From 11b4c48c3a03ed04859231e40fb7d5f36724b137 Mon Sep 17 00:00:00 2001
From: Marco Realacci <marco@marcorealacci.me>
Date: Fri, 17 Jan 2025 21:06:51 +0100
Subject: [PATCH] move legacy code to separate branch

---
 .github/workflows/update-data.yml             |    27 -
 .github/workflows/validate-questions.yml      |    15 -
 .../AccessControl/AccessManager.cs            |     0
 {legacy/Bot => Bot}/ModuleLoader/IModule.cs   |     0
 .../Bot => Bot}/ModuleLoader/ModuleLoader.cs  |     0
 .../Bot => Bot}/Modules/OttoLinux/BotGame.cs  |     0
 .../Modules/OttoLinux/OttoReverse.cs          |     0
 .../Modules/OttoLinux/OttoScore.cs            |     0
 .../Modules/OttoLinux/PhotoServer.cs          |     0
 .../Bot => Bot}/Modules/OttoLinux/Question.cs |     0
 .../Modules/OttoLinux/WebReverse.cs           |     0
 {legacy/Bot => Bot}/Program.cs                |     0
 {legacy/Bot => Bot}/SoUnBot.csproj            |     0
 {legacy/Bot => Bot}/Telegram/TelegramBot.cs   |     0
 {legacy/Bot => Bot}/run.sh                    |     0
 {legacy/Data => Data}/Images/25.png           |   Bin
 {legacy/Data => Data}/Images/26.png           |   Bin
 {legacy/Data => Data}/Images/27.png           |   Bin
 {legacy/Data => Data}/Images/35.png           |   Bin
 {legacy/Data => Data}/Images/36.png           |   Bin
 {legacy/Data => Data}/Images/37.png           |   Bin
 {legacy/Data => Data}/Images/38.png           |   Bin
 {legacy/Data => Data}/Images/39.png           |   Bin
 {legacy/Data => Data}/Images/40.png           |   Bin
 {legacy/Data => Data}/Images/56.png           |   Bin
 {legacy/Data => Data}/Images/57.png           |   Bin
 {legacy/Data => Data}/Images/58.png           |   Bin
 {legacy/Data => Data}/Images/59.png           |   Bin
 {legacy/Data => Data}/Images/60.png           |   Bin
 {legacy/Data => Data}/Images/61.png           |   Bin
 {legacy/Data => Data}/Images/62.png           |   Bin
 .../Images/FDS/1positive0negative.png         |   Bin
 .../Data => Data}/Images/FDS/accuracy80.png   |   Bin
 .../Images/FDS/matrixwhatcanwesay.png         |   Bin
 .../Questions/Domande Sicurezza.old           |     0
 .../Questions/diritto_unive_inf.txt           |     0
 .../Questions/ingsw/0000_102/correct.txt      |     0
 .../Questions/ingsw/0000_102/quest.txt        |     0
 .../Questions/ingsw/0000_102/wrong1.txt       |     0
 .../Questions/ingsw/0000_102/wrong2.txt       |     0
 .../Questions/ingsw/0000_2/correct.txt        |     0
 .../Questions/ingsw/0000_2/quest.txt          |     0
 .../Questions/ingsw/0000_2/wrong1.txt         |     0
 .../Questions/ingsw/0000_2/wrong2.txt         |     0
 .../Questions/ingsw/0000_3/correct.txt        |     0
 .../Questions/ingsw/0000_3/quest.txt          |     0
 .../Questions/ingsw/0000_3/wrong1.txt         |     0
 .../Questions/ingsw/0000_3/wrong2.txt         |     0
 .../Questions/ingsw/0000_32/correct.txt       |     0
 .../Questions/ingsw/0000_32/quest.txt         |     0
 .../Questions/ingsw/0000_32/wrong1.txt        |     0
 .../Questions/ingsw/0000_32/wrong2.txt        |     0
 .../Questions/ingsw/0000_4/correct.txt        |     0
 .../Questions/ingsw/0000_4/quest.txt          |     0
 .../Questions/ingsw/0000_4/wrong1.txt         |     0
 .../Questions/ingsw/0000_4/wrong2.txt         |     0
 .../Questions/ingsw/0000_7/correct.txt        |     0
 .../Questions/ingsw/0000_7/quest.txt          |     0
 .../Questions/ingsw/0000_7/wrong1.txt         |     0
 .../Questions/ingsw/0000_7/wrong2.txt         |     0
 .../Questions/ingsw/0000_8/correct.txt        |     0
 .../Questions/ingsw/0000_8/quest.txt          |     0
 .../Questions/ingsw/0000_8/wrong1.txt         |     0
 .../Questions/ingsw/0000_8/wrong2.txt         |     0
 .../Questions/ingsw/0120_0/correct.txt        |     0
 .../Questions}/ingsw/0120_0/quest.txt         |    20 +-
 .../Questions/ingsw/0120_0/wrong1.txt         |     0
 .../Questions/ingsw/0120_0/wrong2.txt         |     0
 .../Questions}/ingsw/0120_1/correct.txt       |    32 +-
 .../Questions/ingsw/0120_1}/quest.txt         |     4 +-
 .../Questions}/ingsw/0120_1/wrong1.txt        |    34 +-
 .../Questions/ingsw/0120_1/wrong2.txt         |    34 +-
 .../Questions}/ingsw/0120_10/correct.txt      |    32 +-
 .../Questions/ingsw/0120_10}/quest.txt        |     6 +-
 .../Questions/ingsw/0120_10/wrong1.txt        |    32 +-
 .../Questions}/ingsw/0120_10/wrong2.txt       |    32 +-
 .../Questions/ingsw/0120_11/correct.txt       |     0
 .../Questions/ingsw/0120_11/quest.txt         |     0
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 .../Questions/ingsw/0120_11/wrong2.txt        |     0
 .../Questions/ingsw/0120_12/correct.txt       |     0
 .../Questions}/ingsw/0120_12/quest.txt        |    10 +-
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 .../Questions/ingsw/0120_13/correct.txt       |     0
 .../Questions/ingsw/0120_13/quest.txt         |    16 +-
 .../Questions/ingsw/0120_13/wrong1.txt        |     0
 .../Questions/ingsw/0120_13/wrong2.txt        |     0
 .../Questions/ingsw/0120_14/correct.txt       |     0
 .../Questions/ingsw/0120_14}/quest.txt        |    10 +-
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 .../Questions/ingsw/0120_16/correct.txt       |     0
 .../Questions/ingsw/0120_16/quest.txt         |     2 +-
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 .../Questions/ingsw/0120_17/quest.txt         |    16 +-
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 .../Questions/ingsw/0120_18/correct.txt       |     0
 .../Questions}/ingsw/0120_18/quest.txt        |    16 +-
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 .../Questions}/ingsw/0120_2/quest.txt         |    20 +-
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 .../Questions}/ingsw/0120_20/quest.txt        |    14 +-
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 .../Questions/ingsw/0120_21/correct.txt       |     0
 .../Questions/ingsw/0120_21/quest.txt         |     2 +-
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 .../Questions}/ingsw/0120_22/quest.txt        |    62 +-
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 .../Questions/ingsw/0120_23/correct.txt       |     0
 .../Questions/ingsw/0120_23/quest.txt         |    20 +-
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 .../Questions/ingsw/0120_24/correct.txt       |     0
 .../Questions/ingsw/0120_24/quest.txt         |    16 +-
 .../Questions/ingsw/0120_24/wrong1.txt        |     0
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 .../Questions/ingsw/0120_25/quest.txt         |    72 +-
 .../Questions/ingsw/0120_25/wrong1.txt        |     0
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 .../Questions/ingsw/0120_26/correct.txt       |     0
 .../Questions}/ingsw/0120_26/quest.txt        |    16 +-
 .../Questions/ingsw/0120_26/wrong1.txt        |     0
 .../Questions/ingsw/0120_26/wrong2.txt        |     0
 .../Questions}/ingsw/0120_27/correct.txt      |    34 +-
 .../Questions/ingsw/0120_27/quest.txt         |     4 +-
 .../Questions/ingsw/0120_27/wrong1.txt        |    34 +-
 .../Questions/ingsw/0120_27/wrong2.txt        |    34 +-
 .../Questions}/ingsw/0120_28/quest.txt        |     2 +-
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 .../Questions/ingsw/0120_28/wrong2.txt        |    64 +-
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 .../Questions}/ingsw/0120_29/correct.txt      |    30 +-
 .../Questions/ingsw/0120_29}/quest.txt        |     8 +-
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 .../Questions/ingsw/0120_3/correct.txt        |     8 +-
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 .../Questions/ingsw/0120_3}/wrong1.txt        |     8 +-
 .../Questions/ingsw/0120_3}/wrong2.txt        |     8 +-
 .../Questions/ingsw/0120_30/correct.txt       |     0
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 .../Questions/ingsw/0120_34/quest.txt         |     2 +-
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 .../Questions}/ingsw/0120_38/correct.txt      |    34 +-
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diff --git a/.github/workflows/update-data.yml b/.github/workflows/update-data.yml
deleted file mode 100644
index 302f0bd..0000000
--- a/.github/workflows/update-data.yml
+++ /dev/null
@@ -1,27 +0,0 @@
-name: Update bot data
-
-# Configures this workflow to run every time a change is pushed to the branch called `release`.
-on:
-  push:
-    branches: ['main']
-
-jobs:
-  validate-questions:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Validate questions
-        run: python scripts/validate_questions.py
-        shell: sh
-
-  update-data:
-    runs-on: ubuntu-latest
-    steps:
-
-      - name: Configure custom DNS resolver
-        run: echo "nameserver 1.1.1.1" | sudo tee /etc/resolv.conf
-    
-      - name: Call webhook
-        run: curl -X POST ${{secrets.DEPLOY_WEBHOOK}}
diff --git a/.github/workflows/validate-questions.yml b/.github/workflows/validate-questions.yml
deleted file mode 100644
index faeb7b4..0000000
--- a/.github/workflows/validate-questions.yml
+++ /dev/null
@@ -1,15 +0,0 @@
-name: Validate questions
-
-# Configures this workflow to run every time a change is pushed to the branch called `release`.
-on: [pull_request]
-
-jobs:
-  validate-questions:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Validate questions
-        run: python scripts/validate_questions.py
-        shell: sh
\ No newline at end of file
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diff --git a/legacy/Data/Questions/ingsw/0000_8/wrong2.txt b/Data/Questions/ingsw/0000_8/wrong2.txt
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diff --git a/legacy/Data/Questions/ingsw/0120_0/correct.txt b/Data/Questions/ingsw/0120_0/correct.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0120_0/quest.txt b/Data/Questions/ingsw/0120_0/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0120_0/quest.txt
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index 1b78d21..7df72fd 100644
--- a/legacy/Data/ingsw/0120_0/quest.txt
+++ b/Data/Questions/ingsw/0120_0/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_0.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act1 
-Test case 2: act1 act0 act1 act0 act2 
-Test case 3: act0 act2 act2 act1 
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_0.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act1 
+Test case 2: act1 act0 act1 act0 act2 
+Test case 3: act0 act2 act2 act1 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
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rename from legacy/Data/Questions/ingsw/0120_0/wrong1.txt
rename to Data/Questions/ingsw/0120_0/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_0/wrong2.txt b/Data/Questions/ingsw/0120_0/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_0/wrong2.txt
rename to Data/Questions/ingsw/0120_0/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_1/correct.txt b/Data/Questions/ingsw/0120_1/correct.txt
similarity index 94%
rename from legacy/Data/ingsw/0120_1/correct.txt
rename to Data/Questions/ingsw/0120_1/correct.txt
index 279908a..4c63afe 100644
--- a/legacy/Data/ingsw/0120_1/correct.txt
+++ b/Data/Questions/ingsw/0120_1/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and ((y <pre 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and ((y <pre 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_31/quest.txt b/Data/Questions/ingsw/0120_1/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_31/quest.txt
rename to Data/Questions/ingsw/0120_1/quest.txt
index 5922b9f..348f2a5 100644
--- a/legacy/Data/Questions/ingsw/0324_31/quest.txt
+++ b/Data/Questions/ingsw/0120_1/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
+Si consideri il seguente requisito:
+RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_1/wrong1.txt b/Data/Questions/ingsw/0120_1/wrong1.txt
similarity index 94%
rename from legacy/Data/ingsw/0120_1/wrong1.txt
rename to Data/Questions/ingsw/0120_1/wrong1.txt
index 867889a..ff387f8 100644
--- a/legacy/Data/ingsw/0120_1/wrong1.txt
+++ b/Data/Questions/ingsw/0120_1/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_1/wrong2.txt b/Data/Questions/ingsw/0120_1/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0120_1/wrong2.txt
rename to Data/Questions/ingsw/0120_1/wrong2.txt
index a159504..68e8643 100644
--- a/legacy/Data/Questions/ingsw/0120_1/wrong2.txt
+++ b/Data/Questions/ingsw/0120_1/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0120_10/correct.txt b/Data/Questions/ingsw/0120_10/correct.txt
similarity index 94%
rename from legacy/Data/ingsw/0120_10/correct.txt
rename to Data/Questions/ingsw/0120_10/correct.txt
index fffebc7..e70f938 100644
--- a/legacy/Data/ingsw/0120_10/correct.txt
+++ b/Data/Questions/ingsw/0120_10/correct.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0210_43/quest.txt b/Data/Questions/ingsw/0120_10/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0210_43/quest.txt
rename to Data/Questions/ingsw/0120_10/quest.txt
index e11a044..0910f0b 100644
--- a/legacy/Data/Questions/ingsw/0210_43/quest.txt
+++ b/Data/Questions/ingsw/0120_10/quest.txt
@@ -1,4 +1,4 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
+Si consideri il seguente requisito:
+RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_10/wrong1.txt b/Data/Questions/ingsw/0120_10/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0120_10/wrong1.txt
rename to Data/Questions/ingsw/0120_10/wrong1.txt
index c5ef6d8..278e783 100644
--- a/legacy/Data/Questions/ingsw/0120_10/wrong1.txt
+++ b/Data/Questions/ingsw/0120_10/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0120_10/wrong2.txt b/Data/Questions/ingsw/0120_10/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_10/wrong2.txt
rename to Data/Questions/ingsw/0120_10/wrong2.txt
index 06e9d5a..ab50168 100644
--- a/legacy/Data/ingsw/0120_10/wrong2.txt
+++ b/Data/Questions/ingsw/0120_10/wrong2.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_11/correct.txt b/Data/Questions/ingsw/0120_11/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_11/correct.txt
rename to Data/Questions/ingsw/0120_11/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_11/quest.txt b/Data/Questions/ingsw/0120_11/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_11/quest.txt
rename to Data/Questions/ingsw/0120_11/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0120_11/wrong1.txt b/Data/Questions/ingsw/0120_11/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_11/wrong1.txt
rename to Data/Questions/ingsw/0120_11/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_11/wrong2.txt b/Data/Questions/ingsw/0120_11/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_11/wrong2.txt
rename to Data/Questions/ingsw/0120_11/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_12/correct.txt b/Data/Questions/ingsw/0120_12/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_12/correct.txt
rename to Data/Questions/ingsw/0120_12/correct.txt
diff --git a/legacy/Data/ingsw/0120_12/quest.txt b/Data/Questions/ingsw/0120_12/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0120_12/quest.txt
rename to Data/Questions/ingsw/0120_12/quest.txt
index aed3c79..edefd37 100644
--- a/legacy/Data/ingsw/0120_12/quest.txt
+++ b/Data/Questions/ingsw/0120_12/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_12.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
-Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_12.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
+Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_12/wrong1.txt b/Data/Questions/ingsw/0120_12/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_12/wrong1.txt
rename to Data/Questions/ingsw/0120_12/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_12/wrong2.txt b/Data/Questions/ingsw/0120_12/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_12/wrong2.txt
rename to Data/Questions/ingsw/0120_12/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_13/correct.txt b/Data/Questions/ingsw/0120_13/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_13/correct.txt
rename to Data/Questions/ingsw/0120_13/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_13/quest.txt b/Data/Questions/ingsw/0120_13/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_13/quest.txt
rename to Data/Questions/ingsw/0120_13/quest.txt
index c1cd6d0..abc3e1c 100644
--- a/legacy/Data/Questions/ingsw/0120_13/quest.txt
+++ b/Data/Questions/ingsw/0120_13/quest.txt
@@ -1,9 +1,9 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
-int z = x;
-while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
-return (z);
-}
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+int z = x;
+while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
+return (z);
+}
 Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_13/wrong1.txt b/Data/Questions/ingsw/0120_13/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_13/wrong1.txt
rename to Data/Questions/ingsw/0120_13/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_13/wrong2.txt b/Data/Questions/ingsw/0120_13/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_13/wrong2.txt
rename to Data/Questions/ingsw/0120_13/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_14/correct.txt b/Data/Questions/ingsw/0120_14/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_14/correct.txt
rename to Data/Questions/ingsw/0120_14/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_12/quest.txt b/Data/Questions/ingsw/0120_14/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_12/quest.txt
rename to Data/Questions/ingsw/0120_14/quest.txt
index 36947c2..853f496 100644
--- a/legacy/Data/Questions/ingsw/0613_12/quest.txt
+++ b/Data/Questions/ingsw/0120_14/quest.txt
@@ -1,6 +1,6 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (x + 7); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (x + 7); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
 Quale dei seguenti test cases consegue una partition coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_14/wrong1.txt b/Data/Questions/ingsw/0120_14/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_14/wrong1.txt
rename to Data/Questions/ingsw/0120_14/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_14/wrong2.txt b/Data/Questions/ingsw/0120_14/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_14/wrong2.txt
rename to Data/Questions/ingsw/0120_14/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_15/correct.txt b/Data/Questions/ingsw/0120_15/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_15/correct.txt
rename to Data/Questions/ingsw/0120_15/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_15/quest.txt b/Data/Questions/ingsw/0120_15/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_15/quest.txt
rename to Data/Questions/ingsw/0120_15/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0120_15/wrong1.txt b/Data/Questions/ingsw/0120_15/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_15/wrong1.txt
rename to Data/Questions/ingsw/0120_15/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_15/wrong2.txt b/Data/Questions/ingsw/0120_15/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_15/wrong2.txt
rename to Data/Questions/ingsw/0120_15/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_16/correct.txt b/Data/Questions/ingsw/0120_16/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_16/correct.txt
rename to Data/Questions/ingsw/0120_16/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_16/quest.txt b/Data/Questions/ingsw/0120_16/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_16/quest.txt
rename to Data/Questions/ingsw/0120_16/quest.txt
index f6839df..881f5f4 100644
--- a/legacy/Data/Questions/ingsw/0120_16/quest.txt
+++ b/Data/Questions/ingsw/0120_16/quest.txt
@@ -1,2 +1,2 @@
-"Ogni giorno, per ciascuna clinica, il sistema genererà una lista dei pazienti che hanno un appuntamento quel giorno."
+"Ogni giorno, per ciascuna clinica, il sistema genererà una lista dei pazienti che hanno un appuntamento quel giorno."
 La frase precedente è un esempio di:
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_16/wrong1.txt b/Data/Questions/ingsw/0120_16/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_16/wrong1.txt
rename to Data/Questions/ingsw/0120_16/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_16/wrong2.txt b/Data/Questions/ingsw/0120_16/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_16/wrong2.txt
rename to Data/Questions/ingsw/0120_16/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_17/correct.txt b/Data/Questions/ingsw/0120_17/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_17/correct.txt
rename to Data/Questions/ingsw/0120_17/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_17/quest.txt b/Data/Questions/ingsw/0120_17/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_17/quest.txt
rename to Data/Questions/ingsw/0120_17/quest.txt
index fc7cc95..a7da454 100644
--- a/legacy/Data/Questions/ingsw/0120_17/quest.txt
+++ b/Data/Questions/ingsw/0120_17/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_17.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_17.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 3, 4? In altri terminti, qual'è la probabilità che non sia necessario ripetere la seconda fase (ma non la prima) ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_17/wrong1.txt b/Data/Questions/ingsw/0120_17/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_17/wrong1.txt
rename to Data/Questions/ingsw/0120_17/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_17/wrong2.txt b/Data/Questions/ingsw/0120_17/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_17/wrong2.txt
rename to Data/Questions/ingsw/0120_17/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_18/correct.txt b/Data/Questions/ingsw/0120_18/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_18/correct.txt
rename to Data/Questions/ingsw/0120_18/correct.txt
diff --git a/legacy/Data/ingsw/0120_18/quest.txt b/Data/Questions/ingsw/0120_18/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0120_18/quest.txt
rename to Data/Questions/ingsw/0120_18/quest.txt
index fd2ddc5..b4978f3 100644
--- a/legacy/Data/ingsw/0120_18/quest.txt
+++ b/Data/Questions/ingsw/0120_18/quest.txt
@@ -1,9 +1,9 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y - 2 <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
-  else {if (x + 2*y - 5 >= 0)  return (3); else return (4); }
- }  /* f()  */
-Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y - 2 <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
+  else {if (x + 2*y - 5 >= 0)  return (3); else return (4); }
+ }  /* f()  */
+Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_18/wrong1.txt b/Data/Questions/ingsw/0120_18/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_18/wrong1.txt
rename to Data/Questions/ingsw/0120_18/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_18/wrong2.txt b/Data/Questions/ingsw/0120_18/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_18/wrong2.txt
rename to Data/Questions/ingsw/0120_18/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_19/correct.txt b/Data/Questions/ingsw/0120_19/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_19/correct.txt
rename to Data/Questions/ingsw/0120_19/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_19/quest.txt b/Data/Questions/ingsw/0120_19/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_19/quest.txt
rename to Data/Questions/ingsw/0120_19/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0120_19/wrong1.txt b/Data/Questions/ingsw/0120_19/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_19/wrong1.txt
rename to Data/Questions/ingsw/0120_19/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_19/wrong2.txt b/Data/Questions/ingsw/0120_19/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_19/wrong2.txt
rename to Data/Questions/ingsw/0120_19/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_2/correct.txt b/Data/Questions/ingsw/0120_2/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_2/correct.txt
rename to Data/Questions/ingsw/0120_2/correct.txt
diff --git a/legacy/Data/ingsw/0120_2/quest.txt b/Data/Questions/ingsw/0120_2/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0120_2/quest.txt
rename to Data/Questions/ingsw/0120_2/quest.txt
index 7cf45ee..6a6482f 100644
--- a/legacy/Data/ingsw/0120_2/quest.txt
+++ b/Data/Questions/ingsw/0120_2/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_2.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act1 act0 act1 act0 act2 
-Test case 2: act0 act2 act2 act0 act1 
-Test case 3: act0 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_2.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act1 act0 act1 act0 act2 
+Test case 2: act0 act2 act2 act0 act1 
+Test case 3: act0 act0 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_2/wrong1.txt b/Data/Questions/ingsw/0120_2/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_2/wrong1.txt
rename to Data/Questions/ingsw/0120_2/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_2/wrong2.txt b/Data/Questions/ingsw/0120_2/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_2/wrong2.txt
rename to Data/Questions/ingsw/0120_2/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_20/correct.txt b/Data/Questions/ingsw/0120_20/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_20/correct.txt
rename to Data/Questions/ingsw/0120_20/correct.txt
diff --git a/legacy/Data/ingsw/0120_20/quest.txt b/Data/Questions/ingsw/0120_20/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0120_20/quest.txt
rename to Data/Questions/ingsw/0120_20/quest.txt
index 173901c..57ba121 100644
--- a/legacy/Data/ingsw/0120_20/quest.txt
+++ b/Data/Questions/ingsw/0120_20/quest.txt
@@ -1,8 +1,8 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
-  else {if (2*x + y - 5 >= 0)  return (3); else return (4); }
- }  /* f()  */
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
+  else {if (2*x + y - 5 >= 0)  return (3); else return (4); }
+ }  /* f()  */
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_20/wrong1.txt b/Data/Questions/ingsw/0120_20/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_20/wrong1.txt
rename to Data/Questions/ingsw/0120_20/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_20/wrong2.txt b/Data/Questions/ingsw/0120_20/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_20/wrong2.txt
rename to Data/Questions/ingsw/0120_20/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_21/correct.txt b/Data/Questions/ingsw/0120_21/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_21/correct.txt
rename to Data/Questions/ingsw/0120_21/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_21/quest.txt b/Data/Questions/ingsw/0120_21/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_21/quest.txt
rename to Data/Questions/ingsw/0120_21/quest.txt
index 5e04a05..05ba3b0 100644
--- a/legacy/Data/Questions/ingsw/0120_21/quest.txt
+++ b/Data/Questions/ingsw/0120_21/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_21.png
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_21.png
 Si consideri la Markov chain in figura con stato iniziale 0 e p in (0, 1). Quale delle seguenti formule calcola il valore atteso del numero di transizioni necessarie per lasciare lo stato 0.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_21/wrong1.txt b/Data/Questions/ingsw/0120_21/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_21/wrong1.txt
rename to Data/Questions/ingsw/0120_21/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_21/wrong2.txt b/Data/Questions/ingsw/0120_21/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_21/wrong2.txt
rename to Data/Questions/ingsw/0120_21/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_22/quest.txt b/Data/Questions/ingsw/0120_22/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_22/quest.txt
rename to Data/Questions/ingsw/0120_22/quest.txt
index 306d75a..81a7fa1 100644
--- a/legacy/Data/ingsw/0120_22/quest.txt
+++ b/Data/Questions/ingsw/0120_22/quest.txt
@@ -1,32 +1,32 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_22/wrong1.txt b/Data/Questions/ingsw/0120_22/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_22/wrong1.txt
rename to Data/Questions/ingsw/0120_22/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_22/wrong2.txt b/Data/Questions/ingsw/0120_22/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_22/wrong2.txt
rename to Data/Questions/ingsw/0120_22/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_22/wrong3.txt b/Data/Questions/ingsw/0120_22/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_22/wrong3.txt
rename to Data/Questions/ingsw/0120_22/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0120_23/correct.txt b/Data/Questions/ingsw/0120_23/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_23/correct.txt
rename to Data/Questions/ingsw/0120_23/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_23/quest.txt b/Data/Questions/ingsw/0120_23/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_23/quest.txt
rename to Data/Questions/ingsw/0120_23/quest.txt
index 6f49368..434d90a 100644
--- a/legacy/Data/Questions/ingsw/0120_23/quest.txt
+++ b/Data/Questions/ingsw/0120_23/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_23.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act2 act1 act2 act1 
-Test case 2: act1 act0 act2 
-Test case 3: act2 act1 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_23.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act2 act1 act2 act1 
+Test case 2: act1 act0 act2 
+Test case 3: act2 act1 act0 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_23/wrong1.txt b/Data/Questions/ingsw/0120_23/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_23/wrong1.txt
rename to Data/Questions/ingsw/0120_23/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_23/wrong2.txt b/Data/Questions/ingsw/0120_23/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_23/wrong2.txt
rename to Data/Questions/ingsw/0120_23/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_24/correct.txt b/Data/Questions/ingsw/0120_24/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_24/correct.txt
rename to Data/Questions/ingsw/0120_24/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_24/quest.txt b/Data/Questions/ingsw/0120_24/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_24/quest.txt
rename to Data/Questions/ingsw/0120_24/quest.txt
index 702f202..6e41adb 100644
--- a/legacy/Data/Questions/ingsw/0120_24/quest.txt
+++ b/Data/Questions/ingsw/0120_24/quest.txt
@@ -1,9 +1,9 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 0)   { if (x + y - 2>= 0)  return (1); else return (2); }
-  else {if (2*x + y - 1>= 0)  return (3); else return (4); }
- }  /* f()  */
-Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 0)   { if (x + y - 2>= 0)  return (1); else return (2); }
+  else {if (2*x + y - 1>= 0)  return (3); else return (4); }
+ }  /* f()  */
+Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_24/wrong1.txt b/Data/Questions/ingsw/0120_24/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_24/wrong1.txt
rename to Data/Questions/ingsw/0120_24/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_24/wrong2.txt b/Data/Questions/ingsw/0120_24/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_24/wrong2.txt
rename to Data/Questions/ingsw/0120_24/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_25/quest.txt b/Data/Questions/ingsw/0120_25/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_25/quest.txt
rename to Data/Questions/ingsw/0120_25/quest.txt
index 5e6a3b5..83988f9 100644
--- a/legacy/Data/Questions/ingsw/0120_25/quest.txt
+++ b/Data/Questions/ingsw/0120_25/quest.txt
@@ -1,37 +1,37 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_25/wrong1.txt b/Data/Questions/ingsw/0120_25/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_25/wrong1.txt
rename to Data/Questions/ingsw/0120_25/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_25/wrong2.txt b/Data/Questions/ingsw/0120_25/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_25/wrong2.txt
rename to Data/Questions/ingsw/0120_25/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_25/wrong3.txt b/Data/Questions/ingsw/0120_25/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_25/wrong3.txt
rename to Data/Questions/ingsw/0120_25/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0120_26/correct.txt b/Data/Questions/ingsw/0120_26/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_26/correct.txt
rename to Data/Questions/ingsw/0120_26/correct.txt
diff --git a/legacy/Data/ingsw/0120_26/quest.txt b/Data/Questions/ingsw/0120_26/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0120_26/quest.txt
rename to Data/Questions/ingsw/0120_26/quest.txt
index 458f85c..b2616cb 100644
--- a/legacy/Data/ingsw/0120_26/quest.txt
+++ b/Data/Questions/ingsw/0120_26/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_26.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_26.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 2, 3, 4 ? In altri terminti, qual'è la probabilità che sia necessario ripetere sia la fase 1 che la fase 2 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_26/wrong1.txt b/Data/Questions/ingsw/0120_26/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_26/wrong1.txt
rename to Data/Questions/ingsw/0120_26/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_26/wrong2.txt b/Data/Questions/ingsw/0120_26/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_26/wrong2.txt
rename to Data/Questions/ingsw/0120_26/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_27/correct.txt b/Data/Questions/ingsw/0120_27/correct.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_27/correct.txt
rename to Data/Questions/ingsw/0120_27/correct.txt
index 5f37ecc..f8d8a20 100644
--- a/legacy/Data/ingsw/0120_27/correct.txt
+++ b/Data/Questions/ingsw/0120_27/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_27/quest.txt b/Data/Questions/ingsw/0120_27/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_27/quest.txt
rename to Data/Questions/ingsw/0120_27/quest.txt
index 864cc93..498beed 100644
--- a/legacy/Data/Questions/ingsw/0120_27/quest.txt
+++ b/Data/Questions/ingsw/0120_27/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
+Si consideri il seguente requisito:
+RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
 Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_27/wrong1.txt b/Data/Questions/ingsw/0120_27/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_27/wrong1.txt
rename to Data/Questions/ingsw/0120_27/wrong1.txt
index 8856598..137f01e 100644
--- a/legacy/Data/Questions/ingsw/0120_27/wrong1.txt
+++ b/Data/Questions/ingsw/0120_27/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_27/wrong2.txt b/Data/Questions/ingsw/0120_27/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_27/wrong2.txt
rename to Data/Questions/ingsw/0120_27/wrong2.txt
index 2057e11..59d1095 100644
--- a/legacy/Data/Questions/ingsw/0120_27/wrong2.txt
+++ b/Data/Questions/ingsw/0120_27/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0120_28/quest.txt b/Data/Questions/ingsw/0120_28/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0120_28/quest.txt
rename to Data/Questions/ingsw/0120_28/quest.txt
index 5826ea3..4f79494 100644
--- a/legacy/Data/ingsw/0120_28/quest.txt
+++ b/Data/Questions/ingsw/0120_28/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_28.png
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_28.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_28/wrong1.txt b/Data/Questions/ingsw/0120_28/wrong1.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_28/wrong1.txt
rename to Data/Questions/ingsw/0120_28/wrong1.txt
index bfb3c5d..77f4d59 100644
--- a/legacy/Data/ingsw/0120_28/wrong1.txt
+++ b/Data/Questions/ingsw/0120_28/wrong1.txt
@@ -1,38 +1,38 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_28/wrong2.txt b/Data/Questions/ingsw/0120_28/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_28/wrong2.txt
rename to Data/Questions/ingsw/0120_28/wrong2.txt
index c5d8c6b..c8b6f58 100644
--- a/legacy/Data/Questions/ingsw/0120_28/wrong2.txt
+++ b/Data/Questions/ingsw/0120_28/wrong2.txt
@@ -1,33 +1,33 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_28/wrong3.txt b/Data/Questions/ingsw/0120_28/wrong3.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_28/wrong3.txt
rename to Data/Questions/ingsw/0120_28/wrong3.txt
index 40db007..d52af85 100644
--- a/legacy/Data/ingsw/0120_28/wrong3.txt
+++ b/Data/Questions/ingsw/0120_28/wrong3.txt
@@ -1,33 +1,33 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_29/correct.txt b/Data/Questions/ingsw/0120_29/correct.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_29/correct.txt
rename to Data/Questions/ingsw/0120_29/correct.txt
index 080c618..5015021 100644
--- a/legacy/Data/ingsw/0120_29/correct.txt
+++ b/Data/Questions/ingsw/0120_29/correct.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0613_23/quest.txt b/Data/Questions/ingsw/0120_29/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_23/quest.txt
rename to Data/Questions/ingsw/0120_29/quest.txt
index 576af1a..0d3faca 100644
--- a/legacy/Data/Questions/ingsw/0613_23/quest.txt
+++ b/Data/Questions/ingsw/0120_29/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_29/wrong1.txt b/Data/Questions/ingsw/0120_29/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_29/wrong1.txt
rename to Data/Questions/ingsw/0120_29/wrong1.txt
index 5ea42fe..1c57f89 100644
--- a/legacy/Data/Questions/ingsw/0120_29/wrong1.txt
+++ b/Data/Questions/ingsw/0120_29/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0120_29/wrong2.txt b/Data/Questions/ingsw/0120_29/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_29/wrong2.txt
rename to Data/Questions/ingsw/0120_29/wrong2.txt
index a55c0a4..3b177dc 100644
--- a/legacy/Data/ingsw/0120_29/wrong2.txt
+++ b/Data/Questions/ingsw/0120_29/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_3/correct.txt b/Data/Questions/ingsw/0120_3/correct.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0120_3/correct.txt
rename to Data/Questions/ingsw/0120_3/correct.txt
index e940faa..c496119 100644
--- a/legacy/Data/Questions/ingsw/0120_3/correct.txt
+++ b/Data/Questions/ingsw/0120_3/correct.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 3) || (y > 3)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 3) || (y > 3)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_3/quest.txt b/Data/Questions/ingsw/0120_3/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_3/quest.txt
rename to Data/Questions/ingsw/0120_3/quest.txt
index 2758118..42b0d46 100644
--- a/legacy/Data/Questions/ingsw/0120_3/quest.txt
+++ b/Data/Questions/ingsw/0120_3/quest.txt
@@ -1,4 +1,4 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-Si consideri la funzione C
-int f(int x, int y) { ..... }
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+Si consideri la funzione C
+int f(int x, int y) { ..... }
 Quale delle seguenti assert esprime la pre-condizione che entrambi gli argomenti di f sono non-negativi ed almeno uno di loro è maggiore di 3 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_33/wrong1.txt b/Data/Questions/ingsw/0120_3/wrong1.txt
similarity index 95%
rename from legacy/Data/ingsw/0613_33/wrong1.txt
rename to Data/Questions/ingsw/0120_3/wrong1.txt
index ad32d88..5dcb186 100644
--- a/legacy/Data/ingsw/0613_33/wrong1.txt
+++ b/Data/Questions/ingsw/0120_3/wrong1.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x >= 3) || (y >= 3)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x >= 3) || (y >= 3)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_33/wrong2.txt b/Data/Questions/ingsw/0120_3/wrong2.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0613_33/wrong2.txt
rename to Data/Questions/ingsw/0120_3/wrong2.txt
index 642ec6b..21886fc 100644
--- a/legacy/Data/Questions/ingsw/0613_33/wrong2.txt
+++ b/Data/Questions/ingsw/0120_3/wrong2.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x > 0) && (y > 0) && ((x >= 3) || (y > 3)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x > 0) && (y > 0) && ((x >= 3) || (y > 3)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_30/correct.txt b/Data/Questions/ingsw/0120_30/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_30/correct.txt
rename to Data/Questions/ingsw/0120_30/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_30/quest.txt b/Data/Questions/ingsw/0120_30/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_30/quest.txt
rename to Data/Questions/ingsw/0120_30/quest.txt
index dbd72c0..20501b2 100644
--- a/legacy/Data/Questions/ingsw/0120_30/quest.txt
+++ b/Data/Questions/ingsw/0120_30/quest.txt
@@ -1,22 +1,22 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a - 100 >= 0) && (b - c - 1 <= 0) )
-          return (1);    // punto di uscita 1
-      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0)
-)
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a - 100 >= 0) && (b - c - 1 <= 0) )
+          return (1);    // punto di uscita 1
+      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0)
+)
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_30/wrong1.txt b/Data/Questions/ingsw/0120_30/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_30/wrong1.txt
rename to Data/Questions/ingsw/0120_30/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_30/wrong2.txt b/Data/Questions/ingsw/0120_30/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_30/wrong2.txt
rename to Data/Questions/ingsw/0120_30/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_31/correct.txt b/Data/Questions/ingsw/0120_31/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_31/correct.txt
rename to Data/Questions/ingsw/0120_31/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_31/quest.txt b/Data/Questions/ingsw/0120_31/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_31/quest.txt
rename to Data/Questions/ingsw/0120_31/quest.txt
index 6c8f77e..fcb9f0a 100644
--- a/legacy/Data/Questions/ingsw/0120_31/quest.txt
+++ b/Data/Questions/ingsw/0120_31/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_31.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act1 act0 act0 
-Test case 2: act2 act0 act1 
-Test case 3: act0 act0 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_31.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act1 act0 act0 
+Test case 2: act2 act0 act1 
+Test case 3: act0 act0 act0 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_31/wrong1.txt b/Data/Questions/ingsw/0120_31/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_31/wrong1.txt
rename to Data/Questions/ingsw/0120_31/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_31/wrong2.txt b/Data/Questions/ingsw/0120_31/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_31/wrong2.txt
rename to Data/Questions/ingsw/0120_31/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_32/correct.txt b/Data/Questions/ingsw/0120_32/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_32/correct.txt
rename to Data/Questions/ingsw/0120_32/correct.txt
diff --git a/legacy/Data/ingsw/0120_32/quest.txt b/Data/Questions/ingsw/0120_32/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0120_32/quest.txt
rename to Data/Questions/ingsw/0120_32/quest.txt
index dcec721..3e3f55d 100644
--- a/legacy/Data/ingsw/0120_32/quest.txt
+++ b/Data/Questions/ingsw/0120_32/quest.txt
@@ -1,8 +1,8 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (2*x); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
-Si consideri il seguente insieme di test cases:
-{x=-20, x= 10, x=60}
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (2*x); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
+Si consideri il seguente insieme di test cases:
+{x=-20, x= 10, x=60}
 Quale delle seguenti è la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_32/wrong1.txt b/Data/Questions/ingsw/0120_32/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_32/wrong1.txt
rename to Data/Questions/ingsw/0120_32/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_32/wrong2.txt b/Data/Questions/ingsw/0120_32/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_32/wrong2.txt
rename to Data/Questions/ingsw/0120_32/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_33/correct.txt b/Data/Questions/ingsw/0120_33/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_33/correct.txt
rename to Data/Questions/ingsw/0120_33/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_37/quest.txt b/Data/Questions/ingsw/0120_33/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0613_37/quest.txt
rename to Data/Questions/ingsw/0120_33/quest.txt
index e5fbc81..6906b2b 100644
--- a/legacy/Data/Questions/ingsw/0613_37/quest.txt
+++ b/Data/Questions/ingsw/0120_33/quest.txt
@@ -1,16 +1,16 @@
-Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
-block Monitor
-input Real x;  
-output Boolean y;
-Boolean w;
-initial equation
-y = false;
-equation
-w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
-algorithm
-when edge(w) then
-y := true;
-end when;
-end Monitor;
-</pre>
+Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
+block Monitor
+input Real x;  
+output Boolean y;
+Boolean w;
+initial equation
+y = false;
+equation
+w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
+algorithm
+when edge(w) then
+y := true;
+end when;
+end Monitor;
+</pre>
 Quale delle seguenti affermazioni meglio descrive il requisito monitorato?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_33/wrong1.txt b/Data/Questions/ingsw/0120_33/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_33/wrong1.txt
rename to Data/Questions/ingsw/0120_33/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_33/wrong2.txt b/Data/Questions/ingsw/0120_33/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_33/wrong2.txt
rename to Data/Questions/ingsw/0120_33/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_34/quest.txt b/Data/Questions/ingsw/0120_34/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_34/quest.txt
rename to Data/Questions/ingsw/0120_34/quest.txt
index 29d0647..89108f1 100644
--- a/legacy/Data/Questions/ingsw/0120_34/quest.txt
+++ b/Data/Questions/ingsw/0120_34/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_34.png
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_34.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_34/wrong1.txt b/Data/Questions/ingsw/0120_34/wrong1.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_34/wrong1.txt
rename to Data/Questions/ingsw/0120_34/wrong1.txt
index 160702f..92a1693 100644
--- a/legacy/Data/Questions/ingsw/0120_34/wrong1.txt
+++ b/Data/Questions/ingsw/0120_34/wrong1.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_34/wrong2.txt b/Data/Questions/ingsw/0120_34/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_34/wrong2.txt
rename to Data/Questions/ingsw/0120_34/wrong2.txt
index 3c05a37..dfac680 100644
--- a/legacy/Data/Questions/ingsw/0120_34/wrong2.txt
+++ b/Data/Questions/ingsw/0120_34/wrong2.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_34/wrong3.txt b/Data/Questions/ingsw/0120_34/wrong3.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_34/wrong3.txt
rename to Data/Questions/ingsw/0120_34/wrong3.txt
index bdfb644..d7ee3c7 100644
--- a/legacy/Data/ingsw/0120_34/wrong3.txt
+++ b/Data/Questions/ingsw/0120_34/wrong3.txt
@@ -1,37 +1,37 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_41/quest.txt b/Data/Questions/ingsw/0120_35/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0613_41/quest.txt
rename to Data/Questions/ingsw/0120_35/quest.txt
index 99379e6..e060808 100644
--- a/legacy/Data/ingsw/0613_41/quest.txt
+++ b/Data/Questions/ingsw/0120_35/quest.txt
@@ -1,4 +1,4 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-Si consideri la funzione C
-int f(int x, int y) { ..... }
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+Si consideri la funzione C
+int f(int x, int y) { ..... }
 Quale delle seguenti assert esprime l'invariante che le variabili locali z e w di f() hanno somma minore di 1 oppure maggiore di 7 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_41/wrong1.txt b/Data/Questions/ingsw/0120_35/wrong1.txt
similarity index 94%
rename from legacy/Data/ingsw/0613_41/wrong1.txt
rename to Data/Questions/ingsw/0120_35/wrong1.txt
index cbf1814..3957de1 100644
--- a/legacy/Data/ingsw/0613_41/wrong1.txt
+++ b/Data/Questions/ingsw/0120_35/wrong1.txt
@@ -1,6 +1,6 @@
-int f(in x, int y) 
-{ 
-int z, w;
-assert( (z + w <= 1) || (z + w >= 7));
-.....
+int f(in x, int y) 
+{ 
+int z, w;
+assert( (z + w <= 1) || (z + w >= 7));
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_35/wrong2.txt b/Data/Questions/ingsw/0120_35/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_35/wrong2.txt
rename to Data/Questions/ingsw/0120_35/wrong2.txt
index 03b9f52..dff55b1 100644
--- a/legacy/Data/Questions/ingsw/0120_35/wrong2.txt
+++ b/Data/Questions/ingsw/0120_35/wrong2.txt
@@ -1,6 +1,6 @@
-int f(in x, int y) 
-{ 
-int z, w;
-assert( (z + w < 1) || (z + w > 7));
-.....
+int f(in x, int y) 
+{ 
+int z, w;
+assert( (z + w < 1) || (z + w > 7));
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_35/wrong3.txt b/Data/Questions/ingsw/0120_35/wrong3.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_35/wrong3.txt
rename to Data/Questions/ingsw/0120_35/wrong3.txt
index 6fcb8b5..d56f4a5 100644
--- a/legacy/Data/Questions/ingsw/0120_35/wrong3.txt
+++ b/Data/Questions/ingsw/0120_35/wrong3.txt
@@ -1,6 +1,6 @@
-int f(in x, int y) 
-{ 
-int z, w;
-assert( (z + w > 1) || (z + w < 7));
-.....
+int f(in x, int y) 
+{ 
+int z, w;
+assert( (z + w > 1) || (z + w < 7));
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_36/correct.txt b/Data/Questions/ingsw/0120_36/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_36/correct.txt
rename to Data/Questions/ingsw/0120_36/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_36/quest.txt b/Data/Questions/ingsw/0120_36/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_36/quest.txt
rename to Data/Questions/ingsw/0120_36/quest.txt
index 6f256c3..b432df1 100644
--- a/legacy/Data/Questions/ingsw/0120_36/quest.txt
+++ b/Data/Questions/ingsw/0120_36/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_36.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act2 act1 act2 
-Test case 2: act0 act2 act0 
-Test case 3: act0 act0 act0 act1 act1 
-
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_36.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act2 act1 act2 
+Test case 2: act0 act2 act0 
+Test case 3: act0 act0 act0 act1 act1 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_36/wrong1.txt b/Data/Questions/ingsw/0120_36/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_36/wrong1.txt
rename to Data/Questions/ingsw/0120_36/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_36/wrong2.txt b/Data/Questions/ingsw/0120_36/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_36/wrong2.txt
rename to Data/Questions/ingsw/0120_36/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_37/correct.txt b/Data/Questions/ingsw/0120_37/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_37/correct.txt
rename to Data/Questions/ingsw/0120_37/correct.txt
diff --git a/legacy/Data/ingsw/0120_37/quest.txt b/Data/Questions/ingsw/0120_37/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0120_37/quest.txt
rename to Data/Questions/ingsw/0120_37/quest.txt
index cdbc688..f8fae00 100644
--- a/legacy/Data/ingsw/0120_37/quest.txt
+++ b/Data/Questions/ingsw/0120_37/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_37.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-
-Test case 1: act2 act1 act2 act2 
-Test case 2: act2 act0 act2 act0 act2 
-Test case 3: act2 act0 act2 act0 act1 
-
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_37.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+
+Test case 1: act2 act1 act2 act2 
+Test case 2: act2 act0 act2 act0 act2 
+Test case 3: act2 act0 act2 act0 act1 
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_37/wrong1.txt b/Data/Questions/ingsw/0120_37/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_37/wrong1.txt
rename to Data/Questions/ingsw/0120_37/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_37/wrong2.txt b/Data/Questions/ingsw/0120_37/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_37/wrong2.txt
rename to Data/Questions/ingsw/0120_37/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_38/correct.txt b/Data/Questions/ingsw/0120_38/correct.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_38/correct.txt
rename to Data/Questions/ingsw/0120_38/correct.txt
index 25ac15c..1ab78ce 100644
--- a/legacy/Data/ingsw/0120_38/correct.txt
+++ b/Data/Questions/ingsw/0120_38/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and ((x >= 30) or (x <= 20)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and ((x >= 30) or (x <= 20)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0324_46/quest.txt b/Data/Questions/ingsw/0120_38/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_46/quest.txt
rename to Data/Questions/ingsw/0120_38/quest.txt
index b420aaf..6938bd6 100644
--- a/legacy/Data/Questions/ingsw/0324_46/quest.txt
+++ b/Data/Questions/ingsw/0120_38/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
+Si consideri il seguente requisito:
+RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
 Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_38/wrong1.txt b/Data/Questions/ingsw/0120_38/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_38/wrong1.txt
rename to Data/Questions/ingsw/0120_38/wrong1.txt
index 157567e..ceb178e 100644
--- a/legacy/Data/ingsw/0120_38/wrong1.txt
+++ b/Data/Questions/ingsw/0120_38/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) or ((x >= 20) and (x <= 30)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) or ((x >= 20) and (x <= 30)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_38/wrong2.txt b/Data/Questions/ingsw/0120_38/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_38/wrong2.txt
rename to Data/Questions/ingsw/0120_38/wrong2.txt
index d021c3b..b763753 100644
--- a/legacy/Data/Questions/ingsw/0120_38/wrong2.txt
+++ b/Data/Questions/ingsw/0120_38/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and (x >= 20) and (x <= 30) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and (x >= 20) and (x <= 30) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_39/quest.txt b/Data/Questions/ingsw/0120_39/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_39/quest.txt
rename to Data/Questions/ingsw/0120_39/quest.txt
index 4777dbc..49d8ee1 100644
--- a/legacy/Data/Questions/ingsw/0120_39/quest.txt
+++ b/Data/Questions/ingsw/0120_39/quest.txt
@@ -1,35 +1,35 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_39/wrong1.txt b/Data/Questions/ingsw/0120_39/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_39/wrong1.txt
rename to Data/Questions/ingsw/0120_39/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_39/wrong2.txt b/Data/Questions/ingsw/0120_39/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_39/wrong2.txt
rename to Data/Questions/ingsw/0120_39/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_39/wrong3.txt b/Data/Questions/ingsw/0120_39/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_39/wrong3.txt
rename to Data/Questions/ingsw/0120_39/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0120_4/correct.txt b/Data/Questions/ingsw/0120_4/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_4/correct.txt
rename to Data/Questions/ingsw/0120_4/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_4/quest.txt b/Data/Questions/ingsw/0120_4/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_4/quest.txt
rename to Data/Questions/ingsw/0120_4/quest.txt
index d1cf8cb..2aa8dba 100644
--- a/legacy/Data/Questions/ingsw/0120_4/quest.txt
+++ b/Data/Questions/ingsw/0120_4/quest.txt
@@ -1,16 +1,16 @@
-Si consideri il monitor seguente che ritorna true appena i requisiti per il sistema monitorato sono violati.
-block Monitor
-input Real x;  
-output Boolean y;
-Boolean w;
-initial equation
-y = false;
-equation
-w = ((x < 0) or (x > 5));
-algorithm
-when edge(w) then
-y := true;
-end when;
-end Monitor;
-</pre>
+Si consideri il monitor seguente che ritorna true appena i requisiti per il sistema monitorato sono violati.
+block Monitor
+input Real x;  
+output Boolean y;
+Boolean w;
+initial equation
+y = false;
+equation
+w = ((x < 0) or (x > 5));
+algorithm
+when edge(w) then
+y := true;
+end when;
+end Monitor;
+</pre>
 Quale delle seguenti affermazioni meglio descrive il requisito monitorato.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_4/wrong1.txt b/Data/Questions/ingsw/0120_4/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_4/wrong1.txt
rename to Data/Questions/ingsw/0120_4/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_4/wrong2.txt b/Data/Questions/ingsw/0120_4/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_4/wrong2.txt
rename to Data/Questions/ingsw/0120_4/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_40/correct.txt b/Data/Questions/ingsw/0120_40/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_40/correct.txt
rename to Data/Questions/ingsw/0120_40/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_40/quest.txt b/Data/Questions/ingsw/0120_40/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_40/quest.txt
rename to Data/Questions/ingsw/0120_40/quest.txt
index 74a4d32..4944b43 100644
--- a/legacy/Data/Questions/ingsw/0120_40/quest.txt
+++ b/Data/Questions/ingsw/0120_40/quest.txt
@@ -1,8 +1,8 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y - 6 <= 0)   { if (x + y - 3 >= 0)  return (1); else return (2); }
-  else {if (x + 2*y -15 >= 0)  return (3); else return (4); }
- }  /* f()  */
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y - 6 <= 0)   { if (x + y - 3 >= 0)  return (1); else return (2); }
+  else {if (x + 2*y -15 >= 0)  return (3); else return (4); }
+ }  /* f()  */
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_40/wrong1.txt b/Data/Questions/ingsw/0120_40/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_40/wrong1.txt
rename to Data/Questions/ingsw/0120_40/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_40/wrong2.txt b/Data/Questions/ingsw/0120_40/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_40/wrong2.txt
rename to Data/Questions/ingsw/0120_40/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_41/correct.txt b/Data/Questions/ingsw/0120_41/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_41/correct.txt
rename to Data/Questions/ingsw/0120_41/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_41/quest.txt b/Data/Questions/ingsw/0120_41/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_41/quest.txt
rename to Data/Questions/ingsw/0120_41/quest.txt
index 47201e7..332256a 100644
--- a/legacy/Data/Questions/ingsw/0120_41/quest.txt
+++ b/Data/Questions/ingsw/0120_41/quest.txt
@@ -1,4 +1,4 @@
-Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
-Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
-P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
+Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
+Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
+P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
 dove b è una opportuna costante note da dati storici aziendali. Si assuma che b = 0.0001,  C = 1000000, ed il rischio ammesso è R = 1000. Quale dei seguenti valori meglio approssima il costo S per lo sviluppo del software in questione.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_41/wrong1.txt b/Data/Questions/ingsw/0120_41/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_41/wrong1.txt
rename to Data/Questions/ingsw/0120_41/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_41/wrong2.txt b/Data/Questions/ingsw/0120_41/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_41/wrong2.txt
rename to Data/Questions/ingsw/0120_41/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_42/correct.txt b/Data/Questions/ingsw/0120_42/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_42/correct.txt
rename to Data/Questions/ingsw/0120_42/correct.txt
diff --git a/legacy/Data/ingsw/0120_42/quest.txt b/Data/Questions/ingsw/0120_42/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0120_42/quest.txt
rename to Data/Questions/ingsw/0120_42/quest.txt
index 4650bbb..c6b6c5f 100644
--- a/legacy/Data/ingsw/0120_42/quest.txt
+++ b/Data/Questions/ingsw/0120_42/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_42.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act1 act2 act1 act2 act2 
-Test case 2: act2 act0 
-Test case 3: act0 act0 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_42.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act1 act2 act1 act2 act2 
+Test case 2: act2 act0 
+Test case 3: act0 act0 act0 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_42/wrong1.txt b/Data/Questions/ingsw/0120_42/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_42/wrong1.txt
rename to Data/Questions/ingsw/0120_42/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_42/wrong2.txt b/Data/Questions/ingsw/0120_42/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_42/wrong2.txt
rename to Data/Questions/ingsw/0120_42/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_43/quest.txt b/Data/Questions/ingsw/0120_43/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0120_43/quest.txt
rename to Data/Questions/ingsw/0120_43/quest.txt
index 8636c5a..aa56c3d 100644
--- a/legacy/Data/ingsw/0120_43/quest.txt
+++ b/Data/Questions/ingsw/0120_43/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_43.png
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_43.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_43/wrong1.txt b/Data/Questions/ingsw/0120_43/wrong1.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_43/wrong1.txt
rename to Data/Questions/ingsw/0120_43/wrong1.txt
index 0ca6415..8210d86 100644
--- a/legacy/Data/ingsw/0120_43/wrong1.txt
+++ b/Data/Questions/ingsw/0120_43/wrong1.txt
@@ -1,32 +1,32 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_43/wrong2.txt b/Data/Questions/ingsw/0120_43/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_43/wrong2.txt
rename to Data/Questions/ingsw/0120_43/wrong2.txt
index a5879aa..78c7bc5 100644
--- a/legacy/Data/ingsw/0120_43/wrong2.txt
+++ b/Data/Questions/ingsw/0120_43/wrong2.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_43/wrong3.txt b/Data/Questions/ingsw/0120_43/wrong3.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_43/wrong3.txt
rename to Data/Questions/ingsw/0120_43/wrong3.txt
index b4f56fb..f20fbac 100644
--- a/legacy/Data/Questions/ingsw/0120_43/wrong3.txt
+++ b/Data/Questions/ingsw/0120_43/wrong3.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_44/correct.txt b/Data/Questions/ingsw/0120_44/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_44/correct.txt
rename to Data/Questions/ingsw/0120_44/correct.txt
diff --git a/legacy/Data/ingsw/0120_44/quest.txt b/Data/Questions/ingsw/0120_44/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0120_44/quest.txt
rename to Data/Questions/ingsw/0120_44/quest.txt
index e44e320..6f570ef 100644
--- a/legacy/Data/ingsw/0120_44/quest.txt
+++ b/Data/Questions/ingsw/0120_44/quest.txt
@@ -1,21 +1,21 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a  + b - 6 >= 0) && (b - c - 1 <= 0) )
-          return (1);    // punto di uscita 1
-      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0))
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a  + b - 6 >= 0) && (b - c - 1 <= 0) )
+          return (1);    // punto di uscita 1
+      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0))
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_44/wrong1.txt b/Data/Questions/ingsw/0120_44/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_44/wrong1.txt
rename to Data/Questions/ingsw/0120_44/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_44/wrong2.txt b/Data/Questions/ingsw/0120_44/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_44/wrong2.txt
rename to Data/Questions/ingsw/0120_44/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_45/quest.txt b/Data/Questions/ingsw/0120_45/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_45/quest.txt
rename to Data/Questions/ingsw/0120_45/quest.txt
index 4818a62..fce8644 100644
--- a/legacy/Data/Questions/ingsw/0120_45/quest.txt
+++ b/Data/Questions/ingsw/0120_45/quest.txt
@@ -1,35 +1,35 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_45/wrong1.txt b/Data/Questions/ingsw/0120_45/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_45/wrong1.txt
rename to Data/Questions/ingsw/0120_45/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_45/wrong2.txt b/Data/Questions/ingsw/0120_45/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_45/wrong2.txt
rename to Data/Questions/ingsw/0120_45/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_45/wrong3.txt b/Data/Questions/ingsw/0120_45/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_45/wrong3.txt
rename to Data/Questions/ingsw/0120_45/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0120_46/correct.txt b/Data/Questions/ingsw/0120_46/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_46/correct.txt
rename to Data/Questions/ingsw/0120_46/correct.txt
diff --git a/legacy/Data/ingsw/0120_46/quest.txt b/Data/Questions/ingsw/0120_46/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0120_46/quest.txt
rename to Data/Questions/ingsw/0120_46/quest.txt
index 6205846..43e4597 100644
--- a/legacy/Data/ingsw/0120_46/quest.txt
+++ b/Data/Questions/ingsw/0120_46/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_46.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_46.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 3 ? In altri terminti, qual'è la probabilità che non sia necessario ripetere nessuna fase?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_46/wrong1.txt b/Data/Questions/ingsw/0120_46/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_46/wrong1.txt
rename to Data/Questions/ingsw/0120_46/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_46/wrong2.txt b/Data/Questions/ingsw/0120_46/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_46/wrong2.txt
rename to Data/Questions/ingsw/0120_46/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_47/correct.txt b/Data/Questions/ingsw/0120_47/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_47/correct.txt
rename to Data/Questions/ingsw/0120_47/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_47/quest.txt b/Data/Questions/ingsw/0120_47/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_47/quest.txt
rename to Data/Questions/ingsw/0120_47/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0120_47/wrong1.txt b/Data/Questions/ingsw/0120_47/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_47/wrong1.txt
rename to Data/Questions/ingsw/0120_47/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_47/wrong2.txt b/Data/Questions/ingsw/0120_47/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_47/wrong2.txt
rename to Data/Questions/ingsw/0120_47/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_15/correct.txt b/Data/Questions/ingsw/0120_48/correct.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0210_15/correct.txt
rename to Data/Questions/ingsw/0120_48/correct.txt
index 519c7fd..98a62af 100644
--- a/legacy/Data/Questions/ingsw/0210_15/correct.txt
+++ b/Data/Questions/ingsw/0120_48/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x > 5) or (x < 0));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x > 5) or (x < 0));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_48/quest.txt b/Data/Questions/ingsw/0120_48/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_48/quest.txt
rename to Data/Questions/ingsw/0120_48/quest.txt
index 22c683f..d26958e 100644
--- a/legacy/Data/Questions/ingsw/0120_48/quest.txt
+++ b/Data/Questions/ingsw/0120_48/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
+Si consideri il seguente requisito:
+RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_15/wrong1.txt b/Data/Questions/ingsw/0120_48/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0210_15/wrong1.txt
rename to Data/Questions/ingsw/0120_48/wrong1.txt
index 5229f7e..776f12c 100644
--- a/legacy/Data/Questions/ingsw/0210_15/wrong1.txt
+++ b/Data/Questions/ingsw/0120_48/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z =  (time > 0) and ((x > 0) or (x < 5));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z =  (time > 0) and ((x > 0) or (x < 5));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0120_48/wrong2.txt b/Data/Questions/ingsw/0120_48/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_48/wrong2.txt
rename to Data/Questions/ingsw/0120_48/wrong2.txt
index c2e617d..b78ed91 100644
--- a/legacy/Data/ingsw/0120_48/wrong2.txt
+++ b/Data/Questions/ingsw/0120_48/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and (x > 0) and (x < 5);
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and (x > 0) and (x < 5);
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_49/correct.txt b/Data/Questions/ingsw/0120_49/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_49/correct.txt
rename to Data/Questions/ingsw/0120_49/correct.txt
diff --git a/legacy/Data/ingsw/0120_49/quest.txt b/Data/Questions/ingsw/0120_49/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0120_49/quest.txt
rename to Data/Questions/ingsw/0120_49/quest.txt
index 2d6940a..6d49b6f 100644
--- a/legacy/Data/ingsw/0120_49/quest.txt
+++ b/Data/Questions/ingsw/0120_49/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_49.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
-Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_49.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
+Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_49/wrong1.txt b/Data/Questions/ingsw/0120_49/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_49/wrong1.txt
rename to Data/Questions/ingsw/0120_49/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_49/wrong2.txt b/Data/Questions/ingsw/0120_49/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_49/wrong2.txt
rename to Data/Questions/ingsw/0120_49/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_5/quest.txt b/Data/Questions/ingsw/0120_5/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0120_5/quest.txt
rename to Data/Questions/ingsw/0120_5/quest.txt
index 3e68301..e28d958 100644
--- a/legacy/Data/ingsw/0120_5/quest.txt
+++ b/Data/Questions/ingsw/0120_5/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_5.png
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_5.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_5/wrong1.txt b/Data/Questions/ingsw/0120_5/wrong1.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_5/wrong1.txt
rename to Data/Questions/ingsw/0120_5/wrong1.txt
index 6c46c45..073f2e0 100644
--- a/legacy/Data/Questions/ingsw/0120_5/wrong1.txt
+++ b/Data/Questions/ingsw/0120_5/wrong1.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_5/wrong2.txt b/Data/Questions/ingsw/0120_5/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_5/wrong2.txt
rename to Data/Questions/ingsw/0120_5/wrong2.txt
index 39e7bfc..a96f58c 100644
--- a/legacy/Data/Questions/ingsw/0120_5/wrong2.txt
+++ b/Data/Questions/ingsw/0120_5/wrong2.txt
@@ -1,37 +1,37 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_5/wrong3.txt b/Data/Questions/ingsw/0120_5/wrong3.txt
similarity index 95%
rename from legacy/Data/ingsw/0120_5/wrong3.txt
rename to Data/Questions/ingsw/0120_5/wrong3.txt
index 93e29a3..e598e03 100644
--- a/legacy/Data/ingsw/0120_5/wrong3.txt
+++ b/Data/Questions/ingsw/0120_5/wrong3.txt
@@ -1,28 +1,28 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_6/correct.txt b/Data/Questions/ingsw/0120_6/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_6/correct.txt
rename to Data/Questions/ingsw/0120_6/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_6/quest.txt b/Data/Questions/ingsw/0120_6/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_6/quest.txt
rename to Data/Questions/ingsw/0120_6/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0120_6/wrong1.txt b/Data/Questions/ingsw/0120_6/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_6/wrong1.txt
rename to Data/Questions/ingsw/0120_6/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_6/wrong2.txt b/Data/Questions/ingsw/0120_6/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_6/wrong2.txt
rename to Data/Questions/ingsw/0120_6/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_7/correct.txt b/Data/Questions/ingsw/0120_7/correct.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_7/correct.txt
rename to Data/Questions/ingsw/0120_7/correct.txt
index b559d4a..5575173 100644
--- a/legacy/Data/ingsw/0120_7/correct.txt
+++ b/Data/Questions/ingsw/0120_7/correct.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_7/quest.txt b/Data/Questions/ingsw/0120_7/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_7/quest.txt
rename to Data/Questions/ingsw/0120_7/quest.txt
index 031c331..1dbc484 100644
--- a/legacy/Data/Questions/ingsw/0120_7/quest.txt
+++ b/Data/Questions/ingsw/0120_7/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+Si consideri il seguente requisito:
+RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_7/wrong1.txt b/Data/Questions/ingsw/0120_7/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_7/wrong1.txt
rename to Data/Questions/ingsw/0120_7/wrong1.txt
index 4b8db59..fa35b30 100644
--- a/legacy/Data/Questions/ingsw/0120_7/wrong1.txt
+++ b/Data/Questions/ingsw/0120_7/wrong1.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0120_7/wrong2.txt b/Data/Questions/ingsw/0120_7/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_7/wrong2.txt
rename to Data/Questions/ingsw/0120_7/wrong2.txt
index 05ce544..45fd47a 100644
--- a/legacy/Data/ingsw/0120_7/wrong2.txt
+++ b/Data/Questions/ingsw/0120_7/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_8/correct.txt b/Data/Questions/ingsw/0120_8/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_8/correct.txt
rename to Data/Questions/ingsw/0120_8/correct.txt
diff --git a/legacy/Data/ingsw/0120_8/quest.txt b/Data/Questions/ingsw/0120_8/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0120_8/quest.txt
rename to Data/Questions/ingsw/0120_8/quest.txt
index 2809138..f96034d 100644
--- a/legacy/Data/ingsw/0120_8/quest.txt
+++ b/Data/Questions/ingsw/0120_8/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_8.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act1 act0 act1 act0 act2 
-Test case 2: act0 act2 act2 act0 act1 
-Test case 3: act0 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_8.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act1 act0 act1 act0 act2 
+Test case 2: act0 act2 act2 act0 act1 
+Test case 3: act0 act0 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_8/wrong1.txt b/Data/Questions/ingsw/0120_8/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_8/wrong1.txt
rename to Data/Questions/ingsw/0120_8/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_8/wrong2.txt b/Data/Questions/ingsw/0120_8/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_8/wrong2.txt
rename to Data/Questions/ingsw/0120_8/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_9/correct.txt b/Data/Questions/ingsw/0120_9/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_9/correct.txt
rename to Data/Questions/ingsw/0120_9/correct.txt
diff --git a/legacy/Data/ingsw/0120_9/quest.txt b/Data/Questions/ingsw/0120_9/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0120_9/quest.txt
rename to Data/Questions/ingsw/0120_9/quest.txt
index 4962ecf..64fc81c 100644
--- a/legacy/Data/ingsw/0120_9/quest.txt
+++ b/Data/Questions/ingsw/0120_9/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_9.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_9.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 2, 3? In altri terminti, qual'è la probabilità che non sia necessario ripetere la prima fase (ma non la seconda) ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_9/wrong1.txt b/Data/Questions/ingsw/0120_9/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_9/wrong1.txt
rename to Data/Questions/ingsw/0120_9/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0120_9/wrong2.txt b/Data/Questions/ingsw/0120_9/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0120_9/wrong2.txt
rename to Data/Questions/ingsw/0120_9/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0121_34/correct.txt b/Data/Questions/ingsw/0121_34/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0121_34/correct.txt
rename to Data/Questions/ingsw/0121_34/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0121_34/quest.txt b/Data/Questions/ingsw/0121_34/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0121_34/quest.txt
rename to Data/Questions/ingsw/0121_34/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0121_34/wrong1.txt b/Data/Questions/ingsw/0121_34/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0121_34/wrong1.txt
rename to Data/Questions/ingsw/0121_34/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0121_34/wrong2.txt b/Data/Questions/ingsw/0121_34/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0121_34/wrong2.txt
rename to Data/Questions/ingsw/0121_34/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_0/correct.txt b/Data/Questions/ingsw/0210_0/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_0/correct.txt
rename to Data/Questions/ingsw/0210_0/correct.txt
diff --git a/legacy/Data/ingsw/0210_0/quest.txt b/Data/Questions/ingsw/0210_0/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0210_0/quest.txt
rename to Data/Questions/ingsw/0210_0/quest.txt
index 2d895ca..025eb8d 100644
--- a/legacy/Data/ingsw/0210_0/quest.txt
+++ b/Data/Questions/ingsw/0210_0/quest.txt
@@ -1,22 +1,22 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a >= 100) && (b - c <= 1) )
-          return (1);    // punto di uscita 1
-      else if ((b - c <= 1)  || (b + c >= 5)
-)
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a >= 100) && (b - c <= 1) )
+          return (1);    // punto di uscita 1
+      else if ((b - c <= 1)  || (b + c >= 5)
+)
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_0/wrong1.txt b/Data/Questions/ingsw/0210_0/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_0/wrong1.txt
rename to Data/Questions/ingsw/0210_0/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_0/wrong2.txt b/Data/Questions/ingsw/0210_0/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_0/wrong2.txt
rename to Data/Questions/ingsw/0210_0/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_1/quest.txt b/Data/Questions/ingsw/0210_1/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0210_1/quest.txt
rename to Data/Questions/ingsw/0210_1/quest.txt
index 89110fc..10903ae 100644
--- a/legacy/Data/ingsw/0210_1/quest.txt
+++ b/Data/Questions/ingsw/0210_1/quest.txt
@@ -1,5 +1,5 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_1.png
-Si consideri la seguente architettura software:
-
-
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_1.png
+Si consideri la seguente architettura software:
+
+
 Quale dei seguenti modelli Modelica meglio la rappresenta ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_1/wrong1.txt b/Data/Questions/ingsw/0210_1/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0210_1/wrong1.txt
rename to Data/Questions/ingsw/0210_1/wrong1.txt
index 0487745..003ee5e 100644
--- a/legacy/Data/Questions/ingsw/0210_1/wrong1.txt
+++ b/Data/Questions/ingsw/0210_1/wrong1.txt
@@ -1,6 +1,6 @@
-block SysArch;
-SC1 sc1
-SC2 sc2;
-SC3 sc3;
-SC4 sc4;
+block SysArch;
+SC1 sc1
+SC2 sc2;
+SC3 sc3;
+SC4 sc4;
 connect(sc1.input4, sc
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_1/wrong2.txt b/Data/Questions/ingsw/0210_1/wrong2.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0210_1/wrong2.txt
rename to Data/Questions/ingsw/0210_1/wrong2.txt
index 6b9f4b0..93e7db8 100644
--- a/legacy/Data/Questions/ingsw/0210_1/wrong2.txt
+++ b/Data/Questions/ingsw/0210_1/wrong2.txt
@@ -1,3 +1,3 @@
-output4);
-connect(sc1.output4, sc2.input4);
+output4);
+connect(sc1.output4, sc2.input4);
 connect(sc1.input5, sc
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_1/wrong3.txt b/Data/Questions/ingsw/0210_1/wrong3.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_1/wrong3.txt
rename to Data/Questions/ingsw/0210_1/wrong3.txt
index bf32c35..fe11933 100644
--- a/legacy/Data/Questions/ingsw/0210_1/wrong3.txt
+++ b/Data/Questions/ingsw/0210_1/wrong3.txt
@@ -1,40 +1,40 @@
-output5);
-connect(sc1.output5, sc3.input5);
-connect(sc1.input6, sc4.output6);
-connect(sc1.output6, sc4.input6);
-connect(sc2.input1, sc3.output1);
-connect(sc3.input2, sc4.output2);
-connect(sc4.input3, sc2.ouput3);
-end SysArch
-2.
-block SysArch;
-SC1 sc1
-SC2 sc2;
-SC3 sc3;
-SC4 sc4;
-connect(sc1.input4, sc2.output4);
-connect(sc1.output4, sc2.input4);
-connect(sc1.input5, sc3.output5);
-connect(sc1.output5, sc3.input5);
-connect(sc1.input6, sc4.output6);
-connect(sc1.output6, sc4.input6);
-connect(sc2.input1, sc3.output1);
-connect(sc3.input2, sc4.output2);
-connect(sc4.output3, sc2.input3);
-end SysArch
-3.
-block SysArch;
-SC1 sc1
-SC2 sc2;
-SC3 sc3;
-SC4 sc4;
-connect(sc1.input4, sc2.output4);
-connect(sc1.output4, sc2.input4);
-connect(sc1.input5, sc3.output5);
-connect(sc1.output5, sc3.input5);
-connect(sc1.input6, sc4.output6);
-connect(sc1.output6, sc4.input6);
-connect(sc2.output1, sc3.input1);
-connect(sc3.output2, sc4.input2);
-connect(sc4.output3, sc2.input3);
+output5);
+connect(sc1.output5, sc3.input5);
+connect(sc1.input6, sc4.output6);
+connect(sc1.output6, sc4.input6);
+connect(sc2.input1, sc3.output1);
+connect(sc3.input2, sc4.output2);
+connect(sc4.input3, sc2.ouput3);
+end SysArch
+2.
+block SysArch;
+SC1 sc1
+SC2 sc2;
+SC3 sc3;
+SC4 sc4;
+connect(sc1.input4, sc2.output4);
+connect(sc1.output4, sc2.input4);
+connect(sc1.input5, sc3.output5);
+connect(sc1.output5, sc3.input5);
+connect(sc1.input6, sc4.output6);
+connect(sc1.output6, sc4.input6);
+connect(sc2.input1, sc3.output1);
+connect(sc3.input2, sc4.output2);
+connect(sc4.output3, sc2.input3);
+end SysArch
+3.
+block SysArch;
+SC1 sc1
+SC2 sc2;
+SC3 sc3;
+SC4 sc4;
+connect(sc1.input4, sc2.output4);
+connect(sc1.output4, sc2.input4);
+connect(sc1.input5, sc3.output5);
+connect(sc1.output5, sc3.input5);
+connect(sc1.input6, sc4.output6);
+connect(sc1.output6, sc4.input6);
+connect(sc2.output1, sc3.input1);
+connect(sc3.output2, sc4.input2);
+connect(sc4.output3, sc2.input3);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_10/correct.txt b/Data/Questions/ingsw/0210_10/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_10/correct.txt
rename to Data/Questions/ingsw/0210_10/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_10/quest.txt b/Data/Questions/ingsw/0210_10/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_10/quest.txt
rename to Data/Questions/ingsw/0210_10/quest.txt
index d1cf8cb..2aa8dba 100644
--- a/legacy/Data/Questions/ingsw/0210_10/quest.txt
+++ b/Data/Questions/ingsw/0210_10/quest.txt
@@ -1,16 +1,16 @@
-Si consideri il monitor seguente che ritorna true appena i requisiti per il sistema monitorato sono violati.
-block Monitor
-input Real x;  
-output Boolean y;
-Boolean w;
-initial equation
-y = false;
-equation
-w = ((x < 0) or (x > 5));
-algorithm
-when edge(w) then
-y := true;
-end when;
-end Monitor;
-</pre>
+Si consideri il monitor seguente che ritorna true appena i requisiti per il sistema monitorato sono violati.
+block Monitor
+input Real x;  
+output Boolean y;
+Boolean w;
+initial equation
+y = false;
+equation
+w = ((x < 0) or (x > 5));
+algorithm
+when edge(w) then
+y := true;
+end when;
+end Monitor;
+</pre>
 Quale delle seguenti affermazioni meglio descrive il requisito monitorato.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_10/wrong1.txt b/Data/Questions/ingsw/0210_10/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_10/wrong1.txt
rename to Data/Questions/ingsw/0210_10/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_10/wrong2.txt b/Data/Questions/ingsw/0210_10/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_10/wrong2.txt
rename to Data/Questions/ingsw/0210_10/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_11/quest.txt b/Data/Questions/ingsw/0210_11/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0210_11/quest.txt
rename to Data/Questions/ingsw/0210_11/quest.txt
index 57dc789..57d0b65 100644
--- a/legacy/Data/Questions/ingsw/0210_11/quest.txt
+++ b/Data/Questions/ingsw/0210_11/quest.txt
@@ -1,4 +1,4 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_11.png
-Si consideri la seguente architettura software:
-
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_11.png
+Si consideri la seguente architettura software:
+
 Quale dei seguneti modelli Modelica meglio la rappresenta.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_11/wrong1.txt b/Data/Questions/ingsw/0210_11/wrong1.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_11/wrong1.txt
rename to Data/Questions/ingsw/0210_11/wrong1.txt
index 157d205..4b28f65 100644
--- a/legacy/Data/Questions/ingsw/0210_11/wrong1.txt
+++ b/Data/Questions/ingsw/0210_11/wrong1.txt
@@ -1,9 +1,9 @@
-block SysArch
-OS os_c;
-WS ws_c;
-WB wb_c;
-connect(os_c.input1, ws_c.output1);
-connect(os_c.output1, ws_c.input1);
-connect(wb_c.input2, ws_c.output2);
-connect(wb_c.output2, ws_c.input2);
+block SysArch
+OS os_c;
+WS ws_c;
+WB wb_c;
+connect(os_c.input1, ws_c.output1);
+connect(os_c.output1, ws_c.input1);
+connect(wb_c.input2, ws_c.output2);
+connect(wb_c.output2, ws_c.input2);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_11/wrong2.txt b/Data/Questions/ingsw/0210_11/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_11/wrong2.txt
rename to Data/Questions/ingsw/0210_11/wrong2.txt
index 04886bb..7d1273e 100644
--- a/legacy/Data/Questions/ingsw/0210_11/wrong2.txt
+++ b/Data/Questions/ingsw/0210_11/wrong2.txt
@@ -1,9 +1,9 @@
-block SysArch
-OS os_c;
-WS ws_c;
-WB wb_c;
-connect(os_c.input1, wb_c.output1);
-connect(os_c.output1, wb_c.input1);
-connect(wb_c.input2, ws_c.output2);
-connect(wb_c.output2, ws_c.input2);
+block SysArch
+OS os_c;
+WS ws_c;
+WB wb_c;
+connect(os_c.input1, wb_c.output1);
+connect(os_c.output1, wb_c.input1);
+connect(wb_c.input2, ws_c.output2);
+connect(wb_c.output2, ws_c.input2);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_11/wrong3.txt b/Data/Questions/ingsw/0210_11/wrong3.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_11/wrong3.txt
rename to Data/Questions/ingsw/0210_11/wrong3.txt
index 903ba76..5c1559b 100644
--- a/legacy/Data/ingsw/0210_11/wrong3.txt
+++ b/Data/Questions/ingsw/0210_11/wrong3.txt
@@ -1,9 +1,9 @@
-block SysArch
-OS os_c;
-WS ws_c;
-WB wb_c;
-connect(os_c.input1, ws_c.output1);
-connect(os_c.output1, ws_c.input1);
-connect(wb_c.input2, os_c.output2);
-connect(wb_c.output2, os_c.input2);
+block SysArch
+OS os_c;
+WS ws_c;
+WB wb_c;
+connect(os_c.input1, ws_c.output1);
+connect(os_c.output1, ws_c.input1);
+connect(wb_c.input2, os_c.output2);
+connect(wb_c.output2, os_c.input2);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_12/quest.txt b/Data/Questions/ingsw/0210_12/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_12/quest.txt
rename to Data/Questions/ingsw/0210_12/quest.txt
index 86ee3d4..86a19bb 100644
--- a/legacy/Data/Questions/ingsw/0210_12/quest.txt
+++ b/Data/Questions/ingsw/0210_12/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_12.png
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_12.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_12/wrong1.txt b/Data/Questions/ingsw/0210_12/wrong1.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_12/wrong1.txt
rename to Data/Questions/ingsw/0210_12/wrong1.txt
index c7f67fe..4738d06 100644
--- a/legacy/Data/ingsw/0210_12/wrong1.txt
+++ b/Data/Questions/ingsw/0210_12/wrong1.txt
@@ -1,38 +1,38 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_12/wrong2.txt b/Data/Questions/ingsw/0210_12/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_12/wrong2.txt
rename to Data/Questions/ingsw/0210_12/wrong2.txt
index b84dfd6..f85f2de 100644
--- a/legacy/Data/ingsw/0210_12/wrong2.txt
+++ b/Data/Questions/ingsw/0210_12/wrong2.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_12/wrong3.txt b/Data/Questions/ingsw/0210_12/wrong3.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_12/wrong3.txt
rename to Data/Questions/ingsw/0210_12/wrong3.txt
index 162b572..fec0854 100644
--- a/legacy/Data/ingsw/0210_12/wrong3.txt
+++ b/Data/Questions/ingsw/0210_12/wrong3.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_13/correct.txt b/Data/Questions/ingsw/0210_13/correct.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0210_13/correct.txt
rename to Data/Questions/ingsw/0210_13/correct.txt
index 25ac15c..1ab78ce 100644
--- a/legacy/Data/Questions/ingsw/0210_13/correct.txt
+++ b/Data/Questions/ingsw/0210_13/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and ((x >= 30) or (x <= 20)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and ((x >= 30) or (x <= 20)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0210_13/quest.txt b/Data/Questions/ingsw/0210_13/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_13/quest.txt
rename to Data/Questions/ingsw/0210_13/quest.txt
index b420aaf..6938bd6 100644
--- a/legacy/Data/Questions/ingsw/0210_13/quest.txt
+++ b/Data/Questions/ingsw/0210_13/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
+Si consideri il seguente requisito:
+RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
 Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_13/wrong1.txt b/Data/Questions/ingsw/0210_13/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0210_13/wrong1.txt
rename to Data/Questions/ingsw/0210_13/wrong1.txt
index d021c3b..b763753 100644
--- a/legacy/Data/ingsw/0210_13/wrong1.txt
+++ b/Data/Questions/ingsw/0210_13/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and (x >= 20) and (x <= 30) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and (x >= 20) and (x <= 30) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0210_13/wrong2.txt b/Data/Questions/ingsw/0210_13/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0210_13/wrong2.txt
rename to Data/Questions/ingsw/0210_13/wrong2.txt
index 157567e..ceb178e 100644
--- a/legacy/Data/ingsw/0210_13/wrong2.txt
+++ b/Data/Questions/ingsw/0210_13/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) or ((x >= 20) and (x <= 30)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) or ((x >= 20) and (x <= 30)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0210_14/correct.txt b/Data/Questions/ingsw/0210_14/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_14/correct.txt
rename to Data/Questions/ingsw/0210_14/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_14/quest.txt b/Data/Questions/ingsw/0210_14/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0210_14/quest.txt
rename to Data/Questions/ingsw/0210_14/quest.txt
index c1cd6d0..abc3e1c 100644
--- a/legacy/Data/Questions/ingsw/0210_14/quest.txt
+++ b/Data/Questions/ingsw/0210_14/quest.txt
@@ -1,9 +1,9 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
-int z = x;
-while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
-return (z);
-}
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+int z = x;
+while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
+return (z);
+}
 Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_14/wrong1.txt b/Data/Questions/ingsw/0210_14/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_14/wrong1.txt
rename to Data/Questions/ingsw/0210_14/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_14/wrong2.txt b/Data/Questions/ingsw/0210_14/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_14/wrong2.txt
rename to Data/Questions/ingsw/0210_14/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_48/correct.txt b/Data/Questions/ingsw/0210_15/correct.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_48/correct.txt
rename to Data/Questions/ingsw/0210_15/correct.txt
index 519c7fd..98a62af 100644
--- a/legacy/Data/ingsw/0120_48/correct.txt
+++ b/Data/Questions/ingsw/0210_15/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x > 5) or (x < 0));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x > 5) or (x < 0));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0324_8/quest.txt b/Data/Questions/ingsw/0210_15/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_8/quest.txt
rename to Data/Questions/ingsw/0210_15/quest.txt
index 22c683f..d26958e 100644
--- a/legacy/Data/Questions/ingsw/0324_8/quest.txt
+++ b/Data/Questions/ingsw/0210_15/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
+Si consideri il seguente requisito:
+RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_15/wrong1.txt b/Data/Questions/ingsw/0210_15/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0210_15/wrong1.txt
rename to Data/Questions/ingsw/0210_15/wrong1.txt
index 5229f7e..776f12c 100644
--- a/legacy/Data/ingsw/0210_15/wrong1.txt
+++ b/Data/Questions/ingsw/0210_15/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z =  (time > 0) and ((x > 0) or (x < 5));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z =  (time > 0) and ((x > 0) or (x < 5));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0613_15/wrong2.txt b/Data/Questions/ingsw/0210_15/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0613_15/wrong2.txt
rename to Data/Questions/ingsw/0210_15/wrong2.txt
index 2029293..8086f1c 100644
--- a/legacy/Data/Questions/ingsw/0613_15/wrong2.txt
+++ b/Data/Questions/ingsw/0210_15/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and (x > 0) and (x < 5);
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and (x > 0) and (x < 5);
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_31/correct.txt b/Data/Questions/ingsw/0210_16/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_31/correct.txt
rename to Data/Questions/ingsw/0210_16/correct.txt
index 293ebbc..ca92838 100644
--- a/legacy/Data/Questions/ingsw/0324_31/correct.txt
+++ b/Data/Questions/ingsw/0210_16/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_1/quest.txt b/Data/Questions/ingsw/0210_16/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_1/quest.txt
rename to Data/Questions/ingsw/0210_16/quest.txt
index 5922b9f..348f2a5 100644
--- a/legacy/Data/Questions/ingsw/0120_1/quest.txt
+++ b/Data/Questions/ingsw/0210_16/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
+Si consideri il seguente requisito:
+RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_43/wrong1.txt b/Data/Questions/ingsw/0210_16/wrong1.txt
similarity index 94%
rename from legacy/Data/ingsw/0613_43/wrong1.txt
rename to Data/Questions/ingsw/0210_16/wrong1.txt
index d7890b2..c0e3311 100644
--- a/legacy/Data/ingsw/0613_43/wrong1.txt
+++ b/Data/Questions/ingsw/0210_16/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_16/wrong2.txt b/Data/Questions/ingsw/0210_16/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_16/wrong2.txt
rename to Data/Questions/ingsw/0210_16/wrong2.txt
index d50b268..434e904 100644
--- a/legacy/Data/Questions/ingsw/0210_16/wrong2.txt
+++ b/Data/Questions/ingsw/0210_16/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_17/correct.txt b/Data/Questions/ingsw/0210_17/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_17/correct.txt
rename to Data/Questions/ingsw/0210_17/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_17/quest.txt b/Data/Questions/ingsw/0210_17/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0210_17/quest.txt
rename to Data/Questions/ingsw/0210_17/quest.txt
index 5fa40ee..be8341e 100644
--- a/legacy/Data/Questions/ingsw/0210_17/quest.txt
+++ b/Data/Questions/ingsw/0210_17/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_17.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act1 act2 
-Test case 2: act2 act0 act1 act0 act0 
-Test case 3: act2 act0 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_17.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act1 act2 
+Test case 2: act2 act0 act1 act0 act0 
+Test case 3: act2 act0 act2 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_17/wrong1.txt b/Data/Questions/ingsw/0210_17/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_17/wrong1.txt
rename to Data/Questions/ingsw/0210_17/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_17/wrong2.txt b/Data/Questions/ingsw/0210_17/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_17/wrong2.txt
rename to Data/Questions/ingsw/0210_17/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_18/correct.txt b/Data/Questions/ingsw/0210_18/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_18/correct.txt
rename to Data/Questions/ingsw/0210_18/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_18/quest.txt b/Data/Questions/ingsw/0210_18/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_18/quest.txt
rename to Data/Questions/ingsw/0210_18/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0210_18/wrong1.txt b/Data/Questions/ingsw/0210_18/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_18/wrong1.txt
rename to Data/Questions/ingsw/0210_18/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_18/wrong2.txt b/Data/Questions/ingsw/0210_18/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_18/wrong2.txt
rename to Data/Questions/ingsw/0210_18/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_19/correct.txt b/Data/Questions/ingsw/0210_19/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_19/correct.txt
rename to Data/Questions/ingsw/0210_19/correct.txt
diff --git a/legacy/Data/ingsw/0210_19/quest.txt b/Data/Questions/ingsw/0210_19/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0210_19/quest.txt
rename to Data/Questions/ingsw/0210_19/quest.txt
index e786bcf..6837d89 100644
--- a/legacy/Data/ingsw/0210_19/quest.txt
+++ b/Data/Questions/ingsw/0210_19/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_19.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act1 act1 act2 act2 
-Test case 2: act1 act1 act0 act1 
-Test case 3: act0 act0 act2 act1 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_19.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act1 act1 act2 act2 
+Test case 2: act1 act1 act0 act1 
+Test case 3: act0 act0 act2 act1 act0 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_19/wrong1.txt b/Data/Questions/ingsw/0210_19/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_19/wrong1.txt
rename to Data/Questions/ingsw/0210_19/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_19/wrong2.txt b/Data/Questions/ingsw/0210_19/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_19/wrong2.txt
rename to Data/Questions/ingsw/0210_19/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_2/quest.txt b/Data/Questions/ingsw/0210_2/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_2/quest.txt
rename to Data/Questions/ingsw/0210_2/quest.txt
index f9f8976..9392429 100644
--- a/legacy/Data/ingsw/0210_2/quest.txt
+++ b/Data/Questions/ingsw/0210_2/quest.txt
@@ -1,36 +1,36 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_2/wrong1.txt b/Data/Questions/ingsw/0210_2/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_2/wrong1.txt
rename to Data/Questions/ingsw/0210_2/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_2/wrong2.txt b/Data/Questions/ingsw/0210_2/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_2/wrong2.txt
rename to Data/Questions/ingsw/0210_2/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_2/wrong3.txt b/Data/Questions/ingsw/0210_2/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_2/wrong3.txt
rename to Data/Questions/ingsw/0210_2/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0210_20/correct.txt b/Data/Questions/ingsw/0210_20/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_20/correct.txt
rename to Data/Questions/ingsw/0210_20/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_20/quest.txt b/Data/Questions/ingsw/0210_20/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_20/quest.txt
rename to Data/Questions/ingsw/0210_20/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0210_20/wrong1.txt b/Data/Questions/ingsw/0210_20/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_20/wrong1.txt
rename to Data/Questions/ingsw/0210_20/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_20/wrong2.txt b/Data/Questions/ingsw/0210_20/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_20/wrong2.txt
rename to Data/Questions/ingsw/0210_20/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_21/correct.txt b/Data/Questions/ingsw/0210_21/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_21/correct.txt
rename to Data/Questions/ingsw/0210_21/correct.txt
index ad21063..a2f00b3 100644
--- a/legacy/Data/Questions/ingsw/0210_21/correct.txt
+++ b/Data/Questions/ingsw/0210_21/correct.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_21/quest.txt b/Data/Questions/ingsw/0210_21/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_21/quest.txt
rename to Data/Questions/ingsw/0210_21/quest.txt
index 031c331..1dbc484 100644
--- a/legacy/Data/Questions/ingsw/0210_21/quest.txt
+++ b/Data/Questions/ingsw/0210_21/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+Si consideri il seguente requisito:
+RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_21/wrong1.txt b/Data/Questions/ingsw/0210_21/wrong1.txt
similarity index 94%
rename from legacy/Data/ingsw/0210_21/wrong1.txt
rename to Data/Questions/ingsw/0210_21/wrong1.txt
index b14ac60..0696007 100644
--- a/legacy/Data/ingsw/0210_21/wrong1.txt
+++ b/Data/Questions/ingsw/0210_21/wrong1.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_34/wrong2.txt b/Data/Questions/ingsw/0210_21/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_34/wrong2.txt
rename to Data/Questions/ingsw/0210_21/wrong2.txt
index e4201ab..76b58b0 100644
--- a/legacy/Data/Questions/ingsw/0324_34/wrong2.txt
+++ b/Data/Questions/ingsw/0210_21/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_22/correct.txt b/Data/Questions/ingsw/0210_22/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_22/correct.txt
rename to Data/Questions/ingsw/0210_22/correct.txt
diff --git a/legacy/Data/ingsw/0120_33/quest.txt b/Data/Questions/ingsw/0210_22/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_33/quest.txt
rename to Data/Questions/ingsw/0210_22/quest.txt
index e5fbc81..6906b2b 100644
--- a/legacy/Data/ingsw/0120_33/quest.txt
+++ b/Data/Questions/ingsw/0210_22/quest.txt
@@ -1,16 +1,16 @@
-Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
-block Monitor
-input Real x;  
-output Boolean y;
-Boolean w;
-initial equation
-y = false;
-equation
-w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
-algorithm
-when edge(w) then
-y := true;
-end when;
-end Monitor;
-</pre>
+Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
+block Monitor
+input Real x;  
+output Boolean y;
+Boolean w;
+initial equation
+y = false;
+equation
+w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
+algorithm
+when edge(w) then
+y := true;
+end when;
+end Monitor;
+</pre>
 Quale delle seguenti affermazioni meglio descrive il requisito monitorato?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_22/wrong1.txt b/Data/Questions/ingsw/0210_22/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_22/wrong1.txt
rename to Data/Questions/ingsw/0210_22/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_22/wrong2.txt b/Data/Questions/ingsw/0210_22/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_22/wrong2.txt
rename to Data/Questions/ingsw/0210_22/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_23/correct.txt b/Data/Questions/ingsw/0210_23/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_23/correct.txt
rename to Data/Questions/ingsw/0210_23/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_23/quest.txt b/Data/Questions/ingsw/0210_23/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0210_23/quest.txt
rename to Data/Questions/ingsw/0210_23/quest.txt
index adede32..64036f7 100644
--- a/legacy/Data/Questions/ingsw/0210_23/quest.txt
+++ b/Data/Questions/ingsw/0210_23/quest.txt
@@ -1,9 +1,9 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 2)   { if (x + y >= 1)  return (1); else return (2); }
-  else {if (x + 2*y >= 5)  return (3); else return (4); }
- }  /* f()  */
-Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 2)   { if (x + y >= 1)  return (1); else return (2); }
+  else {if (x + 2*y >= 5)  return (3); else return (4); }
+ }  /* f()  */
+Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_23/wrong1.txt b/Data/Questions/ingsw/0210_23/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_23/wrong1.txt
rename to Data/Questions/ingsw/0210_23/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_23/wrong2.txt b/Data/Questions/ingsw/0210_23/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_23/wrong2.txt
rename to Data/Questions/ingsw/0210_23/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_24/correct.txt b/Data/Questions/ingsw/0210_24/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_24/correct.txt
rename to Data/Questions/ingsw/0210_24/correct.txt
diff --git a/legacy/Data/ingsw/0210_24/quest.txt b/Data/Questions/ingsw/0210_24/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0210_24/quest.txt
rename to Data/Questions/ingsw/0210_24/quest.txt
index d188da2..e6995f6 100644
--- a/legacy/Data/ingsw/0210_24/quest.txt
+++ b/Data/Questions/ingsw/0210_24/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_24.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
-Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_24.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
+Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_24/wrong1.txt b/Data/Questions/ingsw/0210_24/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_24/wrong1.txt
rename to Data/Questions/ingsw/0210_24/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_24/wrong2.txt b/Data/Questions/ingsw/0210_24/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_24/wrong2.txt
rename to Data/Questions/ingsw/0210_24/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_7/correct.txt b/Data/Questions/ingsw/0210_25/correct.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0324_7/correct.txt
rename to Data/Questions/ingsw/0210_25/correct.txt
index 43dc0c9..af4c387 100644
--- a/legacy/Data/Questions/ingsw/0324_7/correct.txt
+++ b/Data/Questions/ingsw/0210_25/correct.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 0) || (y > 0)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 0) || (y > 0)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_25/quest.txt b/Data/Questions/ingsw/0210_25/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0210_25/quest.txt
rename to Data/Questions/ingsw/0210_25/quest.txt
index f6744fd..5fd3b47 100644
--- a/legacy/Data/ingsw/0210_25/quest.txt
+++ b/Data/Questions/ingsw/0210_25/quest.txt
@@ -1,4 +1,4 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-Si consideri la funzione C
-int f(in x, int y) { ..... }
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+Si consideri la funzione C
+int f(in x, int y) { ..... }
 Quale delle seguenti assert esprime la pre-condizione che entrambi gli argomenti di f sono non-negativi ed almeno uno di loro è positivo ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0722_2/wrong 1.txt b/Data/Questions/ingsw/0210_25/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0722_2/wrong 1.txt
rename to Data/Questions/ingsw/0210_25/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_25/wrong2.txt b/Data/Questions/ingsw/0210_25/wrong2.txt
similarity index 95%
rename from legacy/Data/ingsw/0210_25/wrong2.txt
rename to Data/Questions/ingsw/0210_25/wrong2.txt
index 3f63933..506df9d 100644
--- a/legacy/Data/ingsw/0210_25/wrong2.txt
+++ b/Data/Questions/ingsw/0210_25/wrong2.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x > 0) && (y > 0) && ((x > 1) || (y > 1)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x > 0) && (y > 0) && ((x > 1) || (y > 1)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_26/correct.txt b/Data/Questions/ingsw/0210_26/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_26/correct.txt
rename to Data/Questions/ingsw/0210_26/correct.txt
diff --git a/legacy/Data/ingsw/0210_26/quest.txt b/Data/Questions/ingsw/0210_26/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0210_26/quest.txt
rename to Data/Questions/ingsw/0210_26/quest.txt
index d318528..50adf73 100644
--- a/legacy/Data/ingsw/0210_26/quest.txt
+++ b/Data/Questions/ingsw/0210_26/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_26.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
-Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_26.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
+Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_26/wrong1.txt b/Data/Questions/ingsw/0210_26/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_26/wrong1.txt
rename to Data/Questions/ingsw/0210_26/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_26/wrong2.txt b/Data/Questions/ingsw/0210_26/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_26/wrong2.txt
rename to Data/Questions/ingsw/0210_26/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_27/quest.txt b/Data/Questions/ingsw/0210_27/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_27/quest.txt
rename to Data/Questions/ingsw/0210_27/quest.txt
index 75e942b..2bc823a 100644
--- a/legacy/Data/Questions/ingsw/0210_27/quest.txt
+++ b/Data/Questions/ingsw/0210_27/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_27.png
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_27.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_27/wrong1.txt b/Data/Questions/ingsw/0210_27/wrong1.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_27/wrong1.txt
rename to Data/Questions/ingsw/0210_27/wrong1.txt
index c296b22..745fe08 100644
--- a/legacy/Data/ingsw/0210_27/wrong1.txt
+++ b/Data/Questions/ingsw/0210_27/wrong1.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_27/wrong2.txt b/Data/Questions/ingsw/0210_27/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_27/wrong2.txt
rename to Data/Questions/ingsw/0210_27/wrong2.txt
index d21df5d..26dfa21 100644
--- a/legacy/Data/ingsw/0210_27/wrong2.txt
+++ b/Data/Questions/ingsw/0210_27/wrong2.txt
@@ -1,32 +1,32 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_27/wrong3.txt b/Data/Questions/ingsw/0210_27/wrong3.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_27/wrong3.txt
rename to Data/Questions/ingsw/0210_27/wrong3.txt
index 421d23f..ba2919d 100644
--- a/legacy/Data/ingsw/0210_27/wrong3.txt
+++ b/Data/Questions/ingsw/0210_27/wrong3.txt
@@ -1,37 +1,37 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_28/quest.txt b/Data/Questions/ingsw/0210_28/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0210_28/quest.txt
rename to Data/Questions/ingsw/0210_28/quest.txt
index 932f11d..63c727e 100644
--- a/legacy/Data/Questions/ingsw/0210_28/quest.txt
+++ b/Data/Questions/ingsw/0210_28/quest.txt
@@ -1,38 +1,38 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_28/wrong1.txt b/Data/Questions/ingsw/0210_28/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_28/wrong1.txt
rename to Data/Questions/ingsw/0210_28/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_28/wrong2.txt b/Data/Questions/ingsw/0210_28/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_28/wrong2.txt
rename to Data/Questions/ingsw/0210_28/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_28/wrong3.txt b/Data/Questions/ingsw/0210_28/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_28/wrong3.txt
rename to Data/Questions/ingsw/0210_28/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0210_29/correct.txt b/Data/Questions/ingsw/0210_29/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_29/correct.txt
rename to Data/Questions/ingsw/0210_29/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_29/quest.txt b/Data/Questions/ingsw/0210_29/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_29/quest.txt
rename to Data/Questions/ingsw/0210_29/quest.txt
index f6839df..881f5f4 100644
--- a/legacy/Data/Questions/ingsw/0210_29/quest.txt
+++ b/Data/Questions/ingsw/0210_29/quest.txt
@@ -1,2 +1,2 @@
-"Ogni giorno, per ciascuna clinica, il sistema genererà una lista dei pazienti che hanno un appuntamento quel giorno."
+"Ogni giorno, per ciascuna clinica, il sistema genererà una lista dei pazienti che hanno un appuntamento quel giorno."
 La frase precedente è un esempio di:
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_29/wrong1.txt b/Data/Questions/ingsw/0210_29/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_29/wrong1.txt
rename to Data/Questions/ingsw/0210_29/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_29/wrong2.txt b/Data/Questions/ingsw/0210_29/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_29/wrong2.txt
rename to Data/Questions/ingsw/0210_29/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_30/quest.txt b/Data/Questions/ingsw/0210_3/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_30/quest.txt
rename to Data/Questions/ingsw/0210_3/quest.txt
index 985c244..e2e738a 100644
--- a/legacy/Data/Questions/ingsw/0324_30/quest.txt
+++ b/Data/Questions/ingsw/0210_3/quest.txt
@@ -1,4 +1,4 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente specifica funzionale per la funzione f.
-La funzione f(int *A, int *B)  prende come input un vettore A di dimensione n  ritorna come output un vettore B ottenuto ordinando gli elementi di A in ordine crescente.
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente specifica funzionale per la funzione f.
+La funzione f(int *A, int *B)  prende come input un vettore A di dimensione n  ritorna come output un vettore B ottenuto ordinando gli elementi di A in ordine crescente.
 Quale delle seguenti funzioni è un test oracle per la funzione f ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_3/wrong1.txt b/Data/Questions/ingsw/0210_3/wrong1.txt
similarity index 97%
rename from legacy/Data/ingsw/0210_3/wrong1.txt
rename to Data/Questions/ingsw/0210_3/wrong1.txt
index ed5ad19..9e00c22 100644
--- a/legacy/Data/ingsw/0210_3/wrong1.txt
+++ b/Data/Questions/ingsw/0210_3/wrong1.txt
@@ -1,14 +1,14 @@
-#define n 1000
-int TestOracle1(int *A, int *B)
-{
-int i, j, D[n];
-//init
-for (i = 0; i < n; i++) D[i] = -1;
-// B is ordered
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-// B is a permutation of A
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; D[j] = 1; break;}
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-// B ok
-return (1);
+#define n 1000
+int TestOracle1(int *A, int *B)
+{
+int i, j, D[n];
+//init
+for (i = 0; i < n; i++) D[i] = -1;
+// B is ordered
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+// B is a permutation of A
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; D[j] = 1; break;}
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+// B ok
+return (1);
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_3/wrong2.txt b/Data/Questions/ingsw/0210_3/wrong2.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0210_3/wrong2.txt
rename to Data/Questions/ingsw/0210_3/wrong2.txt
index 69b9722..59b5927 100644
--- a/legacy/Data/Questions/ingsw/0210_3/wrong2.txt
+++ b/Data/Questions/ingsw/0210_3/wrong2.txt
@@ -1,14 +1,14 @@
-#define n 1000
-int TestOracle2(int *A, int *B)
-{
-int i, j, D[n];
-//init
-for (i = 0; i < n; i++) D[i] = -1;
-// B is ordered
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-// B is a permutation of A
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; break;}
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-// B ok
-return (1);
+#define n 1000
+int TestOracle2(int *A, int *B)
+{
+int i, j, D[n];
+//init
+for (i = 0; i < n; i++) D[i] = -1;
+// B is ordered
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+// B is a permutation of A
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; break;}
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+// B ok
+return (1);
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_3/wrong3.txt b/Data/Questions/ingsw/0210_3/wrong3.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_3/wrong3.txt
rename to Data/Questions/ingsw/0210_3/wrong3.txt
index a26ce6e..5c3b08a 100644
--- a/legacy/Data/ingsw/0210_3/wrong3.txt
+++ b/Data/Questions/ingsw/0210_3/wrong3.txt
@@ -1,15 +1,15 @@
-#define n 1000
-
-int TestOracle3(int *A, int *B)
-{
-int i, j, D[n];
-//init
-for (i = 0; i < n; i++) D[i] = -1;
-// B is ordered
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-// B is a permutation of A
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if (A[i] == B[j]) {C[i][j] = 1; D[j] = 1; break;}
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-// B ok
-return (1);
+#define n 1000
+
+int TestOracle3(int *A, int *B)
+{
+int i, j, D[n];
+//init
+for (i = 0; i < n; i++) D[i] = -1;
+// B is ordered
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+// B is a permutation of A
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if (A[i] == B[j]) {C[i][j] = 1; D[j] = 1; break;}
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+// B ok
+return (1);
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_30/correct.txt b/Data/Questions/ingsw/0210_30/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_30/correct.txt
rename to Data/Questions/ingsw/0210_30/correct.txt
diff --git a/legacy/Data/ingsw/0210_30/quest.txt b/Data/Questions/ingsw/0210_30/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0210_30/quest.txt
rename to Data/Questions/ingsw/0210_30/quest.txt
index a27fc55..ecefa2d 100644
--- a/legacy/Data/ingsw/0210_30/quest.txt
+++ b/Data/Questions/ingsw/0210_30/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_30.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act1 act1 act2 act2 
-Test case 2: act1 act1 act0 act1 
-Test case 3: act0 act0 act2 act1 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_30.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act1 act1 act2 act2 
+Test case 2: act1 act1 act0 act1 
+Test case 3: act0 act0 act2 act1 act0 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_30/wrong1.txt b/Data/Questions/ingsw/0210_30/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_30/wrong1.txt
rename to Data/Questions/ingsw/0210_30/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_30/wrong2.txt b/Data/Questions/ingsw/0210_30/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_30/wrong2.txt
rename to Data/Questions/ingsw/0210_30/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_31/correct.txt b/Data/Questions/ingsw/0210_31/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_31/correct.txt
rename to Data/Questions/ingsw/0210_31/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_22/quest.txt b/Data/Questions/ingsw/0210_31/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0324_22/quest.txt
rename to Data/Questions/ingsw/0210_31/quest.txt
index 65cfd2d..eb6c210 100644
--- a/legacy/Data/Questions/ingsw/0324_22/quest.txt
+++ b/Data/Questions/ingsw/0210_31/quest.txt
@@ -1,9 +1,9 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 0)   { if (x + y >= 2)  return (1); else return (2); }
-  else {if (2*x + y >= 1)  return (3); else return (4); }
- }  /* f()  */
-Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 0)   { if (x + y >= 2)  return (1); else return (2); }
+  else {if (2*x + y >= 1)  return (3); else return (4); }
+ }  /* f()  */
+Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_31/wrong1.txt b/Data/Questions/ingsw/0210_31/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_31/wrong1.txt
rename to Data/Questions/ingsw/0210_31/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_31/wrong2.txt b/Data/Questions/ingsw/0210_31/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_31/wrong2.txt
rename to Data/Questions/ingsw/0210_31/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_32/correct.txt b/Data/Questions/ingsw/0210_32/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_32/correct.txt
rename to Data/Questions/ingsw/0210_32/correct.txt
diff --git a/legacy/Data/ingsw/0210_32/quest.txt b/Data/Questions/ingsw/0210_32/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0210_32/quest.txt
rename to Data/Questions/ingsw/0210_32/quest.txt
index cb591da..4426034 100644
--- a/legacy/Data/ingsw/0210_32/quest.txt
+++ b/Data/Questions/ingsw/0210_32/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_32.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act1 act2 
-Test case 2: act2 act0 act1 act0 act0 
-Test case 3: act2 act0 act2 
-
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_32.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act1 act2 
+Test case 2: act2 act0 act1 act0 act0 
+Test case 3: act2 act0 act2 
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_32/wrong1.txt b/Data/Questions/ingsw/0210_32/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_32/wrong1.txt
rename to Data/Questions/ingsw/0210_32/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_32/wrong2.txt b/Data/Questions/ingsw/0210_32/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_32/wrong2.txt
rename to Data/Questions/ingsw/0210_32/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_33/correct.txt b/Data/Questions/ingsw/0210_33/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_33/correct.txt
rename to Data/Questions/ingsw/0210_33/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_33/quest.txt b/Data/Questions/ingsw/0210_33/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_33/quest.txt
rename to Data/Questions/ingsw/0210_33/quest.txt
index cf9113a..bde312e 100644
--- a/legacy/Data/Questions/ingsw/0210_33/quest.txt
+++ b/Data/Questions/ingsw/0210_33/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_33.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_33.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 2, 3, 4 ? In altri terminti, qual'è la probabilità che sia necessario ripetere sia la fase 1 che la fase 2 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_33/wrong1.txt b/Data/Questions/ingsw/0210_33/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_33/wrong1.txt
rename to Data/Questions/ingsw/0210_33/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_33/wrong2.txt b/Data/Questions/ingsw/0210_33/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_33/wrong2.txt
rename to Data/Questions/ingsw/0210_33/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_34/quest.txt b/Data/Questions/ingsw/0210_34/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_34/quest.txt
rename to Data/Questions/ingsw/0210_34/quest.txt
index 33e1f49..89bfba9 100644
--- a/legacy/Data/ingsw/0210_34/quest.txt
+++ b/Data/Questions/ingsw/0210_34/quest.txt
@@ -1,34 +1,34 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_34/wrong1.txt b/Data/Questions/ingsw/0210_34/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_34/wrong1.txt
rename to Data/Questions/ingsw/0210_34/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_34/wrong2.txt b/Data/Questions/ingsw/0210_34/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_34/wrong2.txt
rename to Data/Questions/ingsw/0210_34/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_34/wrong3.txt b/Data/Questions/ingsw/0210_34/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_34/wrong3.txt
rename to Data/Questions/ingsw/0210_34/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0210_35/correct.txt b/Data/Questions/ingsw/0210_35/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_35/correct.txt
rename to Data/Questions/ingsw/0210_35/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_35/quest.txt b/Data/Questions/ingsw/0210_35/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_35/quest.txt
rename to Data/Questions/ingsw/0210_35/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0210_35/wrong1.txt b/Data/Questions/ingsw/0210_35/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_35/wrong1.txt
rename to Data/Questions/ingsw/0210_35/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_35/wrong2.txt b/Data/Questions/ingsw/0210_35/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_35/wrong2.txt
rename to Data/Questions/ingsw/0210_35/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_36/correct.txt b/Data/Questions/ingsw/0210_36/correct.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0210_36/correct.txt
rename to Data/Questions/ingsw/0210_36/correct.txt
index 3f63933..506df9d 100644
--- a/legacy/Data/Questions/ingsw/0210_36/correct.txt
+++ b/Data/Questions/ingsw/0210_36/correct.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x > 0) && (y > 0) && ((x > 1) || (y > 1)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x > 0) && (y > 0) && ((x > 1) || (y > 1)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_36/quest.txt b/Data/Questions/ingsw/0210_36/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0210_36/quest.txt
rename to Data/Questions/ingsw/0210_36/quest.txt
index 595ab5d..db01158 100644
--- a/legacy/Data/ingsw/0210_36/quest.txt
+++ b/Data/Questions/ingsw/0210_36/quest.txt
@@ -1,4 +1,4 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-Si consideri la funzione C
-int f(in x, int y) { ..... }
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+Si consideri la funzione C
+int f(in x, int y) { ..... }
 Quale delle seguenti assert esprime la pre-condizione che entrambi gli argomenti di f sono positivi ed almeno uno di loro è maggiore di 1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_2/wrong 1.txt b/Data/Questions/ingsw/0210_36/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0722_2/wrong 1.txt
rename to Data/Questions/ingsw/0210_36/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0722_2/wrong 2.txt b/Data/Questions/ingsw/0210_36/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0722_2/wrong 2.txt
rename to Data/Questions/ingsw/0210_36/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_37/quest.txt b/Data/Questions/ingsw/0210_37/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_37/quest.txt
rename to Data/Questions/ingsw/0210_37/quest.txt
index 5743032..fd7fa1d 100644
--- a/legacy/Data/Questions/ingsw/0210_37/quest.txt
+++ b/Data/Questions/ingsw/0210_37/quest.txt
@@ -1,36 +1,36 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_37/wrong1.txt b/Data/Questions/ingsw/0210_37/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_37/wrong1.txt
rename to Data/Questions/ingsw/0210_37/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_37/wrong2.txt b/Data/Questions/ingsw/0210_37/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_37/wrong2.txt
rename to Data/Questions/ingsw/0210_37/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_37/wrong3.txt b/Data/Questions/ingsw/0210_37/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_37/wrong3.txt
rename to Data/Questions/ingsw/0210_37/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0210_38/correct.txt b/Data/Questions/ingsw/0210_38/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_38/correct.txt
rename to Data/Questions/ingsw/0210_38/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_33/quest.txt b/Data/Questions/ingsw/0210_38/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0324_33/quest.txt
rename to Data/Questions/ingsw/0210_38/quest.txt
index b2bed72..793c90f 100644
--- a/legacy/Data/Questions/ingsw/0324_33/quest.txt
+++ b/Data/Questions/ingsw/0210_38/quest.txt
@@ -1,21 +1,21 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a  + b >= 6) && (b - c <= 1) )
-          return (1);    // punto di uscita 1
-      else if ((b - c <= 1)  || (b + c >= 5))
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a  + b >= 6) && (b - c <= 1) )
+          return (1);    // punto di uscita 1
+      else if ((b - c <= 1)  || (b + c >= 5))
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_38/wrong1.txt b/Data/Questions/ingsw/0210_38/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_38/wrong1.txt
rename to Data/Questions/ingsw/0210_38/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_38/wrong2.txt b/Data/Questions/ingsw/0210_38/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_38/wrong2.txt
rename to Data/Questions/ingsw/0210_38/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_39/correct.txt b/Data/Questions/ingsw/0210_39/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_39/correct.txt
rename to Data/Questions/ingsw/0210_39/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_14/quest.txt b/Data/Questions/ingsw/0210_39/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_14/quest.txt
rename to Data/Questions/ingsw/0210_39/quest.txt
index 36947c2..853f496 100644
--- a/legacy/Data/Questions/ingsw/0120_14/quest.txt
+++ b/Data/Questions/ingsw/0210_39/quest.txt
@@ -1,6 +1,6 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (x + 7); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (x + 7); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
 Quale dei seguenti test cases consegue una partition coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_39/wrong1.txt b/Data/Questions/ingsw/0210_39/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_39/wrong1.txt
rename to Data/Questions/ingsw/0210_39/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_39/wrong2.txt b/Data/Questions/ingsw/0210_39/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_39/wrong2.txt
rename to Data/Questions/ingsw/0210_39/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_4/correct.txt b/Data/Questions/ingsw/0210_4/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_4/correct.txt
rename to Data/Questions/ingsw/0210_4/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_4/quest.txt b/Data/Questions/ingsw/0210_4/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0210_4/quest.txt
rename to Data/Questions/ingsw/0210_4/quest.txt
index 84d1f53..13ec01c 100644
--- a/legacy/Data/Questions/ingsw/0210_4/quest.txt
+++ b/Data/Questions/ingsw/0210_4/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_4.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act2 act0 act1 act2 act0 
-Test case 2: act1 act2 act1 
-Test case 3: act1 act2 act1 act0 act0 
-
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_4.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act2 act0 act1 act2 act0 
+Test case 2: act1 act2 act1 
+Test case 3: act1 act2 act1 act0 act0 
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_4/wrong1.txt b/Data/Questions/ingsw/0210_4/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_4/wrong1.txt
rename to Data/Questions/ingsw/0210_4/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_4/wrong2.txt b/Data/Questions/ingsw/0210_4/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_4/wrong2.txt
rename to Data/Questions/ingsw/0210_4/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_40/correct.txt b/Data/Questions/ingsw/0210_40/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_40/correct.txt
rename to Data/Questions/ingsw/0210_40/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_40/quest.txt b/Data/Questions/ingsw/0210_40/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0210_40/quest.txt
rename to Data/Questions/ingsw/0210_40/quest.txt
index a550159..84a9274 100644
--- a/legacy/Data/Questions/ingsw/0210_40/quest.txt
+++ b/Data/Questions/ingsw/0210_40/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_40.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act1 act0 act0 act0 act0 
-Test case 2: act2 act0 
-Test case 3: act0 act0 act1 act0 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_40.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act1 act0 act0 act0 act0 
+Test case 2: act2 act0 
+Test case 3: act0 act0 act1 act0 act2 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_40/wrong1.txt b/Data/Questions/ingsw/0210_40/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_40/wrong1.txt
rename to Data/Questions/ingsw/0210_40/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_40/wrong2.txt b/Data/Questions/ingsw/0210_40/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_40/wrong2.txt
rename to Data/Questions/ingsw/0210_40/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_41/correct.txt b/Data/Questions/ingsw/0210_41/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_41/correct.txt
rename to Data/Questions/ingsw/0210_41/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_41/quest.txt b/Data/Questions/ingsw/0210_41/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0210_41/quest.txt
rename to Data/Questions/ingsw/0210_41/quest.txt
index cdbd481..a02928c 100644
--- a/legacy/Data/Questions/ingsw/0210_41/quest.txt
+++ b/Data/Questions/ingsw/0210_41/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_41.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act1 act0 act0 act0 act0 
-Test case 2: act2 act0 
-Test case 3: act0 act0 act1 act0 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_41.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act1 act0 act0 act0 act0 
+Test case 2: act2 act0 
+Test case 3: act0 act0 act1 act0 act2 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_41/wrong1.txt b/Data/Questions/ingsw/0210_41/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_41/wrong1.txt
rename to Data/Questions/ingsw/0210_41/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_41/wrong2.txt b/Data/Questions/ingsw/0210_41/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_41/wrong2.txt
rename to Data/Questions/ingsw/0210_41/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_42/quest.txt b/Data/Questions/ingsw/0210_42/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0210_42/quest.txt
rename to Data/Questions/ingsw/0210_42/quest.txt
index 8e91c31..b862100 100644
--- a/legacy/Data/ingsw/0210_42/quest.txt
+++ b/Data/Questions/ingsw/0210_42/quest.txt
@@ -1,5 +1,5 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_42.png
-Si consideri la seguente architettura software:
-
-
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_42.png
+Si consideri la seguente architettura software:
+
+
 Quale dei seguenti modelli Modelica meglio la rappresenta ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_42/wrong1.txt b/Data/Questions/ingsw/0210_42/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0210_42/wrong1.txt
rename to Data/Questions/ingsw/0210_42/wrong1.txt
index 512c141..e98e420 100644
--- a/legacy/Data/Questions/ingsw/0210_42/wrong1.txt
+++ b/Data/Questions/ingsw/0210_42/wrong1.txt
@@ -1,6 +1,6 @@
-block SysArch;
-SC1 sc1
-SC2 sc2;
-SC3 sc3;
-SC4 sc4;
+block SysArch;
+SC1 sc1
+SC2 sc2;
+SC3 sc3;
+SC4 sc4;
 connect(sc1.input1, sc
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_42/wrong2.txt b/Data/Questions/ingsw/0210_42/wrong2.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0210_42/wrong2.txt
rename to Data/Questions/ingsw/0210_42/wrong2.txt
index 77d39c1..b578dde 100644
--- a/legacy/Data/Questions/ingsw/0210_42/wrong2.txt
+++ b/Data/Questions/ingsw/0210_42/wrong2.txt
@@ -1,3 +1,3 @@
-output1);
-connect(sc1.output1, sc2.input1);
+output1);
+connect(sc1.output1, sc2.input1);
 connect(sc1.input2, sc
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_42/wrong3.txt b/Data/Questions/ingsw/0210_42/wrong3.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_42/wrong3.txt
rename to Data/Questions/ingsw/0210_42/wrong3.txt
index b9a8baf..dcb6c25 100644
--- a/legacy/Data/Questions/ingsw/0210_42/wrong3.txt
+++ b/Data/Questions/ingsw/0210_42/wrong3.txt
@@ -1,39 +1,39 @@
-output2);
-connect(sc1.output2, sc3.input2);
-connect(sc1.input3, sc4.output3);
-connect(sc1.output3, sc4.input3);
-connect(sc2.input4, sc3.output4);
-connect(sc3.input5, sc4.output5);
-end SysArch
-2.
-block SysArch;
-SC1 sc1
-SC2 sc2;
-SC3 sc3;
-SC4 sc4;
-connect(sc1.input1, sc2.output1);
-connect(sc1.output1, sc2.input1);
-connect(sc1.input2, sc3.output2);
-connect(sc1.output2, sc3.input2);
-connect(sc1.input3, sc4.output3);
-connect(sc1.output3, sc4.input3);
-connect(sc2.output4, sc3.input4);
-connect(sc3.output5, sc4.input5);
-end SysArch
-3.
-block SysArch;
-SC1 sc1
-SC2 sc2;
-SC3 sc3;
-SC4 sc4;
-connect(sc1.input1, sc2.output1);
-connect(sc1.output1, sc2.input1);
-connect(sc1.input2, sc3.output2);
-connect(sc1.output2, sc3.input2);
-connect(sc1.input3, sc4.output3);
-connect(sc1.output3, sc4.input3);
-connect(sc2.input4, sc3.output4);
-connect(sc2.output4, sc3.input4);
-connect(sc3.input5, sc4.output5);
-connect(sc3.output5, sc4.input5);
+output2);
+connect(sc1.output2, sc3.input2);
+connect(sc1.input3, sc4.output3);
+connect(sc1.output3, sc4.input3);
+connect(sc2.input4, sc3.output4);
+connect(sc3.input5, sc4.output5);
+end SysArch
+2.
+block SysArch;
+SC1 sc1
+SC2 sc2;
+SC3 sc3;
+SC4 sc4;
+connect(sc1.input1, sc2.output1);
+connect(sc1.output1, sc2.input1);
+connect(sc1.input2, sc3.output2);
+connect(sc1.output2, sc3.input2);
+connect(sc1.input3, sc4.output3);
+connect(sc1.output3, sc4.input3);
+connect(sc2.output4, sc3.input4);
+connect(sc3.output5, sc4.input5);
+end SysArch
+3.
+block SysArch;
+SC1 sc1
+SC2 sc2;
+SC3 sc3;
+SC4 sc4;
+connect(sc1.input1, sc2.output1);
+connect(sc1.output1, sc2.input1);
+connect(sc1.input2, sc3.output2);
+connect(sc1.output2, sc3.input2);
+connect(sc1.input3, sc4.output3);
+connect(sc1.output3, sc4.input3);
+connect(sc2.input4, sc3.output4);
+connect(sc2.output4, sc3.input4);
+connect(sc3.input5, sc4.output5);
+connect(sc3.output5, sc4.input5);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_43/correct.txt b/Data/Questions/ingsw/0210_43/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_43/correct.txt
rename to Data/Questions/ingsw/0210_43/correct.txt
index 4c75070..9be970c 100644
--- a/legacy/Data/Questions/ingsw/0210_43/correct.txt
+++ b/Data/Questions/ingsw/0210_43/correct.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_28/quest.txt b/Data/Questions/ingsw/0210_43/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0324_28/quest.txt
rename to Data/Questions/ingsw/0210_43/quest.txt
index e11a044..0910f0b 100644
--- a/legacy/Data/Questions/ingsw/0324_28/quest.txt
+++ b/Data/Questions/ingsw/0210_43/quest.txt
@@ -1,4 +1,4 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
+Si consideri il seguente requisito:
+RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_28/wrong1.txt b/Data/Questions/ingsw/0210_43/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_28/wrong1.txt
rename to Data/Questions/ingsw/0210_43/wrong1.txt
index 6dafe94..8b6fcac 100644
--- a/legacy/Data/Questions/ingsw/0324_28/wrong1.txt
+++ b/Data/Questions/ingsw/0210_43/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_28/wrong2.txt b/Data/Questions/ingsw/0210_43/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_28/wrong2.txt
rename to Data/Questions/ingsw/0210_43/wrong2.txt
index a3d79a4..40a2ce0 100644
--- a/legacy/Data/Questions/ingsw/0324_28/wrong2.txt
+++ b/Data/Questions/ingsw/0210_43/wrong2.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_44/quest.txt b/Data/Questions/ingsw/0210_44/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_44/quest.txt
rename to Data/Questions/ingsw/0210_44/quest.txt
index 5c4c81d..c94348c 100644
--- a/legacy/Data/Questions/ingsw/0210_44/quest.txt
+++ b/Data/Questions/ingsw/0210_44/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_44.png
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_44.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_44/wrong1.txt b/Data/Questions/ingsw/0210_44/wrong1.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_44/wrong1.txt
rename to Data/Questions/ingsw/0210_44/wrong1.txt
index 421b38f..1297a9a 100644
--- a/legacy/Data/Questions/ingsw/0210_44/wrong1.txt
+++ b/Data/Questions/ingsw/0210_44/wrong1.txt
@@ -1,34 +1,34 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_44/wrong2.txt b/Data/Questions/ingsw/0210_44/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_44/wrong2.txt
rename to Data/Questions/ingsw/0210_44/wrong2.txt
index f385f1c..c73ac0f 100644
--- a/legacy/Data/ingsw/0210_44/wrong2.txt
+++ b/Data/Questions/ingsw/0210_44/wrong2.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_44/wrong3.txt b/Data/Questions/ingsw/0210_44/wrong3.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_44/wrong3.txt
rename to Data/Questions/ingsw/0210_44/wrong3.txt
index 1034e02..ec07178 100644
--- a/legacy/Data/Questions/ingsw/0210_44/wrong3.txt
+++ b/Data/Questions/ingsw/0210_44/wrong3.txt
@@ -1,32 +1,32 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_45/correct.txt b/Data/Questions/ingsw/0210_45/correct.txt
similarity index 94%
rename from legacy/Data/ingsw/0210_45/correct.txt
rename to Data/Questions/ingsw/0210_45/correct.txt
index 4a8e634..a3a4b67 100644
--- a/legacy/Data/ingsw/0210_45/correct.txt
+++ b/Data/Questions/ingsw/0210_45/correct.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_39/quest.txt b/Data/Questions/ingsw/0210_45/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_39/quest.txt
rename to Data/Questions/ingsw/0210_45/quest.txt
index 576af1a..0d3faca 100644
--- a/legacy/Data/Questions/ingsw/0324_39/quest.txt
+++ b/Data/Questions/ingsw/0210_45/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_23/wrong1.txt b/Data/Questions/ingsw/0210_45/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0613_23/wrong1.txt
rename to Data/Questions/ingsw/0210_45/wrong1.txt
index 68aa37a..32c00bc 100644
--- a/legacy/Data/ingsw/0613_23/wrong1.txt
+++ b/Data/Questions/ingsw/0210_45/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_23/wrong2.txt b/Data/Questions/ingsw/0210_45/wrong2.txt
similarity index 94%
rename from legacy/Data/ingsw/0613_23/wrong2.txt
rename to Data/Questions/ingsw/0210_45/wrong2.txt
index a43796b..9f7ef6a 100644
--- a/legacy/Data/ingsw/0613_23/wrong2.txt
+++ b/Data/Questions/ingsw/0210_45/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_46/correct.txt b/Data/Questions/ingsw/0210_46/correct.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_46/correct.txt
rename to Data/Questions/ingsw/0210_46/correct.txt
index 001b1d9..610cf7c 100644
--- a/legacy/Data/ingsw/0210_46/correct.txt
+++ b/Data/Questions/ingsw/0210_46/correct.txt
@@ -1,9 +1,9 @@
-block SysArch
-DB db_c;
-S1 s1_c;
-S2 s2_c;
-connect(db_c.input[1], s1_c.output);
-connect(db_c.output[1], s1_c.input);
-connect(db_c.input[2], s2_c.output);
-connect(db_c.output[2], s2_c.input);
+block SysArch
+DB db_c;
+S1 s1_c;
+S2 s2_c;
+connect(db_c.input[1], s1_c.output);
+connect(db_c.output[1], s1_c.input);
+connect(db_c.input[2], s2_c.output);
+connect(db_c.output[2], s2_c.input);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_46/quest.txt b/Data/Questions/ingsw/0210_46/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0210_46/quest.txt
rename to Data/Questions/ingsw/0210_46/quest.txt
index 9f5199d..4f513a6 100644
--- a/legacy/Data/Questions/ingsw/0210_46/quest.txt
+++ b/Data/Questions/ingsw/0210_46/quest.txt
@@ -1,4 +1,4 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_46.png
-Si consideri la seguente architettura software:
-
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_46.png
+Si consideri la seguente architettura software:
+
 Quale dei seguenti modelli Modelica meglio la rappresenta ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_46/wrong1.txt b/Data/Questions/ingsw/0210_46/wrong1.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_46/wrong1.txt
rename to Data/Questions/ingsw/0210_46/wrong1.txt
index fc95495..f80fc3b 100644
--- a/legacy/Data/ingsw/0210_46/wrong1.txt
+++ b/Data/Questions/ingsw/0210_46/wrong1.txt
@@ -1,9 +1,9 @@
-block SysArch
-DB db_c;
-S1 s1_c;
-S2 s2_c;
-connect(db_c.input[1], s2_c.output[1]);
-connect(db_c.output[1], s2_c.input[1]);
-connect(s1_c.input[2], s2_c.output[2]);
-connect(s1_c.output[2], s2_c.input[2]);
+block SysArch
+DB db_c;
+S1 s1_c;
+S2 s2_c;
+connect(db_c.input[1], s2_c.output[1]);
+connect(db_c.output[1], s2_c.input[1]);
+connect(s1_c.input[2], s2_c.output[2]);
+connect(s1_c.output[2], s2_c.input[2]);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_46/wrong2.txt b/Data/Questions/ingsw/0210_46/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_46/wrong2.txt
rename to Data/Questions/ingsw/0210_46/wrong2.txt
index eaf9272..dc15bde 100644
--- a/legacy/Data/Questions/ingsw/0210_46/wrong2.txt
+++ b/Data/Questions/ingsw/0210_46/wrong2.txt
@@ -1,9 +1,9 @@
-block SysArch
-DB db_c;
-S1 s1_c;
-S2 s2_c;
-connect(db_c.input[1], s1_c.output[1]);
-connect(db_c.output[1], s1_c.input[1]);
-connect(s1_c.input[2], s2_c.output[2]);
-connect(s1_c.output[2], s2_c.input[2]);
+block SysArch
+DB db_c;
+S1 s1_c;
+S2 s2_c;
+connect(db_c.input[1], s1_c.output[1]);
+connect(db_c.output[1], s1_c.input[1]);
+connect(s1_c.input[2], s2_c.output[2]);
+connect(s1_c.output[2], s2_c.input[2]);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_47/correct.txt b/Data/Questions/ingsw/0210_47/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_47/correct.txt
rename to Data/Questions/ingsw/0210_47/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_13/quest.txt b/Data/Questions/ingsw/0210_47/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0324_13/quest.txt
rename to Data/Questions/ingsw/0210_47/quest.txt
index 4344b75..3fa8769 100644
--- a/legacy/Data/Questions/ingsw/0324_13/quest.txt
+++ b/Data/Questions/ingsw/0210_47/quest.txt
@@ -1,8 +1,8 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (2*x); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
-Si consideri il seguente insieme di test cases:
-{x=-100, x= 40, x=100}
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (2*x); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
+Si consideri il seguente insieme di test cases:
+{x=-100, x= 40, x=100}
 Quale delle seguenti è la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_47/wrong1.txt b/Data/Questions/ingsw/0210_47/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_47/wrong1.txt
rename to Data/Questions/ingsw/0210_47/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_47/wrong2.txt b/Data/Questions/ingsw/0210_47/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_47/wrong2.txt
rename to Data/Questions/ingsw/0210_47/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_48/correct.txt b/Data/Questions/ingsw/0210_48/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_48/correct.txt
rename to Data/Questions/ingsw/0210_48/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_18/quest.txt b/Data/Questions/ingsw/0210_48/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0324_18/quest.txt
rename to Data/Questions/ingsw/0210_48/quest.txt
index d3a9fe2..f6bb83a 100644
--- a/legacy/Data/Questions/ingsw/0324_18/quest.txt
+++ b/Data/Questions/ingsw/0210_48/quest.txt
@@ -1,8 +1,8 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 0)   { if (x + y >= 1)  return (1); else return (2); }
-  else {if (2*x + y >= 5)  return (3); else return (4); }
- }  /* f()  */
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 0)   { if (x + y >= 1)  return (1); else return (2); }
+  else {if (2*x + y >= 5)  return (3); else return (4); }
+ }  /* f()  */
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_48/wrong1.txt b/Data/Questions/ingsw/0210_48/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_48/wrong1.txt
rename to Data/Questions/ingsw/0210_48/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_48/wrong2.txt b/Data/Questions/ingsw/0210_48/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_48/wrong2.txt
rename to Data/Questions/ingsw/0210_48/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_49/correct.txt b/Data/Questions/ingsw/0210_49/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_49/correct.txt
rename to Data/Questions/ingsw/0210_49/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_49/quest.txt b/Data/Questions/ingsw/0210_49/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0210_49/quest.txt
rename to Data/Questions/ingsw/0210_49/quest.txt
index 8cb7d37..c0aea8e 100644
--- a/legacy/Data/Questions/ingsw/0210_49/quest.txt
+++ b/Data/Questions/ingsw/0210_49/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_49.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act0 act1 act2 act0 
-Test case 2: act1 act2 act1 
-Test case 3: act1 act2 act1 act0 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_49.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act0 act1 act2 act0 
+Test case 2: act1 act2 act1 
+Test case 3: act1 act2 act1 act0 act0 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_49/wrong1.txt b/Data/Questions/ingsw/0210_49/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_49/wrong1.txt
rename to Data/Questions/ingsw/0210_49/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_49/wrong2.txt b/Data/Questions/ingsw/0210_49/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_49/wrong2.txt
rename to Data/Questions/ingsw/0210_49/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_21/correct.txt b/Data/Questions/ingsw/0210_5/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/1122_21/correct.txt
rename to Data/Questions/ingsw/0210_5/correct.txt
index e582263..2771487 100644
--- a/legacy/Data/Questions/ingsw/1122_21/correct.txt
+++ b/Data/Questions/ingsw/0210_5/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_27/quest.txt b/Data/Questions/ingsw/0210_5/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_27/quest.txt
rename to Data/Questions/ingsw/0210_5/quest.txt
index 864cc93..498beed 100644
--- a/legacy/Data/Questions/ingsw/0324_27/quest.txt
+++ b/Data/Questions/ingsw/0210_5/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
+Si consideri il seguente requisito:
+RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
 Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_5/wrong1.txt b/Data/Questions/ingsw/0210_5/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_5/wrong1.txt
rename to Data/Questions/ingsw/0210_5/wrong1.txt
index 0f38391..44ac950 100644
--- a/legacy/Data/Questions/ingsw/0210_5/wrong1.txt
+++ b/Data/Questions/ingsw/0210_5/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_5/wrong2.txt b/Data/Questions/ingsw/0210_5/wrong2.txt
similarity index 94%
rename from legacy/Data/ingsw/0210_5/wrong2.txt
rename to Data/Questions/ingsw/0210_5/wrong2.txt
index 590f7e1..d681803 100644
--- a/legacy/Data/ingsw/0210_5/wrong2.txt
+++ b/Data/Questions/ingsw/0210_5/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_17/correct.txt b/Data/Questions/ingsw/0210_6/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_17/correct.txt
rename to Data/Questions/ingsw/0210_6/correct.txt
index c37d6ae..56ad9f9 100644
--- a/legacy/Data/Questions/ingsw/0613_17/correct.txt
+++ b/Data/Questions/ingsw/0210_6/correct.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_45/quest.txt b/Data/Questions/ingsw/0210_6/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_45/quest.txt
rename to Data/Questions/ingsw/0210_6/quest.txt
index 003d1dd..316ed3f 100644
--- a/legacy/Data/Questions/ingsw/0324_45/quest.txt
+++ b/Data/Questions/ingsw/0210_6/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_6/wrong1.txt b/Data/Questions/ingsw/0210_6/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0210_6/wrong1.txt
rename to Data/Questions/ingsw/0210_6/wrong1.txt
index 14bd900..976ab6a 100644
--- a/legacy/Data/Questions/ingsw/0210_6/wrong1.txt
+++ b/Data/Questions/ingsw/0210_6/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_17/wrong2.txt b/Data/Questions/ingsw/0210_6/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_17/wrong2.txt
rename to Data/Questions/ingsw/0210_6/wrong2.txt
index edea147..3184bf8 100644
--- a/legacy/Data/Questions/ingsw/0613_17/wrong2.txt
+++ b/Data/Questions/ingsw/0210_6/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_7/correct.txt b/Data/Questions/ingsw/0210_7/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_7/correct.txt
rename to Data/Questions/ingsw/0210_7/correct.txt
diff --git a/legacy/Data/ingsw/0210_7/quest.txt b/Data/Questions/ingsw/0210_7/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0210_7/quest.txt
rename to Data/Questions/ingsw/0210_7/quest.txt
index d649932..21834a5 100644
--- a/legacy/Data/ingsw/0210_7/quest.txt
+++ b/Data/Questions/ingsw/0210_7/quest.txt
@@ -1,8 +1,8 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 6)   { if (x + y >= 3)  return (1); else return (2); }
-  else {if (x + 2*y >= 15)  return (3); else return (4); }
- }  /* f()  */
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 6)   { if (x + y >= 3)  return (1); else return (2); }
+  else {if (x + 2*y >= 15)  return (3); else return (4); }
+ }  /* f()  */
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_7/wrong1.txt b/Data/Questions/ingsw/0210_7/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_7/wrong1.txt
rename to Data/Questions/ingsw/0210_7/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_7/wrong2.txt b/Data/Questions/ingsw/0210_7/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_7/wrong2.txt
rename to Data/Questions/ingsw/0210_7/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_8/correct.txt b/Data/Questions/ingsw/0210_8/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_8/correct.txt
rename to Data/Questions/ingsw/0210_8/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_5/quest.txt b/Data/Questions/ingsw/0210_8/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0324_5/quest.txt
rename to Data/Questions/ingsw/0210_8/quest.txt
index 236ccc7..3ce3c9b 100644
--- a/legacy/Data/Questions/ingsw/0324_5/quest.txt
+++ b/Data/Questions/ingsw/0210_8/quest.txt
@@ -1,10 +1,10 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
-int z, k;
-z = 1;   k = 0;
-while (k < x)  { z = y*z;  k = k + 1; }
-return (z);
-}
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+int z, k;
+z = 1;   k = 0;
+while (k < x)  { z = y*z;  k = k + 1; }
+return (z);
+}
 Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_8/wrong1.txt b/Data/Questions/ingsw/0210_8/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_8/wrong1.txt
rename to Data/Questions/ingsw/0210_8/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0210_8/wrong2.txt b/Data/Questions/ingsw/0210_8/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0210_8/wrong2.txt
rename to Data/Questions/ingsw/0210_8/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_9/quest.txt b/Data/Questions/ingsw/0210_9/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_9/quest.txt
rename to Data/Questions/ingsw/0210_9/quest.txt
index fcfd787..ee6ee4c 100644
--- a/legacy/Data/Questions/ingsw/0210_9/quest.txt
+++ b/Data/Questions/ingsw/0210_9/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_9.png
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_9.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_9/wrong1.txt b/Data/Questions/ingsw/0210_9/wrong1.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_9/wrong1.txt
rename to Data/Questions/ingsw/0210_9/wrong1.txt
index acd5e00..36648c1 100644
--- a/legacy/Data/Questions/ingsw/0210_9/wrong1.txt
+++ b/Data/Questions/ingsw/0210_9/wrong1.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_9/wrong2.txt b/Data/Questions/ingsw/0210_9/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_9/wrong2.txt
rename to Data/Questions/ingsw/0210_9/wrong2.txt
index 298890c..e3eaa51 100644
--- a/legacy/Data/ingsw/0210_9/wrong2.txt
+++ b/Data/Questions/ingsw/0210_9/wrong2.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_9/wrong3.txt b/Data/Questions/ingsw/0210_9/wrong3.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_9/wrong3.txt
rename to Data/Questions/ingsw/0210_9/wrong3.txt
index 3b3e08a..3abcf61 100644
--- a/legacy/Data/ingsw/0210_9/wrong3.txt
+++ b/Data/Questions/ingsw/0210_9/wrong3.txt
@@ -1,32 +1,32 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0221_18/correct.txt b/Data/Questions/ingsw/0221_18/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0221_18/correct.txt
rename to Data/Questions/ingsw/0221_18/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0221_18/quest.txt b/Data/Questions/ingsw/0221_18/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0221_18/quest.txt
rename to Data/Questions/ingsw/0221_18/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0221_18/wrong1.txt b/Data/Questions/ingsw/0221_18/wrong1.txt
similarity index 100%
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similarity index 100%
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diff --git a/legacy/Data/Questions/ingsw/0221_28/correct.txt b/Data/Questions/ingsw/0221_28/correct.txt
similarity index 100%
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diff --git a/legacy/Data/Questions/ingsw/0221_28/quest.txt b/Data/Questions/ingsw/0221_28/quest.txt
similarity index 100%
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rename to Data/Questions/ingsw/0221_28/quest.txt
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similarity index 100%
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similarity index 100%
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similarity index 100%
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diff --git a/legacy/Data/Questions/ingsw/0321_31/wrong 2.txt b/Data/Questions/ingsw/0321_31/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_31/wrong 2.txt
rename to Data/Questions/ingsw/0321_31/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/0321_32/correct.txt b/Data/Questions/ingsw/0321_32/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_32/correct.txt
rename to Data/Questions/ingsw/0321_32/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0321_32/quest.txt b/Data/Questions/ingsw/0321_32/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_32/quest.txt
rename to Data/Questions/ingsw/0321_32/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0321_32/wrong 1.txt b/Data/Questions/ingsw/0321_32/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_32/wrong 1.txt
rename to Data/Questions/ingsw/0321_32/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/0321_32/wrong 2.txt b/Data/Questions/ingsw/0321_32/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_32/wrong 2.txt
rename to Data/Questions/ingsw/0321_32/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/0321_36/correct.txt b/Data/Questions/ingsw/0321_36/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_36/correct.txt
rename to Data/Questions/ingsw/0321_36/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0321_36/quest.txt b/Data/Questions/ingsw/0321_36/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_36/quest.txt
rename to Data/Questions/ingsw/0321_36/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0321_36/wrong 1.txt b/Data/Questions/ingsw/0321_36/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_36/wrong 1.txt
rename to Data/Questions/ingsw/0321_36/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/0321_36/wrong 2.txt b/Data/Questions/ingsw/0321_36/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_36/wrong 2.txt
rename to Data/Questions/ingsw/0321_36/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/0321_37/correct.txt b/Data/Questions/ingsw/0321_37/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_37/correct.txt
rename to Data/Questions/ingsw/0321_37/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0321_37/quest.txt b/Data/Questions/ingsw/0321_37/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_37/quest.txt
rename to Data/Questions/ingsw/0321_37/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0321_37/wrong 1.txt b/Data/Questions/ingsw/0321_37/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_37/wrong 1.txt
rename to Data/Questions/ingsw/0321_37/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/0321_37/wrong 2.txt b/Data/Questions/ingsw/0321_37/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_37/wrong 2.txt
rename to Data/Questions/ingsw/0321_37/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/0321_38/correct.txt b/Data/Questions/ingsw/0321_38/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_38/correct.txt
rename to Data/Questions/ingsw/0321_38/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0321_38/quest.txt b/Data/Questions/ingsw/0321_38/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_38/quest.txt
rename to Data/Questions/ingsw/0321_38/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0321_38/wrong 1.txt b/Data/Questions/ingsw/0321_38/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_38/wrong 1.txt
rename to Data/Questions/ingsw/0321_38/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/0321_38/wrong 2.txt b/Data/Questions/ingsw/0321_38/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_38/wrong 2.txt
rename to Data/Questions/ingsw/0321_38/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/0321_4/correct.txt b/Data/Questions/ingsw/0321_4/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_4/correct.txt
rename to Data/Questions/ingsw/0321_4/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0321_4/quest.txt b/Data/Questions/ingsw/0321_4/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_4/quest.txt
rename to Data/Questions/ingsw/0321_4/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0321_4/wrong 1.txt b/Data/Questions/ingsw/0321_4/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_4/wrong 1.txt
rename to Data/Questions/ingsw/0321_4/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/0321_4/wrong 2.txt b/Data/Questions/ingsw/0321_4/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_4/wrong 2.txt
rename to Data/Questions/ingsw/0321_4/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/0321_40/correct.txt b/Data/Questions/ingsw/0321_40/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_40/correct.txt
rename to Data/Questions/ingsw/0321_40/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0321_40/quest.txt b/Data/Questions/ingsw/0321_40/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_40/quest.txt
rename to Data/Questions/ingsw/0321_40/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0321_40/wrong 1.txt b/Data/Questions/ingsw/0321_40/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_40/wrong 1.txt
rename to Data/Questions/ingsw/0321_40/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/0321_40/wrong 2.txt b/Data/Questions/ingsw/0321_40/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_40/wrong 2.txt
rename to Data/Questions/ingsw/0321_40/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/0321_8/correct.txt b/Data/Questions/ingsw/0321_8/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_8/correct.txt
rename to Data/Questions/ingsw/0321_8/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0321_8/quest.txt b/Data/Questions/ingsw/0321_8/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_8/quest.txt
rename to Data/Questions/ingsw/0321_8/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0321_8/wrong 1.txt b/Data/Questions/ingsw/0321_8/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_8/wrong 1.txt
rename to Data/Questions/ingsw/0321_8/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/0321_8/wrong 2.txt b/Data/Questions/ingsw/0321_8/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_8/wrong 2.txt
rename to Data/Questions/ingsw/0321_8/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/0321_9/correct.txt b/Data/Questions/ingsw/0321_9/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_9/correct.txt
rename to Data/Questions/ingsw/0321_9/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0321_9/quest.txt b/Data/Questions/ingsw/0321_9/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_9/quest.txt
rename to Data/Questions/ingsw/0321_9/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0321_9/wrong 1.txt b/Data/Questions/ingsw/0321_9/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_9/wrong 1.txt
rename to Data/Questions/ingsw/0321_9/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/0321_9/wrong 2.txt b/Data/Questions/ingsw/0321_9/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0321_9/wrong 2.txt
rename to Data/Questions/ingsw/0321_9/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_0/correct.txt b/Data/Questions/ingsw/0324_0/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_0/correct.txt
rename to Data/Questions/ingsw/0324_0/correct.txt
diff --git a/legacy/Data/ingsw/0324_0/quest.txt b/Data/Questions/ingsw/0324_0/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0324_0/quest.txt
rename to Data/Questions/ingsw/0324_0/quest.txt
index 858d9c6..dcebb30 100644
--- a/legacy/Data/ingsw/0324_0/quest.txt
+++ b/Data/Questions/ingsw/0324_0/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_0.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_0.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 3 ? In altri terminti, qual'è la probabilità che non sia necessario ripetere nessuna fase?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_0/wrong1.txt b/Data/Questions/ingsw/0324_0/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_0/wrong1.txt
rename to Data/Questions/ingsw/0324_0/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_0/wrong2.txt b/Data/Questions/ingsw/0324_0/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_0/wrong2.txt
rename to Data/Questions/ingsw/0324_0/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_1/quest.txt b/Data/Questions/ingsw/0324_1/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0324_1/quest.txt
rename to Data/Questions/ingsw/0324_1/quest.txt
index a4a7e01..b46ed7b 100644
--- a/legacy/Data/ingsw/0324_1/quest.txt
+++ b/Data/Questions/ingsw/0324_1/quest.txt
@@ -1,4 +1,4 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_1.png
-Si consideri la seguente architettura software:
-
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_1.png
+Si consideri la seguente architettura software:
+
 Quale dei seguneti modelli Modelica meglio la rappresenta.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_27/wrong1.txt b/Data/Questions/ingsw/0324_1/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0613_27/wrong1.txt
rename to Data/Questions/ingsw/0324_1/wrong1.txt
index 4bcd55f..d698ca8 100644
--- a/legacy/Data/Questions/ingsw/0613_27/wrong1.txt
+++ b/Data/Questions/ingsw/0324_1/wrong1.txt
@@ -1,8 +1,8 @@
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
 connect(sc1.output12, sc
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_1/wrong2.txt b/Data/Questions/ingsw/0324_1/wrong2.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0324_1/wrong2.txt
rename to Data/Questions/ingsw/0324_1/wrong2.txt
index a3caf2e..7509ae5 100644
--- a/legacy/Data/Questions/ingsw/0324_1/wrong2.txt
+++ b/Data/Questions/ingsw/0324_1/wrong2.txt
@@ -1,2 +1,2 @@
-input12)
+input12)
 connect(sc2.output23, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_1/wrong3.txt b/Data/Questions/ingsw/0324_1/wrong3.txt
similarity index 94%
rename from legacy/Data/ingsw/0324_1/wrong3.txt
rename to Data/Questions/ingsw/0324_1/wrong3.txt
index 1d08fb4..f0ac66f 100644
--- a/legacy/Data/ingsw/0324_1/wrong3.txt
+++ b/Data/Questions/ingsw/0324_1/wrong3.txt
@@ -1,46 +1,46 @@
-input23)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output31, sc1.input31)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output43, sc3.input43)
-
-
-end SysArch;
-
-2.
-
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc2.input12)
-connect(sc1.output13, sc3.input13)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output21, sc1.input21)
-connect(sc3.output31, sc1.input31)
-connect(sc3.output32, sc2.input32)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-
-
-end SysArch;
-
-3.
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output14, sc4.input14)
-connect(sc3.output31, sc1.input31)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-
-
+input23)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output31, sc1.input31)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output43, sc3.input43)
+
+
+end SysArch;
+
+2.
+
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc2.input12)
+connect(sc1.output13, sc3.input13)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output21, sc1.input21)
+connect(sc3.output31, sc1.input31)
+connect(sc3.output32, sc2.input32)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+
+
+end SysArch;
+
+3.
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output14, sc4.input14)
+connect(sc3.output31, sc1.input31)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+
+
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_10/correct.txt b/Data/Questions/ingsw/0324_10/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_10/correct.txt
rename to Data/Questions/ingsw/0324_10/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_10/quest.txt b/Data/Questions/ingsw/0324_10/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_10/quest.txt
rename to Data/Questions/ingsw/0324_10/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0324_10/wrong1.txt b/Data/Questions/ingsw/0324_10/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_10/wrong1.txt
rename to Data/Questions/ingsw/0324_10/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_10/wrong2.txt b/Data/Questions/ingsw/0324_10/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_10/wrong2.txt
rename to Data/Questions/ingsw/0324_10/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_11/quest.txt b/Data/Questions/ingsw/0324_11/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0324_11/quest.txt
rename to Data/Questions/ingsw/0324_11/quest.txt
index 26df850..32ca7ba 100644
--- a/legacy/Data/ingsw/0324_11/quest.txt
+++ b/Data/Questions/ingsw/0324_11/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_11.png
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_11.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_11/wrong1.txt b/Data/Questions/ingsw/0324_11/wrong1.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0324_11/wrong1.txt
rename to Data/Questions/ingsw/0324_11/wrong1.txt
index 2f7168f..8d9c242 100644
--- a/legacy/Data/Questions/ingsw/0324_11/wrong1.txt
+++ b/Data/Questions/ingsw/0324_11/wrong1.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_11/wrong2.txt b/Data/Questions/ingsw/0324_11/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0324_11/wrong2.txt
rename to Data/Questions/ingsw/0324_11/wrong2.txt
index c3b40d2..db35f26 100644
--- a/legacy/Data/Questions/ingsw/0324_11/wrong2.txt
+++ b/Data/Questions/ingsw/0324_11/wrong2.txt
@@ -1,34 +1,34 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_11/wrong3.txt b/Data/Questions/ingsw/0324_11/wrong3.txt
similarity index 96%
rename from legacy/Data/ingsw/0324_11/wrong3.txt
rename to Data/Questions/ingsw/0324_11/wrong3.txt
index 9116c62..1ecf634 100644
--- a/legacy/Data/ingsw/0324_11/wrong3.txt
+++ b/Data/Questions/ingsw/0324_11/wrong3.txt
@@ -1,37 +1,37 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_12/correct.txt b/Data/Questions/ingsw/0324_12/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_12/correct.txt
rename to Data/Questions/ingsw/0324_12/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_12/quest.txt b/Data/Questions/ingsw/0324_12/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0324_12/quest.txt
rename to Data/Questions/ingsw/0324_12/quest.txt
index c1cd6d0..abc3e1c 100644
--- a/legacy/Data/Questions/ingsw/0324_12/quest.txt
+++ b/Data/Questions/ingsw/0324_12/quest.txt
@@ -1,9 +1,9 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
-int z = x;
-while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
-return (z);
-}
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+int z = x;
+while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
+return (z);
+}
 Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_12/wrong1.txt b/Data/Questions/ingsw/0324_12/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_12/wrong1.txt
rename to Data/Questions/ingsw/0324_12/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_12/wrong2.txt b/Data/Questions/ingsw/0324_12/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_12/wrong2.txt
rename to Data/Questions/ingsw/0324_12/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_13/correct.txt b/Data/Questions/ingsw/0324_13/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_13/correct.txt
rename to Data/Questions/ingsw/0324_13/correct.txt
diff --git a/legacy/Data/ingsw/0210_47/quest.txt b/Data/Questions/ingsw/0324_13/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0210_47/quest.txt
rename to Data/Questions/ingsw/0324_13/quest.txt
index 4344b75..3fa8769 100644
--- a/legacy/Data/ingsw/0210_47/quest.txt
+++ b/Data/Questions/ingsw/0324_13/quest.txt
@@ -1,8 +1,8 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (2*x); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
-Si consideri il seguente insieme di test cases:
-{x=-100, x= 40, x=100}
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (2*x); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
+Si consideri il seguente insieme di test cases:
+{x=-100, x= 40, x=100}
 Quale delle seguenti è la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_13/wrong1.txt b/Data/Questions/ingsw/0324_13/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_13/wrong1.txt
rename to Data/Questions/ingsw/0324_13/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_13/wrong2.txt b/Data/Questions/ingsw/0324_13/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_13/wrong2.txt
rename to Data/Questions/ingsw/0324_13/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_14/correct.txt b/Data/Questions/ingsw/0324_14/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_14/correct.txt
rename to Data/Questions/ingsw/0324_14/correct.txt
diff --git a/legacy/Data/ingsw/0324_14/quest.txt b/Data/Questions/ingsw/0324_14/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0324_14/quest.txt
rename to Data/Questions/ingsw/0324_14/quest.txt
index b9ba678..c0b0f0e 100644
--- a/legacy/Data/ingsw/0324_14/quest.txt
+++ b/Data/Questions/ingsw/0324_14/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_14.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_14.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 2, 3, 4 ? In altri terminti, qual'è la probabilità che sia necessario ripetere sia la fase 1 che la fase 2 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_14/wrong1.txt b/Data/Questions/ingsw/0324_14/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_14/wrong1.txt
rename to Data/Questions/ingsw/0324_14/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_14/wrong2.txt b/Data/Questions/ingsw/0324_14/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_14/wrong2.txt
rename to Data/Questions/ingsw/0324_14/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_15/correct.txt b/Data/Questions/ingsw/0324_15/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_15/correct.txt
rename to Data/Questions/ingsw/0324_15/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_15/quest.txt b/Data/Questions/ingsw/0324_15/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0324_15/quest.txt
rename to Data/Questions/ingsw/0324_15/quest.txt
index 2d895ca..025eb8d 100644
--- a/legacy/Data/Questions/ingsw/0324_15/quest.txt
+++ b/Data/Questions/ingsw/0324_15/quest.txt
@@ -1,22 +1,22 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a >= 100) && (b - c <= 1) )
-          return (1);    // punto di uscita 1
-      else if ((b - c <= 1)  || (b + c >= 5)
-)
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a >= 100) && (b - c <= 1) )
+          return (1);    // punto di uscita 1
+      else if ((b - c <= 1)  || (b + c >= 5)
+)
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_15/wrong1.txt b/Data/Questions/ingsw/0324_15/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_15/wrong1.txt
rename to Data/Questions/ingsw/0324_15/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_15/wrong2.txt b/Data/Questions/ingsw/0324_15/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_15/wrong2.txt
rename to Data/Questions/ingsw/0324_15/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_16/correct.txt b/Data/Questions/ingsw/0324_16/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_16/correct.txt
rename to Data/Questions/ingsw/0324_16/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_16/quest.txt b/Data/Questions/ingsw/0324_16/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_16/quest.txt
rename to Data/Questions/ingsw/0324_16/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0324_16/wrong1.txt b/Data/Questions/ingsw/0324_16/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_16/wrong1.txt
rename to Data/Questions/ingsw/0324_16/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_16/wrong2.txt b/Data/Questions/ingsw/0324_16/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_16/wrong2.txt
rename to Data/Questions/ingsw/0324_16/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_17/correct.txt b/Data/Questions/ingsw/0324_17/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_17/correct.txt
rename to Data/Questions/ingsw/0324_17/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_17/quest.txt b/Data/Questions/ingsw/0324_17/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0324_17/quest.txt
rename to Data/Questions/ingsw/0324_17/quest.txt
index 1f51ab1..4ce08bf 100644
--- a/legacy/Data/Questions/ingsw/0324_17/quest.txt
+++ b/Data/Questions/ingsw/0324_17/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_17.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act1 act2 
-Test case 2: act2 act2 act2 act2 act1 
-Test case 3: act2 act2 act2 act2 act0 
-
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_17.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act1 act2 
+Test case 2: act2 act2 act2 act2 act1 
+Test case 3: act2 act2 act2 act2 act0 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_17/wrong1.txt b/Data/Questions/ingsw/0324_17/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_17/wrong1.txt
rename to Data/Questions/ingsw/0324_17/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_17/wrong2.txt b/Data/Questions/ingsw/0324_17/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_17/wrong2.txt
rename to Data/Questions/ingsw/0324_17/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_18/correct.txt b/Data/Questions/ingsw/0324_18/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_18/correct.txt
rename to Data/Questions/ingsw/0324_18/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_48/quest.txt b/Data/Questions/ingsw/0324_18/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0210_48/quest.txt
rename to Data/Questions/ingsw/0324_18/quest.txt
index d3a9fe2..f6bb83a 100644
--- a/legacy/Data/Questions/ingsw/0210_48/quest.txt
+++ b/Data/Questions/ingsw/0324_18/quest.txt
@@ -1,8 +1,8 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 0)   { if (x + y >= 1)  return (1); else return (2); }
-  else {if (2*x + y >= 5)  return (3); else return (4); }
- }  /* f()  */
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 0)   { if (x + y >= 1)  return (1); else return (2); }
+  else {if (2*x + y >= 5)  return (3); else return (4); }
+ }  /* f()  */
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_18/wrong1.txt b/Data/Questions/ingsw/0324_18/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_18/wrong1.txt
rename to Data/Questions/ingsw/0324_18/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_18/wrong2.txt b/Data/Questions/ingsw/0324_18/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_18/wrong2.txt
rename to Data/Questions/ingsw/0324_18/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_19/correct.txt b/Data/Questions/ingsw/0324_19/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_19/correct.txt
rename to Data/Questions/ingsw/0324_19/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_19/quest.txt b/Data/Questions/ingsw/0324_19/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0324_19/quest.txt
rename to Data/Questions/ingsw/0324_19/quest.txt
index b7a608e..d1b1ff4 100644
--- a/legacy/Data/Questions/ingsw/0324_19/quest.txt
+++ b/Data/Questions/ingsw/0324_19/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_19.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act0 
-Test case 2: act2 act2 act0 
-Test case 3: act1 act1 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_19.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act0 
+Test case 2: act2 act2 act0 
+Test case 3: act1 act1 act0 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_19/wrong1.txt b/Data/Questions/ingsw/0324_19/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_19/wrong1.txt
rename to Data/Questions/ingsw/0324_19/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_19/wrong2.txt b/Data/Questions/ingsw/0324_19/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_19/wrong2.txt
rename to Data/Questions/ingsw/0324_19/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_2/correct.txt b/Data/Questions/ingsw/0324_2/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_2/correct.txt
rename to Data/Questions/ingsw/0324_2/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_2/quest.txt b/Data/Questions/ingsw/0324_2/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0324_2/quest.txt
rename to Data/Questions/ingsw/0324_2/quest.txt
index adede32..64036f7 100644
--- a/legacy/Data/Questions/ingsw/0324_2/quest.txt
+++ b/Data/Questions/ingsw/0324_2/quest.txt
@@ -1,9 +1,9 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 2)   { if (x + y >= 1)  return (1); else return (2); }
-  else {if (x + 2*y >= 5)  return (3); else return (4); }
- }  /* f()  */
-Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 2)   { if (x + y >= 1)  return (1); else return (2); }
+  else {if (x + 2*y >= 5)  return (3); else return (4); }
+ }  /* f()  */
+Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_2/wrong1.txt b/Data/Questions/ingsw/0324_2/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_2/wrong1.txt
rename to Data/Questions/ingsw/0324_2/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_2/wrong2.txt b/Data/Questions/ingsw/0324_2/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_2/wrong2.txt
rename to Data/Questions/ingsw/0324_2/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_20/correct.txt b/Data/Questions/ingsw/0324_20/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_20/correct.txt
rename to Data/Questions/ingsw/0324_20/correct.txt
diff --git a/legacy/Data/ingsw/0324_20/quest.txt b/Data/Questions/ingsw/0324_20/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0324_20/quest.txt
rename to Data/Questions/ingsw/0324_20/quest.txt
index 9d685ad..8625c93 100644
--- a/legacy/Data/ingsw/0324_20/quest.txt
+++ b/Data/Questions/ingsw/0324_20/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_20.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-
-Test case 1: act0 act2 
-Test case 2: act1 
-Test case 3: act0 act2 
-
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_20.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+
+Test case 1: act0 act2 
+Test case 2: act1 
+Test case 3: act0 act2 
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_20/wrong1.txt b/Data/Questions/ingsw/0324_20/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_20/wrong1.txt
rename to Data/Questions/ingsw/0324_20/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_20/wrong2.txt b/Data/Questions/ingsw/0324_20/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_20/wrong2.txt
rename to Data/Questions/ingsw/0324_20/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_21/correct.txt b/Data/Questions/ingsw/0324_21/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_21/correct.txt
rename to Data/Questions/ingsw/0324_21/correct.txt
diff --git a/legacy/Data/ingsw/0324_21/quest.txt b/Data/Questions/ingsw/0324_21/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0324_21/quest.txt
rename to Data/Questions/ingsw/0324_21/quest.txt
index d649932..21834a5 100644
--- a/legacy/Data/ingsw/0324_21/quest.txt
+++ b/Data/Questions/ingsw/0324_21/quest.txt
@@ -1,8 +1,8 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 6)   { if (x + y >= 3)  return (1); else return (2); }
-  else {if (x + 2*y >= 15)  return (3); else return (4); }
- }  /* f()  */
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 6)   { if (x + y >= 3)  return (1); else return (2); }
+  else {if (x + 2*y >= 15)  return (3); else return (4); }
+ }  /* f()  */
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_21/wrong1.txt b/Data/Questions/ingsw/0324_21/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_21/wrong1.txt
rename to Data/Questions/ingsw/0324_21/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_21/wrong2.txt b/Data/Questions/ingsw/0324_21/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_21/wrong2.txt
rename to Data/Questions/ingsw/0324_21/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_22/correct.txt b/Data/Questions/ingsw/0324_22/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_22/correct.txt
rename to Data/Questions/ingsw/0324_22/correct.txt
diff --git a/legacy/Data/ingsw/0210_31/quest.txt b/Data/Questions/ingsw/0324_22/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0210_31/quest.txt
rename to Data/Questions/ingsw/0324_22/quest.txt
index 65cfd2d..eb6c210 100644
--- a/legacy/Data/ingsw/0210_31/quest.txt
+++ b/Data/Questions/ingsw/0324_22/quest.txt
@@ -1,9 +1,9 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 0)   { if (x + y >= 2)  return (1); else return (2); }
-  else {if (2*x + y >= 1)  return (3); else return (4); }
- }  /* f()  */
-Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 0)   { if (x + y >= 2)  return (1); else return (2); }
+  else {if (2*x + y >= 1)  return (3); else return (4); }
+ }  /* f()  */
+Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_22/wrong1.txt b/Data/Questions/ingsw/0324_22/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_22/wrong1.txt
rename to Data/Questions/ingsw/0324_22/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_22/wrong2.txt b/Data/Questions/ingsw/0324_22/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_22/wrong2.txt
rename to Data/Questions/ingsw/0324_22/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_23/correct.txt b/Data/Questions/ingsw/0324_23/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_23/correct.txt
rename to Data/Questions/ingsw/0324_23/correct.txt
diff --git a/legacy/Data/ingsw/0324_23/quest.txt b/Data/Questions/ingsw/0324_23/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0324_23/quest.txt
rename to Data/Questions/ingsw/0324_23/quest.txt
index c9ea208..cffeb06 100644
--- a/legacy/Data/ingsw/0324_23/quest.txt
+++ b/Data/Questions/ingsw/0324_23/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_23.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act0 act2 
-Test case 2: act1 act0 act0 act2 
-Test case 3: act0 act0 act2 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_23.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act0 act2 
+Test case 2: act1 act0 act0 act2 
+Test case 3: act0 act0 act2 act2 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_23/wrong1.txt b/Data/Questions/ingsw/0324_23/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_23/wrong1.txt
rename to Data/Questions/ingsw/0324_23/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_23/wrong2.txt b/Data/Questions/ingsw/0324_23/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_23/wrong2.txt
rename to Data/Questions/ingsw/0324_23/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_24/correct.txt b/Data/Questions/ingsw/0324_24/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_24/correct.txt
rename to Data/Questions/ingsw/0324_24/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_24/quest.txt b/Data/Questions/ingsw/0324_24/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_24/quest.txt
rename to Data/Questions/ingsw/0324_24/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0324_24/wrong1.txt b/Data/Questions/ingsw/0324_24/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_24/wrong1.txt
rename to Data/Questions/ingsw/0324_24/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_24/wrong2.txt b/Data/Questions/ingsw/0324_24/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_24/wrong2.txt
rename to Data/Questions/ingsw/0324_24/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_25/correct.txt b/Data/Questions/ingsw/0324_25/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_25/correct.txt
rename to Data/Questions/ingsw/0324_25/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_25/quest.txt b/Data/Questions/ingsw/0324_25/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_25/quest.txt
rename to Data/Questions/ingsw/0324_25/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0324_25/wrong1.txt b/Data/Questions/ingsw/0324_25/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_25/wrong1.txt
rename to Data/Questions/ingsw/0324_25/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_25/wrong2.txt b/Data/Questions/ingsw/0324_25/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_25/wrong2.txt
rename to Data/Questions/ingsw/0324_25/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_26/quest.txt b/Data/Questions/ingsw/0324_26/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0324_26/quest.txt
rename to Data/Questions/ingsw/0324_26/quest.txt
index aef871e..edb1cc1 100644
--- a/legacy/Data/Questions/ingsw/0324_26/quest.txt
+++ b/Data/Questions/ingsw/0324_26/quest.txt
@@ -1,19 +1,19 @@
-Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
-
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc2.input12)
-connect(sc1.output13, sc3.input13)
-connect(sc2.output23, sc3.input23)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output42, sc2.input42)
-
-
+Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
+
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc2.input12)
+connect(sc1.output13, sc3.input13)
+connect(sc2.output23, sc3.input23)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output42, sc2.input42)
+
+
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_26/wrong1.txt b/Data/Questions/ingsw/0324_26/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_26/wrong1.txt
rename to Data/Questions/ingsw/0324_26/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_26/wrong2.txt b/Data/Questions/ingsw/0324_26/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_26/wrong2.txt
rename to Data/Questions/ingsw/0324_26/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_26/wrong3.txt b/Data/Questions/ingsw/0324_26/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_26/wrong3.txt
rename to Data/Questions/ingsw/0324_26/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0613_16/correct.txt b/Data/Questions/ingsw/0324_27/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_16/correct.txt
rename to Data/Questions/ingsw/0324_27/correct.txt
index e582263..2771487 100644
--- a/legacy/Data/Questions/ingsw/0613_16/correct.txt
+++ b/Data/Questions/ingsw/0324_27/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_5/quest.txt b/Data/Questions/ingsw/0324_27/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_5/quest.txt
rename to Data/Questions/ingsw/0324_27/quest.txt
index 864cc93..498beed 100644
--- a/legacy/Data/Questions/ingsw/0210_5/quest.txt
+++ b/Data/Questions/ingsw/0324_27/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
+Si consideri il seguente requisito:
+RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
 Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_16/wrong1.txt b/Data/Questions/ingsw/0324_27/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_16/wrong1.txt
rename to Data/Questions/ingsw/0324_27/wrong1.txt
index 590f7e1..d681803 100644
--- a/legacy/Data/Questions/ingsw/0613_16/wrong1.txt
+++ b/Data/Questions/ingsw/0324_27/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_16/wrong2.txt b/Data/Questions/ingsw/0324_27/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_16/wrong2.txt
rename to Data/Questions/ingsw/0324_27/wrong2.txt
index 0f38391..44ac950 100644
--- a/legacy/Data/Questions/ingsw/0613_16/wrong2.txt
+++ b/Data/Questions/ingsw/0324_27/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_43/correct.txt b/Data/Questions/ingsw/0324_28/correct.txt
similarity index 94%
rename from legacy/Data/ingsw/0210_43/correct.txt
rename to Data/Questions/ingsw/0324_28/correct.txt
index 4c75070..9be970c 100644
--- a/legacy/Data/ingsw/0210_43/correct.txt
+++ b/Data/Questions/ingsw/0324_28/correct.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_10/quest.txt b/Data/Questions/ingsw/0324_28/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_10/quest.txt
rename to Data/Questions/ingsw/0324_28/quest.txt
index e11a044..0910f0b 100644
--- a/legacy/Data/Questions/ingsw/0120_10/quest.txt
+++ b/Data/Questions/ingsw/0324_28/quest.txt
@@ -1,4 +1,4 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
+Si consideri il seguente requisito:
+RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_49/wrong1.txt b/Data/Questions/ingsw/0324_28/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_49/wrong1.txt
rename to Data/Questions/ingsw/0324_28/wrong1.txt
index 6dafe94..8b6fcac 100644
--- a/legacy/Data/Questions/ingsw/0613_49/wrong1.txt
+++ b/Data/Questions/ingsw/0324_28/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_49/wrong2.txt b/Data/Questions/ingsw/0324_28/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_49/wrong2.txt
rename to Data/Questions/ingsw/0324_28/wrong2.txt
index a3d79a4..40a2ce0 100644
--- a/legacy/Data/Questions/ingsw/0613_49/wrong2.txt
+++ b/Data/Questions/ingsw/0324_28/wrong2.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_29/correct.txt b/Data/Questions/ingsw/0324_29/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_29/correct.txt
rename to Data/Questions/ingsw/0324_29/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_29/quest.txt b/Data/Questions/ingsw/0324_29/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_29/quest.txt
rename to Data/Questions/ingsw/0324_29/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0324_29/wrong1.txt b/Data/Questions/ingsw/0324_29/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_29/wrong1.txt
rename to Data/Questions/ingsw/0324_29/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_29/wrong2.txt b/Data/Questions/ingsw/0324_29/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_29/wrong2.txt
rename to Data/Questions/ingsw/0324_29/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_3/correct.txt b/Data/Questions/ingsw/0324_3/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_3/correct.txt
rename to Data/Questions/ingsw/0324_3/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_3/quest.txt b/Data/Questions/ingsw/0324_3/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0324_3/quest.txt
rename to Data/Questions/ingsw/0324_3/quest.txt
index b865ed9..7893985 100644
--- a/legacy/Data/Questions/ingsw/0324_3/quest.txt
+++ b/Data/Questions/ingsw/0324_3/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_3.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act0 act0 act0 act2 act2 
-Test case 2: act2 act0 act2 
-Test case 3: act1 act0 act0 act2 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_3.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act0 act0 act0 act2 act2 
+Test case 2: act2 act0 act2 
+Test case 3: act1 act0 act0 act2 act2 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_3/wrong1.txt b/Data/Questions/ingsw/0324_3/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_3/wrong1.txt
rename to Data/Questions/ingsw/0324_3/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_3/wrong2.txt b/Data/Questions/ingsw/0324_3/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_3/wrong2.txt
rename to Data/Questions/ingsw/0324_3/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_30/quest.txt b/Data/Questions/ingsw/0324_30/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0324_30/quest.txt
rename to Data/Questions/ingsw/0324_30/quest.txt
index 985c244..e2e738a 100644
--- a/legacy/Data/ingsw/0324_30/quest.txt
+++ b/Data/Questions/ingsw/0324_30/quest.txt
@@ -1,4 +1,4 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente specifica funzionale per la funzione f.
-La funzione f(int *A, int *B)  prende come input un vettore A di dimensione n  ritorna come output un vettore B ottenuto ordinando gli elementi di A in ordine crescente.
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente specifica funzionale per la funzione f.
+La funzione f(int *A, int *B)  prende come input un vettore A di dimensione n  ritorna come output un vettore B ottenuto ordinando gli elementi di A in ordine crescente.
 Quale delle seguenti funzioni è un test oracle per la funzione f ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_30/wrong1.txt b/Data/Questions/ingsw/0324_30/wrong1.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0324_30/wrong1.txt
rename to Data/Questions/ingsw/0324_30/wrong1.txt
index 69b9722..59b5927 100644
--- a/legacy/Data/Questions/ingsw/0324_30/wrong1.txt
+++ b/Data/Questions/ingsw/0324_30/wrong1.txt
@@ -1,14 +1,14 @@
-#define n 1000
-int TestOracle2(int *A, int *B)
-{
-int i, j, D[n];
-//init
-for (i = 0; i < n; i++) D[i] = -1;
-// B is ordered
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-// B is a permutation of A
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; break;}
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-// B ok
-return (1);
+#define n 1000
+int TestOracle2(int *A, int *B)
+{
+int i, j, D[n];
+//init
+for (i = 0; i < n; i++) D[i] = -1;
+// B is ordered
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+// B is a permutation of A
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; break;}
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+// B ok
+return (1);
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_30/wrong2.txt b/Data/Questions/ingsw/0324_30/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0324_30/wrong2.txt
rename to Data/Questions/ingsw/0324_30/wrong2.txt
index a26ce6e..5c3b08a 100644
--- a/legacy/Data/Questions/ingsw/0324_30/wrong2.txt
+++ b/Data/Questions/ingsw/0324_30/wrong2.txt
@@ -1,15 +1,15 @@
-#define n 1000
-
-int TestOracle3(int *A, int *B)
-{
-int i, j, D[n];
-//init
-for (i = 0; i < n; i++) D[i] = -1;
-// B is ordered
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-// B is a permutation of A
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if (A[i] == B[j]) {C[i][j] = 1; D[j] = 1; break;}
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-// B ok
-return (1);
+#define n 1000
+
+int TestOracle3(int *A, int *B)
+{
+int i, j, D[n];
+//init
+for (i = 0; i < n; i++) D[i] = -1;
+// B is ordered
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+// B is a permutation of A
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if (A[i] == B[j]) {C[i][j] = 1; D[j] = 1; break;}
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+// B ok
+return (1);
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_30/wrong3.txt b/Data/Questions/ingsw/0324_30/wrong3.txt
similarity index 97%
rename from legacy/Data/ingsw/0324_30/wrong3.txt
rename to Data/Questions/ingsw/0324_30/wrong3.txt
index ed5ad19..9e00c22 100644
--- a/legacy/Data/ingsw/0324_30/wrong3.txt
+++ b/Data/Questions/ingsw/0324_30/wrong3.txt
@@ -1,14 +1,14 @@
-#define n 1000
-int TestOracle1(int *A, int *B)
-{
-int i, j, D[n];
-//init
-for (i = 0; i < n; i++) D[i] = -1;
-// B is ordered
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-// B is a permutation of A
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; D[j] = 1; break;}
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-// B ok
-return (1);
+#define n 1000
+int TestOracle1(int *A, int *B)
+{
+int i, j, D[n];
+//init
+for (i = 0; i < n; i++) D[i] = -1;
+// B is ordered
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+// B is a permutation of A
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; D[j] = 1; break;}
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+// B ok
+return (1);
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_16/correct.txt b/Data/Questions/ingsw/0324_31/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_16/correct.txt
rename to Data/Questions/ingsw/0324_31/correct.txt
index 293ebbc..ca92838 100644
--- a/legacy/Data/Questions/ingsw/0210_16/correct.txt
+++ b/Data/Questions/ingsw/0324_31/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_16/quest.txt b/Data/Questions/ingsw/0324_31/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_16/quest.txt
rename to Data/Questions/ingsw/0324_31/quest.txt
index 5922b9f..348f2a5 100644
--- a/legacy/Data/Questions/ingsw/0210_16/quest.txt
+++ b/Data/Questions/ingsw/0324_31/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
+Si consideri il seguente requisito:
+RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_31/wrong1.txt b/Data/Questions/ingsw/0324_31/wrong1.txt
similarity index 94%
rename from legacy/Data/ingsw/0324_31/wrong1.txt
rename to Data/Questions/ingsw/0324_31/wrong1.txt
index d50b268..434e904 100644
--- a/legacy/Data/ingsw/0324_31/wrong1.txt
+++ b/Data/Questions/ingsw/0324_31/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_31/wrong2.txt b/Data/Questions/ingsw/0324_31/wrong2.txt
similarity index 94%
rename from legacy/Data/ingsw/0324_31/wrong2.txt
rename to Data/Questions/ingsw/0324_31/wrong2.txt
index d7890b2..c0e3311 100644
--- a/legacy/Data/ingsw/0324_31/wrong2.txt
+++ b/Data/Questions/ingsw/0324_31/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_32/correct.txt b/Data/Questions/ingsw/0324_32/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_32/correct.txt
rename to Data/Questions/ingsw/0324_32/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_32/quest.txt b/Data/Questions/ingsw/0324_32/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_32/quest.txt
rename to Data/Questions/ingsw/0324_32/quest.txt
index 5d96d42..513dba7 100644
--- a/legacy/Data/Questions/ingsw/0324_32/quest.txt
+++ b/Data/Questions/ingsw/0324_32/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_32.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
-Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_32.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
+Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_32/wrong1.txt b/Data/Questions/ingsw/0324_32/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_32/wrong1.txt
rename to Data/Questions/ingsw/0324_32/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_32/wrong2.txt b/Data/Questions/ingsw/0324_32/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_32/wrong2.txt
rename to Data/Questions/ingsw/0324_32/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_33/correct.txt b/Data/Questions/ingsw/0324_33/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_33/correct.txt
rename to Data/Questions/ingsw/0324_33/correct.txt
diff --git a/legacy/Data/ingsw/0210_38/quest.txt b/Data/Questions/ingsw/0324_33/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0210_38/quest.txt
rename to Data/Questions/ingsw/0324_33/quest.txt
index b2bed72..793c90f 100644
--- a/legacy/Data/ingsw/0210_38/quest.txt
+++ b/Data/Questions/ingsw/0324_33/quest.txt
@@ -1,21 +1,21 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a  + b >= 6) && (b - c <= 1) )
-          return (1);    // punto di uscita 1
-      else if ((b - c <= 1)  || (b + c >= 5))
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a  + b >= 6) && (b - c <= 1) )
+          return (1);    // punto di uscita 1
+      else if ((b - c <= 1)  || (b + c >= 5))
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_33/wrong1.txt b/Data/Questions/ingsw/0324_33/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_33/wrong1.txt
rename to Data/Questions/ingsw/0324_33/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_33/wrong2.txt b/Data/Questions/ingsw/0324_33/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_33/wrong2.txt
rename to Data/Questions/ingsw/0324_33/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_2/correct.txt b/Data/Questions/ingsw/0324_34/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/1122_2/correct.txt
rename to Data/Questions/ingsw/0324_34/correct.txt
index ad21063..a2f00b3 100644
--- a/legacy/Data/Questions/ingsw/1122_2/correct.txt
+++ b/Data/Questions/ingsw/0324_34/correct.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_35/quest.txt b/Data/Questions/ingsw/0324_34/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_35/quest.txt
rename to Data/Questions/ingsw/0324_34/quest.txt
index 031c331..1dbc484 100644
--- a/legacy/Data/Questions/ingsw/0613_35/quest.txt
+++ b/Data/Questions/ingsw/0324_34/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+Si consideri il seguente requisito:
+RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_34/wrong1.txt b/Data/Questions/ingsw/0324_34/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_34/wrong1.txt
rename to Data/Questions/ingsw/0324_34/wrong1.txt
index b14ac60..0696007 100644
--- a/legacy/Data/Questions/ingsw/0324_34/wrong1.txt
+++ b/Data/Questions/ingsw/0324_34/wrong1.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_35/wrong2.txt b/Data/Questions/ingsw/0324_34/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_35/wrong2.txt
rename to Data/Questions/ingsw/0324_34/wrong2.txt
index e4201ab..76b58b0 100644
--- a/legacy/Data/Questions/ingsw/0613_35/wrong2.txt
+++ b/Data/Questions/ingsw/0324_34/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_35/quest.txt b/Data/Questions/ingsw/0324_35/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0324_35/quest.txt
rename to Data/Questions/ingsw/0324_35/quest.txt
index 627c57e..e5bd153 100644
--- a/legacy/Data/Questions/ingsw/0324_35/quest.txt
+++ b/Data/Questions/ingsw/0324_35/quest.txt
@@ -1,39 +1,39 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_35/wrong1.txt b/Data/Questions/ingsw/0324_35/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_35/wrong1.txt
rename to Data/Questions/ingsw/0324_35/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_35/wrong2.txt b/Data/Questions/ingsw/0324_35/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_35/wrong2.txt
rename to Data/Questions/ingsw/0324_35/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_35/wrong3.txt b/Data/Questions/ingsw/0324_35/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_35/wrong3.txt
rename to Data/Questions/ingsw/0324_35/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0324_36/correct.txt b/Data/Questions/ingsw/0324_36/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_36/correct.txt
rename to Data/Questions/ingsw/0324_36/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_36/quest.txt b/Data/Questions/ingsw/0324_36/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_36/quest.txt
rename to Data/Questions/ingsw/0324_36/quest.txt
index 36471c2..628380c 100644
--- a/legacy/Data/Questions/ingsw/0324_36/quest.txt
+++ b/Data/Questions/ingsw/0324_36/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_36.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
-Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_36.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
+Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_36/wrong1.txt b/Data/Questions/ingsw/0324_36/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_36/wrong1.txt
rename to Data/Questions/ingsw/0324_36/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_36/wrong2.txt b/Data/Questions/ingsw/0324_36/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_36/wrong2.txt
rename to Data/Questions/ingsw/0324_36/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_37/correct.txt b/Data/Questions/ingsw/0324_37/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_37/correct.txt
rename to Data/Questions/ingsw/0324_37/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_37/quest.txt b/Data/Questions/ingsw/0324_37/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0324_37/quest.txt
rename to Data/Questions/ingsw/0324_37/quest.txt
index fc6a5e1..eba16a3 100644
--- a/legacy/Data/Questions/ingsw/0324_37/quest.txt
+++ b/Data/Questions/ingsw/0324_37/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_37.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-
-Test case 1: act1 act2 act2 act2 
-Test case 2: act0 act2 act1 act2 act0 
-Test case 3: act0 act2 act1 act2 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_37.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+
+Test case 1: act1 act2 act2 act2 
+Test case 2: act0 act2 act1 act2 act0 
+Test case 3: act0 act2 act1 act2 act2 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_37/wrong1.txt b/Data/Questions/ingsw/0324_37/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_37/wrong1.txt
rename to Data/Questions/ingsw/0324_37/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_37/wrong2.txt b/Data/Questions/ingsw/0324_37/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_37/wrong2.txt
rename to Data/Questions/ingsw/0324_37/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_38/correct.txt b/Data/Questions/ingsw/0324_38/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_38/correct.txt
rename to Data/Questions/ingsw/0324_38/correct.txt
diff --git a/legacy/Data/ingsw/0324_38/quest.txt b/Data/Questions/ingsw/0324_38/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0324_38/quest.txt
rename to Data/Questions/ingsw/0324_38/quest.txt
index d24403f..9de598d 100644
--- a/legacy/Data/ingsw/0324_38/quest.txt
+++ b/Data/Questions/ingsw/0324_38/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_38.png
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_38.png
 Si consideri la Markov chain in figura con stato iniziale 0 e p in (0, 1). Quale delle seguenti formule calcola il valore atteso del numero di transizioni necessarie per lasciare lo stato 0.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_38/wrong1.txt b/Data/Questions/ingsw/0324_38/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_38/wrong1.txt
rename to Data/Questions/ingsw/0324_38/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_38/wrong2.txt b/Data/Questions/ingsw/0324_38/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_38/wrong2.txt
rename to Data/Questions/ingsw/0324_38/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_45/correct.txt b/Data/Questions/ingsw/0324_39/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_45/correct.txt
rename to Data/Questions/ingsw/0324_39/correct.txt
index 4a8e634..a3a4b67 100644
--- a/legacy/Data/Questions/ingsw/0210_45/correct.txt
+++ b/Data/Questions/ingsw/0324_39/correct.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_45/quest.txt b/Data/Questions/ingsw/0324_39/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_45/quest.txt
rename to Data/Questions/ingsw/0324_39/quest.txt
index 576af1a..0d3faca 100644
--- a/legacy/Data/Questions/ingsw/0210_45/quest.txt
+++ b/Data/Questions/ingsw/0324_39/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_39/wrong1.txt b/Data/Questions/ingsw/0324_39/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_39/wrong1.txt
rename to Data/Questions/ingsw/0324_39/wrong1.txt
index a43796b..9f7ef6a 100644
--- a/legacy/Data/Questions/ingsw/0324_39/wrong1.txt
+++ b/Data/Questions/ingsw/0324_39/wrong1.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_39/wrong2.txt b/Data/Questions/ingsw/0324_39/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0324_39/wrong2.txt
rename to Data/Questions/ingsw/0324_39/wrong2.txt
index 68aa37a..32c00bc 100644
--- a/legacy/Data/Questions/ingsw/0324_39/wrong2.txt
+++ b/Data/Questions/ingsw/0324_39/wrong2.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_4/correct.txt b/Data/Questions/ingsw/0324_4/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_4/correct.txt
rename to Data/Questions/ingsw/0324_4/correct.txt
diff --git a/legacy/Data/ingsw/0324_4/quest.txt b/Data/Questions/ingsw/0324_4/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0324_4/quest.txt
rename to Data/Questions/ingsw/0324_4/quest.txt
index 40b7789..390dc53 100644
--- a/legacy/Data/ingsw/0324_4/quest.txt
+++ b/Data/Questions/ingsw/0324_4/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_4.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_4.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 3, 4? In altri terminti, qual'è la probabilità che non sia necessario ripetere la seconda fase (ma non la prima) ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_4/wrong1.txt b/Data/Questions/ingsw/0324_4/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_4/wrong1.txt
rename to Data/Questions/ingsw/0324_4/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_4/wrong2.txt b/Data/Questions/ingsw/0324_4/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_4/wrong2.txt
rename to Data/Questions/ingsw/0324_4/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_40/correct.txt b/Data/Questions/ingsw/0324_40/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_40/correct.txt
rename to Data/Questions/ingsw/0324_40/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_40/quest.txt b/Data/Questions/ingsw/0324_40/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_40/quest.txt
rename to Data/Questions/ingsw/0324_40/quest.txt
index fbee794..b4d7a22 100644
--- a/legacy/Data/Questions/ingsw/0324_40/quest.txt
+++ b/Data/Questions/ingsw/0324_40/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_40.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_40.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 2, 3? In altri terminti, qual'è la probabilità che non sia necessario ripetere la prima fase (ma non la seconda) ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_40/wrong1.txt b/Data/Questions/ingsw/0324_40/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_40/wrong1.txt
rename to Data/Questions/ingsw/0324_40/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_40/wrong2.txt b/Data/Questions/ingsw/0324_40/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_40/wrong2.txt
rename to Data/Questions/ingsw/0324_40/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_41/quest.txt b/Data/Questions/ingsw/0324_41/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0324_41/quest.txt
rename to Data/Questions/ingsw/0324_41/quest.txt
index bfb2790..ed4c07b 100644
--- a/legacy/Data/ingsw/0324_41/quest.txt
+++ b/Data/Questions/ingsw/0324_41/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_41.png
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_41.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_41/wrong1.txt b/Data/Questions/ingsw/0324_41/wrong1.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0324_41/wrong1.txt
rename to Data/Questions/ingsw/0324_41/wrong1.txt
index 1fad89a..07dfe0b 100644
--- a/legacy/Data/Questions/ingsw/0324_41/wrong1.txt
+++ b/Data/Questions/ingsw/0324_41/wrong1.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_41/wrong2.txt b/Data/Questions/ingsw/0324_41/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0324_41/wrong2.txt
rename to Data/Questions/ingsw/0324_41/wrong2.txt
index 882ae3e..37f0e6d 100644
--- a/legacy/Data/ingsw/0324_41/wrong2.txt
+++ b/Data/Questions/ingsw/0324_41/wrong2.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_41/wrong3.txt b/Data/Questions/ingsw/0324_41/wrong3.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0324_41/wrong3.txt
rename to Data/Questions/ingsw/0324_41/wrong3.txt
index e5618fa..6a88923 100644
--- a/legacy/Data/Questions/ingsw/0324_41/wrong3.txt
+++ b/Data/Questions/ingsw/0324_41/wrong3.txt
@@ -1,34 +1,34 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_42/quest.txt b/Data/Questions/ingsw/0324_42/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0324_42/quest.txt
rename to Data/Questions/ingsw/0324_42/quest.txt
index 071ac68..5f2cac3 100644
--- a/legacy/Data/Questions/ingsw/0324_42/quest.txt
+++ b/Data/Questions/ingsw/0324_42/quest.txt
@@ -1,35 +1,35 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_42/wrong1.txt b/Data/Questions/ingsw/0324_42/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_42/wrong1.txt
rename to Data/Questions/ingsw/0324_42/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_42/wrong2.txt b/Data/Questions/ingsw/0324_42/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_42/wrong2.txt
rename to Data/Questions/ingsw/0324_42/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_42/wrong3.txt b/Data/Questions/ingsw/0324_42/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_42/wrong3.txt
rename to Data/Questions/ingsw/0324_42/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0324_43/correct.txt b/Data/Questions/ingsw/0324_43/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_43/correct.txt
rename to Data/Questions/ingsw/0324_43/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_43/quest.txt b/Data/Questions/ingsw/0324_43/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0324_43/quest.txt
rename to Data/Questions/ingsw/0324_43/quest.txt
index 710edb6..c2ef886 100644
--- a/legacy/Data/Questions/ingsw/0324_43/quest.txt
+++ b/Data/Questions/ingsw/0324_43/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_43.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act1 act0 act1 act0 
-Test case 2: act1 act0 act2 act2 
-Test case 3: act2 act2 act1 act2 act1 
-
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_43.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act1 act0 act1 act0 
+Test case 2: act1 act0 act2 act2 
+Test case 3: act2 act2 act1 act2 act1 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_43/wrong1.txt b/Data/Questions/ingsw/0324_43/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_43/wrong1.txt
rename to Data/Questions/ingsw/0324_43/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_43/wrong2.txt b/Data/Questions/ingsw/0324_43/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_43/wrong2.txt
rename to Data/Questions/ingsw/0324_43/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_44/correct.txt b/Data/Questions/ingsw/0324_44/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_44/correct.txt
rename to Data/Questions/ingsw/0324_44/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_39/quest.txt b/Data/Questions/ingsw/0324_44/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_39/quest.txt
rename to Data/Questions/ingsw/0324_44/quest.txt
index 36947c2..853f496 100644
--- a/legacy/Data/Questions/ingsw/0210_39/quest.txt
+++ b/Data/Questions/ingsw/0324_44/quest.txt
@@ -1,6 +1,6 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (x + 7); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (x + 7); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
 Quale dei seguenti test cases consegue una partition coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_44/wrong1.txt b/Data/Questions/ingsw/0324_44/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_44/wrong1.txt
rename to Data/Questions/ingsw/0324_44/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_44/wrong2.txt b/Data/Questions/ingsw/0324_44/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_44/wrong2.txt
rename to Data/Questions/ingsw/0324_44/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_6/correct.txt b/Data/Questions/ingsw/0324_45/correct.txt
similarity index 94%
rename from legacy/Data/ingsw/0210_6/correct.txt
rename to Data/Questions/ingsw/0324_45/correct.txt
index c37d6ae..56ad9f9 100644
--- a/legacy/Data/ingsw/0210_6/correct.txt
+++ b/Data/Questions/ingsw/0324_45/correct.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_17/quest.txt b/Data/Questions/ingsw/0324_45/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_17/quest.txt
rename to Data/Questions/ingsw/0324_45/quest.txt
index 003d1dd..316ed3f 100644
--- a/legacy/Data/Questions/ingsw/0613_17/quest.txt
+++ b/Data/Questions/ingsw/0324_45/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_45/wrong1.txt b/Data/Questions/ingsw/0324_45/wrong1.txt
similarity index 94%
rename from legacy/Data/ingsw/0324_45/wrong1.txt
rename to Data/Questions/ingsw/0324_45/wrong1.txt
index edea147..3184bf8 100644
--- a/legacy/Data/ingsw/0324_45/wrong1.txt
+++ b/Data/Questions/ingsw/0324_45/wrong1.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_45/wrong2.txt b/Data/Questions/ingsw/0324_45/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0324_45/wrong2.txt
rename to Data/Questions/ingsw/0324_45/wrong2.txt
index 14bd900..976ab6a 100644
--- a/legacy/Data/ingsw/0324_45/wrong2.txt
+++ b/Data/Questions/ingsw/0324_45/wrong2.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_6/correct.txt b/Data/Questions/ingsw/0324_46/correct.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0613_6/correct.txt
rename to Data/Questions/ingsw/0324_46/correct.txt
index a98afd2..d3daba4 100644
--- a/legacy/Data/Questions/ingsw/0613_6/correct.txt
+++ b/Data/Questions/ingsw/0324_46/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and ((x >= 30) or (x <= 20)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and ((x >= 30) or (x <= 20)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_6/quest.txt b/Data/Questions/ingsw/0324_46/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_6/quest.txt
rename to Data/Questions/ingsw/0324_46/quest.txt
index b420aaf..6938bd6 100644
--- a/legacy/Data/Questions/ingsw/0613_6/quest.txt
+++ b/Data/Questions/ingsw/0324_46/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
+Si consideri il seguente requisito:
+RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
 Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_6/wrong1.txt b/Data/Questions/ingsw/0324_46/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0613_6/wrong1.txt
rename to Data/Questions/ingsw/0324_46/wrong1.txt
index 66064fe..5bdf9c8 100644
--- a/legacy/Data/Questions/ingsw/0613_6/wrong1.txt
+++ b/Data/Questions/ingsw/0324_46/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and (x >= 20) and (x <= 30) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and (x >= 20) and (x <= 30) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_6/wrong2.txt b/Data/Questions/ingsw/0324_46/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0613_6/wrong2.txt
rename to Data/Questions/ingsw/0324_46/wrong2.txt
index c71f1f5..169ec7c 100644
--- a/legacy/Data/Questions/ingsw/0613_6/wrong2.txt
+++ b/Data/Questions/ingsw/0324_46/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) or ((x >= 20) and (x <= 30)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) or ((x >= 20) and (x <= 30)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_47/quest.txt b/Data/Questions/ingsw/0324_47/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0324_47/quest.txt
rename to Data/Questions/ingsw/0324_47/quest.txt
index 0240bc8..ca6ba26 100644
--- a/legacy/Data/Questions/ingsw/0324_47/quest.txt
+++ b/Data/Questions/ingsw/0324_47/quest.txt
@@ -1,18 +1,18 @@
-Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc2.input12)
-connect(sc1.output13, sc3.input13)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output31, sc1.input31)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output42, sc2.input42)
-connect(sc4.output43, sc3.input43)
+Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc2.input12)
+connect(sc1.output13, sc3.input13)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output31, sc1.input31)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output42, sc2.input42)
+connect(sc4.output43, sc3.input43)
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_47/wrong1.txt b/Data/Questions/ingsw/0324_47/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_47/wrong1.txt
rename to Data/Questions/ingsw/0324_47/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_47/wrong2.txt b/Data/Questions/ingsw/0324_47/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_47/wrong2.txt
rename to Data/Questions/ingsw/0324_47/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_47/wrong3.txt b/Data/Questions/ingsw/0324_47/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_47/wrong3.txt
rename to Data/Questions/ingsw/0324_47/wrong3.txt
diff --git a/legacy/Data/ingsw/0324_48/quest.txt b/Data/Questions/ingsw/0324_48/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0324_48/quest.txt
rename to Data/Questions/ingsw/0324_48/quest.txt
index 1109458..8cd20e6 100644
--- a/legacy/Data/ingsw/0324_48/quest.txt
+++ b/Data/Questions/ingsw/0324_48/quest.txt
@@ -1,4 +1,4 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_48.png
-Si consideri la seguente architettura software:
-
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_48.png
+Si consideri la seguente architettura software:
+
 Quale dei seguneti modelli Modelica meglio la rappresenta.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_1/wrong1.txt b/Data/Questions/ingsw/0324_48/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0324_1/wrong1.txt
rename to Data/Questions/ingsw/0324_48/wrong1.txt
index 4bcd55f..d698ca8 100644
--- a/legacy/Data/Questions/ingsw/0324_1/wrong1.txt
+++ b/Data/Questions/ingsw/0324_48/wrong1.txt
@@ -1,8 +1,8 @@
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
 connect(sc1.output12, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_48/wrong2.txt b/Data/Questions/ingsw/0324_48/wrong2.txt
similarity index 97%
rename from legacy/Data/ingsw/0324_48/wrong2.txt
rename to Data/Questions/ingsw/0324_48/wrong2.txt
index 19be218..8c2a586 100644
--- a/legacy/Data/ingsw/0324_48/wrong2.txt
+++ b/Data/Questions/ingsw/0324_48/wrong2.txt
@@ -1,2 +1,2 @@
-input12)
+input12)
 connect(sc1.output13, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_48/wrong3.txt b/Data/Questions/ingsw/0324_48/wrong3.txt
similarity index 95%
rename from legacy/Data/ingsw/0324_48/wrong3.txt
rename to Data/Questions/ingsw/0324_48/wrong3.txt
index 3387be9..702e46b 100644
--- a/legacy/Data/ingsw/0324_48/wrong3.txt
+++ b/Data/Questions/ingsw/0324_48/wrong3.txt
@@ -1,49 +1,49 @@
-input13)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output23, sc3.input23)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output32, sc2.input32)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output42, sc2.input42)
-
-
-end SysArch;
-
-2.
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output13, sc3.input13)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output23, sc3.input23)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output31, sc1.input31)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output42, sc2.input42)
-connect(sc4.output43, sc3.input43)
-
-
-end SysArch;
-
-3.
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output13, sc3.input13)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output32, sc2.input32)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output43, sc3.input43)
-
-
+input13)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output23, sc3.input23)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output32, sc2.input32)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output42, sc2.input42)
+
+
+end SysArch;
+
+2.
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output13, sc3.input13)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output23, sc3.input23)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output31, sc1.input31)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output42, sc2.input42)
+connect(sc4.output43, sc3.input43)
+
+
+end SysArch;
+
+3.
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output13, sc3.input13)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output32, sc2.input32)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output43, sc3.input43)
+
+
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_49/correct.txt b/Data/Questions/ingsw/0324_49/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_49/correct.txt
rename to Data/Questions/ingsw/0324_49/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_49/quest.txt b/Data/Questions/ingsw/0324_49/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_49/quest.txt
rename to Data/Questions/ingsw/0324_49/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0324_49/wrong1.txt b/Data/Questions/ingsw/0324_49/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_49/wrong1.txt
rename to Data/Questions/ingsw/0324_49/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_49/wrong2.txt b/Data/Questions/ingsw/0324_49/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_49/wrong2.txt
rename to Data/Questions/ingsw/0324_49/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_5/correct.txt b/Data/Questions/ingsw/0324_5/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_5/correct.txt
rename to Data/Questions/ingsw/0324_5/correct.txt
diff --git a/legacy/Data/ingsw/0210_8/quest.txt b/Data/Questions/ingsw/0324_5/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0210_8/quest.txt
rename to Data/Questions/ingsw/0324_5/quest.txt
index 236ccc7..3ce3c9b 100644
--- a/legacy/Data/ingsw/0210_8/quest.txt
+++ b/Data/Questions/ingsw/0324_5/quest.txt
@@ -1,10 +1,10 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
-int z, k;
-z = 1;   k = 0;
-while (k < x)  { z = y*z;  k = k + 1; }
-return (z);
-}
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+int z, k;
+z = 1;   k = 0;
+while (k < x)  { z = y*z;  k = k + 1; }
+return (z);
+}
 Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_5/wrong1.txt b/Data/Questions/ingsw/0324_5/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_5/wrong1.txt
rename to Data/Questions/ingsw/0324_5/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_5/wrong2.txt b/Data/Questions/ingsw/0324_5/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_5/wrong2.txt
rename to Data/Questions/ingsw/0324_5/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_6/correct.txt b/Data/Questions/ingsw/0324_6/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_6/correct.txt
rename to Data/Questions/ingsw/0324_6/correct.txt
diff --git a/legacy/Data/ingsw/0324_6/quest.txt b/Data/Questions/ingsw/0324_6/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0324_6/quest.txt
rename to Data/Questions/ingsw/0324_6/quest.txt
index f6ffda4..80dab68 100644
--- a/legacy/Data/ingsw/0324_6/quest.txt
+++ b/Data/Questions/ingsw/0324_6/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_6.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-
-Test case 1: act0 act0 act0 act0 act1 
-Test case 2: act2 act2 
-Test case 3: act0 act0 act2 act1 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_6.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+
+Test case 1: act0 act0 act0 act0 act1 
+Test case 2: act2 act2 
+Test case 3: act0 act0 act2 act1 act2 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_6/wrong1.txt b/Data/Questions/ingsw/0324_6/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_6/wrong1.txt
rename to Data/Questions/ingsw/0324_6/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_6/wrong2.txt b/Data/Questions/ingsw/0324_6/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_6/wrong2.txt
rename to Data/Questions/ingsw/0324_6/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_25/correct.txt b/Data/Questions/ingsw/0324_7/correct.txt
similarity index 95%
rename from legacy/Data/ingsw/0210_25/correct.txt
rename to Data/Questions/ingsw/0324_7/correct.txt
index 43dc0c9..af4c387 100644
--- a/legacy/Data/ingsw/0210_25/correct.txt
+++ b/Data/Questions/ingsw/0324_7/correct.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 0) || (y > 0)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 0) || (y > 0)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_7/quest.txt b/Data/Questions/ingsw/0324_7/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_7/quest.txt
rename to Data/Questions/ingsw/0324_7/quest.txt
index f6744fd..5fd3b47 100644
--- a/legacy/Data/Questions/ingsw/0324_7/quest.txt
+++ b/Data/Questions/ingsw/0324_7/quest.txt
@@ -1,4 +1,4 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-Si consideri la funzione C
-int f(in x, int y) { ..... }
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+Si consideri la funzione C
+int f(in x, int y) { ..... }
 Quale delle seguenti assert esprime la pre-condizione che entrambi gli argomenti di f sono non-negativi ed almeno uno di loro è positivo ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_7/wrong1.txt b/Data/Questions/ingsw/0324_7/wrong1.txt
similarity index 95%
rename from legacy/Data/ingsw/0324_7/wrong1.txt
rename to Data/Questions/ingsw/0324_7/wrong1.txt
index 3f63933..506df9d 100644
--- a/legacy/Data/ingsw/0324_7/wrong1.txt
+++ b/Data/Questions/ingsw/0324_7/wrong1.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x > 0) && (y > 0) && ((x > 1) || (y > 1)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x > 0) && (y > 0) && ((x > 1) || (y > 1)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_7/wrong2.txt b/Data/Questions/ingsw/0324_7/wrong2.txt
similarity index 95%
rename from legacy/Data/ingsw/0324_7/wrong2.txt
rename to Data/Questions/ingsw/0324_7/wrong2.txt
index 6a97baf..392cc67 100644
--- a/legacy/Data/ingsw/0324_7/wrong2.txt
+++ b/Data/Questions/ingsw/0324_7/wrong2.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 1) || (y > 1)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 1) || (y > 1)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_8/correct.txt b/Data/Questions/ingsw/0324_8/correct.txt
similarity index 93%
rename from legacy/Data/ingsw/0324_8/correct.txt
rename to Data/Questions/ingsw/0324_8/correct.txt
index b8bf06e..a068be6 100644
--- a/legacy/Data/ingsw/0324_8/correct.txt
+++ b/Data/Questions/ingsw/0324_8/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x > 5) or (x < 0));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x > 5) or (x < 0));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_15/quest.txt b/Data/Questions/ingsw/0324_8/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_15/quest.txt
rename to Data/Questions/ingsw/0324_8/quest.txt
index 22c683f..d26958e 100644
--- a/legacy/Data/Questions/ingsw/0613_15/quest.txt
+++ b/Data/Questions/ingsw/0324_8/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
+Si consideri il seguente requisito:
+RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_8/wrong1.txt b/Data/Questions/ingsw/0324_8/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0324_8/wrong1.txt
rename to Data/Questions/ingsw/0324_8/wrong1.txt
index 2029293..8086f1c 100644
--- a/legacy/Data/ingsw/0324_8/wrong1.txt
+++ b/Data/Questions/ingsw/0324_8/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and (x > 0) and (x < 5);
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and (x > 0) and (x < 5);
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_8/wrong2.txt b/Data/Questions/ingsw/0324_8/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0324_8/wrong2.txt
rename to Data/Questions/ingsw/0324_8/wrong2.txt
index bc8720d..c22f966 100644
--- a/legacy/Data/ingsw/0324_8/wrong2.txt
+++ b/Data/Questions/ingsw/0324_8/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z =  (time > 0) and ((x > 0) or (x < 5));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z =  (time > 0) and ((x > 0) or (x < 5));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_9/correct.txt b/Data/Questions/ingsw/0324_9/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_9/correct.txt
rename to Data/Questions/ingsw/0324_9/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_9/quest.txt b/Data/Questions/ingsw/0324_9/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_9/quest.txt
rename to Data/Questions/ingsw/0324_9/quest.txt
index 47201e7..332256a 100644
--- a/legacy/Data/Questions/ingsw/0324_9/quest.txt
+++ b/Data/Questions/ingsw/0324_9/quest.txt
@@ -1,4 +1,4 @@
-Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
-Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
-P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
+Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
+Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
+P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
 dove b è una opportuna costante note da dati storici aziendali. Si assuma che b = 0.0001,  C = 1000000, ed il rischio ammesso è R = 1000. Quale dei seguenti valori meglio approssima il costo S per lo sviluppo del software in questione.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_9/wrong1.txt b/Data/Questions/ingsw/0324_9/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_9/wrong1.txt
rename to Data/Questions/ingsw/0324_9/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0324_9/wrong2.txt b/Data/Questions/ingsw/0324_9/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0324_9/wrong2.txt
rename to Data/Questions/ingsw/0324_9/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0422-16/correct.txt b/Data/Questions/ingsw/0422-16/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0422-16/correct.txt
rename to Data/Questions/ingsw/0422-16/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0422-16/quest.txt b/Data/Questions/ingsw/0422-16/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0422-16/quest.txt
rename to Data/Questions/ingsw/0422-16/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0422-16/wrong1.txt b/Data/Questions/ingsw/0422-16/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0422-16/wrong1.txt
rename to Data/Questions/ingsw/0422-16/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0422-16/wrong2.txt b/Data/Questions/ingsw/0422-16/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0422-16/wrong2.txt
rename to Data/Questions/ingsw/0422-16/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_0/quest.txt b/Data/Questions/ingsw/0613_0/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0613_0/quest.txt
rename to Data/Questions/ingsw/0613_0/quest.txt
index 1f3419c..623ac16 100644
--- a/legacy/Data/Questions/ingsw/0613_0/quest.txt
+++ b/Data/Questions/ingsw/0613_0/quest.txt
@@ -1,35 +1,35 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_0/wrong1.txt b/Data/Questions/ingsw/0613_0/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_0/wrong1.txt
rename to Data/Questions/ingsw/0613_0/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_0/wrong2.txt b/Data/Questions/ingsw/0613_0/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_0/wrong2.txt
rename to Data/Questions/ingsw/0613_0/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_0/wrong3.txt b/Data/Questions/ingsw/0613_0/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_0/wrong3.txt
rename to Data/Questions/ingsw/0613_0/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0613_1/correct.txt b/Data/Questions/ingsw/0613_1/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_1/correct.txt
rename to Data/Questions/ingsw/0613_1/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_1/quest.txt b/Data/Questions/ingsw/0613_1/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_1/quest.txt
rename to Data/Questions/ingsw/0613_1/quest.txt
index 654955e..04740a2 100644
--- a/legacy/Data/Questions/ingsw/0613_1/quest.txt
+++ b/Data/Questions/ingsw/0613_1/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_1.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_1.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 3, 4? In altri terminti, qual'è la probabilità che non sia necessario ripetere la seconda fase (ma non la prima) ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_1/wrong1.txt b/Data/Questions/ingsw/0613_1/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_1/wrong1.txt
rename to Data/Questions/ingsw/0613_1/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_1/wrong2.txt b/Data/Questions/ingsw/0613_1/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_1/wrong2.txt
rename to Data/Questions/ingsw/0613_1/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_10/correct.txt b/Data/Questions/ingsw/0613_10/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_10/correct.txt
rename to Data/Questions/ingsw/0613_10/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_10/quest.txt b/Data/Questions/ingsw/0613_10/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0613_10/quest.txt
rename to Data/Questions/ingsw/0613_10/quest.txt
index 9e4d3a9..3aba807 100644
--- a/legacy/Data/Questions/ingsw/0613_10/quest.txt
+++ b/Data/Questions/ingsw/0613_10/quest.txt
@@ -1,31 +1,31 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x[3])
-{
-  if (-x[0] + x[1] - x[2] < -7)
-    { return (0); }
-  else if (-3*x[0] +3*x[1] - 5*x[2] > 7)
-        { 
-      if (-x[0] + x[1] - x[2] > 10)
-                { return (1); }
-      else
-                { return (0); }      
-    }
- else
-           { 
-             if (3*x[0] - 5*x[1] + 7*x[2] > 9)
-               { return (1); }
-      else
-                { return (0); }      
-    }
-
-} /* f()  */
-----------
-ed il seguente insieme di test cases:
-
-Test 1: x[0] = 0, x[1] = 0, x[2] = 1, 
-Test 2: x[0] = 3, x[1] = 1, x[2] = 5, 
-Test 3:  x[0] = 0, x[1] = 4, x[2] = -2, 
-Test 4:  x[0] = -4, x[1] = 5, x[2] = -2, 
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x[3])
+{
+  if (-x[0] + x[1] - x[2] < -7)
+    { return (0); }
+  else if (-3*x[0] +3*x[1] - 5*x[2] > 7)
+        { 
+      if (-x[0] + x[1] - x[2] > 10)
+                { return (1); }
+      else
+                { return (0); }      
+    }
+ else
+           { 
+             if (3*x[0] - 5*x[1] + 7*x[2] > 9)
+               { return (1); }
+      else
+                { return (0); }      
+    }
+
+} /* f()  */
+----------
+ed il seguente insieme di test cases:
+
+Test 1: x[0] = 0, x[1] = 0, x[2] = 1, 
+Test 2: x[0] = 3, x[1] = 1, x[2] = 5, 
+Test 3:  x[0] = 0, x[1] = 4, x[2] = -2, 
+Test 4:  x[0] = -4, x[1] = 5, x[2] = -2, 
 Test 5:   x[0] = 1, x[1] = -4, x[2] = 4,
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_10/wrong1.txt b/Data/Questions/ingsw/0613_10/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_10/wrong1.txt
rename to Data/Questions/ingsw/0613_10/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_10/wrong2.txt b/Data/Questions/ingsw/0613_10/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_10/wrong2.txt
rename to Data/Questions/ingsw/0613_10/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_11/correct.txt b/Data/Questions/ingsw/0613_11/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_11/correct.txt
rename to Data/Questions/ingsw/0613_11/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_11/quest.txt b/Data/Questions/ingsw/0613_11/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_11/quest.txt
rename to Data/Questions/ingsw/0613_11/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0613_11/wrong1.txt b/Data/Questions/ingsw/0613_11/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_11/wrong1.txt
rename to Data/Questions/ingsw/0613_11/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_11/wrong2.txt b/Data/Questions/ingsw/0613_11/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_11/wrong2.txt
rename to Data/Questions/ingsw/0613_11/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_12/correct.txt b/Data/Questions/ingsw/0613_12/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_12/correct.txt
rename to Data/Questions/ingsw/0613_12/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_44/quest.txt b/Data/Questions/ingsw/0613_12/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_44/quest.txt
rename to Data/Questions/ingsw/0613_12/quest.txt
index 36947c2..853f496 100644
--- a/legacy/Data/Questions/ingsw/0324_44/quest.txt
+++ b/Data/Questions/ingsw/0613_12/quest.txt
@@ -1,6 +1,6 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (x + 7); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (x + 7); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
 Quale dei seguenti test cases consegue una partition coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_12/wrong1.txt b/Data/Questions/ingsw/0613_12/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_12/wrong1.txt
rename to Data/Questions/ingsw/0613_12/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_12/wrong2.txt b/Data/Questions/ingsw/0613_12/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_12/wrong2.txt
rename to Data/Questions/ingsw/0613_12/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_13/correct.txt b/Data/Questions/ingsw/0613_13/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_13/correct.txt
rename to Data/Questions/ingsw/0613_13/correct.txt
diff --git a/legacy/Data/ingsw/0613_13/quest.txt b/Data/Questions/ingsw/0613_13/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0613_13/quest.txt
rename to Data/Questions/ingsw/0613_13/quest.txt
index 6f20250..b2e5e25 100644
--- a/legacy/Data/ingsw/0613_13/quest.txt
+++ b/Data/Questions/ingsw/0613_13/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_13.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act0 act2 act0 
-Test case 2: act1 act0 act1 act2 act1 
-Test case 3: act1 act2 act0 act2 act1 
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_13.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act0 act2 act0 
+Test case 2: act1 act0 act1 act2 act1 
+Test case 3: act1 act2 act0 act2 act1 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_13/wrong1.txt b/Data/Questions/ingsw/0613_13/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_13/wrong1.txt
rename to Data/Questions/ingsw/0613_13/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_13/wrong2.txt b/Data/Questions/ingsw/0613_13/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_13/wrong2.txt
rename to Data/Questions/ingsw/0613_13/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_14/quest.txt b/Data/Questions/ingsw/0613_14/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0613_14/quest.txt
rename to Data/Questions/ingsw/0613_14/quest.txt
index b95c7d3..98c0282 100644
--- a/legacy/Data/Questions/ingsw/0613_14/quest.txt
+++ b/Data/Questions/ingsw/0613_14/quest.txt
@@ -1,13 +1,13 @@
-Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
-block SysArch  //  System Architecture
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-connect(sc1.output12, sc2.input12)
-connect(sc1.output13, sc3.input13)
-connect(sc1.output14, sc4.input14)
-connect(sc3.output31, sc1.input31)
-connect(sc3.output32, sc2.input32)
-connect(sc4.output42, sc2.input42)
+Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
+block SysArch  //  System Architecture
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+connect(sc1.output12, sc2.input12)
+connect(sc1.output13, sc3.input13)
+connect(sc1.output14, sc4.input14)
+connect(sc3.output31, sc1.input31)
+connect(sc3.output32, sc2.input32)
+connect(sc4.output42, sc2.input42)
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_14/wrong1.txt b/Data/Questions/ingsw/0613_14/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_14/wrong1.txt
rename to Data/Questions/ingsw/0613_14/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_14/wrong2.txt b/Data/Questions/ingsw/0613_14/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_14/wrong2.txt
rename to Data/Questions/ingsw/0613_14/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_14/wrong3.txt b/Data/Questions/ingsw/0613_14/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_14/wrong3.txt
rename to Data/Questions/ingsw/0613_14/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0613_15/correct.txt b/Data/Questions/ingsw/0613_15/correct.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0613_15/correct.txt
rename to Data/Questions/ingsw/0613_15/correct.txt
index b8bf06e..a068be6 100644
--- a/legacy/Data/Questions/ingsw/0613_15/correct.txt
+++ b/Data/Questions/ingsw/0613_15/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x > 5) or (x < 0));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x > 5) or (x < 0));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_15/quest.txt b/Data/Questions/ingsw/0613_15/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_15/quest.txt
rename to Data/Questions/ingsw/0613_15/quest.txt
index 22c683f..d26958e 100644
--- a/legacy/Data/Questions/ingsw/0210_15/quest.txt
+++ b/Data/Questions/ingsw/0613_15/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
+Si consideri il seguente requisito:
+RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_15/wrong1.txt b/Data/Questions/ingsw/0613_15/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0613_15/wrong1.txt
rename to Data/Questions/ingsw/0613_15/wrong1.txt
index bc8720d..c22f966 100644
--- a/legacy/Data/Questions/ingsw/0613_15/wrong1.txt
+++ b/Data/Questions/ingsw/0613_15/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z =  (time > 0) and ((x > 0) or (x < 5));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z =  (time > 0) and ((x > 0) or (x < 5));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_15/wrong2.txt b/Data/Questions/ingsw/0613_15/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0210_15/wrong2.txt
rename to Data/Questions/ingsw/0613_15/wrong2.txt
index 2029293..8086f1c 100644
--- a/legacy/Data/Questions/ingsw/0210_15/wrong2.txt
+++ b/Data/Questions/ingsw/0613_15/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and (x > 0) and (x < 5);
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and (x > 0) and (x < 5);
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_5/correct.txt b/Data/Questions/ingsw/0613_16/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_5/correct.txt
rename to Data/Questions/ingsw/0613_16/correct.txt
index e582263..2771487 100644
--- a/legacy/Data/Questions/ingsw/0210_5/correct.txt
+++ b/Data/Questions/ingsw/0613_16/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_16/quest.txt b/Data/Questions/ingsw/0613_16/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_16/quest.txt
rename to Data/Questions/ingsw/0613_16/quest.txt
index 864cc93..498beed 100644
--- a/legacy/Data/Questions/ingsw/0613_16/quest.txt
+++ b/Data/Questions/ingsw/0613_16/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
+Si consideri il seguente requisito:
+RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
 Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_27/wrong1.txt b/Data/Questions/ingsw/0613_16/wrong1.txt
similarity index 94%
rename from legacy/Data/ingsw/0324_27/wrong1.txt
rename to Data/Questions/ingsw/0613_16/wrong1.txt
index 590f7e1..d681803 100644
--- a/legacy/Data/ingsw/0324_27/wrong1.txt
+++ b/Data/Questions/ingsw/0613_16/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_27/wrong2.txt b/Data/Questions/ingsw/0613_16/wrong2.txt
similarity index 94%
rename from legacy/Data/ingsw/0324_27/wrong2.txt
rename to Data/Questions/ingsw/0613_16/wrong2.txt
index 0f38391..44ac950 100644
--- a/legacy/Data/ingsw/0324_27/wrong2.txt
+++ b/Data/Questions/ingsw/0613_16/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_6/correct.txt b/Data/Questions/ingsw/0613_17/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_6/correct.txt
rename to Data/Questions/ingsw/0613_17/correct.txt
index c37d6ae..56ad9f9 100644
--- a/legacy/Data/Questions/ingsw/0210_6/correct.txt
+++ b/Data/Questions/ingsw/0613_17/correct.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_6/quest.txt b/Data/Questions/ingsw/0613_17/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_6/quest.txt
rename to Data/Questions/ingsw/0613_17/quest.txt
index 003d1dd..316ed3f 100644
--- a/legacy/Data/Questions/ingsw/0210_6/quest.txt
+++ b/Data/Questions/ingsw/0613_17/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_17/wrong1.txt b/Data/Questions/ingsw/0613_17/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0613_17/wrong1.txt
rename to Data/Questions/ingsw/0613_17/wrong1.txt
index 14bd900..976ab6a 100644
--- a/legacy/Data/Questions/ingsw/0613_17/wrong1.txt
+++ b/Data/Questions/ingsw/0613_17/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_17/wrong2.txt b/Data/Questions/ingsw/0613_17/wrong2.txt
similarity index 94%
rename from legacy/Data/ingsw/0613_17/wrong2.txt
rename to Data/Questions/ingsw/0613_17/wrong2.txt
index edea147..3184bf8 100644
--- a/legacy/Data/ingsw/0613_17/wrong2.txt
+++ b/Data/Questions/ingsw/0613_17/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_18/correct.txt b/Data/Questions/ingsw/0613_18/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_18/correct.txt
rename to Data/Questions/ingsw/0613_18/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_18/quest.txt b/Data/Questions/ingsw/0613_18/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_18/quest.txt
rename to Data/Questions/ingsw/0613_18/quest.txt
index 91edad5..82a9faa 100644
--- a/legacy/Data/Questions/ingsw/0613_18/quest.txt
+++ b/Data/Questions/ingsw/0613_18/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_18.png
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_18.png
 Si consideri la Markov chain in figura con stato iniziale 0 e p in (0, 1). Quale delle seguenti formule calcola il valore atteso del numero di transizioni necessarie per lasciare lo stato 0.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_18/wrong1.txt b/Data/Questions/ingsw/0613_18/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_18/wrong1.txt
rename to Data/Questions/ingsw/0613_18/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_18/wrong2.txt b/Data/Questions/ingsw/0613_18/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_18/wrong2.txt
rename to Data/Questions/ingsw/0613_18/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_19/quest.txt b/Data/Questions/ingsw/0613_19/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0613_19/quest.txt
rename to Data/Questions/ingsw/0613_19/quest.txt
index 052028b..7278f88 100644
--- a/legacy/Data/Questions/ingsw/0613_19/quest.txt
+++ b/Data/Questions/ingsw/0613_19/quest.txt
@@ -1,37 +1,37 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_19/wrong1.txt b/Data/Questions/ingsw/0613_19/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_19/wrong1.txt
rename to Data/Questions/ingsw/0613_19/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_19/wrong2.txt b/Data/Questions/ingsw/0613_19/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_19/wrong2.txt
rename to Data/Questions/ingsw/0613_19/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_19/wrong3.txt b/Data/Questions/ingsw/0613_19/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_19/wrong3.txt
rename to Data/Questions/ingsw/0613_19/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0613_2/quest.txt b/Data/Questions/ingsw/0613_2/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0613_2/quest.txt
rename to Data/Questions/ingsw/0613_2/quest.txt
index fcb1323..cf64e3c 100644
--- a/legacy/Data/Questions/ingsw/0613_2/quest.txt
+++ b/Data/Questions/ingsw/0613_2/quest.txt
@@ -1,19 +1,19 @@
-Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc2.input12)
-connect(sc1.output13, sc3.input13)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output42, sc2.input42)
-
-
+Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc2.input12)
+connect(sc1.output13, sc3.input13)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output42, sc2.input42)
+
+
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_2/wrong1.txt b/Data/Questions/ingsw/0613_2/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_2/wrong1.txt
rename to Data/Questions/ingsw/0613_2/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_2/wrong2.txt b/Data/Questions/ingsw/0613_2/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_2/wrong2.txt
rename to Data/Questions/ingsw/0613_2/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_2/wrong3.txt b/Data/Questions/ingsw/0613_2/wrong3.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_2/wrong3.txt
rename to Data/Questions/ingsw/0613_2/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0613_20/correct.txt b/Data/Questions/ingsw/0613_20/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_20/correct.txt
rename to Data/Questions/ingsw/0613_20/correct.txt
diff --git a/legacy/Data/ingsw/0613_20/quest.txt b/Data/Questions/ingsw/0613_20/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0613_20/quest.txt
rename to Data/Questions/ingsw/0613_20/quest.txt
index 79b69ac..6e1ee0b 100644
--- a/legacy/Data/ingsw/0613_20/quest.txt
+++ b/Data/Questions/ingsw/0613_20/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_20.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
-Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_20.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
+Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_20/wrong1.txt b/Data/Questions/ingsw/0613_20/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_20/wrong1.txt
rename to Data/Questions/ingsw/0613_20/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_20/wrong2.txt b/Data/Questions/ingsw/0613_20/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_20/wrong2.txt
rename to Data/Questions/ingsw/0613_20/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_21/correct.txt b/Data/Questions/ingsw/0613_21/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_21/correct.txt
rename to Data/Questions/ingsw/0613_21/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_21/quest.txt b/Data/Questions/ingsw/0613_21/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_21/quest.txt
rename to Data/Questions/ingsw/0613_21/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0613_21/wrong1.txt b/Data/Questions/ingsw/0613_21/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_21/wrong1.txt
rename to Data/Questions/ingsw/0613_21/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_21/wrong2.txt b/Data/Questions/ingsw/0613_21/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_21/wrong2.txt
rename to Data/Questions/ingsw/0613_21/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_22/correct.txt b/Data/Questions/ingsw/0613_22/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_22/correct.txt
rename to Data/Questions/ingsw/0613_22/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_22/quest.txt b/Data/Questions/ingsw/0613_22/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0613_22/quest.txt
rename to Data/Questions/ingsw/0613_22/quest.txt
index aef94a6..f520cd6 100644
--- a/legacy/Data/Questions/ingsw/0613_22/quest.txt
+++ b/Data/Questions/ingsw/0613_22/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_22.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act2 act1 act0 
-Test case 2: act2 act2 act0 act2 act2 
-Test case 3: act1 act1 act2 act2 
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_22.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act2 act1 act0 
+Test case 2: act2 act2 act0 act2 act2 
+Test case 3: act1 act1 act2 act2 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_22/wrong1.txt b/Data/Questions/ingsw/0613_22/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_22/wrong1.txt
rename to Data/Questions/ingsw/0613_22/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_22/wrong2.txt b/Data/Questions/ingsw/0613_22/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_22/wrong2.txt
rename to Data/Questions/ingsw/0613_22/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_39/correct.txt b/Data/Questions/ingsw/0613_23/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_39/correct.txt
rename to Data/Questions/ingsw/0613_23/correct.txt
index 4a8e634..a3a4b67 100644
--- a/legacy/Data/Questions/ingsw/0324_39/correct.txt
+++ b/Data/Questions/ingsw/0613_23/correct.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_29/quest.txt b/Data/Questions/ingsw/0613_23/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_29/quest.txt
rename to Data/Questions/ingsw/0613_23/quest.txt
index 576af1a..0d3faca 100644
--- a/legacy/Data/Questions/ingsw/0120_29/quest.txt
+++ b/Data/Questions/ingsw/0613_23/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_23/wrong1.txt b/Data/Questions/ingsw/0613_23/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0613_23/wrong1.txt
rename to Data/Questions/ingsw/0613_23/wrong1.txt
index 68aa37a..32c00bc 100644
--- a/legacy/Data/Questions/ingsw/0613_23/wrong1.txt
+++ b/Data/Questions/ingsw/0613_23/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_45/wrong2.txt b/Data/Questions/ingsw/0613_23/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_45/wrong2.txt
rename to Data/Questions/ingsw/0613_23/wrong2.txt
index a43796b..9f7ef6a 100644
--- a/legacy/Data/Questions/ingsw/0210_45/wrong2.txt
+++ b/Data/Questions/ingsw/0613_23/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_24/correct.txt b/Data/Questions/ingsw/0613_24/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_24/correct.txt
rename to Data/Questions/ingsw/0613_24/correct.txt
diff --git a/legacy/Data/ingsw/0613_24/quest.txt b/Data/Questions/ingsw/0613_24/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0613_24/quest.txt
rename to Data/Questions/ingsw/0613_24/quest.txt
index 9534ab3..edbe473 100644
--- a/legacy/Data/ingsw/0613_24/quest.txt
+++ b/Data/Questions/ingsw/0613_24/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_24.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act2 
-Test case 2: act1 
-Test case 3: act2 act0 act2 act0 act2 
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_24.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act2 
+Test case 2: act1 
+Test case 3: act2 act0 act2 act0 act2 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_24/wrong1.txt b/Data/Questions/ingsw/0613_24/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_24/wrong1.txt
rename to Data/Questions/ingsw/0613_24/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_24/wrong2.txt b/Data/Questions/ingsw/0613_24/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_24/wrong2.txt
rename to Data/Questions/ingsw/0613_24/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_25/correct.txt b/Data/Questions/ingsw/0613_25/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_25/correct.txt
rename to Data/Questions/ingsw/0613_25/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_25/quest.txt b/Data/Questions/ingsw/0613_25/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0613_25/quest.txt
rename to Data/Questions/ingsw/0613_25/quest.txt
index c1cd6d0..abc3e1c 100644
--- a/legacy/Data/Questions/ingsw/0613_25/quest.txt
+++ b/Data/Questions/ingsw/0613_25/quest.txt
@@ -1,9 +1,9 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
-int z = x;
-while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
-return (z);
-}
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+int z = x;
+while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
+return (z);
+}
 Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_25/wrong1.txt b/Data/Questions/ingsw/0613_25/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_25/wrong1.txt
rename to Data/Questions/ingsw/0613_25/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_25/wrong2.txt b/Data/Questions/ingsw/0613_25/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_25/wrong2.txt
rename to Data/Questions/ingsw/0613_25/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_26/correct.txt b/Data/Questions/ingsw/0613_26/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_26/correct.txt
rename to Data/Questions/ingsw/0613_26/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_26/quest.txt b/Data/Questions/ingsw/0613_26/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0613_26/quest.txt
rename to Data/Questions/ingsw/0613_26/quest.txt
index dcec721..3e3f55d 100644
--- a/legacy/Data/Questions/ingsw/0613_26/quest.txt
+++ b/Data/Questions/ingsw/0613_26/quest.txt
@@ -1,8 +1,8 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (2*x); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
-Si consideri il seguente insieme di test cases:
-{x=-20, x= 10, x=60}
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (2*x); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
+Si consideri il seguente insieme di test cases:
+{x=-20, x= 10, x=60}
 Quale delle seguenti è la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_26/wrong1.txt b/Data/Questions/ingsw/0613_26/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_26/wrong1.txt
rename to Data/Questions/ingsw/0613_26/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_26/wrong2.txt b/Data/Questions/ingsw/0613_26/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_26/wrong2.txt
rename to Data/Questions/ingsw/0613_26/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_27/quest.txt b/Data/Questions/ingsw/0613_27/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0613_27/quest.txt
rename to Data/Questions/ingsw/0613_27/quest.txt
index 35670bc..7a4bedd 100644
--- a/legacy/Data/ingsw/0613_27/quest.txt
+++ b/Data/Questions/ingsw/0613_27/quest.txt
@@ -1,4 +1,4 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_27.png
-Si consideri la seguente architettura software:
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_27.png
+Si consideri la seguente architettura software:
+
 Quale dei seguenti modelli Modelica meglio la rappresenta.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_9/wrong1.txt b/Data/Questions/ingsw/0613_27/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0613_9/wrong1.txt
rename to Data/Questions/ingsw/0613_27/wrong1.txt
index 4bcd55f..d698ca8 100644
--- a/legacy/Data/Questions/ingsw/0613_9/wrong1.txt
+++ b/Data/Questions/ingsw/0613_27/wrong1.txt
@@ -1,8 +1,8 @@
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
 connect(sc1.output12, sc
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_48/wrong2.txt b/Data/Questions/ingsw/0613_27/wrong2.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0324_48/wrong2.txt
rename to Data/Questions/ingsw/0613_27/wrong2.txt
index 19be218..8c2a586 100644
--- a/legacy/Data/Questions/ingsw/0324_48/wrong2.txt
+++ b/Data/Questions/ingsw/0613_27/wrong2.txt
@@ -1,2 +1,2 @@
-input12)
+input12)
 connect(sc1.output13, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_27/wrong3.txt b/Data/Questions/ingsw/0613_27/wrong3.txt
similarity index 95%
rename from legacy/Data/ingsw/0613_27/wrong3.txt
rename to Data/Questions/ingsw/0613_27/wrong3.txt
index 29daf30..ed27dd6 100644
--- a/legacy/Data/ingsw/0613_27/wrong3.txt
+++ b/Data/Questions/ingsw/0613_27/wrong3.txt
@@ -1,49 +1,49 @@
-input13)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output23, sc3.input23)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output32, sc2.input32)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-
-
-end SysArch;
-
-2.
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc2.input12)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output23, sc3.input23)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output31, sc1.input31)
-connect(sc3.output32, sc2.input32)
-connect(sc3.output34, sc4.input34)
-
-
-end SysArch;
-
-3.
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc2.input12)
-connect(sc1.output13, sc3.input13)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-
-
+input13)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output23, sc3.input23)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output32, sc2.input32)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+
+
+end SysArch;
+
+2.
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc2.input12)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output23, sc3.input23)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output31, sc1.input31)
+connect(sc3.output32, sc2.input32)
+connect(sc3.output34, sc4.input34)
+
+
+end SysArch;
+
+3.
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc2.input12)
+connect(sc1.output13, sc3.input13)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+
+
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_28/correct.txt b/Data/Questions/ingsw/0613_28/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_28/correct.txt
rename to Data/Questions/ingsw/0613_28/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_28/quest.txt b/Data/Questions/ingsw/0613_28/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0613_28/quest.txt
rename to Data/Questions/ingsw/0613_28/quest.txt
index 32aecd3..7e792d0 100644
--- a/legacy/Data/Questions/ingsw/0613_28/quest.txt
+++ b/Data/Questions/ingsw/0613_28/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_28.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act2 act0 act1 act0 
-Test case 2: act2 act0 act0 
-Test case 3: act2 act0 act2 act1 act1 
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_28.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act2 act0 act1 act0 
+Test case 2: act2 act0 act0 
+Test case 3: act2 act0 act2 act1 act1 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_28/wrong1.txt b/Data/Questions/ingsw/0613_28/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_28/wrong1.txt
rename to Data/Questions/ingsw/0613_28/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_28/wrong2.txt b/Data/Questions/ingsw/0613_28/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_28/wrong2.txt
rename to Data/Questions/ingsw/0613_28/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_29/correct.txt b/Data/Questions/ingsw/0613_29/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_29/correct.txt
rename to Data/Questions/ingsw/0613_29/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_29/quest.txt b/Data/Questions/ingsw/0613_29/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_29/quest.txt
rename to Data/Questions/ingsw/0613_29/quest.txt
index 47201e7..332256a 100644
--- a/legacy/Data/Questions/ingsw/0613_29/quest.txt
+++ b/Data/Questions/ingsw/0613_29/quest.txt
@@ -1,4 +1,4 @@
-Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
-Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
-P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
+Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
+Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
+P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
 dove b è una opportuna costante note da dati storici aziendali. Si assuma che b = 0.0001,  C = 1000000, ed il rischio ammesso è R = 1000. Quale dei seguenti valori meglio approssima il costo S per lo sviluppo del software in questione.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_29/wrong1.txt b/Data/Questions/ingsw/0613_29/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_29/wrong1.txt
rename to Data/Questions/ingsw/0613_29/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_29/wrong2.txt b/Data/Questions/ingsw/0613_29/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_29/wrong2.txt
rename to Data/Questions/ingsw/0613_29/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_3/correct.txt b/Data/Questions/ingsw/0613_3/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_3/correct.txt
rename to Data/Questions/ingsw/0613_3/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_3/quest.txt b/Data/Questions/ingsw/0613_3/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_3/quest.txt
rename to Data/Questions/ingsw/0613_3/quest.txt
index d8bc097..11102b4 100644
--- a/legacy/Data/Questions/ingsw/0613_3/quest.txt
+++ b/Data/Questions/ingsw/0613_3/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_3.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_3.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 3 ? In altri terminti, qual'è la probabilità che non sia necessario ripetere nessuna fase?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_3/wrong1.txt b/Data/Questions/ingsw/0613_3/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_3/wrong1.txt
rename to Data/Questions/ingsw/0613_3/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_3/wrong2.txt b/Data/Questions/ingsw/0613_3/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_3/wrong2.txt
rename to Data/Questions/ingsw/0613_3/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_30/correct.txt b/Data/Questions/ingsw/0613_30/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_30/correct.txt
rename to Data/Questions/ingsw/0613_30/correct.txt
diff --git a/legacy/Data/ingsw/0613_30/quest.txt b/Data/Questions/ingsw/0613_30/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0613_30/quest.txt
rename to Data/Questions/ingsw/0613_30/quest.txt
index 56ab57a..67a9861 100644
--- a/legacy/Data/ingsw/0613_30/quest.txt
+++ b/Data/Questions/ingsw/0613_30/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_30.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act0 act0 act0 act1 act1 
-Test case 2: act1 act0 act1 
-Test case 3: act0 act2 act2 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_30.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act0 act0 act0 act1 act1 
+Test case 2: act1 act0 act1 
+Test case 3: act0 act2 act2 act2 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_30/wrong1.txt b/Data/Questions/ingsw/0613_30/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_30/wrong1.txt
rename to Data/Questions/ingsw/0613_30/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_30/wrong2.txt b/Data/Questions/ingsw/0613_30/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_30/wrong2.txt
rename to Data/Questions/ingsw/0613_30/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_31/correct.txt b/Data/Questions/ingsw/0613_31/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_31/correct.txt
rename to Data/Questions/ingsw/0613_31/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_31/quest.txt b/Data/Questions/ingsw/0613_31/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0613_31/quest.txt
rename to Data/Questions/ingsw/0613_31/quest.txt
index 9f9ed74..991828f 100644
--- a/legacy/Data/Questions/ingsw/0613_31/quest.txt
+++ b/Data/Questions/ingsw/0613_31/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_31.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act1 act2 act2 act2 act0 
-Test case 2: act1 act0 act1 act1 act1 
-Test case 3: act1 act2 act2 act2 act2 
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_31.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act1 act2 act2 act2 act0 
+Test case 2: act1 act0 act1 act1 act1 
+Test case 3: act1 act2 act2 act2 act2 
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_31/wrong1.txt b/Data/Questions/ingsw/0613_31/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_31/wrong1.txt
rename to Data/Questions/ingsw/0613_31/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_31/wrong2.txt b/Data/Questions/ingsw/0613_31/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_31/wrong2.txt
rename to Data/Questions/ingsw/0613_31/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_32/correct.txt b/Data/Questions/ingsw/0613_32/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_32/correct.txt
rename to Data/Questions/ingsw/0613_32/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_32/quest.txt b/Data/Questions/ingsw/0613_32/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0613_32/quest.txt
rename to Data/Questions/ingsw/0613_32/quest.txt
index 1724f1c..90ea8cc 100644
--- a/legacy/Data/Questions/ingsw/0613_32/quest.txt
+++ b/Data/Questions/ingsw/0613_32/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_32.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act0 act2 act0 act0 act2 
-Test case 2: act1 act0 act0 act0 
-Test case 3: act0 act2 act2 act0 act2 
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_32.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act0 act2 act0 act0 act2 
+Test case 2: act1 act0 act0 act0 
+Test case 3: act0 act2 act2 act0 act2 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_32/wrong1.txt b/Data/Questions/ingsw/0613_32/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_32/wrong1.txt
rename to Data/Questions/ingsw/0613_32/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_32/wrong2.txt b/Data/Questions/ingsw/0613_32/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_32/wrong2.txt
rename to Data/Questions/ingsw/0613_32/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_33/correct.txt b/Data/Questions/ingsw/0613_33/correct.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0613_33/correct.txt
rename to Data/Questions/ingsw/0613_33/correct.txt
index e940faa..c496119 100644
--- a/legacy/Data/Questions/ingsw/0613_33/correct.txt
+++ b/Data/Questions/ingsw/0613_33/correct.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 3) || (y > 3)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 3) || (y > 3)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_3/quest.txt b/Data/Questions/ingsw/0613_33/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0120_3/quest.txt
rename to Data/Questions/ingsw/0613_33/quest.txt
index 2758118..42b0d46 100644
--- a/legacy/Data/ingsw/0120_3/quest.txt
+++ b/Data/Questions/ingsw/0613_33/quest.txt
@@ -1,4 +1,4 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-Si consideri la funzione C
-int f(int x, int y) { ..... }
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+Si consideri la funzione C
+int f(int x, int y) { ..... }
 Quale delle seguenti assert esprime la pre-condizione che entrambi gli argomenti di f sono non-negativi ed almeno uno di loro è maggiore di 3 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_3/wrong1.txt b/Data/Questions/ingsw/0613_33/wrong1.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0120_3/wrong1.txt
rename to Data/Questions/ingsw/0613_33/wrong1.txt
index ad32d88..5dcb186 100644
--- a/legacy/Data/Questions/ingsw/0120_3/wrong1.txt
+++ b/Data/Questions/ingsw/0613_33/wrong1.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x >= 3) || (y >= 3)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x >= 3) || (y >= 3)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_33/wrong2.txt b/Data/Questions/ingsw/0613_33/wrong2.txt
similarity index 95%
rename from legacy/Data/ingsw/0613_33/wrong2.txt
rename to Data/Questions/ingsw/0613_33/wrong2.txt
index 642ec6b..21886fc 100644
--- a/legacy/Data/ingsw/0613_33/wrong2.txt
+++ b/Data/Questions/ingsw/0613_33/wrong2.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x > 0) && (y > 0) && ((x >= 3) || (y > 3)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x > 0) && (y > 0) && ((x >= 3) || (y > 3)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_34/correct.txt b/Data/Questions/ingsw/0613_34/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_34/correct.txt
rename to Data/Questions/ingsw/0613_34/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_34/quest.txt b/Data/Questions/ingsw/0613_34/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_34/quest.txt
rename to Data/Questions/ingsw/0613_34/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0613_34/wrong1.txt b/Data/Questions/ingsw/0613_34/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_34/wrong1.txt
rename to Data/Questions/ingsw/0613_34/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_34/wrong2.txt b/Data/Questions/ingsw/0613_34/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_34/wrong2.txt
rename to Data/Questions/ingsw/0613_34/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_35/correct.txt b/Data/Questions/ingsw/0613_35/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_35/correct.txt
rename to Data/Questions/ingsw/0613_35/correct.txt
index ad21063..a2f00b3 100644
--- a/legacy/Data/Questions/ingsw/0613_35/correct.txt
+++ b/Data/Questions/ingsw/0613_35/correct.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_34/quest.txt b/Data/Questions/ingsw/0613_35/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_34/quest.txt
rename to Data/Questions/ingsw/0613_35/quest.txt
index 031c331..1dbc484 100644
--- a/legacy/Data/Questions/ingsw/0324_34/quest.txt
+++ b/Data/Questions/ingsw/0613_35/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+Si consideri il seguente requisito:
+RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_35/wrong1.txt b/Data/Questions/ingsw/0613_35/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_35/wrong1.txt
rename to Data/Questions/ingsw/0613_35/wrong1.txt
index b14ac60..0696007 100644
--- a/legacy/Data/Questions/ingsw/0613_35/wrong1.txt
+++ b/Data/Questions/ingsw/0613_35/wrong1.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_21/wrong2.txt b/Data/Questions/ingsw/0613_35/wrong2.txt
similarity index 94%
rename from legacy/Data/ingsw/0210_21/wrong2.txt
rename to Data/Questions/ingsw/0613_35/wrong2.txt
index e4201ab..76b58b0 100644
--- a/legacy/Data/ingsw/0210_21/wrong2.txt
+++ b/Data/Questions/ingsw/0613_35/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_36/correct.txt b/Data/Questions/ingsw/0613_36/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_36/correct.txt
rename to Data/Questions/ingsw/0613_36/correct.txt
diff --git a/legacy/Data/ingsw/0613_36/quest.txt b/Data/Questions/ingsw/0613_36/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0613_36/quest.txt
rename to Data/Questions/ingsw/0613_36/quest.txt
index 58782d5..d1c0c6a 100644
--- a/legacy/Data/ingsw/0613_36/quest.txt
+++ b/Data/Questions/ingsw/0613_36/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_36.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_36.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 2, 3, 4 ? In altri terminti, qual'è la probabilità che sia necessario ripetere sia la fase 1 che la fase 2 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_36/wrong1.txt b/Data/Questions/ingsw/0613_36/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_36/wrong1.txt
rename to Data/Questions/ingsw/0613_36/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_36/wrong2.txt b/Data/Questions/ingsw/0613_36/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_36/wrong2.txt
rename to Data/Questions/ingsw/0613_36/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_37/correct.txt b/Data/Questions/ingsw/0613_37/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_37/correct.txt
rename to Data/Questions/ingsw/0613_37/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_22/quest.txt b/Data/Questions/ingsw/0613_37/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_22/quest.txt
rename to Data/Questions/ingsw/0613_37/quest.txt
index e5fbc81..6906b2b 100644
--- a/legacy/Data/Questions/ingsw/0210_22/quest.txt
+++ b/Data/Questions/ingsw/0613_37/quest.txt
@@ -1,16 +1,16 @@
-Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
-block Monitor
-input Real x;  
-output Boolean y;
-Boolean w;
-initial equation
-y = false;
-equation
-w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
-algorithm
-when edge(w) then
-y := true;
-end when;
-end Monitor;
-</pre>
+Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
+block Monitor
+input Real x;  
+output Boolean y;
+Boolean w;
+initial equation
+y = false;
+equation
+w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
+algorithm
+when edge(w) then
+y := true;
+end when;
+end Monitor;
+</pre>
 Quale delle seguenti affermazioni meglio descrive il requisito monitorato?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_37/wrong1.txt b/Data/Questions/ingsw/0613_37/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_37/wrong1.txt
rename to Data/Questions/ingsw/0613_37/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_37/wrong2.txt b/Data/Questions/ingsw/0613_37/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_37/wrong2.txt
rename to Data/Questions/ingsw/0613_37/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_38/quest.txt b/Data/Questions/ingsw/0613_38/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_38/quest.txt
rename to Data/Questions/ingsw/0613_38/quest.txt
index 230115c..647970e 100644
--- a/legacy/Data/Questions/ingsw/0613_38/quest.txt
+++ b/Data/Questions/ingsw/0613_38/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_38.png
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_38.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_38/wrong1.txt b/Data/Questions/ingsw/0613_38/wrong1.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0613_38/wrong1.txt
rename to Data/Questions/ingsw/0613_38/wrong1.txt
index 00b636b..a910206 100644
--- a/legacy/Data/Questions/ingsw/0613_38/wrong1.txt
+++ b/Data/Questions/ingsw/0613_38/wrong1.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_38/wrong2.txt b/Data/Questions/ingsw/0613_38/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0613_38/wrong2.txt
rename to Data/Questions/ingsw/0613_38/wrong2.txt
index dc39134..1ec9f1a 100644
--- a/legacy/Data/ingsw/0613_38/wrong2.txt
+++ b/Data/Questions/ingsw/0613_38/wrong2.txt
@@ -1,34 +1,34 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_38/wrong3.txt b/Data/Questions/ingsw/0613_38/wrong3.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0613_38/wrong3.txt
rename to Data/Questions/ingsw/0613_38/wrong3.txt
index 6a9ef82..02b44cb 100644
--- a/legacy/Data/Questions/ingsw/0613_38/wrong3.txt
+++ b/Data/Questions/ingsw/0613_38/wrong3.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_39/correct.txt b/Data/Questions/ingsw/0613_39/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_39/correct.txt
rename to Data/Questions/ingsw/0613_39/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_39/quest.txt b/Data/Questions/ingsw/0613_39/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_39/quest.txt
rename to Data/Questions/ingsw/0613_39/quest.txt
index 24a64fe..b598314 100644
--- a/legacy/Data/Questions/ingsw/0613_39/quest.txt
+++ b/Data/Questions/ingsw/0613_39/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_39.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
-Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_39.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
+Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_39/wrong1.txt b/Data/Questions/ingsw/0613_39/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_39/wrong1.txt
rename to Data/Questions/ingsw/0613_39/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_39/wrong2.txt b/Data/Questions/ingsw/0613_39/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_39/wrong2.txt
rename to Data/Questions/ingsw/0613_39/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_4/correct.txt b/Data/Questions/ingsw/0613_4/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_4/correct.txt
rename to Data/Questions/ingsw/0613_4/correct.txt
diff --git a/legacy/Data/ingsw/0613_4/quest.txt b/Data/Questions/ingsw/0613_4/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0613_4/quest.txt
rename to Data/Questions/ingsw/0613_4/quest.txt
index 5cf5cae..c4fdcb7 100644
--- a/legacy/Data/ingsw/0613_4/quest.txt
+++ b/Data/Questions/ingsw/0613_4/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_4.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act0 act0 act0 
-Test case 2: act1 act0 act2 act1 act0 
-Test case 3: act1 act2 act2 act0 act2 
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_4.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act0 act0 act0 
+Test case 2: act1 act0 act2 act1 act0 
+Test case 3: act1 act2 act2 act0 act2 
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_4/wrong1.txt b/Data/Questions/ingsw/0613_4/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_4/wrong1.txt
rename to Data/Questions/ingsw/0613_4/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_4/wrong2.txt b/Data/Questions/ingsw/0613_4/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_4/wrong2.txt
rename to Data/Questions/ingsw/0613_4/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_40/quest.txt b/Data/Questions/ingsw/0613_40/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0613_40/quest.txt
rename to Data/Questions/ingsw/0613_40/quest.txt
index 2959407..344d34d 100644
--- a/legacy/Data/ingsw/0613_40/quest.txt
+++ b/Data/Questions/ingsw/0613_40/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_40.png
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_40.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_40/wrong1.txt b/Data/Questions/ingsw/0613_40/wrong1.txt
similarity index 96%
rename from legacy/Data/ingsw/0613_40/wrong1.txt
rename to Data/Questions/ingsw/0613_40/wrong1.txt
index f919b6b..3a0f2d2 100644
--- a/legacy/Data/ingsw/0613_40/wrong1.txt
+++ b/Data/Questions/ingsw/0613_40/wrong1.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_40/wrong2.txt b/Data/Questions/ingsw/0613_40/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0613_40/wrong2.txt
rename to Data/Questions/ingsw/0613_40/wrong2.txt
index fc9e0aa..b7a0fcd 100644
--- a/legacy/Data/ingsw/0613_40/wrong2.txt
+++ b/Data/Questions/ingsw/0613_40/wrong2.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_40/wrong3.txt b/Data/Questions/ingsw/0613_40/wrong3.txt
similarity index 96%
rename from legacy/Data/ingsw/0613_40/wrong3.txt
rename to Data/Questions/ingsw/0613_40/wrong3.txt
index e537817..8a45805 100644
--- a/legacy/Data/ingsw/0613_40/wrong3.txt
+++ b/Data/Questions/ingsw/0613_40/wrong3.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_35/quest.txt b/Data/Questions/ingsw/0613_41/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_35/quest.txt
rename to Data/Questions/ingsw/0613_41/quest.txt
index 99379e6..e060808 100644
--- a/legacy/Data/Questions/ingsw/0120_35/quest.txt
+++ b/Data/Questions/ingsw/0613_41/quest.txt
@@ -1,4 +1,4 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-Si consideri la funzione C
-int f(int x, int y) { ..... }
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+Si consideri la funzione C
+int f(int x, int y) { ..... }
 Quale delle seguenti assert esprime l'invariante che le variabili locali z e w di f() hanno somma minore di 1 oppure maggiore di 7 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_35/wrong1.txt b/Data/Questions/ingsw/0613_41/wrong1.txt
similarity index 94%
rename from legacy/Data/ingsw/0120_35/wrong1.txt
rename to Data/Questions/ingsw/0613_41/wrong1.txt
index cbf1814..3957de1 100644
--- a/legacy/Data/ingsw/0120_35/wrong1.txt
+++ b/Data/Questions/ingsw/0613_41/wrong1.txt
@@ -1,6 +1,6 @@
-int f(in x, int y) 
-{ 
-int z, w;
-assert( (z + w <= 1) || (z + w >= 7));
-.....
+int f(in x, int y) 
+{ 
+int z, w;
+assert( (z + w <= 1) || (z + w >= 7));
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_41/wrong2.txt b/Data/Questions/ingsw/0613_41/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0613_41/wrong2.txt
rename to Data/Questions/ingsw/0613_41/wrong2.txt
index 6fcb8b5..d56f4a5 100644
--- a/legacy/Data/Questions/ingsw/0613_41/wrong2.txt
+++ b/Data/Questions/ingsw/0613_41/wrong2.txt
@@ -1,6 +1,6 @@
-int f(in x, int y) 
-{ 
-int z, w;
-assert( (z + w > 1) || (z + w < 7));
-.....
+int f(in x, int y) 
+{ 
+int z, w;
+assert( (z + w > 1) || (z + w < 7));
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_41/wrong3.txt b/Data/Questions/ingsw/0613_41/wrong3.txt
similarity index 93%
rename from legacy/Data/ingsw/0613_41/wrong3.txt
rename to Data/Questions/ingsw/0613_41/wrong3.txt
index 03b9f52..dff55b1 100644
--- a/legacy/Data/ingsw/0613_41/wrong3.txt
+++ b/Data/Questions/ingsw/0613_41/wrong3.txt
@@ -1,6 +1,6 @@
-int f(in x, int y) 
-{ 
-int z, w;
-assert( (z + w < 1) || (z + w > 7));
-.....
+int f(in x, int y) 
+{ 
+int z, w;
+assert( (z + w < 1) || (z + w > 7));
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_42/correct.txt b/Data/Questions/ingsw/0613_42/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_42/correct.txt
rename to Data/Questions/ingsw/0613_42/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_42/quest.txt b/Data/Questions/ingsw/0613_42/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0613_42/quest.txt
rename to Data/Questions/ingsw/0613_42/quest.txt
index 2bda796..46e2ad5 100644
--- a/legacy/Data/Questions/ingsw/0613_42/quest.txt
+++ b/Data/Questions/ingsw/0613_42/quest.txt
@@ -1,29 +1,29 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x[3])
-{
-  if (x[0] + x[1] - x[2] < -7)
-    { return (0); }
-  else if (2*x[0] -3*x[1] + 4*x[2] > 7)
-        { 
-      if (x[0] + x[1] + x[2] > 10)
-                { return (1); }
-      else
-                { return (0); }      
-    }
- else
-           { 
-             if (2*x[0] + 3*x[1] + 4*x[2] > 9)
-               { return (1); }
-      else
-                 { return (0); }     
-    }
- } /* f()  */
-ed il seguente insieme di test cases:
-
-Test 1: x[0] = 1, x[1] = 1, x[2] = 1, 
-Test2: x[0] = 2, x[1] = 3, x[2] = 3, 
-Test 3: x[0] = -4, x[1] = -4, x[2] = 0, 
-Test 4: x[0] = 3, x[1] = 0, x[2] = 4, 
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x[3])
+{
+  if (x[0] + x[1] - x[2] < -7)
+    { return (0); }
+  else if (2*x[0] -3*x[1] + 4*x[2] > 7)
+        { 
+      if (x[0] + x[1] + x[2] > 10)
+                { return (1); }
+      else
+                { return (0); }      
+    }
+ else
+           { 
+             if (2*x[0] + 3*x[1] + 4*x[2] > 9)
+               { return (1); }
+      else
+                 { return (0); }     
+    }
+ } /* f()  */
+ed il seguente insieme di test cases:
+
+Test 1: x[0] = 1, x[1] = 1, x[2] = 1, 
+Test2: x[0] = 2, x[1] = 3, x[2] = 3, 
+Test 3: x[0] = -4, x[1] = -4, x[2] = 0, 
+Test 4: x[0] = 3, x[1] = 0, x[2] = 4, 
 Test 5:  x[0] = 3, x[1] = 3, x[2] = 5.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_42/wrong1.txt b/Data/Questions/ingsw/0613_42/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_42/wrong1.txt
rename to Data/Questions/ingsw/0613_42/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_42/wrong2.txt b/Data/Questions/ingsw/0613_42/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_42/wrong2.txt
rename to Data/Questions/ingsw/0613_42/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_43/correct.txt b/Data/Questions/ingsw/0613_43/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_43/correct.txt
rename to Data/Questions/ingsw/0613_43/correct.txt
index 293ebbc..ca92838 100644
--- a/legacy/Data/Questions/ingsw/0613_43/correct.txt
+++ b/Data/Questions/ingsw/0613_43/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_43/quest.txt b/Data/Questions/ingsw/0613_43/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_43/quest.txt
rename to Data/Questions/ingsw/0613_43/quest.txt
index 5922b9f..348f2a5 100644
--- a/legacy/Data/Questions/ingsw/0613_43/quest.txt
+++ b/Data/Questions/ingsw/0613_43/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
+Si consideri il seguente requisito:
+RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_43/wrong1.txt b/Data/Questions/ingsw/0613_43/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_43/wrong1.txt
rename to Data/Questions/ingsw/0613_43/wrong1.txt
index d7890b2..c0e3311 100644
--- a/legacy/Data/Questions/ingsw/0613_43/wrong1.txt
+++ b/Data/Questions/ingsw/0613_43/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_43/wrong2.txt b/Data/Questions/ingsw/0613_43/wrong2.txt
similarity index 94%
rename from legacy/Data/ingsw/0613_43/wrong2.txt
rename to Data/Questions/ingsw/0613_43/wrong2.txt
index d50b268..434e904 100644
--- a/legacy/Data/ingsw/0613_43/wrong2.txt
+++ b/Data/Questions/ingsw/0613_43/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_44/correct.txt b/Data/Questions/ingsw/0613_44/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_44/correct.txt
rename to Data/Questions/ingsw/0613_44/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_44/quest.txt b/Data/Questions/ingsw/0613_44/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_44/quest.txt
rename to Data/Questions/ingsw/0613_44/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0613_44/wrong1.txt b/Data/Questions/ingsw/0613_44/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_44/wrong1.txt
rename to Data/Questions/ingsw/0613_44/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_44/wrong2.txt b/Data/Questions/ingsw/0613_44/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_44/wrong2.txt
rename to Data/Questions/ingsw/0613_44/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_45/correct.txt b/Data/Questions/ingsw/0613_45/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_45/correct.txt
rename to Data/Questions/ingsw/0613_45/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_44/quest.txt b/Data/Questions/ingsw/0613_45/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_44/quest.txt
rename to Data/Questions/ingsw/0613_45/quest.txt
index e44e320..6f570ef 100644
--- a/legacy/Data/Questions/ingsw/0120_44/quest.txt
+++ b/Data/Questions/ingsw/0613_45/quest.txt
@@ -1,21 +1,21 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a  + b - 6 >= 0) && (b - c - 1 <= 0) )
-          return (1);    // punto di uscita 1
-      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0))
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a  + b - 6 >= 0) && (b - c - 1 <= 0) )
+          return (1);    // punto di uscita 1
+      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0))
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_45/wrong1.txt b/Data/Questions/ingsw/0613_45/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_45/wrong1.txt
rename to Data/Questions/ingsw/0613_45/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_45/wrong2.txt b/Data/Questions/ingsw/0613_45/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_45/wrong2.txt
rename to Data/Questions/ingsw/0613_45/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_46/correct.txt b/Data/Questions/ingsw/0613_46/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_46/correct.txt
rename to Data/Questions/ingsw/0613_46/correct.txt
diff --git a/legacy/Data/ingsw/0613_46/quest.txt b/Data/Questions/ingsw/0613_46/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0613_46/quest.txt
rename to Data/Questions/ingsw/0613_46/quest.txt
index 03acbcc..3187441 100644
--- a/legacy/Data/ingsw/0613_46/quest.txt
+++ b/Data/Questions/ingsw/0613_46/quest.txt
@@ -1,30 +1,30 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-
-int f(int x[3])
-{
-  if (-x[0] + x[1] - x[2] < -7)
-     if (-x[0] + x[1] - x[2] > 10)
-                { return (0); }
-      else
-                { return (1); }
-      else if (-3*x[0] +3*x[1] - 5*x[2] > 7)
-        { 
-      return (0);      
-  }
- else
-           { 
-             if (3*x[0] - 5*x[1] + 7*x[2] > 9)
-               { return (1); }
-      else
-                { return (0); }      
-    }
-} /* f()  */
-----------
-ed il seguente insieme di test cases:
-
-Test 1: x[0] = 2, x[1] = -3, x[2] = 4, 
-Test 2: x[0] = 1, x[1] = 0, x[2] = 2, 
-Test 3:   x[0] = -3, x[1] = -4, x[2] = -3,
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+
+int f(int x[3])
+{
+  if (-x[0] + x[1] - x[2] < -7)
+     if (-x[0] + x[1] - x[2] > 10)
+                { return (0); }
+      else
+                { return (1); }
+      else if (-3*x[0] +3*x[1] - 5*x[2] > 7)
+        { 
+      return (0);      
+  }
+ else
+           { 
+             if (3*x[0] - 5*x[1] + 7*x[2] > 9)
+               { return (1); }
+      else
+                { return (0); }      
+    }
+} /* f()  */
+----------
+ed il seguente insieme di test cases:
+
+Test 1: x[0] = 2, x[1] = -3, x[2] = 4, 
+Test 2: x[0] = 1, x[1] = 0, x[2] = 2, 
+Test 3:   x[0] = -3, x[1] = -4, x[2] = -3,
 Test 4: x[0] = 3, x[1] = -1, x[2] = -3.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_46/wrong1.txt b/Data/Questions/ingsw/0613_46/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_46/wrong1.txt
rename to Data/Questions/ingsw/0613_46/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_46/wrong2.txt b/Data/Questions/ingsw/0613_46/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_46/wrong2.txt
rename to Data/Questions/ingsw/0613_46/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_47/correct.txt b/Data/Questions/ingsw/0613_47/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_47/correct.txt
rename to Data/Questions/ingsw/0613_47/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_47/quest.txt b/Data/Questions/ingsw/0613_47/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_47/quest.txt
rename to Data/Questions/ingsw/0613_47/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0613_47/wrong1.txt b/Data/Questions/ingsw/0613_47/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_47/wrong1.txt
rename to Data/Questions/ingsw/0613_47/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_47/wrong2.txt b/Data/Questions/ingsw/0613_47/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_47/wrong2.txt
rename to Data/Questions/ingsw/0613_47/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_48/correct.txt b/Data/Questions/ingsw/0613_48/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_48/correct.txt
rename to Data/Questions/ingsw/0613_48/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_48/quest.txt b/Data/Questions/ingsw/0613_48/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_48/quest.txt
rename to Data/Questions/ingsw/0613_48/quest.txt
index adccf3a..ab4b6cc 100644
--- a/legacy/Data/Questions/ingsw/0613_48/quest.txt
+++ b/Data/Questions/ingsw/0613_48/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_48.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_48.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 2, 3? In altri terminti, qual'è la probabilità che non sia necessario ripetere la prima fase (ma non la seconda) ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_48/wrong1.txt b/Data/Questions/ingsw/0613_48/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_48/wrong1.txt
rename to Data/Questions/ingsw/0613_48/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_48/wrong2.txt b/Data/Questions/ingsw/0613_48/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_48/wrong2.txt
rename to Data/Questions/ingsw/0613_48/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_28/correct.txt b/Data/Questions/ingsw/0613_49/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_28/correct.txt
rename to Data/Questions/ingsw/0613_49/correct.txt
index 4c75070..9be970c 100644
--- a/legacy/Data/Questions/ingsw/0324_28/correct.txt
+++ b/Data/Questions/ingsw/0613_49/correct.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_49/quest.txt b/Data/Questions/ingsw/0613_49/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0613_49/quest.txt
rename to Data/Questions/ingsw/0613_49/quest.txt
index e11a044..0910f0b 100644
--- a/legacy/Data/Questions/ingsw/0613_49/quest.txt
+++ b/Data/Questions/ingsw/0613_49/quest.txt
@@ -1,4 +1,4 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
+Si consideri il seguente requisito:
+RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_43/wrong1.txt b/Data/Questions/ingsw/0613_49/wrong1.txt
similarity index 94%
rename from legacy/Data/ingsw/0210_43/wrong1.txt
rename to Data/Questions/ingsw/0613_49/wrong1.txt
index 6dafe94..8b6fcac 100644
--- a/legacy/Data/ingsw/0210_43/wrong1.txt
+++ b/Data/Questions/ingsw/0613_49/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_43/wrong2.txt b/Data/Questions/ingsw/0613_49/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_43/wrong2.txt
rename to Data/Questions/ingsw/0613_49/wrong2.txt
index a3d79a4..40a2ce0 100644
--- a/legacy/Data/Questions/ingsw/0210_43/wrong2.txt
+++ b/Data/Questions/ingsw/0613_49/wrong2.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_5/correct.txt b/Data/Questions/ingsw/0613_5/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_5/correct.txt
rename to Data/Questions/ingsw/0613_5/correct.txt
diff --git a/legacy/Data/ingsw/0613_5/quest.txt b/Data/Questions/ingsw/0613_5/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0613_5/quest.txt
rename to Data/Questions/ingsw/0613_5/quest.txt
index 579b39b..1e9d8fc 100644
--- a/legacy/Data/ingsw/0613_5/quest.txt
+++ b/Data/Questions/ingsw/0613_5/quest.txt
@@ -1,29 +1,29 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x[3])
-{
-  if (-x[0] + x[1] - x[2] < -7)
-     if (-x[0] + x[1] - x[2] > 10)
-                { return (0); }
-      else
-                { return (1); }
-      else if (-3*x[0] +3*x[1] - 5*x[2] > 7)
-        { 
-     if (3*x[0] - 5*x[1] + 7*x[2] > 9)
-               { return (0); }
-      else
-                { return (1); }      
-  }
- else
-           { 
-             return (0);       
-    }
-
-} /* f()  */
-----------
-ed il seguente insieme di test cases:
-
-Test 1: x[0] = 1, x[1] = 5, x[2] = 3,
-Test 2: x[0] = 4, x[1] = -2, x[2] = 2, 
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x[3])
+{
+  if (-x[0] + x[1] - x[2] < -7)
+     if (-x[0] + x[1] - x[2] > 10)
+                { return (0); }
+      else
+                { return (1); }
+      else if (-3*x[0] +3*x[1] - 5*x[2] > 7)
+        { 
+     if (3*x[0] - 5*x[1] + 7*x[2] > 9)
+               { return (0); }
+      else
+                { return (1); }      
+  }
+ else
+           { 
+             return (0);       
+    }
+
+} /* f()  */
+----------
+ed il seguente insieme di test cases:
+
+Test 1: x[0] = 1, x[1] = 5, x[2] = 3,
+Test 2: x[0] = 4, x[1] = -2, x[2] = 2, 
 Test 3:   x[0] = 5, x[1] = 3, x[2] = -4.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_5/wrong1.txt b/Data/Questions/ingsw/0613_5/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_5/wrong1.txt
rename to Data/Questions/ingsw/0613_5/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_5/wrong2.txt b/Data/Questions/ingsw/0613_5/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_5/wrong2.txt
rename to Data/Questions/ingsw/0613_5/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_31/correct.txt b/Data/Questions/ingsw/0613_6/correct.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/1122_31/correct.txt
rename to Data/Questions/ingsw/0613_6/correct.txt
index a98afd2..d3daba4 100644
--- a/legacy/Data/Questions/ingsw/1122_31/correct.txt
+++ b/Data/Questions/ingsw/0613_6/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and ((x >= 30) or (x <= 20)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and ((x >= 30) or (x <= 20)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_38/quest.txt b/Data/Questions/ingsw/0613_6/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_38/quest.txt
rename to Data/Questions/ingsw/0613_6/quest.txt
index b420aaf..6938bd6 100644
--- a/legacy/Data/Questions/ingsw/0120_38/quest.txt
+++ b/Data/Questions/ingsw/0613_6/quest.txt
@@ -1,3 +1,3 @@
-Si consideri il seguente requisito:
-RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
+Si consideri il seguente requisito:
+RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
 Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_46/wrong1.txt b/Data/Questions/ingsw/0613_6/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0324_46/wrong1.txt
rename to Data/Questions/ingsw/0613_6/wrong1.txt
index 66064fe..5bdf9c8 100644
--- a/legacy/Data/Questions/ingsw/0324_46/wrong1.txt
+++ b/Data/Questions/ingsw/0613_6/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and (x >= 20) and (x <= 30) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and (x >= 20) and (x <= 30) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_6/wrong2.txt b/Data/Questions/ingsw/0613_6/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0613_6/wrong2.txt
rename to Data/Questions/ingsw/0613_6/wrong2.txt
index c71f1f5..169ec7c 100644
--- a/legacy/Data/ingsw/0613_6/wrong2.txt
+++ b/Data/Questions/ingsw/0613_6/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) or ((x >= 20) and (x <= 30)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) or ((x >= 20) and (x <= 30)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_7/correct.txt b/Data/Questions/ingsw/0613_7/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_7/correct.txt
rename to Data/Questions/ingsw/0613_7/correct.txt
diff --git a/legacy/Data/ingsw/0120_30/quest.txt b/Data/Questions/ingsw/0613_7/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0120_30/quest.txt
rename to Data/Questions/ingsw/0613_7/quest.txt
index dbd72c0..20501b2 100644
--- a/legacy/Data/ingsw/0120_30/quest.txt
+++ b/Data/Questions/ingsw/0613_7/quest.txt
@@ -1,22 +1,22 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a - 100 >= 0) && (b - c - 1 <= 0) )
-          return (1);    // punto di uscita 1
-      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0)
-)
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a - 100 >= 0) && (b - c - 1 <= 0) )
+          return (1);    // punto di uscita 1
+      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0)
+)
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_7/wrong1.txt b/Data/Questions/ingsw/0613_7/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_7/wrong1.txt
rename to Data/Questions/ingsw/0613_7/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_7/wrong2.txt b/Data/Questions/ingsw/0613_7/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_7/wrong2.txt
rename to Data/Questions/ingsw/0613_7/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_8/correct.txt b/Data/Questions/ingsw/0613_8/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_8/correct.txt
rename to Data/Questions/ingsw/0613_8/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_8/quest.txt b/Data/Questions/ingsw/0613_8/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_8/quest.txt
rename to Data/Questions/ingsw/0613_8/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0613_8/wrong1.txt b/Data/Questions/ingsw/0613_8/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_8/wrong1.txt
rename to Data/Questions/ingsw/0613_8/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0613_8/wrong2.txt b/Data/Questions/ingsw/0613_8/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0613_8/wrong2.txt
rename to Data/Questions/ingsw/0613_8/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_9/quest.txt b/Data/Questions/ingsw/0613_9/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0613_9/quest.txt
rename to Data/Questions/ingsw/0613_9/quest.txt
index 89f55eb..b56062c 100644
--- a/legacy/Data/ingsw/0613_9/quest.txt
+++ b/Data/Questions/ingsw/0613_9/quest.txt
@@ -1,4 +1,4 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_9.png
-Si consideri la seguente architettura software:
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_9.png
+Si consideri la seguente architettura software:
+
 Quale dei seguenti modelli Modelica meglio la rappresenta.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_48/wrong1.txt b/Data/Questions/ingsw/0613_9/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0324_48/wrong1.txt
rename to Data/Questions/ingsw/0613_9/wrong1.txt
index 4bcd55f..d698ca8 100644
--- a/legacy/Data/Questions/ingsw/0324_48/wrong1.txt
+++ b/Data/Questions/ingsw/0613_9/wrong1.txt
@@ -1,8 +1,8 @@
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
 connect(sc1.output12, sc
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_9/wrong2.txt b/Data/Questions/ingsw/0613_9/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0613_9/wrong2.txt
rename to Data/Questions/ingsw/0613_9/wrong2.txt
index 2c10a10..daecacc 100644
--- a/legacy/Data/Questions/ingsw/0613_9/wrong2.txt
+++ b/Data/Questions/ingsw/0613_9/wrong2.txt
@@ -1,4 +1,4 @@
-input12)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output24, sc4.input24)
+input12)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output24, sc4.input24)
 connect(sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_9/wrong3.txt b/Data/Questions/ingsw/0613_9/wrong3.txt
similarity index 94%
rename from legacy/Data/ingsw/0613_9/wrong3.txt
rename to Data/Questions/ingsw/0613_9/wrong3.txt
index 7ddc09e..fc21af3 100644
--- a/legacy/Data/ingsw/0613_9/wrong3.txt
+++ b/Data/Questions/ingsw/0613_9/wrong3.txt
@@ -1,46 +1,46 @@
-output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output43, sc3.input43)
-
-
-end SysArch;
-
-2.
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc2.input12)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output23, sc3.input23)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output31, sc1.input31)
-connect(sc3.output32, sc2.input32)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output43, sc3.input43)
-
-
-end SysArch;
-
-3.
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output13, sc3.input13)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output23, sc3.input23)
-connect(sc3.output32, sc2.input32)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output42, sc2.input42)
-
-
+output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output43, sc3.input43)
+
+
+end SysArch;
+
+2.
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc2.input12)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output23, sc3.input23)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output31, sc1.input31)
+connect(sc3.output32, sc2.input32)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output43, sc3.input43)
+
+
+end SysArch;
+
+3.
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output13, sc3.input13)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output23, sc3.input23)
+connect(sc3.output32, sc2.input32)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output42, sc2.input42)
+
+
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0621_0/correct.txt b/Data/Questions/ingsw/0621_0/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_0/correct.txt
rename to Data/Questions/ingsw/0621_0/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0621_0/quest.txt b/Data/Questions/ingsw/0621_0/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_0/quest.txt
rename to Data/Questions/ingsw/0621_0/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0621_0/wrong0.txt b/Data/Questions/ingsw/0621_0/wrong0.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_0/wrong0.txt
rename to Data/Questions/ingsw/0621_0/wrong0.txt
diff --git a/legacy/Data/Questions/ingsw/0621_0/wrong1.txt b/Data/Questions/ingsw/0621_0/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_0/wrong1.txt
rename to Data/Questions/ingsw/0621_0/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0621_1/correct.txt b/Data/Questions/ingsw/0621_1/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_1/correct.txt
rename to Data/Questions/ingsw/0621_1/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0621_1/quest.txt b/Data/Questions/ingsw/0621_1/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_1/quest.txt
rename to Data/Questions/ingsw/0621_1/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0621_1/wrong1.txt b/Data/Questions/ingsw/0621_1/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_1/wrong1.txt
rename to Data/Questions/ingsw/0621_1/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0621_1/wrong2.txt b/Data/Questions/ingsw/0621_1/wrong2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_1/wrong2.txt
rename to Data/Questions/ingsw/0621_1/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0621_10/correct.txt b/Data/Questions/ingsw/0621_10/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_10/correct.txt
rename to Data/Questions/ingsw/0621_10/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0621_10/quest.txt b/Data/Questions/ingsw/0621_10/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_10/quest.txt
rename to Data/Questions/ingsw/0621_10/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0621_10/wrong0.txt b/Data/Questions/ingsw/0621_10/wrong0.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_10/wrong0.txt
rename to Data/Questions/ingsw/0621_10/wrong0.txt
diff --git a/legacy/Data/Questions/ingsw/0621_10/wrong1.txt b/Data/Questions/ingsw/0621_10/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_10/wrong1.txt
rename to Data/Questions/ingsw/0621_10/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0621_13/correct.txt b/Data/Questions/ingsw/0621_13/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_13/correct.txt
rename to Data/Questions/ingsw/0621_13/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0621_13/quest.txt b/Data/Questions/ingsw/0621_13/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_13/quest.txt
rename to Data/Questions/ingsw/0621_13/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0621_13/wrong0.txt b/Data/Questions/ingsw/0621_13/wrong0.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_13/wrong0.txt
rename to Data/Questions/ingsw/0621_13/wrong0.txt
diff --git a/legacy/Data/Questions/ingsw/0621_13/wrong1.txt b/Data/Questions/ingsw/0621_13/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_13/wrong1.txt
rename to Data/Questions/ingsw/0621_13/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0621_14/correct.txt b/Data/Questions/ingsw/0621_14/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_14/correct.txt
rename to Data/Questions/ingsw/0621_14/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0621_14/quest.txt b/Data/Questions/ingsw/0621_14/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_14/quest.txt
rename to Data/Questions/ingsw/0621_14/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0621_14/wrong0.txt b/Data/Questions/ingsw/0621_14/wrong0.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_14/wrong0.txt
rename to Data/Questions/ingsw/0621_14/wrong0.txt
diff --git a/legacy/Data/Questions/ingsw/0621_14/wrong1.txt b/Data/Questions/ingsw/0621_14/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_14/wrong1.txt
rename to Data/Questions/ingsw/0621_14/wrong1.txt
diff --git a/legacy/Data/Questions/ingsw/0621_17/correct.txt b/Data/Questions/ingsw/0621_17/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_17/correct.txt
rename to Data/Questions/ingsw/0621_17/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0621_17/quest.txt b/Data/Questions/ingsw/0621_17/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_17/quest.txt
rename to Data/Questions/ingsw/0621_17/quest.txt
diff --git a/legacy/Data/Questions/ingsw/0621_17/wrong0.txt b/Data/Questions/ingsw/0621_17/wrong0.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_17/wrong0.txt
rename to Data/Questions/ingsw/0621_17/wrong0.txt
diff --git a/legacy/Data/Questions/ingsw/0621_17/wrong1.txt b/Data/Questions/ingsw/0621_17/wrong1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/0621_17/wrong1.txt
rename to Data/Questions/ingsw/0621_17/wrong1.txt
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diff --git a/legacy/Data/Questions/ingsw/0210_25/wrong1.txt b/Data/Questions/ingsw/0722_2/wrong 1.txt
similarity index 95%
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index 6a97baf..392cc67 100644
--- a/legacy/Data/Questions/ingsw/0210_25/wrong1.txt
+++ b/Data/Questions/ingsw/0722_2/wrong 1.txt	
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 1) || (y > 1)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 1) || (y > 1)) );
+.....
 }
\ No newline at end of file
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similarity index 100%
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diff --git a/legacy/Data/Questions/ingsw/10/correct.txt b/Data/Questions/ingsw/10/correct.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/10/quest.txt b/Data/Questions/ingsw/10/quest.txt
similarity index 94%
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index 6befac6..b474c0a 100644
--- a/legacy/Data/ingsw/10/quest.txt
+++ b/Data/Questions/ingsw/10/quest.txt
@@ -1,26 +1,26 @@
-Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato:
-<pre>
-block Monitor
-
-input Real x;  
-output Boolean y;
-Boolean w;
-
-initial equation
-
-y = false;
-
-equation
-
-w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
-
-algorithm
-
-when edge(w) then
-y := true;
-end when;
-
-end Monitor;
-</pre>
-
+Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato:
+<pre>
+block Monitor
+
+input Real x;  
+output Boolean y;
+Boolean w;
+
+initial equation
+
+y = false;
+
+equation
+
+w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
+
+algorithm
+
+when edge(w) then
+y := true;
+end when;
+
+end Monitor;
+</pre>
+
 Quale delle seguenti affermazioni meglio descrive il requisito monitorato?
\ No newline at end of file
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diff --git a/legacy/Data/Questions/ingsw/11/wrong.txt b/Data/Questions/ingsw/11/wrong.txt
similarity index 100%
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diff --git a/legacy/Data/Questions/ingsw/1122_1/correct.txt b/Data/Questions/ingsw/1122_1/correct.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/1122_1/correct.txt
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index fd39f0f..8e30daa 100644
--- a/legacy/Data/Questions/ingsw/1122_1/correct.txt
+++ b/Data/Questions/ingsw/1122_1/correct.txt
@@ -1,44 +1,44 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-
-algorithm
-
-
-when initial() then
-x := 0;
-
-
-elsewhen sample(0,T) then
-
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-
-end when;
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+
+algorithm
+
+
+when initial() then
+x := 0;
+
+
+elsewhen sample(0,T) then
+
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+
+end when;
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_1/quest.txt b/Data/Questions/ingsw/1122_1/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/1122_1/quest.txt
rename to Data/Questions/ingsw/1122_1/quest.txt
index df0e6be..abd4f55 100644
--- a/legacy/Data/ingsw/1122_1/quest.txt
+++ b/Data/Questions/ingsw/1122_1/quest.txt
@@ -1,2 +1,2 @@
-img=https://i.imgur.com/jS97TUd.png
+img=https://i.imgur.com/jS97TUd.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_1/wrong 1.txt b/Data/Questions/ingsw/1122_1/wrong 1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/1122_1/wrong 1.txt
rename to Data/Questions/ingsw/1122_1/wrong 1.txt
index febcca9..8578489 100644
--- a/legacy/Data/Questions/ingsw/1122_1/wrong 1.txt	
+++ b/Data/Questions/ingsw/1122_1/wrong 1.txt	
@@ -1,77 +1,77 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_1/wrong 2.txt b/Data/Questions/ingsw/1122_1/wrong 2.txt
similarity index 93%
rename from legacy/Data/ingsw/1122_1/wrong 2.txt
rename to Data/Questions/ingsw/1122_1/wrong 2.txt
index 94279c9..8ddcb58 100644
--- a/legacy/Data/ingsw/1122_1/wrong 2.txt	
+++ b/Data/Questions/ingsw/1122_1/wrong 2.txt	
@@ -1,67 +1,67 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_10/correct.txt b/Data/Questions/ingsw/1122_10/correct.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/1122_10/correct.txt
rename to Data/Questions/ingsw/1122_10/correct.txt
index e940faa..c496119 100644
--- a/legacy/Data/Questions/ingsw/1122_10/correct.txt
+++ b/Data/Questions/ingsw/1122_10/correct.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 3) || (y > 3)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 3) || (y > 3)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_10/quest.txt b/Data/Questions/ingsw/1122_10/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_10/quest.txt
rename to Data/Questions/ingsw/1122_10/quest.txt
index c939cfb..210b7c7 100644
--- a/legacy/Data/ingsw/1122_10/quest.txt
+++ b/Data/Questions/ingsw/1122_10/quest.txt
@@ -1,7 +1,7 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-
-Si consideri la funzione C
-
-int f(int x, int y) { ..... }
-
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+
+Si consideri la funzione C
+
+int f(int x, int y) { ..... }
+
 Quale delle seguenti assert esprime la pre-condizione che entrambi gli argomenti di f sono non-negativi ed almeno uno di loro è maggiore di 3?
\ No newline at end of file
diff --git a/Data/Questions/ingsw/1122_10/wrong 1.txt b/Data/Questions/ingsw/1122_10/wrong 1.txt
new file mode 100644
index 0000000..11dc1a3
--- /dev/null
+++ b/Data/Questions/ingsw/1122_10/wrong 1.txt	
@@ -0,0 +1,10 @@
+
+int f(in x, int y) 
+
+{ 
+
+assert( (x >= 0) && (y >= 0) && ((x >= 3) || (y >= 3)) );
+
+.....
+
+}
\ No newline at end of file
diff --git a/Data/Questions/ingsw/1122_10/wrong 2.txt b/Data/Questions/ingsw/1122_10/wrong 2.txt
new file mode 100644
index 0000000..c98f275
--- /dev/null
+++ b/Data/Questions/ingsw/1122_10/wrong 2.txt	
@@ -0,0 +1,9 @@
+int f(in x, int y) 
+
+{ 
+
+assert( (x > 0) && (y > 0) && ((x >= 3) || (y > 3)) );
+
+.....
+
+}
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_11/correct.txt b/Data/Questions/ingsw/1122_11/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_11/correct.txt
rename to Data/Questions/ingsw/1122_11/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_11/quest.txt b/Data/Questions/ingsw/1122_11/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/1122_11/quest.txt
rename to Data/Questions/ingsw/1122_11/quest.txt
index b46cb2b..51be506 100644
--- a/legacy/Data/Questions/ingsw/1122_11/quest.txt
+++ b/Data/Questions/ingsw/1122_11/quest.txt
@@ -1,17 +1,17 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-
-Si consideri la seguente funzione C:
-
------------
-
-int f(int x, int y)  {   
-
-int z = x;
-
-while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
-
-return (z);
-
-}
-
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+
+Si consideri la seguente funzione C:
+
+-----------
+
+int f(int x, int y)  {   
+
+int z = x;
+
+while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
+
+return (z);
+
+}
+
 Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_11/wrong 1.txt b/Data/Questions/ingsw/1122_11/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_11/wrong 1.txt
rename to Data/Questions/ingsw/1122_11/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_11/wrong 2.txt b/Data/Questions/ingsw/1122_11/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_11/wrong 2.txt
rename to Data/Questions/ingsw/1122_11/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_12/correct.txt b/Data/Questions/ingsw/1122_12/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_12/correct.txt
rename to Data/Questions/ingsw/1122_12/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_12/quest.txt b/Data/Questions/ingsw/1122_12/quest.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/1122_12/quest.txt
rename to Data/Questions/ingsw/1122_12/quest.txt
index 464b817..f13bd49 100644
--- a/legacy/Data/Questions/ingsw/1122_12/quest.txt
+++ b/Data/Questions/ingsw/1122_12/quest.txt
@@ -1,76 +1,76 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente?
-
-
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente?
+
+
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_12/wrong 1.txt b/Data/Questions/ingsw/1122_12/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_12/wrong 1.txt
rename to Data/Questions/ingsw/1122_12/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_12/wrong 2.txt b/Data/Questions/ingsw/1122_12/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_12/wrong 2.txt
rename to Data/Questions/ingsw/1122_12/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_13/correct.txt b/Data/Questions/ingsw/1122_13/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_13/correct.txt
rename to Data/Questions/ingsw/1122_13/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_13/quest.txt b/Data/Questions/ingsw/1122_13/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/1122_13/quest.txt
rename to Data/Questions/ingsw/1122_13/quest.txt
index d57a552..3ad0e09 100644
--- a/legacy/Data/Questions/ingsw/1122_13/quest.txt
+++ b/Data/Questions/ingsw/1122_13/quest.txt
@@ -1,8 +1,8 @@
-img=https://i.imgur.com/pBLLwD1.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-
-Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-
-Uno scenario è una sequenza di stati.
-
+img=https://i.imgur.com/pBLLwD1.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+
+Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+
+Uno scenario è una sequenza di stati.
+
 Qual è la probabilità dello scenario: 1, 3, 4? In altri terminti, qual è la probabilità che non sia necessario ripetere la seconda fase (ma non la prima)?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_13/wrong 1.txt b/Data/Questions/ingsw/1122_13/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_13/wrong 1.txt
rename to Data/Questions/ingsw/1122_13/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_13/wrong 2.txt b/Data/Questions/ingsw/1122_13/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_13/wrong 2.txt
rename to Data/Questions/ingsw/1122_13/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_14/correct.txt b/Data/Questions/ingsw/1122_14/correct.txt
new file mode 100644
index 0000000..633b308
--- /dev/null
+++ b/Data/Questions/ingsw/1122_14/correct.txt
@@ -0,0 +1,19 @@
+<pre>
+#define n 1000
+int TestOracle1(int *A, int *B)
+{
+int i, j, D[n];
+//init
+
+for (i = 0; i < n; i++) D[i] = -1;
+
+// B is ordered
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+// B is a permutation of A
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; D[j] = 1; break;}
+
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+// B ok
+return (1);
+}
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_14/quest.txt b/Data/Questions/ingsw/1122_14/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_14/quest.txt
rename to Data/Questions/ingsw/1122_14/quest.txt
index bd20578..3d717ca 100644
--- a/legacy/Data/ingsw/1122_14/quest.txt
+++ b/Data/Questions/ingsw/1122_14/quest.txt
@@ -1,7 +1,7 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-
-Si consideri la seguente specifica funzionale per la funzione f.
-
-La funzione f(int *A, int *B)  prende come input un vettore A di dimensione n  ritorna come output un vettore B ottenuto ordinando gli elementi di A in ordine crescente.
-
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+
+Si consideri la seguente specifica funzionale per la funzione f.
+
+La funzione f(int *A, int *B)  prende come input un vettore A di dimensione n  ritorna come output un vettore B ottenuto ordinando gli elementi di A in ordine crescente.
+
 Quale delle seguenti funzioni è un test oracle per la funzione f?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_14/wrong 1.txt b/Data/Questions/ingsw/1122_14/wrong 1.txt
similarity index 94%
rename from legacy/Data/ingsw/1122_14/wrong 1.txt
rename to Data/Questions/ingsw/1122_14/wrong 1.txt
index 189d31d..6068762 100644
--- a/legacy/Data/ingsw/1122_14/wrong 1.txt	
+++ b/Data/Questions/ingsw/1122_14/wrong 1.txt	
@@ -1,29 +1,29 @@
-<pre>
-#define n 1000
-
-int TestOracle2(int *A, int *B)
-
-{
-
-int i, j, D[n];
-
-//init
-
-for (i = 0; i < n; i++) D[i] = -1;
-
-// B is ordered
-
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-
-// B is a permutation of A
-
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; break;}
-
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-
-// B ok
-
-return (1);
-
-}
+<pre>
+#define n 1000
+
+int TestOracle2(int *A, int *B)
+
+{
+
+int i, j, D[n];
+
+//init
+
+for (i = 0; i < n; i++) D[i] = -1;
+
+// B is ordered
+
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+
+// B is a permutation of A
+
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; break;}
+
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+
+// B ok
+
+return (1);
+
+}
 </pre>
\ No newline at end of file
diff --git a/Data/Questions/ingsw/1122_14/wrong 2.txt b/Data/Questions/ingsw/1122_14/wrong 2.txt
new file mode 100644
index 0000000..7a0753f
--- /dev/null
+++ b/Data/Questions/ingsw/1122_14/wrong 2.txt	
@@ -0,0 +1,29 @@
+<pre>
+#define n 1000
+
+int TestOracle3(int *A, int *B)
+
+{
+
+int i, j, D[n];
+
+//init
+
+for (i = 0; i < n; i++) D[i] = -1;
+
+// B is ordered
+
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+
+// B is a permutation of A
+
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if (A[i] == B[j]) {C[i][j] = 1; D[j] = 1; break;}
+
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+
+// B ok
+
+return (1);
+
+}
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_15/correct.txt b/Data/Questions/ingsw/1122_15/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_15/correct.txt
rename to Data/Questions/ingsw/1122_15/correct.txt
diff --git a/legacy/Data/ingsw/1122_15/quest.txt b/Data/Questions/ingsw/1122_15/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/1122_15/quest.txt
rename to Data/Questions/ingsw/1122_15/quest.txt
index 0d7fe08..f820e09 100644
--- a/legacy/Data/ingsw/1122_15/quest.txt
+++ b/Data/Questions/ingsw/1122_15/quest.txt
@@ -1,12 +1,12 @@
-img=https://i.imgur.com/mMq2O4x.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-
-Si consideri lo state diagram in figura
-Si consideri il seguente insieme di test cases:
-
-Test case 1: act1 act2 act0 
-
-Test case 2: act0 act1 act0 act0 
-
-Test case 3: act1 act0 act2 act2 act0
+img=https://i.imgur.com/mMq2O4x.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+
+Si consideri lo state diagram in figura
+Si consideri il seguente insieme di test cases:
+
+Test case 1: act1 act2 act0 
+
+Test case 2: act0 act1 act0 act0 
+
+Test case 3: act1 act0 act2 act2 act0
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_15/wrong 1.txt b/Data/Questions/ingsw/1122_15/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_15/wrong 1.txt
rename to Data/Questions/ingsw/1122_15/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_15/wrong 2.txt b/Data/Questions/ingsw/1122_15/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_15/wrong 2.txt
rename to Data/Questions/ingsw/1122_15/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_16/correct.txt b/Data/Questions/ingsw/1122_16/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_16/correct.txt
rename to Data/Questions/ingsw/1122_16/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_16/quest.txt b/Data/Questions/ingsw/1122_16/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_16/quest.txt
rename to Data/Questions/ingsw/1122_16/quest.txt
index db10798..96b152b 100644
--- a/legacy/Data/Questions/ingsw/1122_16/quest.txt
+++ b/Data/Questions/ingsw/1122_16/quest.txt
@@ -1,7 +1,7 @@
-Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
-
-Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
-
-P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
-
+Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
+
+Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
+
+P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
+
 dove b è una opportuna costante note da dati storici aziendali. Si assuma che b = 0.0001,  C = 1000000, ed il rischio ammesso è R = 1000. Quale dei seguenti valori meglio approssima il costo S per lo sviluppo del software in questione.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_16/wrong 1.txt b/Data/Questions/ingsw/1122_16/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_16/wrong 1.txt
rename to Data/Questions/ingsw/1122_16/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_16/wrong 2.txt b/Data/Questions/ingsw/1122_16/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_16/wrong 2.txt
rename to Data/Questions/ingsw/1122_16/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_19/correct.txt b/Data/Questions/ingsw/1122_19/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_19/correct.txt
rename to Data/Questions/ingsw/1122_19/correct.txt
diff --git a/legacy/Data/ingsw/1122_19/quest.txt b/Data/Questions/ingsw/1122_19/quest.txt
similarity index 94%
rename from legacy/Data/ingsw/1122_19/quest.txt
rename to Data/Questions/ingsw/1122_19/quest.txt
index 28aa70c..4a339f6 100644
--- a/legacy/Data/ingsw/1122_19/quest.txt
+++ b/Data/Questions/ingsw/1122_19/quest.txt
@@ -1,75 +1,75 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente?
-
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente?
+
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_19/wrong 1.txt b/Data/Questions/ingsw/1122_19/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_19/wrong 1.txt
rename to Data/Questions/ingsw/1122_19/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_19/wrong 2.txt b/Data/Questions/ingsw/1122_19/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_19/wrong 2.txt
rename to Data/Questions/ingsw/1122_19/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_34/correct.txt b/Data/Questions/ingsw/1122_2/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_34/correct.txt
rename to Data/Questions/ingsw/1122_2/correct.txt
index ad21063..a2f00b3 100644
--- a/legacy/Data/Questions/ingsw/0324_34/correct.txt
+++ b/Data/Questions/ingsw/1122_2/correct.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_2/quest.txt b/Data/Questions/ingsw/1122_2/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_2/quest.txt
rename to Data/Questions/ingsw/1122_2/quest.txt
index 2ef9a23..c0c6fa2 100644
--- a/legacy/Data/Questions/ingsw/1122_2/quest.txt
+++ b/Data/Questions/ingsw/1122_2/quest.txt
@@ -1,9 +1,9 @@
-Si consideri il seguente requisito:
-
-RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-
-se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
-
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
-
+Si consideri il seguente requisito:
+
+RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+
+se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
+
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+
 Quale dei seguenti monitor meglio descrive il requisito RQ?
\ No newline at end of file
diff --git a/Data/Questions/ingsw/1122_2/wrong 1.txt b/Data/Questions/ingsw/1122_2/wrong 1.txt
new file mode 100644
index 0000000..b37deaa
--- /dev/null
+++ b/Data/Questions/ingsw/1122_2/wrong 1.txt	
@@ -0,0 +1,17 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+
+initial equation
+
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/Questions/ingsw/1122_2/wrong 2.txt b/Data/Questions/ingsw/1122_2/wrong 2.txt
new file mode 100644
index 0000000..e0b32cd
--- /dev/null
+++ b/Data/Questions/ingsw/1122_2/wrong 2.txt	
@@ -0,0 +1,17 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+
+initial equation
+
+wy = false;
+equation
+wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_20/correct.txt b/Data/Questions/ingsw/1122_20/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_20/correct.txt
rename to Data/Questions/ingsw/1122_20/correct.txt
diff --git a/legacy/Data/ingsw/1122_20/quest.txt b/Data/Questions/ingsw/1122_20/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/1122_20/quest.txt
rename to Data/Questions/ingsw/1122_20/quest.txt
index 139b0a2..8e66453 100644
--- a/legacy/Data/ingsw/1122_20/quest.txt
+++ b/Data/Questions/ingsw/1122_20/quest.txt
@@ -1,19 +1,19 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-
-Si consideri la seguente funzione C:
-
------------
-<pre>
-int f(int x, int y)  {   
-
-int z, k;
-
-z = 1;   k = 0;
-
-while (k < x)  { z = y*z;  k = k + 1; }
-
-return (z);
-
-}
-</pre>
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+
+Si consideri la seguente funzione C:
+
+-----------
+<pre>
+int f(int x, int y)  {   
+
+int z, k;
+
+z = 1;   k = 0;
+
+while (k < x)  { z = y*z;  k = k + 1; }
+
+return (z);
+
+}
+</pre>
 Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_20/wrong 1.txt b/Data/Questions/ingsw/1122_20/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_20/wrong 1.txt
rename to Data/Questions/ingsw/1122_20/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_20/wrong 2.txt b/Data/Questions/ingsw/1122_20/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_20/wrong 2.txt
rename to Data/Questions/ingsw/1122_20/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_27/correct.txt b/Data/Questions/ingsw/1122_21/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_27/correct.txt
rename to Data/Questions/ingsw/1122_21/correct.txt
index e582263..2771487 100644
--- a/legacy/Data/Questions/ingsw/0324_27/correct.txt
+++ b/Data/Questions/ingsw/1122_21/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_21/quest.txt b/Data/Questions/ingsw/1122_21/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_21/quest.txt
rename to Data/Questions/ingsw/1122_21/quest.txt
index 9f10cbc..934fb10 100644
--- a/legacy/Data/Questions/ingsw/1122_21/quest.txt
+++ b/Data/Questions/ingsw/1122_21/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-
-RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
-
+Si consideri il seguente requisito:
+
+RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
+
 Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/Data/Questions/ingsw/1122_21/wrong 1.txt b/Data/Questions/ingsw/1122_21/wrong 1.txt
new file mode 100644
index 0000000..7b3b2f2
--- /dev/null
+++ b/Data/Questions/ingsw/1122_21/wrong 1.txt	
@@ -0,0 +1,19 @@
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+
+initial equation
+
+y = false;
+equation
+z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/Questions/ingsw/1122_21/wrong 2.txt b/Data/Questions/ingsw/1122_21/wrong 2.txt
new file mode 100644
index 0000000..92e0b1f
--- /dev/null
+++ b/Data/Questions/ingsw/1122_21/wrong 2.txt	
@@ -0,0 +1,19 @@
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+
+initial equation
+
+y = false;
+equation
+z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_22/correct.txt b/Data/Questions/ingsw/1122_22/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_22/correct.txt
rename to Data/Questions/ingsw/1122_22/correct.txt
diff --git a/legacy/Data/ingsw/1122_22/quest.txt b/Data/Questions/ingsw/1122_22/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/1122_22/quest.txt
rename to Data/Questions/ingsw/1122_22/quest.txt
index a116140..97b4244 100644
--- a/legacy/Data/ingsw/1122_22/quest.txt
+++ b/Data/Questions/ingsw/1122_22/quest.txt
@@ -1,14 +1,14 @@
-img=https://i.imgur.com/VZQnGCY.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-
-Si consideri lo state diagram in figura
-
-ed il seguente insieme di test cases:
-
-Test case 1: act1 act2 act0 act1 
-
-Test case 2: act1 act0 act1 act1 act2 act2 act0 
-
-Test case 3: act1 act2 act0 act0 
-
+img=https://i.imgur.com/VZQnGCY.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+
+Si consideri lo state diagram in figura
+
+ed il seguente insieme di test cases:
+
+Test case 1: act1 act2 act0 act1 
+
+Test case 2: act1 act0 act1 act1 act2 act2 act0 
+
+Test case 3: act1 act2 act0 act0 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_22/wrong 1.txt b/Data/Questions/ingsw/1122_22/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_22/wrong 1.txt
rename to Data/Questions/ingsw/1122_22/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_22/wrong 2.txt b/Data/Questions/ingsw/1122_22/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_22/wrong 2.txt
rename to Data/Questions/ingsw/1122_22/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_23/correct.txt b/Data/Questions/ingsw/1122_23/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_23/correct.txt
rename to Data/Questions/ingsw/1122_23/correct.txt
diff --git a/Data/Questions/ingsw/1122_23/quest.txt b/Data/Questions/ingsw/1122_23/quest.txt
new file mode 100644
index 0000000..83f906a
--- /dev/null
+++ b/Data/Questions/ingsw/1122_23/quest.txt
@@ -0,0 +1,29 @@
+Si consideri il monitor seguente che ritorna true appena i requisiti per il sistema monitorato sono violati.
+<pre>
+block Monitor
+
+input Real x;  
+
+output Boolean y;
+
+Boolean w;
+
+initial equation
+
+y = false;
+
+equation
+
+w = ((x < 0) or (x > 5));
+
+algorithm
+
+when edge(w) then
+
+y := true;
+
+end when;
+
+end Monitor;
+</pre>
+Quale delle seguenti affermazioni meglio descrive il requisito monitorato.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_23/wrong 1.txt b/Data/Questions/ingsw/1122_23/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_23/wrong 1.txt
rename to Data/Questions/ingsw/1122_23/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_23/wrong 2.txt b/Data/Questions/ingsw/1122_23/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_23/wrong 2.txt
rename to Data/Questions/ingsw/1122_23/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_24/correct.txt b/Data/Questions/ingsw/1122_24/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/1122_24/correct.txt
rename to Data/Questions/ingsw/1122_24/correct.txt
index 293ebbc..ca92838 100644
--- a/legacy/Data/Questions/ingsw/1122_24/correct.txt
+++ b/Data/Questions/ingsw/1122_24/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_24/quest.txt b/Data/Questions/ingsw/1122_24/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_24/quest.txt
rename to Data/Questions/ingsw/1122_24/quest.txt
index 0accc5f..404b24d 100644
--- a/legacy/Data/Questions/ingsw/1122_24/quest.txt
+++ b/Data/Questions/ingsw/1122_24/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-
-RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
-
+Si consideri il seguente requisito:
+
+RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
+
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/Data/Questions/ingsw/1122_24/wrong 1.txt b/Data/Questions/ingsw/1122_24/wrong 1.txt
new file mode 100644
index 0000000..fc17559
--- /dev/null
+++ b/Data/Questions/ingsw/1122_24/wrong 1.txt	
@@ -0,0 +1,19 @@
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+
+initial equation
+
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/Questions/ingsw/1122_24/wrong 2.txt b/Data/Questions/ingsw/1122_24/wrong 2.txt
new file mode 100644
index 0000000..eb22c69
--- /dev/null
+++ b/Data/Questions/ingsw/1122_24/wrong 2.txt	
@@ -0,0 +1,19 @@
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+
+initial equation
+
+wy = false;
+equation
+wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_25/correct.txt b/Data/Questions/ingsw/1122_25/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_25/correct.txt
rename to Data/Questions/ingsw/1122_25/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_25/quest.txt b/Data/Questions/ingsw/1122_25/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_25/quest.txt
rename to Data/Questions/ingsw/1122_25/quest.txt
index 4087608..750b638 100644
--- a/legacy/Data/Questions/ingsw/1122_25/quest.txt
+++ b/Data/Questions/ingsw/1122_25/quest.txt
@@ -1,10 +1,10 @@
-img=https://i.imgur.com/jQT3J83.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
-
-Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
-
-Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
-
-Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
-
+img=https://i.imgur.com/jQT3J83.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
+
+Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
+
+Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
+
+Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
+
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_25/wrong 1.txt b/Data/Questions/ingsw/1122_25/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_25/wrong 1.txt
rename to Data/Questions/ingsw/1122_25/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_25/wrong 2.txt b/Data/Questions/ingsw/1122_25/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_25/wrong 2.txt
rename to Data/Questions/ingsw/1122_25/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_26/correct.txt b/Data/Questions/ingsw/1122_26/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_26/correct.txt
rename to Data/Questions/ingsw/1122_26/correct.txt
diff --git a/legacy/Data/ingsw/1122_26/quest.txt b/Data/Questions/ingsw/1122_26/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/1122_26/quest.txt
rename to Data/Questions/ingsw/1122_26/quest.txt
index 514a3fa..2ec2008 100644
--- a/legacy/Data/ingsw/1122_26/quest.txt
+++ b/Data/Questions/ingsw/1122_26/quest.txt
@@ -1,15 +1,15 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-
-Si consideri la seguente funzione C:
-
------------
-<pre>
-int f(int x, int y)  {   
-
- if (x - y - 6 <= 0)   { if (x + y - 3 >= 0)  return (1); else return (2); }
-
-  else {if (x + 2*y -15 >= 0)  return (3); else return (4); }
-
- }  /* f()  */
-</pre>
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+
+Si consideri la seguente funzione C:
+
+-----------
+<pre>
+int f(int x, int y)  {   
+
+ if (x - y - 6 <= 0)   { if (x + y - 3 >= 0)  return (1); else return (2); }
+
+  else {if (x + 2*y -15 >= 0)  return (3); else return (4); }
+
+ }  /* f()  */
+</pre>
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_26/wrong 1.txt b/Data/Questions/ingsw/1122_26/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_26/wrong 1.txt
rename to Data/Questions/ingsw/1122_26/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_26/wrong 2.txt b/Data/Questions/ingsw/1122_26/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_26/wrong 2.txt
rename to Data/Questions/ingsw/1122_26/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_27/correct.txt b/Data/Questions/ingsw/1122_27/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_27/correct.txt
rename to Data/Questions/ingsw/1122_27/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_27/quest.txt b/Data/Questions/ingsw/1122_27/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_27/quest.txt
rename to Data/Questions/ingsw/1122_27/quest.txt
index 59f8742..1a2c042 100644
--- a/legacy/Data/Questions/ingsw/1122_27/quest.txt
+++ b/Data/Questions/ingsw/1122_27/quest.txt
@@ -1,13 +1,13 @@
-img=https://i.imgur.com/TXCFgeI.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-
-Si consideri lo state diagram in figura
-ed il seguente insieme di test cases:
-
-Test case 1: act1 act2 act0 
-
-Test case 2: act2 act2 act2 act2 act2 act2 act0 
-
-Test case 3: act2 act0 act2 act0 act1 act2 act2 act2 act2 act2 act1 act0 act0 act2 act2 act2 act1 act2 act2 act2 act2 act2 act1 act2 act2 act2 act0 
-
+img=https://i.imgur.com/TXCFgeI.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+
+Si consideri lo state diagram in figura
+ed il seguente insieme di test cases:
+
+Test case 1: act1 act2 act0 
+
+Test case 2: act2 act2 act2 act2 act2 act2 act0 
+
+Test case 3: act2 act0 act2 act0 act1 act2 act2 act2 act2 act2 act1 act0 act0 act2 act2 act2 act1 act2 act2 act2 act2 act2 act1 act2 act2 act2 act0 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_27/wrong 1.txt b/Data/Questions/ingsw/1122_27/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_27/wrong 1.txt
rename to Data/Questions/ingsw/1122_27/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_27/wrong 2.txt b/Data/Questions/ingsw/1122_27/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_27/wrong 2.txt
rename to Data/Questions/ingsw/1122_27/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_28/correct.txt b/Data/Questions/ingsw/1122_28/correct.txt
similarity index 91%
rename from legacy/Data/ingsw/1122_28/correct.txt
rename to Data/Questions/ingsw/1122_28/correct.txt
index c3fc7c1..ba8750b 100644
--- a/legacy/Data/ingsw/1122_28/correct.txt
+++ b/Data/Questions/ingsw/1122_28/correct.txt
@@ -1,9 +1,9 @@
-<pre>
-int f(in x, int y) 
-
-{ 
-int z, w;
-assert( (z + w < 1) || (z + w > 7));
-.....
-}
+<pre>
+int f(in x, int y) 
+
+{ 
+int z, w;
+assert( (z + w < 1) || (z + w > 7));
+.....
+}
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_28/quest.txt b/Data/Questions/ingsw/1122_28/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_28/quest.txt
rename to Data/Questions/ingsw/1122_28/quest.txt
index 733c0cb..37bd848 100644
--- a/legacy/Data/ingsw/1122_28/quest.txt
+++ b/Data/Questions/ingsw/1122_28/quest.txt
@@ -1,7 +1,7 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-
-Si consideri la funzione C
-
-int f(int x, int y) { ..... }
-
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+
+Si consideri la funzione C
+
+int f(int x, int y) { ..... }
+
 Quale delle seguenti assert esprime l'invariante che le variabili locali z e w di f() hanno somma minore di 1 oppure maggiore di 7 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_28/wrong 1.txt b/Data/Questions/ingsw/1122_28/wrong 1.txt
similarity index 88%
rename from legacy/Data/ingsw/1122_28/wrong 1.txt
rename to Data/Questions/ingsw/1122_28/wrong 1.txt
index 1b8fa8b..521f650 100644
--- a/legacy/Data/ingsw/1122_28/wrong 1.txt	
+++ b/Data/Questions/ingsw/1122_28/wrong 1.txt	
@@ -1,13 +1,13 @@
-<pre>
-int f(in x, int y) 
-
-{ 
-
-int z, w;
-
-assert( (z + w <= 1) || (z + w >= 7));
-
-.....
-
-}
+<pre>
+int f(in x, int y) 
+
+{ 
+
+int z, w;
+
+assert( (z + w <= 1) || (z + w >= 7));
+
+.....
+
+}
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_28/wrong 2.txt b/Data/Questions/ingsw/1122_28/wrong 2.txt
similarity index 88%
rename from legacy/Data/ingsw/1122_28/wrong 2.txt
rename to Data/Questions/ingsw/1122_28/wrong 2.txt
index b0705b4..c02f435 100644
--- a/legacy/Data/ingsw/1122_28/wrong 2.txt	
+++ b/Data/Questions/ingsw/1122_28/wrong 2.txt	
@@ -1,13 +1,13 @@
-<pre>
-int f(in x, int y) 
-
-{ 
-
-int z, w;
-
-assert( (z + w > 1) || (z + w < 7));
-
-.....
-
-}
+<pre>
+int f(in x, int y) 
+
+{ 
+
+int z, w;
+
+assert( (z + w > 1) || (z + w < 7));
+
+.....
+
+}
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_29/correct.txt b/Data/Questions/ingsw/1122_29/correct.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/1122_29/correct.txt
rename to Data/Questions/ingsw/1122_29/correct.txt
index 2d46409..b647f40 100644
--- a/legacy/Data/Questions/ingsw/1122_29/correct.txt
+++ b/Data/Questions/ingsw/1122_29/correct.txt
@@ -1 +1 @@
-img=https://i.imgur.com/SXM3yWp.png
+img=https://i.imgur.com/SXM3yWp.png
diff --git a/legacy/Data/Questions/ingsw/1122_29/quest.txt b/Data/Questions/ingsw/1122_29/quest.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/1122_29/quest.txt
rename to Data/Questions/ingsw/1122_29/quest.txt
index 52863ce..8e0d6f4 100644
--- a/legacy/Data/Questions/ingsw/1122_29/quest.txt
+++ b/Data/Questions/ingsw/1122_29/quest.txt
@@ -1,70 +1,70 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente ?
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente ?
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_29/wrong 1.txt b/Data/Questions/ingsw/1122_29/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_29/wrong 1.txt
rename to Data/Questions/ingsw/1122_29/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_29/wrong 2.txt b/Data/Questions/ingsw/1122_29/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_29/wrong 2.txt
rename to Data/Questions/ingsw/1122_29/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_3/correct.txt b/Data/Questions/ingsw/1122_3/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_3/correct.txt
rename to Data/Questions/ingsw/1122_3/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_3/quest.txt b/Data/Questions/ingsw/1122_3/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_3/quest.txt
rename to Data/Questions/ingsw/1122_3/quest.txt
index ddcf7c8..4eb39a7 100644
--- a/legacy/Data/Questions/ingsw/1122_3/quest.txt
+++ b/Data/Questions/ingsw/1122_3/quest.txt
@@ -1,3 +1,3 @@
-"Ogni giorno, per ciascuna clinica, il sistema genererà una lista dei pazienti che hanno un appuntamento quel giorno."
-
+"Ogni giorno, per ciascuna clinica, il sistema genererà una lista dei pazienti che hanno un appuntamento quel giorno."
+
 La frase precedente è un esempio di:
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_3/wrong 1.txt b/Data/Questions/ingsw/1122_3/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_3/wrong 1.txt
rename to Data/Questions/ingsw/1122_3/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_3/wrong 2.txt b/Data/Questions/ingsw/1122_3/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_3/wrong 2.txt
rename to Data/Questions/ingsw/1122_3/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_30/correct.txt b/Data/Questions/ingsw/1122_30/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_30/correct.txt
rename to Data/Questions/ingsw/1122_30/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_30/quest.txt b/Data/Questions/ingsw/1122_30/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/1122_30/quest.txt
rename to Data/Questions/ingsw/1122_30/quest.txt
index 8b8cea7..476a9fd 100644
--- a/legacy/Data/Questions/ingsw/1122_30/quest.txt
+++ b/Data/Questions/ingsw/1122_30/quest.txt
@@ -1,10 +1,10 @@
-img=https://i.imgur.com/jQT3J83.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
-
-Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
-
-Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
-
-Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
-
+img=https://i.imgur.com/jQT3J83.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
+
+Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
+
+Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
+
+Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
+
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_30/wrong 1.txt b/Data/Questions/ingsw/1122_30/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_30/wrong 1.txt
rename to Data/Questions/ingsw/1122_30/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_30/wrong 2.txt b/Data/Questions/ingsw/1122_30/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_30/wrong 2.txt
rename to Data/Questions/ingsw/1122_30/wrong 2.txt
diff --git a/legacy/Data/ingsw/0324_46/correct.txt b/Data/Questions/ingsw/1122_31/correct.txt
similarity index 93%
rename from legacy/Data/ingsw/0324_46/correct.txt
rename to Data/Questions/ingsw/1122_31/correct.txt
index a98afd2..d3daba4 100644
--- a/legacy/Data/ingsw/0324_46/correct.txt
+++ b/Data/Questions/ingsw/1122_31/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and ((x >= 30) or (x <= 20)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and ((x >= 30) or (x <= 20)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_31/quest.txt b/Data/Questions/ingsw/1122_31/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_31/quest.txt
rename to Data/Questions/ingsw/1122_31/quest.txt
index 7314e2c..682b4f6 100644
--- a/legacy/Data/Questions/ingsw/1122_31/quest.txt
+++ b/Data/Questions/ingsw/1122_31/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-
-RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
-
+Si consideri il seguente requisito:
+
+RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
+
 Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/Data/Questions/ingsw/1122_31/wrong 1.txt b/Data/Questions/ingsw/1122_31/wrong 1.txt
new file mode 100644
index 0000000..0eefcf1
--- /dev/null
+++ b/Data/Questions/ingsw/1122_31/wrong 1.txt	
@@ -0,0 +1,19 @@
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+
+initial equation
+
+y = false;
+equation
+z = (time > 20) and (x >= 20) and (x <= 30) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/Questions/ingsw/1122_31/wrong 2.txt b/Data/Questions/ingsw/1122_31/wrong 2.txt
new file mode 100644
index 0000000..f36cefa
--- /dev/null
+++ b/Data/Questions/ingsw/1122_31/wrong 2.txt	
@@ -0,0 +1,19 @@
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+
+initial equation
+
+y = false;
+equation
+z = (time > 20) or ((x >= 20) and (x <= 30)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_33/correct.txt b/Data/Questions/ingsw/1122_33/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_33/correct.txt
rename to Data/Questions/ingsw/1122_33/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_33/quest.txt b/Data/Questions/ingsw/1122_33/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/1122_33/quest.txt
rename to Data/Questions/ingsw/1122_33/quest.txt
index 6283906..5ccd93d 100644
--- a/legacy/Data/Questions/ingsw/1122_33/quest.txt
+++ b/Data/Questions/ingsw/1122_33/quest.txt
@@ -1,17 +1,17 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-
-Si consideri la seguente funzione C:
-
------------
-<pre>
-int f(int x, int y)  {   
-
- if (x - y - 2 <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
-
-  else {if (x + 2*y - 5 >= 0)  return (3); else return (4); }
-
- }  /* f()  */
-</pre>
-Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
-
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+
+Si consideri la seguente funzione C:
+
+-----------
+<pre>
+int f(int x, int y)  {   
+
+ if (x - y - 2 <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
+
+  else {if (x + 2*y - 5 >= 0)  return (3); else return (4); }
+
+ }  /* f()  */
+</pre>
+Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
+
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_33/wrong 1.txt b/Data/Questions/ingsw/1122_33/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_33/wrong 1.txt
rename to Data/Questions/ingsw/1122_33/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_33/wrong 2.txt b/Data/Questions/ingsw/1122_33/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_33/wrong 2.txt
rename to Data/Questions/ingsw/1122_33/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_34/correct.txt b/Data/Questions/ingsw/1122_34/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_34/correct.txt
rename to Data/Questions/ingsw/1122_34/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_34/quest.txt b/Data/Questions/ingsw/1122_34/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/1122_34/quest.txt
rename to Data/Questions/ingsw/1122_34/quest.txt
index 09970ee..0a0cca1 100644
--- a/legacy/Data/Questions/ingsw/1122_34/quest.txt
+++ b/Data/Questions/ingsw/1122_34/quest.txt
@@ -1,13 +1,13 @@
-img=https://i.imgur.com/cXPjiw9.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-
-Si consideri lo state diagram in figura 
-Si consideri il seguente insieme di test cases:
-
-Test case 1: act2 act1 act1 act2 act1 act1 act0 
-
-Test case 2: act2 act0 act2 act2 act1 act1 act0 act2 act2 act2 act0 
-
-Test case 3: act1 act2 act2 act2 act1 act0 act1 act2 act2 act0
-
+img=https://i.imgur.com/cXPjiw9.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+
+Si consideri lo state diagram in figura 
+Si consideri il seguente insieme di test cases:
+
+Test case 1: act2 act1 act1 act2 act1 act1 act0 
+
+Test case 2: act2 act0 act2 act2 act1 act1 act0 act2 act2 act2 act0 
+
+Test case 3: act1 act2 act2 act2 act1 act0 act1 act2 act2 act0
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_34/wrong 1.txt b/Data/Questions/ingsw/1122_34/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_34/wrong 1.txt
rename to Data/Questions/ingsw/1122_34/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_34/wrong 2.txt b/Data/Questions/ingsw/1122_34/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_34/wrong 2.txt
rename to Data/Questions/ingsw/1122_34/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_35/correct.txt b/Data/Questions/ingsw/1122_35/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_35/correct.txt
rename to Data/Questions/ingsw/1122_35/correct.txt
diff --git a/legacy/Data/ingsw/1122_35/quest.txt b/Data/Questions/ingsw/1122_35/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_35/quest.txt
rename to Data/Questions/ingsw/1122_35/quest.txt
index 215b21b..d56e26d 100644
--- a/legacy/Data/ingsw/1122_35/quest.txt
+++ b/Data/Questions/ingsw/1122_35/quest.txt
@@ -1,3 +1,3 @@
-img=https://i.imgur.com/jQT3J83.png
-Si consideri la Markov chain in figura con stato iniziale 0 e p in (0, 1). Quale delle seguenti formule calcola il valore atteso del numero di transizioni necessarie per lasciare lo stato 0.
-
+img=https://i.imgur.com/jQT3J83.png
+Si consideri la Markov chain in figura con stato iniziale 0 e p in (0, 1). Quale delle seguenti formule calcola il valore atteso del numero di transizioni necessarie per lasciare lo stato 0.
+
diff --git a/legacy/Data/Questions/ingsw/1122_35/wrong 1.txt b/Data/Questions/ingsw/1122_35/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_35/wrong 1.txt
rename to Data/Questions/ingsw/1122_35/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_35/wrong 2.txt b/Data/Questions/ingsw/1122_35/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_35/wrong 2.txt
rename to Data/Questions/ingsw/1122_35/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_36/correct.txt b/Data/Questions/ingsw/1122_36/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_36/correct.txt
rename to Data/Questions/ingsw/1122_36/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_36/quest.txt b/Data/Questions/ingsw/1122_36/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_36/quest.txt
rename to Data/Questions/ingsw/1122_36/quest.txt
index da5d010..f896e42 100644
--- a/legacy/Data/Questions/ingsw/1122_36/quest.txt
+++ b/Data/Questions/ingsw/1122_36/quest.txt
@@ -1,26 +1,26 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-<pre>
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-</pre>
-
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-<pre>
-int f(int a, int b, int c)
-{    if ( (a - 100 >= 0) && (b - c - 1 <= 0) )
-          return (1);    // punto di uscita 1
-      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0)
-)
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
-</pre>
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+<pre>
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+</pre>
+
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+<pre>
+int f(int a, int b, int c)
+{    if ( (a - 100 >= 0) && (b - c - 1 <= 0) )
+          return (1);    // punto di uscita 1
+      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0)
+)
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
+</pre>
 Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_36/wrong 1.txt b/Data/Questions/ingsw/1122_36/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_36/wrong 1.txt
rename to Data/Questions/ingsw/1122_36/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_36/wrong 2.txt b/Data/Questions/ingsw/1122_36/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_36/wrong 2.txt
rename to Data/Questions/ingsw/1122_36/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_45/correct.txt b/Data/Questions/ingsw/1122_37/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_45/correct.txt
rename to Data/Questions/ingsw/1122_37/correct.txt
index c37d6ae..c642439 100644
--- a/legacy/Data/Questions/ingsw/0324_45/correct.txt
+++ b/Data/Questions/ingsw/1122_37/correct.txt
@@ -1,15 +1,17 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+
+initial equation
+
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_37/quest.txt b/Data/Questions/ingsw/1122_37/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_37/quest.txt
rename to Data/Questions/ingsw/1122_37/quest.txt
index 843e4e9..99e8239 100644
--- a/legacy/Data/Questions/ingsw/1122_37/quest.txt
+++ b/Data/Questions/ingsw/1122_37/quest.txt
@@ -1,9 +1,9 @@
-Si consideri il seguente requisito:
-
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-
-se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
-
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
-
+Si consideri il seguente requisito:
+
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+
+se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
+
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/Data/Questions/ingsw/1122_37/wrong 1.txt b/Data/Questions/ingsw/1122_37/wrong 1.txt
new file mode 100644
index 0000000..1441001
--- /dev/null
+++ b/Data/Questions/ingsw/1122_37/wrong 1.txt	
@@ -0,0 +1,19 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+
+initial equation
+
+wy = false;
+equation
+
+wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/Questions/ingsw/1122_37/wrong 2.txt b/Data/Questions/ingsw/1122_37/wrong 2.txt
new file mode 100644
index 0000000..f1efe0e
--- /dev/null
+++ b/Data/Questions/ingsw/1122_37/wrong 2.txt	
@@ -0,0 +1,20 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+
+initial equation
+
+wy = false;
+equation
+
+wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
+
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_38/correct.txt b/Data/Questions/ingsw/1122_38/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_38/correct.txt
rename to Data/Questions/ingsw/1122_38/correct.txt
diff --git a/Data/Questions/ingsw/1122_38/quest.txt b/Data/Questions/ingsw/1122_38/quest.txt
new file mode 100644
index 0000000..d19e9da
--- /dev/null
+++ b/Data/Questions/ingsw/1122_38/quest.txt
@@ -0,0 +1,29 @@
+Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
+<pre>
+block Monitor
+
+input Real x;  
+
+output Boolean y;
+
+Boolean w;
+
+initial equation
+
+y = false;
+
+equation
+
+w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
+
+algorithm
+
+when edge(w) then
+
+y := true;
+
+end when;
+
+end Monitor;
+</pre>
+Quale delle seguenti affermazioni meglio descrive il requisito monitorato?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_38/wrong 1.txt b/Data/Questions/ingsw/1122_38/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_38/wrong 1.txt
rename to Data/Questions/ingsw/1122_38/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_38/wrong 2.txt b/Data/Questions/ingsw/1122_38/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_38/wrong 2.txt
rename to Data/Questions/ingsw/1122_38/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_39/correct.txt b/Data/Questions/ingsw/1122_39/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_39/correct.txt
rename to Data/Questions/ingsw/1122_39/correct.txt
diff --git a/legacy/Data/ingsw/1122_39/quest.txt b/Data/Questions/ingsw/1122_39/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/1122_39/quest.txt
rename to Data/Questions/ingsw/1122_39/quest.txt
index 5826af4..cfa5093 100644
--- a/legacy/Data/ingsw/1122_39/quest.txt
+++ b/Data/Questions/ingsw/1122_39/quest.txt
@@ -1,14 +1,14 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-
-Si consideri la seguente funzione C:
------------
-<pre>
-int f(int x, int y)  {   
-
- if (x - y <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
-
-  else {if (2*x + y - 5 >= 0)  return (3); else return (4); }
-
- }  /* f()  */
-</pre>
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+
+Si consideri la seguente funzione C:
+-----------
+<pre>
+int f(int x, int y)  {   
+
+ if (x - y <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
+
+  else {if (2*x + y - 5 >= 0)  return (3); else return (4); }
+
+ }  /* f()  */
+</pre>
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_39/wrong 1.txt b/Data/Questions/ingsw/1122_39/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_39/wrong 1.txt
rename to Data/Questions/ingsw/1122_39/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_39/wrong 2.txt b/Data/Questions/ingsw/1122_39/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_39/wrong 2.txt
rename to Data/Questions/ingsw/1122_39/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_4/correct.txt b/Data/Questions/ingsw/1122_4/correct.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/1122_4/correct.txt
rename to Data/Questions/ingsw/1122_4/correct.txt
index 9ddc3d7..6162c46 100644
--- a/legacy/Data/Questions/ingsw/1122_4/correct.txt
+++ b/Data/Questions/ingsw/1122_4/correct.txt
@@ -1,45 +1,45 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-
-algorithm
-
-
-when initial() then
-x := 0;
-
-
-elsewhen sample(0,T) then
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-else x := pre(x); // default
-end if;
-
-
-end when;
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+
+algorithm
+
+
+when initial() then
+x := 0;
+
+
+elsewhen sample(0,T) then
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+else x := pre(x); // default
+end if;
+
+
+end when;
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_4/quest.txt b/Data/Questions/ingsw/1122_4/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/1122_4/quest.txt
rename to Data/Questions/ingsw/1122_4/quest.txt
index 4181719..46b6886 100644
--- a/legacy/Data/ingsw/1122_4/quest.txt
+++ b/Data/Questions/ingsw/1122_4/quest.txt
@@ -1,2 +1,2 @@
-img=https://i.imgur.com/1yUsW7d.png
+img=https://i.imgur.com/1yUsW7d.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_4/wrong 1.txt b/Data/Questions/ingsw/1122_4/wrong 1.txt
similarity index 93%
rename from legacy/Data/ingsw/1122_4/wrong 1.txt
rename to Data/Questions/ingsw/1122_4/wrong 1.txt
index c92e243..8b1c8db 100644
--- a/legacy/Data/ingsw/1122_4/wrong 1.txt	
+++ b/Data/Questions/ingsw/1122_4/wrong 1.txt	
@@ -1,67 +1,67 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_4/wrong 2.txt b/Data/Questions/ingsw/1122_4/wrong 2.txt
similarity index 93%
rename from legacy/Data/ingsw/1122_4/wrong 2.txt
rename to Data/Questions/ingsw/1122_4/wrong 2.txt
index ef7bdb0..06eb215 100644
--- a/legacy/Data/ingsw/1122_4/wrong 2.txt	
+++ b/Data/Questions/ingsw/1122_4/wrong 2.txt	
@@ -1,69 +1,69 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_40/correct.txt b/Data/Questions/ingsw/1122_40/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_40/correct.txt
rename to Data/Questions/ingsw/1122_40/correct.txt
diff --git a/legacy/Data/ingsw/1122_40/quest.txt b/Data/Questions/ingsw/1122_40/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/1122_40/quest.txt
rename to Data/Questions/ingsw/1122_40/quest.txt
index 62c01e2..954468a 100644
--- a/legacy/Data/ingsw/1122_40/quest.txt
+++ b/Data/Questions/ingsw/1122_40/quest.txt
@@ -1,13 +1,13 @@
-img=https://i.imgur.com/ZjBToOi.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-
-Si consideri lo state diagram in figura ed il seguente insieme di test cases:
-
-Test case 1: act2 
-
-Test case 2: act1 act0 act1 act2 act1 act0 act0 act0 
-
-
-Test case 3: act0 act0 
-
+img=https://i.imgur.com/ZjBToOi.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+
+Si consideri lo state diagram in figura ed il seguente insieme di test cases:
+
+Test case 1: act2 
+
+Test case 2: act1 act0 act1 act2 act1 act0 act0 act0 
+
+
+Test case 3: act0 act0 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_40/wrong 1.txt b/Data/Questions/ingsw/1122_40/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_40/wrong 1.txt
rename to Data/Questions/ingsw/1122_40/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_40/wrong 2.txt b/Data/Questions/ingsw/1122_40/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_40/wrong 2.txt
rename to Data/Questions/ingsw/1122_40/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_42/correct.txt b/Data/Questions/ingsw/1122_42/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_42/correct.txt
rename to Data/Questions/ingsw/1122_42/correct.txt
diff --git a/legacy/Data/ingsw/1122_42/quest.txt b/Data/Questions/ingsw/1122_42/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/1122_42/quest.txt
rename to Data/Questions/ingsw/1122_42/quest.txt
index 67b07dc..685ad76 100644
--- a/legacy/Data/ingsw/1122_42/quest.txt
+++ b/Data/Questions/ingsw/1122_42/quest.txt
@@ -1,17 +1,17 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-
-Si consideri la seguente funzione C:
-
------------
-<pre>
-int f(int x, int y)  {   
-
- if (x - y <= 0)   { if (x + y - 2>= 0)  return (1); else return (2); }
-
-  else {if (2*x + y - 1>= 0)  return (3); else return (4); }
-
- }  /* f()  */
-</pre>
-Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
-
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+
+Si consideri la seguente funzione C:
+
+-----------
+<pre>
+int f(int x, int y)  {   
+
+ if (x - y <= 0)   { if (x + y - 2>= 0)  return (1); else return (2); }
+
+  else {if (2*x + y - 1>= 0)  return (3); else return (4); }
+
+ }  /* f()  */
+</pre>
+Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
+
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_42/wrong 1.txt b/Data/Questions/ingsw/1122_42/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_42/wrong 1.txt
rename to Data/Questions/ingsw/1122_42/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_42/wrong 2.txt b/Data/Questions/ingsw/1122_42/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_42/wrong 2.txt
rename to Data/Questions/ingsw/1122_42/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_43/correct.txt b/Data/Questions/ingsw/1122_43/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_43/correct.txt
rename to Data/Questions/ingsw/1122_43/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_43/quest.txt b/Data/Questions/ingsw/1122_43/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_43/quest.txt
rename to Data/Questions/ingsw/1122_43/quest.txt
index 1045bb8..fc279af 100644
--- a/legacy/Data/Questions/ingsw/1122_43/quest.txt
+++ b/Data/Questions/ingsw/1122_43/quest.txt
@@ -1,11 +1,11 @@
-img=https://i.imgur.com/5ZmMM3r.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-
-Si consideri lo state diagram in figura 
-Si consideri il seguente insieme di test cases:
-
-Test case 1: act0 act2 act2 act1 act2 act2 act0 act2 act2 act0 act2 act0 act0 act2 act2 act2 act2 act1 
-
-Test case 2: act2 act1 act0 act2 act2 act0 act0 act1 
-
+img=https://i.imgur.com/5ZmMM3r.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+
+Si consideri lo state diagram in figura 
+Si consideri il seguente insieme di test cases:
+
+Test case 1: act0 act2 act2 act1 act2 act2 act0 act2 act2 act0 act2 act0 act0 act2 act2 act2 act2 act1 
+
+Test case 2: act2 act1 act0 act2 act2 act0 act0 act1 
+
 Test case 3: act0 act1 act0 act0 act0 act2 act1 act0 act2 act2 act2 act0 act1 
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_43/wrong 1.txt b/Data/Questions/ingsw/1122_43/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_43/wrong 1.txt
rename to Data/Questions/ingsw/1122_43/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_43/wrong 2.txt b/Data/Questions/ingsw/1122_43/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_43/wrong 2.txt
rename to Data/Questions/ingsw/1122_43/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_44/correct.txt b/Data/Questions/ingsw/1122_44/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_44/correct.txt
rename to Data/Questions/ingsw/1122_44/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_44/quest.txt b/Data/Questions/ingsw/1122_44/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/1122_44/quest.txt
rename to Data/Questions/ingsw/1122_44/quest.txt
index 6428a0e..764ee2f 100644
--- a/legacy/Data/Questions/ingsw/1122_44/quest.txt
+++ b/Data/Questions/ingsw/1122_44/quest.txt
@@ -1,15 +1,15 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-
-Si consideri la seguente funzione C:
-
-int f1(int x)  { return (2*x); }
-
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-
-{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
-
-Si consideri il seguente insieme di test cases:
-
-{x=-20, x= 10, x=60}
-
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+
+Si consideri la seguente funzione C:
+
+int f1(int x)  { return (2*x); }
+
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+
+{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
+
+Si consideri il seguente insieme di test cases:
+
+{x=-20, x= 10, x=60}
+
 Quale delle seguenti è la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_44/wrong 1.txt b/Data/Questions/ingsw/1122_44/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_44/wrong 1.txt
rename to Data/Questions/ingsw/1122_44/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_44/wrong 2.txt b/Data/Questions/ingsw/1122_44/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_44/wrong 2.txt
rename to Data/Questions/ingsw/1122_44/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_45/correct.txt b/Data/Questions/ingsw/1122_45/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_45/correct.txt
rename to Data/Questions/ingsw/1122_45/correct.txt
diff --git a/legacy/Data/ingsw/1122_45/quest.txt b/Data/Questions/ingsw/1122_45/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/1122_45/quest.txt
rename to Data/Questions/ingsw/1122_45/quest.txt
index 1454704..90e2bab 100644
--- a/legacy/Data/ingsw/1122_45/quest.txt
+++ b/Data/Questions/ingsw/1122_45/quest.txt
@@ -1,8 +1,8 @@
-img=https://i.imgur.com/47sr1ne.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-
-Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-
-Uno scenario è una sequenza di stati.
-
+img=https://i.imgur.com/47sr1ne.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+
+Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+
+Uno scenario è una sequenza di stati.
+
 Qual è la probabilità dello scenario: 1, 3 ? In altri terminti, qual è la probabilità che non sia necessario ripetere nessuna fase?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_45/wrong 1.txt b/Data/Questions/ingsw/1122_45/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_45/wrong 1.txt
rename to Data/Questions/ingsw/1122_45/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_45/wrong 2.txt b/Data/Questions/ingsw/1122_45/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_45/wrong 2.txt
rename to Data/Questions/ingsw/1122_45/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_46/correct.txt b/Data/Questions/ingsw/1122_46/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_46/correct.txt
rename to Data/Questions/ingsw/1122_46/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_46/quest.txt b/Data/Questions/ingsw/1122_46/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/1122_46/quest.txt
rename to Data/Questions/ingsw/1122_46/quest.txt
index 5bf1a08..b3403d8 100644
--- a/legacy/Data/Questions/ingsw/1122_46/quest.txt
+++ b/Data/Questions/ingsw/1122_46/quest.txt
@@ -1,14 +1,14 @@
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-
-Si consideri lo state diagram in figura
-Si consideri il seguente insieme di test cases:
-
-Test case 1: act2 act1 act1 
-
-Test case 2: act0 act0 act2 act1 
-
-Test case 3: act2 act0 act2 act2 act0 
-
-
-
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+
+Si consideri lo state diagram in figura
+Si consideri il seguente insieme di test cases:
+
+Test case 1: act2 act1 act1 
+
+Test case 2: act0 act0 act2 act1 
+
+Test case 3: act2 act0 act2 act2 act0 
+
+
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_46/wrong 1.txt b/Data/Questions/ingsw/1122_46/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_46/wrong 1.txt
rename to Data/Questions/ingsw/1122_46/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_46/wrong 2.txt b/Data/Questions/ingsw/1122_46/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_46/wrong 2.txt
rename to Data/Questions/ingsw/1122_46/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_47/correct.txt b/Data/Questions/ingsw/1122_47/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_47/correct.txt
rename to Data/Questions/ingsw/1122_47/correct.txt
diff --git a/legacy/Data/ingsw/0722_12/quest.txt b/Data/Questions/ingsw/1122_47/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0722_12/quest.txt
rename to Data/Questions/ingsw/1122_47/quest.txt
diff --git a/legacy/Data/Questions/ingsw/1122_47/wrong 1.txt b/Data/Questions/ingsw/1122_47/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_47/wrong 1.txt
rename to Data/Questions/ingsw/1122_47/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_47/wrong 2.txt b/Data/Questions/ingsw/1122_47/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_47/wrong 2.txt
rename to Data/Questions/ingsw/1122_47/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_48/correct.txt b/Data/Questions/ingsw/1122_48/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_48/correct.txt
rename to Data/Questions/ingsw/1122_48/correct.txt
diff --git a/legacy/Data/ingsw/1122_48/quest.txt b/Data/Questions/ingsw/1122_48/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_48/quest.txt
rename to Data/Questions/ingsw/1122_48/quest.txt
index 4fc3c18..a2719a1 100644
--- a/legacy/Data/ingsw/1122_48/quest.txt
+++ b/Data/Questions/ingsw/1122_48/quest.txt
@@ -1,24 +1,24 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta in espressioni boolean più semplici. Ad esempio, <code>(x + y <= 3)</code> è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-<pre>
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-</pre>
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-<pre>
-int f(int a, int b, int c)
-{    if ( (a  + b - 6 >= 0) && (b - c - 1 <= 0) )
-          return (1);    // punto di uscita 1
-      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0))
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
-   Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta in espressioni boolean più semplici. Ad esempio, <code>(x + y <= 3)</code> è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+<pre>
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+</pre>
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+<pre>
+int f(int a, int b, int c)
+{    if ( (a  + b - 6 >= 0) && (b - c - 1 <= 0) )
+          return (1);    // punto di uscita 1
+      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0))
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
+   Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_48/wrong 1.txt b/Data/Questions/ingsw/1122_48/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_48/wrong 1.txt
rename to Data/Questions/ingsw/1122_48/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_48/wrong 2.txt b/Data/Questions/ingsw/1122_48/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_48/wrong 2.txt
rename to Data/Questions/ingsw/1122_48/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_49/correct.txt b/Data/Questions/ingsw/1122_49/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_49/correct.txt
rename to Data/Questions/ingsw/1122_49/correct.txt
diff --git a/legacy/Data/ingsw/1122_49/quest.txt b/Data/Questions/ingsw/1122_49/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_49/quest.txt
rename to Data/Questions/ingsw/1122_49/quest.txt
index e591c8c..c2267f6 100644
--- a/legacy/Data/ingsw/1122_49/quest.txt
+++ b/Data/Questions/ingsw/1122_49/quest.txt
@@ -1,12 +1,12 @@
-img=https://i.imgur.com/rZnqUL9.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-
-Si consideri lo state diagram in figura ed il seguente insieme di test cases:
-
-Test case 1: act1 act0 act2 act0 act0 act0 act2 act1 act1 act0 act2 act0 act2 act2 act1 act1 act0 act2 act2 act2 act1 act1 act2 act0 act1 act0 act1 act2 
-
-Test case 2: act1 act0 act0 act0 act2 act2 act2 act2 act2 act1 act1 act0 act0 act0 act2 act2 act2 act0 act1 act1 act1 act0 act2 act0 act0 act0 act1 act1 act2 act0 act1 act0 act0 act0 act2 act0 act1 act2 act2 act2 act0 act1 act2 act0 act1 act0 act1 act2 
-
-Test case 3: act1 act0 act0 act1 act1 act1 act1 act2 act2 act0 act1 act2
-
+img=https://i.imgur.com/rZnqUL9.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+
+Si consideri lo state diagram in figura ed il seguente insieme di test cases:
+
+Test case 1: act1 act0 act2 act0 act0 act0 act2 act1 act1 act0 act2 act0 act2 act2 act1 act1 act0 act2 act2 act2 act1 act1 act2 act0 act1 act0 act1 act2 
+
+Test case 2: act1 act0 act0 act0 act2 act2 act2 act2 act2 act1 act1 act0 act0 act0 act2 act2 act2 act0 act1 act1 act1 act0 act2 act0 act0 act0 act1 act1 act2 act0 act1 act0 act0 act0 act2 act0 act1 act2 act2 act2 act0 act1 act2 act0 act1 act0 act1 act2 
+
+Test case 3: act1 act0 act0 act1 act1 act1 act1 act2 act2 act0 act1 act2
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_49/wrong 1.txt b/Data/Questions/ingsw/1122_49/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_49/wrong 1.txt
rename to Data/Questions/ingsw/1122_49/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_49/wrong 2.txt b/Data/Questions/ingsw/1122_49/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_49/wrong 2.txt
rename to Data/Questions/ingsw/1122_49/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_5/correct.txt b/Data/Questions/ingsw/1122_5/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_5/correct.txt
rename to Data/Questions/ingsw/1122_5/correct.txt
diff --git a/legacy/Data/ingsw/1122_5/quest.txt b/Data/Questions/ingsw/1122_5/quest.txt
similarity index 93%
rename from legacy/Data/ingsw/1122_5/quest.txt
rename to Data/Questions/ingsw/1122_5/quest.txt
index bcbb3d8..bd4a8a1 100644
--- a/legacy/Data/ingsw/1122_5/quest.txt
+++ b/Data/Questions/ingsw/1122_5/quest.txt
@@ -1,68 +1,68 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente?
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente?
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_5/wrong 1.txt b/Data/Questions/ingsw/1122_5/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_5/wrong 1.txt
rename to Data/Questions/ingsw/1122_5/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_5/wrong 2.txt b/Data/Questions/ingsw/1122_5/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_5/wrong 2.txt
rename to Data/Questions/ingsw/1122_5/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_50/correct.txt b/Data/Questions/ingsw/1122_50/correct.txt
similarity index 93%
rename from legacy/Data/ingsw/1122_50/correct.txt
rename to Data/Questions/ingsw/1122_50/correct.txt
index 7470aaf..6fc3465 100644
--- a/legacy/Data/ingsw/1122_50/correct.txt
+++ b/Data/Questions/ingsw/1122_50/correct.txt
@@ -1,69 +1,69 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_50/quest.txt b/Data/Questions/ingsw/1122_50/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/1122_50/quest.txt
rename to Data/Questions/ingsw/1122_50/quest.txt
index 971e607..de6a1fb 100644
--- a/legacy/Data/Questions/ingsw/1122_50/quest.txt
+++ b/Data/Questions/ingsw/1122_50/quest.txt
@@ -1,2 +1,2 @@
-img=https://i.imgur.com/fyv5jqF.png
+img=https://i.imgur.com/fyv5jqF.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_50/wrong 1.txt b/Data/Questions/ingsw/1122_50/wrong 1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/1122_50/wrong 1.txt
rename to Data/Questions/ingsw/1122_50/wrong 1.txt
index e77e043..afe907d 100644
--- a/legacy/Data/Questions/ingsw/1122_50/wrong 1.txt	
+++ b/Data/Questions/ingsw/1122_50/wrong 1.txt	
@@ -1,69 +1,69 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 2) then x := 1;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 2) then x := 1;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_50/wrong 2.txt b/Data/Questions/ingsw/1122_50/wrong 2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/1122_50/wrong 2.txt
rename to Data/Questions/ingsw/1122_50/wrong 2.txt
index 03c4dea..b91d652 100644
--- a/legacy/Data/Questions/ingsw/1122_50/wrong 2.txt	
+++ b/Data/Questions/ingsw/1122_50/wrong 2.txt	
@@ -1,71 +1,71 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_6/correct.txt b/Data/Questions/ingsw/1122_6/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_6/correct.txt
rename to Data/Questions/ingsw/1122_6/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_6/quest.txt b/Data/Questions/ingsw/1122_6/quest.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_6/quest.txt
rename to Data/Questions/ingsw/1122_6/quest.txt
diff --git a/legacy/Data/Questions/ingsw/1122_6/wrong 1.txt b/Data/Questions/ingsw/1122_6/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_6/wrong 1.txt
rename to Data/Questions/ingsw/1122_6/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_6/wrong 2.txt b/Data/Questions/ingsw/1122_6/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_6/wrong 2.txt
rename to Data/Questions/ingsw/1122_6/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_7/correct.txt b/Data/Questions/ingsw/1122_7/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_7/correct.txt
rename to Data/Questions/ingsw/1122_7/correct.txt
diff --git a/legacy/Data/ingsw/1122_7/quest.txt b/Data/Questions/ingsw/1122_7/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_7/quest.txt
rename to Data/Questions/ingsw/1122_7/quest.txt
index 8db1ade..f22c6a8 100644
--- a/legacy/Data/ingsw/1122_7/quest.txt
+++ b/Data/Questions/ingsw/1122_7/quest.txt
@@ -1,10 +1,10 @@
-img=https://i.imgur.com/5TP66IN.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
-
-Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2.
-F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR.
-F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
-
-Uno scenario è una sequenza di stati.
-
+img=https://i.imgur.com/5TP66IN.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
+
+Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2.
+F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR.
+F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
+
+Uno scenario è una sequenza di stati.
+
 Qual è la probabilità dello scenario: 1, 2, 3? In altri terminti, qual è la probabilità che non sia necessario ripetere la prima fase (ma non la seconda)?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_7/wrong 1.txt b/Data/Questions/ingsw/1122_7/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_7/wrong 1.txt
rename to Data/Questions/ingsw/1122_7/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_7/wrong 2.txt b/Data/Questions/ingsw/1122_7/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_7/wrong 2.txt
rename to Data/Questions/ingsw/1122_7/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_8/correct.txt b/Data/Questions/ingsw/1122_8/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_8/correct.txt
rename to Data/Questions/ingsw/1122_8/correct.txt
diff --git a/legacy/Data/ingsw/1122_8/quest.txt b/Data/Questions/ingsw/1122_8/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/1122_8/quest.txt
rename to Data/Questions/ingsw/1122_8/quest.txt
index 1f66143..d9efe1d 100644
--- a/legacy/Data/ingsw/1122_8/quest.txt
+++ b/Data/Questions/ingsw/1122_8/quest.txt
@@ -1,8 +1,8 @@
-img=https://i.imgur.com/5TP66IN.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-
-Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
-
-Uno scenario è una sequenza di stati.
-
+img=https://i.imgur.com/5TP66IN.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+
+Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
+
+Uno scenario è una sequenza di stati.
+
 Qual è la probabilità dello scenario: 1, 2, 3, 4 ? In altri terminti, qual è la probabilità che sia necessario ripetere sia la fase 1 che la fase 2?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_8/wrong 1.txt b/Data/Questions/ingsw/1122_8/wrong 1.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_8/wrong 1.txt
rename to Data/Questions/ingsw/1122_8/wrong 1.txt
diff --git a/legacy/Data/Questions/ingsw/1122_8/wrong 2.txt b/Data/Questions/ingsw/1122_8/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/1122_8/wrong 2.txt
rename to Data/Questions/ingsw/1122_8/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_9/correct.txt b/Data/Questions/ingsw/1122_9/correct.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/1122_9/correct.txt
rename to Data/Questions/ingsw/1122_9/correct.txt
index a7a3133..d48268a 100644
--- a/legacy/Data/Questions/ingsw/1122_9/correct.txt
+++ b/Data/Questions/ingsw/1122_9/correct.txt
@@ -1,44 +1,44 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-
-algorithm
-
-
-when initial() then
-x := 0;
-
-
-elsewhen sample(0,T) then
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-
-end when;
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+
+algorithm
+
+
+when initial() then
+x := 0;
+
+
+elsewhen sample(0,T) then
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+
+end when;
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_9/quest.txt b/Data/Questions/ingsw/1122_9/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/1122_9/quest.txt
rename to Data/Questions/ingsw/1122_9/quest.txt
index 0e4c593..09ad5cb 100644
--- a/legacy/Data/ingsw/1122_9/quest.txt
+++ b/Data/Questions/ingsw/1122_9/quest.txt
@@ -1,2 +1,2 @@
-img=https://i.imgur.com/Jq6EzV9.png
+img=https://i.imgur.com/Jq6EzV9.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_9/wrong 1.txt b/Data/Questions/ingsw/1122_9/wrong 1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/1122_9/wrong 1.txt
rename to Data/Questions/ingsw/1122_9/wrong 1.txt
index ea67dd7..3ca0d3a 100644
--- a/legacy/Data/Questions/ingsw/1122_9/wrong 1.txt	
+++ b/Data/Questions/ingsw/1122_9/wrong 1.txt	
@@ -1,71 +1,71 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_9/wrong 2.txt b/Data/Questions/ingsw/1122_9/wrong 2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/1122_9/wrong 2.txt
rename to Data/Questions/ingsw/1122_9/wrong 2.txt
index 578bb6b..0ffb41c 100644
--- a/legacy/Data/Questions/ingsw/1122_9/wrong 2.txt	
+++ b/Data/Questions/ingsw/1122_9/wrong 2.txt	
@@ -1,76 +1,76 @@
-<pre>
-
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/12/correct.txt b/Data/Questions/ingsw/12/correct.txt
similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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diff --git a/legacy/Data/Questions/ingsw/19/wrong 2.txt b/Data/Questions/ingsw/19/wrong 2.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/19/wrong 2.txt
rename to Data/Questions/ingsw/19/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/19/wrong.txt b/Data/Questions/ingsw/19/wrong.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/19/wrong.txt
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diff --git a/legacy/Data/Questions/ingsw/2/correct.txt b/Data/Questions/ingsw/2/correct.txt
similarity index 100%
rename from legacy/Data/Questions/ingsw/2/correct.txt
rename to Data/Questions/ingsw/2/correct.txt
diff --git a/legacy/Data/Questions/ingsw/2/quest.txt b/Data/Questions/ingsw/2/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/2/quest.txt
rename to Data/Questions/ingsw/2/quest.txt
index 78e700c..54a3411 100644
--- a/legacy/Data/Questions/ingsw/2/quest.txt
+++ b/Data/Questions/ingsw/2/quest.txt
@@ -1,2 +1,2 @@
-Si consideri un software sviluppato seguendo un approccio plan-driven implementato con tre fasi: F1, F2, F3 ciascuna con costo A. Le "change request" possono arrivare solo al fine di una fase e provocano la ripetizione (con relativo costo) di tutte le fasi che precedono. Si assuma che dopo la fase F3 (cioè al termine dello sviluppo) arriva una change request. Qual è il costo totale per lo sviluppo del software in questione.
+Si consideri un software sviluppato seguendo un approccio plan-driven implementato con tre fasi: F1, F2, F3 ciascuna con costo A. Le "change request" possono arrivare solo al fine di una fase e provocano la ripetizione (con relativo costo) di tutte le fasi che precedono. Si assuma che dopo la fase F3 (cioè al termine dello sviluppo) arriva una change request. Qual è il costo totale per lo sviluppo del software in questione.
 Scegli un'alternativa:
\ No newline at end of file
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similarity index 100%
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@@ -1,2 +1,2 @@
-img=https://i.imgur.com/2gg5nIM.png
+img=https://i.imgur.com/2gg5nIM.png
 Lo State Diagram in figura descrive (in modo semplificato) una macchina distributrice di bevande. Quale delle seguenti frasi è corretta riguardo allo State Diagram in figura?
\ No newline at end of file
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diff --git a/legacy/Data/Questions/ingsw/69420/correct.txt b/Data/Questions/ingsw/69420/correct.txt
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@@ -1,2 +1,2 @@
-img=https://i.imgur.com/a8kMXoW.png
+img=https://i.imgur.com/a8kMXoW.png
 Serafina che tagga Sabrina
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@@ -1,2 +1,2 @@
-img=https://i.imgur.com/F4evurl.jpg
+img=https://i.imgur.com/F4evurl.jpg
 Gioele che tagga Sabrina
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@@ -1,2 +1,2 @@
-img=https://i.imgur.com/qyKmPIA.png
+img=https://i.imgur.com/qyKmPIA.png
 Deco che disegna un Hentai in aula studio
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--- a/legacy/Data/Questions/ingsw/69420/wrong.txt
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@@ -1,2 +1,2 @@
-img=https://corsidilaurea.uniroma1.it/sites/default/files/styles/user_picture/public/pictures/picture-23550-1602857792.jpg
+img=https://corsidilaurea.uniroma1.it/sites/default/files/styles/user_picture/public/pictures/picture-23550-1602857792.jpg
 Tronci
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/8/correct.txt b/Data/Questions/ingsw/8/correct.txt
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@@ -1,815 +1,815 @@
-1) Quale delle seguenti affermazioni sulle directory di un file system è vera?
-> È sempre necessario identificare un file di un file system fornendone il path assoluto
-> È sempre necessario identificare un file di un file system fornendone il path relativo alla directory corrente
-> È sempre possibile dare lo stesso nome a file diversi
-v Nessuna delle altre opzioni è vera
-
-2) UNSAFE Quale delle seguenti affermazioni sulla concorrenza tra processi o thread è falsa?
-v La disabilitazione delle interruzioni impedisce la creazione di nuove interruzioni
-> L'abuso della disabilitazione delle interruzioni fa diminuire la multiprogrammazione, a parità di numero di processi
-> Se un processo può disabilitare le interruzioni tramite un'istruzione macchina dedicata, allora può far diminuire l'uso del processore
-> La disabilitazione delle interruzioni non funziona su sistemi con più processori o più core
-
-3) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni è vera? 
-> Lo scheduler ha, tra i suoi obiettivi, quello di minimizzare il numero di processi che rispettano la propria deadline
-> Lo scheduler ha, tra i suoi obiettivi, quello di minimizzare il volume di lavoro nel tempo
-> Lo scheduler ha, tra i suoi obiettivi, quello di massimizzare il tempo di risposta
-v Lo scheduler ha, tra i suoi obiettivi, quello di minimizzare il tempo di inattività del processore
-
-4) Quale delle seguenti affermazioni sul modello dei processi in UNIX SVR4 System V Release 4 è falsa?
-> Se un processo è Zombie, allora è terminato ma il suo process control block è ancora in memoria
-> Asleep in Memory coincide con Blocked
-v Ha anche uno stato Zombie: serve per tutti i processi che sono terminati
-> Ha 9 stati (10 con Exit)
-
-5) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è falsa? 
-> Quando un indirizzo non viene trovato nel translation lookaside buffer, è necessario consultare la normale tabella delle pagine
-> Il translation lookaside buffer è una particolare cache, ma non è completamente trasparente al sistema operativo
-v Il translation lookaside buffer permette di accedere direttamente al contenuto degli indirizzi di memoria virtuali usati più di recente
-> In assenza di translation lookaside buffer, l'accesso ad un indirizzo virtuale può richiedere almeno 2 accessi in memoria
-
-6) Quale delle seguenti affermazioni sugli obiettivi di sicurezza di un sistema operativo è vera?
-> Per "disponibilità" dell'hardware si intende la garanzia che le workstation restino sempre fisse in un posto
-> Per "confidenzialità" dei dati si intende la garanzia che essi non possano essere generati automaticamente
-v Nessuna delle altre opzioni è vera
-> Per "integrità" dei dati si intende la garanzia che essi non vengano mai modificati
-
-7) Quale delle seguenti affermazioni sul buffering dell'I/O è vera? 
-> Nessuna delle altre opzioni è corretta
-> Avviene direttamente su disco, altrimenti si rischia il deadlock per interferenze con il DMA
-> Nel caso ci siano più buffer, vanno gestiti come nel problema dei lettori/scrittori
-v Può consistere nel completare un'istruzione di output I (è una i) dopo che alcune istruzioni successive ad I siano state eseguite 
-
-8) Quale delle seguenti affermazioni, riguardanti il joint progress diagram di 2 processi, è vera?
-v Nessuna delle altre opzioni è vera
-> Può essere usato per visualizzare le possibilità di deadlock, ma solo se i processi richiedono al massimo 2 risorse
-> Può essere usato per determinare quando uno dei due processi va in esecuzione a discapito dell'altro
-> Può essere usato per determinare quando uno dei due processi sperimenta un page fault
-
-9) Quale delle seguenti affermazioni sulla gerarchia della memoria è vera?
-> Nessuna delle altre opzioni è corretta
-> Andando dall'alto in basso, cresce il costo
-> Andando dall'alto in basso, diminuisce la capacità
-v Andando dall'alto in basso, diminuisce la frequenza di accesso alla memoria da parte del processore
-
-10) Quale dei seguenti elementi non fa parte del process control block?
-> Il puntatore alla tabella delle pagine
-v L’identificatore del thread
-> Lo stato o modalità
-> L’identificatore del processo
-
-11) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sugli algoritmi di scheduling è vera?
-v Nessuna delle altre opzioni è vera
-> Il quanto di tempo ottimale per lo scheduler round-robin è maggiore del tipico tempo di completa esecuzione di un processo interattivo
-> Lo scheduler First Come First Served favorisce i processi I/O-bound
-> Anche assumendo che tutti i processi prima o poi terminino, lo scheduler First Come First Served soffre di starvation
-
-
-14) Quale delle seguenti affermazioni sulla segmentazione della memoria è falsa? 
-> Diversi segmenti possono avere diverse lunghezze
-v Differentemente dalla paginazione, il programmatore assembler di un processo non interagisce esplicitamente con la gestione dei segmenti
-> Per accedere ad un indirizzo contenuto in un segmento di un processo, tale segmento dovrà essere posizionato in memoria principale
-> Un indirizzo di memoria principale va visto come un numero di segmento più uno spiazzamento all'interno di tale segmento
-
-15) Quale delle seguenti affermazioni sull'algoritmo per il rilevamento del deadlock visto a lezione è vera?
-> Richiede in input, per ogni processo p e per ogni risorsa r, il numero massimo di istanze di r che p chiederà nel corso della sua esecuzione
-> Se al passo 3 viene trovato un processo non marcato che soddisfi la condizione Qik ≤ wik, allora c'è un deadlock
-v I processi marcati sono quelli che non sono coinvolti in un deadlock
-> Nessuna delle altre opzioni è vera
-
-16) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sul long-term scheduler è falsa? 
-v Viene chiamato in causa esclusivamente quando viene creato un nuovo processo
-> Avendo le necessarie informazioni, una tipica strategia è mantenere una giusta proporzione, stabilita a priori, tra processi I/O-bound e CPU-bound
-> Avendo le necessarie informazioni, una tipica strategia è ammettere in memoria principale i processi che richiedono dispositivi di I/O diversi da quelli richiesti dai processi già attivi
-> Decide quali processi, tra quelli appena creati, possono essere ammessi in memoria principale per l'esecuzione
-
-17) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera? 
-v Il difetto principale del prepaging è che potrebbe portare in memoria pagine cui poi non si fa riferimento
-> Placement policy e replacement policy sono sinonimi ed indicano lo stesso insieme di metodologie
-> Nessuna delle altre opzioni è corretta
-> Il difetto principale del paging on demand è che causa molti page fault dopo alcuni secondi di esecuzione
-
-18) Quale dei seguenti requisiti deve soddisfare un meccanismo che offra la mutua esclusione?
-v Non deve essere fatta alcuna assunzione sulla velocità di esecuzione dei processi coinvolti
-> Se un processo fa richiesta di entrare nella sezione critica, deve poterlo fare subito
-> Se un processo non fa richiesta di entrare nella sezione critica, deve comunque accordarsi all'esecuzione degli altri processi
-> Si può assumere che un processo che non sia nella sezione critica prima o poi ci entri
-
-19) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera? 
-> Il principio di località afferma che poche pagine saranno sempre sufficienti per eseguire ogni processo senza thrashing
-> Il thrashing si verifica quando l'overhead dovuto alla gestione della paginazione è molto basso
-v Nessuna delle altre opzioni è corretta
-> La paginazione con memoria virtuale funziona bene nonostante il principio di località
-
-20) UNSAFE (sbagliata secondo Gabriele Pelissetto) Quale delle seguenti affermazioni sullo scambio messaggi per la gestione della concorrenza è vera? 
-> Nessuna delle altre opzioni è vera
-> L'implementazione delle primitive per lo scambio messaggi non è garantita atomica dal sistema operativo
-v Se un processo chiama receive, finché il messaggio non viene ricevuto, tutti gli altri processi che proveranno a chiamare receive verranno bloccati
-> Per garantire la mutua esclusione, occorre ricorrere al busy waiting se sia invio che ricezione sono non bloccanti
-
-21) Quali delle seguenti affermazioni sui file system è vera?
-> I dati possono essere ricavati dai metadati
-> I metadati possono essere ricavati dai dati
-v I file system, che adottano il metodo journaling, mantengono un log per le operazioni di sola scrittura da effettuare, realizzandole in seguito
-> Un volume coincide sempre con un disco, quindi se un computer ha 2 dischi avrà 2 volumi
-
-22) (UNSAFE secondo Gabriele Pelissetto vd. gruppo 5 di slide, slide 11) Quale delle seguenti affermazioni sui dispositivi di I/O è vera?
-v Nessuna delle altre opzioni è corretta
-> Il data rate confronta le velocità di 2 diversi dispositivi di I/O
-> Ciascun dispositivo di I/O può essere usato solo da un ben determinato tipo di applicazioni
-> Tutti i dispositivi di I/O scambiano informazioni con la CPU in blocchi, per motivi di efficienza
-
-23) Quale delle seguenti affermazioni sui metodi di gestione dello spazio libero su disco è vera?
-v Se viene usata la lista di blocchi liberi, c'è un overhead di spazio, contrariamente alla concatenazione di blocchi liberi
-> Nessuna delle altre opzioni è vera
-> Se ci sono blocchi da 1kB, e il disco contiene 1TB, l'occupazione dovuta alla lista di blocchi liberi è dell'1%
-> Se viene usata la lista di blocchi liberi, una parte viene memorizzata su disco ed una parte in memoria principale
-
-24) UNSAFE Quale delle seguenti azioni va effettuata sia per un process switch che per un mode switch, assumendo di essere in un SO nel quale le funzioni di sistema sono eseguite all'interno dei processi utente?
-v Salvataggio del contesto del programma
-> Aggiornamento delle strutture dati per la gestione della memoria
-> Spostamento del process control block nella coda appropriata (ready, blocked, ready/suspend)
-> Scelta di un altro processo da eseguire
-
-25) Quale delle seguenti affermazioni è vera?
-> Nessuna delle altre opzioni è corretta
-> Nell'algoritmo di sostituzione basato su frequenza a 2 segmenti della page cache, un blocco passa da un segmento ad un altro esclusivamente per scorrimento
-> L'algoritmo di LFU della page cache ha buone performance quando un settore viene acceduto molto spesso in poco tempo, per poi non essere più usato
-v L'algoritmo di sostituzione basato su frequenza a 2 segmenti della page cache può non avere buone performance quando un settore viene acceduto spesso, ma tra il primo accesso e quelli successivi ci sono N accessi ad altri settori, diversi tra loro, con N pari alla dimensione del segmento nuovo
-
-26) Quale delle seguenti affermazioni sul kernel di un sistema operativo è vera?
-> È responsabile dell'accensione del computer 
-> Viene swappato dal disco alla memoria principale ad ogni context switch 
-v È responsabile, tra le altre cose, della gestione dei processori
-> Nessuna delle altre opzioni è corretta
-
-27) Quale delle seguenti affermazioni sul controllo di accesso è vera?
-> Nel controllo di accesso basato su ruoli, ad ogni ruolo è assegnato un utente
-> Nessuna delle altre opzioni è vera
-> Nel controllo di accesso basato su ruoli, prima di stabilire se un'operazione è lecita, è necessario consultare una tabella soggetti-ruoli-oggetti
-v Nel controllo di accesso discrezionale, prima di stabilire se un'operazione è lecita, è necessario consultare una tabella soggetti-oggetti
-
-28) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sulla preemption è vera?
-> Se uno scheduler è non-preemptive, permette sempre ai suoi processi di essere eseguiti sul processore, senza interruzioni, fino al loro completamento
-v Se uno scheduler è non-preemptive, è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready
-> Se uno scheduler è preemptive, non è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready
-> Per avere un trattamento equo sui processi, è sufficiente usare uno scheduler preemptive
-
-29)  Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sugli algoritmi di scheduling è vera?
-> Con lo scheduler Shortest Process Next, i processi con una grande immagine su RAM potrebbero soffrire di starvation
-v Lo scheduler round-robin virtuale migliora il round-robin classico, facendo sì che i processi I/O-bound non vengano sfavoriti
-> Lo scheduler First Come First Served "degenera" nello scheduler round-robin se il quanto di tempo è troppo lungo
-> Nessuna delle altre opzioni è vera
-
-30) Quale delle seguenti affermazioni sugli indirizzi di memoria principale è vera?
-> Un indirizzo �sico fa sempre riferimento alla memoria secondaria
-> Per rispettare il requisito di rilocazione, occorre trasformare indirizzi �sici in logici
-v Gli indirizzi relativi sono usati nella paginazione
-> Nessuna delle altre opzioni è corretta
-
-31) Quale delle seguenti affermazioni sui termini tipici della concorrenza è falsa?
-v Una sezione critica è una porzione di memoria che contiene almeno una variabile condivisa tra più processi
-> Una operazione atomica è una sequenza di istruzioni macchina tale che, se un processo la esegue, allora arriverà a termine senza interruzioni da altri processi
-> Il requisito di mutua esclusione prevede che un solo processo possa eseguire un certo segmento di codice o accedere ad una determinata risorsa
-> Una race condition è una violazione della mutua esclusione || È possibile che 2 distinti processi chiamino la stessa funzione atomica
-
-32) Quale dei seguenti elementi fa parte del process control block?
-v Nessuna delle altre opzioni contiene elementi del process control block
-> Le informazioni sul contesto del processo, aggiornate ad ogni istruzione eseguita
-> L'intera immagine del processo in memoria
-> La tabella delle pagine di secondo livello
-
-33) Quale delle seguenti informazioni non è presente in una tipica entry di una directory di un �le system?
-v Il gruppo cui appartiene l'utente che ha creato il �le
-> La data di creazione del �le
-> Autorizzazioni per l'accesso al �le
-> Dimensione del �le
-
-34) Quale delle seguenti affermazioni sugli algoritmi di scheduling per i dischi è vera?
-> L'algoritmo random ha la stessa funzione dell'algoritmo ottimo dei rimpiazzamenti di pagina: ha delle prestazioni ottime non raggiungibili dagli altri algoritmi
-> Nessuna delle altre opzioni è corretta
-v L'algoritmo C-SCAN deriva da SCAN, ed è stato sviluppato per evitare di favorire le richieste di tracce ai bordi del disco
-> Per valutare le prestazioni dell'algoritmo con priorità è necessario fornire il ruolo dell'utente
-
-35) Quale delle seguenti affermazioni sugli algoritmi di scheduling per i dischi è vera?
-> Nessuna delle altre opzioni è corretta
-v L'algoritmo C-SCAN deriva da SCAN, ed è stato sviluppato per evitare di favorire le richieste di tracce ai bordi del disco
-> Per valutare le prestazioni dell'algoritmo con priorità è sufficiente fornire il ruolo degli utenti dei processi che effettuano le richieste
-> L'algoritmo random ha la stessa funzione dell'algoritmo ottimo dei rimpiazzamenti di pagina: ha delle prestazioni ottime non raggiungibili dagli altri algoritmi
-
-36) Quale delle seguenti affermazioni sul metodo di allocazione contigua dei file è vera? 
-> È possibile che ci sia frammentazione interna
-v La compattazione permette di memorizzare file che altrimenti non potrebbero esserlo (pur essendo la loro dimensione minore di quella dello spazio libero)
-> Non è necessaria la preallocazione
-> La tabella di allocazione dei file necessita di memorizzare, per ogni file, il solo blocco di partenza
-
-37) Quale delle seguenti affermazioni sulla paginazione della memoria è vera? 
-v Frame e pagine devono avere la stessa dimensione
-> Tutte le pagine di un processo dovranno essere, prima o poi, posizionate in un frame
-> Nessuna delle altre opzioni è corretta
-> Soffre del problema della frammentazione interna, e quindi necessita compattazione
-
-38) Quale delle seguenti affermazioni sul controllo di accesso è vera? 
-> Nel controllo di accesso basato su ruoli, ad ogni ruolo è assegnato un utente
-> Nel controllo di accesso basato su ruoli, prima di stabilire se un'operazione è lecita, è necessario consultare una tabella soggetti-ruoli-oggetti
-v Nel controllo di accesso discrezionale, prima di stabilire se un'operazione è lecita, è necessario consultare una tabella soggetti-oggetti
-> Nessuna delle altre opzioni è vera
-
-39) Quale delle seguenti affermazioni è falsa? 
-> Nel caso delle risorse riusabili, in un grafo dell'allocazione delle risorse ci possono essere più archi tra lo stesso nodo-processo e lo stesso nodo-risorsa
-> Nel caso delle risorse riusabili, in un grafo dell'allocazione delle risorse ci possono essere archi sia da nodi-processi a nodi-risorse che viceversa
-v Un grafo dell'allocazione delle risorse è un grafo diretto aciclico
-> In un grafo dell'allocazione delle risorse, all'interno di un nodo rappresentante una risorsa, c'è un pallino per ogni istanza di quella risorsa
-
-40) Quali delle seguenti affermazioni è vera? 
-> La confidenzialità di un sistema operativo consiste nel fatto che la shell del sistema operativo deve essere intuitiva e dare del tu agli utenti
-v La disponibilità (availability) di un sistema operativo consiste nel fatto che il sistema operativo deve essere sempre pronto a rispondere alle richieste di un utente
-> La disponibilità (availability) di un sistema operativo consiste nel fatto che devono esistere delle repository online che permettano sia di installare che di aggiornare il sistema operativo
-> La confidenzialità di un sistema operativo consiste nel fatto che il sistema operativo deve essere sempre pronto a rispondere alle richieste di un utente
-
-41) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera? 
-v Il difetto principale del prepaging è che potrebbe portare in memoria pagine cui poi non si fa riferimento
-> Nessuna delle altre opzioni è corretta
-> Il difetto principale del paging on demand è che, dopo una prima fase di assestamento, causa molti page fault
-> Placement policy e replacement policy sono sinonimi ed indicano lo stesso insieme di metodologie
-
-42) Quale delle seguenti affermazioni sui dispositivi di memoria di massa è vera? 
-v Nessuna delle altre opzioni è corretta
-> Un settore di un disco magnetico a testina mobile è l'area di una corona circolare del disco stesso
-> Una traccia di un disco magnetico a testina mobile è l'area compresa da 2 raggi del disco stesso
-> Per selezionare un settore su una traccia di un disco magnetico a testina mobile, è sufficiente posizionare la testina sulla giusta traccia
-
-44) Quale delle seguenti affermazioni sui semafori per la gestione della concorrenza è falsa? 
-> Semafori generali e semafori binari hanno lo stesso potere computazionale (ovvero, permettono di risolvere gli stessi problemi)
-> Le primitive sui semafori sono in grado di mettere un processo in blocked, senza usare, a tal proposito, il busy-waiting
-v Per implementare le primitive sui semafori, servono un contatore ed una coda, che saranno condivisi da tutti i semafori usati
-> L'implementazione delle primitive sui semafori è garantita atomica dal sistema operativo
-
-45) Quale delle seguenti affermazioni sugli algoritmi di scheduling per i dischi è falsa? 
-> Nell'algoritmo F-SCAN, immediatamente prima che vengano scambiati i contenuti delle code F ed R, la coda F è vuota, mentre la coda R contiene le richieste arrivate mentre si servivano le richieste dentro F
-> L'algoritmo Minimum Service Time può portare alla starvation di un processo, che non verrà quindi mai selezionato, se la richiesta era bloccante, per andare in esecuzione sul processore
-v L'algoritmo LIFO è il più equo nei confronti dei processi che effettuano le richieste al disco
-> Gli algoritmi Minimum Service Time, SCAN, C-SCAN, N-steps-SCAN ed F-SCAN non sono ottimizzati per essere usati su dischi con testine multiple selezionabili elettronicamente
-
-46) Quale delle seguenti affermazioni sui meccanismi per la gestione della concorrenza è vera? 
-v Senza usare né semafori, né scambio messaggi, né istruzioni macchina atomiche, è possibile scrivere processi che non soffrano di starvation per garantire la mutua esclusione tra 2 processi
-> Disabilitando gli interrupt, è possibile scrivere processi che non soffrano di starvation
-> Usando i semafori di qualsiasi tipo, è possibile scrivere processi che non soffrano di starvation
-> Usando le istruzioni macchina exchange e compare_and_swap, è possibile scrivere processi che non soffrano di starvation
-
-47) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sul long-term scheduler è falsa? 
-> Decide quali processi, tra quelli appena creati, possono essere ammessi in memoria principale per l'esecuzione
-> Avendo le necessarie informazioni, una tipica strategia è mantenere una giusta proporzione, stabilita a priori, tra processi I/O-bound e CPU-bound
-v Viene chiamato in causa esclusivamente quando viene creato un nuovo processo
-> Avendo le necessarie informazioni, una tipica strategia è ammettere in memoria principale i processi che richiedono dispositivi di I/O diversi da quelli richiesti dai processi già attivi
-
-48) Quale delle seguenti affermazioni sui metodi di gestione dello spazio libero su disco è vera? 
-> Se ci sono blocchi da 1kB, e il disco contiene 1TB, l'occupazione dovuta alla lista di blocchi liberi è dell'1%
-> Se viene usata la lista di blocchi liberi, tale lista viene interamente mantenuta in memoria principale
-> Nessuna delle altre opzioni è vera
-v Se viene usata la lista di blocchi liberi, c'è un overhead di spazio, contrariamente alla concatenazione di blocchi liberi
-
-49) Quale delle seguenti affermazioni sulle directory di un file system è vera?
-> È sempre necessario identificare un file di un file system fornendone il path relativo alla directory corrente
-> È sempre possibile dare lo stesso nome a file diversi
-v Nessuna delle altre opzioni è vera
-> È sempre necessario identificare un file di un file system fornendone il path assoluto
-
-50) Quale delle seguenti affermazioni sulla memoria cache è vera? 
-> La memoria cache è direttamente indirizzabile in assembler
-> Nessuna delle altre opzioni è corretta
-v È possibile che, in un dato istante, la cache e la memoria RAM non siano coerenti tra loro
-> L'algoritmo di rimpiazzamento per la cache stabilisce quale blocco di RAM deve essere sostituito da un blocco di cache
-
-51) Quale delle seguenti affermazioni sui problemi dei produttori/consumatori e dei lettori/scrittori, nelle accezioni viste a lezione, è vera? 
-v Per il problema dei produttori/consumatori, non deve essere mai possibile che più consumatori accedano contemporaneamente al buffer, mentre nel problema dei lettori/scrittori deve sempre possibile che più lettori, in assenza di scrittori, accedano all'area di memoria
-> Per il problema dei produttori/consumatori, non deve essere mai possibile che più produttori accedano contemporaneamente al buffer, mentre nel problema dei lettori/scrittori deve essere sempre possibile che più scrittori (in assenza di lettori) accedano all'area di memoria
-> Nessuna delle altre opzioni è corretta
-> Per il problema dei produttori/consumatori, deve essere sempre possibile che più consumatori accedano contemporaneamente al buffer, mentre nel problema dei lettori/scrittori non deve essere mai possibile che più scrittori accedano all'area di memoria
-
-52) Quale delle seguenti affermazioni, riguardanti il joint progress diagram di 2 processi, è vera? 
-> Può essere usato per determinare quando uno dei due processi sperimenta un page fault
-> Può essere usato per visualizzare le possibilità di deadlock, ma solo se i processi richiedono al massimo 2 risorse
-v Nessuna delle altre opzioni è vera
-> Può essere usato per determinare quando uno dei due processi manda un segnale all'altro
-
-53) Quale delle seguenti affermazioni sui (vecchi) metodi per il partizionamento della memoria è vera? 
-> Con il partizionamento fisso, le partizioni devono avere tutte la stessa dimensione
-> Con il buddy system, ogni indirizzo di memoria può ricadere in 2 porzioni
-> Con il partizionamento fisso, ci possono essere al massimo N processi attivi (ovvero, accettati per l'esecuzione), dove N è il numero di partizioni
-v Con il partizionamento dinamico, si manifesta il problema della frammentazione esterna
-
-54) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sulla preemption è vera? 
-> Se uno scheduler è preemptive e vi è più di 1 processo ready, non è possibile che un processo monopolizzi il processore
-> Per avere un trattamento equo sui processi, è sufficiente usare uno scheduler preemptive
-> Se uno scheduler è non-preemptive, permette sempre ai suoi processi di essere eseguiti senza interruzioni sul processore fino al loro completamento
-v Se uno scheduler è non-preemptive, è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready
-
-55) Nel modello dei processi a 5 stati, quali delle seguenti transizioni non è possibile? 
-v Blocked ==> Running
-> Running ==> Ready
-> Blocked ==> Exit
-> Blocked ==> Ready
-
-56) Quale delle seguenti affermazioni sul metodo di allocazione indicizzata dei file è vera? 
-> Il consolidamento permette sempre di ridurre la dimensione dell'indice
-v Se usato con porzioni di dimensione variabile, i blocchi indice devono contenere anche la lunghezza di ogni porzione
-> Nessuna delle altre opzioni è vera
-> Non c'è modo per il sistema operativo di distinguere tra blocchi con dati e blocchi con indici
-
-57) Quale delle seguenti affermazioni sul requisito di rilocazione nella gestione della memoria è vera? 
-v Nessuna delle altre opzioni è corretta
-> Se viene realizzato tramite sostituzione degli indirizzi nel programma sorgente (al momento della creazione del processo), allora il relativo processo dovrà cominciare sempre allo stesso indirizzo; tale indirizzo dovrà essere uguale per tutti i processi
-> Se viene realizzato tramite sostituzione degli indirizzi nel programma sorgente (al momento della creazione del processo), allora il relativo processo potrà trovarsi in diverse posizioni della memoria in diversi momenti del sua esecuzione
-> Se viene realizzato tramite sostituzione degli indirizzi nel programma sorgente (al momento della creazione del processo), serve hardware speciale
-
-58) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sulla preemption è vera?
-> Se uno scheduler è non-preemptive, permette sempre ai suoi processi di essere eseguiti sul processore, senza interruzioni, fino al loro completamento
-v Se uno scheduler è non-preemptive, è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready
-> Se uno scheduler è preemptive, non è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready
-> Per avere un trattamento equo sui processi, è sufficiente usare uno scheduler preemptive
-
-59) Quale dei seguenti requisiti deve soddisfare un meccanismo che offra la mutua esclusione? 
-v Non deve essere fatta alcuna assunzione sulla velocità di esecuzione dei processi coinvolti
-> Se un processo non fa richiesta di entrare nella sezione critica, deve comunque sincronizzarsi all'esecuzione degli altri processi
-> Se un processo è nella sezione critica, occorre che rilasci subito la sezione critica stessa
-> Se un processo fa richiesta di entrare nella sezione critica, deve poter entrare subito nella sezione critica stessa
-
-60) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sul dispatcher è falsa? 
-> Il resource balancing è un criterio di sistema non prestazionale
-> Il rispetto delle deadline è un criterio utente prestazionale
-> Il throughput è un criterio di sistema prestazionale
-v La predictability è un criterio utente prestazionale
-
-61) Quale delle seguenti affermazioni sugli interrupt (o eccezioni) è falsa? 
-> Devono essere gestiti da opportuno software di sistema
-v Una volta gestito l'interrupt o l'eccezione, quando (e se) si torna ad eseguire il processo interrotto, l'esecuzione ripartirà sempre dall'istruzione successiva a quella dove è stato ricevuto l'interrupt o l'eccezione
-> Normalmente, non vengono gestiti dal programmatore dell'applicazione che li ha causati
-> Possono essere creati direttamente dai dispositivi di I/O
-
-62) Quale delle seguenti affermazioni sulle istruzioni macchina speciali per la gestione della concorrenza è vera? 
-> Sono basate sul busy-waiting, ovvero sul fatto che un processo si mette autonomamente in stato blocked
-v Nessuna delle altre opzioni è vera
-> Non riescono ad evitare il manifestarsi del deadlock, a meno che non sia presente un sistema a priorità
-> Come per la disabilitazione delle interruzioni, non funzionano per architetture con più processori o core
-
-63) Quale delle seguenti affermazioni sui processi è vera? 
-> Nessuna delle altre opzioni è vera
-v Per la terminazione normale di un processo, è tipicamente prevista un'apposita system call, come ad esempio exit
-> Un processo può morire quando si effettua il process spawning
-> Un processo può essere creato dal modulo di gestione della memoria per gestire la traduzione da indirizzi virtuali a fisici
-
-64) (UNSAFE le decisioni dello scheduler possono influenzare la risposta) Quale delle seguenti affermazioni sui meccanismi software per la gestione della concorrenza è vera? 
-> Sia l'algoritmo di Dekker che quello di Peterson possono mettere in blocked uno dei 2 processi, quando ciò si rivela necessario
-> Sia l'algoritmo di Dekker che quello di Peterson non funzionano se l'hardware sottostante riordina gli accessi in memoria
-> Nell'algoritmo di Peterson, se la variabile turn è inizializzata ad 1, allora il processo 1 sarà sicuramente il primo ad entrare nella sezione critica nella prima iterazione
-v Nell'algoritmo di Dekker, se la variabile turn è inizializzata ad 1, allora il processo 1 sarà sicuramente il primo ad entrare nella sezione critica nella prima iterazione
-
-65) Quale delle seguenti affermazioni sugli i-node di Unix è falsa? 
-> Ogni directory è identificata da un i-node
-v Per modificare una directory, un utente deve aprire il file speciale corrispondente e poi modificarlo opportunamente
-> Ogni directory è un file speciale, organizzato come una lista di entry, ciascuna delle quali contiene il nome di un file ed il relativo i-node number
-> Ogni directory può contenere molti i-node
-
-66) Quale delle seguenti affermazioni è falsa? 
-v Nel caso di un sistema operativo a kernel separato, la gestione dei process switch è a sua volta un processo
-> Nel caso di un sistema operativo in cui le funzioni del sistema operativo vengono eseguite all'interno dei processi utente, non c'è bisogno di un process switch per eseguire una funzionalità del sistema operativo
-> Nel caso di un sistema operativo in cui le funzioni del sistema operativo vengono eseguite all'interno dei processi utente, se un processo effettua una syscall e poi può continuare ad essere eseguito, non avviene alcun process switch
-> Nel caso di un sistema operativo in cui le funzioni del sistema operativo vengono eseguite come processi separati, c'è sempre bisogno di un process switch per eseguire una funzionalità del sistema operativo
-
-67) (corretta secondo @loryspat e @notherealmarco, da controllare) Quale delle seguenti affermazioni sulla paginazione della memoria è vera? 
-> La differenza tra paginazione semplice e paginazione con memoria virtuale è che nella seconda viene richiesto che tutte le pagine di un processo siano in memoria principale, affinché il processo stesso possa essere eseguito
-> Con la paginazione con memoria virtuale, una sola pagina di ogni processo ready o in esecuzione è inizialmente caricata in memoria principale
-v La differenza tra paginazione semplice e paginazione con memoria virtuale è che nella prima viene richiesto che tutte le pagine di un processo siano in memoria principale, affinché il processo stesso possa essere eseguito
-> Nessuna delle altre opzioni è vera
-
-
-68) Quale delle seguenti affermazioni sul metodo di allocazione concatenata dei file è vera? 
-> Il consolidamento permette di memorizzare file che altrimenti non potrebbero esserlo (pur essendo la loro dimensione minore di quella dello spazio libero)
-> La tabella di allocazione dei file deve contenere l'intera catena
-v Nessuna delle altre opzioni è vera
-> Viene usato con porzioni di dimensione variabile, ma piccola
-
-69) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera? 
-> Nel caso di una tabella delle pagine a 2 livelli, viene tipicamente richiesto che tutte le tabelle delle pagine di secondo livello entrino in una pagina
-> Il numero di bit di un indirizzo virtuale è necessariamente diverso a seconda che si usi una tabella delle pagine ad 1 o a 2 livelli
-v Il numero di bit di una entry di una tabella delle pagine di ultimo livello è uguale al numero di bit di controllo più il logaritmo (arrotondato all'intero superiore) del massimo numero di frame in memoria principale
-> Nessuna delle altre opzioni è corretta
-
-70) (corretta secondo Simone Sestito e @loryspat) Quale delle seguenti affermazioni sul deadlock è falsa? 
-> Affinchè ci sia un deadlock, sono necessarie le condizioni di attesa circolare, hold-and-wait, mutua esclusione e no preemption
-v Per prevenire il deadlock, è necessario cercare di impedire almeno una delle 3 condizioni di mutua esclusione, hold-and-wait e no preemption
-> Affinchè il deadlock sia possibile, sono necessarie le condizioni di mutua esclusione, hold-and-wait e no preemption
-> Per prevenire il deadlock impedendo l'hold-and-wait, si può in alcuni casi imporre ai processi di richiedere tutte le risorse fin dall'inizio
-
-71) Quale delle seguenti affermazioni è vera? 
-> La modalità di un processo utente è sempre la modalità di sistema
-> La modalità di un processo utente è inizialmente la modalità utente; può diventare modalità sistema nel momento in cui va in esecuzione il dispatcher
-v Nessuna delle altre opzioni è vera
-> La modalità di un processo utente è sempre la modalità utente
-
-72) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera? 
-v Nessuna delle altre opzioni è corretta
-> Per ogni processo, il resident set contiene lo stesso numero di pagine
-> Un tipico algoritmo per il replacement scope è quello dell'orologio
-> La gestione del resident set tramite politica dinamica mira ad ampliare il numero di pagine di un processo durante l'esecuzione del processo stesso
-
-74) Quale delle seguenti affermazioni sulla concorrenza tra processi o thread è vera?
-v L'istruzione exchange non può ricevere costanti in input su nessun suo argomento, mentre per l'istruzione compare_and_swap questo non vale
-> Le istruzioni speciali exchange e compare_and_swap sono garantite atomiche dal sistema operativo
-> Per realizzare opportunamente l'istruzione compare_and_swap è sufficiente disabilitare le interruzioni
-> Nessuna delle altre opzioni è vera
-
-75)  Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sul dispatcher è falsa?
-> Il response time è un criterio utente prestazionale
-> Il turnaround time (normalizzato o no) è un criterio utente prestazionale
-v Il throughput è un criterio di sistema non prestazionale
-> La fairness è un criterio di sistema non prestazionale
-
-76) Quale delle seguenti affermazioni sul file system FAT è vera?
-> Usa il metodo di allocazione contiguo
-> Ogni cluster del disco contiene sia dati del disco che l'indirizzo del prossimo cluster (o l'indicazione che si tratta dell'ultimo cluster)
-> La tabella di allocazione dei file contiene tante righe quanti sono i file memorizzati sul disco, più una riga speciale per i blocchi liberi
-v Nessuna delle altre opzioni è vera
-
-77) UNSAFE Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è falsa?
-> Il translation lookaside buffer, su alcuni processori, contiene un campo per il PID dei processi
-> Il translation lookaside buffer funziona correttamente solo se tutti i frame validi contenuti al suo interno fanno riferimento a pagine effettivamente in RAM, e non swappate su disco
-v Il mapping associativo permette al translation lookaside buffer di trovare una data pagina semplicemente sommando il numero della pagina con l'indirizzo di partenza del translation lookaside buffer stesso
-> Quando un indirizzo viene trovato nel translation lookaside buffer, non è necessario consultare la normale tabella delle pagine
-
-78) Quale dei seguenti elementi non è una delle parti che definiscono un processo?
-> Il contatore di programma
-> La priorità
-> I dati contenuti nella porzione di memoria a lui dedicata
-v Informazioni sullo stato delle risorse
-
-
-79) UNSAFE Quale delle seguenti affermazioni, riguardanti la classificazione delle risorse di un sistema operativo e la loro relazione con il deadlock, è vera?
-> Nel caso delle risorse consumabili, se c'è un deadlock allora è stata richiesta almeno una risorsa già detenuta da un altro processo
-v Nel caso delle risorse consumabili, se c'è un deadlock allora c'è una successione circolare di processi, ciascuno dei quali richiede una risorsa al processo successivo, che però la deve ancora creare
-> Nel caso delle risorse riusabili, se c'è un deadlock allora è stata richiesta almeno una risorsa non ancora creata
-> Nel caso delle risorse riusabili, se c'è un deadlock allora c'è una successione circolare di processi, ciascuno dei quali richiede una risorsa al processo successivo, che però la deve ancora creare
-
-80) UNSAFE Si supponga che ci siano N processi attivi, giostrati da uno scheduler round-robin su un sistema monoprocessore. Quale delle seguenti affermazioni è vera?
-> Dal punto di vista del processore, ogni processo esegue sempre le proprie istruzioni senza interruzioni
-v Per realizzare correttamente un process switch, il SO avrà necessità di usare le informazioni sul contesto contenute nel process control block
-> Dal punto di vista di ogni processo, l'esecuzione avviene in interleaving con gli altri processi
-> Nessuna delle altre opzioni è vera
-
-81) Quale delle seguenti affermazioni sulla traduzione di un indirizzo virtuale in fisico, in un sistema con memoria virtuale con paginazione (avente tabella delle pagine ad 1 livello), è falsa?
-v L'hardware deve anche cercare il numero di pagina nelle entries della tabella delle pagine del processo in esecuzione. 
-> L'hardware deve anche estrarre dall'indirizzo virtuale il numero di pagina virtuale; tale operazione è equivalente ad una divisione intera
-> L'hardware deve anche usare il numero di pagina per accedere alla tabella delle pagine del processo in esecuzione. A tal proposito, deve conoscere l'inizio di tale tabella, che viene definito dal software (sistema operativo). Tale indirizzo può cambiare durante l'esecuzione del processo: sta al sistema operativo mantenerlo aggiornato
-> L'hardware deve anche usare il numero di frame ottenuto dalla tabella delle pagine per comporre, insieme con l'offset originale, l'indirizzo fisico. Tale operazione è equivalente ad uno shift seguito da una somma
-
-82) Quale delle seguenti operazioni non è tipicamente effettuata su un file?
-> Apertura
-v Connessione
-> Posizionamento (seek)
-> Lock/Unlock
-
-83) Quale delle seguenti affermazioni è falsa?
-v Diversi thread di uno stesso processo condividono lo stesso thread identifier
-> Tra le funzioni di sistema per i thread, è tipicamente prevista una funzione per bloccare e sbloccare esplicitamente i thread stessi
-> Diversi thread di uno stesso processo condividono lo stesso process identifier
-> Diversi thread di uno stesso processo condividono i file aperti
-
-84) Quale delle seguenti affermazioni sulla page cache è falsa?
-v Nell'algoritmo di sostituzione basato su frequenza a 3 segmenti della page cache, i contatori vengono sempre incrementati, tranne quando sono nel segmento vecchio
-> Nell'algoritmo di sostituzione basato su frequenza a 3 segmenti della page cache, i settori che possono essere sostituiti sono solo quelli del segmento vecchio
-> Nell'algoritmo di sostituzione basato su frequenza a 3 segmenti della page cache, l'unico segmento in cui i contatori non vengono incrementati e i settori non possono essere sostituti è quello nuovo
-> L'algoritmo di sostituzione basato su frequenza a 3 segmenti della page cache può avere buone performance anche quando dei settori vengono acceduti spesso, ma tra il primo accesso e quelli successivi ci sono molti altri accessi ad altri settori
-
-85) Quali delle seguenti affermazioni sulla efficienza di un sistema operativo è falsa?
-> Deve minimizzare il tempo di risposta, tenendo presenti eventuali priorità
-> Deve servire il maggior numero di utenti possibile, tenendo presenti eventuali livelli di accesso
-v Deve dare accesso alle risorse in modo equo ed egualitario tra tutti i processi
-> Deve massimizzare l'uso delle risorse per unità di tempo, tenendo presenti eventuali priorità
-
-88) Quale delle seguenti affermazioni sui metodi di gestione del deadlock è vera?
-> Nessuna delle altre opzioni è vera
-> L'unico metodo, che richiede di conoscere in anticipo il massimo numero di risorse che un processo dovrà chiedere, è quello per rilevare il deadlock
-> Il metodo più permissivo nei confronti delle richieste di risorse è quello che consiste nel prevenire il deadlock
-v L'unico metodo che non prevede mai la preemption delle risorse è quello che evita il deadlock
-
-89) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni è falsa?
-> Lo scheduler ha, tra i suoi obiettivi, quello dell'equità tra i processi, a meno che non siano definite delle priorità
-> Lo scheduler va scritto in modo che il suo overhead sia basso
-v Lo scheduler ha, tra i suoi obiettivi, quello di evitare il deadlock
-> Lo scheduler ha, tra i suoi obiettivi, quello di massimizzare il volume di lavoro dei processi nel tempo
-
-90) Quale delle seguenti affermazioni sugli scheduler per architetture multiprocessore è vera?
-> Con l'assegnamento statico, si dà un processore a caso tra quelli liberi ai processi che mantengono un uso della RAM pressoché costante
-> Assegnando i processi del sistema operativo con l'assegnamento dinamico, si rischia di creare un bottleneck su un solo processore
-> Uno svantaggio dell'assegnamento statico è il suo overhead maggiore rispetto a quello dinamico
-v Nessuna delle altre opzioni è vera
-
-91) Quale delle seguenti affermazioni sull'algoritmo del banchiere per evitare il deadlock visto a lezione è falsa?
-v La matrice C - A può contenere elementi negativi, ma le matrici C ed A contengono solo elementi non negativi
-> Richiede in input, per ogni processo p e per ogni risorsa r, il numero massimo di istanze di r che p chiederà nel corso della sua esecuzione
-> All'inizio e alla fine di ogni invocazione dell'algoritmo, Vi = Ri - ∑j = 1, ..., nAi, j
-> Se si procede da uno stato ad un altro, necessariamente è stata fatta almeno una richiesta ad almeno una risorsa da parte di almeno un processo
-
-92) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sul dispatcher è falsa?
-> Il throughput è definito come il numero di processi completati per unità di tempo
-v Il turnaround time è definito, per un dato processo, come il tempo che intercorre tra la sua prima esecuzione sul processore e il suo completamento
-> Un dispatcher con buone prestazioni sul response time deve tipicamente sia minimizzare il valore medio di sistema del response time, sia massimizzare il numero di utenti con un basso valore per il response time
-> Il processor utilization è definito come il rapporto tra il tempo in cui il processore viene usato ed il tempo totale del sistema
-
-93) Quale delle seguenti affermazioni sugli i-node di Unix è vera?
-> Per ogni file-system su disco organizzato con i-node, tutti gli i-node di tutti i file su tale file-system sono memorizzati esclusivamente su disco
-v I puntatori a tripla indirezione di un i-node vengono usati solo se la dimensione del file lo richiede
-> Nessuna delle altre opzioni è vera
-> Ad ogni file effettivamente memorizzato su disco può essere associato un solo numero di i-node
-
-94) UNSAFE Quale delle seguenti affermazioni sul modello dei processi a 7 stati è vera?
-v Nessuna delle altre opzioni è vera
-> Gli stati Ready, New e Blocked del modello a 5 stati vengono sdoppiati, e ne viene creata una versione Suspend
-> Un processo è Suspend quando scade il timeout del dispatcher
-> È possibile la transizione Ready/Suspend ==> Blocked/Suspend
-
-95) Quale delle seguenti affermazioni sui dischi magnetici a testina mobile è vera? 
-> Per selezionare un settore su una traccia di un disco magnetico a testina mobile, bisogna prima far ruotare il disco fino ad arrivare alla giusta traccia, e poi posizionare la testina sul giusto settore
-> Una traccia di un disco è l'area compresa tra 2 raggi del disco stesso
-v Il tempo di accesso ad un disco magnetico a testina mobile tiene conto sia del tempo che occorre per posizionare la testina che del tempo che occorre per far ruotare il disco, ma non del tempo che occorre per effettuare effettivamente il trasferimento di dati
-> Nessuna delle altre opzioni è corretta
-
-98) UNSAFE Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sugli algoritmi di scheduling è vera?
-v L'exponential averaging permette di stimare la dimensione dell'immagine di un processo, a partire dalle precedenti immagini di quello stesso processo
-> La funzione di decisione dello scheduler Highest Response Ratio Next considera tanto il tempo di esecuzione stimato quanto il tempo trascorso in attesa
-> L'exponential averaging è una tecnica applicabile dal solo scheduler Short Process Next
-> La funzione di decisione dello scheduler Shortest Remaining Time considera tanto il tempo di esecuzione richiesto quanto il tempo trascorso in attesa
-
-100) Quale delle seguenti affermazioni è vera sulla memoria virtuale con paginazione a segmentazione?
-v Sia la tabella dei segmenti che quella delle pagine di un processo contengono, in ciascuna entry, un bit per indicare se la pagina o il segmento sono stati modificati
-> Un indirizzo virtuale contiene anche un bit per indicare se la pagina corrispondente è o no in memoria principale
-> La tabella delle pagine di un processo contiene una pagina speciale dove è memorizzato il process control block del processo stesso
-> Ogni entry di una tabella delle pagine contiene un numero di pagina ed un offset
-
-99) (risposta corretta secondo @notherealmarco, Simone Sestito e Gabriele Pelissetto, non verificata da nessuna parte) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera?
-v Per avere un overhead accettabile, occorre demandare la traduzione degli indirizzi all'hardware, mentre al software resta da gestire prelievo, posizionamento e sostituzione delle pagine
-> Per avere un overhead accettabile, occorre demandare la traduzione degli indirizzi e la politica di sostituzione delle pagine all'hardware, mentre al software resta da gestire prelievo e posizionamento delle pagine
-> Per avere un overhead accettabile, occorre demandare all'hardware la traduzione degli indirizzi ed il prelievo, il posizionamento e la sostituzione delle pagine
-> Per avere un overhead accettabile, occorre demandare al software anche la traduzione degli indirizzi
-
-96) (risposta corretta secondo @notherealmarco e Gabriele Pelissetto, non verificata da nessuna parte) Riguardo alle differenze tra sistemi batch e sistemi time sharing (degli anni 60/70), quale delle seguenti affermazioni è falsa? 
-v I sistemi time-sharing puntavano a minimizzare l'uso del processore
-> Nei sistemi time-sharing, le direttive al sistema operativo arrivavano dai comandi digitati su terminali
-> Nei sistemi batch, le direttive al sistema operativo arrivavano dai comandi del job control language, che erano non-interattivi
-> I sistemi batch puntavano a massimizzare l'uso del processore
-
-img=https://i.imgur.com/orqyjeh.png
-12) Considerare un insieme di cinque processi P1, P2, P3, P4, P5 con i seguenti tempi di arrivo e tempi di esecuzione in millisecondi: Quale delle seguenti affermazioni è falsa?
-> Non ci sono sufficienti informazioni per determinare come si comporterebbe l'algoritmo di scheduling a feedback classico di Unix
-> Non ci sono sufficienti informazioni per determinare come si comporterebbe l'algoritmo di scheduling Virtual Round-Robin
-> Non ci sono sufficienti informazioni per determinare come si comporterebbe l'algoritmo di scheduling Round-Robin
-v Non ci sono sufficienti informazioni per determinare come si comporterebbe l'algoritmo di scheduling SRT
-
-img=https://i.imgur.com/orqyjeh.png
-13) Considerare un insieme di cinque processi P1, P2, P3, P4, P5 con i seguenti tempi di arrivo e tempi di esecuzione in millisecondi: Assegnare questo insieme di processi ad un processore usando l'algoritmo di scheduling SRT, �no a che non terminano tutti. Quale delle seguenti affermazioni è falsa?
-v Gli unici 2 processi che non sono serviti subito (ovvero, appena arrivati) sono P3 e P5
-> Il tempo medio di attesa è tra 10 ed 11 ms
-> Il processo con il più lungo tempo di attesa è P1
-> Il tempo medio di turnaround è tra 2 e 3 ms
-
-img=https://i.imgur.com/5nWWwyF.png
-101) Si consideri il seguente modo di implementare la mutua esclusione: Quale delle seguenti affermazioni è vera?
-> La soluzione non implementa correttamente la mutua esclusione, ma può essere corretta nel seguente modo: int bolt = 0; void P(int i) { int key; while(true) { do (exchange(key, bolt) == 0) while(key != 0); critical_section(); bolt = 0; key = 1; } } 
-> La soluzione non implementa correttamente la mutua esclusione, in quanto key deve essere una variabile globale
-v La soluzione non implementa correttamente la mutua esclusione, ma può essere corretta nel seguente modo: int bolt = 0; void P(int i) { int key; while(true) { key = 1; do (exchange(key, bolt) == 0) while(key != 0); critical_section(); bolt = 0; } } 
-> La soluzione implementa correttamente la mutua esclusione
-
-103) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è falsa?
-> Diminuire la dimensione delle pagine ha effetti positivi sul numero di pagine che possono trovarsi in memoria principale
-v Aumentare la dimensione delle pagine ha effetti positivi sulla frammentazione interna
-> Diminuire la dimensione delle pagine ha effetti negativi sulla dimensione della tabella delle pagine
-> Aumentare la dimensione delle pagine ha effetti negativi sulla multiprogrammazione
-
-104) Quale delle seguenti affermazioni sulla concorrenza tra processi o thread è falsa?
-v La disabilitazione delle interruzioni impedisce la creazione di nuove interruzioni
-> Se un processo utente può disabilitare le interruzioni tramite un'istruzione macchina dedicata, allora può far diminuire l'uso utile del processore
-> La disabilitazione delle interruzioni non funziona ai fini della concorrenza (gestione sezioni critiche) su sistemi con più processori o più core
-> L'abuso della disabilitazione delle interruzioni fa diminuire la multiprogrammazione, a parità di numero di processi
-
-105) Quale delle seguenti affermazioni non è vera?
-> il kernel rimane in memoria durante l'intera sessione del computer
-v il kernel è costituito da vari moduli che non possono essere caricati nel sistema operativo in esecuzione
-> il kernel è la prima parte del sistema operativo a essere caricata in memoria durante l'avvio
-> Il kernel è il programma che costituisce il nucleo centrale del sistema operativo.
-
-106) UNSAFE In generale, la CPU puo’ eseguire un'istruzione soltanto quando gli operandi si trovano:
-> In RAM, o in un livello qualsiasi della cache o nella memoria secondaria o nei registri CPU
-> In RAM o in un livello qualsiasi della cache o nei registri CPU
-> Nella cache di livello 1 (L1 cache) o nei registri CPU
-v Nei registri della CPU
-
-107) Il PCB (Process Control Block) e’:
-> Un campo dello stato di un processo che definisce quali operazioni di controllo dei dispositivi a blocchi sono state fatte dal processo
-v Una struttura dati mantenuta dal sistema operativo che contiene tutte le informazioni necessarie all’esecuzione, sospensione e ripresa dell’esecuzione di un processo
-> Una struttura dati mantenuta dal sistema operativo che contiene l’intera immagine di un processo
-> Un’interfaccia di controllo dei processi del sistema operativo
-
-108) Considera un Sistema Operativo con esecuzione all’interno dei processi utente. Quando un processo utente fa una chiamata di sistema, quale delle seguenti affermazioni e’ corretta
-> Il sistema operativo deve effettuare un process switch ed un mode switch per eseguire la funzione richiesta
-> Il sistema operativo deve effettuare soltanto un process switch per eseguire la funzione richiesta
-v Il sistema operativo deve effettuare soltanto un mode switch per eseguire la funzione richiesta
-> Il sistema operativo deve creare un nuovo processo e fare switch ad esso per eseguire la funzione richiesta
-
-109) Quale delle seguenti affermazioni e’ vera:
-> Il dispatcher e’ una componente del medium term scheduler
-> Il dispatcher si occupa di decidere l’ordine di sospensione dei processi
-v Il dispatcher si occupa di scambiare i processi in esecuzione sulla CPU (process switch)
-> Il dispatcher si occupa di scambiare i processi dalla memoria principale alla memoria secondaria
-
-110) In un sistema operativo con I/O buffering, quando c’e’ una scrittura su dispositivo di I/O quale delle seguenti affermazioni e’ vera:
-> Il sistema operativo copia immediatamente il contenuto della scrittura dalla memoria del processo direttamente alla memoria del dispositivo di I/O
-v Il sistema operativo copia immediatamente il contenuto della scrittura dalla memoria utente alla memoria del sistema operativo, e dalla memoria del sistema operativo alla memoria del dispositivo di I/O quando piu’ opportuno
-> Il sistema operativo copia quando piu’ opportuno il contenuto della scrittura dalla memoria del processo direttamente alla memoria del dispositivo di I/O
-> Nessuna delle altre opzioni e’ corretta
-
-111) L’algoritmo di scheduling C-SCAN:
-> Scrivere le richieste su disco in modo tale che il braccio meccanico si muova sempre in una direzione, fino a raggiungere l’ultima traccia, e poi torna indietro scrivendo tutte le richieste fino a raggiungere la prima traccia
-> Puo’ portare a starvation per alcuni processi
-> E’ meno fair (equo) dell’algoritmo SCAN
-v Non favorisce le richieste ai bordi rispetto a SCAN
-
-112) Quale dei seguenti sono requisiti per un File Management System?
-> Ogni utente dev’essere in grado di creare, cancellare, leggere, scrivere e modificare un file
-> Ogni utente deve poter accedere, in modo controllato, ai file di un altro utente
-> Ogni utente deve poter mantenere una copia di backup dei propri file
-v Tutte le opzioni sono requisiti
-
-113) Una sezione critica è un segmento di programma:
-> Che e’ racchiuso tra una coppia di operazioni di semaforo semWait e semSignal
-v In cui si accede a risorse condivise
-> Che evita i deadlock
-> Che deve essere eseguito in un determinato lasso di tempo.
-
-114) Quale dei seguenti NON è vero riguarda il Algoritmo di Dekker per gestire la concorrenza?
-> Garantisce la non-starvation
-> Non richiede nessun supporto dal SO.
-v Richiede supporto dal SO
-> E' deterministico.
-
-115) Quale delle affermazioni è vera riguardo al Translation lookaside buffer per la gestione della memoria?
-> Nel Translation lookaside buffer ci sono tag e chiavi con l'aiuto dei quali viene effettuata la mappatura.
-> Il TLB hit è una condizione in cui la voce desiderata viene trovata nel TLB.
-> Se la voce non viene trovata nel TLB (TLB miss), la CPU deve accedere alla tabella delle pagine nella memoria principale e quindi accedere al frame effettivo nella memoria principale.
-v Tutte le opzioni sono vere.
-
-116) Quale delle seguenti affermazioni sul long-term scheduler e’ vera:
-v Si occupa della decisione di quali processi debbano essere ammessi all’esecuzione nel sistema
-> Si occupa dell’organizzazione di lungo termine dell’ordine di esecuzione dei processi nella CPU
-> Si occupa dell’implementazione della funzione di swapping dei processi alla memoria secondaria
-> Si occupa della transizione dei processi tra gli stati running ed exit
-
-117) Nel modello dei processi a 5 stati, quale affermazione e’ falsa:
-v Un processo puo’ essere spostato allo stato suspended dallo stato blocked e ready
-> Un processo puo’ essere spostato dallo stato running allo stato ready o exit
-> Un processo puo’ essere spostato dallo stato blocked solo allo stato ready
-> Un processo puo’ essere spostato dallo stato ready allo stato running, blocked o exit
-
-118) Riguardo l’efficienza dal punto di vista dell’utilizzo utile della CPU, quale dei seguenti modelli di I/O e’ piu’ efficiente dal punto di vista dell’uso della CPU e perche’?
-> I/O programmato, perche’ consente al programmatore di fare uno scheduling esatto delle operazioni di I/O nei momenti piu’ opportuni
-> I/O basato su DMA (Accesso Diretto alla Memoria), perche’ la CPU deve soltanto occuparsi del trasferimento dei dati
-> I/O basato su interruzioni, perche’ il processore non deve controllare attivamente lo stato del dispositivo di I/O dopo aver effettuato la richiesta
-v I/O basato su DMA (Accesso Diretto alla Memoria), perche’ la CPU deve soltanto occuparsi di inviare la richiesta di I/O e leggere il risultato
-
-119) Dati due processi A e B e due risorse R1 ed R2, si ha sicuramente una situazione di deadlock se:
-v A richiede ed ottiene accesso ad R1, B richiede ed ottiene accesso ad R2. A richiede accesso ad R2, B richiede accesso ad R1
-> A richiede ed ottiene accesso ad R1, B richiede accesso ad R2. A richiede accesso ad R2. B richiede accesso ad R1
-> A richiede ed ottiene accesso ad R2, B richiede accesso ad R1 ed R2. A richiede ed ottiene accesso ad R1
-> B richiede ed ottiene accesso ad R1, A richiede ed ottiene accesso ad R2. B richiede accesso ad R2
-
-120) Quali delle seguenti affermazioni e' vera riguardo la preallocazione rispetto all'allocazione dinamica dello spazio per i file?
-> la preallocazione è più efficiente nell'utilizzo dello spazio su disco
-v nessuna delle opzioni è corretta
-> l'allocazione dinamica rischia di sprecare spazio disco in caso gli utenti/applicazioni sovrastimino la dimensione dei file, mentre questo non è il caso con la preallocazione
-> L'allocazione dinamica impone un overhead di gestione minore per il sistema operativo
-
-121) Quale delle seguenti affermazioni sul file system NTFS è vera?
-v NTFS può, ove possibile, includere direttamente i dati di un file nella master file table
-> NTFS non prevede la possibilità di avere record estesi
-> nessuna delle altre opzioni è vera
-> In NTFS, le informazioni relative alla sequenza di blocchi che contengono il file è interamente contenuta nel record base
-
-122) Quale delle seguenti affermazioni riguardo la rilocazione degli indirizzi di memoria è vera?
-> Nei sistemi con hardware dedicato per la rilocazione, il base register (registro base) viene impostato una sola volta, quando il programma viene caricato in memoria per la prima volta
-> In un sistema con rilocazione a run time, i sistemi di protezione che verificano che un processo non vada ad accedere alla memoria di un'altro processo possono essere eseguiti a tempo di compilazione, prima di eseguire il programma
-> In un sistema a rilocazione con indirizzi logici, non è necessario avere hardware dedicato per effettuare la rilocazione
-v In un sistema a rilocazione con indirizzi assoluti, se si conosce l'indirizzo di memoria dove verrà caricato il programma, il compilatore può inserire direttamente gli indirizzi di memoria corretti nel codice oggetto (programma compilato)
-
-123) Quale delle seguenti affermazioni è vera riguardo il concetto di Thrashing?
-> Il SO impiega la maggior parte del suo tempo a swappare pezzi di processi, anziché ad eseguire istruzioni
-> provoca il deterioramento o il crollo delle prestazioni del computer
-> quasi ogni richiesta di pagine da luogo ad una page fault
-v Tutte le opzioni sono vere
-
-124) Il sistema di partizionamento fisso per la memoria principale:
-> Permette di avere partizioni di lunghezza diversa e di modificarle a runtime
-> Nessuna delle opzioni è vera
-> Consente una efficiente della memoria se ci sono molti processi di piccole dimensioni 
-v Impone un numero massimo di processi che possono essere in memoria principale
-
-125) Quale delle seguenti non è un vantaggio dell’attacco dizionario?
-> Semplice da effettuare
-> Versatilità
-v Velocità di computazione in real time degli hash
-> Disponibilità di molti tool per automatizzazione
-
-126) Nello scheduler a breve ed a lungo termine la distizione principale è:
-> Il tipo di processi che gestiscono
-v La frequenza di esecuzione
-> La lunghezza delle loro code
-> Nessuna delle opzioni è corretta
-
-127) Quale dei seguenti NON è un vantaggio della multiprogrammazione?
-> Riduzione dei tempi di risposta
-> Possibilità di assegnare priorità ai lavori
-> Aumento del throughput
-v Riduzione dell’overhead del sistema operativo
-
-128) ___> fornisce l’indirizzo della prossima istruzione che deve essere eseguita dal processo corrente?
-> Lo stack del processo
-> Il bus di sistema
-> Nessuno 
-v Program Counter
-
-129) Quale dei seguenti NON è un valido schema di prevenzione del deadlock?
-> Rilasciare tutte le risorse prima di richiederne una nuova
-v Non chiedere mai una risorsa dopo averne rilasciate altre
-> Si definisce un ordinamento crescente delle risorse, una risorsa viene data solo se esegue quelle che il processo già detiene
-> Richiedere e allocare tutte le risorse necessarie prima dell’esecuzione
-
-130) UNSAFE Quale dei seguenti NON è vero riguardo l’algoritmo di Dekker per gestire la concorrenza?
-v Non usa busy waiting
-> Garantisce la non-starvation
-> Tutte le opzioni elencate
-> Garantisce il non-deadlock
-
-131) Quale delle seguenti non è una tabella di controllo del sistema operativo?
-v Tabella dei processi sospesi
-> Tabelle di memoria
-> Tabelle di controllo di accesso
-> Tabelle di I/O
-
-132) In un sistema con modello di interruzioni (interrupt) annidate, se un interrupt (I-2) è ricevuto durante la gestione di un altro interrupt(I-1)
-v La cpu sospende l’esecuzione del codice corrente, ed avvia l’handler del nuovo interrupt ricevuto
-> La cpu completa l’esecuzione del codice corrente, e successivamente avvia l’handler del nuovo interrupt ricevuto
-> La cpu gestisce entrambi gli handler in parallelo
-> La cpu termina (aborts,kills) l’esecuzione del codice corrente, ed avvia l’handler del nuovo interrupt ricevuto
-
-133) Il numero di processi completati per unità di tempo è chiamato _____
-> Produzione
-v Throughput
-> Capacità
-> Nessuno
-
-134) Quale dei seguenti sono obiettivi per un file Management System?
-v Tutte le opzioni elencate
-> Fornire supporto per l’I/O da più utenti in contemporanea
-> Minimizzare i dati persi o distrutti
-> Fornire un insieme di interfacce standard per i processi utente
-
-135) In un sistema operativo con allocazione dei file indicizzata, quale delle seguenti opzioni è vera:
-> La tabella di allocazione contiene soltanto l'indirizzo di un blocco, e questo blocco contiene sempre tutte le entry per ogni porzione allocata al file
-> La tabella di allocazione contiene l'indirizzo del primo blocco del file, e ciascun blocco contiene l'indirizzo del prossimo blocco del file
-v La tabella di allocazione contiene soltanto l'indirizzo di un blocco, e questo blocco contiene le entry delle porzioni di file allocate oppure l'indirizzo di altri blocchi usati a loro volta per indicizzare le porzioni di file allocate
-> La tabella di allocazione dei file contiene l'indirizzo di un blocco e la lista dei blocchi del file
-
-136) Quale delle seguenti affermazioni riguardo algoritmi di scheduling del disco è vera
-> L'algoritmo SCAN può portare a starvation delle richieste
-> L'algoritmo FSCAN è una versione di SCAN che rimuove il problema della starvation delle richieste, ma che rende l'algoritmo meno fair rispetto a SCAN
-> L'algoritmo Minimo Tempo di Servizio non richiede di conoscere la posizione della testina del disco per operare
-v N-step-SCAN è una generalizzazione di FSCAN che è fair e può avere prestazioni molto simili a quelle di SCAN
-
-137) Quali dei seguenti NON è un tipo di scheduling dei sistemi operativi:
-> Short term scheduling
-> Long term scheduling
-> Disk scheduling
-v File scheduling
-
-138) Nei sistemi operativi che usano paginazione SEMPLICE per la gestione della memoria
-> ai processi devono essere allocati frame di memoria necessariamente contigui per poter consentire l'esecuzione del processo
-> il sistema operativo deve utilizzare la tabella delle pagine per tradurre gli indirizzi. Qualora una pagina non sia presente in memoria principale, il sistema la deve caricare dinamicamente per consentire il proseguimento dell'esecuzione di un processo
-> non c'è necessità di traduzione degli indirizzi, in quanto tutte le pagine di un processo sono sempre caricate in un frame nella memoria principale
-v nessuna delle altre opzioni è corretta
-
-139) Nei sistemi operativi che usano journaling logico
-> non c'è possibilità di perdita dei dati in quanto, in caso di arresto imprevisto, il sistema operativo può usare il journal per ricostruire interamente le operazioni non andate a buon fine
-> il sistema operativo usa il journal solo per copiare i dati prima di farne la scrittura anche nel file system, ma non lo utilizza per i metadati
-v il sistema operativo usa il journal solo per copiare i metadati prima di aggiornare le strutture del file system, ma non lo utilizza per i dati
-> nessuna delle opzioni è corretta
-
-140) Il sistema operativo linux per la gestione dei file
-v nessuna delle altre opzioni è corretta
-> utilizza un sistema misto di allocazione contigua e concatenata in modo da minimizzare l'overhead di sistema e massimizzare le performance
-> utilizza un sistema di allocazione concatenata basato sulla struttura dati conosciuta come inode
-> usa gli inode per tenere traccia dei blocchi su disco allocati a ciascun file. Ogni inode contiene al suo interno la lista completa di tutti i blocchi su disco che compongono il file corrispondente
-
-141) Nei sistemi Unix
-> gli hard links sono dei file speciali che contengono il cammino completo sul file system di un altro file, effettivamente creando un "puntatore" a quel file
-v gli hard link sono puntatori diretti al descrittore di un file (inode). Un contatore viene utilizzato per tenere traccia di quanti hard link puntino ad un determinato inode. Questo fa si che il file non possa essere cancellato fintantoché ci sono hard link che continuano a puntarlo
-> possono esistere hard link a file non più esistenti, ad esempio se il file a cui l'hard link puntava viene cancellato
+1) Quale delle seguenti affermazioni sulle directory di un file system è vera?
+> È sempre necessario identificare un file di un file system fornendone il path assoluto
+> È sempre necessario identificare un file di un file system fornendone il path relativo alla directory corrente
+> È sempre possibile dare lo stesso nome a file diversi
+v Nessuna delle altre opzioni è vera
+
+2) UNSAFE Quale delle seguenti affermazioni sulla concorrenza tra processi o thread è falsa?
+v La disabilitazione delle interruzioni impedisce la creazione di nuove interruzioni
+> L'abuso della disabilitazione delle interruzioni fa diminuire la multiprogrammazione, a parità di numero di processi
+> Se un processo può disabilitare le interruzioni tramite un'istruzione macchina dedicata, allora può far diminuire l'uso del processore
+> La disabilitazione delle interruzioni non funziona su sistemi con più processori o più core
+
+3) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni è vera? 
+> Lo scheduler ha, tra i suoi obiettivi, quello di minimizzare il numero di processi che rispettano la propria deadline
+> Lo scheduler ha, tra i suoi obiettivi, quello di minimizzare il volume di lavoro nel tempo
+> Lo scheduler ha, tra i suoi obiettivi, quello di massimizzare il tempo di risposta
+v Lo scheduler ha, tra i suoi obiettivi, quello di minimizzare il tempo di inattività del processore
+
+4) Quale delle seguenti affermazioni sul modello dei processi in UNIX SVR4 System V Release 4 è falsa?
+> Se un processo è Zombie, allora è terminato ma il suo process control block è ancora in memoria
+> Asleep in Memory coincide con Blocked
+v Ha anche uno stato Zombie: serve per tutti i processi che sono terminati
+> Ha 9 stati (10 con Exit)
+
+5) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è falsa? 
+> Quando un indirizzo non viene trovato nel translation lookaside buffer, è necessario consultare la normale tabella delle pagine
+> Il translation lookaside buffer è una particolare cache, ma non è completamente trasparente al sistema operativo
+v Il translation lookaside buffer permette di accedere direttamente al contenuto degli indirizzi di memoria virtuali usati più di recente
+> In assenza di translation lookaside buffer, l'accesso ad un indirizzo virtuale può richiedere almeno 2 accessi in memoria
+
+6) Quale delle seguenti affermazioni sugli obiettivi di sicurezza di un sistema operativo è vera?
+> Per "disponibilità" dell'hardware si intende la garanzia che le workstation restino sempre fisse in un posto
+> Per "confidenzialità" dei dati si intende la garanzia che essi non possano essere generati automaticamente
+v Nessuna delle altre opzioni è vera
+> Per "integrità" dei dati si intende la garanzia che essi non vengano mai modificati
+
+7) Quale delle seguenti affermazioni sul buffering dell'I/O è vera? 
+> Nessuna delle altre opzioni è corretta
+> Avviene direttamente su disco, altrimenti si rischia il deadlock per interferenze con il DMA
+> Nel caso ci siano più buffer, vanno gestiti come nel problema dei lettori/scrittori
+v Può consistere nel completare un'istruzione di output I (è una i) dopo che alcune istruzioni successive ad I siano state eseguite 
+
+8) Quale delle seguenti affermazioni, riguardanti il joint progress diagram di 2 processi, è vera?
+v Nessuna delle altre opzioni è vera
+> Può essere usato per visualizzare le possibilità di deadlock, ma solo se i processi richiedono al massimo 2 risorse
+> Può essere usato per determinare quando uno dei due processi va in esecuzione a discapito dell'altro
+> Può essere usato per determinare quando uno dei due processi sperimenta un page fault
+
+9) Quale delle seguenti affermazioni sulla gerarchia della memoria è vera?
+> Nessuna delle altre opzioni è corretta
+> Andando dall'alto in basso, cresce il costo
+> Andando dall'alto in basso, diminuisce la capacità
+v Andando dall'alto in basso, diminuisce la frequenza di accesso alla memoria da parte del processore
+
+10) Quale dei seguenti elementi non fa parte del process control block?
+> Il puntatore alla tabella delle pagine
+v L’identificatore del thread
+> Lo stato o modalità
+> L’identificatore del processo
+
+11) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sugli algoritmi di scheduling è vera?
+v Nessuna delle altre opzioni è vera
+> Il quanto di tempo ottimale per lo scheduler round-robin è maggiore del tipico tempo di completa esecuzione di un processo interattivo
+> Lo scheduler First Come First Served favorisce i processi I/O-bound
+> Anche assumendo che tutti i processi prima o poi terminino, lo scheduler First Come First Served soffre di starvation
+
+
+14) Quale delle seguenti affermazioni sulla segmentazione della memoria è falsa? 
+> Diversi segmenti possono avere diverse lunghezze
+v Differentemente dalla paginazione, il programmatore assembler di un processo non interagisce esplicitamente con la gestione dei segmenti
+> Per accedere ad un indirizzo contenuto in un segmento di un processo, tale segmento dovrà essere posizionato in memoria principale
+> Un indirizzo di memoria principale va visto come un numero di segmento più uno spiazzamento all'interno di tale segmento
+
+15) Quale delle seguenti affermazioni sull'algoritmo per il rilevamento del deadlock visto a lezione è vera?
+> Richiede in input, per ogni processo p e per ogni risorsa r, il numero massimo di istanze di r che p chiederà nel corso della sua esecuzione
+> Se al passo 3 viene trovato un processo non marcato che soddisfi la condizione Qik ≤ wik, allora c'è un deadlock
+v I processi marcati sono quelli che non sono coinvolti in un deadlock
+> Nessuna delle altre opzioni è vera
+
+16) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sul long-term scheduler è falsa? 
+v Viene chiamato in causa esclusivamente quando viene creato un nuovo processo
+> Avendo le necessarie informazioni, una tipica strategia è mantenere una giusta proporzione, stabilita a priori, tra processi I/O-bound e CPU-bound
+> Avendo le necessarie informazioni, una tipica strategia è ammettere in memoria principale i processi che richiedono dispositivi di I/O diversi da quelli richiesti dai processi già attivi
+> Decide quali processi, tra quelli appena creati, possono essere ammessi in memoria principale per l'esecuzione
+
+17) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera? 
+v Il difetto principale del prepaging è che potrebbe portare in memoria pagine cui poi non si fa riferimento
+> Placement policy e replacement policy sono sinonimi ed indicano lo stesso insieme di metodologie
+> Nessuna delle altre opzioni è corretta
+> Il difetto principale del paging on demand è che causa molti page fault dopo alcuni secondi di esecuzione
+
+18) Quale dei seguenti requisiti deve soddisfare un meccanismo che offra la mutua esclusione?
+v Non deve essere fatta alcuna assunzione sulla velocità di esecuzione dei processi coinvolti
+> Se un processo fa richiesta di entrare nella sezione critica, deve poterlo fare subito
+> Se un processo non fa richiesta di entrare nella sezione critica, deve comunque accordarsi all'esecuzione degli altri processi
+> Si può assumere che un processo che non sia nella sezione critica prima o poi ci entri
+
+19) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera? 
+> Il principio di località afferma che poche pagine saranno sempre sufficienti per eseguire ogni processo senza thrashing
+> Il thrashing si verifica quando l'overhead dovuto alla gestione della paginazione è molto basso
+v Nessuna delle altre opzioni è corretta
+> La paginazione con memoria virtuale funziona bene nonostante il principio di località
+
+20) UNSAFE (sbagliata secondo Gabriele Pelissetto) Quale delle seguenti affermazioni sullo scambio messaggi per la gestione della concorrenza è vera? 
+> Nessuna delle altre opzioni è vera
+> L'implementazione delle primitive per lo scambio messaggi non è garantita atomica dal sistema operativo
+v Se un processo chiama receive, finché il messaggio non viene ricevuto, tutti gli altri processi che proveranno a chiamare receive verranno bloccati
+> Per garantire la mutua esclusione, occorre ricorrere al busy waiting se sia invio che ricezione sono non bloccanti
+
+21) Quali delle seguenti affermazioni sui file system è vera?
+> I dati possono essere ricavati dai metadati
+> I metadati possono essere ricavati dai dati
+v I file system, che adottano il metodo journaling, mantengono un log per le operazioni di sola scrittura da effettuare, realizzandole in seguito
+> Un volume coincide sempre con un disco, quindi se un computer ha 2 dischi avrà 2 volumi
+
+22) (UNSAFE secondo Gabriele Pelissetto vd. gruppo 5 di slide, slide 11) Quale delle seguenti affermazioni sui dispositivi di I/O è vera?
+v Nessuna delle altre opzioni è corretta
+> Il data rate confronta le velocità di 2 diversi dispositivi di I/O
+> Ciascun dispositivo di I/O può essere usato solo da un ben determinato tipo di applicazioni
+> Tutti i dispositivi di I/O scambiano informazioni con la CPU in blocchi, per motivi di efficienza
+
+23) Quale delle seguenti affermazioni sui metodi di gestione dello spazio libero su disco è vera?
+v Se viene usata la lista di blocchi liberi, c'è un overhead di spazio, contrariamente alla concatenazione di blocchi liberi
+> Nessuna delle altre opzioni è vera
+> Se ci sono blocchi da 1kB, e il disco contiene 1TB, l'occupazione dovuta alla lista di blocchi liberi è dell'1%
+> Se viene usata la lista di blocchi liberi, una parte viene memorizzata su disco ed una parte in memoria principale
+
+24) UNSAFE Quale delle seguenti azioni va effettuata sia per un process switch che per un mode switch, assumendo di essere in un SO nel quale le funzioni di sistema sono eseguite all'interno dei processi utente?
+v Salvataggio del contesto del programma
+> Aggiornamento delle strutture dati per la gestione della memoria
+> Spostamento del process control block nella coda appropriata (ready, blocked, ready/suspend)
+> Scelta di un altro processo da eseguire
+
+25) Quale delle seguenti affermazioni è vera?
+> Nessuna delle altre opzioni è corretta
+> Nell'algoritmo di sostituzione basato su frequenza a 2 segmenti della page cache, un blocco passa da un segmento ad un altro esclusivamente per scorrimento
+> L'algoritmo di LFU della page cache ha buone performance quando un settore viene acceduto molto spesso in poco tempo, per poi non essere più usato
+v L'algoritmo di sostituzione basato su frequenza a 2 segmenti della page cache può non avere buone performance quando un settore viene acceduto spesso, ma tra il primo accesso e quelli successivi ci sono N accessi ad altri settori, diversi tra loro, con N pari alla dimensione del segmento nuovo
+
+26) Quale delle seguenti affermazioni sul kernel di un sistema operativo è vera?
+> È responsabile dell'accensione del computer 
+> Viene swappato dal disco alla memoria principale ad ogni context switch 
+v È responsabile, tra le altre cose, della gestione dei processori
+> Nessuna delle altre opzioni è corretta
+
+27) Quale delle seguenti affermazioni sul controllo di accesso è vera?
+> Nel controllo di accesso basato su ruoli, ad ogni ruolo è assegnato un utente
+> Nessuna delle altre opzioni è vera
+> Nel controllo di accesso basato su ruoli, prima di stabilire se un'operazione è lecita, è necessario consultare una tabella soggetti-ruoli-oggetti
+v Nel controllo di accesso discrezionale, prima di stabilire se un'operazione è lecita, è necessario consultare una tabella soggetti-oggetti
+
+28) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sulla preemption è vera?
+> Se uno scheduler è non-preemptive, permette sempre ai suoi processi di essere eseguiti sul processore, senza interruzioni, fino al loro completamento
+v Se uno scheduler è non-preemptive, è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready
+> Se uno scheduler è preemptive, non è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready
+> Per avere un trattamento equo sui processi, è sufficiente usare uno scheduler preemptive
+
+29)  Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sugli algoritmi di scheduling è vera?
+> Con lo scheduler Shortest Process Next, i processi con una grande immagine su RAM potrebbero soffrire di starvation
+v Lo scheduler round-robin virtuale migliora il round-robin classico, facendo sì che i processi I/O-bound non vengano sfavoriti
+> Lo scheduler First Come First Served "degenera" nello scheduler round-robin se il quanto di tempo è troppo lungo
+> Nessuna delle altre opzioni è vera
+
+30) Quale delle seguenti affermazioni sugli indirizzi di memoria principale è vera?
+> Un indirizzo �sico fa sempre riferimento alla memoria secondaria
+> Per rispettare il requisito di rilocazione, occorre trasformare indirizzi �sici in logici
+v Gli indirizzi relativi sono usati nella paginazione
+> Nessuna delle altre opzioni è corretta
+
+31) Quale delle seguenti affermazioni sui termini tipici della concorrenza è falsa?
+v Una sezione critica è una porzione di memoria che contiene almeno una variabile condivisa tra più processi
+> Una operazione atomica è una sequenza di istruzioni macchina tale che, se un processo la esegue, allora arriverà a termine senza interruzioni da altri processi
+> Il requisito di mutua esclusione prevede che un solo processo possa eseguire un certo segmento di codice o accedere ad una determinata risorsa
+> Una race condition è una violazione della mutua esclusione || È possibile che 2 distinti processi chiamino la stessa funzione atomica
+
+32) Quale dei seguenti elementi fa parte del process control block?
+v Nessuna delle altre opzioni contiene elementi del process control block
+> Le informazioni sul contesto del processo, aggiornate ad ogni istruzione eseguita
+> L'intera immagine del processo in memoria
+> La tabella delle pagine di secondo livello
+
+33) Quale delle seguenti informazioni non è presente in una tipica entry di una directory di un �le system?
+v Il gruppo cui appartiene l'utente che ha creato il �le
+> La data di creazione del �le
+> Autorizzazioni per l'accesso al �le
+> Dimensione del �le
+
+34) Quale delle seguenti affermazioni sugli algoritmi di scheduling per i dischi è vera?
+> L'algoritmo random ha la stessa funzione dell'algoritmo ottimo dei rimpiazzamenti di pagina: ha delle prestazioni ottime non raggiungibili dagli altri algoritmi
+> Nessuna delle altre opzioni è corretta
+v L'algoritmo C-SCAN deriva da SCAN, ed è stato sviluppato per evitare di favorire le richieste di tracce ai bordi del disco
+> Per valutare le prestazioni dell'algoritmo con priorità è necessario fornire il ruolo dell'utente
+
+35) Quale delle seguenti affermazioni sugli algoritmi di scheduling per i dischi è vera?
+> Nessuna delle altre opzioni è corretta
+v L'algoritmo C-SCAN deriva da SCAN, ed è stato sviluppato per evitare di favorire le richieste di tracce ai bordi del disco
+> Per valutare le prestazioni dell'algoritmo con priorità è sufficiente fornire il ruolo degli utenti dei processi che effettuano le richieste
+> L'algoritmo random ha la stessa funzione dell'algoritmo ottimo dei rimpiazzamenti di pagina: ha delle prestazioni ottime non raggiungibili dagli altri algoritmi
+
+36) Quale delle seguenti affermazioni sul metodo di allocazione contigua dei file è vera? 
+> È possibile che ci sia frammentazione interna
+v La compattazione permette di memorizzare file che altrimenti non potrebbero esserlo (pur essendo la loro dimensione minore di quella dello spazio libero)
+> Non è necessaria la preallocazione
+> La tabella di allocazione dei file necessita di memorizzare, per ogni file, il solo blocco di partenza
+
+37) Quale delle seguenti affermazioni sulla paginazione della memoria è vera? 
+v Frame e pagine devono avere la stessa dimensione
+> Tutte le pagine di un processo dovranno essere, prima o poi, posizionate in un frame
+> Nessuna delle altre opzioni è corretta
+> Soffre del problema della frammentazione interna, e quindi necessita compattazione
+
+38) Quale delle seguenti affermazioni sul controllo di accesso è vera? 
+> Nel controllo di accesso basato su ruoli, ad ogni ruolo è assegnato un utente
+> Nel controllo di accesso basato su ruoli, prima di stabilire se un'operazione è lecita, è necessario consultare una tabella soggetti-ruoli-oggetti
+v Nel controllo di accesso discrezionale, prima di stabilire se un'operazione è lecita, è necessario consultare una tabella soggetti-oggetti
+> Nessuna delle altre opzioni è vera
+
+39) Quale delle seguenti affermazioni è falsa? 
+> Nel caso delle risorse riusabili, in un grafo dell'allocazione delle risorse ci possono essere più archi tra lo stesso nodo-processo e lo stesso nodo-risorsa
+> Nel caso delle risorse riusabili, in un grafo dell'allocazione delle risorse ci possono essere archi sia da nodi-processi a nodi-risorse che viceversa
+v Un grafo dell'allocazione delle risorse è un grafo diretto aciclico
+> In un grafo dell'allocazione delle risorse, all'interno di un nodo rappresentante una risorsa, c'è un pallino per ogni istanza di quella risorsa
+
+40) Quali delle seguenti affermazioni è vera? 
+> La confidenzialità di un sistema operativo consiste nel fatto che la shell del sistema operativo deve essere intuitiva e dare del tu agli utenti
+v La disponibilità (availability) di un sistema operativo consiste nel fatto che il sistema operativo deve essere sempre pronto a rispondere alle richieste di un utente
+> La disponibilità (availability) di un sistema operativo consiste nel fatto che devono esistere delle repository online che permettano sia di installare che di aggiornare il sistema operativo
+> La confidenzialità di un sistema operativo consiste nel fatto che il sistema operativo deve essere sempre pronto a rispondere alle richieste di un utente
+
+41) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera? 
+v Il difetto principale del prepaging è che potrebbe portare in memoria pagine cui poi non si fa riferimento
+> Nessuna delle altre opzioni è corretta
+> Il difetto principale del paging on demand è che, dopo una prima fase di assestamento, causa molti page fault
+> Placement policy e replacement policy sono sinonimi ed indicano lo stesso insieme di metodologie
+
+42) Quale delle seguenti affermazioni sui dispositivi di memoria di massa è vera? 
+v Nessuna delle altre opzioni è corretta
+> Un settore di un disco magnetico a testina mobile è l'area di una corona circolare del disco stesso
+> Una traccia di un disco magnetico a testina mobile è l'area compresa da 2 raggi del disco stesso
+> Per selezionare un settore su una traccia di un disco magnetico a testina mobile, è sufficiente posizionare la testina sulla giusta traccia
+
+44) Quale delle seguenti affermazioni sui semafori per la gestione della concorrenza è falsa? 
+> Semafori generali e semafori binari hanno lo stesso potere computazionale (ovvero, permettono di risolvere gli stessi problemi)
+> Le primitive sui semafori sono in grado di mettere un processo in blocked, senza usare, a tal proposito, il busy-waiting
+v Per implementare le primitive sui semafori, servono un contatore ed una coda, che saranno condivisi da tutti i semafori usati
+> L'implementazione delle primitive sui semafori è garantita atomica dal sistema operativo
+
+45) Quale delle seguenti affermazioni sugli algoritmi di scheduling per i dischi è falsa? 
+> Nell'algoritmo F-SCAN, immediatamente prima che vengano scambiati i contenuti delle code F ed R, la coda F è vuota, mentre la coda R contiene le richieste arrivate mentre si servivano le richieste dentro F
+> L'algoritmo Minimum Service Time può portare alla starvation di un processo, che non verrà quindi mai selezionato, se la richiesta era bloccante, per andare in esecuzione sul processore
+v L'algoritmo LIFO è il più equo nei confronti dei processi che effettuano le richieste al disco
+> Gli algoritmi Minimum Service Time, SCAN, C-SCAN, N-steps-SCAN ed F-SCAN non sono ottimizzati per essere usati su dischi con testine multiple selezionabili elettronicamente
+
+46) Quale delle seguenti affermazioni sui meccanismi per la gestione della concorrenza è vera? 
+v Senza usare né semafori, né scambio messaggi, né istruzioni macchina atomiche, è possibile scrivere processi che non soffrano di starvation per garantire la mutua esclusione tra 2 processi
+> Disabilitando gli interrupt, è possibile scrivere processi che non soffrano di starvation
+> Usando i semafori di qualsiasi tipo, è possibile scrivere processi che non soffrano di starvation
+> Usando le istruzioni macchina exchange e compare_and_swap, è possibile scrivere processi che non soffrano di starvation
+
+47) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sul long-term scheduler è falsa? 
+> Decide quali processi, tra quelli appena creati, possono essere ammessi in memoria principale per l'esecuzione
+> Avendo le necessarie informazioni, una tipica strategia è mantenere una giusta proporzione, stabilita a priori, tra processi I/O-bound e CPU-bound
+v Viene chiamato in causa esclusivamente quando viene creato un nuovo processo
+> Avendo le necessarie informazioni, una tipica strategia è ammettere in memoria principale i processi che richiedono dispositivi di I/O diversi da quelli richiesti dai processi già attivi
+
+48) Quale delle seguenti affermazioni sui metodi di gestione dello spazio libero su disco è vera? 
+> Se ci sono blocchi da 1kB, e il disco contiene 1TB, l'occupazione dovuta alla lista di blocchi liberi è dell'1%
+> Se viene usata la lista di blocchi liberi, tale lista viene interamente mantenuta in memoria principale
+> Nessuna delle altre opzioni è vera
+v Se viene usata la lista di blocchi liberi, c'è un overhead di spazio, contrariamente alla concatenazione di blocchi liberi
+
+49) Quale delle seguenti affermazioni sulle directory di un file system è vera?
+> È sempre necessario identificare un file di un file system fornendone il path relativo alla directory corrente
+> È sempre possibile dare lo stesso nome a file diversi
+v Nessuna delle altre opzioni è vera
+> È sempre necessario identificare un file di un file system fornendone il path assoluto
+
+50) Quale delle seguenti affermazioni sulla memoria cache è vera? 
+> La memoria cache è direttamente indirizzabile in assembler
+> Nessuna delle altre opzioni è corretta
+v È possibile che, in un dato istante, la cache e la memoria RAM non siano coerenti tra loro
+> L'algoritmo di rimpiazzamento per la cache stabilisce quale blocco di RAM deve essere sostituito da un blocco di cache
+
+51) Quale delle seguenti affermazioni sui problemi dei produttori/consumatori e dei lettori/scrittori, nelle accezioni viste a lezione, è vera? 
+v Per il problema dei produttori/consumatori, non deve essere mai possibile che più consumatori accedano contemporaneamente al buffer, mentre nel problema dei lettori/scrittori deve sempre possibile che più lettori, in assenza di scrittori, accedano all'area di memoria
+> Per il problema dei produttori/consumatori, non deve essere mai possibile che più produttori accedano contemporaneamente al buffer, mentre nel problema dei lettori/scrittori deve essere sempre possibile che più scrittori (in assenza di lettori) accedano all'area di memoria
+> Nessuna delle altre opzioni è corretta
+> Per il problema dei produttori/consumatori, deve essere sempre possibile che più consumatori accedano contemporaneamente al buffer, mentre nel problema dei lettori/scrittori non deve essere mai possibile che più scrittori accedano all'area di memoria
+
+52) Quale delle seguenti affermazioni, riguardanti il joint progress diagram di 2 processi, è vera? 
+> Può essere usato per determinare quando uno dei due processi sperimenta un page fault
+> Può essere usato per visualizzare le possibilità di deadlock, ma solo se i processi richiedono al massimo 2 risorse
+v Nessuna delle altre opzioni è vera
+> Può essere usato per determinare quando uno dei due processi manda un segnale all'altro
+
+53) Quale delle seguenti affermazioni sui (vecchi) metodi per il partizionamento della memoria è vera? 
+> Con il partizionamento fisso, le partizioni devono avere tutte la stessa dimensione
+> Con il buddy system, ogni indirizzo di memoria può ricadere in 2 porzioni
+> Con il partizionamento fisso, ci possono essere al massimo N processi attivi (ovvero, accettati per l'esecuzione), dove N è il numero di partizioni
+v Con il partizionamento dinamico, si manifesta il problema della frammentazione esterna
+
+54) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sulla preemption è vera? 
+> Se uno scheduler è preemptive e vi è più di 1 processo ready, non è possibile che un processo monopolizzi il processore
+> Per avere un trattamento equo sui processi, è sufficiente usare uno scheduler preemptive
+> Se uno scheduler è non-preemptive, permette sempre ai suoi processi di essere eseguiti senza interruzioni sul processore fino al loro completamento
+v Se uno scheduler è non-preemptive, è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready
+
+55) Nel modello dei processi a 5 stati, quali delle seguenti transizioni non è possibile? 
+v Blocked ==> Running
+> Running ==> Ready
+> Blocked ==> Exit
+> Blocked ==> Ready
+
+56) Quale delle seguenti affermazioni sul metodo di allocazione indicizzata dei file è vera? 
+> Il consolidamento permette sempre di ridurre la dimensione dell'indice
+v Se usato con porzioni di dimensione variabile, i blocchi indice devono contenere anche la lunghezza di ogni porzione
+> Nessuna delle altre opzioni è vera
+> Non c'è modo per il sistema operativo di distinguere tra blocchi con dati e blocchi con indici
+
+57) Quale delle seguenti affermazioni sul requisito di rilocazione nella gestione della memoria è vera? 
+v Nessuna delle altre opzioni è corretta
+> Se viene realizzato tramite sostituzione degli indirizzi nel programma sorgente (al momento della creazione del processo), allora il relativo processo dovrà cominciare sempre allo stesso indirizzo; tale indirizzo dovrà essere uguale per tutti i processi
+> Se viene realizzato tramite sostituzione degli indirizzi nel programma sorgente (al momento della creazione del processo), allora il relativo processo potrà trovarsi in diverse posizioni della memoria in diversi momenti del sua esecuzione
+> Se viene realizzato tramite sostituzione degli indirizzi nel programma sorgente (al momento della creazione del processo), serve hardware speciale
+
+58) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sulla preemption è vera?
+> Se uno scheduler è non-preemptive, permette sempre ai suoi processi di essere eseguiti sul processore, senza interruzioni, fino al loro completamento
+v Se uno scheduler è non-preemptive, è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready
+> Se uno scheduler è preemptive, non è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready
+> Per avere un trattamento equo sui processi, è sufficiente usare uno scheduler preemptive
+
+59) Quale dei seguenti requisiti deve soddisfare un meccanismo che offra la mutua esclusione? 
+v Non deve essere fatta alcuna assunzione sulla velocità di esecuzione dei processi coinvolti
+> Se un processo non fa richiesta di entrare nella sezione critica, deve comunque sincronizzarsi all'esecuzione degli altri processi
+> Se un processo è nella sezione critica, occorre che rilasci subito la sezione critica stessa
+> Se un processo fa richiesta di entrare nella sezione critica, deve poter entrare subito nella sezione critica stessa
+
+60) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sul dispatcher è falsa? 
+> Il resource balancing è un criterio di sistema non prestazionale
+> Il rispetto delle deadline è un criterio utente prestazionale
+> Il throughput è un criterio di sistema prestazionale
+v La predictability è un criterio utente prestazionale
+
+61) Quale delle seguenti affermazioni sugli interrupt (o eccezioni) è falsa? 
+> Devono essere gestiti da opportuno software di sistema
+v Una volta gestito l'interrupt o l'eccezione, quando (e se) si torna ad eseguire il processo interrotto, l'esecuzione ripartirà sempre dall'istruzione successiva a quella dove è stato ricevuto l'interrupt o l'eccezione
+> Normalmente, non vengono gestiti dal programmatore dell'applicazione che li ha causati
+> Possono essere creati direttamente dai dispositivi di I/O
+
+62) Quale delle seguenti affermazioni sulle istruzioni macchina speciali per la gestione della concorrenza è vera? 
+> Sono basate sul busy-waiting, ovvero sul fatto che un processo si mette autonomamente in stato blocked
+v Nessuna delle altre opzioni è vera
+> Non riescono ad evitare il manifestarsi del deadlock, a meno che non sia presente un sistema a priorità
+> Come per la disabilitazione delle interruzioni, non funzionano per architetture con più processori o core
+
+63) Quale delle seguenti affermazioni sui processi è vera? 
+> Nessuna delle altre opzioni è vera
+v Per la terminazione normale di un processo, è tipicamente prevista un'apposita system call, come ad esempio exit
+> Un processo può morire quando si effettua il process spawning
+> Un processo può essere creato dal modulo di gestione della memoria per gestire la traduzione da indirizzi virtuali a fisici
+
+64) (UNSAFE le decisioni dello scheduler possono influenzare la risposta) Quale delle seguenti affermazioni sui meccanismi software per la gestione della concorrenza è vera? 
+> Sia l'algoritmo di Dekker che quello di Peterson possono mettere in blocked uno dei 2 processi, quando ciò si rivela necessario
+> Sia l'algoritmo di Dekker che quello di Peterson non funzionano se l'hardware sottostante riordina gli accessi in memoria
+> Nell'algoritmo di Peterson, se la variabile turn è inizializzata ad 1, allora il processo 1 sarà sicuramente il primo ad entrare nella sezione critica nella prima iterazione
+v Nell'algoritmo di Dekker, se la variabile turn è inizializzata ad 1, allora il processo 1 sarà sicuramente il primo ad entrare nella sezione critica nella prima iterazione
+
+65) Quale delle seguenti affermazioni sugli i-node di Unix è falsa? 
+> Ogni directory è identificata da un i-node
+v Per modificare una directory, un utente deve aprire il file speciale corrispondente e poi modificarlo opportunamente
+> Ogni directory è un file speciale, organizzato come una lista di entry, ciascuna delle quali contiene il nome di un file ed il relativo i-node number
+> Ogni directory può contenere molti i-node
+
+66) Quale delle seguenti affermazioni è falsa? 
+v Nel caso di un sistema operativo a kernel separato, la gestione dei process switch è a sua volta un processo
+> Nel caso di un sistema operativo in cui le funzioni del sistema operativo vengono eseguite all'interno dei processi utente, non c'è bisogno di un process switch per eseguire una funzionalità del sistema operativo
+> Nel caso di un sistema operativo in cui le funzioni del sistema operativo vengono eseguite all'interno dei processi utente, se un processo effettua una syscall e poi può continuare ad essere eseguito, non avviene alcun process switch
+> Nel caso di un sistema operativo in cui le funzioni del sistema operativo vengono eseguite come processi separati, c'è sempre bisogno di un process switch per eseguire una funzionalità del sistema operativo
+
+67) (corretta secondo @loryspat e @notherealmarco, da controllare) Quale delle seguenti affermazioni sulla paginazione della memoria è vera? 
+> La differenza tra paginazione semplice e paginazione con memoria virtuale è che nella seconda viene richiesto che tutte le pagine di un processo siano in memoria principale, affinché il processo stesso possa essere eseguito
+> Con la paginazione con memoria virtuale, una sola pagina di ogni processo ready o in esecuzione è inizialmente caricata in memoria principale
+v La differenza tra paginazione semplice e paginazione con memoria virtuale è che nella prima viene richiesto che tutte le pagine di un processo siano in memoria principale, affinché il processo stesso possa essere eseguito
+> Nessuna delle altre opzioni è vera
+
+
+68) Quale delle seguenti affermazioni sul metodo di allocazione concatenata dei file è vera? 
+> Il consolidamento permette di memorizzare file che altrimenti non potrebbero esserlo (pur essendo la loro dimensione minore di quella dello spazio libero)
+> La tabella di allocazione dei file deve contenere l'intera catena
+v Nessuna delle altre opzioni è vera
+> Viene usato con porzioni di dimensione variabile, ma piccola
+
+69) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera? 
+> Nel caso di una tabella delle pagine a 2 livelli, viene tipicamente richiesto che tutte le tabelle delle pagine di secondo livello entrino in una pagina
+> Il numero di bit di un indirizzo virtuale è necessariamente diverso a seconda che si usi una tabella delle pagine ad 1 o a 2 livelli
+v Il numero di bit di una entry di una tabella delle pagine di ultimo livello è uguale al numero di bit di controllo più il logaritmo (arrotondato all'intero superiore) del massimo numero di frame in memoria principale
+> Nessuna delle altre opzioni è corretta
+
+70) (corretta secondo Simone Sestito e @loryspat) Quale delle seguenti affermazioni sul deadlock è falsa? 
+> Affinchè ci sia un deadlock, sono necessarie le condizioni di attesa circolare, hold-and-wait, mutua esclusione e no preemption
+v Per prevenire il deadlock, è necessario cercare di impedire almeno una delle 3 condizioni di mutua esclusione, hold-and-wait e no preemption
+> Affinchè il deadlock sia possibile, sono necessarie le condizioni di mutua esclusione, hold-and-wait e no preemption
+> Per prevenire il deadlock impedendo l'hold-and-wait, si può in alcuni casi imporre ai processi di richiedere tutte le risorse fin dall'inizio
+
+71) Quale delle seguenti affermazioni è vera? 
+> La modalità di un processo utente è sempre la modalità di sistema
+> La modalità di un processo utente è inizialmente la modalità utente; può diventare modalità sistema nel momento in cui va in esecuzione il dispatcher
+v Nessuna delle altre opzioni è vera
+> La modalità di un processo utente è sempre la modalità utente
+
+72) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera? 
+v Nessuna delle altre opzioni è corretta
+> Per ogni processo, il resident set contiene lo stesso numero di pagine
+> Un tipico algoritmo per il replacement scope è quello dell'orologio
+> La gestione del resident set tramite politica dinamica mira ad ampliare il numero di pagine di un processo durante l'esecuzione del processo stesso
+
+74) Quale delle seguenti affermazioni sulla concorrenza tra processi o thread è vera?
+v L'istruzione exchange non può ricevere costanti in input su nessun suo argomento, mentre per l'istruzione compare_and_swap questo non vale
+> Le istruzioni speciali exchange e compare_and_swap sono garantite atomiche dal sistema operativo
+> Per realizzare opportunamente l'istruzione compare_and_swap è sufficiente disabilitare le interruzioni
+> Nessuna delle altre opzioni è vera
+
+75)  Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sul dispatcher è falsa?
+> Il response time è un criterio utente prestazionale
+> Il turnaround time (normalizzato o no) è un criterio utente prestazionale
+v Il throughput è un criterio di sistema non prestazionale
+> La fairness è un criterio di sistema non prestazionale
+
+76) Quale delle seguenti affermazioni sul file system FAT è vera?
+> Usa il metodo di allocazione contiguo
+> Ogni cluster del disco contiene sia dati del disco che l'indirizzo del prossimo cluster (o l'indicazione che si tratta dell'ultimo cluster)
+> La tabella di allocazione dei file contiene tante righe quanti sono i file memorizzati sul disco, più una riga speciale per i blocchi liberi
+v Nessuna delle altre opzioni è vera
+
+77) UNSAFE Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è falsa?
+> Il translation lookaside buffer, su alcuni processori, contiene un campo per il PID dei processi
+> Il translation lookaside buffer funziona correttamente solo se tutti i frame validi contenuti al suo interno fanno riferimento a pagine effettivamente in RAM, e non swappate su disco
+v Il mapping associativo permette al translation lookaside buffer di trovare una data pagina semplicemente sommando il numero della pagina con l'indirizzo di partenza del translation lookaside buffer stesso
+> Quando un indirizzo viene trovato nel translation lookaside buffer, non è necessario consultare la normale tabella delle pagine
+
+78) Quale dei seguenti elementi non è una delle parti che definiscono un processo?
+> Il contatore di programma
+> La priorità
+> I dati contenuti nella porzione di memoria a lui dedicata
+v Informazioni sullo stato delle risorse
+
+
+79) UNSAFE Quale delle seguenti affermazioni, riguardanti la classificazione delle risorse di un sistema operativo e la loro relazione con il deadlock, è vera?
+> Nel caso delle risorse consumabili, se c'è un deadlock allora è stata richiesta almeno una risorsa già detenuta da un altro processo
+v Nel caso delle risorse consumabili, se c'è un deadlock allora c'è una successione circolare di processi, ciascuno dei quali richiede una risorsa al processo successivo, che però la deve ancora creare
+> Nel caso delle risorse riusabili, se c'è un deadlock allora è stata richiesta almeno una risorsa non ancora creata
+> Nel caso delle risorse riusabili, se c'è un deadlock allora c'è una successione circolare di processi, ciascuno dei quali richiede una risorsa al processo successivo, che però la deve ancora creare
+
+80) UNSAFE Si supponga che ci siano N processi attivi, giostrati da uno scheduler round-robin su un sistema monoprocessore. Quale delle seguenti affermazioni è vera?
+> Dal punto di vista del processore, ogni processo esegue sempre le proprie istruzioni senza interruzioni
+v Per realizzare correttamente un process switch, il SO avrà necessità di usare le informazioni sul contesto contenute nel process control block
+> Dal punto di vista di ogni processo, l'esecuzione avviene in interleaving con gli altri processi
+> Nessuna delle altre opzioni è vera
+
+81) Quale delle seguenti affermazioni sulla traduzione di un indirizzo virtuale in fisico, in un sistema con memoria virtuale con paginazione (avente tabella delle pagine ad 1 livello), è falsa?
+v L'hardware deve anche cercare il numero di pagina nelle entries della tabella delle pagine del processo in esecuzione. 
+> L'hardware deve anche estrarre dall'indirizzo virtuale il numero di pagina virtuale; tale operazione è equivalente ad una divisione intera
+> L'hardware deve anche usare il numero di pagina per accedere alla tabella delle pagine del processo in esecuzione. A tal proposito, deve conoscere l'inizio di tale tabella, che viene definito dal software (sistema operativo). Tale indirizzo può cambiare durante l'esecuzione del processo: sta al sistema operativo mantenerlo aggiornato
+> L'hardware deve anche usare il numero di frame ottenuto dalla tabella delle pagine per comporre, insieme con l'offset originale, l'indirizzo fisico. Tale operazione è equivalente ad uno shift seguito da una somma
+
+82) Quale delle seguenti operazioni non è tipicamente effettuata su un file?
+> Apertura
+v Connessione
+> Posizionamento (seek)
+> Lock/Unlock
+
+83) Quale delle seguenti affermazioni è falsa?
+v Diversi thread di uno stesso processo condividono lo stesso thread identifier
+> Tra le funzioni di sistema per i thread, è tipicamente prevista una funzione per bloccare e sbloccare esplicitamente i thread stessi
+> Diversi thread di uno stesso processo condividono lo stesso process identifier
+> Diversi thread di uno stesso processo condividono i file aperti
+
+84) Quale delle seguenti affermazioni sulla page cache è falsa?
+v Nell'algoritmo di sostituzione basato su frequenza a 3 segmenti della page cache, i contatori vengono sempre incrementati, tranne quando sono nel segmento vecchio
+> Nell'algoritmo di sostituzione basato su frequenza a 3 segmenti della page cache, i settori che possono essere sostituiti sono solo quelli del segmento vecchio
+> Nell'algoritmo di sostituzione basato su frequenza a 3 segmenti della page cache, l'unico segmento in cui i contatori non vengono incrementati e i settori non possono essere sostituti è quello nuovo
+> L'algoritmo di sostituzione basato su frequenza a 3 segmenti della page cache può avere buone performance anche quando dei settori vengono acceduti spesso, ma tra il primo accesso e quelli successivi ci sono molti altri accessi ad altri settori
+
+85) Quali delle seguenti affermazioni sulla efficienza di un sistema operativo è falsa?
+> Deve minimizzare il tempo di risposta, tenendo presenti eventuali priorità
+> Deve servire il maggior numero di utenti possibile, tenendo presenti eventuali livelli di accesso
+v Deve dare accesso alle risorse in modo equo ed egualitario tra tutti i processi
+> Deve massimizzare l'uso delle risorse per unità di tempo, tenendo presenti eventuali priorità
+
+88) Quale delle seguenti affermazioni sui metodi di gestione del deadlock è vera?
+> Nessuna delle altre opzioni è vera
+> L'unico metodo, che richiede di conoscere in anticipo il massimo numero di risorse che un processo dovrà chiedere, è quello per rilevare il deadlock
+> Il metodo più permissivo nei confronti delle richieste di risorse è quello che consiste nel prevenire il deadlock
+v L'unico metodo che non prevede mai la preemption delle risorse è quello che evita il deadlock
+
+89) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni è falsa?
+> Lo scheduler ha, tra i suoi obiettivi, quello dell'equità tra i processi, a meno che non siano definite delle priorità
+> Lo scheduler va scritto in modo che il suo overhead sia basso
+v Lo scheduler ha, tra i suoi obiettivi, quello di evitare il deadlock
+> Lo scheduler ha, tra i suoi obiettivi, quello di massimizzare il volume di lavoro dei processi nel tempo
+
+90) Quale delle seguenti affermazioni sugli scheduler per architetture multiprocessore è vera?
+> Con l'assegnamento statico, si dà un processore a caso tra quelli liberi ai processi che mantengono un uso della RAM pressoché costante
+> Assegnando i processi del sistema operativo con l'assegnamento dinamico, si rischia di creare un bottleneck su un solo processore
+> Uno svantaggio dell'assegnamento statico è il suo overhead maggiore rispetto a quello dinamico
+v Nessuna delle altre opzioni è vera
+
+91) Quale delle seguenti affermazioni sull'algoritmo del banchiere per evitare il deadlock visto a lezione è falsa?
+v La matrice C - A può contenere elementi negativi, ma le matrici C ed A contengono solo elementi non negativi
+> Richiede in input, per ogni processo p e per ogni risorsa r, il numero massimo di istanze di r che p chiederà nel corso della sua esecuzione
+> All'inizio e alla fine di ogni invocazione dell'algoritmo, Vi = Ri - ∑j = 1, ..., nAi, j
+> Se si procede da uno stato ad un altro, necessariamente è stata fatta almeno una richiesta ad almeno una risorsa da parte di almeno un processo
+
+92) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sul dispatcher è falsa?
+> Il throughput è definito come il numero di processi completati per unità di tempo
+v Il turnaround time è definito, per un dato processo, come il tempo che intercorre tra la sua prima esecuzione sul processore e il suo completamento
+> Un dispatcher con buone prestazioni sul response time deve tipicamente sia minimizzare il valore medio di sistema del response time, sia massimizzare il numero di utenti con un basso valore per il response time
+> Il processor utilization è definito come il rapporto tra il tempo in cui il processore viene usato ed il tempo totale del sistema
+
+93) Quale delle seguenti affermazioni sugli i-node di Unix è vera?
+> Per ogni file-system su disco organizzato con i-node, tutti gli i-node di tutti i file su tale file-system sono memorizzati esclusivamente su disco
+v I puntatori a tripla indirezione di un i-node vengono usati solo se la dimensione del file lo richiede
+> Nessuna delle altre opzioni è vera
+> Ad ogni file effettivamente memorizzato su disco può essere associato un solo numero di i-node
+
+94) UNSAFE Quale delle seguenti affermazioni sul modello dei processi a 7 stati è vera?
+v Nessuna delle altre opzioni è vera
+> Gli stati Ready, New e Blocked del modello a 5 stati vengono sdoppiati, e ne viene creata una versione Suspend
+> Un processo è Suspend quando scade il timeout del dispatcher
+> È possibile la transizione Ready/Suspend ==> Blocked/Suspend
+
+95) Quale delle seguenti affermazioni sui dischi magnetici a testina mobile è vera? 
+> Per selezionare un settore su una traccia di un disco magnetico a testina mobile, bisogna prima far ruotare il disco fino ad arrivare alla giusta traccia, e poi posizionare la testina sul giusto settore
+> Una traccia di un disco è l'area compresa tra 2 raggi del disco stesso
+v Il tempo di accesso ad un disco magnetico a testina mobile tiene conto sia del tempo che occorre per posizionare la testina che del tempo che occorre per far ruotare il disco, ma non del tempo che occorre per effettuare effettivamente il trasferimento di dati
+> Nessuna delle altre opzioni è corretta
+
+98) UNSAFE Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sugli algoritmi di scheduling è vera?
+v L'exponential averaging permette di stimare la dimensione dell'immagine di un processo, a partire dalle precedenti immagini di quello stesso processo
+> La funzione di decisione dello scheduler Highest Response Ratio Next considera tanto il tempo di esecuzione stimato quanto il tempo trascorso in attesa
+> L'exponential averaging è una tecnica applicabile dal solo scheduler Short Process Next
+> La funzione di decisione dello scheduler Shortest Remaining Time considera tanto il tempo di esecuzione richiesto quanto il tempo trascorso in attesa
+
+100) Quale delle seguenti affermazioni è vera sulla memoria virtuale con paginazione a segmentazione?
+v Sia la tabella dei segmenti che quella delle pagine di un processo contengono, in ciascuna entry, un bit per indicare se la pagina o il segmento sono stati modificati
+> Un indirizzo virtuale contiene anche un bit per indicare se la pagina corrispondente è o no in memoria principale
+> La tabella delle pagine di un processo contiene una pagina speciale dove è memorizzato il process control block del processo stesso
+> Ogni entry di una tabella delle pagine contiene un numero di pagina ed un offset
+
+99) (risposta corretta secondo @notherealmarco, Simone Sestito e Gabriele Pelissetto, non verificata da nessuna parte) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera?
+v Per avere un overhead accettabile, occorre demandare la traduzione degli indirizzi all'hardware, mentre al software resta da gestire prelievo, posizionamento e sostituzione delle pagine
+> Per avere un overhead accettabile, occorre demandare la traduzione degli indirizzi e la politica di sostituzione delle pagine all'hardware, mentre al software resta da gestire prelievo e posizionamento delle pagine
+> Per avere un overhead accettabile, occorre demandare all'hardware la traduzione degli indirizzi ed il prelievo, il posizionamento e la sostituzione delle pagine
+> Per avere un overhead accettabile, occorre demandare al software anche la traduzione degli indirizzi
+
+96) (risposta corretta secondo @notherealmarco e Gabriele Pelissetto, non verificata da nessuna parte) Riguardo alle differenze tra sistemi batch e sistemi time sharing (degli anni 60/70), quale delle seguenti affermazioni è falsa? 
+v I sistemi time-sharing puntavano a minimizzare l'uso del processore
+> Nei sistemi time-sharing, le direttive al sistema operativo arrivavano dai comandi digitati su terminali
+> Nei sistemi batch, le direttive al sistema operativo arrivavano dai comandi del job control language, che erano non-interattivi
+> I sistemi batch puntavano a massimizzare l'uso del processore
+
+img=https://i.imgur.com/orqyjeh.png
+12) Considerare un insieme di cinque processi P1, P2, P3, P4, P5 con i seguenti tempi di arrivo e tempi di esecuzione in millisecondi: Quale delle seguenti affermazioni è falsa?
+> Non ci sono sufficienti informazioni per determinare come si comporterebbe l'algoritmo di scheduling a feedback classico di Unix
+> Non ci sono sufficienti informazioni per determinare come si comporterebbe l'algoritmo di scheduling Virtual Round-Robin
+> Non ci sono sufficienti informazioni per determinare come si comporterebbe l'algoritmo di scheduling Round-Robin
+v Non ci sono sufficienti informazioni per determinare come si comporterebbe l'algoritmo di scheduling SRT
+
+img=https://i.imgur.com/orqyjeh.png
+13) Considerare un insieme di cinque processi P1, P2, P3, P4, P5 con i seguenti tempi di arrivo e tempi di esecuzione in millisecondi: Assegnare questo insieme di processi ad un processore usando l'algoritmo di scheduling SRT, �no a che non terminano tutti. Quale delle seguenti affermazioni è falsa?
+v Gli unici 2 processi che non sono serviti subito (ovvero, appena arrivati) sono P3 e P5
+> Il tempo medio di attesa è tra 10 ed 11 ms
+> Il processo con il più lungo tempo di attesa è P1
+> Il tempo medio di turnaround è tra 2 e 3 ms
+
+img=https://i.imgur.com/5nWWwyF.png
+101) Si consideri il seguente modo di implementare la mutua esclusione: Quale delle seguenti affermazioni è vera?
+> La soluzione non implementa correttamente la mutua esclusione, ma può essere corretta nel seguente modo: int bolt = 0; void P(int i) { int key; while(true) { do (exchange(key, bolt) == 0) while(key != 0); critical_section(); bolt = 0; key = 1; } } 
+> La soluzione non implementa correttamente la mutua esclusione, in quanto key deve essere una variabile globale
+v La soluzione non implementa correttamente la mutua esclusione, ma può essere corretta nel seguente modo: int bolt = 0; void P(int i) { int key; while(true) { key = 1; do (exchange(key, bolt) == 0) while(key != 0); critical_section(); bolt = 0; } } 
+> La soluzione implementa correttamente la mutua esclusione
+
+103) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è falsa?
+> Diminuire la dimensione delle pagine ha effetti positivi sul numero di pagine che possono trovarsi in memoria principale
+v Aumentare la dimensione delle pagine ha effetti positivi sulla frammentazione interna
+> Diminuire la dimensione delle pagine ha effetti negativi sulla dimensione della tabella delle pagine
+> Aumentare la dimensione delle pagine ha effetti negativi sulla multiprogrammazione
+
+104) Quale delle seguenti affermazioni sulla concorrenza tra processi o thread è falsa?
+v La disabilitazione delle interruzioni impedisce la creazione di nuove interruzioni
+> Se un processo utente può disabilitare le interruzioni tramite un'istruzione macchina dedicata, allora può far diminuire l'uso utile del processore
+> La disabilitazione delle interruzioni non funziona ai fini della concorrenza (gestione sezioni critiche) su sistemi con più processori o più core
+> L'abuso della disabilitazione delle interruzioni fa diminuire la multiprogrammazione, a parità di numero di processi
+
+105) Quale delle seguenti affermazioni non è vera?
+> il kernel rimane in memoria durante l'intera sessione del computer
+v il kernel è costituito da vari moduli che non possono essere caricati nel sistema operativo in esecuzione
+> il kernel è la prima parte del sistema operativo a essere caricata in memoria durante l'avvio
+> Il kernel è il programma che costituisce il nucleo centrale del sistema operativo.
+
+106) UNSAFE In generale, la CPU puo’ eseguire un'istruzione soltanto quando gli operandi si trovano:
+> In RAM, o in un livello qualsiasi della cache o nella memoria secondaria o nei registri CPU
+> In RAM o in un livello qualsiasi della cache o nei registri CPU
+> Nella cache di livello 1 (L1 cache) o nei registri CPU
+v Nei registri della CPU
+
+107) Il PCB (Process Control Block) e’:
+> Un campo dello stato di un processo che definisce quali operazioni di controllo dei dispositivi a blocchi sono state fatte dal processo
+v Una struttura dati mantenuta dal sistema operativo che contiene tutte le informazioni necessarie all’esecuzione, sospensione e ripresa dell’esecuzione di un processo
+> Una struttura dati mantenuta dal sistema operativo che contiene l’intera immagine di un processo
+> Un’interfaccia di controllo dei processi del sistema operativo
+
+108) Considera un Sistema Operativo con esecuzione all’interno dei processi utente. Quando un processo utente fa una chiamata di sistema, quale delle seguenti affermazioni e’ corretta
+> Il sistema operativo deve effettuare un process switch ed un mode switch per eseguire la funzione richiesta
+> Il sistema operativo deve effettuare soltanto un process switch per eseguire la funzione richiesta
+v Il sistema operativo deve effettuare soltanto un mode switch per eseguire la funzione richiesta
+> Il sistema operativo deve creare un nuovo processo e fare switch ad esso per eseguire la funzione richiesta
+
+109) Quale delle seguenti affermazioni e’ vera:
+> Il dispatcher e’ una componente del medium term scheduler
+> Il dispatcher si occupa di decidere l’ordine di sospensione dei processi
+v Il dispatcher si occupa di scambiare i processi in esecuzione sulla CPU (process switch)
+> Il dispatcher si occupa di scambiare i processi dalla memoria principale alla memoria secondaria
+
+110) In un sistema operativo con I/O buffering, quando c’e’ una scrittura su dispositivo di I/O quale delle seguenti affermazioni e’ vera:
+> Il sistema operativo copia immediatamente il contenuto della scrittura dalla memoria del processo direttamente alla memoria del dispositivo di I/O
+v Il sistema operativo copia immediatamente il contenuto della scrittura dalla memoria utente alla memoria del sistema operativo, e dalla memoria del sistema operativo alla memoria del dispositivo di I/O quando piu’ opportuno
+> Il sistema operativo copia quando piu’ opportuno il contenuto della scrittura dalla memoria del processo direttamente alla memoria del dispositivo di I/O
+> Nessuna delle altre opzioni e’ corretta
+
+111) L’algoritmo di scheduling C-SCAN:
+> Scrivere le richieste su disco in modo tale che il braccio meccanico si muova sempre in una direzione, fino a raggiungere l’ultima traccia, e poi torna indietro scrivendo tutte le richieste fino a raggiungere la prima traccia
+> Puo’ portare a starvation per alcuni processi
+> E’ meno fair (equo) dell’algoritmo SCAN
+v Non favorisce le richieste ai bordi rispetto a SCAN
+
+112) Quale dei seguenti sono requisiti per un File Management System?
+> Ogni utente dev’essere in grado di creare, cancellare, leggere, scrivere e modificare un file
+> Ogni utente deve poter accedere, in modo controllato, ai file di un altro utente
+> Ogni utente deve poter mantenere una copia di backup dei propri file
+v Tutte le opzioni sono requisiti
+
+113) Una sezione critica è un segmento di programma:
+> Che e’ racchiuso tra una coppia di operazioni di semaforo semWait e semSignal
+v In cui si accede a risorse condivise
+> Che evita i deadlock
+> Che deve essere eseguito in un determinato lasso di tempo.
+
+114) Quale dei seguenti NON è vero riguarda il Algoritmo di Dekker per gestire la concorrenza?
+> Garantisce la non-starvation
+> Non richiede nessun supporto dal SO.
+v Richiede supporto dal SO
+> E' deterministico.
+
+115) Quale delle affermazioni è vera riguardo al Translation lookaside buffer per la gestione della memoria?
+> Nel Translation lookaside buffer ci sono tag e chiavi con l'aiuto dei quali viene effettuata la mappatura.
+> Il TLB hit è una condizione in cui la voce desiderata viene trovata nel TLB.
+> Se la voce non viene trovata nel TLB (TLB miss), la CPU deve accedere alla tabella delle pagine nella memoria principale e quindi accedere al frame effettivo nella memoria principale.
+v Tutte le opzioni sono vere.
+
+116) Quale delle seguenti affermazioni sul long-term scheduler e’ vera:
+v Si occupa della decisione di quali processi debbano essere ammessi all’esecuzione nel sistema
+> Si occupa dell’organizzazione di lungo termine dell’ordine di esecuzione dei processi nella CPU
+> Si occupa dell’implementazione della funzione di swapping dei processi alla memoria secondaria
+> Si occupa della transizione dei processi tra gli stati running ed exit
+
+117) Nel modello dei processi a 5 stati, quale affermazione e’ falsa:
+v Un processo puo’ essere spostato allo stato suspended dallo stato blocked e ready
+> Un processo puo’ essere spostato dallo stato running allo stato ready o exit
+> Un processo puo’ essere spostato dallo stato blocked solo allo stato ready
+> Un processo puo’ essere spostato dallo stato ready allo stato running, blocked o exit
+
+118) Riguardo l’efficienza dal punto di vista dell’utilizzo utile della CPU, quale dei seguenti modelli di I/O e’ piu’ efficiente dal punto di vista dell’uso della CPU e perche’?
+> I/O programmato, perche’ consente al programmatore di fare uno scheduling esatto delle operazioni di I/O nei momenti piu’ opportuni
+> I/O basato su DMA (Accesso Diretto alla Memoria), perche’ la CPU deve soltanto occuparsi del trasferimento dei dati
+> I/O basato su interruzioni, perche’ il processore non deve controllare attivamente lo stato del dispositivo di I/O dopo aver effettuato la richiesta
+v I/O basato su DMA (Accesso Diretto alla Memoria), perche’ la CPU deve soltanto occuparsi di inviare la richiesta di I/O e leggere il risultato
+
+119) Dati due processi A e B e due risorse R1 ed R2, si ha sicuramente una situazione di deadlock se:
+v A richiede ed ottiene accesso ad R1, B richiede ed ottiene accesso ad R2. A richiede accesso ad R2, B richiede accesso ad R1
+> A richiede ed ottiene accesso ad R1, B richiede accesso ad R2. A richiede accesso ad R2. B richiede accesso ad R1
+> A richiede ed ottiene accesso ad R2, B richiede accesso ad R1 ed R2. A richiede ed ottiene accesso ad R1
+> B richiede ed ottiene accesso ad R1, A richiede ed ottiene accesso ad R2. B richiede accesso ad R2
+
+120) Quali delle seguenti affermazioni e' vera riguardo la preallocazione rispetto all'allocazione dinamica dello spazio per i file?
+> la preallocazione è più efficiente nell'utilizzo dello spazio su disco
+v nessuna delle opzioni è corretta
+> l'allocazione dinamica rischia di sprecare spazio disco in caso gli utenti/applicazioni sovrastimino la dimensione dei file, mentre questo non è il caso con la preallocazione
+> L'allocazione dinamica impone un overhead di gestione minore per il sistema operativo
+
+121) Quale delle seguenti affermazioni sul file system NTFS è vera?
+v NTFS può, ove possibile, includere direttamente i dati di un file nella master file table
+> NTFS non prevede la possibilità di avere record estesi
+> nessuna delle altre opzioni è vera
+> In NTFS, le informazioni relative alla sequenza di blocchi che contengono il file è interamente contenuta nel record base
+
+122) Quale delle seguenti affermazioni riguardo la rilocazione degli indirizzi di memoria è vera?
+> Nei sistemi con hardware dedicato per la rilocazione, il base register (registro base) viene impostato una sola volta, quando il programma viene caricato in memoria per la prima volta
+> In un sistema con rilocazione a run time, i sistemi di protezione che verificano che un processo non vada ad accedere alla memoria di un'altro processo possono essere eseguiti a tempo di compilazione, prima di eseguire il programma
+> In un sistema a rilocazione con indirizzi logici, non è necessario avere hardware dedicato per effettuare la rilocazione
+v In un sistema a rilocazione con indirizzi assoluti, se si conosce l'indirizzo di memoria dove verrà caricato il programma, il compilatore può inserire direttamente gli indirizzi di memoria corretti nel codice oggetto (programma compilato)
+
+123) Quale delle seguenti affermazioni è vera riguardo il concetto di Thrashing?
+> Il SO impiega la maggior parte del suo tempo a swappare pezzi di processi, anziché ad eseguire istruzioni
+> provoca il deterioramento o il crollo delle prestazioni del computer
+> quasi ogni richiesta di pagine da luogo ad una page fault
+v Tutte le opzioni sono vere
+
+124) Il sistema di partizionamento fisso per la memoria principale:
+> Permette di avere partizioni di lunghezza diversa e di modificarle a runtime
+> Nessuna delle opzioni è vera
+> Consente una efficiente della memoria se ci sono molti processi di piccole dimensioni 
+v Impone un numero massimo di processi che possono essere in memoria principale
+
+125) Quale delle seguenti non è un vantaggio dell’attacco dizionario?
+> Semplice da effettuare
+> Versatilità
+v Velocità di computazione in real time degli hash
+> Disponibilità di molti tool per automatizzazione
+
+126) Nello scheduler a breve ed a lungo termine la distizione principale è:
+> Il tipo di processi che gestiscono
+v La frequenza di esecuzione
+> La lunghezza delle loro code
+> Nessuna delle opzioni è corretta
+
+127) Quale dei seguenti NON è un vantaggio della multiprogrammazione?
+> Riduzione dei tempi di risposta
+> Possibilità di assegnare priorità ai lavori
+> Aumento del throughput
+v Riduzione dell’overhead del sistema operativo
+
+128) ___> fornisce l’indirizzo della prossima istruzione che deve essere eseguita dal processo corrente?
+> Lo stack del processo
+> Il bus di sistema
+> Nessuno 
+v Program Counter
+
+129) Quale dei seguenti NON è un valido schema di prevenzione del deadlock?
+> Rilasciare tutte le risorse prima di richiederne una nuova
+v Non chiedere mai una risorsa dopo averne rilasciate altre
+> Si definisce un ordinamento crescente delle risorse, una risorsa viene data solo se esegue quelle che il processo già detiene
+> Richiedere e allocare tutte le risorse necessarie prima dell’esecuzione
+
+130) UNSAFE Quale dei seguenti NON è vero riguardo l’algoritmo di Dekker per gestire la concorrenza?
+v Non usa busy waiting
+> Garantisce la non-starvation
+> Tutte le opzioni elencate
+> Garantisce il non-deadlock
+
+131) Quale delle seguenti non è una tabella di controllo del sistema operativo?
+v Tabella dei processi sospesi
+> Tabelle di memoria
+> Tabelle di controllo di accesso
+> Tabelle di I/O
+
+132) In un sistema con modello di interruzioni (interrupt) annidate, se un interrupt (I-2) è ricevuto durante la gestione di un altro interrupt(I-1)
+v La cpu sospende l’esecuzione del codice corrente, ed avvia l’handler del nuovo interrupt ricevuto
+> La cpu completa l’esecuzione del codice corrente, e successivamente avvia l’handler del nuovo interrupt ricevuto
+> La cpu gestisce entrambi gli handler in parallelo
+> La cpu termina (aborts,kills) l’esecuzione del codice corrente, ed avvia l’handler del nuovo interrupt ricevuto
+
+133) Il numero di processi completati per unità di tempo è chiamato _____
+> Produzione
+v Throughput
+> Capacità
+> Nessuno
+
+134) Quale dei seguenti sono obiettivi per un file Management System?
+v Tutte le opzioni elencate
+> Fornire supporto per l’I/O da più utenti in contemporanea
+> Minimizzare i dati persi o distrutti
+> Fornire un insieme di interfacce standard per i processi utente
+
+135) In un sistema operativo con allocazione dei file indicizzata, quale delle seguenti opzioni è vera:
+> La tabella di allocazione contiene soltanto l'indirizzo di un blocco, e questo blocco contiene sempre tutte le entry per ogni porzione allocata al file
+> La tabella di allocazione contiene l'indirizzo del primo blocco del file, e ciascun blocco contiene l'indirizzo del prossimo blocco del file
+v La tabella di allocazione contiene soltanto l'indirizzo di un blocco, e questo blocco contiene le entry delle porzioni di file allocate oppure l'indirizzo di altri blocchi usati a loro volta per indicizzare le porzioni di file allocate
+> La tabella di allocazione dei file contiene l'indirizzo di un blocco e la lista dei blocchi del file
+
+136) Quale delle seguenti affermazioni riguardo algoritmi di scheduling del disco è vera
+> L'algoritmo SCAN può portare a starvation delle richieste
+> L'algoritmo FSCAN è una versione di SCAN che rimuove il problema della starvation delle richieste, ma che rende l'algoritmo meno fair rispetto a SCAN
+> L'algoritmo Minimo Tempo di Servizio non richiede di conoscere la posizione della testina del disco per operare
+v N-step-SCAN è una generalizzazione di FSCAN che è fair e può avere prestazioni molto simili a quelle di SCAN
+
+137) Quali dei seguenti NON è un tipo di scheduling dei sistemi operativi:
+> Short term scheduling
+> Long term scheduling
+> Disk scheduling
+v File scheduling
+
+138) Nei sistemi operativi che usano paginazione SEMPLICE per la gestione della memoria
+> ai processi devono essere allocati frame di memoria necessariamente contigui per poter consentire l'esecuzione del processo
+> il sistema operativo deve utilizzare la tabella delle pagine per tradurre gli indirizzi. Qualora una pagina non sia presente in memoria principale, il sistema la deve caricare dinamicamente per consentire il proseguimento dell'esecuzione di un processo
+> non c'è necessità di traduzione degli indirizzi, in quanto tutte le pagine di un processo sono sempre caricate in un frame nella memoria principale
+v nessuna delle altre opzioni è corretta
+
+139) Nei sistemi operativi che usano journaling logico
+> non c'è possibilità di perdita dei dati in quanto, in caso di arresto imprevisto, il sistema operativo può usare il journal per ricostruire interamente le operazioni non andate a buon fine
+> il sistema operativo usa il journal solo per copiare i dati prima di farne la scrittura anche nel file system, ma non lo utilizza per i metadati
+v il sistema operativo usa il journal solo per copiare i metadati prima di aggiornare le strutture del file system, ma non lo utilizza per i dati
+> nessuna delle opzioni è corretta
+
+140) Il sistema operativo linux per la gestione dei file
+v nessuna delle altre opzioni è corretta
+> utilizza un sistema misto di allocazione contigua e concatenata in modo da minimizzare l'overhead di sistema e massimizzare le performance
+> utilizza un sistema di allocazione concatenata basato sulla struttura dati conosciuta come inode
+> usa gli inode per tenere traccia dei blocchi su disco allocati a ciascun file. Ogni inode contiene al suo interno la lista completa di tutti i blocchi su disco che compongono il file corrispondente
+
+141) Nei sistemi Unix
+> gli hard links sono dei file speciali che contengono il cammino completo sul file system di un altro file, effettivamente creando un "puntatore" a quel file
+v gli hard link sono puntatori diretti al descrittore di un file (inode). Un contatore viene utilizzato per tenere traccia di quanti hard link puntino ad un determinato inode. Questo fa si che il file non possa essere cancellato fintantoché ci sono hard link che continuano a puntarlo
+> possono esistere hard link a file non più esistenti, ad esempio se il file a cui l'hard link puntava viene cancellato
 > nessuna delle altre risposte è corretta
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diff --git a/legacy/Data/ingsw/0000_2/quest.txt b/Data/ingsw/0000_2/quest.txt
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rename from legacy/Data/ingsw/0000_2/quest.txt
rename to Data/ingsw/0000_2/quest.txt
diff --git a/legacy/Data/ingsw/0000_2/wrong1.txt b/Data/ingsw/0000_2/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_2/wrong1.txt
rename to Data/ingsw/0000_2/wrong1.txt
diff --git a/legacy/Data/ingsw/0000_2/wrong2.txt b/Data/ingsw/0000_2/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_2/wrong2.txt
rename to Data/ingsw/0000_2/wrong2.txt
diff --git a/legacy/Data/ingsw/0000_3/correct.txt b/Data/ingsw/0000_3/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_3/correct.txt
rename to Data/ingsw/0000_3/correct.txt
diff --git a/legacy/Data/ingsw/0000_3/quest.txt b/Data/ingsw/0000_3/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_3/quest.txt
rename to Data/ingsw/0000_3/quest.txt
diff --git a/legacy/Data/ingsw/0000_3/wrong1.txt b/Data/ingsw/0000_3/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_3/wrong1.txt
rename to Data/ingsw/0000_3/wrong1.txt
diff --git a/legacy/Data/ingsw/0000_3/wrong2.txt b/Data/ingsw/0000_3/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_3/wrong2.txt
rename to Data/ingsw/0000_3/wrong2.txt
diff --git a/legacy/Data/ingsw/0000_32/correct.txt b/Data/ingsw/0000_32/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_32/correct.txt
rename to Data/ingsw/0000_32/correct.txt
diff --git a/legacy/Data/ingsw/0000_32/quest.txt b/Data/ingsw/0000_32/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_32/quest.txt
rename to Data/ingsw/0000_32/quest.txt
diff --git a/legacy/Data/ingsw/0000_32/wrong1.txt b/Data/ingsw/0000_32/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_32/wrong1.txt
rename to Data/ingsw/0000_32/wrong1.txt
diff --git a/legacy/Data/ingsw/0000_32/wrong2.txt b/Data/ingsw/0000_32/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_32/wrong2.txt
rename to Data/ingsw/0000_32/wrong2.txt
diff --git a/legacy/Data/ingsw/0000_4/correct.txt b/Data/ingsw/0000_4/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_4/correct.txt
rename to Data/ingsw/0000_4/correct.txt
diff --git a/legacy/Data/ingsw/0000_4/quest.txt b/Data/ingsw/0000_4/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_4/quest.txt
rename to Data/ingsw/0000_4/quest.txt
diff --git a/legacy/Data/ingsw/0000_4/wrong1.txt b/Data/ingsw/0000_4/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_4/wrong1.txt
rename to Data/ingsw/0000_4/wrong1.txt
diff --git a/legacy/Data/ingsw/0000_4/wrong2.txt b/Data/ingsw/0000_4/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_4/wrong2.txt
rename to Data/ingsw/0000_4/wrong2.txt
diff --git a/legacy/Data/ingsw/0000_7/correct.txt b/Data/ingsw/0000_7/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_7/correct.txt
rename to Data/ingsw/0000_7/correct.txt
diff --git a/legacy/Data/ingsw/0000_7/quest.txt b/Data/ingsw/0000_7/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_7/quest.txt
rename to Data/ingsw/0000_7/quest.txt
diff --git a/legacy/Data/ingsw/0000_7/wrong1.txt b/Data/ingsw/0000_7/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_7/wrong1.txt
rename to Data/ingsw/0000_7/wrong1.txt
diff --git a/legacy/Data/ingsw/0000_7/wrong2.txt b/Data/ingsw/0000_7/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_7/wrong2.txt
rename to Data/ingsw/0000_7/wrong2.txt
diff --git a/legacy/Data/ingsw/0000_8/correct.txt b/Data/ingsw/0000_8/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_8/correct.txt
rename to Data/ingsw/0000_8/correct.txt
diff --git a/legacy/Data/ingsw/0000_8/quest.txt b/Data/ingsw/0000_8/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_8/quest.txt
rename to Data/ingsw/0000_8/quest.txt
diff --git a/legacy/Data/ingsw/0000_8/wrong1.txt b/Data/ingsw/0000_8/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_8/wrong1.txt
rename to Data/ingsw/0000_8/wrong1.txt
diff --git a/legacy/Data/ingsw/0000_8/wrong2.txt b/Data/ingsw/0000_8/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0000_8/wrong2.txt
rename to Data/ingsw/0000_8/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_0/correct.txt b/Data/ingsw/0120_0/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_0/correct.txt
rename to Data/ingsw/0120_0/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_0/quest.txt b/Data/ingsw/0120_0/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_0/quest.txt
rename to Data/ingsw/0120_0/quest.txt
index 1b78d21..7df72fd 100644
--- a/legacy/Data/Questions/ingsw/0120_0/quest.txt
+++ b/Data/ingsw/0120_0/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_0.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act1 
-Test case 2: act1 act0 act1 act0 act2 
-Test case 3: act0 act2 act2 act1 
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_0.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act1 
+Test case 2: act1 act0 act1 act0 act2 
+Test case 3: act0 act2 act2 act1 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_0/wrong1.txt b/Data/ingsw/0120_0/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_0/wrong1.txt
rename to Data/ingsw/0120_0/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_0/wrong2.txt b/Data/ingsw/0120_0/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_0/wrong2.txt
rename to Data/ingsw/0120_0/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_1/correct.txt b/Data/ingsw/0120_1/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0120_1/correct.txt
rename to Data/ingsw/0120_1/correct.txt
index 279908a..4c63afe 100644
--- a/legacy/Data/Questions/ingsw/0120_1/correct.txt
+++ b/Data/ingsw/0120_1/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and ((y <pre 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and ((y <pre 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/Data/ingsw/0120_1/quest.txt b/Data/ingsw/0120_1/quest.txt
new file mode 100644
index 0000000..348f2a5
--- /dev/null
+++ b/Data/ingsw/0120_1/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_1/wrong1.txt b/Data/ingsw/0120_1/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0120_1/wrong1.txt
rename to Data/ingsw/0120_1/wrong1.txt
index 867889a..ff387f8 100644
--- a/legacy/Data/Questions/ingsw/0120_1/wrong1.txt
+++ b/Data/ingsw/0120_1/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0120_1/wrong2.txt b/Data/ingsw/0120_1/wrong2.txt
similarity index 94%
rename from legacy/Data/ingsw/0120_1/wrong2.txt
rename to Data/ingsw/0120_1/wrong2.txt
index a159504..68e8643 100644
--- a/legacy/Data/ingsw/0120_1/wrong2.txt
+++ b/Data/ingsw/0120_1/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_10/correct.txt b/Data/ingsw/0120_10/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0120_10/correct.txt
rename to Data/ingsw/0120_10/correct.txt
index fffebc7..e70f938 100644
--- a/legacy/Data/Questions/ingsw/0120_10/correct.txt
+++ b/Data/ingsw/0120_10/correct.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/Data/ingsw/0120_10/quest.txt b/Data/ingsw/0120_10/quest.txt
new file mode 100644
index 0000000..0910f0b
--- /dev/null
+++ b/Data/ingsw/0120_10/quest.txt
@@ -0,0 +1,4 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_10/wrong1.txt b/Data/ingsw/0120_10/wrong1.txt
similarity index 94%
rename from legacy/Data/ingsw/0120_10/wrong1.txt
rename to Data/ingsw/0120_10/wrong1.txt
index c5ef6d8..278e783 100644
--- a/legacy/Data/ingsw/0120_10/wrong1.txt
+++ b/Data/ingsw/0120_10/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_10/wrong2.txt b/Data/ingsw/0120_10/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_10/wrong2.txt
rename to Data/ingsw/0120_10/wrong2.txt
index 06e9d5a..ab50168 100644
--- a/legacy/Data/Questions/ingsw/0120_10/wrong2.txt
+++ b/Data/ingsw/0120_10/wrong2.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0120_11/correct.txt b/Data/ingsw/0120_11/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_11/correct.txt
rename to Data/ingsw/0120_11/correct.txt
diff --git a/legacy/Data/ingsw/0120_11/quest.txt b/Data/ingsw/0120_11/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_11/quest.txt
rename to Data/ingsw/0120_11/quest.txt
diff --git a/legacy/Data/ingsw/0120_11/wrong1.txt b/Data/ingsw/0120_11/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_11/wrong1.txt
rename to Data/ingsw/0120_11/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_11/wrong2.txt b/Data/ingsw/0120_11/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_11/wrong2.txt
rename to Data/ingsw/0120_11/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_12/correct.txt b/Data/ingsw/0120_12/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_12/correct.txt
rename to Data/ingsw/0120_12/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_12/quest.txt b/Data/ingsw/0120_12/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_12/quest.txt
rename to Data/ingsw/0120_12/quest.txt
index aed3c79..edefd37 100644
--- a/legacy/Data/Questions/ingsw/0120_12/quest.txt
+++ b/Data/ingsw/0120_12/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_12.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
-Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_12.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
+Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_12/wrong1.txt b/Data/ingsw/0120_12/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_12/wrong1.txt
rename to Data/ingsw/0120_12/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_12/wrong2.txt b/Data/ingsw/0120_12/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_12/wrong2.txt
rename to Data/ingsw/0120_12/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_13/correct.txt b/Data/ingsw/0120_13/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_13/correct.txt
rename to Data/ingsw/0120_13/correct.txt
diff --git a/Data/ingsw/0120_13/quest.txt b/Data/ingsw/0120_13/quest.txt
new file mode 100644
index 0000000..abc3e1c
--- /dev/null
+++ b/Data/ingsw/0120_13/quest.txt
@@ -0,0 +1,9 @@
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+int z = x;
+while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
+return (z);
+}
+Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_13/wrong1.txt b/Data/ingsw/0120_13/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_13/wrong1.txt
rename to Data/ingsw/0120_13/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_13/wrong2.txt b/Data/ingsw/0120_13/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_13/wrong2.txt
rename to Data/ingsw/0120_13/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_14/correct.txt b/Data/ingsw/0120_14/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_14/correct.txt
rename to Data/ingsw/0120_14/correct.txt
diff --git a/Data/ingsw/0120_14/quest.txt b/Data/ingsw/0120_14/quest.txt
new file mode 100644
index 0000000..853f496
--- /dev/null
+++ b/Data/ingsw/0120_14/quest.txt
@@ -0,0 +1,6 @@
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (x + 7); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
+Quale dei seguenti test cases consegue una partition coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_14/wrong1.txt b/Data/ingsw/0120_14/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_14/wrong1.txt
rename to Data/ingsw/0120_14/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_14/wrong2.txt b/Data/ingsw/0120_14/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_14/wrong2.txt
rename to Data/ingsw/0120_14/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_15/correct.txt b/Data/ingsw/0120_15/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_15/correct.txt
rename to Data/ingsw/0120_15/correct.txt
diff --git a/legacy/Data/ingsw/0120_15/quest.txt b/Data/ingsw/0120_15/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_15/quest.txt
rename to Data/ingsw/0120_15/quest.txt
diff --git a/legacy/Data/ingsw/0120_15/wrong1.txt b/Data/ingsw/0120_15/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_15/wrong1.txt
rename to Data/ingsw/0120_15/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_15/wrong2.txt b/Data/ingsw/0120_15/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_15/wrong2.txt
rename to Data/ingsw/0120_15/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_16/correct.txt b/Data/ingsw/0120_16/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_16/correct.txt
rename to Data/ingsw/0120_16/correct.txt
diff --git a/legacy/Data/ingsw/0120_16/quest.txt b/Data/ingsw/0120_16/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0120_16/quest.txt
rename to Data/ingsw/0120_16/quest.txt
index f6839df..881f5f4 100644
--- a/legacy/Data/ingsw/0120_16/quest.txt
+++ b/Data/ingsw/0120_16/quest.txt
@@ -1,2 +1,2 @@
-"Ogni giorno, per ciascuna clinica, il sistema genererà una lista dei pazienti che hanno un appuntamento quel giorno."
+"Ogni giorno, per ciascuna clinica, il sistema genererà una lista dei pazienti che hanno un appuntamento quel giorno."
 La frase precedente è un esempio di:
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_16/wrong1.txt b/Data/ingsw/0120_16/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_16/wrong1.txt
rename to Data/ingsw/0120_16/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_16/wrong2.txt b/Data/ingsw/0120_16/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_16/wrong2.txt
rename to Data/ingsw/0120_16/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_17/correct.txt b/Data/ingsw/0120_17/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_17/correct.txt
rename to Data/ingsw/0120_17/correct.txt
diff --git a/legacy/Data/ingsw/0120_17/quest.txt b/Data/ingsw/0120_17/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0120_17/quest.txt
rename to Data/ingsw/0120_17/quest.txt
index fc7cc95..a7da454 100644
--- a/legacy/Data/ingsw/0120_17/quest.txt
+++ b/Data/ingsw/0120_17/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_17.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_17.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 3, 4? In altri terminti, qual'è la probabilità che non sia necessario ripetere la seconda fase (ma non la prima) ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_17/wrong1.txt b/Data/ingsw/0120_17/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_17/wrong1.txt
rename to Data/ingsw/0120_17/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_17/wrong2.txt b/Data/ingsw/0120_17/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_17/wrong2.txt
rename to Data/ingsw/0120_17/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_18/correct.txt b/Data/ingsw/0120_18/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_18/correct.txt
rename to Data/ingsw/0120_18/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_18/quest.txt b/Data/ingsw/0120_18/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_18/quest.txt
rename to Data/ingsw/0120_18/quest.txt
index fd2ddc5..b4978f3 100644
--- a/legacy/Data/Questions/ingsw/0120_18/quest.txt
+++ b/Data/ingsw/0120_18/quest.txt
@@ -1,9 +1,9 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y - 2 <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
-  else {if (x + 2*y - 5 >= 0)  return (3); else return (4); }
- }  /* f()  */
-Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y - 2 <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
+  else {if (x + 2*y - 5 >= 0)  return (3); else return (4); }
+ }  /* f()  */
+Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_18/wrong1.txt b/Data/ingsw/0120_18/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_18/wrong1.txt
rename to Data/ingsw/0120_18/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_18/wrong2.txt b/Data/ingsw/0120_18/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_18/wrong2.txt
rename to Data/ingsw/0120_18/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_19/correct.txt b/Data/ingsw/0120_19/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_19/correct.txt
rename to Data/ingsw/0120_19/correct.txt
diff --git a/legacy/Data/ingsw/0120_19/quest.txt b/Data/ingsw/0120_19/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_19/quest.txt
rename to Data/ingsw/0120_19/quest.txt
diff --git a/legacy/Data/ingsw/0120_19/wrong1.txt b/Data/ingsw/0120_19/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_19/wrong1.txt
rename to Data/ingsw/0120_19/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_19/wrong2.txt b/Data/ingsw/0120_19/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_19/wrong2.txt
rename to Data/ingsw/0120_19/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_2/correct.txt b/Data/ingsw/0120_2/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_2/correct.txt
rename to Data/ingsw/0120_2/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_2/quest.txt b/Data/ingsw/0120_2/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_2/quest.txt
rename to Data/ingsw/0120_2/quest.txt
index 7cf45ee..6a6482f 100644
--- a/legacy/Data/Questions/ingsw/0120_2/quest.txt
+++ b/Data/ingsw/0120_2/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_2.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act1 act0 act1 act0 act2 
-Test case 2: act0 act2 act2 act0 act1 
-Test case 3: act0 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_2.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act1 act0 act1 act0 act2 
+Test case 2: act0 act2 act2 act0 act1 
+Test case 3: act0 act0 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_2/wrong1.txt b/Data/ingsw/0120_2/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_2/wrong1.txt
rename to Data/ingsw/0120_2/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_2/wrong2.txt b/Data/ingsw/0120_2/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_2/wrong2.txt
rename to Data/ingsw/0120_2/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_20/correct.txt b/Data/ingsw/0120_20/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_20/correct.txt
rename to Data/ingsw/0120_20/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_20/quest.txt b/Data/ingsw/0120_20/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_20/quest.txt
rename to Data/ingsw/0120_20/quest.txt
index 173901c..57ba121 100644
--- a/legacy/Data/Questions/ingsw/0120_20/quest.txt
+++ b/Data/ingsw/0120_20/quest.txt
@@ -1,8 +1,8 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
-  else {if (2*x + y - 5 >= 0)  return (3); else return (4); }
- }  /* f()  */
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
+  else {if (2*x + y - 5 >= 0)  return (3); else return (4); }
+ }  /* f()  */
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_20/wrong1.txt b/Data/ingsw/0120_20/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_20/wrong1.txt
rename to Data/ingsw/0120_20/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_20/wrong2.txt b/Data/ingsw/0120_20/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_20/wrong2.txt
rename to Data/ingsw/0120_20/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_21/correct.txt b/Data/ingsw/0120_21/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_21/correct.txt
rename to Data/ingsw/0120_21/correct.txt
diff --git a/legacy/Data/ingsw/0120_21/quest.txt b/Data/ingsw/0120_21/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0120_21/quest.txt
rename to Data/ingsw/0120_21/quest.txt
index 5e04a05..05ba3b0 100644
--- a/legacy/Data/ingsw/0120_21/quest.txt
+++ b/Data/ingsw/0120_21/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_21.png
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_21.png
 Si consideri la Markov chain in figura con stato iniziale 0 e p in (0, 1). Quale delle seguenti formule calcola il valore atteso del numero di transizioni necessarie per lasciare lo stato 0.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_21/wrong1.txt b/Data/ingsw/0120_21/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_21/wrong1.txt
rename to Data/ingsw/0120_21/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_21/wrong2.txt b/Data/ingsw/0120_21/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_21/wrong2.txt
rename to Data/ingsw/0120_21/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_22/quest.txt b/Data/ingsw/0120_22/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_22/quest.txt
rename to Data/ingsw/0120_22/quest.txt
index 306d75a..81a7fa1 100644
--- a/legacy/Data/Questions/ingsw/0120_22/quest.txt
+++ b/Data/ingsw/0120_22/quest.txt
@@ -1,32 +1,32 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_22/wrong1.txt b/Data/ingsw/0120_22/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_22/wrong1.txt
rename to Data/ingsw/0120_22/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_22/wrong2.txt b/Data/ingsw/0120_22/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_22/wrong2.txt
rename to Data/ingsw/0120_22/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_22/wrong3.txt b/Data/ingsw/0120_22/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_22/wrong3.txt
rename to Data/ingsw/0120_22/wrong3.txt
diff --git a/legacy/Data/ingsw/0120_23/correct.txt b/Data/ingsw/0120_23/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_23/correct.txt
rename to Data/ingsw/0120_23/correct.txt
diff --git a/legacy/Data/ingsw/0120_23/quest.txt b/Data/ingsw/0120_23/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0120_23/quest.txt
rename to Data/ingsw/0120_23/quest.txt
index 6f49368..434d90a 100644
--- a/legacy/Data/ingsw/0120_23/quest.txt
+++ b/Data/ingsw/0120_23/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_23.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act2 act1 act2 act1 
-Test case 2: act1 act0 act2 
-Test case 3: act2 act1 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_23.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act2 act1 act2 act1 
+Test case 2: act1 act0 act2 
+Test case 3: act2 act1 act0 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_23/wrong1.txt b/Data/ingsw/0120_23/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_23/wrong1.txt
rename to Data/ingsw/0120_23/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_23/wrong2.txt b/Data/ingsw/0120_23/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_23/wrong2.txt
rename to Data/ingsw/0120_23/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_24/correct.txt b/Data/ingsw/0120_24/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_24/correct.txt
rename to Data/ingsw/0120_24/correct.txt
diff --git a/legacy/Data/ingsw/0120_24/quest.txt b/Data/ingsw/0120_24/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0120_24/quest.txt
rename to Data/ingsw/0120_24/quest.txt
index 702f202..6e41adb 100644
--- a/legacy/Data/ingsw/0120_24/quest.txt
+++ b/Data/ingsw/0120_24/quest.txt
@@ -1,9 +1,9 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 0)   { if (x + y - 2>= 0)  return (1); else return (2); }
-  else {if (2*x + y - 1>= 0)  return (3); else return (4); }
- }  /* f()  */
-Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 0)   { if (x + y - 2>= 0)  return (1); else return (2); }
+  else {if (2*x + y - 1>= 0)  return (3); else return (4); }
+ }  /* f()  */
+Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_24/wrong1.txt b/Data/ingsw/0120_24/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_24/wrong1.txt
rename to Data/ingsw/0120_24/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_24/wrong2.txt b/Data/ingsw/0120_24/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_24/wrong2.txt
rename to Data/ingsw/0120_24/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_25/quest.txt b/Data/ingsw/0120_25/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_25/quest.txt
rename to Data/ingsw/0120_25/quest.txt
index 5e6a3b5..83988f9 100644
--- a/legacy/Data/ingsw/0120_25/quest.txt
+++ b/Data/ingsw/0120_25/quest.txt
@@ -1,37 +1,37 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_25/wrong1.txt b/Data/ingsw/0120_25/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_25/wrong1.txt
rename to Data/ingsw/0120_25/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_25/wrong2.txt b/Data/ingsw/0120_25/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_25/wrong2.txt
rename to Data/ingsw/0120_25/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_25/wrong3.txt b/Data/ingsw/0120_25/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_25/wrong3.txt
rename to Data/ingsw/0120_25/wrong3.txt
diff --git a/legacy/Data/ingsw/0120_26/correct.txt b/Data/ingsw/0120_26/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_26/correct.txt
rename to Data/ingsw/0120_26/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_26/quest.txt b/Data/ingsw/0120_26/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_26/quest.txt
rename to Data/ingsw/0120_26/quest.txt
index 458f85c..b2616cb 100644
--- a/legacy/Data/Questions/ingsw/0120_26/quest.txt
+++ b/Data/ingsw/0120_26/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_26.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_26.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 2, 3, 4 ? In altri terminti, qual'è la probabilità che sia necessario ripetere sia la fase 1 che la fase 2 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_26/wrong1.txt b/Data/ingsw/0120_26/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_26/wrong1.txt
rename to Data/ingsw/0120_26/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_26/wrong2.txt b/Data/ingsw/0120_26/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_26/wrong2.txt
rename to Data/ingsw/0120_26/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_27/correct.txt b/Data/ingsw/0120_27/correct.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_27/correct.txt
rename to Data/ingsw/0120_27/correct.txt
index 5f37ecc..f8d8a20 100644
--- a/legacy/Data/Questions/ingsw/0120_27/correct.txt
+++ b/Data/ingsw/0120_27/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/Data/ingsw/0120_27/quest.txt b/Data/ingsw/0120_27/quest.txt
new file mode 100644
index 0000000..498beed
--- /dev/null
+++ b/Data/ingsw/0120_27/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
+Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_27/wrong1.txt b/Data/ingsw/0120_27/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_27/wrong1.txt
rename to Data/ingsw/0120_27/wrong1.txt
index 8856598..137f01e 100644
--- a/legacy/Data/ingsw/0120_27/wrong1.txt
+++ b/Data/ingsw/0120_27/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0120_27/wrong2.txt b/Data/ingsw/0120_27/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_27/wrong2.txt
rename to Data/ingsw/0120_27/wrong2.txt
index 2057e11..59d1095 100644
--- a/legacy/Data/ingsw/0120_27/wrong2.txt
+++ b/Data/ingsw/0120_27/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_28/quest.txt b/Data/ingsw/0120_28/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_28/quest.txt
rename to Data/ingsw/0120_28/quest.txt
index 5826ea3..4f79494 100644
--- a/legacy/Data/Questions/ingsw/0120_28/quest.txt
+++ b/Data/ingsw/0120_28/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_28.png
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_28.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_28/wrong1.txt b/Data/ingsw/0120_28/wrong1.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_28/wrong1.txt
rename to Data/ingsw/0120_28/wrong1.txt
index bfb3c5d..77f4d59 100644
--- a/legacy/Data/Questions/ingsw/0120_28/wrong1.txt
+++ b/Data/ingsw/0120_28/wrong1.txt
@@ -1,38 +1,38 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_28/wrong2.txt b/Data/ingsw/0120_28/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_28/wrong2.txt
rename to Data/ingsw/0120_28/wrong2.txt
index c5d8c6b..c8b6f58 100644
--- a/legacy/Data/ingsw/0120_28/wrong2.txt
+++ b/Data/ingsw/0120_28/wrong2.txt
@@ -1,33 +1,33 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_28/wrong3.txt b/Data/ingsw/0120_28/wrong3.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_28/wrong3.txt
rename to Data/ingsw/0120_28/wrong3.txt
index 40db007..d52af85 100644
--- a/legacy/Data/Questions/ingsw/0120_28/wrong3.txt
+++ b/Data/ingsw/0120_28/wrong3.txt
@@ -1,33 +1,33 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_29/correct.txt b/Data/ingsw/0120_29/correct.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_29/correct.txt
rename to Data/ingsw/0120_29/correct.txt
index 080c618..5015021 100644
--- a/legacy/Data/Questions/ingsw/0120_29/correct.txt
+++ b/Data/ingsw/0120_29/correct.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/Data/ingsw/0120_29/quest.txt b/Data/ingsw/0120_29/quest.txt
new file mode 100644
index 0000000..0d3faca
--- /dev/null
+++ b/Data/ingsw/0120_29/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_29/wrong1.txt b/Data/ingsw/0120_29/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_29/wrong1.txt
rename to Data/ingsw/0120_29/wrong1.txt
index 5ea42fe..1c57f89 100644
--- a/legacy/Data/ingsw/0120_29/wrong1.txt
+++ b/Data/ingsw/0120_29/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_29/wrong2.txt b/Data/ingsw/0120_29/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_29/wrong2.txt
rename to Data/ingsw/0120_29/wrong2.txt
index a55c0a4..3b177dc 100644
--- a/legacy/Data/Questions/ingsw/0120_29/wrong2.txt
+++ b/Data/ingsw/0120_29/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0120_3/correct.txt b/Data/ingsw/0120_3/correct.txt
similarity index 95%
rename from legacy/Data/ingsw/0120_3/correct.txt
rename to Data/ingsw/0120_3/correct.txt
index e940faa..c496119 100644
--- a/legacy/Data/ingsw/0120_3/correct.txt
+++ b/Data/ingsw/0120_3/correct.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 3) || (y > 3)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 3) || (y > 3)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_33/quest.txt b/Data/ingsw/0120_3/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0613_33/quest.txt
rename to Data/ingsw/0120_3/quest.txt
index 2758118..42b0d46 100644
--- a/legacy/Data/ingsw/0613_33/quest.txt
+++ b/Data/ingsw/0120_3/quest.txt
@@ -1,4 +1,4 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-Si consideri la funzione C
-int f(int x, int y) { ..... }
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+Si consideri la funzione C
+int f(int x, int y) { ..... }
 Quale delle seguenti assert esprime la pre-condizione che entrambi gli argomenti di f sono non-negativi ed almeno uno di loro è maggiore di 3 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_33/wrong1.txt b/Data/ingsw/0120_3/wrong1.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0613_33/wrong1.txt
rename to Data/ingsw/0120_3/wrong1.txt
index ad32d88..5dcb186 100644
--- a/legacy/Data/Questions/ingsw/0613_33/wrong1.txt
+++ b/Data/ingsw/0120_3/wrong1.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x >= 3) || (y >= 3)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x >= 3) || (y >= 3)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_3/wrong2.txt b/Data/ingsw/0120_3/wrong2.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0120_3/wrong2.txt
rename to Data/ingsw/0120_3/wrong2.txt
index 642ec6b..21886fc 100644
--- a/legacy/Data/Questions/ingsw/0120_3/wrong2.txt
+++ b/Data/ingsw/0120_3/wrong2.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x > 0) && (y > 0) && ((x >= 3) || (y > 3)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x > 0) && (y > 0) && ((x >= 3) || (y > 3)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_30/correct.txt b/Data/ingsw/0120_30/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_30/correct.txt
rename to Data/ingsw/0120_30/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_7/quest.txt b/Data/ingsw/0120_30/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0613_7/quest.txt
rename to Data/ingsw/0120_30/quest.txt
index dbd72c0..20501b2 100644
--- a/legacy/Data/Questions/ingsw/0613_7/quest.txt
+++ b/Data/ingsw/0120_30/quest.txt
@@ -1,22 +1,22 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a - 100 >= 0) && (b - c - 1 <= 0) )
-          return (1);    // punto di uscita 1
-      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0)
-)
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a - 100 >= 0) && (b - c - 1 <= 0) )
+          return (1);    // punto di uscita 1
+      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0)
+)
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_30/wrong1.txt b/Data/ingsw/0120_30/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_30/wrong1.txt
rename to Data/ingsw/0120_30/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_30/wrong2.txt b/Data/ingsw/0120_30/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_30/wrong2.txt
rename to Data/ingsw/0120_30/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_31/correct.txt b/Data/ingsw/0120_31/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_31/correct.txt
rename to Data/ingsw/0120_31/correct.txt
diff --git a/legacy/Data/ingsw/0120_31/quest.txt b/Data/ingsw/0120_31/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0120_31/quest.txt
rename to Data/ingsw/0120_31/quest.txt
index 6c8f77e..fcb9f0a 100644
--- a/legacy/Data/ingsw/0120_31/quest.txt
+++ b/Data/ingsw/0120_31/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_31.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act1 act0 act0 
-Test case 2: act2 act0 act1 
-Test case 3: act0 act0 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_31.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act1 act0 act0 
+Test case 2: act2 act0 act1 
+Test case 3: act0 act0 act0 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_31/wrong1.txt b/Data/ingsw/0120_31/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_31/wrong1.txt
rename to Data/ingsw/0120_31/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_31/wrong2.txt b/Data/ingsw/0120_31/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_31/wrong2.txt
rename to Data/ingsw/0120_31/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_32/correct.txt b/Data/ingsw/0120_32/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_32/correct.txt
rename to Data/ingsw/0120_32/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_32/quest.txt b/Data/ingsw/0120_32/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_32/quest.txt
rename to Data/ingsw/0120_32/quest.txt
index dcec721..3e3f55d 100644
--- a/legacy/Data/Questions/ingsw/0120_32/quest.txt
+++ b/Data/ingsw/0120_32/quest.txt
@@ -1,8 +1,8 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (2*x); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
-Si consideri il seguente insieme di test cases:
-{x=-20, x= 10, x=60}
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (2*x); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
+Si consideri il seguente insieme di test cases:
+{x=-20, x= 10, x=60}
 Quale delle seguenti è la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_32/wrong1.txt b/Data/ingsw/0120_32/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_32/wrong1.txt
rename to Data/ingsw/0120_32/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_32/wrong2.txt b/Data/ingsw/0120_32/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_32/wrong2.txt
rename to Data/ingsw/0120_32/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_33/correct.txt b/Data/ingsw/0120_33/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_33/correct.txt
rename to Data/ingsw/0120_33/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_33/quest.txt b/Data/ingsw/0120_33/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_33/quest.txt
rename to Data/ingsw/0120_33/quest.txt
index e5fbc81..6906b2b 100644
--- a/legacy/Data/Questions/ingsw/0120_33/quest.txt
+++ b/Data/ingsw/0120_33/quest.txt
@@ -1,16 +1,16 @@
-Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
-block Monitor
-input Real x;  
-output Boolean y;
-Boolean w;
-initial equation
-y = false;
-equation
-w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
-algorithm
-when edge(w) then
-y := true;
-end when;
-end Monitor;
-</pre>
+Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
+block Monitor
+input Real x;  
+output Boolean y;
+Boolean w;
+initial equation
+y = false;
+equation
+w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
+algorithm
+when edge(w) then
+y := true;
+end when;
+end Monitor;
+</pre>
 Quale delle seguenti affermazioni meglio descrive il requisito monitorato?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_33/wrong1.txt b/Data/ingsw/0120_33/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_33/wrong1.txt
rename to Data/ingsw/0120_33/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_33/wrong2.txt b/Data/ingsw/0120_33/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_33/wrong2.txt
rename to Data/ingsw/0120_33/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_34/quest.txt b/Data/ingsw/0120_34/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0120_34/quest.txt
rename to Data/ingsw/0120_34/quest.txt
index 29d0647..89108f1 100644
--- a/legacy/Data/ingsw/0120_34/quest.txt
+++ b/Data/ingsw/0120_34/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_34.png
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_34.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_34/wrong1.txt b/Data/ingsw/0120_34/wrong1.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_34/wrong1.txt
rename to Data/ingsw/0120_34/wrong1.txt
index 160702f..92a1693 100644
--- a/legacy/Data/ingsw/0120_34/wrong1.txt
+++ b/Data/ingsw/0120_34/wrong1.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_34/wrong2.txt b/Data/ingsw/0120_34/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_34/wrong2.txt
rename to Data/ingsw/0120_34/wrong2.txt
index 3c05a37..dfac680 100644
--- a/legacy/Data/ingsw/0120_34/wrong2.txt
+++ b/Data/ingsw/0120_34/wrong2.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_34/wrong3.txt b/Data/ingsw/0120_34/wrong3.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_34/wrong3.txt
rename to Data/ingsw/0120_34/wrong3.txt
index bdfb644..d7ee3c7 100644
--- a/legacy/Data/Questions/ingsw/0120_34/wrong3.txt
+++ b/Data/ingsw/0120_34/wrong3.txt
@@ -1,37 +1,37 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_35/quest.txt b/Data/ingsw/0120_35/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0120_35/quest.txt
rename to Data/ingsw/0120_35/quest.txt
index 99379e6..e060808 100644
--- a/legacy/Data/ingsw/0120_35/quest.txt
+++ b/Data/ingsw/0120_35/quest.txt
@@ -1,4 +1,4 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-Si consideri la funzione C
-int f(int x, int y) { ..... }
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+Si consideri la funzione C
+int f(int x, int y) { ..... }
 Quale delle seguenti assert esprime l'invariante che le variabili locali z e w di f() hanno somma minore di 1 oppure maggiore di 7 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_41/wrong1.txt b/Data/ingsw/0120_35/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_41/wrong1.txt
rename to Data/ingsw/0120_35/wrong1.txt
index cbf1814..3957de1 100644
--- a/legacy/Data/Questions/ingsw/0613_41/wrong1.txt
+++ b/Data/ingsw/0120_35/wrong1.txt
@@ -1,6 +1,6 @@
-int f(in x, int y) 
-{ 
-int z, w;
-assert( (z + w <= 1) || (z + w >= 7));
-.....
+int f(in x, int y) 
+{ 
+int z, w;
+assert( (z + w <= 1) || (z + w >= 7));
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_35/wrong2.txt b/Data/ingsw/0120_35/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_35/wrong2.txt
rename to Data/ingsw/0120_35/wrong2.txt
index 03b9f52..dff55b1 100644
--- a/legacy/Data/ingsw/0120_35/wrong2.txt
+++ b/Data/ingsw/0120_35/wrong2.txt
@@ -1,6 +1,6 @@
-int f(in x, int y) 
-{ 
-int z, w;
-assert( (z + w < 1) || (z + w > 7));
-.....
+int f(in x, int y) 
+{ 
+int z, w;
+assert( (z + w < 1) || (z + w > 7));
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_35/wrong3.txt b/Data/ingsw/0120_35/wrong3.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_35/wrong3.txt
rename to Data/ingsw/0120_35/wrong3.txt
index 6fcb8b5..d56f4a5 100644
--- a/legacy/Data/ingsw/0120_35/wrong3.txt
+++ b/Data/ingsw/0120_35/wrong3.txt
@@ -1,6 +1,6 @@
-int f(in x, int y) 
-{ 
-int z, w;
-assert( (z + w > 1) || (z + w < 7));
-.....
+int f(in x, int y) 
+{ 
+int z, w;
+assert( (z + w > 1) || (z + w < 7));
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_36/correct.txt b/Data/ingsw/0120_36/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_36/correct.txt
rename to Data/ingsw/0120_36/correct.txt
diff --git a/legacy/Data/ingsw/0120_36/quest.txt b/Data/ingsw/0120_36/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0120_36/quest.txt
rename to Data/ingsw/0120_36/quest.txt
index 6f256c3..b432df1 100644
--- a/legacy/Data/ingsw/0120_36/quest.txt
+++ b/Data/ingsw/0120_36/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_36.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act2 act1 act2 
-Test case 2: act0 act2 act0 
-Test case 3: act0 act0 act0 act1 act1 
-
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_36.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act2 act1 act2 
+Test case 2: act0 act2 act0 
+Test case 3: act0 act0 act0 act1 act1 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_36/wrong1.txt b/Data/ingsw/0120_36/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_36/wrong1.txt
rename to Data/ingsw/0120_36/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_36/wrong2.txt b/Data/ingsw/0120_36/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_36/wrong2.txt
rename to Data/ingsw/0120_36/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_37/correct.txt b/Data/ingsw/0120_37/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_37/correct.txt
rename to Data/ingsw/0120_37/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_37/quest.txt b/Data/ingsw/0120_37/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0120_37/quest.txt
rename to Data/ingsw/0120_37/quest.txt
index cdbc688..f8fae00 100644
--- a/legacy/Data/Questions/ingsw/0120_37/quest.txt
+++ b/Data/ingsw/0120_37/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_37.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-
-Test case 1: act2 act1 act2 act2 
-Test case 2: act2 act0 act2 act0 act2 
-Test case 3: act2 act0 act2 act0 act1 
-
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_37.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+
+Test case 1: act2 act1 act2 act2 
+Test case 2: act2 act0 act2 act0 act2 
+Test case 3: act2 act0 act2 act0 act1 
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_37/wrong1.txt b/Data/ingsw/0120_37/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_37/wrong1.txt
rename to Data/ingsw/0120_37/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_37/wrong2.txt b/Data/ingsw/0120_37/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_37/wrong2.txt
rename to Data/ingsw/0120_37/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_13/correct.txt b/Data/ingsw/0120_38/correct.txt
similarity index 93%
rename from legacy/Data/ingsw/0210_13/correct.txt
rename to Data/ingsw/0120_38/correct.txt
index 25ac15c..1ab78ce 100644
--- a/legacy/Data/ingsw/0210_13/correct.txt
+++ b/Data/ingsw/0120_38/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and ((x >= 30) or (x <= 20)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and ((x >= 30) or (x <= 20)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/Data/ingsw/0120_38/quest.txt b/Data/ingsw/0120_38/quest.txt
new file mode 100644
index 0000000..6938bd6
--- /dev/null
+++ b/Data/ingsw/0120_38/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
+Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_38/wrong1.txt b/Data/ingsw/0120_38/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_38/wrong1.txt
rename to Data/ingsw/0120_38/wrong1.txt
index 157567e..ceb178e 100644
--- a/legacy/Data/Questions/ingsw/0120_38/wrong1.txt
+++ b/Data/ingsw/0120_38/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) or ((x >= 20) and (x <= 30)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) or ((x >= 20) and (x <= 30)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0120_38/wrong2.txt b/Data/ingsw/0120_38/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_38/wrong2.txt
rename to Data/ingsw/0120_38/wrong2.txt
index d021c3b..b763753 100644
--- a/legacy/Data/ingsw/0120_38/wrong2.txt
+++ b/Data/ingsw/0120_38/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and (x >= 20) and (x <= 30) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and (x >= 20) and (x <= 30) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0120_39/quest.txt b/Data/ingsw/0120_39/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_39/quest.txt
rename to Data/ingsw/0120_39/quest.txt
index 4777dbc..49d8ee1 100644
--- a/legacy/Data/ingsw/0120_39/quest.txt
+++ b/Data/ingsw/0120_39/quest.txt
@@ -1,35 +1,35 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_39/wrong1.txt b/Data/ingsw/0120_39/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_39/wrong1.txt
rename to Data/ingsw/0120_39/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_39/wrong2.txt b/Data/ingsw/0120_39/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_39/wrong2.txt
rename to Data/ingsw/0120_39/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_39/wrong3.txt b/Data/ingsw/0120_39/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_39/wrong3.txt
rename to Data/ingsw/0120_39/wrong3.txt
diff --git a/legacy/Data/ingsw/0120_4/correct.txt b/Data/ingsw/0120_4/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_4/correct.txt
rename to Data/ingsw/0120_4/correct.txt
diff --git a/legacy/Data/ingsw/0210_10/quest.txt b/Data/ingsw/0120_4/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_10/quest.txt
rename to Data/ingsw/0120_4/quest.txt
index d1cf8cb..2aa8dba 100644
--- a/legacy/Data/ingsw/0210_10/quest.txt
+++ b/Data/ingsw/0120_4/quest.txt
@@ -1,16 +1,16 @@
-Si consideri il monitor seguente che ritorna true appena i requisiti per il sistema monitorato sono violati.
-block Monitor
-input Real x;  
-output Boolean y;
-Boolean w;
-initial equation
-y = false;
-equation
-w = ((x < 0) or (x > 5));
-algorithm
-when edge(w) then
-y := true;
-end when;
-end Monitor;
-</pre>
+Si consideri il monitor seguente che ritorna true appena i requisiti per il sistema monitorato sono violati.
+block Monitor
+input Real x;  
+output Boolean y;
+Boolean w;
+initial equation
+y = false;
+equation
+w = ((x < 0) or (x > 5));
+algorithm
+when edge(w) then
+y := true;
+end when;
+end Monitor;
+</pre>
 Quale delle seguenti affermazioni meglio descrive il requisito monitorato.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_4/wrong1.txt b/Data/ingsw/0120_4/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_4/wrong1.txt
rename to Data/ingsw/0120_4/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_4/wrong2.txt b/Data/ingsw/0120_4/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_4/wrong2.txt
rename to Data/ingsw/0120_4/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_40/correct.txt b/Data/ingsw/0120_40/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_40/correct.txt
rename to Data/ingsw/0120_40/correct.txt
diff --git a/legacy/Data/ingsw/0120_40/quest.txt b/Data/ingsw/0120_40/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0120_40/quest.txt
rename to Data/ingsw/0120_40/quest.txt
index 74a4d32..4944b43 100644
--- a/legacy/Data/ingsw/0120_40/quest.txt
+++ b/Data/ingsw/0120_40/quest.txt
@@ -1,8 +1,8 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y - 6 <= 0)   { if (x + y - 3 >= 0)  return (1); else return (2); }
-  else {if (x + 2*y -15 >= 0)  return (3); else return (4); }
- }  /* f()  */
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y - 6 <= 0)   { if (x + y - 3 >= 0)  return (1); else return (2); }
+  else {if (x + 2*y -15 >= 0)  return (3); else return (4); }
+ }  /* f()  */
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_40/wrong1.txt b/Data/ingsw/0120_40/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_40/wrong1.txt
rename to Data/ingsw/0120_40/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_40/wrong2.txt b/Data/ingsw/0120_40/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_40/wrong2.txt
rename to Data/ingsw/0120_40/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_41/correct.txt b/Data/ingsw/0120_41/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_41/correct.txt
rename to Data/ingsw/0120_41/correct.txt
diff --git a/legacy/Data/ingsw/0120_41/quest.txt b/Data/ingsw/0120_41/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0120_41/quest.txt
rename to Data/ingsw/0120_41/quest.txt
index 47201e7..332256a 100644
--- a/legacy/Data/ingsw/0120_41/quest.txt
+++ b/Data/ingsw/0120_41/quest.txt
@@ -1,4 +1,4 @@
-Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
-Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
-P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
+Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
+Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
+P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
 dove b è una opportuna costante note da dati storici aziendali. Si assuma che b = 0.0001,  C = 1000000, ed il rischio ammesso è R = 1000. Quale dei seguenti valori meglio approssima il costo S per lo sviluppo del software in questione.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_41/wrong1.txt b/Data/ingsw/0120_41/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_41/wrong1.txt
rename to Data/ingsw/0120_41/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_41/wrong2.txt b/Data/ingsw/0120_41/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_41/wrong2.txt
rename to Data/ingsw/0120_41/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_42/correct.txt b/Data/ingsw/0120_42/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_42/correct.txt
rename to Data/ingsw/0120_42/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_42/quest.txt b/Data/ingsw/0120_42/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_42/quest.txt
rename to Data/ingsw/0120_42/quest.txt
index 4650bbb..c6b6c5f 100644
--- a/legacy/Data/Questions/ingsw/0120_42/quest.txt
+++ b/Data/ingsw/0120_42/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_42.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act1 act2 act1 act2 act2 
-Test case 2: act2 act0 
-Test case 3: act0 act0 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_42.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act1 act2 act1 act2 act2 
+Test case 2: act2 act0 
+Test case 3: act0 act0 act0 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_42/wrong1.txt b/Data/ingsw/0120_42/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_42/wrong1.txt
rename to Data/ingsw/0120_42/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_42/wrong2.txt b/Data/ingsw/0120_42/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_42/wrong2.txt
rename to Data/ingsw/0120_42/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_43/quest.txt b/Data/ingsw/0120_43/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_43/quest.txt
rename to Data/ingsw/0120_43/quest.txt
index 8636c5a..aa56c3d 100644
--- a/legacy/Data/Questions/ingsw/0120_43/quest.txt
+++ b/Data/ingsw/0120_43/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_43.png
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_43.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_43/wrong1.txt b/Data/ingsw/0120_43/wrong1.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_43/wrong1.txt
rename to Data/ingsw/0120_43/wrong1.txt
index 0ca6415..8210d86 100644
--- a/legacy/Data/Questions/ingsw/0120_43/wrong1.txt
+++ b/Data/ingsw/0120_43/wrong1.txt
@@ -1,32 +1,32 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_43/wrong2.txt b/Data/ingsw/0120_43/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0120_43/wrong2.txt
rename to Data/ingsw/0120_43/wrong2.txt
index a5879aa..78c7bc5 100644
--- a/legacy/Data/Questions/ingsw/0120_43/wrong2.txt
+++ b/Data/ingsw/0120_43/wrong2.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_43/wrong3.txt b/Data/ingsw/0120_43/wrong3.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_43/wrong3.txt
rename to Data/ingsw/0120_43/wrong3.txt
index b4f56fb..f20fbac 100644
--- a/legacy/Data/ingsw/0120_43/wrong3.txt
+++ b/Data/ingsw/0120_43/wrong3.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_44/correct.txt b/Data/ingsw/0120_44/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_44/correct.txt
rename to Data/ingsw/0120_44/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_45/quest.txt b/Data/ingsw/0120_44/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0613_45/quest.txt
rename to Data/ingsw/0120_44/quest.txt
index e44e320..6f570ef 100644
--- a/legacy/Data/Questions/ingsw/0613_45/quest.txt
+++ b/Data/ingsw/0120_44/quest.txt
@@ -1,21 +1,21 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a  + b - 6 >= 0) && (b - c - 1 <= 0) )
-          return (1);    // punto di uscita 1
-      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0))
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a  + b - 6 >= 0) && (b - c - 1 <= 0) )
+          return (1);    // punto di uscita 1
+      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0))
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_44/wrong1.txt b/Data/ingsw/0120_44/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_44/wrong1.txt
rename to Data/ingsw/0120_44/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_44/wrong2.txt b/Data/ingsw/0120_44/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_44/wrong2.txt
rename to Data/ingsw/0120_44/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_45/quest.txt b/Data/ingsw/0120_45/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_45/quest.txt
rename to Data/ingsw/0120_45/quest.txt
index 4818a62..fce8644 100644
--- a/legacy/Data/ingsw/0120_45/quest.txt
+++ b/Data/ingsw/0120_45/quest.txt
@@ -1,35 +1,35 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_45/wrong1.txt b/Data/ingsw/0120_45/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_45/wrong1.txt
rename to Data/ingsw/0120_45/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_45/wrong2.txt b/Data/ingsw/0120_45/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_45/wrong2.txt
rename to Data/ingsw/0120_45/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_45/wrong3.txt b/Data/ingsw/0120_45/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_45/wrong3.txt
rename to Data/ingsw/0120_45/wrong3.txt
diff --git a/legacy/Data/ingsw/0120_46/correct.txt b/Data/ingsw/0120_46/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_46/correct.txt
rename to Data/ingsw/0120_46/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_46/quest.txt b/Data/ingsw/0120_46/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_46/quest.txt
rename to Data/ingsw/0120_46/quest.txt
index 6205846..43e4597 100644
--- a/legacy/Data/Questions/ingsw/0120_46/quest.txt
+++ b/Data/ingsw/0120_46/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_46.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_46.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 3 ? In altri terminti, qual'è la probabilità che non sia necessario ripetere nessuna fase?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_46/wrong1.txt b/Data/ingsw/0120_46/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_46/wrong1.txt
rename to Data/ingsw/0120_46/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_46/wrong2.txt b/Data/ingsw/0120_46/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_46/wrong2.txt
rename to Data/ingsw/0120_46/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_47/correct.txt b/Data/ingsw/0120_47/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_47/correct.txt
rename to Data/ingsw/0120_47/correct.txt
diff --git a/legacy/Data/ingsw/0120_47/quest.txt b/Data/ingsw/0120_47/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_47/quest.txt
rename to Data/ingsw/0120_47/quest.txt
diff --git a/legacy/Data/ingsw/0120_47/wrong1.txt b/Data/ingsw/0120_47/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_47/wrong1.txt
rename to Data/ingsw/0120_47/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_47/wrong2.txt b/Data/ingsw/0120_47/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_47/wrong2.txt
rename to Data/ingsw/0120_47/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_48/correct.txt b/Data/ingsw/0120_48/correct.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_48/correct.txt
rename to Data/ingsw/0120_48/correct.txt
index 519c7fd..98a62af 100644
--- a/legacy/Data/Questions/ingsw/0120_48/correct.txt
+++ b/Data/ingsw/0120_48/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x > 5) or (x < 0));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x > 5) or (x < 0));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/Data/ingsw/0120_48/quest.txt b/Data/ingsw/0120_48/quest.txt
new file mode 100644
index 0000000..d26958e
--- /dev/null
+++ b/Data/ingsw/0120_48/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_48/wrong1.txt b/Data/ingsw/0120_48/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_48/wrong1.txt
rename to Data/ingsw/0120_48/wrong1.txt
index 5229f7e..776f12c 100644
--- a/legacy/Data/ingsw/0120_48/wrong1.txt
+++ b/Data/ingsw/0120_48/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z =  (time > 0) and ((x > 0) or (x < 5));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z =  (time > 0) and ((x > 0) or (x < 5));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_48/wrong2.txt b/Data/ingsw/0120_48/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_48/wrong2.txt
rename to Data/ingsw/0120_48/wrong2.txt
index c2e617d..b78ed91 100644
--- a/legacy/Data/Questions/ingsw/0120_48/wrong2.txt
+++ b/Data/ingsw/0120_48/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and (x > 0) and (x < 5);
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and (x > 0) and (x < 5);
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0120_49/correct.txt b/Data/ingsw/0120_49/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_49/correct.txt
rename to Data/ingsw/0120_49/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_49/quest.txt b/Data/ingsw/0120_49/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_49/quest.txt
rename to Data/ingsw/0120_49/quest.txt
index 2d6940a..6d49b6f 100644
--- a/legacy/Data/Questions/ingsw/0120_49/quest.txt
+++ b/Data/ingsw/0120_49/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_49.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
-Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_49.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
+Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_49/wrong1.txt b/Data/ingsw/0120_49/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_49/wrong1.txt
rename to Data/ingsw/0120_49/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_49/wrong2.txt b/Data/ingsw/0120_49/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_49/wrong2.txt
rename to Data/ingsw/0120_49/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_5/quest.txt b/Data/ingsw/0120_5/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_5/quest.txt
rename to Data/ingsw/0120_5/quest.txt
index 3e68301..e28d958 100644
--- a/legacy/Data/Questions/ingsw/0120_5/quest.txt
+++ b/Data/ingsw/0120_5/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_5.png
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_5.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_5/wrong1.txt b/Data/ingsw/0120_5/wrong1.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_5/wrong1.txt
rename to Data/ingsw/0120_5/wrong1.txt
index 6c46c45..073f2e0 100644
--- a/legacy/Data/ingsw/0120_5/wrong1.txt
+++ b/Data/ingsw/0120_5/wrong1.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_5/wrong2.txt b/Data/ingsw/0120_5/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_5/wrong2.txt
rename to Data/ingsw/0120_5/wrong2.txt
index 39e7bfc..a96f58c 100644
--- a/legacy/Data/ingsw/0120_5/wrong2.txt
+++ b/Data/ingsw/0120_5/wrong2.txt
@@ -1,37 +1,37 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_5/wrong3.txt b/Data/ingsw/0120_5/wrong3.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0120_5/wrong3.txt
rename to Data/ingsw/0120_5/wrong3.txt
index 93e29a3..e598e03 100644
--- a/legacy/Data/Questions/ingsw/0120_5/wrong3.txt
+++ b/Data/ingsw/0120_5/wrong3.txt
@@ -1,28 +1,28 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_6/correct.txt b/Data/ingsw/0120_6/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_6/correct.txt
rename to Data/ingsw/0120_6/correct.txt
diff --git a/legacy/Data/ingsw/0120_6/quest.txt b/Data/ingsw/0120_6/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_6/quest.txt
rename to Data/ingsw/0120_6/quest.txt
diff --git a/legacy/Data/ingsw/0120_6/wrong1.txt b/Data/ingsw/0120_6/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_6/wrong1.txt
rename to Data/ingsw/0120_6/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_6/wrong2.txt b/Data/ingsw/0120_6/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_6/wrong2.txt
rename to Data/ingsw/0120_6/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0120_7/correct.txt b/Data/ingsw/0120_7/correct.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_7/correct.txt
rename to Data/ingsw/0120_7/correct.txt
index b559d4a..5575173 100644
--- a/legacy/Data/Questions/ingsw/0120_7/correct.txt
+++ b/Data/ingsw/0120_7/correct.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/Data/ingsw/0120_7/quest.txt b/Data/ingsw/0120_7/quest.txt
new file mode 100644
index 0000000..1dbc484
--- /dev/null
+++ b/Data/ingsw/0120_7/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_7/wrong1.txt b/Data/ingsw/0120_7/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0120_7/wrong1.txt
rename to Data/ingsw/0120_7/wrong1.txt
index 4b8db59..fa35b30 100644
--- a/legacy/Data/ingsw/0120_7/wrong1.txt
+++ b/Data/ingsw/0120_7/wrong1.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0120_7/wrong2.txt b/Data/ingsw/0120_7/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_7/wrong2.txt
rename to Data/ingsw/0120_7/wrong2.txt
index 05ce544..45fd47a 100644
--- a/legacy/Data/Questions/ingsw/0120_7/wrong2.txt
+++ b/Data/ingsw/0120_7/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0120_8/correct.txt b/Data/ingsw/0120_8/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_8/correct.txt
rename to Data/ingsw/0120_8/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_8/quest.txt b/Data/ingsw/0120_8/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0120_8/quest.txt
rename to Data/ingsw/0120_8/quest.txt
index 2809138..f96034d 100644
--- a/legacy/Data/Questions/ingsw/0120_8/quest.txt
+++ b/Data/ingsw/0120_8/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_8.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act1 act0 act1 act0 act2 
-Test case 2: act0 act2 act2 act0 act1 
-Test case 3: act0 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_8.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act1 act0 act1 act0 act2 
+Test case 2: act0 act2 act2 act0 act1 
+Test case 3: act0 act0 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_8/wrong1.txt b/Data/ingsw/0120_8/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_8/wrong1.txt
rename to Data/ingsw/0120_8/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_8/wrong2.txt b/Data/ingsw/0120_8/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_8/wrong2.txt
rename to Data/ingsw/0120_8/wrong2.txt
diff --git a/legacy/Data/ingsw/0120_9/correct.txt b/Data/ingsw/0120_9/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_9/correct.txt
rename to Data/ingsw/0120_9/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0120_9/quest.txt b/Data/ingsw/0120_9/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0120_9/quest.txt
rename to Data/ingsw/0120_9/quest.txt
index 4962ecf..64fc81c 100644
--- a/legacy/Data/Questions/ingsw/0120_9/quest.txt
+++ b/Data/ingsw/0120_9/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_9.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_9.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 2, 3? In altri terminti, qual'è la probabilità che non sia necessario ripetere la prima fase (ma non la seconda) ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_9/wrong1.txt b/Data/ingsw/0120_9/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_9/wrong1.txt
rename to Data/ingsw/0120_9/wrong1.txt
diff --git a/legacy/Data/ingsw/0120_9/wrong2.txt b/Data/ingsw/0120_9/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0120_9/wrong2.txt
rename to Data/ingsw/0120_9/wrong2.txt
diff --git a/legacy/Data/ingsw/0121_34/correct.txt b/Data/ingsw/0121_34/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0121_34/correct.txt
rename to Data/ingsw/0121_34/correct.txt
diff --git a/legacy/Data/ingsw/0121_34/quest.txt b/Data/ingsw/0121_34/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0121_34/quest.txt
rename to Data/ingsw/0121_34/quest.txt
diff --git a/legacy/Data/ingsw/0121_34/wrong1.txt b/Data/ingsw/0121_34/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0121_34/wrong1.txt
rename to Data/ingsw/0121_34/wrong1.txt
diff --git a/legacy/Data/ingsw/0121_34/wrong2.txt b/Data/ingsw/0121_34/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0121_34/wrong2.txt
rename to Data/ingsw/0121_34/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_0/correct.txt b/Data/ingsw/0210_0/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_0/correct.txt
rename to Data/ingsw/0210_0/correct.txt
diff --git a/legacy/Data/ingsw/0324_15/quest.txt b/Data/ingsw/0210_0/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0324_15/quest.txt
rename to Data/ingsw/0210_0/quest.txt
index 2d895ca..025eb8d 100644
--- a/legacy/Data/ingsw/0324_15/quest.txt
+++ b/Data/ingsw/0210_0/quest.txt
@@ -1,22 +1,22 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a >= 100) && (b - c <= 1) )
-          return (1);    // punto di uscita 1
-      else if ((b - c <= 1)  || (b + c >= 5)
-)
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a >= 100) && (b - c <= 1) )
+          return (1);    // punto di uscita 1
+      else if ((b - c <= 1)  || (b + c >= 5)
+)
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_0/wrong1.txt b/Data/ingsw/0210_0/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_0/wrong1.txt
rename to Data/ingsw/0210_0/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_0/wrong2.txt b/Data/ingsw/0210_0/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_0/wrong2.txt
rename to Data/ingsw/0210_0/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_1/quest.txt b/Data/ingsw/0210_1/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0210_1/quest.txt
rename to Data/ingsw/0210_1/quest.txt
index 89110fc..10903ae 100644
--- a/legacy/Data/Questions/ingsw/0210_1/quest.txt
+++ b/Data/ingsw/0210_1/quest.txt
@@ -1,5 +1,5 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_1.png
-Si consideri la seguente architettura software:
-
-
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_1.png
+Si consideri la seguente architettura software:
+
+
 Quale dei seguenti modelli Modelica meglio la rappresenta ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_1/wrong1.txt b/Data/ingsw/0210_1/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0210_1/wrong1.txt
rename to Data/ingsw/0210_1/wrong1.txt
index 0487745..003ee5e 100644
--- a/legacy/Data/ingsw/0210_1/wrong1.txt
+++ b/Data/ingsw/0210_1/wrong1.txt
@@ -1,6 +1,6 @@
-block SysArch;
-SC1 sc1
-SC2 sc2;
-SC3 sc3;
-SC4 sc4;
+block SysArch;
+SC1 sc1
+SC2 sc2;
+SC3 sc3;
+SC4 sc4;
 connect(sc1.input4, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_1/wrong2.txt b/Data/ingsw/0210_1/wrong2.txt
similarity index 97%
rename from legacy/Data/ingsw/0210_1/wrong2.txt
rename to Data/ingsw/0210_1/wrong2.txt
index 6b9f4b0..93e7db8 100644
--- a/legacy/Data/ingsw/0210_1/wrong2.txt
+++ b/Data/ingsw/0210_1/wrong2.txt
@@ -1,3 +1,3 @@
-output4);
-connect(sc1.output4, sc2.input4);
+output4);
+connect(sc1.output4, sc2.input4);
 connect(sc1.input5, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_1/wrong3.txt b/Data/ingsw/0210_1/wrong3.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_1/wrong3.txt
rename to Data/ingsw/0210_1/wrong3.txt
index bf32c35..fe11933 100644
--- a/legacy/Data/ingsw/0210_1/wrong3.txt
+++ b/Data/ingsw/0210_1/wrong3.txt
@@ -1,40 +1,40 @@
-output5);
-connect(sc1.output5, sc3.input5);
-connect(sc1.input6, sc4.output6);
-connect(sc1.output6, sc4.input6);
-connect(sc2.input1, sc3.output1);
-connect(sc3.input2, sc4.output2);
-connect(sc4.input3, sc2.ouput3);
-end SysArch
-2.
-block SysArch;
-SC1 sc1
-SC2 sc2;
-SC3 sc3;
-SC4 sc4;
-connect(sc1.input4, sc2.output4);
-connect(sc1.output4, sc2.input4);
-connect(sc1.input5, sc3.output5);
-connect(sc1.output5, sc3.input5);
-connect(sc1.input6, sc4.output6);
-connect(sc1.output6, sc4.input6);
-connect(sc2.input1, sc3.output1);
-connect(sc3.input2, sc4.output2);
-connect(sc4.output3, sc2.input3);
-end SysArch
-3.
-block SysArch;
-SC1 sc1
-SC2 sc2;
-SC3 sc3;
-SC4 sc4;
-connect(sc1.input4, sc2.output4);
-connect(sc1.output4, sc2.input4);
-connect(sc1.input5, sc3.output5);
-connect(sc1.output5, sc3.input5);
-connect(sc1.input6, sc4.output6);
-connect(sc1.output6, sc4.input6);
-connect(sc2.output1, sc3.input1);
-connect(sc3.output2, sc4.input2);
-connect(sc4.output3, sc2.input3);
+output5);
+connect(sc1.output5, sc3.input5);
+connect(sc1.input6, sc4.output6);
+connect(sc1.output6, sc4.input6);
+connect(sc2.input1, sc3.output1);
+connect(sc3.input2, sc4.output2);
+connect(sc4.input3, sc2.ouput3);
+end SysArch
+2.
+block SysArch;
+SC1 sc1
+SC2 sc2;
+SC3 sc3;
+SC4 sc4;
+connect(sc1.input4, sc2.output4);
+connect(sc1.output4, sc2.input4);
+connect(sc1.input5, sc3.output5);
+connect(sc1.output5, sc3.input5);
+connect(sc1.input6, sc4.output6);
+connect(sc1.output6, sc4.input6);
+connect(sc2.input1, sc3.output1);
+connect(sc3.input2, sc4.output2);
+connect(sc4.output3, sc2.input3);
+end SysArch
+3.
+block SysArch;
+SC1 sc1
+SC2 sc2;
+SC3 sc3;
+SC4 sc4;
+connect(sc1.input4, sc2.output4);
+connect(sc1.output4, sc2.input4);
+connect(sc1.input5, sc3.output5);
+connect(sc1.output5, sc3.input5);
+connect(sc1.input6, sc4.output6);
+connect(sc1.output6, sc4.input6);
+connect(sc2.output1, sc3.input1);
+connect(sc3.output2, sc4.input2);
+connect(sc4.output3, sc2.input3);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_10/correct.txt b/Data/ingsw/0210_10/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_10/correct.txt
rename to Data/ingsw/0210_10/correct.txt
diff --git a/legacy/Data/ingsw/0120_4/quest.txt b/Data/ingsw/0210_10/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0120_4/quest.txt
rename to Data/ingsw/0210_10/quest.txt
index d1cf8cb..2aa8dba 100644
--- a/legacy/Data/ingsw/0120_4/quest.txt
+++ b/Data/ingsw/0210_10/quest.txt
@@ -1,16 +1,16 @@
-Si consideri il monitor seguente che ritorna true appena i requisiti per il sistema monitorato sono violati.
-block Monitor
-input Real x;  
-output Boolean y;
-Boolean w;
-initial equation
-y = false;
-equation
-w = ((x < 0) or (x > 5));
-algorithm
-when edge(w) then
-y := true;
-end when;
-end Monitor;
-</pre>
+Si consideri il monitor seguente che ritorna true appena i requisiti per il sistema monitorato sono violati.
+block Monitor
+input Real x;  
+output Boolean y;
+Boolean w;
+initial equation
+y = false;
+equation
+w = ((x < 0) or (x > 5));
+algorithm
+when edge(w) then
+y := true;
+end when;
+end Monitor;
+</pre>
 Quale delle seguenti affermazioni meglio descrive il requisito monitorato.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_10/wrong1.txt b/Data/ingsw/0210_10/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_10/wrong1.txt
rename to Data/ingsw/0210_10/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_10/wrong2.txt b/Data/ingsw/0210_10/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_10/wrong2.txt
rename to Data/ingsw/0210_10/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_11/quest.txt b/Data/ingsw/0210_11/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0210_11/quest.txt
rename to Data/ingsw/0210_11/quest.txt
index 57dc789..57d0b65 100644
--- a/legacy/Data/ingsw/0210_11/quest.txt
+++ b/Data/ingsw/0210_11/quest.txt
@@ -1,4 +1,4 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_11.png
-Si consideri la seguente architettura software:
-
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_11.png
+Si consideri la seguente architettura software:
+
 Quale dei seguneti modelli Modelica meglio la rappresenta.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_11/wrong1.txt b/Data/ingsw/0210_11/wrong1.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_11/wrong1.txt
rename to Data/ingsw/0210_11/wrong1.txt
index 157d205..4b28f65 100644
--- a/legacy/Data/ingsw/0210_11/wrong1.txt
+++ b/Data/ingsw/0210_11/wrong1.txt
@@ -1,9 +1,9 @@
-block SysArch
-OS os_c;
-WS ws_c;
-WB wb_c;
-connect(os_c.input1, ws_c.output1);
-connect(os_c.output1, ws_c.input1);
-connect(wb_c.input2, ws_c.output2);
-connect(wb_c.output2, ws_c.input2);
+block SysArch
+OS os_c;
+WS ws_c;
+WB wb_c;
+connect(os_c.input1, ws_c.output1);
+connect(os_c.output1, ws_c.input1);
+connect(wb_c.input2, ws_c.output2);
+connect(wb_c.output2, ws_c.input2);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_11/wrong2.txt b/Data/ingsw/0210_11/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_11/wrong2.txt
rename to Data/ingsw/0210_11/wrong2.txt
index 04886bb..7d1273e 100644
--- a/legacy/Data/ingsw/0210_11/wrong2.txt
+++ b/Data/ingsw/0210_11/wrong2.txt
@@ -1,9 +1,9 @@
-block SysArch
-OS os_c;
-WS ws_c;
-WB wb_c;
-connect(os_c.input1, wb_c.output1);
-connect(os_c.output1, wb_c.input1);
-connect(wb_c.input2, ws_c.output2);
-connect(wb_c.output2, ws_c.input2);
+block SysArch
+OS os_c;
+WS ws_c;
+WB wb_c;
+connect(os_c.input1, wb_c.output1);
+connect(os_c.output1, wb_c.input1);
+connect(wb_c.input2, ws_c.output2);
+connect(wb_c.output2, ws_c.input2);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_11/wrong3.txt b/Data/ingsw/0210_11/wrong3.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_11/wrong3.txt
rename to Data/ingsw/0210_11/wrong3.txt
index 903ba76..5c1559b 100644
--- a/legacy/Data/Questions/ingsw/0210_11/wrong3.txt
+++ b/Data/ingsw/0210_11/wrong3.txt
@@ -1,9 +1,9 @@
-block SysArch
-OS os_c;
-WS ws_c;
-WB wb_c;
-connect(os_c.input1, ws_c.output1);
-connect(os_c.output1, ws_c.input1);
-connect(wb_c.input2, os_c.output2);
-connect(wb_c.output2, os_c.input2);
+block SysArch
+OS os_c;
+WS ws_c;
+WB wb_c;
+connect(os_c.input1, ws_c.output1);
+connect(os_c.output1, ws_c.input1);
+connect(wb_c.input2, os_c.output2);
+connect(wb_c.output2, os_c.input2);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_12/quest.txt b/Data/ingsw/0210_12/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0210_12/quest.txt
rename to Data/ingsw/0210_12/quest.txt
index 86ee3d4..86a19bb 100644
--- a/legacy/Data/ingsw/0210_12/quest.txt
+++ b/Data/ingsw/0210_12/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_12.png
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_12.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_12/wrong1.txt b/Data/ingsw/0210_12/wrong1.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_12/wrong1.txt
rename to Data/ingsw/0210_12/wrong1.txt
index c7f67fe..4738d06 100644
--- a/legacy/Data/Questions/ingsw/0210_12/wrong1.txt
+++ b/Data/ingsw/0210_12/wrong1.txt
@@ -1,38 +1,38 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_12/wrong2.txt b/Data/ingsw/0210_12/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_12/wrong2.txt
rename to Data/ingsw/0210_12/wrong2.txt
index b84dfd6..f85f2de 100644
--- a/legacy/Data/Questions/ingsw/0210_12/wrong2.txt
+++ b/Data/ingsw/0210_12/wrong2.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_12/wrong3.txt b/Data/ingsw/0210_12/wrong3.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_12/wrong3.txt
rename to Data/ingsw/0210_12/wrong3.txt
index 162b572..fec0854 100644
--- a/legacy/Data/Questions/ingsw/0210_12/wrong3.txt
+++ b/Data/ingsw/0210_12/wrong3.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_38/correct.txt b/Data/ingsw/0210_13/correct.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_38/correct.txt
rename to Data/ingsw/0210_13/correct.txt
index 25ac15c..1ab78ce 100644
--- a/legacy/Data/Questions/ingsw/0120_38/correct.txt
+++ b/Data/ingsw/0210_13/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and ((x >= 30) or (x <= 20)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and ((x >= 30) or (x <= 20)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/Data/ingsw/0210_13/quest.txt b/Data/ingsw/0210_13/quest.txt
new file mode 100644
index 0000000..6938bd6
--- /dev/null
+++ b/Data/ingsw/0210_13/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
+Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_13/wrong1.txt b/Data/ingsw/0210_13/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0210_13/wrong1.txt
rename to Data/ingsw/0210_13/wrong1.txt
index d021c3b..b763753 100644
--- a/legacy/Data/Questions/ingsw/0210_13/wrong1.txt
+++ b/Data/ingsw/0210_13/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and (x >= 20) and (x <= 30) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and (x >= 20) and (x <= 30) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/Questions/ingsw/0210_13/wrong2.txt b/Data/ingsw/0210_13/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0210_13/wrong2.txt
rename to Data/ingsw/0210_13/wrong2.txt
index 157567e..ceb178e 100644
--- a/legacy/Data/Questions/ingsw/0210_13/wrong2.txt
+++ b/Data/ingsw/0210_13/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) or ((x >= 20) and (x <= 30)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) or ((x >= 20) and (x <= 30)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0210_14/correct.txt b/Data/ingsw/0210_14/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_14/correct.txt
rename to Data/ingsw/0210_14/correct.txt
diff --git a/Data/ingsw/0210_14/quest.txt b/Data/ingsw/0210_14/quest.txt
new file mode 100644
index 0000000..abc3e1c
--- /dev/null
+++ b/Data/ingsw/0210_14/quest.txt
@@ -0,0 +1,9 @@
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+int z = x;
+while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
+return (z);
+}
+Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_14/wrong1.txt b/Data/ingsw/0210_14/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_14/wrong1.txt
rename to Data/ingsw/0210_14/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_14/wrong2.txt b/Data/ingsw/0210_14/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_14/wrong2.txt
rename to Data/ingsw/0210_14/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_15/correct.txt b/Data/ingsw/0210_15/correct.txt
similarity index 93%
rename from legacy/Data/ingsw/0210_15/correct.txt
rename to Data/ingsw/0210_15/correct.txt
index 519c7fd..98a62af 100644
--- a/legacy/Data/ingsw/0210_15/correct.txt
+++ b/Data/ingsw/0210_15/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x > 5) or (x < 0));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x > 5) or (x < 0));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/Data/ingsw/0210_15/quest.txt b/Data/ingsw/0210_15/quest.txt
new file mode 100644
index 0000000..d26958e
--- /dev/null
+++ b/Data/ingsw/0210_15/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_48/wrong1.txt b/Data/ingsw/0210_15/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0120_48/wrong1.txt
rename to Data/ingsw/0210_15/wrong1.txt
index 5229f7e..776f12c 100644
--- a/legacy/Data/Questions/ingsw/0120_48/wrong1.txt
+++ b/Data/ingsw/0210_15/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z =  (time > 0) and ((x > 0) or (x < 5));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z =  (time > 0) and ((x > 0) or (x < 5));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0210_15/wrong2.txt b/Data/ingsw/0210_15/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0210_15/wrong2.txt
rename to Data/ingsw/0210_15/wrong2.txt
index 2029293..8086f1c 100644
--- a/legacy/Data/ingsw/0210_15/wrong2.txt
+++ b/Data/ingsw/0210_15/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and (x > 0) and (x < 5);
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and (x > 0) and (x < 5);
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/Data/ingsw/0210_16/correct.txt b/Data/ingsw/0210_16/correct.txt
new file mode 100644
index 0000000..ca92838
--- /dev/null
+++ b/Data/ingsw/0210_16/correct.txt
@@ -0,0 +1,17 @@
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0210_16/quest.txt b/Data/ingsw/0210_16/quest.txt
new file mode 100644
index 0000000..348f2a5
--- /dev/null
+++ b/Data/ingsw/0210_16/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_16/wrong1.txt b/Data/ingsw/0210_16/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_16/wrong1.txt
rename to Data/ingsw/0210_16/wrong1.txt
index d7890b2..c0e3311 100644
--- a/legacy/Data/Questions/ingsw/0210_16/wrong1.txt
+++ b/Data/ingsw/0210_16/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_16/wrong2.txt b/Data/ingsw/0210_16/wrong2.txt
similarity index 94%
rename from legacy/Data/ingsw/0210_16/wrong2.txt
rename to Data/ingsw/0210_16/wrong2.txt
index d50b268..434e904 100644
--- a/legacy/Data/ingsw/0210_16/wrong2.txt
+++ b/Data/ingsw/0210_16/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_17/correct.txt b/Data/ingsw/0210_17/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_17/correct.txt
rename to Data/ingsw/0210_17/correct.txt
diff --git a/legacy/Data/ingsw/0210_17/quest.txt b/Data/ingsw/0210_17/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0210_17/quest.txt
rename to Data/ingsw/0210_17/quest.txt
index 5fa40ee..be8341e 100644
--- a/legacy/Data/ingsw/0210_17/quest.txt
+++ b/Data/ingsw/0210_17/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_17.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act1 act2 
-Test case 2: act2 act0 act1 act0 act0 
-Test case 3: act2 act0 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_17.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act1 act2 
+Test case 2: act2 act0 act1 act0 act0 
+Test case 3: act2 act0 act2 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_17/wrong1.txt b/Data/ingsw/0210_17/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_17/wrong1.txt
rename to Data/ingsw/0210_17/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_17/wrong2.txt b/Data/ingsw/0210_17/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_17/wrong2.txt
rename to Data/ingsw/0210_17/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_18/correct.txt b/Data/ingsw/0210_18/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_18/correct.txt
rename to Data/ingsw/0210_18/correct.txt
diff --git a/legacy/Data/ingsw/0210_18/quest.txt b/Data/ingsw/0210_18/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_18/quest.txt
rename to Data/ingsw/0210_18/quest.txt
diff --git a/legacy/Data/ingsw/0210_18/wrong1.txt b/Data/ingsw/0210_18/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_18/wrong1.txt
rename to Data/ingsw/0210_18/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_18/wrong2.txt b/Data/ingsw/0210_18/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_18/wrong2.txt
rename to Data/ingsw/0210_18/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_19/correct.txt b/Data/ingsw/0210_19/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_19/correct.txt
rename to Data/ingsw/0210_19/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_19/quest.txt b/Data/ingsw/0210_19/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0210_19/quest.txt
rename to Data/ingsw/0210_19/quest.txt
index e786bcf..6837d89 100644
--- a/legacy/Data/Questions/ingsw/0210_19/quest.txt
+++ b/Data/ingsw/0210_19/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_19.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act1 act1 act2 act2 
-Test case 2: act1 act1 act0 act1 
-Test case 3: act0 act0 act2 act1 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_19.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act1 act1 act2 act2 
+Test case 2: act1 act1 act0 act1 
+Test case 3: act0 act0 act2 act1 act0 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_19/wrong1.txt b/Data/ingsw/0210_19/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_19/wrong1.txt
rename to Data/ingsw/0210_19/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_19/wrong2.txt b/Data/ingsw/0210_19/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_19/wrong2.txt
rename to Data/ingsw/0210_19/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_2/quest.txt b/Data/ingsw/0210_2/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_2/quest.txt
rename to Data/ingsw/0210_2/quest.txt
index f9f8976..9392429 100644
--- a/legacy/Data/Questions/ingsw/0210_2/quest.txt
+++ b/Data/ingsw/0210_2/quest.txt
@@ -1,36 +1,36 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_2/wrong1.txt b/Data/ingsw/0210_2/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_2/wrong1.txt
rename to Data/ingsw/0210_2/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_2/wrong2.txt b/Data/ingsw/0210_2/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_2/wrong2.txt
rename to Data/ingsw/0210_2/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_2/wrong3.txt b/Data/ingsw/0210_2/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_2/wrong3.txt
rename to Data/ingsw/0210_2/wrong3.txt
diff --git a/legacy/Data/ingsw/0210_20/correct.txt b/Data/ingsw/0210_20/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_20/correct.txt
rename to Data/ingsw/0210_20/correct.txt
diff --git a/legacy/Data/ingsw/0210_20/quest.txt b/Data/ingsw/0210_20/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_20/quest.txt
rename to Data/ingsw/0210_20/quest.txt
diff --git a/legacy/Data/ingsw/0210_20/wrong1.txt b/Data/ingsw/0210_20/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_20/wrong1.txt
rename to Data/ingsw/0210_20/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_20/wrong2.txt b/Data/ingsw/0210_20/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_20/wrong2.txt
rename to Data/ingsw/0210_20/wrong2.txt
diff --git a/Data/ingsw/0210_21/correct.txt b/Data/ingsw/0210_21/correct.txt
new file mode 100644
index 0000000..a2f00b3
--- /dev/null
+++ b/Data/ingsw/0210_21/correct.txt
@@ -0,0 +1,15 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0210_21/quest.txt b/Data/ingsw/0210_21/quest.txt
new file mode 100644
index 0000000..1dbc484
--- /dev/null
+++ b/Data/ingsw/0210_21/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_21/wrong1.txt b/Data/ingsw/0210_21/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_21/wrong1.txt
rename to Data/ingsw/0210_21/wrong1.txt
index b14ac60..0696007 100644
--- a/legacy/Data/Questions/ingsw/0210_21/wrong1.txt
+++ b/Data/ingsw/0210_21/wrong1.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_21/wrong2.txt b/Data/ingsw/0210_21/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_21/wrong2.txt
rename to Data/ingsw/0210_21/wrong2.txt
index e4201ab..76b58b0 100644
--- a/legacy/Data/Questions/ingsw/0210_21/wrong2.txt
+++ b/Data/ingsw/0210_21/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_22/correct.txt b/Data/ingsw/0210_22/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_22/correct.txt
rename to Data/ingsw/0210_22/correct.txt
diff --git a/Data/ingsw/0210_22/quest.txt b/Data/ingsw/0210_22/quest.txt
new file mode 100644
index 0000000..6906b2b
--- /dev/null
+++ b/Data/ingsw/0210_22/quest.txt
@@ -0,0 +1,16 @@
+Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
+block Monitor
+input Real x;  
+output Boolean y;
+Boolean w;
+initial equation
+y = false;
+equation
+w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
+algorithm
+when edge(w) then
+y := true;
+end when;
+end Monitor;
+</pre>
+Quale delle seguenti affermazioni meglio descrive il requisito monitorato?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_22/wrong1.txt b/Data/ingsw/0210_22/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_22/wrong1.txt
rename to Data/ingsw/0210_22/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_22/wrong2.txt b/Data/ingsw/0210_22/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_22/wrong2.txt
rename to Data/ingsw/0210_22/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_23/correct.txt b/Data/ingsw/0210_23/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_23/correct.txt
rename to Data/ingsw/0210_23/correct.txt
diff --git a/legacy/Data/ingsw/0210_23/quest.txt b/Data/ingsw/0210_23/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0210_23/quest.txt
rename to Data/ingsw/0210_23/quest.txt
index adede32..64036f7 100644
--- a/legacy/Data/ingsw/0210_23/quest.txt
+++ b/Data/ingsw/0210_23/quest.txt
@@ -1,9 +1,9 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 2)   { if (x + y >= 1)  return (1); else return (2); }
-  else {if (x + 2*y >= 5)  return (3); else return (4); }
- }  /* f()  */
-Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 2)   { if (x + y >= 1)  return (1); else return (2); }
+  else {if (x + 2*y >= 5)  return (3); else return (4); }
+ }  /* f()  */
+Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_23/wrong1.txt b/Data/ingsw/0210_23/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_23/wrong1.txt
rename to Data/ingsw/0210_23/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_23/wrong2.txt b/Data/ingsw/0210_23/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_23/wrong2.txt
rename to Data/ingsw/0210_23/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_24/correct.txt b/Data/ingsw/0210_24/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_24/correct.txt
rename to Data/ingsw/0210_24/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_24/quest.txt b/Data/ingsw/0210_24/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_24/quest.txt
rename to Data/ingsw/0210_24/quest.txt
index d188da2..e6995f6 100644
--- a/legacy/Data/Questions/ingsw/0210_24/quest.txt
+++ b/Data/ingsw/0210_24/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_24.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
-Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_24.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
+Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_24/wrong1.txt b/Data/ingsw/0210_24/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_24/wrong1.txt
rename to Data/ingsw/0210_24/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_24/wrong2.txt b/Data/ingsw/0210_24/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_24/wrong2.txt
rename to Data/ingsw/0210_24/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_25/correct.txt b/Data/ingsw/0210_25/correct.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0210_25/correct.txt
rename to Data/ingsw/0210_25/correct.txt
index 43dc0c9..af4c387 100644
--- a/legacy/Data/Questions/ingsw/0210_25/correct.txt
+++ b/Data/ingsw/0210_25/correct.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 0) || (y > 0)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 0) || (y > 0)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_25/quest.txt b/Data/ingsw/0210_25/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_25/quest.txt
rename to Data/ingsw/0210_25/quest.txt
index f6744fd..5fd3b47 100644
--- a/legacy/Data/Questions/ingsw/0210_25/quest.txt
+++ b/Data/ingsw/0210_25/quest.txt
@@ -1,4 +1,4 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-Si consideri la funzione C
-int f(in x, int y) { ..... }
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+Si consideri la funzione C
+int f(in x, int y) { ..... }
 Quale delle seguenti assert esprime la pre-condizione che entrambi gli argomenti di f sono non-negativi ed almeno uno di loro è positivo ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_36/wrong1.txt b/Data/ingsw/0210_25/wrong1.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0210_36/wrong1.txt
rename to Data/ingsw/0210_25/wrong1.txt
index 6a97baf..392cc67 100644
--- a/legacy/Data/Questions/ingsw/0210_36/wrong1.txt
+++ b/Data/ingsw/0210_25/wrong1.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 1) || (y > 1)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 1) || (y > 1)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_25/wrong2.txt b/Data/ingsw/0210_25/wrong2.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0210_25/wrong2.txt
rename to Data/ingsw/0210_25/wrong2.txt
index 3f63933..506df9d 100644
--- a/legacy/Data/Questions/ingsw/0210_25/wrong2.txt
+++ b/Data/ingsw/0210_25/wrong2.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x > 0) && (y > 0) && ((x > 1) || (y > 1)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x > 0) && (y > 0) && ((x > 1) || (y > 1)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_26/correct.txt b/Data/ingsw/0210_26/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_26/correct.txt
rename to Data/ingsw/0210_26/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_26/quest.txt b/Data/ingsw/0210_26/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_26/quest.txt
rename to Data/ingsw/0210_26/quest.txt
index d318528..50adf73 100644
--- a/legacy/Data/Questions/ingsw/0210_26/quest.txt
+++ b/Data/ingsw/0210_26/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_26.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
-Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_26.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
+Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_26/wrong1.txt b/Data/ingsw/0210_26/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_26/wrong1.txt
rename to Data/ingsw/0210_26/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_26/wrong2.txt b/Data/ingsw/0210_26/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_26/wrong2.txt
rename to Data/ingsw/0210_26/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_27/quest.txt b/Data/ingsw/0210_27/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0210_27/quest.txt
rename to Data/ingsw/0210_27/quest.txt
index 75e942b..2bc823a 100644
--- a/legacy/Data/ingsw/0210_27/quest.txt
+++ b/Data/ingsw/0210_27/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_27.png
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_27.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_27/wrong1.txt b/Data/ingsw/0210_27/wrong1.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_27/wrong1.txt
rename to Data/ingsw/0210_27/wrong1.txt
index c296b22..745fe08 100644
--- a/legacy/Data/Questions/ingsw/0210_27/wrong1.txt
+++ b/Data/ingsw/0210_27/wrong1.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_27/wrong2.txt b/Data/ingsw/0210_27/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_27/wrong2.txt
rename to Data/ingsw/0210_27/wrong2.txt
index d21df5d..26dfa21 100644
--- a/legacy/Data/Questions/ingsw/0210_27/wrong2.txt
+++ b/Data/ingsw/0210_27/wrong2.txt
@@ -1,32 +1,32 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_27/wrong3.txt b/Data/ingsw/0210_27/wrong3.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_27/wrong3.txt
rename to Data/ingsw/0210_27/wrong3.txt
index 421d23f..ba2919d 100644
--- a/legacy/Data/Questions/ingsw/0210_27/wrong3.txt
+++ b/Data/ingsw/0210_27/wrong3.txt
@@ -1,37 +1,37 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_28/quest.txt b/Data/ingsw/0210_28/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0210_28/quest.txt
rename to Data/ingsw/0210_28/quest.txt
index 932f11d..63c727e 100644
--- a/legacy/Data/ingsw/0210_28/quest.txt
+++ b/Data/ingsw/0210_28/quest.txt
@@ -1,38 +1,38 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_28/wrong1.txt b/Data/ingsw/0210_28/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_28/wrong1.txt
rename to Data/ingsw/0210_28/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_28/wrong2.txt b/Data/ingsw/0210_28/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_28/wrong2.txt
rename to Data/ingsw/0210_28/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_28/wrong3.txt b/Data/ingsw/0210_28/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_28/wrong3.txt
rename to Data/ingsw/0210_28/wrong3.txt
diff --git a/legacy/Data/ingsw/0210_29/correct.txt b/Data/ingsw/0210_29/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_29/correct.txt
rename to Data/ingsw/0210_29/correct.txt
diff --git a/legacy/Data/ingsw/0210_29/quest.txt b/Data/ingsw/0210_29/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0210_29/quest.txt
rename to Data/ingsw/0210_29/quest.txt
index f6839df..881f5f4 100644
--- a/legacy/Data/ingsw/0210_29/quest.txt
+++ b/Data/ingsw/0210_29/quest.txt
@@ -1,2 +1,2 @@
-"Ogni giorno, per ciascuna clinica, il sistema genererà una lista dei pazienti che hanno un appuntamento quel giorno."
+"Ogni giorno, per ciascuna clinica, il sistema genererà una lista dei pazienti che hanno un appuntamento quel giorno."
 La frase precedente è un esempio di:
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_29/wrong1.txt b/Data/ingsw/0210_29/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_29/wrong1.txt
rename to Data/ingsw/0210_29/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_29/wrong2.txt b/Data/ingsw/0210_29/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_29/wrong2.txt
rename to Data/ingsw/0210_29/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_3/quest.txt b/Data/ingsw/0210_3/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_3/quest.txt
rename to Data/ingsw/0210_3/quest.txt
index 985c244..e2e738a 100644
--- a/legacy/Data/Questions/ingsw/0210_3/quest.txt
+++ b/Data/ingsw/0210_3/quest.txt
@@ -1,4 +1,4 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente specifica funzionale per la funzione f.
-La funzione f(int *A, int *B)  prende come input un vettore A di dimensione n  ritorna come output un vettore B ottenuto ordinando gli elementi di A in ordine crescente.
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente specifica funzionale per la funzione f.
+La funzione f(int *A, int *B)  prende come input un vettore A di dimensione n  ritorna come output un vettore B ottenuto ordinando gli elementi di A in ordine crescente.
 Quale delle seguenti funzioni è un test oracle per la funzione f ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_3/wrong1.txt b/Data/ingsw/0210_3/wrong1.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0210_3/wrong1.txt
rename to Data/ingsw/0210_3/wrong1.txt
index ed5ad19..9e00c22 100644
--- a/legacy/Data/Questions/ingsw/0210_3/wrong1.txt
+++ b/Data/ingsw/0210_3/wrong1.txt
@@ -1,14 +1,14 @@
-#define n 1000
-int TestOracle1(int *A, int *B)
-{
-int i, j, D[n];
-//init
-for (i = 0; i < n; i++) D[i] = -1;
-// B is ordered
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-// B is a permutation of A
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; D[j] = 1; break;}
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-// B ok
-return (1);
+#define n 1000
+int TestOracle1(int *A, int *B)
+{
+int i, j, D[n];
+//init
+for (i = 0; i < n; i++) D[i] = -1;
+// B is ordered
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+// B is a permutation of A
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; D[j] = 1; break;}
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+// B ok
+return (1);
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_3/wrong2.txt b/Data/ingsw/0210_3/wrong2.txt
similarity index 97%
rename from legacy/Data/ingsw/0210_3/wrong2.txt
rename to Data/ingsw/0210_3/wrong2.txt
index 69b9722..59b5927 100644
--- a/legacy/Data/ingsw/0210_3/wrong2.txt
+++ b/Data/ingsw/0210_3/wrong2.txt
@@ -1,14 +1,14 @@
-#define n 1000
-int TestOracle2(int *A, int *B)
-{
-int i, j, D[n];
-//init
-for (i = 0; i < n; i++) D[i] = -1;
-// B is ordered
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-// B is a permutation of A
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; break;}
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-// B ok
-return (1);
+#define n 1000
+int TestOracle2(int *A, int *B)
+{
+int i, j, D[n];
+//init
+for (i = 0; i < n; i++) D[i] = -1;
+// B is ordered
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+// B is a permutation of A
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; break;}
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+// B ok
+return (1);
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_3/wrong3.txt b/Data/ingsw/0210_3/wrong3.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_3/wrong3.txt
rename to Data/ingsw/0210_3/wrong3.txt
index a26ce6e..5c3b08a 100644
--- a/legacy/Data/Questions/ingsw/0210_3/wrong3.txt
+++ b/Data/ingsw/0210_3/wrong3.txt
@@ -1,15 +1,15 @@
-#define n 1000
-
-int TestOracle3(int *A, int *B)
-{
-int i, j, D[n];
-//init
-for (i = 0; i < n; i++) D[i] = -1;
-// B is ordered
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-// B is a permutation of A
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if (A[i] == B[j]) {C[i][j] = 1; D[j] = 1; break;}
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-// B ok
-return (1);
+#define n 1000
+
+int TestOracle3(int *A, int *B)
+{
+int i, j, D[n];
+//init
+for (i = 0; i < n; i++) D[i] = -1;
+// B is ordered
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+// B is a permutation of A
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if (A[i] == B[j]) {C[i][j] = 1; D[j] = 1; break;}
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+// B ok
+return (1);
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_30/correct.txt b/Data/ingsw/0210_30/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_30/correct.txt
rename to Data/ingsw/0210_30/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_30/quest.txt b/Data/ingsw/0210_30/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0210_30/quest.txt
rename to Data/ingsw/0210_30/quest.txt
index a27fc55..ecefa2d 100644
--- a/legacy/Data/Questions/ingsw/0210_30/quest.txt
+++ b/Data/ingsw/0210_30/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_30.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act1 act1 act2 act2 
-Test case 2: act1 act1 act0 act1 
-Test case 3: act0 act0 act2 act1 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_30.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act1 act1 act2 act2 
+Test case 2: act1 act1 act0 act1 
+Test case 3: act0 act0 act2 act1 act0 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_30/wrong1.txt b/Data/ingsw/0210_30/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_30/wrong1.txt
rename to Data/ingsw/0210_30/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_30/wrong2.txt b/Data/ingsw/0210_30/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_30/wrong2.txt
rename to Data/ingsw/0210_30/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_31/correct.txt b/Data/ingsw/0210_31/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_31/correct.txt
rename to Data/ingsw/0210_31/correct.txt
diff --git a/legacy/Data/ingsw/0324_22/quest.txt b/Data/ingsw/0210_31/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0324_22/quest.txt
rename to Data/ingsw/0210_31/quest.txt
index 65cfd2d..eb6c210 100644
--- a/legacy/Data/ingsw/0324_22/quest.txt
+++ b/Data/ingsw/0210_31/quest.txt
@@ -1,9 +1,9 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 0)   { if (x + y >= 2)  return (1); else return (2); }
-  else {if (2*x + y >= 1)  return (3); else return (4); }
- }  /* f()  */
-Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 0)   { if (x + y >= 2)  return (1); else return (2); }
+  else {if (2*x + y >= 1)  return (3); else return (4); }
+ }  /* f()  */
+Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_31/wrong1.txt b/Data/ingsw/0210_31/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_31/wrong1.txt
rename to Data/ingsw/0210_31/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_31/wrong2.txt b/Data/ingsw/0210_31/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_31/wrong2.txt
rename to Data/ingsw/0210_31/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_32/correct.txt b/Data/ingsw/0210_32/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_32/correct.txt
rename to Data/ingsw/0210_32/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_32/quest.txt b/Data/ingsw/0210_32/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0210_32/quest.txt
rename to Data/ingsw/0210_32/quest.txt
index cb591da..4426034 100644
--- a/legacy/Data/Questions/ingsw/0210_32/quest.txt
+++ b/Data/ingsw/0210_32/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_32.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act1 act2 
-Test case 2: act2 act0 act1 act0 act0 
-Test case 3: act2 act0 act2 
-
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_32.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act1 act2 
+Test case 2: act2 act0 act1 act0 act0 
+Test case 3: act2 act0 act2 
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_32/wrong1.txt b/Data/ingsw/0210_32/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_32/wrong1.txt
rename to Data/ingsw/0210_32/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_32/wrong2.txt b/Data/ingsw/0210_32/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_32/wrong2.txt
rename to Data/ingsw/0210_32/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_33/correct.txt b/Data/ingsw/0210_33/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_33/correct.txt
rename to Data/ingsw/0210_33/correct.txt
diff --git a/legacy/Data/ingsw/0210_33/quest.txt b/Data/ingsw/0210_33/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0210_33/quest.txt
rename to Data/ingsw/0210_33/quest.txt
index cf9113a..bde312e 100644
--- a/legacy/Data/ingsw/0210_33/quest.txt
+++ b/Data/ingsw/0210_33/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_33.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_33.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 2, 3, 4 ? In altri terminti, qual'è la probabilità che sia necessario ripetere sia la fase 1 che la fase 2 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_33/wrong1.txt b/Data/ingsw/0210_33/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_33/wrong1.txt
rename to Data/ingsw/0210_33/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_33/wrong2.txt b/Data/ingsw/0210_33/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_33/wrong2.txt
rename to Data/ingsw/0210_33/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_34/quest.txt b/Data/ingsw/0210_34/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_34/quest.txt
rename to Data/ingsw/0210_34/quest.txt
index 33e1f49..89bfba9 100644
--- a/legacy/Data/Questions/ingsw/0210_34/quest.txt
+++ b/Data/ingsw/0210_34/quest.txt
@@ -1,34 +1,34 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_34/wrong1.txt b/Data/ingsw/0210_34/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_34/wrong1.txt
rename to Data/ingsw/0210_34/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_34/wrong2.txt b/Data/ingsw/0210_34/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_34/wrong2.txt
rename to Data/ingsw/0210_34/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_34/wrong3.txt b/Data/ingsw/0210_34/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_34/wrong3.txt
rename to Data/ingsw/0210_34/wrong3.txt
diff --git a/legacy/Data/ingsw/0210_35/correct.txt b/Data/ingsw/0210_35/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_35/correct.txt
rename to Data/ingsw/0210_35/correct.txt
diff --git a/legacy/Data/ingsw/0210_35/quest.txt b/Data/ingsw/0210_35/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_35/quest.txt
rename to Data/ingsw/0210_35/quest.txt
diff --git a/legacy/Data/ingsw/0210_35/wrong1.txt b/Data/ingsw/0210_35/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_35/wrong1.txt
rename to Data/ingsw/0210_35/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_35/wrong2.txt b/Data/ingsw/0210_35/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_35/wrong2.txt
rename to Data/ingsw/0210_35/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_36/correct.txt b/Data/ingsw/0210_36/correct.txt
similarity index 95%
rename from legacy/Data/ingsw/0210_36/correct.txt
rename to Data/ingsw/0210_36/correct.txt
index 3f63933..506df9d 100644
--- a/legacy/Data/ingsw/0210_36/correct.txt
+++ b/Data/ingsw/0210_36/correct.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x > 0) && (y > 0) && ((x > 1) || (y > 1)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x > 0) && (y > 0) && ((x > 1) || (y > 1)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_36/quest.txt b/Data/ingsw/0210_36/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0210_36/quest.txt
rename to Data/ingsw/0210_36/quest.txt
index 595ab5d..db01158 100644
--- a/legacy/Data/Questions/ingsw/0210_36/quest.txt
+++ b/Data/ingsw/0210_36/quest.txt
@@ -1,4 +1,4 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-Si consideri la funzione C
-int f(in x, int y) { ..... }
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+Si consideri la funzione C
+int f(in x, int y) { ..... }
 Quale delle seguenti assert esprime la pre-condizione che entrambi gli argomenti di f sono positivi ed almeno uno di loro è maggiore di 1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_36/wrong1.txt b/Data/ingsw/0210_36/wrong1.txt
similarity index 95%
rename from legacy/Data/ingsw/0210_36/wrong1.txt
rename to Data/ingsw/0210_36/wrong1.txt
index 6a97baf..392cc67 100644
--- a/legacy/Data/ingsw/0210_36/wrong1.txt
+++ b/Data/ingsw/0210_36/wrong1.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 1) || (y > 1)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 1) || (y > 1)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_36/wrong2.txt b/Data/ingsw/0210_36/wrong2.txt
similarity index 95%
rename from legacy/Data/ingsw/0210_36/wrong2.txt
rename to Data/ingsw/0210_36/wrong2.txt
index e607157..2fde3f0 100644
--- a/legacy/Data/ingsw/0210_36/wrong2.txt
+++ b/Data/ingsw/0210_36/wrong2.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x > 0) && (y > 0) && (x > 1) && (y > 1)  );
-.....
+int f(in x, int y) 
+{ 
+assert( (x > 0) && (y > 0) && (x > 1) && (y > 1)  );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_37/quest.txt b/Data/ingsw/0210_37/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_37/quest.txt
rename to Data/ingsw/0210_37/quest.txt
index 5743032..fd7fa1d 100644
--- a/legacy/Data/ingsw/0210_37/quest.txt
+++ b/Data/ingsw/0210_37/quest.txt
@@ -1,36 +1,36 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_37/wrong1.txt b/Data/ingsw/0210_37/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_37/wrong1.txt
rename to Data/ingsw/0210_37/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_37/wrong2.txt b/Data/ingsw/0210_37/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_37/wrong2.txt
rename to Data/ingsw/0210_37/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_37/wrong3.txt b/Data/ingsw/0210_37/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_37/wrong3.txt
rename to Data/ingsw/0210_37/wrong3.txt
diff --git a/legacy/Data/ingsw/0210_38/correct.txt b/Data/ingsw/0210_38/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_38/correct.txt
rename to Data/ingsw/0210_38/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_38/quest.txt b/Data/ingsw/0210_38/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0210_38/quest.txt
rename to Data/ingsw/0210_38/quest.txt
index b2bed72..793c90f 100644
--- a/legacy/Data/Questions/ingsw/0210_38/quest.txt
+++ b/Data/ingsw/0210_38/quest.txt
@@ -1,21 +1,21 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a  + b >= 6) && (b - c <= 1) )
-          return (1);    // punto di uscita 1
-      else if ((b - c <= 1)  || (b + c >= 5))
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a  + b >= 6) && (b - c <= 1) )
+          return (1);    // punto di uscita 1
+      else if ((b - c <= 1)  || (b + c >= 5))
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_38/wrong1.txt b/Data/ingsw/0210_38/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_38/wrong1.txt
rename to Data/ingsw/0210_38/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_38/wrong2.txt b/Data/ingsw/0210_38/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_38/wrong2.txt
rename to Data/ingsw/0210_38/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_39/correct.txt b/Data/ingsw/0210_39/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_39/correct.txt
rename to Data/ingsw/0210_39/correct.txt
diff --git a/Data/ingsw/0210_39/quest.txt b/Data/ingsw/0210_39/quest.txt
new file mode 100644
index 0000000..853f496
--- /dev/null
+++ b/Data/ingsw/0210_39/quest.txt
@@ -0,0 +1,6 @@
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (x + 7); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
+Quale dei seguenti test cases consegue una partition coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_39/wrong1.txt b/Data/ingsw/0210_39/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_39/wrong1.txt
rename to Data/ingsw/0210_39/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_39/wrong2.txt b/Data/ingsw/0210_39/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_39/wrong2.txt
rename to Data/ingsw/0210_39/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_4/correct.txt b/Data/ingsw/0210_4/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_4/correct.txt
rename to Data/ingsw/0210_4/correct.txt
diff --git a/legacy/Data/ingsw/0210_4/quest.txt b/Data/ingsw/0210_4/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0210_4/quest.txt
rename to Data/ingsw/0210_4/quest.txt
index 84d1f53..13ec01c 100644
--- a/legacy/Data/ingsw/0210_4/quest.txt
+++ b/Data/ingsw/0210_4/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_4.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act2 act0 act1 act2 act0 
-Test case 2: act1 act2 act1 
-Test case 3: act1 act2 act1 act0 act0 
-
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_4.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act2 act0 act1 act2 act0 
+Test case 2: act1 act2 act1 
+Test case 3: act1 act2 act1 act0 act0 
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_4/wrong1.txt b/Data/ingsw/0210_4/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_4/wrong1.txt
rename to Data/ingsw/0210_4/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_4/wrong2.txt b/Data/ingsw/0210_4/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_4/wrong2.txt
rename to Data/ingsw/0210_4/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_40/correct.txt b/Data/ingsw/0210_40/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_40/correct.txt
rename to Data/ingsw/0210_40/correct.txt
diff --git a/legacy/Data/ingsw/0210_40/quest.txt b/Data/ingsw/0210_40/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0210_40/quest.txt
rename to Data/ingsw/0210_40/quest.txt
index a550159..84a9274 100644
--- a/legacy/Data/ingsw/0210_40/quest.txt
+++ b/Data/ingsw/0210_40/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_40.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act1 act0 act0 act0 act0 
-Test case 2: act2 act0 
-Test case 3: act0 act0 act1 act0 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_40.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act1 act0 act0 act0 act0 
+Test case 2: act2 act0 
+Test case 3: act0 act0 act1 act0 act2 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_40/wrong1.txt b/Data/ingsw/0210_40/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_40/wrong1.txt
rename to Data/ingsw/0210_40/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_40/wrong2.txt b/Data/ingsw/0210_40/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_40/wrong2.txt
rename to Data/ingsw/0210_40/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_41/correct.txt b/Data/ingsw/0210_41/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_41/correct.txt
rename to Data/ingsw/0210_41/correct.txt
diff --git a/legacy/Data/ingsw/0210_41/quest.txt b/Data/ingsw/0210_41/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0210_41/quest.txt
rename to Data/ingsw/0210_41/quest.txt
index cdbd481..a02928c 100644
--- a/legacy/Data/ingsw/0210_41/quest.txt
+++ b/Data/ingsw/0210_41/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_41.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act1 act0 act0 act0 act0 
-Test case 2: act2 act0 
-Test case 3: act0 act0 act1 act0 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_41.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act1 act0 act0 act0 act0 
+Test case 2: act2 act0 
+Test case 3: act0 act0 act1 act0 act2 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_41/wrong1.txt b/Data/ingsw/0210_41/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_41/wrong1.txt
rename to Data/ingsw/0210_41/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_41/wrong2.txt b/Data/ingsw/0210_41/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_41/wrong2.txt
rename to Data/ingsw/0210_41/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0210_42/quest.txt b/Data/ingsw/0210_42/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0210_42/quest.txt
rename to Data/ingsw/0210_42/quest.txt
index 8e91c31..b862100 100644
--- a/legacy/Data/Questions/ingsw/0210_42/quest.txt
+++ b/Data/ingsw/0210_42/quest.txt
@@ -1,5 +1,5 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_42.png
-Si consideri la seguente architettura software:
-
-
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_42.png
+Si consideri la seguente architettura software:
+
+
 Quale dei seguenti modelli Modelica meglio la rappresenta ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_42/wrong1.txt b/Data/ingsw/0210_42/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0210_42/wrong1.txt
rename to Data/ingsw/0210_42/wrong1.txt
index 512c141..e98e420 100644
--- a/legacy/Data/ingsw/0210_42/wrong1.txt
+++ b/Data/ingsw/0210_42/wrong1.txt
@@ -1,6 +1,6 @@
-block SysArch;
-SC1 sc1
-SC2 sc2;
-SC3 sc3;
-SC4 sc4;
+block SysArch;
+SC1 sc1
+SC2 sc2;
+SC3 sc3;
+SC4 sc4;
 connect(sc1.input1, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_42/wrong2.txt b/Data/ingsw/0210_42/wrong2.txt
similarity index 97%
rename from legacy/Data/ingsw/0210_42/wrong2.txt
rename to Data/ingsw/0210_42/wrong2.txt
index 77d39c1..b578dde 100644
--- a/legacy/Data/ingsw/0210_42/wrong2.txt
+++ b/Data/ingsw/0210_42/wrong2.txt
@@ -1,3 +1,3 @@
-output1);
-connect(sc1.output1, sc2.input1);
+output1);
+connect(sc1.output1, sc2.input1);
 connect(sc1.input2, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_42/wrong3.txt b/Data/ingsw/0210_42/wrong3.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_42/wrong3.txt
rename to Data/ingsw/0210_42/wrong3.txt
index b9a8baf..dcb6c25 100644
--- a/legacy/Data/ingsw/0210_42/wrong3.txt
+++ b/Data/ingsw/0210_42/wrong3.txt
@@ -1,39 +1,39 @@
-output2);
-connect(sc1.output2, sc3.input2);
-connect(sc1.input3, sc4.output3);
-connect(sc1.output3, sc4.input3);
-connect(sc2.input4, sc3.output4);
-connect(sc3.input5, sc4.output5);
-end SysArch
-2.
-block SysArch;
-SC1 sc1
-SC2 sc2;
-SC3 sc3;
-SC4 sc4;
-connect(sc1.input1, sc2.output1);
-connect(sc1.output1, sc2.input1);
-connect(sc1.input2, sc3.output2);
-connect(sc1.output2, sc3.input2);
-connect(sc1.input3, sc4.output3);
-connect(sc1.output3, sc4.input3);
-connect(sc2.output4, sc3.input4);
-connect(sc3.output5, sc4.input5);
-end SysArch
-3.
-block SysArch;
-SC1 sc1
-SC2 sc2;
-SC3 sc3;
-SC4 sc4;
-connect(sc1.input1, sc2.output1);
-connect(sc1.output1, sc2.input1);
-connect(sc1.input2, sc3.output2);
-connect(sc1.output2, sc3.input2);
-connect(sc1.input3, sc4.output3);
-connect(sc1.output3, sc4.input3);
-connect(sc2.input4, sc3.output4);
-connect(sc2.output4, sc3.input4);
-connect(sc3.input5, sc4.output5);
-connect(sc3.output5, sc4.input5);
+output2);
+connect(sc1.output2, sc3.input2);
+connect(sc1.input3, sc4.output3);
+connect(sc1.output3, sc4.input3);
+connect(sc2.input4, sc3.output4);
+connect(sc3.input5, sc4.output5);
+end SysArch
+2.
+block SysArch;
+SC1 sc1
+SC2 sc2;
+SC3 sc3;
+SC4 sc4;
+connect(sc1.input1, sc2.output1);
+connect(sc1.output1, sc2.input1);
+connect(sc1.input2, sc3.output2);
+connect(sc1.output2, sc3.input2);
+connect(sc1.input3, sc4.output3);
+connect(sc1.output3, sc4.input3);
+connect(sc2.output4, sc3.input4);
+connect(sc3.output5, sc4.input5);
+end SysArch
+3.
+block SysArch;
+SC1 sc1
+SC2 sc2;
+SC3 sc3;
+SC4 sc4;
+connect(sc1.input1, sc2.output1);
+connect(sc1.output1, sc2.input1);
+connect(sc1.input2, sc3.output2);
+connect(sc1.output2, sc3.input2);
+connect(sc1.input3, sc4.output3);
+connect(sc1.output3, sc4.input3);
+connect(sc2.input4, sc3.output4);
+connect(sc2.output4, sc3.input4);
+connect(sc3.input5, sc4.output5);
+connect(sc3.output5, sc4.input5);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_49/correct.txt b/Data/ingsw/0210_43/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_49/correct.txt
rename to Data/ingsw/0210_43/correct.txt
index 4c75070..9be970c 100644
--- a/legacy/Data/Questions/ingsw/0613_49/correct.txt
+++ b/Data/ingsw/0210_43/correct.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/Data/ingsw/0210_43/quest.txt b/Data/ingsw/0210_43/quest.txt
new file mode 100644
index 0000000..0910f0b
--- /dev/null
+++ b/Data/ingsw/0210_43/quest.txt
@@ -0,0 +1,4 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_43/wrong1.txt b/Data/ingsw/0210_43/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_43/wrong1.txt
rename to Data/ingsw/0210_43/wrong1.txt
index 6dafe94..8b6fcac 100644
--- a/legacy/Data/Questions/ingsw/0210_43/wrong1.txt
+++ b/Data/ingsw/0210_43/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_43/wrong2.txt b/Data/ingsw/0210_43/wrong2.txt
similarity index 94%
rename from legacy/Data/ingsw/0210_43/wrong2.txt
rename to Data/ingsw/0210_43/wrong2.txt
index a3d79a4..40a2ce0 100644
--- a/legacy/Data/ingsw/0210_43/wrong2.txt
+++ b/Data/ingsw/0210_43/wrong2.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_44/quest.txt b/Data/ingsw/0210_44/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0210_44/quest.txt
rename to Data/ingsw/0210_44/quest.txt
index 5c4c81d..c94348c 100644
--- a/legacy/Data/ingsw/0210_44/quest.txt
+++ b/Data/ingsw/0210_44/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_44.png
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_44.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_44/wrong1.txt b/Data/ingsw/0210_44/wrong1.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_44/wrong1.txt
rename to Data/ingsw/0210_44/wrong1.txt
index 421b38f..1297a9a 100644
--- a/legacy/Data/ingsw/0210_44/wrong1.txt
+++ b/Data/ingsw/0210_44/wrong1.txt
@@ -1,34 +1,34 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_44/wrong2.txt b/Data/ingsw/0210_44/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_44/wrong2.txt
rename to Data/ingsw/0210_44/wrong2.txt
index f385f1c..c73ac0f 100644
--- a/legacy/Data/Questions/ingsw/0210_44/wrong2.txt
+++ b/Data/ingsw/0210_44/wrong2.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_44/wrong3.txt b/Data/ingsw/0210_44/wrong3.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_44/wrong3.txt
rename to Data/ingsw/0210_44/wrong3.txt
index 1034e02..ec07178 100644
--- a/legacy/Data/ingsw/0210_44/wrong3.txt
+++ b/Data/ingsw/0210_44/wrong3.txt
@@ -1,32 +1,32 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_23/correct.txt b/Data/ingsw/0210_45/correct.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_23/correct.txt
rename to Data/ingsw/0210_45/correct.txt
index 4a8e634..a3a4b67 100644
--- a/legacy/Data/Questions/ingsw/0613_23/correct.txt
+++ b/Data/ingsw/0210_45/correct.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/Data/ingsw/0210_45/quest.txt b/Data/ingsw/0210_45/quest.txt
new file mode 100644
index 0000000..0d3faca
--- /dev/null
+++ b/Data/ingsw/0210_45/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_45/wrong1.txt b/Data/ingsw/0210_45/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0210_45/wrong1.txt
rename to Data/ingsw/0210_45/wrong1.txt
index 68aa37a..32c00bc 100644
--- a/legacy/Data/Questions/ingsw/0210_45/wrong1.txt
+++ b/Data/ingsw/0210_45/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_23/wrong2.txt b/Data/ingsw/0210_45/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_23/wrong2.txt
rename to Data/ingsw/0210_45/wrong2.txt
index a43796b..9f7ef6a 100644
--- a/legacy/Data/Questions/ingsw/0613_23/wrong2.txt
+++ b/Data/ingsw/0210_45/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_46/correct.txt b/Data/ingsw/0210_46/correct.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_46/correct.txt
rename to Data/ingsw/0210_46/correct.txt
index 001b1d9..610cf7c 100644
--- a/legacy/Data/Questions/ingsw/0210_46/correct.txt
+++ b/Data/ingsw/0210_46/correct.txt
@@ -1,9 +1,9 @@
-block SysArch
-DB db_c;
-S1 s1_c;
-S2 s2_c;
-connect(db_c.input[1], s1_c.output);
-connect(db_c.output[1], s1_c.input);
-connect(db_c.input[2], s2_c.output);
-connect(db_c.output[2], s2_c.input);
+block SysArch
+DB db_c;
+S1 s1_c;
+S2 s2_c;
+connect(db_c.input[1], s1_c.output);
+connect(db_c.output[1], s1_c.input);
+connect(db_c.input[2], s2_c.output);
+connect(db_c.output[2], s2_c.input);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_46/quest.txt b/Data/ingsw/0210_46/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0210_46/quest.txt
rename to Data/ingsw/0210_46/quest.txt
index 9f5199d..4f513a6 100644
--- a/legacy/Data/ingsw/0210_46/quest.txt
+++ b/Data/ingsw/0210_46/quest.txt
@@ -1,4 +1,4 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_46.png
-Si consideri la seguente architettura software:
-
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_46.png
+Si consideri la seguente architettura software:
+
 Quale dei seguenti modelli Modelica meglio la rappresenta ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_46/wrong1.txt b/Data/ingsw/0210_46/wrong1.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_46/wrong1.txt
rename to Data/ingsw/0210_46/wrong1.txt
index fc95495..f80fc3b 100644
--- a/legacy/Data/Questions/ingsw/0210_46/wrong1.txt
+++ b/Data/ingsw/0210_46/wrong1.txt
@@ -1,9 +1,9 @@
-block SysArch
-DB db_c;
-S1 s1_c;
-S2 s2_c;
-connect(db_c.input[1], s2_c.output[1]);
-connect(db_c.output[1], s2_c.input[1]);
-connect(s1_c.input[2], s2_c.output[2]);
-connect(s1_c.output[2], s2_c.input[2]);
+block SysArch
+DB db_c;
+S1 s1_c;
+S2 s2_c;
+connect(db_c.input[1], s2_c.output[1]);
+connect(db_c.output[1], s2_c.input[1]);
+connect(s1_c.input[2], s2_c.output[2]);
+connect(s1_c.output[2], s2_c.input[2]);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_46/wrong2.txt b/Data/ingsw/0210_46/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_46/wrong2.txt
rename to Data/ingsw/0210_46/wrong2.txt
index eaf9272..dc15bde 100644
--- a/legacy/Data/ingsw/0210_46/wrong2.txt
+++ b/Data/ingsw/0210_46/wrong2.txt
@@ -1,9 +1,9 @@
-block SysArch
-DB db_c;
-S1 s1_c;
-S2 s2_c;
-connect(db_c.input[1], s1_c.output[1]);
-connect(db_c.output[1], s1_c.input[1]);
-connect(s1_c.input[2], s2_c.output[2]);
-connect(s1_c.output[2], s2_c.input[2]);
+block SysArch
+DB db_c;
+S1 s1_c;
+S2 s2_c;
+connect(db_c.input[1], s1_c.output[1]);
+connect(db_c.output[1], s1_c.input[1]);
+connect(s1_c.input[2], s2_c.output[2]);
+connect(s1_c.output[2], s2_c.input[2]);
 end SysArch
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_47/correct.txt b/Data/ingsw/0210_47/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_47/correct.txt
rename to Data/ingsw/0210_47/correct.txt
diff --git a/legacy/Data/ingsw/0324_13/quest.txt b/Data/ingsw/0210_47/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0324_13/quest.txt
rename to Data/ingsw/0210_47/quest.txt
index 4344b75..3fa8769 100644
--- a/legacy/Data/ingsw/0324_13/quest.txt
+++ b/Data/ingsw/0210_47/quest.txt
@@ -1,8 +1,8 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (2*x); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
-Si consideri il seguente insieme di test cases:
-{x=-100, x= 40, x=100}
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (2*x); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
+Si consideri il seguente insieme di test cases:
+{x=-100, x= 40, x=100}
 Quale delle seguenti è la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_47/wrong1.txt b/Data/ingsw/0210_47/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_47/wrong1.txt
rename to Data/ingsw/0210_47/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_47/wrong2.txt b/Data/ingsw/0210_47/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_47/wrong2.txt
rename to Data/ingsw/0210_47/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_48/correct.txt b/Data/ingsw/0210_48/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_48/correct.txt
rename to Data/ingsw/0210_48/correct.txt
diff --git a/legacy/Data/ingsw/0210_48/quest.txt b/Data/ingsw/0210_48/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0210_48/quest.txt
rename to Data/ingsw/0210_48/quest.txt
index d3a9fe2..f6bb83a 100644
--- a/legacy/Data/ingsw/0210_48/quest.txt
+++ b/Data/ingsw/0210_48/quest.txt
@@ -1,8 +1,8 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 0)   { if (x + y >= 1)  return (1); else return (2); }
-  else {if (2*x + y >= 5)  return (3); else return (4); }
- }  /* f()  */
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 0)   { if (x + y >= 1)  return (1); else return (2); }
+  else {if (2*x + y >= 5)  return (3); else return (4); }
+ }  /* f()  */
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_48/wrong1.txt b/Data/ingsw/0210_48/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_48/wrong1.txt
rename to Data/ingsw/0210_48/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_48/wrong2.txt b/Data/ingsw/0210_48/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_48/wrong2.txt
rename to Data/ingsw/0210_48/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_49/correct.txt b/Data/ingsw/0210_49/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_49/correct.txt
rename to Data/ingsw/0210_49/correct.txt
diff --git a/legacy/Data/ingsw/0210_49/quest.txt b/Data/ingsw/0210_49/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0210_49/quest.txt
rename to Data/ingsw/0210_49/quest.txt
index 8cb7d37..c0aea8e 100644
--- a/legacy/Data/ingsw/0210_49/quest.txt
+++ b/Data/ingsw/0210_49/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_49.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act0 act1 act2 act0 
-Test case 2: act1 act2 act1 
-Test case 3: act1 act2 act1 act0 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_49.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act0 act1 act2 act0 
+Test case 2: act1 act2 act1 
+Test case 3: act1 act2 act1 act0 act0 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_49/wrong1.txt b/Data/ingsw/0210_49/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_49/wrong1.txt
rename to Data/ingsw/0210_49/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_49/wrong2.txt b/Data/ingsw/0210_49/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_49/wrong2.txt
rename to Data/ingsw/0210_49/wrong2.txt
diff --git a/Data/ingsw/0210_5/correct.txt b/Data/ingsw/0210_5/correct.txt
new file mode 100644
index 0000000..2771487
--- /dev/null
+++ b/Data/ingsw/0210_5/correct.txt
@@ -0,0 +1,17 @@
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0210_5/quest.txt b/Data/ingsw/0210_5/quest.txt
new file mode 100644
index 0000000..498beed
--- /dev/null
+++ b/Data/ingsw/0210_5/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
+Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_5/wrong1.txt b/Data/ingsw/0210_5/wrong1.txt
similarity index 94%
rename from legacy/Data/ingsw/0210_5/wrong1.txt
rename to Data/ingsw/0210_5/wrong1.txt
index 0f38391..44ac950 100644
--- a/legacy/Data/ingsw/0210_5/wrong1.txt
+++ b/Data/ingsw/0210_5/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_5/wrong2.txt b/Data/ingsw/0210_5/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_5/wrong2.txt
rename to Data/ingsw/0210_5/wrong2.txt
index 590f7e1..d681803 100644
--- a/legacy/Data/Questions/ingsw/0210_5/wrong2.txt
+++ b/Data/ingsw/0210_5/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/Data/ingsw/0210_6/correct.txt b/Data/ingsw/0210_6/correct.txt
new file mode 100644
index 0000000..56ad9f9
--- /dev/null
+++ b/Data/ingsw/0210_6/correct.txt
@@ -0,0 +1,15 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_6/quest.txt b/Data/ingsw/0210_6/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0210_6/quest.txt
rename to Data/ingsw/0210_6/quest.txt
index 003d1dd..316ed3f 100644
--- a/legacy/Data/ingsw/0210_6/quest.txt
+++ b/Data/ingsw/0210_6/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_6/wrong1.txt b/Data/ingsw/0210_6/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0210_6/wrong1.txt
rename to Data/ingsw/0210_6/wrong1.txt
index 14bd900..976ab6a 100644
--- a/legacy/Data/ingsw/0210_6/wrong1.txt
+++ b/Data/ingsw/0210_6/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_6/wrong2.txt b/Data/ingsw/0210_6/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0210_6/wrong2.txt
rename to Data/ingsw/0210_6/wrong2.txt
index edea147..3184bf8 100644
--- a/legacy/Data/Questions/ingsw/0210_6/wrong2.txt
+++ b/Data/ingsw/0210_6/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_7/correct.txt b/Data/ingsw/0210_7/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_7/correct.txt
rename to Data/ingsw/0210_7/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_7/quest.txt b/Data/ingsw/0210_7/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0210_7/quest.txt
rename to Data/ingsw/0210_7/quest.txt
index d649932..21834a5 100644
--- a/legacy/Data/Questions/ingsw/0210_7/quest.txt
+++ b/Data/ingsw/0210_7/quest.txt
@@ -1,8 +1,8 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 6)   { if (x + y >= 3)  return (1); else return (2); }
-  else {if (x + 2*y >= 15)  return (3); else return (4); }
- }  /* f()  */
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 6)   { if (x + y >= 3)  return (1); else return (2); }
+  else {if (x + 2*y >= 15)  return (3); else return (4); }
+ }  /* f()  */
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_7/wrong1.txt b/Data/ingsw/0210_7/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_7/wrong1.txt
rename to Data/ingsw/0210_7/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_7/wrong2.txt b/Data/ingsw/0210_7/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_7/wrong2.txt
rename to Data/ingsw/0210_7/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_8/correct.txt b/Data/ingsw/0210_8/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_8/correct.txt
rename to Data/ingsw/0210_8/correct.txt
diff --git a/legacy/Data/ingsw/0324_5/quest.txt b/Data/ingsw/0210_8/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0324_5/quest.txt
rename to Data/ingsw/0210_8/quest.txt
index 236ccc7..3ce3c9b 100644
--- a/legacy/Data/ingsw/0324_5/quest.txt
+++ b/Data/ingsw/0210_8/quest.txt
@@ -1,10 +1,10 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
-int z, k;
-z = 1;   k = 0;
-while (k < x)  { z = y*z;  k = k + 1; }
-return (z);
-}
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+int z, k;
+z = 1;   k = 0;
+while (k < x)  { z = y*z;  k = k + 1; }
+return (z);
+}
 Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_8/wrong1.txt b/Data/ingsw/0210_8/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_8/wrong1.txt
rename to Data/ingsw/0210_8/wrong1.txt
diff --git a/legacy/Data/ingsw/0210_8/wrong2.txt b/Data/ingsw/0210_8/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0210_8/wrong2.txt
rename to Data/ingsw/0210_8/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_9/quest.txt b/Data/ingsw/0210_9/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0210_9/quest.txt
rename to Data/ingsw/0210_9/quest.txt
index fcfd787..ee6ee4c 100644
--- a/legacy/Data/ingsw/0210_9/quest.txt
+++ b/Data/ingsw/0210_9/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_9.png
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_9.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_9/wrong1.txt b/Data/ingsw/0210_9/wrong1.txt
similarity index 96%
rename from legacy/Data/ingsw/0210_9/wrong1.txt
rename to Data/ingsw/0210_9/wrong1.txt
index acd5e00..36648c1 100644
--- a/legacy/Data/ingsw/0210_9/wrong1.txt
+++ b/Data/ingsw/0210_9/wrong1.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_9/wrong2.txt b/Data/ingsw/0210_9/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_9/wrong2.txt
rename to Data/ingsw/0210_9/wrong2.txt
index 298890c..e3eaa51 100644
--- a/legacy/Data/Questions/ingsw/0210_9/wrong2.txt
+++ b/Data/ingsw/0210_9/wrong2.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_9/wrong3.txt b/Data/ingsw/0210_9/wrong3.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0210_9/wrong3.txt
rename to Data/ingsw/0210_9/wrong3.txt
index 3b3e08a..3abcf61 100644
--- a/legacy/Data/Questions/ingsw/0210_9/wrong3.txt
+++ b/Data/ingsw/0210_9/wrong3.txt
@@ -1,32 +1,32 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
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rename from legacy/Data/ingsw/0321_2/wrong 1.txt
rename to Data/ingsw/0321_2/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_2/wrong 2.txt b/Data/ingsw/0321_2/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_2/wrong 2.txt
rename to Data/ingsw/0321_2/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_20/correct.txt b/Data/ingsw/0321_20/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_20/correct.txt
rename to Data/ingsw/0321_20/correct.txt
diff --git a/legacy/Data/ingsw/0321_20/quest.txt b/Data/ingsw/0321_20/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_20/quest.txt
rename to Data/ingsw/0321_20/quest.txt
diff --git a/legacy/Data/ingsw/0321_20/wrong 1.txt b/Data/ingsw/0321_20/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_20/wrong 1.txt
rename to Data/ingsw/0321_20/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_20/wrong 2.txt b/Data/ingsw/0321_20/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_20/wrong 2.txt
rename to Data/ingsw/0321_20/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_21/correct.txt b/Data/ingsw/0321_21/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_21/correct.txt
rename to Data/ingsw/0321_21/correct.txt
diff --git a/legacy/Data/ingsw/0321_21/quest.txt b/Data/ingsw/0321_21/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_21/quest.txt
rename to Data/ingsw/0321_21/quest.txt
diff --git a/legacy/Data/ingsw/0321_21/wrong 1.txt b/Data/ingsw/0321_21/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_21/wrong 1.txt
rename to Data/ingsw/0321_21/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_21/wrong 2.txt b/Data/ingsw/0321_21/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_21/wrong 2.txt
rename to Data/ingsw/0321_21/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_23/correct.txt b/Data/ingsw/0321_23/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_23/correct.txt
rename to Data/ingsw/0321_23/correct.txt
diff --git a/legacy/Data/ingsw/0321_23/quest.txt b/Data/ingsw/0321_23/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_23/quest.txt
rename to Data/ingsw/0321_23/quest.txt
diff --git a/legacy/Data/ingsw/0321_23/wrong 1.txt b/Data/ingsw/0321_23/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_23/wrong 1.txt
rename to Data/ingsw/0321_23/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_23/wrong 2.txt b/Data/ingsw/0321_23/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_23/wrong 2.txt
rename to Data/ingsw/0321_23/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_24/correct.txt b/Data/ingsw/0321_24/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_24/correct.txt
rename to Data/ingsw/0321_24/correct.txt
diff --git a/legacy/Data/ingsw/0321_24/quest.txt b/Data/ingsw/0321_24/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_24/quest.txt
rename to Data/ingsw/0321_24/quest.txt
diff --git a/legacy/Data/ingsw/0321_24/wrong 1.txt b/Data/ingsw/0321_24/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_24/wrong 1.txt
rename to Data/ingsw/0321_24/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_24/wrong 2.txt b/Data/ingsw/0321_24/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_24/wrong 2.txt
rename to Data/ingsw/0321_24/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_27/correct.txt b/Data/ingsw/0321_27/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_27/correct.txt
rename to Data/ingsw/0321_27/correct.txt
diff --git a/legacy/Data/ingsw/0321_27/quest.txt b/Data/ingsw/0321_27/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_27/quest.txt
rename to Data/ingsw/0321_27/quest.txt
diff --git a/legacy/Data/ingsw/0321_27/wrong 1.txt b/Data/ingsw/0321_27/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_27/wrong 1.txt
rename to Data/ingsw/0321_27/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_27/wrong 2.txt b/Data/ingsw/0321_27/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_27/wrong 2.txt
rename to Data/ingsw/0321_27/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_28/correct.txt b/Data/ingsw/0321_28/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_28/correct.txt
rename to Data/ingsw/0321_28/correct.txt
diff --git a/legacy/Data/ingsw/0321_28/quest.txt b/Data/ingsw/0321_28/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_28/quest.txt
rename to Data/ingsw/0321_28/quest.txt
diff --git a/legacy/Data/ingsw/0321_28/wrong 1.txt b/Data/ingsw/0321_28/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_28/wrong 1.txt
rename to Data/ingsw/0321_28/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_28/wrong 2.txt b/Data/ingsw/0321_28/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_28/wrong 2.txt
rename to Data/ingsw/0321_28/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_29/correct.txt b/Data/ingsw/0321_29/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_29/correct.txt
rename to Data/ingsw/0321_29/correct.txt
diff --git a/legacy/Data/ingsw/0321_29/quest.txt b/Data/ingsw/0321_29/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_29/quest.txt
rename to Data/ingsw/0321_29/quest.txt
diff --git a/legacy/Data/ingsw/0321_29/wrong 1.txt b/Data/ingsw/0321_29/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_29/wrong 1.txt
rename to Data/ingsw/0321_29/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_29/wrong 2.txt b/Data/ingsw/0321_29/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_29/wrong 2.txt
rename to Data/ingsw/0321_29/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_30/correct.txt b/Data/ingsw/0321_30/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_30/correct.txt
rename to Data/ingsw/0321_30/correct.txt
diff --git a/legacy/Data/ingsw/0321_30/quest.txt b/Data/ingsw/0321_30/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_30/quest.txt
rename to Data/ingsw/0321_30/quest.txt
diff --git a/legacy/Data/ingsw/0321_30/wrong 1.txt b/Data/ingsw/0321_30/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_30/wrong 1.txt
rename to Data/ingsw/0321_30/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_30/wrong 2.txt b/Data/ingsw/0321_30/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_30/wrong 2.txt
rename to Data/ingsw/0321_30/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_31/correct.txt b/Data/ingsw/0321_31/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_31/correct.txt
rename to Data/ingsw/0321_31/correct.txt
diff --git a/legacy/Data/ingsw/0321_31/quest.txt b/Data/ingsw/0321_31/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_31/quest.txt
rename to Data/ingsw/0321_31/quest.txt
diff --git a/legacy/Data/ingsw/0321_31/wrong 1.txt b/Data/ingsw/0321_31/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_31/wrong 1.txt
rename to Data/ingsw/0321_31/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_31/wrong 2.txt b/Data/ingsw/0321_31/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_31/wrong 2.txt
rename to Data/ingsw/0321_31/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_32/correct.txt b/Data/ingsw/0321_32/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_32/correct.txt
rename to Data/ingsw/0321_32/correct.txt
diff --git a/legacy/Data/ingsw/0321_32/quest.txt b/Data/ingsw/0321_32/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_32/quest.txt
rename to Data/ingsw/0321_32/quest.txt
diff --git a/legacy/Data/ingsw/0321_32/wrong 1.txt b/Data/ingsw/0321_32/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_32/wrong 1.txt
rename to Data/ingsw/0321_32/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_32/wrong 2.txt b/Data/ingsw/0321_32/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_32/wrong 2.txt
rename to Data/ingsw/0321_32/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_36/correct.txt b/Data/ingsw/0321_36/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_36/correct.txt
rename to Data/ingsw/0321_36/correct.txt
diff --git a/legacy/Data/ingsw/0321_36/quest.txt b/Data/ingsw/0321_36/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_36/quest.txt
rename to Data/ingsw/0321_36/quest.txt
diff --git a/legacy/Data/ingsw/0321_36/wrong 1.txt b/Data/ingsw/0321_36/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_36/wrong 1.txt
rename to Data/ingsw/0321_36/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_36/wrong 2.txt b/Data/ingsw/0321_36/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_36/wrong 2.txt
rename to Data/ingsw/0321_36/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_37/correct.txt b/Data/ingsw/0321_37/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_37/correct.txt
rename to Data/ingsw/0321_37/correct.txt
diff --git a/legacy/Data/ingsw/0321_37/quest.txt b/Data/ingsw/0321_37/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_37/quest.txt
rename to Data/ingsw/0321_37/quest.txt
diff --git a/legacy/Data/ingsw/0321_37/wrong 1.txt b/Data/ingsw/0321_37/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_37/wrong 1.txt
rename to Data/ingsw/0321_37/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_37/wrong 2.txt b/Data/ingsw/0321_37/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_37/wrong 2.txt
rename to Data/ingsw/0321_37/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_38/correct.txt b/Data/ingsw/0321_38/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_38/correct.txt
rename to Data/ingsw/0321_38/correct.txt
diff --git a/legacy/Data/ingsw/0321_38/quest.txt b/Data/ingsw/0321_38/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_38/quest.txt
rename to Data/ingsw/0321_38/quest.txt
diff --git a/legacy/Data/ingsw/0321_38/wrong 1.txt b/Data/ingsw/0321_38/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_38/wrong 1.txt
rename to Data/ingsw/0321_38/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_38/wrong 2.txt b/Data/ingsw/0321_38/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_38/wrong 2.txt
rename to Data/ingsw/0321_38/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_4/correct.txt b/Data/ingsw/0321_4/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_4/correct.txt
rename to Data/ingsw/0321_4/correct.txt
diff --git a/legacy/Data/ingsw/0321_4/quest.txt b/Data/ingsw/0321_4/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_4/quest.txt
rename to Data/ingsw/0321_4/quest.txt
diff --git a/legacy/Data/ingsw/0321_4/wrong 1.txt b/Data/ingsw/0321_4/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_4/wrong 1.txt
rename to Data/ingsw/0321_4/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_4/wrong 2.txt b/Data/ingsw/0321_4/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_4/wrong 2.txt
rename to Data/ingsw/0321_4/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_40/correct.txt b/Data/ingsw/0321_40/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_40/correct.txt
rename to Data/ingsw/0321_40/correct.txt
diff --git a/legacy/Data/ingsw/0321_40/quest.txt b/Data/ingsw/0321_40/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_40/quest.txt
rename to Data/ingsw/0321_40/quest.txt
diff --git a/legacy/Data/ingsw/0321_40/wrong 1.txt b/Data/ingsw/0321_40/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_40/wrong 1.txt
rename to Data/ingsw/0321_40/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_40/wrong 2.txt b/Data/ingsw/0321_40/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_40/wrong 2.txt
rename to Data/ingsw/0321_40/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_8/correct.txt b/Data/ingsw/0321_8/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_8/correct.txt
rename to Data/ingsw/0321_8/correct.txt
diff --git a/legacy/Data/ingsw/0321_8/quest.txt b/Data/ingsw/0321_8/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_8/quest.txt
rename to Data/ingsw/0321_8/quest.txt
diff --git a/legacy/Data/ingsw/0321_8/wrong 1.txt b/Data/ingsw/0321_8/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_8/wrong 1.txt
rename to Data/ingsw/0321_8/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_8/wrong 2.txt b/Data/ingsw/0321_8/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_8/wrong 2.txt
rename to Data/ingsw/0321_8/wrong 2.txt
diff --git a/legacy/Data/ingsw/0321_9/correct.txt b/Data/ingsw/0321_9/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_9/correct.txt
rename to Data/ingsw/0321_9/correct.txt
diff --git a/legacy/Data/ingsw/0321_9/quest.txt b/Data/ingsw/0321_9/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_9/quest.txt
rename to Data/ingsw/0321_9/quest.txt
diff --git a/legacy/Data/ingsw/0321_9/wrong 1.txt b/Data/ingsw/0321_9/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_9/wrong 1.txt
rename to Data/ingsw/0321_9/wrong 1.txt
diff --git a/legacy/Data/ingsw/0321_9/wrong 2.txt b/Data/ingsw/0321_9/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0321_9/wrong 2.txt
rename to Data/ingsw/0321_9/wrong 2.txt
diff --git a/legacy/Data/ingsw/0324_0/correct.txt b/Data/ingsw/0324_0/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_0/correct.txt
rename to Data/ingsw/0324_0/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_0/quest.txt b/Data/ingsw/0324_0/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_0/quest.txt
rename to Data/ingsw/0324_0/quest.txt
index 858d9c6..dcebb30 100644
--- a/legacy/Data/Questions/ingsw/0324_0/quest.txt
+++ b/Data/ingsw/0324_0/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_0.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_0.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 3 ? In altri terminti, qual'è la probabilità che non sia necessario ripetere nessuna fase?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_0/wrong1.txt b/Data/ingsw/0324_0/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_0/wrong1.txt
rename to Data/ingsw/0324_0/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_0/wrong2.txt b/Data/ingsw/0324_0/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_0/wrong2.txt
rename to Data/ingsw/0324_0/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_1/quest.txt b/Data/ingsw/0324_1/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0324_1/quest.txt
rename to Data/ingsw/0324_1/quest.txt
index a4a7e01..b46ed7b 100644
--- a/legacy/Data/Questions/ingsw/0324_1/quest.txt
+++ b/Data/ingsw/0324_1/quest.txt
@@ -1,4 +1,4 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_1.png
-Si consideri la seguente architettura software:
-
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_1.png
+Si consideri la seguente architettura software:
+
 Quale dei seguneti modelli Modelica meglio la rappresenta.
\ No newline at end of file
diff --git a/Data/ingsw/0324_1/wrong1.txt b/Data/ingsw/0324_1/wrong1.txt
new file mode 100644
index 0000000..d698ca8
--- /dev/null
+++ b/Data/ingsw/0324_1/wrong1.txt
@@ -0,0 +1,8 @@
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_1/wrong2.txt b/Data/ingsw/0324_1/wrong2.txt
similarity index 97%
rename from legacy/Data/ingsw/0324_1/wrong2.txt
rename to Data/ingsw/0324_1/wrong2.txt
index a3caf2e..7509ae5 100644
--- a/legacy/Data/ingsw/0324_1/wrong2.txt
+++ b/Data/ingsw/0324_1/wrong2.txt
@@ -1,2 +1,2 @@
-input12)
+input12)
 connect(sc2.output23, sc
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_1/wrong3.txt b/Data/ingsw/0324_1/wrong3.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_1/wrong3.txt
rename to Data/ingsw/0324_1/wrong3.txt
index 1d08fb4..f0ac66f 100644
--- a/legacy/Data/Questions/ingsw/0324_1/wrong3.txt
+++ b/Data/ingsw/0324_1/wrong3.txt
@@ -1,46 +1,46 @@
-input23)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output31, sc1.input31)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output43, sc3.input43)
-
-
-end SysArch;
-
-2.
-
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc2.input12)
-connect(sc1.output13, sc3.input13)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output21, sc1.input21)
-connect(sc3.output31, sc1.input31)
-connect(sc3.output32, sc2.input32)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-
-
-end SysArch;
-
-3.
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output14, sc4.input14)
-connect(sc3.output31, sc1.input31)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-
-
+input23)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output31, sc1.input31)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output43, sc3.input43)
+
+
+end SysArch;
+
+2.
+
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc2.input12)
+connect(sc1.output13, sc3.input13)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output21, sc1.input21)
+connect(sc3.output31, sc1.input31)
+connect(sc3.output32, sc2.input32)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+
+
+end SysArch;
+
+3.
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output14, sc4.input14)
+connect(sc3.output31, sc1.input31)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+
+
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_10/correct.txt b/Data/ingsw/0324_10/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_10/correct.txt
rename to Data/ingsw/0324_10/correct.txt
diff --git a/legacy/Data/ingsw/0324_10/quest.txt b/Data/ingsw/0324_10/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_10/quest.txt
rename to Data/ingsw/0324_10/quest.txt
diff --git a/legacy/Data/ingsw/0324_10/wrong1.txt b/Data/ingsw/0324_10/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_10/wrong1.txt
rename to Data/ingsw/0324_10/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_10/wrong2.txt b/Data/ingsw/0324_10/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_10/wrong2.txt
rename to Data/ingsw/0324_10/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_11/quest.txt b/Data/ingsw/0324_11/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_11/quest.txt
rename to Data/ingsw/0324_11/quest.txt
index 26df850..32ca7ba 100644
--- a/legacy/Data/Questions/ingsw/0324_11/quest.txt
+++ b/Data/ingsw/0324_11/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_11.png
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_11.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_11/wrong1.txt b/Data/ingsw/0324_11/wrong1.txt
similarity index 96%
rename from legacy/Data/ingsw/0324_11/wrong1.txt
rename to Data/ingsw/0324_11/wrong1.txt
index 2f7168f..8d9c242 100644
--- a/legacy/Data/ingsw/0324_11/wrong1.txt
+++ b/Data/ingsw/0324_11/wrong1.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_11/wrong2.txt b/Data/ingsw/0324_11/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0324_11/wrong2.txt
rename to Data/ingsw/0324_11/wrong2.txt
index c3b40d2..db35f26 100644
--- a/legacy/Data/ingsw/0324_11/wrong2.txt
+++ b/Data/ingsw/0324_11/wrong2.txt
@@ -1,34 +1,34 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_11/wrong3.txt b/Data/ingsw/0324_11/wrong3.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0324_11/wrong3.txt
rename to Data/ingsw/0324_11/wrong3.txt
index 9116c62..1ecf634 100644
--- a/legacy/Data/Questions/ingsw/0324_11/wrong3.txt
+++ b/Data/ingsw/0324_11/wrong3.txt
@@ -1,37 +1,37 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_12/correct.txt b/Data/ingsw/0324_12/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_12/correct.txt
rename to Data/ingsw/0324_12/correct.txt
diff --git a/Data/ingsw/0324_12/quest.txt b/Data/ingsw/0324_12/quest.txt
new file mode 100644
index 0000000..abc3e1c
--- /dev/null
+++ b/Data/ingsw/0324_12/quest.txt
@@ -0,0 +1,9 @@
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+int z = x;
+while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
+return (z);
+}
+Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_12/wrong1.txt b/Data/ingsw/0324_12/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_12/wrong1.txt
rename to Data/ingsw/0324_12/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_12/wrong2.txt b/Data/ingsw/0324_12/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_12/wrong2.txt
rename to Data/ingsw/0324_12/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_13/correct.txt b/Data/ingsw/0324_13/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_13/correct.txt
rename to Data/ingsw/0324_13/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_47/quest.txt b/Data/ingsw/0324_13/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0210_47/quest.txt
rename to Data/ingsw/0324_13/quest.txt
index 4344b75..3fa8769 100644
--- a/legacy/Data/Questions/ingsw/0210_47/quest.txt
+++ b/Data/ingsw/0324_13/quest.txt
@@ -1,8 +1,8 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (2*x); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
-Si consideri il seguente insieme di test cases:
-{x=-100, x= 40, x=100}
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (2*x); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
+Si consideri il seguente insieme di test cases:
+{x=-100, x= 40, x=100}
 Quale delle seguenti è la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_13/wrong1.txt b/Data/ingsw/0324_13/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_13/wrong1.txt
rename to Data/ingsw/0324_13/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_13/wrong2.txt b/Data/ingsw/0324_13/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_13/wrong2.txt
rename to Data/ingsw/0324_13/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_14/correct.txt b/Data/ingsw/0324_14/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_14/correct.txt
rename to Data/ingsw/0324_14/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_14/quest.txt b/Data/ingsw/0324_14/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_14/quest.txt
rename to Data/ingsw/0324_14/quest.txt
index b9ba678..c0b0f0e 100644
--- a/legacy/Data/Questions/ingsw/0324_14/quest.txt
+++ b/Data/ingsw/0324_14/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_14.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_14.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 2, 3, 4 ? In altri terminti, qual'è la probabilità che sia necessario ripetere sia la fase 1 che la fase 2 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_14/wrong1.txt b/Data/ingsw/0324_14/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_14/wrong1.txt
rename to Data/ingsw/0324_14/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_14/wrong2.txt b/Data/ingsw/0324_14/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_14/wrong2.txt
rename to Data/ingsw/0324_14/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_15/correct.txt b/Data/ingsw/0324_15/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_15/correct.txt
rename to Data/ingsw/0324_15/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_0/quest.txt b/Data/ingsw/0324_15/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0210_0/quest.txt
rename to Data/ingsw/0324_15/quest.txt
index 2d895ca..025eb8d 100644
--- a/legacy/Data/Questions/ingsw/0210_0/quest.txt
+++ b/Data/ingsw/0324_15/quest.txt
@@ -1,22 +1,22 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a >= 100) && (b - c <= 1) )
-          return (1);    // punto di uscita 1
-      else if ((b - c <= 1)  || (b + c >= 5)
-)
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a >= 100) && (b - c <= 1) )
+          return (1);    // punto di uscita 1
+      else if ((b - c <= 1)  || (b + c >= 5)
+)
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_15/wrong1.txt b/Data/ingsw/0324_15/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_15/wrong1.txt
rename to Data/ingsw/0324_15/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_15/wrong2.txt b/Data/ingsw/0324_15/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_15/wrong2.txt
rename to Data/ingsw/0324_15/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_16/correct.txt b/Data/ingsw/0324_16/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_16/correct.txt
rename to Data/ingsw/0324_16/correct.txt
diff --git a/legacy/Data/ingsw/0324_16/quest.txt b/Data/ingsw/0324_16/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_16/quest.txt
rename to Data/ingsw/0324_16/quest.txt
diff --git a/legacy/Data/ingsw/0324_16/wrong1.txt b/Data/ingsw/0324_16/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_16/wrong1.txt
rename to Data/ingsw/0324_16/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_16/wrong2.txt b/Data/ingsw/0324_16/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_16/wrong2.txt
rename to Data/ingsw/0324_16/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_17/correct.txt b/Data/ingsw/0324_17/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_17/correct.txt
rename to Data/ingsw/0324_17/correct.txt
diff --git a/legacy/Data/ingsw/0324_17/quest.txt b/Data/ingsw/0324_17/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0324_17/quest.txt
rename to Data/ingsw/0324_17/quest.txt
index 1f51ab1..4ce08bf 100644
--- a/legacy/Data/ingsw/0324_17/quest.txt
+++ b/Data/ingsw/0324_17/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_17.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act1 act2 
-Test case 2: act2 act2 act2 act2 act1 
-Test case 3: act2 act2 act2 act2 act0 
-
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_17.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act1 act2 
+Test case 2: act2 act2 act2 act2 act1 
+Test case 3: act2 act2 act2 act2 act0 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_17/wrong1.txt b/Data/ingsw/0324_17/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_17/wrong1.txt
rename to Data/ingsw/0324_17/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_17/wrong2.txt b/Data/ingsw/0324_17/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_17/wrong2.txt
rename to Data/ingsw/0324_17/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_18/correct.txt b/Data/ingsw/0324_18/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_18/correct.txt
rename to Data/ingsw/0324_18/correct.txt
diff --git a/legacy/Data/ingsw/0324_18/quest.txt b/Data/ingsw/0324_18/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0324_18/quest.txt
rename to Data/ingsw/0324_18/quest.txt
index d3a9fe2..f6bb83a 100644
--- a/legacy/Data/ingsw/0324_18/quest.txt
+++ b/Data/ingsw/0324_18/quest.txt
@@ -1,8 +1,8 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 0)   { if (x + y >= 1)  return (1); else return (2); }
-  else {if (2*x + y >= 5)  return (3); else return (4); }
- }  /* f()  */
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 0)   { if (x + y >= 1)  return (1); else return (2); }
+  else {if (2*x + y >= 5)  return (3); else return (4); }
+ }  /* f()  */
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_18/wrong1.txt b/Data/ingsw/0324_18/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_18/wrong1.txt
rename to Data/ingsw/0324_18/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_18/wrong2.txt b/Data/ingsw/0324_18/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_18/wrong2.txt
rename to Data/ingsw/0324_18/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_19/correct.txt b/Data/ingsw/0324_19/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_19/correct.txt
rename to Data/ingsw/0324_19/correct.txt
diff --git a/legacy/Data/ingsw/0324_19/quest.txt b/Data/ingsw/0324_19/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0324_19/quest.txt
rename to Data/ingsw/0324_19/quest.txt
index b7a608e..d1b1ff4 100644
--- a/legacy/Data/ingsw/0324_19/quest.txt
+++ b/Data/ingsw/0324_19/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_19.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act0 
-Test case 2: act2 act2 act0 
-Test case 3: act1 act1 act0 
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_19.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act0 
+Test case 2: act2 act2 act0 
+Test case 3: act1 act1 act0 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_19/wrong1.txt b/Data/ingsw/0324_19/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_19/wrong1.txt
rename to Data/ingsw/0324_19/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_19/wrong2.txt b/Data/ingsw/0324_19/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_19/wrong2.txt
rename to Data/ingsw/0324_19/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_2/correct.txt b/Data/ingsw/0324_2/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_2/correct.txt
rename to Data/ingsw/0324_2/correct.txt
diff --git a/legacy/Data/ingsw/0324_2/quest.txt b/Data/ingsw/0324_2/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0324_2/quest.txt
rename to Data/ingsw/0324_2/quest.txt
index adede32..64036f7 100644
--- a/legacy/Data/ingsw/0324_2/quest.txt
+++ b/Data/ingsw/0324_2/quest.txt
@@ -1,9 +1,9 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 2)   { if (x + y >= 1)  return (1); else return (2); }
-  else {if (x + 2*y >= 5)  return (3); else return (4); }
- }  /* f()  */
-Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 2)   { if (x + y >= 1)  return (1); else return (2); }
+  else {if (x + 2*y >= 5)  return (3); else return (4); }
+ }  /* f()  */
+Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_2/wrong1.txt b/Data/ingsw/0324_2/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_2/wrong1.txt
rename to Data/ingsw/0324_2/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_2/wrong2.txt b/Data/ingsw/0324_2/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_2/wrong2.txt
rename to Data/ingsw/0324_2/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_20/correct.txt b/Data/ingsw/0324_20/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_20/correct.txt
rename to Data/ingsw/0324_20/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_20/quest.txt b/Data/ingsw/0324_20/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0324_20/quest.txt
rename to Data/ingsw/0324_20/quest.txt
index 9d685ad..8625c93 100644
--- a/legacy/Data/Questions/ingsw/0324_20/quest.txt
+++ b/Data/ingsw/0324_20/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_20.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-
-Test case 1: act0 act2 
-Test case 2: act1 
-Test case 3: act0 act2 
-
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_20.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+
+Test case 1: act0 act2 
+Test case 2: act1 
+Test case 3: act0 act2 
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_20/wrong1.txt b/Data/ingsw/0324_20/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_20/wrong1.txt
rename to Data/ingsw/0324_20/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_20/wrong2.txt b/Data/ingsw/0324_20/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_20/wrong2.txt
rename to Data/ingsw/0324_20/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_21/correct.txt b/Data/ingsw/0324_21/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_21/correct.txt
rename to Data/ingsw/0324_21/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_21/quest.txt b/Data/ingsw/0324_21/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0324_21/quest.txt
rename to Data/ingsw/0324_21/quest.txt
index d649932..21834a5 100644
--- a/legacy/Data/Questions/ingsw/0324_21/quest.txt
+++ b/Data/ingsw/0324_21/quest.txt
@@ -1,8 +1,8 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 6)   { if (x + y >= 3)  return (1); else return (2); }
-  else {if (x + 2*y >= 15)  return (3); else return (4); }
- }  /* f()  */
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 6)   { if (x + y >= 3)  return (1); else return (2); }
+  else {if (x + 2*y >= 15)  return (3); else return (4); }
+ }  /* f()  */
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_21/wrong1.txt b/Data/ingsw/0324_21/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_21/wrong1.txt
rename to Data/ingsw/0324_21/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_21/wrong2.txt b/Data/ingsw/0324_21/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_21/wrong2.txt
rename to Data/ingsw/0324_21/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_22/correct.txt b/Data/ingsw/0324_22/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_22/correct.txt
rename to Data/ingsw/0324_22/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_31/quest.txt b/Data/ingsw/0324_22/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0210_31/quest.txt
rename to Data/ingsw/0324_22/quest.txt
index 65cfd2d..eb6c210 100644
--- a/legacy/Data/Questions/ingsw/0210_31/quest.txt
+++ b/Data/ingsw/0324_22/quest.txt
@@ -1,9 +1,9 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
- if (x - y <= 0)   { if (x + y >= 2)  return (1); else return (2); }
-  else {if (2*x + y >= 1)  return (3); else return (4); }
- }  /* f()  */
-Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+ if (x - y <= 0)   { if (x + y >= 2)  return (1); else return (2); }
+  else {if (2*x + y >= 1)  return (3); else return (4); }
+ }  /* f()  */
+Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_22/wrong1.txt b/Data/ingsw/0324_22/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_22/wrong1.txt
rename to Data/ingsw/0324_22/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_22/wrong2.txt b/Data/ingsw/0324_22/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_22/wrong2.txt
rename to Data/ingsw/0324_22/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_23/correct.txt b/Data/ingsw/0324_23/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_23/correct.txt
rename to Data/ingsw/0324_23/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_23/quest.txt b/Data/ingsw/0324_23/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0324_23/quest.txt
rename to Data/ingsw/0324_23/quest.txt
index c9ea208..cffeb06 100644
--- a/legacy/Data/Questions/ingsw/0324_23/quest.txt
+++ b/Data/ingsw/0324_23/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_23.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act0 act2 
-Test case 2: act1 act0 act0 act2 
-Test case 3: act0 act0 act2 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_23.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act0 act2 
+Test case 2: act1 act0 act0 act2 
+Test case 3: act0 act0 act2 act2 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_23/wrong1.txt b/Data/ingsw/0324_23/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_23/wrong1.txt
rename to Data/ingsw/0324_23/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_23/wrong2.txt b/Data/ingsw/0324_23/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_23/wrong2.txt
rename to Data/ingsw/0324_23/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_24/correct.txt b/Data/ingsw/0324_24/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_24/correct.txt
rename to Data/ingsw/0324_24/correct.txt
diff --git a/legacy/Data/ingsw/0324_24/quest.txt b/Data/ingsw/0324_24/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_24/quest.txt
rename to Data/ingsw/0324_24/quest.txt
diff --git a/legacy/Data/ingsw/0324_24/wrong1.txt b/Data/ingsw/0324_24/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_24/wrong1.txt
rename to Data/ingsw/0324_24/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_24/wrong2.txt b/Data/ingsw/0324_24/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_24/wrong2.txt
rename to Data/ingsw/0324_24/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_25/correct.txt b/Data/ingsw/0324_25/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_25/correct.txt
rename to Data/ingsw/0324_25/correct.txt
diff --git a/legacy/Data/ingsw/0324_25/quest.txt b/Data/ingsw/0324_25/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_25/quest.txt
rename to Data/ingsw/0324_25/quest.txt
diff --git a/legacy/Data/ingsw/0324_25/wrong1.txt b/Data/ingsw/0324_25/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_25/wrong1.txt
rename to Data/ingsw/0324_25/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_25/wrong2.txt b/Data/ingsw/0324_25/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_25/wrong2.txt
rename to Data/ingsw/0324_25/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_26/quest.txt b/Data/ingsw/0324_26/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0324_26/quest.txt
rename to Data/ingsw/0324_26/quest.txt
index aef871e..edb1cc1 100644
--- a/legacy/Data/ingsw/0324_26/quest.txt
+++ b/Data/ingsw/0324_26/quest.txt
@@ -1,19 +1,19 @@
-Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
-
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc2.input12)
-connect(sc1.output13, sc3.input13)
-connect(sc2.output23, sc3.input23)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output42, sc2.input42)
-
-
+Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
+
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc2.input12)
+connect(sc1.output13, sc3.input13)
+connect(sc2.output23, sc3.input23)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output42, sc2.input42)
+
+
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_26/wrong1.txt b/Data/ingsw/0324_26/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_26/wrong1.txt
rename to Data/ingsw/0324_26/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_26/wrong2.txt b/Data/ingsw/0324_26/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_26/wrong2.txt
rename to Data/ingsw/0324_26/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_26/wrong3.txt b/Data/ingsw/0324_26/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_26/wrong3.txt
rename to Data/ingsw/0324_26/wrong3.txt
diff --git a/Data/ingsw/0324_27/correct.txt b/Data/ingsw/0324_27/correct.txt
new file mode 100644
index 0000000..2771487
--- /dev/null
+++ b/Data/ingsw/0324_27/correct.txt
@@ -0,0 +1,17 @@
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0324_27/quest.txt b/Data/ingsw/0324_27/quest.txt
new file mode 100644
index 0000000..498beed
--- /dev/null
+++ b/Data/ingsw/0324_27/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
+Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_16/wrong1.txt b/Data/ingsw/0324_27/wrong1.txt
similarity index 94%
rename from legacy/Data/ingsw/0613_16/wrong1.txt
rename to Data/ingsw/0324_27/wrong1.txt
index 590f7e1..d681803 100644
--- a/legacy/Data/ingsw/0613_16/wrong1.txt
+++ b/Data/ingsw/0324_27/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_27/wrong2.txt b/Data/ingsw/0324_27/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_27/wrong2.txt
rename to Data/ingsw/0324_27/wrong2.txt
index 0f38391..44ac950 100644
--- a/legacy/Data/Questions/ingsw/0324_27/wrong2.txt
+++ b/Data/ingsw/0324_27/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/Data/ingsw/0324_28/correct.txt b/Data/ingsw/0324_28/correct.txt
new file mode 100644
index 0000000..9be970c
--- /dev/null
+++ b/Data/ingsw/0324_28/correct.txt
@@ -0,0 +1,16 @@
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0324_28/quest.txt b/Data/ingsw/0324_28/quest.txt
new file mode 100644
index 0000000..0910f0b
--- /dev/null
+++ b/Data/ingsw/0324_28/quest.txt
@@ -0,0 +1,4 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/Data/ingsw/0324_28/wrong1.txt b/Data/ingsw/0324_28/wrong1.txt
new file mode 100644
index 0000000..8b6fcac
--- /dev/null
+++ b/Data/ingsw/0324_28/wrong1.txt
@@ -0,0 +1,16 @@
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0324_28/wrong2.txt b/Data/ingsw/0324_28/wrong2.txt
new file mode 100644
index 0000000..40a2ce0
--- /dev/null
+++ b/Data/ingsw/0324_28/wrong2.txt
@@ -0,0 +1,16 @@
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_29/correct.txt b/Data/ingsw/0324_29/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_29/correct.txt
rename to Data/ingsw/0324_29/correct.txt
diff --git a/legacy/Data/ingsw/0324_29/quest.txt b/Data/ingsw/0324_29/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_29/quest.txt
rename to Data/ingsw/0324_29/quest.txt
diff --git a/legacy/Data/ingsw/0324_29/wrong1.txt b/Data/ingsw/0324_29/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_29/wrong1.txt
rename to Data/ingsw/0324_29/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_29/wrong2.txt b/Data/ingsw/0324_29/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_29/wrong2.txt
rename to Data/ingsw/0324_29/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_3/correct.txt b/Data/ingsw/0324_3/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_3/correct.txt
rename to Data/ingsw/0324_3/correct.txt
diff --git a/legacy/Data/ingsw/0324_3/quest.txt b/Data/ingsw/0324_3/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0324_3/quest.txt
rename to Data/ingsw/0324_3/quest.txt
index b865ed9..7893985 100644
--- a/legacy/Data/ingsw/0324_3/quest.txt
+++ b/Data/ingsw/0324_3/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_3.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act0 act0 act0 act2 act2 
-Test case 2: act2 act0 act2 
-Test case 3: act1 act0 act0 act2 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_3.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act0 act0 act0 act2 act2 
+Test case 2: act2 act0 act2 
+Test case 3: act1 act0 act0 act2 act2 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_3/wrong1.txt b/Data/ingsw/0324_3/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_3/wrong1.txt
rename to Data/ingsw/0324_3/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_3/wrong2.txt b/Data/ingsw/0324_3/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_3/wrong2.txt
rename to Data/ingsw/0324_3/wrong2.txt
diff --git a/legacy/Data/ingsw/0210_3/quest.txt b/Data/ingsw/0324_30/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0210_3/quest.txt
rename to Data/ingsw/0324_30/quest.txt
index 985c244..e2e738a 100644
--- a/legacy/Data/ingsw/0210_3/quest.txt
+++ b/Data/ingsw/0324_30/quest.txt
@@ -1,4 +1,4 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente specifica funzionale per la funzione f.
-La funzione f(int *A, int *B)  prende come input un vettore A di dimensione n  ritorna come output un vettore B ottenuto ordinando gli elementi di A in ordine crescente.
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente specifica funzionale per la funzione f.
+La funzione f(int *A, int *B)  prende come input un vettore A di dimensione n  ritorna come output un vettore B ottenuto ordinando gli elementi di A in ordine crescente.
 Quale delle seguenti funzioni è un test oracle per la funzione f ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_30/wrong1.txt b/Data/ingsw/0324_30/wrong1.txt
similarity index 97%
rename from legacy/Data/ingsw/0324_30/wrong1.txt
rename to Data/ingsw/0324_30/wrong1.txt
index 69b9722..59b5927 100644
--- a/legacy/Data/ingsw/0324_30/wrong1.txt
+++ b/Data/ingsw/0324_30/wrong1.txt
@@ -1,14 +1,14 @@
-#define n 1000
-int TestOracle2(int *A, int *B)
-{
-int i, j, D[n];
-//init
-for (i = 0; i < n; i++) D[i] = -1;
-// B is ordered
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-// B is a permutation of A
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; break;}
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-// B ok
-return (1);
+#define n 1000
+int TestOracle2(int *A, int *B)
+{
+int i, j, D[n];
+//init
+for (i = 0; i < n; i++) D[i] = -1;
+// B is ordered
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+// B is a permutation of A
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; break;}
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+// B ok
+return (1);
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_30/wrong2.txt b/Data/ingsw/0324_30/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0324_30/wrong2.txt
rename to Data/ingsw/0324_30/wrong2.txt
index a26ce6e..5c3b08a 100644
--- a/legacy/Data/ingsw/0324_30/wrong2.txt
+++ b/Data/ingsw/0324_30/wrong2.txt
@@ -1,15 +1,15 @@
-#define n 1000
-
-int TestOracle3(int *A, int *B)
-{
-int i, j, D[n];
-//init
-for (i = 0; i < n; i++) D[i] = -1;
-// B is ordered
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-// B is a permutation of A
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if (A[i] == B[j]) {C[i][j] = 1; D[j] = 1; break;}
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-// B ok
-return (1);
+#define n 1000
+
+int TestOracle3(int *A, int *B)
+{
+int i, j, D[n];
+//init
+for (i = 0; i < n; i++) D[i] = -1;
+// B is ordered
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+// B is a permutation of A
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if (A[i] == B[j]) {C[i][j] = 1; D[j] = 1; break;}
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+// B ok
+return (1);
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_30/wrong3.txt b/Data/ingsw/0324_30/wrong3.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0324_30/wrong3.txt
rename to Data/ingsw/0324_30/wrong3.txt
index ed5ad19..9e00c22 100644
--- a/legacy/Data/Questions/ingsw/0324_30/wrong3.txt
+++ b/Data/ingsw/0324_30/wrong3.txt
@@ -1,14 +1,14 @@
-#define n 1000
-int TestOracle1(int *A, int *B)
-{
-int i, j, D[n];
-//init
-for (i = 0; i < n; i++) D[i] = -1;
-// B is ordered
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-// B is a permutation of A
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; D[j] = 1; break;}
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-// B ok
-return (1);
+#define n 1000
+int TestOracle1(int *A, int *B)
+{
+int i, j, D[n];
+//init
+for (i = 0; i < n; i++) D[i] = -1;
+// B is ordered
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+// B is a permutation of A
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; D[j] = 1; break;}
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+// B ok
+return (1);
 }
\ No newline at end of file
diff --git a/Data/ingsw/0324_31/correct.txt b/Data/ingsw/0324_31/correct.txt
new file mode 100644
index 0000000..ca92838
--- /dev/null
+++ b/Data/ingsw/0324_31/correct.txt
@@ -0,0 +1,17 @@
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0324_31/quest.txt b/Data/ingsw/0324_31/quest.txt
new file mode 100644
index 0000000..348f2a5
--- /dev/null
+++ b/Data/ingsw/0324_31/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_31/wrong1.txt b/Data/ingsw/0324_31/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_31/wrong1.txt
rename to Data/ingsw/0324_31/wrong1.txt
index d50b268..434e904 100644
--- a/legacy/Data/Questions/ingsw/0324_31/wrong1.txt
+++ b/Data/ingsw/0324_31/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_31/wrong2.txt b/Data/ingsw/0324_31/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_31/wrong2.txt
rename to Data/ingsw/0324_31/wrong2.txt
index d7890b2..c0e3311 100644
--- a/legacy/Data/Questions/ingsw/0324_31/wrong2.txt
+++ b/Data/ingsw/0324_31/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_32/correct.txt b/Data/ingsw/0324_32/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_32/correct.txt
rename to Data/ingsw/0324_32/correct.txt
diff --git a/legacy/Data/ingsw/0324_32/quest.txt b/Data/ingsw/0324_32/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0324_32/quest.txt
rename to Data/ingsw/0324_32/quest.txt
index 5d96d42..513dba7 100644
--- a/legacy/Data/ingsw/0324_32/quest.txt
+++ b/Data/ingsw/0324_32/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_32.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
-Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_32.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
+Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_32/wrong1.txt b/Data/ingsw/0324_32/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_32/wrong1.txt
rename to Data/ingsw/0324_32/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_32/wrong2.txt b/Data/ingsw/0324_32/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_32/wrong2.txt
rename to Data/ingsw/0324_32/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_33/correct.txt b/Data/ingsw/0324_33/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_33/correct.txt
rename to Data/ingsw/0324_33/correct.txt
diff --git a/legacy/Data/ingsw/0324_33/quest.txt b/Data/ingsw/0324_33/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0324_33/quest.txt
rename to Data/ingsw/0324_33/quest.txt
index b2bed72..793c90f 100644
--- a/legacy/Data/ingsw/0324_33/quest.txt
+++ b/Data/ingsw/0324_33/quest.txt
@@ -1,21 +1,21 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a  + b >= 6) && (b - c <= 1) )
-          return (1);    // punto di uscita 1
-      else if ((b - c <= 1)  || (b + c >= 5))
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a  + b >= 6) && (b - c <= 1) )
+          return (1);    // punto di uscita 1
+      else if ((b - c <= 1)  || (b + c >= 5))
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_33/wrong1.txt b/Data/ingsw/0324_33/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_33/wrong1.txt
rename to Data/ingsw/0324_33/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_33/wrong2.txt b/Data/ingsw/0324_33/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_33/wrong2.txt
rename to Data/ingsw/0324_33/wrong2.txt
diff --git a/Data/ingsw/0324_34/correct.txt b/Data/ingsw/0324_34/correct.txt
new file mode 100644
index 0000000..a2f00b3
--- /dev/null
+++ b/Data/ingsw/0324_34/correct.txt
@@ -0,0 +1,15 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0324_34/quest.txt b/Data/ingsw/0324_34/quest.txt
new file mode 100644
index 0000000..1dbc484
--- /dev/null
+++ b/Data/ingsw/0324_34/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/Data/ingsw/0324_34/wrong1.txt b/Data/ingsw/0324_34/wrong1.txt
new file mode 100644
index 0000000..0696007
--- /dev/null
+++ b/Data/ingsw/0324_34/wrong1.txt
@@ -0,0 +1,15 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0324_34/wrong2.txt b/Data/ingsw/0324_34/wrong2.txt
new file mode 100644
index 0000000..76b58b0
--- /dev/null
+++ b/Data/ingsw/0324_34/wrong2.txt
@@ -0,0 +1,15 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_35/quest.txt b/Data/ingsw/0324_35/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0324_35/quest.txt
rename to Data/ingsw/0324_35/quest.txt
index 627c57e..e5bd153 100644
--- a/legacy/Data/ingsw/0324_35/quest.txt
+++ b/Data/ingsw/0324_35/quest.txt
@@ -1,39 +1,39 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_35/wrong1.txt b/Data/ingsw/0324_35/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_35/wrong1.txt
rename to Data/ingsw/0324_35/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_35/wrong2.txt b/Data/ingsw/0324_35/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_35/wrong2.txt
rename to Data/ingsw/0324_35/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_35/wrong3.txt b/Data/ingsw/0324_35/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_35/wrong3.txt
rename to Data/ingsw/0324_35/wrong3.txt
diff --git a/legacy/Data/ingsw/0324_36/correct.txt b/Data/ingsw/0324_36/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_36/correct.txt
rename to Data/ingsw/0324_36/correct.txt
diff --git a/legacy/Data/ingsw/0324_36/quest.txt b/Data/ingsw/0324_36/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0324_36/quest.txt
rename to Data/ingsw/0324_36/quest.txt
index 36471c2..628380c 100644
--- a/legacy/Data/ingsw/0324_36/quest.txt
+++ b/Data/ingsw/0324_36/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_36.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
-Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_36.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
+Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_36/wrong1.txt b/Data/ingsw/0324_36/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_36/wrong1.txt
rename to Data/ingsw/0324_36/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_36/wrong2.txt b/Data/ingsw/0324_36/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_36/wrong2.txt
rename to Data/ingsw/0324_36/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_37/correct.txt b/Data/ingsw/0324_37/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_37/correct.txt
rename to Data/ingsw/0324_37/correct.txt
diff --git a/legacy/Data/ingsw/0324_37/quest.txt b/Data/ingsw/0324_37/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0324_37/quest.txt
rename to Data/ingsw/0324_37/quest.txt
index fc6a5e1..eba16a3 100644
--- a/legacy/Data/ingsw/0324_37/quest.txt
+++ b/Data/ingsw/0324_37/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_37.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-
-Test case 1: act1 act2 act2 act2 
-Test case 2: act0 act2 act1 act2 act0 
-Test case 3: act0 act2 act1 act2 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_37.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+
+Test case 1: act1 act2 act2 act2 
+Test case 2: act0 act2 act1 act2 act0 
+Test case 3: act0 act2 act1 act2 act2 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_37/wrong1.txt b/Data/ingsw/0324_37/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_37/wrong1.txt
rename to Data/ingsw/0324_37/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_37/wrong2.txt b/Data/ingsw/0324_37/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_37/wrong2.txt
rename to Data/ingsw/0324_37/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_38/correct.txt b/Data/ingsw/0324_38/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_38/correct.txt
rename to Data/ingsw/0324_38/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_38/quest.txt b/Data/ingsw/0324_38/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_38/quest.txt
rename to Data/ingsw/0324_38/quest.txt
index d24403f..9de598d 100644
--- a/legacy/Data/Questions/ingsw/0324_38/quest.txt
+++ b/Data/ingsw/0324_38/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_38.png
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_38.png
 Si consideri la Markov chain in figura con stato iniziale 0 e p in (0, 1). Quale delle seguenti formule calcola il valore atteso del numero di transizioni necessarie per lasciare lo stato 0.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_38/wrong1.txt b/Data/ingsw/0324_38/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_38/wrong1.txt
rename to Data/ingsw/0324_38/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_38/wrong2.txt b/Data/ingsw/0324_38/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_38/wrong2.txt
rename to Data/ingsw/0324_38/wrong2.txt
diff --git a/Data/ingsw/0324_39/correct.txt b/Data/ingsw/0324_39/correct.txt
new file mode 100644
index 0000000..a3a4b67
--- /dev/null
+++ b/Data/ingsw/0324_39/correct.txt
@@ -0,0 +1,15 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0324_39/quest.txt b/Data/ingsw/0324_39/quest.txt
new file mode 100644
index 0000000..0d3faca
--- /dev/null
+++ b/Data/ingsw/0324_39/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_39/wrong1.txt b/Data/ingsw/0324_39/wrong1.txt
similarity index 94%
rename from legacy/Data/ingsw/0324_39/wrong1.txt
rename to Data/ingsw/0324_39/wrong1.txt
index a43796b..9f7ef6a 100644
--- a/legacy/Data/ingsw/0324_39/wrong1.txt
+++ b/Data/ingsw/0324_39/wrong1.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_39/wrong2.txt b/Data/ingsw/0324_39/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0324_39/wrong2.txt
rename to Data/ingsw/0324_39/wrong2.txt
index 68aa37a..32c00bc 100644
--- a/legacy/Data/ingsw/0324_39/wrong2.txt
+++ b/Data/ingsw/0324_39/wrong2.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_4/correct.txt b/Data/ingsw/0324_4/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_4/correct.txt
rename to Data/ingsw/0324_4/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_4/quest.txt b/Data/ingsw/0324_4/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_4/quest.txt
rename to Data/ingsw/0324_4/quest.txt
index 40b7789..390dc53 100644
--- a/legacy/Data/Questions/ingsw/0324_4/quest.txt
+++ b/Data/ingsw/0324_4/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_4.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_4.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 3, 4? In altri terminti, qual'è la probabilità che non sia necessario ripetere la seconda fase (ma non la prima) ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_4/wrong1.txt b/Data/ingsw/0324_4/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_4/wrong1.txt
rename to Data/ingsw/0324_4/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_4/wrong2.txt b/Data/ingsw/0324_4/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_4/wrong2.txt
rename to Data/ingsw/0324_4/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_40/correct.txt b/Data/ingsw/0324_40/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_40/correct.txt
rename to Data/ingsw/0324_40/correct.txt
diff --git a/legacy/Data/ingsw/0324_40/quest.txt b/Data/ingsw/0324_40/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0324_40/quest.txt
rename to Data/ingsw/0324_40/quest.txt
index fbee794..b4d7a22 100644
--- a/legacy/Data/ingsw/0324_40/quest.txt
+++ b/Data/ingsw/0324_40/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_40.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_40.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 2, 3? In altri terminti, qual'è la probabilità che non sia necessario ripetere la prima fase (ma non la seconda) ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_40/wrong1.txt b/Data/ingsw/0324_40/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_40/wrong1.txt
rename to Data/ingsw/0324_40/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_40/wrong2.txt b/Data/ingsw/0324_40/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_40/wrong2.txt
rename to Data/ingsw/0324_40/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0324_41/quest.txt b/Data/ingsw/0324_41/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0324_41/quest.txt
rename to Data/ingsw/0324_41/quest.txt
index bfb2790..ed4c07b 100644
--- a/legacy/Data/Questions/ingsw/0324_41/quest.txt
+++ b/Data/ingsw/0324_41/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_41.png
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_41.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_41/wrong1.txt b/Data/ingsw/0324_41/wrong1.txt
similarity index 96%
rename from legacy/Data/ingsw/0324_41/wrong1.txt
rename to Data/ingsw/0324_41/wrong1.txt
index 1fad89a..07dfe0b 100644
--- a/legacy/Data/ingsw/0324_41/wrong1.txt
+++ b/Data/ingsw/0324_41/wrong1.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_41/wrong2.txt b/Data/ingsw/0324_41/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0324_41/wrong2.txt
rename to Data/ingsw/0324_41/wrong2.txt
index 882ae3e..37f0e6d 100644
--- a/legacy/Data/Questions/ingsw/0324_41/wrong2.txt
+++ b/Data/ingsw/0324_41/wrong2.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_41/wrong3.txt b/Data/ingsw/0324_41/wrong3.txt
similarity index 96%
rename from legacy/Data/ingsw/0324_41/wrong3.txt
rename to Data/ingsw/0324_41/wrong3.txt
index e5618fa..6a88923 100644
--- a/legacy/Data/ingsw/0324_41/wrong3.txt
+++ b/Data/ingsw/0324_41/wrong3.txt
@@ -1,34 +1,34 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_42/quest.txt b/Data/ingsw/0324_42/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0324_42/quest.txt
rename to Data/ingsw/0324_42/quest.txt
index 071ac68..5f2cac3 100644
--- a/legacy/Data/ingsw/0324_42/quest.txt
+++ b/Data/ingsw/0324_42/quest.txt
@@ -1,35 +1,35 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_42/wrong1.txt b/Data/ingsw/0324_42/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_42/wrong1.txt
rename to Data/ingsw/0324_42/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_42/wrong2.txt b/Data/ingsw/0324_42/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_42/wrong2.txt
rename to Data/ingsw/0324_42/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_42/wrong3.txt b/Data/ingsw/0324_42/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_42/wrong3.txt
rename to Data/ingsw/0324_42/wrong3.txt
diff --git a/legacy/Data/ingsw/0324_43/correct.txt b/Data/ingsw/0324_43/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_43/correct.txt
rename to Data/ingsw/0324_43/correct.txt
diff --git a/legacy/Data/ingsw/0324_43/quest.txt b/Data/ingsw/0324_43/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0324_43/quest.txt
rename to Data/ingsw/0324_43/quest.txt
index 710edb6..c2ef886 100644
--- a/legacy/Data/ingsw/0324_43/quest.txt
+++ b/Data/ingsw/0324_43/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_43.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act1 act0 act1 act0 
-Test case 2: act1 act0 act2 act2 
-Test case 3: act2 act2 act1 act2 act1 
-
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_43.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act1 act0 act1 act0 
+Test case 2: act1 act0 act2 act2 
+Test case 3: act2 act2 act1 act2 act1 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_43/wrong1.txt b/Data/ingsw/0324_43/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_43/wrong1.txt
rename to Data/ingsw/0324_43/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_43/wrong2.txt b/Data/ingsw/0324_43/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_43/wrong2.txt
rename to Data/ingsw/0324_43/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_44/correct.txt b/Data/ingsw/0324_44/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_44/correct.txt
rename to Data/ingsw/0324_44/correct.txt
diff --git a/Data/ingsw/0324_44/quest.txt b/Data/ingsw/0324_44/quest.txt
new file mode 100644
index 0000000..853f496
--- /dev/null
+++ b/Data/ingsw/0324_44/quest.txt
@@ -0,0 +1,6 @@
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (x + 7); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
+Quale dei seguenti test cases consegue una partition coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_44/wrong1.txt b/Data/ingsw/0324_44/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_44/wrong1.txt
rename to Data/ingsw/0324_44/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_44/wrong2.txt b/Data/ingsw/0324_44/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_44/wrong2.txt
rename to Data/ingsw/0324_44/wrong2.txt
diff --git a/Data/ingsw/0324_45/correct.txt b/Data/ingsw/0324_45/correct.txt
new file mode 100644
index 0000000..56ad9f9
--- /dev/null
+++ b/Data/ingsw/0324_45/correct.txt
@@ -0,0 +1,15 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0324_45/quest.txt b/Data/ingsw/0324_45/quest.txt
new file mode 100644
index 0000000..316ed3f
--- /dev/null
+++ b/Data/ingsw/0324_45/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_45/wrong1.txt b/Data/ingsw/0324_45/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_45/wrong1.txt
rename to Data/ingsw/0324_45/wrong1.txt
index edea147..3184bf8 100644
--- a/legacy/Data/Questions/ingsw/0324_45/wrong1.txt
+++ b/Data/ingsw/0324_45/wrong1.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_45/wrong2.txt b/Data/ingsw/0324_45/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0324_45/wrong2.txt
rename to Data/ingsw/0324_45/wrong2.txt
index 14bd900..976ab6a 100644
--- a/legacy/Data/Questions/ingsw/0324_45/wrong2.txt
+++ b/Data/ingsw/0324_45/wrong2.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_46/correct.txt b/Data/ingsw/0324_46/correct.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0324_46/correct.txt
rename to Data/ingsw/0324_46/correct.txt
index a98afd2..d3daba4 100644
--- a/legacy/Data/Questions/ingsw/0324_46/correct.txt
+++ b/Data/ingsw/0324_46/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and ((x >= 30) or (x <= 20)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and ((x >= 30) or (x <= 20)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/Data/ingsw/0324_46/quest.txt b/Data/ingsw/0324_46/quest.txt
new file mode 100644
index 0000000..6938bd6
--- /dev/null
+++ b/Data/ingsw/0324_46/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
+Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_46/wrong1.txt b/Data/ingsw/0324_46/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0324_46/wrong1.txt
rename to Data/ingsw/0324_46/wrong1.txt
index 66064fe..5bdf9c8 100644
--- a/legacy/Data/ingsw/0324_46/wrong1.txt
+++ b/Data/ingsw/0324_46/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and (x >= 20) and (x <= 30) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and (x >= 20) and (x <= 30) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_46/wrong2.txt b/Data/ingsw/0324_46/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0324_46/wrong2.txt
rename to Data/ingsw/0324_46/wrong2.txt
index c71f1f5..169ec7c 100644
--- a/legacy/Data/ingsw/0324_46/wrong2.txt
+++ b/Data/ingsw/0324_46/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) or ((x >= 20) and (x <= 30)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) or ((x >= 20) and (x <= 30)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_47/quest.txt b/Data/ingsw/0324_47/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0324_47/quest.txt
rename to Data/ingsw/0324_47/quest.txt
index 0240bc8..ca6ba26 100644
--- a/legacy/Data/ingsw/0324_47/quest.txt
+++ b/Data/ingsw/0324_47/quest.txt
@@ -1,18 +1,18 @@
-Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc2.input12)
-connect(sc1.output13, sc3.input13)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output31, sc1.input31)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output42, sc2.input42)
-connect(sc4.output43, sc3.input43)
+Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc2.input12)
+connect(sc1.output13, sc3.input13)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output31, sc1.input31)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output42, sc2.input42)
+connect(sc4.output43, sc3.input43)
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_47/wrong1.txt b/Data/ingsw/0324_47/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_47/wrong1.txt
rename to Data/ingsw/0324_47/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_47/wrong2.txt b/Data/ingsw/0324_47/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_47/wrong2.txt
rename to Data/ingsw/0324_47/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_47/wrong3.txt b/Data/ingsw/0324_47/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_47/wrong3.txt
rename to Data/ingsw/0324_47/wrong3.txt
diff --git a/legacy/Data/Questions/ingsw/0324_48/quest.txt b/Data/ingsw/0324_48/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0324_48/quest.txt
rename to Data/ingsw/0324_48/quest.txt
index 1109458..8cd20e6 100644
--- a/legacy/Data/Questions/ingsw/0324_48/quest.txt
+++ b/Data/ingsw/0324_48/quest.txt
@@ -1,4 +1,4 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_48.png
-Si consideri la seguente architettura software:
-
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_48.png
+Si consideri la seguente architettura software:
+
 Quale dei seguneti modelli Modelica meglio la rappresenta.
\ No newline at end of file
diff --git a/Data/ingsw/0324_48/wrong1.txt b/Data/ingsw/0324_48/wrong1.txt
new file mode 100644
index 0000000..d698ca8
--- /dev/null
+++ b/Data/ingsw/0324_48/wrong1.txt
@@ -0,0 +1,8 @@
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_27/wrong2.txt b/Data/ingsw/0324_48/wrong2.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0613_27/wrong2.txt
rename to Data/ingsw/0324_48/wrong2.txt
index 19be218..8c2a586 100644
--- a/legacy/Data/Questions/ingsw/0613_27/wrong2.txt
+++ b/Data/ingsw/0324_48/wrong2.txt
@@ -1,2 +1,2 @@
-input12)
+input12)
 connect(sc1.output13, sc
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_48/wrong3.txt b/Data/ingsw/0324_48/wrong3.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0324_48/wrong3.txt
rename to Data/ingsw/0324_48/wrong3.txt
index 3387be9..702e46b 100644
--- a/legacy/Data/Questions/ingsw/0324_48/wrong3.txt
+++ b/Data/ingsw/0324_48/wrong3.txt
@@ -1,49 +1,49 @@
-input13)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output23, sc3.input23)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output32, sc2.input32)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output42, sc2.input42)
-
-
-end SysArch;
-
-2.
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output13, sc3.input13)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output23, sc3.input23)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output31, sc1.input31)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output42, sc2.input42)
-connect(sc4.output43, sc3.input43)
-
-
-end SysArch;
-
-3.
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output13, sc3.input13)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output32, sc2.input32)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output43, sc3.input43)
-
-
+input13)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output23, sc3.input23)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output32, sc2.input32)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output42, sc2.input42)
+
+
+end SysArch;
+
+2.
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output13, sc3.input13)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output23, sc3.input23)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output31, sc1.input31)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output42, sc2.input42)
+connect(sc4.output43, sc3.input43)
+
+
+end SysArch;
+
+3.
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output13, sc3.input13)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output32, sc2.input32)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output43, sc3.input43)
+
+
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_49/correct.txt b/Data/ingsw/0324_49/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_49/correct.txt
rename to Data/ingsw/0324_49/correct.txt
diff --git a/legacy/Data/ingsw/0324_49/quest.txt b/Data/ingsw/0324_49/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_49/quest.txt
rename to Data/ingsw/0324_49/quest.txt
diff --git a/legacy/Data/ingsw/0324_49/wrong1.txt b/Data/ingsw/0324_49/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_49/wrong1.txt
rename to Data/ingsw/0324_49/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_49/wrong2.txt b/Data/ingsw/0324_49/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_49/wrong2.txt
rename to Data/ingsw/0324_49/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_5/correct.txt b/Data/ingsw/0324_5/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_5/correct.txt
rename to Data/ingsw/0324_5/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0210_8/quest.txt b/Data/ingsw/0324_5/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0210_8/quest.txt
rename to Data/ingsw/0324_5/quest.txt
index 236ccc7..3ce3c9b 100644
--- a/legacy/Data/Questions/ingsw/0210_8/quest.txt
+++ b/Data/ingsw/0324_5/quest.txt
@@ -1,10 +1,10 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
-int z, k;
-z = 1;   k = 0;
-while (k < x)  { z = y*z;  k = k + 1; }
-return (z);
-}
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+int z, k;
+z = 1;   k = 0;
+while (k < x)  { z = y*z;  k = k + 1; }
+return (z);
+}
 Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_5/wrong1.txt b/Data/ingsw/0324_5/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_5/wrong1.txt
rename to Data/ingsw/0324_5/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_5/wrong2.txt b/Data/ingsw/0324_5/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_5/wrong2.txt
rename to Data/ingsw/0324_5/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_6/correct.txt b/Data/ingsw/0324_6/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_6/correct.txt
rename to Data/ingsw/0324_6/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0324_6/quest.txt b/Data/ingsw/0324_6/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0324_6/quest.txt
rename to Data/ingsw/0324_6/quest.txt
index f6ffda4..80dab68 100644
--- a/legacy/Data/Questions/ingsw/0324_6/quest.txt
+++ b/Data/ingsw/0324_6/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_6.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-
-Test case 1: act0 act0 act0 act0 act1 
-Test case 2: act2 act2 
-Test case 3: act0 act0 act2 act1 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_6.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+
+Test case 1: act0 act0 act0 act0 act1 
+Test case 2: act2 act2 
+Test case 3: act0 act0 act2 act1 act2 
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_6/wrong1.txt b/Data/ingsw/0324_6/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_6/wrong1.txt
rename to Data/ingsw/0324_6/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_6/wrong2.txt b/Data/ingsw/0324_6/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_6/wrong2.txt
rename to Data/ingsw/0324_6/wrong2.txt
diff --git a/legacy/Data/ingsw/0324_7/correct.txt b/Data/ingsw/0324_7/correct.txt
similarity index 95%
rename from legacy/Data/ingsw/0324_7/correct.txt
rename to Data/ingsw/0324_7/correct.txt
index 43dc0c9..af4c387 100644
--- a/legacy/Data/ingsw/0324_7/correct.txt
+++ b/Data/ingsw/0324_7/correct.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 0) || (y > 0)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 0) || (y > 0)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_7/quest.txt b/Data/ingsw/0324_7/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0324_7/quest.txt
rename to Data/ingsw/0324_7/quest.txt
index f6744fd..5fd3b47 100644
--- a/legacy/Data/ingsw/0324_7/quest.txt
+++ b/Data/ingsw/0324_7/quest.txt
@@ -1,4 +1,4 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-Si consideri la funzione C
-int f(in x, int y) { ..... }
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+Si consideri la funzione C
+int f(in x, int y) { ..... }
 Quale delle seguenti assert esprime la pre-condizione che entrambi gli argomenti di f sono non-negativi ed almeno uno di loro è positivo ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_7/wrong1.txt b/Data/ingsw/0324_7/wrong1.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0324_7/wrong1.txt
rename to Data/ingsw/0324_7/wrong1.txt
index 3f63933..506df9d 100644
--- a/legacy/Data/Questions/ingsw/0324_7/wrong1.txt
+++ b/Data/ingsw/0324_7/wrong1.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x > 0) && (y > 0) && ((x > 1) || (y > 1)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x > 0) && (y > 0) && ((x > 1) || (y > 1)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_7/wrong2.txt b/Data/ingsw/0324_7/wrong2.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0324_7/wrong2.txt
rename to Data/ingsw/0324_7/wrong2.txt
index 6a97baf..392cc67 100644
--- a/legacy/Data/Questions/ingsw/0324_7/wrong2.txt
+++ b/Data/ingsw/0324_7/wrong2.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 1) || (y > 1)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 1) || (y > 1)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_8/correct.txt b/Data/ingsw/0324_8/correct.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0324_8/correct.txt
rename to Data/ingsw/0324_8/correct.txt
index b8bf06e..a068be6 100644
--- a/legacy/Data/Questions/ingsw/0324_8/correct.txt
+++ b/Data/ingsw/0324_8/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x > 5) or (x < 0));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x > 5) or (x < 0));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/Data/ingsw/0324_8/quest.txt b/Data/ingsw/0324_8/quest.txt
new file mode 100644
index 0000000..d26958e
--- /dev/null
+++ b/Data/ingsw/0324_8/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_8/wrong1.txt b/Data/ingsw/0324_8/wrong1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0324_8/wrong1.txt
rename to Data/ingsw/0324_8/wrong1.txt
index 2029293..8086f1c 100644
--- a/legacy/Data/Questions/ingsw/0324_8/wrong1.txt
+++ b/Data/ingsw/0324_8/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and (x > 0) and (x < 5);
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and (x > 0) and (x < 5);
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_8/wrong2.txt b/Data/ingsw/0324_8/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0324_8/wrong2.txt
rename to Data/ingsw/0324_8/wrong2.txt
index bc8720d..c22f966 100644
--- a/legacy/Data/Questions/ingsw/0324_8/wrong2.txt
+++ b/Data/ingsw/0324_8/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z =  (time > 0) and ((x > 0) or (x < 5));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z =  (time > 0) and ((x > 0) or (x < 5));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_9/correct.txt b/Data/ingsw/0324_9/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_9/correct.txt
rename to Data/ingsw/0324_9/correct.txt
diff --git a/Data/ingsw/0324_9/quest.txt b/Data/ingsw/0324_9/quest.txt
new file mode 100644
index 0000000..332256a
--- /dev/null
+++ b/Data/ingsw/0324_9/quest.txt
@@ -0,0 +1,4 @@
+Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
+Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
+P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
+dove b è una opportuna costante note da dati storici aziendali. Si assuma che b = 0.0001,  C = 1000000, ed il rischio ammesso è R = 1000. Quale dei seguenti valori meglio approssima il costo S per lo sviluppo del software in questione.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_9/wrong1.txt b/Data/ingsw/0324_9/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_9/wrong1.txt
rename to Data/ingsw/0324_9/wrong1.txt
diff --git a/legacy/Data/ingsw/0324_9/wrong2.txt b/Data/ingsw/0324_9/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0324_9/wrong2.txt
rename to Data/ingsw/0324_9/wrong2.txt
diff --git a/legacy/Data/ingsw/0422-16/correct.txt b/Data/ingsw/0422-16/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0422-16/correct.txt
rename to Data/ingsw/0422-16/correct.txt
diff --git a/legacy/Data/ingsw/0422-16/quest.txt b/Data/ingsw/0422-16/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0422-16/quest.txt
rename to Data/ingsw/0422-16/quest.txt
diff --git a/legacy/Data/ingsw/0422-16/wrong1.txt b/Data/ingsw/0422-16/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0422-16/wrong1.txt
rename to Data/ingsw/0422-16/wrong1.txt
diff --git a/legacy/Data/ingsw/0422-16/wrong2.txt b/Data/ingsw/0422-16/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0422-16/wrong2.txt
rename to Data/ingsw/0422-16/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_0/quest.txt b/Data/ingsw/0613_0/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0613_0/quest.txt
rename to Data/ingsw/0613_0/quest.txt
index 1f3419c..623ac16 100644
--- a/legacy/Data/ingsw/0613_0/quest.txt
+++ b/Data/ingsw/0613_0/quest.txt
@@ -1,35 +1,35 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_0/wrong1.txt b/Data/ingsw/0613_0/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_0/wrong1.txt
rename to Data/ingsw/0613_0/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_0/wrong2.txt b/Data/ingsw/0613_0/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_0/wrong2.txt
rename to Data/ingsw/0613_0/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_0/wrong3.txt b/Data/ingsw/0613_0/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_0/wrong3.txt
rename to Data/ingsw/0613_0/wrong3.txt
diff --git a/legacy/Data/ingsw/0613_1/correct.txt b/Data/ingsw/0613_1/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_1/correct.txt
rename to Data/ingsw/0613_1/correct.txt
diff --git a/legacy/Data/ingsw/0613_1/quest.txt b/Data/ingsw/0613_1/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0613_1/quest.txt
rename to Data/ingsw/0613_1/quest.txt
index 654955e..04740a2 100644
--- a/legacy/Data/ingsw/0613_1/quest.txt
+++ b/Data/ingsw/0613_1/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_1.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_1.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 3, 4? In altri terminti, qual'è la probabilità che non sia necessario ripetere la seconda fase (ma non la prima) ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_1/wrong1.txt b/Data/ingsw/0613_1/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_1/wrong1.txt
rename to Data/ingsw/0613_1/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_1/wrong2.txt b/Data/ingsw/0613_1/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_1/wrong2.txt
rename to Data/ingsw/0613_1/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_10/correct.txt b/Data/ingsw/0613_10/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_10/correct.txt
rename to Data/ingsw/0613_10/correct.txt
diff --git a/legacy/Data/ingsw/0613_10/quest.txt b/Data/ingsw/0613_10/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0613_10/quest.txt
rename to Data/ingsw/0613_10/quest.txt
index 9e4d3a9..3aba807 100644
--- a/legacy/Data/ingsw/0613_10/quest.txt
+++ b/Data/ingsw/0613_10/quest.txt
@@ -1,31 +1,31 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x[3])
-{
-  if (-x[0] + x[1] - x[2] < -7)
-    { return (0); }
-  else if (-3*x[0] +3*x[1] - 5*x[2] > 7)
-        { 
-      if (-x[0] + x[1] - x[2] > 10)
-                { return (1); }
-      else
-                { return (0); }      
-    }
- else
-           { 
-             if (3*x[0] - 5*x[1] + 7*x[2] > 9)
-               { return (1); }
-      else
-                { return (0); }      
-    }
-
-} /* f()  */
-----------
-ed il seguente insieme di test cases:
-
-Test 1: x[0] = 0, x[1] = 0, x[2] = 1, 
-Test 2: x[0] = 3, x[1] = 1, x[2] = 5, 
-Test 3:  x[0] = 0, x[1] = 4, x[2] = -2, 
-Test 4:  x[0] = -4, x[1] = 5, x[2] = -2, 
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x[3])
+{
+  if (-x[0] + x[1] - x[2] < -7)
+    { return (0); }
+  else if (-3*x[0] +3*x[1] - 5*x[2] > 7)
+        { 
+      if (-x[0] + x[1] - x[2] > 10)
+                { return (1); }
+      else
+                { return (0); }      
+    }
+ else
+           { 
+             if (3*x[0] - 5*x[1] + 7*x[2] > 9)
+               { return (1); }
+      else
+                { return (0); }      
+    }
+
+} /* f()  */
+----------
+ed il seguente insieme di test cases:
+
+Test 1: x[0] = 0, x[1] = 0, x[2] = 1, 
+Test 2: x[0] = 3, x[1] = 1, x[2] = 5, 
+Test 3:  x[0] = 0, x[1] = 4, x[2] = -2, 
+Test 4:  x[0] = -4, x[1] = 5, x[2] = -2, 
 Test 5:   x[0] = 1, x[1] = -4, x[2] = 4,
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_10/wrong1.txt b/Data/ingsw/0613_10/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_10/wrong1.txt
rename to Data/ingsw/0613_10/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_10/wrong2.txt b/Data/ingsw/0613_10/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_10/wrong2.txt
rename to Data/ingsw/0613_10/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_11/correct.txt b/Data/ingsw/0613_11/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_11/correct.txt
rename to Data/ingsw/0613_11/correct.txt
diff --git a/legacy/Data/ingsw/0613_11/quest.txt b/Data/ingsw/0613_11/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_11/quest.txt
rename to Data/ingsw/0613_11/quest.txt
diff --git a/legacy/Data/ingsw/0613_11/wrong1.txt b/Data/ingsw/0613_11/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_11/wrong1.txt
rename to Data/ingsw/0613_11/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_11/wrong2.txt b/Data/ingsw/0613_11/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_11/wrong2.txt
rename to Data/ingsw/0613_11/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_12/correct.txt b/Data/ingsw/0613_12/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_12/correct.txt
rename to Data/ingsw/0613_12/correct.txt
diff --git a/Data/ingsw/0613_12/quest.txt b/Data/ingsw/0613_12/quest.txt
new file mode 100644
index 0000000..853f496
--- /dev/null
+++ b/Data/ingsw/0613_12/quest.txt
@@ -0,0 +1,6 @@
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (x + 7); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
+Quale dei seguenti test cases consegue una partition coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_12/wrong1.txt b/Data/ingsw/0613_12/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_12/wrong1.txt
rename to Data/ingsw/0613_12/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_12/wrong2.txt b/Data/ingsw/0613_12/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_12/wrong2.txt
rename to Data/ingsw/0613_12/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_13/correct.txt b/Data/ingsw/0613_13/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_13/correct.txt
rename to Data/ingsw/0613_13/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_13/quest.txt b/Data/ingsw/0613_13/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0613_13/quest.txt
rename to Data/ingsw/0613_13/quest.txt
index 6f20250..b2e5e25 100644
--- a/legacy/Data/Questions/ingsw/0613_13/quest.txt
+++ b/Data/ingsw/0613_13/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_13.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act0 act2 act0 
-Test case 2: act1 act0 act1 act2 act1 
-Test case 3: act1 act2 act0 act2 act1 
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_13.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act0 act2 act0 
+Test case 2: act1 act0 act1 act2 act1 
+Test case 3: act1 act2 act0 act2 act1 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_13/wrong1.txt b/Data/ingsw/0613_13/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_13/wrong1.txt
rename to Data/ingsw/0613_13/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_13/wrong2.txt b/Data/ingsw/0613_13/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_13/wrong2.txt
rename to Data/ingsw/0613_13/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_14/quest.txt b/Data/ingsw/0613_14/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0613_14/quest.txt
rename to Data/ingsw/0613_14/quest.txt
index b95c7d3..98c0282 100644
--- a/legacy/Data/ingsw/0613_14/quest.txt
+++ b/Data/ingsw/0613_14/quest.txt
@@ -1,13 +1,13 @@
-Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
-block SysArch  //  System Architecture
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-connect(sc1.output12, sc2.input12)
-connect(sc1.output13, sc3.input13)
-connect(sc1.output14, sc4.input14)
-connect(sc3.output31, sc1.input31)
-connect(sc3.output32, sc2.input32)
-connect(sc4.output42, sc2.input42)
+Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
+block SysArch  //  System Architecture
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+connect(sc1.output12, sc2.input12)
+connect(sc1.output13, sc3.input13)
+connect(sc1.output14, sc4.input14)
+connect(sc3.output31, sc1.input31)
+connect(sc3.output32, sc2.input32)
+connect(sc4.output42, sc2.input42)
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_14/wrong1.txt b/Data/ingsw/0613_14/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_14/wrong1.txt
rename to Data/ingsw/0613_14/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_14/wrong2.txt b/Data/ingsw/0613_14/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_14/wrong2.txt
rename to Data/ingsw/0613_14/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_14/wrong3.txt b/Data/ingsw/0613_14/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_14/wrong3.txt
rename to Data/ingsw/0613_14/wrong3.txt
diff --git a/legacy/Data/ingsw/0613_15/correct.txt b/Data/ingsw/0613_15/correct.txt
similarity index 93%
rename from legacy/Data/ingsw/0613_15/correct.txt
rename to Data/ingsw/0613_15/correct.txt
index b8bf06e..a068be6 100644
--- a/legacy/Data/ingsw/0613_15/correct.txt
+++ b/Data/ingsw/0613_15/correct.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x > 5) or (x < 0));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x > 5) or (x < 0));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/Data/ingsw/0613_15/quest.txt b/Data/ingsw/0613_15/quest.txt
new file mode 100644
index 0000000..d26958e
--- /dev/null
+++ b/Data/ingsw/0613_15/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_15/wrong1.txt b/Data/ingsw/0613_15/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0613_15/wrong1.txt
rename to Data/ingsw/0613_15/wrong1.txt
index bc8720d..c22f966 100644
--- a/legacy/Data/ingsw/0613_15/wrong1.txt
+++ b/Data/ingsw/0613_15/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z =  (time > 0) and ((x > 0) or (x < 5));
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z =  (time > 0) and ((x > 0) or (x < 5));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_15/wrong2.txt b/Data/ingsw/0613_15/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0613_15/wrong2.txt
rename to Data/ingsw/0613_15/wrong2.txt
index 2029293..8086f1c 100644
--- a/legacy/Data/ingsw/0613_15/wrong2.txt
+++ b/Data/ingsw/0613_15/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and (x > 0) and (x < 5);
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and (x > 0) and (x < 5);
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/Data/ingsw/0613_16/correct.txt b/Data/ingsw/0613_16/correct.txt
new file mode 100644
index 0000000..2771487
--- /dev/null
+++ b/Data/ingsw/0613_16/correct.txt
@@ -0,0 +1,17 @@
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0613_16/quest.txt b/Data/ingsw/0613_16/quest.txt
new file mode 100644
index 0000000..498beed
--- /dev/null
+++ b/Data/ingsw/0613_16/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
+Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_27/wrong1.txt b/Data/ingsw/0613_16/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0324_27/wrong1.txt
rename to Data/ingsw/0613_16/wrong1.txt
index 590f7e1..d681803 100644
--- a/legacy/Data/Questions/ingsw/0324_27/wrong1.txt
+++ b/Data/ingsw/0613_16/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_16/wrong2.txt b/Data/ingsw/0613_16/wrong2.txt
similarity index 94%
rename from legacy/Data/ingsw/0613_16/wrong2.txt
rename to Data/ingsw/0613_16/wrong2.txt
index 0f38391..44ac950 100644
--- a/legacy/Data/ingsw/0613_16/wrong2.txt
+++ b/Data/ingsw/0613_16/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/Data/ingsw/0613_17/correct.txt b/Data/ingsw/0613_17/correct.txt
new file mode 100644
index 0000000..56ad9f9
--- /dev/null
+++ b/Data/ingsw/0613_17/correct.txt
@@ -0,0 +1,15 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0613_17/quest.txt b/Data/ingsw/0613_17/quest.txt
new file mode 100644
index 0000000..316ed3f
--- /dev/null
+++ b/Data/ingsw/0613_17/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_17/wrong1.txt b/Data/ingsw/0613_17/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0613_17/wrong1.txt
rename to Data/ingsw/0613_17/wrong1.txt
index 14bd900..976ab6a 100644
--- a/legacy/Data/ingsw/0613_17/wrong1.txt
+++ b/Data/ingsw/0613_17/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_6/wrong2.txt b/Data/ingsw/0613_17/wrong2.txt
similarity index 94%
rename from legacy/Data/ingsw/0210_6/wrong2.txt
rename to Data/ingsw/0613_17/wrong2.txt
index edea147..3184bf8 100644
--- a/legacy/Data/ingsw/0210_6/wrong2.txt
+++ b/Data/ingsw/0613_17/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_18/correct.txt b/Data/ingsw/0613_18/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_18/correct.txt
rename to Data/ingsw/0613_18/correct.txt
diff --git a/legacy/Data/ingsw/0613_18/quest.txt b/Data/ingsw/0613_18/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0613_18/quest.txt
rename to Data/ingsw/0613_18/quest.txt
index 91edad5..82a9faa 100644
--- a/legacy/Data/ingsw/0613_18/quest.txt
+++ b/Data/ingsw/0613_18/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_18.png
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_18.png
 Si consideri la Markov chain in figura con stato iniziale 0 e p in (0, 1). Quale delle seguenti formule calcola il valore atteso del numero di transizioni necessarie per lasciare lo stato 0.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_18/wrong1.txt b/Data/ingsw/0613_18/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_18/wrong1.txt
rename to Data/ingsw/0613_18/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_18/wrong2.txt b/Data/ingsw/0613_18/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_18/wrong2.txt
rename to Data/ingsw/0613_18/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_19/quest.txt b/Data/ingsw/0613_19/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0613_19/quest.txt
rename to Data/ingsw/0613_19/quest.txt
index 052028b..7278f88 100644
--- a/legacy/Data/ingsw/0613_19/quest.txt
+++ b/Data/ingsw/0613_19/quest.txt
@@ -1,37 +1,37 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-end when;
+Si consideri il seguente modello Modelica. Quale dei seguenti UML state diagram lo rappresenta correttamente ?
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_19/wrong1.txt b/Data/ingsw/0613_19/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_19/wrong1.txt
rename to Data/ingsw/0613_19/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_19/wrong2.txt b/Data/ingsw/0613_19/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_19/wrong2.txt
rename to Data/ingsw/0613_19/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_19/wrong3.txt b/Data/ingsw/0613_19/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_19/wrong3.txt
rename to Data/ingsw/0613_19/wrong3.txt
diff --git a/legacy/Data/ingsw/0613_2/quest.txt b/Data/ingsw/0613_2/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0613_2/quest.txt
rename to Data/ingsw/0613_2/quest.txt
index fcb1323..cf64e3c 100644
--- a/legacy/Data/ingsw/0613_2/quest.txt
+++ b/Data/ingsw/0613_2/quest.txt
@@ -1,19 +1,19 @@
-Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc2.input12)
-connect(sc1.output13, sc3.input13)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output42, sc2.input42)
-
-
+Si consideri il seguente modello Modelica. Quale delle seguenti architetture software meglio lo rappresenta  ?
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc2.input12)
+connect(sc1.output13, sc3.input13)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output42, sc2.input42)
+
+
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_2/wrong1.txt b/Data/ingsw/0613_2/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_2/wrong1.txt
rename to Data/ingsw/0613_2/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_2/wrong2.txt b/Data/ingsw/0613_2/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_2/wrong2.txt
rename to Data/ingsw/0613_2/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_2/wrong3.txt b/Data/ingsw/0613_2/wrong3.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_2/wrong3.txt
rename to Data/ingsw/0613_2/wrong3.txt
diff --git a/legacy/Data/ingsw/0613_20/correct.txt b/Data/ingsw/0613_20/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_20/correct.txt
rename to Data/ingsw/0613_20/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_20/quest.txt b/Data/ingsw/0613_20/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_20/quest.txt
rename to Data/ingsw/0613_20/quest.txt
index 79b69ac..6e1ee0b 100644
--- a/legacy/Data/Questions/ingsw/0613_20/quest.txt
+++ b/Data/ingsw/0613_20/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_20.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
-Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_20.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
+Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_20/wrong1.txt b/Data/ingsw/0613_20/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_20/wrong1.txt
rename to Data/ingsw/0613_20/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_20/wrong2.txt b/Data/ingsw/0613_20/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_20/wrong2.txt
rename to Data/ingsw/0613_20/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_21/correct.txt b/Data/ingsw/0613_21/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_21/correct.txt
rename to Data/ingsw/0613_21/correct.txt
diff --git a/legacy/Data/ingsw/0613_21/quest.txt b/Data/ingsw/0613_21/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_21/quest.txt
rename to Data/ingsw/0613_21/quest.txt
diff --git a/legacy/Data/ingsw/0613_21/wrong1.txt b/Data/ingsw/0613_21/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_21/wrong1.txt
rename to Data/ingsw/0613_21/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_21/wrong2.txt b/Data/ingsw/0613_21/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_21/wrong2.txt
rename to Data/ingsw/0613_21/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_22/correct.txt b/Data/ingsw/0613_22/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_22/correct.txt
rename to Data/ingsw/0613_22/correct.txt
diff --git a/legacy/Data/ingsw/0613_22/quest.txt b/Data/ingsw/0613_22/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0613_22/quest.txt
rename to Data/ingsw/0613_22/quest.txt
index aef94a6..f520cd6 100644
--- a/legacy/Data/ingsw/0613_22/quest.txt
+++ b/Data/ingsw/0613_22/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_22.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act2 act1 act0 
-Test case 2: act2 act2 act0 act2 act2 
-Test case 3: act1 act1 act2 act2 
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_22.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act2 act1 act0 
+Test case 2: act2 act2 act0 act2 act2 
+Test case 3: act1 act1 act2 act2 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_22/wrong1.txt b/Data/ingsw/0613_22/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_22/wrong1.txt
rename to Data/ingsw/0613_22/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_22/wrong2.txt b/Data/ingsw/0613_22/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_22/wrong2.txt
rename to Data/ingsw/0613_22/wrong2.txt
diff --git a/Data/ingsw/0613_23/correct.txt b/Data/ingsw/0613_23/correct.txt
new file mode 100644
index 0000000..a3a4b67
--- /dev/null
+++ b/Data/ingsw/0613_23/correct.txt
@@ -0,0 +1,15 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0613_23/quest.txt b/Data/ingsw/0613_23/quest.txt
new file mode 100644
index 0000000..0d3faca
--- /dev/null
+++ b/Data/ingsw/0613_23/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_45/wrong1.txt b/Data/ingsw/0613_23/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0210_45/wrong1.txt
rename to Data/ingsw/0613_23/wrong1.txt
index 68aa37a..32c00bc 100644
--- a/legacy/Data/ingsw/0210_45/wrong1.txt
+++ b/Data/ingsw/0613_23/wrong1.txt
@@ -1,16 +1,16 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) or (delay(x, 10) > 0) or  (y <= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_45/wrong2.txt b/Data/ingsw/0613_23/wrong2.txt
similarity index 94%
rename from legacy/Data/ingsw/0210_45/wrong2.txt
rename to Data/ingsw/0613_23/wrong2.txt
index a43796b..9f7ef6a 100644
--- a/legacy/Data/ingsw/0210_45/wrong2.txt
+++ b/Data/ingsw/0613_23/wrong2.txt
@@ -1,15 +1,15 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y > 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_24/correct.txt b/Data/ingsw/0613_24/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_24/correct.txt
rename to Data/ingsw/0613_24/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_24/quest.txt b/Data/ingsw/0613_24/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0613_24/quest.txt
rename to Data/ingsw/0613_24/quest.txt
index 9534ab3..edbe473 100644
--- a/legacy/Data/Questions/ingsw/0613_24/quest.txt
+++ b/Data/ingsw/0613_24/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_24.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act2 act2 
-Test case 2: act1 
-Test case 3: act2 act0 act2 act0 act2 
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_24.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act2 act2 
+Test case 2: act1 
+Test case 3: act2 act0 act2 act0 act2 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_24/wrong1.txt b/Data/ingsw/0613_24/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_24/wrong1.txt
rename to Data/ingsw/0613_24/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_24/wrong2.txt b/Data/ingsw/0613_24/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_24/wrong2.txt
rename to Data/ingsw/0613_24/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_25/correct.txt b/Data/ingsw/0613_25/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_25/correct.txt
rename to Data/ingsw/0613_25/correct.txt
diff --git a/Data/ingsw/0613_25/quest.txt b/Data/ingsw/0613_25/quest.txt
new file mode 100644
index 0000000..abc3e1c
--- /dev/null
+++ b/Data/ingsw/0613_25/quest.txt
@@ -0,0 +1,9 @@
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+Si consideri la seguente funzione C:
+-----------
+int f(int x, int y)  {   
+int z = x;
+while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
+return (z);
+}
+Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_25/wrong1.txt b/Data/ingsw/0613_25/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_25/wrong1.txt
rename to Data/ingsw/0613_25/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_25/wrong2.txt b/Data/ingsw/0613_25/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_25/wrong2.txt
rename to Data/ingsw/0613_25/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_26/correct.txt b/Data/ingsw/0613_26/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_26/correct.txt
rename to Data/ingsw/0613_26/correct.txt
diff --git a/legacy/Data/ingsw/0613_26/quest.txt b/Data/ingsw/0613_26/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0613_26/quest.txt
rename to Data/ingsw/0613_26/quest.txt
index dcec721..3e3f55d 100644
--- a/legacy/Data/ingsw/0613_26/quest.txt
+++ b/Data/ingsw/0613_26/quest.txt
@@ -1,8 +1,8 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (2*x); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
-Si consideri il seguente insieme di test cases:
-{x=-20, x= 10, x=60}
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+Si consideri la seguente funzione C:
+int f1(int x)  { return (2*x); }
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
+Si consideri il seguente insieme di test cases:
+{x=-20, x= 10, x=60}
 Quale delle seguenti è la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_26/wrong1.txt b/Data/ingsw/0613_26/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_26/wrong1.txt
rename to Data/ingsw/0613_26/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_26/wrong2.txt b/Data/ingsw/0613_26/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_26/wrong2.txt
rename to Data/ingsw/0613_26/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_27/quest.txt b/Data/ingsw/0613_27/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0613_27/quest.txt
rename to Data/ingsw/0613_27/quest.txt
index 35670bc..7a4bedd 100644
--- a/legacy/Data/Questions/ingsw/0613_27/quest.txt
+++ b/Data/ingsw/0613_27/quest.txt
@@ -1,4 +1,4 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_27.png
-Si consideri la seguente architettura software:
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_27.png
+Si consideri la seguente architettura software:
+
 Quale dei seguenti modelli Modelica meglio la rappresenta.
\ No newline at end of file
diff --git a/Data/ingsw/0613_27/wrong1.txt b/Data/ingsw/0613_27/wrong1.txt
new file mode 100644
index 0000000..d698ca8
--- /dev/null
+++ b/Data/ingsw/0613_27/wrong1.txt
@@ -0,0 +1,8 @@
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_27/wrong2.txt b/Data/ingsw/0613_27/wrong2.txt
similarity index 97%
rename from legacy/Data/ingsw/0613_27/wrong2.txt
rename to Data/ingsw/0613_27/wrong2.txt
index 19be218..8c2a586 100644
--- a/legacy/Data/ingsw/0613_27/wrong2.txt
+++ b/Data/ingsw/0613_27/wrong2.txt
@@ -1,2 +1,2 @@
-input12)
+input12)
 connect(sc1.output13, sc
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_27/wrong3.txt b/Data/ingsw/0613_27/wrong3.txt
similarity index 95%
rename from legacy/Data/Questions/ingsw/0613_27/wrong3.txt
rename to Data/ingsw/0613_27/wrong3.txt
index 29daf30..ed27dd6 100644
--- a/legacy/Data/Questions/ingsw/0613_27/wrong3.txt
+++ b/Data/ingsw/0613_27/wrong3.txt
@@ -1,49 +1,49 @@
-input13)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output23, sc3.input23)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output32, sc2.input32)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-
-
-end SysArch;
-
-2.
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc2.input12)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output23, sc3.input23)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output31, sc1.input31)
-connect(sc3.output32, sc2.input32)
-connect(sc3.output34, sc4.input34)
-
-
-end SysArch;
-
-3.
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc2.input12)
-connect(sc1.output13, sc3.input13)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-
-
+input13)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output23, sc3.input23)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output32, sc2.input32)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+
+
+end SysArch;
+
+2.
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc2.input12)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output23, sc3.input23)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output31, sc1.input31)
+connect(sc3.output32, sc2.input32)
+connect(sc3.output34, sc4.input34)
+
+
+end SysArch;
+
+3.
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc2.input12)
+connect(sc1.output13, sc3.input13)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+
+
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_28/correct.txt b/Data/ingsw/0613_28/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_28/correct.txt
rename to Data/ingsw/0613_28/correct.txt
diff --git a/legacy/Data/ingsw/0613_28/quest.txt b/Data/ingsw/0613_28/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0613_28/quest.txt
rename to Data/ingsw/0613_28/quest.txt
index 32aecd3..7e792d0 100644
--- a/legacy/Data/ingsw/0613_28/quest.txt
+++ b/Data/ingsw/0613_28/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_28.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act2 act0 act1 act0 
-Test case 2: act2 act0 act0 
-Test case 3: act2 act0 act2 act1 act1 
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_28.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act2 act0 act1 act0 
+Test case 2: act2 act0 act0 
+Test case 3: act2 act0 act2 act1 act1 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_28/wrong1.txt b/Data/ingsw/0613_28/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_28/wrong1.txt
rename to Data/ingsw/0613_28/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_28/wrong2.txt b/Data/ingsw/0613_28/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_28/wrong2.txt
rename to Data/ingsw/0613_28/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_29/correct.txt b/Data/ingsw/0613_29/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_29/correct.txt
rename to Data/ingsw/0613_29/correct.txt
diff --git a/Data/ingsw/0613_29/quest.txt b/Data/ingsw/0613_29/quest.txt
new file mode 100644
index 0000000..332256a
--- /dev/null
+++ b/Data/ingsw/0613_29/quest.txt
@@ -0,0 +1,4 @@
+Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
+Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
+P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
+dove b è una opportuna costante note da dati storici aziendali. Si assuma che b = 0.0001,  C = 1000000, ed il rischio ammesso è R = 1000. Quale dei seguenti valori meglio approssima il costo S per lo sviluppo del software in questione.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_29/wrong1.txt b/Data/ingsw/0613_29/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_29/wrong1.txt
rename to Data/ingsw/0613_29/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_29/wrong2.txt b/Data/ingsw/0613_29/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_29/wrong2.txt
rename to Data/ingsw/0613_29/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_3/correct.txt b/Data/ingsw/0613_3/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_3/correct.txt
rename to Data/ingsw/0613_3/correct.txt
diff --git a/legacy/Data/ingsw/0613_3/quest.txt b/Data/ingsw/0613_3/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0613_3/quest.txt
rename to Data/ingsw/0613_3/quest.txt
index d8bc097..11102b4 100644
--- a/legacy/Data/ingsw/0613_3/quest.txt
+++ b/Data/ingsw/0613_3/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_3.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_3.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 3 ? In altri terminti, qual'è la probabilità che non sia necessario ripetere nessuna fase?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_3/wrong1.txt b/Data/ingsw/0613_3/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_3/wrong1.txt
rename to Data/ingsw/0613_3/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_3/wrong2.txt b/Data/ingsw/0613_3/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_3/wrong2.txt
rename to Data/ingsw/0613_3/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_30/correct.txt b/Data/ingsw/0613_30/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_30/correct.txt
rename to Data/ingsw/0613_30/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_30/quest.txt b/Data/ingsw/0613_30/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0613_30/quest.txt
rename to Data/ingsw/0613_30/quest.txt
index 56ab57a..67a9861 100644
--- a/legacy/Data/Questions/ingsw/0613_30/quest.txt
+++ b/Data/ingsw/0613_30/quest.txt
@@ -1,11 +1,11 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_30.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act0 act0 act0 act1 act1 
-Test case 2: act1 act0 act1 
-Test case 3: act0 act2 act2 act2 
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_30.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act0 act0 act0 act1 act1 
+Test case 2: act1 act0 act1 
+Test case 3: act0 act2 act2 act2 
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_30/wrong1.txt b/Data/ingsw/0613_30/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_30/wrong1.txt
rename to Data/ingsw/0613_30/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_30/wrong2.txt b/Data/ingsw/0613_30/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_30/wrong2.txt
rename to Data/ingsw/0613_30/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_31/correct.txt b/Data/ingsw/0613_31/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_31/correct.txt
rename to Data/ingsw/0613_31/correct.txt
diff --git a/legacy/Data/ingsw/0613_31/quest.txt b/Data/ingsw/0613_31/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0613_31/quest.txt
rename to Data/ingsw/0613_31/quest.txt
index 9f9ed74..991828f 100644
--- a/legacy/Data/ingsw/0613_31/quest.txt
+++ b/Data/ingsw/0613_31/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_31.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act1 act2 act2 act2 act0 
-Test case 2: act1 act0 act1 act1 act1 
-Test case 3: act1 act2 act2 act2 act2 
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_31.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act1 act2 act2 act2 act0 
+Test case 2: act1 act0 act1 act1 act1 
+Test case 3: act1 act2 act2 act2 act2 
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_31/wrong1.txt b/Data/ingsw/0613_31/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_31/wrong1.txt
rename to Data/ingsw/0613_31/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_31/wrong2.txt b/Data/ingsw/0613_31/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_31/wrong2.txt
rename to Data/ingsw/0613_31/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_32/correct.txt b/Data/ingsw/0613_32/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_32/correct.txt
rename to Data/ingsw/0613_32/correct.txt
diff --git a/legacy/Data/ingsw/0613_32/quest.txt b/Data/ingsw/0613_32/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/0613_32/quest.txt
rename to Data/ingsw/0613_32/quest.txt
index 1724f1c..90ea8cc 100644
--- a/legacy/Data/ingsw/0613_32/quest.txt
+++ b/Data/ingsw/0613_32/quest.txt
@@ -1,13 +1,13 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_32.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-
-ed il seguente insieme di test cases:
-Test case 1: act0 act2 act0 act0 act2 
-Test case 2: act1 act0 act0 act0 
-Test case 3: act0 act2 act2 act0 act2 
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_32.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+
+ed il seguente insieme di test cases:
+Test case 1: act0 act2 act0 act0 act2 
+Test case 2: act1 act0 act0 act0 
+Test case 3: act0 act2 act2 act0 act2 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_32/wrong1.txt b/Data/ingsw/0613_32/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_32/wrong1.txt
rename to Data/ingsw/0613_32/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_32/wrong2.txt b/Data/ingsw/0613_32/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_32/wrong2.txt
rename to Data/ingsw/0613_32/wrong2.txt
diff --git a/Data/ingsw/0613_33/correct.txt b/Data/ingsw/0613_33/correct.txt
new file mode 100644
index 0000000..c496119
--- /dev/null
+++ b/Data/ingsw/0613_33/correct.txt
@@ -0,0 +1,5 @@
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 3) || (y > 3)) );
+.....
+}
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_33/quest.txt b/Data/ingsw/0613_33/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_33/quest.txt
rename to Data/ingsw/0613_33/quest.txt
index 2758118..42b0d46 100644
--- a/legacy/Data/Questions/ingsw/0613_33/quest.txt
+++ b/Data/ingsw/0613_33/quest.txt
@@ -1,4 +1,4 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-Si consideri la funzione C
-int f(int x, int y) { ..... }
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+Si consideri la funzione C
+int f(int x, int y) { ..... }
 Quale delle seguenti assert esprime la pre-condizione che entrambi gli argomenti di f sono non-negativi ed almeno uno di loro è maggiore di 3 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_3/wrong1.txt b/Data/ingsw/0613_33/wrong1.txt
similarity index 95%
rename from legacy/Data/ingsw/0120_3/wrong1.txt
rename to Data/ingsw/0613_33/wrong1.txt
index ad32d88..5dcb186 100644
--- a/legacy/Data/ingsw/0120_3/wrong1.txt
+++ b/Data/ingsw/0613_33/wrong1.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x >= 3) || (y >= 3)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x >= 3) || (y >= 3)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_3/wrong2.txt b/Data/ingsw/0613_33/wrong2.txt
similarity index 95%
rename from legacy/Data/ingsw/0120_3/wrong2.txt
rename to Data/ingsw/0613_33/wrong2.txt
index 642ec6b..21886fc 100644
--- a/legacy/Data/ingsw/0120_3/wrong2.txt
+++ b/Data/ingsw/0613_33/wrong2.txt
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x > 0) && (y > 0) && ((x >= 3) || (y > 3)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x > 0) && (y > 0) && ((x >= 3) || (y > 3)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_34/correct.txt b/Data/ingsw/0613_34/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_34/correct.txt
rename to Data/ingsw/0613_34/correct.txt
diff --git a/legacy/Data/ingsw/0613_34/quest.txt b/Data/ingsw/0613_34/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_34/quest.txt
rename to Data/ingsw/0613_34/quest.txt
diff --git a/legacy/Data/ingsw/0613_34/wrong1.txt b/Data/ingsw/0613_34/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_34/wrong1.txt
rename to Data/ingsw/0613_34/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_34/wrong2.txt b/Data/ingsw/0613_34/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_34/wrong2.txt
rename to Data/ingsw/0613_34/wrong2.txt
diff --git a/Data/ingsw/0613_35/correct.txt b/Data/ingsw/0613_35/correct.txt
new file mode 100644
index 0000000..a2f00b3
--- /dev/null
+++ b/Data/ingsw/0613_35/correct.txt
@@ -0,0 +1,15 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0613_35/quest.txt b/Data/ingsw/0613_35/quest.txt
new file mode 100644
index 0000000..1dbc484
--- /dev/null
+++ b/Data/ingsw/0613_35/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/Data/ingsw/0613_35/wrong1.txt b/Data/ingsw/0613_35/wrong1.txt
new file mode 100644
index 0000000..0696007
--- /dev/null
+++ b/Data/ingsw/0613_35/wrong1.txt
@@ -0,0 +1,15 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0613_35/wrong2.txt b/Data/ingsw/0613_35/wrong2.txt
new file mode 100644
index 0000000..76b58b0
--- /dev/null
+++ b/Data/ingsw/0613_35/wrong2.txt
@@ -0,0 +1,15 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_36/correct.txt b/Data/ingsw/0613_36/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_36/correct.txt
rename to Data/ingsw/0613_36/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_36/quest.txt b/Data/ingsw/0613_36/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_36/quest.txt
rename to Data/ingsw/0613_36/quest.txt
index 58782d5..d1c0c6a 100644
--- a/legacy/Data/Questions/ingsw/0613_36/quest.txt
+++ b/Data/ingsw/0613_36/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_36.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_36.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 2, 3, 4 ? In altri terminti, qual'è la probabilità che sia necessario ripetere sia la fase 1 che la fase 2 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_36/wrong1.txt b/Data/ingsw/0613_36/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_36/wrong1.txt
rename to Data/ingsw/0613_36/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_36/wrong2.txt b/Data/ingsw/0613_36/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_36/wrong2.txt
rename to Data/ingsw/0613_36/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_37/correct.txt b/Data/ingsw/0613_37/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_37/correct.txt
rename to Data/ingsw/0613_37/correct.txt
diff --git a/Data/ingsw/0613_37/quest.txt b/Data/ingsw/0613_37/quest.txt
new file mode 100644
index 0000000..6906b2b
--- /dev/null
+++ b/Data/ingsw/0613_37/quest.txt
@@ -0,0 +1,16 @@
+Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
+block Monitor
+input Real x;  
+output Boolean y;
+Boolean w;
+initial equation
+y = false;
+equation
+w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
+algorithm
+when edge(w) then
+y := true;
+end when;
+end Monitor;
+</pre>
+Quale delle seguenti affermazioni meglio descrive il requisito monitorato?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_37/wrong1.txt b/Data/ingsw/0613_37/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_37/wrong1.txt
rename to Data/ingsw/0613_37/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_37/wrong2.txt b/Data/ingsw/0613_37/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_37/wrong2.txt
rename to Data/ingsw/0613_37/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_38/quest.txt b/Data/ingsw/0613_38/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0613_38/quest.txt
rename to Data/ingsw/0613_38/quest.txt
index 230115c..647970e 100644
--- a/legacy/Data/ingsw/0613_38/quest.txt
+++ b/Data/ingsw/0613_38/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_38.png
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_38.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_38/wrong1.txt b/Data/ingsw/0613_38/wrong1.txt
similarity index 96%
rename from legacy/Data/ingsw/0613_38/wrong1.txt
rename to Data/ingsw/0613_38/wrong1.txt
index 00b636b..a910206 100644
--- a/legacy/Data/ingsw/0613_38/wrong1.txt
+++ b/Data/ingsw/0613_38/wrong1.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_38/wrong2.txt b/Data/ingsw/0613_38/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0613_38/wrong2.txt
rename to Data/ingsw/0613_38/wrong2.txt
index dc39134..1ec9f1a 100644
--- a/legacy/Data/Questions/ingsw/0613_38/wrong2.txt
+++ b/Data/ingsw/0613_38/wrong2.txt
@@ -1,34 +1,34 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_38/wrong3.txt b/Data/ingsw/0613_38/wrong3.txt
similarity index 96%
rename from legacy/Data/ingsw/0613_38/wrong3.txt
rename to Data/ingsw/0613_38/wrong3.txt
index 6a9ef82..02b44cb 100644
--- a/legacy/Data/ingsw/0613_38/wrong3.txt
+++ b/Data/ingsw/0613_38/wrong3.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_39/correct.txt b/Data/ingsw/0613_39/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_39/correct.txt
rename to Data/ingsw/0613_39/correct.txt
diff --git a/legacy/Data/ingsw/0613_39/quest.txt b/Data/ingsw/0613_39/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0613_39/quest.txt
rename to Data/ingsw/0613_39/quest.txt
index 24a64fe..b598314 100644
--- a/legacy/Data/ingsw/0613_39/quest.txt
+++ b/Data/ingsw/0613_39/quest.txt
@@ -1,6 +1,6 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_39.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
-Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
-Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
-Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_39.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
+Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
+Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
+Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_39/wrong1.txt b/Data/ingsw/0613_39/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_39/wrong1.txt
rename to Data/ingsw/0613_39/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_39/wrong2.txt b/Data/ingsw/0613_39/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_39/wrong2.txt
rename to Data/ingsw/0613_39/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_4/correct.txt b/Data/ingsw/0613_4/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_4/correct.txt
rename to Data/ingsw/0613_4/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_4/quest.txt b/Data/ingsw/0613_4/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/0613_4/quest.txt
rename to Data/ingsw/0613_4/quest.txt
index 5cf5cae..c4fdcb7 100644
--- a/legacy/Data/Questions/ingsw/0613_4/quest.txt
+++ b/Data/ingsw/0613_4/quest.txt
@@ -1,12 +1,12 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_4.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-Si consideri lo state diagram in figura 
-
-
-
-Si consideri il seguente insieme di test cases:
-Test case 1: act0 act0 act0 
-Test case 2: act1 act0 act2 act1 act0 
-Test case 3: act1 act2 act2 act0 act2 
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_4.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+Si consideri lo state diagram in figura 
+
+
+
+Si consideri il seguente insieme di test cases:
+Test case 1: act0 act0 act0 
+Test case 2: act1 act0 act2 act1 act0 
+Test case 3: act1 act2 act2 act0 act2 
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_4/wrong1.txt b/Data/ingsw/0613_4/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_4/wrong1.txt
rename to Data/ingsw/0613_4/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_4/wrong2.txt b/Data/ingsw/0613_4/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_4/wrong2.txt
rename to Data/ingsw/0613_4/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_40/quest.txt b/Data/ingsw/0613_40/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_40/quest.txt
rename to Data/ingsw/0613_40/quest.txt
index 2959407..344d34d 100644
--- a/legacy/Data/Questions/ingsw/0613_40/quest.txt
+++ b/Data/ingsw/0613_40/quest.txt
@@ -1,2 +1,2 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_40.png
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_40.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_40/wrong1.txt b/Data/ingsw/0613_40/wrong1.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0613_40/wrong1.txt
rename to Data/ingsw/0613_40/wrong1.txt
index f919b6b..3a0f2d2 100644
--- a/legacy/Data/Questions/ingsw/0613_40/wrong1.txt
+++ b/Data/ingsw/0613_40/wrong1.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_40/wrong2.txt b/Data/ingsw/0613_40/wrong2.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0613_40/wrong2.txt
rename to Data/ingsw/0613_40/wrong2.txt
index fc9e0aa..b7a0fcd 100644
--- a/legacy/Data/Questions/ingsw/0613_40/wrong2.txt
+++ b/Data/ingsw/0613_40/wrong2.txt
@@ -1,36 +1,36 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_40/wrong3.txt b/Data/ingsw/0613_40/wrong3.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0613_40/wrong3.txt
rename to Data/ingsw/0613_40/wrong3.txt
index e537817..8a45805 100644
--- a/legacy/Data/Questions/ingsw/0613_40/wrong3.txt
+++ b/Data/ingsw/0613_40/wrong3.txt
@@ -1,35 +1,35 @@
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-algorithm
-
-when initial() then
-x := 0;
-
-elsewhen sample(0,T) then
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-end when;
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+algorithm
+
+when initial() then
+x := 0;
+
+elsewhen sample(0,T) then
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+end when;
 end FSA;
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_41/quest.txt b/Data/ingsw/0613_41/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/0613_41/quest.txt
rename to Data/ingsw/0613_41/quest.txt
index 99379e6..e060808 100644
--- a/legacy/Data/Questions/ingsw/0613_41/quest.txt
+++ b/Data/ingsw/0613_41/quest.txt
@@ -1,4 +1,4 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-Si consideri la funzione C
-int f(int x, int y) { ..... }
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+Si consideri la funzione C
+int f(int x, int y) { ..... }
 Quale delle seguenti assert esprime l'invariante che le variabili locali z e w di f() hanno somma minore di 1 oppure maggiore di 7 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0120_35/wrong1.txt b/Data/ingsw/0613_41/wrong1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0120_35/wrong1.txt
rename to Data/ingsw/0613_41/wrong1.txt
index cbf1814..3957de1 100644
--- a/legacy/Data/Questions/ingsw/0120_35/wrong1.txt
+++ b/Data/ingsw/0613_41/wrong1.txt
@@ -1,6 +1,6 @@
-int f(in x, int y) 
-{ 
-int z, w;
-assert( (z + w <= 1) || (z + w >= 7));
-.....
+int f(in x, int y) 
+{ 
+int z, w;
+assert( (z + w <= 1) || (z + w >= 7));
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_41/wrong2.txt b/Data/ingsw/0613_41/wrong2.txt
similarity index 93%
rename from legacy/Data/ingsw/0613_41/wrong2.txt
rename to Data/ingsw/0613_41/wrong2.txt
index 6fcb8b5..d56f4a5 100644
--- a/legacy/Data/ingsw/0613_41/wrong2.txt
+++ b/Data/ingsw/0613_41/wrong2.txt
@@ -1,6 +1,6 @@
-int f(in x, int y) 
-{ 
-int z, w;
-assert( (z + w > 1) || (z + w < 7));
-.....
+int f(in x, int y) 
+{ 
+int z, w;
+assert( (z + w > 1) || (z + w < 7));
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_41/wrong3.txt b/Data/ingsw/0613_41/wrong3.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0613_41/wrong3.txt
rename to Data/ingsw/0613_41/wrong3.txt
index 03b9f52..dff55b1 100644
--- a/legacy/Data/Questions/ingsw/0613_41/wrong3.txt
+++ b/Data/ingsw/0613_41/wrong3.txt
@@ -1,6 +1,6 @@
-int f(in x, int y) 
-{ 
-int z, w;
-assert( (z + w < 1) || (z + w > 7));
-.....
+int f(in x, int y) 
+{ 
+int z, w;
+assert( (z + w < 1) || (z + w > 7));
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_42/correct.txt b/Data/ingsw/0613_42/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_42/correct.txt
rename to Data/ingsw/0613_42/correct.txt
diff --git a/legacy/Data/ingsw/0613_42/quest.txt b/Data/ingsw/0613_42/quest.txt
similarity index 96%
rename from legacy/Data/ingsw/0613_42/quest.txt
rename to Data/ingsw/0613_42/quest.txt
index 2bda796..46e2ad5 100644
--- a/legacy/Data/ingsw/0613_42/quest.txt
+++ b/Data/ingsw/0613_42/quest.txt
@@ -1,29 +1,29 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x[3])
-{
-  if (x[0] + x[1] - x[2] < -7)
-    { return (0); }
-  else if (2*x[0] -3*x[1] + 4*x[2] > 7)
-        { 
-      if (x[0] + x[1] + x[2] > 10)
-                { return (1); }
-      else
-                { return (0); }      
-    }
- else
-           { 
-             if (2*x[0] + 3*x[1] + 4*x[2] > 9)
-               { return (1); }
-      else
-                 { return (0); }     
-    }
- } /* f()  */
-ed il seguente insieme di test cases:
-
-Test 1: x[0] = 1, x[1] = 1, x[2] = 1, 
-Test2: x[0] = 2, x[1] = 3, x[2] = 3, 
-Test 3: x[0] = -4, x[1] = -4, x[2] = 0, 
-Test 4: x[0] = 3, x[1] = 0, x[2] = 4, 
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x[3])
+{
+  if (x[0] + x[1] - x[2] < -7)
+    { return (0); }
+  else if (2*x[0] -3*x[1] + 4*x[2] > 7)
+        { 
+      if (x[0] + x[1] + x[2] > 10)
+                { return (1); }
+      else
+                { return (0); }      
+    }
+ else
+           { 
+             if (2*x[0] + 3*x[1] + 4*x[2] > 9)
+               { return (1); }
+      else
+                 { return (0); }     
+    }
+ } /* f()  */
+ed il seguente insieme di test cases:
+
+Test 1: x[0] = 1, x[1] = 1, x[2] = 1, 
+Test2: x[0] = 2, x[1] = 3, x[2] = 3, 
+Test 3: x[0] = -4, x[1] = -4, x[2] = 0, 
+Test 4: x[0] = 3, x[1] = 0, x[2] = 4, 
 Test 5:  x[0] = 3, x[1] = 3, x[2] = 5.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_42/wrong1.txt b/Data/ingsw/0613_42/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_42/wrong1.txt
rename to Data/ingsw/0613_42/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_42/wrong2.txt b/Data/ingsw/0613_42/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_42/wrong2.txt
rename to Data/ingsw/0613_42/wrong2.txt
diff --git a/Data/ingsw/0613_43/correct.txt b/Data/ingsw/0613_43/correct.txt
new file mode 100644
index 0000000..ca92838
--- /dev/null
+++ b/Data/ingsw/0613_43/correct.txt
@@ -0,0 +1,17 @@
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0613_43/quest.txt b/Data/ingsw/0613_43/quest.txt
new file mode 100644
index 0000000..348f2a5
--- /dev/null
+++ b/Data/ingsw/0613_43/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_16/wrong1.txt b/Data/ingsw/0613_43/wrong1.txt
similarity index 94%
rename from legacy/Data/ingsw/0210_16/wrong1.txt
rename to Data/ingsw/0613_43/wrong1.txt
index d7890b2..c0e3311 100644
--- a/legacy/Data/ingsw/0210_16/wrong1.txt
+++ b/Data/ingsw/0613_43/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_43/wrong2.txt b/Data/ingsw/0613_43/wrong2.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_43/wrong2.txt
rename to Data/ingsw/0613_43/wrong2.txt
index d50b268..434e904 100644
--- a/legacy/Data/Questions/ingsw/0613_43/wrong2.txt
+++ b/Data/ingsw/0613_43/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_44/correct.txt b/Data/ingsw/0613_44/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_44/correct.txt
rename to Data/ingsw/0613_44/correct.txt
diff --git a/legacy/Data/ingsw/0613_44/quest.txt b/Data/ingsw/0613_44/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_44/quest.txt
rename to Data/ingsw/0613_44/quest.txt
diff --git a/legacy/Data/ingsw/0613_44/wrong1.txt b/Data/ingsw/0613_44/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_44/wrong1.txt
rename to Data/ingsw/0613_44/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_44/wrong2.txt b/Data/ingsw/0613_44/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_44/wrong2.txt
rename to Data/ingsw/0613_44/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_45/correct.txt b/Data/ingsw/0613_45/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_45/correct.txt
rename to Data/ingsw/0613_45/correct.txt
diff --git a/legacy/Data/ingsw/0613_45/quest.txt b/Data/ingsw/0613_45/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0613_45/quest.txt
rename to Data/ingsw/0613_45/quest.txt
index e44e320..6f570ef 100644
--- a/legacy/Data/ingsw/0613_45/quest.txt
+++ b/Data/ingsw/0613_45/quest.txt
@@ -1,21 +1,21 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a  + b - 6 >= 0) && (b - c - 1 <= 0) )
-          return (1);    // punto di uscita 1
-      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0))
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a  + b - 6 >= 0) && (b - c - 1 <= 0) )
+          return (1);    // punto di uscita 1
+      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0))
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_45/wrong1.txt b/Data/ingsw/0613_45/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_45/wrong1.txt
rename to Data/ingsw/0613_45/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_45/wrong2.txt b/Data/ingsw/0613_45/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_45/wrong2.txt
rename to Data/ingsw/0613_45/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_46/correct.txt b/Data/ingsw/0613_46/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_46/correct.txt
rename to Data/ingsw/0613_46/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_46/quest.txt b/Data/ingsw/0613_46/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0613_46/quest.txt
rename to Data/ingsw/0613_46/quest.txt
index 03acbcc..3187441 100644
--- a/legacy/Data/Questions/ingsw/0613_46/quest.txt
+++ b/Data/ingsw/0613_46/quest.txt
@@ -1,30 +1,30 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-
-int f(int x[3])
-{
-  if (-x[0] + x[1] - x[2] < -7)
-     if (-x[0] + x[1] - x[2] > 10)
-                { return (0); }
-      else
-                { return (1); }
-      else if (-3*x[0] +3*x[1] - 5*x[2] > 7)
-        { 
-      return (0);      
-  }
- else
-           { 
-             if (3*x[0] - 5*x[1] + 7*x[2] > 9)
-               { return (1); }
-      else
-                { return (0); }      
-    }
-} /* f()  */
-----------
-ed il seguente insieme di test cases:
-
-Test 1: x[0] = 2, x[1] = -3, x[2] = 4, 
-Test 2: x[0] = 1, x[1] = 0, x[2] = 2, 
-Test 3:   x[0] = -3, x[1] = -4, x[2] = -3,
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+
+int f(int x[3])
+{
+  if (-x[0] + x[1] - x[2] < -7)
+     if (-x[0] + x[1] - x[2] > 10)
+                { return (0); }
+      else
+                { return (1); }
+      else if (-3*x[0] +3*x[1] - 5*x[2] > 7)
+        { 
+      return (0);      
+  }
+ else
+           { 
+             if (3*x[0] - 5*x[1] + 7*x[2] > 9)
+               { return (1); }
+      else
+                { return (0); }      
+    }
+} /* f()  */
+----------
+ed il seguente insieme di test cases:
+
+Test 1: x[0] = 2, x[1] = -3, x[2] = 4, 
+Test 2: x[0] = 1, x[1] = 0, x[2] = 2, 
+Test 3:   x[0] = -3, x[1] = -4, x[2] = -3,
 Test 4: x[0] = 3, x[1] = -1, x[2] = -3.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_46/wrong1.txt b/Data/ingsw/0613_46/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_46/wrong1.txt
rename to Data/ingsw/0613_46/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_46/wrong2.txt b/Data/ingsw/0613_46/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_46/wrong2.txt
rename to Data/ingsw/0613_46/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_47/correct.txt b/Data/ingsw/0613_47/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_47/correct.txt
rename to Data/ingsw/0613_47/correct.txt
diff --git a/legacy/Data/ingsw/0613_47/quest.txt b/Data/ingsw/0613_47/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_47/quest.txt
rename to Data/ingsw/0613_47/quest.txt
diff --git a/legacy/Data/ingsw/0613_47/wrong1.txt b/Data/ingsw/0613_47/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_47/wrong1.txt
rename to Data/ingsw/0613_47/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_47/wrong2.txt b/Data/ingsw/0613_47/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_47/wrong2.txt
rename to Data/ingsw/0613_47/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_48/correct.txt b/Data/ingsw/0613_48/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_48/correct.txt
rename to Data/ingsw/0613_48/correct.txt
diff --git a/legacy/Data/ingsw/0613_48/quest.txt b/Data/ingsw/0613_48/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/0613_48/quest.txt
rename to Data/ingsw/0613_48/quest.txt
index adccf3a..ab4b6cc 100644
--- a/legacy/Data/ingsw/0613_48/quest.txt
+++ b/Data/ingsw/0613_48/quest.txt
@@ -1,9 +1,9 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_48.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
-Ad esempio lo state diagram in figura
-
-
-
-Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
-Uno scenario è una sequenza di stati.
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_48.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
+Ad esempio lo state diagram in figura
+
+
+
+Rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
+Uno scenario è una sequenza di stati.
 Qual'e' la probabilità dello scenario: 1, 2, 3? In altri terminti, qual'è la probabilità che non sia necessario ripetere la prima fase (ma non la seconda) ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_48/wrong1.txt b/Data/ingsw/0613_48/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_48/wrong1.txt
rename to Data/ingsw/0613_48/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_48/wrong2.txt b/Data/ingsw/0613_48/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_48/wrong2.txt
rename to Data/ingsw/0613_48/wrong2.txt
diff --git a/Data/ingsw/0613_49/correct.txt b/Data/ingsw/0613_49/correct.txt
new file mode 100644
index 0000000..9be970c
--- /dev/null
+++ b/Data/ingsw/0613_49/correct.txt
@@ -0,0 +1,16 @@
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0613_49/quest.txt b/Data/ingsw/0613_49/quest.txt
new file mode 100644
index 0000000..0910f0b
--- /dev/null
+++ b/Data/ingsw/0613_49/quest.txt
@@ -0,0 +1,4 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/Data/ingsw/0613_49/wrong1.txt b/Data/ingsw/0613_49/wrong1.txt
new file mode 100644
index 0000000..8b6fcac
--- /dev/null
+++ b/Data/ingsw/0613_49/wrong1.txt
@@ -0,0 +1,16 @@
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0613_49/wrong2.txt b/Data/ingsw/0613_49/wrong2.txt
new file mode 100644
index 0000000..40a2ce0
--- /dev/null
+++ b/Data/ingsw/0613_49/wrong2.txt
@@ -0,0 +1,16 @@
+<pre>
+class Monitor
+
+InputReal x, y, z;  // plant output
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_5/correct.txt b/Data/ingsw/0613_5/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_5/correct.txt
rename to Data/ingsw/0613_5/correct.txt
diff --git a/legacy/Data/Questions/ingsw/0613_5/quest.txt b/Data/ingsw/0613_5/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/0613_5/quest.txt
rename to Data/ingsw/0613_5/quest.txt
index 579b39b..1e9d8fc 100644
--- a/legacy/Data/Questions/ingsw/0613_5/quest.txt
+++ b/Data/ingsw/0613_5/quest.txt
@@ -1,29 +1,29 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-Si consideri la seguente funzione C:
------------
-int f(int x[3])
-{
-  if (-x[0] + x[1] - x[2] < -7)
-     if (-x[0] + x[1] - x[2] > 10)
-                { return (0); }
-      else
-                { return (1); }
-      else if (-3*x[0] +3*x[1] - 5*x[2] > 7)
-        { 
-     if (3*x[0] - 5*x[1] + 7*x[2] > 9)
-               { return (0); }
-      else
-                { return (1); }      
-  }
- else
-           { 
-             return (0);       
-    }
-
-} /* f()  */
-----------
-ed il seguente insieme di test cases:
-
-Test 1: x[0] = 1, x[1] = 5, x[2] = 3,
-Test 2: x[0] = 4, x[1] = -2, x[2] = 2, 
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+Si consideri la seguente funzione C:
+-----------
+int f(int x[3])
+{
+  if (-x[0] + x[1] - x[2] < -7)
+     if (-x[0] + x[1] - x[2] > 10)
+                { return (0); }
+      else
+                { return (1); }
+      else if (-3*x[0] +3*x[1] - 5*x[2] > 7)
+        { 
+     if (3*x[0] - 5*x[1] + 7*x[2] > 9)
+               { return (0); }
+      else
+                { return (1); }      
+  }
+ else
+           { 
+             return (0);       
+    }
+
+} /* f()  */
+----------
+ed il seguente insieme di test cases:
+
+Test 1: x[0] = 1, x[1] = 5, x[2] = 3,
+Test 2: x[0] = 4, x[1] = -2, x[2] = 2, 
 Test 3:   x[0] = 5, x[1] = 3, x[2] = -4.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_5/wrong1.txt b/Data/ingsw/0613_5/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_5/wrong1.txt
rename to Data/ingsw/0613_5/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_5/wrong2.txt b/Data/ingsw/0613_5/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_5/wrong2.txt
rename to Data/ingsw/0613_5/wrong2.txt
diff --git a/Data/ingsw/0613_6/correct.txt b/Data/ingsw/0613_6/correct.txt
new file mode 100644
index 0000000..d3daba4
--- /dev/null
+++ b/Data/ingsw/0613_6/correct.txt
@@ -0,0 +1,17 @@
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and ((x >= 30) or (x <= 20)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/0613_6/quest.txt b/Data/ingsw/0613_6/quest.txt
new file mode 100644
index 0000000..6938bd6
--- /dev/null
+++ b/Data/ingsw/0613_6/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
+Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_6/wrong1.txt b/Data/ingsw/0613_6/wrong1.txt
similarity index 93%
rename from legacy/Data/ingsw/0613_6/wrong1.txt
rename to Data/ingsw/0613_6/wrong1.txt
index 66064fe..5bdf9c8 100644
--- a/legacy/Data/ingsw/0613_6/wrong1.txt
+++ b/Data/ingsw/0613_6/wrong1.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and (x >= 20) and (x <= 30) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and (x >= 20) and (x <= 30) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0324_46/wrong2.txt b/Data/ingsw/0613_6/wrong2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/0324_46/wrong2.txt
rename to Data/ingsw/0613_6/wrong2.txt
index c71f1f5..169ec7c 100644
--- a/legacy/Data/Questions/ingsw/0324_46/wrong2.txt
+++ b/Data/ingsw/0613_6/wrong2.txt
@@ -1,17 +1,17 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) or ((x >= 20) and (x <= 30)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) or ((x >= 20) and (x <= 30)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_7/correct.txt b/Data/ingsw/0613_7/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_7/correct.txt
rename to Data/ingsw/0613_7/correct.txt
diff --git a/legacy/Data/ingsw/0613_7/quest.txt b/Data/ingsw/0613_7/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/0613_7/quest.txt
rename to Data/ingsw/0613_7/quest.txt
index dbd72c0..20501b2 100644
--- a/legacy/Data/ingsw/0613_7/quest.txt
+++ b/Data/ingsw/0613_7/quest.txt
@@ -1,22 +1,22 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
-in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-int f(int a, int b, int c)
-{    if ( (a - 100 >= 0) && (b - c - 1 <= 0) )
-          return (1);    // punto di uscita 1
-      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0)
-)
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta 
+in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+int f(int a, int b, int c)
+{    if ( (a - 100 >= 0) && (b - c - 1 <= 0) )
+          return (1);    // punto di uscita 1
+      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0)
+)
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
    Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_7/wrong1.txt b/Data/ingsw/0613_7/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_7/wrong1.txt
rename to Data/ingsw/0613_7/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_7/wrong2.txt b/Data/ingsw/0613_7/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_7/wrong2.txt
rename to Data/ingsw/0613_7/wrong2.txt
diff --git a/legacy/Data/ingsw/0613_8/correct.txt b/Data/ingsw/0613_8/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_8/correct.txt
rename to Data/ingsw/0613_8/correct.txt
diff --git a/legacy/Data/ingsw/0613_8/quest.txt b/Data/ingsw/0613_8/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_8/quest.txt
rename to Data/ingsw/0613_8/quest.txt
diff --git a/legacy/Data/ingsw/0613_8/wrong1.txt b/Data/ingsw/0613_8/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_8/wrong1.txt
rename to Data/ingsw/0613_8/wrong1.txt
diff --git a/legacy/Data/ingsw/0613_8/wrong2.txt b/Data/ingsw/0613_8/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0613_8/wrong2.txt
rename to Data/ingsw/0613_8/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_9/quest.txt b/Data/ingsw/0613_9/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/0613_9/quest.txt
rename to Data/ingsw/0613_9/quest.txt
index 89f55eb..b56062c 100644
--- a/legacy/Data/Questions/ingsw/0613_9/quest.txt
+++ b/Data/ingsw/0613_9/quest.txt
@@ -1,4 +1,4 @@
-img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_9.png
-Si consideri la seguente architettura software:
-
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_9.png
+Si consideri la seguente architettura software:
+
 Quale dei seguenti modelli Modelica meglio la rappresenta.
\ No newline at end of file
diff --git a/Data/ingsw/0613_9/wrong1.txt b/Data/ingsw/0613_9/wrong1.txt
new file mode 100644
index 0000000..d698ca8
--- /dev/null
+++ b/Data/ingsw/0613_9/wrong1.txt
@@ -0,0 +1,8 @@
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_9/wrong2.txt b/Data/ingsw/0613_9/wrong2.txt
similarity index 96%
rename from legacy/Data/ingsw/0613_9/wrong2.txt
rename to Data/ingsw/0613_9/wrong2.txt
index 2c10a10..daecacc 100644
--- a/legacy/Data/ingsw/0613_9/wrong2.txt
+++ b/Data/ingsw/0613_9/wrong2.txt
@@ -1,4 +1,4 @@
-input12)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output24, sc4.input24)
+input12)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output24, sc4.input24)
 connect(sc
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0613_9/wrong3.txt b/Data/ingsw/0613_9/wrong3.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/0613_9/wrong3.txt
rename to Data/ingsw/0613_9/wrong3.txt
index 7ddc09e..fc21af3 100644
--- a/legacy/Data/Questions/ingsw/0613_9/wrong3.txt
+++ b/Data/ingsw/0613_9/wrong3.txt
@@ -1,46 +1,46 @@
-output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output43, sc3.input43)
-
-
-end SysArch;
-
-2.
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc2.input12)
-connect(sc1.output14, sc4.input14)
-connect(sc2.output23, sc3.input23)
-connect(sc2.output24, sc4.input24)
-connect(sc3.output31, sc1.input31)
-connect(sc3.output32, sc2.input32)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output43, sc3.input43)
-
-
-end SysArch;
-
-3.
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output13, sc3.input13)
-connect(sc2.output21, sc1.input21)
-connect(sc2.output23, sc3.input23)
-connect(sc3.output32, sc2.input32)
-connect(sc3.output34, sc4.input34)
-connect(sc4.output41, sc1.input41)
-connect(sc4.output42, sc2.input42)
-
-
+output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output43, sc3.input43)
+
+
+end SysArch;
+
+2.
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output12, sc2.input12)
+connect(sc1.output14, sc4.input14)
+connect(sc2.output23, sc3.input23)
+connect(sc2.output24, sc4.input24)
+connect(sc3.output31, sc1.input31)
+connect(sc3.output32, sc2.input32)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output43, sc3.input43)
+
+
+end SysArch;
+
+3.
+block SysArch  //  System Architecture
+
+SC1 sc1
+SC2 sc2
+SC3 sc3
+SC4 sc4
+
+connect(sc1.output13, sc3.input13)
+connect(sc2.output21, sc1.input21)
+connect(sc2.output23, sc3.input23)
+connect(sc3.output32, sc2.input32)
+connect(sc3.output34, sc4.input34)
+connect(sc4.output41, sc1.input41)
+connect(sc4.output42, sc2.input42)
+
+
 end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_0/correct.txt b/Data/ingsw/0621_0/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_0/correct.txt
rename to Data/ingsw/0621_0/correct.txt
diff --git a/legacy/Data/ingsw/0621_0/quest.txt b/Data/ingsw/0621_0/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_0/quest.txt
rename to Data/ingsw/0621_0/quest.txt
diff --git a/legacy/Data/ingsw/0621_0/wrong0.txt b/Data/ingsw/0621_0/wrong0.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_0/wrong0.txt
rename to Data/ingsw/0621_0/wrong0.txt
diff --git a/legacy/Data/ingsw/0621_0/wrong1.txt b/Data/ingsw/0621_0/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_0/wrong1.txt
rename to Data/ingsw/0621_0/wrong1.txt
diff --git a/legacy/Data/ingsw/0621_1/correct.txt b/Data/ingsw/0621_1/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_1/correct.txt
rename to Data/ingsw/0621_1/correct.txt
diff --git a/legacy/Data/ingsw/0621_1/quest.txt b/Data/ingsw/0621_1/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_1/quest.txt
rename to Data/ingsw/0621_1/quest.txt
diff --git a/legacy/Data/ingsw/0621_1/wrong1.txt b/Data/ingsw/0621_1/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_1/wrong1.txt
rename to Data/ingsw/0621_1/wrong1.txt
diff --git a/legacy/Data/ingsw/0621_1/wrong2.txt b/Data/ingsw/0621_1/wrong2.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_1/wrong2.txt
rename to Data/ingsw/0621_1/wrong2.txt
diff --git a/legacy/Data/ingsw/0621_10/correct.txt b/Data/ingsw/0621_10/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_10/correct.txt
rename to Data/ingsw/0621_10/correct.txt
diff --git a/legacy/Data/ingsw/0621_10/quest.txt b/Data/ingsw/0621_10/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_10/quest.txt
rename to Data/ingsw/0621_10/quest.txt
diff --git a/legacy/Data/ingsw/0621_10/wrong0.txt b/Data/ingsw/0621_10/wrong0.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_10/wrong0.txt
rename to Data/ingsw/0621_10/wrong0.txt
diff --git a/legacy/Data/ingsw/0621_10/wrong1.txt b/Data/ingsw/0621_10/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_10/wrong1.txt
rename to Data/ingsw/0621_10/wrong1.txt
diff --git a/legacy/Data/ingsw/0621_13/correct.txt b/Data/ingsw/0621_13/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_13/correct.txt
rename to Data/ingsw/0621_13/correct.txt
diff --git a/legacy/Data/ingsw/0621_13/quest.txt b/Data/ingsw/0621_13/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_13/quest.txt
rename to Data/ingsw/0621_13/quest.txt
diff --git a/legacy/Data/ingsw/0621_13/wrong0.txt b/Data/ingsw/0621_13/wrong0.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_13/wrong0.txt
rename to Data/ingsw/0621_13/wrong0.txt
diff --git a/legacy/Data/ingsw/0621_13/wrong1.txt b/Data/ingsw/0621_13/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_13/wrong1.txt
rename to Data/ingsw/0621_13/wrong1.txt
diff --git a/legacy/Data/ingsw/0621_14/correct.txt b/Data/ingsw/0621_14/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_14/correct.txt
rename to Data/ingsw/0621_14/correct.txt
diff --git a/legacy/Data/ingsw/0621_14/quest.txt b/Data/ingsw/0621_14/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_14/quest.txt
rename to Data/ingsw/0621_14/quest.txt
diff --git a/legacy/Data/ingsw/0621_14/wrong0.txt b/Data/ingsw/0621_14/wrong0.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_14/wrong0.txt
rename to Data/ingsw/0621_14/wrong0.txt
diff --git a/legacy/Data/ingsw/0621_14/wrong1.txt b/Data/ingsw/0621_14/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_14/wrong1.txt
rename to Data/ingsw/0621_14/wrong1.txt
diff --git a/legacy/Data/ingsw/0621_17/correct.txt b/Data/ingsw/0621_17/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_17/correct.txt
rename to Data/ingsw/0621_17/correct.txt
diff --git a/legacy/Data/ingsw/0621_17/quest.txt b/Data/ingsw/0621_17/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_17/quest.txt
rename to Data/ingsw/0621_17/quest.txt
diff --git a/legacy/Data/ingsw/0621_17/wrong0.txt b/Data/ingsw/0621_17/wrong0.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_17/wrong0.txt
rename to Data/ingsw/0621_17/wrong0.txt
diff --git a/legacy/Data/ingsw/0621_17/wrong1.txt b/Data/ingsw/0621_17/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_17/wrong1.txt
rename to Data/ingsw/0621_17/wrong1.txt
diff --git a/legacy/Data/ingsw/0621_19/correct.txt b/Data/ingsw/0621_19/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_19/correct.txt
rename to Data/ingsw/0621_19/correct.txt
diff --git a/legacy/Data/ingsw/0621_19/quest.txt b/Data/ingsw/0621_19/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_19/quest.txt
rename to Data/ingsw/0621_19/quest.txt
diff --git a/legacy/Data/ingsw/0621_19/wrong0.txt b/Data/ingsw/0621_19/wrong0.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_19/wrong0.txt
rename to Data/ingsw/0621_19/wrong0.txt
diff --git a/legacy/Data/ingsw/0621_19/wrong1.txt b/Data/ingsw/0621_19/wrong1.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_19/wrong1.txt
rename to Data/ingsw/0621_19/wrong1.txt
diff --git a/legacy/Data/ingsw/0621_2/correct.txt b/Data/ingsw/0621_2/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_2/correct.txt
rename to Data/ingsw/0621_2/correct.txt
diff --git a/legacy/Data/ingsw/0621_2/quest.txt b/Data/ingsw/0621_2/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_2/quest.txt
rename to Data/ingsw/0621_2/quest.txt
diff --git a/legacy/Data/ingsw/0621_2/wrong0.txt b/Data/ingsw/0621_2/wrong0.txt
similarity index 100%
rename from legacy/Data/ingsw/0621_2/wrong0.txt
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index 3631f63..58ef38e 100644
--- a/legacy/Data/Questions/ingsw/1122_47/quest.txt
+++ b/Data/ingsw/0722_12/quest.txt
@@ -1,11 +1,11 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-
-Si consideri la seguente funzione C:
-
-int f1(int x)  { return (x + 7); }
-
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-
-{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
-
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+
+Si consideri la seguente funzione C:
+
+int f1(int x)  { return (x + 7); }
+
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+
+{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
+
 Quale dei seguenti test cases consegue una partition coverage del 100% ?
\ No newline at end of file
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diff --git a/legacy/Data/ingsw/0722_18/quest.txt b/Data/ingsw/0722_18/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0722_18/quest.txt
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diff --git a/legacy/Data/ingsw/0722_18/wrong 1.txt b/Data/ingsw/0722_18/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0722_18/wrong 1.txt
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diff --git a/legacy/Data/ingsw/0722_18/wrong 2.txt b/Data/ingsw/0722_18/wrong 2.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_19/correct.txt b/Data/ingsw/0722_19/correct.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_19/quest.txt b/Data/ingsw/0722_19/quest.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_19/wrong 1.txt b/Data/ingsw/0722_19/wrong 1.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_19/wrong 2.txt b/Data/ingsw/0722_19/wrong 2.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_2/correct.txt b/Data/ingsw/0722_2/correct.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_2/quest.txt b/Data/ingsw/0722_2/quest.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0210_25/wrong1.txt b/Data/ingsw/0722_2/wrong 1.txt
similarity index 95%
rename from legacy/Data/ingsw/0210_25/wrong1.txt
rename to Data/ingsw/0722_2/wrong 1.txt
index 6a97baf..392cc67 100644
--- a/legacy/Data/ingsw/0210_25/wrong1.txt
+++ b/Data/ingsw/0722_2/wrong 1.txt	
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 1) || (y > 1)) );
-.....
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 1) || (y > 1)) );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/0210_36/wrong2.txt b/Data/ingsw/0722_2/wrong 2.txt
similarity index 95%
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index e607157..2fde3f0 100644
--- a/legacy/Data/Questions/ingsw/0210_36/wrong2.txt
+++ b/Data/ingsw/0722_2/wrong 2.txt	
@@ -1,5 +1,5 @@
-int f(in x, int y) 
-{ 
-assert( (x > 0) && (y > 0) && (x > 1) && (y > 1)  );
-.....
+int f(in x, int y) 
+{ 
+assert( (x > 0) && (y > 0) && (x > 1) && (y > 1)  );
+.....
 }
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_20/correct.txt b/Data/ingsw/0722_20/correct.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_20/quest.txt b/Data/ingsw/0722_20/quest.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_20/wrong 1.txt b/Data/ingsw/0722_20/wrong 1.txt
similarity index 100%
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similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_21/correct.txt b/Data/ingsw/0722_21/correct.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_21/quest.txt b/Data/ingsw/0722_21/quest.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_21/wrong 1.txt b/Data/ingsw/0722_21/wrong 1.txt
similarity index 100%
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similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_22/correct.txt b/Data/ingsw/0722_22/correct.txt
similarity index 100%
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similarity index 100%
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similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_22/wrong 2.txt b/Data/ingsw/0722_22/wrong 2.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_23/correct.txt b/Data/ingsw/0722_23/correct.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_23/quest.txt b/Data/ingsw/0722_23/quest.txt
similarity index 100%
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similarity index 100%
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similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_24/correct.txt b/Data/ingsw/0722_24/correct.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_24/quest.txt b/Data/ingsw/0722_24/quest.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_24/wrong 1.txt b/Data/ingsw/0722_24/wrong 1.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_24/wrong 2.txt b/Data/ingsw/0722_24/wrong 2.txt
similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_25/correct.txt b/Data/ingsw/0722_25/correct.txt
similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_26/correct.txt b/Data/ingsw/0722_26/correct.txt
similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_4/correct.txt b/Data/ingsw/0722_4/correct.txt
similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_7/correct.txt b/Data/ingsw/0722_7/correct.txt
similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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diff --git a/legacy/Data/ingsw/0722_8/correct.txt b/Data/ingsw/0722_8/correct.txt
similarity index 100%
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similarity index 100%
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diff --git a/legacy/Data/ingsw/0922_10/correct.txt b/Data/ingsw/0922_10/correct.txt
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diff --git a/legacy/Data/ingsw/0922_11/correct.txt b/Data/ingsw/0922_11/correct.txt
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diff --git a/legacy/Data/ingsw/0922_12/correct.txt b/Data/ingsw/0922_12/correct.txt
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diff --git a/legacy/Data/ingsw/0922_14/correct.txt b/Data/ingsw/0922_14/correct.txt
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rename to Data/ingsw/0922_15/wrong 2.txt
diff --git a/legacy/Data/ingsw/0922_16/correct.txt b/Data/ingsw/0922_16/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_16/correct.txt
rename to Data/ingsw/0922_16/correct.txt
diff --git a/legacy/Data/ingsw/0922_16/quest.txt b/Data/ingsw/0922_16/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_16/quest.txt
rename to Data/ingsw/0922_16/quest.txt
diff --git a/legacy/Data/ingsw/0922_16/wrong 1.txt b/Data/ingsw/0922_16/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_16/wrong 1.txt
rename to Data/ingsw/0922_16/wrong 1.txt
diff --git a/legacy/Data/ingsw/0922_16/wrong 2.txt b/Data/ingsw/0922_16/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_16/wrong 2.txt
rename to Data/ingsw/0922_16/wrong 2.txt
diff --git a/legacy/Data/ingsw/0922_17/correct.txt b/Data/ingsw/0922_17/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_17/correct.txt
rename to Data/ingsw/0922_17/correct.txt
diff --git a/legacy/Data/ingsw/0922_17/quest.txt b/Data/ingsw/0922_17/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_17/quest.txt
rename to Data/ingsw/0922_17/quest.txt
diff --git a/legacy/Data/ingsw/0922_17/wrong 1.txt b/Data/ingsw/0922_17/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_17/wrong 1.txt
rename to Data/ingsw/0922_17/wrong 1.txt
diff --git a/legacy/Data/ingsw/0922_17/wrong 2.txt b/Data/ingsw/0922_17/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_17/wrong 2.txt
rename to Data/ingsw/0922_17/wrong 2.txt
diff --git a/legacy/Data/ingsw/0922_18/correct.txt b/Data/ingsw/0922_18/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_18/correct.txt
rename to Data/ingsw/0922_18/correct.txt
diff --git a/legacy/Data/ingsw/0922_18/quest.txt b/Data/ingsw/0922_18/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_18/quest.txt
rename to Data/ingsw/0922_18/quest.txt
diff --git a/legacy/Data/ingsw/0922_18/wrong 1.txt b/Data/ingsw/0922_18/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_18/wrong 1.txt
rename to Data/ingsw/0922_18/wrong 1.txt
diff --git a/legacy/Data/ingsw/0922_18/wrong 2.txt b/Data/ingsw/0922_18/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_18/wrong 2.txt
rename to Data/ingsw/0922_18/wrong 2.txt
diff --git a/legacy/Data/ingsw/0922_3/correct.txt b/Data/ingsw/0922_3/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_3/correct.txt
rename to Data/ingsw/0922_3/correct.txt
diff --git a/legacy/Data/ingsw/0922_3/quest.txt b/Data/ingsw/0922_3/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_3/quest.txt
rename to Data/ingsw/0922_3/quest.txt
diff --git a/legacy/Data/ingsw/0922_3/wrong 1.txt b/Data/ingsw/0922_3/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_3/wrong 1.txt
rename to Data/ingsw/0922_3/wrong 1.txt
diff --git a/legacy/Data/ingsw/0922_3/wrong 2.txt b/Data/ingsw/0922_3/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_3/wrong 2.txt
rename to Data/ingsw/0922_3/wrong 2.txt
diff --git a/legacy/Data/ingsw/0922_4/correct.txt b/Data/ingsw/0922_4/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_4/correct.txt
rename to Data/ingsw/0922_4/correct.txt
diff --git a/legacy/Data/ingsw/0922_4/quest.txt b/Data/ingsw/0922_4/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_4/quest.txt
rename to Data/ingsw/0922_4/quest.txt
diff --git a/legacy/Data/ingsw/0922_4/wrong 1.txt b/Data/ingsw/0922_4/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_4/wrong 1.txt
rename to Data/ingsw/0922_4/wrong 1.txt
diff --git a/legacy/Data/ingsw/0922_4/wrong 2.txt b/Data/ingsw/0922_4/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_4/wrong 2.txt
rename to Data/ingsw/0922_4/wrong 2.txt
diff --git a/legacy/Data/ingsw/0922_5/correct.txt b/Data/ingsw/0922_5/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_5/correct.txt
rename to Data/ingsw/0922_5/correct.txt
diff --git a/legacy/Data/ingsw/0922_5/quest.txt b/Data/ingsw/0922_5/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_5/quest.txt
rename to Data/ingsw/0922_5/quest.txt
diff --git a/legacy/Data/ingsw/0922_5/wrong 1.txt b/Data/ingsw/0922_5/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_5/wrong 1.txt
rename to Data/ingsw/0922_5/wrong 1.txt
diff --git a/legacy/Data/ingsw/0922_5/wrong 2.txt b/Data/ingsw/0922_5/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_5/wrong 2.txt
rename to Data/ingsw/0922_5/wrong 2.txt
diff --git a/legacy/Data/ingsw/0922_6/correct.txt b/Data/ingsw/0922_6/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_6/correct.txt
rename to Data/ingsw/0922_6/correct.txt
diff --git a/legacy/Data/ingsw/0922_6/quest.txt b/Data/ingsw/0922_6/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_6/quest.txt
rename to Data/ingsw/0922_6/quest.txt
diff --git a/legacy/Data/ingsw/0922_6/wrong 1.txt b/Data/ingsw/0922_6/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_6/wrong 1.txt
rename to Data/ingsw/0922_6/wrong 1.txt
diff --git a/legacy/Data/ingsw/0922_6/wrong 2.txt b/Data/ingsw/0922_6/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_6/wrong 2.txt
rename to Data/ingsw/0922_6/wrong 2.txt
diff --git a/legacy/Data/ingsw/0922_7/correct.txt b/Data/ingsw/0922_7/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_7/correct.txt
rename to Data/ingsw/0922_7/correct.txt
diff --git a/legacy/Data/ingsw/0922_7/quest.txt b/Data/ingsw/0922_7/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_7/quest.txt
rename to Data/ingsw/0922_7/quest.txt
diff --git a/legacy/Data/ingsw/0922_7/wrong 1.txt b/Data/ingsw/0922_7/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_7/wrong 1.txt
rename to Data/ingsw/0922_7/wrong 1.txt
diff --git a/legacy/Data/ingsw/0922_7/wrong 2.txt b/Data/ingsw/0922_7/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_7/wrong 2.txt
rename to Data/ingsw/0922_7/wrong 2.txt
diff --git a/legacy/Data/ingsw/0922_8/correct.txt b/Data/ingsw/0922_8/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_8/correct.txt
rename to Data/ingsw/0922_8/correct.txt
diff --git a/legacy/Data/ingsw/0922_8/quest.txt b/Data/ingsw/0922_8/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_8/quest.txt
rename to Data/ingsw/0922_8/quest.txt
diff --git a/legacy/Data/ingsw/0922_8/wrong 1.txt b/Data/ingsw/0922_8/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_8/wrong 1.txt
rename to Data/ingsw/0922_8/wrong 1.txt
diff --git a/legacy/Data/ingsw/0922_8/wrong 2.txt b/Data/ingsw/0922_8/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_8/wrong 2.txt
rename to Data/ingsw/0922_8/wrong 2.txt
diff --git a/legacy/Data/ingsw/0922_9/correct.txt b/Data/ingsw/0922_9/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_9/correct.txt
rename to Data/ingsw/0922_9/correct.txt
diff --git a/legacy/Data/ingsw/0922_9/quest.txt b/Data/ingsw/0922_9/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_9/quest.txt
rename to Data/ingsw/0922_9/quest.txt
diff --git a/legacy/Data/ingsw/0922_9/wrong 1.txt b/Data/ingsw/0922_9/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_9/wrong 1.txt
rename to Data/ingsw/0922_9/wrong 1.txt
diff --git a/legacy/Data/ingsw/0922_9/wrong 2.txt b/Data/ingsw/0922_9/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/0922_9/wrong 2.txt
rename to Data/ingsw/0922_9/wrong 2.txt
diff --git a/legacy/Data/ingsw/10/correct.txt b/Data/ingsw/10/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/10/correct.txt
rename to Data/ingsw/10/correct.txt
diff --git a/legacy/Data/Questions/ingsw/10/quest.txt b/Data/ingsw/10/quest.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/10/quest.txt
rename to Data/ingsw/10/quest.txt
index 6befac6..b474c0a 100644
--- a/legacy/Data/Questions/ingsw/10/quest.txt
+++ b/Data/ingsw/10/quest.txt
@@ -1,26 +1,26 @@
-Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato:
-<pre>
-block Monitor
-
-input Real x;  
-output Boolean y;
-Boolean w;
-
-initial equation
-
-y = false;
-
-equation
-
-w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
-
-algorithm
-
-when edge(w) then
-y := true;
-end when;
-
-end Monitor;
-</pre>
-
+Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato:
+<pre>
+block Monitor
+
+input Real x;  
+output Boolean y;
+Boolean w;
+
+initial equation
+
+y = false;
+
+equation
+
+w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
+
+algorithm
+
+when edge(w) then
+y := true;
+end when;
+
+end Monitor;
+</pre>
+
 Quale delle seguenti affermazioni meglio descrive il requisito monitorato?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/10/wrong 2.txt b/Data/ingsw/10/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/10/wrong 2.txt
rename to Data/ingsw/10/wrong 2.txt
diff --git a/legacy/Data/ingsw/10/wrong.txt b/Data/ingsw/10/wrong.txt
similarity index 100%
rename from legacy/Data/ingsw/10/wrong.txt
rename to Data/ingsw/10/wrong.txt
diff --git a/legacy/Data/ingsw/11/correct.txt b/Data/ingsw/11/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/11/correct.txt
rename to Data/ingsw/11/correct.txt
diff --git a/legacy/Data/ingsw/11/quest.txt b/Data/ingsw/11/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/11/quest.txt
rename to Data/ingsw/11/quest.txt
diff --git a/legacy/Data/ingsw/11/wrong 2.txt b/Data/ingsw/11/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/11/wrong 2.txt
rename to Data/ingsw/11/wrong 2.txt
diff --git a/legacy/Data/ingsw/11/wrong.txt b/Data/ingsw/11/wrong.txt
similarity index 100%
rename from legacy/Data/ingsw/11/wrong.txt
rename to Data/ingsw/11/wrong.txt
diff --git a/legacy/Data/ingsw/1122_1/correct.txt b/Data/ingsw/1122_1/correct.txt
similarity index 96%
rename from legacy/Data/ingsw/1122_1/correct.txt
rename to Data/ingsw/1122_1/correct.txt
index fd39f0f..8e30daa 100644
--- a/legacy/Data/ingsw/1122_1/correct.txt
+++ b/Data/ingsw/1122_1/correct.txt
@@ -1,44 +1,44 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-
-algorithm
-
-
-when initial() then
-x := 0;
-
-
-elsewhen sample(0,T) then
-
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-else x := pre(x); // default
-end if;
-
-
-end when;
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+
+algorithm
+
+
+when initial() then
+x := 0;
+
+
+elsewhen sample(0,T) then
+
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+else x := pre(x); // default
+end if;
+
+
+end when;
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_1/quest.txt b/Data/ingsw/1122_1/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/1122_1/quest.txt
rename to Data/ingsw/1122_1/quest.txt
index df0e6be..abd4f55 100644
--- a/legacy/Data/Questions/ingsw/1122_1/quest.txt
+++ b/Data/ingsw/1122_1/quest.txt
@@ -1,2 +1,2 @@
-img=https://i.imgur.com/jS97TUd.png
+img=https://i.imgur.com/jS97TUd.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_1/wrong 1.txt b/Data/ingsw/1122_1/wrong 1.txt
similarity index 94%
rename from legacy/Data/ingsw/1122_1/wrong 1.txt
rename to Data/ingsw/1122_1/wrong 1.txt
index febcca9..8578489 100644
--- a/legacy/Data/ingsw/1122_1/wrong 1.txt	
+++ b/Data/ingsw/1122_1/wrong 1.txt	
@@ -1,77 +1,77 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_1/wrong 2.txt b/Data/ingsw/1122_1/wrong 2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/1122_1/wrong 2.txt
rename to Data/ingsw/1122_1/wrong 2.txt
index 94279c9..8ddcb58 100644
--- a/legacy/Data/Questions/ingsw/1122_1/wrong 2.txt	
+++ b/Data/ingsw/1122_1/wrong 2.txt	
@@ -1,67 +1,67 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/Data/ingsw/1122_10/correct.txt b/Data/ingsw/1122_10/correct.txt
new file mode 100644
index 0000000..c496119
--- /dev/null
+++ b/Data/ingsw/1122_10/correct.txt
@@ -0,0 +1,5 @@
+int f(in x, int y) 
+{ 
+assert( (x >= 0) && (y >= 0) && ((x > 3) || (y > 3)) );
+.....
+}
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_10/quest.txt b/Data/ingsw/1122_10/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_10/quest.txt
rename to Data/ingsw/1122_10/quest.txt
index c939cfb..210b7c7 100644
--- a/legacy/Data/Questions/ingsw/1122_10/quest.txt
+++ b/Data/ingsw/1122_10/quest.txt
@@ -1,7 +1,7 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-
-Si consideri la funzione C
-
-int f(int x, int y) { ..... }
-
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+
+Si consideri la funzione C
+
+int f(int x, int y) { ..... }
+
 Quale delle seguenti assert esprime la pre-condizione che entrambi gli argomenti di f sono non-negativi ed almeno uno di loro è maggiore di 3?
\ No newline at end of file
diff --git a/Data/ingsw/1122_10/wrong 1.txt b/Data/ingsw/1122_10/wrong 1.txt
new file mode 100644
index 0000000..11dc1a3
--- /dev/null
+++ b/Data/ingsw/1122_10/wrong 1.txt	
@@ -0,0 +1,10 @@
+
+int f(in x, int y) 
+
+{ 
+
+assert( (x >= 0) && (y >= 0) && ((x >= 3) || (y >= 3)) );
+
+.....
+
+}
\ No newline at end of file
diff --git a/Data/ingsw/1122_10/wrong 2.txt b/Data/ingsw/1122_10/wrong 2.txt
new file mode 100644
index 0000000..c98f275
--- /dev/null
+++ b/Data/ingsw/1122_10/wrong 2.txt	
@@ -0,0 +1,9 @@
+int f(in x, int y) 
+
+{ 
+
+assert( (x > 0) && (y > 0) && ((x >= 3) || (y > 3)) );
+
+.....
+
+}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_11/correct.txt b/Data/ingsw/1122_11/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_11/correct.txt
rename to Data/ingsw/1122_11/correct.txt
diff --git a/legacy/Data/ingsw/1122_11/quest.txt b/Data/ingsw/1122_11/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/1122_11/quest.txt
rename to Data/ingsw/1122_11/quest.txt
index b46cb2b..51be506 100644
--- a/legacy/Data/ingsw/1122_11/quest.txt
+++ b/Data/ingsw/1122_11/quest.txt
@@ -1,17 +1,17 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-
-Si consideri la seguente funzione C:
-
------------
-
-int f(int x, int y)  {   
-
-int z = x;
-
-while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
-
-return (z);
-
-}
-
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+
+Si consideri la seguente funzione C:
+
+-----------
+
+int f(int x, int y)  {   
+
+int z = x;
+
+while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
+
+return (z);
+
+}
+
 Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_11/wrong 1.txt b/Data/ingsw/1122_11/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_11/wrong 1.txt
rename to Data/ingsw/1122_11/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_11/wrong 2.txt b/Data/ingsw/1122_11/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_11/wrong 2.txt
rename to Data/ingsw/1122_11/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_12/correct.txt b/Data/ingsw/1122_12/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_12/correct.txt
rename to Data/ingsw/1122_12/correct.txt
diff --git a/legacy/Data/ingsw/1122_12/quest.txt b/Data/ingsw/1122_12/quest.txt
similarity index 94%
rename from legacy/Data/ingsw/1122_12/quest.txt
rename to Data/ingsw/1122_12/quest.txt
index 464b817..f13bd49 100644
--- a/legacy/Data/ingsw/1122_12/quest.txt
+++ b/Data/ingsw/1122_12/quest.txt
@@ -1,76 +1,76 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente?
-
-
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente?
+
+
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_12/wrong 1.txt b/Data/ingsw/1122_12/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_12/wrong 1.txt
rename to Data/ingsw/1122_12/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_12/wrong 2.txt b/Data/ingsw/1122_12/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_12/wrong 2.txt
rename to Data/ingsw/1122_12/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_13/correct.txt b/Data/ingsw/1122_13/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_13/correct.txt
rename to Data/ingsw/1122_13/correct.txt
diff --git a/legacy/Data/ingsw/1122_13/quest.txt b/Data/ingsw/1122_13/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/1122_13/quest.txt
rename to Data/ingsw/1122_13/quest.txt
index d57a552..3ad0e09 100644
--- a/legacy/Data/ingsw/1122_13/quest.txt
+++ b/Data/ingsw/1122_13/quest.txt
@@ -1,8 +1,8 @@
-img=https://i.imgur.com/pBLLwD1.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-
-Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-
-Uno scenario è una sequenza di stati.
-
+img=https://i.imgur.com/pBLLwD1.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+
+Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+
+Uno scenario è una sequenza di stati.
+
 Qual è la probabilità dello scenario: 1, 3, 4? In altri terminti, qual è la probabilità che non sia necessario ripetere la seconda fase (ma non la prima)?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_13/wrong 1.txt b/Data/ingsw/1122_13/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_13/wrong 1.txt
rename to Data/ingsw/1122_13/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_13/wrong 2.txt b/Data/ingsw/1122_13/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_13/wrong 2.txt
rename to Data/ingsw/1122_13/wrong 2.txt
diff --git a/Data/ingsw/1122_14/correct.txt b/Data/ingsw/1122_14/correct.txt
new file mode 100644
index 0000000..633b308
--- /dev/null
+++ b/Data/ingsw/1122_14/correct.txt
@@ -0,0 +1,19 @@
+<pre>
+#define n 1000
+int TestOracle1(int *A, int *B)
+{
+int i, j, D[n];
+//init
+
+for (i = 0; i < n; i++) D[i] = -1;
+
+// B is ordered
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+// B is a permutation of A
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; D[j] = 1; break;}
+
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+// B ok
+return (1);
+}
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_14/quest.txt b/Data/ingsw/1122_14/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_14/quest.txt
rename to Data/ingsw/1122_14/quest.txt
index bd20578..3d717ca 100644
--- a/legacy/Data/Questions/ingsw/1122_14/quest.txt
+++ b/Data/ingsw/1122_14/quest.txt
@@ -1,7 +1,7 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-
-Si consideri la seguente specifica funzionale per la funzione f.
-
-La funzione f(int *A, int *B)  prende come input un vettore A di dimensione n  ritorna come output un vettore B ottenuto ordinando gli elementi di A in ordine crescente.
-
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+
+Si consideri la seguente specifica funzionale per la funzione f.
+
+La funzione f(int *A, int *B)  prende come input un vettore A di dimensione n  ritorna come output un vettore B ottenuto ordinando gli elementi di A in ordine crescente.
+
 Quale delle seguenti funzioni è un test oracle per la funzione f?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_14/wrong 1.txt b/Data/ingsw/1122_14/wrong 1.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/1122_14/wrong 1.txt
rename to Data/ingsw/1122_14/wrong 1.txt
index 189d31d..6068762 100644
--- a/legacy/Data/Questions/ingsw/1122_14/wrong 1.txt	
+++ b/Data/ingsw/1122_14/wrong 1.txt	
@@ -1,29 +1,29 @@
-<pre>
-#define n 1000
-
-int TestOracle2(int *A, int *B)
-
-{
-
-int i, j, D[n];
-
-//init
-
-for (i = 0; i < n; i++) D[i] = -1;
-
-// B is ordered
-
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-
-// B is a permutation of A
-
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; break;}
-
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-
-// B ok
-
-return (1);
-
-}
+<pre>
+#define n 1000
+
+int TestOracle2(int *A, int *B)
+
+{
+
+int i, j, D[n];
+
+//init
+
+for (i = 0; i < n; i++) D[i] = -1;
+
+// B is ordered
+
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+
+// B is a permutation of A
+
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; break;}
+
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+
+// B ok
+
+return (1);
+
+}
 </pre>
\ No newline at end of file
diff --git a/Data/ingsw/1122_14/wrong 2.txt b/Data/ingsw/1122_14/wrong 2.txt
new file mode 100644
index 0000000..7a0753f
--- /dev/null
+++ b/Data/ingsw/1122_14/wrong 2.txt	
@@ -0,0 +1,29 @@
+<pre>
+#define n 1000
+
+int TestOracle3(int *A, int *B)
+
+{
+
+int i, j, D[n];
+
+//init
+
+for (i = 0; i < n; i++) D[i] = -1;
+
+// B is ordered
+
+for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
+
+// B is a permutation of A
+
+for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if (A[i] == B[j]) {C[i][j] = 1; D[j] = 1; break;}
+
+for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
+
+// B ok
+
+return (1);
+
+}
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_15/correct.txt b/Data/ingsw/1122_15/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_15/correct.txt
rename to Data/ingsw/1122_15/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_15/quest.txt b/Data/ingsw/1122_15/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/1122_15/quest.txt
rename to Data/ingsw/1122_15/quest.txt
index 0d7fe08..f820e09 100644
--- a/legacy/Data/Questions/ingsw/1122_15/quest.txt
+++ b/Data/ingsw/1122_15/quest.txt
@@ -1,12 +1,12 @@
-img=https://i.imgur.com/mMq2O4x.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-
-Si consideri lo state diagram in figura
-Si consideri il seguente insieme di test cases:
-
-Test case 1: act1 act2 act0 
-
-Test case 2: act0 act1 act0 act0 
-
-Test case 3: act1 act0 act2 act2 act0
+img=https://i.imgur.com/mMq2O4x.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+
+Si consideri lo state diagram in figura
+Si consideri il seguente insieme di test cases:
+
+Test case 1: act1 act2 act0 
+
+Test case 2: act0 act1 act0 act0 
+
+Test case 3: act1 act0 act2 act2 act0
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_15/wrong 1.txt b/Data/ingsw/1122_15/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_15/wrong 1.txt
rename to Data/ingsw/1122_15/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_15/wrong 2.txt b/Data/ingsw/1122_15/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_15/wrong 2.txt
rename to Data/ingsw/1122_15/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_16/correct.txt b/Data/ingsw/1122_16/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_16/correct.txt
rename to Data/ingsw/1122_16/correct.txt
diff --git a/legacy/Data/ingsw/1122_16/quest.txt b/Data/ingsw/1122_16/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_16/quest.txt
rename to Data/ingsw/1122_16/quest.txt
index db10798..96b152b 100644
--- a/legacy/Data/ingsw/1122_16/quest.txt
+++ b/Data/ingsw/1122_16/quest.txt
@@ -1,7 +1,7 @@
-Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
-
-Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
-
-P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
-
+Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
+
+Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
+
+P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
+
 dove b è una opportuna costante note da dati storici aziendali. Si assuma che b = 0.0001,  C = 1000000, ed il rischio ammesso è R = 1000. Quale dei seguenti valori meglio approssima il costo S per lo sviluppo del software in questione.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_16/wrong 1.txt b/Data/ingsw/1122_16/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_16/wrong 1.txt
rename to Data/ingsw/1122_16/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_16/wrong 2.txt b/Data/ingsw/1122_16/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_16/wrong 2.txt
rename to Data/ingsw/1122_16/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_19/correct.txt b/Data/ingsw/1122_19/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_19/correct.txt
rename to Data/ingsw/1122_19/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_19/quest.txt b/Data/ingsw/1122_19/quest.txt
similarity index 94%
rename from legacy/Data/Questions/ingsw/1122_19/quest.txt
rename to Data/ingsw/1122_19/quest.txt
index 28aa70c..4a339f6 100644
--- a/legacy/Data/Questions/ingsw/1122_19/quest.txt
+++ b/Data/ingsw/1122_19/quest.txt
@@ -1,75 +1,75 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente?
-
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente?
+
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_19/wrong 1.txt b/Data/ingsw/1122_19/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_19/wrong 1.txt
rename to Data/ingsw/1122_19/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_19/wrong 2.txt b/Data/ingsw/1122_19/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_19/wrong 2.txt
rename to Data/ingsw/1122_19/wrong 2.txt
diff --git a/Data/ingsw/1122_2/correct.txt b/Data/ingsw/1122_2/correct.txt
new file mode 100644
index 0000000..a2f00b3
--- /dev/null
+++ b/Data/ingsw/1122_2/correct.txt
@@ -0,0 +1,15 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_2/quest.txt b/Data/ingsw/1122_2/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_2/quest.txt
rename to Data/ingsw/1122_2/quest.txt
index 2ef9a23..c0c6fa2 100644
--- a/legacy/Data/ingsw/1122_2/quest.txt
+++ b/Data/ingsw/1122_2/quest.txt
@@ -1,9 +1,9 @@
-Si consideri il seguente requisito:
-
-RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-
-se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
-
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
-
+Si consideri il seguente requisito:
+
+RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+
+se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
+
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+
 Quale dei seguenti monitor meglio descrive il requisito RQ?
\ No newline at end of file
diff --git a/Data/ingsw/1122_2/wrong 1.txt b/Data/ingsw/1122_2/wrong 1.txt
new file mode 100644
index 0000000..b37deaa
--- /dev/null
+++ b/Data/ingsw/1122_2/wrong 1.txt	
@@ -0,0 +1,17 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+
+initial equation
+
+wy = false;
+equation
+wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/1122_2/wrong 2.txt b/Data/ingsw/1122_2/wrong 2.txt
new file mode 100644
index 0000000..e0b32cd
--- /dev/null
+++ b/Data/ingsw/1122_2/wrong 2.txt	
@@ -0,0 +1,17 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+
+initial equation
+
+wy = false;
+equation
+wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_20/correct.txt b/Data/ingsw/1122_20/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_20/correct.txt
rename to Data/ingsw/1122_20/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_20/quest.txt b/Data/ingsw/1122_20/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/1122_20/quest.txt
rename to Data/ingsw/1122_20/quest.txt
index 139b0a2..8e66453 100644
--- a/legacy/Data/Questions/ingsw/1122_20/quest.txt
+++ b/Data/ingsw/1122_20/quest.txt
@@ -1,19 +1,19 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-
-Si consideri la seguente funzione C:
-
------------
-<pre>
-int f(int x, int y)  {   
-
-int z, k;
-
-z = 1;   k = 0;
-
-while (k < x)  { z = y*z;  k = k + 1; }
-
-return (z);
-
-}
-</pre>
+Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
+
+Si consideri la seguente funzione C:
+
+-----------
+<pre>
+int f(int x, int y)  {   
+
+int z, k;
+
+z = 1;   k = 0;
+
+while (k < x)  { z = y*z;  k = k + 1; }
+
+return (z);
+
+}
+</pre>
 Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_20/wrong 1.txt b/Data/ingsw/1122_20/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_20/wrong 1.txt
rename to Data/ingsw/1122_20/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_20/wrong 2.txt b/Data/ingsw/1122_20/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_20/wrong 2.txt
rename to Data/ingsw/1122_20/wrong 2.txt
diff --git a/Data/ingsw/1122_21/correct.txt b/Data/ingsw/1122_21/correct.txt
new file mode 100644
index 0000000..2771487
--- /dev/null
+++ b/Data/ingsw/1122_21/correct.txt
@@ -0,0 +1,17 @@
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_21/quest.txt b/Data/ingsw/1122_21/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_21/quest.txt
rename to Data/ingsw/1122_21/quest.txt
index 9f10cbc..934fb10 100644
--- a/legacy/Data/ingsw/1122_21/quest.txt
+++ b/Data/ingsw/1122_21/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-
-RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
-
+Si consideri il seguente requisito:
+
+RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
+
 Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/Data/ingsw/1122_21/wrong 1.txt b/Data/ingsw/1122_21/wrong 1.txt
new file mode 100644
index 0000000..7b3b2f2
--- /dev/null
+++ b/Data/ingsw/1122_21/wrong 1.txt	
@@ -0,0 +1,19 @@
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+
+initial equation
+
+y = false;
+equation
+z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/1122_21/wrong 2.txt b/Data/ingsw/1122_21/wrong 2.txt
new file mode 100644
index 0000000..92e0b1f
--- /dev/null
+++ b/Data/ingsw/1122_21/wrong 2.txt	
@@ -0,0 +1,19 @@
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+
+initial equation
+
+y = false;
+equation
+z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_22/correct.txt b/Data/ingsw/1122_22/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_22/correct.txt
rename to Data/ingsw/1122_22/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_22/quest.txt b/Data/ingsw/1122_22/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/1122_22/quest.txt
rename to Data/ingsw/1122_22/quest.txt
index a116140..97b4244 100644
--- a/legacy/Data/Questions/ingsw/1122_22/quest.txt
+++ b/Data/ingsw/1122_22/quest.txt
@@ -1,14 +1,14 @@
-img=https://i.imgur.com/VZQnGCY.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-
-Si consideri lo state diagram in figura
-
-ed il seguente insieme di test cases:
-
-Test case 1: act1 act2 act0 act1 
-
-Test case 2: act1 act0 act1 act1 act2 act2 act0 
-
-Test case 3: act1 act2 act0 act0 
-
+img=https://i.imgur.com/VZQnGCY.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+
+Si consideri lo state diagram in figura
+
+ed il seguente insieme di test cases:
+
+Test case 1: act1 act2 act0 act1 
+
+Test case 2: act1 act0 act1 act1 act2 act2 act0 
+
+Test case 3: act1 act2 act0 act0 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_22/wrong 1.txt b/Data/ingsw/1122_22/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_22/wrong 1.txt
rename to Data/ingsw/1122_22/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_22/wrong 2.txt b/Data/ingsw/1122_22/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_22/wrong 2.txt
rename to Data/ingsw/1122_22/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_23/correct.txt b/Data/ingsw/1122_23/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_23/correct.txt
rename to Data/ingsw/1122_23/correct.txt
diff --git a/Data/ingsw/1122_23/quest.txt b/Data/ingsw/1122_23/quest.txt
new file mode 100644
index 0000000..83f906a
--- /dev/null
+++ b/Data/ingsw/1122_23/quest.txt
@@ -0,0 +1,29 @@
+Si consideri il monitor seguente che ritorna true appena i requisiti per il sistema monitorato sono violati.
+<pre>
+block Monitor
+
+input Real x;  
+
+output Boolean y;
+
+Boolean w;
+
+initial equation
+
+y = false;
+
+equation
+
+w = ((x < 0) or (x > 5));
+
+algorithm
+
+when edge(w) then
+
+y := true;
+
+end when;
+
+end Monitor;
+</pre>
+Quale delle seguenti affermazioni meglio descrive il requisito monitorato.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_23/wrong 1.txt b/Data/ingsw/1122_23/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_23/wrong 1.txt
rename to Data/ingsw/1122_23/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_23/wrong 2.txt b/Data/ingsw/1122_23/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_23/wrong 2.txt
rename to Data/ingsw/1122_23/wrong 2.txt
diff --git a/Data/ingsw/1122_24/correct.txt b/Data/ingsw/1122_24/correct.txt
new file mode 100644
index 0000000..ca92838
--- /dev/null
+++ b/Data/ingsw/1122_24/correct.txt
@@ -0,0 +1,17 @@
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+initial equation
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_24/quest.txt b/Data/ingsw/1122_24/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_24/quest.txt
rename to Data/ingsw/1122_24/quest.txt
index 0accc5f..404b24d 100644
--- a/legacy/Data/ingsw/1122_24/quest.txt
+++ b/Data/ingsw/1122_24/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-
-RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
-
+Si consideri il seguente requisito:
+
+RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
+
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/Data/ingsw/1122_24/wrong 1.txt b/Data/ingsw/1122_24/wrong 1.txt
new file mode 100644
index 0000000..fc17559
--- /dev/null
+++ b/Data/ingsw/1122_24/wrong 1.txt	
@@ -0,0 +1,19 @@
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+
+initial equation
+
+wy = false;
+equation
+wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/1122_24/wrong 2.txt b/Data/ingsw/1122_24/wrong 2.txt
new file mode 100644
index 0000000..eb22c69
--- /dev/null
+++ b/Data/ingsw/1122_24/wrong 2.txt	
@@ -0,0 +1,19 @@
+<pre>
+class Monitor
+
+InputReal x, y;  // plant output
+OutputBoolean wy;
+
+Boolean wz;
+
+initial equation
+
+wy = false;
+equation
+wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_25/correct.txt b/Data/ingsw/1122_25/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_25/correct.txt
rename to Data/ingsw/1122_25/correct.txt
diff --git a/legacy/Data/ingsw/1122_25/quest.txt b/Data/ingsw/1122_25/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_25/quest.txt
rename to Data/ingsw/1122_25/quest.txt
index 4087608..750b638 100644
--- a/legacy/Data/ingsw/1122_25/quest.txt
+++ b/Data/ingsw/1122_25/quest.txt
@@ -1,10 +1,10 @@
-img=https://i.imgur.com/jQT3J83.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
-
-Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
-
-Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
-
-Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
-
+img=https://i.imgur.com/jQT3J83.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il costo dello stato (fase) x è c(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi c(1) = 0.
+
+Il costo di una istanza del processo software descritto sopra è la somma dei costi degli stati attraversati (tenendo presente che si parte sempre dallo stato 0.
+
+Quindi il costo C(X) della sequenza di stati X = x(0), x(1), x(2), .... è C(X) = c(x(0)) + c(x(1)) + c(x(2)) + ...
+
+Ad esempio se X = 0, 1 abbiamo C(X) = c(0) + c(1) = c(0)  (poichè c(1) = 0).
+
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_25/wrong 1.txt b/Data/ingsw/1122_25/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_25/wrong 1.txt
rename to Data/ingsw/1122_25/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_25/wrong 2.txt b/Data/ingsw/1122_25/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_25/wrong 2.txt
rename to Data/ingsw/1122_25/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_26/correct.txt b/Data/ingsw/1122_26/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_26/correct.txt
rename to Data/ingsw/1122_26/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_26/quest.txt b/Data/ingsw/1122_26/quest.txt
similarity index 96%
rename from legacy/Data/Questions/ingsw/1122_26/quest.txt
rename to Data/ingsw/1122_26/quest.txt
index 514a3fa..2ec2008 100644
--- a/legacy/Data/Questions/ingsw/1122_26/quest.txt
+++ b/Data/ingsw/1122_26/quest.txt
@@ -1,15 +1,15 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-
-Si consideri la seguente funzione C:
-
------------
-<pre>
-int f(int x, int y)  {   
-
- if (x - y - 6 <= 0)   { if (x + y - 3 >= 0)  return (1); else return (2); }
-
-  else {if (x + 2*y -15 >= 0)  return (3); else return (4); }
-
- }  /* f()  */
-</pre>
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+
+Si consideri la seguente funzione C:
+
+-----------
+<pre>
+int f(int x, int y)  {   
+
+ if (x - y - 6 <= 0)   { if (x + y - 3 >= 0)  return (1); else return (2); }
+
+  else {if (x + 2*y -15 >= 0)  return (3); else return (4); }
+
+ }  /* f()  */
+</pre>
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_26/wrong 1.txt b/Data/ingsw/1122_26/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_26/wrong 1.txt
rename to Data/ingsw/1122_26/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_26/wrong 2.txt b/Data/ingsw/1122_26/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_26/wrong 2.txt
rename to Data/ingsw/1122_26/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_27/correct.txt b/Data/ingsw/1122_27/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_27/correct.txt
rename to Data/ingsw/1122_27/correct.txt
diff --git a/legacy/Data/ingsw/1122_27/quest.txt b/Data/ingsw/1122_27/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_27/quest.txt
rename to Data/ingsw/1122_27/quest.txt
index 59f8742..1a2c042 100644
--- a/legacy/Data/ingsw/1122_27/quest.txt
+++ b/Data/ingsw/1122_27/quest.txt
@@ -1,13 +1,13 @@
-img=https://i.imgur.com/TXCFgeI.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-
-Si consideri lo state diagram in figura
-ed il seguente insieme di test cases:
-
-Test case 1: act1 act2 act0 
-
-Test case 2: act2 act2 act2 act2 act2 act2 act0 
-
-Test case 3: act2 act0 act2 act0 act1 act2 act2 act2 act2 act2 act1 act0 act0 act2 act2 act2 act1 act2 act2 act2 act2 act2 act1 act2 act2 act2 act0 
-
+img=https://i.imgur.com/TXCFgeI.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+
+Si consideri lo state diagram in figura
+ed il seguente insieme di test cases:
+
+Test case 1: act1 act2 act0 
+
+Test case 2: act2 act2 act2 act2 act2 act2 act0 
+
+Test case 3: act2 act0 act2 act0 act1 act2 act2 act2 act2 act2 act1 act0 act0 act2 act2 act2 act1 act2 act2 act2 act2 act2 act1 act2 act2 act2 act0 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_27/wrong 1.txt b/Data/ingsw/1122_27/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_27/wrong 1.txt
rename to Data/ingsw/1122_27/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_27/wrong 2.txt b/Data/ingsw/1122_27/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_27/wrong 2.txt
rename to Data/ingsw/1122_27/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_28/correct.txt b/Data/ingsw/1122_28/correct.txt
similarity index 91%
rename from legacy/Data/Questions/ingsw/1122_28/correct.txt
rename to Data/ingsw/1122_28/correct.txt
index c3fc7c1..ba8750b 100644
--- a/legacy/Data/Questions/ingsw/1122_28/correct.txt
+++ b/Data/ingsw/1122_28/correct.txt
@@ -1,9 +1,9 @@
-<pre>
-int f(in x, int y) 
-
-{ 
-int z, w;
-assert( (z + w < 1) || (z + w > 7));
-.....
-}
+<pre>
+int f(in x, int y) 
+
+{ 
+int z, w;
+assert( (z + w < 1) || (z + w > 7));
+.....
+}
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_28/quest.txt b/Data/ingsw/1122_28/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_28/quest.txt
rename to Data/ingsw/1122_28/quest.txt
index 733c0cb..37bd848 100644
--- a/legacy/Data/Questions/ingsw/1122_28/quest.txt
+++ b/Data/ingsw/1122_28/quest.txt
@@ -1,7 +1,7 @@
-Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
-
-Si consideri la funzione C
-
-int f(int x, int y) { ..... }
-
+Pre-condizioni,  invarianti e post-condizioni di un programma possono essere definiti usando la macro del C assert() (in <assert.h>).  In particolare, assert(expre) non fa nulla se l'espressione expre vale TRUE (cioè non è 0), stampa un messaggio di errore su stderr e abortisce l'esecuzione del programma altrimenti.
+
+Si consideri la funzione C
+
+int f(int x, int y) { ..... }
+
 Quale delle seguenti assert esprime l'invariante che le variabili locali z e w di f() hanno somma minore di 1 oppure maggiore di 7 ?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_28/wrong 1.txt b/Data/ingsw/1122_28/wrong 1.txt
similarity index 88%
rename from legacy/Data/Questions/ingsw/1122_28/wrong 1.txt
rename to Data/ingsw/1122_28/wrong 1.txt
index 1b8fa8b..521f650 100644
--- a/legacy/Data/Questions/ingsw/1122_28/wrong 1.txt	
+++ b/Data/ingsw/1122_28/wrong 1.txt	
@@ -1,13 +1,13 @@
-<pre>
-int f(in x, int y) 
-
-{ 
-
-int z, w;
-
-assert( (z + w <= 1) || (z + w >= 7));
-
-.....
-
-}
+<pre>
+int f(in x, int y) 
+
+{ 
+
+int z, w;
+
+assert( (z + w <= 1) || (z + w >= 7));
+
+.....
+
+}
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_28/wrong 2.txt b/Data/ingsw/1122_28/wrong 2.txt
similarity index 88%
rename from legacy/Data/Questions/ingsw/1122_28/wrong 2.txt
rename to Data/ingsw/1122_28/wrong 2.txt
index b0705b4..c02f435 100644
--- a/legacy/Data/Questions/ingsw/1122_28/wrong 2.txt	
+++ b/Data/ingsw/1122_28/wrong 2.txt	
@@ -1,13 +1,13 @@
-<pre>
-int f(in x, int y) 
-
-{ 
-
-int z, w;
-
-assert( (z + w > 1) || (z + w < 7));
-
-.....
-
-}
+<pre>
+int f(in x, int y) 
+
+{ 
+
+int z, w;
+
+assert( (z + w > 1) || (z + w < 7));
+
+.....
+
+}
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_29/correct.txt b/Data/ingsw/1122_29/correct.txt
similarity index 97%
rename from legacy/Data/ingsw/1122_29/correct.txt
rename to Data/ingsw/1122_29/correct.txt
index 2d46409..b647f40 100644
--- a/legacy/Data/ingsw/1122_29/correct.txt
+++ b/Data/ingsw/1122_29/correct.txt
@@ -1 +1 @@
-img=https://i.imgur.com/SXM3yWp.png
+img=https://i.imgur.com/SXM3yWp.png
diff --git a/legacy/Data/ingsw/1122_29/quest.txt b/Data/ingsw/1122_29/quest.txt
similarity index 94%
rename from legacy/Data/ingsw/1122_29/quest.txt
rename to Data/ingsw/1122_29/quest.txt
index 52863ce..8e0d6f4 100644
--- a/legacy/Data/ingsw/1122_29/quest.txt
+++ b/Data/ingsw/1122_29/quest.txt
@@ -1,70 +1,70 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente ?
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente ?
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 2;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_29/wrong 1.txt b/Data/ingsw/1122_29/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_29/wrong 1.txt
rename to Data/ingsw/1122_29/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_29/wrong 2.txt b/Data/ingsw/1122_29/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_29/wrong 2.txt
rename to Data/ingsw/1122_29/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_3/correct.txt b/Data/ingsw/1122_3/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_3/correct.txt
rename to Data/ingsw/1122_3/correct.txt
diff --git a/legacy/Data/ingsw/1122_3/quest.txt b/Data/ingsw/1122_3/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_3/quest.txt
rename to Data/ingsw/1122_3/quest.txt
index ddcf7c8..4eb39a7 100644
--- a/legacy/Data/ingsw/1122_3/quest.txt
+++ b/Data/ingsw/1122_3/quest.txt
@@ -1,3 +1,3 @@
-"Ogni giorno, per ciascuna clinica, il sistema genererà una lista dei pazienti che hanno un appuntamento quel giorno."
-
+"Ogni giorno, per ciascuna clinica, il sistema genererà una lista dei pazienti che hanno un appuntamento quel giorno."
+
 La frase precedente è un esempio di:
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_3/wrong 1.txt b/Data/ingsw/1122_3/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_3/wrong 1.txt
rename to Data/ingsw/1122_3/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_3/wrong 2.txt b/Data/ingsw/1122_3/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_3/wrong 2.txt
rename to Data/ingsw/1122_3/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_30/correct.txt b/Data/ingsw/1122_30/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_30/correct.txt
rename to Data/ingsw/1122_30/correct.txt
diff --git a/legacy/Data/ingsw/1122_30/quest.txt b/Data/ingsw/1122_30/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/1122_30/quest.txt
rename to Data/ingsw/1122_30/quest.txt
index 8b8cea7..476a9fd 100644
--- a/legacy/Data/ingsw/1122_30/quest.txt
+++ b/Data/ingsw/1122_30/quest.txt
@@ -1,10 +1,10 @@
-img=https://i.imgur.com/jQT3J83.png
-Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
-
-Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
-
-Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
-
-Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
-
+img=https://i.imgur.com/jQT3J83.png
+Si consideri il processo software con due fasi (0 ed 1) rappresentato con la Markov chain in figura. Lo stato iniziale 0 e p è in (0, 1).  Il tempo necessario per completare la fase x è time(x). La fase 0 è la fase di design, che ha probabilità p di dover essere ripetuta causa errori. La fase 1 rappreenta il completamento del processo software, e quindi time(1) = 0.
+
+Il tempo di una istanza del processo software descritto sopra è la somma dei tempi degli stati (fasi) attraversati (tenendo presente che si parte sempre dallo stato 0.
+
+Quindi il costo Time(X) della sequenza di stati X = x(0), x(1), x(2), .... è Time(X) = time(x(0)) + time(x(1)) + time(x(2)) + ...
+
+Ad esempio se X = 0, 1 abbiamo Time(X) = time(0) + time(1) = time(0)  (poichè time(1) = 0).
+
 Quale delle seguenti formule calcola il valore atteso del costo per completare il processo software di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_30/wrong 1.txt b/Data/ingsw/1122_30/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_30/wrong 1.txt
rename to Data/ingsw/1122_30/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_30/wrong 2.txt b/Data/ingsw/1122_30/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_30/wrong 2.txt
rename to Data/ingsw/1122_30/wrong 2.txt
diff --git a/Data/ingsw/1122_31/correct.txt b/Data/ingsw/1122_31/correct.txt
new file mode 100644
index 0000000..d3daba4
--- /dev/null
+++ b/Data/ingsw/1122_31/correct.txt
@@ -0,0 +1,17 @@
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+initial equation
+y = false;
+equation
+z = (time > 20) and ((x >= 30) or (x <= 20)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_31/quest.txt b/Data/ingsw/1122_31/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_31/quest.txt
rename to Data/ingsw/1122_31/quest.txt
index 7314e2c..682b4f6 100644
--- a/legacy/Data/ingsw/1122_31/quest.txt
+++ b/Data/ingsw/1122_31/quest.txt
@@ -1,5 +1,5 @@
-Si consideri il seguente requisito:
-
-RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
-
+Si consideri il seguente requisito:
+
+RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
+
 Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/Data/ingsw/1122_31/wrong 1.txt b/Data/ingsw/1122_31/wrong 1.txt
new file mode 100644
index 0000000..0eefcf1
--- /dev/null
+++ b/Data/ingsw/1122_31/wrong 1.txt	
@@ -0,0 +1,19 @@
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+
+initial equation
+
+y = false;
+equation
+z = (time > 20) and (x >= 20) and (x <= 30) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/1122_31/wrong 2.txt b/Data/ingsw/1122_31/wrong 2.txt
new file mode 100644
index 0000000..f36cefa
--- /dev/null
+++ b/Data/ingsw/1122_31/wrong 2.txt	
@@ -0,0 +1,19 @@
+<pre>
+class Monitor
+
+InputReal x;  // plant output
+OutputBoolean y;
+
+Boolean z;
+
+initial equation
+
+y = false;
+equation
+z = (time > 20) or ((x >= 20) and (x <= 30)) ;
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_33/correct.txt b/Data/ingsw/1122_33/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_33/correct.txt
rename to Data/ingsw/1122_33/correct.txt
diff --git a/legacy/Data/ingsw/1122_33/quest.txt b/Data/ingsw/1122_33/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/1122_33/quest.txt
rename to Data/ingsw/1122_33/quest.txt
index 6283906..5ccd93d 100644
--- a/legacy/Data/ingsw/1122_33/quest.txt
+++ b/Data/ingsw/1122_33/quest.txt
@@ -1,17 +1,17 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-
-Si consideri la seguente funzione C:
-
------------
-<pre>
-int f(int x, int y)  {   
-
- if (x - y - 2 <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
-
-  else {if (x + 2*y - 5 >= 0)  return (3); else return (4); }
-
- }  /* f()  */
-</pre>
-Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
-
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+
+Si consideri la seguente funzione C:
+
+-----------
+<pre>
+int f(int x, int y)  {   
+
+ if (x - y - 2 <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
+
+  else {if (x + 2*y - 5 >= 0)  return (3); else return (4); }
+
+ }  /* f()  */
+</pre>
+Si considerino i seguenti test cases:  {x=1, y=2}, {x=0, y=0}, {x=5, y=0}, {x=3, y=0}.
+
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_33/wrong 1.txt b/Data/ingsw/1122_33/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_33/wrong 1.txt
rename to Data/ingsw/1122_33/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_33/wrong 2.txt b/Data/ingsw/1122_33/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_33/wrong 2.txt
rename to Data/ingsw/1122_33/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_34/correct.txt b/Data/ingsw/1122_34/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_34/correct.txt
rename to Data/ingsw/1122_34/correct.txt
diff --git a/legacy/Data/ingsw/1122_34/quest.txt b/Data/ingsw/1122_34/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/1122_34/quest.txt
rename to Data/ingsw/1122_34/quest.txt
index 09970ee..0a0cca1 100644
--- a/legacy/Data/ingsw/1122_34/quest.txt
+++ b/Data/ingsw/1122_34/quest.txt
@@ -1,13 +1,13 @@
-img=https://i.imgur.com/cXPjiw9.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-
-Si consideri lo state diagram in figura 
-Si consideri il seguente insieme di test cases:
-
-Test case 1: act2 act1 act1 act2 act1 act1 act0 
-
-Test case 2: act2 act0 act2 act2 act1 act1 act0 act2 act2 act2 act0 
-
-Test case 3: act1 act2 act2 act2 act1 act0 act1 act2 act2 act0
-
+img=https://i.imgur.com/cXPjiw9.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+
+Si consideri lo state diagram in figura 
+Si consideri il seguente insieme di test cases:
+
+Test case 1: act2 act1 act1 act2 act1 act1 act0 
+
+Test case 2: act2 act0 act2 act2 act1 act1 act0 act2 act2 act2 act0 
+
+Test case 3: act1 act2 act2 act2 act1 act0 act1 act2 act2 act0
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_34/wrong 1.txt b/Data/ingsw/1122_34/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_34/wrong 1.txt
rename to Data/ingsw/1122_34/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_34/wrong 2.txt b/Data/ingsw/1122_34/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_34/wrong 2.txt
rename to Data/ingsw/1122_34/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_35/correct.txt b/Data/ingsw/1122_35/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_35/correct.txt
rename to Data/ingsw/1122_35/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_35/quest.txt b/Data/ingsw/1122_35/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_35/quest.txt
rename to Data/ingsw/1122_35/quest.txt
index 215b21b..d56e26d 100644
--- a/legacy/Data/Questions/ingsw/1122_35/quest.txt
+++ b/Data/ingsw/1122_35/quest.txt
@@ -1,3 +1,3 @@
-img=https://i.imgur.com/jQT3J83.png
-Si consideri la Markov chain in figura con stato iniziale 0 e p in (0, 1). Quale delle seguenti formule calcola il valore atteso del numero di transizioni necessarie per lasciare lo stato 0.
-
+img=https://i.imgur.com/jQT3J83.png
+Si consideri la Markov chain in figura con stato iniziale 0 e p in (0, 1). Quale delle seguenti formule calcola il valore atteso del numero di transizioni necessarie per lasciare lo stato 0.
+
diff --git a/legacy/Data/ingsw/1122_35/wrong 1.txt b/Data/ingsw/1122_35/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_35/wrong 1.txt
rename to Data/ingsw/1122_35/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_35/wrong 2.txt b/Data/ingsw/1122_35/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_35/wrong 2.txt
rename to Data/ingsw/1122_35/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_36/correct.txt b/Data/ingsw/1122_36/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_36/correct.txt
rename to Data/ingsw/1122_36/correct.txt
diff --git a/legacy/Data/ingsw/1122_36/quest.txt b/Data/ingsw/1122_36/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_36/quest.txt
rename to Data/ingsw/1122_36/quest.txt
index da5d010..f896e42 100644
--- a/legacy/Data/ingsw/1122_36/quest.txt
+++ b/Data/ingsw/1122_36/quest.txt
@@ -1,26 +1,26 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-<pre>
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-</pre>
-
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-<pre>
-int f(int a, int b, int c)
-{    if ( (a - 100 >= 0) && (b - c - 1 <= 0) )
-          return (1);    // punto di uscita 1
-      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0)
-)
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
-</pre>
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta in espressioni boolean più semplici. Ad esempio, (x + y <= 3) è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+<pre>
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+</pre>
+
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+<pre>
+int f(int a, int b, int c)
+{    if ( (a - 100 >= 0) && (b - c - 1 <= 0) )
+          return (1);    // punto di uscita 1
+      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0)
+)
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
+</pre>
 Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_36/wrong 1.txt b/Data/ingsw/1122_36/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_36/wrong 1.txt
rename to Data/ingsw/1122_36/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_36/wrong 2.txt b/Data/ingsw/1122_36/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_36/wrong 2.txt
rename to Data/ingsw/1122_36/wrong 2.txt
diff --git a/Data/ingsw/1122_37/correct.txt b/Data/ingsw/1122_37/correct.txt
new file mode 100644
index 0000000..c642439
--- /dev/null
+++ b/Data/ingsw/1122_37/correct.txt
@@ -0,0 +1,17 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+
+initial equation
+
+wy = false;
+equation
+wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_37/quest.txt b/Data/ingsw/1122_37/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_37/quest.txt
rename to Data/ingsw/1122_37/quest.txt
index 843e4e9..99e8239 100644
--- a/legacy/Data/ingsw/1122_37/quest.txt
+++ b/Data/ingsw/1122_37/quest.txt
@@ -1,9 +1,9 @@
-Si consideri il seguente requisito:
-
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-
-se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
-
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
-
+Si consideri il seguente requisito:
+
+RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
+
+se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
+
+Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
+
 Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/Data/ingsw/1122_37/wrong 1.txt b/Data/ingsw/1122_37/wrong 1.txt
new file mode 100644
index 0000000..1441001
--- /dev/null
+++ b/Data/ingsw/1122_37/wrong 1.txt	
@@ -0,0 +1,19 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+
+initial equation
+
+wy = false;
+equation
+
+wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/Data/ingsw/1122_37/wrong 2.txt b/Data/ingsw/1122_37/wrong 2.txt
new file mode 100644
index 0000000..f1efe0e
--- /dev/null
+++ b/Data/ingsw/1122_37/wrong 2.txt	
@@ -0,0 +1,20 @@
+<pre>
+class Monitor
+InputReal x, y; 
+OutputBoolean wy;
+Boolean wz;
+
+initial equation
+
+wy = false;
+equation
+
+wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
+
+
+algorithm
+when edge(wz) then
+wy := true;
+end when;
+end Monitor;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_38/correct.txt b/Data/ingsw/1122_38/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_38/correct.txt
rename to Data/ingsw/1122_38/correct.txt
diff --git a/Data/ingsw/1122_38/quest.txt b/Data/ingsw/1122_38/quest.txt
new file mode 100644
index 0000000..d19e9da
--- /dev/null
+++ b/Data/ingsw/1122_38/quest.txt
@@ -0,0 +1,29 @@
+Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
+<pre>
+block Monitor
+
+input Real x;  
+
+output Boolean y;
+
+Boolean w;
+
+initial equation
+
+y = false;
+
+equation
+
+w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
+
+algorithm
+
+when edge(w) then
+
+y := true;
+
+end when;
+
+end Monitor;
+</pre>
+Quale delle seguenti affermazioni meglio descrive il requisito monitorato?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_38/wrong 1.txt b/Data/ingsw/1122_38/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_38/wrong 1.txt
rename to Data/ingsw/1122_38/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_38/wrong 2.txt b/Data/ingsw/1122_38/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_38/wrong 2.txt
rename to Data/ingsw/1122_38/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_39/correct.txt b/Data/ingsw/1122_39/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_39/correct.txt
rename to Data/ingsw/1122_39/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_39/quest.txt b/Data/ingsw/1122_39/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/1122_39/quest.txt
rename to Data/ingsw/1122_39/quest.txt
index 5826af4..cfa5093 100644
--- a/legacy/Data/Questions/ingsw/1122_39/quest.txt
+++ b/Data/ingsw/1122_39/quest.txt
@@ -1,14 +1,14 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-
-Si consideri la seguente funzione C:
------------
-<pre>
-int f(int x, int y)  {   
-
- if (x - y <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
-
-  else {if (2*x + y - 5 >= 0)  return (3); else return (4); }
-
- }  /* f()  */
-</pre>
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+
+Si consideri la seguente funzione C:
+-----------
+<pre>
+int f(int x, int y)  {   
+
+ if (x - y <= 0)   { if (x + y - 1 >= 0)  return (1); else return (2); }
+
+  else {if (2*x + y - 5 >= 0)  return (3); else return (4); }
+
+ }  /* f()  */
+</pre>
 Quale dei seguenti test sets consegue una branch coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_39/wrong 1.txt b/Data/ingsw/1122_39/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_39/wrong 1.txt
rename to Data/ingsw/1122_39/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_39/wrong 2.txt b/Data/ingsw/1122_39/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_39/wrong 2.txt
rename to Data/ingsw/1122_39/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_4/correct.txt b/Data/ingsw/1122_4/correct.txt
similarity index 96%
rename from legacy/Data/ingsw/1122_4/correct.txt
rename to Data/ingsw/1122_4/correct.txt
index 9ddc3d7..6162c46 100644
--- a/legacy/Data/ingsw/1122_4/correct.txt
+++ b/Data/ingsw/1122_4/correct.txt
@@ -1,45 +1,45 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-
-algorithm
-
-
-when initial() then
-x := 0;
-
-
-elsewhen sample(0,T) then
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-else x := pre(x); // default
-end if;
-
-
-end when;
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+
+algorithm
+
+
+when initial() then
+x := 0;
+
+
+elsewhen sample(0,T) then
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+else x := pre(x); // default
+end if;
+
+
+end when;
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_4/quest.txt b/Data/ingsw/1122_4/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/1122_4/quest.txt
rename to Data/ingsw/1122_4/quest.txt
index 4181719..46b6886 100644
--- a/legacy/Data/Questions/ingsw/1122_4/quest.txt
+++ b/Data/ingsw/1122_4/quest.txt
@@ -1,2 +1,2 @@
-img=https://i.imgur.com/1yUsW7d.png
+img=https://i.imgur.com/1yUsW7d.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura?
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_4/wrong 1.txt b/Data/ingsw/1122_4/wrong 1.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/1122_4/wrong 1.txt
rename to Data/ingsw/1122_4/wrong 1.txt
index c92e243..8b1c8db 100644
--- a/legacy/Data/Questions/ingsw/1122_4/wrong 1.txt	
+++ b/Data/ingsw/1122_4/wrong 1.txt	
@@ -1,67 +1,67 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_4/wrong 2.txt b/Data/ingsw/1122_4/wrong 2.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/1122_4/wrong 2.txt
rename to Data/ingsw/1122_4/wrong 2.txt
index ef7bdb0..06eb215 100644
--- a/legacy/Data/Questions/ingsw/1122_4/wrong 2.txt	
+++ b/Data/ingsw/1122_4/wrong 2.txt	
@@ -1,69 +1,69 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_40/correct.txt b/Data/ingsw/1122_40/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_40/correct.txt
rename to Data/ingsw/1122_40/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_40/quest.txt b/Data/ingsw/1122_40/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/1122_40/quest.txt
rename to Data/ingsw/1122_40/quest.txt
index 62c01e2..954468a 100644
--- a/legacy/Data/Questions/ingsw/1122_40/quest.txt
+++ b/Data/ingsw/1122_40/quest.txt
@@ -1,13 +1,13 @@
-img=https://i.imgur.com/ZjBToOi.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-
-Si consideri lo state diagram in figura ed il seguente insieme di test cases:
-
-Test case 1: act2 
-
-Test case 2: act1 act0 act1 act2 act1 act0 act0 act0 
-
-
-Test case 3: act0 act0 
-
+img=https://i.imgur.com/ZjBToOi.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+
+Si consideri lo state diagram in figura ed il seguente insieme di test cases:
+
+Test case 1: act2 
+
+Test case 2: act1 act0 act1 act2 act1 act0 act0 act0 
+
+
+Test case 3: act0 act0 
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_40/wrong 1.txt b/Data/ingsw/1122_40/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_40/wrong 1.txt
rename to Data/ingsw/1122_40/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_40/wrong 2.txt b/Data/ingsw/1122_40/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_40/wrong 2.txt
rename to Data/ingsw/1122_40/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_42/correct.txt b/Data/ingsw/1122_42/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_42/correct.txt
rename to Data/ingsw/1122_42/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_42/quest.txt b/Data/ingsw/1122_42/quest.txt
similarity index 97%
rename from legacy/Data/Questions/ingsw/1122_42/quest.txt
rename to Data/ingsw/1122_42/quest.txt
index 67b07dc..685ad76 100644
--- a/legacy/Data/Questions/ingsw/1122_42/quest.txt
+++ b/Data/ingsw/1122_42/quest.txt
@@ -1,17 +1,17 @@
-Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
-
-Si consideri la seguente funzione C:
-
------------
-<pre>
-int f(int x, int y)  {   
-
- if (x - y <= 0)   { if (x + y - 2>= 0)  return (1); else return (2); }
-
-  else {if (2*x + y - 1>= 0)  return (3); else return (4); }
-
- }  /* f()  */
-</pre>
-Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
-
+Il branch coverage di un insieme di test cases è la percentuale di branch del programma che sono attraversati da almeno un test case.
+
+Si consideri la seguente funzione C:
+
+-----------
+<pre>
+int f(int x, int y)  {   
+
+ if (x - y <= 0)   { if (x + y - 2>= 0)  return (1); else return (2); }
+
+  else {if (2*x + y - 1>= 0)  return (3); else return (4); }
+
+ }  /* f()  */
+</pre>
+Si considerino i seguenti test cases:  {x=1, y=1}, {x=0, y=0}, {x=1, y=0}, {x=0, y=-1}.
+
 Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_42/wrong 1.txt b/Data/ingsw/1122_42/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_42/wrong 1.txt
rename to Data/ingsw/1122_42/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_42/wrong 2.txt b/Data/ingsw/1122_42/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_42/wrong 2.txt
rename to Data/ingsw/1122_42/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_43/correct.txt b/Data/ingsw/1122_43/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_43/correct.txt
rename to Data/ingsw/1122_43/correct.txt
diff --git a/legacy/Data/ingsw/1122_43/quest.txt b/Data/ingsw/1122_43/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_43/quest.txt
rename to Data/ingsw/1122_43/quest.txt
index 1045bb8..fc279af 100644
--- a/legacy/Data/ingsw/1122_43/quest.txt
+++ b/Data/ingsw/1122_43/quest.txt
@@ -1,11 +1,11 @@
-img=https://i.imgur.com/5ZmMM3r.png
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-
-Si consideri lo state diagram in figura 
-Si consideri il seguente insieme di test cases:
-
-Test case 1: act0 act2 act2 act1 act2 act2 act0 act2 act2 act0 act2 act0 act0 act2 act2 act2 act2 act1 
-
-Test case 2: act2 act1 act0 act2 act2 act0 act0 act1 
-
+img=https://i.imgur.com/5ZmMM3r.png
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+
+Si consideri lo state diagram in figura 
+Si consideri il seguente insieme di test cases:
+
+Test case 1: act0 act2 act2 act1 act2 act2 act0 act2 act2 act0 act2 act0 act0 act2 act2 act2 act2 act1 
+
+Test case 2: act2 act1 act0 act2 act2 act0 act0 act1 
+
 Test case 3: act0 act1 act0 act0 act0 act2 act1 act0 act2 act2 act2 act0 act1 
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_43/wrong 1.txt b/Data/ingsw/1122_43/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_43/wrong 1.txt
rename to Data/ingsw/1122_43/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_43/wrong 2.txt b/Data/ingsw/1122_43/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_43/wrong 2.txt
rename to Data/ingsw/1122_43/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_44/correct.txt b/Data/ingsw/1122_44/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_44/correct.txt
rename to Data/ingsw/1122_44/correct.txt
diff --git a/legacy/Data/ingsw/1122_44/quest.txt b/Data/ingsw/1122_44/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/1122_44/quest.txt
rename to Data/ingsw/1122_44/quest.txt
index 6428a0e..764ee2f 100644
--- a/legacy/Data/ingsw/1122_44/quest.txt
+++ b/Data/ingsw/1122_44/quest.txt
@@ -1,15 +1,15 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-
-Si consideri la seguente funzione C:
-
-int f1(int x)  { return (2*x); }
-
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-
-{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
-
-Si consideri il seguente insieme di test cases:
-
-{x=-20, x= 10, x=60}
-
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+
+Si consideri la seguente funzione C:
+
+int f1(int x)  { return (2*x); }
+
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+
+{(-inf, -11], [-10, -1], {0}, [1, 50], [51, +inf)}
+
+Si consideri il seguente insieme di test cases:
+
+{x=-20, x= 10, x=60}
+
 Quale delle seguenti è la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_44/wrong 1.txt b/Data/ingsw/1122_44/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_44/wrong 1.txt
rename to Data/ingsw/1122_44/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_44/wrong 2.txt b/Data/ingsw/1122_44/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_44/wrong 2.txt
rename to Data/ingsw/1122_44/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_45/correct.txt b/Data/ingsw/1122_45/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_45/correct.txt
rename to Data/ingsw/1122_45/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_45/quest.txt b/Data/ingsw/1122_45/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/1122_45/quest.txt
rename to Data/ingsw/1122_45/quest.txt
index 1454704..90e2bab 100644
--- a/legacy/Data/Questions/ingsw/1122_45/quest.txt
+++ b/Data/ingsw/1122_45/quest.txt
@@ -1,8 +1,8 @@
-img=https://i.imgur.com/47sr1ne.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-
-Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
-
-Uno scenario è una sequenza di stati.
-
+img=https://i.imgur.com/47sr1ne.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+
+Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.2 di dover essere ripetuta (a causa di errori).
+
+Uno scenario è una sequenza di stati.
+
 Qual è la probabilità dello scenario: 1, 3 ? In altri terminti, qual è la probabilità che non sia necessario ripetere nessuna fase?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_45/wrong 1.txt b/Data/ingsw/1122_45/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_45/wrong 1.txt
rename to Data/ingsw/1122_45/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_45/wrong 2.txt b/Data/ingsw/1122_45/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_45/wrong 2.txt
rename to Data/ingsw/1122_45/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_46/correct.txt b/Data/ingsw/1122_46/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_46/correct.txt
rename to Data/ingsw/1122_46/correct.txt
diff --git a/legacy/Data/ingsw/1122_46/quest.txt b/Data/ingsw/1122_46/quest.txt
similarity index 97%
rename from legacy/Data/ingsw/1122_46/quest.txt
rename to Data/ingsw/1122_46/quest.txt
index 5bf1a08..b3403d8 100644
--- a/legacy/Data/ingsw/1122_46/quest.txt
+++ b/Data/ingsw/1122_46/quest.txt
@@ -1,14 +1,14 @@
-La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
-
-Si consideri lo state diagram in figura
-Si consideri il seguente insieme di test cases:
-
-Test case 1: act2 act1 act1 
-
-Test case 2: act0 act0 act2 act1 
-
-Test case 3: act2 act0 act2 act2 act0 
-
-
-
+La state coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) raggiunti almeno una volta.
+
+Si consideri lo state diagram in figura
+Si consideri il seguente insieme di test cases:
+
+Test case 1: act2 act1 act1 
+
+Test case 2: act0 act0 act2 act1 
+
+Test case 3: act2 act0 act2 act2 act0 
+
+
+
 Quale delle seguenti è la migliore stima della state coverage per i test cases di cui sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_46/wrong 1.txt b/Data/ingsw/1122_46/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_46/wrong 1.txt
rename to Data/ingsw/1122_46/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_46/wrong 2.txt b/Data/ingsw/1122_46/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_46/wrong 2.txt
rename to Data/ingsw/1122_46/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_47/correct.txt b/Data/ingsw/1122_47/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_47/correct.txt
rename to Data/ingsw/1122_47/correct.txt
diff --git a/legacy/Data/ingsw/1122_47/quest.txt b/Data/ingsw/1122_47/quest.txt
similarity index 98%
rename from legacy/Data/ingsw/1122_47/quest.txt
rename to Data/ingsw/1122_47/quest.txt
index 3631f63..58ef38e 100644
--- a/legacy/Data/ingsw/1122_47/quest.txt
+++ b/Data/ingsw/1122_47/quest.txt
@@ -1,11 +1,11 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-
-Si consideri la seguente funzione C:
-
-int f1(int x)  { return (x + 7); }
-
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-
-{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
-
+Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
+
+Si consideri la seguente funzione C:
+
+int f1(int x)  { return (x + 7); }
+
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+
+{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
+
 Quale dei seguenti test cases consegue una partition coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_47/wrong 1.txt b/Data/ingsw/1122_47/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_47/wrong 1.txt
rename to Data/ingsw/1122_47/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_47/wrong 2.txt b/Data/ingsw/1122_47/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_47/wrong 2.txt
rename to Data/ingsw/1122_47/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_48/correct.txt b/Data/ingsw/1122_48/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_48/correct.txt
rename to Data/ingsw/1122_48/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_48/quest.txt b/Data/ingsw/1122_48/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_48/quest.txt
rename to Data/ingsw/1122_48/quest.txt
index 4fc3c18..a2719a1 100644
--- a/legacy/Data/Questions/ingsw/1122_48/quest.txt
+++ b/Data/ingsw/1122_48/quest.txt
@@ -1,24 +1,24 @@
-Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta in espressioni boolean più semplici. Ad esempio, <code>(x + y <= 3)</code> è una condition.
-
-Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
-<pre>
-(x + y <= 3) 
-((x + y <= 3) || (x - y > 7))
-</pre>
-Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
-
-1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
-2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
-3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
-
-Si consideri la seguente funzione:
-<pre>
-int f(int a, int b, int c)
-{    if ( (a  + b - 6 >= 0) && (b - c - 1 <= 0) )
-          return (1);    // punto di uscita 1
-      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0))
-           then return (2);   // punto di uscita 2
-           else return (3);   // punto di uscita 3
-}
-   Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
+Una Condition è una proposizione booleana, cioè una espressione con valore booleano che non può essere decomposta in espressioni boolean più semplici. Ad esempio, <code>(x + y <= 3)</code> è una condition.
+
+Una Decision è una espressione booleana composta da conditions e zero o più operatori booleani. Ad esempio, sono decisions:
+<pre>
+(x + y <= 3) 
+((x + y <= 3) || (x - y > 7))
+</pre>
+Un insieme di test cases T soddisfa il criterio di  Condition/Decision coverage se tutte le seguenti condizioni sono soddisfatte:
+
+1) Ciascun punto di entrata ed uscita nel programma è eseguito in almeno un test;
+2) Per ogni decision d nel programma, per ogni condition c in d, esiste un test in T in cui c è true ed un test in T in cui c è false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d è true ed un test in T in cui d è false.
+
+Si consideri la seguente funzione:
+<pre>
+int f(int a, int b, int c)
+{    if ( (a  + b - 6 >= 0) && (b - c - 1 <= 0) )
+          return (1);    // punto di uscita 1
+      else if ((b - c - 1 <= 0)  || (b + c - 5 >= 0))
+           then return (2);   // punto di uscita 2
+           else return (3);   // punto di uscita 3
+}
+   Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_48/wrong 1.txt b/Data/ingsw/1122_48/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_48/wrong 1.txt
rename to Data/ingsw/1122_48/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_48/wrong 2.txt b/Data/ingsw/1122_48/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_48/wrong 2.txt
rename to Data/ingsw/1122_48/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_49/correct.txt b/Data/ingsw/1122_49/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_49/correct.txt
rename to Data/ingsw/1122_49/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_49/quest.txt b/Data/ingsw/1122_49/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_49/quest.txt
rename to Data/ingsw/1122_49/quest.txt
index e591c8c..c2267f6 100644
--- a/legacy/Data/Questions/ingsw/1122_49/quest.txt
+++ b/Data/ingsw/1122_49/quest.txt
@@ -1,12 +1,12 @@
-img=https://i.imgur.com/rZnqUL9.png
-La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
-
-Si consideri lo state diagram in figura ed il seguente insieme di test cases:
-
-Test case 1: act1 act0 act2 act0 act0 act0 act2 act1 act1 act0 act2 act0 act2 act2 act1 act1 act0 act2 act2 act2 act1 act1 act2 act0 act1 act0 act1 act2 
-
-Test case 2: act1 act0 act0 act0 act2 act2 act2 act2 act2 act1 act1 act0 act0 act0 act2 act2 act2 act0 act1 act1 act1 act0 act2 act0 act0 act0 act1 act1 act2 act0 act1 act0 act0 act0 act2 act0 act1 act2 act2 act2 act0 act1 act2 act0 act1 act0 act1 act2 
-
-Test case 3: act1 act0 act0 act1 act1 act1 act1 act2 act2 act0 act1 act2
-
+img=https://i.imgur.com/rZnqUL9.png
+La transition coverage di un insieme di test cases (cioè sequenze di inputs) per uno state diagram è la percentuale di transizioni (archi nel grafo dello state diagram) percorsi almeno una volta.
+
+Si consideri lo state diagram in figura ed il seguente insieme di test cases:
+
+Test case 1: act1 act0 act2 act0 act0 act0 act2 act1 act1 act0 act2 act0 act2 act2 act1 act1 act0 act2 act2 act2 act1 act1 act2 act0 act1 act0 act1 act2 
+
+Test case 2: act1 act0 act0 act0 act2 act2 act2 act2 act2 act1 act1 act0 act0 act0 act2 act2 act2 act0 act1 act1 act1 act0 act2 act0 act0 act0 act1 act1 act2 act0 act1 act0 act0 act0 act2 act0 act1 act2 act2 act2 act0 act1 act2 act0 act1 act0 act1 act2 
+
+Test case 3: act1 act0 act0 act1 act1 act1 act1 act2 act2 act0 act1 act2
+
 Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_49/wrong 1.txt b/Data/ingsw/1122_49/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_49/wrong 1.txt
rename to Data/ingsw/1122_49/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_49/wrong 2.txt b/Data/ingsw/1122_49/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_49/wrong 2.txt
rename to Data/ingsw/1122_49/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_5/correct.txt b/Data/ingsw/1122_5/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_5/correct.txt
rename to Data/ingsw/1122_5/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_5/quest.txt b/Data/ingsw/1122_5/quest.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/1122_5/quest.txt
rename to Data/ingsw/1122_5/quest.txt
index bcbb3d8..bd4a8a1 100644
--- a/legacy/Data/Questions/ingsw/1122_5/quest.txt
+++ b/Data/ingsw/1122_5/quest.txt
@@ -1,68 +1,68 @@
-Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente?
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+Si consideri il seguente modello Modelica. Quale dei seguenti state diagram lo rappresenta correttamente?
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_5/wrong 1.txt b/Data/ingsw/1122_5/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_5/wrong 1.txt
rename to Data/ingsw/1122_5/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_5/wrong 2.txt b/Data/ingsw/1122_5/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_5/wrong 2.txt
rename to Data/ingsw/1122_5/wrong 2.txt
diff --git a/legacy/Data/Questions/ingsw/1122_50/correct.txt b/Data/ingsw/1122_50/correct.txt
similarity index 93%
rename from legacy/Data/Questions/ingsw/1122_50/correct.txt
rename to Data/ingsw/1122_50/correct.txt
index 7470aaf..6fc3465 100644
--- a/legacy/Data/Questions/ingsw/1122_50/correct.txt
+++ b/Data/ingsw/1122_50/correct.txt
@@ -1,69 +1,69 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 2;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_50/quest.txt b/Data/ingsw/1122_50/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/1122_50/quest.txt
rename to Data/ingsw/1122_50/quest.txt
index 971e607..de6a1fb 100644
--- a/legacy/Data/ingsw/1122_50/quest.txt
+++ b/Data/ingsw/1122_50/quest.txt
@@ -1,2 +1,2 @@
-img=https://i.imgur.com/fyv5jqF.png
+img=https://i.imgur.com/fyv5jqF.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_50/wrong 1.txt b/Data/ingsw/1122_50/wrong 1.txt
similarity index 93%
rename from legacy/Data/ingsw/1122_50/wrong 1.txt
rename to Data/ingsw/1122_50/wrong 1.txt
index e77e043..afe907d 100644
--- a/legacy/Data/ingsw/1122_50/wrong 1.txt	
+++ b/Data/ingsw/1122_50/wrong 1.txt	
@@ -1,69 +1,69 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 2) then x := 1;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 2) then x := 1;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 3;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_50/wrong 2.txt b/Data/ingsw/1122_50/wrong 2.txt
similarity index 93%
rename from legacy/Data/ingsw/1122_50/wrong 2.txt
rename to Data/ingsw/1122_50/wrong 2.txt
index 03c4dea..b91d652 100644
--- a/legacy/Data/ingsw/1122_50/wrong 2.txt	
+++ b/Data/ingsw/1122_50/wrong 2.txt	
@@ -1,71 +1,71 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 1;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 2;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 1;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_6/correct.txt b/Data/ingsw/1122_6/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_6/correct.txt
rename to Data/ingsw/1122_6/correct.txt
diff --git a/legacy/Data/ingsw/1122_6/quest.txt b/Data/ingsw/1122_6/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_6/quest.txt
rename to Data/ingsw/1122_6/quest.txt
diff --git a/legacy/Data/ingsw/1122_6/wrong 1.txt b/Data/ingsw/1122_6/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_6/wrong 1.txt
rename to Data/ingsw/1122_6/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_6/wrong 2.txt b/Data/ingsw/1122_6/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_6/wrong 2.txt
rename to Data/ingsw/1122_6/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_7/correct.txt b/Data/ingsw/1122_7/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_7/correct.txt
rename to Data/ingsw/1122_7/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_7/quest.txt b/Data/ingsw/1122_7/quest.txt
similarity index 98%
rename from legacy/Data/Questions/ingsw/1122_7/quest.txt
rename to Data/ingsw/1122_7/quest.txt
index 8db1ade..f22c6a8 100644
--- a/legacy/Data/Questions/ingsw/1122_7/quest.txt
+++ b/Data/ingsw/1122_7/quest.txt
@@ -1,10 +1,10 @@
-img=https://i.imgur.com/5TP66IN.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
-
-Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2.
-F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR.
-F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
-
-Uno scenario è una sequenza di stati.
-
+img=https://i.imgur.com/5TP66IN.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del processo software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo per lasciare lo stato. 
+
+Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2.
+F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR.
+F1 ha una probabilita dello 0.4 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.3 di dover essere ripetuta (a causa di errori).
+
+Uno scenario è una sequenza di stati.
+
 Qual è la probabilità dello scenario: 1, 2, 3? In altri terminti, qual è la probabilità che non sia necessario ripetere la prima fase (ma non la seconda)?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_7/wrong 1.txt b/Data/ingsw/1122_7/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_7/wrong 1.txt
rename to Data/ingsw/1122_7/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_7/wrong 2.txt b/Data/ingsw/1122_7/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_7/wrong 2.txt
rename to Data/ingsw/1122_7/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_8/correct.txt b/Data/ingsw/1122_8/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_8/correct.txt
rename to Data/ingsw/1122_8/correct.txt
diff --git a/legacy/Data/Questions/ingsw/1122_8/quest.txt b/Data/ingsw/1122_8/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/1122_8/quest.txt
rename to Data/ingsw/1122_8/quest.txt
index 1f66143..d9efe1d 100644
--- a/legacy/Data/Questions/ingsw/1122_8/quest.txt
+++ b/Data/ingsw/1122_8/quest.txt
@@ -1,8 +1,8 @@
-img=https://i.imgur.com/5TP66IN.png
-Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
-
-Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
-
-Uno scenario è una sequenza di stati.
-
+img=https://i.imgur.com/5TP66IN.png
+Un processo software può essere rappesentato con uno state diagram in cui gli stati rappresentano le fasi (e loro iterazioni) del prcoesso software e gli archi le transizioni da una fase all'altra. Gli archi sono etichettati con le probabilità della transizione e gli stati sono etichettati con il costo  per lasciare lo stato. 
+
+Ad esempio lo state diagram in figura rappresenta un processo software con 2 fasi F1 ed F2. F1 ha costo 10000 EUR ed F2 ha costo 1000 EUR. F1 ha una probabilita dello 0.3 di dover essere ripetuta (a causa di errori) ed F2 ha una probabilità 0.1 di dover essere ripetuta (a causa di errori).
+
+Uno scenario è una sequenza di stati.
+
 Qual è la probabilità dello scenario: 1, 2, 3, 4 ? In altri terminti, qual è la probabilità che sia necessario ripetere sia la fase 1 che la fase 2?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_8/wrong 1.txt b/Data/ingsw/1122_8/wrong 1.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_8/wrong 1.txt
rename to Data/ingsw/1122_8/wrong 1.txt
diff --git a/legacy/Data/ingsw/1122_8/wrong 2.txt b/Data/ingsw/1122_8/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/1122_8/wrong 2.txt
rename to Data/ingsw/1122_8/wrong 2.txt
diff --git a/legacy/Data/ingsw/1122_9/correct.txt b/Data/ingsw/1122_9/correct.txt
similarity index 95%
rename from legacy/Data/ingsw/1122_9/correct.txt
rename to Data/ingsw/1122_9/correct.txt
index a7a3133..d48268a 100644
--- a/legacy/Data/ingsw/1122_9/correct.txt
+++ b/Data/ingsw/1122_9/correct.txt
@@ -1,44 +1,44 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-/* connector declarations outside this block:
-connector InputInteger = input Integer;
-connector OutputInteger = output Integer;
-*/
-
-
-InputInteger u; // external input
-OutputInteger x; // state
-parameter Real T = 1;
-
-
-algorithm
-
-
-when initial() then
-x := 0;
-
-
-elsewhen sample(0,T) then
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
-else x := pre(x); // default
-end if;
-
-
-end when;
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+/* connector declarations outside this block:
+connector InputInteger = input Integer;
+connector OutputInteger = output Integer;
+*/
+
+
+InputInteger u; // external input
+OutputInteger x; // state
+parameter Real T = 1;
+
+
+algorithm
+
+
+when initial() then
+x := 0;
+
+
+elsewhen sample(0,T) then
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 0;
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 0;
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 3;
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 1;
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 0;
+else x := pre(x); // default
+end if;
+
+
+end when;
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_9/quest.txt b/Data/ingsw/1122_9/quest.txt
similarity index 99%
rename from legacy/Data/Questions/ingsw/1122_9/quest.txt
rename to Data/ingsw/1122_9/quest.txt
index 0e4c593..09ad5cb 100644
--- a/legacy/Data/Questions/ingsw/1122_9/quest.txt
+++ b/Data/ingsw/1122_9/quest.txt
@@ -1,2 +1,2 @@
-img=https://i.imgur.com/Jq6EzV9.png
+img=https://i.imgur.com/Jq6EzV9.png
 Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_9/wrong 1.txt b/Data/ingsw/1122_9/wrong 1.txt
similarity index 93%
rename from legacy/Data/ingsw/1122_9/wrong 1.txt
rename to Data/ingsw/1122_9/wrong 1.txt
index ea67dd7..3ca0d3a 100644
--- a/legacy/Data/ingsw/1122_9/wrong 1.txt	
+++ b/Data/ingsw/1122_9/wrong 1.txt	
@@ -1,71 +1,71 @@
-<pre>
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 2;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
-
-elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 2;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 1;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 1;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 3;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_9/wrong 2.txt b/Data/ingsw/1122_9/wrong 2.txt
similarity index 93%
rename from legacy/Data/ingsw/1122_9/wrong 2.txt
rename to Data/ingsw/1122_9/wrong 2.txt
index 578bb6b..0ffb41c 100644
--- a/legacy/Data/ingsw/1122_9/wrong 2.txt	
+++ b/Data/ingsw/1122_9/wrong 2.txt	
@@ -1,76 +1,76 @@
-<pre>
-
-block FSA  //  Finite State Automaton
-
-
-
-/* connector declarations outside this block:
-
-connector InputInteger = input Integer;
-
-connector OutputInteger = output Integer;
-
-*/
-
-
-
-InputInteger u; // external input
-
-OutputInteger x; // state
-
-parameter Real T = 1;
-
-
-
-algorithm
-
-
-
-when initial() then
-
-x := 0;
-
-
-
-elsewhen sample(0,T) then
-
-
-
-if (pre(x) == 0) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
-
-elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
-
-elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
-
-elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
-
-elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
-
-elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
-
-else x := pre(x); // default
-
-end if;
-
-
-
-end when;
-
-end FSA;
+<pre>
+
+block FSA  //  Finite State Automaton
+
+
+
+/* connector declarations outside this block:
+
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+*/
+
+
+
+InputInteger u; // external input
+
+OutputInteger x; // state
+
+parameter Real T = 1;
+
+
+
+algorithm
+
+
+
+when initial() then
+
+x := 0;
+
+
+
+elsewhen sample(0,T) then
+
+
+
+if (pre(x) == 0) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 0) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 1) and (pre(u) == 2) then x := 3;
+
+elseif (pre(x) == 2) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 1) then x := 3;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 3) and (pre(u) == 0) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 1) then x := 4;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 0) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 1) then x := 0;
+
+elseif (pre(x) == 4) and (pre(u) == 2) then x := 2;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
 </pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/12/correct.txt b/Data/ingsw/12/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/12/correct.txt
rename to Data/ingsw/12/correct.txt
diff --git a/legacy/Data/ingsw/12/quest.txt b/Data/ingsw/12/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/12/quest.txt
rename to Data/ingsw/12/quest.txt
diff --git a/legacy/Data/ingsw/12/wrong 2.txt b/Data/ingsw/12/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/12/wrong 2.txt
rename to Data/ingsw/12/wrong 2.txt
diff --git a/legacy/Data/ingsw/12/wrong.txt b/Data/ingsw/12/wrong.txt
similarity index 100%
rename from legacy/Data/ingsw/12/wrong.txt
rename to Data/ingsw/12/wrong.txt
diff --git a/legacy/Data/ingsw/16/correct.txt b/Data/ingsw/16/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/16/correct.txt
rename to Data/ingsw/16/correct.txt
diff --git a/legacy/Data/ingsw/16/quest.txt b/Data/ingsw/16/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/16/quest.txt
rename to Data/ingsw/16/quest.txt
diff --git a/legacy/Data/ingsw/16/wrong 2.txt b/Data/ingsw/16/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/16/wrong 2.txt
rename to Data/ingsw/16/wrong 2.txt
diff --git a/legacy/Data/ingsw/16/wrong.txt b/Data/ingsw/16/wrong.txt
similarity index 100%
rename from legacy/Data/ingsw/16/wrong.txt
rename to Data/ingsw/16/wrong.txt
diff --git a/legacy/Data/ingsw/17/correct.txt b/Data/ingsw/17/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/17/correct.txt
rename to Data/ingsw/17/correct.txt
diff --git a/legacy/Data/ingsw/17/quest.txt b/Data/ingsw/17/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/17/quest.txt
rename to Data/ingsw/17/quest.txt
diff --git a/legacy/Data/ingsw/17/wrong 2.txt b/Data/ingsw/17/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/17/wrong 2.txt
rename to Data/ingsw/17/wrong 2.txt
diff --git a/legacy/Data/ingsw/17/wrong.txt b/Data/ingsw/17/wrong.txt
similarity index 100%
rename from legacy/Data/ingsw/17/wrong.txt
rename to Data/ingsw/17/wrong.txt
diff --git a/legacy/Data/ingsw/19/correct.txt b/Data/ingsw/19/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/19/correct.txt
rename to Data/ingsw/19/correct.txt
diff --git a/legacy/Data/ingsw/19/quest.txt b/Data/ingsw/19/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/19/quest.txt
rename to Data/ingsw/19/quest.txt
diff --git a/legacy/Data/ingsw/19/wrong 2.txt b/Data/ingsw/19/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/19/wrong 2.txt
rename to Data/ingsw/19/wrong 2.txt
diff --git a/legacy/Data/ingsw/19/wrong.txt b/Data/ingsw/19/wrong.txt
similarity index 100%
rename from legacy/Data/ingsw/19/wrong.txt
rename to Data/ingsw/19/wrong.txt
diff --git a/legacy/Data/ingsw/2/correct.txt b/Data/ingsw/2/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/2/correct.txt
rename to Data/ingsw/2/correct.txt
diff --git a/legacy/Data/ingsw/2/quest.txt b/Data/ingsw/2/quest.txt
similarity index 99%
rename from legacy/Data/ingsw/2/quest.txt
rename to Data/ingsw/2/quest.txt
index 78e700c..54a3411 100644
--- a/legacy/Data/ingsw/2/quest.txt
+++ b/Data/ingsw/2/quest.txt
@@ -1,2 +1,2 @@
-Si consideri un software sviluppato seguendo un approccio plan-driven implementato con tre fasi: F1, F2, F3 ciascuna con costo A. Le "change request" possono arrivare solo al fine di una fase e provocano la ripetizione (con relativo costo) di tutte le fasi che precedono. Si assuma che dopo la fase F3 (cioè al termine dello sviluppo) arriva una change request. Qual è il costo totale per lo sviluppo del software in questione.
+Si consideri un software sviluppato seguendo un approccio plan-driven implementato con tre fasi: F1, F2, F3 ciascuna con costo A. Le "change request" possono arrivare solo al fine di una fase e provocano la ripetizione (con relativo costo) di tutte le fasi che precedono. Si assuma che dopo la fase F3 (cioè al termine dello sviluppo) arriva una change request. Qual è il costo totale per lo sviluppo del software in questione.
 Scegli un'alternativa:
\ No newline at end of file
diff --git a/legacy/Data/ingsw/2/wrong 2.txt b/Data/ingsw/2/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/2/wrong 2.txt
rename to Data/ingsw/2/wrong 2.txt
diff --git a/legacy/Data/ingsw/2/wrong.txt b/Data/ingsw/2/wrong.txt
similarity index 100%
rename from legacy/Data/ingsw/2/wrong.txt
rename to Data/ingsw/2/wrong.txt
diff --git a/legacy/Data/ingsw/20/correct.txt b/Data/ingsw/20/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/20/correct.txt
rename to Data/ingsw/20/correct.txt
diff --git a/legacy/Data/ingsw/20/quest.txt b/Data/ingsw/20/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/20/quest.txt
rename to Data/ingsw/20/quest.txt
diff --git a/legacy/Data/ingsw/20/wrong 2.txt b/Data/ingsw/20/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/20/wrong 2.txt
rename to Data/ingsw/20/wrong 2.txt
diff --git a/legacy/Data/ingsw/20/wrong.txt b/Data/ingsw/20/wrong.txt
similarity index 100%
rename from legacy/Data/ingsw/20/wrong.txt
rename to Data/ingsw/20/wrong.txt
diff --git a/legacy/Data/ingsw/21/correct.txt b/Data/ingsw/21/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/21/correct.txt
rename to Data/ingsw/21/correct.txt
diff --git a/legacy/Data/ingsw/21/quest.txt b/Data/ingsw/21/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/21/quest.txt
rename to Data/ingsw/21/quest.txt
diff --git a/legacy/Data/ingsw/21/wrong 2.txt b/Data/ingsw/21/wrong 2.txt
similarity index 100%
rename from legacy/Data/ingsw/21/wrong 2.txt
rename to Data/ingsw/21/wrong 2.txt
diff --git a/legacy/Data/ingsw/21/wrong.txt b/Data/ingsw/21/wrong.txt
similarity index 100%
rename from legacy/Data/ingsw/21/wrong.txt
rename to Data/ingsw/21/wrong.txt
diff --git a/legacy/Data/ingsw/22/correct.txt b/Data/ingsw/22/correct.txt
similarity index 100%
rename from legacy/Data/ingsw/22/correct.txt
rename to Data/ingsw/22/correct.txt
diff --git a/legacy/Data/ingsw/22/quest.txt b/Data/ingsw/22/quest.txt
similarity index 100%
rename from legacy/Data/ingsw/22/quest.txt
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diff --git a/legacy/Data/ingsw/5/correct.txt b/Data/ingsw/5/correct.txt
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index 4ce9b89..41acfaa 100644
--- a/legacy/Data/ingsw/5/quest.txt
+++ b/Data/ingsw/5/quest.txt
@@ -1,2 +1,2 @@
-img=https://i.imgur.com/2gg5nIM.png
+img=https://i.imgur.com/2gg5nIM.png
 Lo State Diagram in figura descrive (in modo semplificato) una macchina distributrice di bevande. Quale delle seguenti frasi è corretta riguardo allo State Diagram in figura?
\ No newline at end of file
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diff --git a/legacy/Data/ingsw/69420/correct.txt b/Data/ingsw/69420/correct.txt
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+++ b/Data/ingsw/69420/correct.txt
@@ -1,2 +1,2 @@
-img=https://i.imgur.com/a8kMXoW.png
+img=https://i.imgur.com/a8kMXoW.png
 Serafina che tagga Sabrina
\ No newline at end of file
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index 670e7eb..b97a6ba 100644
--- a/legacy/Data/ingsw/69420/wrong 2.txt	
+++ b/Data/ingsw/69420/wrong 2.txt	
@@ -1,2 +1,2 @@
-img=https://i.imgur.com/F4evurl.jpg
+img=https://i.imgur.com/F4evurl.jpg
 Gioele che tagga Sabrina
\ No newline at end of file
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--- a/legacy/Data/ingsw/69420/wrong 3.txt	
+++ b/Data/ingsw/69420/wrong 3.txt	
@@ -1,2 +1,2 @@
-img=https://i.imgur.com/qyKmPIA.png
+img=https://i.imgur.com/qyKmPIA.png
 Deco che disegna un Hentai in aula studio
\ No newline at end of file
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+++ b/Data/ingsw/69420/wrong.txt
@@ -1,2 +1,2 @@
-img=https://corsidilaurea.uniroma1.it/sites/default/files/styles/user_picture/public/pictures/picture-23550-1602857792.jpg
+img=https://corsidilaurea.uniroma1.it/sites/default/files/styles/user_picture/public/pictures/picture-23550-1602857792.jpg
 Tronci
\ No newline at end of file
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diff --git a/legacy/Data/motd.txt b/Data/motd.txt
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diff --git a/legacy/Dockerfile b/Dockerfile
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rename to Dockerfile
diff --git a/README.md b/README.md
index e1f05a4..a936ac3 100644
--- a/README.md
+++ b/README.md
@@ -1,19 +1,44 @@
-### so-un-bot
-
-� [@so_1_bot](https://t.me/so_1_bot) is an italian Telegram bot to help students of Sapienza, University of Rome with some exams.
+# WARNING
+**We are working on a completely new version of the bot, written in Python. Stay tuned!**
 
 ---
-🇮🇹 Il repository raccoglie domande di esami a risposta multipla di corsi di laurea dell'università Sapienza, con lo scopo di renderle disponibili tramite il bot Telegram [@so_1_bot](https://t.me/so_1_bot).
+# so-un-bot
+The official https://t.me/so_1_bot questions repository
 
-Le domande disponibili sul bot vengono automaticamente sincronizate con questo repository; per aggiungere nuove domande o correggere domande esistenti siete liberi di inviare Pull Requests.
+🇮🇹 Raccolta di domande con risposta multipla utili per esercitarsi in molteplici esami!
 
-[@so_1_bot](https://t.me/so_1_bot) è un'istanza di [pIm-a-bot](https://github.com/WAPEETY/pIm-a-bot), progetto mantenuto da [@notherealmarco](https://github.com/notherealmarco) e [@WAPEETY](https://github.com/WAPEETY), per ulteriori informazioni si rimanda al repository ufficiale.
+### Struttura del repository
 
----
-La directory `legacy` contiene il sorgente della precedente versione del bot, non piu in uso; pertanto pull requests che aggiornano domande in `legacy/` verranno chiuse.
+In Data/Questions sono presenti tutte le domande attualmente presenti nel bot, il nome del file corrisponde al nome del comando sul bot.
+Per aggiungere o correggere domande potete fare una Pull Request a questa repo.
 
-Le domande utilizzate dal bot si trovano in `data/questions`
+In Utils trovate script, sviluppati da vari studenti del corso, per creare o validare i file delle domande.
 
----
-mirror disponibili:
-- [https://git.marcorealacci.me/marcorealacci/so-un-bot](https://git.marcorealacci.me/marcorealacci/so-un-bot)
+**Nota per gli admin di appinfosapienza:**
+Al momento non sono presenti dei test CI che testano l'integrità del repository prima di un deploy.
+Quando accettate una Pull Request, entro due minuti verrà lanciata una nuova build sul server di produzione e al termine eseguito il bot con la nuova versione.
+
+Non essendoci test CI, se sono presenti errori, un commit contenente errori può mandare offline il bot (ad es. se il bot non riesce a fare il parsing di tutte le domande all'avvio).
+
+**Per i contributori:** 
+### Struttura dei file
+
+Il bot accetta le domande sia in un singolo file (utilizzato ad esesempio da Sistemi Operativi 1 e 2), che in file multipli (utilizzato da Ingegneria del Software).
+È in programma l'implementazione del supporto al formato JSON.
+
+#### Singolo file
+
+- Le domande sono separate da una riga vuota
+- Le risposte possono essere su una riga sola
+- Le risposte devono iniziano per `> ` se sono errate, per `v ` se sono corrette
+- Non ci possono essere righe vuote nella stessa domanda
+- Solo la domanda può contenere un'immagine, non le risposte
+
+#### File multipli
+
+- Ogni domanda è in una directory separata
+- La directory ha come nome il numero della domanda
+- Il testo della domanda è nel file `quest.txt`
+- La risposta corretta è nel file `correct.txt`
+- Gli altri file contengono solo risposte errate
+- Sia domande che risposte possono contenere immagini (max un'immagine per domanda e una per ciascuna risposta)
diff --git a/legacy/Utils/check-ingsw-photos.sh b/Utils/check-ingsw-photos.sh
similarity index 100%
rename from legacy/Utils/check-ingsw-photos.sh
rename to Utils/check-ingsw-photos.sh
diff --git a/legacy/Utils/find_duplicates.py b/Utils/find_duplicates.py
similarity index 100%
rename from legacy/Utils/find_duplicates.py
rename to Utils/find_duplicates.py
diff --git a/legacy/Utils/make_questions.py b/Utils/make_questions.py
similarity index 100%
rename from legacy/Utils/make_questions.py
rename to Utils/make_questions.py
diff --git a/legacy/Utils/moodle-scraper/README.md b/Utils/moodle-scraper/README.md
similarity index 100%
rename from legacy/Utils/moodle-scraper/README.md
rename to Utils/moodle-scraper/README.md
diff --git a/legacy/Utils/moodle-scraper/scraper.py b/Utils/moodle-scraper/scraper.py
similarity index 100%
rename from legacy/Utils/moodle-scraper/scraper.py
rename to Utils/moodle-scraper/scraper.py
diff --git a/data/config/motd.txt b/data/config/motd.txt
deleted file mode 100644
index be8625f..0000000
--- a/data/config/motd.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-"Benvenuto 👑
-Con questo bot puoi esercitarti con le domande di alcuni esami del corso di Informatica della Sapienza! 🤞.
-
-✅ Il bot è mantenuto da @notherealmarco con l'aiuto di alcuni studenti del corso, un enorme grazie a @simone_s0, @loryspat, @Deco71, @mmatex123ab, Raffaele Ruggeri e sicuramente ne scordo qualcuno, perdonatemi 😢
-
-Per contribuire (aggiungere o correggere domande), il bot si sincronizza con il seguente repository:
-https://github.com/appinfosapienza/so-un-bot
-Pull requests sono ben accette! 🫂��
-
-🆘 Per segnalare errori, per proporre nuove domande �, o semplicemente se questo bot ti fa schifo 😢, non esitare a contattarmi: @notherealmarco
-(Oppure puoi correggere errori in autonomia inviando una PR al repository GitHub)
-
-⭕� Informativa sulla privacy: https://so-un-bot.mrlc.cc/privacy.pdf
-
-Per esercitare il diritto all'eliminazione dei dati personali, puoi inviare il comando /unregister.
-I dati verranno immediatamente eliminati, l'azione del comando non è reversibile.
-
-
-👷�♀� To start a quiz, use one of the following commands:
-
-Corso di laurea in Informatica 🇮🇹:
-/so1 -> Sistemi Operativi I, prof. Melatti (canale I)
-/so2 -> Sistemi Operativi II, prof. Casalicchio (canale II)
-/ogas -> Organizzazione e Gestione per lo Startup Aziendale, prof.ssa Castaldo
-/sicurezza -> Sicurezza, prof. Casalicchio. Domande in inglese (l'esame usa le stesse domande ma tradotte in italiano)
-
-Master Degree in Computer Science 🇬🇧:
-/fds -> Foundation of Data Science, prof. Galasso. Updated to 2022
-/fds_llm -> Foundation of Data Science, questions generated by an LLM. NOT questions from past exams.
-
-To stop a quiz, please use the /leave command
diff --git a/data/questions/diritto_unive_inf.json b/data/questions/diritto_unive_inf.json
deleted file mode 100644
index 87790bb..0000000
--- a/data/questions/diritto_unive_inf.json
+++ /dev/null
@@ -1,1244 +0,0 @@
-[
-  {
-    "quest": "1) Esiste un codice di diritto dell’informatica?",
-    "answers": [
-      {
-        "text": "No.",
-        "image": ""
-      },
-      {
-        "text": "Si",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "2) Cosa si intende con principio di neutralità della rete?",
-    "answers": [
-      {
-        "text": "Che opera nella rete non deve discriminare politicamente",
-        "image": ""
-      },
-      {
-        "text": "Che chi opera nella rete non deve discriminare tra tecnologie di accesso.",
-        "image": ""
-      },
-      {
-        "text": "Che chi opera nella rete non deve discriminare i consumatori",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "3) Quando in Italia la riservatezza è divenuto diritto tutelato delle leggi? ",
-    "answers": [
-      {
-        "text": "A metà degli anni ‘70",
-        "image": ""
-      },
-      {
-        "text": "A metà degli anni ‘90",
-        "image": ""
-      },
-      {
-        "text": "Dal 2023",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "4) Perché di solito un internet provider non chiede un canone all’utente?",
-    "answers": [
-      {
-        "text": "Perché vende le informazioni sugli interessi dell'utente",
-        "image": ""
-      },
-      {
-        "text": "Perché riceve dallo Stato un apposito finanziamento per far funzionare l rete",
-        "image": ""
-      },
-      {
-        "text": "Perché ricava gli utili da altri servizi che offre agli utenti",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "5) Un file è un documento valido?",
-    "answers": [
-      {
-        "text": "Si",
-        "image": ""
-      },
-      {
-        "text": "Si ma solo se firmato digitalmente.",
-        "image": ""
-      },
-      {
-        "text": "No",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "6) Cosa è la dematerializzazione dei titoli finanziari?",
-    "answers": [
-      {
-        "text": "Il fatto che ormai nessuno è più interessato a questi documenti",
-        "image": ""
-      },
-      {
-        "text": "Il fatto che si sta passando dalla moneta cartacea a quella digitale (bitcoins e simili)",
-        "image": ""
-      },
-      {
-        "text": "Il fatto che i titoli di carta sono stati sostituiti da scritturazioni elettroniche.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "7) Cos’è l’informatizzazione dei registri immobiliari?  ",
-    "answers": [
-      {
-        "text": "Il fatto che i registri di carta sono ora digitali",
-        "image": ""
-      },
-      {
-        "text": "Il fatto che chiunque può accedere telematicamente ai registri digitali.",
-        "image": ""
-      },
-      {
-        "text": "Il fatto che i notati si trasmettono gli atti ai registri in formato digitale",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "8) I bitcoins sono una moneta?",
-    "answers": [
-      {
-        "text": "Si, digitale",
-        "image": ""
-      },
-      {
-        "text": "Si ma privata",
-        "image": ""
-      },
-      {
-        "text": "No, sono un mezzo di scambio.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "9) Il commercio elettronico riguarda?",
-    "answers": [
-      {
-        "text": "Consumatori e/o imprese.",
-        "image": ""
-      },
-      {
-        "text": "solo i consumatori",
-        "image": ""
-      },
-      {
-        "text": "solo le imprese",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "10) Per effettuare una comunicazione commerciale:",
-    "answers": [
-      {
-        "text": "occorre il preventivo consenso del destinatario.",
-        "image": ""
-      },
-      {
-        "text": "occorre che siano dirette solo ad imprese, non a consumatori",
-        "image": ""
-      },
-      {
-        "text": "occorre che chiariscano di essere comunicazioni commerciali",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "11) A cosa servono i marchi di qualità?",
-    "answers": [
-      {
-        "text": "Per attestare la qualità del prodotto",
-        "image": ""
-      },
-      {
-        "text": "Per acquistare la fiducia dei cliente sulla bontà del prodotto",
-        "image": ""
-      },
-      {
-        "text": "Per rispettare le norme sulla etichettatura dei prodotti",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "12) Si possono utilizzare tecniche digitali per bloccare l’accesso a proprie opere d’ingegno?",
-    "answers": [
-      {
-        "text": "Si sempre.",
-        "image": ""
-      },
-      {
-        "text": "Si ma solo se sono opere che hanno carattere creativo",
-        "image": ""
-      },
-      {
-        "text": "Si ogni volta che sono brevettate",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "13) Esistono norme penali contro l’uso improprio del software?",
-    "answers": [
-      {
-        "text": "Si ma riguardano solo gli hacker",
-        "image": ""
-      },
-      {
-        "text": "Si ma riguardano solo la violazione del diritto d’autore.",
-        "image": ""
-      },
-      {
-        "text": "Si e riguardano anche i mezzi di pagamento",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "14) I provider devono controllare il materiale che viene inserito dagli utenti?",
-    "answers": [
-      {
-        "text": "Si ",
-        "image": ""
-      },
-      {
-        "text": "Si se si tratta di tutela dei minori o di terrorismo",
-        "image": ""
-      },
-      {
-        "text": "No",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "15) Quali regole disciplinano i social network?",
-    "answers": [
-      {
-        "text": "Il contratto ed eventuali norme di legge",
-        "image": ""
-      },
-      {
-        "text": "L’apposita disciplina legale e poi il contratto.",
-        "image": ""
-      },
-      {
-        "text": "Non ci sono regole",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "16) Da quando è la legge scritta a tutelare la privacy?",
-    "answers": [
-      {
-        "text": "Dagli anni ‘50",
-        "image": ""
-      },
-      {
-        "text": "Dagli anni ‘70",
-        "image": ""
-      },
-      {
-        "text": "Dagli anni ‘90",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "17) Chi è il \"responsabile\" nel trattamento dei dati personali?",
-    "answers": [
-      {
-        "text": "Chi paga i danni se non ci sono le autorizzazioni",
-        "image": ""
-      },
-      {
-        "text": "Chi è titolare delle modalità di trattamento",
-        "image": ""
-      },
-      {
-        "text": "Chi fa apporre la firma per il consenso al soggetto interessato",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "18) Dove si trovano le regole sulla trasparenza nelle comunicazioni elettroniche?",
-    "answers": [
-      {
-        "text": "Nel codice delle telecomunicazioni",
-        "image": ""
-      },
-      {
-        "text": "Nel codice penale",
-        "image": ""
-      },
-      {
-        "text": "Nel codice civile",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "19) Le regole del codice dell'amministrazione digitale sui documenti informatici valgono per i privati?",
-    "answers": [
-      {
-        "text": "No",
-        "image": ""
-      },
-      {
-        "text": "Si, ma solo se autorizzati",
-        "image": ""
-      },
-      {
-        "text": "Si",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "20) Si può imporre ad un'amministrazione di rispondere via pec alle istanze dei privati?",
-    "answers": [
-      {
-        "text": "Si",
-        "image": ""
-      },
-      {
-        "text": "No",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "21) Quali mezzi di comunicazione hanno la data certa opponibile a tutti?",
-    "answers": [
-      {
-        "text": "La mail e il fax",
-        "image": ""
-      },
-      {
-        "text": "La pec",
-        "image": ""
-      },
-      {
-        "text": "La pec ed il fax.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "22) Cosa è il processo civile telematico?",
-    "answers": [
-      {
-        "text": "Il processo civile in cui il deposito degli atti avviene in forma telematica.",
-        "image": ""
-      },
-      {
-        "text": "Il processo civile che ha per oggetto una lite informatica",
-        "image": ""
-      },
-      {
-        "text": "Il fatto che nella società moderna vi è un processo di sostituzione della carta con il mezzo informatico",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "23) Cos'è la moneta elettronica?",
-    "answers": [
-      {
-        "text": "Il bitcoin",
-        "image": ""
-      },
-      {
-        "text": "Carte di debito e di credito",
-        "image": ""
-      },
-      {
-        "text": "Un valore monetario memorizzato elettronicamente.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "24) I bitcoins hanno valore stabile?",
-    "answers": [
-      {
-        "text": "Tendenzialmente si",
-        "image": ""
-      },
-      {
-        "text": "Tendenzialmente no;",
-        "image": ""
-      },
-      {
-        "text": "Non esistono dati rilevati",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "25) I pagamenti mediante bitcoins sono tracciati?",
-    "answers": [
-      {
-        "text": "Si, ma non i loro autori",
-        "image": ""
-      },
-      {
-        "text": "No",
-        "image": ""
-      },
-      {
-        "text": "No ma sono tracciati i loro autori",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "26) Si possono fornire ai consumatori beni non richiesti?",
-    "answers": [
-      {
-        "text": "No, occorre un previo ordine di acquisto",
-        "image": ""
-      },
-      {
-        "text": "No, a meno che il venditore li offra gratuitamente",
-        "image": ""
-      },
-      {
-        "text": "No, a meno che il venditore si impegni a ritirarli gratuitamente a richiesta",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "27) A cosa servono i marchi di qualità?",
-    "answers": [
-      {
-        "text": "È una dichiarazione di un terzo circa l'affidabilità di un soggetto",
-        "image": ""
-      },
-      {
-        "text": "È un marchio che protegge un software o un sito web",
-        "image": ""
-      },
-      {
-        "text": "È un marchio rilasciato dallo stato ad imprenditori che hanno certe qualifiche",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "28) Le banche dati sono tutelate?",
-    "answers": [
-      {
-        "text": "Si sempre.",
-        "image": ""
-      },
-      {
-        "text": "Si ogni volta che hanno carattere creativo",
-        "image": ""
-      },
-      {
-        "text": "Si ogni volta che sono brevettate",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "29) Esiste un' autorità centrale che governa Internet?",
-    "answers": [
-      {
-        "text": "Si",
-        "image": ""
-      },
-      {
-        "text": "Si ma non in Italia",
-        "image": ""
-      },
-      {
-        "text": "No",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "30) Cosa si intende per privacy?",
-    "answers": [
-      {
-        "text": "Il diritto di mantenere il controllo sulle proprie informazioni",
-        "image": ""
-      },
-      {
-        "text": "Il diritto alla proprietà privata",
-        "image": ""
-      },
-      {
-        "text": "Il diritto alla non ingerenza nella sfera sessuale",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "31) I privati possono raccogliere i dati personali altrui?",
-    "answers": [
-      {
-        "text": "No",
-        "image": ""
-      },
-      {
-        "text": "Si, ma solo se autorizzati",
-        "image": ""
-      },
-      {
-        "text": "Si ma solo se non sono destinati alla diffusione.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "32) Quando non si adottano misure minime di sicurezza nella conservazione dei dati vi è una responsabilità?",
-    "answers": [
-      {
-        "text": "Civile",
-        "image": ""
-      },
-      {
-        "text": "Penale.",
-        "image": ""
-      },
-      {
-        "text": "Amministrativa",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "33) I documenti informatici?",
-    "answers": [
-      {
-        "text": "Sono validi nei soli casi previsti dalla legge",
-        "image": ""
-      },
-      {
-        "text": "Sono validi se con firma digitale.",
-        "image": ""
-      },
-      {
-        "text": "Sono validi a tutti gli effetti",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "34) Quando un documento inviato a mezzo posta certificata si considera consegnato?",
-    "answers": [
-      {
-        "text": "Quando viene inviato dal proprio server",
-        "image": ""
-      },
-      {
-        "text": "Quando arriva al server del destinatario",
-        "image": ""
-      },
-      {
-        "text": "Quando viene letto dal destinatario",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "35) Chi emette i bitcoins?",
-    "answers": [
-      {
-        "text": "Le autorità dei vari paesi controllano le loro emissioni",
-        "image": ""
-      },
-      {
-        "text": "Le banche centrali",
-        "image": ""
-      },
-      {
-        "text": "Colui che li ha inventati",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "36) I bitcoins sono convertibili in denaro?",
-    "answers": [
-      {
-        "text": "Si",
-        "image": ""
-      },
-      {
-        "text": "No",
-        "image": ""
-      },
-      {
-        "text": "Solo se la conversione è autorizzata dalle banche centrali",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "37) Il commercio elettronico disciplinato dal codice del consumo vale:",
-    "answers": [
-      {
-        "text": "Per qualsiasi transazione effettuata con strumenti telematici",
-        "image": ""
-      },
-      {
-        "text": "Per le transazioni con strumenti telematici tra consumatori",
-        "image": ""
-      },
-      {
-        "text": "Per le transazioni con strumenti telematici tra consumatori ed imprese.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "38) In caso di acquisti online si può recedere dall'acquisto?",
-    "answers": [
-      {
-        "text": "Per qualsiasi transazione effettuata con strumenti telematici",
-        "image": ""
-      },
-      {
-        "text": "Per le transazioni con strumenti telematici tra consumatori",
-        "image": ""
-      },
-      {
-        "text": "Per le transazioni con strumenti telematici tra consumatori ed imprese",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "39) Il commercio elettronico disciplinato dalla disciplina delle società dell'informazione riguarda:",
-    "answers": [
-      {
-        "text": "Qualsiasi transazione effettuata con strumenti telematici con imprese",
-        "image": ""
-      },
-      {
-        "text": "Le transazioni con strumenti telematici tra consumatori",
-        "image": ""
-      },
-      {
-        "text": "Le transazioni con strumenti telematici tra consumatori ed imprese",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "40) Quando inizia il trattamento dei dati personali?",
-    "answers": [
-      {
-        "text": "Da quando inizia la raccolta dei dati",
-        "image": ""
-      },
-      {
-        "text": "Da quando l’interessato rilascia il consenso",
-        "image": ""
-      },
-      {
-        "text": "Da quando i dati vengono elaborati",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "41) I codici di autoregolamento sono obbligatori?",
-    "answers": [
-      {
-        "text": "Si",
-        "image": ""
-      },
-      {
-        "text": "Si, se lo dice la legge",
-        "image": ""
-      },
-      {
-        "text": "No",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "42) Scannerizzo ed appongo la mia firma ad un documento, questa è una firma elettronica avanzata?",
-    "answers": [
-      {
-        "text": "Si",
-        "image": ""
-      },
-      {
-        "text": "Si se poi non la contesto",
-        "image": ""
-      },
-      {
-        "text": "No",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "43) Si può concludere un contratto via mail?\t",
-    "answers": [
-      {
-        "text": "Si.",
-        "image": ""
-      },
-      {
-        "text": "Si se poi non lo contestano",
-        "image": ""
-      },
-      {
-        "text": "No",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "45) Se compro il pc ed il software è in licenza:",
-    "answers": [
-      {
-        "text": "posso acquistare il software pagando un ulteriore somma",
-        "image": ""
-      },
-      {
-        "text": "Il software non è mio.",
-        "image": ""
-      },
-      {
-        "text": "ho pagato e quindi anche il software è mio",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "46) Cos’è il fascicolo telematico?",
-    "answers": [
-      {
-        "text": "È il fascicolo digitale del processo civile.",
-        "image": ""
-      },
-      {
-        "text": "Il fascicolo delle comunicazioni digitali con la pubblica amministrazione",
-        "image": ""
-      },
-      {
-        "text": "Il fascicolo digitale dove l’università conserva tutti i dati dello studente",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "47) Si può fare una copia di un programma che si ha in licenza?",
-    "answers": [
-      {
-        "text": "Sì pagando un’apposita royalties",
-        "image": ""
-      },
-      {
-        "text": "Si ma solo se pattuito all’inizio del contratto",
-        "image": ""
-      },
-      {
-        "text": "Si ma senza commercializzarlo e per l’uso del programma stesso.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "48) Cosa è una wireless community network?",
-    "answers": [
-      {
-        "text": "Un insieme di persone che sostiene il diritto alla libertà su internet",
-        "image": ""
-      },
-      {
-        "text": "Un insieme di persone che crea una rete di comunicazioni wireless.",
-        "image": ""
-      },
-      {
-        "text": "È un modo di designare gli utenti dei social network",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "49) Le regole di comportamento previste nella piattaforma di un social network?",
-    "answers": [
-      {
-        "text": "Non sono regole giuridiche",
-        "image": ""
-      },
-      {
-        "text": "Sono regole locali",
-        "image": ""
-      },
-      {
-        "text": "Sono regole contrattuali.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "50) Cliccando su accetto sull’iscrizione ad un social network si conclude un contratto?",
-    "answers": [
-      {
-        "text": "Si",
-        "image": ""
-      },
-      {
-        "text": "Si ma poi serve un documento scritto",
-        "image": ""
-      },
-      {
-        "text": "No",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "51) Quando sono protette le banche dati?",
-    "answers": [
-      {
-        "text": "Sempre se hanno carattere creativo",
-        "image": ""
-      },
-      {
-        "text": "Sempre se sono brevettate",
-        "image": ""
-      },
-      {
-        "text": "Sempre se sono rese pubbliche.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "52) Cosa si intende per deterritorializzazione?",
-    "answers": [
-      {
-        "text": "La perdita di sovranità derivante della tecnologia informatica.",
-        "image": ""
-      },
-      {
-        "text": "Il fatto che tra gli stati stanno venendo meno i conflitti",
-        "image": ""
-      },
-      {
-        "text": "Il fatto che ognuno può installare un provider nello stato che preferisce",
-        "image": ""
-      },
-      {
-        "text": "Il fatto che gli stati non riescono a controllare gli illeciti compiuti in rete",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "53) Un nome di dominio è un bene?",
-    "answers": [
-      {
-        "text": "Si",
-        "image": ""
-      },
-      {
-        "text": "No",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "54) Un documento elettronico privo di firma digitale?",
-    "answers": [
-      {
-        "text": "Non è valido",
-        "image": ""
-      },
-      {
-        "text": "È valido",
-        "image": ""
-      },
-      {
-        "text": "È valido ma un giudice può anche ritenerlo inidoneo.",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "55) Un internet provider è responsabile degli insulti pubblici dai suoi utenti?",
-    "answers": [
-      {
-        "text": "No se svolge un ruolo meramente passivo nelle loro attività.",
-        "image": ""
-      },
-      {
-        "text": "No ma solo se ha messo delle regole contrattuali che li vietino",
-        "image": ""
-      },
-      {
-        "text": "Si",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "56) Cos’è il trattamento dei dati personali?",
-    "answers": [
-      {
-        "text": "La raccolta dei dati personali di un soggetto tramite strumenti informatici",
-        "image": ""
-      },
-      {
-        "text": "La raccolta, l’elaborazione e la conservazione dei dati personali di un soggetto tramite strumenti informatici.",
-        "image": ""
-      },
-      {
-        "text": "Praticamente ogni attività che coinvolga i dati personali di un soggetto",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "57) È legittimo prendere una decisione (es. di tipo contrattuale) automatizzata utilizzando un algoritmo?",
-    "answers": [
-      {
-        "text": "Si",
-        "image": ""
-      },
-      {
-        "text": "Si ma solo se vi è coinvolgimento umano nella decisione",
-        "image": ""
-      },
-      {
-        "text": "No",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "58) Le criptovalute sono monete?",
-    "answers": [
-      {
-        "text": "Si",
-        "image": ""
-      },
-      {
-        "text": "No, sono beni digitali.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "59) Cosa si intende con dematerializzazione?",
-    "answers": [
-      {
-        "text": "il fatto che esistono beni immateriali",
-        "image": ""
-      },
-      {
-        "text": "Il fatto che sempre più beni stanno assumendo forma digitale anziché materiale.",
-        "image": ""
-      },
-      {
-        "text": "il fatto che nella legge si progetta la futura sostituzione di beni materiali con beni digitali",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "60) Cosa vuol dire che il contratto è fonte delle regole?",
-    "answers": [
-      {
-        "text": "Che molti rapporti digitali sono regolati quasi interamente da contratti , mancando regole specifiche.",
-        "image": ""
-      },
-      {
-        "text": "che il contratto fa parte delle fonti legali di regole",
-        "image": ""
-      },
-      {
-        "text": "che molti internet provider utilizzano regole uguali nei rapporti con gli utenti",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "61) Cosa si intende con / cos'è il principio di neutralità tecnologica?",
-    "answers": [
-      {
-        "text": "l’obbligo di utilizzare una medisca tecnologia informatica",
-        "image": ""
-      },
-      {
-        "text": "L’obbligo di non discriminare tra diverse tecnologie.",
-        "image": ""
-      },
-      {
-        "text": "il divieto di discriminazione di ogni tipo tramite social network",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "62) Cos’è il digital divide?",
-    "answers": [
-      {
-        "text": "La scarsa distribuzione di risorse e conoscenze informatiche.",
-        "image": ""
-      },
-      {
-        "text": "L’utilizzo di pc e softwares non aggiornati",
-        "image": ""
-      },
-      {
-        "text": "l’insieme dei rischi connessi all’utilizzo di strumenti informativi",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "63) L’acquisto di competenze digitali:",
-    "answers": [
-      {
-        "text": "È oggetto di leggi che lo agevolano.",
-        "image": ""
-      },
-      {
-        "text": "dipende solo dalla volontà di ognuno",
-        "image": ""
-      },
-      {
-        "text": "è affidato alle società che trattano big data",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "64) Si può caricare su un social network l’immagine di una persona?",
-    "answers": [
-      {
-        "text": "si sempre ma solo se è maggiorenne",
-        "image": ""
-      },
-      {
-        "text": "si ma va tolta se lo chiede",
-        "image": ""
-      },
-      {
-        "text": "Si ma dopo aver avuto il suo consenso.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "65) Il cyberbullismo: ",
-    "answers": [
-      {
-        "text": "è una questione di maleducazione",
-        "image": ""
-      },
-      {
-        "text": "È un comportamento vietato dalla legge",
-        "image": ""
-      },
-      {
-        "text": "è un comportamento regolato dai singoli social network",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "66) Cos’è lo SPID?",
-    "answers": [
-      {
-        "text": "È un sistema pubblico di identificazione di oggetti.",
-        "image": ""
-      },
-      {
-        "text": "è un sistema pubblico di attribuzione di posta certificata",
-        "image": ""
-      },
-      {
-        "text": "è un sistema pubblico per accedere a determinati servizi di trading on line",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "67) Cos’è un domicilio digitale?",
-    "answers": [
-      {
-        "text": "l’indirizzo pec che indica dove siamo residenti",
-        "image": ""
-      },
-      {
-        "text": "l'indirizzo pec che dobbiamo aver per i nostri rapporti con il fisco",
-        "image": ""
-      },
-      {
-        "text": "L’indirizzo pec che vale per comunicazioni aventi valore legale.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  }
-]
\ No newline at end of file
diff --git a/data/questions/fds.json b/data/questions/fds.json
deleted file mode 100644
index c207f09..0000000
--- a/data/questions/fds.json
+++ /dev/null
@@ -1,2693 +0,0 @@
-[
-    {
-        "quest": "What class does the Naive Bayes classifier predict for a given observation?",
-        "answers": [
-            {
-                "text": "The class maximizing the joint predictors probability",
-                "image": ""
-            },
-            {
-                "text": "The class minimizing the joint predictors probability",
-                "image": ""
-            },
-            {
-                "text": "The class maximizing the joint predictors/labels probability",
-                "image": ""
-            },
-            {
-                "text": "The class minimizing the joint predictors/labels probability",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "If your dataset has two variables \ud835\udc99, \ud835\udc99\u2032 such that \ud835\udc99 = \ud835\udc82 \u22c5 \ud835\udc99\u2032 for some constant a > 0, then you have:",
-        "answers": [
-            {
-                "text": "overfitting",
-                "image": ""
-            },
-            {
-                "text": "underfitting",
-                "image": ""
-            },
-            {
-                "text": "multicollinearity",
-                "image": ""
-            },
-            {
-                "text": "supercollinearity",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "A na\u00efve Bayes classifier can deal with previously unseen feature-label combination through:",
-        "answers": [
-            {
-                "text": "Laplacian smoothing",
-                "image": ""
-            },
-            {
-                "text": "Bootstrapping",
-                "image": ""
-            },
-            {
-                "text": "Stratified cross-validation",
-                "image": ""
-            },
-            {
-                "text": "Repeated sampling",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "For a linear regression model, the expected squared error can be decomposed in:",
-        "answers": [
-            {
-                "text": "Variance and covariance",
-                "image": ""
-            },
-            {
-                "text": "SSE and SST",
-                "image": ""
-            },
-            {
-                "text": "Underfit and overfit",
-                "image": ""
-            },
-            {
-                "text": "Bias and variance noise",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": " What is the key assumption of the Na\u00efve Bayes Classifier?",
-        "answers": [
-            {
-                "text": "The predictors and labels are independent",
-                "image": ""
-            },
-            {
-                "text": "Each predictor follows a Gaussian distribution",
-                "image": ""
-            },
-            {
-                "text": "The predictors are independent conditionally on the label",
-                "image": ""
-            },
-            {
-                "text": "The number of predictors is at most poly(n)",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "Which one of the following performance indicates the best model for prediction?",
-        "answers": [
-            {
-                "text": "\ud835\udc45' = 0.2 on training, \ud835\udc45' = 0.1 on test",
-                "image": ""
-            },
-            {
-                "text": "\ud835\udc45' = 0.7 on training, \ud835\udc45' = 0.7 on test",
-                "image": ""
-            },
-            {
-                "text": "\ud835\udc45' = 0.8 on training, \ud835\udc45' = 0.1 on test",
-                "image": ""
-            },
-            {
-                "text": "\ud835\udc45' = 0.9 on training, \ud835\udc45' = \u22120.9 on test",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "You want to predict the market price of a team\u2019s merchandising (t-shirts, hats..), according to the team\u2019s seasonal performance. You suggest using:",
-        "answers": [
-            {
-                "text": "Linear regression",
-                "image": ""
-            },
-            {
-                "text": "Logistic regression",
-                "image": ""
-            },
-            {
-                "text": "Linear programming",
-                "image": ""
-            },
-            {
-                "text": "Clustering",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "When is the accuracy a misleading classifier performance measure?",
-        "answers": [
-            {
-                "text": "When the population label proportions are unbalanced",
-                "image": ""
-            },
-            {
-                "text": "When the population label proportions are balanced",
-                "image": ""
-            },
-            {
-                "text": "When the sensitivity is high",
-                "image": ""
-            },
-            {
-                "text": "When the specificity is low",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "The goal of linear regression is to?",
-        "answers": [
-            {
-                "text": "Make America great again",
-                "image": ""
-            },
-            {
-                "text": "Group similar observations together",
-                "image": ""
-            },
-            {
-                "text": "Learn a linear function from data",
-                "image": ""
-            },
-            {
-                "text": "Evaluate the amount of noise in the data",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "In the bias-variance decomposition of the expected squared error, what does high bias suggest?",
-        "answers": [
-            {
-                "text": "Noisy data",
-                "image": ""
-            },
-            {
-                "text": "Overfitting",
-                "image": ""
-            },
-            {
-                "text": "Underfitting",
-                "image": ""
-            },
-            {
-                "text": "Crossfitting",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "Social network users often form communities according to their tastes. If you had access to their personal data, you may verify this intuition by:",
-        "answers": [
-            {
-                "text": "Linear Regression",
-                "image": ""
-            },
-            {
-                "text": "Logistic Regression",
-                "image": ""
-            },
-            {
-                "text": "Clustering",
-                "image": ""
-            },
-            {
-                "text": "Linear programming",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "R^2 is a measure of:",
-        "answers": [
-            {
-                "text": "Reliability of predictions",
-                "image": ""
-            },
-            {
-                "text": "Goodness of fit",
-                "image": ""
-            },
-            {
-                "text": "Significance of estimates",
-                "image": ""
-            },
-            {
-                "text": "Model complexity",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "A company wants to relate the monthly revenue to productivity parameters such as total number of working hours, etc. They could use:",
-        "answers": [
-            {
-                "text": "Linear regression",
-                "image": ""
-            },
-            {
-                "text": "Logistic regression",
-                "image": ""
-            },
-            {
-                "text": "Clustering",
-                "image": ""
-            },
-            {
-                "text": "Linear programming",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "How do you perform a linear regression in R?",
-        "answers": [
-            {
-                "text": "lm(y ~ x, data)",
-                "image": ""
-            },
-            {
-                "text": "lm(y ~ x, data, family = \u201cbinomial\u201d)",
-                "image": ""
-            },
-            {
-                "text": "predict(y ~ x, data)",
-                "image": ""
-            },
-            {
-                "text": "predict(y ~ x, data, binomial)",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "Your friend proposes to cluster 300 observations by trying all possible clustering and taking the one that minimizes intra cluster variance. You observe that:",
-        "answers": [
-            {
-                "text": "This is the only possible approach",
-                "image": ""
-            },
-            {
-                "text": "This does not produce a good clustering",
-                "image": ""
-            },
-            {
-                "text": "This does require a few seconds",
-                "image": ""
-            },
-            {
-                "text": "This does require a centuries",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "Single-linkage clustering works by",
-        "answers": [
-            {
-                "text": "Repeatedly recomputing the centroids of clusters",
-                "image": ""
-            },
-            {
-                "text": "Repeatedly merging smaller clusters into larger ones",
-                "image": ""
-            },
-            {
-                "text": "Enumerating all possible clustering of the given points",
-                "image": ""
-            },
-            {
-                "text": "Enumerating all possible points in a cluster",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "In linear regression, a high value of \ud835\udc79\ud835\udfd0 on the training set suggests:",
-        "answers": [
-            {
-                "text": "A small error of the model on the fitted data",
-                "image": ""
-            },
-            {
-                "text": "A small error of the model on future predictions",
-                "image": ""
-            },
-            {
-                "text": "A large error of the model on the fitted data",
-                "image": ""
-            },
-            {
-                "text": "A large error of the model on future predictions",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "A logistic regression gives the following scores, preceded by the actual label: (Y, 0.85), (Y, 0.75), (N,0.6), (Y,0.5), (N, 0.4), (N, 0.2). For a sensitivity of at least 2/3, the best choice is to predict Y when the score is at least:",
-        "answers": [
-            {
-                "text": "0.9",
-                "image": ""
-            },
-            {
-                "text": "0.75",
-                "image": ""
-            },
-            {
-                "text": "0.6",
-                "image": ""
-            },
-            {
-                "text": "0.45",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "Look at the confusion matrix below. What we can say?",
-        "answers": [
-            {
-                "text": "The sensitivity is < 0.80%",
-                "image": ""
-            },
-            {
-                "text": "There are less positives than negatives",
-                "image": ""
-            },
-            {
-                "text": "The accuracy is > 90%",
-                "image": ""
-            },
-            {
-                "text": "The classifier predicts 1 on 60% of the times",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": 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"
-    },
-    {
-        "quest": "A set of observations (\ud835\udc99\ud835\udfcf, \ud835\udc9a\ud835\udfcf), (\ud835\udc99\ud835\udfd0,\ud835\udc9a\ud835\udfd0)\u2026(\ud835\udc99\ud835\udc8f, \ud835\udc9a\ud835\udc8f) obeys the law \ud835\udc9a\ud835\udc8a \u2254 \ud835\udc82\ud835\udc99\ud835\udc8a + \ud835\udc83 + \ud835\udf3a\ud835\udc8a, where \ud835\udf3a\ud835\udc8a is some random noise. The task of estimating a and b from the dataset is called:",
-        "answers": [
-            {
-                "text": "Logistic regression",
-                "image": ""
-            },
-            {
-                "text": "Linear regression",
-                "image": ""
-            },
-            {
-                "text": "Linear programming",
-                "image": ""
-            },
-            {
-                "text": "Logistic programming",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "Laplacian smoothing aims at:",
-        "answers": [
-            {
-                "text": "Producing readable plots by using an average window",
-                "image": ""
-            },
-            {
-                "text": "Reducing the model\u2019s dependence on the noise",
-                "image": ""
-            },
-            {
-                "text": "Improving the feature quality by removing outliers",
-                "image": ""
-            },
-            {
-                "text": "Avoid penalizing previously unseen observations",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "A dataset of points (\ud835\udc99\ud835\udfcf, \ud835\udc9a\ud835\udfcf), (\ud835\udc99\ud835\udfd0,\ud835\udc9a\ud835\udfd0)\u2026(\ud835\udc99\ud835\udc8f, \ud835\udc9a\ud835\udc8f) has been generated by the model \ud835\udc9a\ud835\udc8a \u2254 \ud835\udc82\ud835\udc99\ud835\udc8a + \ud835\udc83 + \ud835\udf3a\ud835\udc8a, where \ud835\udf3a\ud835\udc8a is gaussian noise. Linear regression aims at estimating:",
-        "answers": [
-            {
-                "text": "a and b",
-                "image": ""
-            },
-            {
-                "text": "a and \ud835\udf00",
-                "image": ""
-            },
-            {
-                "text": "x and b",
-                "image": ""
-            },
-            {
-                "text": "x and y",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "Which one of the following R commands selects only the rows of data where X equals 0?",
-        "answers": [
-            {
-                "text": "select(data, X == 0)",
-                "image": ""
-            },
-            {
-                "text": "filter(data, X==0)",
-                "image": ""
-            },
-            {
-                "text": "summarize(data, X==0)",
-                "image": ""
-            },
-            {
-                "text": "table(data, X == 0)",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "If an algorithm has exponential complexity, then we can assume that:",
-        "answers": [
-            {
-                "text": "In practice it is still fast enough to be useful",
-                "image": ""
-            },
-            {
-                "text": "It admits a polynomial-time algorithm",
-                "image": ""
-            },
-            {
-                "text": "It can be solved by finding an optimal clustering",
-                "image": ""
-            },
-            {
-                "text": "No technological progress will ever make it practical",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "If you have n points, what is the number of clusters that minimizes the within-cluster sum of square?",
-        "answers": [
-            {
-                "text": "1",
-                "image": ""
-            },
-            {
-                "text": "k",
-                "image": ""
-            },
-            {
-                "text": "n",
-                "image": ""
-            },
-            {
-                "text": "We cannot say",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "Which regression model has smaller squared error in fitting a real function \ud835\udc87(\ud835\udc99)?",
-        "answers": [
-            {
-                "text": "A simple linear regression",
-                "image": ""
-            },
-            {
-                "text": "A logistic regression",
-                "image": ""
-            },
-            {
-                "text": "A polynomial regression of degree 2",
-                "image": ""
-            },
-            {
-                "text": "A polynomial regression of degree 10",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "A doping screening is tested on a pool of 800 athletes of which 796 are clean. The test is correct in 99% of the cases. What can we say about it?",
-        "answers": [
-            {
-                "text": "It may have missed all of the doped athletes",
-                "image": ""
-            },
-            {
-                "text": "It may have missed all of the clean athletes",
-                "image": ""
-            },
-            {
-                "text": "It identified all of the doped athletes",
-                "image": ""
-            },
-            {
-                "text": "It identified all of the clean atheletes",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "In linear programming, the space of feasible solution is:",
-        "answers": [
-            {
-                "text": "An arbitrary set",
-                "image": ""
-            },
-            {
-                "text": "A subset of \ud835\udc45'",
-                "image": ""
-            },
-            {
-                "text": "A convex polytope",
-                "image": ""
-            },
-            {
-                "text": "None of the above",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "The explained variance of a clustering equals:",
-        "answers": [
-            {
-                "text": "Within-cluster SSE divided by total sum of squares",
-                "image": ""
-            },
-            {
-                "text": "Total sum of squares divided by within-cluster SSE",
-                "image": ""
-            },
-            {
-                "text": "Within-cluster SSE divided by between-cluster SSE",
-                "image": ""
-            },
-            {
-                "text": "Total sum of squares divided by between-cluster SSE",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "Gradient descent is a technique we have used to:",
-        "answers": [
-            {
-                "text": "Compute the optimal number of clusters",
-                "image": ""
-            },
-            {
-                "text": "Reduce the noise in the training set",
-                "image": ""
-            },
-            {
-                "text": "Find the local minima of a function",
-                "image": ""
-            },
-            {
-                "text": "Estimate the probability of false positive",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "Which of these models is probably overfitting?",
-        "answers": [
-            {
-                "text": "\ud835\udc45' = 0.1 on training, \ud835\udc45' = 0.1 on test",
-                "image": ""
-            },
-            {
-                "text": "\ud835\udc45' = 0.8 on training, \ud835\udc45' = 0.7 on test",
-                "image": ""
-            },
-            {
-                "text": "\ud835\udc45' = 0.7 on training, \ud835\udc45' = 0.7 on test",
-                "image": ""
-            },
-            {
-                "text": "\ud835\udc45' = 0.8 on training, \ud835\udc45' = 0.1 on test",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "Laplacian smoothing aims at:",
-        "answers": [
-            {
-                "text": "Improving the feature quality by removing outliers",
-                "image": ""
-            },
-            {
-                "text": "Producing readable plots",
-                "image": ""
-            },
-            {
-                "text": "Reducing the model\u2019s dependence on the noise",
-                "image": ""
-            },
-            {
-                "text": "Avoid penalizing previously unseen observations",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "To visualize a hierarchical clustering one can use:",
-        "answers": [
-            {
-                "text": "a dendrogram ",
-                "image": ""
-            },
-            {
-                "text": "a ROC curve",
-                "image": ""
-            },
-            {
-                "text": "a boxplot",
-                "image": ""
-            },
-            {
-                "text": "a histogram",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "The goal of linear regression is to:",
-        "answers": [
-            {
-                "text": "bring peace to the world",
-                "image": ""
-            },
-            {
-                "text": "group similar observations together",
-                "image": ""
-            },
-            {
-                "text": "learn a linear function from data ",
-                "image": ""
-            },
-            {
-                "text": "evaluate the amount of noise in the data",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "Naive Bayes classiers work well for:",
-        "answers": [
-            {
-                "text": "linear programming",
-                "image": ""
-            },
-            {
-                "text": "spam filtering ",
-                "image": ""
-            },
-            {
-                "text": "k-center clustering",
-                "image": ""
-            },
-            {
-                "text": "speech recognition",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "The explained variance of a clustering equals:",
-        "answers": [
-            {
-                "text": "(total variance)/(within-cluster variance)",
-                "image": ""
-            },
-            {
-                "text": "(within-cluster variance)/(between-cluster variance)",
-                "image": ""
-            },
-            {
-                "text": "(between-cluster variance)/(total variance) ",
-                "image": ""
-            },
-            {
-                "text": "(within-cluster variance)/(total variance)",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "A binary classifier on 6 points gives the probabilities: 0.9, 0.85, 0.75, 0.5, 0.4, 0.3; the correct labels are 1,1,0,1,0,0. What is the best probability threshold, if we need FPR <= 1/3?",
-        "answers": [
-            {
-                "text": "0.45 ",
-                "image": ""
-            },
-            {
-                "text": "1.0",
-                "image": ""
-            },
-            {
-                "text": "0.95",
-                "image": ""
-            },
-            {
-                "text": "0.25",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "Mark the wrong statement about gradient descent:",
-        "answers": [
-            {
-                "text": "batch gradient descent approximates \u25bdf using a mini-batch",
-                "image": ""
-            },
-            {
-                "text": "stochastic gradient descent approximates \u25bdf with a single example",
-                "image": ""
-            },
-            {
-                "text": "there is no guarantee to nd the global minimum",
-                "image": ""
-            },
-            {
-                "text": "increasing the learning rate damps oscillations ",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "Which task does not require to learn a model?",
-        "answers": [
-            {
-                "text": "Clustering ",
-                "image": ""
-            },
-            {
-                "text": "Linear Regression",
-                "image": ""
-            },
-            {
-                "text": "Classication",
-                "image": ""
-            },
-            {
-                "text": "Logistic Regression",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "For two sets A, B the probability that the first element in a random permutation of A U B is in A \u2229 B:",
-        "answers": [
-            {
-                "text": "is J(A,B) / |A \u2229 B|",
-                "image": ""
-            },
-            {
-                "text": "is J(A,B) ",
-                "image": ""
-            },
-            {
-                "text": "is 1/|A|+1/|B|",
-                "image": ""
-            },
-            {
-                "text": "is 1/(|A||B|)",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "The R^2 and the p-values of a regression:",
-        "answers": [
-            {
-                "text": "are always equivalent",
-                "image": ""
-            },
-            {
-                "text": "cannot be both positive",
-                "image": ""
-            },
-            {
-                "text": "measure different aspects ",
-                "image": ""
-            },
-            {
-                "text": "are negatively correlated",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "xXmini=1k||x-ci||22 is the objective function of:",
-        "answers": [
-            {
-                "text": "k-squares",
-                "image": ""
-            },
-            {
-                "text": "k-medians",
-                "image": ""
-            },
-            {
-                "text": "k-centers",
-                "image": ""
-            },
-            {
-                "text": "k-means",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "Classification accuracy is misleading when:",
-        "answers": [
-            {
-                "text": "the label proportions are unbalanced ",
-                "image": ""
-            },
-            {
-                "text": "the label proportions are balanced",
-                "image": ""
-            },
-            {
-                "text": "the dataset is too small",
-                "image": ""
-            },
-            {
-                "text": "the dataset is too large",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "A binary classifier on 6 points gives the probabilities: 0.85, 0.75, 0.65, 0.5, 0.4, 0.2; the correct labels are 1,1,1,0,0,0. What is the best probability threshold?",
-        "answers": [
-            {
-                "text": "0.3",
-                "image": ""
-            },
-            {
-                "text": "0.6 ",
-                "image": ""
-            },
-            {
-                "text": "0.7",
-                "image": ""
-            },
-            {
-                "text": "0.9",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "An algorithm is considered practical if its running time, as a function of the input size, is:",
-        "answers": [
-            {
-                "text": "exponential",
-                "image": ""
-            },
-            {
-                "text": "polynomial ",
-                "image": ""
-            },
-            {
-                "text": "linear",
-                "image": ""
-            },
-            {
-                "text": "logarithmic",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "The naive Bayes classier learns:",
-        "answers": [
-            {
-                "text": "the marginal distribution of predictors",
-                "image": ""
-            },
-            {
-                "text": "the joint distribution of predictors",
-                "image": ""
-            },
-            {
-                "text": "the joint distribution of predictors and labels ",
-                "image": ""
-            },
-            {
-                "text": "the marginal distribution of labels",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "k-PCA differs from k-means in that xi is:",
-        "answers": [
-            {
-                "text": "any PCA component",
-                "image": ""
-            },
-            {
-                "text": "any linear combination of PCA components ",
-                "image": ""
-            },
-            {
-                "text": "orthogonal to all PCA components",
-                "image": ""
-            },
-            {
-                "text": "any a convex combination of PCA components",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "In logistic regression, the estimated probability of xi being a positive is:",
-        "answers": [
-            {
-                "text": "1/(1+e-Tx) ",
-                "image": ""
-            },
-            {
-                "text": "1/(1+|x|2)",
-                "image": ""
-            },
-            {
-                "text": "log(Tx/(1-Tx))",
-                "image": ""
-            },
-            {
-                "text": "log(xi)",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "A sports betting agency wants to predict whether the Italian national football team will or not qualify for the World Cup championship. They should use:",
-        "answers": [
-            {
-                "text": "Clustering",
-                "image": ""
-            },
-            {
-                "text": "Logistic regression ",
-                "image": ""
-            },
-            {
-                "text": "Linear programming",
-                "image": ""
-            },
-            {
-                "text": "Linear regression",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "A problem X  NP is said to be NP-complete if:",
-        "answers": [
-            {
-                "text": "X can be reduced to every Y  NP in polytime",
-                "image": ""
-            },
-            {
-                "text": "every Y  NP can be reduced to X in polytime",
-                "image": ""
-            },
-            {
-                "text": "no Y  NP can be reduced to X in polytime",
-                "image": ""
-            },
-            {
-                "text": "none of the others",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "The quadratic loss of linear regression is:",
-        "answers": [
-            {
-                "text": "i=1m(yi-yi)2",
-                "image": ""
-            },
-            {
-                "text": "i=1m(xi-xi)2",
-                "image": ""
-            },
-            {
-                "text": "i=1m(xi-yi)2",
-                "image": ""
-            },
-            {
-                "text": "i=1m(yi2-yi2)2",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "What is the best threshold value for turning probability scores into binary predictions?",
-        "answers": [
-            {
-                "text": "the one that maximizes sensitivity",
-                "image": ""
-            },
-            {
-                "text": "it depends on the problem ",
-                "image": ""
-            },
-            {
-                "text": "the one that maximizes accuracy",
-                "image": ""
-            },
-            {
-                "text": "the one that maximizes specificity",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "In Human coding, the encoder:",
-        "answers": [
-            {
-                "text": "processes whole runs of identical input symbols",
-                "image": ""
-            },
-            {
-                "text": "works by solving a clustering problem",
-                "image": ""
-            },
-            {
-                "text": "works by solving a regression problem",
-                "image": ""
-            },
-            {
-                "text": "processes each input symbol individually ",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "The Maximum Likelihood Estimator for the parameters of a linear model with independent Gaussian noise is:",
-        "answers": [
-            {
-                "text": "the OLS solution vector *  ",
-                "image": ""
-            },
-            {
-                "text": "the square root of the OLS solution *",
-                "image": ""
-            },
-            {
-                "text": "it depends on the dataset",
-                "image": ""
-            },
-            {
-                "text": "the vector  of the generating process",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "Consider the LP: min f(x,y)=x+y; x+y2; x,y0. The corresponding polytope is:",
-        "answers": [
-            {
-                "text": "degenerate",
-                "image": ""
-            },
-            {
-                "text": "bounded",
-                "image": ""
-            },
-            {
-                "text": "unbounded",
-                "image": ""
-            },
-            {
-                "text": "empty ",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "Min-hashing maps each document to:",
-        "answers": [
-            {
-                "text": "one hash signature ",
-                "image": ""
-            },
-            {
-                "text": "a distance matrix",
-                "image": ""
-            },
-            {
-                "text": "the set of most frequent terms",
-                "image": ""
-            },
-            {
-                "text": "a real vector",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "How do you do a linear regression in R?",
-        "answers": [
-            {
-                "text": "predict(y  x, data)",
-                "image": ""
-            },
-            {
-                "text": "lm(y  x, data) ",
-                "image": ""
-            },
-            {
-                "text": "predict(y  x, data, family=\"binomial\")",
-                "image": ""
-            },
-            {
-                "text": "lm(y  x, data, family=\"binomial\")",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "How do you measure the significance of an estimate?",
-        "answers": [
-            {
-                "text": "with its magnitude",
-                "image": ""
-            },
-            {
-                "text": "with R^2",
-                "image": ""
-            },
-            {
-                "text": "with its p-value",
-                "image": ""
-            },
-            {
-                "text": "with its sign",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "A manufacturing company wants to nd out the relationship between the budget spent in advertising and the total sales of the next semester. They could use:",
-        "answers": [
-            {
-                "text": "Linear Regression",
-                "image": ""
-            },
-            {
-                "text": "Logistic Regression",
-                "image": ""
-            },
-            {
-                "text": "Clustering",
-                "image": ""
-            },
-            {
-                "text": "Linear Programming",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "The company wants to predict if a machine will have a technical failure in the next 10 days. This could be done with:",
-        "answers": [
-            {
-                "text": "Linear Regression",
-                "image": ""
-            },
-            {
-                "text": "Logistic Regression",
-                "image": ""
-            },
-            {
-                "text": "Clustering",
-                "image": ""
-            },
-            {
-                "text": "Linear Programming",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "Moreover, items from the same production line are similar while those from different lines are radically different. You suggest to check by using:",
-        "answers": [
-            {
-                "text": "Linear Regression",
-                "image": ""
-            },
-            {
-                "text": "Logistic Regression",
-                "image": ""
-            },
-            {
-                "text": "Clustering",
-                "image": ""
-            },
-            {
-                "text": "Linear Programming",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "What is the true positive rate aka sensitivity?",
-        "answers": [
-            {
-                "text": "the fraction of negatives that are incorrectly classified",
-                "image": ""
-            },
-            {
-                "text": "the fraction of negatives that are correctly classified",
-                "image": ""
-            },
-            {
-                "text": "the fraction of positives that are incorrectly classified",
-                "image": ""
-            },
-            {
-                "text": "the fraction of positives that are correctly classified",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "Single-linkage clustering works by:",
-        "answers": [
-            {
-                "text": "repeatedly recomputing the centroids of clusters",
-                "image": ""
-            },
-            {
-                "text": "repeatedly merging smaller clusters into larger ones",
-                "image": ""
-            },
-            {
-                "text": "enumerating all possible clustering of the given points",
-                "image": ""
-            },
-            {
-                "text": "enumerating all possible points in a cluster",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "You have a set of observations (x; y) with x; y 2 R. Which one of the following gives the highest R2?",
-        "answers": [
-            {
-                "text": "Simple linear regression",
-                "image": ""
-            },
-            {
-                "text": "Polynomial regression of degree 2",
-                "image": ""
-            },
-            {
-                "text": "Polynomial regression of degree 10",
-                "image": ""
-            },
-            {
-                "text": "Logistic regression\t",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "Which one of the following performances indicates the best model for prediction?",
-        "answers": [
-            {
-                "text": "R2 = 0:2 on training, R2 = 0:1 on test",
-                "image": ""
-            },
-            {
-                "text": "R2 = 0:7 on training, R2 = 0:7 on test",
-                "image": ""
-            },
-            {
-                "text": "R2 = 0:8 on training, R2 = 0:1 on test",
-                "image": ""
-            },
-            {
-                "text": "R2 = 0:9 on training, R2 = \udbc0\udc000:9 on test",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "Which task does not require a training set (i.e. a dataset used for learning a model)?",
-        "answers": [
-            {
-                "text": "Linear Regression",
-                "image": ""
-            },
-            {
-                "text": "Logistic Regression",
-                "image": ""
-            },
-            {
-                "text": "Classification",
-                "image": ""
-            },
-            {
-                "text": "Clustering",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "If you have n points, what is the number of clusters that minimizes the within-cluster sum of squares?",
-        "answers": [
-            {
-                "text": "1",
-                "image": ""
-            },
-            {
-                "text": "k",
-                "image": ""
-            },
-            {
-                "text": "n",
-                "image": ""
-            },
-            {
-                "text": "we cannot say",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "In the bias-variance decomposition of the expected squared error, what does a high bias suggest?",
-        "answers": [
-            {
-                "text": "noisy data",
-                "image": ""
-            },
-            {
-                "text": "overtting",
-                "image": ""
-            },
-            {
-                "text": "undertting",
-                "image": ""
-            },
-            {
-                "text": "crosstting",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "A set of observations (x1; y1), (x2; y2), \u2026,(xn; yn) obeys the law yi := axi + b + i where  i is some random noise. The task of estimating a and b from the dataset is called:",
-        "answers": [
-            {
-                "text": "logistic regression",
-                "image": ""
-            },
-            {
-                "text": "linear regression",
-                "image": ""
-            },
-            {
-                "text": "linear programming",
-                "image": ""
-            },
-            {
-                "text": "logistic programming",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "A regression model (M1) on a training set gives R^2 = 0.5 while a second model (M2) gives R^2 = 0.9. What can we say about predictions on a test set?",
-        "answers": [
-            {
-                "text": "M2 will have error smaller than M1",
-                "image": ""
-            },
-            {
-                "text": "M2 will have error larger than M1",
-                "image": ""
-            },
-            {
-                "text": "M2 will have the same error as M1",
-                "image": ""
-            },
-            {
-                "text": "we cannot say",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "You developed a clinical test to distinguish sick patients from healthy patients. In the population, on average 998 out of 1000 people are healthy, and the test gives an incorrect prediction in 0.5% of the cases. This means the test:",
-        "answers": [
-            {
-                "text": "identifies all the healthy patients",
-                "image": ""
-            },
-            {
-                "text": "identifies all the sick patients",
-                "image": ""
-            },
-            {
-                "text": "could miss all the healthy patients",
-                "image": ""
-            },
-            {
-                "text": "could miss all the sick patients",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "How would you describe overfitting?",
-        "answers": [
-            {
-                "text": "the model is too complex and follows the noise",
-                "image": ""
-            },
-            {
-                "text": "the model is too complex and discards the noise",
-                "image": ""
-            },
-            {
-                "text": "the model is too simple and follows the noise",
-                "image": ""
-            },
-            {
-                "text": "the model is too simple and discards the noise",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "You have to convert the scores given by a logistic regression model into binary predictions. What is the best threshold?",
-        "answers": [
-            {
-                "text": "the one that maximizes accuracy",
-                "image": ""
-            },
-            {
-                "text": "the one that maximizes TPR",
-                "image": ""
-            },
-            {
-                "text": "the one that maximizes FPR",
-                "image": ""
-            },
-            {
-                "text": "it depends on the requirements",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "Given a linear regression model, the expected squared error can be usefully decomposed in:",
-        "answers": [
-            {
-                "text": "SSE and SST",
-                "image": ""
-            },
-            {
-                "text": "underfit, overfit and noise",
-                "image": ""
-            },
-            {
-                "text": "bias, variance, and error",
-                "image": ""
-            },
-            {
-                "text": "variance and covariance",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "Look at the confusion matrix below (1=positive=true,0=negative=false). What can we say?",
-        "answers": [
-            {
-                "text": "the specificity is 2/3",
-                "image": ""
-            },
-            {
-                "text": "the sensitivity is 2/3",
-                "image": ""
-            },
-            {
-                "text": "the accuracy is 2/3",
-                "image": ""
-            },
-            {
-                "text": "none of the above",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": 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"
-    },
-    {
-        "quest": "You are using k-means, and notice that different executions give different results. This happens since:",
-        "answers": [
-            {
-                "text": "k-means is randomized",
-                "image": ""
-            },
-            {
-                "text": "clustering can take exponential time",
-                "image": ""
-            },
-            {
-                "text": "this is unsupervised learning",
-                "image": ""
-            },
-            {
-                "text": "you are using the wrong value for k",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "You have 6 observations; their class (Positive or Negative) and the score given by a logistic regression are as follows: (P,0.9), (P,0.85), (N,0.75), (P,0.5), (N,0.4), (N,0.3). If you do not want the false positive rate of your classier to exceed 1/3, the best choice is to predict \u201cY\" whenever the score is at least:",
-        "answers": [
-            {
-                "text": "1.2",
-                "image": ""
-            },
-            {
-                "text": "1.0",
-                "image": ""
-            },
-            {
-                "text": "0.45",
-                "image": ""
-            },
-            {
-                "text": "0.25",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "Logistic regression finds the parameters that maximize: ",
-        "answers": [
-            {
-                "text": "the mean square error of the input data",
-                "image": ""
-            },
-            {
-                "text": "the skewness of the input data",
-                "image": ""
-            },
-            {
-                "text": "the inter-cluster distance of the input data",
-                "image": ""
-            },
-            {
-                "text": "the log-likelihood of the input data",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "What does the Bayesian Optimal Classier need to know in order to work?",
-        "answers": [
-            {
-                "text": "the marginal distribution of each variable",
-                "image": ""
-            },
-            {
-                "text": "the marginal distribution of the label",
-                "image": ""
-            },
-            {
-                "text": "the joint distribution of variables and label",
-                "image": ""
-            },
-            {
-                "text": "the joint distribution of the variables",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "Which one of the following classifiers has the best performance?",
-        "answers": [
-            {
-                "text": "TPR=0.2, FPR=0.2",
-                "image": ""
-            },
-            {
-                "text": "TPR=0.2, FPR=0.8",
-                "image": ""
-            },
-            {
-                "text": "TPR=0.8, FPR=0.2",
-                "image": ""
-            },
-            {
-                "text": "TPR=0.8, FPR=0.8",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "Your boss calls you to tell your new regression model seems completely useless for prediction, in spite of the high R^2 of the t. You realize that probably there is:",
-        "answers": [
-            {
-                "text": "underfitting",
-                "image": ""
-            },
-            {
-                "text": "overfitting",
-                "image": ""
-            },
-            {
-                "text": "correlation",
-                "image": ""
-            },
-            {
-                "text": "no tomorrow",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "From the confusion matrix below, what can we say?",
-        "answers": [
-            {
-                "text": "R^2 = 0:67",
-                "image": ""
-            },
-            {
-                "text": "accuracy = 80%",
-                "image": ""
-            },
-            {
-                "text": "all good things must come to an end",
-                "image": ""
-            },
-            {
-                "text": "sensitivity < 80%",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": 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"
-    },
-    {
-        "quest": "Consider the LP: max f(x,y)=x+3y; x10; y3. The value of the optimal solution is:",
-        "answers": [
-            {
-                "text": "19",
-                "image": ""
-            },
-            {
-                "text": "23",
-                "image": ""
-            },
-            {
-                "text": "12",
-                "image": ""
-            },
-            {
-                "text": "40",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "Your friend proposes a novel clustering algorithm that tries all possible clusterings of the data. This algorithm:",
-        "answers": [
-            {
-                "text": "has exponential complexity",
-                "image": ""
-            },
-            {
-                "text": "is efficient but gives poor clusterings",
-                "image": ""
-            },
-            {
-                "text": "has polynomial complexity",
-                "image": ""
-            },
-            {
-                "text": "is efficient and gives good clusterings",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "In a binary classier build by thresholding the scores of a logistic regression model, the positive observations:",
-        "answers": [
-            {
-                "text": "have a score strictly higher than all the negatives",
-                "image": ""
-            },
-            {
-                "text": "have higher density than the negatives",
-                "image": ""
-            },
-            {
-                "text": "are at least as many as the negatives",
-                "image": ""
-            },
-            {
-                "text": "are separated from the negatives by a hyperplane",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "The class NP contains all problems whose solution:",
-        "answers": [
-            {
-                "text": "can be verified in polytime",
-                "image": ""
-            },
-            {
-                "text": "requires exponential time",
-                "image": ""
-            },
-            {
-                "text": "none of the others",
-                "image": ""
-            },
-            {
-                "text": "can be computed in polytime",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "Lloyd's algorithm for k-means works by:",
-        "answers": [
-            {
-                "text": "evaluating all possible points in a cluster",
-                "image": ""
-            },
-            {
-                "text": "evaluating all possible clustering of the points",
-                "image": ""
-            },
-            {
-                "text": "repeatedly merging clusters",
-                "image": ""
-            },
-            {
-                "text": "repeatedly adjusting the centroids of clusters",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "You want to learn how your revenue depends on parameters such as number of working hours, etc. You could use:",
-        "answers": [
-            {
-                "text": "Linear Regression",
-                "image": ""
-            },
-            {
-                "text": "Linear Programming",
-                "image": ""
-            },
-            {
-                "text": "Logistic Regression",
-                "image": ""
-            },
-            {
-                "text": "Clustering",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "In least squares, R^2 can be seen as:",
-        "answers": [
-            {
-                "text": "the norm of the parameter vector",
-                "image": ""
-            },
-            {
-                "text": "none of the others",
-                "image": ""
-            },
-            {
-                "text": "the gain over a baseline model",
-                "image": ""
-            },
-            {
-                "text": "the inverse of the SSE",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "The ROC curve shows:",
-        "answers": [
-            {
-                "text": "specificity versus sensitivity",
-                "image": ""
-            },
-            {
-                "text": "specificity versus FPR",
-                "image": ""
-            },
-            {
-                "text": "TPR versus sensitivity",
-                "image": ""
-            },
-            {
-                "text": "TPR versus FPR",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "Can feature scaling improve the model fitted via least squares?",
-        "answers": [
-            {
-                "text": "yes, in terms of p-values",
-                "image": ""
-            },
-            {
-                "text": "no",
-                "image": ""
-            },
-            {
-                "text": "yes, in terms of interpretability",
-                "image": ""
-            },
-            {
-                "text": "yes, in terms of R2",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "Can a clustering on n points achieve 0 within-cluster sum of squares?",
-        "answers": [
-            {
-                "text": "yes, with 1 cluster",
-                "image": ""
-            },
-            {
-                "text": "yes, with k clusters",
-                "image": ""
-            },
-            {
-                "text": "yes, with n clusters",
-                "image": ""
-            },
-            {
-                "text": "no, never",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "In linear regression, if the p-value for the estimate i is small enough, then we:",
-        "answers": [
-            {
-                "text": "accept the null hypothesis i = 0",
-                "image": ""
-            },
-            {
-                "text": "reject the null hypothesis i = 0",
-                "image": ""
-            },
-            {
-                "text": "use a model with more features",
-                "image": ""
-            },
-            {
-                "text": "use a model with more parameters",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "Texts written in the same language have a similar letter frequency distribution. You can check this fact by:",
-        "answers": [
-            {
-                "text": "Logistic Regression",
-                "image": ""
-            },
-            {
-                "text": "Linear Programming",
-                "image": ""
-            },
-            {
-                "text": "Linear Regression",
-                "image": ""
-            },
-            {
-                "text": "Clustering",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "Texts written in the same language have a similar letter frequency distribution. You can check this fact by:",
-        "answers": [
-            {
-                "text": "Logistic Regression",
-                "image": ""
-            },
-            {
-                "text": "Linear Programming",
-                "image": ""
-            },
-            {
-                "text": "Linear Regression",
-                "image": ""
-            },
-            {
-                "text": "Clustering",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "Two classifiers, C1 and C2, have accuracy respectively 98\\\\% \\\\and 95%. Which one is the best?",
-        "answers": [
-            {
-                "text": "C1",
-                "image": ""
-            },
-            {
-                "text": "They are equivalent",
-                "image": ""
-            },
-            {
-                "text": "We cannot say",
-                "image": ""
-            },
-            {
-                "text": "C2",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "Correlation clustering asks to minimize:",
-        "answers": [
-            {
-                "text": "The root mean squared error",
-                "image": ""
-            },
-            {
-                "text": "The number of disagreements",
-                "image": ""
-            },
-            {
-                "text": "The intra-cluster variance",
-                "image": ""
-            },
-            {
-                "text": "The running time",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "If you increase the complexity of your linear regression model, eventually the SSE on the test set will:",
-        "answers": [
-            {
-                "text": "Approach zero",
-                "image": ""
-            },
-            {
-                "text": "Cancel the training error",
-                "image": ""
-            },
-            {
-                "text": "Exceed the training error",
-                "image": ""
-            },
-            {
-                "text": "Become negative",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "Classification accuracy is misleading when:",
-        "answers": [
-            {
-                "text": "The label proportions are unbalanced",
-                "image": ""
-            },
-            {
-                "text": "The dataset is too small",
-                "image": ""
-            },
-            {
-                "text": "The label proprtions are balanced",
-                "image": ""
-            },
-            {
-                "text": "The dataset is too large",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "The worst-case running time of the k-means algorithm on the n points is:",
-        "answers": [
-            {
-                "text": "Polynomial in n",
-                "image": ""
-            },
-            {
-                "text": "Superpolynomial in n",
-                "image": ""
-            },
-            {
-                "text": "Linear in n",
-                "image": ""
-            },
-            {
-                "text": "Unbounded in n",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "In linear regression, the expected squared error s the sum of:",
-        "answers": [
-            {
-                "text": "The good the bad and the ugly",
-                "image": ""
-            },
-            {
-                "text": "Squared bias and variance and noise",
-                "image": ""
-            },
-            {
-                "text": "Underfit and overfit the noise",
-                "image": ""
-            },
-            {
-                "text": "Variance and covariance and noise",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "Your friend proposes an innovative clustering algorithm that enumerates all possible clusterings of the points. This algorithm:",
-        "answers": [
-            {
-                "text": "Has exponential complexity",
-                "image": ""
-            },
-            {
-                "text": "Has polynomial complexity",
-                "image": ""
-            },
-            {
-                "text": "Is efficient but gives poor clustering",
-                "image": ""
-            },
-            {
-                "text": "Is efficient and gives good clusterings",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "A high R^2 on a given dataset means:",
-        "answers": [
-            {
-                "text": "A large error on new data",
-                "image": ""
-            },
-            {
-                "text": "A large error on that data",
-                "image": ""
-            },
-            {
-                "text": "A small error on that data",
-                "image": ""
-            },
-            {
-                "text": "A small error on new data",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "Multicollinearity arises if the features vectors are:",
-        "answers": [
-            {
-                "text": "absolutely orthonogal",
-                "image": ""
-            },
-            {
-                "text": "linearly dependent",
-                "image": ""
-            },
-            {
-                "text": "linearly independent",
-                "image": ""
-            },
-            {
-                "text": "positive semidefinite",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "A logistic regression model learns:",
-        "answers": [
-            {
-                "text": "The conditional distribution of predictors",
-                "image": ""
-            },
-            {
-                "text": "The conditional distribution of labels",
-                "image": ""
-            },
-            {
-                "text": "The marginal distribution of predictors",
-                "image": ""
-            },
-            {
-                "text": "The marginal distribution of labels",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "Consider the LP: min f(x,y) = x + y; x+y >= 2; x, y <= 0. The corresponding polytope is:",
-        "answers": [
-            {
-                "text": "Bounded",
-                "image": ""
-            },
-            {
-                "text": "empty",
-                "image": ""
-            },
-            {
-                "text": "Degenerate",
-                "image": ""
-            },
-            {
-                "text": "Unbounded",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "To measure the efficiency of algorithms we use:",
-        "answers": [
-            {
-                "text": "convex analysis",
-                "image": ""
-            },
-            {
-                "text": "asymptotic analysis",
-                "image": ""
-            },
-            {
-                "text": "squared analysis",
-                "image": ""
-            },
-            {
-                "text": "clinical analysis",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "Everything else being equal, what does suggest a good clustering?",
-        "answers": [
-            {
-                "text": "a high p-value",
-                "image": ""
-            },
-            {
-                "text": "a low within-cluster sum of squares",
-                "image": ""
-            },
-            {
-                "text": "a large number of observations",
-                "image": ""
-            },
-            {
-                "text": "a small number of clusters",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "The set cover problem:",
-        "answers": [
-            {
-                "text": "Can be solved in constant time",
-                "image": ""
-            },
-            {
-                "text": "is part of linear programming",
-                "image": ""
-            },
-            {
-                "text": "is NP-Complete",
-                "image": ""
-            },
-            {
-                "text": "is P-Complete",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "A company must allocate 5M\u20ac so that each department receives a minimum amount. You can use:",
-        "answers": [
-            {
-                "text": "Linear regression",
-                "image": ""
-            },
-            {
-                "text": "Logistic Regression",
-                "image": ""
-            },
-            {
-                "text": "Clustering",
-                "image": ""
-            },
-            {
-                "text": "Linear Programming",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "With hierarchical clustering on n points you can get:",
-        "answers": [
-            {
-                "text": "Between 1 and n clusters",
-                "image": ""
-            },
-            {
-                "text": "No satisfaction",
-                "image": ""
-            },
-            {
-                "text": "Up to 2^n clusters",
-                "image": ""
-            },
-            {
-                "text": "At most log(n) clusters",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    },
-    {
-        "quest": "The standard assumption of linear regression is that the noise across the observations:",
-        "answers": [
-            {
-                "text": "is fast and furios",
-                "image": ""
-            },
-            {
-                "text": "is always bounded",
-                "image": ""
-            },
-            {
-                "text": "is Gaussian and correlated",
-                "image": ""
-            },
-            {
-                "text": "is Gaussian and independent",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "The ROC curve is used to measure:",
-        "answers": [
-            {
-                "text": "The amount of overfitting and underfitting",
-                "image": ""
-            },
-            {
-                "text": "The noise in the training dataset",
-                "image": ""
-            },
-            {
-                "text": "The performance of binary classifiers",
-                "image": ""
-            },
-            {
-                "text": "The MSE obtained by a linear regression\"",
-                "image": ""
-            }
-        ],
-        "correct": 2,
-        "image": ""
-    },
-    {
-        "quest": "Geometrically, each constraint of a linear program corresponds to:",
-        "answers": [
-            {
-                "text": "a vector",
-                "image": ""
-            },
-            {
-                "text": "A double-space",
-                "image": ""
-            },
-            {
-                "text": "a cone",
-                "image": ""
-            },
-            {
-                "text": "a half-space",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "Many well-known clustering problems are:",
-        "answers": [
-            {
-                "text": "impossible to solve",
-                "image": ""
-            },
-            {
-                "text": "NP-hard",
-                "image": ""
-            },
-            {
-                "text": "easy to solve",
-                "image": ""
-            },
-            {
-                "text": "infeasible\"",
-                "image": ""
-            }
-        ],
-        "correct": 1,
-        "image": ""
-    },
-    {
-        "quest": "A polytope is:",
-        "answers": [
-            {
-                "text": "The difference of half-spaces",
-                "image": ""
-            },
-            {
-                "text": "the greatest gift of all",
-                "image": ""
-            },
-            {
-                "text": "the union of half-spaces",
-                "image": ""
-            },
-            {
-                "text": "The intersection of half spaces",
-                "image": ""
-            }
-        ],
-        "correct": 3,
-        "image": ""
-    },
-    {
-        "quest": "Everything else being equal. What does suggest good clustering?",
-        "answers": [
-            {
-                "text": "Few clusters",
-                "image": ""
-            },
-            {
-                "text": "low within-cluster sum of squares",
-                "image": ""
-            },
-            {
-                "text": "high p-value",
-                "image": ""
-            },
-            {
-                "text": "large number of points",
-                "image": ""
-            }
-        ],
-        "correct": 0,
-        "image": ""
-    }
-]
\ No newline at end of file
diff --git a/data/questions/fds_llm.json b/data/questions/fds_llm.json
deleted file mode 100644
index c5f23c0..0000000
--- a/data/questions/fds_llm.json
+++ /dev/null
@@ -1,6749 +0,0 @@
-[
-  {
-    "quest": "Autoencoders: What is the purpose of the hidden layer in an autoencoder, which typically has a smaller dimensionality than the input layer?",
-    "answers": [
-      {
-        "text": "To expand the input data to a higher-dimensional space.",
-        "image": ""
-      },
-      {
-        "text": "To learn a compressed and efficient representation of the input data.",
-        "image": ""
-      },
-      {
-        "text": "To apply a non-linear transformation to the input.",
-        "image": ""
-      },
-      {
-        "text": "To add noise to the input data for robustness.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: According to the source, why is the constraint of a smaller hidden layer important in an autoencoder?",
-    "answers": [
-      {
-        "text": "It allows the network to memorize the input.",
-        "image": ""
-      },
-      {
-        "text": "It forces the network to learn meaningful patterns and structures.",
-        "image": ""
-      },
-      {
-        "text": "It speeds up the training process.",
-        "image": ""
-      },
-      {
-        "text": "It prevents overfitting.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: Which of the following is NOT a typical use case for autoencoders mentioned in the source?",
-    "answers": [
-      {
-        "text": "Dimensionality reduction for visualization.",
-        "image": ""
-      },
-      {
-        "text": "Data compression.",
-        "image": ""
-      },
-      {
-        "text": "Feature learning for downstream tasks.",
-        "image": ""
-      },
-      {
-        "text": "Supervised classification with labeled data.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: What does the source say about using autoencoders with unlabeled data?",
-    "answers": [
-      {
-        "text": "They are not suitable for unlabeled data.",
-        "image": ""
-      },
-      {
-        "text": "They require labeled data to extract useful patterns.",
-        "image": ""
-      },
-      {
-        "text": "They can leverage large amounts of unlabeled data to extract useful patterns.",
-        "image": ""
-      },
-      {
-        "text": "They are only useful for supervised learning tasks",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: In a linear autoencoder, what is the relationship between the input data (x) and the reconstructed output (x̂), according to the source?",
-    "answers": [
-      {
-        "text": "x̂ = VxU",
-        "image": ""
-      },
-      {
-        "text": "x̂ = UVx",
-        "image": ""
-      },
-      {
-        "text": "x̂ = x²",
-        "image": ""
-      },
-      {
-        "text": "x̂ = x + noise",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: What is the encoded representation of x in a linear autoencoder?",
-    "answers": [
-      {
-        "text": "UVx",
-        "image": ""
-      },
-      {
-        "text": "x̂",
-        "image": ""
-      },
-      {
-        "text": "Vx",
-        "image": ""
-      },
-      {
-        "text": "CCTx",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: According to the source, what is the main drawback of a linear autoencoder?",
-    "answers": [
-      {
-        "text": "It cannot be used for dimensionality reduction.",
-        "image": ""
-      },
-      {
-        "text": "It performs poorly with non-linear data.",
-        "image": ""
-      },
-      {
-        "text": "It requires extensive computational resources.",
-        "image": ""
-      },
-      {
-        "text": "The mapping is linear, limiting its ability to capture complex non-linear data.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: In the context of the source, what is Principal Component Analysis (PCA) equivalent to?",
-    "answers": [
-      {
-        "text": "A deep nonlinear autoencoder.",
-        "image": ""
-      },
-      {
-        "text": "A sparse autoencoder.",
-        "image": ""
-      },
-      {
-        "text": "A linear autoencoder with a single hidden layer and linear activation functions.",
-        "image": ""
-      },
-      {
-        "text": "A denoising autoencoder.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: What are Eigenfaces, as described in the source?",
-    "answers": [
-      {
-        "text": "A specific type of autoencoder architecture.",
-        "image": ""
-      },
-      {
-        "text": "Randomly generated facial images.",
-        "image": ""
-      },
-      {
-        "text": "The principal components (or eigenvectors) of a large set of facial images.",
-        "image": ""
-      },
-      {
-        "text": "Non-linear transformations of facial images.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: According to the source, what does the value of the eigenvalues in PCA represent?",
-    "answers": [
-      {
-        "text": "The amount of noise present in the image.",
-        "image": ""
-      },
-      {
-        "text": "The size of the dataset.",
-        "image": ""
-      },
-      {
-        "text": "The amount of variance each eigenface captures.",
-        "image": ""
-      },
-      {
-        "text": "The total number of faces in the dataset.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: How are the weights (or coefficients) for each eigenface found, according to the source?",
-    "answers": [
-      {
-        "text": "By random initialization.",
-        "image": ""
-      },
-      {
-        "text": "By applying a non-linear activation function.",
-        "image": ""
-      },
-      {
-        "text": "By projecting the original face onto the eigenfaces.",
-        "image": ""
-      },
-      {
-        "text": "By calculating the mean of the image.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: Which of the following is a limitation of using eigenfaces for face recognition as mentioned in the source?",
-    "answers": [
-      {
-        "text": "They are not sensitive to facial expressions.",
-        "image": ""
-      },
-      {
-        "text": "They are highly effective with any data set",
-        "image": ""
-      },
-      {
-        "text": "They can capture any non-linear variation of a face",
-        "image": ""
-      },
-      {
-        "text": "They are sensitive to variations in lighting, pose, and facial expressions.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: What distinguishes a deep autoencoder from a linear autoencoder?",
-    "answers": [
-      {
-        "text": "A deep autoencoder uses a single hidden layer with linear activation.",
-        "image": ""
-      },
-      {
-        "text": "A deep autoencoder projects data onto a non-linear manifold, instead of a subspace.",
-        "image": ""
-      },
-      {
-        "text": "A deep autoencoder is only useful for supervised learning tasks",
-        "image": ""
-      },
-      {
-        "text": "A deep autoencoder uses only labeled data",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: What is the key characteristic of an undercomplete autoencoder?",
-    "answers": [
-      {
-        "text": "The hidden layer has more units than the input layer.",
-        "image": ""
-      },
-      {
-        "text": "The embedded space has a lower dimensionality than the input space.",
-        "image": ""
-      },
-      {
-        "text": "The model overfits to the training data.",
-        "image": ""
-      },
-      {
-        "text": "The hidden layer uses non-linear activation.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: What is the main challenge with overcomplete autoencoders?",
-    "answers": [
-      {
-        "text": "They are difficult to train.",
-        "image": ""
-      },
-      {
-        "text": "They perform poorly with complex data.",
-        "image": ""
-      },
-      {
-        "text": "Without proper constraints, they can overfit by simply copying the input to the output.",
-        "image": ""
-      },
-      {
-        "text": "They cannot capture the data's hidden structure.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: What is a stacked autoencoder?",
-    "answers": [
-      {
-        "text": "An autoencoder with a very small hidden layer.",
-        "image": ""
-      },
-      {
-        "text": "An autoencoder consisting of multiple encoding and decoding layers.",
-        "image": ""
-      },
-      {
-        "text": "An autoencoder that can only learn linear features.",
-        "image": ""
-      },
-      {
-        "text": "An autoencoder that operates exclusively on labeled data.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: According to the source, what does the layer-wise training of a stacked autoencoder achieve?",
-    "answers": [
-      {
-        "text": "It leads to overfitting.",
-        "image": ""
-      },
-      {
-        "text": "It makes training more complex.",
-        "image": ""
-      },
-      {
-        "text": "It simplifies optimization and ensures meaningful feature learning at each step.",
-        "image": ""
-      },
-      {
-        "text": "It eliminates the need for pre-training.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: What is a denoising autoencoder (DAE) designed to do?",
-    "answers": [
-      {
-        "text": "To compress data without loss.",
-        "image": ""
-      },
-      {
-        "text": "To learn robust, noise-resistant representations by reconstructing clean data from noisy inputs.",
-        "image": ""
-      },
-      {
-        "text": "To reduce the dimensionality of the input data.",
-        "image": ""
-      },
-      {
-        "text": "To only use clean data as input.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: How does a denoising autoencoder achieve noise-resistance?",
-    "answers": [
-      {
-        "text": "By reducing the size of the hidden layer.",
-        "image": ""
-      },
-      {
-        "text": "By training on noisy data and reconstructing the clean version.",
-        "image": ""
-      },
-      {
-        "text": "By adding random noise to the output.",
-        "image": ""
-      },
-      {
-        "text": "By adding labels to the input data.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: What is a key characteristic of a sparse autoencoder?",
-    "answers": [
-      {
-        "text": "The hidden layer has fewer units than the input.",
-        "image": ""
-      },
-      {
-        "text": "Most hidden units should have zero activation.",
-        "image": ""
-      },
-      {
-        "text": "It uses only labeled data.",
-        "image": ""
-      },
-      {
-        "text": "It maps the data onto a linear subspace.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: What is the purpose of the sparsity penalty in a sparse autoencoder?",
-    "answers": [
-      {
-        "text": "To increase the complexity of the model.",
-        "image": ""
-      },
-      {
-        "text": "To encourage most hidden units to be inactive.",
-        "image": ""
-      },
-      {
-        "text": "To increase the amount of noise",
-        "image": ""
-      },
-      {
-        "text": "To reduce the reconstruction error to zero.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: According to the source, what does the Kullback-Leibler (KL) divergence measure in the context of sparse autoencoders?",
-    "answers": [
-      {
-        "text": "The reconstruction error.",
-        "image": ""
-      },
-      {
-        "text": "The amount of noise in the input data.",
-        "image": ""
-      },
-      {
-        "text": "The difference between the average activation of hidden units and a target sparsity value.",
-        "image": ""
-      },
-      {
-        "text": "The dimensionality of the latent space.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: a) Classify data into predefined categories.",
-    "answers": [
-      {
-        "text": "Classify data into predefined categories.",
-        "image": ""
-      },
-      {
-        "text": "Reconstruct its input by predicting an approximation.",
-        "image": ""
-      },
-      {
-        "text": "Generate new data samples similar to the input.",
-        "image": ""
-      },
-      {
-        "text": "Reduce the dimensionality of the data for visualization purposes only.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: The 'bottleneck' in an autoencoder architecture refers to:",
-    "answers": [
-      {
-        "text": "The activation function used in the output layer.",
-        "image": ""
-      },
-      {
-        "text": "The hidden layer with a significantly smaller dimensionality than the input.",
-        "image": ""
-      },
-      {
-        "text": "The initial weight matrices of the encoder and decoder.",
-        "image": ""
-      },
-      {
-        "text": "The loss function that the network tries to minimize.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: What is the primary purpose of the dimensionality constraint in autoencoders?",
-    "answers": [
-      {
-        "text": "To increase the computational speed of the network.",
-        "image": ""
-      },
-      {
-        "text": "To force the network to learn meaningful patterns and structures in the data instead of memorising the input.",
-        "image": ""
-      },
-      {
-        "text": "To allow the network to handle high dimensional input data.",
-        "image": ""
-      },
-      {
-        "text": "To reduce the risk of overfitting.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: Which of the following is NOT a typical application of autoencoders?",
-    "answers": [
-      {
-        "text": "Data compression.",
-        "image": ""
-      },
-      {
-        "text": "Feature learning for downstream tasks.",
-        "image": ""
-      },
-      {
-        "text": "Supervised classification with labelled data.",
-        "image": ""
-      },
-      {
-        "text": "Dimensionality reduction for visualisation.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: A linear autoencoder, which uses linear activation functions, is functionally equivalent to:",
-    "answers": [
-      {
-        "text": "A deep neural network.",
-        "image": ""
-      },
-      {
-        "text": "Principal Component Analysis (PCA).",
-        "image": ""
-      },
-      {
-        "text": "A convolutional neural network.",
-        "image": ""
-      },
-      {
-        "text": "A recurrent neural network.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: In a linear autoencoder, the reconstruction of the input is computed as a linear transformation expressed as:",
-    "answers": [
-      {
-        "text": "x̂ = U + V + x",
-        "image": ""
-      },
-      {
-        "text": "x̂ = UVx",
-        "image": ""
-      },
-      {
-        "text": "x̂ = U * V * x",
-        "image": ""
-      },
-      {
-        "text": "x̂ =  V / U * x",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: According to the source, a linear autoencoder learns to choose a subspace that:",
-    "answers": [
-      {
-        "text": "Maximises the distance of the data points from the projections.",
-        "image": ""
-      },
-      {
-        "text": "Minimises the variance of the projections.",
-        "image": ""
-      },
-      {
-        "text": "Minimises the squared distance from the data to the projections and maximises the variance of the projections.",
-        "image": ""
-      },
-      {
-        "text": "Is randomly generated each training cycle.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: Eigenfaces are:",
-    "answers": [
-      {
-        "text": "The result of applying non-linear transformations to faces.",
-        "image": ""
-      },
-      {
-        "text": "The principal components (or eigenvectors) of a large set of facial images.",
-        "image": ""
-      },
-      {
-        "text": "A set of facial images created by averaging the original dataset.",
-        "image": ""
-      },
-      {
-        "text": "A specific type of neural network.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: What is the purpose of centering the data (subtracting the mean) before performing PCA on a set of face images?",
-    "answers": [
-      {
-        "text": "To increase the variance of the data",
-        "image": ""
-      },
-      {
-        "text": "To reduce the noise in the data",
-        "image": ""
-      },
-      {
-        "text": "To standardise the range of pixel values",
-        "image": ""
-      },
-      {
-        "text": "To ensure that the data is centered around zero",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: When recognising faces using eigenfaces, each face is represented as:",
-    "answers": [
-      {
-        "text": "A new image composed from a random set of pixels",
-        "image": ""
-      },
-      {
-        "text": "A set of binary digits",
-        "image": ""
-      },
-      {
-        "text": "A compressed image that has a lower resolution",
-        "image": ""
-      },
-      {
-        "text": "A weighted sum of eigenfaces",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: Which of the following is a limitation of using eigenfaces for facial recognition?",
-    "answers": [
-      {
-        "text": "Their high computational complexity.",
-        "image": ""
-      },
-      {
-        "text": "Their inability to generalize to different people.",
-        "image": ""
-      },
-      {
-        "text": "Their effectiveness is not dependent on the training data quality.",
-        "image": ""
-      },
-      {
-        "text": "Their sensitivity to variations in lighting, pose, and facial expressions.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: Deep autoencoders project the data onto a:",
-    "answers": [
-      {
-        "text": "Linear subspace",
-        "image": ""
-      },
-      {
-        "text": "Nonlinear manifold",
-        "image": ""
-      },
-      {
-        "text": "Randomly generated vector space",
-        "image": ""
-      },
-      {
-        "text": "Discrete set of points",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: An undercomplete autoencoder is characterized by:",
-    "answers": [
-      {
-        "text": "A hidden layer larger than the input layer",
-        "image": ""
-      },
-      {
-        "text": "The absence of regularization techniques",
-        "image": ""
-      },
-      {
-        "text": "A hidden layer smaller than the input layer",
-        "image": ""
-      },
-      {
-        "text": "A tendency to overfit the data.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: What is the main challenge associated with overcomplete autoencoders?",
-    "answers": [
-      {
-        "text": "Difficulty in learning any meaningful features.",
-        "image": ""
-      },
-      {
-        "text": "Tendency to underfit the data due to low capacity.",
-        "image": ""
-      },
-      {
-        "text": "Overfitting by simply copying the input to the output",
-        "image": ""
-      },
-      {
-        "text": "Difficulty with using regularization techniques",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: A stacked autoencoder introduces which property to the network?",
-    "answers": [
-      {
-        "text": "Linear transformations",
-        "image": ""
-      },
-      {
-        "text": "Reduced computational costs",
-        "image": ""
-      },
-      {
-        "text": "Hierarchical representations of the input data",
-        "image": ""
-      },
-      {
-        "text": "The ability to only use labelled training data",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: In the simplified training of a stacked autoencoder:",
-    "answers": [
-      {
-        "text": "All layers are trained simultaneously",
-        "image": ""
-      },
-      {
-        "text": "The layers are only trained with labelled data",
-        "image": ""
-      },
-      {
-        "text": "The output of one layer is used as input to train the next",
-        "image": ""
-      },
-      {
-        "text": "All layers share the same weight matrices.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: What is a key feature of denoising autoencoders (DAE)?",
-    "answers": [
-      {
-        "text": "They only use labelled training data.",
-        "image": ""
-      },
-      {
-        "text": "They are trained to generate new data",
-        "image": ""
-      },
-      {
-        "text": "They are trained to directly reconstruct the input without using any noise",
-        "image": ""
-      },
-      {
-        "text": "They are trained to reconstruct clean data from noisy inputs",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: Sparse autoencoders enforce sparsity by:",
-    "answers": [
-      {
-        "text": "Ensuring that all hidden units have non-zero activation.",
-        "image": ""
-      },
-      {
-        "text": "Removing some hidden units during training.",
-        "image": ""
-      },
-      {
-        "text": "Reducing the size of the hidden layer.",
-        "image": ""
-      },
-      {
-        "text": "Encouraging most hidden units to have zero activation",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: The Kullback-Leibler (KL) divergence in sparse autoencoders is used to:",
-    "answers": [
-      {
-        "text": "Minimise the reconstruction error",
-        "image": ""
-      },
-      {
-        "text": "Match the average activation of hidden units to a target sparsity value",
-        "image": ""
-      },
-      {
-        "text": "Maximise the number of active hidden units",
-        "image": ""
-      },
-      {
-        "text": "Increase the dimensionality of the hidden layer",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Autoencoders: What are the weights in the context of face representation using eigenfaces?",
-    "answers": [
-      {
-        "text": "The pixels that represent the image",
-        "image": ""
-      },
-      {
-        "text": "The coefficients found by projecting the original face onto the eigenfaces",
-        "image": ""
-      },
-      {
-        "text": "The eigenvectors of the faces",
-        "image": ""
-      },
-      {
-        "text": "The eigenfaces themselves",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "VAE: According to the source, the joint probability of observed data 'x' and latent variable 'z' in a VAE is expressed as:",
-    "answers": [
-      {
-        "text": "p(x,z) = p(x|z) + p(z)",
-        "image": ""
-      },
-      {
-        "text": "p(x,z) = p(x|z)p(z)",
-        "image": ""
-      },
-      {
-        "text": "p(x,z) = p(z|x)p(x)",
-        "image": ""
-      },
-      {
-        "text": "p(x,z) = p(x) / p(z)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "VAE: The term 'p(x|z)' in the VAE framework represents:",
-    "answers": [
-      {
-        "text": "The prior distribution of the latent variable.",
-        "image": ""
-      },
-      {
-        "text": "The likelihood of observing 'x' given a specific value of 'z'.",
-        "image": ""
-      },
-      {
-        "text": "The posterior distribution of 'z' given 'x'.",
-        "image": ""
-      },
-      {
-        "text": "The marginal distribution of 'x'.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "VAE: In the VAE model, the probability of the observed data 'x' is obtained by:",
-    "answers": [
-      {
-        "text": "Maximizing p(x|z) over all possible values of 'z'.",
-        "image": ""
-      },
-      {
-        "text": "Integrating p(x|z)p(z) over all possible values of 'z'.",
-        "image": ""
-      },
-      {
-        "text": "Calculating the product of p(x|z) and p(z).",
-        "image": ""
-      },
-      {
-        "text": "Minimizing the squared difference between 'x' and its reconstruction.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "VAE: According to the source, the encoder in a VAE aims to approximate:",
-    "answers": [
-      {
-        "text": "p(x|z)",
-        "image": ""
-      },
-      {
-        "text": "p(z|x)",
-        "image": ""
-      },
-      {
-        "text": "p(z)",
-        "image": ""
-      },
-      {
-        "text": "p(x)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "VAE: The decoder network in a VAE is parameterised by:",
-    "answers": [
-      {
-        "text": "q(z|x)",
-        "image": ""
-      },
-      {
-        "text": "p(x|z)",
-        "image": ""
-      },
-      {
-        "text": "p(z)",
-        "image": ""
-      },
-      {
-        "text": "p(x)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "VAE: The encoder in the VAE is denoted as:",
-    "answers": [
-      {
-        "text": "p(x|z)",
-        "image": ""
-      },
-      {
-        "text": "q(z|x)",
-        "image": ""
-      },
-      {
-        "text": "p(z)",
-        "image": ""
-      },
-      {
-        "text": "p(x)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "VAE: In a VAE, the Evidence Lower Bound (ELBO) is introduced as a:",
-    "answers": [
-      {
-        "text": "Direct computation of the marginal likelihood p(x).",
-        "image": ""
-      },
-      {
-        "text": "Tractable approximation to the log-likelihood of the observed data.",
-        "image": ""
-      },
-      {
-        "text": "A method to calculate the exact posterior p(z|x).",
-        "image": ""
-      },
-      {
-        "text": "A way to eliminate the need for integration over the latent space.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "VAE: The ELBO is represented by the expression:",
-    "answers": [
-      {
-        "text": "E[log p(x|z)] + D_KL(q(z|x)||p(z))",
-        "image": ""
-      },
-      {
-        "text": "E[log p(x|z)] - D_KL(q(z|x)||p(z))",
-        "image": ""
-      },
-      {
-        "text": "E[log q(z|x)] - D_KL(p(z|x)||q(z))",
-        "image": ""
-      },
-      {
-        "text": "E[log p(z|x)] + D_KL(p(z|x)||p(z))",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "VAE: The first term in the ELBO, E[log p(x|z)], represents:",
-    "answers": [
-      {
-        "text": "The regularization term of the model.",
-        "image": ""
-      },
-      {
-        "text": "The distribution of the latent variables",
-        "image": ""
-      },
-      {
-        "text": "The reconstruction accuracy of the decoder.",
-        "image": ""
-      },
-      {
-        "text": "The deviation of the posterior from the prior.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "VAE: The second term in the ELBO, D_KL(q(z|x)||p(z)), represents:",
-    "answers": [
-      {
-        "text": "The Kullback-Leibler divergence between the approximate posterior and the prior.",
-        "image": ""
-      },
-      {
-        "text": "The cross-entropy between the input and output.",
-        "image": ""
-      },
-      {
-        "text": "The variance of the latent space.",
-        "image": ""
-      },
-      {
-        "text": "The reconstruction accuracy of the encoder.",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "VAE: What is the purpose of the KL divergence term in the VAE loss function?",
-    "answers": [
-      {
-        "text": "To increase the reconstruction error.",
-        "image": ""
-      },
-      {
-        "text": "To force the approximate posterior distribution of the latent variables to be close to the prior distribution.",
-        "image": ""
-      },
-      {
-        "text": "To make the latent variables deterministic.",
-        "image": ""
-      },
-      {
-        "text": "To reduce the computational complexity.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "VAE: The source mentions that if q(z|x) is equal to p(z) then:",
-    "answers": [
-      {
-        "text": "The model will underfit the data",
-        "image": ""
-      },
-      {
-        "text": "The ELBO will be equal to log(p(x))",
-        "image": ""
-      },
-      {
-        "text": "The reconstruction of x will have a lot of noise",
-        "image": ""
-      },
-      {
-        "text": "The KL-divergence term will be infinite",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "VAE: According to the source, the approximate posterior q(z|x) is often assumed to be a:",
-    "answers": [
-      {
-        "text": "Uniform distribution",
-        "image": ""
-      },
-      {
-        "text": "Bernoulli distribution",
-        "image": ""
-      },
-      {
-        "text": "Gaussian distribution",
-        "image": ""
-      },
-      {
-        "text": "Poisson distribution",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "VAE: In a VAE, if we assume that q(z|x) is a Gaussian distribution, then what are the two outputs of the encoder network?",
-    "answers": [
-      {
-        "text": "The mean and the variance of p(x|z)",
-        "image": ""
-      },
-      {
-        "text": "The mean and the variance of p(z)",
-        "image": ""
-      },
-      {
-        "text": "The mean and the variance of q(z|x)",
-        "image": ""
-      },
-      {
-        "text": "The reconstruction and the original input",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "VAE: The reparameterisation trick in VAEs allows us to:",
-    "answers": [
-      {
-        "text": "Calculate the reconstruction error without using samples from the latent distribution.",
-        "image": ""
-      },
-      {
-        "text": "Backpropagate through the sampling process of the latent variable z.",
-        "image": ""
-      },
-      {
-        "text": "Directly use the mean and variance of q(z|x) during training.",
-        "image": ""
-      },
-      {
-        "text": "Calculate the KL divergence without the need for approximations.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "VAE: The reparameterisation trick expresses the latent variable ‘z’ as:",
-    "answers": [
-      {
-        "text": "z = μ + σ",
-        "image": ""
-      },
-      {
-        "text": "z = μ + σ ⊙ ε, where ε ~ N(0,I)",
-        "image": ""
-      },
-      {
-        "text": "z = μ * σ * ε",
-        "image": ""
-      },
-      {
-        "text": "z = μ / σ + ε",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "VAE: In the reparameterisation trick the random variable ε is drawn from:",
-    "answers": [
-      {
-        "text": "A uniform distribution",
-        "image": ""
-      },
-      {
-        "text": "A standard normal distribution",
-        "image": ""
-      },
-      {
-        "text": "The posterior distribution q(z|x)",
-        "image": ""
-      },
-      {
-        "text": "The prior distribution p(z)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: What are the two key requirements for probabilities when using the Softmax classifier?",
-    "answers": [
-      {
-        "text": "They must be less than zero and sum to one.",
-        "image": ""
-      },
-      {
-        "text": "They must be greater than or equal to zero and not sum to one.",
-        "image": ""
-      },
-      {
-        "text": "They must be greater than or equal to zero and sum to greater than one.",
-        "image": ""
-      },
-      {
-        "text": "They must be greater than or equal to zero and sum to one",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: In the context of training a classifier, what does Maximum Likelihood Estimation aim to do?",
-    "answers": [
-      {
-        "text": "To minimise the likelihood of the observed data.",
-        "image": ""
-      },
-      {
-        "text": "To choose weights to maximise the likelihood of the observed data.",
-        "image": ""
-      },
-      {
-        "text": "To calculate the cross-entropy loss.",
-        "image": ""
-      },
-      {
-        "text": "To regularise the model.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: Cross-entropy is described as the sum of two components. Which of the following correctly identifies these components?",
-    "answers": [
-      {
-        "text": "Entropy and regularisation.",
-        "image": ""
-      },
-      {
-        "text": "Entropy and model complexity.",
-        "image": ""
-      },
-      {
-        "text": "Entropy and KL-divergence.",
-        "image": ""
-      },
-      {
-        "text": "KL-divergence and regularisation.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: What is the primary effect of L2 regularization on the weights of a model?",
-    "answers": [
-      {
-        "text": "It increases the magnitude of the weights.",
-        "image": ""
-      },
-      {
-        "text": "It \"spreads out\" the weights.",
-        "image": ""
-      },
-      {
-        "text": "It makes the model more complex.",
-        "image": ""
-      },
-      {
-        "text": "It has no effect on the weights.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: When using gradient descent to find the best weights (W), what is the relationship between the data loss and the regularization term?",
-    "answers": [
-      {
-        "text": "They are independent of each other",
-        "image": ""
-      },
-      {
-        "text": "They are used separately, data loss first then regularization",
-        "image": ""
-      },
-      {
-        "text": "Data loss is applied only if the regularization loss is too high",
-        "image": ""
-      },
-      {
-        "text": "They are combined.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: Why is deriving gradients on paper considered a bad idea for complex models?",
-    "answers": [
-      {
-        "text": "It's too simple for non-linear functions",
-        "image": ""
-      },
-      {
-        "text": "It is very tedious, requires lots of matrix calculus and needs to be re-derived if loss changes.",
-        "image": ""
-      },
-      {
-        "text": "It only works for linear score functions.",
-        "image": ""
-      },
-      {
-        "text": "It is not possible.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: In the context of backpropagation, what are the two gradients that are multiplied using the chain rule?",
-    "answers": [
-      {
-        "text": "Upstream and downstream gradients",
-        "image": ""
-      },
-      {
-        "text": "Upstream and local gradients.",
-        "image": ""
-      },
-      {
-        "text": "Input and output gradients",
-        "image": ""
-      },
-      {
-        "text": "Weight and bias gradients",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: In a computational graph, what is the behavior of the \"add gate\" regarding gradients?",
-    "answers": [
-      {
-        "text": "It swaps multipliers.",
-        "image": ""
-      },
-      {
-        "text": "It adds gradients.",
-        "image": ""
-      },
-      {
-        "text": "It distributes gradients.",
-        "image": ""
-      },
-      {
-        "text": "It routes gradients.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: In a computational graph, what is the behavior of the \"mul gate\" regarding gradients?",
-    "answers": [
-      {
-        "text": "It adds gradients.",
-        "image": ""
-      },
-      {
-        "text": "It distributes gradients",
-        "image": ""
-      },
-      {
-        "text": "It \"swaps multiplier\".",
-        "image": ""
-      },
-      {
-        "text": "It routes gradiens",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: In a computational graph, what is the behavior of the \"copy gate\" regarding gradients?",
-    "answers": [
-      {
-        "text": "It swaps multipliers.",
-        "image": ""
-      },
-      {
-        "text": "It distributes gradients",
-        "image": ""
-      },
-      {
-        "text": "It adds gradients.",
-        "image": ""
-      },
-      {
-        "text": "It routes gradients.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: In a computational graph, what is the behaviour of the \"max gate\" regarding gradients?",
-    "answers": [
-      {
-        "text": "It swaps multipliers.",
-        "image": ""
-      },
-      {
-        "text": "It distributes gradients",
-        "image": ""
-      },
-      {
-        "text": "It adds gradients.",
-        "image": ""
-      },
-      {
-        "text": "It routes gradients.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: In a modularized implementation of backpropagation, what is a key function of a gate/node/function object?",
-    "answers": [
-      {
-        "text": "To only compute the result of an operation.",
-        "image": ""
-      },
-      {
-        "text": "To only calculate upstream gradient",
-        "image": ""
-      },
-      {
-        "text": "To cache some values for use in backward pass and multiply upstream and local gradients.",
-        "image": ""
-      },
-      {
-        "text": "To apply the chain rule on forward pass.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: When dealing with vector derivatives, what is the derivative called when mapping from a vector to a scalar?",
-    "answers": [
-      {
-        "text": "Jacobian",
-        "image": ""
-      },
-      {
-        "text": "Hessian",
-        "image": ""
-      },
-      {
-        "text": "Gradient",
-        "image": ""
-      },
-      {
-        "text": "Laplacian",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: When dealing with vector derivatives, what is the derivative called when mapping from a vector to a vector?",
-    "answers": [
-      {
-        "text": "Gradient",
-        "image": ""
-      },
-      {
-        "text": "Hessian",
-        "image": ""
-      },
-      {
-        "text": "Jacobian",
-        "image": ""
-      },
-      {
-        "text": "Laplacian",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: In backpropagation with vectors, what is the loss (L) considered to be?",
-    "answers": [
-      {
-        "text": "A vector.",
-        "image": ""
-      },
-      {
-        "text": "A matrix.",
-        "image": ""
-      },
-      {
-        "text": "A tensor.",
-        "image": ""
-      },
-      {
-        "text": "A scalar.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: In backpropagation with vectors, what is the relationship between the shape of dL/dx and x?",
-    "answers": [
-      {
-        "text": "dL/dx is always larger than x.",
-        "image": ""
-      },
-      {
-        "text": "dL/dx is always smaller than x.",
-        "image": ""
-      },
-      {
-        "text": "dL/dx always has the same shape as x.",
-        "image": ""
-      },
-      {
-        "text": "Their shapes are unrelated.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: When backpropagating through a matrix multiplication, what is the primary challenge in dealing with Jacobians?",
-    "answers": [
-      {
-        "text": "They are difficult to compute",
-        "image": ""
-      },
-      {
-        "text": "They are always sparse",
-        "image": ""
-      },
-      {
-        "text": "They are always dense",
-        "image": ""
-      },
-      {
-        "text": "They take too much memory, so we must work with them implicitly",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: When performing backpropagation with matrices, what does an element of X affect in the output Y (considerando che Y = X x W)?",
-    "answers": [
-      {
-        "text": "Only one element of y.",
-        "image": ""
-      },
-      {
-        "text": "Only one column of y.",
-        "image": ""
-      },
-      {
-        "text": "The whole row of y.",
-        "image": ""
-      },
-      {
-        "text": "It doesn't affect y.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: What is the core principle of backpropagation?",
-    "answers": [
-      {
-        "text": "To optimise the loss function directly",
-        "image": ""
-      },
-      {
-        "text": "To compute gradients on forward pass",
-        "image": ""
-      },
-      {
-        "text": "To recursively apply the chain rule along a computational graph to compute the gradients of all inputs/parameters/intermediates",
-        "image": ""
-      },
-      {
-        "text": "To implement forward pass without saving any intermediates",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: What are the two essential methods that nodes implement in a modularized backpropagation system?",
-    "answers": [
-      {
-        "text": "Train() and Predict()",
-        "image": ""
-      },
-      {
-        "text": "Input() and Output()",
-        "image": ""
-      },
-      {
-        "text": "Loss() and Regularisation()",
-        "image": ""
-      },
-      {
-        "text": "Forward() and Backward()",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: In a modularized backpropagation system, what is the purpose of the forward() method of a node?",
-    "answers": [
-      {
-        "text": "To apply the chain rule",
-        "image": ""
-      },
-      {
-        "text": "To compute gradients",
-        "image": ""
-      },
-      {
-        "text": "To compute the result of an operation and save any intermediates needed for gradient computation.",
-        "image": ""
-      },
-      {
-        "text": "To update parameters.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: In a modularized backpropagation system, what is the purpose of the backward() method of a node?",
-    "answers": [
-      {
-        "text": "To compute the result of an operation",
-        "image": ""
-      },
-      {
-        "text": "To save intermediates",
-        "image": ""
-      },
-      {
-        "text": "To update parameters",
-        "image": ""
-      },
-      {
-        "text": "To apply the chain rule to compute the gradient of the loss function with respect to the inputs",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: What is the primary difference between a linear classifier and a two-layer neural network?",
-    "answers": [
-      {
-        "text": "A neural network does not include a linear score function.",
-        "image": ""
-      },
-      {
-        "text": "A neural network has a simpler architecture.",
-        "image": ""
-      },
-      {
-        "text": "A neural network is linear.",
-        "image": ""
-      },
-      {
-        "text": "A neural network introduces a non-linear transformation with an activation function.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: According to the sources, what is the consequence of building a neural network without an activation function?",
-    "answers": [
-      {
-        "text": "It becomes a more powerful non-linear classifier",
-        "image": ""
-      },
-      {
-        "text": "It becomes computationally intractable",
-        "image": ""
-      },
-      {
-        "text": "It ends up being a linear classifier",
-        "image": ""
-      },
-      {
-        "text": "It cannot learn anything",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: What does the Universal Approximation Theorem state in the context of Neural Networks?",
-    "answers": [
-      {
-        "text": "Neural networks always find a global minimum",
-        "image": ""
-      },
-      {
-        "text": "Neural networks always learn a linear function",
-        "image": ""
-      },
-      {
-        "text": "A sufficiently large neural network can approximate any discontinuous function",
-        "image": ""
-      },
-      {
-        "text": "A sufficiently large neural network can approximate any continuous function",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: What does the source say about training of Multi-Layer Perceptrons (MLPs)?",
-    "answers": [
-      {
-        "text": "It is convex.",
-        "image": ""
-      },
-      {
-        "text": "It is generally easy.",
-        "image": ""
-      },
-      {
-        "text": "It is always optimal.",
-        "image": ""
-      },
-      {
-        "text": "It is highly non-convex, with multiple local minima.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: What does the source state is a good default choice for an activation function?",
-    "answers": [
-      {
-        "text": "Sigmoid",
-        "image": ""
-      },
-      {
-        "text": "tanh",
-        "image": ""
-      },
-      {
-        "text": "ReLU",
-        "image": ""
-      },
-      {
-        "text": "ELU",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: What does the source say about using the size of a neural network as a regularizer?",
-    "answers": [
-      {
-        "text": "It is the best approach to regularize a network",
-        "image": ""
-      },
-      {
-        "text": "It is better to use implicit regularization",
-        "image": ""
-      },
-      {
-        "text": "It is always possible",
-        "image": ""
-      },
-      {
-        "text": "It is not a good idea; stronger regularization methods are preferred",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: What are the key factors contributing to the advancements in deep learning, according to the provided text?",
-    "answers": [
-      {
-        "text": "Only massive parallel compute power",
-        "image": ""
-      },
-      {
-        "text": "Only availability of large datasets",
-        "image": ""
-      },
-      {
-        "text": "Only advances in machine learning over the years",
-        "image": ""
-      },
-      {
-        "text": "Availability of large datasets, massive parallel compute power, and advances in machine learning",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: According to the sources, what is a limitation of hand-crafted features used in traditional machine learning?",
-    "answers": [
-      {
-        "text": "They are very efficient to compute",
-        "image": ""
-      },
-      {
-        "text": "They are very efficient to train",
-        "image": ""
-      },
-      {
-        "text": "They are often task specific",
-        "image": ""
-      },
-      {
-        "text": "They might be too general or too specific",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: What is a key characteristic of features in deep learning, in contrast to traditional approaches?",
-    "answers": [
-      {
-        "text": "They are fixed",
-        "image": ""
-      },
-      {
-        "text": "They are hand-crafted",
-        "image": ""
-      },
-      {
-        "text": "They are trainable (parameterized)",
-        "image": ""
-      },
-      {
-        "text": "They are non-differentiable",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: In deep learning, what does \"end-to-end\" training refer to?",
-    "answers": [
-      {
-        "text": "The separation of feature extraction and classification",
-        "image": ""
-      },
-      {
-        "text": "Hand-crafting of features",
-        "image": ""
-      },
-      {
-        "text": "Training each layer of a network separately",
-        "image": ""
-      },
-      {
-        "text": "The joint training of feature extraction and classification as a single pipeline",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Backpropagation: What does the source emphasize about how complex systems in deep learning are built?",
-    "answers": [
-      {
-        "text": "They use extremely complicated individual blocks",
-        "image": ""
-      },
-      {
-        "text": "They are built by hand-crafting the individual components",
-        "image": ""
-      },
-      {
-        "text": "They require an extraordinary amount of data",
-        "image": ""
-      },
-      {
-        "text": "They are built via composition of simple building blocks",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest":"ConvNets: In the context of Convolutional Neural Networks, a fully connected layer applied to a 32x32x3 image involves stretching the image into a 3072x1 vector. What is the primary drawback of this approach?",
-    "answers": [
-      {
-        "text": "It increases the computational complexity of the network.",
-        "image": ""
-      },
-      {
-        "text": "It destroys the spatial structure of the image.",
-        "image": ""
-      },
-      {
-        "text": "It requires more memory than convolutional layers.",
-        "image": ""
-      },
-      {
-        "text": "It is difficult to implement.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest":"ConvNets: A convolutional layer processes a 32x32x3 image using a 5x5x3 filter. What does the output of a single filter application produce?",
-    "answers": [
-      {
-        "text": "A 32x32x1 activation map.",
-        "image": ""
-      },
-      {
-        "text": "A 28x28x1 activation map.",
-        "image": ""
-      },
-      {
-        "text": "A single number, representing a dot product of a 5x5x3 chunk of the input with the filter, plus bias",
-        "image": ""
-      },
-      {
-        "text": "A 5x5x3 feature map.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest":"ConvNets: In a convolutional layer, if you have multiple filters, say six filters, what is the result of applying those filters to a single input image?",
-    "answers": [
-      {
-        "text": "A single activation map with increased depth.",
-        "image": ""
-      },
-      {
-        "text": "Six activation maps which are then stacked up to get a new image of the same size but with a different depth.",
-        "image": ""
-      },
-      {
-        "text": "Six activation maps that are averaged to form a single map.",
-        "image": ""
-      },
-      {
-        "text": "Six separate images of the same spatial dimension as the input.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest":"ConvNets: What does it mean for a convolutional filter to \"slide\" over the image during the convolution operation?",
-    "answers": [
-      {
-        "text": "The filter moves across the image, changing its weights at each position.",
-        "image": ""
-      },
-      {
-        "text": "The filter is applied to different channels of the input volume sequentially.",
-        "image": ""
-      },
-      {
-        "text": "The filter computes dot products with small overlapping patches of the image at each location, resulting in an activation map",
-        "image": ""
-      },
-      {
-        "text": "The filter moves in a predetermined pattern, similar to a pooling operation.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest":"ConvNets: What is a key difference between a convolutional layer and a fully connected layer in terms of how they process spatial information?",
-    "answers": [
-      {
-        "text": "A convolutional layer destroys spatial information, whereas a fully connected layer preserves it.",
-        "image": ""
-      },
-      {
-        "text": "A fully connected layer performs dot products between an input vector and a row of weights, while a convolutional layer does not",
-        "image": ""
-      },
-      {
-        "text": "A convolutional layer preserves the spatial structure of the input, whereas a fully connected layer stretches the input into a vector.",
-        "image": ""
-      },
-      {
-        "text": "A fully connected layer uses filters, whereas convolutional layers do not.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest":"ConvNets: What is the consequence of having 6 separate 5x5 filters in a convolutional layer that acts on a 32x32x3 input?",
-    "answers": [
-      {
-        "text": "A single 28x28x6 activation map will be obtained.",
-        "image": ""
-      },
-      {
-        "text": "Six 28x28x1 activation maps are obtained which are then stacked to get a 28x28x6 \"new image\".",
-        "image": ""
-      },
-      {
-        "text": "A 28x28x3 activation map is obtained.",
-        "image": ""
-      },
-      {
-        "text": "Six different 32x32x3 images are obtained.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest":"ConvNets: What are the four hyperparameters that a convolutional layer needs?",
-    "answers": [
-      {
-        "text": "Filter size, stride, number of pooling layers, and number of fully connected layers",
-        "image": ""
-      },
-      {
-        "text": "Filter size, stride, padding, and number of pooling layers.",
-        "image": ""
-      },
-      {
-        "text": "Number of filters, the filter size, the stride and the zero padding",
-        "image": ""
-      },
-      {
-        "text": "Input size, filter size, stride, and number of output channels.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest":"ConvNets: What are the two primary functions of a pooling layer in a CNN?",
-    "answers": [
-      {
-        "text": "It adds learnable parameters and introduces spatial variance.",
-        "image": ""
-      },
-      {
-        "text": "It reduces the size of the representation and introduces spatial invariance.",
-        "image": ""
-      },
-      {
-        "text": "It increases the depth of the feature maps and makes the network deeper",
-        "image": ""
-      },
-      {
-        "text": "It adds non-linearity and performs non-linear combinations of features.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest":"ConvNets: How does a max pooling layer with 2x2 filters and a stride of 2 operate?",
-    "answers": [
-      {
-        "text": "It averages the values in each 2x2 region.",
-        "image": ""
-      },
-      {
-        "text": "It takes the maximum value in each 2x2 region.",
-        "image": ""
-      },
-      {
-        "text": "It multiplies the values in each 2x2 region by a scalar",
-        "image": ""
-      },
-      {
-        "text": "It applies a learnable function to each 2x2 region.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest":"ConvNets: What is a trend in recent CNN architectures regarding pooling and fully connected layers?",
-    "answers": [
-      {
-        "text": "A trend towards more pooling layers and larger fully connected layers",
-        "image": ""
-      },
-      {
-        "text": "A trend towards larger filters and wider architectures",
-        "image": ""
-      },
-      {
-        "text": "A trend towards smaller filters and deeper architectures",
-        "image": ""
-      },
-      {
-        "text": "A trend towards getting rid of pooling and fully connected layers (just CONV layers)",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest":"ConvNets: According to the source, what is a typical structure of CNN architectures historically, before recent advancements like ResNet/GoogLeNet challenged it?",
-    "answers": [
-      {
-        "text": "A sequence of convolutional layers followed by a single fully connected layer",
-        "image": ""
-      },
-      {
-        "text": "A sequence of pooling layers followed by a sequence of convolutional layers",
-        "image": ""
-      },
-      {
-        "text": "A repeating pattern of (CONV-RELU)N followed by an optional POOL, repeated M times, followed by (FC-RELU)K, and a final SOFTMAX activation.",
-        "image": ""
-      },
-      {
-        "text": "A sequence of fully connected layers followed by a sequence of pooling layers.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: During the training phase, how does dropout modify the activation of a neuron with a dropout rate of p?",
-    "answers": [
-      {
-        "text": "The neuron's activation is scaled by a factor of p.",
-        "image": ""
-      },
-      {
-        "text": "The neuron's activation is multiplied by 1-p.",
-        "image": ""
-      },
-      {
-        "text": "The neuron's activation is set to zero with a probability of p, otherwise its activation is preserved.",
-        "image": ""
-      },
-      {
-        "text": "The neuron's activation is always set to zero.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: During inference (testing or validation), what adjustment is typically made to neuron activations in a network that uses dropout?",
-    "answers": [
-      {
-        "text": "No adjustments are made; neurons are used as they are.",
-        "image": ""
-      },
-      {
-        "text": "Neuron activations are multiplied by the dropout rate p.",
-        "image": ""
-      },
-      {
-        "text": "Neuron activations are scaled down by multiplying by (1-p).",
-        "image": ""
-      },
-      {
-        "text": "Neuron activations are set to zero.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the purpose of scaling the activations by 1-p during inference when using dropout?",
-    "answers": [
-      {
-        "text": "To increase the magnitude of neuron activations.",
-        "image": ""
-      },
-      {
-        "text": "To compensate for the fact that fewer neurons were active during training.",
-        "image": ""
-      },
-      {
-        "text": "To introduce more randomness during the inference phase.",
-        "image": ""
-      },
-      {
-        "text": "To ensure the network learns different features during inference.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: In the \"inverse dropout\" formulation, when is the scaling applied to the activations, and what is the key benefit?",
-    "answers": [
-      {
-        "text": "Scaling is applied during inference, ensuring faster inference.",
-        "image": ""
-      },
-      {
-        "text": "Scaling is applied before training, resulting in faster convergence.",
-        "image": ""
-      },
-      {
-        "text": "Scaling is applied during training, ensuring the expected value of the activations remain consistent between training and inference.",
-        "image": ""
-      },
-      {
-        "text": "Scaling is applied after the backpropagation, for better generalization.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the \"vanishing gradient\" problem in deep neural networks?",
-    "answers": [
-      {
-        "text": "A situation where gradients become very large, causing instability in training.",
-        "image": ""
-      },
-      {
-        "text": "The tendency of neurons to deactivate randomly during training.",
-        "image": ""
-      },
-      {
-        "text": "A phenomenon where gradients become increasingly small as they propagate backward, making training difficult.",
-        "image": ""
-      },
-      {
-        "text": "A problem that only occurs in shallow networks.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the main purpose of residual connections (skip connections) in ResNets?",
-    "answers": [
-      {
-        "text": "To reduce the number of layers needed in a network.",
-        "image": ""
-      },
-      {
-        "text": "To add more non-linearity to the network.",
-        "image": ""
-      },
-      {
-        "text": "To address the vanishing gradient problem by allowing gradients to flow more easily through the network.",
-        "image": ""
-      },
-      {
-        "text": "To speed up training by reducing the number of computations.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: How does a residual connection work mathematically?",
-    "answers": [
-      {
-        "text": "It replaces the layer's output with the original input.",
-        "image": ""
-      },
-      {
-        "text": "It multiplies the layer's output by the input.",
-        "image": ""
-      },
-      {
-        "text": "It adds the layer's input directly to its output h = F(x) + x.",
-        "image": ""
-      },
-      {
-        "text": "It subtracts the input from the layer's output.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What happens if the parameters in a residual unit are set such that F(x) = 0?",
-    "answers": [
-      {
-        "text": "The residual unit outputs a zero vector.",
-        "image": ""
-      },
-      {
-        "text": "The residual unit's output becomes exponentially large.",
-        "image": ""
-      },
-      {
-        "text": "The residual unit passes the input x through unmodified.",
-        "image": ""
-      },
-      {
-        "text": "The unit passes through a zero vector.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the key advantage of residual connections in backpropagation?",
-    "answers": [
-      {
-        "text": "It simplifies the backpropagation process.",
-        "image": ""
-      },
-      {
-        "text": "It ensures that the gradients vanish more quickly.",
-        "image": ""
-      },
-      {
-        "text": "It means the derivatives don't vanish as ∇ₓ h = ∇ₓ (F(x) + x) = ∂F / ∂x + I.",
-        "image": ""
-      },
-      {
-        "text": "It forces the network to learn different features for every layer.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What was the impact of Residual Networks (ResNets) on image classification performance on ImageNet?",
-    "answers": [
-      {
-        "text": "ResNets reduced the performance of image classification.",
-        "image": ""
-      },
-      {
-        "text": "ResNets achieved similar results to previous state-of-the-art models.",
-        "image": ""
-      },
-      {
-        "text": "ResNets achieved significantly lower error rates than previous models and even human performance, with a 152-layer ResNet achieving 4.49% top-5 error.",
-        "image": ""
-      },
-      {
-        "text": "ResNets could only be trained with a limited number of layers",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What does \"standard scaling\" aim to achieve when preprocessing data?",
-    "answers": [
-      {
-        "text": "It ensures that each feature has a different mean.",
-        "image": ""
-      },
-      {
-        "text": "It ensures each feature (column) has zero mean and unit variance.",
-        "image": ""
-      },
-      {
-        "text": "It scales the data between 0 and 1.",
-        "image": ""
-      },
-      {
-        "text": "It increases the variance of each feature.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: In standard scaling, what do μ and σ² represent?",
-    "answers": [
-      {
-        "text": "μ is the sum and σ² is the variance of each feature/column.",
-        "image": ""
-      },
-      {
-        "text": "μ is the median and σ² is the standard deviation of each feature/column.",
-        "image": ""
-      },
-      {
-        "text": "μ is the mean and σ² is the variance of each feature/column.",
-        "image": ""
-      },
-      {
-        "text": "μ is the mean and σ² is the standard deviation of each feature/column.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the main goal of Batch Normalization (BN) in neural networks?",
-    "answers": [
-      {
-        "text": "To normalize the input data before training.",
-        "image": ""
-      },
-      {
-        "text": "To learn an optimal mean and variance for each unit of the network's layers during training.",
-        "image": ""
-      },
-      {
-        "text": "To reduce the number of parameters in the network.",
-        "image": ""
-      },
-      {
-        "text": "To simplify backpropagation.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: How does Batch Normalization (BN) approximate the mean and variance?",
-    "answers": [
-      {
-        "text": "By calculating statistics over the entire dataset during each training step.",
-        "image": ""
-      },
-      {
-        "text": "By using the data in a mini-batch.",
-        "image": ""
-      },
-      {
-        "text": "By using a pre-defined set of values.",
-        "image": ""
-      },
-      {
-        "text": "By using the moving average of the previous layer.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: During BN training, how is the output H standardized, and what does ε do?",
-    "answers": [
-      {
-        "text": "H is standardized by subtracting its mean, and ε adds a small value to prevent division by zero.",
-        "image": ""
-      },
-      {
-        "text": "H is standardized by dividing by its variance, and ε increases the variance.",
-        "image": ""
-      },
-      {
-        "text": "H is standardized by multiplying by its standard deviation, and ε reduces the mean.",
-        "image": ""
-      },
-      {
-        "text": "H is standardized by adding its mean, and ε adjusts the variance.",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: During BN training, what trainable parameters are introduced to set a new mean and variance for each column j?",
-    "answers": [
-      {
-        "text": "Two scalars, α and β.",
-        "image": ""
-      },
-      {
-        "text": "Two vectors, μ and σ.",
-        "image": ""
-      },
-      {
-        "text": "Two 2d values, αj and βj.",
-        "image": ""
-      },
-      {
-        "text": "Two matrices, W and b.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What are two common solutions used during inference to avoid the output depending on the mini-batch when using BN?",
-    "answers": [
-      {
-        "text": "Training with larger batch sizes and adjusting learning rates.",
-        "image": ""
-      },
-      {
-        "text": "Applying a different set of trainable parameters and using dropout.",
-        "image": ""
-      },
-      {
-        "text": "Post-training statistics calculation and moving average of statistics.",
-        "image": ""
-      },
-      {
-        "text": "Re-training with the entire dataset and applying a different activation function.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: How does Batch Normalization (BN) work with convolutional outputs that have dimensions (b, h, w, c)?",
-    "answers": [
-      {
-        "text": "?",
-        "image": ""
-      },
-      {
-        "text": "It normalizes across all dimensions, including the batch size.",
-        "image": ""
-      },
-      {
-        "text": "The mean and variance are computed per channel, normalizing independently across spatial dimensions and batch.",
-        "image": ""
-      },
-      {
-        "text": "It normalizes only across the batch size.",
-        "image": ""
-      },
-      {
-        "text": "It normalizes across spatial dimensions only.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is a limitation of Batch Normalization when using small batch sizes?",
-    "answers": [
-      {
-        "text": "Batch Normalization becomes more accurate with small batch sizes.",
-        "image": ""
-      },
-      {
-        "text": "The variance in the computed mean and variance estimates can become excessively high, leading to unstable training.",
-        "image": ""
-      },
-      {
-        "text": "It makes the network simpler to train.",
-        "image": ""
-      },
-      {
-        "text": "It decreases the computational overhead.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: How does Layer Normalization differ from Batch Normalization?",
-    "answers": [
-      {
-        "text": "Layer Normalization normalizes across the batch, while Batch Normalization normalizes across features.",
-        "image": ""
-      },
-      {
-        "text": "Layer Normalization normalizes across features, while Batch Normalization normalizes across the mini-batch.",
-        "image": ""
-      },
-      {
-        "text": "Layer Normalization introduces learnable parameters, whereas Batch Normalization does not.",
-        "image": ""
-      },
-      {
-        "text": "Layer Normalization does not require the calculation of mean and variance.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: In what type of networks is Layer Normalization commonly used?",
-    "answers": [
-      {
-        "text": "CNNs for image classification.",
-        "image": ""
-      },
-      {
-        "text": "Forecasting neural networks working with time series and transformers.",
-        "image": ""
-      },
-      {
-        "text": "Generative adversarial networks (GANs).",
-        "image": ""
-      },
-      {
-        "text": "Recurrent neural networks (RNNs) for language modeling.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the primary goal of data augmentation?",
-    "answers": [
-      {
-        "text": "To reduce the size of the training dataset.",
-        "image": ""
-      },
-      {
-        "text": "To decrease the complexity of the training data.",
-        "image": ""
-      },
-      {
-        "text": "To increase the size of the training dataset effectively by applying random transformations.",
-        "image": ""
-      },
-      {
-        "text": "To make the training process faster by using simpler examples.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What are the typical transformations used in data augmentation?",
-    "answers": [
-      {
-        "text": "Only geometric transformations like flipping, cropping, and rotating.",
-        "image": ""
-      },
-      {
-        "text": "Only color and lighting adjustments like brightness, contrast and saturation.",
-        "image": ""
-      },
-      {
-        "text": "A combination of geometric transformations, color and lighting adjustments, noise and distortion, cutout/masking, and combination techniques.",
-        "image": ""
-      },
-      {
-        "text": "Only noise addition",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the main purpose of using 1D convolutions for time-series data?",
-    "answers": [
-      {
-        "text": "To extract global features or patterns that evolve over time.",
-        "image": ""
-      },
-      {
-        "text": "To extract local features or patterns that evolve over time.",
-        "image": ""
-      },
-      {
-        "text": "To make the time-series data stationary.",
-        "image": ""
-      },
-      {
-        "text": "To increase the dimensionality of the input.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: How does a 1D convolution capture local dependencies in time series data?",
-    "answers": [
-      {
-        "text": "By averaging all time steps.",
-        "image": ""
-      },
-      {
-        "text": "By applying a filter of a fixed size that slides over the time series, detecting trends or repeated patterns.",
-        "image": ""
-      },
-      {
-        "text": "By only considering the first and last time steps.",
-        "image": ""
-      },
-      {
-        "text": "By considering the entire sequence at once.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What does it mean for a 1D convolution to have parameter sharing?",
-    "answers": [
-      {
-        "text": "The parameters change over time, allowing to learn specific behaviour for certain time-steps.",
-        "image": ""
-      },
-      {
-        "text": "The same filter is applied across all time steps, reducing the number of parameters and improving generalization.",
-        "image": ""
-      },
-      {
-        "text": "Different filters are applied to different time steps.",
-        "image": ""
-      },
-      {
-        "text": "The parameters are only used for a specific subset of the input.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What does translation invariance mean in the context of 1D convolutions?",
-    "answers": [
-      {
-        "text": "The model is sensitive to shifts in the time domain.",
-        "image": ""
-      },
-      {
-        "text": "The model does not consider the time order of the input.",
-        "image": ""
-      },
-      {
-        "text": "It helps in identifying features that are present at different time steps, making it robust to shifts in the time domain.",
-        "image": ""
-      },
-      {
-        "text": "It means the model can only detect patterns at a fixed time step.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the key characteristic of a causal convolution?",
-    "answers": [
-      {
-        "text": "The output at each time step depends on future time steps.",
-        "image": ""
-      },
-      {
-        "text": "The output at each time step depends only on the current and previous time steps.",
-        "image": ""
-      },
-      {
-        "text": "The output at each time step is independent of other time steps.",
-        "image": ""
-      },
-      {
-        "text": "The output at each time step is influenced by future and past time steps",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: In the context of time series forecasting with a causal model, what is one way to train a model?",
-    "answers": [
-      {
-        "text": "Pool the output representation H over all time steps and apply a regressor head to predict xn.",
-        "image": ""
-      },
-      {
-        "text": "Use a non-causal model to train.",
-        "image": ""
-      },
-      {
-        "text": "Pool only the first few steps in the time series.",
-        "image": ""
-      },
-      {
-        "text": "Disregard the time dependencies between the series.",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the difference between how 1D convolution and self-attention model sequences?",
-    "answers": [
-      {
-        "text": "1D convolution captures global dependencies, while self-attention captures local dependencies.",
-        "image": ""
-      },
-      {
-        "text": "1D convolution has quadratic complexity, while self-attention is more efficient.",
-        "image": ""
-      },
-      {
-        "text": "1D convolution captures local patterns using a sliding filter, while self-attention computes interactions between all elements.",
-        "image": ""
-      },
-      {
-        "text": "1D convolution uses weights for each pair of inputs, while self-attention shares weights.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: In self-attention mechanisms, what are the three transformed vectors derived from each token's embedding?",
-    "answers": [
-      {
-        "text": "Input, output, and hidden vectors.",
-        "image": ""
-      },
-      {
-        "text": "Weight, bias, and activation vectors.",
-        "image": ""
-      },
-      {
-        "text": "Query, Key, and Value vectors.",
-        "image": ""
-      },
-      {
-        "text": "Gradient, loss, and prediction vectors.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: In the analogy with a web search, what corresponds to the \"Query\" vector in self-attention?",
-    "answers": [
-      {
-        "text": "The titles of web pages.",
-        "image": ""
-      },
-      {
-        "text": "The content of web pages.",
-        "image": ""
-      },
-      {
-        "text": "The search term you type—what you're looking for.",
-        "image": ""
-      },
-      {
-        "text": "The search engine algorithm.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the role of \"masking\" in masked self-attention?",
-    "answers": [
-      {
-        "text": "To amplify the attention scores of future tokens.",
-        "image": ""
-      },
-      {
-        "text": "To ensure the model only focuses on past tokens when predicting the next token.",
-        "image": ""
-      },
-      {
-        "text": "To randomize the attention scores to avoid bias.",
-        "image": ""
-      },
-      {
-        "text": "To ignore the past tokens and focus only on future tokens.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the purpose of passing the concatenated outputs of multiple self-attention heads through a Multilayer Perceptron (MLP) layer?",
-    "answers": [
-      {
-        "text": "To reduce the dimensionality of the output.",
-        "image": ""
-      },
-      {
-        "text": "To enhance the model's representational capacity after capturing diverse relationships.",
-        "image": ""
-      },
-      {
-        "text": "To compute attention scores.",
-        "image": ""
-      },
-      {
-        "text": "To apply positional embeddings.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the \"Add\" operation in a Transformer block?",
-    "answers": [
-      {
-        "text": "A fully connected layer",
-        "image": ""
-      },
-      {
-        "text": "A pooling layer",
-        "image": ""
-      },
-      {
-        "text": "A residual connection",
-        "image": ""
-      },
-      {
-        "text": "A layer normalization",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the \"Norm\" operation in a Transformer block?",
-    "answers": [
-      {
-        "text": "A fully connected layer.",
-        "image": ""
-      },
-      {
-        "text": "A residual connection",
-        "image": ""
-      },
-      {
-        "text": "A Batch Normalization",
-        "image": ""
-      },
-      {
-        "text": "A Layer Normalization",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the first step in representing text as input for a transformer?",
-    "answers": [
-      {
-        "text": "Applying a softmax function to the input text.",
-        "image": ""
-      },
-      {
-        "text": "Dividing text into tokens and converting them into numerical vectors called embeddings.",
-        "image": ""
-      },
-      {
-        "text": "Normalizing the text using a standard scaler.",
-        "image": ""
-      },
-      {
-        "text": "Applying data augmentation techniques to the text.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What are the main issues with word encoders?",
-    "answers": [
-      {
-        "text": "They are difficult to train and implement.",
-        "image": ""
-      },
-      {
-        "text": "They require huge computational power to represent words.",
-        "image": ""
-      },
-      {
-        "text": "They need to detect boundaries of words and treat different forms of the same word as separate types.",
-        "image": ""
-      },
-      {
-        "text": "They don't capture the semantic meaning of the words",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What are the characteristics of character encoders?",
-    "answers": [
-      {
-        "text": "They increase the complexity of the model and are easy to use.",
-        "image": ""
-      },
-      {
-        "text": "They reduce the complexity but are almost impossible to use.",
-        "image": ""
-      },
-      {
-        "text": "They make the model more robust and efficient.",
-        "image": ""
-      },
-      {
-        "text": "They are ideal for most NLP tasks.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the byte pair encoding (BPE) algorithm used for?",
-    "answers": [
-      {
-        "text": "To represent each word as a single byte.",
-        "image": ""
-      },
-      {
-        "text": "To represent each character in a text as an integer.",
-        "image": ""
-      },
-      {
-        "text": "To create subword tokens by merging frequent character sequences.",
-        "image": ""
-      },
-      {
-        "text": "To compress text data into smaller files.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the first step in the BPE algorithm?",
-    "answers": [
-      {
-        "text": "Count the frequency of each character pair in the data",
-        "image": ""
-      },
-      {
-        "text": "Merge the characters into one symbol.",
-        "image": ""
-      },
-      {
-        "text": "Form a base vocabulary of all characters that occur in the training data.",
-        "image": ""
-      },
-      {
-        "text": "Tokenize the data.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the purpose of positional encodings in Transformer models?",
-    "answers": [
-      {
-        "text": "To reduce the dimensionality of the input.",
-        "image": ""
-      },
-      {
-        "text": "To inject order into the model by embedding position-specific information.",
-        "image": ""
-      },
-      {
-        "text": "To increase the variance of the input data.",
-        "image": ""
-      },
-      {
-        "text": "To prevent the model from overfitting.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: Why are positional encodings needed in transformers?",
-    "answers": [
-      {
-        "text": "Transformers do not need positional encodings because they can infer the order of the input.",
-        "image": ""
-      },
-      {
-        "text": "Because Transformers process all tokens simultaneously, they need positional encodings to be aware of the sequence information.",
-        "image": ""
-      },
-      {
-        "text": "Positional encodings are only needed for time-series data.",
-        "image": ""
-      },
-      {
-        "text": "Because Transformers can easily capture the order of the sequence.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: How do relative positional embeddings work?",
-    "answers": [
-      {
-        "text": "They add information about the absolute position of the tokens.",
-        "image": ""
-      },
-      {
-        "text": "They only use static positional information.",
-        "image": ""
-      },
-      {
-        "text": "They consider the relative distance between tokens instead of their absolute positions.",
-        "image": ""
-      },
-      {
-        "text": "They only encode the first and last positions of the tokens.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What are the outputs of the transformer block and what are they used for?",
-    "answers": [
-      {
-        "text": "Logits, which represent probabilities of the next token, are used to select the most probable token or sample one.",
-        "image": ""
-      },
-      {
-        "text": "Embeddings, which are used for classification tasks.",
-        "image": ""
-      },
-      {
-        "text": "Attention scores, which are used for image generation.",
-        "image": ""
-      },
-      {
-        "text": "Key and value matrices, used for backpropagation",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "MoreNN: What is the role of the \"temperature\" hyperparameter when generating text from a language model?",
-    "answers": [
-      {
-        "text": "The \"temperature\" is used to adjust the learning rate of the model",
-        "image": ""
-      },
-      {
-        "text": "It is used to adjust the size of the model",
-        "image": ""
-      },
-      {
-        "text": "It is used to control the size of the vocabulary used by the model",
-        "image": ""
-      },
-      {
-        "text": "It controls the randomness of the output by sharpening or softening the probability distribution.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "LR: What is the primary goal of the cost function in linear regression?",
-    "answers": [
-      {
-        "text": "To maximize the difference between predicted and actual output values.",
-        "image": ""
-      },
-      {
-        "text": "To identify the optimal number of features for a model.",
-        "image": ""
-      },
-      {
-        "text": "To minimize the error between the predicted values and the actual target values.",
-        "image": ""
-      },
-      {
-        "text": "To determine the correlation between input and output variables.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "LR: In the context of gradient descent, which statement accurately describes the effect of the learning rate (α)?",
-    "answers": [
-      {
-        "text": "A larger learning rate guarantees faster convergence to the global minimum.",
-        "image": ""
-      },
-      {
-        "text": "A smaller learning rate might cause the gradient descent to diverge from the minimum.",
-        "image": ""
-      },
-      {
-        "text": "If α is too small, gradient descent will be slow, and if α is too large, gradient descent might overshoot the minimum and even diverge.",
-        "image": ""
-      },
-      {
-        "text": "The learning rate should automatically decrease over time as gradient descent approaches a local minimum.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "LR: What is the key distinction between \"batch\" gradient descent and stochastic gradient descent (SGD)?",
-    "answers": [
-      {
-        "text": "Batch gradient descent is an online method, while SGD is an offline method.",
-        "image": ""
-      },
-      {
-        "text": "Batch gradient descent updates parameters after each training example, whereas SGD does it using all training examples.",
-        "image": ""
-      },
-      {
-        "text": "Batch gradient descent calculates the gradient using all training examples in each iteration, while SGD uses only a single training example to update the gradient in each iteration.",
-        "image": ""
-      },
-      {
-        "text": "SGD is slower and requires more iterations than batch gradient descent.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "LR: Which of the following is NOT a method to calculate simple linear regression?",
-    "answers": [
-      {
-        "text": "Gradient Descent",
-        "image": ""
-      },
-      {
-        "text": "Normal equation",
-        "image": ""
-      },
-      {
-        "text": "Principal Component Analysis",
-        "image": ""
-      },
-      {
-        "text": "Software packages, e.g., NumPy polyfit",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "LR: What is the purpose of feature scaling in linear regression, and how is mean normalization typically applied?",
-    "answers": [
-      {
-        "text": "To increase the magnitude of the features and make the gradient descent faster.",
-        "image": ""
-      },
-      {
-        "text": "Feature scaling ensures that the features are on a similar scale and mean normalization replaces the feature value with *x*<sub>*i*</sub> - *μ*<sub>*i*</sub>, to have approximately zero mean.",
-        "image": ""
-      },
-      {
-        "text": "To add random noise to the features in order to prevent overfitting.",
-        "image": ""
-      },
-      {
-        "text": "Feature scaling is not required if we are using the normal equation.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "LR: What does the normal equation provide in the context of linear regression?",
-    "answers": [
-      {
-        "text": "An iterative approach to find the parameters that minimise the cost function.",
-        "image": ""
-      },
-      {
-        "text": "A direct analytical method to compute the parameters (Θ) that minimise the cost function.",
-        "image": ""
-      },
-      {
-        "text": "An alternative to gradient descent that is faster regardless of the number of features.",
-        "image": ""
-      },
-      {
-        "text": "A way to determine the appropriate learning rate for gradient descent.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "LR: When might the normal equation be computationally inefficient and what might be a workaround?",
-    "answers": [
-      {
-        "text": "When the number of training examples is very high, one should use gradient descent instead",
-        "image": ""
-      },
-      {
-        "text": "When the number of features is very high, and as a work-around, delete some features or use regularization",
-        "image": ""
-      },
-      {
-        "text": "The normal equation is always computationally efficient, irrespective of the number of features",
-        "image": ""
-      },
-      {
-        "text": "It is inefficient when there is correlation, therefore it requires an alternative method",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "LR: According to the sources, what does a Pearson correlation coefficient (r) of -1 signify?",
-    "answers": [
-      {
-        "text": "No correlation between the variables",
-        "image": ""
-      },
-      {
-        "text": "A moderate positive correlation between the variables",
-        "image": ""
-      },
-      {
-        "text": "A maximum negative correlation between the variables",
-        "image": ""
-      },
-      {
-        "text": "A maximum positive correlation between the variables",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "LR: What does the coefficient of determination (R²) measure in regression analysis?",
-    "answers": [
-      {
-        "text": "The correlation between variables.",
-        "image": ""
-      },
-      {
-        "text": "The goodness-of-fit of a line or curve to the data points.",
-        "image": ""
-      },
-      {
-        "text": "The slope of the regression line.",
-        "image": ""
-      },
-      {
-        "text": "The complexity of the regression model.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "LR: What is a key characteristic that distinguishes locally-weighted regression from linear regression?",
-    "answers": [
-      {
-        "text": "Locally-weighted regression uses a fixed set of parameters",
-        "image": ""
-      },
-      {
-        "text": "Locally-weighted regression parameters grow with the data making it a non-parametric learning algorithm, whilst linear regression uses a fixed set of parameters.",
-        "image": ""
-      },
-      {
-        "text": "Locally-weighted regression is faster than linear regression.",
-        "image": ""
-      },
-      {
-        "text": "Locally weighted regression uses gradient descent.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "LR: According to the sources, what is the primary assumption underlying the probabilistic interpretation of least squares?",
-    "answers": [
-      {
-        "text": "That the target value y is equal to Θ<sup>T</sup>x plus some random error.",
-        "image": ""
-      },
-      {
-        "text": "That the input features are normally distributed.",
-        "image": ""
-      },
-      {
-        "text": "That the parameters Θ are fixed and known.",
-        "image": ""
-      },
-      {
-        "text": "That the regression line has zero error.",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "LR2: In the context of the normal equation, what does the term 'X' represent?",
-    "answers": [
-      {
-        "text": "The vector of predicted values.",
-        "image": ""
-      },
-      {
-        "text": "The matrix of target variables.",
-        "image": ""
-      },
-      {
-        "text": "The matrix of input features.",
-        "image": ""
-      },
-      {
-        "text": "The vector of errors.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "LR2: What does θ represent in the normal equation?",
-    "answers": [
-      {
-        "text": "The error term",
-        "image": ""
-      },
-      {
-        "text": "The predicted values",
-        "image": ""
-      },
-      {
-        "text": "The parameter vector that we aim to find",
-        "image": ""
-      },
-      {
-        "text": "The input features",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "LR2: Given the cost function J(θ) = 1/2 * ||Xθ − y||², which statement correctly describes how the normal equation is derived?",
-    "answers": [
-      {
-        "text": "The gradient of J(θ) is set to a non-zero constant to minimise the cost function.",
-        "image": ""
-      },
-      {
-        "text": "The cost function is directly minimised by setting its partial derivative to the identity matrix.",
-        "image": ""
-      },
-      {
-        "text": "The gradient of J(θ) is set to zero to find the optimal parameter vector.",
-        "image": ""
-      },
-      {
-        "text": "The cost function is minimised by setting the second derivative to zero.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "LR2: The normal equation solution θ = (XᵀX)�¹Xᵀy can be computed when:",
-    "answers": [
-      {
-        "text": "Xáµ€X is a singular matrix.",
-        "image": ""
-      },
-      {
-        "text": "X has more rows than columns",
-        "image": ""
-      },
-      {
-        "text": "Xáµ€X is invertible",
-        "image": ""
-      },
-      {
-        "text": "X has linearly dependent columns",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "LR2: What is the rank condition for Xáµ€X to be invertible?",
-    "answers": [
-      {
-        "text": "X must have a rank equal to the number of rows.",
-        "image": ""
-      },
-      {
-        "text": "X must be a symmetric matrix.",
-        "image": ""
-      },
-      {
-        "text": "X must have linearly independent columns which equals the number of features (m).",
-        "image": ""
-      },
-      {
-        "text": "X must be a square matrix with a determinant of 1.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "LR2: In the equation for θ, θ = (XᵀX)�¹Xᵀy what does the (XᵀX)�¹ term represent?",
-    "answers": [
-      {
-        "text": "The pseudo-inverse of the feature matrix",
-        "image": ""
-      },
-      {
-        "text": "The transpose of the feature matrix",
-        "image": ""
-      },
-      {
-        "text": "The inverse of the matrix product X transpose times X.",
-        "image": ""
-      },
-      {
-        "text": "The dot product of X with itself.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "LR2: If Xáµ€X is not invertible, what can be inferred about the feature matrix X?",
-    "answers": [
-      {
-        "text": "The feature matrix is not real",
-        "image": ""
-      },
-      {
-        "text": "The feature matrix contains all zeros",
-        "image": ""
-      },
-      {
-        "text": "The feature matrix has linearly dependent columns",
-        "image": ""
-      },
-      {
-        "text": "The feature matrix contains no features",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "LR2: What is a key difference between linear regression and locally weighted regression (LWR)?",
-    "answers": [
-      {
-        "text": "Linear regression uses a kernel function, while LWR does not.",
-        "image": ""
-      },
-      {
-        "text": "LWR assigns weights to data points based on their proximity to the query point, while linear regression does not.",
-        "image": ""
-      },
-      {
-        "text": "Linear regression uses a constant for error calculation while LWR does not.",
-        "image": ""
-      },
-      {
-        "text": "LWR computes global parameters, while linear regression computes local parameters.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "LR2: In Locally Weighted Regression (LWR), what is the purpose of the kernel function?",
-    "answers": [
-      {
-        "text": "To perform a linear transformation of the input data.",
-        "image": ""
-      },
-      {
-        "text": "To reduce the dimensionality of the feature matrix.",
-        "image": ""
-      },
-      {
-        "text": "To give higher weights to points closer to the query point and lower weights to points farther away.",
-        "image": ""
-      },
-      {
-        "text": "To transform all data into a standard normal distribution.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "LR2: What does the term 'Ï„' (tau) represent in the context of the weighting function for Locally Weighted Regression (LWR)?",
-    "answers": [
-      {
-        "text": "The weighting parameter",
-        "image": ""
-      },
-      {
-        "text": "The inverse of the feature matrix",
-        "image": ""
-      },
-      {
-        "text": "The variance of the data",
-        "image": ""
-      },
-      {
-        "text": "The bandwidth parameter, controlling the width of the kernel.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "LR2: What is an advantage of locally weighted regression compared to standard linear regression?",
-    "answers": [
-      {
-        "text": "LWR is always faster to compute than linear regression.",
-        "image": ""
-      },
-      {
-        "text": "LWR is not affected by outliers.",
-        "image": ""
-      },
-      {
-        "text": "LWR can model non-linear relationships between the input features and the target variable.",
-        "image": ""
-      },
-      {
-        "text": "LWR always has a unique solution and no risk of overfitting.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "LR2: Based on the diagram in the source, which of the following can be described as 'overfitting'?",
-    "answers": [
-      {
-        "text": "The model underfits the data.",
-        "image": ""
-      },
-      {
-        "text": "The model perfectly fits all data points including the noise in the data.",
-        "image": ""
-      },
-      {
-        "text": "The model has very high flexibility and thus it captures the random variations and noise in the training data, not generalising well to unseen data.",
-        "image": ""
-      },
-      {
-        "text": "The model gives a completely inaccurate fit to the data.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass: Given a binary classification scenario, which of the following is the correct interpretation of the notation 'y = 1'?",
-    "answers": [
-      {
-        "text": "It represents a negative outcome",
-        "image": ""
-      },
-      {
-        "text": "It represents an positive outcome",
-        "image": ""
-      },
-      {
-        "text": "It indicates an unknown outcome",
-        "image": ""
-      },
-      {
-        "text": "It symbolizes the probability of an event",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass: In the context of logistic regression, what does the notation 'p(y|x; θ)' represent?",
-    "answers": [
-      {
-        "text": "The probability of observing feature 'x' given the parameters 'θ'.",
-        "image": ""
-      },
-      {
-        "text": "The probability of the parameters 'θ' given the label 'y' and the feature 'x'.",
-        "image": ""
-      },
-      {
-        "text": "The probability of label 'y' given feature 'x' and the parameters 'θ' .",
-        "image": ""
-      },
-      {
-        "text": "The likelihood of feature 'x' being present",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass: Based on the notes, what is the primary purpose of the sigmoid function, denoted as 'g(z)'?",
-    "answers": [
-      {
-        "text": "To directly predict the class label.",
-        "image": ""
-      },
-      {
-        "text": "To map the output of a linear combination of features to a probability between 0 and 1.",
-        "image": ""
-      },
-      {
-        "text": "To calculate the error in the classification.",
-        "image": ""
-      },
-      {
-        "text": "To optimize the parameters 'θ'",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass: According to the notes, how is the decision boundary determined in the context of binary classification using a linear model?",
-    "answers": [
-      {
-        "text": "By maximizing the probability p(y|x;θ)",
-        "image": ""
-      },
-      {
-        "text": "By setting the sigmoid function g(z) to 0",
-        "image": ""
-      },
-      {
-        "text": "By finding the line where g(z) = 0.5 which occurs when θ₀ + θ�x� + θ₂x₂ = 0",
-        "image": ""
-      },
-      {
-        "text": "By minimizing the cost function J(θ)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass: In the provided material, what does the notation 'J(θ)' represent?",
-    "answers": [
-      {
-        "text": "The probability of observing the features given the parameters",
-        "image": ""
-      },
-      {
-        "text": "The cost function used to evaluate the model's performance",
-        "image": ""
-      },
-      {
-        "text": "The gradient of the model's parameters",
-        "image": ""
-      },
-      {
-        "text": "The model's prediction",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass: What is the primary goal of the optimization process with respect to J(θ)?",
-    "answers": [
-      {
-        "text": "To maximize J(θ)",
-        "image": ""
-      },
-      {
-        "text": "To calculate the Hessian matrix",
-        "image": ""
-      },
-      {
-        "text": "To minimize J(θ)",
-        "image": ""
-      },
-      {
-        "text": "To find the gradient of J(θ)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass: According to the notes, what is the significance of the term 'yi' in the cost function J(θ)?",
-    "answers": [
-      {
-        "text": "It represents the predicted label",
-        "image": ""
-      },
-      {
-        "text": "It is a feature value",
-        "image": ""
-      },
-      {
-        "text": "It is the learning rate",
-        "image": ""
-      },
-      {
-        "text": "It represents the true label",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass: Which of the following best describes the update rule for θ using gradient descent, as per the source?",
-    "answers": [
-      {
-        "text": "θ = θ + α∇J(θ)",
-        "image": ""
-      },
-      {
-        "text": "θ = θ - α∇J(θ)",
-        "image": ""
-      },
-      {
-        "text": "θ = α∇J(θ)",
-        "image": ""
-      },
-      {
-        "text": "θ = ∇J(θ)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass: In the context of the provided notes, what is the purpose of the expression (sigmoid(z(i)) − y(i)) in the gradient calculation?",
-    "answers": [
-      {
-        "text": "To calculate the total number of training examples",
-        "image": ""
-      },
-      {
-        "text": "To represent the regularization term",
-        "image": ""
-      },
-      {
-        "text": "To measure the difference between the predicted probability and the true label",
-        "image": ""
-      },
-      {
-        "text": "To compute the Hessian matrix",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass: According to the provided material, what does the Newton-Raphson method aim to accomplish?",
-    "answers": [
-      {
-        "text": "It uses the gradient to reach an optimum.",
-        "image": ""
-      },
-      {
-        "text": "It finds the minimum by directly inverting the Hessian",
-        "image": ""
-      },
-      {
-        "text": "It is used to find the roots of a function by updating the parameters using the Hessian matrix",
-        "image": ""
-      },
-      {
-        "text": "It is used to calculate the gradient of the cost function",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass: What does the term H in the update equation θ := θ − H�¹∇J(θ) refer to (Newton method)?",
-    "answers": [
-      {
-        "text": "?",
-        "image": ""
-      },
-      {
-        "text": "The gradient of the cost function",
-        "image": ""
-      },
-      {
-        "text": "The learning rate",
-        "image": ""
-      },
-      {
-        "text": "The Hessian matrix of the cost function",
-        "image": ""
-      },
-      {
-        "text": "The sigmoid function",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass: What is a key drawback mentioned in the notes regarding the Newton-Raphson method?",
-    "answers": [
-      {
-        "text": "It converges very slowly.",
-        "image": ""
-      },
-      {
-        "text": "It always finds the global minimum.",
-        "image": ""
-      },
-      {
-        "text": "It requires the calculation of the inverse of the Hessian matrix which is expensive to compute",
-        "image": ""
-      },
-      {
-        "text": "It only works for linear models.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass: According to the material, what algorithm is suggested as a practical alternative to Newton's method?",
-    "answers": [
-      {
-        "text": "Stochastic gradient descent.",
-        "image": ""
-      },
-      {
-        "text": "BFGS (Broyden-Fletcher-Goldfarb-Shanno algorithm)",
-        "image": ""
-      },
-      {
-        "text": "Linear Regression.",
-        "image": ""
-      },
-      {
-        "text": "The conjugate gradient method.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass: What is the relationship between the gradient and the direction of steepest ascent of a function J(θ)?",
-    "answers": [
-      {
-        "text": "The gradient points in the direction of the steepest decrease",
-        "image": ""
-      },
-      {
-        "text": "The gradient points in the direction of the steepest increase",
-        "image": ""
-      },
-      {
-        "text": "The gradient is orthogonal to the direction of the steepest ascent",
-        "image": ""
-      },
-      {
-        "text": "The gradient provides no information about the direction of the steepest ascent",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass2: According to the source, what does \"Log Loss\" or \"Negative Log Likelihood\" (NLL) measure in the context of classification?",
-    "answers": [
-      {
-        "text": "The accuracy of the model’s predictions",
-        "image": ""
-      },
-      {
-        "text": "The sum of squared errors.",
-        "image": ""
-      },
-      {
-        "text": "The difference between predicted probabilities and true labels",
-        "image": ""
-      },
-      {
-        "text": "The margin of separation between classes.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass2: What does the term 'logits' refer to in the document?",
-    "answers": [
-      {
-        "text": "The output of the Softmax function.",
-        "image": ""
-      },
-      {
-        "text": "The predicted probabilities of each class",
-        "image": ""
-      },
-      {
-        "text": "The raw, unnormalized scores that are input to the Softmax",
-        "image": ""
-      },
-      {
-        "text": "The loss calculated during backpropagation.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass2: According to the source, what is the effect of the exponential function within Softmax?",
-    "answers": [
-      {
-        "text": "To normalize values between 0 and 1.",
-        "image": ""
-      },
-      {
-        "text": "To produce a weighted average of the inputs.",
-        "image": ""
-      },
-      {
-        "text": "To ensure that all scores are positive",
-        "image": ""
-      },
-      {
-        "text": "To amplify the differences between the raw scores",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass2: In the context of the provided document, what is the significance of the term \"cross-entropy\"?",
-    "answers": [
-      {
-        "text": "It measures the complexity of the model.",
-        "image": ""
-      },
-      {
-        "text": "It measures the average number of bits needed to encode data.",
-        "image": ""
-      },
-      {
-        "text": "It measures the difference between probability distributions",
-        "image": ""
-      },
-      {
-        "text": "It quantifies the uncertainty of predictions for multiple classes",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass2: What does the diagram with the red, green and blue points with decision boundaries represent?",
-    "answers": [
-      {
-        "text": "The training of a linear regression model.",
-        "image": ""
-      },
-      {
-        "text": "The concept of bias, variance and underfitting and overfitting.",
-        "image": ""
-      },
-      {
-        "text": "The function of gradient descent",
-        "image": ""
-      },
-      {
-        "text": "A graphical representation of the Softmax function",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass2: Based on the document, what is the effect of \"overfitting\" on the model?",
-    "answers": [
-      {
-        "text": "The model generalizes well to new, unseen data.",
-        "image": ""
-      },
-      {
-        "text": "The model memorizes the training data instead of learning the underlying patterns",
-        "image": ""
-      },
-      {
-        "text": "The model is too simple to capture the complexity of the data.",
-        "image": ""
-      },
-      {
-        "text": "The model has a high bias.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass2: What does 'bias' in the context of model training refer to in the document?",
-    "answers": [
-      {
-        "text": "The model's tendency to consistently make incorrect assumptions",
-        "image": ""
-      },
-      {
-        "text": "The variability in the model’s predictions.",
-        "image": ""
-      },
-      {
-        "text": "The amount of training data used.",
-        "image": ""
-      },
-      {
-        "text": "The complexity of the model architecture.",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass2: According to the document, what does 'variance' in the context of model training refer to?",
-    "answers": [
-      {
-        "text": "The model's ability to make consistent predictions.",
-        "image": ""
-      },
-      {
-        "text": "The model’s sensitivity to changes in the training data.",
-        "image": ""
-      },
-      {
-        "text": "The model's tendency to make consistent errors.",
-        "image": ""
-      },
-      {
-        "text": "The bias in the model's assumptions.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass2: What does the source suggest about the relationship between model complexity and bias and variance?",
-    "answers": [
-      {
-        "text": "Increasing model complexity always reduces bias and variance.",
-        "image": ""
-      },
-      {
-        "text": "Increasing model complexity may reduce bias but increase variance.",
-        "image": ""
-      },
-      {
-        "text": "Decreasing model complexity always reduces both bias and variance.",
-        "image": ""
-      },
-      {
-        "text": "Bias and variance are not affected by the model complexity.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass2: What do the dashed lines in the model representation diagrams indicate?",
-    "answers": [
-      {
-        "text": "Hyperplanes that separate the classes.",
-        "image": ""
-      },
-      {
-        "text": "The decision boundaries of an overfitted model.",
-        "image": ""
-      },
-      {
-        "text": "The margin of separation between classes.",
-        "image": ""
-      },
-      {
-        "text": "Areas of underfitting.",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "BinaryClass2: What does the concept of 'Expected Error' (E(Err)) in the context of the document represent?",
-    "answers": [
-      {
-        "text": "The bias of the model.",
-        "image": ""
-      },
-      {
-        "text": "The variance of the model.",
-        "image": ""
-      },
-      {
-        "text": "The sum of Bias and Variance.",
-        "image": ""
-      },
-      {
-        "text": "The irreducible error of the data.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "BiasVariance: In the context of bias-variance tradeoff, what does \"high variance\" typically indicate about a machine learning model?",
-    "answers": [
-      {
-        "text": "The model is too simple and underfits the training data.",
-        "image": ""
-      },
-      {
-        "text": "The model is too complex and overfits the training data.",
-        "image": ""
-      },
-      {
-        "text": "The model has a strong bias towards a specific class.",
-        "image": ""
-      },
-      {
-        "text": "The model has low statistical efficiency.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "BiasVariance: What is the primary purpose of regularization in machine learning?",
-    "answers": [
-      {
-        "text": "To increase the model's complexity and reduce bias.",
-        "image": ""
-      },
-      {
-        "text": "To reduce the model's complexity and prevent overfitting.",
-        "image": ""
-      },
-      {
-        "text": "To improve the model's performance on the training data.",
-        "image": ""
-      },
-      {
-        "text": "To increase the model's variance.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "BiasVariance: Why is choosing hyperparameters based solely on the training data considered a bad practice?",
-    "answers": [
-      {
-        "text": "It can lead to a decrease in the model's variance.",
-        "image": ""
-      },
-      {
-        "text": "It does not provide information about how the algorithm will perform on new, unseen data.",
-        "image": ""
-      },
-      {
-        "text": "It will always choose the least complex model.",
-        "image": ""
-      },
-      {
-        "text": "It would always lead to choosing the most complex model.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "BiasVariance: What is the primary advantage of using k-fold cross-validation, compared to a single hold-out validation set?",
-    "answers": [
-      {
-        "text": "It is less computationally expensive.",
-        "image": ""
-      },
-      {
-        "text": "It is better suited for large datasets.",
-        "image": ""
-      },
-      {
-        "text": "It makes better use of small datasets.",
-        "image": ""
-      },
-      {
-        "text": "It always chooses the most complex model.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "BiasVariance: According to the sources, which of the following represents the correct sequence of steps when using a hold-out cross validation method?",
-    "answers": [
-      {
-        "text": "Train each model on Sdev, choose the model with lowest error on Strain, optionally evaluate on Stest.",
-        "image": ""
-      },
-      {
-        "text": "Split S into Strain, Sdev and Stest, train each model on Strain, choose model with lowest error on Sdev, optionally evaluate on Stest.",
-        "image": ""
-      },
-      {
-        "text": "Choose hyperparameters that work best on the test data.",
-        "image": ""
-      },
-      {
-        "text": "Split the data into train and test, and choose hyperparameters on the test data.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "BiasVariance: What does the term \"empirical risk\" refer to in the context of machine learning?",
-    "answers": [
-      {
-        "text": "The risk associated with the variance of a model.",
-        "image": ""
-      },
-      {
-        "text": "The risk associated with the bias of a model.",
-        "image": ""
-      },
-      {
-        "text": "The error of the model on the training data.",
-        "image": ""
-      },
-      {
-        "text": "The generalization performance of the model on new, unseen data.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "BiasVariance: According to the sources, what is the relationship between model complexity and error?",
-    "answers": [
-      {
-        "text": "As model complexity increases, error always decreases.",
-        "image": ""
-      },
-      {
-        "text": "As model complexity decreases, error always decreases.",
-        "image": ""
-      },
-      {
-        "text": "There is an optimal level of model complexity that results in the lowest error, typically, increasing complexity will initially decrease error and then will increase it.",
-        "image": ""
-      },
-      {
-        "text": "Model complexity does not affect error.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "PCA: What is a manifold in the context of PCA?",
-    "answers": [
-      {
-        "text": "A high-dimensional space where data points are randomly scattered.",
-        "image": ""
-      },
-      {
-        "text": "A topological space that locally resembles Euclidean space, where data may reside.",
-        "image": ""
-      },
-      {
-        "text": "A set of basis vectors used for representing data points.",
-        "image": ""
-      },
-      {
-        "text": "A non-linear transformation applied to the data.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "PCA: What is a 'chart' in the context of manifolds?",
-    "answers": [
-      {
-        "text": "A visual representation of data in a scatter plot.",
-        "image": ""
-      },
-      {
-        "text": "A function that provides a one-to-one correspondence between open regions of a surface and subsets of Euclidean space.",
-        "image": ""
-      },
-      {
-        "text": "A method for reducing the dimensionality of the data.",
-        "image": ""
-      },
-      {
-        "text": "A way to visualize the principal components of a dataset.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "PCA: What is the key property of a chart mapping (Ï•)?",
-    "answers": [
-      {
-        "text": "It must be non-invertible.",
-        "image": ""
-      },
-      {
-        "text": "It must be discontinuous.",
-        "image": ""
-      },
-      {
-        "text": "It must be smooth and invertible (a diffeomorphism).",
-        "image": ""
-      },
-      {
-        "text": "It can be any arbitrary mapping.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "PCA: In the context of unsupervised learning, what is the primary goal?",
-    "answers": [
-      {
-        "text": "To predict labels for input data.",
-        "image": ""
-      },
-      {
-        "text": "To uncover meaningful structures or representations within the data.",
-        "image": ""
-      },
-      {
-        "text": "To train a model with labeled outputs.",
-        "image": ""
-      },
-      {
-        "text": "To use a supervised learning approach.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "PCA: What is a basis in the context of vector spaces?",
-    "answers": [
-      {
-        "text": "A set of random vectors used for representing points.",
-        "image": ""
-      },
-      {
-        "text": "A set of linearly independent vectors that can be used to reconstruct any point in the space.",
-        "image": ""
-      },
-      {
-        "text": "A single vector that captures the variance of the data.",
-        "image": ""
-      },
-      {
-        "text": "A set of vectors that overlap and point in similar directions.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "PCA: What is the implication of mean-centering a dataset before applying PCA?",
-    "answers": [
-      {
-        "text": "It increases the variance of the data.",
-        "image": ""
-      },
-      {
-        "text": "It shifts the data away from the origin.",
-        "image": ""
-      },
-      {
-        "text": "It ensures the data is centered at the origin, simplifying calculations.",
-        "image": ""
-      },
-      {
-        "text": "It makes the data more noisy.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "PCA: Why must basis vectors be linearly independent?",
-    "answers": [
-      {
-        "text": "To make computations easier.",
-        "image": ""
-      },
-      {
-        "text": "To ensure the basis vectors point in similar directions.",
-        "image": ""
-      },
-      {
-        "text": "To ensure they span the entire space and can reconstruct any point in the space without overlap or redundancy.",
-        "image": ""
-      },
-      {
-        "text": "Linear dependence is required in PCA.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "PCA: In a standard basis, what is unique about the weights when representing a data point?",
-    "answers": [
-      {
-        "text": "The weights must be numerically solved for.",
-        "image": ""
-      },
-      {
-        "text": "The weights are zero for every dimension.",
-        "image": ""
-      },
-      {
-        "text": "The weights are simply the values of the data point itself.",
-        "image": ""
-      },
-      {
-        "text": "They require complex mathematical functions for computation.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "PCA: What is the significance of an orthonormal basis in the context of PCA?",
-    "answers": [
-      {
-        "text": "It requires more complex calculations to find the weights.",
-        "image": ""
-      },
-      {
-        "text": "It makes the representation of a point more complicated.",
-        "image": ""
-      },
-      {
-        "text": "It simplifies the calculation of the weight vector; it can be expressed directly in terms of the spanning set and the data itself.",
-        "image": ""
-      },
-      {
-        "text": "It provides a non-unique basis for representing the data.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "PCA: What is the role of the operation CC^T (where C is the basis matrix) in the context of an orthonormal basis?",
-    "answers": [
-      {
-        "text": "It is a non-linear transformation.",
-        "image": ""
-      },
-      {
-        "text": "It scales the data.",
-        "image": ""
-      },
-      {
-        "text": "It acts as a projection matrix, ensuring data is represented by the orthonormal basis.",
-        "image": ""
-      },
-      {
-        "text": "It adds noise to the data.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "PCA: What happens when the number of spanning vectors (K) is less than the dimensionality of the data space (D)?",
-    "answers": [
-      {
-        "text": "All points can still be perfectly represented.",
-        "image": ""
-      },
-      {
-        "text": "The data becomes more accurate.",
-        "image": ""
-      },
-      {
-        "text": "Points can only be approximated but not perfectly represented in the subspace.",
-        "image": ""
-      },
-      {
-        "text": "The spanning vectors become linearly dependent.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "PCA: What does the projection of a data point onto a subspace represent?",
-    "answers": [
-      {
-        "text": "A random transformation of the original data point.",
-        "image": ""
-      },
-      {
-        "text": "The 'dropping' of the data point perpendicularly onto the subspace defined by the basis vectors.",
-        "image": ""
-      },
-      {
-        "text": "A transformation that moves the data point away from the subspace.",
-        "image": ""
-      },
-      {
-        "text": "An increase in the dimensionality of the data.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "PCA: What is learned in Principal Component Analysis (PCA) besides weights?",
-    "answers": [
-      {
-        "text": "Only the weights are learned.",
-        "image": ""
-      },
-      {
-        "text": "An appropriate basis (principal components) is also learned alongside the weights.",
-        "image": ""
-      },
-      {
-        "text": "Non-linear transformations of the input data.",
-        "image": ""
-      },
-      {
-        "text": "The eigenvalues of the data matrix are minimized.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "PCA: What is the relationship between the PCA least squares cost function and the autoencoder?",
-    "answers": [
-      {
-        "text": "They are unrelated mathematical concepts.",
-        "image": ""
-      },
-      {
-        "text": "The simplified PCA least squares cost function under orthogonality constraint is known as the autoencoder.",
-        "image": ""
-      },
-      {
-        "text": "The autoencoder is only used for supervised learning.",
-        "image": ""
-      },
-      {
-        "text": "The cost function is always minimized by using non-orthogonal matrices.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "PCA: What are principal components?",
-    "answers": [
-      {
-        "text": "Randomly chosen vectors that span the data space.",
-        "image": ""
-      },
-      {
-        "text": "The elements of the orthonormal basis that point in the directions of the greatest variance in the dataset.",
-        "image": ""
-      },
-      {
-        "text": "The weight vectors used to represent each point.",
-        "image": ""
-      },
-      {
-        "text": "The eigenvalues of the data covariance matrix.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "PCA: How are principal components computed?",
-    "answers": [
-      {
-        "text": "By random selection from the dataset.",
-        "image": ""
-      },
-      {
-        "text": "As the eigenvectors of the data's correlation matrix (or covariance matrix).",
-        "image": ""
-      },
-      {
-        "text": "By a complex non-linear optimization process.",
-        "image": ""
-      },
-      {
-        "text": "Using only the standard basis.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: In the context of decision trees, what is the primary purpose of a 'splitting variable'?",
-    "answers": [
-      {
-        "text": "To randomly select a subset of the data to be classified.",
-        "image": ""
-      },
-      {
-        "text": "To reduce the number of neighbours in the k-NN algorithm",
-        "image": ""
-      },
-      {
-        "text": "To divide the feature space into mutually exclusive regions",
-        "image": ""
-      },
-      {
-        "text": "To assign weights to all the samples according to their importance.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: What is the role of the 'misclassification rate' in the context of building a decision tree?",
-    "answers": [
-      {
-        "text": "It helps to reduce the data dimensionality.",
-        "image": ""
-      },
-      {
-        "text": "It determines the appropriate number of nearest neighbors in a k-NN.",
-        "image": ""
-      },
-      {
-        "text": "It quantifies the performance of a given split",
-        "image": ""
-      },
-      {
-        "text": "It evaluates the overall performance of the trained model",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: What is the primary distinction between 'Tree-based methods' and 'Linear regression models' as described in the text?",
-    "answers": [
-      {
-        "text": "Tree-based methods use Euclidean distance, while linear regression does not.",
-        "image": ""
-      },
-      {
-        "text": "Linear regression models are more robust to outliers.",
-        "image": ""
-      },
-      {
-        "text": "Tree-based methods partition the input space into rectangles whilst linear regression creates a single partitioning",
-        "image": ""
-      },
-      {
-        "text": "Linear regression models are more computationally efficient.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: In the context of bagging, what is 'bootstrapping'?",
-    "answers": [
-      {
-        "text": "The random division of the data into training and test sets.",
-        "image": ""
-      },
-      {
-        "text": "The technique used to visualize the decision boundaries in tree-based methods",
-        "image": ""
-      },
-      {
-        "text": "A method of randomly sampling with replacement from the original dataset",
-        "image": ""
-      },
-      {
-        "text": "A process of feature selection that reduces the complexity of the model.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: How does 'random forest' method build individual trees that are less correlated?",
-    "answers": [
-      {
-        "text": "By using only a subset of the input samples for the training.",
-        "image": ""
-      },
-      {
-        "text": "By pruning the trees to reduce their complexity.",
-        "image": ""
-      },
-      {
-        "text": "By randomly choosing a subset of the features at each split",
-        "image": ""
-      },
-      {
-        "text": "By weighting the importance of the features.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: What is the fundamental idea behind 'Boosting' as described in the text?",
-    "answers": [
-      {
-        "text": "To average the predictions of multiple decision trees.",
-        "image": ""
-      },
-      {
-        "text": "To make every tree independent from other trees.",
-        "image": ""
-      },
-      {
-        "text": "To build an ensemble of models, where each model corrects the errors of its predecessor",
-        "image": ""
-      },
-      {
-        "text": "To select the best performing features among all available ones.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: In the context of boosting, how are the weights of the training samples adjusted after each boosting step?",
-    "answers": [
-      {
-        "text": "Weights are assigned to the samples based on their Euclidean distance from the decision boundary.",
-        "image": ""
-      },
-      {
-        "text": "The weights are randomly re-distributed to ensure variety in the training data.",
-        "image": ""
-      },
-      {
-        "text": "Weights are adjusted to increase the importance of misclassified samples",
-        "image": ""
-      },
-      {
-        "text": "The weights of the training samples remain unchanged throughout the boosting process.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: What is the objective function that is being optimized when fitting a single tree in a boosting model?",
-    "answers": [
-      {
-        "text": "The misclassification rate.",
-        "image": ""
-      },
-      {
-        "text": "A sum of weights of the misclassified examples",
-        "image": ""
-      },
-      {
-        "text": "The entropy",
-        "image": ""
-      },
-      {
-        "text": "The variance of the labels.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: In the context of gradient boosting, what does the ‘gradient’ refer to?",
-    "answers": [
-      {
-        "text": "The direction of maximum increase of the loss function",
-        "image": ""
-      },
-      {
-        "text": "The set of all training samples.",
-        "image": ""
-      },
-      {
-        "text": "The change in the feature space",
-        "image": ""
-      },
-      {
-        "text": "The number of nodes in the decision tree.",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: Which of the following best describes the core idea behind the k-Nearest Neighbour (k-NN) algorithm as presented in the source?",
-    "answers": [
-      {
-        "text": "It partitions the feature space into rectangles.",
-        "image": ""
-      },
-      {
-        "text": "It determines class membership by identifying the k-nearest data points to a given instance.",
-        "image": ""
-      },
-      {
-        "text": "It applies boosting techniques to improve accuracy.",
-        "image": ""
-      },
-      {
-        "text": "It uses a linear combination of basis functions.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: In the context of tree-based methods, which of the following is NOT a typical criterion for splitting nodes?",
-    "answers": [
-      {
-        "text": "Maximizing information gain.",
-        "image": ""
-      },
-      {
-        "text": "Minimising impurity.",
-        "image": ""
-      },
-      {
-        "text": "Maximising the number of features.",
-        "image": ""
-      },
-      {
-        "text": "Minimizing the weighted average impurity of child nodes",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: What is the purpose of the 'margin' in the context of support vector machines as described in the source?",
-    "answers": [
-      {
-        "text": "To ensure each data point is correctly classified.",
-        "image": ""
-      },
-      {
-        "text": "To find a decision boundary that minimizes the number of misclassifications.",
-        "image": ""
-      },
-      {
-        "text": "To minimize the computational complexity.",
-        "image": ""
-      },
-      {
-        "text": "To maximize the separation between classes.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: The source mentions that the decision rule for k-NN is updated based on which aspect of the k neighbours?",
-    "answers": [
-      {
-        "text": "Their distances to the decision boundary.",
-        "image": ""
-      },
-      {
-        "text": "Their feature values.",
-        "image": ""
-      },
-      {
-        "text": "Their class labels.",
-        "image": ""
-      },
-      {
-        "text": "Their position in feature space.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: What is the primary focus of 'Boosting' algorithms, according to the source?",
-    "answers": [
-      {
-        "text": "To independently fit many decision trees.",
-        "image": ""
-      },
-      {
-        "text": "To iteratively fit weak learners while focusing on misclassified instances from previous iterations.",
-        "image": ""
-      },
-      {
-        "text": "To linearly separate data into different classes.",
-        "image": ""
-      },
-      {
-        "text": "To find the optimal decision boundary using a kernel trick.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: In the context of boosting, what is meant by “reweighting� training data?",
-    "answers": [
-      {
-        "text": "It’s where the values of features are adjusted.",
-        "image": ""
-      },
-      {
-        "text": "It is the process of re-assigning training samples to different classes.",
-        "image": ""
-      },
-      {
-        "text": "It’s adjusting the weights of the linear function.",
-        "image": ""
-      },
-      {
-        "text": "It means increasing the weight of instances that are harder to classify.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: According to the source, what is a 'weak learner' in the context of boosting algorithms?",
-    "answers": [
-      {
-        "text": "A model that achieves very low training error.",
-        "image": ""
-      },
-      {
-        "text": "A model that performs slightly better than random guessing.",
-        "image": ""
-      },
-      {
-        "text": "A complex model with high capacity.",
-        "image": ""
-      },
-      {
-        "text": "A model that is prone to overfitting.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: What is the main objective of the 'objective function' mentioned in the section about boosting?",
-    "answers": [
-      {
-        "text": "To minimise the number of training samples.",
-        "image": ""
-      },
-      {
-        "text": "To maximize the margin between classes.",
-        "image": ""
-      },
-      {
-        "text": "To minimise the empirical risk (loss) based on the training data.",
-        "image": ""
-      },
-      {
-        "text": "To maximize the number of iterations.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Clustering: According to the source, what is the rationale behind 'regularization' when building the objective function in boosting?",
-    "answers": [
-      {
-        "text": "To speed up training time.",
-        "image": ""
-      },
-      {
-        "text": "To simplify the data.",
-        "image": ""
-      },
-      {
-        "text": "To avoid overfitting.",
-        "image": ""
-      },
-      {
-        "text": "To convert linear to non-linear problems.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "An image is defined as:",
-    "answers": [
-      {
-        "text": "A collection of coloured dots.",
-        "image": ""
-      },
-      {
-        "text": "A two-dimensional array of numerical values.",
-        "image": ""
-      },
-      {
-        "text": "A function that maps locations to pixels.",
-        "image": ""
-      },
-      {
-        "text": "A visual representation of objects.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "How many colour combinations are possible for a single pixel in an RGB image, where each colour channel (Red, Green, Blue) has values ranging from 0 to 255?",
-    "answers": [
-      {
-        "text": "256",
-        "image": ""
-      },
-      {
-        "text": "65,536",
-        "image": ""
-      },
-      {
-        "text": "16,777,216",
-        "image": ""
-      },
-      {
-        "text": "1,048,576",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "What is the primary function of image filtering?",
-    "answers": [
-      {
-        "text": "To change the colour palette of an image.",
-        "image": ""
-      },
-      {
-        "text": "To alter the pixel locations within an image.",
-        "image": ""
-      },
-      {
-        "text": "To apply a function to the pixels of an image, without changing their positions.",
-        "image": ""
-      },
-      {
-        "text": "To compress the size of an image.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Which of the following is NOT a typical application of image filtering?",
-    "answers": [
-      {
-        "text": "Image deblurring.",
-        "image": ""
-      },
-      {
-        "text": "Improving contrast.",
-        "image": ""
-      },
-      {
-        "text": "Noise reduction.",
-        "image": ""
-      },
-      {
-        "text": "Increasing image resolution.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "What is the mathematical operation at the core of 2D convolutions?",
-    "answers": [
-      {
-        "text": "Subtraction and division.",
-        "image": ""
-      },
-      {
-        "text": "Element-wise matrix multiplication and summation.",
-        "image": ""
-      },
-      {
-        "text": "Vector dot product.",
-        "image": ""
-      },
-      {
-        "text": "Matrix inversion.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "If the kernel was not flipped in a 2D convolution, the operation would be a:",
-    "answers": [
-      {
-        "text": "Matrix transpose.",
-        "image": ""
-      },
-      {
-        "text": "Cross-correlation.",
-        "image": ""
-      },
-      {
-        "text": "Dot product.",
-        "image": ""
-      },
-      {
-        "text": "Linear transformation.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "What is the purpose of padding in 2D convolutions?",
-    "answers": [
-      {
-        "text": "To reduce the computational cost of the convolution.",
-        "image": ""
-      },
-      {
-        "text": "To maintain the same output dimensions as the input.",
-        "image": ""
-      },
-      {
-        "text": "To sharpen the image.",
-        "image": ""
-      },
-      {
-        "text": "To blur the image.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "An identity kernel in image filtering will:",
-    "answers": [
-      {
-        "text": "Sharpen the edges of an image.",
-        "image": ""
-      },
-      {
-        "text": "Leave the image unchanged.",
-        "image": ""
-      },
-      {
-        "text": "Blur the image significantly.",
-        "image": ""
-      },
-      {
-        "text": "Invert the colours of the image.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "A mean blur kernel works by:",
-    "answers": [
-      {
-        "text": "Multiplying the pixel value by a constant.",
-        "image": ""
-      },
-      {
-        "text": "Amplifying the difference between a pixel and its neighbors.",
-        "image": ""
-      },
-      {
-        "text": "Averaging a pixel with its surrounding neighbours.",
-        "image": ""
-      },
-      {
-        "text": "Giving greater weight to the centre pixel.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Which filter is known for weighting nearby pixels more heavily than distant ones, leading to a more natural-looking blur?",
-    "answers": [
-      {
-        "text": "Mean blur filter.",
-        "image": ""
-      },
-      {
-        "text": "Sharpening kernel.",
-        "image": ""
-      },
-      {
-        "text": "Identity kernel.",
-        "image": ""
-      },
-      {
-        "text": "Gaussian blur filter.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Which of the following is a property of a Gaussian filter?",
-    "answers": [
-      {
-        "text": "Non-rotational symmetry.",
-        "image": ""
-      },
-      {
-        "text": "It weights distant pixels more than nearby ones.",
-        "image": ""
-      },
-      {
-        "text": "Rotational symmetry.",
-        "image": ""
-      },
-      {
-        "text": "It enhances the noise in an image.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "What is the separable property of a filter?",
-    "answers": [
-      {
-        "text": "Applying the filter only to certain parts of the image.",
-        "image": ""
-      },
-      {
-        "text": "First convolving rows with a 1D filter, then columns with a 1D filter.",
-        "image": ""
-      },
-      {
-        "text": "Applying different filters to different image channels.",
-        "image": ""
-      },
-      {
-        "text": "Convolution with multiple kernels",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "In the context of a Gaussian pyramid, what is the first step after starting with the original image?",
-    "answers": [
-      {
-        "text": "Downsampling the image.",
-        "image": ""
-      },
-      {
-        "text": "Applying a Gaussian blur.",
-        "image": ""
-      },
-      {
-        "text": "Upsampling the image.",
-        "image": ""
-      },
-      {
-        "text": "Applying a sharpening filter.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "How does downsampling change the image size in each level of a Gaussian pyramid?",
-    "answers": [
-      {
-        "text": "Reduces the size by a factor of 2.",
-        "image": ""
-      },
-      {
-        "text": "Reduces the size by a factor of 3.",
-        "image": ""
-      },
-      {
-        "text": "Reduces the size by a factor of 4.",
-        "image": ""
-      },
-      {
-        "text": "Reduces the size by a factor of 8.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "What is the primary reason for applying a Gaussian blur before downsampling in a Gaussian pyramid?",
-    "answers": [
-      {
-        "text": "To increase the resolution of the image.",
-        "image": ""
-      },
-      {
-        "text": "To sharpen the image before resizing.",
-        "image": ""
-      },
-      {
-        "text": "To act as a low-pass filter and prevent aliasing.",
-        "image": ""
-      },
-      {
-        "text": "To make the image more colourful.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "What is aliasing, as discussed in the context of image downsampling?",
-    "answers": [
-      {
-        "text": "The effect of making the image sharper.",
-        "image": ""
-      },
-      {
-        "text": "Distortions in the downsampled image caused by undersampling high-frequency components.",
-        "image": ""
-      },
-      {
-        "text": "The effect of applying a blur.",
-        "image": ""
-      },
-      {
-        "text": "A form of image compression.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: Which of the following best describes the fundamental concept of computer vision, as presented in the material?",
-    "answers": [
-      {
-        "text": "Generating arrays of numbers that resemble real-world objects, like fruits.",
-        "image": ""
-      },
-      {
-        "text": "Solving the 'inverse graphics' problem by inferring the structure of the world from visual cues.",
-        "image": ""
-      },
-      {
-        "text": "Creating digital images using a pinhole camera model and digitizers.",
-        "image": ""
-      },
-      {
-        "text": "Recognising objects by matching 2D image fragments and their configurations.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: The 'trompe l’oeil' examples in the text primarily illustrate which aspect of computer vision?",
-    "answers": [
-      {
-        "text": "The challenges of object recognition in cluttered scenes.",
-        "image": ""
-      },
-      {
-        "text": "The use of color and shading to create realistic images.",
-        "image": ""
-      },
-      {
-        "text": "The exploitation of depth-perception cues and their mathematical modeling.",
-        "image": ""
-      },
-      {
-        "text": "The importance of prior expectations in image interpretation.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: According to the provided text, what is a key characteristic of 'basic level categories' in object recognition?",
-    "answers": [
-      {
-        "text": "They represent the most detailed classification of objects.",
-        "image": ""
-      },
-      {
-        "text": "They are the categories that are most difficult for humans to identify quickly.",
-        "image": ""
-      },
-      {
-        "text": "They are culturally dependent without any consistency.",
-        "image": ""
-      },
-      {
-        "text": "They represent the highest level at which category members have similar perceived shapes and are easily recognized by humans.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What is the primary purpose of image filtering, as described in the material?",
-    "answers": [
-      {
-        "text": "To increase the amount of noise in an image to make edges more apparent.",
-        "image": ""
-      },
-      {
-        "text": "To create 3D models of objects from 2D images.",
-        "image": ""
-      },
-      {
-        "text": "To enhance image quality, extract features, and reduce noise.",
-        "image": ""
-      },
-      {
-        "text": "To generate new images using the principles of graphics.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: In the context of 2D convolution, which of the following steps is essential?",
-    "answers": [
-      {
-        "text": "Rotating the filter kernel by 90 degrees.",
-        "image": ""
-      },
-      {
-        "text": "Mirroring the filter kernel before applying it to the image.",
-        "image": ""
-      },
-      {
-        "text": "Applying a non-linear function to the local image patch.",
-        "image": ""
-      },
-      {
-        "text": "Only summing the values without multiplication.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What does the text say about linear systems in the context of image processing?",
-    "answers": [
-      {
-        "text": "They are used only for non-linear filtering.",
-        "image": ""
-      },
-      {
-        "text": "They are characterized by a lack of superposition.",
-        "image": ""
-      },
-      {
-        "text": "They exhibit properties such as homogeneity, additivity, and superposition.",
-        "image": ""
-      },
-      {
-        "text": "They cannot be represented by matrix operations.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: According to the source material, why is Gaussian averaging preferred over a simple box filter for smoothing?",
-    "answers": [
-      {
-        "text": "Because box filters are computationally more expensive.",
-        "image": ""
-      },
-      {
-        "text": "Because box filters do not reduce noise effectively.",
-        "image": ""
-      },
-      {
-        "text": "Because Gaussian averaging gives more weight to nearby pixels, modelling probabilistic inference.",
-        "image": ""
-      },
-      {
-        "text": "Because box filters are not separable and therefore cannot be implemented efficiently.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What is the main problem caused by subsampling without average filtering, according to the text?",
-    "answers": [
-      {
-        "text": "It makes the image smoother.",
-        "image": ""
-      },
-      {
-        "text": "It increases the resolution of the image.",
-        "image": ""
-      },
-      {
-        "text": "It leads to aliasing, introducing artifacts in the image.",
-        "image": ""
-      },
-      {
-        "text": "It preserves high-frequency information more accurately.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What is the significance of the 'derivative of Gaussian' in edge detection, according to the source?",
-    "answers": [
-      {
-        "text": "It is used to enhance noise and amplify variations in the image.",
-        "image": ""
-      },
-      {
-        "text": "It directly extracts lines and edges without the need for smoothing.",
-        "image": ""
-      },
-      {
-        "text": "It is an approximation of the optimal edge detector under certain assumptions (linear filtering and additive Gaussian noise).",
-        "image": ""
-      },
-      {
-        "text": "It is a simplified method used to avoid complex calculations in edge detection.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: In the context of edge detection, what is the role of \"non-maximum suppression\"?",
-    "answers": [
-      {
-        "text": "To amplify the noise near edges.",
-        "image": ""
-      },
-      {
-        "text": "To smooth out the detected edges.",
-        "image": ""
-      },
-      {
-        "text": "To thin edges by choosing the largest gradient magnitude along the gradient direction.",
-        "image": ""
-      },
-      {
-        "text": "To detect edges at different scales and combine them into a single map.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What is the Laplacian operator, as presented in the text, and what is it used for?",
-    "answers": [
-      {
-        "text": "It is a filter that calculates the gradient magnitude of an image.",
-        "image": ""
-      },
-      {
-        "text": "It is a smoothing filter that reduces high-frequency information.",
-        "image": ""
-      },
-      {
-        "text": "It is a linear filter used to detect edges by identifying zero-crossings of the second derivative.",
-        "image": ""
-      },
-      {
-        "text": "It is a filter that is used for color histogram generation.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What is a primary motivation for using color histograms for object recognition?",
-    "answers": [
-      {
-        "text": "They are sensitive to geometric transformations.",
-        "image": ""
-      },
-      {
-        "text": "They require perfect segmentation of objects.",
-        "image": ""
-      },
-      {
-        "text": "They are computationally expensive, however, this is offset by the quality of recognition they provide.",
-        "image": ""
-      },
-      {
-        "text": "They are relatively invariant to object translations, image rotations, and partial occlusions.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: According to the source, what is a limitation of using color histograms for object recognition?",
-    "answers": [
-      {
-        "text": "They cannot be used for deformable objects such as pullovers.",
-        "image": ""
-      },
-      {
-        "text": "They require a large number of training views per object.",
-        "image": ""
-      },
-      {
-        "text": "They can be sensitive to changes in illumination conditions.",
-        "image": ""
-      },
-      {
-        "text": "They perform poorly when objects are partially occluded.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: Which of the following statements accurately describes the 'Intersection' method for comparing histograms?",
-    "answers": [
-      {
-        "text": "It calculates the differences between corresponding histogram cells.",
-        "image": ""
-      },
-      {
-        "text": "It gives a higher score when there is minimal overlap between histograms.",
-        "image": ""
-      },
-      {
-        "text": "It measures the common part of both histograms, with a range between 0 and 1.",
-        "image": ""
-      },
-      {
-        "text": "It weights all histogram cells equally regardless of their significance.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: In the context of performance evaluation, what does a confusion matrix help to determine?",
-    "answers": [
-      {
-        "text": "The optimal parameters for an image filtering algorithm.",
-        "image": ""
-      },
-      {
-        "text": "The best method for comparing color histograms.",
-        "image": ""
-      },
-      {
-        "text": "The number of true positives, true negatives, false positives, and false negatives for a given classifier and threshold.",
-        "image": ""
-      },
-      {
-        "text": "The area under the ROC curve for a specific model.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What does the term \"recall\" measure in the context of performance evaluation?",
-    "answers": [
-      {
-        "text": "The proportion of correctly identified negative cases.",
-        "image": ""
-      },
-      {
-        "text": "The proportion of actual positives that are correctly identified.",
-        "image": ""
-      },
-      {
-        "text": "The overall accuracy of the classification model.",
-        "image": ""
-      },
-      {
-        "text": "The proportion of false alarms in the classification process.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: In the context of ROC curves, what does the True Positive Rate (TPR) represent?",
-    "answers": [
-      {
-        "text": "The rate of false alarms for a given threshold.",
-        "image": ""
-      },
-      {
-        "text": "The proportion of correctly identified negative cases.",
-        "image": ""
-      },
-      {
-        "text": "The proportion of actual positives that are correctly identified.",
-        "image": ""
-      },
-      {
-        "text": "The overall accuracy of the classification model.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What is the significance of the Area Under the ROC Curve (AUROC)?",
-    "answers": [
-      {
-        "text": "It represents the trade-off between precision and recall for a classification model.",
-        "image": ""
-      },
-      {
-        "text": "It indicates how well a classifier distinguishes between two classes, with a higher AUROC suggesting better performance.",
-        "image": ""
-      },
-      {
-        "text": "It helps choose the best comparison method for color histograms.",
-        "image": ""
-      },
-      {
-        "text": "It is used to determine the optimal threshold for object detection algorithms.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: According to the material, why is the precision-recall curve preferred for detection tasks?",
-    "answers": [
-      {
-        "text": "Because it does not require any threshold.",
-        "image": ""
-      },
-      {
-        "text": "Because it is less sensitive to noise than other performance metrics.",
-        "image": ""
-      },
-      {
-        "text": "Because it is better suited when the number of true negatives is not well-defined, such as in detection tasks.",
-        "image": ""
-      },
-      {
-        "text": "Because it gives more importance to the true negative rate.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: Leonardo da Vinci's observations about the camera obscura, as described in the text, highlight which fundamental principle of image formation?",
-    "answers": [
-      {
-        "text": "The principle of digital image processing.",
-        "image": ""
-      },
-      {
-        "text": "The formation of a reversed and reduced image through a small aperture.",
-        "image": ""
-      },
-      {
-        "text": "The concept of linear filtering in image enhancement.",
-        "image": ""
-      },
-      {
-        "text": "The use of color histograms for object recognition.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: The text refers to computer vision as the problem of 'inverse graphics'. What does this imply about the goals of computer vision?",
-    "answers": [
-      {
-        "text": "Computer vision aims to generate images that are indistinguishable from real-world scenes.",
-        "image": ""
-      },
-      {
-        "text": "Computer vision seeks to create digital images by using the pinhole camera model.",
-        "image": ""
-      },
-      {
-        "text": "Computer vision tries to infer the properties of the world from images, reversing the process of graphics which creates images from the world.",
-        "image": ""
-      },
-      {
-        "text": "Computer vision focuses on the analysis of color histograms for object identification.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: According to the text, what is the significance of the 'pictorial structure' model in object recognition?",
-    "answers": [
-      {
-        "text": "It only uses 3D models for object recognition.",
-        "image": ""
-      },
-      {
-        "text": "It relies on color histograms to identify objects.",
-        "image": ""
-      },
-      {
-        "text": "It represents objects as combinations of 2D image fragments and their configurations.",
-        "image": ""
-      },
-      {
-        "text": "It is a simple method that can overcome all complexities of object recognition.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What does the material say about the challenges of visual categorization?",
-    "answers": [
-      {
-        "text": "They are limited to problems with occlusions.",
-        "image": ""
-      },
-      {
-        "text": "They are not affected by multi-scale, multi-view variations.",
-        "image": ""
-      },
-      {
-        "text": "They include issues such as multi-scale, multi-view, multi-class, varying illumination, occlusion, cluttered backgrounds, articulation, and high intraclass variance/low interclass variance.",
-        "image": ""
-      },
-      {
-        "text": "They are easily solved by basic linear filtering techniques.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What is the role of a 'filter kernel' in the context of image filtering?",
-    "answers": [
-      {
-        "text": "It is used to digitize analog images.",
-        "image": ""
-      },
-      {
-        "text": "It is only useful for non-linear operations.",
-        "image": ""
-      },
-      {
-        "text": "It is a small matrix that is used to apply some function to a local image patch during convolution.",
-        "image": ""
-      },
-      {
-        "text": "It represents the output image, after applying the convolution.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: According to the source material, what is the primary goal of using linear filtering for smoothing an image?",
-    "answers": [
-      {
-        "text": "To enhance the edges and details in an image.",
-        "image": ""
-      },
-      {
-        "text": "To create a sharper version of the image.",
-        "image": ""
-      },
-      {
-        "text": "To reduce noise and fill in missing information.",
-        "image": ""
-      },
-      {
-        "text": "To perform non-linear operations on an image.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: Why is the concept of 'separability' important in the context of Gaussian filtering?",
-    "answers": [
-      {
-        "text": "Because it makes the filter non-linear.",
-        "image": ""
-      },
-      {
-        "text": "Because it allows for efficient implementation of the filtering operation by applying 1D filters sequentially.",
-        "image": ""
-      },
-      {
-        "text": "Because it increases the smoothing effect on an image.",
-        "image": ""
-      },
-      {
-        "text": "Because it reduces the computational cost of applying a box filter.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What is the main idea behind using a Gaussian pyramid for multi-scale image representation?",
-    "answers": [
-      {
-        "text": "To reduce the resolution of images for easier processing.",
-        "image": ""
-      },
-      {
-        "text": "To apply linear filtering in a single scale.",
-        "image": ""
-      },
-      {
-        "text": "To represent an image at different scales by repeated smoothing and subsampling.",
-        "image": ""
-      },
-      {
-        "text": "To compute the 2nd derivative of an image.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: In the context of edge detection, why is smoothing an image prior to computing derivatives beneficial?",
-    "answers": [
-      {
-        "text": "It enhances the noise, to see edges more clearly.",
-        "image": ""
-      },
-      {
-        "text": "It ensures edges are not affected by lighting changes.",
-        "image": ""
-      },
-      {
-        "text": "It reduces the impact of noise and small variations, which can interfere with detecting true edges.",
-        "image": ""
-      },
-      {
-        "text": "It makes the edges thicker and more visible.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: According to the text, what does the magnitude of the gradient measure in edge detection?",
-    "answers": [
-      {
-        "text": "The direction of the edge.",
-        "image": ""
-      },
-      {
-        "text": "The noise level around an edge.",
-        "image": ""
-      },
-      {
-        "text": "The strength of an edge.",
-        "image": ""
-      },
-      {
-        "text": "The scale of the image where edges are more evident.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What is the main advantage of using the Canny edge detector over other edge detection methods, according to the text?",
-    "answers": [
-      {
-        "text": "It is faster and less computationally intensive than other methods.",
-        "image": ""
-      },
-      {
-        "text": "It is an approximation of the optimal edge detector under the assumptions of linear filtering and additive Gaussian noise, offering a good trade-off between detection and localization.",
-        "image": ""
-      },
-      {
-        "text": "It does not require any parameter tuning for different images.",
-        "image": ""
-      },
-      {
-        "text": "It is simpler to implement and more robust in noisy conditions.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: In the context of edge detection using the Laplacian, what are 'zero-crossings' and what do they indicate?",
-    "answers": [
-      {
-        "text": "They indicate the location of the maximum gradient value.",
-        "image": ""
-      },
-      {
-        "text": "They indicate the strength of an edge in an image.",
-        "image": ""
-      },
-      {
-        "text": "They are used to calculate color histograms in an image.",
-        "image": ""
-      },
-      {
-        "text": "They represent points where the second derivative changes sign, which indicates the location of edges.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: According to the text, what is a key characteristic of appearance-based object identification/recognition?",
-    "answers": [
-      {
-        "text": "It relies on explicit 3D models of objects.",
-        "image": ""
-      },
-      {
-        "text": "It requires perfect segmentation of the object in the image.",
-        "image": ""
-      },
-      {
-        "text": "It represents objects by a collection of 2D images without the need for a 3D model, and it is sufficient to compare the 2D appearances.",
-        "image": ""
-      },
-      {
-        "text": "It is invariant to changes in the viewing angle.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What does the material say about the use of color in object recognition?",
-    "answers": [
-      {
-        "text": "Color changes under geometric transformations and therefore is not a reliable feature.",
-        "image": ""
-      },
-      {
-        "text": "Color is a global feature that is robust to occlusions.",
-        "image": ""
-      },
-      {
-        "text": "Color is a local feature that remains relatively constant under geometric transformations and is robust to partial occlusions.",
-        "image": ""
-      },
-      {
-        "text": "Color cannot be used for recognition because it is very sensitive to light variations.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What does a 3D (joint) color histogram represent?",
-    "answers": [
-      {
-        "text": "The 1D count of pixels of individual R, G, B colors, and luminance.",
-        "image": ""
-      },
-      {
-        "text": "The color normalized by intensity.",
-        "image": ""
-      },
-      {
-        "text": "The count of pixels for each combination of RGB values.",
-        "image": ""
-      },
-      {
-        "text": "A 2D representation of color, for example, using two parameters, r and g.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: According to the text, what is the significance of using a 'chromatic representation' of color?",
-    "answers": [
-      {
-        "text": "It ensures that the color histograms do not change under rotation.",
-        "image": ""
-      },
-      {
-        "text": "It guarantees that the color histogram is robust to occlusion.",
-        "image": ""
-      },
-      {
-        "text": "It normalizes colors by intensity, focusing on the color itself rather than its brightness.",
-        "image": ""
-      },
-      {
-        "text": "It generates an intensity image that is later used to extract color information.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What does the Euclidean distance measure in the context of histogram comparison?",
-    "answers": [
-      {
-        "text": "It measures the differences between the histograms, weighting each cell equally.",
-        "image": ""
-      },
-      {
-        "text": "It measures the common part of both histograms.",
-        "image": ""
-      },
-      {
-        "text": "It measures if two distributions are statistically different, with a focus on outliers.",
-        "image": ""
-      },
-      {
-        "text": "It only measures the distance between the central cells of two histograms.",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: In the context of histogram comparison, what does the Chi-square measure primarily aim to test?",
-    "answers": [
-      {
-        "text": "The overlap between the histograms of known objects and a test image.",
-        "image": ""
-      },
-      {
-        "text": "The distances between the centers of two histograms.",
-        "image": ""
-      },
-      {
-        "text": "Whether two distributions are statistically different.",
-        "image": ""
-      },
-      {
-        "text": "If the two images can be considered the same object.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: What is the 'nearest-neighbor' strategy for object recognition using histograms, as described in the text?",
-    "answers": [
-      {
-        "text": "It measures the distance between objects using the Euclidean distance.",
-        "image": ""
-      },
-      {
-        "text": "It focuses on the differences between histograms using a Chi-squared measure.",
-        "image": ""
-      },
-      {
-        "text": "It looks for the perfect overlap of two histograms.",
-        "image": ""
-      },
-      {
-        "text": "It compares a test histogram to a set of known object histograms and selects the one with the best matching score.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "CV Basics: According to the material, what is the 'color constancy problem' that affects color histograms?",
-    "answers": [
-      {
-        "text": "It refers to the fact that colors cannot be used in image recognition.",
-        "image": ""
-      },
-      {
-        "text": "It describes a scenario where objects have the same color distribution.",
-        "image": ""
-      },
-      {
-        "text": "It is the problem of pixel colors changing due to the illumination conditions.",
-        "image": ""
-      },
-      {
-        "text": "It is a problem of color histograms that arises because not all objects can be identified by their color distribution.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  }
-]
\ No newline at end of file
diff --git a/data/questions/ium_unive.json b/data/questions/ium_unive.json
deleted file mode 100644
index 7c811de..0000000
--- a/data/questions/ium_unive.json
+++ /dev/null
@@ -1,3081 +0,0 @@
-[
-  {
-    "quest": "1) L’interaction framework descrive 4 fasi del ciclo interattivo, tra le quali troviamo:",
-    "answers": [
-      {
-        "text": "l’osservazione e la memorizzazione",
-        "image": ""
-      },
-      {
-        "text": "la sintesi e l’articolazione",
-        "image": ""
-      },
-      {
-        "text": "l’articolazione e la presentazione",
-        "image": ""
-      },
-      {
-        "text": "la prestazione e la virtualizzazione",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "2) Qual èil significato dell’euristica visibilità dello stato del sistema?",
-    "answers": [
-      {
-        "text": "che i dispositivi di input del sistema devono essere posizionati in modo da essere sempre visibili agli utenti",
-        "image": ""
-      },
-      {
-        "text": "che in ogni momento il sistema deve tenere informato l’utente su quello che sta succedendo",
-        "image": ""
-      },
-      {
-        "text": " che i dispositivi di output del sistema devono essere posizionati a non più di 2 metri dall’utente",
-        "image": ""
-      },
-      {
-        "text": "che il sistema deve fornire un feedback all’utente quando ha esaurito tutti gli altri compiti computazionali",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "3) Nell’ambito dell’informatica pervasiva si intende per ambient display:",
-    "answers": [
-      {
-        "text": "l’uso dell’ambiente fisico come interfaccia per l’informazione digitale",
-        "image": ""
-      },
-      {
-        "text": "l’utilizzo esclusivo di schermi di schermi di grande dimensione, per permettere una comunicazione parallela a più utenti",
-        "image": ""
-      },
-      {
-        "text": "l’utilizzo esclusivo della visualità per comunicare l’output di un sistema pervasivo",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "4) Il paradigma a manipolazione diretta è caratterizzato da:",
-    "answers": [
-      {
-        "text": "invisibilità di una parte degli oggetti e feedback veloce",
-        "image": ""
-      },
-      {
-        "text": "sostituzione di linguaggi di comando basati su numero con linguaggi di comando basati su parole comuni",
-        "image": ""
-      },
-      {
-        "text": "reversibilità delle azioni",
-        "image": ""
-      },
-      {
-        "text": "impossibilità di attuare azioni incrementali",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "5) Nell’ambito dell’informatica pervasiva, che cosa si intende per principio di intelligenza appropriata?",
-    "answers": [
-      {
-        "text": "che il sistema deve essere percepito dall’utente come una controparte umana",
-        "image": ""
-      },
-      {
-        "text": "che il sistema informatico non deve mai effettuare previsioni sbagliate",
-        "image": ""
-      },
-      {
-        "text": "che il sistema informatico deve eseguire previsioni il più spesso possibile corrette",
-        "image": ""
-      },
-      {
-        "text": "che il sistema informatico deve avere un livello minimo di intelligenza misurata utilizzando il test di Turing",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "6) Il termine mixed realityintrodotto da Milgrim e Kishino si riferisce a:",
-    "answers": [
-      {
-        "text": "paradigmi di interazione in cui vengono presentati all’utente in sequenza elementi reali ed elementi virtuali",
-        "image": ""
-      },
-      {
-        "text": "paradigmi di interazione in cui vengono presentati all’utente contemporaneamente sia elementi reali che virtuali",
-        "image": ""
-      },
-      {
-        "text": "tutta la gamma di combinazioni possibili tra realtà e virtualità",
-        "image": ""
-      },
-      {
-        "text": "paradigmi di interazione completamente immersivi in cui l’utente si sente ‘frullato’ (da qui il termine mixed) e proiettato con il corpo in un mondo virtuale",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "7) In uno degli articoli proposti nella bibliografia del corso (Comparative Feedback in the Street: Exposing Residential Energy Consumption on House Façades) gli autori descrivono uno studio sull’impatto sui comportamenti dovuti alla condivisione dei dati dei consumi energetici sulle facciate esternedelle abitazioni, e concludono che:",
-    "answers": [
-      {
-        "text": "è fondamentale dare il dettaglio dei consumi in tempo reale",
-        "image": ""
-      },
-      {
-        "text": "è importante mostrare dati sui consumi a lungo termine",
-        "image": ""
-      },
-      {
-        "text": "non c’è alcun vantaggio dal punto di vista del miglioramento delle abitudini derivante dalla competizione tra i vicini",
-        "image": ""
-      },
-      {
-        "text": "è importante dare informazioni sui consumi, ma non è importante dare informazioni su come cambiare i comportamenti, visto che la maggior parte delle famiglie è consapevole dei suoi punti di debolezza",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "8) Nell’ambito delle tecniche di prototipazione di un’interfaccia, che si intende perbranching storyboard?",
-    "answers": [
-      {
-        "text": "una storyboard caratterizzata da uno schema lineare per mostrare i cambiamenti di stato dell’interfaccia a seguito dell’azione dell’utente o di altri eventi",
-        "image": ""
-      },
-      {
-        "text": "una storyboard nella qualevengono proposti gli screenshot dell’interfaccia senza alcun ordinamento particolare",
-        "image": ""
-      },
-      {
-        "text": "una storyboard nella quale viene data una descrizioneil più possibilecompleta di tutti glistati dell’interazionee delle loro relazioni",
-        "image": ""
-      },
-      {
-        "text": "una storyboard nella quale viene descrittanon solo l'interfaccia ma anche il contesto in cui avviene l’interazione",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "9) Le interfacce per l’eco-feedbacksi caratterizzano per:",
-    "answers": [
-      {
-        "text": "richiedere un consumo di energia molto basso, di provenienza esclusivamente rinnovabile",
-        "image": ""
-      },
-      {
-        "text": "fornire agli utenti informazioni riguardo alle conseguenze per l’ambiente del loro comportamento",
-        "image": ""
-      },
-      {
-        "text": "fornire agli utenti un feedback sonoro per il quale vengono utilizzati processori di ecoo di riverbero, atti a simulare luoghi chiusi e aperti molto ampi",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "10) Nell’ambito dell’informatica pervasiva, che cosa si intende per prossemica (proxemics)?",
-    "answers": [
-      {
-        "text": "lo studio dei tempi di interazione con un sistema pervasivo",
-        "image": ""
-      },
-      {
-        "text": "lo studio del significato della gestualità umana",
-        "image": ""
-      },
-      {
-        "text": "lo studio delle distanze interpersonali",
-        "image": ""
-      },
-      {
-        "text": "lo studio delle distanze tra le componenti dell’interfaccia",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "11) Tra le caratteristiche positive dei nuovi stili di interazione gestuale pensati per tablet e smartphone possiamo indicare:",
-    "answers": [
-      {
-        "text": "la disponibilità e l’omogeneità di operazioni non distruttive, come l’undo",
-        "image": ""
-      },
-      {
-        "text": "la disponibilità di linee guida riconosciute per il controllo gestuale",
-        "image": ""
-      },
-      {
-        "text": "la disponibilità di gesti consistenti e omogenei attraverso le diverse piattaforme di utilizzo",
-        "image": ""
-      },
-      {
-        "text": "l’elevata espressività del linguaggio di input",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "12) Nell’ambito del modello di interazione proposto da Don Norman (ciclo di esecuzione e di valutazione) il golfo di valutazione indica:",
-    "answers": [
-      {
-        "text": "la differenza tra la formulazione delle azioni dell’utente e le azioni consentite",
-        "image": ""
-      },
-      {
-        "text": "la differenza tra la presentazione dello stato del sistema e le aspettative dell’utente",
-        "image": ""
-      },
-      {
-        "text": "la differenza tra la presentazione dello stato del sistema e le azioni consentite",
-        "image": ""
-      },
-      {
-        "text": "la differenza tra la formulazione delle azioni dell’utente e le aspettative dell’utente",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "13) In uno degli articoli proposti nella bibliografia del corso (Comparative Feedback in the Street: Exposing Residential Energy Consumption on House Façades) gli autori descrivono uno studio sull’impattosui comportamenti dovuti alla condivisione dei dati dei consumi energetici sulle facciate esternedelle abitazioni, e concludono che:",
-    "answers": [
-      {
-        "text": "è fondamentale dare il dettaglio dei consumi in tempo reale",
-        "image": ""
-      },
-      {
-        "text": "è importante dare informazioni sui consumi, ma non è importante dare informazioni su come cambiare i comportamenti, visto che la maggior parte delle famiglie è consapevoledei suoi punti di debolezza",
-        "image": ""
-      },
-      {
-        "text": "è importante mostrare dati sui consumi a lungo termine",
-        "image": ""
-      },
-      {
-        "text": "non c’è alcun vantaggio dal punto di vista del miglioramento delle abitudini derivante dalla competizione tra i vicini",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "14) Nell’ambito dell’informatica pervasiva per input implicito si intende:",
-    "answers": [
-      {
-        "text": "le azioni che non richiedono un’interazione verbale da parte dell’utente",
-        "image": ""
-      },
-      {
-        "text": "le azioni che non vengono viste dall’utente come un’interazione con un sistema informatico, ma che vengono interpretati come tali da un sistema informatico presente nell’ambiente",
-        "image": ""
-      },
-      {
-        "text": "le azioni che non richiedono un’interazione gestuale da parte dell’utente",
-        "image": ""
-      },
-      {
-        "text": "le azioni compiute dagli utenti che non vengono viste dalle altre persone presenti nell’ambiente",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "15) Il termine mixed reality introdotto da Milgrim e Kishino si riferisce a:",
-    "answers": [
-      {
-        "text": "paradigmi di interazione in cui vengono presentati all’utente in sequenza elementi reali ed elementi virtuali",
-        "image": ""
-      },
-      {
-        "text": "paradigmi di interazione in cui vengono presentati all’utente contemporaneamente siaelementi reali che virtuali",
-        "image": ""
-      },
-      {
-        "text": "tutta la gamma di combinazioni possibili tra realtà e virtualità",
-        "image": ""
-      },
-      {
-        "text": "paradigmi di interazione completamente immersivi in cui l’utente si sente ‘frullato’ (da qui il termine mixed) e proiettato con il corpo in un mondo virtuale",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "16) Nell’ambito delle tecniche di prototipazione di un’interfaccia, che si intende per narrative storyboard?",
-    "answers": [
-      {
-        "text": "una storyboard caratterizzata da uno schema lineare per mostrare i cambiamenti di stato dell’interfaccia a seguito dell’azione diretta dell’utente",
-        "image": ""
-      },
-      {
-        "text": "una storyboard nella quale vengono proposti gli screenshot dell’interfaccia senza alcun ordinamento particolare",
-        "image": ""
-      },
-      {
-        "text": "una storyboard nella quale viene data una descrizione il più possibile completa di tutti gli stati dell’interazione e delle loro relazioni",
-        "image": ""
-      },
-      {
-        "text": "una storyboard nella quale viene descritta non solo l'interfaccia ma anche il contesto in cui avviene l’interazione",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "17) Qual è il significato dell’euristica ‘Controllo dell’utente e libertà?",
-    "answers": [
-      {
-        "text": "che agli utenti deve essere impedito di selezionare per errore le funzionalità del sistema",
-        "image": ""
-      },
-      {
-        "text": "che tutti i sistemi dovrebbero fornire la libertà di essere utilizzati,anche da parte di utenti non registrati",
-        "image": ""
-      },
-      {
-        "text": "che agli utenti deve essere fornite uscite di emergenza nel caso in cui selezionino determinate funzionalità per errore",
-        "image": ""
-      },
-      {
-        "text": "che non devono essere supportate le funzionalità di Redo e Redo multiplo",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "18) I punti di forza delle interfacce tangibili (TUI) includono:",
-    "answers": [
-      {
-        "text": "la versatilità e la collaborazione",
-        "image": ""
-      },
-      {
-        "text": "il parallelismo spaziale e la scalabilità",
-        "image": ""
-      },
-      {
-        "text": "il pensiero tangibile e l’uso delle affordances",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "19) I parametri che definiscono l’engagement dell’utente comprendono:",
-    "answers": [
-      {
-        "text": "l’attenzione focalizzata e la durabilità",
-        "image": ""
-      },
-      {
-        "text": "l’usabilità percepita e l’effervescenza",
-        "image": ""
-      },
-      {
-        "text": "il coinvolgimento percepito e l’accessibilità",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "20) Il cosiddetto affective computing denota:",
-    "answers": [
-      {
-        "text": "la computazione che influenza le emozioni degli utenti",
-        "image": ""
-      },
-      {
-        "text": "l’attrazione emotiva degli utenti verso i nuovi prodotti hardware",
-        "image": ""
-      },
-      {
-        "text": "la computazione che sorge dalle emozioni dell’unità di calcolo",
-        "image": ""
-      },
-      {
-        "text": "l’attrazione emotiva degli utenti verso i prodotti hardware vintage",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "21) Le nuove interfacce gestuali da un certo punto di vista rappresentano un passo indietro dal punto di vista dell’usabilità, a causa:",
-    "answers": [
-      {
-        "text": "dell’incapacità dei cosiddetti nativi digitali ad effettuare manipolazioni con entrambe le mani",
-        "image": ""
-      },
-      {
-        "text": "della mancanza di linee guida riconosciute per il controllo gestuale",
-        "image": ""
-      },
-      {
-        "text": "dellalimitata espressività delle interfacce gestuali",
-        "image": ""
-      },
-      {
-        "text": "dell’incapacità degli utenti ad effettuare manipolazioni con una mano singola",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "22) Nell’ambito delle attività di prototipazione la tecnica del mago di Oz denota:",
-    "answers": [
-      {
-        "text": "l’implementazione fittizia di interfacce, costruita in modo tale che l’utente abbia l’impressione di interagire con un sistema realmente funzionante",
-        "image": ""
-      },
-      {
-        "text": "l’utilizzo di tecniche di storytelling (narrazione interattiva) per la didattica dell’informatica",
-        "image": ""
-      },
-      {
-        "text": "un’implementazione ridondante e fantasiosa di un’interfaccia utente, effettuata allo scopo di permettere molteplici modalità di interazione",
-        "image": ""
-      },
-      {
-        "text": "l’implementazione completa delle interfacce, realizzata in tempi molto rapidi",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "23) Tra le caratteristiche dei nuovi stili di interazione gestuale pensati per tablet e smartphone possiamo indicare:",
-    "answers": [
-      {
-        "text": "la disponibilità e l’omogeneità di operazioni non distruttive, come l’undo",
-        "image": ""
-      },
-      {
-        "text": "la ridotta espressività del linguaggio di input",
-        "image": ""
-      },
-      {
-        "text": "la disponibilità di gesti consistenti e omogenei attraverso le diverse piattaforme di utilizzo",
-        "image": ""
-      },
-      {
-        "text": "la mancanza di linee guida riconosciute per il controllo gestuale",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "24) Nell'ambito delle interfacce WIMP le cosiddette finestre modalisi caratterizzano per:",
-    "answers": [
-      {
-        "text": "bloccare la possibilità di interazioni ulteriori fino a quando l’utente non ha dato un OK o ha annullato le operazioni consentite dall’interfaccia della finestra",
-        "image": ""
-      },
-      {
-        "text": "permettere di svolgere la stessa azione attraverso modalità diverse, scegliendo ad esempio una modalitàgrafica piuttosto che una a linea di comando",
-        "image": ""
-      },
-      {
-        "text": "permettere di selezionare altre funzionalità dell'applicazione esterne alla finestra modale, senza la necessità di dover concludere prima l'interazione all'interno della finestra stessa",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "25) L'euristica Consistenza e Standard indica che:",
-    "answers": [
-      {
-        "text": "gli oggetti, le azioni e le opzioni che fanno parte dell’interazione vanno resi visibili",
-        "image": ""
-      },
-      {
-        "text": "non vanno seguitele convenzioni delle piattaforme su cui si sta lavorando",
-        "image": ""
-      },
-      {
-        "text": "i widget utilizzati per l'interazione dovrebbero essere di grandi dimensioni e posti a breve distanza",
-        "image": ""
-      },
-      {
-        "text": "gli utenti non dovrebbero preoccuparsi di dover capire se parole, situazioni e azioni diverse significano la stessa cosa",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "26) Nell’ambito delle tecniche di prototipazione di un’interfaccia, la cosiddetta narrative storyboardsi differenzia dalla sequential storyboard:",
-    "answers": [
-      {
-        "text": "per la descrizione del contesto in cui avviene l’interazione",
-        "image": ""
-      },
-      {
-        "text": "per la presentazione degli screenshots dell’interfaccia senza alcun ordinamento particolare",
-        "image": ""
-      },
-      {
-        "text": "per la definizione di percorsi non-lineari che descrivono le transizioni di stato dell’interfaccia",
-        "image": ""
-      },
-      {
-        "text": "per una migliore accuratezza nella rappresentazione dell’interfaccia",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "27) Nell’ambito della percezione uditiva, che cosa si intende per effetto mascheramento?",
-    "answers": [
-      {
-        "text": "l’incapacità del sistema uditivo di distinguere, in determinate condizioni, suoni di livello diverso vicini nel tempo",
-        "image": ""
-      },
-      {
-        "text": "la capacità del sistema uditivo di filtrare i suoni in un ambiente rumoroso",
-        "image": ""
-      },
-      {
-        "text": "l’incapacità del sistema uditivo di distinguere suoni bassi con frequenze molto diverse",
-        "image": ""
-      },
-      {
-        "text": "l’incapacità del sistema uditivo di focalizzarsi in una conversazione in un ambiente rumoroso",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "28) La tastiera Michela è un particolare tipo di dispositivo per l’input del testo:",
-    "answers": [
-      {
-        "text": "che ha lo stesso layout fisico di una tastiera QWERTY",
-        "image": ""
-      },
-      {
-        "text": "che ha il layout e lo stesso numero di tasti di una tastiera di pianoforte",
-        "image": ""
-      },
-      {
-        "text": "per avviare alla digitazione del testo i bambini di età pre-scolare",
-        "image": ""
-      },
-      {
-        "text": "fondato sulla scomposizione del testo da digitare in sillabe",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "29)Nell’ambito del modello definito come Interaction Framework, che cosa succede durante la fase di articolazione?",
-    "answers": [
-      {
-        "text": "Il linguaggio dei compitidell’utente viene tradottoinlinguaggio di input",
-        "image": ""
-      },
-      {
-        "text": "Dopo l’esecuzione il sistema cambia stato e lo comunica attraverso il linguaggio di output",
-        "image": ""
-      },
-      {
-        "text": "L’utente osserva l’output e valuta i risultati",
-        "image": ""
-      },
-      {
-        "text": "L'inputvienetradotto nel linguaggio di base del sistema per attivare l’esecuzione",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "30) E’ corretto dire che, nella progettazione di dispositivi fisici, l’applicazione del concetto di affordance dovrebbe portare a:",
-    "answers": [
-      {
-        "text": "utilizzare forme specifi che suggeriscano come svolgere la funzione",
-        "image": ""
-      },
-      {
-        "text": "utilizzare in forma seriale le stesse forme per mappare funzioni diverse",
-        "image": ""
-      },
-      {
-        "text": "utilizzare solo dispositivi sostenibili economicamente",
-        "image": ""
-      },
-      {
-        "text": "utilizzare solo dispositivi che richiedano una forza fisica limitata",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "31) Che cosa si intende per scheumorfismo?",
-    "answers": [
-      {
-        "text": "E’ un ornamento apposto su un oggetto (digitale) allo scopo di richiamare le caratteristiche di un altro oggetto",
-        "image": ""
-      },
-      {
-        "text": "E’ un termine che indica le interfacce visuali caratterizzate da uno stile astratto",
-        "image": ""
-      },
-      {
-        "text": "E’ un termine che indica le interfacce realizzate in economia di mezzi",
-        "image": ""
-      },
-      {
-        "text": "E’ un termine utilizzato per caratterizzare tutte le tipologie di interfacce tangibili",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "32) E’ corretto dire, secondo gli studi di O’Brien, che i sei parametri che definiscono l’engagement:",
-    "answers": [
-      {
-        "text": "non sono indipendenti l’uno dall’altro",
-        "image": ""
-      },
-      {
-        "text": "comprendono il coinvolgimento percepito e la reversibilità delle azioni",
-        "image": ""
-      },
-      {
-        "text": "comprendono la consistenzae la durabilità",
-        "image": ""
-      },
-      {
-        "text": "non comprendono l’usabilità percepita",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "33) Con il termine interazione a manipolazione diretta:",
-    "answers": [
-      {
-        "text": "i si riferisce ad un paradigma di interazione basato sull’invisibilità degli oggetti",
-        "image": ""
-      },
-      {
-        "text": "ci si riferisce ad un paradigma di interazione nel quale le azioni sono irreversibili",
-        "image": ""
-      },
-      {
-        "text": "ci si riferisce ad un paradigma di interazione basato su WIMP",
-        "image": ""
-      },
-      {
-        "text": "ci si riferisce ad un paradigma di interazione basato su oggetti fisici",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "34) Nell'ambito delle tecniche di progettazionedi interfacce, è corretto dire che una storyboard sequenziale è assimilabile a:",
-    "answers": [
-      {
-        "text": "un prototipo orizzontale",
-        "image": ""
-      },
-      {
-        "text": "un prototipo verticale",
-        "image": ""
-      },
-      {
-        "text": "uno scenario",
-        "image": ""
-      },
-      {
-        "text": "una combinazione di un prototipo orizzontale con un prototipo verticale",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "35) E’ corretto dire che, nell’ambito delle interfacce olfattive,la diffusione di sistemi di output per la riproduzione di una grande varietà di odori è correntemente limitata:",
-    "answers": [
-      {
-        "text": "dal grande costo delle essenze necessarie",
-        "image": ""
-      },
-      {
-        "text": "dall’impossibilità di ottenere il risultato utilizzando una miscela di pochi odori di base",
-        "image": ""
-      },
-      {
-        "text": "dalla grande diffusione di allergie e dal conseguente rischio di shock anafilattici conseguenti all’uso di questo tipo di interfacce",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "36) Tra le caratteristiche dei nuovi stili di interazione gestuale pensati per tablet e smartphone possiamo indicare:",
-    "answers": [
-      {
-        "text": "la disponibilità e l’omogeneità di operazioni non distruttive, come l’undo",
-        "image": ""
-      },
-      {
-        "text": "la disponibilità di gesti consistenti e omogenei attraverso le diverse piattaforme di utilizzo",
-        "image": ""
-      },
-      {
-        "text": "la mancanza di linee guida riconosciute per il controllo gestuale",
-        "image": ""
-      },
-      {
-        "text": "la ridotta espressività del linguaggio di input",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "37) Il layout del tastierino numerico delle tastiere di computer deriva:",
-    "answers": [
-      {
-        "text": "da uno dei primi studi sul layout dei tastierini numerici di Bell Labs",
-        "image": ""
-      },
-      {
-        "text": "dal layout dei calcolatori meccanici",
-        "image": ""
-      },
-      {
-        "text": "dal layout delle tastiere telefoniche",
-        "image": ""
-      },
-      {
-        "text": "dal fatto che 0 e 1 sono i numeri più utilizzzati",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "38) E’corretto dire che, nella progettazione di dispositivi fisici, l’applicazione del concetto di affordance dovrebbe portare a:",
-    "answers": [
-      {
-        "text": "utilizzare forme specifiche che suggeriscano come svolgere la funzione",
-        "image": ""
-      },
-      {
-        "text": "utilizzare in forma seriale le stesse forme per mappare funzioni diverse",
-        "image": ""
-      },
-      {
-        "text": "utilizzare solo dispositivi sostenibili economicamente",
-        "image": ""
-      },
-      {
-        "text": "utilizzare solo dispositivi che richiedano una forza fisica limitata",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "39) L'euristica Consistenza e Standard indica che:",
-    "answers": [
-      {
-        "text": "gli oggetti, le azioni e le opzioni che fanno parte dell’interazione vanno resi visibili",
-        "image": ""
-      },
-      {
-        "text": "non vanno seguitele convenzioni delle piattaforme su cui si sta lavorando",
-        "image": ""
-      },
-      {
-        "text": "i widget utilizzati per l'interazione dovrebbero essere di grandi dimensioni e posti a breve distanza",
-        "image": ""
-      },
-      {
-        "text": "gli utenti non dovrebbero preoccuparsi di dover capire se parole, situazioni e azioni diverse significano la stessa cosa",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "40) Che cosa si intende per scheumorfismo?",
-    "answers": [
-      {
-        "text": "E’ un termine che indica le interfacce visuali caratterizzate da uno stile astratto",
-        "image": ""
-      },
-      {
-        "text": "E’ un ornamento apposto su un oggetto (digitale) allo scopo di richiamare le caratteristiche di un altro",
-        "image": ""
-      },
-      {
-        "text": "E’ un termine utilizzato per caratterizzare tutte le tipologie di interfacce tangibili",
-        "image": ""
-      },
-      {
-        "text": "E’ un termine che indica le interfacce realizzate in economiadi mezzi",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "41) Definire la relazione tra usabilità ed engagement:",
-    "answers": [
-      {
-        "text": "il concetto di engagementè utilizzato solo nella progettazione di sistemi di gioco interattivi",
-        "image": ""
-      },
-      {
-        "text": "sono due concetti separati che vengono utilizzati in aree di applicazione completamente diverse",
-        "image": ""
-      },
-      {
-        "text": "l'usabilità percepita è parte della definizione del concetto di engagement(secondo la definizione di O'Brien)",
-        "image": ""
-      },
-      {
-        "text": "L’engagementè parte della definizionedel concetto di usabilità (secondo la definizione ISO)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "42) Nell’ambito del modello definito come Interaction Framework, che cosa succede durante la fase di prestazione?",
-    "answers": [
-      {
-        "text": "Il linguaggio dei compitidell’utente viene tradottoinlinguaggio di input",
-        "image": ""
-      },
-      {
-        "text": "Dopo l’esecuzione il sistema cambia stato e lo comunica attraverso il linguaggio di output",
-        "image": ""
-      },
-      {
-        "text": "L’utente osserva l’output e valuta i risultati",
-        "image": ""
-      },
-      {
-        "text": "L'inputvienetradotto nel linguaggio di base del sistema per attivare l’esecuzione",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "43) Un’interfaccia tangibile",
-    "answers": [
-      {
-        "text": "è un’interfaccia utente nella quale l’utente interagiscecon l’informazione digitale trasmettendo l’input attraverso oggetti fisici",
-        "image": ""
-      },
-      {
-        "text": "è un’interfaccia utente nella quale l’utente interagisce con gli oggetti tangibili trasmettendo l’input attraverso la mediazione dioggetti digitali",
-        "image": ""
-      },
-      {
-        "text": "è un’interfaccia che va oltre lo stadio prototipale, presentandosi come un sistema interattivo ben costruito",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "44) I menu pull-down si differenziano rispetto ai menu fall-down perché",
-    "answers": [
-      {
-        "text": "vengono aperti automaticamente al passaggio del puntatore sui titoli",
-        "image": ""
-      },
-      {
-        "text": "vengono aperti posizionando il puntatore sui titoli e facendo click",
-        "image": ""
-      },
-      {
-        "text": "vengono aperti posizionando il puntatore sui titoli e rilasciando il tasto del mouse",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "45) Un campoX3D",
-    "answers": [
-      {
-        "text": "è un’area in cui viene partizionato lo spazio della scena 3D",
-        "image": ""
-      },
-      {
-        "text": "è un’entità di secondo livello che definisce lo stato di un nodo",
-        "image": ""
-      },
-      {
-        "text": "è un’entità di primolivello per definire primitivegeometrichedi interazione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "46) In un prototipo verticale di un'interfaccia",
-    "answers": [
-      {
-        "text": "viene ridotto il livello di funzionalità del sistema, dando luogo a un'interfaccia con features non completamente implementate",
-        "image": ""
-      },
-      {
-        "text": "viene ridotto il numero di features considerate, mostrando il funzionamento del sistema solo lungo percorsi precedentemente pianificati",
-        "image": ""
-      },
-      {
-        "text": "viene ridotto il numero di features considerate, ma quelle selezionate vengono pienamente implementate",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "47) Le interfacce industriali si distinguono dalle interfacce a manipolazione diretta perché",
-    "answers": [
-      {
-        "text": "l'utente riceve un doppio output, dall'interfaccia e dal sistema",
-        "image": ""
-      },
-      {
-        "text": "l'utente opera un doppio input, verso l'interfaccia e verso il sistema",
-        "image": ""
-      },
-      {
-        "text": "l'utente riceve un unico output derivante dall'interfaccia",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "48) Il ciclo di esecuzione e di valutazione di Norman",
-    "answers": [
-      {
-        "text": "definisce un modello di interazione uomo calcolatore basato sulla specifica di 7 fasi corrispondenti alle attività dell’utente",
-        "image": ""
-      },
-      {
-        "text": "definisce un modello di interazione uomo calcolatore basato sulla specifica di 7 golfi corrispondenti ai punti critici dell’interazione",
-        "image": ""
-      },
-      {
-        "text": "definisce un modello di interazione uomo calcolatore basato sulla definizione di 4 componenti principali, ognuna delle quali è caratterizzata da un proprio linguaggio",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "49) Comparando i sistemi interattivi basati sull’uso del mouse e quelli basati sull’utilizzo delle gestures,è generalmente corretto affermare che:",
-    "answers": [
-      {
-        "text": "i secondi sistemi si differenziano dai primi per un’espressività più limitata",
-        "image": ""
-      },
-      {
-        "text": "i primi sistemi si differenziano dai secondi per una maggiore standardizzazione",
-        "image": ""
-      },
-      {
-        "text": "non ci sono differenze significative dal punto di vista dell’espressività e della standardizzazione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "50) Le applicazioni context-aware vengono utilizzate in molti domini applicativi, e per scopi diversi, tra i quali",
-    "answers": [
-      {
-        "text": "la comunicazione di un maggior numero di informazioni",
-        "image": ""
-      },
-      {
-        "text": "la diminuzione del coinvolgimento emotivo dell’utente",
-        "image": ""
-      },
-      {
-        "text": "la diminuzione del carico cognitivo dell’utente",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "51) Nell’ambito dell’informatica pervasiva, sono utilizzati studi teorici a supporto della progettazione e della valutazione dell’esperienza. Tra i vari studi, èconsiderata la teoria delle attività, nella quale:",
-    "answers": [
-      {
-        "text": "si da spazio all’aspetto dell’improvvisazione tipico del comportamento umano",
-        "image": ""
-      },
-      {
-        "text": "le azioni derivano da scopi pre-pianificati",
-        "image": ""
-      },
-      {
-        "text": "l’uomo viene considerato come parte di un sistema più ampio",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "52) Nell’ambito della descrizione delparadigma a manipolazione direttasi fa riferimento alla metafora mondo modello, per la quale:",
-    "answers": [
-      {
-        "text": "l’interfaccia non viene percepita comeuna mediazione con il sistema sottostante, ma come il sistema",
-        "image": ""
-      },
-      {
-        "text": "l’interfaccia dovrebbe essere costruita come un modello in scala del mondo",
-        "image": ""
-      },
-      {
-        "text": "e azioni non sono reversibili, come succede appunto nel mondo reale",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "53) La tastiera DVORAK",
-    "answers": [
-      {
-        "text": "condivide con la tastiera QWERTY lo stesso layout fisico dei tasti",
-        "image": ""
-      },
-      {
-        "text": "diminuisce la fatica di input e raddoppia la velocità di input",
-        "image": ""
-      },
-      {
-        "text": "permette di eseguire il 50% delle sequenze senza spostare le dita",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "54) I punti di forza di un’interfaccia tangibile includono:",
-    "answers": [
-      {
-        "text": "lascalabilità",
-        "image": ""
-      },
-      {
-        "text": "a mancanza di relazione con il contesto",
-        "image": ""
-      },
-      {
-        "text": "il parallelismo spaziale",
-        "image": ""
-      },
-      {
-        "text": "la mancanza di affordance",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "55) Una finestra di dialogo modale",
-    "answers": [
-      {
-        "text": "permette di continuare in ogni momento l’interazione con le altre componenti dell’interfaccia che non fanno parte della finestra di dialogo",
-        "image": ""
-      },
-      {
-        "text": "non permette di continuare l’interazione con le altre componenti dell’interfaccia che non fanno parte della finestra di dialogo fino alla conferma di una delle opzioni contenute nella finestra di dialogo",
-        "image": ""
-      },
-      {
-        "text": "permette di continuare l’interazione con le altre componenti dell’interfaccia, ma solo nel caso di un imminente crash di sistema",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "56) La mixed reality",
-    "answers": [
-      {
-        "text": "è un altro modo per riferirsi alla realtà virtuale immersiva",
-        "image": ""
-      },
-      {
-        "text": "descrive tutto il continuum nel quale si collocano tutte le diverse miscele di realtà e virtualità",
-        "image": ""
-      },
-      {
-        "text": "è un altro modo per riferirsi alla realtà aumentata",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "57) L’affordance di un oggetto consiste",
-    "answers": [
-      {
-        "text": "nella capacità dell’oggetto di suggerire, attraverso il proprio nome, le possibilità di interazione",
-        "image": ""
-      },
-      {
-        "text": "nel grado di convenienza economica dell’oggetto",
-        "image": ""
-      },
-      {
-        "text": "nella capacità dell’oggetto di suggerire, attraverso la propria forma, le possibilità di interazione",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "58) Qual è il significato dell’euristica ‘Riconoscimento anziché ricordo’?",
-    "answers": [
-      {
-        "text": "che è vantaggioso per l’interazione rendere gli oggetti e le azioni visibili",
-        "image": ""
-      },
-      {
-        "text": "che è vantaggioso per l’interazione richiedere all’utente di memorizzare azioni e opzioni",
-        "image": ""
-      },
-      {
-        "text": "che è vantaggioso per l’interazione riconoscere il linguaggio con il quale è stata costruita l’applicazione.",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "59) La legge di Fitts afferma che è vantaggioso per l’interazione:",
-    "answers": [
-      {
-        "text": "definire oggetti interattivi di piccole dimensioni e posti a grande distanza tra di loro",
-        "image": ""
-      },
-      {
-        "text": "definire oggetti interattivi di grandidimensioni e posti a piccoladistanza tra di loro",
-        "image": ""
-      },
-      {
-        "text": "definire oggetti interattivi di grandidimensioni e posti a grande distanza tra di loro",
-        "image": ""
-      },
-      {
-        "text": "definire oggetti interattivi di piccole dimensioni e posti a piccola distanza tra di loro",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "60) Il meccanismo di routing per i mondi X3D serve a:",
-    "answers": [
-      {
-        "text": "definire un meccanismo di navigazione per gli utenti del mondo interattivo",
-        "image": ""
-      },
-      {
-        "text": "definire un meccanismo di rotazione automatica per il nodo a cui è applicato",
-        "image": ""
-      },
-      {
-        "text": "definire un meccanismo di trasmissione degli eventi attraverso i nodi che definiscono il mondo X3D",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "61)Qual è il significato dell’euristica ‘Consistenza e standard’?",
-    "answers": [
-      {
-        "text": "che è necessario seguire nella progettazione unnumero consistente di standard",
-        "image": ""
-      },
-      {
-        "text": "che è necessario seguire le convenzioni delle piattaforme su cui si lavora",
-        "image": ""
-      },
-      {
-        "text": "che è possibile utilizzare anche etichette diverse per fare riferimento ad uno stesso oggetto purché si seguano gli standard",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "62)I punti di forza delle interfacce tangibili (TUI) includono",
-    "answers": [
-      {
-        "text": "la versatilità e la malleabilità",
-        "image": ""
-      },
-      {
-        "text": "la relazione con il contesto e l’utilizzo delle affordances",
-        "image": ""
-      },
-      {
-        "text": "il pensiero tangibile e la scalabilità",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "63)La realtà aumentata",
-    "answers": [
-      {
-        "text": "prevede l’esclusiva possibilità di sincronizzazione gli elementi virtuali con l’occhio dell’utente",
-        "image": ""
-      },
-      {
-        "text": "prevede la possibilità di sincronizzazione gli oggetti virtuali con una telecamera non coincidente con il punto di vista dell’utente",
-        "image": ""
-      },
-      {
-        "text": "prevede solo forme di sincronizzazione di oggetti reali",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "64)La definizione di golfo dell’esecuzione nel ciclo di esecuzione di Norman (uno dei modelli di interazione uomo calcolatore più famosi) indica",
-    "answers": [
-      {
-        "text": "la differenza tra l’osservazione della risposta del sistema e la prestazione",
-        "image": ""
-      },
-      {
-        "text": "la differenza tra la formulazione delle azioni dell’utente e le azioni consentite",
-        "image": ""
-      },
-      {
-        "text": "la differenza tra la presentazione dello stato del sistema e l’aspettativa dell’utente",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "65)In un’interfaccia WIMP i menu linearisono preferibili ai menu a tortaperché",
-    "answers": [
-      {
-        "text": "permettono di aumentare leggermente la produttività",
-        "image": ""
-      },
-      {
-        "text": "diminuiscono lo spazio necessario per l’interfaccia",
-        "image": ""
-      },
-      {
-        "text": "permettono di accedere nello stesso tempo ad un numero molto elevato di elementi",
-        "image": ""
-      },
-      {
-        "text": "garantiscono un aumento della soddisfazione soggettiva degli utenti",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "66)In un prototipo orizzontaledi un'interfaccia",
-    "answers": [
-      {
-        "text": "viene ridotto il numero di features considerate, mostrando il funzionamento del sistema solo lungo percorsi precedentemente pianificati",
-        "image": ""
-      },
-      {
-        "text": "viene ridotto il numero di features considerate, ma quelle selezionate vengono pienamente implementate",
-        "image": ""
-      },
-      {
-        "text": "viene ridotto il livello di funzionalità del sistema, dando luogo a un'interfaccia con features non completamente implementate",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "67) Nell’ambito delle interfacce per Apple Watch con il termine complicazione ci si riferisce a:",
-    "answers": [
-      {
-        "text": "piccoli widget che forniscono informazioni aggiuntive oltre all’indicazione del tempo",
-        "image": ""
-      },
-      {
-        "text": "una situazione di blocco dell’interfaccia che provoca problemi persistenti agli utenti",
-        "image": ""
-      },
-      {
-        "text": "app particolarmente complesse che richiedono il pieno utilizzo delle capacità computazionali del dispositivo",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "68) Nell'ambito della definizione di engagement, la durabilità esprime:",
-    "answers": [
-      {
-        "text": "ilcoinvolgimento emotivo del soggetto coinvolto",
-        "image": ""
-      },
-      {
-        "text": "l’impegno cognitivo del soggetto coinvolto",
-        "image": ""
-      },
-      {
-        "text": "Il grado di novità dell’esperienza con il sistema interattivo percepito dal soggetto coinvolto",
-        "image": ""
-      },
-      {
-        "text": "la propensione del soggetto coinvolto a ripetere l’esperienza",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "69) Nell’ambito dell’informatica pervasiva si intende per ambient display:",
-    "answers": [
-      {
-        "text": "l’uso dell’ambiente fisico come interfaccia per l’informazione digitale",
-        "image": ""
-      },
-      {
-        "text": "l’utilizzo esclusivo di schermi di schermi di grande dimensione, per permettere una comunicazione parallela a più utenti",
-        "image": ""
-      },
-      {
-        "text": "l’utilizzo esclusivo della visualità per comunicare l’output di un sistema pervasivo",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "70) La tastiera DVORAK si differenzia dalla tastiera MICHELA per:",
-    "answers": [
-      {
-        "text": "l'utilizzo della sola mano sinistra",
-        "image": ""
-      },
-      {
-        "text": "una minore velocità nella digitazione",
-        "image": ""
-      },
-      {
-        "text": "l'utilizzo della sola mano destra",
-        "image": ""
-      },
-      {
-        "text": "la maggiore adattabilità a bambini di età pre-scolare",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "71) I punti di forza di un’interfaccia tangibile includono:",
-    "answers": [
-      {
-        "text": "la scalabilità",
-        "image": ""
-      },
-      {
-        "text": "la mancanza di relazione con il contesto",
-        "image": ""
-      },
-      {
-        "text": "il parallelismo spaziale",
-        "image": ""
-      },
-      {
-        "text": "la mancanza di affordance",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "72) Nell’ambito delle tecniche di prototipazione di un’interfaccia, che cosa si intende per branching storyboard?",
-    "answers": [
-      {
-        "text": "è uno storyboard nel quale vengono proposti gli screenshots dell’interfaccia senza alcun ordinamento particolare",
-        "image": ""
-      },
-      {
-        "text": "è uno storyboard che utilizza uno schema non-lineare per mostrare i cambiamenti di stato dell’interfaccia a seguito dell’azione dell’utente o di altri eventi",
-        "image": ""
-      },
-      {
-        "text": "è uno storyboard che utilizza uno schema lineare per mostrare i cambiamenti di stato dell’interfaccia a seguito dell’azione dell’utente o di altri eventi",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "73) Per quanto riguarda i tempi di risposta ad uno stimolo sensoriale, è corretto affermare che:",
-    "answers": [
-      {
-        "text": "il tempo motorio è funzione del canale sensoriale e aumenta in caso di segnali misti (es. segnale uditivo + segnale visivo)",
-        "image": ""
-      },
-      {
-        "text": "il tempo motorio è funzione dell’età e della salute dell’individuo",
-        "image": ""
-      },
-      {
-        "text": "il tempo motorio è funzione del canale sensoriale e diminuisce in caso di segnali misti (es. segnale uditivo + segnale visivo)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "74) Nell’ambito della progettazione ergonomica, si indica come keyhole effect (effetto buco della serratura):",
-    "answers": [
-      {
-        "text": "la situazione positiva derivante dalla possibilità di concentrarsi solo sul proprio compito senza essere distratti da visualizzazioni di flussi informativi che devono essere gestiti da altri operatori",
-        "image": ""
-      },
-      {
-        "text": "la situazione negativa derivante all’incapacità di avere una visione complessiva dello stato del sistema con cui più operatori interagiscono",
-        "image": ""
-      },
-      {
-        "text": "la pericolosa distrazione derivante dall’osservazione del comportamento dei colleghi che operano nelle postazioni adiacenti o nella sala di controllo vicina",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "75) Nell’ambito del modello definito come Interaction Framework, la fase di articolazione indica:",
-    "answers": [
-      {
-        "text": "la fase di conversione del linguaggio di base in linguaggio di output",
-        "image": ""
-      },
-      {
-        "text": "la fase di conversione del linguaggio del compito in linguaggio di input",
-        "image": ""
-      },
-      {
-        "text": "la fase di conversione del linguaggio di output in linguaggio del compito",
-        "image": ""
-      },
-      {
-        "text": "la fase di conversione del linguaggio di input in linguaggio del compito",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "76) Nell'ambito delle tecniche di prototipazione, che cosa caratterizza la tecnica del Mago di Oz?",
-    "answers": [
-      {
-        "text": "l'utilizzo di schemi su carta (paper mockup) anziché programmi funzionanti, con un esperto che fa la parte del computer e mostra lo schema di interfaccia successivo quando colui che prova l'applicazione seleziona un’azione sullo schema corrente",
-        "image": ""
-      },
-      {
-        "text": "la necessità, da parte dei valutatori, di indossare i costumi dei protagonisti del noto romanzo, al fine di aumentare l'engagement dei bambini che provano l'interfaccia",
-        "image": ""
-      },
-      {
-        "text": "l'utilizzo di una configurazione di computer più potente di quella che verrà utilizzata come target per il rilascio dell’applicazione",
-        "image": ""
-      },
-      {
-        "text": "l'utilizzo di un umano dietro le scene che si prenda carico delle operazioni troppo difficili da programmare",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "77) Indicare quale delle seguenti affermazioni contrasta con le 10 euristiche di usabilità di Nielsen:",
-    "answers": [
-      {
-        "text": "il sistema dovrebbe parlare il linguaggio dell'utente",
-        "image": ""
-      },
-      {
-        "text": "nel processo di interazione gli oggetti, le azioni e le opzioni vanno rese visibili",
-        "image": ""
-      },
-      {
-        "text": "ogni elemento informativo presentato nella finestra di output compete con gli altri e ne diminuisce la visibilità",
-        "image": ""
-      },
-      {
-        "text": "le indicazioni di aiuto non dovrebbero essere troppo focalizzate sui compiti dell'utente",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "78) Nell’ambito delle tecniche di prototipazione di un’interfaccia, la cosiddettanarrative storyboardsi differenzia dallasequentialstoryboard:",
-    "answers": [
-      {
-        "text": "per la definizione di percorsi non-lineari che descrivono le transizioni di stato dell’interfaccia",
-        "image": ""
-      },
-      {
-        "text": "per la descrizione del contesto in cui avviene l’interazione",
-        "image": ""
-      },
-      {
-        "text": "per la presentazione degli screenshots dell’interfaccia senza alcun ordinamento particolar",
-        "image": ""
-      },
-      {
-        "text": "per una migliore accuratezza nella rappresentazione dell’interfaccia",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "79) Nell’ambito del modello definito come Interaction Framework, che cosa succede durante lafase di prestazione?",
-    "answers": [
-      {
-        "text": "Dopo l’esecuzione il sistema cambia stato e lo comunica attraverso il linguaggio di output",
-        "image": ""
-      },
-      {
-        "text": "L’utente osserva l’output e valuta i risultati",
-        "image": ""
-      },
-      {
-        "text": "Il compito dell’utentevienearticolato all’interno del linguaggio di input",
-        "image": ""
-      },
-      {
-        "text": "L'inputvienetradotto nel linguaggio di base del sistema per attivare l’esecuzione",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "80) Nell’ambito dell’informatica pervasiva si considera il concetto di engagement, che tra i parametri che lo definiscono comprende:",
-    "answers": [
-      {
-        "text": "l’esteticae la visibilità degli oggetti",
-        "image": ""
-      },
-      {
-        "text": "la durabilità e le azioni incrementali",
-        "image": ""
-      },
-      {
-        "text": "il coinvolgimento percepitoe l'attenzione focalizzata",
-        "image": ""
-      },
-      {
-        "text": "l’usabilità percepita e la correttezza sintattica",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "81) Nell’ambito della risposta sensoriale ad uno stimolo esterno, è corretto affermare che il tempo di reazione:\ndiminuisce quando lo stimolo avviene su un canale misto (es. uditivo e visivo)",
-    "answers": [
-      {
-        "text": "è indipendente dal canale sensoriale",
-        "image": ""
-      },
-      {
-        "text": "dipende dalla salute del soggetto",
-        "image": ""
-      },
-      {
-        "text": "dipende dall'età del soggetto",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "82) Le interfaccea manipolazione diretta si caratterizzano per:",
-    "answers": [
-      {
-        "text": "l'irreversibilità delle azioni",
-        "image": ""
-      },
-      {
-        "text": "l'invisibilità degli oggetti",
-        "image": ""
-      },
-      {
-        "text": "l'utilizzo di linguaggi di comando",
-        "image": ""
-      },
-      {
-        "text": "la correttezza sintattica di tutte le azioni",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "84) Il concetto di affordanceesprime:",
-    "answers": [
-      {
-        "text": "la capacità di un oggetto fisico di suggerire, tramite la propria forma, la funzione a cui è preposto",
-        "image": ""
-      },
-      {
-        "text": "la possibilità di produzionedi un oggetto fisico a basso costo, per permettere una distribuzione ampia",
-        "image": ""
-      },
-      {
-        "text": "la capacità di un oggetto fisico di suggerire, tramite etichette apposte sull’oggetto stesso, la funzione a cui è preposto",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "85) Nell’ambito delle attività di valutazione dell’usabilità dell’interfaccia le tecniche di ispezione:",
-    "answers": [
-      {
-        "text": "prevedono che un elevato numero di utenti esaminino gli aspetti di usabilità di un prodotto",
-        "image": ""
-      },
-      {
-        "text": "prevedono un’accurata analisi del dispositivo prima della release finale, ottenuta anche attraverso il disassemblaggio e l’ispezione delle sue componenti",
-        "image": ""
-      },
-      {
-        "text": "prevedono che un numero limitato di specialisti esaminino gli aspetti di usabilità di un prodotto",
-        "image": ""
-      },
-      {
-        "text": "prevedono che i valutatori dell’usabilità raccolgano dati attraverso un numero sostanzioso di questionari composti di domande aperte e chiuse",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "86) Lo studio “Why don’t Families Get along with Eco-Feedback Technologies?� analizza le tecnologie di eco-feedback in un contesto familiare.Quali delle seguenti affermazioni corrisponde a indicazioni che si possono derivare dallo studio?",
-    "answers": [
-      {
-        "text": "il sistema di eco-feedback dovrebbe dare solo informazioni generali, lasciando agli utenti il compito di elaborare strategie di azione giornaliere",
-        "image": ""
-      },
-      {
-        "text": "non ci sono benefici nel rendere il sistema di eco-feedback accessibile atutta la famiglia, dal momento che le decisioni vengono comunque prese dagli adulti",
-        "image": ""
-      },
-      {
-        "text": "il sistema di eco-feedback dovrebbe includere una vista a volo d’uccello dei consumi della famiglia",
-        "image": ""
-      },
-      {
-        "text": "il sistema di eco-feedback, per ragioni di privacy, non dovrebbe stimolare una mutua consapevolezza dei consumi individuali",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "87) Indicare quale delle seguenti affermazioni, relative all'informatica pervasiva e all'informatica interattiva, è vera.",
-    "answers": [
-      {
-        "text": "l'informatica pervasiva richiede che l'utente comunichi esplicitamente al sistema che cosa fare",
-        "image": ""
-      },
-      {
-        "text": "l'informatica interattiva richiede necessariamente un ambiente dotato di sensori",
-        "image": ""
-      },
-      {
-        "text": "l'informatica interattiva prevede che l'utente possa essere ignaro del fatto che stia",
-        "image": ""
-      },
-      {
-        "text": "nell'informatica pervasiva è possibile che l'output sia implicito",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "88) Indicare quale delle seguenti affermazioni, relative ai concetti di usabilità ed engagement, è vera.",
-    "answers": [
-      {
-        "text": "usabilità ed engagement sono due concetti separati che vengono utilizzati in ambiti applicativi completamente diversi",
-        "image": ""
-      },
-      {
-        "text": "il concetto di usabilità viene compreso nella definizione del concetto di engagement (secondo la definizione di O'Brien)",
-        "image": ""
-      },
-      {
-        "text": "il concetto di engagement viene compreso nella definizione del concetto di usabilità (secondo la definizione ISO)",
-        "image": ""
-      },
-      {
-        "text": "il concetto di engagement viene utilizzato solo nell'ambito della progettazione di sistemi interattivi ludici",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "89) Indicare quale delle seguenti affermazioni contrasta con una delle 10 euristiche di usabilità di Nielsen:",
-    "answers": [
-      {
-        "text": "vanno predisposte uscite di emergenza per lasciare lo stato dell'interazione in cui ci si trova",
-        "image": ""
-      },
-      {
-        "text": "l'interfaccia non deve seguire le convenzioni del mondo reale",
-        "image": ""
-      },
-      {
-        "text": "gli utenti non devono preoccuparsi se le parole o icone diverse usate nell'interfaccia indicano la stessa cosa",
-        "image": ""
-      },
-      {
-        "text": " i messaggi di errore non dovrebbero usare un linguaggio per esperti",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "90) Nell’ambito delle tecniche di prototipazione di un’interfaccia, una narrative storyboard si distingue per:",
-    "answers": [
-      {
-        "text": "l'utilizzo di una voce narrante che descrive lo scenario dell'interfaccia",
-        "image": ""
-      },
-      {
-        "text": "l'utilizzo di uno schema non lineare per mostrare i cambiamenti di stato dell’interfaccia a seguito delle azioni dell'utente",
-        "image": ""
-      },
-      {
-        "text": "l'utilizzo di uno schema lineare per mostrare i cambiamenti di stato dell’interfaccia",
-        "image": ""
-      },
-      {
-        "text": "l'utilizzo di una voce narrante che descrive il contesto dell'interfaccia",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "91) Le interfacce industriali si distinguono dalle interfacce a manipolazione diretta:",
-    "answers": [
-      {
-        "text": "per avere solamente un’interfaccia di input",
-        "image": ""
-      },
-      {
-        "text": "perché il feedback per l’operatore non deriva solo dall’interfaccia di output",
-        "image": ""
-      },
-      {
-        "text": "perché il feedback per l’operatore deriva solo dall’osservazione diretta del mondo reale",
-        "image": ""
-      },
-      {
-        "text": "per avere solamente un’interfaccia di output",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "92) Il paradigma a manipolazione diretta si distingue dal paradigma linguistico:",
-    "answers": [
-      {
-        "text": "perché è più difficile da apprendere",
-        "image": ""
-      },
-      {
-        "text": "per la possibilità di applicare contemporaneamente un determinato comando a più oggetti",
-        "image": ""
-      },
-      {
-        "text": "per l'implicita correttezza sintattica di tutte le azioni che si possono compiere",
-        "image": ""
-      },
-      {
-        "text": "per l'invisibilità degli oggetti che si usano nell'interazione",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "93) Il layout del tastierino numerico usato in telefonia e basato su una matrice rettangolare nella quale i numeri 1 2 3 stanno nella riga superiore deriva:",
-    "answers": [
-      {
-        "text": "dalla trasposizione del layout utilizzato nelle calcolatrici meccaniche",
-        "image": ""
-      },
-      {
-        "text": "da uno studio specifico sulla fatica di utilizzo svolto da IBM",
-        "image": ""
-      },
-      {
-        "text": "dalla versione estesa della tastiera DVORAK",
-        "image": ""
-      },
-      {
-        "text": "da uno studio specifico sulle preferenze da parte degli utenti svolto da Bell Labs",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "94) Il concetto di eco-feedback:",
-    "answers": [
-      {
-        "text": "si riferisce alle modalità di utilizzo del canale sonoro per la comunicazione a grande distanza in sistemi pervasivi",
-        "image": ""
-      },
-      {
-        "text": "si riferisce alla possibilità di fornire agli utenti informazioni sulle conseguenze delle loro azioni per l'ambiente",
-        "image": ""
-      },
-      {
-        "text": "si riferisce alla possibilità di regolare automaticamente le condizioni ambientali per gli utenti attraverso un sistema di controllo",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "95) Nell’ambito del modello definito come Interaction Framework, il mapping tra il linguaggio del compito e il linguaggio di input dell'interfaccia viene svolto nella fase di:",
-    "answers": [
-      {
-        "text": "presentazione",
-        "image": ""
-      },
-      {
-        "text": "prestazione",
-        "image": ""
-      },
-      {
-        "text": "articolazione",
-        "image": ""
-      },
-      {
-        "text": "osservazione",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "96) In uno degli articoli proposti nella bibliografia del corso (Comparative Feedback in the Street: Exposing Residential Energy Consumption on House Façades) gli autori descrivono uno studio sull’impatto sui comportamenti dovuti alla condivisione dei dati dei consumi energetici sulle facciate esterne delle abitazioni, e concludono che:",
-    "answers": [
-      {
-        "text": "è essenziale dare informazioni sui consumi in tempo reale",
-        "image": ""
-      },
-      {
-        "text": "è importante dare informazioni chiare su come cambiare i comportamenti",
-        "image": ""
-      },
-      {
-        "text": "è importante mostrare comparazione sui consumi, ma solo a lungo termine",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "97) Nell'ambito dell'informatica pervasiva il principio di intelligenza appropriata:",
-    "answers": [
-      {
-        "text": "indica che il sistema deve eseguire almeno il 5% di previsioni corrette ed utili",
-        "image": ""
-      },
-      {
-        "text": "indica che è necessario un quoziente intellettivo minimo (espresso come valore QI) per poter interagire con il sistema",
-        "image": ""
-      },
-      {
-        "text": "indica che il sistema non deve causare problemi nel caso in cui l'azione del sistema derivi da una previsione sbagliata",
-        "image": ""
-      },
-      {
-        "text": "indica che il sistema deve essere percepito come una controparte umana",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "98) Nell’ambito del modello definito come Interaction Framework, il mapping tra il linguaggio di base e il linguaggio di output dell'interfaccia viene svolto nella fase di:",
-    "answers": [
-      {
-        "text": "osservazione",
-        "image": ""
-      },
-      {
-        "text": "articolazione",
-        "image": ""
-      },
-      {
-        "text": "presentazione",
-        "image": ""
-      },
-      {
-        "text": "prestazione",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "99) Definire quale dei seguenti parametri non viene considerato nella definizione di engagement secondo O'Brien:",
-    "answers": [
-      {
-        "text": "coinvolgimento percepito",
-        "image": ""
-      },
-      {
-        "text": "costo",
-        "image": ""
-      },
-      {
-        "text": "durabilità",
-        "image": ""
-      },
-      {
-        "text": "attenzione focalizzata",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "100) Nell’ambito delle tecniche di prototipazione di un’interfaccia, una branching storyboard si distingue per:",
-    "answers": [
-      {
-        "text": "l'utilizzo di uno schema lineare per mostrare i cambiamenti di stato dell’interfaccia a seguito dell’azione dell’utente o di altri eventi",
-        "image": ""
-      },
-      {
-        "text": "l'utilizzo di uno schema non lineare per mostrare i cambiamenti di stato dell’interfaccia a seguito delle sole azioni esplicite dell'utente",
-        "image": ""
-      },
-      {
-        "text": "l'utilizzo di uno schema non lineare per mostrare i cambiamenti di stato dell’interfaccia, anche a seguito dei cambiamenti di valore di alcune variabili del contesto",
-        "image": ""
-      },
-      {
-        "text": "l'utilizzo di uno schema non ordinato per mostrare i cambiamenti di stato dell’interfaccia a seguito dell’azione dell’utente o di altri eventi",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "101) Correntemente le interfacce basate sullo stile di interazione WIMP si distinguono da quelle basate sullo stile di interazione touch-based per:",
-    "answers": [
-      {
-        "text": "la maggiore espressività nell'input",
-        "image": ""
-      },
-      {
-        "text": "la maggiore consistenza",
-        "image": ""
-      },
-      {
-        "text": "la minore scopribilità",
-        "image": ""
-      },
-      {
-        "text": "la minore scalabilità",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "102) Nell’ambito dei sistemi di input, la motivazione per la quale tuttora la tastiera QWERTY è la più utilizzata è:",
-    "answers": [
-      {
-        "text": "la maggiore velocità rispetto alle soluzioni concorrent",
-        "image": ""
-      },
-      {
-        "text": "la minore fatica rispetto alle soluzioni concorrent",
-        "image": ""
-      },
-      {
-        "text": "l'ordinamento ottimale del layout",
-        "image": ""
-      },
-      {
-        "text": "'inerzia tecnologica",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "103) Le interfacce industriali si distinguono dalle interfacce a manipolazione diretta:",
-    "answers": [
-      {
-        "text": "per avere solamente un’interfaccia di input",
-        "image": ""
-      },
-      {
-        "text": "perché il feedback per l’operatore non deriva solo dall’interfaccia di output",
-        "image": ""
-      },
-      {
-        "text": "per avere solamente un’interfaccia di output",
-        "image": ""
-      },
-      {
-        "text": " perché il feedback per l’operatore deriva solo dall’osservazione diretta del mondo reale",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "104) In uno degli articoli proposti nella bibliografia del corso (SINAIS from Fanal) gli autori descrivono la progettazione e la valutazione di un sistema di eco-feedback giungendo alla conclusione che:",
-    "answers": [
-      {
-        "text": "le soluzioni di eco-feedback basate su semplici rappresentazioni numeriche alla fine sono le più efficaci nel suscitare per lungo tempo l'attenzione degli utenti",
-        "image": ""
-      },
-      {
-        "text": "il feedback basato su rappresentazioni artistiche è un'ottima alternativa alla visualizzazione di informazione dettagliata",
-        "image": ""
-      },
-      {
-        "text": "il feedback basato su rappresentazioni artistiche dovrebbe comunque essere associato alla visualizzazione dettagliata dell'informazione",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "105) E' corretto affermare che il numero di colori a cui viene assegnato un nome :",
-    "answers": [
-      {
-        "text": "dipende dalle culture.",
-        "image": ""
-      },
-      {
-        "text": "non cambia a seconda delle culture.",
-        "image": ""
-      },
-      {
-        "text": "non cambia (ma possono cambiare i nomi a seconda delle culture).",
-        "image": ""
-      },
-      {
-        "text": "é correlato alle condizioni climatiche.",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "106) E' corretto dire che il software Graffiti per il riconoscimento della scrittura:",
-    "answers": [
-      {
-        "text": "permette il riconoscimento di qualsiasi stile di scrittura individuale.",
-        "image": ""
-      },
-      {
-        "text": "è basato esclusivamente sulla semplificazione dei caratteri.",
-        "image": ""
-      },
-      {
-        "text": " si avvale della standardizzazione del tratto.",
-        "image": ""
-      },
-      {
-        "text": "necessita di un lunghissimo periodo di apprendimento da parte dell'utente.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "107) Nell’ambito dell’informatica pervasiva si considera il concetto di engagement, che tra i parametri che lo definiscono comprende:",
-    "answers": [
-      {
-        "text": "la durabilità e la visibilità degli oggetti",
-        "image": ""
-      },
-      {
-        "text": "il coinvolgimento percepito e le azioni incrementali",
-        "image": ""
-      },
-      {
-        "text": "l'estetica e l'attenzione focalizzata",
-        "image": ""
-      },
-      {
-        "text": "la correttezza sintattica e la novità",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "108) Indicare quale delle seguenti affermazioni contrasta con le 10 euristiche di usabilità di Nielsen:",
-    "answers": [
-      {
-        "text": "è bene permettere all'utente di personalizzare lo svolgimento delle azioni frequenti",
-        "image": ""
-      },
-      {
-        "text": "nell'interazione è preferibile il riconoscimento al ricordo",
-        "image": ""
-      },
-      {
-        "text": "il sistema non dovrebbe corrispondere al mondo reale",
-        "image": ""
-      },
-      {
-        "text": "va supportata la funzione di Redo",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "109) Lo studio “Why don’t Families Get along with Eco-Feedback Technologies?� analizza le tecnologie di eco-feedback in un contesto familiare. Quali delle seguenti affermazioni corrisponde a indicazioni che si possono derivare dallo studio?\nconsapevolezza dei consumi individuali",
-    "answers": [
-      {
-        "text": "il sistema di eco-feedback dovrebbe includere una vista a volo d’uccello dei consumi della famiglia",
-        "image": ""
-      },
-      {
-        "text": "il sistema di eco-feedback, per ragioni di privacy, non dovrebbe stimolare una mutua",
-        "image": ""
-      },
-      {
-        "text": "il sistema di eco-feedback dovrebbe dare solo informazioni generali, lasciando agli utenti il compito di elaborare strategie di azione giornaliere",
-        "image": ""
-      },
-      {
-        "text": "non ci sono benefici nel rendere il sistema di eco-feedback accessibile a tutta la famiglia, dal momento che le decisioni vengono comunque prese dagli adulti",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "110) Nell’ambito dell’informatica pervasiva si considera il concetto di engagement, che tra i parametri che lo definiscono comprende",
-    "answers": [
-      {
-        "text": "la durabilità e la visibilità degli oggetti",
-        "image": ""
-      },
-      {
-        "text": "il coinvolgimento percepito e le azioni incrementali",
-        "image": ""
-      },
-      {
-        "text": "l'estetica e l'attenzione focalizzata",
-        "image": ""
-      },
-      {
-        "text": "la correttezza sintattica e la novità",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "111) Indicare quale delle seguenti affermazioni contrasta con le 10 euristiche di usabilità di Nielsen:",
-    "answers": [
-      {
-        "text": "è bene permettere all'utente di personalizzare lo svolgimento delle azioni frequenti",
-        "image": ""
-      },
-      {
-        "text": "nell'interazione è preferibile il riconoscimento al ricordo",
-        "image": ""
-      },
-      {
-        "text": "il sistema non dovrebbe corrispondere al mondo reale",
-        "image": ""
-      },
-      {
-        "text": "va supportata la funzione di Redo",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "112) Lo studio “Why don’t Families Get along with Eco-Feedback Technologies?� analizza le tecnologie di eco-feedback in un contesto familiare. Quali delle seguenti affermazioni corrisponde a indicazioni che si possono derivare dallo studio?\nconsapevolezza dei consumi individuali",
-    "answers": [
-      {
-        "text": "il sistema di eco-feedback dovrebbe includere una vista a volo d’uccello dei consumi della famiglia",
-        "image": ""
-      },
-      {
-        "text": "il sistema di eco-feedback, per ragioni di privacy, non dovrebbe stimolare una mutua",
-        "image": ""
-      },
-      {
-        "text": "il sistema di eco-feedback dovrebbe dare solo informazioni generali, lasciando agli utenti il compito di elaborare strategie di azione giornaliere",
-        "image": ""
-      },
-      {
-        "text": "non ci sono benefici nel rendere il sistema di eco-feedback accessibile a tutta la famiglia, dal momento che le decisioni vengono comunque prese dagli adulti",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "113) Nell’ambito delle tecniche di prototipazione di un’interfaccia, la cosiddetta narrative storyboard si differenzia dalla branching storyboard:",
-    "answers": [
-      {
-        "text": "per la descrizione del contesto in cui avviene l’interazione",
-        "image": ""
-      },
-      {
-        "text": "per la definizione di percorsi lineari che descrivono le transizioni di stato dell’interfaccia",
-        "image": ""
-      },
-      {
-        "text": "per una migliore accuratezza nella rappresentazione dell’interfaccia",
-        "image": ""
-      },
-      {
-        "text": "per la presentazione degli screenshots dell’interfaccia senza alcun ordinamento particolare",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "114) Nell’ambito della risposta sensoriale ad uno stimolo esterno, è corretto affermare che il tempo motorio:",
-    "answers": [
-      {
-        "text": "è dipendente dal canale sensoriale",
-        "image": ""
-      },
-      {
-        "text": "dipende dalla salute del soggetto",
-        "image": ""
-      },
-      {
-        "text": "diminuisce quando lo stimolo avviene su un canale misto (es. uditivo e tattile",
-        "image": ""
-      },
-      {
-        "text": "è indipendente dall'età del soggetto",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "115) Nell’ambito del modello definito come Interaction Framework, che cosa succede durante la fase di presentazione?",
-    "answers": [
-      {
-        "text": "il compito dell’utente viene articolato all’interno del linguaggio di input",
-        "image": ""
-      },
-      {
-        "text": "dopo l’esecuzione il sistema cambia stato e lo comunica attraverso il linguaggio di output",
-        "image": ""
-      },
-      {
-        "text": "l’utente osserva l’output e valuta i risultati",
-        "image": ""
-      },
-      {
-        "text": "l'input viene tradotto nel linguaggio di base del sistema per attivare l’esecuzione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "116) Nell’ambito delle attività di valutazione dell’usabilità dell’interfaccia le tecniche di ispezione:",
-    "answers": [
-      {
-        "text": "prevedono che un elevato numero di utenti esaminino gli aspetti di usabilità di un prodotto",
-        "image": ""
-      },
-      {
-        "text": "prevedono che un numero limitato di specialisti esaminino gli aspetti di usabilità di un prodotto",
-        "image": ""
-      },
-      {
-        "text": "prevedono che i valutatori dell’usabilità raccolgano dati attraverso un numero sostanzioso di questionari composti di domande aperte e chiuse",
-        "image": ""
-      },
-      {
-        "text": "prevedono un’accurata analisi del dispositivo prima della release finale, ottenuta anche attraverso il disassemblaggio e l’ispezione delle sue componenti",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "117) Le interfacce a righe di comando si distinguono per:",
-    "answers": [
-      {
-        "text": "la manipolazione di oggetti visibili",
-        "image": ""
-      },
-      {
-        "text": "l’impossibilità di accedere in modo diretto alle funzioni del sistema",
-        "image": ""
-      },
-      {
-        "text": "la possibilità di applicare lo stesso comando contemporaneamente a più oggetti",
-        "image": ""
-      },
-      {
-        "text": "la correttezza sintattica di tutte le azioni",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "118) Nell’ambito dell’informatica pervasiva, per iHCI si intende:\nnel corso del quale il sistema pervasivo acquisisce anche input implicito",
-    "answers": [
-      {
-        "text": "l’interazione dell’uomo con un dispositivo smart, basata sull’uso di un set di app",
-        "image": ""
-      },
-      {
-        "text": "l’interazione di un uomo con il suo ambiente e con gli artefatti inseriti all’interno di esso,",
-        "image": ""
-      },
-      {
-        "text": "la formalizzazione del processo di interazione, noto anche come Interaction Framework",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "119) Per quanto riguarda le capacità percettive del nostro apparato uditivo, che cosa si intende per effetto cocktail?",
-    "answers": [
-      {
-        "text": "la capacità del sistema uditivo di comprendere una conversazione che comprende una miscela di termini appartenenti a lingue diverse",
-        "image": ""
-      },
-      {
-        "text": "la capacità del sistema uditivo di filtrare i suoni ricevuti, per permettere ad esempio di focalizzarsi su una conversazione in un ambiente rumoroso",
-        "image": ""
-      },
-      {
-        "text": "l’incapacità del nostro del sistema uditivo di distinguere una conversazione in un ambiente rumoroso",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "120) Nell’ambito della risposta sensoriale ad uno stimolo esterno, è corretto affermare che il tempo motorio:",
-    "answers": [
-      {
-        "text": "è dipendente dal canale sensoriale",
-        "image": ""
-      },
-      {
-        "text": "dipende dalla salute del soggetto",
-        "image": ""
-      },
-      {
-        "text": "diminuisce quando lo stimolo avviene su un canale misto (es. uditivo e tattile",
-        "image": ""
-      },
-      {
-        "text": "è indipendente dall'età del soggetto",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "121) Nell’ambito delle tecniche di prototipazione di un’interfaccia, la cosiddetta narrative storyboard si differenzia dalla branching storyboard:",
-    "answers": [
-      {
-        "text": "per la descrizione del contesto in cui avviene l’interazione",
-        "image": ""
-      },
-      {
-        "text": "per la definizione di percorsi lineari che descrivono le transizioni di stato dell’interfaccia",
-        "image": ""
-      },
-      {
-        "text": "per una migliore accuratezza nella rappresentazione dell’interfaccia",
-        "image": ""
-      },
-      {
-        "text": "per la presentazione degli screenshots dell’interfaccia senza alcun ordinamento particolare",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "122) Nell’ambito del modello definito come Interaction Framework, che cosa succede durante la fase di presentazione?",
-    "answers": [
-      {
-        "text": "il compito dell’utente viene articolato all’interno del linguaggio di input",
-        "image": ""
-      },
-      {
-        "text": "dopo l’esecuzione il sistema cambia stato e lo comunica attraverso il linguaggio di output",
-        "image": ""
-      },
-      {
-        "text": "l’utente osserva l’output e valuta i risultati",
-        "image": ""
-      },
-      {
-        "text": "l'input viene tradotto nel linguaggio di base del sistema per attivare l’esecuzione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "123) Nell’ambito delle attività di valutazione dell’usabilità dell’interfaccia le tecniche di ispezione:",
-    "answers": [
-      {
-        "text": "prevedono che un elevato numero di utenti esaminino gli aspetti di usabilità di un prodotto",
-        "image": ""
-      },
-      {
-        "text": "prevedono che un numero limitato di specialisti esaminino gli aspetti di usabilità di un prodotto",
-        "image": ""
-      },
-      {
-        "text": "prevedono che i valutatori dell’usabilità raccolgano dati attraverso un numero sostanzioso di questionari composti di domande aperte e chiuse",
-        "image": ""
-      },
-      {
-        "text": "prevedono un’accurata analisi del dispositivo prima della release finale, ottenuta anche attraverso il disassemblaggio e l’ispezione delle sue componenti",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "124) Le interfacce a righe di comando si distinguono per:",
-    "answers": [
-      {
-        "text": "la manipolazione di oggetti visibili",
-        "image": ""
-      },
-      {
-        "text": "l’impossibilità di accedere in modo diretto alle funzioni del sistema",
-        "image": ""
-      },
-      {
-        "text": "la possibilità di applicare lo stesso comando contemporaneamente a più oggetti",
-        "image": ""
-      },
-      {
-        "text": "la correttezza sintattica di tutte le azioni",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "125) Nell’ambito dell’informatica pervasiva, per iHCI si intende:\nnel corso del quale il sistema pervasivo acquisisce anche input implicito",
-    "answers": [
-      {
-        "text": "l’interazione dell’uomo con un dispositivo smart, basata sull’uso di un set di app",
-        "image": ""
-      },
-      {
-        "text": "l’interazione di un uomo con il suo ambiente e con gli artefatti inseriti all’interno di esso,",
-        "image": ""
-      },
-      {
-        "text": "la formalizzazione del processo di interazione, noto anche come Interaction Framework",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "126) Per quanto riguarda le capacità percettive del nostro apparato uditivo, che cosa si intende per effetto cocktail?",
-    "answers": [
-      {
-        "text": "la capacità del sistema uditivo di comprendere una conversazione che comprende una miscela di termini appartenenti a lingue diverse",
-        "image": ""
-      },
-      {
-        "text": "la capacità del sistema uditivo di filtrare i suoni ricevuti, per permettere ad esempio di focalizzarsi su una conversazione in un ambiente rumoroso",
-        "image": ""
-      },
-      {
-        "text": "l’incapacità del nostro del sistema uditivo di distinguere una conversazione in un ambiente rumoroso",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "127) L’interaction framework descrive 4 fasi del ciclo interattivo, tra le quali troviamo:",
-    "answers": [
-      {
-        "text": "la sintesi e l’articolazione;",
-        "image": ""
-      },
-      {
-        "text": "l’elaborazione e la manipolazione;",
-        "image": ""
-      },
-      {
-        "text": "la sequenza e la presentazione;",
-        "image": ""
-      },
-      {
-        "text": "l’osservazione e la prestazione",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "128) Qual è il significato dell’euristica controllo dell’utente e libertà?",
-    "answers": [
-      {
-        "text": "che gli utenti non devono avere la possibilità di selezionare funzionalità del sistema per errore;",
-        "image": ""
-      },
-      {
-        "text": "che agli utenti devono essere disponibili funzioni di undo;",
-        "image": ""
-      },
-      {
-        "text": "che agli utenti non devono essere disponibili funzioni di redo;",
-        "image": ""
-      },
-      {
-        "text": "che gli utenti devono avere a disposizioneuscite di emergenza, ma ben celate per non disturbare il normale flusso dell’interazione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "129) Nell’ambito degli studi legati alla percezione umana, per effetto mascheramentosi intende:",
-    "answers": [
-      {
-        "text": "l’impossibilità di riconoscere la vocedi una persona in ambienti molto rumorosi;",
-        "image": ""
-      },
-      {
-        "text": "l’incapacità del sistema uditivo di differenziare suoni vicini per intensità e frequenza;",
-        "image": ""
-      },
-      {
-        "text": "l’incapacità del sistema visuale di discriminare l’identità di una persona in ambienti poco illuminati;",
-        "image": ""
-      },
-      {
-        "text": "l’incapacità del sistema uditivo di differenziare suoni con frequenza diversa ma di uguale intensità",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "130) In uno degli articoli proposti nella bibliografia del corso (Why don’t families get along with eco-feedback Technologies) gli autori affrontano con uno studio il tema dell’assorbimento dell’informazione data dal sistema di eco-feedback (incorporation), e notano che:",
-    "answers": [
-      {
-        "text": "gli utenti che hanno fatto parte dello studio si sono sentiti a disagio quando sono stati dati loro come punto di riferimento i costi legati ai comportamenti correnti;",
-        "image": ""
-      },
-      {
-        "text": "gli utenti che hanno fatto parte dello studio hanno avuto difficoltà nell’agire a seguito dell’informazionericevuta,nel caso in cui l’informazione fossestata fornita con un basso grado di dettaglio;",
-        "image": ""
-      },
-      {
-        "text": "gli utenti che hanno fatto parte dello studio si sono sentiti a disagio quando sono stati dati loro come punto di riferimento la comparazione con comportamenti ottimali;",
-        "image": ""
-      },
-      {
-        "text": "gli utenti che hanno fatto parte dello studio hanno avuto difficoltà nell’agire a seguito dell’informazionericevuta,nel caso in cui l’informazione fossestata fornita con un elevato grado di dettaglio",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "131) Nell’ambito dell’informatica pervasiva, i concetti di informatica quotidianae interazione continuasi riferiscono:",
-    "answers": [
-      {
-        "text": "allo svolgimento di attività che hanno un inizio ed una fine ben definiti;",
-        "image": ""
-      },
-      {
-        "text": "alla promozione di attività informali e non strutturate;",
-        "image": ""
-      },
-      {
-        "text": "all’uso giornaliero e continuativo di alcune applicazioni nel contesto lavorativo;",
-        "image": ""
-      },
-      {
-        "text": "allo svolgimento di attività con un elevato grado di coordinamento",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "132) Nell’ambito dell’ergonomia, che cosa si intende per keyhole effect (effetto buco della serratura)?",
-    "answers": [
-      {
-        "text": "una situazione che si verifica quando l’utente deve accedere ad un’interfaccia con un visore monoculare;",
-        "image": ""
-      },
-      {
-        "text": "una situazione che si verifica quando l’operatore si astrae volontariamente dalla visualizzazione generale dell’interfaccia per concentrarsi sul suo lavoro;",
-        "image": ""
-      },
-      {
-        "text": "una situazione che si verifica quando l’utente ha accesso a informazioni riservate;",
-        "image": ""
-      },
-      {
-        "text": "una situazione che si verifica quando l’utente ha accesso solo a visualizzazioni parziali del sistema",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "133) Il paradigma a manipolazione direttaè caratterizzato da:",
-    "answers": [
-      {
-        "text": "irreversibilità delle azioni;",
-        "image": ""
-      },
-      {
-        "text": "azioni non incrementali;",
-        "image": ""
-      },
-      {
-        "text": "presenza di widget che vengono utilizzati come oggetti di input e di output;",
-        "image": ""
-      },
-      {
-        "text": "invisibilità degli oggetti che fanno parte dell’interazione",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "134) Secondo la ricerca scientifica, le attuali interfacce basate sull’uso di gestures sono caratterizzate da:",
-    "answers": [
-      {
-        "text": "una bassa espressività;",
-        "image": ""
-      },
-      {
-        "text": "la disponibilitàdi linee guida riconosciute;",
-        "image": ""
-      },
-      {
-        "text": "la proposta di nuove convenzioni che ignorano le esistenti;",
-        "image": ""
-      },
-      {
-        "text": "il profondo legame con la ricerca nell’interazione uomo calcolatore",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "135) L’interaction framework descrive 4 fasi del ciclo interattivo, tra le quali troviamo:",
-    "answers": [
-      {
-        "text": "l’osservazione e la prenotazione;",
-        "image": ""
-      },
-      {
-        "text": "l’analisi e la collaborazione;",
-        "image": ""
-      },
-      {
-        "text": "la sintesi e l’articolazione;",
-        "image": ""
-      },
-      {
-        "text": "la presentazionee la prestazione",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "136) Qual è il significato dell’euristica riconoscimento anziché ricordo?",
-    "answers": [
-      {
-        "text": "che,per favorire l’interazione,gli oggetti e le azioni dell’interazionestessavanno resi visibili;",
-        "image": ""
-      },
-      {
-        "text": "che è auspicabile che sistema interattivo sia in grado di riconoscere i pattern di interazione dell’utente;",
-        "image": ""
-      },
-      {
-        "text": "che è auspicabile che sistema interattivo sia in grado di riconoscere il profilo dell’utente;",
-        "image": ""
-      },
-      {
-        "text": "che va favorita l’interazione cooperativa",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "137) Nell’ambito dell’ergonomia, che cosa si intende per keyhole effect(effetto buco della serratura)?",
-    "answers": [
-      {
-        "text": "una situazione che si verifica quando l’utente ha accesso a informazioni riservate;",
-        "image": ""
-      },
-      {
-        "text": "una situazione che si verifica quando l’operatore si astrae volontariamente dalla visualizzazione generale dell’interfaccia per concentrarsi sul suo lavoro;",
-        "image": ""
-      },
-      {
-        "text": "una situazione che si verifica quando l’utente ha accesso solo a visualizzazioni parziali del sistema;",
-        "image": ""
-      },
-      {
-        "text": "una situazione che si verifica quando l’utente deve accedere ad un’interfaccia con un visore monoculare",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "138) Nell’ambito degli studi legati alla percezione umana, per effetto cocktailsi intende:",
-    "answers": [
-      {
-        "text": "uno stato di frastornamento legato all’eccesso di stimoli uditivi di un ambiente;",
-        "image": ""
-      },
-      {
-        "text": "la capacità del sistema sensoriale di miscelare stimoli da canali diversi (es. visuale e tattile) per aumentare la velocità di ricezione dell’informazione;",
-        "image": ""
-      },
-      {
-        "text": "uno stato di frastornamento legato all’eccesso di stimoli visuali di un ambiente;",
-        "image": ""
-      },
-      {
-        "text": "la capacità del sistema uditivo di filtrare i suoni ricevuti per focalizzarsi su una conversazione",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "139) Secondo la ricerca scientifica, le attuali interfacce basate sull’uso di gestures sono caratterizzate da:",
-    "answers": [
-      {
-        "text": "una bassa espressività;",
-        "image": ""
-      },
-      {
-        "text": "la proposta di nuove convenzioni che ignorano le esistenti;",
-        "image": ""
-      },
-      {
-        "text": "la disponibilità di linee guida riconosciute;",
-        "image": ""
-      },
-      {
-        "text": "il profondo legame con la ricerca nell’interazione uomo calcolatore",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "140) In uno degli articoli proposti nella bibliografia del corso (Why don’t families get along with eco-feedback Technologies) gli autori affrontano con uno studio il tema dell’assorbimento dell’informazione data dal sistema di eco-feedback (incorporation), e notanoche:",
-    "answers": [
-      {
-        "text": "gli utenti che hanno fatto parte dello studio hanno avuto difficoltà nell’agire a seguito dell’informazionericevuta,nel caso in cui l’informazione fossestata fornita con un elevato grado di dettaglio;",
-        "image": ""
-      },
-      {
-        "text": "gli utenti che hanno fatto parte dello studio si sono sentiti a disagio quando sono stati dati lorocome puntodi riferimento la comparazione con comportamenti ottimali;",
-        "image": ""
-      },
-      {
-        "text": "gli utenti che hanno fatto parte dello studio hanno avuto difficoltà nell’agire a seguito dell’informazionericevuta,nel caso in cui l’informazione fossestata fornita con un basso grado di dettaglio;",
-        "image": ""
-      },
-      {
-        "text": "gli utenti che hanno fatto parte dello studio si sono sentiti a disagio quando sono stati dati loro come puntodi riferimento i costi legati ai comportamenti correnti",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "141) Nell’ambito dell’informatica pervasiva, è corretto dire che:",
-    "answers": [
-      {
-        "text": "l’output esplicito costituisce un canale di comunicazione diretto tra utente e ambiente, che non passa attraversol’applicazione di informatica pervasiva;",
-        "image": ""
-      },
-      {
-        "text": "l’output esplicitonon dovrebbe mai affiancare l’output esplicito dell’interfaccia;",
-        "image": ""
-      },
-      {
-        "text": "l’input implicito viene comunque visto dall’utente come un’interazione con il sistema informatico;",
-        "image": ""
-      },
-      {
-        "text": "l’input implicito può affiancare l’input esplicito dell’utente",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "142) Ilparadigma a manipolazione diretta è caratterizzato da:",
-    "answers": [
-      {
-        "text": "sostituzione di azioni per la manipolazione di oggetti visibili con linguaggi di comando;",
-        "image": ""
-      },
-      {
-        "text": "correttezza sintattica di tutte le azioni;",
-        "image": ""
-      },
-      {
-        "text": "chiara separazionedel linguaggio di input e del linguaggio di output;",
-        "image": ""
-      },
-      {
-        "text": "uso esclusivo di widget fisici",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  }
-]
\ No newline at end of file
diff --git a/data/questions/ogas.json b/data/questions/ogas.json
deleted file mode 100644
index ef1a12a..0000000
--- a/data/questions/ogas.json
+++ /dev/null
@@ -1,1066 +0,0 @@
-[
-  {
-    "quest": "1) I manager sono dei visionari e hanno un'alta propensione al rischio",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "2) Il prototitpo di un modello di business può presentarsi come un foglio di calcolo che simula il funzionamento del nuovo business",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "3) Il budget è l'output dell'attività di programmazione",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "4) Nel processo di pianificazione di un business il meccanismo di controllo assume caratteristiche più rilevanti rispetto a quello della programmazione",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "5) La propensione al rischio, che secondo alcuni studiosi è tipica dell'orientamento imprenditoriale, è l'inclinazione a intraprendere nuovi progetti avventurandosi in ambienti incerti",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "6) I primi tasselli indispensabili per la formazione di uno schema di Business Plan sono i 'Ricavi' e gli 'Investimenti durevoli'",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "7) Il business plan è in grado di fornire una stima della probabilità di successo di una startup",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "8) La fattibilità economica è quella fase della pianificazione il cui output è il piano operativo",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "9) Il pensiero visuale è una tecnica di progettazione dei modelli di business",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "10) Il business model CANVAS è strutturato in sette blocchi",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "11) Gli scenari possono rivelarsi utili come guide per la progettazione di nuovi modelli di business in quanto aiutano gli innovatori a riflettere sul modello più appropriato per ciascuna delle ambientazioni future (Scenario Planning)",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "12) Il manager, così come l'imprenditore, potrebbe rischiare il proprio capitale",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "13) Il business plan non è in grado di fornire una stima della probabilità di successo di una startup",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "14) La condizione di vitalità dell'impresa è RICAVI >= COSTI",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "15) Il business model è composto da elementi che mostrano la logica con cui un'azienda intende creare valore",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "16) La struttura organizzativa rappresenta il sistema di processi e di attività di un'organizzazione, cioè il suo modo di funzionare",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "17) L'equilibrio finanziario è rappresentato dall'equazione ∑ Ricavi = ∑ Costi + margine di profitto equo",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "18) In un sistema di impresa, compito della direzione è la ricerca di una coordinazione fra sub-sistemi, come condizione per la coordinazione generale",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "19) Quando combinata con il CANVAS la SWOT Analysis permette una precisa valutazione del modello di business di un'organizzazione e dei suoi elementi di base",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": 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"
-  },
-  {
-    "quest": "20) I modelli di business sono progettati in ambienti specifici che possono essere considerati \"spazi di progettazione\"",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "21) Il capitale di rischio rappresenta i mezzi monetari apportati da terzi finanziatori",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "22) L'attitudine all'imprenditorialità si manifesta",
-    "answers": [
-      {
-        "text": "nell'ideazione dell'attività intrapresa dall'imprenditore",
-        "image": ""
-      },
-      {
-        "text": "nell'avviamento e gestione dell'attività oggetto dell'impresa",
-        "image": ""
-      },
-      {
-        "text": "nell'ideazione, ma anche nell'avviamento e gestione dell'attività intrapresa dall'imprenditore",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "23) Nelle società per azioni, il rischio aziendale",
-    "answers": [
-      {
-        "text": "rimane in capo ai soci, nei limiti delle azioni sottoscritte",
-        "image": ""
-      },
-      {
-        "text": "viene delegato al consiglio di amministrazione",
-        "image": ""
-      },
-      {
-        "text": "è assunto dal top management",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "24) L'interesse patrimoniale consiste",
-    "answers": [
-      {
-        "text": "nel compenso spettante al titolare per la messa a disposizione del capitale",
-        "image": ""
-      },
-      {
-        "text": "nel compenso per il rischio sopportato con l'investimento",
-        "image": ""
-      },
-      {
-        "text": "nel compenso per l'attività che il titolare svolge in azienda",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "25) La redditività coincide con l'economicità",
-    "answers": [
-      {
-        "text": "in ogni caso",
-        "image": ""
-      },
-      {
-        "text": "nel caso in cui il reddito realizzato sia anche equo",
-        "image": ""
-      },
-      {
-        "text": "nel caso in cui i ricavi coprano esattamente tutti i costi della gestione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "26) L'ordine di composizione si riferisce",
-    "answers": [
-      {
-        "text": "all'ottimale combinazione dei fattori produttivi",
-        "image": ""
-      },
-      {
-        "text": "all'ordinata sequenza ed alla correta attuazione delle operazioni di gestione",
-        "image": ""
-      },
-      {
-        "text": "all'instaurazione di corretti rapporti con l'ambiente di riferimento",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "27) Il budget è",
-    "answers": [
-      {
-        "text": "il documento sintetico che scaturisce al termine del processo di programmazione",
-        "image": ""
-      },
-      {
-        "text": "il documento sintetico che scaturisce al termine del processo di pianificazione",
-        "image": ""
-      },
-      {
-        "text": "uno strumento di analisi dei problemi gestionali",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "28) Il gruppo autarchicoera caratterizzato da una politica rivolta",
-    "answers": [
-      {
-        "text": "all'oggi e al domani proprio e all'oggi e al domani altrui",
-        "image": ""
-      },
-      {
-        "text": "all'oggi proprio e al domani altrui",
-        "image": ""
-      },
-      {
-        "text": "all'oggi proprio e al domani proprio",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "29) La fase cognitiva, nell'ambito dell'attività umana per il soddisfacimento dei bisogni, si riferisce",
-    "answers": [
-      {
-        "text": "all'attuazione della produzione, mediante il principio del minimo mezzo",
-        "image": ""
-      },
-      {
-        "text": "all'acquisizione dei beni necessari per il consumo",
-        "image": ""
-      },
-      {
-        "text": "alla selezione dei bisogni e all'individuazione delle vie più convenienti per la produzione",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "30) Le imprese sono",
-    "answers": [
-      {
-        "text": "aziende che producono per il mercato",
-        "image": ""
-      },
-      {
-        "text": "aziende che producono per gli associati",
-        "image": ""
-      },
-      {
-        "text": "aziende che producono per la collettività",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "31) Lo scambio è una fase economica che ha origine",
-    "answers": [
-      {
-        "text": "nel momento dell'introduzione della moneta",
-        "image": ""
-      },
-      {
-        "text": "nel momento in cui il gruppo economico da chiuso diventa aperto",
-        "image": ""
-      },
-      {
-        "text": "nelle epoche in cui la produzione avveniva nei limiti del consumo",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "32) Le associazioni sono aziende nelle quali prevale",
-    "answers": [
-      {
-        "text": "lo scopo di lucro",
-        "image": ""
-      },
-      {
-        "text": "l'elemento personale",
-        "image": ""
-      },
-      {
-        "text": "l'elemento patrimoniale",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "33) Il concepimento delle linee strategico-tattiche si riferisce all'impegno umano in azienda",
-    "answers": [
-      {
-        "text": "di tipo esecutivo",
-        "image": ""
-      },
-      {
-        "text": "di tipo volitivo",
-        "image": ""
-      },
-      {
-        "text": "di tipo direttivo",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "34) Il passaggio da capitale generico a capitale disponibile si attua",
-    "answers": [
-      {
-        "text": "con la progettazione dell'azienda",
-        "image": ""
-      },
-      {
-        "text": "con l'ingresso del capitale in azienda",
-        "image": ""
-      },
-      {
-        "text": "con l'ingresso in azienda dei fattori produttivi",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "35) La cognizione post-operativa, si riferisce",
-    "answers": [
-      {
-        "text": "alla fase del controllo dell'esito della gestione",
-        "image": ""
-      },
-      {
-        "text": "alla fase esecutiva della gestione",
-        "image": ""
-      },
-      {
-        "text": "alla fase di pianificazione della gestione",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "36) Il passaggio da capitale inerte a capitale attivato si ha",
-    "answers": [
-      {
-        "text": "quando il capitale monetario viene rimborsato al titolare",
-        "image": ""
-      },
-      {
-        "text": "quando il capitale monetario viene convertito nei fattori produttivi con esso acquisiti",
-        "image": ""
-      },
-      {
-        "text": "con l'applicazione delle capacità potenziali del lavoro alle utilità del capitale",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "37) Il soggetto istituzionale è",
-    "answers": [
-      {
-        "text": "colui che svolge il lavoro direttivo in azienda",
-        "image": ""
-      },
-      {
-        "text": "colui che progetta l'azienda e le linee fondamentali dell'attività",
-        "image": ""
-      },
-      {
-        "text": "colui che svolge il lavoro esecutivo in azienda",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "38) Il soggetto giuridico, in un'azienda individuale",
-    "answers": [
-      {
-        "text": "coincide sempre con il soggetto economico",
-        "image": ""
-      },
-      {
-        "text": "non può mai coincidere con il soggetto economico",
-        "image": ""
-      },
-      {
-        "text": "è rappresentato dal titolare dell'azienda",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": 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"
-  },
-  {
-    "quest": "39) Nelle società per azioni, il rischio aziendale",
-    "answers": [
-      {
-        "text": "rimane in capo ai soci, nei limiti delle azioni sottoscritte",
-        "image": ""
-      },
-      {
-        "text": "viene delegato al consiglio di amministrazione",
-        "image": ""
-      },
-      {
-        "text": "è assunto dal top management",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "40) Il capitale di rischio rappresenta",
-    "answers": [
-      {
-        "text": "mezzi monetari apportati da terzi finanziatori",
-        "image": ""
-      },
-      {
-        "text": "mezzi monetari apportati dal titolare",
-        "image": ""
-      },
-      {
-        "text": "l'insieme dei mezzi monetari e del lavoro del titolare",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "41) Le combinazioni di processi sono",
-    "answers": [
-      {
-        "text": "raggruppamenti di operazioni di gestione",
-        "image": ""
-      },
-      {
-        "text": "raggruppamenti di funzioni amministrative",
-        "image": ""
-      },
-      {
-        "text": "operazioni collegate sotto il profilo spaziale",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "42) Nelle società per azioni, la titolarità dei diritti e degli obblighi è in capo",
-    "answers": [
-      {
-        "text": "ai soci",
-        "image": ""
-      },
-      {
-        "text": "al consiglio di amministrazione",
-        "image": ""
-      },
-      {
-        "text": "alla società",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "43) La redditività coincide con l'economicità",
-    "answers": [
-      {
-        "text": "in ogni caso",
-        "image": ""
-      },
-      {
-        "text": "nel caso in cui il reddito realizzato sia anche equo",
-        "image": ""
-      },
-      {
-        "text": "nel caso in cui i ricavi coprano esattamente tutti i costi della gestione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "44) L'ordine di composizionesi riferisce",
-    "answers": [
-      {
-        "text": "all'ottimale combinazione dei fattori produttivi",
-        "image": ""
-      },
-      {
-        "text": "all'ordinata sequenza ed alla corretta attuazione delle operazioni di gestione",
-        "image": ""
-      },
-      {
-        "text": "all'instaurazione di corretti rapporti con l'ambiente di riferimento",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "46) Affinché si abbia equilibrio economico, i ricavi",
-    "answers": [
-      {
-        "text": "devono limitarsi a reintegrare i costi storici",
-        "image": ""
-      },
-      {
-        "text": "devono coprire anche i costi prospettici di ricostituzione",
-        "image": ""
-      },
-      {
-        "text": "devono coprire anche i costi prospettici di sviluppo",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "47) L'ordine",
-    "answers": [
-      {
-        "text": "è sinonimo di economicità",
-        "image": ""
-      },
-      {
-        "text": "è il presupposto dell'economicità",
-        "image": ""
-      },
-      {
-        "text": "è dipendente dall'economicità",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "48) DOMANDA APERTA: Come dovrebbe evolvere il modello di business in un ambiente che cambia?",
-    "answers": [
-      {
-        "text": "So rispondere",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "49) DOMANDA APERTA: Progettare un modello di business nuovo o innovativo e rappresentarne uno esistente: descrivere la differenza principale.",
-    "answers": [
-      {
-        "text": "So rispondere",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "50) DOMANDA APERTA: Il business plan: descrizione e scopi",
-    "answers": [
-      {
-        "text": "So rispondere",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "51) DOMANDA APERTA: Progettare un modello di business. Finalità e tecniche",
-    "answers": [
-      {
-        "text": "So rispondere",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "52) Il business plan è",
-    "answers": [
-      {
-        "text": "non conosco la risposta (a)",
-        "image": ""
-      },
-      {
-        "text": "il documento sintetico che scaturisce al termine del processo di pianificazione",
-        "image": ""
-      },
-      {
-        "text": "non conosco la risposta (c)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "53) Il break-even operativo",
-    "answers": [
-      {
-        "text": "rappresenta il punto di pareggio tra costi totali e ricavi totali, espresso in volumi di vendita",
-        "image": ""
-      },
-      {
-        "text": "non conosco la risposta (b)",
-        "image": ""
-      },
-      {
-        "text": "non conosco la risposta (c)",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "54) Organizzazione è un concetto polisemico",
-    "answers": [
-      {
-        "text": "con molteplici significati e due accezioni prevalenti",
-        "image": ""
-      },
-      {
-        "text": "con molteplici significati e un' accezione prevalente",
-        "image": ""
-      },
-      {
-        "text": "con molteplici significati e molte accezioni",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "55) Le due grandi dimensioni del problema organizzativo",
-    "answers": [
-      {
-        "text": "divisione del lavoro e controllo",
-        "image": ""
-      },
-      {
-        "text": "divisione del lavoro e coordinamento",
-        "image": ""
-      },
-      {
-        "text": "coordinamento e potere",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "56) DOMANDA APERTA: Si descrivano le funzioni del business plan",
-    "answers": [
-      {
-        "text": "So rispondere",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "57) Cosa hai appreso durante il corso?",
-    "answers": [
-      {
-        "text": "Se non investi in azioni non sei nessuno",
-        "image": ""
-      },
-      {
-        "text": "Se non hai l'Audi non sei nessuno",
-        "image": ""
-      },
-      {
-        "text": "Se non hai la Amex Platinum non sei nessuno",
-        "image": ""
-      },
-      {
-        "text": "Miriam",
-        "image": ""
-      },
-      {
-        "text": "Tutte le risposte sono corrette",
-        "image": ""
-      }
-    ],
-    "correct": 4,
-    "image": ""
-  },
-  {
-    "quest": "58) Cosa hai appreso durante il corso?",
-    "answers": [
-      {
-        "text": "Berlusconi usa delle scorciatoie",
-        "image": ""
-      },
-      {
-        "text": "I conquistatori inglesi sono violenti",
-        "image": ""
-      },
-      {
-        "text": "Saremo schiavi della Cina",
-        "image": ""
-      },
-      {
-        "text": "Il voto dell'esame è espresso in trentesimi",
-        "image": ""
-      },
-      {
-        "text": "Gli ospedali offrono servizi di problem solving",
-        "image": ""
-      },
-      {
-        "text": "Tutte le risposte sono corrette",
-        "image": ""
-      }
-    ],
-    "correct": 5,
-    "image": ""
-  },
-  {
-    "quest": "45) Cosa hai appreso durante il corso?",
-    "answers": [
-      {
-        "text": "Gli imprenditori hanno le visioni, in pratica pippano",
-        "image": ""
-      },
-      {
-        "text": "L'attività del progettista comprende pratiche di proiezione astrale per \"estendere il pensiero\"",
-        "image": ""
-      },
-      {
-        "text": "Uno di noi sarà il nuovo Steve Jobs (no niente poi ci ha ripensato e ha detto che non starebbe ad informatica)",
-        "image": ""
-      },
-      {
-        "text": "POPI (https://popipopi.win) è la miglior idea del 2023",
-        "image": ""
-      },
-      {
-        "text": "Tutte le risposte sono corrette",
-        "image": ""
-      }
-    ],
-    "correct": 4,
-    "image": ""
-  },
-  {
-    "quest": "59) Cosa hai appreso durante il corso?",
-    "answers": [
-      {
-        "text": "Che la prof vuole un personal trainer figo, un azzardo",
-        "image": ""
-      },
-      {
-        "text": "Che il cane della prof ce l'ha con gli attrezzi da palestra",
-        "image": ""
-      },
-      {
-        "text": "I foulard Gucci sono meglio dei Rolex e delle Mercedes",
-        "image": ""
-      },
-      {
-        "text": "I vegani sono facilmente abbindolati dai guru",
-        "image": ""
-      },
-      {
-        "text": "I lavoratori hanno troppi diritti",
-        "image": ""
-      },
-      {
-        "text": "La televisione è un mezzo del mezzo",
-        "image": ""
-      },
-      {
-        "text": "I mezzi sono essi stessi un mezzo",
-        "image": ""
-      },
-      {
-        "text": "Tutte le risposte sono corrette",
-        "image": ""
-      }
-    ],
-    "correct": 7,
-    "image": ""
-  },
-  {
-    "quest": "60) L'orale è:",
-    "answers": [
-      {
-        "text": "Obbligatorio",
-        "image": ""
-      },
-      {
-        "text": "Facoltativo",
-        "image": ""
-      },
-      {
-        "text": "FACOLTATIVO",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  }
-]
\ No newline at end of file
diff --git a/data/questions/sicurezza.json b/data/questions/sicurezza.json
deleted file mode 100644
index 45a34bc..0000000
--- a/data/questions/sicurezza.json
+++ /dev/null
@@ -1,11975 +0,0 @@
-[
-  {
-    "quest": "473) Developed by IBM and refined by Symantec, the __________ provides a malware detection system that will automatically capture, analyze, add detection and shielding, or remove new malware and pass information about it to client systems so the malware can be detected before it is allowed to run elsewhere",
-    "answers": [
-      {
-        "text": "Intrusion Prevention System (IPS)",
-        "image": ""
-      },
-      {
-        "text": "Firewall",
-        "image": ""
-      },
-      {
-        "text": "Encryption tool",
-        "image": ""
-      },
-      {
-        "text": "digital immune system",
-        "image": ""
-      },
-      {
-        "text": "Rootkit",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "342) In a a __________ attack the slave zombies construct packets requiring a response that contains the target's IP address as the source IP address in the packet's IP header. These packets are sent to uninfected machines that respond with packets directed at the target machine\nSelect one:",
-    "answers": [
-      {
-        "text": "reflector DDoS",
-        "image": ""
-      },
-      {
-        "text": "blended",
-        "image": ""
-      },
-      {
-        "text": "internal resource",
-        "image": ""
-      },
-      {
-        "text": "direct DDoS",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "302) ____________detection involves the collection of data relating to the behavior of legitimate users over a period of time. Statistical tests are applied to observed behavior to determine with a high level of con�dence whether that behavior is not legitimate user behavior",
-    "answers": [
-      {
-        "text": "Signature-based",
-        "image": ""
-      },
-      {
-        "text": "Statistical anomaly",
-        "image": ""
-      },
-      {
-        "text": "Heuristic",
-        "image": ""
-      },
-      {
-        "text": "Machine learning",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "469) A __________ is when a user views a Web page controlled by the attacker that contains a code that exploits the browser bug and downloads and installs malware on the system without the user's knowledge or consent",
-    "answers": [
-      {
-        "text": "Phishing attack",
-        "image": ""
-      },
-      {
-        "text": "drive-by-download",
-        "image": ""
-      },
-      {
-        "text": "Cross-site scripting (XSS)",
-        "image": ""
-      },
-      {
-        "text": "Denial of Service (DoS) attack",
-        "image": ""
-      },
-      {
-        "text": "Social engineering attack",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "311) The ________ is an audit collection module operating as a background process on a monitored system whose purpose is to collect data on security related events on the host and transmit these to the central manager\nSelect one:",
-    "answers": [
-      {
-        "text": "central manager module",
-        "image": ""
-      },
-      {
-        "text": "host agent module",
-        "image": ""
-      },
-      {
-        "text": "intruder alert module",
-        "image": ""
-      },
-      {
-        "text": "LAN monitor agent module",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "826) A(n) __________ is an action, device, procedure, or technique that reduces a threat, a vulnerability, or an attack by eliminating or preventing it, by minimizing the harm it can cause, or by discovering and reporting it so that correct action can be taken\nSelect one:",
-    "answers": [
-      {
-        "text": "protocol",
-        "image": ""
-      },
-      {
-        "text": "attavk",
-        "image": ""
-      },
-      {
-        "text": "countermeasure",
-        "image": ""
-      },
-      {
-        "text": "adversary",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "441) _________ attacks can occur in a binary buffer copy when the programmer has included code to check the number of bytes being transferred, but due to a coding error, allows just one more byte to be copied than there is space available",
-    "answers": [
-      {
-        "text": "SQL injection",
-        "image": ""
-      },
-      {
-        "text": "off-by-one",
-        "image": ""
-      },
-      {
-        "text": "Cross-site scripting (XSS)",
-        "image": ""
-      },
-      {
-        "text": "Integer overflow",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1145) the __________ approach is unsuitable for a connectionless type of application because it requires the overhead of a handshake before any connectionless transmission, effectively negating the chief characteristic of a connectionless transaction.",
-    "answers": [
-      {
-        "text": "timestamp",
-        "image": ""
-      },
-      {
-        "text": "backward reply",
-        "image": ""
-      },
-      {
-        "text": "challenge-response",
-        "image": ""
-      },
-      {
-        "text": "replay",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "416) A buffer _________ is a condition at an interface under which more input can be placed into a buffer or data holding area than the capacity allocated, overwriting other information",
-    "answers": [
-      {
-        "text": "underflow/underrun/underwrite",
-        "image": ""
-      },
-      {
-        "text": "overflow/overrun/overwrite",
-        "image": ""
-      },
-      {
-        "text": "bypass/overwrite/override",
-        "image": ""
-      },
-      {
-        "text": "breach/infiltration/compromise",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "417) A consequence of a buffer overflow error is __________ ",
-    "answers": [
-      {
-        "text": "loss of data connectivity and communication",
-        "image": ""
-      },
-      {
-        "text": "corruption of data used by the program, unexpected transfer of control int he program, and possible memory access violation",
-        "image": ""
-      },
-      {
-        "text": "system shutdown and restart",
-        "image": ""
-      },
-      {
-        "text": "network congestion and slow performance",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "286) The _________ is the predefined formally documented statement that defines what activities are allowed to take place on an organization's network or on particular hosts to support the organization's requirements",
-    "answers": [
-      {
-        "text": "incident response plan",
-        "image": ""
-      },
-      {
-        "text": "access control list",
-        "image": ""
-      },
-      {
-        "text": "security policy",
-        "image": ""
-      },
-      {
-        "text": "encryption protocol",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "88) Because of the opportunities for parallel execution in __________ mode, processors that support parallel features, such as aggressive pipelining, multiple instruction dispatch per clock cycle, a large number of registers, and SIMD instructions can be effectively utilized",
-    "answers": [
-      {
-        "text": "CBC",
-        "image": ""
-      },
-      {
-        "text": "CTR",
-        "image": ""
-      },
-      {
-        "text": "CFB",
-        "image": ""
-      },
-      {
-        "text": "ECB",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "439) __________ is one of the best known protection mechanisms that is a GCC compiler extension that inserts additional function entry and exit code",
-    "answers": [
-      {
-        "text": "Address Space Layout Randomization (ASLR)",
-        "image": ""
-      },
-      {
-        "text": "Data Execution Prevention (DEP)",
-        "image": ""
-      },
-      {
-        "text": "Control Flow Integrity (CFI)",
-        "image": ""
-      },
-      {
-        "text": "stackguard",
-        "image": ""
-      },
-      {
-        "text": "Stack smashing protection",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "474) __________ technology is an anti-virus approach that enables the anti-virus program to easily detect even the most complex polymorphic viruses and other malware, while maintaining fast scanning speeds",
-    "answers": [
-      {
-        "text": "Encryption key",
-        "image": ""
-      },
-      {
-        "text": "Generic decryption",
-        "image": ""
-      },
-      {
-        "text": "Firewall",
-        "image": ""
-      },
-      {
-        "text": "Intrusion Detection System (IDS)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "344) Unlike heuristics or �ngerprint based scanners,the _________ integrates with the operating system of a host computer and monitors program behavior in real time for malicious actions\nSelect one:",
-    "answers": [
-      {
-        "text": "mobile code",
-        "image": ""
-      },
-      {
-        "text": "digital immune system",
-        "image": ""
-      },
-      {
-        "text": "generic decryption",
-        "image": ""
-      },
-      {
-        "text": "behavior blocking software",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "5) The principal objectives of computer security are to prevent unauthorized users from gaining access to resources, to prevent legitimate users from accessing resources in an unauthorized manner, and to enable legitimate users to access resources in an authorized manner",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "422) __________ can prevent buffer overflow attacks, typically of global data, which\nattempt to overwrite adjacent regions in the processes address space, such as the global offset table",
-    "answers": [
-      {
-        "text": "secure coding practices",
-        "image": ""
-      },
-      {
-        "text": "guard pages",
-        "image": ""
-      },
-      {
-        "text": "encrypted tunnels",
-        "image": ""
-      },
-      {
-        "text": "intrusion detection systems (IDS)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "129) Assuming that Alice and Bob have each other?s public key. In order to establish a shared session key, Alice just needs to generate a random k, encrypt k using Bob?s public key, and send the encrypted k to Bob and then Bob will know he has a key shared with Alice",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "314) A ________ is used to measure the current value of some entity. Examples include the number of logical connections assigned to a user application and the number of outgoing messages queued for a user process\nSelect one:",
-    "answers": [
-      {
-        "text": "Gauge",
-        "image": ""
-      },
-      {
-        "text": "Resource utilization",
-        "image": ""
-      },
-      {
-        "text": "Counter",
-        "image": ""
-      },
-      {
-        "text": "Interval timer",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "414) Traditionally the function of __________ was to transfer control to a user commandline interpreter, which gave access to any program available on the system with the privileges of the attacked program",
-    "answers": [
-      {
-        "text": "Firewall",
-        "image": ""
-      },
-      {
-        "text": "Shellcode",
-        "image": ""
-      },
-      {
-        "text": "Antivirus software",
-        "image": ""
-      },
-      {
-        "text": "Virtual private network (VPN)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "284) The _________ (RFC 4766) document defines requirements for the Intrusion Detection Message Exchange Format (IDMEF)",
-    "answers": [
-      {
-        "text": "Intrusion Detection Message Exchange Requirements",
-        "image": ""
-      },
-      {
-        "text": "Network Security Protocol Standards",
-        "image": ""
-      },
-      {
-        "text": "Firewall Configuration Best Practices",
-        "image": ""
-      },
-      {
-        "text": "Data Encryption Algorithms",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "430) A __________ can occur as a result of a programming error when a process attempts to store data beyond the limits of a fixed-size buffer and consequently overwrites adjacent memory locations",
-    "answers": [
-      {
-        "text": "buffer overflow",
-        "image": ""
-      },
-      {
-        "text": "Null pointer dereference",
-        "image": ""
-      },
-      {
-        "text": "Division by zero",
-        "image": ""
-      },
-      {
-        "text": "Integer overflow",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "110) An encryption scheme is _________ if the cost of breaking the cipher exceeds the value of the encrypted information and/or the time required to break the cipher exceeds the useful lifetime of the information",
-    "answers": [
-      {
-        "text": "vulnerable",
-        "image": ""
-      },
-      {
-        "text": "computationally secure",
-        "image": ""
-      },
-      {
-        "text": "unbreakable",
-        "image": ""
-      },
-      {
-        "text": "reversible",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "277) __________ is a security service that monitors and analyzes system events for the purpose of finding, and providing real-time warning of attempts to access system resources in an unauthorized manner",
-    "answers": [
-      {
-        "text": "Anti-virus software",
-        "image": ""
-      },
-      {
-        "text": "Data encryption",
-        "image": ""
-      },
-      {
-        "text": "Intrusion Detection",
-        "image": ""
-      },
-      {
-        "text": "Firewall",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "404) The function of ________ was to transfer control to a user commandline interpreter,which gave access to any program available on the system with the privileges of the attacked program",
-    "answers": [
-      {
-        "text": "Cryptographic hash function",
-        "image": ""
-      },
-      {
-        "text": "Shellcode",
-        "image": ""
-      },
-      {
-        "text": "Key exchange algorithm",
-        "image": ""
-      },
-      {
-        "text": "Digital signature",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "444) In the classic __________ overflow, the attacker overwrites a buffer located in the local variable area of a stack frame and then overwrites the saved frame pointer and return address",
-    "answers": [
-      {
-        "text": "Heap buffer overflow",
-        "image": ""
-      },
-      {
-        "text": "Integer overflow",
-        "image": ""
-      },
-      {
-        "text": "Format string vulnerability",
-        "image": ""
-      },
-      {
-        "text": "stack buffer",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "113) \"The input to the encryption algorithm is the XOR of the next 64 bits of plaintext and the preceding 64 bits of ciphertext\" is a description of the ________ mode of operation",
-    "answers": [
-      {
-        "text": "Stream Cipher (SC)",
-        "image": ""
-      },
-      {
-        "text": "Counter (CTR)",
-        "image": ""
-      },
-      {
-        "text": "Cipher Block Chaining (CBC)",
-        "image": ""
-      },
-      {
-        "text": "Electronic Codebook (ECB)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "512) Modifying the system's TCP/IP network code to selectively drop an entry for an incomplete connection from the TCP connections table when it overflows, allowing a new connection attempt to proceed is _______",
-    "answers": [
-      {
-        "text": "poison packet",
-        "image": ""
-      },
-      {
-        "text": "slashdot",
-        "image": ""
-      },
-      {
-        "text": "backscatter traffic",
-        "image": ""
-      },
-      {
-        "text": "random drop",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "1120) the __________ mechanism assures that a received packet was in fact transmitted by the party identified as the source in the packet header and assures that the\nPacket has not been altered in transit.",
-    "answers": [
-      {
-        "text": "confidentiality",
-        "image": ""
-      },
-      {
-        "text": "authentication",
-        "image": ""
-      },
-      {
-        "text": "security",
-        "image": ""
-      },
-      {
-        "text": "key management",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "81) The output of the encryption function is fed back to the shift register in Output Feedback mode, whereas in ___________ the ciphertext unit is fed back to the shift register",
-    "answers": [
-      {
-        "text": "Electronic Codebook mode",
-        "image": ""
-      },
-      {
-        "text": "Cipher Block Chaining mode",
-        "image": ""
-      },
-      {
-        "text": "Counter mode",
-        "image": ""
-      },
-      {
-        "text": "Cipher Feedback mode",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "111) The _________ was issued as a federal information-processing standard and is intended to replace DES and 3DES with an algorithm that is more secure and efficient",
-    "answers": [
-      {
-        "text": "Data Encryption Standard (DES)",
-        "image": ""
-      },
-      {
-        "text": "Rivest Cipher 4 (RC4)",
-        "image": ""
-      },
-      {
-        "text": "Blowfish",
-        "image": ""
-      },
-      {
-        "text": "Advanced Encryption Standard (AES)",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "1170) The __________ strategy is when users are told the importance of using hard to guess passwords and provided with guidelines for selecting strong passwords.",
-    "answers": [
-      {
-        "text": "reactive password checking",
-        "image": ""
-      },
-      {
-        "text": "computer-generated password",
-        "image": ""
-      },
-      {
-        "text": "proactive password checking",
-        "image": ""
-      },
-      {
-        "text": "user education",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "709) The __________ strategy is when users are told the importance of using hard to guess passwords and provided with guidelines for selecting strong passwords",
-    "answers": [
-      {
-        "text": "reactive password checking",
-        "image": ""
-      },
-      {
-        "text": "proactive password checking",
-        "image": ""
-      },
-      {
-        "text": "computer-generated password",
-        "image": ""
-      },
-      {
-        "text": "user education",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "305) The simplest statistical test is to measure the _________ of a parameter over some historical period which would give a reflection of the average behavior and its variability\nSelect one:",
-    "answers": [
-      {
-        "text": "mean and standard deviation",
-        "image": ""
-      },
-      {
-        "text": "Markoprocess",
-        "image": ""
-      },
-      {
-        "text": "multivariate",
-        "image": ""
-      },
-      {
-        "text": "time series",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "281) ________ detection techniques detect intrusion by observing events in the system and applying a set of rules that lead to a decision regarding whether a given pattern of activity is or is not suspicious",
-    "answers": [
-      {
-        "text": "Signature",
-        "image": ""
-      },
-      {
-        "text": "Statistical",
-        "image": ""
-      },
-      {
-        "text": "Heuristic",
-        "image": ""
-      },
-      {
-        "text": "Machine learning",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "429) Traditionally the function of __________ was to transfer control to a user command-line interpreter, which gave access to any program available on the system with the privileges of the attacked program",
-    "answers": [
-      {
-        "text": "Ransomware",
-        "image": ""
-      },
-      {
-        "text": "Spyware",
-        "image": ""
-      },
-      {
-        "text": "shellcode",
-        "image": ""
-      },
-      {
-        "text": "Rootkit",
-        "image": ""
-      },
-      {
-        "text": "Keylogger",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "90) Both __________ produce output that is independent of both the plaintext and the ciphertext. This makes them natural candidates for stream ciphers that encrypt plaintext by XOR one full block at a time",
-    "answers": [
-      {
-        "text": "CBC and ECB",
-        "image": ""
-      },
-      {
-        "text": "OFB and CTR",
-        "image": ""
-      },
-      {
-        "text": "ECB and OFB",
-        "image": ""
-      },
-      {
-        "text": "CTR and CBC",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "322) The _________ prevents duplicate passwords from being visible in the password �le. Even if two users choose the same password, those passwords will be assigned at different times\nSelect one:",
-    "answers": [
-      {
-        "text": "honeypot",
-        "image": ""
-      },
-      {
-        "text": "salt",
-        "image": ""
-      },
-      {
-        "text": "rule based intrusion detection",
-        "image": ""
-      },
-      {
-        "text": "audit record",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "407) __________ can prevent buffer overflow attacks, typically of global data, which attempt to overwrite adjacent regions in the processes address space, such as the global offset table",
-    "answers": [
-      {
-        "text": "Intrusion Prevention System (IPS)",
-        "image": ""
-      },
-      {
-        "text": "Honeytokens",
-        "image": ""
-      },
-      {
-        "text": "Guard pages",
-        "image": ""
-      },
-      {
-        "text": "Captcha",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "54) A __________ uses macro or scripting code, typically embedded in a document and triggered when the document is viewed or edited, to run and replicate itself into other such documents",
-    "answers": [
-      {
-        "text": "boot sector infector",
-        "image": ""
-      },
-      {
-        "text": "file infector",
-        "image": ""
-      },
-      {
-        "text": "macro virus",
-        "image": ""
-      },
-      {
-        "text": "multipartite virus",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "509) In both direct flooding attacks and ______ the use of spoofed source addresses results in response packets being scattered across the Internet and thus detectable",
-    "answers": [
-      {
-        "text": "SYN spoofing attacks",
-        "image": ""
-      },
-      {
-        "text": "indirect flooding attacks",
-        "image": ""
-      },
-      {
-        "text": "ICMP attacks",
-        "image": ""
-      },
-      {
-        "text": "system address spoofing",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "316) A _________ is a legitimate user who accesses data, programs, or resources for which such access is not authorized, or who is authorized for such access but misuses his or her privileges\nSelect one:",
-    "answers": [
-      {
-        "text": "Misfeasor",
-        "image": ""
-      },
-      {
-        "text": "Emissary",
-        "image": ""
-      },
-      {
-        "text": "Clandestine User",
-        "image": ""
-      },
-      {
-        "text": "Masquerader",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "582) The __________ cloud infrastructure is a composition of two or more clouds that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability",
-    "answers": [
-      {
-        "text": "hybrid",
-        "image": ""
-      },
-      {
-        "text": "community",
-        "image": ""
-      },
-      {
-        "text": "private",
-        "image": ""
-      },
-      {
-        "text": "public",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "262) A ________ monitors network traffic for particular network segments or devices and analyzes network, transport, and application protocols to identify suspicious activity",
-    "answers": [
-      {
-        "text": "host-based IDS ",
-        "image": ""
-      },
-      {
-        "text": "security intrusion",
-        "image": ""
-      },
-      {
-        "text": "network-based IDS ",
-        "image": ""
-      },
-      {
-        "text": "intrusion detection",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "461) A __________ uses multiple methods of infection or propagation to maximize the speed of contagion and the severity of the attack",
-    "answers": [
-      {
-        "text": "Man-in-the-middle attack",
-        "image": ""
-      },
-      {
-        "text": "Social engineering attack",
-        "image": ""
-      },
-      {
-        "text": "blended attack",
-        "image": ""
-      },
-      {
-        "text": "Phishing attack",
-        "image": ""
-      },
-      {
-        "text": "Denial of Service (DoS) attack",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "823) __________ assures that individuals control or influence what information related to them may be collected and stored and by whom and to whom that information may be disclosed\nSelect one:",
-    "answers": [
-      {
-        "text": "Privacy",
-        "image": ""
-      },
-      {
-        "text": "System Integrity",
-        "image": ""
-      },
-      {
-        "text": "Avvailability",
-        "image": ""
-      },
-      {
-        "text": "Data Integrity",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "279) Copying a database containing credit card numbers, viewing sensitive data without authorization, and guessing and cracking passwords are examples of _________ ",
-    "answers": [
-      {
-        "text": "firewall configuration",
-        "image": ""
-      },
-      {
-        "text": "intrusion",
-        "image": ""
-      },
-      {
-        "text": "network segmentation",
-        "image": ""
-      },
-      {
-        "text": "vulnerability scanning",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "383) The function of ___________ was to transfer control to a user command-line interpreter, which gave access to any program available on the system with the privileges of the attacked program",
-    "answers": [
-      {
-        "text": "stacking",
-        "image": ""
-      },
-      {
-        "text": "shellcode",
-        "image": ""
-      },
-      {
-        "text": "no-execute",
-        "image": ""
-      },
-      {
-        "text": "memory management",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "60) __________ will integrate with the operating system of a host computer and monitor program behavior in real time for malicious actions",
-    "answers": [
-      {
-        "text": "Fingerprint-based scanners",
-        "image": ""
-      },
-      {
-        "text": "Behavior-blocking software",
-        "image": ""
-      },
-      {
-        "text": "Generic decryption technology",
-        "image": ""
-      },
-      {
-        "text": "Heuristic scanners",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "243) A _____ monitors network traffic for particular network segments or devices and analyzes network, transport, and application protocols to identify suspicious activity",
-    "answers": [
-      {
-        "text": "Host-based IDS",
-        "image": ""
-      },
-      {
-        "text": "Intrusion Prevention System",
-        "image": ""
-      },
-      {
-        "text": "Firewal",
-        "image": ""
-      },
-      {
-        "text": "Network-based IDS",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "273) The _______ is the ID component that analyzes the data collected by the sensor for signs of unauthorized or undesired activity or for events that might be of interest to the security administrator",
-    "answers": [
-      {
-        "text": "data source ",
-        "image": ""
-      },
-      {
-        "text": "sensor",
-        "image": ""
-      },
-      {
-        "text": "operator ",
-        "image": ""
-      },
-      {
-        "text": "analyzer",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "834) ________ assures that a system performs its intended function in an unimpaired manner, free from deliberate or inadvertent unauthorized manipulation of the system\nSelect one:",
-    "answers": [
-      {
-        "text": "Data Integrity",
-        "image": ""
-      },
-      {
-        "text": "Confidentiality",
-        "image": ""
-      },
-      {
-        "text": "Availability",
-        "image": ""
-      },
-      {
-        "text": "System Integrity",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "1140) ________ is a procedure that allows communicating parties to verify that the contents of a received message have not been altered and that the source is authentic.",
-    "answers": [
-      {
-        "text": "Identification",
-        "image": ""
-      },
-      {
-        "text": "Message authentication",
-        "image": ""
-      },
-      {
-        "text": "Verification",
-        "image": ""
-      },
-      {
-        "text": "User authentication",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "502) The ______ attacks the ability of a network server to respond to TCP connection requests by overflowing the tables used to manage such connections",
-    "answers": [
-      {
-        "text": "DNS amplification attack",
-        "image": ""
-      },
-      {
-        "text": "SYN spoofing attack",
-        "image": ""
-      },
-      {
-        "text": "basic flooding attack",
-        "image": ""
-      },
-      {
-        "text": "poison packet attack",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "16) __________ implements a security policy that specifies who or what may have access to each specific system resource and the type of access that is permitted in each instance",
-    "answers": [
-      {
-        "text": "Audit control",
-        "image": ""
-      },
-      {
-        "text": "Resource control",
-        "image": ""
-      },
-      {
-        "text": "System control",
-        "image": ""
-      },
-      {
-        "text": "Access control",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "1140) A (n)__________ uses a microcontroller, is not programmable once the program logic for the device has been burned into ROM, and has no interaction with a user.",
-    "answers": [
-      {
-        "text": "deeply embedded system",
-        "image": ""
-      },
-      {
-        "text": "constrained device",
-        "image": ""
-      },
-      {
-        "text": "lattice device",
-        "image": ""
-      },
-      {
-        "text": "embedded system",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "460) A _________ is a set of programs installed on a system to maintain covert access to that system with administrator (root) privileges while hiding evidence of its presence",
-    "answers": [
-      {
-        "text": "Encryption tool",
-        "image": ""
-      },
-      {
-        "text": "Spyware",
-        "image": ""
-      },
-      {
-        "text": "rootkit",
-        "image": ""
-      },
-      {
-        "text": "Firewall",
-        "image": ""
-      },
-      {
-        "text": "Antivirus software",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "368) A buffer ____________ is a condition at an interface under which more input can be placed into a buffer or data holding area than the capacity allocated, overwriting other information",
-    "answers": [
-      {
-        "text": "overwrite",
-        "image": ""
-      },
-      {
-        "text": "overflow",
-        "image": ""
-      },
-      {
-        "text": "overrun",
-        "image": ""
-      },
-      {
-        "text": "all of these options",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "434) An essential component of many buffer overflow attacks is the transfer of execution to code supplied by the attacker and often saved in the buffer being overflowed.\nThis code is known as _________ ",
-    "answers": [
-      {
-        "text": "Exploit",
-        "image": ""
-      },
-      {
-        "text": "shellcode",
-        "image": ""
-      },
-      {
-        "text": "Payload",
-        "image": ""
-      },
-      {
-        "text": "Malware",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "326) _________ detection focuses on characterizing the past behavior of individual users or related groups of users and then detecting signi�cant deviations\nSelect one:",
-    "answers": [
-      {
-        "text": "Threshold",
-        "image": ""
-      },
-      {
-        "text": "Pro�le-based anomaly",
-        "image": ""
-      },
-      {
-        "text": "Statistical anomaly",
-        "image": ""
-      },
-      {
-        "text": "Action condition",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "89) __________ mode is suitable for parallel operation. Because there is no chaining, multiple blocks can be encrypted or decrypted simultaneously. Unlike CTR mode, this mode includes a nonce as well as a counter",
-    "answers": [
-      {
-        "text": "XTS-AES",
-        "image": ""
-      },
-      {
-        "text": "S-AES",
-        "image": ""
-      },
-      {
-        "text": "3DES",
-        "image": ""
-      },
-      {
-        "text": "OFB",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "264) __________ involves an attempt to define a set of rules or attack patterns that can be used to decide if a given behavior is that of an intruder",
-    "answers": [
-      {
-        "text": "Profile based detection ",
-        "image": ""
-      },
-      {
-        "text": "Signature detection",
-        "image": ""
-      },
-      {
-        "text": "Threshold detection ",
-        "image": ""
-      },
-      {
-        "text": "Anomaly detection",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "241) The _____ is the IDS component that analyzes the data collected by the sensor for signs of unauthorized or undesired activity or for events that might be of interest to the security administrator",
-    "answers": [
-      {
-        "text": "Agent",
-        "image": ""
-      },
-      {
-        "text": "Collector",
-        "image": ""
-      },
-      {
-        "text": "Analyzer",
-        "image": ""
-      },
-      {
-        "text": "Logger",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "345) _________ is a mass mailing e-mail worm that installs a backdoor in infected computers thereby enabling hackers to gain remote access to data such as passwords and credit card numbers\nSelect one:",
-    "answers": [
-      {
-        "text": "Sobig.f",
-        "image": ""
-      },
-      {
-        "text": "Mydoom",
-        "image": ""
-      },
-      {
-        "text": "Slammer",
-        "image": ""
-      },
-      {
-        "text": "Code Red",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "339) _____technology enables the antivirus program to easily detect even the most complex polymorphic viruses while maintaining fast scanning speeds",
-    "answers": [
-      {
-        "text": "File signature matching",
-        "image": ""
-      },
-      {
-        "text": "Generic Decryption",
-        "image": ""
-      },
-      {
-        "text": "Behavioral analysis",
-        "image": ""
-      },
-      {
-        "text": "Heuristic scanning",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "347) _________ antivirus programs are memory resident programs that identify a virus by its actions rather than its structure in an infected program\nSelect one:",
-    "answers": [
-      {
-        "text": "First generation",
-        "image": ""
-      },
-      {
-        "text": "Fourth generation",
-        "image": ""
-      },
-      {
-        "text": "Second generation",
-        "image": ""
-      },
-      {
-        "text": "Third generation",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "419) The function of ________ was to transfer control to a user command-line interpreter, which gave access to any program available on the system with the privileges of the attacked program",
-    "answers": [
-      {
-        "text": "ransomware",
-        "image": ""
-      },
-      {
-        "text": "shellcode",
-        "image": ""
-      },
-      {
-        "text": "rootkit",
-        "image": ""
-      },
-      {
-        "text": "keylogger",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "472) Countermeasures for malware are generally known as _________ mechanisms because they were first developed to specifically target virus infections",
-    "answers": [
-      {
-        "text": "Firewall",
-        "image": ""
-      },
-      {
-        "text": "Encryption tool",
-        "image": ""
-      },
-      {
-        "text": "Rootkit",
-        "image": ""
-      },
-      {
-        "text": "anti-virus",
-        "image": ""
-      },
-      {
-        "text": "Intrusion Detection System (IDS)",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "433) \"Smashing the Stack for Fun and Profit\" was a step by step introduction to exploiting stack-based buffer overflow vulnerabilities that was published in Phrack magazine by _________ ",
-    "answers": [
-      {
-        "text": "Aleph One",
-        "image": ""
-      },
-      {
-        "text": "L0phtcrack",
-        "image": ""
-      },
-      {
-        "text": "Acid Burn",
-        "image": ""
-      },
-      {
-        "text": "The Mentor",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "825) A flaw or weakness in a system's design, implementation, or operation and management that could be exploited to violate the system's security policy is a(n) __________\nSelect one:",
-    "answers": [
-      {
-        "text": "vulnerability",
-        "image": ""
-      },
-      {
-        "text": "countermeasure",
-        "image": ""
-      },
-      {
-        "text": "risk",
-        "image": ""
-      },
-      {
-        "text": "adversary",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "331) The _________ worm exploits a security hole in the Microsoft Internet Information Server to penetrate and spread to other hosts. It also disables the system �le checker in Windows\nSelect one:",
-    "answers": [
-      {
-        "text": "Mydoom",
-        "image": ""
-      },
-      {
-        "text": "Warezov",
-        "image": ""
-      },
-      {
-        "text": "Slammer",
-        "image": ""
-      },
-      {
-        "text": "Code Red",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "432) A ___________ overflow occurs when the targeted buffer is located on the stack, usually as a local variable in a function's stack frame",
-    "answers": [
-      {
-        "text": "Heap buffer overflow",
-        "image": ""
-      },
-      {
-        "text": "Global buffer overflow",
-        "image": ""
-      },
-      {
-        "text": "stack buffer",
-        "image": ""
-      },
-      {
-        "text": "Data section buffer overflow",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "84) The __________ method is ideal for a short amount of data and is the appropriate mode to use if you want to transmit a DES or AES key securely",
-    "answers": [
-      {
-        "text": "cipher feedback mode",
-        "image": ""
-      },
-      {
-        "text": "counter mode",
-        "image": ""
-      },
-      {
-        "text": "electronic codebook mode",
-        "image": ""
-      },
-      {
-        "text": "output feedback mode",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "303) A ________ is an individual who seizes supervisory control of the system and uses this control to evade auditing and access controls or to suppress audit collection\nSelect one:",
-    "answers": [
-      {
-        "text": "Clandestine User",
-        "image": ""
-      },
-      {
-        "text": "Mole",
-        "image": ""
-      },
-      {
-        "text": "Masquerader",
-        "image": ""
-      },
-      {
-        "text": "Misfeasor",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "300) What are possible locations for NIDS sensors?",
-    "answers": [
-      {
-        "text": "inside the external firewall",
-        "image": ""
-      },
-      {
-        "text": "between the external firewall and the Internet",
-        "image": ""
-      },
-      {
-        "text": "before internal servers and database resources",
-        "image": ""
-      },
-      {
-        "text": "before the workstation networks",
-        "image": ""
-      },
-      {
-        "text": "All of the above ",
-        "image": ""
-      }
-    ],
-    "correct": 4,
-    "image": ""
-  },
-  {
-    "quest": "580) An end user who operates on database objects via a particular application but does not own any of the database objects is the __________",
-    "answers": [
-      {
-        "text": "application owner",
-        "image": ""
-      },
-      {
-        "text": "end user other than application owner",
-        "image": ""
-      },
-      {
-        "text": "foreign key",
-        "image": ""
-      },
-      {
-        "text": "administrator",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "710) A __________ strategy is one in which the system periodically runs its own password cracker to find guessable passwords",
-    "answers": [
-      {
-        "text": "user education",
-        "image": ""
-      },
-      {
-        "text": "proactive password checking",
-        "image": ""
-      },
-      {
-        "text": "reactive password checking",
-        "image": ""
-      },
-      {
-        "text": "computer-generated password",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "154) ________ is a term that refers to the means of delivering a key to two parties that wish to exchange data without allowing others to see the key",
-    "answers": [
-      {
-        "text": "Private key",
-        "image": ""
-      },
-      {
-        "text": "Key exchange protocol",
-        "image": ""
-      },
-      {
-        "text": "Key distribution technique ",
-        "image": ""
-      },
-      {
-        "text": "Public key",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "260) A _________ is a security event that constitutes a security incident in which an intruder gains access to a system without having authorization to do so",
-    "answers": [
-      {
-        "text": "intrusion detection ",
-        "image": ""
-      },
-      {
-        "text": "IDS",
-        "image": ""
-      },
-      {
-        "text": "criminal enterprise ",
-        "image": ""
-      },
-      {
-        "text": "security intrusion",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "824) An assault on system security that derives from an intelligent act that is a deliberate attempt to evade security services and violate the security policy of a system is a(n) __________",
-    "answers": [
-      {
-        "text": "risk",
-        "image": ""
-      },
-      {
-        "text": "vulnerability",
-        "image": ""
-      },
-      {
-        "text": "asset",
-        "image": ""
-      },
-      {
-        "text": "attack",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "1297)________ includes data processing and storage equipment,transmission and networking facilities,and offline storage media.",
-    "answers": [
-      {
-        "text": "Supporting facilities",
-        "image": ""
-      },
-      {
-        "text": "Physical facilities",
-        "image": ""
-      },
-      {
-        "text": "Information system hardware",
-        "image": ""
-      },
-      {
-        "text": "Infrastructure facilities",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "500) A ______ triggers a bug in the system's network handling software causing it to crash and the system can no longer communicate over the network until this software is reloaded",
-    "answers": [
-      {
-        "text": "echo",
-        "image": ""
-      },
-      {
-        "text": "reflection",
-        "image": ""
-      },
-      {
-        "text": "poison packet",
-        "image": ""
-      },
-      {
-        "text": "flash flood",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "86) \"Each block of plaintext is XORed with an encrypted counter. The counter is incremented for each subsequent block\", is a description of ___________ mode",
-    "answers": [
-      {
-        "text": "Cipher Block Chaining",
-        "image": ""
-      },
-      {
-        "text": "Counter",
-        "image": ""
-      },
-      {
-        "text": "Cipher Feedback",
-        "image": ""
-      },
-      {
-        "text": "Electronic Codebook",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "715) A __________ is when an adversary attempts to achieve user authentication without access to the remote host or to the intervening communications path",
-    "answers": [
-      {
-        "text": "client attack",
-        "image": ""
-      },
-      {
-        "text": "eavesdropping attack",
-        "image": ""
-      },
-      {
-        "text": "host attack",
-        "image": ""
-      },
-      {
-        "text": "Trojan horse attack",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "338) A _________ is a secret entry point into a program that allows someone who is aware of it to gain access without going through the usual security access procedures\nSelect one:",
-    "answers": [
-      {
-        "text": "multipartite",
-        "image": ""
-      },
-      {
-        "text": "backdoor",
-        "image": ""
-      },
-      {
-        "text": "hatch",
-        "image": ""
-      },
-      {
-        "text": "Trojan horse",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1142) _________ is a program flaw that occurs when program input data can accidentally or deliberately influence the flow of execution of the program.",
-    "answers": [
-      {
-        "text": "PHP attack",
-        "image": ""
-      },
-      {
-        "text": "Format string injection attack",
-        "image": ""
-      },
-      {
-        "text": "XSS attack",
-        "image": ""
-      },
-      {
-        "text": "Injection attack",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "105) Cryptographic systems are generically classified by _________",
-    "answers": [
-      {
-        "text": "the type of operations used for transforming plaintext to ciphertext",
-        "image": ""
-      },
-      {
-        "text": "the number of keys used",
-        "image": ""
-      },
-      {
-        "text": "the way in which the plaintext is processed",
-        "image": ""
-      },
-      {
-        "text": "all of the above",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "706) Presenting or generating authentication information that corroborates the binding between the entity and the identifier is the ___________",
-    "answers": [
-      {
-        "text": "identification step",
-        "image": ""
-      },
-      {
-        "text": "verification step",
-        "image": ""
-      },
-      {
-        "text": "authentication step",
-        "image": ""
-      },
-      {
-        "text": "corroboration step",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "317) A _________ is an individual who is not authorized to use the computer and who penetrates a system's access controls to exploit a legitimate user's account\nSelect one:",
-    "answers": [
-      {
-        "text": "Clandestine User",
-        "image": ""
-      },
-      {
-        "text": "Masquerader",
-        "image": ""
-      },
-      {
-        "text": "Sniffer",
-        "image": ""
-      },
-      {
-        "text": "Misfeasor",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "646) __________ houses cross-connects and active equipment for distributing cable to the equipment distribution area",
-    "answers": [
-      {
-        "text": "Main distribution area",
-        "image": ""
-      },
-      {
-        "text": "Equipment distribution area",
-        "image": ""
-      },
-      {
-        "text": "Horizontal distribution area",
-        "image": ""
-      },
-      {
-        "text": "Zone distribution area",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "280) _________ anomaly detection focuses on characterizing the past behavior of individual users or related groups of users and then detecting significant deviations",
-    "answers": [
-      {
-        "text": "Profile-based",
-        "image": ""
-      },
-      {
-        "text": "Statistical",
-        "image": ""
-      },
-      {
-        "text": "Behavioral",
-        "image": ""
-      },
-      {
-        "text": "Signature-based",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "238) _____ involves an attempt to define a set of rules or attack patterns that can be used to decide if a given behavior is that of an intruder",
-    "answers": [
-      {
-        "text": "Traffic Analysis",
-        "image": ""
-      },
-      {
-        "text": "Payload Inspection",
-        "image": ""
-      },
-      {
-        "text": "Signature Detection",
-        "image": ""
-      },
-      {
-        "text": "Anomaly Detection",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "784) IPsec can assure that _________",
-    "answers": [
-      {
-        "text": "a router advertisement comes from an authorized router",
-        "image": ""
-      },
-      {
-        "text": "a routing update is not forged",
-        "image": ""
-      },
-      {
-        "text": "a redirect message comes from the router to which the initial packet was sent",
-        "image": ""
-      },
-      {
-        "text": "all of the above",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "83) The __________ algorithm will work against any block encryption cipher and does not depend on any particular property of DES",
-    "answers": [
-      {
-        "text": "counter mode attack",
-        "image": ""
-      },
-      {
-        "text": "ciphertext stealing",
-        "image": ""
-      },
-      {
-        "text": "meet-in-the-middle attack",
-        "image": ""
-      },
-      {
-        "text": "cipher block chaining",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "288) The __________ is the human with overall responsibility for setting the security policy of the organization, and, thus, for decisions about deploying and configuring the IDS",
-    "answers": [
-      {
-        "text": "hacker",
-        "image": ""
-      },
-      {
-        "text": "administrator",
-        "image": ""
-      },
-      {
-        "text": "analyst",
-        "image": ""
-      },
-      {
-        "text": "auditor",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "06) If the only form of attack that could be made on an encryption algorithm is brute-force, then the way to counter such attacks would be to __________ ",
-    "answers": [
-      {
-        "text": "use longer keys",
-        "image": ""
-      },
-      {
-        "text": "use shorter keys",
-        "image": ""
-      },
-      {
-        "text": "use more keys",
-        "image": ""
-      },
-      {
-        "text": "use less keys",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1087) A common technique for masking contents of messages or other information traffic so that opponents can not extract the information from the message is __________ .",
-    "answers": [
-      {
-        "text": " integrity",
-        "image": ""
-      },
-      {
-        "text": "encryption",
-        "image": ""
-      },
-      {
-        "text": " analysis",
-        "image": ""
-      },
-      {
-        "text": " masquerade",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "010) A __________ is created by using a secure hash function to generate a hash value for a message and then encrypting the hash code with a private key",
-    "answers": [
-      {
-        "text": "digital signature",
-        "image": ""
-      },
-      {
-        "text": "keystream",
-        "image": ""
-      },
-      {
-        "text": "one way hash function",
-        "image": ""
-      },
-      {
-        "text": "secret key",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "101) __________ is a term that refers to the means of delivering a key to two parties that wish to exchange data without allowing others to see the key",
-    "answers": [
-      {
-        "text": "Session key",
-        "image": ""
-      },
-      {
-        "text": "Subkey",
-        "image": ""
-      },
-      {
-        "text": "Key distribution technique",
-        "image": ""
-      },
-      {
-        "text": "Ciphertext key",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "014) Combined one byte at a time with the plaintext stream using the XOR operation, a __________ is the output of the pseudorandom bit generator",
-    "answers": [
-      {
-        "text": "keystream",
-        "image": ""
-      },
-      {
-        "text": "digital signature",
-        "image": ""
-      },
-      {
-        "text": "secure hash",
-        "image": ""
-      },
-      {
-        "text": "message authentication code",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "385) To exploit any type of buffer overflow, the attacker needs to identify a buffer overflow vulnerability in some program that can be triggered using externally sourced data under the attacker's control",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "829) A ________ level breach of security could be expected to have a severe or catastrophic adverse effect on organizational operations, organizational assets, or individuals\nSelect one:",
-    "answers": [
-      {
-        "text": "moderate",
-        "image": ""
-      },
-      {
-        "text": "high",
-        "image": ""
-      },
-      {
-        "text": "normal",
-        "image": ""
-      },
-      {
-        "text": "low",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "268) The purpose of the ________ module is to collect data on security related events on the host and transmit these to the central manager",
-    "answers": [
-      {
-        "text": "central manager agent ",
-        "image": ""
-      },
-      {
-        "text": "LAN monitor agent",
-        "image": ""
-      },
-      {
-        "text": "host agent ",
-        "image": ""
-      },
-      {
-        "text": "architecture agent",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "1115) If the analyst is able to get the source system to insert into the system a message chosen by the analyst,then a ________ attack is possible.",
-    "answers": [
-      {
-        "text": "known-plaintext",
-        "image": ""
-      },
-      {
-        "text": "chosen-plaintext",
-        "image": ""
-      },
-      {
-        "text": "chosen ciphertext",
-        "image": ""
-      },
-      {
-        "text": "chosen text",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "158) ________ are analogous to a burglar guessing a safe combination by observing how long it takes to turn the dial from number to number",
-    "answers": [
-      {
-        "text": "Collision attacks",
-        "image": ""
-      },
-      {
-        "text": "Preimage attacks",
-        "image": ""
-      },
-      {
-        "text": "Timing attacks",
-        "image": ""
-      },
-      {
-        "text": "Side-channel attacks",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "1083) the algorithm will produce a different output depending on the\nspecific secret key being used at the time.the exact substitutions\nand transformations performed by the algorithm depend on the\nkey.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "376) _________ can prevent buffer overflow attacks, typically of global data, which attempt to overwrite adjacent regions in the processes address space, such as the global offset table",
-    "answers": [
-      {
-        "text": "MMUs",
-        "image": ""
-      },
-      {
-        "text": "Heaps",
-        "image": ""
-      },
-      {
-        "text": "Guard Pages",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "1091) The _______ category is a transitional stage between awareness and training.",
-    "answers": [
-      {
-        "text": "roles and responsibilities relative to IT systems",
-        "image": ""
-      },
-      {
-        "text": "security basics and literacy",
-        "image": ""
-      },
-      {
-        "text": "education and experience",
-        "image": ""
-      },
-      {
-        "text": "security awareness",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "585) T/F: To create a relationship between two tables, the attributes that define the primary key in one table must appear as attributes in another table, where they are referred to as a foreign key",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "223) 5.0 Points\nSince the responsibility for IT security is shared across the\norganization, there is a risk of inconsistent implementation of security and a loss of central monitoring and control",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "261) A _________ monitors the characteristics of a single host and the events occurring within that host for suspicious activity",
-    "answers": [
-      {
-        "text": "host-based IDS ",
-        "image": ""
-      },
-      {
-        "text": "security intrusion",
-        "image": ""
-      },
-      {
-        "text": "network-based IDS ",
-        "image": ""
-      },
-      {
-        "text": "intrusion detection",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "259) _________ are either individuals or members of a larger group of outsider attackers who are motivated by social or political causes",
-    "answers": [
-      {
-        "text": "State-sponsored organizations ",
-        "image": ""
-      },
-      {
-        "text": "Activists",
-        "image": ""
-      },
-      {
-        "text": "Cyber criminals ",
-        "image": ""
-      },
-      {
-        "text": "Others",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "153) Which of the following would allow an attack that to know the (plaintext of) current message must be the same as one previously transmitted because their ciphtertexts are the same?",
-    "answers": [
-      {
-        "text": "CBC",
-        "image": ""
-      },
-      {
-        "text": "CTR",
-        "image": ""
-      },
-      {
-        "text": "OFB",
-        "image": ""
-      },
-      {
-        "text": "ECB",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "464) Sometimes known as a \"logic bomb\", the __________ is the event or condition that determines when the payload is activated or delivered",
-    "answers": [
-      {
-        "text": "Firewall",
-        "image": ""
-      },
-      {
-        "text": "Router",
-        "image": ""
-      },
-      {
-        "text": "Antivirus software",
-        "image": ""
-      },
-      {
-        "text": "Encryption key",
-        "image": ""
-      },
-      {
-        "text": "trigger",
-        "image": ""
-      }
-    ],
-    "correct": 4,
-    "image": ""
-  },
-  {
-    "quest": "013) The purpose of the DSS algorithm is to enable two users to securely reach agreement about a shared secret that can be used as a secret key for subsequent symmetric encryption of messages",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "716) A __________ is directed at the user file at the host where passwords, token passcodes, or biometric templates are stored",
-    "answers": [
-      {
-        "text": "eavesdropping attack",
-        "image": ""
-      },
-      {
-        "text": "denial-of-service attack",
-        "image": ""
-      },
-      {
-        "text": "client attack",
-        "image": ""
-      },
-      {
-        "text": "host attack",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "468) __________ code refers to programs that can be shipped unchanged to a heterogeneous collection of platforms and execute with identical semantics",
-    "answers": [
-      {
-        "text": "Obfuscated",
-        "image": ""
-      },
-      {
-        "text": "Scripting",
-        "image": ""
-      },
-      {
-        "text": "Legacy",
-        "image": ""
-      },
-      {
-        "text": "Mobile",
-        "image": ""
-      },
-      {
-        "text": "Open-source",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "276) The _________ to an IDS enables a user to view output from the system or control the behavior of the system",
-    "answers": [
-      {
-        "text": "command-line interface",
-        "image": ""
-      },
-      {
-        "text": "graphical user interface",
-        "image": ""
-      },
-      {
-        "text": "administrator console",
-        "image": ""
-      },
-      {
-        "text": "user interface",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "465) The four phases of a typical virus are: dormant phase, triggering phase, execution phase and __________ phase",
-    "answers": [
-      {
-        "text": "Initialization phase",
-        "image": ""
-      },
-      {
-        "text": "Recovery phase",
-        "image": ""
-      },
-      {
-        "text": "propagation",
-        "image": ""
-      },
-      {
-        "text": "Termination phase",
-        "image": ""
-      },
-      {
-        "text": "Mutation phase",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "265) _________ involves the collection of data relating to the behavior of legitimate users over a period of time",
-    "answers": [
-      {
-        "text": "Profile based detection ",
-        "image": ""
-      },
-      {
-        "text": "Signature detection",
-        "image": ""
-      },
-      {
-        "text": "Threshold detection ",
-        "image": ""
-      },
-      {
-        "text": "Anomaly detection",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "013) A __________ is to try every possible key on a piece of ciphertext until an intelligible translation into plaintext is obtained",
-    "answers": [
-      {
-        "text": "mode of operation",
-        "image": ""
-      },
-      {
-        "text": "hash function",
-        "image": ""
-      },
-      {
-        "text": "cryptanalysis",
-        "image": ""
-      },
-      {
-        "text": "brute-force attack",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "012) A __________ is directed at the user file at the host where passwords, token passcodes, or biometric templates are store",
-    "answers": [
-      {
-        "text": "eavesdropping attack",
-        "image": ""
-      },
-      {
-        "text": "denial-of-service attack",
-        "image": ""
-      },
-      {
-        "text": "client attack",
-        "image": ""
-      },
-      {
-        "text": "host attack",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "642) A(n) __________ is a user who has administrative responsibility for part or all of the database",
-    "answers": [
-      {
-        "text": "administrator",
-        "image": ""
-      },
-      {
-        "text": "database relations manager",
-        "image": ""
-      },
-      {
-        "text": "application owner",
-        "image": ""
-      },
-      {
-        "text": "end user other than application owner",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "96) There are _____ modes of operation defined by NIST that are intended to cover virtually all the possible applications of encryption for which a block cipher could be used",
-    "answers": [
-      {
-        "text": "three",
-        "image": ""
-      },
-      {
-        "text": "five",
-        "image": ""
-      },
-      {
-        "text": "seven",
-        "image": ""
-      },
-      {
-        "text": "nine",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "30) The __________ component deals with the management and control of the ways entities are granted access to resources",
-    "answers": [
-      {
-        "text": "resource management",
-        "image": ""
-      },
-      {
-        "text": "access management",
-        "image": ""
-      },
-      {
-        "text": "privilege management",
-        "image": ""
-      },
-      {
-        "text": "policy management",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "325) _________ involves counting the number of occurrences of a speci�c event type over an interval of time\nSelect one:",
-    "answers": [
-      {
-        "text": "Threshold detection",
-        "image": ""
-      },
-      {
-        "text": "Rule-based detection",
-        "image": ""
-      },
-      {
-        "text": "Resource usage",
-        "image": ""
-      },
-      {
-        "text": "Pro�le-based system",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "282) _________ simulate human brain operation with neurons and synapse between them that classify observed data",
-    "answers": [
-      {
-        "text": "Antivirus software",
-        "image": ""
-      },
-      {
-        "text": "Intrusion prevention systems",
-        "image": ""
-      },
-      {
-        "text": "Neural networks",
-        "image": ""
-      },
-      {
-        "text": "Genetic algorithms",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "239) A _____ monitors the characteristics of a single host and the events occurring within that host for suspicious activity",
-    "answers": [
-      {
-        "text": "Network-based IDS",
-        "image": ""
-      },
-      {
-        "text": "Intrusion Prevention System",
-        "image": ""
-      },
-      {
-        "text": "Firewall",
-        "image": ""
-      },
-      {
-        "text": "Host-based IDS",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "832) A(n) _________ is an attempt to learn or make use of information from the system that does not affect system resources\nSelect one:",
-    "answers": [
-      {
-        "text": "active attack",
-        "image": ""
-      },
-      {
-        "text": "inside attack",
-        "image": ""
-      },
-      {
-        "text": "outside attack",
-        "image": ""
-      },
-      {
-        "text": "passive attack",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "507) ______ attempts to monopolize all of the available request handling threads on the Web server by sending HTTP requests that never complete",
-    "answers": [
-      {
-        "text": "HTTP",
-        "image": ""
-      },
-      {
-        "text": "Reflection attacks",
-        "image": ""
-      },
-      {
-        "text": "SYN flooding",
-        "image": ""
-      },
-      {
-        "text": "Slowloris",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "443) Gaps, or __________ , are flagged in the MMU as illegal addresses, and any attempt to access them results in the process being aborted",
-    "answers": [
-      {
-        "text": "Stack frames",
-        "image": ""
-      },
-      {
-        "text": "Heap blocks",
-        "image": ""
-      },
-      {
-        "text": "guard pages",
-        "image": ""
-      },
-      {
-        "text": "Code sections",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "1107) If the PRF does not generate effectively random 128-bit output values it may be possible for an adversary to narrow the possibilities and successfully use a brute force attack.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "633) Network security is extremely important in a facility in which such a large collection of assets is concentrated in a single place and accessible by external network connections",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "07) __________ is a procedure that allows communicating parties to verify that received or stored messages are authentic",
-    "answers": [
-      {
-        "text": "Cryptanalysis",
-        "image": ""
-      },
-      {
-        "text": "Decryption",
-        "image": ""
-      },
-      {
-        "text": "Message authentication",
-        "image": ""
-      },
-      {
-        "text": "Collision resistance",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "85) _________ mode is similar to Cipher Feedback, except that the input to the encryption algorithm is the preceding DES output",
-    "answers": [
-      {
-        "text": "Counter",
-        "image": ""
-      },
-      {
-        "text": "Cipher Block Chaining",
-        "image": ""
-      },
-      {
-        "text": "Output Feedback",
-        "image": ""
-      },
-      {
-        "text": "Cipher Feedback",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "463) Sometimes referred to as the \"infection vector\", the __________ is the means by which a virus spreads or propagates",
-    "answers": [
-      {
-        "text": "Exploit",
-        "image": ""
-      },
-      {
-        "text": "Encryption algorithm",
-        "image": ""
-      },
-      {
-        "text": "infection mechanism",
-        "image": ""
-      },
-      {
-        "text": "Payload",
-        "image": ""
-      },
-      {
-        "text": "Backdoor",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "1122) the key exchange protocol is vulnerable to a __________ attack because it does not authenticate the participants.",
-    "answers": [
-      {
-        "text": "one-way function",
-        "image": ""
-      },
-      {
-        "text": "time complexity",
-        "image": ""
-      },
-      {
-        "text": "chosen ciphertext",
-        "image": ""
-      },
-      {
-        "text": "man-in-the-middle",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "718) An institution that issues debit cards to cardholders and is responsible for the cardholder's account and authorizing transactions is the _________",
-    "answers": [
-      {
-        "text": "cardholder",
-        "image": ""
-      },
-      {
-        "text": "auditor",
-        "image": ""
-      },
-      {
-        "text": "issuer",
-        "image": ""
-      },
-      {
-        "text": "processor",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "378) A consequence of a buffer overflow error is:",
-    "answers": [
-      {
-        "text": "possibly memory access violation",
-        "image": ""
-      },
-      {
-        "text": "corruption of data used by the program",
-        "image": ""
-      },
-      {
-        "text": "unexpected transfer of control in the program",
-        "image": ""
-      },
-      {
-        "text": "all of these options",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "310) An operation such as login, read, perform, I/O or execute that is performed by the subject on or with an object is the _________ audit record �eld",
-    "answers": [
-      {
-        "text": "Action",
-        "image": ""
-      },
-      {
-        "text": "Subject",
-        "image": ""
-      },
-      {
-        "text": "Resource-usage",
-        "image": ""
-      },
-      {
-        "text": "Object",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1077) the XtS-AES standard describes a method of decryption for data\nstored in sector-based devices where the threat model includes\npossible access to stored data by the adversary.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "462) A computer __________ is a piece of software that can \"infect\" other programs or any type of executable content and tries to replicate itself",
-    "answers": [
-      {
-        "text": "Trojan horse",
-        "image": ""
-      },
-      {
-        "text": "Adware",
-        "image": ""
-      },
-      {
-        "text": "virus",
-        "image": ""
-      },
-      {
-        "text": "Worm",
-        "image": ""
-      },
-      {
-        "text": "Spyware",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "511) It is possible to specifically defend against the ______ by using a modified version of the TCP connection handling code",
-    "answers": [
-      {
-        "text": "three-way handshake",
-        "image": ""
-      },
-      {
-        "text": "UDP flood",
-        "image": ""
-      },
-      {
-        "text": "SYN spoofing attack",
-        "image": ""
-      },
-      {
-        "text": "flash crowd",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "015) A _________ protects against an attack in which one party generates a message for another party to sign",
-    "answers": [
-      {
-        "text": "data authenticator ",
-        "image": ""
-      },
-      {
-        "text": "strong hash function",
-        "image": ""
-      },
-      {
-        "text": "weak hash function ",
-        "image": ""
-      },
-      {
-        "text": "digital signature",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "644) __________ is the process of performing authorized queries and deducing unauthorized information from the legitimate responses received",
-    "answers": [
-      {
-        "text": "Perturbation",
-        "image": ""
-      },
-      {
-        "text": "Inference",
-        "image": ""
-      },
-      {
-        "text": "Compromise",
-        "image": ""
-      },
-      {
-        "text": "Partitioning",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "283) A ________ IDS monitors traffic at selected points on a network or interconnected set of networks",
-    "answers": [
-      {
-        "text": "host-based (HIDS)",
-        "image": ""
-      },
-      {
-        "text": "cloud-based (CIDS)",
-        "image": ""
-      },
-      {
-        "text": "application-based (AIDS)",
-        "image": ""
-      },
-      {
-        "text": "net-work based (NIDS)",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "27) __________ provide a means of adapting RBAC to the specifics of administrative and security policies in an organization",
-    "answers": [
-      {
-        "text": "Constraints",
-        "image": ""
-      },
-      {
-        "text": "Mutually Exclusive Roles",
-        "image": ""
-      },
-      {
-        "text": "Cardinality",
-        "image": ""
-      },
-      {
-        "text": "Prerequisites",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "160) The principal attraction of ________ compared to RSA is that it appears to offer equal security for a far smaller bit size, thereby reducing processing overhead",
-    "answers": [
-      {
-        "text": "AES",
-        "image": ""
-      },
-      {
-        "text": "ECC",
-        "image": ""
-      },
-      {
-        "text": "Blowfish",
-        "image": ""
-      },
-      {
-        "text": "RC4",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "393) At the basic machine level, all of the data manipulated by machine instructions executed by the computer processor are stored in either the processors registers or in memory",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1124) For determining the security of various elliptic curve\nciphers it is of some interest to know the number of\npoints in a finite abelian group defined over an elliptic\ncurve.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "366) In 2004 the ________ exploited a buffer overflow in Microsoft Windows 2000/XP Local Security Authority Subsystem Service",
-    "answers": [
-      {
-        "text": "Code Red Worm",
-        "image": ""
-      },
-      {
-        "text": "Slammer Worm",
-        "image": ""
-      },
-      {
-        "text": "Morris Internet Worm",
-        "image": ""
-      },
-      {
-        "text": "Sasser Worm",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "694) User authentication is a procedure that allows communicating parties to verify that the contents of a received message have not been altered and that the source is authentic",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "421) __________ aim to prevent or detect buffer overflows by instrumenting programs when they are compiled",
-    "answers": [
-      {
-        "text": "threat modeling",
-        "image": ""
-      },
-      {
-        "text": "compile-time defenses",
-        "image": ""
-      },
-      {
-        "text": "runtime patching",
-        "image": ""
-      },
-      {
-        "text": "post-incident analysis",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "308) Metrics that are useful for pro�le-based intrusion detection are: counter, gauge, resource utilization, and _______",
-    "answers": [
-      {
-        "text": "network bandwidth",
-        "image": ""
-      },
-      {
-        "text": "packet loss rate",
-        "image": ""
-      },
-      {
-        "text": "system uptime",
-        "image": ""
-      },
-      {
-        "text": "interval timer",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "1440) __________ is a data collection technology that uses electronic tags attached to items to allow the items to be identified and tracked by a remote system.",
-    "answers": [
-      {
-        "text": "RFID",
-        "image": ""
-      },
-      {
-        "text": "NtRU",
-        "image": ""
-      },
-      {
-        "text": "EPC",
-        "image": ""
-      },
-      {
-        "text": "CRYPtOREC",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "827) An example of __________ is an attempt by an unauthorized user to gain access to a system by posing as an authorized user\nSelect one:",
-    "answers": [
-      {
-        "text": "repudiation",
-        "image": ""
-      },
-      {
-        "text": "masquerade",
-        "image": ""
-      },
-      {
-        "text": "inference",
-        "image": ""
-      },
-      {
-        "text": "interception",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "822) __________ is the insertion of bits into gaps in a data stream to frustrate traffic analysis attempts\nSelect one:",
-    "answers": [
-      {
-        "text": "Traffic padding",
-        "image": ""
-      },
-      {
-        "text": "Traffic integrity",
-        "image": ""
-      },
-      {
-        "text": "Traffic control",
-        "image": ""
-      },
-      {
-        "text": "Traffic routing",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "242) _____ involves the collection of data relating to the behavior of legitimate users over a period of time",
-    "answers": [
-      {
-        "text": "Signature Detection",
-        "image": ""
-      },
-      {
-        "text": "Statistical Analysis",
-        "image": ""
-      },
-      {
-        "text": "Log Monitoring",
-        "image": ""
-      },
-      {
-        "text": "Anomaly Detection",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "375) Even through it is a high-level programming language, Java still suffers from buffer overflows because it permits more data to be saved into a buffer than it has space for",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "775) ______ is the recommended technique for wireless network security",
-    "answers": [
-      {
-        "text": "Using encryption",
-        "image": ""
-      },
-      {
-        "text": "Using anti-virus and anti-spyware software",
-        "image": ""
-      },
-      {
-        "text": "Turning off identifier broadcasting",
-        "image": ""
-      },
-      {
-        "text": "All of the above",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "269) A(n) ________ is inserted into a network segment so that the traffic that it is monitoring must pass through the sensor",
-    "answers": [
-      {
-        "text": "passive sensor ",
-        "image": ""
-      },
-      {
-        "text": "analysis sensor",
-        "image": ""
-      },
-      {
-        "text": "LAN sensor ",
-        "image": ""
-      },
-      {
-        "text": "inline sensor",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "57) __________ is malware that encrypts the user's data and demands payment in order to access the key needed to recover the information",
-    "answers": [
-      {
-        "text": "Trojan horse",
-        "image": ""
-      },
-      {
-        "text": "Ransomware",
-        "image": ""
-      },
-      {
-        "text": "Crimeware",
-        "image": ""
-      },
-      {
-        "text": "Polymorphic",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "510) In a _______ attack the attacker creates a series of DNS requests containing the spoofed source address for the target system",
-    "answers": [
-      {
-        "text": "SYN flood",
-        "image": ""
-      },
-      {
-        "text": "DNS amplification",
-        "image": ""
-      },
-      {
-        "text": "poison packet",
-        "image": ""
-      },
-      {
-        "text": "UDP flood",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "830) A __________ is any action that compromises the security of information owned by an organization\nSelect one:",
-    "answers": [
-      {
-        "text": "security attack",
-        "image": ""
-      },
-      {
-        "text": "security mechanism",
-        "image": ""
-      },
-      {
-        "text": "security policy",
-        "image": ""
-      },
-      {
-        "text": "security service",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "466) During the __________ phase the virus is activated to perform the function for which it was intended",
-    "answers": [
-      {
-        "text": "Encryption phase",
-        "image": ""
-      },
-      {
-        "text": "Stealth phase",
-        "image": ""
-      },
-      {
-        "text": "Payload phase",
-        "image": ""
-      },
-      {
-        "text": "triggering",
-        "image": ""
-      },
-      {
-        "text": "Replication phase",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "394) Even though it is a highlevel programming language, Java still suffers from buffer overflows because it permits more data to be saved into a buffer than it has space for",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "46) A program that is covertly inserted into a system with the intent of compromising the integrity or confidentiality of the victim's data is __________",
-    "answers": [
-      {
-        "text": "Adobe",
-        "image": ""
-      },
-      {
-        "text": "Animoto",
-        "image": ""
-      },
-      {
-        "text": "Malware",
-        "image": ""
-      },
-      {
-        "text": "Prezi",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "106) A symmetric encryption scheme has five ingredients: plaintext, encryption algorithm, ciphertext, decryption algorithm and _________",
-    "answers": [
-      {
-        "text": "password",
-        "image": ""
-      },
-      {
-        "text": "hash",
-        "image": ""
-      },
-      {
-        "text": "secret key",
-        "image": ""
-      },
-      {
-        "text": "digital signature",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "648) _________ is an organization that produces data to be made available for controlled release, either within the organization or to external users",
-    "answers": [
-      {
-        "text": "Client",
-        "image": ""
-      },
-      {
-        "text": "Data owner",
-        "image": ""
-      },
-      {
-        "text": "User",
-        "image": ""
-      },
-      {
-        "text": "Server",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "114) Unlike ECB and CBC modes, ________ mode requires only the implementation of the encryption algorithm and not the decryption algorithm",
-    "answers": [
-      {
-        "text": "block",
-        "image": ""
-      },
-      {
-        "text": "counter (CTR)",
-        "image": ""
-      },
-      {
-        "text": "stream",
-        "image": ""
-      },
-      {
-        "text": "substitution",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "714) To counter threats to remote user authentication, systems generally rely on some form of ___________ protocol",
-    "answers": [
-      {
-        "text": "eavesdropping",
-        "image": ""
-      },
-      {
-        "text": "Trojan horse",
-        "image": ""
-      },
-      {
-        "text": "challenge-response",
-        "image": ""
-      },
-      {
-        "text": "denial-of-service",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "1104) Plaintext is recovered from the ciphertext using the paired key and _____________ .",
-    "answers": [
-      {
-        "text": "a digital signature",
-        "image": ""
-      },
-      {
-        "text": "a recovery encryption",
-        "image": ""
-      },
-      {
-        "text": "a decryption algorithm",
-        "image": ""
-      },
-      {
-        "text": "an encryption algorithm",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "115) The most powerful, and most common, approach to countering the threats to network security is ________",
-    "answers": [
-      {
-        "text": "authentication",
-        "image": ""
-      },
-      {
-        "text": "firewall implementation",
-        "image": ""
-      },
-      {
-        "text": "intrusion detection",
-        "image": ""
-      },
-      {
-        "text": "encryption",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "442) The _________ is typically located above the program code and global data and grows up in memory (while the sack grows down toward it)",
-    "answers": [
-      {
-        "text": "Data section",
-        "image": ""
-      },
-      {
-        "text": "Cache",
-        "image": ""
-      },
-      {
-        "text": "heap",
-        "image": ""
-      },
-      {
-        "text": "Register file",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "369) _________ aim to prevent or detect buffer overflows by instrumenting programs when they are compiled",
-    "answers": [
-      {
-        "text": "Run-time defenses",
-        "image": ""
-      },
-      {
-        "text": "Compile-time defenses",
-        "image": ""
-      },
-      {
-        "text": "Shellcodes",
-        "image": ""
-      },
-      {
-        "text": "All of these answers",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "821) Masquerade, falsification, and repudiation are threat actions that cause __________ threat consequences\nSelect one:",
-    "answers": [
-      {
-        "text": "unauthorized disclosure",
-        "image": ""
-      },
-      {
-        "text": "disruption",
-        "image": ""
-      },
-      {
-        "text": "deception",
-        "image": ""
-      },
-      {
-        "text": "usurpation",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "348) _________ are used to attack networked computer systems with a large volume of traf�c to carry out a denial-of-service attack\nSelect one:",
-    "answers": [
-      {
-        "text": "Bots",
-        "image": ""
-      },
-      {
-        "text": "Exploits",
-        "image": ""
-      },
-      {
-        "text": "Keyloggers",
-        "image": ""
-      },
-      {
-        "text": "flooders",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "275) A ________ is a hacker with sufficient technical skills to modify and extend attack toolkits to use newly discovered vulnerabilities",
-    "answers": [
-      {
-        "text": "script kiddie",
-        "image": ""
-      },
-      {
-        "text": "journeyman",
-        "image": ""
-      },
-      {
-        "text": "novice",
-        "image": ""
-      },
-      {
-        "text": "expert",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1101) The appeal of HMAC is that its designers have been able to prove an\nexact relationship between the strength of the embedded hash function and the strength of HMAC.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "21) A concept that evolved out of requirements for military information security is ______ ",
-    "answers": [
-      {
-        "text": "reliable input",
-        "image": ""
-      },
-      {
-        "text": "mandatory access control",
-        "image": ""
-      },
-      {
-        "text": "open and closed policies",
-        "image": ""
-      },
-      {
-        "text": "discretionary input",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "287) 14.________ are decoy systems that are designed to lure a potential attacker away from critical systems",
-    "answers": [
-      {
-        "text": "Antivirus software",
-        "image": ""
-      },
-      {
-        "text": "Honeypots",
-        "image": ""
-      },
-      {
-        "text": "Firewalls",
-        "image": ""
-      },
-      {
-        "text": "Intrusion Detection Systems",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "48) A __________ is code inserted into malware that lies dormant until a predefined condition, which triggers an unauthorized act, is met",
-    "answers": [
-      {
-        "text": "logic bomb",
-        "image": ""
-      },
-      {
-        "text": "trapdoor",
-        "image": ""
-      },
-      {
-        "text": "worm",
-        "image": ""
-      },
-      {
-        "text": "Trojan horse",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "315) To be of practical use an intrusion detection system should detect a substantial percentage of intrusions while keeping the false alarm rate at an acceptable level",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "118) For symmetric encryption to work the two parties to an exchange must share the same _____, which must be protected from access by others",
-    "answers": [
-      {
-        "text": "username",
-        "image": ""
-      },
-      {
-        "text": "key",
-        "image": ""
-      },
-      {
-        "text": "password",
-        "image": ""
-      },
-      {
-        "text": "certificate",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "380) The potential for a buffer overflow exists anywhere that data is copied or merged into a buffer, where at least some of the data are read from outside the program",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "471) A bot can use a __________ to capture keystrokes on the infected machine to retrieve sensitive information",
-    "answers": [
-      {
-        "text": "Antivirus software",
-        "image": ""
-      },
-      {
-        "text": "Encryption key",
-        "image": ""
-      },
-      {
-        "text": "keylogger",
-        "image": ""
-      },
-      {
-        "text": "Firewall",
-        "image": ""
-      },
-      {
-        "text": "Rootkit",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "828) The assurance that data received are exactly as sent by an authorized entity is __________\nSelect one:",
-    "answers": [
-      {
-        "text": "data integrity",
-        "image": ""
-      },
-      {
-        "text": "data confidentiality",
-        "image": ""
-      },
-      {
-        "text": "authentication",
-        "image": ""
-      },
-      {
-        "text": "access control",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "833) The _________ prevents or inhibits the normal use or management of communications facilities\nSelect one:",
-    "answers": [
-      {
-        "text": "passive attack",
-        "image": ""
-      },
-      {
-        "text": "denial of service",
-        "image": ""
-      },
-      {
-        "text": "masquerade",
-        "image": ""
-      },
-      {
-        "text": "traffic encryption",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1128) Intrusion detection is the process of collecting information about\nevents occurring in a computer system or network and analyzing them for signs of intrusions.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "504) _______ bandwidth attacks attempt to take advantage of the disproportionally large resource consumption at a server",
-    "answers": [
-      {
-        "text": "Application-based",
-        "image": ""
-      },
-      {
-        "text": "System-based",
-        "image": ""
-      },
-      {
-        "text": "Random",
-        "image": ""
-      },
-      {
-        "text": "Amplification",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "499) ______ relates to the capacity of the network links connecting a server to the wider Internet",
-    "answers": [
-      {
-        "text": "Application resource",
-        "image": ""
-      },
-      {
-        "text": "Network bandwidth",
-        "image": ""
-      },
-      {
-        "text": "System payload",
-        "image": ""
-      },
-      {
-        "text": "Directed broadcast",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "440) A _________ value is named after the miner's bird used to detect poisonous air in a mine and warn miners in time for them to escape",
-    "answers": [
-      {
-        "text": "Sparrow",
-        "image": ""
-      },
-      {
-        "text": "Falcon",
-        "image": ""
-      },
-      {
-        "text": "Hawk",
-        "image": ""
-      },
-      {
-        "text": "canary",
-        "image": ""
-      },
-      {
-        "text": "Eagle",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "384) The buffer overflow type of attack has been known since it was first widely used by the _______ Worm in 1988",
-    "answers": [
-      {
-        "text": "Alpha One",
-        "image": ""
-      },
-      {
-        "text": "Code Red Worm",
-        "image": ""
-      },
-      {
-        "text": "Slammer Worm",
-        "image": ""
-      },
-      {
-        "text": "Morris Internet Worm",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "423) _________ is a form of overflow attack",
-    "answers": [
-      {
-        "text": "heap overflows, return to system call, and replacement stack frame",
-        "image": ""
-      },
-      {
-        "text": "Cross-site scripting (XSS)",
-        "image": ""
-      },
-      {
-        "text": "SQL injection",
-        "image": ""
-      },
-      {
-        "text": "Directory traversal",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "412) A buffer overflow in Microoft Windows 2000/XP Local Security Authority Subsystem Service was exploited by the _________",
-    "answers": [
-      {
-        "text": "Melissa Worm",
-        "image": ""
-      },
-      {
-        "text": "Sasser Worm",
-        "image": ""
-      },
-      {
-        "text": "Nimda Worm",
-        "image": ""
-      },
-      {
-        "text": "Sobig Worm",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "240) A(n) _____ is inserted into a network segment so that the traffic that it is monitoring must pass through the sensor",
-    "answers": [
-      {
-        "text": "Active Sensor",
-        "image": ""
-      },
-      {
-        "text": "Probe",
-        "image": ""
-      },
-      {
-        "text": "Inline Sensor",
-        "image": ""
-      },
-      {
-        "text": "Passive Sensor",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "868) The Common Criteria for Information Technology and Security Evaluation are ISO standards for specifying security requirements and defining evaluation criteria",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "228) The relative lack of success in bringing cybercriminals to justice has led to an increase in their numbers, boldness, and the global scale of their operations",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "579) The basic building block of a __________ is a table of data, consisting of rows and columns, similar to a spreadsheet",
-    "answers": [
-      {
-        "text": "relational database",
-        "image": ""
-      },
-      {
-        "text": "query set",
-        "image": ""
-      },
-      {
-        "text": "DBMS",
-        "image": ""
-      },
-      {
-        "text": "perturbation",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "329) A ______ attack is an attempt to prevent legitimate users of a service from using that service",
-    "answers": [
-      {
-        "text": "Man-in-the-middle",
-        "image": ""
-      },
-      {
-        "text": "Phishing",
-        "image": ""
-      },
-      {
-        "text": "Denial of service (DOS)",
-        "image": ""
-      },
-      {
-        "text": "Social engineering",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "506) Bots starting from a given HTTP link and then following all links on the provided Web site in a recursive way is called _______",
-    "answers": [
-      {
-        "text": "trailing",
-        "image": ""
-      },
-      {
-        "text": "spidering",
-        "image": ""
-      },
-      {
-        "text": "spoofing",
-        "image": ""
-      },
-      {
-        "text": "crowding",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "271) _________ is a document that describes the application level protocol for exchanging data between intrusion detection entities",
-    "answers": [
-      {
-        "text": "RFC 4767 ",
-        "image": ""
-      },
-      {
-        "text": "RFC 4766",
-        "image": ""
-      },
-      {
-        "text": "RFC 4765 ",
-        "image": ""
-      },
-      {
-        "text": "RFC 4764",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "581) __________ is an organization that receives the encrypted data from a data owner and makes them available for distribution to clients",
-    "answers": [
-      {
-        "text": "User",
-        "image": ""
-      },
-      {
-        "text": "Client",
-        "image": ""
-      },
-      {
-        "text": "Data owner",
-        "image": ""
-      },
-      {
-        "text": "Server",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "06) Modes of operation are the alternative techniques that have been developed to increase the security of symmetric block encryption for large sequences of data",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "343) A _________ virus is a form of virus explicitly designed to hide itself from detection by antivirus software\nSelect one:",
-    "answers": [
-      {
-        "text": "stealth",
-        "image": ""
-      },
-      {
-        "text": "polymorphic",
-        "image": ""
-      },
-      {
-        "text": "encrypted",
-        "image": ""
-      },
-      {
-        "text": "metamorphic",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "157) ________ attacks have several approaches, all equivalent in effort to factoring the product of two primes",
-    "answers": [
-      {
-        "text": "Mathematical ",
-        "image": ""
-      },
-      {
-        "text": "Statistical",
-        "image": ""
-      },
-      {
-        "text": "Brute-force",
-        "image": ""
-      },
-      {
-        "text": "Social engineering",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "841) Computer security is essentially a battle of wits between a perpetrator\nwho tries to find holes and the administrator who tries to close them\nTrue or False",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "897) An attacker can generally determine in advance exactly where the targeted buffer will be located in the stack frame of teh function in which it is defined",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1043) Which stages does a virus have?",
-    "answers": [
-      {
-        "text": "Dormant phase",
-        "image": ""
-      },
-      {
-        "text": "Propagation phase - i.e. attachment to email",
-        "image": ""
-      },
-      {
-        "text": "Triggering phase",
-        "image": ""
-      },
-      {
-        "text": "Execution phase",
-        "image": ""
-      },
-      {
-        "text": "All viruses have these four stages",
-        "image": ""
-      }
-    ],
-    "correct": 4,
-    "image": ""
-  },
-  {
-    "quest": "267) The _________ module analyzes LAN traffic and reports the results to the central manager",
-    "answers": [
-      {
-        "text": "LAN monitor agent ",
-        "image": ""
-      },
-      {
-        "text": "host agent",
-        "image": ""
-      },
-      {
-        "text": "central manager agent ",
-        "image": ""
-      },
-      {
-        "text": "architecture agent",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1134) Message authentication protects two parties who exchange\nmessages from any third party, however, it does not protect the\ntwo parties against each other.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "645) A ___________ is the portion of the data center that houses data processing equipment",
-    "answers": [
-      {
-        "text": "computer room",
-        "image": ""
-      },
-      {
-        "text": "main distribution area",
-        "image": ""
-      },
-      {
-        "text": "entrance room",
-        "image": ""
-      },
-      {
-        "text": "horizontal distribution",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "377) The ________________ used a buffer overflow exploit in the \"fingerd\" as one of its attack mechanisms",
-    "answers": [
-      {
-        "text": "Morris Internet Worm",
-        "image": ""
-      },
-      {
-        "text": "Sasser Worm",
-        "image": ""
-      },
-      {
-        "text": "Code Red Worm",
-        "image": ""
-      },
-      {
-        "text": "Slammer Worm",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "470) A __________ is a collection of bots capable of acting in a coordinated manner",
-    "answers": [
-      {
-        "text": "botnet",
-        "image": ""
-      },
-      {
-        "text": "Firewall",
-        "image": ""
-      },
-      {
-        "text": "Encryption algorithm",
-        "image": ""
-      },
-      {
-        "text": "Intrusion Detection System (IDS)",
-        "image": ""
-      },
-      {
-        "text": "Rootkit",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "11) A user program executes in a kernel mode in which certain areas of memory are protected from the user's use and certain instructions may not be executed",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1116) The BLP model effectively breaks down when (untruste> low classified\nexecutable data are allowed to be executed by a high clearance (truste>  subject.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1089) To emphasize the importance of security awareness,an organization\nshould have a security awareness policy document that is provided to all employees.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "76) In the first instance of multiple encryption plaintext is converted to __________ using the encryption algorithm",
-    "answers": [
-      {
-        "text": "ciphertext",
-        "image": ""
-      },
-      {
-        "text": "S-AES mode",
-        "image": ""
-      },
-      {
-        "text": "Triple DES",
-        "image": ""
-      },
-      {
-        "text": "block cipher",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "161) Intrusion detection is based on the assumption that the behavior of the intruder differs from that of a legitimate user in ways that can be quantified",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "92) The exact substitutions and transformations performed by the algorithm depend on the ________",
-    "answers": [
-      {
-        "text": "ciphertext",
-        "image": ""
-      },
-      {
-        "text": "decryption algorithm",
-        "image": ""
-      },
-      {
-        "text": "secret key",
-        "image": ""
-      },
-      {
-        "text": "encryption algorithm",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "127) A hash function such as SHA-1 was not designed for use as a MAC and cannot be used directly for that purpose because it does not rely on a secret key",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "109) A ________ cipher processes the input elements continuously, producing output one element at a time as it goes along",
-    "answers": [
-      {
-        "text": "substitution",
-        "image": ""
-      },
-      {
-        "text": "block",
-        "image": ""
-      },
-      {
-        "text": "stream",
-        "image": ""
-      },
-      {
-        "text": "transposition",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "1078) Once the plaintext is converted to ciphertext using the\nencryption algorithm the plaintext is then used as input and the algorithm is applied again.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "692) Depending on the details of the overall authentication system, the registration authority issues some sort of electronic credential to the subscriber",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "713) Each individual who is to be included in the database of authorized users must first be __________ in the system",
-    "answers": [
-      {
-        "text": "verified",
-        "image": ""
-      },
-      {
-        "text": "authenticated",
-        "image": ""
-      },
-      {
-        "text": "identified",
-        "image": ""
-      },
-      {
-        "text": "enrolled",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "397) An attacker is more interested in transferring control to a location and code of the attackers choosing rather than immediately crashing the program",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "307) Password �les can be protected in one of two ways: One-way function or ______",
-    "answers": [
-      {
-        "text": "biometric authentication",
-        "image": ""
-      },
-      {
-        "text": "access control",
-        "image": ""
-      },
-      {
-        "text": "encryption",
-        "image": ""
-      },
-      {
-        "text": "two-factor authentication",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "719) __________ allows an issuer to access regional and national networks that connect point of sale devices and bank teller machines worldwide",
-    "answers": [
-      {
-        "text": "EFT",
-        "image": ""
-      },
-      {
-        "text": "POS",
-        "image": ""
-      },
-      {
-        "text": "BTM",
-        "image": ""
-      },
-      {
-        "text": "ATF",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "012) Digital signatures and key management are the two most important applications of __________ encryption",
-    "answers": [
-      {
-        "text": "private-key",
-        "image": ""
-      },
-      {
-        "text": "public-key",
-        "image": ""
-      },
-      {
-        "text": "preimage resistant ",
-        "image": ""
-      },
-      {
-        "text": "advanced",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "647) __________ encompasses intrusion detection, prevention and response",
-    "answers": [
-      {
-        "text": "Intrusion management",
-        "image": ""
-      },
-      {
-        "text": "Security assessments",
-        "image": ""
-      },
-      {
-        "text": "Database access control",
-        "image": ""
-      },
-      {
-        "text": "Data loss prevention",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "820) A threat action in which sensitive data are directly released to an unauthorized entity is __________\nSelect one:",
-    "answers": [
-      {
-        "text": "disruption",
-        "image": ""
-      },
-      {
-        "text": "exposure",
-        "image": ""
-      },
-      {
-        "text": "corruption",
-        "image": ""
-      },
-      {
-        "text": "intrusion",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "12) Any program that is owned by, and SetUID to, the \"superuser\" potentially grants unrestricted access to the system to any user executing that program",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "08) The purpose of a __________ is to produce a “fingerprint� of a file, message, or other block of data",
-    "answers": [
-      {
-        "text": "secret key ",
-        "image": ""
-      },
-      {
-        "text": "digital signature",
-        "image": ""
-      },
-      {
-        "text": "keystream",
-        "image": ""
-      },
-      {
-        "text": "hash function",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "04) On average, __________ of all possible keys must be tried in order to achieve success with a brute-force attack",
-    "answers": [
-      {
-        "text": "one-fourth ",
-        "image": ""
-      },
-      {
-        "text": "half",
-        "image": ""
-      },
-      {
-        "text": "two-thirds ",
-        "image": ""
-      },
-      {
-        "text": "three-fourths",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "759) A traditional packet filter makes filtering decisions on an individual packet basis and does not take into consideration any higher layer context",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "270) A(n) ________ event is an alert that is generated when the gossip traffic enables a platform to conclude that an attack is under way",
-    "answers": [
-      {
-        "text": "PEP ",
-        "image": ""
-      },
-      {
-        "text": "DDI",
-        "image": ""
-      },
-      {
-        "text": "IDEP ",
-        "image": ""
-      },
-      {
-        "text": "IDME",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1172) __________ defines user authentication as \"the process of verifying an identity claimed by or for a system entity\".",
-    "answers": [
-      {
-        "text": "RFC 2828",
-        "image": ""
-      },
-      {
-        "text": "RFC 2493",
-        "image": ""
-      },
-      {
-        "text": "RFC 2298",
-        "image": ""
-      },
-      {
-        "text": "RFC 2328",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "370) In 2003, the _______ exploited a buffer overflow in Microsoft SQL Server 2000",
-    "answers": [
-      {
-        "text": "Slammer worm",
-        "image": ""
-      },
-      {
-        "text": "Sasser worm",
-        "image": ""
-      },
-      {
-        "text": "Morris Internet Worm",
-        "image": ""
-      },
-      {
-        "text": "Code Red Worm",
-        "image": ""
-      },
-      {
-        "text": "Slammer Worm",
-        "image": ""
-      }
-    ],
-    "correct": 4,
-    "image": ""
-  },
-  {
-    "quest": "1118) Multilevel security is of interest when there is a requirement to maintain a\nresource in which multiple levels of data sensitivity are defined.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "410) The __________ used a buffer overflow exploit in fingerd as one of its attack mechanisms",
-    "answers": [
-      {
-        "text": "Conficker Worm",
-        "image": ""
-      },
-      {
-        "text": "Morris Internet Worm",
-        "image": ""
-      },
-      {
-        "text": "Stuxnet Worm",
-        "image": ""
-      },
-      {
-        "text": "ILOVEYOU Worm",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "108) A ________ cipher processes the input one block of elements at a time, producing an output block for each input",
-    "answers": [
-      {
-        "text": "substitution",
-        "image": ""
-      },
-      {
-        "text": "block",
-        "image": ""
-      },
-      {
-        "text": "stream",
-        "image": ""
-      },
-      {
-        "text": "transposition",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "212) A cookie can be used to authenticate a user to a web site so that the user does not have to type in his password for each connection to the site",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1163) The countermeasure to tiny fragment attacks is to discard packets with\nan inside source address if the packet arrives on an external interface.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "140) Which of the following scenario requires a security protocol:",
-    "answers": [
-      {
-        "text": "log in to mail.google.com",
-        "image": ""
-      },
-      {
-        "text": "connecting to work from home using a VPN",
-        "image": ""
-      },
-      {
-        "text": "All the previous answers",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "274) The broad classes of intruders are: cyber criminals, state-sponsored organizations, _________ , and others",
-    "answers": [
-      {
-        "text": "terrorists",
-        "image": ""
-      },
-      {
-        "text": "script kiddies",
-        "image": ""
-      },
-      {
-        "text": "activists",
-        "image": ""
-      },
-      {
-        "text": "hackers",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "1095) Performing regular backups of data on a system is a critical control\nthat assists with maintaining the integrity of the system and user data.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "594) T/F: Business continuity consists of security services that allocate access, distribute, monitor, and protect the underlying resource services",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "01) __________ defines user authentication as “the process of verifying an identity claimed by or for a system entity�",
-    "answers": [
-      {
-        "text": "RFC 4949",
-        "image": ""
-      },
-      {
-        "text": "RFC 2298",
-        "image": ""
-      },
-      {
-        "text": "RFC 2493",
-        "image": ""
-      },
-      {
-        "text": "RFC 2328",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "427) The buffer is located __________ ",
-    "answers": [
-      {
-        "text": "in the heap",
-        "image": ""
-      },
-      {
-        "text": "in the stack",
-        "image": ""
-      },
-      {
-        "text": "in the data section of the process",
-        "image": ""
-      },
-      {
-        "text": "in the register",
-        "image": ""
-      },
-      {
-        "text": "All of the above",
-        "image": ""
-      },
-      {
-        "text": "1,2,3 are correct",
-        "image": ""
-      }
-    ],
-    "correct": 5,
-    "image": ""
-  },
-  {
-    "quest": "162) To be of practical use an IDS should detect a substantial percentage of intrusions while keeping the false alarm rate at an acceptable level",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "132) In Kerberos, each human user has a master key shared with the authentication server, and the key is usually derived from the user's password",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "712) __________ systems identify features of the hand, including shape, and lengths and widths of fingers",
-    "answers": [
-      {
-        "text": "Signature",
-        "image": ""
-      },
-      {
-        "text": "Hand geometry",
-        "image": ""
-      },
-      {
-        "text": "Fingerprint",
-        "image": ""
-      },
-      {
-        "text": "Palm print",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "155) Which of the following feature can only be provided by public-key cryptography?",
-    "answers": [
-      {
-        "text": "Data integrity",
-        "image": ""
-      },
-      {
-        "text": "Confidentiality",
-        "image": ""
-      },
-      {
-        "text": "Digital signatures",
-        "image": ""
-      },
-      {
-        "text": "None of the above",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "401) The buffer overflow type of attack has been known since it was first widely used by the __________ Worm in 1988",
-    "answers": [
-      {
-        "text": "Morris",
-        "image": ""
-      },
-      {
-        "text": "Slammer",
-        "image": ""
-      },
-      {
-        "text": "Code Red",
-        "image": ""
-      },
-      {
-        "text": "Heartbleed",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "707) Recognition by fingerprint, retina, and face are examples of __________",
-    "answers": [
-      {
-        "text": "face recognition",
-        "image": ""
-      },
-      {
-        "text": "dynamic biometrics",
-        "image": ""
-      },
-      {
-        "text": "static biometrics authentication",
-        "image": ""
-      },
-      {
-        "text": "token",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "306) The three classes of intruders identi�ed by Anderson are: Masquerader, Misfeasor, and____",
-    "answers": [
-      {
-        "text": "Insider threat",
-        "image": ""
-      },
-      {
-        "text": "Social engineer",
-        "image": ""
-      },
-      {
-        "text": "clandestine",
-        "image": ""
-      },
-      {
-        "text": "Cybercriminal",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "513) When a DoS attack is detected, the first step is to _______",
-    "answers": [
-      {
-        "text": "identify the attack",
-        "image": ""
-      },
-      {
-        "text": "analyze the response",
-        "image": ""
-      },
-      {
-        "text": "design blocking filters",
-        "image": ""
-      },
-      {
-        "text": "shut down the network",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "373) Buffer overflows can be found in a wide variety of programs, processing a range of different input and with a variety of possible responses",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "309) Two types of audit records used are Detection-speci�c audit records and ____ audit records",
-    "answers": [
-      {
-        "text": "system uptime",
-        "image": ""
-      },
-      {
-        "text": "native",
-        "image": ""
-      },
-      {
-        "text": "network bandwidth",
-        "image": ""
-      },
-      {
-        "text": "packet loss rate",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "102) A ________ is a key used between entities for the purpose of distributing session keys",
-    "answers": [
-      {
-        "text": "permanent key",
-        "image": ""
-      },
-      {
-        "text": "session key",
-        "image": ""
-      },
-      {
-        "text": "distribution key",
-        "image": ""
-      },
-      {
-        "text": "all of the above",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1074) A __________ is a set in which you can do addition, subtraction, multiplication and division without leaving the set.",
-    "answers": [
-      {
-        "text": "record",
-        "image": ""
-      },
-      {
-        "text": "standard",
-        "image": ""
-      },
-      {
-        "text": "field",
-        "image": ""
-      },
-      {
-        "text": "block",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "202) In a wireless network, traffic is broadcasted into the air, and so it is much easier to sniff wireless traffic compared with wired traffic",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1113) Defensive programming is sometimes referred to as _________.",
-    "answers": [
-      {
-        "text": "variable programming",
-        "image": ""
-      },
-      {
-        "text": "secure programming",
-        "image": ""
-      },
-      {
-        "text": "interpretive programming",
-        "image": ""
-      },
-      {
-        "text": "chroot programming",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "18) _________ is the granting of a right or permission to a system entity to access a system resource",
-    "answers": [
-      {
-        "text": "Authorization",
-        "image": ""
-      },
-      {
-        "text": "Authentication",
-        "image": ""
-      },
-      {
-        "text": "Control",
-        "image": ""
-      },
-      {
-        "text": "Monitoring",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1119) IPSec can guarantee that all traffic designated by the network\nadministrator is authenticated but cannot guarantee that it is\nencrypted.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "33) Metamorphic code is software that can be shipped unchanged to a heterogeneous collection of platforms and execute with identical semantics",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "381) Memory is requested from the ______ by programs for use in dynamic data structures, such as linked lists of records",
-    "answers": [
-      {
-        "text": "ROM",
-        "image": ""
-      },
-      {
-        "text": "heap",
-        "image": ""
-      },
-      {
-        "text": "address space",
-        "image": ""
-      },
-      {
-        "text": "shell",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "117) With ______ encryption each vulnerable communications link is equipped on both ends with an encryption device",
-    "answers": [
-      {
-        "text": "network",
-        "image": ""
-      },
-      {
-        "text": "end-to-end",
-        "image": ""
-      },
-      {
-        "text": "link",
-        "image": ""
-      },
-      {
-        "text": "transport",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "351) The success of the digital immune system depends on the ability of the virus analysis machine to detect new and innovative virus strains",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "91) _________ is the original message or data that is fed into the algorithm as input",
-    "answers": [
-      {
-        "text": "Plaintext",
-        "image": ""
-      },
-      {
-        "text": "Encryption algorithm",
-        "image": ""
-      },
-      {
-        "text": "Decryption algorithm",
-        "image": ""
-      },
-      {
-        "text": "Ciphertext",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1166) Signature-based approaches attempt to define normal,or expected,\nbehavior,whereas anomaly approaches attempt to define proper behavior.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "143) A brute-force approach involves trying every possible key until an intelligible translation of the ciphertext into plaintext is obtained",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1138) the __________ generation is usually thought of as the Iot and is marked by the use of billions of embedded devices.",
-    "answers": [
-      {
-        "text": "second",
-        "image": ""
-      },
-      {
-        "text": "third",
-        "image": ""
-      },
-      {
-        "text": "fourth",
-        "image": ""
-      },
-      {
-        "text": "fifth",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "1158) A denial-of-service attack is an attempt to compromise availability by\nhindering or blocking completely the provision of some service.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "321) Intrusion detection involves detecting unusual patterns of activity or patterns of activity that are known to correlate with intrusions",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "577) Encryption can be applied to the entire database, at the record level, at the attribute level, or at the level of the individual field",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "266) A (n) __________ is a hacker with minimal technical skill who primarily uses existing attack toolkits",
-    "answers": [
-      {
-        "text": "Master ",
-        "image": ""
-      },
-      {
-        "text": "Apprentice",
-        "image": ""
-      },
-      {
-        "text": "Journeyman ",
-        "image": ""
-      },
-      {
-        "text": "Activist",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "424) The __________ used a buffer overflow exploit in \"fingerd\" as one of its attack",
-    "answers": [
-      {
-        "text": "Code Red Worm",
-        "image": ""
-      },
-      {
-        "text": "Stuxnet Worm",
-        "image": ""
-      },
-      {
-        "text": "Morris Internet Worm",
-        "image": ""
-      },
-      {
-        "text": "ILOVEYOU Worm",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "632) Site security of the data center itself includes barriers to entry, coupled with authentication techniques for gaining physical access",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "285) 12.The functional components of an _________ are: data source, sensor, analyzer, administration, manager, and operator",
-    "answers": [
-      {
-        "text": "IDS",
-        "image": ""
-      },
-      {
-        "text": "IPS",
-        "image": ""
-      },
-      {
-        "text": "SIEM",
-        "image": ""
-      },
-      {
-        "text": "Firewall",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "139) The purposes of a security protocol include:",
-    "answers": [
-      {
-        "text": "Authentication",
-        "image": ""
-      },
-      {
-        "text": "Key-exchange",
-        "image": ""
-      },
-      {
-        "text": "Negotiate crypto algorithms and parameters",
-        "image": ""
-      },
-      {
-        "text": "All the previous answers",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "1106) there are well-defined tests for determining uniform distribution\nand independence to validate that a sequence of numbers is\nrandom.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1082) The first widely used occurrence of the buffer overflow attack was the _______.",
-    "answers": [
-      {
-        "text": "Code Red Worm",
-        "image": ""
-      },
-      {
-        "text": "Morris Internet Worm",
-        "image": ""
-      },
-      {
-        "text": "Sasser Worm",
-        "image": ""
-      },
-      {
-        "text": "Slammer Worm",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "29) Subject attributes, object attributes and environment attributes are the three types of attributes in the __________ model",
-    "answers": [
-      {
-        "text": "DSD",
-        "image": ""
-      },
-      {
-        "text": "RBAC",
-        "image": ""
-      },
-      {
-        "text": "ABAC",
-        "image": ""
-      },
-      {
-        "text": "SSD",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "63) A mode of operation is a technique for enhancing the effect of a cryptographic algorithm or adapting the algorithm for an application",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "272) The rule _______ tells Snort what to do when it finds a packet that matches the rule criteria",
-    "answers": [
-      {
-        "text": "protocol ",
-        "image": ""
-      },
-      {
-        "text": "direction",
-        "image": ""
-      },
-      {
-        "text": "action ",
-        "image": ""
-      },
-      {
-        "text": "destination port",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "640) A _________ is defined to be a portion of a row used to uniquely identify a row in a table",
-    "answers": [
-      {
-        "text": "foreign key",
-        "image": ""
-      },
-      {
-        "text": "query",
-        "image": ""
-      },
-      {
-        "text": "primary key",
-        "image": ""
-      },
-      {
-        "text": "data perturbation",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "211) Since Android is open-source, each handset vendor can customize it, and this is good for security (hint: consider security updates)",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "010) The strength of a hash function against brute-force attacks depends\nsolely on the length of the hash code produced by the algorithm",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "138) The DSS makes use of the _______ and presents a new digital signature technique, the Digital Signature Algorithm (DSA) ",
-    "answers": [
-      {
-        "text": "AES",
-        "image": ""
-      },
-      {
-        "text": "SHA-1 ",
-        "image": ""
-      },
-      {
-        "text": "MD5",
-        "image": ""
-      },
-      {
-        "text": "RSA",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "428) _________ is a tool used to automatically identify potentially vulnerable programs",
-    "answers": [
-      {
-        "text": "Code obfuscation",
-        "image": ""
-      },
-      {
-        "text": "Encryption",
-        "image": ""
-      },
-      {
-        "text": "fuzzing",
-        "image": ""
-      },
-      {
-        "text": "Penetration testing",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "80) __________ modes of operation have been standardized by NIST for use with symmetric block ciphers such as DES and AES",
-    "answers": [
-      {
-        "text": "Nine",
-        "image": ""
-      },
-      {
-        "text": "Seven",
-        "image": ""
-      },
-      {
-        "text": "Three",
-        "image": ""
-      },
-      {
-        "text": "Five",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "467) A __________ virus is explicitly designed to hide itself from detection by anti-virus software",
-    "answers": [
-      {
-        "text": "Adware",
-        "image": ""
-      },
-      {
-        "text": "Spyware",
-        "image": ""
-      },
-      {
-        "text": "Rootkit",
-        "image": ""
-      },
-      {
-        "text": "stealth",
-        "image": ""
-      },
-      {
-        "text": "Ransomware",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "116) With _________ encryption the encryption process is carried out at the two end systems",
-    "answers": [
-      {
-        "text": "point-to-point",
-        "image": ""
-      },
-      {
-        "text": "intermediary",
-        "image": ""
-      },
-      {
-        "text": "centralized",
-        "image": ""
-      },
-      {
-        "text": "end-to-end",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "119) All encryption algorithms are based on two general principles: substitution and _________",
-    "answers": [
-      {
-        "text": "compression",
-        "image": ""
-      },
-      {
-        "text": "expansion",
-        "image": ""
-      },
-      {
-        "text": "transposition",
-        "image": ""
-      },
-      {
-        "text": "permutation",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "01) The original message or data that is fed into the algorithm is __________",
-    "answers": [
-      {
-        "text": "encryption algorithm ",
-        "image": ""
-      },
-      {
-        "text": "secret key",
-        "image": ""
-      },
-      {
-        "text": "decryption algorithm ",
-        "image": ""
-      },
-      {
-        "text": "plaintext",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "100) ______ mode is typically used for a general-purpose block-oriented transmission and is useful for high-speed requirements",
-    "answers": [
-      {
-        "text": "ECB",
-        "image": ""
-      },
-      {
-        "text": "OFB",
-        "image": ""
-      },
-      {
-        "text": "CFB",
-        "image": ""
-      },
-      {
-        "text": "CTR",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "323) System administrators can stop all attacks and hackers from penetrating their systems by installing software patches periodically",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "217) In XSRF, the malicious site can send malicious script to execute in the user?s browser by embedding the script in a hidden iframe",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "634) Security specifically tailored to databases is an increasingly important component of an overall organizational security strategy",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "836) Computer security is protection of the integrity, availability, and\nconfidentiality of information system resources\nTrue or False",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1137) A major characteristic of a good security program is how quickly\nthe Iot system can be recovered after an incident has occurred.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1121) Additional padding may be added to provide partial traffic-flow\nconfidentiality by concealing the actual length of the payload.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "03) Cryptanalytic attacks try every possible key on a piece of ciphertext until an intelligible translation into plaintext is obtaine",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "69) A typical application of Output Feedback mode is stream oriented transmission over noisy channel, such as satellite communication",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "650) __________ specifies the minimum requirements for telecommunications infrastructure of data centers",
-    "answers": [
-      {
-        "text": "TIA-492",
-        "image": ""
-      },
-      {
-        "text": "RFC-4949",
-        "image": ""
-      },
-      {
-        "text": "NIST-7883",
-        "image": ""
-      },
-      {
-        "text": "RSA-298",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "147) Using PKCS (public-key cryptography standard), when RSA encrypts the same message twice, different ciphertexts will be produced",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "943) Four stages of viruses",
-    "answers": [
-      {
-        "text": "Dormant phase",
-        "image": ""
-      },
-      {
-        "text": "Propagation phase - i.e. attachment to email",
-        "image": ""
-      },
-      {
-        "text": "Triggering phase",
-        "image": ""
-      },
-      {
-        "text": "Execution phase",
-        "image": ""
-      },
-      {
-        "text": "All of the above",
-        "image": ""
-      }
-    ],
-    "correct": 4,
-    "image": ""
-  },
-  {
-    "quest": "437) __________ defenses aim to detect and abort attacks in existing programs",
-    "answers": [
-      {
-        "text": "Code signing",
-        "image": ""
-      },
-      {
-        "text": "run-time",
-        "image": ""
-      },
-      {
-        "text": "Compile-time defenses",
-        "image": ""
-      },
-      {
-        "text": "Patch management",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1162) The firewall may be a single computer system or a set of two or more\nsystems that cooperate to perform the firewall function.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "82) The simplest form of multiple encryption has __________ encryption stages and __________ keys",
-    "answers": [
-      {
-        "text": "three, two",
-        "image": ""
-      },
-      {
-        "text": "four, two",
-        "image": ""
-      },
-      {
-        "text": "two, three",
-        "image": ""
-      },
-      {
-        "text": "two, two",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "304) Statistical approaches attempt to de�ne proper behavior and rule-based approaches attempt to de�ne normal or expected behavior",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "17) __________ is verification that the credentials of a user or other system entity are valid",
-    "answers": [
-      {
-        "text": "Adequacy",
-        "image": ""
-      },
-      {
-        "text": "Authentication",
-        "image": ""
-      },
-      {
-        "text": "Authorization",
-        "image": ""
-      },
-      {
-        "text": "Audit",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "711) The most common means of human-to-human identification are __________",
-    "answers": [
-      {
-        "text": "facial characteristics",
-        "image": ""
-      },
-      {
-        "text": "signatures",
-        "image": ""
-      },
-      {
-        "text": "retinal patterns",
-        "image": ""
-      },
-      {
-        "text": "fingerprints",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1155) In relational database parlance,the basic building block is a __________,which is a flat table.",
-    "answers": [
-      {
-        "text": "attribute",
-        "image": ""
-      },
-      {
-        "text": "tuple",
-        "image": ""
-      },
-      {
-        "text": "primary key",
-        "image": ""
-      },
-      {
-        "text": "relation",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "1159) Using forged source addresses is known as _________.",
-    "answers": [
-      {
-        "text": "source address spoofing",
-        "image": ""
-      },
-      {
-        "text": "a three-way address",
-        "image": ""
-      },
-      {
-        "text": "random dropping",
-        "image": ""
-      },
-      {
-        "text": "directed broadcast",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1432) \"Each block of 64 plaintext bits is encoded independently using the\nsame key\" is a description of the CBC mode of operation.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "126) Cryptographic hash functions generally execute faster in software than conventional encryption algorithms such as DES and AES",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "95) The most widely used encryption scheme is based on the _________ adopted in 1977 by the National Bureau of Standards",
-    "answers": [
-      {
-        "text": "AES",
-        "image": ""
-      },
-      {
-        "text": "3DES",
-        "image": ""
-      },
-      {
-        "text": "CES",
-        "image": ""
-      },
-      {
-        "text": "DES",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "939) If we find that a botnet server is located in country X, we can be certain that criminals within country X control the botnet",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "169) The strength of a hash function against brute-force attacks depends on the length of the hash code produced by the algorithm",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "05) The most important symmetric algorithms, all of which are block ciphers, are the DES, triple DES, and the __________",
-    "answers": [
-      {
-        "text": "SHA",
-        "image": ""
-      },
-      {
-        "text": "RSA",
-        "image": ""
-      },
-      {
-        "text": "AES",
-        "image": ""
-      },
-      {
-        "text": "DSS",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "229) The purpose of the privacy functions is to provide a user protection against discovery and misuse of identity by other users",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1123) the __________ cryptosystem is used in some form in a number of standards including DSS and S/MIME.",
-    "answers": [
-      {
-        "text": "Rabin",
-        "image": ""
-      },
-      {
-        "text": "Rijnedel",
-        "image": ""
-      },
-      {
-        "text": "Hillman",
-        "image": ""
-      },
-      {
-        "text": "ElGamal",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "1052) TCB Design Principles",
-    "answers": [
-      {
-        "text": "Least Privilege",
-        "image": ""
-      },
-      {
-        "text": "Economy",
-        "image": ""
-      },
-      {
-        "text": "Open Design",
-        "image": ""
-      },
-      {
-        "text": "Complete Mediation",
-        "image": ""
-      },
-      {
-        "text": "Fail-safe defaults",
-        "image": ""
-      },
-      {
-        "text": "Ease of Use",
-        "image": ""
-      },
-      {
-        "text": "All of the above",
-        "image": ""
-      }
-    ],
-    "correct": 6,
-    "image": ""
-  },
-  {
-    "quest": "438) The __________ project produces a free, multiplatform 4.4BSD-based UNIX-like operating system",
-    "answers": [
-      {
-        "text": "Linux",
-        "image": ""
-      },
-      {
-        "text": "Windows",
-        "image": ""
-      },
-      {
-        "text": "OpenBSD",
-        "image": ""
-      },
-      {
-        "text": "macOS",
-        "image": ""
-      },
-      {
-        "text": "FreeBSD",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "58) A __________ attack is a bot attack on a computer system or network that causes a loss of service to users",
-    "answers": [
-      {
-        "text": "spam",
-        "image": ""
-      },
-      {
-        "text": "phishing",
-        "image": ""
-      },
-      {
-        "text": "DDoS",
-        "image": ""
-      },
-      {
-        "text": "sniff",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "328) Stealth is not a term that applies to a virus as such but, rather, refers to a technique used by a virus to evade detection",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "411) In 2003 the _________ exploited a buffer overflow in Microsoft SQL Server 2000",
-    "answers": [
-      {
-        "text": "Code Red Worm",
-        "image": ""
-      },
-      {
-        "text": "Mydoom Worm",
-        "image": ""
-      },
-      {
-        "text": "Blaster Worm",
-        "image": ""
-      },
-      {
-        "text": "Slammer Worm",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "1131) A recipient in possession of the secret key cannot generate an\nauthentication code to verify the integrity of the message.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "831) A loss of _________ is the unauthorized disclosure of information\nSelect one:",
-    "answers": [
-      {
-        "text": "integrity",
-        "image": ""
-      },
-      {
-        "text": "availability",
-        "image": ""
-      },
-      {
-        "text": "confidentiality",
-        "image": ""
-      },
-      {
-        "text": "authenticity",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "335) An encrypted virus is a virus that mutates with every infection, making detection by the signature of the virus impossible",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "934) The best defense against being an unwitting participant in a DDos attack is to prevent your systems from being compromised",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "112) ______ was designed in 1987 by Ron Rivest and is a variable key-size stream cipher with byte-oriented operations",
-    "answers": [
-      {
-        "text": "DES",
-        "image": ""
-      },
-      {
-        "text": "RC4",
-        "image": ""
-      },
-      {
-        "text": "AES",
-        "image": ""
-      },
-      {
-        "text": "RSA",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1090) Security awareness,training,and education programs may be needed to\ncomply with regulations and contractual obligations.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "77) Triple DES makes use of __________ stages of the DES algorithm, using a total of two or three distinct keys",
-    "answers": [
-      {
-        "text": "twelve",
-        "image": ""
-      },
-      {
-        "text": "six",
-        "image": ""
-      },
-      {
-        "text": "nine",
-        "image": ""
-      },
-      {
-        "text": "three",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "436) __________ defenses aim to harden programs to resist attacks in new programs",
-    "answers": [
-      {
-        "text": "Machine code",
-        "image": ""
-      },
-      {
-        "text": "Obfuscated",
-        "image": ""
-      },
-      {
-        "text": "Self-modifying",
-        "image": ""
-      },
-      {
-        "text": "compile-time",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "150) Just like RSA can be used for signature as well as encryption, Digital Signature Standard can also be used for encryption",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "55) __________ is the first function in the propagation phase for a network worm",
-    "answers": [
-      {
-        "text": "Propagating",
-        "image": ""
-      },
-      {
-        "text": "Fingerprinting",
-        "image": ""
-      },
-      {
-        "text": "Keylogging",
-        "image": ""
-      },
-      {
-        "text": "Spear phishing",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "717) A __________ attack involves an adversary repeating a previously captured user response",
-    "answers": [
-      {
-        "text": "client",
-        "image": ""
-      },
-      {
-        "text": "replay",
-        "image": ""
-      },
-      {
-        "text": "Trojan horse",
-        "image": ""
-      },
-      {
-        "text": "eavesdropping",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "578) A(n) __________ is a structured collection of data stored for use by one or more applications",
-    "answers": [
-      {
-        "text": "attribute",
-        "image": ""
-      },
-      {
-        "text": "database",
-        "image": ""
-      },
-      {
-        "text": "tuple",
-        "image": ""
-      },
-      {
-        "text": "inference",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "837) Data integrity assures that information and programs are changed only\nin a specified and authorized manner\nTrue or False",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "216) XSRF is possible when a user has a connection to a malicious site while a connection to a legitimate site is still alive",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1125) Limited characteristics make it impossible for hash functions to be\nused to determine whether or not data has changed.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "120) The three most important symmetric block ciphers are: 3DES, AES, and _____",
-    "answers": [
-      {
-        "text": "Serpent",
-        "image": ""
-      },
-      {
-        "text": "Data Encryption Standard (DES)",
-        "image": ""
-      },
-      {
-        "text": "Blowfish",
-        "image": ""
-      },
-      {
-        "text": "RSA",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "795) The principal objective for developing a PKI is to enable secure, convenient, and efficient acquisition of private keys",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "278) An IDS comprises three logical components: analyzers, user interface and _____",
-    "answers": [
-      {
-        "text": "sensors",
-        "image": ""
-      },
-      {
-        "text": "firewalls",
-        "image": ""
-      },
-      {
-        "text": "routers",
-        "image": ""
-      },
-      {
-        "text": "encryption algorithms",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1129) One limitation of a firewall is that an improperly secured wireless\nLAN may be accessed from outside the organization.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "41) A Trojan horse is an apparently useful program containing hidden code that, when invoked, performs some harmful function",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "698) Depending on the application, user authentication on a biometric system involves either verification or identification",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "382) A stack buffer overflow attack is also referred to as ______",
-    "answers": [
-      {
-        "text": "buffer overrunning",
-        "image": ""
-      },
-      {
-        "text": "stack framing",
-        "image": ""
-      },
-      {
-        "text": "heap overflowing",
-        "image": ""
-      },
-      {
-        "text": "stack smashing",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "78) Another important mode, XTS-AES, has been standardized by the __________ Security in Storage Working Group",
-    "answers": [
-      {
-        "text": "NIST",
-        "image": ""
-      },
-      {
-        "text": "IEEE",
-        "image": ""
-      },
-      {
-        "text": "ITIL",
-        "image": ""
-      },
-      {
-        "text": "ISO",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "72) It is possible to convert a block cipher into a stream cipher using cipher feedback, output feedback and counter modes",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1105) A major advance in symmetric cryptography occurred with the\ndevelopment of the rotor encryption/decryption machine.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1108) A widely used technique for pseudorandom number generation is\nan algorithm known as the linear congruential method.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "26) A __________ is a named job function within the organization that controls this computer system",
-    "answers": [
-      {
-        "text": "user",
-        "image": ""
-      },
-      {
-        "text": "role",
-        "image": ""
-      },
-      {
-        "text": "permission",
-        "image": ""
-      },
-      {
-        "text": "session",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1126) the Secure Hash Algorithm design closely models, and is based on, the hash function __________ .",
-    "answers": [
-      {
-        "text": "MD5",
-        "image": ""
-      },
-      {
-        "text": "FIPS 180",
-        "image": ""
-      },
-      {
-        "text": "RFC 4634",
-        "image": ""
-      },
-      {
-        "text": "MD4",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "09) __________ is a block cipher in which the plaintext and ciphertext are integers between 0 and n-1 for some n",
-    "answers": [
-      {
-        "text": "DSS",
-        "image": ""
-      },
-      {
-        "text": "RSA",
-        "image": ""
-      },
-      {
-        "text": "SHA",
-        "image": ""
-      },
-      {
-        "text": "AES",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "349) Malicious software that needs a host program is referred to as _________ \nSelect one:",
-    "answers": [
-      {
-        "text": "blended",
-        "image": ""
-      },
-      {
-        "text": "parasitic",
-        "image": ""
-      },
-      {
-        "text": "logic bomb",
-        "image": ""
-      },
-      {
-        "text": "flooders",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "701) Identifiers should be assigned carefully because authenticated identities are the basis for other security services",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "79) The _________ and _________ block cipher modes of operation are used for authentication",
-    "answers": [
-      {
-        "text": "OFB, CTR",
-        "image": ""
-      },
-      {
-        "text": "CBC, CFB",
-        "image": ""
-      },
-      {
-        "text": "CFB, OFB",
-        "image": ""
-      },
-      {
-        "text": "ECB, CBC",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "865) A subject can exercise only accesses for which it has the necessary authorization and which satisfy the MAC rules",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1111) Data representing behavior that does not trigger an alarm cannot serve as input to intrusion detection analysis.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1112) Security flaws occur as a consequence of sufficient checking and validation of data and error codes in programs.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "107) _________ is the process of attempting to discover the plaintext or key",
-    "answers": [
-      {
-        "text": "Cryptanalysis",
-        "image": ""
-      },
-      {
-        "text": "Steganography",
-        "image": ""
-      },
-      {
-        "text": "Cryptography",
-        "image": ""
-      },
-      {
-        "text": "Hashing",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "133) In Kerberos, the purpose of using ticket-granting-ticket (TGT) is to minimize the exposure of a user?s master key",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1135) the main work for signature generation depends on the message\nand is done during the idle time of the processor.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "02) The __________ is the encryption algorithm run in reverse",
-    "answers": [
-      {
-        "text": "decryption algorithm",
-        "image": ""
-      },
-      {
-        "text": "plaintext",
-        "image": ""
-      },
-      {
-        "text": "ciphertext ",
-        "image": ""
-      },
-      {
-        "text": "encryption algorithm",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "15) An ABAC model can define authorizations that express conditions on properties of both the resource and the subject",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1169) A bot propagates itself and activates itself,whereas a worm is initially\ncontrolled from some central facility.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "560) T/F: SQL Server allows users to create roles that can then be assigned access rights to portions of the database",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "320) Unauthorized intrusion into a computer system or network is one of the most serious threats to computer security",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "567) T/F: A view cannot provide restricted access to a relational database so it cannot be used for security purposes",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1160) Flooding attacks take a variety of forms based on which network\nprotocol is being used to implement the attack.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "589) T/F: The database management system makes use of the database description tables to manage the physical database",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "595) T/F: An IPS incorporates IDS functionality but also includes mechanisms designed to block traffic from intruders",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "340) Mobile phone worms communicate through Bluetooth wireless connections or via the _________ \nSelect one:",
-    "answers": [
-      {
-        "text": "SQL",
-        "image": ""
-      },
-      {
-        "text": "TRW",
-        "image": ""
-      },
-      {
-        "text": "PWC",
-        "image": ""
-      },
-      {
-        "text": "MMS",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "367) ____________ is a form of overflow attack",
-    "answers": [
-      {
-        "text": "Heap overflows",
-        "image": ""
-      },
-      {
-        "text": "Replacement stack frame",
-        "image": ""
-      },
-      {
-        "text": "Return to system call",
-        "image": ""
-      },
-      {
-        "text": "All of the above",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "1102) HMAC can be proven secure provided that the embedded hash function\nhas some reasonable cryptographic strengths.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1149) A loss of _________ is the unauthorized disclosure of information.",
-    "answers": [
-      {
-        "text": "confidentiality",
-        "image": ""
-      },
-      {
-        "text": "authenticity",
-        "image": ""
-      },
-      {
-        "text": "integrity",
-        "image": ""
-      },
-      {
-        "text": "availability",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "149) A key exchange protocol is vulnerable to a man-in-the-middle attack if it does not authenticate the participants",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "319) The main advantage of the use of statistical pro�les is that a prior knowledge of security flaws is not required",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1100) The Diffie-Hellman algorithm depends for its effectiveness on the\ndifficulty of computing discrete logarithms.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "014) An important element in many computer security services and\napplications is the use of cryptographic algorithms",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "937) the domain name of the command and control server of a botnet are pre-determined for the lifetime of the botnet",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "52) The __________ is when the virus function is performed",
-    "answers": [
-      {
-        "text": "dormant phase",
-        "image": ""
-      },
-      {
-        "text": "propagation phase",
-        "image": ""
-      },
-      {
-        "text": "triggering phase",
-        "image": ""
-      },
-      {
-        "text": "execution phase",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "596) T/F: The CSP can provide backup at multiple locations, with reliable failover and disaster recovery facilities",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "131) In Kerberos, the authentication server shares a unique secret key with each authorized computer on the network",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1171) In a biometric scheme some physical characteristic of the individual is\nmapped into a digital representation.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "418) A stack buffer overflow is also referred to as ___________ ",
-    "answers": [
-      {
-        "text": "data leakage",
-        "image": ""
-      },
-      {
-        "text": "stack smashing",
-        "image": ""
-      },
-      {
-        "text": "heap hijacking",
-        "image": ""
-      },
-      {
-        "text": "code injection",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "87) The __________ mode operates on full blocks of plaintext and ciphertext, as opposed to an s-bit subset",
-    "answers": [
-      {
-        "text": "ECB",
-        "image": ""
-      },
-      {
-        "text": "CFB",
-        "image": ""
-      },
-      {
-        "text": "CBC",
-        "image": ""
-      },
-      {
-        "text": "OFB",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "214) XSS is possible when a web site does not check user input properly and use the input in an outgoing html page",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1075) the Rijndael developers designed the expansion key algorithm to\nbe resistant to known cryptanalytic attacks.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "898) It is possible to write a compiler tool to check any C program and identify all possible buffer overflow bugs",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "838) Availability assures that systems works promptly and service is not\ndenied to authorized users\nTrue or False",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "207) The App Store review process can guarantee that no malicious iOS app is allowed into the store for download",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1142) A major weakness of the public announcement of public keys is\nthat anyone can forge a public announcement.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "137) Issued as RFC 2104, _______ has been chosen as the mandatory-to-implement MAC for IP Security",
-    "answers": [
-      {
-        "text": "SHA-256",
-        "image": ""
-      },
-      {
-        "text": "HMAC",
-        "image": ""
-      },
-      {
-        "text": "MD5",
-        "image": ""
-      },
-      {
-        "text": "AES",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1084) Restoring the plaintext from the ciphertext is __________ .",
-    "answers": [
-      {
-        "text": "deciphering",
-        "image": ""
-      },
-      {
-        "text": " transposition",
-        "image": ""
-      },
-      {
-        "text": " steganography",
-        "image": ""
-      },
-      {
-        "text": " encryption",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "74) OFB mode requires an initialization vector that must be unique to each execution of the encryption operation",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "05) Triple DES takes a plaintext block of 64 bits and a key of 56 bits to produce a ciphertext block of 64 bits",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "590) T/F: The cloud carrier is useful when cloud services are too complex for a cloud consumer to easily manage",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "08) A message authentication code is a small block of data generated by a\nsecret key and appended to a message",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "49) The term \"computer virus\" is attributed to __________",
-    "answers": [
-      {
-        "text": "Herman Hollerith",
-        "image": ""
-      },
-      {
-        "text": "Fred Cohen",
-        "image": ""
-      },
-      {
-        "text": "Charles Babbage",
-        "image": ""
-      },
-      {
-        "text": "Albert Einstein",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "167) Two of the most important applications of public-key encryption are digital signatures and key management",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "842) Security mechanisms typically do not involve more than one particular\nalgorithm or protocol\nTrue or False",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "227) The IT security management process ends with the implementation of controls and the training of personnel",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "913) each layer of code needs appropriate hardening measures in place to provide appropriate security services",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1117) The Biba models deals with confidentiality and is concerned with\nunauthorized disclosure of information.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "215) XSS can perform many types of malicious actions because a malicious script is executed at user?s browser",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "163) An inline sensor monitors a copy of network traffic; the actual traffic does not pass through the device",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1114) It is possible to convert any block cipher into a stream cipher by using\nthe cipher feedback (CF> mode.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "37) Many forms of infection can be blocked by denying normal users the right to modify programs on the system",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "28) __________ refers to setting a maximum number with respect to roles",
-    "answers": [
-      {
-        "text": "Cardinality",
-        "image": ""
-      },
-      {
-        "text": "Prerequisite",
-        "image": ""
-      },
-      {
-        "text": "Exclusive",
-        "image": ""
-      },
-      {
-        "text": "Hierarchy",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "152) is the original message or data that is fed into the encryption process as input",
-    "answers": [
-      {
-        "text": "Hash",
-        "image": ""
-      },
-      {
-        "text": "Key",
-        "image": ""
-      },
-      {
-        "text": "Plaintext ",
-        "image": ""
-      },
-      {
-        "text": "Ciphertext",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "1092) The approach taken by Kerberos is using authentication software tied\nto a secure authentication server.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "25) __________ is based on the roles the users assume in a system rather than the user's identity",
-    "answers": [
-      {
-        "text": "DAC",
-        "image": ""
-      },
-      {
-        "text": "RBAC",
-        "image": ""
-      },
-      {
-        "text": "MAC",
-        "image": ""
-      },
-      {
-        "text": "URAC",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "245) Activists are either individuals or members of an organized crime group with a goal of financial reward",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "246) Running a packet sniffer on a workstation to capture usernames and passwords is an example of intrusion",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "409) A buffer can be located _________",
-    "answers": [
-      {
-        "text": "in the heap",
-        "image": ""
-      },
-      {
-        "text": "on the stack",
-        "image": ""
-      },
-      {
-        "text": "in the data section of the process",
-        "image": ""
-      },
-      {
-        "text": "All of the above",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "226) It is likely that an organization will not have the resources to implement all the recommended controls",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "47) __________ are used to send large volumes of unwanted e-mail",
-    "answers": [
-      {
-        "text": "Rootkits",
-        "image": ""
-      },
-      {
-        "text": "Spammer programs",
-        "image": ""
-      },
-      {
-        "text": "Downloaders",
-        "image": ""
-      },
-      {
-        "text": "Auto-rooters",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "263) The ________ is responsible for determining if an intrusion has occurred",
-    "answers": [
-      {
-        "text": "analyzer ",
-        "image": ""
-      },
-      {
-        "text": "host",
-        "image": ""
-      },
-      {
-        "text": "user interface ",
-        "image": ""
-      },
-      {
-        "text": "sensor",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "899) The OpenSSL heartbleed vulnerability would have been prevented if OpenSSL had been implemented in Java",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "220) Using an input filter to block certain characters is an effective way to prevent SQL injection attacks",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "103) The _______ module performs end-to-end encryption and obtains session keys on behalf of users",
-    "answers": [
-      {
-        "text": "PKM",
-        "image": ""
-      },
-      {
-        "text": "RCM",
-        "image": ""
-      },
-      {
-        "text": "SSM",
-        "image": ""
-      },
-      {
-        "text": "CCM",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "1130) the primary benefit of a host-based IDS is that it can detect both\nexternal and internal intrusions.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "93) The _________ is the encryption algorithm run in reverse",
-    "answers": [
-      {
-        "text": "decryption algorithm",
-        "image": ""
-      },
-      {
-        "text": "ciphertext",
-        "image": ""
-      },
-      {
-        "text": "plaintext",
-        "image": ""
-      },
-      {
-        "text": "secret key",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "840) The more critical a component or service, the higher the level of\navailability required\nTrue or False",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1099) If a computer's temperature gets too cold the system can undergo\nthermal shock when it is turned on.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "708) A __________ is a password guessing program",
-    "answers": [
-      {
-        "text": "password hash",
-        "image": ""
-      },
-      {
-        "text": "password cracker",
-        "image": ""
-      },
-      {
-        "text": "password biometric",
-        "image": ""
-      },
-      {
-        "text": "password salt",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1088) Integrity can apply to a stream of messages, a single message, or\nselected fields within a message.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1489) __________ controls access based on comparing security labels with security clearances.",
-    "answers": [
-      {
-        "text": "MAC",
-        "image": ""
-      },
-      {
-        "text": "DAC",
-        "image": ""
-      },
-      {
-        "text": "RBAC",
-        "image": ""
-      },
-      {
-        "text": "MBAC",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "124) The additive constant numbers used in SHA-512 are random-looking and are hardcoded in the algorithm",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "508) A characteristic of reflection attacks is the lack of _______ traffic",
-    "answers": [
-      {
-        "text": "backscatter",
-        "image": ""
-      },
-      {
-        "text": "network",
-        "image": ""
-      },
-      {
-        "text": "three-way",
-        "image": ""
-      },
-      {
-        "text": "botnet",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "313) Penetration identi�cation is an approach developed to detect deviation from previous usage patterns",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "935) Botnet command and control must be centralized( i.e. all bots communicate with a central server(s))",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "880) A virus that attaches to an executable program can do anything that hte program is permitted to do",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "691) Identification is the means of establishing the validity of a claimed identity provided by a user",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "198) In IPSec, if A uses DES for traffic from A to B, then B must also use DES for traffic from B to A",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "34) A virus that attaches to an executable program can do anything that the program is permitted to do",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "779) The most significant source of risk in wireless networks in the underlying communications medium",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "36) A logic bomb is the event or condition that determines when the payload is activated or delivered",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "53) During the __________ the virus is idle",
-    "answers": [
-      {
-        "text": "dormant phase",
-        "image": ""
-      },
-      {
-        "text": "propagation phase",
-        "image": ""
-      },
-      {
-        "text": "triggering phase",
-        "image": ""
-      },
-      {
-        "text": "execution phase",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "503) TCP uses the _______ to establish a connection",
-    "answers": [
-      {
-        "text": "zombie",
-        "image": ""
-      },
-      {
-        "text": "SYN cookie",
-        "image": ""
-      },
-      {
-        "text": "directed broadcast",
-        "image": ""
-      },
-      {
-        "text": "three-way handshake",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "97) For stream-oriented transmission over noisy channel you would typically use _______ mode",
-    "answers": [
-      {
-        "text": "ECB",
-        "image": ""
-      },
-      {
-        "text": "CTR",
-        "image": ""
-      },
-      {
-        "text": "OFB",
-        "image": ""
-      },
-      {
-        "text": "CBC",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "866) One way to secure against Trojan horse attacks is the use of a secure, trusted operating system",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "593) T/F: An IDS is a set of automated tools designed to detect unauthorized access to a host system",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "350) The challenge in coping with DDoS attacks is the sheer number of ways in which they can operate",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "66) Given the potential vulnerability of DES to a brute-force attack, an alternative has been found",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "758) A packet filtering firewall is typically configured to filter packets going in both directions",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1085) the process of converting from plaintext to ciphertext is known as\ndeciphering or decryption.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "99) For general-purpose stream-oriented transmission you would typically use _______ mode",
-    "answers": [
-      {
-        "text": "CTR",
-        "image": ""
-      },
-      {
-        "text": "CFB",
-        "image": ""
-      },
-      {
-        "text": "ECB",
-        "image": ""
-      },
-      {
-        "text": "CBC",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "10) The default set of rights should always follow the rule of least privilege or read-only access",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "03) __________ is the scrambled message produced as output",
-    "answers": [
-      {
-        "text": "Plaintext",
-        "image": ""
-      },
-      {
-        "text": "Ciphertext",
-        "image": ""
-      },
-      {
-        "text": "Secret key ",
-        "image": ""
-      },
-      {
-        "text": "Cryptanalysis",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "42) Packet sniffers are mostly used to retrieve sensitive information like usernames and passwords",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "236) The primary purpose of an IDS is to detect intrusions, log suspicious events, and send alerts",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "696) User authentication is the basis for most types of access control and for user accountability",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "59) The ideal solution to the threat of malware is __________",
-    "answers": [
-      {
-        "text": "identification",
-        "image": ""
-      },
-      {
-        "text": "removal",
-        "image": ""
-      },
-      {
-        "text": "detection",
-        "image": ""
-      },
-      {
-        "text": "prevention",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "98) For general-purpose block-oriented transmission you would typically use _______ mode",
-    "answers": [
-      {
-        "text": "CBC",
-        "image": ""
-      },
-      {
-        "text": "CTR",
-        "image": ""
-      },
-      {
-        "text": "CFB",
-        "image": ""
-      },
-      {
-        "text": "OFB",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "695) A good technique for choosing a password is to use the first letter of each word of a phrase",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "128) It is a good idea to use sequentially increasing numbers as challenges in security protocols",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "210) In Android, an app will never be able to get more permission than what the user has approved",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "893) Security mechanisms typically do not involve more than one particular algorithm or protocol",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1127) Big-endian format is the most significant byte of a word in the\nlow-address byte position.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "341) Backdoors become threats when unscrupulous programmers use them to gain unauthorized access",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "894) The first step in devising security services and mechanisms is to develop a security policy",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "67) A number of Internet based applications have adopted two-key 3DES, including PGP and S/MIME",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "386) Buffer overflow exploits are no longer a major source of concern to security practitioners",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "936) Both static and dynamic analyses are needed in order to fully understand malware behaviors",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "151) In general, public key based encryption is much slower than symmetric key based encryption",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "011) Transmitted data stored locally are referred to as __________ ",
-    "answers": [
-      {
-        "text": "ciphertext ",
-        "image": ""
-      },
-      {
-        "text": "DES",
-        "image": ""
-      },
-      {
-        "text": "data at rest ",
-        "image": ""
-      },
-      {
-        "text": "ECC",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "727) Hardware is the most vulnerable to attack and the least susceptible to automated controls",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1109) The foundation of a security auditing facility is the initial capture of\nthe audit data.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "597) T/F: Encryption is a pervasive service that can be provided for data at rest in the cloud",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "699) Enrollment creates an association between a user and the user's biometric characteristics",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "222) Organizational security objectives identify what IT security outcomes should be achieved",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "221) SQL injection is yet another example that illustrates the importance of input validation",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "203) Compared with WEP, WPA2 has more flexible authentication and stronger encryption schemes",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "165) Network-based intrusion detection makes use of signature detection and anomaly detection",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "125) The strong collision resistance property subsumes the weak collision resistance property",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "13) Traditional RBAC systems define the access rights of individual users and groups of users",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "218) It is easy for the legitimate site to know if a request is really from the (human) user",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "200) Most browsers come equipped with SSL and most Web servers have implemented the protocol",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "690) User authentication is the fundamental building block and the primary line of defense",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "6) Security labels indicate which system entities are eligible to access certain resources",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1096) A malicious driver can potentially bypass many security controls to\ninstall malware.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "639) In a relational database rows are referred to as _________",
-    "answers": [
-      {
-        "text": "relations",
-        "image": ""
-      },
-      {
-        "text": "attributes",
-        "image": ""
-      },
-      {
-        "text": "views",
-        "image": ""
-      },
-      {
-        "text": "tuples",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "312) Password crackers rely on the fact that some people choose easily guessable passwords",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1161) An important aspect of a distributed firewall configuration is security\nmonitoring.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1133) An important characteristic of the MAC algorithm is that it needs\nto be reversible.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1086) A loss of integrity is the unauthorized modification or destruction\nof information.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "3) An auditing function monitors and keeps a record of user accesses to system resources",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "224) Legal and regulatory constraints may require specific approaches to risk assessment",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "568) T/F: Two disadvantages to database encryption are key management and inflexibility",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1146) SSO enables a user to access all network resources after a single\nauthentication.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "333) Viruses, logic bombs, and backdoors are examples of independent malicious software",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "225) One asset may have multiple threats and a single threat may target multiple assets",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "371) A stack overflow can result in some form of a denial of service attack on a system",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "14) A constraint is a defined relationship among roles or a condition related to roles",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1167) The __________ is what the virus \"does\".",
-    "answers": [
-      {
-        "text": "infection mechanism",
-        "image": ""
-      },
-      {
-        "text": "trigger",
-        "image": ""
-      },
-      {
-        "text": "logic bomb",
-        "image": ""
-      },
-      {
-        "text": "payload",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "56) Unsolicited bulk e-mail is referred to as __________",
-    "answers": [
-      {
-        "text": "spam",
-        "image": ""
-      },
-      {
-        "text": "propagating",
-        "image": ""
-      },
-      {
-        "text": "phishing",
-        "image": ""
-      },
-      {
-        "text": "crimeware",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "575) The two commands that SQL provides for managing access rights are ALLOW and DENY",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "71) Cipher Block Chaining is a simple way to satisfy the security deficiencies of ECB",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "197) In IPSec, packets can be protected using ESP or AH but not both at the same time",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1110) Although important,security auditing is not a key element in computer\nsecurity.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "933) A bot is a computer compromised by malware and under the control of a bot master",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "379) A buffer overflow error is not likely to lead to eventual program termination. ",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "700) An individual's signature is not unique enough to use in biometric applications",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "505) _______ is a text-based protocol with a syntax similar to that of HTTP",
-    "answers": [
-      {
-        "text": "RIP",
-        "image": ""
-      },
-      {
-        "text": "DIP",
-        "image": ""
-      },
-      {
-        "text": "SIP",
-        "image": ""
-      },
-      {
-        "text": "HIP",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "431) Data is simply an array of _________ ",
-    "answers": [
-      {
-        "text": "characters",
-        "image": ""
-      },
-      {
-        "text": "integers",
-        "image": ""
-      },
-      {
-        "text": "floating-point numbers",
-        "image": ""
-      },
-      {
-        "text": "bytes",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "915) The default configuration for many operating systems usually maximizes security",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "141) Symmetric encryption is also referred to as secret-key or single-key encryption",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1081) The buffer overflow type of attack is one of the least commonly seen\nattacks.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "23) A(n) __________ is a resource to which access is controlled",
-    "answers": [
-      {
-        "text": "object",
-        "image": ""
-      },
-      {
-        "text": "owner",
-        "image": ""
-      },
-      {
-        "text": "world",
-        "image": ""
-      },
-      {
-        "text": "subject",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "159) _________ was the first published public-key algorithm",
-    "answers": [
-      {
-        "text": "ElGamal",
-        "image": ""
-      },
-      {
-        "text": "DSA",
-        "image": ""
-      },
-      {
-        "text": "Diffie-Hellman",
-        "image": ""
-      },
-      {
-        "text": "RSA",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "1132) A __________ is an algorithm that requires the use of a secret key.",
-    "answers": [
-      {
-        "text": "DAA",
-        "image": ""
-      },
-      {
-        "text": "SHA",
-        "image": ""
-      },
-      {
-        "text": "GCM",
-        "image": ""
-      },
-      {
-        "text": "MAC",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "914) it is possible for a system to be compromised during the installation process",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1136) the digital signature function does not include the authentication\nfunction.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "19) __________ is the traditional method of implementing access control",
-    "answers": [
-      {
-        "text": "MAC",
-        "image": ""
-      },
-      {
-        "text": "RBAC",
-        "image": ""
-      },
-      {
-        "text": "DAC",
-        "image": ""
-      },
-      {
-        "text": "MBAC",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "1157) T F 4.The value of a primary key must be unique for each tuple of its table.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "31) Malicious software aims to trick users into revealing sensitive personal data",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1147) The authentication function determines who is trusted for a given purpose.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "73) Cipher Feedback Mode conforms to the typical construction of a stream cipher",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "168) The secret key is one of the inputs to a symmetric-key encryption algorithm",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1080) Buffer overflow attacks result from careless programming in\napplications.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "70) Cipher Feedback (CFB  is used for the secure transmission of single values)",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "586) T/F: The value of a primary key must be unique for each tuple of its table",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1103) Much of the theory of public-key cryptosystems is based on\nnumber theory.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "22) A __________ is an entity capable of accessing objects",
-    "answers": [
-      {
-        "text": "group",
-        "image": ""
-      },
-      {
-        "text": "object",
-        "image": ""
-      },
-      {
-        "text": "subject",
-        "image": ""
-      },
-      {
-        "text": "owner",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "334) In addition to propagation a worm usually performs some unwanted function",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "388) The buffer overflow type of attack is one of the most common attacks seen",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "104) Public-key encryption was developed in the late ________",
-    "answers": [
-      {
-        "text": "1950s",
-        "image": ""
-      },
-      {
-        "text": "1970s",
-        "image": ""
-      },
-      {
-        "text": "1960s",
-        "image": ""
-      },
-      {
-        "text": "1980s",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "164) A common location for a NIDS sensor is just inside the external firewall",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1165) Those who hack into computers do so for the thrill of it or for status.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "9) An access right describes the way in which a subject may access an object",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "4) External devices such as firewalls cannot provide access control services",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1094) The authentication server shares a unique secret key with each server.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "233) The IDS component responsible for collecting data is the user interface",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "130) In security protocol, an obvious security risk is that of impersonation",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "50) Computer viruses first appeared in the early __________",
-    "answers": [
-      {
-        "text": "1960s",
-        "image": ""
-      },
-      {
-        "text": "1970s",
-        "image": ""
-      },
-      {
-        "text": "1980s",
-        "image": ""
-      },
-      {
-        "text": "1990s",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "24) The final permission bit is the _________ bit",
-    "answers": [
-      {
-        "text": "superuser",
-        "image": ""
-      },
-      {
-        "text": "kernel",
-        "image": ""
-      },
-      {
-        "text": "set user",
-        "image": ""
-      },
-      {
-        "text": "sticky",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "1079) the XtS-AES mode is based on the concept of a tweakable block\ncipher.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "591) T/F: Fixed server roles operate at the level of an individual database",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1154) Public-key algorithms are based on simple operations on bit patterns.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "40) In addition to propagating, a worm usually carries some form of payload",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1144) For end-to-end encryption over a network, manual delivery is\nawkward.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1093) X.509 provides a format for use in revoking a key before it expires.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "387) Shellcode must be able to run no matter where in memory it is located",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "563) T/F: Encryption becomes the last line of defense in database security",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "324) One important element of intrusion prevention is password management",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1164) Snort can perform intrusion prevention but not intrusion detection.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "68) The sender is the only one who needs to know an initialization vector",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "693) Many users choose a password that is too short or too easy to guess",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "374) Stack buffer overflow attacks were first seen in the Aleph One Worm",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "199) In IPSec, the sequence number is used for preventing replay attacks",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "372) There are several generic restrictions on the content of shellcode",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "583) T/F: A query language provides a uniform interface to the database",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "166) Symmetric encryption is used primarily to provide confidentiality",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "900) ASLR(if implemented correctly) can prevent return-to-libc attacks",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "399) Shellcode is not specific to a particular processor architecture",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1143) Manual delivery of a key is not reasonable for link encryption.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "839) The \"A\" in the CIA triad stands for \"authenticity\"\nTrue or False",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "389) Buffer overflow attacks are one of the most common attacks seen",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "206) In iOS, an app can run its own dynamic, run-time generated code",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "336) Macro viruses infect documents, not executable portions of code",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "732) Like the MAC, a hash function also takes a secret key as input",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "235) Intruders typically use steps from a common attack methodology",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "587) T/F: A foreign key value can appear multiple times in a table",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "209) In Android, all apps have to be reviewed and signed by Google",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "121) SHA is perhaps the most widely used family of hash functions",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "735) The advantage of a stream cipher is that you can reuse keys",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "015) Some form of protocol is needed for public-key distribution",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "584) T/F: A single countermeasure is sufficient for SQLi attacks",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "205) In iOS, each file is encrypted using a unique, per-file key",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "213) Malicious JavaScripts is a major threat to browser security",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "142) The ciphertext-only attack is the easiest to defend against",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "318) Insider attacks are among the easiest to detect and prevent",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "244) An intruder can also be referred to as a hacker or cracker",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1139) the main elements of a RFID system are tags and readers.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "219) SQL injection attacks only lead to information disclosure",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "879) External attacks are the only threats to dataase security",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "938) Some APT attacks last for years before they are detected",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1) Access control is the central element of computer security",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "65) S-AES is the most widely used multiple encryption scheme",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "631) A data center generally includes backup power supplies",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1168) Programmers use backdoors to debug and test programs.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "864) ?No write down? is also referred to as the *-property",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1141) the Iot depends heavily on deeply embedded systems.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "135) Kerberos does not support inter-realm authentication",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1151) The \"A\" in the CIA triad stands for \"authenticity\".",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "136) SHA-1 produces a hash value of _______ bits",
-    "answers": [
-      {
-        "text": "256",
-        "image": ""
-      },
-      {
-        "text": "512",
-        "image": ""
-      },
-      {
-        "text": "160",
-        "image": ""
-      },
-      {
-        "text": "128",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "39) E-mail is a common method for spreading macro viruses",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "35) It is not possible to spread a virus via a USB stick",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1097) Lower layer security does not impact upper layers.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "62) There are no practical cryptanalytic attacks on 3DES",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "32) Keyware captures keystrokes on a compromised system",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "04) The secret key is input to the encryption algorithm",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "253) Anomaly detection is effective against misfeasors",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "883) A macro virus infects executable protions of code",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "38) A macro virus infects executable portions of code",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1098) The direct flame is the only threat from fire.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "702) A smart card contains an entire microprocessor",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "346) Malware is another name for Malicious Software",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "641) A _________ is a virtual table",
-    "answers": [
-      {
-        "text": "tuple",
-        "image": ""
-      },
-      {
-        "text": "query",
-        "image": ""
-      },
-      {
-        "text": "view",
-        "image": ""
-      },
-      {
-        "text": "DBMS",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "7) Reliable input is an access control requirement",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "835) Threats are attacks carried out\nTrue or False",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "201) Even web searches have (often) been in HTTPS",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "134) The ticket-granting ticket is never expired",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "146) Timing attacks are only applicable to RSA",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "1076) InvSubBytes is the inverse of ShiftRows.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "208) In iOS, each app runs in its own sandbox",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1153) Public-key cryptography is asymmetric.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "327) Bot programs are activated by a trigger",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1148) A user may belong to multiple groups.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "122) SHA-1 is considered to be very secure",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "703) Keylogging is a form of host attack",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "697) Memory cards store and process data",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "44) Every bot has a distinct IP address",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "1150) Threats are attacks carried out.",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "144) AES uses a Feistel structure",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "787) Search engines support HTTPS",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "204) iOS has no vulnerability",
-    "answers": [
-      {
-        "text": "True",
-        "image": ""
-      },
-      {
-        "text": "False",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  }
-]
\ No newline at end of file
diff --git a/data/questions/sicurezza_appello1.json b/data/questions/sicurezza_appello1.json
deleted file mode 100644
index 26227a8..0000000
--- a/data/questions/sicurezza_appello1.json
+++ /dev/null
@@ -1,754 +0,0 @@
-[
-  {
-    "quest": "1) L'input affidabile (reliable) è un requisito per il controllo degli accessi",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "2) I / Le __________ forniscono un mezzo per adattare RBAC alle specifiche delle politiche amministrative e di sicurezza in un'organizzazione",
-    "answers": [
-      {
-        "text": "cardinalità (cardinality)",
-        "image": ""
-      },
-      {
-        "text": "vincoli (constraints)",
-        "image": ""
-      },
-      {
-        "text": "ruoli che si escludono a vicenda (mutually exclusive roles)",
-        "image": ""
-      },
-      {
-        "text": "prerequisiti (prerequisites)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "3) _______________ è il metodo tradizionale per implementare il controllo degli accessi",
-    "answers": [
-      {
-        "text": "MBAC",
-        "image": ""
-      },
-      {
-        "text": "RBAC",
-        "image": ""
-      },
-      {
-        "text": "DAC",
-        "image": ""
-      },
-      {
-        "text": "MAC",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "4) Qualsiasi programma di proprietà e con SetUID del \"superutente\" potenzialmente concede l'accesso illimitato al sistema a qualsiasi utente che esegue quel programma",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "5) Le minacce (thrats) sono attacchi effettuati",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "6) Masquerade, falsificazione e ripudio sono azioni di minaccia che causano conseguenze di minaccia di __________",
-    "answers": [
-      {
-        "text": "Inganno (deception)",
-        "image": ""
-      },
-      {
-        "text": "Usurpazione (usurpation)",
-        "image": ""
-      },
-      {
-        "text": "Divulgazione non autorizzata (unauthorized disclosure)",
-        "image": ""
-      },
-      {
-        "text": "Interruzione (disruption)",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "7) La \"A\" nella triade CIA sta per \"autenticità\"",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "8) Un difetto (flow) o debolezza (weakness) nella progettazione, implementazione o funzionamento e gestione di un sistema che potrebbe essere sfruttato per violare la politica di sicurezza del sistema è un / una __________",
-    "answers": [
-      {
-        "text": "Contromisura",
-        "image": ""
-      },
-      {
-        "text": "Vulnerabilità",
-        "image": ""
-      },
-      {
-        "text": "Avversario",
-        "image": ""
-      },
-      {
-        "text": "Rischio",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "9) La presentazione o la generazione di informazioni di autenticazione che confermano il legame tra l'entità e l'identificatore è la ___________",
-    "answers": [
-      {
-        "text": "Fase di autnticazione (authentication step)",
-        "image": ""
-      },
-      {
-        "text": "Fase di conferma (confirmation step)",
-        "image": ""
-      },
-      {
-        "text": "Fase di identificazione (identification step)",
-        "image": ""
-      },
-      {
-        "text": "Fase di verifica (verification step)",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "10) L'autenticazione dell'utente è la base per la maggior parte dei tipi di controllo degli accessi (Access control) e della responsabilità dell'utente (User accountability)",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "11) Un attacco __________ coinvolge un avversario che ripete una risposta dell'utente acquisita in precedenza",
-    "answers": [
-      {
-        "text": "Eavesdropping",
-        "image": ""
-      },
-      {
-        "text": "Client",
-        "image": ""
-      },
-      {
-        "text": "Replay",
-        "image": ""
-      },
-      {
-        "text": "Trojan horse",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "12) L'autenticazione dell'utente è una procedura che consente ai soggetti comunicanti di verificare che i contenuti di un messaggio ricevuto non siano stati alterati e che la fonte sia autentica",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "13) Due svantaggi della crittografia dei database sono la gestione delle chiavi e la poca flessibilità",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "14) Il / La / L' __________ è il processo di esecuzione di query autorizzate e deduzione di informazioni non autorizzate dalle risposte legittime ricevute",
-    "answers": [
-      {
-        "text": "compromesso (compromise)",
-        "image": ""
-      },
-      {
-        "text": "inferenza (inference)",
-        "image": ""
-      },
-      {
-        "text": "perturbazione (perturbation)",
-        "image": ""
-      },
-      {
-        "text": "partizionamento (partitioning)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "15) Un SQL Server consente agli utenti di creare ruoli a cui è possibile assegnare diritti di accesso a porzioni del database",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "16) La crittografia è l'ultima linea di difesa nella sicurezza del database",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "17) Un ______ attiva un bug nel software di gestione della rete del sistema, provocandone l'arresto anomalo e il sistema non può più comunicare sulla rete fino a quando questo software non viene ricaricato",
-    "answers": [
-      {
-        "text": "pacchetto di echo (echo packet)",
-        "image": ""
-      },
-      {
-        "text": "reflection attack",
-        "image": ""
-      },
-      {
-        "text": "attacco flash flood (flash flood attack)",
-        "image": ""
-      },
-      {
-        "text": "pacchetto avvelenato (poison packet)",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "18) Gli attacchi Reflector e Amplifier utilizzano sistemi compromessi che eseguono i programmi dell'aggressore",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "19) Un attacco DoS che prende di mira le risorse dell'applicazione in genere mira a sovraccaricare o arrestare in modo anomalo il software di gestione della rete",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "20) Il ______ attacca la capacità di un server di rete di rispondere alle richieste di connessione TCP sovraccaricando le tabelle utilizzate per gestire tali connessioni",
-    "answers": [
-      {
-        "text": "poison packet attack",
-        "image": ""
-      },
-      {
-        "text": "SYN spoofing attack",
-        "image": ""
-      },
-      {
-        "text": "DNS amplification attack",
-        "image": ""
-      },
-      {
-        "text": "basic flooding attack",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "21) Il codice metamorfico (metamorphic code) è un software che può essere spedito invariato a un insieme eterogenea di piattaforme ed eseguito con semantica identica",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "22) I / Gli / La __________ si integrerà con il sistema operativo di un computer host e monitorerà il comportamento del programma in tempo reale per azioni dannose",
-    "answers": [
-      {
-        "text": "scanner basati su fingerprint (fingerprint-based scanners)",
-        "image": ""
-      },
-      {
-        "text": "scanner che bloccano il comportamento (behavior-blocking scanners)",
-        "image": ""
-      },
-      {
-        "text": "tecnologie di decrittazione generica (generic decryption technology)",
-        "image": ""
-      },
-      {
-        "text": "scanner euristici (heuristic scanners)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "23) Un macro virus infetta porzioni eseguibili di codice",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "24) Un / Una __________ è un codice inserito in un malware che rimane dormiente finché non viene soddisfatta una condizione predefinita, che attiva un'azione non autorizzata",
-    "answers": [
-      {
-        "text": "worm",
-        "image": ""
-      },
-      {
-        "text": "trojan horse",
-        "image": ""
-      },
-      {
-        "text": "logic bomb",
-        "image": ""
-      },
-      {
-        "text": "trapdoor",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "25) La / Le __________ possono impedire attacchi di buffer overflow, in genere a dati globali, che tentano di sovrascrivere regioni adiacenti nello spazio degli indirizzi dei processi, come la tabella di offset globale",
-    "answers": [
-      {
-        "text": "MMU",
-        "image": ""
-      },
-      {
-        "text": "pagine di protezione (guard pages)",
-        "image": ""
-      },
-      {
-        "text": "Heaps",
-        "image": ""
-      },
-      {
-        "text": "Tutto quanto elencato precedentemente",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "26) Una conseguenza di un errore di buffer overflow è la / il / __________",
-    "answers": [
-      {
-        "text": "corruzione dei dati utilizzati dal programma",
-        "image": ""
-      },
-      {
-        "text": "trasferimento imprevisto del controllo del programma",
-        "image": ""
-      },
-      {
-        "text": "possibile violazione dell'accesso alla memoria",
-        "image": ""
-      },
-      {
-        "text": "tutto quanto elencato precedentemente",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "27) Un overflow dello stack può provocare, su di un sistema, una qualche forma di attacco denial-of-service",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "28) Java, anche se è un linguaggio di programmazione di alto livello, soffre ancora della vulnerabilità di buffer overflow perché consente di salvare più dati in un buffer di quanti ne possa contenere",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "29) Un _________ controlla le caratteristiche di un singolo host e gli eventi che si verificano all'interno di tale host per attività sospette",
-    "answers": [
-      {
-        "text": "rilevamento delle intrusioni (intrusion detection)",
-        "image": ""
-      },
-      {
-        "text": "IDS basato sulla rete (network-based IDS)",
-        "image": ""
-      },
-      {
-        "text": "intrusione nella sicurezza (security intrusion)",
-        "image": ""
-      },
-      {
-        "text": "IDS basato sull'host (host-based IDS)",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "30) Il _________ comporta la raccolta di dati relativi al comportamento degli utenti legittimi in un periodo di tempo",
-    "answers": [
-      {
-        "text": "rilevamento basato sul profilo (profile-based detection)",
-        "image": ""
-      },
-      {
-        "text": "rilevamento delle anomalie (anomaly detection)",
-        "image": ""
-      },
-      {
-        "text": "rilevamento della firma (signature detection)",
-        "image": ""
-      },
-      {
-        "text": "rilevamento di soglia (threshold detection)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "31) Un sensore NIDS è solitamente posizionato all'interno del firewall esterno",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "32) Gli approcci di intrusion detection basati sulle firme (signature-based) tentano di definire i comportamenti normali, o attesi, mentre gli approcci basati su anomalia (anomaly-based) tentano di definire il comportamento corretto",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "33) SHA-1 produce un valore di hash di ______ bits",
-    "answers": [
-      {
-        "text": "384",
-        "image": ""
-      },
-      {
-        "text": "160",
-        "image": ""
-      },
-      {
-        "text": "180",
-        "image": ""
-      },
-      {
-        "text": "256",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "34) Una funzione hash come SHA-1 non è stata progettata per essere utilizzata come MAC e non può essere utilizzata direttamente a tale scopo perché non si basa su una chiave segreta",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "35) Gli attacchi di tipo Timing (timing attacks) sono applicabili solo a RSA",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "36) Gli attacchi _________ usano diversi approcci, tutti equivalenti, in termini di costo, a fattorizzare il prodotto di due numeri primi",
-    "answers": [
-      {
-        "text": "matematici",
-        "image": ""
-      },
-      {
-        "text": "a testo cifrato scelto",
-        "image": ""
-      },
-      {
-        "text": "di Timing",
-        "image": ""
-      },
-      {
-        "text": "di forza bruta",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "37) Per la trasmissione orientata al flusso (stream-oriented) su un canale rumoroso, in genere si utilizza la modalità di cifratura a blocchi _______",
-    "answers": [
-      {
-        "text": "CBC (Cipher Block Chaining)",
-        "image": ""
-      },
-      {
-        "text": "OFB (Output Feedback)",
-        "image": ""
-      },
-      {
-        "text": "ECB (Electronic Code Book)",
-        "image": ""
-      },
-      {
-        "text": "CTR (Counter)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "38) Per la trasmissione generica orientata ai blocchi (block-oriented), in genere si utilizza la modalità di cifratura a blocchi ________",
-    "answers": [
-      {
-        "text": "CFB (Cipher Feedback)",
-        "image": ""
-      },
-      {
-        "text": "OFB (Output Feedback)",
-        "image": ""
-      },
-      {
-        "text": "CBC (Cipher Block Chaining)",
-        "image": ""
-      },
-      {
-        "text": "CTR (Counter)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "39) È possibile convertire qualsiasi cifrario a blocchi in un cifrario a flusso utilizzando la modalità Cipher Feedback (CFB)",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "40) AES usa una struttura di Feistel",
-    "answers": [
-      {
-        "text": "V",
-        "image": ""
-      },
-      {
-        "text": "F",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  }
-]
\ No newline at end of file
diff --git a/data/questions/so1.json b/data/questions/so1.json
deleted file mode 100644
index ec7f18f..0000000
--- a/data/questions/so1.json
+++ /dev/null
@@ -1,3475 +0,0 @@
-[
-  {
-    "quest": "1) Quale delle seguenti affermazioni sulle directory di un file system è vera?",
-    "answers": [
-      {
-        "text": "È sempre necessario identificare un file di un file system fornendone il path assoluto",
-        "image": ""
-      },
-      {
-        "text": "È sempre necessario identificare un file di un file system fornendone il path relativo alla directory corrente",
-        "image": ""
-      },
-      {
-        "text": "È sempre possibile dare lo stesso nome a file diversi",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "2) Quale delle seguenti affermazioni sulla concorrenza tra processi o thread è falsa?",
-    "answers": [
-      {
-        "text": "La disabilitazione delle interruzioni impedisce la creazione di nuove interruzioni",
-        "image": ""
-      },
-      {
-        "text": "L'abuso della disabilitazione delle interruzioni fa diminuire la multiprogrammazione, a parità di numero di processi",
-        "image": ""
-      },
-      {
-        "text": "Se un processo può disabilitare le interruzioni tramite un'istruzione macchina dedicata, allora può far diminuire l'uso del processore",
-        "image": ""
-      },
-      {
-        "text": "La disabilitazione delle interruzioni non funziona su sistemi con più processori o più core",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "3) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni è vera? ",
-    "answers": [
-      {
-        "text": "Lo scheduler ha, tra i suoi obiettivi, quello di minimizzare il numero di processi che rispettano la propria deadline",
-        "image": ""
-      },
-      {
-        "text": "Lo scheduler ha, tra i suoi obiettivi, quello di minimizzare il volume di lavoro nel tempo",
-        "image": ""
-      },
-      {
-        "text": "Lo scheduler ha, tra i suoi obiettivi, quello di massimizzare il tempo di risposta",
-        "image": ""
-      },
-      {
-        "text": "Lo scheduler ha, tra i suoi obiettivi, quello di minimizzare il tempo di inattività del processore",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "4) Quale delle seguenti affermazioni sul modello dei processi in UNIX SVR4 System V Release 4 è falsa?",
-    "answers": [
-      {
-        "text": "Se un processo è Zombie, allora è terminato ma il suo process control block è ancora in memoria",
-        "image": ""
-      },
-      {
-        "text": "Asleep in Memory coincide con Blocked",
-        "image": ""
-      },
-      {
-        "text": "Ha anche uno stato Zombie: serve per tutti i processi che sono terminati",
-        "image": ""
-      },
-      {
-        "text": "Ha 9 stati (10 con Exit)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "5) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è falsa? ",
-    "answers": [
-      {
-        "text": "Quando un indirizzo non viene trovato nel translation lookaside buffer, è necessario consultare la normale tabella delle pagine",
-        "image": ""
-      },
-      {
-        "text": "Il translation lookaside buffer è una particolare cache, ma non è completamente trasparente al sistema operativo",
-        "image": ""
-      },
-      {
-        "text": "Il translation lookaside buffer permette di accedere direttamente al contenuto degli indirizzi di memoria virtuali usati più di recente",
-        "image": ""
-      },
-      {
-        "text": "In assenza di translation lookaside buffer, l'accesso ad un indirizzo virtuale può richiedere almeno 2 accessi in memoria",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "6) Quale delle seguenti affermazioni sugli obiettivi di sicurezza di un sistema operativo è vera?",
-    "answers": [
-      {
-        "text": "Per \"disponibilità\" dell'hardware si intende la garanzia che le workstation restino sempre fisse in un posto",
-        "image": ""
-      },
-      {
-        "text": "Per \"confidenzialità\" dei dati si intende la garanzia che essi non possano essere generati automaticamente",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "Per \"integrità\" dei dati si intende la garanzia che essi non vengano mai modificati",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "7) Quale delle seguenti affermazioni sul buffering dell'I/O è vera? ",
-    "answers": [
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "Avviene direttamente su disco, altrimenti si rischia il deadlock per interferenze con il DMA",
-        "image": ""
-      },
-      {
-        "text": "Nel caso ci siano più buffer, vanno gestiti come nel problema dei lettori/scrittori",
-        "image": ""
-      },
-      {
-        "text": "Può consistere nel completare un'istruzione di output I dopo che alcune istruzioni successive ad I siano state eseguite",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "8) Quale delle seguenti affermazioni, riguardanti il joint progress diagram di 2 processi, è vera?",
-    "answers": [
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "Può essere usato per visualizzare le possibilità di deadlock, ma solo se i processi richiedono al massimo 2 risorse",
-        "image": ""
-      },
-      {
-        "text": "Può essere usato per determinare quando uno dei due processi va in esecuzione a discapito dell'altro",
-        "image": ""
-      },
-      {
-        "text": "Può essere usato per determinare quando uno dei due processi sperimenta un page fault",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "9) Quale delle seguenti affermazioni sulla gerarchia della memoria è vera?",
-    "answers": [
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "Andando dall'alto in basso, cresce il costo",
-        "image": ""
-      },
-      {
-        "text": "Andando dall'alto in basso, diminuisce la capacità",
-        "image": ""
-      },
-      {
-        "text": "Andando dall'alto in basso, diminuisce la frequenza di accesso alla memoria da parte del processore",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "10) Quale dei seguenti elementi non fa parte del process control block?",
-    "answers": [
-      {
-        "text": "Il puntatore alla tabella delle pagine",
-        "image": ""
-      },
-      {
-        "text": "L’identificatore del thread",
-        "image": ""
-      },
-      {
-        "text": "Lo stato o modalità",
-        "image": ""
-      },
-      {
-        "text": "L’identificatore del processo",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "11) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sugli algoritmi di scheduling è vera?",
-    "answers": [
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "Il quanto di tempo ottimale per lo scheduler round-robin è maggiore del tipico tempo di completa esecuzione di un processo interattivo",
-        "image": ""
-      },
-      {
-        "text": "Lo scheduler First Come First Served favorisce i processi I/O-bound",
-        "image": ""
-      },
-      {
-        "text": "Anche assumendo che tutti i processi prima o poi terminino, lo scheduler First Come First Served soffre di starvation",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "14) Quale delle seguenti affermazioni sulla segmentazione della memoria è falsa? ",
-    "answers": [
-      {
-        "text": "Diversi segmenti possono avere diverse lunghezze",
-        "image": ""
-      },
-      {
-        "text": "Differentemente dalla paginazione, il programmatore assembler di un processo non interagisce esplicitamente con la gestione dei segmenti",
-        "image": ""
-      },
-      {
-        "text": "Per accedere ad un indirizzo contenuto in un segmento di un processo, tale segmento dovrà essere posizionato in memoria principale",
-        "image": ""
-      },
-      {
-        "text": "Un indirizzo di memoria principale va visto come un numero di segmento più uno spiazzamento all'interno di tale segmento",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "15) Quale delle seguenti affermazioni sull'algoritmo per il rilevamento del deadlock visto a lezione è vera?",
-    "answers": [
-      {
-        "text": "Richiede in input, per ogni processo p e per ogni risorsa r, il numero massimo di istanze di r che p chiederà nel corso della sua esecuzione",
-        "image": ""
-      },
-      {
-        "text": "Se al passo 3 viene trovato un processo non marcato che soddisfi la condizione Qik ≤ wik, allora c'è un deadlock",
-        "image": ""
-      },
-      {
-        "text": "I processi marcati sono quelli che non sono coinvolti in un deadlock",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "16) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sul long-term scheduler è falsa? ",
-    "answers": [
-      {
-        "text": "Viene chiamato in causa esclusivamente quando viene creato un nuovo processo",
-        "image": ""
-      },
-      {
-        "text": "Avendo le necessarie informazioni, una tipica strategia è mantenere una giusta proporzione, stabilita a priori, tra processi I/O-bound e CPU-bound",
-        "image": ""
-      },
-      {
-        "text": "Avendo le necessarie informazioni, una tipica strategia è ammettere in memoria principale i processi che richiedono dispositivi di I/O diversi da quelli richiesti dai processi già attivi",
-        "image": ""
-      },
-      {
-        "text": "Decide quali processi, tra quelli appena creati, possono essere ammessi in memoria principale per l'esecuzione",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "17) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera? ",
-    "answers": [
-      {
-        "text": "Il difetto principale del prepaging è che potrebbe portare in memoria pagine cui poi non si fa riferimento",
-        "image": ""
-      },
-      {
-        "text": "Placement policy e replacement policy sono sinonimi ed indicano lo stesso insieme di metodologie",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "Il difetto principale del paging on demand è che causa molti page fault dopo alcuni secondi di esecuzione",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "18) Quale dei seguenti requisiti deve soddisfare un meccanismo che offra la mutua esclusione?",
-    "answers": [
-      {
-        "text": "Non deve essere fatta alcuna assunzione sulla velocità di esecuzione dei processi coinvolti",
-        "image": ""
-      },
-      {
-        "text": "Se un processo fa richiesta di entrare nella sezione critica, deve poterlo fare subito",
-        "image": ""
-      },
-      {
-        "text": "Se un processo non fa richiesta di entrare nella sezione critica, deve comunque accordarsi all'esecuzione degli altri processi",
-        "image": ""
-      },
-      {
-        "text": "Si può assumere che un processo che non sia nella sezione critica prima o poi ci entri",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "19) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera? ",
-    "answers": [
-      {
-        "text": "Il principio di località afferma che poche pagine saranno sempre sufficienti per eseguire ogni processo senza thrashing",
-        "image": ""
-      },
-      {
-        "text": "Il thrashing si verifica quando l'overhead dovuto alla gestione della paginazione è molto basso",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "La paginazione con memoria virtuale funziona bene nonostante il principio di località",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "20) Quale delle seguenti affermazioni sullo scambio messaggi per la gestione della concorrenza è vera? ",
-    "answers": [
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "L'implementazione delle primitive per lo scambio messaggi non è garantita atomica dal sistema operativo",
-        "image": ""
-      },
-      {
-        "text": "Se un processo chiama receive, finché il messaggio non viene ricevuto, tutti gli altri processi che proveranno a chiamare receive verranno bloccati",
-        "image": ""
-      },
-      {
-        "text": "Per garantire la mutua esclusione, occorre ricorrere al busy waiting se sia invio che ricezione sono non bloccanti",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "21) Quali delle seguenti affermazioni sui file system è vera?",
-    "answers": [
-      {
-        "text": "I dati possono essere ricavati dai metadati",
-        "image": ""
-      },
-      {
-        "text": "I metadati possono essere ricavati dai dati",
-        "image": ""
-      },
-      {
-        "text": "I file system, che adottano il metodo journaling, mantengono un log per le operazioni di sola scrittura da effettuare, realizzandole in seguito",
-        "image": ""
-      },
-      {
-        "text": "Un volume coincide sempre con un disco, quindi se un computer ha 2 dischi avrà 2 volumi",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "22) Quale delle seguenti affermazioni sui dispositivi di I/O è vera?",
-    "answers": [
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "Il data rate confronta le velocità di trasferimento dati tra 2 diversi dispositivi di I/O",
-        "image": ""
-      },
-      {
-        "text": "Ciascun dispositivo di I/O può essere usato solo da uno specifico tipo di applicazione",
-        "image": ""
-      },
-      {
-        "text": "Tutti i dispositivi di I/O scambiano informazioni con la CPU in blocchi, per motivi di efficienza",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "23) Quale delle seguenti affermazioni sui metodi di gestione dello spazio libero su disco è vera?",
-    "answers": [
-      {
-        "text": "Se viene usata la lista di blocchi liberi, c'è un overhead di spazio, contrariamente alla concatenazione di blocchi liberi",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "Se ci sono blocchi da 1kB, e il disco contiene 1TB, l'occupazione dovuta alla lista di blocchi liberi è dell'1%",
-        "image": ""
-      },
-      {
-        "text": "Se viene usata la lista di blocchi liberi, una parte viene memorizzata su disco ed una parte in memoria principale",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "24) Quale delle seguenti azioni va effettuata sia per un process switch che per un mode switch, assumendo di essere in un SO nel quale le funzioni di sistema sono eseguite all'interno dei processi utente?",
-    "answers": [
-      {
-        "text": "Salvataggio del contesto del programma",
-        "image": ""
-      },
-      {
-        "text": "Aggiornamento delle strutture dati per la gestione della memoria",
-        "image": ""
-      },
-      {
-        "text": "Spostamento del process control block nella coda appropriata (ready, blocked, ready/suspend)",
-        "image": ""
-      },
-      {
-        "text": "Scelta di un altro processo da eseguire",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "25) Quale delle seguenti affermazioni è vera?",
-    "answers": [
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "Nell'algoritmo di sostituzione basato su frequenza a 2 segmenti della page cache, un blocco passa da un segmento ad un altro esclusivamente per scorrimento",
-        "image": ""
-      },
-      {
-        "text": "L'algoritmo di LFU della page cache ha buone performance quando un settore viene acceduto molto spesso in poco tempo, per poi non essere più usato",
-        "image": ""
-      },
-      {
-        "text": "L'algoritmo di sostituzione basato su frequenza a 2 segmenti della page cache può non avere buone performance quando un settore viene acceduto spesso, ma tra il primo accesso e quelli successivi ci sono N accessi ad altri settori, diversi tra loro, con N pari alla dimensione del segmento nuovo",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "26) Quale delle seguenti affermazioni sul kernel di un sistema operativo è vera?",
-    "answers": [
-      {
-        "text": "È responsabile dell'accensione del computer ",
-        "image": ""
-      },
-      {
-        "text": "Viene swappato dal disco alla memoria principale ad ogni context switch ",
-        "image": ""
-      },
-      {
-        "text": "È responsabile, tra le altre cose, della gestione dei processori",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "27) Quale delle seguenti affermazioni sul controllo di accesso è vera?",
-    "answers": [
-      {
-        "text": "Nel controllo di accesso basato su ruoli, ad ogni ruolo è assegnato un utente",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "Nel controllo di accesso basato su ruoli, prima di stabilire se un'operazione è lecita, è necessario consultare una tabella soggetti-ruoli-oggetti",
-        "image": ""
-      },
-      {
-        "text": "Nel controllo di accesso discrezionale, prima di stabilire se un'operazione è lecita, è necessario consultare una tabella soggetti-oggetti",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "28) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sulla preemption è vera?",
-    "answers": [
-      {
-        "text": "Se uno scheduler è non-preemptive, permette sempre ai suoi processi di essere eseguiti sul processore, senza interruzioni, fino al loro completamento",
-        "image": ""
-      },
-      {
-        "text": "Se uno scheduler è non-preemptive, è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready",
-        "image": ""
-      },
-      {
-        "text": "Se uno scheduler è preemptive, non è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready",
-        "image": ""
-      },
-      {
-        "text": "Per avere un trattamento equo sui processi, è sufficiente usare uno scheduler preemptive",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "29) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sugli algoritmi di scheduling è vera?",
-    "answers": [
-      {
-        "text": "Con lo scheduler Shortest Process Next, i processi con una grande immagine su RAM potrebbero soffrire di starvation",
-        "image": ""
-      },
-      {
-        "text": "Lo scheduler round-robin virtuale migliora il round-robin classico, facendo sì che i processi I/O-bound non vengano sfavoriti",
-        "image": ""
-      },
-      {
-        "text": "Lo scheduler First Come First Served \"degenera\" nello scheduler round-robin se il quanto di tempo è troppo lungo",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "30) Quale delle seguenti affermazioni sugli indirizzi di memoria principale è vera?",
-    "answers": [
-      {
-        "text": "Un indirizzo �sico fa sempre riferimento alla memoria secondaria",
-        "image": ""
-      },
-      {
-        "text": "Per rispettare il requisito di rilocazione, occorre trasformare indirizzi �sici in logici",
-        "image": ""
-      },
-      {
-        "text": "Gli indirizzi relativi sono usati nella paginazione",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "31) Quale delle seguenti affermazioni sui termini tipici della concorrenza è falsa?",
-    "answers": [
-      {
-        "text": "Una sezione critica è una porzione di memoria che contiene almeno una variabile condivisa tra più processi",
-        "image": ""
-      },
-      {
-        "text": "Una operazione atomica è una sequenza di istruzioni macchina tale che, se un processo la esegue, allora arriverà a termine senza interruzioni da altri processi",
-        "image": ""
-      },
-      {
-        "text": "Il requisito di mutua esclusione prevede che un solo processo possa eseguire un certo segmento di codice o accedere ad una determinata risorsa",
-        "image": ""
-      },
-      {
-        "text": "Una race condition è una violazione della mutua esclusione || È possibile che 2 distinti processi chiamino la stessa funzione atomica",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "32) Quale dei seguenti elementi fa parte del process control block?",
-    "answers": [
-      {
-        "text": "Nessuna delle altre opzioni contiene elementi del process control block",
-        "image": ""
-      },
-      {
-        "text": "Le informazioni sul contesto del processo, aggiornate ad ogni istruzione eseguita",
-        "image": ""
-      },
-      {
-        "text": "L'intera immagine del processo in memoria",
-        "image": ""
-      },
-      {
-        "text": "La tabella delle pagine di secondo livello",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "33) Quale delle seguenti informazioni non è presente in una tipica entry di una directory di un �le system?",
-    "answers": [
-      {
-        "text": "L'utente che ha creato il �le",
-        "image": ""
-      },
-      {
-        "text": "La data di creazione del �le",
-        "image": ""
-      },
-      {
-        "text": "Autorizzazioni per l'accesso al �le",
-        "image": ""
-      },
-      {
-        "text": "Dimensione del �le",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "34) Quale delle seguenti affermazioni sugli algoritmi di scheduling per i dischi è vera?",
-    "answers": [
-      {
-        "text": "L'algoritmo random ha la stessa funzione dell'algoritmo ottimo dei rimpiazzamenti di pagina: ha delle prestazioni ottime non raggiungibili dagli altri algoritmi",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "L'algoritmo C-SCAN deriva da SCAN, ed è stato sviluppato per evitare di favorire le richieste di tracce ai bordi del disco",
-        "image": ""
-      },
-      {
-        "text": "Per valutare le prestazioni dell'algoritmo con priorità è necessario fornire il ruolo dell'utente",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "35) Quale delle seguenti affermazioni sugli algoritmi di scheduling per i dischi è vera?",
-    "answers": [
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "L'algoritmo C-SCAN deriva da SCAN, ed è stato sviluppato per evitare di favorire le richieste di tracce ai bordi del disco",
-        "image": ""
-      },
-      {
-        "text": "Per valutare le prestazioni dell'algoritmo con priorità è sufficiente fornire il ruolo degli utenti dei processi che effettuano le richieste",
-        "image": ""
-      },
-      {
-        "text": "L'algoritmo random ha la stessa funzione dell'algoritmo ottimo dei rimpiazzamenti di pagina: ha delle prestazioni ottime non raggiungibili dagli altri algoritmi",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "36) Quale delle seguenti affermazioni sul metodo di allocazione contigua dei file è vera? ",
-    "answers": [
-      {
-        "text": "È possibile che ci sia frammentazione interna",
-        "image": ""
-      },
-      {
-        "text": "La compattazione permette di memorizzare file che altrimenti non potrebbero esserlo (pur essendo la loro dimensione minore di quella dello spazio libero)",
-        "image": ""
-      },
-      {
-        "text": "Non è necessaria la preallocazione",
-        "image": ""
-      },
-      {
-        "text": "La tabella di allocazione dei file necessita di memorizzare, per ogni file, il solo blocco di partenza",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "37) Quale delle seguenti affermazioni sulla paginazione della memoria è vera? ",
-    "answers": [
-      {
-        "text": "Frame e pagine devono avere la stessa dimensione",
-        "image": ""
-      },
-      {
-        "text": "Tutte le pagine di un processo dovranno essere, prima o poi, posizionate in un frame",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "Soffre del problema della frammentazione interna, e quindi necessita compattazione",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "38) Quale delle seguenti affermazioni sul controllo di accesso è vera? ",
-    "answers": [
-      {
-        "text": "Nel controllo di accesso basato su ruoli, ad ogni ruolo è assegnato un utente",
-        "image": ""
-      },
-      {
-        "text": "Nel controllo di accesso basato su ruoli, prima di stabilire se un'operazione è lecita, è necessario consultare una tabella soggetti-ruoli-oggetti",
-        "image": ""
-      },
-      {
-        "text": "Nel controllo di accesso discrezionale, prima di stabilire se un'operazione è lecita, è necessario consultare una tabella soggetti-oggetti",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "39) Quale delle seguenti affermazioni è falsa? ",
-    "answers": [
-      {
-        "text": "Nel caso delle risorse riusabili, in un grafo dell'allocazione delle risorse ci possono essere più archi tra lo stesso nodo-processo e lo stesso nodo-risorsa",
-        "image": ""
-      },
-      {
-        "text": "Nel caso delle risorse riusabili, in un grafo dell'allocazione delle risorse ci possono essere archi sia da nodi-processi a nodi-risorse che viceversa",
-        "image": ""
-      },
-      {
-        "text": "Un grafo dell'allocazione delle risorse è un grafo diretto aciclico",
-        "image": ""
-      },
-      {
-        "text": "In un grafo dell'allocazione delle risorse, all'interno di un nodo rappresentante una risorsa, c'è un pallino per ogni istanza di quella risorsa",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "40) Quali delle seguenti affermazioni è vera? ",
-    "answers": [
-      {
-        "text": "La confidenzialità di un sistema operativo consiste nel fatto che la shell del sistema operativo deve essere intuitiva e dare del tu agli utenti",
-        "image": ""
-      },
-      {
-        "text": "La disponibilità (availability) di un sistema operativo consiste nel fatto che il sistema operativo deve essere sempre pronto a rispondere alle richieste di un utente",
-        "image": ""
-      },
-      {
-        "text": "La disponibilità (availability) di un sistema operativo consiste nel fatto che devono esistere delle repository online che permettano sia di installare che di aggiornare il sistema operativo",
-        "image": ""
-      },
-      {
-        "text": "La confidenzialità di un sistema operativo consiste nel fatto che il sistema operativo deve essere sempre pronto a rispondere alle richieste di un utente",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "41) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera? ",
-    "answers": [
-      {
-        "text": "Il difetto principale del prepaging è che potrebbe portare in memoria pagine cui poi non si fa riferimento",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "Il difetto principale del paging on demand è che, dopo una prima fase di assestamento, causa molti page fault",
-        "image": ""
-      },
-      {
-        "text": "Placement policy e replacement policy sono sinonimi ed indicano lo stesso insieme di metodologie",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "42) Quale delle seguenti affermazioni sui dispositivi di memoria di massa è vera? ",
-    "answers": [
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "Un settore di un disco magnetico a testina mobile è l'area di una corona circolare del disco stesso",
-        "image": ""
-      },
-      {
-        "text": "Una traccia di un disco magnetico a testina mobile è l'area compresa da 2 raggi del disco stesso",
-        "image": ""
-      },
-      {
-        "text": "Per selezionare un settore su una traccia di un disco magnetico a testina mobile, è sufficiente posizionare la testina sulla giusta traccia",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "44) Quale delle seguenti affermazioni sui semafori per la gestione della concorrenza è falsa? ",
-    "answers": [
-      {
-        "text": "Semafori generali e semafori binari hanno lo stesso potere computazionale (ovvero, permettono di risolvere gli stessi problemi)",
-        "image": ""
-      },
-      {
-        "text": "Le primitive sui semafori sono in grado di mettere un processo in blocked, senza usare, a tal proposito, il busy-waiting",
-        "image": ""
-      },
-      {
-        "text": "Per implementare le primitive sui semafori, servono un contatore ed una coda, che saranno condivisi da tutti i semafori usati",
-        "image": ""
-      },
-      {
-        "text": "L'implementazione delle primitive sui semafori è garantita atomica dal sistema operativo",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "45) Quale delle seguenti affermazioni sugli algoritmi di scheduling per i dischi è falsa? ",
-    "answers": [
-      {
-        "text": "Nell'algoritmo F-SCAN, immediatamente prima che vengano scambiati i contenuti delle code F ed R, la coda F è vuota, mentre la coda R contiene le richieste arrivate mentre si servivano le richieste dentro F",
-        "image": ""
-      },
-      {
-        "text": "L'algoritmo Minimum Service Time può portare alla starvation di un processo, che non verrà quindi mai selezionato, se la richiesta era bloccante, per andare in esecuzione sul processore",
-        "image": ""
-      },
-      {
-        "text": "L'algoritmo LIFO è il più equo nei confronti dei processi che effettuano le richieste al disco",
-        "image": ""
-      },
-      {
-        "text": "Gli algoritmi Minimum Service Time, SCAN, C-SCAN, N-steps-SCAN ed F-SCAN non sono ottimizzati per essere usati su dischi con testine multiple selezionabili elettronicamente",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "46) Quale delle seguenti affermazioni sui meccanismi per la gestione della concorrenza è vera? ",
-    "answers": [
-      {
-        "text": "Senza usare né semafori, né scambio messaggi, né istruzioni macchina atomiche, è possibile scrivere processi che non soffrano di starvation per garantire la mutua esclusione tra 2 processi",
-        "image": ""
-      },
-      {
-        "text": "Disabilitando gli interrupt, è possibile scrivere processi che non soffrano di starvation",
-        "image": ""
-      },
-      {
-        "text": "Usando i semafori di qualsiasi tipo, è possibile scrivere processi che non soffrano di starvation",
-        "image": ""
-      },
-      {
-        "text": "Usando le istruzioni macchina exchange e compare_and_swap, è possibile scrivere processi che non soffrano di starvation",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "47) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sul long-term scheduler è falsa? ",
-    "answers": [
-      {
-        "text": "Decide quali processi, tra quelli appena creati, possono essere ammessi in memoria principale per l'esecuzione",
-        "image": ""
-      },
-      {
-        "text": "Avendo le necessarie informazioni, una tipica strategia è mantenere una giusta proporzione, stabilita a priori, tra processi I/O-bound e CPU-bound",
-        "image": ""
-      },
-      {
-        "text": "Viene chiamato in causa esclusivamente quando viene creato un nuovo processo",
-        "image": ""
-      },
-      {
-        "text": "Avendo le necessarie informazioni, una tipica strategia è ammettere in memoria principale i processi che richiedono dispositivi di I/O diversi da quelli richiesti dai processi già attivi",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "48) Quale delle seguenti affermazioni sui metodi di gestione dello spazio libero su disco è vera? ",
-    "answers": [
-      {
-        "text": "Se ci sono blocchi da 1kB, e il disco contiene 1TB, l'occupazione dovuta alla lista di blocchi liberi è dell'1%",
-        "image": ""
-      },
-      {
-        "text": "Se viene usata la lista di blocchi liberi, tale lista viene interamente mantenuta in memoria principale",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "Se viene usata la lista di blocchi liberi, c'è un overhead di spazio, contrariamente alla concatenazione di blocchi liberi",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "49) Quale delle seguenti affermazioni sulle directory di un file system è vera?",
-    "answers": [
-      {
-        "text": "È sempre necessario identificare un file di un file system fornendone il path relativo alla directory corrente",
-        "image": ""
-      },
-      {
-        "text": "È sempre possibile dare lo stesso nome a file diversi",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "È sempre necessario identificare un file di un file system fornendone il path assoluto",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "50) Quale delle seguenti affermazioni sulla memoria cache è vera? ",
-    "answers": [
-      {
-        "text": "La memoria cache è direttamente indirizzabile in assembler",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "È possibile che, in un dato istante, la cache e la memoria RAM non siano coerenti tra loro",
-        "image": ""
-      },
-      {
-        "text": "L'algoritmo di rimpiazzamento per la cache stabilisce quale blocco di RAM deve essere sostituito da un blocco di cache",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "51) Quale delle seguenti affermazioni sui problemi dei produttori/consumatori e dei lettori/scrittori, nelle accezioni viste a lezione, è vera? ",
-    "answers": [
-      {
-        "text": "Per il problema dei produttori/consumatori, non deve essere mai possibile che più consumatori accedano contemporaneamente al buffer, mentre nel problema dei lettori/scrittori deve sempre possibile che più lettori, in assenza di scrittori, accedano all'area di memoria",
-        "image": ""
-      },
-      {
-        "text": "Per il problema dei produttori/consumatori, non deve essere mai possibile che più produttori accedano contemporaneamente al buffer, mentre nel problema dei lettori/scrittori deve essere sempre possibile che più scrittori (in assenza di lettori) accedano all'area di memoria",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "Per il problema dei produttori/consumatori, deve essere sempre possibile che più consumatori accedano contemporaneamente al buffer, mentre nel problema dei lettori/scrittori non deve essere mai possibile che più scrittori accedano all'area di memoria",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "52) Quale delle seguenti affermazioni, riguardanti il joint progress diagram di 2 processi, è vera? ",
-    "answers": [
-      {
-        "text": "Può essere usato per determinare quando uno dei due processi sperimenta un page fault",
-        "image": ""
-      },
-      {
-        "text": "Può essere usato per visualizzare le possibilità di deadlock, ma solo se i processi richiedono al massimo 2 risorse",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "Può essere usato per determinare quando uno dei due processi manda un segnale all'altro",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "53) Quale delle seguenti affermazioni sui (vecchi) metodi per il partizionamento della memoria è vera? ",
-    "answers": [
-      {
-        "text": "Con il partizionamento fisso, le partizioni devono avere tutte la stessa dimensione",
-        "image": ""
-      },
-      {
-        "text": "Con il buddy system, ogni indirizzo di memoria può ricadere in 2 porzioni",
-        "image": ""
-      },
-      {
-        "text": "Con il partizionamento fisso, ci possono essere al massimo N processi attivi (ovvero, accettati per l'esecuzione), dove N è il numero di partizioni",
-        "image": ""
-      },
-      {
-        "text": "Con il partizionamento dinamico, si manifesta il problema della frammentazione esterna",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "54) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sulla preemption è vera? ",
-    "answers": [
-      {
-        "text": "Se uno scheduler è preemptive e vi è più di 1 processo ready, non è possibile che un processo monopolizzi il processore",
-        "image": ""
-      },
-      {
-        "text": "Per avere un trattamento equo sui processi, è sufficiente usare uno scheduler preemptive",
-        "image": ""
-      },
-      {
-        "text": "Se uno scheduler è non-preemptive, permette sempre ai suoi processi di essere eseguiti senza interruzioni sul processore fino al loro completamento",
-        "image": ""
-      },
-      {
-        "text": "Se uno scheduler è non-preemptive, è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "55) Nel modello dei processi a 5 stati, quali delle seguenti transizioni non è possibile? ",
-    "answers": [
-      {
-        "text": "Blocked ==> Running",
-        "image": ""
-      },
-      {
-        "text": "Running ==> Ready",
-        "image": ""
-      },
-      {
-        "text": "Blocked ==> Exit",
-        "image": ""
-      },
-      {
-        "text": "Blocked ==> Ready",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "56) Quale delle seguenti affermazioni sul metodo di allocazione indicizzata dei file è vera? ",
-    "answers": [
-      {
-        "text": "Il consolidamento permette sempre di ridurre la dimensione dell'indice",
-        "image": ""
-      },
-      {
-        "text": "Se usato con porzioni di dimensione variabile, i blocchi indice devono contenere anche la lunghezza di ogni porzione",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "Non c'è modo per il sistema operativo di distinguere tra blocchi con dati e blocchi con indici",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "57) Quale delle seguenti affermazioni sul requisito di rilocazione nella gestione della memoria è vera? ",
-    "answers": [
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "Se viene realizzato tramite sostituzione degli indirizzi nel programma sorgente (al momento della creazione del processo), allora il relativo processo dovrà cominciare sempre allo stesso indirizzo; tale indirizzo dovrà essere uguale per tutti i processi",
-        "image": ""
-      },
-      {
-        "text": "Se viene realizzato tramite sostituzione degli indirizzi nel programma sorgente (al momento della creazione del processo), allora il relativo processo potrà trovarsi in diverse posizioni della memoria in diversi momenti del sua esecuzione",
-        "image": ""
-      },
-      {
-        "text": "Se viene realizzato tramite sostituzione degli indirizzi nel programma sorgente (al momento della creazione del processo), serve hardware speciale",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "58) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sulla preemption è vera?",
-    "answers": [
-      {
-        "text": "Se uno scheduler è non-preemptive, permette sempre ai suoi processi di essere eseguiti sul processore, senza interruzioni, fino al loro completamento",
-        "image": ""
-      },
-      {
-        "text": "Se uno scheduler è non-preemptive, è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready",
-        "image": ""
-      },
-      {
-        "text": "Se uno scheduler è preemptive, non è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready",
-        "image": ""
-      },
-      {
-        "text": "Per avere un trattamento equo sui processi, è sufficiente usare uno scheduler preemptive",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "59) Quale dei seguenti requisiti deve soddisfare un meccanismo che offra la mutua esclusione? ",
-    "answers": [
-      {
-        "text": "Non deve essere fatta alcuna assunzione sulla velocità di esecuzione dei processi coinvolti",
-        "image": ""
-      },
-      {
-        "text": "Se un processo non fa richiesta di entrare nella sezione critica, deve comunque sincronizzarsi all'esecuzione degli altri processi",
-        "image": ""
-      },
-      {
-        "text": "Se un processo è nella sezione critica, occorre che rilasci subito la sezione critica stessa",
-        "image": ""
-      },
-      {
-        "text": "Se un processo fa richiesta di entrare nella sezione critica, deve poter entrare subito nella sezione critica stessa",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "60) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sul dispatcher è falsa? ",
-    "answers": [
-      {
-        "text": "Il resource balancing è un criterio di sistema non prestazionale",
-        "image": ""
-      },
-      {
-        "text": "Il rispetto delle deadline è un criterio utente prestazionale",
-        "image": ""
-      },
-      {
-        "text": "Il throughput è un criterio di sistema prestazionale",
-        "image": ""
-      },
-      {
-        "text": "La predictability è un criterio utente prestazionale",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "61) Quale delle seguenti affermazioni sugli interrupt (o eccezioni) è falsa? ",
-    "answers": [
-      {
-        "text": "Devono essere gestiti da opportuno software di sistema",
-        "image": ""
-      },
-      {
-        "text": "Una volta gestito l'interrupt o l'eccezione, quando (e se) si torna ad eseguire il processo interrotto, l'esecuzione ripartirà sempre dall'istruzione successiva a quella dove è stato ricevuto l'interrupt o l'eccezione",
-        "image": ""
-      },
-      {
-        "text": "Normalmente, non vengono gestiti dal programmatore dell'applicazione che li ha causati",
-        "image": ""
-      },
-      {
-        "text": "Possono essere creati direttamente dai dispositivi di I/O",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "62) Quale delle seguenti affermazioni sulle istruzioni macchina speciali per la gestione della concorrenza è vera? ",
-    "answers": [
-      {
-        "text": "Sono basate sul busy-waiting, ovvero sul fatto che un processo si mette autonomamente in stato blocked",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "Non riescono ad evitare il manifestarsi del deadlock, a meno che non sia presente un sistema a priorità",
-        "image": ""
-      },
-      {
-        "text": "Come per la disabilitazione delle interruzioni, non funzionano per architetture con più processori o core",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "63) Quale delle seguenti affermazioni sui processi è vera? ",
-    "answers": [
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "Per la terminazione normale di un processo, è tipicamente prevista un'apposita system call, come ad esempio exit",
-        "image": ""
-      },
-      {
-        "text": "Un processo può morire quando si effettua il process spawning",
-        "image": ""
-      },
-      {
-        "text": "Un processo può essere creato dal modulo di gestione della memoria per gestire la traduzione da indirizzi virtuali a fisici",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "64) Quale delle seguenti affermazioni sui meccanismi software per la gestione della concorrenza è vera? ",
-    "answers": [
-      {
-        "text": "Sia l'algoritmo di Dekker che quello di Peterson possono mettere in blocked uno dei 2 processi, quando ciò si rivela necessario",
-        "image": ""
-      },
-      {
-        "text": "Sia l'algoritmo di Dekker che quello di Peterson non funzionano se l'hardware sottostante riordina gli accessi in memoria",
-        "image": ""
-      },
-      {
-        "text": "Nell'algoritmo di Peterson, se la variabile turn è inizializzata ad 1, allora il processo 1 sarà sicuramente il primo ad entrare nella sezione critica nella prima iterazione",
-        "image": ""
-      },
-      {
-        "text": "Nell'algoritmo di Dekker, se la variabile turn è inizializzata ad 1, allora il processo 1 sarà sicuramente il primo ad entrare nella sezione critica nella prima iterazione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "65) Quale delle seguenti affermazioni sugli i-node di Unix è falsa? ",
-    "answers": [
-      {
-        "text": "Ogni directory è identificata da un i-node",
-        "image": ""
-      },
-      {
-        "text": "Per modificare una directory, un utente deve aprire il file speciale corrispondente e poi modificarlo opportunamente",
-        "image": ""
-      },
-      {
-        "text": "Ogni directory è un file speciale, organizzato come una lista di entry, ciascuna delle quali contiene il nome di un file ed il relativo i-node number",
-        "image": ""
-      },
-      {
-        "text": "Ogni directory può contenere molti i-node",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "66) Quale delle seguenti affermazioni è falsa? ",
-    "answers": [
-      {
-        "text": "Nel caso di un sistema operativo a kernel separato, la gestione dei process switch è a sua volta un processo",
-        "image": ""
-      },
-      {
-        "text": "Nel caso di un sistema operativo in cui le funzioni del sistema operativo vengono eseguite all'interno dei processi utente, non c'è bisogno di un process switch per eseguire una funzionalità del sistema operativo",
-        "image": ""
-      },
-      {
-        "text": "Nel caso di un sistema operativo in cui le funzioni del sistema operativo vengono eseguite all'interno dei processi utente, se un processo effettua una syscall e poi può continuare ad essere eseguito, non avviene alcun process switch",
-        "image": ""
-      },
-      {
-        "text": "Nel caso di un sistema operativo in cui le funzioni del sistema operativo vengono eseguite come processi separati, c'è sempre bisogno di un process switch per eseguire una funzionalità del sistema operativo",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "67) Quale delle seguenti affermazioni sulla paginazione della memoria è vera? ",
-    "answers": [
-      {
-        "text": "La differenza tra paginazione semplice e paginazione con memoria virtuale è che nella seconda viene richiesto che tutte le pagine di un processo siano in memoria principale, affinché il processo stesso possa essere eseguito",
-        "image": ""
-      },
-      {
-        "text": "Con la paginazione con memoria virtuale, una sola pagina di ogni processo ready o in esecuzione è inizialmente caricata in memoria principale",
-        "image": ""
-      },
-      {
-        "text": "La differenza tra paginazione semplice e paginazione con memoria virtuale è che nella prima viene richiesto che tutte le pagine di un processo siano in memoria principale, affinché il processo stesso possa essere eseguito",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "68) Quale delle seguenti affermazioni sul metodo di allocazione concatenata dei file è vera? ",
-    "answers": [
-      {
-        "text": "Il consolidamento permette di memorizzare file che altrimenti non potrebbero esserlo (pur essendo la loro dimensione minore di quella dello spazio libero)",
-        "image": ""
-      },
-      {
-        "text": "La tabella di allocazione dei file deve contenere l'intera catena",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "Viene usato con porzioni di dimensione variabile, ma piccola",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "69) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera? ",
-    "answers": [
-      {
-        "text": "Nel caso di una tabella delle pagine a 2 livelli, viene tipicamente richiesto che tutte le tabelle delle pagine di secondo livello entrino in una pagina",
-        "image": ""
-      },
-      {
-        "text": "Il numero di bit di un indirizzo virtuale è necessariamente diverso a seconda che si usi una tabella delle pagine ad 1 o a 2 livelli",
-        "image": ""
-      },
-      {
-        "text": "Il numero di bit di una entry di una tabella delle pagine di ultimo livello è uguale al numero di bit di controllo più il logaritmo (arrotondato all'intero superiore) del massimo numero di frame in memoria principale",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "70) Quale delle seguenti affermazioni sul deadlock è falsa?",
-    "answers": [
-      {
-        "text": "Affinchè ci sia un deadlock, sono necessarie le condizioni di attesa circolare, hold-and-wait, mutua esclusione e no preemption",
-        "image": ""
-      },
-      {
-        "text": "Per prevenire il deadlock, è necessario cercare di impedire almeno una delle 3 condizioni di mutua esclusione, hold-and-wait e no preemption",
-        "image": ""
-      },
-      {
-        "text": "Affinchè il deadlock sia possibile, sono necessarie le condizioni di mutua esclusione, hold-and-wait e no preemption",
-        "image": ""
-      },
-      {
-        "text": "Per prevenire il deadlock impedendo l'hold-and-wait, si può in alcuni casi imporre ai processi di richiedere tutte le risorse fin dall'inizio",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "71) Quale delle seguenti affermazioni è vera? ",
-    "answers": [
-      {
-        "text": "La modalità di un processo utente è sempre la modalità di sistema",
-        "image": ""
-      },
-      {
-        "text": "La modalità di un processo utente è inizialmente la modalità utente; può diventare modalità sistema nel momento in cui va in esecuzione il dispatcher",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "La modalità di un processo utente è sempre la modalità utente",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "72) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera? ",
-    "answers": [
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "Per ogni processo, il resident set contiene lo stesso numero di pagine",
-        "image": ""
-      },
-      {
-        "text": "Un tipico algoritmo per il replacement scope è quello dell'orologio",
-        "image": ""
-      },
-      {
-        "text": "La gestione del resident set tramite politica dinamica mira ad ampliare il numero di pagine di un processo durante l'esecuzione del processo stesso",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "74) Quale delle seguenti affermazioni sulla concorrenza tra processi o thread è vera?",
-    "answers": [
-      {
-        "text": "L'istruzione exchange non può ricevere costanti in input su nessun suo argomento, mentre per l'istruzione compare_and_swap questo non vale",
-        "image": ""
-      },
-      {
-        "text": "Le istruzioni speciali exchange e compare_and_swap sono garantite atomiche dal sistema operativo",
-        "image": ""
-      },
-      {
-        "text": "Per realizzare opportunamente l'istruzione compare_and_swap è sufficiente disabilitare le interruzioni",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "75)  Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sul dispatcher è falsa?",
-    "answers": [
-      {
-        "text": "Il response time è un criterio utente prestazionale",
-        "image": ""
-      },
-      {
-        "text": "Il turnaround time (normalizzato o no) è un criterio utente prestazionale",
-        "image": ""
-      },
-      {
-        "text": "Il throughput è un criterio di sistema non prestazionale",
-        "image": ""
-      },
-      {
-        "text": "La fairness è un criterio di sistema non prestazionale",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "76) Quale delle seguenti affermazioni sul file system FAT è vera?",
-    "answers": [
-      {
-        "text": "Usa il metodo di allocazione contiguo",
-        "image": ""
-      },
-      {
-        "text": "Ogni cluster del disco contiene sia dati del disco che l'indirizzo del prossimo cluster (o l'indicazione che si tratta dell'ultimo cluster)",
-        "image": ""
-      },
-      {
-        "text": "La tabella di allocazione dei file contiene tante righe quanti sono i file memorizzati sul disco, più una riga speciale per i blocchi liberi",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "77) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è falsa?",
-    "answers": [
-      {
-        "text": "Il translation lookaside buffer, su alcuni processori, contiene un campo per il PID dei processi",
-        "image": ""
-      },
-      {
-        "text": "Il translation lookaside buffer funziona correttamente solo se tutti i frame validi contenuti al suo interno fanno riferimento a pagine effettivamente in RAM, e non swappate su disco",
-        "image": ""
-      },
-      {
-        "text": "Il mapping associativo permette al translation lookaside buffer di trovare una data pagina semplicemente sommando il numero della pagina con l'indirizzo di partenza del translation lookaside buffer stesso",
-        "image": ""
-      },
-      {
-        "text": "Quando un indirizzo viene trovato nel translation lookaside buffer, non è necessario consultare la normale tabella delle pagine",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "78) Quale dei seguenti elementi non è una delle parti che definiscono un processo?",
-    "answers": [
-      {
-        "text": "Il contatore di programma",
-        "image": ""
-      },
-      {
-        "text": "La priorità",
-        "image": ""
-      },
-      {
-        "text": "I dati contenuti nella porzione di memoria a lui dedicata",
-        "image": ""
-      },
-      {
-        "text": "Informazioni sullo stato delle risorse",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "79) Quale delle seguenti affermazioni, riguardanti la classificazione delle risorse di un sistema operativo e la loro relazione con il deadlock, è vera?",
-    "answers": [
-      {
-        "text": "Nel caso delle risorse consumabili, se c'è un deadlock allora è stata richiesta almeno una risorsa già detenuta da un altro processo",
-        "image": ""
-      },
-      {
-        "text": "Nel caso delle risorse consumabili, se c'è un deadlock allora c'è una successione circolare di processi, ciascuno dei quali richiede una risorsa al processo successivo, che però la deve ancora creare",
-        "image": ""
-      },
-      {
-        "text": "Nel caso delle risorse riusabili, se c'è un deadlock allora è stata richiesta almeno una risorsa non ancora creata",
-        "image": ""
-      },
-      {
-        "text": "Nel caso delle risorse riusabili, se c'è un deadlock allora c'è una successione circolare di processi, ciascuno dei quali richiede una risorsa al processo successivo, che però la deve ancora creare",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "80) Si supponga che ci siano N processi attivi, giostrati da uno scheduler round-robin su un sistema monoprocessore. Quale delle seguenti affermazioni è vera?",
-    "answers": [
-      {
-        "text": "Dal punto di vista del processore, ogni processo esegue sempre le proprie istruzioni senza interruzioni",
-        "image": ""
-      },
-      {
-        "text": "Per realizzare correttamente un process switch, il SO avrà necessità di usare le informazioni sul contesto contenute nel process control block",
-        "image": ""
-      },
-      {
-        "text": "Dal punto di vista di ogni processo, l'esecuzione avviene in interleaving con gli altri processi",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "81) Quale delle seguenti affermazioni sulla traduzione di un indirizzo virtuale in fisico, in un sistema con memoria virtuale con paginazione (avente tabella delle pagine ad 1 livello), è falsa?",
-    "answers": [
-      {
-        "text": "L'hardware deve anche cercare il numero di pagina nelle entries della tabella delle pagine del processo in esecuzione. ",
-        "image": ""
-      },
-      {
-        "text": "L'hardware deve anche estrarre dall'indirizzo virtuale il numero di pagina virtuale; tale operazione è equivalente ad una divisione intera",
-        "image": ""
-      },
-      {
-        "text": "L'hardware deve anche usare il numero di pagina per accedere alla tabella delle pagine del processo in esecuzione. A tal proposito, deve conoscere l'inizio di tale tabella, che viene definito dal software (sistema operativo). Tale indirizzo può cambiare durante l'esecuzione del processo: sta al sistema operativo mantenerlo aggiornato",
-        "image": ""
-      },
-      {
-        "text": "L'hardware deve anche usare il numero di frame ottenuto dalla tabella delle pagine per comporre, insieme con l'offset originale, l'indirizzo fisico. Tale operazione è equivalente ad uno shift seguito da una somma",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "82) Quale delle seguenti operazioni non è tipicamente effettuata su un file?",
-    "answers": [
-      {
-        "text": "Apertura",
-        "image": ""
-      },
-      {
-        "text": "Connessione",
-        "image": ""
-      },
-      {
-        "text": "Posizionamento (seek)",
-        "image": ""
-      },
-      {
-        "text": "Lock/Unlock",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "83) Quale delle seguenti affermazioni è falsa?",
-    "answers": [
-      {
-        "text": "Diversi thread di uno stesso processo condividono lo stesso thread identifier",
-        "image": ""
-      },
-      {
-        "text": "Tra le funzioni di sistema per i thread, è tipicamente prevista una funzione per bloccare e sbloccare esplicitamente i thread stessi",
-        "image": ""
-      },
-      {
-        "text": "Diversi thread di uno stesso processo condividono lo stesso process identifier",
-        "image": ""
-      },
-      {
-        "text": "Diversi thread di uno stesso processo condividono i file aperti",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "84) Quale delle seguenti affermazioni sulla page cache è falsa?",
-    "answers": [
-      {
-        "text": "Nell'algoritmo di sostituzione basato su frequenza a 3 segmenti della page cache, i contatori vengono sempre incrementati, tranne quando sono nel segmento vecchio",
-        "image": ""
-      },
-      {
-        "text": "Nell'algoritmo di sostituzione basato su frequenza a 3 segmenti della page cache, i settori che possono essere sostituiti sono solo quelli del segmento vecchio",
-        "image": ""
-      },
-      {
-        "text": "Nell'algoritmo di sostituzione basato su frequenza a 3 segmenti della page cache, l'unico segmento in cui i contatori non vengono incrementati e i settori non possono essere sostituti è quello nuovo",
-        "image": ""
-      },
-      {
-        "text": "L'algoritmo di sostituzione basato su frequenza a 3 segmenti della page cache può avere buone performance anche quando dei settori vengono acceduti spesso, ma tra il primo accesso e quelli successivi ci sono molti altri accessi ad altri settori",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "85) Quali delle seguenti affermazioni sulla efficienza di un sistema operativo è falsa?",
-    "answers": [
-      {
-        "text": "Deve minimizzare il tempo di risposta, tenendo presenti eventuali priorità",
-        "image": ""
-      },
-      {
-        "text": "Deve servire il maggior numero di utenti possibile, tenendo presenti eventuali livelli di accesso",
-        "image": ""
-      },
-      {
-        "text": "Deve dare accesso alle risorse in modo equo ed egualitario tra tutti i processi",
-        "image": ""
-      },
-      {
-        "text": "Deve massimizzare l'uso delle risorse per unità di tempo, tenendo presenti eventuali priorità",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "88) Quale delle seguenti affermazioni sui metodi di gestione del deadlock è vera?",
-    "answers": [
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "L'unico metodo, che richiede di conoscere in anticipo il massimo numero di risorse che un processo dovrà chiedere, è quello per rilevare il deadlock",
-        "image": ""
-      },
-      {
-        "text": "Il metodo più permissivo nei confronti delle richieste di risorse è quello che consiste nel prevenire il deadlock",
-        "image": ""
-      },
-      {
-        "text": "L'unico metodo che non prevede mai la preemption delle risorse è quello che evita il deadlock",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "89) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni è falsa?",
-    "answers": [
-      {
-        "text": "Lo scheduler ha, tra i suoi obiettivi, quello dell'equità tra i processi, a meno che non siano definite delle priorità",
-        "image": ""
-      },
-      {
-        "text": "Lo scheduler va scritto in modo che il suo overhead sia basso",
-        "image": ""
-      },
-      {
-        "text": "Lo scheduler ha, tra i suoi obiettivi, quello di evitare il deadlock",
-        "image": ""
-      },
-      {
-        "text": "Lo scheduler ha, tra i suoi obiettivi, quello di massimizzare il volume di lavoro dei processi nel tempo",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "90) Quale delle seguenti affermazioni sugli scheduler per architetture multiprocessore è vera?",
-    "answers": [
-      {
-        "text": "Con l'assegnamento statico, si dà un processore a caso tra quelli liberi ai processi che mantengono un uso della RAM pressoché costante",
-        "image": ""
-      },
-      {
-        "text": "Assegnando i processi del sistema operativo con l'assegnamento dinamico, si rischia di creare un bottleneck su un solo processore",
-        "image": ""
-      },
-      {
-        "text": "Uno svantaggio dell'assegnamento statico è il suo overhead maggiore rispetto a quello dinamico",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "91) Quale delle seguenti affermazioni sull'algoritmo del banchiere per evitare il deadlock visto a lezione è falsa?",
-    "answers": [
-      {
-        "text": "La matrice C - A può contenere elementi negativi, ma le matrici C ed A contengono solo elementi non negativi",
-        "image": ""
-      },
-      {
-        "text": "Richiede in input, per ogni processo p e per ogni risorsa r, il numero massimo di istanze di r che p chiederà nel corso della sua esecuzione",
-        "image": ""
-      },
-      {
-        "text": "All'inizio e alla fine di ogni invocazione dell'algoritmo, Vi = Ri - ∑j = 1, ..., nAi, j",
-        "image": ""
-      },
-      {
-        "text": "Se si procede da uno stato ad un altro, necessariamente è stata fatta almeno una richiesta ad almeno una risorsa da parte di almeno un processo",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "92) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sul dispatcher è falsa?",
-    "answers": [
-      {
-        "text": "Il throughput è definito come il numero di processi completati per unità di tempo",
-        "image": ""
-      },
-      {
-        "text": "Il turnaround time è definito, per un dato processo, come il tempo che intercorre tra la sua prima esecuzione sul processore e il suo completamento",
-        "image": ""
-      },
-      {
-        "text": "Un dispatcher con buone prestazioni sul response time deve tipicamente sia minimizzare il valore medio di sistema del response time, sia massimizzare il numero di utenti con un basso valore per il response time",
-        "image": ""
-      },
-      {
-        "text": "Il processor utilization è definito come il rapporto tra il tempo in cui il processore viene usato ed il tempo totale del sistema",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "93) Quale delle seguenti affermazioni sugli i-node di Unix è vera?",
-    "answers": [
-      {
-        "text": "Per ogni file-system su disco organizzato con i-node, tutti gli i-node di tutti i file su tale file-system sono memorizzati esclusivamente su disco",
-        "image": ""
-      },
-      {
-        "text": "I puntatori a tripla indirezione di un i-node vengono usati solo se la dimensione del file lo richiede",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "Ad ogni file effettivamente memorizzato su disco può essere associato un solo numero di i-node",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "94) Quale delle seguenti affermazioni sul modello dei processi a 7 stati è vera?",
-    "answers": [
-      {
-        "text": "Nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "Gli stati Ready, New e Blocked del modello a 5 stati vengono sdoppiati, e ne viene creata una versione Suspend",
-        "image": ""
-      },
-      {
-        "text": "Un processo è Suspend quando scade il timeout del dispatcher",
-        "image": ""
-      },
-      {
-        "text": "È possibile la transizione Ready/Suspend ==> Blocked/Suspend",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "95) Quale delle seguenti affermazioni sui dischi magnetici a testina mobile è vera? ",
-    "answers": [
-      {
-        "text": "Per selezionare un settore su una traccia di un disco magnetico a testina mobile, bisogna prima far ruotare il disco fino ad arrivare alla giusta traccia, e poi posizionare la testina sul giusto settore",
-        "image": ""
-      },
-      {
-        "text": "Una traccia di un disco è l'area compresa tra 2 raggi del disco stesso",
-        "image": ""
-      },
-      {
-        "text": "Il tempo di accesso ad un disco magnetico a testina mobile tiene conto sia del tempo che occorre per posizionare la testina che del tempo che occorre per far ruotare il disco, ma non del tempo che occorre per effettuare effettivamente il trasferimento di dati",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "98) Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sugli algoritmi di scheduling è vera?",
-    "answers": [
-      {
-        "text": "L'exponential averaging permette di stimare la dimensione dell'immagine di un processo, a partire dalle precedenti immagini di quello stesso processo",
-        "image": ""
-      },
-      {
-        "text": "La funzione di decisione dello scheduler Highest Response Ratio Next considera tanto il tempo di esecuzione stimato quanto il tempo trascorso in attesa",
-        "image": ""
-      },
-      {
-        "text": "L'exponential averaging è una tecnica applicabile dal solo scheduler Short Process Next",
-        "image": ""
-      },
-      {
-        "text": "La funzione di decisione dello scheduler Shortest Remaining Time considera tanto il tempo di esecuzione richiesto quanto il tempo trascorso in attesa",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "100) Quale delle seguenti affermazioni è vera sulla memoria virtuale con paginazione a segmentazione?",
-    "answers": [
-      {
-        "text": "Sia la tabella dei segmenti che quella delle pagine di un processo contengono, in ciascuna entry, un bit per indicare se la pagina o il segmento sono stati modificati",
-        "image": ""
-      },
-      {
-        "text": "Un indirizzo virtuale contiene anche un bit per indicare se la pagina corrispondente è o no in memoria principale",
-        "image": ""
-      },
-      {
-        "text": "La tabella delle pagine di un processo contiene una pagina speciale dove è memorizzato il process control block del processo stesso",
-        "image": ""
-      },
-      {
-        "text": "Ogni entry di una tabella delle pagine contiene un numero di pagina ed un offset",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "99) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è vera?",
-    "answers": [
-      {
-        "text": "Per avere un overhead accettabile, occorre demandare la traduzione degli indirizzi all'hardware, mentre al software resta da gestire prelievo, posizionamento e sostituzione delle pagine",
-        "image": ""
-      },
-      {
-        "text": "Per avere un overhead accettabile, occorre demandare la traduzione degli indirizzi e la politica di sostituzione delle pagine all'hardware, mentre al software resta da gestire prelievo e posizionamento delle pagine",
-        "image": ""
-      },
-      {
-        "text": "Per avere un overhead accettabile, occorre demandare all'hardware la traduzione degli indirizzi ed il prelievo, il posizionamento e la sostituzione delle pagine",
-        "image": ""
-      },
-      {
-        "text": "Per avere un overhead accettabile, occorre demandare al software anche la traduzione degli indirizzi",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "96) Riguardo alle differenze tra sistemi batch e sistemi time sharing (degli anni 60/70), quale delle seguenti affermazioni è falsa? ",
-    "answers": [
-      {
-        "text": "I sistemi time-sharing puntavano a minimizzare l'uso del processore",
-        "image": ""
-      },
-      {
-        "text": "Nei sistemi time-sharing, le direttive al sistema operativo arrivavano dai comandi digitati su terminali",
-        "image": ""
-      },
-      {
-        "text": "Nei sistemi batch, le direttive al sistema operativo arrivavano dai comandi del job control language, che erano non-interattivi",
-        "image": ""
-      },
-      {
-        "text": "I sistemi batch puntavano a massimizzare l'uso del processore",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "12) Considerare un insieme di cinque processi P1, P2, P3, P4, P5 con i seguenti tempi di arrivo e tempi di esecuzione in millisecondi: Quale delle seguenti affermazioni è falsa?",
-    "answers": [
-      {
-        "text": "Non ci sono sufficienti informazioni per determinare come si comporterebbe l'algoritmo di scheduling a feedback classico di Unix",
-        "image": ""
-      },
-      {
-        "text": "Non ci sono sufficienti informazioni per determinare come si comporterebbe l'algoritmo di scheduling Virtual Round-Robin",
-        "image": ""
-      },
-      {
-        "text": "Non ci sono sufficienti informazioni per determinare come si comporterebbe l'algoritmo di scheduling Round-Robin",
-        "image": ""
-      },
-      {
-        "text": "Non ci sono sufficienti informazioni per determinare come si comporterebbe l'algoritmo di scheduling SRT",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": 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"
-  },
-  {
-    "quest": "13) Considerare un insieme di cinque processi P1, P2, P3, P4, P5 con i seguenti tempi di arrivo e tempi di esecuzione in millisecondi: Assegnare questo insieme di processi ad un processore usando l'algoritmo di scheduling SRT, �no a che non terminano tutti. Quale delle seguenti affermazioni è falsa?",
-    "answers": [
-      {
-        "text": "Gli unici 2 processi che non sono serviti subito (ovvero, appena arrivati) sono P3 e P5",
-        "image": ""
-      },
-      {
-        "text": "Il tempo medio di attesa è tra 10 ed 11 ms",
-        "image": ""
-      },
-      {
-        "text": "Il processo con il più lungo tempo di attesa è P1",
-        "image": ""
-      },
-      {
-        "text": "Il tempo medio di turnaround è tra 2 e 3 ms",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": 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"
-  },
-  {
-    "quest": "101) Si consideri il seguente modo di implementare la mutua esclusione: Quale delle seguenti affermazioni è vera?",
-    "answers": [
-      {
-        "text": "La soluzione non implementa correttamente la mutua esclusione, ma può essere corretta nel seguente modo: int bolt = 0; void P(int i) { int key; while(true) { do (exchange(key, bolt) == 0) while(key != 0); critical_section(); bolt = 0; key = 1; } } ",
-        "image": ""
-      },
-      {
-        "text": "La soluzione non implementa correttamente la mutua esclusione, in quanto key deve essere una variabile globale",
-        "image": ""
-      },
-      {
-        "text": "La soluzione non implementa correttamente la mutua esclusione, ma può essere corretta nel seguente modo: int bolt = 0; void P(int i) { int key; while(true) { key = 1; do (exchange(key, bolt) == 0) while(key != 0); critical_section(); bolt = 0; } } ",
-        "image": ""
-      },
-      {
-        "text": "La soluzione implementa correttamente la mutua esclusione",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": "iVBORw0KGgoAAAANSUhEUgAAAP8AAACICAYAAAAoLz2zAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAABldSURBVHhe7Z0JtFVTGMe3oSSkDIkylqRBZEiDWoZKoQEVSywqomURimQsCokGLUM0miMqaZKWZC4qRUlEFDKHZHzu73N2nc7b591z37vv9c4932+ts95759177rnnnG/Ye3//vbfLS2EURUkc23s/twJ/sGnTJu+v4uX11183t912m/eXoiglhdP4P/nkE9O7d2/zyy+/eHuigyFj0CXJ33//bR566CFz0EEHmSOOOMJMnjxZHJiiKOE4jb9GjRpm5MiRZrfddvP25Kc4Ivbjjz8uW6YsWbJEtqVLl5rp06ebqVOnmjVr1nj/VRTFhdP4P/roI3PttdeaH374QTKAAQMGSERlmz9/vhho06ZNzc033+x0AI899pipVauWOe6448xbb70l+7799ltzySWXmAoVKpgzzzzTLF68WPZbOE6XLl1ky9QBrFixwjRp0kScVbVq1czBBx9svvzyS/P000+bwYMHe69SFMWP0/j9/Prrr6ZKlSpmwYIFZtiwYWbatGmmY8eO5rXXXhOncNNNN3mv3MLxxx9vPvzwQzN69GjzyCOPiBMhk2jWrJn58ccfzS233GKGDh1q1q9f773DyHFwGmznnXeet9eY33//3Vx66aVmu+2227xFzTg6dOhgevXq5f2lKIqftMYPderUMTvttJOpWrWqtKX/+ecf7z9uaDZsv/328nOPPfaQPgQif6tWrcwOO+xg6tataw488EDzxRdfeO8IZ+eddzYPPvigfK7dXA7HRdmyZWVTFCU/kYy/sERxFOkoSuRXFCWcIhk/ho2BB1m1apX5999/JeL//PPPpnr16mbvvfc2s2bNkvcsW7bMfP7552b//ff33rGFoLOIEvnpX6ADktEJ2vqrV6+Wtj+Og01RlPwU2vgxrnnz5pnbb7/d2/M/u+yyi1m5cqU55JBDTLdu3Uz37t0l9b/88svNq6++aipVqmT69+9vrrrqKlO5cmXvXf+DEQ8aNCjjDr/69evLVq9ePdOmTRvTtm1bc8ABB5jnnntONkVR8pPTFX4TJkwwFStWFGegKMrWFGubf1uyceNG6WRs1KiRt0dRFD9a268oCSVnI7+iKAWjxq8oCUWNX1ESSlaM/48//pBx/WxAJx3j+ozPc9zCwPv++usvM378ePPNN994e/OD7oCagd9++83boyjJocjGj+6/X79+UrgThEq8oLyXfbZSD5FP165dN5f5YrT333+/ad68uaj00A5gxJlgz2f58uXmlFNOMaNGjXIW+nz33Xdm7NixpmfPnlKboChJo8jGX65cOXPPPfeI4i8qiHcYZPjpp59M+/btxSEQfTF4ioAo9kEcNHDgQFOmTBnvXflxSYD954MWAVHSG2+84f13C99//73ZfffdpQ5AUZJIPuPHKDE6G7HXrVsn1Xko85gkA6Ni0ozhw4dvLp9F/osMmN/vuOMOs+eee5rOnTuLsq8gEP8gxeUnEfvNN980Rx99tGQFdr4AjPSiiy4yt956q3wu0ZzP4n8uCbD/fKBhw4ZyrKJqDBQl18hn/BheixYtRLKLIyB9PvTQQ81XX31lJk2aZGbMmCH7qNkPls4SYamt/+yzz8y9997rTLf90E/w7rvvyk8+a+3atWavvfby/rsFHE+DBg1EK3DhhRfKhB1hEuAg++67r7wfabKFz/r0009FN6CqPyWpONN+jB3jxmgWLVpkTjzxRGnT4xRIpTEa0nUi+59//um9y5h33nlHIj6TavA6orgLojVOhpQbvX+fPn1E6kvq7zL+8uXLm8MOO0xew0QdfkNOh80qmOrLgpO64YYb5Dw4pqIkEafxY5S0lUnDmXwDg8smts2/YcMGM3HiRHE2JUnjxo2lacN5aHNASSpO4ycq0xZ/4IEHTM2aNSWSMwHHSy+9JKk56Tzt/9q1a2+VNjNtF1NnIa3ldaT0UaGjjl53euEzIZ3x0qTg2DvuuKO35//vh+qQ7+HPXBQlSTiNHzB69PbHHHOM/I2hn3XWWaZ169bm8MMPl55y5uLzw7x+vIeOuauvvlqaB1HhtTQVMjH+KBJg+iqQFO+6667eHkVRoFQJexYuXChNDUYXiM7ZgLF8tP0nn3yyt+d/cDLUAzCfIE5HUZJGaOTfFhx55JHS6cdsvNmApgejD7Txg9CxSIERTRut8FOSSKmT9FLey5AiY/tMGlpY6AugQ+/UU081++yzj7dXURSL6vkVJaGUqrRfUZSSQ41fURJKRsZfWOkuQ3HU4iuKUnqIbPwFSXdLE3RhfP3117I82Pnnny/CIEVR8hPZ+KNIdzE0VtfZlgbHgiGsC4Cy0F/VpyjK1kQ2fr9UljS+b9++UuGHkaHgQ+RDIQ3r5KOyczkAovITTzxh7rrrLmlCfPzxx3IMJvVgBV9W8aHA54MPPpDXI8WlCCeT+nt0Aozdt2zZcquhQmbtYaEQMhhFUYrQ4cf0WBg6hkpTgLr8l19+2fTo0UOcA07BD4ZP3T9GiIGjsnv44YfNnXfeKZN6oNNHIsw8+7yG17/yyiuysq9feeefCYiNTCOddBgoU2auATIYRVGKYPwIY1h/jxp/quXSRVS7dBZr+uMoqLybPXv2ZqkuUmCm8zr22GMli+B3sgeW4fKDjh/HYDfm+4uiIaAJoIavKFsotPFnCmIfluUmK8BogWYCpbXWkGk+8BrSdRSEvCeYQRQ28iuKsjVZN36GAl3DgUzswVRctOtZsJMZdojsTKzB69esWSP9CRg+k4cQ4ZnHDwP3U9jIj3Q3k0lAFCXXyarxI5slUocNsZHuX3zxxebRRx+VaM1sOszWS9rfq1cvmXUHkAx36NBBmgTZYsGCBWbo0KHeX4qilLrafk5n5syZMlaPuCdbkG0wuQg9/oqilGCbPyqs90+/AOvsZwuGChmVOP300709iqKoqk9REkqpi/yKopQMavyKklDU+BUloZSI8VspsF2CKx1M4kmPf7bgePPnz/f+yg8ag2uuuUamEFOUpJA14/cLf/xkKgVmqa9p06aZE044wdtTdCgZxgFg5EE4b4RATOZJubKiJIVij/xRpMAWBh6mTp1q2rVrJwVBUTOFdFDXz0SezzzzTD6F4MaNG2VjhSJFSRKRjZ91+JDiYqCU6FKAw9z3lM1SqUdkZy19quj8q+mGZQRBOS8pN8t3UeaLaIixfhYBQQiEA+D9V155penUqZP8z27g/wyOw/E4Lse30Z4lx1h6DDWioigZGD8GaQ105cqV8jsr3bKgJ+IcFsZApktxjl1NN7iKr4XXB+W8TLNNVR8lvkR95gRgpeABAwZIPT+wcCjin7BVeYnqLPx5wQUXyHEpGR43bpw4KEqPUSCygo8fHJkq/pQkEtn4K1WqJBtS28WLF0uNPuWyNlKznh8CG/9quv6Vcf2EyXnXr18vETtMqIMYKKjy88MagfPmzZP+Ao7bvHlzcVRkBgiEyFr80mMyhZ49e5pzzz1XHI6iJInIxo8x1atXT1R4GBGKO9J+BDMo9oLqu3S45LxlypTx/lt4yEBwIva4tPOJ+C5wPgiLnnzySV21R0kcGXX4YSxjxoyRxThZBYcFMIngmS7hHSbnJbOgWUGktpDKY8RBOAfq9RlGJGugbU/0JjOYMWOGZB00Td5///3NjgAHFUzvmT+A1+r0XkrSyMj499tvP1lPz/bcM4TGnHnM5JMJpPYuOS897jgDG4WrVasmaTxinyBkGxg+w3McC6Mmc2B1YPoKcEysKkxPPkaPlp/+CRyPoiilTNjDqeAQcCqs9Z9NyBIYRmRUAIdjIcvAedBBGWU4UlFyhYwif3FDhG7fvr0U+WSzDU5a/+KLL5qzzz57K8MHOhcvu+wyM378eOkrUJSkUColvVTjkaJTmJMNOB4OIJtVg4oSd1TPrygJpVSl/YqilBxq/IqSUBJj/LRuJk+ebBYtWuTtiSf0XzBqwXoFCJ+2FS7RFZ20o0ePLpGCKepCuA7ZbLUyzMzwcZgeJQjfk6njKWenSjTd60sC6mLovI6iYYmt8fuFPVGgFJlSX6oUi0q21IaZYuXOjRs39vZkj2x8J4qsGKYdO3ZsPvVkUXCJtWrWrClCLQrFMiXsuyJeQ0uCQC0dOB10I5xHxYoVvb1Fh3OLuhCNDWgMUSOmY/k89qGVGTVqVNpjJCLy480xmo4dO8Z25V5uql/uXJrwO+I6deqIUa5YsUL+zgaIvpo0aSLHRYuBeAsDbdGihXnhhReylmlQsj5w4MBIZeY4YpwS55Vtypcvn29I2gWVsTwT06dPN0uXLjVLliyRrWrVqlIwl84xxsb4GaojnbFyYTwkBL3f8OHD83k81II8LFaz75ITU6Z84403yudwbCoFeahcrwUyCfZTTowugfehOqRgiH2cD0uOcS69e/eWiMI+Njur0HvvvSffhfPG27OmAK93faZf7uyHc+Rc586dK2lrMBJQ3szCqLyOa8XDzYKqLvhOZ5xxhpw/E5zwndg4DsfjuE899ZTssxBBu3TpIhsOgIcWI6LKMhtwPVBickyOfdRRR8kwMCIuSswxFNJuC46e+2hTcCTm1jHx/ZCIc51c98+VEbiiK6/F2Khu9a8EDaTb3bt3F4l7MGPBIbLqtL3+nCMydb/zolqW0vWyZct6e8JBIEdpPZWwCOtwRNbpNmzYUL4PGRgL5A4ePFj2+4mN8XOh5syZI1/o+eef9/YaWdRz0qRJUs+/fPlyeTCCUmIuEuW/FPRwoV1yYhSA3OhZs2bJA45qEXAmGBdRh4vLawEdgl2lGG9LJKCUGIUiN52INHHiRPkMSotxPIighg0bJlkIxyM1Y5YjHl7mLkBfEHZ+frmzhePyoLMCMUuc8f35DPoFOC+uGa/hQeL80FPgQGrXru0dYWsolWYJdV5rIwnl1TgQ/uZ3Fj/xRxTk1pwfm5Va8/CuXbtWpNQWjBgHRyGX3TA0zpGScf9+69jTgTPAIDlfC8bIik8cFweBZJtzxylwTbhWXEfX/XPB8xW8pjbC8tl+cFJ9+vSRFas4h6C8nJWquE9cQ4xy4cKF4lTCMjmuj/+6RG0OAGXsBCPuP6tf8flBYmP8eFLSPFIa6+WC+zFuKgS5Yf4HD+O3ETNMTszFxeCHDBkiD2OtWrXktTxMDRo0kNfycOPJgePZVYqJQNxMHigcE//jweC4RBkgHeZYnCdOBuMgvaSNzLH5yfdif1S5M9kEn33OOefI+SO1HjRokMxdwEbUQ3mJY2HZc+Zf4NzCZi1CEck5+KMI7WDOgX18Px4komZBcC44Mn+7n/Omc4zvbjccB9+T8/bvzySV5hph5H7o1+EZWL16talevbpEd5wejsD2+bjunwvXNeW4OHq/poW/CRLMZ0EgweBd8nIiNf/jnuKsuTdhFHZdSsDBcQ/IUnD+rkwiEW3+IC45MRcHr0rzgBvpgv6CgtrbRHT+z03F6IKRIQjZCA9GkKhyZzr+eDhpPlhoGtn3sbVt21ZSZYyBZgiRB+NMR5gxFJbiiPxhoNTEIZChkP7iYN5++205dpjjK4jgNXWt/ESwoT+GJoLNIlzycvbxXJBhck+Dzwj7cPJQlMgfhdgYf926deXhJTJyY+0D4t/PheHik9b6PR1tIqIehMmJeS9pGl4ez06qyPtIFzEujIHmBc2AMHgtEZLPXrVqlTRBwuAh5HsQkTk2qSV/sz+q3JkshzYkEYG2JtkFnT+8h/cyrEnqxwNH1kHEpp3Kg0o6GIT3cQ72vMh+EFjRZmQfTRUyGxSVQfzOggc96NgKG/l5H/fMXie+E9Ga6wzss79bMB76AtBzEOHr169vnn32WTlO1MhpcV1TggPHx8Fb6PEnA0MnMmLECIm8ZBxBeTng3JnXkiwk2GeAM6D/BNJFfp5PnlUyVO4PNsE9A86Ve0DA4plxOY3YGD9fqlWrVvJgkHpaMHSku61bt5Z2Fg8GnSt+uEikQFwAHoygnBivykNduXJlOT4KPy40DxYdMkRejI80jvZcGJwDaTLHmTBhgjgBHIILMgQ6+HA2pKZ0kHGzXOfnkjtbuC4nnXSSRCceVB5AIhMp7ZQpU+Th4jikn2x8DxyNa8ViHmCuLQ8gDyZGQ7pKu5S/+b1Zs2b5hho5B76H7VgjpaV540o1CwPGwIPNuXNd6FDj2NwfomwwenI/cVpkPLyHa1ejRg3JMDKFa+C6pux39RPw/PB5GD331yUv53zRrbicKP0A9BtEmV8Cp05mR1PD3i82oP+Bz6WpQh+Yc0q9lEfJeVIXMi+VQuWlor+3p3SQiiZ5qewgL/UQ540bNy4v5WS8/+QnZfh5I0eOzEulr96e6PD9BwwYkLds2TL5e/bs2Xkp5ya/Z5tUlMtLOYLNn1WcrFu3Lq9fv355qSzG21NypCKuXFOubaakMjC5Rlyr4mLMmDF5c+bMkd9TgSEv5bTkdz+JaPPjqfGQRHdSsNJA6trLkB4RhYhLZLPpngsiRmHkzjQhiDpkGqTYREs+lyyqOKBHnDTcpp/FBdePjlGiXkH9MMUF/QpklJn2T5AdUT1Idurq78kGNIFpCpChkW3Q5GjUqJH33y0kRtXH1yRl46aRDsaVbMudswlOiWFS0uTiNkj6QdioS8Axbgv4vgzfMRpC86I0gHNn2JXng1GMglBJr6IklEQO9SmKosavKIkl54yfoTWGxIKE7S8IhgajSDuzBecXNjQYBTp2GApDVqvLkinpyCnjZ2zUtSJw2P7SRiaS0iA4DQyfCj46e6JIOpVkk1PGT5GMXRGYIS7KIfnp31/aYOjHFsdkIikNgtCEHmeG2KJKOpVkExvjZ1DCJa2k+u7666+X8XLGN0nTZ86cKSWUvAYxjt1P+s572M8xOBbH5NiUmDL2yrARQzeMk6aD8XKGmqjKs9LbsPN0fS612345LGPG7AOO5ZeDcn5E8jB5MEOA/mXTokg6lWQTG+NHmEKtNoUdVm7Kxlgr0Y5JDShnBJRTaKZ79OghRmX3AyW6qKsoRqFmm/dRpkkJ7X333SftbmSYjN8WBPXxzFhD/TUqLQoqiLRhElDX5/I5QTksYLAjR46UUlrq6anfpxwXh0BNPlGdz7DyYMb9cXB+lVkUSaeSbGJj/EQ+atN58Kkgs8IIoG4b440CBmpnw6FCC4NHf45gglpstAFkEekqAaktp1iIOnOME504WQDnSZ27XwKKYbo+l/TcBQIeDJ0qPKrAEC9RnGSzkaA8mCounKDf+KNIOpVkE9s2P8aajQcaY7QpOVNEEcWjzkLDtGCk3XSwMfkGE3iAS1YbhM+N65RiSm4QG+NHkYWhMaMNUe66665LO6kEKXxweI/0nDJfjsGxrrjiCkn7cQC0r+lpJzVPBxJKRhCIxtRNd+rUSWTDYbJa1+cyuQSQ5vvhPFCQofnmf4xS8FoyBhdkATgTv8SUz04n6VSSTWyMH+nuaaedZlq2bCkGxt9WvuiClBs9NRJcjM+CLJUZVzkGx+KYCF6oR2d0gBl16MjDCQSN0g9NDyI6+m2EObS96bgLk4C6PhdJZlAOC6T6zLvHdE/04Pfv31+aJBzPBVkQTQC/8UeSdCqJRmv7I0CnHSMAdNxZ6EykPZ/p5BDFBTpwOhlxGvT40xmJc2HUg7kFcFCu5oeSXGLb5i9JXLPNBGdV2dbQLKJJwbx7USWdSrLRyJ9DYOTME0czg2ZHOkmnkmzU+BUloWjarygJRY1fURJKzho/PfTU8+v4tqK4iY3xbytjdq2bpyi5QCLSfr+8NxMQ77CQB2W/wbX6FCXuxMr4WQwCJRxRGDUd49oMVvgltMFVelHS+eW9mTgASnhRzCHgoeoOvT2Vc2xdu3bdanVYRYkbsTJ+6tSttBVRD7JdJLQFrdJLSa1f3kvJrwW9O9VwdqNQhuZFOlD+IbllKShFiSuxMn406hgvRSwsUUW5LaIX1yq9UabCIo0nc7AbVXxU86UDR8FabfxUlLgS2zZ/QaKbqBQ28itKLhAr46etTZudySqZGQcFXdgqvcF58Fzy3nSRH+UeKT6yXJwNK8WSfZB50LwIHk9R4kSsjB+Jat++fUXaSk88HXlRVun1y3sz6fCjX4GZepgVF2ktmQKyXXr+mU+An4oSV7S2vxDQ4cgsu+jscTaKEkdi2+bfljB8SJNBDV+JMxr5FSWhaORXlISixq8oCUWNX1ESSmTjnzNnjhkxYoRKZBUlR4hs/IypU+iiiz8qSm4Q2fgpf61cubIsOKEoSvzJqM2vy0spSu6QkfGzICZLWVFbryhKvMnI+KmjZ007XetdUeJPRsaPTp7Un7n0FEWJNxkZ/6pVqzavDa8oSrzJyPhJ+RVFyQ0iGz/6n/Xr15sqVap4exRFiTORjZ9JMJm3rmnTpt4eRVHijEp6FSWhZNTmVxQld1DjV5SEosavKAkln/Gz2MWQIUPM3LlzpYdfUZTcJJ/xM999t27dzJQpU1TBpyg5jDPtL1eunFTxbdiwwdujKEqu4TR+tPusSqsoSu4SGvmZuINafm33K0pu4jR+aNeunbn77rt12i5FyVFCK/xYB5+17du0aSPNAEVRcgtn5N+0aZOIeJi5Rw1fUXITp/GTDGRj/XtFUUovoZGfefoqVKjg7VEUJdfIZ/xU+I0ePdp07txZtfuKksOopFdREkroUJ+iKLmNGr+iJBQ1fkVJJMb8B1dVOx5GmGfIAAAAAElFTkSuQmCC"
-  },
-  {
-    "quest": "103) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è falsa?",
-    "answers": [
-      {
-        "text": "Diminuire la dimensione delle pagine ha effetti positivi sul numero di pagine che possono trovarsi in memoria principale",
-        "image": ""
-      },
-      {
-        "text": "Aumentare la dimensione delle pagine ha effetti positivi sulla frammentazione interna",
-        "image": ""
-      },
-      {
-        "text": "Diminuire la dimensione delle pagine ha effetti negativi sulla dimensione della tabella delle pagine",
-        "image": ""
-      },
-      {
-        "text": "Aumentare la dimensione delle pagine ha effetti negativi sulla multiprogrammazione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "104) Quale delle seguenti affermazioni sulla concorrenza tra processi o thread è falsa?",
-    "answers": [
-      {
-        "text": "La disabilitazione delle interruzioni impedisce la creazione di nuove interruzioni",
-        "image": ""
-      },
-      {
-        "text": "Se un processo utente può disabilitare le interruzioni tramite un'istruzione macchina dedicata, allora può far diminuire l'uso utile del processore",
-        "image": ""
-      },
-      {
-        "text": "La disabilitazione delle interruzioni non funziona ai fini della concorrenza (gestione sezioni critiche) su sistemi con più processori o più core",
-        "image": ""
-      },
-      {
-        "text": "L'abuso della disabilitazione delle interruzioni fa diminuire la multiprogrammazione, a parità di numero di processi",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "105) Quale delle seguenti affermazioni non è vera?",
-    "answers": [
-      {
-        "text": "il kernel rimane in memoria durante l'intera sessione del computer",
-        "image": ""
-      },
-      {
-        "text": "il kernel è costituito da vari moduli che non possono essere caricati nel sistema operativo in esecuzione",
-        "image": ""
-      },
-      {
-        "text": "il kernel è la prima parte del sistema operativo a essere caricata in memoria durante l'avvio",
-        "image": ""
-      },
-      {
-        "text": "Il kernel è il programma che costituisce il nucleo centrale del sistema operativo.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "106) In generale, la CPU puo’ eseguire un'istruzione soltanto quando gli operandi si trovano:",
-    "answers": [
-      {
-        "text": "In RAM, o in un livello qualsiasi della cache o nella memoria secondaria o nei registri CPU",
-        "image": ""
-      },
-      {
-        "text": "In RAM o in un livello qualsiasi della cache o nei registri CPU",
-        "image": ""
-      },
-      {
-        "text": "Nella cache di livello 1 (L1 cache) o nei registri CPU",
-        "image": ""
-      },
-      {
-        "text": "Nei registri della CPU",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "107) Il PCB (Process Control Block) e’:",
-    "answers": [
-      {
-        "text": "Un campo dello stato di un processo che definisce quali operazioni di controllo dei dispositivi a blocchi sono state fatte dal processo",
-        "image": ""
-      },
-      {
-        "text": "Una struttura dati mantenuta dal sistema operativo che contiene tutte le informazioni necessarie all’esecuzione, sospensione e ripresa dell’esecuzione di un processo",
-        "image": ""
-      },
-      {
-        "text": "Una struttura dati mantenuta dal sistema operativo che contiene l’intera immagine di un processo",
-        "image": ""
-      },
-      {
-        "text": "Un’interfaccia di controllo dei processi del sistema operativo",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "108) Considera un Sistema Operativo con esecuzione all’interno dei processi utente. Quando un processo utente fa una chiamata di sistema, quale delle seguenti affermazioni e’ corretta",
-    "answers": [
-      {
-        "text": "Il sistema operativo deve effettuare un process switch ed un mode switch per eseguire la funzione richiesta",
-        "image": ""
-      },
-      {
-        "text": "Il sistema operativo deve effettuare soltanto un process switch per eseguire la funzione richiesta",
-        "image": ""
-      },
-      {
-        "text": "Il sistema operativo deve effettuare soltanto un mode switch per eseguire la funzione richiesta",
-        "image": ""
-      },
-      {
-        "text": "Il sistema operativo deve creare un nuovo processo e fare switch ad esso per eseguire la funzione richiesta",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "109) Quale delle seguenti affermazioni e’ vera:",
-    "answers": [
-      {
-        "text": "Il dispatcher e’ una componente del medium term scheduler",
-        "image": ""
-      },
-      {
-        "text": "Il dispatcher si occupa di decidere l’ordine di sospensione dei processi",
-        "image": ""
-      },
-      {
-        "text": "Il dispatcher si occupa di scambiare i processi in esecuzione sulla CPU (process switch)",
-        "image": ""
-      },
-      {
-        "text": "Il dispatcher si occupa di scambiare i processi dalla memoria principale alla memoria secondaria",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "110) In un sistema operativo con I/O buffering, quando c’e’ una scrittura su dispositivo di I/O quale delle seguenti affermazioni e’ vera:",
-    "answers": [
-      {
-        "text": "Il sistema operativo copia immediatamente il contenuto della scrittura dalla memoria del processo direttamente alla memoria del dispositivo di I/O",
-        "image": ""
-      },
-      {
-        "text": "Il sistema operativo copia immediatamente il contenuto della scrittura dalla memoria utente alla memoria del sistema operativo, e dalla memoria del sistema operativo alla memoria del dispositivo di I/O quando piu’ opportuno",
-        "image": ""
-      },
-      {
-        "text": "Il sistema operativo copia quando piu’ opportuno il contenuto della scrittura dalla memoria del processo direttamente alla memoria del dispositivo di I/O",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre opzioni e’ corretta",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "111) L’algoritmo di scheduling C-SCAN:",
-    "answers": [
-      {
-        "text": "Scrivere le richieste su disco in modo tale che il braccio meccanico si muova sempre in una direzione, fino a raggiungere l’ultima traccia, e poi torna indietro scrivendo tutte le richieste fino a raggiungere la prima traccia",
-        "image": ""
-      },
-      {
-        "text": "Puo’ portare a starvation per alcuni processi",
-        "image": ""
-      },
-      {
-        "text": "E’ meno fair (equo) dell’algoritmo SCAN",
-        "image": ""
-      },
-      {
-        "text": "Non favorisce le richieste ai bordi rispetto a SCAN",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "112) Quale dei seguenti sono requisiti per un File Management System?",
-    "answers": [
-      {
-        "text": "Ogni utente dev’essere in grado di creare, cancellare, leggere, scrivere e modificare un file",
-        "image": ""
-      },
-      {
-        "text": "Ogni utente deve poter accedere, in modo controllato, ai file di un altro utente",
-        "image": ""
-      },
-      {
-        "text": "Ogni utente deve poter mantenere una copia di backup dei propri file",
-        "image": ""
-      },
-      {
-        "text": "Tutte le opzioni sono requisiti",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "113) Una sezione critica è un segmento di programma:",
-    "answers": [
-      {
-        "text": "Che e’ racchiuso tra una coppia di operazioni di semaforo semWait e semSignal",
-        "image": ""
-      },
-      {
-        "text": "In cui si accede a risorse condivise",
-        "image": ""
-      },
-      {
-        "text": "Che evita i deadlock",
-        "image": ""
-      },
-      {
-        "text": "Che deve essere eseguito in un determinato lasso di tempo.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "114) Quale dei seguenti NON è vero riguarda il Algoritmo di Dekker per gestire la concorrenza?",
-    "answers": [
-      {
-        "text": "Garantisce la non-starvation",
-        "image": ""
-      },
-      {
-        "text": "Non richiede nessun supporto dal SO.",
-        "image": ""
-      },
-      {
-        "text": "Richiede supporto dal SO",
-        "image": ""
-      },
-      {
-        "text": "E' deterministico.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "115) Quale delle affermazioni è vera riguardo al Translation lookaside buffer per la gestione della memoria?",
-    "answers": [
-      {
-        "text": "Nel Translation lookaside buffer ci sono tag e chiavi con l'aiuto dei quali viene effettuata la mappatura.",
-        "image": ""
-      },
-      {
-        "text": "Il TLB hit è una condizione in cui la voce desiderata viene trovata nel TLB.",
-        "image": ""
-      },
-      {
-        "text": "Se la voce non viene trovata nel TLB (TLB miss), la CPU deve accedere alla tabella delle pagine nella memoria principale e quindi accedere al frame effettivo nella memoria principale.",
-        "image": ""
-      },
-      {
-        "text": "Tutte le opzioni sono vere.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "116) Quale delle seguenti affermazioni sul long-term scheduler e’ vera:",
-    "answers": [
-      {
-        "text": "Si occupa della decisione di quali processi debbano essere ammessi all’esecuzione nel sistema",
-        "image": ""
-      },
-      {
-        "text": "Si occupa dell’organizzazione di lungo termine dell’ordine di esecuzione dei processi nella CPU",
-        "image": ""
-      },
-      {
-        "text": "Si occupa dell’implementazione della funzione di swapping dei processi alla memoria secondaria",
-        "image": ""
-      },
-      {
-        "text": "Si occupa della transizione dei processi tra gli stati running ed exit",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "117) Nel modello dei processi a 5 stati, quale affermazione e’ falsa:",
-    "answers": [
-      {
-        "text": "Un processo puo’ essere spostato allo stato suspended dallo stato blocked e ready",
-        "image": ""
-      },
-      {
-        "text": "Un processo puo’ essere spostato dallo stato running allo stato ready o exit",
-        "image": ""
-      },
-      {
-        "text": "Un processo puo’ essere spostato dallo stato blocked solo allo stato ready",
-        "image": ""
-      },
-      {
-        "text": "Un processo puo’ essere spostato dallo stato ready allo stato running, blocked o exit",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "118) Riguardo l’efficienza dal punto di vista dell’utilizzo utile della CPU, quale dei seguenti modelli di I/O e’ piu’ efficiente dal punto di vista dell’uso della CPU e perche’?",
-    "answers": [
-      {
-        "text": "I/O programmato, perche’ consente al programmatore di fare uno scheduling esatto delle operazioni di I/O nei momenti piu’ opportuni",
-        "image": ""
-      },
-      {
-        "text": "I/O basato su DMA (Accesso Diretto alla Memoria), perche’ la CPU deve soltanto occuparsi del trasferimento dei dati",
-        "image": ""
-      },
-      {
-        "text": "I/O basato su interruzioni, perche’ il processore non deve controllare attivamente lo stato del dispositivo di I/O dopo aver effettuato la richiesta",
-        "image": ""
-      },
-      {
-        "text": "I/O basato su DMA (Accesso Diretto alla Memoria), perche’ la CPU deve soltanto occuparsi di inviare la richiesta di I/O e leggere il risultato",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "119) Dati due processi A e B e due risorse R1 ed R2, si ha sicuramente una situazione di deadlock se:",
-    "answers": [
-      {
-        "text": "A richiede ed ottiene accesso ad R1, B richiede ed ottiene accesso ad R2. A richiede accesso ad R2, B richiede accesso ad R1",
-        "image": ""
-      },
-      {
-        "text": "A richiede ed ottiene accesso ad R1, B richiede accesso ad R2. A richiede accesso ad R2. B richiede accesso ad R1",
-        "image": ""
-      },
-      {
-        "text": "A richiede ed ottiene accesso ad R2, B richiede accesso ad R1 ed R2. A richiede ed ottiene accesso ad R1",
-        "image": ""
-      },
-      {
-        "text": "B richiede ed ottiene accesso ad R1, A richiede ed ottiene accesso ad R2. B richiede accesso ad R2",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "120) Quali delle seguenti affermazioni e' vera riguardo la preallocazione rispetto all'allocazione dinamica dello spazio per i file?",
-    "answers": [
-      {
-        "text": "la preallocazione è più efficiente nell'utilizzo dello spazio su disco",
-        "image": ""
-      },
-      {
-        "text": "nessuna delle opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "l'allocazione dinamica rischia di sprecare spazio disco in caso gli utenti/applicazioni sovrastimino la dimensione dei file, mentre questo non è il caso con la preallocazione",
-        "image": ""
-      },
-      {
-        "text": "L'allocazione dinamica impone un overhead di gestione minore per il sistema operativo",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "121) Quale delle seguenti affermazioni sul file system NTFS è vera?",
-    "answers": [
-      {
-        "text": "NTFS può, ove possibile, includere direttamente i dati di un file nella master file table",
-        "image": ""
-      },
-      {
-        "text": "NTFS non prevede la possibilità di avere record estesi",
-        "image": ""
-      },
-      {
-        "text": "nessuna delle altre opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "In NTFS, le informazioni relative alla sequenza di blocchi che contengono il file è interamente contenuta nel record base",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "122) Quale delle seguenti affermazioni riguardo la rilocazione degli indirizzi di memoria è vera?",
-    "answers": [
-      {
-        "text": "Nei sistemi con hardware dedicato per la rilocazione, il base register (registro base) viene impostato una sola volta, quando il programma viene caricato in memoria per la prima volta",
-        "image": ""
-      },
-      {
-        "text": "In un sistema con rilocazione a run time, i sistemi di protezione che verificano che un processo non vada ad accedere alla memoria di un'altro processo possono essere eseguiti a tempo di compilazione, prima di eseguire il programma",
-        "image": ""
-      },
-      {
-        "text": "In un sistema a rilocazione con indirizzi logici, non è necessario avere hardware dedicato per effettuare la rilocazione",
-        "image": ""
-      },
-      {
-        "text": "In un sistema a rilocazione con indirizzi assoluti, se si conosce l'indirizzo di memoria dove verrà caricato il programma, il compilatore può inserire direttamente gli indirizzi di memoria corretti nel codice oggetto (programma compilato)",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "123) Quale delle seguenti affermazioni è vera riguardo il concetto di Thrashing?",
-    "answers": [
-      {
-        "text": "Il SO impiega la maggior parte del suo tempo a swappare pezzi di processi, anziché ad eseguire istruzioni",
-        "image": ""
-      },
-      {
-        "text": "provoca il deterioramento o il crollo delle prestazioni del computer",
-        "image": ""
-      },
-      {
-        "text": "quasi ogni richiesta di pagine da luogo ad una page fault",
-        "image": ""
-      },
-      {
-        "text": "Tutte le opzioni sono vere",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "124) Il sistema di partizionamento fisso per la memoria principale:",
-    "answers": [
-      {
-        "text": "Permette di avere partizioni di lunghezza diversa e di modificarle a runtime",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "Consente una efficiente della memoria se ci sono molti processi di piccole dimensioni ",
-        "image": ""
-      },
-      {
-        "text": "Impone un numero massimo di processi che possono essere in memoria principale",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "125) Quale delle seguenti non è un vantaggio dell’attacco dizionario?",
-    "answers": [
-      {
-        "text": "Semplice da effettuare",
-        "image": ""
-      },
-      {
-        "text": "Versatilità",
-        "image": ""
-      },
-      {
-        "text": "Velocità di computazione in real time degli hash",
-        "image": ""
-      },
-      {
-        "text": "Disponibilità di molti tool per automatizzazione",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "126) Nello scheduler a breve ed a lungo termine la distizione principale è:",
-    "answers": [
-      {
-        "text": "Il tipo di processi che gestiscono",
-        "image": ""
-      },
-      {
-        "text": "La frequenza di esecuzione",
-        "image": ""
-      },
-      {
-        "text": "La lunghezza delle loro code",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle opzioni è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "127) Quale dei seguenti NON è un vantaggio della multiprogrammazione?",
-    "answers": [
-      {
-        "text": "Riduzione dei tempi di risposta",
-        "image": ""
-      },
-      {
-        "text": "Possibilità di assegnare priorità ai lavori",
-        "image": ""
-      },
-      {
-        "text": "Aumento del throughput",
-        "image": ""
-      },
-      {
-        "text": "Riduzione dell’overhead del sistema operativo",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "128) ___> fornisce l’indirizzo della prossima istruzione che deve essere eseguita dal processo corrente?",
-    "answers": [
-      {
-        "text": "Lo stack del processo",
-        "image": ""
-      },
-      {
-        "text": "Il bus di sistema",
-        "image": ""
-      },
-      {
-        "text": "Nessuno ",
-        "image": ""
-      },
-      {
-        "text": "Program Counter",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "129) Quale dei seguenti NON è un valido schema di prevenzione del deadlock?",
-    "answers": [
-      {
-        "text": "Rilasciare tutte le risorse prima di richiederne una nuova",
-        "image": ""
-      },
-      {
-        "text": "Non chiedere mai una risorsa dopo averne rilasciate altre",
-        "image": ""
-      },
-      {
-        "text": "Si definisce un ordinamento crescente delle risorse, una risorsa viene data solo se esegue quelle che il processo già detiene",
-        "image": ""
-      },
-      {
-        "text": "Richiedere e allocare tutte le risorse necessarie prima dell’esecuzione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "130) Quale dei seguenti NON è vero riguardo l’algoritmo di Dekker per gestire la concorrenza?",
-    "answers": [
-      {
-        "text": "Non usa busy waiting",
-        "image": ""
-      },
-      {
-        "text": "Garantisce la non-starvation",
-        "image": ""
-      },
-      {
-        "text": "Tutte le opzioni elencate",
-        "image": ""
-      },
-      {
-        "text": "Garantisce il non-deadlock",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "131) Quale delle seguenti non è una tabella di controllo del sistema operativo?",
-    "answers": [
-      {
-        "text": "Tabella dei processi sospesi",
-        "image": ""
-      },
-      {
-        "text": "Tabelle di memoria",
-        "image": ""
-      },
-      {
-        "text": "Tabelle di controllo di accesso",
-        "image": ""
-      },
-      {
-        "text": "Tabelle di I/O",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "132) In un sistema con modello di interruzioni (interrupt) annidate, se un interrupt (I-2) è ricevuto durante la gestione di un altro interrupt(I-1)",
-    "answers": [
-      {
-        "text": "La cpu sospende l’esecuzione del codice corrente, ed avvia l’handler del nuovo interrupt ricevuto",
-        "image": ""
-      },
-      {
-        "text": "La cpu completa l’esecuzione del codice corrente, e successivamente avvia l’handler del nuovo interrupt ricevuto",
-        "image": ""
-      },
-      {
-        "text": "La cpu gestisce entrambi gli handler in parallelo",
-        "image": ""
-      },
-      {
-        "text": "La cpu termina (aborts,kills) l’esecuzione del codice corrente, ed avvia l’handler del nuovo interrupt ricevuto",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "133) Il numero di processi completati per unità di tempo è chiamato _____",
-    "answers": [
-      {
-        "text": "Produzione",
-        "image": ""
-      },
-      {
-        "text": "Throughput",
-        "image": ""
-      },
-      {
-        "text": "Capacità",
-        "image": ""
-      },
-      {
-        "text": "Nessuno",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "134) Quale dei seguenti sono obiettivi per un file Management System?",
-    "answers": [
-      {
-        "text": "Tutte le opzioni elencate",
-        "image": ""
-      },
-      {
-        "text": "Fornire supporto per l’I/O da più utenti in contemporanea",
-        "image": ""
-      },
-      {
-        "text": "Minimizzare i dati persi o distrutti",
-        "image": ""
-      },
-      {
-        "text": "Fornire un insieme di interfacce standard per i processi utente",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "135) In un sistema operativo con allocazione dei file indicizzata, quale delle seguenti opzioni è vera:",
-    "answers": [
-      {
-        "text": "La tabella di allocazione contiene soltanto l'indirizzo di un blocco, e questo blocco contiene sempre tutte le entry per ogni porzione allocata al file",
-        "image": ""
-      },
-      {
-        "text": "La tabella di allocazione contiene l'indirizzo del primo blocco del file, e ciascun blocco contiene l'indirizzo del prossimo blocco del file",
-        "image": ""
-      },
-      {
-        "text": "La tabella di allocazione contiene soltanto l'indirizzo di un blocco, e questo blocco contiene le entry delle porzioni di file allocate oppure l'indirizzo di altri blocchi usati a loro volta per indicizzare le porzioni di file allocate",
-        "image": ""
-      },
-      {
-        "text": "La tabella di allocazione dei file contiene l'indirizzo di un blocco e la lista dei blocchi del file",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "136) Quale delle seguenti affermazioni riguardo algoritmi di scheduling del disco è vera",
-    "answers": [
-      {
-        "text": "L'algoritmo SCAN può portare a starvation delle richieste",
-        "image": ""
-      },
-      {
-        "text": "L'algoritmo FSCAN è una versione di SCAN che rimuove il problema della starvation delle richieste, ma che rende l'algoritmo meno fair rispetto a SCAN",
-        "image": ""
-      },
-      {
-        "text": "L'algoritmo Minimo Tempo di Servizio non richiede di conoscere la posizione della testina del disco per operare",
-        "image": ""
-      },
-      {
-        "text": "N-step-SCAN è una generalizzazione di FSCAN che è fair e può avere prestazioni molto simili a quelle di SCAN",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "137) Quali dei seguenti NON è un tipo di scheduling dei sistemi operativi:",
-    "answers": [
-      {
-        "text": "Short term scheduling",
-        "image": ""
-      },
-      {
-        "text": "Long term scheduling",
-        "image": ""
-      },
-      {
-        "text": "Disk scheduling",
-        "image": ""
-      },
-      {
-        "text": "File scheduling",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "138) Nei sistemi operativi che usano paginazione SEMPLICE per la gestione della memoria",
-    "answers": [
-      {
-        "text": "ai processi devono essere allocati frame di memoria necessariamente contigui per poter consentire l'esecuzione del processo",
-        "image": ""
-      },
-      {
-        "text": "il sistema operativo deve utilizzare la tabella delle pagine per tradurre gli indirizzi. Qualora una pagina non sia presente in memoria principale, il sistema la deve caricare dinamicamente per consentire il proseguimento dell'esecuzione di un processo",
-        "image": ""
-      },
-      {
-        "text": "non c'è necessità di traduzione degli indirizzi, in quanto tutte le pagine di un processo sono sempre caricate in un frame nella memoria principale",
-        "image": ""
-      },
-      {
-        "text": "nessuna delle altre opzioni è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "139) Nei sistemi operativi che usano journaling logico",
-    "answers": [
-      {
-        "text": "non c'è possibilità di perdita dei dati in quanto, in caso di arresto imprevisto, il sistema operativo può usare il journal per ricostruire interamente le operazioni non andate a buon fine",
-        "image": ""
-      },
-      {
-        "text": "il sistema operativo usa il journal solo per copiare i dati prima di farne la scrittura anche nel file system, ma non lo utilizza per i metadati",
-        "image": ""
-      },
-      {
-        "text": "il sistema operativo usa il journal solo per copiare i metadati prima di aggiornare le strutture del file system, ma non lo utilizza per i dati",
-        "image": ""
-      },
-      {
-        "text": "nessuna delle opzioni è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "140) Il sistema operativo linux per la gestione dei file",
-    "answers": [
-      {
-        "text": "nessuna delle altre opzioni è corretta",
-        "image": ""
-      },
-      {
-        "text": "utilizza un sistema misto di allocazione contigua e concatenata in modo da minimizzare l'overhead di sistema e massimizzare le performance",
-        "image": ""
-      },
-      {
-        "text": "utilizza un sistema di allocazione concatenata basato sulla struttura dati conosciuta come inode",
-        "image": ""
-      },
-      {
-        "text": "usa gli inode per tenere traccia dei blocchi su disco allocati a ciascun file. Ogni inode contiene al suo interno la lista completa di tutti i blocchi su disco che compongono il file corrispondente",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "141) Nei sistemi Unix",
-    "answers": [
-      {
-        "text": "gli hard links sono dei file speciali che contengono il cammino completo sul file system di un altro file, effettivamente creando un \"puntatore\" a quel file",
-        "image": ""
-      },
-      {
-        "text": "gli hard link sono puntatori diretti al descrittore di un file (inode). Un contatore viene utilizzato per tenere traccia di quanti hard link puntino ad un determinato inode. Questo fa si che il file non possa essere cancellato fintantoché ci sono hard link che continuano a puntarlo",
-        "image": ""
-      },
-      {
-        "text": "possono esistere hard link a file non più esistenti, ad esempio se il file a cui l'hard link puntava viene cancellato",
-        "image": ""
-      },
-      {
-        "text": "nessuna delle altre risposte è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "142) Quale delle affermazioni è vera riguardo alla Segmentazione per la gestione della memoria?",
-    "answers": [
-      {
-        "text": "Permette al programmatore di vedere la memoria come un insieme di spazi di indirizzi",
-        "image": ""
-      },
-      {
-        "text": "Non permette di condividere dati",
-        "image": ""
-      },
-      {
-        "text": "Non permette di proteggere dati",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle opzioni è vera",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "143) Quale opzione non appartiene alla triade della sicurezza?",
-    "answers": [
-      {
-        "text": "Integrità",
-        "image": ""
-      },
-      {
-        "text": "Disponibilità",
-        "image": ""
-      },
-      {
-        "text": "Confidenzialità",
-        "image": ""
-      },
-      {
-        "text": "Autenticità",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "144) Quale dei seguenti elementi non e contenuto nel Process Control Block (PCB)?",
-    "answers": [
-      {
-        "text": "Stack del processo",
-        "image": ""
-      },
-      {
-        "text": "Codice del programma",
-        "image": ""
-      },
-      {
-        "text": "Programma Bootstrap",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle opzioni e contenuta nel PCB",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "145) Il multiprocessore viene utilizzato perché:",
-    "answers": [
-      {
-        "text": "Capacità distribuita",
-        "image": ""
-      },
-      {
-        "text": "Aumentano l'affidabilità",
-        "image": ""
-      },
-      {
-        "text": "Consentono di risparmiare denaro rispetto a più sistemi singoli",
-        "image": ""
-      },
-      {
-        "text": "Tutte queste cose",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "146) Considerando solo risorse riusabili, quali delle seguenti condizioni non è necessaria per avere deadlock?",
-    "answers": [
-      {
-        "text": "Mutua esclusione",
-        "image": ""
-      },
-      {
-        "text": "Hold-and-wait: richiesta di una risorsa quando già se ne detiene una",
-        "image": ""
-      },
-      {
-        "text": "Preemption delle risorse",
-        "image": ""
-      },
-      {
-        "text": "Attesa circolare",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "147) Il buffering dell'I/O:",
-    "answers": [
-      {
-        "text": "non introduce alcun overhead nei sistemi operativi che lo usano rispetto a quelli che fanno I/O diretto senza buffering",
-        "image": ""
-      },
-      {
-        "text": "è utile per appianare le differenze tra diversi dispositivi di I/O rispetto alla loro velocità ed al tipo di trasferimento dati (blocchi, stream)",
-        "image": ""
-      },
-      {
-        "text": "richiede l'uso di buffer aggiuntivi nell'area di memoria dedicata ai dispositivi di I/O e nell'area di memoria dedicata ai processi utente, ma non nell'area del sistema operativo",
-        "image": ""
-      },
-      {
-        "text": "nessuna delle opzioni è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "148) I sistemi operativi che usano partizionamento dinamico della memoria:",
-    "answers": [
-      {
-        "text": "non soffrono mai di frammentazione interna",
-        "image": ""
-      },
-      {
-        "text": "non soffrono mai di frammentazione esterna",
-        "image": ""
-      },
-      {
-        "text": "soffrono in alcuni casi di frammentazione interna, quando diversi processi a cui erano assegnate diverse aree di memoria vengono rimossi e sostituiti da altri processi con dimensioni diverse",
-        "image": ""
-      },
-      {
-        "text": "necessitano dell'uso di tecniche di compattazione della memoria per risolvere il problema della frammentazione interna",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "149) Quale dei seguenti è un tipo di partizionamento della memoria?",
-    "answers": [
-      {
-        "text": "Partizionamento fisso",
-        "image": ""
-      },
-      {
-        "text": "Partizionamento dinamico",
-        "image": ""
-      },
-      {
-        "text": "Segmentazione con memoria virtuale",
-        "image": ""
-      },
-      {
-        "text": "Tutte le opzioni sono vere",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "150) Quale delle seguenti affermazioni sulla concorrenza tra processi o thread è vera?",
-    "answers": [
-      {
-        "text": "La disabilitazione delle interruzioni non impedisce la creazione di nuove interruzioni, ma solo la loro gestione",
-        "image": ""
-      },
-      {
-        "text": "Se un processo utente può disabilitare le interruzioni tramite un'istruzione macchina dedicata, allora può far diminuire l'uso utile del processore",
-        "image": ""
-      },
-      {
-        "text": "La disabilitazione delle interruzioni non funziona ai fini della concorrenza (gestione sezioni critiche) su sistemi con più processori o più core",
-        "image": ""
-      },
-      {
-        "text": "Tutte le opzioni sono vere",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "151) Quale delle seguenti affermazioni sulla traduzione di un indirizzo virtuale in fisico, in un sistema con memoria virtuale con paginazione (avente tabella delle pagine ad 1 livello), è vera?",
-    "answers": [
-      {
-        "text": "Il numero di frame dell'indirizzo fisico è contenuto già nell'indirizzo virtuale",
-        "image": ""
-      },
-      {
-        "text": "L'hardware deve usare il numero di pagina per accedere alla tabella delle pagine del processo in esecuzione. A tal proposito, deve conoscere l'inizio di tale tabella, che viene definito dal software (sistema operativo). Tale indirizzo può cambiare durante l'esecuzione del processo: sta al sistema operativo mantenerlo aggiornato",
-        "image": ""
-      },
-      {
-        "text": "L'hardware deve usare il numero della pagina per comporre, insieme con l'offset originale, l'indirizzo fisico. Tale operazione è equivalente ad uno shift seguito da una somma",
-        "image": ""
-      },
-      {
-        "text": "L'hardware deve effettuare una ricerca sequenziale del numero di pagina nelle entries della tabella delle pagine del processo in esecuzione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "152) Il file system NTFS",
-    "answers": [
-      {
-        "text": "utilizza un sistema di lista concatenata per tenere traccia dei blocchi del disco assegnati ai diversi file",
-        "image": ""
-      },
-      {
-        "text": "utilizza un sistema di allocazione simile all'allocazione indicizzata basato su record di attributi per ciascun file, dove un il tipo di attributo DATA contiene puntatori individuali a tutti i blocchi che compongono un dato file",
-        "image": ""
-      },
-      {
-        "text": "utilizza un sistema di allocazione simile all'allocazione indicizzata basato su record di attributi per ciascun file, dove un il tipo di attributo DATA contiene puntatori a sequenze di blocchi contigue (runs) che compongono un dato file",
-        "image": ""
-      },
-      {
-        "text": "utilizza un sistema di allocazione simile all'allocazione indicizzata simile agli inode di UNIX, in cui un campo DATA all'interno dell'inode del file contiene la sequenza completa dei blocchi che compongono un dato file",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "153) Nei sistemi operativi con memoria virtuale paginata, tipicamente",
-    "answers": [
-      {
-        "text": "il sistema usa sempre e comunque l'allocatore di memoria standard a pagine, senza consentire richieste speciali come ad esempio allocazioni contigue di memoria. Questo per assicurarsi di evitare i problemi di frammentazione interna od esterna",
-        "image": ""
-      },
-      {
-        "text": "il sistema operativo, in casi speciali, può usare allocatori di memoria specializzati che consentano ad esempio allocazioni di porzioni di memoria contigue per richieste particolari (come ad esempio per il DMA)",
-        "image": ""
-      },
-      {
-        "text": "il sistema può modificare dinamicamente la dimensione delle pagine in base alla quantità di memoria richiesta da un processo, in modo tale da minimizzare la frammentazione interna",
-        "image": ""
-      },
-      {
-        "text": "nessuna delle altre opzioni è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "154) Nel sistema di partizionamento fisso variabile della memoria",
-    "answers": [
-      {
-        "text": "la memoria è divisa dinamicamente a tempo di esecuzione dal sistema operativo in base alle richieste di allocazione effettuate dai processi in esecuzione",
-        "image": ""
-      },
-      {
-        "text": "la memoria viene suddivisa in un numero fisso e predefinito di partizioni, tuttavia diversi gruppi di partizioni hanno dimensioni diverse (ad es. N partizioni da 2MB, M partizioni da 4MB, e cosi via)",
-        "image": ""
-      },
-      {
-        "text": "la memoria è divisa a tempo di esecuzione dal sistema operativo in partizioni della stessa dimensione, in base alla dimensione media di memoria richiesta dai diversi processi",
-        "image": ""
-      },
-      {
-        "text": "il sistema operativo deve mantenere una tabella di traduzione degli indirizzi che consentano di tradurre un indirizzo relativo nel codice del programma ad un indirizzo assoluto, in base alla dimensione della partizione di memoria assegnatagli",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "155) Nei sistemi Unix",
-    "answers": [
-      {
-        "text": "i symbolic links sono dei simboli speciali che possono essere usati nelle chiamate di sistema per collegare le aree di memoria di diversi processi",
-        "image": ""
-      },
-      {
-        "text": "i symbolic links sono dei puntatori diretti ai descrittori di un file (inode) che consentono di creare un collegamento a quel file in una qualsiasi directory del file system",
-        "image": ""
-      },
-      {
-        "text": "ogni volta che viene creato un symbolic link tra due processi, deve essere incrementato il contatore che tiene traccia del numero di collegamenti creati. Questo per poter consentire di de-allocare le aree di memoria collegate quando tutti i processi sono terminati",
-        "image": ""
-      },
-      {
-        "text": "i symbolic links sono dei file che contengono il cammino completo sul file system di un altro file, effettivamente creando un puntatore a quel file",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-{
-    "quest": "156) Tipicamente, nei processori moderni la memoria cache",
-    "answers": [
-      {
-        "text": "è strutturata a livelli gerarchici ed il processore può caricare i dati e le istruzioni nei registri da uno qualsiasi dei livelli della cache",
-        "image": ""
-      },
-      {
-        "text": "è strutturata a livelli gerarchici ed il processore può caricare i dati e le istruzioni nei registri solo se si trovano nel primo o secondo livello della cache",
-        "image": ""
-      },
-      {
-        "text": "non ha struttura gerarchica, in quanto una struttura piatta consente una maggior efficenza e velocità nel trasferimento dei dati",
-        "image": ""
-      },
-      {
-        "text": "è strutturata a livelli gerarchici ed il processore può caricare i dati solo dalla cache di livello 1 dati, e le istruzioni solo dalla cache livello 1 istruzioni.",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-{
-    "quest": "157) Il file system FAT32",
-    "answers": [
-      {
-        "text": "utilizza una lista concatenata con cluster di dimensione fissa per l'allocazione dei file",
-        "image": ""
-      },
-      {
-        "text": "utilizza una bitmap per tenere traccia dei cluster liberi sul disco",
-        "image": ""
-      },
-      {
-        "text": "occupa poco spazio di memoria, e lo spazio occupato è indipendente dalla dimensione del disco e numero dei cluster",
-        "image": ""
-      },
-      {
-        "text": "nessuna delle opzioni è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  }
-]
\ No newline at end of file
diff --git a/data/questions/so1_new.json b/data/questions/so1_new.json
deleted file mode 100644
index 55a2e89..0000000
--- a/data/questions/so1_new.json
+++ /dev/null
@@ -1,2927 +0,0 @@
-[
-  {
-    "quest": "Il sistema operativo",
-    "answers": [
-      {
-        "text": "Coincide con il kernel",
-        "image": ""
-      },
-      {
-        "text": "Costituisce l'interfaccia tra la macchina fisica (hardware) e le applicazioni utente",
-        "image": ""
-      },
-      {
-        "text": "È soggetto alle politiche di scheduling",
-        "image": ""
-      },
-      {
-        "text": "Risiede in memoria principale anche in seguito allo shutdown della macchina",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "In un sistema operativo microkernel",
-    "answers": [
-      {
-        "text": "Alcune delle funzionalità sono implementate in spazio utente anziché all'interno del kernel",
-        "image": ""
-      },
-      {
-        "text": "I processi utente possono interagire direttamente con il sistema,evitando l'uso di system call",
-        "image": ""
-      },
-      {
-        "text": "La comunicazione tra le varie componenti del sistema è più efficiente",
-        "image": ""
-      },
-      {
-        "text": "Non sono previsti meccanismi di protezione  ",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "In un sistema operativo strutturato secondo un approccio microkernel",
-    "answers": [
-      {
-        "text": "Non necessita di avere due modalità di utilizzo della CPU (user vs.kernel mode)",
-        "image": ""
-      },
-      {
-        "text": "Non necessita di meccanismi di comunicazione tra porzioni diverse del sistema operativo",
-        "image": ""
-      },
-      {
-        "text": "E' più efficiente di un sistema monolitico",
-        "image": ""
-      },
-      {
-        "text": "Ad eccezione delle funzionalità fondamentali, implementa tutto il resto in spazio utente",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "L'insieme di istruzioni del livello macchina:",
-    "answers": [
-      {
-        "text": "Sono composte da un codice operativo e da zero o più operandi",
-        "image": ""
-      },
-      {
-        "text": "Sono definite da uno specifico linguaggio macchina",
-        "image": ""
-      },
-      {
-        "text": "Sono un'astrazione dell'architettura hardware",
-        "image": ""
-      },
-      {
-        "text": "Tutte le risposte precedenti sono corrette",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "I registri interni della CPU e la cache sono unità di memoria:",
-    "answers": [
-      {
-        "text": "Non volatili",
-        "image": ""
-      },
-      {
-        "text": "Gestite interamente dall'architettura a livello hardware",
-        "image": ""
-      },
-      {
-        "text": "Gestite interamente dal sistema operativo",
-        "image": ""
-      },
-      {
-        "text": "Molto economiche e altamente performanti",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "La transizione da user a kernel mode avviene quando:",
-    "answers": [
-      {
-        "text": "Un programma esegue una chiamata di funzione",
-        "image": ""
-      },
-      {
-        "text": "Si avvia il computer (bootstrap)",
-        "image": ""
-      },
-      {
-        "text": "Si esegue la prima istruzione di un programma",
-        "image": ""
-      },
-      {
-        "text": "Scade il quanto di tempo assegnato al processo in esecuzione",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Il device controller di un dispositivo di I/O:",
-    "answers": [
-      {
-        "text": "Contiene dei registri che ne indicano lo stato",
-        "image": ""
-      },
-      {
-        "text": "Contiene dei registri che ne consentono il controllo da parte della CPU",
-        "image": ""
-      },
-      {
-        "text": "Contiene dei registri per lo scambio di dati con la CPU",
-        "image": ""
-      },
-      {
-        "text": "Tutte le risposte precedenti sono corrette",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Le chiamate di sistema:",
-    "answers": [
-      {
-        "text": "Sono sempre bloccanti",
-        "image": ""
-      },
-      {
-        "text": "Causano la terminazione del processo in corso e l'avvio di un nuovo processo",
-        "image": ""
-      },
-      {
-        "text": "Devono essere implementate in spazio utente",
-        "image": ""
-      },
-      {
-        "text": "Devono essere implementate in spazio kernel",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Una chiamata di sistema bloccante",
-    "answers": [
-      {
-        "text": "Sposta in coda pronti (ready) il processo che la esegue",
-        "image": ""
-      },
-      {
-        "text": "Interrompe definitivamente il processo che la esegue",
-        "image": ""
-      },
-      {
-        "text": "Interrompe temporaneamente il processo che la esegue",
-        "image": ""
-      },
-      {
-        "text": "Necessità che il processo che la esegue ne verifichi periodicamente l'esito (polling)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Il system call handler:",
-    "answers": [
-      {
-        "text": "È invocato dallo scheduler del sistema operativo",
-        "image": ""
-      },
-      {
-        "text": "Viene invocato alla scadenza del quanto temporale",
-        "image": ""
-      },
-      {
-        "text": "Viene eseguito in spazio utente",
-        "image": ""
-      },
-      {
-        "text": "Gestisce le chiamate di sistema tramite la system call table",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": " Il codice generico del system call handler:",
-    "answers": [
-      {
-        "text": "Viene eseguito in spazio utente",
-        "image": ""
-      },
-      {
-        "text": "È indicizzato tramite la interrupt vector table (IVT)",
-        "image": ""
-      },
-      {
-        "text": "Viene invocato alla scadenza del quanto temporale",
-        "image": ""
-      },
-      {
-        "text": "Viene invocato dallo scheduler del sistema operativo",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "L'interrupt vector table(IVT):",
-    "answers": [
-      {
-        "text": "Si aggiorna dinamicamente ad ogni interruzione",
-        "image": ""
-      },
-      {
-        "text": "E' una struttura dati che contiene puntatori ai vari gestori(handler) delle interruzioni",
-        "image": ""
-      },
-      {
-        "text": "E' una struttura dati che è associata a ciascun processo",
-        "image": ""
-      },
-      {
-        "text": "E' una struttura dati che contiene puntatori a codici di errori",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "La system-call table:",
-    "answers": [
-      {
-        "text": "Contiene tante entry quanto sono le chiamate di sistema supportare",
-        "image": ""
-      },
-      {
-        "text": "Contiene tante entry quante sono le interruzioni supportare",
-        "image": ""
-      },
-      {
-        "text": "Contiene tante entry quanti sono i dispositivi di I/O presenti nel sistema",
-        "image": ""
-      },
-      {
-        "text": "Contiene tante entry quanti sono i processi in esecuzione",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "La system-call table è una struttura dati gestita:",
-    "answers": [
-      {
-        "text": "Dai dispositivi di I/O",
-        "image": ""
-      },
-      {
-        "text": "Dal processo utente",
-        "image": ""
-      },
-      {
-        "text": "Sia dal kernel del sistema operativo che dal processo utente",
-        "image": ""
-      },
-      {
-        "text": "Dal kernel del sistema operativo",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Se si cambia l'implementazione di una chiamata di sistema esistente:",
-    "answers": [
-      {
-        "text": "E' sempre necessario modificare il codice utente che ne fa uso",
-        "image": ""
-      },
-      {
-        "text": "Non è mai necessario modificare il codice utente che ne fa uso",
-        "image": ""
-      },
-      {
-        "text": "Non è necessario modificare il codice utente che ne fa uso, a patto che cambi anche l'interfaccia (API) della chiamata di sistema",
-        "image": ""
-      },
-      {
-        "text": "Non è necessario modificare il codice utente che ne fa uso, a patto che non cambi anche l’interfaccia (API) della chiamata di sistema",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Un processore impiega 5 cicli di clock per eseguire un'istruzione (CPI = 5), ossia per completare l'intero ciclo fetch-decode-execute. Assumendo che la frequenza di clock del processore sia pari a 5 MHz, quante istruzioni è in grado di eseguire in un secondo? (Si ricordi che 1 MHz = 1*10^6 cicli al secondo)",
-    "answers": [
-      {
-        "text": "1*10^3",
-        "image": ""
-      },
-      {
-        "text": "Decido di NON rispondere a questa domanda",
-        "image": ""
-      },
-      {
-        "text": "25*10^3",
-        "image": ""
-      },
-      {
-        "text": "1*10^6",
-        "image": ""
-      },
-      {
-        "text": "25*10^6",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Data una CPU multicore con �unità(cores), il numero di processi/thread che ad un certo istante si trovano nella “coda� di esecuzione(running):",
-    "answers": [
-      {
-        "text": "Può essere superiore a �",
-        "image": ""
-      },
-      {
-        "text": "E’ esattamente pari a �",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficienti per rispondere alla domanda",
-        "image": ""
-      },
-      {
-        "text": "E' al massimo pari a �",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "La creazione di un nuovo processo da parte di un processo avviene tramite:",
-    "answers": [
-      {
-        "text": "Una chiamata di sistema",
-        "image": ""
-      },
-      {
-        "text": "Una chiamata di funzione",
-        "image": ""
-      },
-      {
-        "text": "L'invio di un interruzione",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle risposte precedenti è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "Il sistema operativo tiene traccia dello stato di un processo tramite:",
-    "answers": [
-      {
-        "text": "Un'apposita area dedicata e protetta della memoria principale",
-        "image": ""
-      },
-      {
-        "text": "Un apposito registro interno della CPU",
-        "image": ""
-      },
-      {
-        "text": "Un'apposita area dedicata e protetta della memoria cache",
-        "image": ""
-      },
-      {
-        "text": "Un apposito campo all'interno del process control block (PCB)",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Un processo in esecuzione sulla CPU passa in stato ready quando:",
-    "answers": [
-      {
-        "text": "Riceve un segnale di interruzione da parte di un dispositivo di I/O",
-        "image": ""
-      },
-      {
-        "text": "Fa richiesta di input da parte dell’utente",
-        "image": ""
-      },
-      {
-        "text": "Fa richiesta di una pagina che non è presente in memoria principale",
-        "image": ""
-      },
-      {
-        "text": "Esegue una chiamata di funzione",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "Un processo in esecuzione sulla CPU passa in stato waiting quando:",
-    "answers": [
-      {
-        "text": "Riceve un segnale da parte di un dispositivo di I/O",
-        "image": ""
-      },
-      {
-        "text": "Termina il quanto di tempo ad esso assegnato",
-        "image": ""
-      },
-      {
-        "text": "Apre una connessione di rete (ad es., un socket TCP)",
-        "image": ""
-      },
-      {
-        "text": "Esegue una chiamata di funzione",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Un processo in esecuzione sulla CPU passa in stato waiting quando:",
-    "answers": [
-      {
-        "text": "Fa richiesta di input da parte dell'utente",
-        "image": ""
-      },
-      {
-        "text": "Esegue una chiamata di funzione",
-        "image": ""
-      },
-      {
-        "text": "Termina il quanto di tempo ad esso assegnato",
-        "image": ""
-      },
-      {
-        "text": "Riceve un segnale di interruzione da parte di un dispositivo di I/O",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "Un processo in esecuzione sulla CPU passa in stato waiting quando:",
-    "answers": [
-      {
-        "text": "Termina il quanto di tempo ad esso assegnato",
-        "image": ""
-      },
-      {
-        "text": "L'utente trascina il dispositivo di puntamento(e.g. mouse)",
-        "image": ""
-      },
-      {
-        "text": "Esegue una chiamata di funzione",
-        "image": ""
-      },
-      {
-        "text": "Riceve un segnale di interruzione da parte di un dispositivo di I/O",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Quanti processi saranno presenti nel sistema a seguito di queste chiamata: pid_1 = fork(); pid_2 = fork(); pid_3 = fork();?",
-    "answers": [
-      {
-        "text": "8",
-        "image": ""
-      },
-      {
-        "text": "7",
-        "image": ""
-      },
-      {
-        "text": "4",
-        "image": ""
-      },
-      {
-        "text": "3",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "I processi CPU-bound che non eseguono richieste di I/O:",
-    "answers": [
-      {
-        "text": "Hanno una priorità alta",
-        "image": ""
-      },
-      {
-        "text": "Hanno una priorità bassa",
-        "image": ""
-      },
-      {
-        "text": "Sono processi mediamente brevi",
-        "image": ""
-      },
-      {
-        "text": "Possono non rilasciare mai la CPU volontariamente",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Lo scheduler della CPU si attiva:",
-    "answers": [
-      {
-        "text": "Quando un processo tenta di eseguire una scrittura su discord",
-        "image": ""
-      },
-      {
-        "text": "Quando il codice di un programma esegue una divisione per zero",
-        "image": ""
-      },
-      {
-        "text": "Quando scade il quanto di tempo",
-        "image": ""
-      },
-      {
-        "text": "Tutte le risposte precedenti sono corrette",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Lo scheduling preemptive(basato su time slice o quanto temporale):",
-    "answers": [
-      {
-        "text": "Da la priorità ai processi CPU-bound",
-        "image": ""
-      },
-      {
-        "text": "Si attiva solamenta alla scadenza del quanto temporale(time slice)",
-        "image": ""
-      },
-      {
-        "text": "Si attiva solamente a fronte di una chiamata di sistema",
-        "image": ""
-      },
-      {
-        "text": "Fornisce un limite superiore al tempo di CPU assegnato a ciascun processo",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "In un sistema uniprocessore (single core) time-sharing in cui i processi in esecuzione sono tutti puramente CPU-bound:",
-    "answers": [
-      {
-        "text": "L'impiego dei multi-threading consente di migliorare la latenza del sistema",
-        "image": ""
-      },
-      {
-        "text": "L'impiego del multi-threading consente di diminuire il tempo di completamente di ciascun processo",
-        "image": ""
-      },
-      {
-        "text": "L'impiego del multi-threading consente di migliorare il throughput del sistema",
-        "image": ""
-      },
-      {
-        "text": "L'impiego dei multi-threading non costituisce alcun vantaggio",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "In caso di scheduling preemptive, lo scheduler interviene:",
-    "answers": [
-      {
-        "text": "Quando un processo passa dallo stato running allo stato waiting",
-        "image": ""
-      },
-      {
-        "text": "Quando un processo passa dallo stato running allo stato ready",
-        "image": ""
-      },
-      {
-        "text": "Quando un processo passa dallo stato waiting allo stato ready",
-        "image": ""
-      },
-      {
-        "text": "Tutte le risposte precedenti sono corrette",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Se un processo arriva nella coda dei pronti all'istante t.0 = 2e termina all'istante t.f = 15, il suo tempo di turnaround equivale a",
-    "answers": [
-      {
-        "text": "13",
-        "image": ""
-      },
-      {
-        "text": "2",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficienti per rispondere alla domanda",
-        "image": ""
-      },
-      {
-        "text": "15",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "Se un processo arriva nella coda dei pronti all’istante �0 = 3 e termina all’istante �� = 25, il tempo di attesa equivale a",
-    "answers": [
-      {
-        "text": "3",
-        "image": ""
-      },
-      {
-        "text": "22",
-        "image": ""
-      },
-      {
-        "text": "25",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficienti per rispondere alla domanda",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "I thread di uno stesso processo condividono:",
-    "answers": [
-      {
-        "text": "Lo stack",
-        "image": ""
-      },
-      {
-        "text": "Le variabili globali",
-        "image": ""
-      },
-      {
-        "text": "I valori dei registri della CPU",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle informazioni elencate sopra",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Lo user thread:",
-    "answers": [
-      {
-        "text": "Necessita del supporto di una opportuna thread table a livello kernel",
-        "image": ""
-      },
-      {
-        "text": "E' la più piccola unità schedulabile sulla CPU dal sistema operativo",
-        "image": ""
-      },
-      {
-        "text": "E' gestito in spazio utente tramite un'apposita libreria",
-        "image": ""
-      },
-      {
-        "text": "Coincide sempre con uno ed un solo kernel thread",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Nel modello di thread mapping cosiddetto one-to-one:",
-    "answers": [
-      {
-        "text": "Consente di gestire i thread tramite un'apposita libreria a livello utente",
-        "image": ""
-      },
-      {
-        "text": "Può essere implementato solo su sistemi multiprocessore",
-        "image": ""
-      },
-      {
-        "text": "Causa il blocco di tutti i thread di un processo se anche uno solo di questi thread esegue una chiamata di sistema bloccante",
-        "image": ""
-      },
-      {
-        "text": "Consente di gestire i thread a livello del kernel del sistema operativo",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Nel modello di thread mapping cosiddetto many-to-one:",
-    "answers": [
-      {
-        "text": "Molti user thread possono essere distribuiti su più CPU (se presenti)",
-        "image": ""
-      },
-      {
-        "text": "L'effetto di una chiamata bloccante da parte di uno user thread non blocca gli altri thread da cui è composto il processo",
-        "image": ""
-      },
-      {
-        "text": "Molti user thread sono mappati su un singolo kernel thread",
-        "image": ""
-      },
-      {
-        "text": "Molti kernel thread sono mappati su un singolo user thread",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Il modello di thread mapping considerato many-to-many",
-    "answers": [
-      {
-        "text": "Non prevede alcun limite al numero di kernel thread",
-        "image": ""
-      },
-      {
-        "text": "Può essere implementato solo su sistemi multiprocessore",
-        "image": ""
-      },
-      {
-        "text": "Causa il blocco di tutti i thread di un processo se anche uno solo di questi thread esegue una chiamata di sistema bloccante",
-        "image": ""
-      },
-      {
-        "text": "E' il compromesso tra un'implementazione dei thread puramente user level e una puramente kernel level",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Si parla di parallelismo quando:",
-    "answers": [
-      {
-        "text": "Vengono eseguiti processi single-threaded su CPU multicore",
-        "image": ""
-      },
-      {
-        "text": "Vengono eseguiti processi multi-threaded su CPU single core",
-        "image": ""
-      },
-      {
-        "text": "Vengono eseguiti processi multi-threaded su CPU multicore",
-        "image": ""
-      },
-      {
-        "text": "Tutte le risposte precedenti sono corrette",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Si parla di concorrenza quando:",
-    "answers": [
-      {
-        "text": "Vengono eseguiti processi multi-threaded su CPU single core",
-        "image": ""
-      },
-      {
-        "text": "Vengono eseguiti processi single-threaded su CPU single core",
-        "image": ""
-      },
-      {
-        "text": "Vengono eseguiti processi single-threaded su CPU multicore",
-        "image": ""
-      },
-      {
-        "text": "Vengono eseguiti processi multi-threaded su CPU multicore",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "La comunicazione tra thread dello stesso processo rispetto a quella tra processi diversi:",
-    "answers": [
-      {
-        "text": "È più lenta poiché i thread sono gestiti da librerie di alto livello",
-        "image": ""
-      },
-      {
-        "text": "È più veloce poiché i thread non eseguono context switch",
-        "image": ""
-      },
-      {
-        "text": "È più veloce poiché i thread condividono lo stesso spazio di indirizzamento",
-        "image": ""
-      },
-      {
-        "text": "Non c'è alcuna differenza sostanziale in termini di performance",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Il kernel thread:",
-    "answers": [
-      {
-        "text": "Coincide sempre con uno ed un solo user thread",
-        "image": ""
-      },
-      {
-        "text": "È gestito in spazio utente tramite un'apposita libreria",
-        "image": ""
-      },
-      {
-        "text": "È la più piccola unità schedulabile sulla CPU dal sistema operativo",
-        "image": ""
-      },
-      {
-        "text": "È il termine con cui si identificano i processi propri del sistemaoperativo (i.e., non i processi utente)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "L'uso di una primitiva di sincronizzazione lock prevede che:",
-    "answers": [
-      {
-        "text": "La lock sia inizialmente libera",
-        "image": ""
-      },
-      {
-        "text": "La lock venga acquisita prima dell'ingresso nella sezione critica",
-        "image": ""
-      },
-      {
-        "text": "La lock venga rilasciata dopo l'uscita dalla sezione critica",
-        "image": ""
-      },
-      {
-        "text": "Tutte le condizioni precedenti devono essere verificate",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "L'acquisizione di una lock:",
-    "answers": [
-      {
-        "text": "Deve avvenire in modo atomico, evitando che lo scheduler interrompa l'acquisizione",
-        "image": ""
-      },
-      {
-        "text": "Necessita obbligatoriamente del supporto di istruzioni hardware atomiche",
-        "image": ""
-      },
-      {
-        "text": "Necessita obbligatoriamente che il sistema operativo disabiliti le interruzioni",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle risposte precedenti è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "Un semaforo può essere utilizzato per:",
-    "answers": [
-      {
-        "text": "Forzare le politiche di scheduling tra processi/thread",
-        "image": ""
-      },
-      {
-        "text": "Accedere al codice del kernel",
-        "image": ""
-      },
-      {
-        "text": "Lo scambio di messaggi tra processi/thread",
-        "image": ""
-      },
-      {
-        "text": "Gestire le interruzioni che giungono alla CPU",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "L'invocazione del metodo wait() su un semaforo il cui valore è pari a 2:",
-    "answers": [
-      {
-        "text": "Lascia invariato il valore del semaforo a 2 e fa proseguire il processo che ha eseguito l'invocazione (al netto delle politiche di scheduling)",
-        "image": ""
-      },
-      {
-        "text": "Decrementa il valore del semaforo a 1 e blocca il processo che ha eseguito l'invocazione",
-        "image": ""
-      },
-      {
-        "text": "Incrementa il valore del semaforo a 3 e fa proseguire il processo che ha eseguito l'invocazione (al netto delle politiche di scheduling)",
-        "image": ""
-      },
-      {
-        "text": "Decrementa il valore del semaforo a 1 e fa proseguire il processo che ha eseguito l'invocazione (al netto delle politiche di scheduling)",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "L'istruzione test-and-set:",
-    "answers": [
-      {
-        "text": "È un'istruzione atomica che consente di implementare le primitive di sincronizzazione",
-        "image": ""
-      },
-      {
-        "text": "È un'istruzione atomica che consente di disabilitare le interruzioni",
-        "image": ""
-      },
-      {
-        "text": "È un'istruzione atomica che consente di aggiornare i valori di più registri simultaneamente",
-        "image": ""
-      },
-      {
-        "text": "È un'istruzione atomica che consente di resettare il valore di un semaforo",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "La differenza tra deadlock e starvation risiede nel fatto che:",
-    "answers": [
-      {
-        "text": "Si riferiscono a codice utente e codice di sistema (rispettivamente)",
-        "image": ""
-      },
-      {
-        "text": "Nel caso di starvation tutto il sistema è completamente bloccato",
-        "image": ""
-      },
-      {
-        "text": "Non vi è alcuna differenza",
-        "image": ""
-      },
-      {
-        "text": "Nel caso di deadlock tutto il sistema è completamente bloccato",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Con il termine address binding si intende:",
-    "answers": [
-      {
-        "text": "Il processo di traduzione da indirizzi logici a indirizzi fisici",
-        "image": ""
-      },
-      {
-        "text": "Il processo di inizializzazione delle variabili globali di un programma",
-        "image": ""
-      },
-      {
-        "text": "Il processo di collegamento tra il codice compilato ed eventuali librerie esterne",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle risposte precedenti è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "Lo swapping consente di:",
-    "answers": [
-      {
-        "text": "Implementare la rilocazione dinamica del codice di un processo",
-        "image": ""
-      },
-      {
-        "text": "Risolvere il problema della frammentazione esterna",
-        "image": ""
-      },
-      {
-        "text": "Trasferire temporaneamente su disco i processi che non sono attualmente in esecuzione",
-        "image": ""
-      },
-      {
-        "text": "Scambiare le aree di memoria occupate da due o più processi",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "La gestione 'paginata' della memoria (paging):",
-    "answers": [
-      {
-        "text": "Prevede che lo spazio di indirizzamento logico di un processo sia non-contiguo e suddiviso in blocchi di dimensioni fissate (pages)",
-        "image": ""
-      },
-      {
-        "text": "Non richiede alcun supporto hardware per essere implementata in modo efficiente",
-        "image": ""
-      },
-      {
-        "text": "Prevede che lo spazio di indirizzamento fisico di un processo sia non-contiguo e suddiviso in blocchi di dimensioni fissate (frames)",
-        "image": ""
-      },
-      {
-        "text": "Risolve il problema della frammentazione interna",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "La cache TLB (Translation Look-aside Buffer)",
-    "answers": [
-      {
-        "text": "E' condivisa tra tutti i processi del sistema",
-        "image": ""
-      },
-      {
-        "text": "Consente una traduzione mediamente più rapida degli indirizzi logici",
-        "image": ""
-      },
-      {
-        "text": "Contiene un sottoinsieme delle entry della page table",
-        "image": ""
-      },
-      {
-        "text": "Tutte le risposte precedenti sono corrette",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "La dimensione (i.e., il numero di entry) della page table:",
-    "answers": [
-      {
-        "text": "È direttamente proporzionale alla dimensione (fissata) delle pagine",
-        "image": ""
-      },
-      {
-        "text": "Si adatta a seconda delle richieste di accesso alla memoria di ciascun processo",
-        "image": ""
-      },
-      {
-        "text": "Dipende dalla dimensione (fissata) delle pagine",
-        "image": ""
-      },
-      {
-        "text": "Varia dinamicamente a seconda del processo",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "La dimensione (i.e., il numero di entry) della page table:",
-    "answers": [
-      {
-        "text": "Varia dinamicamente a seconda del processo",
-        "image": ""
-      },
-      {
-        "text": "E' direttamente proporzionale alla dimensione (fissata)",
-        "image": ""
-      },
-      {
-        "text": "E' inversamente proporzionale alla dimensione (fissata) delle pagine",
-        "image": ""
-      },
-      {
-        "text": "Si adatta a seconda delle richieste di accesso alla memoria di ciascun processo",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Un compilatore genera l'indirizzo logico 576 per riferirsi ad una certa locazione di memoria fisica. Assumendo che la traduzione degli indirizzi avvenga tramite rilocazione statica con indirizzo fisico base = 24, quale sarà l'indirizzo fisico corrispondente?",
-    "answers": [
-      {
-        "text": "576",
-        "image": ""
-      },
-      {
-        "text": "552",
-        "image": ""
-      },
-      {
-        "text": "600",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficienti per rispondere al problema",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Si consideri un processo di dimensione pari a 2,488 bytes e un blocco di memoria libero di dimensione pari a 2,699 bytes. In questo caso, assumendo il vincolo di allocazione contigua della memoria, la scelta più conveniente è:",
-    "answers": [
-      {
-        "text": "Allocare l'intero blocco al processo, sprecando 211 bytes(frammentazione interna)",
-        "image": ""
-      },
-      {
-        "text": " Allocare la porzione del blocco necessaria al processo e aggiungere alla lista dei blocchi liberi i 211 bytes rimanente(frammentazione esterna)",
-        "image": ""
-      },
-      {
-        "text": "Attendere che vi sia un blocco di dimensione multipla rispetto a quella del processo",
-        "image": ""
-      },
-      {
-        "text": "Attendere che vi sia un blocco di dimensione inferiore adatto a contenere il processo",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "Si consideri un processo di dimensione pari a 4,996 e un blocco di memoria libero di dimensione pari a 5,016 bytes. In questo caso, assumendo il vincolo di allocazione contigua della memoria, la scelta più conveniente è:",
-    "answers": [
-      {
-        "text": "Attendere che vi sia un blocco di dimensione inferiore adatto a contenere il processo",
-        "image": ""
-      },
-      {
-        "text": "Allocare l'intero blocco al processo, sprecando 20 bytes(frammentazione interna)",
-        "image": ""
-      },
-      {
-        "text": "Attendere che vi sia un blocco di dimensione multipla rispetto a quella dei processi",
-        "image": ""
-      },
-      {
-        "text": "Allocare la porzione del blocco necessaria al processo e aggiungere alla lista dei blocchi liberi i 20 bytes rimanenti(frammentazione esterna)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Si supponga che un processo P necessiti di un'area di memoria libera pari a 99 KiB per essere allocato in modo contiguo in memoria principale. Se la lista dei blocchi di memoria libera contiene i seguenti elementi: A, B, C, D le cui dimensioni sono rispettivamente 102 KiB, 99 KiB, 256 KiB e 128 KiB, quale blocco verrà allocato per P assumendo una politica Worst-Fit?",
-    "answers": [
-      {
-        "text": "blocco A",
-        "image": ""
-      },
-      {
-        "text": "blocco C",
-        "image": ""
-      },
-      {
-        "text": "blocco B",
-        "image": ""
-      },
-      {
-        "text": "blocco D",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": " Si supponga che un processo P necessiti di un'area di memoria libera pari a 99 KiB per essere allocato in modo contiguo in memoria principale. Se la lista dei blocchi di memoria libera contiene i seguenti elementi: A, B, C, D, E, F le cui dimensioni sono rispettivamente 300 KiB, 600 KiB, 350 KiB, 200 KiB, 750 KiB e 125 KiB, quale blocco verrà allocato per P assumendo una politica Worst-Fit?",
-    "answers": [
-      {
-        "text": "blocco B",
-        "image": ""
-      },
-      {
-        "text": "Non è possibile soddisfare la richiesta, pertanto P dovrà attendere",
-        "image": ""
-      },
-      {
-        "text": "C e i restati 25 KiB vengono allocati su A",
-        "image": ""
-      },
-      {
-        "text": "blocco E",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Si supponga che un processo P necessiti di un'area di memoria libera pari a 128 KiB per essere allocato in modo contiguo in memoria principale. Se la lista dei blocchi di memoria libera contiene i seguenti elementi: A, B, C, D le cui dimensioni sono rispettivamente 105 KiB, 916 KiB, 129 KiB e 80 KiB, quale blocco verrà allocato per P assumendo una politica First-Fit?",
-    "answers": [
-      {
-        "text": "blocco A",
-        "image": ""
-      },
-      {
-        "text": "blocco D",
-        "image": ""
-      },
-      {
-        "text": "blocco B",
-        "image": ""
-      },
-      {
-        "text": "blocco C",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Si supponga che un processo P necessiti di un'area di memoria libera pari a 115 KiB per essere allocato in modo contiguo in memoria principale. Se la lista dei blocchi di memoria libera contiene i seguenti elementi: A, B, C, D,E,F le cui dimensioni sono rispettivamente 300 KiB, 600 KiB, 350 KiB, 200 KiB,750 KiB e 125 KiB quale blocco verrà allocato per P assumendo una politica First-Fit?",
-    "answers": [
-      {
-        "text": "blocco A",
-        "image": ""
-      },
-      {
-        "text": "blocco F",
-        "image": ""
-      },
-      {
-        "text": "blocco E",
-        "image": ""
-      },
-      {
-        "text": "blocco D",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "Si supponga che un processo P necessiti di un'area di memoria libera pari a 375 KiB per essere allocato in modo contiguo in memoria principale. Se la lista dei blocchi di memoria libera contiene i seguenti elementi: A, B, C, D,E,F le cui dimensioni sono rispettivamente 300 KiB, 600 KiB, 350 KiB, 200 KiB,750 KiB e 125 KiB quale blocco verrà allocato per P assumendo una politica Best-Fit?",
-    "answers": [
-      {
-        "text": "blocco B",
-        "image": ""
-      },
-      {
-        "text": "blocco C e i restanti 25 Kib vengono allocati su A",
-        "image": ""
-      },
-      {
-        "text": "blocco E",
-        "image": ""
-      },
-      {
-        "text": "Non è possibile soddisfare la richiesta, pertanto P dovrà attendere",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "Si supponga che un processo P necessiti di un'area di memoria libera pari a 34 KiB per essere allocato in modo contiguo in memoria principale. Se la lista dei blocchi di memoria libera contiene i seguenti elementi: A, B, C, D le cui dimensioni sono rispettivamente 36 KiB, 90 KiB, 42 KiB e 35 KiB, quale blocco verrà allocato per P assumendo una politica Best-Fit?",
-    "answers": [
-      {
-        "text": "blocco A",
-        "image": ""
-      },
-      {
-        "text": "blocco B",
-        "image": ""
-      },
-      {
-        "text": "blocco C",
-        "image": ""
-      },
-      {
-        "text": "blocco D",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Si supponga di avere una memoria M di capacità pari a 4 KiB, ossia 4,096 bytes. Assumendo che l'indirizzamento avvenga con lunghezza di parola (word size) pari 2 bytes e che M utilizzi una gestione paginata con blocchi di dimensione pari a S = 128 bytes, quanti bit sono necessari per identificare l'indice di pagina (p) e l'offset (interno alla pagina), rispettivamente?",
-    "answers": [
-      {
-        "text": "p=6; offset=5",
-        "image": ""
-      },
-      {
-        "text": "b.p=7; offset=5",
-        "image": ""
-      },
-      {
-        "text": "p=5; offset=7",
-        "image": ""
-      },
-      {
-        "text": "p=5; offset=6",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Si consideri una memoria M di capacità pari a 512 bytes con frame di dimensione pari a 16 bytes. Dato l'indirizzo del byte 197, quale sarà l'indirizzo di pagina (p) e l'offset (interno alla pagina):",
-    "answers": [
-      {
-        "text": "p=5; offset=12",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficienti per rispondere alla domanda",
-        "image": ""
-      },
-      {
-        "text": "p=13; offset=0",
-        "image": ""
-      },
-      {
-        "text": "p=12; offset=5",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Si consideri una memoria M di capacità pari a 100 bytes con frame di dimensione pari a 10 bytes. Dato l’indirizzo del byte 37, quale sarà l’indirizzo di pagina (p) e l’offset (interno alla pagina).",
-    "answers": [
-      {
-        "text": "p=3; offset=7",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficienti per rispondere alla domanda",
-        "image": ""
-      },
-      {
-        "text": "p=7; offset=3",
-        "image": ""
-      },
-      {
-        "text": "p=0; offset=37",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "Si consideri un processo di dimensione pari a 2,097 bytes e un blocco di memoria libero di dimensione pari a 2,104 bytes. In questo caso, assumendo il vincolo di allocazione contigua della memoria, la scelta più conveniente è:",
-    "answers": [
-      {
-        "text": "Attendere che vi sia un blocco di dimensione multipla rispetto a quella del processo",
-        "image": ""
-      },
-      {
-        "text": "Allocare l'intero blocco al processo, sprecando 7 bytes (frammentazione interna)",
-        "image": ""
-      },
-      {
-        "text": "Attendere che vi sia un blocco di dimensione inferiore adatto a contenere il processo",
-        "image": ""
-      },
-      {
-        "text": "Allocare la porzione del blocco necessaria al processo e aggiungere alla lista dei blocchi liberi i 7 bytes rimanenti (frammentazione esterna)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Si supponga di avere una memoria M di capacità pari a 2 KiB, ossia 2,048 bytes. Assumendo che l’indirizzamento avvenga con lunghezza di parola (word size) pari a 4 bytes, quanti bit sono necessari ad indirizzare le parole contenute in M?",
-    "answers": [
-      {
-        "text": "2",
-        "image": ""
-      },
-      {
-        "text": "9",
-        "image": ""
-      },
-      {
-        "text": "11",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficienti per rispondere al problema",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Si supponga di avere una memoria M di capacità pari a 4 KiB ossia 4,096 bytes. Assumendo che l’indirizzamento avvenga con lunghezza di parola (word size) pari a 2 bytes, quanti bit sono necessari ad indirizzare le parole contenute in M?",
-    "answers": [
-      {
-        "text": "10",
-        "image": ""
-      },
-      {
-        "text": "11",
-        "image": ""
-      },
-      {
-        "text": "12",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficienti per rispondere alla domanda",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Si supponga di avere una memoria M di capacità pari a 8 KiB, ossia 8,192 bytes. Assumendo che l'indirizzamento avvenga con lunghezza di parola (word size) pari al singolo byte e che M utilizzi una gestione paginata con blocchi di dimensione pari a S = 128 bytes, quale dimensione (intesa come numero di entry) ha la corrispondente page table T?",
-    "answers": [
-      {
-        "text": "I dati sono insufficienti per rispondere al problema",
-        "image": ""
-      },
-      {
-        "text": "13",
-        "image": ""
-      },
-      {
-        "text": "64",
-        "image": ""
-      },
-      {
-        "text": "7",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Si supponga di avere una memoria M di capacità pari a 8 KiB, ossia 8,192 bytes. Assumendo che l’indirizzamento avvenga con lunghezza di parola (word size) pari a 4 bytes e che M utilizzi una gestione paginata con blocchi di dimensione pari a S = 256 bytes, quale sarà il numero di entry della corrispondente page table T?",
-    "answers": [
-      {
-        "text": "32",
-        "image": ""
-      },
-      {
-        "text": "2048",
-        "image": ""
-      },
-      {
-        "text": "8",
-        "image": ""
-      },
-      {
-        "text": "5",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "Si supponga di avere una memoria M di capacità pari a 16 KiB, ossia 16,384 bytes. Assumendo che l’indirizzamento avvenga con lunghezza di parola (word size) pari a 4 bytes e che M utilizzi una gestione paginata con blocchi di dimensione pari a S = 64 bytes, quale sarà il numero di entry della corrispondente page table T?",
-    "answers": [
-      {
-        "text": "a",
-        "image": ""
-      },
-      {
-        "text": "b",
-        "image": ""
-      },
-      {
-        "text": "c",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficienti per rispondere al problema",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Si consideri un sistema operativo che utilizza indirizzi logici da 21 bit, indirizzo fisico da 16 bit e memoria paginata in cui ciascuna pagina ha dimensione 2 KiB(2048 bytes). Qual è la dimensione massima di memoria fisica supportata dal sistema?",
-    "answers": [
-      {
-        "text": "32 KiB",
-        "image": ""
-      },
-      {
-        "text": "64 KiB",
-        "image": ""
-      },
-      {
-        "text": "2 MiB",
-        "image": ""
-      },
-      {
-        "text": "Non esiste un limite fisico alla memoria supportata dal sistema",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "La memoria virtuale consente di:",
-    "answers": [
-      {
-        "text": "Aumentare l'efficienza delle operazioni di I/O",
-        "image": ""
-      },
-      {
-        "text": "Mantenere allocate in memoria fisica solo alcune pagine dello spazio di indirizzamento logico di un processo",
-        "image": ""
-      },
-      {
-        "text": "Diminuire il grado di multiprogrammazione del sistema",
-        "image": ""
-      },
-      {
-        "text": "Eseguire un processo direttamente dai dispositivi di memoria secondaria (e.g., disco)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Se un'istruzione idempotente genera un page fault:",
-    "answers": [
-      {
-        "text": "Il processo di cui fa parte l'istruzione termina",
-        "image": ""
-      },
-      {
-        "text": "Le istruzioni idempotenti non possono generare page fault",
-        "image": ""
-      },
-      {
-        "text": "L'istruzione non verrà più eseguita una volta effettuato il ritorno dalla gestione del page fault",
-        "image": ""
-      },
-      {
-        "text": "L'istruzione verrà nuovamente eseguita al ritorno dalla gestione del page fault",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": ".Il problema della frammentazione esterna:",
-    "answers": [
-      {
-        "text": "Necessita di un supporto hardware per essere risolto",
-        "image": ""
-      },
-      {
-        "text": "Non è risolvibile a meno di un riavvio del sistema",
-        "image": ""
-      },
-      {
-        "text": "E’ una conseguenza del vincolo di allocazione contigua della memoria",
-        "image": ""
-      },
-      {
-        "text": "Causa un’interruzione hardware",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Il problema della frammentazione esterna",
-    "answers": [
-      {
-        "text": "Non è risolvibile a meno di un riavvio del sistema",
-        "image": ""
-      },
-      {
-        "text": "Causa un’interruzione hardware",
-        "image": ""
-      },
-      {
-        "text": "Necessita di un supporto hardware per essere risolto",
-        "image": ""
-      },
-      {
-        "text": "E’ dovuto all’ allocazione/deallocazione di blocchi contigui di memoria",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Il working set è:",
-    "answers": [
-      {
-        "text": "Fissato per ogni quanto di tempo",
-        "image": ""
-      },
-      {
-        "text": "Relativamente grande rispetto all’intero spazio di indirizzamento di un processo",
-        "image": ""
-      },
-      {
-        "text": "Relativamente piccolo rispetto all’intero spazio di indirizzamento di un processo",
-        "image": ""
-      },
-      {
-        "text": "Fissato per l’intera esecuzione di un processo",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Si consideri un sistema che implementa la politica LRU per la sostituzione dei frame mediante l’uso di un timestamp. Ad ogni richiesta di accesso ad un determinato frame occorre:",
-    "answers": [
-      {
-        "text": "Incrementare una variabile di tipo contatore",
-        "image": ""
-      },
-      {
-        "text": "Aggiornare il valore del timestamp con quello corrente",
-        "image": ""
-      },
-      {
-        "text": "Impostare un bit di validità",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle precedenti risposte è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Data una memoria composta da 3 frame fisici e un processo composto da 5 pagine virtuali: A, B, C, D, E, si calcoli il numero di page fault che si verificano a fronte delle seguenti richieste da parte del processo: B, C, C, B, A, E, B, A, E, D, B. Si assuma che nessuna pagina del processo sia inizialmente caricata in memoria e che si utilizzi un algoritmo LRU di sostituzione delle pagine.",
-    "answers": [
-      {
-        "text": "4",
-        "image": ""
-      },
-      {
-        "text": "5",
-        "image": ""
-      },
-      {
-        "text": "6",
-        "image": ""
-      },
-      {
-        "text": "7",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Data una memoria composta da 3 frame fisici e un processo composto da 5 pagine virtuali: A, B, C, D, E, si calcoli il numero di page fault che si verificano a fronte delle seguenti richieste da parte del processo: D, B, A, C, C, E, A, D, B, E, D, A. Si assuma che nessuna pagina del processo sia inizialmente caricata in memoria e che si utilizzi un algoritmo LRU di sostituzione delle pagine.",
-    "answers": [
-      {
-        "text": "10",
-        "image": ""
-      },
-      {
-        "text": "7",
-        "image": ""
-      },
-      {
-        "text": "9",
-        "image": ""
-      },
-      {
-        "text": "6",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Data una memoria composta da 3 frame fisici e un processo composto da 5 pagine virtuali: A, B, C, D, E, si calcoli il numero di page fault che si verificano a fronte delle seguenti richieste da parte del processo: C,B,C,B,A,E,B,A. Si assuma che nessuna pagina del processo sia inizialmente caricata in memoria e che si utilizzi un algoritmo LRU di sostituzione delle pagine.",
-    "answers": [
-      {
-        "text": "2",
-        "image": ""
-      },
-      {
-        "text": "4",
-        "image": ""
-      },
-      {
-        "text": "5",
-        "image": ""
-      },
-      {
-        "text": "1",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Data una memoria fisica composta da 3 frame fisici e un processo composto da 5 pagine virtuali: A, B, C, D, E, si calcoli il numero di page fault che si verificano a fronte delle seguenti richieste da parte del processo: A, B, E, C, E, D, D, A, B. Si assuma che nessuna pagina del processo sia inizialmente caricata in memoria e che si utilizzi un algoritmo FIFO di sostituzione delle",
-    "answers": [
-      {
-        "text": "6",
-        "image": ""
-      },
-      {
-        "text": "7",
-        "image": ""
-      },
-      {
-        "text": "4",
-        "image": ""
-      },
-      {
-        "text": "8",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Data una memoria composta da 3 frame fisici e un processo composto da 5 pagine virtuali: A, B, C, D, E, si calcoli il numero di page fault che si verificano a fronte delle seguenti richieste da parte del processo: D, A, C, B, B, A, C, B, D, E, A. Si assuma che nessuna pagina del processo sia inizialmente caricata in memoria e che si utilizzi un algoritmo FIFO di sostituzione delle pagine.",
-    "answers": [
-      {
-        "text": "6",
-        "image": ""
-      },
-      {
-        "text": "7",
-        "image": ""
-      },
-      {
-        "text": "5",
-        "image": ""
-      },
-      {
-        "text": "4",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Data una memoria composta da 3 frame fisici e un processo composto da 5 pagine virtuali: A, B, C, D, E si calcoli il numero di page fault che si verificano a fronte delle seguenti richieste da parte del processo: E, B, E, C, D, E, A, B, E. Si assuma che nessuna pagina del processo sia inizialmente caricata in memoria e che si utilizzi un algoritmo FIFO di sostituzione delle pagine.",
-    "answers": [
-      {
-        "text": "7",
-        "image": ""
-      },
-      {
-        "text": "8",
-        "image": ""
-      },
-      {
-        "text": "6",
-        "image": ""
-      },
-      {
-        "text": "5",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "L'allocazione contigua di un file su disco:",
-    "answers": [
-      {
-        "text": "È ottima sia per l'accesso diretto (random) che per quello sequenziale",
-        "image": ""
-      },
-      {
-        "text": "Presenta il problema della frammentazione",
-        "image": ""
-      },
-      {
-        "text": "Necessita il mantenimento dei blocchi liberi all'interno di una opportuna struttura dati",
-        "image": ""
-      },
-      {
-        "text": "Tutte le risposte precedenti sono corrette",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "L’allocazione contigua di un file su un disco è la scelta preferibile quando il disco è:",
-    "answers": [
-      {
-        "text": "Un CD/DVD-ROM in sola lettura",
-        "image": ""
-      },
-      {
-        "text": "Un disco magnetico",
-        "image": ""
-      },
-      {
-        "text": "Un disco a stato solido",
-        "image": ""
-      },
-      {
-        "text": "In nessuno dei casi precedenti",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "In un disco magnetico, il seek time:",
-    "answers": [
-      {
-        "text": "È il tempo necessario al disco per posizionare le proprie testine su uno specifico settore",
-        "image": ""
-      },
-      {
-        "text": "Include il tempo di trasferimento alla memoria principale",
-        "image": ""
-      },
-      {
-        "text": "È il tempo necessario al disco per posizionare le proprie testine su uno specifico cilindro",
-        "image": ""
-      },
-      {
-        "text": "È trascurabile rispetto all'intero tempo necessario al trasferimento dei dati",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Un disco è composto da 15 cilindri, ciascuno di capacità pari a 500 MB. Qual è la capacità totale del disco?",
-    "answers": [
-      {
-        "text": "7.5 GB",
-        "image": ""
-      },
-      {
-        "text": "75 GB",
-        "image": ""
-      },
-      {
-        "text": "750 MB",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficienti per rispondere al problema4",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "Si supponga che il tempo di accesso alla memoria fisica sia tMA = 50 nsec. e che il tempo per la gestione di un page fault tFAULT  sia pari a 15 msec. Assumendo che la probabilità che si verifichi un page fault sia p = 0.0002, qual è il tempo complessivo atteso di accesso alla memoria?",
-    "answers": [
-      {
-        "text": "~30.5 nsec",
-        "image": ""
-      },
-      {
-        "text": "~30.5 microsec",
-        "image": ""
-      },
-      {
-        "text": "~3.05 microsec",
-        "image": ""
-      },
-      {
-        "text": "~305 nsec",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Si supponga che il tempo di accesso alla memoria fisica sia tMA = 25 nsec. e che il tempo per la gestione di un page fault  tFAULT sia pari a 30 msec. Assumendo che la probabilità che si verifichi un page fault sia p = 0.005, qual è il tempo complessivo atteso di accesso alla memoria?",
-    "answers": [
-      {
-        "text": "~150.025 microsec",
-        "image": ""
-      },
-      {
-        "text": "~15.025 nsec",
-        "image": ""
-      },
-      {
-        "text": "~150.025 nsec",
-        "image": ""
-      },
-      {
-        "text": "~15.025 microsec",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "Si supponga che il tempo di accesso alla memoria fisica sia tMA = 50 nsec. e che il tempo per la gestione di un page fault tFAULT sia pari a 25 msec. Assumendo che il tempo medio di accesso alla memoria sia pari a 0.5 microsec, qual è la probabilità p che si verifichi un page fault?",
-    "answers": [
-      {
-        "text": "~0.02%",
-        "image": ""
-      },
-      {
-        "text": "~0.2%",
-        "image": ""
-      },
-      {
-        "text": "~0.002%",
-        "image": ""
-      },
-      {
-        "text": "~0.0002%",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Si supponga che il tempo di accesso alla memoria fisica sia tMA  = 60 nsec. e che il tempo per la gestione di un page fault tFAULT  sia pari a 5 msec. Quale dovrà essere il valore della probabilità che si verifichi un fault () se si vuole garantire che il tempo atteso di accesso alla memoria sia al più il 20% più lento di tMA ? (Si ricordi che 1 msec = 10^3 microsec = 10^6 nsec)",
-    "answers": [
-      {
-        "text": "I dati sono insufficienti per rispondere alla domanda",
-        "image": ""
-      },
-      {
-        "text": "~0,00024%",
-        "image": ""
-      },
-      {
-        "text": " ~0,000024%",
-        "image": ""
-      },
-      {
-        "text": " ~0,0000024%",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Si consideri un disco magnetico composto da 128 cilindri/tracce, numerati da 0 a 127 (0 indice del cilindro/traccia più esterno/a rispetto al centro del disco), la cui testina si trova inizialmente sul cilindro 42. Si calcoli il numero di cilindri/tracce attraversate dalla testina del disco, assumendo che la sequenza di richieste: 74, 50, 32, 55, 81 venga gestita da un algoritmo di scheduling SSTF (Shortest Seek Time First) e trascurando il tempo di rotazione.",
-    "answers": [
-      {
-        "text": "86",
-        "image": ""
-      },
-      {
-        "text": "49",
-        "image": ""
-      },
-      {
-        "text": "123",
-        "image": ""
-      },
-      {
-        "text": "88",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Si consideri un disco magnetico composto da 128 cilindri/tracce, numerati da 0 a 127 (0 indice del cilindro/traccia più esterno/a rispetto al centro del disco), la cui testina si trova inizialmente sul cilindro 87. Si calcoli il numero di cilindri/tracce attraversate dalla testina del disco, assumendo che la sequenza di richieste: 43, 81, 36, 25, 127 venga gestita da un algoritmo di scheduling FCFS (First Come First Served) e trascurando il tempo di rotazione.",
-    "answers": [
-      {
-        "text": "290",
-        "image": ""
-      },
-      {
-        "text": "240",
-        "image": ""
-      },
-      {
-        "text": "238",
-        "image": ""
-      },
-      {
-        "text": "265",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Il tempo di trasferimento totale per un'operazione di I/O da disco magnetico è pari a 30 msec. Sapendo che: il seek time complessivo è pari a 18 msec, il rotational delay complessivo è pari a 7 msec e che il transfer rate è pari a 1.5 Gbit/sec, qual è la quantità totale di dati trasferita? (Si ricordi che 1 B = 1 byte = 8 bit e 1 MB = 10^3 KB = 10^6 B)",
-    "answers": [
-      {
-        "text": "9.375 MB",
-        "image": ""
-      },
-      {
-        "text": "7.5 MB",
-        "image": ""
-      },
-      {
-        "text": "937.5 KB",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficienti per rispondere alla domanda",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Il tempo di trasferimento totale per un'operazione di I/O da disco magnetico è pari a 40 msec. Sapendo che: il seek time complessivo è pari a 18 msec, il rotational delay complessivo è pari a 7 msec e che il transfer rate è pari a 5 Gbit/sec, qual è la quantità totale di dati trasferita? (Si ricordi che 1 B = 1 byte = 8 bit e 1 MB = 10^3 KB = 10^6 B)",
-    "answers": [
-      {
-        "text": "9375 MB",
-        "image": ""
-      },
-      {
-        "text": "70 MB",
-        "image": ""
-      },
-      {
-        "text": "70 KB",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficienti per rispondere alla domanda",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "Il tempo di trasferimento totale per un'operazione di I/O da disco magnetico è pari a 36 msec. Sapendo che il seek time complessivo è pari a 13 msec e che sono stati trasferiti 2MB ad una velocità pari a 1 Gbit/sec qual è il rotational delay del disco?(Si ricordi che 1 B = 1 byte = 8 bit)",
-    "answers": [
-      {
-        "text": "7 msec",
-        "image": ""
-      },
-      {
-        "text": "2 msec",
-        "image": ""
-      },
-      {
-        "text": "16 msec",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficiente per rispondere alla domanda",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "La tabella globale dei file aperti (global open file table):",
-    "answers": [
-      {
-        "text": "È condivisa tra tutti i processi",
-        "image": ""
-      },
-      {
-        "text": "Contiene una entry per ciascun file in uso",
-        "image": ""
-      },
-      {
-        "text": "Mantiene un contatore per ciascun file in uso",
-        "image": ""
-      },
-      {
-        "text": "Tutte le risposte precedenti sono corrette",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "La tabella locale dei file aperti (local open file table):",
-    "answers": [
-      {
-        "text": "Contiene informazioni di protezione di ciascun file riferita da un processo",
-        "image": ""
-      },
-      {
-        "text": "Contiene un puntatore alla locazione sul disco di ciascun file riferito da un processo",
-        "image": ""
-      },
-      {
-        "text": "Contiene un puntatore alla tabella globale dei file aperti per ciascun file riferito da un processo",
-        "image": ""
-      },
-      {
-        "text": "E’ condivisa tra più processi",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Un possibile esempio di applicazione che necessita accesso sequenziale ad un file è:",
-    "answers": [
-      {
-        "text": "Un compilatore",
-        "image": ""
-      },
-      {
-        "text": "Un sistema di ricerca all'interno di una base di dati",
-        "image": ""
-      },
-      {
-        "text": "Un sistema di ricerca di contatti telefonici",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle risposte precedenti è corretta",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "L’allocazione di un file indicizzata è preferibile quando il file in questione:",
-    "answers": [
-      {
-        "text": "E’ di piccole dimensioni,indipendentemente dal modo in cui viene acceduto",
-        "image": ""
-      },
-      {
-        "text": "E’ di grandi dimensioni, indipendentemente dal modo in cui viene acceduto",
-        "image": ""
-      },
-      {
-        "text": "E’ di grandi dimensioni ed è tipicamente acceduto in modo sequenziale",
-        "image": ""
-      },
-      {
-        "text": "E’ di grandi dimensioni ed è tipicamente acceduto in modo casuale(diretto)",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "L’allocazione di un file basata su linked list(liste puntate) è preferibile quando il file in questione",
-    "answers": [
-      {
-        "text": "E’ di piccolo dimensioni, indipendentemente dal modo in cui viene acceduto",
-        "image": ""
-      },
-      {
-        "text": "E’ di grandi dimensioni ed è tipicamente acceduto in modo casuale(diretto)",
-        "image": ""
-      },
-      {
-        "text": "E’ di grandi dimensioni, indipendentemente dal modo in cui viene acceduto",
-        "image": ""
-      },
-      {
-        "text": "E’ di grandi dimensioni ed è tipicamente acceduto in modo sequenziale",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "In un sistema UNIX-like, un file che ha i seguenti privilegi: 101000000 ",
-    "answers": [
-      {
-        "text": "Consente al solo proprietario del file di esercitare diritti di lettura e un sistema UNIX-like, un file che ha i seguenti privilegi: 101000000:",
-        "image": ""
-      },
-      {
-        "text": "Consente al solo proprietario del file di esercitare diritti di lettura ed esecuzione (sul file)",
-        "image": ""
-      },
-      {
-        "text": "Consente al solo proprietario del file di esercitare diritti di scrittura ed esecuzione (sul file)",
-        "image": ""
-      },
-      {
-        "text": "Non dà alcun diritto al proprietario del file",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "In un sistema UNIX-like, un file che ha i seguenti privilegi: 011000000 ",
-    "answers": [
-      {
-        "text": "Consente al solo proprietario del file di esercitare diritti di lettura e scrittura (sul file)",
-        "image": ""
-      },
-      {
-        "text": "Consente al solo proprietario del file di esercitare diritti di lettura ed esecuzione (sul file)",
-        "image": ""
-      },
-      {
-        "text": "Non dà alcun diritto al proprietario del file",
-        "image": ""
-      },
-      {
-        "text": "Consente al solo proprietario del file di esercitare diritti di scrittura ed esecuzione (sul file)",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "In un sistema UNIX-like, un file che ha i seguenti privilegi: 111101101",
-    "answers": [
-      {
-        "text": "Consente al proprietario del file di esercitare tutti i diritti(sul file) e fornisce agli altri utenti solo diritti di scrittura ed esecuzione",
-        "image": ""
-      },
-      {
-        "text": "Consente al proprietario del file di esercitare tutti i diritti(sul file) e fornisce agli altri utenti solo diritti di lettura ed scrittura",
-        "image": ""
-      },
-      {
-        "text": "Consente al proprietario del file di esercitare tutti i diritti(sul file) e fornisce agli altri utenti solo diritti di lettura ed esecuzione",
-        "image": ""
-      },
-      {
-        "text": "Consente a chiunque di esercitare tutti i diritti (sul file)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Il comando UNIX ln file_1 file_2",
-    "answers": [
-      {
-        "text": "Crea un hard link con il file file_2(sorgente) in cui nome è file_1(destinazione)",
-        "image": ""
-      },
-      {
-        "text": "Crea un hard link con il file file_1(sorgente) il cui nome è file_2(destinazione)",
-        "image": ""
-      },
-      {
-        "text": "Crea un soft link con il file file_1(sorgente) il cui nome è file_2(destinazione)",
-        "image": ""
-      },
-      {
-        "text": "Crea un soft link con il file file_2(sorgente) il cui nome è file_1(destinazione)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Il comando UNIX ln -s file_1 file2:",
-    "answers": [
-      {
-        "text": "Crea un hard link con il file file_2(sorgente) in cui nome è file_1(destinazione)",
-        "image": ""
-      },
-      {
-        "text": "Crea un hard link con il file file_1(sorgente) il cui nome è file_2(destinazione)",
-        "image": ""
-      },
-      {
-        "text": "Crea un soft link con il file file_1(sorgente) il cui nome è file_2(destinazione)",
-        "image": ""
-      },
-      {
-        "text": "Crea un soft link con il file file_2(sorgente) il cui nome è file_1(destinazione)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "Si consideri un file system organizzato con file descriptor indicizzati multi-livello (multi-level indexed files), contenente i riferimenti diretti a 10 blocchi a cui si aggiunge un livello di riferimento indiretto a 100 blocchi e un ulteriore doppio livello di riferimento indiretto, sempre da 100 blocchi ciascuno. Assumendo che ciascun blocco abbia dimensione pari a 2 KiB, qual è la dimensione massima del file supportata?",
-    "answers": [
-      {
-        "text": "~20.2 KB",
-        "image": ""
-      },
-      {
-        "text": "~20.2 MB",
-        "image": ""
-      },
-      {
-        "text": "~20.7 KB",
-        "image": ""
-      },
-      {
-        "text": "~20.7 KB",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Si consideri un disco magnetico composto da 200 cilindri/tracce, numerati da 0 a 199(0 indice del cilindro/traccia più esterno/a rispetto al centro del disco), la cui testina si trova inizialmente sul cilindro 53. Si calcoli il numero di cilindri/tracce attraversate dalla testina del disco, assumendo che la sequenza di richieste: 98,183,37,122,14,85,67 venga gestita da un algoritmo di scheduling FCFS (First Come First Served) e trascurando il tempo di rotazione.",
-    "answers": [
-      {
-        "text": "595",
-        "image": ""
-      },
-      {
-        "text": "558",
-        "image": ""
-      },
-      {
-        "text": "650",
-        "image": ""
-      },
-      {
-        "text": "638",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Si consideri un disco magnetico composto da 200 cilindri/tracce, numerati da 0 a 199(0 indice del cilindro/traccia più esterno/a rispetto al centro del disco), la cui testina si trova inizialmente sul cilindro 53. Si calcoli il numero di cilindri/tracce attraversate dalla testina del disco, assumendo che la sequenza di richieste: 98,183,37,122,14,65,67 venga gestita da un algoritmo di scheduling FCFS (First Come First Served) e trascurando il tempo di rotazione.",
-    "answers": [
-      {
-        "text": "650",
-        "image": ""
-      },
-      {
-        "text": "522",
-        "image": ""
-      },
-      {
-        "text": "638",
-        "image": ""
-      },
-      {
-        "text": "595",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "Si consideri un disco magnetico composto da 100 cilindri/tracce, numerati da 0 a 99 (0 indice del cilindro/traccia più esterno/a rispetto al centro del disco), la cui testina si trova inizialmente sul cilindro 11. Si calcoli il numero di cilindri/tracce attraversate dalla testina del disco, assumendo che la sequenza di richieste: 24, 16, 77, 49, 82 venga gestita da un algoritmo di scheduling SCAN (non-ottimizzato), che la testina si stia muovendo verso l'esterno (i.e., verso i cilindri con numeri più bassi) e trascurando il tempo di rotazione.",
-    "answers": [
-      {
-        "text": "76",
-        "image": ""
-      },
-      {
-        "text": "87",
-        "image": ""
-      },
-      {
-        "text": "46",
-        "image": ""
-      },
-      {
-        "text": "93",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "Data la porzione di codice in figura, indicare quale sarà il valore della variabile value che verrà stampato alla line 18:",
-    "answers": [
-      {
-        "text": "5",
-        "image": ""
-      },
-      {
-        "text": "20",
-        "image": ""
-      },
-      {
-        "text": "15",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficiente per rispondere alla domanda",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": 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yZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE+Cs5g7goAAAAcaURPVAAAAAIAAAAAAAABWQAAACgAAAFZAAABWQAApZ+DUl3VAABAAElEQVR4AeydB1wURxvGH0ERBQRBEcGOvfeuqNh777FEjSaaYhJrrIlRY0z/kpho7L3G3o0Ne+8dLKgoFlQQQfSbd8gcu8cd3CkQyjv5fXe7s7Ozs/89/ObZt0y6osUrvQYXJsAEmAATYAJMgAkwASbABJhAIhBIx4IjEahyl0yACTABJsAEmAATYAJMgAlIAiw40vAPwSWjI3xylcKj8GfYHXj6PyFRIUchVHQvpLv2jpsncflxoK4uoXeKu+ZBWXdvnA0OwMlg/4TunvtLowRq+eRBvfr5cfTQHaxbeymNUuDbZgJMgAkwASagJ5BmBEf6DPbInb8cXN3zSQKXTu/A05AgPY0UvGdva4cPyjTHzaf3sezyHjiI++1fqin8Q+5i1dV9Ju+sTPYC+L3eINwOfYj26yaYbJPYlR+Xa41OhWvrLjP16AqsvOKnq0voHWLTs3h9rLi8F98dW5nQ3ae5/rwLZsWsea3x+tVrtGy6GCEh4ToGdLxSZS9cvfIQhw/d1h1LTTuTv60P3wb5ceb0PfR+Z3VqujW+FybABJgAE2ACb0wg1QsOu4yZkdu7InLmLoF06WwMoM4cXYdH968b9lP6RmWPIvjRpz82Xz+K8QcWoE6u0phYoxf+vrofU44sM3l7yUFwONs5wMXeQY5veMWOoDGx4DD5uJJ15fRZLVC2nAduXA9Bu1ZLY4119LjaaNm6CK5cfoguHVbEOp5aKlhwpJYnyffBBJgAE2ACCUkg1QuOIqXqw92riGT2NOQenJzd5XZqExwflW2FzkV8pNgg0TGsYge08q6GYXtnYk/gGZO/mfQ2tsiWKQsiol7iYfhTk22SsvLbWn1Rw7M4C46khJ4A13LP4YB1m7oKQQ98MmgT/PbejNUrC45YSLiCCTABJsAEmECaIZDqBUfhUr6wF5PqgEv78eTxXdRoOAA2YqKdGgSHY4ZMINFAZZrvh8jjlB3dN30rxcPshp/CPbMLOqz/GqGRL/AkIgyvXr+SbT0dXMXkUMwO/y0voiIR/PyJ2jV8Z7BJL/sMj4pA4LMHyJ7JGZVyFJbHj9+/ijvCFctcSYd0yJMlOwo6e8JN8Ke2R4MuI+zlC3OnwBLBkcsxG2xtbHDjyX28Fv+pQi5kJJ6eRjyPJZ5shGWrcFYvlHDLK+4jGCfv+6NHMd84Xaqc7DKJc3KhQBYPOfazD6/LWBd1vcT6dnDMgNoiDmDj+qu6S3h6OcFXxAbkyeuMsLBI+F97jE0briA8/KWhXe7cWeCVKwvu3n2GAP/Hhnq14eJij6LFsuHZswjp8qPq6duS/rXttdvf/dgQtevkRXBwGJrUX2A4pK5HFT16lUalKl5ybF+P32No8+BBGC5fiv4dqfahoZE4fSq2u6ONTTpUFn1QOXQwEK+E+xaNO08eZ9y8GYKQxy/QoFEBFCmaTR7fu/sGIiKiDNfSblBf5SvkFGPyRObMGXBEuHmRUHr5MvpvRNvWmm1jCweNt65vPty98wwb1l/B/Xuhhu4qV/XEvaBQ8axCDHW8wQSYABNgAkwgNRJI9YKD3Khe/zvRpgeYmgTH2pbj5GTekh/mxzun4XDQJZCI2NVhiu4UczEcJd3y4c/6H+Fe2GPsunUaHQrX0p037sB8bLl+TFdHOyQKJtXsDW/nnLpjUeI5fHlgIbbeiH0ONbREcOzr9L3ss/GqUVJEqQt0FHEgn4h4EBrnCL9ZqhouGR3wc533UdDF01BHFp39d87LgHlTMRwUU0KxJcblh2OrZHyMcX1C7Fer7oXefUtLQUAiomHdRYZuh42sgfYdixv21cZrobfatVwiJtvRYnHsVz5o3qIwwsSE3afGbNXM8P3L701QtVou3L8fhqYNYoSBpf0bOtJsODraYfvuHkLEp8OE8buxetVFw9EBAyuiT79yhn1TG0F3Q9G88UJ5qFqN3Pj518Zyu1mjhXIyrj2nXYdiGP5FTfH3DNSuNkuKLXVPN2+EIKenE9Knj3GbfPo0Au90WYnAW3rrXZGibvjtj2bI4pxR2z2ehLzAgH7rDAJId9DCHSU4Ll54gBwewl1QiDxVoqJe4cP3NxpiWL770RdKdCxfdhHLFp9/a8GjrsXfTIAJMAEmwASSE4FULziMYacmwTG4fBt4OrghbxZ3OcknYXD58W3kd/YQ9a4ICnuEK4/vSAS/nVwL/ydBoLf9A0TAtDBAwEucWzd3GbNB40pwKIYbAg7jyYsw4apVFZnSZ5SuWA1WjkDkq5i3yK72TljdcixsxXVCIkKx+uoBYT0JkRP+ZvkrY9757fjz9EbVpe47MQTH9PofS8vGc2FZWXDhHzmubkXrwc42vby2seDoU6IR+pRsJI9tDDiCsw+uS+tIywJVZd2Y/fOw7cZx3bjfdMfZJSP6vlcWDRvnl2/ZVT/Xrj5Gz25r5S5ZLVas6STdlSg+4vixu2LC/RolS7mjYCFXdO+8EjS5pUKuTes3d5XbNLE9sP+W3KYPEgR+h96VE/Jpvx7BX9Oj78Ga/g2daTbGjPdBi1aFERoagTo15miOQMZ0dOpaQtaVKpVDTsDJOrPPL8bl6tLFB5g144ThvF1+vZDZIQPWrr6EL8fuMtTTxrJVHZAvv4suIFsJDjpOQmTPruvSqlGnXj55r48fh6NBnXl0WJZs2TJj/ZaukgcJu507AvBSCIF6vtHP4Pnzl6hXa84bT/yV4KCLkQXmoHgGbuKahYu4yevfEZaOlk2ixeQHH1ZA567F5bOlg1FRr3H08B1M++34W4keeSH+YAJMgAkwASaQjAiw4EhGD+NNh/Jh2ZboUqSOIX5jqIjfaC3iN4bvnRVnutv4gsa1gmPGmU2YeXaLHCKl093Q+ku5PWD7LzilSSv7VfUe8M1dVoqYHsK9S+tCRS5ZHg5ZcVqkojVVElpwFHXNjZkNBstLdd4wGTee3pPbhVy8MKfRZ3JbKzjIJWtVizFSlEw+vBRrrh0wDFNZPZ5FhqPhypGG+jfZ8KmbBz17l5KCQXm2RUa+EpPTQMz486TI5PTI0O2kKb6o37CAyQk9CYyHD57rJscrVneUblcnjt9Fv97RooU6a9OuKEaOriUnwbWEdSDiRbRItLZ/w8DEhp2dLXbt6yUn9r//7whmzjAvxCyN4VDWFhImPtVnGy6XNas9tvzzjtz/aOAm7P9XtGgFx8hh27F18zXZpljxbJi7sI3cHvb5NuzY5i+3VXA7uVr51p5rcEmzt0+PnX49YWtrg8ULz+C7Kftle2s/tIJDO56+/cuj//sVZHeVyk43dOssrCw9epVCo6YFQNuq0HNdufwiFs4/h8jIGEGvjvM3E2ACTIAJMIGURIAFR0p6WmbGOq/xEOm+1GLNODwQsRjLm4+SFo6GK7/As8jnZs6CzAgVV1pcreCg2JBrIXcMfSl3LuM3/ns6TpUT9s/3zMC+2+cM7S3ZSGjB0UukvX1PWHPIStFv20+6Icxt9Lm0umgFR/tCNfFp+bYg16/OGybJb4pFoZJZWHSIMxVKIUxuaNaW/h+UQ/sORUVMUbR1hd7IUyzGwvlnsXlj9ETZuM/3B1XEu33LSVHRovEiGSdh3Ea736pNEYwaWztaWFQVwuLfGIalwjqQX1gHjh29g/591hlOsbZ/w4liY9DHlYVwKiPFC4kYeqNvrlgqOMgFiYQFCTGtsPh8WHV06lIilruYEhym3MjWbuoCDw9HacUY8ulWObQDR/tIUbFPxGv89r/Dso6sP1SGjayJEiWzw1/Ev3Rss0zWWfuhBAexqFbxLwMTr1xO+HtdZ9kdpQ2+c/tprK5JJPV5rwwqVPQwuIZRPyeOB+HzT3aw8IhFjCuYABNgAkwgpRBgwZFSnpSZcdL6GzvaT5YBzc1Wj5Hrb2xtO9Gsm5S2G0stHBTzUGf5UO2pMgUvpeLVWgLchDvV2lbjZTvfFSNAbkzWlIQWHCMrd0Lz/FWEW9d+fGOUGnh0la5okq+ibh2Ozyu0Q9uCNeId8hAhpvysFFPU6fotHZElS/RbbL+9t/D1eD9QnEFchWIkduzpKSfgJFAoyPrcmfuYPfOk2cDqvQffRYYMNvj158Oi3QkZR7B1Z7R1gMQGiQ5VrO1fnUffew70BlkGliw6i6nf7NMeirVtqeCgExcubYdChV1x6mQQ+vRcI/vatusdYQGwx6oVFzDxq5igcyU4/K89Qse2y3XX/e3PZmLtD09cOB8sYjlW6TjoGhrtmHIPM2pidlcJDoqradtiia7dwWN9pStXj66rcP5csO6YdocEUNfuJfDe++UM7lbNGy+VQfHadrzNBJgAE2ACTCClEGDBkVKelNE46Q09Ffv0djJ+g97K0yJ/mUW2JorfUPuvxCy115bvjM6O3rVUcFBsSOu10S5UqqPvavdDtZzF5BoftNYHlSIiq9MskR2LSvUl0d9yx8KPhBYcqr9FF3filxPRE1c1lMHl2sggeK2FQ7W/JTJZ7Qk8q5rG+l5zbT+uP4l2z4p1MI6KH39pgAqVPAwtKKvSls3+mDPzlFgoz7w4q1krD4Z9UUO+rTecLDZokt1dTKSVe5Q6pia9lK2KrCKfDa0mYgVK4smTF9KNSLVT39b2T+d171EaH39aRcQdvELt6rNjjUH1rb6tERyUaWriN74G969CIlZl9vzoIP5GvvOlG5nqVwmO06fu4d0eq1W1/J4qsmf5iOxZtwOfolWzxShTNgdmzG4pj1EsjLL+6E4SO9ReK2qMj8e1r9ibWvhPCY5e3f/GWSEajQsJjZatC6F9p6LIK7KRqULPrV3LlQb3L1XP30yACTABJsAEUgoBFhwp5UkZjVNlazKqNrlrbvKf0IIji11mbGoTvWK5cRYpkwMzqlQTflr5m4SAqaLum6w5j8KfGZpQAH2HQrV0WaqGVGyPNt7Vsc7/ICYe0r9tHle1OxrmLa+zcKhVz40zXRkukgAblLnovQFlRRrZPNI6QF2S5YICwpcsOo/1a68Y3HCML5fd3QEtRYB2m3bFZAA2Hf/p+4OYP/eUrimlzqVYDiqtmy/GnAWtpXUgvtgES/unfrfv6iGzPG3ZdBVfDN9BVXEWawQHdaSsJxQbQkKhes3cJl2dlOC4HhCC9q2X6sYwbUZz4Z6UU07uaZJP6W8p5oRKj65/CytD7Em/PGjig4LVKQuWKhQMbsqF7E0ER+ky7jJDWdlyOXSuVGdO38f0aSekS5W6Ln8zASbABJgAE0iJBFhwpMSnphnzgsZDZVaq5qvHyvUnVrUYjRyZs6KBCGwOFQHOcZWEFhx0rZ3tp8gMUBMOLgJltbKmjKrSBU3zVYpzdfTt7SbJDFnGMSJLm42Ulh6tWOhatC4GlWkhLT/dNk3RDUXFuWgtHJSJaniljjIOpuWa8bp1PnQnJ9BOg0b5haWgJAp4uxh6pHUgdmwLwFfj/Ax1pjZUcLi5lbvXbOyCnDkdZUYnymhFooayNYWExP2bUNeKq//GTQriq0l1ZZ8N684DZYKKr1A6W0pra7xWh7nzVIpfWrciq6tYb0akux0/ZhfWrbmkO0UJDrLy1KgyU3ds847ucBXnbtt6DSOGbJfH9h/pI/uiLF2UrcvSMmteK5kZTLUn9y2yMBkXawRH85YFMVBkqnJ0sjN08+hRuMjQdRnzZp9hi4aBCm8wASbABJhASifAgiMFP8FMwp1qe7vJcoJMAePKwkAuQR3XT4z3zhJDcChXJcrm1Fu4ctGCgaqQyxUtAmgumLx7sXr4oHRzeT89tkzVWTBUH4ubjpCLEVIGKYofoZLHyR2Lmw6X21rBkVO4lq0QAfRUBu74FbRYIZXqtJq5WNWcilZw0EKK60QMCqXM/eP0Bsw5t022UR+0vkjFHIWkIFJ1CfHt6mqPfgPKoX6DfDKgnFKzqnU4mjQriAfBz+VCdtprKQuDNs5Be7xr91IY/HlVQxUtrte14wrDvtp4k/5pVXGy1Bw8EIhBAzaoruL87tKtJD4dUk2KlMb19W5Rpk7Upvil45TFq3qlv2I1VYKDDmjT/dYTiyR+M7W+bK+NW/n2hwaoUzefdKfq3nmVTjTYZbSVAfpHD982rJUhOxAfiSE41DocMjD8WBD++P04zp01H9uhxsLfTIAJMAEmwARSGoE0ITiq+vYRwZfRC4KlF5N0Kq/E2hH0PyoXTmzCo+CYtQFkZQr4oBgKiqVY738IXx9ajHpiTY0J1XtiuXBH+l64JZkqA0o3Q14xQadCaWBp9W2K99iriVlYLGIeTopUtypLlaUxHNQnrc+xssUoONs5yH4P3Lkg1wOhRQBJ4Mw+t9XsOhx0zrrW42WWK+rrilhThGJQJhxaJLeprqfIPNWf1hERhdYZufTothAQxQznaAUHtfla8KC1Ruged946JdvVyVWaDsmiFRxUQUHmFGxOheI0TgiRYmebQa7FQfdA12yz9it5PDE+fISrVfWauTBpQnQQtnILokBmchsiS0UB76zIJDJd0bZ24T/teCgeQK27QfVjvvhHrF5+RdtEblvbP62c/esf0fzJXct4Ub1YF/i3wtUtEzZu7SaDpqnq4cPnCBfC6tzZ+xgxNNr6YHyusrJQ/a6d10Wmpui0zNp2WsFB9bTCOqWRLVjITQZcGwdvU5A7BdDTN/G7HvBYWl1yiGxWXmLVcuKmgu2110kMwdGxSzFkEuPg1Lda0rzNBJgAE2ACqZFAmhActRoPjPPZnTu2AQ/u+cfZJjkeJHchchsavW8utt88ARWzEFcWJeV6FNf9ULwDxT0Ud82DGQ0+MTnJVkHj2ixVqk+yFAyr1EGux6Hq6JtiLsYfXIBDd2NWo9Yep+3CWb0wqnIX6SZGiwdSGfTPbzh2L3qyTFadqUJklcvuLY/Rx8n71+B355y0jpCoGOk3m6plSW9ji7FVu+nGQhago0GXxQKG1UyKswrCivFl1XeQ1d5RdSO/aSHDpZf2YNa/65HoDibSzseDq6CzsA5oV9CmS9Eb/z9+O4I5s06avfIffzVH+Qo55dv8GpX17kbqJGv7V+l1SSj07Pa36saib7IsfPJZVXiKib1af0S70rhxJyownerJOkNWGuOiBActfkj9OmnckyguhmI1SKxpC2W7+kmsaE4pcI0LuXBRTAoFlWvLzLktUap0DkNVB5E2l8SNcVHrmpw+FSSC2PWJClTQeHxZqoz75H0mwASYABNgAimdQJoQHCn9IaXU8dNkn9ydyJJyW7hWkXvVa/FfQhRyl/J0dBMWjlt4GmF+rRF1LQeRvYtcumgMZKWwpJDFpYBYtf2VsI7cEeeQpee/KpQm1tvbVbpcUdwGpXqleI+4Cr3Jp3UttDEM5tpb0j+tlr1gSVvZhbVB1+auG1f9D780AmXQiivuQwmOI8IN6v1+6+FdMKsQEu7Yv+8WSDzEVWjhQopvIYsRiRMSUc+e6cVJXOfzMSbABJgAE2ACTMAyAiw4LOPErZhAiiLQtHkhjJ9QR465XaulckL9tjcw9ksfNGpSUFgaHlht3bD22hTDsVYEvpOLU1yrmBsLDmuvw+2ZABNgAkyACTCBxCfAgiPxGfMVmECSEaBA6WIlsssMVXTRN3F9SrLBmrhQ3/7l0VCsw5Evf1bpdhUe/hI+1WebTEFLp7PgMAGRq5gAE2ACTIAJJDMCLDiS2QPh4TCBtyGg1q+gPijGoFunlWYXuHub6yTWudrgbIq96P3OGl0mKePrsuAwJsL7TIAJMAEmwASSHwEWHMnvmfCImMAbEyhWPBscHOzEonbBsYKl37jTJDzRK5eTsM444caNENwLijsGg4ZFi/FRdqlAsTr4ndtPk3CkfCkmwASYABNgAkzAUgIsOCwlxe2YABNgAkyACTABJsAEmAATsJoACw6rkfEJTIAJMAEmwASYABNgAkyACVhKgAWHpaS4HRNgAkyACTABJsAEmAATYAJWE2DBYTUyPoEJMAEmwASYABNgAkyACTABSwmw4LCUFLdjAkyACTABJsAEmAATYAJMwGoCLDisRsYnMAEmwASYABNgAkyACTABJmApARYclpLidkyACTABJsAEmAATYAJMgAlYTYAFh9XI+AQmwASYABNgAkyACTABJsAELCXAgsNSUtyOCTABJsAEmAATYAJMgAkwAasJpCnBYWNjKwG9ehVlNajUeIJLRkf45CqFR+HPsDvw9H9yixVyFEJF90K6a++4eRKXHwfq6hJ6p7hrHpR198bZ4ACcDPZP6O5TfH/58rug57tlcCfwGf6cdtTs/XToVBx58jrj75UXcPXKI7PtEutALZ88qFc/P44euoN1ay8l1mW4XybABJgAE2ACTOAtCKR6weGWowBy5i4BZ1cvKMHx8mUE7gVeQMDlg4gS26mh2Nva4YMyzXHz6X0su7wHDhns0b9UU/iH3MWqq/tM3mKZ7AXwe71BuB36EO3XTTDZJrErPy7XGp0K19ZdZurRFVh5xU9Xl9A7xKZn8fpYcXkvvju2MqG7/8/78y6YFZUqewkR8BCHD922ejzNWxTG2K98EBYaCZ8as82ev3l7d7i6ZcL/fjqEObNOmm2XWAcmf1sfvg3y48zpe+j9zurEugz3ywSYABNgAkyACbwFgVQtONKlS4eajT4w4Hn9+hXSpbMx7Ee8CMOR3fMRFRVpqEupG5U9iuBHn/7YfP0oxh9YgDq5SmNijV74++p+TDmyzORtJQfB4WznABd7Bzm+4RU7gsbEgsPk47KqcvS42mjZugiuXH6ILh1WWHUuNWbBYTUyPoEJMAEmwASYABMwQyDVC44aDQcg8Pop3A44hRfhT2Gb3g7unoVRsLiPRBIYcBLXLuw1gyflVH9UthU6F/GRYoNEx7CKHdDKuxqG7Z2JPYFnTN5IeuFili1TFkREvcRDwea/Lt/W6osansVZcCTAg0gqweHp5QQnJzvcvPEEYWFJL9zZwpEAPxbuggkwASbABJhAIhNI1YKD2JEblamYjXLVO8IxS3aEPg3GMb8liYw5cbp3zJAJJBqoTPP9EHmcsqP7pm+leJjd8FO4Z3ZBh/VfIzTyBZ5EhOGVsPBQ8XRwFZaedHKbPl4IC0/w8yeGfbWRwSa97DM8KgKBzx4geyZnVMpRWB4+fv8q7ghXLHMlHdIhj+Bb0NkTbkLUUNujQZcR9vKFuVNgieDI5ZgNtjY2uPHkPl6L/1QhFzIST08jnscSTzbCqlU4qxdKuOUV9xGMk/f90aOYb5wuVU52mcQ5uVAgi4cc+9mH12Wsi7peYn07OGZAbRGXsHH9Vd0laGLvK2IVKF6CJvb+1x5j04YrCA9/aWjn4mKPosWyyf0evUqjUhUv3L37DF+P32No8+BBGC5fiv3cypTNAZ+6+RD+/KW49mWUKeth1qUqu7sD3N0zG/qkjQD/EISGmndPzOnphJq1cqOAd1ZcDwjBnt3XEXjr7UWuseCoLO65rm8+3L3zDBvWX8H9e6GGcVau6ol7QaFyrIZK3mACTIAJMAEmwAQSnUCqFxzmCOYvUh258pcTE6wnOLxrnrlmybp+bctxcjJvySA/3jkNh4MugUTErg5TdKeYi+Eo6ZYPf9b/CPfCHmPXrdPoULiW7rxxB+Zjy/VjujraIVEwqWZveDvn1B2LEoLnywMLsfVG7HOooSWCY1+n72WfjVeNkiJKXaCjiAP5RMSD0DhH+M1S1XDJ6ICf67yPgi6ehjqy6Oy/c14GzJuK4aCYEootMS4/HFsl42OM6xNiv1p1L/TuW1oKBhIRDesuMnQ7bGQNtO9Y3LCvNl4LvdWu5RLcvBktFgcMrIg+/cqpwya/g+6GonnjhYZj6dPbYPqsFihZyt1QRxsnTwQJ0ZHDZAzH79OboWKlGJ7UPq4YDnPjX7v6Er4cu4tOf+OiBMfFCw+Qw0O45wnRpUpU1Ct8+P5GQwzLdz/6QomO5csuYtni83j5MlqEq3P4mwkwASbABJgAE0h4AmlWcJSp2g5ZXDwQFHgel07vSHiySdDj4PJthLXCDXmzuMtJPgmDy49vI7+zh7RiBIU9wpXHd+RIfju5Fv5PgkBv+weIgGlhgICXOLdu7jJmg8aV4FC3siHgMJ6IuJdW3lWRKX1G6YrVYOUIRGqyfrnaO2F1y7GwFdcJiQjF6qsHhPUkRE74m+WvjHnnt+PP0xtVl7rvxBAc0+t/LC0bz4VlZcGFf+S4uhWtBzvb9PLaxoKjT4lG6FOykTy2MeAIzj64Lq0jLQtUlXVj9s/DthvHdeN+0x1nl4zo+15ZNGycH5kzZzB0c+3qY/Tstlbu586dBSvWdBIWKeDG9RAcP3YXr4XSIIFQsJArundeCZpsUylbzgOdupaQ26VK5ZATcLKG7PO7Kevo49LFB5g144Rh/5up9WWWJ6o4cvi2tIjU840Zj6mg8TbtiqJO3Xx0ipjAe4FEiznB0evdshj4USXZliwbp04GoXiJ7KCgdiozZxzH7/87Irff5EMJDjr31avXOLj/FtyyZUbhIm6yuzvC0tGySbR4++DDCujctbhkSQejol7j6OE7mPbbcZNWH9kBfzABJsAEmAATYAJvTSBNCg7X7PlQokIzCe/E/uV4GhL01iD/yw4+LNsSXYrUMcRvDBXxG61F/MbwvbPiTHcbX9C4VnDMOLMJM89ukbdJ6XQ3tP5Sbg/Y/gtOadLKflW9B3xzl5Uipodw79K6UJFLlodDVpwODjCJK6EFR1HX3JjZYLC8VucNk3Hj6T25XcjFC3MafSa3tYKDXLJWtRgjRcnkw0ux5toBwziV1eNZZDgarhxpqH+TDZ+6edCzdykpGJRnW2TkKzFZDsSMP0/q0stOmuKL+g0LSHelOjXm6C7nnsMBDx88N/mW3pIYDgcHO+zY00O4HabDkkVnMfWb6GxmZCXYuK2bFBKmBId2EPFlqdq1r5cUU8ZZpH77s5nIouWJiBdRqFVtlhQL2n4t3dYKjpHDtmPr5mvy1L79y6P/+xXkdqWy0w3dOTtnRI9epdCoaQHQtirEceXyi1g4/xwiIzlttuLC30yACTABJsAEEoJAmhMcGYRvfiWfd2BrmwG3/I/D/2L0JCshYP5XfcxrPES6L7VYMw4PhIvY8uajpIWj4cov8CzyudlhWSM4KDbkWki0tYQ6VO5cxm/893ScKifsn++ZgX23z5m9tqkDCS04eom0t+8Jaw5ZKfpt+0l3ybmNPpdWF63gaF+oJj4t3xbk+tV5wyT5TbEoVDILiw5xpkIphMkNzdrS/4NyaN+hKOwzRVtXyCWKYjEWzj+LzRujJ8rGfb4/qCLe7VtOiooWjRchODjMuInJfUsEB8U6TPmugbCYAHVqzNYFff/8a2NUq5HbpEuV9oJxCQ5Kl0vHqfTq/jfOnrlvOJXW+Vi2qoPc79h2ueDwZmt4KMFB1o1qFf8yCBevXE74e11n2X/Lpotx5/ZTw7XVRrHi2dDnvTKoUNFDiiuqp35OHA/C55/sYOGhQPE3E2ACTIAJMIG3JJCmBAeJjHI1OiFTZmc8E0HHZN2gVLkpudD6GzvaT5YBzc1Wj5Hrb2xtO9Gsm5T2Xi0VHBTzUGf5UO2pMgUvpeLVWgLchDvV2lbjZTvfFSNAbkzWlIQWHCMrd0Lz/FWEW9d+fGOUGnh0la5okq+ibh2Ozyu0Q9uCNeId8hAhpvysFFPU6fotHZElS/Rbdb+9t0Qwtx+ePo2I83qOjmSF6CndgEgYUND3OTFxnz3zJE6fMm+Zs0RwKCvAcxEoXltYGbSl/wcVhLtX+bcSHCRYSLhQ0VoZ1HUOHusrrSujRuwQgksfJK/axPetBAfFsbRtoU/+oPrv0XUVzp8LNtsVWXi6di+B994vZ3C3at54KUIeW/f7NXsBPsAEmAATYAJMII0TSDOCI53I5lSmSls4ObvL9LhH9y4Si/4lfRrPhPq90Rt6KvYizS8FadNbeVrkL7PI1kRZqNT+KzFL7bXlO5OXtVRwUGxI67XRLlSqo+9q90O1nMXkGh+01geVIiKr0yyRHYtK9SXR33LHwo+EFhyqv0UXd+KXE2t0oxhcro0MgtdaOFT7WyKT1Z7As7r22p011/bj+pNo9yxtfXzbP/7SABUqeRia0YR2y2Z/zJl5CiEh5ie3NWvlwbAvasDDw9FwLm2QVaB7l1XSLUl3QOxYIjiGf1ET7ToUwxNxbV+fubouOnctic+GVnsrwUF90zUoeLtqhb90/dPOvsN9kCGD+fiPWCeYqFCCw9hli5oqwWFsXVHdkNBo2boQ2ncqirwi+5cqT568EMH4K3UZwNQx/mYCTIAJMAEmwASsJ5AmBAelgC1RoQWyZsst3CTCcWzPIkSINLEpuahsTZbcg7nJf0ILjix2mbGpTfSK5cZZpCwZp5rw08rfJARMFXXfZM15FP7M0IQC6DsUqqXLUjWkYnu08a6Odf4HMfGQ/u33uKrd0TBveZ2FQ616bpzpynCRBNigTErvDSiL2nXywN4+xrWKAsKXLDqP9WuvGNyCjC9H6WhbtiqMNu2KyYBwOv7T9wcxf+4p46YWCY53epbGR4OryIl1rap6CwcFelPA99vEcFCMBsVqUDFl4Th0vJ+0KAz9bCv+2R4g26kPcrmiVLqqUDA4uTsZlzcRHKXLuMuMYGXL5dC5Up05fR/Tp52QLlXG1+F9JsAEmAATYAJM4M0JpAnBUbRsI2T3KChXFCfLxovnsf253xzhf3vmgsZDZVaq5qvHyvUnVrUYjRyZs6KBCGwOFeIqrpLQgoOutbP9FJkBasLBRaCsVtaUUVW6oGm+SnGujr693SSZIcs4RmRps5HS0qMVC12L1sWgMi2k5afbpim6oag4F62FgzJRDa/UUcbBtFwzXrfOh+7kBNpp0Cg/uvcoKdamcDH0SGlad2wLwFfj/Ax1pjZWrO4o1+Qwt5K4sl5QzEeT+gtMdSFjNJTLU8O68/DoUczvZcbslmbT4mo7iyuGg9zHtu/uIZt/MmgT/PbGZMuilLt0DSqtmi3G7UD93+Ssea10qXrNxXlYIziatyyIgSJTlaNYqFAVuue1qy9j3uwzbNFQUPibCTABJsAEmEACE0j1gqNQibrwyF1cvB2NEgv8Lcbz0McJjPC/6y6TcKfa3m6ynCBTwLiyMJBLUMf1E+MdWGIIDuWqRNmcegtXLlowUBVyuaJFAM0Fk3cvVg8flG4u76fHlqk6C4bqY3HTEXIxQsogRfEjVPI4uWNx0+FyWys4cgrXshUigJ7KwB2/ghYrpFKdVjMXq5pT0QoOWkhxnYhBoZS5f5zegDnntsk26oNc1yrmKCQFkapLiG9XV3v0G1AO9RvkkwHlFFOh1uFo0qwgHgQ/x6GDgbpLbd/VA1lEliVKM9unp95djBp26VYSnw6pJgPCG9efL7NZ6ToQO+RStOdAb9jZ2WL3zuv47JPoLGSFCrti/uK28vjbWDjoept3dIerayYpKNqIGAuyUtB1F69oj/zCikExLPVq6bNv0XmJITjUOhwyMPxYEP74/TjOnTUf20Hj4MIEmAATYAJMgAm8PYFULTjsxeSWMlKpEiVW1DYur0RA9IEdM42rU8Q+xVBQLMV6/0P4+tBi1BNrakyo3hPLhTvS98ItyVQZULoZ8ooJOhVKA0urb1O8x15NzMJiEfNwUqS6VWlxLY3hoD5pfY6VLUbB2c5B9nvgzgXQeiC0CCAJnNnntppdh4POWdd6vMxyRX1dEWuKUAzKhEOL5DbV9RSZp/rTOiKiUL+XHt0WAqKY4Ryt4KA2XwsetNYI3ePOW6dkuzq5StMhWbSCgyooyJyCzalQnMYJIVLsRLIBWqmc7oGu2WbtV/J4Ynz4CFer6jVzYdKE6Oxp02Y0F1mUcsq0uLSOhcAhV+vOJDJd0bZ24T/teChD1Mat3eTknuofPnwuVxE/d/Y+Rgzdbmg6+POqImC6lNynNSseiXZFirqJLG42ss5YcFAgOJ2jCsU+kIAICQkX14i2kFCK2QF918kmTZsXwvgJdeQ2rUR+9cojkLuUCp6fMskPy5acU90ZvhNDcHTsUgyZhBsbp741YOYNJsAEmAATYAJJQiBNCQ5TRClL1d7Nv5s6lOzryF2I3IZG75uL7TdPQMUsxJVFSbkexXVzFO9AcQ/FXfNgRoNPTE6yVdC4NkuV6pMsBcMqdZDrcag6+qaYi/EHF+DQ3Yvaat02TexHVe4i3cRo8UAqg/75DcfuXZHbZNWZKkRWuezecp8+Tt6/Br8756R1hETFSL/ZVC1LepEsYGzVbrqxkAXoaNBlsYBhNZPirIKwYnxZ9R1ktdcHadNChksv7cGsf9cjUddIzO+PRYxFZ2GtoMX1tIXW7fjjtyOYM+uktlq3XaduPnzyWVV4ejkZsi8ZrzROJ4waWxut2hQxnEsWAMoaRdYVY8FB4kQrOAwnaTZobNUrxQSJ00KBtNq4EjHUlK7x43cHsGjBGc2ZMZsz57ZEqdI5DBUd2ixDgH9s66Rap4Qydr3bQ2/pUUHj8WWpMlyEN5gAE2ACTIAJMIFEIZCqBUeiEONOLSZAk31ydyJLym3hWkXuVa/FfwlRyF3K09FNWDhu4WmE+bVG1LUcRPYucumiMZCVwpJCFpcCYtX2V0KU3hHnkKXnvyrk5uTt7Spdrihu48L5YJML/r3p+Owy2oKyYZE7l7kA7TftW51HlhNaZfzC+QciTW3MmhzqOH8zASbABJgAE2ACqZMAC47U+Vz5rpgAE2ACTIAJMAEmwASYQLIgwIIjWTwGHgQTYAJMgAkwASbABJgAE0idBFhwpM7nynfFBJgAE2ACTIAJMAEmwASSBQEWHMniMfAgmAATYAJMgAkwASbABJhA6iTAgiN1Ple+KybABJgAE2ACTIAJMAEmkCwIsOBIFo+BB8EEmAATYAJMgAkwASbABFInARYcqfO58l0xASbABJgAE2ACTIAJMIFkQYAFR7J4DDwIJsAEmAATYAJMgAkwASaQOgmw4Eidz5XvigkwASbABJgAE2ACTIAJJAsCLDiSxWPgQTABJsAEmAATYAJMgAkwgdRJgAVH6nyufFdMgAkwASbABJgAE2ACTCBZEGDBkSweAw+CCTABJsAEmAATYAJMgAmkTgIsOFLnc+W7YgJMgAkwASbABJgAE2ACyYJAmhIcNrbpgdev8epVVLKA/18PwiWjI3xylcKj8GfYHXj6PxlOhRyFUNG9kO7aO26exOXHgbq6hN4p7poHZd29cTY4ACeD/RO6+/+sv2LFs6Nf//K66588cRdzZp3U1aXWnWrVc6F6zdw4dvQO/tkeEOs26/rmQ+06eXHoQCA2rr8S63hKq4jvfhPifmxs0iFLlox4/Dg8VndeuZzQV/ze7gWF4vf/HYl1nCvSHoF3epZGgYJZsXzJOZw9cz/tAeA7ZgJMwCSBVC848nhXRDYPb2R2dEW6dDYSQlRUJO7fuYyASwcQGfHcJJiUVmlva4cPyjTHzaf3sezyHjhksEf/Uk3hH3IXq67uM3k7ZbIXwO/1BuF26EO0XzfBZJvErvy4XGt0Klxbd5mpR1dg5RU/XV1C7xCbnsXrY8Xlvfju2MqE7v4/669j5xIYMry67vrXA0LQvvVSXZ3xjreYIFSq7IWrVx7i8KHbxoeTbL9+gwIY86UPIiOj4Ft7rtXXnfpjQ/gIQXHyRBD69loT6/zvxHESHCeO30W/3mtjHU9pFfHd79vcz6ixteFTNy+cne3Fv53yXQ2C74fifz8fxoZ1l2XXDRoVwMRvfBEe/hK1qs56m8vFOnfD1m7ImNFW1n8yaDNOnwrStRkxqibqNyyAoLvP0LVj0v0NL1rWDl65smD50nP4+YeDujHxDrBxWzdky5YZ0349gr+mH2ckTIAJMAFJINULjip1e8MuY2Z5syQ0bG0zGB59ZGQ4juyej5eRLwx1KXWjskcR/OjTH5uvH8X4AwtQJ1dpTKzRC39f3Y8pR5aZvK3kIDic7RzgYu8gxze8YkfQmFhwmHxcFlU6ONihcBFX2fadXmVQq3YeWCI4Ro+rjZati+DK5Yfo0mGFRddKjEYdOhXH0BE1yBCJyuWmW32J+CbgLDjiR0q/obkLWyNPXmfZmJ4FCUA7u+jJ/8ULD9C9c/QEPzEFx6Hj/aTQoUFcuvgA3TrpRcXv05uhYiVPhIVFwqf6bDnWpPjYvruHtPhs2XQVXwzfkRSXTFHXYMGRoh4XD5YJJBmBVC84cuUvj1Dx1v/xg0AxiXkFGxtbZM2eF8XLNZGQb18/havn9yQZ8MS60EdlW6FzER8pNkh0DKvYAa28q2HY3pnYE3jG5GXTCxbZMmVBRNRLPAx/arJNUlZ+W6svangWZ8GRQNCHjayB9h2LpynB4eJijxweDngQ/BzBwWGxSKY2wRHf/cYCYEGFYkRN6Q31n78fFW6or+HoaIexX/nI7/f7rZc9JZXgoIt1bLsc/tceyevSBwsOA4pktcGCI1k9Dh4ME0g2BFK94DBHunj5pnBzz49nT+7j+L643U3M9fFf1ztmyAQSDVSm+X6IPE7Z0X3Tt1I8zG74Kdwzu6DD+q8RKiw4TyLC8EoILiqeDuReJnwk/i0vhOUn+PkTtWv4zmCTXvYZHhWBwGcPkD2TMyrlKCyPH79/FXeEK5a5kg7pkCdLdhR09oSbEDXU9mjQZYS9NG9NskRw5HLMBlsbG9wQz+21+E8VciEj8fRUuMgZiycb4UpXOKsXSrjlFfcRjJP3/dGjmG+cLlVOdpnEOblQIIuHHPvZh9dlrIu6XmJ9OzhmQG2fPCK+4KrhEvb26aXriLd3VmR2yCD9og/svyX95g2NTGzEJzhoslq0WDZ5Zo9epVGpihfuCveUr8fHCPAHD8Jw+ZL+OZNPf2XRNiIiSsZK0H6devlQtVou3LwRghXLzsu3zmpI1oz/TSwcmTNnQP4CLupy8vvevTDcvxeqq6MdNZkmlypy06nfMD8o7uXokdvYtfM6Il6Yju8qWcpdTrRPnQyS90b7TZoVxLNnEVi3+hJu3tT//RCT8hVyCqaeoPEdEW5qfntv4uXL6L9B44FZ078196uuQ/dYpaqXcAVyElzC5HMjBsbjod/Eln/ekZaF1asuYsL43aoLk9/GgiN/gaxo2rygbLtjmz/Onws2eR5VkhtftRq5kTt3FuEudQ97dt1ASEhMnIjWwkHtjd3k4hIcxL+K+D1WqJhTvGRKh6OHb+OgiNsxvl/ql6wk6dPb4NDBQLhktUfTZoUkpwP7bsnfBLXRlje1cGR3d0D1GrlQoqQ7Avwfi74DEHjL9IseGk8t8e9Alaq5xN/fA2zb4g/b9OmQM6cjTP22rW1P90O/CYoBcnXLJP5NuQe/PTfx5In5f5+1DOLa1gqOeXNPwbd+fpQt54EL54Oxfu1l+e+GOr9RkwI4sC9QPPe3v67qk7+ZABNIngTSrOAoUaE5XIWl41HwTZw5EtvXO3k+Lv2o1rYcJyfz+lrTex/vnIbDQZdAImJXhym6RuZiOEq65cOf9T/CvTDxf463TqND4Vq688YdmI8t14/p6miHRMGkmr3h7ZxTdyxKCJ4vDyzE1huxz6GGlgiOfZ2+l302XjVKiih1gY4iDuQTEQ9C4xzhN0tVwyWjA36u8z4Kunga6siis//OeRkwbyqGg2JKKLbEuPxwbJWMjzGuT4j9atW90LtvaSkAyB++Yd1FslsSDe06FDe4lmivtW7tJYwfvUtbpduOT3AMGFgRffqV051jvBN0NxTNGy/UVXt6OWH1+s6y7qOBm/DjL43kpE41okl7jSoz5a61438TwdG5W0l8NqSaurz8Np6cqoNKcNBEjywhNFFThVxzenVfrXuLro75HXxXuGbaYuJXe/DJp1Wl8FPH6Jvcfcjth0qRom747Y9myOKcUe6rjydiUjWg37pYAo6OW9O/NfdLk+1d+3qBRJ9xCQuNxHt91oJcpFQhjtQ/lYZ15+HRoxgBoNpov7WCg0RDpcoxf2fUbsafx/DHb0e1p0jhNm1Gc8lJeyAqSvz7MHa3IT5ECY7jx+6iXHkP2bRl08W4czt6km5OcJDomTGrRSz+T59GYEDfdYbnpK59+EQ/uTl/zil0FwHP2mLqd/QmgoPiYVq1KaLtWm6vWnFB/qa0Bzw8HLFgSVvd+ImN/7XHKFjIVYrF/n3WGU6xtj0JVmJXvER2Qx+0QdeY+NVerPn7oq7e2h0lOMjlzLdBfuHGHPM3Rn8DbVsuNQjL9Vs6wskpo3TlnPXXKSE6b1p7OW7PBJhACiGQ5gRH+gwZ4eqeD0VK1ZeP6MLJLTKAPIU8L90wB5dvI6wVbsibxV1O8kkYXH58G/mdPaQVIyjsEa48viPP+e3kWvg/CQK97R8gAqaFAQJe4ty6ucuYDRpXgkNddEPAYTx5ESZctaoiU/qM0hWrwcoRiNRk/XK1F5PRlmNhK64TEhGK1VcPCOtJiJzwN8tfGfPOb8efpzeqLnXfiSE4ptf/WFo2ngvLyoIL/8hxdStaD3aUsUwUY8HRp0Qj9CnZSB7bGHAEZx9cl9aRlgWqyrox++dh242ECYR0dsmIvu+VRcPG+eVbcHkB8XHt6mP07BYd0PzrH02lNYHeiJKlgXzpaVJHb0up/L3yAr7+MsYiISv//YhPcNBbx05dS8jWpUrlkBNwmnTv84v5P32aRM+acULbLbSCIzLylZy0B/g/Ei5Mz1FAWBroLXHVCn/Jc6wd/5sIDrIOqMxcxUtmB72lNzVRpAEpwUHbFJuwd/cNGfBMFpoMGWwQGhqBOjXm0GFdUYKA7pfa0aT3lhAt7uI55M3nLAPQyWJAwbLrt3SVAoyE484dAXgpJnL1fKOf8fPnL1Gv1pxYb9ot7Z8GZc39UtyF36F3Qdc9I4Kub9x4gkKFXeXbbboPmmTWFvEPyrLzv2lNpSWEMlI1qDNPx8DUjhIc6ti5s/el1YcsCzTRJFcsiq8gFqpsEkHFboIT8ffbe0MEfYfKzGL09p6KEm9KcKxcfl5a38gSsl9YHT76IPrfD3OCgwQWTarJmkHPl8ZQyyevfG50n7WqzZJ1ajxKcNA+WcUosxJZRzJliv434pNBm6R1SrW3VnB8M7U+6om3/FTI4kPWBBKlpUrnkHV/TjuK6dNiXsKoCTuNe/eu6/I+6orfp5q4UwY2reCwtv1fc1qidJnoax8Rlh/KMFajVm6ZHIAGRPE5WhEqB2nFhxoPnUK/u/3i35NSpd0N/2ZpX5T88VcTIXyirayq/fatAYLHcTx8GLfYtWJI3JQJMIFkQCDNCA6K2ciSNScyCFcZVYICL+DymX9kbIeqS4nfH5ZtiS5F6hjiN4aK+I3WIn5j+N5Zcaa7jS9oXCs4ZpzZhJlnt0g8lE53Q+sv5faA7b/glCat7FfVe8A3d1kpYnoI9y6tCxW5ZHk4ZMXp4ACTmBNacBR1zY2ZDQbLa3XeMBk3nt6T24VcvDCn0WdyWys4yCVrVYsxUpRMPrwUa64dMIxTWT2eiUQDDVeONNS/yYZP3Tzo2buUfFupPNtoIntwf6B4I3xSZIqK8VMntxxy1zF2D/rjr+bSZcfcBJnGFZ/g0I7dmqBxY8HRs9sq3Vt7ehtNb6WpWDv+NxEc2vuwNGiczhnzxT+G1Ljk3rN4eXvZFVkx6M2ztihBQHXjx+zCujWXDIcLF3GTrjH0LKaLN+sk5MjdjLJsqYk2WRh2+vWUk8bFC8/guyn7DefThqX9604SO/HdL1k46C3z1s3XdKeSYN0ghBGV77/dj0ULzsjt5X93lALqxvUQtGu1VNbF9aEVHNu3+mP4kG2yufY3MmrEDmzeGO0iSClTPxpcRbZ5t8caXeapv9d1lq5M/kJcd2yzDFrBQe5WPwhLGomURvWiLS+mBEfP3mUw6OPKsv+uHVcYfpcUAE/3Rn9vM2cc16XwVYKDYn6a1F9guN09B3pLyxCJxiGfbjXUWyM4yNqyZEV7ed3FgvF3grUqyupBwqhaxWiBThnW6JlS+eC99YaMcfR3RH/zVLSCw9r2JNpWru0k+9FmkSJhulFkBSOrnDYpgGxo5YcSHPSsSFyT2yGVWfNaSbH8QHBurOGcL7+zfPFSvaaXEIW2hqvRi5d5c04Ld7IAQx1vMAEmkHIJpBnBUb5GJzg4xbxJoYxVZ4+uQ8jD/y4FaEL9bOY1HiLdl1qsGYcHIhZjefNR0sLRcOUXeBb53OxlrBEcFBtyLSTaWkIdKncu4zf+ezpOlRP2z/fMwL7b58xe29SBhBYcvUTa2/eENYesFP22/aS75NxGn0uri1ZwtC9UE5+Wbwty/eq8YZL8plgUKpmFRYc4U6EUwuSGZm3p/0E5tO9QFPb/vjml/0MmN4mF88+KCZl+Qmiqb3prS2/T6U1nNeEL/t6ACvINtbImGJ+TFIJj9l8n8Osvh40vbXLfkvEnleCgN6+1xZtubaGJGE3IyNd/0IAN2kMGQRDfZOzA0T7y+ewT8Rq//S+aC036qQwbWVP472eHmlBrL6AER3z9a8+h7fgEh3F7EgKurtEvXX76X2M5wVy6+Cy+nbxPNt268x1pITpz+h56v7Pa+PRY+1rBMbD/BhkHoRqpe9K+wV+xuqPMfvXwwXN8NHCjbKr4NGjkDRIkagKuFRyTJuwFpcnNnj0ztm29hhFDtpsMGldv700xnreojXRZNM54pQQHuRJ9NS4mZmXOgtbS7YgsHr26/61uC9YIjuFf1BQukcWkUHq3x2ppoVT3S+mGf/k9OnlJj65/C+vHfUya4ivjtYwn5XTxzTu6y2enFRzWtu/1blkM/KiSZFyj8kydpUe5WJJYpmNvWpTguB34FK2aLTZ0Q/9e9RtQPs4Uyr7186FbjxK6lzEk2teuviJSEFv274zhgrzBBJhAsiKQZgQHvWIid6IsWT3h7lkIObyKyQdx5dwu3LkR/XYvWT0ZCwdD62/saD9ZBjQ3Wz1Grr+xte1Es25S2m4tFRwU81Bn+VDtqTIFL6Xi1VoC3IQ71dpW42U73xUjQG5M1pSEFhwjK3dC8/xVhFvXfnxjlBp4dJWuaJKvos6l6vMK7dC2YI14hzxEiCk/K8UUdUr+yrSAGhW/vbdEcLYfyK88rkKT9LFiXQpaO0Ibb6DOIdFiLn1sUggOegtOb8PNFWvHn1SCw9QbfHqDXrNWHhnQ20G8YdcWNXk2fjuubUOuXDRhj6+YskpZ0r+pfi0RHOTO8/nQagaXFuN+Nm24gtEj/5HVFJtDosTUhN34PNpXgoPcf6qUn6FrovrSvtnf5SfcnUTig/gKubX9s7entAyQSxUJDgrS//LrunKSXK/WXCG2GsRKi7tmYxcZWK1EifY6yopnPJlXgoPclGgyr4pqT5mxKEOWKtYIjj9ntjDEn6jzTX0rK9Ps+a2lKDXlEqiOaQWHqrO0PWUZa96isMzgprXm0JgoEQS5QFKhZ0nP9E2KEhzG8SmqfyUo4+rbQfxGho2shrq+eWUzekGg4triOo+PMQEmkHwJpB3BYfQMPPOUgnfx2gh7JrIn7V1kdDT579Ibeir26e1k/Aa9ladF/jKLbE2UhUrtvxIz0l5bvjN5Q5YKDooNab022oVKdfRd7X6olrOYXOOD1vqgUkRkdZolsmNRqb4k+lvuWPiR0IJD9bfo4k78ckKfGGBwuTYyCF5r4VDtb4lMVnsCz5od9Zpr+3H9SbR7ltlGJg78+EsDVKgUHfxKh0Mev8CWzf6YM/OU2SwtahJM7elNH62p8VBkjnIQKUqVH3alsqbXq0gKwWHu2jReKtaOP6kEB/nS9+i6KnqQ/37SAnY0gb5/PwxNG8S41tBhJQi0bli6k8VOmbI5MGN2S1lNLmX0pthUoTe/5LalLZb0r22vtuMTHBRQTJNwciUicUpuQzSBjhJuPGXL55RxCtrJuZogG0/KZsOIwgAAQABJREFU1fWMv5XgoN+m8cJ/ymK0ZNFZTP0m2oKiLEDEwDizl+r7tRjoZ59swd4D7+oEBx3ftusdGWtAGbQo45bxOhzbd4k1MoRbEGVKm/z1XtWl/CZXK3K5Ml63QwmOZo0W6jK/fTGmFlq3LRpLfFkjOJRFhwL0Twurkbnylwiup9+Mak9xD5SQQVuUG6VWcFjbniwqlE3OlOAm96+lK9vLS1qSMEA7Nu22Ehxaly06Ts+K3ODiEhyUpa9nr1Jo1NRbWHPsDd2eOX0f7/fT8zAc5A0mwARSBIE0Kzgy2juicp2e8iEd2PGXWHE8PEU8MDVIla1J7cf1bW7yn9CCI4tdZmxqM0EOxTiLVFzjU8fUhJ9W/iYhYKqo+yZrzqPwZ4YmFEDfoVAtXZaqIRXbo413dazzP4iJh5YY2tLGuKrd0TBveZ2FQ616bpzpSnfiW+5QZqT3BpQVFos8hsxBNBGkCcCSRedF2sgrhjeL2rgCWtF4nsiio4rWd9vcpF8JDlOTC9WP+lZvcy1Z+E/rn2/u2tTvm4w/qQQHTXbbttD/JtRkjILzyf9fW5QgIFcemqCbKmTNoYBlKspFRu5Y8GFJ/6a6iU9wKL95mvDWFf702rfW6zZ1lYkCtIJjzHgftGhVWLYjAWFONKmxWCs4duzpKbIS2cFUHIvqU30bu1RRfeeuIiOZsNbQpJXcviheRisgVBzIzn9E3MXgmLgLOnf8hDoiZW8hGQ/VtOFCqpIlMQXHb8JiQOmmLbUYqbgUSiH7The9IF64tJ0M+NcKDmvbq7gRcmlr5DtfIZDfNWrmxo/CzY7+PTJlNaUgd0fx7Kg8F8klKEmCqfImgqNJM2/xbIvL9NYxcW1RImj+pshydlwkaYj5t97UNbmOCTCB5E8gzQqOLC4eKFO1nXxCB3eKLC3hsXP2J//HByxoPFRmpWq+eqxcf2JVi9HIkTkrGojA5lAR4BxXSWjBQdfa2X6KzAA14eAiUFYra8qoKl3QNF+lOFdH395uksyQZRwjsrTZSGnp0YqFrkXrYlCZFtLy023TFN1QVJyL1sJBmaiGV+oo42BarhmvW+dDd3IC7TRolB/de5REAe+YNSRoIrVjW4DwJfeTb2PprSxlEjKO01CuETQUc5N+5TOtnZCZG7ryNTcOnDXV3lLBoQJ4rRl/UgkOCtKvXik6UFfdo4oR2CMyG3360WZVLb8tFQT7j/SRrm+0YB694bW0WNq/cX/xCQ6VsUkbp0F9kDgilyWKJ9AKDspgRRNbKhSDYpyhTB7QfFgrOOgNOr1JNzWh1nQrN00JDjqg3LLod0XxTNrft7IC3LnzDC2b6C3XyhpAmbR6douJyUhMwTH486ro2r2UzAJmnB3L+H5pf8Sommjbvphsr1JLq3aUbYyCu7WCw9r23XuUxsefVokWlCKGSWUno2uoFxTatNbq2vStguhp29TfD9VTsUZwkNsUrcVBGdNUuS5eviyYe1a3FpE6xt9MgAmkXAKpWnBkdnRF1mx5EBR4Hi/F4neqOIj6ouUaC1/irOIfznAc2K6feKh2yf07k3Cn2t5uspwgU8C4sjCQS1DH9RPjHX5iCA7lqkTZnHoLVy5aMFAVcrmiRQDNBZN3L1YPH5RuLu+nx5apOguG6mNx0xFyMULKIEXxI1TyOLljcdPhclsrOHIK17IVIoCeysAdv4IWK6RS3VOsZi5WNaeiFRy0kOI6EYNCKXP/OL0Bc85FZ9yRDcUHrS9SMUchKYhUXUJ8k+tAvwHlUL9BPhlQrvyV69TNh29/aCAvQe4/ahG1YsWzyYwvKk2mOcFB8Qjk0kTFOLWnrNR8dBFrL3wq1mCgt5uN688Xblvmkw1YKjjeZPxJJTjo1rWigBZZ+/6naFaUdpXSr2qLpYKAnhfdN1kGundepVvTg9bxeLdvObkI3eFDt7XdG1y24rKg6E74dyc+wUGZqCgjlbHlSr15p260goP2aQ0IyrxFE3paHJEWmVSFVq6nlK4qFbO1gkPFYVB/o0f8g00br6iu5TfFFzhlsZNZs8wJjr79y6P/+xUM52kFB1kwyJJBhSwcZOmgQm5EKkCbMoSRhUWVxBQc2jTJtC7FF8N3qMvKb7IaNGribXA507rAaRdeHDK8Ojp2LiHP0QoOa9tnFSmrN++IXthRm56WxkkxN/Qbpd8mZcgyLokhOFRcG7nk/bP9Oqb/cUIuTGl8bd5nAkwg5RNI1YKDFvajBf6oPA8LwYvnT+Ho7C7eQEabhan+/InNCL6r/z89qk8JhWIoKJZivf8hfH1oMeqJNTUmVO+J5cId6XvhlmSqDCjdDHnFBJ0KpYGl1bcp3mOvJmZh8cWdOClS3aq0uJbGcFCftD7Hyhaj4GznIPs9cOcCaD0QWgSQBM7sc1vNrsNB56xrPV5muaK+rog1RSgGZcKhRXKb6nqKzFP9aR0RUajfS49uCwFRzHCOVnBQm68FD1prhO5x561Tsl2dXKXpkCxawUEVFGROweZUKE7jhBApdrYZ5FocdA90zTZrv5LHE+PDR7haVa+ZSwTJ7pMuV+SCQm//yBWG3vy9FOtwFCzkJi+tXA/MCQ5qpHzaaZuClenNJK2M/ZNw0dIWWm2Y0mKqDDoPHz5HuAjUpLfBI4Zu1zbVrcMR17UpFay141eCgy5IE15ThUSRSiNKE6T5i9samtEEjNZPoAl/oIgTUIWyTtF6A9p1OOgYxcS8ePFSuKq4yXgBU2/GqZ2lgoPumQLH6ZvGeT3gsYybyCHG5SWCsYnvrz8fxuyZ+rVNLO3f2vsl9yNyQ6JCCQpovZS8eV10i8oZCw6yQMxd2Nrg8kdrcjwWCwDSPRBbbSYtawUHjWPh0raSN23TGhy3bj2RmbEoQxjdH63m3qfnGl1aXAoaV4UY7tnfW7alOq3goH3tOh9XrzyUz4EWzKO/F1p4ztdnLjUzlDcVHPR8X70y/RulNMBjR+2U1/hYpAFWCwrSM6AYGkr/Sr8HijcxzpimRCudTPdGMS0ODnbyPugetIKD2ljd/nshisW6HlTo907xOkWKZpP/ztA9UWYptbiibPTvR2IIjiHDq+LYkbvYLqy6XJgAE0jdBFK14JCWjLKNQJYO4/JC+P9ThqqH9wKMD6WYfXIXIreh0fvmYvvNE1AxC3FlUVKuR3HdJMU7UNxDcdc8mNHgE5OTbBU0rs1SpfokS8GwSh3kehyqjr4p5mL8wQU4dPeitlq3XTirF0ZV7iLdxGjxQCqD/vkNx+5Fi0Ky6kwVIqtcdm/DeSfvX4PfnXPSOkKiYqTfbMOx9Da2GFu1m24sZAE6GnRZLGBYzaQ4qyCsGF9WfQdZRZyPttBChksv7cGsf9cj0R5LrG3yq54sFg6jCawq5PIwbsxOUJAzTRBM+VurtmSN+OW3JsglJnNKTJhyGaL29Gb+k8+qSkGhxIyplcZzejphzYbO8V6b+rR2/JRClNy74itK6DiK4HlyDYqvqDUZ1OSMJs0eOR0Mi53R+RTX0UP4zat1A7R9qsnW0M+2ijexAdpDsbYp3elPvzaW2YaMD9J6KvSWW61Too5b2r+190v9a60Z6npkwaGMaZSml9boGDlMLyrpHqbPboF8+VzkRF2dRyJw4fwzIkVptGClDFi0sJ3xpJnaq6BxbZYqqqffIbkCtWpTVNc3HSNRPOPP46BVv5WFw1QAuHJVonMoPsWnxmzalIXui4LfKYZIWyiWqW+vNbFWT1eCg7I2kUuhKiNH10KbdiJo3DhL1b+B6aqdqe/dO6/LwHd1jFYZHzqihnSJUnX0TS8Adu0MkGl+tfUUU9W4aUHZnpifOxssXgBEyngQsjh9+H50SmF1jrXtKdsXWZu0hThSqmLKeGWq7BYiTy2GSILeXOpc5Zpo7JKn1hKJK2jc1HW5jgkwgdRBIFULDvWIbMUb6oyZnMQbsczijVQUwkNDxBvQmP9jUe34O2EJ0GSf3J3IknJbuFaRe9Vr8V9CFHKX8nR0ExaOW3gaYd79R13LQWTvIpcuGgNZKSwpZHEpIFZtfyWsI3fEOWTp+S8KTdAoA1K+/C5ynYNAscJ1SirJefz0Np9WQaYJuPHiim/LmPztaVXwAt5ZZVIAtQr32/b7Jue753AArf5NYmq/3y0ZdG1JP/TsKDCb1n+h1e5Pn7pn8bmW9E+uWyR6HolVpc+JdSjICpVQhcQZuVLRewtaVPPJkxi32oS6hrX9kAWOsjXRpPvy5Qe6RT5N9UXCLyQkOhZPxb9os34Zn2NNe0qzTSIgp6ejdKOizGFcmAATYAKJRSBNCI7Egsf9MgEmwASYABNISALkWvZIuLBpLW2UAnjV2s7SIjT4w83Yu+eG4ZLWtjecyBtMgAkwgSQkwIIjCWHzpZgAE2ACTIAJxEXgq0l10ahxQRlv43/tMQoJKxCJCioUW9Gyaczq3VRnbXs6hwsTYAJMIKkJsOBIauJ8PSbABJgAE2ACZghog8y1TSjT2ECR/MA4g5y17bV98jYTYAJMIKkIsOBIKtJ8HSbABJgAE2ACFhCgdVIovoKSPpDAOHrkdqxgd2031rbXnsvbTIAJMIGkIMCCIyko8zWYABNgAkyACTABJsAEmEAaJcCCI40+eL5tJsAEmAATYAJMgAkwASaQFARYcCQFZb4GE2ACTIAJMAEmwASYABNIowRYcKTRB8+3zQSYABNgAkyACTABJsAEkoIAC46koMzXYAJMgAkwASbABJgAE2ACaZQAC440+uD5tpkAE2ACTIAJMAEmwASYQFIQYMGRFJT5GkyACTABJsAEmAATYAJMII0SYMGRRh883zYTYAJMgAkwASbABJgAE0gKAiw4koIyX4MJMAEmwASYABNgAkyACaRRAiw40uiD59tmAkyACTABJsAEmAATYAJJQSDNCY70GTKiTJW2sE2fAU8e3cWFk1uSgjNfgwkwASbABJgAE2ACTIAJpEkCaU5wFCndAO6eheXDDn36AMf8FqfJB883zQSYABNgAkyACTABJsAEkoJAmhIczq6eKF25jYErCw4DCt5gAkyACTABJsAEmAATYAKJQiDNCI50Nrao7NMDGezscefGGXjmLQ0WHInym+JOmQATYAJMgAkwASbABJiAgUCaERz5C1dDrgLl4X9xP9KlA/KJfRYcht8BbzABJsAEmAATYAJMgAkwgUQhkCYER2ZHV1So2QUvwp/h8K65yJW/HAuORPk5cadMgAkwASbABJgAE2ACTEBPIE0IjvI1OsPByQ2nDv2NkIeByC0sHWzh0P8QeI8JMAEmwASYABNgAkyACSQGgVQvODzzlIJ38dp4cM8f545tkAxZcCTGT4n7ZAJMgAkwASbABJgAE2ACsQmkasFhlzEzKtfpKe/60M45iHgRJrdZcMT+IXANE2ACTIAJMAEmwASYABNIDAKpWnAUKlEXHrmL48njuwgMOGHgl92jELJ5eEsBcvX8bhnb8fRxkOE4bzABJsAEmAATYAJMgAkwASaQMARSteAoVraxFBbxoQp5dBunDq6KrxkfZwJMgAkwASbABJgAE2ACTMBKAqlacGT3KAgnF49YSGgBQMcs2REVFYm7N88h9NkDBN06H6sdVzABJsAEmAATYAJMgAkwASbwdgRSteAwh4ZjOMyR4XomwASYABNgAkyACTABJpCwBFhw+C1OWKLcGxNgAkyACTABJsAEmAATYAIGAmlScOTKXx75i9BK48E45rfEAIM3mAATYAJMgAkwASbABJgAE0hYAmlScCQsQu6NCTABJsAEmAATYAJMgAkwAXMEWHCYI8P1TIAJMAEmwASYABNgAkyACbw1ARYcb42QO2ACTIAJMAEmwASYABNgAkzAHAEWHObIcD0TYAJMgAkwASbABJgAE2ACb02ABcdbI+QOmAATYAJMgAkwASbABJgAEzBHgAWHOTJczwSYABNgAkyACTABJsAEmMBbE2DB8dYIuQMmwASYABNgAkyACTABJsAEzBFgwWGODNczASbABJgAE2ACTIAJMAEm8NYEWHC8NULugAkwASbABJgAE2ACTIAJMAFzBFhwmCPD9UyACTABJsAEmAATYAJMgAm8NQEWHG+NkDtgAkyACTABJsAEmAATYAJMwBwBFhzmyHA9E2ACTIAJMAEmwASYABNgAm9NgAXHWyPkDpgAE2ACTIAJMAEmwASYABMwR4AFhzkyXM8EmAATYAJMgAkwASbABJjAWxNgwfHWCJNPBy4ZHeGTqxQehT/D7sDTyWdg/+FI2rRuijp1auhGsGz5Guzde1BXxztMgAkwASbABJgAE2ACiUMg1QuOvIWqIIdXEbP0Du+ah9evX5s9npIOlMleAL/XG4TboQ/Rft2EBB16efeCyGibAUeCLiHyVVSC9p2Ynf0xbSqqVC6vu8TSpasxcfJPurq0utOhzqcoW9DH7O2PntkWr1LQ8zZ7I3yACTABJsAEmAAT+M8IpHrBUaR0A7h7FjYLeO/m34XgeGX2eEo6kJiCY2f7KbCzTY82a79EUNjjFIPFw8MdnjlzyPFOnjQa7u7ZwIIj5vENaPktCueuEFNhtDVkWkNERb00quVdJsAEmAATYAJMgAlYTiDNCI6b144iMOCkEZnXiIwIN6pLubvpbWyRLVMWRIgJ4sPwpwl6IylVcGghrF41F3nz5mLBoYGiBMfBcxuw8eAszZHozSdhD2LVcQUTYAJMgAkwASbABKwhkGYEh/+l/bh17Zg1bFJMW08HV6RLl84w3hdRkQh+/sSwrzYy2KRHHqfsCI+KQOCzB8ieyRmVckRbf47fv4o7whVLW7wc3WBvayerZjf6DLbpbPDp7j9xPyzE0OzG03tv7GJVunRxODhkxtmzF/HkSWyBVLRoIWTN6ozLl/0RHBwz8c2RIzvq+9ZG3jy5EBEZiePHT+PgoWN49izUMC5TG5YIDhpTeltbnDh5RrgSxbjaublllde7H/wQN28GxuqeLCc1qldGyZJF4e9/Azt3+uFW4J1Y7ZJbhRIcu04sw2q/3y0eXrZsbsifP7ds//x5OM6cuWDxudyQCTABJsAEmAATSFsEWHCk8OdNImJXhym6uzAXw1HSLR/+rP8R7gmXqF23TqND4Vq688YdmI8t12NE2dqW4+AmLCZxlXe3/oALD2/G1cTssR3bVsLV1QUbNmzDyFETde1sbGxw+OBm2IrJ/+RvfsbiJX/L40sXT0fhwt66trRD4mDCxO+xcuX6WMdUhSWC48SxHbJ5oyadEBR0X52KMaM/Q9s2zYSYuI427Xob6mlj7JjPQcHpxmXFynX4asL3xtXJav9NBcdX44ehRYtG8l4iI1+iUpWGyeq+eDBMgAkwASbABJhA8iGQZgRHZGQ4Xr96JeI1ohD69AGCAi8i+O6V5PMk3nAkNsLqMKCUmOwKA4eXgxvq5i5jNmhcCQ51qQ0Bh/HkRRhaeVdFpvQZpStWg5UjDBaLVt7V4GrvJJv3K9lYfi+7tAePI2IsCX9f3SezYqk+rfkeOmQQunZpi9DQMNSo1Vx3Kk3gaSJPQqJy1UZ4+TI6jmD3ztVwdHTE2XMXcPVqgBQs5cqWgpOTozz//YFDsX//EV1faicxBMfUb8dJawtd49z5Szhz+jzIMkOWEirT/pgj/yd3rPwYXL4NirlGWxHiOtU/JAiTDi+Jq4nZY0pwRLx8If82KED8/uNAHDy/AfvPrjN7HgsOs2j4ABNgAkyACTABJmBEIM0IDqP7lrsP7wfg7FHzb8RNnZOc6+ILGtcKjhlnNmHm2S3ydiid7obWX8rtAdt/walg/1i3mRgxHOSmtH3rCnmtd3oMxOkz5w3XXbzwDzlxP3X6HHr0HGSo961XS6a0fRERYaijDb8966R71pEjJ9D3vU91x9ROQguOAgXyYsWymdKdbcHCFfh26q/qUgarBwmlipXf7O3/vMZD4O2c09CnuQ1zFi1z7bX1SnBo69T2jaAL+HH5B2pX982CQ4eDd5gAE2ACTIAJMIE4CKR6weGZt7SYENrgUfB1RL54joyZsyCPd0W4ueeXWK6c3Yk7N8/GgSjlHLJGcHTf9C2uhcTEGCj3qTH752HbjeOxbjoxBAddZP3aBfDyyol//tmLwZ+Nkde1s8uAA/s2wcYmHYYMG4+tW3fFGg9VuLg4I1eunMK4kw6DBw9A+XKl4B9wA23a9jLZPqEFxxcjB6ND+xYyrTKJokghLmz+jaVxdsmC33+dIsfRtfv7OHfuoskxxVXp4ZAVmdPbx9VEHgsX1gkSHW9SGlfpDVvhlnfyyk6EhAbD3SUPmld/D3lzFJPdbTg4E9uOzI/VNVmgOnVsJesfPHyEgYOGx2rDFUyACTABJsAEmAATIAKpXnCYe8ylKrWEi1tuhIvgalqLIzUUSwUHZbGqs3yo7pZ/9OmPyh5FMPnwUqy5dkB3jHYSS3D07dMdgwa+i/DwF6havYm8bvdu7fH5Zx/AVGwAWRUmfDUCxYTbkjZQXg34btA9NG7SWe3qvhNacMz86ycpcnQXMbFDlg+ygKSk8nnnGfB0K4DQ8CcY/VfrlDR0HisTYAJMgAkwASaQzAikWcFBFo7i5UXsgyh7NsW4wiSz52PVcCwVHBQ03lqsp6Et39Xuh2o5i2HKkWX4++p+7SG5nViCw94+I/b7bZDiof/7Q3Dw4FGsWjkb+fPlwV6/Qxj0of7N+c4dq6RlgwZFma2uXgtAWNhzFMifFznFehuUzap+ww6xxk8VCS04VH8Ug3JaxG6YK39On4tjIpOWtYVc3WixxfhKxKvIN46jMdd3paKN0cU3WpR+9qsvXov/uDABJsAEmAATYAJM4E0IpFnB4ezqidKV20hme7dMEwHlKWf1bHMPOiUKDroXlXnqwIGj+GzIWOzdvVYKkN7vfozjJ2Im6v36dsfAD96Vt9+i1Tu69LSTvv4CTZr4WiY4lq3BxEk/msSoslQ1b9FNl9Z2+h/fo1KlsrosVWoVc1OZq0x2bmVlUsRwmBtS8bxV0bd5dOawodMa46VIpcyFCTABJsAEmAATYAJvQiDNCo7cBSogX+Gqwm0nHAe2//Um7JLdOYkpOLa3myQzWfXb9hPOPrieoPeuMlKRC9VfMxdgQP+eJjNXzZ75M8qWLWkyTmPj+kXxWjgWzPsdJUoUwYkTZ9Dr3Y9M3sPRw1tlKl5jNygVlK4VF+T2Re5fFMBeTbiDadftMNm5lZUjK3dCCde88Z51NeQuxuyfG287axq0rjkQtcu0A2WvGv5HtKub9vwmjeth+LCPZVVUVBTq1W+rPczbTIAJMAEmwASYABMwEEjVgiOjSOnqmj0P7t2+hCixGJ4qTs45UKZqW/EW3UYeu3hqqzqUor8TU3AsbjpCLhq48MI/+N/JtQnKiYLDDx/cIif6lNUpffr0WLd+K0aNnqS7zuRJo9C4UT0Z21GlWiPDBL9b13YY8vlA2TYul6qvJ4xAs6YNpEBoJ9bSMLUwnxIWp06J7Fi9orNjUdrdWTN/kv1rBQctfrdl01IZ3L5p8w4MHzFBN15KjduksS++mfKLrj657GR3yYUyBetgz8mVeBEZZhhWoVzlMKDlVGllunTzKKatGWI4pjZ69uyEwR/3V7soW76eYZs3mAATYAJMgAkwASagJZCqBUeWrDlRpkpbkUXoFcKePULEi1BktHdEZkdXyYBEyKGdc/FSWDlSahlQuhnyOrnL4WcTi/SVcMuLKHG/ewNjMm8tvrgTJ0WqW5UW901iOGgdjt4lotO7PhO8zj+8IbMzfSNiPoxXKH8TljP+/B4VK5Y1nNqqTQ9cv37LsE8bFSqUwV/Tf5B1ZA25ePEKXMRK5LlElitV4hIcHh7uIEuICjYPCXki7+GLUZPgt++Q7EKJGtqhFbRp8b+8eXMZztEKDmoz+JMB6NmjI23i6dNnuHbtOjKILFtenjnh7Owk+6hWIzpWSDZKRh9F81TGey0mSwZPwh7gWdgjOGV2RRaxnguVKJFcYPycjnj2/HGsUffu1QUff9TPUM+Cw4CCN5gAE2ACTIAJMAEjAqlacGQWaUVLC0tGhgyxU4s+un8dl87+g4jwUCMkKWt3abORyOWYLc5BTzy0BOv8D6K4ax7MaPAJgsTEss3ar3TnqKBxc1mqaEXzLkV80KmwD7IK0aZK7y3f4+IjvTBQx6z5rlmzCv73c7RFI/jBQ9Rv0N7k6R+83xuU2YqsIqpQZqrNm3fKif+9e8Fo2DhaAKjj2u9atapiwpfDkSWLk0FEjBk3BWvWbJLNqH7pkunwyBEt4qiS+jx85Li0jpCgaNu+t7ZLucr4iOEfg9L5aguJop07/WRqX219ctn2yl4IH7b9GXZi0Ufjcuv+JczeNB4Pn9wxPiT3f/juS9StW1Nuk3DzqcuZrEyC4komwASYABNgAkwgbaTFtbN3gL1wr0qfQaymLVbWDgt9hFfi7S2XlEnAwSEzKgprB60uvnvPAZmtKqHvpHixwihSpKC0fJDgsKSQBaVSpXJyVfTLl6/hyhV/S077z9u4ZsmJ7M5ecMzkgodPg3A7+IpwsXoe57h2bFspV3mnRqPHfoO1azfH2Z4PMgEmwASYABNgAmmXQKq2cKTdx8p3zgQSj0DmzJmwb+96eYG4rFGJNwLumQkwASbABJgAE0hJBFhwpKSn9X/27gM8imKBA/g/vRcICZ3QO4YWegstVOkgiigqCNj7Q5+9YRcFFVH0YQGp0lvonUBoofdOGiE9pL6diXvcxbtLh9u7/75Pbsvs3uxv8r4v/+zODOtKAQsQEMMDf/1lXgf5t5XX0cI2bLWAWrEKFKAABShAAQpYqgADh6W2DOtFAQpQgAIUoAAFKEABKxBg4LCCRuQtUIACFKAABShAAQpQwFIFGDgstWVYLwpQgAIUoAAFKEABCliBAAOHFTQib4ECFKAABShAAQpQgAKWKsDAYaktw3pRgAIUoAAFKEABClDACgQYOKygEXkLFKAABShAAQpQgAIUsFQBBg5LbRnWiwIUoAAFKEABClCAAlYgwMBhBY3IW6AABShAAQpQgAIUoIClCjBwWGrLsF4UoAAFKEABClCAAhSwAgEGDitoRN4CBShAAQpQgAIUoAAFLFWAgcNSW4b1ogAFKEABClCAAhSggBUI2GTgcHB0RnZWhhU0H2+BAhSgAAUoQAEKUIACli1gM4GjQsXaqFa7FTy9K8DOzl62SnJiDE4eWo/UlHjLbiXWjgIUoAAFKEABClCAAhoVsInAUaNuMALrttE1UZbydMNRecohlmMRqxAXfV53jCsUoAAFKEABClCAAhSgQOkJWH3g8PT2R4sOI6XY1QuHcPH0HmRnZypPOexQoVJdpCTG8glH6f088UoUoAAFKEABClCAAhQwELD6wNG4RV/4Ka9TxVw/jROH1hncPDcoQAEKUIACFKAABShAgbIVsO7AoTzF6Bw6WQpG7JiHlKS4stXk1SlAAQpQgAIUoAAFKEABAwGrDhzOLh5oG/IocnKysX/bn6haqznK+wfK7eSEGOX1qt1IT0syAOEGBShAAQpQgAIUoAAFKFB6AlYdOLx8K6J5u+EyYIh+G05OrgZyubk5OLz3byTGXzfYzw0KUIACFKAABShAAQpQoHQErDpwiKFwGyl9OMQiwsWxA6txM+YiXJQnH01aDYCHlx/SUhOwb+vvpaPJq1CAAhSgAAUoQAEKUIACBgJWHTh8/aqhWfAgecPR107h5OH1upt38/BF684PyW0ROETw4EIBClCAAhSgAAUoQAEKlK6AVQcOd8/yaNVptBQ7fnANYm+cNdDr2Hsi7O0dcHT/Cvnkw+AgNyhAAQpQgAIUoAAFKECBEgtYdeBwcnZFu+6PS6SDuxci6VaUAVj7nuPlBIBiuFwxbC4XClCAAhSgAAUoQAEKUKB0Baw6cAgqNVRcOrtPTvqn8tk7OKJjryfl5oGd85GcGKMe4icFKEABClCAAhSgAAUoUEoCVh84ajVoj2q1WuJ2ehL2KUPj5mRnSboadYMRWLeN7Ey+K+wnOft4KZnyMhSgAAUoQAEKUIACFKDAPwJWHzhcXD3RussY2VcjMyMNN6MvwNXDBz7lqkiCC6d24/K5/fyBoAAFKEABClCAAhSgAAXKQMDqA4cwc3X3UUaruh+ubt4GhBdO7VLCRoTBPm5QgAIUoAAFKEABClCAAqUnYBOBQ+VydHKBp1cFiCcdqSm35OtU6jF+UoACFKAABShAAQpQgAKlL2BTgaP0+XhFClCAAhSgAAUoQAEKUMCcAAOHOR0eowAFKEABClCAAhSgAAVKJMDAUSI+nkwBClCAAhSgAAUoQAEKmBNg4DCnw2MUoAAFKEABClCAAhSgQIkEGDhKxMeTKUABClCAAhSgAAUoQAFzAgwc5nR4jAIUoAAFKEABClCAAhQokQADR4n4eDIFKEABClCAAhSgAAUoYE6AgcOcDo9RgAIUoAAFKEABClCAAiUSYOAoER9PpgAFKEABClCAAhSgAAXMCTBwmNPhMQpQgAIUoAAFKEABClCgRAIMHCXi48kUoAAFKEABClCAAhSggDkBBg5zOjxGAQpQgAIUoAAFKEABCpRIgIGjRHw8mQKGAhUr+mPco6Nx+vQ5LFq8wvDgP1sVKvjhiccfwtmz57Fg4XKjZUprp729HUY/MBTBwS3g51dO1uu9978orcvzOhSgAAUoQAEKUKBAAQaOAom0U8DXxRNdqzVDfHoytl49op2K38WaNnmkAewc7HD0fyeRm51b6t/8/YxP0b59a6xYuR7/ffNjo9d3dXXBzu2rYGcHhPYdhejoWKPlSmPn0iVzEBhYTXepG1HR6NP3Ad02VyhAAQpQgAIUoEBZC1h14PDyqYhGLUILNDy0ZwlupyUVWM7SCwT518b33Z/GtZSbGL7ig1KtbsuAunBxcMK+qFPIzMku1WvfzYs9vH+4/Lq/ui1FRlJmqX61eHKxfu18ec3uPYchPv6WyetP/3YqOnVsg23bduOZ5143Wa4kB8TTlrWr/5KX+P2Phfhz7mLExsYhI6N077skdeS5FKAABShAAQpYv4BVB45yFaqjaev7C2zF3Rt+RmZmeoHlLL1AWQaOzcM/hbODI4Ysfw9RqaZ/kbZ0o7IMHF9/9QG6de2AQ4eO4pFxz5ilEE8dxNOH3NxcFBROzF7IzMHQ3iH4ZOqbyM7ORqvgXmZK8hAFKEABClCAAhQoOwGrDhx2yjsrjk6uRvVqN+qEgMr1cSvuCo6ELzVaRms7He0dUMHNGxnZWbiZXrpPbKwlcHhV9wSUV5mSLiWXavP6+vpg04bFymtSdhgzdjIiI08UeP0li35BrVqBCNuwFS+/8k6B5YtaYPCgvnjn7VeQnn4b7Tr0LerpLE8BClCAAhSgAAVKRcCqA4cpITs7e3ToNQH2yi/oxw+sRmzUOVNFNbG/ikd5+YuuWtnb2ZmITUtUN3WfTvaOqOHlj/TsDFxNjoO/mw+CK9aXxw/EnMV15VUs/aWqpx9cHZzlrl9DX4KD4vbi1h8Rk5qgK3YpKbrYr1j51vGGo5sjUm6kolbfGki/eRsX11+Bk6cjaobWUPpY5ODcqkvITDZ8Bci1vAsqt60Ir2qeyM7MRtzReMQevYms1CxdvfRXPCq7w8ndUbcrNwdIOP9vH1HAr3E52CkdrcX1XLycUa1LZXhU9kBURAyi9sforpF/ZfKkcZgw/mEkJCSha8ig/IeNbo8ccT9en/I8bmdkoG27PkbLlGSnGjjS0tLRvmO/klyK51KAAhSgAAUoQIFiC9hk4PCvVBcNm4ciR+mLsHP9j8prLcpvoBpdRIjYMuJTg9qb6sPR1K8mfuz5LKKVV6K2XDmCEfU7G5z3zu7fse5ihG7f8vvfgZ/yxMTc8tj6r3Di5mVzRUweG7nxfrj4OMvO26Ijt1gSziXCo5I7HP8JCGkxaVjYZ6XuGr1mdkWl1v66bXVFdADf+p/duLTxqrpL9zli/UCIkKK/mOrDob5yFTHtCFo+10z/FFzefA2bX9ppsE/dmDF9Kjp2aINTp85i5APj1d1mP+vWrYWF83+WZUTgEMGjNJcHRw/Fq688DQaO0lTltShAAQpQgAIUKKqATQaOoHbD4O1bCdcvReLMsS1FNbOo8vbKU4eJzZS/Xiu/r1f18ENI9SCTncbVwKHewKoL4Ui8nYpBddrBzdFFvorVa/EU3ROLQXXao7yrlyw+vmneX+AXnNqGWxkp6iXw99mdclQs3Y4irKiB43ZCBmIOxqJa1yry7JzMHBkcAntVk08bVj28AXHH4uWx4Wv6K+HBFdd2RyH+1C25XrVTJbj55b06t/qRjYiNNHxS0+zxRvCqobxKpSx1BgTKz4IChyiUcCFJPtWoqdTD2TvvSc/axzYh+lCcvIb+PyuX/4GqVStj/foteOW1d/UPmVwXQ9ZG7Nsgjz856RXs2bPfZNniHJj5/Wdo27ZVkZ66FOd7eA4FKEABClCAAhQwJ2BzgcNF+QW6Tbex0iRi+1ykJBv+cmoOy9KPFdRpXD9w/BS5BrOPrpO3JIbTXTX4Pbk+ccO3OBx7/l+3WhZ9ONTAseGpbTJAiDDh5u+GyNkncGBGJEK+6ihfaQr/7CBOzDsj61QpOADRSjgRoUR/GRGmPMUo54KLYVew9bXd+ocM1tUnGAUFDtHH4+8ha3TnPrBlkPKqlxNOLTqHPR/deQqkFtizew1cnJ0xfcZs/PTz7+ruAj9371wNMUzuDzP/J/8r8IRCFmjatCF++fkbODk5Ym/4AUx48qVCnsliFKAABShAAQpQoHQFbC5w1GrQHtVqtURayi3s2/ZH6Wre46sVJXCMWfMZziVc19VYfX3qrV2/IezSAd1+daUsA8ffg9Yg6Uoy+v/ZE+Ub+GLnu/twdtkFtJ3SAvWH18HhH4/h0MxjalV0nyIAeFR0k9tBE5ugRveqiDkchzXjNunK5F8pbOCI/EUJPdMjdaf3+LYTqnSohKs7bmDjs9t1+9WVgxEb5erzL76JzZt3qLsL/Fy3Zj4CAipg46btePGltwosX1CBHdtWwN3dTfbpycrKwoEDkXj51XeUpxzG+6wUdD0epwAFKEABClCAAiUVsK3AoYwg1L7HE3B0dMbZ48pf1S8eLqmfRZ1f2MAhRrHqtvBVg7p/3fVJtKnUAFPD52PZuX8/ISjLwLGg1wqlw3g6+swOgX+Qn3xCIZ5UtHrhPjQeUx/H/zyNfV8ckvX1rOqBtq+3ROU2AfJ1K4ObUDZEH5BlI/Ke3OQ/JrYLGzhWPhSGmyfuDP/b4ummaDquoXydSrxWlX/Zt3ed8nPliM8+n4E//lyU/7DJbREQPDzc5Tni3JIuy5f+hooVA+Ds7CQvdf7CJUxUXteKijLd4b2k38nzKUABClCAAhSggDkBmwoc5f1rokmr/tJj14afkJV525yN5o4VNnCITuODlfk09JcvuoxH+8qN8Om+BUq/jF36h+R6WQaO+d2XQfTjCFUCR4ASOLa8skv24Wj5TDM0ebSBfJ1KvFYllqEr+imjRrnLddGhPEbpr5GVkoXyDX3hW9dHDner/yqULKj3T2EDx4Jey+WoWeqpzSc3gegLEqP031hjJHCErVsAMfHfsmVr8NY7hp341WsY+zywf4N8GiH6fYj+H6W13H9/H7z13xdlCDp4MBKPPvZsaV2a16EABShAAQpQgAJFErCpwNEseBB8/arhZswFHN2/skhQWihs7YGj/rDa8umGaItF/VYiNSpN1yytXwpCowfr3bPAMfvnaWjZohmORB7Hw2Of0tXL3EqlSgFYs2qeLNKt+xDcunVnuGFz5xX22MwfPkfbNi3l61RdQwYX9jSWowAFKEABClCAAqUqYDOBw9nFHW1Dxkm8yH3LER97qVQhLeFiZRk4Ngz7WI5kNT5sGo7GXSyV21U7jRf2CUe3LzqgercqRvtpDPyr1z19wvHaq89g9ANDEBsbh569RxTKR50JXPS1aN2md6HOKUohdZ4PDotbFDWWpQAFKEABClCgtAVsJnDUqBuMwLptkJWVAfE6lTL5Rmlb3vPrlWXgmNdvipw08M8TmzD90PJSudeiBo7gl4PQcHQ9ZKVnY26nJUob5lVDTBrY6YM2ciP/6FL5K1pWr1Q1bFgP8/6cqfxY5aJX6EgZPPJ/d/5tddjakyfPYNToCfkPl3ibE/+VmJAXoAAFKEABClCgFARsJnC06/4YnJzdcOXCQZw/UfhRhErBuEwvMfG+/gj0CpDfUUGZpK+JXyCylYkMt189qvveeSc345Ay1K06LG5x+nCIeTjGNcn7K3xyZjqO37wkf7n+ROnzkX+Gct0XF7BS1MAh+mn0/6OnvKoIHWLmb7cKrnJkK/Wr8geOhg/URc3e1dXDslO62BDzeqhD64qRp478fFyWUQNJUftwiJMXzP8J9erWxurVGzDljQ/l9Uz9IzqKb9uyXJnt3g5jxk5GZOQJU0WLvV99gpKZmYXgtqX/BKXYFeOJFKAABShAAQrYlIBNBA7vcpUR1HaobNjwrb8jPbV035W/lz8x8/u/jmqeFcxW4aO9f2HF+T1oXL4Gfur1PKJS4zFk+fsG56idxk2NUiVmNB/doCtG1e+Kcq55k+iJC4xb9yVOxl8xuFZhN9S5M+aHKJ3GEzPQe1Y3VGxZQc7mLWb1VkeG0h+lSgSI1i8GQZ2ZXHyXGJnq7PKLcmZwMVnfsmFrdVXo8kk7BPaspts2thIVEYt14zfLQ2rgUF/zUssHTWqC+55oZHKUKlGuefOm+HX2N8pTtCy0UWYOz8kxnCtEvZb4/M9rz+KBUYNx/vxFDBmW96qf/vHSWNefyVw8QRFPUrhQgAIUoAAFKECBuy1gE4HjbqPy+8pWwNHVAX6Ny8HR3QnRETHITM0q2y8swtWXLpmDwMBqygSAPysTAP5h8sxdO1bBzc0Vj49/Afv35w35a7JwMQ+Ipyfbt+bNyyEukZKSioOHIvHU0/8p5hV5GgUoQAEKUIACFCi6AANH0c14BgVMCgQHN1eGo30JEQeO4G0Tw+PKQPLNxzh3/hKee/4Nk9cqjQONGzeQw+NWrVpZBpxLl65i6PCyeaJSGvXlNShAAQpQgAIUsD4BBg7ra1PeEQUoQAEKUIACFKAABSxGgIHDYpqCFaEABShAAQpQgAIUoID1CTBwWF+b8o4oQAEKUIACFKAABShgMQIMHBbTFKwIBShAAQpQgAIUoAAFrE+AgcP62pR3RAEKUIACFKAABShAAYsRYOCwmKZgRShAAQpQgAIUoAAFKGB9AgwcGm3TgAB/3E6/jYTERI3eAatNAQpQgAIUoAAFKGALAgwcGm1lBg6NNhyrTQEKUIACFKAABWxMgIFDow3OwKHRhmO1KUABClCAAhSggI0JMHBotMEZODTacKw2BShAAQpQgAIUsDEBBg6NNjgDh0YbjtWmAAUoQAEKUIACNibAwKHRBmfg0GjDsdoUoAAFKEABClDAxgRsLnA4ODojOytD883MwJHXhANrt4W9nT2Wn9uDnNycQrdr4/I10DygDo7GXsCh2POFPk9rBUeNHIzAwGpYvGQlzpwp/H3WqlkDj40bjavXbuCHmf/T2m2zvhSgAAUoQAEKWJCATQQOn3JVULtRJ3h4+cFO+eU0V/nFNDU5HudObMetuCsW1ByFrwoDR57VzlFfypWei6YgNet2oQGfbNYPjzTuiUWnt+OLiMWFPs9Uwbq+VeDv5oNT8VcQl55kqthd379h/SL4+ZXDtG9m4Zdf5xb6+wcODMX7776GlJRUdOw8oNDnsSAFKEABClCAAhTIL2D1gaNchepo2vp+3X3fVn4ZdHH10m1H7l+B+JiLum2trDBw5LWUpQSOr7s+iTaVGuCjvX9hxfk9FvNjxMBhMU3BilCAAhSgAAVsVsDqA0erTqPh7lkeaakJOLx7MTIyUuHk7IpmwYPlE4/0tESEb/lNcz8ADBx5TVbR3VdZsUNUanyR2rC0n3BYauCoWrUyvLw8cenSFaSmphXaiE84Ck3FghSgAAUoQAEKFCBg9YGjc5+nJMH5Eztw5cJBHUel6o1Rr0mI3N6+9jvlNatc3TEtrFhL4KjlU0mJC3Y4l3AdVT390CqgHpIz03Ao5pzJV5P83Lzh6uCkaybRdtdSbuq286+IPh71y1VFE79AXE2OVa59HmMb9SjxK1V+ypMyXxdP+XWvtxmFRkq/kF+PrceGS3d+zmLSEpCohNziLo0b1YePrzeOHj0JT08P9O/XUwaIzZt3IOLAEaOXDQioAPGf/nL+/CX5epT+Pv315s2bIqRrR6Slp2PlqjCIbXOvVFWo4IdatarLS6SlpSMy8oT+5bhOAQpQgAIUoAAFdAJWHzjadBsrX6H6d+BoogSObsjMSMPujbN1IFpZsYbAYW9nh+0jv5DkC05vw4h6nQ34P923AH+f3WWwT2zM6zcFNbz8Dfab6sPh6+KBb7pNguhjoS4Z2VnYdf04ulZrVqI+HG+1fRB9arZWL2v084fDKzHn+Aajxwqzc8umpfDx8cKhQ0cRFNTE4JQTJ05j7KNPK0/tMg32z5r5JYKDmxvsM9WHw9HREb/MnoZmTRsZlD94MFKGDlN9OEQYEU9BxJKZmYXgtr0NzucGBShAAQpQgAIUUAWsPnAE1m2DGnWD816p2rsEGekpcHZxV16pGiRftbqqPPU4pzz90NpibYFD+IunGpuvHEZz/zoyDIh9T2/6DhHRZ8Sqbhlcpz2qKE9DxDKmYXf5aSpwzOr5nHyykaZ0KP/jxCY4KE87HlLOcXZwlOeVpNO46LMhnpqIZVDtdghQXu/aff0EjsRdkPvEPyLYnLh5Wbdd1BU1cIjz0tNvY8PGrajgVx5t27aSl9q6bReefe4Ng8sOGzoAISGd5L52bVtChApTgePzz95Bzx5dZNnw8IO4cSMKPZRtd3c3uY+Bw4CWGxSgAAUoQAEKFEPA6gOHg/LqTc0Gyi+oNZpJnozbqTJwiI2kW1EQncazMtOLQXdvT7G2wHFYGZp20obpyFX+J5a32z2E0MBWEPsnbvjWJLa5TuMNy1fH7F4vyHMfWDUVl5Ki5Xo936r4X+hLcr0kgUO/UmXVh0MNHDk5uejdZyRiY+Pk1z7z9BN4/LEHIfZ36XY/kpNT9KujWzfXadzDwx3btiyHvb0d5s5bgk8+zXP29fVB2LoFMqgwcOgouUIBClCAAhSgQDEFrD5w2Cmv7dSs1w7Varc0IBJD4544tB6xNwz/em5QyII3rC1wvLZ9NrZdjdSJiz4Xv/bOCwUd/npRtz//irnA8agy7O0EZfjbo3EXMT5smsGpc0Jflq9ZaSVw7N9/CI+PzwtP4kacnZ2we+caGRamvP4BVq/ZaHB/6oa5wNGje2d88fm7sv+SGPpWv1P5jOlT0bFDG5PD4g4Z3A+jRg6SXxN3Mx5PPf0f9Sv5SQEKUIACFKAABQwErD5w1GsagkrVGiM7OxPXLh5WhsC9BF9lqNwqgfcpf8F1RvTVkzh5JMwARQsb1hY4hq34ANf1On472Ttgy4jPZFMMWPo2bpqY28Jc4BAduQfUaoulSj+QT5T+IPrLm0r/i75K/wutBI4//lyEzz6foX8LytOJZbID+ayffseM74z3QzIXOJ6cMBaTJj4K0em7fcd+BteePGkcJox/2GTgMCjMDQpQgAIUoAAFKGBGwLoDh/J0o2OvJ5W/Ajvg1JGNiLp6XEfhX7keGgb1lpMA7lg3U37qDmpgxdoCR+iSN5CkdODXX9Qw8fCaz3BWGcXK2KKWMdaH47POT6BjlcaYe3Izvj24zOD0F1oMwYj6nTUTOKbPmI2ffv7d4B7UMLF02Rq8/c6nBsfUDbWMsT4cb7z+AkYMH4iEhCR0Dcl7WqGe9+DooXj1lacZOFQQflKAAhSgAAUoUGwBqw4c3uUqI6jtUImzM+xHZGfdGc1HhJCOvSfKY5H7liM+9lKxEe/FidYWOEau/AhXlCFr1cXVwRkbh0+Vm/3+fhO3bhvvo2AucLzSejiG1OkgJ+ITE/LpL++0G4PegS01Ezjmz1+Kj6Yavha2Y9sKiH4YP8z8n/xP//7UdXOB45FHRuGF556UndHbdeirniI/n31mPB4bN5qBw0CFGxSgAAUoQAEKFEfAqgOH/izjEdvnIiX5zlwNrm5eCO46VpodP7AasVHniuN3z86xtsDx7u4/sPbifp2nGKnqu+5Pye3i9uF4sGEIng4aiPMJN/DQGsMnAAsH/BdVPMqXWuBQn6bMilyDX46u091HSVfUTuNiCNwHHnxSdzkxJ4d4pUr0UXrltXexfv0W3TH9FXOBQ/TREH01xBLSYyji42/pTv119jdmh8Xt26c7/vPac7J8dnY2uvfMC/a6C3CFAhSgAAUoQAEK/CNg1YHDydkN7bo/Jm816spxnD66STfBX+2GnVC1ZpA8tnfLHNxOS9LUD4W1BY5LSTEYu/YziDkyxPwc34ZMRgsldOy4dgyvbPvJZNuYe8JRWQQKJViI5amNM3Ag5qxc76C8ZvW58rqVWEqrD8cLLZVXtJR5RE7FX8Vj679CjjIoQWksauAQkxs+9PBkHDt2Ul526sf/RZ/Q7sjKykKHTv3/NReH+t3mAoe9vb3S8Xy17IC+ectOPP9CnlX9erUxb+4s2SHd1ChV6tMR9Xuat8wbnljd5icFKEABClCAAhRQBaw6cIibFLOJi1nFxSI6jt+KvQwfv2qyw7jYF33tFE4eXi9WNbVYW+AQ+NGpt7D7xgk086sJMQO5WMau/Rxnbl2T6+If8RpUSLW8oCi2xeR9YtmpBJPMnGy5vke5hjph4IcdHkFI9SBkKwFAzPEh5uHoVu0+WU78U1qBI8i/Nr7v/rS8rvguMZxvphKeFp3ZYTD6lu6LC7miBg5RXAyBe+Lkafh4e6Fq1cryCouXrMR773+hu5p4avHyS5N024GBNWRwSEhIxE1lNCmxxMXF44kJL8r1l1+ajDEPDZfr169HKWVuoWHDunBwcJD7TAWOcY+OxnPPjpdlxD8MHDoKrlCAAhSgAAUokE/A6gOH6KsRWK8NqtVqme/WATHp34VTu5Vf5PJ+Uf1XAQveYW2B46uIJRBPCdRFPOkQTzbCo06pu+Tniy2HYni9Tgb78m9suHwQb+6cI3c7Ku0v5vToUf3OzNuir8j+qNMYpEwguPD0dnwZsTj/JYq13bNGc4xr3FsXlsRFvldmGv+tFGYaXx+2BT26d5HhQa3cmrUb8Z8pH6ib8lOEBxEizC35ZwZ/+62XIYa5VRcRbFavCUP/fr1M9uH46ov3dJMLijDTNWSwejo/KUABClCAAhSggIGA1QcO9W5F8HB194GLqwduK7ONp6cmaDJoqPdjbYGj64JXlVvLRaPyNZCsTMR4IfGG8lpS3iSA6j2X5NPDyRUNylXD1eQ4RKXm/aW/JNe7W+eqTzjEkLhz5y2WM4x7e3lh2/bdBvNmlLQ+Ls7O6Ny5nRwid9fucPk0xdw1N4YtRvnyvrLIm29/guXL15orzmMUoAAFKEABCtiwgM0EDmtrY2sMHJk5WfesmcQTEE+lz09hl93XT9yV4KIfOMRcHJawuLu7Yef2lbIqsXE30bNX3itZllA31oECFKAABShAAcsTYOCwvDYpVI0YOArFVOhCq4e8Dx9nj0KXF69siVe3ynqxxMARHNwcX3+Z9yqXmP8jbMPWsmbg9SlAAQpQgAIU0LAAA4dGG4+Bo3Qb7uFGPeDr4lnoi644txvnE6MKXb64BS0xcBT3XngeBShAAQpQgAK2KcDAodF2t4bAIehreleULXDhLvzyrsWmbtCgLry9PXH8+GkkJxuf/FCL98U6U4ACFKAABShgOwIMHBpta2sJHBrlZ7UpQOUJr28AACVTSURBVAEKUIACFKAABQopwMBRSChLK8bAYWktwvpQgAIUoAAFKEABChgTYOAwpqKBfQwcGmgkVpECFKAABShAAQpQAAwcGv0hYODQaMOx2hSgAAUoQAEKUMDGBBg4NNrgDBwabThWmwIUoAAFKEABCtiYAAOHRhucgUOjDcdqU4ACFKAABShAARsTYODQaIMzcGi04VhtClCAAhSgAAUoYGMCDBwabXAGDo02HKtNAQpQgAIUoAAFbEyAgUOjDc7AodGGY7UpQAEKUIACFKCAjQnYXOBwcHBCdnam5puZgUPzTcgboAAFKEABClCAAjYhYBOBw9evOuo27gI3D1/ZqJkZaYiPuYTTxzYjJztLkw3NwKHJZmOlKUABClCAAhSggM0JWH3gCKhcHw2CesmGFU82MtJTdMEjNSUeB3cu0OQTDwYOm/v/Km+YAhSgAAUoQAEKaFLAqgOHnb0D2vd4HOI1qpjrp3Hy8Hrk5ubC1d0HLTuOkvvPHt+GaxcPa67xGDg012SsMAUoQAEKUIACFLBJAasOHB7eFdCywyjZsLs3/IzMzHRdI9drGoJK1RojLeUW9m37Q7dfKysMHFppKdaTAhSgAAUoQAEK2LaAVQcO/8r10DCoN7KyMrArbJZBS5f3D0STVgPkvp3rf9Tca1UMHAbNyQ0KUIACFKAABShAAQsVsO7AUakuGjYPLTBwhG/5DelpiRbaRMarxcBh3IV7KUABClCAAhSgAAUsS8CqA4entz9adBgpxXcpr1Rl6b9S1UR5pap6Y3ns0O5FSLx1w7JapoDaMHAUAMTDFKAABShAAQpQgAIWIWDVgcNe6TTe7p9O49HXTymdxsOg9Bo36DQuWuHo/pW4GXPBIhqksJVg4CisFMtRgAIUoAAFKEABCtxLAasOHAK2YtWGqN+shzSWw+LeToWbMkpVbm4O7Ozs5X4+4ZAM/IcCFKAABShAAQpQgAKlLmD1gUOIlfeviTqNO8PVzVsCJifG4NyJHWgWfL8MHXs2/yrn5yh13TK8IJ9wlCEuL00BClCAAhSgAAUoUGoCNhE4VC07OzsZMHJysuHi5oU2XcfKQ9vWfidftVLLaeGTgUMLrcQ6UoACFKAABShAAQrYVODQb+7aDTuhas0gJMZfx6E9i/UPaWKdgUMTzcRKUoACFKAABShAAZsXsPrA4eziDjHj+O20JF1jB1SujwZBveR25L5liI+9rDumlRUGDq20FOtJAQpQgAIUoAAFbFvA6gNHhUp10Kh5H6SmxCv9NJLh7ukHEULEIkamEiNUaXFh4NBiq7HOFKAABShAAQpQwPYErD5weJerjPvaDNaNSCWaWIxQdeX8AVw4tVuzLc7AodmmY8UpQAEKUIACFKCATQlYfeAQrSmGv3X3LCefbKSlJiI9NUHzjczAofkm5A1QgAIUoAAFKEABmxCwicBhjS3JwGGNrcp7ogAFKEABClCAAtYnwMCh0TZl4NBow7HaFKAABShAAQpQwMYEGDg02uAMHBptOFabAhSgAAUoQAEK2JgAA4dGG5yBQ6MNx2pTgAIUoAAFKEABGxNg4NBogzNwaLThWG0KUIACFKAABShgYwIMHBptcAYOjTYcq00BClCAAhSgAAVsTICBQ6MNzsCh0YZjtSlAAQpQgAIUoICNCTBwaLTBGTg02nCsNgUoQAEKUIACFLAxAQYOjTY4A4dGG47VpgAFKEABClCAAjYmwMCh0QZn4NBow7HaFKAABShAAQpQwMYEGDg02uAMHBptOFabAhSgAAUoQAEK2JgAA4dGG5yBQ6MNx2pTgAIUoAAFKEABGxNg4NBogzNwaLThWG30qxksFVZdCDep0bdma9gp/zNXxuTJPEABClCAAhSggEUJMHBYVHMUvjIMHHlWL74wEY6Ojvjyqx+QlZVVaMAe3TujW9cO2Ll7H1av3lDo8+51QXsne9ToXhU+tbzg4OKA3JxcHPg28q5Vy6eWN+rcH4iUa6k4ueBskb+3TaUG+Lrrk9gXdRrPbv7e5PnTQyajZUBdPL9lJvbeOGmyHA9QgAIUoAAFKGD5ApoNHI5OrqheqwXKB9SUyqeObERSQpRRcTs7e9Ru1Al+AbXg4uqJ2+nJiI+5iLPHtyEnJ9voOZa+k4Ejr4UORmyUKyE9hiI+/lahm23a1x+ia5f22L//EB4f/0Khz7uXBd0quGLQkj5wcnc0qMZvrRYabJflRr0htdDuv62QFpeOhb1XFPmr5oS+jLq+VfD0pu8QEX3G5PmtKtbDt90m4VT8VTy67guT5XiAAhSgAAUoQAHLF9Bc4HB2cUf1Oq1RuXoTiCChLpH7V8gQoW6rn3b2DrgveBC8y1WWu7KzM+Hg4CTXkxNjcGjPYuRkF/4v4+p17/UnA0deC9hS4OgytR0Ce1VDVmoWTi0+h1tnEwHlCcfZFRfv2o9jSQJHc/86+K77U4hKjceQ5e8XWOclA99ERfdymLxxBg7GFP1pSoFfwAIUoAAFKEABCtwVAc0FjgbNeiKgagOJk5QQDS+fALluKnBUrNYI9Zt2l2UO7l6IpFtR8PCugBbtR8jAIp5yXLt4+K5gl+aXMHDkadavVxt29vY4edL0X8uNuWvxCcfgv/vAq7onDv94DIdmHjN2W2W+rySBY1bP59DELxBTw+dj2bndBdZ1UJ32eK31CByNu4jxYdMKLM8CFKAABShAAQpYpoDmAkf9Zj3g6uaNC6d2IfHWDXTsPRH2ylMMU4GjRYeR8PT2x9ULh3DuxHbZCs7O7mgT8ogMHGkpt7Bv2x+W2TpmamUtgaNNcAsZGPbs2Y9mTRshNDQEt24lYn3YZly8eMWoQK2aNeDh6a47lqP8lf/YMdPv+Ys+Hp07t0P7dq1x9uwFrFm7Ee+9+5omXqnyquEJF29nea89v+8iX6c6MD0SN8Kj5T7RhyPuWLzOQl0Rr19VbOkP75peSLqUjKiIGKRGp6mHDT7t7O3g17gccrJycPOE8lqaHVCpdQAqtvZH8tUUXFh7Gdm38149NBc4fOv6wNHVAenxt+V5+l9S3tULKwa9K3d1XfAqMnMKfqro7OCIzcM/lef0X/oW4pVXIU0tLs7OuC+ose7w/v2Hldclc3TbXKEABShAAQpQ4N4JaC5wiNeocnPv/CJhLnCIV6c69JogdQ/vXYKEm9fkepNW/VHev6ZOfffGn5GZka7b1sKKNQQOe+XJRMS+MMkdHn4QwcHNDejnzluCTz791mCf2NixbQU8PO4EDrHPVB+OKlUqYe4fP8DHx1sUk4sIKBcvXkKtWoEW34djsNJnQ4QOc0v+Phz3TWiMoCfv/PKtnhv56wmjHcxFOBm+doAstuaxTQid1Q12Dkrq+GfJVF7hmtf5b7llKnC0fjEIjR6qJ8tsfW03LoYZhsXWSp+Mb5Q+GQkZKei75E310gV+rh7yPnycPWQHc9HR3NTSsUMbzJg+VXd46PBxOHfu7r1qpvtirlCAAhSgAAUo8C8BzQWO/HdgLnC4uvsguMsYecrO9T9C9N8o5x+Ipq0GIDX5Jtw9y8tjETvmISUpLv+lLXrb2gKHwI6OjsWOnXvRPKiJDANi31vvfIply9aIVd3y2qvPoHr1qnK7U8c28tNU4AhbvxAV/MorbZ+NjZu2w9HBAV27dlSeiuX9Ql3cTuNiyNaZPZ/R1cncyurz+7Dk7E5zRUweqz+sNnzr5IWlekNrQ4xSdX1PNBIvKP03lCU3Fwj/7KDu/LqDa6H9m63kdmzkTdzYF42AoAoIaFFB7gv//BBOzDX8xV0/cGSlZ8PB2R4xR24iNSoVfo3KwaOSO/5ot1iebyxwtHujJUTdxNOWzS/txJWt13X1UVceaNAVzzYfhMi4C5gQ9o26u8DPH3s+i6Z+NfHNwaWYd3KLyfIMHCZpeIACFKAABShwzwWsOnCIjuJBbYdK5G1rZsBeeUWjTbdH4KSMcCVeo2rVabR8rSpy33LEx166541RlApYW+CIiYlDaN+Rymswym/QyjLvz5lo2LAexP5eoSNM0pjrNN6tW0d8/WVe5+Rxjz2HAwePyOt06tQW07/5WK6XJHDsGFW40ZNE2PhsX8lHkhoRNhCu5Vyw6YUdRn+pFzc0cuP9cPFxxtUdN7Dx2bxXCMX+zh+3Rc3e1SGfVnRRnlbkMYtDyB84VjywHkmX77y+VL6BL26evCXL5g8cnT5sg1p9asiwETZ5m+5VL1lY75832z4IMbfG8nN78HH4X3pHzK++3mYUBtRqK+fj+GDPXJOFGThM0vAABShAAQpQ4J4LWHXgqFCxNhq16Ctfwdq+9nvUadQZVQLvw6Wz+3Dx9B75upV47erk4TBEXzPdB+Cet5KRClhb4BDzaMz5bb7uTkWfi2+nfSS3m7fM6/SvO6i3Yi5wfDL1TYT2DkFs3E307DVc7yxgy6a/5WtWxQ0c4mK1fCrJyekMLmxkI/52ktn+B0ZOMbqroMAh+nqM3HS/PHf5yHV5o1j9cyWPyu4YuqKf3FoycDWSr6XovkM/cOz74hCO/2n4BERXUFlRA0f6zduIPhSLGiFVkZudi7XjNyPmkOmnhGqH8R8Or8Sc4xv0L2l2/ZHGPfFks34FdhyvXTsQH33wuu5ak556rUjDJOtO5AoFKEABClCAAqUuYNWBw9evGpopQ+KKRbw21bLjA3IOjvAtv8kQor6OdSxiFeKiz5c6blle0NoCx4MPTcSx46d0ZK6uLti9c7XcHjjoYVy+fFV3TH/FXOD4bc4M2RH90KGjeGSc4etPfyr9Oho3qm/xfTj077WgwOEf5Ic+s0PkKfn7dYidY/YOk30zNjyzHdd23tBdWj9wLO6/Cik3UnXH8q+ogUN/vwgfC/uskMFDf7/++kcdH0W3avdhwelt+Cpiif4hs+svtByCEfU6Y/OVw3h9x69my/IgBShAAQpQgAKWKWDVgUP00RCvTYnldnqSMumfF9ShccW+zn2eEh84sGsBkpUhdrW0WFvg6N1npOzDod8GapiY9NSr2LVrn/4h3bpaxlgfjqVL5iAwsJo8V1xDf/lx5hcQI2SV5AlHgLtvoZ5wJGemISWz5IMSFBQ4AntWQ5dP2iEnM0fX50L/nkfvGCJHkdr1/n6c+ftOwNYPHMaCiv419AOH2t9DjHJ1buVF7HgrXL+owfq4Jr0xvmkfhEedwnObfzA4Zm5DdDQXHc5nRa7BL0fXmSvKYxSgAAUoQAEKWKiAVQcOMRt5+x6P6+hvXD6G00c3yW1XNy8Edx0r1/du/p988qErqIEVawscYx99GocP35lbwtPTA9u3Lpct0W/Ag7h27c5f5PWbx1zg+GX2NLRo3gxHIo/j4bF54VI996+5P6JBg7rFDhyi07il9eEQQ9v2+62HvEVjwWFMuPKEQwkH6yduNehrUZzAIULNkkGrUaV9JV0nddFh/PLmvJHgVGf1s3PVpvik02OITr2FwcvfU3cX+Pn3wLcggt1r22dj29XIAsuzAAUoQAEKUIAClidg1YFDcLcNGQcxO3lWVgb2bvpVjlQl9lep0Qx1GneR+3eFzRK7NLVYW+D46effMX3GbF0bDOjfCx+8P0VuF7cPx/vKXBsDB4YiMTEJXbrlvVqnfsHO7Svh7u5WosAxp8/LsFeCR0GLmOTur1NbCypW4PGCnnA4uTvigW2D5XXWKsPbRuv1qShXzwcD5vWSxxaGrkBa7J0nLsUJHGlx6VjYe4W8Xo9vO6FKh0ryycriAasMrq3eVFVPPyzo/4bc7PDXi+pus5/6oW7Eyg9xNdl0HxExTPKqFXc6lb/6n/cg5nbhQgEKUIACFKDAvRew+sBRo05rBNZrq4x+lI3wzXOQkZEKB0dn5enGw3K0qivnInBemURQa4u1BY6UlFSI16rEp5ifY9XKP1GpYoCc/G/QkLwnUcbayNwTDtFHQ/TVEMubb3+C5cvXyvWHHhyGV17Oe+JRkleq5MXu4j8FBQ5RlSHL+8KzioccVWrVmA1y9CiRifr8HALRx0M/KKhVL2ngcFSCjpjHQwSehHOJWDbi368+ifCw7P634adM2vn8lpnYe6PgQRraVW6IL7tMkB3uByx9WxlYS29oLbXy/3z6+vpg88Y7fUPe+O/HWLlqfb5S3KQABShAAQpQ4F4IaDJwtFNekxITAIrFUQkPYhGBQvwnlhMH1yjD3F6W607ObmitzMUhyol5OBJuXoWXbyUZNsT2vq1/IOP2nRF75Eka+MfaAocgT0+/jVOnzqJmzerw9vaSrfDU0/+Rc3OoTTJ50jj06tlF3YSYvE8sly5dkXNtiPXdeyJ0EwYunP8z6tatpQwSkIszZ8/DQZmVXoxopC7WFjiqdqqM7tM6ytsTnbljj8TJWcTd/N3kvm1T9uDCurz/b6gGJQ0c4joBSpgJ/afD+tE5JxExLW8IYvU7xKcY3lYMc3sg5iye2jhD/5DR9e+7P40g/9pyGF0xnK65pVw5X2zakDdXiCjHwGFOi8coQAEKUIACd1dAk4FD7extiir/qFOis3izNoPgpkwEqC6iE/nhvUuRnpqg7tLUp7UFjkWLV2DY0LzZrkVDiPk4PvzoK4j9+stPP36J1q2b6+/61/rpM+cwYuQTcr+LszN+//071KtbW1dOPEU5c+Y8gpQJBvftO4gnJhTuFR/dBe7RivqEY+NzO3B1+3WTtajRoyo6f9hWThKoFhJD1+58JxznVl1Sd+k+1cAhJu77PXiRbr+xFXViQWNPSlq9cB8aj6kvT1v5UBhunsibu0O9jr3yR4KVg9+VM4ePXPkRriTHqof+9VnDyx/z+k2RM5P3//tt5OTm/KuM/o6+fXvg4w/zXtkS4TK076h/DUKgX57rFKAABShAAQrcPQFNBo7i8jg5u8LDq4KcVTwzI624l7GI86wtcHTo1F8JGTkIUSbruxmfgPDwA3K7tLD9/MqhY8e2OHnyjPyvtK5rydfxDvSCb11v5TWnJCScT7SIqg6r1wkvtRwK0a9lavideVfyV25K8CgMrN0WX0QsxqLTdyYwzF9O3f76qw/QrWsHubll6y4893xe+FCP85MCFKAABShAgXsnYFOB494xl/43W2PgSE3Vdggs/Va2vis6Kq+0vd9+LFKzbuP9PX+avEExM7m7owve3DUHWf+8KmmysHJA9N8Q/TjEk7EevYZx0j9zWDxGAQpQgAIUuMsCDBx3Gby0vo6Bo7QkeR1rEBAzxzs6OmLN2o14/4MvreGWeA8UoAAFKEABqxFg4NBoUzJwaLThWG0KUIACFKAABShgYwIMHBptcGsIHIK+Vasg2QJitCguFKAABShAAQpQgALWJ8DAodE2tZbAoVF+VpsCFKAABShAAQpQoJACDByFhLK0YgwcltYirA8FKEABClCAAhSggDEBBg5jKhrYx8ChgUZiFSlAAQpQgAIUoAAFwMCh0R8CBg6NNhyrTQEKUIACFKAABWxMgIFDow3OwKHRhmO1KUABClCAAhSggI0JMHBotMEZODTacKw2BShAAQpQgAIUsDEBBg6NNjgDh0YbjtWmAAUoQAEKUIACNibAwKHRBmfg0GjDsdoUoAAFKEABClDAxgQYODTa4AwcGm04VpsCFKAABShAAQrYmAADh0YbnIFDow3HalOAAhSgAAUoQAEbE9Bs4HB0ckX1Wi1QPqCmbLJTRzYiKSHKZPMVtbzJC1nIAQYOC2kIVoMCFKAABShAAQpQwKyA5gKHs4s7qtdpjcrVm8DOzl53c5H7VyA+5qJuW10pann1PEv/ZOCw9BZi/ShAAQpQgAIUoAAFhIDmAkeDZj0RULWBbL2khGh4+QTIdVOBo6jl5cU08A8DhwYaiVWkAAUoQAEKUIACFNBe4KjfrAdc3bxx4dQuJN66gY69J8Le3gGmAkdRy2vlZ4KBQystxXpSgAIUoAAFKEAB2xbQ3BMO8RpVbm6OrtUKChxFLa+7sIWvMHBYeAOxehSgAAUoQAEKUIACUkBzgSN/uxUUOEpaPv/5lrLNwGEpLcF6UIACFKAABShAAQqYE2DgMKdjwccYOCy4cVg1ClCAAhSgAAUoQAGdAAOHjkJbKwwc2mov1pYCFKAABShAAQrYqgADh0ZbnoFDow3HalOAAhSgAAUoQAEbE2Dg0GiDM3BotOFYbQpQgAIUoAAFKGBjAgwcGm1wBg6NNhyrTQEKUIACFKAABWxMgIFDow3OwKHRhmO1KUABClCAAhSggI0JMHBotMEZODTacKw2BShAAQpQgAIUsDEBTQaOdj0eh5jQTyyOjs7yMycnG+I/sZw4uAbxsZfluvinqOV1J1rwCgOHBTcOq0YBClCAAhSgAAUooBPQZODo3Ocp3Q0YWzkWsQpx0ed1h4paXneiBa8wcFhw47BqFKAABShAAQpQgAI6AU0GDl3tbXiFgcOGG5+3TgEKUIACFKAABTQkwMChocbSryoDh74G1ylAAQpQgAIUoAAFLFWAgcNSW6aAejFwFADEwxSgAAUoQAEKUIACFiHAwGERzVD0SjBwFN2MZ1CAAhSgAAUoQAEK3H0BBo67b14q38jAUSqMvAgFKEABClCAAhSgQBkLMHCUMXBZXZ6Bo6xkeV0KUIACFKAABShAgdIUYOAoTc27eC0GjruIza+iAAUoQAEKUIACFCi2AANHsenu7YkMHPfWn99OAQpQgAIUoAAFKFA4AQaOwjlZXCkGDotrElaIAhSgAAUoQAEKUMCIAAOHERQt7GLg0EIrsY4UoAAFKEABClCAAgwcGv0ZYODQaMOx2hSgAAUoQAEKUMDGBBg4NNrgDBwabThWmwIUoAAFKEABCtiYAAOHRhucgUOjDcdqU4ACFKAABShAARsTYODQaIMzcNydhvN2dkeP6s1xJTkW4VGn7s6X8luKLNC3ZmvYKf9bdSG8yOcW9YS6vlXQuWpTVHDzhoOdPaaGzy/qJVieAhSgAAUoYFMCNhU47O0dZOPm5GRrvpGtNXAMrN0W9sovccvP7UFObk6B7eTr4oHm/nVQ2aMc7JTzbqTcxMbLhwo8r7AFnml+P0Y36IbvDq3A7yc2Fva0QpcbNXIwAgOrYfGSlThz5nyhz6tVswYeGzcaV6/dwA8z/1fo87RWUISIXOV/BS3TQyajZUBdPL9lJvbeOFlQ8WIfH1GvM15oOUR3frbyM9p5/su6ba5QgAIUoAAFKPBvAc0GDkcnV1Sv1QLlA2rKuzp1ZCOSEqL+dYd+FWujcvUm8ClfFWrgyMrKQPTVE7hweg+ylXUtLtYaOHaO+lI2R89FU5Caddts07RQgsY3IZPkX5nVgmduXcPYtZ+rmyX69HRyw+oh78trhC5+o8D6FOfLNqxfBD+/cpj2zSz88uvcQl9i4MBQvP/ua0hJSUXHzgMKfZ5WCoa2eQTtGveHj0cFZGdn4VrcWfwR9jGi4y8ZvYVWFevh226TcCr+Kh5d94XRMqWxc8nAN1HRvZx8kjLz8Crcup2CzJys0rg0r0EBClCAAhSwWgHNBQ5nF3dUr9NahgjxF211idy/AvExF9VN+WlnZ4dOoZN1+3KVv0bqn5NxOxX7tv6u/EKTqSujlRUGDmBevymo4eWPE/FX8PeZnfKXv/jbSTgSe6FUmnFCs354tHFPLD27C5/sW1Aq18x/EQaO/CLAyJCXZNgQR8TTSN0fCrIz8OX8ibhx88K/T1L2qGFg8sYZOBhz1miZku5UA/GIlR/ianJcSS/H8ylAAQpQgAI2IaC5wNGgWU8EVG0gGycpIRpePgFy3VTg6Nh7Iq5ePIxrFw7jdnoSHBydEVClPuo27irPu3rhEM6d2K65xrbWwFHR3VdpCztEpcabbRPxqs2OUXl/yX5g1VRcSoo2W76oB90cXbBu6Ify6UlZ/nJZtWpleHl54tKlK0hNTSt0Na31CYe/bzVMeWiOdNgQMRcrd82Ch6s3Xh/zG9xcvHA5+iS+WjDJqNOgOu3xWusROBp3EePDphktU9KdauDos+S/SMxILenleD4FKEABClDAJgQ0FzjqN+sBV6Wz5oVTu5B46wZEoBB/ATUWOEQLimPG+my06DASnt7+SEmKRcSOvzTX2NYUOPyU9nR1cNK1QW5uLq4pfTGMLbW8K8o+Hq5KcJzV8zlZ5OE1n+mKpmSlK/04zIcVXWEzK70DW+KddmNwNuE69K+f/5Q2wS1gZ2+PPXv2o1nTRggNDcGtW4lYH7YZFy9eyV9cbgcEVID4T385f/6SfD1Kf5/+evPmTRHStSPS0tOxclUYxLY1vlI1qvsraNuoL5JSb+LtX4ZLAhEu3350Prw9/OT2W7OHIjntlj6PXHd2cMTm4Z/K9f5L30J8evK/ypR0hxo4Qpe8gaSMwgfEkn4vz6cABShAAQpoWUBzgUO8EiVejVKXggKHWi7/Z60GHVBN6QOSnpaI8C2/5T9s8dvWFDjUV6P00U314VB/4dMvq79eWn04ngoaiIcahiDs0gG8tcv4z4e9EjQi9oXJrw8PP4jg4Ob6VcHceUvwyaffGuwTG7Nmfvmvsqb6cDg6OuKX2dNkmNG/0MGDkTJ0WFsfjvceWwxPN1/sPrYS8zflPcEKDX4Eok+HuizaMg07Ipeqmwafos+Nj7MHnt38PfZFnTY4VtINJ3tHbBmRF2gYOEqqyfMpQAEKUMCWBDQXOPI3TnEDR1C7YfD2rYSoq8chOpxrbbGmwDFYeRWmimfeX6/HNOwum8JU4BjTqDvEL37Oyn+PKP0rxDIrco38FP9cU96rX3txv267uCtfd30SbSo1kNf+5eg6o5fRDxyiQHR0LHbs3IvmQU1Qq1agPOetdz7FsmV36id2Dhs6ACEhneTxdm1bQoQKU4Hj88/eQc8eXWRZEWpu3IhCD2Xb3d1N7itJ4BCjLTUqX11ex9w/55XBGD4O/0uOzlSU8uauaerYZxPXwUF5UjF3wycIP7EWvp7+eHPsPFk8IysNLk7u2Ki8arVCedXK2PJjz2fR1K8mvjm4FPNObjFWpNj72lVuiC+7TJDnd13witJZXPuj3RUbgydSgAIUoAAFiiBgk4GjvH9NNGnVXzId3LXQ6OhWRTC8J0WtKXDoA6pPMEwFDrWsm/JK1YZhU+Vmh79eVHeX2ufy+9+BeNXrvzv/Z3KYXf3AERMTh9C+I5XX9/KGcJ3350w0bFgPYn+v0BEm62Wu07iHhzu2bVmuvBZoZ/C0xNfXB2HrFsigUpLA8VufV1DHp7LJuqkHxOttw1d8gKKWV88vyueXT+WF/1krpuD4xT14fvh3qFGxoRIwfkRwg1BULB+IiNMb8fu6D4xe9vU2ozCgVls5itQHewo/6pfRi+ntdFf69HzW5QmIkdEuJkZj9Oq8nz29IlylAAUoQAEKUMCEgM0FDidnNwR3fVj5K6oTrpw/gPMnd5qgsezdDBxlGzjU4PPY+q9w4uZloz8M+oHjy69+wJzf5uvKde7cDt9O+0huN2+Z99RGd1BvxVzg6NG9M774/F3lFcJcOfStfqfyGdOnomOHNiUaFreSMneJu6OrXm2Mr6YrwxOL0FHU8savZnqvm4snPnximSwwbeHT8FPC0JhebyAu8To+/O0hPDtsOmpWaoxTl/fjh2WvGL2QeOr1pDK6WGl1HBcBpneNVhD9Q8Sy9eoRfHtwGUeoMqrPnRSgAAUoQAHjAjYVOETIaNFxFNzcfZCcGAPxdEO/P4hxIsvcy8BRtoFj3dCP4KnM9fLytp+w89oxoz8E+oHjwYcm4tjxOzORu7q6YPfO1fK8gYMexuXLV41ew1zgeHLCWEya+CjS0tLRvmM/g/MnTxqHCeMfLlHgMLigBWyICR8/n5zXJ+bXNe/gwZ5T4Kw8Wfh07mNyKNyXRv2IqhXq4vDZrRDHjS3iNTExOd/mK4fx+o5fjRUp0r4nmvZBzxot5PDL4kTRR+i9PX/KzyJdiIUpQAEKUIACNixgM4HDThmtKqjtUDmMrhged//2ucqkf9qbf0P9WWXgKNvAofYFEH/Nnntys8pu8KkfOHr3GSn7cOgXOBiR93rQpKdexa5d+/QP6dbNBY43Xn8BI4YPREJCErqGDNKdI1YeHD0Ur77ydIkCh6/yRMFFb3Qwgy/Q28jIyZQjPhW1vN4lCr36+aT1cmS59IwUuCqdv7cdXowl26bL8/879k+U96qErYcW4e/tM4xe8xtl8r/WyiSAol+Pqb43Rk8sYKeY7+Xtdg8pfV5qyKc94hUzLhSgAAUoQAEKFE7AJgKHmACwSauBKFehOjIz0xGxbS4yND6GPgNH2QaOl1sNw9C6HbHi/B58tNf4sMn6gWPso0/j8OE7T0I8PT2wfety+f/CfgMexLVrN4z+P9Jc4HjkkVF44bknkZ5+G+069DU4/9lnxuOxcaNLFDiK2iejqOUNKlzIjY/GL5dBQxRPSU9UhsYdphvW+tOJa+Do4IzlO3/ApgN3Xl/Tv/TfA99CgDKXy2vbZ2Pb1Uj9QyVeb1+5Eb7oMl5ep/P8l5GtN1peiS/OC1CAAhSgAAWsWMAmAkfD5qHwr1RXzigunmzcTkvSfJMycJRt4BAjZ72qTCIXGXcBE8K+Mfrzoh84fvr5d0yfMVtXbkD/Xvjg/Slyu7h9OEQfDdFXQywhPYYiPv7O3BO/zv6mxMPiiv4JTZRO2AUtZxNuKEMDz0FRyxd0XWPHJ97/GepXbyUPzVjyPM5eOyzXxatU4pUqsaivWMkNvX/0J4Msi8kaHZRXvraN/Fx+I4fF1YPnKgUoQAEKUKAAgf8DAAD//8RoYcQAAEAASURBVOydB3wURRvGn/TeC4FUOoReQu+hhU6oIiJFFAUVEZSOFMFPFAVFEBQRRRSkhw6B0EJL6L0l9ASSkB5Sv52Ju7lL7lKP5PbyDj9up+3Mu/9ZdJ6dplfL2ycLMnatu46Dvr4BroQEIOZ5eJ4nqV6nI1zcvZGZmYHQE38jOfFlnjxyjHB2dsKrlFeIjYuTo/lqbT45ZAlP67x5GpLSX6nNZ2ZojEMDvuLprf6ZpDZfcRNcLOywpdcsZGRlwm/rLCSkJecpSl9fH6HnDvL4xMQkdO0+GOzK4nfv+gsuFZwRHv4IffuPyHOvGHHowGY4ONhh6bLV+G3tBjGaX1k5p07ugbGxEY4EncTET2by+BrVq+DvDauFevR4fa3b9lK6T86BGu5NMK7PYv4Ia/fMwaV7x7h/0uCVcHOqgZj4CMxf94bKR2xRsRaWtHsXMSkJ6LV9DrKEP5p24vvZbesMxKfmfSc0XR+VRwSIABEgAkRAFwjoyVFwtPAdAz09fc7fUOh4MscEBfvL3I0LexHz4iFMzazh0/4tHsd+MjLSJL/oycxIx6nANWJQNlddERxdPRujo1sDiXt7t3rcf/LJNaT9156nn93AtrvBUh7med2Cg9Uxv9UI+Lo3xMpLu7Du+iEWpeQUBQdLSBEE4K1bd+Hl5Q5rayued/yEqThx8ox0X+tWzTD50/elsKenBxcOsbFxiI6O4fFRUTF4591sETX50w8w/M2BPP7p0wghz0vUqlUNBgYGPI4JHF0SHOyhpg5bC2c7D2RlZeFp9H1Ym9vD0syWP+/v++bi4p0g7s/9s6LTBDRwqoJFZ//BznuncydrJCwKjn475yEySTc+XmgEDBVCBIgAESACRCAfArIUHG27j8/nkYBrobsRFXk/j+BQdVOW8AX7+L4VqpK0Ok5XBMekxv4YWL1NvqwPPbyAWSfXKeUxNTBG4MDXN8LBKvOwcsLfPaYJoxsp6C580c4UOsCKTlFwbN4SgAH+OSMNmZlZ+HLhd2Dxio6JByYi8nNpaenwad5VyjJn9mT079dDCrOy9+w9iJ49uujcCAd7SEOhbT/0Xwp355oKz5yBDYFfI+TmASlO0SO2VWxqInpumyO0VaZissb8e/rPh42xBeaeWo994SEaK5cKIgJEgAgQASKgywRkKTh0uUEK+2y6IjgK+7xllW9x23fQupI3Zpz8HYcfXlQyQ1FwtGrTUxhhy0THDq0RHROLs2fP87DSDSUImBgbo23bFkhOTkHwqbNC2cripwRFa+2tJkZmqFqpAWISIvE06l6+dk7zGYLeVZrj29At2Hz7eL55S5L4edNB6Fu1JS8iIikGTxKjMT5weUmKpHuJABEgAkSACOg8ARIcMm1iEhyl03CVrStgQsM+OBdxCxtuKk/lyS04kpJoTn/ptEreWmY1HwZzQxPMCl6H9P+m4uXNVfIYM6GOUd5d0MylJpzMbHiBPbfPLnnBVAIRIAJEgAgQAR0mQIJDpo1LgqPsG44ER9m3AVlABIgAESACRIAIaD8BEhza30YqLSTBoRJLqUaS4ChV3FQZESACRIAIEAEiIFMCJDhk2nAkOLSj4Zo0yd5hKyREeX2HdlhHVhABIkAEiAARIAJEoOwJkOAo+zYolgUkOIqFjW4iAkSACBABIkAEiAARKGUCJDhKGbimqiPBoSmSVA4RIAJEgAgQASJABIjA6yRAguN10n2NZZPgeI1wqWgiQASIABEgAkSACBABjREgwaExlKVbkNwFh9ztL93WptqIABEgAkSACBABIiBfAiQ4ZNp2cu+wy91+mb42ZDYRIAJEgAgQASJABEqdAAmOUkeumQrl3mGXu/2aaUUqhQgQASJABIgAESACuk+ABIdM21juHXa52y/T14bMJgJEgAgQASJABIhAqRMgwVHqyDVTodw77HK3XzOtSKUQASJABIgAESACRED3CZDgkGkby73DLnf7ZfrakNlEgAgQASJABIgAESh1AuVKcOgbGAJZWcjMzCh10JquUO4ddrnbr+n2VFeet3dNvPfuCKXkCxeu4Le1G5TiKEAEiAARIAJEgAgQAW0lIFvBYWhkCvfKjWDv7MXZ3rociPjYiDycPao2haNLVZhb2kNPT5+nZ2Sk4fnT2wi7dQppqcl57pFDhNw77HK3v7TekaFD+mHq5x8pVRcW9hD9/N9WiqNA0QgYCB8fOjQcjFZ1esNA3xDBVwOw7+zveQppXMMXA9op81fMtG7ffNx8eE4xivxEgAgQASJABIhALgKyExzGJuZwF0RERfc6koBgz3QlJAAxz8NzPR7QvOMosHuYY0LDwMBIypOWloJzR/9EetorKU4uHrl32OVuf2m9JxYW5qhZoyqvbuTIoWjXtiVIcBSfvomRGbo0HY52DQbA0MBYKuj87UD8sX+BFBY9LF+/NuPFYJ4ru4fdS44IEAEiQASIABFQT0B2gqNmvc5wdq3Jnyg+NhJWNs7cr05wuFVujMT453gZ9ViYTZUJfX0D2Dl5wruRH7/vSfgl3L1+TD0hLU2Re4dd7vaXxWsxfdpEDB7UhwRHCeC3rd8f/dt+yEt4lZbMP1oYG5pw0ZCf4HgR+xg/bpmYp+bElFhkZKbniacIIkAEiAARIAJEIIeA7ARHjXq+MDWzFqZDBSPu5TO07jqOiwh1giPnUZV93o17wMG5MhLinuP8yY3KiTIIyb3DLnf72StSv7432AjE1as3ERcXn+etqVWrOuzsbHD79n28eBElpVeo4ITOvu3g6eGG1LQ0nD9/GafPhCIhIVHKo8pTGMFRpYonbG2scedumJJNxsZGqFunFlJepeLatZt5ijc3N0Orlj5o0qQB4uMTcPzEaVy6dC1PvpJEVLaugFkt3sTJJ9fwy5W9JSmq2PcywdG92UjsOf0bTl7ZgU8GrYCrU/UCBcez6DB8vWF0oes1MTZG/QbeUv6QkEvC2rFMKUweIkAEiAARIALliYDsBAdbh8FGKkRXXMFRp0kv2AsjHTEvHuLKuR1icbK5yr3DLnf72YsSeHAL7O1tsXv3QUyfuVDp3dHX18fZ0/uEKXwG+Op/y/D3P9t4+sa/V6PGf1OkFG/IzMzCgoVLsGXLLsVoJX9hBMfB/Zvg6OiA5T+twepf/pTu9/FpiNU/L0F6ejqaNusqxTNPv75+mDVzErdVMeH6jdt4Z+wnSExMUowutr+BY2Ws8P0QJwTBMeXYL8UupyQ3svUaiiMSnw7++bUIjtatmmH5j19JpvoPHIV79/JO+ZQykIcIEAEiQASIgA4TkJ3gyN0WRRUchkYmfKE5m5rF3I2L+/kC8tzlantY7h12udvP3o/PpkzAsDf8eYe8ddteSq9M/349MGf2ZOGrdhaatejGO/osw9Ej22FpaYmr127grjAKwQRLo4b1YGVlye9/f/xnCA5WvQj5dQiOnj264MsF03jdkZEvcOz4KTjY26Fdu1bCyKEerly5geEjPuDpJf0pSHB80rg/atu7F1jNfWFziEVn/ykwX2EyFFZwsJ3tXqUl8dHUmPhIvlA8IHiVsC5M9XQqEhyFoU95iAARIAJEoLwQKDeCg63ZsLarCCNjM6ltIx7fwO0rh5VGTKRELffIvcMud/vZ6+HgYIdDBzbzN+WtEeNx+cp16a35+6+fwaZUXbp8DSPeniDF+3Zqi+PHT+NVaqoUxzwnjgXw6Vnnzl3AO+9OUkoTA5oWHExQHAvayetlX9/ZV3jRNW/eBD+vWMyDI0ZO0Mj0qoIExx/dp6CqTUXRBLXXJ4nRGBiQd4G32hvySSis4FBVREpqIr5aPxJxSTnT5cR8JDhEEnQlAkSACBABIgCUG8HRuPUQWFg5Sm3Odqy6KuxsFRv9RIqTk0fuHXa52y++K7t2roera0UcPnwcn3w6m0ez9RKnTu7lIwRTPp+LAweCxOxKV1tbG7i5VYSe8OeTT8ahcaN6uB/2AP39RyrlEwOaFhxsZOW3NUt58V8u/E4QTDegr6cnVofVq5ZwMfLXhi34evGPUnxhPUxg1FQYsfC0dkb/qq3ABMPGW0elYrKEs3E23T4GFws7mBuaSvHqPCnpr3gZ6tKLEl+Q4KhTuRUaVevIt819/vIRrCzs0aZuXzT37sGrCXt2Fcs2Zy9CV6yXraVZuGC6FPX++M8RE/NSCpOHCBABIkAEiEB5IlBuBIewHY3QmdIXRjkqwblSdVRwrc3b+c61IDx9cEV2bS73Drvc7RdfmHfGDMeE8aORkvIKLVr58ejhbw7E5E8/QFpaOnyaK6+XYB3RBfOnobYw+qGn0LkXy3sWEYnufkPFoNJV04Lj7RGD8cnEcUp1qAqcPXsBY99TPeqiKr8YN9VnMPpUaSEG8722+qfo5edbYCETCxIc6orp1XIsOjV+gyfP+KUPkl8lqMtK8USACBABIkAEyj2B8iM4cjV1JY96qOrdDkkJ0Qg5Lr9Tm+XeYZe7/eLrZGpqguATu7l4eO/9KTh9OgRbt6xFZS8PYaenM5jw4VQxK78eCdwKNrLBHNvZ6u69MCQlJaNKZU9UrFiB72bVuesgnp77R9OCQyyPrTM5I+ySpc4dPRYMNspRVNfMpSbqC6MconOzdERXz8aITHqJgPtnxGhkCptArLm6H7YmljBROCdHypDLk5qZhpgUzXTwiys4TI0tsHDsTm7Zyh1TcOthSC4rKUgEiAARIAJEgAiIBMqt4DAxtUSzDtmnNZ8K/FU4cTxFZCKLq9w77HK3X/ElEXeeOnUqBJ9OmYPjR3dyATJq9Mc4f+GylHXsO8Mx/oPsrVV7930LDx8+ltIWfTkDfn6+hRIc4eGP0Lf/COleRc+BfZvg5OSAVav/wE8rfpOS3hw2AFMmj1fapUo8xVzVzlXSjRr0yHENh7rHZ6Ol33xwkCevP7gIITcPqMtK8USACBABIkAEyj2Bcis4rG1d0KDFAP4CnD6yFqkp+Z+BoG1vitw77HK3X/F9EHekYlOofl2zHuPee1vlzlVr1yxDw4Z1Va7T2LNrQ4EjHKxc9peNiLRq01PRBMm/fes6eHq6Ifc0qDW/LuVrRBTFBVvUzha3Mzdw8BjcuXNfKud1eAoSHNObDUEde88Cq74b+wyzg9cVmK8wGYo7wlG1UgOM7/8dr2LR+hFg6zsUHTufZXdAzsjpZ1Pn8dEvxTzkJwJEgAgQASJQXgjotOAwt7SHnaMHIh5fR3raK6lNLYT4Wo26w1xYpJqWloJTh36V0uTikXuHXe72K74nbLens6f383MsWIfe0NAQAbsOYOasRYrZ8NWimejerRNf29G8ZTe+ZS7LII4+MD87IFDdlKq2bVvgh6ULWTZM+Gga3+2KBxR+2Fkb7MwNdnZGJ19/vhuWjXAQIBv5YIvZFQUHu+3ggX/h6GCPx4+fYvDQsUpnbrADCicIIzJfLvqer1FRqKZY3oIER7EKLeFNBQmOvq3fx9FLWxATHyHVZG/lgomDfoKlma2wVW4ypq3KK/7YtDk2fU50M2Yuwq7dNAoi8qArESACRIAIlC8CshQcLXzHCFNW9HlLGRoa8yvbJ5/9Ze7Ghb38QD92sB874I+55KRYvEqOh6WNs9AhzL6HxV+/sA8vnt1hXlk5uXfY5W5/7pflF2FHp6ZNG0rRbMoTm/qk6Ngp3r+uzv4qzkZDbt68A1vhJHI3YZcr0eUnOFieoMPbYWNjxbMzUZEmnFS+fcc+fPf9Sh7XsWMbfPftPO5nazPCwx8IO2G5wsjIkMflFhzMJmY7W8DObLp79z6SU1LgWqkinJ0d+T2+XQYgKiqG+0vyU822Er5qMxpBjy7hhwtld9jmxwOXw9WxKn8UQ4Ps/xawnbIyhLUhzJ0TpkdtPPwt9y8ZH8ivCckvEZv4AmztBhMc4oL/XwKm41r4KZ5H8cfOzhaHD+WseyHBoUiH/ESACBABIlDeCMhScLTtPj7fdroWuhtRkffBRzIadgMb6cjtXgmLTtkOVdGRYbmTZBGWe4dd7vbnfknatGmOH5dlj2i8iIpG5y4Dc2fh4Q/eHwW2sxUbFREd25lq374jYLtGscP3unYfLCblubIteH/68X9wd3eVymCLuj/6eIaUd9nSL9GubUspnJqahmU/rOY7Z+UWHCyTt3dNPnLCzhVRdKwTzg4nZAf/sV24dMVNH/4HHG1c1T7OpXvHsHbPHJ7+xchNsLZwyJOXCZD1BxbyAwDzJAoRbD0OW5fDHOPYzW8Ib1seQT9EgAgQASJABMoZAVkKjqK2kYGw842JmRWMTcz5KEhKYixSU5OKWoxW5Zd7h13u9pfkZWDz+5sKIwvsdPGjx07x3apKUp6qeytVckEzn0a4fuM2H0lRlSd3nKWlhTAdqxFshSlYbPesa9duSSek585bnsJs6pSLvReszO359rePnt8CExz5ue+/W4AO7VvxLEFHg/HxxBxBmN99lEYEiAARIAJEQBcJlAvBoYsNJ/cOu9zt18V3ip5JcwTE7Y/ZtDY2JY0O/dMcWyqJCBABIkAE5EeABIf82oxbLPcOu9ztl+lrQ2aXEoGgw9v45gF79wVi/oIlpVQrVUMEiAARIAJEQDsJkODQznYp0Cq5d9jlbn+BDUQZiAARIAJEgAgQASJABDgBEhwyfRHk3mGXu/0yfW3IbCJABIgAESACRIAIlDoBEhyljlwzFcq9wy53+zXTilQKESACRIAIEAEiQAR0nwAJDpm2sdw77HK3X6avDZlNBIgAESACRIAIEIFSJ0CCo9SRa6ZCuXfY5W6/ZlqRSiECRIAIEAEiQASIgO4TIMEh0zaWe4dd7vbL9LUhs4kAESACRIAIEAEiUOoESHCUOnLNVCj3Drvc7ddMK1IpRIAIEAEiQASIABHQfQIkOGTaxnLvsMvdfpm+NmQ2ESACRIAIEAEiQARKnQAJjlJHrpkK5d5hl7v9mmlFKoUIEAEiQASIABEgArpPgASHTNtY7h12udsv09eGzCYCRIAIEAEiQASIQKkTIMFR6sg1U6HcO+xyt18zrUilEAEiQASIABEgAkRA9wmQ4JBpG8u9wy53+2X62sjObD3oIUv4Q44IEAEiQASIABGQLwHZCg5DI1O4V24Ee2cvTv/W5UDEx0YU2BKGRiZo0NwfBoZGiIt5hhsX9xd4jzZmkHuHXe72l/Y74Wxui6o2FfEsKQb3Y5+VdvWlXl+3Zm+jhXdP2Fg4IiMjHU+i7mL9wUWIjHlQ6rZQhUSACBABIkAEiEDJCMhOcBibmMO9alNUdK8DPT196emvhAQg5nm4FFbnqVm/C5wr1eDJifFRCD3xt7qsWh0v9w673O0v7ZdjaM32+KhhXxx6eAGzTq4r7epLtb7BHT/lYoNVmpmZAX19A15/ekYqlmwch2fRYTxMP0SACBABIkAEiIA8CMhOcNSs1xnOrjU53fjYSFjZOHN/YQSHjX0l1G/WX2oZEhwSilL3kOAoGvLyIjicbN0w7c1sQXUodAN2Ba+Ghak1pg//A2YmVngYeRPfbXq/aPAoNxEgAkSACBABIlCmBGQnOGrU84WpmTXCbgUj7uUztO46jn8BLUhw6AlfSZu1HwEjY1M8fXAFlTzrgwRH2b17uiQ4zAxNUMnCHglpKYgQpjwZGxiiiXN1uFk64s7LJzj//G4e0FbGZqhh54Yq1i54mhiNq9HhiElJUMonlssie1VpjiE12iE08g6+C90q5UtISxbqfCmFHYV/GxbCdMNIIS45PVWKNxBGA92sHJHGpicJ9Sk6fT09VBbsyMjKRFhchLBqQg+17d1Rx8GTlx389DrSMtP5LRXM7WAplM/KYHEsTw1bV34fs42VURI3pNMUNK/th/ikaMz5bSAvitkzZ+RGWFs48PDsNf5ISM55ZhZpYmyM+g28eTr7CQm5JIyOlMwWqTDyEAEiQASIABEgAiUiIDvBwaZRZSl0agorOCrXaAm3Ko1x/2awMBUL8BLCJDhK9O6U6GZdEhxdPBpjbsvhuBIVhl33z+LzpoOU2OwOO4sFpzdIcUw4fNyonxQWPUxIbLp9TAyim2cTzGnxphRW5WFiZnzgcilpVeePUNfBi0+7YtOvRFfZugLW+33ORVHXLdPFaH61NbHE7n7zuH/Evm/wS+eJXDSJmaKS49B7xxc8KJb/8+XdGFHbF0wUie5u7FO8e3CZIHReiVFFvs4bvQWWZrY4dW0XNh7+lt/fzedtsDUdotsctBQnrmwXg/zaulUzLP/xKynOf+Ao3LtX8BRL6QbyEAEiQASIABEgAq+NgOwER24ShREc5pb2aNLmDbwSviCfDVoHN2GxOQmO3CRLN6yLgiNVGD1goxtspCL42XWwcMuKtYRRjqeYcuwXDnhMnW4YU7cb9+8JO4erUeHCSIcr+lRpweNmB/+Bgw/Oc7+HlTN8PRpyf9MK1dHIqSofWdh1/wyPYz9sROLww4tSWBQEbJ1HcQRHbGqiMIJhhmOPr+B5Uiy8HTxQxcYFvpun8TrE8lmAjeYE3DsDZlsDpyo8/ceLO/HXjcPcX5yfxeP2w0BguOHQ/3D2xj7YWjph1ojsdVap6ckwMTJHoDDVKkCYaqXoSHAo0iA/ESACRIAIEAHtIlAuBEfj1kNhYeWAS2e2ITb6MdyFkQ4SHGX7Iuqi4GBE2bSiSUdXcbHBwmw6kLuVEx7ER4JNd9raezbY9Kavzm7EjnunWBbuxFEPNi0r9wgEy1DYNRyiICiJ4Bi571ulaVqe1s4Ij4vkdorls6lTXTZPR4qwkJu56c2GoFfl5lxAjT24lMcV52fJ+EB+2+qAabgefhoTB/4Ejwq1BIGxCj41u6GCvSdCbwfiz/0LlIonwaGEgwJEgAgQASJABLSKgM4Ljkoe9VDVux2iIu/jWuhuDp8ER9m/g7oqOPrvnM+//KsiPLB6G0xq7M/XOQzdvYhfmSBhzlyYmvRH9yncPzBgQZ51FqUlOOaf/gts5EWdEwXHmWc3MTHoZymbOK2MrR3ptzN7epaUWEiPmTC168t3dvDcS/+dAAdhG+DhXWYgKu4pvvzjTXw04Ed4uXjj1sMQrNyRzUosukoVTyxckDNV7P3xnyMmRnmdh5iXrkSACBABIkAEiEDpEtBpwcG20G3WIXvu95kjvyP1VRKnS4KjdF8yVbXpouBgU6l6bp+t6nF53OQmA+BfrbXadDGBTb868eSaGOTX0hIcvbfPQVRKvFLdigFRcOReb1JFEAd/CoJJ3QiNYhnq/PrCyM83HxzkyWv3foFhnafBWBBiX28YzbfC/XTIKrg6VsOlu0fB0skRASJABIgAESAC8iCg04Kjep2OcHH35rtZPQ7LWUDr5FIdji5VuQC5e/0oX9sR/7LgQwO1qUnl3mGXu/2K74L4df969AOMOfC9YpKSf3Hbd9C6kjceJbwQ1khcVUpTDOy4FyxNYRLjS0twtPpnklilyqsoOIoyZUtlQWoiv3n/AN91LkVYS2JqbIFjl7Zg67Efee6ZI/6CvZULjl7cjG3HcxbKqymKookAESACRIAIEAEtIaDTgqN2w+5cWBTEOjbmCS6dztlqtKD82pAu9w673O1XfAdEwcF2qWK7NKlzbGcqtlYj6NFlTDvxm7psKuMLKzhW+n6I+o6V8cWpP7E/PFQqiwkdJnhUjUAo7lJV1oJj4didXGgwwxNT4oStcQfww/9Y+Otxe2FoYIydJ1fi8PmNLIocESACRIAIEAEiIAMCOi04nFyqwcrWJU8zsAMALa2dkJGRhmcPryExIQoRj67nyafNEXLvsMvdfsV3o7CCg+1ENdVnMNg2s312zEWW8KewTlz/UZCoEUdR2Pa6iud1iNO5tF1wjOuzGDXcm3Asy7dOxN0nl7ifTaViU6qYE6dY8cB/PxYW5tgdkLP18GdT5+H06RDFLOQnAkSACBABIkAEyoiATgsOdUxpDYc6MqUXXx4FB9tuNqDvXL51LjvH4vdr2esVROrsoEC2xey2u8FilHQVRyjY7lDdt85EorCblSr3SeP+GFS9LZ+2NWzP/5CemcHPyvi35wzYmVpq/QgHExtMdDC3ds8cXLqXfS7JpMEr4eZUAzHxEZi/7o08j25ra4MjgTmjlDNmLsKu3Qfy5KMIIkAEiAARIAJEoPQJyFJwtPAdIxzep89pGRoa82um0LFif5m7cWEvYl485H5VPyQ4VFEp3bjyKDgYYbZ1LNtCljm21ewF4eA+YwMjfhZHVWHhNTvbgu10ldux08N39Z0nHch3I+YR4l4lCvffw9prOR3rWsIJ4Wu6fMJvZ+eAsFPCGzpXgY2wHoI5bR/hYDZOHbYWznYewgGfWXgafR/W5vb8MECW9vu+ubh4J4h5lZydnS0OH9oixZHgkFCQhwgQASJABIhAmROQpeBo2318vuDY9rdsG1x1zq1yY1SuyU4af4HQE/+oy6bV8XLvsMvdfsWXo7NHI8xr+RYuCp3/9wOzFzgrpuf2NxFGMea1eIuPOCimsUP3Nt46ht+u7leMlvxMTLxb1w8+LjX4WR4sIfdJ4yxuXP2e/BRw5meO7Z616Ow/+LrtGDWCw0I4aXw+36a37cbJ2Tep+RXXiMw4+bvSgYNewknmf6k5yVxNUWqj2TqND/2Xwt25ppSHfUzYEPg1Qm7miCspUfD4+fli0ZczeBQTKt38hiAy8oViFvITASJABIgAESACZURAloKjjFhpVbVy77DL3X5NvAxs1IGd4p0pTJN6KoxssDMsNOVsTVjZFfGYrU8SypajMxGmoFWt1AAxCZF4GnUv30f4/rsF6NC+Fc8TdDQYH0/MFh/53kSJRIAIEAEiQASIQKkQIMFRKpg1X4ncO+xyt1/zLUolloQAW7/B1nFkZmbBt8sAOvSvJDDpXiJABIgAESACGiZAgkPDQEurOLl32OVuf2m1M9VTOAJBh7fB0NAQe/cFYv6CJYW7iXIRASJABIgAESACpUKABEepYNZ8JXLvsMvdfs23KJVIBIgAESACRIAIEAHdJECCQ6btKvcOu9ztl+lrQ2YTASJABIgAESACRKDUCZDgKHXkmqlQ7h12uduvmVakUogAESACRIAIEAEioPsESHDItI3l3mGXu/0yfW3IbCJABIgAESACRIAIlDoBEhyljlwzFcq9wy53+zXTilQKESACRIAIEAEiQAR0nwAJDpm2MXXYZdpwZDYRIAJEgAgQASJABMoZARIcMm1wEhwybTgymwgQASJABIgAESAC5YwACQ6ZNjgJDpk2HJlNBIgAESACRIAIEIFyRoAEh0wbnASHTBuOzCYCRIAIEAEiQASIQDkjQIJDpg1OgkOmDUdmEwEiQASIABEgAkSgnBEgwSHTBtcVwTGsVkd4WDkhMuklbsQ8QvCT68gS/pAjAkSACBABIkAEiAAR0A0CJDhk2o66Ijh+7TIRte09pFbYFx6CuafWS2HyEAEiQASIABEgAkSACMibgGwFh6GRKdwrN4K9sxdvgVuXAxEfG5GnNTyrN0cF15p54sWIs0F/ICtLfl/UdUVwGOjpw8rYHJ09GmJSY3/eLL6bpyI5PVVsIroSASJABIgAESACRIAIyJiA7ASHsYk53Ks2RUX3OtATOquiuxISgJjn4WJQutas3wXOlWpI4dye4/tWCIIjM3e01od1RXAogj4y8GsYGxhi2J7/ISwur3hUzEt+IkAEiAARIAJEgAgQAXkQkJ3gqFmvM5z/G7GIj42ElY0zJ12Q4Hh4LwSPwy7mapUspKWm5IqTR1AXBceuvvNgZ2qJdw8uw5WoMHk0BFlJBIgAESACRIAIEAEikC8B2QmOGvV8YWpmjbBbwYh7+Qytu46Dvr4BChIc94X8j+6F5gtDTokkOOTUWmQrESACRIAIEAEiQATKLwHZCQ42jUpxChQJjjideXvFEY4Jh39CaOQdnXkuehAiQASIABEgAkSACJRnArITHLkbq7CCIy0tBVmZmYJYyUBifBQiHt/Ei2fy7dTq4gjHBr+p8LR2xtprB7Hq8u7cTU1hIkAEiAARIAJEgAgQARkSKDeCQ1XbRD8Pw9WQXaqStD5OFwXHlKYD0b9qK0Qlx2HsoaV4lhij9e1ABhIBIkAEiAARIAJEgAjkT0DnBUclz/p8N6uYF+FIe5UME3NreAi7XDk4V+Zk7lw9gqcPr+ZPSQtTdVFwmBuaYFz9nuju1RSWwrbHqRnpeJjwHG/tXayFLUAmEQEiQASIABEgAkSACBSGgM4LDnUQ6vn0ga2DO1KEr+nsLA65OV0UHGyHqg/q9+KCg53PwUY6rkaHY+rx3+TWPGQvESACRIAIEAEiQASIwH8Eyq3gYCMc3o17cAzH9i6X3Quhi4JjTos30c2zCWJTE7nIuPj8nuzahQwmAkSACBABIkAEiAARUCZQbgWHjX0l1G/Wn9M4vn+lsKA8Q5mMlod0UXD822smKlnYY931Q1h5SZ5ra7T8tSHziAARIAJEgAgQASJQ6gTKreBwr9IEXjVagO1ederQr6UOvqQV6qLgELfF/SBwOS48v1tSRHQ/ESACRIAIEAEiQASIgBYQ0GnBYWJqBXsnD0Q+uYWMjDQJt5VNBTRo4c8Xk7O0m5cOSGly8eiy4KCTxuXyFpKdRIAIEAEiQASIABEomIAsBUcL3zFcLLDHMzQ05k+ZKUyJYn+Zu3FhL2JePIS1XUU0aO7PDwpMSohB6qtEmAgLk80t7Xk+JkLOHFmHdGGUQ25OFwXHoQGLYCbsVPXOge9xLfqB3JqE7CUCRIAIEAEiQASIABFQQUCWgqNt9/EqHiUn6lrobkRF3oe5hR3qCyMZRsIWq7ldzPNw3Lp6GKkpibmTZBHWNcHhZumIjT2nc/Y9t89GTEqCLNqBjCQCRIAIEAEiQASIABHIn4AsBUf+j5Q31djUAqbC9CpDIxNhlCMJScKBcpnCGQ9ydroiOD5p3B/NXWrBw8qJN8ejhBcYvGuhnJuGbCcCRIAIEAEiQASIABFQIFAuBIfC8+qMV1cExzdt30F9pyqITonHpRf38OuV/YhIohPGdeZFpQchAkSACBABIkAEyj0BEhwyfQV0RXDIFD+ZTQSIABEgAkSACBABIlBIAiQ4CglK27KR4NC2FiF7iAARIAJEgAgQASJABFQRIMGhiooM4khwyKCRyEQiQASIABEgAkSACBABkOCQ6UtAgkOmDUdmEwEiQASIABEgAkSgnBEgwSHTBifBIdOGI7OJABEgAkSACBABIlDOCJDgkGmDk+CQacOR2USACBABIkAEiAARKGcESHDItMFJcMi04chsIlBKBEaOGFCkmtau21yk/JSZCBABIkAEiEBhCZDgKCwpLctHgkPLGoTMIQJaRoAJjrV/byyUVSOHDgYJjkKhokxEgAgQASJQDAIkOIoBTRtuIcGhDa1ANhAB7SVAgkN724YsIwJEgAiUNwIkOGTa4iQ4ZNpwZDYRKCUCJDhKCTRVQwSIABEgAgUSIMFRICLtzECCQzvbhawiAtpCgASHtrQE2UEEiAARIAIkOGT6DpDgkGnDycDsHl4+3MrdYWdlYK1umNi0QnW4Wzrh4MPziE9N1shDqRMcDqbW6OrWBC7mdrA2tsDKqzvh17crreHQCHUqhAgQASJABFQRKJeCw8DQGBnpqap4yCauvAqOJkLHrKlzdaV2Cnx4EbdfPlaKk0ugRcVaqCB0/IKfXkdk0ssCzTbUN4BPhRpws3SEkYEhsrIyseFmUIH3FTZDM5ea+L79ezgXcRsfHVlR2NsKnW/I4H7w9HTDlq27cOfO/ULfxzJ+MWcK9PX0MG/BEqSnpxfp3rLKrAc9ZAl/CnLDa3fCB/V7CW15BD9c2FFQ9kKlqxIc1Wxcsa7TZ0r3jzq8GC27tyLBoUSFAkSACBABIqBJArIVHIZGpnCv3Aj2zl6cx63LgYiPjVDLxrFCFbhVaQJLa0fo6enzfAlxz3Hz4gEkJcaovU9bE8qr4Pi4UT8MqdFOqVm+CdmMLXdOKMXJJfBrl4mobe+B6SfW4sijS/mabWtigT+6TYGDmbVSvlb/TFIKlySwrttkVLOthAmHf0Jo5J2SFKXy3kMHNsPBwQ5Ll63Gb2s3qMyjLvJCaCBPatehL+Li4tVl04r4bs3eRgvvnrCxcERGRjqeRN3F+oOLEBnzQKV95oYm2Of/JU/rvnUmEtNSVOYrSqQqwTGt8Rvo7dkC9+OeYcaZNYhIikFyRipol6qikKW8RIAIEAEiUFQCshMcxibmcK/aFBXd60jCgT30lZAAxDwPV/n8HtV84FmtmZSWLoxuGAqjHMxdC92NqMiifWmVCipDT3kVHDbCFBBbUwtOfmrTwWjgVAXlRXB80rg/BlVvi5iUBP4l/EH8c/71/NjjKxp5Exs6VcVPncbzTmj/nfM1UmbuQsqD4Bjc8VMuNtizZ2ZmQF8YlWIuXejYL9k4Ds+iw3g49880nyHoXaU51l47iFWXd+dOLnJYleBY3WES6th5YvGFjdh6P0ekk+AoMl66gQgQASJABIpAQHaCo2a9znB2rckfMT42ElY2ztyvTnBYWjuhUavBPM/jsIsIv31a+OKYJogVPTi6VENi3Asa4SjCC6NNWRe3fQetK3nLWnDYmljC1NAIUcnxSMvMf5rQ6s4fo46DJ5aEbsG/t49rvCnE8r86uxE77p3SePmsQFfXirCyssSDB4+QlFS0tQpyGOFwsnXDtDfXcXaHQjdgV/BqWAhrJqYP/wNmJlZ4GHkT3216XyVbNk1uY8/pyBCmyXXdMgPJ6a9U5itspCrBsbbjFNQQbJx1Zi0OPT4vFUWCQ0JBHiJABIgAEXgNBGQnOGrU84WpMKUk7FYw4l4+Q+uu4/gXRHWCw7uRHxyE6VTPn97GjYv7XwPCsilS10Y4rIzNUMPODVWsXfA0MRpXo8P5l/z86BZFcHhYOaGmnbswHckKsa8ShSklEbgZ/Ujt/Pri2JOfrYpprGxrY3PFKDxPjkWqMPUmt2PrOyyF6YPMsbUVbDoVEwRXo7JH8zKFJ7gf+yz3bUUO25taIaDvXH5f+02fqRU/3rVrwMbWGlev3oSlpQV69ujMBcSRIycQev6yynqdnR3B/iq6+/cfIDExSTFKye/u7ooundvB0dEBBw4E4fyFyyip4GBTxVjbM9avyw3pNAXNa/shPikac34byKth6zjmjNwIawsHHp69xh8JyarX66zv/hkq27jgi1N/Yn94aInMzE9wzDzzGwIfX5DKJ8EhoSAPESACRIAIvAYCshMcbP0FWygrunwFhzCK0bbbBzxr6Im/kRgfJd4m+6suCQ62JoOtzcjtvgvdik23j+WOlsKFFRzzWr6Fzh6NpPtEz6OEFxi8a6EYlK7FtUcqoACPODVKMZu6NRyrOn+Eug5eilnz+DWxhoPtkrSsw/uITU2E39ZZeeoQI4IOb4eNjRUuXryKBg3qiNH8euPGbYwYOQGpqWlK8at/XgIfn4ZKcfmt4fjk4/fw9ttDlPIzwdGoYT0eV9w1HBMa9MawWh1x/vld/HPzKI4/uYpMhf+WKFVYzMC80VtgaWaLU9d2YePhb3kp3XzeBlvTIbrNQUtx4sp2Mah0nddyhPCuNsT6G4ex/OJOpbSiBlQJjg2dp8PTqgJIcBSVJuUnAkSACBCBkhCQneDI/bD5CQ5jYZFt844j+TzqkGN/wbVyQ9g7efJwQuxzYXrVKaQIU1nk6HRFcIyp0w1j6nbjTbAn7Bz/cl/DzhV9qrTgcbOD/8DBBzlTPxTbqjCCg025YvnYNJU9wjavV6MewEYYXWjjWgfuwqhH7s51SexRtC0/P9tlyselBs/Sr2orPoKhTnD4ujeEh3X2tMHhQmfZjC0uDg8BW7/BHBPfbM5/Sd3Qmu3xUcO+uBIVhncPLlNbnCg4WIaUlFc4FHgUjg72aN68Cb/n6LFgfPTxDKX7B/j3QseObXhci+aNhfVThmoXjft174RFC2fyvGzaVUjoJbQQyq5YsYJUZnEFR6/KzfG5zyAY/LdpBJuytPXOSfx75zieaWjjiMXj9sNA2D1sw6H/4eyNfbAVtrqdNeJvbntqejJMjMwRKEy1ChCmWqlyo+t0xTt1u+PMs5uYGPSzqiyFjsstONi7c6j31/z+iSdW4EzkDaksGuGQUJCHCBABIkAEXgMBnRYcVrYV0LDFQC4w2LoNo/+mpogcWWft0pltiIt5KkbJ5qoLgsNRmB60tfds3gHMvW5AHGVIEHbr6bplusp2KYzgWOn7Ieo7VuYjJWzERNGx9RMvXyVIUSW1RyqoCJ6i7FLF5vezef7jA5fzr/RFqKbArLOaD4OfV1PsvHcai87+oza/KDgyM7PQtftgvHiRPWr44YR3MGb0MOHfWhbadeiDhIRElWUUtGh8756/4VLBGffvh6P/gFFSGbsD/kKlSi48XFzBwW5mne4uwmjXwOpt+G5cYgVsitrfwvbCbKcwJk6L65aMz95Ja3XANFwPP42JA3+CR4VagsBYBZ+a3VDB3hOhtwPx5/4FKqtgAnN+qxHCmp449N7xhco8hY1UFBz6gsh6p3Z3jBRsYM5v13Q+miWWRYJDJEFXIkAEiAAReB0EdFpwsK1wawtrOJhj4uLa+T2IFnayMhFGPuo06QULKwckJ8Xi3NE/Xwfb11qmLggO1umb1Nifd/CG7l7Er2y+O3Nsm9A/uk/h/oEBC/BEWNeR2xVGcMxp8Sa6eTbB2YhbmBS0Kt/OZEntyW1fYcLaIjjEBeMrL+3CuuuH1JouCo6QkIsYM/YTKZ+xsRFOndwrrKfSw7TpC7Bnb3bHW8rwn6cgwXE+5BDf0OHzqfOxb/9h6XZ//56YPfNTHi6J4JAKFDyulg5gI0x9q7aU1smwdTTb7wVj2fnt+b4riuWIfjNBwH75zg4eXPrvBDjYVMTwLjMQFfcUX/7xJj4a8CO8XLxx62EIVu7IfrfFe8VrLXt3rOmSzbWkU+WY4Ig4eR+TGwziQovVcTUmHD9fDcC557fEKvmVBIcSDgoQASJABIiAhgnotOCwdXBDPZ++HFnkk1u4eemAhM/MwhZN277Jw0xwMOEhJ6cLgmNykwHwr9a6QOxTjv2CE0+u5clXGMHBts1d0WkCv5d1JpnwCI28LXzJP4OENOVdkkpqTx4DCxGhLYJjYeuR6OBWX+VIkOJjiIJj/V+bsfib5YpJOBa0gy8gX/3Ln1j+0xqlNDGQn+BwcXHG3t3Z0498uwxAVFSMeBvchN2tAnau52FNCQ6xcCZy27vV4yML4nQr383TirxLFBtF+OaD7Olta/d+gWGdp8FYEM5fbxjNt8L9dMgquDpWw6W7R8HSVTlxCmB+I3uq7lMVxwTHnSPX8K53D7AD/5hja3S+vbgJBx8pT1MkwaGKIMURASJABIiApgjotOAwt7RHkzZvcFbXL+zFi2d3lbiJ6z+uCmd4sJEPOTldEByiYGCLt489vqoW/w7hi3N4XGSedPH+gs7hYB3pjxv15Sd6i4WwaTOrLu/BHwpf88XyimuPWHZRrtoiOEYJawfGCmsHmCD7+MhKtY8gCo4fl6/BL78qjwyKYmL7jr2Y80X2WoHcBYl5VC0aZ4vCf1uzlN/SsHEnpVtNjI1x+tReHqcpwcFOLW8qrKfxr9YK7VyzF6SzCu7GPsXo/UuEnboylGwoTOCb9w/wXfNShI69qXBmzLFLW7D12I/81pkj/oK9lQuOXtyMbceVxZpYNlvUzha3F7SWRsyf31VxShXbFW2cdy/0q5wt8HvunomYVznr10hw5EeS0ogAESACRKCkBHRacBgZm6JFpzGc0YVT/yL+ZYQSr5adx/IDANl2uWzbXDk5XRAc4qnhQY8uY9qJ34qMXxQI3wrnUmwuxLkUFcxthS/Z9fn8fbYWgjl2qnNcavb2rCW1p8gPINygLYKjrWtd/K/NaEQmvUS/nfPUPoooODZu3I6FX2WLAzHziWMBsLAwx8qff+d/xXjFa36Cw9bWBkcCs9fZdPMbgoiI7IXx7P4qVTyx5d/sd6SkgsPDylmYStUSvYRD9sQth9no166wM9h06xjChC2Ti+sWjt3JhQa7PzElTtgadwBfQ8bCX4/bC0MDY+w8uRKHz29kUXnc9GZDwBa3b7lzgp8vkydDESIUBQe7jU1TPPjfovGPBMGjOK2KBEcRwFJWIkAEiAARKDIBnRYcjIYoKh7cPccP/RMJ6Qs7ybTu8h4Pnj+5EQlxOZ0bMY82X3VBcLCdqKb6DOYLZPvsmKv2TAx17TCz+Rvo4eWDbXeD8fW5Teqy5Yk3FE5+3t1vPu9sLjzzDwLun+Z5SmpPnooKEaEtgoOtZ9jUcwa3OL+1A6LgYFvgDh2W/e+H3cTO5GBTqtiBmlM+n8vPzlD1+PkJDpZfXMOx6Ktl+GfjNqmIUSPfwMcfjeXh4gqOJsLWv5MbD4Dnf7t+scJuxDwStsgNQuDDC8Ua0ZAM/M8zrs9i1HDP3rFr+daJuPvkEk9hU6nYlCrmxClWPJDrR1xLw95n9l6XxOUWHKys9b7TUFk464a2xS0JWbqXCBABIkAEikpA5wVH5Zot4Va5MV6lxOOcsDVu5n+Hq3lU84FntWZ8MXnwwV/46eNFhVeW+XVBcFgamfHD5owF8ffz5d34Pdf2rmwUgp0Poa7jNbx2J3xQvxcXLCP2f6PyoEA2nerSi/uIFtpfdOzgPSY42Hz96SfW8p2JWFpJ7RHLL8pVWwQHW8ewo88cfrAg246VbcuqyomCIysrC2++9QGuXcvO99WimejerRPS09PRqk3PPGdxiGUVJDg2/vMLalSvghdR0ejuN5SXZ2oqbAW8ZyM//4OVU1zBIZ7DwReGC535jbeP4nFC9i5bon0lvTKxwUQHc2v3zMGle9nnyEwavBJuTjUQEx+B+euyp3nmrou9l/v6f8mj/QPml3irXlWCQzxpnARHbvoUJgJEgAgQgddJQJaCo4XvGOFLqj7nYmhozK+Zwnxr9pe5G8J6jZgXD7nfxNQSTdsN5/Oq01KTER0ZBlMLG9jYVeLpYbdO4eG9EO6X048uCA7Gm00fYdNImGPrNC4Ih7IZGxgJp467oqqwy09EUgz675zP03P/2Ahz5AP6zZXOVbjz8olwkFsWFpzZAOZnTtxKls2Jv/PyKWzZ2SwuNfmuPay+YXv+pzSyUhJ7ctunKsxOu2ZnfYiuZcXawvMacnvFzi87CXuJME0stxOf5XVsi8vqEp+dHYzH6lDlRMHB0tgWuDdu3oaNtRVchUXdzG3Zugvz5n/L/eyndatmmPzp+1LY09OD72QVGxuH6OjsReFscfg7707ieRo3qoc1v2ZP1WInkd+9G4bqggAxM8s+bZ1lKq7gaFOpDkyEd6ukW99KD6PGM3XYWjjbeQgfM7LwNPo+rM3t+WGALPvv++bi4p0glXeO9O6Md+v1wCFhtGXWyXUq8xQlUpXgWN1hEurYeeLL0L+wS9i2V3Q0pUokQVciQASIABF4HQRkKTjadh+fL4trobsRFXlfymNqbiPsVtUHpsK5D4ou7FawIDZCFaNk49cVwcGAs6ku81q8BTtBHCo6tqPORmFO/W9X9ytGK/mZMJnZ7A1UtnGRhMeEwz8JO1Hd4fk+azoIvYW5+uLuQ+LNbGHwbKFTd1/FfP2S2COWr+7KOr1ft81eV6Quj7rTvkXB8YEgBpgw07RjuyztEgQcE3LsBHa2eD63EwXHgYNB8O3UjosHMc/efYGYOk35fInhbw4UBMcHYhaV17S0dPg07yql9ezRBfPnTVUq+9/NOzFwQG+ep0273mrP+ZAKKUMPW6fxof9SuDvXlKxgH0M2BH6NkJsHpDhFD2O/t/8CPs2PbREtHuyomKeoflWCY57PSHR2a4R9D89h7rk/pCJJcEgoyEMEiAARIAKvgYAsBUdxORgamcDSyhFspCMp8SWfTlXcssr6Pl0SHCJL1tGtIgiHTGEHqafCyAZbwKwJx3YjchNOfHYShGe60PF7IkyjYaMIBbnXZU9B9ZZl+gDhbJRPhbNRdtw7BXYYY24nCg62Je6Gv7fwE8atraxw7PgpJCUpbzOc+96ihNl5Ho0a1YezsyOCgk5qtOyi2FGSvCbClMGqlRogJiEST6Pu5VtUJ/cGWNDqbb79M9sGWhNOleDoINizsPloXjwTts+TX2La6TXo0rsT1q7brIlqqQwiQASIABEgAnkIlCvBkefpZRyhi4JDxs2hM6azBfXzW45AUvorzD/9V57nUhQc7CwOcpoh8EbN9nyL3h8v7FA56lacWlQJDlZOX69W6OXVAk6mNrASRNG7Qd+jbY82JDiKA5nuIQJEgAgQgUIRIMFRKEzal4kEh/a1SXmwiASHfFpZneBQ9QQ0pUoVFYojAkSACBABTREgwaEpkqVcDgmOUgZO1XECJDjk8yKQ4JBPW5GlRIAIEAFdJ0CCQ6YtTIJDpg0nc7Nr1qwGa2tLXL9+W6sXbsscs0bMJ8GhEYxUCBEgAkSACGiAAAkODUAsiyJIcJQFdaqTCMiHAAkO+bQVWUoEiAAR0HUCJDhk2sIkOGTacGQ2ESglAiQ4Sgk0VUMEiAARIAIFEiDBUSAi7cxAgkM724WsIgLaQoAJjqI42ha3KLQoLxEgAkSACBSFAAmOotDSorxyFxytWjVH1Iso3LyVfUCfFqElU4gAESACRIAIEAEiQAQ0SIAEhwZhlmZRJDhKkzbVRQSIABEgAkSACBABIlBcAiQ4ikuujO8jwVHGDUDVEwEiQASIABEgAkSACBSKAAmOQmHSvkwkOLSvTcgiIkAEiAARIAJEgAgQgbwESHDkZSKLGBIcsmgmMpIIEAEiQASIABEgAuWeAAkOmb4CJDhk2nAyMLvj2Pe5lYdXr5CBtcUzscOYcdDT14MuP2PxyNBdRIAIEAEiQAQ0T4AEh+aZlkqJJDhKBbPWVeLV1R2WbhYI3/8I8Y8SNG5fo9790H3iFMRGPMNPw4q2rarGjXmNBY7fsAXWzhWw9/vFOL9z22usiYomAkSACBABIkAEZCs4DI1M4V65EeydvXgr3rociPjYCKUWtbKpgNqNuinFqQpcPL0Vr5LjVSVpbRwJDq1tmtdqWP8dfrB0tUDw/BDc2XZfbV1WbpZwrGuPuAfxiLoWozZf7oSJW3fDzNoGu79ZhIt7AqTkVnX7oGvTt3j4zpOL+HP/AimNeQwMDDHzrb+gJ/z5O3Axbjw4g4oOVTCh/3c835zfBiE9I1XpnvljtkFfTw8/7/wcDyJu8LQa7k3wdrfZSEtPxRdrBynlVxUoan6xjAZ+vdBj8jQkx8bie/8eYjRdiQARIAJEgAgQgddAQHaCw9jEHO5Vm6Kiex3o6elLSK6EBCDmebgUZh47R3fUbdpHKU5V4NShX5GWlqIqSWvjSHBobdO8VsMKKzgaTaiLuqNqISL0BfaPPVIom+p26Y7eU2chLSUF3/T0VbqnT+tx6NBwsBS3aP0IPH/5SAobG5rgq/f28PDfgV/jzPW9qFyxLj70X8bjpv7sh9T0V1J+5lkyPpCHf97xGW4+PMf9Dat3xIius5CVlYVPf1K2gWfI9VPU/Iq3T951CEamptj51XxcObBXMYn8RIAIEAEiQASIgAYJyE5w1KzXGc6uNTmC+NhIWNk4c78qwaEnfD1lIyGqXJXabeBcsQZeRj3C5bPbVWXR6jgSHFrdPK/NODNHUxhZGCHxWRIyXmWorac4guOjTTtgYe+AM//+g0MrsoWCWEFuwXE9/AxWB0wVkyFHwdFl/EQ09R+ExOgoLBtU8IcJ6WHJQwSIABEgAkSACBSJgOwER416vjA1s0bYrWDEvXyG1l3HQV/fAKoEhzoSbGSkVZd3+X3Xz+/Bi4h76rJqbTwJjrJtGtuq1jA0M0TM7dh8O/5sapOJrTEBhVnPAABAAElEQVTiHyTgVVz2lCJTexNUbF4BLC0jLQNRV2Pw4mo00pPSVT6UqZ0J2D2KLuFxItJTlAWHkaURbLyseLbaw6rDq5s7Xt6NQ/C87NEDlvAqNhXxD/Ou/bBzdcO4df/we7/t1RmpycncL/7kFhwsft7vQ/Ay4TnPIkfBYWJugUk793P7VwwfhJdPn3C/qh8TY2PUb+AtJYWEXEJmZqYUJg8RIAJEgAgQASKgnoDsBAcTC1lZOf+jL47gcHKphloNuwkdhgycPLBKqTz1qLQrhQRH2bbHwL09YeZkhhOzz+LeLuWpfIqW+e/uAYsK5tKaiy4/t4dLUyfFLNyflZGFo1NP4UHg4zxp7b5qAc8ubkrxqtZw1BpaDT5TGirlyx2IvReHHYOyO9mKaQ179oHfpM+RkZ6Gr7t1UEziflFwsH8zDyJvwsvFGxfuHMG6ffN4uhwFBzP8s31HYGBohD1L/ocLu3bwZ1H107pVMyz/8SspyX/gKNy7p77dpYzkIQJEgAgQASJABCA7wZG7zYojOBq0GABrWxc8fXAFd64F5S5SFmESHGXbTKIIuBcQjhNzzqo0Rs9AD2+e8ufbr/7bLQDJL1LAhIqpvSmenIpAzK2X3O/axgVmDtlT//a8HYgXV6KVyvPwdYVbu0o8zqurGwyMDSQBo5jRvqYtqvX14lEuPs6wqWLNRzTC9j6QsrERmdtb8y42Zwuo2ULqhKgo/DA47/QiRcGxbMuHmDjwJ77OYs5vA5CQ/FKWU6oYlA837oClgwNfIM8WyqtzJDjUkaF4IkAEiAARIAIFEyh3gsPE1ArNOozgZEKPb0BignLnrmBk2pGDBEfZtkPV3l5o9UVTPj1pWz/VC46dGjig+5qOSBOmSv3dNnvrVSYEIi+8QGZazigde5JBB3uDTZ0KP/gIRz8/pfbhXtei8VEr1sClRk08vnYV6z58N0/9ioJj8ooumDL0F74L1alru7Dx8LeyFRxv/7galWp74+nN61j7wTt5nluMIMEhkqArESACRIAIEIGiEyh3gqNyzZZwq9wYyYkvce7Y+qIT05I7SHCUbUOwNRWDDvRGVmYW/my2GcgC/AOE6VMVzXF8xhncF0YV6r/rjQbveePZuec48F7ekTS25sKighl/kAbj6sCjkyueX4rC3lGH1T7c6xIc4oLxq4f2Y8fCuXnqzy04qrs1wvt9v+XTEmf80lcY7ciQ3S5V7CH7zpwL746dC1w4XqWKJxYumC5xeX/854iJeSmFyUMEiAARIAJEgAioJ1C+BIewa1VL33dgaGiMu9eP4Un4JfVktDyFBEfZN9DQoL5gomHXsINIeJKIIUf6cqMeBT3B4Ukn0XVVe1Ro4oTQHy7j6tqbPI2dodF8emNUbObMp1rlfgp1ayzEfK9LcIxdsx6Onl4IvxCKvz79UKxOuuYWHCxhxlvr4WBdEUEXNmHP6TWyFBzDvv0Bng0b40V4GFaPflN6XvIQASJABIgAESACmiNQrgSHvZMX6jTpyekFH/oF6WnK5wJoDuvrL4kEx+tnXFANnX9qJ+w25YzQZZf5WomWs5rwEQ+2exSbQiUKkp2D9/Pdolh54igI8yc/T8ZzYb1GemI67GvZwraaDd/Nalt/1VO02D2vS3D0nz0ftdp3Quyzp/jpzYGsKiWnSnDUr9IWI/3mIiMjHXPWDsCCMdnbS4vncHhUqMXXerCC8juHY8X2T3H70XleX1HP1ShqfqWHEgIf/LUZNhVccCMoEFvnzcqdTGEiQASIABEgAkRAAwTKleCo59MXtg5uiH4ehqshuzSAr+yKIMFRduzFmuuMqInGH9fDk5PPIByZzUctrv15CyyeLf72+70T2O5TfMqVcFONAVX46Aa7f3OPXUiKyNl6tumnDcC2smXb55aF4Gg+eBg6vTceaa+EQ/965D1wT5XgYM8xd9S/sDK3x6HQDfBt/AaLEk4azz74j500ztZ6MDfjlz5IfpWzHa++sNvcNx8c5GlL/x2P8Ijr3F9UAVHU/LwShZ/Ju4XD/0xMEfjzcpze+JdCCnmJABEgAkSACBABTREoN4KDnVDevOMozu3KuZ2IeZGzc4+mYJZmOSQ4SpO26rpsKlujz79dkRLzip/JEXs/DidmnkGfzd0QdT0GDrXtEH3zJZ9yxUro8G0ruHeopHKdRu9/umh0hIOtHWFrSAoSMOKTVarljbeXr+bBRb6txWjpqk5wNKvdHUM7fcZHOQwMDHl+UXBYmdsJgkRY3yK4hX++hRexOVv+WprZYt7oLTztyz+HIyo2+wyMogqIoubnFf73w7bYnnrwGA/9Pn4snty4ppis5LewMMfugA1S3GdT5+H06RApTB4iQASIABEgAkRAPYFyIzg8qvnAs1ozpKengk2nEla5qqcigxQSHNrRSGzbW30jfW6MuFZj6LF+MDLP7nyzEY+Q77LXCvlMboBab1TnB/ZtaLOVLzRnN1b280CbBc14GQUJhMJOqarSwwOt5zfjU7w2+e6UDh3klaj4YZ1v9rXfUDjgbu/3i3F+Z/auWmJWdYJDD3r4cuwOmBpbiFmlEQ4W8fW4vTA0MMble8fx257ZUp43O09Dk5pd+KJztuuV6IoqIIqaX6yHXZv0HYCuH00SDl9Mw2I/YTRK4XwfxXzMb2trgyOBQpv952bMXIRduw+IQboSASJABIgAESAC+RCQpeBo4TsGrIPEHFsAzhw7kIz9Ze7Ghb3CCMZD7hd/WnQaDSNjMzwKu4D7N06I0bK9kuDQjqbr8acvH8lg1mz2E6ZJRSaj/dctwc7OYO7AuKN4djaS+9k6jZ7rO3M/W+cREfIcZo6mYOdniC634GBb6zb5uL6YDMe69mDneyRGJCHpWfaUrCRhLUjurXRNrI35VrssL3NsUXtqfBoiz7/A2cUXpPIUPWxKFZtaFfc8EsuH9ldMgjrBwTK1azAA/dqMl/KLIxwsok29fvBv9xFPS0yJw/OXD+Fo4wo2wsFcQPAqBIb+zf3sRxQQzM/WhqhyWcjEZyu786Si5lcsb8I/22Dl6ITTmzYgcOWPikl5/HZ2tjh8KHtEhiWS4MiDiCKIABEgAkSACKglIEvB0bZ7TudG1ZNdC92NqMicw82s7SqiQXN/nvXs0T+RkhSr6jZZxZHg0I7mavxhPdQZWZNPq9rUeSc3ik2bYtOn2Ja561ts4es4RGvZaeBNJzXgokGMYztT3d0ZzteDxIbFY8eAfWISqvT0ROt5PlJYlYeJlw2tc76+i3nYDllsbYhddRtpR6z8dsHSNzTE5F0H+cnb6z58TziT44pYFHq1HItOwhoNJgKmrOwqxTOPvr4BFo4N4GdxsPCGQ1/j7I2che/dfN5Gl6bDeT6WzlyWMMIYKKz72HUqe41HdizQoFp7vN1tjhhUe520vBNPK2p+sUC3uvXx1tIV/GT1b3p2Rma6anEj5vfz88WiL2fwILO9m98QREa+EJPpSgSIABEgAkSACORDQJaCI5/nKTdJJDjk29SGpgZw8LaDobkRIkOf84MBteVpun08GY379Odig4kOTTkDfUO42HvB0dZVWMvxBM+i7iMjM/9OvqbqVlXOiB9WwdW7DkJ3bMW+pd+oyqIU9/13C9ChfSseF3Q0GB9PzBYfSpkoQASIABEgAkSACKgkQIJDJRbtjyTBof1tJEcL2RqO0T//hrSUV/jt/dFyfIRC2cxOVjcyNcGa90YhPTW1wHvY+g22jiNTGLXy7TKADv0rkBhlIAJEgAgQASKQQ4AERw4LWflIcMiquchYmRMIOrxNWC9miL37AjF/wRKZPw2ZTwSIABEgAkSgdAmQ4Chd3hqrjQSHxlBSQUSACBABIkAEiAARIAKvkQAJjtcI93UWTYLjddKlsokAESACRIAIEAEiQAQ0RYAEh6ZIlnI5JDhKGThVRwSIABEgAkSACBABIlAsAiQ4ioWt7G8iwVH2bUAWEAEiQASIABEgAkSACBRMgARHwYy0MgcJDq1sFjKKCBABIkAEiAARIAJEIBcBEhy5gMglSIJDLi1FdhIBIkAEiAARIAJEoHwTIMEh0/YnwSHThiOziQARIAJEgAgQASJQzgiQ4JBpg5PgkGnDkdlEgAgQASJABIgAEShnBEhwyLTBSXDItOHIbCJABIgAESACRIAIlDMCJDhk2uAkOGTacGQ2ESACRIAIEAEiQATKGQESHDJtcBIcMm04Mhsdx77PKRxevYJoEAEiQASIQCEIdBgzDnr6eqD/bhYCFmXRSgLlTnAYGBojIz1VKxujKEaR4CgKLd3J69XVHZZuFgjf/wjxjxLK/MH0jfTh0ckVNpWtYGBigKzMLJz/4Ypauxr17ofuE6cgNuIZfho2IE++Zi0qoX4DZyQmpGLD+ms83auyDTp3rcz9f/5+BSkp6XnuowgiQARKRqC6WyPU8miGO4/P43r4Gamwfv414Ohkjrt3YnD4UDiPb93GDbXrOCL2ZQo2/XNDyqsLHn19fSz5dh709PSkx4l9GYvZX3wthcvCM37DFlg7V8De7xfj/M5tZWEC1UkESkRAtoLD0MgU7pUbwd7ZiwO4dTkQ8bERKmHY2FVCldptYGHlIPxHRB9ZWZlISojBvRvH8TLqkcp7tD2SBIe2t9Drsa//Dj9YulogeH4I7my7r7FKrNws4VjXHnEP4hF1LaZQ5Zo5mqLv1u4wMjdUyv9Hk3+VwoqBiVt3w8zaBru/WYSLewIUk7h/6fIuaNzEBRkZWejQ+k8eN+qd+hj9TgPuHz5kB8LDY6X7JnzcDIOH1pHCzJOenokXL5JwOvgRli45zcNKGf4L/PxrL9T2duKhzyYdwCkhf2535MTbYB0Q0TFB9VLoZN2+HY3/LTyB55GJYhK/qrJHKYMQ2Pj3VaHjdh/LfvLjSa8EAdWnx9957Pzzb39UrGQp5A3DgrlH8dOqnqhbzzl3cSrDd25HYfSIHSrTNBX5+fTW6Nm7BmKik9G3598qi920dRAquFji0IF7mDs7SCnPuPFNMWx4PR63fcsNfLs4WCmdBQwN9RF47G0eP3HCXoSGPM2TRzFi1ZreqFXbEYcOCvXNUq5PtPfunWiMemu7dNuhoBEwMjaQwrk9aakZ8G2/Lne01oSNDU3g6lQdUbFPEZcUVWy7hnScjObePXDp7lGs3fuFVM7eQ0NhYWGEB+FxeHNINrdVa/yEfzuOSEvLQKe2f0l5dcFjYmyM06f2Kj1KRkYGmvh0UYor7UADv17oMXkakmNj8b1/j9KunuojAiUmIDvBYWxiDveqTVHRvQ4XDyKBKyEBiHme/fVFjGNXO0d31G3aR4p6lRIPE1MrKazuPimDlnpIcGhpw7xms16X4Gg0oS7qjqqFiNAX2D/2SKGeot1XLeDZxQ3pSem4teUeXt6NA4QO+d2AvP8OWYF1u3RH76mzkJaSgm96+qqso6iCY/6ijujuV01lWSwyKSkNvbtvQFzcK6U8xkIH8/jp0cJ/Q7KjjwU9wKSP9ynlYYGzF8bmiRMjsrIAJlSOHA4To1CQPSzj3j13sHfXHXz/Y3fpvrVrLmD5srNSmHmY2LGwMMaF888wdtRObNk5BO7u1kp51AWYEOrR9fV2BHv1qYE589pzExjjZ8+UR9xsbExxMOgtnr5EEBMb1iuPfAXsHSaIEQueHi2Ilm6dsgWm4jOxdjpxZjSPmvKJMmvFfKJ/x543ULGiJc6dfYL3x+4So/n1ux+6oU1bDy5G/Tqvl9LOnB8rvQdSpIKHtXOzRqsVYrTH27nJm+jRYoxk0MPIm/hp2yS8SkuW4grrIcGRQ6pRw3r8nahduwamTB4vfAApe8HBrJu86xCMTE2x86v5uHJAWRTlWE8+IqCdBGQnOGrW6wxn15qcZnxsJKxssr/4qRMOTdq8AXNLeyQnxeLSqS1ITU0SvmaZop5PPz7ikZIch7NBf2hn6+RjFQmOfODocBIbVTASvjYmPktCxqsMjT1pcQRHv23dYeVuiUurruHiz9nTn/Iz6KNNO2Bh74Az//6DQyuWqcxaXMERH5+KfsJXdn1hjnPX7lXRz78Wqtew53VcvBCBd0Yqf+1n6TNmt5VsSEpMQ/vWa6Ww6BEFB/v6vnvnbXjXccLAId5o286D15UqfP1u3WyNmF0SHKy8NwZvluIVPS9jUtCosYuS4GDTxNq3WotMQbCJLrfgYB14C0sjMRmVKllhxeqePDz980O4euW5lMbqZyMxr9MpirZVK0OwemWoUnVs9OKTyS14XIfWvyMxMWcqq7W1CQ4dHaGUn4kANjKl6EpTcLBn2CW0cW7HRswiI5RHsnLnKYtw5Yp18aH/MqEznI6Ld4PgVbEO7K1ccOHOEazbN6/IJpHgyIusYcO6WLuGMdYOwdFl/EQ09R+ExOgoLBuU8yE1r+UUQwS0j4DsBEeNer4wNbNG2K1gxL18htZdxwn/4zeAOsHRtvt4Tv3+jRN4FHZBagEXd29Ur9ORh4/v+0mYZpXzP3opkxZ7SHBoR+M4eNsJX8L08OJqNGyrWsO1bUWkxqXhyclnXBSostJI6DTaeFnhVVwq4h8kwNDUAO4dXGHtZcmnMz06qjxtxNTOBKb2JkpFJTxORHpKXsFh5WEJE2tjxIbFc0FSsZkzHOvZI+Z2LB4FPUWm0HkSnWgHC9ceVh1e3dz5KEXwvHNiFryKFWx8mPPlWiyfZei8oh2fTnX+xyt4djaS38OmHKmakmXn6oZx6/7heb7t1Rmpyaq/wBZXcLARDN92ytNeWIeWdWxTBWHWunmOKGBG/Pp7H2GtSAX+JbxJ00r8a+Zg/39x/57ydDJRcMz/4ih2bLvJ7Wc/Xy3uDN8ulXl4xLBtuH4tu7MvjnCoske6WfC0buOuJDhY2o9Lz+D33y5K2XILDinhP09FQXDs2D2Uh9j0qcuXVE8pFe9r0LACjIwMePCOMCVME4JEHHW5eSMKw4duEavi11/W9gGrU9Voy8jRDTH+Ix/Exb7iU8nsHcyw9ldhlOeHs0pllKbgYFPk/t1YsHBWMrCIgfcnNOVi+Pc1F/HXn5eLeLdydv92H6FNvX74Y/8CnL8dCH1huvBX7+1GRmYGpgnT7/Jz7P+ZVSrWQ9VKDRDx8gFuhJ9G39YfaP2UqspeHnCp6IyHDx7j0WPl/06y561UyQWenm6IiHiOe/dyRlpNTU3QtWtHVK3iJYwamuHKlRsIPnWO58uPU2EEh7OzI9xcKyIi8gUe57Kpbt1aMDYywqXL14X3XHntGZuq2aRJfTRv1hjm5uY4c/Y8jh9nU0CV8ynaZ2JugUk79/OoFcMH4eXTJ4rJSn42Nax+A28pLiTkkvBBI+e//1ICeYhAKRGQneAQ12CIfAoSHM06jOBTqPIKjjqC4OiAtNRknApU7oyIZWvzlQRH2bcO2zFk+Nnshc/hBx7x6UWKVl346Sou/3pdMYr7aw6uimafN+Ji487OMDQaX1cpDxMrhz48LsWJU5ekCMGjbg1HP2FNBRMFNzbcRo2BVcEWdYsuKTIZOwbuR5rw9Zu5WkOrwWdKQzFZ5TX2Xhx2DMr+HxzLIJavMvN/karWcDTs2Qd+kz4XNmxIw9fdOqi9XZOCY/5CYbpVj+zpVj4NlafEnDw7Ruh862P2jMP4UFgH4uRsga2bb2Dh/GNKtqkTHM2au2L5z9nzqNn6iu1bs8VIcQQHm/7T1KcSHwFgIwGi07TgUJw6xIQNEzgldR9Pao7hI+oLc/kz0crnV6Xijp0aBVNTQ5VcN2wagGrV7RF0JBxsDQsblXr4MA7+vbNFqViQrgmOxd91QYeOXti86Tq++jLn37j4vEW5Dmw/Ea3q9sFve2bj8r3j0BP+LBIEB3NTf/ZTW5S1uQMmDloOW0tnKU+mIFIeRN6Al0sdrV7D8f13C9ChfSs8i4gUplJmi23pIQTPjm3r4OHhhtNnQvHeuMk8afrUjzFIGA1QXAQu3rNz5z7MmvM/MZjnWhjBsfjrOejSuT0uX7mOt0Zkf+AUCwo9d4iPhA4f8QEXOWJ8rVrV8fOKb2BjkzO9m6XFxsZj7Luf4Nbte2LWPNfP9h2BgaER9iz5Hy7sUh65VczculUzLP/xKynKf+AoJREmJZCHCJQSAdkJjtxcChIcntWawaOaT/aUqjNbkZqSCLYOpJ5PXz7V6rEw6nFPGP2QmyPBUfYtpig4mDVsVOFh4GNUaOIEpwYO3MDDk04KIwvKX6FEwZElLIzWM9DjayAiL7xARmomKjR2RJwworBnRKD0gB6+rnBrV4mHvbq6wUCY116Q4GCZU4URlLD9D1GhqTMfUWFxV9fdROjS7C+r9jVtUa2vF4uGi4+Qp4o1H9EI2/uAx7EfNjJye+t9KVxjQBU+ksMiqvtX4YLm6elIxIXF8TxsoPDs4pyRRPFGttiRLXpMiIrCD4PVTwWY+2U7tGvvjuTkdPTokt35HDi4lvA1vAkv6o1B2/Hsac6IS34d/M3bB8PD00YYvVSeg9+ipRt+WOHH41knefqstujdt0aeuf2sQnWCY9YX7dCnX/bUTjayIq4REe1hU6S+nKssXkQWR4PClaZUffTBHrD1BQYG+lzwMOHDnBwEh6ubFbYFZHf82AgHG+lgjq01YaMfzL05ZAtu3cyOZ2E27S343Jj/t3cfcFWVbxzAfwiioKIo4F6YC3Nr7sytlZbZX8vShmWa7axcaY60TDMrs6FmWlpWau6JuTUV907FgQNxgezh/zwvncO9yEXQC9xz+b194p7x3nPe831R73PfpV7ff3e19ndzglEfzRvPUC1Skk9SdgYcy7VxNVs2nU2+scXPkJCIO7YeWWRPd9OeAYfMKtX/sYmIS4jFP4eXo3LpuihetLw2w9R2/LBksCpH9fIPoF2DXijuXQ7Dpj2OW9p/Q56dDZ/CpVVXrK2HlsA1jxsaVe+kegrIm1IPGp83v6sWkHvi0MEwDHgleZzT+Imt0bBRSe33Pg6PPfx7us9sz5PSYvDzrG/UJdu2/5/2Zzbl98rLqxA2/J08qL3/gPexdWtyS+13Uz9Do0b1cSr4DI4dO6EFxwl4oGFdSMuEpPkLlmLU6IlqO/WPrAg4fHyKYdWKeer3PyYmFoGBG1WXrTZtHtRaOjy0v/ti0KJlF5stHa/PW4SCxYqpSTdk8g1biQGHLRkezykBpw84XF3zokLVJihVrqYyjouNUgGH7ERcv6S6YiXEx+SU/13flwHHXdPZ7Y2WAUf46Qj81U37x1j7cCup7TcPomQjP6jjTyT/I518BtADDtmXgdbLe69N6R7lAhStUgRXj17Xs1u93mnQuN4CIV2bfm35lwpm5AKtv2yO0s1KqO5RCx9fYXVN2bmbMRz/W9MZ0t1r3dtad8VU3cBS3+CFqTNQokpVhBw6iFmv9019+q739Q/4ll2Y5EPqk90DjPEDqbv06B/6zmsfJGV2Jen2I91/JLVtOVv7ljHl7wM94JBvo6UVo9J93mpWrIcfraxmUNKvoT+AXh59P63Xvi8u1j5Y5DW6VMm+jCmRa1oOnjZDwCHPp5dTZt/67JMt6pEHvN4Qz/epo6YwbtH4RysGGeQvTjJepUmD6ep1264+KuBK3a0pOwMOq0Ja7Jw9cwNPdJlnceTuN/XfPXu0cEgpZMC4DBzXU+i1M5i+bBgaBzyCJjUeRX73Aqq78PFzQdrMUyNQ2qcyBnSdpLJP/K0vQsL+Vdv6eBDZSR1wqAwO9GPj+kUoVKgg5v66AJ+O/8oo2WsDXsRLfZ5VH9ibNEtufZST9evX1lrPQhCqdXmyTNN/mKTORUZGoVmLRy1PGdtZEXD8OGMyZFB6XFw8Hnyoi/ZnJFbdT7p9bd6oBYCurpgzdz7Gf/a1UQ7Ljee+/gGlqgfgwtHDmPnqS5anrLYZcFhxcMcBBJw+4JBm1AqVG6OMfz0rbpka98je1Qi7mPwXrtVJE+ww4Mj5SrIMOLaM2GE1O5NfHR90mP6QKmTqLkaWAcfCritU16qMPk1GAw4JWJb2XGNcVr9n/M14FYgYJ/7byOqAQx8wfnDtKiwaOzL17e96X/+AL60YV65EaZ1KtICtmKcak6FfVKZT3WzxzbX+AVn668sHXEl6F6vvpu7CtO+C9LcaLRzGAYuNkHMRGNBvKeRVT5bluXA+5bh+Xl5f11o0pAVAn6VKAg5Z42D1373Ut56D31uLNdo0sno59VmqLK8h25kdw/HDj53h4ZE86HzGtN0IXHMq9SXvav/zyR20b2TL4Yw2XXG3x5I/mOtdptKaLUoGuksXsrTyH9gfajVlbXYGHDKe5Ka2/kvqJFPxpp7SN3UeW/sv96unfZNewDgtY3ek+54EMbt2poxBEIvZP+0z8mVmo0B+L5QrXg1+3uVRu9KDqluUvP9m9HVs3v8X1u/9AzFxyYPeu7Z4DS1qPYGwGyEY+3Mvq9sMf+5X1c3K0QOOj0a8h8cf66T9eb+mjaPqZjzDiuW/okRxP6xevR7vfZD23zHSglChfFn1ob5ZswfQ75Xn0h0QnhUBx64dq9X9N23+B19PSe6GmEf7nCJpyOC3IK04p06dRtduLxjPZrnx2LCRCGjV9o4Dx/39y2PsmCHGW/sP+ADXrqX9RZaRiRsUyEIBpw84Kt/fCiXKBGh/qWgDeU/v06bOPYMi2lS5pcrX0r6hdEdoyFEc3Z/ywSwLre16aQYcduW8q4tZBhyLtNYN6VKlJ+n21HNrV7X758NLEXUpZZC0/uFfppOd22Kh/pYMvWY04Ng37TD2Tj1oXNOnRlF0mtVa67aViDlNFhjH9Y2sDjhenvELfMpXwOk9QZjz7uv6be/5Vf+An/pCEoCEXY7EaK1b09bNKd1kKlQsAlkbQpJlV59Zc7qqdQVOnbqO7l1TuojoLRyJiUna+JNbWuuoq3GrX7VpXlOvHaGXx7LFxXiDxYbloHEJOHYHXcSEL9qj5UPl1fSyMs2svQMOi9vbdVPKLGWXFotG9aapa0uXKVlDQ9bCWLL4mNX9NmvTEYvjz7P2qXVS5OQrr9bHS33rqQHk0uqhp+wMOFK3ruhluJfXDVtf0II8tzteQgbwt3to9h3zpc5Q0KMIWtXtYdWacezcLpwI2au1bngiKjYCO4+sNtbnePnRcahevhEOntqiWkIsr/dKl/GoWraBw7dwlNYGaC9d/IsqeufHeqnWC+keJd2UJHXv8ZLVGAgJMkaN/ECN/XBzu70uZMKYuvXbqPem/mHvgKNIkcL4O/D2v39T3ze9VpeeE79C+Tr1EHY6GD+8mNK6lfoa3KeAowk4d8ChfWvQrN0rqm+qLAx4KSRlAK9vycqoVru91tychM2rvlOvjlY56ZWHAUd6OtlzzjLg+L3tYsRcS24a1+/ea9eTanP5c4EIO3BVP2x0qZKZphZ0WW4cz8hGRgOO7WODcOzPlIGHMpvWw7Pb5FjA0XX4aFRr2Ro3Ll7AN88ku2Tkee+UR/+AL+ttvNYv2fLq1SirVgfLa8jib9LdSpLM1KQnmWLWU5tuWD40yzS3MhWqJD3g0GepklmvZKpXWYNC0rDBgVi5/ITalh96ee4m4PDx8cSy1c+o1pnX+y/HJxPaWK3DYdzkv43MtnCkfr+99mVMxpYdL6ouUW8MWIEobfpb6aImQZ+MkdEt5X6W3dekq9sNrVVBUoGC7mr9DNm2bJHKbMDx19KnUKp0oXTX4bh8OUobH5T8gVXupw+mz4qA4933m6CEtvChnmRmNJmR69LFSGNmMzkXrAW6qWfo0t9j61UGjMvAcUnSmrFh33xs3DsfnZv2VYPJ9ffJgPCpfw3EifN78daT36jWkF1HV+OXNdb9/3u3/xB1Krdy+IBDnmvNqt8hYyEW/rUcH438DIM+eANP9Xhc+30KR8tWj+uPrl6/mjwWLVo0VtvSfSlYG8tx5eo1FCxQALVr11DH69RrbfUefcfeAYd+Pbl+0O792nil21vU5FzI+QsYPeZz2bwtvTrnTxQuXgJH1gdiwagPbzvPAxRwVAGnDji8vEuidqMnlP2WNd+rGXL0ipBpAWXAuaQDOxfjWljKQFk9jyO/MuDI+dqxDDgW91iN6//eMArlpq2+/fTG5H/4/uiwBNFhKeMC9BYOmRJXulRlJpk14GjUvSdavzIA8bHaon8Pp/1tYmYc9LwZ/YCv51+2qqfq0qLvp/Uq3WeWLEr+Vj51wKHnl1YSaS1JHVhktDxptXDItWU18YYPlMLp4Bvw8fUwRcAh5da7UK1Y9q8atyFjUtIa+/DxJ63VjFTyHltp4wZtEcY3ksc9ZTbg+GNhd5SvUBhprb0iEwXIhAGpx91kZcCR+hntOYajRNEKWnDxClbv/BnBF5NbM0sV88fAp6YhWmvZmL/hK/h6l0V7bdC4BCTDZzyBFzqNQk3/5lYDy/UyygB0GYju6F2qpLxvvvEyXnj+ae3PX4Q2DuIxbSX7P1GsmDfmaWv9jB33hf5IuO++itpUx8ktZpMmf4effkqZBe2hh5rhi89Hq7z3EnCM/3Q42rd7CIcOH0PPZ5I/U8hFvb2LYN3a+er6+ixV0tqyZdNSdazns/1x6NBRtZ2ZHwOXaYv/5cuPwO+mYPu8OZl5K/NSIEcFnDrgsFxlPGjTXETeTPlGM79HITRs2VvhH969HGGXUr4NztEayeDNGXBkECoLs1kGHDsm7FVT0eq3K928JFpPbqZ2bY3hcKSAo/YrAajVN0CNJ8loEJSZQeOlqgXguSnJU9OOa5Psolvdy2tGP+DLPSwXm5M1EGRshGWShQBlcT3p3iTdnCTZCjiqB/hAumFJslxFO6PlsRVwWM7uJK0t0npgrzEcK9Y8Y4zhkG/TZZC3vVKfl+ui34AGkNaDhPhENb5ExiR8OWm71S1k5XExlrEas2butTr3TK9aqgXEchHGzAYcP85+DPfX9FOtV0//70+r6+vnjh+7ip4WizKaNeCwerj/dlrU6oquLV7H4i3fYt3u5C5GEoBIIDLkh0fVjFXSBSs88go+mpnctVC/zug+CyHjQewVcHTq2FpreXhTXV4WzmvdNvnLP/1+9/JauLAX1q9bqC4x8L2PMOGzj9R2+47drQaHS1AiwUlaC/eN1rpZde7cQb3PVsBh2X0r9bXVG7UfMu6iuzbtbupuUM/17o6330oOQPSAQ96z859VWndDN/ww7WdM+WaGfpkMvcqyAIPWbFR5fxrwMs4fsb1uTIECWovpkrnGdd8fNArbt+8y9rlBgewWcOqAI6+7Bxq3flGZXjp3GMcPrlMzdsgB/2rarD0Vaqtz/6yfhdjolP736qCD/2DAkfMVZBlwxFyNxYLOy9RsU3K882/t1DSzsiCg5RS3UmpHbOHwf7gcmo1+ADK71e9tFqtFCe8knJmAQ/6hlG/m3LTFqFZ88Rl2L07+sHCne9zpfEY/4Mt1XnipDl59raHq6tNcWwhQVgm3TKM+boVOj9ynjuurh9sKOOR98+Y/iYr+3lbrZ2S0PLYCDrmu/sFYtiXZK+DQP1jLNe21DodcS5K3d36sWtdLbUtXKhkD2+XhX2E5cN6yC5isjL56pfWXPM1blFPTA8tF9EUYMxtwSHc3WeFcgrWXnl9sTGdbtVox/PTL46rb18L5R/DxqOQPbXIv3SUrulTJ9S2TPVs4LK+rb9er0gbPthuKnVqXqTlalylZm2Ns38XIl9cTA79pC58iZTCo50yVffrSoTgYvFVt16/SFs+0Sx5gbK+A47nneuDtN1/RiwZbH+qNDJnc+GvBLLXInyyUJx/gL1y4pP35fdrqKq1aNcekiaPUMWl9kFYISQHVq2D2rClq8Lbsp1e23bvWqjU8/lq0AiM+Gi/ZrdIzPbvhvYED1LFHOz9jLEi4YP5MyEKFkiwDDimPlEtmqXqqZ1+rtTFksb6XXnoWO7RFAGUhwNSp/mPd0P6Nd5AYH4/POrVOtyt46vEiQ4eNw9Jlq1NfkvsUyDYBUwYcjdv00f4CyKOQZOC3JOmnKv9LOrJnhdZF6qzaltXEZVVxSTJw/Lp2vHCxMmrAuBwLPX8MR/eZ7w8hAw6pvZxNlgGHlERmgLoUFIZi1YvAQ+sOI2nFi+twee8Vta3/yEzAIet51H+zlv5W+NxfVK3dEXkpClEXkweiR12OxoYPtqk8+rS4mR3DIauTSwAh64JIunk+EnER8QjdHZbmuhqSJzMBh+SXLlXStSr8ciimPJXcOiDH7yVl9AO+3EPv9nPx4k3IoOzUqW69Evh+Rmd1uP/LS9U4gPQCDun6JF2gJE0cvxW/zjlgjOGQYzLQPK10YP9l/KjNEmU5S5W0quipStVi+OW3lG+DzRBwSNlXBj6LokWTf+8jIuLQusVP+iOpV32RQAlIGtefpoICqwzajj49rh4UWAYcEkTIAN+00pAPAtWsWzJQPXDjc2qgtmSVupYIs0TJQioIkiBT1k2RdVL0pAcckt/WSswywYDMJHav6d33mqBL16qYOWOP9juw514vd9v7JbAY+eKfcHfLh+s3Q+GZv7DaPnz6H21tjkEq/2tdJ8O/VE1lefLCfrVCuUyLqyd7BRx664J+3fQ+1Ot5MvP6rDYWbOC7rxpv+fa7nyD/WyaZanbj+sXaIp9u6vft9Okzah2OypX9VTZ9McD0yvbNlE/RtElDlV+ChKioKLWmxwsvvqmOSUuC3ENaIyXJFLzSAiPrgujJMuCQMv0duFBbFDOfqoPg02e1CS6uamN9fFG6dCl1nS+/+gEzfrz976jXfluIQj6+2P77XAR+m/a0ufo9Lbt0yTEGHLoMX3NKwJQBR4uOyd8m2EI7FLQMV0JPqdMyVqN85QdQpmK927LLon/Bx7YZgcptGRz4AAOOnK8cy4BDZoWq9VJ1o1CyqN+GwdtwZm2IcUzfqPo/baXxQXXVrFYyu1V6yf+R8mg2KvkfO1v5EmISMbfZAnX6sfkd4FW+ELaN2WW1YF/RakXwyC9tbQ4alzfLgoUN3q0N78qFIc8mKfVK4+rgfz/0gCPwzc0I2XTB8lSa23m0byEHLl2jVsmd9for2pocB9LMl5mDequETGnapuWsdN+6Pegl9Y+5/mE2rcz67EoyFuHDIeuMLlWW4zos37do+dNqsLOs3SFreOjlscyTevvUyWuYNGEbvvymkzr18guLb+ve9esfT6o1PySDZRcvy2vJYOTFK5K/0X3+2YU4eOCy5enbtvXnlxMzp+/J9CDl2y6Y6oDlyu4btBXE331rlVWO+Yu6o2y5wtoHsttXFNcz6s8tg6of7ThHzXQldXKnNGLY31r3keMqm3R3G/95O6vB2nIiLCwK77+zxmj10K+pBxz6flqvvXsu0AZ5W6/jkFY+Rzgm3aee7zQSxbxKqX/bDgZvUQPE47UFAiW5urqhX5cJqFQq5YsMGeNx+uIh1KjYFHtPrMdPK0be86N8rn2T31r7Jl9SWoO57/UG7u55sW3LCvVnWgLR5g92Vt2aUl+3efNGmDD+I/UBXz8XGxeH4cM/xaeffKg+9NuapUryy30mT/oYDRvWUS0pcuz69Rt4qHXKlya9e0n3qVdUS4icl+D4q6+n4fXXkv/OsWxdkfMSkEz56hM1Ba7sWyZZL2TQ4NFqULnl8TL310KvyVPVWNQJj7RFktayk17q1KkNxn08VGURnw6delh1N0vvvTxHgawQMGXAcTcQEnjk9yyMfPkLIFZbbTwm6oYpAw392Rlw6BI592oZcMxpqn3g174hLd7AF3E34nDl8DXVPSnnSueYd+7w5kDU69JVBRsSdDBRIKsE/IoXUCu6S7eivXsvWXXvyqp7mum6nvkLoWLJmpDFAi9fP2f3ousDueXCw7WuSIu0Lkk5lfLkyYM62oxUFSuWV+MYzoXc+QuSzJZVWjSaNG6greUSia3bdqig407XkGCmZs0AVNLWzDh9JgQHDx5R70/rfb2/+h6lA2ogaNECrJw8Ia0sVse+mDRGTQUsB9dv2Io330oOPqwycYcC2SiQawKObDTNllsx4MgW5nRvkjrgSIy1HhOQ7ptz6UkZw/Hidz8iXpue8sf+L+ZSBT42BZxbQMYibN+WHGCkXqDPuZ88657uhakzkFfrhjXjlReQoLXQ3CnJeh8yjkNaW2SBRC76dycxns9qAQYcWS2cRddnwJFFsJm4LAOOTGAxKwUokGsEHn2kHcaMHqye94NBo7Fy1bpc8+yO8qAyi5cMpl+xMtDmmh6OUlaWI3cIMOAwaT0z4Mj5imPAkfN1wBJQgAKOJ1CqVAm00ta5iNC6F+VkVyrHk2GJKJB7BRhwmLTuGXA4RsXJYGxJV49cd4wCsRQUoAAFKEABClDAwQQYcDhYhWS0OAw4MirFfBSgAAUoQAEKUIACOSnAgCMn9e/h3gw47gGPb6UABShAAQpQgAIUyDYBBhzZRm3fGzHgsK8nr0YBClCAAhSgAAUokDUCDDiyxjXLr8qAI8uJeQMKUIACFKAABShAATsIMOCwA2JOXIIBR06o854UoAAFKEABClCAApkVYMCRWTEHyc+Aw0EqgsWgAAUoQAEKUIACFEhXgAFHujyOe5IBh+PWDUtGAQpQgAIUoAAFKJAiwIAjxcJUWww4TFVdLCwFKEABClCAAhTItQIMOExa9Qw4TFpxLDYFKEABClCAAhTIZQK5LuBwdc2LxMR401czAw7TVyEfIAMCLnDBLe0/JgpQgAIUoAAFzCtg2oDDLW9+lK1YF0X9Kij9Y/sDEXHjUpo1UaRYWdwX8CA8ChRR5+PjonHt8hkcP/Q3khIT0nyPox9kwOHoNWTf8vl5FkGlwiVxMeoaTt24aN+LO+DVOjzwHBoHPILCBXy0LwgScP7KCfyyZhxCr51xwNKySBSgAAUoQAEKpCdguoDDPZ8nylZqgJJla8DFJY/xbAd2LdGCiNPGvr7hV7IKqtZup3alZSMuJtIIPKIir2HPlt9N2eLBgEOv4dzx+lTVlnijzmNYe3YPPtwyy6kfunurd1WwIQ+ZlJSIPHlc1fMmJMbh83n9cPFqsNrnDwpQgAIUoAAFzCFguoCjas228CtdVelG3AhFocJ+ajutgMNF+6DSpE0fSDeqyxeO4+i+1bh16xbyexZGvWY91PEThzfi/Ol95qgti1Iy4LDAyAWbuSXg8C1SBoOfSQ6o1gbNxdKtP6BAfi8MeXY2PPIVwtnQo5j0e/9cUON8RApQgAIUoIDzCJgu4KhSsw3ye3gh+NhWhF+/iGbt+6lvQNMKOAp4+aBe0x6qtratnY74+Bij5irf3wolygQgOvI6dm78xThulg0GHI5TUx5u+VCqQFHc1H6/Lmldntxd3VDfrzLKFPTBv9fPY/flE7cVtpC7B6p4l4G/VwlciLyKg1dP41rMTat8+nXl4KP+jdCjyoMICv0Xk4IWGPluxkdr97xu7PtofzYKaN0NQ7Vj0QlxxnFXrTWwTCEfxEv3JO1+limPiwsqauVIvJWE4PBL2qgJF1QvWhY1ipVX19564TDik5K7Hhb39EZB7fpyDTkmeaoUKa3eJ2WTa9xL6tH6PTSq3gkRUVcx4scn1aWkPCOenwevAsXU/vAZT+BmdMozy8F87u6oVTtAnZcfu3bt01pH7q0sxsW4QQEKUIACFKDAPQmYLuCQblS3LD7UpBdw+JasjGq12yNB++C1dc0PVlBFfcujRv1H1bEtq783XbcqBhxW1ZmjO+3K1cPIJs/iwJVgLD21Ax80+J9VeZYF78CY7XONYxI4vFn3cWNf35BA4vfjG/VddChfHyMaP2Psp7UhwcyAwCnGqe/bvoH7i1VQ3a6k+5WeKnoVxy+dPlBBUfv5Q/TD6rVIvoJY9vgotd175QRMa/uWCpr0TFeiw9F50UdqV7/+d/uXoXf1NpCgSE8nblxA3zVfaoFOrH4o06+jXpyPgh5FsO3QUsxbN1G9v0PD5yBjOvT05/rJ2HzgL31XvTZr+gCmfP2JceyJJ1/AyZO3d7E0MnCDAhSgAAUoQIFsEzBdwJFaJt2Ao8R9qFanwx0Djh3rZyNG+1BlpsSAw3FqSw844rTWA2ndkJaKrRcPQ/ablKymtXJcwHsbp6kC96nRAX3u76C2lwfvxMErp7WWjtLo4t9YHRu+dTbWnNmttssV8kObcnXUdoPilVHXt5JqWVh66h91TH5Ii8S6s3uNfT0gkHEedxNw3IiL1FowPLAx5AAuR91AQLFy8C9cAm3+HKzuoV9fdqQ1Z8nJfyBlq+3rr85/vXcx5hxZp7bv5sdn/VZpXR3dMHftp9hxZCWKFPTFh71/VZeKS4hGvryeCNS6Wi3RulpZJgYclhrcpgAFKEABCjiWgFMHHAW9fFG3aXclvlXrUpVg2aWqhtalqmxyF4y92/5U3bMcq2rSLw0DjvR9svOsHnDIPaVb0TsbvlfBhuxLd6CyhXxxJiIU0t1pQefhkO5Nn+yYh0Unt0kWlfRWD+mWlboFQjJkdAyHHhDcS8Dx/MqJVt20ynv54XR4qCqnfn3pOtXuzyGI0QZySxryQA88WrGRCqBeXjNZHbubH58PCFRv+2HJYBw+vR1vPfkNyhWvpgUY36Nh1Q4oXrQ8go4H4udVY6wuz4DDioM7FKAABShAAYcScOqAQ2a3afzfoPHQC8e0QeNroPXHsho0LrVxcNdSXL0c7FAVc6fCMOC4k1D2nbcMOLouHq2++U/r7k9Wbo536j2hxjk8tWycepWARJKn1jVpdsf31PaTS8bcNs4iuwKO0dvnQFpebCU94Pjn4lG8tf47I5tuIGNHHl+c3D3LOJnBDQ+ta9fHLy1SuSf/8RqKadMAP9tuKK6EX8DHs5/BG92+RoUSATh2dhe+XZRspV/a3788xo5J6SrWf8AHuHbNepyHnpevFKAABShAAQpkr4BTBxxCWbx0NchAc0lqWtzYKHhos1TJOBB9Wl22cCiebP3RtGkjXAm7gqPH/s3W+2bFzfQP29KV6pG/htu8xcD63fDEfc1sntdPSPerzecP6bvqNbsCjs5/jcCVmAire1vu6AFH6vEm/lpw8LMWMNlqobG8hq3tPFrLz4RXtS8FtDRzxUfo2XYw3LVAbPzcF9VUuO/2+B6lfe7DvhMb1Hlb1+FxClCAAhSgAAUcS8DpAw7hLupbAZUCWqjZrWT/ZvhlnDyyGTUbdlFBx/a/Z6r1OeScWRJbOBynpvSA4/DVM+iz+gubBfusxUtoVioA526GaWMkDtrMt+jkVqMLk54puwKOpr+9o98yzVc94MhMl600L2Tj4IT+q9WsczHaWJL87gWwcd98LNj4tco9rPccFC1UAhv2/omFm1IGytu4FA9TgAIUoAAFKOAgArki4NCtXbTpP6VVQxYTy+dRCA+07K1ObVz5jepqpeczwysDDsepJT3gkFmqZJYmW0lmppKxGuvP7cfgzT/aypbm8YwGHN+2eR21fCrio20/Y9XpIONaEuhIwJNWC4TlLFU5HXCMfXmxCjSk4JEx4drUuN3Un1fZH99vBdxc3bF4y7dYt3ueHGKiAAUoQAEKUMAEArkq4LCsD/9qzVG6Qm2EX7uAvdvnW54yxTYDDseppowGHDIT1aCG3SHTzHZZNBK3tP8ymvTxH3cKavRWFJle13K9Dr07l6MHHP26fIYqZesrlikL3sKJ8/vUtnSlki5VkvQuVmrnvx8FCnhi2ZKUqYffHzQK27fvsszCbQpQgAIUoAAFckjA6QMO93yekBXHY6NT+qX7layCqrXbKfIDOxfhWtjZHOK/+9sy4Lh7O3u/M6MBh0w3u+SxkWrqXFnH4qdDyeMV9PLIQoEyxezCE1v1Q8ar3kIhs0N1XDAMkRYzrhmZtI2363XF/yq3UN22ei7/FAlaa56slfHHI0Phnb+gw7dwSLAhQYekmctHYN/J5HVJ3un+Lcr4VsG1iEsYPetpdd7yR5EihfF3YMqCiEOHjcPSZasts3CbAhSgAAUoQIEcEjBlwCEzT+kDvt3c3BWddJOS/yUd2bPCCCJ8SlRC9TodERV5TRuncROeBYtBghBJMjOVzFBlxsSAw3FqLaMBh5RYpo6VKWQlyVSze7SF+9xd86q1OCppA69lbQuZ6Sp1ktXDlz42yliQ78i1cwiPjdTefxIzD6V8sK6mrRA+o93b6u2yDoisEl7Hzx+FtfEQkhy9hUPKOKjnTPh5l9MmdriFC1dPwcuzqFoMUM79tHIk9v67Xjatkrd3Eaxbm9JSyYDDioc7FKAABShAgRwVMGXA0aLjgHTRDgUtw5XQUyqPl3dJ1HrgcSNAkYMyQ9W5U7sRfCxlHYR0L+iAJxlwOE6ltC1XF6Oa9MJe7cN//8DkAc7pla6+1ooxqnEv1eJgmU8W3Zt3bCN+PLjK8rCxLcFE3/s7oWGJKmotDzmReqVxOdav1iNqFXDZliSzZ43b8RvGt+hjI+AooK00PlpN09ti3sDkN9n4qY8RGbrlJ6sFBytoK5nPsbGSuY1L2Tws4zRef2IyyvpVNfLIlwlzA8dj19GU4Mo4qW106tQG4z4eqg5JoNKhUw+EhoZZZuE2BShAAQpQgAI5JGDKgCOzVtIa4lnQW7VsREeFI0ZbQdnsiQGH2WsQqtVBVvFO0gLgC1rLhqxhYa9UJF8BbYXwkgi5ecXmuiD2uldWXSef1gWtUqnauHYzFBeunEz3Nl9MGoOHWjZVedZv2Io330oOPtJ9E09SgAIUoAAFKJAtArki4MgWyWy+CQOObAbn7RxaQMZvyDiOpKRbaNOuGxf9c+jaYuEoQAEKUCC3CTDgMGmNM+AwacWx2FkisH7dQri5uWHFykCMHvN5ltyDF6UABShAAQpQ4O4EGHDcnVuOv4sBR45XAQtAAQpQgAIUoAAFKJABAQYcGUByxCwMOByxVlgmClCAAhSgAAUoQIHUAgw4UouYZJ8Bh0kqisWkAAUoQAEKUIACuVyAAYdJfwEYcJi04lhsClCAAhSgAAUokMsEGHCYtMIZcJi04lhsClCAAhSgAAUokMsEGHCYtMLr1GmAuLg4REREmvIJ2rSqk6lyV6la2cg/9dvfjW1uUIACFKAABShAAQo4tgADDseuH5ulc4aAY9WG7Tafz/JE+wcbgQGHpQi3KUABClCAAhSggHkEGHCYp66sSsqAw4qDOxSgAAUoQAEKUIACDirAgMNBK+ZOxbIMOM6ePXGn7A53/vne3TDz13kZKtfzT3W3auEYMvSTDL2PmShAAQpQgAIUoAAFcl6AAUfO18FdlYABx12x8U0UoAAFKEABClCAAtkswIAjm8HtdTtnCTh6Vm6NcgX9EBp9HUeun8XWi4dwS/vPMrGFw1KD2xSgAAUoQAEKUMBcAqYOODw88iMp6RZiY2MzrO7i4oJbt6w/0Gb4zQ6U0VkCjukPvYvq3uUM2ZVnd2LkztnGvmww4LDi4A4FKEABClCAAhQwlYDpAo6OHdugVctm8PEpCgkeJCUmJuL48ZP49vufEB4ecVsF5M2bF++81Q8VK5aDu7u7mk72zNkQTPx8aqaCldsunIMHnCXgcHXJg0Lunmhbui7eqf2kEm2z6D1EJ8YZugw4DApuUIACFKAABShAAdMJmCrgcHV1xQ/ffW4gS0uFHnTIwfj4eLz73gjcvJmyNoUEGJ+MHYYiRQqr9yUlJSFPnjxqOzwiAoOHfIzo6Gi1b6YfzhJwWJr/3WUC3F3zoueasQiOuGScYsBhUHCDAhSgAAUoQAEKmE7AdAHH999OxKFDR/H7H4sgrRReXoXQpXNHtG7VXOHLuQmff2NURPf/PYaOHVqr/anf/ogdO/egVs0AvPlGXxWs/L1+M2bNzthsScZFHWDDGQOOpQ+PgXe+Qui7fhIOXA02lBlwGBTcoAAFKEABClCAAqYTMFXAIbr58uVLsxvUl1+MRcGCBVTr6k7uLgAAFeBJREFUxhtvDTEqQj9+6PAxTJg4RR338/PBuI+HqYAjOjoGA17/wMhvlg0GHGapKZaTAhSgAAUoQAEK5G4B0wUctqrrbW2MRs37q6tgpP+A91W2Ap6e+OrLcWp72vSfsWXrDrU99uOhKFHcz7jU+4NGIizsqrFvhg1nDjhe2/Q1gi4fN6qBLRwGBTcoQAEKUIACFKCA6QScJuCYNHE0Chf2wqlTpzH64+RxHv7+5TFsyDuqUl5/YzAio6LQpnULPNPzSbUtAYmkzydNxYGDR9S2WX44Y8Axt+0QlC9UHDOPrsL3h5YaVcGAw6DgBgUoQAEKUIACFDCdgFMEHO3aPYSne3RV+FO+mYFdQXvVduNG9dH35d5q+8WX3oSHhwcmT/oYbm6uGPfpZAx6/w3VrernX/5A4LqNpqo8Zww43qvTHV0rNsOVmHC8rI3juBiV3OrEgMNUv5osLAUoQAEKUIACFLASMH3A4etbTI3HkJmnpJVCWiv09HCntniyW2e17kafl9/CewMHoHq1Kti9Zz+++noapn0/Sc1YtWTpKsxfkPKNuv5+R351xoDD0y0f+tXojI5lG6BgXg/EJcbjbORlrPU9hSpVKxvVMWToJ8Y2NyhAAQpQgAIUoAAFHFvA1AGHDBIf/8kI5M+fTw0WlylxZWpcPbVo0RgvPPe02pVA5J23+6s1OF7TulclJCRg+g9fqBaO3+YtxMpV6/S3meLVGQMOmaHqVQk4yjWErM8hLR0Hr53GkQrhDDhM8VvJQlKAAhSgAAUoQIHbBUwbcMj6GuM/Ga6mxY2NjYMM/I6IuGn1hNWqVcb7A19TxyRPvnzu0KfGlYMzpk1W576eMg1Bu/erbbP8cMaAY0SDXuigtW7ciIvEoG3TsffKCVUd7FJllt9KlpMCFKAABShAAQrcLmDKgEMWABynzTTl41NMa6lIxLDhYxEaGnbb03l7F8HEz0Yax0+cCMbH4yap/Qrly2L4hwPV9ocjPkFIyAUjnxk2nDHg+KPDcJTyLIZZx1bj24NLjGpgwGFQcIMCFKAABShAAQqYTsB0AYesLD5yxPsoU6YUZNXwUWMm4syZczbhv5s6AXnz5kViYiLefvdDYxXy3s92x0MPNVPH+/Z7V43zsHkRBzzhjAGHvvDfqxu/xJ6w5NYNoWfA4YC/gCwSBShAAQpQgAIUyKCA6QKOoYPfRqVKFVSA8Mn4L3H8+Ml0H7V/vxfQsEEdlX/w0DGqJURWJ58wfqSarWrfvoP44svv072GI5505oCDK4074m8cy0QBClCAAhSgAAXuTsBUAUeFCuUwfNi7xpNKC0fqJMekxUJPMovV2DFDId2w5Fzo5TD4FCumgg3ZH/7Rpzh//qKe3TSvzhhwrO08Hh7aTFUv/f05DmmDxfXEFg5dgq8UoAAFKEABClDAfAKmDjjS4r516xZkClzLVK5saQz64E01m5V+XAaRj5/wtVooUD9mpldnCzjKFPDBvPYfqip4ZNkwXIuNMKqDAYdBwQ0KUIACFKAABShgOgFTBRz3quvjUxTVq1fBkSPHcfnylXu9XI6+31kCjrdrdUOj4tVQrqCf8jwXGYbuq0Zb2TLgsOLgDgUoQAEKUIACFDCVQK4KOExVM3corLMEHBOa9EWtYv64qrVo7LtyEtOPrMClqGtWT8+Aw4qDOxSgAAUoQAEKUMBUAgw4TFVdKYV1loAj5YlsbzHgsG3DMxSgAAUoQAEKUMDRBRhwOHoN2SgfAw4bMDxMAQpQgAIUoAAFKOBQAgw4HKo6Ml4YBhwZt2JOClCAAhSgAAUoQIGcE2DAkXP293Rny4Djni6UQ29u06oOVm3YnqG7t3+wEapUrWzknfrt78Y2NyhAAQpQgAIUoAAFHFuAAYdj14/N0jHgsEnDExSgAAUoQAEKUIACDiTAgMOBKiMzRXGGgCMzz8sWjsxoMS8FKEABClCAAhRwHAEGHI5TF5kqiZ+fL2JjYnEjPDxT72NmClCAAhSgAAUoQAEKZKcAA47s1LbjvRhw2BGTl6IABShAAQpQgAIUyDIBBhxZRpu1F2bAkbW+vDoFKEABClCAAhSggH0EGHDYxzHbr8KAI9vJeUMKUIACFKAABShAgbsQYMBxF2iO8BYGHI5QCywDBShAAQpQgAIUoMCdBBhw3EnIQc8z4HDQimGxKEABClCAAhSgAAWsBEwdcOTJ46oeJikp0eqhcsMOA47cUMt8RgpQgAIUoAAFKGB+AdMFHMWK+6Nk2RooXLQ09IAjISEOoSFHEHx8OxK17bSSW978KFuxLor6VVCnj+0PRMSNS2llNcUxBhymqCYWkgIUoAAFKEABCuR6AVMFHC4uLmje4VWj0m7dSoKLSx5jPy42Cjs3/IzExHjjmHs+T5St1EAFKZZ5D+xagmuXTxv5zLbBgMNsNcbyUoACFKAABShAgdwpYLqAo1n7fgg5vQ/ng/dpC99FwNXNHX6lquC+gJaqBkOC9+LkkU1GbVat2RZ+pauq/YgboShU2E9tM+AwiLhBAQpQgAIUoAAFKECBLBMwVcAhCtKNKq0xG3WbdkdBL19ERoQhaPNvBliVmm2Q38MLwce2Ivz6RUjAItdgwGEQcYMCFKAABShAAQpQgAJZJmC6gMOWRMWqTVFGG6MREx2OHetnG9mkG5V0vdITAw5dgq8UoAAFKEABClCAAhTIegGnCThqN+4GryIlcCnkMGRAuK3EgMOWDI9TgAIUoAAFKEABClDA/gJOEXAU9a2AGvUfUTp7tv6R7uxTDDjs/0vEK1KAAhSgAAUoQAEKUMCWgOkDjrzuHmjYshdcXfPi3KndOHV0i61nVccZcKTLw5MUoAAFKEABClCAAhSwq4CpAw4JMuo26wEPz8K4GX4Z0rphOV4jLSkGHGmp8BgFKEABClCAAhSgAAWyRsC0AYeLNtNU7UZPqGluZXrcXZvmaov+pay/YYuLAYctGR6nAAUoQAEKUIACFKCA/QVMGXDIAoA16neGt09ZxMfHIGjjXMTFRWVIhwFHhpiYiQIUoAAFKEABClCAAnYRMGXAUa1OB/iWuE+tKC4tG7HRERnGYMCRYSpmpAAFKEABClCAAhSgwD0LmC7gqFyjFUqUDVCL/wVt/hXRkdczhcCAI1NczEwBClCAAhSgAAUoQIF7EjBVwCErhsuMVHpKTLx9zEZSYgK2Bc7Qs6jXxm36QBYAlOTm5q5eZbVyfcXyI3tW4FrYWXXcLD/8/HwRGxOLG+HhZikyy0kBClCAAhSgAAUokAsFTB1wpFVfMkvVppVTrU616DjAaj/1zqGgZbgSeir1YYfeZ8Dh0NXDwlGAAhSgAAUoQAEK/CdgqoCDtZYiwIAjxYJbFKAABShAAQpQgAKOK8CAw3HrJt2SMeBIl4cnKUABClCAAhSgAAUcRIABh4NURGaLwYAjs2LMTwEKUIACFKAABSiQEwIMOHJC3Q73ZMBhB0ReggIUoAAFKEABClAgywUYcGQ5cdbcgAFH1rjyqhSgAAUoQAEKUIAC9hVgwGFfz2y7GgOObKPmjShAAQpQgAIUoAAF7kGAAcc94OXkWxlw5KQ+700BClCAAhSgAAUokFEBBhwZlXKwfAw4HKxCWBwKUIACFKAABShAgTQFGHCkycKDFKAABShAAQpQgAIUoIA9BBhw2EOR16AABShAAQpQgAIUoAAF0hRgwJEmCw9SgAIUoAAFKEABClCAAvYQYMBhD0VegwIUoAAFKEABClCAAhRIU4ABR5osPEgBClCAAhSgAAUoQAEK2EOAAYc9FHPwGr/Mnoro6GiEhoZh+cpAbNy4LQdLw1tTgAIUoAAFKEABClDAWsDUAUeePK7qaZKSEq2fysZeZvPbuIxDHd4TFGhVnukz5uCrr6dZHeMOBShAAQpQgAIUoAAFckrAdAFHseL+KFm2BgoXLQ09gEhIiENoyBEEH9+ORG3bMmU2v+V7zbDt6emBMqVLYsyYIahS2R+RkVFo1uJRMxSdZaQABShAAQpQgAIUyAUCpgo4XFxc0LzDq0a13LqVBBeXPMZ+XGwUdm74GYmJ8epYZvMbFzLhRpMmDTB1yngkJd1CvQZtTPgELDIFKEABClCAAhSggDMKmC7gaNa+H0JO78P54H2IjYmAq5s7/EpVwX0BLVX9hATvxckjm9S2BByZyW/mCvb3L4/5f/yoHqFOvdZmfhSWnQIUoAAFKEABClDAiQRMFXCIu3SjSmvMRt2m3VHQyxeREWEI2vybUUWZzW+80WQbFSuUw4L5M1WpGXCYrPJYXApQgAIUoAAFKODEAqYLOGzVRcWqTVGmYl3ERIdjx/rZtrIZxzOb33ijg25YBhwNHmiPhIQEBy0pi0UBClCAAhSgAAUokJsEnCbgqN24G7yKlMClkMM4tt965qa0KjSz+dO6hiMdk8HjWzYtVUUa9uE4LFm62pGKx7JQgAIUoAAFKEABCuRSAacIOIr6VkCN+o+oKtyz9Q9E3LiUbnVmNn+6F3OgkxJwSOBxLuQCnnm2P27cCHeg0rEoFKAABShAAQpQgAK5UcD0AUdedw80bNkLrq55ce7Ubpw6uiXdesxs/nQv5mAna9UKwKiPPkCFCmVVyWTGqt2796HPy287WElZHApQgAIUoAAFKECB3CJg6oBDgoy6zXrAw7MwboZfhrRuyFS5tlJm89u6jqMeb/lgEwz64A2ULFlcFVHW5Ni4aRsGDR7jqEVmuShAAQpQgAIUoAAFnFzAtAGHizZbVe1GT6BQYT81Pe6uTXO1Rf+S199Iq84ymz+tazj6sX+2rYS7e16EhV3BW+98iAMHjjh6kVk+ClCAAhSgAAUoQAEnFzBlwCHra9So3xnePmURHx+DoI1zERcXZbOqMpvf5oUc+IS3dxGsWztflXD4R+OxaNEKBy4ti0YBClCAAhSgAAUokFsETBlwVKvTAb4l7lMrikvLRmx0RLr1ldn86V7MQU9aTotbr0Fbba0S213LHPQRWCwKUIACFKAABShAAScUMF3AUblGK5QoG6AW/wva/CuiI6+nWy2ZzZ/uxRz4pGXAwYX/HLiiWDQKUIACFKAABSiQywRMFXDk9/BSM1LpdZSYePuYjaTEBGwLnKGyZDa/fl0zvsoMVbNmfq2KzoDDjDXIMlOAAhSgAAUoQAHnFDB1wJFWlcgsVZtWTlWnUgccd8qf1nmzHHtv4AA807MbYuPi0KhxR7MUm+WkAAUoQAEKUIACFHByAVMFHE5eF3f1eGtW/4ECnp7w8Miv3h+0ez9e7PPmXV2Lb6IABShAAQpQgAIUoIC9BRhw2Fs0m6+3859V2niWW4iOjsE/O4IwesznCA9PfxB9NheRt6MABShAAQpQgAIUyMUCDDhyceXz0SlAAQpQgAIUoAAFKJDVAgw4slqY16cABShAAQpQgAIUoEAuFmDAkYsrn49OAQpQgAIUoAAFKECBrBZgwJHVwrw+BShAAQpQgAIUoAAFcrEAA45cXPl8dApQgAIUoAAFKEABCmS1AAOOrBbm9SlAAQpQgAIUoAAFKJCLBRhw5OLK56NTgAIUoAAFKEABClAgqwUYcGS1MK9PAQpQgAIUoAAFKECBXCzAgCMXVz4fnQIUoAAFKEABClCAAlktwIAjq4V5fQpQgAIUoAAFKEABCuRiAQYcubjy+egUoAAFKEABClCAAhTIagEGHFktzOtTgAIUoAAFKEABClAgFwuYOuDIk8dVVV1SUmKGqjCPqxtw6xYymj9DF2UmClCAAhSgAAUoQAEKUMCmgOkCjmLF/VGybA0ULloaesCRkBCH0JAjCD6+HYnatmUqV6kBfEpUgmfBonBxyaNOJSbG4/KF4wg+tg3xcdGW2blNAQpQgAIUoAAFKEABCthRwFQBh4uLC5p3eNV4/Fu3kowgQg7GxUZh54afIQGFnhq1egHu+TzVrhx3dc2rn0J8fIzKnxAfaxzjBgUoQAEKUIACFKAABShgPwHTBRzN2vdDyOl9OB+8D7ExEXB1c4dfqSq4L6ClUgkJ3ouTRzYZQmUq1kNkxGVcvxKi9aZKUq0i3r7lEVC3k8pzXrvWicMbjfzcoAAFKEABClCAAhSgAAXsJ2CqgEMeW7pRpTUGo27T7ijo5asFF2EI2vzbHYUC6j2MYn4VcTP8MnZvmXfH/MxAAQpQgAIUoAAFKEABCmRewHQBh61HrFi1KcpUrIuY6HDsWD/bVjbjeI36j6Ko1tJxLewsDuxcZBznBgUoQAEKUIACFKAABShgPwGnCThqN+4GryIlcCnkMI7tD7Qp5JY3H4r6VUDVmm1VniN7V6kB5DbfwBMUoAAFKEABClCAAhSgwF0LOEXAUdS3AmrUf0Qh7Nn6ByJuXLoNRMZseHmXRF53D+PcJW1mq+MH1qmxHcZBblCAAhSgAAUoQAEKUIACdhMwfcAhAUTDlr3U7FPnTu3GqaNb0sSp16wHChTyMc7JjFUHdy3BjavnjWPcoAAFKEABClCAAhSgAAXsK2DqgEOmuK2rBRIenoXV4G9p3ZCZqNJM2pS6ebR1OLy8S2mzWlVG8dLVVbZ/D63HhTMH0nwLD1KAAhSgAAUoQAEKUIAC9yZg2oDDRZutqnajJ1CosJ+aHnfXprnaon8p62/ciaVUuZqoFPAgom5ehbyXiQIUoAAFKEABClCAAhSwv4ApAw5ZALBG/c7w9imrFu8L2jgXcXFRmdLJl78gHnjoOfWebYHTtRXHYzL1fmamAAUoQAEKUIACFKAABe4sYMqAo1qdDvAtcZ9aUVxaJ2KjI+78pKlyyIxWMrOVpO1/z0RcTGSqHNylAAUoQAEKUIACFKAABe5VwHQBR+UarVCibIBa/C9o86+Ijrxu08CzYFGtFaScmio3IT7WyFdAO16tbkd4FvBWLSTb1k43znGDAhSgAAUoQAEKUIACFLCfgKkCjvweXmpGKv3xZaap1CkpMQHbAmeow7KwnyzwJyk66oZqCSmojflwc3NXx+TH4T0rEXbxX2OfGxSgAAUoQAEKUIACFKCA/QRMHXCkxSCzVG1aOVWdUi0ZWvcraelInWJjbkJmqLoaGpz6FPcpQAEKUIACFKAABShAATsJmCrguNtnlulz83kUgns+T9UVKybyRqYHmd/tvfk+ClCAAhSgAAUoQAEK5GaBXBFw5OYK5rNTgAIUoAAFKEABClAgJwUYcOSkPu9NAQpQgAIUoAAFKEABJxdgwOHkFczHowAFKEABClCAAhSgQE4KMODISX3emwIUoAAFKEABClCAAk4uwIDDySuYj0cBClCAAhSgAAUoQIGcFGDAkZP6vDcFKEABClCAAhSgAAWcXIABh5NXMB+PAhSgAAUoQAEKUIACOSnAgCMn9XlvClCAAhSgAAUoQAEKOLkAAw4nr2A+HgUoQAEKUIACFKAABXJSgAFHTurz3hSgAAUoQAEKUIACFHByAQYcTl7BfDwKUIACFKAABShAAQrkpMD/ARpcfV/jNKMtAAAAAElFTkSuQmCC"
-  },
-  {
-    "quest": "Data la porzione di codice in figura, indicare il corrispondente albero dei processi generati:",
-    "answers": [
-      {
-        "text": "A",
-        "image": ""
-      },
-      {
-        "text": "B",
-        "image": ""
-      },
-      {
-        "text": "C",
-        "image": ""
-      },
-      {
-        "text": "D",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": 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TY3JlZW5zaG90PC9leGlmOlVzZXJDb21tZW50PgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4Ku1NoFAAAABxpRE9UAAAAAgAAAAAAAAK+AAAAKAAAAr4AAAK+AANF7yUSYtMAAEAASURBVHgB7F0HnF5Ftf9vL+kdQghJSN8skNBCFSmiFOlCICEgoatIIAkKiFKlY0FURAELTxQBpVhAIy0UaQlJNoX0vrtp2/s7Z+aeb+be7367m2STbJJzke/OnD7/u/6e57wzM2lN9EAfRUARUAQUAUVgKxGYOeszXDj+SjTU1yGN/kl+mtAU0KNc93+A3Ej009LTMWrUSJSWlGLVqjVEZhm24GRjZ43kLSMDg4YfhZy8Dlg8731Ulq9PxCY66ZmZGDLyWGNv6aKPUFW2Ad16D8A+Aw9Ep859kJaWgfr6KhSvmI+Vyz5BXU0VuU9DU1MD8vK7YN9Bh6Bbr/7Iys5DI6195dKZWDp/BtLSM22YZNk+PioSO9lJcEXOUoQeMSJCiXcUS2Hw/1lP4ziJIDLOpqOJ/I58h+OwM5+2LbHErTV5tc17szbEUsvRuL9slm2FHrtnMfmfXvSdEmM2wQ/T9FEEtgGBysoaTJo0DtOmTkZWVtY2WFJVRUARUAQUgbZCIE0LL20FpdpRBBQBRWDPRGDmzM8wYeJVqK2tJQD8IoOHByeaLSaUnMZaC/xOJ/mCwmjhxdk0+Wsgz8UY0W5qakSXbvtg5OivYEPpMsyb9W80cTHGT3jRSEWTgRh24ElYu3Iuli78gIonNRhWeBLyOvVAZVkpmhoa0bFTd3To3BtrVnyGz4veMnaycnJI7kR07toXGzesRG1VGTKp+FJVuZEKL+956+RV8BPFxK6SOW7EM37sKuyYf8NJuKw5meM02EKcR5EIWxTqjnkLIuyNS0Oyfp/uR+Kv1+o4ruiIjKxL6FZSqDwLc5wlN3LSdiQaju5kLY9/mWvfcXJOQ0aBVVEVMr9DtKi1ENPX8sa+THTMYi7WsDOW5Uf4cWOmRW1GY2SZrX38GLbWhuoxAlVVtZh02QWYqoUX/YNQBBQBRaDdIKCFl3bzKTQQRUARUAR2TQRmzZqNC8ZdbjpBwomZW490YThKqpFN7Pg3PT2NOl4KUFJS4nW8OD0uLrgyhSSATGnC0IIT0KP3fphPRZfSdYup6JJBOaVN7GyzQSNGjDmFOlt6oujT11G2kTtqQMWUvVFbV03zVWhsqKMCTj8cePjZqK+twtyZ/6KCzHrs3b8AA6mbZjV1uHw+5w1KcjYiO68zcnM7UtdPvQvQJKk89csgLrl0I08loWORDOtaOdaT1fqazY3jfTWnsX144Tjc12uNNykJyNrDtpwFoVuKSPMszHEabuRLM8qsEaY52a0aJQzSIBj7zS/OG2Pj/91slTdV2kMRMB0vVHiZNu0G7XjZQ/8GdNmKgCLQ/hDQwkv7+yYakSKgCCgCuxQCs2ir0QUXXkHdIA0UtySUQboqWaVZEdGazWI5E5X0mJJOKrwUNlN4McZMJw3rpRtdCgL5XXph5IEnmw6Uok//hYa6usCvtd9ABZVuPffDsANOoALLWlOcaWygggm1xDQ01pp0Nz2N2vNp3thYj4OPHkfdNxkooiJOVUUJBg8/Bt37DMKsD16kQkstMjIzUF1djrrqSioW8TYjjsX6skHJry0V2ZlN6nmcDIkUJMIcmYllmYv1VG+Rj/eVSmtH0P11cpS8Ij/acAxx6xUtloyOrXaclthN9hWWDs9Ea6vfCXc0YNMSsLyNYfFJREOX+VZ7VcU9EAEtvOyBH12XrAgoAu0eAS28tPtPpAEqAoqAItC+EeCtRhdPvIa2GtVQoEHCGBsyJZHN5pGSiAfKVPjgwktpaSlW0xkvIa6frEqCSkUYPtuFz17ZZ79CLJw9HcVrPndJLps1sk10tstx6LHXIMz99J/YWLKMaiwUmOGlo4ljpDE3yHTsuhdGHXwqNlEHzLyZryGDzp0ZUvBF5Hfqic3rV6IDFXmyMnNQUVaClctnG1vuzA5ZLBt2j6Wm7qYISzu96EisR+nRuW+vtTq+DdZP1oun+nqtG9vo/BhFj30K3ffv03gsvOjY2hGuWPXf1lKyn+Z0fP0tHJsiZODNd5EI3BHlb33bel4ShlMEatcf/i9IEJ/RaG7MAr79VGNxvaW+RE/fW4OAbDWaMmUysrP1jJetwVB1FAFFQBFoawS08NLWiKo9RUARUAT2MATMVqOLaKtRA3e80CM5lhtYOhc3Uj5WKaFKcn7Hy+pVa8kab74IzLNgwh5NuFrSVIcOdO7KKNpCVLZ5LeZ9+hoauJOFtbiK0kj/oY6WnnsPxdBRx1NRZj5tFXqb9ChuY8vFx50uubR9qGD0qXR+SzZtR/oXNm9YTYf1dsZwOhemS/e+KN+4DksWzqAulywMGnG0sT/vs9exsXQ50WhrU4rHeXEjuyo79zFIYcKQfe3m5Jhn4GpJKMIXHXlH2G04tSu2v2zWrcyNmB6WC/OEG9aW2J1tGbG2jNn2tpU42ELyQ/YTLoJok4IOBCJ0pm6PiJJjVMruiIDteKHDdafp4bq74/fVNSkCisCuiYAWXnbN76ZRKwKKgCLQbhDgwsv54yZRkkmVDXlCW4wCYqJQIkL+W/5//I7mF15WUeGFs9houmyliWr8NWAQdbL07jsMi2b/F2tXFdkbhpxJ09ky9IAT0a17PxR98g86fHc5yQTblBIJf5O5qWhI4fHo3GVvLKabilYvm2mKKdk5HcwWpS7d+lIx5p90MG8R1Xya0H/QYRg47Ag6pPd9LFvwPtIz6P/LbHJq+okk1W7qRl6IZhik41Fy0jzZgk3ZkwSbIbTka2t8pIxCjCU5lbJaONBkcacoPNFwHIHcSTiejBxP9PkdT/UltmTMvsgi/22m/NuXeDy7ROI6ohZePEx0uEUI6FajLYJLhRUBRUAR2CEIaOFlh8CsThQBRUAR2H0RsLcaXYM6s9UoWGdiWwXPOfHkl8kmAwH/lVx0MSp8uG4hbTUyh+vawos1xLqcnQY2jG0gt0MnHHjoWais2ITZH71kDsflokqT2TtkE9lO3ffGyIO+jA3FS7Bw7pvmjBY/Eh7zDUWDRxyHrr36YcXnH2Plko/JBnXFkJ0MatsfSgWZbj3649MZf6YtRnT7ERWcevQZiBHUHbOCZJfQ7UdpXHgxdahg7Z4TDtc94Zmju+X5tLhx2AKD0jxFYIuz1TLNFr9a4yMcRSSsFEH4ZF/f0d3I53PcjmO/tb8Wn+fTo+OozSi/dXPfG1nkaUrDvqyzblVSKjlBHSkCMQjoVqMYUJSkCCgCisBORkALLzv5A6h7RUARUAR2dQRm0uG64ydchXo+xDYpy5TEsrkkMkXhJcPeapS68BLYpI4T3ubENw316TfCdJ2sXvIJdbJQ8UPc0juNDuAdPOJYOhi3Pz6f+w5KV39uakGJG5e4WETNL4OHH0fbkQZj7Yo51O3yDl0zTduVqAjENyNxF87AoUeh736jMPfjV1GydqEpvOzTfzT2LzgWyxb9j66UttuP3HflIAQHF5LlC08CdVpOw9H8UbIGc1krzIlSku1KDL71VONo4SVqPZVeQG+Fq1QWXdx2ZFfpDDo+I2C50WjsX5rjOW0n6biOtmUjPxLRTGU1TrYlHeHrWxGIR8B0vEwah6l6xks8QEpVBBQBRWAnIKCFl50AurpUBBQBRWB3QsC/TprTSD5Oxfz4OWXKrRZcKvAFHTJpGekoLBhJ10mXetdJ+6myJLNNdB5LR4wcfQqd6dKAzz56GQ10/bPb3kFydGZLJ9oeNIy2GVVWbMSCz/5NHTokYxJ0E7C5wWi/oYej38CDUVe1mbpXZqKRulnS6JaiRrq9aNP6FaiprkDPvQaTneNpvhorl31q+AMGH262Jy2gA335+mq+Bcn6D6/NFQSsT7fa5JGvyXoOJ6crCPjaoic8mfsyWzdmi2yNiy88lnnUmqPbSN08LBmluzUyxz1WLryO8MzJyogjDNu3GqIX9uC07Ap5Hi8hks29Ax/mRVbsfyEChXBMYW8uQht7nI+ofnQep8O0tpZL5act6NFYo3PxEaVH59tLTuzKO5Vf4e+IdzgGt9XoRrpOmm5a00cRUAQUAUVgpyOghZed/gk0AEVAEVAEdm0EuOPlwvFX0rad+nCyarYb0do4JwhzEgt2qWaClJBN561Go0aimAovfKuRS1KdLOtzJ8ve+xVQJ8oRWDT3bayh24Vc0YVlmyi2OjoA91gjt5DOfylZvcBoMpcf3i6UnplptiH16DUoCLfRlBl4UkcFl3mz/mO2KPH10fsMHIN+A0abYg0XWbgowue7rFoyi0ZcrOFFS+pvV2kchX4MMLHIpNJw6lbCT9AthX/Ddh3daYdHVp5pPLLyYYnwTAovYarMUuk7LyKZ/E6lu2WS4fWzbjwGyRH560/mJkcRR7EdVAmn8jkC0YjV0H9HAhEKVs94iUNWaa1FQDpepk3Vw3Vbi5nKKQKKgCKwvRHQwsv2RljtKwKKgCKwmyPAZ7yMn3gVdZDURYoInO5GEs0IFskJsZNPp+JFQeFIOuMlep20yFC5gwzk5nXA8ANOogNtMzGHDsytqSwPuW1qbEBmVg4KD/0qdcTU0aG4r1OsFRQJH6prIzBvOg+GbzLKyMg1hZM006lApigL5sJMZdVGNNTVkmgT0rOykJ/XDbn5XWj7EVBZvhGVlevRVM83JPEiXYw8S/1YOZEWOYlK5vJmOR8zXzuqIzZdp4xYiXv7lhzft+l88+qYIx6cvIycnoz8EpFIydvacnGmtisca1Vsix15i5TM5Z1a3tcQKZ8mFlrzlnWk1hcOeWrRmci2xrPKKAIWATnjZaoWXvRPQhFQBBSBdoOAFl7azafQQBQBRUAR2DURMFuNLqTrpKnA4Z4go+RXs9uMWEOyTx67RDODzngpKCigrUYlpuPFJrSOz9J8gm1uh84YNOxYs8Vn7Yq5ZI5OtSWfIslxZed2ohuPjsU6uulo/dolRtPGxVLOvzlElyj0fxyJynQuzvBDXR50RXRCmn0YMlPo3zReO8km1sp0fpxtO4//lWibl3adJlbO/qbSDUcQtcxcnyZWwvGxx7Cc8EVX9JyccKykzJxcqsKE0K2eRC/+HFUshuOKlw9r88xpi0UbmaW7X2fPjZKtRSlGn3+oaNe8nnCtx6gdOxcZfjcnF6+9a1F1jW35vXSrUVuiqbYUAUVAEWgbBLTw0jY4qhVFQBFQBPZYBJILL5xE2W06NnW0v3EA2XRSksqwXPg66TUpSgCBBSqEmC4MPlvFJKmUTnMRhAsoLJJG8dAgrSkojgSFGVdYMEIsaPQlEmvd0hLJryHSD7e6+A87EsWkaMWGr+CPiW/0EwYM081cWYILBTYuFpEE36dZu6JrZZ2G5Sb/Gnm7xyXBbF6LuVJQSah4sTFNLDg5oYiGxMmyjueo1oY/F02xH/BsOEZc6l8eyVcKjX3L1r+LIiRoJrwO1oiXEWr4a6SWT7bvU/zIfLqOFYHmEXCFlxvojBc6ZFwfRUARUAQUgZ2OgBZedvon0AAUAUVAEdi1EeCtRhdPvAa1/nXSZkkuDU21QisRleM5JZ30n8JRdJ10KW814uukucvEJqOiYe06K+FUlWciyW++WpoKFSYr5zml0CTC9Q4rF9ZmKj9hGUuzv1F58SV0mfs6zYxtSCQg+jKK2rF8R00otsY4yYhmjB9jytFFshnDzbB8bVd4YYUwx1Ec3cVgHTAnShO9gE4iImGkHdmaaOaXRZ1vN4pTCa/El2hez5ds/VhW1HoNlVQEZKvRFL3VSP8YFAFFQBFoNwho4aXdfAoNRBFQBBSBXRMB7ngZd9HlaKQbhcKPTUT5N1Wy6iRYM5xk2o4XvtWohG414sKLtcSS4bHM+S2e/O4JSw/rWM/MsY+k3vJmqo3HUkQ+ym+JHphv8WV9RcV8qr96lhPPUR2Z+5HGrz0swXr2Ah7fa8t+rD+JxtcVmrFsxSK/TppHcpixCDkuU8IziUtQoTd368iTGApfGKnfrOIidiNfQ/66fFryOF43Wa4lSmIRLQkqXxEIIWA7XsZh2jQ9XDcEjE4UAUVAEdiJCGjhZSeCr64VAUVAEdgdELDXSU+iq5cbEmUPsy7bShJkzPFJZHMpqttqVOwVXnzEfJtiKY7GITCdZehN/zFlGVExJkXPJ4ZpgbYXQMAnhumKSaTtoueJJng+zR8n6/gUicqnsbYtL0Wp1i5To+Uns3jD9lcj1p2ek7O0sISluV+f68eSTHdc5rmZsxWOOVHoIHGjEVFx62eBgBmSYa1UT0gwSYi5Yt9KmgiS5MIE6681kmG9uFnz8cVpKE0RYAR0q5H+HSgCioAi0P4Q0MJL+/smGpEioAgoArsUAnyd9PgJV6Kujm81SpUsSipqE1NeoJxOYhfr823Km0a3DPF10qWhjheBJupH7Fq6WLPpOusQPaFCXBE35hIMmjlNw0r8SAqeINCA9IwdWrUxwTJi2LfJOkKP6CemUb7VFythrp0xz40CQ0TkrhWmW76VSLhJGsR7iH7HqJWwbz5fR+yw74i0BcdKGFaE7+lyeJYbyFAxxaibaeDVuXKexKTHE0tJS04QQsIJKiNnOWLUY7U43BqdeKPOko0mVbTx2krdkxEwhZdJ4zBVtxrtyX8GunZFQBFoZwho4aWdfRANRxFQBBSBXQ0BW3i5CvV81XIoiabUMdGF4NLI8PrkGNIYPhVeCltReGFNSUr57RJ/oQYeDTPqx5fxLUWj9O36PE7SrVdHtXYSnohtXScoJOr7dZq2WBDmycxqW9tOLpiHhXyDNPb9RliJOMIyUnoQacsNfAkx8nZfUoKhVVLVxHwPUaW3yDn1QMYRaMQ2PCWa221QIsQ8fgJrsd1VImPlfBys5eQvZ0waqzLit8Th01KNPZ+iJu9UKj5d1Gn5ZpjQjX4RX0nHikAYAS28hPHQmSKgCCgC7QEBLby0h6+gMSgCioAisAsjIFuNmuhmIZs3Br0sUnQJqIlcOhgk2Lz2RILpgEjLoMJLQfSMF+YHWanJ7ynxNgfmWj1DoqEtvtgZ/5q4EgPrzKb7osH60SCic5bhx1izQ4ol7CXhJCFl+PTDcYYLCr4tlnK6zofRDjiiH7jml6jI22PJsBmWiNDbfrPklbkylidMQxtXmGa1GRE7sgcZs2SjGKaJ4QZz8zI/hkGSniAb56lxZQdmaMiBXOKPyM55mmZ/RJHeTGNT9A/dbpWWuCKcrfG/7tBmluXH+aFJUNSxhzIbdjM/EpeImIC8ZbHlQEZEzDuGLqYClvUflYvOQ0a9SWvlPBUz5CBYd0c+0Vijc4klSo/Ot5ec2JV3Kr/C3xHvcAxuq9GNdKtR5o4IQH0oAoqAIqAItICAFl5aAEjZioAioAgoAs0jwB0vF42/Eg319fGCJneTLNKJGIqfL/DYfxIdL6V0xssa4nBxIEjqE/mgb0CUA1+8R0WGrGdEmRAQjXjUKfOYxro0Fp1gu0xY1xgI5KPpqdixMsnli6hfTv75Yb/2ZaZJPyYgSzVD+glaQXhqHtJnE/Ik6GYxTI2LxmpYTpyG1eP4Et/AWGKK743lWD8ovvHUk0gzsRq2+bGa9lpu41VM0UQiStRF6BvYyBIcErIU86mJnJHO3zkNjY3+Qc9WJoP+nhrpm5oCGJuQWAKrRAkea9+swwztfOsKL2KT3jYMj9DcUIStb/6V1TenpTxFgBGQjpdpU/VwXf2LUAQUAUWgvSCghZf28iU0DkVAEVAEdlEE3HXS1bQCP2GUMS/MJpCyxKCJILU4C1ICXVhYQLcaueuk/aTfiJC/cLGgCXmZWcjNyMWm2kpKtG1BgyNJLjf4MQWx8otUbD5PxQOb8bKryGMt+kQ/MRbL8X4DX6xMwwa6DapHt67o3LUTVq5YTQUs0o445sICQ8hr6NmzO/Lz8rB2bTFqaml7l+FZm4GYH1biiyQGxk6gxkbpsdo+RkyhFXGhQphG0kk3NaYhp2sGsrtkIz0zDeUrq9BYQ+AFBQ1R80swCV4QgzUpyHEs1q/pYspKQ26PHDRV1aNmExVSjEGW8b6kDZ8jRS3qcfagI5CVlokXFr9rvj3T2VZ+Th7xxmLxxnV4Y81sZFhjxr3EaWMR25bKv+ZvlQauwBTWsHr+b2hxPiNYA5OM5TAvNAv4DH7kbyEklpi0ZC8huAsPWrvGtpbbkZC1Tex6nfSO/GbqSxFQBBSB1iGghZfW4aRSioAioAgoAikQ4K1G5194OZoabceL2+rBCpxI8BNkyHaSguIxaWi2Go0qQHFxCVYHHS8uYZVk3epwcswecjLT8aUBh2JM7+H47WevYvHmdUEIgX8TDv0Y+VBJgD0aY9zZ0CO3MyXnQHF1WVC8YX1ZixELfoTOthzfFCu45mNINpn3+b6Fojlrcd9938QJJx2P6759C9atWYt06s4ITuy156TQArmLoyktHdOmXIOhg/fHY4/9GjPe+wS5udnGN3sxhYYYrL3ILJfWaOsjEpuLyG2J4vBZk9ZoKj/uK9IFVtjv9H0w8MR9kd89jySa8M4dH2DTvE1IyyTkEpUi8cw4+U9g2ZApbpIPhsG4CZ0H5uOgqw/Emo/WYOGfF6OxjvTJXMgif3giVDfUYXi3fnj4C1fik7ULMOXd36FTekZQNGlCl+x83HrEReic3RF3vPM7LC8vJWPWr7Uo8Vk8uNrES2Cq+LPRJ1MsPfpLcmIybMATZIYIeWQzZOcUi9G1BuxvVG5nzTmaVLFvTUyttdfWclsT69bqtHXsqe3pVqOt/UaqpwgoAorA9kNACy/bD1u1rAgoAorAHoGAK7zwddKcjKVOCKKANJe68XXSo0aN8m41st0r1oZN3HlsCgUmcbfdLhcMOwHH7XcQHnz3D5hZuhxUq0jkiJxWG83gPJo0ZnJXTJD4My8/IxM3HHI+sjOy8JOP/4J1lZtNAszdHyYbtwGwYzPn1crYDCUhDaDg+HiLSzqtx/4j4ZAmyRTNXYZf/vJ2nHralzFhwjexYsWKUOHFmm9CbT3dGtWUiV89fh9GDB+Cu+58EC+89Do6dcgjERMFB8Li5gmNpIogTP9NsTFbHl6WmGOys2OFGsrr0OeY3hh708Ho2CcPy2esRXVpFWb/dj6qVlWZgpm1RWiTstWiAa2fjTEGtrxBc1OcYiLR6MUCFqMm9DigG465eyyW/ncFPv7RbDTVkYAVIjm2ahRo2ISims146phJGLt3Aa5+7VEsK1uLTCpeWd9W+sv7jcG1Y86mgtw/8OzCtxPdUCFbJgQKqon+Ljje4DGjxGIcXfjhdxAXE3ko4v5YFERUZBI6QSFK1mhQESV9KwLNI+AKLzfQGS9ZzQsrVxFQBBQBRWCHIKCFlx0CszpRBBQBRWD3RYC3Gk2YeC1qa6uDHFOyyeQ1p+Yky3IyPoo6XtaX8hkva0kgvvDCmmw3lwomWfRvh6wcdKCtRivKS1BD23gMlxLb3IxsOgMkA3WNteiQmYvczGyUUcwV9dWUz9Ohq+QvLysbndJy8f2jLjaFl/s/+BNWV5aigU6HrayvpQiSV+DnzCYWStBzc7OQn5+H6po6dKdtRDwvLl6PzWVV5Cs4f4TaGfI75FBilI1ePbuiG8l9/MlsupabC1j0BD+ZdDhmr549kJeXjQ0bNuP2H0zBsCGD8cN7f4S/vvwfdOyQG+DOCi4+OwrmYRZbN5LGhZm5H6E5SwGPCOnZ6Wiqb8SQcYNw0KRRWPqvpfj08florK1DXTl15JhCCoVOTS9Z+Vm0FSkbDXWNqNtUg/oqLrdwUcM6T6PtSdkdM1Ff24hG2l6V1y0HGfkZqC6pQV0Zbb8q7IyjfnAYlr+1Gp/+5DPyQcUr4mflpBudhmrCkTCspu95WO/BuOnQCzC3dCluff+PyE4USRhGu/ie+Z1x29iJyKG/kSlv/AIbaiqptkI8s1C72pycLHTq2BF19Q0oK6sIzoqJImLtuYqKw86NrD03TzHyxUJmxWcKve1AFvfbwbSa3MEI6FajHQy4ulMEFAFFoBUIaOGlFSCpiCKgCCgCikBqBMytRhddgaYGOVzXzybDCX6Yk9omc9KoY2GUuU46ReFFjFHGWEvFjK8NOQbDu+9D9Zk0Ot+lAi9QV8PKyg2UdjdRct6AC4cci30790JJxUbs37Uvuud3wYKNq/Higrewsnw9uuTk4qt0Rsi+nXpS58RI03Xy8bqFqKqrIX4p/rbofayvKSNrtivDc29zd/NLhYCaGhx79GE49dSTsWnTJgwdsj86duyAuUUL8NyfX8C8BUup+NKEjZvL8c1rLqE1DqfVNtF5LY148KFHsXH9JtMdw1uL0qhQNOGic3HkkYehc5dOWLF8JQbs14+KNN1w9w8fxt+o8NKpI3e82AKD7SQJ4uMAqRuEX1wP4XJPPc34nxyqjNRTDA30TzZ10WTxvqNgew2JGR1+u69HWhRP74N7YeDJ+6L78O7oMawL1s/fhM3LK4jXiE9/MRflS8tJJw17U0fMgJP7o/PeHamo0oD1Czdi/vOLUL6ogqo3JEKFrLw++Tjg8gJsXLKRTsUF9hrdB3ldc7B25jrMeaoI+X074Ng7j8DyN1fio4dnGUyGXrg/ug7sjCX/XIF175aYQtiy2jLcXHgqLj3wdNzx9m/x2vJPqMDm2pxshw2wrr4KPz7qMhzRdwRu+O/P8Rl1Q3FXjFksYcF4n3D8sTjl1C9hDW33evrp/0NxyXrDjztY18DLEMU+zN3aZ8cXXbY20rbREyTl3TZW92QrtuNlHKZN08N19+S/A127IqAItC8EtPDSvr6HRqMIKAKKwC6HABdevjbuckpQg04NL22PLmZL0lE+42VUQUGKrUZBoSHI1WrofJmrKPke3Wsg9uvUh7pIcnDrG4/jf8WLTAjFDTV4/NgrcdyAg7F84yqsrihF34690LfzXvjVpy/hj3Nfp26XHIwbdhwGdO6Dwl6DqAMmHfM2LKMtPvVm68r/zXsDJdW07chY9JJEGVLyzgl6GRVULrn0fPzg+99FRUUZFi5chOycbAwZPBSvvPoP/OSnj9MhumtRun4z7rxjCsaMKUT/ffuhV69e+PJXvoYlS5chI4M6P6hb5uKLzsT1k7+JqsoqLFm8BAMH7mcKMCUlG3D3PQ9Rx8u/0aVTh6CngwOz0fGICw5ccBrapQ/OGnwEeuZ0IWLQ18BiXGih7TQryovx67mvoYa2MtnH2bBztsSftwF7HdEbQ88djC4DO6Jz/050oG4lKlZT4YXW/t79n2LDzI3odXh3HHnLIeg2uCuKZ5YiMy8D3YZ1w5J/LcMH932K2o11plDTbXhXnPLUSXR2Cx2eu7kW1ZtrkE7rbqQOmbdun2GuwT2Wthotnr4Cs35WhILLhmDkeUNRMnc9Pv7pTKyfXUbracIa6li6ffSZuGDUSfjGaz/FR+s+D20z4jIUy22g9d025iw6A+gw/PST5/Hc4g/QIZ22YdByueOpproekydfjm9+8xosJqy/ff00zJ+/hIHk1dPLDHiSQNlRDDnyE8Uxwk45bd5qSjVlKAIBAhWVNbj8sguo8KLXSesfhSKgCCgC7QUBLby0ly+hcSgCioAisIsiwNdJj59wFerrUiXubmFbkopyx0shdbzIrUZmm0qQ/LoTMIIklQynU6GkY3YOJow4AV/sfzAdovokPilZYpxvoJNZHzliIo7ofxCenvkyXvh8BoZRgeXWYyfhM5J5jBLxVeUbqasC6JyehzuPuZS2ImXh9hl/oK6ZUlNY4E4RPjg4sTuFLEuK7K+rvKyczmo5F7fcPAUvv/J3OgT3N8ihwsu0adfh0EPGUEJ/C/4z/S1kUpEhh7YgrV9fiXvuuhFnfPVUnH3e17F06XIqQKSjG3W43HnHd3HQQQfg+z+4B2+99T5GHzQSd955G92EVI+77n4QL786nbYa2Y4XVw4I8KZYK2gbztheA3BxwZfRM48KL/5jMEujbp4S3EHbcyqpgCHrSYgxIVgcN8WwQG1pHQquHYbRVxRg5m+KMP8ZKm5l0Nepoe1Yc6pxzNOHYchpgzDnD0WY/esFyKSbjw678SDse8w++Oc3/4vi99ZTEYcOvB3aGaf85gRTaPnolzOxYvpK45a3J21eUoluQzrh6NsPx+qP1qG+oh5DTxuIFe+uxcc/m4XyxRVIz6Fg6GPQJjF879Cv4ah9CnH9fx7D7A3LkZE42CdYEb2qCLNxg4/BpQVfwquL38c9n/wV3egcH/tBueOoDpddegG+9a0rqPCyDDd953Z6U0xm4bT4mI6gwHoCLjsIAItQWz+Nt9p6fZXc0xGQ66SnTpmM7Gz6G9dHEVAEFAFFYKcjoIWXnf4JNABFQBFQBHZtBGzh5WoqvNDVxuYJMnTJ2IkmlEDA43CSGZeoppmtJeGtRslyXAyxD9mhrou8rFxcMuJEHD/wENz59m+o8LKY2GlYT4WXHx15KQ7Yaxi+O/3nmL1xBfrkdMS9X7iCznmhLSgfPU83IK2hHDyDzn/JxN1HX2bOhLmDDuhdQd0x4oMLPuKRaTZFDlPLyysw8eLzcPmkS3H/Aw/jtdfexPqNVbj+WxNw7bXX4I4778NfXngV2eSHDRTNWYynnnoAZ555Os4xhZeV5kDggpGD8cB9P6Czc2pw7TemUUFmFS0xE3/5y+Po0b0b7rz7Ibz0Cm01ShReOCKJjiNjTGiTERUnsmjLkr9dhrs3+LwalmqgrpgGczgL6zNFbLgpU/nh1dcuq8WIG4dhzBWFmPWb2Sh6mjDm4gQJNRbV4wsvHYm9DuqDv533Lzp0t4YKMg0Y9vVBOOz6MZhx9/tY9CKtg3x2GdwZX3niBKz5eB3evIGuf6btR8Z98O4+qivG3joanXp1QGY+ncezsgKvf+st6rKhoksQEEeaRQWs2w6/iLqUBuLG/zyOeZvocGJTeDEh2x/CgAsvZ9LfxbUHnoHptB3plvefRXfqcjKFNIKpqameDi0ehiOPOhwbNq7Hv/71BiroW7IPfqxLQcLS7K9IuFmclK/R/HjbtJu33Z65jOOeuva2/S5aeGlbPNWaIqAIKAJtgYAWXtoCRbWhCCgCisAejIA544W2GvF2DZu0hxNRhiZVSpUs6YDkro/wViNX9LDpGRU8qNjC1nlbEI95uxAXXk4ceChuf4sKL6VLjMGSxho8euTXMbLnQFz1+o+wpnITXTGcg/uowEJHwuKnH71I571Q4YU6XjrRobt3Hf11U3j5/nu/p04YOucjlBCmXg0XNMrKyqjw8jVccskE3HTT9/DpzDlYXlKG6648DzdNnYJfPfE0nnjiDxQz2aEWm6LZ8/HkU4/g7DNPw1nnUsfLMup4oUBGjxmJRx68E8uWrcS079B7+SqynoZfP/EweMvRXVR4+SvdasRbjdwjiFqE+DalfLrVZO/8bnSOS6a5XYkrDRn0bwNjR/+pbarDirJSKsTw94s+kgiLXZKfW4uRtw2lwssB1PEyB0VPLqTqB30LOssljW4DOv4XR9H5Lz3wp6NeQlN2Ax2KS4fxjt8PY6cdhk8em425Ty9AQ20DulHHy8m/OoFuLVqKN85/HzkjuPuE/dMPues+sjOO/P6h6NSvIxqqKTrqkvnwJ59gyT9WoqmGC0o21jLq6vnemHPwVTrD5xuv/QQz1y8JOl5YgOO27zK6cvo66vw5e+gxeJq2lv26iA4mTs8mdrA2elVVVWPpkiWkk4/9h+yNbDrYmPX5Ly9wR3N5hBLoCzn0lhiEGJmLKld/OFbqqrGOInKiHqwlMU2aCyeqH52LXPTdWrmoXlvOozFE5+IrSo/Ot5ec2JV3Kr/C3xHvcAzuViPdarQj0FcfioAioAi0BgEtvLQGJZVRBBQBRUARSIkAF17G0eG6jYnDdeNEExlmwLRzocZp2K1GBbTVqASrza1GYR1ONfixSTEVXqhwkJeZg4kjTsJJAw7B7cFWI85pS+iMl59w4aX7frhm+qN0xst6dM3Oww+PnmT0fvzhC1i4iQovdMhrJ7r9yBZecvCDd3+PlYmOF/YmCY54D/JkZnHiTHy+DWfixHMx6bJLqThyH6a/MQPr1ldg8jcn4PrrvkldMI/gj398CRl0qw/LF81ZRIWXB6jwcgoVXiZhybIV5nDYUQVD8OD9d6CsnIo2376Z6KvJQxr+/MzP0adPL+p4eRAvmcN1841vwTIRGRHKqdPn2D5DcCUdPNszvztpG5emSMUdMFzgWbZ5NW566ymU8y0/ppWEvXCxIRA2b7FOhZdFVHi5aShtNeKOl7koeupzs9WIFRpWNeCLzx6NPqN64vnT/4G6CroJim5pGjpxAMZOORTv/vAjfP7cUnOLUdehnfDlx4+nwssyvHHBB8gZKVsi6IuSrR4ju+AYOly3pGg91n64BoWXjEL1xmq8e8+HWP/ZJvpWtBo6V2hFfSVuLfwqJh5wGm6a/gu8vXZuUHgxgdMPr7oJxfU1eHDsBBxNW5Jun/FbvL2myHQCsRRL8OG63bp2Qb/++6KOuoyWLVtlCjHMFDRY1j2sJY/DRyiteouamOK5KcK0SluFFIFYBNzhunydNBcP9VEEFAFFQBHY2Qho4WVnfwH1rwgoAorALo4AXyd9cXCd9JYuxfWwxGhSAlpYGBReVq8jAcpKJVGlgc1VKSU2ZHsCTAc6l4XP8Dhhv0Nx19tP4kM+44U6Gsqoq+OhIy4xhZdvTX/MbB/qnpOPu2j7EW+5+fHHtvDC1wvn0XXDtx4+HkO698W9tB3lf3RYq4mTE3DuSAg8G8cmCFemYF5FRQXGjz8XU6dehz/TLUY/ffQ3VGRJx2233oDjjvsCbrzxVrz+nzfNFhkufsyds4IKL/fgrLNOxbnnTcKiJctMQWTvvXrR1dHTMHzYENw49Xt4683/4QA64+UnP7rHFALuoVuNXnplOjrk51BMcQ93tTSiH3W7HNOvkDp8OiS24HC3C+f3VGfC2qpNeHnJ+6gNCmdmScYD20wAbhwwr3ZVHUZ8ewjGXFmImb8uwrzfLrT4UPdO9ZwqHPO7w7H/yQPw0aOf0vkvS5Gel4bDph2I/U8dgNeufwNrZ5RwvQRd6QyXk4PCy5uXUOFlYBY1fLA//qZN6EFbjY6hM16Wv7UKnzz6GYZfPBQHXDwCy95YiQ/u+Qi1m+rBV1KXUVHtawMOx7UHn41ni/6L3xS9RkfOUKQcrLzZKi34LrrVaEjXvXHdv39iCmq8tcyuFyij73YOFb9uv/1mc6vRnbQl7H8fzaJY5Pva2MiUWa/o8dw9xqmbphw1J+dbbk7ON96cnIvb19j+Y4lJ/ob8dfnet1TO140bi704nk9rrZyvs73HrY2peTm9Tnp7fye1rwgoAorAliOghZctx0w1FAFFQBFQBDwEzFajC+k6abpZaEuf5gov5lajUVR4KaaOl9VrTDLOKbAtBgRvyuk4oR7ZvR++TF0uubR1ZDCNB9B10TNWfEbXSpdj/vrleGbRO7hv7ESM6DEQ107/sdla04M6Xu6lm44aqArwyIfPm6ulueEjk85DOWfwWIwfeSKW05aktZuLqStkHV5cNAPr6Zpqk8wHBQlJaTkaebjj5eLx5+B7t03DqtWrMXfufDpcNwcHHXgAPvroUzz8yGO0/agIxx17qLm6uGPHfAwbOhSD9h+IN998B9XV1Vi+YiV+/vOncNYZX8HVV1+CFSvXYvmyZejXb18qxOxPXTAVuPOuB/D8i6/RVqP8SHlEouKImuja6AZTnKijIkw9n59CD1+jzFuL6BZodKQOn84ZubZQEViyOBtRQ7FoW7u1C2pQcOswHHzlgVR4mY05v1lAFRySoKJJw+Z69D1pbxz87QOR1yUHq/63Dpm5Geh76F7mLJf3fvghqtfWmMJLl2GdcMoTJ2HJG0sx/dx3kTtCtv1Q4YXi7HlgVxx715FY+sYKfPzwZ6ZQdNgtYzDoS/ti3vOfY+bP56Jucx1tFWugW6Cycf+RF6M3dfVcSWf4VNC13xnm3mpaiQm+CYf1GYrJh3wNH68rou/9It34VEs8vnaaUKKlbaqg26guOo8OMp6G1YT39267Gx/879MEtv43lr9bn2bR4l/jMFbPyTi5EM1AHLVq7YXkdvqktTG1d7kdCeSOw0K3Gu3I76q+FAFFQBFoHQJaeGkdTiqlCCgCioAikAKBbSm8cGHAlgKSjfNWo1Hc8RIUXkx2HIiZHgSTpNIPdSQc1HMAvjzoEGSnUdcE/UO9EFQLsJbnly7Hn6hocjp1wezVoTuemf9vbKqpQh5dJXzOkKNNx8trSz82V0WbpJn0sqjr5Uv9DkJhn2FUzEmnQk2xuQmJr5NOPF4exUNeC+uXlVVRB9A5uIIO150+/b/Yt39f5OXmY968hfjDH56josoqlFVW4oQvjMWXTj4BXej2ovr6RtTRrVBcoMmgIsaqVevwi18+hdLiTfj6Zefh0EMPRdcuHbFuXbHBg8+GefGvr2LW7AXIoS4fU1wIYrCrNpOYHwnae3PVwRkgHbsOKyHW7AqZ11jegD7H9Ub/L/TDijdXYe27JXRWDbsiWfoWfLtRr7HdMfiMAejYu4M5m2Xj4k0o+t0CVK6mm5PMFqEm5PbOQ8Elw1AyrwSLfk83EXUiI+TG+CVT+X3zMPjsgVg/fyNW/nsN6uka6k6DO2LI+QNJKB3z//g5qlZXGt/FtE3q9H6j8d0jLsALC2bgx5+9Que30BYLiocLQnl0zs03aDvSaNp2ddeM39HNR3RbET18zg73TvEBw1yEueXmb2Pc+WdSwWUmnc9zB9atlbVxXPZv1cRntJnmP/4s/Hftc3yNxFhgZostCie0dKAIxCLgCi+81Ui28MWKKlERUAQUAUVgByGghZcdBLS6UQQUAUVgd0Vgu241OoC3GpVSB8Iags9mpzYvpV8qGDCF55xf87Yay6POC+Kl0yw90fXAaXOjkeGrhrlLhpVI0shylwtvb+GCDvM4Ea9La0Q6ydgbdNgW3wxkv2LwCiLyc+U0lFdU0uG65+LCC8/H9ZO/Q/FvQH5eDtauoy025CODijr88JkiFDK9ebMTn7NL8dDAylDhiAoHvMK6+jp06doZnTp2oEJAKRVoqFODo6HbfMw6SMa+2Wo44fcjYy7zQ08wFX2fy2v051aPKEzkf7l7hsFhUAQQI0RIU/MTN5Pk9cpFEx2uW72eYk6nxRow+cuQOv1D8Bt9W4zhsZhiLAgXOlCXO5+Cj8BXMJlCDu+R4phl/YwZF9u+c8jZxvL9H/6NeGSf6BxqN9pm9e0xZ2HRhlV4ct5/6Lsat8SxS6muqcPee/fEzx99AH336YuHHvoJ/vTcy3TlN1eU7OL4r4VtycN/K/bxqUxhupMVKSub4teYIMlWCaewoWRFIEBAtxrpn4IioAgoAu0PAS28tL9vohEpAoqAIrBLIcDXSV80nrbsUIHAPq3PHm3KGk1cAyvU1VE4aqQpvKxaZQsvIcuhZFVsWInwzCbX8iuJMb85meaHCx7mTXNO5nkuRRbL8X+DKIwSJ/9i2RZueKvRJZeejwnjL8T1N34Hc6krJSuLNr5QcSeavFs/nNzzI1HbWWIeBMc3FKXzmSRBJ48tHJFsEE5iLYE6W3PFgYhNo0QSpiNEePHvwLwgFRFKFTuZ5gCC2Ll7iUstBluqh5hHDCfWLQR+G+VAjuYcp8gl1m/ZgRC5akTnnA7IocLW+upye4NToJNORareuZ2xmTqdKuqo60ZcGeU01NTWYa8+PXDvD3+AjRs24N4H6OarNeuRlcmn4MjfCcckj2/Apwt/S9++vS3VVXlFIIyA6XiZNA7Tpk7WjpcwNDpTBBQBRWCnIaCFl50GvTpWBBQBRWD3QMBeJ023A/GJqeZpOYnkVFWkbNqanLxyp0NhAXe8lNDWGy68cEeLaLEjGoua2TLiCg0JMkm53oNAh00EBQGmxD5swHdlHFmCpOHWBs0COauShsrqKpx84hdw0kkn4NHHnsDKlauoO8V1TiR8i2JsABEix+vHQ4WIxJQGZjlSkAipWin+FUx8tlFhfZ/Y0jhhzK5YxI0N4sWGwUIkwF0wqaF39hIuSIkPNBbb4su+DdXwGA36HzR0NbZpoaHbiqQgxJJsDaijG56414Y7iayS1Wc+d8zwLDs7AzXVvO3IdiBZTYnGyRuDNio73OZf62mbzTRrgOPfEX6aDUKZOwCBisoaXH7ZBZg2Ta+T3gFwqwtFQBFQBFqFgBZeWgWTCikCioAioAikQoA7XsZPuBL1dEZJ+Emd5EkKG5aXmeWaM16o46XUFF7WEjNaeBF5mxgHWkIM3lFPElOUHlbj4oGREPEQ2/qzJFsAYVmhcgEqNy8PHTrkY8OGTbRdhlo8jB2REGOGSBOJxdkSCfsWvk8VXZ8WHUtULt22lizdt5DsQSi+lGc/FUAibtR5InZEl+aG7PNESWTo7bGF61tybKGKlNhgOv/LdJ8X0Lj645GlGMTbvmzRxRXKjBj/iFBCkW211eMF01Ym1c4ei4B0vEydMpmKiXrGyx77h6ALVwQUgXaFgBZe2tXn0GAUAUVAEdj1ELCFl6uo8FIbBN+6JFLSYklfnZblpNF5IAV0uG5pcTFWr7KFF+vASQpaJicmMpcuxJ7lyczTMeaT6awp1MTAU3O+RcpnEi1Bth0UTJBbc0TS8yChJ9627CJRizFbH3Az5jtrVlrmdtb8r43AatAvD/xtPAnlsEfnMxAwhZeElbCWsSkka0dKSjKzXN+HteUwtBIhU2Iy6c12An3D8+2KcEv8QC7hMDEg0xS9MenbtX8tvlXxtPXvtrW29XGIpoeBkPTdAgLtAzMtvLTwmZStCCgCisBOQEALLzsBdHWpCCgCisDuhIBsNWoMtnm0Nn3001jBw+paTjptF+HCi7nVKDjjJagUBOLhJIdPWEn9sM0oP0yz1oKogldYxdf3dWPG1pgtxtDYaaYqvfhlF1aTAOJW5KzFcZNpfjBRbnO2/BgicoHJCNXWcNiFqAYFGivnryowkAjHrd+q8m/UekI4MojKivOIWMJeKr7Ik19TbOF4k2UlqqhX0Y5/+1pRCeFF6TpXBLYOAb3VaOtwUy1FQBFQBLYnAlp42Z7oqm1FQBFQBPYABGbRVqNxF12Bhga6yobPHmlFHpmczlqgrKpNd3mrUeEod8ZLtJclCm248GLTYgnF+hOvQmULQuOx0H2aT+dx8w9bMNEzCIEZa9VOopatNfHr245K+jLC82m+btxYdOJ41o5vzUm7kcOHbLAwsZJ0AnrCCxVe+IwWfuKKGAm5kCVHbd3Ij5E1/Kh4HuUzzX+YTzqJ2EVf9AK+lTKKwhFJ39qWj9vGSst+3TpallWJXRkBW3ihw3Wn8XXS9ha1XXk9GrsioAgoArsDAlp42R2+oq5BEVAEFIGdiIC9Tvoauhmm2kThehdSByWJq5NIpqRT8aKACi+lpXSddGirkdPysmVKryWBFVvhSPzE33KsnEizLf7HyQlH7Dq/YTlHdyNfV8YBl82FSMn2nZ3WjkIGPaUkZ8SL0qx/PwprjSlyBZFn0uhbK5Zq7YUjcDOLteg7dIXibIhN/xuEpVLPnD+xIrI2Op75MsKVN/MEAXmLvM9zUlYzKuu8ieX4ty8nNuIkfbk4vtBaKyfyO+Ld2pjau9yOwEp8tA0Wep204KlvRUARUATaDwJaeGk/30IjUQQUAUVgl0TAbDW68HI0NTaY22H4yuCWHklpw3JhKt8EVBDcarQ6sdXIaoik9eSSFaFbKZfy23Rf5HwtW66xekK3cq5E4OxIvGE/Qm3NO6opPq1ulNsai2FNG3vLer5cOAbWDcfhz5JlrS9rz5e0VmyJyslY6yznRxAtZ1j5LfkNexZ7NlrnM7VFiUgkwtFZajIt/JchMTivVk/oYlveUTmh707vZMziV7ez5OKj2T7UHbdGt9VIbzXaPt9SrSoCioAisOUIaOFlyzFTDUVAEVAEFAEPAd5qdP6FV5jCi90O1HJCmZyKJlP4jJdRZqtRMV0nvZYSdVcKYfdxXpKtxCU7vqZo+DS7OLsWWWiYL1rCTX6zRFgnWcZRWDKq0bIPpx8tlfic1o2jJYSo9/BawjPnIaolcUWti4aVT2VNpCJvAStEjnp2Nu3I8qNSIRNbPAmvyqqzB+fbmUzlOU7WaelIEdgaBFzhhbca6a1GW4Oh6igCioAi0NYIaOGlrRFVe4qAIqAI7GEI8FajCROvQm1tbZByti6ZbCkVNluN6HDd9YnrpMMacV5Spbf2k8RpxH0sayUsHZ4174dtikRYL84b06JSop1KPpm+5RphG34RIVzgcnK+jKPKKHUEzBHdZCnbc8QIRLc1RVExZqy7hJnEwNIjv9ZrWCY8iyhswVRW1LJKcx5j1tiyQZVQBJpFQLcaNQuPMhUBRUAR2CkIaOFlp8CuThUBRUAR2H0Q4OukLwwO1+U0khPp1qaTqVJ8Rscdrms7Xqxli5ukslE/QrdSxkowFE5Uw0m6kcgShYZm5xSTvC1UnoRTS4yi3Nb4TCgnsItacRLRUSpJ9tt6nvtqVieq6fhR/3YelXdS0b+IsKQtvDhpG3NrMQvb8q3I2FmK9kyJROq36MZ5aQkP36r8nYs9x0umOJ6OFIGtQ8B0vEyiw3WnTtaOl62DULUUAUVAEWhzBLTw0uaQqkFFQBFQBPYsBOx10nTGC18n3UQpqlegaAkJSUjj5NLojBez1ai4ODhc16bofhIcl7Y6fhzXemKOk+ORyDqqxGQ5wrfUZCmR5rdvz6c3NxaLYT/NabCkaPkjq+PbcVLN2xOd5K/ifFkZkYzaC3uKavnSTnLrCy/Ohm/Zjp1vnodnydJxFFljnBfLE4k47ZZoYnVbbLTkQ/l7KgIVlTW4/LIL6FYjPeNlT/0b0HUrAopA+0NACy/t75toRIqAIqAI7FIIcMfL+AlXob6ujvL/IKFsZfElOcV3S+eOl1GjRqKEthqtoTNe4mTj0laX0lquzJ1lf9Q8lyXjOhta1vJ9tDSOWnOrklFUwsbl7MbxW8u1cv4q4605qkQlms4Tj5ycjHzbIut4MrIcmQU+eGqGQmcpJvhzpkUfKxNYMcyWNKIWWprHrSq1TmIhgYhE40eYWls5isCWICAdL1OnTEZ2tp7xsiXYqawioAgoAtsLAS28bC9k1a4ioAgoAnsIAvaMl6tRV1NLKw4SyqTCSzTxtODEFVMSsJGNQjrjhQsv9jppSVYTEjYnd1MzslI2LfY1JF33U93m/Ds5NxJXvl2hJdbulWqsHP8m27B68ZZE3tcKS4a374R5LiIXk0+LG/tlhHhrlupHFGdHIg97TqWV2lNUI14yPoItp7K3LfFgo4vGuOV+VUMR2B4IyBkvU3Wr0faAV20qAoqAIrBVCGjhZatgUyVFQBFQBBQBQSCx1Yiuk06kr0HhxS9sxCerTiKa/LozXkroVqM1zjY75iyZFOJTX0uVRDpeRiIVKTbqnmSddHIZL2u1wjyrL/ciMU8oUWnRE4/RuWgGsVFHUROLGpOuWCJagZQIBG9HjR+Jb8t1szirjhtvKxJvIGTCTaXgycSLJMcRL9cyVaIXizLnqJv/vs62r+OoOlIE2g8CeqtR+/kWGokioAgoAoKAFl4ECX0rAoqAIqAIbBUCfJ30uIuuRGN9PekHKW1s4cWlrM6RlZdE2NEpFU5Po61Go1BqbjXa+sIL29wyz8nyyeeQ+JHyOHYFRsiWX8IRxElbi8Jx8m5EPP4PE+htIbZc0bI24n6bk3AeRDOVVYdD1J6z4UZirXXvqEWnlZrjZMLfLJWGHxvLuLkWXnwsdbxrI2ALL3S47jS+Tjpz116MRq8IKAKKwG6CgBZedpMPqctQBBQBRWBnIcBbjS6eeC1qa6opBC/lNZWBoI+AyGlBMSYcZzN9BiRfOKoApaXc8bI2bDvImlNtKHIFAuvNl7MRenGGAuJU3PIkKY+qjxPVAABAAElEQVTaEosi59SDoByBRtae2AqxaJIchVCchhuJtvVjkWuu60Xk5e3bljHzkj04ii/H8QrHpwtN/DiLYSmW8ylOPkyNyIUObA5LOgutGyVHavXsupqz7WJyNtzIeXdyjhY3aq1cnG4cra3txfnYUlprY2rvclu67m2RbxssZKvRFD3jZVs+huoqAoqAItCmCGjhpU3hVGOKgCKgCOx5CJitRhdegaZG6niRw3UZhqDwwkObO3NS4T/NFF1IjG81KiywZ7zYwkujr2zG1qJLVqKps/PoRlbGlxSe0LiY4cZJTgNCc8m6b1HGvh2xHi5EsCRzkjV8ihRcrD2Owue21LkhnsWXROVsuLUzz8mJpmjYt+g5OaFE5S3dyYmdqJzQ3ZslUll1UuGR/YZxtsUSy1vLLWEmlm3sTt+NRGLL3tbelum0F+nWxt7e5XYknjsOC7fVSG812pFfWH0pAoqAItAcAlp4aQ4d5SkCioAioAi0iEDijBe5Tlo0Wiy8cOIbTo391MSd8VJMHS/ryCoXXpwEj6JP2Fo4XXfllHipcMGBLcd5sB6jFqJxpNaM6sdZaklbvFldv/ASZ02ko57DdN+n62sJy4TLRJbn64UpNhb+tTLJklY+dcw+R7R9mtWP/01deImXd9TUfydWRiKRdTlNHSkC7QMBV3jhrUZ6q1H7+CoahSKgCOzpCGjhZU//C9D1KwKKgCKwjQjYW42uQV1tDVmixDgmN2ZSdKuRFYsRlnh4qxHdasRnvPCtRlykcUWGZL1kikuN43jWTTSNtpL863xJQOF3apvOb1jDzZyuG1mui8dJx42cnh+nK2SF7diZ1XGacXYdzVmQklScppMSTaH43vwYRU7ecVYtTzhisWUNkbD+HBpCb81bYvW9JywxMQhH5FpjU2UUgR2JgG412pFoqy9FQBFQBFqHgBZeWoeTSikCioAioAikQGAmHa570fir0FBfF0hIykpTM6RMNZo7CyuuShNYMR0vfJ10cbF3nbQY8nwkbAWKkRdrhKUjAkFw8ZZjQzcGnE0eiXbUtp1HuWFdlolKxNuJo4pmojhghITqW072GmfP0px+WMbZaF5OtKR7JpU9/jZx+Pl+rC7/Okm32jA97luLb9+mxBf3jiupOH9Ww8UUZ6H90Pw1Cw7tJzqNZPsgYDpeJtHhunqd9PYBWK0qAoqAIrAVCGjhZStAUxVFQBFQBBQBh0Boq5GQE2e9BMleipwvms6KOr/NGS90uG5Jsb1O2vZdWEOsZ0f8m9qKuPXTT9+HX5aIkxVaWMfNUtt1MlEbrdFx2s2PQrYJc7ZtdnjFFnLCnsOzVH5CHkgoqhXlh+343yzM4ZmJNsmilfP9WB/8azWSv7gfha/pf99ku5bifp0VNwq4jG0igLjCjLPS3kbuvyscWdLK2lu4Gk8bIFBRWYPLL7uAbjXSM17aAE41oQgoAopAmyCghZc2gVGNKAKKgCKw5yLAHS8XBh0vLq2j9DeRARPVMUJApS6ZkEp6Ol0nPZK2GpXSGS98nbRvJmHc0KUM4zkNuQxLG5XgxwXmRj7ft+j7Ty3jc9hmat++ZAvjFg05L6nLAk6GvYVn4j8VCsKPajUnL7KtjUh8yJv1k+1bKMS2lfWlUnN8uzL239aKb0u4TeZ0aJqZkFKvR+Tb01v+O5Z6de0pWo2lLRDQjpe2QFFtKAKKgCLQtgho4aVt8VRrioAioAjscQjYM16uQm1tLa1d0jvKUBMZMNFisllJCFMCljjjRQovJusNfNgbjsTs1hRevEhNCGIrGk9iGR7DydqYojLCj9I9E80OWT+hG5rEqSUkA6YUBpieWjmVVvMexKaVknXyTOw5j0JhrsTEY/vY7y8WRFbmsX8ySZqixQzRdDShBGrm5bg+1Y6TYxQZ/281tZRIt583xx1GITxrP5FqJG2JgJzxMlW3GrUlrGpLEVAEFIFtQkALL9sEnyorAoqAIqAI8Faj88dNoiujGyj7prQ0jZNbSfD8cRgrP5kNc+zM3WpktxrZ1N7ZjUuAfZsiKbajKTfzLS3OkmjZd1SXqXLkLI/FDo/5Ed9xelai+V8XWyDXrEH2YgVEzGpZNMI05jDFRubHF4tCIGC22RijTEg3+sl23RcKW3eSMvK/kzGb+BEJWVGC4Q3C1oUhmkHIRHYrcjSWDs+cvlgQyq7/5pXS/9ALPjmtb/db4q7/kbbDCvRWo+0AqppUBBQBRWAbEdDCyzYCqOqKgCKgCOzpCMyirUbjLroCDQ31BIVLdpvHJXXqLXotFV7ifPmlBptjMoVHJgUV04l3a/LQaJou1sSInYuUW79QUvkW/ehbYnL6YYkwX6SEGuTYRkV4Vt9JhO25mZWwWoEuvfjMmGjfBOv49sQT00L6Eewdny2IFo/lsVZ928Lhd9i2z7F/DWG+/RbOixuJpsTTvFeR3pXfvPZUqO7K69LY4xCwhRc6XHcaXyedGSeiNEVAEVAEFIEdjIAWXnYw4OpOEVAEFIHdDQHeanTxxGtpq1G1SYxbTu/C6XFKPHirER2uW1rKW43Wkpgkjy6Bbi61lqTa2g90RTUIMhqrK9KkjCqRvoopJ2kp1mZyP4yRM1WMIBYi2BidJXPldjA1LyvgXBgdq+9KV+FVOBVn1zcQxczxJGbRs5aklCRUkRevYczipIQW55l5Ysm3bHUSHBsK/QWIrUA2oIdtiEzUn9CtbsK2uE2KI8GgQcKRT4wZt3e5mJC3G2lnYdHaBbU2vtbaawu5tolJthpNmTIZ2dlZbRGY2lAEFAFFQBHYRgS08LKNAKq6IqAIKAJ7OgK81Yg7XhpNx0tr0HAJsBsl6yUfrsvSnJi4R9IUofr2hGelA13zEmnmUNnA3Fjj05z9uBGXJ+I6QKysjYBTfjuyvwk7kcKL9HAY4SCEVIWOhI3AspvHxR7x64QjCAqDbSSVNYhm6fxu3ksqf6LFfBkHPs0WmAgtYCVwCXRYynpoyY+L2JiSbTZR32Qt2XMyJRGODhSBXQgB1/EymTpetPCyC306DVURUAR2YwS08LIbf1xdmiKgCCgCOwKB2OukW3TcfBrN6rbwQh0vJXzGC3e88IG6khzbRN4l5H7CbW274gdbs/I8cjZ4zHQrn0wXXyznHqEarcCsi8PaMr4Ds1xriT4Bi8iBgYQAFwSkaJMgBgPR8g3G0Vhc6FEbtEqPZYbGnFtBWMP5Eono2/kSWVvAMTM+HEbIvmEuiPBD4PCNQRYjX9ALMjDAXCkNMVekLZ2N8UMzYnLnkJO1mAZcI2V/xIdY8lg6VAR2YQT0jJdd+ONp6IqAIrDbIqCFl9320+rCFAFFQBHYMQjYW42uQV1tTSsd2oRX0t54JUqGKXkuLJTrpG3hxS9KSLosCba1E1CbpEjDduI9GCoHkTgMOC4iqywmRILn5sDZRjPiWZDss1VO9P3Hn3G03jy2I0O8iA3xQXOuUBCbJcSKvFnaanr6viAL0NN84cW3ZuXllzlSL0k4DzzaaJxf65Y0zMAPQr4Wf0l+7Nx5lZHYkrkRNvIysm+xzXKBjvn0NKf/mLqPHzSJGIvmx1mSqXh1HB0pArseArrVaNf7ZhqxIqAI7P4IaOFl9//GukJFQBFQBLYrAjPpcN2Lxl+Jhno+XDfV46e0nOZKAp5KnhLk9HQ642UkSkpKsTroeJGihSTKrO0sWWoj3a7EuXZGGt2+Y0iWznKJwg0JcH7eQLIZVMzIMDf1kJwRlVitHvtwo0R6b6iNZCU/IxvpZKOqvjaIRYoKrBmOj+2IdcOkH+7NqK9vQF1DA3Kys01txfFEPtAk5brGBjTUNZJslpE158JEFAQTo8UOgwWYF1UjbAxBJIYYCIidmDdL2BoG6XEBKPJw5wo/Nh6KwFSmAprlEJPn/C9hTt8nJzMLFXW1RCZaKA4rY9RCP0ynR9zz1Iim0bekAcXVO7cjOmTlYE3FRtQSVu4hvokpoFAs8qV8c05eR4rAromA6XiZRIfr6nXSu+YH1KgVAUVgt0RACy+75WfVRSkCioAisOMQcFuNuJTBWXCqx2TICaaVTCVPKXEGdbwUFFDhJW6rEefenC5ziSFst1N2DnIzclFaVYZG7nyJdLTUE62a/u2d2wV9cjuhpqEWpdWbUUPFD/MkWkLYvvVhGfwbnvfO64gzBh2NZWVr8drST9CQxhjwk06SHJeskvX4Ef3gTa+62jr022dv9NmrF2bPnkcFLIrDiPMKrZxYaaRB/333QdcuHbFwwRJUVVebQofdWmM92F+rEXhJMKLzBCMRl6PwiP1bS2G6XZcJMmBwoSXwSQUVvlq8oqYM1Y2bkZmWSwWWfOTSv2Y9VBxpJNmeuR3w9cKv4JM1C/DC0v+hU0ZWwpe1zL59BPzovahoWIcGdM/pjHP3PxKFvQYhLysb973/LBZtXmsscJ2I/07SuOiU+HuwofuriK5y2+d+zGwtOhcPUXp0vr3kxG57fu8oLKJ+ovNUGLVWLpV+W9DDMbitRjfqrUZtAa/aUAQUAUWgDRDQwksbgKgmFAFFQBHYkxGwHS9XUcGgrgUYJFm2SYLMUimFD9eNnvEiWoEVk1k3ITszE6cPGIvD9xmFxz9+EfM3rQo6MKx8fWM9OuV2xoQhX8DovQajU3Y+FmxYiadn/wuLNlGSnu4nMDzmh33IOJhR4SAjLQOnDTocF486CY9/8iL+vuRjEiM5I2rl7a8tDfk2rBW2nYaiOevw0MPX4aSTTsA3vvEdrF6zFukmDmeDI2gw3RvpuOXm6zB0yCD89NFf4e23P0JeTrYx7fD043VU9rblj40hioEfP4+NF8LEdLvQJDenA44pPAv79BqCxsY6zFv6IWbMfZU6g4hJGDVR4atLTkfcdfSl5hry22b8FptqKg3PxiglF+ffrcSN7F4ioKhmM24vPB0XFRyPOcXLMKt4Ef625D2sJ5uBS9vsklBNDMidLe60jI0p3RgxiaplHZVQBHY8AtrxsuMxV4+KgCKgCLSEgBZeWkJI+YqAIqAIKALNItD6M1442ZWU1U98XRofckQJemEhH64r10lzN4mvHx5zWpxLhZezBx+Ho/YtxGP/+zNmb1gRmCQudTtsbKjBhYPH4uoxZ2PR+hV4d1URVpYXY2bJEpv4cwlBzCaCSSKY7o5+Hbvj+jHnkM8s3P3eH7CqcmNCg5s/uLiQRp0v3HXD/2ak04YmU1AJxLhIQ0sqmrsaP3v0Jpx40pdw5VU30EHCVCyibVb2YUMkRkWXmroGZGbm4LFH78KI4YNxzz2P4K8v/wcdO+SQaHKMCVRJ3xSE6GUOsmVZETc8wyA3VIDw6UQOy7EwP34XivXCaoZL68nMzMDXTpiKkf0PQ31dJRVbaN20Hev1j57Bfz95juRIiL4F+zpn8JGYQF0vv/r4r3iFijMNhJN9bGCuJBIUd4wX8SZxEIaV6/DMcddhbL+RuPb1n+HzTSuDKEVGFhqYNy9ZD0/swu2vL+OPJQZBwOfpWBFoPwjIGS9TdatR+/koGokioAjs8Qho4WWP/xNQABQBRUAR2DYE3FYj/zyNqE0/yY3ygqQ9QrZnvMhWozXEFRuSeLMCjek/zKmjpJ2LB1lZGdSNkolG2sJDm1oMjwsh6VT44MLLTQecjjOHfwF3vPN7PLHsAwygokBeeqYplNgE3LfPPtzDaTyn33zOyin7jcG3Dzkff5rzOh6d+w90IPusz8WNbIqBY1mybAOOOmI48nPz8cknRRREE3Jy+HpXLgQZcTrfBejSqQM60/ahtcXFaKonTbOmJuoGacSa1RU4+OAh6N6jC/738Tw8/tidVHgZirvufgh/e+k/6Nw5LwiQizWCEZPc2PgyBQ+mM2aBc54Fy7XS9Guc84y2DBlpWrWRpwnrBr+uDEEEI5iG8uoN+OIB5+L0o6/FipJ5ePofd6BP1/64+owHsIG2/fz6lVtRTEURflh/aNe+mHbY+dhQVY67338GG0zXi2EHnuyYzUdHdm5jm1u2Gn88/kYcus9InPa3H6CurtqcISPxBgGKkRhbbl0RITO1/vmL+pasTpy8wYMZzYjE6ilREWgDBNxWoxv0Ouk2wFNNKAKKgCLQFgho4aUtUFQbioAioAjswQhw4WXcRVegsaE1h+tKJhpNZZMBjC+8hPWtFh1s21SPbx5wGkb3HEi5bhpKqjbjN3P+gcWbqZBBBZmqxkZcXXAyDus7DHvld6PzRboSbyWdQVJPW4xW45m5b2B52TpbdEik18kxMYULKw0Uxg0HfRVf3Hc0HqbOmleWz0SHDCq8UNGiqroGX/7SsRg37jxs2LgBA/bbFx3y87Bs6Qo8/sTvMGvWXOqAAUqKN+Guu6Zi9EEj2Shqqcgy7aY7UbqulGoetoBTX9+Im6ZejUMPG4O83DyUlm5Al66dyV4+7rn3EbxEHS+dOuYGgXpdMl7oHG8tnYHCPhupgMJFmEw6h4WOIDZryaYxl4L43JsG4mdSh0o94cVI83Yq0kRWE8mQnDTshIounq81mxfhujMexYFDjsOfpz+Cf3/6DL527I044eALkZ2Rg1+8MBVFKz6wGuSAC2Q3HXaB2fZ1/Ws/xtLy9YEP/oq2yGGF+e8lTDF0IvF5MvMqS/HsFyfjkL4FOP2l2+jcnGqyw3iwTvgRClu0j9iOk5YYnLSLTCyJHZa1xRljyajQjxFLlnVaOlIE2hYBW3ihw3WnceEls22NqzVFQBFQBBSBrUJACy9bBZsqKQKKgCKgCAgCvNXo4onXopaS3dSPS1xtJpoqdfcsUBGjcBRtNSrlrUbS8SIJrNizcy4inDHwcAzv3g8ju+9L54d0wZ0znsbHJYvJHd14Qx0qJ/U/CMN77IcDew7A0G774o0Vs7CuagPdfrMBb6ycheKKMlPwsKl22L4XlSlWUPsMHjn2cirgdMId7/6BtjQtR1bQ8VJeUYHx48/B926egs1lmzHns3nIy8tBAa3lnXfexUMPPYZly1ahrLwCV189EQceWIAhgwdh3333whlnfR3Lly03W43qaGvRFXQzyaRJF1MBZxPmzJ6PwYMHon//vQmTTbjnh1R4eZW2GuVL4SUUJU3SUEOFpQLqKjl72DHoRZgY7Ln4wgUC3u5D6+CC08uL3sN5w45FDzr/hg++ZVTN8cBmS1AaltLhwc/Om441VNDix349xoilHKWSzlq55qsPYRht9br795ejU35XTDjpFuRnd0TXTr3xh9fvw/vz/k4VINKlOKoovu9Q19Dx9G1ufvNxfFqyzBReuLhhvwD/sofgbYaWwyQzIju5WXm4/8iJGNh1X5z+8vfRVEfdTty5E0THEfJjY7Vj/g3/FVquyARejJTT8G2IpHCttbAV5vl0kY3qCl3fisC2IyBbjaZMmYxsuv1MH0VAEVAEFIGdj4AWXnb+N9AIFAFFQBHYpRFoXccLL5FTWZdwhpPeZAhsx4u9TnpVcJ20r2/tiV4a+LaivMxsTCr4Ek7c71Dc8faT+ITObuHCC7vlq583U1fOzaPPxMkDD8PtM36P19d8hk60zYh6OciclROLEq9ELNHzFdS1VGR54SvfxcaaTbjujSdQVctnmbAkbbcpL8eE8efSIbg34tk/PY977/sZHTabRWey3IxjjjkKN9x4C/71zzcpIaLtUGRrftFHePLJJ3H2Wafh7HMnYemyFXQeTBp69OyKO2//LhVshuPb130XL/3zbRx9eCF+8fOHTPHnzrsfxMuvcMdLfiJkP1bubKlorMVhVGwaP+IkdMvrTNHZcgJjz8UN7gpZWV6CP89/CxcXnEgFqw6mTEC1DPMwJLRxCyvK1+HXn/0dy+mKZtfxIYiwKF2JTcWtTNpidcUp92FAn/1x85PnYeKJt6FPtwH4oOgVnDr2cvz9g6fw2od/MGfW8PalTVR4mTzqNJwz/Fg8Soch/+Xzd5BD38MVXmwc7ltbjMkd3WRUh6Fd9saBPQZSQW1fHNxnOIpKl2Lau79Ftok/QENAsSsi2+4JlukIweqYEOb5M4uApxQMowUW8UR0Ujd/Hgkl4SUIOlAE2gwB1/EyWbcatRmqakgRUAQUgW1DQAsv24afaisCioAisMcj0PwZL+GEVYoZ/PY5cSC6rUbF1PFC24CocGIqKJ6wLSCwJT6PpAn5VHiZSEWGk7iw8tZv8GnpYlJxW3DKKNG/6cAz8KWBh+L2d3+Pt1fPoUQ/y3ZHmOxYjHNizHZdki3xNtK2pnrSef7Um1FauR7X/vdxOoulJuj9SENZWQUmXnwerrj8UjqH5X5Mn/4OVq+rwNTJ43Hddd/C3fc8iOeee8kcQsv2i+bOwVNP/RRnnXkazjr3MuqGWWEKIgUFg/HgA3egsrIK3/rWd/D5kuV0Hkwa/vr8E+jZqwfuvOtB/O3lf9P5MFx4iU/kubGEtwrxJrAmyvztOTjUIUJbfOqo6MOn8vDRvDmEUSWfkUNvLnvUEI+fPCow1XJBhcznmhXG+bHI1BG2OXSV96Sv3IN9e+2Pf3/yf/jiQRfi7+/+2hRazqWtQP/88Gm89sHvqAOp0XS2lNFV3t8sOAVfG34cfj7rZTy74E1TePHXY7+EoE8zHtKrrL4KZw08AhMKTsLgLn1RUr0Rd8z4P7y3poi2MHlxmqHom2WZHyYnU9m0lKdENiplbXseki0FMYqF+HfYQryMUhWBLUdAz3jZcsxUQxFQBBSB7Y2AFl62N8JqXxFQBBSB3RwBd6tRc1uNkkGIprNJEpQ8u1uNeKtRqocLMtyx0oS8rBxcQoWXEwYegrveeQofFy82nQZ28wxMQWEyHa578iDqiKGOl7coSc82HRbRaMJJMc+MBPngG4oaMrLwyy9ehY6Zubjt3d9hAV1bnUXnoXDaXl5RhYkTzqHiy3jccusd+PgT2sa0vgLXXnkebpg8GU888RSe+PUzJEtx01afotmLqfDyAM4+8xScfd7lWLKUtxql4eDRBXjw/tuxbMUaOqvh+1i+ci2dk0J+f3kvBgwYQFuN6HBd6njpSOfHmEdCtoEaEteSOmbnon+nnnT7UrY9U5c4pmBF+PKtS+W1VdT1sh4DuvQmLDLMLiAKyyyYTfGWnUraRraEtiRVN8iV4eLMuDHC3FFU21CBq059GCMGHI56KqoULXufznp5GKP3Px5nH/cN/GX6jzBjzt8Nhny1dBVdNT3t4K/hRPpe33/rSby/ZoHp9hGrFnX2ZRZF8QqFjk2mxXXMykXPnE44ap8ROGfosdhEnUcTXvsRsui8HCNsDFldZ7OlUfLa4jSiUnEyLdPaxkrLflRiT0JAtxrtSV9b16oIKAK7CgJaeNlVvpTGqQgoAopAO0Vg5qzPcNH4q9BQL0m5BJoq4bXpcyquaIc7XtYKOfJmKzZ55UN0ufAyceSXcNKAQ3Dn20/iIzrjJZ2rCMYZJfrUyXHjAV/Flwcdhh9Q4eXtNXOpw4ILJvzY7pa4uPz0mA+r5Q6SaQefhaP6FuKBD/+E11bORD51wbCN8opKXDLhXFxxxWV44MFH8PKr07FhfRWm3HgJrr3mKtxx57147vlXzc1HHFfR3M/x5FMPUeHlVJx19iQspjNeMumg3hHDB+K+e29HE3WcXPuNafh8EXXC0NXVzz/3OLp37Yq7qHPmpVem03XSeQYBEzfvEZIF0Js7So6nA4W/Mfps9MnvzuUp0/nCQrxaLqos2rgKT8z6B66j9fSiM1kYR8GU5fiA3QUbl+P+9/+EhZvXGV/2/BQSMw87tAitLVtqDtc9fMRXULJ5FZ799/1YsPJjnELbjE4YMw6/fvlmfLZkhimGmUDJ9ncOPR+H0Dah6//zEyrulJi+msAc2RXbNl7XJ+W+FRd85tPVUH/7wlUo6DMIJ7/wPWrzqTNxy3dzejbi5n9FS6QEUJnLalkumWekJGynQqOofNRPSFgnisBWI2A6Xuh8qGl6nfRWY6iKioAioAi0NQJaeGlrRNWeIqAIKAJ7GAJuqxEn7NFENDp34KTm2IQ0LcMerltMVyyvNme8+NmsaFtZvjS6hgoinbPzcMVI6nihwssPaKvReyWLaOtMOp1Swol6E22nqccU2mr05UGHU+Hld9TxMge5pmDi4gqPrJ6lWf+cxPOWnbMGjcXVY87Eb6lo8au5r6EjdaNwcs2H5l5MZ7zcestUvPji33DnD3+KrMwMPPzgbTjooANx44234d//ecscellb24DFn/MZL0/h7LPpmuuzL8F/P1yAvl1zsc9e3XD7D27CmIMPIlt34bHHXqHbo47BQw/cTluO6nH3nQ/iRep4sVuNGHlJ7B02vKWnR24HHNZnKLpk5VNRhVAgMb6diLthqO5CBwxvxszSRTi891B0ymYZXq1dK8vRf7C2aiPeL16ASjq01j6WL3IBEZsq1+HUI6/AaYdPwsp1C/DIX65F9059ccVp9yCH/P/qb9/F6g2LjTgXb/bp2BPfoeukGeXvv/MkiqvLicce5THBBBRLd+skGWLzGuaWrcAzJ0zF4fsU4FS61ai+hq6TNp1MNkL5FavyZovWg1D47fsXeljKSsRrs6Thm4GV5GKdLVbx3C9sif2oreh8S+VEfnd4bysWUf3oXDCK0qNzkYu+WysX1WvLeTgGt9XoRr3VqC1hVluKgCKgCGwDAlp42QbwVFURUAQUAUUASN3xwuiEE1Yfr9QcTiIoeaWsv7BwJIqLS0zhJdG1QGx7+KuVY9kD6ADZ0/cfiw609ad/5z7oS1trZq5ZiDLaRjN7/RK88Pl7qKqvQTWdanLjgV+lrUb2cF3b8SLXrVJEXJUwD405ow/GPLLxWlojdaEM7ro3plDRYHN1Je55/xk6aLfS6FTQrUYXXXQ2brtlChVhyrF8+Qq61Yji6t8fr732X/zkp7/E7KJFOPGLR2D8hPPQrUsX9OndB3369MS8+QtRR0WVBfM/x/0PPIpTTzkR11z7ddRTwYMPGO7dpzd69ehubjm66+6HTTdNxw65XmGCk3qJ1QzNeSrlfJ4LFWH4Oml++DDhBioesXTHjEw6yyXdHDxsrpGmdZurp2nF2URnuVzqTMlPo7NwqGXGIsBlLLZlO0/YJj98e1QHOsSXbzLav+8BWLthGd041IFuNOqJNz99Dq++/xTJ1NmiD+meQluMrh5zBn5PxavnFr5Dx+XS2TL+N7BmA5+8LvFux/Q/Yki8CUUV6/DMF6+jwssBuPif92JN+QZzZXYQIlmxsdp3YDTyspaZGD3jRQT9vwChOa0k28al8MW/6OlbEdh+CGjHy/bDVi0rAoqAIrC1CGjhZWuRUz1FQBFQBBQBg4A746UmBSI2YY1jxnOCZJUKAFx4KSmxhZdEYpsoiATpNFUJxlJHxxmDj0RORjal2GTVGOZRGj6jA3b/uOAtVNRVm4T/9EGH0NaWIfh90X8xj66BzqTCQ9DmQW/xbQsMNj4/SpdIZ1EfzblDj8E5w76AH33wJ7xJ3TNcmSinjpeJtNXo8kmX4s233saAAf2Ql5uHhQsX4be/+yMW0ZahjZvLcdpXjsNpp30luJXInlnC1rm4sWbNOjzy41/Q2jfhyisuwtixhyA/vwNKi0tRsn49snNy8Oyzz+Ojj+fQliVbOHKRSdT8DhdGWMatxvKTv4tIkDQXaszUROZpizdPO5Dljpm9uvfHF0efj950vTOf9bJ87UL8439PobbOFqf4y3SgK6BvO+xCdKOOnDve+z8sKytO1Lqs9agP+z09jyY2ps6rLsODB5+LM4cehVcWf4CikqV4r3g+nV9TY0o1LCMPW3UzR7XemOP8huXczEm4kbUqc5JNmBKa+NK3IrB9EZAzXqbqVqPtC7RaVwQUAUVgCxDQwssWgKWiioAioAgoAskIuK1G9iacZAmXsEZ5yRyXpHLHy6jCApSEthqRBSm8mG4HWxuooDM9ltOtNjZn5qSXYuEKRlM6HUibiwF0TXI6H35LOhvoNpy19O/+OZ2RRdtR/CeNCy8uBMMKxcg8kjH9HmRrYOfeGD/yBMwqXYIXF75rcu2yzRW45JLz6ErpC3HJ16/B4kWr0aVrJ7o2ejkGDaYDbLPlLJhyrFjKhwaXBiF0oHdFMO6D4cP3M4tb8v/sfQeAVtXR9mzfpS1LVxERFRVYC3YsqFE0lqhoFBA7IqifRqTYFSvWWFPsGo0aS4wxatSosUcTu4AgCFKWskvf3r6ZM3fuOee+9919F5aFhbkf3nvOnGfKed79/++f+U9ZWAKZ2dmw9RadYNo3M3GcYq6FbbbdwqykwUNgEBfEbYL1Ig7s0ScyMSk/kC5VSwQSp44yM+uoCdYK3qKYZla1LF4zH7q37wkVyHV59RrolNs9PG+H1tp0x/Nk6HyXTxdMhQdnvAcdcatWaJ5Mye+cYB3DNTJ+U1wVuIpmuw494GzcZrZrt+3M1eJXfvA4nkmzMNC2H/IhkYqU/cpIGIUZdn0l4kVCX0KKLrbpH3b90peL17YysH4YsFuNLtXrpNcPxWpVGVAGlIEmM6CFlyZTpgrKgDKgDCgDLgOm8DJiNOb/dORssofT1+hoojRIXLEQkJaRbgovJVh4oW02iemya81Nb2UbjDsuniQx5hTZroRw9V29SNsk02KLT+vYMreDMbaofLUBr8HrpE8/82Q4Awsv48ZfDt9P/REyMtMhi1bWkPtA3TSpjQUGRxym7gSU+OpxmxBtFTKrcwhsiiViC414RQobHzk0QyhypagZ8zSEcPghWxRDaDFJ9ITDAhhp0kHJIZ7k2MvDg4K75eXDorIV5rakDNz+FF67RObDxzgLe6RLj/FKBSPDBZ7xg39/3fLaQ+/2XSE/Mw8PVp4La/Cab/uIppVIi2fnzkNG6Mt6rraNyLYYJ31BS9+1p21lYP0yoIWX9cuvWlcGlAFlYG0Y0MLL2rCmOsqAMqAMKAMhA7TV6PQzLoAqvHI4+SOJqEUkSmiMEtVgBBPqwgH9oaSkuMHCCyfNVKKQlvXhb5XxPRJaJOyVe9xmW44l06RDUk2IMoBdPD0Fe2l4iw4VF9KgvKISjjx8MAw54jC4/4GHYP78hZCBRSSjiMbNqhr0LL7FVPRrY8KWAQcaNBANIqrs9Y2CJ0nsSDSIpX/YlQhJ2z1bxcRiTAqCo2ELZsBoUF961p+VEJfEHTGTnpbpYcUyo/l3orZE6baMbbO0hK76rsWDj1mbD032oxDvbMdao0hlzMbqtwTNON+uj6SeoKndmGXCbCxPY/PaWOLUOBpiQLYaTZgwLlhh1xBax5QBZUAZUAZaggEtvLQEy+pDGVAGlIFNmAFa8TL81NFQV7uuK14oQbUJK62SGIBnvNC5JgsX0pYcO+bSGWphg4oaggrlJAkSc7YhI0ExJNQQTbHuJ+NUczEPqePDN9VwmwX0ZtsZeIsRXWNdXYWcUBXDPDjG/8KpyAiPSy9wEGjZTzDOy02sLQQkFISsErZ8e9ITbyYY7PhXRJMUhQZEQZNWHVsyXJIDtsBv6idaTpQFGCFTluMYbfJDZrlwEmeNvCQ8FIABS4M69B8fNCwxiD3Sj0ZOMjtuWySPf8RX/GjrlTIzLhutdy6bb+S84gWvk540Trcabb5/BjpzZUAZ2MgY0MLLRvaDaDjKgDKgDLQ2BprvjBdJeDn5o8JLoTnjBVe8LMDCS1jASGSINfktqyUoeQwtUqIvHU89TsjrOzi19sdNvcATyVoQSeZxkBTNjUH1WHzBc2XMw9aCDn7i+q7YcxIMkHainGZp50yYZE9UV3AYC/6TwgtFxvEFH1McwTbyb36CSOiMJx3m29MnsffYGKjlxU1+jLLlVFStD5HYr/lpA7NsM+iwMQRK3+pwy1plPZLavxlBC0qsSJ/RgtpEvuH/OZHZbiLz2symoVuNNrMfXKerDCgDrYIBLby0ip9Jg1QGlAFlYONlILVbjSSRk7Q1WbHApsC0yoIKLyV4q1FjZ7yIdUqcJZmXFFo8hsWEkEqrFYpMw2pIIi7JPY+446IZxC1DThGBV4sQTgZFJ/iGK0gIghgTVmAvadFAbCSxKcMJX56zWOdhtsEFHJSEg1RoQT6dghdrJxg1MxO+ZZ7RyNi+SENvqCtWZYztx0uT+Ra5q8UR8d+DyAVHX99fQHwIIA0XIRbiZKGSoyN4d0zbykBLMKBbjVqCZfWhDCgDykDTGNDCS9P4UrQyoAwoA8pAhIFvvv0ORp42FmqqqyIj0nVTVZHxl5PuJON4q1HhgAFYeEl+uG5icusXXnzLfi+aaNvIEnH4vyzNcL1Z/REdp6EgkgBnii0ECwN0dAwGB8SWKbw4+p4Od0RkighhocaxaaKLvkjLYrgnlhgrPYtybIjQgLgThoywUCxGHFVqBhphizTcX4fhhBID3KaeL2VLjOe3YKwuyQUno75dV5/bgqeeYBNRrkQsi8zVcq3FjYus9Xyjs13XyJvb3rrGQ/obKqb169eseBk1HCbqGS/N8UeiNpQBZUAZaBYGtPDSLDSqEWVAGVAGNl8G7FYjOlMjLv2MyjjpiEqjDKZlYOGlP14n7ax4iUvdRY/skWV52L4jkaYUPgTofaNRcaw0LaMe2IiirGcEkH2jJkquAzMQCBxjCWLxwJgAiWGQXMDyde1TW9DUZjsisT0qeVEZxFpjrCBFV/oukmUyIj5Igx9nBJu0YEZWIdnYxD9H5OqRtkjZUqAdFpzIisgQbFYXoYZ1S8LgP7LkW0NB8Iicup6yALyvIEhL2i7AtSZyWXMjfdaMR9o4CU0e1gVnPXIrmb21xUX1mrMfjTXaF19RebS/vnBiV77J/Mp4S3z9GOxWo/F4xgtdP6+PMqAMKAPKwIZmQAsvG/oXUP/KgDKgDLRyBmjFy6kjx0BtTXU4Ezc55fTRJpGUItBjJdyPvtNwxcsAutUICy9FeJ00pdpSLBBtsSX2ov3Qplmqgb2kTt0BseLK2BKN8Cod6su44BnDbxyTYSNAjLNlhzGBnuCMcdLDRjoJjcB8GSnlC5GzFXpzgm/70hLTkWCCYbZq3FCwITiQE0qKVBi7+OAoGMPvQFH0hWsJgswExsWGkQQu0wwvgU+aOz3Bh5v8y9fX4dXUdHOUGRMAGcEyDN5mRHI+p0bG5EtWKDi3LzL60hMdY4lMKR5hFMOXixVholUZ0a8ysP4YkBUvkybq4brrj2W1rAwoA8pA0xjQwkvT+FK0MqAMKAPKQISBhs548ZNR7kky6o9FjGI3HRP4/sEZL7bwwjhJ/sUWScke90Uq6T7r8MqIxIQ6GPUssCwuQrFttRKxFlNfVwd5bXIgJzsbVq0qwxjlph1XH/FB8Hl52ZCZmQmlayqgFg/oFUv8jczHmLClDNditB2nKWxFsVRsoTpGelY6ZLbjQkdNWR3UV2OQJhDhhX2Hts0wAiRoMym0HsDNh8YIh79tZvsMqKvCK6XL0TYWmth0AKCgjABfiM/KzYFt99obSpcvg4XTpuLZxcQjY7PbtYU+e+8Dy+fPh6WzZuHtWny9N5kgn8aMBBH0aIzl1LIo6rkPjdBjsdyPews2biwV/Ti9jUPmzqx1z2Tj4HP9RyFnvEzUwsv6J1s9KAPKgDKQIgNaeEmRKIUpA8qAMqAMxDNgthqNGI3JcOJ10m7Kxgku26CUPUzY481CBm416t9/QLDViK+TprSPbdJbig4ijU8KyQ+PJI778UkgYo99sDSIFkVy+0+Yr8vWIgIag6hvVnGkw4pVq+CUk46B3XYthEcfewaWLC22+tYAKqbhloAMOPHEY2DLLbaAF1/6O8yduyAwKAWO0AE1zMM8So+s8OPOi2fjSgjjIM0WHupjMYPaOJ/2fdpCz8FbQYee7YzBWa/NgSWflZhiDGvL70d24+IjO4RE7rHIYq67NorYxqJJmx5tYMeTt4NVC1bDzKfmQEab9OAQX7uyRuipr66GnY84Eg45azR8987b8OnTj0N1JV3TTQYB8rfcEk64ajKUr14Jr94xBdYsXgxp4W1SjAnfwW/FvJEBvHkKDXl/iyw2Kjw70XYGRBT7de0580nApm4vIDPBQssI3L8dipme5o59Q9nj2bTMu+XmqFuNWuYXVS/KgDKgDDSFAS28NIUtxSoDyoAyoAwkMNBQ4YXAbtpme40nIel4nTRtNeIzXrjwEnXuWuH0P4pwIyC0308WGyM5HecknZFhjUW205iFFzjmBcLapDdt6nx45NEb4ZCDB8O4S6+E76f+gAUlOnNBPCO2jvXz2uTCbbddBzts3wduuvkO+OCD/yJOVsiITdIkXZ4tW7F9HnXHJDRG0uxFYr4iZvOoXg+5XXNgj9/sClsftBWsmL0SypdVwvTnZ0DRO4shPQ9XwNBkRU/MBTFRl2xgtYW/hCXbZo7UwDU/NXXQdbfOcNCUQVDy4zJ484j3IXfnbMShnmvX2KqF7Dbt4ZhJV8CW/frCyzfeBAu++5ZXvJA5NF5bXg5HTLgM9jj6OHj5lskw/b1/4XKdDOOWEPwEnFBs9MjvJ00jplcQbzQO0mnCI7+RTIi9N8HAxga1f/gbW2QaTwwDtvByKRZ0s2IQKlIGlAFlQBloaQa08NLSjKs/ZUAZUAY2MQZoq9HpZ1wAVVUVsTNLzGETJbGKmBwX0hkvJSV4nXR84SWqZ3JxTJ45heZ3FCPJMEfBGoxx4gqapn6Ag4Ki9QtUEMrMTIPqqlrIycmCTDy8sqKiEm91oi0uFpyekY4rM6ph0KA9YZtttoY333wXlq9YHeT8gUX0k5ubDVnZuO0Gayy33noN7LBdH7h5yp3w7/c+CxxTMDIzjp56Nlpp4VcCpjhSemwcdHwKFQy67dMFDrp2X1g5ZyV89dD3ULqkEiqLcesTbjfC5SHGcTrOPyMbix4oqsftQmYLlSlqoL0g3DSsL9XXYp/UcpC3LMSX1kFddR10GdgJBk/ZDwsvK+Ctwz+A7J0QnIG2yT4ZCHYLVa5cDXuceBIcMno0zPnyS3j9jluhBjnldSToF81Xl5XD9vsPgl/+ZqLZivT0JRchl9W46gXXstD5L2QSH7O9CbdPUZg1+NuFPyqNhSiDpFejj9glYMBioINxeYOtvuyC85IJ+TMNJqyfjYwB2Wo0QW812sh+GQ1HGVAGNmcGtPCyOf/6OndlQBlQBpqBAVrxMvzU0Xi2RuJWIzIvKZt1lSixY9LCZNVcJ90PV7zEFV7ERrJEkOTuNg/Bi/24uOyYBB2tY9RhdWSXXfrDrrv0g/nzF8CA/jtCx4ICmDp1Brz37w9h8ZJlkI2FGCrOHHTgPrD11lthEaAOysor4J9v/hvPbik1Z9fQ1pvSsko4YP/dYdB+e0NBpwL48cefYMjhB0PPnlvCjTfdAe9/8HkQEMXuJ+9mNvii76raKqim82BwyplYPanBNslpzUcbjCMnPcmtJlS1oIdWmtSlQUH/DtB1l07QYZv20HfodrD4f0thwSdFplBS9NkSWDWz1BQU2m6dB1vu3x3ye7XHIkgdlEwrgUUfL4Wacjz8Fn8ziiqnYxZs/YutYNm05ZCZnwXddukC2W2zYMl3xTDv1YVceLl1EBTPXA5vDvk3ZPfOhE57FkC3ws6wAosxxV8tN37LFy6FY2+5AfY49gh47qrrYdaHH0JGLv3/4AexY1WHihwrp82Gs559FvrstQc8Mvpsc95LGha+giqXKcIU4oqZ3XffBXlfg0Ww92DlylIcNpUetEfn6YhN7DbyEL/yJGrxqBRf6Hex8YqWfpWB9ccAr3gZDpMm6eG6649ltawMKAPKQNMY0MJL0/hStDKgDCgDykCEAXuddLDiwxun1RDR1NRNWz2w05HCS8NbjRyFhCZ7Fd++T7dHCLdvOigUTTIs45UV1XDhhefAqLNPhWXLSzBxz4AMLDbktcmDv/39Nbjrtw/hyp8qs4rl0nEXwt577Q75+e2gCgsUF140AX6YMQsyMzKgZNlqOOrIA2HcuLGw1ZY9YdmyEmjfviNk48qXstJSuP5G3Gr0IW01Is9+AYljodUmddAppz0c3HMAZGKBheTm/Jkg46f2rBUL4auSn52tOcFMpOiCOlR4qa+shz5De8EOv+oDuZ2yIX+7DlBRXAlrFpZBLa4O+e7P02H+60VmG9LASwqh1yFbQ9nScshpl2VWsnzz+HSY8Zef8ABeXN6CkXQemA8H33IgrJ6/GvK65mGxKR0ycMVLdWUt/O34f0Ln/vkw+Lb9oXjGcvj7EW/DDqf3goH/VwiZOZnwxe+/haL3F+MtWbW4yqYcTrnzXui9567w1MUX4zaj7yArL4eiDh6aTzosmvoZnPXoi9DvoEPh7d/fA1+8/DfIQFv0K1KRi1YVnTvqdDgbf7fFeAbMb35zBUydNjPY9sV/AVQzkmKJWJdvwJr5m5C2jLl/JySTv3iDoxcCmlLUEbsb1zeYyMYVlEaThAHdapSEGBUrA8qAMrABGdDCywYkX10rA8qAMrApMEDXSY88bQxutbHXSdt5URoqj6SoViIjcV97nXTcipc4DZHFpbleOizAmC/FyPHZlsDSzJaiSy8ZA2PGnAkzZs6EP/7hcTy+pA77Z0N+hw5w192/h7/85VVo36EN7LTjDlhMaQPDTzkBdtttIIw5fxwm+z+Y1TCrVpfDfb+9Do448hfwxOPPwf/+9yUM3GM3OOP0X8PKVWvgBiq8vI8rXgxlnPQyexwbFQjK66pg107bwpWDTof87FxTNKjHIgotYDFXLGMl4cN5X8EDX78K5TVVZqWNzM3s0QltIx5XvNDZLu22bgNdBnSE3cfuCj+/Mx9+fHUOFl7qYPXc1VBeVAHbntAL9rpoN1j43yUw67W5kIcrWwacuTNk52XBWxd9AKtmrMKlNunQbS8svNx6IOQW5MC052ZC0eeLzTaidt3bwA9PzYbOu3SEwbfuDyUzS+Cbh6fDvpN2h+x2ufDlH76Bn/+5EMOsxZVCtViIyYWRd/8B2uGqoueuuBSWzPwRtzjheTDmYS6ouWLqPPjVHTfAXsefDN/+6zV47ZZbIBPPzOGnHvKw8HLeeWfD6HNPh0WLF8HYsROCwgvtb5LHLXCJjL/Wky/n38SXuX/xPBL39+jrbPQ9IiBusht94JtngKbwMmo4TNStRpvnH4DOWhlQBjZKBrTwslH+LBqUMqAMKAOthwEuvIzFwktVJGhOV/ntZm1uGktyt++YwMJB4YABUFJcnPIZL5wd+gl0YiLs+PCaNkaOysbFI1h4qayCcVh4OeOMEfDA7x6CBx/+kznrZdRZp+BKmDHw3LPPwZWT74UeXTuYgsyMaQvwcN1bYMiQITD2gvEwbfoMs/Vo553wAN0broBu3brAr44/C5bjNcndunaGp578HZ4bkwvX33AbHq4bV3ixdNUib+2x4LJzwda4xQgLCCZIiZk6abCkbBnMXV1sVsdwRcabMFMfLPWop1UweGjLFod2h4Nv2A9+eGkWfHn392bFiNmRg6YLx/aD3odtAx9d/x9YhtuBqhZXwx7XF0K/YTvCB9d9DgveXABpmbjiZY8COOyug2DZjBXw0Q3/hbK5ZRw4nbFSgYfr4hkvB03Z11wnXV1RC20758KHt/wXFv6riOeBq3Xq8O+pTX4+nPa7B6GqvBL+du3VsGzhXEjPDLYa0RRpulhpKi0qgQPGnguDzxgN877+Ep699BLIapcXTLbe3Bh1xJBD4fjjf4lb15abAtmioqWmCMYgy5uhMdCUj4xKn4L0z4WxI/z3xlYYE2fR4lt/S36IxmaSKq4xOzreGANaeGmMIR1XBpQBZaDlGdDCS8tzrh6VAWVAGdikGLBbjXibiTs5v+ghySgh/BFXR9p0Rkdh/+hWo8aTN+tFfMTpRGXSZ22Jgb+cdlOqXY4FgPHjz4fhw07EW4ouh0/+8wVQkrPf3rvD/fffCW++9SZcfc1duLWFtrnUw3RcifHoY7fi2S2H4YqX38DU6bPMIbyHDN4bJl93Ga5uWQ3Dh58P1XhOS4d2beD++6bA1njGy+QbbsetRlh4cR43MikE0AyLayqgBFeHYBUB2uFtPmvoWm9cEpOXkQVd8KSXPDzllmorXNGg03BlroEIP1R0obNI6vH2oS0O6QYH37gfzHgRCy/3TsXFJ6SD58e0yYA9x+8KBTt2gncu/gAqS6qgYlolDLiuL+wxdjf4+pFpMP3JH4z5rrjV6PB7D4EfXvkRvr5vGp7/UmO2QdFKnTrcdtVtTzxcF894aYvXSqfj1quyJWXwARZzFvyzCK+WRu4wljrkJAsLS6fd9yDkdWgPz066FIpn41atnBwKh6I2bwKvmPozHHPrdbD30JNh2vvvwCvXXgfZHfLMKiBjDO1V19bilq4KyMnNgI4dcDWM2VdEXNBDxTp5uCXWfanrVUYa+pIt1xKVYrhv/ZF+Is7XS+Yjqie4qDzaF1z0myouqrc++8liisqjfYkpKo/2m4oTvHyT2ZPxlvj6MditRuOx6JjkjKeWCEt9KAPKgDKgDIQMaOElpEIbyoAyoAwoA2vDAK14OXXkGDyTI26rEVvkZJOSA0oxJfXksWRvupVmwIB+uOLF3WrENhpLSv00JJmHZHLxIeOSOKdBZUUVjL90LAzDwsuESdfARx/9ByrK8eai/QbC3XffCm+88QZcM/keU3ghK9On4oqXx26CIw4/HFe80FajH03h5aCDBsLka6+A0tIyPJh4LFTiuTAd2ufB7x+4Dbbo0QNuMIfr4q1GkcdEhi86t6QCDzPu22FLuGjg8dAui889ITkVUJhlgM+KpsFTM/4NVfjb8EGyiYWXcHbYwF1TuOIFCy+44mUGrXi5D1e81NAA1nWwYLHnuF3xfJbO8M4lH+FNR+VceJm8I+w5Zlf48vffwQ/PzDK/btc98+Gwew6B6S/OhK8fmIY3H6FhfGgrVD0ujOq6e4E546UKuZv79nzoi2fLrJq7Et6//DOoKKmEtAz8K8ECUj2eL3Pybb+FXrsWwtPjL4IFeJBzZi7N1UwSv2QXz8yZ+jWMfOQp6Df4MHj3kQfg8+deCM94EWg9bl+qxJunMvGw4axMvt1IxtAIPrxSSvhgWfxbvMePJpe6ttfWRnLrOqIMMAOy4mXSRD1cV/8mlAFlQBnYWBjQwsvG8ktoHMqAMqAMtFIGGrtOmqZFpRZ70gWnn24SGjt1rCDwddK01QjPCHEe0rXbOBItUVLrSxMljjmnKThOi7knltLwkNwquAS3Gp155gh4/LE/wW13PIgHxtbAJZeeA+ecdTo888xzMPnGB6BzQVtcYVEDs2fOhYcfuRO3Gh0OF2Dh5cuvp+LqkwzYeedt4cbrr8QbjLaAoSeeAz/PWwi9e/fArUZ/MIfvxhZeTEhBLLhCpRLPeNkhfysYN/AEyMvkc0+o8CIPFVo+WvAd/HnGByYWLsjgOOry7xEgeaqmYwovh+BWo8n7wg8v/whf4WoVU3ghDK6GGTBmJ9juiG3h0ymfw+JPi6FmWR3seWMh7HxiX3jv8k9h4XtFuNUICyt7doRf/PZgmPbXmfDN/bhqBq+c5ooQesZCTpfdaMXLvrB89kp4+5SPYN87d4c+R/aG2W/Mhs9v+Q7jo1ua6qAKr98++prrYLejDoPnrrwKZn/yGWRm41YjngwGRfOhW41+htOeeQK232sv+PO4i6FoxnSEYNAUt3GdBkccMdgcsLti+Sq4F1fRfPnVt7hIiFfXmMkjMHEDESqbh/96iTd6ArMGbwThS0ZCgdcQa2SB/3694ZhOw/ZiFDYiUaqxb+y4lqS0ebjQ66Rb8jdTX8qAMqAMpMaAFl5S40lRyoAyoAwoA0kYMFuNRow2KxSSQGLEksLGDAWi5FuNTC5tkl+rTUkzy60s2mocwRqEi3kwa67AFS/jxp0HY8ecBfPmzcNCy4tQi1tYTjttGNYz0uCO2++Fl15+C/r0Yabf9wAAQABJREFU3hIGDdoLMjPTYMhhv4DCXQbA008/A8tXrIQlS4vh90+/AY/fcxWccMIv4fU33oWvv/oGr6kuhF8dMwQxq2Hy9XjGy4eRFS8mfE7d6W2ixNdKLPCUBwWXXCy2VGPRAmskgJcIQfv0LMAjV7gIYYoGRosnx6a4TStRqEUrXsxWo0HBViNc8VJLBnAMtxz1HLIl7HPpQFg+awXMfedn3M6TAzuf1BdqcEXLvy7+ENbMXg1peI5Lpz3y4Yh7D4VpL80whZc6KrwE/utwFUvX3TvBwbcPwsN1V8I/Dn8POu6RB4PvGgSdduxoDtid+dwcChrKkat9zzwbDj7nLJjx0Sfwz9/eYc7Isb80FsPKSqHX7nvCsROvwO1JtfDI6SOhPjsNiypycC4WOfDWaDrk+MorxsHylctxO9gUeP31dyGHDuo1RRyOz5ZDDBsYszw0QiSwnNlyuAxgLLE40Zava9W1J+Px3+T24vGbsjRVLlLFtSRXqca07jjdatSSv6v6UgaUAWUgNQa08JIaT4pSBpQBZUAZSMLA2hReGi+7YD6MW41oxUtx8dJgxQulrZSUUPobrKAxOQqns5KuMMLAAqxtJ2tJEmwT40QJ+S6nrUbjxsDZZ42AWbNmQY/u3fEckiwoqyiHJ558Fg/SfQ6LENUw+MC94YorL8WbjtqbMxaysrJwS1KFiXv2Tz/DmLGToOdWXWDChIvwxqMBuGqGtmmlQWlZGdRg8WDKrXfjNia6Tjr62AhpRBipw200pI/lhkAqY8yGqS0ECL9EhesugqINYcyKl4O6wgHX7AszXpkF3/4OV6vgkTG4mwdwAQoebJuGq152hp1O2M4UOTLwNqPy4gr44g/fwrw38WBcsoVGCgZ2hMNuPxhXzcyEb/+I577g1dA0ZHzgdp/Ou3SBA26kFS/L4O2jP4Ks7dKhx6AusM9le0F1aTV8dscXsOzbFahTC20KOsExk66ELr16wotXXw1LfvqJ/WCxiJ7KlavgF7+5FPYfdir8467b4bvXXqFAccSuYMnF7UnnjT4NbzY6E4oWLYaL8Drpr77Eq6nxd7EP2ZO/IiuVFv9FcM9w5v2h2Y5tiSZFkmg5Dmc1tKUMrD0DtvByaeRvfO1tqqYyoAwoA8rAujGghZd140+1lQFlQBnY7BloeKuRpJw+TakUXihL97caWVt+4YVtcyJLGPv4PSt3W5JQM9b64A05PrICz2IZf8lYOPnk4+HyyyfjYD0UdCrAIsxP8OOsOVBVgQUUDKRNmxzYptdWkJGRicUDPt+EBuiK7ErcrjTrp/mIrYSttuoGfftuDx075sO8ufNh1erVeNhsOizArVUV5dFbojiWxLikxBDEKpMOM3sUOAfq0hYcl38Dp3EsZFAtI60NHtKL10rT4blVJXJuT2AUr53OaJsOHbZtC+17tsdDc2thJV41XTavHOstNE+yg/9wxUm73nlsYxkd9huM0SAux0nPTYc2PXOhFm84Kp9XgYUSVMWiTpstcyEDx8rnV6Bt5q0Or8Le7dgT4ICRZ8LX/3wdPnn2CTygtxb/PPivIH+LHnD8VdeZgtert9wEKxdjAYgqPFTuoG1V6Ltbt85w553Xw2679Id/vvUeXDf5DlizppRhhjbC0xPMkzvem/5O7Ci1REdgVhY3QqioXDT1qww0JwO61ag52VRbyoAyoAw0DwNaeGkeHtWKMqAMKAObLQO04mX4qaNxBQQtjUjt4QTWprGc8PppKa946YcrXuSMF8Lzig6v8ILSYPFDrHO3yJAIkLKLm3JLAu3GR07wOmlctTJh/IVwChZexoy9xJzZQlcGZZptLRg/qlBBgB6zwsO0qIP/hdPDhsGwoB5vJKIVFHS7T/hQ4QB1zDyxbZCmTwi2TzJuhVqxDcYFaFIwRQmCiraxjl3uGwgN28Uz1PMfU9BAJP7js1SwQXgszLCQP0YpMG/kpgCEUoqBCjWkQ7Mz82V7JEsz9mmIZXST0T4nD4cVSxbB1HffNofuGhVUbdulM+x70ikw+3+fw5z/4ioh2meFgfHKHmyhn15bbwmPPXYfVGHh7Mab7oSPP/4fOebHxGde2Cfdxh7CEk50CG/7rpRG6HGtxmkzSt/KQPMwwCtehsOkSXq4bvMwqlaUAWVAGVh3BrTwsu4cqgVlQBlQBjZrBux10rTdJfWHk1E3JfV17RkvzlYjk20TLkh0gyy34cKL2I3zxUUNLs5Iyiw46Ys+bmuprITfXDQaD8Q9Di66eAJ8//0MPEuEqgeMDaIK4iM9lAcFDQqdHrEuOqHQnYQUIswgxki6qGh1zQALpUlfU+0JHLlyEx/OEg2EpSYDI4sSWGDd+CIgywnPI8G44MUf9aWQZCCiK3gnEBEZ09Shhvhxf4XIqhwsnpQtWYZXWudCDm7fsg/q4Nas0vlLIadbPmS1yTNFG2aK7VJRq12HNnDyicfAosVL4b33PoWy8vLAr5RnonO0Hig+PxoaY9sWJXOxEhch07ajiRbcMW0rA+vCQClecX/uOcOw8KLXSa8Lj6qrDCgDykBzMqCFl+ZkU20pA8qAMrAZMkDXSY88bQxekyzbUlIjgZPRuJSU9c110oW04qUEihYuskUFo0IvTG2D7NatWSTzzml94qgUImwkthU6CNQoie/bd1vYbrtt4cOPPoU1q8tsHG5cBu/aYUuJEgKKFNN7tO/exuOOGZMJL9F1B5yUnyot9KCIWsyTM05St2vA9pXITTDmuvX0ccA4sjbiWqQii1+ShSAuxJz5khL+Yxm+TcMNgLcn0VIaI8X5E4SeEjy0OCszC9q1zcFFSijF26WMbwaGOEbT27VLfbGUbMzibcvXIk15XIzI9KsMNAcDcp30xAnjIJtuAdNHGVAGlAFlYIMzoIWXDf4TaADKgDKgDLRuBrjwMhYLL/FnkjQ2O5saR5B0uC4WXkqWlgSH62JSbVaCEC5IgrFvElg3J46YkW4yP0mLC6KIX/JhCgXkxxQzKBZM7tG/1DYYzoHQ2ybW4oGilkBl1EeyDX4bn4jnL8lEh9qiJ/ZIJlIHJ8GJKChcWFtUqBAPISjwxH3fg3HDLwnBscAR0DDNme3aObMaj3Lbs41w2mJED8u9UVYIIuMOcSMrVViLPVI78I7z54U7Ps4yZTWiFtgKvSUebvOb4+S2xGllthWny4y71rStDDQnA1p4aU421ZYyoAwoA83DgBZemodHtaIMKAPKwGbLgN1qREm8JKEN0WGT3bikXDTtViM54yVaeMHk2poStdhvYiJvFTlJTsfI42JnHKXtXAKRfnSm1h6vWnHDYA8GQS5kW47xx2MuWtp2RHzjiISINrjoIALWkp7VFWuBLlchHF3RIJyJkBVQ7B/CS2POOKPwTfpBkcMGZ0YT0FQEIiE9xi3PgGRcb6EGDzsNFHhKxhvD6E1joVKgzDLRIgz/thbLjJJ/VxaoiwzNBnRFPFjLEQ3pmi+jxLcMWX8ksZZ8efy8RMOdb1Qv3k9ye4KXbzJ7Mt4S32gM0b7EEJVH++sLJ3blm8yvjLfE14/B3mqkW41agn31oQwoA8pAKgxo4SUVlhSjDCgDyoAykJSBtTlcV4xxQiw9/2uvk3YKL2Gqykk3pRupPJyqsg7jbfLKKUtjlhw8ZuNUXLFPRNe4oZdpBDDBuDJq0yNj3PMk6MegDIRxgva5I6nYEzsWKZJEX65OHF40ZUz6pGd92paMC15wKDdN159gg68xYi35LcZYGc+e+v6cfPuMZxRb4PKZj+IR+xY8ogjI+7OwKYUai6SWg/YHUuiJbgpQhSgDKTNgD9el66TpanV9lAFlQBlQBjY0A1p42dC/gPpXBpQBZaCVM9DwddINT44T4CRpMBY4+Dpp2WrEOEpWJe1OPXGllQeyrUZiMlm1sWbTZ7YuCP668bkeMRUPizC+3OrH2eN1FjIL8R1Fcl9821HSZqmMBVMgp7JEg5pGzBhZ2SE9gnJVgVv8Jg16HLumL3LTCV4uxsbmIry2gQe4OHMEDs2EDcdEVIn+AoRHgbkxiUH6klz0BRPng1BUVMMvDhstKnx5K5TMNUxkNMkTbzcRbHHUSv5YXHIMjaSKa9hK846mGlNz45p3Fi1rLVUuGo5Kr5NumB8dVQaUAWVgQzCghZcNwbr6VAaUAWVgE2LAbDUaMRqv7U39OmmZPqfBkgxL+kjJB7YzsPDSv39wnTQeruuk2lx4Yby8jVKSl/WQBGDEJtX2AFTi8HXdxMiWM1ypNcDz4L5rBeXGlS9LsCEVADTAYxgLXcFM5QFzk5KjL0282jpYoBHELQOmpIAy9k1SW6OxGD9W8oqh4rDFMoLfokc9N3rW40k6+NApcyooBxHaca3xuEiEc18rsWeceWK6tpsmYiwEEzK/bwjFEWyb6OrrsEyHHbyaOj0DVwyge4mAW57pSIc82L8bqxeBYZfG3IdCiT4mXoplo3oampUbaHPjXNutrZ0qF+s+L91qtO4cqgVlQBlQBpqbAS28NDejak8ZUAaUgc2MgW/xVqNhpvBC10lTghhNJ5MTYtNJ22J0Gl7TnAYDBtjCCyezYpvx3PNlcf6j1uMjIhTZcm1LL0bDHFSLCTbCaeULxUePKW4ELVOxQDFtTaJUPr2eD+T1rBpdoxBY4LaVYExkGj85bduhrTqoKqXblIxj/gbj5rAUrB7YWbAVywnFi8UbjCeNbvUxBQjCswaZsQ/OCHG1WLDIwEIPzdHaIZSP5i5iCIaP5YP7jb8DxUDXt27HyI70CMNn6qAEY63H3ySNqicGQC+2kpaRAXlt20NlWSnU0iHQdDAyGcLH84Od+voayMzNhbwOHSEzMxtWLV0EdTVYVHTnb5TwZWRsJ+LSt8uQhHc0BhuxhQrGSrSlDDTMgC280FYjvdWoYbZ0VBlQBpSBlmFACy8tw7N6UQaUAWVgk2VgbbcaeQmvk6ZyEYBz2v5YeCkpCa6TRoxX1EBG45NSX0p+4hLaxB+EI0oNy9r5OW2gS04+LCgtgYpaudVJ/LO93PRM6NKmI7TPyjNFjJ/XFCOWr972kX5E7hitdOm2ww6w19BhsHTWDPjshedxUgEbAhR1mbD0nW8d2uneoytsuUV3mDtnAaxYscqQQyaIXZcpqil069oVevbsAbN/mgvLlyEWi2EGZmzy/MR8NAyS+whBNvblaCwq0bJFoAf8l47XRGem8VkWtfW1UIOFElOAwWIMLhOCvE6d4YgLLoaFP0yD//71RVtICQo1EicVcbbYcScoPHwIbNV/V8jMzoAXr70SiufMhQyTwArSRsecyRvlQXBxSNKS2GVWghO5WJa+4ESuX2WgMQZ0q1FjDOm4MqAMKAMtz4AWXlqec/WoDCgDysAmxQBdJ33qyPOgllYFrMXDiaekn2SAU01e8dIPtxrRGS+LUEoYHuMigfSiTm2qmmibdGQtRpw++xD71rLYtHHSaR8Hb707HLndvvDYV6/AtBULwvgoTNrakpmRDcf32RsG9tgJOmTlwqrKMnj8u7fgh5ULsIZBkVAsYlO+7ItHAgQuUtn/rDNh0MnD4e3f/x6+eOWvWFgIYsKvRE0N0zZjLOU5UBtg+rylMHnCKDjqqCPh8cefgdf/+Q5kZ/JWGhoXbmhVDBUhRgw/EY499ih4+OHH4NXX3oW2bXIJhg/b4za9KZaojOQiC2IlkQeNGSdM+Dh6RkZ4ilIsp0EuFrR23WEw9Oy8reF0fvFc+GLmO1BZXcognEdeQQEMvfYGaN+5E7w85SZYNHUqmgls4zi3kUcsZh116UTY7cijYd6076Fo2rfw8dN/hrLly3GRDK4QwlVL7NuNILBj4vNf9NtytFF5hAYcTm7F15WZszR1ragV7W+6DJgVL6OGw6SJ43TFy6b7M+vMlAFloJUxoIWXVvaDabjKgDKgDGxsDPB10qMwUafzR5r+cOotCbjVz8hIh/4JZ7zwOKOlTGB14tJXTn4tRlbNSMoqnqVPyMSYZJTmiFdPY7Kehkn4L/vsD0N3Ggy/++xZ+KJkTpA8Ixb/LasugyO33AUm7TPcrHD5X9F0KClfCW/O+xIWrsFEXhJ/Gxo6Rs/iysixg6tUOm/bG4697GojefGaK2D14iW8+gQjrcdzSGSOFFct/pdJW4PMOTBijLYXpcH0afPh9jvGw5FHDoEnn3wOnn/pVcjNlq0IiCV99FdNK0YQP2HC+TB8+Elw++13wyOPvQAFHdthDMKYCaeBl/iOQkifxug/11YgNx+L4bKSjyQ1oo9Qv9z7LNiv8Fjkqdps58rNag/vffUC/vc8VFStMX4Iu8vRR8GQ8y+Bz158AT584hHcckTbh8gCPUE8WE07Zcqt0H3b3vDi9dfDjDffhbyuBbiihlbT2L8c4pueuKKKGTAv17YvJW/0sB3psczvsUzfykBTGCgtq4RzzxkGkybpddJN4U2xyoAyoAysTwa08LI+2VXbyoAyoAxsBgzQipeRp42BmmrePtP0KUsa62umY+Gg/4B+UBKseImk3gacmKQmSkwxg9DhUNgwNuLSY5GxT3eFBceagweu0taWdlk50CmnHcxZvZS3D5FpLF5k4fkplVgGGLn9gXDabsfAI1+9Cq/89KkJpRbPEKnDYok7n2gCH/pHW6Q08Pjj4PDzL4APn3kePnj0QTzwNcNMh+zk5WZDXl4ulJdXQPctukI2bolZuGgJlK4uNys4TEhYTMnFc0uyc7KgU6dO0L17J5gxYw6sWL4y2D7EHonzrl06Q8eOHcxKo1OGDYXhpwyFO++6Dx5/8q/QMb+N4cy+hMsgYipkmJDdGSEG/9FU8GgVc2At6ZMGFZ9Izj36kh4BqU2Py71IeLi6rgp6ddsZzjrqeqisKoO/vv87ozbyiCvxTJpM+OMrE2HekhkIRnv4r2PPreCEq6+DnLy28PT4i2Hl4qXmHCG2GthGv6fceivkd+sKz152JawsWoj6EjwFGgYWqLnzdC0RVp6ojp1t2EK4FJIYnagj1vSrDDTGgKx4mThhHGSHhdXGtHRcGVAGlAFlYH0yoIWX9cmu2lYGlAFlYDNgoDkKL0STm6qaBBfPEykMCy+LEZG4osZPT6lH//mWEvu+lqBdKcmsJbtdhMouVXiGyHHb7gX9u/TG4ksWVOJ5LS/NeB/mri422XM13u60W9dt4Yjee8EO+T1g5y7bwf8WTYOlZcuxOFMDL838BOauWYL22bObukf90naljIwsGDr5Zui1627wxv2/hW///nfIwGILrWCprqmCQwYfAEOGHAKrVq2C7bbrDW3a5sEPP/wIL774CvwwfTYWVtJx5UclDD3uaNh334GQg4lYDdp9+eXX4NNPv0D7mVj8wKN/0d4pJx0NBx40CIszBbC4aBHk5uXB7rvvCnfcfg88/tTfIL+DbDUSdlzWkDScVU4WFoXM1HAMqwlUIqoNVkNRvakG+crGM1m40ECFF+aXjrutS6uD2to6E58tx4kPyxe1lpcthhGHXA6DdzkBPv7uH/D8B7+FfXc6Ck4cfBHkt+0Mf357Cnwy9R+GJ4qsckUpDLtnCvQZuC+uZrkOfvzkYzw8F+dOgeBDK5joEN5TptwGHbt3gWcnXQkrFs7DEaoW0VzYP2FNP/w7C4pFPJDkLXPgYWspWEVDAh8SJwhsi3aCQjC+Nh83gGib7DWnr7WJT3WawoAWXprClmKVAWVAGWgZBrTw0jI8qxdlQBlQBjZZBnir0bmYQNOtRk19OImUVNLVpq0yhc6tRokFFEwHJR/EogGPi8C11HBbfMdpSnRSHKFiQAUWVn7dZxDs0q0P9O64Jf63FVz93h/hE1xdQTao8LJr5z5wVJ+9oE9+dy68FE2DZbjNiA7VfeHHj2DuqmJc/RF4lgBMmBwFvclXXS0VXrJh1KOPQyaeEfPqHTfBnM//Cxk5uPUF51xeVg6jRo2AKy4fDwvmz4dp06ebYskuuxTCp5/8B268+R5YurQYqqtr4dcnHwsHDNoLdt65L/TqtTXcdtt98MeH/gzt2uZCWTkWZk4YAuMuOR9XLtXCd99PNStf+vbdAf2nwc233A1P/OmlYKsRBcrFBhs6xoJz261TL/jVDvtBAa4CwnupeBYIopUt9N/81UvgrTlfwgl9D4AO2bR6BgsPDg10e9LcVYvg2en/hhI8D4d/XwLIryMe02Duym/g8pOehV167wN/eutWWLTsJxh5+GXQsW0PyG/XCT75/jV4/t93oQfURq4q8CDh42+8HgYc+kv4+Nmn4J3774WcDrR1CksueDBy+dxi2HK/3WH47ahTWwFPTxgPpcXLKHgTJ75MFBIB9fmRwovESNIoyh0L1BBjVgFJ1/3yxF2JtpWBlBnQW41SpkqByoAyoAy0GANaeGkxqtWRMqAMKAObJgN0nfRwvE66DldRNP2xCaptsZVUCi+E5JRWEluyIm2209hb/Ea1RM5JNJdezCoMHKik7UKocPqOv4DTdzkSbnz/Efh0yY/sGcdpbEb5Mriq/9Fw9h4nwnXvPwRP/vw5bJHVBvLNIa1clPBjsxFQi3zVU+ElMwcuevE5WLawGF7GG3ZWLlmMKzN4FQZtLzr7rGHwm4vH4pktz8B9DzyBZ7Zkwi1TroQDDzgAzjn3Ivj8v1+jLBtXklTDzBlfwh//8CCceNKv4P77H4GHH/sLHpibg97S4NZbroKDDt4Prr3mZrjvwZdhtwHbwNNP3AdbbdXDFF6efOpF3GpEhQpCc4TUNjxRYaO+CgoLtoajtt0b8rPb88/Ag9gmjXQoKl0K7837Bo7Zfl9on9EWdWUVk5ktriBKh/lrlsLzMz+CZZWl5rBc8kGP7zENfl41Ha486WnYqdcu8MDfLoODCofCDj0HYsHl7zB415NgDq4yeuS1y7G4gb5RuWpNBQw+bwwMGj4SZnzyETw79kJos1UXY7hn/wGw3b77Qdde20LP/oXw3b9eg3cfehDqqvBvGqnm2ov59TkYJx6Xi3AwaYNmQQ8Rw6t9qCdSaoePFl9CKrTRNAa48IKH606i66T5tq+mWVC0MqAMKAPKQHMzoIWX5mZU7SkDyoAysJkxwNdJj4WqykqcOaaQsVlkHCmcfHKJIXGcVkgMCK6TXriQtxqRaZPLh3ArsW5tK4SZoHxNGRMpa7E9lskIIXmUJGaFBm6JoRUnw3Y8BIYXDoGbP8DCy2IsvATJMn2W4K06F+x0GJy5+1C4/sNH4L3530BuumyxEe/Wti/hJJ+KWVl4a8+Fzz8DK4qWwV+vvwZWLlyAhReKB1eZVFTCqLNHwPBhv4abbr4NPv74v7AUt9RMvORsGH3uKLjm2hvh9Tf+jVtqcCMPBjV96jx48MEbYOjQY+H+3z0Ej+OBubl52bAFXjF9401XwDa9esIILKItWLgUsvA8mBuumwiHHnIQ3HbHvfDUUy9D+/Z56NWw4IaLEpQhH3RTUxYWhdKwgBJQEa7qoMJLDR6AW4PQbNzSQ/Hj/yPEbPWhq5/5rBc0gyunKnG7kaGcXCV5Fq+ZDZee8KgpvHz2w7swcIdD4f2vX4DlqxbDCQdeCHOKpsJjb1yD9tAI0lVbVgUHjjkP9jt5BPzw8Qfw/KXjIbdTPo7VQ+GQX8KgEcOhY7cuUL6mBv6N5+h88zpuU6J54UTk15dwuC+B2R61+JeTMedLygZKL0aSeeODPt6D4yFWvHqASCewF5G2jm6qsW8oXEuy2Dxz1OukW/I3U1/KgDKgDKTGgBZeUuNJUcqAMqAMKANJGAi3GuEWG/NIxp0Ez2I/mfR7jIjeaiRnoiRiOVmhNz+2JRIrT9QWiavFsrgRtERiTNYpHT+572AYWYgrXj54GFe8zOJc2TirhznVa2DiTkPgzIFD4YYPH4W3538NbdOzTUEiMTn3vVOP3FDhJR23Go1+9E+QgYfm/v22m2Dufz83bTJEK15GnTMCfn3SULjssmth6rQfYMZPy+Gay8/AVTAX4aG498Jzz+OZMFQMwf+ZPvUnLLzchIUXXPHywMPw8KN/MVuNtum9FUy5+SoowIN3Txs5BooW41YoLKRcfvmFcNyvjoFb8VajJ3GrUUfcmkNFEqoKhOyY4gltsaqFnm06wR7d+0CbjDzDgnnhZEiFCj/LK1fDN3jd8149+kJeZjbeyESIwBKe6UO4kopV8OmiGVCG59c4hFp7gdHZK7+Cq056Bnbf/mCUpMNPRd/AE69Phn69B8HQwRfCFzP+Bc+/+1uqB5m5VK5YCcfdeD0UHnoUfPyXp+Ff994NuR06GB+ZeC5NZm5b6Hfo4XDIOedC0exp8MJVV0HVarwVyft75lj515JfiXt+gMGcXCGJPFvYl2qTi6O2hxM/UdCG7Kca08aOa0kOW44Lu9VIbzVqyV9YfSkDyoAy0BADWnhpiB0dUwaUAWVAGWiUAdpqNGyYnPFCyQX+Z5LzhlT9xNTvsR7dsEMrXoqLi2HhwkVBqi8Jf6IGeebHtkTS0FcsRbVceVgooSzePLStCgsvuOLlNCy83PA+Fl7MihebM/+MK17G74yFF1rxgiti3pn/JbTBwgsuCzEW6C0+DGdGyi8Zq8cVImm4SubXN98KPfsNgDfuw8N1X30ViwR4yC0m5+Xl5XAOrng54/QRMOW2u+Ctf30Ai5aUwg3XjoWzzzwTxk+4Et7+10d4oC4eIoumf8DCzB//eAcMPZG2Gj0MDz38rFnFUtCpI0y56Urou+P2eH30aJg7byFk4qG79993A+yzz54w5ZZ74ImnsPCS39bUQjhujtK8kZfVteVw6Bb94dzdjoauuR3xTF/co4OFhTr8Lx3nTKWfGSsWwjNT34Hzd/8VdM7rYFajkD49VIPIwJUw00t+hrv+9yIsLF1h6g/0iwdH0DIQ31REKi5dAGccPhkO3v0kWFO2Aossd8GXs9+BIQNPh2MHjYa/fvgAfPjNX828CV++dDkMu/9O2H7PQfDKLTfCD+++B+m4BQuHsABUC7WVFdCmSzcYgWe85LTNhmcmTIJVi3FbF/4PH8BLs+aZUyAk5370S6PJHgebrOgSqiKW4PooA01kwBZeaKuRXBffRCMKVwaUAWVAGWhWBrTw0qx0qjFlQBlQBjY/Bmir0Wl4nXR1TarXSdvkVdiKSijfNFuNCrnwUmS2GjHKvv2s1PZsS+zHfaM+CUOayeQ0bm7gwW00uZl0V08anLDdgTBswGEw5aMn4X9LfySI2SZDtwQtwsLLRf0Ox8LLCVh4eRTeXfAV5FHhJQjPJPShNxuzHwNtaQLYZ9gIGIyFlPef+BN8/OTjWDDgZIrPeDkFJk64GF7669/hD398HPLz28Pll10CO+LBuKPPuwS++moqXiWNV07jIbqLlyyHyVf/Bo4//hjEPgYvvPgqrqhJh2Ula+CGyePhmGMPxxUxT8CTT70A22+/Ldx1x414w1F7PDPmHlzx8jLeakRbjYQjZorngQfUYvGiILcd7FzQE/LwJiaumuAowqjGQIs4VuC1z7NWFkG/gl4GE9ax0CbZIeDyqlKYunwe8lgTUJVYeCGeq2rKof82+8Gph1+BhZdieBYLL2UVa+CUQy+BXt13hodeuQxmL/qeoMZ/u86dYeh110J+1x7w1CUXwYoFC7DS455/wTMbdhvdatQVnrv8ali+AG81wt+bfk9vEQpHS5aN/UZfAiMX9Eife0neCBZ8EoSKlYE4BnSrURwrKlMGlAFlYMMyoIWXDcu/elcGlAFloNUzQNdJn3rqeXh4a7DVqMEZxWeccVJe8dIPV7yUQBGueHGzVV6B4meltmdbDYUStcFakuRTRGIH20GXrkOmK6KH7rA/3tyTDz3ad4I+eKvRd1h0WYUrJn5euQie//EDs13mZywgTOg3BM7AFS834BkvstWIVwORQToglx5q00P+eAuTSEhah1u4tujXH46/4lpYU7wEXrpxMpSX4G07uKKEbjU668xTYNKEi2AlXie9YEER5OXlQs+ePeFvf/sH3HffI7AUb+bps902cPFF5+JNRV1giy26Q7duXaFo0SLkdjksW1YCEy+/Afbec1eYOP5CyO/YEebOmQ8FBfnQtVtnLDqkwe14xstjT7yAW43oJiJ6JFaKmKJlruqwQEHFKdpBRLMzO4mCNuHSzdkv6VBbhzgcdVeymPNxUJdKWulpeAaMqXQIE/y1ntgf6Yz4xQQo7HMALMcbkyiMLvlbwH+mvQWvf/IIlFauCmKrh/5DjoBf/t8l8N27/4K37r8HavGKbYkbG9xGe6dMmQJdem0Ff7nqWpj/xZeQ3a59cKYOowIkfiQ2K09sMU8J8lRUScmv9iSYUYEyEMeAWfEyCg/XnThOV7zEEaQyZUAZUAY2AANaeNkApKtLZUAZUAY2JQbMGS/DRplE2uSinBM3MEU/6/R7Vo1u7insH7fVKFHDd+n3rMX4llhztdxiAskZg1IsDPTp0B2O7rMPdMxuZ24vqsVsPwuLCLSZZv7qpfC32R/jVcilUIq3CB2MZ5kM2mZveHXG+/DDigWQRdtvgses8EDL4l/kVAzA/+VsumaLC7VR75DR58HAY4+DV2+/FaZj8SANb0eSrUYjTx0G7773HhZctoLcnFyYPn0GPPeXl7EQswTPXqmGbc2huSdC504FUFNTh0WyOlzskY7biTJgzZo1cMdvfw+LFi+HX594BBwy+CAszHSBEizu/DTnJ9xe1AneePNt+PQ/X6Ftu21B+HLjp7ZsBjPzwD7L8CuNYKKW10CAmuZBHNUbbFGGFF1l4owLVGS0XV4B3mJ0AmzRZXvju6hkNrz39UuwBs+KMStVEJOLq4COHncZbNlvZ3j55hth3pdfshO0HMYbFDmOvfwa2OmAg+DHzz6FRT/Ngv+9+Bcox6JWOvIdYk2sFFOyR9ih8QBnpuDMuiF1MauFF2FCv01goLSsEs49ZxjeaqRnvDSBNoUqA8qAMrBeGdDCy3qlV40rA8qAMrDpM0BbjUaOPA8TetpqFCScbt4ZS4HNOm3LB9J10gMG9IOSyIoXFy9prO/O7/lW/R7ZirPBPmSUdTjZx1UcKK6srzbnflC7BhP7TEyQKSnPwOJLDq7WMKs3sF+LN/RU4yqQXNpiZB62LD5J5LYFYmfAqX4dFnG2KtwFDj57DMz77mv44LGHjY/yCjxcd9SpcMJxx8L/XTQJVq5cbf5/uJcuLcEDeNMhg66uDqZB2w9q8HrqWuxnZqbh70XFCyzA4Lagdm3woFuUV1VXQR4Wbgrwth+yUVVZg/Org7zcLGOLt1pxdFKE4BmZyCV8MyeRJMxPBryvnTGJmU2xLF9PIZgXRoFXe9fgqqC2eR2N39LyVSZWKkzRmhv8CaB9924w9Orrkbtv4b2H/oBnuuBaHFPU4PKOeWMIafiD9tpzL7z56FTojlut6uvK4Klxk2DprFmQQefBIJky+7Cg4oRlI+WymgwZ3qjjFlKI8GSPi0uGUbkykIQBWfEyccI4yA62JSaBqlgZUAaUAWWghRjQwksLEa1ulAFlQBnYVBkwZ7ycjme8VOEtNPSklDT6SaffYzNkpxDPeKHCCx2ua5PexJTXpu22FVgJP7y2xE+IZZD8u5qMlVH62lFKoo3EiOgV1Wa0QeHL0IEw1mP/bIHfxpj7CsRMo5Q30A6uUGlX0Bnq8LafNcvx4FnUKS/H66SDwsulk66E2TPnmgNqE36DwL+dvTihuGgKOEIGjdVgjD50bbZ56GhcmqkROmyILIAZDFtx32wxsGuh2DJOHYnbR7xx5+oF41K0oKDddmhJdIwBPMsl3ZztUlm2Bipw9QpfdURgKbxQm23TmTdt8Xan7Da8rWpF0SI8eBe3JTmhJfBI6viIVwI7cI7REBzEI2irYCThi3+MsKsNZaApDMgZLxN1q1FTaFOsMqAMKAPrlQEtvKxXetW4MqAMKAObPgMJtxp5GWdD8+ckNLHIwTq04qXQudVI0loyH81X2WXDjslPMpzY822zlKOMmYcZRg3PLflgga0HJHpONmfjRYJxXQZJOx1ga+4HMjcjpZkVKr8ccjAM2n8/+MODj8LiRUuDLTFOwSaIj+MR4xw3yWz4tpXIMAVDutaY1AbEog2XMCIVNmiUYpJCDvfl7VgOcKJPCHl8lEi9uIyQ+XZnY8S4aogOyk3D7VU+FzTqoGmQlsng1/yatHImLJoQNJhTGGLYMG74xfybtwyTDWoHrox1c7qwAEQdAU44ItWvMpAqA3qrUapMKU4ZUAaUgZZjQAsvLce1elIGlAFlYJNkgAovw/Fw3bqUDteNUpC8BJGs8OJZoAQV81Y3vffGpWNy2yDBlYoBqwoi8o0mwzQcOBOkQIJE3F07YSDhuCi4Xxm0soh1P0nHIgBvX7IZOSX1VCPIys6AzKxsc9CuWCM+yINBByqEZaM0gMJICNZywhAp4hNYxI+xLnZ50HnTgBh3rRJE5NSOjlmJixK3pBH/CFrsUV/apIH9mPkyKqJroPiigohjwv6V8l8a6wa26ZPwIENMuDFjvJA9Y18Ms9XEm9d93wmmVaAMNMIAF17wcN1JdJ20e3NXI4o6rAwoA8qAMrDeGNDCy3qjVg0rA8qAMrB5MEBbjU4/4wKoqqpYiwnblDZBGZNlWvFSUkJbjRbjsLtaIkCbhNqktZjgSkKbYCnI9xnHMMYGkhgFVxSHQn1MrM2KiAbc2jSd7bEl316oTuKg4yPcWKhNIERQYm/woQVRdxSCMfxIISCuCGEtGMuBPkrlLujQIs7IxOlGaEpAISJs0G8TfYJiBItjxnGApEFJIqqdpO/H4oMCjvBjfisaFLdGjV4okDbGLMPWDmMMJFCXv9pELEkQyf+sCWoFYP6IpgMMVBkn4yL0TSX2UsUlam54SaqxbyhcSzLUPHOUrUYT9IyXlvzx1JcyoAwoAw0yoIWXBunRQWVAGVAGlIHGGDC3Go04Fw8ixe0ZTX44neW3rxy91SjIjn2Q6VGyEua1pp3wMgl/kNQ4BQHym5jqsITfgmCLubk5eCgtHuZajVt+EBAXt/gmfXnicbxGhjFkDFGOkukG/Xj9wLoJhBBUNCCFKJrtkj1BRWftuE3Qdu3F26c4khRfKB5TvCEMPW587FV8c9T8tlhuJXuzNdah2KwN36q1hwj5/UNXbCWcZyCXA5Ktb7YvMzXTQlXx5OLIlvmTQ6FYF7c2FmoxztgIDYUNhoYWgu5G8ZFZNRbMxo5rLP7mHG85LuxWI73VqDl/QbWlDCgDysC6MKCFl3VhT3WVAWVAGVAGwBRehmPhhc7FaPIjawcSFeO3GiVLXrgkkGglkJhk2km6XXGMEnmRx42wc+cueKBtKZSVlkcyblcjMkTJdZL0PEz2w/GwGhAMoV3858aAHb/oI4UX/JrVKBI4fskaRebph74IKAhqC5bb/LbxuDM00mDIuHdVUmxHfzE/RjLieqR+YqwkZTl9Gc92RFd0gmAJ5j2Mo7fnX+Bm2LVllVmH+4KQUfN7G9f4MtuW6IsRevaCyEM5NhLmLBZbw5fipyfKBkv13XIM2MILbTWyV8C3XATqSRlQBpQBZSDKgBZeooxoXxlQBpQBZaBJDJhbjc6gW43oOum1ebyU1xrALJVvNSoOthrxZhFO7yTJc+Cm2fSkL9GSTR3dsTSsanQq6ISFl3IoK6PCC436/rgnMqvNLZFLzG45RsYsF6YwEbiwUtalMbZpfVBWL4UXRxo4E4n4IbHIAog3FxojrI8RbV9qWYjKxXL0K3ZYzj1/jj7Cj4PHogjB+DG4KH+Eo6Zx3sJmkcJtNOqovp23iySU/X0YI3NjH/xma4wmfevftdba2nY+rS3yTSle3Wq0Kf2aOhdlQBnYVBjQwsum8kvqPJQBZUAZ2EAMfIOH65468jyoxS04a/NIUhrVNSteCvtB8dJiKMIzXhgn6Smnra6Om9S68sbaUUvigfRkTGSdCgqgrIILL7x6wbeeGANb4LdYER1fKr5k1HzNrTfYkopKOCi2fC1TrEGMLyUlkYieK6M2PazNyHg8acuIUQleYjVuzMVxm9GiQzLfp6CkABK1mqjPGvFWeIze8XZoxI+FcK6Ee0n/Th2sjcDqU0t05fchn/T4EYlf/loLjG0Nb5lBa4h1U47RrHgZhYfr6nXSm/LPrHNTBpSBVsaAFl5a2Q+m4SoDyoAysLExsC5bjTjx5LedF6ecaRl0uC4VXuhw3UU47KZ1rMNJrSTGki43LWV1vUc1XY8UX6eCzlh4wa1GuOJFCi++DvdcmzyvqCWSJqIYG32TzShWvIqc+1GpteTjEuUkkcJLMiwhZMxacFsNjwpSopS+fH1tQflSwvpzZW2SEVJKHCwVLPeilsSDRTGC3r4Pq2lbbDMRZ61KDP6v545ba7YlWtEijcj1qww0xkBpWSWce84wvNVIz3hpjCsdVwaUAWWgpRjQwktLMa1+lAFlQBnYRBlYlxUvnG4mJp1EFa14GYArXkqw8FKEhZe4FS/RJNb2basx2qPeXU3jEwEmCcaBgk4FZqtReVlFeM6KLUawZtReov/GEYk6UYkfpVcooCE82ZU3MkV9uXpiUzB2TFoyIkguIfCoYNyyQiLeatqW1bQyajWgTUOhWtiwosBQtOziKMXYt3ZIXXoShfQD05HofFQUKzru15tCMCBWuOv3tPDisqftpjCgK16awpZilQFlQBloGQa08NIyPKsXZUAZUAY2WQb4jJfz8YyXygbmGJd2SqrtJpxOCmvOeMHCS7G/1YgQkmA7aJMYc9+VNhCSM+RGQGKx4N5MQ8KCTp3N4bpUeAlB5Bn/8S04bDRqz3HlNONQ5NmVh5E4eiIjkWDjZI6KaboYdyzOhjtu22JBNOxIfJnHjoumlcS3rGXSCHvUMCYS7VhciI6Ytgh/wNqyLfLJzkRLxhqy7tuN74VTiBlm23EetPwSQ5eKGmFAzniZqFuNGmFKh5UBZUAZaDkGtPDSclyrJ2VAGVAGNkkGkm41oqpFkLXapFPSWKYiMeGUcUzkccVL4YD+UIyFF9lqJGloXOGFLYp+06j244jYwHnErXjhuaGmnRzj0HWD9rzQGOmJTMePwe2JBsmEh8b0E8dFItaoz+y6EnvFtTDPeh7GiBIljKS3G72VxrfEjvUnc2Qrdn2Rry96vrThnliMosgWj6ViNdnsRDfZeJxX+cshHRtFqhaiFrW/uTKgtxptrr+8zlsZUAY2Zga08LIx/zoamzKgDCgDrYABKrwMP3U01NVGD9fF1NFmjzgTP4GUxJSnKD1JOREdU3ghG4xgvG+RLCVKxHL8qOsdkQQ2Jhw7wbIXWtFSgIfrlpvDdXmrkVEIdaIeknu21q2yFBjYikVwhIlvtm71LUL8JrFBh9MYCL0ES9qMFy2G4DsQeHLjLFFixAkvwSUMxAjIqy26GEBYwOOii1hzI0/8HdhOjANHxJbEHg+4VpMVeRwT2PT17ZhYSjZukW5LtFxZ0yy4mtrePBngwgserjuJrpPO3DxJ0FkrA8qAMrCRMaCFl43sB9FwlAFlQBlobQzQVqPTz7gAqqpw+030CYoWRiyn0QYYP8WUniSZ2KetRrjipaTEXidt01ze2sJ90hW9aACUksu4+HAxUshBmYkV7URvEArUTOGFznjBg3XLy2mrUdReshjEP/tNhpJICeVj/B6Ns2fxHx2nvoxZW6HEK7zQuF/yIfv8sB1rLdl2IosQTf9L4/Q0hhNUchzNIX40nB0bSenNcfGbFKI23MKL79XqsCOJy41OrEWxrOHbY5m8RVP68RZklL8N2fOR67dnmXC5aNhnqrFvKFzD0TfvaPPMUbYaTZgwDrKzs5o3RLWmDCgDyoAysFYMaOFlrWhTJWVAGVAGlAFhIPmKF0SY1QqSTNA38eE0U5JNi0lLT4MBeKtRSbF7q5Gvb9EiT5S4ZQWTW4cQalDhhRDoX0IgU4LxZGl4q1FwuK4pvIjP1L5kksyJ6eRarlNB+VrenAhC1077EFFs8MsqZC1GGcOQWpmJCH9Lr+9ZJv24uAnEtl0PyZAWTa3kj6/v95JrRUeicSXaieUFzbhziVrlvvxCkdU78eCIlHWj0UVA2lUGkjJgV7yMwxUvWnhJSpQOKAPKgDLQggxo4aUFyVZXyoAyoAxsigwkPeMlhcnaVFdaNqVd/4UXSqDZn0l1JQSKW8JwZFR0KCjoxLcapVh4ETNCBZmLymTMfh2noRC1AmWxIak9y1FKxReyHnUQKJDY6JgVLyy0q11cJRojsKNojEoZgi0xSN7WkkjsV1i2ksCDFWBLrLqReACnk6ifKHHgSZrsyfqL2uCRqJSMWZ0kplEsv0/i7JPr2BHymooXq6EtZUAY0DNehAn9KgPKgDKw8TCghZeN57fQSJQBZUAZaJUMpHarUfzUbFIrLSfZxGZhIW41MofrLkYDkowy1kHimPQE4/rjFJgQ4sWOclpsEO6ga07AKOvUKSi8eLcaCSDxK2ZoRMy7skSNOGSgiR/TQgNkQ+yZhpGlG6kpj5iVRmSLkHXBV1hyiiSmuELDZJyw2DTLWthDyIuxz3qMMtDgJRIpNcgYy2VUpPIN43esiCyZTjJdlou2oBr7RuOL6vN4VEpWo/ERJlHGmlp4aex30PHmZkC3GjU3o2pPGVAGlIF1Z0ALL+vOoVpQBpQBZWCzZuCbb7+DU0eOgdqa6gZ4kPTVT09F6pQR0EaAwa1Ghc2w1YhsWz9+iDYpDjAE9EO0oaG8E614wcN16Trp5Ntu2IdrRvy7Mj8StydoV8btsE6C3aBOAnV19dC+fVvos20vyMvNhpLlK2H2T/OhvrbOiTENcrIzoW/fPpCTkw0rVq6CH2f9jDUZ2j5Ec0efaDA9MwN26rsdtMnLhTKc48xZs6G6mg5Nlsj92EQq45ZpHrHjMXNJFIUS0mNPVEaigo94CCHBuO37LT9Of4x6NjLbEi+iyyPSS7ThS6wdkYs96af+JR7t32bqesKaO7+maDcdu/ZzbLov1UiVAbPiZRQerqvXSadKmeKUAWVAGVjvDGjhZb1TrA6UAWVAGdi0GUhpq5FZgSGpKX05nbVJrbQEg4lnRjoM6C8rXhYFOlaXWBW02yK5+3ASayWcKrqrN9iKLRpYLNmtx9gNAl92xUs5Dlnvrga3aUzmZEc5mXblcTh3XHQjOIRI4aWyshoOOGAPOO/cc6BLlwKYMfNHuHbynVC6phQysHhF1uoRvMP2PeHKyy+FLl07w6zZc2Dc+OugjooqtFAGZ1hXV4cFlzZw3723QI8eXaCoaClcN3kKLFiwGG+YIv/0Hz20goYe7ouUZfyWGViESFwU/wZWQugIjrqOA7EX/1tZS2wnxl4IsUZtKxxMaESiShgnQSp2GBVnraFYXXdRXLQv2Kg82hdc9JsqLqrXnP1oDNG++IrKo/31hRO78k3mV8Zb4uvHYLcajddbjVqCfvWhDCgDykAKDGjhJQWSFKIMKAPKgDKQnAFvxUt0SYbJByTRxA71nUdGbMItACx2mOuk+0FxuNVIFK2WoGUkwUE0kbfA2Ja1nDhMY52DrUa0GsSvu9BoYjRsxY4lQ8R786USG9kwbWM2Dc+cqYKjjxoM11wzCbp37Qrffz8Vzjl3HCxbvgIycAULgeln2XW3vnDf3bdCt249YNasWXDccWdATW0VFrgIkwa1uEKmbYc28OY/noGuXXtA0cIiGHXe/yH2Z/NbWCqN9yC4xHUZ/qg/B7fn4ly5bbuI1JmL1xepsBdvL14a8I0mRFusud9kui5G28pASzCgK15agmX1oQwoA8pA0xjQwkvT+FK0MqAMKAPKQIQB74wXKbxEMKbrVypCBKfXkmQ76SviCwv5VqOihYsx7+eVFm7y66BDe34BROw6ww00G0PLipcyvFKaz0JJbkxi49UZtkBBPmQsubZN9t0W4wMG0BDZqsJVKwcP3gcuOH+0KQxRUeXKq2+C5bjlKD0diyrkrb4OdtppW7j2uiuhCxaP5s6ZC2MvnAi0WiY9WM1SV1cL7XDL0kN/vAe6de0EixcvhcuvmAw//1yEhRfyLFH7LLGU3+6IoEmzscfVY2xU4lujXhTRmI/Gxl0Prm3xJePumNiUMenrVxnYkAzIGS8TdavRhvwZ1LcyoAwoAx4DWnjx6NCOMqAMKAPKQFMZ8LYaRQsvkpFSttrEwgsVBAYM6G9WvBQtXISJNqe8kghTnGK+qTGzZlwKnSyhZ69SeCnHwgs7Tx6BjNi4WeJ6FYwbvzvuyrlNoxEt5Dw9KwM65neADFy9UllRCatXr+FLiQIDpJGRkQYd2ucDZOLqlupaWIXnvJifiwbpPzrvBTnv2KkjpONvRStgCFNXiz4dl9SUOcmADLuxiwzhKT+s71oRVd+a9OKQotHUr9gUvahtGbcxOhISImciERv6VQY2BAN2q9Glep30hvgB1KcyoAwoAzEMaOElhhQVKQPKgDKgDKTOwLd4uO7wU0dDXQ0dwtrIE1N8sYks6VLqypJ03GrEhZelsBBXvIicUPJEE12T/3rpL9sSvP1aP1YW54FGxUs9nvFSABV4lTSteGGxHbM4tsgjbolCJK7HqBaP8TySxePok0msntTj+SxVWEwh6qhglZWV5Rs2BuvNChcqtiC1kJ2VHWLoZzFFGDRQVVVtDuylFT052WSH4+ZopO3EkKSZOtIaoDD5sS3uW2u2xRGJRnN8xXbUO9mWMWrzr0oSF9mShRfx60ZFkemjDAD+30+VMOocPFx3EhVeMpUSZUAZUAaUgY2AAS28bAQ/goagDCgDykBrZoC2Gp1+xgVQVYnFCHmiOWko9xNFSR9tAuuMY9PcalSyDAsvyQ/XtTbYCae/UakEwN+GR32sTbm58FJezrca2UxcYnatShQiE0zUtrWeOGJZiRuLysiDePPH7IiJQkDYkQKCqa2gnIa4bQtGbMto+mYTeo5hspMwnpqArYgt0bHWbCvZfEVnfX+dGKmJxLmxrW/val8ZSMaAbDWaMGEcZFPxVB9lQBlQBpSBDc6AFl42+E+gASgDyoAy0LoZoK1GZsVLLV4n7eSiJoM3fUcYrp6wc+ZRB2OGMInFlRtUeCkuLrGFF9KP2gxNUdpLNxDRl9vhUNiI9xYOhw1fX7bkdMYVL+W44qW8FItMDZx7EpoxDbKV/GlolKJ1x6MsuVZp3vYcHEcLm7yaJbAVNeoacdpu6YVt02AyZTcy65tbgSWE8M9vxx13YZMtJdpLpuUiQyMxDdJPFUvqqeGtRf67i3GsImWghRmwK17G6VajFuZe3SkDyoAykIwBLbwkY0blyoAyoAwoAykxYM94wa1GNg+N140UXixcWja95sILn/ESrnhpMBvmwcYSYLegEB8kSjFO/F+QznTQNv7rXECFl3Iow8ILX7HMFjh6mYNrlWJq+GkcYfXjPNhRbrn24vDuuOh6OOzwz2Sl3CJNasVZEEvul4tgIYt0/3WaFMZcXGI71MGhuN9TorARWhsSnTvmyqRNGi7GWmhCiypaNK2UOWmCbYUqA2vJgJ7xspbEqZoyoAwoA+uRAS28rEdy1bQyoAwoA5sDA/ZWo4r4TDbMdMOGoSUx6SWJg8HsX241CgsvZtzRJLjpil7jiT1rOzZS+pE4tc7Pbw8VFdX4H87VuAwDCKy4diWmhh2khmIbrnUr8S24EblticJHi1SsEX/uQ6tool59hIumth0lPdR3l9uwCKUWFdW3ffGbiJURi228FbUShGIU18YeKUZtNh6FIpSB9c+AbjVa/xyrB2VAGVAGmsqAFl6aypjilQFlQBlQBjwGvsHDdU8deR7U1uBWI/eRbDbMTsNGiBKIrdhYTOxWI0/TYknMPV8Wwp1GYiHBGUzaZLsZWAyq81bCsAKN8lzsjHgvktu3xhuP0mLdVrw1QsiIjSRYtvCyAAcAAEAASURBVGKGHGkjxQKxw179Hsm4ZJIoZ7ydl0VQy/VvrbDO2ryt9Ya1Xb82NtaRuMSCtRkdEYT9Rm3ZEW0pAxueAbPiZRQerqvXSW/4H0MjUAaUAWUgYEALL/qnoAwoA8qAMrBODPBWo1G4sqEO7WDSajNYthtmqWHD82fhfsKbhtcfF/aPbDXyNN0OFQR8fXfUbXPhhWKxnqU0YCO0LcEbiSm6YIv24phtJmyDvROC+n4cIpUYqG+f6Kgd8VuMcyPmcZH4Vk0MJkbfCvWiSJKJFbeVGJnV5Ja/GsYfJYtilS25vfgoGEfxNPxYnNiMw0uJiDA2Not05b4dd8TipRVnS8YSvzZWHov2RSMqj/bXF07syjeZXxnfEN9kMUXl0b7EGpVH+03FCV6+yezJeEt8/RjsVqPxeqtRS9CvPpQBZUAZSIEBLbykQJJClAFlQBlQBpIzQCteRuCKlzpZ8eJnsSbrNbtNMDfgZNi3ZeFuwotbXsx10v2gxDlcV/TjVq1Q6hGfYlt/cXoyyvp+z8ZmLdPWGXOIbTCptPQMHqQzTMJig9hxv2xN5uCONKXtxpRcD1FSHIqUHfx5soU4mzyb+BHXhotw5Q1zQX59dPK5JB9xfVt/bLdh6/YvgXBsJ06DRiyC2owSefLYWnKEZyOx+Z43rkj92LS3fhjQFS/rh1e1qgwoA8rAujCghZd1YU91lQFlQBlQBoDPeBkD1VVVyIYkr5juUcZHD4o4+ZMxFstbYNIPv1g4iJ7xIqmlTZtDdOA5zof1YFtWTxJrX5N7IR67eC4s0KKetl07Q5fevaEabzdaVbQQVhcvhbQMKr4gKFSw9v11ISRn2/y2OKssI2RM2hYX48IOhq0oytqxrRAcF3Yw6NthXd+Ci7AjrtT6SWxZjcQxsZEcIwjLHVlhvGgRRtrsg7WicmuLUcnevi1CJUqS6Ta/3M4lGgmPcGy8Gky8b8h4JQb9rk8G5IyXibrVaH3SrLaVAWVAGWgSA1p4aRJdClYGlAFlQBmIMmC3GtXyUDSDxTyPRJICuj1SsHCLImm6WfHibzXyCy+SQLIFa5/DsG/rwfcnCNJ0bARwc0yvMUplHmygvL6uFgYceRTs++vhRqNk7k/wn788AwunfgfpWVloJz2Yp6xBsL5ti/2ZuVjXQTAWxQITQDDGnyjCGzSdOIS1Y1tWM5mGW+Dy9fye6LNUetZ+8pZvx8eJnYYw8sv5WFeDRty+aLi+RNuVuW3ST7RDCN+yq7Pe22bFlYQgpRU3Sts2ozJJKhDqs0kzYLcaXarXSW/Sv7ROThlQBloTA1p4aU2/lsaqDCgDysBGyMC3uNVo2IjRWJSoMckpvUxuF+Z9ydNWmo7kg35qm7zwwnir5VLCKaWbWCbiEiVsIdRCgMGECSr1OLFNq6uHLn22gW123xO233s/6D1wd3j/iSfg/Ycfhqy8bDNxtiNGjKozRzdashp6DQaMZwcUWnNkjTWjNghv/diWtcMz5L5oE04KL4k6UYloWZuJLdFxsSJLRK8fieubPSRKEj1TlILzI/Z7iZqpScS2oBuz6v8uwd+miZEsiTa3pWfOJCIH4d81dfTZFBnQwsum+KvqnJQBZaC1M6CFl9b+C2r8yoAyoAxsYAZoq9HpZ5wPVVUVTnIapnuNRsdJp6SeVo/ywwED+kNJSQkULVxkigBUqPDxvnnWtjZsusw48eJr2Z5oRi9VZjtSfKmF2qoa2P6A/eHXN1wPX7/xFrx5391QX4vWAwMSJVluzKd4F9/c516qumIjuTe2Jz7IrtsmfdsXryThtowRzj523Moaa0V14i03ZmXtxmVeVlsk0agswuWFpfERx0tdOw21JQ7BNG5NVlSRBv218V8s2YnOhW250satSxz6bZ0MyFajCRPGQXY2rcTTRxlQBpQBZWBDM6CFlw39C6h/ZUAZUAZaOQO01Wj4qedCXS2veKGzUMIs3sxNkr74hC+adDIdsuKlHxQ7h+uSYbYWr8Ue4v1QESFeK+4HcG2wFu3sSAtWC9TVVEHvvfaCU266Bb5/99/wz9/eDrXVeACMO/lwK0hj5SLr38Yf+LRDpmUtRQbMzCTm6CxFHvlZoiawH9UkCGvLiNiiPrVFTsiGHtEjjKvjyhvSb44x1y/bcyXMLUmi8/L7iREnSpoarRsH6aZmUbSCgiDqcQFGyn48F2tLijVW0tQ4Fd86GOAVL3id9KRxutWodfxkGqUyoAxsBgxo4WUz+JF1isqAMqAMrE8G/DNeMKkz+aAkhdgN8jz+0JsTQonJQaIoACOGbjUqxBUvxcXFsBBXvPh6VkvscElGenHfuMKL+CN81KbEKrYYS++6mkrotdfeMPzmW2H6h5/A63feioftlmH4NIqJLxZdqOnHxPajXsQ6fdmDSKiXiE5cjUN4wSW34I+ID/6Kti/lntVzUVZqfcdpNyZz7USx4q8hTFTH9kXbSqhlpWJVJPJbST+KFbnoWbv8e5sfL7jZKqjPISQRbfX8ltgnaUpaRiHQIofUDAt/ZCEYi+LIgT6bNAO61WiT/nl1csqAMtBKGdDCSyv94TRsZUAZUAY2FgbsrUbVTkiY7QV5nxGaTDI+nXRhjgGTfRYW4lYjU3hZjEOEFBu+liTNnn5CJ9XCi01aE7wZt5ho43k2W/YvhF/feDOUrlgFr905BZbM+hHqcNVLPV19FMTqrj2gcKgvs/BnYIMVn4K2fcawXrzU8sPYKMp6sa1kcQjCt9EYWrRS/frWfS3x1RDG15CeaErffpOPuH9DjIrHSjQ0ym37K5MN+oUFIwjrP77lerK68VhPKquqyKNRdC0FyFAkGM+CdjZBBnSr0Sb4o+qUlAFloNUzoIWXVv8T/j973wGnV1W0/2zfTe+VBAKEkGRDTSgBMRQVUARUkBAQ6SB+Sk0oFjrIB4jg99mwgAh+iH/sBZXeawgkhCSEkED6pm9v/5kzd95z7n3v++5uIGWXuST3nDPlmTlzX346wyk2AYuARcAisG0jMIsO1z3l1HPR1NgUL7a4EgP5RvleuE0nzdtMbphhEsWteBlHhZcqWvHChRcuaLjsklrWiPd1RIwcT1rhJRQNEcLkWWU0wSbrdLtRzyGDMeX0c7Hb5IPwzksvoGrJIix8+WUsmTnT3XAUogmCnyXz/Mjj+3nFuXEs70ccNy7FvGyK2vJt3JKnay+OkU9aJUWG30zRVvCUwiOVF85H9c7nodhIl5DCi/jnJXxP/dMCjfs1EZu3n/EKJ1ntRA31eeGLzG7LzFF9yfyInBl+ZfsbI21hdzJ+WWebRsCteDlrKqbbGS/b9DuYcYuARcAiEEbACi9hNKxvEbAIWAQsAh2OgGw1OpuSz/A66SABzCR7mU7MRiAZ0KnwUURbjcbn22oUJpqaDgcQbXTFrlrP9k0oymewqODBJEquS3t2xx6fORqHnHYmbYtqxUY6BPiFBx/Eiw/+H8p6dGMhVqInwiYdOSNG6PzWFRJOLM9LZuf1RDTb5yRE2xLey6Sujj0G99gH8UPmpX2V5lblVNKPHY+LFCzl9+O4cagXEXLQQ7y4ZNybXHJxKUGI/36yJZTi5bSM5ylxX9JHuXxK0pNjRUvSk+OOyqm8trnwlL812qQPybH6kKQnx7nklJ5sk/rJcVJex+2VU/kt0cZ98FuNLqUzXoq3hEHDtAhYBCwCFoEORsAKLx0MmIlbBCwCFgGLQDwCvOJl2innormJVrxkHkpUfa4aUTk5iD8qEqfKqKCwgG414hUvfMZLrq1GHtP30tDy0TiFzq3N5RauFagEy7bQVqOhY8fhC9+5DiXlZXj617/CwldfxiZandNYXUOFGJWO5pIpRvgZc4+lwjbNyziSSiRXvSjdt+l6ns89702cHo7ScdqjmR+/YwWL0KPc/c33ys+Se9k4QhGfua9SXi+3V8axCGzdCOiKlxnT7XDdrRt5s2YRsAhYBHJHwAovuWNjHIuARcAiYBFoRwTkjJevobGhnjLWMCllZUpMXY4a0RN5qqSzOYzQiogJE3SrUdrhunGw+CgdU73zXPEgX+HFz0mmwu+W5gaM2GdfnHzzLVj02uv4y3/fhJq1a4mV7oUvMrC99FmnU9Wm95h7ef2NRNM9SeLEx2mjdJxc3gpCFpcJChQxs1e8pFnvGE3NZNmPwaRx1TkR5FFcSiPuOWIrrhczYwOLwDaKgJ7xMt0KL9voC5hZi4BFwCKQHQErvGTHxCgWAYuARcAi0IEIuK1GJ5+DVrpOWtLVMBmNp6/JwoTnShobmi0sohUvma1GaWe8sDTbkmQ4tBriaF9seYteXvQ9p5BU/Mh1M8Ii29xYR9dJ861GN+PN/9B10ndE10mzap4nA5ORCewQLT7KqlUEtYsQibV0LP4xvFK4Hz5iw8uFvOw+l4ySXrFUkubxkhyPGXmU2XLlOVuqF/clPvI245HSUVJa6aon66SSVOVuTssW0/CYlvRmc/A/Sp32+rS9y32UMWkLa+vFwrYatfUtjG8RsAhYBLZ+BKzwsvVjbhYtAhYBi0CXikDOwovLMxIJY8qKEJEIk07pF7qtRnrGS77CS3Y42XT4eBueKjIqKTZFzsvEeu7EVEq36WDdlvoGjJ4yBSdccw1m/uNfeOSO26LtSMR3kIobQ0hNq0Ui3UNB4RQ/+Si+cnTs5bIpoSUvF+8xXlwzPlJptatj36ZzAhQSSPkZeICol+1JlkiMIHZ1ZYqwhKa2ZRRTcgPlCyccqUZIy+i7/WepnIxIxzps7aPE65h1k+46EfCFl0vojJeSrjMxm4lFwCJgEejEEbDCSyf+eOa6RcAiYBHYHiLAW42+ctoFaKivS7ijaauSKalM5JVeQnsq0IpCys7HV9J10nRo7TI646U1561Giu9bRVGKoKsNpfoNQEyJlzcSCKTaytUCOkB40K67YhRtMxoxrhKjJk7E0/fdj6d+9XOUVJQ7FEFP6AsxOf2I6ptsD8O1Dkmu10sGNt26yOdGSePEY6QIKql2eMx9pYtc7ncaaigd4qiNkB/vi7T6EPJCnJDu++nofi6C4KUCTmQwnEtoz+t4a/l7EWB+IeNaBNqMgG41usxuNWozViZgEbAIWAS2VgSs8LK1Im12LAIWAYtAF40Ar3iZOu0cOveEr5MOU8/khCkVTWSjIp2tw2IFdJ00H667mg7XlcILr2bQjS+qkwCMTCap6XYYjSWVG2EqtOOxI0yQ7UetdIDw+KOPxsFTp7nC0Adz5+O5396LlQvmo7C4NIOVNVGGob9tPWo6Kat08TWNG6fFR2LVY/BYRyypfZFLvsPCAvPiBSqRbhslRE0ihrxsb9LmEtfgkXgV/57ZUkLJjxhyGTU+9t+RoyY8lZA4Cl1puXwwukVgy0VAVrxMxYwZdrjulouyIVsELAIWgY5FwAovHYuXSVsELAIWAYtAIgKx66SThRfOP10+Sp2UXNSnqnFQJ0qFlwm04oULL0uXLid1SWmzdbKBk5S0YgFbjJcAImRpHNd5FYLR/Cp690SPQUNpy1ELNq1agWq6yaiotCyanyqHSvGps0Sc66zkfSlqulAcMQ07Wz+bko6djJGUONpnIw1RNFU/7rnIp3mm8tmIXlqw+FcixRLPSWrlRlNJL6EFnXhByPOZHv6K1GoooajWWgS2TgRsq9HWibNZsQhYBCwCHYmAFV46Ei2TtQhYBCwCFoGsCPB10qeceh6aGhuFlyy+qEbiYA9NUX1Kq8mqcHTFC18nzSteRC654oXBVU8NZVME0Vv0WqLLnBiyE41wteF5Ub+FCi5NDY1uxUtBcSEKC4uIrqfqqo1IybvkvPRpfLaPgajrik9CVdSkjOd6e77npbP1syleOt4LywrenpdRP/Mjxr3Skep6NP9rCGncV5043Vv1vbiEjNK10ySZptJasPNjj+Z+5sQQXppELnSjWwS2bARc4eWsqZhuW422bKAN3SJgEbAIdCACVnjpQLBM1CJgEbAIWASyIxArvGg26sSiVJhzUncwbbauSIQpsxOWZNYdrlsJX3hpIYBYeSQC1KQ3Gz9OCe1Iwuw3koQ91go3rdCIVFvdliPhucU3UVcLQs7pzPzZVppf6kN2OYPh9FEpN5aQRKwQN5QSW2kWWTGUFKAQJ12CqR5VnchGErw0G4qQtKUavg39zmUhlPGa3BONXHoiG2qrP6FGyJd5h6jKDVe3aBFNf5HejuKrb6otEl3z3ZE5a9w5Lsm+xiqkJ+PYlq0kn/XVluIzres+Vnjput/WZmYRsAh03ghY4aXzfjvz3CJgEbAIbBcR8FuNuDBCSU+Y96R6KEkQiyUfTY+Y7q6T5q1Gq1bRVqOVRJHCSzxZSyLoWNE10dKx8LWs4qlM8SNO1PxqhzR/44UTTcIdOokXUKFJ9dUjbj21MLLnhIlDtiPzvgTkFFhNHlfU0UF662YbTsOJsY0MfLqio2YccPJMcnjRW0b8DehxosylThZ2RI/pZTlFuirHgDzy0RGKvEM6a8T1BDcN3cuJVtsRUDnRZEsZdOoU0Iot/gSJhVssFntaSUhkGI//ht4lx6qapCfHW0pOcbXNZVf5W6NN+pAcqw9JenKclFO+tsrXNklPjlUu2bZXLqn3UY7jPvitRpfSrUbFH6Uhw7IIWAQsAhaBzYyAFV42M3CmZhGwCFgELAISAV7xMu2U89DcFG016kBgwpSU1Xz6QBx3xss4t+JlabDVSOBVkzWSj/KU7lGVwm2cml14CWW5HxZSwsUvwhM/1BvnQTAIuhEsS6iO9zdWdHGSnhcp5m1cOShRoBFf1AOvrhTm818ue7ieKPj4UBVBZT0/wvGMWHlBuAEzEv+wTRzROZpiN7QSaoh8yPX9UE6/jMRF31oAEkl+p+H54pGX81asZxHYGhHQFS8zptvhulsj3mbDImARsAi0JwJWeGlPlEzGImARsAhYBHJGIHOddEPyOumcKjFGWvrqklpK+PlwXb5OWgovuuKF1VUrnjALcD6e103T9Cm3IGXeBMm7peRhfBq4Age1EV3ZsdUvysz4qxjcqp8KIaUPL+H5KtEWz/mQLLw4onrnEbJ7kT1qRJr8yVSYvLTYiMYBNmvzMPTaa3EvP9fLpss5U16IemIptz0WDrXySyp0oszkyB5FMZRCX5tJNBRKWHhRGUUOW+YpVkhP9j9quST+9jBu7xw/al+3ld1882ivT/nl7DrpfDE2nkXAImAR2DYRsMLLtom7WbUIWAQsAl0mAm6r0cnn0C0/dJ30Zjy50s+CogJUjqfCy2reauQP1xUToRYnIeGjvCRdkxXhJ7mMwJww8VbUMLnOrHwJzq1RLJbjYkWIISslFF0R42PxTFFURufB43w8zxcpKQa4PhWvHIq+hEhOtqKltcWhFmQOBg7MkLzXj8+HrbmHMcVg1HFGhEfv+EjJrJDOUYl8bcZchNE+JNXKJR3nh99ONUSC30pRLzO/BiVQSKT4EuJkmLFOGl5MIBq0Vy5Nd0vRtkefttRcPyrc9sbsw8vZVqOP6psZjkXAImAR+OgiYIWXjy6WhmQRsAhYBD6WEdgyhRdK9mmrUSWveGmz8MJh52RFH02OQ5ryuM3e0BNyGStEcH3/ypiS9Ci0IVr8dsm3q9YIMp8P4lwkpiTkkrA7DFeoiVJ6p0xUalnCozvJyM2w7z1X2cAsGYuojMugPKZbmdgH92Y20XgUSbiWX7zYxdeWFD3DblfHmW1TkqXah++lBLl9+KqVJs280L4UmXJJZk/FS8ZReEZqN1vLKBaBLRkBX3i5hM54KdmSpgzbImARsAhYBNoZASu8tDNQJmYRsAhYBCwC6RHYMluNyBblrX6r0XIiBKktVxdieW04COTSXSZVlkl/hBPiqRxxAjV32GpIyDjUSsfTFKJPSXeU8FXT9GxsrENtU4P4TL5r2YXrIK6vuDR2BQ816Vr1JaNFVFWICcogZHlVAqZBSyMq+vbHbgcdgh0q96RVSo147rcPYO2SxaIbrdaR8gOTFEyBRCzfWzVyyShSW3JeXyW1lCFx8Py2emqR5RSL+yGdx20/+TU8tvTU37Zxu44Ezzx/lLrOXLffmdhWo+3325hnFgGLwMc3AlZ4+fh+e5u5RcAiYBH4SCLAK16mTjsHLc2JrUaZ4kj+REyS1OyErYCuk55QOQ6rV/MZL4nCSyyBDqeR35ZK5iu8cOIoPqk0t4wbUWNLSiIZZ1ZsN9OWq5G9BuHE3T6JfmU9neoz77+JR5bMRDOvNnFioYUAm+ywdeEKXmQh0XiJEMkJKcEZogGPnYkCbHp3OT599RWY+PnjUbNuHareX4wnfnU31ixaFOHrKhuJQT4PIoWsRs1nMSJCONs0GbUpOHG0OC9NO0lTjSR988a50eJ+yihdOp2a2x9FZj3ts3RHcXJbME5Xi4CseJmKGTPscN2u9m1tPhYBi0DnjYAVXjrvtzPPLQIWAYvAdhEBf510c9wfV3jJnx6GiWRCGQVFhZhAZ7ysXr06u/CSKeqEWvltqaRI5bKc5IarTCIEVnXLUpjHhYpCYbAq8VpamzG8+wB8duf9sUufIdh70G7456IX8f1Zf0ZrUzMVXlhe7XNLKA5TV0joPLRtIX4kT8UUpTqjTs/1olfkhBuppOjSkS5oqq7GCbd+H6MPmIzff/cKLHz+JZoLFcwKZGWO+KV6AhmOIi/iPoTmqa8uqWyCHdNNk4nbi0swL05JosfHiWg5Zsf043j5RvJLIQk2INW1VPFwfqkCKcRcPms8NgczxYyRukgEqmvqcfaZJ1Hhxa6T7iKf1KZhEbAIdIEIWOGlC3xEm4JFwCJgEdiWEeDrpE859Tw0NX7466T9PKigEV0nzSteltGKF0lsoxQzKrxo4un18qeg+bkeRXtpCS/9DydaqXZSQIWMsuISV0ioa2pK3ABUiNrWBhwyZCym73sinl82G7dT4aXFFV7UCz7gthWlhcUoKSomkwVobG5EYwsXZ1iG/rp5UixoXFbItlpRTyuLmt0JLVHBhzN9dTQr4VcGidBqm/KK7jj26uswvHICfnDM0Wii7U8FxcVuDmJLfdMIJFvGSytneDmR8C4xJw3Ve+Z14z0uavn1P4rTtl6Iku1re/TZX5mHvEPEtvpt4afFYnMx1c9Qf3PwQ33rd/4I6HXS0y+7GKWldsZL5/+iNgOLgEWgK0TACi9d4SvaHCwCFgGLwDaMQHrhJS0lTHcyV6JaSFuNKqOtRsvoViMqU0SJOONwUi5PXF/tKjcSipp0alwmbaTpN7eNtKJlRLd+2H/YWFrZ0g/FtIJl0YZVeHbpHCyvXY9i8pufTc0NOGjw7rhyvy9T4eWtaMVLU1RUIRwqsIzrOwKTBo/G4B793C1DVdXr8fKKeZi9bimKaIaNNGe2dSDZGkYyhUR7d8MKPP3BHKyu20C2tPhCBjUQUfFFalNEjCbd2tyMsh69cPx3rsOwseNw55eOQ1NDDRW4eLWLzpC6LK9Y1I0/npFd0hBJL+E10+KeLRdSFF1o/FaMUKoNZ50DgiQFnLxT8+5memozQ2hHJ+5ftsLmYDJKW7hqaXPxVd/aDxOBjv7CPoyt3LpWeMkdG+NYBCwCFoFtFQErvGyryJtdi4BFwCLQRSLgtxrxzTjtSQ8lOcktyfwWFNJWI75Omrca5Vrxkh3C9LQznRpqi08hhf2TpD1aa0MEWteC4VQAObfys9h7yK5YQkUQ1hzeazCeWvw6fjL7H9hQvwnFVDbZ0FyLybTi5Vv7neQKL7e9/icUNPMZLwV01kszKkrKcO2Bp6BywCi8u34Z6psaqbjSH08vnoWbZv0RPQtLMbLnIJwz4WjsMXAUFpOtIirqDO0xEI8vfg0/euNvqGuqJ1u8/EbOZAn9d99CJuFWuzTU1mIYrXQ5Zvq30GfwENx1wudplRKteIkOAJZv11ak9KtpYSTLootZZttNEMO4JI/iq1mEr/gyEm+8XHIsUv7NfEVIehiFwgmn9VXPo0lZJxx3pB/i+aiGHoZoSXp8HGKFWuKhcL0NlojrZ4/jKH6U1POcbdfL5VOSnhyrx0l6cpxLTumdr/W3GtlWo8739cxji4BFoKtGwAovXfXL2rwsAhYBi8BWisAbtNVo6rRzsw/XdSkwJznpT+5kUuTdipcJVHhZlVJ4yaTXSew0e351jJdOk/Nc9c1LUfLPW4yowPHVcYdj6tjD8dcFT+NPC190236mjTkMB+wwAfe8/lfcu+Ap9CoqwSYqikweOhZXTZIVL7dT4YUqLlToKMCaxlp8aaf98c29j8WrK+fjp28+QluXmqnQMhgbG6oxa+1iFNK5K+dUHoUvjjkEf3z7Kfxp0QsootU1p449DPsOHY8fvfIQHn7vFfQgW+xnVB6KJqGlD+LQzUVl3XujmLYcTDj6czjwpGlY9c47eOCyC9HcSCtwohU6YcnCRyLsaRx9VEKu9jV2OuY2l0aarPcjRJC+zDOdrhLxOMRl1Q+2G2KF/bhGbt+TcslxODe1m5RpzzjECeWTPm+uDY1FiG39zh0Bf7guXyfN2xjtsQhYBCwCFoFtHQErvGzrL2D2LQIWAYtAJ49A7uukw5QxOy0MuRqCMJnkesB4KrxUuTNeZKuRT4NVO9RglKSdsFigOqE17WsreJq8Z9BIlWl9yrrj8kknUeFjDO6f8y+sqluPFjo7ZVTvwfjc6EPwyDvP4pbX/gAuhfBWo08MGUfyJ+D55W/hjtf9GS/VzfX45LDxmLHvCVhavQYPznsKy6tXY3F1FZ3zQofdUpFnYEUvXL7fydhz4M64d84jWFO/kWy1YnTvoThq9MH43Zx/0wqbf9NWJ56Xzk3jwd7ymo8CrH/vAxz97W9jryOPRrfefdBQX4d/3Hkr3n7i8UhNdTkGmRnzgJ6Qp9ykjNpM0xAav5NanpO0wpw0u6IR5wjNY0svTUYkc/uRS0cQ+Z1LQpG59XKhtGDE5eQ35mkqE9fzq31UUuV47GX5S/uRyrbVqobHVIpqMkdpXkq50jI/lFNuLnnmKyb388kxn580G7n00nwRlPi7vXJxrS07aq9P+eXsOukt+5UM3SJgEbAIbE4ErPCyOVEzHYuARcAiYBHIRMBtNTr5HNrOkrhOOiOR3smVSmlKVlDE10nLipf810lrEsJt9uMTUpVTmXR54ap3ftRKa1uGd++Payefhh17DsHCDUvduSyS8hKXVrO8sHwOfkOrU1po9cpGKq4cNGR3WfGyfC5up8ILn7NSyNuC6IqhJqosfYO2LH1mp4ng1T3LNlXhraol+PPC5zB33TKMpu1LV08+lbY2DaRzXZaRLfaF0nXWbW7F4x+8joffec4VfuQwXk1nNYLie8OmaoyeMgU77rk3RuyxF4aNHotZj/wV/7zj+4QVHOTrktu4blpCzfPNfpKxVV+8ZJqW50rPW/c9LSmIfliuEEouXI8Qt5Itn13ciGtoaYA1c6F6/yU+IhdKh3aF3jaeoGbLKVYu/KT/7R+HiEkttcr00KdcOiwTyilemnyIrXLahvIhXj6dUE5xtnXbXp8+vJxtNdrW39rsWwQsAhaB7AhY4SU7JkaxCFgELAIWgQ5EYHMLL6EJTa3ClIO3wLjCS9p10qkJMGtnP1J4CZHD9DldRyRCHnlIBY8h3fri6gNORTmdz/LfL/3OrULhoodbdEKmNzbVYkNDHcm20oqXehxIK2O+RStkXlm+AN+b+bDbjlXkzmShQg0VZxhn336jMIqunT5g+HjsNWhX/GXBs7j+lYexcw+ydeCpVKgpxO0vP4Squk1uWxAfp0vw2EjblTY2si1Kb8kH7mgcHTEKBRcCGuvqULNwOfY47QQc+V+XoLiiDP879UQ0N9QTZqHTk9l6hEg9qxG5MDZZIo6QjRRSpDyR1AwllJe05GeZjqF6aVjKk1YlkhbiUjyKS/Avig961sf3GDEcheutVJpbtRzS2tv3+KKhNrVtL066XD7PkpYVIZdOR+RzybKNjuKrXx/f1hdeeKuR3Wr08f0l2MwtAhaB7SkCVnjZnr6G+WIRsAhYBDphBHJvNWr/ZFJTK8rFuPBSVVWF/Cte2E564haneiu+l6Yb53rsVpQXldK5LMfRLUPjcOPzv6HzWRa6a6RLCorRq6wCDbRNaB3dFMQ6dS31GN93JK6YdCJdAd2I6176P1TVrqMVKnzUS7PbtsRyXEBhiyPp0N67Dv06HbS7FNc9/wCqSf/SfU7A3oN2wbXP3YtZqxc7V/gWpd5l3VBLmBsbankCqQ9jStFJ2K20Iqeibx8cd9U1GDp2LO6iW40a62pd4cUlt6JAShQ1ruzkeHxMfS8pmq2dTeGyiZoM9bMl3bSdiPBEK7f13Km6t5PPipcKe6E9Lf4wLfRG+qKlMux9qKuYaR4oL1+rWGpL23w67efl80otJ9Fy6aTJ55JlzM2V/2gjkJxdZxzbVqPO+NXMZ4uARaCrR8AKL139C9v8LAIWAYvAFo4AXyc97ZTz0Ey38mzuk5aOyYqXcXSrUVuFl7SETTyJczRB8ylxerLHukmPGIlXqbTi0yP3wpl7HoOVm1bhebpCegMVPwbRSpjBtELlebrm+Z/vv04FmhIqsDShpLgUV+z7JRw4vBKPvz8Tq+i66BeXzcVTqxbgxB0noXLwLoSzhgooNdip9yB8bufJeGzxq7j11T+gjgorx9A2pDPoVqP3N67EC3QlNa9wGdytDwZ174vH3puJJ2kLUwXdSpT0Vmbv3857KvYUlZbh+GtuwogJe+AHxx/tCi9F7r+IkwQXW6KVM14zvZdeSvCycX/iI5ESBObEv5Fw0zQ8erpOyG9LP/37hgjZ/dBP/gXFx+wTrxyK0xklbY5t+5dtXylsV/W1H/qicpvXKnKo3RZ6mg7rp+nlks0lz/RcOmn4LG+PW/Fy1lTMmH6xrXixn4NFwCJgEdhOImCFl+3kQ5gbFgGLgEWgs0bAXyfd7KegKyZcIu/JyV6ulIrl/Bkvq2jFywqisLQmW6Gm0oSbzmFE/0jpxet5jvYEhSW0R/+DSRtMeFyAo6kg8sXdDkJ5YQkaW5vcSpjVdRtwHx12y8WQ8sJikmpFNRVP9hu4K04ccyh27TfEndPy27mP49fznsBxIydi2vgj0I2KNA1UFOlRUo536WyXu9/4O95ctwSldKsR2zpm5wNw/K4HopQwm+hMljKSX1mzDnfP+jteWf0u2SqiiWfPJU6hGdNE6qrW44Tbf4AxBx2Mh6+5AisXvYuatWvd7UY6c2l9FBUnSQnp2lcML+t7yuNWCzfMTeoyP12LOV4+lAkxQrpoJN9pEoygdEXjsfZ9z9WnWJpYmRVFJMo3Xunjex5VeVuiDe21H5+1+BfNrc69Le2kpXx6SVnGDuWTdtuST/qWJp+U+XiOq2vqcfaZJ2HGDLtO+uP5C7BZWwQsAttjBKzwsj1+FfPJImARsAh0ogjwipdTTj0PTY3RihctuugcgoRUSdqGaZgkZT6Z4hUvlZXj6FYjvk6abzVi6WilhGa/DijQoXGI6TksKPpOJSGntFjLysm5EIkPuW2i815Gde+HAWW90K2sHBvqa7Cidi2q6mnbkPPN22om2V5UVOlX0g3FRUVYXbcRG+iq6SIqrAwt70mrZfqggviM8QHdarSSznIpoXNX2N0WerGtndhWRR90LynF+vpqLK9ZS+fL0PkuxHfJP8WYLerjXHcDvwKD+TV0NfdBZ52N/b80FaUVpdi4qgp///4tWD5vnqpSKzqKpzHUsS9B+B7zVI6BRNZrMC18tPAS0sJ+tmbSQigtfT9nHitC6FVIj0Qy7FA77LMOP4HHEXTSIy7EhE8aSsjvaF/hdWahvvJCWtI/5Xl9kRBd/bdLpXK1oSWPlC4dyqpEPp2OyjNmmo7a+vi2uuJl+mUXo5SukbfHImARsAhYBLZ9BKzwsu2/gXlgEbAIWAQ6dQTSCy+cEEVJVp7CC0/cp2Lck0TKvTOFl6qo8MIHmnKBgeQyovHEK8NnYHriXKHx29v0tKweK4eFF55HRlEcaOUDW4js0nJtA3yG4McVY7gt4Dnw8bj60Hy4kkMklhX/lRe1zCCRFirASJcQqFNAmb4k++JLJOaU4n0ZtdIJwOxvERV/xkw5HP1G7EjjZrz5yD+wYcWKCJux4g9r8+M5ShF6+FaOl/W9uJxKhtSwH33nDIlx0nSYFtoI+6ysOiqX5LNM+Kh8KOe+bihE35Pk9ERl5rhim+rGRD+CAfvifQg9U/Ck5TQZlY23vjAXp/OIUdOQkvTkWJGSXjFd5uJx02RUjttc2MzjJ5e+cD+ubyu8fFy/vM3bImAR2J4jYIWX7fnrmG8WAYuARaATRCB7q5FP1jTNSkuQvBRPUkc+keIrliv5Omm34mV5JMNFCy69qDzrSlLqbcXRQkmWzvfE0rxowLZ82ivazparelDPuxxBiydKFl8VWXiyxYOrLURnkgNxnQgjf6NoOtPQvySKu/HIEYXDV0jXbaxG4/vrQEtw0GOXwSgsKo4imtQO/FBWG4U07xvrqlKAQ93Q3zjHj2KarsihsfIy8V5MI2KxN+GTJsP8fHJxb9MRmJrECO22p6/IHod7SaqOQ0SvIdQ0mVA+3s9XfIlLdmyU9Iq10zxTOeXprJWuVpWv4yRf6dbarUb2G7AIWAQsAttfBKzwsv19E/PIImARsAh0qgi8QVuNpk47112VHHdcEiV+x1NXkYqnUTryyZS/TlrOeOE1EJrcphVe4rb9SJE9pZ09dSUGwMTID9dETGoyNZQI3qtHMi4x1z4LqYS2IS8CcQ3TVSakc1952gpfpD2NeyzLdPeWjqNKVyQcIflKsgScpBQkQ8jgcSdUEwmmxGWdQualGvKlM+SkioplBLIIGU77O6GREE/o2VyVCTntt+a/W6iTjcnoTA2tqFSoGfKZniYTysf7Ip3EiMtsziiJmM8rlk3y26OflNkcP7uejhRe6HDdGXyddHHXm6DNyCJgEbAIdMIIWOGlE340c9kiYBGwCGxPEci+TtonUNyT1Cg7QfJSPBsd+RINFzLGR9dJ8xkvKiOSXl7o6VZUyhcJ2Fa+h3ECLRqGu428JvsZL/94nu8JmsdzwcgMxedwRY23zkLCZzTteYtK8bakJ3oh1+sESCzgJhZKCoKflxMi21EhxEFnnBdh9xY59jJRMslEUr6qygWqqV0v56zpULf2xFxgpj4xhhJztKqnOjpmcaWpKvP4b5KeHHs55oSIgpSGIZzcb6+TjRdqebmkV6FUWj/5zdJkNo+W9DiXZ+q7yofRC4qFzgnFUNk0zxQvjRfS2isX6mzpfnt9yi9n10lv6e9k+BYBi4BFoOMRsMJLx2NmGhYBi4BFwCIQRMBtNTr5bHdeSCY5jeVHnCTkflRUdEWW34VFBRg/XrYaLV2qW40Uy2slU9w0CbHOHE3kfCot1NA/T3FY9NLiS1ERXRlMgxY6l8XxHKLX9ZpKC/0kWihAlSU5+4WxFE16Yo/owbYeQQrxVEdtJSPBdCm7OLOKpZNxahGGE/D6UgySaEk5heyy6YzJ0A8GEoAM22EnX+lcXxjyUQg1vSXqiZmAHWKqpAoleeE4gHBd1lW+4kQkOUgnqSBjjqWqOQoP5LtquL2iCgb4npmnx3r+95ZH0LFyoeey3vHCSy6kpGcsJ74LJ5dnzFVMkcyWT+OHssl+aDfJ21bj9vr04eX8ViO71WhbfW2zaxGwCFgEkhGwwksyIja2CFgELAIWgQ5FQAov51DhpUn0Yok9kbIz0Bh+rnQsPONFrpPWq29ZPVsrLW3PlvKmk6lcKJvkqVafPr1RV1eH2lq6UcgJiSQnr2H5ROSZJ6iZ4gLHIhMfz1d8BhVET9Fe6J/gxiXjI9XSljxwtnkcRxIJ9SVuX/xWZNYL+6IZvpUb0jLWyD79n44Agz3JcKNwEgKRlMp43gelCrp4yly1qnwdqxe56Ek+j8WicvwwomsRRk2EhRc2k/Vbl1+FeJBRiuDVLx4qL2KlNG1LpCjlIKllj6mUHAoxsmq1paNyqqzyTNc+83Ss8spTuuqH42RfZazlCPjCC281sluN7FdhEbAIWAS2hwhY4WV7+Armg0XAImAR6MQR4K1Gp572NTQ21MssXN4UJE+aT+WYo0pKMhYIUxI7YQJfJ12FLVF4YXcCa5F37E2cmvGPtrr07zsANbXVqK2pTYh5Hd/zMwoLDJGhnE289OHFMn54UqYX2kw4RjKiGb4zilkdbz0j727wYcF8HggQ+9EeqbicL52o777YonZVQ2eqOlzyUp744N6uIKKyIUa8zxJ8ZbdKsu/aZ8msJyy8OGGZrXvTy9ddCMXxsxACgugKIa9VJxJKcD/UDkA/ZLct1NALNpVPviOyjNXWrJSvuGpbx4xhD0fAthrZ78AiYBGwCGx/EbDCy/b3Tcwji4BFwCLQqSLA10lPO+U8NDc1xv3mvEhvo4lzYiNNn2JEVqVbjSZUjqNbjbjwkn+rkWyXSCZmmhaqBZ+g+V7SanIcL5n069uPkpoaKr7UBUk268QR46M4RtJCfOwLH+p9iC0zcYGNqak9aePR4O1MBXSBkl5pzYpZdQmHpihOwlGoKiH1A75Myj3ZtpWTbBlNI+95oY10qp+j8lUnsk1NgZ73QnF38o6m8ml2maceRTiROF+zzSB8Pbd0VFYE+OuJB+pHRG+mlq8Hp9+pj6fItDaLQxz32BM3zUYjZxWbBcJH6eK9cpialFQet/qLC39NIT93Px+q98Xrd0Q+n6xHTO+p7TQM5aVrCjWp1x6dfHjbN8+teDmLDtedfrGteNm+P5V5ZxGwCHyMImCFl4/Rx7apWgQsAhaBLRGB7OukQyuc4CSTnpAvfZHgt0+ICuiMlwlZZ7yEuiTLfzLw2hEMHYl9j8sI8VGIGfaj9DVwyxVe6mppKX9t3sKL2hAfwjUZbNl75q1FHgUsoRAh5iwXGlgoJGp6HSjH+GSRiy+kwm1La4s7o6a4qIhgQhz1hnDYrCigvrkJJYWFVGAIqwiqF9pUfWnjEjqKy+go5Aqi4jKH5kxn6pDjkV9Eot9GfIosJ1vRuKfa1I0eH6Ps+HFcSMy9CJYKKYzAX417mYdlIgKL9tixG4rLirBhwUbnG2/lcv+UF6Lv6J6o/qAWtavqwb/jNI8yuLFOtudsNOaHdyOj6bWk58dp2hm1lE6omWQnvVB+Lp1QPpeMYrSnVTzG4r622m8PxsdDprqmHmefeRLdamRnvHw8vrjN0iJgEegMEbDCS2f4SuajRcAiYBHYjiPAK15OOfU8NDUmVrxk0l9NmPJPIpmacQJcSSteZKtRrhUvmpSG2mJPKNn09nkjvkalFzdgS3379aUVL8nCSzpi6IWgRaszMnFJxoM0InfjiOEconQzKJgIlzW0uKLWhMYjV1OgIW+ROvjgidh5l1H40x//ibr6BimwiIrLZ12RhhVIftz43an4tTuee+5lvLtoMYq4WOMexuYn9I0jFC9rKNdzWEe+mfKEwm95MnSuqLWQHZprSZ8SDBjXF92HVoDrP+89shR1a6mo4WpBoS8e25VOmEUYUkQibxmTaTw/t4WKPW6hg5wLMfyQoWhpasHSp+i3pmw3P8WP/KP6Tlm/Eux35T6oq6rFyze9QSdBR8U10qsYWo79p++DNe+sw+y756K5jjYzkV3nmbMtOOHbfU626eyFHO7LnLKpnkKq0SM9Hcc9j49UI96qZpwqozT99srnk0uzlYuWI4Duo+bSCensR9o8QpnO39cVL9MvuxilpXbGS+f/ojYDi4BFoCtEwAovXeEr2hwsAhYBi8A2jEDWGS+hL+3Mc9LSMk6WKzNnvGjhJQSXBErLGZ7jE6s0XJXzUkrJbsNEmOX79uOtRlp4yY0Q52QHIcR1VtXRSNGl2kqLCaiPJBjJqhgPXT2BWk7kI7YquJR+7pzF+Nnd1+Oww6bgK6ddgFWrqlCYEY6UCKSlhVePtOKM00/BCV/8PO76nx/jod//Ez16VDg6vehxFqUbe0uhQOYYeiGeMld9VjUnRS/2nx9BJgTazlPWrxSVZ+6OHSYPQ0tjM5rqmvHUd17ApoUbUVBChSDW4YKK0yVNBxYBycBhipx0vSEpFRVXFOOQGw9EU0MTnp7+gpz7ojgOX/UKUF/ViMqv7YY9zhiLt/5vPmb/dD75wXxSoAmU9i7GvhfvhUF7DcCz176AlS+vkcKPw4twtHFucsFM/FByvPWKvicSPNaZCkVGSvPyvuc1QppaVE0dh22aPPNz6STlWY5pueQZq71PEru9eh8POT3jZbptNfp4fHCbpUXAItApImCFl07xmcxJi4BFwCKw/UYg91YjTbTa9j0tFSuiFS/jK5PXScexNMWPU3kkiVkarqZs7fHOF0gkYeyXo/AiXLWa7Q1T1J74lPBMh845mVU8P1UBxSZB+sPU1pZmNNP2Idbia675L197LVuJWN6BEr0Z8+YuxbXXfg377L0Prrvhdqxbtz7YQiQFl+bmZjQ0yuEuM6afj6knnYjbbvs+fv7L36Nv3x7qQAZX8LlQI48rIiTddSx2mOS40JN8iMRUKbxEyrRSpWF5A0ZN2wkHXLoXamh1yfw/vIu6NQ1Y8dJKNG6kW7RopYk7h4YKRa3NhEC/mQI6d8W1FINMPYaAW7mYREtknJ1mlqeCB8vQVqCibkX41I+moLGuCf8570nyI/KBW16tUsgrfSg+Ta0oLinG4T89GBX9K/CPaY+jvprO+yG7zHcPzW/Ep4biwEv3xTuPvIdXb3+D9IJpsyg/hN3U1OSKLkVFxc6OMNLekZLzJ/p9pIiJB/IWDTWmwnGJkMscHutvPqmvfEXyrWCKtlLTpJWm8iorVnXUvjbUEU/bp/fxkLJbjT4e39lmaRGwCHSuCFjhpXN9L/PWImARsAhsdxF4g7YaTZ12LlroLJD4owlWmBhp8pUmKTyVlq1GXHhZRYfrriQFn9yrtqSgakep3ApKLk5IV3uhtvZDOab1i201Es2kflJH+XF6fKT22G+VzyTyKpphZDru7JN+/Xuhf/9+WL1qLfbYaxwqysox6423sGzZSikgkHgzFWOGDxmE7r0qUFpShuLiYsye8zatIOHDYcVSMxVmKsrLMH7sGAzfYRjeeeddfPrTh+KELx2L2269E7/89cPo07tb5Cr7wI6pL9qnggYVB1oyBQKRC6dQ6PYHiZ5708tp08ttBWIL9KlLe5eguLwUY04ahTFf2BXv/P1dzHtwkSu2bFxc7Xzn4gUrlw+uQN8xfaiI0Yq18zegdinfOsW/F5ocF06KC9FreDfUU7GmcUMjeo/piW5Du2P9vPXYuKQGRaUFOOJHn0RTLRVevvYUHRTdhLJepbS1qRutgmlBzdI6MkUFqRVNGHPmzpj4jb2w8B+L8PJ1s1BQQROQ6ZA9LltQcaaiBIf94CAqznTDI2c+RluS6MYv3hZFvrAwx2hA/74YMmwwqjdV4/0lS9FEBa/kw7CsIQakx31ZM5Qx6qS8nLMQ6bBu+IiUyoYILKUWuCe2kxLiSYjYvr4iC2pcJ40Wl8gehTrZPmbLMyX0IV2iq1Cl8EKH687g66SpqGePRcAiYBGwCGzzCFjhZZt/AnPAImARsAh07gjwViPettLQULfZE9H/yh6mdbyIgFe8VFWF10nHTbRVeGFpTTK1FyKkpWxheiZ9foslPeOltobmmqLMJNUXO5Ikx2nxUbY/CuzlojpGJKpWCuiMlnp84fijcfLJJ6K6ehP6D+jriierVq3BPb+6H0889QKaaXXHxg01uPa6S7HnhEo684FXVwDnnncZ1lTRNhhazcGrYbr36IEL/+tMHHDgRLcKo5YO6OQKyA47jMAtVHi5776H0bMnbzXiR32I+m7YivKiEgzq1hslBWxD5u7eNAE+UqW2qQHLata5wgOr5Hp4dcrww4dhzPG7oPdOvdB9SAWql9NtUqtq3MqWZ697CRsXbqItPgXY+ZgR2O0Lu6Cke6mLfROtWpn/x4V4929L0FIjxYxy0p9y8wFY9+56FJNcn5E9UNKtBJuWV+Ol22aiekk1DruLCi+01eg/5zyFsgHFmHD2OPQb0xfv/mMxFjz8Hq1aocLLu02YePseqDx5LB6/7Bkse2qFFFQyE+FZtaB5TTM+efdkDN17EJ684gWseJYKh+Qrh40PCqbjjTHtpC/ilFNOxPz5C3HLbXdi2QeExU8sMDzwvwPHdyKhEPN1q5LIu19rlqrH0Z6Ti0CVpjbS2tBqGj9OSzqQtKB8bePa2aNcckwPn1xyoQz32yuX1NuS44/GJ91qdJmd8bIlP5ZhWwQsAhaBDkXACi8dCpcJWwQsAhYBi0AyAm6r0clnU0KZ/V/sk7JpY7etI5M7aUe2y4x3txrxihdOSiVxC1MTkQ4pakFxVCuZZjGW11YtKQB5epgqMlUKL3XukFqfIKtfohfqMK5QvR9hT+1KK4WKpIbD48oLPe6Q2CBh5PNmvnr6VHz7qstohcpCPP740+jevRs+85kjsIAS+qu+fQMW82qKxiZ8/vOfxoiRIzDlkAMwdvfR+MzRJ2PlihV0sGwRNtGhu+edfSrOPfcrWPLe+1SweRYjqOBy2GGHuMNsb/ze7biXV7z00q1GyVkVYFNzAyYP2gWnjDsCfSp6kZdRPKjQ4PymQszi9ctx48u/o9UdjY4frtyQGfKb9Gi+PXfugYHj+mGHKcMx/MAheP+ZZVg1q4rqGq149+9LUEMrVQZO7o+DrtkPJeVFWPBXKo40NGPno3ailTLFeGzGs1g7a50Ld/eduuGY+49w24RWz1mLVbNXo5W2DZX1LsVbD8zDxkU1bsVLY10jXrrhVYw/YwyGHTgM7z2+BG/8bC7q18hBvi2bWnHg9/bBLkfvjL995d9YN3e93LDkvo68eNYNK5tw4K0kd+ROeP3ns/HmnW+juJ8UvLio1ER/L/rGObjggnMxe/YcXDrjKixa+AF/YNKWSHjIkKZR9VzuJTWEK6tWpO8xQtkQLaSLTvqbkeQ32h4Nbzfby5AX9tPtCjUpx+O0JymXJrO1ae316cPL+a1GdqvR1v7KZs8iYBGwCOSKgBVeckXG6BYBi4BFwCLQrgjkPuOF1Tk500SC2+wnV+GlkK6vqcyc8cKFF9lqpGiMpP1s5GxKdpooFJGUd1h48Z7KHFiio4UX9SKXbW+De2EazGOv5Ve86MoGmXsNFV7OoMLLN75xHn5+9z3435/+mq5+Lsb3br6KiiaH4uxzvokXXpyJ8rISNNL2mXfmv4bf/OY3OOqoT+OYY7+CFStXylkwZPuWm7+Ngw7eD1fMuA4//dmfsXvlcDxw3/9g1KgdcOONd+De+35PW4248BL303lJr7rWBozrMwxHjNwXPUpoSxJNPvSWT1dZWbMGv377SbfCRooxMk2Ok9tmRMtiNGZ8Vk3N3Drsc/0E7HnmOMz80RuY+7MFKKTrmlFWiDri7f+jvTH2hF0x+7fzMPPmtwDaOrXHjN2x1zkT8NIdr2PefQsJuAVcePncbz6FZloB8/ytr2LJnz+gwgtQTLclcZwL6AiXI350CG1JKqKtRxsweO+BePc/izH7l/NQt6IWhaWyH6u0ZwkOunE/DJrQH3+nLUTuKml3vgvBkOduHrziZUMLxtMBvHueMQ4L/7UYT536EsrHki0qrLSSP81UeDn5pOPx1a9Oxbx57+Dmm+/E8uW0KkYnz3BZj2f6XvgrUQX+IuKJyMWl/a9KzYV8xYjjCprKe5n290KrqqWoOt6cNt33zUHqSjq+8MJbjdzJz11pejYXi4BFwCLQKSNghZdO+dnMaYuARcAisP1EIPetRpRs+RyQsrb0JElSMk3MRIbfnJiPz9xqJIUXpqtkGIFs5JAiTsT1dMTJsvDT+P7sAABAAElEQVQZL7vwEsgRZN++fJ10+1e8hF4Ifuh1dj8ur7adYy5+QpE3e86Fl7POOBknnXQCrrv+Zjz//KtYunwTrpxxOs4//zx89+ob8be/PUpnurgDRjD3rbl44P4f4agjP43Pf+E0LFu+AkW01WjokIFUXLmKthUNp21L5+D9D6ggQ2dDXHfNZfjUEYe6rUb3/vr/oXev7onagC+tcBj5XJ4SPrDW/cPzk2KElMx4sUozGmnrk3AS8ebzT3g/knvkSzQsasSEq8Zir9PH4fWfvIm3f/suQ7rtPXVz6jDloYMx4sChePLK57DiudVoqW/CTl8aiQMu2QfvPfYBXrxuJtmkwsuO3XHMrz+FVW+uppuGXkbtCjoQl3+PHEqyW0KH637yjoNcQYUP3K1eXotn6EaiVS/TChtnkJ1qoW1KVHi5bj8M2XcA/kaFl43zNtKKF50ly8icmjdRAegbdBX3KWMx/2+L8Nwlr6B0iKx4YWl3xsvAvhgxfBg2VW/Ee4s+oMKYRomdSj4aF++NSsSl46O4Zxpv0fSIPI6PRMIFR7ofyVvxkj62Bc6+JXWUluZ3W3hdn29bjbr+N7YZWgQsAp0vAlZ46XzfzDy2CFgELALbVQRm0eG60045jw4kbUz4RclSmGt1oPDCigW04mVC5Tg6XJfPeEm7Ttqby06/sinx1E1GUhbwONmFF+bJJBjRFV7qammrEZ/xEkfU5DXbcojv+2m9NF3nE5lyvpKA+CjjGioCnXnmyTjxS1/Ald+6Bm/SoboLl6zDt6Z/lVbBfB230tkhDz30Z1cQKaBDbefOmYX77/8lPnv04fjs50/DClplUUSrPEbuOAw33fAtOqS3P6adei7R+ZrpQlxx+ddpi9Jncct/34F77vsj+tAZL8nPqFFopKt7RvYcgAOH7I7uJeXkKDkbTIjDVVW/Ef9Y9BrdwkTb0mLx84I8P8ciUsM7VHj5DhVezhiP137yBubdTytYikW2Zg5tD/rDIRi27xD85xtPYu2cDWiub8HIz9GNQjMmYtmrq/DMZS+6eHUfxYWXI7DkyaV4/rpXaUtSVPwhKF5NVFxRhEPvPAj9duuNNQvWo9/OfbCYZF+76w3Urqx1tx9x5BurWjD5lr0x+rhd8bfTaavRmxtI2U8zE4ulzfjEjydipyk74KW7ZmHePQtR1JOKX871qFhFhtdtrHUrjrrTYbyF7uYk/VUoko5lzvJmT3I92Rz5jTM9KJK5URJD0ZUuOjpqu9VfJltKYrF2tm9tY7YlkWanLZ2uz3crXs6iw3XtOumu/7FthhYBi0CniYAVXjrNpzJHLQIWAYvA9hmBtrcakd+ZHC6eKPlUTHvK5+0fVHhxZ7yszlt4UY14dCIqJbetyUqBExR7YYIoFH6LLr8lldRxKxVe+qGOCi81QeGFMUI5ho+sczf26CxjRBrkkhc50nKKJEV/NL1lu7zi5Uxa8XLG6adSkeUH+NOf/o1VdO3yzTd+E6eecjIuvPAKPPb4s+5AXd66M3/eK/jN/b/BZ488gs54mUYFllUooW1Iffr2wi03fRtjx+3uimhz5y5EaXEZfnb3zZi471646SYqvLhbjbqTD/EYsY9cvNjU0oBDBo/B6ROOdAfsslgr79CJznjhOS5Ytwzfee4+WvXC1yjzdHg2vBqHH42im6SzUzenAXveMI627HDhZRbm/2YRHVLLenSmzJxqHHr/ZIw6fCRe/O9X8M5DH6C5thG7n7MzJl20L95+cD5eu2O2W13SfScpvCx+4gO8cDUVfmirj9hn3+k8obIid8YL95+8/EXsc+F4jKSVNDN/Pgdzfznf2WQ32Z99f1CJPU6rxNPfeQFL/v6BFIJ4ctHDvjUsaMThfzwYgysH4tELn8bq1+kQY7o5iWfo+PUNmDRpTxx3/GexeuVq/L+H/0a3UK3iiUUPF4Z44D581I9YORuV9QICJzjMzcB7EddzPPfKJZFQyBombKdiOSJpJmSzsNpDkDnlnlF7MLquTDUdjH32mSfRrUZ2xkvX/co2M4uARaCzRcAKL53ti5m/FgGLgEVgO4tA7hUvoaOadIW0MAXTZMwnfnKd9DhUpa548XiioXpJHE3mxa5yZcQpcEhXrkcUCr9Zlgov/ajwQsWOsPDiE2RFU/QIPGjUgpJYQ9CVErYqTVJc2YgKSDIjlqNbjehQ3NNPPwmXX34R/vrXR/Djn96DXnQA7lVXXoQhgwbj/Asuw8zX5xCtO3r07I6l76/GrbdeiSMOPwRnnHkJlq9cgWY6eHf5inW45rsX4XgqBDzwwEP4yU/vpxulRuPG67/ldG+86ft0uO4f0KcPFV6iJzZbGnBhpyetdNm592C63ajUxUvmpgUHYH19Dd6mA3b9HLTAkB6JukX12ONb49xWo5m01WjeA+/S1h6JS0MVFVnOGo39vrEnlr22Ei/fOsvd4LTvNyux46Ej8cRVz+KDf9MWNYpdNz5cl7YaLX5iCV68eqYrvPCCHC75sC9FdDjvEf9zCJrrWvHIqY9j4EF9ceBVE1FMW5B4a9LK56goQtu1mtY1YMcvjsSBl+2DD15ciWcvf5kKKvGA8EwqBpfjELpFqai8BP86+3HU0xXW7hZtJ0pFo5oanEJnvFx99QwsXvwBrrn2FrzyyiwBYgB6NCL5fiEiqW+Ji460jeDcUL6HcgRZR6Gc0LIpKhtv/ddUumgm9dP9U53Na5M2Ng+lq2nZipeu9kVtPhYBi0BXiIAVXrrCV7Q5WAQsAhaBbRiB3Ge8tO2UT8W0FyRSVGiYkDnjJbnVKEohSTy2YyXFJK8xiKeYLOSLLjwS63EfklpaeOGbhGqp4MGwLMOPaOpIaPGR0Lysl4lm4gVcL+4Lr8RwZ5IQTziiVVdXh9NP+zIuuuh8rF+/ERs3bkB5RQV69ujpthj94hf3YfH7y3HcsZ/CKdNOpCJKTwzo34fOaulF57gspwJEM2bR9qSbb74L48bviksvvgA7jBjuVmF061aBsvJyFBUV47bbf0grZf5IuBXRpDl+UjRRT9ltnbN4x75SLyS6QajhJhu9SFAVI0r90gZMuHR3WmFCZ7z8/E3M/+0iweOKCa1aKelZjP2vnEi3Hg3GJjoEl+PUY3A3LKWiyMu3v04H49KWMKJV7FCBz917BN5/is59ueF1KjaR74zBDxWMSnoU49DvfwLNdJ30o19/Bk0bmrDb6aOw91mVWP/uBjx/06t06G41ydKvoLYVU34yGQN274tHzn0CG98jOv8Io8JYYVEBdqHizN7n7IHZD7yN2b+Y5/ziMLjyHflTW9eAs86cSlu5von5Cxbh29+5GW+8OcfxnU+xl8ZLAxkyEwGLfh2hhGrJvwfMUTyRUn6oI0GOU9JGghTiMZqM4/+GsXYol4a2ObR07zcHqSvp6Bkv022rUVf6rDYXi4BFoJNHwAovnfwDmvsWAYuARWBbR6DtrUaajGUnST4V056X4RUvEzK3GuUovNDkvUYYCaUKbqb4kjHDfKaqnKaFLCA0kWBMocULL3LGi5dhOX68royz3+pCyBGtkJK7L/riU50742UqTvryiXj44T9g5MihdINRN8x+ax7+8pdHsGbNelRTcebgA/fFwZP3Q0VFORUBmukvWaQzdPhCnmV0zsuDv/szbVHahMMP3Q+HTTkYA/sPoBuPVmDRe4sxcNAgPProE5g1ez7K6MDdKBxB5DIkN32qKxDPe+lnQjTntlY8RIb5PP/YKBq01Da7K6MHTxyEpc8ux/o3N9L2JWIy3ynRldADyzDqcyPRZ6dejrZhyUYs/MNi1FU1kBwVWGiuJb2KMObkXbGWzm9Z+ugKt/0pNFpUUoBRx45ECx1wu/CPS9Da3EwrVIqw8xdGoPvA7lhCW5TWzaWDdKnAwkWZUV8YiUnf3BPv0Qqal6+fjYLyyCFqygeWYP8rJqI7FYCe+u7zdPMRF2b4D//eCtFAV2l3r+iOG66fTtd+H4ZHHnkSV19zCzZu2OTO4uF4uMdB8ksfnnBbTyjvzAZxza2bhcy/jyxitr5YC22yko7Df7tYV+nZOJtPaYeTmw/eaTXtVqNO++nMcYuARaALR8AKL13449rULAIWAYvA1ogAF16mTjsHLXRuR+bh7JufKC+SUTIR01QsTMh8IpWr8BKmdmJE3l4zpEqfLcStE8URRSv0IK4tmkxjSb5OOtxqlO0LW/E6cSwZ5baVCVeWGq8cUf9Fn990pTKf8XLWyTj+2M/ja1+/GO+/T2e2FBdj3fpNtLqlglarSJGjiQ4+XrumFlW0VQZUZOjRrRib6uiAWypMDO5bgb69ZQtRLZ0NQbdOu2ujq9ZsIP0WbNjUhAFUfCjja2mlquJi4Z0U73gcj0eumZIUrw5xvxGZl59ThMqJP8eRGeQmFzuKehWjkA/WpSnxyhZnjdpWuiWpcW0rSvvx4S90vgoVXEr6FqGA5un0nSgdjLuyCYXdClHUna8hYnx+yL6aIRt8Q1FRdyouuacVLXQ7EW8/Yjw51JeEadVLIWHv+V/j6Dffilk/nEthlDNjuM5TMbQMe39tD6x4fQWdO7OY/GUH2AjZJLt1tNplxIih+MXdd6CsrBTXXn8bHn3sGZRyUSsT2cgp56b6Kl7xO0lhC/L4nlJ8q1rpMvL7Yh7JRY3X9b2QJUjy9r9PGTOOWhRt1VS+x+x4j5EVr+PaXV1DCi90uO4Mvk5af89dfdY2P4uARcAisH1HwAov2/f3Me8sAhYBi8B2HwHeavSV0y5AQwOtAuHHJcXS1QTXbTlRUpSO+fTL9ySllISKc+NKWvFSVcWH6/J10pykS7oVQVHjKdzL9fiyhUqoTdHXkccL0YTLlPQzXhRTWtFUfW49uu/FdXSkWjrWVgsvoq8olMTTipfTz5iKzx/zWVx+5XfxzjuLXbElmfI6L/i7uIIDj3ibkDwsq4hSWCG+J0RdRWQG8flP1GUU+b6Mm/YoWMR3DdEcOcKNsDI4Wg3JqIouvyPRyK/4yM0v8/sj6Uift2k5bBpn+s5VLrwQz03GTYt0onEk66hc4In8dmGkABR1pwoQ4TbXUNGFZBlD3S6lIlFzXYu7PSkTdvaAQBppxcuggQPw9QtOp9/1Svz+93/CmrUbaQGSzNFHkJ1P0oQbUllK5LgnI0cKqDJWrbhMKJvLnspwy9qMFKLEkZWjvxnW4kfpMvLvJJrnxHtpcmo5lEyTC/nab6+cym+Ntr0+5ZfTrUaXXXYxHaxNBUl7LAIWAYuARWCbR8AKL9v8E5gDFgGLgEWgc0cga8VLJvGN5kU5gk+54smYp2uPEwpJ7PRwXblOmgsvlOA6bogXESIdPxKZMD1JWpaE2COqB4Lh6SFmPzpcNzzjJeRpXzRVX6nSxm3EeTpK02Q9pYcYDY0N+OTB+2PSfhPx6/sexOqqNSiiLURxadVUC9wqivD4rRTHpYHj0EsKDUSlfkYuFGYFV7hwGhkZGTEz/khxRWhOxlU0BFCKG6F8xiIR5QuKfvA1SVULJ67AxlACHE2KZbW4RMwUx1xZho27eRRGIgwkYGwz9IQ9zBRsqPgiBReWZXhCky4NRCs0yawWWiGznlYllZQUgc/SKeSVScRIyjlA92KtkCscQY+kMjal46V17pFc7Et7KcGPQCJzIVe1uVVTIS3mS8igvuBEoKnaqpAPRWW4VTlBDjnWBx3+Xe/OEJox42L6jVnhxX4TFgGLgEVge4iAFV62h69gPlgELAIWgU4cgawzXjRT1zxL55YZ+2TJJ3Da8wkVrwDgFS+rV+uKF1mloRIM65HUSL5WpF1S7EDIJifNpBIWAwQhRFbfgLYKL17L95IeebQkJ3s+SVnx1XvYEmX4TXQmSTHFq8AVXZgfSuo4pDKy9zEp7XJjxyY5V5BgjODJBJFoKufYHlOlkxSdk9CjEZthBS5URHMSXJZSjdD/NNSQ5nXYf+WIDTakfMF370zhxctLHMJST/RLYfWoqMJeKX4cl+24kg53nIyUb3ik9sO+R9Gel9Keclgv8ZAIc3kajC+S/Jae/40rFssJj3sf9mGkEDkbz3M/nNUPp53tV9ej2BkvXe+b2owsAhaBzh8BK7x0/m9oM7AIWAQsAts0ArlvNfKpnkuVOO9yHUmcJA3zyZhMwidVvCWk0t1qpIWXlFUHHZo5Y6tP0hdrPjn2cOpH4B+R+vXNv+JFtXIltAGaN5XoKUZuWeIok4Vdn16uEJAAiw19gUCjoH7S/xkQGGecXoypjmSMsS0iOjp/i/hKCiarW96sB8rAZZhkM3BbdFWKRlJBIHsRRtSwlaTt0HKAkLGkM9eaDs8hXKXCY3kEWaJB/agY4yw6+z5yKu81tZdxNBYPNiG/YJZjGfrL34zriW6OTBdHRJbH+nhMpaS1os3IEiORSaIxVuiLSH3Yd9KK4nm62GW6+qkym99+dEib78P2p2lbjba/b2IeWQQsAhYBK7zYb8AiYBGwCFgEPlQEZr3xJqadch6a6QBXeXyCpT2XHmUGkizpMK7jE6mOrnhhPK8tqa2O1ZYm4N4mSySTacYRzZDDGP258FLnr5MWHHmrrbSRynk/Qqvpmiqrur5ljvgtNOq7CoaXCHsiKTbkHdoOJJ3BSEIFHTvdEydChQnnTVT0kXgxR3UcN2MkBhtgO2kBEtlMhYSGTslr+l4Gljrhl2I6S6kP4fdknjxS64j0MqDxwosUmmQOKsIa0pe3tyy6kcORdfVBflFuWgqkjrjWywk5OY4JJwZJ/5gthZe2UIQv+gnQaI5hFJMS8TFPS+2FHJ2u2tJxKJO7n4baMYTc2F2X41a8nEWH69p10l33I9vMLAIWgU4XASu8dLpPZg5bBCwCFoHtKwJ+qxH/p3t/aGv+dC6ZpGnK5pOqwiJa8TJ+XLTVaGUMWyPgpT1eLFVLOKEFFdWXVoTkphyh8GobfsQr/+6f84wXTbpFX5NvHXmsUM6n8KEc92NzCJmZAks0czeO+k6OfJXpuJFytFTgkVUoaHWFCWuGDuiKE8WPkB12ZE6ydKLE/GFsfhRM5y4rjJw+OSu+MZDKiVbm7b6F+sloIheis2xYAMnopnYiLAdA1sWRjKTiOr9pkOSLYIRBg4xd8r8gFiuVFFnxOsccnajHlO1WjMf44ZOmH+FTI58wOdaYMY6ghW+met98j+n8iKz0873jnmXjiG570BQp2YbWmZd8VL4t+oeVaws/yd8a4/ic/FajS+1Wo60RfrNhEbAIWATaEQErvLQjSCZiEbAIWAQsArkjkLbiRdMrSZJZVync90mTUJM8GfN5JZWV41C1uopuf1mewGAcTRh9K9TQgiQkasH7o5LakoT8cYSOF15Cm4rp56kU9kOp4pO8PVUlfavySnEFIiIyXbWVx3m/ozkmFxVE2xdeWJIlhK56mujHyBmxTEfEdcitPg7OGSWKYouAjHRbUlRmcUs/VFSBSDLT5X40YADts70CPjw4/jiRtHk5McYhCRbSJ8ILSRlWTDCu5mW4F+HKXiHSEjQtxOg4rsMjlhMpp5GZs0r6NvRPxTyXeyGVpDVO9N3db4FIDoNfQWFLtOTt+BkkGSmNLfCjVhwMjbV1zOgVp7FGMgKhtCKGtM3pJz3dHIyup2MrXrreN7UZWQQsAp0/AlZ46fzf0GZgEbAIWAS2aQTSznjR5JMd04QsLQ2T9CtMwoJEipLHCe6Ml7DwonzRcSsCQnUXicgSJ6Ek4HJRp8ZJv+qnhIzEBSosWLCcN5DzcF0xFYCm22GkkKP24tQAJuqGOt4foUqsI2RqOL9WeT/f4ItEMXGLM9SUUxBl7opf1FEgltNMnvssQX8KSuQGpdYmv9LJsenlVWWFi9KlZQsq0YzWFo45YfHf6LtlvBBTbvUH1zh4JlyUEz8FR5HURlBjiKwE84+EYjqBO848y/CPK8eTRPNxjvwjPf+705lGxSf3gVpRWFqAlkZCItvuLBm2VaBx9La1xy5mPRqriFlYUUg3JBEuXWXd2sTSDE4N/6Uui3m8SIlo/hGuynh6vJfRDAEzIsINY5JhxToZlBjVD9SLNDnleWnr+QjoGS/TbauRD4r1LAIWAYvANo6AFV628Qcw8xYBi4BFoLNHwG81ak6ZimRmLlnOpHxezKdU2vMJFV8nPSFzq5GueFG+ynussMdJXyY5di6wnujkSwhluxFJqBlGITVZOdKKvrTVqKa2FnU1tS6DVTFnInQgZa4xdjAQr7IRApEATaSFJ9YdhZxspapEeUkpClGMuqYGN//QP54//9PU2oJuRWUoLS6lEVDXWE/XG9O3o0mzPM/XPaocDTNpe8QvKi/EjkfugNo19Vj2NG0Fc4GKhBUjo5ur04qS0hJ0Ly+js3PqUVfXENzMRCAOhxyhog+5jR49u6GirBRr1mxECxH8dyJ853ykEpmLSDTK7ZAWoFyxhkE0AAE44/iHoyiP0OXt1LhLnWaqerB/DEelEBQWFJGvslKHyYVlBRg+ZRjqKXarXlpDclHBRaC8KeopSW3GmDxgPCq2DJo0AEMPGITiimIs+OsirJu9gXik5SYYFX0CPFb1M+GRWFJ7TMn1sC8sl5mzCioj4is5f5tzZvnVMlyO64fFyIB1iY7fanSJXSfdJb6oTcIiYBHoChGwwktX+Io2B4uARcAisA0jwIWXqdPOQUuz+0/sHfZEUiZNnHzal154ScC77C9BSx16wbyFF5fAxSV8Ht7qbjWq5sIL/Q3y8kza571nJ+KjVLcioo9BUoc5UUEksuIltLgklJrmOhyz4/7Yuf8O+N1bj2Ft/SbykXgC4Sxxd88BO2G/wWMwoFtvVDfW4vfznsbSmnU+d9VKhKTWWfYZqLWhBSOo6LL3eRPw7j/fw5u/mCdJPufAXENwLpE1tc9KsYdXuAD1DY347FGH4xOfmIw//fkfeP75V1BcVBTpRY7zFKha0dTcgq+c8kWMHzcOP/zh3ViydBkVj0hW48w26WMRsrMkbzEaLzAILf3tQIgVb0Ms1vMhFQ6P3UOdkuJiDB0wGgN6DXSFiaoNy7Fk5TzS4cBQgKLVPXteOB79RvfBcze8ipol1Sig4ol7AmNB19kUAfZOCimM2UqrW4YdPgwTv7EnisuLsPqtNXj7wXlY/dpaV6ziqYQ4isGt+J3xnigimUs+1M3oh+qRQPjvRlInfZwCki6YQm2vtymqXZQkhRc6XHcGF16Ku+gsbVoWAYuARaBzRcAKL53re5m3FgGLgEVgu4sAbzX6ymkXoKGhLss3n05xOhxPkDyP1XQUyFCXV7xUVYVbjRImWFxVQxbTXNKvaTiJZaCTnnhFSdDj/LDwwiteamm1S21QeAnNZ0w4yPjIW8nuhRgh1yP40oHSYjpEXEWFlmsnnYzKYRNww9M/waINqzjNlzhQ09TajN16DcPl+5+E/uW9sXDdUqyuW4cH3noCi6tXZ8zq2ojWaL8O25Mu9bgoQ/WDEkrw97t6X/TfrS+e/u6LWPPGOrQW+lhrAs/fxpVCnNOszxBsQaozm2pqcOlF5+O44z6Hn/70l3jgt39AGa2AkUfkGaOZfC8qKMF1103HoVMOwZlnXoBZb76NsjKWdeDR5+aoRN9PyOSAflX2pT2P6IcRZyg/zlgI8ASZf3L7jD4CU/Y6Af169XOFjzUbV+M/rzyAN959hmousiqspbYFOxw9DJO+uQ/eun8u5j24SLZbsXtsTAzKzKgf+s0sFpDfZSsa1zfjoFv2wc6f3hFz/98CzP7ZXDTWkR1SEhj9PYumoglmNrKgOyPteum/H0nhjhVfQj+SSPnGOqd8Mh8/nm41uuyyi1Ga+ffp4xcHm7FFwCJgEdieImCFl+3pa5gvFgGLgEWgE0Yg94qXMFWViWkKGE4zngD6REquk+ZbjfIUXgIgTTI1seS006MlrXiOQjiJKIvUrUXMkwIEoVHRwRVeqOjCxRdNLDVlzEaMU1hOihrZdPUhbONSzBEElRG78uYyRm1LIw4cNAbDegzEUx/MwqYG9ZFkaKvLpuYGnLrLQfjGvl/EA3Mfx/8teAqbaEsSr1TiFSX8OJscA5qcJvbCEG/43bi2ETt+YQT2++beWPbKcjx/3etUNGh2W4R4qxavVCokfU7+evYoR7du3bBu7QY0kUwxrVDhuPGZLiSIpqYm7LNPJd1eNQYvvTgTc+ctQFEh/Rd6NkQyDY1N6Nmzh0seq2h70c03zsDhh0+hQt/5mD1nPsrLS517LM8q0eeTgeOQHSK6khB1M48KszP8hIoRFhGJzH6SzyLk3gweabkvwnL8tFJRpVe3vjj/2NtQXtYLj736IG0BAj534NlYvnoR7nvkBqxY9z4teiFcOhOn+449MOWmyWjc1ICnvvsS6lYEhcvIgFt8RH1xL5pA5DOL8G+zYV4TDvvj/hi61xA8ffVLeP+RD1DUkwwTT/10DiZejCa/R2V46ciSMvLisJCTD5UIyqNlYPJ0QuU8YlmsjlnJUu+iBL/i5WLbatRFv7FNyyJgEeh8EbDCS+f7ZuaxRcAiYBHYriKQ74yXsPSSKw2UlEsTL02k6PBRd6vR+Og6aT3jJffURZNTScJiOEpQFU20kjbiVKcUifjbgEjGZb2ExYWXvrTihQsvKSte1L5HTbMergphyXjqK7r+HSJILInCf5yfMleqc2DS4N0wvEd/OqulhbYY1eKFFW/R2S2NTraaCi6fGDIGO/Uegsp+O+LIUfvhwXlPYOmmVailosvT78/G2obqKHsW22xXQqEBiXyiYeOKZky6cQ+MOX40nrjyOSz993IUdqe1NaS0YUMNDj/0AOw2ZjesWL4Cu4/ZBYMGDcRbc+fjsceewbJlq6jeQqs0qODyiYMPwJjRO/NmGRfPV1+ZifnvvCeFF7ZDMmN33wVHHXUE+vfri/nz38EB+0/CxP32xrRTzsWc2VR4qSiLHCaFTKEh+tVFk2ikohKXlaKZOHm3I4q+KxeISmhdEJ3IQqtq6MwY6hOMSJMC85lW7Fqx4QDkByYfghXog9Q1bsJnJn4Vn550KmYtfALXPDQVh485CRccfweKqZj04KO345X5j7kiFceqsLwAB127HwaO64/nv/cKlj4pV6aTqegnJ79fV/sRj1yMZSbOSdetf6sJR/zlQAzZazCe+s4LeP/RZSjuJluwIin1VlyPsJjHkVKZCNzJZgSp4/khNb0vBSJRyqWn30Hsc+iIogOGzaWYbnJzFHIidRWGnfHSVb6kzcMiYBHoShGwwktX+po2F4uARcAisA0ikHarkbiRKRVEuWJ2RqVJmAiwlsjwm4sf47NuNRJkfUepaZBY+sKLFk9UNnIiGqplthQl6koiitclHtM58ebCS5+2Ci8RvGvi8xX4MNEVWW82Ls9cpXgZmYXbQkR8ppdSgerLux2K3elsl9G9h6OxqBSXP/pDvEeFFS7KrKBzXM4m/kQqvgzr0Rvj+u+EmSvfwfr6GmxorMZ9sx/DkmCrEYO65J8dcBZcx/V5p0xp9xIcQNuMhkwciH99/WmsmbnOFRFYae7s9/DLX30Px37+s1i/fiOq1qxxhYYddhiOF154Gbfe/r9USFtLBxTX49xzTsEnD5mMYUMH06G53XHXXT/Br+/7g1vFwof9VlbujgsvPA/Dhw7BipWr0KtXDwzs3x/dulfgpGnnU+GFtxrxihd2mBv5NfCY48LfrUdZNxxARam+5b1ELpoPSxTTSpb1DTV4dcUC7N5vBwzu3pcW2XDxhcEcoOuvo6LU00tno7axIcKIGhbhx1VqWglrNf7rmDsxZuS++N1jd2De+y/ihCmX0HgiSuibPPH6/8O/Xv41nWtT5+JLDmCvC8Zh9DGj8Pov38K837xDPtPvg3DlNyfw6ktm5Ao9PEt6uKJEC2UO+8Vk9B/dF4/PeA4rnqfvXsaFMJmD6vvoKBK3DiUz25CjfZ2mjtvTsv/OfJYw24v7FRNRdozY1mBzPGwLs3PzbatR5/5+5r1FwCLQNSNghZeu+V1tVhYBi4BFYKtFYNYbb9IKhPPQ3EQrLDr4SNrHStrTJEpXvMS3GvnkUeQ1hVOzOhYUxVIu6STNRCxHjniSMHpd2W7ClqMVL3XxrUaKHm+9vtIFPl/hRSWzdZUjLSNpJGhKNCwrKcE6KrBct9/J2HtoJS569E68t3ElnYtCKzioWlJSVIImgj151IE4f9/j8d1n7sWzy+cSTivq6btFizYcPBclHDrhtvLSGldYYBZx6FDd3mN74RM3HEAyhXhi+rNYv2ADJfq0woLw586eh3vuuQsnnHAsZs6cjWtvuBWN9Q244Gun48gjj8D0Gd/Fv/7zNEPRFqRyOhy3DF/72lfwuWM+g7vu/Cl+dc/vUVZeQluLinDB+Wfhy18+Hj/60c/x57/8CwccsA8uokJM/369ccJJ52HOnHlUpPFnvDjnGdg9dKUy/bND9wE4o/JI7EQH3bpARdUA3upUVFRMq35W47dvP06rgCZhbL+RpM1no0TxJyjeNrVoQxXufO33WEeFKr8+iWW8LcZeU7sU07/0K4ykg4v/98+X4oDdjsSEnQ/G3Pdfwl67Ho633nsRDz95J6rr1hMw6dOfnT8/ks552Rtv/2EBZt75pkAqNLfuyXRoFNlsLkD5oBL0Hd8XvXfsRauPdkXNqmo8eenzqF9XD/rwJKtA3BMM9Vhw2/cOrbdPI7+U/PskMg6bnfpQRj6Ucn5nOynXrXg5iw7XteukO+kXNLctAhaBrhgBK7x0xa9qc7IIWAQsAlsxAn6rUXJDRy4nOFGSFDCeCMYzML4it3K8bDVatnQ5aUQFAaftCxiCIcmXJMa5EjGSlD+U96oPvnUJIWFnF15kHrnOeIm4zivfl174Vv+55Uc9ZqeEotLeJ6WINEvxX0mjRUfGfGjrvLoNuPvgM7H/iL3xzX9/H0s2VlH+LVaYX9Vcj/NHT8E3J30Z05/4KZ5Y9iaVTgpoGwxvS+FH7Ua+EbTUIMge90mihQ5t7btXH0y5ebJL8J+4/AVUv1+NwhLWpRUvc2bi3nt/geOPOwoP/N8fMP1btxJ+Ac4/+0RcfvmFuPvu3+DHP7nXbYniYsWiJWvxg9tm4IQTj6PCy8+o8PIgiqnoMmTwQHzrikswdvzuuOSSK/ECnf8yYsRQfO+mb2OPPcfjpKnn4i0+46WCV7zIHMVDGmaeAld4GtCtF7oVUuGJ6C5aJK6FpcaWJtqatRG9aGVMWWGp+/buTB/yjX8jHL56Ogdnec1a0mY7jMCP2uS+BGdt3Qe47Ev3YsSg0fjbC7/AEftMxTtLZ2H2u8/jy4ddhgUfvIbfP34HNtYSFgNTUEccMQyTL5+EeX9ZiNe+/0bkIEMKn9HZlloT63Q0cX0Lhh40GHufX4leVHgpKGzB6z97C3N/vYCKLqzC+tTyPLjDY+qr94ya/jilGIspH/ZRu4KuI3JLuzEjSR+SY/VG6aqsY+Vrm6Qnxx2VU3ltc+Epf2u0cR/8VqNL7VajrRF+s2ERsAhYBNoRASu8tCNIJmIRsAhYBCwCuSMgK17OpRUvbV0nzVkWJwj+0bxLMk6mC5/fvNpgfOU4VNHhur7wIrqqF6YbHtn3RFp1VEvGoVScw14olzjuDyfh6We8hDbifcVIt6+yoZQvJyk3vVUdn0oXYAkVEO6cfBomUeHl4n/fgcW0moMLKxLTFmxsacCZu34SX594Aq548m48vWw2zYn49Jf+z4Ak5RwIBc+YlmiwREtjC3qM7I5PXL8/ug2owH8ufArr525AAW9tIcW5c+bgnnt/jGM+9ync9T+/oP7v0EzXQH/5xKNx5eUX4Z+P0Nkn191Kh+o2u/NT+EyYa75zIb74xWNw51284uUhV8TZddROuO6aKzCQzoe55JKr8Oact9GtogLXXzcDBx88GdNO5RUvCzKH66rL7GOmT/MqpH/6lvegokqR25XjpsQC0ZybmpuxgbYbdS+l1TdUeMlUmNxsWLoQ9XRGztq6TTx7JkSPWOG3RK4AVTVLqfDyc4wZsQ+qNqygM2rqaMvR7RhA279OOvwyzFzwOP70zI9RU7fRYRTQVqPdTtwZe59TidkPzscbP37LHSisFvxEiMLfiedGrTuYmL5TxZByDN5/AAZVDsSOh+2ANW+vxeOXPY+m6kb6d4e3Grk/pKYfNfrGceCMOe2IJR1FGH7YZk+t5RLUb+Tl2KI+TO3oE+rn0g1x2yOfC6dz0G3FS+f4TualRcAi8PGKgBVePl7f22ZrEbAIWAQ+8gjkPuMlNOXTrJDK/TAlUh6nRpxktn3GS6ih/VxtPh+SXkSll5AcFV5q6GDduuBw3XRryeQuTDfTNYQa19NR6IZGLMNz02rFYioO/IAKL7zi5ZJHf4B3N6ykY0T0NJgCutWoHmeM/iQumPglXPXkL/CUK7xkW43bYn7GEviMlyK6meiAq/fCyE/sgEf+60msfrmKzniRM0XmzllIW43uwLHHHkntA7j9jl+4gsWp047FjBnfxEMP/ZnOeflxtC2tEOs31uDa71yEL37pGPyAthrdc8/vHP6IHYbi6u9Mx0477YjLLvs2Xnv9TTrfZQCuv34GJk3ah7a20a1GtK2pTG81ynipZRDeatRKW4364awJR9NWo8EUNqaIIH/dAjrjZRkVp+5/6zEctfN+GNufthrRBHW2PG8u3SzauAq3v/p7rKf4uvoHYziefgketWJj3Wqc89lbse/uR6CWrvb++/O/wlOzfofJ44/DFw+9AP95+UH857UH0NhAZ8XQb6mgpBD7XjQBoz61I2b+7E3Mf3BhAKheSCEp/k3Yd54OzZWKKsXlxTjshwfTlqMeVAh7FlVv0Pcokq1fjCIRcU6nvtQSz0If95OSaLk3R8JHVqXS27bk1B/xjUNBPTeIZundSDcQo3ZIOKbZlQd6xst022rUlT+zzc0iYBHoZBGwwksn+2DmrkXAImAR2N4i4LcaUVa+mU88sRQQuU46/61GnBBKttqeBEzSyTQXxT69MyKCLLlnRKRkuR/dalSdLLyIcpi3kom4P5psptkWmpf3vbi0mhEn2YJI8oKG2tYmvFe3Dnd/4hzsN3wvfP3ft2EeFQy60W06JVR8IddpxUs9Th99CL4x6QRc/sTdeGb5WzEv1a63o/aZw1T6S0ly4weN2Pv6CRh/whg8c9PLeO9P76Pw/7P3JnB+HcWdeGlGGt23D9my8ImNLcnYxgcGEwjmDsdyBJAlmyz45F6D7eVMuBLyZ5NwZJP8dxcIJOQmQAJkIYRAggEbbIPvE9+yrrF1WHMfW9X1vq+6+733m9+MZqSRVC/Se91V3/pWdb2fyKfL3f3msg+uBtx5+x1cPPkTet3rXkk/uvYndPnbPxjOePnwB99BF130BvrIR36f/vHr3+GtRkPUx9uWHn5gJ/3pn72fXs9bjT7zmf+fPvqRL9CKVfPp8EMW05Xvfiu97DdeSB/72O/TX3zlW/TiFzyT7f87rVx5GP3mGy7lrUa84iVsNZLYEL20JNvaX9o1j36Dz29ZwQWYZKsNq+X8m209u+h7D99E54SvPh0eCi+wldFL2WpLz07+9PaPqJe3HAm3XrE/lfQO7aLnrH4dvfa5b6MtTzxKn/7Ht4YtVevPfx+tPf5c+qt//RR/7ehaZuADfJmmc0EnnffxZ9KSYxfRTz7xM9pyfTcTFfxlhUfeMheM0iEyrvDP8r47BuiF3zqPDn/6ofSj376eHv23zdQ5jyMvqIIzDbH2Xh0JDCMKi4w9m0VKGP/Kx0YJIgwr3OCAO030qbOoN26DyPbAbNpWo/f456QPzFfso/IMeAb2wwx44WU/fGkesmfAM+AZmE4ZCIWXCy7l/wLfvNVIp3IypU0nSSqvH41sNVq7pnXhRf7Tv0xi9UIDU2/1FUsVl8Zg1kDK3K/AhBmvILiPwksPr3jhA3bD3NhMaiaM5ieeksKfPQUnf40s7aXUxsXj5FUcK+YtpecctSasbvn1VbwSZclR9O17eAsQH7b7wI7H6NrH7qZhfjc7eavRJSc+l9559uvp6h/8L/5SDxdeyhDFt06FLQpEqNnAoaiDOwfpqBcdSWdfeTptv38nXfvBn/GZI1x04+0td/KWoC99+Y/pda95Be3YybofX8e56uevFz2b+gf76b3v+RDdfue9dNjhh9Bv8vaiRQsW8teLTqFTTz2Zrr/+RrrvVw/Sli3b6Gtf/xY969xzeJvRFfTIw4/SrbfdQccdewydeeYZfCgu8ZkwF4fPSc+eK4frysUDKcYScldUKiRX/byKZah4j0Pcl9HM5L/yCenZvOplDm9D6uP89HMuZ3KpRdbFDBe4WQFNNIdx8nsMV5Yg/NpGuPg1e9Ycevt/+QwtX3Qk3frAj3g7VQetPvZZ9MiWe+hvvv8/6IldmzXOoRFaeOJCeu7vPpt2b9pN13LBZGA7//spXKij6I7fob2wUtl/+xA9/5+eSUeecRj9+BM30ANfe5hmLeHIOd5wXk34B6JBh9yUltpQl7HjbIAZvi5I+00CXP4LgmDsp7i1f8xj40tEHHspPKgbXng5qF+/D94z4BmYphnwwss0fTEelmfAM+AZ2F8yIFuNLnrT22iAP5M7nqs6vQszL6Pg+ZQUXrq7u2kjH64bFyZKUDERtflaxsETaJ0omjeV1E3WWBOUmQ6mdYUXCQR6aSemSaeAxWAxwJVipWdIbWlZRFzYNHd4ZIRWL1tFb1r9YlrCB8R2ds4O53sMDfbx4pQOunXrPfT5278XPoXcz4WBV/P5I69b+yL67A1fpZu3PBAmusKu3hED/MUxBGgAhgIMr3w5+4On8Selj6CffpJXa/yUV2vwIos7b3+IV7x8il7+8pfQPffcRwsXLqAu/uTzE0/spH/82j/RN7/5Xerr76fVJ59IV1xxCR1yyNJQnOicOZOLQ1y84WNU5F1/5rN/Gj47vWHDa+kF5/96OMvl8ce3U39/L82du4A+9KFP0H33P1gcHBrFzb+HkDsegmWJ21xUkcvqFsWKEAabNbdQ4JBAwiUreYpmeCSdUgJfcojxyUedTeeftY6WzF8eYti2fRP968++QvdvvpW5xAe/QS5UHf3qp9Bpl6zhQ3FvpQe/+ageimsvg7mjDuIK7jWG8Jb4NrRzmM7+8Kn01FceQ/f9y0N011fvo/6t/TT4pIwB4wih1t6qJZLAXItVIfwzTqDaTfBVzkTdojOW79y0xnkOOQj72Gp01VVX8hfCUJw8CBPhQ/YMeAY8A9MoA154mUYvw0PxDHgGPAP7YwZkxcu69ZfSyHDzipemcaXTrHQWpyte0s9J2yyvsJSJdklSNtgdJmTKKRNjkRiibmqo0+dUwxZyBoVcReFFznjpjc94MdLcrRgFU4OghfiCunKzWA0vBRe9VBt6fOvglSZdM3lyFSb1o8Qfh6YOjrmD+6NczBgo3wvngFdBzGLs4CCv/SiKEYgxDyIu8KQ69s+H4x561qF02mWn0KM/3Ux38Nd0uK5Dd96hn5N+OW8R+v1PfY5uvOlmOmLF4XT33ffR9h18CG+Rj86ZHVw0mR1GNMJnlch2mg5eeSIhyej6B/p5m45sXRulY495Ci1bvox+dd8DtHNnL5/rMlP1fDAuD6hgFCQnQ3IgF5KjvdBVjd5FHVp8k99PgBdYewCVa5UDOOmFX1dUHJHPZB+y+Ej+NzFCjz+5iYuSPexIPjfEA2TcDP6U9ZrLn0YLjphPN/zRzaFQIoftjn0hJkYW8NH+UVp+xlI67fLVtGDlAurZvJtu+eIdunWJ4SgKKXdkHygyn2GofMvEaVyqTHljhOpbUsTwpB0CSCRjdybmaWze/RehK174c9LXXOlbjfbf1+iRewY8AwdYBrzwcoC9UB+OZ8Az4BnY2xlo54wXTKfqpkjQ6fTXEFp4ab3VSCfywiB2KZPITGo6yKt54qlkoEJ5QChhx0zjKbyU5CiXBOJCGnGWOB2BdGNkpE6aygAe+RiOtCGVp+ZR6hDyZR/0ZbI8wtUNOd9ErwLHHWODpn5qjcNQ5XDXxSctDF862vUAFxb4uvP22+grX/k/vOLlxfS7v/eH3P4GzZrdSZ1cHOrkPULBmxSEOF4trBTjDc4576GKJkUjjo+LRCO8omeYC0dSkJHPi8u5P/J/UmzSS+MPLMIrQr5BilGVYxNF3En6okuMWSArRmJfMC4MpSgnzRBVkS8ZGwcsG5Y4Uh6GFFyA099YBxeeFh83n3q3DlD/43zYLlwIVbhiP7lM+uJU/jJO/PHWpfl8Ns7i4xbQDOZ+4s4d1LelX8cacio2whmClU649Jce+xI+VhXUBUjB5R0csCsVoaFSxQCZIlr16jlbWeRjao09OLS+1ejgeM8+Ss+AZ2D/yoAXXvav9+XRegY8A56BaZeB9r5q1By2TbWkZVM12ZaxZq1+Trp+q5FOG22mKD5SDvNqXmIf0KuW74U5mGsLL3zGS29fX1EkKBgKO/DpU8di5QsbW4pLI6qlYgPhKeMCQSgUaMFB2M0XAHhiii3seqkfyCG1p7CW6DgoLjZIXSRsOeJFJ6NS3eHJvbyvjZu66V3v5C8rnXU2/d3ff5V++tOboiIJ2CzSPCPoK7LoyRjDxf3wp+jLQ3wXF1DoQxNGIZ0YUNgFMctNHYPUBDwxqtoWr2obXokGGmR4ZxKHIJSP71LTkatSdGGZknDDvINfTBK5FFbkj/DxChshDKtnyoKL4PnS8FJT1dTfI3zULLBxXCICQlqms1a9i6rUeKq6WCLMwI7fS8x0ILZ9q9GB+FZ9TJ4Bz8D+ngEvvOzvb9Dj9wx4BjwD+zgDN99yK2248AoaGuT/ct94ySRJJkjxhMnAmEJBEpC8umE1H7zavU3OeOFDScuZKlDJHJKFzfw2SRNbwaVXVGIICkyWawsvTVuNElrp6Kh0IhqPMAEW/uRhpY4qgtmKyXgZm3gIMqDhwxAyLtXKvdBzM7SkgCJuS7+wD0LVsQhSRTOjGgU71Wrcoh/lFSpLliyiRYsX0tatj4eDdZUNLNrTdwAZCONMiUz0/OQxSlFHhwpf4LGnvUPlM1bBgM/wsQhYy4XgEB9sDAVJHUZzXoctKMuCSAMmkMN3jIFMAJAXOUKu5CmqGCpw9IOZAcAikPQqNMVvLjEPwDpLReEXB746JHT2hAeT2EDA0ISJbbwtGQgrXi5eR1f7GS/+g/AMeAY8A9MmA154mTavwgPxDHgGPAP7ZwZsq5H81/a6yZGMS+SYQMnTcGkLxQD+7/a8tWT1atlqtLUovBhSGOUCY3WSpnq7F9NypggewooHiRex6IQeeIuCJXDLE+Zly5ZRz25Z8VJ81QgGJU8pCA2YqlR8hfJESWn+UzvpYWwph0xrJdbikomxIAMYUkZwPxRlAlqGoLqq94jLWAN5TAlm9QPnslSj1KiQ4xGJnB8TDpItz2BhqRR6Aqla1cciOP5b4EKVh9tlt3CNvnTFn1wiQ1v7KollIs8vcFVxuQTImCHHiK7AiarOJESZKfSFITFsCF7JEnqQsSBIJZ+Sb5ZbNUyUxRUFEEz5xmTGF4eXxQOK8Ix4orgSSNQJrqJ+K2aDxVZxhIZIW3WsTXa5PO+DOZfnfeDyZ7u43G4y+2kMttXovcUB1JPpy7k8A54Bz4BnYCIZ8MLLRLLmNp4Bz4BnwDNQZkBWvKzfcDkNDw2WsnYbNt1CSyYQesl5HrbiBV81gl7x6NkUUuQmBZdMXVMPggFWNdCLjU53pcXSoGC8FF6WcuElX/EisIYr5sxYGyzGIx6l2V1d4RyUwUE5aFa9hdg53FC/KIapmjQa8ySg+EpxBUUACHeeydISZsGAO4FWucM91osR90WEYkwhUkX5DgMi6ikJ3o/2xLJwx0+1CKLQ1lb1rpGl8phPmVK99XJrtVTf9Tq1FV0WYSi6GHNdC1aqQ5SFNDzkpjmwSAoJC3B2TgBVsqK2qmvnHvuv4qGNNUWkpajqsc5K4LklKKoM0PjTVrxcc7Ufruu/B8+AZ8AzMF0y4IWX6fImPA7PgGfAM7CfZkDPeLmCBgdabTWqH5xOt+JJl02o5Ks8qytnvOiUG5N/Q8f89dIw4YUrhujaEdiBUfuY2IdeaTO+wgvM4CFmFh2irMelVnlPVrPIAbNLFi/izzMPUG9PD0+uwZijta8jjDGpZxuz5cLQ1lIr2EIufbRj/xGOmyVCGtwXT3HcdZ71TRmneUIRSFlLboOKi9qrDlsFmrW1YlTMogi5jx0N7Awdsza1YSV6Xc3EjSBMPVok3BrVA2TSnwZ+CcY4vkjUg8ZpHNqXtzX21b6VIFsxVpnG9n7gI3DGy9VeeDnwX7aP0DPgGdhvMuCFl/3mVXmgngHPgGdgemYgbDW64FI+32P8n5OWEem0CpMrnUjJ3bYabeOtRrbiRadiijc0ciNylUKiz0Iuy0BEHyAxtpjysyguBARblgUkVryEw3Vlq5GQiCa+4NtKCKZVHawMaYimFrCiF2YZRii8LFkczlGRwkvdJDzm04hi73oaRxhbAMKLIQvSmKYYMcYNmwRSdApMeKCsw/hiZY6CzLuiC5vkHSoGZRaxA1urSMFUBFP70OjlbkywUx16Yh5HADpFaQ/YWCYayOtsIGt+6lhTDvyM662AlWccC5igRxbrWZql4MUzRYJdpeJDcxuj4qhMnlrCPs1fvaVxoCW4Oj7o98Wz3Zj2HOdbjfbF+3WfngHPgGegdQa88NI6P671DHgGPAOegTEy0F7hBZOg6sQJGp0omb4zbDUa63PScXBmG0uTNmasASqeYVNMDlmkqwMKOfcxJZftGmGrUVl4SZiLjthhRMph0074MjtIYGGatAWcSIUvFF46ZcULCi+7EXhpqDawNA9oQaMG2hPu0BJQBCikpcjGJNYRUJpwYI0SEyFlFKWcG3rJwEpQ2YCWn4ikgPNDUSm2DKHUR2GpaXkXSxtPHZtCDVOaciP1G2usHUcDqXpttq/JjXgDVeQWIjC3HqmgzGJipRfYSxAYR7N3hAorQULWbGWa+laVoR53cEqt8PIePuNl1sGZBB+1Z8Az4BmYZhnwwss0eyEejmfAM+AZ2N8yIFuNLnrT22hgoK9F6DLtap4sxZMykEgBZM2a1dTdLV81wooXaPWZMqa9FMkewgGkhScml4m0Wshd9IVFEBZckIkqL7wEWWHT8IjN68ZfeCld19EAIzrl4zuPpWNmBy1axFuN+vqpr5dXvGRXbJepsq4hhV8yE19pL9bUtIWqYmD8aKVFl8gATQDxm2G5FsTEJ0DaUmhpEIIyhGU9lgVQeUs1TcWIloUXcc80iCJlFEdVibqHhfbsLviqriqxHKiHJj/GnLeaxpvj4vFrHHXR5Fbt9vO4wY085HrwAoe+PyUD2Gp0lX/VyH8QngHPgGdg2mTACy/T5lV4IJ4Bz4BnYP/MgKx4Wbf+UhoZbt5qhGlT3TQJunz0M3jFy1r+nPS28DnpusJL3ZQRE7WILYj4JoWXcskAd1mUbCsKq2HYzmb4SiL24Sq+ajTmihcBB6fFdBsE1dFDAoT6sTv0JlFmIZbCy2I+46W3lwsv5VYjQ9bZmhatJpRFJC1BmURsYZeWUERTfykeVoIxPmsFWzgEUfHFnmAbboaPoSqNPYCg6Wk8KaLudwWE2qSW5lNaqS7OlHDEWrMTTZ1tLk8tRJsy5j1FNN+Vr441s+GwR4t/Oyk67WVWE+zGOWqiEL+Cmwr/TT73H7mueFlH11zjh+vuP2/NI/UMeAYO9Ax44eVAf8M+Ps+AZ8AzMMUZsM9J85d1xnmlU6x0IqWFl9ZbjWTipSs0MAEDI/oIiOWh8CJ9YKQd4RoLL1ZcCJ+TRuElM5dufsWeVCf+YmnkPzNu1jADU3TyVqNFS3jFCxde7IyXlKSOA95VY9UFVAAAQABJREFUV4dIOdIe1j3EdmAUJOSxDAxW0BCtIMGmCJUAnTzZoO5MnaoX+E+sazpVyxhkkcZSa1fjNl3cQjTwZmNu7SHGC1/ehw/I0ddnvTTFWE8jgQ0iNn1oiTouWhZqHY9hW4/KcGO3EM9YyIZ4xzI7wPW+1egAf8E+PM+AZ2C/zIAXXvbL1+ZBewY8A56B6ZMB+Zz0hgsvp6HBPfmctIwnzO7KgUnhZQ2veOlOVrxgolVMzGR1CjdVGk/WMlyVniXAFC6DuZDFcu7rn4De88JLObyiEftKdc0aDolj6pStRuWKl95kYgymOg42DZfprAW75BkM+BbBsEnLcFVWfZ+GEAJQAJ1ioBUbQ8TSmC1FxRqxEPuJWRpTK5609FLnq867jkqyV6dVzzbylDWWW4xpK94m1h4+tU895jrpYwSFTrrRULQZCQpYuw+xzDPb2nbivlrz7t/aUHi5eB1d7VuN9u8X6dF7BjwDB1QGvPByQL1OH4xnwDPgGdj7GdDCyxVceGn9OelsjlYGahPEDFFsNdLCy2bGj/BfTLQKKy6ShMlmsR1FSWMemcYBE5kHILjUKlQzpJkUXrjPVY7AyPClS5fx6pJe6u2TrxoVdmM8tEhRxBuwYhhL64nqpeqsLLzwGS+9csYLx6SpSf1gIp5mxAJWH6086VQbPLGlebKWvR9BxnJocibFSAR1aPVnGkQqErWBJTRqgajRqz6NM9ZpQQTsoonbQKosLRDk/jHeZnuw1T3FCheYYxl0dU/JcGsscoYYYxZ4i2XSBqOyx6j0lxxrco52+mIv/87bvfbUX7t+9i+cF172r/fl0XoGPAMHRwa88HJwvGcfpWfAM+AZmLIM2FYjmTBhgpa6E6lOuWWiJH8Nl7ZsIiWfk16zOt9qBL0yhuJHSYAG+IGVWFhXFme4XVs1EYyEF9tx1KNa8Gn9VSP4FF9yaV/o0gvc6XQ1xWgPSOuBjSfWPBaseAlbjXq58BLltI4vlxm/tXJMOoUXnMYwI9q2hahyW+sXha/SOtZYO86ISrUMIu04DmXLveZjEH0uU1a95/YqRaRmCZxJ0mjAaXppqZWMyOTwAC0s6+VN5RNjF7u6XMBjnDP8HlOfal/vP0fGeUCbR4Jm4bQ63pyn3T6Im/AYZaxPc2OaXJ73gczleR+4/NkuLrebzH4ag201ei9/1WjmZDpyLs+AZ8Az4BmYYAa88DLBxLmZZ8Az4BnwDGgGZMXL+g2X0/BQq61GMpGSyUH9VTfN6gifk863GqX2OtGDdTN/OdktoVWsTFTDMRZx4YXx5QSWTeRz0rUrXoQ3o4SrNGL04sjVODaHbSyDpTzDihc544W3GslXjXprvmoU45vaKX/aC36KEkJiz8EFZAFHrAlmUjpxPM1etPjQ7BAsKUPaU2srG4hW7epw5a8pcqpo86VlJANAY5KxWvWex7LSuMvfbAUucSizjbYCahAgK2lkKJilGWigaBSnnAqLc1anF1SMaSQ/6BRY8XLN1X647kH38n3AngHPwLTNgBdepu2r8cA8A54Bz8D+kYE9/Zx0PqXCVErqH6vLz0nXbzWy8gWsMK2sm5KxJzgLcLORTI9yNSPwafWlTL7IMZ2VM15qCy8lOm+Ij3wajKgLf7lJsKiLX4FhCHzr4MLL4sULufAy0PJwXQzZGDUmiSvNAOTiRzV55BqB3g3NLcmZOYphZVtG3YrPWEuTqNFMrrwGRVx4iiZuA2ky4dZ3Ijp4En3aUw30QR1uipQm3qzZAWUYSMZ6Vv20stDRwEt9pNAaT1ViuuYWIhvLWvTANrPp22lne1HM18p3jBvLbzvxteKYbF27sbf265+Tbp0f13oGPAOegX2RAS+87Iusu0/PgGfAM3AAZSBsNbrgEhod2ZOvGmECZBOqTt5qtDrbaoTpMSbwitYJvU16pa/TuUqaUUThQgHKDspRMLJh8olpRgUT4WOgnPHSw+ep9IUzXuCXlY0OLYLqCEVn3jE2sxBaJVaUaoKMt/rIiqAlSxaHFS89xeekhaMYSUwTtZXJciWqamTGIXjotSm6oZFR6uDEiGZmZyfJQcghTyFcvvGfIf49yC6tGR2M6egMCSzZwvatURocZg42lJzPmilbIgIBP+WK2+gHRXlDzgQZX3G+RB7rY53KNWuwB1ZxOkZty137io2ZcglYwFrF6rsXnPyt6o1B/YJpfM96bnBUvUJjOatiWnMag7TGE7uN2Dhy+7q+oa2V40yz71p7LybfarTv3rJ79gx4BjwDTRnwwktTZlzuGfAMeAY8A21l4BbeavTGCy6d5MILT+w7OvirRukZLzbRRtkkniBiYiPPsS5M8hRbTqdZrDuNwCGFAeFiz1ysCYUXPk+lT85UYYigRB0WyShM7m1fMgpEoGxmWsZUYlQX5JXCy+5QvBAERmZMcUu96R1yWJg09g2UPOW8m67ZXbTyyMNpdtds3uLUR488solGRqXoZvayGueolUfQnDldNDg4RPff/wgbSxWmYGOXglm16kiaPXs29fX30cZHN/EBzcwDTIAmHYmgINAHsie9WJNbxUYxTuStsGpnFtZKLY0jRag97oISvaHTqIGTp2GbGIGIrcbbjiPJbeE3xUAKdKqFdPzPnBcM7YxysmKAz/3/aYWX9/AZL7P2/wH5CDwDngHPwAGQAS+8HAAv0YfgGfAMeAb2ZQba22pUjTCdaqGnkyhZtyGrIGyr0SYm0DKFIvWeT8tgXfWWS8QeE7aizFCEkBZe2Gspbyi8sF5tch+t+0prI0ARoXDHxmjZqFTCd/6jW43kjBfdaiQxwKLZM8aM0cPC5GKrGdGIrAhD1M9FlNNOfRpd/OY30SGHLA1Fl0/9wefoicd3hFUv8Ltq1Qp65zsup8MOW0a7n+yht7/zAzQ8PFgUh2bQCK+YWbJkAX38ox+gpcsW06ZNW+j3Pvlp6u5+oiwgIUJw2hMxQ5LGnvaA0WduKdIcD0wqVyl0ymYIa6UIxdV5EVkTNsXXoeAPOunjnYl1uxd4cjx4RZ5imjU5x/j6MW9sqSOLJWk7jS7VHbw932p08L57H7lnwDMwfTPghZfp+248Ms+AZ8AzsF9kQLYarVt/KY0MD7WIFxOr6kRJNaleULJ9Zc2aU2jbtm7auNEKL+IkLgbETlG8iGV5W2wFh6fq2b/+0YlmWUlhlHzBh69yxUux1SibkebdYJOOKogSOTpwF4o86JQB5TmTmIg6eUXQoiWLqJ8LL33F4brwZ57ylnFZK8dYSUAwkqdw5zzs7umjF77wPPrkJz7MRZXD6cEHH6ILNlxGW7ds468s8Xai4lp9yvH0x5/9FK04YgWvitlNp5x2PnXxsiApFslUfnh4mA5fcSh98+tfocVLltFjjz1K//Utb6cHeGXMDB5XeYnrSqD5KFNA2iuZQkPHIs1WKLUJq5gSmPnVFgpT7bApZ/VunMoiDk2m8SZBtIgco0vxuU94aI2yKKo4ia8qzf1MvB/z5+2cdSrjyH3ty76M034XY0WiK17W0TXX+OG6Y+XK9Z4Bz4BnYG9lwAsveyvT7scz4BnwDBygGbDPSY//jBdJSdN0YuzPSVcT2k7hJfZpU2eOogyEJzmYz0XisQovwhuZheDQD53oZgUWdVCuVpEYYqO8X3CIvWzFWsKFl7Dipe2vGhm5taLAwC9P8c1XKFEJmPu9XOR55jNPoyvf/TYuvBxCDz28ka7+77/DK162hzNnggFbHH/cSvrQB6+hww4/jHp276ZXvfZi6pwxFGIWzPDwSFgx8yf/83/QsmVLubC2md7/gY/ydqPNycoZDcLeqoZUBKbO+J6OJO2VoNBAaUKlORK8OlgtvOQYtRQkNHFbteO5wydswIq+PtvzkY4uZTBea6WIvNeez9xqMvsSaZ6fvC/+2h3RZMY2vbl29/TTJW95Ixde/HPS0/tNeXSeAc/AwZQBL7wcTG/bx+oZ8Ax4BqYgA/I56Q0XXs7nczR/TlqmhDo9SidJmNzVTaeIV7ys5RUv3dGKl1bh1/HLxE254Tf1JFKsfymALGFpAQ/W2YoXfNUImNLOzCphhkKLMBerWfRLSeqqtBerwq80G+Wi4mHEhReseMnNpJ9e5sBaKSLNUNqTLUILF86nE088jubw2SxP7u6j2++8i4YGhsOKIGWawee/zKSnnXIizenqomE+2+WGn9/CR7zEZ7zwYbpdnXTq2pP5DIouPuNlgG6//S4aGMBvCH41ynDnm+YROsSdjiTtASPpbG1nSGnV/15TjPbA2uS3zsZksBbJxBhirpjN5HHLilixdHLa4n08Y4jxeeTCE8vyPiIejz/YTMUTsSLOfRsXPid99VVXUleXn/EyFW/cOT0DngHPwHgz4IWX8WbM8Z4Bz4BnwDOQZEALL1dw4WUgkeedeJoV6zBl0YlWNGEJhZfVXHjZVm41iu3q2phWKmc6edaJN9a4lOUWpilwZSAcQxGGFl7Uk9RM5HDdSuFF1Gxb7hBSeHTXbU0isPhKZwEncsSszrkHSJQSJWUsFzM6+EtB8YqXGAZTxeMeI8AEnT1TW/TEVtr85OrHCBdRwteI+B11zJAvFol9ih2RrxoF0QzeFiUYzbOiwMMYMWUCxUhbJJG/sqdIQdiV4iAP4aBTPKuFlxigFsoG/8WwYtg0bmt26nKEoPHrQ39fPvM48R4nElPd254Iz4Fj44WXA+dd+kg8A56BAycDXng5cN6lj8Qz4BnwDOyTDNhWI17RUE6+W4Vik6x0+iU9nUSFO39Oei1/TnorF14eK854sQl5PT+slVfvkNVZwH/AaJWgCEGtxAarU6SwsmwZf056dy/1hM9JF4yBhJVFYUGlYi9TfS2pIJIghR9GhKkw21lJQhDFBVwQKR9UkmY5C6UsvPDnpAHDmEps0oj4I3ksrdqLRBDyF9q4DSLIgIdcLLXsoQjgRC/t+MptNYNA5BmFvMojktiPIjUKs6q2YKNx5dFV8WNJwAdc3m+Sjw9XHVdu327RJbdDfJP5xG+oiTOPIe/DDnJ5yoW+9uyey/M+kLk87wOXP9vF5XaT2U9jsK8a+Vajycyyc3kGPAOegT3JgBde9iR7busZ8Ax4BjwD1M7huphqyfQgvnI5+oKRw3XXZp+Tjm1DO5pvGDem+flULJ58iqcUl3IbGwovMq9bVrfipQi6uuIFHFouEH7zaN4EZeOGjehNamhrdcwoDtft76feJ3vDiptiV5SBopaMvhUjPFcxkAAB0rHkwOl7kJ5aNPEIItYZfymVg1f0j0IBEdPENu8FQLglJpWMiCcgitINF8CwRcxYJqclnsqx7QFltfBSRxb//uv0e0OG3E6mr8nI4GTGM1lvdeIx2eG68jnpmRMnckvPgGfAM+AZmLQMeOFl0lLpRJ4Bz4Bn4ODMwJ5+TtqmuumERQoZa9bKViP5qtFmTq6sqMEkSydwMiEuCyORVnDpZNS8xG8JbCJT72qn8qJQEa08kRUvYatRb28eSujHfAowv8qfRmWxwKNKdHR6N4y1ZAo9g1cELV5cHK67m1e8SC7CKNSToMt4WKRFmVJiZEULGvMqEukZX8WoVmAMsVpittHDW4xAG36lr74NXXAXAnk1Ma+0zSo0KzdlUF5TFrwsAAd8KzKOyayqrfZxlovoPRWEiKYYZtVNrSRmzAE2qlwzdf26XGBksdc6XKxHuwmXZ6kJBx4828UBvzee7cbUGuefk94b78p9eAY8A56B8WXACy/jy5ejPQOeAc+AZyDLQNhqdMGlfHjqWJ+TlslC/ZVPxwQphYU1suJla/MZL8oYT+jTSWzKWz9ZiaNSvE7lJdLQBwkDQ+GFiy49/ElpXQXBSugl5pisKAIoj4IStSj40m1G2hZ9RKfCGokcntLBn29etHAh9Q30Ux9/5rmMOqwKERadbIe6UUwcgkgjiXup/5K1LooiPnnAQWodAbJm7DFThW4Rf6BNOVWT2gChrEXBjCFNXoA3FpXAXuVFIYNV+q5DMKyCdRM7cMZe36r+bsEMvMUjGvlb71MiLd52MDUejUV1YJ2MZ/tjtHzBr0UHif1+TFLfwvjBgX6Obje+3G4q++3GtOc432o0le/RuT0DngHPwMQy4IWXieXNrTwDngHPgGegyMAt/FWjN4bCS6vPSTdPlKDJE9rBW41C4SUcrosVLznK+vEUrInT0NYa0w5kDAxbjfh8Fy28RNNFwbBeuADXntxtJULsyyIQG9VAbxxAxZLCC1d5Orn4MspfGhoZznOvARVIJWGRrorRLnzBQ90TXhOeAIw1sIQM/fSZcozlXbiAQXZSvrgXozXjqgVDjEXboi1a/LDCWRqtxRJbt2IHrvXTYqjHpR4UHVb5hNVNZqOlpjxm0acMZrEvWhJ/XYzjjSXnmE5jHO9YpgZvhRfZauRfNZqaLDurZ8Az4BkYXwa88DK+fDnaM+AZ8Ax4BrIMTHSrkdC0mnjKJFgKL93d3Xy47mbGylajqbnyqZx4KWXFfFH6S2WrUbnipRqLYOSKxwUe6BQR323aHNvCzpBY1SD86qEIzSARgRURLJ7xrnyIx6FOYkk+oliXhhQjU1SsgY0gcrla5dJ6C82n6JrwsZ1lR/BiAf9xpGMxgbH9Z8zeyso8461X0Yap6sbOQp3NVMjaHbH4xnsYK47WIx/L+kDV+1ajA/XN+rg8A56B/TkDXnjZn9+ex+4Z8Ax4BqZBBuRz0us3XEbDQ622GtUH2moqZofrbg2Fl3iC3Mqu3lOzFFO3nLOc+rECRYzwOeloxUvOWtrkiqIPX6k6LbykOht1bKuxyp2l8mlnXvUSigadHSziAhUf6BLwIfB4wj6+0kuek/poEHEVDU01dmjkaeUO5Y/RojfeXCNauYpMaCfcm5ARpGiqLXyIXczW1K7yjFcCj2oX9yx2a4Gd32UcUiGu4oCXZ2ttjJy6djy+yfQyHcY2meOZHK6w4uXidXTN1Vf6ipfJSamzeAY8A56BPc6AF172OIVO4BnwDHgGDu4MyBkvb1h3Mc9X8TlpTIZqZohZquLpWGwlMDnjRT4nvS3aapRjMrpyihnz5pi838Qp8sDDt7zwIgfsQhb7quMqeQrHwFgc7a/QEBs5JJf/nze32I6fHXM6aPbiLhrpGaX+7f1EHazjbVoSvMQYxyf24yu9iAU4YiYZhfTliQv9GKe61ihoYYe+2EKWeoLH+mdsX0VYlFhBZD7Ui/aBqzKIpLWPepuq1Dxby1D1byocJg33CJKf+D2aPVoAo7+vnnVj3NNYpsvY9nQck2u/u6efLnnLG+maa/xz0pObWWfzDHgGPAMTz4AXXiaeO7f0DHgGPAOeAc6ArHjZcOFlNDg4WMlH/dQxhTVNx2TFy5o1p4SvGmGrEaZZjTYRdRMGEHChn+NFH2SFQia22Go0VuFFOWMPQiIFFpxXEuuqJQzEFD/L+NhUzs+Va2RwiFacdzit3nAiDfUO06Ybt9L9336YBrYP8ue4FVO9p29FI6uicknpPyjSnmJlTHVyGbnqoG1CmtxaYi28cm/vao2UGFIEoqpjV991mpylHtOeVNckISqMFf2UI16/pBqNsR4N29ZaoKb+2SrX8K7jQa/6jPXTZVzVKPelBCterr7qSurq8jNe9uW7cN+eAc+AZwAZ8MILMuFPz4BnwDPgGZhQBrTwcjkXXgaCvU6LcB97YqRTsep0WFZtrC0KL/Y56dYhYoIPVKtpXl1kwEMX+kVoIgtbjfiMl95e/opQUf3IMfBtE3OxBDOe6gE9tVGcaowFLcGqTgs4wilbjBYdv5COfuFRdNR5R9LCw+fRD97/E9p87dawEga26ZOzpK5S8Ri9NNa0p6aIvE6nsUPTGils1QDVJpfnfdhqRHV3FC6s/ISocluRq1fjATaXG2IirbTwIl6b/cTxw07xdbmIoxH9WJgYPxVtjKuJO85rjq2LPcY3ce5NeV2Me9O/+vLCy97PuXv0DHgGPANjZcALL2NlyPWeAc+AZ8Az0DID2Go0Opp/WQfT1taTo3h6FU9b7IyX+HPS4CqsYgOOUrWGEZT1bBiQmVZ0mNKKVBHBS9jWwzIWSeGlhwsvffwXWzvK+LkBWcprfkMrCqq0zSBx1yKxqMVOohWJ7PDqv3OAzvjUWlp70Sn0sz+6ge7724doxtygLUaCqbw5N7bYW7ttjTyNP2aEnxQBdiBVaz1p5RbhXaDIhbNrKigwyxN8sSxth9yxI9m2VVIVZvbuJb+ct2JblxTa4tiAS5njnqDHjgUWyq027VvBWp5xdLEc7Ymxwnpynq1irIsvxos+709OVAcai3/V6EB7oz4ez4Bn4EDIgBdeDoS36GPwDHgGPAP7MAPyOel16y9t/KTxWKHlUyn0rfCylXTFi2gwOStQ2VxMtXpXNNgQRWyPNnQ8reNJthZPjENOM8XkXz4nLYUX+bIRiizBA8JhM/UrnOBHDLYuwTymU8lYXm2nTnDWh3jpu32ATv7QCXTWW0+nmz9/B9355/cQzWI8K0PxoEqWSBBpImzsYDwKsF7MIlLpmzangxYFjLSsYWj5BHbgYSotNQlzM6/6Nfu6Vu4rjjyNG4UXZmFQFVfHvq9krXISx5SOItZMfbsuRolH5HVx1eERZR0euoP7qYUXPlz3Gvmc9MyDOxk+es+AZ8AzME0y4IWXafIiPAzPgGfAM7C/ZkA/J/1WGhjgg10neMXTrnKqxfOqtcXnpJu3GmHSpo51KhZPyMAGHHQsD6fURgEDKqIYVkzyZf7fsvDCephZNCIxYrRiHGRq08YdlGJYrMIYfGyQnvYOKbycRrf+xR102+e58CKrRELRIiq9BFspWxReuV8sJqlxDEe5Ko8YfILPrxhbz4fCi+RJ0cDpsyxwiVI6/JzUwovwiavsCu5YJk/xWPVZYxR+AWoRzBpvOrZGdaloFycG7fgFcV3s0E3Wsy72uhiBq4spxgOH+OrwostxwOfPdnG53VT2242pNc4/Jz2V78i5PQOeAc/AxDLghZeJ5c2tPAOeAc+AZ6DIgGw1euMFl/KWl6Fy6ifTgnavpqlV+lWjTUwXI2N29VbvEzbQypNlxfaRwFKIjJ8FgAua4aHLFYpQeOEvGvXyJ6XLlRhCIm7C7B3+RBiRFJAgDVhpmUftxZLU1vR5S/0NbBykp156LJ39rtNp001b6dr3/YwGHx+kzkXyeWkrbeTWY/cljnhMYoG+xoheay6gcr44NhRewJRjRZ7igUyfGlcqS3viCShdz4T4gDMtNOpZY1QtMLCpPsV2bFTVLpUIA6JINWmvHUxssSeRtRtTjktjtPy0iiW2ifla2cTjnC7tOPZWMe05zrYa+VeNWmXadZ4Bz4BnYG9mwAsvezPb7ssz4BnwDByAGYgLLzZBbH9SFE+r4vTYVqP0jBesr4ix0laPbfoNhZfCKsxzsijiJRAo0lQKLxYBpvFtejdDbmWeIwmziTIiDaGW1mY5zF80WrJ6MZ3xjlNp2VOX0D3fepB6t+ymh7//GPV3D4QvHCmNMWTUJetYDfUK3wgu7zexABfrc45YV21b6aWOS/Dgq9qKRK3wxnIM8mMcKAeZJH4tsRRcdXHV4YCfrGed3ybuKYpHQpAlVOFZ50Nj1LvGZihrpVHH6FjThI8xB2fbCi+y1ci/anRw/gp81J4Bz8B0y4AXXqbbG/F4PAOeAc/AfpYB2Wp04ZveSoMDfVnk7U2MbCqW4bn4sXatfk5640Zd8YJJdzwZxrRMrTOOJCJYydxQcIWlFFnKQkxhMI7CC/zLhL+V9ySUqGPjj4ShKXExqz5Kbg0dXsUjtwXHX4E65c0n0inrTgz9nif66UcfvI62376DZszsMHuMmy0tXvCJY5NKL78MKS1gTWqy3FL6MS7WF+OIRS3aORpRqIn1miKUOEpUDCp9llr+aTCAu5CkcEhLQ2aOuEtxFVeqJq0hkbV7TVE8Ra40ijofGmMcqaGslY4iRseaJnyMOTjbvtXo4HzvPmrPgGdgemfACy/T+/14dJ4Bz4BnYNpnQD4nvX7DZTQ8NJjF2t7ECNMqQaMtRB1cSFjDn5Petq2bD9ednMJLGWBZeGGvwXHsmVHjKLwIp0y12xttGUGlgQjyPOTAVK9Ww08O0aHnHUZnvevpNG/5HLr97++lx+/cTk/c9gQN7R7i8fCWI8luYQwOeaZZl75KpZVfiDGXpxywBxr93Ar6XD7+furBes0eVCNIaakFeuIfbVtfpRjR6aV2uVR0dV7rcAXRpD3q/ObkNq5cs0f94JpvxTClm/+LQEFKdXHe4Xlv5Ai+DuxnWPFyMR+ue/WVvuLlwH7VPjrPgGdgP8qAF172o5floXoGPAOegemYgbDVaN0lvDKAJ/hhAsZRhjlUexMpmORj6+jkwsvq1Vx4sa1GmMxh7Yp4gL3qMK3L2aTPulLNluG/zhcxtlzxUnA1bTUSWqZRpoKvzv0EZBJufokHk2ur/wH+qtGVT6Wz33E6PfjDR+knH7iRCy4DfMZLZ1gJU3IUxmmUwpFKSnzWML+ZoujiANoU14o7RdaztpbWsyOSJtvILze1ziZMkIOV+7kooQQOQoDVTLU5BtjJfprv9pgnMy71LXdhxdN6FlH4p5a5tq61zMJb483A7p5+uuQtb+SvGvkZL+PNneM9A54Bz8BUZcALL1OVWef1DHgGPAMHSQZkxcuGCy+noUFe8YICRrxipI08pNM2NZAVL6t5xUt3tOJFJnI6sbPtHGqrUzy1bJq8MTIGh1UvbBEIoSiCjeMvx1T/VSPh1MJLk9+CcwIPjUruxj2DC0AICZT9DwzSKVedQM+4/DS6+c/voDu+cC/RzBEuKKhdGGLCAkt5gh/PWFdta0xVuXoqtAWV9vDOcpsmJosot6jrW2ZMC78myVuRb27Gr9tyHWHYPO6ZT2tVPagFioW5fvL7cYTtsjfH3y6D4rQUmrBJOCGxKJMqUo6AKX/ORchWuFSMAdD353gygBUvV191JXV1+Rkv48mdYz0DngHPwFRlwAsvU5VZ5/UMeAY8AwdJBvSMl8v5jJeBhhHLdCyebaWwYu6VCrnXwZO21W2f8YLpbb0fk3KrdFhME3EYaBwBZuIllpVY8dLLXzXiv1UIYoiJ6toWTZ3WZLFzSLGSo+DgIcwYGaW+OwZpzUdPojMuPZVu+rNb6K6/up/PdTH78g1Erq1pLXipexpbnVZliA49IOttW0uLtwOKMZ8YowFbMajv2KauPFQfoXnQlrLU2Zs+t5mKfnvRmudW+TFUO624tIKc4mn2RZ4kTLgOxU+25r6KoDArb40/Azjj5WrfajT+5LmFZ8Az4BmYogx44WWKEuu0ngHPgGfgYMmAbjW6mFdhDLcx5PqJVd2Use6rRihtYKInbPE8DtM3BGK8xQqZIGCrEEZhmS8fEeOkqlLgUHgpPyetXuADscF3/RNo0ca5gBwy9FMWlRpmdIRo3oo5tGztcjruJU+hlWeuoP/8yPX08L88Qh1zgSueBWUyNKtCcTTFOTCpy7LXKiKA6nIwlp1EV48B60SfGH+9vRaJ5B6f0INIzFYkFmOsR9v4zcpk2mrW5Mg971fjquecxJjwbyiizKOo+22EuAKQb/hh1gfr0nFkwL9qNI5kOdQz4BnwDOylDHjhZS8l2t14BjwDnoEDNQO38FajdesvpeHhobKUgEkX5mHS1zYklg1gTaKtusKLsAhDtfDSmrdpJUbpMwQRRYJJYCQqV7xkhRfhUFjj1LJ0AyQEoK9GbwjbVAWUWLEvfgwPDdORv7aCTrtsLXUtnEndd2+nm/74Vtr94G5e8QIOtmvpSJVx9Jpfk8AcjNWnIAwPfb1dKhUrlaRycOz5E3nLmeAPEVRxhlBb9HMmJDjNAH6lgk41VfvJkjRHWPVQHW8VM16J+pd7lR2STIuQoR6vS8dXMqCFFz5c9xr5nHT5PwQVnAs8A54Bz4BnYO9lwAsvey/X7skz4BnwDByQGZCtRhfx56T7K5+Trg63bvqJeZegZe5V9rmzds1q6u6WrxptZg0v7yinc4rSKbOWJkSCuVvJUUiCHP9VXmQAsk15BX2hiPUsD2sisOIl32pUONNaTWwozNK3aNK29XKr2KKMD42SkhuckjlHzKHla5bR6PAI7fzVLnry0R6uyEQMAY9+7gmktupDkXJXR5pj4OqeihNNzA6PVYtcAyvIja9quycS+BEO+83E48zZEZFh7J0ZNkbBh8nSrMCq3TGOFxf7ha+mJ2Jt0otc+NrBFVD81oOJriYSloQBIYowtAttAhKr/CoNckXWbwc3jnGNJwdJJOPxAcN2Yhdsaxy2Gl3lZ7wgsf70DHgGPAP7PANeeNnnr8AD8Ax4BjwD+3cGwlajCy6hkRH+qlHLq1p2wRwMZvF0YgZ/1Wht9lUjm8KpJRjBY1M9YbSSQZgCFedJBF9Y0QLH8iwLM7zlRpaT4EIThZdyxYtFG/jT6SWsoyeIosm7GirG1DbMoBE/+cVgHIzBZ7wM98ono3mzUBd/xSj8B261gaVSxw6ED1ppxzqRax/5zRHSzy9Y4ZnqwV+nFZlcwGiveq+zVVQzA2yAACvGJ/16TDUa/T1V5WkMxgdfTU/4bdJD3i5O8IhuLBvR78nVwB+7b6KPMdIOoTTwVTgmG1dxMA0Eez5G22rkXzWaBi/UQ/AMeAY8AyEDXnjxH4JnwDPgGfAM7FEGcMbLSHHGS6tpQzyRh9N4Hiayss9fNZIVL/HnpK1YoKg6XyKTq5zTYWJdCoBQXHkvCy+sBySRjdKypcuop1jxIhiBIV7hqRtfyR8aio5tInEKRQxlMKk6HmGqYXZxEBeXQj9F1ffilSBAlIFAUHnqZqEUZ2O0VsWwUQCu2FZkcd+M69CqhQ2eZoO3hQ1B8bur91J93ylb3NtX7abI6+JB1up0E5SJ+0BbNlgQtwveGtEEPbpZTQas8CJbjfyrRjUpcpFnwDPgGdjrGfDCy15PuTv0DHgGPAMHVgbwVaOBxq8a2XjjyS2kMgervbhwsDb7qlExq2O4ztx0Om0MMT+kOr3kXljxwqaN803BFJGgaFFTeNnNhZe+mq8axb5rxxOE6gBuKrhYkcWpXZyHgjjZIIwLYNmO1XQB06QXeRxAEw48ikVP0dZTbTt8hR+BluZosDBqxhGJGOwNkJgwMrVtVSrMT7RJeeEjIqhtIoZa5R4KEUPdmKvUQFc1JtFoY+SkxM//XvDPDL6Ud1LYQenPMTLgW43GSJCrPQOeAc/APsiAF172QdLdpWfAM+AZOJAycDMfrrt+w2V80OtgNixMtuLpnUAgN3iOEE0Hr3hZw4WXbVu76bGNm3iSbRNmW6Vgk2SxiYsfyil3kfIzzAj5iaKKGJSX6MuOYYJMbhxzsdWotvDCkBm1vOCMyVNXQIQnYNUUhRhEDIjgpS2yYoSFBAiQoM/q8oKuFEQN4IGJ+1gRIzqVKwpYo8E7MknaggXYw0CSToSXrV+lTsccaStNg8JLDNExQBKw8tvQPyxWa/u1QQKL5ie8CQPahkZUVY1h0hYsIBVLyJpZgIAVnrG18FQZqhLYtvlEoZKJ4ijif5dtMjlsDzIQVrxczIfr+uek9yCLbuoZ8Ax4BiY3A154mdx8OptnwDPgGTjoMoCtRhP9nLRN0KSlUz+5yxkva2Sr0dZt9Nijm1gAvU3pBWf2knqb4sVy2woDDjHkdiiW8DMGBxqNQxSjPCnXmsooLZWtRn284oXPeRFZbAbPsUyoAoqFcV2milFkWV+AexXzXQVyb7ItoSXC0KkNIjULawHJtuqypUPJq1goI2zNxCIwD0ps2FhjbXUuvKPhvRf4yCzmRqiwj2AsirWqiSVikxYhzDrOlEnhpf6JuHIflhWxq9fm0lY+c6xFg3diktQf/v3UM4gUfusRMW/WDv+mhIAt+b0Zz7iZMmLvjicDu3v66ZK3vJG/auRnvIwnb471DHgGPANTmQEvvExldp3bM+AZ8AwcBBloXvGCwWMqVz/5wuRMp3uKkbt8TnrNGl7xsk1XvMR6TOnALJ5iduU0reqsH9Dhv86zrKx2IF4hU4vgR8i4L0WGpcuWUa+c8VJTeAmcEYU2bXTSV9Y6jelSC7OKo1eGuntsDQuUR0bYPx8cHC7WFZPjkNcwxpRPVvAENr4h7iq7SeowKSN6iEuIOR6hkL+hwBJhArVgxmaOEWAHk0YfyEyUtVBgUZRh9a1n4DG6iKUKM17VpUhoIUW/ylNnDVSdFRiB0XRbL20BLUxop4jx9CaHZTweHUvkK178V+AZ8Ax4BqZfBrzwMv3eiUfkGfAMeAb2qwzoGS9X0KCc8VJuNRjflK1uutjBE+7VxRkvstWomJ2H3NQVXuKkifewioHj0Xk74ok8SeFBrrzwEsSqC2i+KUfrwkt1kh75Uk+Nd0QngNgKBYFcHmbEMTAwVwQsZQYWy6qRkZERfnYEU/FnW6PEDhE0cAR+iw3ZidGtGdQ2xRQ9/iqTFl6kz23Uhgqf+oCl9GKvBiqsa1IDTYq1nrbqWUUX+86tqv1mdJ2HFC0ISOrQsTfgYll9btpHplwWSy73/vTOAM54udq3Gk3vF+XReQY8AwdVBrzwclC9bh+sZ8Az4BmY/AwkW40wW0zmetKJp5RpDLGJobhg0tGhW4228VajMQovWvQAk04YpfBiYRhzGUvYFoHYihAltGCkllLgCQsxQuWFv2rEK17CV43aWvFi8aQjtp7FZzJYaWQxIh4D8E0y6ItVK6E7g0ZmjFCHFF/ETM7hDQ3Fqr/Yu8i1j0zCW8hLqVV7uWu0sU95C/GlvdEi96NcdOmY1UmHPmMZLT95GdftRumh7z1Cux/pCYthNCYh5pbYtLgCtgiwtEu8m73o9bKW9BVhOIyoALd8mM/cKubLKVL/sbaVFXDwmbLklqbNNeDB05DIRT4WIP05nTPgXzWazm/HY/MMeAYO1gx44eVgffM+bs+AZ8AzMEkZuIUP1123/lIaGR4agzGe1hk0nwwKSib2HWXhZStt3Lg5SK0AoFaK1clhzKNy9ad30Wqr9Bwm8iwTcTypDzBg2S5aGSOFF9lq1FNTeLHYxEMejcoiVhFkVxNDCLDAggGm6keLTBijZM/KTn0jQ/TUxUfSi55yGi3smkvDoyN03cZ76Gdb7uHay3AggmdljWMXSe4/18eQKhM2Omnkepf45M/IwAid8Ppj6OQ3nkTDA8O044En6Y6/upOeuG07zZgpWFn+wsA4BJbgUjbpWUt6GqHezbiOpBovrJVR7zGT8ONqkoMDuOZnGrfgwNlsU9XUs9RLq9axRLybnbVijLenewa08MKH614jn5OeOd3D9fg8A54Bz8BBkQEvvBwUr9kH6RnwDHgGpi4DstXooje9jQYGejMnmLZhKol+ClNtjuHCC69yWM2H63Z3dxeFl+qnkmXaHCbxKSXPXtlX5M6a0cQyalrhRZCs0L1FyloWaHTFixZe+hiiMesd7vJ4zLPwhl7hV2jhJuUYa/KtnFpckRClL395KxH/nRHGrhjR9nPh5eSlq+glx5xBqxYcSk8/9Hj6519dR392y7+E7UeIQbB2cURcoOG3UMaoOvAWg5BciW8R84BQ7kE5Q9+Njk5lLAlYxXfwpPDs951GT3neUfSzT99EW37RTX1b+mi4XwtCmomCXwJgKjFPL0gQk1rhnlpoLLBHnOirFphYa+8V3sTGkGCIpbEsb8csqQ6cKhVcKknRQOU4san6aGaCfWpXZajzDlnO3q41fIOn6bmvcE3xTIV8csaIrUZXXXUldXXNmopAndMz4BnwDHgGxpkBL7yMM2EO9wx4BjwDnoE0A7LVaN36SyZtxQvY5XDdtcXhuhvLrUbQ6rN+moIpvmF08iroFley6iXCYj7JhZZ8xUsLtmi6bFGCta7oEnPJCLRoIVJYAaEB6RRZuWUVyyD/ncWrhIa5PjXC7dkdnWG7lmznmdnZSV0zZ9Ezlh9L7znz9fSDR35Bn/3FN7nwMsSFFeFQH2I3ODJMnWyrZRwuvbCj2R0zi3h4u9LocPA1k2VyPIv0ZzFeCmXRoJlRY5Ooy7Gom1DnkrrV7CVddM6Hz6TlJy6l71zx77T7Qd5i1MnRCJUMMLsKc5YaN2KPoWoKArNKAiwijO2kDStpwzKW1cmBE13KYJIUI/KqJPej1mPfq0ytbZr9QGOM0hKpSZq4YQv92BZA+nNyM2ArXq7kFS9eeJnc7DqbZ8Az4BmYWAa88DKxvLmVZ8Az4BnwDBQZwBkvMgEf64rXEABr07V0eqeFF/6cNJ/x0lR4AUf+tMniOCZ/5UyfbWKzIBdZU+FFwDYKxFKsbym6qg+03MTuJWCrTwSQ5YSBoYgRuZNiyeI5C2j1sqPpuEWH0RC/h9ufeJhu736EtxXJO9H4ekYG6dxDjqcPP/Mi+o/HbqbP3vRNLm4MF4UXLdYs6JpPpx5yHB2/+DCaxXaberfTrd0P0UO7usOmH9maNGfWbFq77Fg6YcnhLOuke7dvpF90308DwwNsYSfjilf1HILmMUdBF0WariWz6NzfPpOWHreYvvu2H1LPQ7xqqkNWNhVbjHi0+M1E1qyXCxrt6bkxKi8k+ghRFM3wMM5Y2qod+47fjNigb/aGtlYdrpWlsdlvq4qPUa21MdLaFp+1VFvPVi8Vi9xeZM1o0fo1dRnwM16mLrfO7BnwDHgGJpoBL7xMNHNu5xnwDHgGPAMhA/pVo8t5qxF/1ajllU6UY6hO2+QeTda4uXYtbzXiz0mPt/ACbvWY8UKZP1F4CStAIqXIRcZ/li1dmpzxEqEqTR1TRRwEGGUrTJKLhAZWuvrksLlL6M2rX0ynHnY8be/fRbM7Z/EKlC76u7t/SN976AbqH5JVLURy1su5h55A7z97A/3nplvoc7ziZZRXt8i4hHGY75ev+Q164dFn0Pa+HdQz1E9LZy+kW7bdRx/7+VdpHpPM4lUz6056Pp8XczrtGuwJtou7FtC3f/Vz+up9P6KeQdmCpYTxGKWtUctde5LWRcfOp2d94Cyafcgc+lcuvPQ+0ld+1Ujt1UqGby3pyaWIlFs11btizaqKqJNYtKkWPo011luksI+1aTtlMEugckmKB0qezZoYZW1jtlY7LOYntjNeaxnSZN7aGxnwrUZ7I8vuwzPgGfAMjC8DXngZX74c7RnwDHgGPANZBm7mw3Uv2HAZDQ8NZpq8Wy28tJy6FVuNusOKFz1cN2XExK7KknuSVSK5LOWSHvMIlRQO4guFF1YuW8qH6/bJ4bp2xotOVqsxiNyk1orZTRo7jBGQCzKVi0RWGb3quHPpt9a+hP7j4V/Q9x++meZx4eXCU15Ai+YspA9f+2VekfJo2ArUxytensmFlw+cs4F+tPEW+uwvZauRrHjhoguvmjl60eH0yfPeTH1DA/SHN36NBgYH6OjFh9IAF25++Nit7GyEnrNyLV1+2svprscfof97/8+pZ6Sf1p/063Ti8qPpd3/6Fbpuy71hpYzmGmfyaC+s1BFnLO5c2EmzFsyklc8+nM649FTqvucJ+vFHrqe+zVy8KxbNhNFGw05zZbmQlupShGbOcNJPe4poutexAduaxyylhfiqNlWJWcJTLqnaAGkaeIWm+kxZ456xVK1MYqjY1vTaMlSu8f7UZiCseLmYD9f1z0lPbaKd3TPgGfAMjCMDXngZR7Ic6hnwDHgGPAPVDMhWozesu5i3rchEu/VETKdicjdc2oKOJ+udfMbL6lN4q1HTihdM7IwB0aVFlkgvzWAGW1jIE7hYx+1yJYxuNZIvGvX2xoUXsRUb2FvfthtBZzj1Ep/lInZyqcbasJWn6Ya5aDJ/9hz6b6f9Fzp75Wr6y1u/Q1t6dtAgF0pewF8wesEJz6SP/uhL9J2HbqJZ/AnpvtFBOocP1v3QORfSf8pWo198qzhcd5S3J43Q8YtW0Keeexnt6NtJH7/+r2kbPzf37aKZ7HMO23fwmS4Xr3kpvfz4Z9JX7/ohPbRjM+0e6qNnrTyZfuOE8+grt/0bfeH27zGaD/gNwWvceBd6zgtrnxymEy8+no59/iqad/g86uzsoBv++Jf08Pcfo5F+/g2Jsfwt6zbS0UyCMdAXN/WVZl970Bi6KjFd3kLWc7n063lSC+kJLpaiBCUcKYshDW8txeNe7z0u8ykyxhm/6KrMuSS2FV1sH7fVE+6xlcogAQeQrZ45f96HbS7P+8DlzyZcLs/7OQ/67eKAn4pnGoNtNXqvf9VoKtLtnJ4Bz4BnYAIZ8MLLBJLmJp4Bz4BnwDNgGZAVL+trV7zIZEAuTOrQVynuqo0x2p4RPid9yoS2Guk0RP2ZV+Yt3ZgUcURKE0mrtNEVL1J46eNVLw2z78S2GElEYmpEAHrV5DGbe9GLFviB4UE6YsFy+p1zLqATlh1Fv9rxKA3Kybp8zZ4xkxbMXcjFkO/Svzx4ExuNUj8XXmSr0Qd5xYuc8fK5UHiRrUY8ZefCyxAzX33mb9Jzj1zNX0IapPu3b6Jfbr2Prt14F92781Fayito3nX6q+h5R51O9/EZMgNc+JGvKM3m7UfzOufSv/Nqm7+44/s0FA7sxcgxSombZXy4zQh/sWjVK1bSqmevpOUnL6V5y+fQLV+8g+7+h/tppI+LNmwi1mGsoaFcYWDlTXmNvVQEW2UQWYpIe2YjrTovKSLtgUtD5DsEEQycokI7UhcmqSEKVPUWsI5tQgSlh1ij6KpE5Gk8aS8dTMwPrhyvnuQOhEnQatYAMXVPxLsvY5i60eXMvuIlz4j3PQOeAc/Avs+AF172/TvwCDwDngHPwH6dAZzxMtjGGS/5QDEdSuUqlbNC1oz7jBeZWGGdia4BwFRLJrThU8viDMLEMaLJlKW4KLz0yoqX3mRHkkAyq8AM08RNY0cZch7liDxwEUVWTgyODtGKecvow1x4mcVfLfrKnf/O57IM8HG3uopGMA/u2kxbe3eGSfYAF1PO5hUv7+fCy0823Uafuemfw+G7gpNzb+Xw2+VcrDnrsJPo+CUraM0hx/D2oxX0Q14x8/u89WjezC668ozX0knLnkJfuPlbtHOwl3cFyfoWGfsor7bZTo/sfpy3P4nE3gGGa+PgFq9y6ZjZQauev4LOeufp9OTm3fSDq3/CW436o7xqCQL29rQMWcu06sf6casOL/pmG9E0WalGC0rczmDNnBZRZhIUagdNE4vq5Z4XamBpXqSVSo3VWq3wqgNWvaZ466WeTK6t1tocPXl9xA7GfRUH/E/tE2e8XO1bjaY20c7uGfAMeAbGkQEvvIwjWQ71DHgGPAOegWoG8FUj+UJO/YWJWv1kR6dEmBgBy0d9FCte4q1GUiTAVFN8GRqe1YfeIYuesZtIrM0GZXzGy7Jl1COFl+SMlwpREICtXlsntaithaJAxBaaXPDgAse8rjn0rtNeRU9dupLe/6Mv0B1caJEjUob47+Gz58shMGE7kXgb4ELN6qWr+KtGG+ix3d303679AvXyqpkFM2aFAorke5jLKFsGdvNnqGfS6UtW0sfOvTBsU/oYn99y2xOP0hWnvpye85Sn0+/95Mt0Y/cDvIpmmHo5jlVdC3nVTOErBK9vSvzKhaKXjoLvguHVL3MOn03n/c5ZtPDIBfSdt/+g+KqRWCADjBUjdEUVdazEFBThpj6sH7cSmljB7aodJK2shEQKevyIYLAUrSnqpYqxu6KELMUbQlrqTFGGi0JI4XFwrElLWnW+GpjEVVCZz8xR5inWNnDGkClr5/HuaSzMF3KxpzxTM2DbavQe/5z01KTYWT0DngHPwLgz4IWXcafMDTwDngHPgGcgzsAtvNVo3frLaGSYp/tSpJAr/8//Kq29q0VhV07bRiuFF6xfifEy7YGlkqcT/kaHDfOlUCAoYyisxUHA6xkvstolPVy31ksQprE14zCRbkZkTCGmGbxiZZR+4+gz6S2nvoxu2HQn/eSx28Oql8PnLeVPQq+gf7j3Wnp451ZeYNIRDuLt4FUrn+TPSa855Gj667v+k7bx56Jv2fYAPbx7G53ChZZnHHEybevpZvkOOpxX0/wWfy1pS88T9JGffIVXs3TTrx15Cl3Gh+s+tutx+o9Hf8m4XXTo3EV00vJj6Bv3/pju27GROmd0hmEk7yaEH42h+H3MXjSLzvnwM2j5iUvoO2+tK7wwVRhrZMsvQ18fZPYyraBgsoKgTG2sgRBM6Oe/qtbvp2qtPOJJdLFHw8ZS8wvPsI01aKul2dePGmh9xmiR5HEB3SQHi8Zvo4CdPc2TydLW2IgUPxm9pognGgv4Jmo/GWNq5tDCCx+ue40UXmY2A13jGfAMeAY8A3stA1542WupdkeeAc+AZ+DAzIBsNbroTW/jz0nzp4DLwouMtfWkBFMXzQp6YqPtDp6cr17Dn5Pu7qbHNm5iqW6xUS3waq13TMhjGXCtY1ELncDWsYBx2bL2PycNG0SAfvXZXmx1drLKZNmc+fxlofPpWVwU6eOvDA3wGStL+RPP2/p30Cev+wd6YOfmUHiR02qfZN2LVz6dNpz8fDqEizMD/MnoL932PfrOwzfSaVw8uYLPcFnQOZueHNpNS5ijn79k9De8helr91/H58bMoJmdM+m1JzybXn7sOeF99AzyJ6fnLOCVMyP0ieu/Qnc+/jDNDIUXyaIVBDT2LBO84mXm3A465/1n0BFnHUH/8aEf0/b7dtLQriEaGeTtSuwvZIbNZBuUXcrN9Hwxgv9IU7B4g6IZ6wrcDIqZ1cbYtA9kE2OVIUXG9oaNpTEem7RUZnjDqKXZW55NZmhtmUYZ5W6yHN3UF4s0orQHu2bmZg1sp+ZZF+fEY4kzPnGWqRmpsGKr0VVXXUldXbOmzpEzewY8A54Bz0DbGfDCS9upcqBnwDPgGfAM1GVAthqtW38pr3jhz0ljflPORspGnWmQqQkMFSZWOFw33mok3xrWCX2KNytMJ21qpLpCzpP95vkmpu01MYs7Fo+38FIXJeKJY05lGiJsJZp4NNoXC41TPim9YNZ8OmnJkbRy4XKazZ+Tfpy/RvTgk9vokV1baCCsRGI0w4VTahon8Rkuy+csopkdnXwo7ybazCtf5rDdCcxx1PxDaBF/LWk3F1Xu5y8X3bV9Y3FgLttzoaeLcSfy6phVi5bTwlnzaHvvbnqUV8nIapc+9oXClUYXgpdgZWdRdOno5CtGqy87iVa/8Wm08+FdtOP+HXTH39xDO+7ZFb5qJQYw04KE2KkEcsFAqqwiGfvK7dWiHQZY1mNFaxpgEbXoTFsXZVFWKlRNWOPN48418NFc0GmygGX8lHgE3xSXYpsZRdPKHrzNDOphvHfwxnZ74gP/WyF8e8ITx9Oq3SpnVTtb8XKlbzWqpsclngHPgGdgn2TACy/7JO3u1DPgGfAMHDgZ0DNeLuVJORdekqu9CYlOiTAxsglGRwcfrssrXrTwspmZ9RhXdQF84pA7Ou0PC2+SVRLF9AhmUoWovQSQ6ZQsiJctXapnvITDdYEDaZWwWQOscoAJ0vxZnazDQlcBDfHKFPlctKwSkkD7+dwV+QT0HD6rRS+RazQyHDnvpS98lYhoYcessCJG5D1cPBMK4RlkgXxKejZz8KsIWRFvIyyXs2F49xJfM7goM8Lbi2bQXOaRA5HjK2wPY97gOaigDxIaHRqluSvm0DEvewotPWExDewaoLu/eh/tvIfPmeHPicsKKuGMp7ngT5kQH7Stn2KrEeS4GqmI4CyH1/Sr3LlxjY+Epx18M6ZRI+8mcR13cisJCPpYJzLpQye45iu2NFRVWrKG9y3IKsbsJ9LK450Yv7KkXCg0TiSqqbLxM16mKrPO6xnwDHgGJp4BL7xMPHdu6RnwDHgGPAOcAf2q0VtpcKCfe5iUtD+xgYXail0h4SK9wfkAAEAASURBVIni2vKrRmMVXswfWnGxAjJQh+pCu2+PJ4NhIsgkofDCn5Pu7eurobCRgLoqgaaMqK0ppo6liCNQwF4LLyhMlGNmaCiCSOwBKrc4GumrzMoaHWFFS1nggl0YfwEXm8Atj5RPV2rEsrgdgg4koRjDLdUyHx8ALNeMruAwFGOCMhRxRFcU0wLKbooGj8nbbym34TUO6xetHMZi8a1oa8GuKhGNRqs6+KlHAqt8wII9fipnHa5eE0vBA37VyR0SbeU2ok1RYLKn2VhLtMacjjHWKGbyixmxb4kljUwk7V7KJP/ucFkLkn399K1G+/oNuH/PgGfAM1DNgBdeqjlxiWfAM+AZ8AyMIwM38+G66zdcTsNDuuJFJibjnYrk0yKZimGrUfe2btrIZ7ykE0FYiKeqR3xlptSEAgRjY7PKGFlZtxUpKbwUXzXKPiddoSocwV1VrxmqyxNsYp3KoAFbjkAfZRjFQRp6QlGsIAlN6Ya3ZdzaglUhB1gpi7tgzA5MolRprCtMoAR96Tsr22BfUomz31Q8ujyCyMsYzYbYxrAydRRYEIIPcvTrLFB6El0VZxatWzr2en+Qph7qpepFdXLXiOK4Yl0sR3wxL2Qxq7ZRqEvRmguVMbfQh4JbyrPnvfHFPZY/ZdP7xItEYbBjuZqQPqx4uZgP1/XPSU8of27kGfAMeAamIgNeeJmKrDqnZ8Az4Bk4iDKgW40u4dUSshWoboKTJ0OmWYZLWzYtk60ma1afQs2FF8bKHyYwDvVlLJlvAMvVFNCzRSiwMCCZ+IlctziJeNmy5bzVqIe/atSbwsoCAvjkialmLMsjkz6CwlPx6YQO61LAJVjY4gkdP4Mavox3xiivaqnbgiWmwQYxVzmz0kjhzKLUNQC4w6fqrcf8wY+Ym49KpDAIMNUCIyKoUwZIwWxasbHLcCZrpyV2iAJP+NK8WTZyPvVZ1ef5Nl4bZcxVjInpArL4rYJFkMqgv5cCXUpLJvwGilRYXPidFYpgYFqVpjqBmJ/SQ2FpfTkfSMIVa40Rutw61bZmz2OJ+zG//BvGBX9j+QEOdk3PdnFN9pMhT2OwrUbv9a8aTUZ6ncMz4BnwDExCBrzwMglJdArPgGfAM3AwZ0BWvFyw4TJe8SIHq+qF6Q/6rfIDbAXDB4usXSOFl2284kW2GgkSjNqOe7F9LEc71te2QyB8SwovjESlgImWLW13xYt4wGRcIzefTRGFAAzGY42RuVaAOpFlFIpGpTUscyvIGSg2TBBJiogDc8Gk9gHDTSxEUaVayl1Rei8Mo4f5iAsEEaBgkIOT9cpxM+C4AKSezCrlRC9FQzr+p/DoWLS8JD29xuvB7MZraQ4DB278LjVFiK+Ot/AaHqxPIIWuoM+ULAWvAGBoNmhBAxrIzaoozDGw1JUNQSUd0EzCE5EJf9zW3lR5nYTAJ0ThK14mlDY38gx4BjwDU5oBL7xMaXqd3DPgGfAMHPgZ0DNeLufPSQ9UBqsTmrGnNTIViqdEgYiLAmvXtlrxgumgTaUQgG5gwKRqbP/BLhQvpMX42CSap9nhunVnvMB7/FRjUJgmdiDSKgJBxEhD8QSWT7ddMG8+DQwOUH+/nK/TzgU2MDUVRVJc2hM/KpF7XiRpJ4o6TCiuFI6qnPHEP7euRmcIjNMk9a06Dh2d4WXFRjhRWOtcrIgRqNeVPyMzzFrtxiRmeD9ZRpgiaLByhZHGWjeWLIQk8lzX1K9/B1VvEolKU50UXfhfZohdfBQRh8Qhelg0xTARueaunln/l6JepxE26SYSidlYjkw2eS2c8XK1bzWavKQ6k2fAM+AZ2MMMeOFlDxPo5p4Bz4Bn4GDPgG41upjPSB2upALTxooiEmDKFYlCc0ZY8SJfNZIVL/VnvIC/yhFLgMo9pP2wFUI9Y97IMy/w8PSL/4y38CJTPpm4gcU8xtO5qlZxiomRIg9ovnVw4WXp0sXUw0WX3id3W8zmpI1WmArXxCemiDyPQGmhrRtdG45TCFKQu5JlHLmsjAsU+n41L3xP8CAGNn0KVN9QKq/2wJP/llAQMTmQVQ5IFKG+IWv1rEfqKqDCGxcvEAmYwtegwu8XCRk7MtjWP8GjWkQVS9WD3E0KHH4nZaasApPg631XpfBV8lUhhaSu8GLxISsmaSRKFGAdr11CMkUd22r0Hv+c9BTl2Gk9A54Bz8B4M+CFl/FmzPGeAc+AZ8AzkGTgFt5q9MYLLqWRkaFELp2xJ0WYjlVMeUWHbDWKCy8xRid3mPRg8mSIWNJeFKixBE78V/iShqX8p/VXjcy7tfLpsGnGbmF0KVKkEqt8bnvxksXU18uFl14pvNTjU+u0F7hSUUNPueGhTEtApz2ddOeyBlpJajwBTwKSDgTCJ9hCFGm4yRpdSREAYlJezXEEmxI3VsMcgx4SPOPYMCmvZ0VMGFs9qpSWDkQiOTB7K7cI10gofs6Y0ck/Bekb/yh/8jtkKXwD3Ow1oawKWHnW6UQul/DpZS1I9KnWMUesV3nlX6OIE8KKICYJbWWyomaFM7EY622o8ySExF47iEp9i0wlY9nVUE25yAsvU55id+AZ8Ax4BsadAS+8jDtlbuAZ8Ax4BjwDcQZkq9FFb3orbzXKt7tgqhKjrW0TGMhSvMwd13Dhpbt7Gz3GZ7yk0yfdthBboh2eJZXiUP4IOzPCTCneYgAtW7KdqvmeB8ii8a54EZKcJolzzE6IphYlK14WL17IhZcBLrz0ZJPXWpNIKLzjiUzjQDSpZdpTJ/XIKIBirl3EwY+onqAw0MoPIbS1VCJdrSMUtjFpYxtkMUDsx77iN6g7eziOIqTwLKjxizJPaBV+ym7ZYOdoZ3GEofFvt+Yw5AQJcw5k/uyF1NU1n3bu3krDI/KFMY2os6OTFs87hIaGB2hX73YWcxEmHAgTnAQ6zWzCzB2QQ16Mg7vWgk6fZoEWkOiLLWSpbbUHmya86kPdLkDqcAWGyeu04lPLN+q9GaP6unuTTR12b8mw1eiqq67k38SsveXW/XgGPAOeAc9Aiwx44aVFclzlGfAMeAY8A2NnQLYarVvPK16GqytexraOp3cyScI0ZjRspVnDh+vKVqO48CIITIYxicMUTfyBQdoBGSaZPL1SQxVzW0syBUYfwVjtC5ZQDSja/Fi6bCkXOXqpt6f9M17i2NT5eO6F78SER89jksLLkiWLOJ5+Lr6g8CL4djwqzibc7dkgmioaEvgHUgIXHfrAiZw1I6O06qgj6SlHr6R7772ftmzpZqi+VbxjBTKWvy51yskn0mErDqXrr/sl7e7pCat+gr68gR/+oIAcfXnmmEJXhptEEJQhMv5NzOjsoEVHz6P5KxdQ55wO6r5lO/Vs6mVKNg7VGIHDp/gpSNEs+yyXP/w3/D5DTMw/k+jwMw6hvicGaPvdu1hZFEtCyFHc4ffJK13Y70vP/K+8rWQOfefnf85FUPl9dnCBapjmzJlH5615Dc3qnEXfvfEr4d8p/t2EQfFNw1JeGXXoh5giXyFmWMgzZ7ERx63Yot4qR7TZL5PGgYYrjjXm0PfYrDWNtWL7VqPR3KXofd/TFS/8OelrrvStRvv+dXgEngHPgGcgZMALL/5D8Ax4BjwDnoE9yoB9Trp6xks7xJg26fTGpj6ylUZWvOCMF6woEE5MiatTP/UITmEL7TBJ0w4muJhgioViuFG4N17WBCUr+M+eFV7KaMRlG1cRjCATU42pg1cyLOLCS38fbzXqmdhWo3LAZTRhsGXPGhoLIlKU3GOJtCVzktlIh9yXWIYVk2YpYF122QZ6+ctfSl/4wl/SP/3zd/i/0HeZW+YRxhEuugwNjdJ73nM5nXPWGfS+93+c7rnvAZrdxRWKmDdYmm+JCL+ViDRpasQQwVaeuIxFzlUZ2j5EJ1/2VFr1a0dS1/xZJDvsbv3CHbTx2i28/6soWgRT5pAfm+7yARk/9Zenv+fQNV34cY7S7KWz6DkfP5e23tFNN//JHTQ6zCQShlxSSAxt5uc/gyP9dNwRT6eLXvA++tXm2+nPv/PbNLtznkIlf4x90VkX0ekn/Br95fc+RQ9vvoM6uLIzg4s15ShLzmBWRKjtpjvyVoZVeNTgSuYac7WAXQ2gPVGl8FJvhkjgT/rStic0Zg+JYUxX1wK+TrcvZL7VaF9k3X16BjwDnoHWGfDCS+v8uNYz4BnwDHgGxsiAftXorTRY2WoUG2IKE8u0LRprRVMYnjHiq0aP8eG6mK4KNm4XxtlDWeFVp+/oZdAwBWPOIpAw97VZrs7QpM9/JnLGi3hTahlb4SQPodKP81BjxjSy4sUKL1jxUiEaQxD5Ccim+JpwkIudtPkZ/siEv4Ob0tHKA74IJICAZHEfHwy8fv1r6AXnP4/+/u+/Qd/53g9o1sx4a4TYj+rZJTSTPv7x99Hzn/9cetNvvZVuvfUuXs0RF2mKobJJcCC+x7oChKORgEp8iK6wDIqgE+go7+CZd+gceu4fnEsLVs6ne/7pfurd1kubr9tCOx+IDzjmsgqPG9ahwR1ZqYSCS1k3EE/cCWHz1iK55qycTS/93+fTYzdsoes/eiMXd6SoKXnUnHaEryupjRxqfcH576MTVz2D/u7fPkW3PPBjXt0yu3gdozTMQT915en0qvOuoHsfuZm+ff0X9NPvEo84iy6NWUtnUrEp448waGIkwChXzCiauA9LeSq3aGEfa9tpy2HYoaoU+ajjiiOoj6h5nLFtGhM06rHOb4rfuz3farR38+3ePAOeAc9AOxnwwks7WXKMZ8Az4BnwDDRm4GY+XHfDhVfQEH/WuPlKJyo5TrVytymMHK4bznjZtrXcaoQpUjFNjdAxI/PoHxUyZTvTvLa/atTDW4362t1qpCFovHGMdW0be502DFYTFcbXwdtdFi/mrUa84qWPt90k+kAlNxjUMrJQMHU4sQskmWHOpxgZn7RG+PAVOXdm4aIF9Hj3Dlq1akUoVt3/wMP8DreQxBwKW0yzePF8mr9gAS1atIiWsM19vIJly5YnuKCkLod5KckM6qSnPe24wPnggxvp7W97C51//nNpw0VX0G133ENzZ3OBIR5jUdgYDYexhIjCahkpWcg8PRQTQkN9SDFI/k9WfxQSZtOxFILwkNrRzPmdNHPeLFpxxnJ6xrtPo91beui639WiSO+WPhrczaDAPUpdS2fTgqPmBtsnH95N/d0DYfuQ5DTUCzpn0Jxls2hwx3DI2cJj5zP3THrywR4a4O1Fc47oopf+nxfQphu30k+l8DIwTDO6OmjeYV28VWiU+jbzvzVZscKBHXXoSbThhe+jJ/n8lj/7xlV8lstg+NQ44hd/s7vm0Bue/15atvBQ+uvv/QFtfuJhfQ8MkiwNF+9tyZKFNMz8W7duo8HBoZBZfcNg06fKUo1mMCSZQXjCDn08IVf/1mu/pYWXFJ9GpDrxiEv0cR/e6+zEBtGmNmkv5ah6gO+9+QwrXi5eR1f7GS97M+3uyzPgGfAMtMyAF15apseVngHPgGfAMzBWBmyrUZje1sIxgVFlOjnJpzEgmMGT07WrZavRVv6c9GYWK4taWxv4+JlPyqqFlzQGnTwxJybluuwlnaWxyTI+46VntxReenniKhy4cj7IdXR6B157isjtUFpSrSEZJ8UBCPgZDtflrUZ9YatRVngR8zI+GClnekdMqbS+h1gLvpKf0SFvM+iBjdvove94Ez3r2efQju076YgjDqOFC+bzAclP0De+8W36/r//JBQL+voH6KKLfpPOPecsPoOik3r7++jrX/sW/eCHP9UzKZhvFm8jeu2rX0bnv+B5NGd2Fz3++BN02GGH0rHHHUvrLriMbr+dCy9zpfASXRyabaGRTgfN4bNNZvK2LPlNyC9Uwg5Z5r6E3TvUz2mVMclfXJoXLcLMoOH+IVrxrBV03EueQkuOXUjLTllKA9sHaMstjwsZ3fW399KWn3XTKBdIjnzhkXTCK4+huUu6whk2Pd19dN+3HqTHfrglFF9GuKAx+5C5dMY71tCOB3fRwpVzadGqRTSLty113/kE3fL5OznsEXrp519IG3nFy3W/fSPvLhqlp114PB2yZjk98v1H6IFvPSKLX8JqlvPWvJpe8axL6ce3fpP+6cd/ymL+qhEXLfXfgL6zgZEBes2z30FnnPh8+vqP/oR+ee8POA9FhYsH8NCj3fTud6yn5z332Xw+zBD91V/9Pd1ww62MCcnipGhugo3kjf9PcmhP5E1w6lOfsRxt0adXKoE9MHkf0cTvS7CKq6JzSbUvEtiHZnHLPag4l5p1bLt32+mYbKvRe/nfk2zH88sz4BnwDHgG9nUGvPCyr9+A+/cMeAY8A/t5BmTFy/oNl/P2BfmSSvOFKVmMyKcwsU5XvJxC3du6ufCyiVUxg7Z1yhNbNbdlkihXWtrI8DITlwkciENfrUQ28a1GJWHmMO62jKwEhlHwLS28FGe86BAZG42hGHdJUDY0JkRWiotGSZUrwMeT8rBQJKy8YBD377ztXvrzL32GXvfaV9L2Hbvopht/Gb52de4zz6RNm7fSBz74cbr//keof2CQXvnKF9FZfF7L2lNOoqOPOYr+6NN/Rl/60lfD9qGR0SF64QueS//tyrfSkzt30W233ckH8K6i1ac8jbpmzaLXr7uUCy93MxYrXuKx8Jvm4OV9r5i3mNad/Ou0cv6hxSstfgW8MmYmr77Z3LOdvnDL/6XNvTtCQQZDDVuCmBJHn4wMjnCxZQkd+ewjaPnTltDKc1dQD69yeeTajcHk/n95kLpv204Ln7KAnvOJc2jB4XPpoR8+Gg7hXXHmCurt5tUx/98NtOPuJ2m0d5jmHrOQXvb550ldiHZv7qGdDz3JRRku8PQN0y1fvJP/LQ3wVqMX0WM/30w3/dHNdNxrjqETX308bbvtcbr1i7fTjrvkwF3eRsSrgl52zpvp/DPX0T/8+2fp+ru+zfHwCp4QlYxVW4NceDn/9HX0Yj7r5bs/+0v6wS/+ljWd4Z1Jpu68/V764hf/gN7whteEw5p/75OfoW/weTvyG1MGIWQ2KcQUuQ0uihuKMLEsbRtLKucQckEbff1t6r0N+BiQ+n93zey5pt5+DKdTqsaKl2uu9sN1pzTRTu4Z8Ax4BsaRAS+8jCNZDvUMeAY8A56BagbaO+OlageJTWOkFU3DeJKHM17SwotgwtqFAA+Tf5BVnuAsJtzMH3mooMOMXSaXuNgcDGK4dCmveOGtRn1tbzUSa+UAZfWp/iKvVUgkCSPhikBZeOGvGtV/TlrHWheB+NI1CxFx1lS7TFh2NSvFaSCFtIMn8L/iwssf0ete9wq67rob6GMf+8Nw9s+ll1xIr/vNV9GHP/xJ+ta3/43f2yivYpnNhYcOeufb38xFmJfSZz/3v+nLX/6H8PnbOXNn0bvecSm94hUvpU9/+k/ou//6Qzr99DV01XvfQUceeRi9/g284uWOu2l2sdXI3qqNVlZ8HD53Eb3ihGfTkfOW83iLMkRRPJCixxN9u+iv7/w+beNnqLywjY3b3kj4jc3iPm/DWfWCI+jsd5/Oq1266fqP38RfFBqhYS6m9G8coDN+51Q65XVPpXu/fT/d9JnbqGvhLDpp/fF08utOpBv/5y/o3q8/REM7Bmn+MQvopV/8dT4zZph+/rlbeLWMrIbhUsgcXv2zrZ+3KnXRS/7X86n7jsdp58O76JgXPYW23LSNbvvynbTzV1y8KQqCHTyWVz/rCjpnzcvo89/8MN39yM95HLziJbyRcsQ0xIWXM098Mb3++e+m//jFP9K3r/tiWBmj1aYRfm8b6dOfvfr/sfcdcH5VVf4nM+kJaXQIQZqUJAsqgiJ/QJQiYAWVDkoVFVdKWEXFvu6q6y6KoIIgit11RYpdQVQUEKQjTQRSIKEEkpnMZCb/873nnnfufeU3v8lMEgjnCe/de/r9vvfDzzlzC735TW/gzZq76fNfOI9+99s/MjvOiolQAGdDSPERXA234iNJGiqbkGLT3h0Ira3k2iVZdNlNiZqr1PbyCFSk2U6ZU6+vdtbEU/d4meOFlzUBv/t0BBwBR6AWAS+81MLiREfAEXAEHIF2EQhLjQ4/kZdVDP446XIKk/q0pUYLG2e8xAxTZl6kykUbHtKkr9wvBKWBhDYtvICqQbIZK7xgqRGSUmWKen4v89KkNZdEr930LVjlOHGq0eTiOGk91QhjVb/140YUOSbw3nSJPbGoembfRgQ5nvFy5x30jUsvoDe+cX+67Fvfp8989iscTj8dc/Rb6N/mvJ8uu+xH9N9f+hrvU8J7lrDGwieeoU9//HQ6mIsyXzz3q1y0+QGNHN1Jm268EX3kQ2fQVltvSWeceTbd9Nc7aMMN16V//9TZ9NKX7ihLje66L8yOyXHTSLkYwMUpzFjBUouRPLsDC41kqREXrdg7n2TNVz8t601naunYgCIixIWWtJc90UObHzSddjubZ/DwMqDrTr+BRoxjLrO77uqhfa/egzb+l/Xod2ddT/N+v4A6xo6gGa+bTq86exe678oH6W/n3x72Z5nAR1EfcMlraMHfFvLmuTdRz2LsARPfDNsau9FY2veCV9MoLsSMGjeS94FZQb9+3+9p4Q1P0Ag+vlrC4ri4cPWWV51Cu8x8HX3tpx+k+x69hVnMZxv2boh6eN+Xnbd5LR2xz+k82+XHdOX1F1nhhb+lru4e2nHHbWibLbfiPWKW0403/Y2Xhz0lnwkgkeGL33BXguGVMJOmyoFUa4hNq8xAtmADsqmclYEazENpgEv8axQqnHpRmjxTjkSvlLKNXK8ce8613tDlfKmRoektR8ARcASeKwh44eW58iY8DkfAEXAEnqcItFt4kb+/V1MTTVrKw8dSo9nJcdJ54gYtthVzlKrVsjXuh6IK6A3S4BdslWFaIHOf/7HCS6vNdaMdsRbvYi9JEzOuuFWfFVZCiOk0u8CMlykovBR7vEC/7Fttgi5tpSRGB2zWxa0Js3jEHTNe7qBLv3E+HfT6femLX7qILrnk+6HAcejbXkcf+MDp9POf/5Y+9snP80bMfRz/CHr66Wfp4+ecRgfzDJlzz72QLv7G96mTZ5ZsveWL6BMf+yCtv/56dNrpZ9Ntd95D48eMpU996t9o9913482cT+alRii88KyZmHFjVkrYVFcCCu2xI8fQ9tOm06SR40IcGAdmiQADFGGwv8vtix6irj4ufBQocAs2IiF8t2hzFaeX93WZceBm9MoPvjRsfHvdGTdycUUUUXjZ/5d70PrbTaOrj/0dYVNd1EA23Xsj2v3Du9Ajf5xPN37+Zuqau4zGv2gCLzXam/d+eYBuO+9unjWDIor4QGRjNxpH+56/J41fdww9M6+LJk2fSPf87308i+ZOWrEcBU4OiMeBJUSv3/V42vulh9J3f/N5+uvffxXo5e+gh8f36n95Gx34qnfSr268jH5907fZBs+MQcUo4tXFJ0w9+dRSLrzwEq311uGZR7I3iOxlJEJyj8DIsBF0gCqaKah5IwDIpLJuVSqlwGadRuorLTCJl7JGKp1a17bJa6u1hnJVWu3Ux2rc1deywsvpsmfS6nPtnhwBR8ARcAQaEPDCSwMwTnYEHAFHwBFoDwEsNTr6mHfzXh7dLRSQrFQTFVXQVEb74cniKLwsWtS0x4tIi9Vm22azdQyhMKPCSEj10uCYhD1elnTxUqOutPCiAqpQ/xxIqmkcadRig+9YasR7lEyJpxp18fInyaHBY/9F+NowolIQpVHrYxZqXdlFOFZ8EUt33/kQ79PyOXrTG19H3/3+/9J/fvZrfEpOLx179JvptPe/l779nR/RueddFI8z7qBnn+3mmS2n0sEHH0T/88ULw1KjUaM6aKON1qcPnX0GvXibbegDHzyHbrjpVtps+ib0sY+eSTvtOJuOOOoU3veFlxqN5qOnUbSwAWvIXL5YQTMmrkvv2vH1tOWkjQI9FFHQYhA6RoykuUufoM/95fv08NJF0UZEBA9cKVjc7l3cSzP235QLLy+jBTc9Tn/44E1cv5Blb8vu6qXXXLEbbfqyDenas/9Cj16zgEaMWkGbHzidXnnWzlxk+Qfdev4d1PV4D03YkpcafXUvuufH99OdF/2dXwQ75EJUeCF8G7fxWNrvK3vT0/94mm7n5UWz37k9TdtqMt30pVvpH1c/Siu4OIKrjwsve/7L2+mA3d4RlhBdzUuIcKWfL/p9/b30+le+i3bebh+64k9fo5vu+aUUXcDkQfb29NCs2dvS9tttxwWgEfS3W26jBx78Z+AJBOJP7iko0BcqWgNfZd1co8xVy010aEvhTe3kkmnUIlsXreloS/2q1fypXJU2bpVivNXZ0qVGZ/qpRqsTdvflCDgCjkBLBLzw0hIeZzoCjoAj4AgMhABmvBx2xIm8fKTVUiMkK/VpiaYxuR9ZRjF71g58qpEVXrBIBKmTlgJg0SzX2ze76qksx3RlqXCRuQovpPasFma8lAovaSyq3vQsu0nlJKo8NrGd00KwKLxwgjxlyuRwqhH2nWm38JL61HbZg9LlKVHnsZejBbeD7uKZKd/4xpforTyD5eZbbqWPf+LzIT4cA33AgftwEeXj9Mtf/D7APWXKRN5PpI9Ofe876I1vOoC++tVL6Qc/vCJ8Jr28+e4p73onvZWXIJ133lfpyit/Ra94xcvo/e9/F6237lTe4wWb694blhrJOjMuvSCkECSjFp4jaNyo0bTjulvRlLF8tHOc0QQWREfwXihLe7vohsfu4+eyMDtFZJrR6OH9WWYcwEuNMOOFCy/XnXkjL/3hr4NVlj2+nF4yZ3v6l2N3oAd//TDd8Km/0Sg+Mnr2cdvR1q/bgv7yhb+G04iwx8t43uNl/4v2or/zLJY7L7yP62j9oeDBYYU4x240mmfE7EPzcKoRHye9/sunsc+dadmzvfSHj95AT93GmwGPRslhOW21yU502Gv/jR5/6mH6yk/mhMEVe7MEg1wb4rEe9tozaaNpL6Lv/OZz9CiPWQBjAY797jvm0n/915l02KEHc7+DvvKVr9Nl3/kxdXbyBrxBJAAqwoFiN+UEQ+EFGMWkmjE1GbGQ9tVSWVvpkC3zUn1t43ekcqKrPUiAgr48dQaNFvNSXyIDHVypjXJPJNbUXWa8HEZnneWb666pd+B+HQFHwBEoI+CFlzIi3ncEHAFHwBEYFAJ2nHTfoPQgbEmNtjSZwR4mHTSrZqmRJkZlZ5oolenmRxIr5ctfymPCxawsgqLwwtKcsAdNFp06dRphdklX2FxX6GZPWwM/1ZdK6qjT9C23rpLyxKk7nVx4mcyFl2W8P8fSpfE46WIUuXxqt8zRvsWgFDwtUmuBnqKtHCm8XHrpl3iT1gNoKReoHnlkHvX29tC2272Ybr/jLvroOf9JD/1zLu/dsjmdysWYKVOn0PRNNqHNZmxCDzzwD1rER0Y//PBcLrZcRDNmTKcz57yXJowby3bm0rrrrkfTedbLmDGjeGnSsXT77Vx44U145ZLodQwBOw4LkfXwCUmYk9LHPfQhg+1n+/i9juL3PBqb0abvm/kybLPJcIer90kuvLxhM9r9Iy/jwstCuuZ9f+alRmyV9Vcs55kq646m3T/zCj72eWo4gQgoTdl6Cj1+5yK64bO30JKHl/JGvMvD5roH8R4vmPFy+/n30IoORCZ28L1hxsuB35DCyx8/dCP1P9tHM095Mf3LO2fSY7fyTJsP30jdfEw1BrOCeumd+3+KNttgW7rk5x+j++feSqNHjpYxMB+zYjbfcCa9da/30T8X3MNHTn+F30lvqLvAK667uYh1wVf+g448/JAw9P/6wpfpm9/6MY0cicKLFS0EvaBS3NRGQQgWrCetCGCZXOqnUmW7Ka+k1mY3tSi//qAYdk5OrDP+1uvg0UAP/xoVeuilFpWG55q+fKnRmn4D7t8RcAQcgSoCXnipYuIUR8ARcAQcgUEggOOksefG8myT0vYMSOKSpi+W3GBGx0ye8VI+TloSnlQn9ZUWBMqJUa4DSVCKFEzZIQSLg/OwcCE3x4yXLi4odIWlRsLI1NJQVqotfnPLNYZYoINnI0zmpUbdYY+XrpgXajRlHRtPmWN9JJxAQ20oQiKhVJPP+QjgLj7iGTNeXn/QvvR/l18VlmShOPTo3HnhlKM777ifN4rtow02mEp77PEqmjBhXCh6oMi2gjfhRQRPPbWYT9T5PT27pJte/rJZtMsuO9M6kybS3EfnMW0JF1vG0+WXX01PPvl0KM5ZPNLCSLNYY9UED0UBY0T0QRLCafJdMQC7ooziyvjp42mT3TegJXO7aP51j4PJbC7twA7XHqfMmkQb7boB788yjsc0gp5d8CwtuGEhLb6XT07iC8uERq4zkl60/3R68p6n6Ik7FrMqz3gJ8QQR6hjTQVscNIN9LKEF1y+k/uX9NHpKJ01/zSbUycUQLGPCcdb4JlFY2WKjWXT0fh+hB7jo8vWffYTGjuIZPjwDCacfoai0Px8jveNWe/A+MP9FDy64jVc1ofQUv3zG/Qne2+W8L36MDjpgH35XC+ikd51Jjzw8L87CETQBiyAYAAs9K2WFbryJfEoRvZxS1xMfwkmtpPQ6vcHRLH7oiR++Ryf4FEwi/z2onzQe0VdOYcYIa6AVCi/HH0ZzfKnRGkDfXToCjoAjUI+AF17qcXGqI+AIOAKOQJsIDL3wAkeavkhKgzv2mrDCywKmWHJqxQHo2qXJZH1iFNOp+JC0E5LRdxFCqs3cUHlhy0xeucJL4sNCrWmpXEyINa6KJPM51s64xwsKL1hqJImjDgJKak/bFUMDEFCc0MtsmQcpFei7wPKWu+64l081OpcO5AT+s5/9Il199W9p7ISxtIQLJn1ctAiB654sYTdZtQ+r8CfFArUJ+XHjxvBGr6N5jN3hxJ2wFwoXOKRYofroyyUxS8JsUVur0AiVGNUqqA0N1g+i/ISpTu70RRoXXRBvQEOrO50j+DSiTt7QdwX1LUVRhuVDRh91wgyX2A52wVfX0sD3FnCATb1CEzeWh17g8VweLlzt85K305R1NqAreZ+XJV1clGJ8V6zoo7GjJ9FrX3Y4LwXsoV/c9E0+Wjq+1xATzwjigun2223F++f8G++psxWd9+WL6Otf/x4XeNSvPLWnoeAZQg+BS0spqYwNLMadM7Ne6kMtqkCqncopX5+qVyfTzGOko0I4DSuOTGKHlno3q9pSm/CvNI1lTT298LKmkHe/joAj4Ag0I+CFl2ZsnOMIOAKOgCPQBgK21AgLOtI0JFXW5AU0TWKELxqqZ6lLByevs2bO5D1e7DjpPNVP7Ws76sekUuSVV6TzHIH5kZi5Dx1cyHiLC0UOoYM8bRpvrruEN9cNS40gxDaZnZ78IqqwoWMSSj09lUv9qrbaSOUQqi41msJLjVB40aVG6gt6uT3l4Jlba5JU36opWkbVtwE+qDjV6C7eIPfLfKrRfvSZ//gf+uH3r5TTcVg1lCZ0ZokaKfogsBD+YUBDaUDcRdvC5nu84FuNCMl6+nbFgL111YUb5YGGNi6zIP14lwqIsFU0FFHANzvKEhq+Gy4UGjExid8JF0WgDX5/FApYRDGQinCyjgiwoohLgQljxD4uOLFpOfsNH2U0hXhGjRzFJyDxsdlyhnbkiIPe5X201Zab8f4ubwkFrnPP/RqfbpTOJlI8C4OVRhGqBZ3IxPExpWYkiZw083cDWlVLLAo94Bit2LtOYzZ9iRM8ixiqoRfE1Fr6jMazh9mXMpbYk7gyQe6Yf+OofaMMZ8uWGp0RjlMfTttuyxFwBBwBR2DlEPDCy8rh5lqOgCPgCDgCEQHMeDniyJN5RgMndo1X60RD0pZcGTNeZpWWGtWlMKmWpFScChXuLBWSpEz6RoU2C6cBlLLl8owXzC7JCi8xgNxmGpW1i7CMlLSqFiyRlPQNwrAhhZcOmsTHSaPwgn1nQn5XWAtljmxYBYsb8KRDrnqFpHLRlqtKgZ08hX1y8TP0Jl5mtONOM/no6N/Q3XfdxzOXZENktRO8p8bqAzDxUNniLqYkJLKp59QcFIu0GAzWSXFUblnHxgwnwo3qYoTvxWUiBaloBFUW4I9Q/IoVqEgwMRqIFEpJg8UDrkUhRqVgR2zlmkILy4oCn/FmXXs3zA/FQziELbWhPjkertUs5yVg4HZ2jmQxyOBS33U9oeEeR2SEolW8iYKChlpPW/CVejOZTDV0dBQ5p6qhFquc8sjEkslZSzh1HsvRqrc0KthRuTqbqezwtXXGy1lzfHPd4UPVLTkCjoAjMDQEvPAyNPxc2xFwBByBFzwC7R0n3QyTpSNpksLynK/YcdKy1KhqJU+ItIe8UfJ0TXqgaZ4sGYr0gsXyqQq0YtKKnDUsNeIih2yuC11Ylb95S698hzExri5y83mvrK2zbZAwq6TGg+Okwx4vXVx44X1nEJ/6gJ1CvmRULKWSqUBKhwXpp1ST1phk1oXSR48ZSWPHjOZiUDf18t4kcqUWNDLVSJ4QK7OZFsaW8EQktamRQj0xwCKyhERljact5VTjTOKCTSjkwoUnJZdFpCAhRZDUmg1SNUtcDlriVjrk0oi1HfkFQCqfPtWH6oitECs3UWSRL1jkwj3QVV59sKyIpMa53Vx2QcwlKzXyIRKmm2ytm2BJOKqRB6K89HsUm3X26jhVOVCqI8j9oqe+q5xmSp3d+pFVbbSW8+Okq4g5xRFwBByBNY2AF17W9Btw/46AI+AIPM8RCEuNDj+R/2refJy0JjR1qQaGr/wUihG81Gh2aamRJUGpRjUJsRQutShtSTNLOpm5PMpQd2FV5LbhOOkw4wWFDp3JkSrn/sBJramk0FJOrqc9LbLAt14hzeWsHBvSTuYZL7q5rspIGlxFoOpbKWo5fSoPjqWtFJEqp60qx0kvpk+wjiy/ioGnIKZuym11ouNFn9tpN9iuUMyQyKqG0K00YHS01J1pKwVc/IuxqCSeFovRYUf1cgnjAC+WQTUlVC/QB1ZMy6oZ5kMhC+6D5+hRXYlo5EhgwVwqG9qqgI4pWSuPPqpnslAL8qmpQrBZ376SJhk1IpLo1bpQsRBFWiZSBrQQoWprO0QtsSdcaAkHrfxSC0YtU3LbIicyTTZFRvXMcn0UdXKpjrab5XypkWLkT0fAEXAEnjsIeOHlufMuPBJHwBFwBJ6XCLRTeGkaWJ7SoGepC5YaYcZLusdLnlw1WTWpajrXkKykgYQQYhycyWpKlRde5EQZiUBkLHKLq2xW+xaXyda1kJgH2cI40ncuBrChjg4+1QiFl2TGC2zARyFeZ7QtmkZqwjml6gGxjho9ikaPGhVOfVrRz3FqNSiYSS1E/SRYUFA4KAdvnqr6KUWT7hxbkQj3UPSw8UhI0UKIE55QaMGlXsHP2+iVqeobmiaPdrRf2AAtuTBgNW8NFmB6cKLeoCOCIVQeS/gOMh21qz5Np4ijwEBlgJa0lRL8pG6jmUCKQhJJ7s/0YUGsmpz+jlSn/LS3ZtGYdiod4kgJMX7FR1gxmjhesYQYcptpL40/Mx86KTeNQC0IX3tV/SbK4DWaLKV0K7ycznu86JHrqYS3HQFHwBFwBFY3Al54Wd2Iuz9HwBFwBNYyBFZ2qVGayggkoCSJCDdtqdF85qX8qnYKK6wECb5Z7p/YDsJqLz5D1h+tqBKzwMUF0jQ+TnppcZy00KvpnNBxV12joGVJZk6v9sozXsSeBBUKL3qcdFxqVLWwspQ88ryX4ggO90HiY5LHjh/L/46jZ55+hvf84aOHgEAqXoSTE7WX+lFaoRIaKiFc7SnSQjVNTfcD4hBmFvaXlT1MVC4yMvviVTipnNDlPhAdUqntVFd5Ka3JHmSUl8qXbaOPS2Xr+OCZnEianH7LIqF2gtHkZlzTNKsQ1C9c8U+UJTwxkZEt7hK50m32WhHNCLGIydFJ0QoeMWsNlwRUG1bCD83ipviYllIKkXIjDT3wBtQoW2ir70uN2oLJhRwBR8ARWK0IeOFltcLtzhwBR8ARWPsQwIyXw444kY+rHWipkSY+OQaWtuR0mfGyA894WURz5zYVXpC4VC1Y4qc2RUbpYWqFFlcKEbXDNjUf4mKM/pUc4vlx0qKYJpcajT7VdP4sosjJNb0w46UUZ4iSb8VSo8qMF0i07yN3qxjkVPTEKp4KTkqFABdexqHwMp6eWbyYCy+YmQN0xKYsPYJO9VKLqfcUQ+WXNU0++igERFv5ioYUsuSdWjxRCo/gSD2rtiXnyincVBrBQDQEP3IEOvwrDsnHlWtjdgawUhMZNyWyTIg1pUE4xhtmw7CZuIRJfKfGTC56DD6lHXkQ1+8uIaVWCn8QDeMzrkZmY44hQyR1GmKGAxD1XwgxDYXQYMjwN5+BIXIQD1dK07byoMnvImIDKoYn30VoBH8aCfho25X3hA4f+FdnScVwTanaSh0ELvSH/5IZL4fRWWf55rrDj65bdAQcAUdg5RDwwsvK4eZajoAj4Ag4AhEBO04aMxwGf1lKk2clTXu8WAoraVOa3Il3oeeRmJeQHiGpQ9KU5j2BBq2EzmJqH4l6vseLKquEeVQOKKlnkaiLz3RVJ4wzKLN8NCi2QlCNS41yS4PtVaOtWkBk+YXw+nkqybgw4wWFl6dj4SXKpYDkqqGn7LJdFVW+9tNnGf1UVuwZ3ii8yDHPSOajZLHHivShA5miaMJ7+YiFPDpI5xRElXhnX/3EJwXxMc+hyFN8X5ArX6zHxsRmtApSLMb0cTw4Kroj7CvEurDFfZE3Wyv4HfTx/zoRMz4aDVA/oCDKxECP+jASr/S3VQxFbahQ9hQmLKViatLejVF0lGZGeUYJrRV9PDtpBX/n6VKZ1Auk0r7aAS1tC1z9K3rDuwgYsohAAjmTLTQjSQp18FN3mZ5ylaL9yrNwoJwBNVRwUM8lS5fRCccdyoUXP056UMC5sCPgCDgCqxABL7ysQnDdtCPgCDgCLwQEcJz0kUedTMt7V+44aeQidVd6nPQ8nvGCJE7TlDR/KetLOplbFF1oI/HWVCtaC+SSlZiohsQxJL+SqNXNeAk2E3cao5LUsniH77KEStpT/GqfNRIVzd+bZryo1uCfGmmTpgWRSiq1n48jHjdhPBdfxtPip7nw0os9XppsCT1lpzbrtFLZnC9vV2j5N1J4gTKSeC5IjO0cRT08O2s5J/Z4F2ChzKKySPZHdXbSGJZD8aJ7eQ/18TIqOf0HUiobVbJBgscW2Qbszt5qd+rkwsHN9/6ayXHmhqpDINhCI3TMdNyAFwWgzpGdtM7ECbRsWW/YSBla4YqutAsTo0Z10kTGHydKdff0MAt2U4faZk5ocsEkuBZ6jEJM8tj1Wyt8xIbIha80UICiWS5GwzRQM6tBHgUp7AEk6Mt+RRbrCBo5chRtN2NXDr2f7vnnDdTbx/9tUQeZuTqi+lQeLHfQ1pvMpq2mv4QmT1iXbrr7V3TvvFsYg05mpnLsJrXfBEDEofxIVcu8+v7gNert5FQ9TnrOmafRaN53yS9HwBFwBByBNY+AF17W/DvwCBwBR8AReF4j0H7hBcOsJhqS9mjyk/DD5ro70CJeajRv7gLOuyxR05QP0qopIKYUbeszes8UmIfEK6GhGWYLBNvgQR+0+qVGUFZ1kQziyQ1UkQi2azBIhEOzsMgKGkshwzTY6WB8poQ9XnrCvjNSnAFHr/polJs/U72coz2V0GTZ+iKBRHoc7++CwsvTmPHSUHjRqFQf2oaQesMTEiot9LwnNJUrMAtaKASoB46YFVFo2WHyZvSqzWbRbY89QH+cfw+NQjEkAAdZ1uDHdlOm084bb0sbjp8SeFc98Ge664lHJZIQgMaV2I+hCIVnnfAJX9PX346OfO1p9PBj99NFP/sojR8zgW3oshkUNVDMgWL9qMBZzgWHF2+zFR1++CH097/fT9/57o+5kKM2IBEvNoHZLrvs8i/0hjccSL/9zbX0u2uvp34s90oKCyIt/rRUoqNAHIpaeMcQC4UHkQ946tCZJVRBP0Yhj0RGpYIs38KMHMZm0/W3pE3W3YpGd46hp5Y+QQ88eisXw7pYP46NZQ/Z/T1cKHkpfeuXn6GHH7+LZ/GMjO80OoDRojASPCRh6KjgdAUXXXait+79/oD5osXz6I+3X0G3PXAt/4ZGBh0NOZgEJbNdEIJs801/GSqhMWg/faonPIf/8sLL8GPqFh0BR8ARGCoCXngZKoKu7wg4Ao7ACxwBW2qEvQ5aJRsKlCYdqbTqWSJiS40e5z1eFoSkEBmRSMrdLKnt9ClclTHLkOFeMMHPUHhR/0wGCfx46XIDFECmTePNdfk46a6wmW0iEyzCBhJHPM0ed5LLdISIflW2KBowq0iC1QrToIEZL1P4VKOu0h4vKjbwE1Zy/6DolUYqkpaWqwyeKoekGkuNwowXLrwsD4UX9ZFqSBvjwjhVH5aKcQcRjcYkQEav2WquI/ZEYxnPyDlgxkvoyFn70s8fuIEuvOvXNJFPhorVD7bZTxM6x9KcXd5OL9lga7qfiy0Llz1DP7n/D3T7on+yX/UsUVi09rWI9xVcRFhGR+w9h2ZuuRv94Lf/TTfffw3PohkdbOB7Ckto2Bye+Hckv0t8X7K0CPYFFxwVvteer6QPfeQsuvmmm+nMsz5JY8diBoPFEmzw2KB/+KFvpPe//xS6+OJv09cv+Q71LuPlf0nhRSMNcbJfFH4kZtjTERpF3YACCb30PUEj1CXZll4SmdyFZu2+/l7acqOd6HW7HUMbTJ4eikjLepfzDJRf0s9v+hYDgng7qKd/Ge267b705t3fTTffdw19+zf/QeNGTohj0UjwxH9ztC8ticQi7udCz367HEv773I0/favP6Tf3vIdWtL9DOupjGiEsSDgEG6ZB4ZiJW27BwXuKrrKEbvaq39a7PX8dqkag8jbqUa+1KhdBF3OEXAEHIFVjYAXXlY1wm7fEXAEHIG1HIHbeKnRYUec1HJz3SYILDXRliUidcdJF8lRki6qZtmHpEGaIgrXrEdpqbJIJyaPaeElaAcHbI2V65YaBbWY3AafkA+OSpEFeiWCGIilgSAUUcf4Mi12CFM41ahaeGFOOqbCel2jFF/wa3Kpz6qkyinK3OfCC041Gj+OZ7w8ozNeVDO1JrqggKsclRQu7lVKzIorKa7plFpsHGjxihVaxjNeXjRxGu203lb0wOL5dNdTjxLvvsIKKHisYH4vbbHOhvTvux9PT3Ji/uW/XU5PdC+mxT1LqRdHYxdXjBovPgSvI9DoiKavtzUdsc8H6JmuJ+min36Ilyst4e8HRbkRYS8cFAz62ebIUaN5KchIXhq0jFkrwt4sYUlSeMd91MMnQ83YdFPaa6/d6JFHHqXfX3dD2OtFQ+lbsZzHNoImTBxPy7hI89ZDDqAzzngPXXLJd+nCr3+bCy+8RCeGJw+Ui/TiVhyKUFgCQvh+ohS++UIhdNAPiHID4ii8wA4E0ReDuMulDuQ5euQYevurz6RZW76Cfn/rT2jR4rm028w30jrjp9KFV32IHp5/B+M0kj8lxptNvv/gL9K4MZPpgp+exbKPhGVbMUh1UPKjZPGH+JZzEeyQPd9HL9/+ALr8uvPp+ruuCu8be+ZInFLkshKgUGFJR6VW8TSutUCXXwJaeuV8peZP8ZDTht6zzXVxnLTM6hm6VbfgCDgCjoAjMBQEvPAyFPRc1xFwBBwBR4DkOOlTqKeHk8fGS5OQaqIhnCofed6sWTNp0SKcarSALad/3dY0CcmT6ppzeFEJUKWvlBiDqoUud0KfO6GIEmVgBwk20iom1RZe1D+LhWVBIg7NwjM3EvuhV7mpNATRtquU0jET/E5eajSZlxp1dffEGTjQiExNks1I0sLYYAH/5ldKEa9GsZbgKZqwJe+gmPHChZfFKLxgmUvmwzBNvSpV7Wt0uW6uoTopFaiFmCMz2OMbZrJsPGEq7bT+NjQSyTa/z7uffJjufWo+FzHYAhcu1h8/kXZYd3Paasqm9KatX0n/eHoBXcPLX7qXL6dbHr+PHn12UeEKPjRWJYpLUNFawcWEN9DrdzuRfnnjZfSbm78bvyHmcEHhxdtuSTvtOCu8s0023YTWW29d+seDD9Mfrrue/vHQw/wNdYZZMJtssgG98pUvp3Fjx1IH7zlz33330fV//GsEnws4XEjafMamtPvur6CtttqC5j+2kKZNWYfe/KYD6dJLv0cXXnwZF174pDGEhO+BC2PhCsGGW4gVAsABn3n4bLiIErCUDp8Szhv78v+gITUZ+R3przHgwb+ZMHq+6a9MnOk9MHj51bZ0wkEfo0cWPkA/+N0X2GEHF6j+jWfBzKLr77yKLvv1Z2h0x9igtKxvCb3l/51Ku2z3OvrdLd+jq/7ydRo7ap0Qh1q1Z/Bu3dDiSHgMy3mPnrftdRq99MWvpcv/cAH95e8/D0ueivGwbKgdRR0xIvjIXSh6hye5rKUU06ryVMaeddZBa1e3Wc6PkzaUveUIOAKOwHMFAS+8PFfehMfhCDgCjsDzFIGw1OjwEzmZaT5OeqCh1aUQHZ0jaNbMmXyc9MLkOOnUUloaSC2kdJGXVDkmNclf9AO3nOuEhNP8hLoLd0FuLLywe5iRi1uhk8bEnKKrfJWXp5ZboFqIBpaMp5BmJvj5UqOlIckUx7l2oVdpVOVSShiCpNusWY1KzFkRYgUvE8FR0pjxEgovxVKj1LFYraOI7zSCVApt6EocuZUUOZFQTVjD7Intp86g12/zCpoxcT3afJ316bv3XEtfu+uXNKFjdABz80nr0Wt4GdIWkzeiV2y0Lc3lQstdTzzMM12W05UP3kD3PPmommx4atwjeN+SUbTvzkfSni85hC65+hN0xz/+VATVu7yXjjr8YDr++KPDxJYHH/gnv8sVNGOzzen+Bx6kj5zzHzRvHhcZeYAve8lMOvTQQ2jTTTakbbbekn57ze/ptNM/wYWYUTxbZjlNmjyZPv3Js2nmzB3ogQfv5/ffQS9+8daM/1j66tcupUu+8R3qKS01suBRuOE9eUaNoUO2ehVtNmn9UBQCXwop3OAhPcuzfX50//W034ydaCOelRLhFzP8g8CskUU8O+jH9/2B5i99KhS2wORtjEVGrMEUE/to1x0OpLfudSr97uYf0c9vvJRe/4oTeQPiPWjCuEn0+FOP0IcufQtNHgU/XDDhZUmzXrQbHbHvB+iOB/5E5115Jq07bsNgPRrnBwMVKkZG0RbGt6x3CW+MvJzOeNtX6UUb70Df+fXn6NYHrwuFlzDAEFn63wtEiiJTLCSpseSpb9pIQsm/SeNaCxKpdpNGWc4s5K1mOV9qlCPlPUfAEXAEngsIeOHlufAWPAZHwBFwBJ7HCLRTeEG6YcWP6mDTdES52DwWM17ywosmSZrsaIqkWvIMyWNCCr7jcgjkajETVOE8H4IAZOJVnvGCPV66u7tDIUZEYpJWDEL1IwGPwic0lC/aeg9+OOEMoppMRl2hRknmYVZB2FyX93jp7ko311Vr7Tw1sFxWRpNCgIhkAMqDBii44ijD0hns71IUXiozXkTeNKWf21FrKlt+irTqGFcjy5BiNttjTEfxyULjuciwz/Qd6cgd9qb/vfdP9D+3XUlTRnLhha9OnGI0YjRtP21TOnPng+mmBffSJbwHDI7IXtyzpLTUSMwGADSQEDZuI2idsZPpLXucSltvtiNdcuXH6L55t8q3wuxlfNLQO9/xdvrX951MDz30SCi0PP74E7wvy0m05x670xe+8GX67g8uD3ufjBs7hpeSTabdX/Vy+tf3n0x//vONdNK7zqaJE0bRwscXs4130kknH0M3//Vv9IlP/Q9tuukGdMrJ76CX7fxS+upXLqaLQ+ElzngJkcVvizHB94NlShNHT6CTZ7+KWVh7AABAAElEQVSOtpyycYBKOCzH48In+FT3s3Txnb+ig7fejWZM2iAOXN45ii6YUfLYkqfpwjt+Rv98+jHGkYsWAQsBJjSj7xXsb9+XH0P77HwUXXX9RbR4yWO0/67H0d//eSNvortzsP3Rbx3C72FcsIsNiqdN2pBOf+uX6R/z/07n//QMPmlqTLCWfXz6DpgTPsYY/Pixk2gznmEzlZePvfblh9HTzy6g7/zm87TwqXlRUBXwhBGJFihp3ODgKrsQqtL129O+cgd6plYHkh0c3wovWGrkpxoNDj2XdgQcAUdg1SDghZdVg6tbdQQcAUfgBYOALDV6Ny816h7UmMvJTcyazAbnJbOLpUbzmQ4NTVaq2qYoUkgccYU8DE8UXiQrNFPRpNY5CoXQkFswwzfYCTNesLluUniRZBWyGpsqSwAhDrBgI4iV5TQcyAtPij1iJyxfkqbc2aAUXnhz3cnrlJYaQcTspGpDbcfRZGYklYdHXtATTjVqp/ACE6qZmQt2ckpdTzDKUUyTX+XgafQunkHxphe9nE6YfSD93/1/ogu4WDCRCzLxpdCyvh7afspm9LHdjqTr599F5916Fc+W6At76VgqrnFHjPFRwAteciRNGjeV3r73mbTJ+lvRN3/2CXpg7q2YRMHXCOrlpUvvOPpt9N73nEDXXPsneu+pH6aH/vEUffTjJ9B7TjmOrrnmj/SJT36BupfJsr0l/K3t85o96JOf/AAXWG6hd596Do0d3UF337WArrzyPNp5l53py+ddSF86/zJab9o6dNw7DqeTT3oHb6z7TbqIN9gtlhoF74hBLnmXCKuDNpo4hcbxxr8h/gCGcDGyXp41soBnskwbOzHIxCGGtxeQZaFenuk0f+mTvKEwb4zLRux7ZdTCxy8QY2nUAbseT3vudDD94daf8gyUbXnGzMiwce4x+36Ej5AeR//+naPCikLYgPz4MevQB4+8mE81up8uvOKDPFOFTzaLP+hi4pq8gjgwdDhKltliw1l08J7vovUmb8iLzcbQtX/7EV39l4s4dt5Qubh0REqQscePQonhGS0XtMwtxl1w6hplbci01qiz0i7Nlxq1i5TLOQKOgCOw+hDwwsvqw9o9OQKOgCOwViKA46SPOPJk3tODN/Ic5KVpjqjlSZBsrrsDz3jBHi+DK7ykYVh6U5P8pC65HboxmVYbRfLI9OpSI0nsxUeNfRhJfQSjFpH6kCcE7Qq9SLJklvlJ4WVymPGyLNnjBfoVh2Z0CC0JJQYU7VgZYjCFF9WqC8YKJYpmGS1NcXO66cFqqK/hPfI/Mfenbi4QvHGLl9GJs1B4uZ6+dMdVNImXGim2vf0ovMygc7jw8ud5d4fCC5J/iTbFVCMIngLaxUjY2Tg+NvoNrzqZdtxqT7r4qo/S3x+5iZcVSbTLenvp+GMPpZN5ZsrVV/+aTyv6Tz56u5uOPeaNdBZvinvXXffS+04/J2yUi7hQgNnnNf+PPv7xs+ivXHg5+ZQP8YyikXTPXf+g3/zme7T99i+msz/07/TrX19HY8eNpsMPexPPnnk3ff3r34qFFzvVCBFkb487WJ60yYQpND7MJJE+9qHBhQU3PTxL5RE+7nkjnj0yjk984i2BAx2Y9AdrfIITz0yZu/TpsCwLHhC3+DHw4Rt7xbzmJYfSa3c+gouFz/ApT6PoJ7znym0PXcub6H6VizCd9JFvHsx+JqJuwkdy99Em621J733T5+i+R++gL15+Kk0cMw2vlN8v3je30CmuwOEeOCNoIi9f2mKTWbT5+jvQK/idz110H132q/+gp555PGqk71SNKEKwhbaeUqZ8eWZuWS7VyiXRU1tlTm6lzB1KP8x4Of4wOmvOaT7jZShAuq4j4Ag4AsOIgBdehhFMN+UIOAKOwAsRATtOGn/xHtylCYtooWfJiB0n3bTHywC+CnOS+IQZL5oiITlERs4s88j2gk5GKRJ3JIMovJSXGmnaVS0nwBibhJtoUtq5/SAkkllT/6If7KYqIenklJBPNaorvEhKmiqY2aG04mgaTbQ/46WKVGpU8UxpaMuINKnXvklJfNaXlwk5aHIRg2e8vH6Ll9NJsw7iI6L/SOdy4WUKZnoEPp9qxPyZk2fQR1/FhZf5d9MX/3aFvLzIVzn1AKviUyMTAvbe2edlh9Frdj6cvvGzT9PtD1zLL4tnWbDwsl5eanTs2+m97z6Bfvbz39AZZ32CCwJL6Iz3HkFnnvYuuvnm2+n0OR+LpxFxzHxS0X777EEf+9gcuomXFJ10Ci81Gj+a7r7zfvrVr77LM8K2p0/9+xfohz/6GU1aZyzvCfMWOv2099BFF13Ks16+zXu86Oa6RbDckHhR2JjIRaLTX/Jm2m7a9LDnC8YT0OKPD+/hST5O+9xbr6Cjt92bXsTLfnQT3iDHH3UnF27mLVlE595yBT349DxeIgUDQDy+JwEo2IS9l2y9N715z1NpChd7ruV9Xr5z7edpyw1n0zH7nkNLexbTxy87lCaOmsJGKBzJveu2+9HBe7yPbmUMv3L1HJoybmOJL0jgJnFKV8YFoIN/LvT08ElVI3lWzalv+R/aaNoW9N3ffpbu5D13sIRM3p69uxiqmGIbuc3YbaDqN6sRCIqpDW3DgEnlVoevt4RPyTrhuEPprLP8OOnhQ9UtOQKOgCMwNAS88DI0/FzbEXAEHIEXPAKY8XLkUSfTcv5rfqsLqUddypGmJKk+ZgnMmrUDLcpmvKQSLdrBmSVVIcnSSgaiqAskmKtGiWIJLi28dHXxUqOufI+XKCGP4l4zssJ8NQAUguAjaKkcP0PhIBVnGthI8K3wgs11xTF4UUsIw3QXu3KvM1kpvLTYXDcdTp0tTWSrvIBGRkZEZXumzxwWwH4mS7nQ8GZeanTyjgfR/973R/rv26+kaTzTY2QHH7fLMkjSd5gync55JQov94QZLyt4xkt61fkSPuJCFOL5lXx08et51ss1t/yAfnHTt6TIx99fL5+wcwwvNXrfqSfRrbfdQccefxrdf/d8Ov+CD9Pb3/oW+tZl36MvX3Apm1lBvX29tGRJF+2/76vp05/+IP31ppvp8GPPonUnj6EH73+KLrzoHHrTG/fnJUe/pGOO+TDtuNOmdPbZp9GBB+xDX+PNdbPCSzqIECcXLNjH2JEjaf/Nd+ZZL9NCgRAsW43Xz5vrdtPPecbOHhvPpg3GTQ6nGqFmIWcc4RskLs4soasfuome6HqGCzFqAPYNGbRwutQm625Fx7zuo7TupI3ogv87g67/+5V0+Ks/QHu95G10Pe+586Pr/pv34+FTjdjO0mVP0JGvPYeLNXvSz264lH59y2W8x8sEZqXfgGEurWygweuy3mV0JJ+ctNM2r6af/P58uuHeX/I+v3riFmvhdwc1/QEFLQ0e/MiKpPgQHejxhbcebEiX79qDdNpWAaVpf3ifOuNlzpmn8XHlvsfL8KLr1hwBR8ARWDkEvPCycri5liPgCDgCjkBEQAov7+LCS08LTDQBQcKhqYuJVymcrmSFFz1O2nTqW9G+GozJVPz7e1SpT3o0WU/TOijIUiOmslp1qVEQELvBbGq7CAJCIhPuqUxCDk2RC3e+iclUXhJmiBanGvGsiK6lUngxLxgF9IwSzA/xphjVmRm+wgtixhuri12wKBDhhib4FpPqiVQf9dGh2+xFk0ePpy2nbky7brQdn1j0T7p5wf20pLeLfvnQX/lknmel8MKnH31yt6PoTzzjBbM4ZNkN7FlqbRGoH/OsSfa6kzemd+z3YVrO+56cf/mZ1M0b9OKY6B4uTh7LhZdTTnkHm+yna6/7MxdXumn33XYJBYXTzziHj2e/k0+HGkVv40LMjM02oo033phetduu9Mijc+kvf/lrWFZ2xU9/zsuQenh/l89xYt3J+8X8kSZMnEB78NHSkyatQxdccAld9PXLaBnPeBnBlRSJFJHjXxkP2uiN5FkroZSiLObjWw9SDO5yjhMyWmwp6pesFQzwo5f3XkFhRUnlMoQyOnnmz1t2fw/tsv2BvHyIizXPzKNtZ+xCI3nm0flciJn/xD9CVFjSNG7MWDrljVyI6RxH5/34vbSkazH74yg4OMSW/6Yl3jAEYQaXkOpdvowO2fN9tMvMA+jy319Af7zj/9gGxsOzkMJ/HxAtNJMrdKMhDLgwbH4SaW5CFlcQlGZ2T+lor9rLCy+rFl+37gg4Ao7AyiDghZeVQc11HAFHwBFwBAoE2ltqlCYmhWpoKCen8kkzXHiZWXOqkSU5ZQ30Y4ITcqZKOsWqzAjJVqobhCWZq+MzLaRmbHrqtKlc5MhnvISZKuo6S+CaRlZNvCytT0aHUEOYfE9VQjwovHTyqTd8qhEXXpYuRWKfpqI1Y0+HvBJtGU15TOIH8aPwMj6eavT0M0/znj86q6AAp/CaDqcgFg0pFEAm9yZaqW7glwSzLgt082yXT+x2DG3CxyGPHMmnF/Emrj2cjGPj3Md549gLeabFP59dyBvJLqct19mYTt3pDXTLY/fTZff+nj8X7PGil7bgVb0oLcoEVj/PVumht/y/U2nnbV9LP/3D1+i6O3/Ms0smhqVGx/EeL8cfdyQ99dTTtHx5H40aPYYLNMvp+9/9EX37u5eHI5on8LKhs878V9p6qxdxrWEkjeFZC1ga1MOnIi3lIts3v/l9+vnPrqFDD3sjHXXUoTSGj5hexrOwupctpbFjxtEPf/gT+vH//Yzl4xHvsulNCFIjR0fagnfoh/hx439DhUXHJzu6QAZaUq+I2vzAMj75/qCLS/WCQXmRTOrn5VzrTd6EXrfrCbTZBluGfV36mPabv/6Abvw7z0TB7491ly3vor12PIQO3PWddN1tl9Plf7qAZ7uMw1dW+s6jJw03aItvHRVOR9r7pYfSm171Li66XEE33fMrXt71AGO1ROJE/NwKHxs3JAK+gxgwALP1BV/BRqNYMBa5rSUbTQyC4acaDQIsF3UEHAFHYDUh4IWX1QS0u3EEHAFHYG1F4DZeanTYESdRPyePg72Q5MilLUlKcJcZLzhO+nHeXBczXiQlkhRG5KUdTcSH0upLD7kfURG7aIciSrkww+wgwYbTGS/ItIIvThbxTG7o8aW+pGf3IF10Q66JxC0hB3/qF5IJU5NTOU56clZ4KYxKMNYdhpaNxlpx0GydU8/kVKNnuPDSG5cape9BNZOhZpEJX+4qozrmK1OpdFQPDNVdlzdaHckzTvo4cUeS3Mn/w0wQFF+wVAab6EIWMzImjxpP3bzMZykvUQkWEuxhM1x4acGRjE79CLGfiyTLeUnQpnTsfmfTwqfn09euOpvGjRzPBYXlYXPdE044mn7x89/SD398BU2dMoUenTefHn5kHvX3YjNc/of/XXe9aTzbg5dB8eyMvvjbwrHXeP+Ln14cCoAd3N90041p8xdtxkvyFtL8+Qt5OxleptO1jLqXdoexhoFFUPCQ7018wJf2w1gxLNDiMPHQ08ACDTcWiObEfpQNtgMnCMWWWIKG6KN00s/FoQm0/qTpNJ5nIS3kY56fWvxYGJcaHsFrmN7+6vfTxlM3p2//6j9p/pP/5CJNR+YvumUV8Yy+eEZLIsS7xvg2XXdLeute76P12R5mH13154vphruv4veN46nlEitmQVEw6yontlUPT/ipUlWiakE5w/+U+KXwwpvrnoXjpPkb8ssRcAQcAUdgjSPghZc1/go8AEfAEXAEnt8IyHHSp/Bf1+UI3MGMBmmCXNqy9AUbeWLGy6JFi2heKLxgBoXwLdFBQqe6yoVFsyP2y3fVgRzaJp/3wBYP+It+0+a6oo272ivbZ1ZxqS/1pLKFgETEZJUI2TCzQyRRvNjjpbunWGpkFtBSPznVYizTy30di9DFbXSeiEpSnc94WcyFl+U840XSbY0jt5eYKJpm3RJZo6m+2ivUuKFSVhRQrn0fGinE2UYwI3rCUQ31wzywQ6Eh/85Sf9aGPuvGUPB+tth4JlNG0P3zbguFBZxqdMI7D6MTjj+KrrjiF/Spz5wb9uDoT/cc0TCi72g1UFG20JhCg2XwXXaM4oIM28CsI7nC4GSIkYJH+v1AT2jhEW8x+IIkeIZiX5QHKzRZVLEVqUIpa0BG+SLPS5t4+RKW+4RZMuH3hXFF3Nk4CmAzNtwunF700IK7uZDF/BBuACWzj046Ev1ygrVYVYK9TdffljacOj34fWj+nbSAizkyEFiQGCU+sQYq6OVLuKlMvZzphUisW7TKNsBoki2U2mr4cdJtweRCjoAj4AisVgS88LJa4XZnjoAj4AisfQiEpUaHn8i51PDNeAFKnfyX+5kzMeNlIRde5ockL0/gNE2xBCZP9GuwjokYEi5L4WKLHyFJTBLMYCHQJUdLZ7yoWNAJJiwO8Qxi3VWWi34T0aCZqGuSDBGZ8WJ7vHRz4QXLTyQeKRAoTonJlWxaImjhWEvegNz7eSnMuLjUSGe8YFYJuKqBuAJeDdGInNwNJdNvUGOyeoCEaKq+JNNREyDpNxBIiEhiihL8UFtmR2yoRZWEnNJMR7CXpTmY3TFiBXZH4f+xaA/PeHn7IQfRkUceQj/7xTX0xS9dTKN5+ZMeN62WwzOalHefjcLECrcaizwRlbJMGC2mxuUz9v2qRKoh4ypGJ2aDoNLEg/XUSv7U953HX2COdxECKdkJYUqRCTNdcGkIeNZd+pUIH5jDCEuyaXhH0YrrPfF3IkUfEYCI+K+3XaaqdF0UZVnIwHYMJDxVrzRmJRfyBWHQDVtq5KcaDRo8V3AEHAFHYBUh4IWXVQSsm3UEHAFH4IWCQHuFF01IqsmGciw5kVQFS2mqe7zkqGpKA6okc6KbSyU9Tbo52UMBI0QTb4EVCwVZlMwIfw9nYii8dPMeL7yMQxPXEHfIqzItdmojSyLgZlkOkiKrnNBL1FsVXvDX7a6uZI8XTqw1Wc/9Dq1n4Vgrtah7vIwbN56exVKjuMeLvpdUVseZ0qyd2hfJlGJy2ipzzbq2FF/DHjrCxT23wNk5b72KSyRUW60FVummFiwpVy2xI2WBFXwk0NQp69Cm0zfiguKTtICXBoUzmkvW0pjSdkks6bJ/fJAaRohcO3jmsec2VY7FClEbR+IkEVCd3G4um4RjgZkLxFT8HqEpPgvL3Ajt4EJmpEBKcK36zQsvwsdd7LEWN/C7EJpywFUarOsldPTEkr5N6cldZdOneEspze1mK8067XGs8IKlRn6qUXuouZQj4Ag4AqsWAS+8rFp83boj4Ag4Ams9AlhqdNQxp1Bvy6VGlsjUAVJOV5CSIEmaObv1cdKQU12kT81eolQqoIpIrULBRW1JIqZxygwTprHYtKlTef+MfHNdSx4RTXoVDlIit8ty6lfkwS00uRGk4RwX9zUFbFpqBN18BEFzWG4Sl9zLBiunGhWb61bHW6WUrakPk1RKWTJBK2GZnhBVW96jAZxrQ0vxDXqmxt2yTbEsdxWUHvAXO/ImAleMswB63AnLndDOdfXtlaliGUUhGErsCiNQgw6zg6vUgFQdgiR4uIxtLeHkd5VPqWUNk5FWmZ/rxoInEyGtsjIi6as95Zl+laKyKlOVUI48Uz+tvYnlNEa1VPYJun43dTzVy5/tS+Z6A/d8qdHAGLmEI+AIOAKrGwEvvKxuxN2fI+AIOAJrGQI4TvqII0/mU2x6BxgZUqJqstGUKGH5xexZO/DMgEW8ue581i1LavLJdPwlmx9IfjSxqgQT1HHTGAIhdkFj7cDOLVQKLzjVqDuZ8VL81V7tqudoX7vFsywXRxaSYwknaCbqxYwXpmmCZ8dJY4+X0owX+Kq6KSJY2YaElARWOJE9XnSpEfZ4sVONqoFUKa0jSj1WJZu4qReVyd8tbClH7OY985XaMmreUl2TTUuB4Eo5xviZdxEIUiqhFsVP2quOAzIqAX1tm65atU9DZHJJkcdd5E0LNP360M6vNKImi9CQ36jpmn1pQddoMg7001iVX5ZVq2X/pm+2yzKqa0/1AkrVU8oVHcGmSleLzRyVGK5nmPFyPG+uO+c0n/EyXKC6HUfAEXAEhoiAF16GCKCrOwKOgCPwQkegveOk61IXTaYUwTy5GcF7vMyOe7zUF16QRMVkBg9W17RQ6WoxJHvo4Iqy0snvKh+mt0RWWlcJM14qhRcIsqbOSilMqsOCEBvVBEz8mnxoWTeaZr0YDFh54UX3eGFG3MNDoSl7H0pfQkoCS4wN74yXxHBs1nu1RFrEymm92oG24W6t8OZUKD7LnlJpiCjf6GgJNfcDaZXSbxO0lBq6mUnVCJzSTQRFQmfVlESiR4upzBfv6lIjNynzby3jSquKs8qa3bKO9psk1ALkIKO/YtFL8VNJkRJ++V71IhS1WuWzhcxg6kWsQ1f1wA3ifJPJS9yo/DegHNXq6S9ZuoxOOO5QPtXI93hZPYi7F0fAEXAEBkbACy8DY+QSjoAj4Ag4Ai0QwIyXI486mZbziS2DvTSJEb2QxhQm5Djp8lKjUjIUZ7qovtrT5EpSo2hSmYWHaiOIBBdiISR7UQ+zTmoLL1kqltpscqhjSGXRNvnQsq7lc0xTcrHUKOzxkhRegtkmH2Wfg++nCXCqXSm8xOOkrchg0isbnY5dLbWyU42zlbRaxBNeIKveUj2lQU7oyk054NZfqVTqA9KpJeUpTa2JvlKlpzaVKk/claPa+lTJJj7kVEZ16p6pfit/dbpZdJkz7kTDKbn6Puutpr/9ND5IB3sgmgsxooKpQ+HU3CHECrHAGXtskmnPkcKLzniZc+Zp4dSsmkE4yRFwBBwBR2A1I+CFl9UMuLtzBBwBR2BtQ6C9PV7qR635Ti2Xk5jZA+zxgkSH/4+M1fUv0WLRki9YlpQtbuMi2VeD40AOyZcWXoI6jIScqrbwEtyHW5CTW4ODQqIuw8t1dKYNVCSfY53SjJfJUyZRd7nwskpnvOQxCpiIsNVSozAC3IqrbvQFs0WjyXu9Spqqi8fUb9lWbkO5qYZK5DyVUKpK1T9bSamlsiZ0wDNdlTSKykBXuWjXX7m1qkzFQoOC+a/aaJ/CVgqHRUO3XUrMpO8zIWfN/Jevv33BLvLwG4oFEo0/8RqsteNJCy9QEHh4mWO0m4W0Bjq6x8scX2q0BtB3l46AI+AI1CPghZd6XJzqCDgCjoAj0CYC7Sw1kgSnfv+VNPnRNlzLHi9ynHSrpUZ5kiQplCZSai+UZUJBAhRJk4rhha7QRJ4TtGhA6hwxbqbVF15YK8hHpWBYPaMDetpXGp64ZASmjWhZPlXhgAK/VHiZMnUyn2i0rNjjReyxJHTNoJCH5W5BWUsMV2a8FJvrVh2X0+OqRD2l7BNSUnLDYJVrA7dvQ2jGMemqJ9hRe6mGSqof9DESyMjx0SJR1snlRUbvyivrKB9PlTFavXT8bkI8JlvXUv2qZZNWGaPkrbKuypfpuVZdD5px0+BghC1EI4ItOmpd9O29lu2Vv6yyJH7LuHJ7YkVoFr+1hF+6a4ET5KCq8s0eShZWWddPNVpl0LphR8ARcARWGgEvvKw0dK7oCDgCjoAjAARQeDnsiBOpv2/5SgOiKUtqoJ3CSyovbUvVUl7rwktMwtIgUhIXO0KXix/VwgsrQU8EUpfcVoNgajsViU4iT8yApgk0U1StKLyAJMRiqVG3ba4r1tVu6mu42hqQhaaWK4WXYqmRStiznB4bZ+CWRSBIiUZ1zCKn0sIvS+UysFSWaIpH7UIDOqkl61e127Vf1RRKWjiwGNK4U2qTFY3ColaKaVQpGoHJpC2Vb8d/qpdbhRWzoDbrx2dyZg9vA3TT1N+LycRCqhFK8sqos688ee+hDlq4UvmhfN1mfygtKbzw5rpn4TjpkUMx5bqOgCPgCDgCw4SAF16GCUg34wg4Ao7ACxUBLDU6+ph3U09PdwsI8mQoFdR0JaWhzYca0cxZM2nRIpxqtIAp+Kt4fkmalVqIaXDJXZGMZZlS1EPiVJiIWVR8BHIovDCB/6kUXoI91o/ySSMaBaMwzu3mC1KasokG34VonEASYgcDVLvUyIJpdtYWJ40dPvPLKCJXKbxUZryIHO5yWUspRjfrOa/cEyzKVOmnKbf4yj2m/NSCSqW2QSvHJH2VlnevRTPYK8uDptJ19sAvX6mc+lMbKgu60eq8qqQ+U6vJR6bs8BSLKUbmIxPkTsox/7mXJh0pJUHLNFPZMPsszC4Re7mUFqJUW39BqQXwci2JV+OTnmrkkkrVp3Ljf2eC3VwfkmpZ26o98DPVbCXdWk6XGp3pe7y0AtF5joAj4AisVgS88LJa4XZnjoAj4AisfQiEpUaHn0Ar+vtWanCSylhCAyNIKzr4VKOZA55qhKQqT0KQEkmiVaVrehY8pEUTdR9GwHpQ5QvkdG8YK7x08XKkDuZWi0HQW5kr+CpiZwtJTOneEXK8tZxqFAovYcaLbq6rY44DWJlAMh21lwST8I06gvr5/WfHSdfOeBF7Et1wxahRqD3tK4RK18CFD2q5JRLKKespHVKW8IsO7iqv359x1JNRVNa0wEM8qReTtzHVlRbsezd7pmFW2mlZZGLB7BinHGXKgQ/Vsd9i1bPqQBbtcnFENERKZSFZllOe+YSmWBQbejdN1RE58CWKFEfVGsyzzqv5atdSnZU63WY5W2rkpxrVIec0R8ARcATWBAJeeFkTqLtPR8ARcATWIgTa2eOlabiaLKWpmspiRscsnvGycOFCnvEyn8lIdiWN0RSqnHoIV+5iRyWRYql0fAbn3EZlxQIRtfBn9kgOM16Y3LDUCKqpRzFQd1f/dTylmYzWhcAZuPCyJJNpNyL12vpZBieXVm5lxktt4UV0Ba/2UBONVihrBAl2RYipD5UrmA0NtZPqiqhR6mwJt44j2nUc0TG7dZJlPf0VpLKQqdoqa4pG9V72n/4gzEZVCpbqqKZT9VVHERv2WzWZurGaTxu1aTS3ylFp5Ea3lvLqrSnX5OvllKry2l/VTyu8YKnRqFXtzu07Ao6AI+AItIGAF17aAMlFHAFHwBFwBJoRGJ5TjTSFsRSlgwsds/hUo4ULF9E8LrwgLdM0TNoSU6qJtsmgLcmcWJW7pndhJksssARLeaUjDpgtFg7q9nhhMehZ2FGv7gEhNVbHB81kNBwJUWOHCbGBPV6m8KlGXdmMF7XbVkAq3PAcKFZTg2Sl8FJZamTyEt1wxGg2BVu1mX4hkGkaC+iqo7bKfaHn1Dp7VQmliHSzjsppBOlTv1ejtZKuxlrn1WxVRy8807JW1W+VUtZOPWnbLIKS2mjmqG7+THVzTlNPPZQ1U5T1vyDVr6asBS9qsckj6Kke5NN+K72V5flSo5VFzvUcAUfAEVh1CHjhZdVh65YdAUfAEXhBIHDrbbfTEUeeRH3LW22uq+lJnnIo1dIXS3qwue6sWVJ40RkvSFlgIU2TyiALH1RtQcfSHSzVkSj4Lg0xEYKJcpEeekkFxJYadWMCTLyConZW+hk9x0ROYobrrPASXImkFV5wqhGWGhUBrXQMuWJwxiTY1bZI5D2hDVvhJQwv3MRweNv6DkFqb5x5jHkvGh7gIX509LnXJnsmlUvkPXNsPoxmrebvXH8HJqsti0AorT2rVv6E39SO2Egprd+C+oQG2vosf0e5xTJXYxIps9Hat2rVP9PIcgnFWpA1nmqAksdrnBwt063qYIypdG4x11z5XpjxcjxvruvHSa88iK7pCDgCjsAwI+CFl2EG1M05Ao6AI/BCQ6C9pUaaftWjYymM8JGOtLPHS521NEVXvhVe2FPlGNgoFYKIccZ8KCRjTArJGNOs8II9XjRpCorqqs1nVVc8S6SFkVB5if41zFgI6ujopMmTJ1F3NxdeuhBPoTVMjeZx1XGGpfASDPMtjMUGJEmxvlmjtxqoxWitOnlYq5dQjqbikFLf9Rqp/UYJZaipaLPopkZiO+zrwwIik0umUZVV1VVKz7VTjrWtCCE0sWPeW/mERp3f1BLaTXFUdXO/rXTFR/1d7Mpd32guCV4eVTmWOq5ahC3lD6RnnlQjj2SovSVLl9EJxx3Kpxr5Hi9DxdL1HQFHwBEYLgS88DJcSLodR8ARcAReoAjIjJeTecZLb0sELNnIxcpJinKxx8tMnvGyiJca2YwX5eIp6ZMmicbhZKZwJo220psQSFSMCs2Fl3TGCzwXDmPboqlvpRGxbvDNkiH0PC1MLUNOixDFcdJdeeElk6933iZVg1JxxCy0MgcSlcLLyuzxEgzzrcAfls1vJEcaeM1Xc6SiY7bQl0VpOa3ZdpkDX2Vd8V+WrO/jjdfZKKS52LaCK2tlHwW/RaMcR9VG1bMWemC2Pa/ixXxVvei3IzabAzYbKlO1VaWobP0zt4neYEZlNs1vbrH69k2nroXfsNgyi3VyK0vzGS8ri5zrOQKOgCOw6hDwwsuqw9YtOwKOgCPwgkCgnT1epFhQTXfK6UsGGCeas3mPl7zwgkQl1Sr3YcHSWJUcIK0Vtywc4mSTmpbJ5BKxghkuNuOlXHgRE3ZXz0bJW0nCFWewgI/JOAlHtnNJ4sHQFctOzHgJe7xgqZHNeJGkLrWSe26/1zyGOk6l8NJijxfEoBin8SjemL0T2skw0FS/dbpqR2Wkn/dUBs/EdOylsqm3VKu+Dc3cnsVar5FTq+Nhi/KPzGQKDsoechvD29OvrP495b4QnFzWqou1NRcWTCLFszQLjOXqrEsE1XtqM+UOxkaqJ+2y1cFYE2xFYzB61SiaKLrHyxxfatQEkdMdAUfAEVjtCHjhZbVD7g4dAUfAEVi7EGhvqVH9mPP0BT1LRLDHy+zSqUaEI5xDRi6axXKfaEhTGrNS77eJqn/p10Q4mI2FkcEVXuAhBlXrrCZC+EnI0W2gaTySjIvdsNSICy/dq23Giw5EE+F8fMNSeAkuol01X2ACFLQgUCCiQRVPVWvCH+YgU7WgHDVllpTS9BR7Ve5AFoqhZaoxjsxovWSmNoyd8Cny7tPybNe3jNbGXNZr5qShq5Rp6/dmUsYzWlNL7ZX5g7FR1sUXpHbFzmCtQXuwOtUomih+qlETMk53BBwBR2DNIeCFlzWHvXt2BBwBR2CtQACFl8OOOJH6+5o315U0o5psaPIiQOT82sJLkayYbEhfcGNSc+EFzChUKJThV23Qg5DUeEKKxWk6k9qf8QIbiLHuEtsZp1R0AS9oRxNpgUlTPhRewqlGpcJLzJYz8yvXaYq/fmSVwkuLpUaIp1r4kCh1fPIOZElGNZIm7brYytrQBc3eg/VUVnnGabJskaiujAP3KsV4aKmXnCo90zUPdXLt0dJxDKyBd4DYcG8Vo1qSWC1ioZc1hV+mqg19qhXxL9+J0lRmIBsqh2dZV3mDsaE6+rRvVN/hUKyp1cE8MaqqTx1r19JuOv443lzX93gZDKgu6wg4Ao7AKkXACy+rFF437gg4Ao7A2o8Alhodfcy7qaene6UGq8mCpEhJMsFNzHhZtAh7vCxg2/38r/JFK6SFIEUjakulEBBoIdHWwguIQSCVgiBLcnVF5CHEF3ckyWJPLD74wgt8wGJ6lfyCFUT4VmKBnKW+TNB4wqlGk9eR46TTzXWDraAIy0O81BjM2Fg0LRfjQq8UXipLjUxf9UrDFTLfU69CBEWQkFlJGSpRw6zl+nlP7BnUKVcsmB2VlWcqqZw62Tq5fEyIXpP3OgtivZmj3u0J2Xq/JtN+C5YG413HIh5UsxyTxKdciyaXax5FLlf+Atq1Z/5ze6Y/cCsdr9gzqwNrt5JoI6YCoKIRlyjGb4rJ3d29XHg5lM488zQaPXpUK4fOcwQcAUfAEVhNCHjhZTUB7W4cAUfAEVhbEWhnxoskhc3JiaUQhpLMeKkeJ20JZpp6iYXyX+jNbuSguIILVZQiUZV24ODGPIuUU6x4ChJUpk6dGvZT6erGHi9BGNYahhd9iURyN+sJMRqxqAIvi4c78g+zRlBxnHQ646UYH7Tr/UhUcg8SaKaixUYzIgNL5auOM3DhpWwlxTnn1dlXibw0Vicpg6lyqhRIplSDwVriN5WySLTVLGMSdRbALXsyjWaOyTw3WunYNOqUZlFKyc761Va9Xi4HH+Xfei5hvdSefHEpxeQG08oLLzriwVgYgmwIX8cgSOhPVqldfKqRzHg5nUaN8sLLENB2VUfAEXAEhg0BL7wMG5RuyBFwBByBFyYC7e3xoilBNUlRjqTAwsddTjWaSQsXLkxONdLUSbTQQxKkWtLOfYhkQ+EFzFiEsThKBQEuZgRtNltbeAmKfAt20m/ALKbUplQ7TeYK+WC2NJ5YXKksNQpK8Bnlc7XCJBrqqyISQgYVjjOVrBPEMkp+qtEzzzxNywdYagQHLVwggprL3rUwq1I5JfWQc6AfR1r4SaUNAOjlnPRbLZQbIlZ+6j31W7as8lWf4NTFYhprqpWOrVUMUjRrJSG8duy1/npa+RgeDOv+W9PK67DyigKrfEkZXtxZysXYE8JSIy+8DCvubswRcAQcgSEg4IWXIYDnqo6AI+AIOAJE7ZxqVIdTliwEAUmINBHFviYzw6lGKLxgqVEsnoSWyaYJUNqu8xkSV1GNKax400KE6mRJXanwspSX9XR36YyXEIyoDVh4kSSpPqFGZDGwYA1tluRH5RjhGI8sNZrES43icdKJXvChQAZ6fhOcAhpJSYEVNKGDeIO+RJbbQ29lZrxAL3WjCDT5yOXrpau68jYLOhxyR/2m715p8JNfzRyTKzwYqZ0W1GrN1xLbsbhaZdLwB0KgncLLQDZscNmv1MgDttTD8wPfAYdTI9DFhRfMePGlRjXgOMkRcAQcgTWEgBde1hDw7tYRcAQcgbUFgVtvu52OOPIk6lvevLlu01glBdJECFKaDK0IS2lmzZKlRvPmzg+FCU21NFmGtGpbW6WavIKuZQ7xp/ZUI7MQCh0cGRdWMOPFCi8qzREgiAELLyqvY9R+3VNsxlVOjEqikxZecJx03VIjyCcqdR5Aw7hNDC1FUzVyWpmrUnimhZcw46Wyx0sqjbZ4Nv9V72UNSJhWNRqjoKWS8jSeWFW/Sq+X0ghUWvvlp1op0wfoq1qDebCzdz+AuTXBToeg7eY49P2ZhOooBNo3iaYWNKwYm0qlNtSu8VtzTe752wqnGh3Pm+v6cdLP35fokTsCjsBah4AXXta6V+oDcgQcAUdg9SJgS42w+W2a1DTFIQkTuCatLU2TuPDS2UGzZuZLjTQJlYKB2BFN42jC3eRd6enR0WKD72iwWbUWOkXhhTfXnTaVlizhGS9hjxdYijqipKbjU6waUcdmFIm1LCf9YvJJFg/rlgovOE4axaBQ98mVUkdJ2/yhJSgKu4gwiATHzFB5WdZlhlLNvPDyLC816i2WGuVypo+WIY12uQAmsqaPVh4x+hpf2hI5mWEhWiYFnliRltzzIlTKQVtslKnWL1tXjsUulFJf1eTlqRI/Va7sV+kqWu430ZvkVH54njqcOmsyklYSqlX+DppiF4tyV118A1LcCZ64cmn10NQOuGXNlA975b75yFvtyuVaq7Jnx0mfwXu8jFyVrty2I+AIOAKOQJsIeOGlTaBczBFwBBwBR6AeAZnxcjLPeOmtFwjUukRHxCUVk3uaDGGPF5nxYkuNwJc0R+yhLem2tMTiwHf1aWk/U+QfSccsW2N6LPOwi7DHCxc5usJSo9SPxJNSJK6cIr12YmWfEmRMHBOdloWXxF+iklC5GQ3zSDVqTVaLZNOm2kRV1TFttQk34K7o76dx48fT+HHjCTNerPCiktWn4S8881JEkik1DilIYRR6aVS5h8BXFouGDZLDWE0TrXo/iWJwk0qZvkYw0FM1Uiu5TjMnl3s+9XTUrWMuSynyOSJ5T75CK9pUdVQe1rXdOo7nJpfjV4Dw3yn894AfGJH+N6Orq4eOP/5QnvHie7w8N9+hR+UIOAIvRAS88PJCfOs+ZkfAEXAEhhGB9vZ4aU52JIcoMokisg5OKmSPFxwnPZ/pKqMiaVLdbF+ly09N08UK66fms8KLsEAKS42WpjNeylbTfmowpSNFGvgK8UUTWOZUXK0KLxhE0GH5RKXQDQ2NKy28lMRNJKoqIYcptZsWXhZjc90BlhpJeHmQ5sXiERrudRppBOXYSjraZTOaoKpGHoX2xHPuQTRUosxL+/XaOgr1LBr19uqpqY/22zr4gTSaoobecMSj9mFL280xqYRK5xHkPZFVjTTaXK79N9gc1xrl2MfLYcjYVvBmUGjp6Lu58HIcNtf1pUZr9FW5c0fAEXAEUgS88JKi4W1HwBFwBByBQSNgS436BtBFWlBOgjRZ0JRB+ZxIdHTQ7Fn5UiPTF/m09DK0hIrtaQgYRU2hQwsvXbUzXuqGnhpM+TrGlFZtNxdeECr2wOmkKXV7vLD5+p0v4ENjkhjQay8ajU/LVdoXfdjRwss4nvGCwktfUXiBB/WrevmbU2rdUzxKlAPFal60ZRraUnvoi5TcrS+SUpaqRgRptVXlqs06jtBUVyMEVWmmBYpKVLkm106rbKfVCFS2zu5Q46izCVqzT4sUvlNMmmypPZXVJ+ho41J/2hfq8+fO8RdD4DGgEJsOhXlhxstxPOPlLJ/x8vx5rx6pI+AIrO0IeOFlbX/DPj5HwBFwBFYxAii8HHbEidTf13pzXeQKaX6AsDR/sJZJjOClRpXCC6ofQSlqcp//jyyxnLbhYRBX+pfkovDC9jirCVbZNfZ4wX4qXUtxqlG7tqGdXu0qckIVVHmMqQoSLUYSS7GmTJnM+83wHi9Ll7IMhBBvCJklUiX1r7Eor9xP5VRGaXhK2SKlaBuFl7FxqVEovBR7vKhE+qyPTiQkJo1MaBJLXUSpVbRzbdUAFW38m1uu9lkkudQCSGolYRfNstWC0UZDfagNQcd6bZhoIaJ2UhH1mNLQrpNVmSYd5Q/l2cov7A7sG3s2ye9EZWFTZ3VJWyJUXyon1LXhriPrWopTjVB48T1e1ob36mNwBByBtQMBL7ysHe/RR+EIOAKOwBpDAEuNjj7m3dTT071SMUiyoClDkgxxE4WXRYuSpUbIrIIobtyGODfxsKRReoE0mJvaDepmI2zCy3ZQ2AiFF15qVN3jpc5RMFjDMNs1zIQkhZdsmRG4sfDSyYWXyTzjpbu7JxZeoiq7lT0fyn7SeJSnNO2re9DLNPCaCy/pqUb5jBe1aU+xrPZTXxqPvU3RalWoMbtolS1Ac2TnaOaMoL7+XupfwTOzdFMMljbL9WNLo4R99M0HKDVXMqQ6+TJNfZgllahyTKbdVlO0dbabZOGrTr7dGAaSG7pfe3v6RpOXUPvGVuV4BhrvquXrjBc/TnrV4uzWHQFHwBEYDAJeeBkMWi7rCDgCjoAjUEGg3RkvFUUmWLqlLUuGZMaLHCdte7woX+WRDgpN0iy51/kakCbTS0RM/nQeIlwR5/GDNC0eJ91e4UU9WqzRuDIGeEoqKaPTcbNKKLxQOG4bS42wn8PSLsx4MXOCQkKoIC2orQxaluCaP7QGV3hJYzM7Eo/gFV5HIaZv2WTrW5jhwKdrhcIK22Ej64yfSvvt+g7qGNFJjzx2N/3tvmtpSfdiVmfjcW8MtSWetSdPeDY6CjVyGS2Xb6cHG6l+3egU5zpeOz5MJvWkVB2F9vEEDSeTNV11Ok2yg6XXxag22vOrp5Th3YdNk4M6dOtsK7092xrJ8+UppxrxHi9nncanGo16voTtcToCjoAjsFYj4IWXtfr1+uAcAUfAEVj1CLS7xwsSybokUtIiTY4sEapdapQNp2wNNkw/Ex2ww7ryD5vI7cqMFyRzKLxMo6XdS3mpEY5vHsiXjqnsfCA9ldfUG6NSHYuzg/fAmTI5znjpWpLFA8+qodbkiSJCPbdePteWEck959QUXlosNZLxwCMSffNskdnY09GXfVb7Glsc54p+WmfCNHrty46kTdffhjacOoOu+duP6Lc3fZ/6VvQGz+YJcai+WjbklSIxipy0hVPWVPmBnzZ+yGrP4hocAvX+6qJTT6lGnZzy6+SVN9TnUPxCF7Hpk1uo2iWk5sibOUMd0ZrUt+OkfY+XNfke3Lcj4Ag4AikCXnhJ0fC2I+AIOAKOwKARGPqpRnVJFydEXNiYPXsHWrQwWWpUis7SYqSp1iuJ1XZFAyzJ0MIMi9g1S5L+oo86yxSe8RI2121rj5e6cak/PJsv0TT9Ij2MJDw6ufAyuSi82IwX8Ar56EIt6bgsqc8l8141PrGj1nL+4Ga8pLrwqpYtQpEYKCKzUx+VUEePHEcv3uyl9OY93ksLn3qELr76HOpaxsWq4BnvFnKpBfGr0ZgXbVURTLVVanDP1JvZFxspbzBWEZXhm2vWYdtqFHXyucWV762cX0WpjI6NWuy2jrw1d+XHtOY0u7qwx8th5EuN1tw7cM+OgCPgCJQR8MJLGRHvOwKOgCPgCAwKgVtvu52OOPJkPsWmt4WepkJVEUmN5G4lAy6jFJvrDlR4abZd9WYUSdo0ZYspXDAliZimY6kclhp1dXfxniqrdnPdHBNDJUTPTPDDjJdiqZHNeBFd09E+dDFa6efUYDfwtVV9moa1Uql2Cy+Ka6qr7bCQh83LZCL4aSWtWvIMUWlooZCS6HJ/g8mb0VH7fSTs8XLBT+bQ0mVPs/WOxAiUxadoJvqJlDYLV4GgPXSgJ3bQSzno21XPkS9SSwomrV+qUdpp1fuwGGFD4y236+y3xqROY1XSFCWJymIrRh2rqfI9peNMozK9lPp8bocZL8f7cdLP53fosTsCjsDah4AXXta+d+ojcgQcAUdgtSJgS42wbKRIeVrEkCdAoqF6xhvRycdJz2w+Trp1utSai+AsaRNZ7Uvgmubq0hxJT5tPNbK4beA6JqOIlXK/KpfHAq3cPvLJovBS3lyXzcOiIlC1rv7VpkqajkrkT8VCLaq+SFUKL8VSo1wO0uKxTNflQcyXTDm6L8tFsj44HI1ISLEXbIgnLGlaf/KmdAwXXuD9y5efQUu6uPAyQgsvagHPjgI7dTHUp1qXkVvPIk/HqG/b5GQUuKdyiKrc10iVDhto4zJ7A+uJRvWutqqc4aNo7Gqx3Fc6njymZIgyQv3tJoxUKKiLpFjSMZX9lPsiXb23K1fVHD5KHoMtNfJTjYYPY7fkCDgCjsDQEPDCy9Dwc21HwBFwBF7wCMiMl5N4xkvr46TrgJL0p5wESX8EL6WZNUuWGs2bu4BTp3Q/kCjDRlUbqYddec/o2tLSRpqkpbZMXyRFbtq0dXmp0VKe8YI9XtRW01MjK/MHVOQxRd34yP5izzSQ88JL/YyXqF4OoNS3eKxVEondIq4adqXwsry5ENfKj8Ws76bGmcZjwokQiOwhOGEbvNnqCuqjqZM2pGP3/zBNHDeVzv3R++jJZ+Yzhp0si+ILdERvYK8sOsirNsxgo5mD8JW7cjGpdl2wqfWU30Qvy6T9NdfG9xhecxaCUlqNP1VQ+ZT2/G77jJfn9/vz6B0BR2DtRMALL2vne/VROQKOgCOw2hCQPV7eRb09PSWfSHxaJzWWGmkL8rHN1YbZs/k4ad7jZd7c+ZEu9rQAkEgH38KVeyA03syCWoSoRWE2Ao1vKH5I4WVJm0uN1Fo5CLNd5qT9EGE0kc8AYQ7TtfDSxTNeupbaHi+wUe8Zfus4Es9AUYlmnb5EnRVeFi/mQhwf2xyW/Agfd3TDgUNGSloSQephoJignO7NE4ypAVaGvrzpfurgI6UPffW/0vYzXkl/uP1yuuehG+nJJQtp8TOPhZiCHHQQYHHBmPbTdiEwqIaGlivVUyV28562cv2mntpVS01yg6ErFoPRWZWyOsbUR6sYByuf2n3+tPU46Tlz/FSj589b80gdAUdgbUfACy9r+xv28TkCjoAjsIoRkKVGx3MCzIl27dUqEdJSgCZEliSme7yg8ILEWC2VpVVL+CpVG0xBlIRc5xJwrzCqNBUFQ2w2F15MRrTUmNpIn4OIL5jheFIVDhRkKbxM5hk4y/hfm/ECT2k0glpKUQk87ZL9ToJDIyYt4TTzByq86BDKkZgLlTDKYFpFZNyQNxgaAQvY6e1bRttOfxm95f+9hzaYthk9u/QJ+v2tP6Hrbv0x9eLb1cCCGjp6Ia5yX3mDe6ZWTLNKNY/a0m9SZRUr5YOuNLWsstpveqqNJn6ZXvZT5q/uvo1TUKiLT/Ex2SpeqzvuVefPlhr5qUarDmW37Ag4Ao7A4BDwwsvg8HJpR8ARcAQcgRICt/HmuocefiL198tSozSN03SxpJJ1JRXShMi0sdRoNi81WpicaqQpVVVaTbbjUWSlfCFJemgXRsWL+ULpQnrTpvJSIz5OurrUCMqqAftqXXylnFxO+E33stUglxVe+Djprh5amhReimFEo1auSr2olNF0jOKD73nQkVzVUwtp4eUZnvGyvDTjpcacqjJamfeC3m7DouJWP3uSf/jJ76GYYrOCDnrlO2mXHV5Hjy54gO579Ga699Fb6OHH7mX//WG48tYQi1kUIMr9diMzudSCUbWVcg0LUKu41VGbaGq/1RMeUv8Dybbirxle+dfWjFk6zqrUmol++L164WX4MXWLjoAj4AgMFQEvvAwVQdd3BBwBR+AFjgCWGh19zCm0rKe7goSlkBVWQZBUSBOiJBni5uxZvNRoUdOpRmJ94KSrcNXYKGwgjDi9RCNJI6vf40UlzLxS1IZx0Kqn5jLWCyFZl3NkibbVcdIQVy/tFl6CDjvTlTb27mQ0OqY0lLS9or+fxo0fT+PGjafFi5/mpUa8x0uy1EjjMZ2couNUPznXtOpaqpMWEKAf6NHwlIkb8Oa6H+S9Xjamr13+AXrw8btoVMdoxkn3eIFljNqsBRADIAmtCGAwEaaRFQZKDQl0YKt1scBUWbNJruRWBlkmNvTLPhrEVjM5jBRT1kJ49uVaGGUsnpvjsHiH1tKlRn6c9NBwdG1HwBFwBIYTAS+8DCeabssRcAQcgRcgAlhqdNgRJ1BfH5YaaYKDxGbgeQwqnesJiLLUKJ/xMhC8kk6tXFIVyhkIKKhb8qYxgjx12rSwuW5Xdpy0SjRHpxKDiU90cLdYgofSjBckWdjwN12OJFqKaoqHRqKx5jzdgwVci9UiUa26Z1Z4eYYLL8WpRqmtOk2haWRpRM3SOUd1lSrlE7xRKTuBv8Hk6XT0fh/mJVoj6Lz/PZ2WLHuGOhg0kVohZxkBRCTwcfRqT57qZfARqmZuz3rtW2xlKbXSSs782jgHK5/aeK60wy84BFP6xTCtaXwpZsM1DvhaFXbbj09mvPBx0mf5Hi/to+aSjoAj4AisWgS88LJq8XXrjoAj4Ais9QjYcdJNe7w0Q2DpkLYsYbE9XhbS3GxzXZWt2hVts1GVaE3R1C1N3FbERBw5+coUXtJoBxsffOcb63L8pcJLeakRRgif8FWd7VKNJs9JIz9CKDgoKrDcfIXCC892wayXZ7nw0hsLLymWddppROBH1xVRHVOZUdY3vsSNYtLyFb208bQt+VSjc6ivv5cu+MkcWtKthRfTQEtmvDRFkcu222uOUSwMzluTtdRKk0w54qHqlO2t2X584zXfUCs8UgzWbPzD5d2XGg0Xkm7HEXAEHIHhQ8ALL8OHpVtyBBwBR+AFiYCcanQKn2q0rMjhB5PKWEqEVqLJlY7Zs+U46bzwAphNKwVdtBMbKXOANpI2nfGRFgvSwguWGmE/lYFmvGh0iETbcD+U+IrwQ+FlBHXyzI3Jk3mPFz7VaGlpxktwikoRX6n/vJfgFISiZAw6TP5g/dgNtqo3s7Giv69YahQKL+E4aR1zVRPe6mybRdPRMVR5eaoderZWioe7gjpHjqT1Jm1CW26yE+2/67H0zwV30WW//DR19yyJEYgfiUWi0riq/iymwbQ0/iad9v20K7hrCQAAQABJREFUspRaaSWXRjFYnVQ+tfNcb7fC4/k6pmbMfalRMzbOcQQcAUdgTSHghZc1hbz7dQQcAUdgLUHgVt5c98ij3kXLe3GctCY4g0tmRAv3RI8LC9hcF8dJz527gHm8Z0jBVz9Mipdoqn7Jlgo1PCV9Z2Y0m84yCYUXrkLAsmyuWz5OuhyLLF+BK40pFHVACJfGqP1BPtkdPKLwMmmKbK5bXmokFsVPHl3aK8WB2TVQLMg6jlbzXgph3ly5j8bHPV5sxkuMBGKp62pXBPluFgtSVIWBrCQW+vruiihZDG3QsWnu5Inr0aF7z6ENps4Iy+F+d8v36C93/YxnvmBDXZYJ1Tb1JYHm4aY+VW7wz9Lwg4G6sba2nFrJo8yRS+XKFlWv7L2so3JN+qBDpkmurNduH3FobGl7IH2NX3VVXunaT58ae1knlRlsW2NO/Q6n/YHjCTNejj+M5px5Go0ePWpgBZdwBBwBR8ARWOUIeOFllUPsDhwBR8ARWLsRSJca5Ulw07g12RF+mp6YBhcBOvlUo5kz+VSjpqVGmsyYhboUWdMgs42WxVBoc+FBLyu8cGq+Agk6y/M/64YZL3aqkWigYGH2NGk0LKTmAAnloVUfFzh1V2KfFaGLvUqmcOGli2e8dC0t7/FiSEiMavP/s/cmUJZdV5XgjTnnSanBNjbGUxllZlUBNhjLMg0GPMBqoFmAUpJtWBoxw6JlSyrmqQow1aymFqu7arGabqC63AYMdBWDTbvAUNiSAbcH2bQZbDxLSilTyjEy5uiz73nn3XPvf+/Hj4j/I3782E8Z/05nuvuF4r27/x3ykuEQY6vsxq5EcY3YNDUCr5+3G/GyD5vrylKjpbjUSLRBWkVsoZvreGsWC/zlUqBHQLqV9bGq4UO00Rfxi3u3d2Z/eNmNrwvTk3vCF879U/jHz30gLCwvuhsgtvNAsruZ/OZRNThuqIKONx6pnlouWeyUy/VqlYZMbj8JlPW+bFIpggRIk5zJI7VYva5v70e+KQbzW9q3ep+WMt6eyXkZ1JVXk1wpg3Kvck26/arLY0hLjd4SpqYm++WEdogAESACRGATCJB42QR4VCUCRIAIEIEQMOPlttvvlVNsMOPFX02DGd+ueT8kSq1CZshx0ifrGS+PS5MRHMipVhpuqK/SY5Ntk4ENT0843kXGUlmL+NZyeaqRxZHi7syZP99icSUvvtXlk2CqlDpU5zNertR7waAfEr5c6tlMJAPNuTZKoFPaW0y9A/GC/V1AvFyqiRfIQgZY26Vl1HtL1mqpyutd0h5bj0yiLa00o2v1sCT7umgkskRrbEKxihUWS5st1K95l7opd7RpHB3VPVZ4beTt6qUfXhZ5b8vKSNdjy+SR9vPyfduIXf0dsN+b3i2YXu8awyhpM14efICb6w7j/WFMRIAI7E4ESLzszvvOXhMBIkAE+oaA3+MFe2rEK45fehvENA+xZKAu7MHJU3KcdFxq1Ey8pE7o4DgnU5qHXRqVSIrjNLOlKotBbBHih9qxS7FOlhodOyr7qVyNe7z4o5JTHJ25JhSsz95PpyaCqWq9EQkINAaOk45Ljebm3YyXRJ+oSndyw/s0F+bSt7XloaPyMidFlu5gqdG+vXtlxsvFsLSEFvvJMTV77b4SUeNlLEbTb05VCijVV7Xvi93v1GYy3jLqfLks1VY3lOm0vh4zFm+pg3i1zUvY/xWldCr7fnrNJLF2zttYW7q7RFsM5qOtvbvV9taEW5IxX6lmp+Vsj5cHSLzstFvHeIkAERhhBEi8jPDNZdeIABEgAluBQFxqdOtdAZurJuKl98FLGkohl/TGJ4R4KZYaGVGRBs7ooQ6e0KaURC9kAwb25stFIFkbnBt2nng5KsTL1Yp4waySPA7T6Ew1Qq33XjVvNZ16jTUSIyLGUqPDRw7L5rqeeLHhd1PvkjXDymosgoSEb/G1yOdRW6sSL3sj+XLhghwnvYzlQdYKvMcro1bXdp8S6WJRJK9W0y213tj9SeWUM/3cskVmrZamHrdJmCRSSA9Crjeb9juZ+totniTVPebShi97Gx6H9WBhemv10fs1+0hLvVLO7Jdpk1y3/pT66yk3+WrS37wclxo14co6IkAEiMD2IkDiZXvxp3ciQASIwI5H4KOy1OiWW+8W4mWppS82kGgf0JTDJgykxuNSo3yPF6MT/ODSdBOZ0O4nDdA0Ji8Z7eBDKs0POuSPdD56tCJers6JkHlu6barNj+mYeU4XozrgirHTidmo4J86NqhqkrKMoPD9niJpxrNuqVGlY2sD3WdZsxbNC9ViMfylWhDYhIafd2HqCt3RBiqmZlp+ZkJs7NXK+IFZkyvMilFI7eKlkpAkzZaJhPqKPiopFGKkTirYvCYqKpGgE/9/ekwGCsKq81CA6/thpY5R6S+T6hPdSaVUt+zbvbVhuKk2invbSTLG8+1xdHWj7b6XiJo0u13f3qJo78yiXh5s+zxws11+4surREBIkAENoYAiZeN4UYtIkAEiAARqBDAUqM3vPFNYWFByIg1r85BTRpmpaEcBo8YnJ88KUuNzuFUo86lRmZJ9XVInWy1BeJ96JBUJUEcVDrRcBqiWz24DxAvWGo0F4kXyJtSpduSaKxKJVjcmcO6sjBg5qt2LconiBeZEXREjpMuN9dNKm1GCx+xCCpL5ZOWWVJ5X0roWFvVKiCNrY6LLZvtAmtmOaFl+t6mWso/NRYvZdFZndr3dzK3AJ8mixbVT1Z8W9laWtruch7rWtFA2vrZKduEW3f7pb283O6p03cvNd1jSRbMbynfVp80m3Om19y6U2ptqdH9PNVop9wyxkkEiMAuQIDEyy64yewiESACRGCQCGCp0enb7g4rOCkmu2wQ4wdFVpcJZkNjaxmrjpM+22WPF1gz65pX+75e7ZkUSirjc9oqn/JPJ5eYDAiZiiaQKk+8QM4P6k0DdmtvyEhDPLFYDSfvxuhEAWg1XJW+b4k+pR4zgg7jOOnGU43g1kfkLTTlNWL9NF0rNcmnulqq2kclujXXsREkzLhUI3JriLAknJK5mEtSvgHGrMW8WtnLad4k6lJU9/JOwmVtNk6nxc3UmAPvf732zEZvepDu7q1s7W7fWk2r2b619hZjs5R5am7Na70/r9dWD220eVmz6HWsbmemOuPldHjwQW6uuzPvIKMmAkRgFBEg8TKKd5V9IgJEgAhsIQJ6nPSdQlDIHi/VZcMaP5RBXRMZYLI6GEoaSrzkS41g3g+bfF5dJ30t22fyUg5HTcMIDTjwcWIJTayR+l6IF+8J47vIt6iJSDyYvxgZbFeEjEWapZWer9N4lHjBcdJ+qRHE7fJ9sLrmVLVqVzGDKL21bppoE/kojnQlLF5aklOpZLnYvolIEHWSGWpbPzNE4v1t9tZWCyu5DUiqbdORUhQr5SopJ9wZq9nYTGoOSv/rsWk21qPTJtsURz/sN9lti6GXesTUq02Lv5S3evOH9rIObaWeye+8lEuNdt49Y8REgAiMPgIkXkb/HrOHRIAIEIGBIoDjpG9//T1hcbGc8VK6baYCmoZA0ATxosdJn5WlRmekpk0ySuOj4TKdpsGWDrTScEtk8U8qUqRCx1QzOcCP+D1elC+JdI0bsqk1rXXhiF0M6BFN8ufam7KRlLGGpNVMvMzW/A184FKNpKe1TZ9VtFVwOjsnycGC2UStt5jqpRYF+Zk5Ph2OnTgaVhdXwvm/vxjmnpqr+676qqWfirTm0Zrb15pun6bpo8rlTSKvRalowcymuG9PaQtyZV2nte415mszdsxGd0/ra/XxePuo9+X1WPU216O3VbJN/Rr2mNeHTSRe7jwdHuBSo/UBR2kiQASIwAARIPEyQHBpmggQASKwGxBQ4uVeIV4WYnd1yNY0cMMgu6nehngYELkBULXUSI+TBvGCJStrXU4/itogq/Sb5FLOSJRUg8h0RRAG5SXxYrbbYoIdo2C0522SzfWmC1RSTBqPbj7ctNTIourEOtko/SVPeYvXMLuQMH4ixlILjYXlK0vhWa96ZvgXd3xpmDo0HeaFdPmbf/eRcP7jF0QJgrbYKN86V1uS79pkqtp0zsdvv3Fm1OObtUEpCwaFMlqzMug070Gnt17j8nLI21Xa93Im05Q2yXm7TTpbXVf2zfwPW5wW1+ZSEi+bw4/aRIAIEIFBIEDiZRCo0iYRIAJEYBchgKVG33UaS41sQ9W2ztvwNh+opSERcmkgNCabx56Kx0k/6Wa85LrNnsyGWS7L0LK6lDPywaI020p0iJywDceOHYsn9lyVzXV1doQO03snOXqJXzzH0KuIRCXFJHVVt7DHS1pq1DbjxXqBNPU51aoxQyrVay6PNpXMEvS0VnNYbbbn2HTY/6x94QXf8tzwwm97XvjIf/jb8LFf+0fByxFnIGFExftFH9Fjs51iUQ/t5dSS50q93J/Kmj+T9d59dKqrrV4m99hZUrvar256Kpf0yzJaFOO8F01ykC3ryzJkcKHerib71raR1Ntej34Za1k2W2V9WfZy7nevERvIlvpl2eyVaa9ypV4/y3kMaanRW+RUo8l+OqItIkAEiAAR2CACJF42CBzViAARIAJEQBHAjJdbb78nLC/pUqN8CJBQSuRBqkMuDW9t4KftaalROtUoDRRVq81X7gGlUhLlZA15W8KT7/FhRIdEL8TL0aPHwlU51aiTeIGF8lIfZW0qSx90XVOqslzFrtTYCFGh1iyetMdL06lGzZ6bamsP7j5oECadJCw4S0FaVFFFIckLRqhZemo5POc7nhVe9sBXhEfffyY8/BMflFoZ/EKkUk92rUYb8lIl3KdEfeITXox08cbbvBt1YrpeZ+08tOGzzfraFiCh0fcm2ybVLYJ+2Pd+u/nycluR79a3YYqzP1jYjJcHH+Dmuv1BlFaIABEgAptHgMTL5jGkBSJABIjArkZAj5P+PjlO+mqBgw1obNBj5VxMW70M5KQsyan6OOnmpUYYyuqgNreZSt6n+UCr1vtWHdh21ugUE/EE4kVmvFydBfFyVcrqxaw2D6shZBIqnz6tPvdZtwv5EiVis1mXukqt26lGtY0sY358TBZDJhj9qsfOdtSYJdWSe2BBVWZWri6HZ33jM8LL/tVLw5OPnA3v/ZEPCO8imuPVzAMzUs188d7b/HqZjea1N/oJG3k/mmvs9wJ3I8mnXB4L6pN9bbPfULuH5qeUyy1pydvrJu/lmuyUdU3xd7Nf6vdSbvLRpNdr7JuRa+qb2es1zqbY+11nMa1lt7scj5NeCz+2EwEiQAS2HgESL1uPOT0SASJABEYKgXiq0a13yWE2S2v0q3mA0zQkgqG01Mg21/XLBdSVDj+6DUJ8W5lPNpBrGh7H2npz3Yp4wYwXIV90ZoxGj08/rFbLm/hsmfESY6wAayZefB+b/KMdl8k1o1/Wom/aR49TRTFEYdgTKSGn8N/KleXwzFc9I3z1j740XPzMhfCnd74vjM2IvJx0FFYq3yBdLIwYk36garOXxVraiaHGnqhrbfdBtHjH/YgkUZWWhjdU9n67GTA5jb6b5NptZqupn+uxb3a6eYRML3KwsVVyTX2E72G6No8FlxoN0/1kLESACBABRYDEC38TiAARIAJEYFMIfFSWGt1y691KvGBc0zGOsYFER0P02zQUQkN+nHTbjJdq8B8tNX2YzyYv2pYkTMZTKEIjxGolFeJSozkjXtRfk1ZTJOupwwwSxGW2UajLVWVJvHgZ61Onz7Kl9iCivg31SrZ4G0lC9Sp+SDWrKUCoW5lfCkdPHA6v+NmXhT1HZ8IffOu7w8UPXQozL5qJhJruzgsPuY9k33tVHNrackktafRNLbCVZq6Uctorvf++TfPy2bY0rNlVn2s1ut6NArFuOiWi3WR795okS/upZetz6FsbHsMUZ3+QScTLm2WPl6n+GKUVIkAEiAAR2BQCJF42BR+ViQARIAJEQJcavUmWGs3LOK8avFWD8F7RUS0bHKkWTJyMS43OhsfkOGkMmNd/tQ22YEkHXDbs8jFYHaRSl8oZL2hNQ1tP12jLZj8r0qcKBkmMsYJhfGI8HD58KGCPl7nZzs114d33I0VjtR5Pq0tSvtVqk5S2ps/UEr0KaKsLq+FL73hhuPG7XhiunLka5i7Oh0f+94+Hpz8mJxxVl0yQUfFY1gIsqd0UfyVWtagvk4mq8uEjSPLeVqlhmiktJdSmt5xktzZXRtYv775vg/Dh7fcr5o3YaevbsMS3kT6163CpUTs2bCECRIAIbBcCJF62C3n6JQJEgAiMCAJYanT6trvCyrIsNbLxTTaeQQENWWXde1MpZcblOOmTJ28MZ8+mzXVtLkLSqc20ZMx3U7PGY1ElYsdTKCA/pByFto54UbSUeCk5LCOCyhkvKocZJIqO9Sv1vKxROfVVtqVbmfRxB1UHdTFnxUxdClK/cnkpvFiIl1Pf/aWC4Wq4KkdL//UvfSic+9B50RaZyLqYpcxA/E2BaavN+2S1zTHCIq4kleR8XapVefu0LlnZ/zZY3fakZWS9RGE9btK1Nm+nSc63ryffZH89+v2UberXMMXXz74GOXltPtx5x+nw4IPcXLe/yNIaESACRGDjCJB42Th21CQCRIAIEAFBIO7xEo+TlrOE4/gGA5pqoNMxtumoMMlKJ7WPy3HSJ+Nx0n6PF2tvGkg13Q7It8mqrWTR5PxQW4mXyBOInfalRl6nKY6GOjAoJasSxZRmsLhKzWbi5YqYGi9ErT+obrVW6ORFb6G0UraZJpBYWVgJh77kQPjqn/qKcORLDoW/+NG/CWfedzaEyep3JMYKC/hpji2vNepFo0Bbm39I2GU22r10WvE12V014GE8Gjbr5m0rUh9dL/5KpCzmNkTWa78pBvNpvppktrqu7NcwxdZ/LK4I8XLXHbcI8cLjpPuPLi0SASJABDaGAImXjeFGLSJABIgAEagQwHHSt7/+3rC0qMdJl8D4ITPassFsJeyHRTYksuOkdcaL7fFiraqBktdV+76uSaJyWg34a4vVwLrzOGmVyI+TTqcaqTWzYrbXSiXqGLjodag2ES8mpL1FqBOy1OiQLDWak6VGutnvWj6b2tVeU4vVeQmLAsHrTil6N2PETnBZTjV6xtfeEL76h18SZh+7Et5911+qxoRYhZEoK5lk0NzVaWpKhjWnLfhMLbVanUn6Sc7XqWBuwZeSrORq0gW1IhUbk0TtdOAZH2E3Z1WcjSLW1hT/Wva9znpkGwPZwsoyVt+PLQxji1zZcdIP3H9fmJ7mHi9bBDvdEAEiQAS6IkDipSs8bCQCRIAIEIG1EKiJl4WKeMnGNJ52aaJc0qDY/Jh6L8SL6VhqNICViy1qrdqlRUwYYBezUPSoZJHDcdJH5TjpYnPdruyB8+SzkbawsWDmL+FlOKgeSrVC5AES8TJfEy9JwnvrJW+azbLWajH5KGNkImDdgOzyxaXwRa99ZvhqOU767MfOhr988AMSvcx2gXA0opZsjxd4NR/I41IJzftP9Y37phIpFi/Vrp9LoeQ9+8VUZqNqjwl8SsY622lswDU+1tJVE2JN8lUfGhH28ibn/ZQ+IN+LnLfB/KARIPEyaIRpnwgQASKwfgRIvKwfM2oQASJABIiAQyAuNbrlTiEDZGCNy4/NqnGZDudsqJwP1MqhnhqRT1lqdEo2133ySd1cV3YNcca9Vq2RuUZtB/FSxeMHixYVpJV38R2QgXg12wHEy7Fjx2T/BDnVSI6UTmNvyOd96ixbjCqnYVTB2GyKyq31TItVZUYOaL/GZbnO4SOY8bJZ4sU8RsAs0JRKCBopYlFZIz9MaCye9gP8ILwa5v5uIbzoh54bXv7DLw2f+dPPh4d/+kMCidwNWQ2ltqCp2AJHtQr7uPSuaR6fyS9KFoHWoya/kjTurErnEm2lZDmqiSGLKGqgOd30NiNFfYpGG8qyiZf1ZdnkkPo+eTnk7fL1MXBpKPVMtky9nG/z9q3e2/Y+0d4kb3rd0iY7TTFtl1wZexlH2b4V5TyGdKoRlxptBfr0QQSIABHoBQESL72gRBkiQASIABFoRQDHSZ++7e6wsiSb6+L9H5eNk6wcK7NCrLEPG75ZOabYXPfUiXB2E8QLAookgTmwtHaUaBdUQTavqboievWMFyFdOomX2mBPmTwMlNRPO0KdZsfHJsKhvhAvlXcNo9NRQ1AWf9x4GLHHqStCjc0th2v+5bHwpbe+KFxz4mg4/OxD4eGf+5vwyd/7bP274bmLyDnBvvmoDFfmOmOBL/kvv0w5r7VS91aTarKb2pKNlEutW5kr++59t8XWptMk3yYLPyYPGcub/ya9UsZkmQ4agbS5Lo6Tnhy0O9onAkSACBCBHhAg8dIDSBQhAkSACBCBdgTq46Tn51QI461qbIaBdRpo5wOxtYZq0DsB4kVONXrsMdnjpR6lY+ZLdUEo1ncOBVFjpAv2bbF81KxizPdzMaN5agN9qByVGS/YTyUnXiCP1vVeOQIVZIURs5vLwh9OfSr3ePFSplkYbCmKpleOUrAgva8MddpLNYrtWFi+uhhuuOn6cPINLw6T+yfD4x98IvzD2z8Z5p9edL8HMK7O9FPswJT5l3slLycQkst8WDlGpE31p8nUFVmme6uKqnX9jKEgGzOaRjLOx9Rq1JSyEBoKm5FLWCTDrQGJyHrkm2TNS5uPNp02ebNn6WawMBs+7bc9b3vQ+f7EzuOkB32faJ8IEAEisH4ESLysHzNqEAEiQASIgENATzW6K6yuYKmRDMJAhtSXH5T5+logGxZ6CZxqdKKHU43SDBXvC/YrazJgji0xLsmZmDQn3RRPmcuIF+zxUs94gX0zBi0ffWnFl72Or+89j3k5EyBeMOPlKjbXnY24+4i6R+MlK0x8FUKJBirCqgot2Uw535toYmUlLD66LPu8rIap54yHif1pR92Id1RVrXJRkSGQrFtNSu1+9IJ3NzvJovXRoom9kGbU6xVzkYGSmmi0m2XTTx42lyvtWVSl1baYmuTXI2t+vI6PqZt9L2d2mtLtkmuKZVB1W9dHLjUa1D2kXSJABIjAxhEg8bJx7KhJBIgAESACgkAkXm69O6wuY6kRBmEYYNhlZR0utxEdfuhmwxMQCydkj5ezZ+04aS+l+dyebzf/eSxRwol1znhBo+loJDbQRylurlsTL+bDUtPzNqzNUufcqhrSUsosQzS2CQkwNjkWjsRTjWSPlytXHSGQ/Hs97yZJVBa9QyhVAr4akmrPrKqQysgnYpKmuCeOV4ziuFNS6Ug5LwLb/jIPvg551Sk126Qt3tJKXk7WPPGiMmhT65IzQdeH3NJWlCyIJl9tOJQ6bXKwWcp6P216TTptst4e84NCIBEvWGrEU40GhTPtEgEiQATWgwCJl/WgRVkiQASIABHoQKBearQwj1G3ttvskljCIAz17YOxcugGSZgA8XLunCw1evSMWPBSaq/TopeB10oiVudtiKd5DJ3LWQmW4lIjEC+y3KhZV4Ti1RmZ1pu1SqxIurUqdWESutToMIiXeZnxckVnvCRzkNMYykjMAmTTnA4pJBU0xcvLoiKz6ORLOdiCbLxnLgDT75CP3vIPp1Y3qF6p3SRpsdaqMeM1oeXLECgtob2ui7/bUqoroMGLCAwfAlxqNHz3hBERASJABEi88HeACBABIkAENoUAjpO+7fZ7wvJSdZx0h7W1R6pNA2DsYXLi5I1xj5dHhXgZc6ca+fkzRsiUXkqbdVjWEMfQa2s58TjjZU6Ok8bJRv0kXsxHHWMc3XfWpvYQxifGw6FDh8L8nC41qmfviFrUdF1zWSVDKkO+3ttGvvQO4kdrk5bn2eIGu6aE2S9RuqowFWuvwDOL8FdepgKfNYEWhcyIaSTJlLO2PC0181ZyKiUeo1W2u7/Wb8lW9RrxDCaWOOPlztPhwQfu44yXrbqd9EMEiAARWAMBEi9rAMRmIkAEiAAR6I5AvcdLPE7aBjfQ6X1Q4bXM20TDHi86nLehOOwb7QJvWoZ+k71YmURESjQiAeClLW+xe/shHJM9XmaFeMlnvGRGo13E0HyZ/by1s7a0mcujhNinZRnByvJyWFxOGw5b5JDxdq0+9QgSelmblX1tt4kevi2zWxMvQFmj8LG0s1ZJKo/JSpmXGKb9TuSx56VkNa8vS+alrGeZCOwkBK7Mzoe77rglPPggj5PeSfeNsRIBIjDaCJB4Ge37y94RASJABAaOAGa83P76e8PSosx4qadAmFsbymLoa3lrS2nTwBgzXk5WM14ee/RxEU42bPiNQXfKd7eXWn2uHLZbJIgVPys1eYHSMTnVaHYDS400crPt/ad891aTQxQmWeZNxqde1u6AIZbkYCm/XA1MuGIuZ/YRlQmpQjoS2smIiJdKpdyqt6YtuVayaK3WXtrJy6Ve3mq2mmpZRwR2DgI24+WB++8L09Pc42Xn3DlGSgSIwCgjQOJllO8u+0YEiAAR2AIEIvFyO4iXheQNM0mMhLHaanmJEgf5QLkcEKN1LCNesMcLZnWYXqmhTpRG6SQWLITOtCReIAHbyY95QvjHjjTNePFWTc/XwaJZSZZzCZQSiaSlJJFIjVQHT7C7KqcIrSzJiVLjWH40IZNJ7BQhyNpMGB9XigUShhnyzZfp5nqlrLaabNVPVMYqp1vVpZo8gmSh9JDKhic0kW+6i0m6OZf8p/ZefCdp5ojAcCJA4mU47wujIgJEYHcjQOJld99/9p4IEAEisGkEPiozXm65RU41qpcaYfgqw9qY2FAWZct3umwaBOuMF3+qEUgEYRdgO/502ik9NNnNtcohe6cGaowaOHrsqBwnPVcsNcotWiknS5LdtWNUCf0UvRiALdgx65LG+tUwObMnHDh+LCzIyUYXHzsTxmcmwvj4pBOEpeQ/z1e8iJPOs3UU0YaWcgkr5f0t7Xr/0ZSqVb8TsStVdTcf5sv6kKw23cfulpJustpdI8kxRwSGGQGeajTMd4exEQEisFsRIPGyW+88+00EiAAR6BMCIF5On747rKzIrAtlA6oUDjCUrYa4cVTbPLRtHATLjJdT2XHSvcx4gU+9mmxaW0r9gF3nUWiEFqfNrdCelKcalT7MmrfUFA2sQ9fSFI/lcksGYdSo1/CMh2U5SepLv+7rw81vvCNcvXQh/NMH/iZ85L/8fpi9eF5mDCn5kvsoI9YYzGuZdkqrhNlEikvlrKR1+Ew1zpJlHRGHqiSb9Nty6a6oRLNuc61F2+mzm3xbW1uErCcC24eAEi+yue6DOE7ak7DbFxM9EwEiQAR2OwIkXnb7bwD7TwSIABHYJAL1cdLzc90tuYF2KWhj8axexrogXnCcNE41sgGzypgGBsSWzwfvqRYauZza0Po0pMZwHoQHNK02p1B6IV4SKQAb+LHlPvCnF2rr+CxjLk1I0kyuqk91kltZCtd+yfPCja96TXjRTTeHQ9ceC+/4yR8Pn3j4/WFqZloMaH/UhXfk8zBczldBXb70CTX+SuGaXKpplvO1KV9G0tzrJI9YTcdq1+e51O5uxVp7SxFJm31vod9y3vaw5Pvdx+2yt5V49qePPE56K+8ZfREBIkAEekOAxEtvOFGKCBABIkAEWhCIpxrdepfsNbJUSVSDBxt/RsIFBdQ3XybqW8fkVKNTJ8qlRmbDa6TBirV6O0kyyaV2m1mCGiNZvBXVxnY16IYnXnJSw+gC04WezyeP683BSqJFUm+iBzk9KMgSryufORu+4cceCDe//rvCu//9r4YP/PbvhHFsqmkhRKdJN8VgAtpmJd/eSXPkZts0zUZuE6U8DivlcqbdlOYRteu1tZhHs20xtcmXclZuS81eW7vV9ypn8sOU9hr7sMttJaZbh0VaasRTjbbyDtMXESACRKAbAiReuqHDNiJABIgAEVgTgbjHy62yxwuIFxABGF9kg+tY0dVOORSGMDbXzZcaeSmfT6a9p2aJJKs51cCnH8531IqxSLzIcdJX3XHS3X1YNC1SqDaRMqxY1qgam+p2bb306S+EV/7A94Wv/967wkNve3t432/8umyHI/pj2BPHLnEYfZpTCwCpXtaCUmdrJdQ18RaSYHNt/lsC6Ta5ZAk5f6dSS6duZ02nx6Tfq3evwTwRGEYEEvGCpUY81WgY7xFjIgJEYPchQOJl991z9pgIEAEi0FcEsNTo9W/83rC4IKca2Wi99qAVNmOkrq4zOuhPMzrqBhkHC/Fy6sZw7qwtNXJ7vESDTrbK+qG2Wu6UyWuaNdI8GDfIF9FjIF7icdJzEp5ry432Fo0F6EPI7KDBhLKGojAWLj9+Jrzynu8NX3/PHeGh3/7d8N7/49cEP8QnxIuYQKx6lc6sPpn0Xk26UyrJd+ZMK7V01qQ2s91NJkn7nGp21y+tmrS3Y/lS1uqZEoGdhQCXGu2s+8VoiQAR2B0IkHjZHfeZvSQCRIAIDAwBHCd92+33hOVFmfFiA/xi5ks8WVqIlM6hbUleJAmd8XJjODtQ4qUNFo21jC4SL3HGSy/Ei9lGn7oN+Cs5iKTuF4UWfamGyuwTT4aX33l3eNW994RPf+jD4Xd++C3hyhMXwv7rj1Sb7HYYj07VXdnL2FR/+JDKKNBW1tWKdabpvteNUd/7SC0pZz5yOa21Nkjn7WWNl0y281ynhbydJSIw/AjEGS93yua6D9zHGS/Df7sYIREgArsEARIvu+RGs5tEgAgQgUEhEPd4OY09XnCqUXFVREwn8ZIGwSkH3TTwnZA9Xk7Ue7w8Lm2QrNq7zHgxqdwubK/vAl1QUhKeeKlJplazqS9JZL1RwcZaOmNhYXY2fPFLXiLEy5vC0RueGT78zj8Oly88FT7+p/81XH36gpAv/hhui0vtrulBxLDMqiOKNRXNT33XEgwuB7smaT6sDDGrMxXfhlbfnrd5DS9l9U1ps4UmSdYRgWFF4MrsfLjrjlvkVCPu8TKs94hxEQEisPsQIPGy++45e0wEiAAR6CsCmPFy++33hqXFxdxuNobFANlmPqRBcMp5VVUclz1KTpy0pUZrEy+Zu8pcs33vqy3fOeMF9vPNddt0rb6/EcEqLDb1aUVWYa0uL4ZXfs9d4WXfeTpM75sJi/Pz4f+8738MX/jYI2Ei7vNgmrmVvGSxa1r2wCzE1m6KmRm771llXTCbvZjL41FNfOb1tekNZPpnaQPOqUIE+oKAzXh54P77wjQ22eZFBIgAESAC244AiZdtvwUMgAgQASKwsxHQPV7eJHu8zEtH/DDa+mV1Vk5pe8uY7A0rxEvc4+WsO066Ghg3zHhpGjK3208x9JqD/fURL2bZR7beiEy3QQ9VsXkszM9eDi94+SvCq+75wXD0GdeHD/z+O8JnH/lIeOzv/y7MX5mNM1b03uT2UGqwbIE3Ehrd5GvFLKM+zbM1Wfhmb61YTA+pHvnta/qZLyPtp23aIgKDR8D2eHmAS40GDzY9EAEiQAR6RIDES49AUYwIEAEiQASaEehYatQ4brVhttmw4XbTwF8NYMbLyZN2nLTOeBE6JqzGdUui7/y4rDmIafKSVfdQMIu5hWPHbHPdqzXp0dSD5MDsWE1uz2q7p7ABvSqN2cpuJKDGw5XHHg833XW3nGqEPV4+Ev7Lz/50mLt0SY+UjnrYmBjX+uMpNdSORBTXH/XSH7Xg7ZiWr4NdqzcfTWmu04tGk5W2utx6mxTricAwI8BTjYb57jA2IkAEdisCJF52651nv4kAESACfUIAx0mfvu2esLIkm+viysauOjCOXEm2uW5VrxruMyl3Hie9IqZlrkNUlY8kGrOoRpVadiY3lDXjubVEvOjmuvCY7zJSOkvLbNRSbq+U1nLp28qVNEy4KpxYdPEzj4av+YHvD19/rxwn/XY51ejXfjWsTozLTBcIRtQSLqjKwsgKlZOUOFepEgHIPyXBXHVrttVKppEisVyul5eganKZmaIArV7koNbpoTDGIhEYegSUeJHNdR/EcdKTQx8vAyQCRIAI7AYESLzshrvMPhIBIkAEBogAlhq94Y1vCgsNS41yUiKRECmcXCIb+MoY+JTMeDl3zo6T1l1iokac6QErvQ6q+yE3Fo4dO+qOk9ZelD1IfdP4bCivQ/+1CACTzq00lmAK4rKp8YW/+0L4uh/5ofCqu+8I733bb4WH/+NvCtUgAtFcaRPlPA6VyOt6xVaPAi91myL2cZh8/juhWJbxJb2UM/tmx8ptaWmzm1xT23r0e4lpu+w19W1QddvVx377HRQ+TXb7E7stNbqfe7w0gcw6IkAEiMC2IEDiZVtgp1MiQASIwOggEJca3VqdaoQxJ8YO1YWBdJxvUY1F4wQMa5Q0DVEtl5TH5FSjU/WpRmdEGstlqvaaeIGx/gxWYEmvZnuI8Bq31GhsTJY91T2w+M2GpYlYUIlCDsXU5Uqpo0LqC8GquLq6FA5dd0N4zpd9eXjxTV8bXnTTV4bf/9f/JnzsXe8Kk3umKz2YtT61227eN0Vn9JS9gEW9kt2EhbWVaZPvvPsJHc0lv6UtKycNq8nT0meTfOpDrluWTK6sH3R5u/z2o1+9xr5dcv3oY682tq6PaakRTzXq9e5QjggQASIwaARIvAwaYdonAkSACIw4ArrHy52y7ATHSWNwgUsHuPi0wbPmY2P9kQ+Dc4mmpUY1gZCL1vYGnbGlRrOzWGrkveU9SS0qlIZcbXJJI2FodaYDJPOlTUvzc+HUa18XXvODMsCa2Rs+//89Et75S78Yzn7202Fi0k4zMbCSHbs/NZ7xvuncFXgtJe0eakTWavF1pt0lPHBKzJmFUs9Lmkyelhp5ayeWaG/TWdtbaZ1lIjCMCCTiBUuN7O/AMEbKmIgAESACuwcBEi+7516zp0SACBCBgSCgpxrdK6caLdT2/dA2DWdTrhaUTJJFzskIs3EqnmpkS43cjBen5W0NNC+hHTuqm+tujHhJPZVtWWJXdb+aKurYddf/jj76tkpHlhntv/Z4uP6FLw6rcqb0U5/5bLh45nHRTL6aMenWbjSLyZR+rb7ZMmrXlvC65s/X9Zrv5qmM22y26bTJmx5TIrAzEOBSo51xnxglESACuwsBEi+7636zt0SACBCBviPwiGyue9vt94SlpcWaNvFD2zScTTkfRJJFLsnojJcbw9mzGyVeYMusW76cM1KWfWQ+r/qReJm7GmZnrxYzXrysz6f+aK3Fk2QssrwGJZMtbVRt0qyzbmQWjDA4kMZGu0FOfurtMvteWn1ZTE2eU1xerznf6cEse3kSLx4N5onAZhGIM17ulM11eZz0ZqGkPhEgAkSgbwiQeOkblDREBIgAEdidCNhSoxUsNZKRtp6ko1jowFs/24bXaXCOXBrqd93jpSYl2jBPdpJE8pTqEhGQcqlVcxo5qI1rZMbLbEW8INTaSx56YaCWkvoUQ3MtVMsWX1bTth+LWpP2Dv9SBxImCnQ0qhEXi1mtGrKk03s0msk0F/IoVQbWTF8td9o3a21xWztSs+XrLN9muU2nTd7srTc1P/22u944KL/bELgyOx/uuuMWOdWIe7zstnvP/hIBIjC8CJB4Gd57w8iIABEgAjsCAZvxsiwzXmyonHYLQRfSALRpCGqtZWcx4+XkyRvDuQ3NeOnVk814gXxbJIhM2uXfNUf1VKMrxYyXmuNoclsTKbn9UjS1trcoRmhP0p1ETRVvfTdUq/PT2zAdteZbymhy351WtUa11I635uVVptO+yZheu0T3WNr0zK75sbRN3to3ksLXIOxuJBbq7BYEOONlt9xp9pMIEIGdhACJl510txgrESACRGAIEfB7vKQhrQ02Uw0GoFZr3fCtVlen2R4vj0u1H8T6fK2xRkZ05B9m5EDbBu0+Jq1HG2pTSfVCOHToUJibmw/z9dHZVZ8gWtiFFbWjOf1Um96n1qu/vDWVNJfsFNFJg7fopX190k85k01ype3UkrRSLt9NRmWThlnPsKyVS+m6wWVgIdlzDTsiu7Oj3xEQM8gGBGyPlwe41KgBHVYRASJABLYHARIv24M7vRIBIkAERgaBbKlR3SsMlm3YnQbOKaeCJlGruUx+qlE/iBcNSfdFsejyuTnq3hMzqR9xbozthus6ErdVgWJlOPWpbdjd5FM949O0kp3UhlxyLbkqPOzx4hqcQpJ2lV2zlcko04u2j7NJ3reXjpvkSxmWiQARWB8CPNVofXhRmggQASKwFQiQeNkKlOmDCBABIjDCCHxUNtc9fdvdYWV5qaZaUnfzoXVeUqm2gXm/iZdm3+o9bzPiJfXCaBqt6ZQ2PgbkC1rVKj5zWdXvTryojNmwUrLk8Yqzd2JFk8cm38neWjmv7X2anrX7NqszmZSaVLtEkmWOCBCBzSCgxItsrvsgjpOe3Iwp6hIBIkAEiECfECDx0icgaYYIEAEisFsRwFKjN7zxTbL8Zs5B0D7AthYbiicl1LhWITFOnTwRzp07Gx599Iy0Fe1RFvKdlpJNzZlVX6/WTLdJQqVVwk4NElJmZTUshZV4dhAmuYzXpwipDbOkeiiZD4slL/uYfN5LecvZ4p5qlo0ca5ShY568vZSPc3fqovdTV0qmpJB8m8+rtWTFeuxxsLzXa8+bhXaJrW/pd0y92uu33NYj1z+PvWLRP49bb6k/fbSlRvfff1+Ynp7a+m7QIxEgAkSACHQgQOKlAxJWEAEiQASIwHoQwFKj07fdFZaX5VSjjuE/BuQYTOiVciUdYRJIVWe82lz37NluxIvXS7pa630rMaBkgvnGzBMb6FiKNh9lbh/0xjP2Hgk3Hnt2uLw4Hz5x8fHw+OzTYXp8QgSTXsqZL7NjcVjZUvNvaamncqm13UOSNNtI1a/RKb7FWs2iR83LlfVmETIWV7KhrZ2kjEl4y6OaNzJsN/V5O+6l/fZth++t8rm+PqYZL/fJjBcSL1t1l+iHCBABItANARIv3dBhGxEgAkSACKyJgO3xsrq6ksn6gbk1+CFoZztqbIAxFpR4ORES8QL7ZsFkzbKlnVatRdNc30pexobLvs7yS3Jk9tc+81T4zhd/TTg4tSc8MXsh/PrH3hU+/NTnwszYRFiNBjUGZD21Yza0NpU0p9Kp/4kqKSWTPHJt/fU9Exn5p5NjfH2nZVjzkXRK9FaT9pwRa27/GSO+erOy86UMz53fE/ZgJyHAPV520t1irESACOwWBEi87JY7zX4SASJABAaEgJ5q9KawGE/66d0JBqXpspIN+0EUyHHSp2SpUetx0kYimG6y1luumQZQ4gUWzL5khTxAPCvy36HpfeE5B46HV37RqfAt/+y/C7/10XeF/+Vv3x32TeisF9shRqNyNuqgdE6NRd0kAVFrr9XqTKnRJOllpF3+9Ua8mK1mbOoQ1spUS5+iUxAvuCSkTVpVOzvqE33392JHBc9gdygCXGq0Q28cwyYCRGCkESDxMtK3l50jAkSACAwegUdkc93bbr83LC8trsuZDfHbKIax8XHZ4+VGmfFyTvZ4sVONvAsb0CZLvnVzeaEI3Ji5JGMWlhfCS65/YfiFV94T/uSTD4Vf/NB/DjMSrw6yLR6Lr4zE2lP9+iQ79ZOl9pz6aPekVo0agdzG/GgE0FVf3kqb9/ao2UIEiMB6EYgzXu6UzXV5nPR6oaM8ESACRGBgCJB4GRi0NEwEiAAR2B0I6FKju2RSCJYC+WF2W/91UJ8kU85rjE0I8XICS42e7LK5LjSa9b2tznxJLBglYLasDCLCaBe0af3SylL4iuteEH7+5jvCuz/1ASFe/u8Qd1LQaSXOXac9jdfXa948OuWYbW41fYtItVJtbiXZTrlMAjNUqiajXrL2WICA91CWTaOsL8uDkjO7lrb5tfbtSNtiKuvLssVa1pfl9cqZvKVt9qx9K9IyhrJsMZT1ZXlQcmbX0ja/1r4VaR5DWmr0Fp5qtBXw0wcRIAJEoAcESLz0ABJFiAARIAJEoB2B7jNeMFDHoKD50mF8OZiHrCzHkRkkJ2XGiy41shkvZsvb9frNfnqr9YMX8+M1Kz9CriyvLId/efx5MuPlzvDQFz4W3vqBd4QlOU47XrVqnfFGJN8Zb5skFJO0SaUatFst8rjyVq1LMimnLW0aqG+STVrrzyGyfttcfxS9aeykWHvrEaV2DwKc8bJ77jV7SgSIwM5BgMTLzrlXjJQIEAEiMJQIdNvjBXNFbM5IOZNCCYKSJnADcyE4Tp3qhXjxsJT2fFuZN19NOtYGnbJdiBfZZPcFh28IP/ay28KEbKr7Kx/43fDJi2fClaW5sCBt2ldvw/tO9tokmqV9bWfebCXrScbamomPBg1URaWkmax1yUW9WrkQbKsvxFgkAkRgUwjYHi8PcKnRpnCkMhEgAkSgnwiQeOknmrRFBIgAEdiFCKSlRjhOurxsmQ7qPfXi671OGuiPyXHSp2Rz3bNP4jjpYsYLNmztWNYDOxjcr++Cx06tFIe2eilt2zMxFW5+1o3hjlOvFQuT4cnZc+H3/v6/hT/63IfCfmlTm6UdT334NsQMDe+nKS7INV/eWtkf3+Yj6PDgQ4hKuWazZ1cb74uVS10zbu1MiQARGAQCaanRm3mc9CAApk0iQASIwAYQIPGyAdCoQgSIABEgAgkBEC+nb7s7rNhSm9QkuZxgSdSLUgOdBIGdCRTccdINe7wUxIsN8XNvWSCtBeiWcTSREyYXU9kQ5fDMvvA/vPCm8K0vuCl89vLZ8I/nPhv+7LMfDB88++kwPa4nHCWnpq0nGmk96tquFFHKtcnm9WbV61mdSlrJS+Q2UslkU033nLMZs6Jf71KMivXa6+5tcK07KdbBoUDLOxMBJV5kc90HQbxM7sxOMGoiQASIwIghQOJlxG4ou0MEiAAR2GoEsNToDW/8vrCwMNfiWumQtiG3DdW1XT9RJxNewomTcpz0OX+qUWUFxAsG8VUxd2wW89r1lZoMm10hh2Qj4RcdeWb46Ze/PszKaU7/7oO/Hz7+9OfCEjYYjrHBW6KZkE+kkJEvTT4sSvOl5bxkMs0prJby3Tw1W7HaHjWzmS6ma2mPNkycKREgAptCwJYa3X//fWF6Om77vSl7VCYCRIAIEIHNI0DiZfMY0gIRIAJEYFcj0H3Gi0GzNvmiw/M0SB8X5uVEPE4aS43OiCHQCdIeWYUqH8WrvLnqoB3qhnVkYLjN7ljAqUZffu3zw1tlc92/+NxHwi988PeCTPkRFdGLMVWuUIzxVmG7CLyYq66ylVJjqVN6rZruvrppN2lKbDU00h5DlQ8TjW1W6GabbUSACAwCgTTj5T4uNRoEwLRJBIgAEdgAAiReNgAaVYgAESACRCAh0H2PF4zLbYZH0rFcHLNXBR2q61Ij5LHHy0mZ8XL2bLnHi2jFwb1ZQeoH+t6ql1lPflyE1Y6fq6IWxsLi6mJ4ybUvDD//yrvDf/2nvwr/04f/cxi3oBBKEUJDlURsQhY7yk2SHeY0jB4+zZp56EGlEGnQtBk9sUk+YjeqvqCu7lbVFuUKsywSASIwMAS4x8vAoKVhIkAEiMCGESDxsmHoqEgEiAARIAJAYO1TjWQsLoPxsWwz3LTwxqOo4/ZqiY7In+x6qlGpibKN+n3b+vLGE9h5TKotMYnpldiImS2r4WU3nAw/8YrvDn8sG+r+0sf+MMygf9WMl9iPOhQlnrTolx/BMmrNo3oq+1CbseZ1pDGODg9W24uhIrYYTBVRbKraIxlT2q3aqqQXb5QhAkRg8whwqdHmMaQFIkAEiEC/ESDx0m9EaY8IEAEisMsQeOSjHwu33X5vWJa9TsorLTBSGsPadejenVIYGx8Pp+KMl4bNdaMh09eRfRr2W7156y31/ECkSqoKv1RoRfZw+QpZYvTtL/qa8ALZ42XP1Ez4tY/8YXj7Jx+KJxl1ekqxpCg7pdprmgmqdvn2lpLyKQmeZk2PikjE7shHTaKhPfUx7W9j1qS9MGEtTIkAERgMAnHGy52yuS6Pkx4MwLRKBIgAEdgAAiReNgAaVYgAESACRCAhkJYaycayfhAeSxiUy8hbZkSkGS86WNfhuhu0J5MxNzYhxMuJcqlRIVQXdXSvluvKKlPFUFdb2XybrtWLoFZpRmI3yRXJfeXx54dve+HNYWV1Ofz1mb8Pf/TpD8meL4uyGbCnNmyBkmnCOdp9hM4fmlsubyGJ+FpvM0mUOR9davN2Uq3mahCqBinHmS2iE5uq9lgHEZS9PSnXcr6+Mtckn+mvV87kLS3jsfqtTHuNoZQryxZzWV+W1ytn8pa22bP2rUjLGMqyxVDWl+VByZldS9v8WvtWpHkMaanRW3iq0VbATx9EgAgQgR4QIPHSA0gUIQJEgAgQgXYEus94ScNxDA3KK1EaZYvoyYyXk7K57rmz/lSjTjmtabJushj028DE5KzOZBqsOFGlDYw8GA+gmNCMGTA4OFpngJQ2TR4CeuXkRylvUpr61k5LuWxOeJRtWs59exln3TtV1sQLar4mWjqbIiqGW1Mz64gAERg4ApzxMnCI6YAIEAEisG4ESLysGzIqEAEiQASIgEeg2x4vXq4p3414AZlx6pQcJ90T8dJk3deBDchYBd8Y8934AovTyAtHVeRmaxeZROYr92NxmYi1wqPlra1b2u7PtNRak81K10zUInXGTGjaRLxE0Rb5XJslIkAEBoyA7fHyAJcaDRhpmicCRIAI9I4AiZfesaIkESACRIAINCCQlhrJprPrvIzQ6FQTikNONQLx8uSTZ8Njjz4uIsYMdEqvXdOdFOjWajEa6YI4NJKklXIWpY/VtyLSfL8b09A5NGodGr0SL2rdxWTEiBopJqGUsWg8+IwXwq5F6kzVWCVm32rjpsKiWO/7Yg1MiQAR2A4E0lKjN/M46e24AfRJBIgAEWhAgMRLAyisIgJEgAgQgd4RiMTLrXeH1ZWl3pUqSaMLIpNRj/M1E4mXjuOkzQVkZLAv/+YXF8PK0mpc/iOrk2T5j+yvIkuAwAOMj43LwGMiTMp+MY5RMCNZmrkHARGvOsKKj1C/aK4iiDlYx4UlSHrVBqRYW3a57nKrIDeiWtI1jTKtJaIOSt63SddSVlGlTbIm0qJjsUHVyJaY97ZadM00UyJABAaGAImXgUFLw0SACBCBDSNA4mXD0FGRCBABIkAEgACWGr3hjW8KCwvz6wakpjXiwN3Uq0G7JDjV6Nw57PFyRgb5Fa2BI5vlAjmB3Dd8w8vD1OSUVMjWt5UqMsiOTUyEf/rkZ8LHPv6PYWJ8EmqNl6nFRhQch1DHKNVp1gtETGgsgHjR6KwuWqo+kvWU8+2mo61W0iCaNby2SWg8iBAWUJtSs+njVxvW4i36vFn3dWa3qDNTUaVJz8szTwSIwKAQsKVG999/X5ielr+NvIgAESACRGDbESDxsu23gAEQASJABHY2Apjxcvq2u8LKMma8rG/AbWP1eFpOrVrRA1hqJJvrnpU9Xh4T4mU1Ei8QEq04qwVpCK997c3h//mLv5CZLqvxZCFFczwsywycFz//+eHw/oPhvQ9/OExPgXgxJ/BseZ9Tbf/pCRbUawTQt0vjTTUpV1pOHk3X0jweI1GstVta26xNWMZS1c5LZhG1dmnPUFLZ2rIJxBSx+RazYMdua6OXyNRZIAJEYMAI6IwXOU76wfu41GjAWNM8ESACRKBXBEi89IoU5YgAESACRKARAd3j5c54vDIEdMjd28DbBu061E/ayI0L8XKyXmokM17sLCGYFpIFK15W5Od1r3ll+KM/+9MgDzRZ+aIkCE5EWlxeFuLleeHowSPhfe//cJjJiBd4gPe1o00xQgcaRj106peyqpF/tiGTrCEyX8r1y1KbvVKuF4vaM+thp2Xtn36iVXPqKUmnXBkDy0SACAweAS41GjzG9EAEiAARWC8CJF7WixjliQARIAJEIENATzX63ipVzNAAAEAASURBVLAoS41sIN65pCVTqQsmbySIDeaRgkQ5ecrNeHE7qES+JJ7pPBZe8+qbwp8//P5w+OABWe60GK7MXg2LiwthaWUl/LPnPS8cEeLlob+SGS9YjlRfyfNGyJfaTJHx8XsPhVhF96TabrJJqj3XH6rD6J60WKnTo0Zaxqv++xNFp0/WEAEisB4EuNRoPWhRlggQASKwNQiQeNkanOmFCBABIjCyCDzy0Y+F219/b1gSssMG5PniG09HdMJgOmULNtc96ZYa5TNehHWJ+7iMhW989cvDnn37wi3f+e3hCTkB6b3vezj84bveFS5cuhJufOEL6hkvttRI6QFPIPROG7TFarF76sFkrfcmY6mXRZ3JW3uvaWmnV71OOYugF4uQ9T3rRafTI2uGGQF/f4c5zs3Etl19HKzfOOPlztPhAe7xsplfDuoSASJABPqKAImXvsJJY0SACBCB3YeALTXCSUK2CKdEQYfpNjjPBx023C91xiZkj5cTJ2SPl7Oyua4dJ202RAtrjeTfq1/zinD+0uXwLf/968JLv/zL4maS//aXfyX8+tveEV72Zf9ciJej4b0PfSjMzKQ9XtSKzvCAX7OaYrAapBqhfiYJqzdt01CJZj0QUoYR7OUEVbLd6UvtmQ/VTfLNud6kal3gaacU1ZU+k/qktWXZZMv6stwmZ/X9Stv89st+L3bKGMqy2Sjry3KbnNWXaZt+KVeWN6pX2tlMudcYepUrY2nTa6sv9Ye/nJYavUX2eGnfVHz4e8IIiQARIAKjgwCJl9G5l+wJESACRGBbEMCMl1tvvycsLy1G/xi+6GU5W8KCWqtTiU6CQeujZDXj5ZxsrttEvETeRQy87jU3hd//k3eHK3Pz4cEffFO443veGP7T294WfuBnfjF888teGme8PPTQh8PU9EQkFnwERr34OhdBylbki1boUpxEvFivcitl39Dq63Lp5MrLpFrksKlt0oJcKuWSvXkqdVgmAkRgFBCwGS8PPsDNdUfhfrIPRIAIjAYCJF5G4z6yF0SACBCBbUNA93i5V/ZXWYgxNJEBkUzA0qCisZ1kEFMifEr2eOlGvIDJeO1rbg7vfM+fyb4uy+G13/i14Sd/5MfC7/zu74Qf+pm3htd81Utkj5fD4eGHPhImK+IFQSKMJtIlJzMs2DzKvARrRoCYvNaVctaa+1DZps9SHzJmo0medUSACBABIGB7vDxA4oW/EESACBCBoUGAxMvQ3AoGQgSIABHYmQjEpUa33hVW5fjmta+cOmgiF8zGWkuNbMbLa2Wp0Tv/7D1hcUmIl6//2vBTP/5j4bff8dvh+3/858M33/xV4eiBw+EhIV6m4lIjs45UvecRoR41zZHltSZXzUORImJqujp9NEnldd5U7/oWU26rszTscp0RD65m2LHoNb7BIdRpudeYhl2us2eDq9k6LLjUaHB3kZaJABEgAhtFgMTLRpGjHhEgAkSACEQEPipLjW659e6MeIkEBMYZ1aVZV1HVJ3IBubwdm+ueqo+TfrxoXZWjpPEzJkuNQLz8ucx4WQyvvOmrwi+99RfDX/zle8Kbf+Snw4ue+8Xh+NHj4f1//UiYnpKlRtEK/OBIJL1yr1bbnKZ4U7st//ELqlJryq3HT9JijggQASKwPgQS8fJm2ePFn+a2PjuUJgJEgAgQgf4hQOKlf1jSEhEgAkRgVyKApUZveOObZKnRXOy/JydANljZCAoPkrX5ujovyiBezp07Fx6TzXWN2FACQ4mXEMblOOlXhD/58z8PS0tL4YXP/5Lw1p/9mXDo8MHwn37rt8OsnGx07tyF8Cfv+m9hZg8GIJ7+UO8aV9dI6pCapSqbSBoFUNnU+9osM0SACBCBviFgS43u56lGfcOUhogAESACm0WAxMtmEaQ+ESACRGCXI4ClRqdvuyusLKelRkY12D4qClEiHxr5iQJHnfFyo5xq1Ea8yLwVMfQ62ePlXe95jyzzWQkH9x8It97y7eHO7/nuuOzn4oVL4Tf+4/8V/udf/o1w3fED4kHYkbjRDCLQKJQ2qciTOobmCJtrTam0YfWmlfpvLbszBR7AytLdiQJ7TQQGhYDOeDkdHnyQm+sOCmPaJQJEgAisFwESL+tFjPJEgAgQASKQIaB7vNwpS42Wq/pyUJ3ol5J6MEoiM2hWiqVGfqAO7mRFWBclXmSpkRAvS7K57o0vekH4Nz/zk+Haa46GP3jnn4QnzpwNn//8Y+GPZcbL/j0zarne4VeXGyldop8pjjyyvJSkKoNlRVE27bL3hRiLRIAIEIE+IMClRn0AkSaIABEgAn1GgMRLnwGlOSJABIjAbkMAx0nfLsdJL+I4aeEYxmpiwyMh5EvkH4R8qDgOoyO8lM9jxsvJkzjV6KwsNTojpqFhBAns4Vjn1fDaV+uMl8WFJdnj5WXh3/7Cz4X3v//h8KM//XPhhuuuDcePXCN7vHy02uPFe9AIzGKyDRn15qVTexl5spDLW6mSr8In/WK4MCUCRGAQCETi5c7T4QEuNRoEvLRJBIgAEdgQAiReNgQblYgAESACRMAQiMTL6++VGScLkVxR4gVkhCcoLJ+TFFZrtnw6XhMv58KjkXjBDJWkDyIHG+x+02teGf64Ok76Na/6mvDTP/Hj4R1ynPSbfuqt4Ztf9hXh6MEj4X0PfThMT0+KOvTx0765breYfHyaT/F0tqEG1kwm5a2mWYe1RIAIEIGNI0DiZePYUZMIEAEiMCgESLwMClnaJQJEgAjsEgTiUqPTstRI9lhJZEsTtZCIB4OmG8kxPoEZL7K57pNnI/GiZEmya8QLjpP+3Xe+K3z+7z8e3vLDPxz+1f1vDm//rbeH7/+JXwjfdNNLlXh5+MNhBsRLTYIgAvEu/3QGDuy2zXLxUaoctPWyeJrqE7mjfmFH5U0r1Zu9trS03yY3yPoyhrJsvsv6sjwoObNraZtfa9+KtIyhLFsMZX1ZHpSc2bW0za+1b0VaxlCWLYayviwPSs7sWtrm19q3Is1jSEuN3iKnGuHvHi8iQASIABHYbgRIvGz3HaB/IkAEiMAORwAzXm6VpUYrstSoiVrQ7pXkRWdtCUNaaoTNdXWpEYYXuJBif5cx+fnGV788fOoLnw9f/ZUvDa+8+aZw8sSN4T/86v8WfvyX/9fwDS/58nDk4OHw8PtlxkscgJiFFI/VwG6qRamXy2s3yZtFyCGv8mtpNVnqf12Kp3fbG9Hp3ToliQAR2DwCNuPlwQe4ue7m0aQFIkAEiEB/ECDx0h8caYUIEAEisGsR8MdJe5ohB6STajDZXE5LkaaQj3Sc9Blp8Bq6x8u41H6DEC+HjxwN99x9V5icXA1/8/9+KPzKv//V8A+f+FQ48aIXKvHyVx8JU5PlN79KImhkZjt6jkFYjUaEz9TWmU9SnbnKkrqLdtRnkqybUpXLdW91gj1mfT+6qQy7XLfY+9027Fj0Gl+/celmr9eYhl2uWx/73dYfLHicdL/vC+0RASJABDaPAImXzWNIC0SACBCBXY2Anmp0l5xqVBwnXfENOskDA4rmKxPzIrLU6NSJE3KctG6ui6VGJhvFpIBZL1hq9Fcf/kh41jNvkH1mlsITIo8jqLEU6cUveH4kXt4XlxpNeesSli4t0s1uvWWNVWt8faYuBT9Iau+fJ4w2trFuUwxt/nxMZby+POxyPtZB54cdi17jGzRO3n6vMQ27nO/ToPNbhwWXGg36XtI+ESACRGD9CJB4WT9m1CACRIAIEAGHwEdlqdEtt94txzsvKccibSA9MMyIV8zUJattTGuKAfoTmPFyMhIvjz76eCRKoORlcID1N73m5vAH7/6zsLy0EE9UmpqcCtgfZmV5Jbzo+SBeZHPdSLyIwXhhngysrIpNi6u2qiJVvdZCzvmtJPLE7OS1WjLbyVuTVHud6ZcS3XyWsiwTASKwWxBIxMubZY+XnHDeLRiwn0SACBCBYUOAxMuw3RHGQwSIABHYYQjYUqP5hbk68kgJeL7AzpCuJSwDISUQVFw+K70x4UdOnjopx0nLHi+PyVKjuHmv6cl8FWF3QIm89tU3hXe+588lVylW9lZWliPxcvgA9nh5RE41AvHiyQr4EjKkrsr1zZMFhFaImlRqt5wSK53tqlm7MfGe006Lqrpxiz27piARIAI7DgEuNdpxt4wBEwEisAsQIPGyC24yu0gEiAARGCQCWGp0+ra7ZIaJLjWKNEPJFSR2ozWUqIKpMrhEfkxmrfyLf34yPPnE2fCFzz8mVVUbEmkH8YJzg771m78ufPhv/1Zy0hC5CCUkQLw84/rrZQPeifCX7/1gmJkpv/lVe2vPQ6n8igdceUnr8OnteJnN0yPeWvJXddZXME8EiAARCDrj5XR48EFurstfByJABIjAsCBA4mVY7gTjIAJEgAjsUATScdJY+KPEhHEkscKRIbFcfCRaQXIyA6WelCJGvuS5XxzOn78Ynn7qfKxXEgMa1YwXmQVz8yteGpZX9ChrkDHxkukyq6vLcpLRTHjs8TPh43/3qYbNdStRTdyn2k8VarOynKqLnMaW5t2gWesKwZ6LvXns2RwFiQAR2BUIXJmdD3fdcYsQLzxOelfccHaSCBCBHYEAiZcdcZsYJBEgAkRgeBHAcdK3v/5e2dh2UYI0skAoB5Agvc50KbtX6YK4MIsQMSIj1a2GiQnbu0XdGfcyLr6x+S72etFtdEsnyV7eYtZzb1abyyYbaPezXkq59ZfbPJoli8/KZaoRlbUsEwEiMNoI2HHSD9x/nyyxLGf6jXbf2TsiQASIwLAiQOJlWO8M4yICRIAI7BAElHj5XiFeFjYUcU4vWCknFVCyFu8k1ecnFMVtc1OjKmcmzVoTVWJt3lOzf0iYWdWCPe/YJJpt5h6stJast2/eTVdTb6FZwst7e75+N+Z3Axbb1cft8ruVv8fD0UcSL1t5z+mLCBABItAbAiReesOJUkSACBABItCCgC41kuOk4+a3fsjfouCIiU5p1GDw0u0SmVqsRdaNf1y2Muq9GvHipfJ2o1y01supOdRozHVQ2tD4WeqXZVPyMaAOS5jKOq1X/xZlqW/9s/rNpGWsZdlst9Vbu6WlXFler5zJW9pmz9q3It1oDG16bfVlX3qV65deaWcz5V5j71WujKVNr62+1B/+cjrViEuNhv9uMUIiQAR2CwIkXnbLnWY/iQARIAIDQkA315XjpKvNdXt1k2iElFNdDIDyy2pUcg35XDg3FEvQt0FWEzFR2m8wsWaVBbGmYCHQ6buzplBZg6jaaCSlF5aJABHYGQikzXVxnPTkzgiaURIBIkAERhwBEi8jfoPZPSJABIjAoBH4yCMfDbfccneYn5+TE58rmmBchvuSxWa3Y7bPi+3bIk0ryONH5DSrciAJognRiSakERv1jlX2VjCrRtpUDg0wgb1cYE/ykJMrzg7BUdHRHuTUnsmNyea7clp11IEDadbTqiVFvJi9A5N57FUbAqzlRA8xwkC059qw3y/q0STVnXISn7Stxo2BRabqo5dDPuqLoMWOPsbuRr9ohhGpgyv5ifhJ2zg6iMooJykuKIpQ9Iu8/DO/1tYqByVc3kbEItnTprXkYKLEzLDQ+NbGDIHL1YBZFl/Rx1a/hZzZiD60U8QMYGwGi+pemY1WbPl7JhBt7v/Ny7K57g9+3+tlc903yx4v0xFqfhABIkAEiMD2IkDiZXvxp3ciQASIwI5H4FOf+nT4zd98mwwWVpQckB5FMkDpjziGRg3IkDgkl4FVHFhEOR37GwjQsyU1SoDoALu2V43LIV8NvWVAnNuDE4xbcOX28nq060AcXERFbEgdiBzUw0RFIUR7Ur22XKXj7dWxxza1aFjkciIgV+ZXylGj6KP1HfJ5Hz0WFZYijNghadjmfaxikj6vR87H3s3emnISFSLwcr1jBk1/r7SPZi/dx3XKEbP4e7UubInZ0GC2IoToiRM3hu/4jm/LNh/HXwFeRIAIEAEisD0IkHjZHtzplQgQASIwMghgcDs/v1CRGBjgGqGh+bKsZMfg5OAPg/hOvytSH+e5IEAlOBrl0mwMxIqr2d7g5Dpjz31ZPP2S244+9hvbfmHRK7bErPP/sbXuATHbOsxw2htPNMJvHC8iQASIwHAgQOJlOO4DoyACRIAIEAEiQASIABEgAkSACBABIkAERhABEi8jeFPZJSJABIgAESACRIAIEAEiQASIABEgAkRgOBAg8TIc94FREAEiQASIABEgAkSACBABIkAEiAARIAIjiACJlxG8qewSESACRIAIEAEiQASIABEgAkSACBABIjAcCJB4GY77wCiIABEgAkSACBABIkAEiAARIAJEgAgQgRFEgMTLCN5UdokIEAEiQASIABEgAkSACBABIkAEiAARGA4ESLwMx31gFESACBABIkAEiAARIAJEgAgQASJABIjACCJA4mUEbyq7RASIABEgAkSACBABIkAEiAARIAJEgAgMBwIkXobjPjAKIkAEiAARIAJEgAgQASJABIgAESACRGAEESDxMoI3lV0iAkSACBABIkAEiAARIAJEgAgQASJABIYDARIvw3EfGAURIAJEgAgQASJABIgAESACRIAIEAEiMIIIkHgZwZvKLhEBIkAEiAARIAJEgAgQASJABIgAESACw4EAiZfhuA+MgggQASJABIgAESACRIAIEAEiQASIABEYQQRIvIzgTWWXiAARIAJEgAgQASJABIgAESACRIAIEIHhQIDEy3DcB0ZBBIgAESACRIAIEAEiQASIABEgAkSACIwgAiReRvCmsktEgAgQASJABIgAESACRIAIEAEiQASIwHAgQOJlOO4DoyACRIAIEAEiQASIABEgAkSACBABIkAERhABEi8jeFPZJSJABIgAESACRIAIEAEiQASIABEgAkRgOBAg8TIc94FREAEiQASIABEgAkSACBABIkAEiAARIAIjiACJlxG8qewSESACRIAIEAEiQASIABEgAkSACBABIjAcCJB4GY77wCiIABEgAkSACBABIkAEiAARIAJEgAgQgRFEgMTLCN5UdokIEAEiQASIABEgAkSACBABIkAEiAARGA4ESLwMx31gFESACBABIkAEiAARIAJEgAgQASJABIjACCJA4mUEbyq7RASIABEgAkSACBABIkAEiAARIAJEgAgMBwIkXobjPjAKIkAEiAARIAJEgAgQASJABIgAESACRGAEESDxMoI3lV0iAkSACBABIkAEiAARIAJEgAgQASJABIYDARIvw3EfGAURIAJEgAgQASJABIgAESACRIAIEAEiMIIIkHgZwZvKLhEBIkAEiAARIAJEgAgQASJABIgAESACw4EAiZfhuA+MgggQASJABIgAESACRIAIEAEiQASIABEYQQRIvIzgTWWXiAARIAJEgAgQASJABIgAESACRIAIEIHhQIDEy3DcB0ZBBHYXAqvS3bHd1WX2lggQASJABIgAESACRIAIEIHdiQCJl91539lrIrC9CJB42V786Z0IEAEiQASIwNAggJcCf/GbGY8G80SACIwGAiReRuM+shdEgAiMBAL+5ZMvniNxS9kJIkAEiAAR6AEBPv96AIkiRIAI7GAESLzs4JvH0IkAESACRIAIEAEiQASIwGggYOQLv3gYjfvJXhABIuARIPHi0WCeCBABIkAEiAARIAJEgAjsNASMswiSGRPiYrVKUeamajvtbjJeIkAERhABEi8jeFPZJSJABIYBgfJltywPQ4yMoRUB3q5WaNhABIjAECKAv1n+4qQRjwbzRIAIEIFtR4DEy7bfAgZABIgAESACQ4cAiZehuyUMiAjsOgRKMgUAgFCp/j7Vk1pQX8p64sXaTBfydsU6EcAsmdqIVzZBpkSACBABIrAZBEi8bAY96hIBIkAEiAARIAJEgAgQgUEgYISJ2bZyxYusWL2k4y7fU9bzLCBcMq4lK/RkjkJEgAgQASLQHYEeiBf8lbc/wD7f3TBbiQARIAJEgAgQASJABIgAEVgHAkauRBUt+CrlS/S9vKz3XlTO15R51R6TmS6rMnUmWlSzIlhnSiWWiQARIAJEYIMIkHjZIHBUIwJEgAgQASJABIgAESACfUPAmBRjTSz1DqQORElYlUzM+8aUz1Qh6m1XYpCJHAvs2RUrUagz1sKUCBABIkAENoFAC/GCP8D2B7ctvwmvVCUCRIAIEAEiQASIwK5CwL9P+Y631XuZQea9f58fpM/B2va9QB6XvdVqaZs/LagNhLFq+7CIjdpMla/7WDdUHZcGa1uVGS6Wt9RZKiKqJJJg0c4iESACRIAI9IoAiZdekaIcERhKBPzbFQLk29FQ3iYGRQSIABEgAkSgQiDOWPFo1I9yIUXwGLdHeVWPJBIuMWPUC4ScQFUy1dpINCiN0YLKY3lRdJOEq711xXZlMukjZ4JVoxXretjnRQSIABEgAt0QaCFeuqmwjQgQASJABIgAESACRIAIDCcCxh3U/MBQhenJjSowCxjFmiipqI+qE6srSrhYWnapNlFnki3ZxUXFxRZIF1z4RDaWYp3IVGJIQLZURZWBUl0TBewDDbyIABEgAkRgDQRIvKwBEJuJwPAgoK9CwxPPbojEXjutr/rCaiWmRIAIEIGNI8C/6RvHrl3T/mr7v9a9Im1yTTbaPfbYUhm1+SpgMxBj3K6ldghSpopc6iIxUgmtgHiRupVqSoqlq3K0EdRh1+xZRDXJIkcema2x1fGacBkfxwwb/IhGdCsfsC8VlZtoyppQCV9RPrXEHD+IABEgAkSgOwK7jHiJj4vuiLCVCAwdAvy9bb8lwAZXfC3ULD+JABEgAkRg1yKw0aeC6QG4QTxRjMiA7bjUSDLeZ7xhQrqsGJMirZFcESHIg3hZWVkJS8uoXwkrSIV0ifloEx/4Z1bHwnh1xvQ4yBUhWZCOT4yFCaTSOAXiJZIvQsaAnMF/VVxSLRfKVZ2YHZMdeq0fVauCBZdRHrW8iAARIAJEoAmBXUa8NEHAOiIwzAjwbab73bEXTEjxra87VmwlAkSACIw+Aht9Kvinrdno21NFDCabiRrB3TC/IDTwA7JFZ7co0YKlRSBcFpZWwvIyiJelsLS0GvPL0rYsbatCxEhTvEDSRJsgV6QDIFsmJsaFaJEUP5Pjkk6ESfmZmh6XVMpoF0JmTNgXyONxCs6mnjETgZD2uhexMXUKnqMMMryIABEgAkSgCQESL02osI4IDA0C9ko2NAENUSDAprz45lciwjIRIAJEYDchYE+GpqdBt7YSow0/fWsnlYWqbHSLchjyifpIkiidAdJlOc5kWRYSRcmWJZAt8rO4tCzEy1LMzy8uhoVFIWJAwkgbfkDWrAoZo/8pgQMiRAmXEKaEWJnATJcJIVumJsL05ESYkZ+p6akwJUTM1JQQMJJiFsyE/ICkGQcJIyGCh4Et0C6YFdPEsPjNgqM8FHgRASJABIhAhgCJlwwOFogAESACRIAIEAEiQASIwAYQAHtSXbqcSAkIzByJS4ikLYqIGFLMZlleVqIFZMv84pKQKkKyzGNWy3K4uiBlIVbm5qUOZAvqpG1eZBaFeFlYwIyXZV1yJPrRptAv1aSVOHtFeBSZ5TIRiZepybEwPTUppMt42CMEzN4ZyUs6M42fyUjCTEt+cnJS5ISYEblJkDYgY8SoEjKRhSn4F+wvg31h4BvtJF7s94ApESACRMAQIPFiSDAlAkSACBABIkAEiAARIAIbRSCyKlBWEgQEBIgIW/5j9dibJZIucSaLkCiY1SJkytzCYpidWwxX5xbC/MJKmL0KkkVSIVuuLgj5Ij9I50DOgHiRWS7Cu0TCBTbNPnKYnYKlRvAv3ImQKWNCooQwPTEppIsQL0KwHJipyBfJ79kzJeTLWDiwb1pmwIiMkDL4AQGDGTIgX7BESSbNRJvwYRNbwLPYrBcsV+JFBIgAESACnQiQeOnEhDVEgAgMAAFMgdbvwDq/CcN3hKhN3xXW73NdIzG9TMgb8Q2dbn0r80SACBABIkAE1o+APYgkXZXNZyMLUlupHjzxuYQlRWlvliWZ6bIoy4euzsmsFpnZMitky8VZ+bkoP1cWYvnSlcVwRUiXy1eWJF0REmY1XJFZLpgFsyib6y4uyUwakDh4gmLGiz1JqweqEi8VASNLjSLxMhXCjOT3ChFzZN942LtnMuzfOxkO7ZsKeyU9cnBPTA8emA4H90xLXn5EZgrLlORnUozozBfpJBiX7MKMFxIvGSQsEAEiQAQqBEi88FeBCBCB9SEQXyDjh+rZe1dVpQk+rSG9m9k3YtqmMvqqKLJJXO3Gbw6tEqlzYNUq2fBpAkb2VCJW3eGswQSriAARIAJEgAishYCf5VLLysNGHll4vsVLkmUhSrARLma3zAmZslDNbrk0Oy8ky1K4NLsYLlyZF+JlPpwX4gWEzCUhXBZk5stlIV2uynKjeZnhItUy20XsCeEi3E1YkecZjqAG55MuFPDAAxEC4qVKq5kvIF5kMks4OIMlRyBfxsOBvZIX4uXogT1h/z4hYoR4ObRvJhzcPyU/03FJ0l4QMTNTQuCAgMGeMbofDDwlDgYlfWLHZUextNM+DL+dFjfjJQJEYJgRIPEyzHeHsRGBYUQgvtzJh84tToRJrNeXreqVy0WvjUq86EuZl4QR1Mq7Y3x5hDS+G0zGnQ6Eqst/sWbTubXJZEi8GFZMiQARIAJEYAAIgHjBI0dSfdLBh1RIATUyESUuK8KeLdgIF6TLlavzQqwsysyW+XDh0mIkXS5J3YXLi+H8ZSFfhHDBkqKrcyBqZO8XIV4WMMNFyJb5hRCkSjbixSQXkC7wjR977sE/KuxKpAtmwGCp0KQQL7K6KOyRnxkhXmbkdKOZaSlLevjgjBAxE+GwkC+HZfbL0QMzQsJMhX0y6+WgEDEH5Cdu0IslSLIvTDwpSQyPDvFi2Hk8DUumRIAIEIGNIzC0xAv+7PFP3sZvLDWJwMAQyF4ukxf//2t8bZG3sLiuXReexxdQfTHU/7vjyyJqpehfV6NFMaZUjNrP81IntuEPL3rxGzVLIW7xxSBQITLjcBKNaoXWujyz/UZA73K/rdIeESACRGCbEXDPFo3EKvInWXy2SRM2zV2S6Smz84tCoAjpcmUuPHVhVgiX+fDkBcmfV8IFBMyF2WVZSiTkjJArso1L4k/MhVRFsqVqWsW5Q9aGh2K88IWDfnWBRstpqkJjmAEjsrVKVQAxs2ePLEOSvV72yc/RQ5PhmoPT4dhhIV9k1suxQ3vC8aN7hYSZDvtkCdL+vULCyNIjbMKLTXz1qq2qB1d0WRMestTAtLCGP2KLlCkRIALDjwCJl+G/R4yQCAwXAsqYuJjsxURfWPBpS4ogGo+51MpIsMSXUcmtxBMYpAH/nE3kYXFM3uKMcIkEC94IUS8J1pDjhAWQLjqVWV4/q/YoEyWj6WjDXoejhfRRSTEhAkSACBABItAjAnieyWXPFeTxWLEynlH4vgHPPiwvwtIinEZ06bIsH5LpKhcuz4Wz52bD05fmIvHy9Hns6bIk+7ssh8tXZUmR7OMyj71bxA/IDBzrLKt69HkXfcUC3OLxGa9smZEGY0E5KclKmz5udSYOlOEnxipHWePCEiTZW1dmv6yGI/snw7GDU7Lvi8x+2T8Tjh3ZG647tk9mvUzJMiRZinRgn8yUkQ165WdCyJcxeQ4jXnumGwEETGKs8oH8zr3Qix3dgZ0LPSMnAiOAwNASLyOALbtABEYXASNK8P5RT2+21068dCqxguMz7QV0VTLIL0sd3u+WxYaRLzjdAa9l8cUs5vXYSrygRZIlvsxJQWauTEjluEyTRv0ECJiYx8se6kQeL6oRefueL90GJWlSmTkiQASIABEgAutCID6o9IllxH/8wkAeQEjxo89AWRYkx0Njr5ZZIVyevoAlREjnwhNPzYanZMbLOclfuISlR8uyz8tqmBXSRbZ+CcsyuMfMlilZBjQpz79p7KWCZ58847C0x1/xuRcr8nqjCOqnK57VUsCTFo9wkC1YsrQkz9w4K0cIIjynsRRpYnxFfK7IjJYJIViqH9nr5ZrDe8Pxa/YJITMts2Gmw3EpY+YLTkICAYM9X3D6EQgY9ZUiBQmDC/HyIgJEgAjsRgRIvOzGu84+E4GNIIA3teqNabX6Zgxm4vwUvMxFYkXIFZEDqbIk86Sxnh0vdZhmvSjf+C3LW96y5JewyaDsChhf9vDNoOjqC6vYge34gqkb9+EFDkdh4qUTL6A4UWEyvtzp9GasNZ/ARn8TQsrg5RTHXYocZsDURIu86CGP9z3EB1u8iAARIAJEgAj0jIA853BVXxHUzxc8u/RSQgNlHA2NZ97lq4tyIhFmtMyHx568EsmWM2evhnOygS5muWBpEU4qWpYNc/E8xIa58tiUDXPlmSX/4TkWv2yononwg6eX/fgVtEZsQMYiQh6PO4SoYWpL/BRlfM8BX/hZlnOp8fxVgkdSsTIhX2xMVjNg9sm+Lzj56NgRWXIke74cP7ovPOuavXET3qOH9oYDshcMjp2ekX1f9NSj9JyFvxjzjnz2WvTSCV5EgAgQgU0gMLTEC//MbeKuUpUIDAoB/I8ZXztjJr7d4cUNL5o4rQEECzYQxOkLCwsL8s2dfIuH0xtQlvw8UpAx8oOp10tCxEQSJpI2eGnVH7ybgVwZj9+caR5ryOOGgEK0II9N/fbMTMh0aKRT8ZvBPdNTsR4vf3baQpz9IvZqEkZ6oO9+6aVwUHDRLhEgAkSACIwQAspexIeIZeODULqI55fN9pyX/VxmhXQ5L8uKnro4F54WouWzj10MZyU9c3ZBTi2SI6Jl49yrspcLHkiT8mPERPV0lbI+o7Q+YYh2e3r551qSaMlVAdf2hXjRhb3aAyOU6h3uhY6JxAz6JYTMtKx32iMnHx2SE5Cw98v1R/eE51x/IByVPWCuk1kwIF/27pkK+2X/l0l5RtszOLJAEjD6gxD0+dsS49BXe/SHPlgGSASIwJAhMLTEy5DhxHCIwC5EACRIfFuKfU9ryI0gwYumzl6JU5aFPIlHZIJswYkN8uIZj8OUde1zOJ0BP7NCxkg6LzNeQMbEmTBx9guWIcEuXgDlVVDScSFe8E0bvjmbjqSLlGXh+R7Z8A/fqM0I6YKN/fbJi94+OQJzn5Ave2Vx+h556QMhMxNnxkzEpUggX+KMl/gGW720drz92aus3Wq+YBkSTIkAESACux4BPBLwhKrZA6ES5LERy9KCLx8wo3NRnnFX5Fl3WY6JPnPuSjgry4mePD8XPv3oJSFhFsOZC1h+JM9LWVK0JHu5TAmhMS1LirB0ViaYxGU6kVARP9FldJvydZ3UGzmjTIx7hnmh2kpWCavVpf2IT8bKRHwWV8/3BXyhIs/1cYlTHqthz+SKkC2T4bjMfHn2dUK4HJkJzzp2INxwfH888eiAHEONL0TilyXy7MZVx4lcxEzqXLhVIEOaALcdE+yQYsiwiAARAAJDS7zwzxx/QYnA9iNgL5SIBBwM3t+wH8uKvIihDcuF5uR8S8xguSoL03FE5mw8IlOOx5Rv9K5I/sLFq5GQuSonNVyVr/fiaQ0ytRpTsePGgzLHGXYi8RJfbuDElg1h1ou8mMrPJF765AfHXuIlFTNeDso68wNYXy4ppkEflGnQ2ABwvxx7uQffvAkRMy3rzvHtG2bPwBZeaLFhoZ1y1PmNYdVRdNriqfMxw4+eEOBf8Z5gohARIAI7AwH8SauIl/jckGcJCIpYJ5+Y7TkvpMuCzPK8KCcXYfPczz9+OZwV0uUJ+fnCGTnJSJYWPX1ZliHJiUXyGBV97OGixztjU1r7kgBW/ZMIZaNemv+yirRnMmJc0MIioqYL1s0DaBFRgL4k0U/VhBiXZAnSkpBKuOQRGvd/OSB7vxw5MBGecXxPOCxEy7Ov2x+eee3+eBT1kYN75UuRqfgFSDztSOxiTzb4iy6q2Hy40Tg/iAARIAIjjgCJlxG/weweEdgMAhnxIm+YeMkEQYIXTKxfj0djCpmC2S2XZFr1BXnRvCxkyznZRBDEy+zcspzgMC/LjHCyQ4gzX7B+HYQL3uNgC0Zttkt8EYQfCTpukhtf2HQzQZTxsouTE7CHC0iYfUKs7IszXyaEeBmPL4BHZNozNv47IBsB4oVw38xMJGH2CAGDYy+xZj2evICXzsp+O0bV22d8QW2XYgsRIAJEgAiMOAKRMMDzCQt08GyIPEUs4Rk2LxvoXpHn4Kx8u/DUhavhiXNXwyc+d15mu8zLrJeF8MTT+KJiNS4vwnMoPsdgSU3Fxx9somhVdU4q8Pzt9TL9XlVKOYsJPvEegP/ksS1kEZYdrcrGuzLzZSbIaUey78v+ifAcmfnynGcclBOQZsL1curRkQN75IuRmTgzNe73gmXDda80OvPRa58oRwSIABHY6QiQeNnpd5DxE4E1EbBXKnsV664A6SiJFy75DxdevJQcwSyXVZnBshi/2cML5vlLV2XzQCFdZPNATKm+JBsJPhGJF5luPb8i3/xhLxchWcQUSBekcDAm+fJVDH7Vo6SugBc0Edfv7kxf6mZkvxdZWRT2TI3JzJcxme0yIacuyMZ/h/fIVOgZWXMu38ZJul/eEPfLzBgsRYrfwMlLb5wBI4a7vvxZMOK7uyAEeOUIVDcqr2SJCBABIrAzEaieB/ZcRCfis1HqsWwWy4vwPLwoXzacOTsbHpOfT3z+ghwZLUTMJXw5MV49C2Xmpjy7cFKRrsQRi5Hc0OcfHn14EIKoiM9BKeI5JSL1FWXqUlMGEk7BieS1asn3SUVVCrNwzFec/SLLj7BP25icMIiZo3unV2Sp71h41vG94Yuv3x+OyTP3WdfuC9cd3SvP331hv8xCxTNX93tJT/yahDHjLj5miQARIAKjisDQEi+jCjj7RQS2HgG8QPX+dgOCQ/dzET28DEqCF67llaW4ES5OYLh06YrMclkK52WjwCfPy4umkC3n5Bs+HI158fJSOHtpIR6NObc4Jvu9yDIinI8QpxrryyROTRjDN34SFjbMxfshpjBjMjIkEDG+U0Qs+kKKtfM6UybGIi9/WE8/LXYm5QVwakqOvZwR4kWmPh/eNymnLijpckyOusQ3cMcOzcg3cDNx/fleWYY0gyVI8TQkmdpdMy/wWhAxqLIrQhg/rCamqpVVsUBk+DtABIjAqCFQPQ+MpMCzCc8hfCmxKGTEeZnx+eR5OSZafh59UomXTz06K0uL5GQjeQ7OL07I80yfMdgAHhvq4uhl2MHXCv5xE2QJUnww2iMHz6kop6BaNZ5ZUU6r5bMsW0OqVz9WRoor865V8olZpiqBE5Bktqv0dWkVpx+JhrwoTI4vC4k0Fq47Ni3kyx457UiWHcmGuzccF/LlyP5wUJ692G9tRvZnqzfbjdaLZ23tkRkiQASIwOgiQOJldO8te0YENoRAfP2yF7xIcIDwkI0AF5ZkpotsnCtLic6evxKPyMSSoiefEuJlVpYXyUvnJSFdZmUvl4uzS/IiOh7XsS/KyUV6cgJe4OQVTl7kIslSpfKlH97g4vsiXvDwoocY4k/1gUSnOEu9xBZfdiXFMZsT8vI3IUdJY+oz9n6R/XbjEqOD+yeFbJkO18iLH76FQ3r00HSc/qx7wkzH/V/w4otv7mIA+NS3TFToVX/laBVMiQARIAJEYFcggIdPdVk2PhLk2YhnEZbdLgvpgo3kQbp84YnL4VHZUPdROTr68afmwqNnsfRIZonKRrog+bGBLmaR4LmFZw2eifq4razXz586I96RN+8WTb9S85Pbt9rkReOUua/xCxEsO4oEjBBP2HD30IHxcO2hKVl2NBmee8N+IV8Oxj1fjsWTjmRJsOz5okt9J2KfYbd+1gKAqoAoOn2nKJgjAkSACOxkBIaWeOEf3538a8XYdzIC8ds8/A8oL0N6apGc0iAvlhfldCJMpb4gs1see/KybByIExuuxmnUV4RoOS/LjbB+fVHeyBblpIb4IiVvqDClM1mACl6pIv0in0bHiKv6pUvaYh6+IS8fMdVXsRibVGAmDBrUWizoh7zM4rVwj2zAux+b7Up69OCULD2aCdfK0ZfXHtkTjkt6XJYiHZQZMHtl+RFeBnF0tfqtXvrUXWVYCoghq+uocEEwSwSIABEgAiODQHwM2d98pPJIkAcUNpydj19IyMbystT2MSFbPvX4pfDZM5fl2TgnS2/lC4mLWJqDLy/GZFkOlhZhj7JqNgtMRdt4uKhdPGfqR41k9DkIj1ZbyaGqvqytrlhHBvZy/eoRXNvwHqtHdZz9sij9j8uOJHacNrh/nyz53TMWvviGffJzMHzR8QPhelmChGW+h2XPlz1Y6iszTbHMFx7ND57r9vytnTJDBIgAERhBBIaWeBlBrNklIjCUCODFDi8+uuQmFiLhsiIvVVi3Pi+nFl2VmS5PXbgiU6ZlSdH5hfDZxy7KsZhyUoMQLzihYVb2crl8dSksBzn/Wd6mMBNlUqa1xBkp4/INF96y7A3Sv8XViGglCJW0orxuzDPRmFT5d0UhePASvCCnL8zLhjKgdqZlCdM+mQlzaP+4zHyZikdfXn9sb7hOpkA/U469BAmzXzb/wzdx09U0aDiKy6DgA/GarzwClogAESACRGAXIKDLgPKOog7LbhYxE1T2Obsix0ZflBmfOLXoE1+4GD79xJXw5NOLsvQI+7pgXzP9mmGf7HWC5+I4pnmKPh4x8ujFv/rKHjnVY6hubM34h2GrUEsDvOf6MYYqKIvNpNAGaVt2hKOm8f6ALs1MCQEjS36ffd1MeK4sN3rWdQfk1KO98aTBY4f3yQb403GZL04ZxJW86uwfXxMF+EEEiAARGDEESLyM2A1ld4jARhCwlyp7C8RSnmWZQr0gPzilATNdMH36KVla9MTT83E69XnZx+XseWmbw7demEotVrCPi/zgvUqXAWEPF5mHjCu+ZeJVC69pMcFndWkEKQ6rz9P6W7Hq5Q+t0MHcF2z6uyBxz8tmMPKHLfqflh1898qL4IF9uvfL8aPT4fpr9oUvkhMYrhUSBpvv4uQFfCM3I8dTx9MX5M0y+kG82VtwHgtLRIAIEAEiMLoINJIu8rTBni543swvyKbyl+dkr7PZ8IRspPu5M1fCJx+9HD7/pNRd1tOL8HyaGJNZHvJclK1Q5JmCmupxiCeXe8ZoS2xWUkLk42NTq5o/4yMVHxu94LV3fSVelDTCciM8zbFh/lIMVJYdybP/uqMT8uXGTHjmsT3h2TLz5ajMLn3mtQfiLFMs852alGetuMTztvYcMzpDtq7baJeoJwis774SMiJABLYGgaElXvgnY2t+AehlsAjslN9j/4KJ96dlIS+W5OeSzHC5eGVOZrXMh089eik8Ifu5PIblRU8J4SKbBV66Igt7quVEeIMyrgKvUzpzRGeQ6IudGO76FmmLj3BPQKUUV1Xh64EvLvjDS6D+CPEi/+E7tTFZh49Z3ZOTsgGvTMbBsZfXCvnyRdfuDzfIyQs3CPlyrZy+cOTgXiFnpmWvGDluGi+D1cuwpfoSo57wyYsIEAEiQARGF4H4bHHPKyXjZaYHvpTAZroy0+PK1flw/oKQLk/JRrpPXgqfe/xK+PTjMvPlnO7rgiVGM7Lv2ITM+pSdXeS5AqugKvAU06cXnpPqpvpCwkGKx5ALwbW4bHwgQtDV9StrNsV8dCN2UYW8jxYxzsuGu0vyBQxiPnZoLFx/eEoImJnwHFl2dFw2uQcBc0T2ezmEI6blSw5stIuN9aPd6nnbr7BphwgQASIwrAgMLfEyrIAxLiIwkgjIixNepEDAYHnRIpYXyWaBT128LCcVyTHRMsvlHz73dDgjKX4uyrr1eXnJWpBZLlPxxVK+zRufrF7Gqre1jjdGe3VbP4KVxeolTfX15S+9EMZacy0FTIVGCOgTdn5Bv3D09NFDkzLr5f9n7z27G1mSNE0nCUmCIrW+qrtr++yZ//99d3Znp6d7VKnuW1W3rkitmNSQJPd5zcMQARBkkikpLDIR4eHaLcAww+sm2IFDBfo+AMwDtF/uXltMq3ICiEAofy/S0jFBm0HUZRYOq2N9+Fry7OMcFAgKBAWCAueRAv7Or25I2DwpyNou4n8jCxv9dmMHE6MdtF0205OXvfT49RAnuzK/pQWbEp2WQinLrwlRgQBtxEWyj7LMT6T54eNo3IlDbOZIJnlH2E+RMa6rRLXS9P3EKDNvrKuiP83RuuNe3NQnJdhEfFI5/f0RZsmZ5xJFGt9qC+nGMo5277XTDTY3vru7gnlv5rPmaBc+qzDT482NAnwphpyY/cwJRmZQICgQFLiAFDi3wItevlW2cQFpG1MOClwoCkgoFFAxRKCUT5cdzItevNkGdMGXy0Y//e3JJmZGAz4jABeADDkMPMCXCvrTchgowMKADsQw/e26MOlEcB2Wyb9rq+lVTrhKuCt6mOzgyHsij50FRNPGKUJPyxZdfl/kAPDmWj3duQ74gtnRN3eW0j20XxR6uoPJUbtF9AUTlFGVqY5FurwtUydMOoqCAkGBoEBQ4AJSQOYz4iJZIyUDCuIA+/gRGw5H+DTDh8sWGqA41P31xVb6+Sn8kQhGb7bxd9bXBgagBCGhFWpZwIVwBVgRPfLhRhqZdoiViHHqYufK6Vg2Y43KiuOG3kAZnlY1r1DN8/zpPOVPtrAaRbU8VfUnflwCL+ZUXzIE//ChmxaJLriMo91Hd9EqxZn99w9WzMz3JuCLHNs3CUPYQg11DLwYumND22n2rMryy5CafkqXYU2xhqBAUOBkCgTwcjJ9ojQocKkpUMh7JhRKI0RhowdDmRD1sV3vpp8BWxS56DlaLr89x7nu7j6hoiVyITIgVMoR7jzq0xJOBb4cKMQmFJslNM0GXj6CvBpEgxVHsWFmYzsApJlIgNa8RiTm5wk7XZdQSLSjDpov1xtEXyD0JVovcry7isnR6vIiDgDrZqs+p+gLjGN08kXR31hY9MHjGhQICgQFggKXgwK841FWKV78umbgX8xA4aN7+HaRM911zIzk10Wgy98eb6IdyoZFj80LQJdD+KPgFfYjDHgxwjgjcWZVUEtsrBihyCkuznMmc4/eqQM7vEHu0XNZwFR5WXJcyluo3Hv1uvleNcpx9qGRIgpqw2UO32p1HNsr0tH9W4om2Ew/PFhO99jouHtjqYhwVMfZbh1Hw4JvOArgxXucHtPHjmtQICgQFLjIFAjg5SI/vZh7UOBjKICEIyFHZ4EiI1RYRoSN3gF02SAUw5vNXvqPnzfw6bJL9CJMjDZG5kgXly/sVhGxCFCijtNAmfHoMJXjvE14VFLLNexsdccpn0Mlo0iWgpdSeaZey0Ecvy/Ly3qWKm4l1GZHgJqnBMNDQknPp5uoQz/CBl3AywMEwtsIhjcwO5IdutSgzfkfa9ThsnLW5NFOXzl6pIICQYGgQFDgclDA8BFbimu6cCNeAjgwQNtlrzvAt8suZrd76ZfnO+knQJe/Pt5Om2i7jA6kKQnvML6R+YT4j4H1Y/ZUMo9xVpXHlcWnI2jZSaV+tZNZFarllWbvS9LMW4oXjmnlmbSXVhBnZIQD29CQX7V/uI9ZL9EE76BdegOtFzm077Sb5utFvNQ3MzRTu3/fPKI8KBAUCApcQAoE8HIBH1pMOSjwSSiAhGOaK3QmvETq04rSsLHVZ+dO4TB76U8/b2JutGdpTNmJXJSIYCSHgTisNfMigJdCy8XEsSPAS0UaG09aO1ylIFimVLd6N3k/bj7R2nO9Xb76nbrLM8grVb6ExX3AokZtDqe6C+zCNXG2S/QFoi7cRTDUR9GOGvUFnPLKNj+AF6dyXIMCQYGgwGWngPGPAlFwYCFf50zbZZvdhzfr2/DGbvoV4OWvhJD+6cmOhY5OAC7yEaZ/AhDEeQx4MaKJG5V8LfOp8n5MV2Wpnga1dHFf4Y/jul5vnOH9jRtSkkeqdDau/SEJH8FWV+naHQWPDtjEwexIOjA30Sq9sVwnvHQ7PSDE9MNbbcCYJUx+m7bBkXksukHIEzrU3ecFXmwEGytOsygwTZ/qfTU9q23kBQWCAu+jQAAv76NQlAcFziUFPp4BqgcdAiIOUAfZ6w/YyRtiWrSVXmNe9IxIDX/8+zagSz+92xpihiRTIu3mZfHNzYskWFpfLqHmbjlXBT/PLEU2z5m+5lbVejnt852ub/fj6mUtpXJf3iJXktNdhb6UCvhiG/AFk6Nb+Hx5hECoyAvfIBzeXGsVIaYxOSrs0L0v0cuUoz3Du49rUCAoEBQIClwKCmR2Bm+zBNAJDEU8xbRdsLd9ijPdZ/h3+QUT3J+f76WfiWa0hzaofJ014RnamBCAYA2dR05xpKOEyjxqIt/4zIx8r1SyPHKq9axhUcsrVfO8g4+5ij65vYElGWlKQzRe+phkCXy5tlLHrHchfXO7CY9dMp9qD24vpQ5apYom2LAIR9KgzRsc6s7opuvHTC3aBgWCAkGBc0iBAF7O4UOJKQUFvgQFJOAcoqEiwVJhILcJjbmNgfrj55tmXvTszW76999kckRI6b391FyooT6NzTrikMlaSEWyYi9gl6kpT4tMui8ktImaXs/Lynpe4uJXruH16KSoarb4RZ/jNi4NVseiqbfep1zmRjU0d9rNOWzOa0Q5aqbv7y2nH+7hbBezo5VOOy0tNVIdpzACmazvcb/cjQerDhLpoEBQICgQFLi4FBCXyEBLXoP8lrivMPgkprjv3gG4PH6bnrzeSb+8ROvl1TD99nqQejjVbWKiuoh32XoBvBhwUzCekleJeTg3mqZUtUx8ZlY9Zz6UWXG1zUn9eZm393tdfZxZZRR7tlUruH4xvIqyA+FMt8H+furiK24A+LIK6KLP/et1wJdFfKqh9cLmxrVOK11bW0otaFWDZgKsxoP4WEWOhqxkcRdHUCAoEBS4mBQ4t8DLxSRnzDoocHEoIGFGoTEFvshh4MY2vl1wqPvTb+/YydtOz1Cj/utzhMxt2bQf4NellhrsStWQsKQ1MnlILKrm6X46b7LF8Xe5nc7TR3WEYjuxGLU0X7LWNr+p8WksXRWdbf7y9YKst4ATwM6STI4a6Qcc7f5wf9ls0a8RE3MVkyM52jVfL7Qs50SqvJmeZtwHBYICQYGgwIWkgLhMfrkbG+EkC1rxDEX+29zqpdfru+mvv7xOT17tEj66l56u76dnRPsbDmSGO5/aMlMVWA9/yRozIsRxmxTvIVLBZ3Sp8r/x/ThznHO6Do/U8o6KAafLK91rJTp0dp5qsAlrFs0UQbCLz7gBGzrLi/P4cplPd9cW0kO0XqRR+u2dlXRttYmvl4452G1IQwjT3+znhYGKKVSGtPGKbEvHKSgQFAgKXEQKBPByEZ9azPkKU8CFI5Hg48QQCZP77EwpklGf8NBv2cV7s7GX/vT31+kpAuWLtwOiGR2mXXbx+kOc0WpXCtAlRy/KItfxD0JzkyBVFTyPr31cSXWF1ZVb/aJQ+YJdJsRSq3ykhTWTYJiDXqodjoFp2mzNp1srtfToTit9h8bLg+tEOUIt+iaC4TJaL9qNswhH1kMxXnVyRX5cggJBgaBAUOBTUyC/5T+8V+cF/tI+XX+urSKesQ/DVHS89Y1uevF6K/3557eYG+2wSTFIL3f206stogKO5ojmA/BCpB4pcGT+V87aZ1HmnCLlU56uOtGZV5rInG7xkfflGGVKXeY7gTHmYJ8pCHjp4ah/CM2WWpj0Nubx8zLPhgZRBO8swWMBXjDnvYWvl2Uc7DbRelmAYNrgyD1yLQaRbo3nWmGcPjMFTvsd0lPxusXDsntPf+ZpRvdBgQtKgQBeLuiDi2lfZQqI2X08cxPwIoe6Al929/DtAvDy6u1e+p8/vkKYxKHuxjADxG+HAABAAElEQVRt7s6nPgEKRuhINxGKFpAkF7jKoW4+fB6ZAUtA1dwkcOZDiVzmOe+/lm28m9k9lPVO7nOydZ5jnlUGYHC0W5evl3kLL/3wZhsHgIAv7Mzdu7Wcrq9gh84uppz/SbD06AsnjxmlQYGgQFAgKHC+KFDlBc5djs5Q2i0ZzBc3wxm7QBdYnnjlayIZPX21lf7w40vTeHmxPkrre4eJPQvqALwAuDQEvIhX0LXxm4IhlqN7qjoHz5uaT7VKtWiiuleayKzWrqRtVpX745LT9fK9j+StSlhEY+dSAS59/MaN2Nho11IianRaXUzpHo52v7mzmL6/vwbw0kTLlI2NThNzX0x6cWQvDaExf4Vwthq69GfhY8Y1KBAUCApcVAoE8HJRn1zM+4pSwAWrafHn9ORw4EHAy2AwSEPMjDa3e2i47BGlYTf9249v0rOXe0Q2GiI81QjDnLfukJ/M14ntSlknPpdybO97LDyVRR+U8lUeHeks3R1tLWFRc0WcNhVy3NegEp3STRwB3r6OE0Cc/32Lo91H2KPfxA69jZnVvO3IQQuJgT6xs0wj6gYFggJBgaDAV6SAeIFe3s4TZr/IDXjRS55qDryM0OIYDvbTG4CXX3Gs+7/+9CI9frWX3myN0nZvLu0M5jHbVbQ/wBfazsuUlatpzRiiUIAwtvpZ43tehTyzp5crTFWv3p7UrNL7ByWn+54AXgqajRAuUKJNQwjYWmBjA3PepeYh5kY4sRfw8nAt3cDU6D4bHCs42F1sNcwh8Ry0UxfjQ/TjXwAvY4pEIigQFLjgFAjg5YI/wJh+UOBUFHCpDKHGbc6RI1Ov1yc85ii9I3z0k9fd9ITQ0f/6pzfpxXofMGYf9V+FxJQ7Xe3gqZMsFVVlo+r4lWGq2TPT3oe3mVXpNHVmtZvMyyNM9lU4S5RYh7S8MI9TRMyNVhdr6ea1OirRzfQP91fS9w9W0s3VJaIf4d/GwksLfvKeNEoWrifHO+udU6Dab+57cqyz9hv1gwJBgaDAeaPAce+7zznPs43pPNJmxGt5JLMZnMVKQ/TVW6IYPd5O//Yfz9ITHOu+3cSXCRsUg8OagQTilii8ZC5ReaX7DCZXWalgBUWt6ezJRgYITWfp3sd4X/NZbc+Sl/ufHqWcu2QLFGVtc2OelGSHxsJ+urPWwLFuK33/aM0iBz7A2e41djwU4ajZIAB3EVLaN27Uo6fPMr/PX9dmdswwJ5Ud0ySygwJBgStDgQBersyjjoVeaQoUMlG+ZBVeCyEN8NLtDXEWuJN+e7EH+LKX/tuP69wPiN5AJCMi+sinyziqD0Q8uv9UdG5lmcrTItk07avlZevpWp/qPo8wOaZ20iQYokbOR7uTdcyNlnAEeG05Rzj6x4cr6Z8eLKMBgx36YpOwlzhMJAySC4ITO3MfPNXq6qszVIcqm8774IGiYVAgKBAUCAqcggLiC1lbRZXR3kArdADw0tvDnwu88u9Pd9K//OGFbVZs4t9l30AXgfKZO6K4wZub93fBJKpv+cnhp9/vRc3p7MlGmTVM533h+6NaKL5K+cKR9zRp+4gE+6QOiIC4n26h8fLwVjt992g53VptWXSjm2ttzI1aqYU9kptnGY912hXP4gsvL4YLCgQFggKfhQIBvHwWskanQYFzSAGEIIvmg0AoEUkRjXb2+ubf5dnLjfTL8x1s1rvpv/9t0/y77BLJqNNGBRgTGzmXtV1A9SHh0sI1+BrV2+mPaZny/a3V4v21MkhR1lOqOlZOl7lKiR4CXtS/nPs1mymtLM1baOnfIRz+7uFyukNo6VUEw0UEw3q9YQJ5dT4mgFYHorfTHRr3fccHdfy+TqM8KBAUCAoEBY6hQNZ4KTgGGpECXvpohnZ3B5jk7qS/PdlO/98f8PFCRKOdPdQ75jBF5aPQz3gCK3rl/e7JMjE1IhVUZ8wKisS43VR1vx3X94xZ14mOZ1Uo8j603jT0Uk5K8FPBVpEX0H0xn3D76QamvA9vtdK3DzpsaOBL7Xa+ri63Uhtzo3H0wKldDd/sOGERxxRpTu8j5jFNIzsoEBQICnwGCgTw8hmIGl0GBc4bBUwIYlJZoBR0IkeBh+bbZXunl35+tpF+fbaTnhJC+g+/7Kb1bcJB9vF70sKpLuZGAiWOAC9a5FjWcgFnnKFSO7J4lsGeLAK5IJTrli2mBcDp+7LHysBkzq7nM/LRvLXaSjDUIbqMEAq1tgX0w+s4Aeyg9XKH0NK/u7ec/gng5d4t1KEJK91ZBIQCmZGT3Tx+7sPORwex/j/uVFIm9/NZBvm4KUbroEBQIChwySjgwIu9geEPBrr0R2lnZy89xxz3r0+303/533JC3087vUM2JmqpiTakabrwms7tM8/LpKm+u5V27kSS22pprp/LnVNlTuDtiua5Yu6gZMQz7scVSeSeco76q/RZrXbKdJ53Ppd951n7hsQIh8SKBiUzrWtEDnxwq4kJ7xIRjRYttPQ9NjZW2NhQ9EADXgBdpGGbj6xB5JpDx02rQk2qTN4d1+bD86dpWO3Jy3z+1bJIBwWCAkEB3rowCH9TBD2CAkGBS04B/bXLp4kBDhhir29209ZON/31l3VMjbbT87e99OfH/bSxe4jvFwde5sfAi7VHsEE0qlCq+gqRwFG9d9Eu50/vkamTXNvbTLefvq8Me8qkepg8NFb28SIRURoveg3KsV+dTcvF9ly6g5+Xf7zfMVOjBwJeVtsmHLYJfZlBqLJH6//oIGWFD045TbyDzzKIdx7XoEBQICgQFIAC4nM68oXQyIAue13MbwFenmKO+9cnW+n//QNO6N/0ycc/GIh9q4bWi5zDqp14StE6c0DrrnKqvMtJVu4qdTR+yQOsX9X0yU3UzKVl1vS9l5T9HT+q133/VaOU/ZR9K+W8fnSwn0aEhBoAvKyZGW8jfSeNl2vt9O29TroP8LKGxkun0zDNWrXLDvzV9+mAF9Usjzx6eR+poEBQIChwfihwboGXeHWeny9JzORyUeAAIUhmRorS8Ga9mza2u+nPP71Mvz7fwn59mH56uZ+2uoldvkM0XuZxigfwgkBpoiR/mLoeD7xUaVUV/nLahbFqLf2t58P/6sucnJ/Fu7JOUf3IZXa9aq43keyqVWgtAl6kA6TdygWiL4CtpNurtfQ9QuE/4lz3we02jgDbhJVeTO3FlgEvau0C57j/ccJH+RTXKi0+ywCfYpLRR1AgKBAUuBQUsDe78Yfy3dtlF2IXFdCNrQy8/PhkM/2X36+npzih7/UULrmeFkHta2iHGodkc0MASdlDAcgYhabe49xO5YzpWO3BMscdegvP0L2nvbnX0b2X6aq55GvOnVXP+6he318v16jUIynQZYhz4t5gyObFQrpzs2G89e6NdvqBzQ0BL9rYWO5Av3k588ennJixDuSUsZnRWAsmFx13zmuqrqwyn+MaRX5QICgQFPhCFDi3wMsXWn8MExS4UhRAjsHcOpvWDCw05i5RGbrpj395AfCyk169G6TH7/D9gjA5HMyh/TFvoTHBXgAnONSef6cDXmaR9ij0kkWkWXU977gaEqi8rJr2di7QVstKAMlrmcaL1kQ1RTdqAbxcZ2fuu7uEvby3hL+XxXQX4fDGWseiL9RqCNdFhCeTBX0K6lBDnXio8nsrFT1UO1aWtztLH0VXcQkKBAWCAkGB91JAb1fTgOR9a5orACjdfgG8EP3vKdH//gONl//nD+/QEB0AvBymRRyvdxoNQPmie4EudJTf4OKXendn3lNOwN/nlJTJspiUAS+5k4l8u/E243Jl6EYfL7SalZOXHTenSlXrwzv3vqvlnq6W5XTGTTK8M0TjZYCPnL3eKAMvNzA1ur+Y7qHx8gPO6++jUXp9pZ1Wl6VNumBmRm5qNAefPcRR7xh88SHjGhQICgQFLigFAni5oA8uph0U+BAKGPCCRCitlwHOAl/iKPDtxl76/b+j8fIC4GVjmF5uprQ3nE8EcUhtIv3UkAoNeHEZ7IhQNy44xZSyQJjPLpieotlElVnjVYW/srKPU82RMCuh2A9pu+heQp6AFwI5WWSjR7cREAFfHgC63LvTIcz0spkb1WqgUHQ8FgYrfR0hjQ/yQVd1XF3X0dV8ULfRKCgQFAgKBAWOUMBf5fbm5eTAy55rvAC8KPLfjwAv//kPG/h7GaR+/yAtNWup0wR4cQtcGIp4inhNPgS9nAC8TM+keO0fCuD3Lqbr6L7KHsblanAcr6h2NmtO406mEjMHmqqj21zPgRflDABe+ggTu5hrLaPxcu96Bl7uXGulf8SH2v1bbGoY8NLKpkbIGwa8+BKY8pjXqsM4ggJBgaDABabAuQVeTmIdF5jeMfWgwFelgIRBmRkJeJHq74s32+n12930P//4PP36spve7IzSu5251B8t4HR2LjUQJIUzSJASaFMeFamozDxFytsdX9VrTAw3Ud1LVNPTExWmxM5cr1pTdPBDwrHuteu4MHeAn5e5tIaA+OB2Hed/7fTgeic9uEtY6ZsAL9iiN+pSh1Yb9SvhtXJM3FTyPzqpCX+2zj96dtFBUCAoEBS4DBTQm1aAix1clM7AyyC929hKjwFe/vIY4OWPW6bx0u8BvLRqablJOGQxSmuaeUrRi3Wlt3f1Pg8weZ5+w7+v/tHW72tRLZ8ebbK3zG+q9afL/f7oygx4KdR4BjjX7Ql4QeNleWkB7dFm+u4eWqTXW+kfAF4eCnjB1GgNB7vynyZfawtyYA/dNbp6D+DFaR3XoEBQ4KJT4NwCLxedsDH/oMB5ooALMLoqmtEB/l2kPi3g5eXbvfQ/BLw83yOa0Sht9ebZpZrH94mAF4AFJB8XrXTNR5l6vzipFtX6RReVy6zSUuQrU5UmJyan+5vVg+VxkniXgRc0XmhYx8/LKvbm92/W0qPbLa4AL3cIK31rhagMbSIfAbxIJDTBcgp40aymBz9xplEYFAgKBAWCAueBAmO8xRNMSs7o97pDfLwAvGxuArz00o+/7aT/+0/v4J+DNOgfArwslMCLFiLQwHhLuSqxBeM5ZdaRlLOO99U70vDUGdWeM++q5py6m/dUNOClYITSeOlhaiTgpbNUQ+MFHy+YGgl4kanRQ0x5b6wuptUlNF7qAC8QwYAXxtDcBLo4Xd4zbBQHBYICQYFzT4EAXs79I4oJBgU+ngJV4Wof0OVAu1D9IeEwd9JLwmP+2x+fpccAL28BXnYGNQNeDhB3sDQybZejMzhOFKqOVG11XP1cZ1Zp7um4/qp9H01P9zerF8vjVO6rHRCr6RDgJaWVpXkAl3oBvKAOjanRvdur6To7c8cCLxpUnU4PfnR6kRMUCAoEBYIC54wCjreMNV6Yn4CXLqDBzp6Alw1Mjfrpx8c76f/643p6gY+XgWm81NMyNqqzfbzkRQo+yHwon2ct3VnH8TVmtfqwvM85VgZeNK85MzXq4Vx3tzfMwMuNBk51F4kciMYLwMuDm1nj5Tjgxf29fNgqo1VQICgQFDhfFDi3wEv8fjlfX5SYzcWmgARKU9BgGQJe9vdHJkw+fUUkI4CXf/398/T4pTRe2Jka1tIICOIAjRc2oI7BEVxsm0WXWWLjSfU1xtE2R3NmjfXheerfBe3cSwZe5Dt3FeDl7o06u3Gt9ABVaGm83L+zAvCymIEXlmPqzyKqT1RLVPrkpeahPvm5OolP3nl0GBQICgQFLg8FjnlPO+CSzUi1XGmu4FwX0MA0XjY2AV566S9P0Hj5w9v0DOCljyP6paY0Xo5zrqt+XLvE39PKu7yHyxpat/l4MeAFjRfz8TIJvDwEeNGGxiqmRvKfpram8ULCH9NXYamX9/HEyoICQYGvSIFzC7x8RZrE0EGBC0aBqjD3fhFFZkZDgJc9hMlnL7dRl95N//L7F+npi730TsDLfg0zowWEHmyt8WXiPZbClMjjuceRqjqn99Uvx1DN6ZZ5rKO5R0fWnE5fTzUdeFEaqIlVHeLg7zCtLOIEEODlwS2AFwRD+Xi5L40Xwkq7jxeM0Wk1Qw36faShlQ6N6VXLWXu0CavynpO38l7eUz2KgwJBgaBAUOBYChxUGUJRyzVeBLxsbG2lp68wNXqybcDL87d9gBdFNWrgXLeWfbzQzrQoeT37G9r5RHlfpo6dzEcXnI0fvn+40/cnH2h2IDTIx4s+e3Kui48XOdf97gGmRkQ1+gfCSYu/Xl9ZxHF9cxJ4oQP1EqZGmZRf7nya76bLHPaE/EmdYYre7gxNompQ4JJQIICXS/IgYxlXmQLOxPx6Mi2k8TIS8IIg+fQVpkY4C/yXPzxPTwBepPHSA3gZJYAXNF5K4AWAwYWpMVxw0jg+F111OKPOd5Pnk4CX0wp7kz2e5k4zy3J2NjbKMEoGXlYXs8ZLCbx0poAXGgO8jJ3+VZd70lIrE/MmnjV97/nHX9VCxykHzJXjHBQICgQFggIzKFBqvOQf/Prlf3C4jz+0ofHLja0cTvovAC//+Q+v09M3Al6I/teopSWZGuEUNnMsacpoAOdfrvHig/q72++9nt8fd/3U9Y4b52Py4ee2S5M1VoYF8NIliuIavtPkXPf7+218vBBO+l6OanRtuZ0jBuJkTU3n5VxXR7Hbo1XH8aUocNJ3c9b3z/PO8pQ0xlnqf6m1xzhBgc9PgQBePj+NY4SgwGekgDMwZ5ZHmZlv4hUyjJkaCXjZIUTm85cZePmvAl7w8fJuByFTGi/HAi/TAuT7lubzm67n853Odybu+dP3nv/xV5+BCdvcmF06UY0UUnoNJ4B3r6PxgqnR/VuL6aFpvOBcd6zxIgFRc8v0sNlUp6r0KY88D50nG03eVTvzmXve8TW9RlyDAkGBoEBQ4GQK+Jv1kKh/5fsYU6MCeNna3kvPMM39i8JJ//4N6V7qdQ9Ts15LLQEvc4p4J0DfexJ/KN/tnlvVhckz0ju8LC1nmfPLHk6uN92uvP+yKeONogNkHOJcd8RmT384NODlHsDLD2i83L7RSt/fXU330HiR0/rOEhovAC/iw/OFkxgjIwTVquMICgQFggKXgQIBvFyGpxhrCAoYBUrxrEoQlwGngReFyHz2cje9QpD8F6IaPX6+a8DL3nDBgJeDw3kESZnfIPbY/w8Rf2bNqSpgep+ep3tP+yq8ju6ny7yOrmerl3vKNvxqbXtsBrwkgJd5TI0a2dQI4OW+fLwQ1UimRuZcl6Hmpk2NqsOrw1Me1RV9YBenHCmqBQWCAkGBoMBJFND7eAJ4gSf0MJMR+LK1lYGXvz0tgBcc7Xa7B6leqxn4Ml8BXvxdnq/5XL7ry9TRuaiul+e07nIPXtvvJut5ab56Hd15vckan7xeMaRDJQcMuw/wsg8CMxDwsqTNDEyNHuJc9/pi+o4NDQEvq2i8dBbrAC/Zx0sVeBGIIyArb3TMmn/kBQWCAkGBi0OBAF4uzrOKmQYFjqFAVaiqCltldYEv08BLF+DlxatdfLwAvPzpBRovAC9ENdodLKQRPl4EuSi8svVo19l9Twp1Xqc6p3Iep0upj9O0r9arpqdHqZZV07meTKpkbKSzzKlqpvFS+niRxosBL9WoRvJ946ZG1amq+zMe1eZqevou1PL0tc84rageFAgKBAUuFQVO88ZUHdNYEcO0XYtDQINR6gO+bO300vO3u+mnp9vpv/z+JVEB+zjdPURLcoFPbQwOTL+VHYgo3/Vl6jQEPs28My84Tb+anderpqdnUpTZYjj5Ds50NeNB6s9n6dfc5ADQRX5yRgejdGMF4MU0XjrpJqZG3wC83L5BKGkc63bahakWQ82NwyJpXLq2ORwZODI+CwWmCV69P036s0wqOg0KXBoKBPByaR5lLCQocDwFqsDLwf4hqr/Zbv2FRTXqpn/99+fpt2cAL1vDtNOvpyHaLopqJC0QmVtLuWOOex1ivfkoU57z9a6a22nmU9az1dBErXJLohoJeMG57iqmRvfReHl0u53u3WpbVKO7aLxcUzhpdjcF0GgHznbhqsNmEp2ZDJ+gizOPGQ2CAkGBoEBQoKSAv4d1df4g7jAc4iAW8GVnr5deru+mv+Hj5b/KIT0RjrZ3VRsTIwEv4pjwBh2TrCBDL96/cxyreIrTeD6nqHu2KpplOauZbW0hnI4FXrxV2U+1qnlQI+PwcJhurjbSw1v4eHm4nG7hXFcmvLfWBLw0UrtFVKhs71sCL97lJDF9wLh+cQp8vm/iF19KDBgU+EoUCODlKxE+hg0KfCkKmOzCyTVeDtD/3Qd46Q2G6cXr7fTq7V76H2i8/PpiN70FeNnYqxH1aB714LlUQ+BB+9eAl2SaIT5rZ8DWu2cec5XUdFy9alk1Pd1VtayaPqnedFn1nj4K6VAzy7MTmILaOOGkrxH28uGtRvr2DtEXbrYtlPTtG8tpFVt0RTXKG3KaRyFg56TdxykoEBQICgQFLh4FnEuZaUsBnYhvDkejNBopMk/fTHP//ngn/esfn6XHr7tsVsiUpoaGaM2YrLECTrDNyjHNIHykShUlVW1cNHFzQsVPXW9qqInb48fKJXnyMjHK7FWbE2xosKj5uVG6vVZPD++00/ePVgBciBiICe8NzIyWAV5areaYZm5WNDYxUudxBAWCAkGBS0CBAF4uwUO8OksQU3cOPJZOZuR5natDmZNWapTiNAm8HOLsbpBevtlJr9/tpv8p4AVTo9dbo7S+M596ozlss1Oqo+4iIEI21+Zv0AayPazxkzhp7PNapk1J/wYphKhcKWqnUqZV9XpK1wFeHt1upm/vYWZ0cxGtl066dR3gBSGxVhPwotb5e2bn+Mqd10cd8woKBAWCAqeigPOEcWUypN04GikSIH5K2Kx4vb6HP7Tt9G///iw9ebkHzxQgI56JlijQgW1WwA/mzRSVngyByC52vV+xi8xFPecTXX0BH8WPSt52lln5kGo94qQNHoyMoEc23201DtO9NbRI7y6m7x+spBsCXuCrKyvNtNRuphbhuB1w8b7y+NxNZpxlWlE3KBAUCAqcKwqcW+Dlw17954q2MZnPSgH/hvhVgyldParculqvWufqpfcxNRLYIPXpV+s7aX1jN/3+x5cALzvp1btRerpxiJ8XhCekp8XGgoEvMjdSGz88WaWwl33dq2ZUzvOkuSjigg4Jh/onYGoe/y6N5ly6vlxL37Ez94MBL5gb4QDwOsBLZwngRQ4AbYzynEGt80eNvMI4BwWCAkGBoMD7KHCEc1gGGqKmJQr4grnR63cALy+2jGf+Juf074ZpszeXtgfwgwMBL3PwTLRF+YgvmL+YgmP4+JlLHRnNiz/86l1+FCtSJ2fvwFoUpyH0GiJnyKluE9NdxIiEGxciBTbTN+KrD1bT9dVWund7GW2XZlps1dAkrY2jGU2Mnpnrh9MkWgYFggJBgXNEgXMLvJwjGsVUzjUFqkJCNT096ZPKpute7ntprhxwGmJu9Jbdu3dEavjT316mX57tpBfrg/Tz6wNCTQuYOURYEvCC40AEJwdblBA1z/fhM3QRTlfPyzP39UioFvgi+Y6lovKc0k2cAH5/r53+6f6SabzcvraUbvBpL7YMeLG+XCCko5z0sc43ZWJ2QYGgQFAgKDBJgSp38De5eIQcw8oB+774Hg5i32700tNX2+nPf32RHuOc/tlrtGD2DtKbHYAGNEUb7FK0UHupG0APGFMg/NX+M7AxmTM5mzPeTXflC3hvN6rojf2qRqfuYGIE01ihaR/QpY9plvzJ4TOXz1y6tjyHY91W+vZuJ337YNmAl9vXO2xmNFITZKaBkCGnuupDNMt90Zmm9WHTmZhb3AQFggJBgfNAgQBezsNTiDmckgKzOHA177i0uq+WnXK4S1hNu28WxYfrEMFofaObNrf30n/8/VX6BY2XZ+v99Ndn7ODtEkJzmNIKUlMLAbKGDU5u67TMxBFVy8Olo8ncstxT76un8urzOq6/ar3pOtV7H8/Hz1fVAHMxTR4J1RL0FNGo1Z5Lt681AF0W0+8eLKHt0k43cQC4trKY2qhEy9Qo72KW/TkGExJiSZNIBQWCAqelgN5Gs99Tp+0h6n0aCow5hyV0Apg/4NmQVGjkja0u5kY76T9+eYup0Q7gSy8929hPz/kMh3OpCejSxj5XWi/SjbSoPtaLP2F3tOsj+XPP9zp7zuSKPLdo583HlVRetCYJx7YSOFtRwxt4PWUX9YuaRUUuqlOtp5KiH0yHLEVTtZY8oZR4oAMvAwCXLhq10nrBOjcttebS3VUc1t8U8LJs4Mt1HNVfW1uijFDcDckYMjWiD+gm/mpj2LDjlO7iODUFqs/21I2iYlAgKPCZKRDAy2cmcHQfFDhfFJCZEYINWi8K77ixRVSG7V76+cl6+vnZdnr6tpt+/2uPsNJEPULrZW2R3aiahCJsjWznT6spGbp0X3SnI4tHOnuOZR9z+tT1fJiTxq4IcEh4yHYF6HJgVwl9dYTmpcW5dOdmM/0ObZf/g525B4STvoZT3eUOjnWxQ5e/myw6F4KmDa1xK/37dOIaFAgKBAXGFDjuPXFc/rhhJL4ABfwp6GoHTMJAdvFMMgWibMEv327spJ8ebwC6bKffXvbSb28G6fGbIZGPEvxyIS3W5Rtt/hjgRZyC/ib4ZMkPfQ4+hclrWW+i+WQlMSYbQdklhDFeVaV2dbRq+SxeVuQ58KK+aaKPDgNNdGLkAf5wBLwIgFlZYgNncSHdv15Pj9jE+AaNF4WRXltuECUQLdImIBU0MxNe60Q9FIf15zdxPRsFqs/2bC2/bO2LMs8vS5UY7fJS4NwCL/GneHm/dLGyr0kBxD127+SvRbt32zt9QmT202/PN8zHy5PXRDj6+256szFMe92DtLrYNEFSQpGpTOsP0yStLBodIoQpy48MSFRzyhKJgOWh9tV7L5nOn773etWr15nV33Q93VOf/xKojQ7FVdhSozGHoDhvwItAl98R9vIBznXlVHcJEKreyA4AbWdPXTFkKRtqHnEEBYICQYEqBfRe8neDv6N0P53vdaptI/01KCDeYGyOwed5wStteRil7uwN07tN/Lw820hPXu+mn4kG+NPzXvr7827qsVnRwlfJUmGiKz6RTZUKPskjFo/0p1+urcypfivy9yZ/Z/K3I9eznJxddFG0z5XIqwIv5ShKebNc1e8m65R31Vp53nJC78NkuuTa4oPOF3sALz384QiAuba8QJTAWvpGUQJvLxnocp/rCvxUGxoNzIwWYL7Zd1ru2XiqDzIerZxVpIICQYGgwEWlQAAvF/XJxbyDAmeggMQrk2NI2A4e9yP8vHS7/bTbHWCnvpV+Q4B8+qqb/vUvmzjdHaTt3ZHZXzfmayYYlcBLZWCTkI4T3jSil1XTlfZjoeqkemMJrNJftY+clmhbrek1yp7LUk1bQqOc/5nTYG4W0OxpoxK92pkHbMG/CyEvf/eoYz5elvHt0mjUUw0VcgmXLmBqeUYCG6zs38eOa1AgKBAUKCmgt5G/J45Ll7Uj9XUoIN7gvKuAG4xfyDdatz9MW7v99PzVZnr+Zi/9jInuT8/20l+f7tlmxfzcQmoJTCCqkUAb9WOwS/G4c385f9bqxMWkD5OPsl71W2Pfococ7R5ApHqM522ZZZmncn9+V21ZTVdreY8qL9t5SjQTX5T57uAQMyNMmbXum2u1dGMFZ/W32+nRnaX08BbmuzeWUodNnVW0XmS6K+BlXh78i66Nbr7gnBvnoEBQIChwKShwboGXS0HdWERQ4JxQwIWjLC/lOwmRPRy5SJBc39xNjwmN+RRB8r/96U16/raXNnf2sb1uIEDK6V0RyceacjK77mJxn1xAUofjGZ9Awcl6JtxSO4utJQjjPU1OM99JdVzAi0S+Jk51lxfn043VOmBLO/3jo9X0j2i93MKp7iK7cy4gqqUDL1lArvZcTZ8w9SgKCgQFriAF9Dbyd8Rx6StIlnO25AwiwIVI5KdVar300eTYYbPi7bvt9AJ++QuRjf76ZJvPLoAMDmXRKF3AS7sMjcQ1FSkvQxbqybmRL7j6Xch54mNViKNac6L1xA21CpCnWl/p6Wp5RZ5bHcvz1Erzqs73uLTqaoxswqxAgaKdbWikfdae0r1rdUJH46yeMNIP0XS5f2Mx3bqOb5d2w7Re5g10EZ0CeDFixikoEBS41BQ4t8CLWICzpEv9BGJxQYEvSAEJRS6KCXjpD0Y4BBwBvHTRdiFCw1uAlz8KeOni52WEMFRHngN40T/bUSv+KnNHNnMHISYFtdMuqirQndTm/fWy2JjP7+tJ5aqZVcoBXlhbuz2f1tB2kWNdmRf98HAlfX9/Nd281kYTpk7EI3bm8O+iQ2u2GUkwN4HXslXiibgGBYICQYGgwAWmgAEver/DLMTylBzgu6SHM5etHfl66aIpugPospF+fLyZ3mxqI2MObVKFAcwhpes0mkf7JXejjrJT90m+QSaHs1XVPflQhdzmrPWmW713KB/AKnLSJPnvt0ooa8R6R2i6CHxB78Uc1Tdqh+nR7RabFy3jp/dvtNNdgJdryy14qkyympmX0sckPSRv+MBxDQoEBYICl4cCAbxcnmd5iVYi0SC47skP9MNoVLZSpIaEOvAojRAkt3aH6cXb7fQSwOV//Pg6PXtJ5IbNQdobLLB7pVCaOJ5lQyrv4LH7J31ie0YOPFSfl8rKY4728gUz+1A7L6ump2tXy6rp6Xqz7l0XpiyzyE42LmUMX6vhABDQ5RaRFx7cavLJtuiP7qxih96ycJce6tIgF6agWZSHz2kytyyPVFAgKBAUCApcKAqIOWTEZDztEX5LBL7s9gb4euliprtDRMCt9NMTTI/WcUxPWOndniIg0QTe15IpTQG8iNlIx8TMfelX3MI5hwYwTlUMOVmi0upRbVVNV+soXS3zNOMb78t1lTtZz++toHISny76EF1IGzhClu6GaPoM90fWd4M1txs5otGjO610e62Vvn+wlu4AvNwkmtFKp4E5FtGMmmzsqCfviLQOu83JOAcFggJBgUtFgQBeLtXjjMUEBWZRQGKRjixi5XSO6HOwv58kSHb7o/R2fS+9ereb/vBXwmTiNPA5oaXfbibAl0Oc5KXUVnQjND4WUAmWtowfcwiV5ZHHqo5kMpoqVDPLBl8gVRUz83AyLzJtF26RjVOzPoeJ0QL25430jWzRb+Hb5XYHQXE5dTpNi1BhwEuxiNmC4Vdb4BegYQwRFAgKBAWuGAXEvIqXvfMx8b5hwTMV4egVoaUfo/Xy6/NNiwr48h0apFuAMyN4iwEv88YzpSw53rCosArBL86hM4Tx+Wg8OU6eRGUqlYHJrRQo6fzSKkEMzdvxEtFmyEnOdFWvg3PhFaIDrnXm0jd32mi8LKbv7q6k29fbaQ2Huh1MdxvIE3V8pjl91Z8fs/mrl8Y1KBAUCApcXAoE8HJxn13MPChwSgq4uFUKNmooAUlC5AHaK3KE926T3bqtvfQfv6ybIPn0TTf98nqUtvcAZojWsIgw1QB0UfSBjLtk8GIW8FKRJCcEuKMTNpFOs+EzOb+ybrXM65elp0tlGjgl9lmzwJc52d8j5bUwM7p7DQeAd1GJvt8BeFlCWOyk62uLqEQ3WbNG0dh5/FIwPG7Op5tV1AoKBAWCAkGB80oBOIaxn0JLhWmKb47gH6OhIhxJ62UPXy+7bFYotDQADNqiL97K5Ej15lIbcEGbFQswjfF+BekMt2T+Mbk1MM1TnGvNplG19vE1VctH8Vq5ZbV9HoGc/N9uvTYLpwfVJke8U/90y4dbNF72Ux85QoeiGN1aWTDHut/eW+bawrHucrqOtstqp5VazZrJETXAFx0aw4AX9WfpfI1zUCAoEBS4bBQI4OWyPdFLvx4XAwoOfenXO73ALKK4qDJdOnnvtJrM9Tvtxgl4kdC0z1W7d1s7gC1Pt9KTNzs42t1Nf3ncw/9LBl/aTRcgSw0XtS3cnpjwpFN+MhV15olHpZuT55XnV62n+hOd+BKKfO+v2qZSpZI00ZPqcv6no7ZwSIjoubS8VEv3bjZwALiUfrjXMVOjNUJILyMkNuvy7zI5vt2V6EtlhEgGBYICQYGgwOWgAMyiYC/OZWzDAsYnU6IemqJb2/hDA3x5Cc/8Gc2Xvz2Dd76El7JhQTE8BpABrVCxEPFL42WkM0fJ59y3zvl+knaen2tNlk22mKyhviZzptvm++l63Of/4+rWS3kq85VHXdFkX5GMIAr7MpgW1dPd6/o00zf3V9KNZcx30SBdhacuEc1I2i7ypTt2qGs9loOSiiMoEBQIClxKCgTwcikf62VelAsSwZrf/5SdVl5TNPO8Qr0ZgcmEJoTIPUJLbxMm8wU7dy9wsvsM4OWPP++k1xuDtLGDo12c7CpOgyStedAW9WbYQ9Gl7YZZusiYeER+o7Jqmtsjx/vqeHtdvW4xJjmeW3Y7maOw2PusW4Jfq5GIrrCAH5cFwJZW+u4eDnXvdtJtHAB22k20fIjqhLqL1puPPJ7dnRV48Sl6V+UEIxUUCAoEBYIC54YC/rLWhHhhi0/mlF0zzzzEOf1B2oVv7rBhsQ748turvfT3p9vpyYvd9Abn9NIWHR3AaGgsjinn7PKTJrNV68jWW/Di8QhiENXxPe2MI9/7nXVhLbydyqulSmujwfO8nl8pOnJ43WpBMW5RBEngo/h/Q9NF89WSFMGpXpvDOX0Ds912usfnkQEuDcyMFEIa3y74dZHWrNjntENdn+Ks0asziXRQICgQFLioFDi3wMtFJWjMOyhwvihQFcI8nQUonSVAmpMTLr3+AJOiIdGMdrFb7xLZaC/94aeN9PwN0RuI1jAazZugpSYWKNMELQmVWXtE/VlfToAJ6clvrBY1dO9pbzDrOquO9zWrPj3TRK1cVJ7GRwS86J8c6nYwMbq+vIBZEYIiAuJ3d5dNULyBTbqpQ9vOHFGdxp3k+WS16JPncWR2vpQzNjvST2QEBYICQYGgwGekgF7WxYtaDI/3v3ilv7qNbcJhBpgb9VFr6fa0aUFkwNc9c7T7+CWO6tcxQ2LDYmNHTuzhRpgdNfFQL/BlQWohdGv96GQcizwdGssSPl6+y2PrnO+tSnE6mjO7XrXN0fSMNjY38lVUHM4K5V9/WJgpK68B4LLUmmOzYg5e2sa3SwfwZRHwZTEtEzp6Fd8ubUyMGpheSdNFbbT5oc5NDtEgxTjFxYeMa1AgKBAUuDQUOLfAixhJvHwvzfcsFvLVKFAVyfwvKufZ2Yu5DggrrdDSW7Jb3+oa+PLnn9exX99Lr98N0ya7dz3s1qU+naMbAb8oWoMDL5KkvD+t14c709rVqNrJ6RuPh6N57iHboStdgYayadT8Ab5b5tK1pQUc6NbTA6It3Meh7j18u9y9IZVoHOoqGgVCsvmwYW3ev/rztM1O6/7o40iv9Dgr76MHig6CAkGBoEBQ4FQU4B1sr+HZwMs+pjXyjybeKY3RF/hF+/X5dnr6ahvn9F3Al2F6+U7hp/H3wsbFYjP7SJPPF+0QGOsE3NAQY64CP8n34l85VZaK13ipcv0o83PO9L3XO/3VevDhdcOhi7N5+UgbALz08SKsvKXWvPl2WWnPpUeY7H6Db5eH4qfX8ZPWrqeVJRzqEskoa7vkDtVOmxoZ1NKNDROnoEBQIChwaSkQwMulfbSxsKBAlQKSoFyqmSG4sRs3IhSkfL30+vtpcwffLlv99NNv64SYVrQj7eYNCTu9n3ZwtGuAi3SLgWAWTBA8Tmhyya06l2Jnz9tZkddTn572NnnePut8pzKvqxIBI5X7oossuuLLBiFRWi6ashzl4rYlLS/Op9toujzCxOjRncV092Yn3WBX7hoOdTvs0Mm8aB6h0MaThHjccVLZcW0iPygQFLiCFMjvqiu48Iu/ZOMpY8YCl8oO2uWofQT40usP0/pGDxPdHTYtdg2EefGunx7j72W7e2ibFhm1yKa6crYr0EEampnJOIkEtzgYo0LXfKnyIE8X8xnzPu9DV6+jtNdTevLwkSy3qGbzGvdZ9MNFCjACSfAMB0+VqZEyDlIDnnptBYe619FswV/at3cXMTdSVMAlc6jbwj/cYksmRoXZLn357EybVON6RjU9OdW4CwoEBYICF54C5xZ4ufCUjQUEBc4VBSTNTB7VHAl/Al0kROq6u4d9+u4QW/V3gC5yHLiX/vJk1xztrmO7DvRiQqOkpZokJglNDkAoLYHs2MMqq9IRcVAlxx2TPbow6rVtULsxQdbGB3AhW+2ykLhvDg4bhI7uoO1yHUHxLqDLPzzAvOjOUrpDFKPlpSbO/+rszOFQt+ja5uRr8+H8qnyNdVy514trUCAoEBSwt5G9UaCF3kyz0kGmc0kBY0B2skfn4Ijxln0cyw60YdFPbzbgkwAwL+CbMtn96Vkvbe6iScqmRR+NUaoay5DJjfl8Ee/I/23ZhmXw3ZjmjsUWwCclTV5NsSb1XCQz8KIMgUO65iJtYCiikzR9cO9ipkKKCN3CQf2da3W0Rlvp2nI2272LFunN1cW0gvao1iptFwObTIvUtVxshDxAMc7En0UuiXNQICgQFLg0FDi3wIve//4evjTUjoUEBb4aBfwvalqcyxOSUCfARUKfwkv3ECJ3MTl69XY7vVkHeEGQ/MuvWwiVg/Qafy+DocAMtQEA4Q81C4WCKvJf7XxGP46sNpfqXFWiLqS9onV5V22uNrnEBEFNVAfXQ5cM7TYLdFZWnCQgZjH2EDv0ZDboa4sLpu1yH1Xo7+8vE3FBO3OLlNXMt8sCO3O+rkyxQuzNC8hTUQWbB1fPrw78wekqBT5pxx88o2gYFAgKfCgFqn/P6sP/po/L/9BxLle7WdSZzqtS84usvuA75pPEHqMiA2buYnyzNwB8GaSd7iDzTTRFf3wCD0Xz5e3mIL3d2McfDODF8DC12vJ3Mp/qOK0vWZhzqsx1bE3Fok0zZLxIDV4UOFH8azWuo0Sl3kR+vsnbF4UhLv14F6XGC3k2uTwf8XuFjB4gH9Tw6SK/Lp0OUQHhp49ut3BOT8jolRYRApeIZNQidHQzteCp8mkjEyMtVJqkzr41Xrn2GROMrKBAUCAocMkoEMDLJXugsZygwMkUkJQm0MOltaI26ITlIREJUJFg1cV2+9273fQWfy9vN/rpb79tplcAMC/X+wlMJvURHntEdTDRzrpTKjvN03aYgS8AMBmy8KvPzkU83efZeL2c4/X8qtLJOedxpfRMSsIc8qNqSP1ZV9W3GXHCFQ0hoRF2m3NpBSHx5iqhLq8RdeHmYnqItstdVKKvISg2QWbqCMM5ihFjIhWa/XkhfKrXfBTXPFCRF5egQFAgKBAU+JQUqL5ip9Max9/In3LME/ty8KWo5CCC+ESfHYm9npztwju39tio6FmI6dcAMG82++nVa4EyB9RBZ5T40mbOKt3K6UXAb5RV5YmTVcTdRA0O7SzYRke+Pe482b6s5f34OlSSe55ukU2rtE59CE6UFuGlN3BOv4x50YPbi+kbIgKuoeFyZ23JtEc7+HZpYIdka5U/OHVppzz+9Ag5N85BgaBAUODyUiCAl8v7bGNlQYEZFHAxS0WIVxLa7OAqgYq0qROPDtBq2U8b+HmRv5d3XH95xs7dBgLkxh5gDMIj4MxOj3CZADCH9KN26TALV3JIK+DF5CypFtNvPtsw3FHXh9b4GnjGUWY7cKOc3FDym4a0g5tDduNGZAy1Dj7zdCr/M/LpsoCQKDvzDoKiTIzurLXTnevtdHe1le7gVPc6fl2Wl+qpjjBcI5KR7fjZMOUk885fMZ5PvpyOF9jVp6WbsoeJKh99c8zQH91vdBAUCAoEBc4LBarvuem05vjh79dqb+pJ9368p1fxGK9aXMnCBGeUeop0BG/chm9u8Hn6EgBms4uDetKEm97G5GhzZx+fajLvzaNmnRN1lHlPBmTgmEwjzyTzUh9SY2u82YcKpufv/HO6RbEOmqiFxAFbB/fqxcZw7VVuNB/5PRNPVUTA1Y4iAjbTWkdmRovpPpqj0nK51mmnJUCXlpzpUtmjF6l9FXjRbJR17DG9FN3rOLFRrnI5z9MEuZyrjFUFBS4zBQJ4ucxPN9YWFJhBgVJkhIkXwItAEeULsDgwR7tEaxDwgs26TI42d7Vb1zVBcn0btWmAmG127rbwBdMFfBkAvgyJ3CABUiCMiQcIR+rXZCRL58noXuPokCCm+rlWzrMCTtZHcc1iZ86x/qySjUZKOXlMgT8DVF/ATxJa0EQmOiSEpUJc1mx3bhUB8To26DdWmzjSbZFumvO/laUWgmJW/c7Ai+Ym0yldRRdmWA5s41mhT9JuJk8nFE1WjLugQFAgKBAUuDgU0MvdOJSf89Sx1oV/AqqAqkhjdBeTo3dbA3NUv47W6OOXW3b/Cq3RTfjqLjy0i98XWC0bF5gcwbgWACrqmOWgeCldmMx3bLw8ho/oPFS8L39K+MZrVq8l+6qkLFncc9Ew7F8AIDEnFjPiqn/8UGCUfTRC5+GnAC44ppcvl5trckzftiiAt2+00s0VzHVxTC/QpV7LJkbmiJ++nX/6tTq3SAcFggJBgatCgXMMvOhFXzCEq/I0Yp1BgS9AAQlSdlSEOf2l6VbCnEVpQOAasHO3BfCyh/C41x2ygyc1aiIbkRbwssP1HYDM5lYGX3YIN227ePSha3kUf8fjP2cJchLEHG5B5bqsXKQyEOTZEgbzdJUoQCJgHnPWpwJp19CfDI/2SSvKQhPJdRHQZbm1kFYAXFaX66YGvdZpYG7UYMeujgZMvkpYbDdr2dQIxGYBAXhSwyWLtuUrabwYn+IXv9qyv/ioMWBQICgQFAgKwCwniGB3nA4A/uUvTRqjihC4A8CyvTtIG2xYPHu9A/Aic120YQBktvb2bfOCgEiEZpbWqPgOkQLREhVPzHxxktfkLYZy6JmSMk1yK4NNqMzExtOt9qdMr5n7VI42MEwW4GqbGJjp1vgoHHZnscaGxQLASz3dQlNUftKW4a830B5dQdOlBR+VpsvCfI5gZHx+PJ/xcHmwOAcFggJBgStGgQBertgDj+UGBVwCy3KjhC6JbpxN2ErsvknbhbDSMiVCYFSYTGm/aDdvxG7eADOkLYTIHfKlEfNcu3c72LTjeLdLmz7lPWzYtf+WHfCK5oiLhdaIdryQK01lWdCLBEmNr0/lVMi1GSZSDXP+S12ZFAmI0Uf++iQYyqRofp4PGi7KaKPhYoDL0jw26I10a4XdueuL6RpRi9qEtZTdeR0HuhIO5SRQO4wtjNZV1sQkqY65kdSjDXxhIYcCdsbzm0jo5qscEpB12LxyMs5BgaBAUCAoYBTwN6Ru9Jas3nuerh94ZAY62ZghBP6Ll47ggyNCGHUBX7RxIe2XF2920zvMj+Sw/g3OdsU3N7Yp66P5MoL3ojUq86Nyvr7JkLmkgBitJL/z/czV/mdeWtxQy9cr3ntg/JhMDiqL15Pyc+4x56uGmQlbnwAuMtMlalGLaIBLgC5rnRqaLQ3CR8NTAVtu4R9NmxhrHbRG2ciQE91GnViH5tNFfmxsoDyEBqXfT3J8yr4+yYQ+VydXZqGfi4DRb1DgXFHg3AIv54pKMZmgwKWjQAYvXAYSa5dkpsgMQ9RV3E59F42WEZLg/ggtEsAKmd1ILtzZ7ZkDwc29YXpByMxNhMe32LGb6REOd3e7hNfcn0P4ZCcPEEf9o0RjV4mHAl7oymQwzUF5dhT5+SbXt7mRb9oshbCbI0lgSoRQVwfJWQB0qSMgNtBwqaMOvUxI6BU+13D6d2MV4AUB8QZq0QoX3UIo1HGIPxr70LP8wSiE9KLs0gFtFG2iJt80Qok07wBe/JHENSgQFAgKXFEKGDeaWnvBu4w3CXTJ9+JRzk/7w5E5rN+Gb24DwkjbZR2n9Vtc33B9hzbpNma7Mt/dgXf2MD+S8/oRfFebFzAi2FC5GSCNTGl7auNhjvLsQ6Xkp+MJ0lQbFAawcBXv1r14qeYmR/QHjJHLxYX3rV+ZE8EOMdFNaRkT3FXAFmm4LKMZurqSzXSNx7KpsYZPlzabFq1Ww/jmPJORHxiZGNmRpz+eUiSCAkGBoMBVpsC5BV7ECDL70uMpU1f5YcXazycFJIod9w09qexrr0bqxD5vzfMAZES23X20W/qAJ3IQ2O31LU/mPR2cz8r/yQK4RRfhsY8N+w7RG16/20tbaMbIhn0DoGaXHb6tbcyP2MXrI9R1e9i8I/Ht04cBJpL+dDBo3sfTLLiRgFnMyHIkyBZZ1oR0fi+olWYM0MJ80GzGrIgoCwBDi+0Fdt0W0uoSAiIgy7UVfLpgWiR79FWiFkmjpYami3YVJdQOJdyyK6kdSmnAtBAsDXwR8IK5kkc3MlMmG7E4CZA59aG5nqX+yR3nlec6n67Xk8eM0qBAUCAoEBQ4jgK8lYsXs7+fdTXtTG1c8JHfF0U86olvCnzZ7mK+S4jpzR4gTA+fL2i/AMRsYn60zWcPEEb8k30LA18OATIEZWgzQMCL0tL2FA9wduQbGOKQznNsHtzpMBBGYAvlShvwIlVWDvUhzVGZ6coJ/WJrPq205wBd4KFot1wHcFlebBofvYZvNPFJ+U7rwDPrdbRE+ShstOYn4MXnkju3c5yCAkGBoMCVp8AFAF7ip8WV/5YGAT4LBQx4kbQlQYwP+APCoUJdsjuHtCcb9cFwCFiSzYIEZDQRxuqoHKtsSF0BNJs73ez7BaFxAwBmB53pdbRftgFcdvnINwyypgmQAjtGdCiB9BCQx4AUSYaMYTCQ0n6QPkSTRZKlpinHg0qYCZBs4LldxCyozXy0Q9dhZ25NgAtAy5qAFtLy7dLhugjgIkFR4IoEQmnjSLNH8x8QAlRaPTI7krmR6rVRrTaQSbt32lW0CTAx6GST8Tl+xatIBQniCAoEBYICQYFTUeDzvDV9I0BTcMBBYL7uDuF3B8XH+Cp8U+a7O3vyk4afF/jmW0x3tXkhPzAb8MsNNi620Hwx0138v4g3a7MgwxnilHrzq/988bu8leIbKiV3UE3taygngzK5tfUgbU54nDQ8pQzahJ+uoSm6hAPdFUyLrsFPr8sRPZot4o0rgC5yRq+NDn1aqMbMA7jU4M9z0hK1f5pYMWAmgzLiCAoEBYICV54C5xZ4yU8m3thX/hsaBPhsFMiCYRbADHgpzIIUxUjAyj4Cony6SJBaAH1YReAy/ycIW4dIgnIiOARIke16H/Cii+Nd7djJAe/6Ng4FMUMSCLOxC/ACkGMgB1czXQJ4OUCSpAvDMvyqxZq8ylXCoPAOyW+mUl0IdrIhFxgiB4SKRLQE+KKP2ZnLga4AF4CXlQJwaTVRgUYtRtoxsjuXvbuAF0Vi6vfZgUSrRyAM8JI515WpUZs1ytyobqEwJVDaNKhTHDYxv/k613g7fh26x6hBgaBAUKBKAQMwPMMZmN9re4EK4rfiqXJeLz9qAl+kNWqO6y3CEb5e5GxXDuvRhFHUI/mH2YGPild18f+ijRH5OiNqNXwUHmp8NG+cyCTJ5qGxxDQ58n5BvhMPEx8V7yTgkPFPaahoI0WO6E1jlA2KFrxUWi6KTLSClusqfluWuS5zlZlukzpyRF8Tf2QjQxqkGk3aoRrPNlOU8zl4pBM6L8/WGKegQFAgKHCRKHBugZfxy/siUTPmGhSYosB5lhMkCPr8JMwNB/LHgoaK/LoAkChP8o3CW0r7Q5okEs6kUmwaKxKxqDPAfl3aLz1ADIE2XTndFfAC6LKHYLmFENlDs0SfPho0qivVa5k1geuYRo3SNh8TGhEUuZrKMmDLPB+L8lCY/iwwF+2uCRjpSENFQAkAjNIr2KKvSkhEaNTuXBPQReDJAvZR2o2TBCz1au0gykmwgJcuwJHWIMFVgqR82UgQ1U5eUwIm4x4FXvSgQ/qb+rrHDlNY5wAAQABJREFUbVAgKBAUuLIUMH4Kf5k8dJ95jwElxn+0aYHWKPxWYae7PfFIhZ/GDImNCmmOKhKSNjHk+0VlMtntY74rwEUbGc5HR4AyphHDMBl8EQDj0Iu0VfEOw/DioYpMVANokfP4OjxUvLwpx7nwOTmkX8JPizmmZ+OiA8CyjOaLABelzSk9iI1kAMkD2gAxk6c5MUiNU4xrbNFOk2T4FHdO2s/U/aeYYvQRFAgKBAVOosA5Bl7iZ81JDy7KggIfS4ES6JCJEUIdwpyFv8R0SCZB+gsUCNGQ/TbhIZfMzKhmQpsAFx1suOUdOICUITc9hEMTJvt9hEX1CfjCLp6bLknI1Fgj6grckdZLdt6rnbLcn0Ad3WXNFmmbSGAEgCH6kK4SFiX4NUBEtPPWlrkR1yb5Eg7l6K+Bg12BJ9qNk7M/gTh5N07Yi8AXNzVC8BXwAgDDVNB5kf087ehffSnMdF3hpZmD7SeyWzh5hAQ4SY+4CwoEBYICV5sC4jHVI3MJbS4UfA4eZ85t4aPyMSa+OxAPhg9lsEXaL2iRajMDfzACXvbY1DCtUTYMBvBWRR4UDyWZ0/Av8WCxzzy+eJZ4JhouTEBmRHnDAr4Oz1Q0vyZ8UvzTIvppE0NaLfB6Oc6V5qf8t1gZeaorniyeavwQXpgjF+WVaiz9/+yHSPslxvnsC7lIAzjRdfXDH8J0WTVfdf1eaa+rdBxBgatJgXMLvFzNxxGrDgp8bgqUjE/CmYQ0fYaEH+qxmyYBcJfIC9JIkSBlgphADX0M+JBzXZnrqB8JeGqv/bV8dU0WCYXSIhHIIlBjiHBp0ZIEvEhgZNfOIjYgeA6VZrwsMOY+bZcOwEQ7dQJeFO55QQCIabpox04hn6WZImAIMyIEQ4Elus8aLtkcyUAX5uZCoWZqDg/J0ZgSYKWpI4FXatwZ9MngiwMv2hE0jRuBL1PAi9ZtfXONIygQFAgKBAWuNgWcN1apkH96CnjJKfE6/QhVXWl7yqTXeKX4ZsGTBLbYxgU+14bSGEWTtIeGjGm8YHakDQ61kdmSmfLKES981rRH876IbTbIxFaAS0umQUrDR7V5Yaa0Al5IN+Gf2mRpsdkgjRbbvCBfoEuddtrksI0Qdi/Eg43riTdrN6M4nMf6/We5Gt3ouRz2swwTnU5TwAlfzddDOC5f9bys+rCUV71XvTiCAleLAucWeNEPpPhBc7W+jJ9mtR/7Yv+Q9rMYzKdZzdFePnYsCXtlr8hstvMmE6BuT6Y3irwwME0UgQ3L+HURANEE3HC7cIWJ1N9nZqC5M7tDCBMIoyIBMHIoqKsJhwa2ICCOhhaaWmZMypfmSxY8aYbvFc1HbFny3DgsJWAHuIsJfBL+ZA4kIKYmkEUfhMkF7cJJuwWh0CIq0F592DskT9FmrJVnoTdr6oxM1TvvNFqEI+bE1MwOXkLoEjt+pjkj0EdAkAMvmiSHrTsEiUyMOAcFggJBgaDABAVKICZzC3El5YmFiI+KZUrzUz7TMl8EQGETQJ/MO/OmhTRiZB4rfjmAT2vDIvNOmfFSxkcRCeVDRhEKfQzxMYEsbW1OwB/l0yWbCmkDA60XNlXET20zA34qjRZpjGqDpWE8VRsg+sBbtRPC4RqkdlOcqiBMNf+TpgteHiz3k1L1FJ35d9cfgJrkb/DRxoVwNJa4/P5ozcgJClxFCpxj4CX/WV/FhxJrDgocTwFngMfXOLlEWi4F8+Qi57gCRyzMpbQ+AEgkxM1hF14DZVhZJZKBCW41QA1sxelcYIaDF6YBUuziadzcM0KfhD/G0cdBGFOtRiC0CA8qk38XrlYXsIMU95mZawwDTkzYA3RR5AWERv0zAIS0gCEDW3TlY2rPds1tNR9j+YyhzlwALlZvAMu+hFmEVfmf0dVUuMmTENmoy65djnYRQM1sCeFTczDwJc9TfWlOcQQFggJBgaBAUOAIBcR/iqNgb47Ye67diz+Jr2aemfmmOeJFc1RXmSQ5LyWruAekYafATHkBY6S1mp3X0yVtYIeYywKsCHghRHQDni7wxfy2weOkvSIQRua1iuonh7ni+7aBwTX7cBF/E5fjH0kDXcYzL9f2RYCXYty4fGkKlM85j1yVeY4rU3613peec4wXFDifFAjg5Xw+l5jVlaHAWZiT13VG9yFMLe+EiSFKyDMnfQAOOzjy6wO47CPcgYEkxSmQRsnKCs722Cmro/EyP6cSP4qxC1CjZLB5bj5DCZMuUFraulBeBlpM9Vp1DHhR396yGAdJTyMhF0ris/Ks0ULaygoNlwIMsSpqWkzPkxq7ekhINAG32Fk0R7usv2ehtEfMD+AF9WrZwnvEB/m6kYDq0RvUn/oNgbNK2UgHBYICQYGgwJgCFd4z5kJKiEcVfErO5I0/FvnjTQruPRT1/mE2M5I2i7QyM8Ainq0NFGm7HOCvjM0TykdUOIS3iTctEd1P/lkUpQhsxTYpanPwc21Y6L7YxFBIaOWZpqk2MPgH9jKeqE1XPFcJzdMumf8FH8z0uBzn4ks4sZjigVte8aUdl1fLlFktn+5r+n7cSSSCAleGAucWeLkyTyAWGhQ4NQU+DdOSICdFZ4EdshmXwLa91TXtF6k7S7tEQIvMeBQlSJofNUAIoArjqRLIXJY0gVEnHePpZcabAYkMsmRmPK6Q6xdna+0dqhPn21ZQ3Ejgm2ilGyqoDgUaK3dRCoJHqquqSY00K8bTDmLeVZSmDx+iSCg0NrIrO39yQCj7diIctWQHL7t4oiMxngTUTydsFouYNeHICwoEBYICQYFLQQHnO5mH6r2vj58tySnzFmeJphXKTd6cKDYtdM/HoiSxg2Jam6jByFTYHNcDuqhMvHG53TT/LQa8AKQYuCJ+Ct+Gjdl44ov2oXycVknBL/OcdF/yV2uam+tcHONcz4hrUCAoEBQIClQoEMBLhRiRDApcdgpIWMufOQMc5ExXmi5b213sx7Mg2JJpDVou8uuyiHqybL9l983e21gQoxs7ZopZhTDn4mR2KCioRwdCpV2nT2Wupyb7nrzzvg3zUZEJiGVLrTELjeTl/5W5e70sxLqjX9nHdwW84MRQtvNqbxGO2An0cJtyQJgF1zx/g4MceCpWOL2y99/7fKbX+P6WUSMoEBQICgQFLggFjBf5+372nJ23eqnxa91YM3hWkbYUN/KL5v5duvDz/X1Mk+Dl0noRD1tabMK/FlKHiEVyWp/ZpYAWOhLvsg4z78l5BQhDvvFQiqbnlGurvSYTR1AgKBAUCAqclgIBvJyWUlEvKPBVKCCp6OOkm2oPEuKkpqxdNO2MKaKPQkBvb/UIS3kAqHCYlpcUSpKPnO7hUbcuVWSz9aGnQgIr+2RuJq1NEUcVOLKYaPBEXkaR70vSfMojr1Nnz1XXk3VUW6VZOLT55GYq4Ji44T7XVT3v06rZKZdpZ1Bq23JmaL5uAF/k68anJnv3VqthqtrmdJB7aWBLKNVovvxiJOVY737JN6c5+wyn13Catl+qTrnKLzVijBMUCAoEBS4FBYpXvLiR+IfzNgM4WKDfT6zV69FWoL8f6kr1pdiiCEdyuLtn0fnwD4PpkRz1qt+lJYAXNDY78DDxKv+oH5tHwRuN31Foc+EmX320zEn9Tn3EcRkoUHwhzy6sXIbFxxqCAl+FAgG8fBWyx6DngwJiOrMkiOPyP82sndV5b+UMvKTM8Tofcq32NoYdyJQ2h8xrpOmyZxoe8vHSkyhopjVrK4VDXbQ75MjWQylLyJsUuE6Yp0lxLkhW6k10wGQq95VaR5Zbnf+4UGPQyOCPSj/jcktQh//jg3q2jiJDRerGgCiEVTkWlhbQgFCe8ndjmi/ALA1AqDY28jI3EghVRwA2x4MShH2t9GVrOHYu41lMJaoT9KKTqOF1yqv3cLZWZftIBQWCAkGBoMDXoUDBQhicN7n+8yI/zbtc733xM3euu8cmijYRFA3pQN7r4UVLi63UBHhZlo+XolMHesSq1N4PyQDVw++MpZXVTjc5OvIm3k+170gHBYICQYGrSIEAXq7iU481f3UKuECiiZRCyezcD52selPfudfiDiFLkYy0Q9bDpEaCmtIysdFumkIzr660iISgEJQCXrQDJvmtACzGElg562PnNxboct0MnpTtrKtjG7+noOjb12grLbuuNM6rNyJoQJ9Tkc6l7mg3h+PsExlCZkdDbOYlwLLPCCBVY9cQ8ytUtkUjfWrQRjTLJKms7swLK+ZYeVp5ATMXVFlbmaz2UOZGKigQFAgKBAXOPQX8Bc5EDQjh1W9vf3hJFRjxdRg/hqmJneljEfmk8TILeJHGCxsHHQderO8qb8lwi03BeVdlPsrSGHk+miCfanOf1Iyrd3PK6jN6iKygQFAgKHC5KBDAy+V6nrGaCQq4hODsf6Jw6ubziwY+C410XDpPqlo6Nc0z3HovaiLhLasOy7wIYAHQZXdvYBoeUkkeUV5Hu6UBwKBIRouADHKuq4lK8BoLX2MyjRNnmNGXrFpdfTFuNauyIAmV+miHUOZGtnuosNrQqI/qtsRSha0WPaTxUq/P4f8m+77J2kC5/zGNz7jMI4K16M0/QTmmzXOK/jT/icP6mMjhZrqSl3+ZZ6k5iuxxBAWCAkGBoECFAsWrOb/zi/zKy9J4ROUF6horqq9sbRQM4Ot7XcxkFdWoovHSAXhpw8/lXNe7HL+GnXEUBeN+9a6uTG+cnJk5Lh0niuVYH0qfstm4fSS+FAXi6XwpSsc4QQGnQAAvTom4BgU+lgIubaifGZJGtXjWUN5kLF95Ay+Y1eiYPG9aFpOj//h32SVksgS0nd1+6gM0SHKz6D040xWwsNzJ2i41hZccd0Bjl9rGeR8wsXHbz5k4uvo8WjFfFzbJrNZUtCeZYI3QCOorwpH5vxmY80IJpGZiBH1q+HhZajfQCELrRf5eIJJ1WTy4PArnk8ijZzEevToLJmVNOXm20V0Nju8w91UpL5wmKkfdjEuOPEMKz3xM9Hjq1gV5Tl0/KgYFggJBgStFAX/na9H+0q7mjYmR3/gqUiTC4RBzI3jWrgEvbKQAxIif6cg+XmppeRHgRRqa5M1P8IHpAXxga/5Bp0/f4wdNIxoFBYICQYFzR4EAXs7dI4kJXVgKVKWNGbJLtdjXqGpH8i2jou1Q6cvrVrK8q4mr1/PMwxyL0hzrSjjb2RumrZ2+gQwSxtotRe5pGPCy1JZTXUAXmdJ4B5rlhLCmgvfNYtz4CyemVz9jrgX4Uq2pXUXJqgJepPnSBaDqovUim3khK/PydwMYVavNESGCEJ0ALwu1RPht0YEPdeaMYBJuuT+JPAws0VlNS42XooE3tcnlGeZk+TSmu/daAojy0pSTc231NpCmedKkVPM0h/o9ez+a1ycZ/jRTjDpBgaBAUOCiUcBf2dXXazWvSIt3eLb8k5mWJsBL1njJwIuiGuk93VlqmJnsClGN9P7VZxJ4EZG8t+rAZyee9zLd8uN6ne4t7oMCQYGgwMWlQAAvF/fZxcy/JAWO/dWIAPQeaUM/wksxKYs4ed9pcgESTqyr8Ykc/S/6zxf9WM923+WP2FKsKapOdKwf9ocIYQrrrAg+291B2t4Zps3tHvc41MV0ZrXTtJ2xJmBCA2Ah+3bJ2hzqTH3MZVTBZ8m1HFd1Ps3hK1Dfnp7u+X3jTrebUb94aPmS6+vMKg18MeAFB7s9wBftHsrJrqYj7RZFOWoTIUIRjgTCyEQLjMp2E3W1g4fjySJjvJpx/jiRI1Oogp6pz0lgmeakk19zXzrTP3X1TPR9sK7G/ancWlSuSqqNGqn8yx9al4aPIygQFAgKBAU+nAIF++ItL96RNV6kpSngZYiZ0T4bBwJexM+WBbzg42V5qWaAi97B82Ne/uFzmNXSuc50Wbz2pykS90GBoMBVpUAAL1f1yce6z0YBk3QQHyRBSLqYkiQyMJHLsvChX5n6r4qVymSbxUilj0opdTlUp1KeB1RB/rGdi3TOJcXP7nGbXFIUqiP+SziTGY0c6W7tYWqEf5et3QFgwTz+SubTjbVFoh/Ux45jbdY2sdxefdqPdg1qh+X4zWe++li6+jFNNS9TfjXt9WddBUiRX+nKyMVJWi4SZBX5SZ8hPl8sGhR9q3pDznUFUvFpYqIlTRhFOVLobWkQjQEYe5AawwdhbvznUdgsMyimKFMCxjI8JxVxpfVALV9zZFQBZzospKiSfBbmFkzLRoK0ZXEywVoAEQkDZVRAqf3ziVme8qsHY1ov1bxPl9aSxmT4dN1GT0GBoEBQ4MpRwFgEXESbKQP5JoNfdQW8KKoRfH5C46VRAC/GmzJf+BwEEwfRIfbiab/XNY6gQFAgKHDVKRDAy1X/BsT6T0cBSTnVH6WVH65WNBYz8o9n6bjoV6b+jduRlX9cFyKJNBD4gTzuyhKcrJ4LLkVdsnOxnUkrP2u+2K/Zskv9XtdANpZPy0NMDgARdol+IE2Ovf4AHyU4jMVR7PW1tkUyyj5LpEVRjJcXl0eb+NWcx2ekL3AUi5sYKdNhIssXO5E5qx4VfF2VZWh5YB527E8BL31o1ifKkTRhBGIJ0BDYIgCmJe0XaNhGA6Zh4aazxlDuKc89g1YCegBTGFvCskAV25nU7mQRulp5GsO0XeQkkXuBNIl5ZcgnAy+GnzDf2vwCGjho3AD41PgycWtaOfWafNFkcGhBoIwEbv1TQ5FEn3xSgqNKYyvM2Z/wLJJPfIU+Yd/RVVAgKBAUuCoUKNgXb+3MS7QxIHMj03iBl4xIZ42XhG+XHNXINF4+M/Ai+lc5iT+Pz8NRvPe4BgWCAkGBi0OBAF4uzrOKmX4NCkiKkNTAdSxQjBOVCbk9UCVr/LvW2urHNH3YqewrCyR5B0o/itW18vKQuXRyuHznNtrWXzGmStS9wAPEMfuRrzxpamhHrI/pzACHsaUa8mFq4KSkjsbG6nLTQiXLlMbgojy09Typ6VIM9qUuebmMNk5k4hwZP1NsMtsXUWlbULba3UQbCGg0A+1Q2O0szI6IANVP2/jF0dXAqwEwCMCZwI1rgFadRZwSozG0jMlWqylNmBogQzGu0AaSelYHADrD0ZA+ZMY0sGfSGwwtnLd2LE3LBpBHQrNChIL15DYydRIoJMBHwjNAi0AWAT/yx9MAaGlqXIAfaTAtMheBQa1m3T56xjWetb5j9jw1p2JempxTytLKt8Ovxe1HXvTdHHf9kX1F86DAl6FA8Tc8/gvR/ay/i+Pyv8wsY5SrRQG9S3XoIhA/Ay+Eky6c60qzVSC+DnOuK1OjxeL9T578lX3Oo5jeeIhZfzHjwkicQwr4E7wsT07r8bVctrWdw69PTOlECgTwciJ5ojAoAAWK97S/ru1HtN7hJv3kXNNkIaksaTXYTpQVkSruVSZtCW7t0CWDHDJN0Q/i/MNUP4z5fUxZVofRUAa0SKPBNWSU6RMTQ+G/fj4fABbIpEjC2GgE0KIru189ftTrx/5wOLR6NQaQmUy7KSexC2i71LgyB5PHrPP8A13DnItjgmjMKM+xpMH0JL18Ov+Yez0cPQCuSoqGAqz6RItQ2G35w3m70U/vtvpEhdpP27tDK1c0qHu322mt08Bcq5WurTSNlm0EXZ6GPSG64nnw3KXBAvAidfBuv29RpfZ6w7QHALOt0N6kLZISYUHNwS/Cs6JO0cTuzSyK6Qt0Ae8x06YmzyybOwGyEDK0iUp5u7mQOgBACiHaadWJwNRKDQCYNukaAI3AF/+Yjxj61NLzITrrZpxR5MclKBAUCAoEBc4DBcSjdOgi+cKc68IvuvAQMzVCs9VNjZbcx4sDLyZfxPvdCBinK0IBl2t8udP3nh/XoMDnp0AAL5+fxjHCRaNAIdRMT9uzM8iSwRXlHfLLWKYp5gOE68hMRwSAENIRCUn5hzIlobKBIiY1FYIPF4Eq+mD1Yz+IZcIinx3yGSKARKYkNRAXmQXph37WWtCPZfVBp7pS3wADHOvx33bAdnCiu4eGi4CCPp/h/si0JwSwLNoOGCABkQ6amMi00JTQD3h1VdV/0A9wG2aaGF/8vqC+Llr2GBgo8u3e09Vypd9/lJpDMt+CBqAcA0CXLiDINtGf3mzspRdvulyl9TJK77b3EGzx7wIdv723mG6tNtPt6y3AlybaL43UAcg64PnrmWv3cYDqijRlFClp27RnBmlju8/zEfCiKFMAL9JIAiQb9gXQ5F1M7WRq41LgiwMvAscEvAiE0zOrN7KD5BYAmszGBKStLLfMoWKnXU9raDMtCoQhBLY5BaZNExBG/mj0XVpAE6Z8xqKhEdiEegGDn+oQXctxPlWv0U9QICgQFLhaFDARgiVnXjUDeIHnCOTX27yzVJgaFcCLKCWNl0/3ZlePcQQFzjMFSrlGEm4+4i/gPD+xyzy3AF4u89ONtZ2aAhOvZX8vGwTBy5n/JuAUvRnwwi6TtBj2AVRkwtPHX4qZj/BDWiGIe/IJYk5ZpbUgsxVFHqA+aduJ0q/nYtAFhCAsQfjgnNVMR/jxzI9pRSJQhKFFfIjIVKTFj2X9cK7XsnmQCU/iHcY/sr8XOdUbAhjsdffTS8CCZ4AFL971AF4ODNhp1FO6jlnM2koj3bmxSDQjfqALdEFLwhzDarnFr2ObnuZ5Lo7qE/r0EzLgRevWMDxsRYaQltCOQBa0XJ6+3E2/veim5297Fop7E9BEdGoDbPzzNyvpzq1Wun9nMd0x4AWTI4CO/rDPB586aLhsbXUJ3z1Aa6aX3tD27fYgvdrE1IhnlT+AdABmJlDbHLRGAXYC+Dj4vmVwqADC9Jz0aABOhGYYQMI9WIo956a0XwBYFjE/urNS5zk30k0AmFuAQwKGVkhL26kp0yS0ZDKYR+OpQ2u08fMIuVQZR6tOtYzboEBQICgQFPhcFHCZRHzBnOsic5jGizZ+4F/SfNF7utOWGXEtrSiqkfgFRwAvn+upRL/nkwJVoSVLNCHEnM8ndRVmFcDLVXjKscYzUcB+/FoL6X5kQUU/f2W+k6/ymyIQBZ8caDL0ej2ADkxH0FrQj2xFDJI5yR7aDSrv4w9EmgwyG5EmgzRi7KWvH/ocdYQhgS4CWaSNIhCkxUcgizQYTFOBH/jL7Fxp96oBetKkTBowpiVDPxKkpHIs7QoBPFuYwvz8fCf99Gw3/fJiL/XRflH/HXa97t1op9sABA/vLqYbq23GAeDhx7dAH00pz8p+9nN/XoAXI9UXOQlQEw33EGS3CLstE6NfX+ykn5/uGvCyvTtKW5geieai5//5w6qZG30D8HL7WgvTnlpaai3wXUCjBW2WrZ1uev12DwCnl1683k0vt4bp9dbIrsPRHAJyDvOtb5s0n2p89AwcV9Gi7ZkgL+iagxvpm8j3kBOufs2oicdv30+dGyAwAvP4mqT7q/V0bblmz/z+zbb587l1nShWmCDpO9YGiJGvGAGA9vx14nAATqNqrJxrRR900t9V0fUHtY9GQYHzQYFP8ddwPlYSs7hYFChlk3Le5uMFeUQO4LXpo00e24AR8MKxVDjXFa+SfzC91w1oL7uIVFDgHFJA79nq8TESSPWd7f1+TH/VeUU6KHA2CgTwcjZ6Re1LToFJwaZ4QZMppd1sQpR/lEvLRc5Ru5jy7Mh0BG2GTcIzb/NjuysnrAAv0nqRg9QB5ioD03qR+q80XiBiERpYP0QXcMIqEyJzkmrAC6Y/Al4AQ+SktcNOlbRe1jptNBX4oQwIs8S9/LLUpbEgDRg+6iv7BpGWxiD9+Hgr/fnX7fTnX7YBhkaJLgFvaum72x20M9rphwdL6S4gjHx/yBSlDsizYL+M87q1k6Zw01frcP84+GJBS2gTkOXtZjc9eQGABYj1bL0HqDVKG5vSeEGo5Rn883cr6d6tdnogQOs6YBlaJvXaIX5h+D7sot3ybi+9Wt9L62jOvHzbTa+299P6zii95rqPudKBvgt8RHuBH7WFw2xqBukFxGgcU2wpoA8D/3hEej4CXvSRWvk+fXgoanotNJwO0XippTW0nG4Cvty72TLTo7s3F9PyMt8ngLwVrjI903dNpkt5TAbVwH5oHp7+wKv+tqpdfmA30SwoEBQICnwyCvBaskPvN08r42Pfd9bpJz7pHZoPJfIMbcMFoaKHhq2iFUrTxTZg0MbVMQZekBvMKTvNXPNF5Wdb53gCalocZ+vBW8U1KBAUCApcRQoE8HIVn/qFXvPHM34XXmb9CMzmHBJGtMvPj3CGsx+4/Lrd3t1LPTRHtnd7+OcYpHebOFxFi2F9mx/iu2hHCHABZFE0HO1CKYKNaclYH/STVRLG5iRaieYgsUW/vRWlRrtRUjJR+F/9CJeABB7CD2P8dizhr2NpIa3iLO8mjlxvrTaIotNIqyttAJWWgS/0wo/wfeY3TH/++V36/d+37fN6o2saFAJYvr29mL5F2+Wfv+ukbwFgFI1nFUCn1WjwY10/9GW2pC8JwMsEkSxTBcVxCQQuX9LUUuSXRyCWhNktfLy8BWh5BejyEu2XbbRgXmFypLIFQJIHd5bQKGmk6zybzqKAC2k27aen1HnDd0S+YXYBvva6h2nHtKASQFxKfFXM5498t+RwzxnckLmQfQ/GIrF9U+wbqWn6lP0qP0ICBvU9tbyiQFGVhJu16FBhwqX9InXzxfYCToDr6SaOgK/jm+bh7aV0ne9Qh51RRWVShCRFazLHuxXcbfK74N+BuAYFggJBgYtLAX+PagXV9+sUSzg3C3TenGcLPyBDQEsJvOR78SBpUS6h0aiNHPn7ysBL1nipLuh0a61S6uytqy0iHRQICgQFrioFAni5qk/+wq67yvxPJy5MLzULLvpxO1nioMs4l6GkSWAfdpTWN7bT9jYOVtFgkBbEq3V8fvBj/M0mwMseGg5ddp34sc7PXbrQj2hpMWRtBdMiUK6AFROHNIomoB/MMhfJH4Yzh7y6t/noxzN1wEtSpzmXbnQIX8yP5ruYtNzjB/MNfjzfxlfLtdWl1MJ5am2ujuYDc0ED588/b6T/9dNW+re/bOFPpGs+RJpo1nx3p5V+uL+U/tP3y+n7e0sG5NzgB/gi7QW82Nw9FLLNkazx4fSfIt64/IIljlmOaJ+d2srBLg5xZV4kAEbAC5pNT193zf+LbOtvrEkjaSEtIdzW56TFQlhPtKF+ebmXXq730ZIhBPVQgBhmRfh0kbNkOU+eoKB9L6Bd8eUcl1lCJ59oQd+pW2Jbj79VuWoBwlB9H2TH1kPfKLaYOVtncQ7To3q6zXP/4dFKuovp0Y0VojKtLZr5kWk/gQSaSnoxGYOFxhMr5hGXoEBQICgQFPiiFKjKKkrLvKgPn+oB8JvGCxsHMocWLxdfb8KbpJ2ZN1bknL1E1MVyxmxmxipKVqOUcQGuZW7RekbLyAoKfCwFPuZ7Vm2rebjw4vl+/7FzjPZBgbNRIICXs9Eran91CvhLUxM5+4uz+F07XoULHC7IjK9op0hu6Q2GCDRymDtKr95so+WCs9o3O6bJ8BKNBjlbfYvZCH5s0zbRaAYH2k2qEYUIrQGEGzmsVQQa+XDRj23/IWu4RjF9acVY+GKubFzZD2VdhwhPCj+dDkf4gTlMS/VDtCpSutapYSKEM1fMRm6sYeJya8XAF2kstJoNtGfmTMPix9+20v/+23b673/bwp9I3wCdFsDL97db6R+IxPOfvltO3wC8rLEjdgMgp03IYWlGiKxZu0G0LiY5ppgSx+VPVLoYN6dYioC3LmZjcli8hWNchX5+9monbfDcu0SLqtcx60Ezif/Y1g8AV4ZpE/OzX5+jIYMD3WfrQ6iIV2MD5GRGhkmPABAc9KiNSOzfO6lYKUuwiadEyOq3Xg1Up8zNd9aX5WefLGojLas9OVqUYI7fGsF7Mm1rMOfbq/L70kj/9M0S36HldAvQ5S4gXgftqez3p46gruhaAoryGDaRImlDxSkoEBQIClw5CpyCcXwJmjANaTzKDFp+5Ny5rsCXA+QHvarbAC8NTJINeEEWsc0fmIW9xk0gInXMO32S78yq5nTwmsd09CVoEWMEBSYo4N/Nicy4CQp8dQoE8PLVH0FM4OwU+PAXqgMvukpEsN+T3OR//FAlbREBEGLkx+XN+nY2J8Kk6AkaDG8x4XnzjjDN/OCW81X9IFdUmgHaDGA0ACgINHQssyEDWkibyQZX+fDQmH54WquRNbYEKM1Lc8mmSvqZbBkANodmcmQaC4A4i2i/LBL5aFnOcnGYKh8jilyjj3y2yN+HTF1+frqXfgEA6IHkyCfMGhoy31D3Dj+4H2JydB1zJZkfLdFGwpn/wA6zkvwc9Kz0LKSpYuGl5dcHc6G3mG5Jq2gD7ZetHZwry4munOmy4yg7+10+W7tERcJPDMUcevb5u7EwJ1OebEqWv38q17cgfyf1/Itb04TyfKswfSq+RP5dqvRgNYXbSeNFIOKIK0uha7RuAF+WWnK2PJ9u4QNGQJ5Mjx7d6hgIdw3TI4WkbgskwpmzvhcGGk58gxmCgfWd1aG1KFnOxbLjFBQICgQFLhkFvvKbzl66+U2rTRsBL+YQHj6kjQIB7eJbIO8AL5iPSuMFPp/f48gmbAr5e9r4TXEzKaHk97kenIqL17w9R29bvvG9tCyxinEKCnw1Cnzlv9Gvtu4Y+LxTIICX8/6EYn4zKPDhL1T/kajrWENAN8gLWfOEnSM5zpXTXHy5/PpsM71Y30nPX+2mx68JB4w/lzdEpDk4ZOtIjln4AZuFlUJ1V13Zr2mmXVxNFJkxZRdRVJSlGjQVikwJQz7XnKlx+PXMeAKH2MxKc5i0tAFfpLVw/1adCEXy/ZKd8C7gbFcOXF++xTfJxhDQZh7tmFa6I5BGDlYBaOSXpIN/F/nzqJtfDwED8u/CWL6GGdS/Kln2XIrnoN1DAWNylqww4dlxLtpOhOr+7dXW/8/ee77HkSRpng6ZiYQGAYKaxarqru7e2Znd2y834r+/m76ZnWfnbnu6p1VJapAgoRMiAfB+r1lYRGQiEwBZBAtAeZAZ4dLc3cLhbmFuIq3CiFlB9UzGeHdxQ72D2plsG8pw7tERRC6TrZgh4BYJEiERHMccqJO1Qn28e+8DRc+N9KghiHqX9YowkCDGpYomSaoRpG7cflCC6TKGzaAxs/8jqZdFVI9uLU6ZJy0x8kYknQOxboy5GswIxnRR/Px9rfcthzMGMgYyBq4KBsqVr2+HL2odLNfzIqB2xFQXo6XDvqRDAUm7HCHleAh9oKvVlCdEGC8c0gTjJVSNfJ8HgF2s3MVC3m8NjxGrqMtURqnIqccj7JB/uvtFvYmfbkS55dMwUH/f9fBpdXJexsCnxUBmvHxafOfWfhQGYoMPIO+/uZeEi0BYpIIp1Z4O7p93UBPZRprhzVY7/fWHNSRdNtPjF20kSDpIOBynNzsJL0SjaWJsDImAYVw+VypF7hVIoJ1x4u0V4aLb0WLZe9sfxPQoPloJWJ7ihPlG5kNZH9LYmyGwJ1fHqL1IymJkBBsj00PpzvwojBdJLjR4NnE33YAgG4Z5BBPpIJlHpNtLDTMEu7SAFxsM7TXxYiNbHmpX9kxC7dsZL+qs9aLo9Yc+bHAfWvknrefvSewLXX4/YH7oRFF2XuRS+tXqTvrj92/Tc56PV3aNMddG5WyXn7wEyeX3OOJP8LYMzw5TsDQneHbNweq9W5NWykPeuoeDTFbM0h0osSLAI3qtAmIwWjlumkeYIUIlTh621Dwno6mDutkQXo9gvGD/5x4ej27fmEwP7s3gAQnJF1TYxpF8GYU5NyJRLrt8TgeDzoZhjQSmimL5kTGQMZAxcK0woHW2WOxszY3wJxqk1ncWXEm66LBI+5EM68qd9A5qsLI7JkaMXEprJ5BXo3H2ee35Y9AqYsLI1psZ8aeEVEnLK2iPMkEQTl79R1wv2b/ESUg5JWPgx2Kg/vf4Y2Hl+hkDF4+BzHi5eBznFj46BmKDP//m7gwQdaSoWyTocQTXQQyXNlIuYro8f7llEgwv37TT49ft9HoD9SKkRnYPUCfCG83hAaoacCnkAloqI/qwlY0PMTBCkkHNmJpQrclAQ/Q+4np2jcSIHz5su1ONOSKY6q+JE9OPIVSQJPXSarwz9aMp3Aa3msNmo2MK2y1yEyyPR7ewBbOIOsnS/CRuhBswi2AI0H/1O674iLaBRKLhqypTJl/jQLwfnyLECCgsYncffTLZ+3mzgdrZGvZ+cA/9zdP19GptH0PLB8wfeSyS6tk7cAt+pVbEU3NDM6LnTQ/AYuA7etJdTDMjroJVE9FyepfvssohpNmD3BTVJTElnxcyBCwX1NDmiKIjFYXakYzu3p7H3fi9WbMhtDDbSjNISJltGtSONAaNJ3rpTXhaV3M5kjGQMZAxcK0wUK291bC6V8Iq/f1Dgj4ImrVcNC/bXTKee8jaLQnMNpIuu3hc3G5LWtelMuWVTxtXCy+IDZgtUktuQA/Iw9EkTJgwtCtp1+ryhb3eh+hT0bQVredXdesl+5eoyuZQxsDHwkDMu48FL8PJGLhYDGTGy8XiN0O/MAy852IbVIOp6tApCBJ9nJpRW50YYUB3G2Mc63gt+uvjtfT89U569qqdXmzgmnkbo6q7+qbVh7TMpOrzFRZLwWyR2K19iOoW7cS4RX/0pPVErWQXmVKrUy9rYfrtaa7KorB4J2LAjMgOzCiSFvwmxofTTQzmSm3kDnY77uIBSS6o5+Q2eLLhTAEbQtWyf6wLWHReT7XQlVDPvJZhxy8jt4CYLvxM6ghpo30xVw5guMCcQ7XoOcy5754RxrPVq/VD1NQ4gVRZfm5MWWpFjlN/cw59yNTUBqEv8B096S2n/AF5JJsYuI4zyyvKVj3QDDbmi05N3x3BHHID0FMTyWy+3FwYT1/cmUnLMOoWkYC5YfNGzJdxmI4Fk5H5Ltz4lRkvJbpzIGMgY+CaYiDWu0HDi7V7UP7p6YI+CEK11mp/wUPeIfsNm40MvG8hobu5s5/WUX3dlcoR0i4HqLyKyT4zhXQrkrkz7Pszk9hzw9juPPa8RrEVJ2GXMZjp0aZoAN8l1I9I9T6f1rfTR5VzMwYuGgN5dl40hjP8j4eBzHj5eLjMkC4zBrQu20UgmBd8nLo3ABlC3U8bSDGsoDLyu7+u4JFmG6O0O2n9YDy1OyNpHzsdUxNIkyCqOzUGQQIMyS+IsNHpky54OievbtqlzB9UVOllXhnwauLriBaSnrYHkV6g7QMxjorTr0PUkI6PdpFeeJc+X55KX302m375YC49vDeNTRcM8PIBLWO6gqBvZrPZIbClnpG34S2+/70f0aZhDEDD+zfwCWpEf4PQNds/UkODyG3DdNmWx6Jnsv2zk15IxQjX0mtbuBvnJ6S6NZd4Q4ydoGDqFy/X8FFDSgStzAeOMWDUmiFIqrhGxVVKwpCsaSvmixvd5c0xgUeY1bOTMO1wkf3FnWnsvEjtqAXjbjrNYmx3qtV0KS8ZBxaRrklkg3KpHsXq/Yh28zNjIGMgY+DqY0ArXP2K1S7SI14v83HCsR8Jmhj7Mv6/D4Nlp83hEMb/36yj+grjZQcD79uoIkvyRXvXPLa7Wki7LM02jOEyj8TrTWx4VYwXjH2xaovlUl4Mo5fxUublQMbApcJAzNuL+9u7VMPNnbnyGMiMlyv/CvMAzsRArMt68nHp3oMkqourXeyk7OCN5sXKNoZo9SGNXZcnuF9ePzAVo4NjPkUxpKuP1AYiuWMwKOC92NellnkRNkav2JofpAsf30Vy2bfYE5B0UFACD3GpW8YF0bNMt1JKKa/IEgRjwpAjRofpevMBLKOpUnM5PpIx3Xeoi4zhJhhPNcut9PltpBeQermJP+rZmZYZ25Vud/khbjAruGWjZwSs70WZ3h5Hf88AcWmzRei6q06pGKGGtoubaESf1pGM+ubZOm7FcRf9dj+93mQOtXXyCDaMKSb1M3/PvYMTW6a6HHv1lCrPp1U9Pijcr371XpQrxktVyueOM96UozksJST7waRBMt2YL3JXfnOugcesVvoMJswi7qY1dyYQW5eanQw2xzwMaalo11tTrGqXSL4yBjIGMgauMAZihTvPED7u2ldnvGjP30flVdIu2zudtILa6yskMH94KQPveNjb7mAHTlKxx+kmhvcl7XIHb4a3se+2jMrxbaRgx6UmLSlZUx/VeBibMdK1jYkW+Lj9Pw/GcpmMgffHQPxN5vn6/rjLNX4KDGTGy0+B9dzmp8WA1uVybeYDE6JF/+Q2ehMjqVv6kH68jmrRZnrGx/TjlT2kFw7T2q7c7kpRRCpGcgftXl0kHKIl3kCKUFGAhOqjWowXMWGiUeUXNUjSJ/lgxksJmUrdl3Li8s96h2+jISgeUIcPZ41rZOg4LUyOpKW5EdSNxtMjPpzvL0+nu7dmcBcM0YWNF+l2y9uO9Uf9Kgit96G3vAfRq+5nvb/dOZczVtCc/qp4dzZPGOAB+BTTZZu5srreTmsbe+kvT9aM6fIKuy7rMF32MaYrppfmilS/uhgvQlKBjGqOCAeeEey6bqz0zB8D0A/b9vZqVdWQz++y0VquBSni89MzxFzS3DEmEzZfxjkAnZyQt6xR85J1B09Yn9+ds1PSGzBfphBV9/nDWMWE1AwCphkL9mgx3NrAval8zxjIGLg2GIi/73hem4GdMhCNddDla2+Vq/iPv4Lhor3DPBsC0ozsI9EiI/ub2/umFv3k1W76+vFmesNBwOomqkd4NZTDgAe3m6zjjfQ5qqP3lzCefnMiPUICdkw23uiiPNbFnh+jU1yt5Stj4PJj4LT1p5zRl38YuYc/GwxkxsvP5lX/vAcqdSAjLrjpFEgnRgcYSn3Lh/Qbfv/fH1+kb5/hvegVIrs7I6gXDaPCg5FaToPQMEpNfiEVoPrxsenEiQgUZ7NYKNZ6I1yKiNEw3PjItTIWr95J/aO/rF5lW8ir0A4FooyxhfjS18e+eiX7IjK4J7sdjWFJLxyZx5pH96fSl/fn0pcP5jnxmsXDAR/PjG3cxHeoK9nl4pLaUU/3IuvEM/pxIoOEfjBUvl96v/qfMk39El6FR7/EjJDHiJT2kIpax8PVKtIur5CKWkWs+4/fbiAd1cHI7pERtzYlGNkYzDkZLHY4jp06Q6L/6E+yY7wP6ky8F+NwEHeYnh93launR71yMFHQSxVF9Zb9b0IGpv23f4T1aNqU0ejpKcTUsQlwC6mp33w2l+4sIjEF024eqZdJXEw3+aPQ/HeGnc9/NWSz52TTZR9yIGMgYyBj4GpioL7O9htBfS3+OItgxXipdg9J60rNKOy7PENS9wle9b5+usWehLTuOqrTSL10OAx4dLfFug3j5e5MuosUoyRfPrszxRrvhwQu+RpjKfrM4+P0PuDmZ8ZAxkDGQMaAMJAZL3keXEsMiDwywqEIiPESJNOBdKP5rW3u8CHtIrq/++trCJd2ermG+O7hCGo7qBfxEd3gC3qMn4yP6jLpgOJb2E+FPF336vM5WlIPIhzl6uRMd153zFk6Xiv6HiWAYUH/wBdE9UVJ6p9UjkSswWLiROvIvB7dvdlMj25PQXwh+cJzBiuq8zOTfDzj3cC4BA5bH9Gq6YyD6PPpz+hVvVR9lPX0yxoGXcXlAeFPuOxA3G5h00WSLi9X3ZiudOm/hkknpssmdl2ONdNMhczxJmmXQhDEYJbzonhHelEVfsqGi/aVE2n1cJFtj6h9VjkV7oURdYs+EFWKGZnmb+QIiSlBNUPNqKtNIzW1OD+evnowA+OlhfSUmC94xsJgo7xmjeJe2v4OdLOB1ectSfnKGMgYyBi4khjQSlitlz6EWHMHDahYB7uye2F0ZQ6MaE8aMtXoKFL1B8FEO0CS3bFtbLq8QtXoOcyX75/vpjdbMF6Ib2x1rMznMFkW55B6vTuJBOwEng1ROWItN69GgDapRXWx2LdihB/W6+hrfmYMZAxkDGQM9MNAZrz0w8rPKu36brNGuBTvUh/R+i7UaOV2cYffy9UNRHRxCQzB8ofv1tKLNwcQLXBV7GNyBKaFDIhK3Ugf1IV6EB+n/LeLpC6yrEguWoyHyJf+OVHCn17OS3pY97g8XXdPre7+oWuKKSSKYWA/Sh7zEX105B5rlvBS8+BmI312q5UeonZ088YEqiOzfEDj3cBOvoCtAUkHimfv2KIfvU/vV29q9PJk+idJqdB07uZsehgKfETCYQdxlwPcjG9sy21028S5nzJf3mztYXi5jSi33HiKraK3wekh98Cb0hTW9a4IFJBtOnhWUeBc88NhDb4LlrcwuIxyijbpk+aMX8xppr1YLjF3BEuMO0m1LMyMpS/vTUKsTxrz5e7SZJpj3sxOw3gZHfO/Dzh1Gqbqh8pawC7bLFrLj4yBjIGrhoFYK2LNqvdfef3S62WuanjQ2Aala5y9uOqN9+LC86NUWb0oFunv2M+1autyNVhWaNbtPSR3V5FwecWBwA8vcQiw0UmvNiTxsp+OYMx8fkceDRt28CJvRnMY112E+aLDFTtoQTrW1mzbE4pGeVhaNK5mi7AeXW+7K1LVz6GMgU+DgfqMjAkbLcfkrKdHWpTJz4yBT4uBzHj5tPi+xK3FwnR9FqX6R6B/WHOiDyGyIQ9GqI785Ye36funm5wU7aY/P0WVZOsdDBm8FnGa38JzkdSLrJ6p7jjdYUt88SHd72Ua9ihk2CxQqYfg2HUGeq2YGYAhRFkvHhIvASKA+KezxqkW9eHvtkVcE3wXqmwXA3uyUTKL2sjt+ZF0/yYf0agbPbg5g573PPZeJlJjbDQNI9Jjw7IOBHxnOnmrg+9WpU92QOmTdbFJ6tD7Nk4dMeeMeUD1QxhWknbZxbbL2802BO12+u7lVhLjZQ0d+hdr+2lPuO1gnHAY/MGsg3/Fa3BxqAonVUeqUDH8aCwo2iL54h/1nnhPjaBXMlF5Ojpizu8dHcCUw9BuayQ9gGF3B6OM929Npc9uT9vJ6Q1OT8cxCDM8hLFdVJNsOIbDk/B9TPX0ix9lbiFjIGPgU2BAa8jP7W/7fcZ8Vtlqf9c6bDGqOM0gvBZ7CmnBLDeILLg6BOqwVq+hXvQWZssLaJmNNiqwOAeQ7RftYw9vwXiZGU8Plyexz4WkItbTp6aazngBuqQc7e2VN1/LyzcaATXKZW170O+RX0/L4YyBT4aB+owsJqm1nSfmJ3sFuaH3wkBmvLwXuq5z4fridfXH6cwIH4c+CKUTXdl12TXVkf/49k36/tkWEi/76eU6Lhj3htCbTmmiMZoafEjK6r/g6IPciaCC6OhZz09gLtZ+lQsmirrSU897F/eC4FK0Xp+ogYliRdyj9rlcFleaoATxhmfptMdNzKZGA7WRZkJ6YTh9cY+PZwzt/fqzedwFT+JeeiyNjaFyZF5q1Jp31EIe9ObOuJ/AwxnlLyz7QzpCHUM745W9Gxko3mMyyGPE6rqYLjvp2+dufHkDSZc3nCruH0gqJqVxZLVH7AeMYqIYLBvgSQRWKRGqSn84TgTrPHCinD+jhqtGkUaC5ruYL23U8UZlaLcxjF2AcSSkxtM97AN8eXcWO0EtmC94OMJWkIw0SmrKZyPdEOh8ZQxkDGQMZAwUGHAaondpjPW3XDPLBFWL0tRlLxdDRrSMiBH9s92eh/b5TfYpGX9/g/0xM7hLfAfbZLJHtzzXxKvRWFrGo1EL2kb0zQQMGDukAZ4kHUNKMZ7WOnmxnyk+6KrXGVQmp2cMfBoM+N+Ft1UPf5rWcysZA+fBQGa8nAdL176MFihdsdF77KreNRoRDDEa6Ul3kEyQ6sj+AfrPhdvof//LKjrR2HlZF5EyhGQIJ0gQMWMwXEbtQ1psDKN4jMwZhI/AnvKjzbLsqZleql6krHdKINowIoxy1UgVKWIUErOJIbskB4Z2R0eOIboSTBd9PE+lv/l8Id1DgmGy1eDjeiyNwnwZNuMu9hlusDx+SmdqWfVxRB9r2Z8uqI6ctwO1Tps6GvU0dw46chF9mLZQK1pZ2zFpl2+ei0mHF6z2EWLckiSSZyxJvLi0yyi4MybdiZF2d6Y7dqLwJ02I4euNaz4xdPsdHg9h6+hQWnepNT6MaLo8ZI2accZfYqT5FoyXZWy9TOGmdEwesviZUV31vhigYMUcBWqVoTL5yhjIGMgYuFIYqK9h9fBZg1BZ31d8afS7p3pdZ6R4uCI2ioW0qKv11NdRfyiq/UY0y665lob5goTm3oFUYN2OnWzbzSDdMtkYMdXQBiKM4+P6SVLRJVsER+u0/UxuVq1E7yqmDMX6Xpnh3hctOfGTYiDma2+j1d9Qb06OZwz8VBjIjJefCvO53QvFgH1E04ITEzBV+JDehzjRh/TT15vp6cpO+uffv07fv9zFdTQegMaaSLjIrgufoOg8O4EjiRcnQk471THCpe9otOgP2hBUwfNPKxFgT98+PNeIJTpshBDMJv4THqYHSPLgrWYXXfDOu0Oz9fLVvVb6H18tpC+QfBFhNjM3iRHecZN6qYguV12KPlyrZ4F0jTVwK7FtMekkGSVPRpvMlbUN7Lu83UvfvdhM3/BbeSvi9phTxSFz6al5FhIv4lmJaXPyfUYLjsHu2MfEqloGejTQ1ZG+iWXjmjP6W7H+I6UFPyntysMRfw4yMD03hbTU9Ah2Xprpq4fycCTVI4w0I0bVhGnX5BQ1Woi/Ff3tCJ/eoXiWTeZAxkDGwKXBQP3vszfc28n4S+9Nv57xwEY832eUqlPSANqbbZElSSg0aVjlRil/9m4gWkf102WMfcXZoyyJDKUdIvayj/jlHlKKktaU0V39VGaC9XkcxvgEzBephEo6cVSMcmgd9cekZPVknRczRpdBL9pUul1FXtkZT+26C5qX7UrOkYyBC8SAJmox73r/eLrSo8wFdiWDzhg4BwYy4+UcSMpFrh4GtBTrI1KXiIG9fQzq4p3m7dpWeoK9jicY1P3XP66nJ6+x97JzzKn+eGpyGiRJFxmx87rOeBEHw8mTghIxqKfd6hvBaeWqvLMgn75lRC7kEoD8w9dtvigs2PsQY9s6BeNj+u6N8fT57Wb6b1/OpF/g5WhuppkWcTE9NdlEtQQRB2o4jGo7q3p6TUIFwoPo1ah8ukDIIp7d3pPO/K55h3iJ56vvX2ykb19s46bTDepKJU3qOELuKASsaFMZYy6SSiTpzXhT3aGywMcOxFQQXG+41kLVh1qiFdP8jqrCg4w27kvai7k/xk+SUnNTMF4WxtIvUVG7i4raPYzszs9MpInmOO7JJS3lOIy543MxOhHQ6y3ncMZAxkDGwOXGgFawWDnfdxXz1S/WQJ4Fs8X25SK5zuRwxrczU2RnS0wVqQsds9mIkSKmiqvCokKsfBgwh5JsFaMFm2RyMR1xlXtHew2M1Y2jDtpE0kVqoWOic2C8yJvhCIdNYsZIVVZSi2Zon7AkXdXHIe1tEocUBorBG6OmeGUxMo9We0iULYrlR8bABWJAszD+MuszMtIusOkMOmPgAzCQGS8fgLRc5fJjIJgutgxDfLR399IW3mmevVxPj1/tmIeaf/9uB09GSDDgmWYKA7PGeOE0SIwXST943fqSXlvUB63p/vUOggYVEO5qcApUVnIXRYLVr8oNhqacqnYVogdiGBkzANUZCLOdPeyWQLjdRGXk4c3x9JsHrfQlrqUXMJJ6984CoshuLJUqVk89Gdxu9PNqPuM1aZ5ovLpszoByGSTcxgCzGC/PX2uOyLjuVvoBd+OvcSG9vw+hiyqOiFrhXieHok2r00JB8yvwV73Ji8IpLcRArGlajkFGvOiT+lxdPgb1quwr2ap6CONFZ6bDzKPxUf5GJoaxFzCavhLj5WYLyanJdGMWVTXsvExisFEEunfBIfnfoM/Bixp1NY4cyhjIGMgY+PgYqK+Wgh7r5HlastFq4/oAAEAASURBVLrFOiwGi6+ysT46hGC8RFkxU8RU6aDqeQjTZQ9JlkMkWfZRIeoguSuV6b1dPO6RL6aMq1GLAaM6MGbYv3j4Kq+1G9pmDLpmDJXRhp5SN0L6RUwYScI0sPE2rrRxSS6icgwzRmWGYcgMs6irz7au0+3YYowXQ/fV5/o2E/kaWTDgFc5XxsDFYUCzMP4q7a+o1lSk15JyMGPgJ8ZAZrz8xC8gN//xMSBCQASAnsGA2djaTevrGEh9ssZHNKpGb/bSn57spdd4MmofpDTNaZCIEtnpEDkB3eOES9m93gW9yLB13etYihXTzTLK2icDyqeR4hJJ5lcQZwGjBjsKn/EMCPpgFvEjJtIBxFh7X+LI73AnOcQH9Eh6tIyHI1xN3pyHAfNoCTseU7gPHqOOS3AYBqgfPTuj2auVXaBXRG4Qi35CiLFCTg03YbysvN1Oz2HSPUc66vEKUlIvUT1CLQ0a2HCkd6x/diJYUp/188BAiRo7a0b0w7LXCygOoTetyvUZ60Syz3vBHDx/4s0WqKCsy/9EuiR6PEXslyOkwoZwSzqWfvFwNt1dcsbLrYUWLslRVcO9tE5J46RUvcqMF2EhXxkDGQPXAQPVOnn2aMpVutgXbHVnowl6xDYdbcxcuhutQsgkWNDz7MBscemVIyR191AhwrYYe9Le7oGpE21DtEh1WnvVAft6B6aLmDWSdqnTPSJnXIrFn2K0SKq1iRSMVJCaDaQVsdMlA/tTLcLEG9h/keqoyo3CnBENYQyY2Cjpp5FJRd/rO1swWzROhX3/6CqhIZ9+vQ+iT4eUc689Bk6bLPW8evjaIyUP8JJjIDNeLvkLyt17fww4reMEiGpLXHdtDXfRGEn98/er6YcXW+YO+LuVw7S2I2kQDIjqxEcnPBAqIpJEE2mpLi8BNepJj4qg8HxlROl4Kq2erpK98SjreYoJcnWp/PtfwXixHnATxM4xrpFRj9HpGIItaX5qKN1ZGElf3G6lmxhK/c2XS2l5ccZURiR+HISVCMQP68X79/uT1wAxmhtBT4rxYkwqThbXN3eRdJH7aElH7aQnuJF+gVraOh6NoHWdGDUGFXPGOt5LYjrW/G3qjdQvxc7znutlVL+3XsDsl6663a1G6XjGm61K+hi8Fgw7CoqAPkYC7Ai7Rw1crM9PI/HycBqJl0mTeLmDytEsTJdZ1NXMsxGVRaTrMuJb4zQEn94Xq5BvGQMZAxkDVw4D1QoaXVeKrfEeYCX29S92CVtbjUniqkSSVJHEyr4kW2Co7KDq2kZCdXf/CGPuMFxItyd5B2xAbTbzA3kslJoRe7rskpnqkUm6eKNqUb9hDpS0BOsJHyWNmKQL0i+SdkHKZRKmS7M5iuTiWJqG+SJbMNN4QWogKTMhaRjK6WdSMKztkAemmlRtLz42jTj2nNhTY7xViSrk5fM9Y+CiMFD/u6yHL6q9DDdj4HwYuEaMl/yHdb5Xfn1LGbkhBklcBENH+tVbvBchwfD7v77Ck9EWnmo6qI0MJQ6RzB3jOJwGSbtISkTEg8CUkCzMDZpBZIMTUUFSRGPxjFpesgalKKB0lYlyUe+sp+r1u6r0eo+89Sqvw4dz52gI4iyhUvUOYiulG5MpPbjTTLdwM/l3v7qV7i9Pu+TCCB4PwIXX5lmB6deBK50WEi9iEugnAlYi3W9h1D2F8fJY0lEwXZ692sPIbsc8GnXkyQgiVoaYxaAK9PgbVaxIJMHTzmK8CIUBJdCpmkpzCJHqzygbeYPK+YyIN9kNQ9ADTrTi8Iz5x0u3vwGSxHTZZ+KMjw7BZBlJv7w/aTZeHtzCzsvyJLZfGmbrRTYCZCtAc0dXxXhRTPCu91zSKPOVMZAx8HPDgNZZX/NsJbZl1NdSz1Fqkc/D9xnZ0TpKR7LNAlOljSSLmC5iuGzhFnp9c59DoQ7pR2kN6Za2GDCUMbUimC3ao45Qd5Vkrk4LbKWnSYt37RmsudrLrXlu/NfyPGJGdodMxaiBCtJEYxiaYDTNTY7YcwFG+tQE8WmkYGTDCwaMnqNjbgtGTBhdkmoxyRZtFgCPdvzpY43dpY6DCBuQ89wc/HlK5jIZAwUG/G+wQof9ERCN9IhXJXIoY+BTYCAzXj4FlnMbnwQDtpxCANhyys5/zCnSEXGJ475c3ebjeSv9rz/hyejFTnqzCVGDC+nO0TB0C8blqBR6y0EUxPLc23n7MCWxOz9iylUYIsQq6h55llC71dPr5fqF1TuHW8EryhWPyI0GlBxXQZ/Zx3QaOsYF8hHE1jEGUpt4qJlM/+NvbqZHd2f4iMbOCyddIzAW3GZJBSWIqYB5lZ8ifuM9axxm1wcCtsMpolxzvtnYhumylb7D+9UT2Xl5vZdW32L7BY9GYtY0OTUcKdxIOyy9lcBVPA2yblxKq79vS6zd+tU5R7USQgXfW/G7Q63DrsrF+CPX522RystWuuaNbN60+SgQsT7dGk5f3Jtg3kykB7em00N+izONdGOu5S5KqWWMlwKo/a1YODNeyleVAxkDGQPXCgOxdtqgasu8BW2vUY5TDko7Yk09QJ1IKkWSXtlCFXprWwyXdnq10U4vUG99zeHQZvswrW5Bqxy8SzjZM2nLI+gVhBBhqEATsE7LOK5TL6y9on+0UQ+rlaI9MWb4aS1XP7V/6VI1yTWOUlZqpJMwX+ZaMNdbo+kW3usWYLoscSizgAH1hfkmTPcJDPQiBYOKUgNnBCFJY+1pb9Oeoa707q3WWvSmoIusYJHR+/Cu96bmeMbAAAwMmjCRrqcuTU5dvXFPzfeMgU+FgWvEePlUKMvtXGYM2Cl7sakfFeK7InCewnSRC+n/+Z8r2Hhpp7ebGKY7HIHsYDHWSTyDimU5SIRB43Ryplq+vVws5opVkAbBqNLr9arU7tAgeErvV9/LRz8FS6WcMeCqI5B+EGzHSC400m0+ov/hb2+lR3emkVxoGlGlEy19aLsBWUFgVIO64dlX6t6P8SJDhYdIs7RxJb26LmO6O+kbpKOevG7DuNtPb9bQt0efXoTrhPTfJXYN7Ruwut9E77vpjfeiqxe53dB6Sw+K96vVC7mq6zlVz7y2pXLTX4VoaKmpbXPiKjp/amIofXF3wtxKP7w9jXesKRgvE2kRWy8SW5dnp8x4qTCcQxkDGQPXHwP9GC+xFpvrZyLaJw7ZO1ylqIPtFtlrKSRc1vfNrtjaJgx+86aHxAsMly0kXjb4IQiTEIRh12ZhFuMFlLrHIR0aoUokqRal2T8LKGLrt9rVOi5pTj2trwoICs9RDmIwcQdDBcZ6EwYM9l+W5lAfRbrxxizSjLMT6QZr/DzhSWy/SCVpsjXBeu/2YmSk1yRk2SDMoD+QrXF7WitFitqkh9eJkCjGmB8/JQZ8Xg3ugfJ16S9EV2/cU/M9Y+BTYSAzXj4VpnM7F4aB+rIrIsOWVzb3A07p5X5xH1fSj19LgmE7/cvvX2EoFVsdm3xIDhXissWC3L0sR6xft2PhrufV01RXv3pavWzkRc8HleutU4/XwwU8PbiGIMx0OTFYwObBJzEEmj6kIf7e4bMGFZI7C+NIvDTTP/3dcvry3rSdbk2i5z1mUi8jRqgFrPDaY8Cv+M0IUOGkwJkkXswlp7w/cQL5Zn07fYdk1NcwXp6u7qQVvF+9Wed0EsaLCOkWItejXRIveMOSmlqB7sHoKd7V4AJFzmnl6nlVuLdpJ8UjtSp3WtOBD5XWKapU9Q5hvGxib0BzZ7o5nD67A7MOl+SfwXT5/O5sWoIwX8JAszxiSNVIzCgh1ghsO5oVLGfiBXyl5CtjIGMgY+CqYiBW1lj0td76nqu1rth92Su0hsp+mBj7stuyf4AnxZ1dmPswW7YO0qoxW/C62EbCZfsQqUpsu+gHs1supPdREZZdMc6Pyu1FB0baj/VPDHEX19Wq7Su3s2Ycs9rrVFESL3TGE3UvMti5kF7B+C6qpI0RGDEcuEy1ZNsFFSQYMbPYe5mbGse+17jZfnFJmCZMmLE0gUe7aYzz2kGN1n6TvqE79O1dsQcIF7q8ZxYs993uVM/L94yBj4sBzT/NvtrctwbqM/LjtpihZQychoFrxHiJP67ThpvzriUGivU0llVfTgvPANKbxjjddy83kHTZSb/9w6oxXjbbGAuF2hgfHvUPRCEGAE4uKTJ4UQ749RkXbfviPriuIFdXVatKGxQ6B0wVAaSIMV3VWHw09iFMljwgyEXlwfFhWp4fS3dvNNI//t1S+tX9KU64JtLM9JRJL8hYqtSw/IKhVZyqFQldjxhJlO7KvIQRPwUEU0WHg/Ei6SiJfr+C8fLti+309VMYL69302vsu7zdIA+RbxGvLU77ZOdF+vK6NH7DQSDCUvvcVL6rTGBMifVwn7pnJHWBLcoGxDOqVtlUUB2vpw8Il3jZ3IXqZ7AtMV5ujcGsa6BmNIVXrGnzinVzgTkjTxjgRMyXciiCVXQi23ip0JxDGQMZA1cbA9V6S6iKlEExXLTPHMsArg6A2Fs20RfaxlbLKjbnXryROtFeevF2F9VWmDGose7sYr8FJotJ4bJu+mEHByYkaV+vNWPrquKxViu3nl9h10tUZYvDKZXW/yJDj4AgP3bqu2BOToykmclhGDDDHMyMp1uoHd1bmoQRI9teqCDNOtO9ocMIJEGNIWSLftEzf5Tdif2gTChGUMVzKGPgY2BAM7p+xUSM9IjXy+RwxsDFYyAzXi4ex7mFi8ZAsY52LadIH8g7wL6J8u6l75Bc+B6Jl9/+4Q0eaiB+2sepMTrKCQ/2TApKQARIEB5BzvTreizXKluFo6R6EamRdp5n9F5lVb83fg4YRbWS8aIBFV3Rw1VAkASCCJTNjr1DMV5G0x0YL//0t4vpKxgvS3MTaW522txMBuNF6DEibADj5QN6eo7BXGyRk4wXGHXgRYYOt3f3IYhxPQ7j5a/PttIz7Lu8WjtIaxucQorxAl5b8vJgjBdHunBgeCiRUSDehlEmnny18YKsdr1OjL9WN5IGPHtL9oN2smqUqmrrfXtqxXjZ4qRWPLgJMV5wQ377xhgSL9PpCxgvy6gZLS1MI6ou47qIn4MX57Y4zLABkBkvJ7GfUzIGMgauJgaqFZNQFSmDUikyF8/sK3vQInvoC71FjWgdKZeXb2RzbgcD/3vp+RvUipDA3dl/RzltLxiwhTYZEyODtZQH0pXOhAmmRa25sm2lWbr2fa6gA4pI+QgYKqx1Hr6Q7e966id1qIMDjP4i6XgIjdBEBWlSKkitlBanx7D/gn2vmy3Uj5ppUdKOMN1leHeykJKRfTgx333drw43rAPcyvYjodwDy4QcyBjIGMgYuLYYyIyXa/tqf0YDg1jQpY9p7eqxsYvQaaNmtLW5g8QLjJeVdvrt799C8BwY46XJh6IYL7LVIZJF1SX5oEtqOboK0Bbufzu7RP96p6WKHBLcOmz/FB5cS2W9TJQs6C9LhmdS2t7Yw/2kGC/t/Q7SCqOmNvKPf3sj/eb+tDFeZuemjZCS+HAJQ9BruK3347ReVr2q1/jpwzZXxEkQsuik3ntHBg9NQuoARks7fYPb8a+fbaZnq7t4xDo09TRnvOB+PBgvPk1QP9IZYWA+njHOOoYiLZ69ZSM9nqfVjTL+9Pa7yxfDswInW+pN8bpK9b+hYLwcY9jRGS9NCPDPljRnxtKjQtXoJgYYb94Q40U2XvTRAFIAEIxJJ8CF44Db3e8cyxjIGMgYuEoY8H1RKy6rJf9tP2EAekqbR2vfASpF8lTURlpQrqA3tvehPbZhsuyn52/30tv1g7SBetHalttwkSrRMZ6KdI3gs1nL6AgbtyRI9DQtHs+mhALFWh8Pa1e1PaHOePH1XHndl5Us6qltU4si3mE/NJtnMI8gkThkkCc/pF8wrj43NZqWkHyZx6j68vxEurs0hUTMGNIwTdSOxs32SwMjvMMF88UOfKxZ73y5H9DPcji1/O4e5ljGwMfEgGZ896z7mNAzrIyB82DgGjFezjPcXObaYcAohyA1xDxxxouMvO2K8YJY74YYL0gvfI+x1N/+fi09X3XGS0uMF06WSsYLPBcRTH6dvjhHrpdXLOr1Yjjyqhq9Jar2YlMYBEslA14vlKgb24o+mgs4VKkYL0OIPB9jLFVqM4cwWkbS7fnx9A9/M5d+fX8m3Vxopvn5WcSL0dvGzouNqwRdMbXqrdd7Wx9lvaeRXq/3McPRh3O3QwVDDxVUx04mITIPkJDaxsf4q7Xd9A0SUl8/3UjPJfGCfZe3nEruw7RSvRZMKXMpXTJeqpkjiIIZfaqHusd8nt4GlCgb8W5IarPqQXfr3SUjz58O1e+qLwJdsSDaNdYOTKXNPb4KyJABxgc3C4kXVI2+wMaLGC/Li0hJMV9G9cFgjBdv1WAVVL9gDfoA6O5jjmUMZAxkDFwuDNj6aOss/bJl+ORaLMPrR2Ja8NzDSLsMta8h5fJmfQ91IvaUJ2sYzz1IK+synEuZTkr7h6y2gDIGCwumnsMyqGWN+J4b66hWeb+Uoqta9cssW8E9z1byKGrlI72MmIckY4FQTmu0tyC4bp/GJWEwKs+/Mbg/jTGkX8YTXo9GWfux9YVnuwXUjm4vttIi3u0msfvSwgCv1I7MO6IkIAW47FfVtkLBiOlOzbGMgYvAQMzDeF5EGxlmxsDpGMiMl9Pxk3MvOwZYP7WE6nJGQ0GoQES0kdttc9K0CePlG1RGjPHyhw10qp3xIuv9DWy8mGqEyIwuxosgimIJ6BHXs05C1AgfzzrlLnguUVOHcEqF98iqNhK1ostoHQVIqBgvwxB7Emt2xsviDK4jF8bSP/56Nv3qMzFe8GAA46XVaphbaeE04OmredCHcx1LalKX6kV6CcNyPs4tYAva+8L3cRVkLICOefkSDR/EeFmB8SJx8PdhvKhf3sd6T6O3SjtPr+t16xgVnO7LZyLviH/nnZX1HliPSFCapwOHRGe8mOEBM7ZYMl6WJzGuO5eWb7T4TZpdoLGC8SJiWn0wWIMmTXf3cyxjIGMgY+DSYSBW6q41tUosGQfaU47gUhwiOSmD5DtIs2wh5bJitlwkNbmT/vzDujFeXuFVcf9wGAYNVm1hsoxLpQimxrgkXLRZc1X0iBrzy3Mi5s/oSndqvaTCQXd0l1LM6QTW66KK7SCEtWxrTJzTpH0YSdofhYNhuEQj6QDX06NmI+6z25OoHDVhyE+m2zen0zS0wxTGeBtjMF+QDDWPR4JnTRf3Yk+wfTjvDydfSk65QAzE35PPxQtsKIPOGOiLgcx46YuWnHhlMMAaGsuoExBOMGgv38FWhxgvG5sYSn2GxIuM6/4RxsubA4iid7jGHU1NxGGHdSIjkkIywhayBzctzAFdadVCXYWUrqsqV4X61RcBdLK2gfigW9VawHXoQSZ6TLScxJVF5O0VEi+7SLwszsJ4wcDu//mbaWy8OONlaQGJl8nmezFe1PV6T3qH4r3oTf2w+Gnt9EIc3C74sQlDCQCa9x5jvLjHiRWMHn5rEi+bSLzsckLpjBepaana5DkkXrwv8R6iZ4N7FCW6n/XRqm49HiXr6Q6/t9UoOehZ9oqAwh6X6LkzXqRqpLbl5eIBNl5uycYLhPYXxniRxMsUEi/u1WgMYttPMQumXSasB6E9p2cMZAxcegz4mmv3+vIbi2aRJmmXDocau0hN7h/gBnprH3WiPWyEbWGcvY3U5F56srKL2+hDVIxgZGDLRSyMIexiuR2XIZ7F4QYwBdZAB2FjeCoa64uzqBsd690toq4/SwZ9wXExzVuqGuOlgK+Skt45oA9SPbJ9hfBw6uDVCLUj2XxBYlbPezen0l32hFm8Hy1g+0VSs83xMVM9kmF+Y8D0oX3K3uZ9ou9bzYkfGwPxd1DOvI/dQIaXMXAqBjLj5VT05MxLjwHW0FhG40Na+7dUjaRmJI9GmzBe5J3m+5ft9C9/2kjPcA1sjJcmjBdOmeQ+UXVF3wSs/uP2hbr/cq2akROfvYoPgnhanloPWAoPgqG8uKr2o2bUUlw4qRgvR6gaYWAXqRcxXuTZ6O9/M5V+9WAWyQUM5s3PfRDjJXqiZ9Wb7nC9zMcIxxgDVow94gOfetmBY4ISDz/ktFISL5ozK6gayRjzX5k3z1bbaQUbL5J42dXxH+VbqNNIusN4dkCK+VO1r1DMg3ovqhL11MHh3hGqZAFD8umW7W11w6i30w9GlO4pR1QpnurupGVkcQv3pkpsTgzBcEHVaBER82VUje7NmCvp5RswXtDrl4vtsPGizhmcgqAWyotgNJ6fGQMZAxkDVwIDznQ42VWtrlrbpK66i+6QVFV16PMab0X6ffN0Pb3Grou84m3syJ10Yh+h0pAzqHXso31EDA9SbJ2tt9K9i/ha7veqlK/XfleqYOlSOYVO1qpDUAkvL7rJrhB/IaJDCSx82VOA1B/ZRIO/DrN9KM1gdHcaz0d3lvB2ZGpHzbS81MLr0USaRvKlJdtfDNAM7sruCzDVirfIU5tC3hwM7fmWMZAxcP0xkBkv1/8dX+8RGiHgQ+xmvKBqVEi8rG9su6oRjJf/508YSy0kXqaReGlwEiMR3/hwLsiOATgriJO+uU4eBTFRwVFKFesmOeqA6uXq4XoZhQflqQ3lVfcg2ETYSPHDGC+E9g5l48XVjULipWS8YONlcUGMF6kajRleHKoAA6eMWFMDb/URq9A5qw2ENygj2hH8CgODSlfpNlcgLm08VHRVIxgvHbn1dBsv3z33efPUGC/S1ddpJqd+lG9xRBmMF7Wt007983EWElRVc4Sih0r0Ul3ZXZEYVVfiyUgdZBf8k0WrlHqlCEd/aJegYk64u7FFZ7y4jZcmBPajZTxhLTbNnfQXd92dtDxbyLjuKAg1Gy9Fg4bfYtJk2rp6CzmUMZAxcDUwEPuobTBFl8sVmoDWNdgSxriXatHaRhsm/S6ei9rpOZ6L/vT9enq73UHSBdsvaZTycCyQGZE3RRNCrQPuQkltXSa9bLOrTESUG+Xrociv11e5/tB8qSZfg1IpngaVDKV4KvuCGZOnnaEjk36ZgAFze7GR7i23zMjuA+x/3YQZfwObLzK4K2a87H/Jj8EQUrfv1FDAtpaUbi0VsfzIGMgYyBi4nhi4RowXbQl54b6e0/T0URU0ghEJmgPav924LsbtOH1aR+LlG1SNvitUjZ53MV70Ae3wg5gYPI98fnXPMidFHIKIk3p8UL8F4eLKRf+8BfABsSNWgBF5ZEpqo2K8ICqMjZe/R9XoV6gaLcvGC6pGU8Z4EZFYwwaIfR/aqD7C6NMgjHxoer0NwTh3OwxMhpijgk7wpMPewbXEDpJSr/FqJIPM3z7fSY9Xd9IKBpnXNo+xjQPjBSaLeTWCihwZ9h4ITxXjxXtR9U3xKvahY63qfSx43s8KrvdRqf6eIZAZWJ3xMoFXi89vj+PVqIlRRRnXlcQLqkYLk6aaZu6kZVxXF3UNzvtMGq+Z7xkDGQMZA5cGA7av15ZwrffaO/SUCo6kXfbZO9bX2zBccBONxOSL1+30Ao94UnEW06WNu+gRbMrZQQibsozo+gpcB+w0RAHeG6GQlxM6PKdWo0hTXr2cxz2lu7Tn1CFGa7VyFoy49n1nvHhd7Z38EP48HmI/POrAWEnpxuw4ErPjaWZqLD3E492dxUmz/bWA5Iu8HDWRMJbUi6RfDJ/W3woDwke+MgYyBjIGrjsGrhHj5bq/qjy+0zBghFDx6avtW3u4eTWSjReTeIHxsrKd/vk/sdmBcd2d9nGa5oS+CRGAuQ67XGohWgkiIIgPpXta5HhJz/e7cqJ8Pewlq3s9rx6uSnioJ0+glXTiqsr1ZlsVI/DQy4bzIgIqbLzsyMYLxnVvG+NlNv36gRgvTWO8yLhuA/3ssHtjTQpObwMn+uIJA7s6oPzHSH6vNikc5fUU40UEdOcQFSy8Yb2GgP7hxQ6Ml630GAJ6BfW01TWYMkgKCScTcOvGREQKH/wchu66TkOS8qKcFR5wO61cPa833Asu2qqXizJKq19eVqn+nsPGC9JReDUSA0/uRD+/00x3xHi5M50ewXxZmptISzBe5CFMc0yuUIUChw7MYtLob/S886feqxzOGMgYyBj4aTAgFoFWMt1hPjihYQwXnWaYFyMkSA8OD03N6A0GdJ+iovr8zTZMlz32DVxHc9AjScn9jtSaxXwoJF18gbRhFWDNu5FWYf3UnoWKRbNWvGcHUWm/6mWqlMgv4FlGVdKZILW8KF7ArIpHHQroP502FSSMCesAYnpyGGmXMZ4jSENOpvsYX7+N7a9F9odWc9y8HZmno9GAo8MgZ+7bxgDMvD/UkZ7DGQMZA9cRA9eI8aLdolrQr+PLymM6HQN2ClOQK5J42ZM76bbcSWPjpWZcV6pG27vHaQbGQpPTeVfBQVi4UBfxVopT+x4SJ8ihqiead04oVWkXEPJmaP70ed73L4BEpRvjBWbBHkTg9j52buROenbEjOP9429wJ/0AtZEbE2lhYaZyJx3takhQRecljE7v5QXgB5Dv02YQutETMV4k8dSBiG6jhL8K4+UxJ5XfPttMTzCuKxfkq2vgDCOzmidivIwb40Vi05CuNB6neI7tEjKBvm8lCnzEZ7926i+wt6ne8l5Wqf6eK8ZL+6BjJ7RTYrzcm0j3FifsVFN2XhYxpHjDGC+IklNRJ5oxUeyclDSDzO2886e3pzmeMZAxkDHw6TFQrZ/1PcPXe5d2kZTkLvbStpCufb6yhXTtJnvHBkwXpCQxpLu1k/BypLWUtXFIjBcxHPrBVRqrb++yXCYU+SeQUME6kVVL6AZbxap9q1aYoErEOCNHaVFTrcrVtLw4DSH5Mj6KJCg2wCbGh9OD2+wP2Hu5tzSVbi1N4shgPM3PNFODw64xGC/OVAJWsSH4yIBcAI82ot38zBjIGMgYuC4YuEaMl+vySvI4PhQDfhpVbOns3MZ4QW1kC3fS3yK98D361v/8h7d4GSANiZephmy8SOJFTBZnvPjns7b92PpPEjWRU+/nyVL13I8QjgY+gPGiqlIzEuOFG8b9YLzwIS3jujcXkHjBuO4//JcbxnhZnm+muflpZ7wgChTNWgAi6bp8OMe4JC7tY3KvDUcYkt3FI8UaouKPX8kg83Z6KsaLPBvBeNG8kWTMOFIdY5zWmbAUaBW8ioDtN0M+whw4E0S/dmOk/Sr3lvey9hdkY3KGkhhSu3xcSJBlZnok/eLuJGLkLbPx8hCJl4XpZlrgVNO8GfF3I/OJQyYKpDaBGZOmFuzXm5yWMZAxkDFwuTAQ62cl7aL+GUOC24G8GMmIP4c861u7eC3aSV8/3kjfo6ZqxnTbh+ngiH1U9sS0NpqKke/HPk7BrY9Yi2Q9rnAkaIeJcL2MA+jOqcdOy692rTrEaNX7VjDOi54Y5OKw4YhxHb87tL1vZPgYWuodkrLDJhH58FYr3cXmyx2YL3MY2b0xO2lSL41x2XpxI7tDPDUk0W6x70Tbvf3J8YyBjIGMgeuAgcx4uQ5vMY/BMBCMF0VM4gWpjn2kF7YgiL6FEJKu9T//4U16+koSL0fY6Ri1D+hRGBIiTVytJoibIFyUo7ATLwScQLC4p1c5yu131evXw71l63n1cFHOuqKbGEW6LKF4WoL11EO6e1kbER+/xniB8NuHWJQEQxuJl+V5GC/oZf/T3yylXz+cNLWRuRkMpXJCNWaMl6IfgJJLyOtyCTO6RFj6qArGCwn74GV9Azegq9vpB04wn73aJQzjhRPMja3D1OGYT957RkVEOxhBQvIlImWgSFA8Wowy/Z6Dyg1K74XR227ke9sVlAh1l9dfQZSMEvqbksenztEhJ5pDaRbVtF/cg6A2xgsnmuY+FMYLUi9iYBrTRYyWcq4IwUU7tWD0LD8zBjIGMgYuJwZ8NfS+OYPEaQztG35QswuNsbm9y74gD0ZISeIuWh4Uf+C5sX2EdKnOOmAp4MFIa6OWRa2GviT6uhiqTGqnYnF4q/UeeEp1780rVlmDUpWKUG/pSNdTNU/mO7zoY1W+noLwp3k7ckfTgoH5YAoszo2nu0vyftdMD27OpLkZ7IJh82VmSm6moS2QGBVexIjyq6d9EOTtVO3mUMZAxkDGwHXAwDVivGjhzkv1dZiUHzoG+2wsv37dQ80+Uh07O7vpO2x1yJ30b/9zFcYLLqbbR9h3ccaLCADNntKeiU2jYi7py/zEpbx6em+8XiHyCnhd9QaVq8Oulzk7HK2opPAhSCJhJNLrxuuGOIET44UfUgx3boykuzca6Z/+9ibupPFCMNtEqgHXwEgDVYwXhxZiwYpdl0uEo4hhXZLskDTLAXZeNrb20wv09J8hJfUMGy9POcl8voqhZjFecPCT3kFGx6kdWDYQJfLLgME9/RZlz3rnZ5WL/H6tOWyVqIh8j3npIHKD8eKzBnTY/LGPjaFOauDJaX4KxgsMOp1kPliattNMuQydw4DiqPABWHmtqJZiYPlXhgGLYL9e5rSMgYyBjIHLgwHWLlsKxXQp9lLicq+stbGDXRe5jV7bxAbYa/YKmPRivHy/wt7x9jBt72GwnXJSSzWmNBvNkDac4vJQfd0u9pGu/CitZ5T1mlXpgBRrer1cPRyw6uWq/kRu9ayXU6rKMgarUtSjiPqh2D7M+UMhhmsBey83UWNeQpr24e1p1FHl8Wgqzc+2oC+auKF2F9PhAc976XeDmDcKw2O+ZQxkDFw/DGTGy/V7pz/PEbHz+weiEwtiEnTw3iP3wHu7e+mHl9jq4OP5n//zNfrXu2md06jRYTZ/PhJhSTjOjMkCIOrGp6hlCPaZWD27hBNO5yl3VmOCEUTKoLKU8f9WVvhwWmYIDwyHaQ/mi4gkGda9u9Q0iZevHs6Y5MLUZNNcSYf7abUg3F4riRdwI3x0M16EMLz4wHjZ2pFnI5gtENTPcSf9eKXN/NlLbzaQojqA8GbOiOAUESqyU8ybYOB4irDW+44o3PdSuSKvFjxZ9NRMHxCVrFRXU12Rk2CLOp7BBwbFJUIetaS/PzJ8lCabw+kGxhN/9XAaxgvMl5u4DJ2HkGa+TLbGTXzcPHWoA/zsz0lY8onXt92cmDGQMZAxcHkxwCpo/7UespaxYciuyRG3AxgNm1vsCdgDE9PlyYv19ATpyCerh+n1BuqZ7BNSMWo1YLzAlJaqpjYcXxdjxddiGZd2lOqK9bdKUUglIifWaMVFsUR6bznFz3PVYQ8oXzRR9tNIJ1omfQ96QjSX+jczkWDSY2wX1dTP70ziVnoi3ZeL6XmkXshsNWXrBbxgYy/2h4rmynvGAOzn5IyBjIFrgIFrxHi5Bm8jD+HDMcDGXzFeRIbwAQ1xJMNvnf19mC0QRng1+tc/rJgY8BqSC+/e4QNRVjokvVDSHMdGwjiB492pkzODO9ivVAm0qNYb7w+tDqkkcPoXHZgqIrGiw8R0CUjoZKdDiEf8EfC7syiR4In0j//lVvry/jQnUk3UjJpGFNUZLwYrpIIHtnq1MoIAVq+FHjvVBG2HHFPqJPMtBPUK4uMv3rTT98+ldtROqxvyXgFD70jeGHy88ijtBhM9XpHPgXNPr16I0ovKltUb9/JOSp9dzucq5RhECale7URbvf2J2e49FzNKJ7UmPg5AvfbGOMaoMay7NDeGStocRhNhvKDDf2PG9fYn8FrhIvXAKjhQgZ9y6kWz+ZkxkDGQMXCZMWCLl+2iLKu+1tvhA+lSvTzQHoHh/rdIuzxHKvKHF0jUPt1IL9YO0put44TmEQx8Fk/+T6LS7PuDGBQuEdK1PJ+Kh3JFL0rV4wr3XgG5Xq63TL94wIr6XibW8PJQgYQoYTW4ibZQuQ4GhDvQW9rb8B6Nkd2UpltD6cFyE4mXZnqE5MvyUistwnyZmsRrIpJAkqzVvqHLJCUVAFbeM4SIfGUMZAxcRwxkxst1fKs/0zEZ48U+PYUApBJEJMF86WDP5NkKbqRfbad/+xOMFyReVjc7SMMMkz9COXk2YrM3ikKEkRMCQWBE/HS0qnSc2XhN/4ytoHh9h+3h3rz653jAUsmT5by+7mfAM8qJMhA3Cr6DOBoawguBdLFHD9P9m010ryfS3//XW+nRHeliI+0yLvsu2OsQQVWOqaepqgNXOmTosREwUsZr7kHhPOztSa1IEi7t9BK3oHIr/Q12gl6vY0gRj1h7+5IcAp+caI5RT+40TWUNgF04M9jxjuI9Kh7hAn19k4T/nnKnwbNmoq0Crh4GooBTp2iLJLUh2jdqHulUl3myB2NO1ziZU62U5hAfX8Ye0G8ezptx3ftIvMygYtTkRHei0WD8wBD8AlDgNppUPMIGON8yBjIGMgYuIwZYq2Lt1XKmpTLWM0lE7suY7vaeeb/7Qd7vnm6mb55sQlfIdbTTFO90oEM92ZAzNV8iAcOHXCzAHqndo0Ul1cO1IhZUXu9Vh1nP70+NeO16OaXUYCjYm21FHJ7neQGlaO/QPx3vjEBnjI0ew6TH6x02Xx7dRTX1xqR5OZpD6mWiOWpupuUFz7YNqaha09TPG4WwnK+MgYyBa4iBzHi5hi/1ZzskNu2SWGLjFtPFTu/RxX6FvY6V1a30H395nb7DU80KOtgbbT6wD4btdF/SHZIE9v3eqQ2jAbqQ2ZcC6SrxYyP1Nvu1dh74wbKRgWEReoIJKuwnFezRkWN0rI/S7MS79ODWBMRQK/2339wx948z2OsYHfVTKCN+YCyUNNCHdug8nf6gMr3YUvx8nSxrErCTzFo1MV/2YdZtbcuY7q5JvGjOfIvUy4s3e5xmvsNuEAw9MSjgTYxjaFeMF3hVxrixoZRIUyyAl61akZO37nKKnVWjhBFVy4RaoA6k3i+NvWiBrtt7VlExlA4JSB1NtPAEHooWZqRmNIIh5glUjeZg1rVQU2ul6Skx6nCtzamuPFXoiq4IllhHMYEy48XQk28ZAxkDlx0DvnjZ3uDcElvJ6DU20lBfbreRiNzYSS8lDVl4Mvr6yXZax330MZK0Q7iNNg9GVkOMBNZGFsbew6Efh4ZYaQOKOj34UunBJQLW4BJdkFnMrUZUU4y9RYddGqMke45RaR5GRfXWvBgvY6gcYZS9cC+9xN7RQiRmCunaUbmXpm7d0G5mvHRhO0cyBjIGrhEGMuPlGr3Mn/1QoBlENujb0jd/Pf1jePXtDl4HttN/fg3jBbHglbcH6eUGtjwQCd7n47nBqYus8Y9QOUgPyb7oCkaGx/rdrdUiI2r3Kxdpolb6l6unerv1lKjf++yGF/0V40WXcKAP6QNJ/8AsmEAMeBqmy/JMSp/dbqVbnEL9za+WsdkxAyGE3RsxXgTSrgJaGY/0y/IM/Lx/B61mdSsHJHwd4NloR67Id/ZxIw1xjbTLN0i9yK20DDNvIE6+D1I5/MQluYuSi/kiA80C2U04et88x5vp39tItU4ZYeuhsmtFQOX65ER1q1mrY0WjPIX8v82LgGNVnW/ijBcmvxgvw4xpgj8MuR1fxlCi1NJ+eXcu3YLxsozIeAvbLqPo6o9jeDdExfX3p+6pRQvXupKDGQMZAxkDVwIDtoYVq3axfGpdbyMNubElSUgY8a/aLg35vM3+sItBXTHgh1ODNXGEp0mNGiPC4UgGxnZUW3AdC/V94fx4KRZZqxDAik52AamX68roiZxeLiA7NVAwXVjhla790qgkiAb9E90kpss+VuiPjo9QTx1NN6bHOORpYWB30tRU76CqOtWSIf9GqdYcjHuDZkOKPllEyfnKGMgYyBi48hjIjJcr/wrzAOoYcCJAKVADBUGgNOliv13n4/mH1fT9Mwymvt1Lf3np+th7B0NpsjGcJiAcRDTZqQ0Qzst4CbKgm4AKoqHeuwjX8+rhostFsSByotbgZzcMr6ePXkOAoQEnRngxct10Gb5bnB5KD5dG0pd3MHiHxMtXj5bT0iJupDF6N4IVQLGfRGQKd5f741lj1Pjf/4qaPmccV4KiuHTVOzBf2u0OhhLbqKehavR8Iz15vYcKUgeDith62ZfnrCFj2jmRzQxwYIa73h55C3o7RVvdr62nuJerelXPrlesh1WmwEXxqNeq/iaKYj3AdSir+pJ4kYTUbucA6ShsFDSHcBvdgEHXSPchmL+8NwvTpYUUDGpGNl9Qt1LBom2bL4Ld04fLP5c0/nxlDGQM/OwxUKyNeoRdFl/Q3qVtGPKy//X05QZe77bxlrjNYc5++g5vRntivCD6aJ6MkITUWuiHQA7Q9mYSY2n01KKxE0hXqfPkfexyJzpS9sJ7Hn0qel9Eg3GiqAz378F4OWQzuYGB3bnJEdSa2T9uttJ9GWZfnk7TGGWfn2nBtHcju27rxTETtIv3JLB1sl85JWMgYyBj4KphIDNertoby/09Fwb8A1gkgDbtobSJPvbGFq6BX2xAKG2lp0gu/O57Tq34gN5pH6fpJnYq+HgcQ/JFKkqqKTO7InwqMolon6siC5wC8XufgudIqtet4A6oWCsczBFPoib/ze8OGfrglfTCDkZhdxHRWIQQundjFPfRjfSLO9PmceDB3cU0PzeJEVWpjKg9b11wPTSgD1c0uURdGdBAPCJ86aTuCIkWuSOXu9AneDd6jOeKZ8ybtzBe9NxG3QgaHBU1TjZh2rnAiMOI9yHsCX9K1a9KJ6IcK+51lPLjL39b3e04VG+qaiveK9pk5WVjp4iYj4ecWjbHhtIs8+XRbVTSsAV0D1Hxh7enjOkyh5rRGESzCG7p6Z+8nHkX6YLdr1+Rn58ZAxkDGQOXAgPFMqlHMF60foky2NzeN493Pzx5k57CePkBqZfHrzrp8WukPGDGS/rPPRlpZ9BaipnyAp5LQsaeUCReigGf3Qn1trZVEPP+y86ZLtENcVgj6dpdGC96zk+wh8B4ub0g5stEesD+cf/2TJqH8XJjbspUVcWsctfSDssOJ7oa64pYe/l2FgZOvrGzauT8jIGMgYvHQGa8XDyOcws/AQZC8kBNixjYQSd7u72XVl5v4uFoG7ePO+nfvuZDevUAQgqPLRNivMjFIRIvnNKgMQJZoc3+7M2rlyQoyJGPMmqH5S343VOMBCqoubJ9AlFeJ0ZK19glubPbgfGyi5QGjJdb86Pp8+Xx9HePptIX98R4aabl5YU0MzVhkgthCDAGUMKPhGvwDCwWCCtGVKbCeGEOQDR2wJsI7ZdvxHSBcYeh3Tebe6ge7aQ11I12sPfiZLQzXsRYcLxXSFJ+CbmGTEu3jDK3qvTBIW+gH4PDm4q2fH5Yb6mi+S5mi3669IExjBvpifFhPF2NpV/cl34+v0VOK5GMmplGTBzGixguso/kHxRVp9W+YDhuPF2g+/WrqpVDGQMZAxkDlwcDIa2iVVHhI2iDNYyur7Af/PlbGC8w458iBfli/Tit8Ot05NEHiRfWzVFJvFg9+YezUNd6+HFHKfi9l6/lvakfGhe07lYixfc33WMfOABPe+yd8iw5DeNlsjGUlrARdhdj/p/BeHmAxMsCno5uLEwhUTlmUi+Smoz67ERluLfVD+1/rpcxkDGQMXAZMJAZL5fhLeQ+fHQMdDNekPhAbWQXTwQbqI3IrbQkGH77+zfmIvgtrqWnG030ssdQrZDrSAgKfvboITV6O9pNiNRz9dkZlxNdHquHIz+e9TwPOwyFB5MfIniiLS9ZlOUrVx+6+qjexVisbJbsHR6khxi5++puK/33X8ynX9ybQaKhkRbmpnEj3TA1I2srAJ3Srspd9avEHKd2CsewjeAGcZJ+2mHevMXOy0t+ci39Zn0//eXZRnq1dogK27Gp5ugFCNfIvpidoOFCzcvxE1Br2CIpmBLWB3uBKhdvslb2nMGAZ8VrTZYQNaFrV50JAp1s4+gg6aM+SHMID9F2UnlzoZF+/dksEi94pEDqZRHx8Clsu0xgD0gGEd1uZK1BtWHRzHipoTsHMwYyBq4QBny5ZHUmYIxpbsd4enu7sYutL+zF/fWVSbw8X0UCZiul1S32C6QkxzBG3pAEByIgvqJr3ZXsS/f62x8VXuNk3qD0KGkLbkQGPOvt1+HVw71VA67qDipXlQnGSYcNZR+X24fgrDU+hATQUEK4BYnJRnq4DONFEi94UFy+MWV7SQO30uNScYaJr8sYL2ovQFcBy8+3jIGMgYyBq4qBzHi5qm8u9/tMDATzRR+YHeybyA1kG6mX5xjFe4bEy2//8Ard7O20utFBXWScD2fUbPSPCi4xIqLLT3O6GwsCpqQKLLses4/pMjWIFiVEXcus3VQ7ylVlqlDXZ7XV8/bUEqGiYP1j2vxjk34E4Sid6wOYLnIl/RmejL66P5P+j69upM9QNZpsNUzfenwc+y4QiwLVBUdxa/H63ozItldgo7eBGsEN4bgP005SLxvbu5x0wnhB4uVPP6zBhDlIKxuIl6PXr5O9wyO5lpZRRXloEBz96lc3Fo1IpUg1V+plI9wLI9IrWB6q4laiFo2/A6+pDMEsavFQ+3IffYRI/AHE8sjIO1MxmsMO0I3Z0XQHDxRfPZBB3cm0NO+SLhOopI3hyUjGqA1UbcIo6L3OjBfHeb5nDGQMXCUM+JpZrJGsZmIgSNrlEMb0awz1P+Xw5j/+vALjZQfj6xzotFPa2EW6FAa21n8xEcJLotbCWMUdYi8mnMbopTR8Z6/XiHBAOwmnN6U7HvUER+F4dpeymGWdkt9VxcvpHoSC9sIOyBDtAU+FnyRf3qVbC+NIu0ylz+7OmMTLHaQn5RlPtuUa7CdGsxTNGrwSZhnravnnE4n3dZ4Rx3sukUegnlZPPw+8XCZjIGPgY2IgM14+JjYzrMuHgWK/kpG3I+yc7O93kFTYSS9QG/m3Pzrj5RWE0+7uiH04H7/zkyqZrDAvNYX+cve2VY9VBEEV0jZXLxNoUYl+6ZE/+Nn9CevlvD0nzwJs9f3rp3T6mJaOua6RYWx2QAB9fq+F6shM+ttfLqJzPc2JUwM9a6lZyb4L5J+6CPDe3np7Bura3URo+7B9lMY8IEG4O4QZsbuHe+k2xpg3ttPaxn760/frGGiWq+kDXIhqXsk1OYwXGFdi3HHo6fgrVL4cYd0Y7Ga8KK93bvTG62ivYPULBQGsGnXGS0As2XhUVj58I5gvMJA40RWRPDkxbF6MlubGOaWcwAjzPJ6NJpCMaiIaPm5zRTr5NoaYdEVHjOdkYbVW9a7e+xzOGMgYyBi4rBjQmhjSG1qXdXChg5sDrNSL8fKYA5t//+OL9OxlG0kX7MTtD6Fag5oy653WfjO2zuC0zmpnqdbd/iPut1L6zt67fioe0OqwesvV8yLcr17k/fin9aDoBlum7Smyj4PJGyQoYeaPHqVlpCfvL7fSZ0jaLqJqJPfSM9MT5k2x2Rgrt4vYUopdlM6dZ3w/fgwZQsZAxkDGwEVjIDNeLhrDHwl+bJmx/URc4CPtIzV1bcAYA0H4AUH6qBQxJQbMOqLC8nL0B3S0v8dTzQvsdjxeOTKbHW2kF6Zk72UcGRjc6IrhIQ3tgndR4KbCvj4yPVZ8ypYvw8mmyPWKyqzqFsDO9VCtEnRPmKE5s4R0qYhIwFm2Og4gFtsY1D08OkSq5Z15F5B9FzFdHt6e5bmAJyM3qDsqKQ24TTaKiurRyMvW6u2TeCWuXmyfNgabL/UCVhkGlhHdiE4j+bKB5MvW9kH69imMlzecfK7i3QId/42dY3MzLZfK+ifJF70wCZgLnX6aWQdevU/Nserq7XGVMzik+tV7Url6e/4OvXZAjxqaOzZ/0pG9a1gwaQpD0wvo45urcZgtd3A3rlPKebwYTU/BpJNBXcZZGtStzRdruxxOd5+8B/meMZAxkDFwuTHgzGpfyCT9KtWZDsZidzm4ec26/x2eEf/nH14g+dKGbsAQ+9FwOnynNd/tumhJV+1YCuvrro/8ZIrSB6cGJD2jlEPye+TX03rD/er1lvnweNkDAmK8eGvYCiM0gr2wkaFDXEs3cCk9kR5BgyzNYaz9ZrGvcPgzMTFaMrtiS9Ge7OES+od3MNfMGMgYyBi4BBjIjJdL8BLO04XYMuvbj9Lq8fPA+TmW0eYd+DvmK7O9u4fqCAZSn62nJy8203O8Evzhh11seHRwFXwEk2LU3EHK2Jvq2c+FRgr0BbQi01L7MV6UoTdUK29l+90+vJwxDKwNmCcwUESoiFG0j4TP7j5EIeLRi7PD6cHSWHp0q5G+uD+fbuEW+N7ybJrDXsco45RHAtdIp3JQPf26+TNIK9+WAqBDc+YdP4may07ODi6mn62sp5W3MF0QNf/25Q7qR4f8JIoO7gspKQmbS/pFakd+7unI05sWYH/G7CAWDZvIiJexomVGpEVBz/V7QItY0XmLenndncnjdzFcJOlyyGTRx8Uov/Hxd2lhaiQtzmFQF8PLt2C6LONufHl+0ozpSiRcXoxMHY+5Vr9+5tOmjooczhjIGLjCGHDGiwbAGgfT4ABDsQemqnyAofXt9C2Ml3/93UtUlneN6f7OGC4yDusMd52EVMzvavWvVkxfkw3+R8FTBXkwuHqbER5c2vt2/nLqgZf2Q6dQ00aJFVzIs1MH1VUkKPFs9AiJl5swXu7fmoI2aaVZGPpNbIZJddV2Rp1YAO3nzXgRNs/zXoWr973ivTr8i2zpfXuWy2cMXHcMZMbLFXnD3ctkbHBV5y9qea5auLqhOuNFH4d7nFq18XK0+naTD+it9HxlJ/2/X2/i6QgjebiX1sflKDrabjiUCiC/LjUQmLB3wi1wb88iEu8ryl7Us7cdSVzYeLkdvcP+CKLR6uC9xRHURZrpSz6mP8euyxJ2OxYWplNrAs80+oDmvz6mndIxqse67CQU6T+jSzjViI34BieGYzFfCOzvY6B4D3HztU3mT9ukXv78dCutrneMcbeLyLnZe4EBw/kdhLiM7QKvZKY4bKEzWC/+DoV7pfIoyv4YrHvdgOCAJbkVl8YiXiLCPOkQwlgGIdE2S/DhMJ47iuerRvrl/TlnujBXZqdbnEiOF3r4gqKx6el/GRqLx5XW/1KbZ5XpXzOnZgxkDGQMfDoMaO2XqpGe+meMFyRetjiweYmq0des+f/6v19hZJdDnN1j9k7Z9mLFL2yk2WZRW29jrf+wEcS6Hev5aVBOKxNwTqv/4XkVe0kwpHalPVQ7BdKUMPePjp3xcmepmb4wGgTGC5KUNxcmjfHSQoXVPeRRpyRBGM/PduPQ+zrtfQrPH3LV54HDv6iWPqR3uU7GwHXHQGa8XKE3XF8ue7t9EctzbxtXNW54K3YW4emQkyu5CZZ76TfYe3mNrY7//Ze35mb65du9hDYJH88jGIeTvZch806gU359qooGiNMwJyrqWBH02lsa+FJ6ytVBdIVPKUczJXhJuRh5yNjgs3QknUHnxof1O0R1aggjumK6zKFeNJvuccokuy4tvBiNYyRVBKbps2tAuq7Z13G8kRJfPspz361+cZO4uWy+bO/sm9TUK+bO18+Rfnmza66mVzc1r3BBjZtpqeJo3oxBRQqlat+ERAJgIRmjjOibiFVdEfdY793ft1JjbFGiu55ilCgSVRahHZspsl1j/ot45yKKp5p4nZgcgQgex5juBJIuE+lzDCDeQA9/Bq9XYtCNIxnlXr8AVM4Rwf9xnxXR9/zMGMgYyBi4LBiQuq6ud3CnDwo1IzFeXqzupL883Uj/8rs36dnr/bQN42UU+2gyDqvDGqtjdb2+4sg9cq/iSvtxV7G2nwqkvhuc1fZ54Kmxk+Vi9XcWvMrQVuCB8sdI3mq/kXH/eZj6dxYbHP5M2eGPPBzdRqJyBsb+NJ7yRjgAsHMg2zCd8WWjKPcbwc/Xj8OA5kJ9bvw4aLl2xkDGwPthIDNe3g9fP3npWDJ7t9G8jA5+NSWuCuRJbUR2O0RMbeChZg2bL39atOKpAAAFH0lEQVTE3svjl0jAoHb0ckP2OlLa7uBSF8mXMTFf+IgWIeb0lEMUoeHqOU5S+edwtMYbqUk5dPdOb6tWrgx3l+pH5JQlrLputGpf84IoGzaczunrmvCkPqbxJLCIvY4v7k5hVHceo3azaRlPAg0YLpLqKe10CE6eRCV6I1BDMyj1eSOVo46YLxjb3dhqp8fPN9Orddl6QQIG16Jvtw/T6w3ZTBGxzQyBwSIRaosZjoFqgNWK5om/acXKZCXWYsqrrnpehPXUew8yWLUjr6pZqRe5JNQIc3QMY7rzqBfdmB7Flksr3V6UalEr3YEonsXjxFSrMrzsXq8E16/3mTP623mf8tFGfmYMZAxkDHxKDJSHKzSqsCQdZd9lYwsGO+v810820v/9u7eE9zC8fmwHGBNj7hVQ/QxJmehztSor5eS6HOW6n1EunvXcelo93FtG8dhVBpVTmdPylB/XyXIam7cQ7UhaqGBAkSdV544cG3Q6aY495vaNRnp0BxVWGPyPbkmdtZXmUTeagvEyZocV2ifUjvfcgx6PXuRnxkDGQMbAVcVAZrxcoTcX21q/LudtqR9WPE14s4++sohj0uy98PG8ubOb/vrdanqyIsbLTvrri056u4URXpgvcoU4DjEQbpbNEmkJR+QKmOe/mB7is3S9I3sp8WaUE+EagA8JajBcfg+ix1PkxahDvgR0FmeG0s3pESQYsNdhki6z6e6tOTzTTNrJkkm6GCTd6FvRvY/UyxLyVQ4I1f7mCpwXuJex5gMIyS3Eo1682khr63vpGUYXv8fYotyTv5Sb6QNJIKF6dCj7Kc54cdxyhxkjgtLi8QRRRSsfjDKDV8JxMEWXgc0/2hV75pC71IvkwXMK5tzS7Fi6if69E8KT6cZCEzsvTgw3G+MGyO0H6RSSfheEcfHwhs64e70zCuXsjIErjwH/Ky4X1Cs/nqs5AL2FWA/PO4J4c1rkIiyJFzFd2ni22+RE5ukq9IIxXpB4gdG+u3+MMX7ZhZOKMhsvFX2tFeu7fw8Cdv/c8/b28paL/UEY6BwfmkcoGaefmUKNVRIvt8V4aZra820M/M/j2WgGOy/ak1RXe40u4cn3mOuKKRvmhd8Mjyda6Z96olhOyBjIGPioGMiMl4+KzosHFhu2Wspb0fnxXf/4tC9H29JH7CSrjcHUp6/W+IDeQWKhnX7/3RZ63BjRQ2phlHImGQInQ5ILxqyAEtC/OBXzXhRvpnwpZeCMTqpc71utx4vqXUkRKT6iiXYgDkXsmYHUEVRHUC+6s9jEoC4/JBekXqQPaTFdJicbiPN6/zUOm0g2pjO6emWyhZ/z4v98g+piGhAR0+7Q3JMjNcUp6DZzaFXMF9yU6/kc1aM3uBndah+TLzYH+Ibj4TPH7enIEKMTlf4+y3dhXSJNyTFx68Px4t5xe33FOyyGYtnUU1XNCUlqmbtozQ/KiCk3jD2XWVxGT7dG0i1cfN5CwuUGHifuigieaZj3oqnJMVMvGhvFoK7a984qYC2VUYudfVN/3rfO2VBziYyBq4+B+p+0RuN/YVd/XFdtBOV70GLFpbskZHf3MKrOGr8O40WHM39+upn+r/+AZkDVSAbsJ5qjaXIciQ3RCVZP669q93uT7AWc0lQ5hIr2qFC7HFKV0BuPnN703vj7lovyH/5UD4xGInCAxIsME+8G44X95rPbqLPOTyCJO53uYOj/hnnMaxaMF3fHrdYNgwJWw5Zi+coYyBjIGLiqGPj/AQAA//+4DWubAABAAElEQVTsvedzJDuT7geatrTj3TGvuWsidOPq//+oe6WQFAp90a605t19zTlzxjuaZrObTs8vE1lV3WxyyBmSQ3JQZHehYBNZaCDxIJGYO9KVynXjOMBLm7txVH9DgnMrP5qTQ03eH+fS3vgg7ezupXcft9LrDzvp5fth+v/+spFevN1Nrz6O0uGhc3ouzae5ozml0z3/eW0I99zsuXoplUPeuCNO020pGl/NsKab5J6eb2jx6ygd6mksGvcODtLc/GHqt+bSSi+lu8uL6afH/fS7x8vpp0cr6dGD5bTc7+jTSp1OK5LXjWiOWpXrTBzI7efg4CgdiO+7o700HO2n7cEovXo/SB82d9PLd96W3m3sqR3tp4PDpLhJ7Ukl6CXOzc2neX3ofquWoRdA66peBGH5vVd+EBgJ5NRr49vv8ifIPjkdwQfyObRyjlJncS4t6tNrq42sLqa7a63088Pl9OhON91Z66aHd5bURtqp211M7dZCWpifT4sLam/NciaIofyzXZDk9J4tfolVOPC9cIDfrP3EvpcKX9N68h7syv2n9aUaX4e74zTYGaePmzvpt7eD9K+/bKT/8c+f1M+P1P9r3FV/udxuWV/Je7R+vcqMHH18rXp7i2QlTX2dFHCS/1Rya0VR8GlpmmFN96z88CPP0+IRJ8Kpq+Lnzn58eJBG+5KzNEauLC2mJ/fa6fdP+unx3V76w9OV9OxBP91b66XVpW5qLWoEVDaMO1xWKtla3tzLVThQOFA4cLM5MKcBInrpm12T74z6GAa/s2p/WXWjhdsA7qJPNPu9vSMJTvvpkybL7yRUvf20m/79r5vppUCYVwJhRpox7+9r8rqnSXM1cbaMRIsLCSYeZCHDi6rfTsSsBYcgZroqxIx0lXiWI+U0dvMc7VcrEIm7yEr7R4epNX+UVlfm0v3+fHq43kq/e7qafn68ln54vJLuri+nbmdRgs2chJo6jznRbaXpXtEaZEyTWJ6NA9F2eF0AKUfi/Z4Ey+F4P334tJ0+be2mN2o7v7wapNefRun5+1EaqZ2N+QjoOzyc10fcVnpYzbcwHBM4AV7mc1vKhdmtfjk8kpaUOZFnYnnl1i23w3O8X6LPzQmiU/vodedTtzWfVvsL6YHayMO1joTg1fTwngTf5U5aE2rn7WTByoEWI8fy0NckIXo++wXJzaqdPWWJWThwuznAT5hfV7m+PQesO1VnZV2syKF/H2pxZjAU8PJpkH4V8PJvv26m/y7g5dW7cRqNDgS8tNKKAS90lCTSvwAb+mF70N1dlnsVx2tLCFcO84cZ35+LV5fliU/KrxnvpDjkEPFmkDLhdTyeixjIFlrcOtxPuxofB7sOvDwW8PKHp30D+//4DOBlKd034KVn8gmYC4sSXFDnYwZllKtwoHCgcODmc6AALzf/HV7bGtigmalrur8VwUyYGb599V+DujRYRpoI70og+CDQZXMwTlvSWtjckZAlMGag+6fNoflvbO2lj1sSHoZHaXukfCQNLPCRhOEigbQXlDef+MZFKOEniQ0en5h+xTO6LJ6KlOGLE4FQoQJdDiTYoYzQbc+lZU2ol3sL6dF9aS2sttODO5309P6S7n2BLktpSdouaC7MQ2+DGJuY5xIa3vL5lhf1vT7UTHMiBHL84SUC9oEBdNJ6kXCOkL6pdvT2/W76oDb19uNu2tgeK0xtaFP33UO1LzStAMwEnOlV87Z5zbSnebVLBFdvUZkTKsj+YAttgPi6aMukFQlqE/5MikW1D2FsSUor0mAR2NJW+1iaT3dWOtKIWkz31nvpzlIrrS2300OtPK5Iy4VV264mEIuLCwb+zM8DvlBHK9TcmZrsLrfCgcKBi+AAv2d+ZeX6thyIfpU+Nt7IoVZcAnj58HGQngt4+XcBL//L/7sh4EWgOhov0iJd0cLG4kJ+i/TRyuI48BL1q0oKDysv2kDdwzeCzdnsi6fDms9njddM86XumurIgfHL+YdGbg28LC8vpCd3Owa8PL4jjZdny+mZ5BSAlzVpvCxqUYAfQmi81GNPlBEllHvhQOFA4cDN5EABXm7me7txVNdizLcjPTQVXCBispyk7YJQta8J8VATYl+9mtN8k1WufWkovP2wrVWuYXqjz6v3AmJ29tP7LQdF2C5iegE2+56zCfAscYq1m1psCNdkzHji7u7wIT5Tbn9mMm4TbfkeiMbO4lFa14SarUV3VlrpqVaPHkt74Z62jjyQMLOy1LdJdUtCITKhCTLcoYisy/VFHGjI5YZ6GBgm9Zfx3n4a7/sK6YY0X7Yy2PLhk4C9bYEx0qx6v0k7OkifBmwRS4qvdyqwBUkdgXXRNxzJrXcuf3tNAC88c0XTyE60ZQ7lieYTAjvxW3rZgC7sKru/sqg2sGDt4+F6VyrdnXRfwMvKUltgXUtaUp3UM22oBYEuAc6hzUVOlKvbBVzw7KLyugByShaFA4UDhQMTHIiulS7P5AR9VRovbDWSxsvztzvSeNlI/13AC1uNAF6W1H8uq7NdUL9r3aWli67aO1DLsyotSsLDw5uuOrR2VUkv1DFJ1clZR7ygNeiKZ1Li5/Fq4EXjm4CrSuNFwMvjCnjpCnhZST88XEr3VjPwYluNtPCgDHz8OZmiElI4UDhQOHATOVCAl5v41m4gzTEkf0vSQ5CCBmy3jMbauy3NlqFsdGxs75oARZx12b5oa+UFrYEXb7YEvgzSK209+pu2jrwxmx3aLkJEwS76Aeku0OVoXpoLDr64AOITYFeY9W0flFsLWaSrL2I7pBKpPcxzd+EG7ZYFhBs9YmuGqXa3m9KjVVaRpLmw3kk/PliVPZe+7HVo64htG2lLfVcaDC4JKaFKYvbrWTYJqN3TYXVIcTU4wKs3oKP5KsU7tE8O2HokII8PWlRv1X42tobp9ced9Fpt6K00qF5/PEhjoSZ89qRF5W9V7zirWfMa5tSuuBywML0YvcL6BWGzKDdBA+IsSJHRgupIiO3K5s/TuwLlpN3yeL2dHt1d1naidrovWy59GXphWxE2fxYWGm3EylNbVkUCKClCsL2G8lU4cE04QH9R9wPXhKhbQwZAuvWrqpEDL9KAta1GWwJehgJetmXj5b1swgl4kQZjL281Oj/w4u9w+k3Wb5cR5qwXuZwl9jnjBXGWdTzMoqkuexbwgo2X5SXXePm9thqh8fLHH7KNF4CX5V6llYu8EgtlZewJXgd/T3sHEbfcCwcKB64rBwrwcl3fTKHrQjkQgzjCFFt02BoyNG0XTX4FvOzogz97i9e0Vacj4GVBk9oPG5owS1Pho2y/oGL8fmusCbPb7NgdS8NBW5WUNGl3iTQXtHXE58aivQZezE7GrNowjjbGUB5JbtoT3BUYE1+2jWgLedIukdTWhLrXWUhtTa6XsOey2tF2ERlH1baRR9o2ckdCzLK0Gvq9TmphIFWTateW8MIiTxVRX1O01AHFNZMD4hcs4/3JfEp9ZSDkMGu/7EmlZWdX29fUhrZt69oovZP7k9rR2y1pxyh8uHco0G/PbAjRPo8ExEiZycC9wwMEUC8n2jBvEWGU9whG43vi9azVVtwLAC5qH932Qupri9F9Gc1d62t7kdrHutr2kgCXFWm5dAW4tBax+yNtqLxSa20u14byrK3MbDB1lU92ZcKr/KD75NglpHCgcOA8HJj8fZ0nZYl7Oge8r82Tf/WDLM4M0F7c2Ey/vRlqq9FW+h//9D69eC8tWckBPQ3OS/pYP0ofzujA67HreKfncErtX7siTdzPA7xEmsu8B6VRuXimzPDDRov7Q/2exkKM6w7He7IjtigZJW81MuO6y7YlGo2XVYAXCTqMbbHVKHJtlnKZtbv+eQePC0eu/7sqFBYOzOZAAV5m86X43jIOuKE7r9Q+GgYSBDZldwP7LrgxksqkFe2QZWkHdDRxZbsGq1zs70Zz4e2HoSbMvl1ke4AdD0450H14mLakbry9S74aGKV2YMOjBDazAyNBghUgm0AHX2P81HM4uR/aphEHYDRN1T9g0JzAFtnq6MiWS8dtudzVBPrOcistazL9QBNrTqTBTgf7pHvY6mDrSEubVrKmy7wqhyxEGWXIjpdwQfd4gVV2EjcB99SoAPj2hMaNhMrx4QStTamsY+9lcyBtK/nRBgfDkbVDjD0T323GCMhT+gqEyQAPr9TAFrXPRTkQVjGazPsGlOM0op62EdEG+vqsqz1zp33YliKFd7DlovRxapGvKnq7tfZhX9Fa4l5V8IyOL013xuxLtMKBwoHCgUviQADdjNu76rd3JAN83NzSqUYCXn7ZSv/rP79LLzjVSON+V6BLvwm8TAz2x0fcrwdeyJP+9XPXZcSjzCi7Wbfo711m8VjS6NQ4iIy1q7Fuja1GskP3+yc92xL9uycrAl5kh07auQG8IO8gr1h68bFovBgr8tcsvjfDi7twoHDgunOgAC/X/Q0V+r6eAxqrGK4QpJi7mhCwe5A2ZG8DYYBJLpPVtiauHWkI9GX3gueW3IcyBMOxwaT5JAO8dqSktF+w3/FuA9svo/R+WzZitg/Sx6FWdUYU5Ns0KNVOqcl3kBc0CmLopGKV2CIH1EW4xQEsQaNB4AtbR9a7C9JamNdEetHAlkdS1cU+x1279wS4aLItLQcm02jukNZLyNoRVWH4l+uLOcDLafIyP5usjb+eufl2ND3S7oSe+PHTaFrtmYYLtoWGIwF7AmFoVwAztMehtGD2BeCNBeYBEkY68uDi3WIoGdDFtsRJEwrNpr7Awo4Quo5AliUdG86R0D1ptfS6HWlw0bZl/FHxAONMC8vq4CuMlnF85brlW/h+9R3+AP6Vq3CgcODyODDdPV1eSbc35wBdvIY6+ZC+Wp/Nbd9q9KdfNtP/JuDlt3e7aUcG99st9bP6xImBPtY3+aN+Vo+8m7iiK6RXd5ghQpr3OkXtIryZW9PdTPul8abzaD4H1U2/cNcUNsGSsWSofYEv4/29tC6Nl6ccJ/3DUnooueX3Al4e312yrdErdgCAj0dzecHIxtIyaDTaDRLiae8g3sV57/W7mxRuzptPiV84UDjwOQ4U4OVzHCrhN5oDDCcMU9irYOJ3oCGM4x9ZweIIabZ6ELakbTk97ePpCWxh4oqRUT6ISsx30V4Y7Gh1SxowaMGg7bKxOUofpAHzUVtGPmobyYYM7+5I7fhQk2Y0FQB0bK4METb5hg45ff4swhhEdaMYVnn0gEkPW/FBGwG7G2g1yEgqW0ZWRd9dabmsalJ9T4DLuo4CXpUWw7K0G5b6XW09wjiqthWRnT7kaUCOykd2uTXyS7xU6nidLuhqXvYS8PAAA2AEpIwF4u1hhJdtagJaRmOMOu+pLbpKNvd9ab2g/QIAQ/vUv+60XmlRCXRRyzAApSMgZUHaLmivdKUNZYCh/DidCDCmrbbcbbcVLiBRpxSxHcnaAe3BW0mmzppo1UYq0pv1Oad7+jUV4OWcDCzRCwcKB66AA82O23s+B15yL6gBGzlhV3305mCYXsi47p90qtH//k+v5caAugyiL6Bt2LKxuyI4BvrocKuAScfpfW1NW+2aTH/yEzlPp5oujfBZ8WblOh1vVl6RTmG5aA4BONDYxbHSd2U/79n9Tvq9TjPiNL2fHy9r61Hf7LssS5axUyLFr9DU9TFjupwoo9wLBwoHCgduHgcK8HLz3lmh+JwcYPw/EuLB5HVPk1q2d4xk6G1jYyTDpgcmdqyt9gVgoCHgR0Ki6qp/n40qHQJAnFjjk+U9WwXDbgcn12wPtRomrQWM9e6Tv8phAm1bTSqtBU2bdfQMpykhlZgpXsAXNBAMaNGEmkm0ntsCUABS2ELCxJmtTyvSxFkRjRxduSybHdjo6LDSJpqJaxNypa0BFgSW6e0jlF2uS+EADa15VfIiDcgDDmWAeV9CKDZgAOcO9WFr0b7AmAO0q9RIaTNoy7jbgT+wOtJw0S5Nk0ovGnCQ7Wzmh9varUBDAXWmFaN2tCjAhfaMhowRIrqaK5JBNuTirsiW+2uu6bwK8PI13CxpCwcKBy6HA9EDkrv3fseBF4HiexrrteDy8u1W+s/fNtL/8f+8kd032e/SVuOFOeSGllI3xt8KeCFf+t7Z1+n9bU2bL9PUz3VukUOETT/XMaN+tU+kqX3O5ooymrEjL+4Kd0vvMvqu8Y4/AS/3VnXy4v12+oOM6t4X8PK7R8vpwR0BLyvd1JfGbtgaK8BLk6/FXThQOHCbOFCAl9v0NktdYn6bJ7ohCLD1h1UXqQyj7YKmgQCSTW0XOtBskEnp3bW+tu0AYmCIVmJDoBeRhe4ID8hSNinOdjh8JcztwLAH3E5JEhAzsjJUjuIBwDgIE3Y/4kWhvyANBoElLdOwwU6HtBbkNjsdACp6RiBhmwhboPpSx/VtUZpQa5UNAQXABa0ZRCHonhSJGsBLFDsZIXyre1QZj89ErdIUxwwOVIzkLfsF+MDFzYR7Obi7oI/bw2hjAIVquBbXti3xrAtDySbg6xvAzt65vaha6Fez8DasOxpUlg5PXdMthGw9xIIv7Gs6X+oWP6sLK6RkVDhQOFA4cB4O5H7Ukkx0fHWPNQG8qAdmqzGfgWy7vX6/lf7826f0f/7TWzvhaEPbjA/TomLJEJs6OEDxrGvqVFmnN1GQ/IOI6JOnKxDh4U+Ox/0itL7XdXC/5nPDTbGWXfjZQ51N5Qq6I14VkB3NdJEp41nmgCWzZYM0fyTgZW0x/fhQNl5+WE3313vpx8dLui/Jvos0jiXn2BClbGLMsjEjRqcgZZqEG/McvAo+BeGfr9hJ3I8cyr1woHDg5nCgAC83510VSs/AgRjaiIqB05iUoumyJ42CgTRTdmXfha0egCNIH/MCOgBelrWFp6ftGpbWviU8aORHjNJcVws4rnHANNUmy0yK5cXJNaM9bHQAuLidDuxzjOSPNsNY24+w0yG5TeCPazM4nWSqU2gkbZh2grYUocGAgd+WjgHGXgtuN5SLBgx2OzCKijaOazFAJkJK5GeAkT9YDUJm8Ydv/Q1hnxcyvjWVF1p+vIupap+FEwidLmzjyBngydvOCEYFtOXnmnbiWUxaq1yknyKCCFd4mRD9bUm4wtqWogoHCgeuJQe8a3TSJvqj6CfVb5q2issCdK0GvNg4f5DevNtMf32+mf6vf3mdfn01lH03bRvdX9BWmgV101oIUT9LGj3lLpceeKIglR1EuP90aB1ec7DOYzJtHQNXXYc6j8i9EYaXZRN5TebiT1WkHBj5NOMqvY1NulfBWmCSny0cqBBs1MGLxfkD2XVppR8EvPzhxzUDXp49XNL2o15aEfCCwXdAq/xvhdiY0fRoFn3j3PC6YpLcwfum3+xKTaecHav4Fg4UDtwEDhTg5Sa8pULjmTnQHMqwjcFAhxDENiGAli2dZIS2i/AQCVfszRbQIa0SjNT2ZbwWDZNY7aoHO6AXhknP3cJtAiw/5YMmzf6B8hSwY9tGBLLYyTRCWurTbbDVUQMvVAi6uExrReCL3RHcpIFj24akzQDAgoYLW5DwbwmMAWhhwh2rQga2eFb6nhrEpx6raFfqcL55kdeCoKurfVR9qtrmHW1I1DSD430S7G1xklxAwGYCa0f68risNkbb8lwtvJlAyaeymCzgkp5qui6pgJJt4UDhQOHAaRyIji/uJ8QFeKEfjj6LbaFsB0Vz9d377fTLy630f//Lq/T89U56r5MNB+MFGUWXMX7li9YG2zodeIme3eFv73mbhUa4+0WsZgxInX1Npp0d5wRfklYZV46pyBORFNYsjzQ5fAp4QU46YEutmAcfO5JjFuePZHz40ICXHx9pi9GzdQEv3QTwsqYTjZZ60uqVMXiXtGL8ghyVEUXxeKuu4HuTr7eqgqUyhQOFAzM4UICXGUwpXjeMA3lgnh7GHHhh3J4z46VsA9rEoK4EqH0JBl3sqGT7KMsyXNsRqIHGia92NQfDcHsJ9WQ4+8ubDSGkA4gxvAc/Ezzk0B0/0umfmOZmQmyClhwIeXwCVDE3YghxLEx3JDr/r0AXZVUpQ/DWQnCxN6i45boGHNA74r01L7ysMXDTx97bVByicFXJLZHiTseTfw6y2LQxbzekno6MXyNPf7yS76DrSgorhRQOFA4UDpzEATrM2V3jRArvs7K9La3W7Gn15N2HQXrxcjP905/epF9eD9LrDzLULwO7G7tzAhx0wpzG6TbAi3XCZOeFNfvokwq/aODlxGpSdyOopmqi4tXDSUxyWaaKJkdsgSVHFrb2ZMuMRametHclZqWV7pFOMGqnHx/1ZVx3Pd1d7+ho6ZW8zchP3YvBzUs9qexmqTfVHXy/zXW8qe+m0F04cLkcKMDL5fK35P4NOBBDGtomAB5omuwKdBnqJCPsumCXhQHeTjKSlguAi23rQbNEqzNc5DE9JCKEERLAy5wMqxybBBNl1qW0ljy+LTP38YJ8Zzju6XKPZZeT5ay0xHYsxhV6QMxnKb5Cem5OUWqW1ihOakPn5SrtnKuS98+bgScv34UD3ykHomMtP5xb2QDi9Ubljr3meizzMT5HkIrhobRYWcjZ18rKB6Esr15tpn/+j7fp19fb6dW7cXo7kP9AgINEi5ZkiI40ac2YOVlYuSy3xHWs4BxQbyaKmNzrdE3f09x1/o0aTSao6JJ30z0Rq85nwjs/WI2MOM/AgBcNPnjBBw4u2BPPltspaRdRWl+a01HSnfTTo6X087NVAS9dnWy0rAMD2nZQQLuFjRz+c7lR8dPJmEXaNfer38rk2711Fb3m76GQVzjwbThQgJdvw/dS6iVwgOEMgYnhCy0ROzVGk1GO5AV0Geo4yG0Zx+PUmHkJRivLXYEvAl7MmC12U/yIXhvvbQwkr3owdOAFwkMi8HICiPGQEJ5IWaedTEUWdR6EeVSPz8S5EtMa0abzszSEN4uZfrbML/Prygu8zMpcad6Twv1k0c1XOhly8lOzSVn6L8nk5OyrkC9949BH2y5X4UDhQOHAN+FAdF5xn0lEHeh9KostvqVY8Ev6tDlKb2Vg91///C799nor/fpuN738eJBefdQW4z0BL9hnw1abZAzGbNOgVXpy9bFavuoHm/11Dp1Jzdd4WplR7NdkdEzIyBRHAZQhgIrFKK49yVi7aBYLfFnpzaXl7ny6L8O6PzwQ8KIjpH96JOBlrZfu31mSUV0OEXB7dshtMMfkuFs9WDQYZ7zluQyO1njKV+HALedAAV7O9IJLp3gmNn3jSLwlm8wyfunhQPquGLXlaOehThraleHboY5/ZksyNlTYW8yxzNh1QdMljjC0wf+0ukhioqxjl/I1gcEGUD1Ah65884f8fTw9PpMxebJ4VeQAdYg6GTdnW90iyemxquhf4ThO91dkVpIWDhQOFA4UDhQOXB4Hzjo4Kp6Pbq45C1ByKHBhc3uUPki95c9/e69TjbbT32Tn5fm7fX0wri8jsgJdepIpWrLPhixxOcBLJR2cyieXGBpyw4mxz5JfSBPOwEngyEUSk50UPNI2Iw4X2Nf9zpJs6PUX0iMzrNs34OXHRytpXQtfgC9xeAD29gJ4EdM+K+OcWJUbF+D8nJb/blw1CsGFA4UDZ+JAAV7OxKYS6SZwgOHLtQigds5OG+LEoSFHSO/u+0lGe25lf1H2XdZX+mmpz9HNMeB7Lc8KvLhQFYOmp62/86qWUVL74pqdAuHI1XQnYzcTZAHqM6BLs4wQlY7leWEe1ObyS7kwcktG34wD35Us/c24XAouHCgcOJEDzcH31GFLEbVC4/IECy248TpMAy3efNR2o19ffEwv3g7SX7Td6G8vR+mvr0eSNRx46aLBsWDnGymd1G5JC1G5zNljvcU4kfQvCYgcj1cVnwj9kpydH808EEumgZcDabzcW11Id5Zb6ekD2Xd54MALRnVXljomg5ltPYEuLIa57JVpO4Oc8yWUX0QaOHecp6flfFKK8G++i/PlfLzUi8zreO7Fp3CgcODrOFCAlzPxr3RkZ2LTFUaK4YoiXctFg5Uko3hTCEl2zLNsu2xpexGnGmHoDYMoqABzkhEDf7eDimse6PJAf7bx3kuinBiBPZfj4Mlpw2gjOVW5guuiS3Q+VEy4ghqUIm4mB/itnO23dTPrV6guHCgcuB0cMJkiD221VOGG83e1ZXlzsJtev9uSfRcBLy+303/+Jg2Y34Zpe6htzJxEKNCFUwgDiHDgxTMEdOHfn3Ihl8I2JI+T8sc/JJPpeOEfRDXzmBEWwQqij+dx/0j2XbA0rK1Zj7TF6O5qS0Z1e/ospWcPltKTe0tpWfLXqo6RRkMIXrHVOy4HYHgit+kyI9ZtugcTqdP3UN/b9O5KXQoHzseBCwBevoeOMeoY9/MxucS+eA4038QRVkU1o2O4MmxFd4SlXWm6DKXpsqWTjEY67lm6LrLn4qcXoeXSk8U3Vls4qpnThAxEUdqzTQ6hwK8mLc1Bc9I/Yn/r+0VTVfOhWfdvXctSfuFA4UDhQOFA4cCXcKDSnNXwVgMvvqgyGuv46OEovf80EPCyk/72ajv9+flW+o/nO2ljcCAjvIAIaLvoLqEE2UKZ6PKcjgMvSC7NcTQonvaffv6aeJRHfqflGflH3Hhu3hWWSWcLNzIURogPpRl0qG1GHCP9+E5bhnRb6XePBbo8Wk5P7/d1rHQ/9XWE9KrAF9/mHdounvf3BbxkBlZs5Z2Uq3CgcOC2cqAAL7f1zX4v9dJIz7AVQxUYDD5otwyH+2mHk4y0J5vVF2y4LHXbqaezDQFc2vq0tOXITzKSMvHZEJcpzjYHzaBiKooezxbreLqb4RO1O7n+N6MehcrCgcKBwoHCge+eA1muqFZjJGHYKCcbL3vYjBuN7YTE9xvacqQjpf/8fCP9x6+b6cPWgWQObXM+nBPwAMQyZ/IFhyVWo6My0qE/tsBT+VUMxyfG08qT2Cf4N+PgbsZruj0espEvUTXDmu7ID7+Trpo+i5Wjep0FPOlvQUkXFw610HWUnt3vyohuN/3uhxW5l9Lje31pwHRTVwtfvU7b5DJEr0r+UvYmilUkVI6TCLoh/sG36fqEf1RjOjz8z3pv5ve1eZ21zBKvcKBw4KwcKMDLmTj1fXdkrGJ8ESZxJt5+ZaQAXjKB+zKoy4rLnizq70jbhWOk2ZN9qHgLUmld7nc12OskI4zfoeKKSjBSAq+4jFFf+TJK8sKBwoHCgcKBwoEbzoEAXlQNxALEA5MC5X+g1Z0xtuOk9fJR2rQv3krr5eVm+svzT+n1+730fvswaa1HpykqpdRA2Na8qEUftF+U3K4DOULcOC5bRYlEbbo9rX9H6pzhzHgRhxQRz1PzZKGWfTOeh5/6nXlDqnyIkdVLopcdIQ2401s8TP32nE40mk8/aIvRQx0dzRHSD6Xt8mC9b5ounTYLYIsGuFSgCwXDG8s8qDgnfZGs3AsHCgcKB64hBwrwcqaXEoPWeQaAamg7UwnXOVIIC4zUTRVcRkcGyPNw5cz1PAPLocvtu4iGLL2Mxwd2XLSdYCTghWe2HBGMQd1ljpDuStPFhCGpAdvJAw0h5FwEXkrNz0zBl0e8PW3zy3lQUl4+B0o7u3welxIKBwoHLpwDCBcSGip5RwXQmwEKxMLOWGdHbwh4efVhmJ6/3Eh/fbGRXr4dpdcbh+njgNMUlULAC9uaF7FhInHBZRYsnwTwggxFzny+Vp4gvVGp+8mXlczRzxYlE3XWsi17oBXSa3uQthJBN3ySIlAaa8GL2i23U1pbmktrOs3opyfLOkpawMvT5fRQtl3WV/taAGvZEdJtbf2egzHNS3mZOGfeU2HNeLfO3Xx3V11vf4+3jqWlQoUD15ADXwm8REdx1Z3EVXOy2Sk13VdNx9WXZ28YaUGXf88SDxyAOSt1kU9kGONrU8hp5hWgStMPN/GNtAy64EYYQhWYLUa7uzrOUIKAFqek9poEtgh46fe0txhtF1ag2IvtbTdnMV3EuZ6reinVbf9FnIsxJfJ3ygF+EeWX8J2+/FLtwoGbywGEiQBechfmsoaAF8kTgC/7+2jTjtOHzaEM7Q7SizdbOl6arUej9OrTnmQQNG+RPaRVq7xc1HApQRiF9YwhM/hGpqvpK61MFeVbjkRIEGFvyx+qMAuWX45TRbX0liCCxJNDGdXl1MiU1pdSeiCjug/udNKPD5fTPQEvT2VY985qL62uSOtYmi6cYlQdIe1Z5e8MvHx3Y0dw9zLaAXlfRr4TL648FA4UDpyBA18JvJyhhFsRpdlpNd23onKnVsKGAvsyOcTADhKwSmOCSE5dgyPq3ImvT06WY+Ruv+r7FWpu97C4OUNzS1CxEKJVaaqszGFG3JSGPzv6Ub5jbS0yo7ragw0AQxwKWhTogk2XJRl067YZ8JW/JIQQME4qY7LE8lQ4UDhQOFA4UDhQOHD7OWCSSCXHHEl7BbkHmYJFH4CGXWnVYkPu0+YgvXk/SL8KePnzi0F6/mYkQ7v7aTQGYlmwRZ5a2pCXZBpytxLyFxokdpkGjDvP+k0WLstYZlPJQoBCUpI7P4ZvVcFGqipMfiZhKVsvI0cKgcnkL2LDEx1gIKd2cGtr0WJ6cr+THt/lJKNlHRvd1nHSOkIajWPJYJxkxKLXAnuVlCbkR0TAyLpBTnEWDhQOFA7cGg4U4OWzrzIGshiKmsNP0z2d0Wlh03Gv77PV3r58AA4uHKM4AhBKCNRXDKKmydIYXAmO6DbKEqa/psaLF+mxYiCu0ig9JxUi+BweIdjIrQQIRCNtKxppe9FwOPbjo5W4taC9xJ15A166Mqzb1koLBnXJlwE/6LSMJsUL9yrfZ+IA76z5js6UqES6xRwoLeIWv9xStcKBG8sBly/OMl5leUY1RU7wC5CBjxZ6BKzs7srQ7vZO+rCxI8BlR6cbbaW/veZ5X8dLs/V5QVKFj4yCG1zzRY+Wnb48W76J43IJvuFDmR4H1+ev42Ow69N4LgqdimCPUwWYX44XdY0oeIfchO08p5QaHmpB60iaxXMyottKPzzopycP+7o78HL/jo6QtoWvVpo3+QstIOWWy2k4TDb7fE1LjMKBwoHCgZvHga8EXppd8c2r/NkojjpG7GqUCI9bc2cAna5dCBvGhfw14c7s4eb+ysVWhEJ08ABf2AihgjslKYXuuGLFw7z1HCOvh+LBRQlO4cGBVpwEunB60b4+wmDs5KIxdl20twijugAxGNDFen4fwMW0XjhlQMAL+rC5sEzJsbpbkeWrcKBwoHCgcKBw4Mo4UI9zV1ZkKegEDkieMTFlcmEIGy38H2gFiO3NOzLgvzVwQ7t/kaHd52+209sP2oa0JXsvW9g/kVFeySxtLQLJ3Is0PSjOZRnkLv7i2X2b0o67mwSGzNL0C3ekj2fu5H788piVzHUsUu0RLpfH/ElVSqqS5C9s6B2lrrZv6+wC0yj+6XEv/fS4n57KpstTATAr/XZa0zajXqdlJ0nOSSCkXNc6Fn2WpecLnSEPHqf5e/Kp+eG1nvVmL5IfUd5ll3ORNJe8CgduHgcK8PLZd0Zn9H10RD74T9aW1Q4DOQA35N7TvmYDOmTCfl9beQ406O5L+Dhg3zNxBYBg9Z+h3m6Ze9UgK1YuSPIA+LCPJBB71j7otgZuB0UULqMsCChugwX+a3UEsESXgSwqe5QBlh3bXqQtRtJ0GeftRYeSCDraUoSGy+pSOy3JmJudZKTVmNhXHIO7DfrK2nO3IspX4UDhQOFA4UDhwDfgwPcjc3wD5p6zSElFeh3ICpMauT5JZXFnT4Zc0LLdlr2X1++306+vttJvAl5ef5Ch3Q/76fXH/TTc1wLRGJlkwU44ii02EOM51a54+35HKqljEGv2ddZ4zdSuCXOy3FOX60tykwtznMzEwteehL55Gdntd2RMd0nbubvzBrr8/Hg5PRPo8ljgS78rOUzbjDqtRW0zWoShIkT56Y7TZLBGPUM2a1L7/blr/hfp9Pt7+6XGt5cDBXj57LuNzu/k4emzWXxVhMsvXxBJg0Kvpw+LgBwCVWSgdl9ABoZqh7KdMh7vpz0JGiMJHHZqkNRt9wSCAMhgTA7QA/DF9kILzmBVgxUecBOObmZ/b0taJ2ijtASOtAWyMCB3ddoQVu7bLYEkcgPCzHMagD6stMRgrGLSUJotgyGfPa0o7UrVVytOegYEIl5bhS0vtbTSspjuyrDbqtxdCT2UOa98GeyJB3dNsNIdv+ZF2JRXM7i4by0H4jcXFSytIDhR7oUDhQOXxYHpfodySt9zWdw+NV9HAhpQgMeu3pAJB4AyLExpEUoiFFq2Hz8O0m9vtwTA6P5apx29201/fjlK27taKBodyqisZJCscctCElBGLkpuZRoF6LUj80yVegrJRtBEuKePDOusJyLZQ5RzPCTAplo2qvMDeNmTnLcnuVBVSmsrC+nBymJaW15Mv9cJRj89WpW2y3K6o6Ok+9J0AXxh0YuTJKka8lddcp0vfCAk5L0Jqupo7l1nMBHtZj9MV7JZm+kKE3farxn/S9yXkeeX0FHSFA7cTg4U4OXav9dmJ3yRHazn64O+DfnGiQAjkAZCqBgbwMKqDlb8RxIw9gR87KWBGY/T6UHyR9sE0AUtmD0dowjwcqBVHtAMBu15LXIsStBA2EDzBEO3bP3pAbzIwj3bgVakmdJtC3RhkO517BQigBKOgYYuTiFK2bjd9mBPYMt+2hI9LyXcvNvEwJ0M6krzpq00fR1jeH+1JSv6rfTwbj/dWekY8ALog4ZNPVZ53W2v8VRbgDfQXq4v4cBltdsvoeVr0tyWenwND0rawoHCgavhAP0Ng07cr6bUUsoUB5B/prz80f0NkFAEtIH3pe67pzvAyuaWjOx+3EkfN3TM9HuOmh6nP7/a0TYkab0oXJKHLl/wkVRkZRwdCYzQK2e7Dm++FjkiHml8cWw2TYRzeUqHMzwv9/dvT9usV5QU6SKHyVQsdFE6Gs1cHpsFLrlFM6ALp0Te1SlGD9c7aVXaxZxg9PhOLz2Q7LWiRS9bWJOch/xlR2uTSOmRCSsZi/zw1D+MKcCLsXvGFwyKi3fSfA7/r7lfRp5fQ09JWzhwuzjwlcDL7WLG9ayND3ZO20V2sOSrQdhvJgAwwCJQoDUyGu+ZZgurOFuDcRroviMtky3tZR7KPcSIrZ5Z7WB7D8KHCyHSn5EQQr6H2oYUQgUShx/fLK0XDb6VxgvuDMQsaUWEYwZZGVlZBoRBbVUgTL+jgRttGKnpKj55o/HyaWssbZex1Hp3BL6M0ttPu2kssAchYLknIeBex8CXHx4vpXurfcu33WY7E6cMNN+2hJRq9G/6F/eXc4CGFdcEs8Pzht2pz22oxw1jeyG3cKBw4IZy4GaNAUatCUTT7AYO8LoAW5hsoy+22Ryw5VofFp7YbrQz1AKQNHCRmd5pIej9p3H65e2OaeSiocspR2zNMc1gHQF0oI+gi7Qo+QOt4AVt2amvXKrRFOXXocddPj7V38286tjUwaozIfM4XNMc4QgmB6unHGi4+AWAdCTtlSS5LNnWom5HC1132gJeeraA9kBaxms6yci2eWu7N3JbW1rNtpAmWS7s7HHIQYAvZu+loinXIoqsya9cBFXRK99v6zCaLoyEkyrffEvNOE3/ryHiYmvxNZSUtIUDt5EDBXi59m81OtaL6lSbFdY6hrIPQYI7oAtbibalSTKUELGt04Heb4zTJ63YbEtw2NyR1ouAl12plqDRwvYjVnuO9GFr0aHSzyFMKDNbIZHbKNeX2WjRSIlFe/9wrDNbj7Q1SAMz4EpfGjBsD1oX8NIX6LKm+50V7Q0GhAGA0Z0cd3RawMY2xutG6a8vd9ILqfW+1AoTR0kvCshZVh5P73el7dJOv3uykh5o9QUwpqP9x4sCXgBanKMFdGm2iItz37bB+7bV5+LedMmpcKBwoHDgOAduVp8ZuIJps+TKMLGPY6QlLFeaH2bfRJq9e5KBzMaL5A40f7E7N5A28FALV1vbB+mjNHNfvtsyWWWD7dD6sGi1syu56XBBYMa88mQhSqcvqrBFtF4oVIIZOivQZEAJgtpnL9LVcs3kQoGnr3KJypoQRBpz5G8HNCIuW8jtYx5QJYqPDrQwxnbu+XQXG3pLWui605WcpSOjey3Zc9EimuS3nsAWNJsDYEF7Gfmsoy3lC5L7WCTz7UeuDQ2SYtWHIsoTnfDDuEFAJipog2Cn/LPMuYERqlrOoD1qTZxwz4hWvAoHCgeuHQcK8HLtXsnlEDTRPTcefFBHZdaN5I61jWiws5s+bqIuO0wftIXn+dtRevNplD5qa8/Gjuy8SOCQbGHgig3Zyo+hkYGSIYAxnTthDNN+USjjpgce2WAqDwWz5RfN07aEj74G81UZaLu/om1CUllFVfXJXVZRWmldVvGXljq2WjLUnmk0Xt5pRek/f9tJf9XnF6n07sjAHasq5PHDk156cr+X/vhsOT19KIFgVWCOVl9aGvwx7OsnLWV6g0yjsnwVDgQHaLelcQQ3yr1woHDgcxzwsa70G8EH+HW9+tBZvXpgEU3gxf0ktciB22zXyQHwsi8ZiK3VaLxwkiIHD4xx6zPSgtSOFqq2tVD1TrIUssqmForYgjTQotGWPjqFWuDLXJISjKAXbYEWixYkRNXbniUU2YUUNYPiYyyVR8XyylEBOTkz3ZQbleEyMCPiUkpTYvM671NXReEkJ5PTpJWzOH+ohax5GdNtSdOlY2DL/fV+uidNlyUtcKG9jJzFx4AVkUa92OYN8NISGMNCHIATGtDki8HhOT0DtPhucLlVLnLaiZfiHmPDiZFvWkC8l6Cbmk77EXZ7ORA1L/fCgdvEgQK83Ka3eda6qO9mgDXDuQAuWtEYykgttlu2dN8ejKQiuyPQRQCMNErefJIB2w3ZVBkepm1JCexVRligw0e7BbXTBQZABknz9sHQh4MQGSiRPx867I5mjBKxT5iBFsGDk4hWunNpXcDJigbwe6sdgS8CXrSKcl+D+rqeUVnF2O+WQCCM6v7t1UgnCWi70duhaeJ0lceqNGV+eNhLj7Td6Kcny+mR7TVmFQZBwI90pEwuZBCRX67CgcKBwoHCgcKBr+QAoxtXGVScD9fvu8IdGqT5W8vfprUrqEERfQu1AywjgSoY00W7V7es5Yt2r/wsU0ANt4dimjACYwbSGt4W0LKtbdrvPuggAC0OYZNuQwtZ29J+GexwJDN5uCwiocgWhgAq+HNARi4ErHy5S+2r0cQqZ6Yj4pp/FejyV4TZPZeLSCfTfAawHOT6cLctQJKV5lV+Dy0XABfJUYAud7Wd6NH9vjSM5ZbMtSrt5C5biiRjOb0UDLUIW9yR98hLz3IAvvgHzWdkMwdj2JqEP5oy9kd0+1MWjYscTSPGHB7QqGoj5k10Uqm4olaNikbQhd+voowLJ7pkWDhwYzhQgJcb86ouhlADPxjkMX4r0GUksGVH6iufBLCwdeetNFs+IRh8FOiifcrbADFSmd0V2LKr4xC1iGMW/CV/6GJ1hD+BJo1B0X1cJoihw+EXBnVPR0rCvItnlccHZAbedjtJ8wXju3PSdJG9F20bWpKmyiOpsj7CQr4AmcW5Ba0wOU1vOLJRAs27jZGEnjkDV+6sdtPjBwJt1jrpyYMlA2z6nJpkx0n7UdVGSv4qwEuTG8VdOFA4UDhQOFA4cLs4YJjEVJWQX0JOMWFGzwAQR5KTAF7YSs3pjWOBKDsCT/zERrd9Ypowig9IwkLOogCFeQMNvBAHbrCJhybxfnrzQZrE2yMZ3t1JbySvmNbulrRjRmxZkmwl+YqlrAVACOUH+NBW3lIKNjd4BbIT30Fzs042PW8AL8g1PDblm0hncclJHnwAftBuQZtnLPt8gEvY7jMNFR2O0Fk8kuHcBW0t0oEFksPuSYP4gbRdHt9bNo3ktb7s8nXbpuEyJ0LJDzAK/mH3j23oJnfKD774JWlRhCxI7Vl4jW1LQkumrcUzNGUcgPFTKB2IUioIJ32uVJVXfo565QJuyQ1+XVXNrrKsW/J6SjUKB87BgQK8nINZ54na7Lqa7vPkcdFxY6h2YUCghXRdtwe7Alx0MtAbrPEP069vtmU3ZS+93pQNFWm4jAW2YO/t6ECdvu4AJ9THr3ogAEZpAi7VGCEgxFI0Rv4YcyMfj8GT+1iuKpQkLQElOujITj96tN5KPz6Q0TatrCx12iaIYLx3W3QOBodm92VRo/cSR0jL0Nt9CQfrWpG5oy1Ky9KgwY4Mx1kjzASt1INS5VWuwoHCgcKBwoHCgcKBM3EgRnAiX/MBNJMaMpBVz/ycbvyRi0y+kT+gC8ADssqethNhu4XtRAAobssOUMGXkVDm4LCARYEH2Kuzj54NKFAY8cbYgNGBAG82ttOGFrleSdZ6LRDmvWStt7KhtznQ9u1dnYykPT2HaLwA4EgAQmukhbRiz7C55rORD60hUFmw+Vr1eCd17JCuclAVAwf1xHILeWWwJHEwArQcCQTRYlZHJ0VqQWxNstWDtXZ6okUwFrcerKFV3Nf2IslkioDGMtuJoILtSfvYwNECGaALWspoC7HgR77GR8VBowb5q6WvRW1BgpecdGk2YCSwsS3ctF+Ur8mZ8ID/RuUoz2rb8JPXRP15vpkXteOaqpx7XsK3c/MSMi5ZFg4UDogDBXi5pGbQ7Lqa7ksq7rPZQgPCBKqjYUB3QzZcPm0NZSeFYw+1tUjCwPN3rMgcpA87RzISh1E1qX4qLYOaD+NRmxgEuAO6ZOAlvIMiRTev+stGd6Mn4tgd0YfLv1l5ISGaMKz8oKlyd2UhPb7LcYWLaU0DfUtaMVjEPxT4crCvuySHnlZcDHiRUHBP4EtfwsCKjPL2tX0JS/qsSjHI+5UdulVeEVTuhQOFA4UDhQOFA9eEAz4yXrexCqoYPeN+TZiVyTAxQjIEwEJcPmEXvZmhgAC2GKU74AqAwJ4WnAAKkCmQlzjZB/AAbZecTNkBjqCt4Vti7MQeCStoabD1mrC5ObYgCbwR+AB4w0LXtjSKP0jb5V1s5bbTGdmS5EZ3Ryr7QHb0Dg2EQVxCbuMEJJeRdNNuJGWuu9dKkhkOewifqC3pcSs/gKKcDj+c0GfbxeVGzjLNHbkXWlpwk/axbOCahjFax8u9BTsq+r62ez8U4LImA7p35V5d6svQ7mLiZCPTUPGMxTvJmipI5m4ke8JHucUHB1/YxoWbOhmBRv58PnHS7xlwiQMYRA+gDtuP5nW3BTSvrlUW2p0RxprMAM87P6iMRoLwLPdzciB4Wnh5TsaV6IUDxoECvFxSQ4iuqZn9t+imgg5bXZD66J4Gf4y+bUkA+O3ddnohoAW1V1ResbiPtgtqr0N9RtJyQWjgqMMYsCqART6RtwEZ+YGx7+QrB7okUKX3+J4B3yYI5eFYuI+p2SIUdHsyvKvtQqy+rJoBN52CJPeyQJieNGC6+qybhX2dhiTghSOpexIGEAp8/7AyR5OmotXrYEKIgk6n3aks34UDhQOFA4UDhQNXyYE8ZFmRpw6xV0lUNYJDERReH8pgQxYzcPol8moKRa//m4xjIIGAFY6FNg0XaaigrYHcxHYbLgCQObQulAkyz+K8bMXlU3nQcgF44ZQeW+AxmQmZwkEd0/YQEDGU1suuToXcHu7awQUcO/1Wx05jT29LW5E4+WibAwyIp5OPxgJgxmiNiASOoPbjpyUjiR60YLhMOjOUx+s3UUeFwwfejgNM/owntJGMc5VYwGqLdolL0jKWdovsuLSkvcKCFfbyOGXSjoeWrb27Kx3TJl7SqUVLPRa12qZNTDpst5gcpXwp1zVb/A7gYuBLBcCgASPNGkAtyaZeN4dhXOIMTRfxWrzl1Evbis5d25EAXprgi58GZQxptEZqXl8hx9Y+xVU4UDhQOHC1HCjAyyXxe7K790LqAfGSCm1ki5BgV0YTbBVHI/eOthdtSsvlzcdB+tPzrfTnl1s6inlXA70Gdw3ygC6s8mgdRMnzwC4nT06/5+vf7uPfXtzkdx1Su4hRc8eG2XgUrRPPOSbB+Bvoo4wWBZ4s6VjoVR1luCYbME/v9dLDe0syviu11/tLCpNAgJaLABkEIl8haQ65VYEQU12ZVdVzcRQOFA4UDhQOFA58aw7EiAUdk2Ppt6SsSdW3o8xlg0muGNhgMlD2r241zaRDE8OOhpbQYzbjADsEvgxl9y40XgAH7KQdCQjYO0HDhYWgBYzIckKPQICWNDVC1pBCxgQgYLQYuOAaxwA72NVj8WtThnc/Cnh5vzmUXAYAw7YjTkBCA4YDD3SggU5wFFaTRpyCJPKRz44EcCARgXIgtxjoQLnyRVqS07/1GMo+LKJRX6Ic6ThoQBfbyiR5SliGjn5mO5G2FQlwWV7WUdBa5FpGq4UTJnWwwd1lLWbJiO76ckefnuy5oOXC6UXaFqTMAJxMRjO6xFsrh7KQJynbNVzQIMLuixktFqIEsGVutGEUGe0iPkhs5IjWDfZeOJYacAsABm1nyqNc6g4P7Is6yVlfKrxxkSeXxW/4F2fhQOFA4cBVcaAAL5fIabp8uvno+icHhEssOGdt4IsKZaBmcBtpFeWjBvj3Ot7w1btB+o/fttJfXnIa0K5WVhRPqyqsOjBSay1EAzMDmhLnUcroZxS1WvEUNTupLpbC4sWA5zHrdAg/VTYRvZEdMauB21RTD0XXkQZ8Gd7Vqgzbjn541E8/yIDuY46e1rHRS1qVWVmSEV4JCaim2srIxEjrgzp5421Vyu5G0cVZOFA4UDhQOFA48M05UI+YTsqMofIb0BhUfRtqTHZo1LqSMWxgb4gVTOb1YZxnvLeJvSb/YwEgEov00V0TfQMAmPybG6gg56F0i2xxQZZgsq/PorY+o92CpggLO6Hl4kAA5SA7OV+MSxLCbMsQ5cnNlibAl10JXhxgsKWTJNFE3pR7UwZ8t2V3j6OoAV42dWf70UBHSQJY7Ik+wBvqI2clvzRY0QAfKN0X0IJfkMUHugEyeguy46K69KXusqTtRGwZQq5CfvLDDbRdWxovy1rM6gn06EuzGBnLgRC0UFzG8ny9zhWzRSQ0cqFVhAYMPGfbE5oubEOy9yGtHvjvYJfi6D1Ar78FrwH8xx4M5Rm/eScsrAGCqS5ow7jMClgjT+qpco0uo4AvybYwTnf8cXIvV+FA4UDhwFVx4IYAL95RXhVTLqKcoJh7XFfdvzO42b5kCRY7WjJhheXXt9vppbYXvZAR3efvR+n1O62yfNpvDN4M0qJUo5G5Yl+O+UYN4t6sXdRy9r0Siiy4ThfCwOxUtfDEAEl9VBUbkBlXMbqLKuwPOr3op4fL6Ym0XQBh1rQig0FdtF4wzmv7lhm0qRdXHm2NiuwVNfIIX/pNjheT05dSUNIVDpzMgdI+T+ZNCSkcuL4csLFqirzvfaSZkB0qBlWjfKXpwXhfbbMRD/c029+TVgvbimzriyb9TP4PNfEPTWEm/PAX2cG0OWQ4l8k+2i1M9pnk+6Tfw5m884mJP68qgBfclq/RAQAB+CDwRXQA/qBdM5Q6i21BkhtNl+2BwBgBMAa8SANmKKBmy7RxHHwZC5ioQAoTjrwMQA7o5hNX0GHHQgeNkp8AjjoSpFjE6qPtIsBlWTbxetJk4ajoXgfNYR1WIJAF47n4t6V+0lG8Llou8ADwQ3yx8mBAXkVrls+rgcSKt/E+xGTfAg8/BCaxlQrwBf6IN9SF05CwBUMefDmf9Y4zCAPowrYj2+YlWQ8gzIAZtiJBhD6WhvR6sDZjmTUp9DgWpXwVDhQOFA5cMgduCPByyVy45Ozp5ye7+csq0EtikKNAH+D3tTqivcNSY323MUj/9utm+utLgS6vB+nT9qEGd46Kdg0QaJzTMc12h8QAXchQo1ct0hDI1awVhZ50NeMRSs2mXQAAQABJREFUpxm36a7TN30jtQ3eNnQyGBN3X4Ns0tGGOu1IgMtTWdf/3ZNlU4t9cJcjpHu2KsPgzN5jDLL5pcSkr0fk7F9uhQO3mQPW6G9zBUvdCgduLQdsyFPtGA+/219yVFx3JtGACjGh58VXMoqYRFQPE6iiWTwyAws3ABwjyURDaZL4thf8sTWiDdbICUprk3flbWCLtDzQoMCvJbcDDq5VQfkxwadEym/SA02Tl789O0VI230AfNBe4ahq7O/togWjk5MAXQBfhqL1o7YjYe9lQwDMruKMVYmRQCNAGzdW6/UT+b6VyGrupVKa1QkZyD6iEDsuqk9HYAunBy1ra1FPW4b6AlnWl7RFW262FGG7ZUkfgJZONp4LP8y+ChmrrhXQZO8DDmRH5oVRYXHNlb8yAKIntKxNC0bEHwC86E6d9lRHswkjN4AYAMyBDP6Z1gylKE80mTFiDD1o33REN5o7bdVrUcCQ00b9+dAaKNcvkx/1oNcN0QTxX67CgcKBwoFL50ABXi6RxdHJU8TnOvXzxD2N5Bj0EQFYPRhLwBjIiv5HthfJiO6//bqh7UWD9PzNUKquWv3RvmGMyLkAwUCq1YsGsTGQmkBjAXmUqoawWdSQQdToJDfpTgqzUquMQ5hqxt7XiI1BtqStUPdWW+mRTjB6eLeb/vBMtl7W++nZgxUBMD1bpWGVClVh6ljVzaqhHMn0wq7gzYVlWDIqHLhADpT2eYHMLFkVDlwZB2I0pcALHbKupAZf2e8oebP+NcnILXryVRjjDEBGxK22EKEdIn9fiGJLC5N7N6JrmjCRofJBTrATc2SzpS1wwk72QbODj8pyo67a2pLliaZx18gGaiuSak+TPyri5B9aOGbYV/QYCCQwhdOPAF+GY201Qn7TFqQRmjHyA3AhHhovADYS8awu5EHNpRxSlR2yDsAQsg8LaaahwrHXBk4IfDGNF2mwCLjgEALAl67sp/TlBszAD3sqJkNlLRLyAoCpL8Amf6KYkEGjpQYddXx3Qa+DReayutQaMIAw/s72BEoBvADImJ2Y3B5IRRmmlSR6eBe+DYmtR9J40QvjYAhoBXTiHfJuoUf/dhlfsjtuQS91CneElXvhQOFA4cDXcuCGAC/ewX5tZa9zemrIFQOCP531u+aPqWVqxGBFZaSBm5WTDx+3DXT5TcDLX37bTL/Jpsubj2MN4KwgaESy5IASGqAag1KUbgMQD0Zck8KgOmLOuhM/4kXaeG7Gb8arUxAjUjVjsDLCh0uG9aUau6DPfPrD0yUztPvzk1XZfNF2I51y1JOx3U5bhnbzoGuJqBSVvdBrul4Xnf+FElsyKxwoHCgcKBy4QRxojjDfzeiiSjfrXb8ufFli4vJvA17kDagBSOHHOEuzBZACf2lVHErTpAYHlFKza9eI1eRcQATgBBohdjSyZAS0KsAZmMjz52CLL+KQ9vjk3KlFxIirioOqRr4IBzywU33YFi5xxrbc2IJZ1mhBu2Vvz7ZHjTJYxBYjtGOIax9bhFJeBjrlQqNsFUeJRqfqYloiaLwIfMFeTYuPNF46aI0IYLGTg+T2E4SkEWPgE9uJArSAV55fXQ+vU11HCozQk+9ed9FttGL/hTrwTL10V1LqtKcHAJnQ7gFQI659TAbMhWGPkIJFCIALdcX4MWAZ75Jt59QXUCZ0Xwx4IQllZVLNnR+qOuWwciscKBwoHPhaDtwQ4OVrq3n909PP0+F/2eWjBN8mUNggPpcGUlv9qGMKn78S2PJmS5+BQJdhere5r+OjGdkoUYcJHtWrAD7QIMzE0ESmivdZ4qIGTsvsekQmp8dhQI6hMFJ4fnrKSVnfweYLHgtayWkvHklFNmnLUS890SlHv3+2nn7UtqP1la5OP+roKGoBL6yCKAtq5iXYw2xSv9g36jZJ+RdnVxIWDhQOFA4UDhQOiAMxukwz40aNNlGJMxJtAIUP2FW1GccDPLFsJLgQzzQmbOLOaUACL7RVZzgcmYaIaVDoEAG22mDvxI5+VrpqYg7oook5gESbyTpAizIHiNF/BliC6KhERZKBG/6GiOPh0BRuj4lkBa2uZezhdYzQgkGzA0wBGcdP/UG7xbVdhDuYn9nvUyQzUisUgqKySCS3lx/AEHcAJOoBIBFGglVV1VP2WnLdW6qz1VcB0Eml4TV3u3kl8nfUkRDnj8fiwbxmfsV7IxC6/DlLZZ6l1Z1w+MMCGxo9ExpMgE9sQ8raTPvwSZrbJhOKF1FfwKQW2ju6cwJTh5OoVFcVW38oKF8UD+mUywd+2UVA8zqlfs1oxV04UDhQODDNgWsLvEQHOE3wbX2Ofv3M/XkesSNd8IWBJ1RqP23rmMJPg/TX55vpl5eb6fnbQXq7sZc2trGor0HFBlNGFkahZk4+fDZ9fDiiFHxnUTkZm5jHr0h3etyTSrByLamn55tBWzWW40ACw2F6dq+jI6XZcrSqz4odMX1nbUlW+TumOutChFPmgsVxKr/ex+mbzafp3IkbfJkOK8+FA4UDhQOFA4UD3wcHDJCYGg8BEUzcaQ6T8vBJu4MTBlJowh2Tb7RCdkcCKjQxH+vkIE4C0r+2rBxKThC4IGABkMG0PkIjwk7IQeMFMAawxSfoBlSobCbr1Vid5a9jb6VJYwTmuC4VeAQXt+rIhHl4/oZeq7fuohm+GHAkN/c9ABfVEdDFTmwirtzEsxzsSxIOMl4uxuQ96i4P6gT4pLUoX5BSXR2U4Q7YokQ5IXlwxd0obdT/KBbu6upY/M99QWvzqp4y7YQRx+y6UE8AGLR85BdGkXm/uM0eDOH28ZygFw0XgDXet53CJO0e/JwXgGpeV2y9UG8uq66ygDyveq6YbuYi++xlCY59fTbCsRTFo3CgcOD74cC1BV6+n1fgNaWr5jq1P/coPu7lYbpOpJTKxI9EPDBL+R82hunl+0H6Tx0b/dfX2+mFthhtDrQCtHukFSANNPxpZLFBtpnfTCqCstMGlahFEPpl91NLsCI47JoVEi4NwgKb2POMgbx764vp4VpLRnb7BrzYEdOy93J3rW8G5EJlFk77oPplNJ491Wm1cfrP+NbPXmSJWThwBRwoLfsKmFyKKBz4jjjARNsm+D64W80dSqg9DFwwbV23Y2c2WzT5RgNivMek3LUjwl6Ia5EovUQYjfoCHrD3wdYTP5nHt6MIbMmTcOQhwuMUICQfBx0aPR6z8llXiEnNsBzVUiN0xIy+kUXtBDxxiYB6GzihZ+4AD4ThNoABN2tOqpPzRIXKw/iFrT7xiPhWbxVrcp4QH6uP1dXrBfhAXRGIDHzR3ckkpsfJDrtRIv/1FfFqn7O4rG6KCG/DXafzPCnI60ud9VG5vE9/t7JRqHdt4AsAjD7Ete1XMIa8+aNefKizKhgyIBpN2ISRHV670w6oN2ksLQ9W0UyLhU16WcRjX5npx/yLR+FA4UDhgPoYdVQTXWhhyg3ggN6YDa5Bqo0LPshygtEYq/jbo/RWtl1evBukf/9tW8DLML36MJJ1fIEuUrfVeGX7YCftukRTyANN5G/3WX7NCJG26Xeae1Z+dR61i4HQLwZE317kfi6gSeBSEx5qDzeD8B3ZebmzuiDjuq30hydLOuloKf309I4M7/Zlob+VTyVggPVBlpz5CbhglQu60Bs1iRpMZxy1PCl8On55Lhy4Phw4rWVD5efCr09NCiWFA4UD14ED1VhM56GP4SvWkzBGQ6FP0rGrz9R6jCFarSKxpYhTfgBesAlyoCNy5rBjojhodSxovLctNNJksa1FBrxIE0IRYgJuWiAqyybqABF2eS/m8kHu0UJkzvTkiGe7VWmVmOymr/C2MC/PNFnwJ20jDQAEl/Es8wU6PVVkDHARbt2NZt3sXkEMela9YRZRFFiFeETzb2YTdDSCLc75vpSjETsFvEBwQz6r6gnIpDCScDcQBk0YvW+OCNe/gS6AMtjAwd4h2jAGvCkt8f3S9nS1AwwG82ELElvLel0HZQTPCKhRTOOREWg8pK5GmvlHXuVeOFA4UDhwPg5cW+DFu7vzVeZ7iW0DcLOyNhBgKFeCiFCVoU4xer8hg7oCXX7T9qI/vdxJv+gUozcf97Qq4AANAwhiia8E5MzwtOukkeUkfxJFWtzEizfY9Ccsrll5nRZXgoGSVnXXgwkHugO4DLW/d6RBViZddBTifHqwNpf+8FhHTD9YSn/44U764cFyWmW7EVb6tcRhwoXSRn6RV1B3sfeT6hWlULNyFQ5cDQfil/m1pX0un8+Ff235JX3hQOHALeFAHiJjQYnxmImywwiu5YB4wnjtNj+4p3ysMicU+SlFLMKwNQeNELQZ4mQbjoHGgC6GZBflxtA+RlYBXtDysLjIFCYTTPOU8TkTKFc1WpujeppOdPw55KtIYlnGA9Hr2k8m9rJNAso8CCCIkDoHl+1qMCSny2kCwLCSRIvlochoALk750WGSoNfyEdOj8Mxnivh7vtl3zmXydupWUW5HBxhvJKHba3X+0bLhZOraBPmFgpDG+DkJ+5uhFhhaAQpnW0n0/sHdMMGjAMwaDqhHaO2ocoBRFFHZGQW6ri8zrwJBTgZ5u+BtfO4K6gn0VVdUSblXWW5V1W/Uk7hwM3kwLUFXm4mO6+G6snB0MtkAGJAGg7HaXN7N718m43pvpG2i7YYvRLo8l5GdY90ipF1wYproIsefDBRPiYYnNRBhz+debib9c2dPEHN/r4Z5UR35HeehPXgJw3TNNaIy5YjaYvqaMSjtNSbSz8/6srmSy/9/c93088Y2l3tyr+tvb4ytKsVDye0FlYqPpxI55cETNcp6kpehDWfvyT/kqZw4OwcaLbGr2l5Z8mntO6zv5cSs3Dgu+HAdOeRO4qQa+iXcPuzj89mw4MxHq0GTbA5pQg7J2wv8VNudA+VBuXHWA6gwNYSPzo52/kAjNHYz8TbJ9lMqolnU2l7BZTvjnBdck/W5AcFU2z4Zbc/whOjzL4CGOFu0eCZh1idqBfxzTaMgQ8uI3oeh1Zn+IORYctDCeId1KXgch7hIv8gj/uFXUa4Uz+RZ4Omo6gQEXJ86hJbyryePGvLPfXVx09/AnwRKENbUYJD6qMs+FaN1Q7UJvT+/ejwDMgpAn7ttm9HQluKQi2e/O0CAMpO97Ao/mX+EQixuOMu54wrQuM+I8o5vTwnWoVTEvScMxuiXxxRX1B4SVI4cLs4UICXG/g+GRwnxiAGE44UVOe4NdhLnzZ20q86yegXgS4v3u5ou5G2HQl02dzRiIQhtPw3T4dMRtUVbnrZ6SvCpv3jeVaaCPvcPfI+Tx4+eDIiaLHD6m4rOjpScGHuQAbUDqXt0tGnm/5RwMsfnq7KzktPpxx1UqcD8ILWC+WpbN1gwwQrPkfymcNn1alZ33CfOcMSsXDgqznQbJWX1QLzr+uraS0ZFA4UDtwiDjQ7n0a1wpvJMJdNqO2OJu++2XDhiGjAlj2z5bKvcV+yjwZu0rCQpF1FWZNFE2atwjBR5jRDPyZa4Xp2Gx9Kk9debB5teVCqT5G5V/RcjmBAEbOvKJhQdc6qYr4qhwfUFBJNT4RP9uakZRsOWh/7urM4h5zINhy3b+JGZ+ET1azLykWS41XXvy6aSuV64VnX0qqa41l99BUaPdQBEIYtWmhDo+2CUV4DY+JZorC1HVuwVFwS6Z+6mu0ftY1FAVLd7qIW8rQlyTSmPNx4ZeTAsExWpsXpIqPwmHU/NbCq2umxZuU77efEXRjwouxnYU3TpZbnwoHCgdM5cG2Bl+n+7PRqfC+huSPVIBFjoY0XGjzCqvvGlrYZfRykP/+2kf72cjv99m6ok4z280lGYP1u3wRhwwWcRveOkyLsCv/wiOcIn3WPuNNhzbQnxSHN2eMR0wWNuix4cajTjQ70kZiRHq0LeLnfSf/4+7X0x6drsvPSS/cEviz1uhLKHHhxfngewdMqR0htklQFnMdxUn2/OuPzEFHiFg5McCBa5WW2wgv5+UxQXR4KBwoHricHokeButN7lUqrgllcxM5JmEQjl5AbMo3+beK8K7t12HMZ6b6nSTT2XPb1jCLCnDQW2DoEkKBdI9JSaOXtRBrdzV/bjDhCWmUAvHBZujzgGz1yE+IQTl2Xbw46sDhUk2O089WkC4CBZ2NnIy5OwIXQ+tjTKU8GSCgAfzRd2tp61eu2DaCyPBQWC3tRWLOs8LuyO5XQizGaVMGQ+fDmintU3mkHXFJMBVp90ZJCRjbwycEYNKZoXxhk5kAKM9AsYIq6su0oDO72O63UEfjCMdTYCqI9mdYL7cWakkvR0GLtJ9PKM9RZq8rxGtR6cI5RPchB1KpO+bkZfj43OTnPjATL/Xw5WGzPxvhpdQ4CPdMzZZizOFPcEqlw4LZz4NoCL7ed8V9WP+/xGFCal+9lxbjcQfqwuZtef9hJf/r1k51k9PLtyECXbWm77GqVCAHF/hBApjNqZnqie1ZvO0XQxPBB/OnwyLwRFj36zKgz4pGFxUXoYOnKrz0Z1WOVYywjw3fXFtOTu+30Dz8tp7/7QcCLth09vLea1mTrpSvhzIUvT2vF56rlm+dfPUQJX3KfrtSFZPolhJQ03zEHaIW0vGiN4T5La4y0sK/p5rlchQOFA+fhQP0Lag7BjEHx3HRP59wMm3BHRNPk5CF+4eGOCFdwj05mqqiYONOJVG6Lo8myxm3XShAwIMCF7cNsD2E8P9Sk2E6vEcgAOGMGZ7W8YhotkmXQYsFoblfIC7ZbAGGYKDORDg0XinF7HU2+TBJo4EPuIR2Kqd/VZMwrepriE++7uiCteVljcI/gLXw0LSEBDYBXYqXaGLZQDgW6yKCsDMsu9QFeIqNcgG7NoiL0m97tx1FT5dWvmRC/nSaNDjQ5Nyo7LwLuaFP7khEBpdAEIh7gi+cmwE68pG1x9DRbkbAFsyBQzww0y93CcDN3hSF+Ik7TdvjQsrnsdZgz01yTLt46YNPwsjSX8UXtvS1fRu4lz8KBwoHzcuDaAi+ls5j1Kr1Dnxxg/EhFTjMa7u4ljpB+8X4n/dsv0nh5PbBtRltDDbpDjpAGeNEAAvhSjwqzCpJfPYCcbQh22mZnNh12fLipVRgVNlnB41laco9HOzFhCnr1j1rtSAPork45WFteSA/vtNLf/9gX8LKaOFr66cPVdIejpXXCkQEvpgHkg6RVOdfcCoVsK+s4Cefzadb/QjI8X/ElduFA5kCzJQZTSosMTpR74cAlcIDxzCZkkXc9sHxuqLPxZ9aPNrJq3KvfcQW8RGAVEh6Xez+F3pDrmPzz55dAF018mfyyYLInOWVfH9NqYVIsjQSLr+iHEhTYIowBVMZvA144qYjJMRoc2G9hsiw5hzCbDCseE09egS/SUK4/ky/XxOtxH33X9JnXt/jKJASvajp5p0FfgzBTeyHEwwBekIUArZAPbduNggC52p2FhEbH0lLLeEYqBw4a+V0np70rXqJX3WvoNMd7JAo8sjB9cbeWhlu8AFpRczJtFwAXA/PEmwNt1dejb29TO8zNQrEBYfhWW1O7os2ZZlXeftShvandKUhh+tLlAIzKm6LXMrEYFmvysfK/eAf1p/2Xq3CgcOB6cODaAi/Xgz3Xj4roRKtOXSQeSFgZ7e2l7cE4vQN40QlG//LrRvrrm0F69X4sg7s68UfaLgw4BrxoZEIosWHJRqjsnqhu0+8snbZlNJFD/TAddjy/CeBFAwX/p181fQy0DCzkwarFrlYxBlKrXe4v6HSjxfRfnnbTH39Y1tHSPRnZXdd2oyWt8iBskJCtVz5wUiS5VtcxjyqkOAoHbiQHZv2sJtr8Z2p1vp/E+WJ/pugSXDhwMzgQPzJ+WOYOj8awZrND+euf8Yc5Wh2rUU3mcjELrLzzL7aRwKLhbWVGQNwBGXKaKo+vdSjvGLSjmJylPxKOh5drxfPsc1MLYzsIDqaFse1jKBsuHA2NJsLuyLeBHEpwcRBFE1/TNpCmgSa+ptnCxFe2XHQzrQTT6DXAxUEZoyDqLlJ8ApppouzKz4gl+ozL488IuDovyDsGqEXx0Jfpp0L2pGeABvF4mI/aZpvW/j7bbwQ/GPCymHqSg1aXOi4L8SaCV5bL9f8KwCUo9d+S6q56VGF6dO6gZaI/WKP6G9gC4GdADHd90LCSLM0x1ACBEq3lD/DHNiROwUKzat6PoM4aQ2zZiiPJ56zt5UauVwENQRMLhBVNmWBrj9egeQX/zn93zvrvfJabHG90Bc/PkpKicOAzHCjAy2cYdJnBdFPn6ZKiW7ORwwjz1HsyPLc7GqeNrZGOjOYI6WH6VwEvv8qw7mudZjQazdkqkkB9E1AYANB6mR4Ejtc1qDuJ0oqiRk2afuRIHpIAJq7It/YMGS6PkHXAGVzICjGAsWd3V4PqcHworZa5dGdpPv10fzH9vYCXJw/66fc/3pWtl2UdOS0Du7aCgaoogumMd3FStc9AU4lSOFA4UH5ApQ18RxyYHvpy1f1XMDuw8pUj3D468jRrUKr5OTl+O6Rgoc1knpnllXOsM/haVyY46I7smN42L57YGkR9mPj6KTScPKMnfdhehHtfi0NovDD5xe4G2hpso6aeC9jd0Dht2z7kBnhpCXDRzUAZ0+JVOEoHVLkJIFTyAXTo4zHqW8MHZ74m61ClieDreq/IhtMADAJedrUwJ0BrR3fABOf/gQ4ZEPDSa9nWaxahpvl2Xat4El2Tv4eIVf8YmuHIwrQtgKnqo/YG+GLbj7AhRLtkK5KBNPAzy4lqUC3xizbH0eS+HckBGTVB14BRHLcHQ1vUh4ZpIEymC0+74n4NW1jVluJ3k0meeWtEPhZe19EaGeGK3vCdejiWQfEoHLhVHPhK4IUf28TP51Yx57Irc17uRfxqAKHzlicW/3eGsuWyOUyvPgzScwEvf3q+lZ5L8+X1hozSjecMwT880OAwLyEmDwoaB3TZ1wlV/dy7baaNuE2/ZraEnxSmoM8lb2Y15baBjQz0D/DCVqOhhLheZy6t9ebTs7vzMq7LNqOl9Mef7qVH95YlbLQ1YLJP11fRoviJrCF3ZsBErPJQOPCdcaD5w4jf9KwfSjPed8aiUt3vjwPxU6Dm/Bzys/8KmoE1a5jMcVl0G5A9tseQ24KZ8PlqedwtXPGrI3YVblEtuU8QbYHGs7cSmjl7/hfwrUzJt3k1gRcPc9px25YitAo0RjNWAwxwZ3uRAQICXDjy12AD3akvf2bDBdsttrVDgIsmvLatCKBFdWQxyUEXPfi/BygnA2GMkCZlIqbizYSzWZXsJnEj8owY18rL6mocNCAB4IVtW4PhnvNfPOcQgo7s4fQFvKzqpEc0Nqih8epaVebLiEFGpkb5tVsmldxMTRUO4ML2I2ttiqhH4w/AC23StLDUHtn2ppvC3QYM8dA+8paZT9ES/+Ah7dAAQbXTlj5oavEBMKQk2qr9hqtq1e2qdlWB385hjKuLn3qsAyqXMaV6cscMv1xJuzUzvVaVn6pGeSwcuGAOfCXwcsHUfGfZNfudZtVP6oOq+ObgS12/ImM0bXswkn0XbS2Slsvzt4P0Hy90otHbXW09Qr10XsALKqYaFNT/m9pus0BzU2pVQg6tKaldHuTD+qw0OSmDW5Vf0x3hcc8554GsHhwjvHmPfPxuKSO5ykNrhj27YwkWIwkanc58WhH48nD1MP3+2ZKOll5O//D7O+nZ/VUTNrBevyCLaTYm5nyapRV34UDhwCwORD/Bj6bpno7rfdS0b3kuHLh1HFBT9zGRmk0NJgrzX4pP1tIcGqBMCvWxWVydwiZl2c95lPM6ts2kzpGSYxLoafy7pofnKZrMK9Nj0c/5WyW6Lm5RB/OgmEZRc3nrixQtLB6T2ZFtI+KEIrRbsD3HRFfbORSJ7RwAKIzJbOEwey16QLMA47ltuS0cjQMBMTaRpUiTH6AgCqduzhkc8If/adYS56Zc53xDzm/xdDgU8CKNl20DXjL4dbRvwAsnGq2v1sCLMVQMMb5Egdy5bhKznGIagK78S8j1iOpElPjNcdQ24Iofuy2wRY1FTVVb+QFhcKOJJbAQO0RozCjctyGJNYAr+gC+oEkEqMWn2oaktosRXm+HToG3RZhqvkZn0MT9SkGwYArkyB2P4aiemwSex618T2s+VmyOcFq88xRZ4hYOXFcOFODlG76Z6Mys08l0hHu684m40RF61+ixhgAv26P07uOWAS+/Cnj5ywsZ1v0wSu83MawmjReQfWm8INDMBl4gIEqltBgMoiT3y2TqNinW1f64mrWoKJ+MYk8Rr5FkRqw6v8lAozZIzrQzMGqcTHsa1WQ7LvVaR+nu0lH63ZNeevZgKf39H+6lHx+upXWt8jAwLtiqBHZePG8Gw3BPllaeCgcKB5wDx/sC/41O82c63nR4eS4cuCUcsGEuDx7mjnHT68eEjosgxhjAitjqgAeTvMRkzsx/ekSLp3HNsuNBl+Z2uviSgVl/sKd5QAhC9IW/fZjpZb8Y1GwyR/n6q8c5z88LshSzf84EiQ6nh4x5znnxwLO+qJv9sa1F4RqS7YNdDdN0EQhgGgUCW7A3Qjhh5DVvhkrdQK5NWjU+2wlFBsL4diOqbRNdCTPUgWKtaCPHwSSjURlWUgwR3dMWaJxSvm/vxXtga5FpvMjm3bYAGN9mJJ5njZcaeBGgZeBecDLzBZ5Ned1IjsW7j0Zglahah9dRcWivvr0tt0t5ABYCvJjdFwAYgELAGDPIi/0XeKS2qBu/O9uGxHY42q4+tj3OtLVcI4b2i60i4jfBlebv0Zqr8g0/L8PJNNJP+LJ4lu8JEU7yVllWjUZ4lImXg6veEII2KmA+RAxCp3Kxfob0+kR9ncbw8aQ82ZUdJ2QXscq9cOBGc6AAL9fg9UUXVHU+osm7uEnion+b7riwVr+xJe2WD1vpZdZ4+cvLQXr5fpQ+bgt4OZjXIIFApJUkOkv1arPy99LqkHBF5zkZju8FXhR2zgyNPn2RjBrxjGCnsTFpIU3aPQJfFg/Tev8o/fSwm37QVqN//KOAl0dr6c5qV8cpsiIhgVVMQUWUy3iMwx9xlatwoHDgRA74r2928Glhs1MU38KBG8UBmriuiTGSsUT+VevXoCIIwsYWxhdAFiZ3TOjiKNtDTeJ49omxj62K4tshyEgfhiSyjs+CJns2gZMfNlB8u40mfoscqczq+6L52dAWq+0Qa/SRKW6/2bd5ZY+mfyMKlXBghfHW8+A7JArcHo72qbZpqA6yVepAiyrENg4DnHJdmzkwdWXyv9Ba1ITVt2wY6CIVF9sKI7rdFolKYwFJz1Y3K58viHb6kHGgI+hSQL4U56S6RZRbdGchymy8CHgZaMsRW7vQ0gDg64jPPdm6u6NFKPgaMlBVfX+x1eNNd1h7yO2CugQIOdEeVGfAKmvDua2bXRy5D8zQLoCLtKoFHu7Lgy1I9jtVfqThMmBQ5ejEaX30W5QbAIa2HB9AxdDcsraqdDRLb7fk4peS2kXW4c5BEzcvWV7E89up8SNxJjke7e7pPUcv139L5AwN8builyKu0y+3AoMHZEQOVXwj3hJPxSMHIvrNEmWnJbFMske5FQ7cEg4U4OUbvkjv2mb2OVU/FOQ143pn6T6Sa0yV9JPsu7x+v2kaL7+92U5/eYlh3XH6JODl8GhBg4OObFRCjmG0Pk5f0ddFGX7H1/NuhnsX69/Ew9UUvDzt1X67UEX3T12c2n2tLLJqIRv0qSXhojV/mJY7B+mHB930TMDL//QP99PPj1fSXR0p3Wm3TIWZVQoGDcvIM7MB42prU0orHLiJHMg/mJmknxY2M0HxLBy4nhw4oSlXE40YNxhE2F7DeMKQxKPGXYAXN+jJyrkfkTwe7+nEmbGeAWD8JB9W1Q2cUDqf1JEOzRHGbmVpYxXaLprEad9wm5V0TehaOmFl0dwLqdttpbaBFwAwvuLuGjCZLjjsQoTTafRltud68BRjqoXkujC2VnXOSQjyyipMDw6saLsvRv81QfWtRaoj4JKQGI/PKUScEuOnEc1nDQHb/qs62raNloMBjM1MUrnD1ijN3ZGb+4sCJyXfqthVtMoRCW7tnfcEaDDIW412tLXr0OyV6F2IT11t38LGC9q/tCHbrhUMvo1cyW2eJm5XQ8Nnoq0r0KPyu/P2bs9ym4aWfo9x6hGaWhjhBTRFCwbgdE8ftrzjtl+RmpyOshDQpd+oPj22IWk7Ehoxbf1u+W0mLRIaDbk8J9DbfLjjPusV5ap5FC/W3LPi8hPhslvFE0pnNqErJzKZ2H0mv3P6yvMzP6lm9GbU8J+kPXyJKZ4ELRTWTFwVXhyFAzePAwV4+cbvjG4m+pNml9Mkq4qTHc2OisF1MBTAsrkrbZcaePnbq2F682ksjRdUIaXxYpn78FJ3ZtMlN0udDuM5KPR4MVi572RYM6e6hvheXDyn0IGXKAMBleEDHglukjFhnW7UOpBdl47Al376r38v4OXZWrq73tMA6MALq4UkgC+k45o5YHnQjfumSvE2bxzxheAbwIGTWthJ/jegSoXEwoEzcMBAiDxmRHT88LITewBZNAlDE5NVcrYp7MpGxFiaH8PxWFoIhOsIZT3baSqaGO8BtNhkzu9WRs6ccckWCjRZ4+hkjMwyaW7rzkSupWX2fm9Rx90uaKLXsnC21BJuQIdW2m2SrfSmMaJ8Qx6YNehRh6iL27TQRFP0WR0VQFrLh8mjLvyZ7POhvl4vnaqjepEPE1W0bu1YaDRyVAcmn9CO1gUnxBhIJKAF8MWBJu71GJZLUm7ytzGbnP2yMI/gHgQd88yRv4Mb72pHtl3iVCP4H9u7OuI7wEtovNC2qrZw23mDoEeFP3M1f3totvDsvwO1Z7Xx+E3wm/XtR5zM5b9zwBeK4QLjweguQKgBLgJGF/RbBCCl3ePPT8jegeIH0Ghpj5FJuyekvqKc8CH8xCpaZMm6lCe6+GVi34aFVCe38a24RKee9punP4g0uLNk6X4qnXj4kje/X/LNtJqf3NZ/oa0HsSwEK8DbnZcb8RWYc8+1Ih+i5Pyy74m3c0Q9MY8SUDhwGRwowMtlcPUceXpXUyc4qU+xTiRHnkgjDQ+Mpn3YEPDyDuBFtl1ebycHXvak8eLAi4ERuSOz7kwF1d3aRI41MZ9xBa1flvozmZ8heLJ8f6LT13igDpraHUh4O0zdBYCXdnr2qJ/+29858HLvTt+Al5aEUhsgrLzIUbypnWeg5PpHgSW3rErXn+m3nsLmL39W6yqt7tY3ge+5gjYZaTBAzZ2xxyZneesQWi1ofbDdY4iGi9zbO/LT5GwgDYRtffa1Us6kbY+jlFk5l00223KE9iYgjIpgxdwmOZqtMEED7LBJnCZtbMtpa/EA47NM6pal8dLXuNZtz+u44MW01NWWkk5Lz1pll19owxDXJkHK3yY+MwY9ACAmVU4XE0oBSLayL38RBnBiAImlRas2awXIwZYWgCby2JfGLbMmvikzQCMM5AIKMfnEj7qQFZ8q3/yspNXl2rbkVl9WB4htXlWUytEMvdVue296D5xmBAi2YzZe0M4Q2Cf5KIAX13iBP65JdauZQuWsiehrRnufrLt+c43mFG5+39h8qUEYtLwATF3zBU0vQBd+MxyTTnmANgAt/G4BHLAtyO+X36zdMyCjm/0+aPsOvjhF1nqVljLnQCHzFVUI2vDGr3rOzd5/Lwqs6hM+7lHFJwM98AfpBgCL+EPajDysP+KZ8PxMWptfkCAKoJ6AK0J27A4II1qok/UZ1F0gMW776Nnqoq85OhZ70G2W1Jrr5GXN6gmoRIS6u3wXDlwnDpwbeOGnVbX761STG0pL8DPuM6tBYOOqHunxAF4k1H38tJNevNlML2Tj5SXAy2tpvOhEow22GqVF6w4Re3h39i1H3alVOTZKIeYs/zqKtwM6vtPjeYrP5/e5eJ5D5BO1IVUMIk6xsUVfczo9YkG0tef3ZVi3pa1G/fQ//92D9LsfpfGyJo2XXkdCn3f8ddnKzSvmXrf8O97cd1TlW/5Gr6J60WqirJNaD/FOCou05V44cE05wEAS7TeasfzcNzycdiZE+DMZOTj0bUNjTcAGOyNtBdYCyNYobQ720qbssX3ckqaLbG5saNxG44XVcjQTDjVZY4JmbuXG/MNIaJBhpalo5l5sGsb2CZM0tujo345YXukuaHvtQlrSZ7m/mNZkx2NlRccG694TKLPEvdN24/ICPlh4AAzhsvJyDXm2U1xUpz1thUJrYih6OY3ItlKILgNMyIMJE+n1cbBGfCAzPgojf2xd2N0AFtfWYdIFaIQ9DCadlg/108dJ4sH+zdMmnhDWoJGnSpZR3HI5B3gXmi+bRvQE8CJQcE9AWE+AFxova9pqhM0ReMr7+D4vuOUXkqW1r9rLAuyx8tPvU26whiP9aA2U0O/VNL0Eqpox3gy+8Jvmt0A8Ayz0TmjflMLvAZ7btkH9DpBHHYjkrlZNPH3x5z2Mp52gReH2M3PyZ3xXRFdhuYZ6xuXAibsyndaPOUjnmnjYtJEGm4DUA+qnzglNPgegvM/y36Rll+nmNyz5GpDFgFX1AdZPATgBAsuWE9sN9Wnp4/aq/JdMvSGNetWXU0gY/QAXvIEvnqpJQdSsTl1chQPXhQPnBl6uC+HfFR3R+dDv4KbjUWfOAx0QKxrvNwS4ALy84T4Q8LKT3hrwgiE1STVK5N2TMrBeDR/vyMhpssviOQqbdvPcvL4kXjP9tLuZn1PRjBGsgN7cRVtw+HOnT4Yv8w3g5ekDabwIePlvAC8/rNpWo36vrcENdWflRAeudJk1zSKLu3CgcOAYB+IXFwHNviT8yr1w4AZzoNnET2jethVHA4drdbANR1uJBExg8H6k0wYBXLa3xxqfMXS/mzYGcm/rFMLdQ4Ewh2lLH9u6o/GcCRpjkDAYG+ZtPJoYpRnvxc9MC2MV4ATjl09ssJeS0ooAl5XunMCX+bQi4GV9WUcGr+iz3EkrS21NtLtpSWMfAExb25FYdWf7EXNuxkG7oEHO2C40VJ12AIqkrbOpOqD1ApHYrOhIi4YtQrFKH1N3lFzwW9THtlTgti1Pvt3CtlfIb0EFz2PjQvG9fMb2fGV6LEy+ExMuYwbsyLGrRJH4+75bW9IEutJ40ftDC8NP45mTBpTAOQFx66tte//sO/l+gRfaCj8uLhrSpJun3NxopPZjdZnRf5MGwCiCby90ABUQxoBLAS0AlWP1DWiOjAFY5Wf5SZA3MFLAF0ax2TqIJhLGeLEJA7BJOSajQpro4tcRvwOenF76Av8lENd8RSZlQG4ujAzIQpc8qUaulANHrtFCH8YHwGUsy9jeB3BIx6G097RNEuBVndU+9oKoi4E0zocolzL50J4cnAVgBXARuKT6Aba2tdWf+rYkg2NvyPsh1dnSeH9EJsrG6wDZmW6rhNUt19XCvGrE5zJa3Fm+CweuFQeuLfBSfjS5ncCIGZd1vAqj48XGy4eNobYYbdqpRr8JePlFAMzbT2i8gLKbRKVc6JJqzlYCi/nNKOQaeEUnGqQ4O/y7op8e3njhtWMsaQIvnfmD9MSAl54BLz9n4GWpi+AJ0h6iopdGduUqHCgcOIkD/vubDJ31oyHeLP/JlOWpcOA6ciDPSYw0a8VVU87tWje20jD5YIKC7RYAly0BExsCW9hStDHIwIu0XLal8YJ26uaOQIzxYdJj2hlpAqN8sLGggdouTX/zXTeF5eGNSJO/JiUJkpjcLGjijOJCT0BIv6O7Hvo9gTACX9B8AYABeFlV4MpSK91d7RgA09U2JOzCsPXIAJi8leFQNA0EHu1Iy2VT9fnEZ0vbmjdHApU0c9SFLZnVvrRotHoNANNVeR22UWjy54CQa8VwgiATqkVNspiAAbrEsGsaN7kiZuC1qlXmMwVFRfGaukwOiPCpsO/5kfbLhLoGXnziz1Yjtn4BvPS1DW1N7cBPjXIQr2b298i9GQ1MbDDf2UEWFn0FC6KH+uLZAAn9TAyUFWgBgAF4YbZgDAAT0KG4NF3TYDMQEq0Qt/viWmyuMQKw6RCDdQMGmACaGOAjCgjlt2hAp2mg+bsLkj0t4Ah9iD9x4MbB4V7WzgFYFdAiGtkqNVK/BtCKQWYDW9S3jQS2Aipbnyf6pQRnIF615Uj5ctGNGQikjovfODTxe6c+yNpdGRYGVOqqnvRV7facQGC2QmJ4WNp4AmQcfAKUURqlp4/gMtk8futWuXgg0KLEzd+Ze9l3I2bDtzgLB66eA9cWeLl6VlzTEqPnDPLoPVhsomeXoMWdVagPOtWIrUYv33LfTr+y1ejTfvo0kP16Gdc9mkATvAu6zh3RSbQ5O/x7JvCiIEIBpNhmtCCtl87CYXpqNl4EvPyXB+knAS931nzVj44dVUcxSZ26BiQlnmBV8P0G3uHDSXy8gdUpJF8bDpy1ZZ013rWpWCGkcKDiAGMBV9WHZseRTaz0oPARNlwETrClaLA7MlDi7cfd9O7TbvoogGJrKK2WoYAL2dfY32OFWFokTL5se5HSC3VxvIX84vfi45AX7uUYERZs691WtoVDn6IwdgG8ANmY9ouGtNaCxj9NdBa1ZaEtEGZZoAgAC/d1AS8P7/bSvdVuWluW1sNy17ad9LUYweSIiRzbIt4LMNoSQvT60yi9/rCb3nwYp7cfRzYpE9YjUKeVHqx1BOJoy4rcd9dbacU0STWuqkxf5WY1H6CFSWS93cjppgKZx/KQQJrHYSob/PA4+rbLx33CnGXUvVzHOUBzYivIQGAgBl+ZSHOqEZN/ABkDXtQe2GpkBpfFRia55Wq0O2uD+efmTe44e9T+AB/gXPQZyOVufBc/th/qw2+ee34HZj9F70VetnWHZ/oC8og27SeV5W15+n1DCSCt5aOEaNLwm+d3tSJgFTtOPQEYAB9Brt1zWjK3XFQQ5e2q/8K21I7ayCct3nIC1iYaeQJY0M6j39oGkBHoAhgD+IKmi2u50E9Bjz87B0SMLuj3D23KgSEDh9W+WOzsqj8Q/iJ7VIsCiedl4Fn9hrSvVk07ryNAUACM+hG00tmWhAaQ4cFisuVLIdZx5t4gN9tmVxD1JypXadnOh/L97TlwbYEXfjTlhyImTPcetBn55e6TftRWNDhO2oCXd9h6CeO6Al4writO0lkbT42pztlJ/vI0qzAKbF7NeE13Mw7uZljTPSsefkZdvp/07ut8Irbnhr/nAD8IQ2gEeFkU8NJd9FONnj3spf+qrUY/PxPwolON+trnbjZeJGjSfZOOq9l5u0/5LhwoHDg/ByZ/pedPX1IUDnw7DjCW+MgySQPbNRh/mXwMDXTRFqLtoU4WHBng8kIG7l8JfHmvUwW3dqXVgnbLSBMQ5begRRBmSuRtK8XKml+JX9OlNZ7rSDY+8QhIwWWx9MXdJmwAMEG8PI9s0qUFCK2Id7W6vNybF/CymJ7e66THd/vpnkCX+/e6AlE60oz5/9l7E99Kliy9L7hd3st9qSrW+qre1t0zPSNL+mdtCDZsAwIk2wPZFmBAAuSZ0Ywh2NLMdL/RdE93v9f9tlpZJIv7TpZ/3zl5crm8ZJEsklWsiiRvZmTseSIzli/Ogr4PxI+0AFf9XvJMSyzEHrOh8+TlTnoG+PJiftsW8ayF0vTYYJoDwLl7o51mWTzd5jo93rHd95Z2s1lkaVE/AAikuoG9sCjUeBsVV511UzyHO/2eehsnUK/ARvwig1raj9Vp70Xx8Lag5ySrRuJkEPfCgcA//CRu5BwvAGbG8UKbFO3zsdKu93PXPjzeWoEMvY66tyz5WP+gL1v//PQt2ZWTABhZlpKiWonzqD2kmFcAiFlHMvEd9TESIZQYH4qQAR8MYcEXhiV09Lh+FZlqV7tJR880egtHAdIEYAjk1EdW1ta+paJWlKf67AOkrNExicNlHeB4cXHTuPUWAGDElbe4hu5IrurDJB4lkNgUa+t5ip+6FpWhZ9NVB4/PwbfOzznYFCJgmDPVUl2HuekACJtIZLs/3aTvEAAsIFibogIDx0fhyBvt0G+5SJKLJooMyoi8o0AvTmeVqJMdHlyP5P4Wx535nCnwTiiQgZd3QvYzFHq037BOzrt2BcIOXDMn/WJhI0nU6Ntnm+nFK1DsDVk1GgBR9xT0e9ZRqwZF/yRncYRPj0IthsK9TE9wmnjHxVEOkZ/n1uvsNToaT4OSnqh+qMN3Vk8BL0xy6ZkFvIxgTvrBrWF0vIymP/581oAXNyfNJJPJobEyFtSwp/NC61l/pG6jxkf67O/qsT8kmn9Iz/Ku3odc7ruigBYTNvLoNdZYw2RfC689xIqkaFbWilYFuKDHxTlcxOkCp+nSFqK/EvNlsQuHyw5WirbYKZaukwFWHkNcyYoclV812ITLilMEDjubh936qYxYBZQLDoVRR3Hl6NDyTYo9tbgDA7Gd4/bwIVwp/WluqpVusOieZpd57kbHdL8INOlILMhMPfehj2aHOcRe+hHQ5enCdnq+uJ1eLu7A5XOQ2FhPk3DO3J7toLwe8Gayle4zxs6weGqbJSWJCsBxw/PaGKu62XMJhFHtrLK4uYrYtUN+tsgt4vlYr7OoVj+ad/WQ99mtp72Mmtfz1eJY7S5OF+nl0FXcFg3gpVCuayJmWqt7w7zPpHsv6ubvYfGONmqkVi3eZS6Fy2LoFddPoIe1gQAUwA+Z+JbOFNOlUoj5BBeJmsP1pPSbXhjpgxJXivoegSBbgDX69jt8Z3ex2ingVObBh7kPK0oq/PC1xMsAawBQBBYLbJHyb3HlydrVCt/5IoCLuNv0zW/ApbeMYQ7potrceZ0kWVgqAQf68M813mB/Sj/7l6q3Wy6PUYXo3qyw0SfA6IKOIerOHHxqFH1UowPWF0kH1XjBhTc10QEohpMHheFSCG6W2QBuSktJemdViv172fEK12tlDVCcLL61jNeuHpbdmQJXQYH3Fni5ioe/FmWo94j+oXCr04vuTV6bAC8rq0yIFtYwKb2RHgt4QdToGWzBr9bcnPQB8pyajIXspZ7dOyC54ojCdH3ToUqdJt6b8nlzeDx+PWbUtO4nOjGGmXJC5hcGurTYaZtovwZ4YVJ4czT9kcxJ3x4DVR+hE4eNkdmoUHjRRYfRtleBjYLyTaZApsCbKdDzK31zshwjU+A9oIDvbhfjglaxHNqp3t6R+I0WJ3tp/tUGnC2I4QBILALASP/JqqwWsXDZ3GaHmHGXNRI71NoI8F3flnFYkhn3voAxp+VfnbzcGIoaX5I8rTpeJ6WJJY7NFfBW3fUnPRACXqi2jXPadR4aRMwEHTBTcKtMwvkiDpi7cL5ox3yWhc4o+mAkCtBifFxGjHmN3xPmEiFmtIio0R4PJb0TyuMW3DJ3xPECiHOPMXZmqo31JBd5kIiTFo4aXkNPgy1aiwcrLrbgD3orrrt5sDKeHEGF+lVPryNy8rvrca7a76LqH5TR88stUZRNVs1aaG+xwD4eeGE2aPMgTvk4FQXsPS42NOsJqjfVZ+l6lw1I5Bv0FuebBEDRjYAU9SniojPghfYSF4x0vwigESijNBLWMbP0tOUyiroNEOEbFGgi7pY2XCOf3hlN03yDN+DmHpWuJQANiSqpnvvIOIrDRTqoVgFWNrcRI4KTTUq/NwDkllg/vEKHk/S8rOC/vQtgR1m67onThUqI00bza/45eEpVTB+rHXZjfhEOPFO81grjEB240G24wm2uYLwmjjjaAYABiemMYIkNjp1xAEEBSLNTw6ZDSiJIk6YcHFAXxFeii6b/hZc2+pX4hqxGUT0ruKiqqsAv1k4Rv4iSL5kCV0aBDLxcGanfoqCi37Ic1KFYf+aeOstc5eraVlp4tY6OFzhe+P3+OSJHCyjdhV3w8BCOF3X6RAZoLkCGo7BLdEt+7a6vujMv00O677vjX9y9SqoOv7NBr5xsFfUiSM+p3y6DlmTcmf+lqZHX6eGcTwp/BsfLg9vjdOptWBglP1qY0ywKUU7N8qqSsytTIFPgLBTIX9NZqJXjvicU0EDJYYv/YmEh4MIWR+wwr65vMtZupkXEiX6UJcEFieGgeHYTDphNV5apoVLZmIgvV/AXWON9zHUOy97PqpJ7jT/H+UcuCu95EKAwjZf+57FkvlmiQoOAMFr83EI3yw2s29xk0abFmxY/2j2XeMEWolJLq2zuMJeQzrh1gCUt6LUAGkO0YRYdLwJfpKdh7saIiQmI46WNKIF2t023i57KHqx4uvoFGpdAC5EMeKGaRZSej+WevZ76zalOyPAKg3rVPYo/2zM0ctJNkdws7PDebrGANlEjNui6gZdOjeNFYiEmohLVeKfX2oO803q8oXAjfqMFygTha6CLvkLaQod3L/oacatfwcMU5QJsmPJdJrBmolqAB+CKuGK2cS/zDQosefxiHfG/A8SDJCK0ZzqZRmnHn30ynm5NY71zTnqbJDo4ZBaEVLB0UYnjaW0DUUHWB6+0UUv/9RTRwTXAmwU49MSVZyDxrtdL9ZNYj/owqx/3VDEewN+zeEh76Yqb+IAVl0O9nkL0fpGYPD2pXlNfj3BvnoRzlb4accHMjMGVx/OII+8mAMwc4PAtfhOIIEkheLvdpn9x3VEhdyRFvkZf67etVErxQ+XJJ46zfWWRKl8zBd6eAhl4eXsaXk0OvXoM72HSNui1OtSFZWTLmQQ+FvDyZD09gSV4cUUmBKWMi+4X+fJBOiQh0RVLaT3jNz1Kd9d1XPze8aKjq0rsHa87V5+ueqpyZ0/PYJ29Yhe+ZMdmgbFU7jBTlhzpCDLts2MAL+wG3L8xmn72+QwiR+M+OZQsOx13neOlu+x8nymQKRBfbHzBmSKZAh8BBTS+Ml5qd1r6F8Q1ICWUC3C5PEOP2gvAlx9frKWX4gZhnF3bQgyARYtZB2IBAKQPkeKnUcq/I1sTFCHdVFSMN35ltc8xnJaP3Sh14WsZcdItFy2edAQEo3UgT8di+zXcKlg9YqE2Dfgyw0JHijqla6GPwVGb89tw8OwgcrANCLOD2IqAqGE2LTqIAEwgFiCwZhzulxtwzYxhOUn6ZIYBXtx6kVXEyvZFqDlFWjscdPGb8POQD/lcNIpTgHPQqO5/uuf3VvUciimh5WZ6PNiA2oKLQUpRxe1g5n/NqhEWqQDOOg3luu8T8HK6Z38/YxUtEg1TVtLbuAIZ/dNUm/nPOdOkOFfflzhU1OfIqpBE+14iwvgK8OXbJ2tpcVWcK4DAKO+WKJ+sU/304Vi6AVBx/2YnzSD2N8F3KFE/cc9sosNlbVMc8DusEQBuUP79Ek69eQAXKc9d2QB0kZUi6qKNSwEuZo3IYBLVM97PeJh4OPnLHVe/O5qimd7uBMT4P9AQXD8QQRopRYwWc/fxkX76k0E4XgbSTfqXOUQabwUwPAIHzMQIXDIOLrlVLokgqSZ2whFl+jXu4gnyNVPgXVEgAy/vivLnKVf9G4dfihvu1TmvA7ysoGD3OQqynqBg93d0zo9fCtWW1nIh6VKWxw4UiLJYno09z7KocruyjkkFRfXrhYafHrLHoQ61itIEXqzjJy/tGIjbZUc7coXyrjvTA+nR7TY7AaPpJw9nUAAI8ALbYgvTln1i+yZdBURZTj1Kz16ZApkCmQKZAh88BbQKKg65NKbsoBdhnYWLRIyevVxHnBfghXH2x5ebcIOgzwWR3k10uezuouhSrPAsHLTJYcObBhgyMuDFPDzzmjOKs/Gtl38ZQY6onmdbBYV/+BQZxUJECz6GRquH6Zlg4aOdde1ETwCgyNrRBGJGM5NuXcREhjA9MihFnSzGzPysxA1Ynako+XcAWMT1Mgb4IsWeU3CSjpCHlNaXloy66xH1s2tU2iOJVB/HEc/d62nPRoQyp3AUyZ3jRcCL9Hmw+OZ6COhSNyddAS+8JYpcw6gAAEAASURBVKQzsbBeVfqg/ESos9H4bI9fa4iuvsTyiWBuwhnRdBXoYqAZ/Y4spgnslGjQvMQZAUu++XE1vUREaBUOtDU47GQtaITv8LMHAC9wrt2TgmuAF5mPVz8k60Wv1qUAXBzwzvGyCICzgPLvZbhmNlG4vLGJOJLEplQ+V5IZoCOdVAIzTk+v8onsUatT0DuuxMMZlp94M+2ZqQqeAoMletSHpbR+9MD0o7h72Kyn3ZzWtW1g79zMOH0N/Q4/FztysCj6O83pK6DXfKvqZFemwDukwHsLvOj7i0/0HdLn/Ssawog2cZZLu3FSsCvgZZ5duCdYIPgNnbMsEcwvo9Genao9AS/8pGBP+kxMp4nlVeXWpLfuPExlHH+cI149SVGoeVlx9cBmqeo66zUqkhIJEIZnUphNkjVg0YPDKW0d973ZwfT5vdF09+ZI+uKTaVgWx9iVA3hBU7wAKPtVmeX3rkn2fJcpkCmQKfDxUKBYBemiXdhddi7W0IsgTpeXbGx8+2w5/TDPzvEiiyHGV+lBsB1hEyVyIN/HWI1MPmIJ9LCpP+NMbag5QtNqfFMsy7WMU1SrvNe+dDVGVylVQFmGVtMcOrOeIzrx+JfbzGLLj0OLK3G+yHjK1BjmphEbujnTNotF0rcwyQ6zdtVbyCX19zFu8mfgEjs5GkdlvUhgi3S7yHy19DgMoF/CxQuiPhTi1fFCC9ropr7xUQR+RBe1XYMw53r2ommhpSfXvRbwZtUIjokN5EgO4aII4GWYNlObTiAqJn09aquPA3g5F3nPkKhoT/tgcdMe5ow2VnBxiOYehgf+ChLwoi9f/mauHuBlA4DlxdIGfdBW+vq7FUSDtkwXywZ6pDR/ldn22+hXGjcz8XCgobtJCrQHBtAZQ7tL4be4XV4iYrTwSqai0RXDT4fAWIGq6qu8x9ELZL0V37q/F14zi26n7rdV9T56RCyFhluuKrZcek77yW1h1Vnc6HQjzOX7AIf70wTg8F04eiTe+NndyTQ7LaXgHePukdiRg73UnY/A1gRRlO6PVjD7ZAq8Ewpk4OWdkP3tC62zKxpLIsj4Goj2CyaH4nj5zQ+ryJ8LiNlBBwyTR7ERslOlAdY4Xmx0Vo+nf3VJ0UNF3ep+dXeEx7UeVndHeFyPCZN3HFaFY+JFnPIaHSm1j5kGV8nD7gC8bDDREKvi7PhAejQ3lL7AhPSd2ZH06b3JdAPgRSi5FI9pYNHja1Pv7Q89QP2B3j7Hi8/hOtTx4p865/i2FMjvzdtSMKe/BhSwMUiLETf3ursv8SJ2ilnoPIXLRTrU/vB0GSX2++w6++Ll4BARXukyYSwxLpf6EFCsqopsGat60UCeEaNXePj5YizujrsG2OHhVYG+nCnuKS5K1NWel2eW6JGsi4jzRWIL92DvnxKbPyz+ukpvxDAiuoNYPdIip5/5hEAmiTRol1o608KCkcbUGKUF7KgcK72q0nGPkP3PQYFYvCqp3jPNEc2qETo8jONFImIGvMiKDqJiAl7gUpqgXevAi6U/R/k5SS8KxFdWhAHO6qh8w+Vfh3UXOHUnt81nERPbQH/U4jLcdfRD36FGYAGOF+lm2UTUSDmK60VmwUdQUDsB6CJuEXF8vz5EqTLtPg9gvCSOl1XpaXqd1kkna0XqtzR/9unv0Q9TooZ+WI28YkSL77oIPHJR7OrQXTPvCnyRP5vCFpl49g9nHn776oP5qd9q0YG0h14n9kvTbfoliVJ9xlz+JkYybkyj2HsaYxlw542gm0oAjPobgYhRbrlGsHLyKVPg3VIgAy/vlv7nKt06NeuhPbk0o++BrEiOcxGul6ewP//28Ur67gWy6Cj925CML8i4zMGZ2UA6Wk0SrWe3bk35NDvGc1XsPInqxdqDnTITm1koLrs0ctsB8AIr7Q4/aYCfZOfu5hTcLnc66csH42b28v7cpFk0Gmm3bLAy4IW06qjfFQm87vmcKfChUUAfdHybH9qz5ef54ChQjD++YGWnGNBFrP5rsOk/Y+Hyw3NEjPj94elaWlhFke6GFJfqFZcFIJmJdvb8oIsvojzT8kt4q8/BExfVjGIa12pB0/C2G09Xr0DhJmBP5mbZsDg4PECx7gBiQ/0otexHHxoLG/S+yFrRTXaXpzEVPdpBMT2cL+JyEdjSx6JPfxpD+1jsxCKnn938+P7LIVo+9SocreZH5FPR5yIeWu+bg2tOdeWuzTYzJ12KGsmCjkwLFzpexPFC+zrwEovV3rXJzdabLif7qhWKw5xOxco3XLoWYbV4Al72AF7M2hAGNJbhvHsyv4kJ+2JeT/8jzjWllGUj6ToR51l/H4CF5sF7slQk4AWrRXC4rMA5s2kbsYhForBX3Glu5p4vuEcDByRj71WsOYin7/1NRzyZ3sp4tkhT9VMO7BR4lAdTDq+tvaPql6SbSrEG+w/Q23iI3pch00f16PYooAt6X1C4e2+OzVRTFN22vljrnAE0iEdf1Kyu1/3NTxC1zddMgYulQAZeLpaeV5KbdWhFJyj3Ib3UPpNEKU8TIi49L797vJq+Y5L4dGEbSwuYkQPhRrH5OYGX6KKsZErUfbjrj9zt331fj1u4i6wtZq8seyQxL40SFr/G8ULAHpPHXQacXbTsTk0MoBV9KP0EMSMBL+qg79zEohEmM6UV3SaNTJpVdgnsH1de9s8UyBQ4IwX0gRYf+BlT5uiZAldOAV5XDavaZdV4uonZ6JVV7TJjvYhNjN//uJa+f76FmNEWiwJ4Q15jqpWrJvesd7j6TmssKnx4snNz6XHsJ6EAxa9H8PR1v/ARfcoUOBopLVIR0y6qlf95Kl86RUlmWUXhPLtEiGRyuqPd5Sl2l9lJvzc3km4zft7gN4sC3ZFOi4XOsC30bNMiWEbJUDlHvrGYs+G6qE6ElZH0IPl4awr4lLAgMrnpXqaK3aqRFu8yJ+3Ai9p7WMp14RCYQH+G2lztIg6mk46y7U6KlMPeSIGylbzRaKvmPFYZePsxt6cNd9k43QIElr6eV+hpERAjy2LgMgBpAkylI8XbdkeikWj5FjizIqW6KMZ+BafLBmsADKBavqlAOtTq2riM79QqrsqpoeUvB//OYa/+wWKYv4Wp1znyUshDfU0cRYaVB0kKP0VRdMpS3t5LKbX6Yc9F/rpHTbABSs7Ngyl7lO7O0j/du9FBhcAkHD+yfjSWxnmfW7zbLcko6cH49w1W5VIdXv/qPrsyBa6KAhl4uSpKX2A51qXV+hDJ8R4KcKAXfsVEUXLo3zypgJelNWlAl1lIEO6iE9Jk0Tu/XhVTT1g/uu+98Oh4a1WpJVKaCKm7a1HkVMdohwSe1PlGmsK7cYl84kpg4TQ5VW41adYgdHCwxwRxEPGi4fRHjybSl/dhS4Qd8cb0KIq52LFjt87NelJqVCGujTI/xJug8UfzwB9iI16DZ9J7lt+xa9BQuYpQQGOPKXtkMNlml2IFTpf5xVV+W+n7Z+tYCtxAmS5+6EjoM1EbOD4EthSDkI2HvO4+rnofG8NZ40vo+UnIMwLq/XO4u5vI42ouEKlKRyMJMWr3cpbxa27tKSsv2z2HU0XiSgPsmt/U7jLiundZ3EiHxJ2Z0TSHrrTxUUzW8pNokcQcZL1IR3f+8ivHV93k4/IoUBA/5lC6FceLLOLILPEGq27di6tJIkiaAznw0jLxDL0X0Y5RSfkpnzjq70745evZKVCnaeMDtaycympHm9dyMq52NhN3ZZ0KAGYfbhW1ocAWMyiBnkcp/t6hrV9haOPl4gY6YbYQh9wzsfs15v8EEbfgcAEt9X6rd92tBvbhFnXRW6D+kejuo++dO25iHVDPKeLpWr5BflOPhjvm356rxSWe9UWKKW9Lp15VAknicPdfa/AQa2xDNsf/6cMp5vttNlbH0vTUCMDwEO92qwSVNNfvPrze3b75PlPg8imQgZfLp/GFlmBdnzqi6EdwixVPE0Yh36sg3UuYlf7+2Sqs0ZvpMSDMC8xdLmB5YQ3EW52tfv5XZENekZ1XNu7U2VWFuW/tXqzE/t/1jIoZ8XQ97oh4hEeRjejhqfS1CabdyY9w/s1CA+G66k8TxgHqdvuG0PDh9LNPp9Ond8Ztp24KbpdRdutMFt2Sexm1MUYZ5yNTIFPgrSkQfcBbZ5QzyBS4dAr4jrEWMq6QdHFlk3F0CWX16+hVQNRoHosgKNRdXkffAPoE2uhTaMHOLmYPgRsG3BS19FHFb0x/AeGln8ZNu4trPJpihJ+uxx0RL8IVt1oIWRYRxFV183x1baat6gTwQjwbQdm8EADD9gXiRgOmpHNuZtjAF4kc3Z8bh5tUYkcdU7Zr5rMBYHQoD42lXqa7LSCfroQCAbqoMDW75kTb21jlQhR9A47oQ3G80LZ6x13Hy5CZDpdohpS0igs4DntTlEnlVXdGtHw9JwVE2vLgg9FmqLefE9zn3t6G4Cu0pbv34egOgFh57NK2AmNevlpng3XP9Dw+ps968mIzPUf59xa6XHb2BwBqVJqUYkssEgBGbHq8H3pRdCmPor1DTKcMio863gLqGzUt0zYcCo2nqF6jMpuIW+ZTxC4KVD+inw55KZ3pooIQtuY52EXp9wAmpofTTx+NmqLd+7fGDXwZZ3NVFtak58UUnRfvtWpjz0Ve8XxWQD5lClwhBd5b4OUKaXBtirJOg67Okd+iyys6KZmFVOe5Duq9jObyJ/NrKNcFeJlH2S47drK+sLwO5G0shnSYXDVVs/5IHVyDCtWdZ+/3fi4KJIUp8eM2fBpZnPUmimxk1vSMO2Xt0VQHyYKKy0Xm8MSKmBhYZNGoj4kiLNKY1vvpoxnMSY/ZZFFste2WK+BSftH5Wt52OmvFc/xMgUyB3hTQV5o/qt60yb7vGwX22VGWXhctYlZg53/8Yj3943dLjKECXbYwJy12fxY6e+LwKKz6iNODQUgLITuKVz7GEy0WipDiS1CIfIqInuotzyfn5QudiKNrdZRfZ6xwCLKddMbSPZ5Jyiw7rb40PT6Y5mZb6QEWRR7dmUwzBfeolLOOIrY7NCRLRzp8oRXjqnnl09VQgKatlrlepNpye0fm0P29Nk6Jg32AF5TroqdHnAFjWMIxRclMntRu5atQe1VKv6t5ko+zlK5PVG0Z7SnQRXN33QsgFvAg7qU9OF82t6ROYCv9CFfeIpaLnjDnf8ac30DiDYneaxQGIOYsbhGJRAomFSBRAnVRtoopqF//ht0vQhTBv3beFktR9iN2p/DqiFQRJ+6rGJ5bva9UmOLHexdppWrXOPzVP4EkYWwNReD9cOINoHB3KD1gnv/Fg1lEjkbSDEY0WsaRh+6b4t0W/TxPf9frdcjuTIGrokAGXq6K0hdQTnTCta7RnOrI1DGp43JlgDtmeu4pFhie8Pv2uSwdSfEurKb76mzVgcNWSiJ1nAa+FHlwabg8Z/etyo37U1wN6KG06DmPTWJPYKGqWyTwZN0+ogQdqP7oRaVQd5dBaJdJRouOeBTQZWa0Lz2Y6wC8oN8FE9IP2KWbGG+hOFCgi3YpQcKLBxfdrJw31tEqlk+ZApkCp6BAfKOniJqjZAq8cwpo51jmW9dZyCyj1+W7p+vpq68XAF7YyEA55f4BWgYOXa8LjC429pieMEYPcYjYKMV4p2HEJvdy4Omj13GPV0Q6Enyc/5GIXkjprXTNQ+Ob1yLCjtZI9S1qzkYGizpb2KDMEkWd4CpYM+pLt6YH031Ejj69N2WWRO7IOiDIzNhoizgyM+11jtzrC7dmjfLdZVEgFtLeEg6iOccLev4AFMUVvS/gBSuX3cCLzQdrunrixbVmLd4g1Tveost6ho8+3+IDivm+X9XH6CvmBAgjcXpZM5URiTVEi16tYeb+6WpaxNqRrJkal/vKLn2ZABpxt0gsEvjFvnNvxaod44t1ypd3xK/iKEwhTR9PUffV2qLMIYKLdEf9Gz7caFYfZ0vMrXyiVMU3rheIIWC41c8mK/3T7ETCmIaAl0762aMb6Rbz/rnZcdPlaGamZcFUO83F4QBj5Bq++ZopcDUUeG+BF31g+bNovgTRSTls4t2gqGR0oiNSxyylshuYv1xGye6LV5so191A3wvWGGRaenmXnQ84RGRams5buIN1xDGyFsV1d7e697KjBs16Hb1TjYq4kaRnYx7XSStHT2BVszy8S46yNM0Vgq9dgH00n8uSkSaMY6DfU5jDnEO/y6M7oyaXrh2627OjyKUzQUTplosZkVrJi/pZaV5kFJGvmQKZApkCmQIfCQW2tnfgatlJq1gPebm8lb7+cTV99btX6fHLnTSPuO4gZpSH0O3i1jIYoUpRWwYRBhLnIS2IpbGEny42csVAcyItPfaJUSywHi8G2EilsDg8XjF8hqdd6+Pqa26k4SXGXFm82ae+4iQVQ88Qi5vh1us0Mz6EdaNhgJcJdpQRObo1miZh59dvBEtIrqxeZTJjqFejUXK+uUwKCHgx0tMAcos7YEtWLWnUbXS8SB/IvnS8wCkhq1TO8eJWjbQw1YaU2k6va1zL+tbatHJW700ZLzsuhAIBoimz+jcsf7d2dACHu0QfN9MC/dXvflih34JTDz1US2tYXVuXMl3egdf9mGJGvEj6qMSgR3p7N4o2Vv7dc35r1aqRFYUj2loBViPzrU6RIMKOi1elcFdtHRBJu6KUmAlZmpgR4bv0T/08y2D/Ic93yPx+gD6pnX7+2Wy6A/By//a4KQDvgBxLl1GYuY+sMzAclMjXq6bAewu8XDUhrkN5VZ9UuVRvExm3BwCEAFXZZudufRO9LsubmJPeTL9n506mpZ8vbKFoC84QwBd0cdnE0eQfu2ZJ3Z3wkQH4nMRq1vpNmRSdOBcNFDrMp/BW5ytxKfX/TCNsYqFduhksGc1qgoh+l0/vYUIaZYD36IBvTI6YubkhtAsPgH7XEW9lX8/7TTXL4ZkCmQKnoYC+2+KDPU30HCdT4B1SQGajl5bXYdFfR18CC5kfV9KvvkNPwhLWArEKMsLkXZwdQxo/bOHhe7u2QPIhCv/ifdel/uoX4Rf7eCdlWi+8uXBTHWzIt+SMo82oZsqV9ToLHMUEhWGCIWBmgoXNzelWenh7xExLP9S4OtXGsoiU7RbiKhJp0G5OcXTnHf75egkUoL30RsY7KLeJGmEJx4CXwqqROCW0SSXgpYM56fGxwjqVAS96t48eNgesefeKUwvOzgugQAAvNv0VwdW+3AhMk84ecTAtMsefp68SUPyb71eY8++kZ692sWLKPB/udsVVRzQIi544Xhx48Xy89/KKWntWp1rtPb171N21KBfgVM7lETe1l8zXON5XiR6KokfzZ0D0Ci6uDjq35mZa6Y8fjqHnZSQ9uDOWbkyMYEyD/glweMBAReXBF0LeGXgpKZ4dV0yBDLxcMcHfprjoj7zbqXJSR+TymnRG7GbIzNwuJuVeoetFnfIfYEGUst2nAC+v1g7TGkj4NgCMWF56AS/qqP1XlVjrA6uCzaWQKl5XYO22hmrjG/nVU7qf59f0r+6qSYV2dGJyiJnE11ia6DtA0VYblujh9Nm9MThe6HinOunWrPS7tEHF2a1kUigZVyvLi7Lal/e1GmdnpkCmwPkpUF8EnD+XnDJT4PIpoLHk1Yo2JxgrH7+ycfMbdCZ8/WQfvQnsLG8eIMKKQl2ZKUVU1cEWn/YrrY0ftWraiIVn6W8etQgWcsSzHsHcWkbH+FrmVY634dOdT/gfyQ4PH4cjRlwjpp5Fhy5yH0jFrsSOmFOMsLCZgZP0E3aVb0yysXF3It2a6WBVZJTxddhEVwb7EDkadK4JVb07f8s8ny6HArRXvc+VG6YAOF72zOKlrBvtYRXnEO5gE9PgPe7wTsv8rhSuioOrnwW6FqX1Q/f2XhTtqXejK0o9enZfEAUCePHsBKJUul02thAjgjtPir+fYUDj2cJm+vqHNThdULKLJaPDQ+k1kV4X52znbG2mdrM5tNrUvvLIXfe9DqXoHdKMfXHx/P0iP0NblC9HrWNywMTfdG3AioNrCz1GQ0Ov0UU1kH5yF0tHKAN/BGfe7RuAw9NjaQZweGhQ838p2yVr+xV5ewn5nClwZRTIwMuVkfpiC6p3mt4vOopbWmZgV2N1Q+ALSreeryJqhJZzOmfJfr5aw/Qc4Ivtjdioql6oXj/d6Fd0uCa37u76oFz4VPHqaerZmbsJvMirVkIRw30Vk/lCeZRshlYfvzMrTtwf0PFKYdjgwCE7OK/NitGtWeTQ744zQRylw+3ABaMdOSYXpttFna7nUS+//lxlwdmRKZApkCmQKfDBU0Dz+ucvtUHxKv3u2wWAl210Jmylp8uv09o2Cix3UTQL6NIGVJAiUg1FWhgpncZBjSVxlo/8YoTzMI8XvgrzpUP4eArd6Yg732iIOw+rh7pPPTxKi7jd1/qoV69xlVOxLYEH+tNY7O2xsNnRwgb9aZOj/YjtDrGT7Byl92bZWb6lhU3H2PlbLG4G4aTQ4ki1Uml5bHXaXvoZgvs75e+A3Aa8wBkhM8TbKNgN4EXiZHqPg+PFgReW53AFFKrvynewrDfZes6lT3ZcOgWsUe0jOgAwk0JdteUK4pBrm9vpD0+Y25sux6303fNt/PfTCkY01I5mREKbq9RR74L6K+9PilZ0NhJCdd/dh8R9PUx+x70B9Xh1N0kax0lh9YgRT9d4r81RA03g8Bf3D7/NrQNAlcOEhft0b1r6qIbSJ3dGWAdMpbvod5y7MQE94FYcHDKRSBeLtKzzKVPgyinw3gIv9QHkyqlyDQoUfezg4t1h0UFJyR+jrXS4bLLTsYKW85ev1kDEpWBXVo7Q9SLwBVR8/xAU/bXv3h1lu/P8ooy4q0+i6rtxJ5NMqcsaH9t1xwDgsT1HpXKlfVGCQvsYgPZNoa7gI+bDiBGhALDTb53t3DSWF2CDvoeG80l6YrFBS8bTTCbyAAG8RI6qmw1I8ZBVQHZlCmQKnJMCuQ8/J+FysiungBYlPz5fSX/4YSn96pt5WPd30vcvUEi/zji6hz4BLIO0Ae6H2TEFV6g2YMvxV1WOAaQa63o/iEabYvzuHaEI9ZEwcj0mauEd+UVsnxU003h+db96bPkrVQAvmhMIeNllMrELAVBvwxjbh5jREJsZg+kzxleZl/6E6w2sHEkUq412+yEBLyz4qhowvtYnDvUKZPfFUQCCN/pcGlc6XXbgdNkFaRHni7ihD0zUSMALHC9YpZLuO82NJIZiwEtRI7Vf4yC/eF8a/vnmFBSovoZTRK6iCNm1A5BBegwBXgSgLa9usIG6nX7/ZAXraxvp8QK6XebFBQNHO9x54uzoYL1TZqM1f5ZC2qJTqfI+tjEjIMqOJMc9g+J3x4009et54lV1sdI5qS/x7oR1APTh9Taa8JRpGOBldlT6qAZM0e4XGNa4D/Byf27C1gDtQfon3nvnfK/XLbszBa6OAhl4uTpan7uk6NKiC6pnZFO8iECA+uno3mSlQXKgUha4gMjRs6VNFO2upSd01PNLOwluRZMDFQAT4LeBHFZQrTT67HLeVIbVCi0rFCWHR/M+UtRyjoi1q0Eg9iCKL2sRNmlTIhMR0iCiCcUecsuIGDExlDbzWVigb/D77O4o7M8j6ROh3DPjKNvVZJCO1rhdNAipqHoN3F0+n4LzkSmQKfDWFGgsAt46t5xBpsDlUUAs6998v5h+/c3L9IvfvEBXwl56ygbF3uEQYwwgAgPEIONP6EmImmi8jX3k8JOPj3UaW9zlYc37ahRq+mt8ihyUrornufQ+RzkRW/fh7p3iqK+DJaFoV2OiOCO0q7yNYwAlu6PDYufvx4TrQHo0N8Ju8iibHBI5GmXzo4W+kCE4T12RpW/MqJTuzZOjJWefC6AATe7vjbe73NLxYSJGJfCCH2CMTPFKx0tbOl7OALyolmd9qy7gyT6qLNSn6PA5aXzXzNWZz8t4xhpIsHS7LGJA45sfpPzbOdnnlxG5YU4vhboD9FVtwJfg7HBz97ScNZ7nKWe3q9m6VdlWIYtdb/0qBw+Pc90/4nfnFXFPf1UOyi1yVEpZaAWLMgDGRLH64Mzr20OEri/dQyTyZ4+m0qO7k+lzOODHRrDA1hk2S0dDKBvOc35RMB/vggIfEPASn+W7IOO7LdM7au/Y1BGZtQWqdMBgu4sW3U0U7S6Djr8AfPk9+l5+fA4HDCYyV4SOb8OCyo6edrk07ervcxlfgR2+88XwTdY2JcPPS4nnbXSBhWfdL+J5Bx8hlkecyNP9I2fuIyxKI4K8XqOdXaCLdifRzc+EGGSfXZobKPy7PdtKc1wl13ljso2M5xhgDOzPaDRXJyuFf4aUF6VFzaL0Lu8IztdMgUyBc1JAE//y+zpnHjlZpsBVUEAL1F99/SL98ncv09/9+mVaXN1Lz+EKHexvwQWArgTGRYH2PlnXIKRa+RveXAqEf1VrjW8OQlgi+yaUstfh34uHhjviKYWPlfIJV/j2zs8rGnG9JpHf0avHi9gqQspZtbDZ5dTPoqY9KMuBfbD0w106N4x5aelSw2ogYr3jI600haLWYThftOMez6z8bPPkaIHZ5xIoEAt3vUWmiNU4XmTdaNdEVTQvFCeMOJPa0vEyBscLYkd6v82qkdqLn71ZOCI/f/cvocI5ywYFjtLbv21xumwZt8u2Kf9eYj7/j4DFT+FkF1C8vNlvRjPEnSeQeJAGNT3XakNNnNWqalhvWZy9eiGLUNTHyy1uinRx5wJMfqc03XEjXuRXlH+KeOr3etUscpQlUyn99v6xWpOI+2X3UOAU/TbceVoP/NGnE+nzB+PpS9YF03C/mwU2wJcWYkdaO+QjU+BdUCADL++C6hdcZnTUAiTUs8bk8IDBVWyle6Dkq5idWwR8+R6Ol8cv+M1vAMTspQWUca0iHymYRVq/bUev3Nkrpn7WpzKNor/TzqBgmui841HqU8XwO+5aZGfBARJVcSnTHsNiubf165jQY/CQjLKeU/LmYvluo/DvLqDLA3bfbqPJ/B4TwGmUxc1MdLDCwCQw2AoZhPrES2vZ0lmTh57Hn5BiVEY+MgUyBS6MAuoTyu/rwnLNGWUKXDwFtED9m189TX/7qxfpv/x2Cd1o+2bJqAMXRytMSBeLhgO917XhqRw8YgyxMbKqo76C5njZSFxG9G9FYRG/DDJHhBwX3owdd1Wq8Dl6jfI8rj8GtcEhbpfYURYr/yBcL21MS4+2+tL9m6109ybACzvKdxA5mgZ0MV0v0KyfVY2Pr/7kTXHhozXIPhdDgZgDKjf1v9LxIlEjmR+W6Ll0hDSBlyHjUnJRIxdLEQBjB9f6q6z2zMflU0A0r9Nabaqtxi2sUm1s7cO9ztwdhbrSQ/Wb715huXTLgOLd/X6+VW1Oah6fbC5vTWbtWO8H5D7uILISWZQqXuWqp4vRvXeoZxTxqzjhsrpZcFlgeSdHxDPPMgRaEBBpddW0XgRjuYPYEWsdNpoFzEwjDvnzT8cAXfg9mEyzKACfnRxNE1g4GobTa9ASWsb5lClwpRR4b4GXK6XCdS+s3kPJTW+krlo9lDopDbZCylfWd9Jj5Nifoozr8fON9B3gy+OlbVgWYXkBTOkX4FKAL0Pa4dMIbP+eTzeZrFhO6vy8zIhhPnZTukoH3pEwose9RhvQbN1WA48XoInxHp2qJhHS6zLCjtsEcpzTY4Pp0zsddLuMY10B1mcmgqPsuImtUMq0zIRc0TFHcXG1YaNerwjI10yBj4gC9r1dwvOqD4qp2SVkn7PMFLgwCmh8+eu/+z79v//1efq7366n5Q1tVuwD3rdMoa4shOjQO82Gcg/gxQYxi9N9qoCXZoiGnkjlw1C3T4R6uuo7PTles5RIVU/TjFHlLpe+2EjjVgMPbUzWUxwaV8RA3z5mtVO6zY7y3Vud9BDrRhp3Z+E0ncPC0chIGwW7zkHhz6Vsc0/gdL7cc2/gZR/dLq/TRsnxgiUY5lLG8aK5EjrwBlmpuxhdBZjR4CXwUs3HLrf+OfcmzemWAM9cb+Pa1l5aYwP1CZbXnpp4ESakv1sFgNmhr9Lmqbjy+O64qr3Kb6/Ry+jb7j6qmM2QKm7lUgzFd5/jUno+3aFVLnJVoZ6f37vb00cpcVcPww36LR/1LHpeAS8SiVyFTq8JGx+VhSOUgN9up58Avsjs/d2bE1hgG0mjZmxDC4N8ZApcPQUy8HL1NL/4EqMXq/o1usXihovYhQVYrIEEzy+specyP8fvRzNFB9siwAsSSQAbdPIM0EpJF85Zkyfv1FxMR1WvFaJbHfIqRubuUHWMjUMejUi64aeL5SFuFL/VVa7XsDgL8dfujUwhatdtcrxFR4pulymZtYTTBZbnm+h2uYWSP5lINI3uAwV4JADJM1OGFHOkVuafT5kCmQIXRwH1JHm5dXH0zDldHgXEHfqX//nb9P989Tz98hsshsAFugbwMoUYRhvWSnEEaIzSO90EXmIsscHqxAr2ilEfDiMnz6Qe4j5K34wj/6PxPHb3+bTxmmVo4VfVWy4GYSgArpLmGH/v3AR4uYfySjheZiTiyzg8PtaGIxW9auIyLaqRx9zu9ric+6PAi8/99pjXbQp44aqNuBJ4AUEbY/NK73cAL8HxEu2ep0uX01bH5hrfHB+PAGH9ZDBjeX2XzdPt9MO8rJTC9bIk4GUdMaNd+itEx+DMUzsOGfjS/d0eWxoB8ZXW40TrV351n+hNeqWsUsgVMarUch2X3nuMZtxmfnHnOejdNJUIXA144bQKZ5D66VGMbXx6azB9cnMofYmOF60NHiAWOTOFWCRiR6YEXNnkI1PgiinwHgMv1cdXfbxXTJ3rVFydXNRbHY/+jVMENtNtBD83YVNcWd2EhXoHjeg7aQnRo6VVfuvIh67SeTPRXEb0aIfBefcAtkVmmLIA1BoaTC1xwthESspui06d/M1dlF1c6hiHhRsZI5Cb5kBeBKgD1M4at/ucNOEzNmf0uEjBriYDLeSQx9reod6aaqPXpZNuzLQRLxqx3bYJJnzTsBEOAbhIZlmshCZeVJTpkxKVn3tba5N8yhTIFMgUyBQwnRf//q+/SX/1y6fpV9/vp3UWMuuAL7PjKGcHeBkaLDYEGJskelMdAS40PKtgc9ngpuH4DYfy8lg6Hz9KeX5vyMxy8DxsNnBi9OPKcjEHJVXN+GMslllbKdqVQvu5G8PpIVaNPpnTjnLbOF+mEfMVt6nEjTRnsNR5zDU6XPYpgBeRW2LhWozuwu28BydAJWrk3MMtxLCHQ8cLHC/icBaHMP8+wSMPzcfiyE0YlLikaxC7ILRutWlqSmOx4rm4ssucHYW6T5bTE7jWnyNq9LsfmM+vHCCCJC5wzCXzzbU1T9dnR3pvvlojnlT1egNbkma66q7qp07KznuwKlXErfsUvYNXtojgfn5Tj1vPL/z1qsqCmg4Tq+O0tsMmLRwvHfRR3ZvuT7enBtIXGN24DTfeo/vTbNCOwfVCHwXoWH9kyySfMgWugALvMfCip4/PS+7656j7fPSkQI1kAl3MnCCgy/Y2Gs8ZgHdgbdnG0tHW7q4NyFK4tgwnjFk6QlnXj7AwrmzQee32px2JSoInd9i9ag+ipJZOHTjGuGDUHjGd8/Ei7pqDdbRbTCjVjDFVVe7R8Xm1YWZmoiDAZYeBxgEY8t1HWRac3hIruj01iP6WASZ7487ePNPBehHszciVy2T0sCwqkKn6YimKaxz5FWqQI99kCmQKZApkCrDAYYX6f/z5b9J//Nvn6ffPpQ8D8dxdOCtH+01PmMa+GOcawAtjjQ8rjGC1sfdYmnaNQbHWivhdweF96ddqM6J4jngcFjB2sAGj+cQBwIvmEKrn1Fg/1oykXw2ulztj7Ch30oNbE2mGa0eLQMSztPlRH4aVW1WWZ/2ms6V5U6STwusZ1N0npbmOYTybz8LijRTwUnG8aK4nkSO966Zcl0mVWzWSqJFzN0spcrGObbzOMU+7jmS5NnUuO4NoP+bCpggZc+DM4ZdWUKKLJaOvH6+kH7FM+hw1Ad8+gwsGMaPtnYTFnsLcvRqQfwdNPS+9GeFq0iN8uz8M3b+bI2qk0o+rRfgrrr+brDC0duC0JT2Q/IH9pukRrLChDPzhHbjxZtH18mgW8UhA4mlfM2TdU++mjT/2Ut9j4CU+rWii+ucYfvl6hAI1smmAldlAKeUS8LJD570PsiFrQIcAG69fS/HuPmyK+4agzy9upO9fbKZX6/vp1cbrtM7kU1wv2gWRzhdNmAaYiAXeraJi2lmvR60KtfCq41c+GmN8kuCx/SwpciHX1FsIDGVJtH4QsKfNjuPN6WE6T8xGT7TSQ3bYbsHWLLnNqbBexERiEG4XHY166dVRAfkVEmnykSmQKZApkClQo4AWon/273+d/uJvn6UfFzHJCuiys9uHpZ4+M8sqDkpN7LWY0RgVh8YyG14aiyYfzSJO41qOQT4olcmKSGVwmcjjlbfHOt4uXgWGUPei+lY3qxBjtwEviCMzd9hCbFniv5PoULg5PZQe3HaulznG4ofoWtOYLD1rMlfswEv1VBrzvazK79hHKgLeeuh+6wzeVMP3JNyazh9WZzWkplGyhiOOly2U6+5javhA8yved+N4YXU6MS4RFekHYX7XA3jB2+ZruubjkilQfXT2Gard9mUgA1PS8yjVfbm8lX773bKbkF7eST+8kJiR+iqAF/Qetmi/oR7AS7nxeaT60ai9PpLw87fpSNKGh/K5mHheI8/vpByjdvZeFnqoIJdt3KqfGWT9MDJ8kGCCRxQSbrwbI+lnn95I9wGJxfUicSPTY9l4jnyTKXD5FHiPgRc9fHx20TlcPkGuewnGaiqy0RGJc2QXxS0bKJuywRfgxWgKOdVZ9Ws3i5803i+sYF4aEaTncL2sIIIkvS/La7vGbr2+CWgDigyTDDslgCPkrV8f7Kk28YSxBInugstEPDKev7detF20pVhgnYXSACDysRktba26M27AWdPHpOA13Ct9KMkdQMHhAGg+bM1wt9xCj4vk7gXCTI13TImuJnmS17RdGzKwqbCKVZFRPM58ZApkCmQKZApkCnRTQDvL/+r/+of05wAv82v9iOYCMqD3bGS4Lw0zpph5ZA1cDCoyZBqHT/rjzocc3VUxqjBzdQV0Ay/12M2opxzMlEhRu49mZh5ai9cTeCGWJwMsYfOFtTqcqIisALxIxf3YSH+6MTnAomY4fYa40Rxj86cAL3PsJo8yXg+1GJdlppiFoOUDsSSqJBDnLEdUs9cjnJhPJFSkSNzL78RMrlEgz3ao56y9VNZmtFcFvPjGm3S+VKJGvmGldnIAxp9ZpLL3+xqR4FpXVe1G+5XNx60BL8zhdwBeni+up3n0uvzjt4tslG6lF4gePVtifr8ja5/JONNbbFAKePHXwEHiePXjqnbtfbw5Ru908n2btM1cz5ZT82lea21SEhF9kCgCbw0cAroMwekymn7++c30Cfpe5uB+mQEg1qZyPjIFrpoC1wR4EVnic7xqEl2j8qwP8o5InbfYSrfptNewZmS7HQAs2rkbgDNkiN9wC3ScjlqU3dxFtwsiSOsbO+h72cZc3Xp6ubCR5ld20o/zOyDt21h6gHNmr5V29zHdxuRJojw+WKNPhXwGzU9cMTJOTYshB+6HJlwMAvysXoA32nXZZ/dMddTwYHpc4MAR4DIKcj8DG/PkKGDL7HC6T4c5CWx9h4ndNJ3lGGJF4zIVjXyynkc6XbRb4yDQRb4nqv9F5leQI18yBTIFMgUyBd4bCgh4+Zf/9qv053/zIi1tDaLnzDcZOoD/g7YgZQwDePG/mKyLe4NHiGGu5jx21LCAKpHGw+OOYvlUBJ80FpGfNlF6HXXv7soqfhFery9PVQaEv8Z5B1600AN4AUAZZVPkBmK/WtQ8YoyeY0f5M0xLzyFqND7SYn6BCIsBLy6+4kWdj+NFVY26yH2qI549EnbfnyqT6xNJ75JtvFFlNbX2tMSxvGMcL8wF2YDTJpsU6wpUHGazynS8yKoR87dewIuePshXOa4PTd77mtbfycKtPkaH2nOP+bt+W7Td88U15uVb6de/X0pPFzZtc/TluotEqj3baLwW6KL+Kj5hYcXWfoWX5Wu5H3dS7KIix0Vp+FvuNZ+T0p4279PF85KDWl4F9ZniSPRvQQY59gBXXrNRO8g6opP+9IvZ9CnKwGXd6BZcLwLU85EpcNUUeI+Bl/iA4yP0z+yqCXStyjOSOd1cqa7MSAt42eYKmymTyzb6WlpMiDTgtodx01mDwZjMr0R8tmFHXVvfSs9frqaXiB7NL22m3z/bpqPfQCHvPhrDhzDZBjcN4ElwvEiJHmM4YIsr0xsQAMNE0IAXqmNdI1fJxqt2B+KawaXJ2x6cNPIVK+RQ356ZqZxgQieLCTMoNryH1YQH7KJNjQ0zsRuD+6WdOgAvw8MtOlRNFrzjLN8Om1xeVKuptmXOF5VpzidTIFMgUyBT4D2igMbG/+nffIWo0fO0ujuEcnnGxEPpd0HUlTFF83ObzNsIFmMO44NNTzR6aayoDh81LLDyVOQugER5FpkU8ap8HHipJT/J6QV6derxIrsiXEHhtKAiPPwU7l7ycQhGflqUS2xlh0W7Ay9wA3X6TN/andmh9PCWlN0DvNybYoMETtQ21qD4DTK5MPEV0legQL005X7y0eMRTk4QofWE4VbY2YqP3N77qy82i9bjGeWqdLwwt0Ok3IEXXeHkKoCXMTa4zKoRnMbdHC966JJcpeO9J8X1qaA1V7ycTuDoS9Se0u0idQAbmzvp2cJ6egHg8g9/WErP4HhZYj6+hEoAm4/zbQ6r/fjOQqG1iKB1gPUjZO39SeReJ5HKjTrIv/s+4nb76777UD694tXz705zvnsvvfk80WeKdtL3sot+SFYa6eZkC46X4fRPv5xJn8Pxcn9uPN25NWXv/flKz6kyBc5PgWsAvHQ/XK+PvTvOR3RPB1Pv0tTRamdOMuu7gBpuzWgH976JB8nMsiwOdNqwl4otURwwzCrFdXKojordkW10wqwDvqzAKSMTdlLitSRRpM09fq8RXQKB3xXLqhBlUoKoOEcLdeHe+V3l9roxFnhfzNVwEiaymsgZlwpzWA0ULQaN4Vaf1WuCicDNiTZcLYNpdmIYtLrNJA9ZZLhcOprMYWVpCGW/ysN2duhlK1Zp97ucN6BO6XgPe/ldTuk510yBTIFMgUyBi6eAWPr/hz/7JRwvz9PWQQvQBQ4PNhJgeIGDk3GLgaY5xY86aKpfjQHhitEhYtVZYzyFx9QCIQ6liTIU5+QjwpUB7i5Ap1alMrxebpl3rXzl40/pNVEcja/ylY82TnYNeGF+QcU7HSm8H0h32E3+hN3kO1gZ/PzeJCalR40rVRsk4qzV/EL5xFGN1eFz/LVevVoWxyeoh9QTy18ZyO/MGSnx+3/oXfL3SfMhf1SBZa7jBXPSW1Ku61apTNQogJcRWcNRO7Fwp728zQtSBc30+O893aLB3/uKVi+TVTnq7d6aQvv30mfcSmq/1Q02Q2VCGuDlq29ewvmCZVJ0Ma7t9KOvR32VdCE66CKQOI7u/kX+Xlq9YSO2rsf51+P0cgfNm8/SzO+kvOvpT4pX1dFTaOXih+6N651n0HPvHu4jFgmXF33WDCKR99Dz8s+/mKKPGk8P706gj2rW3vcieb5kClwZBd5z4CU+QP/Erowq16Wgoo+LyZo6uQNmR1KqK90u23C77DDQ7qJ5S0p1RcV2e9iAF3G7mHgQg60mlX7QidFjmVJegBop4xWnzBpWjzYYtDc2ZY56O61zvwkIsyGlvYgobQPCiLPGNeaDNCM+JEBGiLPqpD8NBmA8XF0sSZw2+g0jViSrSZ3OEDLjLWTDh4xNeWJ8GPN4Q/yGTV5civokk2y7Z5rIFcCN6n2WiZw/50We6wNN0PEi8895ZQpkCmQKZApcJgW0IP0X/+sv0v/9X56n3TTMpgI6TeDchOHFFMoz3LBgUf9Of68u38ZM7/vrvX6xv+zxOGv8swT1SOaHryX3PCyqnaocPG2EdMcLf12LzO1SxOsZnQhlPYhQi1N6k1v5nER2UEkbJeKeCOAFMWHidYZl2WjALA1+Mofie0SBBbzcxWzr6AjjNuO56V5j3NYhkmmD5qzjdVSzXkfL8LQnZXDuxKct5P2Ip3mXjpjSaT64w/xsl/d7qwBeXPffvlnrGsak7hiiRrYBx0su4EXvuh3WXjXShX8R/H5erlljW3N5mxUfCHPwirLiQJcZcHGtP51fR9wI4OV3i+nF0g7WjJh/7w8aSBxPrbYTUByt5t+y7muZ6tYORQz/ursILuPI0SueFVSPXIvX5W239fiR39vEU1rPR2eHh507z4AXxOo2WPsILJ6Ge/72jeH0zz4fp48aQyRyIn324Jb1T71qkP0yBS6TAu8x8KLHrn+c9Y/2MklyTfIuSFN1O15vgR7idhHXyjYKuQSeSImtOFEkltM2MR04XuAakQEgASKGEhfkbYAwpJH2+211XgJyGABWV9bRA4MuGMCX1Y1tdlEAYGBh3aKsXTTmb2MNAu4+6iAARzi8dgv5MRpIn4xYWsXZ0gZIMc4bTESPwH0jvS0TY3C2MFkbY0Y3hrtFHVuYhx4aHLSJn2VWTng1gaPyHGedyFmiCzvld/TCSJkzujAK6K28jB4z3vbLyPvCHj5nlClwRgpozPwX//oX6T/A8YLAK4sfgBc4XgS8SGy2XIxy74AJjuIjePO3QAyJ3iotPztwlO7wK2J4fo3YZQx31MPqbkI9cZF5PaxwR7gyqlWg8naeFy/Hs1OYNjo0nO8yl5AyT2YUiCons2x0e2oAjhdZHKyAlzG4UwW+CHhxjpcoIQMvQdvLuNaBF72nJmokcRXmhWFO2rmhu4AX5mcVx0uxhFWTxTsSzXcZlb6wPFXZa1HR6omjymWnIuMT7qnL1taOAS8rcKA/nd9EBYA4XhZRASDghc3O19j8hDtPSmX16Pp6HXhREboLeijPizoiz3p+vfJXvPCvu+vp5K6H1d2nief56+xvra9zRLtdAS+sWSQeOYnlrjvoi/wnn42mLwW83JtIXzycszVGdyn5PlPgsinwngMv8fj+WcXd2a5vk/ZsJV1ZbB5JTxVHKP3rY3JHX2MAyAbAiCtVk9loR4Gl6G4EUZ1hRI1acJloFyvEdSLHEnghkf6Cg0Y7ggJfNpE13ZEiXoAWccPsiKumGNgFtmiA3+MqsSUHXnzXTCCJTEsOGgADCzd1GYKPWwp+VZd2GzBIVhCoW1v6Z5jVacJmHDJCiDhUVx0hK+4dNmf3tjB/joZH4Z8vmQIfDwU+wF7v42m8/KRXTgGJGv13AC9/DsfLQT8cLwAvWrRoyNHoUx9jNJ6W65mms3e9ew1H5BHZ6BpHFVWuekjE6L4eH68Z0rxrrHXIUqF28KDV+OrPrTD9GNbNqlEALx0sPk1i2WhOwMscOl7Q7fLF/Yl0D46XsZF2GgV8EQeFK9gtShDx6sT0Ustz9xNHveQf7jJydjQpAJG0aDc6cRKpjwAvbI4Z8LKPtRfapg3Hy6g4XjTX4kU3K1RF+0RbvNuNreYjfjB3JXH9iYwTrHi4OvCyCfCyAXf50vpGevZs03QtfvX1YlpY1sanrIsN0UsUfVWR3qyVmvuygBdlftzXGA9WVKa8KP5xYWUkHGeNV+f3V9ci7jz0UVGUAS9sCm/zrkttwW10Uf3ppw68fAHw8tPP72TgpU767L4yClwT4OXK6HE9ClL/VQys1k0xwqqzFugtjpednUM6620Uc6FEjZ28foEeAB0S12kjviMFu9qJ0i6W0pfzIA3a8tChUVsXftpFcX0u0rKOrhjjZkExLpNV+Us0SQO8ACCcVhcp6tXPsiNT5asOUcCOlPGam3sTPzL5YgZ+TdLwk84X6XExEIj4dWVhUT2rnDLNR6ZApkCmQKZApsBbUCCAF4kaHRrwokULYxJ5MhxV4yL3xfBbK+2oTy2wSFwtECzMh9cymo9kOncFlDHqjjfHi/y6Sq0y6crC4xOMoxj6La6GWIXpJ+BllwF+g/lFiBoJeLllOl5kdVA6XibSfel4AXjpwL2qOUdYNrIM33CKp1d5cuuqo+52n3zuRQHTsUeAFqCaE2peZgpamatJZGUfvX8SO9cmWovJl4kajRSiRsWmmBks0EtAHg4IFK0QjdGr4Ox3PgoUL7y+0yCvAZ9qP9pOwMvaxm5aWNlMz14AvsD18tXXS9zjvyW9jEOaWFt7qwLKQz2XH5cJvKiEqHFRnF2i7Aiv39fj1d3K5+zxulPYs3PS2kKv7w670OsCXnjXx0f7AYaH0p8AvHx+d8wA4p9/ec/6p3pNsjtT4Coo8IECL70+yasg5xWVweNVT+g3BrzQEWqQlb6Vja3ttGdiRuxiAHQMAbZIi704S1pwlWhnQ5OqxkFv5V5V7h7uXbkNCLUgOdXBmb+5XbeL7u2vzM/GcJvBmnAQHaPtolCBPtAi7zCryohzJ3ZZVEYVony4izoceQCvbT5/aBTofgs+tOe7Xs+TW+N6tVeu7ZspII7O//Zf1TheGHU0Ggp4GdA4U441DmXUx6RqQFI5zRArufCKYascvyzQTz1S1UKvxhl1KOtJsTYW6woIdcB4XgIvuDtt53i5OTWYHpY6XpzjZVyK8BE1MuBFGy387CBdRUv3cvpF6UU0Lk2fiJuvx1IA0sZcTMQTqR148U0yAS8yTbzPglQbcpoDGscLevWGzLqlG1sIrmd/D/QV0Ha5MY4l+1sFiMjWVprzek4hLmbACxzmq3C8LC5L1Ajg5eUGOl4EvOwa8GIqdcW6XiS+3GY6KXd/W96KFmdIfLQ0vafa2BUpCuAFEaN1MwSynybH4MybHkp//GgkfYFy3S/hzPvjL+8a9/8Zis1RMwUuhAIfIPBy9JP8YEaNspOunsgADnGasB1l3C5MIKXbRfpYNMCqOzLFtHCQSK+KlNkOahfKJkIK5Yj+lPzDGW/Xa0AQG8ylT8VI60BJM5lqoZRc2U0pB3/LpGgPgh1MwaFyBL7wp7g+Ziie8vAJb1GY3YP9W04KjQHG45p3PmUKZApkCmQKZAqcmwJHgBc2BF4zMB0HvNQL0oh14nikgasWRQviOGxMi5v34BpVjfHXx1ztmQC8MM/YKTheBMIE8HIL4EWiRuJ4EQv/PeN4QTl+oeNFlo003tuhhy8WidXj1ohTeB71qWJn1zEUgGjl3Atyi9QGvAhsQcTIgReBMDInXQAvcEDLoMEbgRcVWTThMaVn7/NQQC86h82gC/pWwEsyM9JrGztpEauij5+vY1J6M/19Abysb2tejC2jGseLZVbvbNzjgs4nvQDFg1xQSafNRqXWa9UNvKwBvMj4x/hYH8DLYPr5QwEv4niZTH/ykwy8nJbOOd7FUuADBF6CQNER1D/LCLum16KXsbkLj6BbddgS/RHoIm6XbVkjgp10Fz0s6sC1eyF20mFxvMDpMszOxgA7HS6+Q8dNuFOoF52Uu44oGJfiqwIckcLvzKvHKUKLPIoYkbbKxZ+lgILKvBW9jGuO8q5HWdkrUyBTIFMgUyBT4GwUCFEj6XgJUSOEdAFefAytYwUxokUJpwZelEDDV3cGkdF7dfXx2p9NwAvKdZku7LKxso6o0QEP0hl+7TpeWNA8KMxJf3l3PN0V8ALHiwMvg7bJI/Z/O3oCL0cfvDlbqIcfH1KP9VG6C9IE+CJSaw6o+eAeP+nkk4jRPnNE6eIzjpcCeGlh+EDKdSWCXud48fc+z7ku7X3qajOVE+3Hp2YqA1YxZrG0smXAy1Mp18Wq0dIyBi4EvAwMMYVnTl52UJfdwdTfhfq3KPc7PKiWamY/aKF3X0p1A3gZQ9TIgJdHbRM1+vJbDq5CAABAAElEQVTBZPrTn9zLHC/vsMk+5qI/YOBFzVrvGD6AZi76tgL3sF4G/pJ0wCAq7hZZMpLS2x0NrMySJMYjhbbD7GiIpVSiRhgIMqW1Ng+Cm0WZVF1p5QpqOfQSBdfmjHiVfb3lolOkj2vkomuVR+mWrxTT1I6AepSVhzTDqzJqibLzmlPA38Nr/hC5+pkCmQLXlAJmTvp/cXPSe30yJy0+S7hDGSO1EBU3vw71VOX4W4xpPccpi33cSSmK8fC4KOb/ruLpGb1+gZdoYWeLeFaDrlwXc9JtWTVCdwLKde/fGsGMdAeLIQAvs2MV8CK9bXDXGscLWWp8rxaJJz78MYGqV9ec4JiYH513Qd/6c5s5ael14bcJ8CJRI+np22XOaDpeAF7GmR8OmsXJo8CLXvayvcr5Xb2E7H4bCsR3Vs/D/fx725CoERwvS4ga/SgdLwAvv/ztEhwwu1jswe7aQMvbx9omvgv/dut5nt8defbKIcp5V/3U0XKbHC+HAC/S8fIaHS99SZx5P38I8ALHy5f3p9Kf/jRzvPRq1ex3+RT4wIGXyyfgVZTQ6N40ISo8dFEnrd0LcbuIlVRXgTDS+aJdJokVyZKRLAWJ5be/H3NzhUio+mpxmNShl+7nsYkSMUpApJj0qFyV790yZ//vSh6pIt6R4HgUPUhtAFG84iG7nETqyiTfZgpkCmQKZApkCpyfAgJe/vv/7ZdJynV3X7dQQAqHB/wuQwa8SGGj5x1jru40EvkYqMDaeKVAOyLGcffH+ccY1yvPSHO511gQ1oEXE1spgBf22tEXl9LUGNZCpvpLjhfpTzDgBTEj43jJwMvlNlQ9d16XasaFm3vneBHYgjnpY4AXiRrVOV5MpxH52tsX8zKVk+dedWpfiDu+s6IjsTzDzzleXLnu4vJmegLw8gRRo1/+pgBe0OXY1+/Ai4Nj3f3G2/Q/9b4n8u1+5Hqcethpy62nOa1bZR5XH1MjaesIvfsSizTgxUSNBgBeBgx4cVGjDLycluI53sVTIAMvF0/TC8+x7N6KQdA6ZjzhIjWARYPqtoCXrV12NgBeYLGT+WaJFGlXo8OuxvAwk0jJWtNnydycjaGcvAs7viPTQF7FUU2quOrcdFv59Hp0RdKhWOE2Dz91e/Uc3OuRTi6tlnN2ZgpkCmQKZApkCryRAhI1+h//DI6Xv3metg4AXjAnffB6ILXAVAZivCxy0aZG/TDlo+bR9Pc4zXFPC6RYWDWGRIvmY1uMcNXYW42czdzqtTiLuzuXrnseIxbwCrEf9dZ8Q6JGGyxk9qHBCMp1p8YGsBaCjpdbhTlpEzWC40XAS2fYRFcGhFppXLdiig2Ws1T3yLxBGeXjCAVok/LVhERya/FuHNAF8BKiRuJ4kajRsESN+LVMua7MSQ+UViTtbSaTs3O8KGVuoyPt0/Aw6lbtxW1Mfb1/qThe1k257iYcL+tJokZ/D8eLlOtK1KivD1Ej0NHewIsKtI+uVnL3fQR1+3ffR166et09Za94HnJ156oOAorFoai+ykWNguPFRY3+GI6XL7BqJFGjP8miRlfXRLmkBgUy8NIgxwXfqH9Sn/AWh02A1CmTh2VHp6KJmw2qeGhHY5eJkCwZbW0LeJGJuQPYe2HxZTA1E9Jwu5RmHcnI5kBVhj1r16x6865ngmM9lVZHnRDhh3fN6dHq8cynOEXE48LrcbM7UyBT4MIooE8vf3YXRs6c0ftHgX12R//nf/NV+ou/eZZWd4ew3tMH10tKwyxOB5nNSyeajUCcDl+z4Gk8Qv0u3PXxKtyRqIhjl1pYl9htxPZrFa+7hObHWcVrptddpJS7iBfRu4KOAC88vxbxu8w3NhBnlmnp0U5/mpkYSHdutNLDm8Pp9uxI+vTuZLqLkt1RQJf2cKumyJ/SKUNzl3Ihr2qc+lBF65U8dcKPKqKDehWdJGoksEU6XjZ3du0qcSOZ2m0BsoRVIwde4JAW8GITROituSaNXraX/PNxsRSAzPrWYgtUmat/0bsu0+DriBqtbe6mZTheQtToq69fpYVlAS8yniFz0kqttvH2qZopvhldq6Nelof0jucpos0jTuRTzzPiKKzuH3HjehHx6vU4mh+kMCBK6yO946bjhfdd5qRl1ehPHqEA3MxJT6afZ+W60TD5esUUyMDLGQl+XLdS7wLOmOWJ0WOiIhQ8yjDQhU5ZoMs+A+s2g+oOwMs28owHmh0hnT6EFSMp0m1JqS7cLgJepDwt8lAHKa6Y3sdFP2Wv/Kqa9K5D9s0UyBTIFMgUyBS4fAoIePmX/+ffp7/822dpcXOQSXsy4KUN8DKEbK6AF41YGsk0xlajl1wRclw9e4drgdQYGY8AL43QMvOoSe/QMlpXvXrXoayAgu1QpbQU9NzlrfI0VSiBF4hzABfQGMDLjckhRIuGMCfdKYCXiXR7GuW6I+KikO4QiTeTPlaDmsdodXTmQ/U5T7ozF3StE9SBF7nVZuJ4MatGJfCC5UssGw3TNuJ4GeM3NATowntuHC9qK9q3AbxE+71j6sQ7/8G8CcVrLZAlvpE68CIdLwJeXq0gajS/aSal//4bB17WtthkFfBixLCv1FqnaqqgVrPRFDOO+M7j/uRrla6r5zommeJHHeru7uj1sLq7VzwHpfyhI2+Pp5T26tK/aI20WwIv++icQhfVDMALVo2kh+qLB5iT/uIu6yOUXuYjU+CKKZCBlzMSvPmpe2J98Jd9GPBS9EnqVAS6iD1avx04XsT1IsW6h5oUMmi2TJkuJqThlR5qoTStmACJDc8H55OAl3iai3za7ryugmrxHPmaKZApkCmQKZAp0JsC0vHyr//dP6S/+Nvn6dmyFquJsbUvjQ6zeVEAL76GQJm9wIMym8pVeuGo+3aPfGU8RTo2sIx1xOF5+9mTnyaTYwqLpJ4dZdXiEaa5hPy0KDQdL4gxb7GQZ0mfJgBXbkwNpfs3h9KjOxPpFpwuj26PpZtTo4WJYhb00M7048RqULSz1dGRx8oeb0sBaKu5oR00o9x14GVrt+B4QSw9OF4EvIy0fWNuUFaNSo6X4tUs26t8Qd62lpee3t/YSy/mQgtQWzEzL/OU6/Agpa2tnbSOCoFXa5tYNdow4OUrzEkvwvGyiqnkA4AXtDkS279RtZJ/Xp5XlWOZ9Vs44h04T65Ke5p0tXg1Z73SXgtRqwkbyV+YroBdlSROr7WtPTamD9IYIpF3AF7+yadjplz3i3uT6Y++uA04nIGXOm2z+2ookIGXXnTmq41PWpOE+i5CI7o+8obHMTf0qtHlRPzyPiYkxyWNlEpQlCdLQAa6MAFybhesGAl8YfKoLkecLe1Coa6Zj+ZeWuv7NQOKgnEEwn5M0YV3maC4jyc4OdXR0Ho+583jaK7ZJ1PgY6VA0SV8rI+fnztT4MIoII6X//0//GP6j3/3PH37EnBhVxN3uDracI4itgt+wNjpY1joYPDCT/MVaryrj3/HVft08XzH2sv1XI/L+3T5WdV6DMnCXMSEY0HMU7Sps89PwIs8pd/l1jRiRnC7fALgMjfVSQ9uj6cb0x1MTaPMH7r161fjcPG8jnv+7P+2FBDnhA5NK+UM5brieNkKjhdt1knUiJdaG3QjvOMtOF6k88V1vNjL7pkUYi9lpubIp0uhQNG/qPHkPKRPkvqADTjZVza2UK67np4tbJiOl5foeFnZOEAkcpB21lfF/J509n3xQccn52+Dfa6n6oFOfi7P3eNEzielUPyLj1fVwgGWWF3JP4AXvft7xvGyx7u+n8bHB9O92eH033w+lr64N5E+uzuRfvLolumgOukJclimwGVQ4OMCXtQHVF/tsfR0oOXY4DdmokGvfjTys87V0WmL0xW3kU43RWdcdl84NPHTDp0sGLmmeiZEMikNQg7GglJdAS+YkNakEVEj4S2S21W9oms+DR3qdTmtO+p59LEiJHI6GiNC8jVTIFMgUyBTIFPgqihwwCLn3/7lb9Nf/eJ5+vrZnplMlhnSCQCEtoAXDazFWOzAS9TslOOYonUPgZHFGa9Vib7wuLCMu+rhVfZKa5PG5h0AL1q0ixNmZmIQsGUY0KUD+DIOCNPGrPREmpmWfheAl37NPQpT0mXep93wKRNkx2kpwPtZzjVpL72upgMQUXTp/hPwsstVC1Jt0glokajFCOCLc0VzL46XWLVbuWr/4o3TBLLnES/2ceE9E2XPLgqUbVfQ+1DcZVgq3dwGeFnfBnRx4OUfvl5KL5bQ+wLwsr03AAee6I4eKtrHXHyb1hKcii6ryPFtewrLlbKivbse4IpuoxZ6ZtWkG3gx4Ap/vefr0E/AyyTAy90bw+mffTGRvrg/kT69M5k+/2TWgMYrqnYuJlOgpMDHBbyUj/1mhzpBfeA+7OgD171fy4GoCFduVWeAu36jOOr96n0V4T0BkIhDeDijM4485K+dJ5mQlojRNoi4uF8O0fXyGksM/XDOaUAdwUSgQJch9LwYChydsmraVT/V/6KOqLfyaxajkPCpuy+q5JxPpkCmQKZApkCmwNkpIDGaf/fXX6e/+upp+vUP27D3Y71nG66OEYCXQefeEAvBoc0E6qOcyopx7ezlvk2KmEO81kKLKnXX6m3yVlo9leepswMv0j+xjwyELD3dmBxMd9hFlniROF5m4Xi5c2ssTY93ig0fdOPAVaH5kM+nfKHUPT9SWflwCjilz0eNmCNaaqN5HXiR5UsBL1i9ZCEqUFFiYAJeZNXIdLxIJ2CAjFXjV5XJDVfR4hJc9o3UaCyOl21kHrdRIbC6sZuev1xPL+B4+eqbxTS/uJOW1gEWdvpNEbi4XmBaMs48485T/XiZ/Ptt9lDhdwmPcMlZ6qVsPovu1SfrUKjI51IKbtVoQ1xeAFgzU610DwXg//xzABc4XiQa+fDudAZejHL5dNUU+DiBF4CQ4zof+7T5estOUKBJrTP0dAI58K5nYh+8ms9yONqOFjdSF7GIqnx6HkU2EivyDlTWiojMv8las3OxjULdHSm6E/ACGNPH7tKgdjFQpGtyuwyqUm6n6U48wunEi3rW6FSeQVkHqY6lxqnyypEyBTIFMgUyBTIFLpsC4nj58//8bfpPf/88ffXtRlrdRD/AxmGaHGkBvGAhEO4NDdbxV9XnmPG+inBpLhtjrfiLBV7qTxTzk3huH9/hloAct6a0i9xOj+6NpQc34XSZHE63ZkfT5FjbWPjF7eKKdUU6zUEK4OXSKHL9M9Z0sE7/szyR2soBODnsdW1yvLBJ5xwv+yzWX/NOO/DiokbMHeHqks4LM8IQlYiJ41kqkuOeiwLxjURiiY3tAroY8LK5neYBXeYXttJ//f0C3C87aWFdIkh9CeNGrgicHVa1aQAvyq9+RJM2fesx3kd31LqqW7dP9E3yNzDa+hloJ+68vT36nn3EH4etr/qnX06nz7Fq9GBuIt2fmzKF0lXO2ZUpcDUUuCbAi7qK7s/tFAQ6IVnVJ1XdkDqquDM3NxJx1XRBMoP2geuqePaTH4cyo3pWQ002+PCtEwi/uBZPIYBEh8Y007tid0V63AXWYpmLVVR1MDZfJoeSRTcxI3YsZBZwT4ruivJlQlpmAYcBXDqIGZm8LkCM6uKHT37i7qKvRgvLNFxOh4suJ+eXKZApkCmQKZApcFEU0GbGf/rF9+n/+9V8+sXv19Ly6r6x8sviy/AAGxgsSkN/RjVLUOnV6Hr6uihNjJFVqvCpcuwdr0pRuDSR0Bzgjceb81MuPmPw/JSt5j6Hr1ndcUhx5dDAYeqgsP/2DBwvN1Coi5WQezfHED1qo2x3BAsibdsAEtAS+l18juJP5udeFLAizkVRT3n9z6J60Oe0T9No+rghE+Ulc9I7iKSbEQYUjYrjRdZeduGYNlEj5ocdlCSbfhfAtFYLS1S862aEoahAVR9c1U1ZPZUTR4/gCMrXN1DAvhHixOao7s30N3P89S0BL5tpfmkr/cM3Al6200v6qMX1vrQJ5/s+H6lEIodYW9Ckdih99/uk9qm3l8d82/Npcz1PvKNvlPk0vH09Fi+nPgE944F4YV7vwZ13YLqo7t7spD/9chaT9xPp3o2JNAdILI68fGQKXDUFPkzgpd6zND7QirwxPukT9Q5PrLT6UGWCT79D4yyRCI/c/Bv4oYFMExFNwg4sPm7SaO6jogSqmFwzNxIL9x0flF0xYRmwMK587DLlLFla+Xt8IqtSupCPdZo4DhggZb5SokXbGkA1cNpAij8gjAAZ9R3apRhGrnqYragO5qMFvoj7pQR2lCl5q56XcSj76igKw+OSiquKyq5MgUyBTIFMgUyBt6CAxvxf/OPT9Lf/OJ/+y29epcW1vbS0gsldW4gyljJeM1Rz+HzBtxSqca45/ili06eqGmHHyAVFijONmRE5ElcFncHl9dX0Q4cW3XZwESCluc+exIuYZ7SYZ7SHXyOe0pfuw+1y7+ZIesRC5s4cnC4j7TQN+NLpDNt8RHkoq5hfRbaeeZNCRYkRlK/noEDMGZXU27LPlCHvwhktwEWcE5o/7gO8aC5pwAvzRekCHOIqbpc24umD/qLb5M3ahVOAAT5HtUa1cK9mbr1zNNfRJDb/F23j23DgRe0li1QCXeYXN9Ovv11IjwFe5l/tpafLB4hEwt0BLjoKZ94wHyl7r8y71U+xZqGUZuvo7jSdxVni6VFOk6finfVo1l6pS5+yipRdeB4e8s7bs7Mm4+mH+g/hWDxMd2+2+I2kP/l0Nn1CfzU3O5amAImb+ozOWrccP1PgfBT4IIEX67/q9LAPlI6o7hduZgMCUayTMoAFE21MMmSeWZwlkg/UVeI8Al2k1V+TEYEeewI+lKuxqDg3ySD5iaGlnwnKIJ2gABH+0wCDmmRqJfojUSCFyW0gTAHIFH2H1czAHyolcSLtVui6CU+hlOluYkZOgJA6O1OkKy4XwJbRTsuubdwhq1sCL8Xzdk9+ggxve+1JWzKtP9PblpHTZwpkCmQKZApkClw0BTTe/uqbF+kXv11If/ObhbS0ik6F5T0WoUNMzlESmxirGcxsQUPhFfCimoRvjIL1UU/u8Pe4zXv5veXRXcQbs+tOUNS/qGY38GIbP9CHKQuLmL40NpKw9tQPu37HgRf0Jdy+OZrG4XSZGhtmDgLNRCwOzav0vOXCXbfFEVTxmOGbr+emAAQ1cXQysPks95qnbqGcdc+U6+45BwULeXHBDNGWLYCWNuCiNuq0cdfh3jcIeatpGL3nNmfE7RuM5qiqaI1np8rvPXBd53fLvxkRUUY0fP6/C/Dy8tWW/X79HcDLy630HODl8SLcMAAvLAvSmOnoQXzMGg4K6H2g/czB2Vsp2ioopHJ0VPF637tvM17kFWFxrecdcep+UaOoU6TrdY30VVjpEw4RTM/MRcsiiRjpOxjsf51GAF3GAIrv3ZKo0Uj6o0ez6f6d8XRzdhyFu23b+K5yzq5MgauhwDUBXs5GjOi49F0KbqkGfQEs3gHY7gDh2s3ZFxcLEwvnJPFdgR0pIhPwYkps9+gABbY4Aq2rwBeJ/Qh4UR4+SDmXiw1cTDzEsjkA14lAlqGWTPXRKQK2tBnktMMwZDsMEgliWifuFHbVlI82HFRNMJ+0iQ6XbX4bmyjYglV0dd1/qq/mNp02+lxQADih3+gw7iEz5WhlF9w0QT3lfZmHU/Z0XfNl1iPnnSmQKZApcFUUKBckV1VgLufCKaDx9usfFtNvvl1Kv/zdi7S4vJueLu2gQw3llWysHB4ydrNBIssh2ljRKBdwiyb8Gvs0vNr8ojHQmq8S1I76QByjZi24dJ4yXpRnWb0hv7I6zXgxZ7Kiyc9KhjNH8wzNdXaY64jbZWS4P82M9aXxsf706NaIKdP9BODl1kyHjZ/hNI5OHM1rnNO3fJCGo1my060R4ZJu6uXWKXtJxV1utmqwaPdaSbEhR5NZu4nTZX0Dyy6aQzJ/3NDmnX67e7RTP9wuA2m8zbwRrhfNI8fYvBMgY+JG2hDUu1AQS/Tz9wKHlR8FXxU1T9+CEfOqahaUuIhrfItauwg4M/UCcCstrmymhZXt9Nsfl9OT+Q2Al5307QvWA7JutAPHC2sMmQjX5q+e29c43kvp3mjxBoIE3fQcHjUSRIju627F7D4iXP719O720HqcKpbnFGl0V4tXvIhVLkW84lvQRSJX24BV4vSRifTpziG/vvTJnWEUgY+mLx/OwJ2HyfupsTRKX1WCzF5wPmcKXAkFrgnwoo+v/jGeQBuilp+qOTh1JS3FiEBMpGzMWDABWTY3d829xW7AJmbIdtGjop8UNInrReDLHhrhTfyo6BBVEw1yUQRilkw6JOLD4MUANmBa4h14aQGuSO+KOFL0kzb5MXaHbLeBnQZpmHfARFLWmuwllPzt2Y7FClrNF9mFW1jZAfXetTq0tPs0NpimR4dQbNfCykCHCRF5Mnhqd0q7TqpH1C4GUKdQ1PgEWp4hqKR5UVrcX2wpZ6hQjpopkCmQKXAFFMjAyxUQ+ZKL0KT98fOV9O3jpfRrlFe+ZIHzw4vttLDKvGCXXdQDuEo1fjOeinPVFp7USWN/uMsqVgNt4aVRMEbEMlYPRz1e3d0dtR6GO7T8HymiK57qUPfqztbS1xfaLPwgjICXbeY/MEawwdOfbmFGenpiCGtGo+wij6b7t8exaDQC8KK5zJBx9IoE+omuevZq86s3JVStyz6CPEGCqyjz0p5JhI33rPZgmpvq1riUTLzoIK2s7dgG3vLKLuALHBI7h2mdeWUbEG2Ueej4aAsupqE0yTxyUqJizEWHik1DbSJaMdaWtGP3A5nHEd/uWBd0Hw+q7E4uUzFPjnFBVbqEbKxPsfprc1fgJ1z4gAlLq9tpeW07ffN0JT15IeAF95PttLS2n7a2XtNuzlWvPkqHcfLr27M7nflFX2F+ti1truZJcZ3WnrYZ+mbKRjs1U/ub6akjhufs5Xlsd3eXqPt6H1KGW0aceGZ9EpJCkM4blQWemOYm1F/1p8/ujKL4eyR9+mAKMaNx+q8Om9at8hMq88uOTIEroMA1AV7OQAk+vvjAlar89Bk9xH6mj/NQnCwmQgTAwgAkxVVbIMpLgBqrcJZssDuwxq7ANiyapleFTk/Ay6FxvXAFENFkxDo2Xa166uB8giHxIe2KScdKHwOYFNKJo0UiRsO2yyATfnCoMFGZYbAb5zdKJzDGAGgTF8xXSvZQbIIraC5fY+a3sLKVni1up6fIdj6e3zQUvE1e0xNYEkBj952ZYRRGjaQpBs5J8nHQJ4AXp5/h4CVB3O+izk6DKOeics35ZApkCmQKvN8UyMDL+90+p6md2nBhaSM9nV9O33y3xObGZvrm2Wb68SULV0xLi51fC9I247gsHPkw6tyumnHY2EpBNg5aoE71UfE0tajHOW36rnhdt2WOtnou6tOolhLocE9bl8XCDa996KKNqh3mQExRmF+gUJe5xg02ej7DOshtOF2kWHdqcoSFjrh3tXlULdaDBIERVCVZoeUpalF6ZEdvCpRtVzpsThv01aJd77JEVKRAd5357BJzxzXmuQtwSKyYta69tLy+Y1wuE8w9pwS4sGFnVqmmxbk0yFxVYvIuCm+vQ+O9ijlvUcWy8eIr6F318/qWT6rJe1HWSSWV8SmwrNp5C39jOpV2EaWQj1QWcJj6giJXX2dozXHIWoD1CSDat89X05OXAC+LW+m3P6zDnccahv5J3PQysCHLRvbdQS+tSaJ+RpdaVdWKoqP5q2A7ahG497tmjCJicWnGb4bpLtKqnHB7vtWdp/KcdO4OIX69mCI4YilIOjNFNymO3hDwgv7NMUDi+ygAvzc7hAnpMdZJI6bfZXZ6LE1geU06jRr5ejXyOVPg0inw4QEvkEwDTxzxGYtlVoOS6WZhJ2ALjpYdwJb1TQYjUORNQJZ5drmWEeVZwy3gZQsAxsSPkJ9UusN9y9yAF5OlpRwrKsqzWQtdmYAWfsZtQh/YBxeMuE8ExAyBSo+wyzDGb2pkIM2MD/tOw1gLmcNhJi+ICrVaxv0iRb0BvEi+8zHAyw/PN9P3/AQEacdCLL53Ztrp/s02Jh1H0uwkctbjDryoE1YfbP0YhLDBqt6BBZHyNVMgUyBTIFPgXBSI8abnjty5csyJrpoCGsJfrW2kl4tr6cmTlfRieYvd5LX09bNdFq6ALxtStMvGCYublgb0YhzVTKM5pNYXGAqp5iLNZ6qH1d3NWJ77cXnU456Ux3HxlMaPyuVzGpVo+8aa4/Cnhd8oLPuzU0Pp/q1OusmO8adwvNxg7nFrZtQ2fAS6aNNJwIuOOm3qC5zup6nKtmSXeqrX6VILuszM9bLqF0TV1YiqOa7aSpzZzs29DtAyv7TJHBfROeaPS1IavQ739PI2YmGD6OSBU3q6nWbhYLo5hR4MdPVMsHGnTT21pekgrDcQbhYNVrb6PSuWZ/WqnASHnI8gZf4kV3nqY6PUiy/tPHVUDesEOk8eVRqR1g5d+Yz03WmDV4Y/1uF6X9vYMTGjp4uYlkbZ7m++W0nziEWubmDAw/olUcUpo1o5htoNrkQRUXfFDLeHxdmfLCoV8SJUV49R92m6I23xilp8+YV/M3111ywrXnXlXdLI8pB0gN4LgBcCtg/g/gF4EWfPxGh/+nSulR6gBPynn0ywNmKtdAtuF0BiiURKx2Y+MgXeBQU+POCF75luClry4crNx6ifWNCkYExKxTa2dmwHQOJEK3C5LNOZbeBeUAcmVkyU167z8Qp02RXgcsCnTef3GrY/ffX888NPDiuk1knwwYd8s42HmoPIwVXAi9DoYUwxSh5zAuBllkFO+lnE7ingRbsNE3QKkj/UBGYLNuc1ZHMlc/6EgfPH+a303fMtgCBY6VBwd4dO5e5sBwsDw4AvHQZQKbjDJKAU+FKWixo5OVTLYoSUKx+ZApkCmQKZAm9JAR8H1LVaD/uWueXk74oCq+tbaWl5I714uc5cYCv97vFK+vUPmHBlR3kJ063aIdWu8hALnBjWNQPoXtZoH/l9O7yeXqvKXdWzcmmRx4JP8xzNbfj1MafRM06g1+XmdCs9xILRzalO+gTgRRs9s+wkj48M20LGN5xEHZsZlZRQHqJU+JujOFVl130v1q3S47iK8qKsi75GX2OT0LK/iSdy0MXmuwAv28xh1wEMny1spFerO+mHeUwQI260wEbj/NI288wh2k/c0h10XrTgXmrTpmPMQ4cMeNHGXR140fsQJcVzHaFrUafueBH/rFfLvyykdMSrRH0uqqSz1izi1+oUXo3r6etnywnSqv1iLJHbwBdov8naZAN9Pc+X1tNz2nSezdjf/PCKttylfWX0Q1wflFdUSToj2e+1Q17+Pft90K1Z+6N1dZ9mLM8hzkfTRIhfq7SVqxlDd2/KpXzViRt08gctnoQIYFRph93xLQyR9A+8hotrIH15v40uqk76yYNJOLoAjGfgzkOp7gjse1qP5SNT4F1Q4MMEXvRl8iXrIplXcavsoptFiPEmSMYSLHuLy4AvABqvQP5fcS+LQSt0amLZ20avi35SaiWAI70uUNWi57BOQmyBNpmg2eShMEXlr95JYN3MYCDFUWeqiYmA1mH0s4wAwEx2kLPtyCLREBMb9LNoMKRjmIaVdwS/fUCfTWRyV9ileIFul6cLO6bRnKpZujs3QHH53ZuV8qhOmhbnjIAXCim5boweVQf1Ll60XGamQKZApsCHSAFNjHV0W5D7EJ/1Q36mrZ1d5gjbcAdsppfMC75+vIqVoyU2PHbSi6U9FqGI0fQjToOokZTsatjXHCCAF7s3Asnld8WUgfvKZVGu+KTSo351d1QjwnQvBZUBvGjhNoB1kPbQazZ1BrFe1E6fYo71FsDLPYCXSRT6T8L9YpaMNPHhXwu++lGfD8m/FyXq5dfTZjf0ipVmMY8TTYJeoqV5G6GKdsNDZqOlRFc6AsUhvYB+wG9+BFRE3OgpoMtTzBJrrngT4OzhXdqSOeQDNu4+vzvOwtSBF+Negoupar+q5aJ8Dwx/vokiwEGaYnMSv+JrsVqe56QSxGlTSOOUz9+khBducYtC6u7zlNs7TTxvhKrcbr96WLhPvqoddUSdBVbC6GKHwNBtTBfJuukrNosFvggc/u13i4Aw0kUllQnScSIT4jK8AeiijVcaRP2T8izfI8vR26oo0gupUTWep2jOIrzX5aQYzdybd828TsqlGbNo8TKBOwIsFvCyh07OAYCXG4DEf/RwFDGjcQCYCd53NqUnsL4GSCy9muWmdHcB+T5T4JIpcK2BFxtwyg+woJQ6Z75wmzhw1QAkze6bAllWNwBXQPwBXV4gHym3QJhVdrO0OyClTFu7YtMkHR2e5tP6oCU61E/XJcY078i806oX7V2bgyzyt05G9cAt7MbZdr3T0EQG3IXdMzhf2gJh+kFgET9CUe4YYMtNdiFuwf4pVlCgW6vPOoPoMvpeXrL7Nk/HqxFunLjieLkFi+gcwMttJkOS2R2Da0ZoroCXQM5FHatvjIzyuKBDz1qnxQVl+55k82E/3XtC5FyNTIFrS4EMvFzbpmtUfJcJ+xaLG4Evr1Bk+T06Xv7hD4twDCDmy4bH7h6Lmn0tY5gPsLDRXCB4O2JY9XEwRsO4aj6gceRdHqrL8XUoa8rkSXpdTKkuV6YYcPr0wR3RB5dLC1GUkfQIK0Y3ptrMPUbYHIJjl0mM5hu2kIEQ/kdxZKq5mI6gj9/5uV6bsvx6hI/dXRBI786R1iPM57gBtkAsiO1WqGQIAh2FzGfX2Ez88QWcXIgYff9cwItzu0jsSBt0N5hrPpgbSbeZP96dhTvgDvov4LZG2t30hUh8THNIaz8DGfXuC2RWjdzfwnWnSPLm8PedG70A5l0EePC5z/HOBNhZFmjv9sWUcXLlVAOVEzXpFfvt6hHfjMrQt6RD7brLwmSHtcwKbbm4AhfT8mb6GgtHzwDRXmJaWorAt1m/7KDbR20h4EUAjOdAi5BxVbPwrdXfGjnu63HDr9e1yvFoaJNGzTuPfVLqyK87nfW5kZBAfQchFqn1Vn/fPpZd++yd/unDCYDi8fQpfdYYgMs4QHEHdQ7St5mBl6Bwvl41Ba4t8OKdk3cw1qGo08BTH6kNPsatIsBlH24WwBXEiV4wgXrFQPQS1P8FO1prsGFKma7iSAxJysjEYSKFVjhBm4tJFl+6TSYoQuCLjy8adLq7BMpWOD/VT6HGMMMNlqGL+nmHNkDaQQYvJI7MfN8w+lo6iA6NAsDMoKPlFpOcsVEAFNibVQ/6W5T9wjq68zqtwQEj60hSyntbiu4mWmbVSPpiJKokHTKGdpM/fa9XpNGpqjIXd+g5ox+8uFxzTpkCmQKZAu8/BYwVnD5eyijzcX0psA9XrKwWbkq5PjoxJKLxO1j5df0BhfYrG4y//z97bxpjV3bW/a6a59F2eXZ3ujud5F4iJkEgHxICQYEIoiQvEPReIESCD8wI3kQMQYhIhEgJ4YKALwkBBFEYBJEAiYAQH26IIIAQIiEEujvd7bZddpVrnkff/+9Z+9l7nVOnylXlcrmOvbZdZ6+95v2stdfwX8+wzOEMc388UYYjhK1NOr1Wc2HlKjeiRp6d64ZINeL7bLpbHGJ6vJhq9980nufbKHZxJk4UW6tIvFpuTs4HesVZKyWVIwNt4SyKdGXF6DGsGGnDfloiRn3axKCXDmX+cYNu2yJ7jfQNUvqkNdirVmm8R8MtaqRE88eiG0FfW1fqhzUvG044srfvyHwuh4Rqry1ZwNnW2nfDrHFKrF7r23FxRMyIY/r65IpZxUS/y7iAxEEd9I1Kt8v50/0mQsaa85JMhMN1bSalEaszPYFaR6qj09fRO8iBnh/qsb403SJqIGvjmrpWvd5AmXtsxJQ0sRh8igLL7+YeC9kzeVoDIlL23fy8fntmXBNIG8ec4/6GQDWrwDSNTzIKwr4Frjx0U35J+qgYn8YlbnRT4AvjE1z7NAbWjdokFkm7GFBFxuVFCbGUGq+ahzR+GVCXjjzqL0/n+fs9xvPQKlUMb5RTFQdXEk9OnvgGsPy0qXbgHTt0qN2vMWtAUgQXznaL02VI4nMD4bJ0uwASR9AlHkxn4KWWuvnp+CjQfMCLvtrqw62AFydZBF2EDq9jDhp0eNVAlwkh/ddvL4ZZseTdFofLlLSDrwjIkGqXsCFdLiaSZEBL/JhZeBhEortNMvqs+Wel20fPQqOqSaxVnBi9LoTacCB+QXPbiIpPzIclCi7yx4y0MBbT/zIg8GVIbL2AKF3oapGuFwYJ0OtWsTq3aELsUdiw9MKcErcLCtIwId3fh9UkTZaIGVFH/SlZddnMWD0elYt3S4s5qnxzPpkCmQJOgfyVOSVO2j0DLyetRQ5Xn7hxhVtAegKkYH9G1mBekonpmzpRfuGWTLhOiMVfHKe35+Nms1Xzcofm1MjWzxzIN8rFbCi3PeLr/kkYzmO9fN1RW2isGeuWWEuWKBKw1oGRwBe9xqgAF3TRGUeEOFwujvVLl1xfGO3XukNiKui9YXMeMce4srGFR20xDZ/qqdIw0iPgGTkRROx0EeXEsa6kB92Jx2VtJHCF/ropxGVTIAs6DDEhDafLllns5CBRwIs4packou5WjWa07kXfy5R0vXDINyixotNqxyEd9p2ShcxRHeL1qr27xOWEqBGm0wGUzVqOuVmnqr0Ljmrj/JK/ASuqo3GEF3WNQFGsO68Rr8rlPge5p2SJHxj54ZveD5LjvcRNy63P5wje09q7yEduV32wJs6XBXHqLyyLK+/GgkSNliV6tByeG5eYpLjiZwQQ32FcErd8u+7sM9gHlOOTEZEnvlenqCLVVJmHJKz+9crn/cYrE+yaa03xVfTKVURI47FPA3hZ1ZiNJMGAVDecH2lVn+4QQCyQ+PxQOH8Wi0YDBUBc7afu03aoqm92ZQrsQoHmA170IsX8g6vmtZiYtsSqAkseokVYKpoWS96URHOuTejUSkrzbGBa3DYlupwKbG9JiEiLjPgxc2IZ8yyWEJZ/DJMPQXow9S4W0+MSjYULgTEOPtVIJs8YtZRRjZOo0sjfBwAfILslftQr7hesFiGChChSF8p4YesVV8vgQI+ZjB5motQCqFcnFD0y5QhLsIM0DrjEusfalAUVj/d+iy+bLixTut17/jmHTIFMgUiB+K1lapw8Cjjwgo6XozjZPXlv+GjUiI0s8zIWRLB4yOZmckq6FGRa+sVb8/pblsiRdK1J38vGdpvmcs3YQifgXEXnC+LINfOtyFZM+wUBPbTWt6Sue3u0MuAoHI2BF3ImxMVWeH/WI63axOiMRyJF7eJ2kQlpgS6IGZ0/pb9RKdMVy/6g/kykWRvvuN5g9lfiA9b/UR/ZjOb1dBNRrDuoPQjnH+OM8BW1FdwPEqHXgeEGdx0wYuVyXRZdNiVexAYdIAb/VXFHz0o0hbUwukEAYOZluRPrnT2sL3WANyKwBbF1OKgRP+rWWrMLq0Zad9K+rClNqbS4mgBdMNzQwZ8AGe7ReiZx9Q2o89B/HIixrlDTJQ7YOfbs+kahuhhHmX9d1uWjl0tZ7i4D5bi3OpQ5qt2rvGh/OJoEwAhkWFQ7Lssi6w1xu9wSMMzfMzeWTQk4+l7Q9aKvOIIu5KJGgcve8isKSHM3/5qD5L3erchAucX6pc/medefmCKWkZaEe7eLflVeyoA8jOtL38WG/jrb7+gwuk3W1tBd1CWxuT6BLgPi5urXAXWv9kcdkXNL8zT99B6bqaxKdmQKHJQCTQm88JI2JunTswFeX6BNSkw4oPwCXRYWJUqkwWl8QgsnIfwvye79dYkaLazAiidOF7HjMYnx9YkZr5gsGKhYhsjXvnL/0uMwEUNI41f09ye78z3jnY4SsbJFNPJkMrXfuODBRx74seDR3KZBgj+JH2lyxLRfj8SHRnQ6cUbsvedkvvHUiDTR66RiEOAFzhiZuoxsnyz+eatYjt2KkvE92steNMmy/jkJys5MgUyBTIGHkAKcMHNl4KW5G5c5mIu1BJvXVaweiqV/el46XiRq9OL4gtYRKxLZkBVEifyi82VdG6EWKd/vKHQptGryZ5ZlDo65WZbFj8+/fsc7ieXOMrh01MYrcqtuu8XD3zOtX73ENITy2ga8sKMXv0uH1hsc4vTIfPS5U522MYfTBes36J7DOgiK//u6Oo0rAtSFQyNfX6RLH/O+yw+lpm9wl+gPTXDR3VgN6v0TCsjJE5tsGofxBTdGItDfsam/NXF0r0gkDr1ESwJV1nBjhVN/0ZgEHC+KL2WsiNKvSpTeNuuy2LnCGli6jABRAFkw7tCt9WWvDu/6deDHuhNOJgNetBbtEPiCToxOKSQlrEtxENmAu7pLC1UTNRMIwxrUD/7axHLNGpr3iOvR+marfV/rpolXfezGz/QcvzzxcfSm3cqtr4s/H+xe5q62dzfApoYl2ydsqj+sqr2X1YbTUrA7KbPSN/X37LVFO2S+KVUKK9rfAL6gq4lLqmQNHDXAQc/RO+l39tF6aZak+EnpihfP9fEa+RXJa26eV42n5VebY/I1FGBQmpK6Qxm760eQlP62bJ90Wubun77YK52X0lcE6CKDI0MyHz3Y12P9HToiPWD7u4MOVPXVzs+ZAoekQHMCL/bh8cYgJ7DJysWkpFMqTInNC3SZEpvwzJzML99alGKxVQ1MOqkSe+WyFkwCjLWwioMPHyCLBj5sQ+sLd/Txz70YFhTXF2eKVnslA1ccNoo0Hqt8JE89qNLw2diCh+ficvElwBcdNMhagCY9TWJYQBoV+xxy1pdhndMdEGZY3C994nhhYuQAjguZ8/JKnPGdypB7dFR1rjKqKazyfmhcvPPD/o4PTWPlF3lEKMCYHIHyB/PCDrzYXAL6na+mpEA5tzM3F+ALG1o2rLfFOQvwck3rieduLkhBv5Tdi6V/Xoc4d2TG1cSFNQHr0N+4X5w91nqDL/BtyrQtg+izcx6pXTcQTty7XTEesTzHmKaYq+QZ/T1UERUkQRXdETTQu/K+xTuTarBP3LU6OT411B6uyHT0KSnSvSBOlzM67BkelA4QgS6+EY+5pt9fUg6VypfR28gAaURnrtg6tbTyEKLwB4cD7QKHA5vtNYEqywIDUbA6r/uiuFaWBKpguQiQcE0b8RXd4XbZ0Fp4Q9wvaxiLQJxeYM2qRDIwHIFoxrrCEA8DIBGuIsBE+gbF4YRokbm13myDw8VAGIEuBryIQ0aACyLwA+K+hhs7WuOUMl4BNb3dshajMBOblwWweBBYcMHw0unr8k3EFyYkXmm4+931fiSZ3LWUBxHB38zGJR58j6E+YUZDtImZk+jYrBSB39Z+59mX5s0a2/XJZYkbSRfM8p2wIq5++lK7dL10qEHUzLrkYf+r7xZvitjflcZu1GieU3089/dSPNzv+Kduj4c3/vFibGbvhD4jat3WIm4XWV0bkIjcBRkb+b8eH5Yuqt5wWbpdBqT4G5C4G/PRevn4T8nILskz5px/MwWOhwJNB7wwiFRyiXzI2G/n9EmTEoskrBbNLYcJyT1OSOv31VsLYrWUNaA5nV4tgQLrUyUPTETrw7PvTx8v32C68IluZe9jRfGR3nUpZPnEZGUeZGLcNWmjalIt3sSL4LSMi8mWd6RumEVD0ZnmOcnfypyjzP49dn7A2H3PCs0dHeoT8NKlUwfpfiGNrfQQn7Ks7sPY4rWN+UO1hEju+RDdeV/ekSt9d/eLIfk3U+D+UyD3v0Y0PinAC3VDL0I1XjSqbfY7qRRIgRfcUWeGlOlqM4uVIw5xUGT5pRuzpshyalGmXfW3IUtHLQIxOLgxnQq2rojzt68xyj6hfO9EltgdZGg8o1SrHV9V1M5Dng2rjTg+VL9FjrpVaxGlVgTEq+OShAf+x3+sQUYG26Q3rl2HOzI1LD0JKNI9J+BlVGajBwe6tRaJyikRMWFZRH5xedT4DbyGj+xd9KmWSTwYyWvbxHyhpdaFahizKiUAxbhcBKjAyQ2HA8AL69x5FKwur5vy3HmtedFJBCCzDvCiuOgtxGAE3DGsecX8ov6MiFLhlh9txnDFYZ8BhxLVMJE5cQSIecXEMgBeDHyRR4f+evXX3y3gRZaPAGAA4QalHwZR974eWYyRX6eQnC6ZRUL0qF0L0shhQDnqhTVdpOYh0qigw/5vNcRVsro895/RiYxpvYUf/TFuGP3URxAt429R3P0cNE9rfELRLiam4e6fmotj0xKc/Uor/iW1LRz9XJar3WkTLn7d1zzKn91DYpRG9G6cUxV/r3AvWPnawFI8lx2HcbkAJPWhoNeFvopC3UH1vYtSqPt/Pz4qjpe+cPHMoPXRLnFzwZUVOVJ5V+XdqNpedL5nCtxnCjQ18MLigW8TtuBlKRGbkyF7BiGUTd2YWgoTUjh1bULKdZd1QiD0d1mDEGAHp1LwudggpA+abzB+17gYFAp4pRwfiGOJyuYgBrEbX/Uhim15lRnGZOWgV+StYCZe+2NZJG9Y4xhcULw7Klnrs6Nonu+RrHVfOCcld6dH+iRu1C1FaWL7VWROGdA8b2NWfTUaV/aAvuk7eAHu588HzDJHzxTIFNgHBfJ31ohIjJdcvohsFOd++cWNksbq4srAi1OiOe+0J7gI8zutescOdTjQWRVL/5IsIq7qMGdRul442EHRLlwH4jAQV4Gi2iEQKwvmbTJi3RA5UOM6I1Kl+I7LzQSRd1tP7DanxjyKnGK25a/7xrT+VHwmKiled2QNx6qpnyhWok2MDnjOiLP21JDWGVpfcHI8LB0gp6UjAb0ufdpwI3oCyFT1dXLcrZ5lpR5Zh9OdNuZinLIxC5LhZX/0uChexHp2XQjJmoAT4+JeEbAi0GVOAMuiOBkAXmZRkisLnYsCWhZkuQiRog04WsTJQp/F2tEd/QHcsPGmDvTPO3A56dDR6+RgnYEiqgEHd1TL1pI69IPrRTpaDTxB3wuiSb3iwu4X0NIrAGawt12GINQvtLEdkpGHAf11A9AAwgig69IfwA3gC6CkdXneX2VwsRmmLuaP54G7kedk2R0mA094Yu+8IW3aYoMKJIrtqu2PzEerD6hfzGt8uiqOPLj7r2tsuiVxo4kZgXMC6FC+vCWuPMDheMV2AHCLo5J3xJ0kiOFOY0p2t8f1PP2Ze32cNIwc9wqPfbVMkWRPKg6lTfG37vSbbolESopIaheiLqrHxKH31KWRcEZ6qNDtgghdFJXTBoq3Jb8kz7Kc7MgUOEYKNA3w4otraBO/GwaTqGwMudZ5KcKD0+W2TOVdl14XTqYmZ2WFYArxoqCJjEktpmT8atXkwxUXSP4l+oDgd4uiH8I9jvvVx3F/7vVDS4yb5oCPPVc/ccRRQFzGE0MDo8Lj37Zp7EbR3ZjAF1h/UXZ3QWJHo2L/BXzhpAEZW0xJ+xhrA2daMNW7pyu+S8zCM3Y/f76nAnLiTIFMgR0U8G/MA/xbw9/dHvZo3X1uOAnAS9bz0sR9j5W8rri5KL4pTaRsXlfXtbmRSd4FbXBu6lDnhjY3WBK5ObWizY4OfXS4s6J1xqpEO1hTkDqKLhfAizxYcdjCn1Ai7LjkWfOZpw+eoJGfMgItSi97jG+yzWabMHu/Kh6hthZSEBYRe7taTMTo7KkuE2W+NNangx4BLtpE98uCYm+3RIwkA12eHNviKS30UXJDR2+Tvd/bx6fdYtEsxIFzG24VrNYsCWxBV6GBLNJRuCjulilxNdAHF1GUK3Pn9LklKc9dXuHwUa3J+rbow3QHr116t9b3xWFRIfPD7RELfwMe5bZexCZd4Vpihi6JbPSor/QIhMFs74g4Xvp1H5XOQdx96icjEk/rFyc2B4KIJJlVJPUd0/1CvykKtTE7fhRWalmF0lFU5q43f4sDJ7xrzichQuxD/m5qEbUzfwBsq8YJhdJvGQ+RIuWbUgZ+QzqpxqXTckqc/svaH62ojwjHE3ChHQHc/gXAxhjFZTcnYfLCXmLiVTpj9EYxqowahZYZNHBUKQmsUkfAJX4jcP5ErsE7Av5awmmZRH/8fK/uXTqUltU1iRgNaT80Iu48U/6td8XCk+VW/aTZN6hJ9soUuH8UaB7gxUfq4suE24VBBwViK5qgYAW+cVum1aTdmxOpiWlM5iGbHcEZvrJWbNqLlqZHpciHxZA5yw+y9tOPoRZ4gFaICx1Sxdzib5oLPumzZZ54lunkYOLjn3TZaSJrkUUjzDt2h0tCdx8/129sdSMCX5C3RRa3HQUxyfUgNiRJ8dmZKZApcKQUiKNDHEHcTQE7RpQjLfWkZhYXpYzrx//+lI3MuV8ZeHFKNOGdTau6UOxPsS9xcEH7siFe1ekNoh5T0h13UxYSJ2dQtIvy/nUTZV4QR+3yCv1B727zNqBL1S/hfCFX76b25aZdlk2Rk41AB1PM7SGWKsYqNtB1+2gLs9j0TT35+iEmsleM4JAiIWrCcmG4X6aF+3RqLG6XCxJhRp/LeW1iTg1KR4I20vx1tGM6WgmKqvgJvOf7aN2tUfb1yj4+EZkxKn0mF7gZ4HIxThdxMGCRc0Eo3iwgi3S40N8W5AZ4mdN9SQpysUqEoQh0eKyskyd9p/hHu6oc8A1vLvoh7VY0HU+6ePL+hL5B84wbeoWx2cUPDgM+DX5YOxv4Ii7sbumB6ZdeDUQ8emXGF0symKTul3WkU1LADCfMAOCLrUvbTRyetWnkzI59/eiAl1j3h/WXb7g8TS3azOYeeZu+F4EvU1ICvijAblLAy7iA4euy5DoxJdBOfWhW45Lp+8HaEQOG+getH3sMj7ho5NqLOPHCxWhS+cTY1XMRUbcqn9rQyr+K666idItSpfIU6oHWHzlsbxXwh0ikhqMwonHr7CkU6g6G0eEuO4w+K04X484TYExf06spPnfyJcci/6oYr0S+ZwocCwWaBniBGnwyPmmhTDeKGEW9LpPS5/LSzXnjdrkqywOALpiOnl8mDUrDMKcmhV/62PjEYbKzHG1GIXcLwGFXESo3Lq64fGF42v3yVFWc+JnzW10xNNYCX38nj2HjA/76s9Mq1ZEllCtA6+25I9Y60N3e8OQFWRsQO/AZiRwNydR0j04YMPFHGZ5PC/B2vjIFMgUeEgrUjifVGPWQvN4BX8PHz7iwOmDie4zOptzLJ6sMvNwjQR9gcm9Hn73j0kBzrxxsQOEqAICZFQfCtJTtzuh+S1wvbHRuyVQvbP1zUrjLZnhDnC/o0yAP29LI4fN+41esVhZefvyu6791T02sKixumuOzp48xrAK2emE9QCnSe2qblm5tgiUhogOdFinQ7RL40qkNc3cYk3tEokZnpbwf5ZSIi6A8FW5aX1NQi/J78wK9avmetIyIETtSDVXoU7QW4wfrWKzUIFa0KtBlbkmHhgJZpsXVsiBOl9vi3GbzPC3xomUpc0bJ7pJEjoQByoR05OYW75KBLXEDDeCidtYfoue++qOZaD8AwHh5/4mbXvoQW2vuVNlWnXrYwgWaSKCuVikzBayDw7pDoh5wSnXpPgRwJz2E6IDB8tWIQLshcUoNSVQNpbyAMOgixPoRFpUQP7INMYtyu/we61l45psoYC1VtEsJyOKv9tnWuIR58cVVdP3oEFqW2G4JeIErjwNouKQm57VPkjoGU7cg4IXmVDIbD6A640K84y4uOUq3edEjan086t3uBayyazTL114y9r8yohUXvxX/7dR4hXWtgd5W7YM6wnlZWnvq8oAMjXSaxVe4/3vUB7vU5+y9lIdz9pT54kgHs5qA/JApcH8p0FTAC6QAhWdSwJIEJ1ALQv7n0Ouik6fnx+fDNSmWujaxFk8DhPIuaxHERAPwgqKv+K2RRzWAmEs/xXdvFE9D4wePNzGqEItY/uCf5hADyhRlMoaC+A5lQMMeSgAAQABJREFUUhyWtDY9T+ikIQwOH06Y0PfSLiVoI2KvOy9Tj4+f7ZUctliCAV/EFjyohVKPNPEyl8V3VXl5gKkhdX7IFGh+CqRjRTm4NP9rHeINyg3zAxjnbANlc5KWjirf/w7xGjnJA6dA+k1pzi02mlTL1x122KPNMDoVEP/Amggb4glxv8xoLTItVn8AmEW4EbTR2Zb4B/mgb4PLdGzY/B+/WZvf5e97T3zZJBA7/aqN+UUeMRdC5LL/rE74pzQsjIp0xIifA/0yus2KjQpqF9jSrY0LnAr94lTokSjI2LA4FLRxGRWXwoj+BgXCwKrfp7VEhxKiz6W1WD/FUor8KShfOyjgNLIAtYuTyf231Slsw6wd8AZKUrU5hptqQXLxWKm5KSWpUwLzUKI7pf61IsAFXYXrrjAXYI/Nc7GBVq9RUYw/0WWgBj7yKPuBxaBG8qPzFFesXfShC1kIY5o6HX0X4EWdi/92adtu/RWxIf3XehQR9yCuKPUpcWR3C4jBEtawNsDDA+J+Ud9CEe+ogBj6E1Zmugogj7S2KVY+tk4tyrc6F+UVhcaKOSGLuj9KN2sbaJLSwNolcv8jcrQqJA5xSJTsTgIKAwgLwBufEreUdAItiFtqdaNFXDIChwXacdEbrPfQV4pn8y3KMQkBi1k1QWwaRUjbqIhTxSKzqu+XwQ0ccR5lnKUflrnXpKceAIkD4nJB1A3T9mcEEKMEHM7/wX7Grh5xYUU9VIDFfjF27bhSOu4IzB6ZAvePAk0DvPj3DfLOIogF0JIWPogYwWL30q2l8ML1BbH+LmmQkWk9zOjJ2gALm2hxAPnU+KXZh+0ZMtTsGOUjwf27LKMeoh1I6/nsmrwsoHSUUam/L9bwpKqtLZsaZNpNodSF013S3o0G7z6JHoH6auDRKUNcKBE/DqaWob1nmfUBHPt6iwPkl6NmCmQKHJ4C/j36eMEI436Hz7VZU5YbzkOPb4d/czcl7TlECx4NFnkeId9PMAX8G4rfVdxoxtmbPkb34qQY874c+vC3yEZZG51pcSUAwNyeXgk3tdm5PbceZrVRhutlS5vlbdYinE4ra9vw6G6gi+5sTcjb/9JNEMSKNZDD4kYPOS0v3Qxw4Zn6xnrGurLcSUVOOrVZ6ZSy1C7p5gBwYdMy2i9OBImGjIm7BeAFLgU2x1gB6dW9E8BFux36dc3FI4XWedfEeYQffPNojUL7Qgv92AZTThdfYwO8LJ0tswsYhpDCXIkVTUhP4Q3pKJyQglTAFvrR+pp0eagf2TGiOkqhKtTyLdd48qd/0TDVqs8byQIILOtiD+6THETGOB6fN0kbOb4Zobjoc4A/JoKkaJ0dmPYV94vWp6MCXQBexkYB9brCad1HBeYNmfhRl+LJTLWAlygOEr8BPgLyLkvE4VUpPanho3nFMQl605dEA/3EdoicL4B4ZvVK/WlafzMai2bUr7DINi0Ovak5SQFILxBAHjqpjLT6AfzSV26AGmS2/iM/Izk/SRuYn3sV/p6PvJWYpyStPRHQ6KL28V34NtjbgVHjhzgRw440VhrgAqceY9dpcVVhde0xgS2nAV8E8p2TsZEeiUPCVQU3Ffsf/opaxILT98DHXySG5t9MgWOjQFMBL3yMiBjxcWI+mlOnKYkYTUyvhhfE7XL15qKAF9h/NzQe6atC2RinNEoXP+BIV/Lxi+Emzjmpr4fGbzOGkEvjOFXsnS5SkLL+2uFvlcA3DkRVmggeUbSdQFj4liksGxnQSdVIuynavSgluy87r4FI1gdGBL6YLK2OI2pY7CzTKuf6OuXnTIFMgWaiAOMF16P9TbNgMyqwGj3mqx54YbH3qLfHMTfBERZHP6r6UNGtkvxjP+PwB7EjzLmuYFVE4Atct5j1hUvhprhfAF7mzNpMtEyzps3Oup0y6y6dHMatoOwwjcoVwZdYNj2IVYthHQpOu7XVSdG4k1J7XlsR4PbvAKuN9icuBDgK2AhLWkinxLJKIysffVKEOiBrNIgUIV402NthilFRhtoPx6w2N4grd+jEGBO0FdeECikuyrPaxiq7d74bBeJmGCftBImcTDS3HRxKNARLRZiBRn/LhMTVsFSE3iCAlwlZz5pZUBgcCmviUKC/KTHrOUTHO9Q5zK2c4Tqp7SSxb1A+V7G1LesQfet/01pSX1q40UW8GGbvISd14x8b5PY2iSGpA8NFhcLdft3PSFnzkAC+MzIIwSb5tMTZBnt71M/UF7VZhjPBuGeswxfAixMsrUIjvzT8EXB7q1gr+EMxUDkXFXsjU86sg2lAYcal65PLJr52GzFJ9avF5W1xVwEMF4AwaIdanXydzLRoo8vDTcdTWYdGMb3vVXl6LE+W9jP8oi4XxVchbRJpQxQSs+SIp5lFLbjzRsTZosPly+cE5Imb6rS4XAD2EInkD+MixvFFf/KCeCsqXj7LzXO+MgUeAAWaB3jRB8P4UsrE6rRpdn7JLAuMy7LAl67NhZdkOnpcoAv6XTjp6eADlP16PmImhfSbq/nq7ANMv8LamLXtso94aX6W1T7zKwZQyvNk1J268sxEx8CEgikGoX5p9B7ubw1nz3SHKwJeXn5xIJwf7bPBqKuzw8AX4/JRujvKKK15nKjJtcaXwvKVKZApcCIp4ONI+s3mb5gNp7GpP4A2o2zf8MIZkK9mpkDtt6SmrbniXMw6RLOwEA8/AFqVFRoUoq5IseWcNjtTtrmRuLN0zJllGomQLIrNH6WoyzL3uwwIIzb/zc3I7k85zOs+Q3N38IWVQLEVtUVBtOYR+xnVI13004OGBUYGYSYmWt2lDQsiRZj/BXSBswX9GwPatAzoxPiUOFwQKeqXXrg+gS890t7fjSJU9G8oo6gjhLLS8UaPuoxSeJvDvPJPQoE4JkCclEDRQhbrtzUODmX6F9GiKQEt4zIKMSXxELhcpqSseVaWstgYrwqsg6PE+qKyapV1FuNOUGeMd7WOcRhQeG07UbJd5uDHfdJ4qZ83KHkWabmV6aOnhwEiARyyZ+dbkDydpWPTi1gbZqSFq0jESP1O+l/OaMM8JnH4c1LcfEYb5n6JHw0J6IuKm1G6q75m4Et5i9RTsWTv5SY1e6ScIkF5WUulHqIU+l7od1hhW0cZuEyMo2wXEAZAb0YA37QkBG4J1EPsiD62JJHIdTXguuLCzRfns1hMVMfA6JN0hqIdGKNivyMshiexLAPVprwTZn1YDrtbCD+AifHuXqTi/AJjIv2Irqkf0X8GNW5hvvw03Hlys+8ZUf8ZkIgRfQgdVO3s+eB0UZ6WrWVauLilNKsieNH5nilwLBRoOuBla0tKyDSosKCZFrfLNYkYXZd5x+euLYQb0uA9NS8Za1kyspMeTntK2T4fBiJdfaGefodHTvH0w95vQUk8nGUWcjDRbWrU2tQI2cGiyrTKB2n17gpXZFr66UuD4aJMQI6J6wU9L6C/sN1x1eSFR5nxjgc88pUpkClwYimQfs2p+8RW+L5WLC4WtYB7IMAH9E+vmoE1DcjuE0+BtC214Ugfy7rLswiwTQ7zsTY6GxIDYTNtHAzLWJ9ZlSi0OBYExCwuS+HlrEAZbYDmpRR1TmsXMcqENU3oa9pYs+EhT9vgKHvrQeKAlTo3AmLJ3NKuJTdMshZa3GW00VjyO/Ud9IjLpUvrnz7p3RjSZmXQzP7C0SJlp+I+wPLMiHRwdAO2oPBUJ8kALsamr12Lc7nYOulR3/HGFjjgb2w3+ggXv/whdrah/rIkRagzAloQlccsuVmgEQAzKeBlVnpdlmUUwsA5bYoBWKLIGBvKAnRTZrZhFegSu0Ush7LiFX39qegpDcI8XX18T6n8FaUmVrGr5dVs7CWqxYmx+BVfuvVXNugD/dL/Ik6rUSlCPSdxowvSSYjlrCGBfnBo9yPaJq4XRI/cUpYv2+l/kYaxfrkrersU90hykd8awDyhF2MSf4DCq9I1Nbe4JN0vqGZY0+H0sgAYrCDpWdwvKwKDMVG+ua52A0gTKEy2Sq676I/kgC7raXLCYUXfA3hBjQOhxOfuV6xW7Z6L/mKMNWVMCVsWeRm8S55y8AeHXp/2N0N9USxyBFE1gXXonxpFj4vGsRFxTgG4IGKEzqCI28Wxi3rEvpLWqqhdrFxthb3i+Z4pcAwUaCrgBVY62HvXYO/V4mVyWnpdbsiSkfS6PDcuE9KzmxpYhPIu39GJj0AXgRPIKNvEoa/evzfoujfwwseaxq5viX2G25df5LNXduXQVZRjceOAAd7MxakWiqcYuABe4omCwJfuO2FMJwqXZeHoFVcGpOuFEwVNZqbZW+x3HH+RXv8sR0Y1RsByBiP/WJZFzD+ZApkCmQJNRAFb/GtMezDAS0qoOFbn8TSlSTO5vf2ocy3wwnTJtMlMald8sMMQY/HXhoW1CWz+y5wyr67pcEgcLwJd4HaZkRgJLP/z6PMQIIO+DvTQAdawwQF8iVwDKrlAVHzdQpk+Q1O6bU6pEHWiMjjZsOguhojQIwAFjlhOivsEqAyLmwVT0ZwSD0jx5KCAlz7d+7ulZ0M7nE6x8bPpdRZ9tlKWvfKzi4e6q2YJUReWH6FA7CcRNIhtC3cIAJ0pQJX+jYmZJVnHkj4Xcbtcm4DjRQeH4pJalGUs9BPqfFF9TvpctMtlbONOS5Bz2RfpJDVXfVvFFPW+NUnswXNOQ2IqC7FirOQYIc3Qq6BOSB/mHyaoEY2CI6tXG+hucS4M9SEm0m2i8RfOaBPd12W6OVCKislpAEAXbaMf87Z1q/Zq2ZpW81F3i/4pnehzrk8Ka0ccVC9L5AjOlzmNQZPieJkW9wtikeh9WRIH3jxikQJoEKFcN8XNakP1QfYbWwAvVoZuGgs01FTAi7WSBds+yzpo0R6xTrEN6a/2p7ByNFPfbbc/gTlqb6y2IlaEpSysZPULNB4ZKKxkCXhBYTPKv4cwcS/AeADxIvUZAGPGLhv/irEq3tJO+qh3kvz+J4kCTQO8QLR4sqRTpXWZ2dPAMS6FUc/dQMRoMbw4Li3wmrAWBLqsaCDp1JcM6KI1hY0KNhgZ5esWFcWAYkFFeHQrYNfLpqJdQ8sA+/qLfPbKzkar+ghx0HDghZdAGd+mJmIG1SD5xwi+3NFg1B4uScnuK8TxckkTGpPa0ECfWIp7dJIlk2qMePVXMUDVe+fnTIFMgUyBZqJABl6aqbVOel2ZK4sN345ps5iTdWNKZQq1uZhXwi0EBVHodZ0yr8g88LrEoVfE2YC1mllZO8Ik8KL8ZwXErGqNsiLQxYAX7W5MZENHzeTHCbPtpylffyZCUJTBpqcEXvTAKS+cLhwktWrjgo6hXp3+oj8DjtheRIu0BhgAfNFfn7hf+qQ4gVPibsSRAVyUpo3FhC4OeOwUm1elfO75OgQFIJ5IqI6CC/EP1q+AcQBz07PSRyiwBRGQl3RweGsafRxSfKo1rFS/KJ21tMgfARc1kelOiY0CkGPZU4L+eWO5XwRo4lMalro9rt8bheGXXiorKZfQmmWkHgjnb1PAS9RLw4YaS5wt6nNaq0rZ7rnRLq1XpYtQm+iL0vtyWuJHQzooRKmzKdulH1vHpmwKZM1O3nIXBe6sGXEf3e5qtIkkMCLEOTGOTwDD6xqDAPwAX6Yl4javMWhaSncBXpbVJ7HIhhTBusYlTJajpFcYsXHmwQEDoCZsWGNRLMTani6qZ3Pv6IMxHq1HD6XpuOxmAKJSaazhXBiRyB5x5wG4IBLZJcClTxxS/QLjTkkBOPqohgQUj8gs+TBiRdJF1SnABRPlpsuSehgcpCyTjuGH64TmK1PgJFGgaYAXBpLI7YJiKCG3YuW9cWtBIkYCXiRu9OJNycuuSvO7UFpOCxi4UfLFH4sYPv44ZsRJKfk+42BQtoqHFCNF6X8QB2k9n4Okq43rucQaw7wp0IV3MeBF062KQJnZsFDhs6Md4cnzMi0t60bnxyRDOxotHPVJftsuxY01KuqVjlC1xeanTIFMgYeKAj6SPFQvVb5MXGQ+aI6Xh5vGJbEfagdtWGwOGr6n5k6i7GjqwlNrFPoi4AlrFThT2cBw6gwQs6aND0AL3LrcjTsGFn8DXrYUF7dMw2rDw2JFKjMiB4GyJ182EgawiLOA6btV+jNQSopODfRpuI6DbnG5IGbMJhZdb2xQAFlQUMnpMMp2OV1Gga6biLYMy1eLK44jWMI0pOKj4Rk3m7ZhVX/YgPNAiAoKdOeXpIB5ksPCRYl9rIhjW6JpUna6qvWrVG3YGq9VaJqaSE0Q20JNHJtIfUFOEzOLdKw2tbG9CPW1XoxxNL9WapIVfT5esUQ+C3fxDhEoYc29RUdmEa7DwgFZ0xqRfqGzI5gr7wxXZJnm4pl+WdjqCcODvab8uUP9lr4MwGg5Vj9epPKqyqo878d7p7mfbLe3CGMFrWGXbuikgqMORfCMOwAtUbGzwBf1R3TBoGtoRqJHgMMoBQcYXjSdVKRhLIPzhT/1NzIrirBWkNvvClXT8GSu2Ex6VAqLBDMfQC+4WrvidWncAnQZkigk3HkDvW0FRx4ceuJuEVcL4HEvQIv2Mj1ym9JvxjylQ/SO4qKIWtFfildPuqORIv9kCpwUCjQV8GJsmpq8mLgwv3f1xqKU6s6Gl24uietlPWjM0AABOKGPkX/6IAWmlpd/j4wAilX4c7qVXp6g1jfGcD+Pk6Y7endaGiXyDOZi4yonU/JpZTKTgt0zkp99/JyAF4kcXdRpwrkzA0KIo017Y8FXBrHWRd1tcDz6OuccMwUyBTIFjpMCcaGp8S2PacdJ9oewLGZYZlWfeeMr+rxpocX8S1erYuGKT3HPI/BFj3HTrU2PgBUAFYAXlF6ura3pDmeD/rSeMQtJCmdzvgUIo7/I+aI8lJEePXsdKEWghfLNEoxxBwhI0QbGDpu04MEUtJnqFbDS3i79LejPEOiC+DWbnii6ojfV7scOi3nN4tuhKF874Z2vw1KAPqA/tSsiN6sSj0f58m2UnIrL4PrUYnjhujhdpqTjZUZcUTLtu7mFtStaILYRYjcYhaD/Wbu4gyoBZPgVG82fdCfi/bhi3bwzeg1sBa2HshpWfOxZvA5iR1vQYntTnFjBxN5GJHY0IhH5S2d7w2NnB6WnsDecGuwzJc9Y3kJHI5tzAwZ1N0WvNe+/8x13+twPGpzcPL09bINQ0kqjWdE2cOPBeYVeKbjx1vS3KJvSawKF0UU1Y1x5gIPaS6GYV39LCgOYERNfBF80btGvbYATKWzu1WOL9l02blIJA9lUgfhfdxwKAD2UZ5vGKMYugGHEIXv0N6SOgQUslH4jGtkn0UjTRyXuFsDjLvUJxIoAi9v5LhgHLTv2ebEgxq18ZQo0AwWaBnjhY1/HIoBYd2fnJVY0F4GXZ1+aCdducmqwHrb0QTJpAbzYsK/vkInLRwBcu198tLvF8DAP3+0Dd/+izF3zoxaep993q5mHM6xUgyiDHDwwjKp9OkU4Pdimk4NuU657SQp2L8nK0enhPrFwdtnAZGOTfsrNCR4Hvu72/gfOMCfIFMgUyBS4Jwqw+OPvwet4uafXyIkfMAVs45DUoXaGjE/qZjXLBCRCGoryWj5FDoAnbHr0t70tayPa8ER9MIAtGwUwo82NNkUO0rDeAXDZluUjLitXd6ZtMa3YxoPTXjhXooiRNiRw+QpYMZCFDQp/AC0ALoAs+mP+j2sA6pbyKFBKrO+hlgYkP7EXNCza4hjqSFvZ+szaXOtWcRksaYO7JC7taxIrmpJC3Ws3F8PVW1KmK70uk/ObYWO7OCwUS4C1K+1sbeUV5qgwba/YLzy0uu/3PYnneaTu+pxifqw262noqWM28YnYlqLYZJPbhvo/AOKGWLnMKIQ4rgZ6os6XC2Pd4WXnB03vy9lTiMdL5Egb7y7pKIqcEd5f09rGOpG3fRDmqK9d4fkI3XyMYC5s9A3TQrRDCQiKAwZrbOvitOMON5aJHUkMacXEkiQeKRElQBgsHwEIb5gycPVuxjSNV9YtlLGJIBUVsJ6gJqIOxrVEc+mPccoURcNxp0EMEAXQpU/Pw+JugeMF8aJBgS/0gb4e6XLpQQ8VSr99DIv9wcYxCi1elCLiRaHu5h77ZZ1nGiG7MwWOnQJNA7wwmMCui3k0lOpOSjP3C+J4QdTo2i1p6p6RCWktLOxPJqS5mPwMjefBPsbqi4yfI78G0Vhsou28SOMfr9/xS/1JVf+MX6Pr8PFIyUUtbPCEhVBPyHIP9bdJW3yn/nrCpbH+8PiFAYkf9Yuds0sLLrEU26BHag1c3Lgajc4xJP9mCmQKZAo0BQUy8NIUzXTiK+nAS5wfy1lS9WbGLZ7l9FVA+kKE2npDc6pvgGK41iBKgJ4F+qlxsBgAE/3Qg6HAyOWicNj49d/yYvWiID3IX3+c/lMN23TIO25s4r3N5niC2dTEE2EDWxSf02E2QGx6iMFFtsSNT+blQcVDvu2XAtbuCSXjeAQHC2BaFO3AdPS0DgyfH5/V2lV6XUy/y1aYXZSYx4pKUtvQTu3Kh3aytt1RAW8tWm+vi3ixhatYaZo0PPWvYkeXx6v3r31W17TLa2cdPqEHYkfANnBctIhDu1UfBCLy/RIrGTvVGZ7QIeEFGYW4IDH5USlOHZS+lwFtxAEVrQ/rzuU15VgVmttlfbpwxtsj/+vtEenjtCkoJrKVokcaXBCDBBDeQLeLuLLghFkW6II+mBX0wpiIZFQGbpx5ir8h5btwMcHNBRjjZqwp10op2oRxij7t4pAAwOxDDHTRGNUjcUiMf3QKfKEv9MiNguUeceyZqCTAsf4iABfHNHpCHLVUmPIv+xyu6uGR7wOZACebAk0FvKCkbk4KoW5OL5hm7udlQvrZlxbCtdvr0hC/bsp0O6Qkrh2V6AzM+l/8JB9l/DotqGwb/Gp9yiBz1Icd7Av31AdLVV8nlmH4ASVp0FOmKDCDNbWbEwSJG6Hn5bxYNi+f7QtPXhgM50f7TBN4G2gxg55xBJFDkXcyadW+b37KFMgUyBRoDgpk4KU52umk15JNQzk32hzZoMaad5nPbeos3PWxCKvZ/GieJhH5k9aV5dpGRT8RkCGXCMxEF3O0fMoyqBvlMv8XK4GCpT/6xzBzFyAL9bB66sfTysf+k6/5WSnFDx75OjAFaNOUdIBr/EPJ8ro2sAs6LIRLGzGj/35xJtySGenrEysCXYLEj3SgKDEODse0TNORIZvVogpkvOPykrzUhpGKVB6Xx73i7SjkQB70JS4rrSwy1i/2eL2QHvHRVt02/hJA0ua6JZyRrpfHJW7EuhWLnGMyN42uFwAYAER0ErF5j50VUNN7vxUp/1hgWWzh7bdYC396dO5lm4gw0MB/bUDREwe3xAE84Q6AsiaQ0Djx1Gc31HfX+BMYA4feujhdNvlTnHWJSEZl0QJgBNp4ei8HPS60B2AvopGIzJnFIjj1tA/plPgjHCxwNSEGiX4qdLjAAWPPAmRifMAW59KjqTWO0d4qqGh2Xiw+UDiF5itToAko0FTACwqgZsWmef32gsCXlfA/Al2+dH05jN+WGT6ZkkYjdrc+3k6AF0YDXQAUXCxXqq+VpyKgCC0HJnt+ED+MGlWdqgVgrAv1tUHH3oQFmkAXvSMsgBrHxJbXJpClXfpdug14eVrAyyWdIJwe6RFqzEAWTa6VZVCUBsX9jVVE3l/MWNv8mymQKZApcDwUyMDL8dD5YS/F5lib53we3mXO8+nQoyWEYdORTOM2bRZLkWIGjYkMQCnTu6O+PPnX5+fzcL1/kjSubeShgmM5ysd2Q+RXzPlFkUmyh2yKL17Q2qbmLZPWujenryFj+xYrNkgtoA0lpOvatC7LrPiUuF2wXnRLnNqf/9JsmJAFo5vi0N5YkyEI6Z2lv7A+A18AfPG+oSWeLnkKYKveoHLF2vOe7lfvjjEezG9VJ3dRTxTtbohg6DzSUl2cLS3hoqwcnT0lS0cSkb94us/Al9Mjg+Lk1lpem3NTogpxyEivGCld5FplXlLhwbzvySvVewN3Gwu8ihoD6LPmb37qX3qIenjYW0QABm4YxOQAYXDTZvYnPS8ALrjp5+yxlMTytOzUJmCHgCMtcNrrIYo8ittFezPak2cAGMA1MyEtPS52OCx/l1yIY1ZsdvKlvuQZv7ei9tb++93HkEu+MgUePAWaBniBVLOawGbnlsPViQVpg18Oz7y0GJ6/sSwQRibS5rZlgqwAXvTx8nXGT9O/VD3x1RZXnMqKj9c9a+6EEZ+/veJ5osPG8zrVllHrG8Mi8BLLY8Db0OC3rjsy37169xGZlT5/pitc1gT2SpmWvizg5YxOEDo6OjTYRZY9ry2vFDWBlz7ZkSmQKZAp0JQUAIjOOl6asulOTKWrFYPPvoeoWu00HlcOvjTQvTbnJDJO21V4mUkYXpawSs3mw58spj1YJp5BcteblfFjqmQplMR7mJy70eJo35E+EzeCbP6i28XJlqXXZVGHhTdnZTZ6inXqcvjcl+bD7bkNOyhEF2HQX+RyQcQodgFrG1WfDa11IPzLaleu0quhg8S7XeSxV7inO5p4XmPei809wAv6jJAg6ukS18tQezitPzherkjs6BzGIU4PmMgJFm3i2lWRi4zg3Lb+XDxTW94mefQXyPe70Ib+a5dukQuGPhw576IokcSQxOWyJV1TiEuig8p0VRFH7qjygDRQ31sh3mlv/hCdg2vJxI7sDrgiThjtSVycDHP2hMf44nLBTYuSLZdnbW4euDyw1mlB+SdT4ARToLmAl/llASwr4cVb8+HG7eXw7LV5AS9ohZcJtPktmRuTnKA+5i6AF13oP9nt2j2kPkX6xdeHHcWzDx6xRl6v+Abp9BhPr7zECLyIPVDoMxwvTGCYlT4nmdkrAC+XBbxI5OjMSK9kKKOVA9DlcrBSQRl4cWrme6ZApkAzUyADL83ceiej7uUmRNWxRf9Bq+WTd126mG+1ifAZP53ddyxV2IGwu/SrSlR6+8YkRotx+SVpOc+XGVf51bxbkq8Xdf/v/l73o3DPO6XBUb5RRecqVzgIiqM8oSWYEUcUY0HWN+ekUPf67cVwU8DLuBTrfuHqkgEvs0tbUp4rUEHsAO0yHU2bxXZL6p84q7JSVyP63TVRmsF9cFOn2jrE94o9EiYelO0CvLSIzaJLa9KB3hCGpaPwvPQTPo55aQEvl88NmGLVPlm76ZEYSqss4ZCz/VmG8d09b0qMPnLkq4YC3hqN6FOOeYpEH6btfDxxYIVnB2VoWuZai6l2VCvG+DGR+fu+gryFn4j7RW2nO+UboIJbHtFNu0ZOGItjDZrsdezZquU/uuvCn0r4Reb5yhRoEgo0FfAyMyeN8LNL4YVxAS+ayJ69LpN84+h8kTm0RXG8ALyYqBGfcgRe4rfJU/qV1j95a+2M5yG19/sXz2vJYMVVDoxy7eR4icALklXd4ngZHmgN58S2eVnWjV51eUiTl9g2h3vNvGQH4kYCpTgVtgFWBfkAWftu+SlTIFMgU6C5KJCBl+Zqr5NY22qu1dxYzL9HU88kZ827zPE2l9tmJc7zlFO6Ssc+Svf8FNUOnS0JJeg53pKyijJ8M2Ox9vdTZFXVcX/JjjlWWkvcByHkfqsay3DasgGMG1ZKgwtAwIKsxKyLS8Ctb75wY17i8Mu2Zn1G69U5KdRdXN6OykV10g/4EvPzfgKIU9Sdm7/Wjioq0MLTd62PvGcGSY5HFY980ot1a3ymZnDxiFFCf0Awsa6d7VK0KytHY6NStHu+XxY5BwyAGZSC3aH+HpkWFse2DlPZxNuG3TIsMsWvyKnwiYXl37tSwPvczohOUULUJ62Z6N3Q336jHwEF0emvfAcxNObovdn7Ms2GxS7Lk7yIzEUehBV5mFfxXXla/Ly/WIdKC4qB+TdToGko0FzAizheJqckXnRTExnAi3S8vHhzLdya2QrzSxq8C+Clq9CCbiitNQVfdvqVu7u2nZju9hMvjhSN84g5kg9XGsf9Ykj1S5yqXE/REHixeDElk9e6kOc1sQC2tUXLRgAvZ+F4OdUTXnllSGybAl5G+kJ3tyauAniJA5/y0CCZgZeqFbIrUyBToHkpwKIvBaab901yzR8sBdL5eLc5+/A1jPO7lxFn/sPnVqSssjMP3/Ck+dZuYNKQ/bljvY+ovvsr8hCx0lrWEeUQuTVO4htRhRbdg/2nXXLA6WJKSaUXYwq9LlNL4bnrc8ahfXNqNbw0tRmWVu+E1VVxKUuHCWtVdFpw+cbVHvQT14CNWrOIUSIa6bt6ZTyXam2ZrjM9tLqn8Srfna67xSuIYgnr6xJXxNALngneV3qF5bklfS4hnJK40cvO95mo/BMXZJFzsCuMDvWGwb4es34jxiA7OIzUKsrRLS1xZ32zz/4okPYh2x7QMBHjKJqR+TX2xkhxbR6N+LUpvTT5xv/lvGzRvc8SrUxYOmJif0zvni33R6jBOVC6du2axhWUQenVC/rx7aTPuDs7O0N/f38YHBy0sPxzcinQdMDLhIAX43iZRNRoQfpeKuAFjpdeNGM78MIwEfvnvlrgAFHvkp+PDGmO7leftIpTuXzSZWzCl7Qw9XHF3wi8wPEC8LJtJqWHB9rMstElKdiF48WAF1k26tas1tkIePFBcLeqWXn5J1MgUyBZJZwAYqQjBdXJH3AGXk5At2z6KtR+Vz7zxtc6mm8sllDlfCS5VtlZVeOaobYxDgu8xPpWeR1JfW1NQ55Hk1tVu/tR29o8a9aTRfV9EwQTB/owVrAEIwuck9LpclPiRV98fiaMzyzL8qb85gW6bEiprtZtZkpXKEI7Okv0EmU+xQtFgCHOPNU7Fq7dSFdb3R3JSg/Se9zUXUa4P460b8L9gtgR3EHt4noZ0fr1sbEeGYXoDU9dHJCxCCnd1cEhVo66JG4ULXNGSzlV38mA+9G0FJ2h6lTeF/fOW2mKJGVK71NFwrrHInrkniGKb0HKjPCsT4RfepWFpZ4Pp3tpaSl81Vd9VTh//ryBLt4uKQCDmz/05PT2CqgU8DI2Nha+53u+J3zFV3xFCdY8nBRqzrdqLuBlQScIk3C5SNSoDnhZEMdLX3dUrtutDsjXG1H1+B3Hb9W/2GrAsA++mE1rv3fipj71z/UN7nnX+9/tOS3D61otkyrgRRMMWRFdH5kpKdOktVrP8SKT0pckI/uqywMFx0tv6OnpLjhedIYCq58Vqfzg3bT87lbHHH4yKFDbV2omq5NRwUeoFru1Rb0/JEnHjsOOE41Imz9epwoLEl+MuF++ZwocnAKNvl9yOcrv9uC1OlyKex8fGlHj5FKivraNanoQmtTnp+XSTi8toaKnDqdlBWYjLK3ImpH+sGLEOvU/n52Uez3cXtiQaekWAQ1av+kAu1MHhB1ay5m5ZBpY2aTZx1Vg5XNHcWtjeK9I/C168uxR7J7441SfNh6Gqoia2P5gUf1B97tET2KqBCV2msV8ihWtAgBeMA6BueKWlu0wKD0vV8Y6w4XTXeHJi0NSutstvS+yzClx+R4dHraijFVrVhSzxppTlDJRXvV1rKlEftgfBXbdB8XkaVuajxGd3lC1Rm1B3lOcU0ahFr2utWjCRi1YF60270fjaXFxMQwMDBz6ZT/96U+H1772tcYpduhMcsIjp0BTAS9ziythXBaNHHh5xjhe1iVq5DpeWo19c//AC1+2vnobH5LBYQeZi3jmn7rTiPcySlgFisxiPp6bT+o2NJUzWAReNjVprQC8CEvp7mwJQwMtxvFyWUrKXnll0ICXs1Ku293dLTa0aLqtRtSIkZTLC4tP+ffEUsD7CQ2GOzfcg2sqbwuvgbdFvT/h3l7u9jSN7xMTE8Za6kDCuXPnGkT0crzcBlEeIa8MvDxCjX2sr5q/M8jdHFTwWnoHOaqxscrXl2Csy3yzyNhDDO7La5thUQp1sWYE6HJtYil87pnbYWJ2PcwsbIrDo12AQ6v+BLpo3cYRoQEvlrHqK3/LTc7a2ru/+1Z14m15spDSuoyHe3xipRf+aRx3p3Fwl29pbtLsFpO4MTTmbSWnxSih6wjhnVEFsCbW7eW1Lb22jGNIT+GFUx3hvP6euqhT++HucEnKdsdG+gNKdttZwwqsajfRLMvdyrMT//pq5+d7pIC3cqTzwTLztKTy9Klfo9wUz6N4kkbRHjE/OF4QH+JClAiulle/+tUlFTalTwq9nevr6+E//uM/Sv/U8alPfSq86U1vSr2y+wFToKmAl3lNaOMT8+EFOF6waoSOl4k64EWiRt1CxvmKU44X6OxTiH3hBjrwpWuysA/ev3piRn9cceDw5zRODK1+60eL+rj14Z6ycTyPnU5zrowKThW3agTHS0srOl6ict2z4nhBuS5WjbBudFaiRj09XQnHS5GzivWNXTk2epXy/YRRIO0jtF/6TFW9t+xVbdJ4vNRNGn/2fHeLt1f+j1KY0yl955RmqT9uwvZKU8WfmpoKv/ALvxAAX7gATd/73veGV77ylUWk+rYqvMu29ed8P14KpO3ifeF4a5BLu18U8La9X/mf/Hx99DrZPTutZeo+CvrG/OJa0WfMyg/QhTXZ0goWNpelc3AlXJ9Y1iEhwMt0uD2/EeaWt2RCuUPrNYSIZFJXxDR6ci+q67Wmxk7r1A/ujnjV+BZ+CrMKEqdRuEeri7drlnfJp8gund/SUi3bIgvzFyjUKs4W4rOGhWYb4vxZ1g8gTLcsc44NtobTg+3hqQu9YexUb3js/ICAmP4w0F8cHuqUESMRXjPWx76OtfLKOmXHvVOAVjsMVb0XpGlTP3d7DZN4hy3Ss3rI7inHy+OPP25rwbe+9a1hQ5x16WWWpvQNEf+jH/1o+NCHPlQGf/M3f3P4oz/6ozA6Olr6ZceDpUBTAS8LAl5uTM5pMls04OUZAS9XC+AFTfH9QsyxatRVAC8MysxD1bccP/AUzIjkx59Y/KVXMiCk3qXbw9N0jfxI4P5lYjnSdKm/TU3mkdbVc2CiqQdeeozjJep4uSw52VddklWjs72SkYXjpSvheAF+ipNeVq5bS/OT/1TfX7xH3EvNyfMo8rmXOjRr2pR27q5vI383aOxh7t5J9z//8z8P73rXu8L8/LwlRF73Pe95T/j5n//5IqP6PPDePb8iUb5lCmQKZAocmgLpqHPoTI4tIbU9yjGxyo/1ZLzi2jK643pq00xIb4TpucUwI/DlmgEvi+E/nlnQ83pYXNuWXpcO49hoxRSlMrNa6seBF8+vmitSl0JrgBd/x5jKfsv6NQjzaPVB9c8e7yjuRd5WLf1Qfby8edDxsrrBISm0aQkjffEPJbvnRmVeWpwvF8cGBLx0mbhRh9iEOmx9b7nEGnqeR1HfnMcRUKDshMoraSfLmTD80jgE1MfDL18px8uTTz4ZPvjBD4a3ve1texIGUOZnfuZnwoc//GGL98QTT4SPfexj4fWvf/2e6XLg8VGgqYCXxSXYNwFeFgqOl8Xw4i2U626GeQEvvdLxYuakCx0vfNwg6XzU8bOOvymYUUvquw0GPmDUDxKeLvV3Py8hDXO/+jjuX9U41jWWG3PQu8gRgZdt0/HCAQqiRsODAl6waiSOl1cIeDHlusjHik0zWjWSST5F5qSBy08KqlKz62RTIO0v3p/wc3d97dOw1H3YePXpHuXnlJ64uWgHd5tH8ePtk4a5XxUPdtH3v//94Zd+6ZdKT77Rt7/97eEjH/lIGBkZkX+aRxmtcOzMsz5Gfs4UyBTYPwX8a8tf1v5p9mBjeotRi6NqtSrPncBL5Ji+I826AC+zC+thanZRf8vhpVtL4QX9fe65RflvhBUBL31dmEZuM0Wx1JC1mOVeFOErVR/nq5J3e5cqBvkd/CLf/eRxyHhFMitBP44bccdvU6JGGIjgX6esc/Z3t4TB3hZZN+oV8NIbnpCS3YvnBqT/pTsM2DpW5rfheBHHDM0L/ZgjyYui8tUsFMgttp+WSoGXl7/85eEDH/iArQfvlvbzn/98jUjSX/zFX4Rv//Zvv1uyHH5MFGgq4GVpZV2iRrOR42VqRVaNJHY0HoGXOcnPYtWoWxwvnQ2AF+jpk1oKZjSmM4MCF0M5f/6MH5cP8al/o3gx9s7fRulrYzWKgZ+9gxwV8LIdWsWzCscLVo3On+6UqFFPePrycHgMc9ICXrrMnHScsKpSmLBgec1X81AgnazSvscbpD3G3embpWlT/3r3fuPVp7vXZ38fr/te9dgtbDd/6rZb2G7++3kf0vrl9fbnRvl6/Pq4Mc3nPve58IY3vCEgblR//d3f/W34pm/65sLb80ljkWdaZhqncXlp6uw+KgqkbXBUeeZ8MgUyBQ5GgfQ7TN0HyyXG9rHUx1GetYpEHwvAiR5Zj21KY+7M3GqYmFrQ31J4SfpdXpxYCZ9/finMS8xobS0IPGjX+jSKy8S0cTUal5i1K9Sqpl5u5dPY5fVsHFr5+lxR+TR2HV08r5m/ieUM3UQ84VVhWyw/2Hbq6dgW+HInXJSewvP6e0IHiI9dGNTatkdmpTtDl/S8oK/QDg2LzKA/l4M68Sn/Zgo0PwVSUaODAC8vvfRSuHLlihGgq6srfPKTnwzf+q3f2vwEeUjeoKmAl2UBLzdvI160EMbR8SLlui+My9LR9IZYObdCT9dBgJe9WrB+mqiP69OH+3t8nuvD8EvDeb775bmUKTW7OOhCEdua6I1Nc3PbFI7B7TM61BbOneoSx0tfeLlOCi4LeDkjUSNM8bVjTloTvufLaiHKGt+9LjnGSaQAPYPW9B7iLevPXmf39+eTdL9bXf0d6+t8GH/yaESL3fKqL/Ogz+m7ebm7l7WlRfsnPvGJ8L3f+71W0E//9E+HU6dOhZ/7uZ+z5/e9733h//yfnxb3Ws8BKuLlHiBJjpopkClwSAqk3zxZ5O/vkIQ8gmTpWJu6987aW7C25dy3SuuACz4AMIzfG5tbxukyLsubtwS8XIXjRcDLF69K2e7qdlhbvxOGBLx07Qq8VD2mtsTa2lS1qHfVpqoPPUnP/kYGumgNwz9tRsTNsq3D021xu3SF8zpAfEoHiI9f6A+jAl6G+nskNt8uXTAdFaGSbywDLw+yhel73qoPsh4PV9kp8ILIELpb7iZqBAX+5V/+JXzt136tEQOT0uh9+eqv/uqHizhN/DZNBbysrm4IeJkP16cWw004XqTj5Us3luSWqb5ZtKJLv4vQ8A7Jz0bmw0rUiDba7Tyhaj8GDgaQuw0i9QNMOuHVh5F7fXj6TLiXiztenovXxFgqiWcBWDUSa6vAl3XNXB3tLRKzagunhtvNFN+lM70GvFwSAHNawEsHNBF7Zpsm/FbYMosjAjs18ALzPVPgxFHAe//9rNi9lrFbevy5/EvGvVvcEBYWFsLXfd3XhS984QvhsceuhF/7tf83oN/ljW98Iwntunr1xXD58mW5ydPzj2GNf9OyG8fIvpkCmQJHRQH/vht9m/lbPCoq7y+f+jY4CvoX3ClUQGuouIyK6ynEjNalJPb2zGK4ZsYfsL4p8GVyLTxzY9Ms98gASRiEK1vrsDb0lFgeRZ711a15yYPUvT4j0tb71WRePBxvPH8jVO1SPQOypOdFPDChXRaOxkZlVloi808KeHlCCnbPSN/LyFCP6XkBeAFkYT3va9ja9XHxSvl2jBSgj3mrHmOxD3lRKfACxwvAy1ve8pY93/rq1avhHe94R/inf/oni/d93/d94fd///f3TJMDj5cCTQW8rK1vGgvnzRlYOVfCc+J4efb6Urh+e03Ay4aUc0nMSBNaR2u7qMhA4JOjE9UHht0mIsJjOrTOR7enTe+ej/ul+dWHEcfDPcyfPb2X68/VEEZMC9UkHTleYh4OvDDhd0nhWF9vq7hbOsWeKY6Xsz3hSSklu3CqT1wwPQJd2kObaNMmuVivASX5pFWVml2ZApkCB6OAf8s7v+EqH//q/GuuQtz12c9+1oAXnr/u614TPvWpvw6bOkH93//7/wl/+7d/a9H+5E/+OPyv//UdpqcppvP80joQUv8cY+ffTIFMgftJAf8e0zLyt5hS4/jcTncv0cdgfz7cvYRelL0fYAHAsA5b29gMk7cXw9Wbc+HarXmJGUnPi4CXFya2Tb8LFnwG4co24EX1KfKIAE6j+viccpC6N3pv9/P8vCzPl/D6MI9Tfz9MPC/H86I8LzOGQdft7S2JHW1pXbodzugQ8fyp7vDyxwbDExeGZFK6J4xKbL5fel56JDrP2tXAF37ITdmZq74oC80/mQLNSYFUx8ulS5fCD//wD0vk/JtKq0a+h1uTHCMgzeTkZPj4xz8e/v7v/95eGNPTf/AHfxC+/Mu/vDkJ8JDW+gDACwOlXw9mdONEAaVlE9MLYXJmNTx3XcCLwJdrmtwmZjaktEwaz8Xtgsb41kJNfO2kltY7fR/eizD3457GJdyvNJ77pffd0qVxvBz87pZfms7jS9RIHC9RPhbWTCkk628LZ6RYF9lYgJeXnR805WSjQ73SAaMTFhS6mQk/lagiayCY/VS5vhr5OVMgU0AUSL/llCDpd13vTuNF9w/8wA+E3/md37GHH/mRHwm/+Zu/ae6f+qmfCr/+67+uRel2eM1rvjZ85jOf0bcMsMxVX7Z/yHuNXzFl/s0UyBQ4agrUf3fp9+nf5lGXeZLzq6fHcdY1pT3lHg39dwVeND6vrm2ECYCXG3PS7yLgRaJG1yY3wgu3AWWkA0YcLwPieOnSOqxdXMoM33fEtVxf0/1Tqf6d6nMivN4vzT1Nv1c8T3O3/HaLl5ZDHMriT/4WFLmGAF7WJbJ1586WDhE7TNzo5Vf6ZVp6WBwwveLe7gv9fR3S5dhZAi/k5m1bZBW98u8+KZC2e3077TMLi1a0Z+n2tPeSp+fx6N5T4KW7uzuMjY2F4eFhA30BXRBxxGDK6upqeOaZZ2oI9aM/+qPh3e9+d6nrpSYwPzxQChwSeKHOx/9BIUM7O7cSJmeXBLyshOdvzJu40bXJ1TAuPS/tLYAu0mMipbHUzsDwdFzZUWcfLGoiFQ1CDql//XMRrea2X5qk+dZksMsD+cKGyT2WYcCL6sfE3dPVEob6pN/lTGe4JOAF3S6Pne/XKUF/GBnoDi0AL6JLaz3wQlZOAjnzlSmQKXBQCvi37B+Tp69/Tv3dHe/Xrl2zU4z/+Z//MQ9YRF/zmteY+9Of/v/C6173+jLBP//zP4ev+ZqvKZ79403rQJD7l8myI1MgUyBT4Bgo4GORFxXXK/70YO7peJi6D14bf7uU24U12CbAy7qAl8nF8Py1mfDi+Lwp1x2fXg/XZloUJp18BcdLp9ZhbTKIAGVY1ekMjV9+6q76OaT+2aPX+9c/HzSexz/Kux/1ufh/sZrVupSLt9/Y2pSVTnG96G9E+govSM/Lyy/3h6cfGwpnBbycGe6XWenOMNAL8BLT8aZ+6o97xxIfv3ztQQH/HvxO1NS9R9KaoMOkqckgPzSgQCpq1CB4T6/z588HdAW+613vCqOjo3vGzYHHS4EDAC9eMT4wLhvmovOYftEaP7+wFuYWVsL0vICX8YXwzFXYOldM3Ghru01DhgbkO4AviOZIYKgYjblFH69s7UlDnBb83TyO32Nqy1CK1OLA5GF+t4JqqLJbbtXA5oPV7jEjnQlXfdGiXxRnE7ambTWgTgBawqmhjnDpXHdAv8ul0/o7J4Vk4nYZ7OuKwItQKCYoY8mkqqBSysxyNITK3yPfMwUyBQ5GAf8q01RxPNg5Vrh/Ffe3fuu3wi/+4i+aNaPTp08HNNJzusG1qWNSQJh/+7d/s+ef/MmflP6XXzP37nmn9dlZXpE43zIFMgUyBe4DBU7a+EN9fBxM3Qd/dX8zF20BgNmSSeRNcWmsrm2GmzL88Pw1Wd4cnw3XJWp0Y2Yz3JiVyeSNFoEz4niRWHi7FqXtAl64SE9efrEOrV2ZViE7x3sPS+/k6xmm7jQO7jQsdTeKh5/TzfOuj8ez5+P3Ko7DLuRj/5QN69G49GRdirGIrbCyDtfLppTpStTojICXSwJerkTu7bM6SBzs7wr9Al5aAV5UDCVl4KWi89G4vK0Pktth0hwk/0czbsrxAqcLCnKffPJJ44CGIg4Aw3AwPT1tOgL/67/+q4ZY3/md32lc0wAx+ToZFDgE8PLgKr4lOVosG80vrYVZgS8v3ViUqJEmObF0XhfXy8q6JretVg3gKJENgbkNjs5ieLYBH3fjqaMWlqneUhmY2BIZKWXjxEUZVSpzWdmFX026moe6RP5YlOePKlgHK/r1+pPHHXGySFO+uF3ODHdIvKg7XBzrk56XvnBOfyMCXpCJZXKzSY60JLN6+VQojwy8lFTOjkyB46TA/Px8+PEf//Hwh3/4h8Y2irjRO9/5ThMNpB6IGH3sYx8LP/iDP2jVet3rXmciSU899dQe1eQj50oHoOiTfzMFMgUyBe4/BU7SGOSLHq+Tv/3+xsf6VJa68GQ9BbcLLP8rEjW6ObEYvvTSTLgq4OXaxGq4Kd2DUvkSZBfCzCb3dQp40XqrFnjxerC6i+6G4AvrNCu3YY1ULdISVuVndd3xk8bbLS8SebwdGdR57B0v1ib+Ur/03Qw00XthpXNTwMuygJc1HTaMDHZIx0tneFrAy8svD8haZ284D/Ay0BUGdJgI8BIBF9FM6SFNzavX1TA/7ocCjfqCt9tB0h8kzX7yfXTjpBwvL3vZywLWLd/+9reH9fX1GqIAwLBW3NiQhd+ZmfDBD34w/O7v/m4Zh8O9H/qhHyq+mdI7Ox4QBZoOeEGOdnF5XVwvq+H6rQUp2J2TeWkpMbu1HITFaIJr0cAN4CJ2TqEvOmDQFQeCOKw4cBEp7uM1o3bj4UK+BfeMxTDkolFrNUhNUkW1cmvGNH8oQxtlmPgRb9uAlwi+6LGoU0f7HYkTtYdzkol98kKvNMELdNHfGbFmDskEX69rgGdy0r8MvCRkzc5MgQdMgX/8x38Mb37zm8Ps7KzV5N///d93KEKDA+bKlStlTdFQj6b6nZePKx7CuJGvTIFMgUyBZqGAj2FHOXaRZ7rWqnfvjzZeM2KzjmKzz4ZHmItxJgK8jEvU6DmAlxszBrzcmpVBiHlxvGxKH4M4Xnq1IGVdyp+9odLbmqysgvlSQlw3lv5eXuKxw5m+147AxCONl7qTKOZMw1L3weKRsroiFe8YUhJbBeDkjjaNa1rcLq8BvEjUiDXtqQ4DXZ6WuNGFAngZEMcLXC+IzZOabCIAE0sosi2L85YvPbJjDwqkPdyj1bae++68p2n3m2ZnLtmnlgIp8HIQc9K3bt0y/S4o1uX6+q//+vDHf/zHhVXM2jLy0/FToKmAFxC9tTVpiBeqt7C4HsZvi60TRWa3pNBM4kbT8xJFWtoOS2sCXGStr0MsL52m8yUSFtCi0ZV6Nxwy8LRIpaNRNg38GubmmTWI716ezsEgwKIIvGiON1PSmIWGZbWr6044NSgz0mLL5HQAE9LIww4LdOnv7ZLIAoo4yS/maZOc3sWevBgvNt8zBTIFjo0CnE589KMfNU31FIo8Licavb29NXVAYz1Kdn/7t3/b/Dm5+JVf+ZUwNDRUEy8/ZApkCmQKND8FfEV2VAsUzw/KeJ7u5893p5qnIGYFvGg9JkQFq0YrYmu5IVEjOF5eFPACF3YEXoIdBkoiKfS21wIv6IepvWrrUwO/EFQfvTbxIZ72m6nH8/rtVhGPV1UlpvB0+Fdv5cAJHC/rWt8DvKDnZXigTRwviBr1hVdciRwvF8XxEoEXFzXKwEtF5aNy0a5pW+0338Om22/+j2a8VNQIc9If+MAHjONlP9T467/+azvU87joECSPfD14CjQV8MJkh8m+dbEjLknkaGJmURPcvE4WlsPzAl8mpjcFvmwa+NIhGSNMS3fJwpGj4KpJQMAAAEAASURBVNvJ0UI8b4gTQOmt8WbnkIPPbpNM2oCN4u3MLabYa5ACbFE6K1I/RRY8MkdjyQjWVhkp0vu1yLReq5ToSr/LmW6boC5Ix8vZkV5NUN1SutsZOkGglFl8X2XmxEirnt2ZApkCx06B8fHxcOHChbLc7//+7w+vf/3rS/ldDwBwxprR7/3e77lX+NznPhe+7Mu+rHzOjkyBTIFMgeOlgC1SVORu65x7qQ15H1W+npfXt1G97l5Wmho3zNSsKQFd0POysip9LhNzBry8cF06XgS8TMyJ42WBdWsUNepBxwuHZg11vMRVWlq7CqKQL1VMK5FGPJB7vxk1ipfSySvTKF5VoZiiSlesui0CB4isSVEwvC5RoyUBLyvSQjysw8Tzo+J40WEiwMt56S28KKtGrGud4yWKGCl9Uo365S1BVclVnbJrNwocjmK06c7em5aRNFLqfd/d9eX6MwWf/J6RcrwcFHjBpDSmp/364he/GF7xilf4Y74/QAo0HfCCZSP+llc2w9T8onG7XJ9ckqLdxXBDZqUnJVM7s7BlpqU7Zc2ns7XdsAYG5BrgpfjmAF32Bl7utXXqP27/8Ov9KSeGWUgaTR48av8lJW4ReAFP6ZY1o/6etnBWsrCPyYT001eGNEH1hDPS7dInBWRdne2a4E3WKr6ElPO2FFrko0f+zRTIFHgQFIBF/a/+6q/CW97ylkMV/6u/+qvhx37sx0JHR8eh0udEmQKZApkCDwMF0qVS4/fxGB7KCgu/Rmswj7P3nTUjqVlTbmlhtrl5RyZdBbxMzpmo0Ys3IvByS8ALOl4Qf9/W+qtXh2U1wItO09La+ea18nMX973qe7fwvd/nvoaq2qYm0Qqpe18tzKOoUeR4WRKhVnW4OjLYKY6XjvCUKdcdCBcEvFwQ8DIo4MV0vJiokfIl7xJtIa/aNznBVKmt6EP55H2Xl6Nh0tZI3Q/lyx/JS6XAy0FEjSj8L//yL2vWl5ib3ls34JFUOWeyDwo0F/CiF9qSanhYO9fE9TKzsBRuzSyHm1Mr4QWJHF2dWAnjU+vh9ryOF0zBrtg6sXBUyNO2VqN/MdkxzfmAALVS992ot5+BY6/8CEsv5Rf/Wy3iTwynJKYrJm6b6CV21NPRokmoVWJGnZKF7Q6Xz/VKx8tAODvcY9aM4HRpF1urAy/kweVm+OJT/s0UyBR4EBRAGeM3fMM3hH/4h384dPFzc3NhcHDw0OlzwkyBTIFMgWangK9teI/6VVX1bh7L12S7x6zS7O5y4AUAfUPAy7aAlxWBBuMT8wJeprUebQC8aE3aK5zcgJfiPMzWdlrX+abUa+W1jf6N6uFpGoWlficgXlGF+G6RN4L34xmgBK4XFzUCeIGrPQIvnQJe+mROelAgTI/MSwt46dsDeFE+sYzq/b2cyie7jpcCVU+O5XoL5ZbZTzukwMtBOF5I9973vtesGVEOOl7+5E/+JFy6dGk/xeY495kCTQW8QAtYOhG32dDgvCDrRlNzK2FS4MtViRo9f2PB2DtvCnzZ2kKRrGY3My0dB3csAPnFxMnQX4+Qe/j9v/sAVJSkClW1ixMSIdQTfwNedAd40TQfBiRiNCp2zHOSg70oLpfLsmb02PkBgS49ko/tNsClTeysZnbP0utHV3U6EJ/zb6ZApsDxU+Dzn/98ePWrX10WjIlo2EARK2p0tUm28F//9V/Db/zGb4TJyUmL8qlPfSq86U1vahQ9+2UKZApkCjzUFPD1UrqSwq96rp7idr8KuVfCALiwzecerRohaoRy3ULHC+akJWo0PhOtGiFqtL0tjpfOguOlBF7Io6qN17B2NViFN6VLL+XvRf2hWbwiUIKiXKY9zEkvrQt4warRQLRqZOakE+ClH+BFugtJYyQkY1/ENwBeioLy7YFToGrzWJX65+OqIOWmvfG4yj1cOfU6Xj784Q+Hb/u2b9szsy996UvGTf0TP/ETZbxf/uVfNmW7mUO6JMkDdTQd8AIyjq6TTZ0YL2PdSODL9Hxh4Uj6XrBudEMT3qoU7G4KfFnXHwM0inbheDG2Rj372M+YHYd/PkYfDBq3Sfxk7x4vpr5bPMKLSxkz0cbSC1nJIph3JYAbgItdLdL63t9uul0unukJl8Z6wyVkYMcGwtAgSnU7o9Z8vZzJ0MYsirTpLalD9M6/mQKZAsdAgZ/92Z81RWle1PLycujp6fHHhndOMV7zmteEL3zhCxb+Hd/xHeFP//RPG8bNnpkCmQKZAg8zBeJ6qdpGxRVUXHcZKKJVU1zbpVQg1T2se4pCDXjR+oqDMICXbdPxsh5u3pY56WuzZk76+qQ4sKV38PqsxGg2tG7V8q0XTmSBBvxFAEL3WGWrZOX0t0vrflB3ldveKe9zPGVPCX7Zm9kCvFh5QwsDXqLhjA2t7d2ctAEvlwclTt8jy52IGnWFfsxJe8uSMYt4rppy0hJjcP49aRRI+3hur0atkwIvIyMj4XWve13NAZ2DmOxrObRbWVkJiBT93d/9XZndG97wBtMPmFrGLAOz44FQoOmAF6jEBEYnW5VVkOUVmZeWhaObU1K0exOOl6Xw0sSaABk4YjaFoMcTBdg7GZ99wPaxup7qPlEzZdcCMfXP9Snv/lwNLdHlUIsDK7E8hRHMn8YlgBfglm3NTO3Sz9Ip89G93S3hFOb2TneFy+dlam+0xywZnRrqE+jSIUtGHfaufIyxpOpNqvf2kLvX+2AxGEzvV94Hq0mOnSlwEilw8+bNgCLdv/mbv7HqYaHoPe95j07ximPQPSr97ne/O3zoQx+yGHDMYFr6K7/yK/dIkYMyBTIFMgUePgr4ts1XG+V6qu5Vq1UQAfe4PikKdeCFOzpeEH9fRdRIwMsLAC+35sP1iaVwfXojXJuWOWkBL5id7utsk95BmZPWQaBhD/ox08r+MmXdvZ5pAG+aPnvkev/654PG8/hHdFd1qJFf9gbJa1j7KMK6aLgubpfNbXG8DHdoXYulzgEp2B0IYwJezqJcV6ALB4u2tiUPMvZFrZWDhwd4ifl+MimQdIKaHnIya/sgapWKGlE+/X6vy4EYj/PWt77VdAF+4zd+o3vl+wmgQFMCL0xYnDQgboSuF8CXqbnlcE3iRuNTS7ovS8numnTAbITbi5xGhNDWEq0b0XEBXzT3FZcmPuXnj7Fja1LUP/cjtJrU8U0HDM+Hu6cgnOkkpvIUtQsA5ULButJfy8OzUZjmIgEvcPlshy5ZhsaK0VBvazg32ilLRr3hyvlBm5ROD0nbe1+PFO62hw7pdrH3UD6eVayRMrzLh0t98pUpkClw/yjwyU9+ssYk4H//93+Hp59+el8F/ud//mdpzYgTEOR4MTWdr0yBTIFMgUeZAnEdBQUqF0/16y78Dn0VWcf1IeAJgAri71thTcDLhA4A0Td4TcDLVVnbvH57Lbx4W4p317dlFCKEwS4BLzJ40IbRA6UlfbrSrOoVC/I3ie/gT1WsnS5fbRKSuutjpmGpu1E8/Ch7r3jE8XC/47fbFfPjl/X4HXGjb4qOgC53RMvTAl7OA7xcGQxPXhoMZ0a65SfgRaBLb48OFvlXLeJjISqWkv03eubfk0uBtD/Hlju5dX0wNasHXvZbize/+c3hW77lW8J3fdd3hbNnz+43WY53TBRoSuAF2sAJgoJKU7SrCW9mfiXcmloOt6aXw0vifBmfXpG56XWTsUXkqEW6XjhZ4PNuFQgjbELuYuIrABAbzDUNxNE7HRRiaxhLaDG0M2nufZEbcSjR4yaDi7zQ1sLlofZA/CIJUzL6bKJeF3G6dIYw1N8WTg2322nAlXMD4bFzTEq9Mr/XE3o6O2TlpNXEiwBeSJ9eVnoGXlKSZHemwLFSAJEi5G3f//73W7lve9vbwkc+8pFw6tSpfdWDifiNb3xj+OxnP2vgKuJG6H05d+7cvtLnSJkCmQKZAg8jBXy1E1dZPEWXv+tOHw85wL0oxNdWLAPvGPAigw/iwJ7Uwd/VcYEuAl5eguNF3NdfmpQIwFoBvOjgrEv6ugx4YYVG+iLPaiVYLACtWqk7rae/myduFM/jkM7jpXm4+0HEg4KxXFspC3iBi32bU1Kti89ojXvudHd4WmJGT1zUGne4W+vePlnr7BDHNxzdSpWsZd3NW6Zv42+Y7yeRAmmfzK3WqIXY43Iwh44/QFrv58RNn1N3e3u7ia2PjY3JwIpO6/N14ijQdMBL+qnS2ThtgPNlaWU9zIjrZVrKdsdvY90ocr1cu70alle37W9lQ2CEMuATb2ttA2Ixzhfu5lnMgFaG/QBeMMB7qaTEXT9IuN9u8ZSLgmKo5+HZVGkoiypEDpd4b23ZVl3viIvlThiWXpczQ+3hImyXp3plOrrP/oakTJeTgI62dlM6ZicBZRWZ1oqrdLjHSbtDi90qWR/mdOMddktz0t4v1+dRp8Czzz4b0E7v1yc+8Ynw3d/93f541zsT8Z/92Z+Fd7zjHWXcz3zmM+G1r31t+ZwdmQKZApkCjyIF0lVB+v6+QvBVhN/TOPtyFwUY8CK3PeoHnYPrm1vh9ozAFhMzWpDou8TepW/w2RsbYUlrUMSNhnrbQrc2Ue36Y51n/z2TfVVgr0i8pWW2VySFpfFSd32yNCx1HzZemq4CXljzGj312yJul7bWbVkxkjlpAS8vvzIcHrvYH+DoHhnQ4WJ3p3F1O/DitfKNZ7oxTUs7iBsKkm++7hcFvI9mKt8vCud8TzYFmhp4YY5B2S6T3traVlhYXgvzi6thUlwvN8Xxcnt2RScPS9L3shWmJHY0v0xcgS0o2r0j4EWIebu+fbg+7SonAD2ZOw4MFbC+20DhA0mRz45h2zLzwOpu2ZE25rutO5OQacm3qUiAi0CXLlVSUkThzGCH6XW5MtZvOl3GpNtldKjXlI31dHXoPYpTgKrClvVuta4qclJcTsf6Gu/mf1LqneuRKXB3CgCafPzjHw/vfOc7LXJfX1/49Kc/fWAdLV/84hfDq171qrJARI3e9773BfLLV6ZApkCmwKNMAV8tOA3qVxP4E6eRv6fZ9a6EMf/oMLd+4NRY39wOU7KweWNy3nQOsvZ8cWIl/PeLWpeubkncSIdnEpPpFru1Ay93tH6N+cUSD1WnXSu7nwBKTGuwW5p7iVf/VlV5UYehaCAvDjg7dNDY2b4dLsha54Uz3eGpy0PhsQvRWiempLu720NXZ7ud/Bv4UlQXrnAHY3Z7g+yfKZApkClwEijQdMALAzW4gk0Dxfgd9b0IfNlA38tmmFtYDhPTi9LzIuDlxmK4PbcWbkythZuzm1LgJeBF3C7wgTBQd0jsSNI55XVH7I5xKozcLpRUcbzYUxm3clCRdGJqMNEUdY1pismWaLxM+R7STC2vDcm5AijxT/O0QJfWcEoiRmcl83rlTH94mSaiMXG8DMt0NJwuXQJdmMiNLkXRTifKq68NfifzqiFSUkV/A6dzEpSdmQJNQgFYqeF4QVyIsae7uzs8+eSTobNTMoQHuNBc/9xzz4X19XVLhYnAV77yaYkZko9/KwfIMEfNFMgUyBRoUgqkqwYf/dzPn3k190tfMw1P/Ru7i3WbMooKcXHIj5tWbhsCXqa15rwlcaPJaYkcCXh5QXpevvDccpjVunRlTRwvWtAhatQu8MVWeDFxTd12rVMa0Ohlatagjd8g+pJRwwzqEtXFq3usIu8aUEQhPF6sUZ39mxqIZGHTaCiFw7LW2dNxRwqI78hSZ5e4Xnqk30UcL1rvIkqPNaMuWYXqNOCFpbNlJvrTBhl4KUicb5kCmQInnAJNB7yk0wVzlo29IrKLHK1L7GhhSWamF1bC7MKaxI4kfoS56amVcOP2ulg+Bc5I3nZtQ6cUUtqCCFCbMon/ED0CaNnv9sUnlLRWlR++wDc7c4uTNRwuAC28R8xhy+7oDJPUUOjskPUiiRcN9bVJxKhTWt27bTI6f1agSz8TkU4ANAm1t0fWVZt3lR8XeXI5feLTbr9FZAv2+u8W97j80zp5mSelbl6ffM8UyBTIFMgUyBTIFGgGCrCqYBXh94PVOa7bSMNG3y9ExLGHDKfyrNaat2d12DezGq7eXAzPS9zoc88thtlFWeAU8DLQ1SmODjheBLwoC8/Hc9tzhZMGegKvxIm+VxW3tTXk4p/eAYW6a/rjuFNL2TDYfUd/reHxCwJeRnvDExeGw8Vz/WFwsDv090icHnPcol1LSzwt9ZwhR1zrus+JJkiuXKZApsAjTIGHCnjZlgkgxI6WV2ViemnN9L5MSNfLlACYG5PSMD+1amamZzQJLi7rhAI9Xpo0W7aBXRA5krZ5oR4GvhSdAn8mCWLEuS6d8XyQb+xnk0GRj033im65KIAw0H6AF1egC7dNSyFa1CMulz5NQGNDHWF0QKCLlIuNCXgZG5V295EIuvRqImISQvESdbfLqxSf9vnbqP77THos0WopeSxF5kIyBTIFMgUyBTIFMgUyBYwCFfDiBIncFnGViFnp+cU1WdhcMiubL8m65gvjAC/zZmFzaWVbBhKKNZvWa6wri0Wl32wd6nnvuPvaLl2u7Yh0Ej284gU4Yo8F8KIF8IoWwnCti3E7jPS1hJHelvD4+R4DXh6/MBgujg2EQekx7JViXTi7sdpp6+nyN5IxcsCcxPfPdcoUyBTIFKgo0HTAC1X3eSc5dLCTA/wBL9ClgM6XVf3NLi7L4hGcL4uBiXBKbhTuTkr8aEUyt1tbHUGSPTbhdUrmqNPBF8MxNEMUk4QVagXbdFlVggpV8wpP1TPxBcMT7BaV4oPy0ISzKXmgddUV4MXMRktOuFWKxQbF4XJqsF2AS1u4IgW6pwS6XDzVF0YFvIxoAurv75KSMVkw0gRE7kw4lWm9+spQobtdVlFFshcsIh8mn7uVk8MzBTIFMgUyBTIFMgUyBfamgK9K9o61v9Cjyss5VKpS4zoJ0fBtLeoQdZ+dXw5ziyuyaLRsCnb//ZkprTs3wsLKnSD+ZK3VWLfBtUGtqjVXXClWOe9w2UJyh++J9fAVZPWGxdKY9aoCoSXcLstah8Pz0tPVEs4OtYbTg63hifN94azWvlfODobzZ2RGWmJG3bLaiYgWFqE87+jQcabyagy87Ch9H/QiTVnCPuLnKJkCmQKZAvunQFMDL+VrapwsJ0S50aWwKS26G/pbWJZ40QqnEKvhlhSfzSzIxLTEjgBelsT1sqTJcEXiR8I/jO2RARf+FnSkcHHD/hHDMJMFl02YRXjhga85U2/qFP8pT1O6YlH0g5BRtKikOcQsK7VLrEjziiaXFgEvHTKf1xVODXRItKhXJqQ7w9gwXC6dMqfXKdClXaCLamV6XXy69sp5GQe5e62LPCyp+/GQ+h8k3xw3UyBTIFMgUyBTIFMgU+AhoECxLGJd5xt9X3sCvqysStRdRh6WVlfDDZmTfglRo2dvS9/gmkTfN8VlrYO+O5zqReCFlZVsPejyNVa8k/+9X+S1n3yKeHeNftcIRZVjPH65qIHXwv04KIRuq1qjI/pPDHQZXjzNmrc9PClOlzFxdl+SmNHYSH/o56Cxo91AlzYRzPOpHJRU+vKQr0yBTIFMgRNJgaYEXqCkD+RGVX/QQM5lpw9wvuhveW0zrEr0aH5p1cCX+aWNMCGT05PTa2FxZUPcMAJnpBMGDhlhNGFNbI+IIG1L/AgdMOTYKnlSxI+YLh0ooQY+/DMB88TlVQFnYfLckgM3YJAF6pSDf226d8takeYTIf2tAlzahOq3ht6e9jAy2CXgRdwt4mw5LQBmUGDLMFwuaMTvgtVSokWqSCviRSo7llxMOoeZe6CbpUsT+5vwVqk/z/nKFMgUyBTIFMgUyBTIFHiEKFAsi1jD7QRetH5c3xTosiFFujLmMIlp6UWJGgl40XpzWlwvi2ttMp7QautLJGZMnx8ner7E0pqTi/yjZ/06LH22qEcXj6IbZe/FHPDuK9O44o2J/TUNeNHCeE1c3ss6+ETEHk7vy2cEvJzpCk9dHNIauEdiRn0yJd0XerVQRsyoVQTLwMsBGyJHzxTIFDhRFGha4CWlInMF6LkN9IAI8b8BHltbm1GB18a2AJZVATECW2alAE3gy6zkcSelfHdqZkWnFJvh9sJ2mFlGCZqU7263CzQBlZciNE2QKODlZEKguyZcQA8sIsWJE/CDi6JRG2NAi9woDtuUDBFslBtyI1JEnJawKc32UrTW0xLGJE40JA6X4QJsGRoQ2CJrRadGOsOgwJZB2ZFGgW6PKdGlLrJexITNjM1sndyow+EuVcovJn7ytMv9Sw8PyPdMgUyBTIFMgUyBTIFMgUePAiyNimWRc7wgPr4h1ukVgS+rOvC7Lcua49It+MXnp8PNaR32zazr0G9b4SGsy7gDh23SFas1na+vKk7rCgEhzNdhkLn+GT+uev/65xhrz980SereM9FegWTCVcFI8Vm/CmItvGlmuLeMDsNaCz92rlsWjbBmNBhGhrplUEIi9gM9ZkK6rRCtj2vfmFOx9C6y9fLKUg7pSBrXaH9U+R6yOjlZpkCmwENFgYcCeKFFyqGd0dznKY2XkeNEoIfYWFY0262KFdQ4XRZWJWq0HqZk/m9WIMyCZHNvL2wIjNkKi6vilJHc6YbAGnFCWn4+9ALuuNslTXmmyFhs5JLRYzG5SHGY104R28XlAtdMT2erTAu2hjMD7QJYBLzob3S418SMhgY7BcZ0SZlYlxTsItfaLo3vKNAV1005Scf8KSQ1dx0nYCt9Hz/U2Gvv0f2Zu18ez5/zPVMgUyBTIFMgUyBTIFPg7hR46FYQyQuVwIv8tsQxvWZi7piWXjSz0s9cnQ83ZWJ6XLoFX5iQKNIK4jVBa7s2cT1zmBZXkiiYlTLAuI40/S/Q1ddjkcYUy1WtQisfX4HGGDFW5fZ47pOs7xKn5UHU2mKLRGnE+vw8X+6egcev4rJON2uiFouDSFnyFGLVLTPSp4c7w8su9YbLZ/tkzWgwDEuf4chgnx1Ammi9sjPmoBRtwc+K9rLsIf9kCmQKZAqcWAo8ZMCL6MwY7+O+nExmTIxYPNrQpLguAGZZ4MvSyqrYQQW0zK+bJvpFsYdOz+lZokiYnF5cknJexV1Xmq1NJgvlI1YWdMEwPyI55EO96W/RE8WCi8BGydwASNKiEw2AEUSVAF3apcCXkw7Ei4akpf00XC39bQJcBLbIetH/z96baMeRXFmCD7Fv2MGdyVwlVbWqVN1zev7/A6b7TJ85R1JJJWUyyeQGYgdiXzD3PnNz9whEgBtIAojryggzt92uQ7QX19/SRNkaPiRdanDzXoWKDSMX0azIx8McfsXJcZOdQ7nCpNn8hCv9mIvj58BNh5hXllYqIwSEgBAQAkJACCwpApdJHO8rtVwr6GZEHsqYlAvHkDP5so8v7ehg9wARjn757RSaL217iQib//m8Z4dnlD/H8GlSAeEArReajONyAgdjOFYOSjaJl+UAmI9ZlM9yDedmc73TeSjPUobNzeRmT/H+krFdAEW72NQzbJ/Mk5JIlJspS+elSGi7FIgFHOtuV+DbZdW+ub9q3yJdw8vHjWbD6jC1D5GMwgTRxMu3himSWZKdTt/N3b4KrxiB9MHrGVwxshrudiJwS4iX7P/4nstus7MAuQkIFB5urt44oPNdqH3Ct0u3Dx8vUA9l9CM64z3D/SnscWmr26aPGGjD0AcMSZshzJCGYF2GNCFyW10cJsjzX/9AusAsCTZJ9L5egU0qneaWQbjUERq6Cqe4VXjQXQfZ0gTRsuG+W0C24ABuVEtWg3ZLBaRMjWQLDuQSTZrQl6RNvPxNB26d5smKk2oWxM3PVubLYz6O+iHp7Lgf0neZ2uYxjpjlyyIWsS7eK725CGRia16IDU+Y33z+et439/lq5UJACAgBIDDzT7mTJiimLz8SL3xB1+70nHwh6fIapMurvbb9+ekZfA3iJd/ZAC/U4DAWpuMFvp1LLp4OqbhHJofnRXJk5KWHqzlFklGcGAl5fvsplp/M1xZqPHvJVzJiKoXmm3JffIEJMdoGwKmAeaH0gxePBduCf5f7d2r2/YNV9+vy+G4r1fiuQTamPE0gUmxwR3jCfZw1P1vcwLy6fDvlPx2BiHUcSZhHJJQKgXkI3BLiZXprfl6hiP8o+z8JKOA/BbTB5aES30rQ+a5rtYCEGQxhgsQISL2BdUG0kIDpumPekWvGDL0N2zFiUvDdwsMVQ7gvFyddMAlfXrg5EciXMrRbqCJZgcZKFU5xKyBf6tBiadZBvMBRbguaLavUbIH/Fh4uJF3YvoS+RR7I1HLhJrh4vxI/Nul9LP/QdN4/lLEsDs77mP/Q8dVeCAgBISAEhIAQEAK3GAHIlnlJKWp0kHzpQpY8A8HyFiZHuwcgXkC+/PmfR/AvOLA3iKoJj4H4wGefRzgKVD19CYacC6sugQV/uyxN5NlciqxfQVLLt4g189J8O+SjRsqUhgv75dvNjpOvY55XkLNDjt9BC5zf/sHe6OeQIaT7kKOLcKjbRAjpO+tFmBmV7fGdBny8rNoDONS9t9OAQ11E8MSLygpeQjKYBMejOByuPOqxbDZ9nzazfXT/4QgQ5/yVPqR8ofJCQAgkCNxq4iXsMTsM4lMPhyM0YPhmAswJyRSqhnYHjG40xKFAVVCk1HKBhkt3OPB6hqcegIAZ02kuyJdgdsQxMDL+7aGJEc8HarxA2cXKIFJoJsRPDaZFFWiyVEmygOYnAdPApw5tl6oTLiBp6LUdbdmfkZT4z1dUqwz/tCXECzfySf+2zf5DyQF5fdKgYQh9CwEhIASEgBAQAkLgPRDISyM3SgJJ3vBx/XHdbnKEO1b18eKOL++OTzu2e9S2Nwjk8GeEln6z37cXBz34gil5hKPxhP776GT33LWcw1ggdCIwXjBDvLAyYyEc5biG94A8XTD7+DRxrvfqzEbzO4RVxtpASjllQkIJnyFkboaQJjaUkddhZv8NQ0jvVO2buzAzutey+yBd6O+QLyQpM9PMKMjBHCM3L7MJNheXHdt9ECoXh1HJJQhEjOc1Ee7zUFGZECACt5J4eZ9HywOS/5iTfOHF76guyjONDr9IsPDgpMoovdTT38sIh4YTN4xSRHMjNKazMB8B45HFX3F/LIFIofYKfbMwpbokoxJ5yjw+HhqahA1OXkZLwpevi2qY2ZX9I5bm0kzWSjkhIASEgBAQAkJACNwUBKKkk0hRKYlxY9aPDWTiWPBhwrX7izrY1Zy2+/Al2LE9RNP8r6f79uawZ7/twtdLG0EcupAxR3xZR9PyoP8SX7YF5gUkjsuCgdKIWFFG9SubOLeGpG5RkuuTNonjpQWLMuycvcwM3fidDZrlQrGTUZCRJ2gzoVwNh7pMazDD39koQculYY9BtjyCxsu9bZAua3Xb3KjhRSRfWoKUghY4xwy4zC50aja0+vRrejefPl42wucbOZvjS+Rmn0F+Tj6P27LP/L6UFwJXh8DSEi8fCqH7g4HqKCMh8dCjreoEoar5T0x6CCJP8oRvL0i+eOhn/DvEA8MjElETBiQLiRimriFDooV5pPRoz4SnDBO/vAC5+G9dWpHUKxECQkAICAEhIASEwA1EIIo2cek3TcSh/BfXHN7BhR2NoBJNzWhG0zw969sJCJinL2BqdNi1Z2/o82UAv4JDO+7C3wleyBUhM5Ji4P/SAQEKR/MROU+cKIKVCIas935p+SWZC2OgLQbwOXLd5jWL1bFutk9YeCBm2IbvEkfUEEfDIb9AIpWKE0TpNGvBr8uDrTIc6q7ZQ5AuD6jpAtKFTnWbTUTzhCBNTGhyz+t2EC++E9/Pzfrik84/deYvPv1sT7FtVqKcEBACAQERLx/4l0CNlx6c7gbyJWi8ZP/E8O0ESBUUkHjxD/5x8qMUBw4PDn54jpCg4SEKzsUv/yeMp3ZynyTZ6thYlxAQAkJACAgBISAEbhkC8WfcTZV0KBNyD/RnwstfzsGUnYRDF5E0zxBD+vX+KcJM9+z56zP7+dmpvT7o21uQL+egXKj1ApohkRN9CJcRqZTtI+IrioHEKMySfQfc8uhxJZl2ShgR3/kmPg4K8m8P04azTdnx4nhhBXE97ExTeUTy9OyKDUBAUVu8B9P9IgiXBvy6rDULttkqwbSobr/7ZsMe3Gna/a2GrYJ0aTDIBEyMuFe+lJwKLpGuPc1wFl1fFIH4xC+b9Po+n7j667vCy3BV3W1AQMTLRzxFeq539Un2zf+/GHk/LPx05P+tA9nib0Ry/y8PzP1HTKwuQkAICAEhIASEgBC4ZQjkRambuDUnXiDnQajG8kF6wJyG5At9AfbgO7ALv4Fv4evl8JimRgwzfWKv3/bs1WEfvk9ohhOjGzGoAl/KhRd1HMMvHzaRKyFbRrx8rguAxXYXKtj84pUNdrFupiTrHjpNdw21sQ3l5CFwYBRQ+lGsVs1W4eNwe61kW+slewKfLt9B4+UBTIy2NxsedKIGn4ckXhJhOhA4WMO0xkucYWZxuv0CCEw/8fAHxTI+k1h3fZ/P9V/hF3iEmuKrIiDi5avCr8mFgBAQAkJACAgBIbDcCNzkH0QpN4Lfm5F44dMM/k0YwIGBGeBo96wHny8De/W2Y09fUuOlay/fdu3oFI54B4j2A+0YGB65mU3JtaLzBkSZw92Mdok/cIN2C+cMP3/ZLyLK0qu/4hqyFWDu5KXjCC8nGXQCii5OQPkLycLE1hsFkC4IHb1Ts+31qj1BBKOHCB29vdaw9bWqVRnBCCrhZaROvGDZ6fg+dtxTLL36fc0b8evMOm8lX6ssj0DM59cS/uqyki/7fLJ5lRMC1x8BES/X/xlphUJACAgBISAEhIAQEALXFAGSL24tnvtdyiyJmDH9veDT6U0QMXNgBydtNzd6s09/L2cgX+BsFyZHJ50RyApEwEQYZY9ySZ+B+A0bOQeOF7StSXvwx234wRtoFi4gAcd/9wbSxrPTFeld0jqXxAFyRSn1Ecoi4RIno+9fXwm+ilgvMRiAdelBw2UAMqlYmiCIxLnVy+d2d7MCfy4113TZWq+5X5c7iGC02qpZAxE+GXCCIbWjXxfOyL0TW8cgvyzlvzIC+b8V/gXEi+X5+1iuVAgIASIg4kV/B0JACAgBISAEhIAQEAJC4CMQSDVe0Df/k9N/gqKSZAmjYfaHI5AvIzs57dqr/TOYHsHs6PWpPX/TRX4AzReYJA3hHwXOUOjvJZgbwe8Lx43sC/JxvumlohXn8rZZTX49cXX5n8yRQIk9khE4ihdN92dRvjfyyW1szVs3L4K6C4NSNGoGTRaEjoZfl/vbVXt8twHfLqu20araPTjUXQfp0gTpUkUjBp3gVQCBw0uki8NwTb/yfwezS7z4VzPb4mvcc8XXc2VfAw3N+bUQEPHytZDXvEJACAgBISAEhIAQ+MoI6AfJ1T6ASIwQ10AhBGe7IxARA5Avp4iOyRDThyd9e3PQAflyZrsgYfaPg9ZLp4dIQOMVhF5mP0ZjCOuj+RG5iegNhqWcg594MX/xx2W+BLoyaOR98sXJACSJZkdwPiRROYkmTGwVTKnCWPRFMwK5RN82vIqYhBE+11sFa9WLdnerAuKlDke6LXsIny5riFy0ud4EMQPSBX5dyggbHRdOXZ2o4XJxNT78F/1yrHIzzoEtV3sbsxEB7jzmF+3z+qKTX/n1XeUiXFV+WxAQ8XJbnqT2IQSEgBAQAkJACAgBIfDVEeCPPHIYJF7CDz4QE/DhMnYzHEQ5gq+Xs24PUY7o56Vj+4c9J1/24Gx3D5ovJx1E0ISpDmIBYYRARFShCVMCCwKLHOcoQvjqOH6YL2w8/xMzlKSsBlfD6qlfnpmPmKA1E8yUYrNIvESlG3YN9ApSkC1jbJTmRXSgy+ATZWi4QIkFH2q5gGBZLdsjhIy+t920O1t121lvePSiVWi90JFuGRuiedHUkpJlRwIm7kLp10Zg0d9WLJ/3FL/2mrP5b8Yqs/Uqd/sQ+GjiJf6DfPsg0Y6EgBAQAkJACAgBISAEhMDHIUAZmRohgcLgGCAoQFKAl3CfL93+CNGOQMCcde3NHogXmB+9PejB4W7PXux1bQ/aL2c9OOUdF9ETxAhYliq0QspgQfgJ1jgsjz8lP4B4uWRLNDXKhgyrJ/kxTYCgIGzQSRcSL303LRrbOTbYbKwgelEBWi1FmBXVQLRU7Jv7q3YHkYu2QbqsterQcilZvVpCGG2SLhxfxMslj+UaVWV/b/MXFYmX2C7ez2/9JUvjijjn9VnVl0RAc10HBD6aeOHi+UesP97r8Bi1BiEgBISAEBACQkAICIHrgoD/0Et+7ZHQII0RQkyDhBnB4S4+nd4Q0Y66dnLWR6jpvr1FuGmaH+3BDOmkPXbNlw40X6Ac48SNkyI+JkYDG8IsfcGQtnCfMH5PIiOZ+IPACBI918r/XKslZJ2MiaQMoxbxKhScEgrmT9TEQRToSmkF/lsYLhqaLmvQdEEEo3VottzdbNrGas1Wm3CkW4PmDkkkaLuQdOEVKB5OmyeruI9Qr++vjQD/nvgwmC668g9rXrt8/aIxVC4EbjcCn0S83G5otDshIASEgBAQAkJACAgBIfDhCPhPz+T3Z8jzG1ovSM5pouPmOWOPdNTrQ8OlO4SDXfp66eLTAxkzsF2QMSRgqAHDqEgMOd2DDxhbKULrJbAW1BopIfxRhQSMfxLCAr9z5/7UdTaDi2At1xSv0DoSLKxxogg8ywS+W6DUgjUHk6nCCgJfg3ipIGoRlFfgx6VkG80SohMVQLLU7c5GDeRL1XaQZx2d6DJyEX26MFx0AUQNQ0dnxEpYC+ckCZOVx7UpvR4IhCc0/XeTX1n+byrm82m+rfJCYPkQEPGyfM9cOxYCQkAICAEhIASEgBD4jAg4peFf+JmaqaoEqgP3LCKRMYTD3eGQPl3GIF+g+XLStSOQL0edvr3YhRPe0z78wKD8bGTt/sSO2yBvrATiBQQGGAoojjjxUgORQTKDIajJuLAuY16ShbCA9fiaUoqJ1agh8UKdFq55DO2WkadjG8Lh73BorqmzUoAvFxAvjSo+8OmyDXOi+3Cgu9qqIEw0fLkgTPQ2yZe1htVgUtSol+HPhVouRTcv4hK4vty0KAk/50W8OBTX8ItPi09u9ql9SNk13JaWJAS+IAIiXr4g2JpKCAgBISAEhIAQEAJCYHkQSH+ukmnBRWKBvlB4R3KDmiR0SkvypTuAZgsIl5N21846I9s97NgxiJc3cLp7AKe7Z90JiJgRQlOTFEHoZnb2EfHj1zVYOAM1VDh2yHvCLzThT+R4BaOeUJE09XpqsiTN0w7sF2gS9MINTYoa9OOCTx2fOxtV/7SaFdd22VyvwccLQkXjvgpmiD5dSu7Phdos2UIcC84WF8DFoT6/zrhepV8Lgfhw+FRiPq4llsU0lsdUTzIioVQIEIEc8cL/M+n/IPqzEAJCQAgIASEgBISAEBACV4pA/M2KNFAbJEeo+cIPNEnG0CqB35feYASfLgPrg4Q5gK+Xsw7THjRhGAlpBHOkoZ3CLKkH7ZcOHPD2ByRtQMKM6QcGH4w1pEkQ7jku/vP04l5AcMBaiW3YyPkQhoGGygx9xBRQWALBUqFDXwRXojlT1aMVFUG6FK3VgHlRq2xN+GzZonbLahWaLSiDL5cmzIrq0HKpV8tOuDjpQuKFk8QL2ZR4QZnolgjMdUv5B8KLzy7mvSD5WlQe2+SeeSxSKgSWFIEc8bKkCCz1tvkPqP5BXOo/AW1eCLwTgdl/J6LgpX873gmdGggBISAE8gjwn098IvGS5lBGHRgPz+y+X0YgTibWhvPdXm8EogVaMCBeOiBcGAHpCETMCQiZY5SdwgdMp09HvBMbQBMGXIz1mMIfzARaMD4lvxJ5byUp4627x/V/ykG4gGwh4UJTJYasLsOPC7VamggL3ayuIDx00dZAtqytwaQI9kWbqxXbQqQiEi+ba1WUVUHMgIiBaVEZHzrQdcIF460wEpNrsvhkKSLuIJjMD9sk60srlbkGCPAPh88splyS/zExk1z5ZxrrYp95bWKZUiGwfAiIeFmKZx7/IVy02fw/movaqFwICAEhIASEgBAQAkLgoxBIRbE0479hcxZC0D5JyBcPPU0ihf5fJtaF890uSZj+AFGQoPGCKEh0xnsCMyRqv7TBtpyCeKHz3S6iINEUqQczpBHGcUe+0IRJ+I1kThIyyY9jJCRACiRdSLhAy6XsqVkTmRbJF5oUgUxpgXBZg/lQEykjFK2vluHDBWQLNVyQVsDYuB8X+JqhA91C4kDXzYscNBIwAT3XtPEsCR/JoR/1N/VFOuX+XuPfzDvnTf62vF3Mx2c8O947B1MDIXBrEBDxcmsepTYiBISAEBACQkAICAEhcL0RwA/P+NuT2eT3aNT+SM2PQJoMYX40AoEyoPPd/siJGIagJvHShSbMWQcaLzA/CloxaEPChW1B1jBPrZkQQYnaLwEVEh4hi4ldy4WkiznpQrKkApUXmhhVy0HLpVUtQoulCIIFKcyHWiRZoOVC/y2tBn24oA5lTEvoXyoy4hKd5zJqUdwc585IF19JXA8poNguLFHf1wqB5EEtXBOfMdvEdFHDRfXJ30g6xqL+KhcCNx8BES83/xlqB0JACAgBISAEhIAQEAI3AoHkRyoYENf6iL87fe2xDj9lUclwznS8OwZrwvDT1GChP5f+YGAjaMP0cTOAj5c+yJZOd+ymRn2U9+DgpY8PIyZN0I/Rk9g/jEcNF04WiBBqpTAkNfgS9+9SLsJUCJorNRIqcIpbrSIPM6MqPhU6ysWnDDMi5ssVEi0IDY3OJF3ow4WEC3+D07woMDy8yR7MFMcSf69n1cpdOwTiQ2I678o9XK++rN28utg/zjNvDpUJgduBgIiX2/EctQshIASEgBAQAkJACAiBa48AfmAmvz/n/Qz15af1bEuCJmisUGuFHzriHfNDUgaaLcMRtGEQjpoaLj0nZEDCgHhhnRM3JF1I3uAzOZ+4hon7VMFvXmq5lFagpULiBSQMwz6XYGJEsyGSKxWSLCBdSK6U8SminA54S4kZEfmVaFbEtcdxs8cQf1iHkpR4SfbopdNNsq7KXQME8g9q0XLiA3yftrNj5PvG/Gwb3QuB24GAiJfb8Ry1CyEgBISAEBACQkAICIGbgACIlEwNJPxYTUvITMQbb0fuJWtD4sXNkbhPJ1OgCQNiZQTyhVotg9EQeZgZQQuGEY5cywUZEi+TCcgZpJyCGin067ICDZUiYhhR64UaK3SKW0CevlpocsT7cgFlIFr8A9KFP49dswULZd7JFP/iosJ9WHJoG0qTb3bQdQMRCH+D2cLnPcjZNlnrLMd+i9rNGzPrqZwQuOkIiHi56U9Q6xcCQkAICAEhIASEgBC4IQjwR2fuB2byG5SRjnKlSZtQ6cRLUnnuUYlQjv9Iqrg2jJMqiXYLtWBAwLh5EcqdpElSarsk0zlW9K1CvgTeWEDEBOKFpIsTMExpRuSEC+uDKZH38dWhY7ImT5K8Dxwrkl3kbkO1vm84AvwrmnrgyX7iX1esy7fL52/49rV8IfCRCIh4+Ujg1E0ICAEhIASEgBAQAkJACHwYAnN+gKKIxItfSKLySFJCFReUMRJR0FBxMiUhXcLvX9RxDHbAh+ZFaRsWsS0reCVJzEadFA/5jELOQ+KFa/A6umxhnmXsjv7Mc5ywJqR+EwamBo2vgxPgYlNdtw0BPut5DzZfns/ftv1rP0Lg4xAQ8fJxuKmXEBACQkAICAEhIASEgBD4NAQCXwHqghovJFACyeK/a70uUiasTy6wGWzHK/aJlV6cjJkfI/QkRRIrQ28vj0WcIJkkEiYxZTdq20wxNz57KHMyBqZLnN8JmWTMtL9PpK+bjUD8Q+Eukj+UqQ3l62fbsC72yeenBtCNELjVCIh4udWPV5sTAkJACAgBISAEhIAQuBEIxN+t8ffp1KJj5VRhuMn9jk34mDmNLinK9U9bJWvIL+Xi2KHWv/MN00GUEQJCQAgIgYjALSVe8ofTMp4E807Q+MiVCgEhIASEgBAQAkJACAgBISAEhIAQEAJfCoFrTbxEtcuPByMSMMtIvnw8auopBISAEBACQkAICAEhIASEgBAQAkJACFwNAteaePn0LUrz49Mx1AhCQAgIASEgBISAEBACQkAICAEhIASEwMcicMuJl4+FRf2EgBAQAkJACAgBISAEhIAQEAJCQAgIASHw6QiIePl0DDWCEBACQkAICAEhIASEgBAQAkJACAgBISAE5iIg4mUuLCoUAkJACAgBISAEhIAQEAJCQAgIASEgBITApyPwTuJFXlI+HWSNIASEgBAQAkJACAgBISAEhIAQEAJCQAgsJwIiXq7VcxfNda0ehxYjBISAEBACQkAICAEhIASEgBAQAkLgExF4J/GyaHwFal6EzMeUi3D5GNTURwgIASEgBISAEBACQkAICAEhIASEwHVH4KOJl+u+Ma1PCAgBISAEhIAQEAJCQAgIASEgBISAEBACXxsBES9f+wlofiEgBISAEBACQkAICAEhIASEgBAQAkLg1iIg4uXWPlptTAgIASEgBISAEBACQkAICAEhIASEgBD42giIePnaT0DzCwEhIASEgBAQAkJACAgBISAEhIAQEAK3FgERL7f20WpjQkAICAEhIASEgBAQAkJACAgBISAEhMDXRkDEy9d+AppfCAgBISAEhIAQEAJCQAgIASEgBISAELi1CIh4ubWPVhsTAkJACAgBISAEhIAQEAJCQAgIASEgBL42AiJevvYT0PxCQAgIASEgBISAEBACQkAICAEhIASEwK1FQMTLrX202pgQEAJCQAgIASEgBISAEBACQkAICAEh8LUREPHytZ+A5hcCQkAICAEhIASEgBAQAkJACAgBISAEbi0CIl5u7aPVxoSAEBACQkAICAEhIASEgBAQAkJACAiBr42AiJev/QQ0vxAQAkJACAgBISAEhIAQEAJCQAgIASFwaxEQ8XJrH602JgSEgBAQAkJACAgBISAEhIAQEAJCQAh8bQREvHztJ6D5hYAQEAJCQAgIASEgBISAELiVCJyf57c1dZOv8DxrVy6U5gpWUIsBV5jqEgJC4EYhIOLlRj0uLVYICAEhIASEgBAQAkJACAiBL4HA5TRJWMGKsVWkTFZsAmIkki2TycSrmJ6jcDJhHdoyv8LUs96fI/AipeK0CsgV/g8si60UzAr4It9SKOA+yaPQ23gfNk1H8EzylR85X668EBACXxIBES9fEm3NJQSEgBAQAkJACAgBISAEhMC1RSDSFFxgJFAWLxbaJ16JXq6FAuJlDJIFZZNzpMiTexmNx064jHHj5AsImNAKREwkY9DHx/JxSLSQYAmfQrHgxEuhuGLMF0m8kHQhIUPqBR3ZPlxMYz7uhutD49wVW+SKlBUCQuAzIiDi5TOCq6GFgBAQAkLg9iDAN5aFwrTgent2p50IASEgBITALAI52iKwMOQvUoIj1rIKef6HTyBXJjYiyQLiZTQa2xDEy2R87mVjlLHhGPfeHgQNM/xfIFoS0sUJlRUrknTB2VMs4VMo4X7FyixDWkrqWB81YtLlJZsJBAtHDxepIqdmZhvObl73QkAIXCkCIl6uFE4NJgSEgBAQArcVAREvt/XJal9CQAgIgfkIpHRF4FWcIKGZD8+D8AnmQyRTxtBcGQ5JsiCPT38Q8rwfDEehHgQMCRd0d80XmiWRkHHSheQLyBDyIVHbhWZF/BTxKZWK+BScbKmVSboUrZLcl1HHD0kZkjEka6gRE8gYptn+SMD4rYiXDBTlhMAXQEDEyxcAWVMIgeuGQHzrEc/h2fvrtt7Pu5787mM+zhgRivdKlxkBES/L/PS1dyEgBG4jAtQ4SU/6NBN3GmQCarOQo2Bbbw/iZQwCZQgyZURShQQLCRdotvQHI+vhfoB8p0/CBQQMPr0RiRnco5ykzAgkDZLEHwwGRj5enCs1MaIpEYkXECkkViogVki2NKokXVasVi5atVywOu6r5TLaFKyCfLlcCiQNNGScvMGaV+hTZuoKG/bS3N5z2anWuhECQuDTEBDx8mn4qbcQuJEIxKM3Hq68j/kbuaFPWnQejdn88qLySZDe0s5B+NbfxC19vNqWEBACtxyBeMJzm/yX3O/zhWRVeDlBEf6td6UQFiNDXyzRcS5JF2qx9PtD6/YH1kPa64/srA3CpYsykC8n3YF1e7gfTKyNtuBfrD+CVsyY5kiJxgsy0dmuz42VRVOmQhHTOuli8OliVq2AXAEBUwW5slYrOvnSQBk/zUbZWo2i1Sola9Zr1qhVrVotWRn31JJxDRjfDGbBPp1E4rai9Jc72mKzsB59CwEhcFUIiHi5KiQ1jhAQAkJACNxqBES83OrHq80JASFwyxHIcyzpVvOFrs6S1iADsoUOcpnDF02GaBbElIRLf0CyZWhtEC1tkCx9J15AvnSh9YLy0+4YhMvYemBcOm6CZNCGoZ8XmhqtQFsmEiAYPJI+PmswN6IvXCdeMDlNiKjpUqF2C9JWpWBNfBpVfkogXfCpl60ComWtVQnES6VsjTrIF2i/sLyEMaIPGe6NF4mXSLREKOK9N9CXEBACV4aAiJcrg1IDCQEhIASEwG1GQMTLbX662psQEAI3HQEnSOZsIhIK+SrSDpHrCHm0yml9xFhFI5oIuV+WCTRbhtByAZECwuWsEwiXTm9kp8h3SLYg30ZKDRiaF3V7NDE6h3nRuXWHMC3CFAOQNhgyaLxA88Xn8QXyix8sAkQLQ1STdPFbJFB0sQruy3CiSxOjJgiXKvy81EHE1JCnqVGtVnItl9VGxVZrZZQXbWO1Ai0YEjD4oJ7mSiUQMfQZQz8yNENyDRtfA6dP1oCsLiEgBK4WAREvV4unRhMCQkAICAEhIASEgBAQAkuOwOwP+Nn7q4cnP8N0nne4kiTc8Jv6HrkruaE/r2Duc+6aK0MwJV1os7TPetbpDezkdGiHZ0g7AztGeoS0B38u9O1C06IB8q4VA+2WIU2LQLTAtYsh63kovLjGS1wOgxr55fOHUtc6Se6ZMKAezY1KCflSrcD0yLVg6HQXvl6g/VKpkYyBNgxIl1VoujRgfnR/o458GVowZZAwMD8CCVOvV6AFA0e8jJREJ7w+WVwNJsO9Tx1WpW8hIASuCAERL1cEpIYRAkJACAgBISAEhIAQEAJC4OsgQOogEgb5PFfDqEEXiZcZ6gWdqdkYiJfg+Jb+Wui7hdouJ6ddOyXRcjqwg5M+iJehHbT7dtyGr5feeXCi6452YU4EsmYIxmV8vuLaLXDzAv8w1HRZQUp9FsyFPNNwZTm/50ZQRIe4/IB3cfKFBAzJFzrZhcURtF9wD82XMsgXmiExbSZmR02YID3cqdkGNGA2YH60uV6F1kvFiRf6f6mU6P+lmGi9hFWEuUW85NBQVghcGQIiXq4MSg0kBISAEBACQkAICAEhIASEQIZAJBQiJZLVfM5cmDWZOy4hmZAKHu7OJSE30mpkPCT0CKGfYRLEKEWHJz07g5YLtVpOjvtwngstF5AuJFvOYFp03AYhAy2X8Rg+W2hWBC2XCRgWjs+w0W40BKKFfl04D5VbzhPiJce6+MqS5XiebVPEcuQLtVNodkQzoSI+dMBbKICYQRmiS7sflwq0WWiKVIMD3ntrFVuD9svaatm21mogXcogZlCGTxOaL03UVan94povBSdhspmzVRAzXUJACHwaAiJePg0/9RYCQkAICAEhIASEgBAQAkJgLgL88c7ry/5yD7Mmc8clTDEdYT38jtWwMHJ/Lj34aOmCVOkhNPSbg7YdQsNlFwTMyTFIGGjAnB7jk0QrYtrrB2e5JFRIrKwgJVERyJ0wD/PxYtZLc2WsCy2z9WTV1Hom+MTcAABAAElEQVQJ5dRFmSVBMDt6h9Z0yEtHvNUKQ02v2FYTJAwIGEY82lqvWx3mR2vNiu3ABGkLpkfUhmm6U16SL+Hja4mLSVY1Oyfb6BICQuDDEBDx8mF4qbUQEAJCQAgIASEgBISAEBAClyKQ0QZZs/TXfFLENrNlWetPyV00LUqICTAIHr45nTe5RzViFkFjBeGfOyBYoOHCaEW/7bbtDQiXV3sdO4FZUQeaLqdniFbkPl3g+wVaLrBEwsXYz8FhbQlqKK6BglJqpvgO8cU0v1twJJdeXHFYddYslMF8iUyO/wenvXTYi3skuCaYk5ov5wZFFlttrHjko1ZtxdbXax52er1VtQfbDbu7UbN7KFuHGVIVTmMY/YimRyRZgt+XMK/n40LyG8iWpZwQEALvgYCIl/cASU2EgBAQAkJACAgBISAEhIAQuBkIXCReZtcdGASaBU3AWIyh7jJA9KIuwkIfnXTt8Bj+XGBG9Nte197CxOjVQReRi8Y2hLOWbhfhpOHDhdGJwNO449wVqpqAViFJwUhBbgbEEidjmE7PH29jOl0b7iLXEeviPTkXEi+Be0lMm9CIZlK+b3wXYJ5E0yMEMnLtlzpDT9dD5KNWq2QPtppOvNzfqLrmSwvaLw2QLw045vX1w/SoQPYod/laZzeSq1dWCAiByxEQ8XI5PqoVAkJACAgBISAEhIAQEAJCYAYBEgHziYPFNTNDfLbboNUShs9rb5CsIHfgpAXykwnIFISIHoF86cDEqA1Nl939ju3CxOjotG8vDwa2d4Kyo37w30KihaGh4RgXWffnAr4DF6ID4ZtzMaXWief9PpgK5dHKu9Vl71DnA4XbqW+OyCuSLVx/oFgmScqeYR2sAyOELq75gnLXfoHZUQXmR2U44CUBc3cL2i6bddd42YEfmB3crzWqiH5UQ/sQ7Shov4S56eDXL5hRXWCRQo2+hYAQeAcCIl7eAZCqhYAQEAJCQAgIASEgBISAEHhfBCKBEAmD9+33qe3ivBgn8gRTQ7IwrMkd3IK0IPHS7Q39cwITo8PTnj1/dWq/4UNNlzdHcLB7NrR9hJCGHgvIjKKb4zC0MwmcRM/FtUTCnByfn7CA/Hcoy+oCRZMsNF1nVp8WJWuO9+Q+wvCBfOFtGCvQOSRgWENyCW6CPSVBA50YrNNggnRu22tl+Hmp2MOtqt2D2dGT+y27Ax8w2/jUqmVESioiQlLJ6SSaS3EJTPKrzRNacW1KhYAQWIyAiJfF2KhGCMwgkD9uWMWjTlcegYiQkMmjorwQEAJCQAgIgWVCgNLA15AEohSC6T0biAh+h5rkG8wF/aKQjBghctHJWR+aLn3bP+nYAfy5PAPp8uxN2/aOhiBdJnbWH8PnywTmQyBeYL9TZhQgsBDUKPEPHy3uXSkEZc5xsAxXnDncTX9/DEK5HfroHNFJFx8s0C9OvKAhXb6MoP1CEyQSTIF8wT4QBWmtWbJ1hJ3e2SjZ/c2affdoHRowNbsLLZgWHPDWGHa6VvV9cq8r3Civ3KJFvARI9C0E3hcBES/vi5TaLTkCPOpyp82So6HtCwEhIASEgBAQAkLgeiGQ0BLOfEBmo4oGr4QJcRMc3NIJ7RD+XEZw0tIHqXJw1HbC5dX+me2DeHn+pmPPd/uu6TJAmOghnOeOhiBZ3O8JzHVAvkTSJZoWJcwOJ5tDtgRChEuJsuRFQiasddoEiWUXW/ow6UjxjgXJflmUEC9OupB8oetg2kahgnNAqQUEksG8qAgtl4p9/3DN7sHc6MF2DWRMzVabcLq7GsyOSlCTYcQj7hUUDgcxtzjCl8gXh0NfQuC9EBDx8l4wqdFyI5Ac5CJelvvPQLsXAkJACAgBISAE3hOBL/fCKp3JCZZwFyW3bLGJE1pUTEAf9BAuug/PuNR0ebF7am8OO/bz80OYGg3t9cHIP6ftiZUq0HCBXZGTDyAgnNqgBggGTmeanSz6Q0knzxEiKJu+C+OkTWfqZ4ee3w7kjNsfzdRyIq7F/6NfmozEGYF4GuND7ZdVaL58/6hlD3ZgenSnbo92Wh5u+s5mCwRNwSolEC+IdpQ3OQozEYdkN7Obyi9FeSEgBBwBES/6QxACQkAICAEhIASEgBAQAkLg5iNAx7K4qNeRv+iMlhGMmI6QnrUH1oMz3ePTrj1/fWov99r2t1+P4FAXGjCn53CoC98vA7MGiJcqTItqSKcUShJGxKkMzycFmDRrRw8wWXlcz/TKLraI9Rd7xhFCGtvxLnMmHEuR+n8YJSkiLxWd8fYHgXiiFlC9XrDH9+Bsd6vsGi/Ufrm71bCH22tWr3LvJatU4e+FhBPHTMbjwCJepp+J7oTAZQiIeLkMHdUJASHwQQjkhYT0XP6gEaYbc7yrGGd6VN0JASEgBISAEBACtwUBkg4uK5ARIPHCBAIEP7xifjym1sfY+kMQLggXfYYIRm/2YVr0uo3oRR37z1+Prd2FNsywiAhHIGhgmlOrFqwKMxtqfUR9kTBfkE5YFsvDbCkVEW7nfIeecypyRXl5Klc8N8vx4l7zDVyGQqXPFydFIXgnmE8BB5hakYwqweRovVW09eYKzI6K9vtvNuzRnZZ9e69lzXoZPl8QaroBs6MS/dpA64djxvGSCWVylEdeeSEwHwERL/NxUakQEAIfgUBeUJg5kz9iNAgS6HUV43zU5Op07RGYTCYQ/vgGTn8l1/5haYFCQAgIgc+EQEq8+PhBEonySCRdSDDQpwt9u7S7cKR7iHDRJwgX/ZaOdLv2er9v//m8DVLmHORCCcJHiFdUgjMXuHZxJ7NhTPg5waA8dXj2BNIlUi950oUt4iqmNz59Yl1st7jX4hpOla+NK/KZMYWflbxJ2pGEouaPO9xdmSCCEcNNT+DzpWB/+HbNntxt2Q8PmraxyhDTDVuDvxc6FS7B7CqMxTTbl87hDAvlhMAiBES8LEJG5UJgCRHgoZ07Rz8Ygfyh/ynjxIk/dT1xHKW3EwERL7fzuWpXQkAICIEPQcCJELIArvaRSCK8hfIL71hMooE+XQajkZ22u+7XZf+oa7/+dmjP3w7cp8vfX/TQeMVa1PLApwQtFzdPAsnPORK6BWlGvIR1RpojSD7T8k+8S9aFDrEk7jGriSWLUvZc0HqmOLtlDv0SvzNhbnx7g+Asd4j99YHL5HwMnzbn9t++WbUf7jft949XbRuOdu9sgoDZaLrJFTEpgokixZQnW/IkzKLVX315srerH1gjCoHPgoCIl88CqwYVAjcTgU85wvwMz207HO65AmWFwBUjQOKFVwEOD3UJASEgBITAciIQNVBcBSPx8cL8OTQ6KJtQq2MwhM+W7sh6g6Edn3URMvrEdg+7cKh7hHRs+/Dt8tsuwhfhatRK1oBPE5rWOHmD/hMfN541GZHB9pfLO7F2WkqKpewfVpkviaNO92FpmC1fzn64x39xBNYGaii2m9ZOiaOQLCGxROKlS+IFZliF4rn9+KBhT+7UkNZBujTs/k7T7u6sWRWYVOHrpgTTqyIdDWMAjkFii6kuISAELkdAxMvl+KhWCCwFAvFojpv90PNztj/HiWOwLubj+AvT/EDv3WnhaKq45Qj4G0hIfCJebvmD1vaEgBAQAgsRCFJG1HqJIaNJCpCbJ/kyRqYHZ7KnZ33r9ga2B/8uP784sVdvO3Coe2xnvXPr4HPUdj0OkAvw6wLSheQCL44dTHIomGTCCfVFZi/WhtIsN9vm4n3Yw8XyfEkcL6b5upBnDS8SIdPECwpRyd3x8u9ImjjxMrYOiCn6v1mBZszjrardha+Xh3fgbBcRjp7cW7NvHm6AkCrD2S4c7cLZLsmXAsywpgiX99mGr0BfQmA5ERDxspzPXbsWAikCQUDgEU0RItzFhI3iQe4dwmmd9l2USceZ6j81kgsG8/rzENfZPQ8Zlc1DQOZG81BRmRAQAkJgWRCAxJGILmHH2Q3L3bfLeAK/LgM7Ou7YMRzqvt7r2t8Rwej5btsd6p6f06dLGbIHQyYXjHRLEQQEpZZzF0qCqVHw+4JCVExLNKEM3yiHLJUtgUXJFQt91KQs5ll3YcTYMamL/XPFF7JxvEC+ePXMsCn5gnInTTAstXl6wIjhprmMtSo+dbON5rk9ut+yn55s2U+PNmy1WbFWq4IoRxWrwN+LEy++Yw6WLWaKjMmKlRMCS4+AiJel/xMQAMuOQP64TwmT3PmeO0uTU/r9EItjZf2zHEeYL5igImk23fr95lQrISAEhIAQEAJCYJkQmCVeuPcgxJB04WcAp7qnZz3bO4JD3VM61O3Y358f24u3Pfvr01P4LKE/F5jRIKXsET5hjFQcgtDiJAyHB7MQ26X1eaElLWTjeM0rZKd55bFPTD+8XSpjoWu+9xTxwuExPWglmBtBM8g7nQMHfEpj+Lk5tycPSLxs2u8eryPyUdXWVyuIdFRFtKcKiBdCgUhHpKo4Se4S+ZIDQ1khkCAg4kV/CkJgSRGYOurTEzoHhh/WQQsmHtSZgDBzwua6MRtJF+azllmO5bzSNSAT8njDlDutL/YI/fQtBISAEBACQkAICAG+xUllCcBBGcLNUGFeRFOjITQ5BqMhIhh1YVp0ZodHfXu2e2L/eIl7RDL6Gxzq1km6wHymVSlD4wXyCMcEEcFxoz7wFNKYIyeqTM3v7fILSjvGQko2MZ9WTmXyss/clu8eYmo83mTjZKRRvhS+h/2WJlWIdwTZbWzlwsSewMnu775dR4jplm2sVezOeh0RjhoeYnoFEY4KACKEmM5WncfmwkJUIASWGAERL0v88LX15UUg8izhIMZ3LCAk2dmJLG5y9xcQy07yC1WhgA2igDI9UL6rT89l4H+ReIkH93SvBdOoWAgIASEgBISAEFg+BJwouLjtc7AuJF2GQ5jR9Ie2f9y2Zy/hUJeRjOBY99nu0HaPh/brm6HVQbrUq0VbrYJ4wVBOPlBbhg51IYwEqoJzBJkolUuSjMszeaHmwnIurZxqnY6dlM7tyUZzK6aGWnyDyE1xAJ+Pm06EPQ7bAV7D0dgJmMd369B2QYSjRw3bgg3Sw3tN21pvQfOl4aGliwy3TX8vidAW6CqX/OKQHFyXEBACQEDEi/4MhMBtRiA5mNODEAejO6BLDlgmbALxAlneJO92PBtIEJazT/5K7zwT7vJNImnCPk6k5AviQLMdcO+zY278t/iKk1/aCN3ft93imVQjBISAEBACQkAIXGcEEtmBS6SoEeUVkiaDAT8j64JI2Dts2y8vEMHooGO/vDy1FwcjOzid2Iv9kTVAvNQQracFx7EfSry4KEN5JMocc7G6tHKqR160WdjrnfNNDXnhhpGl49gu+xXgpN7lNDgkxobOekPrw9nuaDyyRyReHtbtp4dV216v2MP7q3ZnY80215rw80LSpQAzreDvhYOm8iYlufxmLqxCBUJg+RAQ8bJ8z1w7XiYEeLLi4IuCSCKVoCg5DRcdivFQ5umcXC5ceD8cq+EGqQ8eDlpvGtoHngWzMINPSBH2N46VDhozSDEXiZfYJleTZjltuuQ0k1ZPZ+LS39VuupfuhIAQEAJCQAgIgeuMQCKDUL6gPBJuIT9AgKBPF17uMBaES78/cse6r/fO7B+/HsK8qI2IRm076JzbadcQRtqcdKkhilENJALFFo4XxqXGC/+jIMEM09zF2yhrZJlcg/nZOErsmh9mtm7+CJeXpmPETGyeTBiIF96EBvAnnGmsoPi0N3LiZTge272Nsn17p2Lf3q9A06Vs3z3cQrSjVdvaaAK3gpXhZLdcBmGVzJXuifeuWZNOE1ehVAgsLQIiXpb20WvjtxEBCgokOSIx4nvMztbkiA1yAouDsJIILbhhP8osDMfIOtpHs7WHUUSOqcs0sR1ugnDC0cIVz/lo87tCNVSyKVgX1+b/QyPKLzzss/vQk17yWcq3L94GX1xLXHyOC0rLwswz39mSLm830023QkAICAEhIASEwDVGAEJB/oinjEB5gRfz/EwQGrkDzY1ud2jtDh3qtu1vTw+cePkFxMtZv2DdYcHg+gWaGwWrwF9JxcWPMFCQo/KzcHQXWphJrtn6WM6U4yyuz2opBWX7ycrzY717vHzrsINQkq4gP7AX8iu0zBMvlPE60BKixgtNtXZWi/Zgs2yPdoq2DR8vPyLC0UOEl74D4qVRL3t0ozJ84zAKFK/wnY7sZVF+Czf6FgLLi4CIl+V99tr5LURginBJ9scjnRePddc8gUSS8CkpoTLG4crTkqF5+WFIQZIsY7ztIPnCgziQMiRa2I4p6pk6O8PRfZIwB7IekhFTB+KFb5ECmUJihW+lKCUxX4Sw43XIU3AqojLcw08+CnzNobnPMUW8oHzhge4LYoPkCjDEO6VCQAgIASEgBITADUQgL+tQRoj3lHdcLsGeKL+ctgcgXvp2gvS312f2119AvOx17OnLMxuMy2hbsL6VEEQaMgscyZYoc0SZicJOkGyQUphBktQxF65ZQSOWX0XKCd9n/IvtfKnJEuIIeZzmrS710YIO3dHISZcRXsKtVQt2Z61od9cM5kVl+/0PO/YNbu5sNW2tUbUqzLPq9QqQCTOl8/kkuZXksvPmV5kQWAYERLwsw1PWHpcGgVT4IIOx6MKpOHJCZYI0EC39AQmWifUHQxvBodoQbzoovFBFd4R0BHf35FdGo0C2kKgh+eIphBMetGHuKLKQQEmIFqyFNsAkWIpQ5S2hvIR8uYQy3FNNtYT6Mt44leCgrQKV1RLqyogyEOyGg+2wEzDY1iU7m95xPP1j6Xt3jB2UCgEhIASEgBAQAtcNgSjrZOsKB3x4MQT5xOWckZ0gdHS7M7DD0x4c6p7ZX34+tNdvu/YLSJiVIsiClSI+JZcr4N0kGQ5yTCpDUZCIwkM+z6azQkbS/aMTzpMfc/Y+Djxbnt0zxyuMkpWH0lib31HcU3gx5z1QNICMR9JljE8Dctpa9dzWm+dOvPzrD9sIMb1u93eaKKtbvVa2RiNEg/J5EuzS2WhuxJu0IKxG30JgGREQ8bKMT117vjUIZEcmtuQnrX/lhIaLW8U5itCKI38bRJJlNJpAHXcAogWe/5HSEV2/D/IFdd0enauFyAAkX4aDQLxQ/XTi5EvUjsH0mDquh29OAvHCt0gkXECukHCBI7YKSRYnVki2rPjbkjLshBlRgCRMrVJxwqWK6AKVctnz1ILxtzEYN5WHLm5tuiRAEQ77uLDpFroTAkJACAgBISAE3huBeLDGDvw1nS+Lv67nlcU+n5gmQyd6tqm846QLVwNZhi+GhsORHZ127BTEywFCSFPL5c+/HCGkdNeevu5aBQ51GY2nUiwn4hMG9v8S7eB0mcme8uq2vobZvacdkkzEgrfJomeb+P2HtOM4i+eNI4XZ5rSbIUUcwyTCUV62gvgH0gXy3srEqth3rTix1frENlbL9scfN+3bhxvw8wLiBZGNmjA3ajZIYoUXb9RyduKKD4SXLxlriYsLpfoWAkuJgIiXpXzs2vRtQWD2rQ/POT9qcQCyjnICzzyaDfH+HMII8x2o3g4glAyg6TIECXPWHXjIxfZZz3oo60DTZQCtlw7soocgZrwdiJcB8iRgSNaMScS4BgzmAJnDeTgHDIWsAI0WnL1uUkTtlvgp481JFZEDyiirQuipIF+vMZoAVFXx1oSCULNZ8jaNWtVqVaixor4M4SiYJFFzJji/K9BeCQKDCwuYy/frDzac7uEbBWnGK/UlBISAEBACQkAIfBQCPOnzFw/YWJbP59vE/BUdxsl0gXgJY8YVkHThSyRq7A4hy+wfd0C+dG3/sGc/Q+Plz7+c2Mu9nj1/04V/korVoGFbh3+SVEYC4bBCwSnPQgSpyosgVYXNQNa5eOX3H/JJa28aewR6Iqw+kdguDvXRJfk1zBkkIUfSGiwqriQtQwkgdP2fsP4J/LeMrFoY29pqyf79p2377tGqPdxp2PZGw1owN1pbrbkvP+6NxAvlM8pkvFYgp50D00yLKJtJOSGwbAiIeFm2J6793ioE4sEWNxVlgXjA4+RzgSKYBOEghZYK86edHsyKRtZzggXquGdDNy86bZN4GYKYgX3vEO1A0JBk6SMkI1OSMU68oM4FGxI5PKDHJHriKki8hNCE4Eug6cIPzY2g7YKbKp3Y0RM+SJcqhJ4miBWSMHW8NalB66UJYYg2w6vNijVqEIzwqdAcCVozJF1oguRaNPGtCgUJSAcLiZdsWTGnVAgIASEgBISAEPhgBNKDfqZnpBgW1bN5bDPT9UNvkylIGPDHPGUPFvkLIHzRbJqaul0QLweHZ3YA77lvD2FeRI2Xp217fdC3X3d7tkGZA7JHA9q1TrzwBRJkGn9jRaEC14UVJ+U5gcfbXfxiz5Sm8epk2emY4T60u9h/toStL6xmtlHSJs40pzoZw0fKTR3Il1w/ZPkajXIVpDzgMsTdCHIZiJffbdgPj9bs0Z2G7Wy2bK1ZtY21qpuMEx6SLnmSJZ0GlT7vvGWpTAgsCQIiXpbkQWuby4FAJD9G45HLBUxHIElcwwVCSJchAmFGdHDSszPkTzoItQhS5RRe/6m90kYEAGq4sA2JFZodkVgZ4YtCDbVcKJdQjZfpud/TMtpPaRQgBdECwyAnQ8IhjHtkggYMCRgQMSBQ3OcLyBj6dqEmjJMrKG/VQMiAmOFhvga74RY+NRAvrSa0Y0DItGBTTNOlWpUmSTRXgo02D3o84qlDHUuZuqYqp2p0IwSEgBAQAkJACLwTAR6sPExnD9h8x1gf01h3RYdwMnUgC8KYTr6g/Pycskt4UdSBjPMWxMv+Ycd28fkZxMtfn/Xs9SEc7R70bBMyBF/2rIJ4obzj8hO+fFwKL7gurhglLPTG3uQdXyQbwoIjYhfHJJo+6CVjsTfbvKtdHOKydqy7uDcSLPEKxAnaoWg4gQb0eIgXbCBeGiX70+/X7UdovDyGqdFd9/NSs+11EC+F4I+vgJRRktKLWJF0wSfuIq1TRggsGQIiXpbsgWu7NwOBeEDnV+tHZawI52a+2vMkQ3iyDUbw2UJToCGIlf7QTo67MB8a2vEJPPxDy+U1hI6T9tD2oenSBTFz1qUfl3PPQ6kFByyFEHw8zU3D8TG3yxxUH02FDxbGQztooKTHOqoow1B71y9X48U9DmY/nFHpGixIwbtYqwozJBAxG80yPiXbbJVhhlS0LbxRaYKE2UYIQ5ItrVbdTZKoEcM3LPQpM3XF+aYKcTPTbLZa90JACAgBISAEhMAiBBYdrovax/IrOnwxvZMjPmwYM5gKoQByCIMD0EddBy+UXh+c2R60XV7vt92h7n+96IN4Gdqrw5Ft0iksIvasQuvWXyb5C6VkbwlzEEmTuAMXZtIbzsebxXi8744vjsCS9+3NNXzKxXk4X7aKEOGIMp5ZH+boXciS9A3YxAuwP/24aj89bDrx8mCnZZswM9rZbEKjOZgYMUgCzY3i5drILgSGsqwmtlAqBJYHAREvy/OstdObggAOOvd1lqw3HonhXAwHY/5QYzOq1vJwg3atCxCdPnyzwIcLNVy6vb692T2FZ/++v/k5g5bLi/2hHZyObPcIRMwQmi4DKJGiL5rjBQdn5FsaaKNAI4VvKWgu5M5ykVK3hEQJL6Z+nmJZnnopxuAekD8nOYP/TSY8wEOkAbe/RiVTCkshZWPOfG710jmIl3PbahRBvBRtZw3mSPWS3duqwbFbze4jjCH9wmyuN9wMaRX2xe4kz4kckDcQmLj29IrZyPxgobEobcMMF8xrbmWo0rcQEAJCQAgIASEQEeDByUMzHqCxPJbFAzW2i/WfkCZTBfIljE+CwF8WReIF5At91716e+pmRq/3z0C8dOyfryAPHY3wGdsG5IhIvNDEiM5kScD4fijQeC6ZzO9CCbfL2rSGky+4grSxuD52u9iCJWENsc3nS+NuslXkiZcuSSy8uOtD46VRL9iffmjajw9b9uhuwx6DeNler9kdEi8wHWe/EI0yW7uIl8/35DTyzUNAxMvNe2Za8W1HAGdfnnhJt8szEQe8q4BmZ5pX029LUK+Fmi20VE7aXffH0sYbnzMQLrsQPk6RHhy2kY7s1f7IiZe3xwO8zTBrD4tO2gzoqwWHPU13SLTQ4z85ljK+UIQUpEYh+G+hQOHEC1bgpAvaMXUBCHUUZLhk8CuBiEkEG4anZohCqvbyQzMmF3a84QQ21xOrYI4NHPBr+JB4aUDj5d52HcRL1R6AeKnBGd7OVt2d4622atB8KbuWDM2X6AOGAgtxcpjiuqKYFMsdudxXlDm8U65cWSEgBISAEBACQuAdCMRDNDb7TIcppokaL26+gvu8xkufvutAFrRBvPz25sTe7nfs1d6ZPYVD3V92B7Z3AhOkY8gYkCvqMGtuwYSZ5ADEESdffPUUwnz5035aXMiJ2wsNuZiFVyKFoP6SRnNr2f4z4bdwtbEiyHicn6vo4CUeTdN7SEm8/Pv3Tfv+QcOJl2/urdodEC/3thrw2VeG3FhwE/D4Us7lQUKZ4sk5MlTijEqFwLIgIOJlWZ609nmjEOBh5SQGV53kL24Aogfq+OnAWW4PbyTO2n1orYxt96ALoYP2zTApOuvbUXuAUNFjNy+iT5cz5Ht9mBb1YQsNYmQ4phM1+nIJxAsPRhItJF+4jhgekKZAFAX8w3JmkkPUCSHe+uKZhEOb66eQFAgYpN4kCDnBoR3rvBEaTkCc4NDHPNACthpUV5s1kilM6YS3YFuwJaaD3rvQgGlBE2ZrFQQMBKedzTo0YKAdg7CGJF9KydsXOuT1y9fKdSXkVSid/sYyfHPTpboTAkJACAgBISAErgMCPKdxBfIlyCiZj5YJZB1oZ0Djhb7tSLy8duKl7cTLryBe9k/H9vbk3Nbh36UBmWIVBEwUQThOGJwCTpglZEJ5Shkk8kRom/TxDjEfhknbUxBKL3bO38/eseF0feianzSUZOXz2s+24f272nG3UUZKiBf4BzyFPNmDqVG9RuKlbt89aIJ4qduTuy27i8hG9zdBvEAOo9k3ZS++BItT8TkRByfJvDBDJb9C5YXAMiAg4mUZnrL2eCMRcP6CZxfOyUBwzG4jEBYkL07gLLcN8uXwpO0RiX7b69rp2cBe7vfsCH5czrpjaMAgjDRIFzrPBTcDPy7URiEBAue4JEnwGYN44eUkCrJOuiT3DLFI7RdfC9fkDdMvZkI/HKxcc9KCGT+Aefjyfy7gOCkT8ihK2jMzcdKF5tXgWkD8QMsGzniZwp+uH+brMD+qlFfs3mbZbbN3NkjAlO3x3VXYH1dsjRowEAAqcJhHjR2GovbLFxyyc7+55ne1mdtRhUJACAgBISAEbgMCN+AgpKhAwcEv/qCn6TJKWAQZwokXkAVnJF5egXiBn5eXb888ktGzvZHtnU5s/2TixAs1Xki8sC+7R+KF1ADvw7dP5F9R/yWVydgoXUvWLubeV6TwYWKnqTSsYqrowg1nWTxC1vz92nHPlAEDUWJ2hmALZyBeutB4ceLlu7p9+6DuGi/fQu66AxnswXYLL8ZIuATiJWpDc24PJZ0IVxmu2aqUEwLLhICIl2V62trrzUEgnrVIo4ARDyxugmQLaxixiCZGe/sncJwLddrdEw8B/fOrjpsS/fqqawdteqSnKdGKDc6p/bECTRKQF2A2aEJUxAHLQ9JZBx8X+XiG8z65DZoiaLbo4hB+uMbOuPWsV3ivmGNxzHsF77wg7JbTRm2YYJpER8FjlMEMqQStGHApDzcRjaBOAqYGk6SS/fTtlq3BFOnudhORCqD50qy5zXG1UnHBLBWUfO7p2WcWE5YUF+l7uLDg0EbfQkAICAEhIASEwJdDAGdylIsoOPA0p38Wyg14nWM9mFj3EZGRZta/vabGS9uJl6cIIU3i5eAMMhM0XjbgWNeJF2i+8Jhnf47DK8pbs8SLV37g14y04b3DLLMDseVsTbzPjzLbbvY+jjtbPns/v10gXmBWjuaE48xJLBAvwLRGjZfvGiBeavZop2bf3gfxAm1jEi81vuxKNF7yxEs6CwYMz43rmJUBYyulQuB2IyDi5XY/X+3uJiMQz9vcHlhEAoQe++nXpTeAp3losezuncBxbtuevT60U5gV/fqmB+FiZM/e0MwIbVdgwwy3uJOVMrRWClCvhW0zzHWoZhu0SWYOQtgD+fR8i8Q5+T80YSuaCoVK1rDEW7J05op1QTBiJUv4xRIe6rx37RqkPgoK3EEwbhhZacS3VySXEOq6g0N/OB4bKCR39nt/fcUjIN3ZqHiIw5+erNnGWt3uILwhQ1BvIOpRHRGPWiBgKAykggDnxeScP1068/Ou/BYXtZnXT2VCQAgIASEgBITA1SOQiBzxRzzPcmrShiADOY0XJ16O4d8lI16e07/d2Tk0XmhqdDnxEhaeTDa1i7wwMK8+Ng7twnfWLstNt5s/H1vH+ZjG+4ujxNGyNLbPSrJcvi6f52x5jZcVa0PjhaZGrvECzP74LU2NSLxUQbysg3iBxsvOKnzvZRovbuKdkFhxzijrxXulQmAZERDxsoxPXXu+OQikZysyOBvHMBMi+XB03LYu3ursHXXg32XoJkVvj/A25zVMjejLpTfBG59z9+VCE6Nz0BU0JSJpQtKjjAOSEYvKkFjoT4V6MNmFuXBgcmq2DTne8C7hKtjEO4Rvzy78Cv1itRMe8QZpHNNTfPGs5oeCVCBhaJIEEgZ7J9mEUidNGHYa3BEiHhUQXnoFYaYrcPxWQloDEVOBQNCyHfiDuQunb4x6xLcxJF/KJZJPnC13zdzmapQVAkJACAgBISAEPjcCeXHisjM5acdIieENSpAX+FKKZampUbtnz2c0Xp7vj13j5QDEyxq0N1KNF4xJuYMaL2HquABOxnwy6VR+FpCrbsfx4/z5sWfnzd+/b7t8n9l8cK7rpkaYnibqJF7oZJd4/RE+Xr69X3Xi5bsHG0Zzb2q81N28m6ZGQdbiSnhR3OKzSYCdwTe00bcQWBYERLwsy5PWPm8OAn5AJUdWPOv98KWmC320wHnuW5gUQah49ebMTjrBlwsjFP0KDZcOHOaCm3HSImqPBEe3JF5AgmDoEtgWmhhR24UzRR7CZ8WcTrakcxM61HhDZL0+pEEoYD0u74yU/ZjPp0llnIe3Ht059kHzeHH77OpvsDwfCmhS5YKV15476UL/bSWYTHEfjWYBNsZF20IUpPVm2X73eM3uI/LR47s0PSIpU3H7Y+bdtIqTxGvOOmKVUiEgBISAEBACQuAzI/C+Z3IqF4AkgSxDuYDyQkq8MPQx/ZKQeKFz3QNovOzCxws0gZ8lGi+H8PGy1oDmb4lRjYKPlzBG2COXEsSC/KLy+2ftorrP2S4/9sfls73N78+dRa1gyo4dmHmfdMbQeBmCXIkaL4F4+fbeht1BoIP7kXgBniW+3MJLrkysCjg5kQPM8jXzV6BSIXB7ERDxcnufrXZ2QxC4SHLwwMeRlZzrrKcKLc942i0zVOKzF3swLera09/2oTLbsxcQJl4fT+yXNwgPPYQJDkiGChyhNCBQlMlKuIAQjzs6040CQ0wDWPGgnF86D9DplvNapBtJK8MskYRJl+L1M+Ph1lvjKwgCYe2gYBCBiZ/EUTDy41Hfiogkvdow21wt2Z++37Qn95v206NVW6XT3dUmNGNKyNdB1GDAOHGcIFlfXFe6XGWEgBAQAkJACAiBz4vAzPHvk/n5PDNtcna77BQkhIR44bE+dhnJfbwgoqMTL4dwrouXVE93uyBexnZ4SlOjMcJJQxMWGhwkXiLp4poZM9PNv+XC5i14tvX1a8dVZ7BeXB9L8sRLG8RL3tTo32hqBI2XhzvQLL636REl78O3XgOhKIvwH1hCVMlFxEuURWdR0r0QWBYERLwsy5PWPq8tAheIFycbcPQlJyPr3bEs1Fc6CJPY7Q3s52cH9hZvcf75DAQMTIxeH03sLYSJZ29HiFhUsHU4ma2CeFmD5kcF9jg0GOIVKBiQFyAsgqBBCiNOFYmZcDSGHuyVlfNu+spaTZdfdpdsLEm4gGyUkHMb42QICgC8aDNME6ERhC7KXT34fRnCD8wAGkA0Q+oPeqifQBUWKsStsv3Hj+v25F7dNV/WW1WEoYbXfWi7bK03XOPHR8VXGJ6ZMKGIl4CDvoWAEBACQmBZEIincHIQfo1txyVw7suWgXap3JS0o0zADx3w96nxgpdU7TaIl9enaVSj1LmuRzWaNjVKiRdmfP64gOQ+lH6Fb84f13I1088fMewzSnvziJdeqvHSCD5etkG8PADxslFNNF7o4wUv+xJTo2y1CYaJcHW1u8lmUU4I3AQERLzchKekNd5aBJLjaPpY9VMx1NCnCT9HJ1146O8helHbjk579svLM7yx6ds/X3bsGOGiB2NELBqBjOhB7RajVSorCL8M0xuogBToxIV+UTgL/uPIProTGGEeAhwtm5mfvuJRPF06fZeNk5Xnj9d8fSwPZfma2DclXnIHdSBg2JdxC7AjkC74z22yGRZ7CKGAdQVovcDUGCEO6WC3hKhHVdterUDzZdOJqCcPVmF2REKqDDVl+HxBmr/i6oiVLiEgBISAEBACQuALIRAFgsvO36SNky++rPBq6RxygPt4SYiXDpzrPn9D4qVtL9zUqGu/xqhGx3CuO+PjxcmXa0e8XD3uhG8aXt6xNNTwLiNe4OMFAQ6CxssQWtT08dJ0jRdGNXpybx0aLzQ1gsaL+3jJNF4wTHIlDwzy3PS8sV6pEFgeBES8LM+z1k6vGQLJUeSrmjqM/OwLte5MF2qebw9PQbh07fXuqR3Aoe5T2CrvnQzt6eueHXdGOMwQX5lH2nkRByZsbHFLUqGIfKbBEWxrw8jJN18RJStgjsdiFGayNWW5pPGcJI6Tr8r3y9fH8nxZ6BfWEPLeKjZF0fSRHUgXf8OFOqaj8QiEDCkZaPZg/81qcLy7DbOjrbWK/fG7LdtsgYCB7xdGOqoh4hFNjioIN03o4pVm00ysUSoEhIAQEAJCQAh8VgTygsC8iRLRIcgq4aAO/t8oC1DjBSbZcARLjZff6OMFL6wY2eiXNzDPfju0fWi87EFLeKMOHy8wNVqFbzgO6b5igggBmSAKAMlkLiTMy88uMN+P+dhnth3vY31M57W5rF0cO875rnHi+IvaZeXULiYElK3o4+Wsm/Px8kPLfnhQtccgXh7fRTjpjboTLzWYGvGFX7GY+HhJ+gcMwnhxBUqFwLIiIOJlWZ+89v3VEZg9MuOCWO7vb+DUbADhoYe3Ny8QJvrNWziHe3FgbyBE/AqTor2ziT3fn1i7d24t2CrXq3Aq2yi5GQ01QChEjJnGgafScMCG90Q8FmMuNGLt9DVbElaZtcnfxxmn++TvQovYLhuFOSdYcp53o0AVW9E9i19JusI417hcaMKeh/jQ90un14fgMIZj3XO7B1XY//sPW3Z3s2F/+mnTQ003Qb6UwVA1kMYhKWQ4YCjwsrQC5THPyXQJASEgBISAEBACXx6BRHTgmU92gEczjab9+MbZPwDxMnCyoA9NlxPbPejYS8hPT9907Oc3kJ3gWHfveAyNl4R4gezkcgYGcH963FFKvPgNvjg6Z2K66HpXfex3Ve3iWjjeu67YJvZZ1J54QiL0l3aUIw1OdYOPl95o6FEk//g9iZeGPb5Ts2/uInok5Kq7my34yym6hnUJchVNw12ewjR8Mhx1CtJF06tcCNxyBES83PIHrO1dXwR4/MWjML9KlrsPFpgYkXTpdBGt6NURtF3O7Jfn+/b6LYQIvK056p7Dt8uKwe2LrYJ4aUBtdr1RRrSfFTe/YaTFYeLLJT9+zHPuSLe8H/Eye2DnVx/rWJbPx9mm97qIDmLr6cMZK8TpnW8fQ0HHdq4Sy1kxLbWEh/hiyG1GfZrA0V6lPLadtar9j9+v2931mv37j1vQgKnZ+lodUZBgjrTWdO2gdKUYw8eOW8mnaSNlhIAQEAJCQAgIgS+OAM7oTCYIP+gZMADFdk7iZTCG6fEIWhp4abV7DH94HWi80Llux/75emhvQbq8PQLxAsKFUXpaSClAUIZg9MRwXQeiIAof70L4attxtDzx0iOWPbwEhDl3A1pCf/pp1b6/T+KljgAGLchTdbuz2XS/giRc6GA3yGkcCdAiCbks9Qp9CYElREDEyxI+dG35eiFAYSEeSjz0qa3ShwNd2icfw7zoBOqyv7yEjTIIl19e4O3NYd+6gxUQDCvW7hfQHr5cyudWQVjlKqL2UCOEY/AauzASR2cJ86EulIY8y2MutErakYHwinwtWyy6svGzXc1ru2A8ny9fF8YLJVl5/u1JIGBQgmpuewzGiX5xunB6Q9JmpUiNoIJ9e7eCMNNF+/FBC+EP6/bw/hrCSVbs4d1113yhLxyaZ3HG+JXffphn3l6SMi7POy+4v6SrqoSAEBACQkAICIH3QABnrdMsLh6EQ5f3PILp52U0GtkQTvfP2gN7tX9qewddpHhx9bpj//VyYLtHQ3tzMERUo3IgXpBmxIuPgpG+BvHiG8oBMHsfq2bLZ+8/vZ1rFmNYklF94NkB8dKnxgtCcP/3H1bth8cte3S3aY92mv4ia2ej6b4Fi5CjCnCwG1+QcSVENC+zxdUpFQLLiICIl2V86trztUIgHEphSe5MF+RLB450T0G6HB537BBOdOlM9/lez34GAfP2cICDjGqc0GyZlJGHg1iQC3jRAPta+njhYZkIIRzcLx7M8UoL/UBkaVbLXFwR0jzzELtPpbH9VGFyk416sTauIbZJ7sPiZ5rPnyMSIXEE7oKjkHQigdWH4MW3YCtgZOpwNnxvA7bcCBv5+B488G/BOdzjDdtA9KdvH22AtAre+DmmO/H1QTPBi8JHnG9mcYtvuZhscYvbqUYICAEhIASEgBB4PwQScSGQL+FliZ/9lHuceIHGC16+tDsDe7N3isiPHTjY7YJ4ObO//9azN4dD3A9hahSIlyacwmamRhwpObwpD8xdUb40Wcx7tGOv+a0/ZLxkbQtGml7G4hnDzuJqLrYLqHI0vAiELNWF1stoPLRVEC//8dO6/fBo1R7dAfFyp2GMGrmz0UCgghB5ksQLCRhiynniij9YhprejO6EwK1AQMTLrXiM2sRNRiCQA+HgHXlo5LEdwJnu/sGp2ye/PYR67KseQkUPEcWobwcnY1utV+BBHj5dEC66CrVOEjYcZwShIxyl2YF62WEXWxE/X0FYRgZnvkFW+pG5/OAzA+cn9yp8JU1m1x97xp1GASFEPUqWBiwAiWNyjhDTKzA5KtnAyan1RgEO4Rr2b3+46z5f/vjjjtVhqlWhthCEBdonEwyXGTAc5+Hywjrye5iBIS4sX3xJ83wz5YWAEBACQkAICIH3QCA5a10GSAQEnte8p6kR5aHRCC+w4OvlLeSo/aMuTIugMYwXV399RhKmby/3B7YG4qVWKcER/4cSL++xxjlNojgwT1SY0/wKi+KMcQXTQ8+WhtYoTTCl6fZoAg1iA/FSL9l//G4zEC/QeLkP7eE1+Mmj+XapBOIFqjKuPZwKbhiHE2CstGh6et0JgaVCQMTLUj1ubfY6IuC2yZAaKDjQLrk/GNou1GNfIYLRs1eHMC3q2vPdkb06HNlvcKp70p7ARKYMny4lpCWYFxXwJgIhlvmmB2k4NMM3T7yLh11yCgKMrNXnRoZz5q/8zMjHJXGxkfFgc1Z519ggW3PcaUq8+CBxDuKZaMCAdDnHZzIeUE/IYF1kD/GW5t9/F5zt/ttPW7BbroB8qTj5UkOo6WTqMFiyniliJ06TT/NbigP42vONlBcCQkAICAEhIAQ+GgE/a5MDFwICc1FsYFQjRoMcgXyhb5J9hJI+PO3YPky0f4bm8F9+hWwF4uX5bt9aOOtreOFSh683185IXtbkBI8pqWJ6vZlMMl0+fRfkkygcZPJL1iqOE9Os5mNyUeTIZrx8lCg/hVaxF6NGciRgCzw9WiRfXhWGttYqu5+8H6Hx8hA+XujbZbVRc+3hYpHyJj402Q6Cm48RV5AWxQKlQmAJERDxsoQPXVv+ughQQMgfQO6TBF7jaR7ThiNdqsc+e3OKtzPH9s/nx/DIP7CTDusmdtqBM92BQdsF5kXw6VLDmxpodYJUCP5NqGbLozMevr7TuTeRtsiwyJoxFw/grP5i7kPaZb2zecJKs5VcNh7Fg7Am6p+EPnGNrMOek1uXF3jPUpRRaCC2vSGAQ0GldA5Nl6r963er7nT3X79ZtTWYHK2vN42RjrbWG75YDucrwgPLhAivev+vbInv30cthYAQEAJCQAgIgfkIxHPVZYKEeElaphovOPOHkKsOjkC8nPWQ9u0piJc/Pz22lyBenr3uQY6qhMiG5Qq0Y+lUF5KFj40vEgjzZ/+g0iA7hAVHcuiDBrjQ2KWSC6WxIK456OmmQMXqqTS0jT1QRSGKXfDBOzxITsi6/DOxIuqqJZhnrYJ4+WHTvn+05i+wtmBi1GpCfsLHtV0o3EZfeTMI5uXeqYXoRggsEQIiXpboYWur1wMBHr7xAAJP4m8UBnAESzOjM/h2OQPx8vOLU/v78yP72zM404XAMOgb3uAYQiTCme64AM0MeI4H4VJOVDtJvJCMcNlhdpu5czVQCWyAiede+fKpjnNbh8J8H5bM9pu+z+5Cv9neiyaK/eb3gpCUDHSODNvSKz+x9jdgEMI6ibPdQmkCwqVsf/imYVt4e/Mv37TwFqdqd3dIwDRsZ2vVn0+6rkRa+ijyJQ4SF79ocyoXAkJACAgBISAE3g+B5FxmYx6z4dRnBtoaNL2GcEUTmaMTylQgXo779uubtv3ll0NEOOrZUzjaLRXw4qrAEMhlyAzJKDireVxzvHBsx0MchX7NO8xn2yRS0GzTpNl06+m7xXMk0y9IFky1oDWK2cGnjvukzBSaE9oBmBdGiBxBqIR1EcyxzhE9cwLNlpL92w9b9uTBuhMvlJmoMdyCKjEstQNmGCiuJ83FgsUrUo0QWAoERLwsxWPWJq8DAvF45aHGA47nEIkXho7uIPRxfzCwY7yZoTPdv4Bw+T//PLQ//wyNF6jINipVt0Vu4Q1NpVgKvkt4aiaDJglGjKdbVjK998vqZ/vEttMjTN/5LqaL0jXMFCflYdQw1+yMsz0W36NnIiWsAMQwTjjiqfHCKrgZRneIU6ikAHGCuNv0gTMGS7XZLNh3d0qefn+v5I7hnjzedtLlm0fb3p/PJoxAmCGGJfOla+KkbKBLCAgBISAEhIAQ+III4ADmGYzLE57PyT1lKide8LbqFFEhqUV8eAo/eSBe/kri5W0XxMsZmtOsuIi05Gc+eAMoaySDpId7vOdMvOYd+kmbWIVbz8b70DFdX2g9O25stGiOfP3F/IKpLjaMJXGBca0ojyIOZabOkMQLTLag99JCcAJYFNlW49w28dLqX77fRhjpVbu/Q20XEC/wldOA2RY1XqbXkbufroirUCoElg4BES9L98i14a+FQDxmI/HCdVA4mOCtzAkiGPVgQ3SIdP+oZ399fmr/3y/H9tenCIWINzUtEC91ONNdrVXh06XoxAuJgeiJP9tT/nTjjLyfTdk6rob5eM2W5ceKbeJ48Z7pvH7z2oXxslEjYZIfK+bz/fP5WB/3xOnD/DzivdQFJzh4QzcvAxNDwuWkDz8vFCTwadXg52WrAOd6K/ZkpwjipW4/fLtj9++s2nffbEOAoAgWVupZvwv3cQVKhYAQEAJCQAgIgYsI5KWCqz85k9FziZ/9WAbP/DFkAooFY8hWDCndgWx1gvQ5yJa//LJvr/c69uurtg1GRcgDBRucIwoPPMB5ZMgCOvp/cdVxZO4xn8/tOSFr2IMteIXe+ZKkLjZIW4b2F7/j/HG0OHc6wFSXhfNNtYo3aB0JptxwBWgJ8yJ2pzDTGkBzaADZaRMBCe6tFe3eutn2esl+/90Wohmt2h1Eh6R/F5q816AWE1ecpjkyLK2MS1AqBJYUAREvS/rgte0vj0DufEsnp0osHeq+3Tu20zN43kfIw1144P8ZarD/9QLho2GHfHQ6gi0y3ihUC9asQOMFvl3AHaT2t4F34FGXn2H2Pk4Zj8TYdlG72D6ml7WLY7FtHD/2y6ehLmuxiHiJc8WW+fHjeCzLtcNtbB0ECohfsQDEC+2Ve/iiI2OSXeXiBHiOjX50t1ZX4CCuZn/8cdse3WvZH77dgikXVJAZYtqjHAVhJDeDLyIOH1ekVAgIASEgBISAEMikkc97TgY5IMhAAXXO5wELkFK+6iKyUa8/tDY0X17uIZz0rwcwNerY01cd+M47R/2KnXRptl10v3lVOIj1C0OHcMiu7xrK8O212RdKpoSP6XZTMllSxXF9FHpQyV8clPvhlazB01gWauZ/x9dO07XTPeOYLOUn3rNPyBcSoYkv9U6A2wAaQ0M41d2GX5dvdir27Z2iba9V4N9l0x4iotE2/OI1EOihCgfFFTgoTmWudESOrUsICIE8AiJe8mgoLwQ+IwLxEKSQEA8o+nXpw/fIy9cHdohwh2/ggX/3oGPP3w7sl128odlDFKPOGIRLEaZGRZgclUAawHcJTsbg+AxHqEsdPDjjDFkuf7SGrcWS2Ha63+LtX0W7OHc85hcRL/lVzJ8335MiB6/4AifKE3E27pQQjVBBrCCL4RpDI2bob7ia9YndA/HyJ0Q3+u7BGgiYDavSpKtU8dDSFeAeV8yevOLY4U7fQkAICAEhIASEQB4Bnr1f6qx0uYqTY8IYZMCd6oNA6A/G1kHggtcILf2PZ0f2Zq9tvyK09P7ZxMkXBJF0v3n0nVeH3zzKZ/SXlxEv2a6mTY65O+wy3WSaSYqIwMyFolA6py5tGsdJxk/LF2Xeh3hh3zhekKCi7BQ3QOKFq+K+T3rAjcQLTLPvboJsuVe1H+5VEDa6Yt892rD7202YHYF4gaaLk1YgX4hbnCHOxlSXEBACGQIiXjIslBMCV4RAPFB5BGUXBQNefqgn+QHUOft4G/OPX99A6+XUXiCa0Uu8jdk7ObdX+OyenkNNdgVmRgaP8gahoAzihRovYQCO6R8fORkU+SwXj1RvwNmTTGyRPyZjG6az5bP3se1s+ez9onaxfDad7T97H9rPI15oWuQXsIm7S0qQQMTwN1kQLKD50h+P7LjTR7sJ8BvBVrlm//Nftu13CJH4f/1+w+oeXpp+dWi7DPBx8S0aL04zLXx5sb6EgBAQAkJACAiBBIF4Dsej+XMCk5evaFLMi2WUsQbQKu7B1Hjv8Myevjiyt4c0NTrDC66h7Z+O/QWXv9yqFfGSiyYzkLHQmQQEnfPn9+FkBYW4edec8kBlhMY+Thws7X+hADVxfKaL6vPlWbvYM9TG8ljKSVkT9haIF9bh4wm+sGdqCJ+5xguCPpwPYVbUgGzUtD88rrvGy6O7Lbuz0YKj3QY0XQpWwsvAkhNWHCRbcbjzIn0JASGQICDiRX8KQuDKEYgH4pxjB1WhNpi8UCDgm5i//fwWDt9O7DfYIL9827FDqMDut82OED66P1qBKifCR0Pxog51WPIHFCh8HKYx7/vwUtRx7pCfXkW8C3VXvvUrGTBb+/Rw2drz1EoQHjJRxQGZ7uh3BRpwQzDi2zC+yTmE6jGJlzGIF4aX/p+/37QfHjbtf/y4Bg/9VajQwq8OSJdWs+5ECxGjVs0c2WrObCoSAkJACAgBISAEvgQClIOyK5MQRqMRzLkn1gcBcwgz7t/eHNsBbIuevz61X1737c3R0H5+2bOam8tAqxhmxjzjfbwc6RLHnj3/g6yVSCFRREHjVAPXO2brCeNEqYWLDgufWn5KvITWn/7NDcVRwpz85nJ9yYwAiTzf55FwGuHDiFCQjuD5ZmTf3Gva775ZBfHSco2X+9t1aLu0qvELmQAAQABJREFUbB3O8op441WEUFqEfMUXUhyHVw6KUKBvISAEHAERL/pDEAJXjkA8esLA5/AxEg8hHuYMdzyBvcvI7Y+HCHU4tD//1y5IlxN43T+157sd6w0K1h2swC8Jjr1zvE3AgQbXLgh/iAMOn6ACi7EmYWSf0bPTc8etzSuNa4pt3pXmx/jQvu8a+7L6OFcQcNhyWoiJ9TGdGssXjZ6oXImCAcoi8UJVZHru31kv25++b9kPd+HrBSkFinXYL7eaNdvaaLpAgQfncwcJBs90VgKbmlg3QkAICAEhIASEwJdAwImSZKIoIaxA1hqNIGslnyMQLq/3T+wIAQt+e9u2f77o2Mv9PvzpIbR0ET5eQL5UETUSFAIEBsgNyaA8+nn6z79Yk9TmGuWJlyA75SWotAf65mtzA8yf7ONLMU22gpCLPl24Gu5xCFUXBiKYwK8L5dQCHA3XSxN7fK9hv3+ybr9/vAotlwq0XepIa3hBVfMgBoxmROIlwJDtIct9/LLVUwjcNgREvNy2J6r9XAMEsuONi0mJF5xCTrzgi75d+GnD2/4ZNC/+z9/e2rNXp3D4BvXX3Tb8kBShiRE+5/S3j758l0ACJvzgD3PMJ14uHndBEGF5traLrS6HLuuZFxou7/Pu2uk1zWuf0VbZ6uetJdtPrtazGfFCBoak1QCC2CGiHPDtDt/sbK6V7I/f1O3Jnar9t2+atg6hYmd71dagSntnq+VhEimZBBw5HmfLZpy3bpUJASEgBISAEBACV4MAj/NFpy5lq1gZX0zxmKaDXY9mOEKkHsha+0endnw6gIPdtv39+QlMu3v2n8/g5GUF4ZBXKGvxQzmLklcyJAfixJgkJ12ggFd+Rfl8VhP6TPecbslxOPbFUtZ88pVMna0g5Jx4cVkmaLowAAFfCE6g6QM3dzC1PkfUIkR+dI2XDfvd4zW8lKrYZouawDWYwBMzhuDGXtPwj9kestwn70ADCIFbg4CIl1vzKLWRa4MAJQAeZkzDf+GHuhfRxAgh+tzmGJ72OwNELera//PnffvHSxAvcPj265u2e4mvQuW1XKyYm8j4OZkMltuoF/v94iMutOE322Q92C3tFTNefbEd2168rqJdNkYgWKbX53MmgkGc3wWseLMwzcYhXcIrCgbsP4CAcdIdJcTLua01V+A4rmgPN4v20/2KbW+s2sP7G7a12UKko/UQXtqHoXDETCTAfGh9CQEhIASEgBAQAl8JgVTswvzhqA4vXOgPjy9YJjjzO72hHZ90rN0d2u4+Ikc+P3Kfen95eoIXMHjRBQ3jPj7UdoF1NzSMC07CBEIEo4b/fIZMDqEMgy6ehLwXXPjyVaWl81pOt0ibIhNr5vXKt8vn0TZZZOydr2Wea47mQcSoC7OsEXDifBurBVtHGOnt9YI9vtO0Hx9v2PcP120V/u9WmyWr16oe/XFlZeJjxBdkcTwfn1+6hIAQmEJAxMsUHLoRAleAQHYi+2A8xoKGBN4qgHTxEIfQdOlCCDht92z/uGv/668H9k+EN/wVn9/g44X2xnybUCtXXIUzONPFSH4mhjcjPILd+sVnedeBzI6hu2fwNdUj3oRmsckXT+Mypia+KuIFdsy86HaPwkUbkQ7cnhkFzdq5PdpEWGkIG9/uFO3OdsuePNwOKUIn0nmcg51bYHymPqi+hIAQEAJCQAgIgc+GAMWT3BE8NY+LXUllFMGotcIznvf+wgsRJE+h6dqD7EW56yledr12zZcjw/svaCCfG/zv+lWBbXeZxAs+LhYlgzLxly9eGJfAFzHMz65uqlFs7OlsSxYubh1r5vWaGnb6JukWXhZx/JQe8ZUG+THUjqHl0oMm8DnMjIrFc3u4BZOizbI9QMoIRk/urdrju2sgXCqQlxg+uuRRH4lxHNUnz8lrH7ja6bXrTgjcUgREvNzSB6ttfU0EeNoFk5a4ingwkXShX5HTsx58u/SQ9uFpv2P/++9H9vPrjj3b7dkL2BzXSyUcboioA/KF4aPpE4aj0vmZUwc4MXmoZcQLy/PX/CMvWZmPNdUCNz6eD4FcImTkR7yYz3qEutn72GO2fPZ+uh1rwzW/Xfr2KTbLrTwtymVSjZdEICCGkC9cyPA3YSigSu2d1RXbrJs92FhBlKOWffdkx+7fXbUfHm8Fj/2Yh2RLlCvOmc/No6wQEAJCQAgIASHweRAI8svlY7NNFF94VntUIhTS/IhOdruIItlHxB5qGj+HefdryF//fHYIh7tjO+6O7dXBCG1XrIJABuUCghmQeHFRJMzOcS4SL5CfXDCYlQjYZ/412zK2ynqE+UJ5LGUvX0xsPidlfYjqREEvd4dbyiyJ3IKKQLngG+3g1QW+70aonUCTxUC01O3BdsXTu+vII5LRve315IUg/A4i2gNJKW6bc8SLOORXHsuVCgEhEBAQ8aK/BCHwWRAIhxmH9gM5OTdpP0vfLsdnXTvD5xCH/y7CF/2//4CZ0Zsewhv27NX+wBp4m9AC8dIE8cIwfecJ8cIDMhz6POpw44d9MvjUPvJHYVYRWobv6RbJeH6EzhsvjhHbxftF6aJ2i8qzcdgiHOVxHdN9Ymnoka/L50NtGIk9wqj/P3vvoR1HkmSJOpBaQVMWWawiq8W0mN59//8Ju+e88/bszsx2l6aGRmoFvHvNwiI8AwlFIkGAsChmhoe7h4sbWWGG62bmvKbyMaUSBsJFFA6cywggt1qBPzMsX7ZaJ+EJTGt/ePEAbkat8OrFBhQRBNyDEsZnaQHpqIxlrbJlPxwBR8ARcAQcAUfgSyFAaU+5rqqRXAkRwPHQ3WgM/YtbTNPa+C02M9jdp+XLHoLsjsL2wST89KEP6xjsJAkdrMS4LynxIhqXNK46GNu2g5oACI1TCgEz4npW33QHK9fzbE1eWYNWYtdZO7Mpaw/17RZUyJIopw6T3MSFJxIuBIwBdSfY4bG4fByqlaXw5xer4duHtfD940bYWEXMu9U6NhpoQRdiEGLoQgjsQn3I5iyYc7z6b3ZYfuUIOAIpAk68pFB4whG4XgS4MhKTLvxDnSsuEwRzPTjoILJ+T8xd3+22w//6GbFdPg7Dm51ReI8Vl2alEJq1kpAvJRIvJAn4n8jIZDUjFcpoOBKtOot8Hq/tyMSwCvb42urkz/n28uW8tj4uau+ietbXVerN69PaQRn+aWtYjUGCtQGp4ol0AX7K5aUxLF+mwPwE2yc2w59fboXnj7GF4stNKGElmN+CfIGWwRUwHtkqGNo27UNK/MsRcAQcAUfAEbgqApRMJveueq/XNwSoJ/Gg7QWTySUIFQSPBelCq+Me4rzsHUIPaw/Ch21uatCD7jUI/3zdDcMRCAlucHCsLsaMD6cBZGktYq1FDUtnfG78JOVWbe7jzOpZsVVnU+cecUW7OelXggqTDOEYrJ7USStC57EZYI4ACpSLdFeEy1AFlr/N6hIC6BbCq29W4FrUDC+eNBBgF3Fd6pWw0qzB0qUIXSghXUi+JIMVfZdXlnHuJLzQEbi/CDjxcn+fvc/8BhBIhVHSF4kXBtbdBfGyj8/uQT+82W6H//1bN7zeHWPVZRI+HkxDowzipcoPXI1IvMD/VkkXrkx8imyLpaFJZA6K+fF1MtBTp9te79SAT2VAr8ABRQNTESwx9yQLxMkxdjCYAOsprI2mQrz8G4iXF09aIGA2xK+ZxAtJF4n3wjbQGlHxwxFwBBwBR8AR+HwEXKp8PoZJC4CStILI+CRLAu3S3RsfWr4cSKBduHvv9eHm3ZWdjv7r1yME30Wg2eFSGIyXsFgWYOUBKxHIfiMctDnpIBtufvHFOr5ASbBiq541eEYqrmg3J3qcEi+JXmL1pE5aEfoj9B6UCeUCKxeSLyRdYOAT1iWgbgEBdUtwsV4LT7bq2Eq6BfeiYqgjtksdgXW57bZau6AfNJsuOrFRjiPr6owJeLYjcL8RcOLlfj9/n/0iERBBxA4ySTRCcLchiJePO4dwMeqEnQME0/3YDv/39Sh8OJyE9yBddtsnoQZB36gsw/KFxAsEpVi8kDCAuDSBmgjbi6fA/tObzql+3fXO6erai06PPUOdnVHZIAYRDlzCYo4oItxyEhoWTG0LhePwAua1f3+5Hr7Hqs9fX62HBlZ7ioi7QxPbEs7xIeRaXumKK3j6liPA38Tsr+WWD9iH5wg4Ao6AI3AWAomSZNI+05lo6QoLD2RMoVNxg4MB4r20273wy/uOLIL918/72G56gpgvJxL3pQfrlyp3mCwth1KZlh6Ia8L/QFaoTmGDmJUh1CuobyTLO1pprp6g9ayV2bPNgHWiekm2lWb3WE5ibZ0W0EpXdUcu3DFiIC1d6GoExQexbKawaFmWQLoP1xBMF8Huvn26Eh7A5frx1gp22aTeQxejopIuMhZt/OL5pYPwhCPgCAABJ178Z+AILAoBysBIVjI9Gk5F2L/f3g/vPh6F7f0uTFw7CKyL7Q0R3G2nHcJBN0DQLyGwbgh1Cnz8nS+KA9oTOYkvNnv6yHUG0Xo3j8vOI66XzfQ0NlpPMAQmUs6vZJcjojQ9nmJ1C9tL43yyNA0vHtXDP16twdy2Ff4O4qXZqIi7ESP50+2IR4ouG4ZCdbpfqeZfjoAj4Ag4Ao6AI3CjCNgilUlmkg3UoZiPDxgIWQjD7obdHty8oYd92OuFn17vwQVpjFh74/Aen3ZvGirJAhjj7YU0QgopB9UCRJ2gBiDEiuapvkEVUK8zxYMgZGPiVXaclZ/V0JTWk/6T5jV/5iK9KaV/UAy6JR13AfFcissnsPI9CevYIvrZ43p4vFaRXYwePaiH9RXEdVlvQgeFpa9Y/CTWLmzZhhr1MiczLfWEI+AIKAJOvPgvwRFYFAJzZCBXV7jK8uY9grm9P4Sg78LEFf7F29Ow1zkOkPvhsLckFi9VrK7UsMpShD8tF2nIunC14vQRZ6ZiXqqdko2nb77FOZxXPAObZ5x3evhnlerdRlpxxUprUnkZI+5OF89lgtWfCcB+AQXkv/2wEn4A8UIChv7NNZjZknSpQgnjwV2N5IznYm2d1bdU9C9HwBFwBBwBR8ARWDACibRPVAaeTDaLlQepBxIvjPcCt6MhFsQ+wAJ5GwrY6w9HsEQehN/eD8Kv+Bx0JpD7GtekIG7ftJqhnsX4L2yYuoTSMbSCyXq6aIrJ4GRklj7rHhu9ldv17H3ZFeaHC14LySRnDlXjuvBMg194D4F0WQortSUEzy2F72Hl8mijHh5uVMMaSBcuOLUY1wWVC/jQ3YqH6Ds2BMnhF3s7lZmWesIRcAQUASde/JfgCCwKgUwKpj30B0q8/PZ2N7x+tw93I2wh/QEmrvvH4aB3Eg76SwEbHYnFSwVWL1UIego8LFLgOIt4SZuXRNztrBjkVVw6e9+XubrsmC5fb5Z6ymallIut/SS7E6FZIjIC8dLpj3Bm8GMQL09qIFxAvDxtynmlWQ3VKsgX+jlTW0lJF97PNtGQ/ss69JQj4Ag4Ao6AI+AI3DACiZ4TnUgWmLWLim8E2p2oToVdlGHl0g1H2GnyPXY6+rg/CP963Yb1SzvsHA5BUqiLzXJhWUiX6clyGFPgg4BhwH2qaGILkxAvuhBDzYAHCtMjGZBdp5dpwkpyZ7o3aR3VY3LF7Ff+qeUt3YiwgZMeIJhgy6t3I49zX4aVC9b0EER3OazWCmFrZRmESzW8erYZHm01woPNJuK6FJIdjHRTgVi/4fx0jvE42GE817jM046AI2AIOPFiSPjZEbhuBBLBZ/KPKxC0dukjmv7Pr3fCb9jC8ONuH6sqnYAwL+FoEEJ7sBS6QwRzg3tRBaRLGdYuJF54L/5nPcPiZXbg1p/lZqKQqXyp1YrPc+pZI7zd0kxwYBcec9pL74nL4nRaIUnEZXH6dD1TUPIlNlKWUz0pwNWIbthUj4YgXo664zCaYPULQfdePK6Gf/zQDK+eNMPfv2/B5LaGOC8gXepVnKvStPpwEwK0R21G/+W79WtHwBFwBBwBR8ARuFEEIPEToc8TZbQSL0yr6kJrF9npaAoXb8R56fQGYXsbu01CGfv1HdzAoZvtHQ3hikwrF3xwhnFMGGO3I6yhqSaBxgr4kHhR3UI6k5myH1EMknFAiZN8++J48ofcgkwr4TV1DbbPvFPECyqwX6mf3MQrrYszxjzVlTuZd7mIYLqIG1iDpctmsxjWm6XwYKUYHm4irss3a+HBWiOs48OFv+JyIRQQ24XzkLnYeHHhxAsehh+OwCcg4MTLJ4DmtzgCV0GAssrkVa8/hD/xKPz8+2749c0ezFt74WcI+L3uUuggin53BHIGkfRLIFzKWF0pQ9ouczklaUCFKXpPBOy8ccRFFNp6MBWXWH7+fNl6+fsWeX3ZMWk9fs87bPYs54fKEt2FqKKQbDnqjRH4GLtOQbt6/rAS/v1lI7x6XAt/e7kaNlaVcGk2QMA0qrgf96FBfSxOvMzD2/McAUfAEbg/CFDCnCV9YhQuWy++5xrTZ3XPfB6XmYLWvP3fENA2LRtsokpJvhEvY7gcHbahm8HqdRfuRl0sju13RuEQpEtnMEKcFyyYgWnp9aEnjLAVNeLwH2LnIxIxsogj3wl0wE9pEpxJULDjZBy4jI78yJL7kxozpSBe9FbqK9TkbF4zteROybFJprU5lhOQKNgqus74gcthpQErl9UKXIyqYWutFtZxpptRE/pNCxa+dC+i7rmMXYx4yNiTdnWhaWYyqMGe83m80w9HwBGIEXDiJUbD047ANSMgQjKRjfQpJunSRiC3n3/bBfmyh+BtiKb/phMOBgUhXAZTbGE4AfECgccP5CSEXzYoaY+XSZtWwkuKPDtb/qedraWL7s7Xy1/b/fn8/PVi6rEXHjmoBCdRlqhJ4B+NcEm8HHYnsHiZhiG0qWdblfDX76rhBxAvf/1+JWxCMWnA13mlWZePtCsNQwGCMmKKiPUpHfuXI+AIOAKOgCNwFxC4HuXhds00ITxsUMobcMEFegEujHhhrJej9gjEyijsHfQl9ssQBMtogl2PRmPJO4Tudng0AiGj5Avj8Y3hqrSExmATIv+RDhGKxAgXdEydwPQCg1iuLdMGF9VjVqq3SCJrnz0o8YKCtFLUiCStAGdxQ0JMlwLci+Bf9Gi1EJq15bDZKmAXI8RzWW8ggG4NMV0q0G1q4k5dQxw7ki4y9kQBxaVgxuZN35ntlX3OmdRsJb9yBO49Ak683PufgAOwaAQojmQ7aCQ6IF3a3UH48Zc9IV/eJRYvnWEBhMsyzFiXYXGBuC6QX0UIb0acl5WGaJAM7KbizQQdz2cdrLmIemf1Z/nWr13b+ax8K7dzXC9OW7md47I4nakAig7LeFBtYZmaB/MiJl5IwAxhV/xsqxz+9l0tvHxUDX950Qqb61gJatXCaqshxAufiSpxSrzwIaWrW9KPfzkCjoAj4Ag4Ao7AjSOQqjxI6D+yBTIMym0Wq4sRg+siuP7wGLH1RqE/HMPliNFbjhFQF+7ecLdZgq+OBtzth+2dbvhwhK2mu9PwDucxLJRPjqkM6OoY27X2TQOhrsGek94lkV3zEv+xibQOEjjSdliQ3c2LxHFIesMVaRg9tA/UThIkS5ZBvCyBdKH7eg2WLi8elMNGowTCpRy+edgKD7ewe9FaMzThQs1dmwp0L4KVyzEWoKwdazcZBvKj1cCkbz3ZSOLM9O4409OOwL1FwImXe/vofeKfj0BeyMwXMObHS7NU+hCTePnXz3vhp1/3sGUhXY3acDEqJaRLAcSLkgK0diEBo5HydbT09dVVlYtGz7Hkxzfvnuusx7Z4XNTvRfVsTFepd7rP7G5rT0fHKyo7hWQlh9srknA5MIsXxHkh8fLXFyBeEOvl375tgHhphBai/K+uNMJaqy4NiWLEL8zXVoCsT+3Jv+8qAvZr8ud5V5+gj9sRuE0I8I3ib5MbeyLyAre3uGoklPnMIeGi8VqOwwCWLmNsJz2AdcsQO07S8qXbn4B4CKFRhUtOFSQEiItduBy1YRFz2EYMGMR/YTy4jyBo6II0BGnTR3y+vljIIJAtYsCgKelL1AM8dlrFkAThL0A+SZpXvOYRj5a0RnadpJHBPPswMZXtLjVPdipCObgT2QmzzvGXC9i5COf6MtyLitidsSjuRKsNkC8rJVjywsoFrkUN7NqoOzcxnssyrF3QCAdvg0O7POQyzUsTWujfjoAjcCkEnHi5FExeyRGYhwBFYHzMF0QkXuzTRuTco84QFi+74UchXvoItNsOvSmIF1i6TI4LEriNhAsF9fnEC/vLj8HGE5fFaSufd77uevP6yOdx/Pl+eX2ZI75PCSneZXerSmM0leGk6he/hXhB9hQY0sXoAEqUuBqBePmGrkYvquH7R5Xwl+cgXjZg6bIK4gUWL/zIChXulRGIgoKU/rvMwL3OLUcg+7Xc8oH68BwBR+AOIMA3ikmmOzDcuz5EYTyySdj7nDlcAJuifAo53wfRMkK0XCNdxrB27eO6hFWvFnb7adSKkj5C8D1uitDGmcF2j+B2tH3YD4edsRA1R3A/avemiP0Ct2V8UFWsUmTRjRbKePQkU+iSxEO+kcn/kn+Sr19qlStpDFzHrlYtTMuH+ZwDz8xh+/iQJCrBsoWbMqyAbFnF+OvVZbgRleBeVIK1biU8gltRExsFrLUqOJdCHfnVckmsXUi48CMWLaLXRMNC0sato5Cr2Qp+5Qg4Ahci4MTLhRB5BUfgLAQoAnEkJ73QVRWmzVKF8otC8hgfki7tjroaKfHSCz8hxkt/XAojmKtOjosQphR8aiIaEy/STa6vXOc6hFPfFJAzN56qoRnXXe+MbmayzxrXVYV6nngRlUZ60h6sH+ZTseEKFLdoNOIF23lzVyPsbsRVsKe0ePm2qhYvz+thaxPEC0mXVVi8wOolHR2aFeWKOfyXFsxM0i8cAUfAEXAEHAFHYNEImKiPdB7Loi5GixcSL1xk6cLKdQiiZYDdJsfH2PuHFXCQiFhtVbHdcjmUYTXC4Lt0vZlOJ2H/sCdWy7v7nbC93wv7iPvybm+ALagn4XCIOH6wgDnsTaDLQdNAcydYUJN4KVAOwImIjkBrZlUVMj1Fc9g/S1RH0VRGunB89GwieUS9Y4KPEi+I4YJ8xgWE8UpowqVoq1FAPJdSWIdb0dPHLZnP5no1bKzVZXfGWrUCyxjqmtYnx6vzxwB0fBwA08lZr/Lf5xbmK/u1I3DvEXDi5d7/BByAT0fAhFQieEQIajoWVCIaad4KaXkEixcSL//6eR8WL/vh3Q5cjd7C4mVSDGMQLtMTEi+MIq+ClCsY1pbKRCUYLhZ1rGHj+/QZXnzn5/Zz1hgvnqEpKDZGa8nuNJVG861U76JrELeT5qFKGIkXmBqDeOlByXq2VRLi5Xu6Ggnx0oSbkboarYN44SHPQ5rFV/KQ7FlJBf9yBBwBR8ARcAQcgZtBwMS8qCW8QCJRCGTxS8gTugOReDkOHSy2kHRpw0SlzPgmICIY54RkSxPuOE1YjJSxnTJ1NyVeuEHCEK5F3J1yANKFFsywgoHly97hKBxx56MBgu9i96MRXI/G0o8uujHO37GYqFAXVP2BnkLUIzhSuj+lKpsMOyFfZPzQB8msQMFYRty/AtLiKS1nEDpgcsr4VDDuBkiXaqUQtmDNsomAuXQv2liFlQu2jW7B4qWFWC5lBHypgFySNtNuE6BwzSO7wsBOKTYcbFZDbvAvR8ARuBQCTrxcCiav5AjMQ4DCJzuMGGFOLKdmiBeQLnQ3IvHyr19AvDDGixAvBaxekHgpRcQLBKzKWulE21fZ7CIvw91S9jQMm7OIF9FfoDSIHzNu5irSEEoYiRfGeqH58TcgXv4Gi5fvEFyXxMsDWLwwvkts8cLnwb5klSh54PFzt3H52RFwBBwBR8ARcAQWiIApANKFXXChiod+T8F4TCDjsb4SBrB0aZN4QUBdxmypg2SpgpCoVklKIDYKYqJUcS6BiDkBQyKxYcCOjLDL0Wg0AfkCNyNsNc0tqI+6CM4LV+V2fxh6aPewM0HcmGSXRIkdQ2sZ3AtrGBIsE7g04Z+2ibGQiGE+z3JgwS3hWYRg4RqRbe/MM12hClAOy9ilqFxeDiWMs1wi6VKEe1QJ48euRdwWGqQL50XrHc6phrlVq2VYunBecHxCW6YvKULxlZWhZEax0ZpOvCTPyk+OwBURcOLlioB5dUcgQ8AEUJQDf14esZwi8aJCG8IZgXU7IF7+GRMvb9TiRYkXdTWiosA2xCQ1koWne5Te8DW/hKXZwYYuUy+7Y/Gpy47p6vVEbcB0xRSXE2ET+NjuQ2bxMo94EVej53Q1YowXdTXKEy9sUsgwYWDY+Oxzlwz/cgQcAUfAEXAEHIGFIiCy2HqwCyhRqfsMRDSJFwbTpcULg+G2YbHSHyJoLoiXtRYtXMoIOFsPRexmRDccWoQsp8qc6k5q/aJaBV2QJnA/4tbTA5AwffmQyAEhA8uYHkiXNtzLaVXTHx9ju2rshMQYMCPspITFngm2o0YTWGzj1tQkZdAuPuySxAstW0i0MGAuSRYSLgyWWwHJQlKoBauWKuO48FNFHBf4GdFSp4JAL00QL1VszURipgzrFsa041x4Fh1IXIwyjZCQsd90uiklY6D62RFwBK4DASdergNFb+OeI6ACWb8BRSLAIMJSXCjwj+FDTGuXlHiBq9F7WryAeOnD1WiEwLp0M2KMFzbCuylwU0GIdtM+0pY9cRYCc4kXVLZVnox44S4EuqvRILF4IfHyt4R4+St2NdrarF8Y4yXxXDprOJ7vCDgCjoAj4Ag4AgtAwHQj6k106yG5wDwjXkiVTGFmwp2LSHL0YX3SBSnSJzkCV6N1ccsB8YLgs2oNQvKDWoS0qO0lhI5ZuYr7EXS7KfSGISxcBmhnCAua3oALbCBb0HaHrkkSRwZuSIn1ywAkzAgEDAmgMcgXWrqMeMYYjXihw7kRL9xlia5QJF+qIIVIvNCCpYUYNHQroiULg+RKsNxqVSxg6rBsKYN4kS2iC0WZhS466Yyw1VL6FKhtsl9zPUoLPOEIOALXjoATL9cOqTd4/xDIBFhMthgOFPgUascQ+kcQwp00xsteeLuLXY3E4qUkrkbc1YiWLxT1DHpGixeufFgPidy3plkLHyuNsk8lr7veqQ4WmPFpY0/UiwSdDKOMeGG7NPE1VyMoT1CgZoLrclcjIV4YXBcxXhhcN9rVSBtH2wk7lpJkC0TjbjVN3BXn7Hdq13drJj5aR8ARcAQcgduJQCrhJZFeie4l+heySLjoDkYkXjSwLomYPkiTdezy02hUwuZqRWK9mJ7A2c5KrLhtugdxUQ3WK/BfojXNCKTKGO0NxwjYC32im5At7Gc0YD2SLoz/Qncjki+J+xHZFzTNsfLg8hvjuNBChR+6B9EKpwqlkO5PRZIwiOei7kZwNaKbFD48F+FGVIbVCwkksdphEF2ZBWai/6SP+Iu9yjxdiYlh8bQjcO0IOPFy7ZB6g/cPgUwQ50U0sVDiRQX0EV2NIuLl3R52NXqNXY2mCK4rW0mrxQvXaki8lLjikkh99nKaeFkE2uwwntNZfeTrcdSz9+nQT9eb3/711juLeCGRxUNMiAEuFR2L8cKAeynxgu2kX4J4+bdvm2GL20knxMs6guzy0GdBUg1PPXlI9qykwr3/st9CAnj627Drew+QA+AIOAKOgCNwjQiYXLYmSYpQxkO0w82H7j6I0TKGdQoIEVqcMPYKy5sgXepJQNplxE9J5boktDU464iOI5ItEW8shnYH8gWLa0ksGLYp1jAoZJ+0dmYeXYuUpCHpoveQfGETtMZJ9SdkUKegriKBdJkmAQPCpYSzpFHIYMAkVhhcl3kkZtI8EDXUR0w34QXnqVYvOh/RVymOkc8xiGTmTX44Ao7AwhBw4mVh0HrDjkCGgAhkSLZ2py9xXn5EYN2fft3T4LrYTro7hqsR3IxGU24pDeEJ2ccVD3M1olDkQSFPs1AVjSYqrVTrzH6z5iLqzfZy+sr6zZeclX9evfPuicsszXOiROA8i44GrWO5Ei+KaUa8wOIFGto33E5aiJdq+MsLuBqBeGk1GWCXOxvVBX8+C7auZyXIXGchJnYY8vo8sidh11bPz46AI+AIOAKOwOcjYHKZLZFo4EfIDuhVDKjL2CtjBMillQv8jkWWUxdo1MuhBncdxnohicF7eHA5iaSFEBiJSEtEv5RKJanDS5ZAvuHEfq1MkhwHiBmmjZiRnY6oQ5C0MYsX3MQ7SahQUnIsksb1MsZLYoUlSxw7KrCOjZEpc3leQrzBE3MnSoZCekWJGN6lh6SyyySXN5zKTMr85Ag4Ap+DgBMvn4Oe3+sInIuACi8T/jzT35dbSv8I0uXn3xHjZacrrkadYSkMQLqQeBlBYNLFqAipWhCBT3GpB4X2rEiMhaPVYl3m56+1jdl8y7Oz3Wdnyz/rfF69uCxOx20xn4eNdV49qxPXk5tyX3av1re7tGVrn4SL3kZlRnLxZTFeRvTVxsqU7Gr0XQ0WL1UQME34fTdCEy5Gq7B6WWnWROHhs8jGDcSpBfmRIJDhPR8Sx2o+Lp7rCDgCjoAj8DkImMakpAtJDVq2BI3nwmC3jMcCi5cK3XKwyiXBamU3IwSlBQFD3YAuyImGQOEOUU+ZlsktpUV0lNZfqnaxutVORKHogbS+Qb6MKyFhRKdjXyjAMGcO6QNt0e2JYxLKRa65LJeMRnREXOBevd3IFQ47GW/SrjVv2dKIfzkCjsCNIuDEy43C7Z3dLwQo5iC6IVSZ4rnbG8HipR9++e0g/Px6P3zY7YZf3hyFg2Ex9MZYkZksIdAufHghGUsQtLKFYCI7iR0FM1vTLH6bKGVpfMRlcTquk09fd718+5e9vuo4WP/0YbmKUIaTKlNQYmhOjNtOQHSReOGWkiReRjALfvagGv79+zp2NSLxsiI7HTSb9dDCTgFNEi+8L+mSz1UUHOsQ+VEyqXXfTobOvHkbeo7SPHQ8zxFwBG4LAnyP+XvqtjyNy47DiBAhOEi6wHhlCkvWLtyMDo14GUxkB6AqYqE04GLEYLWMnUIyJiMmVI5RvkPMy6HpWAdTnYyFUpZoBiRNZBypKMSVGtGgIq1fUCBlyE/q2Fl+c/jZmQENr9k23c+tHznrheTJlzWJhrV+8tu19pOa2fyyWz3lCDgCN4OAEy83g7P3ci8RUGln8pVKQK8/kl2NfoG1yy9vDsLH3U745W077PWXQ2e0HHqjJcQYUcKlDGKA5AssTdODbWTCOc2+IEHhm0jec2ted71zO7umwkSxmNOalYjyE5Ub8bJEfy4cs8QLVsKwKvXtg0r4x6tGePW4Hv723UrYWK1BSdNPA+SL3Ikvebb8EqUo68T6znLuW+qs35shw3JL3zdsfL6OgCPgCDgCi0SAcv/keElipzCWy2TCHYZG4QAWx32QLkPEXtmCW1EDWzBvrNUkgO2yxEzJRqUSirLK5BlzTG5ZXlafugQPEhupbqA58p19kRhhPVbUumFZyRxdyNEeRddjveQ/0f+sPel+dgzSHspZj8PkfTzYzc0eNq4b7/hmp+m9OQKfgIATL58Amt/iCFwOAQofrpRkQqg3GIF8GYZfYO3y+9tDJV7etcPHNuK/9EM4Gi4hCv6SbB1Ygb9R5RTxQqFqyyYiW7WPUwOiwLN+TxXe8ozLjt3qnS/cFQX91ppUcOZYvGA1bDwF8YLAe989roX/9qoVXj1phH9/uRLWENelUa9iy0YoarWqAC+PVZrFl2hR9jxuObw3MjwCY88ln7YBWLld+9kRcAQcAUfAEfg8BIygkF2DENCWAW5H2GWIgXU7+AyxwxB3E9oUuV4K69zJCCtclEjxbkY6ChHy0YBMbsX5zFM5p+peXMZb7Z6smYvJkNk2UjXyVFuoR8IH/zJdE5dJl0bGZD17yhFwBL4kAk68fEn0ve+vGoFYCFIqUg72BuMwGAzDb29BvLwj8dINv71vh7cHJ+GgF8IhyJf2MGDLQJi88gOrDEa1T4xWIVgpXC9DvCwSWs5kVilYZG8Xt51oGDMVs/FlKVN/EuIFuFJfEYsXOIBTIRuBeBljZezFo1r47z+shh+eNsLfX62EVbgXkXipVRGADx8e2grvRw9OvMygf/7vw34/855brhm/dAQcAUfAEXAELkAg1rfoxkO5zyC2A2zxPB5hG2ksqAyhf/VButDtiPXXWpTrCKgL4oVaFiWSiHJjLaTPWINghsmtfD51AblBvkh4ZGOye7Jypma6mS1KrqIG0a+2F7eVlKcnaCVJv9q21pWZxbfN7cszHQFH4CYQcOLlJlD2Pu4tAiYoTeYNhlx1GYW3Hw7wOQof9jrh9w/t8NvOcdjrnATsLh2O8CHxUqsshyqIFwZ/o0SnbGV7mTA3FUC3OLwZkG0msUJwMz2f3YuNKa6RjS9LJXihOu/g+pYQLziPsfrVHY6wCjYJ0xOzeFkNr75phb+/XJPYLrVaJVTL5VCtlOKO9HlAy6GiY33NG9HMTffiwtDIT9bRySPi146AI+AIOAKXRyDWg+QuIxwghWXXIIgfhGwLfbFwAekC3WsMwoXxXiisqVa1uIV0rRhacDlSBUuKhLy4eCQm34wQsWu7M5ZzcdrK8+eMNGEJ78i3aHeIrjG3UDOVfMka0d6TMVxmKNaRnx0BR+DaEXDi5doh9QYdAUXAFAMx9UyE5BCR9Af4A//dx8PwHv5FHw+6IF4Owy8fpnA3Ogm7+Bx0T0K1VADxshTqIGBKCfFCfYFtMsCuyk7aXPCA4JfzTXyZ1L65Hs+flY0nX2ve+FhXwWMqWd8S7CaI6zIYD4HtFBiTeKmHf/ywFl6SeHm1IdYu1XIplPGpIBhfeqAhec7QhJLWpYhpP+Y9A6Li6PhvwxFwBBwBR+DTETDrkoyEyOTNGIwLd2cew82I7kXcwYg7GU2RSZci7hZZhI7VxPbR1WpBrF6kPaoHEE+is116aLgp6Zon3p/KuKxobmtalzoEi2eJl/gGG4/qlMkiT9Jn2jlumG0vuZZ6kcyNknEfnnYEHIGbQcCJl5vB2Xu5hwgY8UIhbLJuDOE/HI3hYgTSBYF1dw974Q0sXv7z7TC825+EDwcTIV9qpeXQgMVLE9YVjLR/AoWB8lOJl1TiitUGoc1yzgKaI7i4lioMl6l3Vj+LyL/M2A1h6//0HKxG4g4tT4XUFZE9wU5GxydjXE2xjfdxePGkGf7+w0b47mkr/OXlOuK6lPEciqEEZa2Ic3wY8RLnWV9x3v1O83k4Kvf7N+CzdwQcgfkImLyyd6S/L+fjlOUa8RLlSJLyeELLFrgZjcYn2MwA7kVY7BrAxYioclGrVCyECraPrpWXQxnX1SqtWG0hi83oczAigzlnH2g1eXzJCXfbc0yLzr49LbG7T9+TtZal0tskkd0b52e1s1Q0tLiqpx0BR+CGEHDi5YaA9m7uHwLziRdsV4wgbzt77bADa5fd/X54u9MJ/+c3EDD74/BubxK2j6ahAcWgjpWYFhSCMokXxHWRraShVMg2hAInAsQmsM4Xu5+KOYX0ZVrM18tfW//5/Pz1ddVju3acHr+VkniR6QkDo8RLgKUL1sdAukzh5nUSnj9uhr+QeHmyAuJlI9SwMlYskHTBB2c77BmLaa9l4mx9RVn3PMnn4ajc8x+BT98RcATmInBaXt2V9+W8keenuIg3f0q8xI1TPxJLF8RxgU/RELFd2t1R6GPBqz+ktUsITRAuJFrq9XLADtLiyl2CFas4c2MyZl3COZB4uVbJlYA1H7MsN0spkjrF5DuebwT0/HuiCucm7e4zGj/3Xi90BByBqyDgxMtV0PK6jsBVEEhkGU8izvA1QXR9Bm/dO+iEw3Yv7B70w3tYvvx/P8PlaHsc3uyOwgcQMI1yMTRAvKjFC1yJwLrErkbaIldoTFCa4LxogKwf181f2/35/Pz1l65n/cdnjpFHPD8iZddJOU4MqAsrZLEgYukyrFwqy5NQKx2HlVoITx81wp++3wzfPl4Jf/x+C3FdQLjARHmZZsrU3qIjbj1OR1U86Qg4Ao6AI+AInIGASY642ORZnHf70meNPM5fxEzyxAsXQZh3jC2kR2PEa4PVyxBkS6cH0gXECy1e6La9KnFdSqHVrEDun+AD8gWLW8dc2cKxzKD7Nvhk4Nc2/qRda37maWIsotelnc+Uapl9nzGguN0zquQbxXXSb3qeU8WzHAFH4NoQcOLl2qD0hhyBOQhApolYS6QgzV8nIF4OEEH3qAPiBdsYcWej//fHdvj1wyi83hnA6kWJlyaCvgnxAmVhmigVoljIrkYmVjPqRXuaMwbJsvq8iMVzvj7rxYI4X56/tvr5fF7HZXE6rst8HjamefWsTlxPbjrja7YNJV6AU9oM8ISSNQT5QksiYlouHIcGSJdG5SSsN0C8PGyEP7zYCt8I8bIJc2SQLmhAiBeQL3bYqHkd95p2ZRXv7Dk/Q07E8i6a5WXr3VlwfOCOgCPgCFwTAvn3Ja8vesdeU9fnNGM8QCY/z65sM7Aads1Z8MO20hlJRlJgN1zljLZopaJ9UJbT2gUf6FgkWriFtMbUA+kCEoZEDK2HV1sVsWBdaZTTsRSgY82bp4w3HfBVBneJujL+rJ7ha1a0UUmatDppxrUkiODnPoxrGYg34gjcCwSceLkXj9kn+SURoCA1gUnzV5Ivne4g9HqDcNgZhG1sZfQ//3kYfnrXD79/7Ic3O334IRfhalQCEYAYL7CwoALABRk9U1DyUI0g0wssn0WJIJUsE6py0zlf113vnK7OLfqUccy7R/P4zcNMiAnJBGD2oJjRbYsfeBKFh62lsFEP4dH6Uni81Qovnm+Gxw+a4ftvNiXOTuDqGFbDSMDoYWe9YruzOUm1O3uy3xNnZel4Ml/XbOOZedoRcAQcgZtF4Mu+Y0/9wU85lwwpFXmXAIQ6Cg+jRJiOl4fmSo25mbzzjAN9WPvWn5AukOvUsXrYQnpE62LsZDTkdtLIn0DvYjyXlSb0Kgj8JlyNtFtblEGLaNf0BPZ83rzZfzyvM0Z6drbMQfuwOWjlBMD0zmSUOOn40oJrSsT9XfVBXNMQvBlH4B4h4MTLPXrYPtUvg0BMvFA5YGT9PraU5jaHR92+uBv9j//cB/HSDb++74XX2z0QL9g9p1IGIVCED7ISLyqcVTnIZhILykiAWrZk8SIqy27Opa67Xq75hV5mY+dM7Ypn/eDbLpA3ghLWxaoYTqKQNatL4RkIlwet5fDtVjE82GyGZ083wiOcnz9d12eA+2mWzGb0yFK8tn6Twjt+st+LzdGu42lZWZznaUfAEXAEHIGrI2ASxN61N/B+RVfWG1NZWkdPYsHIh8v+0Q8VRw/cYEmbCUkNSaPASmOiIxKuc+Cj7mMtWh+0Z+XiSZAFLbUoDqEL/You3SOQLlxYsbu4I2ETli417mSEha10rKhh85SWkwsb95zBXG8WB3gjnRkSN9LZ9WLkrTkCXwkCTrx8JQ/Sp3E3EJAguQjWwp2NRvh0ekOxevkf/7Ebfn7bAfHSDb996CCOSDks41NaLoh7i7j/UjkQ6WzCk8pLLECzfJXi8fXdwEdHyTldZuz5erwnxsNa03xaq/A/Xg3BuBwOxkK6cCvplfpy+MPjYni6UQp/eFINm2uN8PjRSlhfbSDey5r6fUvr9gy07bvzPR+b+eO3ujzPO05jPK+W5zkCjoAj4AjkEbD3az6f1/E792bfsyQ1ZnoX8ZqNIUthmDMXs/M4RY4kxeRMMnIDkvicNmZbjK90hNYWz0KsQKeaTKdwJ8JORrKFNIiXMbeQnqp78BJis0H+lysgXupY1EJU3So+SWsCO8cj1zIuHdwnDTEerqcdAUfAEcgh4MRLDhC/dARUGM/TLVjyOaJYV2wYdX8ygfkrVmS6vRGsXgbhf/7nTvjl7VH4BVYvr7e7UCawZfESTGGXCuhxWXqF3YtaXGAIVDh4nE28aPn9+eZzYfjh08+HVAsJFxIv1PYI3RD+3vsIuneMhzGEwrbRLIQ/PyuH51vl8Kdn9bC+0giP4Ga02qqHh1urSrwA9JnW0dbM9a0GO//bTX5A6Qzs+iqTuDuzv8qsvK4j4Ag4AotDIP8utp7y7+BFvV/Rjwxhtn0hXkyxSIpiCadZaYEN+tSZi0vzjrTphHGZsXSRwmg8UXKmraQRQ4pUEbPYJfWpMUiXAXYy6kGvGkPHYkDdMnYhpLt2ERsWkGypw82IsV7K2Eo6JYnYSDIuFYkzs50Zwt2+MOTOAvhuz85H7wjcBQSceLkLT+kWjdFe2/khXf01Hrd09bvz/d+Ja04ZHyoLx9AU6HbUhw8yrV7+1399DK/fHyHGS1uIl96oEHpDfCbLiEeyBMuXEEpQDIogD8gfGMGQmcoaAjGuWT3VtKzO13rm3O23ZGfmKCYp8QIli2TLQW8sbl9U0LbWSuEfL+vh+4e18PfvVsLKSjWsrjRhllwNG6t16GRsJcHWIEZe1stdw9QmEY+bs5mXH9eJ03d39vEsPO0IOAKOwNeEgLzFk1e58Qn2bo/L0rc9XuVLCDa/BNNa5SCQkRZq0uQf3/pZm7OoxW2rLE5IGN4khUxkR0q+sFNKU/yTZFZlpi/u7hhniC6FLqhLMZbLKNnJqNsfCvEyBvFSg0tRFbHyathGuobdCctFWhFDlypCpktnSd/WZzJEle4yaCvRMUZXnnQEHAFH4KoIOPFyVcS8fiyPUzRmxWmafU7CBNrV7zyn0dtdxCnjQ2VBFQauzoxDD7Fe/uOfH8LbjyBePrTDm4/dcDRcDof95XA0wNaIU5jIIuo+FmuUfElivnCy1EPYbIYir+LDSvL5cZ2vM21OQRnxQpshKnZGvIyQxm4HCMb3EMTLf/9DM7x6XA9/e7kWVrDlZAvWLvVaBQQMiBfeJ3dLQgBLlUa5umtf9qs573dhvx3ObV69uPyuzd/H6wg4Ao7A14dA+qZOEkqSpLnphMk5KMkByZakZZc/1KCMFI4jOYvGYk3YOWtJUxEbw6QQF3JGMc/4LMPlh3ITSaT5jUPKkrTmSKYV6xgTCYQLXvMjLkY8w2pVdikEycKdjEY4D7CgxSC73MmoXmNMF5AuJGBg9VKELkXiRXcygpbArkE6pYf6dYvEy3KzVFrPE46AI+AIfAICTrx8Amj38ZZTsjYHwuXF0nktsRWWX7613DBu9SWVGaocNkPK+pH4IU/CT79uh52dTni7fRTe7XTDh8OT8A6fD4fT0AX5UisvhQpWaGpYrSlBaaBSRKyOuSUyUoqYZM7BwPA8qzy+hXUvUy++Z9Hps8Zk88r6p0IGVUwyUsUumQ8ctSR/egJfcFi8DBFclwrlcvE4PN6ogHhZCy+/aYV/f7khSloNChuDHJN8IcCsa0hLQ8nl6VFI6a36spHb+Xqe8XXOPBvZrQLOB+MIOAKOwB1BgG/RmSN9rWqJvLEhJHlFWUmdhG46PAtRIZa4ICxAZnBbZpIaJDhoUcL6PMsh96oUMYKE+Wx/GcQGD5Isy8skWpCGzrJEKxOUkfAooKyI+HVCwkAs2yKGtqX3Kz2jfbA9dm0x8rhYwmuIcZHlx7imqxGJlil3MzqmGzHvWUqJlwbIFwbX5fjYz+y4tU9VpHSO/E5yZ1Icix+OgCPgCHwqAk68fCpy9/A+FUfZxCmUZoVTVnZ2Kt+K1TQRd/UWrYXbfhbiBdKeCowJ/TEUBgbaff1mL+zsgXDZPQofQbz8vjcNv+5OwpvdaTjqnYQ6iJcafJLrUBxKUByoVPAQZQRnRe8sbKVq8mVPLc67TPqy991kPZ11PHpiq79KxsOxcsVleqIWL9MTje1ycszAeycgV06wixGJl/Xw3dMVWLxswiQZShpWx0pF+IVjdylpVVbCrE3tJu1Catzer/wvI6brsv+L87VsPjZnljNt9Szf6n3O2dr+nDb8XkfAEXAE7i8CKv84f1pyUNewc4wJSZSEVIEiMSVZgfMIsVGG41GYwGJkCkaDusmYZSBgSHRwO2YSHLQu0YC2JHBUBqglixIsBcRPAd8CYgXkChaLSLSUsGBE0qWEhYwi06hDEqQA690C6qkbMM5oTsgYClZOhnNAf5QOJFHoFjwZn6gbkQTSxTi5cxHGf4xaOi/OmcSPumXX65VQ5S5GkOncTpoLXjzoVhWgE2haTrkvl0k5QPzSEXAErgEBJ16uAcT70ARF0LwjkWHzis7IO6slq371Fu3O236mUsDDVneYphLDVZrt3YPQRpDdnb1e2N7vhR/f9sJ/vR6Ef77rhb32KDS5tXS5FFowl60gWBz0H6oZsgKVqgdQJKhw8Ei60jS+M1SzlBTKJVo49VhYEGfmr+XupOWbqWdzsjnGs7LR8KykQoYzR0e8+sCZK3YTrOqViydhDVtIw6glbLUCiJdq+PPLrfDsUTP88fmGKGjFAlbHoAkWsDJHAE/uIPESPxnFaDZHHr/8OmbztW7++7zfQL6uXzsCjoAj4AjcJAImI2dlN64StgEag+gGYt0CcmXCgLRCtDBGyiQM4aJDy5Eh3XYgL0nGkHTRbZpByKADXpMMEfJFBCPEI8kTiAeSKGLVgjNJFaxbCPHCXYR4TQvSEtIlECDq9oM0rmn9QusYqDZCmCyJS5KIXV1cgniiDKcb0QgWLYMBtooGCSNjRt6E5EyyGIVgNbAOJtGDALogfhhMl65GdehOJHyUxsFTob6UWMGqHLzJJ+V9OQKOwH1FwImX+/rkP3He8Z9nny6srJX8H3Kf3uInTudGb1PiRc1crWMSAVxZ2j/ohF63H3YO+2FvfxD+9aYT/s/v3fCfOO8ejWDxosRLE2cqFSRs5D9TNkiSAD4jJaiAEeUMUcXaVqjYv5TJV1JZBqX1tJQtXHRY/Xn1tExb0XRcm+lshNZXXMPa1JoyoWSeWpLkW7XkbLmCBS6IBZW2LhQ0KotUIkm4PGgtYRVsKTxZXw5b69Xwh++3wpMHjfDDsw1R2grQAkmScTWPYN1V4oV4zKJrV4a+IUYArSy+KwE2/TVZHcvnOW4jzve0I+AIOAKOwE0gQMsPHrq4o+9p6hjMpgykJcsxPn1YiSjBMkY8FO4GhJ2AkNcnocEgtSQ4SLzgTIsYtjGltQnbouUL5KgQL+hC3/waN4ULFUXITXAocOFFTDroKrKNM2OrILMKK5cyA9zCerfB3YWwa0CtqLFXSiBFiiXeT/JGiZtlLnrgkPHDrYhjHQynoY0NCTjedn+EHYzgYoQxcSR0Z2J/rTotVguhBSuXRkOJlwaJF+5SgEN0MQyc+0XqnZLtX46AI+AILBwBJ14WDvFt7EAFso7s/D+Y8jXt+vy75s05L97y17zn01uf1+NtyzOLF47LrF6gx4hS0+v3sdqEraXbg7CPz//9vRP+98+H4T9+OwofQMSUimUoLlAmCiVZVaK6wGegnACuoAjpSo7Ze+js9TnxW7E9RbxItU/B3e7Rfs77jmtmI9E7dHzn3X1embVmrWhP9A1XAopYUEGEHzjw6fXhYiRVpmG1vhxePCqE9UYhfI+Auhur1fD88WrY2miGbx6vy+qc9ZyOH91YT1Z2W8/pmHMD1PFbKa8srRV5pXXisjgdN6g14xxPOwKOgCPgCHwZBGTXH3St2gC+QbYwnpks8IBAIXFBK5Eedv3pId0djMNRdxyGIFW6Q5AvIF50dyC68zA4rd7LsGl0R2J7au2iljMqLyhxE50GpIfEc+GCBVyLSMCYqxHzKyBXquLGC1Kkhq2dQY40YY3SqmNhCYQMy8qy7TMsY2AVQ+KGuhItdkj6dDpjGfNBZ4hxj2RR6gh5E7gdQcQHujmVQK5srZRB7BTC5kolrDQZp00tXuhqJCIvEV1s2wX4sogAAEAASURBVKXYl/mteq+OwH1FwImXe/nksz+vvtz0OQYe90ns6ZypIBjxIsQAlBmSLuMxlKDOQBSKH1+3w3/+ehD+49fD8OFgAMWpCMWCSgMUEfglq0ku/KixMkQElXhRnUIx1b4UXakhaJ8mXuw5SPEVnkf+Prv/9DmumY1E6+n44nvyObx7Xl58D9NZy3nihYthDBbIrbsl7u7ScdhsFcIP35TDRqsU/vC0EdZb1fDo0WpYW2mExw9XlLhJBm7jVzIn3+/tvLYxnx6dliiiGWan61mOYT+vRSuzul/n2WZ+P2b7dT5Dn5Uj8DUiQF0iO3CRXItegUJaxlKvIIkyhNtQj5Yi0DUOYUXbxW6KR71xOMBCTw+kSxvlXViT0BoGfAtkplrISIwVClFxV1KCg/2I523SuUgSfJ0kQlIWhOguRMsXIV/oigRrllIQsqUKcmStDtIFVi+r9VJYa5bhCgQCBtYpjSqC2uNTAQlDIoauvpwnrVqO2sPQAeHy8WgYDjCHj3uDsHcIqx24Hy2B6CmC3CHx8uxhLaxhUYWWrButSmjAAqYJciclXjhujNf0MH+3Z78iTzkCjsBiEXDiZbH43tLWKZ2/lKhh3/njS40lP45FXefnzPlmeTTZnUBxoN91bwDFCOazv71vh5/fHYUffz8IH2HxctgNiAGDT58KVJBgcVwNYsBdmvCKOTHaoUUHlRTVfxKrmKi3jHjJUrOzvuyz4Phn58F27O5sdpab5WQp1o/bsLt5jx3WT9I2qpjlEBW/tC3kp3enDIlaAk2xTEcT7PEExAvuKBaOw6P1SvjLdy1ZGfvTixVsH10O62vNUK9Xw/pqXTo3HO1sI7rN5xQPDJJ4UAHnQZwtLRnJl2KmNeP8+WmrF5/n1/yacvOYfk1z87k4Ao7AHUXAXkx4HcvCCwQVSRKJGycuRSfiLtSHwkD3IRIt4qIDsmUfFiMdWIB2YO2yByJjQNJlgA/OQwSvpYWouBexD7S5RNIFeSJHkEcJQNEiZ/mSS5U2rMpinIXYIPFCSxhc09OnAneiKixT1hq0QqHlSwkLHwnxQoIEsrgJX+B6RS1VKiBhuLxEi5c2rFvaGO8OdKIdEC/vtuGefTDCosoUa1IM4kuLmkL47kkjbDSL4Slit23CmrUOcqeFdul+xIFFKgJnovOQlH85Ao6AI7BYBJx4WSy+t7R1isVEWt74CNl3/vhSY8mPY1HXs3PO/yEvpAmUCpIDQ24vDUXp7XY7/P6uE359q8TLh4NjmNUeQ+HAShVWpxgorgwlogHlhMHs2AbbZfwSebrJchT0HDlsBBndkqVmZ33ZZ2EtZndnd+b/xM9KWDt/Z1aapbJWZTZyKaX4MuJlpiEJlMdqqECtCh/AmSikjO0C8mU6gfKHwHulY1HI/vZqLWyuVcO/fbcqq2HNRh2EVgmKX1X6I57xkSprceYtS+eGjNGdzomHLJjGGVdKf97dV+rqC1aOEbwfM/6CYHvXjoAjcDYC8csoqkU3INnZB3oAd/5h4FkGxh3Alag7gM6A8yGC9DMmSgcEzN4hFngGU1i6IE5KBzoHFn7Aw4QeSBcGr2U8GI3hwjeeEi/LCfEi78DkRRi/D21oGIKIYTShRjKoRNlJ4gW8CMgRBLVHolFBfBd8mnAJWgEJ04R1CwkSLoKs4kP3oBVcN7ErkcZ6OUnIo4noQTuYw+sP2Ixgj8QRiRe2ra5Mf3jeChtwN3q+VQ0P1mpoF9Y0sKr5ksSL4RNjFj3Ce5A0BDjV+4vCPXjQPsULEHDi5QKAvs7iRDLKH2Vf+gUYv4wN7S89JhvHdZ1n56jrRtY2CRPk0CYXB3c4ouK0h2C7u/vt8P5jGwF3B7B+6YU326Pwy/shCJgpzHAZKM6IF94LU1t8m483WRh9ysjFv9kRsKfTeWejHpecbikutVazWrOl2vO8b9bL7mKN+Gq2Fb3KiBCtaWbD4oeOrBHJFmFPTrDidhyqy2P4mJ+ETa6EwRT5r6+2ENulHv6EoLrc9YDbXHLnhRICAOYPNpP1ly+9PdcxZjaqWWQTrKxw5jxbc6bowovZJ3Rh9U+qwLHfRD+zgzNMb77n2XH4lSPgCNwjBEToYL72AppN4ip5I9El6JjuQRr8dgBypYsPXWuPOiO4EiEeClgVWom0ce4itstBG+45Yt3CuC/UORjkfykgGcZcxGFfSb9cwxHiBP2RPFE2hXm6eGPvRRItSrrQ0lSHbW2IlxJaZTu0eikvnyDQLmO+4MMFJFi+VGC9SwuYFbgbrcE9qAVXpDWkN0GcVBjvBTd2e7DcwWcPli87e8Pw20fuAjnEXKH/oF22VYUs/zOsWLfWQLxs1sOD9VniReaAsfrhCDgCjsCXQMCJly+B+hfvM5GoMg4Tmzc1KOs77tfybAxxmeXd9XM2x4x40TzRr5K/6mmxwtWrw3YvHB11w8fdjvhg/4ztpV9vD8JPOG/Dp3kZQXbxJW5G3HWHygTpF/pRE73U5oRdICPrPcMxn3c26nFJ/q5U/csalv5nLq92kXSR9pR0n41CUwlkadu8FoUPN5JwGYDAoqWLWLmAS1lvYDcjKG9PN0rhMRSyP363GVZXquHVt9w+msQLgwEyfg6WzqLDlMd8f1GVW5NMMTtzRPbrY03ieN4dGeLn12NncV1eX9dh42R7cfq62r+4nS/T68Xj8hqOgCNwDxCwFxDPyaFJSHmSLjBPESsX7EA0YOBcxHE57MKViPFb4FK0D5ecNtJcwOkkVi5HIC9ItsAIVBZ7wNlgq+glkC64RuMi8/BKpzYh/yFNHcPe8nJmng0IZ0oWEi/c/U8WP5iXjBmheCUmzBIIFxjoaiwW1JPAu3Q94m5GtIQB+cIdiegWtILzGgLj0mKFhEoF1jGjIeT66CQcwkpnHxYv7yzGC0ikAmQ765G8+SOJF1i8PHuggfM1xosG7iWRlE4kGr8nHQFHwBG4CQSceLkJlL2PcxC4D1Iw0T5SFEx9ifM1j4QB3YYGMA/uY9WKwXYPu4Pw+/tOePexG3580w4fsNLTHi4FWNfChLgggfCq2BaZOwbUQCCQfKHZMS1p1OKDSlGEM7vCpfaejUVTlmv5NujkpuRSa+X1F7snKhUtx9qwm+N61m5KFWFgWk5FTo5cG2bZYmfORGrii4oeyasJ4+XAbesYK4EFxHRpVZfDy0e1sAprl5dP6uHhRiM8e7KCnQ8q4cmjlWT7Su1Pto/ODfkuXBrqNlbOJs7T2bE0zuV1vuan5GWt8+7rOWyc1javLX09PXgrjoAj4AjcagTw2jv15sNrUFyUIedIvPRlm+Ux4sMxYC7IFpASRyBf9hCEltYhdC/aOYB7EQiKPtiVbl/1jAArFyFH0AFJE5IuaE4lBPrAko5YqUiQXIBkiw/2FtZlHl5xhGhDPkjLP8h0npM083i/LBCBxeHyBgPvktCRzQKQUaXVi7gfweXIiBfEaamCUKlhtyOIcyGMuthCmrsZfcQ8D7sgkeAiVRRyBtYziBnzh2+a4QGIF1q2rsN6RnY1gusSd0qSAdlEOGg/HAFHwBG4QQSceLlBsL2reQhAGn/1f0xxjnaYysJrm3tWLitGuOROBEOYCtNkmMrUb++PwvsdBNx9fSSR/Hc6IXSw+gNvJMSFwU4BWOUpY8WnJbsALCWxXqhcaT8x8UKdQ5Uhlul4GHpVDz2rQpVkyYn1rE6WimejtWfrzX200kyuHtpOW0/K9Rr1UuJF7yHhwpS1LelkdJyXBBfExLl7AxCA3/eJBPL783ME3EMQvz8/Ww0biO3yYLMhuydsgYShixH+oUn8dweVshQ7fQgCDfMUMc1UnOKaMXJxzaSR9HRRPStPb7jGhM2CTcZj5/Ui+2X7fjgCjoAj8IURwGtPxDiGYW88LqhM6VoEK5cxiJTDTh8WLiBZQERwtx9at9CdaJ9uRrBy6eNzCKsXLtbQqmU4Rktol+0V8W3t2kyzRQ/NyZdbPearuCSBQxnOnOw9naWSO6IMuippfd4zlTtJwJTpglRekhgwLZAlm6tFkC50RyqnfU0mS+Iq1UaMmgmsdjiTGuKz0WWpCfek5yBcVmEt8xC7GjVwXYEbMXdIKjLIjIzvrBmxLT8cAUfAEVgcAk68LA7bBbZs0suFxwJBvsam7XlZk+c/N6lNBgHHCKbDAzAr7z4iyC7cjn55vQ/ipR9+3RmHXaz0vN5GcLzBSWhgNaiKT6uswXap0rCFpBmkTXVTZctGxHJL66hmr2QQ8sVSK8tyZ1tj/lXrWf182zoaaS+ZBGtQyeMKGQ+bm2Qijyt/JK7gYCS7MkxAXi0jtku9cgJz5Ur4f/6wAb/vavj7d+thFatgNVi7MK5LtcrgfVzZsz4ZLwdp/BPXLcuOh2h5OpQv/p0NzVKnlWkdpJXHQ+Zk5uXHdZg+q94iweC4rP38GC0/P06/dgQcAUfg60HA5DQXSLiYQunOhZkhLF1o7XLQ7sOlaBC2D7C9MixbthnPBVYhe0djbA+NOC5gWxhIVy1aliEj+VbFYgNeoWXIPV3MyL9rdTnm7LfsWSX593T2HLREZRPJI15zbhP4JvFqGR9yI0W4BldJvoBwWWuRNIE1L3QbXKo7MMZM16gJPoUljc222oBliwTRLYJwqYnLEndLqtHSJYndxo0IVNadNfZsrJ5yBBwBR2ARCDjxsghUF94mxZULjoXDfG0dqLqhzV383OKny2C7Q5Av7z/uI3p/BzsdHeDcCz9/xKoW/Jx/28aWkL1jrOhAucCnAeXE4rzQUkS4BHxRqUl7Rgeq8qjSc9lpxrOwe1SFmlfCGuzRyuK03Z2dtVZWNx0rq2TZ0qLMidnIt6ITKmHHE+RhJRA308986WQawEWFleZSeLBaCf/4AcQLYrr87bsNKGhl2RWKli7FUgk4waiads9okWhREV0GwyNnG4x1xjHxsHy9+uLfOjwbpD6Z+YOyOnGpTcbKeG1pq5evw3yrZ2VW9zrO7D9u96zxXEdf3oYj4Ag4ArcJAXv/QVonrz7ZuYhx4JDR66k1bAduyTv7fZAs2F55pxd2Ye2ydwg3ZRAvh+0JdjjCYgTup/styYokDL/IvCJkXAlyzxYz4rctKqgEyL92rwJR1CANV+OmOCculJCAmYBNYjw25jEmWwGxYEogSbj1dKuO2C1gYyRwLq7LiAVThstQEeYxdB2qVyjLi2F9taa7IDUrsGwtYcckWMnQ2gVsjcSSQX0R8Ri/bmgQDe4qc/K6joAj4Ah8BgJOvHwGeH6rI3A1BGK1IxP6zOWVlOKLygfJBX4Yr2QMc+LtvTZWtLrhw3ZP/LbfYLejA+xY8PpjH77ckzCcLochzG97owLuR7A6xHzh6k61UBQ3GlWhpAcJoUIlyNyaMnUoHUU0LebpIXdzcNloZYxWfpmz3p20mTWNuaNEO5D2WWTUAU2SeYhFCpVBzE8IFihrdCsaQ2GjvzstXAheoRwQ6yYggG4FZsql8A0C6a6DePnu8YoE7Xu6tQKlrgAFVOknriByIkUCDu6Fi2IFKHpFuh/hogSrGDl0GMldyXi05FZ8J8NLxxLBm+ZpwmqyBtNxzXwZ77B6TOcPK4vbyNe5yrX1z3vybcdlVn6Vtr2uI+AIOAJ3BAGRtTpWSirKPBIvjGdC96IjuBe14V60S0sX7OyzC0uXtzv9cNAF6QKdoNtnLBdKORGLOFNu2txtUYE7FbHEpK2V84z6LJN77F2s+YmwTirPL4OYliNrOek8aU/nRDcqzo1nHDJAjgcymLIYohceRqLLcBGlSgIG5EsdMdto4dusYevpZhVWLXQrqocWLFnXcM2YLhW4XtdQT3cr1MD5OiR0RlmPIxmipP3LEXAEHIGbQMCJl5tA2ftwBAQBahwU9XZWWHiVHryg8gHFgLrBGBYvEyhZe4ddUbQ+7iLYLnYqeI8gL20Ez3v7EWdsEXnUR8yX0ZLEfJkew98ZprklKClV7tZDJkH6THrBJXcdYFbWN1NUkagOxQfvzQ4qf9kcZJhZ4SVS1otV5Ry1SVW+bEQcCf+TAydJyZkj1HvGiLRHwmUMckqIl+lQMKtUAgLsLYdXTxvh0XolfPuoKcrY0wdN4FJCfBfEdIGFy0iC7+r97EfcjXgWsoUra7qFJVfX0gOdEyEhgdLML58gJvGRIBdnRem4dr5mXMZbWG55cTpqzp5TnPXJ6Xl9nTXGfP4nd+o3OgKOgCNwuxAQwcghQb4hLTIOO/UNEaiFW0DvHXbEumX7oB8+7PbDR5Av/BzBAraLmC4jrEMw/lsB5iy03qRVy1y5msxaJWsMwemcWXkQ1z0/nfAcWsle8TgzKZdJBU6Z8pV5iaQXYmh5CbsWFbgrIa1gEMuuCksYWLNstGphC25FK3AzevywEdZWanBNqoGU4cIJFlNQl1atlNeZtEDrSX9Z3vnj91JHwBFwBK4LASdergvJG21HxdKNdumdLQyBVL9KesD/lGqNAvVjAlKBJsKHCJLHXY72sLrV7SK+y0E39LDi9RHRdbsImvfh4CTsdE/C670xyBpYfMD8loHkajTJhcZFPYNKBn85PNI+k3wrMUUkLdfq6bflq94St5hUsQbSO6IEOrf+mZuvquqW1TelD37f3FIBla0+zaY5jimIFw2kizuRMZ2OYKlyglWwZRAtxfCPV+sgXRrh1fNmaCGeS6vZENPkBmK6UIltA0diOxwOcX+CEfzF6XZUhuJWrSJuDvCrIWAfj3TsCQi3iXxJx5aMMlYzDdHZs91hqFqp5dt1fGZdlts5LmM631a+/Lzri/o9714vcwQcAUfg60NAFjoo22jRKaQLLVmwe9EAMn+3i52KBuE9SJcPiOnCHYy290cox2ICFl8mCOhyjLNYb8LHRggYQoTXdCLCkje2vXvtbDiaDLbr5OZMEsYFubTJCD2nkiHJznqyEjtTwqBU/mnMthOsEtFBivHaSnBBEvejWgFkSzk82Khih0KSLeXwHBatm6skX6pwL4KlL9QGWruYxawoQWhXZpUQL7lB+6Uj4Ag4AgtHwImXhUO8iA4otjJBtYgevM1FIjD7/EwJSntEBokB8X0GMUCLjjasXPpQuA5wpuLVxTbTDLp7iFWv/gAxYA6xXeThcfgdW00PYIq8vKx7FRSgYDDmi60h4Uq7wc+HqSWSMjxT00mUEV6rZYtWjcdn6TP1lnk/S6uMLmJyRfvVPvhNVHik5yRBUsQO3k9cWItjlnGzEFWKxSlWuJZgelyQHQ3+hiC632zVw3dPGjA9LsPfuwoXIw1CjA0hYKYN4gXK7IC7HxFvCVqoo6LPOLelrMJPnAH7mGvjMq31/hEvBPq8I3tO59U6XZYie7pIcj613TOa82xHwBFwBG4hAnwT2tuOMpiiThcVaMFC16GRWLq2Ed/lPeK5fIC1y7vtgVi+HGB75T3oARBniOWCMLWQaWytILFNcI5iuYDSQElCz5tQn4PHRW/mObfMzbI5yeQ4p7RWIsEj3UNKUUGsfAQDxn4h8YLAu7B8oQFqE65Eq7B42YRF62OQL6vYOvrFk1XEcquHjfWGkDO0dCnB4jfujd3KWJL+bBzp+NJxecIRcAQcgcUg4MTLYnD1Vh2BcxCIxb0qVyb4VdHSQHhjrnDB1Yj+3D24Ew0Qw4TWLSPsYqB//HMlbCLbSn7ATke72Ery9ccuzIwRVK8bsNsBLGXg6z0YoQ9YcZyIsgGzW/xH9xkGr+OOBhJwDmcW2zhMWTGdbIaIiWcWTyXOT9PaaNYuU3aTVpKypH8pEQKEFi2cH7BIzlPgQR93rOXJmJuIY8M5NOvcShsBdLfK4tv9AObGLQTYe/YASthKJTyA73cFLkYVBh4G0VRYRhwctEPiim1z9ZB+8wPs/kCsBzDlJumy1mR7pbCC1TRiw7HJ6BNQbhPxksJ9I4nZ55d1KU8yu7x06qz22MCntnnpzr2iI+AIOAK3AgGTtzYYBpzlIgzdi/oIonvQRvBcWL0eIpDuGxAv7yH334J4afemkGNT6Afc7QcEBeQi35x0LwogYSjAKMOyQ9+5rKU1Kd+Ql38Vz9yT3X1x6qIbs46ylLaaLA0J4cQyjQFjiyxwOUIFLojQsnVjBTsYrZVFVr/8Zi083ITrEYLsMni+7liIwPkzgyUOszkzxX7hCDgCjsCCEXDiZcEAe/OOwHwEMnWDypYpA0yT5BhB0RoiqC5JAAbXJYnCmCR9kAUMuGs+zGX4MFNb+oiYL3uHPcR8aYcOLGLe7iHuC5SxD3tUyhCEFi40x1DA+GFfdKEpYXeASrIKRvJF1BF+ZUNLkxnxghUqDtIOS87TZUzBQZkVU33iYZYqlp9kykkwQAqeRKJEggtJgwyTeCkuk3g5CVsNWKMgeN4azg24Az3/phWaMD9+iGC6TVipbKzgLMQJLF1gckxFjD0zvgvbGQFXuhr1iTU+XQQlZLqP3SAadRAvIG2aUODWVquiwNLwhuOdGbOM2L/0R/OpyNiPKI/jp7aXb8evHQFHwBG4/QjMiFZcUE7R3biPxQFau2zDwnUbMp3kyzsE0v0IF6P3u0MszFCeYbEF1i6Mns/4brR0FeIFWSRXhFiZA8H5xIu9g/mOZvqsdzUbjsvtPubPO7L2LGW14jtZJh9gQasXzoHBgLmzUR3Eyypk/xYC6K/A+uXVM1i8rMH1iEF2sfBSrZawgFKWxZasbbRueoll+tkRcAQcgRtEwImXGwT7+rrKi6rra/m2t8SZ84iFs+bcve+YwDDihVoGV7j6Q6xgwbKFcV1IuIAjEJ9tWsFQ+VhvIZI/YpCsQMHg6tbO9gF2PeqFdx8OEWh3GH58A/9vmB3/8gHbTCKWyXhSDhOQLnSxoaJVhQWIEC+4l8pZugU1BzBzEGn7vSWqGy6lVr5qfJ88IHzpP5RIhp5y96U+2Lwf1YR4EUWLiidW+7gdJsiXEeY+xQQqBW4TfRKermP+IFwew9JlBVYvf/puM6zAv/vhgxaULrgW4UPLHroNUdeCyqYj5PjxoZsRrWqGsHahG9f+YV+U2w5MtutQ5LawE1ITOyRswHwZRjJwy9LhJzORtuwZps9Pe7iH33yoM8jkrudBkvshnKoSt3eqMMqwdi5bP7rVk46AI+AI3AIEKF0pl5QIUdk3heAbTSZwNYaLEXYweou4Lm92e2FnrwfSBUTMAT5wL55gR8Pj42VZSFjGIgvdbBjbjRae0ijmpy66nCjfk/bOzN7aWQ7r8Jitp3nzvs+rx7L4ON1LXKq1rT294oIHcZmSeMGH+gL1FYSxS+K5Id5LsxRePm2FRyBeHqxXw9ZaXdyL17D4wp0JefCeRBHIJh137mlHwBFwBG4AASdebgBk7+L6EaD4VnF6/W3fVIv2Rzv7sz/coVcI2UD3F1qu9PCR3Xcw2xMEykOp6A6M4s/grwwwx6B5+/ud0OlAGdvtyD2/fOgiHsw4/L4zCPsgXrrDZXGnGcF1iSQGrT6kTwApOCZxVFA0c9i4bJFI6uKLipAcyX1CaUhhkp/UsefEdniP3cZa2TUVTi3RfnQU1JfwgsIX3KTkPyhPsHRplI/FtegpTIxbIFweriFwLoiS756ugigph/WNpgTX43aT3Da6CPJF+sdMOUT2pDFd6MoExRauWMT7AFtDdWHtcgS8uB3lBvzGGRtmHa5L3OmI5Iu4KhkYnIQfCQJENf4B5K/nAWV1eI4Pe0pxe3G5px0BR8ARuNsIyFsvkXv26hRxJ68/bhlNa1dYZGLlgYsq+4jv9jvk+m/4fDzoIZguXIo7U/locHjIKMhj3cEIsg5ySt6g0hFlavY+NcvTWYm8aDxlYnM7EZGaFwNJTcvGGomQR6CkRIYXoQuU4GJMlyNavn77qBoeYbHk4UY9fPu4KdtMP9hAQH1ZeEFwYeIBXYm6huk1cwfjmY6AI+AILBABJ14WCK437QgoAlQdMqWHebq6ZQoAyhLtQoLqQsPg6tZRG25CIAGGUL7KZboGFUJVXGawWxGIAW6rSHNaruj0UX8AH/B2dyBEzUesgjHWy/Yht58eidI2gBUNrTn6aK8L02TGNGEeg/eOJnS/oX84SQ6Mh0oKTiRUqMgtkXjgtRbJbESxkzr4wpF962So6uk8dXpUeKhYkvih7zrTJJrok06lirXYZxGKVQFKVR1KFbglKFUgUGBa3MROBlW6Fq0h6C3m/ghB9FqY/3qrAoKkgB0NaojlgqC4IKXoVsRggiSlCuJixAHqCLlqxtFSWVVTblgWMS4OYuR0oeQetscgbgogddAPzg30Ueb23CBwuFOUuixxvMnBiSRtaw9WcJ/PfKB5NOQh50DJ18kV+6Uj4Ag4Al8pAhQdcsCKg4eIEkpNXI5h6TKAXOICzA6Ctu0fjsKvHzogXjriYnTYnYobcReuxNyJT+Qdz/JKVfmtjfJbZZ5c40tkvFzYAKzkOs7xOz1un/nxtfXFsSJ/XlF0B8esugJDAyPYLupLvJcqd3FcDs8fVMImFkseYXvp779ZwcJJNTzZwiIM5PcyVk2KqCxWL1m3lvKzI+AIOAI3hoATLzcGtXd0PQhQOseC/XpavelWSEjIIdNJ5oO0BJQFEXKE1a3Ddl+sL7ibQR3KA0mAGtxnuJ1iFQRDkTFacCYxMhrBHQkuSQNYbkzgjsPtpwfIO2iPQgc7IGxjhYxuSwfIZxC+XayWdfqoJ8ROALkDNyToZtyGUpUytYgRFxtR6kjAKPGi5AuUJU4AhAOVGSpB6WNBmoYwJFOEbGE9pHWXAjvT8katYEh+sIw9s2KlcAIy6SSslJdCAwrVGuK1MKbNOixcGLtlC7sYkBh5uN4E4VKCnzcw4S5E9QpckJYTTKhkYUgkXmD1IhY1JEfYBZQ8VXhBvCDBoL2M7XIE4qXHnaJATtGHvEbCBfcS9wqC+ZX5AfnCvnmwKTYkODCdkC9M+nEeAoJcUiFF77wbvMwRcAQcga8OAZVDmJa8BimXKE+4IEAXWFi9Iu4YF2DewbVoF9tF//KuHX5DAH3GdukPAhZO8IHsKmAXwxKIBcors+YQacr2VFCl2Onb1967vLI0q2hpWnkmcdl6Mzedc8H2kv7PIV6sASKjOgS21j6ByzUwIl6lUggVyOuHcD0m8bK1Vgt/ebGKMwLsP6KOwIWYIhavSuKWLe3lp22d+NkRcAQcgQUj4MTLggH+tOZdKnwabrflroueH8vlb/Z0wFQqqIQxcC6D6R0geN4BFK4jKFs0N16BVQcDxa1ihx26wZB0IRFSFEUra0sUExAZfWhkEwnGSzIGW09uHyF47DDs7HdBxNAFaSimyh+wisaAfL0h9goCEYIFNhkH1pJEgVtG+yQwuLLElTQlYtT6xSxeqD7JXklIMM15oCkoj0wrxcRxMZ/XJHhk5QpnkjPcmYkWMOK7jfvrRVj44LMupEohPMLOBdw+8slWDYFuK+HRg6ZY+mzC4qUGR+96vSbWQFzxI/fB8fJgX3LmV0KKcMzc0UiUU6bZPwZEs+5D4D0YHIOQGkl1XUVckpUyxtOpg+SipRFdvNJ2kWA/goXk+pciQOyTBzEXEn02WnRevbk3e6Yj4Ag4AnceAXsL6huQcV0gFHFwAaYHOcS4YzvYMvr3bQTPx/n1NraPRjDdvSO6IC9J7DeIeXGjYUyXImUgztIuvkwGSqOUgdZhlpCi6/tKO0CT57/XreZMLbuwwmhgzOJ8KLOJErFi3BfqJlhvQUDdZcR6gwXsSin85bsVWL5Uw4vHjbDWqomeUAUBw4UTm7rJ7QuGGY3Ak46AI+AIfD4CTrx8PoYLaIEixiTQApq/003eBWwuM0atY4oR9S3wLQiSBxIAFhhHcHdpgyg5goUKiZjVhHhZg9VHg1YuYEGoXtGaI+EU5KmyVX5Gw5EEop2AUOgjvXfQgXsR4phAeeP53a7GftlFAF5uN41uUJ8EjMaA4XiEDEJb7MkC4EK/SQ4SKizRcsu1M8fAQ2kXTbMylSYrS3JxDbNhlJHUoWtQowyrF3AbqyReaOECRYq7Fj3cqCGIXjlsYteiSgnxbVYasD6BJRCsgBhMUEaDdmyFL98PgWIeXnoctNTnNckuYs5djeh61QVRJfMXJQ/3AAtuL01rG+52VMfHjrgPNOlHioAgnV7NTxh6jtx8fDzXEXAEvmYEsjegptT6U4mXDtyHd7FQ8n4HcV2EeOFORgNYu0COdybQFWClitugMuiiCJY/GGhfhBtBg/zCEgP+S3qZIV4M1WwEpyWz1YnPbN/uifMtPa9s9v0+rwbvllpWNa0U94dM/MN0E/IFi1QU1BD4jN9SqyxJzLeVZjH89UUT+kI1fPeoIYF2aQ3bbNbnEy9p50z44Qg4Ao7AYhFw4mWx+F6x9VTa4D6TQFdswqvfAgT4HOc9v+T50g8nKqbJLHfsIcHCLaS5lXS/PxGChDsb0VpkFXFLGlixWYfFRw0uLykRklh52C+HnAKPCfzDSXrQmoNkThdKHLdPll2S0P4e45nAvOWAZANivbTR35j15KxbWYv1DaxtzP2JbVsMmgnNfKHz8JrkERW8TCFTZY8uSLQEoWWOEEWShusPr/GhaTTzNW4K3KfgwlNE7JYG5lfBylQLWzm3aOWDALp0JaJCxXgvTShRJJyqKKOVC4Pn8aw7HmAcBoaBHGEt4KCCkDRSkebLUHQxd7ppCUbitsWtvGkJcwzXrGME6qNLU0m2qWzB6ogHuznVtJT41+URcBQvj5XXdAQcga8FAVl0gUzMDshRyCHKVnMXfr/XDm8RTPfnd0cis0m67LWxyxHiukCK4kNaBTKVKcrX2eakLBKIUh9f0WHCkjdaOio+lbxMPWsnHsyphpIMjh/1UXWmtjUR3RaXU9+gXjQ9xs6EVExQCG9k6ATLEmz3L89r4QF0JRIvTx42QwsB8jewUMNFGrF0QbuKPzFM+o47kFz/cgQcAUfg+hFw4uX6Mf2MFmNp41LgM4D8wrfyOc57fsnzlWItZ5IKxBAkCIPoDkGK8I//EQgX+m4PQchQL1lpVCWeyRp8mEm8ZIe2M6O/oZC79fBG6iRcRSOZQ2VOLGrQTxcO4twtgdYv7LcL9yPueMRVtiHy+yAhxiBtGHSWxMsYVjFqjUNlhxYiGhx3jDT+cRIcpnw4Io5HrFjwxdUorsQxCDCD5JJwKSJODQmXEoiWGogTBg+uIUBuGb7YtCqhRQtdq2r40M2ojAC5FWhWGmOFwfI0yK3sMoQ0+2P/8aHkCnIUoqiIqmp2SJBdADUm4YI5a9Bh+tcjEDGwYEBiunitYQvvlZWKWB8Z3nE7WYu3MUV0btto7YndtnHdxufnY3IEHIGvCYFZ4iWR1ZA/3D6aCzF0M3oHa5ffEUz3pzdH2NlohDgvuotRF4sBdIVdxodyTigYWH6IC62BhNervWGzt3/+Xas1snK7+YbP0bBkRPjKsnSMcp0IXuYAKqgd0Gl4AflNVytuMd2oh/CHJyBeoCs9f1QPzx4j0O5qPWwiJhx1CtVUeA8+wI+Hti1J/3IEHAFHYKEIOPGyUHiv2rgKGL0rEztXbcXr31IEyILIgWebPF7mYNEmdODeMoSbC3cxGI1AekCpILnBQlqONGHxQhKCBAAtQ3hQcSNpwCP9tSRNk3ahjpLmSy3RT5ToGTF2DIgdEAsMyksihuTMUacfBrB6YUBeEkB0v+E4+iSDxicShHYKTYc7IR2Liw4tYvDBHKjIcD7sE1yIWLPQdYikS0lIFihGIFwqIFEYBLcK96ESyJQmLFtqFbjwYH4MGtyQYLklBLflltn4oIw7E1kcFe5OoEeEJzJIYNlhdXmd5VrpLC6miImZNwikEebHoIYHsArieR87QzHGziaUt1UQL2urVZmfkS9s1XC2tsyiJqZ4rE42iptM2ZO5yT6vu6+vYQ7XjYm35wg4AncRASFeUslB2czFDboGD/HBTkZwM/p9uwdrl0748W0bcd8gn7EbYR+fIRZCCtypB8QB45vooZJO35KUNpnEoVzKrljbrvQevf/6vy/di1XEEGRE+LIsOyteOkaqPbymzKfYN1mM9RroRyE82ypjt8NSeLpVwQ5Ha+HxVh2fVmhBj2L7SlqxjYR4YSfWkXbh346AI+AILAQBJ14WAqs36gjMQUA0iiQ/EfK0FqEFCXcxonWJEC8gOUicUEPgTshcyanBzYhBXbnFcRlWInZQ6eBhOoN1YWdTSKycdXnPGDsmTLBkJGcqe9wNCedeH7shgQASixcSLpIPRQ9jUlcodT2agBkSFySMPSVepG2uumE8iaWLbOfMNMkXfuBCxB2DSMToFtmweJHtmgsSvLYM65cqlq3K0KDKZexWBCKmhDTvFzIDjc9sCckJJYfN2a7jOVvevLORJeJGBXDoYkVrlwMQLrQI2j8YCBm0BvKFBNgKyC+Oh8/FXKisL2sru7ZU1vPpnKxscSmi82V6XtycvGVHwBFwBO4mArPEi5IIXAzpILZbF3L4wx62jv5I4qUb/gXy5bBDV+ETWGSCoMEiSAnutiVafqbqQOK2o9QCQMm/72MJaWUmF+KyGM/L1uM9rEuCx8YxO4KsB61n9ePemLZ6tniRjiBRdmTEyOQl7G61PtJ0vy5ge0XK5ifY4Wilwbhw5fDH5+twN2qEZw9bYQ1bTAvpQtkNXeJEtgVIRmAd5Qfk146AI+AIXCMCTrxcI5jelCNwLgKZRpFW4x/7dG+RP/JhZdKn1QtiixgxUSUBQSsRWH1wG+Mq3Iw0gF7ShGghWdq6oA6keormUKegFUiiu4BkQewYWqrgwzFwO2oSKXRxooXLeIprmj3TRQl1xlD0JOYL6sh9tHhB08eoI2d+JUqQ6S+6yxBIFxIvVHSoJJKwAPECnTGJ7UL3IebT7QiKpJyx7SMYJ/ngepkfuhPxP52ITNj6SefMOUqJKW82d8tNCq1OeiPVNx7cxhNzwlxIuBySeAEZdoCdpWid0+SW3tzdCOSXbOUt5BFJIY5ND1uVk3EiN+0iKbeT1bfr6zvHPbKX+Jq9WN7iRnB9c/GWHAFHwBH4OhEwWcHZ8W0s8hdy+BBWpyRf3nzsYPfBXvjpPYiXtz1Yu8AdGbsP0u13OqXlKGQmBA0XZ/SgfOf7nh+2mH/HmyyI8y1PW5j9Zr3zyq32bL24dath58u0prY5eQsdtoC79V82NcyX8V6E7oHbEeUug/RvtmAljNhwm9iM4M8v1sJz7G707aMVXNdFnyjAUogEDWqnOpHKbBupnx0BR8ARWAwCTrwsBldv1RE4jQC1DtNREu1EyAwQHTuwquAqVw/K1WAwRZA4uN7gD/0G4pswtgmDywoZA3KCbjwzh7WLTCNWJAt9UBFTv2/VWIx8OYZvkJrp0lRXyR8G+aUlCy1hSLYwWC13RTJihfWpHOpZY7zQ3YjlovCh3IbCfiS4LsZq2zLzWgkYkjD2IQHDOUGJhMJEEoOBc3ltZA3r6hzOJl4SONP+OQ7R0vBNwmbegeHKQdVNDyquqrzyORxiS+8eLH6O4G5EYqjKWDS02KlorJk6zgzWx1g11oMggIZ1vGyVSmH22DWH34s6kknJiPI9x33aiOM8TzsCjoAj4AgsEgHKHf6RL29qE0LokLK1P8BW0XAxOmxjC+kPbWwfPQi/Yiejnz5AP+hNsQBCF2TcC5HFuGdGvJiMoyxOj/wrPuorrXPNiazLLDW/i8RSZW4h7810CasS38EaUivZqIDTZqBdWUJBAeO81KA3rbaK4e/fryHIbit8/7QRHq434N7MhRPIcHxOlhLWakZmW49+dgQcAUfg+hFw4uX6MfUWbz0CFNMU2zd7mN5DpYuWFaIsgLgg2fFxryfbR3cZ0BXEC2OJrCMS//oaY50UlJBISIx01KZkzZkKlRRmW5/aW0JgMNMg4GAucWhX/CbhQnJCyQppCmkpxxevqQQyZp0pg0JCoB/2RBckPaw0uTzzxH7kTpwxK+nI2khuSi5zuVJfa5wuYb62JSl0kdXhs2Fg43ZnIG5Xnf5I8OKIpRrmUAUBs4qtrZsMBMy9r9keUZB/SrzkZ8ihZ73ILQv8st54nnfc3Ejm9X4TeTbzT5npvHsN0ZsYu/fhCDgCXycCIiOTl5JaqOg8ueDB4PY72MmIgXV/e3sU3u4Ow++7I5AvIywCMDg+CAYSL5DBZS5SoB1avFDW8P0kYtleeHY2GOWlln+z2bVVOvvM5i6qnXWZpea3aBoKS/Otak/MndeK5UsZLsi9ENMJLF64wxE1hkoFxBSIl1Z9Ofz7y5Xw8kkzvMLn0UZdYseVS7QgLuLepAfcly2WcEx+OAKOgCOwGAScePlkXO31/8kN+I33EAEqCPzl0FKEK1yMq8Iti/cP+nBr4S46CHgLVyMGc11tVrF9tBIvYiGC+5by5i5sjMc8DUVLPvs7+6VTWRJ6QTQd6ZoTwmHzgpv1zFg4LFq8cHyxYhOTEtoCW9GD91hePC3Ly9eL61jZVc82bOkDXwws3OOOTyBgenA3EisgWgLBOomxbkiGrWOnqRVu7w33IzuuYyzW1vWcF4na9YxwUa3YM/2U9mPU7Jl+TnufMga/xxFwBL4eBBJRKRNK/96nPMWLhbJxDF2gc9QP73cPw+5+P/yCLaTf74/Cu91J+H0PbsjYyQhVsGiDNxLYBsYYg9euEC+6zIJsXQ8RMkJkbgxf+lKL32RpZlwzSfPNl5Xbe9AqzpboVVYnS1n92TP1CKuTtTRbJ3+l9RMNRHZyomLBa2I45g5HmP8xlRCoHHTRrtWWwj++bwrx8senzfDNZgPWMDUJ2F9FMH8e0jsaiPWTfM9+7Qg4Ao7AdSHgxMuVkYyFhAmOKzfiN3xxBPgcb/b5sUcqCFzlmmDnHP4RL1tHw6+bPt3cupg7B01AyLSws0+LxAt3MYJLCxU1GS1JDI7chm4/R7uW0qt+UXFRaw6Ob/6RFHDLSlTITJpV6Unvsfuj8ZhCo2OOCrhUlRx2Gy/TuVnhBeeslQsqnlEsq45Rp2yPz4c7PnFbzwGeD92/5MwYPMgn2bKxWgsbazUocneJeDEQPhc1a+eyZz7hm+kz/i1xdNZrPv+yI4/rWVtxnqcdAUfAEbgMAiL/STmYvEEG/+M/6gKH7UF48/4g7MIC9sc3B+Hj4ShsH0zDe3wYVJdyl7I6YDceGo/yA48Z0SvYv5EZ8q5DGf5lh2Rml1nqzIKsClIzbSUl+TuzOllqppEz7uPIr3LErUuMF9wuFi8kXpA+LhzDSjjAXXsp/P27Rnj5uI4PAuw+aISVZgM7KFYgt3WHI8Ef/XNrbj8cAUfAEVg0Ak68XBlhCgi+9u185Qb8hnuMgCgF+GLQWvlA2RriD/s+rCtGIGJoBQPDCtn2kOTLKrZEZFBd/uIYtZ9R+HmY3iYXn/Wlv2N+n637SKn87E3hSXJwC1LZRToSKpaq0GhWqmimNdhfRvZYEyzm3Hgtc2ZGclgdG4Plf/bZOosaYoBds27hjk5dBD7mFtsMutvpjKG4FcOD9XrYWK9hp6Ny+jyufWzRmD4vaehZKzc90jkg21Cu+XzWTPP587olKufVu2nU5o3R8xwBR+BuIjCPeKGjLvO5oxFdjH5/sx8+Is7LT68Pw84RtpXGNtLbh9QXQCjItPEWSogXvo/gcTRDvPD9lb6nmLCM815s0u75FbRNa1BumHlXpn1K0eyV1s6+T/d0OierfTqlFrN6D8km6lUzxMvyscTCq8Li5W/f1sP3D2pwNapjZ6MGdjZqIkB+BQtbNdxn/Trxchplz3EEHIFFIODEy5VR5Yv6fKFy5Sb9hluCwPU+W1pSmFwn8UBCgX/QM4gtg7Zy++gh3IwGw7G4G7FMD0bkp8WLftLto9EY44/wsFgpbPPTDu3Lxmc9U5MS4gMFck4aV0WHZZ/c4eww0Q+JGe0rURxxQcysD5ble+M483mzDZ++srmded8ZjfJxcDwMgMydjfaPegi4OwyHB6NQw44JD7F6toVgfSvNiih5JMYMp9OjuA05hgTHciYaCxroGSBfsbe4lThtzVgez/HB2VpenI7rMB2XZWm7k+XM9cMRcAQcgasjIPJWXiGUFbTOoI6gcp0WlXsHvfDrm8PwfqcdfkSMl/3DSdhvT8MetpJGMQ4uvPAtpG8itqHyktKUx+e8n+w9Z23YtTSctMyy2XwtzX9bG/l8vZ7fwvxcvSPuN34LM6YLynAriRcYqoqOdJwQLxV4E/35WT282KrC4qUWnoN42VxvhVarBnfummBvIzS9w6797Ag4Ao7AIhBw4mURqHqbQOA8IXpZgM4X3pdt5ex6F43x0/pXFSi7l8qWkQj8I57bNHdgOdHrwcqF1i4gYEikUPBzu2Xu5lPDbjl1+CDX4cZSoiM3D7QjihuSbE8OOyeXZ5+yuTKV3aYpKzUfcXt+an2br22lZ/c2ryRrJbnfOkVlmVdSIZ1brpH4/lzRuZdRN1LvrHYMW2tM7sMXCbGD9hBbfvdlh6MjkC9lbOu9CWuXNZAuzUYJzwym39wmm2faf8uBm5POb5tSlwwr/R3YNYdto9c53P3veG6fN5vbTqx93uz8bkfAEVgsApQxsriQeLUo6aLkfh8LMLv7HbF0eY9tpP/5+2E4AuFyiN2MDkC+kFSAhEmIlox84Ps6e8d9ztvbWrE27FoxsdzLIXR+7dmW2eLpnPP60dnrPXwrE9djEC/w1EaAXbS2BFej5RME2F0Kf3hSCc82q+HFg0p4/qAZHj5cweYFdcTPa8izsH5um4y2cfnZEXAEvi4EnHj5up7nLZqNCsXPH9D5Avzz2o/HyH7ia7b8aX2LJQeVgWhw1pLGdoEy1RmGLuK6DGDxQjejKraOLmGnnEqJWxYvyU45TFfxB36BWxfwiBvUnCsMMbuZKRuPpayUCowQH5aRVZQeeRkXWdqGM+/8/7P33u9xJEmapgPIhNagZpHFEi2nZ3ZP/P+/3z63ezc3PT3dVV3dVdQSWiT0vZ9ZWIRnIAEqAARZHiQyPFy7Rbj63ESkybOydK3EKrfl1Zddnr4v4C0P7TxPy2cg8KK84TJaw6S0uF0kbrTOe+t2h40raRJdL+O4u4iDiTOpa++vMlGp1lSFX7VFXdAkaBHPam74yf0lXHnb1B61r+03qJ0n4xXgZRCdil+hQKHA2ylQzy8afDS4SHQYUP8INtZ9xIy3d/cAXrbT3x+upmevttPfAF42tjikQb+Y7oco1R3C/LHmknqMtkEqH6nqkJMVyqNVoTbnnjoY9gdEzv2+nlGENYWe9GnCTht/B+WsVCfzanx8teXAC8qHcRwqkPuwgJduSt/e7KY7i4Av10bheJlJd27Np/m56bQ0j81pXVYsP7bwca/yWyhQKFAocFEUKMDLRVG25PsZUOC0iT6q3kzv4fMu98g1Ty0/Pe9LeS6LrJX1Hor0dsxs8fbOQZqdHU8T8MVOYf5wEos5XTTDdbAT2UGAuzZkFJm8SyVOjRO1iwhey7ZvhMY9b0v4fez9rDJVXjv8IurQ14aqwJxjSV6sj9MG3EnrgC/bmJbeAHwZZpE2ClA2CpeLrEvIpLTAs3HujZUjElZ56u2XdV0ftS/toX4FA0oc9J0NiGZ9NwDVQeHFr1CgUKBQ4CwKBPCi8ciOZRh8xE15eCjLhrKehz4XdLv8APDy9OVW+uvDDYB+/HcBZXqKS7qhjinUFfTi41p7dDvPWdJr6m0K0Nlnx3Y7vdR8ND29Hu0aR/7tPEUln0BP5tX2kVjwEey64nYR8HIM94vWTViMTg+ud9PtxW66uwjwcnMm3b2zALfLZLq+ONNkr5dTJuiTr6D4FAoUCpw7BQrwci4k1VQSk4QybE8L51JIyeTcKaD39j7Xu73X+Boi5/x5D8sEsly0vLaN3pBt9LuguBXg5RpmDqXXZXa6w71j4kYSW8nOtjy7d6tCFH3KPW+3Z5j7tBOdS5HtTHk+rUyVNyjsourRVzUKPpa8Vb4Iw0+L4k3ek8TCJCamEzXVkQHUevsEoMsUYkfigJme4JjNLm+FsZa3rVFVMb7sm7e/aePHvUGjd5OZ0z97bjvz0gd9U+/n57md6I/tQstzoUChQKHAAAo0wIvPGYoiHS8CXqRgf7uHBaM3m+m/fllPj19sIWq0nrZ66IPbOeaARnPikFnekSSruF40r/RfMaLpfl5XU0bk2vhEGVGuP3u8iO1+J9NE2rjnMfrzG7Se7pueSarU4hw6QmG/rG1L7GgIjpcxpuJ717rp5nwnfbXgwMtXd+exSDiVblwDeOESGW1c76+yhZWfQoFCgUKB86ZAAV7Om6Ilv8+AApqmY3KPu6qdu9vNODkrK5e4ToZ6iOLkctx7iBbJmtH6JpwTWz1AGESNONG6tiizxFgxmmYDj8UccVRoOVGbODytgKjAF3TP6apmXXrT/WiRgr1kLczM8hSgS28PZcgskqWrR6am93mXYhMX2CIrVNL3onsuWuTAS9UKbpfenk/6beRvUy3X86ehQNTkbaVHvEFke1vaQWmKX6FAoUChgCigucQ45zS38CCOlwPmkR4mi7bgpnzxBk6Xn9fTo5eIGj3eAIwhbPc47cLxonFzZBjRY9KKm0PzyuljVWukaj2ekVAFVdfpuUeMQff+ovypyUmu/hiiSP+l8LZfxPC01dTsnpyTiBKHoodMbUMbiRwNw6ra7R6le3C73JgfSbfmu+n+rdn09VcLiBkJeJk1GnomwFrFnHQQudwLBQoFLpACBXi5QOKWrK8yBfIFwGmTfNQ/FgI+6YdvO1V/aBWLSFoQ2EadTbo27XtiK0Z0ZRtletL5oo37IqyvAl5m4HiZEscEacRMXC8GBmYeNfmy7u9E14tsMrQP0CWKMcBsn3fHuxLXkljDd+CCkdiRuGBmAFzm4HiZneJvdswWx7G+jEWisq39IuMv/v7J32ZN4ajJ27pSxKsTZo63pc2iFmehQKFAoUAfBTQHGPBS+Qp42T+AkxLgRWKsL1Cq+58CXtDx8gOcL9sA/QJddplzNHl0DHiRCWlxvMgQ9VkjUhYmp2fhJZ81yFV18wTxEBnE82n3EILKyiZqU1xeicijCQ2f5t4u1/Ptz538ISz4i5ZNNsfuQxsHXo7hdOkAvHQq4GUmPbi3WAEvM3V8F9yqNB43hRdXoUChQKHAuVOgAC8fTNJBE8gHZ1YSfhIKDJrwY0rP32/Ei7CzK5vHluyxrBjZRh2xot1dTEgDwOyxiZefhbNaWETLvsRVpqc6Jq7iKwKVU5X5bkWfXbHPKDSn4VWotphgDvjRuxRYtsO7lJWjlfWdtAWIJi6XhbnxNDczjuK+cRbGvDnemb+2/pentvX7XIUWXmQd8hbn7oss88PzHlRD+en6db03b3P5LRQYTIHSKwbTZYCvkco5VGJ8EVekrQ0EvOyibB8Q//mb7fRngJeHAC9/BXjZQdRoD44XlguItQ6h+20YUJ8/xGic4yUfkeSOd5LXIY+D/2nRIkkdPiiviJTf6wTm6aX5b8TylsfTGfdIdmbR/QKfimp/zM/VcVUtatTlDOvuwki6PptxvAC8XFuYSjevS9TIIZf4PaNmVydIjQ06DaxVEO/MSANTFs9CgUKBi6dAAV4+iMa/noHtrWP8B9HvIxOdC/nzTML9tnoNnsjaiwqdusTSwFiJ2az3AFy0sJJOlz3EVI7xE+hi8tps0mfglhDwYlZysJCTAy9xiqPaBffE22r6OYe338ZgqjctVHzFiXRvi9+kfD+X8reTNV7c1vZBerm8lV6zUN5AbGx6etTMS89MjaeF+QlMgIvV2d+XfwtNrfre5/tV4b1jt2ky6Lmp2XtnnyVQzsop7gqK0uTOrw8tMc87z+983e1S8lZ8aM3Pt4Ylt0KBq0CBvGdEfUoPCUrU9yATpAnxoKAqXO3pAABAAElEQVSSc7zAPQnwIot5z15vpf8EcHmIqJGU6+7siMMywWkJpGDAC+ALi4Bh43jJMq4Ki3yjbI8x2NfjKEyx2nEUGvnL/e6X59TOz/P6sBz7y47aNiW461CnI9BI4RyRmI4XWYi8PT9swMvN+VEXNbq3gGLd6Rp48dwj1/6y9HR6yMm4xadQoFCgUOBtFCjAy9sodGr4r2M4vpKtVKV0NTOvP3/wb2R4WgYq6HRKBPASYEueywEgi0SJdlDMurHZM1GjHhwvI5xc2emV7ljGkZjRGIDLGGr4ZZa4vtpVO7c21yVcSUfe7Lc1OY+rxrwt/sc0WGCZeJrdAsV2er26kzaxdjRpOl5QrjshPS8dlPq5GfBuZ9gsU+Vl5sCL/C+yvkGbKGPQc4Tldfww96Dc85w+tiTl/7F55PUZ7G6XEq1S7IsvfXCdim+hwNWjQPQM9YrcffVq+slqlJMFt9YK8opxJICX7d6uiRo9FfACx8sviBz91y8bHNQAvMD1Io4XgQqjI4gbkViK9wXiNJfnGPmG/2BRpDxdxGynlP9p8eSvv0Fpwrcd5nnlOYa7HVMln3XlJXta/3XgxWsmwSMp1+1WwMvSDBwvKNe9f0uiRksALw3Hi5eV59pf+ukh/fHKU6FAoUChwLtQoAAv70KlX3Gci5t0lDOTJIuJ/BJHh9YTce8Laz8MqFzulbuVVM+6+kuUT8TM7/JvXydTKsbbgBfpBBG3yzrmo7fR8SJRI4Es46NdA1nGRofTBAp1ZUJaJoo7bNb7rqi4PAdXoS/6ZT7kVTutXFU5KJu7Tov/vv7tOpwXiZo6NzWytS7Ayw4cTK/NMlUPPS8HvMuRNIk58HHe5RjmwGVeWqDLWBcQBr/88gVzxdxcVfa86hzlRBnxPKg/nRp2SmXkndPao+U+dY7hGJgiCzzF2c4zSj6lYqfkcl7eeW0+TQ3OqyUln/OngL6OX+tX0W57+/n8qf1Z5pgNILZWqAbjEDUy5boVx8tjgJe/VKJGf4HjxYCX3YRYMpnwf5SDmg7pXcfLSXq3v0QHXrIKXBoB28dQXoeTNVGNT/qerGYTL2I3bWUuxVPmpNUXFS79N8MALx3MSd+B42UJUaObC930dQ28vDvHi+pyskz5lqtQoFCgUOD9KVCAl/enWUnxERTIJ7Bw27TWPNgkOqiIeqINR6TR86CdJX5xKmRJqnTtJUEzrbZLjYLa/u/2rGXAvpTnoiBvY2s3ra5uoVBXel4O4YoYM/PRAlzEITHKJl2LqWFOssRGbFe0L4r7uOpELhd6z6v8GVT3nWgR35Aix7cjrqXVDUAXxIy2Ua4rK1TiYop3ODEO6DIGGAMHzARcTI3BBChk6I2DMfpsL/Kq34eVqcJUfhSK2yrAdqCOSJQItop5GrPS1Bepipenk5eljQxagZZf/ESceL76d7Xm86v11adrqeHnQoHSA977TbWGQAEvORVNFFlWjXaxasThzJPXmyZqJOW6f6k5XlCum4saVWuEY4nWtEakfHzqA10U0KpL05YzA5to5spr3wqqHz2/vC4R1F+F9yk3clAzYhZu/OQSx4sd5JFtDbx0jwFeOg68cBfHyzfG8fJ+wEt/SeWpUKBQoFDgwylQgJcPp11J+QEU6J94lUHlw6auva97e/bZ1I4ze7KktkRgJxgb59jknpy2LWZTl7rgdo51wDs5kDJCEStWCTAZvc4mfWVlE3ZiPR+m+dmJNDs9DugykmZnulgroFZU0Da4aklFlr6CPq46fVld1ENe7c+guu9Ehvh+FDm+HVk5WgdM29zaxyIFfOA0XN/vkX3Hx5iX7qTxcYFqiB+hu0fmPz29vnNR6XKBF70LKzd/QVYj/znpHT6krF7k+7xP/44jj6ygPuf75NiX8JM8qDWfV40/CZlKoV8sBUoPeO9Xmw2BDrmQgxYi1WIngJc9gJcNrBo9w5y0RI0a5bqIGsHxsgvHiwCHLuuEroCXbF2T18nHJ3F9ZAUrggIMcM/9zTNPfq5ur4tnmZfaX8i71qEdL8895l4BL4hgESRRo+GhI0SN4HhBue41cbwAvATHyzXpeLn2mSrX7SdgeSoUKBT4zChQgJfP7IV9ztU9OflmPrYQ6Z9Mra1EyWJVJ+pBhYifx4gw7oG0ZF5yRqqWd/YY+b09ZpbInaovbdFh1J6sGQl4gePFRI02d1CUx0IK4GUB4EUKdScnO2kOU8TamNt6jNrF5v5E3sXj01Ag/xwq9wHvdnsHk+Bwu+j9CmDb4+RS73afu6wcSVGy3vE0XC9DBqxR/SGWz5aHf1unfKLn0s6otmWWPajkeIwvPJ4VUu0J6o7SfI9NrKaCZ7VjUPwmZV1A7lXchQKFAoUCXxoFGApjNHQIxRt4jNIwiRrtArxsVsDLX35ew5z0VvrbL5tpCx0vZk4ajhchCjqg6cA+aVyxlqEs+eiqxmG7B+xycnT3UiN+1Ejxwt3EcFc7D8ULv/64Pk9E607LT2mivLj359M8nRbu5fuv11ylHbHoQrOLVLAxh7moEcym6e6ilOt20m3MSoeOl6WF6XTj2mxTlLU/csy8i7NQoFCgUOCcKVCAl3Mm6K82u1Pm45h+T06hHtLmJvCTck2czlBq4XG6QyY2NfKc58dHXE+bUZ69h7N2tX0RW0sJ3xmThZX2fq+UfI/YlEup7o6ZjcaiEZtxmZHe6e0ZEHPIYmsebpcZ/ibRBzI9OeInWLRC7feWv1+xJfYFUiC+lfgc9I55h7t7+3YSuSdwjfe7jVnpLb1nFCnPwcU0Pdm1dzyD4mQtlCVuFJ9kgBnxfKL2J9GPE1HaHlHNtr89V4Eqr86aAGtS1q5jffsWt+IUI6yuK/FjSe9lEFLlN6gdea9UHv1p894VFfBcy2+hQKHA504BDSKlX9dvMYZVPDQOBmUEvBxK1Ii5ZHN7Nz1fwZrRz6vp0cut9NdHm6wZWD/sMNfsAygwcHc4oRlhsJVIMtlw5cCLj7KiezP2Rg0scvWg0uP95P4RN+4RL551j3S5XxOvceXh4T47NGL5d/O2ejVfl4AWXZqTD92ZDqHV8PARItwp3VsCeJkfSXfnUK57ezZ9/dViWpyfTteN44UEVhQ/gyaxKr9yKxQoFCgUOC8KFODlvChZ8jEKGGDCBBaAigEJ2umxodO0G9Olbf7wkInEehVi4af8xDzsmZwS6SxvZZAlpgLmQ121oFFYhL7//FtVjpuyEsfDHqDLJhtxsQjvYpJAYMsh4inKexjLBFMTY2kKTpcJlLAKfLGrqpNRinwU9/3r4lmV33OkQPV66w/EV2rp0EyCD9m7la6XlXXpfNlNG+u7aWYGYA2ul7mZiTQ3O4b+F5kO12KQeum9VqJGqmX0mf4atwqNx6psrRbdy7/at4v2eLz+Mj7kqa5IlVj5hl/u9nZFCeoXAarGPcL60ze+xVUoUChQKPAlUUDjoI/3Ng0YWHBw6JySW7399GpFnC6r6amAl4dwvCCarL/ernPRDlXAi4H4FWF8raWxN8Zf3dtXjNHyj3htdzvNoHjKJ/w1meXPb8svzz/yyP0GuQfFa9ZqqoqpuqEeLLEAXPjDLzhexhA1+vpaJ91c7KSvZNXo5ky6e2c+LcwBvCxJ1IiLNAaGlcWW06P8FgoUClwoBQrwcqHk/fVlHoBLtPxM4IVI9RRqk6V5eFKtUOIypybg6tKpvE3+8VzdK+8sZhWgW54Gd/3oIFHOZfL+868y8yIE4uyLw6VSviprBD04ImxVQJQOoItMQU7CBaG/MUxHT6CE1RAbzwKnt6AALyLqFbiq19t8cuGhxZ7Meqa0trGbljmtXOW+ttZzTheAF+nymZsb470LeMEMqDA27ucNvASVTv92/ZtSXeWqPjHvB0psntGuOjf316OCKvDU8vDsImJ1t0waPyJGjp6G3m7pBiW2Apq0xVUoUChQKPCFUUDjoEZFQRa6tF6QUlitGSS6+np1O/3w0IGXvz1cx2KegBfCeg4wDFVsk5pLfChtDrk8R/2eNr42MRqX4sYo3fiedEW8t43TEe9kDv0+HxfP1o3Kgstoyo94f7TUMsvbPI1g1UgcL9/e7Kbbi6Nwvoyl+zem0+3bc8zLU+naIsBL1fQCvDgty2+hQKHAxVOgAC8XT+NfTQk2hzEB1mBL1nIP80WHZkrJ48rPuE30bH5Mf3gKvNFdEXSK4Q/+zK9frDx88cHsq//8SeGc7qZPQ+EE2EZP/vzraPebXVYGzypCl8XV3Z7e/tOATFUKbmqXuF16iJwsr+6kfbgipANEFm+6lD/GX7czlMYnumkcwEUmh2VCulyfJwXs2+FnA4sUet8bW3uAMHuYloaTiXfsf1itAmDr6hvA7PQI4Ft8ZfEN6ls+81JBEUf9I9yVpx7rfsMXrcW8LrGyy//oqGI/l5u8ot9ZmDVC/aAqhFveF0JESmV4H8NFBHNbP9Szwuhx6mv2Z8XbT/QTHxeavJsY4arKj8dyLxQoFCgU+JIowBDn4yyNAsjW+GwKdmHV6O0CvADe//BwJT3DqtGPj9bS2tYRul/gnt3SmC5CANkwzop7Mi6Ny7qcp7jxt0HZgyy0dtqckcdrQvpdyrkdT89RYsQeFC/CLuLu5fscpfnNaymuYlndPhRd8ZQ5aabh9P2tsXT32iicL2Pp3o2ZdOvGLFyoU2lxYcoqp/QSV++b9C6i2iXPQoFCgUIBKFCAl/IZnAsFmLb80gSWX8yOmgTDWyygpgSNjaHumizj1OeIxceRhftiRGmOxTtaJbac+dEGzjZ6tunTZk9gD0uSYTa18mNjK/BlhGf310Jl2IAX2xRW9VN+CtdGVPnxv+9qP8caxOpBTLWlLxWZqU2yWrTD6dXyGsAL4kU9FOSNsuGeHB0FbJGZYf547rIZH4EFQuBLua4+BfQZ6nsZdEmsbBVRo00AmPXNfeNkGuW4bXx0yN79+GjXgDdZOuoAvOXfjS0Sq3wFENoV5VQfGz2ov9gIj/hVH9EiXvm5aU2+bVbrcqtvqa/ZP4XzrH/ytz4Z6SlGX6Nlz4/6i5vIrgAV+pX9w7/DdyvdNeLgGgZUMlPo+OsuOvXV2PL3Sissv1SP/KppkHsWd6FAocAnpoD6aavzfuIafXbFQ8J6tKvHXI3HrBOwjreytp1+frKWnr8BeIHzZXXjIK1u4b9xiN44pa1YJm0U1vjsf1VWRo7mLbXfVV3yR5Ctyf0jMvmopHUNquZFqzSn7UGIA/60JhTHyxjz7+/uTKT7S+Ppm1vj6Ss4XpYQMXIR4AlfWlYZ2LpSNWuT7aNqWxIXChQKFAr0U6AAL/30KE9voUAzwftspclKGz2bq9o7qsiLqEpnG72jQ9sI7rHIOMJ9cOD6T2QVRpZiDpF3jo1icMHY6bwf4FuOKpO9nZ242yZPmz/5sRHUJtAADe4jcJcIfBG3iXOcdG0jaRYBom7crU00oJlvvW3R1mhjO4bqLz/bKCoxddACSmJGUrT6enkLSzeyYnRknA8zU5iPngCAwdSwiRxRL29LU3JWreL85BTQd3Dy3eh716V3F9cO1o3E7SJ28S3+FGTgHw4xuBiHE6DLFBaOcg6n4DxRXvavylL8Kbr8N0rpL9NDmzqoXgJahAceHKp/cQJI/1K/kt4hgYLqZwI3ZUlDzTAARukMRBTHlvdl5ar6G5hifU19i2f1JfU37uIgG+VvCPCwoz8DYtTXOtb+ptbqYw6OkmvdKpWvK9oYLcnp6jEu/rddh4svsZRQKPC5UUC9JHrp51b3q1PfGPdi5NOYq789dMGtrgt4WU8vlrfTT49X0RsG5+z6fnoD8EIw4zSirWoKLI+2duB16B5zkgXx42+p/a5ilFOs9qW4Z4VH/Hf9Bt41v8j33e91Darm+Yzl4NUegcxs1hamKA65UvrDV5PpwbXx9O2tyfTVTThd5mfSNIYNZtG/ZnRTdK563mmTzYPLb6FAoUChwLlQoAAv50LGX1cmvnCoZquq6TFXmQgEQRaqu/35pKiT9dgE7u/Jwo+b3tVmUSI5ZoqX+yGbQ1dcSjo2i9oTiiXXJ0ZNs1p0oC/DTtbZBMI5Ii6XUW0K2fyJg0Ta/7vdjgMu3S7+cB/wLOV0Hc3IpJeohF9+J6i6rPb1YkZy1b6w8Xh1mwBeQh+LEqpmat8uoJKAl+UVzEfzLD9ZtpG+j1kU6k4CvjhgpGpEHaLscr86FNB3cPL9xCLX3l0V3BPYBuDS68HtBMv4Ad+tcZjw3aoTTKDlb3y8m6awdDQuwXPS2fdsgAds0ZWYToCCOfCicuoyAT10qS/ZV0qH090BF5km1SLe+5UrbaQufH8yda0+ZQCMnuHKMg4zTgmNy8yqqZwsVxVhdVJ9jJuFviJgRX3LgRf6G/1J4nMCODu49ewcXP59W1Wtjylz8lO3q/uckYVn9XErru6PeRwPufjfaPXJt33xZZcSCgUKBT4fCmisiHEid79rC2Isj/ixntDYvL7ZSw+frKZXAC//eLqWXgO6vF7dTy/XpDdOY+WwjddKMzIka4gaVzU/KDf7iWy5Ry3Dqx0e/p/orup9QJWURNNItE7rLh3qmagR8+2BPYnjZShNjB+nP92fSt/cnEjfCXi5MYWY0QyGDcbSNAdhftDgFannncj4o8hitaxyyN0flWlJXChQKPAFUKAAL1/AS7yQJrQmxBAHiMnJJ3ptnjxinNocsumTe3cfYAVODymN06n6DjpPdNouMZweOk+0UVXYzv6+he8TxuG8bRIddHHARW6VkVfHdEvQaNPlwgxsJ/Fs6mwDiBiHdGnoRH6SDa44S6TAVn+TcBsIdBmTvzaRtnF0HSvKo4uuFbXPdMFoYq8K1d7ZLsLUXJl/1GZWnA37bHSlQFd1PCCi2q+26G+PNocYlCzcCHjRfYoNeL3iUJ48nctc77Usvx9Lgfp912/JcjzrHel97wK47Np7B4TZ1XcuM+L7BnIYhwjfXhcAUNwh+h47/En3y+joMKAg4GH17fUBFFVdrH/xjZmYHqDLvixl8Q2Ku0oLTpm2Vv/aAfjRt9mjfPW/HUDAfUCgHt+oQEwt7tUfZWGrFvEjvYBNXdF0fZGGQ1In1d3EiayP0T94Bmsxzh3pLBo1QGbEdBZJhM7EqayfueifwBn1NwFOAnHUftssWIn6ccrG2HKSzk2tIm6d9Bwcee5Rm/A7WZdzKLBkUShQKPDZU+BDxwitFWKss/WTZcShjYCXrZ307OVaesOhjYCXl4AuL1b20tPlfdZQrDFgstVcoAMfjcni9O1UwEuzSjpt1Ioat0mv+KeF5XHPOR51twVVXkTtjjacrJd8lDSPIT/jlAbEl44X6cDhvC1NTAynPz0Q8DKVvr09ne5cm+Twa5I5CuMGrAerac/yindiVYjM6/q8ryPqrYzCrTw+OuP3rUiJXyhQKHDFKFCAlyv2Qq5MdVpzhS0QqFw+OQmECH8DS9jYiV1Wp/3bqOHfZ1Oo03+J2mxtVXc2hrLysw34InEjhe+zEdSfFhXaRAaIo42hKwf1qcunLApl1rWJt777qY+UlmoxMgr4IsBlUnpU2ORNjHHyAagyzUQrMSQpPB3t6jREz4j+wI3gG2A4Y7QxZKMozhg6h72OmJxVqOomwEgcA+uYDpYi3R10uAh0EQij+kt8Ki7VQwwOZloY4GUajgeV55e3RaWU6TgodgXu/tp9uZS9mMx5opLGVaJvX8AL3/vGNlxPiB8JnOuh9+U4vtUE+IB7cqoDAMi3iPiRzIlPVCChvkd9x3U/q+oS/UJcLFqg7wG0SIxIIk563tjZA+SR9QsHXFS2xNy2MVG6x3e5g1t5yK1vWOALN9wucqR+7J+7tzJ+qaqBJKpPAJzSUSPgRaDLOP1IAOY4wIraMa7+xDcuMEkAo0AZAZ0GyBjwCVBDv3RuNbHN+9dv5akwLv9tk7gixCmh7djv8xw5t9MMrkc7VnkuFCgU+DVSIMaNjxkn+oAXxvbN7d308vWaKWr/+elGer7SS8+Wd9PD1wAvPYBzWyOxHkJZurh69ceSRQuxaizVmzDI/JRXErVWsGqeP5+S5EK9c+qpLlGn8I/6OVdLHcpc4UdW3gLNXXYoYS0CdGHNxRkHXC3D6V++mUkPAF2+vT2FdaNJ1oHjiCBJ3173EoCXNvGiXW3/8lwoUCjwa6FAAV5+LW/6XdvZzHOWIoCVSF5vCMOD+AJd9uBc0aZwFy4PudfWt9Lmpiy87Nqp/PL6LgDFIYri9uEGYGPaYzMI6+y2NpAsJsAv2BhKZII/8tREapOp1ccnKzk12Wp/lrv1rD9tDMFZDHjp4p7SppBN7DTKbKfGh9P8dNc2u3MznHag7n5Wd0SAFpD1lSLUqSnuzNguMqGJ3cu1zClbtDhgw9pjM6sNr9iBN2jPGsrvxPGgdmn5I8BGm8s5uFu0GZ1GvGiaDfbMtJ5lzYgVgfKrWiG36l+uq0UBfX/v+l4EEgp8k/iOgJDVdZTsovNFCnfX6Qc9wJg9vncX8TlCznwCfS8duKD0/cH2LLACUHDClO8C+rVIIUBHJ3oCNtXHNjkZFZfLCgqcxfWysr6TtgB7VlHsK66bjS2xph+ldSxi9ABbtg84UaV++n7VLtft4v3IRPmsvKbUxgUNUFJId4IW9Ahxvehv5BiwBetc/E2q3gAtM5MCMfnu6WcT9LmF2XEzlT4zPUYYfWBa/Yv75ATcPnTUiqVsGJZ5XdYf1LGr6yTt81pFrA+/+xjiNGjncr4ltXMvz4UChQJfAgWy4erEmP3W9pG4HvMY7LSO2t7ZhdtlA2W6vfTo+UZ68pr76530y8s9s27E0op4OgBibcOgrIMd/TmCoNzedeRSvKh97m7XOvI7a7SMNJFP3MO/fc/DI/92nHiOOnptT8bWfGbIE61x8Vvdxzls09/c1Ej64zdz6etbAC93ptONhUnmJB24+TpPc6FP8qJclfvJQqIy73lv6t4kbGcedG1ifLgryosyzjPvD69VSVkoUCjQT4ECvPTTozyJAvl4jVuAgwEuMZ4HlRQGWCLAZWcX7g8QlJ2euEAOMIu4mTZYPGhjKMDlDRtRncavIrO8BUCxtsNmEPGHLTaNAl6c24UFBWWJjVZV0KR4hDsmRPmpctqQ+fmH3+0ZPw5/bIM4ykJE+7opWZThPiPghb/FWT95X5yVnpVOWphD2S3gyOL8lE3GMwJgmJAnsUBjohXkY3mjoDcuiW+YAlV0uDx7tWnA0psNNrtscrfY5Fr9+J2c7KTrbK614Vyw8rrG9aJyA3hRnjbxc1c55fp8KSA1JQIOjwFHJIr2emUXEIRFNCamlwEft3cQ9+Fvu7dnwMyNpSm+h266vjDGH4r+kDkXl4hzY+l78yW094dkokUmSgSHWA/AZWNj2y1nrW7b86s3WFQCEFzmW5Qo3/omYkZ8qwJedgFetvYqbi3VkdydXV30pifRx/wijvnI1y9fylMXwBfvZ+J8AXghwlgX3TUCXuhnE4Ccc3zzU+gvmp/hTsdbnB/nuYsFCYAX/hzg1POUATACcfQnfUdeD5UeZXvf8yfVOGpkUc7tx8eUJruLKaXJv7gKBQoFvhwKxPjxQeMGiTW++uXjs9ZRq1g22mDuePwC4OWNQBf0vTzrwdUoTkdxLQqk8TWOOF5GAWB8HaG1k3LjxxYUTa2acqI0v598ivrk4eFu8vNZJPz771qvNbk0rv5YesrzOxnqPnl6xW+ezUWDNVO6m/nDxIxSmp1mDSbwf66T/vBgId2/DtfLnam0xFpPazwTl+Uuevn6VrUh/3ep0mlVPeEfdY1M9RzuiDzIL8Le9/4u5b1vniV+oUChwHlToAAv503RzzW/GLOpf+asJnKfnDSZHwGy2Mk7mz/fCHKizoZvFRZZcYFscsq/A5iyutYDiNCpv3O8bHICLxGMTW0EAVy0Kdzl9H0Ht05vjNuFu4AWXXUdzKGJ1abFLKSOUW0IlcY3hwJexP0C8wCsuFiUQbJHIhHTcL1IxGgKMQ9tcmcBRSQOMcfJvDhR5jiZH+fEfl53OGCkDFW6KSQqIdEjWaORONH2tliC99ITTqSW13Yx/Uhb8XsDS7Dpz6BqM5z6374xziZ0NF2bH4OzocvfmHHZyLKNZJBjI2tTvi2UrOnl5zOkgDhJTKcRH7LEz5682k6vkNN/ymnlS30fgI4S+5HyXSmPvs/p2zW+h3u3J7C0MGnfyRgLQXG8SIGtuGb0J91I4qbaon+ZuVE4WtTP1rd6hMFttYaIEcjlxgb9DoBzE3Bnj7oI5AT/tP6lZ/U59a3gJlMHUw/yhboIrn4nn/wufy5xpuh7lVN//Agr6cpcJ31Mf1JsPQEAI901stylu/rAGBxfM3zvEj+6Jg4YQM2FOfoXfWsccEacZup7LuInPTdiEZdVJAEvKk11VL1Urj/bQ/bjoZmHxgGrae7XuCO+cgt3E+quwSW1Y5XnQoFCgc+fAhoFPq7Hf0gONqzFAEzxAtnFxbgOUL/FukkHO49fY+XoxU766SkHWTsH6O5SnCETzZZuF/11DbjWW2CdpAFeo1prrJRvc2l0DJ+3jYLvHy/yjpRNubkryn0b3U/PxeYy2qEYfnhw6PMH5xZLc8w7k8Pp+lw3/fbreebY6fQV4Mvi3KSJkpveMtaJzaFD9gW8rUp5M97JrRqee6bvVHKJVChQKHD1KFCAl6v3Tj5NjbL5LXNaXbQ20OZHE90heiWk42RzZ9s2mBsSJ2Iz+BqRii0WBsuc8EusYt1O99FzAfgiSysCXbQ5xZiRiTzskZk4XbQ51MypCVAbQ+mhsE0T81RdjwqM0eTl6wmFKBE3m89w6F49iwVXaxFxu2gjp7sAmDE2hmb9CCBGgMoEAeOciswhAjQB4LIo0SM2vtoQTwPOzCEaId0UkxNjdkIyzl3Ayjay1gKYHiGD/XIZGWxOpFbhNHjCqZSAGal4meXU/86dsXSNTeaNpXFO/0fT9UWAGDaf85z+i2vA1ks0yP61Fkq0plyfEQX0fQtU6fFRbwOW/MJCWaDLIxbNT1k8S+eKWzySTpbj9McHc7A9j6fvZXHh7pR9E2N8g/omdRq3u4t4EvnIyoVEi94Y1wyKFgFatgD91hBxk4jRBmJuB5Stb1J6knbhJjukr+4BtPDoInw865u0hSp+6mvq0953BhHZOlUV4AtbX0x7IvUpfa4SNwJfsT8YXwCMJOoH4Ek/cyXW0pdEPwOIUf+6Tr+SKJKAyCkBMgCc0rs0h79OIbuAMp7OgZio2YcAL0pr40hkkt3VirddOQXeFreEFwoUCnzuFDhzQLyQxtkYbD9kz4Aj4EVK03VgpXWTRJkfv9pK/3y+mf75ZAsOSg62OLiCqRguRq1pHHjpiCO3ysdvtKVaT8RYp/Es3GpMjG8Op/uv/E+/2jmcFlOjrpeUl3cyduQXNTkZQz4VrIKrfzRXjW1NSgzNcyqLFSQ0caW6dxc56JoeSbcWR9P3dxfTreuTHITNMs+Oc4gm5e4SmfV50NOS/9lVUXU+8PISPjBxSVYoUCjwhVGgAC9f2As9r+bEesCmNB5cGac2djpxR3fF2qZtAF++2TLQ5TEsseubh2wyETUCUJFOCW3+JObAHMdE5xr4mfKYIKtNIHdNSbpsum4KNZ881JcKEduSDJwoFYNliJWhH0/hv66BxadyC0GhjACQWXRTSCnotdkR3MPpziIcKoAwN69NYf4XUaQ5saeyUZyZNHBI1gU22fD+8ngtPWdz/fAZNACA+fEfiH2w8e314HiZGUl3vhpNNwBb7lwfTzdvYMrw1pSJlSyx8XQFwNCC2d5FLbxN5ffzpIC4UMR1ssV9DTDyx8eb6ZfnWKZ4vpV+fq7vAg4U/VWAyP/xm7l09/pE+rfvZ9NvH8wijjZqIJ90peib3NjYMT0uz16jK4nTzxcr2/SvfdjOYUWHe8zYzll8sz43IEWgir56iQAZMFI9G2aJ+8h6hPcD7xTqhdWzkVzPVb8xV/ZT96O8F/piXZxbunQ75kdPqotyw76ZPYszBlwl3eT0cZL23ZwH7ETk7vrSpJ1A3r41AwCKtS9AGPWzKXTA6ESyubyMU2pXR8tjye0tqoNrRzueAk6LWycqjkKBQoEvhAL5CBBNGuQXYRdzr0ESstc6QMphpaRdXC895pE3a1vpCYc6v7zYTD89Xudwh7nFuG2dUxgTAIz14hLUOqICT1rNiMf+Fmi0uxiwJS9ncNl5DLnfNvIyh1hGAkZslWhpJJQq/0Nk3Q9wSK+LLOWNdhB/RbfLgxujaYnDr7usvR7cXuDQaypdW5pGvHeMEkU3zZMAL/ojvfxwXtClBlxY5hdU55JtoUChwEVRoAAvF0XZzzTfOF02Fkzmi+NjWT0Rp4qLGG2h22QHZOH18iYiN5g5fL2RXiNu8+QNG0IWBk/f7BnwsnuEWI5EiABhNMFpQyV55DG032qCY7pzCsmNRz0t2STYJl4d2g7omyxJyuWTqLtiI+inIvtM0qxtKhAJ9+FBGsZvEhEkmfO9Blgyg/nB20uALQAvOiWZnkQHB5O29LLMA8AIRgJ7MtDp0ZON9Azld78IeHm1m37455bp8tjZAcxBn8zdewJextLdmxOctjjwcmtpIi2i1yOAF9HmcwJeYgkR9xMv5L08qm+gefvvlfpTR85psA231zp6jDaxYrSCfP7fAV5+Bnj5+1OAFxbP0u8izhWZHtcJ3f/5uzlYnyfTf/vNbPr91zPoPRHo4KJwAg5XUU69trmdniLnLyW9z9/AVQXY9/D5LtwuKM9F1l8cY/sAnOIU0zekBbi+K53mIVHn/SrvW0Znp7n3KP3mrTiFoq3u5znwaw76WHUHY7X+tQ/y4paTGDPoX7JdNAL4ch1gc4pF8U0BnCg9FCi5ND+Z7tyeB2xB+fTkONwy6EKqdC1pIa2iNV70Xyc8LNiq0R/xnZ8G5/jOyUvEQoFCgc+CAhol1Nvbo0WMAOEfzxfXKCvJB0+rkp6lH0wiprJIJ/14z99sIrbqHC8rAPpvMC+9sg5XDNyVrKyqv2b9pLG/eTrZynZrPraVg9IHBdtlffCzrQkpKStMBwniEDrQmk40BHgSZ/PYGJaMxofSb76aTDckysscewcxo3kOzUyXH+C+UajKK7K0+8mJ5oOrXBIWChQKFAqcRoECvJxGmV+Zv01etNmAF+Yx2ziZFRWd0sPiymZyF7kgsb9qk/mKRYHYYW1DCAvsa0RttBhY2UDUgTXBAZrmj/THBDnChlAWlM3UMyfZWhzYqbw2hdVkV99F92oxEpPi6a/CYwheibg26ZNe9wCRJHohHTIwCFAn7igfPWLC3j8AQaGG4xJz4KQEKSBTFLo4g+UVRI0kFiTLM9KELytIdzmZlyiEdoKiwXM21K+W99PTV720jDLVfz6UhRnEO/aHAGlG0r17gDaIGd1h8r/JXeDLEgp9F+dGOdF3Vle124AXq7i3NG/PgF2nRyq/n4wC8V3FN6tXJ46WDek34ruQ/p9/ALg8etkz+fyHL93ikKwOSRxPi+r//u1sunVtIv3pm6n0PYvESRaLzvqsb/WIBTaKqbEE9uzlOmAO/W11D0tg+CNqJFPsO8j5C8A5OORD4hsymXX1M0Tn1LcE3sSp3pABGNFDtOzUlX1w9tz8WLiCcTQ9qwn33qVnIlV9zQBN1YcOJpEnnd5KN43aoj4mzph5OMvGAIboTijfhesF8btZxPyuL0y4Dpg53TtwxEAPFsjSAyN9N+PjY6RXrao6VzdviLemqo15VcNHNFRB9RW5xF0BTQ51tOIoFCgU+CIpEIPHoMZd7qhgNYnBqhqEZNlIfzJYsAoHpQ64pOvlZ8SNZKDgFeuMN2vogYG72NZXrLFYyrCG0FpKY77fo3VntVZxPnbsG5T+bWVG3d71ztThVzUH6DEHXo4hgILGUfY+ycGZDs9+f08WjMbS1zemEPWG0wWx8TnEjMRZ6WsqZjYlgv5xd/93rVWJVyhQKFAo8GEUKMDLh9Hti0ul7ZFtotg4aS2gUxdtnrZQCCrz0KsGtMiSz7ad6r+By2WLE/wXsqbCxnBNSnNlTWVbmy4BHB2bHLVxkywyOjRtYymuF2EX2hRqwrNJz2Zv/TTbvGaytYrph8siWj37nu1B07GHa2OoNsQGOYAXnRGJOyDatk8btWoZZXOHPs80PapTkyE2gyj8RBeFwJcJdFHckglCAJivAV6Mc4e40tfxCi6f5bWD9Awun1U2xg+f7MDRII4aTFcDvNx/MJ5uYNnlFpP/NRYBNxGtkEiJxJiMBobhALywYbbLSWAiG037qzZ7jPJ7BSgQ35V9u9RHX550q2zB7SKuFsno//xs27jAHqLj5THAnMyOb/InXUA9FK78CfGiW3wTf3gwlb69PW6cKvoMpD9Jf28AXWSK+snLLfI7TMtwu8hc+TrgTQ+uGYF7phNJpjTpRzIpKgBvDM4y9S1xmWgRPqI+Zp8X94x24jhTvU1GSHd7sKxqL/Wn6JFVbMW0q35WP8NHSh0P6VumX0abBz0b8KK+KK6XI1O6q3GAz9/0wKh/idNFynYFbKp/yArYHQApsYRPYXFMul+mpyZt7KgrWdXVG9S0St56Ut+n2dEkq2/7x+JV8dth5blQoFDgS6BA30BRNageJd7SwGZceUvEjwqOuSQGZ7c2x9jJGKpxfwVxo5eAL4+ZB95gsECiR1pvLGMd8oB1xkFl4Ui6taTqxRTtWtVpJ/9tXUcNL6o17XyD4h9FlCyx5R+FeJNsXGd6oW3ieDlkvmKuY46bmmDtJqt6cFT+6evZdJN124O7zu0iMdYJdPTJjHRNc6iiufLLv/RWfhUN/fJfZWnhF0GBArx8Ea/x3RpxYvjVDoXLf31glviNTqml2FNWWjbWOa1nI/no6TrcLD1EKHQCz4JgmziADCuIPbC/so2OBnfO2z1PUy6hvLXh0wYQlzaBrfG/efZaKLFHaeLmtbTM9dNEr70inW8KeaJ9kdab6gJOpuvFArQp9OS2xVQdVWPtZXUnUApDl8wc9HD61++mzQrSDGCMFjyouYG7AVqYpZkjNsrw1JjYB6f6C6Ppm/szKNeVrhhtKHW6LzPWWHNhk2lcP1XNGxrUTQkiZB7FeWUoEN9e9i2bVSNTrAyHGJxhL1d6cK3swqnCHS6VF5xU6rTyObqANgBP7iF7LvPm39wcS7evjaZDuMpkJWxzC04yOtYbvqXljUPSYdkIsSIYauiXACrG4KJv2/UDxYeiKqk6Ju/PPb58AzgVltWVx7pfVJ3NPVpxFMm8or1KyJU/qseEj/qSwqxsuatn88E9jLiRYptFL+5pSCeVgJ30sQnE/b6WFTD6yHd3nTPs+iKcL4j63bi2YAtm772WUD9cKk2/UQerrfmd9ROxPHXVxrMSlLBCgUKBL4QC0evf1pwYJd4W7+PCaxCgKk5jpg68jrUGY/21toGIKWuwZyho12HXI/S9GIctwMs2XI9Yn2a+cf0mAl86tnghM/6rpTEWn90ahb4LXS4wXl58VVlbOVZun0t8vebtUo3hXOZfhzlEHMSLAC5zHJotseb6/f35dJ1Dr/s3pxBrnUijrLnGx8VpLF7QpjCVUa5CgUKBQoHLpEABXi6T2p+wrJhq6mlGMxmTTmzQwqze/j5WiGBzNWsq6HVZRqnnJsDCz0/WTPThh0ercLocw+GC8lw2gxu7Akhct4RO3V2HC6XU+atETiQozrAYf2ymPibNuOq6Wc2UCp/as4nXJPbgLCSysmRWhdqHuFVe/fGl1M6poJN5gTJ7tP+IXe4+Cu7Upjl0v0wjJvHHB7CrckI/x0m9uFp6vSFM/KJImIWPmGdksUmLnzH0VCxyin+fDaRO8yU6Ic6ZSU5jxDHTRRhZVambltXRK35aYB6xuD8ZBfQBtV6evjWJ2ei7OeT7WQZsWReHC1wrUrb7DO6o5wAvsnS0Sn9awuKCzJvfXOBbAYDZxVy0OLHWAF42AWZWATTXto7Ti3VAF7hb1BckMiSLQV2+seGh0JXkVNAJoC6Bm/qa+ZSr5/67epWuBqzw8NN+rZlV3pHW0+u3RYQ6E6tt9eRxlIXE+0Qn3cUZY6JItJkeiFnpIYCXsbSAQsTvbwO2LE4AWE4DVqIj6fY1+lTHxPzUvkbxbrTFi9Iiuq4qXio5f/ZYp9c6wsu9UKBQ4EulwKARIW/raWNaHuf83AJetC6JWml81CDpFo6wHsm8sIFlu5er26y/OAB7voHOlx1AfPSJsQ7bNuuRysO5N0ZA5m0ctGZUuUbmJ0bEGCHj/jHtijzi/i55Ka4uKljXkcfKW+0QbfQsuvif4Bab4Tyd5g7E2MdMaTvck3AUL6Hb5Xf35tAfxtxxfdrA+w4szaOsu0Kfns8V+TxFduUqFCgUKBS4BAoU4OUSiHwVirAJPatIfdJCgNwSDxKnyw5KSnbQHvv05QYKZNFjgnI3mYyWiNE6YhIPpSiUk/2d/Y4p9+wdSHSIE2uUZnY5TZB1IJs3VSAOn+AouJpbNWX6f+71lbst2NJFsOUXD/XdcqqfGkcVWzfLVpOrX1qcqEbiNonLTet6XqEUdJeTpgMT+diziX8CvRkyO/3NLcxNS4YYEEbtOjrUwkA6a1AczMQ+3pVeCpSEIiYhcaI7NyexkuScLmMoTu3yJyCn2ThWtdAmua5k1KzcPzcK6JMXAKL+tIkSanGu7NCHJIL0HN1I4np5Qj+S8t0RABq9cllhkOJZWbEQ8NIjjU46WWunLThdVrdgJ+c7kxl0cbtMcHIn4EV9Tv+qj9zK1Oce0IPqoqu6+UPfb5PWvSNmfIjh6/79vgob4FN5eb2sp/VFk6JdjTP7WBPTWKOTWolVHcEuLstH1+ESU9+6syCT06Ppnsx/Arw8uDdPu7uAl1h+ov2TiCB5X45GRsFRste9/Tu4he1Y5blQoFDgy6DAoB4ffmqhxo328/u2XOlPjoXvnkuU73nU6zIy2GNslH496dJbxcrdyoY4XjbS4xdbxkkpfXrrmxI/3Wf8l7VI/rEeYaawdUstMxpFnFIpjaUWJSaNU+Kd7d2mQdAlL/w0eudxBlDT1m2aV31+NT4XiRgZp4sbR5gEVLl/a8K4XK4D2n+DWPjCjMS7ZZGya4CLDsV83vCW2GzRrvbZjcxCo32Z1yd3Bh2jUVexjp+cSKUChQKfnAIFePnkr+DyKhDzquYxuTXJ2x+bIJkxFOfHBicspqMC0aINJvTHnLBsoCz0NSf224gYPUd8AkkKuEIQt8Fq0S6bQulwkVLPru6ALzZPVoXFpB5lB1ySLzCcAhES9LBp0R5iGomQWDA1KU7GsLWQzUPNqUY1fxvngFIYDZQprDiKKo4F6aYQx8uhAS+HJhbRwUTMGH+3l9D5QjtlBUnt6pBOyj9HOXKRNZY5rLFIKegCJy4zbA6lPHUSIGYavRWjpA89HFKE13cV4KWPHJ/zg75rgS8yLy1xPekC6gFkvkD06CViR88BXpZZQO8BsEi0SGCL/vZhHzugH0rvkMSW6IYAMEPGUXZYAy8AnHxHWkBKUbX3EPvIrR873eLZv7Hq6QySxreomE2PUoIIicSeVzx5aB7H+1f4V/cqguUu2gh4EcdLBby4Mkk4ywBDxSYujp7rcJiJC+g+5tfnUYj47VezZmp6Znrc+tfsrHS+NLUdEisdhauMqriopN2j3oPC+iKWh0KBQoEvnAIxGqiZV29E0JrExlHu+6xDNB/IqME6wIuUtz9+KStHKPVf3UmvZeEIJbsrcFcKnJcmLbXJW+VK1rXUUJ5AFll7FWMwHWIGGEyZ3DfS534qJ78iTu6XuyNtxPN7+Dbvx32kTF6cQEeIqKr+IyOIqaIvTHpdpliTfXMbRboL43CRTmAxUAp1x+B6mbIDL+k7E7dLAC+ap8Od16i4CwUKBQoFLpoCBXi5aApfofx9AlaFmMGq2c1EI5jcXafLYXqNGds3iEf8vz+8sROWnx5tmpK3fThbbNPEBG8yQ1od6I9Nj4AETWzKUvob+i/54mf/tTlSuPziauLnk34Dlyie55Gn8tSRtjqxMc/GzwolbZ7O3VUc1Z/QAIG0fxONdDKv+zHAktV7GDkixeRxeBiZYiZ8bfym2C1K9OjafBe54sn0zVdzaRbw5SZa9KXHZZowMxEsFlel5U+T/YkJX9Uh7ARpVGi5rj4Fqs9JFY1vOIBMA1cANaXv5TUKqt+sSNSolx5ignwZhcwvMUf+EgXVR/QbfV9dRGqGBV5aTiiCxix7ZK9PRACFX/Fde2jEidD2c/g3H5p84qPz0KZvKrXyj1w8XuMrl4Af9/EP11OE23Ps/438BUzpsj5mfU39DUWJrKfll44PzDLFg5uAmBI9ujsOKDOKVbEFRPem062b83CYeZ+yfPTDpVrqsjziwb3qsOqxvlVVOTW8jlgchQKFAl8ABfrHLG9Qa7D4hK3U2BW1ETitgyCJfm8BuuhA7AUcyOKefM18IjBfesNercBZCXfkPnrnxLVrazImEx2IaV1m46EBL9Gw8KOsKKwKCuo0dJGPLkUMt3lUP60M8qCB8fMIkTbPVyu//PK6KoaAF63NxMmj9ZfEU6fhQJYo+Czi39/fmUasG4MGgC/XEe+emhhPM7MT0EGAS0XXKvO6jDYB8qKLu1CgUKBQ4AIoUICXCyDqVc3SJnVmHAMaqplHIjX6E6fL7h4n88sAL5zM/98/vjaZ4p8ebXH6Lj0MCNJKrMZ0SzCh4yMAQRO7rnr+8lm+IYGCs3m1Pa3mgbFp9cSxscyzqiptXso0MvYtoMcMv0jXzicLj0oLcVFeVfaa4O1ZFmO4DmFrNZ0UQp5o3xBAjBY1HNCnBZSBSk/HDRSB/u7BYlqcn0AZ6IyJhEyi10Xmp7uIX+lSLQcCLxZafj5rCmSflb5jcY/ZGphvRu5XAC5vBLwgq7+GqNGPDzewirWXnjzH+hFWj/TtSYfLhHFHCVTw/oV2pD6yNF969IUoOO7uH099iU88KG4es90D+xPkMVXhqEHEcp/+WHn+HqJUTUq5pPNFNJII4wF/eyhLkuji1ze7aX5qJH17C/E96PLNvev0ran01e1F61Od4K6LMaiqSD0ENcVkJUZt/R61zaL2RyhPhQKFAl8gBdTzr3Cvp3ri7tBaTXdZyxMXpawcidtlBat3T+F8ec688vglliV7iH/3sISE5bsjDoxkFGCE9ZqLpOr15aNz/zivMdopoV/9xaiodHGd5R9x2vdB+ZD7iQHa41npsSazrODisfbTpoweEhXvcvg1ydwgLskbi2Po3eum39ydxQ3HC+DL9KRMRyOWCvjiHMZNXXJKNJ+ASi9XoUChQKHAxVOgAC8XT+MrVUJMejGpi9NFE/qr5XU7UTHt+QAv//7TGso9DziZ30G5pxSYjRmrpuRlNZkLStAc6crKNJFrkYBnNak201ze/EGT2+CYkcpT6Ffx3jW95+m/ShPpI9f+e+Qac/5x5RC4JEVuYvcVvbY5kj8yVOao4ng5RAdFJ11Hi/6t6xPpX75dgrV1PN29MW36KKbGu6mD+UIDXkgf5Xjp2VPm7K9ZebpSFDjtE4xKVuG6CUjw/qBv5giuFhbMKKrWieXy+nb66TGnlsuwjr9A78uLvTQMy/QwIJ2sXo3BISVRIpmCDkxQRegLEvBi3zWZ2z3Kbt0j7OSnJZ8IbSX6oMfT83Pmt6qeRKs5vbL+ZX52sisl1dJpgDUPWOtHRo7STaxUTE8OAWyiV4kTzd/dv4b43jQAzCyLahfvG4EFaAzdL8FxpybEGKd+H336rKaJGifpdFaKElYoUCjweVPgvHr9eeVzkpqaQ3RpHpEich2MrWPhaEX6XgBenrzaMmtHD1G2u4Yy9g1EwTe3pD8LzkjmEgEvOiDSIKjxTWNtMzZa1jGb2IPHcv93/x00cnq9T82jNXc15TLDqb7VpXYb4IJOMNFCSy+BLh30gbEMRZSow0EXay+AF4l3/+b2LHrCxrEkKWt4iHfDFTk2Jn1gnmG0PS/PAlWQxWnKjjqUe6FAoUChwHlToAAv503RK5qf5ha7zCG2fp0iuBLQzS2Utj1bRYZ4F50um4ga7aa/P9UpCuZsOZWXzolRhGm1GRwdkaIyrPcwR/mEphN5Ze5/UU4UpzIbdzPlyd+vJjR88nv/VNj/9Lb0Z+fclBJcBLExFPCitkl3hLaNYvcVx8s2nEHHuGXqUSFdOF9mUQa6NNdF/8tY+sO3i+kGwMtXtzA7ja6XGU5dRgFeRgFgrOZBHCu6asugJjVVK66rRAF9UGe9L8IDDtE3o4WeTIMesXB8jmz+cxbKT16tI2K0nX55hpnpdfWvfUxP76dhAASdzE0AvJgSZilJVNeKyz5mL1xl+Kc0+AvPfc+qbmR9YXerSFUbKhL9K0YBv+vXOYTE8eIbjAOAlGNAFykfRoZ/itNNdCv98ZtFQM2p9P29GdedNDVpAMzU9ISBwdFW0dzLqnxU9hmNVA3PCj8jaQkqFCgUKBS4EAoEUKDMD1mDSRH51s4uJqZ7KNTdS09Rsiuxo8fSG4bFo9VtrOJhZnqfuHY0pjmEtD62aZyVy8fjACMUGnOWhytBNhr2rVkIO3Flceuwasyvn93hvvkBVKQlxP/3pVB8ptF0DJJkdST6KAvPLnrOGPorbpeJdPvGpBkz+Bq9Lkuz46zHxlh3cUAo8Mlkc72cmH+aQrJC8zY3EYqrUKBQoFDg3ClQgJdzJ+mnzFBTVUxmXo+YN2NekclkTeh7UuLJKcoq4g/LnKL8GdEibQj/+WzTzN2ubaNsFn0u2/vSMTGUsNZnp8qdIXZCdpStsk5eUV5ejTxmNQVmCfPQzPtMZ57L6elPD+nPXEsPoxvZRpp8kaIw+R8KiKGBR0cHtqHe2983ZaBT6HK5iUWWP9yfNK36925OcQLDAmBBnC9jaRorR5Z/lbkWEScXAf11Kk+fDwX0zUf/ilqb0lwWjDItfYBlip+frKaHKKz+x+NlsxS2vD4Ep9kQlou471YgC99fpwJgLB+e/Ut3DhrPO3yipPhi47n5huXTjt3EuixXu37V8l4VawWZSXv8OLC1/rVf0W//cN9OOP/1wRSWjsbS7+/PAMqMsuAGgAHcvLY0C2s9unGqxuYbFmtl9nJyeuTF5/6XRZlSTqFAoUChwGkUsHFMYxcTjLhYpIhcVu90QCZ9L08B8l8AvDx5s40Y625aBXR5g+XJvX1WGIgbCbRwlkmtWzTc+tirsS5A/cYnq0U1GBrYoYSKnwX3P/WHeLR8ZI2EEU9hlbu6WRFWP8VVqfyzZ8XFLSCJuEiWYuTAFenOTA2Z6eibS5PpJtzG8xgzuAcoPz+FPjCsGYnjxyxO6oTQLloazsrHbipoYEAeqbgLBQoFCgXOjwIFeDk/Wl6BnDRR9c8u1bxZzy1+An+Eqds9dCkcpmUQFumf+POPb9IrQBgp/VzbkknbDhP3UDqUUBF3sa1qY1Nb5FFR1QSpIqvtFF4W4PP9qRTJ6+jxT406MMDT++/p6U8P6c80akMz+67mMUoapnlaFMjyDKy/B/tM8MlO48X18v0dNoGzWgBMpAV0vdy+PsfJjBYCyBnb5K7JXyBOAV76CP0ZP0T/UhPy9ZvYwmWNYs/+9gBeNtLPT9cQMVpJL15vp82dYcJlFWyEE0pZAuObMHBTPxVBsg/YvzsrxXt4/XFmkapkV/3moCP1PlF1b5TGEnGY7QBYCRze7mF+G50Ff7yPGN+1bvruNmbasVhx7zbKrOF2uXVzDvGsjrPVW+OrfhYvJ38xhAfpVLzccbek5adQoFCgUKCiwKcaG1SuxnwbK7kLlDaLi8wnWzv7jIn7BuC/RnxVel5kMW8FrpfXKGzf2ZO1PA6IODgTp4x0vgiEj/Px0wAAQABJREFUEXjjazjdXYG78vfxOB+OiWz/VQuNxrrkjitG0Hh+211lDI6jXDVM25+5tTqizhLylielD3MHR4HbJQG4Y7QAC0YSMVpgrXXz2mS6BfgyC/Byc2GK+UCcxsjtaj6t2+aFn1aHwTUrvoUChQKFAhdDgQK8XAxdP1GuPlHlhduGDY+YXPfYzByK0wU9E73dPWSEt9JLTk3+/NMyIMxeevhyl00hymMRKRoyRbodSxsHBzF52ZxIvhJX0mWyue6055MTdRPYuDQh5k/5s/xPma1r/yZtVWjrlufXCtLjgOzzHOkclsgmcEUfhvsHP4kbSVRrB9bfYeSNJQ4xM4G56flhFgOddHtxIl1fnErf3lu0DeIS5g27HNcYeAVS4wsCy7r8fOYUsO+Bz0T9Qu81vp8NLBdt7fCn08mtHqCLxIs24XjZsP4mc+wCNo/FQSZw0zLwG7/+nSk3y1DPTih9svbZVt9ulJf3oyZmE+p+g36V0cl48qmKsEQsY1WrExm0fXyZ3vb1ZE1+lYvFscf0VCKBMCeJNMq0+yZmuKXzZXMH0SP62dfXsSCG3pfbix1ONgE67y8ZwPngq3kAzlH6IeAw/awD94tdNdGakuXv7ehvTX8MT15+CwUKBQoFggIaqy50nKCAGDljZIyypeNEf3uAKjuALjvo5lsxfS9YNUI0fB3xo7VNxI3429w+MCuVPeL2sHa0CweMuJdl+UjrONPRJzGkqjESnNbV37aoSe6vGOEfsfUcbuWiK4+X+Wi+q9O7v1JHjhquJVTkPgJO3IrfKKJFowDvEjedx5iBi3cDsMDdch3dLovodZlGRFdcxpMTY3AZd+COcbBHXC9xRXvjudwLBQoFCgU+BQUK8PIpqH6JZbaBlx6bGVkxerW8kbawZPQMdtXnWDL668/onoBV9Sk6J7bQjj/WcZ0uI+LvZMbSpJVPr76nYTFgs6bC25NtTKcnG3taSJO/XBI4aHxO5iKfdpnt58Gp6nQDsu+rGw9aKjTAizheWBbwI8Bp5+DAaiAxYtS4pMXpY8xLy8JRFyWgU+k3KANdnB8DhJmEnpzIA7rIEovLHZ9Wt+L/OVFA30JcAbzIT4vijQ0sGKEIcU2cZC920qOXiPI9kZJdzEcDthxjNWt4BBEZAD27su9R2doitcrexHCIpCgWrYrblN646u/bc/2gX+WWVceWw/lzZJqXKr9BcTyuwJWI7bGOAV6aMNLirb8Oi2UDNuHI2+WYdhvdBfLHijT9CxbzaUyIsgD/PVbEri9Mpu8ezJvo0SQKd0foY9JHZVe8GxubvG7erua3qsAZ9Y4Y5V4oUChQKHBBFKiGQrsx1jXzCg+Mm6ZcFu4VAS9bAC+7rOPWAfXXMTO9uokb3S/b6H1Zx72JGNImXDGyciSFuzsAL7u7x6QDdGHMDcXtGhFVnlZaujRCqzRdFhIP9mzep/xkEU+JoYJUVj2vVfGsvV6a+dBSChfM71wu0ucyOYGSXICXGaxELsx0zYLRAlzE06y1FuFwmUW0aJoFmLhdJgBexmvgZYDVzdPqV/wLBQoFCgUuiQIFeLkUQvv0ounMLz2H+1Iq4BMsc5rAFrGp/vLotel3efxi3VhVH746YJI+Tq83JB7BZKfJiw1LZ1i6EzQN+vTcLAi83g680Jpqc9O0Jtrc+ITrtJB+irwLjSJF5KjncEdpurf9289VXGtDlr5yetM0iTeLB7k4jOeXSKY3B8W7hwfofHGul3sofPu33yyhcR/xCHS+TLEgmETfyyj8sl0U7tplG0PVJbtaj1lIcX4OFOBzECj39MVqev1mw5Xqvl5PT7Fg9Awluk8ANlc2EeCjXw3B2SFArit59Phu9TmF23yrjzDafkIerhVu8U75viOPC7irFv2fbrsO+XPErFJVAIxuFqIOBw0P+FMXOYRVXjHT8R7PstpxADt5J/3vv19Id2Ez/2+/nU+LnHbOcgI6CvgiE6L945GAYdHVr7z0yqtV9/At90KBQoFCgUugQDU4xRjVzAKMfQyC0u8iIwdmhRIOQAEvPQ7QdhA92tk9RCk5SnUBqsUJs8Eab1VcMAAxOkxb2eSQCDPTq8w7GvtM94kKYlDUXHWAU8C+xkytcQKAiTGT4ybzP0GFurIxoipPZdaMtZFG60SN5QLbaQZRBK74pZWlOBojHRgL8yP6XMDPp8YB2lGWOw34Ms+YL6uREiWaxXiB9L1onToJICOF9FpfmbU75oAOmUgs3nS9RCU0wfDfJoPwK/dCgUKBQoFLpkABXi6F4DbaZyW1n7Og83aqKC6dIEsviVhStzkd+fGf6HRZ2UyPXmyioE3sqpyI7CXAFylxw7qKmbRFgzybQ02QzgLKvUJaNClrls03ifKJK/x9cvVfD4uQiOn3QfH6Y+RPip3TsGpkFsVj5PE8zsmYnsjKt0Y1CwIrQsEemOXuywZZP5J5aW0GZXJ6F705OqHRpvDe9bH0r9/PpzvXJtK9W5PGFjs7g5lDFgxaHNgqpM6RAvKq1v7F8blQQItKYwcHhJNC3YdPVgBfNtNjrIU9eYF40fpBeo3Jz9fr9EH6mMAWmWIfE4dGDbz4Irv+NqrvMf9m7UwSj9rPQIv4eHJq2UdbedSx8wiV+1PEizJVL9y0wXyqavrYEl3EQRfRd2dPeqlgo9/fRbSok/6338ylO0sT6b9/P2eWLJbgfpEy6zn6mWhr4ItlrDK8i+UEyKli0fLA4i4UKBQoFLgsCjAYxXgU41+IcUu0WZaKDuBc0aGZ/qQ/TDpfpLNvn/lGYtEynKA13tY2YMu6Wz6S0t2VzQMTP3q9ShoKIbrpgHFrjUN2gGTLOgqWKJJWfBo7ba6h/bIwF7XrGyejwjWNPFTrIr+IoHrxoLZolJfuLv57W4mmqC7+5OVo/STRojEDUoZMp8s1wBYTJYLLcQGulsmxTpoAlTGT2YzzXTLo8CfQvQsYM0b4uNavzKs2z1p1ssrW9asrXhyFAoUChQKXRoECvFwaqT9NQdoMauLeQyxG+l1WEHvQich//vAqvXyzmX55vsNmEBbVXgdwBoWfB9roDKVxJi0JQEjSSLOk87toApVLE3O0p3aEh0+q1a9P3tmk59lVcZXWwwbFqzOkvIjn26fIL8ruf3Zfn/CbPNwVMRv/yDuWGb4YaMJxVYm87QonDf+1uRN9TdEup0+bKCVmH83mbyjdXcTK0TeT6KMYT7JytMimcHF+BrZYLSLGSE6mWoGQl5XslfZic3dfRcrDpVLA3k+7xOzl6LuoHnUiKVPIh/Sz3b3d9NOjVfrWZvr50XJ69HQjrWLBiK6XNveHAQ58QSiW73EWjF1O5qI/GUeZylXGlrfc9Sdobvt0LFDfT1RBkT2uRzrtN4+Xu9vx87Dc/aHxlE755JfXt2m7h0cs6SJQEp1capOwBjK8zYnuJrpzJsZG0r9+O5Pu0M/+eH8CM+4os0bJ7gwWxK4tzph1KMn5i7Aaz6zPVkVH/nlNirtQoFCgUODSKZAN2Y3TXfrVfKA1BgYU7XBnuweHC8CLuF00jnH0Q6QjGyNtjMMtUaQ1OF42EDl6w12mptfhknnDARuMMuTjnDMwyLAulIJ3yjCOF4EfDr7AL6JR08uwAdPr5DB4NdM0Fe4jWz7WOvASXC4APrRH05slJV+V12F8H+2gK4/zKAEuE+hymZzowCU8DBcjer0Y26fQ4bKIAl0dak2iVE/ipKqWTZXkpnxG4SQW8NIRaEN6WQgcRZSXpaxiepm4+uqnoHIVChQKFApcIgUK8HKJxP4URUmRribuHRTp6u8NWvDFhvofmI9+jlLdRy8wRchp/MFxl8kXhZ8JEQgqytxl92rv0we82HRsk7FaVDvq5vl87L8+dbs7IvQ/ue+geBG//27TbeV1smwFhG9TziBX5KqSNSl7DfL0EcPvsWAgJpO8CtGGkAMnTpCQoYbjZYvFjWSox8aG0vWZEawcjaLvZSzdRezoOqalb16fSYtzmDycQxSCKlk9yevEQiAa0F+B8nSFKbDPQlj6k/bgytjp9dIP6Ez6GcDlH0/W0i9PNtPWvha4w2mPPnaIbpcOel3UtwS6mOAZd712gaT1Zd+BP2e+tmqtn4lj0epE7+OwEt8hwXnFa9fUWxG+6oO64hlcir6hP2eJ35DSSHYLG1g5GmeA+u29ibSkfnZzFD1KE+nr2wuwpTsAM1otwJWBUdbu79DUEqVQoFCgUOCyKODIAaU5pKHxLsAEjY5aX8hCkeYXKRqXNcpdRIv2WG8IWBa2LI6UDqCDdMgJrN4H/Jdid4klraNsd1ncLxwKvVrd4XBI6xSsWQLGyPoR2EzaYo1IdlaH2nqljZoueqQQv/zQTe662lVIfov1TA7SiNNG7dknof5Joa/CpR9PDMCzGCcYHx1CcW4XBbqjpjx9BqBlbmaUA6txwJgOoEs3jSOaO6K5kz879KKthyIS1zC6wcTlIjGjKdKOQo8JQJpQoWaR+PGZtnqKySYCy71QoFCgUOCCKVCAlwsm8GVmr8nQJjvN3tUlRboHzKpr61tMuHvpCcp0Bbz8Pz8iCvFmLz19hQJQWFHHYNHXhNaxEwJOO4yzRZn4dkiTpi4vA0dVRPhbYPWT+zU1Uc3ykDxFnV2V8enxmlTK2eM1E2mkI6wp2JJY2QRHjCafcDW5tJLW5dT6bURf/rRxFpkkxgXDA8p2OXmiBFk5mh47TjdmjjmtkZnDUTuNv//VQrq9NJOuL03ZeZLSO4gTS5Soi2XfPFxJlyh5klJXsqofW6nqo7EWW5PjK2ra3+OUcQM5PS141zd30t8EvGCa/YeHm+nvj7fSkPoVfx0+Dims9vNE+hYdyt6+UAbRE+BF/yznJvvWd6uAvA7hbjf0Q+K188if8/xy/7b77fGMltbKqq1k0bg8P/UP+RrACZ12ZaGDjiZrHVKTBIMLShWH0rXpZLqUfvfNkvWtb+5dM8sWOgFVWvWx2Ax4zpZt7XRiN4/FVShQKFAocCkU0ILKrvYagFmAIOl1cQ4VF2fuoTRX3Mvy7wIwdJg3xOExJt1x4gQByRAQIbFniT9vs+ZbZV4SN+4rATAbroD3xSrW9gBgNlG6KwW8ewL8xfVi/5iJQpdYVI/KxJmAvFQ3D/KxXul0aWmkmUyXL5M8ls1yhKEJz9MRKOCoA6cLeAoiRCNwAmO1aHIsLWEmWhaKpMtlTsp0ZyZon4sXCVjSPwE3OlSUyNU+gJSLMjkIJTBqxtK4DhibR/CzOsbdqyjPchUKFAoUClwqBQrwcqnkvtjCNBnW07dmPa49Tj0OmKjfYLloY7uHvonN9GZjN/1/f19Pz6Tw841OQw7NHKtMHo9yoiClnwG81GBDVXUvg4dqwvJpNSZhj9SeyyJOlcXAWzvNwEineMak39SC3FoZ2nR/ZkWaBI0rCvSEartdVQRTOoyfy0ojzsXKREEdFMVNdI7S/MShaeG/jjjEvZuzSWZv73K/uTRtJzNSXiwz3FJ2F1lXJdiiJdxX864an6TU1azrR9aqejnWYmtyvK2m/VJavbq2xQJ3J62sbqe/Abj8jBjf3x9tp3882/bTSPqWTF3KspW+WeuiOq3Th/VewMtHtueKJQ8qtoEX0acez6izWOPBkdl4wJc3fJQWJo85JcWS2NgRHC/j6Y/focwarjKZmZZ5acn6S5wrwJdodj2GhUdUIJ7LvVCgUKBQ4KIpYNOIzyUxo9RrDMo2ESO4XfbQ7SLxIo17UqC7jxL/Q058xuHo6DCnjKLQfwzRS4npjOrUh9WEDtxM75yU7TIniVNmZW2PNeBeWkMHzCvEjjZ7AC+ILq1tIR4r8IKpSGWaeBPjrE1N3EMvi3GtUFHVUSCMnnXF8CmAI/6cE0cW6ggHiYHJ0yMSIBHtYe5dQKIx9LmgEx0OFxTmGrcL5qHheJmb6qZpiRahE2+K5661U9zYzAgqm8JFj56AFxQHaw2mAzBqRh2O0vT0BNaNyIM5wKZWQkzvF3dVpFoeN5U3//JTKFAoUChw8RQowMvF0/hCS8gnas2DNqFU3Cqah6T9fpfJ+smzFSbeXpIVo2UUrv3nw156gcZ7KVzb5tRjzlgzkY2tlOoeVBOwRB8MtLBjAwpoTbZ6zOugWTgmYjW8ii7nmVeTpnE1Cd4tlyYlrubBsvEcWvnUj63IdcEe4bTQvAzRgHVAdWEKkbOd7tAupzZo45/upAd3Z9JvHyykr+/MpjtsDke70rwv1mDYY1mA9NEwsuFeLxAyv+L8NBRo3lH94VhF9LS6tp1evV5Lyytb6eXKRvrxSQ8T0nvp5xe76dGrPRaYI4jHsBDkVE9iMOLAULpjODgc3NRXFl9aBUHEo+LVYTzUV389au/P1lE1uO/mLVdLw7qRFv068+ymXejIQvt4P91YGDcrYl/dnEl/AICZBniZ5vRUp8CS+885XjQ89vWrjM6fLelKxQsFCgU+LwrY8O1juP9qLaXByJ8EJsh8tESLpLflAHDEuGsZwDQXjcPRJyuJmk/G0YsiIEPWfXQdMa8ovUSUTDxpT0p5Bbxw3z5EvLxHngAviEevYZZ6F/1/AngEYhywkJEi3z3ADYk5iZt3n58D6maHTDwbSNNMiAa4aIzVMpHqmAhRh2e5BXyIu0X3DkCI6qg6j1HnCdowjVWieUxCT6GXRVwuEimaFXBO2yYAT/TnIkRkRrtl6MGAF+ixC7C0C40OBVBRqWMpGkb0agpdX+OUpTylfFfjvQMvHCrGeB93o1j5KRQoFCgUuBwKFODlcuh8YaXUc19MIrWHJkcsFTGpSgziHw+X02s2hY+wsCKZ359eHKYVrKysYmqwBwu/lJaNcjwxxmm8lJH56QaQiyYz1b6erU42JSvS9o5RFcX0JcTJNG2fJk3jauK8Wy5NSlzNg2VjOfRVtMn9ROQ8iBYoK6VvZVnFasrSYkSXJJiPOJU6OtxDNllyy530zd3p9LsHc9xn0lfXp1hMjLHwQDM/4ItOqVQ1kdiqmBWUOT3z8vvJKODfUFY8L6uCJdPrZVkwWgF82UjPMCX9C+bZn64cwVV2kF6uSjRGp3sAmyw2xQ6uS/kZsFm/dL1tK0XB/r3ZB5DzfVhQ9dPEzX0/X3f/1+5PBvtWTarCxSFGZzs87DHGaZOwn64j0vdvmJX+6sZ0+tO3cyhlHEuzs+pnXcQoR+uNgTLqA176i/x8SVdqXihQKPB5UYDhux7dqqHcLALJnzlBAIhAF7dk5LpMbI3BmCWAQ+I3Dk4ALjCnCNgQoKG0BuaTj4EviByJQ0Z6YUxUCS6RTUSQlO8WIMzKpuuD6eG3AxDTA4TZ6cFNwoGcOGEk6rQrYINFoTgO8TIwRnnHpTWjcbvwA1MnumekNB5DA3C0CHwZA1yR7pUJOFgm4c6RcnTpYZGFInG1TE+PEdbl4A89LozZ47RNynFllUiHUwJ1/IDq0LltqMcBa6xdgCLp/doHKOrB2S3ASYsoWY8cgxNoykxNS7zX9b+IS1nivXZpwaWrzAFOh/JbKFAocCkUuALAiwbBMvJ9zNuOecTz8ElFv9qUrAOybAK+/O2nV+n5601O4TcBXnbTy40h5HuxsoK1lQM0208wSUm7vP40gVYYAnNYBbxY5npP+fuqyvKbF08UsYM28fLAxreOnG003S//VT5x9ecTvvm9r9w8KZFsgVNnEfWL1K3I4W33OtGJr1QhVmY1gduNl6EFiRY52+j7EBvwJODLt3cm0x8ezKTv7szU5qUnJ8YBX7TQGPWSqqIsX6p0Vq36qlgeLoUC9ZcQ74n+pe9K68/nr9bTPwE3nzxfSb88XgFsOU4rO0MJPCatbrs8uxagUgor05fWZ0mn9LZItpfNT11I1aTwH9jCPLIi6tkSVO5BifJ4efp23IjX9m8/n3c85R9tkLupo5+o0uNYiEuPgfQXmB4DlIYvznXTv303ne5fn0h/+m7OFDQuYUVsahorYoAwxt5e9VOBXcYBkxejospVKFAoUChwWRRgaLN1SZRnzz43GKAMiLCNTheNcz0AGK0rJDY5PCTlsgAccHOMC8QQl7JxlIirRCsSH9hs5GSikf4T5acxU9b3pPNvxzihAVjIf3MHjhcADLlVXgAv4iRRuQJcZPlIVjEl7klyEz9yjMPHUin4NQtFAlkYnzXHSQeNxKHEcWLrS0CYScAViduqzuGeYv0zbkBJt+bgkaloG7PVnlhfAeRo3tSfK9alboBIOjiURUGJYxlHEHWdogxx1oxrfcVhh/KTuJLo5JMvRK/yDfKXe6FAoUChwGVQ4AoAL5fRzC+3DE1CcWkesU0cHjYxMdGumSb7nfRnrBg9fbkJx8tmWtnYT8s7Mms7hFZ7gBfMSE8wSWpOMuVlyqfaxIUJ5SijuRMpNkVZHfr2TE3k93RF3rrrygtwn0G/vuBQXNJF0iqitafOJvKPXFqRw9vudaIsS0/vJeGG8KK9+fJCTLkdR0OSpx5lsSHz0t/cHE+/uz+dvoPz5d6NCcxKT7IhRFs/pqWnEImIJlppyqi6Mmd4lfsnokD9JcjB39FRdfpGP3v6fC39/ReZjl5J/8CU9DKcZJu94bS+N5w2dgPURCGineB5ejUj+u+xzFyZh9/q3/oDqB11UP3RZD7uVNy6tidCP43H+9Qpb2vTDt9UaIwatgX2JpuEXfrZJgvueawb/es3UwAv4+lPAJyz6AW4eW02zUxPpjksHWkRb+apRZkAXoIQeXHhV+6FAoUChQIXSQGGtr51CYsIzQcCWMS5IR0mAhN2xIkCAKIwcX1oDhnmTwDGOACGRGoY3mz9oTGyXvzgVBr5qByBL/oTt6DyNtBCojqAMPvG5QIgg84XAS4aVyWi3gNlMREk2Fz2EOPZN64X1jjkSzV9lqEQiRVpDWSmoambAS8sKCViK7fqaUAMdbU6j6v+AC2Ei/u3NgUt8Wvi21hNhmqPtUGNoCV2DKiyKVwgkoD3HROPOkaECiCetkkMaYz1bHATT0wg1mScxaqL0BsdK5KhKkxedln+lbvcCgUKBQoFLpACBXi5QOJeRtYBtNgkQoEhunDIJLnH5Lm8uoXVol76X395mR6/3E4PsbSyiob7vYMOWkhkPhrWVCYhlMu7NvpqgkvoTtAV81JM3+ZpP4rYhDb+5+HKZ8GzymjXYdBz1CfP56x4ET/ueTr59ddNYI+fxDurrU6XJA/dY6GyurnPRC/rAyndvzmWvr87nr69PcHmcDItoWB3bnYyzUxNpDkUweWXLca0KODKS8vjFPdlUEDvvnkD9RMO9TvJzx8Cvhzwvh8+XU3/8fc36Z+PV9MPv6yxgMWsJ4DmIWbaMTJuuUj2XLnZ2riyGmG582PvnLD42uRv8v4ByGT1IKi6+mKHJ3dLnT1/Cme7Du3nqFPbv/0c8dh0sCCXNSgtyqWDaoX+1eMdrLLgnp3qpH+5P5HuLnXTH+6No9QaM+4osl6Yn05Li7PGth7Ai4hcda8qc8pUseUqFCgUKBS4LApoHukb8asDM+YTAQtSHNtD9MeAF0R9BECMSa8LnBwSLXIAQ7pSUDprIELFyVfXP3KPeYJn8lU+yt/ACwAYrVcCyJC1JAEX4oqRv0xTG8AhN38KM5PQpBdbdOTsayBx4zh3p8ZaAS0y6Sz3qMAi7jJ77WI/DowICLFniUopLXE0FAtIqgdlG5t9gFaLVKbaoTpr7hVQZCAVQLzEolRPpgrLh2xN7GgCzheJMkncyYEX5c8lzb+6efbmLj+FAoUChQIXSYErCLxoWC2j4Lu+dE2imv5s4pWLyUgcFzrB0MT9Br0uK4gb/c+/vEq/vNhCue4OVowOsBzYJZUmHU3aDro0VMdVb/iUuy7/NWf906QYHB4R3yde+/1fVrlR10H3qFPUpWmPuSCgaKjJXusRLQa0IVzfZssN661MJn7FhvCbm6PpwU02h4hD3MAG7iKiELOIQizOT/rMH8UYrZsyBtWo+F0+BfLXo5NDnRjuHQBi8q5/ebqW/uOHZUxHr6W//rzG4k+Wqnj59DNsSfuIxivVW+UMj+6SvV9zKnf1Ij1Uv1ZgFs9i5D+exnMNd4Tn6dphEUf3TxnPGkgd+usXNer3FaeLgBctyh14WeM0WCam11lwS2nxb++OpVvznfSb26NpfnYs3b81n65hvv3G9Tk2AX7yauOkMGVfb+Pgimr4U/ktFCgUKBS4eAow7jiMoKJCxEj6SgRyALyg/FZcJ3soxpWojyAJARlhwUj6XVy5LoOZ1iDtGptHNoqyWNSBgXxs3ajyqzqICURrFwc0JJbk60gBMAbKMN9JjCfWl5ZPlZfXniGVOoxorqM6GqM1Xo+YzkDdpafFOWEUptOHEZ61bpLoqLxEA3uu3PIRhcyTsjR2W+3xMvEp/FS3PYEt/Amg0mHIHvQz3YTEE1GmxFWMWJZEmwRa+eVzbFiU1KGIRa5Cy61QoFCgUOCiKHAFgBcNeO3LRt62Z3luUUCTpl8+Kcl9yAmGTih66D3Y3t3FbOA24Mtu+h//BfDyfDs9etnDvOChKXaV+WiTFzbUv8osMm29Ap+u5VkX6kUP/M3j5e525Dwsc2v2jcvqk4XZ8uJ96xCZDbrneUd4Vr55nVZetdihvr5g0MLGWYV1AiOrAcPsvYfherk1O5zuLoyke9fH0k1M3965NYcoxAwbxMl0A+4XX1xQWLvoqFK5XzoFjJus731U75t3rAXpNubZZVWht7eX/vl0M/37j8vpx8cb6T9/WaeuOsnjdHKki3UrWJ5svUdC+5T0k2dsnnX7LJQf8837Qh0jHHk65Zc/R5z2PY+Xu88jXjuP057PKrehjC+NmzZpwS4yaoEvjpctlOpK/8DmDsoUkda7d30kXZseTl9zX0DU6JuvrqfbN2bSndvzxtIu8EWXvUVVIS4VkT+Hf7kXChQKFApcEAV8qRXjmwMvB+JUFgcHgMsOf/u4ZT6a6QaAApEd6S2RUl1ZMUJMJ7dkZKByZKc6V4dn5sWPAf54RxSNg/oXz9bMKrKJJRHi4km6A3ZItFZcLqb3j7WOgBgVwz9dKl9js8Zpc2u8BlRxN3c9xzhLvEhniU/8VBG5aR6OZBFNwIvoZxzGgC7ixJHeGtFtjzlZ6y+BMvzHcMQYoAvWkzBRLeBFeUV+qlO4bQ0WBZR7oUChQKHABVHgCgAvect8GM99ivt0CuQTt026RD1khj4A9d9kU7iF0rTnb7YwH91L/9dfljFt20tPXgO8bB6aqdVRgJdRNofDsGroJENXnIgwL7Yuh15anjwqoqc9GZb7vE+8PNvT8n7X/PI6vKu73fjT62Ax+TF66aE6OdKJ1TYLADv9Yb+3NJUSkg/p9uIYpm9HMSu9kG7jsTg/lW4BwMT781WAymvX4V3rXuKdFwXOAl7EUbaxuQP4sgPwgtWwJ1vpf/19zYCXv/yyYSd/kluf6GJdAaV+9mHw2/TZ/P0231fjUmT+TnZEPOPqix2e2V1lvC2Oop9HvLw9yvNdylW8k1fk1M5Bi3qRQ+AL3SttM9btM27t9I7ZjByn2wvDaW6cPrYwlBaxkvH919fS3duz6T59TUqspU9AV93X7IkfFRSFhl+5FwoUChQKnCcFqgFt8ErKgRfjVBaYb9aMJOYj8IBFBQOUAA1ZMpJFIN0lyiPxS1n/acav0wYyCvfJx1rUDHkn48tHc1+AG7Ym5FljruWh+ig7/7G6qXzLiQHax2nP14EXK9LH3WoMV9oM8vAIfb8+1lsRnhWhVqjFUnqJ4qpuopE4dHbEGYQ1JgetULTLAeQRYdNwvEwAvMwY8OJWOzUH+J8XqnlF2dtlD5W73AoFCgUKBc6ZAlcMeDnn1n3h2TXzaMPxcoD4wz5/KxubBr48ebmVXq7tpP/xl7X088u99PTNHifER2kRxa46OZnkBEByrzodUH4CYPTPdjgZ/eJcJOamJqhvxsL7ZAyPm8drUp90fcJ4mXhVU2dcDaF5aOrXt3DAW/O10RASMOdzGn8E8CJ6HKVpNoZzE8fp+mwnLc6OpN98vZTusSG8ASJzBwCmY1xHyl4ZcS/XJ6eAAS/Uwu7VYkyvRs5dxFykP2lD/QyrYf94vpP+50/r6W9Pd9JfH26hrBpzmfSvaUyGT8ISbr3Kuob6GY4qP3UX8z7RWpWkkLM+hggfnIOnjbDI70RBVRkRb1B4+J2Wx6A6njyljFyakgbnF7m149WnpWQkEu4xVnGwCWu5i/MtTh5B6+O0MH6UFmZH0x++u5a+/moezpclYzOXLoR8UxD1eeu9qcjZr+OtGZUIhQKFAr9qCthYYisspgAHDnwO0BoM0IBDs61tdFcBvkjRraIbpx+DosR1BCDLBLNAZFNCC5asezNExejpVG5GWAc6mjlN4U0qPdl6hgSRQ1VVi5XPXxp8Ld/+5JGD3SMTjbd2RabVXUndqV9fv0bdIkEkkVSux/JUiufPSglABDeQaCez0jJ/LdBKSolN7IgJYkZKiFHkK1PWpuTXaOYKioNyUU1rbP3gNSm/hQKFAoUC50mBArycJzUvOS9tPmLyjFNcgS693h5cLpso1d1OT15sp1er6Hj5cT09fLWfni8fpC20wC/AfjkuU8fSfSDghcyU3/sDL5fc6HMsrp7YI8/wiOdYPTiha99w9AEvtniAfqIh6dgTmjiEv58jzCoepanuYVrE+srcdCf97pul9ODOPNwuU+nurVnTQWEnMGJ9LRN/kPiT3puFoDqafxz+6/Lkr95spNU1wBc4X/6JCN+//3Mr/fisl354up0m4CaTycxZgJcpA170JbBI1IfBPYCXE5+WguOywk58lBHKPY+ceX8SZ7uevjgeVJW31TpyasdTt8jDBLroFBbmI/QJ0L9GD9P4CPdx+hmiRn/8bgnQZSH9hr42hXy/Ft/aCEjvwHtdeUWiAu+VQYlcKFAo8GumQDPOM5joIZvjNc/IS1wbu4AGGwD5sjS0s8sYynrATTP7Adk484msGI3CRSkRZmUjU86RX/+apJ/izXzmVfBQ0lZXA7z4IKf56u3XgAGxaWyTvBXNcq6r7YH9UerAJg9c1oaISBTVUcVJBGoXBF4iRrvQbVMWjsQRCRgjk9viEBLwLuDFlBNLTEsckFZX0ZFMLT+RMgroK7o8FAoUChQKnAsFCvByLmS83EyYH/zC4VOEHO6Szont7d30emU9razspCeIGr3k/u//2E6P3+ynl6uSHT5O87BejgG8TAG8SE74kAlIc9Bg4KXZRNVlRx0+xZ2mqrU2Z35E+U4xm289l/Bo55krQrUwp0J/9OodiI7UTmywxpqr2RyOl87QYeqmPQNdZiY76Y/fLqRv7y2kOzcwgXt7hkWANoWu4V+LLUumsvoLsdLLz+VQIL6vISx8mYUh/+oo3E1Xvni1mgS+rG9sp19e7qY/P95Nfwd4+Ym/CRZ1k/zN0M8EvEiZrr4Ek4+3b0RtGPBy/dNSYBXcjpNH8GhX/Tda8LE1j/Ww8lNeh0dOU2FZQJ2Id+0DwBymUe6LWDX6198swVm2kH7/zQIcL5LzH7VT467MjGWXFvNlsZ0RpDgLBQoFzpcCMfgBkth4w4gVXlpzmTJbFgw7HJxtbe0BIri4kcxGy3y0lOjqgEzgsTiVuwJeJGHEYKicdFd+ZwEv0SCV38xtldvyqQ59qor5SqZKVQ26UefISyUOvrQKomYqKAbuusWqq6fTr9W9Hc24j0/mrQNCXZYlTnvCT3OAlN3LKhPqDeEa6hnwIotHNXDFfGxc3uOVUmLoGISIOqg9dXWtpPJTKFAoUChwvhQowMv50vNScvOph6Jw+CQhH5+kerBYbnFi8uL1Kkp14Xh5vQnw0kt//gV9LysH6dW6WDIRe4H9cmx0yDaHmpg0cWlOs4kSx3E9+yjvyF1Fqhz3c99wW7Ts533iKZnyydNkWdXOCI97HdByRHjcW8HVoy9StLx426V8Tl7uS2rLgB+jWSxXtOjwNEgao+fuIA0f7aFbBxGUiZH0LwAv39+fT3dvTKYHd2cxEznGCZZbXtFiq67U4KJPVqb4XAgF/B1ynGYLsmohycvZ2NxNT5+vpBev1tPq+mZ6/Ho//ddTlOwizvfzi10TNRLwMs0iT6JGSq/PwRUSxjfX/3LrhWrekrofhmf1UcVj3135RXju7ovEQx6WuwfFk5/yPCue4kR43P9/9t7zT48cx/Nkem9lS1JJpVLZ7qru3s/u///+7mZu52Znurq8vMmU0nt73x9ARPBxqVQq5SOkfIJBgiAJehAE5Fc/tW+44l3DVK5ILnsEpN5l+TQWCdTGLbmOd+lnKLSGwTkL4+VvX11I334xm76H8TKJhN/khBgvOukcoqvWC2zbCHXQ2RJrfhoKNBT4ACjQNmS8fzlWBvPYHKO/+eAvaQ2ZQD6QZR47OJN1Hilt57CGQzGzZATDZWgIxsvwEOs2rRPcPLOGrVcevzKxbK3Xg1I+1hJo46JlPrKfS1EVp8bQPobGwF1D5EgV9iqkPaon1gonutV5VlhOgAlAM/QB0i6HsnDE1aNNSbzgkFUoozegsjMoc9xTzAPGwOJQhA2QzQWO19PrzEuVzcbRUKChQEOB16ZAw3h5bRK+fQR5GrS5ySeJPJOSFZnUW1/fTY8WljmN30iPFzfSM3S8/PLwAF0vR2lpHTOF+8dp2rj+fbZBFOPFNi+g8QmoXBp4+fKU5JOYTXjyr3LiQNVvnhBjYqz82x0B1+7f/v0u4LzErWUMP8+fbftMGkZ04C8HO3NKGzvRVYsFWZpCUd7BHorxsEqA6OuPX8+kb27OpBtXxtOX12a4fzzCYmqQUy1nvlgKsQIQ+takPQPN71uhgOpPFVDVJ1+r69vp4cOl9PjZcnqO1MuT1cP027MDkyp78OIgjXIcOQrjZYL6HpMJS6tL2gOrbPUu7zmqVK9Ya0t8uT++OMx96noXoGJEhMCEV8tTwvWCUYSAa4nc5eMNwGkxTEohaCa3P2V+c30oANoeaeNyuMfJsXRc7aS5qeH0928upO9gvPz41SyMl2Ez3a6T4uHhYdObEFIu6qPhzgk1r4YCDQUaCpwfBWzo8vErRn8N1zrwknJYmUCWdMaWLPNw3UjWjfYPDkyXiylph0mgqzGaU2SdTYyXaqgH0fmMXzl//nKcWr8UVIg5sPAiG5q9MlQA29pHWcQj1jEM5CbIUkaWux7g20MCq/nH2lTpxWPJ5TzKmqekIEXPLfTjmI4X6LkPQ0amsHXNdwT6zcCUH+Xq6fg4UsbgEvPFUTreyG6kcba3clbn82w4mlgNBRoKfIwUaBgvH2Ct2mSjfGu+qGYyH+Sl6HNtfSc9eMJp/PN1NobraXF1L/3x7DC92DhKK5ua5PuQuujjJIXNoU5OjPECMv035NU0WlEnphALNl/51F8V4Dt1nDZPnaXpnu12fBHPoW1RUZGgppkvgpjQuTLkSosxCckiahezw5IyGmEj/jdjvGBxhatGd65PwngZY0OI5RWJEbM4sMcqgzSVBuus5nlXFKACVAe2mJRY8zG6XdDrcv95evB4KS3QzxbWj9L9F0dIlR2mR/yN0qfsKh91PWJWJ7we66t8Kku0J729IXn/U5gl19bDajiHKH89vuM8Ca6Mc5LbCnwSwCuGnTZPvowX8rJEHYmBTv1MWFUvhyyst/d2Tbx8Z3cXiZfh9A8YLt/fnkk/3JlJM0i8zEyNspGRksUxV1iZ+5QUMxqu81lxd2S18Wgo0FDgE6dADKcM8L5SYOTCrStGIemyD/NlS0wXSW7AeDlEa6ykXYbRTTKGxIvmkTAhLYW6PtaLrm+G8VLWmK9plGVPy5kgJYS7+zK33Mqo8dQmNN7KLk9+FQ73P+m3WFlZ+lZeMMnfZgujqSSHpEPNmVlivEhRsUxL69qWMV+gq5QSSxpSTJcxmPEio/Jk+dJ8UmWwNUcnzkUGGhBlvB7ISpDXdEezek00TfSGAg0F3iIFGsbLWyT2eSVVDvG2+S8Q6wrE8tpWuv9wOT1ZWEXiZSs9X9llU3icljdTWsP0KvMQJ/EyJZ34c5FVncPrv/3xU6bh6DWJlL7t30Um3pqzPQ/t35ER/CsRAvkJTk9ZHn23x2//FkzbAwrf/hkFLbBmvCDcqpMWJCZ2IPr2zi6LKN3X7kt/vzOdvv1cjJexdOfGZJqcnDDGyyhWC6SDomWBo2xalnsvDNpy1XyeJwVs8ag6oLfh1gJvGcbLr38upnsPn6enSJW9gPHyCImyxbVjJMyO6VdisCVOKGG82OkkFUg9ivFijlyfns2inQkm8m6rwOpLGfC4Ed7yDjjBnATXEumED+ETnrM+7Xlo/w687f7t33UuooSKKSgxNvWWrgNtXjZgvEi0fIv3NKZD//HVTPrrren0I4wXLbZnp2FuYt57ckKMF417wqQFO4wX/RO9hbB5Ggo0FGgo8CYoICaBzSe+cpOUhswfr28i6WIWeVic6WEckqq3oawUdpz12iADVpiPNsZLHhCNAdGLY+DYTveb57lynG2PqDGyK9OlLZKPzMxlbQeDLcNry0drSkJXBwdyXYl3dx2W50vlC1pqetWflBO7FBE6DZECD7dMb0+L8SLdazBfRFNZjRrg7TXi+ILRFLnKyfZkzBSzdkRpKUHh2d2pYpWF6g71Fnzfm4y8hbI2STQUeDcUaBgv74buZ0r1ZUOirkRII/7S8gan8ctYNFqrGC9PVvvS+nZf2uCa0T7mV0fFAGDCGdTmRTM8M4tPLkpF00jMAv7ts0K4I/sB4zHCt/P9FuGUVGTTFiP6yJ6VM+cnmDFVnMrRWYTAYSFtcJaeJ2qYSdcmbiZzMV60uNrmzvYm1qaGUJA3iFLjf9yeTN/enDSJl69vTKSpqXG07iMCOzqMpESt/DMm/MiQFSk+mvfbo0BuO2Ki6VTtxdJm+uW3xfT7/cX0aBHrRkiSPd/qgwFD2IYYL1KKCPOFqtTbmw9Lu4rxopbCX9F2vDBa/nlbsvBq4VoW1VoZHgGnsHBHWLzLMLnbn3cJpzwr/ch7e9703Zk/8+HHTEMDISaKdCSsssDeoZ9t7Ow44wXm5l9uTqUfv5wx89LzM2P0La4cTU/aQpto9jjjhZRk0r1MzoOb34YCDQUaCrweBWIiF4OACUTrK416B1jekUGE1TXXSbID82WA9Zgr1e0zxoss8Yyhk0/MFmcS+DsyJNTnsi6ohuM8Hgtpznf28XUNCZfMl27MmIrxYqWsB1Vz1Z9RhFO/q3Q7cPjcquxqit2ThSPmBFmJ2pZ5bphbe8wNmiumkHQZgZElqRdJfctMtyRhoqxVZtqIejKdKwqJOhWKEycUgakc8S5inejsCd+ebk/AE9E3gQ0FGgq8WQo0jJc3S99zxX7yMOonKauYttWm8Pd7MF6errEp3ETJ7m5a3OjHjDR/WfnYqKwZMcH7YTyTlumxILt5Qsvz7bnm/40j65iMe6RYKY8oJqrC2SPWS7xBwH9lwRhZuPqY5LWpk9jwFhP/BoqPIXsaRAri77cn0jcwXG5dHU9fXZ9IM9MT3DvmJGZ0hEXBUMvCxid8L1zbWuAleXr9YJHltGR9/dTeUwy5bej0Tgs/SbwsYi3sXz8/S79y3egh1/nWtlNagvGyCnNTf1okU80wN6lv+lk0OVsVqphWkVDWcOcE8oItvowaXYnfzbOM1S38tLQt8UQc4Wv3f500Au9p35GW58FJx+aD6HIb4wUx8hWsgWwhXr6+C+OFRfU/vpyC8TKZfvhiGmmX4XRxdhyG83Cam5vqwXgB2dvuYKclQQPXUKChABTQGBDjwQdEkLxr1/zhzANJb6BUF2bA1vY+18N3XSqD8UtMAOlw0RUj6SQZRQJ2BIX8xpDReMcYVQ5TduHmtUiSx/b86kpVEqyYHl0B3LMbE6asL8vmGfNqBxK9qh9/kVjBeouuBzBedqDn1qZfN9qBwSW6jUNX0XYUPYduIYor91zzzrEtvh2cZSILZ0x/Jd3lXT8GVX+aSwWVfy5w4Qx83cDM76SfdjyGvkw/0hWSnPZJ+JqwhgINBd4qBRrGy1sl9+slVo63nZiy7gl2gM+RePnt7lJ6+ERXjfSNFMzWAJv//rR9KH0IumLE5pBZpFI/UY3b7nCJl8qzM7nKpxzkK88ujrcI12uuqYqTAUppgiqsS9ZP5QUC/guzGC56KsYLzJctrhqtc/KCURUzC/m3zHi5iXLdr66NsxlEz8voKKcwSL1kxkssYnzN5nnuPfFbkuf+I7L0Iue5J/a+IsxtQ4wXv0d+lBZgvPzzX8/SL3cX0wMYnBu7fWllZwDmWl9awz2A9IQO0RBwMuZmRcRYHVpFZsrKLz+1K3uUbTSAjOXQAVmF1g7hfxW4brBR+yeF1Sm667Tptsdr/460S39GJvOG8cJbbjFe9mC8rGbGyxpX+qYRJf/bnan0/ecT6cdbU2luZgTGy5j1sfn5qerkWJgbiZeSvo27ocCHRwEbQvNwoZcPqZ1jVsypKqG7vaw5qn9YID8RXWNwuLO3bcxr6FdySQJW+ZOUnvS6bG+zNtiQ+WMpCEc/mK4bc0IzipSLdL6JESN9Yc5wOba3zScVN5/kOwrwSlly4JiHYpGhMld4Mw0UBpyvD1vTyEHm2eIuwSp8pWenuyXpItiYL/EtIJ6qTnMWlTdJEmmuFk23oK+YLtL5coCfDhtt7cviV0rXZexgYmwYPBVCR5x/e+WlBajLR8WoghhstDJEEKBIKzvtWlaAteGzMoZfBSNHKz5vlxVAER6Rz+Ndpnse+BocDQU+LQo0jJePob5tIpSI5ZHpnhDj5Zc/X6T7j1bTk8Xd9Hx1N61sD6Tdg/60e9SfUPqeUDNiGxedotjYXY7VTKutjBcN7i0ABdXKsNJdgJjzpLAS9hzhhKosXFUECyCs8miFU7RXfsDFf4nYusSLNoRickmB3lHa1D1uTByK8dLP349fTKSvr4+nW1fQ8fLZZLowNwHTZRT9E6N23UiTdiwoNGf7hKr3K2fsTBHaKXMmJB9LpEwMv2ak+jxEoe5m+s+fn6af/5CC3bW0ud8P8wXmJu9NGJyqezE1dctIf/4IkT6CulVA5RUhBmcLwdonI+Elxl74l/hqCHeVYaW7F1zgbA/v9V3kvwXkpLRKwJfB9cLv+dTYJQiJ5YvxIomXbfrZGlf6Jjkh/pHrfN/fGEfPyySMl9F0eX4cxguSL/MzJl6uOtIjaT/rt+pcvZI0yOanoUBDgfeRAravzX1XL/u2MbKtQ8cnQ0gwB6p3r4IFwgyo0Sfm415ROvx9Ejdvs77DIkxMFyl+FVNgg2uSh2K8sF4YQxpD1tfG0VMl5bpmUhrxSTGYkTvxqcHyklOJMnUk+o49jFBvKg+ZBaM04vGKgUKusFgMdfgv6NbbN91f0qUjZpeP974+m5oYsWtc0zBgwoCB1W2R98IZKb38TX1H1hwfX1WdRYjQFJWHs1z3dSSi+PFEezJUJT5PxsEK+Ih3Lm+l96Zwn0sGGyQNBd5rCjSMl/e6eloz13W40wBvg7xryJfSz0UUTfxLjBc2hI8WdtILGC8be4Pcee1PB8eDMGjYFPbJkgdzjcZP/kIbfWzoWofy1nx82l/lhFNSCTf/bSuYN4Q6WZF0kTFeuGu8KokX/PrR+/HDzXGsGY2Zct0vkXi5fGEKZW9jKNlF6oXNYXVaArF9jvV0y7n3TdRDWSLhL0v7JtJ7n3Cq7GV5q28cqg9nvMhkJdaLUKj7Hz89Sz//vmD9bBuGy9b+YNo5HECqbAA8/cZ46Yd5Ap8NxMJMY8iP09kZB+FXvj1cPrWrDi9zWfu+vqtbWidhfVP5iDQdf6RS5k5+EiyzMaySeNk3ybI1FtqTiJH/8MV4+vbGGIwXJF6mR+hj4yhVHE2XxHjR3f6K8SI2M/+ENBKLLDTvhgINBd4IBdSfT9vdqr5vDv9q9euexQ4GSSRYRS7iZdzdgiyjBHTOv3ncKNB0c5purxx5H6mWA5gu+5iPltJ9KYLdQSeJ1grwXWC8IPGCpMsUUnu6ljzEokFKYTVctZYnFybK1C3hFj+V7NTALTHP9BGEfCNJgtz/d2RN6yWTTOUtmu6Yol10vMB4EaNrT9IwML4IxtLdGOuuwTQ7CeMl14+/PNNR369ahFi/KS/d5/CObFMzpNIjIUNjqJTvDIengec47VE9rN03ZwfvYA1Zup3ZOcEnctMF9wmxmqCGAg0FnAIN4+UDagka7jqGOkZ23xS6ZMXy6k5aXFpPP/3+It17spIeP92F8bKHbpfBtG/SLs54GWBDKNHK3owXpRQD7AdEpDee1bIGSvpAT/skHKJKJiEYLzrF2jTGywF+R8Z4+evnYryMps8vIfEixstFMV7G05QYL4i9xsTtxanTjIXAmypmWSKlUaf8plJ8f/BG2aPM+jY3jmC8iOkinT3PUKj77/9cgPGyCONlFWkySbsMIlE2mLbpZwO0APUt9TPrY23FbE+rLdg+A6ZbWKufcnl66Na4+or4r4rDqNOJ7tx8HH+k4gvF7MdLdNVXLfGy7xIvSJZNjCPxcmsMPUpj6TskXi5Mj6YrFyRVJgbMjOlQ0AmyHtWtFp/Wtxy9+Tc/DQUaCrw5Cmi0OW13K0emOo7WPjl/+R2f8pVEm/VtOnYFF5FLwIxC/d+Vn+MRcFVYreNEQRHdRg482sAtltKMeTzyIUjpHxETYAtJl30xAvIVGEXSdSLpeNMVowmk9qTrxaUnkXZROi0J9UjYUu/2o1y3IOgGdL5+byxJEbeuB2VaTKmgsySNRX8xYKTnRYyuXd67mJl289Iy153S9OQIdB6EAaOrRjWNncHFnCDmvnAXVFGR9JR+7sNvDvS5qvLNDg+0fIrDVj2Byf3sNxYNVcOtgC1hY5ZkFFHuAsJoYQRobTAOongkmV9ltFO6c8LdKXBKHA1YQ4FPlwIN4+UDqvuuAyVi8jbB8D7kb2kZxsuLjfTTH2K8oOPlGcp2YbxsHQzBeBkAZsDO3aV7QsO9T+ZgLu4Lt04aMciWhIqJQu9u4cLNJFiF9YbzXAh3lK47Pk898MTbfTt/IzzenRCt+HqFh7/w9Hoiv7z5bxMik6a2dNrXtUq8iPHCUg0RiL/eHEO3C4yXy6Ppy88m0pWL02liohvjxReNMX/Gu1duXtc/SnNSiV83jfc1/klll9iyK0p2iRdjvPy0kP6FZSNJlu0i8bIDc3MXibIdGC/9rNjUBpzx0tkOIy3RoqZ1DVeGO0SrT3ca1vFPjtML7jRpRMp1rt0ncMY74NrfER7v9vDyWzA1fcrcKSQkXmQKdNeuGgXjZd8YLz/cGk3fIlX23c2JNI/Ey5WLk/iPpEsXZu0EWfH02GJdPVdIPUnzb34aCjQUePsUsL0m/bDsitH35Wfu+sczmAECTp5nmisDQZm4p1D9WhDINW4IvAT1DbvGFN/0C0ZPwOnzgKuq0juyhQSsdJEcMLdoxaD8ysLOyKhfMRrH8mRtyUhyk0QuErO1hpAXfvrs/ng+6rBTRarBz+qKZM87uYKu3bKmYCV9iONAljyZH8Tg0rWubZQZS+rlAKkXXTEag97TU4PMJ7rK5YrxhVNuY7wwTehQrWRwCLeYZNUjD55g/NRrX/f33IRbgIWbdPwpY+HnJ3klYOWu6r7wqZBmdBa9Rl1BqmT6H1kIkAKgcTYUaCjwBinQMF7eIHHPG7UGyo5B0hgvfg1Ck4wYLwtivCDxchfGy5PMeNk+HGKCh/HCBK+rRtI7oXnDth6M0McF48WHZKWkFGN4VmnCT+9wy7/90bTguS2nknaok3GU0Cel9abglKaerlT3oJYwjk8A9cnap0XpetHJizbstcQLEhBwvf7y+SiSLpJ4GUHHC4yXSzBeJPGC6GtIvKh+VC9aRPYJTQkAAEAASURBVGiPqHc51+dMvPKrW4nkF0+UPL4/pbfRIROjpLUWVJJ0sfv51OdTJF7+338+S//87Xm692iNhZz0J3GdT38wN9UCdGiFQXF7Gw1F2Iy7prdT2/tLFdyF5FErdcxWoED+KnCBK+LGtzCHX5lKiTvc3eDKOOE+LVzA6x1plH7utrGLYEFosyIdL8ubwXg54BSzP4nx8s2N0fQdCnYvoOMlJF6uXJyzq0Y6TVaf0hhl/VaoeyfpCTe/DQUaCrwxClh/5Edzp0aj6I7yt/FY/VUf6rXmzt98uG8BlzE4vDApQoHUer1/C3ekhY89lh4/8temO5gqGZPlw9AZYMR3LDZfMPe7Il2tz5RPWTISEwATx0he7MAAMJ0jBA5zpcgs4WHBSBZ29D2Krhdt/G0e0U884Iq8mFcRFCAf0rulSl4x41637ZFiNtXVLfS8yJKnWfPkahfXULfRBbZLHezCkBlHgbEsR8m0tOYRky6iPvtlfIJFlzO+fB6PurbmB80rxosCikd58tbonqpzNTx7yeWf1n6KaNY0HcZSsgZlVVvUr63X1W6zX7SD+u0Y49sbvFAFkohYpvyxuzM9P/ZiNuX7YCjQMF4+mKryITSGz8i2mYFmtNbg7hIv25nxspTucRL/eGHLdLzsHI7YVaODY20F3cStBm/h0zQlZkz9CCKe0h1+JWz4tb4F0S1mK9R5f5021bPCebn9V+WLEsonM15watKzf0zcISkh5bqrW4cm8SLlut9/ritGwXjRVaNgvIzaqbwo45Owv+uJ9vVpFrkOTFGe+P5U3xVdcAS9gxZaTB0cHthCWuLLwXj56TcYnI/W2fj3wXgZyIwXFsxEbGG8BJFzIlValoBai/u0+kfq8RaSkyEc8rRwgTfep8EdsFGg+H7Z+6Q8vSourx/RVzEH0FjtjBdOM+lna5wkT473IVXmjJdvkXi5ODWarl7kqhEm2y9dmjOFlSHxovq0Pitkr56VlxW8CW8o0FDgFBSI+U6gmltjs6hRSeOxwnUVSHOqwn1uxSU/AvUWrG90XbeaPGwjrIB4bH7mA5zW7wmzt5gcDCpya2zR+CAGkPzcopDN6paXQMWs7xvwyKC9lbbnb5fxSGaNtflnr89bCnVhuGjjTxhgpNNnJo6HzHrRgCl7HeJwZgQGjNCJGspDy5PzbH5tQS1wH8CHquZ1iqD6VZ3pLUxRFfrS+liSLZyZGM13YHhtSQk717120LEj5rsxvEznl0x2S58O62MxvqD/6LCUG6vehQ3sVT0ox56utTEL5UdtE1Bq3+o22qVabN1evY1a/gw+8k779UybALpStf8USGWq2iAfsspk7RQGkfyNCaR2yl/QUnlVPOXPnyrE/cP7o3pHYaOsH1XhmsJ8JBRoGC8fUEVqSGkfTmrGi64aSeKlZrzchfHy5NlWWlrbReHnsF01Oj3jJUbsGMh6Eao9R73g3oR/5DFwt3/38j8rnJfVfzWfBW3koyWY148mRLlkTvqYiVUTrjNedNWICRTGy1/QPWE6Xi6OcNUoM1501YgNovRQCLXQ2CSe3YY/EtfHGZ/IdUQ/B5SB6oN+V3TJDqvGXKJ2xsszlOv++389Q7LsuTNeUFy9y98eV430J5pKsa5OqFrWasInKaaq7chD0FXq8shPu3/796vCBXyvd7c89IJVXk562vPa/l3GfRmuEtbdqhtfiPoGya0asahGnFyMl0riBebmtyiyvoiOFzFeJrAcJsbLIMwa9UU9DePF6dD8NhR48xRoHWNso8n4Z5tnJU6/9k20j4q6bmFzIEFMo874Pj5AOSobaunu4C1JRDE0fIONVCJJaM6VtIn6ttZF5fAa43psWOM6jzatYuKKETKktywKDWGIYHDINrTalNsVUgYO24iDyE0Q5/FLRcNPL+XnEL0isla0jX43bfg3s1lj6RxRllh8k54kWwZMyeswm/wpdFPJfPSQlOqSvuUVuHrD72lYikpIT07eP8771wr1GkgjkyUKz3CEOMVerxCiZzyiWXyL8SFFumoDYr5sqR42d2DQY0lqCx071JFZnqS+VL+aEwYx3S3LUjOsxWamRpCKQWMbONUurT5yQrbOs3T5yYkekYjSlFlwa6/82HUya5/eTpUXtd9gzh2SP2Ml+hIy9wXHKfRqj9ZWyZwxhqydimHUT1sZtLczkPCTEmbKobxVeQbHsfKufFt+9Wqlt5dLga3+itI8DQUaCpwvBRrGy5np+fYHqa4pMohr0SKRSg3oy8F4QcfLXZmTXpCOl920ZYwXmZIesEGX8dnmChtm+SF68eTR2XzCLchwF6BdB+pesGU8uc8brh3/eX8rv/XU1Lp5jqnMJ2dNc2K8aOFXMl5sMz5wnCrlul0YL9JDQVXWD26JHMvPJ8g6qHGdPwWC9F7bjl99TGakbTHPoklWjf79v+OqERIvxnhB8oL+tcuflua9GS/de9LrleQ8+lKU/GU5KSlz2nRPwlniOwmuDlM/sIUlXn7V6Bhz0gXjZbQfa0ZIvKDA+jsYLxdYQF+9JB0vzniRpRBtuNSndFJeLapfPSt1phpXQ4GGAqegQOs4Y32QWNJJ4SE+ppg/CxPNs5IM0djrujqQPGQju7u3Z5taSY9IWa0YLwoPvSna3Abj5fgInBm/xg6lwPTsm1ocg7paArNFDA9tYnXVRwyQQZgfUnSrsGEkILTxNaYMhyc+ZkjKwN0587bJVX72kKpY28CwAYzg5fX9tMo6TFeOt7CyY3gQrRgb7kuTY0PpwtwYki4wYLCuM6I8lIwXENsmXzTKCwDR5u2sBbxGSO0U9XoSiPAEjtJ9UpzTh1kuI6tKiaS8XWn9pT9JvBwb42VjY8cUG6+uY+0TKWQz500dWf3TDkZgvExw9ejKRQ7E5jkMm6D+mWys3WjSiXR4q21qbSA/Y+DAcJEC/v0DV5wsxqB0y6iNqm1Kwa9JQHHNyZmGtG/grZ0qk9BI6IQw2pcxXkhXSuRl3UoMObVXtVVJRZm5cdrSoDHsJKHjDBrB++GE2iiNlMdrQL/Ks3+ZJ51Pa9bmaSjQUODNU6BhvJyJxjYyEvPtDlRKtSNFRulgvIjTvoyOl2eYkzblulhbeYSOlyVZNTIdL10YLxlhTB6d5Iiydoa4T0eOegF+BP5e1iixJt360cSlJ0/QejPZ1YyXI64aHTARcrIiiRddNZJVozbGy3SWePHJN2MvKv48FlsFupxA8yop0I0+lSlpLeZZ4D9dwKoR5qR/QsfL3YfoeDHGi+t32YHxQhUbY0DLHa3V2jtuS/2Wib9Td9meX5aR6AUvgztL+Olws660xbA2MXssqpdhvNRXjfrTX7hq9PX1kfQ9V40u0K+uXELiBXPSV5B4aTEnrV0dHcsW1qdL+iyFauI0FGgoUM2ZWrcEOVzmIYK0/9RhhYJNr5Y2ptrAGrNFV0S0ieUNA2PXLASJCSOmuOtPCStBkjrRuK15+hhpB1vjgFMbzNhEawwZZPzQJlUb2mHetrlF+kS6PySFIuWrYtSK8SIYXUEZMkaNNsPa6Do+ScPoUb5lOUd/y6y9xHx5sbSbnq3spnXca0i+DINHeCdgEM9ODqHweyxNTgyluZkRmDHKizbV3Hmxp2a8ZA9enmb9fZ4urxj91sNh7Xq1lBxXZ5yz4uvEJB+lovZUY/V09etMDbWNhNTRflpf207r6HpZhhH2YmXfGGMyQCGGl5gZo9TJHIz6G1cm+ZtIM9PDzqADua+/cIDY1gS0OV3xV9sTM3BPDBddI6NtynqVmC0b5nam4C7KfcUg3INBY1JRiqf2rXaqtYVn2/D30cbEfNH8JotLblZcEli5rdI+R2HWqS2N5rY6DANGuoF0qGAMQ8Sr5R7gDRpjxFRUIi2jl37iD4f54dU8DQUaCrwZCjSMlzPTVSPk2x2iuqXoDBOdCOkvrBo540XKdc2q0UrJeMGcNPkeFIfbBuJMAM1aSiA+rWyFh/l3y4Fo0A4XWMr3u4Ir8/C6bq/vuiSt5fYpyxdJgtEpg+pFJyE66VqB8SJz0rJq9IPpeBlLNy8Np9th1Wh8zJXromnfSAoSVUu4lXuf+OU6+9OtFs+O7eOM2U4j9TNJvGhDYIwXlOv+23/LqtECupSk46UfM9IyJz2QdmDC6A62/roq14167SBd2Z7UgvSUfu5Dy+rhH+HxFo5u8SM83r3gSiq044n8BQ5/e4xe+Fphe3+14o6vMgfqB2Jo6e2MFynXZTOG6PgqSnYnUa5rEi83RtK3KNe9KOW6puNF74bx0pv2TUhDgTdEAevAZS/O6agTM776GkYbWJcOEHNFzAspot1DH8emXdfZ5/rOvvXzbVmokWQJYWGl5oC5dp/NbBx4MFzbHGpXsm0sdAtCStnMB0uSQH/kQZIFOhTRRlVMj+GhY2O+TIzCDGGzO8mVIG1wJQ0xMT6YhmDKTIwioQIjRpth/Qmn1oVi/uiK0Qqb+6XVnfRkcSvdf4LFyeW9tAzzRRv8CTbNkxMD6eLccLoOU3gaBsyVeV1tcd0i40jCaLOsJyRdzB3rTsjW+/GRuDX8VfzKmEqoW9wS5mXuqPcTM90yW50M2ZqesGut5HEiLb5xitEhy1FSpispl8XlTZhh+xxQ7qEPcQdJyf20sLSXpqiPca56zSDhcmFuJN2+Npm+vDGNe9Tah805tBG1T78mJAaLSzZJb8+O2ipMNbXXdVmtEvONdrCxIxi10WNjFCquSbvwVvtUW9U/+C4qhZeDgqgs+rNrRqSryU7SnWL66aqsJK90TW0YRt0of+O0zUkx88ZgutC+Jsew2CTT5GLQ0JYkvaU4Xg6/jqQEgtVSU82ywY8lWX+YT/woZ83TUKChwFko0DBezkK19ykOs402hfrTAL60um3mpP/5B9ZWkHh5/GzHrhptmjnp/nSA/glnvBzljYsPoDSEllK5/Eb4CUZu/bUPuBGm6KVb3+VzUtiHBhf5Dfr4tya0oI/WS1rIaTHJoQenHjXjRTpexHj5ClO3n4vxcg2rRhdq5bqyalTNviRhVWO4YyL09JrfN0uBsrXbwsjubcN4oZ/5VSNnvMic9I4YLzInDeNl2xgvYr7QBlj5afHs3Uu9Si1D2Lo9pW+u8K6QjsMxCK6MV+Itw0p3CSN3r7BeeCNOOx7PSTAgO0PbfXqlG3AKjx7VWUrWoJZzv2qUrRqxwF2DATPJ4vOHW2PpazFepFzXdLxMolx3NF2+NGsLWPVPPca8Vjp8lhscC2x+Ggo0FDgfCnQMJ7VHHFBIcmCbTewuEgn7vKUjZV3KULcPYbzss0neg7HK954fZmzvYqlGVzmM2cJ1EuZabWBtMyv0DLxy20ChcVIDsU/UePEPt3glehvzhWvAGrel28UU3CLNMg1zZYyN7OzYoFnBmZkchEkCMwaLOHpLYfeIJGRgwBgThwleEjg7zPnLSFe8WNlJD55upd/ub8GAgfnCQdgQV4zEvJkG1yU2+bdYA8zBeLl+eTxNI/kyCm5toh2f8pmrIL+V95MfFb4bTLt/+3c7VoXH0w1fhJ3PO1J71ZQUr6paHKKXvvVI6kR/YsovwwR7vLiZVtb2bG38DMaLJMIfL+3DcOlP09TxxTkYYDBbvvpiOn1zay5dnB+Fko5Mc/keDBUxTsQY3Nzas6tKW9tIM8HA2eBvE4bg6jrtFoaL2qbeuzBdcMJ4yTplyJwxXYRWjBfecnoqXnNK0+igwui//YnpApNPzBfa5ijMvlHekpIapw1Owziapl2Nm46aYa5JDcOcEaMQBiHvURiKpjuIuU8HFkIac57SivTDoTRr3xLCAhTYPA0FGgq8IgUaxssrEqwVXMPU2xuAuqUWmwad6GjRssJEL3PS/0LHy31dNVqA8cIks7GH/gk2hPtYNTrij/HaFhi296jG02rYrSatd1ne1rQ/jC9bEEFPyGuLJi36dMIhxouuGknipR8J4h9ujaevrsF4uYxZaZTrXsqMl0lbyGXGixU53/nNzcwnwg+DFh96Lsv+ZoxN5NVVnxJ7f8ri7T8wJ/2vPyTxsgbTBcbL/pBJu2zh1j/rY/21OWnrXVUXo0I7+l0VCOlyhccKqIWYEdbi+QY+Ij9VRos0uudBMbqHFFFP7eyGyfOkEG1K1B9qiReZk4bxYlaNZE56zMxJ/+XWZJqH8SKJF101unhhxhavwXjRpk+P9a3cwbqlbEDNT0OBhgInU6B9EPDuVe/fcmxnPzOe2iY0X8VAmmALZov+JDmwtsGG1nRxsJFlk7vExnaZTe0WDBe8rb/vsqlmX+uHHIdIJMB40ZY1d2tSiwwUzrYOrnVQv8Zq4kk3l0sVJK4ewXhhMzs+0p/m2dBOIhUxNzXM9SCsD8IYmWODPj0B44WN7gQHJqZ/g+seksKRtIOkXZ4vbae7TzbTL39upPusx55y7UiMnUkkK2anBjl0GU23b4g5PJxuXpkynSLjSDJMs2m2ayYsJjTWlQOrJDl8wMrE7HgVZe4I6+bRRpAKpMTTC6YCfm1HpPaqKSme5ug6XmCCUYKkyz5/kkBRfTxa2IQZtpcePt5Mjxa5bgTj5dGLXep0gL/BdHV2MH12aTx9d3s2ffvlnF0Dkx4Wb0dIreiKG4w1Kerd2EJvj5iDtNNVdMesM/dIp89z2ujGNswe2sHKFtIu5A1eIcxE2imYWEZUf2XzlLtihKgB5wLhskdMQh04SCpL64tR2oUxXmhPE7RRtdU52tUE7Wd+diRN0U4lOTU3jQ4hGC9yj0ghL5ElAaNDC2M+Mu8pqUjHky7pmTMQrzxPxmfzbijQUOD0FGgYL6enVRfIGKbq4b4L0Bv1qhgvDJm6AqETludLMF6wtnIP5bqPnzPxw3hZ3+EUBsaLrBodolRrmAHbFhvKnc3i5uDHy6SBt37KIVm+7d8B2e7f/h1wb/PdmoeyWAqpn1a4s5fRN4PCpo2dRJ+1WddVo5rxcsymcAL9E+MwXsbSl1fHzJz0+BhXjZgox5gcq1WjWcABWc5sM9/VNfamXdFWrGXQIeweNnUpRtozrhr9x09IvPy+kB5wpW9rfyBtivEC02Vzn9MkVkjO3MyMF5AEPqtbq8jwrEJqmFw4S9uqP1ztpW73b/8O+Hb/9u+A6/YWrJaL8ei7+xMl6QWh8DpMrojRDV8NWYc6Bp0GGuOFgA6JFzFeuGr0wxdjXDMaw6z0JAtPMV7GObFGue7FGdvQVIwX5YH/XiWeZreU6zw0roYCDQV6UqDo0t5bM6S6mdYaFp4ZLqxZXOmormjougZSLVzFWeXPGC8wWzbY1OoKx4aucIgZsylJgiPGXG2qJXmAVCnzpEnNCH2eM/XufOrNZLg0luiRhSIxXvTN7Qzb4EqqYAppAilcnWZjOzYihskQkhFsYPmbnx4xhbhSjDsnBgxSL5KA0fpJJqRX1yXhspvuI/Hy85+b6QGMl+dYmRzRJhkJl7lpMV7G0hdIvAjXDcaoKRg80t2hqyIm1ZfXau1lqdcC3cvZDu8jr5e1e1in71l8lEK3HJ0GV5m7dhxlmHCV4QozZkF42gLWc2ISLzBetmHqSdLlycIW6+Rd9B9uIf0iHYj76eHzXa4aJWOszVMnn1EP3305m767M4eC3VEYN/scbOpwk6ustEdJtSyLKSgJF9rtJhIvm7TZTSSztpFwEfN/a/eYdsraD+bLAQxBMQf3aO/KWrRRlhLW7ow2Xdurhah07qB8fTAIB1lfqG0g5GJtdRRGzKiYeeinmRrjahHtVYqap/gTw0VtaxxpninalK7KSfJFUlXSA6NrS4NIaYkBY8zQaFg5yUjbGEJGV7JidA5iRx5Pfgvdq8U4GV8T2lDgQ6VAw3j5UGuuzDcjmgZMKelaWdMJy0b6JUu8iPGyyCSztt3PJIASSrT714wXbRBB5McnOKqR1rjx3QdJ+QZc6S4zJHcZVrq7wckvhuXA3Q6n78AT724wZ4HrhSf884SUP5W6P+6qc+Nl0LwlP4kuazEo5ssWjJc17qYP6FRt8Dj9DcbLVzBebl5ByS6LrgtzU0yIY2mCxZuU7lU01iIySINvzImefvP7tiig/mVSL2K8sHhaeL6e/vPnhfQzjJeHT9eQKOvnb5BFVh9vTqNQ5MOBEv1LoutqEN6u7VcVahWJv3tYMRyiW4l8M9AtpNVPrS6wlO5WKG+dp4VT3IBtx1N+R3rxLsNKd4THuwxrdwumfOp8KKRT4kU6H9iYsVmbQnfCj7dZPN8YR9fLtCmtvIKFijE2RRfmp+mHLFxt8IOlpNUvT1SJ6NOesgE0Pw0FGgq8nAJ1N7WRo+xL5TiqfiddKDtsXLeQHFjFzK82rM/Ri/IccRaTHpDODP50bWcXkQFdLdomjq4W7cHgNhPSzJF8WlpK2v9INfspw5EHvRVePXyI4WJxGJc1VuvatW5h2NgN02ME5ouuHY2zmdWYPso1oRGYI1K+O8Mmd35KVzlQkAtzV0yZ+elxkyZQWmtszHWV5SEMl1/vIWHBBv8F0hDS7TJHvHmuGV1lY3+DK0azKHS9Nj/GBtk3ztow9zNGxVUoLQS8HF4Cl4yIklUlyo6WUrYH8t1OiV54ukR9iZdSPiu2MtftOMowZaEMt7AAUIDm2FxGs3YFw0RMurWNAyTCt4158oT3U0mES+IFHTzwIJAegfkCQ+0yzLDv78zanxgvm5vbpm9IbXUBqZkV8DzlbQxC6RpCCmYLpsue9LmgxHeHdiqdLkqbIGufaqPWTnkrd/qzR20Oh2VZb/7KshmM/XgMZxDKYhGKoTHWYAc8tBPLv5QD8zes9or0ixiCo3BnLsB4mYTxMkvZpL9GZZxEompyYtSuIplZapgv1hNsIswZKdKVs8pXwFj46X56l+t08RuohgIfCwUaxsvHUJOMaBq0pV1dJywvljfSb3eX0kNO4h8yoehO8cqWrkL0oX9CUhic4NimUBtEBtO88KiHVW0yuxFGw245fHYFAqaE6wUj/AEn90nPu4BTmno8/2UpIkSh5RbNl2/45IlUc5MWlzLzvYVo6gaLyoEBt2r0t9uTSLxMpM+RdrkDA2Z+ZooFF+LLmJIerRgvICBhqwsWgnKfYb5TNpvnNSmQl+YwXbxOdZ3vv39+hvh4Zrzs9qWVnUFOZPvSuhgvrNyHWBmJ0ab9fdSb96uoSK/f1jaGX25zZZbrdla2xFYIj6f4ek4D1wtG8dvzIdjcCBVcPe1wVUCb4zzh1MfYHOV+FhIvMict/RBrOzuc7A2mv92ZSt+jWPeHzHi5JMbLyAibnSljupj4PrlUH4360VslrendVozms6HAh0aBspurG+rxRu7u8/7N6WnMFKtAjAwfT0gWtyQHdUhkm9IdJFo2ORhiY6sN8TL6W56i9PQpB0jS1SQFtbuSGGATq+sih2xqUa1hc6okCIRafFOXx9MBiYqWCxllrVLXuOFxBGUPr5Aajm+FaaTTGGPXOowJI8kCWT/SwQlMGXa7uo40YxZwJEkwkD67wDyO2MRlpCXGZFmGOWADptEqpqSfvthPvz/cSE+fsxZjoz4Do0a6RC7OjiHxIubLmOl2uYJZaVOYysJMBzDKrxgv8XiRNF6FSyG9RiuVUXC5rAK1p5efAiOOQ75Pv91KETnuyKdPtEYnY87RSPZhvGgNJj0v0hO0uMS1L3Tu6Cq+rH/2mX4fpJ3gronZ8v3taaReZtKF2dG0urYFY4V2ipTLoyXW1DAHH6AfZlUK3WEI7orRAn610yP+DmG2qF2qbUnHX1wvUhn8z+vP8p3rsixDFVpUVS6SYZC3WruYL1Lgj9AKlhS13oAZQ7sUw254BIkY2pEYhRdg8k3TRueQgLlmUlXMg5RrhvY6JeYLeoxUbuXO1q/kSekpa2X/ULrmaaWwL/k0T0OBhgKvQIGG8fIKxOoE1RD67gYfS91GY+WBRQ4emhherGymP+7BeHmKjpfnW+k5jJdn6wlN6zBemBCQuOS0hlMcRm2Y4z6OGgYNso6rs6ztPqeFa4/3Jr9Pm6ezwYneehQ7Hnc7PgtnptKX/rSx07UU/ekkXooCNbcNosLl7zBevmFTeAuThV9dn0zTUxNonh/mKoQrQRN+TXiaBKsqzngV1jxvmQJUgupXdaJTqxdLm2bR6I97i9wZX0PpY0qLMDf1Zl1mi3Rd59MplDM3vaUc0xYMk1Y0VUvyluW/3cvlsfV7ElTEPS1cwL+N92nz1A1Ofnq87PoS+YLBKeth+5xoykzrNiLhG9swXlAm+A8YL9Lv8sPtGdPLcIFNzsjwcJqZnaRvsonKGxrViR7bzAivfTU/DQU+UAqUQ4Qas76jUYc73udZxCJdd2qkC8aLNqJ8w3SRYtIdmA+mJwPptLUtJAjYyD5b3rJrRs+4+rGwLCndbN0IhssB6xb10yMkdtVd7Q/sx3zrMcaL/K2gubAwTqqC4yVf6+OWOfux8dzmVyHRg7fFJq7epvclM3kHNejASLehWxtdNreoezErODILfRV9L7NIvVye40ojjF+ZhBbTSJIQS6sHHIKhyHUNyQj0fMxOI3kwM2zSBxdnh9MlzEhP6eoS1td0rckU+w7BeNH6TOW0zEXelM+8NvDc5t8M1PJSTJUkMLQE5jD5tcMZFdqB3/n3SaWIMCutfXiZKh1CTNx2DQgJcJmVlmTVAswXu37EOtksFlJCxZ+FQfHl51g14nBMV8JWucK/JsXOG9vpCW1zUcwa4kqPi6wliSl4QF3TvP2P+jnSWpos6OCNO3a0TbV/p6tlz6jJd56HWqoIb0F6e/X6t3ZKRGt/AFu43kJh7YS33HhYu6V90oSQ2KI86H2R1aZp3pKwklJomcyeZ06c4Xq7mC/GLIRzo4MMmVdXG9OjPDjzxRmX5q/ELQd6N09DgYYCr0KBhvHyKtTqgNXAZCNQR8jb8KhSzwOkBvV1xHWXVrfSXTFenq2gNMzvGD9ZOWYh08fdVGl5T2hBZ1AWV5zxVQwC4dLgmlGdY/ZFHx/AT0b6vsN1z31Z+15Kn5zk3wddNZmXjBctqMR4+cedSfRPTKWbl9H1cgOFepMwXjjh0smDTFn6RKc0cwqtr+6ZaXzPlQId/Qvs6h9aQL2Au6JrRn/ef46lirW0tIHel40+3jBlNo/pVzoRRbKMa2VS0GirI+KexHhR5iNNvcsnV3/pdQ5uYW1PqRvadwlX5sfzGrQwiRc+KsYL+h+kI2IDiZcZRP7/fmcm/RXLFD/cnjLGyxxXAEZhvEzNjFucTsYLafmqNnpdmXjjbijwYVOgvatHR3rNUtVrhjoB+dmGzXD7uuIQccF9Tn1ksWhlfds3v8tIDUgKAcWkz9jIygzv8jrWgCRJgBSB9Nb5ZtYza3jpnfVIEG4vhPwtzAHMM3dpdztYy287vAJtwwsmYdc/vXMOzHWUmTFirCOkapvbebgwump0ASbKJBItuoqkjbDG/H3WXWvb0vnBNyIzczBe5ma4+sEGeBZpBEnAjLJD1oZ4SFI1ZNrNU3tWLY/g8jxE9lu/LN/kroYqiBBRqncZt4wjgPbvKtJbc5Q5j5yWfmVGFN4SZh/hQ83RaMSoEwPEzHxv0Qa5rrbOQdgLMU+4DrYIs28PZqCFM4fIStAFzHzPo/x4mLWylPHqWtFzDjaXuWYkPUOLXBnbo43a4ZpJwkJ51uBqb6o/Y7LkbERuyny7Wy2rx0NA2XYdLtphEScIYIxGQTmk5YP1vRiENCuzgCQl0Wp7EzD5ZDb7ClJaF2H2XZxBvyDMF7d+pDK7zpegfd2mhD3nocxckZ3G2VCgocDJFGgYLyfT570O1fBaDYw+1iJKiSgrYrr3Hr1AjHLdTOdJudvd5yxm2BwSxOTC/U+UxEkx16i42xVzWxNUgVUDq31HOpokckInUiZmghOBcu7PE9/L0nsD4WTf66BykIg0xePPcVWIVe/A7dra3WMxhvgn0kb/+Ho6fXtzxhgvX12fSpOT46boTEyXIbTOtzNemjnuDdTdS1BGyzRR+VwBfmqLVMvKVvr9LhaNHi6lRfS9LK4fpYfLR2lhHVPTa9QxK27pBhijrqX4LlZQ2kjY0sXwecuJhVJ7diJ9+QdkO0z9LYgyRh3S6npXcK25eLUv8qz/Vry6jNocqTR9DGA6Rd+gf+3Rzzb3djjhG0r/45s5FOvOpB/uTLO5wRIJYtXDMF6kS0lxKsYLY1xstILSwts8DQUaCryEAnRH75F1v1SM+is2vpJaETMFqTQ2vE9ebJolmEfPN2G07MKw3kPKBeWkbGS3kCKQ1SLpyJCkgPqkfjmLtw2tPliyZEGBzo1rnTZA8eQOnV/ha3jzEqfyKx2xyRRO28Drzb89xhu5BhmUtI7CkJFdNxpH8mVGDBjpaeE+9xhz+QhrrGGkX9DGwXUW+Ush72CagUEzDcNF+jZkmnoE8WNJHUhaQfmU8m+lawwsc8TohGd+yrJ62TJgAHwi75IOTjQKnj0jTPUn5bi6ciQdQZJ+0UHlJsy+FfS8SApmS0xB2qLaF1MFazXFPrLr+roGJ0mXjS1dHceq1gZ4JOECiJqpmC4+1XOome+HqU401fvc5VmSn7CqVdt6Wh568HQ/3BHBAjKkR/KlhNK0KI4tJNVdqoZ1vGEWkM9tUi0ghdGSflHbHIMBIybhNa65Xbk4hrQWVrW4ejQ7hbQW0qI6AFT7s7xba7SM+E9dgMKzcTYUaChwWgo0jJfTUuo9hNPAa6MvP75xSJiyQ3QXUcqHT1+kZy/Wubu6afdRf3t2gFI3zE0zabA/Me3tOq0ZY1Gg+6E2efDjjBfwxsViBejTfnNy2f3JvYIIUXCf+SrK2F7avgTIpMXOTmYIpXtnj4vpO2K8cPKgzfjfvppJ39yC8WJXjWZMqa4YLvbHfSQtEkT6qNcadyTevN80BXLTJ5noA75QUh+R2fY/73PN6PGyKdpdWDtK954fJNQqpUdIl2mhI0V9Usgo5qavlojPwk+LLdtPVL2qe0k8VQ9rb3rdY7xrX+WyzPU55Sc3fmOAFSjVN6ynMVYd0M/E2NzjTsLO3q5JuPyPb+bT95J4QfJlFqXVM4hWDyN7PYr1MKEMHS/qa3X/ckr7b5FY42wo0FCglQK5q9t4Vg+WBqMgn7sk9Xloui+26Z87bHK12ZVJ3yX00T1aWOdACAW0SBFoUytz8PvoyBLTxXgb9HCTbONtluLU5+mc6p+SJon5sX3Uaf9Wptr7dPu3YMpiOI78y0vjvklOACdLObo2ZXpgQCRdXtKlMaoNLRvbyXHGfSaBCXa6k3BmdI1oBB1uI5y8yBS1Nr1i0MgEtZSeSsJAujmGmSt8LHKpF8uTfpT7IsPhFO3Lx/0jtAz5ONwqbbfStVCBj5bvquhQiwDpF5KC/AOuB8kctBTmriEFs4ruF5ksl2LnAyRijpDQOjxCEgYpLZmHXt7gTVvd2UGCCanxbdopqKpH7VHSJdFO9a0n3t3y7RCdv2X+W+Llj2inBseP3vLT6sKu4amt6p/yBxNH1+QGpW8OP7VRZ/INoO9lOF29OJKuI/lyaXYc3TaTrEW5eoSIjNqj9BRJorR8LE2hbclYCdG4Gwo0FDiJAg3j5STqvOdhPvj6MBgLkG249uuIQz5ZWE7PUQD6cNEZL/96hJ4X7hkvoetFysCmMbdqgy8TvfSOaLAWPi0mMsaW0scYa5x9QjxtgSjEY+ir9/Ou4Hrn6PR5PwGHFV0/YrRQxkwYO7PSzMS3S70ccMqylyZYnJmpWzTmf31zFmsGE+n2tWnuhDPRcWw2SGXU92trmkX9npCTcwjycpwDog8eRVSr14B9UaZ8QsXnKqLyDx8vpSfP6Gfoe3mysp9+e3qQHiwdpPswYMTQHOMEc4LFt0w7Co/iH4nxQpt4PcZL3S4Mbc+ftwnnJfSx4LTp9sx414BIIROTpOTDH/3Mr/TtIflCP0Ouf45NzT++vZC+uzVrjJcpLDhIlFr9awTup7pm9KmS8eKsnK7JN54NBdooUI8LbQHvx6eyV3Wa882SlbxeBLQgl7fWEepk6lvS5bK9s4tUwaZd11hCR4Z0z0nS5SHvzZ0jM7mrqzjSPyehwGPeWlZIYaj3U/VMbQBDoaihL9L1glq+zLd2OQ2iZxf+QZyu5dBYr4eZnHFGDA7tYeUncM3pES4omfhlGcVG1aVfZHZa0gUTbHInZGoaqzKX5ieQdBlDB4z0aiDtgh4R6XST9aIBpCMVV0p7PZUs0ZPrT+uJ7lXZznpRlXeHVD5P96hkr4vjdCm9CSirF6+cXEeeii/HxHwRQ8IZL7KWJaW4mzBgnnOYssqh5QL3hTd1/c10uiARYxYpkXKBGbhNWz08oD1goEIt4PiYwzXah80nJKPa0zLQr8F6JtRujZpB0jKDLQQQQM54+Eec+G57l01XMfXtb7VX5kXKKhQmieMhtFNaE5nknM/MmevK20WuH6ld3rw8g9LnURQKo+TZpLCQxKJ9lvOlZyEyBi4FNk9DgYYCp6ZAw3g5NaneP8B60K0Hv22495pEFp6vpiWU1T18voGY5E76r7swY5a5l7qG+UYYLzMsBqTEbdy08+fFjgZtkGrgbn00lbuvbxgJrYA06FYfrdFavj5SuLLoxngRNeRJeXm5W44DaIuZW0SRpzDz99c7c+mrz2fTdcxI3rw2wynEEAywAU5MWGrqhIF68IWCE/HNT25RkGYSDUqo/qzVWl1ED1DVcFLGqe3jp8vp2eIqVhI26Wd76ecne+nPhf3057O9NMYGf4JTTC26xwsN1lrw9WJutnQXT7nVq/oq+1LprgCyowwr3SfBtYeV371wyD8eUa8XXAkjdxkvwrq9HV8FnR1HLHrtsUGJBebxPrTlKDLtmfWGH7+G8fIFJkFvz3GKx8kyItTqX8PDMtdO6nnB2DBejBzNT0OBU1PAxsgYF/Xmn+Y699cY6YwJjXdak0jK5SlXMle49vxsGQWlMFxWkB54go4NDBuZxcUtJBCEyTavebNqPZz+7vhxWFr+HSOypQmEHnfXLvOtfwxCn2Uc3erIOc9Y9F1D+TgVEMKtp/42SRh8bDgxmhzZtQ4daI3bQQvmfGeG0q3PZmC+jKfrl1Cmb4xgrhjBBB5F9DjMRmvDrqSVeh6elFiPJ0or6OZppwBVkevZQ0RP91MAElUwUg7g9Onq0SaNcBGGy9LKdnqsdgoD5ilX357SXqV3aPegHwYNMwtcQWekcGCp9R6PGHNRZ7YEtDZAQK4eb9Fen/Jql9wUDn+Ep25Xha+h8tTkK1eGy2nkV/b3/AiywiYA8qV/6pPWj0hrggPYSdaj81P9SLuM2SGgLGxd5frRJMqLxs3gw4hJh1pxq0YZuYEelZ9SbJ6GAg0FXkaBhvHyMgq9x+GaRDS06onBb3eXe6pw65dW19IyHHwtcBZQYPe/f11H+mXfpF62YbzMIvo6ysZwTIwXTlukeEz4AqdjNdT+E+Ns4WUTgEfIvt2AhEn+jjG+WtBUHwHXjqc9NwFXRezh6AUX+NrT6YHmVN7gyjO7TW8koQ2dHn0P9R/yd5DmJrgDPjGQvrs9n27fmEtXkXi5icTLMKs0iXX29btSszJJxdfC8/Uf5ec88Lx+Tt5nDNE6LI/6UD0a2Zx2WqhvYlpyYRFpl+XNtIb0y72FnfR/7m+nP57upF8f7cBsGUwTXKqeQIxc9/7Zhzg6cEW7KOuiJU0HPfOv41JefftwMiKHOxlGoS+Dc9o4npeVRuElvMd62a/1gSLaARZNREuRViLUw30HLIYPueZ1YDpdfvh6Pt25OZ++oa+J8TKB8mpdLxoYQNcCfTVQqX/FU/uGj6hYlz7i1KGNq6HAp0OBqq/UXaal8PK2aS9LFRxyzWgNnRmSEHzwGF1YHAY9Qa/LUxguMsW7iKTg3iFXi44GTF+G9KJxE9cUk2s6zaxVTwPkkayl0ZJygKiHBlQeZbp2Wjx7IWnBW+Pz/SUjhOGr09CVKO1lJaRjkndcUTFz9/1c64D5Ms3G9iKKWr/lavFnMF1ufwbjhU2tGMHSpSFGsKRd9Ni4VOX3pE1tnX6O6a/m1ygQVdtOJSeP+2pOPoTxcsC1sa1dGIJczX++tJ3uc6AiS6D3Fnf5w+oR7TQd6/o38zhtdZB5fZAKk3EKybKouuyvqDfNS/U8r8pVmhWAZaPzK+c2B3Tk3TwUWIaE2/2jjSqpjMbSUhsVpPK0Q5nFdKKxpiH02IzS4aYn+9Il2ujXWHK6cXEiXcfk9Oz0BFfiJJk1ZleoJIxlh4KG0X+E0+R5ysSK8MbZUKChQCcFGsZLJ00+GJ+YXDSkBuNlD7PFOyivW17bYMED934Rxsvqdvr3n9fT/QU4+MswZnbFeGHSF+OF03hNICczXjSKxwAv8miU5bv0Mqp1G31bgfTVDcqiVz/tEK04KrBTOXJeW2ADn8IiPN4B2P7dy19w/qgObMKFMLYQ04/qhj9ZtxkfOkgXJtEqPzWQvv7iQrp1HcbLpYl04+qUSbuYeGoPxotS6LYp9JRP81uW+TTwny5MUKpq3+poVs25BvjcFuPlxRobCjFedtLdhe30n3c302+Pt9MvD7eRJBs0xYq6vy/GS24KvIlcdVwh9XZWpVmR3f2rzwxXf4erE67aHAGiUH864bp04AA+wztS6ixJJ7KAUZz2fHVCB0yMcYIQCVkvG10PIO4gm5zJQTFdjjhFhsGJPpfv71yA8YJk2e0LxnSRAst+Nnb9UmrFU+c48iO/8DWQ6icguodWYI3jk6GAWsSn0hqi9RdDV67nmPPKardrHGzu9rhitMVaZGltK71Af8afj9Y4BOIwCEuLqygm3UHSYA29GcawQGrgSFs4+qeuQmgutPnQxsqa1nVOoH01jmokU10QeooqyZC2pKnxlSWo0US44lSocfgG1+PIbLDgZN1G/1wChsGJEg2yu9dV09nJgXQTJfqXUWB6C8bL3PQw1zuwJMOVI+l/0TVjMYWlTyNwhw4qT6X9N3Im/yhRuNthP61vo0wmj15eOzVt7JoYbUeKdreRdNllzbyBuc9HzzZMGuuPh8tcyT9g3XzAQeUh1+DUupzJIimX/j4OySC53P5PuMv68MMA9/IceOpVC9Jn/WTvCA1M8Q7ACPcrthEa7wwFkMNVDgvQ+kNXoszCFlKh/q0rUVyR45qc9D3O0EY/vzyarsyhcPfCKGvTKRTujsGQmWD9ih4iMZz4U/uk6Dz8gNfaazTanI335xX0sQy/P9lqcvJJU6BhvHzA1W/rjjyexLAipWBivqxtcHeazeHjRa4awXj5t59WYLwwucDFX8dqwAw6RUZgvIwgaSHuvU9GGpxFkHLr1k4gpVQOZqXb43qMyJHCyzgeWv+eFk4xAk+8ayytrgiPd2voy78UL54T8m9gNWy4tPCiGjhZkPI9JikmtqmR44SUcboyg9jxdD8bwovpc64YXWRS+wzGixSy+dwlLEznxUQWtaFJ/tWfqJ9eMc+Csxeuj8vf+wJlyk2gqhK+JVm2AnNzfXPbNhd/PtlM//H7GtIum+mne5tpBJOhIyhy1VUjmRWNqjNldxXiTnp1ry3VUfeQVgwlnJabvh1phdFXK1xv3NE2MgFOzEPACj/wZRLyeoXHMXn+y2gmho+HwrVw3EHsW9YpdriiMDJ0lDioM7OZF3jLZOY3ty/aNb4vkCwbk6l2lC5Yf7S+Vue3O53q8MhDUCG+m3dDgU+HAmr9PLyyy7+7/GpDqzWINrXrmIlehNnynL/fHsJ44erG06VdFJPKuiLXkNjUOj71N52da+7zPm7uKjWFB6w5+fEZUfGrPHV2Wwt17wgUNO4cqYqLbzwlZLufpFniUQ7EYtFjm1nLozbe/IPp0o+nhv8JpF6uX0R3BpvaG1cnjQFzaX40zZuuF5TuSr+brhojVmDljwyAt1wLWEL2k8tQezSuTAGrnUweTbVOSvcISRRJJklB8rqU66L0WVKrD54iiYW1rV/ur5qyXVS3oecF5gxXjMRkGaBihozx4u1UTAtvo0rYUuWt1PyaXes0H5AOJ6jS5XXsPoEJkOKJGLxbELdCq+34Uzns09oooILW+lRXAQ/VVtVoeUviSnqJPpuHITg7BPNlmHY6ky6jdPfaxUm7Dqf5U1fijUEY6GHmVGlWDs/B+/Eb9IkMvx+5anLxaVOgYbx8wPVv428eT2JY0Z1VMV+k52UThXZP0PHynHur/88/l9L9Z0wuMF/W0eA+JcYLK4IhnbRo1GUgFj4f04sFTgt9lIoGskgtBjUBlX7hln8Jo+/yCXylXzf3q8GVKZY5qTG/DF8Zy7F1xWlgDqs5Ry7RTxOb7gJvoZQNARYLENPl8pQmtsF0Vfe9b8yna0xsF2C8XEb8WJO6RbbJS5OZ41WeVRuats/2tOe8/BbGs+I9W24+pFiqS1HH+gSOokoyc3OTfoaOAjYYfzzaSv/xx0r69cFG+unPNRbbQ/Qt3eHnpEiX/RWZ/7bQMYSiRGddOKuk3V+wyomebmHyV7jCXgWuF64Sn9wnPT3SDe8qaodHFdLqUEv3fLXnLhgvWvZKQm9Lp+rWz7AogtqWq3MwXEbpY7OcMMN4+frWxXTjs+l0/RqMF+rBGGAkZifpuZ8pbe9frbmo6Vj7l9StfRtXQ4FPhQL0gNwp2/umBknNWZr7xHiRpIuY0kurG+nJ0202tNvpX/fXkSJAlwbMlyOXa4FwmuuQQuOtq7b+0COrBBgNzO0jo40i+lE8+edhhf1f6/AnkOopACs/OQxRi0/1EekXIHWeanzKt6EJuPyW1I+sOck6U18fki9IFFycHUTyZTB9hl6361wxvnEJKzJYkpmXCV+uHA3BdBlhnAoil/ON5o9Iospj4+hKAdWTGCxBP9HNxni1T+pFkkmyaiSJrNX1rbTBAeUi19/uLWCMgvWy5u8NLBft7A8m1LsAS71QN8OIuQxL1CMqAjxlM7E0CQw/y1zAVpE81GtTGQWqgulWHAW2YPT+EHFagwyV9YUKVWsCRhO85Ku/XfYKYkDJ0tPAAEY3JvrQj8bVONaoXyMt+jlWN7+4Nmntc4I9g6xwDUKLiulE3zVMII7yn1yeKmNvyaFS6gmC+Vfz21DgXVKgYby8S+q/ZtoxpAQaDS1a9BwxkO7sy+wdOl1QGPZ8dTf92389S/c4lRfjZXXzkI2IK5oc5GS+LzNeNIBKHNEfx+7LnUhB7/ZUy7BwC4fgWnFFaP0OOPmU7hrCXWVY6W6H82/PocPp9+SnG772WEELx1SFMtlEbE1ompOZi7HMgCUH6mEdDfiaoPq5/nB1GlHji4Pp1iWZ7xtLN67MIM45laanx9MFTPgJThOX6KAFbEzpVVqWdOvXyeWKUMfZu97OgjNwf+Rvr/aqxauO4xGDcwuGi5gue2jd+/PRevr/flnmVHc1/fcfy8RBfByJFzFfdJLpDQWE/t8+HVdOxD48gdIn0nv5W3FPE7OEK93tKZRhpfuscGW8k/CVo0YrnBgmWuZJHF/Xi1ZFfzY26yygpcTyy8uDMDf705eX2OAgyn/rxgUTl752ZdbMtQ6JAZYfZ+IIf/M0FGgocDoK5MFLwJqjak6EufVt0i7Y2pVel+U1rL0trqdHT3WFYy/dhQEjs9HL/Om6hv401zkjVJZX0KFhGYl1SJFezqCNCNZtWxkRihe92XHkCBlfHoBLLEWMEvZkd+RPUErPchEJ56jai0qy8VhSFawDJP+iMo5x+CIJyCuY8JUeDW1qr2BB5vIMV46wuCbrMRPjSBRkfC1XjYjfmkxZ4pxw8zIKeLNsbQW2nrL2KVPQR6zRYA6iC3FxZdN0IT6F4fKA68JPkMz6/cG2SVEeotfl0BS4w3hhjazrRdKTXy2RWee5O1bJqiH9RSup3e6v7HmY12bksYQTTK/H4SKWoOTT/nhuSijB6RvoiGDB6FZCH5GkRrVmFYNwZAiFuxxe6GrcnRtTXD2asKtxM1jgmkXXywzX43TlaIBGajoJ1S4zTk+hSKM9Y+/sO5f9naXfJNxQoJUCDeOllR4f1JeNnTnHMZ5KhFIma/cO0MSOuO9zlNkto6H93/7rSbr3DMbLE92vRhKDDaEUuSJEaYsfDbo2LlezimOPKUXJ+PBVppoTP/GlnJ0mzvsEF9SMgrXm3+lAWMw42amJORgv22zMV7cPmZzQP4F+l+sXhtJXV4bSF1dhusB4uXJxGksHk2lqaizNcurFyjXTSRNZN8ZLe54ib6d9t5ahjvW6eGtMH50rkywoV1S3nRDt7mK6mH62Tz+7+3g1/ecvK+n3+y/ST3eXYAwMpeN+/vo4weRPVFatCoe7Ra3AHJTz5VF8laFlLYW//OQuwyLu6d6B4WXQbxcuytO+gNRiT2HaoB0wzi0jA77NInp15yBNokfnu2vDSLsMpG+vj5oOheufzWEiczJdujhjp8k6qRMG0Sw2N17ySPFldGjCGwp8yhSg59iA4/2lYrzwecTGTRIe+5ygy4rRCleMFri2oXHxEZIEizBeHq8ccs352MxHqw9r42Y6l0CqcdGZoZ6Eb54Z23KSLVS35H0sUHbi8Vx1jqoRXocEZB1yWpenZ0SwKDkrVXR9+xgjeojloitHCqaM6KEaQpHpBa5yfH55LN2C8fIZjJcrmPGdZUM7jq6XmclRv8rBBl8M5np0B0kxAZ29BMrLeVDC8byPv9F26lLKJWaerBgd2bpYkh7rG1y9X1hHKguFuk/WYLrscRXuwK7ks3zz9skaWW1UEsn60wFZPHFNxyYl82yvFX3XbcXj1fEDTxmrMzSg9A58pV83d2CssdU+PqsKl0LZKrgEELTR9SPtA4akJw29RF/fQDILJbs3rnA4CHPwEtaOLsxO2LUjVzDsDBgvv6dgv5FYt6w1fg0FGgowr1WzZ0OND40CGjhjjIshVpOBTTBwssXVX1rdQuR3N/3vfy6kh0wyD55ssyg6QGGYrAj08adTJt1d1YZGkw3LBt42eYE08Na06fTxsMhJQPaCi3C9Fed14E4bv0zzJHe98W0vjceq8+ouUU70cprphEq014nKDhP76hZWVjhBGOY0/ovLI+lbJrIvPxtPn8N4meeK0czUBCdcKNdjsaWnpSvmDAj/6z11nms8gVNh4a5DG1emQEm6NjJpUS2my6H6GXX98Nl6+omrRvceLadf7y8h7dTP3fB+9JAM8OcLt0Ex4WwRF2QngTINvMvP0t2WfJcqEkQZowuIeb0rOBLvkXTkOsoY7/CPktjdcj60YZPEywoi4tvQfg0GzPTEYPrxCxaJF0bSX2+OIy49kq5emTbLDPMsFgc4sRzUAMejsU1jXJ2hSFF+zdNQoKHAiRTQJi0DxFuHPQeMhTrs2dxCif+LDWO4/HJvibER5bpr+/TTASRB0e1y4LoytNYYlDJ5sNUzr0Yx9Uf9ZeyRSGTKO298vcI7EJWpdY/+shHBzqcCXYEicu5eMF4AFNjh8SHSeZi7hxEzNeZKTG9ddesx2txe4S7yJFc55lBmqitHWlMMZgk9FdfXBjj8f5Hi2Zxl1pXnj+WxdSuFqWhmtFPpnBG2x3yxBWNwG2b9EteM7qHwWdIuvzxawZQ0c8rmMVLiWKXi39jIgBmg8HmDerS1dUkpUZHWWtWJKKk/p25NY/m1P3VowLdCdIsjiIjXmpbHbY3T8pX7rGJ3xlQbldQL+mzov8e0VQnpfnFlFMbLcLp5hYNC2udnlyfTtUvTpudlGKuNJvlCG3WdR55alWbl8Jw1vw0FGgrUFPhkGC8xXNVF9wGo/P7Q3O1l0linDaEmaUm+aCBdxeLK+uZO+q9fF9JjTp7uI+67vI656a0+Btk+FgOK08cdVl8MmdZ2mDCgsaea8CvitKcaAXmkrQZc4HqBRpT34q0MK6Oti7GqGC15rAsUS0/BOeOZl51rAABAAElEQVSF05CK8cIiSxYbWIAOcsI1Al/lSyav729OpK+5L/s5d7xnudc9Mcndbk65xHwxzHnV0Crx0j0nLdk68cPL1rsyXhf/iYl/2IF1dXs5MqlUTeoX2mhInFzi9Y9hav56d5mTs+X0x4OVxGGvMV/Wd/vSBv1M/Urr6BGYBjQJGk1GLlyGXb+tbTBDWOhHUUvthajKbkW0BaFcAVaWX/7qX/qnDZtdNYLxssO1hlX0SUh3wj/uTHOdbyT97YspRKKxzDA/BVMT8WhO68SsGehgvAhr+xOpt/s33w0FPnUKFB2W3abWGno0Hh4cIvlHX9SmVmuORzCiHyygaBxFpVJauoqVmP3jYRgQjHFc3xhCn4TpdWGH12/zXsyowviyPviycOHo9uT8EuQYajweUsfpDKl9FLmCrxxlXGcdmQ+Mdumjkg6Nzb09Y9SPjPQjAavrRmxsUV563XS+TCLtMoLOt0mue8AkhvkyxOY2Uo11mDb5euLtX6/2W2Y58L8ahvcQmkKV5aq+VFe0UzVV1YOkVKXweZM5YwFp8N9hvDyC8fLz3VV0H2KtkLl6bV2HkH2Y+h7gCitSHcwbCIP4ulq6EK1W6jpWXTgdK4clX+fHQ1upVodWeW0F4Otl8RShhCndrSHefjoScKYRBVAZtF/YNCleU7ubriGlfWUGnUSXRlCwy5Wjq9Ppc3SmSR+R6UxDH5Fb4yItIchP7cKj5SMgmndDgU+bAg3j5QOv/3L4VlFsDSMHA54G2zVNMki8/H53MT3jFOr+0y3MO+6ley8Ojbu/usXJPVrbZfZ2CObLGH/a2LBOsKcasIVPPu0JOljx2wtAI3CvsCK6jdSngSvjnNXdOStEymVIuKswNs0ijyZ0hdWMF6xD2UZcp1uIsqLgeHiYe8Hcmf3+xmT68as5pF6muW6EsrKx0erkQAr1RGfh18ZST205IVI379f4cey96+C80nmNLL7vUXMDqLcIopl7PkOXwd0HS+g0WDXmy0P61+L6cXqydsQ7pVEWbyP8TeikiD4W8dRhHYN+JX2WE6khcFl3trf/FPEL307nm4UT9vKpc94j3RyhPTTGrMAX7xqfpyIl4DXjBaayFBuz2Vvf2kYUejj9z29n05ecIv+v7y6kmQlOj2fG0xjKAEdHRmzxXF1lIMFinZiLEKmWJWrcDQUaCgQFNEd5vxHzRJtZXyRorbCLnqttNrNrG9uYjkaJP9eaJeny04NNLCtuo6wUBbMDw8Y8raRcYMLYbWd19BgELDH6Ytfu2NUzsveSdz2ayOWY9OtfdaijqVOKkNpHEOHr0K0+JWRfXkuJ8bK9x7Uj3oNYj7kw2Z8uofft4qwYMFw7+mwmzc6MsMmdSuPoepE0gSyx1eNUrAxIq0ygzsCpXe15f010p073jQPmgvmr/rW2ylpNByQyGy3z5mube2bW/LdHXIVbpJ3+sQozf4A2PcB1JF2DQ5Eu87QU6uLMzdMZOF4BNRWjT/QqX1VzFR5B1vFb3YGlW62UcU6C87gdGIhu6wsLUK4cnzFN8NN1wR2keKXvReyl2fH+NMPf3AQWubhu9OXn0+lLDg6nJKU9MWZz6whSQa6jCXwQIqOOzL3Ft8rSUeK3mH6TVEOB01Pgk2S8qHt+LN3Uh866wmP9oslA7k02JeLu/3nvRXqOfbz7KLp7DuPljwVELTfQk8DfHiYdJ5G8kIjrOMfxNeOFYTojPI4VQHuCddLZ9VKAjhjvzuN0A3VAVSXL0gp53emLI006nG5JAkJ33XfFeMGq1CjSLmOccH17czL97av59I0YL1yB0IZwZBjFq4hBaJElaxBCJNSWXkhEnNtkErkX9nCXlI9Sln6Nu4UCmWw1c0R17hCLMDUfPFqCubmWHiL1cv/5AXfGD9ODlaP0bA2rO6zexjjJHB/Gug6LOVv0EFfMOz1RI/F2rO/3b91iyjLUvh257xXUVugAa/NGR4Loxj+kV2wRvbtjuqw2trfTpdmR9L/EePlsKv3PzHiZRn+SzF+OckKnRWF3xotSU0qRakeuG4+GAp88BWId4OOdb9k0z8lfFmKkaHyLg4alla30DAmCu6wzHi3ucIVjJz1Fd8buLvMgjIRhu/Ln14tEVO99WmeUPRBf647n2SfbR5OySl89nU5s7tOOSeOOQsR4kcJ9XjCfUGA6ktjUYkFmCokCJF+++nzW9GjcvDyFBKyYxYNmQcbGPJDa5rhMtD2hsjgvcQvNa0R/Cfb3Izjaq9qVJLNkyWifNdnaOhaMltaR+t5NDxc30q8oxVc7/df9Tbv2Njig+RlLn9Sb2rpp2eEtPP7Us799E6Y5qftT+9eublgq5G1oOmO1AfDZDiOI2q92lTGjBXi60UbVUPehkxiqWpcMDR7RX13Z7k0YL3duzqD3BWnSqWHa7USanEDyhT7dL+YUaZpOou4Jlok37oYCnzwFPnrGS68hTTX/MYwRreXjyy4fUzYKpzCdQu1wb/Pps+W0srrDZLOJ3heuHj1ggbR6gFZ33Xc9SpPchxmG8TIajBdmGp+0/FTLJn4o5jTT5FNSTynFd2uORGcP6+bvofWvcJwGro5xsqs7vtYUapgoQY3TfcLfJ19iazbmCd4Ic5QxTrhdRO6F/Rjm1SGmBw84GRhAz8RAuo2I5rc35zHNN8VCC3FiNuBiurjuCSk5jlz5Qs3TlF+kjvO1n0ijRHSe+Gu8553zGvO7c1WLLzkgm/cJz8/q2jYmKVHUh5WExaW19Ovj7XRvEYbns12YMHtcMeK+OIyXCU6IZF46qG7i+uDzbhu+Ne5YQPYqdcSImq2/5QrfXrHl/7pw9UjgqUUOcprlZx6bOnPTPZ8abTSOKYuCELMF9UnZRCvXtgYP6GdYDBk4wCzrCBJl81zjm0x//XLeGMlT46NpEFFxF9kXLs+McJXZcuytPq157IzRGt58NRT4eCnQMgblbqIhUIwX29RKkoBrtRsoK11gUyt9V3+woX38gvHv6S4HPdI314dukyH6rMa/QrJPYx+4WnpY7qdO0ZP65avSXKnEI7zt3xH28ncZs4aux8Lw8zHHN/7wp+y6iw5ZRlBgOsLGdhrrMZ+hl+qbL+ZhHo+zTsDSoTa0SCBr/NJmVuQQng5KdHhEqie/I+9njH4y8vcgVOWLNisGQmU+msljhXn6ybNVpLJ20r2nq8zTW+nR0l765eEW0qgw6VmXTZukEeswtU3iG70y0aqmaY6XUbKmcO1yAmWsmVrCI4jAJ+/2GPI7LZxg9RRtpkSnDlc+FubrTgsiWPk7QCdRXzpEUu3QLHB9e2uGNeyUWQy8PCvDEJiXxgqX1rBiVA1IfK1Mp0zjnbjLcr5XGXsn1GgSfX8o8EkxXqLrRXeM7/enOs6WkyiPDcxsbqxcuXA7u/tmaeDF8rppcX/MvesX3MH+j99di/uTF7vogJHCtxG423D7T2S8xLiqQbpO1XMd1Cz95Vd+l+U7KayE6+UWXnFAeuGPeJ3pdI8hupUhUZ4oMynZrIQ/J1aCNsxE0UJKJwXb4rwgpMlaCemG4zQ/eYSSz0HubQ+hoGwa83zz6QYnWpfmJ9D9InFW/5PuivrxXLiP8lOG1VBnc5XlCwzniT9weq28Gcx1Gm/bVa1XskOnkfHoOt/S8iYbkJ20DPPlXw/W0+/oU/r10Vb6E4XWQ+gyGBnkFHNUV43EaPPHFnbCZ6hqfBWTwKos2kFn/eVWWLXc9u9IxxMo4yut8ruGfDVX62aj/NLatCWFnowXpdgCaVmwU0dcorNIpGtFuyyeOVhnMUj/mjjiZJiT44mE9aLR9D0Ml2uYZv/6izm7hz7OqfEAm7x+aO8bl5q+lkDz01CgoQAUUN/r3jeqMc/oBFzecGouFCPUlOrSL1dYUywhRfD42Vq693g5/f6YdQbXLBdWErrOOEVHj5zM1Lquqzi5IGXwRBqGuiMf3fPVu9o6x5HesKcN6cyDp+J0U6jWQzH2tmONtdIx45/WCnaRQ+KyWJCZQBr28txw+oZN7cXZUa5yTKWLM6Om920eS0dScmpKxWV6W4hzViw9J1h7ci/97kahzhK+FM0bBnDanpxIlITc47QY7sxrNWJDbxk72GNtprXwC+bm+4/X0iLX4e7yvs/hyMIaVgk5IBnnGvAYjPpp5mjNPTRvqy/hqOiDv7ujVpXDKrQtu+5fhkaOPbcBXvuGT3ecJVyJtY7V6eqEa13ntuZEUtt61C+36dcHRyiBTAesW8fS19ddR+E8DJerlybTPFd5p6dHWdvAdJEkmw6Uok1GVjuTN/xv9icSL1N5JxkpM9C4GwpUFPjoGS9VSdsc6prqivFuC/6APutBhrHSH942AFJAiVfqVGoTcXxJvzwxiZe99H+ZlaOtdP+ZlO1ijnV8jA2hGC9wsNnoHGphAB6ZmPPHB67W4Sso6HTMgF1eAdcauxVQmQ+41pD6qwwP+Dq0dgVcvOsQxaqfHF54at6Qb+vjPnYdiADNTaLvIH+i7b7djUWZ7vYB/ohoalM42ZduXxlEwedQuj7P3e3L0+nGZ4gTz02neU4LZJaw0uNiRVEanSm35qP5epcUsKWW6srqS7VFfeUqk46DLa6W7cpiAqe//+ePZcSX19I/766kn++tYjVsmIUJipThFAzRz+KxjYchDB9/x/IuEiuXeQGhdyyivAkrM+7yjIXbY9S/rwKnWLnAFe7AFPidCJkUHpg/AsI8ezBeWstQR5eCQ/VH6XZR31vZ3DdTtMtoBOcGEebZB0xXwi3eV+dHYWxeTBfnxtPN6xdNokxXJ1U/lnshqp6aupXXqRxBh1MBN0ANBT4ACkQPLftHzjZBEVq5rB8xGtEfD1hbyDzv3t6hWU9cXJFZ3tX0+8OV9OejDfS6oKhUloz2Bd8P41nXEVBcauOm9yWNa7at5bPatHmPPQPtHGeV1w4MUcaXwSmiYAOuA1EOE0Q5lnSBt0WZ5nrBStJH144O0x6K2fdZW41x0HVhZih9g+L9yygBlw4NWZCZ5i7SJd6DcF60HjPmMXFjGDOqxUe37J3gF7nUO56gTHx/OO9cCuaWqjyidV4Ma/26y1X6XdroNsrYF5Y20h8PV5FK3eRAZCM9XT1k/YtyfMxIj8M4kGLjSaRS1RZ1LczmZ0Oc61nrNiNObrcdhOqkZOlT5bHOrRJxLCVgTkWtpvvTAlyAyL+M0wnnlMI/t00rUQaT6Ww9CtqAUbVHH9e/ayiDvnMVxuBVLHKii+gGB4mXOUCcpc2OmgJiSZa+L4wXK4GVo/7ppEMd1rgaCrxdCnyyjBeROYanD7tLRil8sNSEoVP0OI0/YPaQqOXuDooo2RQ+e7GVltd20/8txgtKxe49QdkuV45GUUZiXOt+5/ZX646KSnnSt8E6KKa3p1/notXHp6uAi3hlIy9jluGlfwkf7jK8jBfhepfpOnwZKyDqIgJfTazteETfHDszXrSAFONFFlYk7bKOWVstLEdQqHtpuh8LRmiFR3ne9Uuj6crFqXTt8kyam5PFlfG8BFMa4BTaahHVqyxlfhr3u6CAllpWV7kZ2EYhV5dMS+/s7rEJ4U07+G8YL788WM2MlxWqVwxNKbAeoY2gkE5VrkLohzYXTUteehytfj2xnKSF+U9AKERLqXYID3fY9rACTU7p1eECZ51O5Mhw8dGRpzMwXoRLjBdZpFjbgvGCPdoVGMijmGj//vpwujQ1kO7Qzy7PjyGmfwEz7WPp2tUL6E3iKh/xlAuzQ0H/ii2SU8xy+Yo/Z4/5igk14A0F3gIFog9HUrkvh7f6sLnDAzjr1+5/wIn4PuZ5t+mTz5EkePZ8M93FLO8vWDL64/EG0mn026Nh5kjhFdMleiBfjHmGzJJWePldug2g+CnyUvjWTuGK5yTY84QLXJ351sZfoTa926ZWFtkOWC9AN5gBg4xjcxOD6H4bNam9O9ensSAzjg4Nt8qma1lal5nknsawSKpyRFlf7d1OmUD7aljeA+g8cWpqUevSGs2YJrwVJPrv0D5lzcjMnHMd+Nf7K2bu/E/aKPyXtLF9nJ5jRnrUrsHJwISudwmXWqXjcewqbzBevMV6Gy6p2UnJ0qdjTjSUOX4JaKSVR4lbnh1ABnm6n4jrONUFPT/y5w8PMV70pVa7jt6mHdrpHgeLV+e4Ln95KN26PJLmsRj4xbVZFEFPpAtcj5MeNTFddKW3kt5WEpHc6TJ3jlDtNAvU7yxDkYHm3VDAKNAwXiDDx9QdNVmUjyYenRbrdGqPhdIyd1s3kNX/P7+/sIXSfRgvz1f32dT0cV0G89JYOBIO6aPQScugKQKth3+JFtsElyf+mnaesH5rl2hbQ5yO0h67LIPHC3/hq1NxuDKNiBlw8e3vwBK+nr923wj1d1UGWyzWYZqCZaVAysj2oYukHoY4wZrCDOFn88Ppu1sTnMSPufloRDJnEc2ckglptMJbGSC055zf11xI1bl6t66SkmWtyL/8fre5PFvqVdmyoyqP+hf1f8BJpvqZFnl/PJJp6fX052OsHKFoEkMK9DsWM1yToZlYvxJfQAtrMQiEyxZ5+ZTNcti1TVSpWoxOqla57FFIT8kDS3c7eBlWuku49lp1OP2qQB05qTxqfOElH4undy53hClEY9gud87F4NrmJG6CPvbjFxOcDtPPPp80xZRX0J00geTexQuTSBdxxYjxS4/iBk59u2+kJp/TPJGbV413GtwNTEOBd0GBaNNl2mrftT9dp+MxKQB8dxjIdmE0b20dIEmwmZ4+32Lcg/Fybw3dVpvMjbJ44oqtYZ/WU5z1R6GNvsQMi9OS6pJeRwY6PCJSa95bwcqw0t0K5Xlqxyd4PeHvX/VvN3zZLxNQ5TPl+7w1V+gahzFekAKaHod5/NkIV41G0u3rk1g5mmBzO4ouuCnTOeKmpVFiajg8Vc0V9bqkzslJrsh9zpmBhlvvD+vx0rS2zyiFFMRSGv60PjPFz0gjb7LufYRC3V/uLqXHtNW7SLxs7PbTjvvSxh5zMgciCG/wJzz8gdz/+bdjD7orxB+9PUzftctDW33qWILLGOzFT0vUIjwjivrOsQw83JGWv+uDmBaUVQIeq4XxQgMVX9BXIpozWa/Qt3c5UJSVI1nhun5Bf4NJV43uoK/w2uWJdGluwq71DqMbZ5irvXnKtWyUc25r/t7Wl8oZdGylxNvKQZNOQ4FuFPikGS/dCPKx+MWEFIwXTUDaFK6u73HtaJfF0RKnVFw1erLJomk3PVtmgkIkc31LAy3ilmO+IZTopQZTDcQaxo5ZNGgwCytHrcOZTyv15CLIEqJ096K0UjnpEQ7LSQHUDW/AFWA5Zunj+Ys0u+Ep5kMLBtb/G020qTs6RhhTkkXY5R7FVKT0ukhh3l/uzKTPuPpw/TIMF0SHpyZlgm/ErKw4EqWrHIC4e9JlVj8Idy9Kekk/iCKcmMkon4Cq5qA2QAdRG1A/E4NTIvdP0Kf0AEV+j7Dy8QILR8/XMTG9eswJmxguKGBmgac75VI4qUe4ZdIx3HWbOEvjKHNqKLv8CO9Z4cp4kT/HZ1/8lBCtHw5XlZOCyif+otyZFPnkUcp19/jj3jkMmCk2K3//chrGy1j6y+1ZxJ9HMR89gZWF4TSD+LNoa1YWSPh8GC9dyNd4NRT4oCnQ0kMpiXqgnto/1hHuTwge8lOf2uZEXEzQdZnmRdrlCZvZ3x6upX/dW08PFrZZNzC2YblvqN+ZBrYuIG5IJQROS5eklbqlXCdfg/R0lcAVhp7QHnBauEAjeD1lWu7jvyfgI4rF5sc2orzFeNlCkmBrV4rBj9Gv159uXhpGt8tgunV1It26MoUkwRjrBqwfspkd0RwBHYOR7DkJBgBflkCZn+7ubrlXVPmfEkV3xO/E10ujtii6qk1FKdTOJPWtNav0EK2jf03X7Fc3MS7xdDP9888lrtxL99oGc/YQF2n6094RB43gkW4XePaV4QQVTZhraofLWrOC256XUVLY2p+yFtrD49vnSMWUT6QSoa0YA18N63CBI8fiZbTLpcu3jAyVyLkF00X6cSQxj7qhBB+QP8xLczXu29sXTV+hrsVJTcEoenHGUJ5dMl6E6N0yX1TOoEVQzIrX/DQUeKcUaBgv75T8by5xG1r5Mel+jaL8Z0/oeii4EvGQDeEaCvFkgeDx4jZXIrhyhJKxJ2h414n8HCOt3XeV1nJGU9tUCk1PxguBPP7bq1w++HWDEY++9xODZwnRDn/SwBphnZNlhMRUFlhrf4W4r09SlNGkiGR6TxO+RBRYQMF8Ge7nJB7Gi+5s32AB9fdvr3DFiKsPl6ZsQ6jrDxIZ1r1ty0kkRgrM9x/Vo6KVRWr//hgKa2s9q0OvyNzNrG4XEGleXsXC0Yt1GJwbpmD33pPd9PMj7pmvHJo4s6RdprBgMW5WFMABAq7+24JR7cNpVlCxcDr9HKI7LT1PnWFC0iushH4ZXImjI2PVgs4wlqB4BLS8LShfQdKiTf0g+gKS+EYLXZcU5HDaR6/SAX1tF2sgw+l/fn8JcefJ9MM3l9IMTM0JTLTrdHgE0WfH4ym19/pI33OiHNQ+fDRPQ4FPlAJtHRUqSCGs+lIwS0yhLuOUGM0bHOCI+bK6toMkwVZ6uLCRfke3y0+Y5n28uGtKxCdghI7aFQRff9iYyUWG1j5XzH9koTMXL6uO08RQHz8t3Ounp5TKUcXcNiiJ8QLTig2trhvJEMw4a4Zrs0gScGBz8/Jouo2C3QtIx964igQfY9oY49kodBQzOR7RsfqqHBHa/R2lF3i4BXnK6N2RvjPfmsLOdKlLEuszHWIcMKHKhLSsbi2to4MI6dOfuAb8hDXvXaSy+mEK9sEgTH2DFR28mkSVsp0GlUrKdSt8wNVh8qljhavVt4aWqz3MyxqYA0NrnNN8BQbBOhb3yb+6masQBfGzR/E19+owiW1AmhrBCheK7GcxJ/2X2xfSTSxw6brR1MRYGqedygx66C3UWvndMl2sKM1PQ4H3kgIN4+W9rJbXz1QeO0EUA6yYBIgHw3SRuL5M30r08jHKdp8ifvmfv2OFgCtHD5iQthhxJ8fGTEv5OJxsYzjYaCx0widWhAZr/Tp+PuzRV+lTDvUnT/FlrIzs3F6BuzU3rehbSyLIGtrjh3UVTfQ68dtlYlcssVFkGnJSE9N4P2YhB9N1Tq2+/+oyuifG01WOCoalgEy6Pdhd9kkRjJ5MSzk12X8MT1BaZSmLJP/y+0Mua1VGHCpTLPy8Or2UK6tcL1pHnxJ/q9wz+uPhZvoD60Y/3dtKj2FuDsmiFUdMWlCPotC6D7PItAxvEsKZe5HjFk7+HHVBupOoWuWygH8Tzh55KPJqdMlJ55LYV5XDzHixBW+Opzjo5DSGrxT8aUE3O6wF4HGaGD5kATiUfvzukvWtb+9cNGWUOhW265HcN9cTCz+XeJGPp+hJFBnsJKyAm6ehwCdEgeiN6hfh/v/Ze+/+NpIkz7tEBwIEvWxL7btnd29n9q/73Pt/Ec+zOzeuWy1LiRQ9CVrd7xuRUZUoAHSiHFklEZU2MjIqbWRkBGNbPwlOtJGFD8rf9p7WCmK8rEqpLtYSn62I8SLzvH9/IZO9spaIFbeuTsHbfYyXGkAD/ykYL/31ON+Xjw/gXPefD6EvhbIHBPFcbO3QY1zTUkB89+LBvCQJZsaLx3fFeHk4pzVEW0rCZ826EYz5jiRlGdviqWNUAo8EQ97DKT8k4VcQFPNj1lSFtdOHq98wBmEYHElqY0Pz75bWumubB4nxsqGrRvum33BySsx6WfNE/xpPrAKBz3xcPeEeRsUqVeBQD/Fc5+XNcw26L4rBYM4IGYTgIenXXiVljUF44ie3kgKSBcGJE13x1TzcnSz+/PNy8b0YLw/vSR+RuDEd9fNZXaG3Jio4Nvc62LS2TZ5ApXk3FLjFFGgYLzf44w9OHuiXODZx/W2dAhwcHkq7+36x+q5X/H+/rUvJ7qEWT9oo7kqcv5gUc2GsONYfDIdJ7Yo4cJmSDhNWYyb4ItoxwdmQmjgHlJlPL1/GcBsYnY+NpVQyWCP9qTUlq45shBG9pN5HWoSyx8PCypzEhR8ujek0gGtGbWO4/PBksZif7ejudscWTdzxriakbLF5g9vgTaxatKa8oZdtJTn2ZFYaKwp7+z272ve7zKv+rmt96D94udbT3WkxFiTRcXQ6IcWxUkyndoTpUBgyiDvz0NbM3LT5FFYWgjOlsbhhPyWWwyKvMaxaoFYlVi4w7fep6BRgNaBaqb6BFNFs7I6khBKx/ENd4eNk+HtZCFumfy1NyIT0VPHTd3eLRZlg/V79rCMGlunK0VgVIvkBFzpWRVAqJTj9oszm3VCgoQAU6O+tzvh1yqgbae0gxgtzoA4dNncOil1d48BSDEyXZ5Ig+F1WEv/++qB4uyEF2GK8zGpDhplel5oVAMa0PkLHSNZfcl8S8/TnqvpvPXww59VDGJy8JOp+HoZnlpPgkOZUm9kDxjX93RnToY0WVloiSDfcmKzHTErPy3zxQLozfpTuqi6SBPxJN5wd2pTUS3RjGAO3/E0hQ55RlCLr1/JUdUgue1U1sO8kJj3WtpDOOpKekjVJnq5LuntVjBf0uvz3b1uSeOmZRU8YBi21z2kx7clr7Z23/jmFK9hOI4s0ZzWnVNRLq+EqoM9lyPaF1D310iKenBF3PpTIlb8jdx5WuS3Wfhw6v0gMYYkLakjLmtrfsV2Rnpcy6L/8slD88GhOeog6di2uq6v0C1030AFdwrhHVYLChhEsT9C4GwrcEgo0jJcb/aHz4drnZxZO/O3J8goWCTa29os1nVj97bcNiWJqMtLmcEOMl/WkbFcSmpqMxnRqNabNoXRS6A0kxGWBzsbQnjSoWpgFePjZw71nvf7fKDXhZphGKREX/uodqW3iUHAoGrNwZbPpR/V1xgsKdd2sLdeLFmcnNRFJ47vej+61TVT4mwfzdgowPzttkw4TTzX5OB7NXFTR/ya5DpKFI94oXX4qCwrPJOb8DymfXFmTCfcd3fM/kLi+lPvtS7kfDE02J9NYB7BjI1FDTeREC8iyJ6Wmm1qOkSva7CDtRscMpv2QkFigOgwvNS/7DMaL9Yf+ssnJkMKppZmy1Fh1cNCT9J2sGD1BWbUUUD5qS8JF1sIeLhRz6luYam9JF8IEx8f639/P+uE3voYCDQXOo0D0X/qu3MnLxtQlXjT/izO6gaJ+6c14JenZP15v66qRrCS+OSx+e3tk0rNIvMy2J8R4QdpTfVrMVGMm9xVfjWZRal906clj00BocXl4mfh6HDE5l0WUjg+CDx2P+WOgE5OAJRVSfOjVeyhp2Z/ESDYrbZJ4meu2iq7Guu6MTEvrGmU1mlYMK0MGkoBeTpohWJKE55xknugL/K3wV8tMHsZ7Z5qAsAeaqXMxXnpivKyuy3qR1rlvdcCIXpf/eSoz0pLI+kPX4eYkTdSSYtgZzR+2lFV2DtWAw0FZPBW98nKJrWIiLe+EWhY0GJJFmnM4pHqqYbAH0wyGnA/dU/i6vsoPbZHwxoiErvlKI86crsTBePlZps+/kcQLCnZnZ6RjTdaOJqSvDjimXy3N7+Sv1r0V5MbVUOC2UqBhvNykL8/sMzC+xnCaVVTpOLFiIbS1u1ds6zrE7882ig1p/eREYHXrsPj7KzFnejKzt8Wgi+Z96U5AJ4UmKMrgJNqmp/TWyGozQjW9uMt/waFygYljleFkzipdHlPBjNDh6SqYkaM/HVvEizyx3kLZGo9JuuitqtqkjDJdxFhPJDE0074jqZaJ4uHydPGfPy1IRHi6+PYBCsdamqDc1N40ssTZY58pwY6ysujG+ZVQgFY22KK87XFaFOLOnLy9fCNJF+lTevryXfFmbad4vnpUrO+eFCsb74s3UriLgl0siHGFpqWNivNe/OQOiNGicViZtYYT8f34RGgQlEVVfwqHVk8X6fP6kW90uipHf6q+HMnjGKDIUAs01cPXt9IjpY5B3zjBOpQYLpgBPRVz8+B41/RN/R/pdPlZVj/+65dF040zP9s13UlzUqwblqHAw/tXhVGz6Kto0bgaClyMAt5Z7Tf1W+ubHNxoY3qkazIbm7rCIamXFytbJePl2epJ8ezdsTa6J9Znu+3xoqOx7TzGy3njkOOcEBkYwyKcVMPHqUjhY8/odF5O9ss4S8XPfYaXm2fLocQmnxF5XMyXKV3jwNjh3VlZOPpW64jlmeLX7+bsCiWK+edm28WkKWGXlEw59nttqjqptD5PXnrlDjwukLTK9IW4wN3x9rkiR6v8THIgjYxi2L2ertWvb0u5/V6xIunu3yR5+tdnuKXnUH/zWpu1W+NFV1fqxVuwGS4U3LOmDRpVDC+VKCSYR/tNoueYOJwqJChehQy6KOti6Wgzl3+iJsqZOQ1OSbh+qKXkreiCCfQDzcUn6vdzuhL3X7/OaS6eLZ5Ix9pDHTTOqY0uS6k917aQdoHx4u20wrVqt/3lNL6GAreNAg3j5SZ9cQbQclKOAS+mqayiSofSPJLv6ioE5qWfvVgTA+ageCqR4TcSyfzb84Nia/998UrWWE6O7+g0Rqfyugsxo0mKKeKEWYpHKwhKikG1GsMtVHH900SM+fE2GOf8RE3OSZbNJ1724PR3VqnZxGfJ5BcYK5uDDzlgvFh9xHhhlr6jyYiF5eKcrj9o8vnPnxftbva3MrPXkaKxblsbQiQY9BeTEPThD3/5qc6rWBP/RVKAtjHYogj1b0ybMUV/6iMrOhV+I0W7r7RJWZVo/u8rvWJNyqxfaJNCH0Nag8UKos9T2qW4WDmMvpB4ESx6XnSwWuPxUnN8IsSwMZzcFRjzztOUSfockbov0Go9LK/DHBZD/kA9zhGrPuCllIwXY2wimccYQ1/raXE8VvxvY7zMifGyZOL3nAJjxpK+hrQQ8ExUPCELiaKvpaDm1VCgocCZFIje632ZpNFviUGSwK5waFP7ThvazR1JyT7fLJ6+3pL1tl7xeuO0eCFm8uYuzOQ7ugIoPVYazybUP09NGX2sCAK+9/3hKFFipMtT5HkG8c1TXtmtIjSi+AgZRZw7Xg7DFQwCAPBwjxlNjfmiRQah4++PTOrx7tx48dO3czrI6WhjOydrbRzg6BqHlO3CeLHNvgY2GzuNNsIzLzYnzYjKBzYRfYEskfSTv8E1xy9wN0qGJ2FVtlOFH0rKFObgriS7V1Bwv47eod3iN1k1+rsU3K9sHOnK71Gx1JE1Hq1pZ2G8KJ/9mTS3SlDBrAH5fkZjKye+oRda4Va5SF9DzROf85tDODspKS9ShqdzWEOgR1CqY5TpwdRZLv4UfyDp+H1d+T2WqoKOLAr+5ZdZtc8ZMV46xTd3u5L4ni6WJfmCwQDm4nFJu1n+jBLuj1Kad0OB20uBhvFyS759TAQxqMZ0diBlEyjIW3m7KWV5hzq9kunbjYPiv/9AIZlOrySOeSgz0+OxGWSDKCCcYLEwGYezndEQd5TFJFZtGz0mT5tlu6IzLw0QdT9hgQ1unkEMYlL1GC0QhTi4S/Wd3rh9ccSiZ+zOqZ1QcfjUnpJOF0koLC9Oyqxtp/h3mbVd1uk7ynTb2hCyKRyTFAPil/4ELoM4pAQf8fU5y76+alGLz0G9i9egTmf5xaR7J8sfW1Lyt6H75jA4//VyW6al1d/Uv15I7Hlffez4hLanjqU/NRsxXwqX5KDCSeyZZR9PMAWjtHhb5Ef/yb9AVrI5+fH46FeGjnk8beRG4g7WCqH0OehEXzvWyZp4lbqmMK5F8R0tit+LITUuxqZMWD7oFv/2/bzMtksyqCWFkxqIsBYWd8ptsxRM4RKPKNEwaX4aCjQUuAQFjJmZOrNd4dCG9lAbsTUxkNdlKebp8w1dUd42xssbSci+1p9udsjKXzBeXO/S+z7GCwhU/RKXjw6BWO6rx1b5InU9dxX+Ia56OWfhFOUMw5VRLseQdUWM5MQwEIrxImHiBV3j+EW6XWC8/CIGDOsJriovSppgCsYL666QJkjoGJZ1VAE74slrQZJLZB0B8XME+xotL5kphIf2eqgrRrTRHZmRfr3KYQdX4mRdUHqI/vHyUIeLHHgcF0u6CgfjZU6MF5uDlF/nadYWhzNe8u9oxcUs4x7lrNM3RZz78u/Abw6h7ncwrEgvks6/LvAcuueu/0a8lxspjblHvxc9scC1L2ZWT4wXrsT9+adu8ZMYL48l3f1kuWvSLndhvOje3ITaZzBeHMsEt29BUMeh8TcUuD0UaBgvt+dbp+Hch2wGYoZD9LzAfOEUfleT1IrMQq5pY/h/n27rGtJR8fvrfQ24Ev3XookD6ENtDhmQW9JJwSnWFKKF8jPA2p/tCBPzQmHcDY0Sg9SkG/7kMT5Yf3i6/okwX+4Y1o604a41jT3UUwd6MqfHvVbl15t8k3dQpquTPN0casF0kdK7xfmp4u7idHF3qV38+v1CsSgFY/clJmwTkBZKSC7YfVeD7HUKE52puE/4ovycxp+w6FtTFDTmL7UmVoMi+Z6YmjA49yRd1js4KH57xpW+nq4g6dqRFFqvbsu8qKyJ6dafKd7FAhbmQ5H0iDbEuqX8egLLApOSCA2XeVNY5fZU4e/3ed8lroJBirIky5an6o+rcoFMnitfZxnuWQAlcN1RRif0JxiMExIPd+bLkZtYlSn2eW1Entydkq6kKZmvRARf1j4ezat/QR/6F+bZk1iz0Ue0gObZ05y0ZcRonA0FLkmBnPFypI0X14w5AV+VJMG7DV1Tfr4uxsuONrZSYLpTFBrSpLtKCvk1fnW0EZNBPxvDXMqWvp73Tx8x+M1DHcU8JEYWwsKdVyTS5nERlqcL98dIF7idVa6n8RSMVayRdC31+FAHNO91pXu8+DUxXv70rcxKL7aLhblpSb5Iuo8xT+sJGC/MCfFAz8uOcTmGFaSA+DW8vQbRlKi/0dLEiKTbRVeMgvHCNd+3a7ImiMSLlD//a0WWuMR4ebt5WixM6yqc5thgvAC1YrzUV645XXKq5dTM0+C+eLoq5XnwmHNJfVa6wCPSJehRiGVN+S1JpCMf0PVPQdaulAzGy47WL/vq/zMcgvwo5c+PxHS53ym+kwVPrhndW5qRjjokXqQPUpYarY2mIgzjbP4P7D7Wm2LzGlEO/uZpKPAlUKBhvHwJX+Ej4hATUxTBAOgDtg9DKMk91u5nXZtAGDBvdRd2U+6/P103xstvOsna2jsutnuazHQSsKmTrPE7mqw0YU1qw4MpXBMtNHBIhDh09FvYRGiMFxWpQTdO6RnUB54IqiNMwhiw0yA+esIJIAHdM9gvUXKwRY2HScUmFsURPUE5+juUHdtD4b+nycaUm4pGY0K+M9Ez049dJmudkDzQqRQnUvck7bKkiee7J1Kmq7uud2UKEkV4JhWUYAK/eiocqrD+FFX49bs+b+lXr0/g/ekodQ6uIFRDxjcVgwthmAwnYuAdq01hzv3Zq02J6ssM69sNKd2V7pfXh1JqfaLF4XGxsXcqZsKkiZbPyoLAlET2J2AyWHtVoeojFG1/YlzgsvUmznOe/gUbyAPFq+FQI6xWMUuTwiLKsyYI8iR/RNP048HNQsz6m/VCmWNXP8Mke49+BrfzBE3eJxpfDu3k9z9+WCjui6n5l5+XTKfLkkx/dGekuDr1r2CUxvgQfZkys6IHfIFT+U54m78/Y5mkcTQUuL0UoIN4x2Ds4gpHT4cxq5IkWNMY9pskXn7XOuH16kHxToxj/nZ7jFko48dSmx8+MPbY9J7N8T7mAJ2Zud4Rc39O/WGdNHA8K0/EkT/cOVzceVy9nIvkqcOr/A4tYOhwKtGDeeHwSIwXMai6ksD49fsZSdBOF//xva4aSWfccrrG0RIHaxyJY9HVLCQm0NDNx9WqrPNcFRbnpbx6PGXUKXh1aPWcXgOaEm2Hxw7J5IeJ39Na9kBtlKv0L6RbbQXGy1tZ3XojPYZvdNC4fVK83TotFiXtwuHGvN7MoUB15br5SpGWSxnRQt1NmYNPHpe76ynzuMqNa9TTP3ePSkV4Ba9KlSD3RVHb/PESPAQrpkqs/9DYGS+Sjtd1o45o9b9+7Bjj5RsxXn6Qnhcsd95f6koSVbrqOAwRk9D0vAT4VHx4P/abmkWRuftjl9vAbyhwEQo0jJeLUOkmpEljLFOHTx8+LB1p83Mkxgt3tQ8OML23X2yjME+bQSRgXrzdKbb3pUhPzJe9g9NiVXe3GYindCLPRhBlWrYhFI1ioHNyeYHlL4O4eTxVf9osc7YoA47nz1MTEv56bIRXOXHZoygH7XkIM631Ea5gmCvEal1pFgcOpeSTjfSEwidk9nGpfSqzg1IwLI7/jBgvy+LwL+r+NadSs7I88FCcf8zaLsy2xPEX1x8rBKo3jKn+p8KhCq+nqWIaV0UBKPelU8pPICucwdglp1C4Kykq/b2SToRNKbNe0RHx67fbunuua0haDJrSXb1PuHKkdoMYNAsYW3CrLZn4s0DTlulS0UMsIi9ywO1tuz8YAPW2GH7iBiltIREcSXMoKcyTZAlIA8IpL4tcdLpgHcV1udDHxHQZUx8bPzErYX/SlaL76lv/+ZMr0+122rpyJOkXzFbCxBFMI4J3bHcn+BYVheFpnoYCN4YC9KusoX/0elXlwXjBVO++rnK8eZsYL8/WTSn/a12b3OiJ8bJ/p8AaIhKxmibtgIY1At00umqgzKgUNaGUiz2RI08dOF4ECvkvmq5eRu4P98XgBYNAW3tlZCyHJlythPl8aAc1M2K8/Om7jinr/7dv5yVNq4McSbzc1VoDs8eMe2Nae3G1MqhgNPTJIBA6853XPGCcmeGKkfFFrpj9nGxeC9qTMZ30hpbGPMkZL3s9O9hYETfwpdayMF6evtU6VxKmb6UAepFDNNZ0mmeB5fmBrW9TYlBvo2d8b8+acp6RroSNo0p31veo8OnLPMRTwasiE+S+qDrEqp7gVGe8bO9rD3B8ZIyq//yhU3z/sF08FuPlx/uzdtj4QFeOpnVfDglUZ7zQRrNyK2Q+uiv/DLn7oxfcFNBQ4AIUuOWMlxh40uBwAYJ91UlUXSYn/+c1OdT1BrS/r8s05KHECDd3JE4ohWQ7u9LtopPoVemk2BdD5u3WUbErKyNv1g9l5pU7tJqkWDBI8S4TFuMrosTaLdpQy5UcBm7EDoPKJKqmsyr0LJoCu56y/3QnTRax8DBkKohWYv55Fc/kyhPTDIqCOeXAWoNtmlkYKc+YmC3in5goamvqTvFoYdLELJe6boJwTso9u7oCMSM9L9NaMM1rQ9iSBJApSNXpFCcp6KCAAeOPFwyKgW6K0CtHsgq9PldQ8WOXc30YD4NELa5ag09LgRzTKNlrpOYmPUq7du0IyyBYXMCa2OauLC28OdCi0JmcSISoi6m90sdg3shtul7E8BQRaENsbnjTzvvpEr4o29/hIzbclSOjeGQfgFrmssT0RQvJgnEafNWTsQa8CYPBIlamXVlE/I2+Bf4TE5LwkRsLYUj3YCXMzbNLx4E2HX/6bsH6VFt3/Ca1+WiJuUm5hqI6kxUdnUqBxBBap0hWu8bZUKChwBUogP6lQx3U7LMWQHeGJAn+9YeuGr3mqtFBoWVEsaFrRntHSMbqSrKkOFAWbuOEdVQVGm8rv/JULiJsBLEUgz/W82vB/blrkfKeBS9Pnaerl3NeGTkc0vbnr3wpTmMfDHmubu2JkYWELJLEf/quXTyQpN+/ady7J8bLfUnV3pVUbWtq0sZLrlgy6Ac8G+vMEyE5HsPdeU0unms4rM8aqooEpW2eYT4wxous8IimOzrceIbEi+ZYJF9+fyNGzNpJ8U6MFyx2zk/rcAPGi9ZrwLH1cSJOrA+rElLEqApbNNSU41yipnSjYCm8ApHmOEsboefn7wcd+SJ0eF2q9Xk/4wXpb2e8HBu9/tf37eKHR9PFY10H/uHBnNppW7oNxXhBAl4SWZOm31D9PhXjbbSOQ+Dycd+g8HlK/rj1aqB/vRRoGC/27W5Bt4wB0DYqTE4+ncB4QSRzQyumAxgv2vwdyn8iN8yIHemj6GkCe7t+oDuex8VrnRzsS/JlZ1finFIIur2v6xPaYOm/HjFZTjXYagHBBmqKAViMFx/1nMYmVZJwsSzZD5Ne/oTXQsmeokvGS/psvPIJI4dRd3u9fSLDygJ4Y7rWroGIocSkPS6GC2K/WIGmHgtdMVE0OT+5L8kWyU4vzk0pfEJSL20p+Bw3pgsWB9o63uNtij818czovjaKdVvsMO3xCoCD1k21ZyCgFv+h3kS8r3wKohZXpdSnoEC04cHvm30/IYKEWa8nZubuvimofP5q15ier9b2JXV2qGt+Wuj0uFetq0nqh0fHY+qPaq/GePFrbCzUzYqACtMavkaXoFLUOi/f23+ERD8Lv73JPqQSFtyXcLiHUt8LbxbDbC5gIMHINTP21gHe63RMfUz9a6YtvUnqXwtYmJAVlOWFlllJeCwR5jlJkn3/eMH6FsolOU2b0gaEp6+G0aki0OIzj+VofhoKNBS4PAXozd6XjnTijYQsa4AVU1rqjJenL8V4kZLwbV0x0vBVSD2cXd1FvwtrAK4Z9o0zgIwJPSFkQcl98Vf08avlvng5l01Z0SxylsOpEUJ4w3jRAInUH4wXxnCUiP/6WIyX5ZauGi1IkqBllhLvLXY0XorxojXVcImXspRwnPnOqRUUPDPDlxapClgdYtwXfjidceJXjVi37mqehfHy2q4aZYyXnVNZFZROHRgvWp8NMl6qFWW9nY4kxTUTtfou/fO1l1/FjsSnLyJPnyOaJVKSYJQQmku87Gru3kHiRfsC1sL/kRgvT+5Oi/GCZNZ08QjGi9qoHTbCeKFBZ7O0sQpzNLKir9tJDaOo3H3d5TTwGgpchQK3mPEybPCJrnoVUn7heVJ1bWLSlKW53jZEMFlQRLYmxktPki27OqoizYLuFjPJwzyBMfEG5btiyjx7s1msSyrmmZSUbe4eF09f7UlMVop3i6niRCuL48LFYWdljaSte54d/bHgiEUHYzGo2NrDfvBEmCOZUO0nKABqEQHTE0akf0MvM50KyUNopEBShw2h1o/FkdymAR/xad1ffS8dE21J60hVS3FfDJdZMU+e3J/WVSKJAP94164ULYo2pvx0TJrwYTYJujNujuVCysWZMPNi0Exr5dmBg8Oj+kaV7cTeUfW45vfrpwANLPum1t68oStYEVmDRam1LbrVtziZe6tNzI7k899KAeA7mQR5uSbTrNrIPJUVBhgwexLfPxTj5Uj96710LLXEgEB55UxLor1iwNiCnCKCxydUvK2lVs9LfxVOObmrTVGGotUl5S4TW7+SL6qJ32VLKNyT+RgjhqYYmTBcjnSiy/WEXY0zx7q+x3iCdbClzlixODNWPF5SP5PU2OP7YrTomt63D+dMoeSs9Lm0JOWCcklXoOtF2BVBcKiKdA8VVmDgnNApcW8cDQUaClyFAvQo702HYrygE45N7Wtd3Xirg5jfnq5JR9Vu8XL1sJBe/mJLf7tHum6gLDBekHixfkn3jOKtk0ZPTeNSxPW9yxwpNPKMCu/L/Bk94FnHEXTQF5fC9WYOOILxIiY8YzcSL78+kfTA8nTx77pqeU8b2ntIvIjx0k6b2nExoEuJP4FivC0Poz5jjT9p0akZOEvC6cnwb3OPSbzIAo8xXvZNGosDw5dvpEvtrcyfS+JlXYwXk3iRlGVbE2hcNfL81KSaR85qnX11jqbp2fuiSg+o5unKiLMcwzKkNnRWtr64PP0weEqsJDnjhWYaVwR3tU9A5QBrZCS5g/HyWIrvf3i4KAahGC/3ZrTmn7LDxrhqVPUBXyUM7RJ9eDaehgI3nwK3nPHCYJQPQvngdDM/visgE7NBiydOn9Hr0pPY8Jb0umCpADf3iFEQi56Stk5gTrVR2pApXCyzvNS97g1dQ3qmu7LbO8fFH7LIstPTlaOTMWNkHOrqEYsArhBw0jWZFhm2MIDUIjEv3xRCY9Knb5B/CqL0pOw+fttVpuqLWZzl8Yw2CZMmfVaGegfir8ipRm8BSLsw0Z5qM2giv1oAIZHTxYStTOY9mJ/Uifu4iVNy9/qn75ftatGcrhSZcjsxXriffaQNJnffd3VFKxZBSL8sioYoIoPxYnW0Yp0+gVFZvwho3jeKAt4e8ip5m2TBbQtFse64gvPu3a4pBHxn5qbVz3RC91aKK3+X7hdEfLfVx6QvUBsaMfnUxiWUZW0QxiiX++xwSe/3qS/Ztb/UFegQqScIEWUkr37K/qHQMkWVkECSph8c/ngSX/JafAbd4VsBZR+3xaz6FhsMrJzRF2Aa3Z0dK5bE3HxyT3pb1L8eScIF5dTfiPHSndG1vSlJlolx25ZeF1PUJ4Ll/eXWbTbiAzTvhgKflAL0cu/1IfHCpvaVNrKr72Ql5g8xXiSxx1WjbV0z2uKqkdYBrCN0OG6HN3RcxrtyeJE7RiCgD3/K1Fl0nnpYfCTN00XYp3pH2Tl+uAkPxov8+h+Ml10xXhAONsbLt2K8SLnuv5mOK1lJNMaLLMaIizUunXpc5TCT0qk6Rsd8YPxU1fyc5YiUNgPpjb4bm0v1w4HaHc0x+0m5LleN/kDiRfPryxXpeAnGi64arW3rqpHWeW1xCCvGi+ACTB+HX38ql/vju9bDFRtBkSRB+LAXQAEYwIF22QLy9DkcYKVHSWJtDHyaFHMs5NgLxotMSiOV+h/S8fLjw5bWxq3i+weSzNKVuEd3xXiZaumQRJKpSeIFGOT39S8Ao7Dm3VDg9lLgljNe6h/+5o8K6DHh70Cnz+h2QX8LEh+7enMqTdyklLct66SFTU9X4v8M+NuawLB6tLImZbs7R8VLnSDsSDrmxVsxYPa4FqErS2JA7OnNlQiYOpr//E8QjMmhH07AGXxtnQCTRJMm/xBLZANJWf4VeJMwvRXDU00ZNu2WgRFemqxUSnRj2Khf5gMLiVBqgaO1i5hDbq1I1bSN67hOPsa5WiRmC2YdH0jMl6tFjyRO2ZFkwTeP5rQwmrJNIRMS0i4oJ3aGlazR6LiPhRRMHKweLenea1v5sFRAnagzG0gjAIjoMTq481p+gw4BjHKbxykQtPmUNPFFXP6dB0tnYbKzs2dX/ra39yXdcigJM5lmleTLsze7dsVvQ0zO/QNd/ZP0C/2MdifhLGvj4mdYM6dvGWM160F2uqpGxpulJO3W3mWjcP9ZDRG6gTU9Dlzx80t55rcADyXceD/KwKaLcscVgFSOhhXbNIifYlf47i6oj0ify2OZiJ7vTKq/dExf0r0lKaluawGn63yIO09I6oVeE30ZJ0/DeHE6NL8NBT4uBejbPm4d6sSbdQNSes54kcTLH+8qiRfpeNk6RMdLYrwg8aK5nfGFsYKxhwf3xR4vt0obGevhVQp3Rbp6ePjJf14a0l4lXcDNcQw4wxkvIfGCqd5fQuIl1/GSrhohTeuMF1sdWWUorfTlRVrsDf1RpW0FaJX3SvuhoupbSrzIqtEZjJd3XDWC8aIDxq6kOGgOPp85Rfn1p3K5P4hcD4/0+Tu+ex42zH1WurycSBc4DIM1LCxPn8PL0hpoj6M9qcvaHAtNhjJeHonxoitx3z/0K3GPlsV4aU0Zc9B0vNDvyyfW82VA42gocGsp0DBeyk+fDxJl4I1zoMgNpghXitC5cCAJl0NJaxxqMQXTgCkcM9GLc23pUhjTBsgZL72eJGK02NrUZnBHG8NVmS/Y0ybwjRgwmJve1KVu/Fu6fsTVgl2d0KM/pqfNImCPxYyBEXPC2wZ0sVkguW3O/A54Wp/5gA8mpDOMeOMSIBZwvHn0YlIwdkp6m18FsuF1ZbmelISqoUFBRQRqV9oT0i0xKVFTTbrocoG5Qp0X56dsI4jCmnByVAAAQABJREFUsK6kVe4u6MqQRICXF7p29aHN1SHDA9PT1FF/YmS9kxluaHgiCRoYL0i8tJUPGPBbqAOMF7/76rgbDRKK1/FKlDFQUKx5KgpAm89BE9qiMwlGY4A02bHMacF0QffL+nav2Nw+LF5LKeC+/O/k3lNf3dAbxZb0ryP1LyyNYYVLfFNr79a/VE+WObRRJLNoeyYxovDoYzAevUXaq6IL2RTk7Sj1NHnw04/pX9SHtzN6Tq1fE2c9UREqjqILqWfRWOJ6XLAG1tEiF500M2LmTqvPLaufLc5OFY+WJBmmPjI31zJG5dw8p7tYR5BIvf6NC4ihS6HZ0zBeMmI0zoYCH40C9DvrgRqjUK6LOWmsGvlVo3/aVSOU63LVSDpeMuW6XDNirqWvGpTUh8s5PMEdjbqXW8VXuFjW/iFByQYCqqzX6gKvs8oKPHP8CYu5wGniEi8a90VPxuQZSRP8IqtGmJP+dxgvusJhV400RrqOF43noin0NJometpYGEVeaz2/UGCqK9WNh/kmGC/oFusdaB4VTXd2e8VzWeg0iZc3WDVCua7WapJ4ebddiPHiOl5muBMHTPsDsrfXgF8rrQyup4yvTfj1PWdBixJ5ezrHqV56pIueTHyV3lIriR2apKzexpRK4aztTbkuOl40d2PV6Ick8WJXjSTxgnLdtuZtmIOTUrLLOrcstXQk4Jd+Da/VpcE0GRoKfAEUuGWMFx9oRtP9g0eH0aCHxFTY2DapluLDcfE52UuxiVklsGiC+bIhs9FIuuzr5BxJF2MGqMiWBk2UWM5qE4Q1Hr8mg76GY7sScaRdHtaPts3q0bEYLQfFli51v5WVFiwhceebDeLr9RNtHI9lFenQmC2943FjvqCngsvMd7R4MCkXDc4sOBBLVpC97XTeFhb400TBzKqJgl97VC0YLtRR1bE3E++xJt3QLXFyioJg4rQ51D8WOYDRXs8U58pQgCRb7hT3VVc2g/cl4TIjK0X3ZboRk7WcwE8jsdKRjgnRgs2gmXMUjXhg5XDNqKdJaVfXQd5qkwwt0WOBpMvSvBZLumo0IykZHd57XtXTpV7AK+ojpMqKOeRagJV3kZ9oU33gLpKxSXMNFID6F6A8jdbac1XkiTE+kTiDkQIj78hMtu7uSbm1uCrvdO1oZ/+gWJGupQ1dC1zdkGJeSZq9XuMu+6kpuT4QJ+TgFOsMMDeN/WFtbUxudWmTPhlTZzMpEjEawdQlYZwhU2GTqiE86WMuRaO3PJjCpr/5n/qamD5hFpoeiZj3+B2uK74vliU5NgMjUwyXBf09lHJqN8Muhqa4MjB352enxdCc1bUijTvaoWEFDctFd4RnjFmqkD2QDJwr0o2idfQCz3ehbxJJm3dDgYYCNQpU45oxXtALp3UE5qRXNS79SxIvv79CgemhrBqJ8aI/qYOz+a6lfjzJBkyd1qAw9unpZ7zQj/M+m/vrfbzCxQAN/ORwBiI/ICDHCTCBV728UenI42ktZ/phDYBy3X1d4cCKIoyXX7+bkXJdMV6eYNVI+l2Sjhf0XXGFm42tX6+pyi7HSoq5DY+qXtXev4at8gjUOm9fjBeMRmzv7RvjZYWrRmK8PBXj5Y9V6TSUjpd3WxXjpau1XRwo8AZiDr8qLb67E9lT4vbUEduf19Ne7BcIo3IPC48SK+ikqkIDXn+Ip656oeeI9ajH0qaYa43xojU9B6z7MF6kkPjP38+YVaNv7rlyXQ4pH2jdzBV7n8P7GS+UdOvaaPVJGldDgT4K3DLGS1/d5akPZNXgVE/5Mfz9A+T1l+BrHJViBWkiUYDrdtH9VqwYaUPX0+SEskus88BUwBzcpE6YZyX6j3ZyBlLuZ6KHAnicyrMx3O8diungJ/Rb2hyuszHUG9O4cMdfy+z0BoyXLVlJOrmjhYUUg2qTJl4P27Nyp8fAzoAMg4VNoFAwv28bPayaRqgIk0M1KYKTs1VsvjUc2Qhq7hXOGK+1LPYzJqW5wJ/TZrAt89BLM3dM1NQYLmKU3NWp0ozqvSyGS1e6JuZnJfWjxc6MdEwwmcB8UfaKcSLoJnZtjJcj0fTAaAJdsGQ0121JgkaWWpCkUV505qAkFFHheMDffFVQRF3pbfVVzmsCdyUcvtRMscyg/XxpDwqfaQswCWF0YGHMGKO63ocC7PWtHelYOhLDZU8MTTFexNCE2ccpM2anN/Yk/i8YUjNkEijH713pM/3Lrvt4x0r9C4sjTgGYMjiDIrSfyu2tif7FQ58CN97gGv2McYVweqXrb3GJsrtSnNuB8aJrewtSTm1X9iRBt7w4Y1eK6B/djiwYzXfF5OWkTF9GHZ++xsYC3MGGE0zexkvyECL0BKbuq35Jnz+j0uVpGndDgYYCwyng/Y+4o/Kq0akUgm8WaxtivDxbL357ua2xqFdsiOmyIUXgEuAzBm9LA80UInfqggYldc0Yi708Znue6Lf013BbRO3nov35ojCuki5wiLyBc7wrlPMUHuprGAY4xk4YLweiqzNe7hS/fj9rOl7+ZIyXVnFXysXvLkna1nS8hNRs0IyxmHE38KnKvdGuRNSStqo+hwTMFe9Fz17S8bK77xIvK+uSyHq7W/y+0pOeFyReTtyqUbpqNKN1GpOa/hs9bd4ZICCljaKzYxKxgVd/+ip0AHQf3FHpRoUDLUquQyZ8VL48Lnc7NJoU7QrGy64YL9taXMB46Yg5+OcfxHh52C5gvPx4f87MnT/UVaNWOpxE4sXWualo+rs3UcppnoYCt5sCN5DxQk+/aOeuD0gXzXd9jcaxvQzOFy+bSSQmE3KxgTrQ6ThSGe90VWhfXBBO1jnF5gQa5kBHEh9M8NNiEiDSOiU3VClhyccmC6kX8iHOua+T+C1ds0G57KYYOojNvpFZ3A1dP1qD8aLNIDpgYLz0VDZXnfTfNojA8qfaDlOeizz6woSR31MxMbIhc5/lkJsBnRD7eqxHDGGFWQCMEmfsaC4w5tKcrv/AeFnuTojxoo3gvCRdxHha0Al8W5Ivi3OSdBGzBeYL2tmnpZTCJiFHtO837rvvaRO8KUkErNUcyMw2umLsFF+iBrxbwBETi1N9JIlYLFVYU6fM21fC5TxOmYv3gMtBPyt1+QXOSvQZ45j6oUtqFJ8RE+tMvgqpsIgPl0KOxDSkn7DRgfHCAvJAHWkNBqeOk7F8BOPl9QYne5Jgk3l3pNf21c+4YncoSaxgknhHSN/HylGfSZ3D2mF0lAqbyiVyOarKKLdyyu8LVCOlwmxfpT7GGwbqNExGtX8U59KvFqW3YF5Wi+4jQaY+sKRT3LakwDrqYzA1ZzptY/ICryzLMFCASjS+i1yxuYjRwBCqMM1cNWIaoll042wo0FDgShRAuS5WjZAmeKvNLActv79YLzAn/VLKdSVYULzdQReVmK8aA9q6youOF5i/aeixcn00DhTo5/XH+349tPLneYAc/nrfjxw5vNwd8fHO43J3xA97n50uMPK5B18VgguF41z3npTECxK4P38nxov0Xv3piTa0GisXdQC0rGvLU5IEZDNr10br84f8YPFpHrD+dKUNrVMiYczqpGG+82WV1rnBeNEV+RdvtmQtcE+MF64aSWH9ylHxVleN3myemCSmmZPWVVckoplrfG2W6pfK8eqOqnckgireFjzE3UPxT+k87qx0ee6qnCo08vKOJ8LCf7l3QKKJGeNF2dkrcNWop3ba0VWjv/zclXLdjiwRtovv7s+qnbY1v8N4canViYl+iZez6Xc5/JrUDQW+dgrcQMbL1/VJRg3l11ELG6Y1kzAZxSaMTRzXjdDR4prfJYKi+EWZPm5J+QkMGKQ1UKzLwIuUB49v0EiqQAFkcwdMpF9gwuxL2gW4e2K+oAtmQ9ePdiQVsyVFvISjgBemC/op8PMHA4irOlwPMnhi5OiWjpUlp+Nt75gQKR8dEr6Q49oEmz0sEYGrKcfVsTg3gVigIF1CGiRNYCohvYM1FaR5uDbEZhD9K3M6jW9pUTMjP4sbmE+8Ee2FaTOB+cYRH4TTKpgtZhlK+je45nQgiRfoxaSFFBHiwS2VgzQNVo5gxFhN+DAGOS2aRhUyouwm+KIUsJ6QaH3RPB8xHd+dBps/0RQSqizGrc+qfcGAoa/Szvb36Ldc8XNzmeh8Qbkz/QzptR0xUulT6G4yU+nJehnSX2qWgkW/AqZrPGKxKa8WnQoHJWuT9ClfRFr/ksf+0dfsL7Vrbawm7I/xwv8YLzrqNy31mfnZSTE4JfmFQmoxOGe79C8xY8TQ9P4FI1L9TOMOEi7Vk38verzH5SmqtMNcef5h8U1YQ4GGAlehgDFeNG+zjnira4/vdGX56ctNKdfd1gZ3v1iV3ow3Ulq6JR1U6Hfp6ICD60ZIsWmoKceX6KFXwcHzxGiQQyIs94+C/rnTBROKsQ2MNRbrWvSkJHLndPDzy7czYrx0jPGyJB0vC2K88OYQjGuiZtGoPn8kf1BlVM1vTHj6zDY7qO5MW6y54g3j5VATHtaiXonxsirGy0sZhvhdVjh/e30kpguHFmK86BCONdms1obnM15GUa9qc0F/D7lsO6vg+LqwXl4eX4+LkmlPzNZnpa3nzf2OM780KWO8CNS+GC4c9PR0EDQjCda//DRb/PhIjJd7Yrw8mNXhJSbPpZtN8zsHthOyvsXaucTKHOBUhuSFNu6GAreKAg3j5TN+7hgar20oGjKu2cZKdTQpE226YIpwWrWmKwtcCTo8ObbN1D3pI0Fp7LwUoISuBUhj+yEhyCbQx3LHFi8PgzybOO5+GyNGmz42iHv7+6abYk/MF07kt7VRpOxtXZdgUkRhKNcp0F/hG0TywYhxHRfgWzJk2CxSlq3cNHQLKa5IwGCBscKJ2oTebAInteFDeS5uZ7RIukWTKoyPOV1rcOWeUparRQyma1u6BkS9JwUM3RJcr/I/LRblZqKAecJjNGDisMqzAGLDKry1mwXvPd2BRaHwoRSeHomDxFURkoNvR9reZ7vagGphheJdamTMGSUwcKkML8mKa36ujQKpsZbwvgwq26LR2lOJmDk83NsbmII9V/yQMHPT5S5KbVf9uPKnfrS1Tf860j1sv+6G8mz6067iTQG0+jp9imtJMEvpozA71V3tDUOGx9uk2qwKpklyUo2y7XHrb6mP0efUv6YkctzS6RdKH02iS227JbGyWfUzGJkwWmBwzqh/8ebaHf10Uuk5tR1XX7UyrJ9ZVwEDkNA7+0Ygc+ZDeh7SRd54W0Tz01CgocAVKEBXjO7H2MA8b4ctWkOsru9K35R0Zojx8od0vDwX4+XN5mnxclOMF119hPEyI30Qbc2tzKFcTbTurZ/YktFLq35bd1tk9hP9+zLpsuwDzhzeQGQWcP3pWMowzvsjHXvvj8zs9sIcVo1mpeOlLQbMfLEk/XNceV7U+owDJMZLV5geUrP4q202mF7sibJJHbkiLPwXg/RZUiVUnYaOL+2ThzdrLxgvKKt/vbpjZs9fSZvub693i3++PCxWNnRN991xMa+1GBIcWK+0tbJA2HUlwYGu9gA33B6SfhMSfWHDPMC5aFryR9r6d8jDwx3l1dMSftFy6+n8qMPrT9vyw8Q9rdWxatbVQeWff+oWv3wzUzy53yke3esWy5IWv7vQ0RyveZ25XYwX8gdWtqYxT4QE3s27ocDto0DDePmM3zyGzmsbigCYAcPLJMJptjE0tMvCbCEbsk0tmDghZzOHmde7C5iC09UbnVJzFYYNF09M6YFrFJDmOJsimOiAQxiTFwpCucLEIH2YTh52JAlzqFN4s9qiSRGGDFIvnMy71AzittoQwnBJf+B+rI0i+FOGlaMyWMSxcWMTB/NDezxjkiDNwl+pr0anGUirtHUCbxtAKcmdlOhzVyfwnB5x3YHNoN1LVT6zoqJ6w6kHsIqwJ2hB+X2P0sKMgfGEJSeU7EJbsxSlzTC0tgWWsnUl7QKjZ1aTVkeTvD0KT2QuHdnn6yuq8XwIBWrfLe8kHwL2A/L6gpHepC9eQy/irEOlBhL9yxioalRHbIDUX7jLfsICM1kdM4ZLOulzHUzqd2IGWj9TevTA0F5N1xN9VWMCbZQ+Cx64kSijWBZOSI7Zn/pEydxM/WxKJ4Xog0KBNFJcMBfpTyio5uSrrXaO7pZp9T8kW+h7DlMMF8FjA2Hl0N/0+K8Q0P++J9GgL6zPU88QkQ4xfM27oUBDgctRgCmP7seb+e9EBzWMQczd7zZ3Tbn+81dbxnh59lZSBe9OiufvTov1Hek50zqiK+ZsR3NwH+PFgPrYN6rnXg7LwdT0/I8FOy+NMrysipmUx+O2+EhokTChGGs15uoZl8TuxJ0j04e1LMbLz9/OFvcl8fKTrhotSr/LnNYN87r+jASvjZf68U2xZbd6hp+yLvbk1IlcERb+i0H6LKmEajlPxsxBu9JDO+UwgjUv0qEra9t2JQ7LRjBe/v6sJ8bLkSRgjoo51oYwXnQ4wFoOEKxh/RGdzSH/0Dko0qXkkTb3mnsUPcmfxw2DNwDsnIAc3jlJB6Kr8g1KqrPRU2uGA6056P9dXR3+r1/nip8ezRZP7sF4mRFzECX5ug7HYYrm8zFtKmiTgQ3fysFFyEDhTUBDgVtDgYbx8hk/dQxz1zUUMWnU5we/wiPdKsYIQSmuJiQkUmCIaPZHBScTOicqSH/M6boN0iI2m4s2wDO4cge+kCwmetzlowSkYQ93rLvgbPyYADmtp3w2i2jv51oOEi/gwckETAuuK+m/TZZKbpMfEyBMmFLxqPz8Q9oFZohvCLVw0QIPnSrBeIGpQp2QckFqhU0gm8NOW5aa8OuUgw3jlE7nYczEfdRgtJSzRXIE84lJkkmI7xW0wM+EDa4HJrGDKUPqKykEMbegPxvdrjajc7KUhBTAjCYuO7VKHytoDB1TEM7muTYK+NeKb8Z3vK4+d20o1gDRruh45RWcQJ4GQv9Sm2LhfqT+Y4zOA/QLqX+pX8OQ2dWC81iMVfo7/Y8rfvQlNkxsnJCccQkaGKZqo8Dnz/gvTh/KhtkSjBckzOxqkRxIvEyqnyFBRn/jpAvJMSRejKGpfmVWOPTmehH9LBq3MTZV1NAxxOgQlcVzkS9F+uiV8b5oXtI1T0OB20iB8/uZjUM2QTEcMZe5pBxz+4b0TG1rjnu1sm1Xjf5Y3SueS3Hp01V0u52YrqdZzXUd9X8YL36A4gc03q2jb8ebb5DjdLVvEtA+HNL55edleLn8VqGBC0G2crAAH2+PGd/1NyHdLq2JE0nCYmERxstCYrzMSim5FJBLCflsVxIvGmt5fNxMpenbsP6IsbQsz1KO+gG/SJm7R6X/csOtfZZ1oY3yx7rRDyZgvKyaLiJJY0nK+zfpIvrrH3syL31YPJdi+jkdDrTt0GDS8ln+tM6ERkGlmLv6KQHt6s+wML7bsPCL5K2nOc9fYnxewlo8+ZwR2B/ha96T91o/6DpcoT/0Iv7518Xix8ddMV78Whxr2yVdN0LinLYYljsDG2rPMFK1u/5SGl9DgdtEgYbx8pm/tg1I14QDk4YPbhVA22BpV4X5Z64Y8ceiiU0Y6dkPwaSYl6ig6V/QdQE2Wz6hAQcMgQuLxofNiIvJ3hJ4suR0ZgMbO1uoGWNFm0SVi7JQNo3oQWFDyIm7S7i4Lgs3Be2MDMoxGJigjslQ5fjArsHdBnid0ItjwpUjdKmAu0nsIAmjBZ9dRdKChY3flJTksgDkdN7FIfVW3c08Y8wQ1CC5s2nX6pX/WHWFHzjiNskX1cUkX7TJRdkuJrupG2FdTfBmxUWMrRlNXGa5RfhTF5HWyUy5Vr+8pMb94RTgC+Uf+MMhfnQIdE4eQ1s/yUsQLQ5GH20Ppp4xKMsrAJKAURyMTJNsSX0sJFzoX7RVTgOBafnlJww/Lx6K9ZMrMV7UZ4zZqXaKG0YMulzoa2PqV0icwdDkqp71M952dY/TL9IhfiyI+osxg2KsahT2wU9C2uAA9XqhfzB6DYCGAl8kBer9ZhBJm+tZVCipjTe6QgsDhXl9Qwr1d6RzakVKS5+u7BRIvDxdOSj+tXIoq2tHYibouqEYLzMaDzQEWD70S8UYYyOADQI+01bYVK5BjM4OyceU0VBijDgb1kViKaOOfZ6vxMcSug8aaBguJIhoB1+T46e6kvW+6LbuFI8WJ4qfvl2Uad5O8b02tki7cGjTnWnbmGofgu9Rf1LYkJh6Svkd6+o9JMlXEGRtU1XhgMDaFNXSw/yIpU7mNq63cyVuXUzCVSxwifHyP7/vFq9k+vyZTEubvj/NYRwYeE79av50t39ZYGrislf/TyqwDAw/acNN5LC8ZaZa2jw8d9dh5nGj3OeVW88HzlU5aUmgMNYa3Pc/1hr71HQi/vmX5eLHb7qyatQp7suMNNLcC5LOYk3gkqxeNvM938nn/YBfL7fxNxS4XRRoGC+f8XvH0HzZ4XEUygyUGuY8Ok0UbLjY/G9o0jETe9pw4TfGgwo2KztiUGDBB251W9dzQvLDJjO44IYgFxA09npgiUI5sJYhcjDYsjrTwybQF2y+WWQyND8bRqF6okUISdk8wmSxt8ogPzWxt7R+Klb5vHxjtMQ1BWEFc8X2dTBgVHZsEqkH+LFRZBNZXSWCK+9xvOME3hDOfnyyyAKSM1HYcLM0ycrL6emYTfhIGWD6Fws0Ls1zImaLrhpJxwtMlxljbqF8jOtMbGxVL8GmfkZqucGreT6EAvGVgAExc3+E8f5Cn75+pvYf7YFOI3f0Q+8T9B/vV8GQgalJHNIwhKm7WR70JtGn6F12lx2/SFD2N7kpKoqjL9HGncnpbxa6MCytb9HxlJq+Rpi1Z+IUjtv7I4sxpQuglKe/UU8kizThH5X+bGjkOh/CaNhNTEOBm0qB6GFRv8F+4uOMh+MOpd/M41uy4gfjZVVXOJ6t6O+NLBy93i/+8bJXvF4/NAXb6M7omKl4n+OBwbhUPjbR1VkXJKDMeJM6z4Q/njydyZRERJXD0I90ZfQIkEPSZVkq58XSlTUz9JVH/2G8wHTh2ieGAVoT74vFmTtSQj5WPLo7KUkCSbzISswPYrzMSF/WDMrJZ6Y1vuqUDPoBRnSLOcBwMtD6ufCT0/My+S5cwMdNmNA3GsTc4ktOoy/6hJj3kKh+tyFdRLs96TXsFb+92i3+/39tFa/XDnQ9bq+UzkT6ORqEM14S+iVpcFBoGVCmTymzVz1dFlU6A14ZIMeob5KH5+mHuQO/YTjkccPy9pfDmgGSsvrW5TgxXY4lxfa+mJfU9l9+WVL7nLNrRijW7eoa/5yuG9maW6lpn/U2aqVbfx9WdhPWUOD2UKBhvNy0b82qxiZnhkuU6UrKRLP8usw897jmIz+KNNs6iZrUKNnR9QAkXVBAy8aJKwNsmJjQyoGTQVSwbFgGfnoinjdl5k/uI4rFBisGTtaJ8wWYL8I8rdyaKC1eaTwd8WTmv+OEH/wok3/a78E5Mbf4K7bh87GdTZ9jZGnlJh+P4Ws5zKMfrytxPFam3p7OggZ+DGfqJDjVc0eMF+nd0GSPdad1TfRI+fREf6Rs0KHDdYxpMbdQRor+C/G67KqUESUBcvwrqI3rKhTwVjWYM/9eg7FfTAjtPutXtP+yjSQrXtY1LBl9IxiT1MAXne7y9swi1Px60QPFe7G39UOLI1SPgPK23qXy6TLef6AbTEKnH6hF//B49S8PJFnKY06gEkQgv16OuTzcMUsBenmqyn++K4dA7txP7stDPL/MJkVDga+dAnk/Gd5HGGPyh3nZD0iKYkcK9G2ek2Tn8zc7xQtJvvzzJTo09ouXbw9M3wNWA6cwLat5zsYgxpcSJvM40PWj/1V4mSAVTaJ6mEdZ9lrssLAzh4DhoFPZo16jcerP4en4pa4UBTP8QCIvHIpJwUshQ5LFvfk7Yr5MFN8+mC5+eDQnKzGyFqN3R7rhfN3QSuseAUhceMbdNGrbeNtf7nm+qDSYfYWPoR91gCSqB8sxHjntwEENiuvtm0hmSeJ7TZY2UQL9379tqH32JJ21J0nNKa0Z1TjvaP1rX8dmWoeTYFWei7gCp4vTNVLGt/RSIhRfwDyv/Hqe3B95CRsFL1sHK8mh2imSWSwPYLjMTJ2IOVjo+ttk8Z8/LxXffTNfPJRVo8VuR/sJrIJ6G6Uk1g/xmMs7egQ174YCt5oCDePlM3z++rBXDVFXQCaAJSDOqNDiSKAYMBG1hPGyJokX7rvCiGHTBdcayyM2sYspMKE/GBN+RxM8lDnBjg1WP3Ypsv+VDbf9qWPTVQstvQ4m/drijGlI/2I1pijWG4RRVdvswXZRuOPHKbs2fyXEusNhe2gQK6VJ3noOYA+rUAkpY7wEjWxRJRoj9bIhxsuhuFwwXrhuwRUoxK8nkz6MtvTOcGUDqSPKKuFS7CicBpBsAm4yBcr2TyWjgaht9DUPaywRGdToSxGB5eaGDRQAA368CSsXTYJrUNIbEeJ4+qD3eSIF736csq6cEiXG6cj8Oaxh7n74ngJgEX5lwMMKa8IaCtw6ClifzboU44QzX1DUf2C62jYl+fJSTBf+/vlip/i/z3ZlWrpnltA6ut47oTkvDnMYgN5rIodlYKOYdVH96H+MD1X/DXJnCERQeg+LIYwnRgH3Dfm9cELy1kuq+4fAz4JIHXP6kRZgPRkNQCcce34sP32zNF4szY4XP9yH4TIvRaXTxeMHYrxwUKO1WUuKy+PAqaxYwj/WHllxN99pHze+sLemki6qPRKdJump9ReGJJBAxgLXs9c7Yry8K1693S9+l/SLbG8p5bjeWv8qHxfq7bBBboOeaBxtM766Bae46yB2wD0bVtR3VKphCNUh1/0By8OjjVL5fUnHY+yC/i41Q2K4FMVSRwzCucni338U4+Uh1rd0zUj6h7DU2RaT0A5fDGTCRa/kUmjlilI/zRu6fa6yP00Nm1K+Lgo0jJfP+L2uZTgQEGNGlCOmJh1xXGC6IA6M9AXMlnUxXvbEDEC3A+XelU4XFM5y/QVpjJjU/c0i4byB6oLYlzNWBq+WFW/+xCaQetn0Zyc87sPPss3Q8x/L6tD7yyDairICIj/JU+osuQEZ9gP+li4SG8QqpbxWRUVTHpMUSoOh+dYOpn6x1gSzyxcCplNGMzuWX7Bu1BLTBWkjniAVbqsajuZpKHAWBfqaI570WHi0WcJslLCmHD2hTI0jNb4qlcPhqlHfk4NMEdFf8RqrZkiaPhjnegKz8wDV0+EnTz383AKbBA0FGgoMoUDMbRalbkVfp3fZoQ6W1KTMe1vSna/f7RSvZLb3n8+2ir8+lb6XlX2l4qBBEgV3XJk8/dJkNEYwXqL4fDyJMO/X4Yv+Hf78ncYMGwbq6XK/EpSTbB6eYJVBDs+9fuiTl1a5rUB5Sel5LE70sjFRwyhrKmJN2bnWBfscyEix7uzMWPH9/cni7ryuGclE77f35oolWYh5eG/WNrQczGAVjvwGg48C+vwr62Cl3Z4f+yD2ozqLrtBDXmuvCjHhF/lPJPGyc3Boklm7u1KoK11E/wPjZXXX9L30DieKI10RPzyVvj/BQKLTbE7KHfMkRA24uP2B9uEOPPATWPcPSxdh8S6BWf7w5ZD64Ua++jtyRngdnwiPNyWQxtPBdOKhvrtaw5qlUXkWxXB5MD9ePFgcLxZl+fTfv18UY3C2uLs0K8tlLTNYgRR92T4Fz9yiRQIpqJXLCvngn8D9LEA5Ba+7/LPKbeIaCoymQMN4GU2bTxJzkaGjjsjgJKDBLc0CtjASUK68YL0EK0KYkn0nceB9mTsmjPHvvkRZufaC+WgYLzaLKDyGpnLDFQElEvlARuBAgjJl6UgID+QUzj65sYRI04olyqe8Eoo7bHZNzmrmS4kqXAxeXqBwyL1kgMk0/MlSZs7Bqiq/4uN7sEeFwQLtYXDtSMGuMV5Ef0ResSgTD8rIZiSyCdMF/S85vlSrhDkKxQDUvG8nBfJ2CQWszdQDa6RRGnpa9ITcPdA5DGRqfLxy0Ck4oNPmeXwMyhZaA/0zcpz3jsJqBQ1ki3QDEQo4L++wPE1YQ4GGAjkFbB6KrqTuxthhYXIfSFE+8xom7N/KYsyKNrO/vdgs/vZU5qV1jePoeEw63Cb1xyZMCrYFB9PJttYwWFUv9VFDgeVDobm/jKg58nRyA19PHlqBCXjEZk+fN+W0pBWUwZx9mQQsUvAmLuC4n6HQNqJ6w3jp6Y/1ANYYF7pjMs3bKu4vtGRCerb45u6MmZG+vzxrV5LdYIDWCAJia4OEOuM3z5WH2QTnRrxE5vgC1CeYd8dc65IFzZ6kvXf2XTLr739sFK/VVn/XtbjNPTEYDu5IB4wUxCN9rDOwaXSVibTR1v0Gk9O6opWXVg+t4nERm2OVx+Zx7h4GazB3PSSHE2XWw/Jy627gVSXHkpg+vq39AoeGR5Lsvjc3UXx3b1JWjCaK5dlW8fOTJdPvsrTQ0fpVUm26Oj8l4lWQvL17/fNQL7+/1DpOF/VfD5SLltakayhwXRRoGC/XRckrwGHY4Bkcljx81C+DInk8v/8G4yWkXZjUsWiEQl0kMBC3RArjvSwEMVEv60Sl00Lhq+5g6+pLOXsLOBM6yjGtgAHkAmuwG4gcRNlWaR6c5ywTCkQsICLM0lklE/yAYV5iq3LzvP0xQr+vwD6PwTh/wRJ5VJ4Bz/xBHAWVqJJMHhQKY9IXE74m/XKAdv0jN/WrONLP6WJ3V3+IEiP9EhOe0YDqpaLOx9FyND+3lQKpnbBIrD82JkQnqLpMmTLvO/W85gdklm9YWCxw8/xluVduvMMKzksId6QbrLunqCMf+Zp3Q4GGAhehgA0f0Y2sm/lIQ78364gwEbSpXYPx8m5POjQ2i3/+salrHHvF3gGn5hO6bguzV9YEdb4zrjEBZfem2FsIOGj5KSjKqSEWvdvHq/DVEuEdkd8m3DJ5JAJOckdQmUaOVIy/SFC5PFk9U8oQGQN2ImCsz6DesSxDHepwBgYMOl2X5yeKXx/PGOPlZzFe7sui0bzudtxb7Coeq4tYhnNpghJFQ0kUqaNRJrhlDpE/vgA1j3kJZdCHXLHXGhhLk6/XkMpaL96uIfGyXaxuyfDE7mnxZgs9JpLQmrpj5s+hK58OOJwr0PYMvtEbl6+T+6lsH6U/yHyjwvuT1tu3ldefRL7hoWVbHkg/KqCOU1r3Kxg8kIvfSXSD8fJocbL46UGr+OGhzEbPTek63IIUQcMg7OgaXNJXaFJZlFfhSCmXx80yNT8NBW4sBRrGy2f8tLFZ8gH3kohobKuGNw2aaQZm0w/zZY9BU6KsTDps/vfEgIEZwMkT9zARZeVeZkcWdqY04cREFVgEvPBf+W0LD+Ga3hrT05McNtBHGO8yQRkYefsXGYPpygzJQZExgfrbKVbS+1wQQeFIiD/cWWlZMpyhfBCTvohqHuo77OtU0Ex66273iRZe8zo1MMaLTgswacg3AUejO0UIeXNnsLMSP4FzRF0/QclVyx5C609S/tdViPWPaCeBOm0p3LxpXNmIUYvNU17K7X2zXjjXkxSWECj724UhB7y+GgzJHekiKuoY7zw83M27oUBDgctQIKZu1ivRs+j3mJRmrcH6AnO9a5KqfbmyLUkCMV+e7RTrbGi3C11x5mqSNrTSE4FCfyQ4gBm91+DL471dv0lqJXCMdB4foYFJ+PXuT5BFyGlAAhJRWeKacyCp0kbd+/L1lRCw450BJZ2CiWETrwswdg2c97RMSN9bnCr+7dtZvdvFT7JkdFdrs5l2q1iWH0tGXH9x6dwKpo2plRegt+eBhvW6J/r6Os9JQRs1C1Jqn34QJtPSYhA+fb5pElq/vdqSCXRZn9w6Lp6+PS66kvzGqmdXRhCYwcQbKw0+MH+WxarsevH9IfaRs+9B6npYFp2cPk9W6SrXYFoPyVMMYuQ45WlGwSGcdGGQwtv7tu0ZdFgrE9JP7k4Vv3zTLn7VVbgladh9xDWjeTEIZ9umo9DaqXQ52RrWoHm5jtUw3CizeRoK3E4KNIyXz/rdY1C87MDk+WzBYvgH4+VOMl98WmxLv8iBdLrA6edkygZE/Uym0xPMR5ulHU00ExJ3zWEB8kKMFx+rr4eClyDFJZIOxy0AROxlyR/5au9qE+r0xGw3V44wabgjxsumFqc93e3GxHRXzBYYL1gsmDVRTbcYg4lpZ7g4Wy42rXy/63yCBINgiRkMvc6yG1gXoMDoD3SBzB8vSaBFCZdpJb5xuUyOs+qQY1FPd11l1OE2/oYCt48C1tNSd6uYD9qmacHgBww+x2FaemN7v1iRgt0/Xm/qytG2NrfHxYt1MWXEgDmV1Eu7pascaf0BJUvYtvjA532XuS4VyUKkTOexZcwFP4bSG8B6viHjBEH1ZKkUggdz5CGRMd6kr+KNUSXmk2vYk1JduSbunJp+l3uLLTFe5qQvQyakH3VtY9vRmmBeSkvHxKRCGnYMLbxwA7Inh58F306nyJ6vmGhSfAkYLzBdTN+h1sKYln7+etuMTTzV+3fpIlrZOCr++aonSRddu5f0RleS4NDW1nMORF/LvyVl9EknX4raAKvaRD1rPYbUZz30jCpNPfdZOT0uamT9IyWv6Cblz8dHYhSqrcJ4uTetNtotftXfoqSxHkj/0IKYLnNyYziCdSvMl3Kdan3au16FyeVxrPI2roYCN4cCDePls37LGDYvOyClfJrIfeitGC9IWKBQd2v7sNjfP7R7xEeSspgSN587rFgtwpJOVycqk7qXyYmLif4GKkaPgHcGcSL9ZVEfBfIS8C6RdHhpASBir6kOZjJbMJEqYtLGesMRUi9itmxLsds6enbkRxJpVvpd0O2Cnp3u9FQSKZYotiYvO90iP7D4d034RXV5BwkGQXupedrG/RkoMPoDfQZkqiIDLUIG206Vru6iNV/fRiHHIkoCm6btBjWad0OB66CA9bTU3XxGSlBtfmOOQ/JFpqX3DmWytyeJgt3ipUxLP5XUy8u1g+Ifrw51nUNz4GFhErasP5B6KQePBFsL0TQnRQDlqE/H5JeC6eM4LzP2WGLLHznjneoSrxhCwp+9R+RQisDECpDfU0YoV7a5VoWkC+Z5kaJAoW5r8tTMSM93x4uHd1vFr08WZFK6XTz5plvMdabTFWSZ56X+kgC6k9Ms4XV942lW0a/VGeQHf5FMzcmekD7G2iQHkBsyLb2ytlVsbB7IwpFMn7+UNa61nixx7ZjZ80npKZme5Oo31BWTxcEZPJhmtPeS7ukjx7f2Eq/+C5y8GmdBijIvmj7oYc1Vmaibdy3VxoApRImO9ccbIxHS0CRpK+0dJt8X3z3oiOkyW/zpu3lJuUzpelxHagqmTb+LMweRytLaVbAMJ+XncdhRk8A6/B/rHVT5VOV9rHo0cG8qBRrGy1f8ZX1sY5Dhj0Hmjkm4oFtkc0umHhPj5Vi6XmYkYTFljBaZM9biB6WuZuJYd1oRZbUnxivg9Y+YHt/8nkkBJiz7DPwkWnLawoS/I8bL5nZPyo6PxRg7Mfq3pvgmMsOnyR6N8JOSPDLGmE4QDFZfaTFB9gU2noYCt5gCdLJmcXWLG0BT9Y9MAZvG0lzmLI/SU85R2tOaLrk9rTc2tqXrZW1PVo22iudv9ou/PkP3iw6BDtjATWqtoatGaYPWj7rgps2ahVu3HmTQlBNrX2YSJ7xsPAh3nigfJ4jP8+Tpwh3x/s4h5pD6xh/Dnw1tSqEXboKhEYcuKHvVlF/IqKTMR4+J2TIpZbrt4sfHi9KXMSWFpbO2mZ1mTaADGVuaAUd0c/o7fl5CKidQvs1v0Zg1kzFFRJb4XsFEgP7o1NmW4iGkXjZ3UbS7V/xdVrh4/8+zbeVHYmMCWSTTRST1OmISiqEgqDDOoD8SIM54gXVBeXrSZ6i+RrjAAndgQ+L6E/HxrseHP+LjHeH19/B471rEiRB6cPHHOj+aK8yWA+0Vjo1RqMPBKUydq61K+fMTMV5+ejxvyp/nZQl1fkZKdXV3cJprWZLK4p8zCQGqxwu09l/V30r0+Oa3ocAtpkDDePmKPz6TgQ3/NshpUJOfqywoEdsUZ3+3d2hillx5WZROkZYmc0wYI/2CiUIkK7hmZANm39wA4E89SAYCn7rc62sAznhx/KMWWJE6lOWHvb3jYntbzDAtvmC8IHWEwjz+0LEzMzNhV7/4RqFzxxdt0IXFHGcvuAPy9eHdQPryKBC9ITC73q9+U9rRTalHfOXm3VDgy6KAjUNpMBrNeLkjqU4kOY+Krd19s6CI2d7nK7vF/8i09ItVrUX2TyXNKY5Dwam4n4zHeU9ZY21qeWweTRyHkokRidKGLrw+H8Y4kBCtIjMXI+hZ8ZF0dLr+3FW6cmwWbuZm7eQOe1MfszCpqy5ceZmael/cnZNpXpnnvb80XTy+1ym+f4QkgSwbLXW12dXhmA7JWlOY4gYvlVzCdDwtuFkLxEezN5JF0V7iW1lbUixMBWi/C3NQksfbMoW+srYvxosks2Th6K+yxHV4JLPSJ+NiPrgexJYYCi0xvFgnGzzBQKqZrxzw82Vy9U0itg+9mofUHz9dWUJy8IKFR8M0fPlxxE1f0z5tVN1QVZX1oqJYkKnzZbXVx/c7xQ9ivHz/zax0Erb0N23S2uwloA90Zx8R9BZUa7JeEj6eVJB7mt+GAreWAg3j5Sv+9DBeGCEZ7Bgo+WNTv7svRa6SruBt3GsNpMtmPnpCukWmNKFrMuHusPbyLlIpOPkMYjSJITPeH0qo64LzoXh8hPy1quVeVz6o619ScsyJ4H7SFM+34yoSosh8hznp3JmWvh0WXZx28fBd70jM2CYzY7wwdTWT10f4gjWQ+ResRX0ib2DAm+fDv3pAcnj+G1Dz0vKwSJuHhTvimndDgYYCN5kC5cjBAiM9bLSY23gIRtEuzJd96ZXbkTbdV7IeA/Plb083dKVjT5ZjZP1IG9uT9/xJYayGESQJbNNmYNI8BzyDGj9s6jzsc488CU1HLEPGnY4187NfLwJn/iFj4PpweqIPG/duZ7x4tDRVfHuvVTxahvEyI0kXrhi1zOgBZnmRDJqakMkjq3uCXZZZOoJIzRsKOJl8spTbvd6uaKMm0SGJY4wc7OtQck2Hk/+S+XPMoP/tj+1iY+9Ubfd9oaZr62IsHE2PIf2iBRrrMIOoX+cPlvApML7IwDL6g78MkKNilwRW0oB8GQwhCdToV8RYH1Z/7onx8l7iQVwxQqHu/YUJtc+p4sHdbvHkfrd4/GDOJLHa0oMD04WDQ/qy1dt+snIo1p6gTvibd0OB202BhvHyFX//nPHCiQp/6HfZ0+JnW8rueCNiyVB4VxryO+0J2+Aj7ZL28TbB2Khrg2ZOjHwAvY6BE3hnwYnyzkqT4/cFuWtVy70wTUzqRQwxmC58n0O5UXp8IN07LF6xRLU83yrasjA1K8YY1qb4aCxuXW9Moo38Wqp+QRW/qagkep/ZXj9u3aMNXR8mAek8vKP/nZU+0pwHq4lvKNBQ4CZQwEYDdmf2VP3fg1ya4OQEfWY6+OkdFavru8VrKdr9lyzIIPnyTnpe1rSp7enK0d6hK/lnJuMKjdTG6mGDbI7yp/Sm4nzuK0OVjojcX2b9CA6kHFRWHUlKKskhDOU2ppTSIYFxorcdfinvHe3YJ8clfWzXi1rFd5IieHhXf1Kqe9/WZy0pLJWyUl0FB84EVmJSHZGyGFiifYRa3hSQ+WeygytVDMYL176x8Hmg6/hbO/syf74jJdC7xT+fb0g/kZTv7hwXz1dhkBVifEnqSFJawXiB/vwZwzE1O2sTZRO4jm+kAvradN0fXygPj1YScSVy3mYjON62jiS/0wS6sJdADw7ucSnTlerB4ocH7eIbMV2e6H1voVM8WJbJ87uzdr1oSgwXrsVj6tzoktqpvQyywzdn89NQoKFAHwVuPuOFMahvDOgPcKmCvgR9BPoSPEwCDG7xBifcPGE+GgWuh5pUDjWh9DSx7Gvxc6ANvlnJUbplmShsi/Eyo409jBfLnuD2ATaozc+HUgD6RqtCxNUsHOkb8U34Voi8skBlIYC56eVFKdTTNbA5WTqC8WKSSEyQKNazb68f+x9QPxTDq+RPjc6ynoFHJIskOTHyYuvhdX+e9pa666S8HjI0hL4eOjZQGgpAgeilQY0Y+MJ/M96xgY2ZLdYk1P7kVCflknqBycAct765W7wV8+W5NrYvpGz3zcZh8WLtsNiSVAF/k3ZSzvzGtQ6HWFFRAfKwqSWMckjh20vcFoojPVXOwM0j8vBIG+/8G10snWOjtMOSUwebr6kT0kDaxEILzfUwX3jaU9rQSorivqRd0Jnx3YOZ4uFiR/62MVyQIpiV5KvpzBAMpF6AxfqsYbzEd7vYu95W8fMP5gLMQWMQSiH0y7fb0vnSM0tcL3UlbmVdFo5e93RYhhJk9BGxFvPvIK+11VMdcHo7452+jd7WRvle5tbLnsu3s/78ASfeAT/eVmpE2ttbm37tv/vKBIaO9yViuFaElU3aayErW+qWRWvivQ4A7xQ/S9Hzt/faxXcPZ8QsnDbz0QuLM1IMLUkXGKbGdHFmY+qkZTGNo6FAQ4HRFLhljJdsELJByQljk9toGn0RMcFocWS8HgyfR5oEmOCRpOiJ6YLp4gNJVjCxnEixK5M4lnIWZ7mTKR0vkqpAv0gdXp0GQal82vgiCPEVIAFtbf4Vrkz4fB/mNZgvfJcjLcaQSNoW8wXJl17vRKYknfES1o5gvBjzBSlXq3P6DcAfmQ7x/euLgAoXUgxpHVXGq2M4BOzVgX3dOYOc55MkUkZ9R+WIdKPiI3/zbijQUODiFLgl/SqrZrWG0OZTHqKY6461a92RElP0zL3RvY2XknxZWd0r/qUrR++2joq1Te1qpeeFy0bvdfWIaQTmiz0ZfILwRlTu8sSf/1fnIsX7NCc7DWC8JEkCGC86WIEyKM5fnr0jpaRjxaP7sl6k60VcMXogxsuSNrWYj8b4AVYOWYtRZ39Xtc8IUat4P5VqkbfSy7cY9sBgQMkuB5Zc/ULRLqbQ3+i60VNJZmHh6K9SuLvfY802LkYi7VRraP3ZISbNtQRNu1e0fpx5qG+Vfa4yWUIk/FmSYSheW5iX4zgZPYSAoZt6lZakxnQ5hvGiOkiARdfd3uvvjhQ/TxR/kqWtx7pe9K1MnHenpddFTMGu/tg/YJDD9gwCL5JUzydan1YFNq6GAl8fBW4242VgpIsAfag0CPHJ6kyHL+0zMrjbAA+uaegU0uazjbw29HvS57K7J50uUuSKRAXR3DWeElca7vScKdeV+WikKRQe8LyuSFUAr/+BWoOh/Wka3yAFgraQ1L+dT9BMbki3YOloQzp40LDP6eB+71QnCpJ00YkX1qc60vXCNxszBbx+2uItlwn+03wRL4+6VS4vmd8IG4JLHhXuQRJVITm4CB0CNqKa9ygK1IndEHEUpZrwhgINBT6QAhpupKKsnAqC8cKbze2edGjAfFnfklUjWY1ZebdX/OPFdrEmiQKkX46kxJTDiKMTH6d8WksMB1BT8OAIxhavPs6RuP5kiNWj+vznpcsxGF6ubcKFPLHM72BtNIAOulqEFSMtwYp2a0xMloliSYpKnyDpstyRfpcZXTGelrSLpJFbmvO188XKIbSI9ZhTJCGdo9NXj8ZTp4AxGmqBkA/GoF+pCfPS+6YLcW1d145gvIhB+Dcp3N3TYdhe744ONDnchDGoL2HMBlgw3jblVXOErRZmpxVAWHqQUvL26u9oQVkSxYfP287F2neUkL2t7QEOeF6eQTY/aHrp3jZFB6USKax9urTLe0lkSSWBFD4vysT5vYWp4qdv5otH0u0C8wXG4IyYgtP6Q9LF6q5yrK2CBg7KSOUR1DwNBRoKDKfAzWW82Djjg02Mf/iY0GIQqkhiQ1Q5dhBu40fKXqVLLh/X3ONZB5JcZwDjWTmmyWFjHAXIYVIuYrRgrnhHopNw8zEfzXWiSW3cmcgnJfXS7U66X4rbgvFicATb6nplhHMi5YQxBK8M9WvOGN8qvltMpviPMC+t7wXjZUdK3npixOxKF8+8bPd1pKyM60Zu6huLU3zD9L2MICZwbK4P+2ajqcvXjK9ob5Ae8XhfyjKMTOft1wFHCV5OWRh5o+CAg795LkiB+ndqiHdBwjXJGgo0FLgsBTTcwHixITsNPayrmC5gOKBoFyncXSnaXdvU37te8Q/pe3mjax0rqwfFlg4begenZmbaN4GSKhAwben8cCHBdrQope6KWTVi8rdhlQeMcHu6Cnp/Mt84exhVdPaK+8s8FiGfiHGsygezxdeYkiJAX4auF81LguC7h21tbKeLJ49m7I3ejLk2FmK0NjMrkzokE/PFmC4C2cd06Uet8V2AAvV1vq2ZxG0wCWQxSzgA29W1fAwfIPXy8o0YhBuSzPpjXf6j4t229BJtoRNG7ZpPTGvXtaMJ/Y0LGFfBeGgX/d/KO4T/egpy54xKQbr0U+XBlZWRr9Eskaf0NSJuMFQvUxaT+FF6V1FgWKlK2i/oetG8GC7fSKHug4WW9Ll0dM1I7VQSWfeWdL1I+wauGGES3qSxk5hL1NHrL9/HWphemlpNhoYCXy4FvkDGS3TlnGg+kOQhadzpC2JcLJ8aGLjSeZ5adJkNRwUmXJ46TiFGJO4Lvk5PLGgcf2riAz2/dsWIqysyVbyljfxJEqOc1YTemoJDjZniMSnEcvFAzBRaPfoIIM+VB8w+QLVqB/1qwTfcW1KkdHiF8XL9C+YLV41glGGFak9XxKb1jWCWYUoabfEt/fHtpvUNOWGwRxNmOcFfmrQ1ZByifs8GVM3pI/KPCC7B4zi7iKpOF0hLceeAA8qNfYLct5kGN/bjNhVrKPAVUSDGIrgtpVv4s7F9L50vXGFAIndTc926mC9PX20VbyT58mpN+l82ZABg70QHEMemF+aUK0c2EyR9GtBBg1y55rJdK4F5SXKHN1u/2NjIT8SRrT5rWKKUwF5RB4vIcsiv/yWouoPkvis3XS5IUzC/jyV9Gd02FgvHdZV4qvjxYdf0uTx5MCszvVN2daMzLSuTLawYaWZXHew6S1YXQ6T5+TAKVIsY+5AoPeaTIaF0JAlxvteepMVXN3aMSfj81aYktcSIWTuQKXRdmRMT5riYKI5OZTZdfxP6PhP6XjBgkPywz0W7KNuG3ISDtf3gGPEYbiTy9jc6uWIEv95KqUeep0LBQ+OXfBTFxTf0ClJ3JF5guKBId8auF01KykWSWEtS+CxprHvSPTQ30ynm59p2VY42itoC6htlUp431wgZUc8muKFAQ4GSAjeD8UKftxGgrJf7+7zBrCChov1lI4jd01VYDB1MgOUwaOk8cbkIMAhZhvB/pLdx7hO+PoAyiApHhe2ZpRw28j1b4Nikooj5rqzktCRFIYW6bOI5UWH/HgqxDFVgGu3k8NHzCjVIiDmgWv6gaC34FniNKkGarL5YOGJBCtMFc9/o5EGsFaVmSCIhnTShDwXThW/Hwgzt8Q7vOhgv0VkCqbO/UdlPHIPIpOZC+wOf1JcCbIADYblhFFlfivgSQuUwZlLkq4Ib1xAKRJNqyDWEOE1QQ4GGAp+UAmmYt41cWbACkfwwqz5iROzsSaJgW1c5Xm+J4SKpAunTeKFN7aYsyKzatSNtCHWdQ+wKgWBkQ+6Fx5kR/WNdjIDpHd4skdgXyqqIiEuw7BU/lj5g+LtK7sCYuQyftDbCV86HyeEbX6VXJHVGkT7KSrFehF6XJel1WZybKO4vt4ofHs1KmqBTPH44W3S5uiHp1ikdrDC/M59SjL+9/EC1eV8DBezj2k/5DVmXsBYzhbtag73b3DFJ5NdvtotVMQpf6NrRH6/3ZV5a67Tj8eJA1+L21U71tZRZ7wYAAEAASURBVIxRNonSXaFm179tLZR9N74lsRbk5Q6tRRYVO5QqHZlJkBLpFckNLDFWhPs8LlIEFMWlfEiWsWY7ElMUxgv/OpLG6ky77qH7C5PFz49nxXiReXNZ2+IK3IxMnHdn2lqfqjbqVnHVvSxfML17REiU27wbCjQUGEWBL5jxEgNIdOh4p6qkaOa/NC96RC2ZBUZa4nCnN/nMqx/g8EQSHzDdXwV6mohLvk/zAkeVhHlhu6cqz4muFHFNBQWtXFvZ1p/RQ+mW5jpFF30hmtxb0hkSnHm7qxoVzzGn4ld6EuE+C1GuhPBnyRTty04adNRgpqUl6YKOF0RdjySaDVPGmWy6GjYzoethUxJRnpYkDAreUluE3Ff6VvGdDFT2c0lgAtMHqewwQ/pKgE44p1fZBTMkLuSMcgPshTI1iRoKNBRoKNBQ4NopYHPVEKhsIJF4Ybx+rznNrhzpkAGJgvXNA21q94tnmJjmesfqvg4fUDpfaE5kEyyLQJJ+4TSeByYKzAhjyWjgj7WezwExE8TM4DlqM5TBiUkzchDIxnPw6YfMXOtzskNlHsdKEe/ID278wewhLf8602Nae40V3yxPFnfnJouH9zq6xpGswyzMmk4XOxAT08UOw3RtJXCjvs1zzRSwT20frfzqfCeYLnxLrh1t6fByT3qJ3m3uFRuSeHkua1zP3mIK/ajY2tfhpq4cbYvzYtJZSGgpH8wV+/76ZHw2fPYl1Raq72iFj66QRfsBlX35UZ+fdMQlcOZM/nQElsqgZi7RYwe0ZIERqjdWMnnG9NYtdulzmSgWdMXorkyc31uYlqWtbnFPps3vyXpRR6IwSGO1pVgXpkvUj/y4o/9X9SSmeRoKNBQ4jwJfEeOFqjDKpCcNPgyaeXBED5u7LG0kyN8JVh4U7mFwhpUX6XlHOT44JfQy1PO0l3ED1+6narLgjuaRGC97MF7sLvWhKWtlIqDcu/Mz2rwj8aJTFSlu89MblLNWE3xf2WlArsJGIVwnFumGfYRhYRX02+Qq24MqHaYlzZqRmC6Y/8YK1Z7uGqMUmQUA5grnpPNlVlJLi3PTuibG98uekaTNv41/lwjx/OlbKbAK95YR0Ie294jknTJG/jwKdx+eeaQiIs/INHn6cCtTiNNG/oi6FJzI1LwbCjQUaCjQUOCDKRAbrwDEeGxjtBynkiSIDdmRDoY4YECZ/O6+NraydvSHNrVcP2Jju6vNbE86X7Z3T3WI5EwYTYW6guQn7OjTYGZh7cKG0ecoVjrDHt905jGebljq+oxCrno60miO1CRu6y/V8FQTEkpzrf5Krpsa0vnx3qQgxsdPtaFFp8tkMadN7ff3ZIZ3cVoSBNLvImkXLMPMz3Zcf5ukCExRKUwXVSroGXQDm+a5JgrYZ9SP/y+BmuSHPqw+pw7AjrQWOzLmy9auJF5WdopXqzvS9XJYvBHzZWNXOl/0dyiFu8fH+pP0C4sTWgxtknbKP9OBYs1IPnt7G0q9o3yBBGubWB/iJ7nnwXexJ5grBph2qmz8UTctJeWm7WrtL+YJ7RRJLAlayZjDmJgtk8X9eel0kVVNGC9Iuizq0HZBf6TjatGk9ETygBcMHsMvQ7ppr0ae5qehwIUp8AUyXsC9Gqjo3z4p5XWy7m8Dyh2NXO+NaeADAultArOMuN1PjveMrgY7wjy+CvOyyOQDoH7tv/lswIlBxt5WboUX4nj++NtQ0CgVE2qk9PqojBEjbJmeutk/5VQ9UIyl9YyJsyIieSApiQOdEqGsdV/MFxTt6gBFf3ckJjhtoqxoIUdnCABK7HwEzVEN1Lyg0kcOFVzmJAI/T0AbFe+pRqeL+FvwTiTzb+ntAcYZ+l4O9Yeel82tfVk3QiGhm5uelxUqrostzE1JzwvfL9FbL5vcJcJE+0n63UoiUkY81derfysmYk/lX89TWntUgLc/pRGO5lZ0TO6cm9DsmdRpC+BiafCmBzjE8wYf3vZPcCjJyuGtOni8nx65NFZAibcAAyfgUa78PP4b6eINIsNjIsWX/v76a3BRCg+r6bCwi8Jr0jUUaCjwqSjg8wTDMeMza6vq8WHa56gT3SNCsuBAYi0cFGEI4Jk2tWuSMHjxarvY1LXbHel7Wd/Uxhdl85KAkVEksyYD7GC83EmHRkgYMAX4NBBjvd4aOphPfASpfius+l0Gw4IsY7hs+iB336MApHCAztx2lOZGJIl1W0M6PwopH3WjBtMtmY5e1Nytw5MfHspktDa0WDFiTu/o6ka7Ne1XN5TP6yFH9vj8CAb94VmSxnkVCiSS5u2WNTXmwPl3LAYhptAP1U7RwbeytqW//WJd7ldSDv12SwyYDbVhrblhEO4d6Puo2Xt7TAwXvprg+ZUcvp/+pO+HRslqxx+FwXHhlUJSAgUpMD0J3fBmZqyzfNkehD5H3cjHr85l5fcwMo9Lie6E3jPSOdRWo51ro0y3patF7eKRlOguS6nu3QWZjpZiou7MtNaWMGtYY2L2Hah6VDec1KXElEbcPA0FGgpcmAJfMOPF62CdPHX2qlZpECAAp/V7JvlsoFFGJkj7pzTOc/FTCrKwqeRhIxluC0g/gLSNIMD5L+D5n4VpxCUdBfOuGC/yGF7ZgEW6EY9PtFWkDXLKT90Bg98U1unNhh2pCBSCIcJr5ok1WeBHYgJLRnCq57rTEhWUclaZKJ6U4lZgGK6pmHKAzwMtjhJ5iMjdFnhGWACKPJE+3hEf/lv0TiSJtkh7YSGKZAv3wVlsruvu+27vyK4g8U3nuWakb4jkS1vfjzbCF9TL2lso44PJlj+UMeypUuFSqhKn1IYjU4Qjmlq2O9qgtwb6Cx7iCLMnIgOG3hZlyIIuCxFDmxrYZE5SGC80M4VYGuLIkrIlaF4jBVc1C7gpRfWiVFJ+3c/NqMVFvsGwmg4LuwisJk1DgYYCn48C6rf2n/6bPRqr32uOI5Q1ysGRNq5cO1rfLTbFgFl5ty9Jl0PpezlUGHrPdAgh5sy2rnYc6GoHGU91/cih2gyR3PlIz5jvM4Sni9SRxuNjZsjTkE2L4BKm5HQS8p4KDxtrUliIfnAdE6FNLEwXlJNOax6emZG+DEmnzsuC5LKkCLgm/ESWYZbmp3TdSCajzeABSvNlt1flcO3D1pSOBBBrT+BSC77V3vguV6dNrFtYRqCXxx9db1M7ZV3G+npv/1D6XnZ15Qjrk4fFS7XTNRmweCMprc3tE2unG7usxa1BaC2kNgozRU/8xoq7al0R5yXSmq2Bu7fMl7x9r6i1BwIx8nqMy7F7SZHW3tbGxBiU0AqCKy3pelyWha1uB7PRE8VDKdK9Py9pLEllLeqgryuJLExHextV22eNlj0wqegIhJYxpSNL2DgbCjQUGEmBL5TxUuHLBo8unvZa9Pmyw9vAYj8pUG68nLBwxxidGSYlojcDKorP/KqO7mmmEwt7JyYMwxbFGSNEg4lvGJ3ja+KD4vzC/Q1FqCEmStiYFKJi+hfkYhxy3BM84QVuxPGudq1VPMFEGhdeb9ucw2hB8Zzw55TlhOsobNgVyHWV2ABbmSISVnFQpDsj/S7T0g0yKTNwbM6tTINvtdRGmOXEWU+ZQ4miRsPSR7pRaSI+8o5KF/H1N/kvm6cO4zP7gwSqBu2Nh1++HZJLiGKv6zRlX8d86H6BETPN3Vr7k8SSGGnRJv0EQqds+rYo4eU9MDkKuk/qURLvQRoGWpYqrUas7QnHU+7ogx8a8Gl3aoMmpUPce/oXfcnrYJIxAKFWAFVRgAMv/tlbbRPGpPUVhYN7uKt+pCtVivN8BtB+cjwJGKxJpM1Tjk4Vqa//nSp//YBvKMT4XvGtGvrd0A/dVOtGUyD1W+vO+tFmj1Ha1ibUO815WrbYGuzw+EgSngem24xrRzBeNnS14430vWzuHxfrutqxamZ8OZzQlSNJF5xIsSlzCqDYKvPW/+zJxxCfI9gz+gxEMnkEAD/5kJIGTd8rpzDFG+PFACue/EqkYyty22M6MuQRL8jmKXgos+K+tCelRFd6MmY7E8U9bWSXJeUy35mSZRiuF03pAEW62rQ2Y66bnITxAnzgpnVYFNBXqzIwld68roMCtCN/5BCJaafeKGhX7BXEJJQ0+Y6uxvV6+tMB52sxXt6J8bKqA7JV6Sna1JWjN7LM1ZN0lq2NxIBhnQST0N4CSTuFGVMx8yjO2Xi0OL5u7GvAh/boAcTg4SGlu1M3Kv0eDxxUCNCyPbW1W9ZetF3WXBKYbksKC+umM2IMPtK1ooUZZw7eX2hLAfSUS8hLGgu9Q1Pi0KDw2U7LvJDy11GEaJRlBZZxjaOhQEOBi1Hgi2a8+HjIwGJd3GuUOdN4FC+TXGHgPJaJONMuz1vcaxgxMCsIZ5MYDA17w4xhNNFfLBTsJF5BznhhsmQQS5tFuX3TyD1J2bTXAMc9yAkNVFNTU75xTDj6dG6AHPf0awM9wSrUBjKroWeizmx6GbxN47oxWYriQPVggw43HnFIY74ozspQ1inKNxwwRzwmKQkxXeSf0MYc6Qgeqx8F6GHTO/rxNFV8qlAVkLki7UXSZNkGnOQfBouws2APAPryAhLNqUlyGo74aad8z00xXvYk8WLmwSXKam1M368lpgumC2l/1h7V/rR2k8IzMWTEYOMd3xeg0basAP0Y5aqfCLa34SMXOLnUF4sI2h6nPsf2PtTVJ/oJJzvWX9T+rI/ppMgWGGqDJlkmIJRdfik5EBG3BYHwp725OUJnUk7IP6GF6IT1HZnTNPeE+o+now/msHLEy/BETOrBYiN5ze3py5R59o/oNkw+IvyvGTS0iYfvkvsjPN6f+rtFuc27oUBDgatTwPt0jMMVnKqvM0dwIIaZaZj6WPbb3t2XdMG+TE33dK1DUjDrutahze2upF64ctST5IunZ77EpK9gCCTl2NpNBTH+22NvzUN6IwVgc6Y2v2kJVI46YGSrL+DIDRzmM2ZMfvlDnwx/k8xfgseFX5ZNE7pSdId5WX/oyViSIvwZbWzv61rw/Bz6Mri2MS0mTEtMF0m6yLpkK83TPo8LkB5oYZIK4Jzwtohq5nPvV/sLFXmscu784F//Rh8MpgTgONKW+OPhxbehfZ6cyty53qvrsniktrmxoWtH65J+EcPwpdrp7p4kzbkWZ7qJuDqOFHOht9qT4NCmoq0CWwsca2EVo8RCjUS62aO304ryHR3eqU0qwHGrWDkwa6ytKxvrQkxdj+v8dwyrWmIGcmg3of3ArFnJHC9mpfPxO+kdWpJE9T1dLVqWJFZXktUznbZZ2NLy0uABExwCn4Rl+bLVmaNahjWOhgINBS5Ggc/OeGEgGdZ/rdMrLgbDqI4NSApkQGOzyBtFZ7y5tgFDgjuaDJZmLUZvC9doyB1jrneYAlOlZ0CEu81GkxHNNH9ToGZXG8z0NkkXjUYMl2aSWRvEFlIGGuV4w5SZNg6xrvXo+AN/MGYwDcxEG9dCOO2wJw1uXkfflEb9QOWEAZ966I1SOhYnmBzeQfxW9YABA86IRsLFZnCd0/1hJnrMEM9IjBAcYqMLDkHPcPMe/vBFeIjP3RY4Iizi/h977/0cSbLk+UVDVkGr1tPdMz0zT9y+3XfHuz+ZpNFoNOMvR2E02hnNaDQa97h7u/vEPjF6WgPd0AXduM/XPTwzq1BAAw3RKhKozMjQ4RnCw8PFSU/ldVyZShdlyX1yvNjenxRLubyr67iWet+ldVZxr72+t/rvinS88J0lbqRvvcf330OBm76xlt4R61cspHzvYfqd9L+0sFYlXT46SYvLCSi8aUXWg6e6dMVRix9d3/pXECA1dnYpT2Xtwgou4ksQ+bZ59zHlHGNS6Kx48lMfNO4yCpDS4CgrPt+gjR9xtogI4wRM9UsjCFLnYREHaYtxZsEtJiKhhTOeRGwyYg3t9nz0rvGk4emwE1zUbwwJUGNVsIIyy2/Uw4L63hxGnqhvhOJ5YRBowjrckbl/T3+LsKZfxCvPAoECgfcbAr7eVPiFDWfN1DGufS1yM76Oq23CWbDR2YabYDutbe6hyHQHs9NbZoZ6YwtdGuh/kW4YbWbRfZq2xfVLdtp+av003KZnutBW1VTSs2AId4sVsq6F0vub7loTbX3OwJUfS48RXka1BuEWnmXiGnC4jEJw0cZW3Cyz6MyYAO+aF+EFUY0FrMLMsLGVyMYY4kXGQcAhXS/KJZho7bKq99T//f7Gp62dw/di11fledHA8j4rPEmX4ch8G+E36hMHHISuYOloE11E4s56AcHlFc/nyx3ewdE5KDP9fCiI7nAw5fpfdHCq/QV4HPmqn/rldffv7pisfAyvqdql3usjxp6kV9WseubmlkEgYqD6pvAlPaVmADpffsqyFvgVfXUSDniZL9fzNmJF6p/qr1PocnHRolE7PFYh3k+9fBVj5fKMy4vm7o7wLs8CgQKBU0LgnRNejqunLaYWGEQDnpoI+TNiC7OaEVt4OmcIVGbYAyvdJ5zYazIMSzHuL8KLNo1OqGGO5J0cmRy18F7Lei20U9Tkc42ZTArdTESCd53Qy92Gs0VEDW0aJcYjqzOjrMjjRnjJhBnCxbYnsSQRXLR51IbTEJJqwvIpbUC8gPnytnkdtfndYhMuawDL68g9b0BMwm9bM7o2ulR6HLOFoyAA09PIaLIJb7eHQAbggqDeqpsIRGqLlRQrC2VViFEU3PVUbFXS6+fuiNDPL8JOekaex8WJfBVeAahPZPUADz8pVp+EV+Z1XEsNsbNmql95ddTv1B9XkB2WXPEGxJcN2K13UJQsNlYhmVq8W3zjUfqf9TWdsiFDLm6XeVhFRQCMq5fwEt9Q/VyX7kICxEprXFPiYCF/cdoI0ZDCZlPWDIYrAqX8nfDCOCOelAEb8ZI6q6wgvCiPIPaoHKEO1+jX6mfqg/z7GMChcWA/OHY0htq0ZxS/EfQRCYEYlXgc7+q/eipujCERcUScURfwOUK9IXoCz6qzWyVUlRMuQSPSnhCtBF0ABLz/dWck2Pd+g2a88m264VXeCgTefwh0jWgbztwYyuKMtPlZ6wY/rRlO7JdhABFfZN1vH6tHsnyE7hc2uiuriB5twBXTcaWmUmy6AQ60D+eLuF9c/FVrgWYSxwxUpNafILgYV0ADbArXJfq89sOBFpl/LMw8B1GKqjMNKSIFxUtjrFcjrMNSTjoFwUVcxbMz0sWGbgzWLinTHUM82PRkQIiRPg1xRcfaJVwyLpVtSxWFapaLYiP843j6l9CXqS+19v27Yr9hXVS9Bwe903qUDmS3wM2EG+kwV7qJNiAQqo9KMbQOyjq8q4+usefY5H0VYiFOcCjhTH7QJWtYoE3+vTNIBA3QHK7AY2pYqY/ozX7Zu+rhdBj1caF+6qPCm4QjtuhzMmWuA9lpOLGmEX1rQ3yZnh5jryDdLkO2Txijv05Klwv9VeLqIg4KTzP8sfGJjAiVB4jtGWKwfJwdVh+iXAUClw6Bd054qaaZGNzWZE0pfvmkI+IIGz4mQG0GZT55WxOhTL+xYG+hkM0mP2Y605rPxLfFxnWTd4no6DTfFLvhtgWfp4rTBlL7RStaT5sS60UwRI5MuzcTjZ3cM8uNMkGJAKOFVVwtkpuUPg6ZD9TmeEJiIJr4MCko4ksLErQWYNMUrs2kCDtMlBKr0IWTy2dZISPS3+KK6A7S2voeGtW3sAKwlZ4swn6rtjP5S4nuGNTtWRRljZtcMWyDkisGCZiSJRwmUtcDoo0v2b9xwhSkz3I1ZuezJPuo4zoMj0KyJg2o+UGA0VP9UYSNJSkZFKKJHLEUDb5EgdsSGvRXWNDVH6YgqE3Qx+anfDG9fXPMkL87NycgwMBbGlcUXn0eIRD0KfqN+pY4wZTfxqZklxk/EHpE5BEioT63zrjaEpcViMMuWMI6iK4RXiRnr74JIYgHeXk75KeRo//ey9ZmIQjURThncGA59w6K3ujD4tYRQiDiiymDZtyIgKmxNQbn1hhjaWKsBYFGHGUoioYII8KMuGHEYeNXbqxezZnfux+91eO9J32fGMXroiAQsFZ++cP0hX/1ES+q4JJPgUCBwLuEQDX0tRL5ZRtd1j+RR/wwLOM8rEnigJHOlzUILi/hglnMIkhSvCulu+usQVqrXKyDdYlMtR5pPvd8zWlcLtUS4cVWy0NVD4+a0S+fl7ReaRM6jOyHNrZt4XfiKm4j2ss6NYvy3FlwrHE7+HCcq4193klwL+n5G2F9kuisrXe2TgnHaxBdcl2aD1srmx4fhTugHPP9u2jU6epgsTKOXPVS8/S6m35I9h/qq+JA14HUBvpf1rB41BEnDH11BbdE5UR8eQknjA5Nd+irHQ7QjFAonInOGn2UzZcBJPc6+mYQX2rMRDFEgImIejdsku6kPQTbC/8JXxJRkP45BVfLGHjUHPuPOQ5kx8EdZ9A7JFxKh3VjLXS5GLHFD7SES5noE+VY3WJ9bgyeo/0zKpXrVh4FAgUCp4bAB0F4cZGGLOoA1Xlrh80hi7M4BMT6p82juATELbAJ1XlVhBiZJWTzqImP/7TDhCcijCjO2oCKsqt5z7SRZ3fz2CE4QjS9aM3UxKODdhFJtClkXWXjCNcLhJQJKMptqMyzUJg1oU3BcdJi8Z3mBEST3jhK1mRdaHJizE4/grps3Cjkb4QXm4MlWhKEFyZuNsDLa3vp0Yut9N3jjfT9c9gdaaeIS6JuT4IQzKMxf5rybs6PphticQUhmJmC80WUbJAAscOen/AiKPiUXz9P3cc+oYj2ETOktIz6e14qKzhocfOfuF1EDDlEvl1sqzrp44dywRcvZcJwF5ZrEUJeZ4LLcLrF955lQb13e5zvPMpzElbRPoQXlabPRh3U37cZM+KsEaeLfqvryCczfjYhuOxAyFvBtKfk6Nc2OLGBECMEVwTLNdhnVUexeEv8DSkkR3Qz0mCnmHY0Q1utPCtSBfs7ndu8GS+i/2kMhdiUWLjppqZhXwit2LX1nGS8CNnViUybvj0DQjsKYhtWIcS+bcgCeTlCkAuuwO1lBsIS1bI6dd2qBF2+5eUyIBCwVt7NLyL/k94voy4lzwKBAoErg0AMca17uVCNeK2B4gKRn9x2CMGhwNY2RBfWpw0UvCy9wpIMOjVWWAfXWac2WAs7IHTivJT+MZYsW5d4JR+tdX6oJgu+8rGLR8ww9mTRiJpYDC0i9l8fEgyBNEHrt5+4WGRZUOZ3J1iXdKA2L9PQrFcLcJ6OI67RYn3SuiR9fyZSztqkSxtaZW7lyiM78vIpn7yG5bpGBAv50G/vS5tUj+oLHAvUqk9UHwfcWX2lelcu4EIQXaQGYAukaBOcbXNrB8ILlrnYi0gESZxbS3Cnm84+OmgHHUUiFMKEb1zDYrOyPHOftZpRRT2D1tHkdrEKW6D3T3UY0y1JH9MhsETgRtExNAYVRv1zJuNPCyh7Nm4s8MNpdLmMsgdp89PTxbidG945WRwsVi/12Qpk/g2tG1sNq4Bj4fjuA7zOp/nm776upQafIgQugPDydp28nssifQ1+DX4jjhCkzaK4XDTRSfRGG0hZf1mTmTcTwYFFlcluXax+Ir7wXIPwIo4XCNBsJmH1Y4LbZtOoExIedmKvicXMv2mB1iynuYanauOTjNdH853PQiyiKKyyjSMTnp5iQRWL34QmPZ2EQHhp4TfFiYjYUOeQpZxgEpxmYW7DlTI3NW7iSqOc6FfsfaRXXpr8NBmGKJSQChFellDm9ddHm+kP36+nf/1x3dq5CQIyofxlDm6ejTjymndvtNOdBZRmSWEW1O1xNq1WBgiDuHPULhpYzUUNVCC3Ty21WIrZuBwCDY/ifAMEYgGPaA7rGrb6DP7zBVl99MXLDRZuZNtBNEVs+Qli2/fPOunREgRGuE5uzQynBfrV/RstU+L38LNJ618P703DSYU9S8ueknPe0t8ityvvBVllzEgUb4tTGiEOZr4aIqWUGm4zVl5C8JEZ61VE2sQpJoIL3owhiDU894ytm/wYL2KZhZZjF4dAthzXbc3+PBSlWsOhuqgXmrJCPHWS2AJhoHui+E3ie5mASb+emh4ylm0RYHSKKBE6scQaQZF+PQ4HjMT+Rhg4QkCkoJehwyXmcsrBz8dzrqTVAz8BRFdzgLtPuV86BOpvYR/n0ssrBRQIFAi8FxBoDn2tCrYouKcIJcJ9xP0rXE/r1TbrzwabWXH2rrAuraB4XrjdDjvXPeKDClrcXd73dbAGUic9fjoYEEdnKHt/7dQYXxNtwWLdyuXnJcIO1Ux3GB6yABO6yMS1Im5M/XQIoAM1HQyIk1iWiybgKtABnNZeGVUYZg3SgZrWHstPbbR/bvnqv+x0AYeYdfxIV55XBwHD3eKT+Ce0wrUnsW7L9wnjHdqXuBVKWaIU9z24kw5GwZlWwK3Uj3WY1YFAY7oZiW868uiLMvphRELhU7wH3mZcMHpRN8g/4Y8qWwe1duBEP5ViZ4lcj0kUm745Sh9tgTuNQ1TRYa/6plQOSAROHMIt/LQfEEeWuLFMdyVjrkl0MYSNYtV8/awLqw4RIKdVyhzv+S0+YrOa1pimR3EXCLwTCLxzwosmuhgOehrRhYlHC7CLNkjXhSY2Fl0IEZtsIKXkapUNo0QyVtB+vy6OFyY6EV9MR8Y2CtmY/PAmHQQX2zhChGEsivDiZagwSuSnP11RkxiyNvHkEAsl2iDsp+wRbaPHOgxRw80Jis1vekILNCcjEjliIpyD82RCytfgTJD4xBz6OLSgS5nVqE5R+Gmx1+JuG8iBIZuEhYDoRGeLSVyKvP7080b652/W0r98A+FFMqX4T6BYdQExozsLo5QznD67OZ7uSVs54kYLc2gqpwwTN4IgJMKLtS9P6P7mdwvomljdp74349W+xXU8BGzxzsHqW80+7t5SCigxN+d2kR6iZ0vrhmS+0AkfrNXfQ3j55kkHAoxO/g7SndkRCC7D6eHNVroJce3h/SmU+LXS1w9mIE6gTU2dlvyEWKp/a4E3oguIqBZ/Fy2Co6UDoQWEQMrixGGzCousEAZp65c+GbHMauysQYQRxwsGJ0yR4R5mEg9EmDSii9wqr16Ge3tJjCG119xEMPOGHOmAMySGC6c16vtwvEC4hAOWMeIEmGlOE0fp31OyCgGSKzZZjRWxzQrhFeeYdL+0SSwERAiFxs/gNRBf8tWY8nK9Fo5cNGqkSOW6Ygg04F/N+FdchVJcgUCBwLuFQM80UBFemJN1WCDiyTYIm7iYhfcZVzNrkhTPG0GFqVtnZUpnh3KsbXuwYepQTUQXVw6vgzvWLVFotMkV3sO7ilZa0ei1BNiBF5tPF4HVARhuiCdaUyQibuIYWmdA9GRYYYz1Rrr8tO7IkII2vlKyKy4Xw99YdyC7GDHHb8Iofa05/ZKjWpb1CSC884uu418CnMW+iPqR+qlZfrSuZX3M8HX1QfqtjGHotyFiC4YS5Kf+vM2hlwg2UsQroov1Xfq6DlqFpwln43HkMkIehdM17YDJ+qoRUNwqpAgrUjswIuIgfVZ4kriv1F8lmt2S6Btu/QZyP3WxInpm7pTRRx1RY5xQnnqhnWV9sF1RLWheH2xDmo0o7o8EAhdAeBEk1MlP07Gbg6E7voewYRQbKdRjsfHt8ltHBGKZU49lWE/XYeGT6M06E1qczK9jzk1WYUwMQosvs6VY+rTwanOoyUzzmRZtvWvujMucummm6Vt/+RPBquoJffPIpISfb/ZYwHHrZ2I9TGbMdT5RsoBL/8sk1OY2m8s5CCU6PbmOSJA2l/NsJCWSNIkm/FH0wEiUQoWpjqbglAn7Obo//vxoNf3Tn9fS7yC+rAKDTX4TiDbNTQ6mu0Z4GUmfXR9Pn8H1Mj87mq4b4cUp3SIAaWJW++OyE6Z4sWcd5t7W4K4Yn9ZLEx7ng0UQYarFDUDqU+jnrNWc8Inw8mLN+vnicia8PBPhZRMxM1k6Okh354chvAylL2614H6B8ALBZZ7vbIQXOK1M0S2dXqbItaivrm6ZCNE6hBYRJB+/lJWIvbS0CuESwt0qxBfTxg8ioDGzB1VS3Cskt7HEUBLOSpiPHblVZz19vPT0CANTs5UerugksH+7sZrb2MHXxg0vdE/zExFTCIY082ts6SkFu+OweEtZ3AKEJymLuwkBU6c7C3No5Acp1vgZYfyM8xTyPALiEUiFFZ9vglHlf77P2sy2uE+EgPeA7igF+N3wKG8FAp8QBBpTQnUooQ0t87N+OxD9xYWpAwNxEujQbR93EDekc4+VyNa8PazuGSc0aYwzWqK0LFoi4uhd+YlIo3VNqfTT7OMn/r6+SDTIFLmzAEk3n7gpxWGpNUYHZRITF1elKX/nKX1jWmckJqU6+f6VnCNzFko5dRmulTe47vOme5WJUr8p8jnCo5xzZPFBJK2+xKlrG5ARvuOXCC/hiw+fxcLyd/V43s9kkVT9TkRD7WP22IxsiwBDvzTCDIdcxqEFzrcPscZFzZ1LS3sY5VWVRDnqY9BMjNtFeLxZfeQpLhcRU6QfT/1RP+PSEnEQ/Md0FBFH/VT1NbxHGVeZOy6k8gxrU1dTWL7MX376fVBXoxFW7w+uAR8UtEtl3w4CF0B4iY4eI/e4jq54HkfU3aaGd1XdTjPwd2sucK6gXE3KcyUS8QqLL4s8Nzb30yJspxsQW6Q1XEqrNhCJ0CRRTRq4TWTIquXTpZZBi6IT+xxR75KntGiqgC4jwPC0JjSn2mYsqYPzOE459uhKIjEKy18TsjLnTRvGyRBFmuDUBELITbgX5mfG0gK/Nif7s2gcF7vq9GRbmdhEKORBsFhEudyffl5L//xXOF74SXO6dNeMtyHkwGEjwosUvX12fcLEjYzwAieETMeJyKPJWBO2YO6Xf4NqA2qeEZaj2MOA0PT4hNy98Hg7WERfE+CaJAl9Cn0PcEIjsIkN9emic7w44WUn/Yg+n2+foFT5BTLEEEfuzDnh5fOboxAeWunL+9MQ2fw5hS4hne7p5GQPDFPKc1cQXRKL6zIEmFVk7h4vIidP33mB3hiNGVmGMDG8PdcrpL5/TT8bEWLJVh01JPDhJfqP/O1SWHYa/sGLIZnZT48I96dDQ5DUT2PIhgk3exLbyrYIjhRoDGmMIVlkYkULU8jXQ2QS4Ul9+9b8OGJH6IARARPkeHKScQSyIWXWQoiVsbKrLhsD2acroIpRHBcKgegBzUwL4JvQKO4CgU8SAnlqiFVBa4AODMSpacpLORjQ4ZMO1dywgsS8JaLK5pMNpylWJ0wEFhFWhC/5k/VPxBY2sc5JAHTz4YHgrPK0KuhPe1L/OaHfTPLaZlWcmFjVoxwRXIzQQrlmXY8Eg4RJWa7qrHVGTbH10dYXleJroT0px64y7TkcPpC7dU/d+G7+6XgxT/nxzfWt1QF0ZX/5qc8ZNxY4mIguwsvcgqQOh+mn9Gn1WRFfpNtP+x5xveBFNvw1+pDyt35KMeLiVf/0pxMHjQhDH5WYm0SIBuRWv1X/VD/lZ3hQ3YhcV8evbOxZM3I7Gm2xdjW87f2DuMVHUmWjAfIL9wfRiFLJjxwCF0B4OQuE6olFmzTNMRoSOonWZlETkxRSSav98hqKZNkoLqJ0dOnVZnqOgrU19E+8WGbjiLKqNcSJttk0bqF7QhPMIMrQoDEw8WiCYsJhoGmh5t8vnnLGa56BcmA8CD1hjCpI4c25UX7aQIMeOAWbCRRit02ukkmWZvyxYX5Ig7BfttP7W5zUX2fTfIONo0Qn9BzjOTcz7hMnsJFYh5CQJUSNvnm0kX733Xr6/XdrEJ2Qf4YDQuIZ82xEb823zIzh3etjiB21zcywrBtNYBHGTmyghOsEJy5rA3XVhO6XfOT2kCaEcoRP9BFwedvm27JmcJarhrf3Hy2w+hmBjdOQF0sduLhQpisdL3Cl/IQVK+l4ebyEmWmQ0Bso8ZtHhO0ehJfruL/8bMIIL5/fnTJlziLeSA+SWLR1uvJ8cQ3Fb+S3vI7C3m0IOLKQtJ+eorRXCgp3DgZY/NXPNFI0bljMGSyyNGQIaCCWTeSCiNFrDCq5y1iUDC4PryN6lByRh40di+QIq7qdWMGFgIhoJDfNYC4QIi6T8Qewy7pZz/lxtyxxe07cYUOI102YDpiFWRFghtJ1xo8IMFJmrT5vrLVUTkiILiubZy7e/MrtqiGgvuC95KpLLuUVCBQIvH8QsHmZKUF6WiROHgYTtEnV2iJcUTO4RHpEDNFhluN1vm5UG16zOuNcM5rstQHWpfw16+inF1uLlS+vdnhGZirHuGAgsGizajgkm10zBY2f4ZKUb3EsvggvXi/PW4VYCSrF1hzVS3HOdimPs6Y5Wwkl9ukgcPRL6Bt72vyoM9J3xlN9TuantZcRN4sTYbAoCT4jHEccMdovGM5jHC7EVb/xfx7eP3mteoGjL9rLCJfRk7GgPY+IkOqb6rP5Z6JIhElEyf5ymsg/uqNjp159Gw/REhWs64Ptgk3IRWM+6AbZ5yi3jwsCV0h48UFg97xAyS1dFzrpEHeLFlrpoljj9xKCg/S5PHuxkZbWtux9DU4PmdiV7hYYYtg8Xks7EChEdJEWelkZGkF5hE5FNDmJfdQpvswjvMtPF412R+PuQTlC9m/GkjveXQkvb5Rtiz7ruyZPyRpz8AKboRZ/6cjYB2F4nVoD/NBhMdUWAWYg3eDEfhbrNBI5mkQHzM35SXTBIEIx7yf2Mj2tMg7QqyHlct8/lXLdjfQnlOvKepMUB09CWFlACelNiDgzKF29BdHlpqwaVSbkXDxjaBDCC/Agu8alyf1N15tjvCmHDzc8vnS04Kyw8PTNXJqLm62z3NR3/JSPfo1el3U4U5ZQrPsSrq7H6Hb5CS6VJ4gISTRoHq6WGYgNt+BwmucbP8Cq0Tz96MFd+g7cTRIjEnurdLhI4duLxQ1DYBdfbZiVpCdLKKRG6fRzCJdbcNDsHqJPiN554Msz/VNjSLpWfDEfYsBAezGEU1AI0l0s3MbQFeDp8+xGODM8MkAcHa4Jr0JS+KdOGj9iERfRhR+3/df7KOB9jVjSYZodc0XWN2bE/j2Ybt+A8ML4mZ8dM51GdyA+ysTnhET3JOcMJVZIiU6BdAnuqv/bIcR9Glm8zgiBGBFnHU9nLKZELxAoEHgPIaDx3xj7vGoDKF/RSIQ/ScRoDXxP4rESNZLIj3GfgNtpTpfeCh0OSG9d6L8w7hfyiNlFuJNdTPjm0q1ZdLxnv0inyArST5fWMFszWPzcL0LqshTB4im+peq+VX6xcHYH93mrapPDqhz6xH17L0G9iZO8fU6fSsrmd/FvIjwirvhK3pvrvqAoEc1jR4zsj6fhJGTkOFXOKTKsClBG3u9sHxN9XOmif5KmO1k+3Io89FQlFKnRH7vTKNKHfqmRcal10ejwK88CgXcLgUsjvFRdPztinIsabMMAD8lCiuAiZZ+v1jZYdPfSkxebuPfSc+mkYKP4DALMtjaKnISwn8xEDTZlLNQiTBxwci+KsCYjowCbmyWFdzvN0LjTT1ddicrLvC0w34jbW/dmsJarmDqrCRWHJmFRsrXoS2dGcMAoNjgCG1jEJYZcsWgL4ouU8EqprjhTZAp6DgWpD25xcs8megpdMEOS2xwaMf0eiy/3IL500g9wQRhnEPnLpPBtiC7ibpli8ykTh9OypgSiIq3mkv8MwpOIT90bYbXRWxmgqdt41KcOkyug86Z43ak+xbeAsdrei+QYFNVnwDilbG0NRFNK2ExJtBEYt9NziDBStLsJF5iQzVG+4xiiaS0U+t3FgtU0/eT2wpgRIZ6/ZNwglvZkac2ULz+XLhdEipZXEduDQ0rEOrG2agwZRwn9lPPE/Dl1sucnKjKPbicrjBW6rPWb6kubI76/f1G1q9lO99U9t1hpSOIocKQ1D8q3aMbxprHj4obUT8H8JPMs+AxBeBFyLStIgIE+zpiinoaU088FB4nsfQURasYImXB8MY6mJ+GAyVaQqrlAZZarQKBAoECgQOCKIcCkrjUnLs3z/Nncz00cm7JktGYGE3Y4iDs0xbbCacxyi/RXSJeFFNpqfTK8RmtUztB2rnphTSJvrUy9eE8U3XyqVroijdzNdU35K8wvL8zXvDpWXu2I4hGjSpamqmDO4o2PqjBiduX0xpSnjaD613U+bapPOd4bvokF15iQfXLrNHw/Xur+E/n4dz3SNRR87Ccn0P8bkRyz8n4XCaMMYXAK8XEQhCL77kSNWJHKIn90t0+jlR/dZ/vIG3RphBfBzbp87vcxwWhz5VMBGyxONLa2pOxTG8z11EGM5ufna+h02eWkH90WbECfsnGEccRORLRAI0VheieUuU7JDyG+GOGF2UpPLbQqS5NJuFUXXSIS26RTz4IewN0mH7u5l1XbblWUyhHeNs3mF2UpZ/O5zwm+PBxJ0OTnnAM6vTc3z3E2jLdlEhrCycPbYyY2Mo0VJClya7dGjei0vAZcFtlU8zO2RMqZhQPitqwYTcE1g5jSBMSWcRMvkgJfKX8TIaqJnDQap3ry5wuvaq2w3BCHBO/HXaeNd1z6T8e/XoZzv2s03aBI3xDHl7ijRHzcgeNLinS3+b1ah+sLBHQRa0PieMGSuXF9SIRGVhduoSdoclxEu5bpNXn0fB2uma3047MVi7+46jqQltdlllxlHNAVhQAYOmpjR7US8UN9UWY0NV7oMtYvRMjUJYJF9xXf330V2u1Tx44FXj5+Chkx/RnwaY6ZcGusm5ub+HLULa1PM2YGAYbqJ8RbsJhoy+TnQPrlvQnTd3RnXmNCnDAQMsfbEDLHSONccHXtiqtAoECgQKBA4KogYJyGjcK0CmiOtzUQZG5Lh2scEMhyoyxX6oBABw1tLAi1ULAu/V0jcPCahRYRXVh5Yp1qZGvrl60tWpzy+iWnLl953C0/vcfTA3OMCFA4eaieykpPXflhad2nDrciI2IuP+K8+Rk5K2bUTM9yvVsINL9L1KTxXap+cTSe9x/H+b1bNNKRVfWWHcqh8oui4pk7oo0l65c95REePiFiHUmrZ44Q8c7cRauM3ndHtFD1PBai73sjSv0+QghcKuFFy1O14cqj22UgOd1ALGIbrd+yWrSKxaIfn6zCXrqTvnu0nl4iTvQY0YhNNoxLa2y0oKjIFr2URolTxE/ktSn0jaGmGjs9t3GmMvt9KXn64PO7R+r1rZdi5VHH8Ry7B68m1OYVk6F7Q/RQ+4mgTaTyEieMmUuEg0ea+rd3dmzDeHMG/S7oa/n6zqiJDZk5aDSKTqFsV/ADTChIRWHqBjpj2HgL8ZhHz8ftm2PouGih72IY7hmJWEnsyoktQd1WyapPXdXuOiv8bJfDJGB5trSfdmxBTtCPZxMaErdRvxUXmMSP3Dw65tLR+yIdLzv6gZhuo59FiKpEzUaxaz4+Jnnew/TD41UTy/vmpxWz/rWyPYxI3jXThSTOMInmqO+0xQmlPiL+VKtJvufPmh+5aiALpK0vheYYdYeq4srhoU7S8wBPL39z4bBRQfpmzuKzjXGrcaUfLSORiFOexixUEGkHjFzEqh1OR8VBJ7PuI8Dib+5LxxG6XxA5msVq2Ge3pyDEoMR6dpIxAzHSRI6cWGN1y01RfXrHste93AsECgQKBAoELgICgZNo3texjy7T78d6J07eDhya4mze2t7ht88cf2gHSmPgNy0Ol4zoYgcPg4bPaO6PNcTy5Na1quRAexxZryg7FhyrSdTJFqjsQ345j3xqZ/7ml9eOOmLlOqejN+PILioS7+X5XkMg963m12y6m1/T3bo3Yphn9pO3Ol3O0w+v69b34i6W1IJx1S91gnBFcSfFibgf9NMA+EG3oFT+44LAFRBeMsCYOLTQuQk1lOhCZOnA6bK8voU4xDYbxxUTt/geRbLLEBlerGiTeYjZW4noiOCCST82jVIYO8gE5aIRTnwR0UWbJ81LNjdxqwg+VnzMMF6XmGfcV0t1d3gdy/0jnvvrzXOwRT4yU2B2iyAkp+csopC3XUQXKdzaElcDhBdx+2jDOIe1oxm4Fx7cwEILJoOvz4ygp6LF5nGcdgrJGDDiC4dBpoNDIhbiirmBmIn0WcgktbgBZFXJN60OB8Fb87VVy26NSsr5VlfAqsrwrXL5FBNFz4lnEwaBBIrIIMKKxO6kXFqnfxITknsHzpcNiA2SfZdJTfUzESYOQEAfP10zBdQ/YgFLcTb2Rol3LXX2EcejB7R1Woj+o3H6CjSIBuGlMW7Ij6wao4GaanBVV6DLRKTcflf41r3DKml5yuXt9PTdSHJApe7D4hRT7xWi8RoCpIgt4OgQlEDKIVBpDIUoEs1LX4pwia6kOzdGGFOj6f6d6TSH3qNb6FBqoR9AJtvF+TLK+FHO0VDNHb3Ii4LLVSBQIFAgUCBwMRCINc5zsxnYCC9az7Y4UBCxRaahw4S01p4xOHnHEB+VMnXT9cIhnAjoSi3CS1xaC30FweFZE9TA7Wwtidj+7K5PtRxkh5VgOUTOka9lFQtdM8uq3KbnWd3NjL0OnsOFZH7WypT4bwsB24iQmM4S/az3y0bW/mWb31rpIjQ/c+LIQ9lr3yKMrLdrV/n15tGTZR4wvb7lvUCgQOCSIXABhJeYCnyUxyQTG5l4VyxtKLe2OM1gY/jkxTJWivaw2tPBgtFO+gYLLiZahBUX9lOIXQwap4gUbZruFhObYdGVOBF/rjQ3yhaU5KYONuGpLs2w3jfFb17HzVDakDXT+lYx2tTMQe5oc1dujfRqv6q3y6ZRG0dxNwhhGMLqEXtBE5mQmejpcfRWjA+n64gTtdFRIc6XCZ469dFzXG6UzE2hF0acQFIeqjKtXCO+RM00Leerd3auQyLGGZ4Z1mdIUaLWENBy2bz0jXydhshgfcStHUnZtMaKENNNxo0sfq0jgiTLRS8wEy2umBeII0kXksSRpJhwHa4o6XLZOxyESCF9Q05WNDODEj+jw4U4UVMpYVUHqqb6ddewWduzuqse2DdhlOO1JArRfZTl/pzfFM8JMNIBI4KLuMccXtd08gkhSpa+pDdpCkLmFITML+6MpRsQXj67Pm4ifFLCOwIBZmZ6wuaUqpFRxboyfetaPAsECgQKBAoEzgaBmFZ9vmVtMaTK15hDDqKk20zrl6zwSSRWhw+6xNncgp1R+rt02GRGE1i/ZOJZl61TLJx+2BSTOAHZ2fCx+Mfduut3XKza/9h8jw2o077ZVdUmR72QTN9cbIlxaRBw3O7N2Xd96a6XnDZ3jd4e0ptznZSY9QvRul56k5X3AoECgSuCwJUQXrQwatMkQsMmVlckv/vj42XEZ3bTU6yvLG/soTgWJaKI4Cwu78EVwgSB1RVdoRRTeTTdTuSIKei4GUkTjcJiWxfxlXOEhVvPPpfNVZHOXsgy3pvxRXmu/XNMSg+XkoEqECVMyclkrjgHrl2DAMOmeBjdFe2WTOZCfEGUROaip8ewgISuirnpNlwuiB/xPt5uGSIyxun+sEwsQo2qSrl0wku0sSqxCYTiPiUEHO1sLoX00dw/9BRnlAgoIrrIPLRMrHew8rWCaWgRXH7C2pf0wPz8AjPr6EaS7iPF3zd9Low39Xm4RGS5SKU4R1RjPOV69n7FICrGV/ZoeuuNmTPoekScSO3v8aao8mm+h1/lS4QgvNRhcvmwM+KLcgBG4oIRq7r8xJoTnHBjjKFx9L48vIvJdiw/3RfhBSKlrCC10BewMDdlYoueKfeotjyUVfNdfuUqECgQKBAoEDgbBPJcatMzc6qeMbUqyNY5cEI7UIDoIqt+8hse8sMk6TMTwaUN8cX0uggHZDkL3RXKwy7SOPElPN7umat7zPr0dnmePVXVKpI2oRXus+dYUry/ELA+F5/80j7xpRfw/gK41KxA4D2EwMUSXmxl7Z49tJDKWyJG2kCurHbSGnpdfv/ti7SMxaKfn22ixwLlsa8kOoBo0RaWf1CgNgZXB1JFaQwZgthsKR/9dMUGUe4gq1jJLM5+xdMT5GQ5TA+FH/U96k88ETMsdk7TL5lyzGV73YiU4/lDhBmPY09yFLeLNtgdmaE2BATTudCbWlhtmWLzuDCe0E+Broob4+nzOzPps1tYbUH8aBqFuq5kddA4f5SPXVZkLq2CQ65EjlI/IlHtU1xXAwEnunTDX2/RbxRuIj98Osm5y6zyOnqQxC32/JVziP3uh2UzNf2771YQ18Pq0bCU7KKQeWQEHS7oceH7qwuIw0WO4KTRWFKPUHn206269OL9pb6HXzNi+FUJT3R4XoriJJX63ZN5ztnXsvb8mZyqfOWy8UWQxVf73JWtH6EHCfFFmaO+du0AeKT0xS3MrKM/6cENmW9vp8/vz0OAaaW716dd3Ag4KU/nnvOiYl6JsVxVoDgKBAoECgQKBE4HgWrq1npjM3ZeWXz+1tSutUhr2wZElzWILqubyJUTty3DAoiFypKRdJONggga4UUlk1WT8OI5e5W03p3nsjWGDKLqyq7pPk/ep08bJSpFNChqdvpcSswPAwLVl43PHp/8Qqt/qZlfaE1LZgUCnwIELoDw0gCTVtOe1U9KQ7Xp20VDrMQmXq1sml6X3/15Mb1c20o/P9nkxP51wpiRiRh1dqQ8Fgs9iNqMsGmUMlAtf8YlYqfbKo/FPOYS3LEBszmrq/wqUrWANmp7aqeyVE4qx7atdbY5D58tvXziRuUsnm90FVGbOeUlxaauIFiiIIiTQHTZMXGSfZCKQ4gvEptIaab9Gn0vo+nerbH08LNZdFZMpQWUhUphqPTdDJtuF20eczV4RNll41jD5H1zeY9ofLRmBa3vEIO+riu4XlbWOiCoW+nZ4hYKp7fSH35cRQH1Vvrj91gBW9uFUDkGgjqcJrGEJQ6oYcaO+kXoQhKBT1mLkKM/lW6/rmroxctt3lUPj+0ud3uM8DnpWcf0kVq/eyqvQvatq6DO3J1tNX5UA7VPkRlDtE3s6ctwAO0x34DOJ/B1dLsMpOvoTLq7gNgeZte/fHADsb3R9OD2tBF2pazbLSNl1vWogleou+zyViBQIFAgUCBwOghoLrW5PFabWFnwJ0y6uaSbTArjpb9sY1P6zHaY0wfSBGLUY1ilGxlBHxfcL3rqoMl5N1m7IJjHymBT9QXN1115nq6VlxTr/anJJTWwZFsgUCBQIPDJQuDCCS/VksGmSPum7Z1ds+CzsrqJiMQOokUo0+X0/l/++gpRo730DNGi3T1ZKtFiKgWaKM9lYZUFFumiYG+kLZYttCIquFhB/l5RmL36pi47ie+by+O/rGEFXcGRXfc6zltDhKiZwPeF3bGb4VZpPJoiSEJGjACTI0pcQhtH/tMudrONcAJCMjjwGjGiA6wcoSx0rpW+uD2R7t2cSNdnEZ/g126PpikIMKOcDmmjrcvr460ohJcM4Pfq4d8mf6EGJkoloxvxEdUHRHg5gBNKpqWldPDp0mpafLUJh9g6el22zerXyuZ+evxyD50vr035tJ0QynoPY48hZJfTJjQWKVX/VrjKUD88CpzwsmhHg6tqRlBvvDel93R1LKWPt8jTfXLOjQKiT/v4qVOKRqV5QcRLKbAWsVdjrt2GYw4CppRXz02NpF/cm0J59ShEzCl0vjB+4H4ZgXg5NtY2WEQ9DFaqVy+A6iLrqhZXgUCBQIFAgUAFgZgmYx6NAPnbZA+RXJb7NFdvcSDXQVRWesukSH5wYDBNSo/dBAdv4nQBARzOlvg0HQsTFHFGl5UTk7b5nPZmNemJ7DjmW2XXk9PFvAYULya3ksv7CoF+fTHq+v70xqhReRYIFAicHwKXSngRp8tmRzopdtOLpRXMRu+yYURUAoWgv/tuPa2hUG1pDUWZ6HQZYMEVJ4is+Ohsw0+0sdwCB4iv1r5pFEGluhrOiKMwLdCKd9ZpK7LoQjkLAABAAElEQVQ7ku6Ih9fAN7FWonscc/clvQ70+sU7oaYA1bl6xAFjXEKHUjK3b2aDr0+PpM9vSUloK92eaxvhZQpxo3l0VUjRrjaPuqz+uVJHNo1RXHm+Qwh4D/O7dVKri96rPb6+Hz+Z19w3LighpuhEwtz606X19P0TrBe93E4rcIl1dg7TyhbmxneloNlNibeGhuu8rEeo86oHeqm5kxwLg+jqOfaReBEeAb3xPLwqzcqOuPUzcvF48VaH93cFabXu24KVtLv4U8RLiWjpNFV+rxlDIty2Rw7T3OQQhBd0JaGw+isIMHOYY5+bQecLhMvZ2QlgRi1I75eQ+z61Ungf70hVngUCBQIFAp8qBKrpM8/HDofw9YlTbyKUi9NlD51kOpjbRjn8ForjO7ilBF5EcXE8m4gR7zpYEmeiT8nKAcJLnocvdDpW1heaoUPg9HdVoHm908o0K1LcBQIFAgUCBQIXBIHzE15isYo1g7XCnHZi/9rEitbWO+n7H1+iDHQr/ciJ/TLElt//iLnc7cO0zk8cLpNwdoywwI7L1i056DREfyLe6IIxhDWxXojMN8q0GN03xVQav+wtXk54viFe5GcZ9ys8IqiIOrzpK3+vu2+GJXIkPRz8G+Fpl01jh033DoqG10FIRtk0zqDr5bN5RCbmB9Od+bF0C+Uv1+cm0oM7c8AN7hc2j8F+K+SkKq+uQt3mKrD2Kq7LhoA+xPGAF60l9vk6JRThYBfTXtso011F9n0N2fdvflxJP2Iy+s/fr6RnL7fS4cAo/XsoveaHQBF9ByKlXIwhlRScYRo+zW5wfC1IdIqrN30z7zq5fBUznnWIu+pc+sfol2vkR85VcHYoiJ90Q8m5T6PFRbaCtbQ9uIYO0fkyNTaQfoWy3VkUVn9918fQZ7dn0zgsMbdvTBvcpIzYs2KM8kGC+NIsLr5Tb4vKe4FAgUCBwKcMgWqe1LzPv+ZKrWeaU3XXU8TxA+E44DZmRhqCi6z3yS09gLJcNA7hZQLDASK8DHEgZ/jRR094Ceg5tHz9/JR7U2l7gUCBQIHAxwmBiyO8CD6sHbHhO4B4oFP7xRWsFmH69q8/vOS5nX563jFlut8vcsrBSf0W+tQGESuaHBtikb2WxlhstVjXhBfy1VqkzPPiHW/mpZfjLkt3XOBJ/r0JY1E8KY3Cmum8vu7bnd5IL3gJGRHBRYiFkBQRYfbYNO6g82ULZKSDmIksHU0gMnFjCmW7kwPpzvV2ugnXy635yfTFZ3OYxsXUNPLQA3Yq5PpelI9d3cW6X7OK7lPu7xoCdVcxooshpnC5bGxsob9lywgI3z5aS49ebKZvflpLL5a30sBQiw4zxElgTXjRp3URI4h6jCFlK2JCsxv452/6nKbxdaepXZ4ukxF7Mmnm30zRdEf67lHTkxGvzbxyaPaqcsuUmCH0AejSHHTAaeoqegN2mYNeY35aZto/v4kJ9jZmpm+NppsQLz9n/Oh09d7tGTtpldJGEVsk4mgDMo+jZg2qMq2kcisQKBD4VCFQ5oXuLx/wCGKLps9wW0w4miViJD1c0umyswMOiIiR/BRPXC2yaNQCF5xsO+FFc3LgR3VpZJwn4gudj2lAHNZdaL51xU9wBfQU5epLP6FiJahAoECgQKBA4AIhcDGEl1whLZ46YRaXys7ODkQVzEUvbaYXnND//js4XlZ305MXMov7Oq3tDvrpB0cggyzILThdjAgx5JwgoVzUzktsTfKFKZak5jLVb29mVYrIpwKYIkeuvQnDXxk1452UcSMPNoaNN5CMSOcElzhZV/kSlYDuYgSYvdcHcDFgXhFWhtbQa36HEF1G0o250XQXfS9f359JN+GAmUITrxSrSimxOB7EQRSLd947RoHhXb8X1xVAQB+82QN6irRg7xSy8iCC5Sv0uSy92mD8bKSXECwfoRvp2cvd9NPiNlxkWL/CgpFOA6UgVjLvnnuTDJJ9qr7mZVpRNctIXRECPKrSdfdX1T1qb+nrVOqxjbc3OSOXN8VrhkeJ3SV5Tj5+YkCJ60uX5iGNsS24hkTEYhiB0CeQ+UPmmWso2h2Ea6ydfvU5hEtMtH91bxoz08OphcieTlxHGEt+eSlVyQymt2lBzqw8CgQKBAoEzg+Bekr0vDQpNafhfpNUb5qoBXEjKLzO+qyKPuqwrIL0r1VF3Ii7u3BCs6Ztw+1iel2gdEuXS4t5dyhbMhqD8CIDAm6E4OjqWeNMZ63t+xY/oB/A6/fx3rc6l/oUCBQIFAgUCLwtBC6U8KKT5r29PdvsrCMeIcLLo2cbJhrxz399yQYSZbqvpGyXE+kkXRTayGhxZWOUN03X0MlgS1C+HU940QKVFyt7+Hvtm116dF11DPfufZdvbyIV0C+e53D8PefDw5EPz0NcCNWF07aL2ct0VFAc+AmEKW0gOQlCVwVv5HHApnE4LcyMpPs3x9Ovv5hNt6+PYWJ6NLWxZqPNo2SkdWqkjacuy1YF5Hf3sKByuzIICPiNb95brgX7B9qD5XoHhYMv0Ofy9Pk6ynRX0suV7fQUJdQvVg9MGfXq5mvT6yPLRbL6oJNC/95OcPDsvbz47FGkFWVBjZAjTo3LhidvUXtLH5nxrIgS5qdYzXQRMfwjl/A/zbPOr3ZF/Ty/qq5NKiORd6Fgak7SD4PaEHilsPowTY7DOTbfSv/mq1mshLXSr77AypEsaaCwWkQXie755flXbSyEl9N8sBKnQKBA4DIhUE+E/UvpN80el4a4CuqXpH/mR32rrI86LLLmZwXpt4chhV30u7xCz18H/HADi0YyFd0aRck5OIzEi0ZHWNcggg9xCHeNtU11663fx0V4MTD1aWX4l2eBQIFAgUCBwMcCgYsjvLAyaoPTQXZod09mozfS+uZO+uHRRnr8opP+/l/ZQKLbZXFtD9gNmFlkWS4all4GTuBfSxcDISI42NW70nZ5V1stfCNB06nE2f+YfDy7Rjxb2ht5eYR8P0s8JVE+jYJxNt6qqlWl+a6ZVBBZ2NyJEGXoBhwNknve5uR+Y3M3bcJFNIsunLmp4fTwTjv99usps3S0gJWjqfE2+l7aZuVICkONuEMBVm6z8KY7t648rhYCPb2Dwl2XkbrBtsyucxL489MV9CItI1r0EmW6iBxtpYTEXlruDKTtvQGIAxo7iObJzCaEteAQg8Gj57LO1O1nfSD3vvzwnhI1qzxzOkd+9dIb0s/HE6mQo7HrMHf1v/em87xEPgmX0olxp1/MoL9U1i8A7C7jaAVratKfcwgB5s78SPr3v5xOt+Ae++3XsxBjZCVsHJEkt3ak/POwdIeILgY3uyn4kq9my7zV/Qu8qvr0L734FggUCFwRBPKUoIfmIp+fYp7I81O8vqFKzWjVDFI5momttKbHse7mfBmEEeM+ZJZWmBkOQIRa+utW1+GIFl4D4UXElnYL8SI4d2XBCBqMc++ytvnhXHeRVs2Y5LuDPsC3+BJ9gf8BtqdUuUCgQKBAoEDgJAhcGOFFy4fY+qVIV5rqny6upTWUW/7wZD09YeP4L99smiWWlQ6cG4cy79p2tn44NLR9ei05Xz1NJ4XxuVi9Y7OjsFiaapeixMIlN8hHtfoTEgkaKRWr3mT6m9+ryLw282zGkfst4uUkVcqcvR7mpzrjEJIiRMMILzyvwf4j+ecdCFmyDLXJhlxKQqcnBtO966PpV/fH0v1bk1hnacEFM4allrE0xgZSpqZJbc2w/O2W29F0Z6/yuGwIVF+6b0GHsDntw50hEaPO1k7aQPnRj+h0+fbHV+mvP62k5ytbaWNnAL0/A6mzN4g4H6eCIKhwZadRiC9Ix5iImsaIOKbs0nemWCmJPXKZl+rElR/+ontOWD3dL3I5Ev1oBnVWPXnUhUVujahHnM2SmnVSxDqsMdwtBzXXc2ccOQUTYgsnrcB3dWPbkP/XyB7dnB1Of/MQJdWYmf7NF5iXhmvsOnqTRLy8Pjtu41ClWF4U4sp2VYTnboVd6i1Kr9taF3dVdahLLK4CgQKBs0EgRvDZUp0QmwxjNjCUwSennEAvEdo/D581/K6Y+vlbM77jIIGLHBerr3+jeK1FcSkviZ9Lga4U6W6j20WHcju8b6HHrt0ahNtlhLl3xDh2RxAHlQit5m9N4XEFMcfeawBE8Af6DDg1GvqBtqRUu0CgQKBAoEDgzRA4N+GlOmlnIZSYxEs4XbbYOH7Haf3LNSnTXUfBLu/PWWQ56ehgxQiTLGitHzFluoPSZMl/5OOcGrEYCQlQI+pFKULqph31qcNOcinP06Rtxmu6e/NuhmV376Y3F6fQky5xAKnh4n45AGGRMrqt7X1TRgdNBSQFjqHpgXRvYTjduzEO0QVlu9cn7ScrR1OTLd90Ajcrq1Fgb5VOqkcJuxwINHFGM33MN97aBhHdQeYdEb01CATfwCn215/XILysMX52EJTBghHjRiJ6smIEF7YhpUOIzqifKE9dFceYv3JXgDpAjiD/nld5+XVsQEQ4Y7yeZNWryjnu6lPfKmqki7Z4fb3t9HWT2SMyFFeFXDNT9LzyLitHGxAuNc+8PthPMxAvH9waxmLYUPrybhuiZSvdvTXNWBpHf9K0KavOo0cZ2nhUNbrHT9RHIRd9RRvflO9l1uFNZZfwAoECga651VfcarbtHZ0xqnv9TwNFETB0uahy5NSbsvY3QkVOo/I8RC4vvY7peWhu0y/Kqec6j99bkudYcx83VYdFHnYYQB2Ex4jIIjEjmZKWmJHwxT30vbQy4WUC4veIuKD5DQW3S5+izauu3NFqfVA+R74CtQ+/Po3/oNpWKlsgUCBQIFAg0AuBCyO8aGGVmMTi0hrcGTvpj9+iTBfrK4+wYvRqA90uq9ew1nMNvQssKiyabVOmy2m9GF64DD8gKJYc94iNTr0AVeGeTCkr1+U7VI/jy6tDs6sXOchJ69YcV2MiklanPdpIi+tF+irEETHCjlusuDAMcVqf0l0IL3Poqfj8zky6d2cSRbtjaRZCTFhoURX0y/iXuY8rtfhfMgT4lrkLVN9BhBd933U4xTqIkq1CrFxG/v3bR+vpzz/z+wkluyilHhpy/T1DQyOmPFnorvqREelU7czeFfnXLZGPYuYQOfUeHUKv1dWIV/n1c5w2Xr+08rNKHBN4tAV1xEjXHSea0nUiSqKsNsqaCojhGNq38XTIC/TJdGMGa2oQMT+DgDkP19jDe/MQLyfM2tEQbESRnw1jbtpMmLuqUNSn8rhAR3cbj8/4MutwfKklpECgQCAg0ByrR8djMzRSOLniaNwItycJm5wjtZ/H8vmJGP0KyJGNeBzFVPHco3olbsxrenbnp1iRQR1P2Vd1qzKq49lBmjLjXxyG0u0isSJxuezu7Bv3iziktSxJn0sbHVtTk1rjJDrrBgIseZ2lirTLvKLC4flRPJuwbro/isaVRhQIFAgUCBQIAIELIbxoQyKujG1O7B89W8b87W76L394mhYhvDx+sZXWtxCXORxFWewAJ/c6loaVlIe5WEC1xMQJia3U9mnkG2/16uuLff1uK7fFPOtNeXgZJ6d8czyvTR3PcjVPD6nyP6Y4h0AVK1fLT/CVRGy6EsQSEUZWjvQburaf2oO76fYChJbp0fQLLLR89QBloZzY38BvAP0w0nERhK3IPZCqjxJviUa+j0++nfp4dAGHv04CX2P6+HVaWl5Hj892WnzZgcNlC46XTvrjj5vpz49lUloiZqMQC4bT9NiwiRmZrhLaKbPr0q3k+dJnerpcBYrKPzuiI1QRrsaheloNdAtgHCn6xMBG7Dqe58s7jsh2IHO8KIGay+GqOTSORgYO0I9zgJWjlGbHDxEzaqfffHUT4uVU+tXDG37iaqzuWWQpl+rEF5XLpYKax7yNDYqFn+sWrajbGLNh3UIVkOtyrrJK4gKBAoG3h4BNBMcmj5GsCDGa30h4yYm08tdjvE7dPQc0i444UZqejRqYM88Z+WExcR9dEoLQTG1J1722xJrj2ZsoppzKn5/Pk6RhDhVnyy4iRjpUkAlp6azTJVGiIVk0kkJzdLyMy5IRB0sivCgsYKRym3WzandXxvL7uG4CZOMDfVyNK60pECgQKBD4ZCFwbsLLAVwYJr+7v49+CnRToBR0Bd0u//jHF+nFK6yxLG1zko91kWujmegCuYVFUxJGumJpqQkqtU8dHn5a03sXJL1f1hV51+X3K+lIKB6eMkJyPrXSma5sTiS8kFSpbXONw+NiZvpwHxjupdtz7TTLSdEvvhDhBfPSvN9cmIDgItPSMjesk/tcn1wNFf7R4y1dEH4PXvQdwR7jEzj8D43bRYjp88XVtLKxlV4sdlCm20k/wCn2zZPt9O3TXfxfYwoZxBRWp6kG4UUEBHGaNZHSakD1Njm6YkToStQb+WLfo82Rq1VFt96AiPDWz+jonkGIGulNzRXXi/iE9BUGmY2GGT8jEGcmILzcmIXw8vVCenB7Kv36y+tsBmQdzPUMSN+A59Fd4fo0Wf4VgC3u+W/dZR2f30WXe3xJJaRAoEDgMiDgY13rQ1ziZjHihXkcP8brFJFSz+Pj+5ybU1FGV8z8Utcjcu+KZciDfCJUk6vc9btqoJmWWJQh3S4dRKWXV9D/B9FlG44XGQDQ/Do6gjJdsyQ3hMgRFvpIol8lNpprqPKUa3U1nJXfB+mooJZrf1UNU7lXVdYH+WFKpQsECgQKBC4FAucmvGwjq6tT+42NTtpExEgiEssbu+kf/vAS87e76QX6XVhnOaV3EYkBVlRtOmsco7nwvG8LQXfd3li7ngjN1DVWcvx37EmekwRyRCiAE9vuAb999FTsIzpxY3YkTY8Pp189mEhf3Z+CAwZdFdfHUbDbwkLAsJ0mDSI2oboYzK1SrhfEatJbaFQvKn9ceMQrz1NDoEZoffOvdykb3OUU8PtHi3C7bKQnSzK/3sFstExH83t1gG6SwzRmJjYHeDo79gFUBKWPcWSfy7tIVZ/4hOGhTyl0uOuKDLo8L/alXz0utoTIrdm2XGoAhiiad3TJTLuLeO25iemB1+nGXCv99hczEF4mULY7a6bZR828KWZOpViJqwZV3aLI8+KR2LoMK7zvrdnevhHec0+18UNvw3sO4lK9DwACPtbr9UFV1rjIszUHNorh4cz5ENvFASvxUud2dMKHR4q0Sl1f1SiDQ09uYRVmqtmQMec+UYCLrooXOdzmsvfqVk967qX5lML0U92ED+owThWQsYQ9WWaE8LK2sQMRRmJGr+FuATfhUEgiRiJyS9yoNZoV6iphXWErw19VwaoWH4mDtlbXVTZO5V5leVUji6NAoECgQOCThsC5CS+bHSyFyGLI2joWjbbTX7DGsoyo0T/8CaWgEF6WVvZZeNFJImW6cGAMio2U+d4WZkCfUYb8EWIh0LO5IL2rbxR1iHp11+OIb49HpK5SHfGoQiqHZUG82CALx5GfyXPzIn0gIrro5KiDaNf1aWSjxwbTL++Pp6/vTaQ7C+10/yZmcScgvmApoMVPJ/axafRnIbxUAH8HDn0DI5rwlaVoUIqT//Ld8/RsaT09foYVMMyvv9p8zU/moxNxYMfG0sMIbGKjIKtDjCETP7N+kjtV7nv5ESpf+rQuYuSgnPwyx1tVRC6ypwZ96ngWr365RYk5jFcfR7zzr02LRCNlQcpH2gGWjEbTv/16Mn1xayL97ZczbAxGsBDWsjEkS0e6YgwFrEIHjAVe2k1tabaxp22XVu5VZNzbtqsos5RRIPC+QcDHtNYEzVNSnuvrP/7i0sNUncJMrFSiqcxdB4dwGhNcEToIPyRMc1T8zOFTHg12QotxlLB+XMt42CAeEusRd6zmM/lL/MfcqoxS5qe/mI9nrbrqNddN9RJRZQeZzl04oFVf/fZwS0edzEhr3VJ+kyg1b6HnbxyFusMcDA1L5AhLfRRv+KHNecRzYlNPHawiXi6ZxdsH+jQIUvcPvR0fKPhLtQsECgQKBK4YAucmvKytbRor6avlNXRR7KQ//SRrRnvpH/+yAcfLQVpZP2DxvIYC2FHbNOqUQ1ZYZD5aGyDfgHqr8zLPixajq16IVF4sgl6f+r1Zl6PxqtDs8Fx64vVmHUVYOxuBldNPuYQoCbcwuWeehsCgoFimpVfgMJrDOstkeyD98rN2+gU/WTm6f2s8TU9PIELR4rS+xYkS2njJV1k7IiPkJ59qRT1U3eYV9ej1b8Yp7tNBQLBswNERY3FdoJAadrCNrb30x788Sz8jpvcT5tcfPd/EbPS1tLk/kLYPhtPea3T1kB681J7qE242mvFzHOLZKM8rKY+eijRrr0rZFfGagedzR87K5Ui1TpX1m+rUzLVJymUDkfPXU9x28jjk1HcbarAIxAcAco9NzPXpofTvMC/91W1Ejh7OoKR6NE1PQcBkDM1MjdlmoQJRnie6NiSnasfbRAroRUt6398mz/clTbQl6hNtjPfyLBD4CCCgbq55R/MG/5qyNZdEb69mrBzPWkwkrdWGJ2UOkl0OW0Tc0EGXiBnGtYdbHCYiJFtcpWFdiblKZag8zVVmnlkEFwgdQywidhAmQj5El2EWFykUFwFGP4vLe6StZ1L/HqpqXKqnlOfuUZcOeMkWeMkmXJwiwOxgyUiHRSISKS+VMYJ40Qymo3UoNAnhRYQeqmGiR8rTMJ8ATi7kaubaaNFVPpuQ7Gn0VVajlHVOCMR37PcNFdbP/5xFluQFAgUCHyQEzk14ebW8AeFlD90Uq5iS3kp/wQTuq8399M/fbqWXEF3WNpzwMjs5UhFeNAcJSdB0JAQhpqymq17oNWHVMXwCa74H3N9VvMaU2phbj9TwiEfUu+dZxXPCi5CVJvFFyM0up0ibYt1Fp8403C4T7WtsGEfTQ34SMxLHy/W5SZCaNsSXdhqVGSQug7VhZE54Ud4VaBt198h2bzQuv5fH2SGgb5rh699AHF86uXTZd5mQ/t1fXqSfnq6iI2kDrpfNtAOxZff1EEQBmZEGEeZDCfkcyqzi8d2gaR5/5TA9olvFuIr3KrH1i3hrpgi/fs/eeL3vkabXv/f9IuIpj95WqRz5vgb80ivlxEapq9xFN9XK5i6wPQTOB2l+aij95kE7fXG9lX79+QREl7Ypqp7AStj83IQRi2sQeTlXsxlQWd4OtcWvfn4R9qE9m9+st50fWltKfQsE+kAgd3F/cG9Mf+ZkYtHconARYYzgollLHCNwkEhURwrYd8CzjOgi8VQjvDB3QUDeF2EGfGqP+CaGpGdUQ8VRiCmrBZEYEJHDCCzXjNAhtzhORuA+GYT6MTIMEUbEF7hkFebcMDosUz7MospMv7oEOwTYhfBiZqLBSWTVcgVx860t1reO8BXNuIdpVOUgTiTz0bIiNyZLRlhkNMILWQ5RtsoxYOjZdansLo+P5KX6UrTno2zgR/Kd3tSM+I79vqHC+vm/Kc8SXiBQIPAxQuDchJfniEeYNSN0VCytbKa/Pumk1c2D9K+P9tJaBwIBC68uicNogddirv3PYUYOPJQINm/F5KVpqjlRyV2H+STWfFcJui46nuf6xnuzqv0i96vqkXhe93pzx7v/8/BNt5CgfeCmXwcRlQ2ILxNwu7RHB9L9hcH02fWhdGe+BdfLWLp7cwZrR2wgp9smR13n65UJjpcj1SgeFw4BwT6QRhMR0gklG38p1d3o7EKc3EI0byn98HQdjpeN9PPiBgjoCONkhIRDfH+RXegD1Mzw3qqGyji/HNfHIrxK0+2hN0PTj6T3kCqZFXQkEsFvG6/Oudt12vy6U/V/U15qXxBefFbR6as2A+uczkrfi9wzk4jr3R5BWfVw+upWi43BeLp9cxJul4l08+Z0RXiJHK+G6NK/VR+Pr/qTQ/TjaVNpyacMgWqGrByCRuMld3fNRCKwaG0QEd7WBV5M5xdr+84+RBbWhx10oogrclsiqVuI7LD2bxsRBu4XJjIdxOzDASsijR1mwcG3r3w1yemiPK0ZzjHLSsLLAAQW6X0TwUNElhHcLQ5nRrAo1ELfyhiEEZl1bqNPTMr5pX9F4sry07ube/aDIRUh7ksRgLbgblmH4PIK60WLGFZY2UChLlzQnW3Nv+CAE8NpHDxwBi7d6/NjiJ+30hwWGUchxkiMVsQeI7wQO9bLGnZNP5X6oV6NvtDVhNwxuvzKS4FAgUCBQIHAxwaBcxNenjxfBTHYTd//uJRevNxM37JxFMHl2xdsKncOQRacaDDO4monGyzuhnSAbGgJCvzAAVsvSopjlz24CUPpeylChDXdzciWyYnxqvIsmecXuTZz6t4o9MQIbCG8q2LDozsnf4tIerMtcG4O/v7Pw2GozZ6QLXAtRCVg6wUhG2uBLKH/49bsQLrD79bcaPoMwsuDO3NpfnYizc6MgeywgVdmqkZl/laIH6/dxVs01aRcFweB6LqCdSDZO+jn2YF1fA1ul+U1EV5epu/gdvnxWYffpimjNuXIA8PoRZKOHj66fb+oV/Sp3g8Y4flZBWdHJOuK1vRUvCio6d+VoPES8RtelbMZ1utWpCgn3Ho24+ldl/ziOqlOvfE8Ly8FS0bW2X3DINb8DTYK4njZA7YS1/v8+mC6NTOUvrgxnBbgcrl7eyYtMIbu3pq1k98ouVlK1Ko8TwOBgKDi+rfp/ranyaPEKRD4MCDQXF9FZLErTx6GA+Elb4kN7UOIlxjRlkR1MFjQ2dlBJwrrw/q+6QDrQNhY76CklsV/i7VfopIS4XE9KsKjnHiDl+FUVXkUqmnPOF705EVuaBwmUjSMWwdi4xzetBA3GgNPm4QTZRQCzARK+0fxmwB/aEF8kciyCDTDxh0jIonmUhGPrlldO3C4rKxvQXTZSk8Wt9PS8q5ZtVznIE5z/ez0CESXoTQ7O8whkcShR9Ot+bYp1VU5o+Rr0qDcRIBptkHzhE3fDsX3567Pmr/p2St1rsRnL66kKBAoECgQKBB45xA4N+Hlx8evONHYTX/5djE9e7HO5nELMZjX6clqSlv7LMjoqtDfGAurFlPWeFuoYlENfOQoJLSasTD1Lmpaqy7kyvmTV28Rkb0XVcfzmCdUIDADi8KNd0+d0xxJ2sw7So2nbxAVQz8RTER4gV7FKb1O7GHt5aRrdOiQ06KErpfDNDeW0o35kXTv+lh6eH8h3b4+mRbmx1FkJ6ss5KLyLTMhNcq0evWXvh51UHGdDQLxuXX6GF3DLT5ANIPTRcpdVzdAUF910t//eRlusU0jvPwE0joxMpImJAPPT3qRjGBDhvpumTxHZfQxo5TeuuUw+975oyuKRdfN+5enon7mcERazkYKj9J1V+hx5XZFPOYlcj8uj9Pm34zXdEex3X6af6JlIl5qA6MNi7hexkYO081JdFGNX0t3FwYwyY6i3c8W0i3G0IPP5uyEOHItz7eFQHzv7u8S3+Rtcy3pCgTeCAGf8t4Y7bwRrIdHN4/MrLuHZzxlkUiEeNZyDq5ESBEHpIwVbOiHGKTWiNV1OCLhdNlEMe0qhGIduGyiO2V7V9aDOIQBB0ASyYgUZGXrQzxVkqwUZZTL8AdN7JoHpcTWlNryMsxh2MSouF14QlyZaruFoRkMIrQhtEyhwH8Cy24ivoxBhBnFFPQIFhMH4VLRn8rZRqfLZmcPbpet9GKJw4On21jn204/Pxbny65BYoFDoTkI2zd43sd63CyEl7s3x6yMtlk2gphDfUTQcY7CgJWSy8+yeb9uBuS3rdK5Er9toSXdFUKgfOErBHYpqkDgA4HAuQkv3/ywhHLQ3fSHv8gqCyf2IryAGLzqDCKHfA0CgZZMTjOguPiC31xMHVHoDyvDVo5aZ2kkP24dbkSh7Ddfx8Vp5uO5eJ2O5phziIwsIbcKU8g5VQo5Iufj8lMJkZlcOT75GbLGK/gXnC8QXTCFOzRwCOJ0CMLE5hHz0ncwKf2LLxbSnZtT6HpB3AgFdgPXZLWgzrYQXo5+xYv20VczOGdgC/5mkQJMeQ2Cy+bmVlrGEtgihJf//JeV9P2z7fTD4k56vLSTJkFuJ/hNZbObNeEFsgvf3nsEGYbOl0blc29xn+hGVd+Tt2JUAR7P7vKrcm749zrreL0hJ79HmRGrq6bh+VZPJx0dX3fbIgAriwEsRGwR4VKblNco122hBmkWwuV0C+Ll9DWIlhPpy/vzNoa+vD9n+gcEM20IbBy9VS1Lom4IxPfv7RfdscpbgcCHAoHo0bG+er3xxUNhpoMFaov0tkiMSApoNzZ3wKGwUgjHyCa41Bo6UkR0kejOKmEdRIq3iLeG2DZMMBxoIR4J5RjVVHC9WNaOF2QgVWUzrIKAoRFmvzzURIxByiiLGznX7CgEmDbcs5OIA4kDRWtPuzWQJlE0Pjk2agSSyUkRYSDIcCDQFgGGNLJUKR0vqufy6hZ4YCf9gMj5Yw4Q/vpzB47OfeJcS3dutNJNDobuoEfrwe1xCC8t5tcxuGyGjNulhdJdEYQ0vyKQnlvjFbe3j2biVU9otK9uaeWKfnRyrCp6cRQIFAgUCBQIfCAQODfh5Q/fPOdUZif97k/P01NEjX5i87jDIry5PwRhQIQCLR0szlpKcQ5AJOi6el6rMOIeF9QM6LcwNdP1C/cyFNKMWZXc43hTPMJ7C7FsuR2HKER4o6RmFkdq1QgUUmXEF9LqOXjtNbCV4rqD1Ib7ZYHTqVvXR9PffHU9fXZ7EkRnMs2aArthq45VifwCObOsdYtCG2U1qlecbwGBgLHk9wV3nT6aOXCQ5pVVRPLWO+nl6nZ6/mo9/dO3IKvPd9NPL3fS01d7EF2GjOV7GoqAc7xAJCBDI7roGfVRvuFuPJuEiIb38c5mJmTefD0+0VlDmrlWLeiTieKdFB5J6njN2M1SFFNEl2Z+gp7GjjjHlE5cL8PMS2MQX8YgYM5AwLyLgupffj6PmNGkPcVm7yZYtTkgP/6VtrcsvMp1aggIgroKFB0O5f6hQ8B6NLfo2WqP5n4RzsXlq7VAYkUSJzKxIkROl8TVsrEHkQXOFizcbUJ00VOEmU38tiG67HDS0oHLZReiC8wliCcpT7hdVFYuT3O+3M3hVM99CvVxZmsR0YZwiANZBBgRXdBty1NzIEpweR+H80X6VyYgwIzza7EWzXGIM8lEOQU3zKS4X5gXR0m4D54nItIrEV4Wt9L3jzvp5xcdrFyi8w8jC0gRpQe32unWgkShW2Z5cQ4Fu7fhzm1RhsSMROwR4cWmV1UyAzGIR179MleoT5WrQKBAoECgQODDhMC5CS//+MfHRnj5JwgvT15sccqxZacy+4fD6cCYXFnNuXSC4Qu+VtMmdhALaV5lLbbfzEfBR4MsQqRsJDFnM/pxcXrTxPsJxUWUMzxz6Wy4u66e1wiLuvYGC266zJ+bnto4GirFab04Ygav7cP9IussI8hNQ3j5eiHdg/Byh5P7uekxk8s2BXtgNYbTWGZdOJqVkXEzd5f7uSBgCLHBOQOb3IR06/dyeZ2TwE2eyMOjoPr3P26lnxb30uPlvfR85QDRl0G4L0BwjfAiETMnFjjhJYYE+dI3rH/URVide17f3I7ofDlm9dqTkfx7vN6c9xXEaNapqntvW/K7CC/6Nj6G1B7Y8RmjQxAxW0OvEfF6nT6D8PKrL+ZhiZ9Mv344D5v9MBZBZGoVxZTaHXCJptxbVi6iPE4FgfhqBYqnAleJ9N5DwAgf1NJEqauFW/ON62CRUvWd3b20Acej9HutIobzdGUnLa5sYwVS+l0QJYJzZAfqirhIpLT2gKcU6ot7mOSIGPFk6BghR5OQ3CozyusLpRhrvl5oxAknk1gPEkMJRhfmt0MjvohIYop38ZQyXSnbbfMTEebG1HCahQNGBBgpxhVxRmJIUs6iOr5c24XjZTt992gTJfFb6V9/7KBwdw8CDZYX76H0H26X+7fG0PEyxoHQKCKdEF4g3oxAdBlB8YyaEL9mM1Tf7snWfJpRPhK3vtPH2raP5BOVZhQIFAgUCJwDAucmvPy///xzWkFz/f//RxFeOpzW73Aaw0ZmMFtkMRGXfNZiWImhCFQ5Fpd41ohBtMd8FHw0qEodcS/ieXxN3jb3nOMpCS/HlRL4VIDBniBcr0XYOtzjB2L3eg+pk700CyvwzdnR9He/XEif35lKd2+MY6FFp0ojnCa5VQI/QbJkRzftAYTjKlP8zwQByfCrAwfMdzmq3OO3+HItLWKK/eUyLNnoRvrDT4gYLe+DhO9zAiqdIwNOeAHZ1eljRXjh4wdyr3w1EPTJaj+vXvQVfzvFvee7V689GYV/j/cpCrj8KKpT1K9ZWvgp3OqdgSXCZVyuvHiPTcgB3GOv071b4+k3X8+lLxhD/+bhLBuPUTYhbt1jSEfEXIXwEtB722f0ovo7vG1OJV2BwPsAAc33IuTaTMME4cQRiCYoUt+BaiIul+3tHVOovgy34zK6XJ6jhPYlXC8iWkh3CypfjMNFeb0GlzILRWQJDcZzZv46UN5qcH7mEqsJ0FEOfEEeFNY1wiyh/ERwtihwzUJ4MW4T/PAUZ/KQiRFBlBFRRDpYeM5NDqAgdwQ8YyQtwEk7CSfMFEQYmaHWfLq6tgcX5y7KdTlQgHvzu+c7cO+8NsLNV59BeOFQ6B4cLzdQqjsNx8w84kZS5CsCT0V4EVrTs6BZ/eubWv6RXke+1kfaztKsAoECgQKBTxMC5ya8/N//8AN6KnbS3//hBRtI2Exf7dqpzPCI9IpwOpwJL46ICMhaWJqLS6AEGRtofIejPnVgpKp9LsalfE8q92ylRC3PmqOns7vdutP7mwJk5UiWWWRScg8kzQkvN2ZG0r/71bwRXrSBXMCkdKvVMuRmWKdKIFaB13hJllX3ZzlbQ0vsYyAQcFawCGiyZKQTz+eLq/zW08uVLViy19JfML/+FE6XF7BlI7GXxqXoUGzeILQcCNr3Mg4NPn6dJy/xAbu7yNv34ZxfZNubkfx7ijqm5VfvHfXqrbvXmbsF6PRZMcX5VcVkDHGyzPgRAWYQzrEHcLz85uvZ9PDuZPrNwxmUSo6yARk2zjFtEPpuaK6+yaXEAoECgfcAAkEosKmF6UU2G0Uwkb9MPodY0RqKc9cRJXoFp+MSimhXILgsru6lZbhCVrD+IxGiXQgvOrxSXtcgrORpSzkal56aqxlMP81j/qw8zFc3xbfU9TRnYSLKeJpMeiZcPHyaDi2qPQmTPz+doYkIIyW80+h/mcIykYgvCxBcJhA7mp8Ev4B4Io5AmbxexYz04spuegER5ueXu3DtJCe83BlH/xwKdSG+LMAtMybRJQgvMlOtQyGJ1KoCNi2rgrgDrjhzgBzlKhAoECgQKBAoEPgwIXBuwst/+vvv09JqJ/1/v3+ZHqEU9AUcL68PUNKGArYhzOAOsaBq8QdliNU+QyqvrFpd7XJUIL/09YmYzTgX51buR+twNP+zxIvUJ+Xbm5+3srEn7FMv4uifSGaGks2iNvR7YDjTk0NpAcLLf/ilTusn7eRehJcJ2IN1Yq+TKTcD6eVYDcHwqk1owztq/66fvdB7D6vYF0SBNCow4KvvJPl+mWF/+mw1vViBJRuOl++fiuhymF5tHqaVTgKRRcZePzb5fDJDwpWfTHfW8MAVWGrtaXXpeVUN+B31tcjNW86vgvEpkjSTX477lHXvbWOuuzYgdsWgUn/Xn81Ngoosg7iyy9cQMg8gwjyAHf7vvp5JX96ZSH/7cBqT7KOw2rtFjxZKJXXpW1RXdjZ8qqDiKBAoEPi4IWBzPdNMTJfiUJRY0b7mFeb8FXSfrKEodwlC+4q4XF520iIcLisQKdZRmtvBatE2YkYmRoRuvAPLCTEgMhxk3vK/DMOYxyyOSvRZp3fuibpU/uGRs7FXC6zTexQjG0Hk4S0nFs4gLpgWynbbWEAab4v7RQrgB9N1RIYmx9ABg4ySuHIkIrXNb532rOwwu3IAJz0xn99swyUDR67wEcVHfGmcn0Q3xWUjBbxxVU0Mj/IsEPgoIaARV/f7j7KJpVEFAgUCXRA4N+Hlf/9/vuF0YwuOl1dsILHQApupFOpKC/4IJyAyWSikwRGT2ALVk43CdHmINpW+9Iev11Zv7u/vup9msuqXrs6hdp02Xp3idK6oY2/dT0rtaboRj6PptZEX54pO0/bgSd4WCzOEl6mJQVOw++9/MZMesmm8f2si3ZhpY5mgnYbRUyFleNJTIZxKl7VcG9EoMKrswe/FXVW1eubne1GpU1ZC/d5AmuG7jTJFWax4/HwlPX66CocYhJfnG+nnpYP0aiMlRP3T+hZKDpGvb8HaPYYAvhQg+vfSiacTXvwz6bt5ReJ79qvW0d7TjBWQzSMq51fFqBJ7vDp2FaPH8eYYnuDi4iknXVHVeDc/88whjQDNOzplVYjmHG2QNjEZcrAPARP350Z4mTLCy999OWXzWbvd4uR2hA0DR8CkF8wtS56iwcS3ULnlKhAoEPjIIZCnFbXS8BuerjdKhFwOQ5jrd6BEdLBUJILLKhwuT1+Jy4UDKsSMFlfhDoHosg1xYg/lueJy0fwu6VSbW5hT4E+F8AJhQoVojtEjP/1NAXEptFEp8w4/w8LyOhLx89MmLmZBzWPKgYMcPXU3he6NaCPDWFLkQGAUUdgZuF9EgFmA+2UaXS+ycmSrndrA3LrLAdwu7kE09orwcm++hX4YxIsmmEfbcHKyvpklI8oXd7RVo2pbLvSND9VUbSxXgcCHCIHSfz/Er1bqXCBwHgicm/DyH/+vvyCjDMfLH1fSo5ewz6KjQsjHLIvrKKf1IyK8sKIG262mmeZ1OsJLM0W4+y22gWQ04/SWqLDTxot8ruIZdfJ2+d3L1cbQt9uqOSH6B6YivMis5C5IXgfCSwdEb3KCk6ipofQfvoLwgsnGzyG83JxtpampcU7sdco0nAkvdTlGHDCsJ7ezWfhVNP0MZTS/5ntczfhwXS1T3U3GHyH+Hx69TD9DeHmBXNEPzzbTs5XXiUNRTgmvpQ1+IyK8MHZGOGUU4SUuR+y9B8uv+dkMNk0A5UTdXoJa06d+74Zn7e/Z+HvEqXPoHy8X3XicI94bdCT11qnr3SqaaxsB1ErjaADAKkTjS+PILIkcSBRs1zhe/u1X0+mr2xPpt19OwknGZmGslcZQJKmfcqiIXZkCY/NcbnGjqOzzpkdA9Owp35RzCS8QKBC4OAhU4z6yzBQLjeADJmiJ/4rbZQ3FuVtYJ5J56MXFTRMtery0CccLelAQLXq1oXWbNBAoxCFTUT58gXcxHyZ4cZtoVoi53t3cY8pQPSJQ7upShOPmk2aYylZ+xpfsYlKkFF6g34GeytOyQvcL65FEj8ZacDZDhJmV6BFEmAmIMeJyHobzZQTitAgug+Acsn6k360ZOGMQL5qG22VYnLfMvxJfikOf6tmsmsotV4FAgUCBQIFAgcBHAoFzE17+5//0Z8SLOunv/7QK4QXN/CsI9LJCzyHa0iS8uDnFblxBMHSUQv55cc+AdXTBlvvs0/sIhELPiNd0N+NH3JPiRRyli3jNPMJ9FfECKl6XujYiwAhRAW5CxvhpwyiOl00IL5sivIAAzSBu9O+/msqEl/F0a66NAjwU7EJ0abNpHJKOCi7wKXITcuX5mWfcms0Mv/fgGbB4T6t3LIQEY/UrEV5kLvSHR6/SD49X0nMQ8R+erqeljWtGdIELPW3tybQnSCncLuC1IN651biVj96i/YGsev79io9xFSm8Hv1iegzdo4Rcbr/IlV/ErzwajmZY092IUrUkyuoTr2tTEfHIIzubo6WZc7gdAnU6laBBJPZ28+W2q41SZ58n5lsP9tL9G+30W8bQl7fH0t9+LpPsYo+H8DLGc7xlWXuOjXzJM96sjKjAKZ5vm+4UWZcoBQIFAhcIgTioiDldg944Q/AQ59wu3CvbcM8twcK4jnjRq9Wd9JR5fgkul6eIY69tMdfIetEO8w6iOeJnMetCZFTNAz5J2Rov8rC9RiBt0bvNezmeeTTCbXLM76Kp6MoPm/sUHmtGLC8WnUjRLr2L+0aVMu4Xc8qX+hBPusegtaBgdyBNslC1eZGIrMxOz0rxLsTqORT7z82NpSkIL1Pjo3DFDKI4foi5l3Yz/+oX7VD9lHtVT9zlKhAoECgQKBAoEPiYIHBuwsv/9H/+iVP7TvrPf14zHS/LKIvT6ckcHC8tTjW0edQirRMdLehaWJvLayyy7t8L2l7fiN0b723elVdv/v3yacZrunvjNsOa7reLpxzicrTL6yp/wdMIL6Ao4nbZ4yfCywaiRhPITM/C8fLvvpxIX8Dx8sVN53iZnRk3wssYm8ehfNIf+evDXIPVt+tqVqAr4N2/9H6196mqUbfeOsXpYYcT0E1OQL//GcLLo5X0DI6X75+speXOIIj4NSO6bHMCKrl+mfR0wkuGOZm6jpeMiAtlrbDmetPf/YVUI9XGaxb1U5zeOjbjKfz4q87v+DjnCenJP1e0x5cmeUAOPrbA7pb7pkFtDcKLQCiLI6sQXnbQbrkD18tnmD39LWPoy1vt9DcQXuYxyT4xrl8LfUntBkT1TZRno5LU5E11ala2+U3C/yzpI015FggUCFweBDTO+154O6cL3KfMHyK6bHb2MKu8llYlVoQi3adwA79E2eyLpV0I7Oh9QYmu5vkD9LloPYbxw+Z85W8zeTUBGFkCXwqx+c7rEMFd804VTih19Zh+Z4LyfGNm8igWr6tNMY+ROqc08adw70kMSS/XzKYiZqjR+4LKq3GkL9sSHUL/ywSHP9cRb56H4HLr+kS6tTCBCHQLsSSJOktZfDYbrWyC8KJKUCdrl+Uvj3IVCBQIFAgUCBQIfFwQODfh5X/8P/7VxCX+85/X02M02K/AQquFWXK8QXhhbTXERCu5Tk58XbUlNtCAapHvBq9iNi9P4z6eSzP08t0qv7dO/Uo9f7zulkZ+joRps23sxyBJIWq0vg2ylwkv0+h5+W9EeGHT+DnWWW7OcfI0O+FiEiK8gPh0IZF8sA+V8NKEU78v8S78+vVMwVvEx87WTtqAreW7H1+l7x4tG+Hlh8fraXUbixB7g4aM7xjhBUScgYPEEd86t4KnIb1VowKZVq+MSFVgdkRt+vfbbl/l4T7H5eaZ1vF6S7v09yNFx8bk+JJjxvG2NQgv2mTk/ER4WdkMjhcnvPztF+Ppy5stI7wscHI7OTFuupKkLynAre/hY4l6KC/96oe/nOIuqOeqWOyczSlSligFAgUCVwEBjdFYN0UciblYfvsHbr1oA6J6h5/Ei36Gk/ElXC7P4AhewtqjrBYtrR2Y4tmDwwEIGs7pYqI7Et+J+cgaEzOAyvE5OeZ4myfMK2YNb32kiDlIvpWfR2lgL55nnbKZe45cPWgtdVAKiR35D8ILmes3RIi4X1pQj6SbbAIFvAsLI+nO/Bg65qbSXXCQaRFepPePiDJNLT4f1U3E77gqV+WIkPIsECgQKBAoECgQ+DggcG7Cy//wv/2ek51O+odvNtITI7ygj58VegE201FOQEYzx4tOhOSv5VsLOOfN3GOxl6vf5THr0OaKHGGRTmH9czm6pbnIeFF+v+dJdWrGPxqvt6Ueu5vwIqRlB+sB4npZx4zjBqJGYyA90+NwvDwcg9ullT6H6+Xm3Gi6Pj8F4UWiEphvhBNJiJROywKpO7J9bVagWdUzugPSzRaeJ+vIT9U4Tz5nbMaZoquOvXUTnCVutwF/uX7f/PAyffvTMsoWO+nHJ+tpfWcYTgspJOTHKahGhwgvOgmtcFPcPobq/L0c2wacoo6KXUOwdvUmVW84JrQri66X3kwa7yfFa4Y13Y3k5myGNd2Chd6Pv7wl0R4RLRUXGKv/56y2EdlbhfAiAkwHcaN710fS3z5oG8fLv7k/zvgR0WXcRPYktqdL38KfPpbk1t5J3ifXSDG7r6hd+J41faQrzwKBAoHLgYBhLjG4Gfw2F3MTQd3Ei1Cku7q6yTyygxUjuBofr0JokZgRinU30ecCM7AsGCGJxKX5J4sYMdglTmSkiGrghyMwJk9TtYx6OCZV+dRzjiahfNWu8NHTZxu/Rwx/U73cR3f55XCcKi8OzmhyDle9xa3jushGhg5N98sc4s63F9rpPjqy7t2AYxAOGHG9tLB2KatwYcFICs67SsrFKfdyFQgUCBQIFAgUCHxsEDg34eW//19/B+FlM/2XbzbheEFLPwiG1uu5SUyvcvoxChsttJe0r5Wa/1i4m8u7gGrr+BHo9vo2V2WFNd+PJH73HlG93macomYBH0WtESz3DY6XXsLLJmYrx0YHUHY3mP7uYTs9uOGElxuz7XRjftJOnMbRURGElwC68hcS2HVF3bs8z/7Sr+kXlPXZK3NFKXp7pn0/+r8Q9I2NHeT+t9M3cLx8A+FFokY/PN5ATGwIggvmOKEE7OkkFIRehDFol85JkQGpR2/+52lWzvZIFh/WN+pf2yAedbdRfd3jx2mrNlDbWBRZRemldL1sIy5wB8LLb+5DvLzVSr++N55uzI2nGcSNpiYhvkw54cU2H5Z5TXgJQNZ7n+7SI7x+el16Y/VvUZ2quAoECgSuEgI+QjVX+OXzuXS7iNtlG2UtUqS7tLyeXq1gNhpOl28eQXhZ2WWO30bxPfM6nIwiuiiPIQgOmn/8j/mjkW8lPlSVFGUe96wS5wj17FG7jqbtTRUx+qdxLISaEs1jmLlprUYGFM2BEF/A8ER8mRbhhQOfz26I6wXCC3rmFhB3noDrZRJdL2q76Xlp4h31pFljdv0rE1W9hGdAJQrW+3FuFR9hl1CVnGWzBpdXSsm5QKBAoECgQOAqIHBuwst/97/8iykI/cdvOunJq17Ci3O8vD3h5SpAcHllCI+wZTzW8jMUdTbCyyEcL1Kuu5/GM8fLb79sGeHlPmy+NxA1urUwadwuE5nwYviS37hfHeHl8tGUMwD5iqIamzbIqSx79RJepHTx+0cbcFkMpR0IL/sQXUR8wZiREVzEhm4cGrkPnbYrNeOdBPNmvCY4TkrTjHd+t0o6rhanzb1/bfsTXmoOIiPAkFR6c5zjJQgvB+kOrPJ/c7/lhBc4XpzwIqILv4kx+zZGeFEV+bbaLNl4z02p9xDNtkVb46nEXvfeWAopV4FAgcC7hkBzZNZ1ERFdP3Hymk4XTEZLafqTRRcvWny1nb59upFere2i42WXgydIEsZe55wu2PQxa3UxTxjnqREwfN4P/VWaHpzYobLruvis0fSp6xZhtc+bXXXOlK+XyKRye4xcRYuQfapaWdTDAw7aXqMLC3GjqeF0a3403YX4cmN2LN25OZlmMLowC+F6EKRwULptsIIUZUnfi+BghCfPvAp7cwsuKkZvwdaqnHnTLa/euBdVh+58ekvtDi1vBQIFAgUCBQIfEgQuhPAijhcRXp4eIbycxPEiMNXkhVjCAnhCNmLjFH6X8RR+0Vv2RZVTI1VvyLG3AlSqhozqFxHcV3DxEyN0vEjUSCIsiBpJz8s4HC9TmHf8tw9FeBlN90R4wZz0zetTcLxgaWC8wfGSqyVkRyy/VTHyD8TrDVU/TXDUPuJeYNaR5Xv1VHt72ygxIyHq6+vbaX2D09CfnOPlKWJ64njp7MHxcjiE/PyAEV/0jfUb0qloZEbGvbDs1/DeOJH8NHEjzklpIs7FPvtB7TQlHF/TCGmOH0FQ8FRYTXgRx4uLGonjpYPIwF0RXu454SVEjabZMExDeNFP6cPih5/4+gluwD7K9i8Wb83QpruO1e17mvaXOAUCBQKXB4HGiDSqg88mxukii4L8RHBZg5NxDRGjH56upcVXW+n58nb6+cV2WmVdfrWO6SLT5yJCgwguPJlBamYPJ8o35ymt9NWs0VwAckOrsD4Nj7A+Qcd6RSsVoea+0VtdKwvTzS5Wp1yQ0rK0WcxDlO9izy3adAAAQABJREFUHBuul2uIPA/C+TzIoc8oOEg7fXF3Ol3neR3Ol1GsGw1zqjAyjGbe3L5opj2V+ds0JNfu7R8BiShc7023ctZ7bzz5X87VrMHllFByLRAoECgQKBC4Kgicm/Dy3/7HfzFTif/0rRNe1rKo0ayJGp3E8aImNpALa3G9oB2/2PQugseB6k3xVLaX4kuo34/mFnWK59EY7nM0PBCJ+pRIMY/Gq9bwyJooihVXVTMydKRI8tTa6Em5rut4McILSJ5EjWRO+rdfjGISdzQ9uDUBsiPCyzRWBUbNIousCjTRKSO81FigF9usQFTkLZ5V3Rtp3y5r5RQpI9d4b2R+amczv1Mn6onYPw997/j2SiD4yk/ElyC8/BVRo28hvsjahThetuB42T0chPCin8S+QGBpnhQuRl7ej9RmlXv85aF1vJOgJPJOv/yOpsnx+kfPlYlUJ9ev+ztGhkoT6cMv3o/LL8L7wyJCPXWdh+CpMCM28tT3ccKLrI0gMoCoUZPw8ut7Y6bjZXraiS6zEF50abPhRBd971xa16Mu0xL0vXmC3pg5GxWQU9U+fbMpngUCBQKXBIE8BnnYXM6Y3Gcu30duaAfx3tWN3bS81kkr6HP5FvGipxBeXsDx8nz9NeJHHIpsO0fp0CAWfUR4YSjHaLZ1WPlW41xNqOd8b5Bixzzg4d3vHuvovTtdlHlSvGYpHs8q15OEnBqZKY2tb3iK8IIKXtP7gioXcA5xvgymm3C+/PKLuXQLkaPbiG1K0a4sHLVaI4bHMBsrdc43t18ZN8rpqcQlvp62YMWL6/IqetraRE3Ks0CgQKBAoEDg/YXAFRBeQseL636pdbwIKCywGTa+hOnNXScvNnW8nPyYx8nx6rJPjndM5sd6R749jetCnboSe5NrLzJwyNSwsEBtwnNcIW8DsOqGVSMRXvQz5bpwvPwdhJcH1yG+oNzu+swoZh0hvMDxInO4siwgBDIuubt0vFQNiBgfw7Nub3drLqKxyrvOR6CNfbjKMmQdz5rjZSv99Ydl43p5huLF75+I8DLYILzI6oM4M+B40TePrMn3uFaonLh640TyCG8+z0x4aSbu61ZpvTXoF7E3ntL0q2lvvD55VcmyIxcf3t2bmvrb6ORZl75PcLzscIJdEV4QNXqIgupfo+tFynWl38U4XniKTmnf2dJ7nvpO0XIvO95Uinzivdet8HIVCBQIvG8Q+K/svXmMbFtVP766u7q6qqvnvvM88QbeAOKXKPxhwCEhJKAxEogxBBUxEqc4xQhoQoIDahDRP4wgAv5EQ2JMSCSiP5X8fl/0C4IaBR6P9959d7493J6nqurh+/msddY5u05V9dz33WGfe7vOPnuvvfbe65w9rb0Gjg3Wl61mfDa7LsZ0WYZ67yyYLnQZPQWDus9enYEXoxUZh22X2cUNGEynAXwaSqe0i0kA66iDoYCjgTNewnZreTro+3gRpu4+HLajEUs2HoUlMqx5NDJLUYokyOwGGRfQhfFkvDBEOnV1QvIFOrNDZLyMFOWxc8NyEp6OTkD1iF6OaGS3D3euPXCMBAYMyvD5LivOqpqU11jv/X7yQrdTmMOyDtuB3++6RnyRApECkQKRAvcjBfbOeIGNl1sTS6ISL9N1iNwag8WM61LihZb7aVw3Y7xkhGrNePEpLZvOGPJY5s4/O8Z8fP55p3AOv7M7S9XLA0nVwxY4SHbnoiVZ5CFfe8aLpZCmneC+NDBeoGrkXo1SxotKvJDx4qpGvWZcN6EnsWm56e4eNfJ6Z5V7AEKbUX+vDSbuDAdeY8YsQQrpyz8yXuaoakTjupch8QKpF5V4SRkvnZB2obpRF9+KoqRXo3ANvlkr/CW1gslqx1AGYV9c9uw4WLjWIY1ozJdGa7vD/K3g8ulemzDeMebz558dLruTPoYpwZugTZ6QlisHCczTpYS177+lxAsZL8d6YFyXjJf+gPFiNl68Bt5/snroq0NyrlzP0EAzr2WaGAORApEC9wAF2K/DS58RZ66jV2UR8y1dR49NL8oYJBcnoV70jatzkHahcd1V2Ooyuy4b8FJHpku3/iXjOcYALonwXy8dBZJTFRsRfFxorENYn92Gw7WFFc6yUI7998ELc1YST6CgGh7keGe15BhKEM5z1qY6bL2sr9vfQE8HDOuC8XK2X04d7lX150PDkHqBBO7oEO29cI1oErxqK4ucKS+EZftlhfnTAdy90O0URNjtwB1ANSPKSIFIgUiBSIH7lgL7y3iZqgdejbqlpO6kYUYO81N7xotNXk2bowaSEsYnxYYEPIRpYTiEszIyHK3gHIb52pXFtAOE84VeWASL1Msi+cuFkzJesECpwaMC3UmnNl5KXTIA3epXJqpG5xJVoyOHjPHS11eGXjUlKtBKlNeS8cLElnVgws6ue3d5crA1C7GTzvxTiRfYd1EbL5R4IeMFNl5M4oUejbpg34V/JnbNhS2ZA50u5gTS+yeSvYXwRdl3a795iGY4QuRhs/6UZ7xk+BpDxNuMxWA8LUtnyBb+jsXr5TCex9N5dxiGHY7h3EWC8UpAPJc9ZvkcrFniZU29GtGdNFWNnqRx3aNgvFDiZSRhvEDdaAjupInD3kWI10u0athvlh7SNoRoFWauVthawca4SIFIgYOjgPdzHcfBPOdhxwrEWBZg02UefzfGF+QmvNNNwJvRc9d5X5XpBRiZVYZCF6QWsQbCXM1Zl+NGOnagk7Ofu60Ue2ocHT3O7u3aGI4U4XiThzc4+w3gguzWVrLcmc4Eg3OQ5vnH1hCEdYy8k/FSh9rmKuxllYodauvlwvGSnIDK0akjFTl+dECG+0tgaPdhPVJQ1WmqYrFIt72VIvRmeCX8Od4jBSIFIgUiBSIF7jMK7J3xojZeEomXbTFeSCGfornIsNm0mfHCeIc7CKpuF38IF4bzdQrTwvBu4cJ8GT7Si9tyX8CR8VKn7RCqGlXXYOMFRu2gakQbL1Q1IuOFxnUPh6pGYLxwEZleCKY2KjySRe7hcuxZzW0ZtweUbbKypN1Udrf5wmo04wjb7ZAZ4wXvie6kaVw3UTW6BVUjGtd1Gy/GeOFbxoIWzaLEC5vnLWw4hdRYL9FL817DHJZmfawZjjkaY7M8TPMyGW68GuEa01o9ZaX4Mr0VVBa3XfwhHMLOoEqKs3Z7G7M6kK68aCeJreT7CSVeVsB4oVejtowXGNnVkhOUzO/9x3FrAfrj5WqOLLoNdR3aAZOq+mO8RwpEChwwBbwPet+zqRIrFDBdyDyvwgYUDepyHJ/BeH719jy8GYHhAlWjy7eWYe+FhrrXpRs2TLrBfCnCc48ONV5vjlMsBBwXLQsFeZkE8XINPEyxGIPweEJ72NP9HqZl4Ub8gA0iOD6ToxxiDJITZjPxM5ZQNqJz3NO8iGfs6voq7GWZHZxC14YM9HbKmcPd8A7XLadGy3LmxIiMwN7LicP9sPVSgKFdMKjAeDEX28CeFOr10MckDujbX8ywHbj2GGJKpECkQKRApECkwIFRYM+Ml//n72hcF4wXdSddx0LEVIpG+1tIvKAZXLjoZIrJkRujbHPkU+xBtJUz8Xbw30tw4eqBNLP6mzY0qIZkLuaqyniBCgsYL3QnTeO6gxV4NVJ30gnjZcgZLzCuCzsv3VsxXsKid/k6WlF7H9C2qA1LOhjMLQrLRbUuuyEWDynjBQtanpAuUtWIjJdrkHiBmh4ZL4v0agSXo5R6oTtpmj/mZr7ARTqa5y20TUDj12x9iKVudRFLI1zjU5bfy8ti9hLKSmGoEXc7ufJ8ec11z0Pos+4ArLzmscVwEET/fDcEVQBjvJg76WWc1J6EJw66k75IVSO4kz4Eg5CpjZeE8cLy/N0a48X6Zct65eiep0KYJ6PWZlBhjhiOFIgU2A8KaN/DIOuM1LDbUsVoFWMDpV3IdJmeo10XjOXXp+UGxvE7s3W5ObUmc0sbsrTcIb0wLEvvPkV47/FhaQ0OjhJ2C+4ciFhr6/He7/2uSQrgMZu1kNA7g2Od/NKcQXYL8jerBWEDkNTQP+NTVDR+hYdVSOFybVKtrUMlWqRc2JBD/SKjAwW4mO6WS2dG1cX0+dNDUoaBXTW0Cw9HZLyQWD40e3lhXVmeXln1POaBunvb2aiUvg9UC2NjIgUiBSIFHj4K7Jnx8pef+2+5DcbLfzy3iEVHXaZhyZ+bw0Mp48VtvCAS/9d858j5NflHsjtjofkVcMoJp6BmCIvZDC5MC8N5XGFaGN4MLp8WPm+GYys45vWrkfFCunFhwj8IuaiqkUq8wMOCejVK3Emfg2HQc8cqchQuHEdHqWrUowZ2qWrkp/R6D6f1sFgvfgf3zd7UHlHvoBYvLShpoG1lAH/69vDd0/DgwmJNFmAXgF6NXrg6rUzLF6/Py0K9W6rrMJaszBdjvLiaERedTjvvPiGddQG/iz4S4mikGDG2T81gWavtwGUwDHlbDE/IeNkMX5gWhrPaWChLM7p4DbM6+KK+gwGCt2S8QOLlXFmN69KdNBkvg1AxGoBHowGqGiGbvQu+V9+ooUQmtL2aW98K1KH83gomxkUKRArsPwXSUQIB9uV0vMXzGrgmdajOUNplZgE2XWbAeJlZkW9hHL8+AUmXhTpcR4ssVaGOVIMhXbhMLsIOWzct4eMiLq5/XDDPmTtpmclY6s+Wi78eo2ja/OwOTsvAj5YQFGNB/ipEOmYHIJqJBuCZ2UrniIsQ/q9jroPTJ6xNWN0N2LdZk77iBqRxYe9lsFseuwAPRzBY/rLTg2BQwdBuuSCVnqIaISZddM4DHi9v83G1DUlidKRApECkQKRApMA9RoE9M17+6nNfg2G5Rfnqtxbl+h0uSFZ1gTHa35PYeGlkvLhXI9JBJ2nO0nr5FJs8PvQ3pwsJkWO8IImLE+pCV+FOOlM1WsXipUOGyHi52Cvnj5HxQlWjHtiowAKnt6iLnIOUeGn3FsPWPDSvlsTAn70926Bz0a4SL1isP39tGkzLRUi8zMt8taCejaprZL7QExg9GokaZEy7CDElBA7pvBfahnjC97IXnCEeC2elMNSIu/GpOe/uYxxzVrrhMps56D/YEGlajvFSXaONlx556lwFxnXh1ehsBS7ZwXDpq0h/fxn3sjZC34X9sENauzbZIYT18Lp568I0j+M9DxemxXCkQKTA/lAg7H92GEG8GB+SAZfpKu0Cg7qUWpyCtMs4DerOLsOTESRexldkbhFMGTBc6qsdYLAImC4cx82wLrFxHoDAb3olI4aNQRobJKZQdyHgg0xQfBBsqICDhpHGe7EcGFVtbNS2Gh3QajCb4GJ6Y1XKXJ/0d8mTF4fg3aiino76yliXYG0yUClD3cgM7abDaBJIn8OC9xz2VrZq1Z6R5xCwrLtRTq7Y+HgPUeDB/wYe/BbeQ59TrMp9S4E9M14+8/dfl9tgvPz7swtyI2C8jPS1Z7wYtfyk2ycjnwRDWoZxDhemP8jhsL0ZHRjLRYieCuHEvlbH6RpsvKg7aYhAV0qdMgRVo2+71ItNY1nOgvFydKQko8P9YLpwgQNVI+hT83Jpl+zEXqPtJyw+iN4qmNW0EXKX6BqR3I9PIAjpS7pwDU8vGIv4e/4qJF6uT6m02JUbczKzUpCleqesgPHCPzJdoDUGA40m3WTrf2O85Gl8P9HWKMEXabW2zUe+Re1etMNtr8UO5bkMK12nW/kUa9c0qHaFqkZkvJyCfSQyXi4eL8sT5/rk0HAvXLH3Sn8f7nCBShT+Thjw02vtnO2qzzz483qFYGEdFXeS2Ao2zBfDkQKRAvtLAbpG9n6nY7cNElAxWpNlMF5moGY0Dk9GN8E0vwM30s9emYV3Oozry+tgMRQwHHBtYwdOxJN4rQ/GDNa3lUxhOAoQ5m5dKLcNZyNpeloRowt+GfBEvyuUQTDdD9Yo/bIGD0cr9RoYKwJvRh3y9MVBjLEVefmFATBiSmBml2Bst6JrEzLEnSmlUonA26Z6ab1iIFIgUiBSIFIgUuB+oMCeGS9/8/8+oxIvX/7GnDJepufr6iJxpFKSHng14qkPp2K37WIbr2RyVgp5ONuS2ZTN2Vxn9ICODutRfM7DMK1dvOfzewgXhj3d72FaGPZ0vzONF+u0GRxhPN3vjAsvx8W4DB9jnfHCFV0d8rxkvCwuw8sCGC/90Csf7i/Iq17WJ+ePV1TihYyXof4+6FJ3g/HSIwWsfrigDC/dOIZRYfEh4BZhR5Fv1S7RbVHaPZ4cEIOGGfm4tAyJF7yry1cnIekyhVPTJblyEwv4pQ68vw49MV0BA6aAxSdsM6qYOoLJSSkw4L+5IDXkRtfNqMs0wvq9Hc083e9bwbVL9/gMT9izrR4OY3dC8rIWOUSWP6t7I4TlagWXpWR4HY5MEsPYkdgjoJFLGoKcX6rDcGZdVrBJOHOkBAPVfcZ4weZgdLBXKhjTKr1lMGBKWkAT42WT3YHX3GrmbfSnfNuz+BiKFIgUuHsUYD/1qbETIxKZMHzm+mUFNtSWVmoq7XIb4/YNZbysQOJlDodPVVmqkalbwF8XRluue6zX27BAVoKPAhyLsj6vD2mMj1OMCMMZlIUMh2HZDI4Qnm53z5lhZL0Y2wjHdK8xw7ycIWKglpqOgwZiMGi0jrNkbmN9Qg9Hi5j7OgsCdWgB42VATo6WwHgZkuHBElSQSmBu96vHRTOwC/ohrw+pfk+KiLdIgUiBSIFIgUiB+5ICe2a8/O0/PSPjU4vypWfIeIEI7kwdC5UOdRVYxAa/G5Mn/ucYL6SVT/9+5/bMJ35PbZ72PaV5SZCnf4Y3n2LPXtZO4PL1CTE7vjCuVXgncJ4/K5e5dUHDH2W8wAUuFjZLK3UY1wXjpbcA2nfJt79sAIyXPmO8YIEzgJP6Uk9BVY0KMPbXxHhJ30dSJgvaxcWatmrhLtHtogb3UJaUGAjofzBWlPFSkys37shVZbwsy5VbizK+AMO7K4J32IF3CbsAdMWO90TP3+Bd6uLf3lkHGC9ERuR22WI4T/X8s0Pn7/sJ5285qxtLa2S8MIYnwrwMrjkXW8S0fIrj9XjeHc7TiNcuhwrHFcL7Ir7Dj6IxXtVgv2FBGS/wxgGJl7NHy/KKSwOQGuuVJy/0y8hAGYyXsjIuK7BJwEtfgf0AJ0pzxFZ8w29z7RqS0wevcxoRA5ECkQJ3jQIq4YJxh/2Q9kvWoTO0BqYL1YyWYUONakZUL6K0y/WxBfVk9Nx1qB1NV6UK2y5FeOjpgTXZLjJ1OTYAUdb3Da/FZGyYxsYRmqVnuRrT+bRVuuXwcS+EZtgvL8mft3UPxzifg7Sq2SjPVqpELgojE2UN6xO64CZjWzrWYUi3Q+1nHT8ExstZMLWHynIIf0cODaqHI7VtxgM70HCLYXVbVY5AkQKRApECkQKRAvcKBfbMePm7/+85GZ9Zki99bQoG5lawEKlhvQGXxjjWoEpLN1wE6mSPSZrzdDg9NxOBixFbcNgCwcLZQsOXDbx7WjOWLGa/4TLMFvL6bKcu+bxbPSe4FbVtFhlkrC1GSKsOeA+Awb+E8bIMxssA7LuMDhbk2x8ZlgsnyXipyBGcKPXDIGixCE9TPd1QX0kYL0DIBRIXmy7Su1Wttpvudd0u/AMLB0LYN23vcxmnpss4Nb0Gpsv12zMq8XIN7khvTG/I9OKGzC7TQ1UH7CPBOCO4LmXoHFE8W20DsA/hnavECzuTXozx73A3VGRex7VZ/t3DZe3PvgrWubGvN9Yia1FYrtczS92sxq3TDIduDihKhIuktBPZetKfwHg53gs7SYNy8URFnrowqKLw5XKP6B+8cDRcRBBuSBoSs4ew9h7OUi0UtnYvrczjPbhntmQnNd0p/MHVPGKOFGimAEYlrF/sItMF6kP4q4LpolKlcB99G0Z1Ke1y7faCTM7V5PmbKzC0C49okHiplApSwpqHbqSViYPPnWO3Y7QQxuzceMFe0TgCWg3uzi9rZzXw8viU1dljGRnEJnOQ5k6yaz5EdICGBO3AGLtOD0dgvMxBTQvUgJcnkUdPleQYXEs/ehrrE9jPOjLaL8cPD0gJUrmkHdco7uGIpYfF8jlekQKRApECkQKRAvcjBfbMePn7//2CMl6+/LU7cm0cJz9gvGCdIgOJLZFunP745p47HJufk1m6aWrPNmMkZjbF69Qe0Df/7En5+PzzTuEcvt2d+Hl5e+xpd7/5uia4FTUXalYKY3URYj+6UVyFDvUyJF7IeBmEmhG9Bnz7I0Ny8WS/nKFxXZwm0S5FdzdO48h4wUk/VV94+QY4Ml6UHPv7k3wWGeNBVFyd7+r6rSm5OTar0mLXcHJ6dWJdphbXZRquSOeWRA1Tl4qdYLyY1AvXuH4am3wSqGtSwI5q3eo7a4XnXoTzerJuu70yHOk3D+KSgbmA90LPJatQNTpPxsulQTAv+9UeQT8YySUwXkrwvFHKMV6UcbmNnUFW8vbe3F5auVvq7DwfWxXWNP+cx7hVeh4+PkcK3D0KsC+nu3wdF2CfBIyDWrUGiY0aDOjWoFa0LFfHMWZj3J6aq8vzt6twJb0qazB031eGOi/GbLqQJiqqKDnjxXoJf4Mygqb56iiIOqBgUocUe/7ZE/Lx+ecAjo31C2MhDe5ySCTjhfSjE4C5Fbo4IuNlQy4e64a3xYJcwjh7DH6mj472yanjdC0NxgvnPJw2GOPFkKaMqqCYhmHHy34A72wyKR+vSIFIgUiBSIH7nwJ7Zrz885dexKJjWb709Ul1qUgjc2vwEkLXgAWc/HRR3xmzhi5oUnr57Nk4nXisg2WpDOVTQyhPawfnmDaDcxjidTgvI7yHcB7fCj6E20464UO4JH+6oLE0XYAkmzxCrOrCDgsb2Kaor9bVo9GhoaL8L0q8nOiX01A3OgSJl7LadilAhxo66GS8EF1aHBg71AeL1/5TADRON+YgMe0ErODk7/bEjNwen5MJGmnEIv6523WoG61BjH1NphZEymC6VOCOtBdSL1Q30tdFXKhh9u4yaRfGW6oGWvyE79egWwAhinBM93trqCx2M7gwLQxnubMlpdepFZzHhTAhDg8DzkGTj9tzECJNQppKvCT9iO/HxOGpJrmG1DVIivXKKy6iD53qlycuDEsf+k8PjmopNca/9Er7JwtgCdu7wnqFORwD0z0cpsdwpECkwAFRIOmULvCCxZFJwEFag4cac9AFnYXEy63xRblCxgvuU/NrCNdhcHdN1ZL6wXghs7wISUUODcp4QXXJiEgvjBPet7PoLJTCpVCMaZXukI5tP+FYHvFuVq6Vr61BYxUyqYpLvFCrlHSoQWVrEb6lae+lq2tDTo92yShUos8fLaqky/Ej/XLh9Kj0gvFSpG1ArlMSiUSWouser4pXK2y2VeWB/GWzH5KmPpDvLzYqUiBSIFIgpMCeGS///1euyPRcVb78jXFIvCzJrakaFitwpVgoQrICpxZqwBLTBmdfXDaBhDNoVh2P9Zh7b7JhjVpNg/maews2u4et2yR/mmSBkPFC7NQ/34DEyyrsUqzBXeNwXxckXIryqkdGsXnsxylSr+pP9/SYUV0a1uVJ0jqYY9kFo4CR8ZKRY59CKbMRr87em0BkHd4dIFkxcQdMlzvzMjm9BMbLvHzzRlVuzq1BemxV7syvS2/CeKlgs9+NBSi7D78A/fMAnvwtMurBv7yV3upciz3awZAcBANaZYwXpSt+1PPGRh0w8EsCOwRnoaL39KUhOXdiAAYgh2FUt6ibAUqNFQqQlQ8vItkHpkuI0sPeJH+O90iBSIEDogC6sY7ZyVxI+dt6HRJwYLzQNtccvBlNz2OcngDTBWpGVyaW8Lwm1yfrMgtpRfBsYdweqkYqsWFSpcSnY1BuIGK/DqMOqEV3Ba2PUWF7lNFEBlPCeKljnbIERwCUtO3qXJfjQ7ADWOmUM0e75QQkXk4eGZILZ0d0nC2R8QLmNj36+ZWGWAjH2jTCIR7cuzb5wW1ebFmkQKRApMBDRYE9M17+/Ws3ZBZ6zl995jY2kDA4N1GVOk431jeK2MLgqJ4G0vCPaxnOlTRYx8s2pc2zp0/ezSmabZc/xOaYN0OxFdxWtcqXsRm+zXARj6Xbr+G1JRxjsD1ElLm95CYSjBNZhXTRmoxA1ejoaI88/bJDcuY49ab7YPOlLN2QnuiEBBKlkIjBNpx8H0YPv9tT/N0PCqSMFyDzM04u5Kt1MFdm5mV6BiemYLzcmpiT/762ggX8qtwA43Jsek36Sl3SD3sB/VAN4+mpewVzLxtWP2L1byOrcfb1ZHEOv3k/0C8jn+mAn3dSprU1a0uuav4RK1gjXViKJ7Pv+PdPehCyA8yWLvShAk5je4vrUNGDq9PzYF5CauyxC6MqAt+F99AFtxxd6EPZZeXwDe/k8lxZHmMGZc/3e0iper83Itb/IaOAjtk+UGCQoFHdGsZsGtWdnVuG84AVte9yBXa5rsGmHRkuY2CW0zA6DzP6e2AUHfZJ6JWO0h3sBWFf91HCWOZhSjtCZ+NjHtpxWc4Mrh2mncFtjiVM9XpYm5hiaxIaUScpeTBExksV60KmcS040ksmVYccH+mU00cGsFYZkkfOQrKwAslC2smB5EsBdPTLylBkNpBbhCfHe6RApECkQKRApMB9QYE9M17+65mbWJBU5T+U8bKkBnZhh06q691gDODUB6pG3JRgz6Jbk4zx0rggIbXyc6ktNBibhQjX+srgmtO5PfVlweZwXpbVZvOlTnM5reDzcZ4r31qP5515svRsc21xTOUCkesYntBzeVLoWJVi55radzl6qEeeuHRYzmDTeGy0IiNgvBRUxYgbRy6GuJFH3qQIvwNNvPaRAi0ZL1jI11dXsYBfkBkYc5mCYepbk/Py35eXYOcFtl8mYUMAhhr7IOlCsfUBLEJpL8BF1o1pkH1T/pVkMdkX7E1xmNbftEPt5k7MYcntcGwGt1laO3xt4v1D1io11ytNRtI69AmUicWRAX2pCwzhbvSfUmFdKmWIwh/tk8fBeCHz8nEwXnrgEaxTDT7C8CMk+ewKy8io3KZ2TdFhbqejM+iagO+7CLZu5zS575oZK/xgUYAcWQwU1jfBeAHTpYYFzcxCTWZmF8EwB+NlAqpGY/Nyfbwq88tQD52DGg08Gm2A8dKLAw4IKxrjBUg4thiuPJnCcS8MbwaXpRFnY+/aDEeWr3EOcAytarh9fE4t4nZMXJdoWaQlGC9cq0DgBRcPiWALB8ztSs8GDP8X5DTs0J07MSiPXjwk/WC8lDHnma0XH2e9rcSFdRwHcq962LQYjhSIFIgUiBSIFLjHKbBnxsszz4/hNKgmX392DCK41H1elpX6hswsdUHypTOZbDt080gGQXaIYTMnNz9+aQx/kqgsxSB2M9fuBw6v39azfb60LGfrUGOL/Mmx8M443zByWcMNIxcxa6DbKv6oFkGJ3AoWMv3wcntoAKK7h3vk0fOH5MTRATky0idDSOjE5p0LFqoUEaeT3XG3rl+M3QsFMsaLb6d5gkq1MHh4mF9Cv1kC82VFDex+9Vtz8uIYvB3hFPXWnRoW8AVIvXTjD4wXdJqM8UJGAWvlXwnD/uUw3JjC58ZUxrS72uNsl2N/472mYT22U4LnC2ARlW4Dko+d7kl58RwarwB2B9gPSE+4OIWkSz82An1guhzu75ATR/rk0plROQl1vUtnRyH6Dkkx9p1Ego94uKXyN8vn3V7h1ixrSRbaLd6YL1IgUmCHFOB4kEyKqsILRjk9GnGcnoYtuzvwaHSd9l2ganQD0r0LK+tyZ1FgA4Zjidnm6sY4UaBYBy6XVNxhLTYF99Fx7yOEY3CMmxbbNtGxGICNuhnjxcZYHhiowIuOmRvw/LQu5e51rFe61A4dGS8vTxgvvb0wUFwuwhmASecSr5dBGt99xgvp4zWwVsbfSIFIgUiBSIFIgd1QYM+Ml+eujMsirP1/41tjcntySV68tQwjahsyudAhK6sdQukXcg5KsC1CBkGXb/yT2m7NePEJzyVWdtbM/JLCse0Mi0NvlTtfmufb6m54Hbtj8enemSNMN6aLGdWtYTVTBOOF6hGwnytDEN89Mkyd6ZK87NxhocG60WEyXnqwaaTKF15FQn/WyPEzHK+DoYCe0PmiDVIVawnjZWFxRRaWIKo+vywTU0vylWdn5MXbK7CThBNVMF7KkHihoUFKvtDLA/Fw8ZqpGvlXwnr7l2NtCFOaUw2m9W+YsxFna/j9jg3LDOuyVTlhPoPVPqOrdDzzjivPeCGDmEnrMM6Adb6Kv7MPHR6ECPwhY7wcO9ovF2H0sQC7AxQtg5NTYlJ8B8d4aW6PFhh/IgUiBQ6cAj7yUFWoBrVQqobOkOkyvaJ/1yfm5QoYLzcnq7KEtc7cEtY6dUrQifTAY08Bgw/dIVOrmnGOLwztpRGO714ZJVrXw2rJX46xXL3pCg4PZMpQrbMb65YhMLnPHa/I+VND8uSlERno61FD5hUYMyfjxfhXZG8nYy7zJ0ytvdBwZ3nZitat3BmeCB0pECkQKRAp8LBTYM+Ml6s3puDKuCbPvjAh42C8PH9rQU+Brk3Rij1OgvC3AZWjgRL0njFhUvdZL0ygnM64MPFLU/ijbgUsPZvwdNp20OSuwLk4PraLz4OGcGG4FZzHbWcSdpigcZ49vXt52Z0hXp7LsXCdoRtJ3OHtVj0Z0T0jjdWVu8F8gR4X1KT1pP74aFFOHi7LuVOH5Mhov4wM90J8F1wZ4jD09qsIc3FhegzvmQIu8aIndAk2lVwB82WpWkW/qarBxsnpZajqTckLtyAxdhsnqhPLUoJxwR66Lu6m+2+KXJNJYH3C8baroH83dudbD2Pa5dpuvOPbCr4dXLv4EB9h/GK/tzxsRfPVjC/MbfAJJZKxBwIuaodqsQpPJKApXoewi5wahsQYPG2cPgwD1XBvevb4sBwBA+b0iUMwqAusjtgrgj7kUc312kmMj3UsYn8w7qT0CBspECnQSAF2cRqCVcZLrQ6V0GU1hH5nCra4YAydqkY3oRZKhgt457BfQgUagboi/tCFO5P5laOXDxfZzM4+nsU2lhymheFGKB8Vtze2Ox6/N+LKnjzd71lK4+DXnM6Y7LK2eQsxxOIy+lh916VjvY714IYMVDoSxssg7NKNwCtjCQZ2wXzBvYdShszKcZb0BB7OfS+NxItWhD/xihSIFIgUiBSIFNg1BfbMeLlxexYucqvy4tU7cI27JM/dXMAJ0Lo8f5sG5zbAhOGGsUMGy3SL24mFCSZRLE64qOF83Mh4weTKYyKdqL1NPqV7ZP7Z4fL3reCYTpzbgXPc+Tp4vN893Z9b3b3cfFq25QqxEJprDl9srEHkhYbqVsB0oZgzN4wlnNafGO6SkzBUd3ykBGN1FTl1YkhGhvpkeLBXPQWwnaS4tjb7SVufr0183jsF0kVi8Jnxu1/HSSrF11eqNTXaOI2V+1e/fkcu35qXq7dg62VsSQpFilrjD8Zc1TMYq4P3ZkyX8AvJdZek2vY1NcI1togfQZgeVLIRMMXYCN8KSD+sHN7dwDke5k2+2ZZYvQ1+t7Ky3B5vbdOTUkSRhjXoGs1jnFKVPbyP/t5OuXikS44PF+Tc0R45PFxRVb1Dw/1y4tiwef3i2IQrHaK4IbAi9/gbGS97JGDMHimw7xTg2qQGpgtdSdMW1yQkEyfvLEMqEeP02AJscxnjZbEGyd51SiWS8ZLYswsGBhvBbAyySto40lxhH6/83gxhMVule779hCMuXu3qbqk6SSlMI1w2itv4u7FWhwS0qUefO1mBK+lBeQoSL8NQi+ZBUT+kdNWlNIq1tY+Vz/mT6yH78TL36+7vyO8hXm+P0yFMi+FIgYOnQPwCD57GsYRIgbtBgT0zXian5mUFi5NbYMBwcfLstXmZWVyV/36xKlNLq7JAN4vYngxWYHQOjJciPILwNGgdGx8OJGpvLWlp682iT3Q+7BCYceFzgqDpdhBwTYUEEdupUwDeMujtZSIpYjhJM24c66Ab/5ZqsBOyvCoD2DBWejrk/LFubBgh7XKoImfAeOFJ/cBAGaK7ZSlDckK3dkn1nInTsvgYeaAU4CvYAPNsFaJL9JixADU9esz4z2cm5MqtWTBeFrCwX5Q1KYAp0I1PANIuKgFmXwK0jhrWnFzsJ681rXf4BWlXYUoeKIVul9CABdAH0ZfalZ1WbgflWg3DnFnYylF1OzSDUi5V9KHZJUq8QJ0A72JkoCCPn+yRszBM/fjpXhlE36HEWH9/rxw+NKBjFmltFxf/+8F0IcI8nb2MeI8UiBR4KSmg40R1VRkv9EA3DsbLxBQkEuHR6OrYoty+U1WJl+U6bdnBDhf6sqoZoUt3JkzadMhIG7LdcTTNcBcCW9Wp1RjV3LLGigbpGCvTJwTI+F5fq0EGhgZ2O1Ti5dKZAXnykVGMwz1qYHegUjaD5iyaYy3+eOlhBgPJM4MHf7H2rWhw8CXHEiIFIgUiBSIFHiwK7JnxMg3PLDwVGhsH4wV60M9dW5Ap6Bj95ws4HZpbhbvFNbX2P9QHt7gQ9S9CbQLmRjDx2ikvpzSflG0r409OaJ/wwnjGhc8hbBi/VzjH63eviz/n72HZ+bStnr2uWKQAjT2RInbCEzJeatCNWAbjZRZ6XMN9XVi8dMolbBovHi/JqcMVeGSpwLZLrzJdKr1YwMBOiEpKBNX3hcxWtYrp+0wBvFuquqyur0FtDAy0lTqYk1X572cn5NrtOTBe6C1jQVbWClJdhYFqeMpYhaoe338X/qjxQgPJ/v3zW8l/dcFrztaLKVCWt7llKRCSGrAkz2G6587jyz8fFJzjze75GithNNLqTcYLm8W+UEUfmlmqKxOmjndBo9RPnYOK3pGSPHmmXwZw8jo8VJF+MC5HIPWi6/y0+Qggoqm8rCrbCDmyvWHZRkERJFIgUmAXFOA4sQJpl+UV80I3PrksY1ALvcFxGlKJt2HjpbpGW3awWELGC7o0vQaS6aKKNdq1OYPz6Mkvhrzve9zdvufrkH/O1yerfZYStqFV/iBdB0/LqfMVftZWjfFC21rnTlTk0ukBefrRhPHSW5LB/jIMmkPaUw8aXkrGC9vRqv0ZJWIoUiBSIFIgUiBSYLsU2DPjZRGbxjpO7qdn5oUqE89em5OpuZp86ZtzMj5TU+bLGhYnIxAd7YG0C6VeOA/z1J8H+ZyIfSGiwaY53Ce9YCJHDo/dbkNDOMPUVFAIomErI4TbqtQMMxE01tjQ5+PyGDVdJRwMnumkF8imEi812Hbhaf0KTuLmwHgZHYTL4XKnPH4Wi5dTYLoc6lXGCzeM1JUul8xInVfG2F3EmS/Zyou/B0wBvGBffPJEle9xEe/xmy9Mys3xObk+Nic3xuHtaEVkegmGG5dpoBqSYuC4dOOvRFF2/Pl3RBzWh7J625vlr0Nlaa1DDhvCt/o+MmaDf0et8e011uuzFZ4t4NAcbQV/8KfGdXHnhqqGMWt2qaoSL2tgvBwZLsq3XRqWCzD0+PRFSLr0FqUCOwO95R4Z7KtoH2RtSCHFyYd47SsFrl+/Ll/+8pdlZmZGqlBf5dXT0wPJvQE5duyYvOY1r8FGDANhvCIF7gIFUsYLJEvvzCyoDbtxGNi9gTH6ym2TeKnBgQCZ5GS8kL1Cnrj+caRIBgrGZyP2ZhVnhnAMbge733CbldMqbTt1BIyDkSC4+EuvjOYtyhgvcGAkZ0/0woD5gLxSGS+makSJQ1U1eskZL6w5G2Jt4FO8IgUiBSIFIgUiBXZLgT0zXpaxaaSL3Ln5RZldWJFvXZ+VO7NV+T/fmJGxGUjC4G8Vno1oNM1UjYzxwuMhTmdc3Pilofz8liU7mN7zYA2JWzy0QdmUq30Z7VIMs6e2Kicf57BeuKYHQOSPkEmi+w3cSesqbFKswBfu0nJdDg8VIJpbkJeD8fLImUE5MVqWM0d7pQ+iuiUaZ4U74gIYXkpn4k0KjIwXp/hdvJP++Etu+k6qeI/LkHp5/sUJGZuEi1IwX25OQqx9YUMm5jdkCvflKhguxQ54zOiEG04wYPDHbqN4EOBJa3jlv6kwbfNwiKg1Fme9HCzjZfNabjsVzWEr1KYUQwmzkcwqMosXV1ZAQ/5bl6NkvLxsWC6e6JdXXBpSd6ZlMF/K9CxVLmvWlDrJmLW3PuTYWtN52228zwFp7+jy5cvygQ98QL7+9a/r3/z8fEOrSiVI8p06JU8//bT85E/+pLz+9a+HseOCjosNgPEhUmA/KJB0TSpDr2BsziRelmRcJV5oXBeMF0i81HCoVAXjpQ6pRA4LlExsZryk026L2rGwe3UMyNfLx6wWzWiKAqyDJ4wXgpBGXIusrtbR6nXphXOAc2C8XDjVD8bLIRmBe0baeBmExGFPD+2bZdIuYRF7G3tDTNsJsyF5WmwnX4SJFIgUiBSIFIgUaKTAnhkvVZzY007C0vKKUPrleahKTM2B8fI/kxDLrcpt/NXg2agILy1d2AF1wWYFp7BgLk5rpPN0fn7zyTuFskAeLJeceyR0higNOZI0ojGbJzfG8slSmtPziAjRuEVtBWH4spRkf2jF8oQI5XGxQgg1Bgr3tzylX8Pi5ehIjwyB8fL4uUF59MyQnDhcAvOlT3pgcbe7u4BTI4g+g9jM6wtDfTDs3hR/iveDogBeQMZkxGIyObivk5EGcfYbt6bgqpTi6wv4W5TrU3V5EcYbb9yBkWp4zeiFh4dSsdPcS0P8iRtWe6dkvNh35lVv/i7DFOZqd4VprbFkjBfHEeQJgttfpzbW3bE235vh0n6CcrOiAzh88PoEQKbz+zdGC2gG+q2u49QV76EIF7DHwLB81SPDcv4ERN4vDkM9ryjFnm7tQz3oR7yyMnbTbcLcii75YQ0fzmsNtnU++clPyo/92I/tiABvfetb5fd+7/eUGbOjjBE4UmAbFPBxmndKJCrjZRYSLxPGeEltvASMF6qDsodzfARfHOubbN63+HzBPk55qj+3g8vH55/b5d8tXD7fTp/RrmResnHaJjwyucn4XsVpnDJeVNUIjJeTfRnjBYd0g/RqlDJerGwyWziGkwvO1sYrUiBSIFIgUiBS4H6jwJ4ZL6t1GKjEkXu1XgPzpaZuFmfmq/Lv35iAITqI5cIA3eIyN4ndukGkhyNOoEUwAzghOwOG8ylXLjpXJ1TUyTWZvDU9oG7zxMuYPJRnaExLl0SsAC+dzRloxNr4xHS/LKU5PV8+IdLSPPOWd6+W1weaRWC44E+lhPCARV1XxxokiFbBeIEtioGiPAamy0V4Bjg2UpbjoxXpwmaxUOhS993pggUlNyxZmhuwZd0iwO4poAt6vEd9BwnjhXZeauhDY+MzcC29nIizL8mLcCn9rZsrcmW8JjMLOBnEItQYL2AGNDFemuvkr7bxi2TJjTHNOTeLyb6eDEsWYs7EpmRDP2aLt9c3dwbn/STtvlr1DIfRgP2P9hdo0Ni8GGlM5zpUH9fBdBGcsHZoP3ry0qicPdovj58fUbtIhQKYxaB1N/oRr8aWWqs0Yds/xOD1C+/bRvDAAJLx9aEPfUh+6Zd+qaFNr3vd6+Rd73qXnD17VuPHx8flC1/4gvzBH/xBA9yP//iPy+/+7u/K8DB8gMcrUmC/KYDxguMKbbwsuY2XO0uJjZd52OMyiRe18bIGGy8bkCpF3+awrowXPG3oAJUfNcKKepqNVGFKY9jTHb4x1Z58PGmVFsbtHxxrYzNCvl7BswKxTIfkQQHG4jokXuDVCEKFKvFy8VSfvCKUeImMl/ClxXCkQKRApECkwANCgT0zXrhx5AKFEhh0kXtzck7mYFz368+B8QKx3Kvjy9g4wjjdfBcMWsI9Yx1TMLgtfdjx0FBogT+4uCklHk7KflmKPWWxnmpbmOxp+yFuvFpfYYmb4Te4RuhdsVhQjdCvky1PuF7TZQpWcdifwIuRyDJUIygd0YXTeR7A9xU3ZKQicB1dllF4Ajh3agi60nAhDd3owzCsS1sIxNPlnC3fpbZueIxVCvh3kX+z+0MeZ7zo200YLyp5gfc6M7coS7A5QgPVU7Aj8K2b8/LM9QX51jWcss7WsEAtghnQjTsYL1A5Yj5e7C+KN61iru7+mDTNH1PwHQccg9Nqxwj2LUO7mjDePneDoEQQmZaLGJ/IJKZhY5g/kqMDMExd6pDjhzqhsleSl50+JMeP9EPsfVjV8zrBdCHTMvahfXtlKaJPfOIT8o53vCN9ph2Xj3/84/J93/d9UqlUdPxiIr/tGkQmr169Ku95z3vkM5/5TJrnl3/5l+WDH/xg+hwDkQL7QoFkDcJbxniB+icYL+OQSrwBqV56NRqjV6O1ThjX7UwYLzhIwsydMV6sNhwpfazKYlrVtBEqg0A8k5J6ZfF3P9Rq1Lda51sZQhLCoWy+oqoRZjEdh88e74Vx3f7EuG5JBqhqRNX0nMSL4yAqw8b2Z6GDoka+ZQdVTsQbKRApECkQKfDgU2DPjBduALke4AawVl/F4mRBFhJjoXfgXvoKDIXegXejW1OwVQGmC5IwVXZIP8T46RqXjBdOne0ZL5qqb8Km8uyZkdm02xhvKeHkrygSPK3jQ2xWyxAuxG+lZmUb7vYMnazsxhAxtGe8UB0Fe3IwXki7NSzwNmDnAyf1MEg3glP6Y0Nd8GJUlkPYNJ4+Piinjg7IEBgvwwO9uvHkptH5LslOtLH4+JSjgL9vf7P55xz4Dh/5jfNrZ39RxgCKUUYAVC4WF1ewyId0y9yK2kh6/sacfP3KrDx7FQv+mSreO1THYNeip9ukMMjms/+8Z/X0UFq1XFP8MU1vGSBUE6YE0jEwPQ+Xf3bk+fj88+7giIVXolBkD/hlfCPjBYxh9KEFqkWC8QJH3uqG/eRwAYv8LrhfL8BINTwanRqRo5AWO3NsWCVdaIyXeMyLVIo0LScGdkeBy5cvy1ve8hb5yle+ogiefPJJtfHy5je/eVOElH555zvfKZ/97GcV7tWvfrV87GMfk6eeemrTfDExUmB3FKCNl1VZgtTL9LS5kx6HO+kbY5B4uWXGdZdhXHcJf/V1k3gp4MCDIXMnbeNcNpLa89Zjq8MFtW4RFaQeQLB1gVlbvEid0fwBd8vHX70QsDwMkOnCtSL+4E6aqkZ95Q45ewzGdSHx8jQlXgYSGy99Pcp44fhr6xjD6FMd4wyzxSelxVukQKRApECkQKTAPU2BPTNebDOJKRATIaVfpmYWdQN59eaUzM6vyOVbsFkxXZNnb9ZkYQkukOGphZPzIIxWcpFS4PEQ9aGV/0AxVNLLJtX8lKpJATkTlk0aY5iz/GlCLuBwIb58WY1ZrD4WZ6UynOX3UAjXAOBZ7Y7frO62GNEEZCcGLirsj5JEkBLCxnyJRljhQhqeFqWfp/QjBbl0rEdOH+uXw9g0HoY3o8PDFTWq20egoHYadqMiWlD8aU2B8D06BOP4Vvbv4uLRMPLdm+h1DYv7Ot7z3EJV5pdW5Plr8/KNF2fkm2C+jEECRqCqtwFxdsgw4c8kMYiEzEt71wlG4NMa49FqzniEEpU9Qm9+JfBtgawcw94WCAnt8LSLz+MK4cJwBsdYlmMttjDrZfFISQKkNyVe5rGJMkmhNUiJFSDh0iMjfQV55HRFjX8fOzwogwMVOXZ00FSmmJEl5BFqbPzZLQWoIvTbv/3bMjU1pSg+97nPyRve8IZtoaMB3ieeeCKF/fSnPy1ve9vb0ucYiBTYLwpwbKb9rWUwbKexriHTZRISL2rjBapGtyjxUu+QRfzVEq9G3Zhn3caL1yMZRfDooWzEchi7c6Dhn8GlYR9/PFqBw8gwj2Fq/PV0z8PUBmQJeAjXKt1zOZxlC7EmiNIxmDlsRAabJZmbWPTGehV0AuOl1GlejWBc96mXHcKhEbyYQeKlXxkv3Tr2UoqIY3AyHOuA3KpMLzveIwUiBSIFIgUiBe5VCuwL40Ubh5lRNzfzS8p4uTUxC09HVXkRKhO3YGD3G1eXZXZxDWpHsAmDTeAA3BxThL8rUYnRpQgn5HR25cTfanr1uNYLg4zQGVwIyVh7tlArNkqGgyHP4XfGMOx4PJSla6JHpw8IaLYMzrHQ7o1iw424baOnD3pCX4dqxDIYL/RkNABJl4HeLjk5UpRHT5aV8cLT+kOw7TKMe2+5JGW4wLVGestxj4yX8E20CTu97M0YEOPC5zZZdxqtReGrx/fOBekqVMn4t7AEW0lQOXoB/ebZqzP4m4XEywrU+DqhatYJdb1O9DN6NoLRZHwo3V04XUU/Ijr9Ay7tQ/YRZTXX8nZayVbwTotWCDdLC3E1wxGbx1oojLGywhIJa/3QF/ZZaoqHAURzSFmDZN4S7AqQ1p1Y8B+BN7DHzvbKaH+3PHZqQAbg7v7QyAAW/OhLME6tF5g1CRntueHBouLvzihAlaF3v/vd8nd/93f6nX7P93yP/O3f/q309SU03wIdXU1TxeijH/2oQjL8vve9T/r7+7fIGZMfTgpwXEhHhJ2RAIceKxgzqtCPnpoxpssE1I1ujFPVaEFu3qmBKSOQpINnIzBe1jEu94Dr0oVxorMzGY84/mj59mxjFm3eeZ3C+iFOjWThzkGLl4PZk45nFpmke3zLOzOHcCGyfHz43BIZIkMmt8GEGMMYm5Eshph1DE6LoNWtGtZ+6zLUC8bL8QqkDQflSXiVG4aKUT8OjvqpatQNRww4WFDpFub15oC+zeVaWfv569X1YkPcd6P8sLwYvlco4F+F1yd+CU6Ju3P3gWCr0rYLtxWemB4psP8U2DPjxTc2nCC54VP30hDTmIYXgEXoFvF06PbUkvznszPwdlSXSbiXxlpGuos9mFS5ecQJPsauDiw4OIRxb8NJOmPA7KTR4SDoHU9ZOikSQjDFL8uRj/XU7dzDRYBhTvEjoJitkDbIfAkGPAEcY/lH1Yj1jTo8GGFjDlWu0cFunNZ3y5njZXn5eXox6pWR/jLsIvRIBYYrirCdQ29GvEIammhumyrE6IACfHvBiwhS9ivI71vfNQO49CQQYUpj0IsG/25PzMGA44K8eGtGJrDoH7uzBpW9dTBh6rKwsoGTQr5neDkq09gumJ7MD3TrUEfTLz5pQvh1tq4/Aa0eLdM1uRU92uXZBJ+j0ayNcIzy5IZ6BJEJuTSZ0WlSGvCcFmHGqEENiNOxL6yv0ovRhkqNnYA3sCfQf6im9/iFYXUfXektg2nZrf0owG5I21bQy4z37VDgS1/6knzHd3xHCvr5z39e7bqkEdsIjI2NCdWOaMeKxnVPnDixjVwRJFJgZxTYwIBKw+dkvEzD9tYdMl8g9XJtfM4YL5Mrqjo9t9wBG2zmTpqMlwIY4VTx5SjEQxUfKbNhKhmjDQJQWUrLGobJjqwlYKvIMHM+3ZERxsN5mPB5O4wXwnPWacTHuYkOAthypnV11KUbY/FoXyfUpCtyHoyXJy6NwENjCVK7PfpHxgtJQ2wNzJewSnchzJZsRsW7UIVYxANDgfg1PTCvMjYkUmCXFNgz48VUhDAxJYyXGg1YgvEyj6OgZdisuIWTobE7y/Jf35qA4dCa3MYpEaR3YYwOhkqQaT1Rm8ABvk5uXLBwaOKip/XVbgrMD2hZfg95Tn8mfo9rXVY+ltBhbkvPNreelixQ8KiMpDya9Jn41rUOzhghBm4w2XxlySiB10ClVfytwQhoEapFRdih6JPHzg/LscN9MtgPKRcYoqP7xQLsgNCbEfEoIt5xOX57ir+tKUCq7eyLaI1nm7EJJ4E3XYojwIU+/yan2G8W5PrYnC74r41XYSdpFYv+qsxAcqwCQz9cmJIBU6CxXeRVPPhwlOGWNoMB/ukXsUXF2sGlyFrk3w7eIFuLIjalesCNZPssu/aM7E1p9XJ1BGxdaWJMF4403Z2rYFIJ1DfyjY0AAEAASURBVIpoUJeMyxE5NFiSR86Pov90S4nqjwAo9cDVBq5N6xU0KQa3TwGqFb3xjW/UDI899pj8zd/8jTz++OPbRxAhIwXuEgV46FHHgQfXNDPzMHoOxguZL1fJeAFT/CbG4sXaBsZjgTQdD0k6BHxbOA2A1AsYMDpWkfGSjFtWbY4q4WhsI5qlhb+N8ek6Atkbx6VGuBCDhZne7rK6NKZuji8re3O4sFS2n/NTxngR9cjY070hhzEWnznRJ2fhHOCJCyPwMAfGS8L87sZYHK5b0nVWiLyx8gfylLX5QNBHpA8VBeLX9FC97tjYSIEWFNg74wXjCCdWHD7qRaYLn+trq2CwrMk4Tu4nwHj52vNjOLmvyg24yV1YgReXxYKqTNSwuOHkStUJiuhS/Yj5eUrNbRaC2QaLoXDSZaJfKjGTJbq0RwhC0AyCoXyqIwvvW8NlOMN8GfZkCZYmaqlB0U471s4WKFikgC5cqHRhw1iESG4ZXozKPetwF12SY8MlOXV8QB67MCojQxUZgFhugfTDYs+8GRndSANduOg9LT4JtKt1Hi4+HzQFlMlIjiMuGoGl2+M5qOzNzi3ArsCSTMPg7vPwcHRlbFmeu7GEflSHa2lIupBJAJfHfOd+mVFH65P8Au0za/+ujYXBLy/4IB1Z070Vnu3ka0K0RQTLIV7cwyLTojxgcNyUGFjWCvYj2syhJBCl93vgDWy4dx10g+toGKU+DptIj50/AomJEtywj0JSrAt05B/7kGFDF0wX/hazRbVj8qYUqEPU8cMf/rCqChHwTW96k/zxH/+xnD59etN8MTFS4KWgAJcgqxhD6DRgBvbqpueM8XJjHGPx7Tm5OQF7XFjLwJGjLFYpsdgBLz1Yy2BA4nzMizh8tOKzjSMWE8YzzVKbY5uTiKUNnALzZzsj1lY4mL41HocIsYUrNwzBOg7DRB3WdVyTbMC5Al1Jr8vRwYIeIp09MSiPXRxVxksvDhN6y0U9QNKWcIAnEq+LF8jEu3AFJd+F0mIRkQKRArunQOytu6ddzHm3KLBnxkvi1RabFasyGS+8VpFQT07up2aX5Nkr0+oq9xrcS8/CvfTtaRiug1rEEmdjSL6UsIk0mxVAhL7jjBPDGvxyEvaLfcyvbTJeCB5g8Nx7umf4Gju9Vy/bDloxGp8kkjFC99p+keGitAMd6c2o2LUuFRzAD0EkdxC2XY6N9oD5Upbjh/uxYRySoUEa1C2C/lDb4qaR+PBH9LrISRAb2RjrZfndS473l4oCIeNFVcvwDSwsgUG5sKyL/em5mrxwY1auw0MYGS9jU1W8Y6iTod+YwV1jWILvhsUq2Xx4/7pQNcmpzduVfIgK1PhNtE5phNHOunkBe0hFWVpcwkBKKpTWIElmexUO6QRh29ex41nBaTUe0CegXoQ+dAx2XSow5nj6cLd6Lzp/ahTGdGGg+sSwuuh2pqUxXowJqozLpAVpuXto0cOcdXl5WX7jN35DaFyX10//9E/Lb/7mb0b7LA/zR3EPt53jsjJeYFuNBs/JAKfUy41JMF7gUvo6GC9zSzhcmoGEb8J46evpxEGJHSRxMCLzlmOSXc7m9ieP38adg0+GKB3v2ufcarQKkbXC4ulb4UmG6KbqMb/l5Xi8CkKsYDjmRcbLUGkN0poCW3Xd8MbYB6kXMF4geUg1ozIkD1X6MGFeaR7Lqb8JWgsf8O/2qXDAFYnoD4gC+TecfbfW4dp9/yHcZlXbLtxmOGJaIwU2o+lmaY1Y4lOkwEtFgT0zXri/0009v3dcG2C4MMitkhnbXZGFxarcnJyHl6Oq6kbfmavKf72wiM0l7FbM1zGPdtoJBybaUlchwZcgJCYPagnhTzAoYjJvuJJHuxEuSw9yJVksxn4zuAZ8TRgyuMZ8GXZdZmnlM1jiTEtjnRNGCeMJT2mHFUgL1bHYo6pWpdwJ1SKcCh0uyUmoR9Cmy7GRXhmGbYqjMAJagSG6ElSMTLLFkDdsFo3jothZhpdu4b38epuy9u4F28OcN+1DIAKpyj+Kt1fx/hdgaHcJtpKu3pyVWxMLcvnmnKruTc1DvB1i7lMLZGAKTlq74Gq6E4wFegszmy/Euw7mnb8po7G/L4/1O1M9zSDbp1j6/vyyzLCkHNYkWVuRgLlEin/n9om7PaRErB3jEG0z8Mi5p3sNtgPgxegMvRcV5REY06VBavafUrkAo7p9Ki3m7ScDxsYcMjFz9YmPu6bA4uKi/MzP/Ix8/OMfVxzvfe975f3vf7+NXbvGGjNGCuwvBazvAyfmZ9pWq9fXZQGq07NQN5qeXYGkyyKkD2FgF9K7MwvrWNusyuyySbz0g7FbwjqmCAY4D6XIdAhHt2w44eFImNKuDVuMj2m2dnBZiSmoBrYq29Pb5c+wOYTl8CeqUCOMAZRrwjoYL0t1QtAYvMiRATBfejvk9CEwXo4MwEnAkFw6NwRbdSXpgfRhT7FoY3LyMhQXiySK5JCPj/GKFIgUiBSIFIgUuJ8osGfGS37tQBF/u7gQwWSLTePSCr0CzGGRAmO7sPlyB4uXL8HYLiVfJmfhaQQeAUqwsdCFE/weMF5cAESncGWoIJSgdexNRFbgILUBnokWYRN4AKeINHOy7fS0LI+X5Sm2QWt8MhjGGS4+5xkv6eKBiQ7md0TR29MqPBjRfTQ93FTrVYjeFuA6ukcuHO+VM0f65ARUJI6OlmH5vwgvRn04HSqoQV1DiRK4UwxwMj5e9xcF+BWtrq7qN0BXpkvLNbkJWy8TdxbhJWxGxuFe+uYUXE/DNfvYNNLBgKnAqDLVZSqlLlXX00U/vkB2x7RLKhn84/Dv1+9M9DTeG7cFnqIo0h+DSx/bBjaDC9PCcIIsjbK2MNYlUry6BOFFJhPl7aimSEPFNfShzq41SLtswAB1tzx+fhBejMB4gaTYCGy7DA6UVWVrcKCU4UR+l0CjYczIeCFl9+eixMuv/MqvyB/90R8pwp/6qZ+S3/md34kSL/tD3ohlHynAMZN9373NLcMw3SzVjRZW5PbkMlSNyHhZkikcHF0br0N1GmPOWgekOLqknDBeiMPGYa9YXuLFRi77tXE4HI0tF1ObYx1jdm8HZ9gzOIYyfB5qhvIUz9kMYSlc1STzjEbYM8vgWoRjKQ/g6jhQWoTICynQBU98p0Y71bDuhWM9Kr174mi/nD85ioOkohqNp5OALkq8JNVIx2E+t6uKVzXeIwUiBSIFIgUiBe5RCuw74yXb5NnGjadFdWwiF7Bg4akRxXQn4B73f//POER36zAYWsXihnYVILWBo4xuMF84WXNu5S/tlvDir+G2Z8Wuk7LPzAqW/SiwwWaRNo/nY+2ZvwmuMEMQttR2cPm8WSn5EEVt/WJoDYsS3vlHxhWZLzhrQ3gV7hW75eSRElzeDsqlE/1gusAF7lAZhnSL6n2lG4ZVdYGCvEq1rDDExOv+pYAtZqmuV4WNgQnYSroDlb3rt6dlcnpZLo/VZAJMy8u34eUIkmNlMl6grsc7F7z2heE3XbHad9sBZoJdBuHfvD0xzeAIo3HJj0uXWN679YvCuXi3ymWFehMa6kgpH/QbLPLt4ghRx8mzyGClE0Z0i/LUI6NyCKpFj58dMpF2uF3v6gLNXGJM247cKc1AA0fXGJ2UEW87oUC1WpUPfvCD8uu//uua7fu///vlIx/5SLTxshMiRtgDp4CraXIYWF1d1/l5BYyXOUjuzpDxApfSV8cWYWQXh0hYw1y+XVPJl3WsY/ogodqLMYXzMg+eOH5kQ4gzXmyMzZ4yCDYue8rG4s0bvRkc08Irw+6xjMlDhbVolep5edfSgcQxG66M8cI1TQ3rQNrD4ahMp4tnj3TJ4YEueQTeGY/CQcCxIxV4ahyF5DNsl4F29MxIe39edjokt65sWJ0YjhR4CSnQ2AvaV2S7cO0x3L8pm3Xidmnt4kmFMC0M378UijV/sCmwd8YL6cNvPbl0o+IzMeKoOqPqM1h08+R+bBqnRLNV+T/PTILxUsXpEVwyYkJeglYAjdOB54B9D+2V0NU0dKUx+ZrdEiDTPoVNpZZnhfoiqWHlEKT7xO31S5YJ2SMy+jY1iERQGxFE2bOVGkSnwTCFsNnFEjQmjebyA83BjzJaIOHC/eIaTuq51uiCXRe6CKbV/xFsGE8d7QXTZUDOQhf68HBZhnFCX+yGVxtICdGIHyUAkhKam5tVI4buOwrQxgD6Dxb/d6YXZB6i7rehsjc1tyQv3IRrUzBenofNF3o5olt27Tcwx6wnjfzW9FuyfhQ2nYwM+wIZyy/Rf/0D5T2Jt86W8CI8XbPgJ4OzmPzzVnB5+PyzFZExXphucbyx77B6rCnF2WnXhdIudBnNw1LwVbAR6oJaUQF/PbAhMAJGZo9cAhOz0ov+Q3F29J1i4n497TzpKh94jQwsLqGBBuPPLiiwhnHuL//yL+Xtb3+75n7qqafkr//6r3fs1eiLX/yi/Oqv/irsGRXk4sWLajfm1KlTu6hRzBIp0EwBX1NwHOX4q9JzYIAvLNZkHsyX29NkuuAPzJdJrGU4Bk/OQkIRno36obpYwndJZwEcmTAcJSNpYznJSKZDjpeXQXgq5fc8nKXuWyhEHYxzht8jQiAv2eMIg5UHx2GE7M+eebik8xDWccp4wRw2s1BTKNiFh7onbNUNF+Sx0xUcJvXLkUMVeGgcUu9yBax9aDjeVUpZ6kvD+GfJ8YoUiBS4fynQPI7Nzs7K3//932/apEqlIocOHZKjR49Kb28vbE6VZGBgYNM8MTFSYLsU2B/GS1ha8p37YoIMBW6G1mC3hG5yZ+dhtwIMmG9dn4bYbk3oIWAacZdvLYEBs4EFzBomWZx44NSIkhxlTMC0WcEw90OZ1yPbeHFS18vvQV24PAgZEt4Fs5UQITw2CzPUeGVpthCyPL4/cwzWZs9tefjrEjx2ggOPTyAKxW9rUI1Yo0oE7HnQoC7VS3pxYjbSx80iJF0O9cB9dBkSLzgRGq3IkeFeqB4VpQ+bRvPAYgwq1uPuL0y81d7eRorFp/2hAL8pnpyurFTxV5O5Bbgwxf2F61A9guTLc9dnwYiB/QHYeoEtXpmch5oaGJgVrG7pGrkCaQ6eIAqYEfpFJotkfvbsMt5PrZ8gQj9qY2i0boFiaU7KfQbE57gbgZP8uKV9Uz8l/VFQC5EdaqGU8Yq6MTfHFCbVwKXV/gMj3Ru0ZbNRY6lYvEP1CiL/546hz8AL2CmIsdO2y4mjFaXHUfQlSgexPzqTl7WxtvPGUuKVUcDeQ/a8G/oQR4f867/+q7z2ta9NUf3DP/yDfO/3fm/6vFWAm+BPfepT8o53vENBf+AHfkBVl06ePLlV1pgeKbAtCoTjFsde8F6wflmTRahN017d2MwSvDMuQsVoQRkvz15bVE9zdfAV6JGnCO9oBRoyScapVMgwKD3tQQhkSxfrI5aRwP4cZEyDjsFheG93EbYFnKNgNs3ucGkEAiEQ48NnK9MY+YZCZxkyvwHGsZVrH0ohVqH2OYvTNY7PPfD89PIzJTkxWpSXn+2XQ8MVGcXa5ujhARw2mYoRGVdqa8sqFsdkkj5ekQKRAjukgI+L2bh1+fJluXDhwo7w/OiP/qj84A/+oLzqVa+SEydO7ChvBI4UyFPg4BgvyYTpmzsyX+o49eDihfrSN8Zn4TIXtismuWhZkWeuLcDN9KrcvFPHRA3GC127YgNEuxUFTOB0Nc1JnOsZoja82IQl5fjNpn/boGVdDXmCB1voMCLslB5uAE0yMq0Z3vdnnjNbQAE8uHTjiGfCE5ZMF7q5JQOGG4k6FiU8rV9dq0MtoksODRXNrgskXbggOYHToNGhXrVJQVFcqpPoaRAQEjevl2az6HTRKsSffaJAnqpc/NehbkTGHO0lUXLsCozsTs8uw9vRDFydUpJsFYaq1+TWHRhmBiOiBGkOMl56i52QDDCmpVXPv9asDzHevqIkkIFoFnsMv3+NbvxRBI4lQZDDk2Ugw4OJgGcWHuymfZEhRpLtYvfsG0+gkFVpQoYu+g/7E/tRZ8cqJMA2pA8Le9pGeuT0gBwHk+XssT7pB+NFVfTAkBqGulF3AeOLls8xhdsBXEkHfWn6Eitwr175F8n3stPL3vf//M//CJklzz//vCJ4y1veosZ2ecK0nWtqakp+4Rd+QT7xiU8oON1Rv/vd795O1ggTKbAtCjjjheMAxxn+cZxxe1uTYLzcnMQfbG5NwN7Ws1i7jE/VZBm2tgo4MNL1CkcX++RlA3iSEQbl5/qODXFBvZJMOiJ5OA+Ufw6yNwQ3gWNSeiVwLG7LK8moWSxDI+MFLUQ0lmy6JiENKY1YxdqPc1cHDgDKpQ558nyfnIKzgCfODsjwcAljcllGYeSc0ocYjpVxpeMwy8Gfrei2rFwK4JRLIw4ocLfKOaDqR7R7okC7t894Xvx4N7va5d8sz72e5m1nPdn+dm1sBRe2bSvaETbEwed8njA9S3vxxRfl/PnzzLDj641vfKP84i/+onz3d3/3jvPGDJECToH9Z7w45uSb1xt/8N1TOoU2K7iJnJqB6gQ8ttyGgbrbOLn/zxemoEO9Ki+MLauRug7BBIxMVKHoAreFpl5wBmKMF6DLupSfi3vBWffThU8WnSY0M15CoBQsiWSHzUpzyFaxzVCOi9C4khttunCvyV/SxGy80K5L3Wy6wIPRGXgvevTkIJgwZTkGg7p9fWWIvNmGsYBNIy/dmnKzmFxZyGPi/X6igH8/vPNdNrxPRJp7Uyxi62BcwtbLNFybXsV9Bm5Or07QXhIkYW7WZQUu2ktQResC46UHTBeevnIhbJeVogtijbBSsrK8Fg4f5kriWoEAAXGESflnx5iVFWZCziSz41DGiyIxFowDYB+k/YdSYtwUMZ6MnGJnHSLqdL0O5gqM6T5xfkhOwn7Aeajp9cLbUwWuSruhBkDmpS7qbSBAxRPicJOAOKZldfRa7+7ubdkvfLurxb2Ti3ZeaOPlwx/+MLx2VbVi//Ef/yGvfOUrt1XJf/u3f5PXvOY1KexnPvMZ+aEf+qH0OQYiBfZKgZTxAkQ+RFANuApD5ys4HKKdl3GoTN8i42VqWb55ZQ5MmGUcKoEJLBhboO7JsQxKn8pEoMSLD78+krWvYzhitFM10kExQRGG81jDtDAMOD7mLy06B5eH8YwKltQVN1uFeV6Mn8m6hDfOW3VIJ67Uahh/wRiH56enHxmWU1jjPAGD52SE98Ob0WB/r0pnMm8nxmRFQZS4DF3yYFGb/oZU3BQwJkYKRAo8VBQIGS9UN3/rW98qjz76qB7e+di/tASTGDjkuX37tjz77LPyzW9+U2lEKbynn35a1Zt5gBSvSIHdUODgGC9em2xu1hj3ErC4aMZ2J3FqfwcSL1+7MivzkIa5cnsJbmAxUUNsF9K98EzC020ybZidEy8nZJ5Sa0gnZJuUFb3++PTcnvHSzKzJcrdakzjGVmmsmJ+YEwtgk9Uab2pODncNY/FmF1wqonI82enCCVABricLWJCUYJNiCF5XjsNr0QkY0b1wvE8XJYfg+rYMgxVFGNSlyhU7v15JtbLaJejj7b6lgH7mqH3rd0omHey+QEJqYnIO9gZo8wUMTIi/XxtfgdQLGS/LOFmECs5Gl35zXPQS5zqf0VF8gUz1PfYjSpKxMN8YtCKc1ylNSyIa4tMKe6xF8JcxaXKCJI33gCMHMOHTPwRUnRAdiFJhOg4oMthxYR9CxSEUh5Nm2HPpNbfaI1DTG4SEC2250APYqcP96jWNfYh9h2pGDWMGHwJ9gIY0r9cu72yHVneX+R/EbJ/73OfkJ37iJ+TGjRvaPC5k/uqv/mpLWy83b96U7/qu70qlZV7xilcIGS8ve9nLHkQyxTa9lBTghJ2MTQxyQU4vaVV6N4Ktl8mZRRkHs2UCzO9nr0xD9WgZB0lrslzvhnQMJGUwnnRjIOEczzHKWbmbM144WvjFwtsxXhyG96SSYVTLcCOcDXesVVCmBhvhHJVDeTuyYjGjaKJDMAdwoO1KNz4isL5OdeoaVMc7ZACq1E9dAuMFNuteDsbLINSneag0WOnV9Q2zqJQwq8I/3vSePFjUpr+szfahN0UVEyMFIgUeIAqEjJfHHntMPvnJT8qrX/3qti3kYc+f//mfy5/8yZ+kME888YTap+M9XpECO6XA3WG8JDMgJ+J1nHyomgCkXlbAVeGmcR4LmWvjS6qGdG18Xp9nZiC6W4VRURgQpQgvXeZyQVODGhKNrxW7qY6ETRQYEVzdcIGj8z1/UA4aZpO1rgf0R5cxTiDdgCLaN6Iez3vjhM0NapKKuzJz0giL1+UL45jI/ywOfxRPXsVOscY7RG2rkPYhw6UHm0QcwEsvjOeWYYtidKAbC48uGRnqAaOlBLsUFbi/7ZGjEMGt4HS+H4uSAnWfuWHEP6pGcNPMYrxqrEn+mXEHe939Eg+2PfcGdn4/jZ+Y0xl3JFK1ZgEGXVaqsPmC/kMR+FvjyzIOBuZVuJ1eXILaEYw9LkPyZQb2X5bQj1Y3Cvj+4flHje+i30AShn2Hf+xCWp5+TPjR8lkW/6Mf8e4fmodxD6/skSEHNghHyyfrPxoKoKyPsd0sR08dEFaVPPYfSIhpX4JnDKoUaZdHHwI/EhI9ZFZCigUqVceO4OS0XJQRqOcN4H78cBkMmB7YS8JJqtoOgMcn9B21eWNV037EYHOtE4B9uB0k7n2o3l1HQXsZP/dzPydUE/Lr/Pnz8tGPfrStCO9XvvIV+f3f/3359Kc/7Vk0/La3vS19joFIgf2lQNJzMTCR+UtDu7RTx/XKLAydT0wtYZxdkeeuzUJ1mkbOIck7L2C+wI4bPByVqeaJsZVMbh8DlHGslcRA58MkE/VKA8GzAyVRemNcHjZM9/AmcIrWcSe4Gm9aPUY5lGPN3zluOwzvNobTyxwlXRjBMRtSmIV1jM8dMjJQkCcujGB8rsijZwex9iliHQTbdQljnHORqZcmqzOdnLZTk6xmO4PO8sVQpECkwINEAY4EftkoFTJeHnnkEVV1Du3OOXR4v3PnjvzFX/yF/PzP/3waTZXnD3zgA2p4N42MgUiBbVDg7jBeUBH//GnrRXWmsYihbRPafKHNCtp5oQ7wLXhtmYXqBHWnF+GtZWwajBmI8M7D8G5trUNW4DmAHn+6cXJCA7zcOJrxWtu82RLANotZ+3nOZJM447wuDKThDDhdRHgUmRyNV4tnRBGMixDDyw1ywnjhxjFgvFRggwKHPNJfEjBWCmC0YMPYV5CjsOUyBIaL2nMBwHB/GbY6CliUQG0E7exEu1kXrU9Sp3xNGut50E9s7Etbg4Nu4b2BP/tK+X2RMUFDu1TZo6FdbgbG4eJ0Cn3o+vgcjOyiH90hQ3MVIvG4QwKmtgbGC1bE9Q3sBCDfQhUkMl968F2RkYGvKvcqWRBbH5SdEqP5nTdDNcL4k99zhVnfUfzoqWgf/+k4gXAdG3WT9MHYgb4EbSH0hw6B+Rb0H7iKBuOlAsblaRjRpS2XQ2C89KJfUVKMjMuBvpLaXugAo5YGH91FfWNzULOkEU3dPQXcecDpkrV75zgexBzj4+PyIz/yI0Ljun6dO3dOT57e8IY3pKK/X/va14SqSP/yL/+iIr8OS7suH/rQh9QzlcfFe6TAflCA40/DGIBOzH5MRjDt1HG9Mg91o6m5ZZnBmHsZ9rauw8vR5CycBcDW1vwS7MGs4GCFntN4SESRl2RwUek9nTNtRGA5KC650oA+cwzUcdmT9/OuxVsdvG6sYmMNsgIdMqlYkuDQrCXCAU7Sis4DVuqkJaR5cch0qB9jdn+XHBspyiPnRuChsVfOnaEaaFFVY3vUdp2pGHl5aQkB7qxW7UPM5zjaQ8WUSIFIgQefAo2jQch4ocTLxz72sQaD/+3oQdWj97///aom7TCXL18WrlviFSmwEwocPOPFa5N8+7qo4ZEI1iLgS+gpEm2cLMNrSw0byYmpObjOXZYbt8CAoSQMVI/uLKyqtxa6nJ5bAcOFp9bYLaohOyxsMFWnZhrSlQNn3WTWtiAWAMmzV6lhdQWgDtbLMzGU1JmxdqWYANWITGF8tYaMTGX7uABZBbOJEi+U8CnC+OdwpUOGKp0y2tcJveZuOX2kIsODJTkH1Yh+2KGgrnMPjICqu1tnuABhw4IQZSXV88rF+wNEAV9065ekGwEu3psvVb/BhzYFF6fz80syBhWkRXSU52/A4waM7r54E/1nHlJja51gYIBxudqJ7wiuyqHexj5U4vcFtM5c9K/aytVuqitYxlua9QHbECCsH35YL8eQT0C8J4Xg7DPZzsNSGMXy0H+4UamjfWTCrILBxD6AdbpKupwa6YJ4eieMTxdlAJucC2cPwwA1GS8VnEIU0YcKGCPgZQR3XopTy0IBWq5GazBfW0uJvwdJATJf3vOe96iky07KobTM+973PhkdHd1JtggbKbA9CnCM8Lk8HbQgwYFxaBXMhBrUjRaWIPWyACkXSBS+eHNeGS8TM1W5fBsMmflVpMOzGg5PeiCZW8IBkV7Ai2EsvXQMbRr+AoAUcjsBjmCe10czfw7zh3BJfC7Kc4XRjtExUaLY4TgJmLF0pto8tao2XSCpXIWMZdc6pFk65fRIQV1InxotyfkzwzJCNdBj5kKaUoi0v6U8KsVnJelwjaC9jnwtDCb+RgpECkQKbJcCIeOFtl3+7M/+bFuMF+L/7Gc/K29/+9vBcJ/R4v70T/9U3vnOd2636AgXKaAUuHuMl5DgmLE5aXNS5WKGmyqe3tPw7iw2j3M4TZrACf4iTpZuQ496ChvIO1jMLEHqZQZ61Gvw2kL7L2Rq2Kk4F0V8xp+uBjBB6xzNBYEtCvgIHo1G+yTOhU+6vkK6ZknqqWiSOtq5ExYaiORyw+8szy/Ck7HDBYiWA2QFiNdyIcETdr9zwzs60AXmS0FVjCjxQjsUZLgcgyFQ2qGgyC0N6FK9iCK37oZaywor6YXH+wNMAf8S2US+fP3S0vba97ihTJdFqB2x/yzDAOR1iL7PLdbV6OPMQl3tD/D0cWGFKm/sc7BDgM6iC1ugXANjhpjZf3hnUf6pmUQZv+3kDylkdtr/PANQcysCDxEdLzJN/MSXG5C0LyGe//xS2y0orAD7RwxTtZD2byG5r5Jufb1gpkDi7cQo1PDgvvXISBl9pgB30QOQcAETBqJkynRBH6KkWBc2PhwHsjK8ZVZi45PXIt7vBgV4ivTP//zP8t73vleeeeaZTYt83etep4Z5v/M7vxPjJPTM4hUpcOAU8HGJHo4guYqxql6DLRdI587DTh1VPa/fXoCNlwWZmK3Kc9cXVc1zdnFdusHw7YJ3RqpEY+hKxk+ONjr44ddGnqwEb4zH5J+3Gqny6Xk8ji93T6rEWM9hmGxsz2IJ4LXW4R8RBrmRnmp16hjPAwG6kK5hrsEZkgxCIvHS8R54ayzC4HkvGC4DMjRUgpSvuZAuYJymFz5is0MA1gRPKE8vTj7xihSIFIgU2CMFQsbLdlWNvMjl5WV585vfLP/4j/+oUZTa/dSnPuXJ8R4psC0KvDSMF1TN51PafGGYqgRkwiyD2bIE9Ql6PKLtihmI707PV/G3grhV3JG+DBUlqFEsIX0BYRrgxToIoq3QrcbfBnZpMLuptlA4d5MRwkMnMjDwX5kZnMZVUgZ3negRocsMne8Rwt02hjSUZxtDjcMP68owhFhwt+0c7zyiJ37ancAaAnYnYMOlpxNqD13Q9+5SF7clGHg5AlsuQ7DkP4oNYgk2XoZwSl/ugUFQlXThphILNkoiABnrRpy6atO6IRyvHVDgQSAa22CXfmcI2uKUcfw4YFdgGRJj6ARUPSID8w7U9ZbAgGF/IQNmCkYgF+mRA31pGaeQs1DjWwITZgVh9p9qnQvmpP8QITYLWgZ2DFTPofFafoe0WWAMS2NaqnFewicXa+O11S6hz+wv7FNkvIDpgzD/KOmmzFP2IQKrFRoyWMBwQXlUx4OmEIzmFlRCZ6ACW0joL8ODPTCWawzLPqjhDdFGEvrP8ECvup831TwyLMlwQT9CX9Ir2Ryw/l5HxhsFDeTh+XUKvPSt57t3DwJf+MIX5LnnnlPDu6VSSV7+8pcLjehSJHhgYEBGRkYenlcUW/oSU8Dmea0Eugkl8Pit0rh5lWqeUOlcVM+Mi3IT9l4m6eHo2hzUpjHe4qAISpHIyrEH0hzIz0MUqknz4qqBQ573QoYsxX4VSGMyCIvjcwjDWD63is/ndTjmCa6maI/AXYNaW2TAg47THEHxxJ/kcggmk3mPUT4Z39eEqtWjMHj+6KkKpBPLchqMF6pV98F23QhcSFNlnIdTXPP4FaC2qLAwB4r3SIFIgUiBHVIgZLzsRNXIi/nhH/7h1NYcPSx+8Ytf9KR4jxTYFgVeMsYLa6cLD6xm7G7SK1XsAmmzYqVW1w0kGTAU56ULx3lsIKchzjsP5swsGDK0YTENNaRlnKogCYsh/EEaBuwPLEMSxgWWCCptgl0j53UyM8hw4SaSywed6hlOZnruzbAn1HUMly2smzFeLMwTL0oKMA5F6WKMwFyQdZDxAoTcMNLTCkxL4CQeG0XYb6HNiREwW3pxQn94pFdtuYxAvagIwAqMyxXVzW0Rki6mQqUMF1SSVbG6sZJ4iNdDSgF+jfzS7Me/V/8o6ug3viGgWhuNVq/A7fQCOsbiMlX4FnWjMKUMmbpM0hYB4smAqUESZrEGxgs4L+DN2BIebtz1e+M3iMLIcAn7D+P4OaZrZVQsrRP7BeuJy4XCVMIFfYT9ZwPSNlyoY/+ijBe1+wRYsGO05/bAwxcZPQNYsA+UoJaH/kPG5fBQEYamoUqE/lPC8+gwXKyjXw32ej+CpBgq1A2uJxlCqe0ndkpe6alsY0dqfDLQB//X39DD2foH//3GFu6ZAugiZCDw4miHoUsvVXvE4EUGNiVf7swtyFji4eiZF2dgXwsSu3AOsIQxdRXqnetQ7yyoTTrYe8H4xNGIqHDOpOOg9UCsH9Ku6BBWXva7VZ91BA6X5dxRKEFjIzzqyDHbG6+IkKLjP8vx2nOdBGYLYkwSGabcO6Bm1LGK8bsbjgJ65HF4MDoNg7qnj1YglYgxHG4cB7FIIpOdWPSgSekc0MKblJSjxcefSIFIgUiBXVJgr4yXn/3Zn5WPfOQjWvorX/lKtUG3y6rEbA8pBV46xkuyNuAGjCsOn191ksfL4DxP7xc1MGDm4EGA+tTzUJmYxsn9FD0K0JjoXB0LHjBgcHI/i0XQAlxQ0wMSbOjjj8wVk2lRdR2UAUESXdxwU8Y4nezxwzseGy5Wz9caymBBhKlHUNKFp/ZcYMBDky5KEhxwmcgTrX5sFnlSP9rXAaZLN1Qhiuoy8fghGv3slsOjfTD4WcZJfUVViqgWQV0KLjx4eV1Yfq5aLSI0yz7+sOWtrqaatAKKcQdMAfYX+3L9PYXvxVLZh6rweMT+U1+F9zBIwYyNL6hdgjtwgzoH20nX4f6Uno8m4DWMXo/mqjC8i1XzCpiYPLHcAOPFFtcszU5q8Ykqo4XPPLhluvYwhnUrETaebBTrQ6ypSbrwzkW83akuyM2HbW7YLvbaDYEjIpUaG+3tlGH8HR2l+h2MMqL/DKHfHIVKXg8kXgYh4VKCpEsZjBeq5tFjGK+EnQoGjz3zl3VNL61v+vSABLSVaIu1uXWjAho0wYVprXPH2EiBh4kCjcyGpOXoXhzDKK3HAyIeFNHD0eQs1iVgatO19G04BhibhqTu3AYOhcDMxrqEa5EC1IyLuKtCTdrdtuqv7dIdAdMZ9ruNxY25wvTGlMb3mYfzZ6K3fFoKxlKm+KXSjCgfR2jKWOdYT9ZRD7wYQWsaRs675RjsujxydhjqoWV4M+rDYROlFinBWNTDKtY6HaJRiIbTCC8p3iMFIgUiBfZGgZDxslNVI5b8W7/1W/Jrv/ZrWglK49L4f8P6cm/Vi7kfAgq8dIyXdsTVCd5Ol7jAqePUfhFqFAuLy6qCRNUjnuZPYaEzC6Oh9CSwAKYLPR8twH3uAjaRazhlUnFXnigBh7FfdGpHqZjVIdJiG0VbPoAIWhtdVKQpFscVgC7AuAjQP5N2UamYpA08UCfTpAu4aXtiAOpF5SKZLlAjgprEyGByUo8T+jJO6IcH4G2lF3Zd9LQHOuDYNNoag2VkhLH6ZM8x9LBQYKs3n3ybSo7gg+G3ze836Tc8eVyjnj108KZmFvR0dpoSLxCRvz21ompId8C8pNvpuWVIoACefYmSKFQFIh6e7vJiKexHTZf3JSYRNKnahn3QmkP7CtIttzGHCJiAqv0W7T8JY6eC/lPCJmUEhnMH6bUIkmHsN/RSNACvRaOQeClCqqUfTBj2HRrS7eSxqTJuWEMu+3HpiTLuKJhlJVVq1QpCPwCXU5RNMWq3b5TDbgXXHkNMiRR4kCmg8z4aaCMW1ww2kDBex9g1k86lrRc6ApiBOvQLN+ZkDCpHY2C+3J7CoRHWJbT3wv5IVedujqI8ZAEqDHE6JmlPxI/1SP56n7S7x3tsM83DFDKw7QoxGQ5PYbrFJKC5G1dHnp7k9EdFYUySZNlkcwXZLoigRCPt3HXDvl0/mOZDsGV3DMbPj8MO14WTw5D2Lanx816oiFIysQenYdy0cL6w2iE/JwyXTvRBO1fD+BgpECkQKbAbCoSMl50a12V5f/iHfyg08M8rMl6UDPFnhxS4hxgvycyuszknXkzGWK+o1Av0qVegR0SvRxTtXUZ4Hp5blqEqQde5C7D1QrsvDNPuSxUbzTpO7nmn2oUa3cUGchWnT9xHmkFf7MvwoAZGSTTE85nLFlef4OJI/3C8z8WBGcmF2gUEVCg1Y25tTbWhG6oPPfyDTsYAbVIgPASdowpUiyqwTdED2y4DOOXhYqOiHle69cSH64quvCoEI1EPUoSheEUKNFIg6SsNX4fF+WKY/YYLYUqU8HSWXjhowJr9qIqTWtpNWoKh6ln0I7VXAPtJtA0zhz5UwykuYdSDB+5k4EBwJlWxU4YO+wqKZN/ih8pTT+1DSQVUbJwMSSygqfKjjJXkW6exXD7rHfwS9hUyUrrhAYTeLfohFUZVon4wWwZg06UfIjBUyetDPyrBUmMf1I0o4ULPX8TBMF1Fb2xQ0J2XMV58Kc8YZf7g/mD3J/8u2OKtWkrYEMbzhnHEE69IgYeTAjaUeb/IegtjlMmAwY9j6zLGSzoCWATz5cY4bbxA1QjMl+duLcoUVKHHYPOFKkcbG5DKQ2ZKv9CjHL0zcn3BgZNltS6JZRn9dVmgQWZyaI5yHja49FczIE2TszxtoJNsBudl2oooK8PqABgESAPCcQqglAsfGNcJhaMKpH4PD1HSpQdeG2FEF57mTp6AQV0wy4fBQOeYzfUU7bvwYjkZhYme9YhXpECkQKTA/lIgZLw8/vjj6lXxta997bYL+cAHPqDOAJjh277t2+SrX/3qtvNGwEgBUuAeZLywVsmky0mdesMQX1nFRlLFe3Ecr0wVZcRQzxrMFhimoCcXGuVdJGMGfzXqXycwdegKYW2EONjBIC48mzFf3JMNpC4gEhUiLgj4jwwYVVNKNorcGHKjR6kWrhdKcLNCmxI05lnEJpDuI4vYOPbBswo3koM4nadKRAmqRMzbA7gCNoh0m6hW/Gk4A+WwDFsdJUsiLmoQzysuP5QM8SelQPKNtPwyuPC1b8klVfhdkflSR78x8Xj2JxinxkaBYvLsO3onw0Vtw6yAOUP1JD7TAC/7DMTqocLHPlNXRib7I5/pDh74UTeq9VgfMkYLP2l+69qHsLvgaa/2HRzzFtiP8O2zH5HRUkYf4clniQxL9Bv2H6rf0eNXL9J68cwFejfC2o8Aq4wbNZ5rTBwyd2grxi7SgD3YLqUYHvw5iY63SIFIgUiBzSkQDrcc4HDx15gOHFvXdUzkmLmEMXN8al6mZ2njBWpH1+dUIvfmZM2M/6/D5gsOfzBiYZ2A8QyDJJYTGJiM7aADKFKtFCYkhesN45lLgSAluzjOJXBZZItQhq8ZmjE+OhqcwmQ/janJ+oQ0sBEXKqKkDbLSul4RNrqG+jrhOpoejMpy5lgf7HKVoXLdL32Q8u3HuqhLVVl56GTlhjVI6+JVatGaGBUpECkQKbAbCoSMl92oGr3rXe8SupHm9frXv17+6Z/+aTfViHkeYgrcY4yXZBLWCT95K4gK599sg8cTeSx6KAVThSFRZb7UlOmyiNP9KpgwC9hcckFUhSoF/5ZpsJdMGCx+yHwxaRjbQGLfqIsobt7ofpbMED+lL4DBwgVCEfciNo69yoDBhhEuEqnq0NdL6ZWi9PWXsXHsVJe23dgccpHBe4H4kJ94yVTy9iTruOzz84UV1zCAi1ekQCMFfHlqHcR+DULDidQJY5L1bJY9+Jz43dVgA4bGeGsQl1+FVMsixOXVnTu9iUEKZolekiDmMg+vSHVwWFagwkemSxV/yKZ9iNJixoihVA0W4VyI444DXXzv7CvGmAR/RJku3eg7PWC2UCqMDBZKgRXxR/fp9FJE+0e809sFPXuVKRlGkXQwYcjEoVQLuwVF9cOLG4DGK2ksb0wKHhvhDuYpKPJgCohYIwUiBe4qBZrHmIz5wjSqc67gb3ZuEY4AoAoNlc4XbszDhtaK3JiEKtLSOqRxN1SlE6bhlOHSDeYD1wUco3izsa1Fs1xcD4A2lJHVkh/zWuRTaINLhsAWuRyPQ+TxWLoPsawBw85wYeVZ7wLWLjywwpANaZdOOQK7LkeHS3LqaK+cPNoHdesSvDZSZdRsu1DqhwMzhnQSUuvlbdMatKtOvnrxOVIgUiBSYAcU2AvjpVaryZve9Cb5/Oc/ryXS0O6HP/zhHZQeQSMFMPNh0eAz771BD1ZnE6aDG8xch1oBT/XJPKGqBKVZquoJKVGngMTLCk70qZ60phIvMHIHGJV4AcOGTBuXeqFEAMlgTB2IymIVxBN0qgBx/ueGkc/KREGY9ieMEdOlJ/dlqBNRXYLuT3mSzxN8urKltyIyW+gmkYwU4uDVjqliJ/aEAex9sfAIP537osJK//v3J6Q3W5E760Sy9+aUNUFmHj8mT0gaTyaLntaC8bKGPrQMRiUlYpbArKTRSKrsKaMFz7T9wjj2nRqYMGSu1NCneGffUoZLUjb7JL8E9g+6cbY7pFxQoS78kBnD/sQ/nvoWwIAhs6UIhmWJkmOI43N3N9TzVFIsY1yS8cILvUPvfrOHxt+wuQfdl7SsLT5/JvPtbQHW2Ii78sRa5a9Wtdyq9lul58uIz5EC9w8FWi2TPI5jHsdOjpcLUDeiTbpZeGF88dYcDJivwMsRnAHAiPk0PMhNzcEgL8ZM2eDagh6OMBZiWOPQpssDrhNyZEljwkENMK16bi5r+ujjTxqxrdzIpRk5xltOSlSqcWGwXth+qpJSagdDtdq164Nq6FClSw2iHxvtlVNH+mFMt1ft2dGuCyWDqW7ta6BwbP6/7Z1LchzHtYaLxIsv0KRNWRGyht6CHd6Bpp54KkU4wuGBZ16CBw7PPHU4whvw1BOvw1vQle+VZIk0KRIkCFL3/0/mqc4uVANooAFUN74i0fnOPPlVZXbW6Xy4qDUZ+MwwYoMABNaKQKt4WfY46X/84x/db37zm+7LL7+MOv/973/vfvWrX61V/RH2+gmsn+IllhNISeKxiz7K/hOe+VJmwPhX/HK0rpUreqlslCye9WIli18kY7Ak08qWongp+Xk4E0oSD4A0EogBkT68ZMLLg+LXe42U7PZsFytpfBS0FSweVMS6Zb9QajSyp5fH2zJjKZHy8o88lnv4i30+BmUgF8OPNRl/tEO/InfWBfMyCLS8S/5FzVHGxx5LZ4zZ3UifKo8VMfpvpWO0H7UPK2CsWHFb8bKjMpNMbj2s9vdyPy9VKorOktbtzm7/uUlGG1IRHpTHbDGVEcrLeP7VjkIJI+WLZr+4jcTeLG5H8nc78qwwK1+Kf1G2WInpcCsxo036NWVWsVrpxZzd1nzNpSleK/2MciSXRatFjubfij4a4Vo8W4mzBmOSOt6Yfwp9WnjGw4TA+hEo381uAWW2ifuj9HP7z/7Q+2PFsmcpXz7/8r/dM818+fLbl90XOmL6K50i9+9vNJtQ45CjI6V/r/YkRYZPQvSf/kdnFf1VNkv59YoX9bWlCWag+5uT2uQizk4zyyNi9U7151GmfatiXxV0sIu36bq+0w9fziXGQerTH+zd0kyXLk5xfPJor/tYypYPf3i/+0izXZ48ftDd06xgLxf1eMh9+rFLGYcy5jzVOZYZHhCAAATGCbSKl2U2132jlRV/+MMfuj/+8Y99xl988UX30Ucf9W4sEDgLgekpXk6ROgc7juZBgDUZRYniX2D8AugXQStk5A5Tbr0Z5uCoxPHLptLJvyhdIqfIy7YY6HjwoRFQDC40UPBgyCen+FepmBGjECtlHMcvlX7Z9IAilyg5fsyYsSWvas380zvNKkWUmX7TNVPalLCpZ3phrphAMl+GdXmiowFYGieVlx7/sLg9ZbuJXzJDEeO2ke3K4cU9M5Ve8cKtjGw6zzD84bah5z4G0moj0TbUWKyIifYhGcpyJLcEtRvNfrEy0uPx0n7UjhQ32l+aFjeEz8/eGSFjHyGKoy2DayyjU/wCpcvRX9rbJFl8IdOGTNF+XinPm26KDJAJAgMCtWGrt4sA903u99IsfZ/20vLsWy9/1n4v/3n6XfdcM2C+eXYQs16++u+b7n++fq0TGL3HVqeZup6xW5ZhWhUR/Z3MWkIVQH1m7fccEn2JPkqfcvE2V6sVfXf2YK5jKcmlqJ+vktiQvr7Gfa8fnL6XolxLi6R0eaRl1w91Ct2PHu51P36s5UVSvDzW8qInj+53j2T6Ryn36dGv67sgvjNqvqUupf5NUVghAAEIrJxAq3hZZo+Xv/71r91vf/vbXh6fbPSnP/0pVjr0nlggcAYCk1e85MtT1qV8YfulLAcfNSS/vTPiMXM2fHAe/gslTbXPojteycwDIV/er8JWmxkWAWf8yDrkYMMvm7WImVnzKiWeMeNriVY5znAWKULw6Ut/LchWVmgL/Wys85mbiVDTLUg+9J6VWF856sMcbUiZhpnTXcJdSoqXCA20fVmpUtprVaYMCylJRj9dvqP3coSld0WabKfDDLLui8KH8c/qni/9rKlKvCWqvlzGK4vt2p0k5TK1PymflQlMRhC4fAL1sa+94Gh57hpjhq1mB3rZ87PnB7Hh/1Ptk/W/37yUAuZ19/nXr3S89Lvumf5evPw+lC85qsh+zkoPzyexWz/1hK30n2VnFQc4zeyShyMPrqFXUaYM2q+c9rHsYdoebn96xkvxLS6HuQ/Xp/68n8sd7dm1rz1dnuxvd48e7nRPpGT58PHd7sMP7nc/uL+nAwbuaK+7vTjFKOqgD/fHzi+vlHPV/XTmP2a25Ts8Zci4p4VnvI0082ZvZOWo1E0n0CpezrLU6ODgoPvLX/7S/f73v+/RPX78uPvnP//Z/fznP+/9sEDgrAQmr3hxRVJxMazU3JflnGMYM3LRR0bSN4sy9a/6Je/iju+b+CiDgxwd3NL0WP8ryTOPsTLG/WbyR+ZzkTwYsW9ey+eeKa/KzDpUMysXgk9f+quidDnlDNifoZBW+VDs9R4tuFVD73zRsH88p/oIM8q2o7ShEs+/Aheh+hcDJfSAugyqS+5+5jN5mOmu3q3h7BycZXqllD1cl1mbbFPM7G3dZ74Xt6UsmVOKP/TP8DQzXrqnaVbAC4U7rZZtwvWocSsxdgicSKA2D/ctfV+nzi7ttlgPbQXMa23E+EZ7zfm0o681++VbKV7+/fV33X+kiPn3t2+6r3TM9AttuhtLjkKnohkwWn7kv6J48fIjLctUM7IOOxQeFq42qzDctw4EHrozeLTlytP+ZQZkMT3HJf5cRyeOypUlpN6ry7NctiXUw/veSHdLs1x2uw810+XJD8pslx9rb5cfPdqXYqZsoO49XbzEqP/+6b8AimSWN+tSfPiEAAQgcDkEWsXLT3/60+7Pf/5z94tf/EKzD4/qOLWML58/fx4nFnmmy7/+9S/16doVvV5/+9vful//+tfpxITAUgTWQvESNaqjhmqMVHI+pHyRZ7SRoUgdVAiABh31l6SaxWwQUAc19Zd7tcqa4XxZ+VY48I3oMWYZDI3mZUsZ19XMWiebda3HZspdh856AsfvT969fIbLMLxlUX4nLUPjWR59vm4/ip5up3RZfu4zdihesg1l1gp32bHlTEaMMP+aOueRKYrZC+zE80G4roJAewMWlceNWUQG/80hkIrdtkZ+8uMHHWkyDr3fnP68qf+z56+659pw9ytttPvtizdSvMj+VJvwaubL4WE5JOCtlh5plVJ3KMXLW3Wq3sjWS3O21FMWxUvpxfvWJUtVP0dXONYyHbf0ze03gGec1NgybLcr03tvr+jTy+BFMpQNdL0Hzd4dHS6gWS7+e/RQJ9Dd3eo+2N/rPnh0p/uRTC8v+uFDLzO6V/buqvt0eWlpzU79diuLCg63S+9rJjsXBCAAgdUTaBUv58ndR0l/9tlncQDEedKTBgIrULzk1/X1fmnGi55EyC/3kKa6T3qPi0cgEsuWVWmfixMSR/QsUIMGD8Rm0TW4SCSZb7rb/LFDYAIE+sd4RJb+OR4JS69+IF89bs12aMwo86bahNN4CJ7NozTApt3Mp8A1CQK+W8OObHYHi4jD8EkIjhAQWDEB9WCDR99K45gNo16tnJpYNv5/oSOmD7Th7jfabPf5d4fd/3mzXe398uylZr289J4wVtDoT4qX11LA2LTixZcODOrHErP9XlxOHbJYq1GvGIPI7jD326WPdaAc8siWOa80cuSSgeO/c4+sDPSpJN4IvZzsuCNBHmhZ0QMpXx7cvS0Fy263r01znzyU0kUKl8f7u93De3e6fe2y++CBTnTU0mxvjB5LtKVht0IqrkYOuy1nL4CtV3RVabLqpb617AxLUZJbujEhAIH1JHBexcunn37a/e53v+t+9rOfRb+4nrVH6ikQWHvFS36Xnwizfmue/cszv3bHU2RoO3jxi2YOcsrLpGKVEUX/xT6znCgtgRC4AIF8Osef3UUZ50Dd0+Tby49wfYxb7wvbS7tNWcuLQmZaZryMzHzJ6MtVLbPFXAkB34ThDWhvzFj4SgomEwhMkkA+/e14wILa7T/vJRenLmr2y8Ghlh690oa7mgHz9Onr7pkUMl8/faVZMIeyH2nj3e+7F2/edy9f+4CAyEWzAtXeVEjf6mR3mf7z5PfoS91Je5aMYjmew/KzT1ct6d6KdZsRsXzUgHrYkhQmPoFOs1w0u+WeTpy7s3ur+0CzXB7vW9my032gWS0P7u92j6WAeWhli5QuXla0K03Nbj0yOvvymTT+PmmV6ylNIwdWCEAAApdE4PPPP+9++ctfdj/5yU+iL8p+2/1S2q0s3tGptPv7+7GPyyeffNJ9/PHHWmK5HWkuSTSyvSEEVqR4ud4vTw884uWwjDbqoKO5gxJvlRLWYuqIp5RTBj8uJwc+KdRqy25qhRUCKycQz3GTa7SbVTaeJu9ZO7KnXU1BsjauJhXW6yUwuE99b5t3axh+vdJSOgQum0Dfj6ktDPvPVvnik+De6lggn3j04sUrKVp0wpFmv3z1VMuPNBPmqewvDt5rFsz7UL4cHb0vyhel8WpobzHg/J2nTxeyXibMOrswVkzHD0DHa+yfhdyhepzkluq/21K8+GXD82WsZInlTPqICTRyb+94PxcpXXY1y0VLiu7ubWkT3Z04ucj7uniWy/5dvZxo89wHd/e6u3fLLJdYHuWTIKOUWpiM/qoyFLcl4YIABCAAAQjcDAIrULwDUAuWAAAQ9klEQVQY1GzoUb7SrxFeFcVGKGMuQRTPbImrqbbLapzFUccU/QDkEmQhy5tIoH3SVjtwbV8cov24qNUWMXfD+pr0lll5l1jsnAzTc7QwUrqWRhve+mfcVZltOW2eY2W2cdvw9E+/dJ+WXxuOHQLTJeAn2k+3zfzFdF5ahei/Z77E7BfPfHl92H338o1mtmj2i/d60cyX/8r9XHu+PD046p4fvI1ZModafvRWG74cHZWjp624eSsFjPeCsdIl/3JzXJdrhUwsU5JQpdV5Hky5rGRxv24lS2zaK48tKVd25NCkFv1paZFmuGzJvSuFy4489+9pL5c72929ve3ug8e7Ojp6R6cV7Wjmy53uzt6uNtjdkVJmt9vZ9a/BLkB53tLONPllYr96xa/KglG8moCMMEHT93U9JJ0gPESCAAQgAIE5AitQvPhradE1ha+ri35tjtVvCvVaxPy8/lnPRXU7Lfy85ZLuOIFk7ZD2frT+maoNT7+rN1vJrl6iLP3qS74YacudMmcdls0x0y+bbhh/mfLbMpdJNyzzNHdbThv3PGWO5TWWz2nxxsJb2bDfTALlWSqfZf+VoorJFl5C3lXli0/IOJRC5Y023n15cNi9evO2O9Dfd9rz5bkVMFK8vNbMGCtkXmkWzJtYfqQ9YLT/i5crHRzp9KNQwpSTkKyAsbLle+X/zo+oi6uPqg0rPOzpWS6xhEhKll0rXKp5TycQ3ZGy5a6WE92/v63lRZ7lsi3Fylb3UAoWz2y5I+XLD7R3ixUt96yI0XnSO1pW5L9tz3CJKTOl0PbHpii6kUFWu4rB5yUSKM8crC8RMVlDAAIQWJLAhiteVvHFk3m0ZDd10NCM1trq9vbTwvuIWG4YgWErudoWkqVfbakXu8VDmdPtXF2P1r2opFXW1+Wdp9yzyLlI/tP8F9VvrMzTZB/La1E+Q7naeGP5DOPjvqkEymzYwTPimR9e1iMonpniJUH2eqcNtXzstBUuh5rCcnj4VkoYKV205Og77QPzSjvsPn1+IPuRFDNafuRNeHUC0ittxCv9jE4+kkJGGhdNoFFet7r33vBFl1YodXX1kVxlOZGVH3EctRUvsnsJkRUve/6TwmVfx0L7pKIHMh9qhouXFXmDXM9yeeSlRLLf2d2JPV32ZO5JUbOr3XY9W2ZLa5OKYkfFKW+3lpnixSczyYPrGgj4Tvjyk8cFAQhAAAJTILACxcsUqoEMEIAABCAAAQhA4PoJxCqb5n23WK11KS/DVsP4lB8vPbLy5UgzYGx6lsurV6+lXPEMGC050qa7MSNGs1y8DOm1zJee8aKZL4dSurzREiTPovEJSla4lKOsy3KjXgUixchtaV5u33ofypdQlkj7ckdHPYfyJRQvmt2iWS73pWh5GDNetjTzRQoWz3i5r/1btJRoVwqXu5oFkzNcbkvp4p1crMjJE5iCfF/vapHRe137rSlqobOKsVzss+Z6mfHK8zVeQt6FjNO6066U61fp8eoe8816DwOaug+Dlna3ZTjf1j2W2arKbsvJcleV95jcY36tDMPwlGnoP3QP443VweUM4w3zSfdY+gzDhMD1EEDxcj3cKRUCEIAABCAAgQ0kMFS8uIqxwW19Z3C4rRFPU2B8mpxnwBwdHXVvDj375Sj2eLHy5bX/tCzppZQxh4fvu1dSvLyOjXe130vs/eJ0UtxIAZP5RN5RpsqV4mVLihfPeollRlKYbEvpsmPFS/27r9kru1K83PWfZrzYf097tmzbrSVFuzrhY0dHHNm9JU2LlS2e5eI8y7tlfcGphnx1zfzmvEvgNX3WG3BNpV9+sa7foivvQsZJ9yD+xiLKeg/qG84FLMainuh3UhljCS+z3FXlPSb3mN+ydR/LY+g3VodlyhlLPywDNwSulgCKl6vlTWkQgAAEIAABCNw4AnphsKZF2opQuFgVUzQvQSJmv0hz4v1fPIOlKGKkVNHsFs+I8WyXQ5lvvCxJipgSrjApYd5aEaMZM++13kg6HC1n8mdRjri8mJlihYk0Lzvaj2VLf9ua6WLli4+AvqulQ96nxbNZvL+LZ7Nsy35L8Xd1hOq2lC5WtPh4aStb2lkudsfapizPBfuq7zx+TZrO608rTWu3wHb7igpV0+6xeFmjYZjjT+FKuYamZbOfr6xDcR37zKTHAtbVIyvU1r+1r6JemZ/zSr7DcrOcDE/3RcxhuVnmRfJcNq3KbMVw8r6KaUm5hhGzrGG89G/NzMN+aT8tvzY9dghcLwEUL9fLn9IhAAEIQAACENh4Au3LQSpfXOnin6cAWQFju/9iBouVMPo7fPc2lCye3WJFTFmm5FOOpHAJZY2UL549Y2WOs/Sf3mO830qZnWLFiRQqqXhpTCtZ7G9zR3GscPGx0E5nZU3ObvFrUShaZDZvVfOuWYTwn9ZHYV1kcm0qpPBow1qp82Uw/do06Tcls61H1tHyZT2G4Qtkv/RqnqeAlD3rskD2hd6Z3hHG2CxMeMaANv9M0paTfjbPW4c2j7QvKjfDr9gMhbLKPHcVk9lYBrWuNvq+Zqz+rvNYevtzQeD6CFxc8dI+7yc848No0Waur96UDAEIQODSCET/po+5fk/9o8cj/VghS5fn99Uzu9BIn+GNOfQfupuoc9axcsf85hKd0WEZ8gUm5R9LWjYeLSF+GTyr7GN52a+UOxtaLc5vcUjJe1H4Iv9FEuEPgZMI+Hlqr5HW0jfK8uxZ8TJTwljZUma6eHZL7u3yXkqZ998XRYzD5YoNfF2SS3Bbc6dz23u91FkvNj17xTNfbt+W0sXKlVC2lNkufR9li/8sV+bnTOOq/gq3jOHdhmW0MKfSlpp74B2IfWnD4dllv9btkL5SNdpZ6zLMJ/PK9GPhtYjRMjMs82ndaR/m2co+DMs0md9ArtihOf3auMv4ZbqUY5g23TYXXXlPWnNR3GF+We4wfsaz/6Kys7xh2tad+Wcei9Is8m/zsn1V8TKfNNty7DeBK/q6Kkfis7MXb0z2odwZuc1gGCfdGTfdmBC4HgKrU7xEG9HDX5/t482gBES0E+pak58QgyAIQAACEDiJQH1Pmo+izrXvf91Vy+EXpr7TduzaAVejST/s0Y/HaCIfsx5XujTlZtYLyz6WXXhksn7crPTzUvUxBhnMYpUY/ix+w5CScOY7yAgnBBoCpz9vTeTemm3DT1m2W88wKW0z/Tz7pcS0f9qdINzVnGU6y9V6Bbf1uGTm7Bfv/WJ/Lxuyny9vwht2nUQUCpvwLR99K3Gcxv+4dRGH4zFdynJX5t2ma/3SPszV8TOstZ83XqZLOZx32jNslWbK3uY5Vt6y8ZZh0ZbdltPmkfYx2Zw+02W8Nk/bx9JlmmHcdA/TnBY/09kcpm3DVmlPmVZZnvNclN9JYaus1xTyWlTX9E9zCrIiAwTUavWl7afyAlcmdweQ9mF2izqHYTzcEIAABCBwcwi0g6Lh98dJ3ynXRYjvsusiv17lts/1WSQvz36OxlrFS6bOoZrNtqWE2x5ZZGbSe+UzW1KlksVaF4dY5xK2Gq21Z9kRyXF7j2UsKdgyaU6Ke9b8zhqvLaswOm9N25zW034eZuetabJ2+tOerDZulrcoTdYh02S8oTvzwVxvAnm/17sWSH9zCKxA8XJzYFFTCEAAAhDYRAI5KG/r5uVQ8/6n/dbfpsYOgWUJpHJlUbr2FTKUM44oRUtR1Oj11REGipfIK5bV5NNcXlS83KjX1mSa3ksW+ynDkCkyPv31OMriAwJLESjP4+lJMp5NX35YT7syjeMtk+60fAmHAAQgcD4CKF7Ox41UEIAABCBwwwjUd9EbVmuqOwUC+drYKlZCrny3lJnWlLdPkx6t2by3NlbFmHfZOfBpckllTsZZHLNJhBUCN5LAae0zW89Yu23D0n4jIZ6x0i1DeJ0RGtGuhACKlyvBTCEQgAAEIAABCEDgYgTaWTHtC0W8aOTbRh/QW2qhOTummPZ0fv3yo2Oqm4vJSmoIQGBGwM1z2CIzdCxszC/jY0IAAutJAMXLet43pIYABCAAAQhAYGMI6DXLb1q+6tKe4pj/7BUluTxoPvjEtMOouCEAAQhAAAIQuDoCKF6ujjUlQQACEIAABCAwGQKp6bBA+Vv08d+Z0ydNx27tdl/mlWWleZllkTcEpkHAT3tei9vmrCW2raO1Zx5pjuWbYZgQgAAELpcAipfL5UvuEIAABCAAAQhMnkD7sjb/cpYhaboqbYysWr4epnsV5lWVswpZyQMCFycw9sQ717LZ+XwbsytbZZopQZtPGy/DMSEAAQhcPQEUL1fPnBIhAAEIQAACEFhjAu1r3bFVPxmo971hWAT5Y/4NslfkzHlXR8nDiUrG3pPltv4cPBd/jXkiOgRaAu9rQ8n9h3jOWzrYIQCBdSWA4mVd7xxyQwACEIAABCAweQJFXSIxe0ux2uk9W4rpasgeRz/Pwu3r3/q97ctW/OmjKl0cxgUBCEAAAhCAwHoQQPGyHvcJKSEAAQhAAAIQuEEEUjHjX/35xf8G3XiqCgEIQAACG0kAxctG3lYqBQEIQAACEIAABCAAAQhAAAIQgMAUCKB4mcJdQAYIQAACEIAABCAAAQhAAAIQgAAENpIAipeNvK1UCgIQgAAEIAABCEAAAhCAAAQgAIEpEEDxMoW7gAwQgAAEIAABCEAAAhCAAAQgAAEIbCQBFC8beVupFAQgAAEIQAACEIAABCAAAQhAAAJTIIDiZQp3ARkgAAEIQAACEIAABCAAAQhAAAIQ2EgCKF428rZSKQhAAAIQgAAEIAABCEAAAhCAAASmQADFyxTuAjJAAAIQgAAEIAABCEAAAhCAAAQgsJEEULxs5G2lUhCAAAQgAAEIQAACEIAABCAAAQhMgQCKlyncBWSAAAQgAAEIQAACEIAABCAAAQhAYCMJoHjZyNtKpSAAAQhAAAIQgAAEIAABCEAAAhCYAgEUL1O4C8gAAQhAAAIQgAAEIAABCEAAAhCAwEYSQPGykbeVSkEAAhCAAAQgAAEIQAACEIAABCAwBQIoXqZwF5ABAhCAAAQgAAEIQAACEIAABCAAgY0kgOJlI28rlYIABCAAAQhAAAIQgAAEIAABCEBgCgRQvEzhLiADBCAAAQhAAAIQgAAEIAABCEAAAhtJAMXLRt5WKgUBCEAAAhCAAAQgAAEIQAACEIDAFAigeJnCXUAGCEAAAhCAAAQgAAEIQAACEIAABDaSAIqXjbytVAoCEIAABCAAAQhAAAIQgAAEIACBKRBA8TKFu4AMEIAABCAAAQhAAAIQgAAEIAABCGwkARQvG3lbqRQEIAABCEAAAhCAAAQgAAEIQAACUyCA4mUKdwEZIAABCEAAAhCAAAQgAAEIQAACENhIAiheNvK2UikIQAACEIAABCAAAQhAAAIQgAAEpkAAxcsU7gIyQAACEIAABCAAAQhAAAIQgAAEILCRBFC8bORtpVIQgAAEIAABCEAAAhCAAAQgAAEITIEAipcp3AVkgAAEIAABCEAAAhCAAAQgAAEIQGAjCaB42cjbSqUgAAEIQAACEIAABCAAAQhAAAIQmAIBFC9TuAvIAAEIQAACEIAABCAAAQhAAAIQgMBGEkDxspG3lUpBAAIQgAAEIAABCEAAAhCAAAQgMAUCKF6mcBeQAQIQgAAEIAABCEAAAhCAAAQgAIGNJIDiZSNvK5WCAAQgAAEIQAACEIAABCAAAQhAYAoEULxM4S4gAwQgAAEIQAACEIAABCAAAQhAAAIbSQDFy0beVioFAQhAAAIQgAAEIAABCEAAAhCAwBQI/D+ydZISJ+KMEwAAAABJRU5ErkJggg=="
-  },
-  {
-    "quest": "Data la porzione di codice in figura, indicare il corrispondente albero dei, indicare il corrispondente albero dei processi generati:",
-    "answers": [
-      {
-        "text": "A",
-        "image": ""
-      },
-      {
-        "text": "B",
-        "image": ""
-      },
-      {
-        "text": "C",
-        "image": ""
-      },
-      {
-        "text": "D",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": 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TY3JlZW5zaG90PC9leGlmOlVzZXJDb21tZW50PgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4KNGFJmgAAABxpRE9UAAAAAgAAAAAAAAMoAAAAKAAAAygAAAMoAAV0KiogLQ8AAEAASURBVHgB7F0HgBbVtT7bG703FaVjpygWBCwgWAArqMnTJEajJvbeABGwRKyxRGzRxNiNXYM9FjSxU5RFkC5td1m2l3fOPXPm3pl//n8XXNh2JvGfe0+/32ySd793S1I1PqCPIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKQJNHIEmJoCb/jXWAioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgYBJQI0j8ERUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUASaCQJKBDWTD63DVAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAiSD9G1AEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFoJkgoERQM/nQOkxFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBJQI0r8BRUARUASaAALr1q2Hl15+FZKTkiElJRmSU1KhorwcR1YNcjdkUhL24t0TmVBpAUpCu9pcNlnLcFDbeIC141BqfBLmdZSJ8gZ0CfIG7BJVFi8GyenxxuWUx/J4v/Hihe1raZdoHK7ObYdT4Yf0/7gS2SXSuTEbs51bu9t2x0dtV+e2E9mFdW4/UYyGbufW7rbduqnt6tx2Iruwzu0nilHXdj16dIfDDx/lhtW2IqAIKAKKgCJQbwgoEVRv0GtiRUARUATqDoHFi3Nh/+EToHvnlpCVmQlZWdlQUJAPlZWVyKHgJN1M1KvwnewlrYYqZCCMDiVEEJEJPUgdoZztuI06imGU1pB1ntywGcRoxNq5eUwIY8URiVQykzGU2VxkRQ/Ho1+xM2Ij55pIJ3ZUsSGpvIEYP7TlTExgUS5ToTNgiSFjtDGwIq8+yUK5xC6JHGX4Xq2unSgpVbJfU8CJKjY6yuPmojjiTzb0L+m7diT36zE+4off16kdR2/8qVz8KzDxyM/4Vrt/F+QfzGX/MPhbiQXZkT9ZS01R2Epu8kNDHIaAZvMYnZM3vh350OPFiBuvruycXHHrIxtnLG5NbpvMfqmd658oXsCutlgkqG+r4lGciG9c11jUNl6g9tqOsbZ2tcOW/vNxzNGjYfr06ymwPoqAIqAIKAKKQL0joERQvX8CLUARUAQUgV+OABFBffqMgH4DuiIRlAVZ2UwEVSERVF2N03WZl5lJEeeTKUwwu28YFHs90Yqv9BNFFFu2CXoYIgJrYhtr6VqxlCREPNiptFiLjOIzMSEaltBv+AnmdaetNftyHbEVhnPYut341srW7ebkMYiV1VArXk5XTp7iZTOIBWukx1mCPZa5v25miezqw+142Ibttn9fqq1phNtSCcWU+In869ouUS7VNVQEqquqYfyEsTBr5tSGWqLWpQgoAoqAItDMEFAiqJl9cB2uIqAINE0EFuciEdQbiaD+SARlWSKIVgSBTwQFJ67BHuFiJ8zSirUJ4id2LI22DkqDHtJjm2hLkbq2sYRPsC7bEy8rid+STGIR7RsrjfYjadBWCC+XKiGLaH+pIqjfWnsbhVvBiljGlUZpXG+uEn+NeU3W1rNGS07vxSU/EdgY29aiOPLUWIUY6lsRqHMElAiqc0g1oCKgCCgCisAvRECJoF8IoLorAoqAItAQEOAVQWNxRVAb3hqWnQX5BQVAK4LoEeqB2u70mPrBZ+smzImthfhwMyTysJVxvezHtADrrIUbM1E7Ub6wnxs92i9aSnHsWAVrjuZ50Iu2spgnaOEJQy83k/VjI+m7LhKTZK7eJEaZ+3b9XHub07asrRmhhLZLzKxBglZsPArkSE1c/HFEwXBxFUEz7SkCDRABIoImThwHM2ZMaYDVaUmKgCKgCCgCzREBJYKa41fXMSsCikCTQ2Bx7hJcEXQYEkEdvK1hQSJIiACaTstcPjEI0RPvaGloUu8H5kyx+WKj2LrsmTcSRqJYG9HESkQT9bZZ+dQasglGcCu11hIrViIawdT1tzqTxSSy+mBe11baibKRjY3FHonsyTae3o1jbaRF5I+0g1ktVedG4FrcX6vlOPTryeglhJKQZG4yN0ygioCiho5JUoONqhWB7YuAWRE0EbeGzZi6fRNpdEVAEVAEFAFFoJYIKBFUS6DUTBFQBBSBhozA4sVIBPUZjkRQN0MEZeMZQXl4WHRVJR0NLA9PioUG4em4zLy5J5aWOAhOpMXa2lGLpGF/sWB5rDY2Uqyk5hhJeGKzcAhiXZu3pTHClTvj9YYVvy7OZMdmW7YG1ztKby255SWtJfFB39JGdVcFheNKn6yj7Fhu0RAbey6TRBB/22cvqdyVBzWMBf4fHkETt+fC5cpriUfARTuKQANBQLeGNZAPoWUoAoqAIqAI+AgoEeRDoQ1FQBFQBBovArw17AgkgtqHVgS5RBCPz5IHwVm39Hia7hIDohF/O723iIm9lVhSgWXB6X8wpnjFSm01YkNv4hJoMQnZB+O6Vl6bDAKBXRqIbWyMoHHALU7o+BW43jZDRBhHFB6RG8MxwyZrYokgsgpmC44pGMXrCTkjK3R8IzcSZ4xfETu5HlwJeVipuW2NTCWQqKTPYUK/Vknmthcy064i0AARICJoAq4Imqkrghrg19GSFAFFQBFonggoEdQ8v7uOWhFQBJoYArwi6HAmgvDq+KysTO+MoCARRBNoQx34s2mUsDAwuZa5uZ3IE2C+k0HPj4WessrIKBwt970EfgaqgGSxj1iShi24EluPaIISjuR6syTeb5gKis0StpBIXLtUH+sndmIhfR9mFJCX6MPjCMpdbbgixtzSepTJekuLpPyQxI0ncvMWIog6hgwSy9g4rl9YK16ujWl78YM8U8KKnBCxf12kDOd2HLSpCDQoBJQIalCfQ4tRBBQBRUARQASUCNI/A0VAEVAEmgACfEYQXR/fBVcEIRGUkwUF+flQSVvDaHbuzZrt1DtCiCKZqLvrTHxnHyfxlbdYsFf0BB2lfnKJHmvpm/gli62fPE6DPOmfIPEVbRymVFyChMYk4wm2yIq1pOfauS9S4xrSiUxySHzXn22ifikjWZKX5GS7IJVWTR/OhJZaOL6NafMaTsZTm9ji4mUxPmF3GyjQcs38MJ6FqyORjCUQoJYdqd41D8d3ddpWBBoSAro1rCF9Da1FEVAEFAFFgBBQIkj/DhQBRUARaAII8NawkR4RRNfHIxFkbg2r4jN0QrNm6roTd0MyEEOACiuXVsjZ4GV1ZqsPndVjPCkSRxfCgb3dGOzLv44cm+RbjY5Gij/UDj6kIRn9Y6wcNfaNOuzjmPhNrlK61kNaNja3eHRkLxpL0UgU523qYGu/6avdHG4bDULGTJ6gUOTyNuMnMeNlDEjnyTmVEXAz8OsHYan/oagW1HkvcQlZizjwlkyh0Tg2onFEtWgGvSQLOwZ7tQhWJyYhcOokpgZp6ggoEdTUv7COTxFQBBSBxoeAEkGN75tpxYqAIqAIxCAQdX18kAiKms7zNJvIBPpXZXUFrghK9mJbe2650247PWcp/1ZBJUZJNrGITRAr8ZQ+JSCZ7YsFys3KFqsRcsnEM2KsFEmnSjxzg2pONuWSgmJIHPFPgiq0q6yugrQUHhdxHiaHw3aIFwbgqv3VNaRx6R6OK/aShfNSTzQcye1jCRjK80/27Fxzcol4fCIopONs+EsxKj1lCr6rUYB5ZIxcvXWmElLTUiA9NQWKSsq4YpQhXQipKMvMSIMtW0rRn8ulNz/UoKzuY/tsZrdw+W6uud8mv6h4voHfsBlIFBtVosRq/BBb0ZBoNbnUtV1N+URf27xivyPeta2podttX6yICNLr47cvxhpdEVAEFAFFYOsQUCJo6/BSa0VAEVAEGiQCTATR1rCu/mHRQgTR/J34gURPZmoaZKdmwKbSQm9FDk3qg9PweJN3ImSIYCivroQUZA4MFYT52NtoMbWQBCK1emnJRD86s/jTKKph1117QEVFFSxbtgLJIBqcDJDeWLlJUw3t2raBTp06wIqVq5HgKDY6/MFH7LknEuPGIYwF9b0utrjHlYSrjI0nkatKqyAFGasWfXIgJSsFCnILoaoE2RshhMQw8i0ViNLrm6Kwje+sDhmQmpMChcuKzbejjy0EDlkbI7SrRjZq+eoCuG3WpUiQVcK0G++E1i1zDGmUX1AIR44ZDhMnHAWPPvIk/PeL7yADSSEmlCS3SYodjipSfpPOfYSCCstdG2pHxQraBC3o78l6SXSykXbQW3qul8ii3g3dLqrm7SVr6FjUtr7thU9U3OiadEVQFFYqUwQUAUVAEahPBJQIqk/0NbcioAgoAnWEwOLcXOjTG7eG9cczgrJoa1g2bg3j6+MNKULzEyEynLkKTaDpn9E77wtjdtsf5nz5L/hq40+QnpTKE27fj/yjHiREkGDISsuADlmtIa+kEArLkHBBvyRin/DfMkmnt/9QUSa2SNwOkQhCtMjE34uCZrQK6Nabp0FRUQlcceU0s5JFovCbbcvLy+H0/zsZjhp3BDz62JPw1twPoYrOTDIP5fdykrnI8M3ZvfLIBms1JmRObmzMHmYcAYkZtxBvNAHsOKQ9DDpnD0jNToNkXJn0nynzYP3nGyClRRqD4wSNgcXJFdushmQklvY9ayC03709fDj9UyhaVmLq84fkOyXBFsTryNEHw/RpV8Pcdz+EUyZdCP0H9MABVcPmzVvggGGDYdasa2H1qtUwfPjp0Ld/e49k84OEGpKFvxEpRUItKw25BbrWIyB2OtbCxdkxwGZ8TdCufno0goZdYf3g0nyyKhHUfL61jlQRUAQUgcaCgBJBjeVLaZ2KgCKgCCRAgFcEjcYVQe34sOjsTHtGkO/HE1KakhqiwsywaUNXNRzXaziM730w3PHfJ+HTdUsg0xBBTIBU48YhWudjzu5BYsTwO+gr0QoqimBMj0Hwuz2OhGd/+BBeXPoppGGS5OpkqBKyxa/BaxhnXrnjcS1GQStQ7BlBHgnEifwIybgF6i/33AJFuMLn/Auvw9U2fjFoQzVjciyyrKwMrrziPDjxxGPhjjsfgH8+/TJUV6KtOdTID4e2lMB9ZNIuJBTpgjZi4XuRwCmjms5MQtIpJTkVDr3nIOi4b3tYNncl5P+4GX58ZRkUrtgCyWmpvjufhYSVm8D4Q+moTU2sz5BqtLsNeSyj8kBLb5kKw64bAl0Gd4Q3//QO5H1XaNzMkNCXQpA/YVJWVg533j4N9t9vCJx19kXw1beLICM11XxPslm0YDU8/eztcOjIEXDxJdfAu+9/hlvFbI0YyjwmNrZoi56XgVcOeeP3rDAjZa/poRjxHvZnC8kVbVubTNGeKlUEtj8CRARNmDgOr4+fsv2TaQZFQBFQBBQBRaAWCCgRVAuQ1EQRUAQUgYaOAN8a5h4WzUSQuTXMLz44mSZ6IDUJz4tJScUzdNKQtEiBPNwaRqtmDM+A+qzUdCRzqqCgtBR2yekAabgcZ1nRRiQXKiEVyaEUJIzKcJvR+N2Gwrl7HwNPLHgHnl3yMWRggKKKUiitxHODPNKCgsqE3Z3+V+GKomxcxdSyRTasWbMO+vbdFTKzMuGH73+EwsIiSE/HlTMUA//JaZFl6m3fvi0UFZfCihWrcNUKMiQUGBmKcszXqnUL6NtnN1j+0wr4v9Mmwcknj4fb77gfSY6XoRK3k/lFGFy8SswLzxQypAl1EB18Ue34P5TGUrZJmXOUSEdSGZCx4B+yTsJtX6k5yZCWnQ7HPnUEbFywCf4z9X9QUVwOZZvLobqCDvZB0qscv0GrdGi5Ww6UFZTDlqVFmLcKSSIKjHqcQKZkJUNqJp7ps7wE0tqnQ5s+LaF0YxkUom16h1Q4iIigIZ3hjfPehrxvC5EsqoaMdulI8CAe+RWmKBrCoEG7w+w/z4BvvpsPf7rgal4dxUMz4ygsKoZDDh4Ct906Hb76+js48dTzoVvHFqYOM04z5iTENwe3jaVDXl4+lNI5Q4S/eejNASNg8WzCLykgLHcj0F8qPa4saB9fE7TTniJQHwiYFUETx8KsGVPrI73mVAQUAUVAEVAEYhBQIigGEhUoAoqAItD4EOAVQYfgiqBu3hlB2ZDvbQ2zo5FJN0+bS/Bw6ON7DoNRO+9jFslsLC+CJxfMhfn5qyAdSZ4tqL9wr6NhpxYdYW1RHgxotxOkpWXC/HWL4cH5b+E2sC0wqOOucGyvA6BXm67Qq3UX+KlgPazdsgnKkCh6OfcTeG/Vt0ge4RIeSh2HCFqxJg8u/tOpMGrkobBq9UoY0L8vZGZmwA8/YJ6HnoD/fTEfWuBWtx7dO8E55/wGOnXuiPGS4etv5sNtdzwAqd5ZOyVIVo0csT9uBzsNunTtCJs25GO9qdC7d0+4/fZ74ennXmEiyALitRgXUyKSDZVYZ7l3DX0K4kAHKdO/k1GOlJRZ6UNEj/twzxIWObtkw5A/7g3pLdOg86COULyuGPJ+LDAun83+CjZ9nWdojZ4Td4a+E3eFjNaZUIErdjZ9nw/fPLAAilZtgSQ81Ll0dTnsfkEf6DqkG6z8aCXsMqoHZHfMQhIH4LunFsCK19bA8Bn7IRHUEV4/Zy5s/HQz9D1jV9jtiF3gxzd+gsXPLzO1E4k19shD4M9IBE274Wb4x5MvYC3OGHjwkJdfAO+/8wKUlpbDb888H1eVIbHkPN27dYHfnflr2GXnnWDOnMfg33M/wK2IGZ6FjWdbjrPXJJTMCifTx8SRxq7Q4hobjSWudTwblSsC9YWAbg2rL+Q1ryKgCCgCikA8BJQIioeMyhUBRUARaEQIMBF0BBJB7f3DovPN9fFypRQNhqbLtEmIp81luEzkkC79YVCX3jCw3c6wS9vucMOHj8C7axbgip5U2FBVDo8deg4M6dwfFmxcCt+tXwZ7d+oNPZHwuf9/L8IjP7wHA1t3g/279oO9O/SCYd36w1drc9F2ubnI6qOV38D8TSv9M5ElL0/rZeqeBAvnr4SHHp6GK3dOgM35m+GTT/9rzjk68MDB8P77H8P0G2fDxo15eOhzOzhl8gnQsWN7GDVqBCxa9AOcdvof8QasZKjElUA5rXLgbw/dAx3xcOhPPp6HK1dawT777AHpaWlwy6134oqgV5FAIcZDzgkiTOyzubIURiAek/odCq0ys8yKIHcfHK3UeR/H9OdvXoNuGS1j+AseEY8uEw9w7ntCL8hqkwF9jusF+cs3w+pPVmPMJFjwj8WQ90Ue9DiyGxw4ZSjg7jFY9vZKaNmjBXTbrxMseWM5fDrjf1BdVgXFC8pg+GNDYMCJ/aBwzRbY/FMhbF5dCK13bgk/fbAcFj2+FIbPRCIIt4a9ce470Ha31jDo7L0h76cC+N9dX0Pegs24YgdJLDyb6PRfnwgXXHAOXHrpNfD6G+/jwLFi+QweDMW4Kuippx+E7t26wuWXXY+HRs9HDWFGr2oYOLAPzLjxeujXry9ce90NMAcPlm7dIseJYwPaFrubEF4ss6uMQnoq5KkSPLgyylpG2iV0j/RQoSKw4xDgrWFjcWvY1B2XVDMpAoqAIqAIKAIJEFAiKAE4qlIEFAFFoLEgwETQGO+MoEw8LDoLVwQVIPFBJwDJPJ2ny/QrE/BkZAnopq+T+42Ak/sfCtM+fBjeX7sQiaBk2ISreh4acTbs3aUf3PLJEzB3xbcwtGMvmDXyTPgXngV0yxcv4goZXDlUVQITex4Afxp0LDz23Vx4avGH5owg2oVFeeiMIapAJuuSmzTUXjR/NTw4ZypMOvl4+Peb78JFl98AnTu0h5tvvhpX8/SGqdNmwRtvfmBWCdFKodSUJHjk4XvMapVTTz8P0nF1zuaiUjjl5GPwTKCL4N9vvwNXX30TdOvSAWbedC3su/deeAjybfDMc68iYUS1UFauhioQVAorkHTp2h8m7HYAZOPKJ9ZZtMjz8zWL4O75b0DndNoy5T5MsMnYkvDcoiRcCZWSmQonvjUOfnp/JXx8xX8Bl1pBJRI8W74rhlFP7A+9ju4Fn8/+An745zLI7pYBB1w7BNr1awuvnz0XNn+/BUqQCDrokSEwcFJ/+HHuMvhsxpdQXlQB6a3TcHtZFVSUVsJB1w+Frvt1hq/mfIeEUV8oWLEZ5t30BWzG84ho2xbVlJmeClOmXgaHHzYCLrvsOnj7nU9QimPjwRvSi1YNlRSXwF133QiHjhqORM+N8Orr73lwYRTccrZbr53h2msugYG4auv6aTfBM8++hqu1Mv04LiLUpq/OaDMy5pebQVOvDu8V1GEv6BJrFSuJCaECRaDeEKAzuCZOGAczZkyptxo0sSKgCCgCioAi4CKgRJCLhrYVAUVAEWikCPAZQbg1rD9eH5+VDdk5WbjNh24NwxVB5nRnGhhNp5mQkYk1kR3lOEn5df9RcNruY2DqB3Pg3bULIBOXqSxFYuT5UefCgA67wWEvXAspleXQu1UnmD3ybPhyXS5Mm/dPDFkFP5cXwqRdD4SLhhwPD3/7Jjz2/XuQQ8tchGUItNxJPVMti+b/hETQdJg06Xj47RkXwqeffQGpaclw8onj4XIkdm695Tb4299fgAw6KwiJp3Q8P+eJJx6Aws1FcPKp50A2Hmi8ELddPf34dDj00BFw330PwdVX3Qs9d20FN954JUwYfxTccssd8CxuDSsndirmYVyoshIkv5ZW4K1nVXi2DolTiLmhc3bQD4mSLqnZ0CbZO7MoFIfICCGP6E1ETRLaTv74WFj+/ip4/+yPIal1svkCFasq4Yjnh0PnvTrC3zo+Cyl9U6C6qAr2uWJ32Oe3e8LbF74Py99eBcXfl8Ehj+wH/Y7pA6/+9t+w4YuNkJKNW+0oF9aX3hoPi756MPQ6qqchiJJTU+DTWz6HL6+bD9kDs0xF9B0ykAi6fsplMGb0oXDJxVfBO+99igGwYo9BMQhgv6QUiaA7Zxgi6Jprb8CVQ++ZVUwMBsKBK4u6du0CLfDva+XK1UjG4RY22iZHS3woiASkZuAxSisJdcXNK8faeS1rHrQI9mLcVKAINAgEdGtYg/gMWoQioAgoAoqAg4ASQQ4Y2lQEFAFFoLEiwCuCRkJ/vD4+01wfb1cE2Qm/3RZGJALN3OlFZ+CcNuBQOHXgaFwR9BC8t2a+2Rq2CreGPTniXOiDW8ZG/GsKtEK7XfC8oJsP+R0sxCvmp3/+pFlhsx6JoJN6HgQXDz0O5nz3Jvx90XuGSHJJgfCE3aQnITYWEhH00I0w+eTj4LgTfgdLliyhxScw4dixMOX6K/Cg57vh4UefQQIIr7RHMiaNiKDH/4pE0BaYdNofzGqXRQvy4V+v3AoHDdsf7rzrAZh5x2PQrV0OXH/dRXDC8UfDn2+9B7eG4RlB/vXxNH56TCX4RhKkqgx2b7sLHNJ9d8giAkhUxg672F+UtxyeWzYP2iAh5Bp4QzGWspEJj1jCLVmpSAQdDcveWwUfnjsPknAhkQlbnAyHP3IgdNy9AzzS6RnI6J8CVSXVsPfFA2HQmXvBe5f/B28XWw6li8vhoIeHQN+j+8Az416Csg0lkIwHR5sHiZtUvDXsgGsGw65jdoYVH66ENru2MSuC3jn/I6jElUPgmdLWsF+degJceskf4ZJLr4LXX/8Ah8xfheuldhLQ1rDnnpmDZyx1Rrvr4MsvF3AqrJosKvFwou9zNwCU49a2njvhaiCPbKJY/EeFdhyXHBnCEJAmoqf0TK2H+IiR+3atWB4rce21rQg0DASUCGoY30GrUAQUAUVAEbAIKBFksdCWIqAIKAKNFgEmgsZC/wFtcAsVbw0rwK1hdHaOmY2bSb9HBOG8HDkE/KnGs3zwX/j+9cDD4JQBR5gzgt5Z+x1eH58GBbQ17JA/QO823eDIV26ENCSGdm3ZCWYd/BtYhGf/zEAiiG7pysNDpifuegBcPOQ4eGrhe3APbp3KxK1leA+ZRzZwXqEDhChgGoqIoLW4Iuh6QwRNn3Eb/OWv/4Q2LbPgkgt/D6ecejJu6/ozbkF6FVcJIVmCZ/zgohd4+umHYTOuRjn51HPxjCCAgsJSuGnGJXDiCePhkceehD+dNw269GgH9//lJjji8JG4zeyOCCJIKqLPnoTjKIaxO+0DZ+59JLRJa0kAGejIig43rsJ/vbH0v3Dl/56GXhmtjR5/8JENUAw1S9AXoU/G4k764GhYTkTQBZ9BUgZGQ+xLFpTDEa8Oh+77dYUXJ74GW5YXIWmUBIOv2Qv6HdcX3jx3Lqz7PA/KFpbDgY8Mhr5H9YHnjn3FHDqdnE63c1FNAGktU+AAujUMzxaae9n70HFgJ9j79IGw6IXF8MXs74wd4DY1Gsi4saPgplnT4KabZ8PjTzxHEagUM0bzg38jxVsK4a23noXiklK8Yv5ivBlsM1ugPz3puPrqjNMnQ69dd4WXXnkT3nn3Q/wedAgRRjJ/VLFYWFQ4Bv/6mU2Xeu7jpXNF2A5bRUlCLlvVDdYU37Wh28WvvP40tcWsriusr7zBcRARNBGvj9etYUFctKcIKAKKgCJQfwgoEVR/2GtmRUARUATqDAFZEdRvQBdzWHQ23rIlt4aZhRo0H8LHvFBAc/Y0PBC6U04rJG1S4cjd9oOjeh0Id37+jDnseVPZFviyeCM8N/I86NO6K4x4eRq0xMlMz5ayImiFvyKoEK+JH9a5L0w74FewDm8Xe+jb12Fz6RZzzXxheYnJyen5vBhbCU/3F85fzlvD8IygxblLzTaulq1awuWXX4DETxXQFqV5n38NPfDGqp136mq2J1111SW4IqgYVwvdg6uHqmDh90uM/sEH7oLvf1gE997/MHTu1Bn+cPbp5uDjGTNvg+eeew0q4h4WzVfHZ6amQcv0bKR8qGIknfCMnUocN1FZdNpOGW6XyysrMucq+XjygEK/6FOBXilpMOmjY8wZQe+fMw9SiF9C0qRkfikMvmMv3Aa2Oyx9axl89+hCaNWrFQw9bx+oKK+EN85+B0rWlBu7gx8dCr3H9Yanx/4LyjeWmnOHDLmC8KUgEXQgnRFkDovGc4WWlMDBM/fHQ6e7wH+mz4Olzy2H5CwcC5I8e+/VD26fPQMWfZ8LZ511sTnDyBSNcWil1aZN+YYsmnXT9fD551/CCadcADt3RcIL/16IR6SvlYnXxv/1gdthyJBBiPGDMHPmPZCdTSu1iJwSis9ENfbcol/+1rYf3eK/k0TWYsH+wV50TGtZmxooYkO2SzRG1TVUBHRFUEP9MlqXIqAIKALNFwElgprvt9eRKwKKQBNCIDd3CR6sTNfH4xlBeONVtn9YNJ+JY6e22DIT+2Tojzd+nTd4ArRNz4G2mS2hQ3YbvP59HRTh7Vmv4NXvU3Blz5uHnY9bw3rAgXhGUIuqKtitVWc8I+gcmL9hKVw/73Gz1aoK46WnpsL5ex8Do3fdDypw5dDG4kL42/y58NKyz8zB08yrxE7bqa5FeGvYHDosevLxsG5dHqTikp+UlBQ86ygd7n/gMbjv/idwPGlwztlnwNFHjzFfrXvXrkjqVMLP637Gq87L4LkXXoHLL7kLnnzqZjj2mDF4y9gmSMOaMjIyME4mroK5E/751L+8rWEWDfsnILXhKikklmhM9PD6FmpTC/+FjEiKIT1sDKYOmCoiH6KNzC8SQSl4Y9kpn0+An95dCe/9Ds8IMkQQbscrB8hqnwHDpg6CnYd3h4LlhZDRKh3JGTyQ+s6vIPfpZQC40qkYCaORTwyDPnhG0D8Pfx5K15UisePt90KCKrVVKgyfvr85LPrVs/6N18fnQbfRXeDga/fDM4Oq4J2LPoC87wsgGbfTbSkqh3vvngb7D9sP/vCHi+Hz/+LKr0zaAsdjWbhgKTz11F9gzJjD4I9/uhzPEZoHOXQQtBAjaNala0e48/aZsOeee8JNt8zGbXgPQ6sWdFg048e/FM8l/bCLMRgnam/rw9GjvONroqxVpgjsWASUCNqxeGs2RUARUAQUgZoRUCKoZozUQhFQBBSBBo8Arwga7d0almVuDaOtYbSiRh6e7tsJeau0bNin427mPB+aSFfhZB7/R4Hm8LAkfy18n78a9u3QE1qn5cB7q/GsGNyLlIln3gzu0Bvyywphft4KwyFwXIC2GTkwoE0PaJWegatoABbkrYQVW9Yb8oTJBgwsM3bjRD90ffwaXBF0HUyefBJcdNE10BPPnsnCFU0LFy3Ca+C/gKIiXIGDeQcMxKvrd+4ByXQTFpI1Hn9h2kuW/ARff7sIOnfuAAcfNBR22Xln2LBhPaxe8zNuZ0qH+fN/wMON1yIfQXhwXsEl8TtQsGdKq19k1NabiCJ65JY0OugoCa+27354ZyhaXQwbv8rH1UDkh/+gaXVlEmR3yYJOQ9tBq+6toHQzrjbKLYB1n22AKiTdkvF7VOGtYG32ag0td2oFK99dA9XlWL+JgWGqcJVSahK02aM1ZHbIhLWf/AwVhZV4vnUKtN+nNWS0zYR1n66H0k3IOqFPaWk5DNt/Hzwv6Qb4z0ef4ja686H/wB4UCAoLS2DEIfvB9BuugMWLl8KkX50PHdrk8EHQpl4kdnA848cfCZdf+kcT68JLroaPPvoCcnIy+e8GI/HD2PBfWlAmPXlHoSu62Ddb028YfYkT66MSRaD+EaD/7EzArWEzZ0yp/2K0AkVAEVAEFAFFABFQIkj/DBQBRUARaAII8K1ho5AI6owrgjIDK4J4hQoNUibSOKk3M2lnOh2eXZs+TeW9lR3eTFs2SZmZOB1SE3iwLyLPntR+DO4Qn+SZkTERQSvxsOgb4JRJJ8DIw46DfLztLBVXBJmDnc1KE3bgkiSBE4ya5qHNW1xzChIwVXhIT7XZCkZKGjPq/LpMNHaT3wgRq8SJ63V7riu1Q9Vx34BN5BGSOMbZRqCzh6oREDrMmeqjc4VMrUm0Oc3amb180nWysA2OEzeu8Rk9XKNx9YtBR8xB8fPzSuCGaRdQErxO/nZo2ZKvfi8oKIZxR46AkyZNxO1j98IPP+Tiyiy8ot5U4cVEnwsvPAvO/N1v4IG/PgSzb7vPbHMzoJoxesMz0dlHSvD+ikzl/PfAGhmS2BnXuD9sTb8cPZ5hYm08L5UrAtsLAUOiThwLs2ZM3V4pNK4ioAgoAoqAIrBVCCgRtFVwqbEioAgoAg0TAV4RNBwPi+6ORBBeH49bevL8FUE8MZbJNk/EkZSgg4LM3Fr0NO2nx/0lagUf35Y6ZEE2ns5I6IczkIZuIiObYCQ2ZBn5UouIoBXwwIPXw0knHAejj5oEeRs2IbeA16wb1oZspHL25/rIX+qlMET0sMbYE6lC+TmZiWCq8YyionrFeukop+fsvzm/SKUq7rM9y7htCC/+wVCetVGhh3kzRkTQ2Ad1Ziyoo8B+vexDn8Er0LzM1e00VvN90AdDUVRu0FvMKR6TYes3FeLNa2nQmrZ0kbWRA5SVV8CSVQXQb+c23kogysUB+EsmwaiRw2DofoPgedyKt3BhLq7Owv1rZjBky9bcCv+6Y7S6aKnVB1tUi32CPSvXliLQ0BDQrWEN7YtoPYqAIqAIKAJKBOnfgCKgCCgCTQABJoIORSKok39GkCWCeIA06eZpvTf9NgJ3Oi3TciFwiEyxD7WRnmGiAX/poig5S8daeTlMbCuliBJLMkp/SxEecHzQINhj9/7wz6dfgtKSMkNOsDf7WW+S8iiYgOAahaiwGSW6lVg/loXrYD3pon1JSj6SnSwlhviwp0ij4lgvMyY0ZR4oOqrEtVl5pPJlGFepDFcRISFkshKLJGJscEWsM9wSCdyU1GEWyRFLABMR5UlQhN9m2foi6NUlB89gSqPBeA/bUFDORWKRiU3su2YL18dGFmmsRDT6VgQaDgK8NWwsbg2b2nCK0koUAUVAEVAEmjUCSgQ168+vg1cEFIGmgoCcEdR/QPvA9fHuGUE0ViEQmAWwo48+9cZaE5VAk26ZeNMEXrgGj8kwwYzc2Fpfm4VjRE3+KRad/UP1Wj1Hs/7hFunFRioL24T7NnrYw2rCPm4/7CW62nmLtSDhe1HDhKYf6hB+nMu1pbZbge9vfCi6oxWlrEbydaKgN9k7Ptimnvl62PC4ISMxKTxTspIoqPQelrjRRBN+S+bYGGFLtx8bmSQSy7XUtiLQkBCgVX8TJ+j18Q3pm2gtioAioAg0dwSUCGrufwE6fkVAEWgSCCzOzYU+vUf4t4Zl5WRBQX6Bd0uWnerzxFtoHZ5EkzZ2Wh+cXrsTdjv5Ril2eEWLA6NxlUl72BPtjN6zpxUsaJpkDnFGGTFCFJQes3SFm/aXdBTAjStaz89LwJY2me37GcTRfwv54gtiGpJX6gi/YxyCAgMW+pCb/0gMEtOXiM4hIzHW2DFbuvwYno8n5wQs8zS+JTdI6tVhcMa29zZUkKnTupgYRi8yqsJ52MCGRJXwTyaTY25MSY//SNuJlKDpBPGsJEasJkEYVSkCOxgB3Rq2gwHXdIqAIqAIKAI1IqBEUI0QqYEioAgoAg0fAV4RJIdF21vDzIHLWL5MlGXijf/ljwSMSD29N6sWIkLoIU9sQDCrgIi5oYOP8eG+aUb8SHzJGjYRvZuBbWIlrm+8eK5NsM2ZLMkimd1I1iboG9sTL4liLYRQYwvSi61n43Zj3W0g04rwFwvDnkmH3jYjf02uhFOwzv1lT9Ei/RWoBTtena7YE7lJbTtAEokl/gVhk3rB+J4M5UbnvW2wqJZbidWTVGJYqbYUgYaFgBJBDet7aDWKgCKgCCgC+H+b4XJV+r+h9FEEFAFFQBFoxAgwETQOVwS1NmcEZeFh0XR9fCVeQ86Tepky8yBtj6fRdpodJEuEFCIvs1IEZ/bmfzS8/+WwcVzwbLTgLD3e/9w49k4YK5WaXH+rtVQAyVwbCsZ915qJEndknFSIL+7Rb2w8jkO+NqLBJZQ3XIXUwVGDdJHNV8sWBTfpbQ3iaSXU8g1N260pQODFmko48+aYbOTGcMdkHdiOfaw0tsV2Ig/GJakbwR0He7haifHL38Ga4sdr6HbxK697TUPHorb11T0ybkQigibi9fEz9Pp4FxZtKwKKgCKgCNQjAkoE1SP4mloRUAQUgbpCgImgkUgEdfGIoGwkgvL9rWE2j0yqXSqDtDRhCj48ObdTdHdVR/z/F0IwjqwI4ci08kT0sURMsAapT+yjaYdgxfF6HEtGLlZmZDI8k8bmEpvwWyzEjfQic21dPctZwrb8G4MAiWMd3bAR7XB2CsDUVIRxdPga8lo1xw7GjS04XJFFyEbiGBwvBodIRINZqRebJ9ZGJYpAQ0BAVwQ1hK+gNSgCioAioAi4CCgR5KKhbUVAEVAEGikCublLoLc5I0iIIDwjiFYE4eHL0Y8QLaKNnVaHJ+hiIXRALAUgk3O29O2wYSQSQFIiLRGMYQ1syzd2bMUrykpqYD+xFIbF9TA6MTAKV8txRC1VuBauzpWTrasL+sqYyUOs6B2OIF7ht2sn/kHihzMEZW4U8SKZW4VrU5u2VCIjEh+JafXxayEftx6JsTVvybM1PmqrCOxIBJQI2pFoay5FQBFQBBSB2iCgRFBtUFIbRUARUAQaOAK8ImgMrghq660IqokIogElIoPC0/utBcCdnlMe27cTf9vi6GxjLSVnvFpcS4llY7gSPwIKZVES6f0VS8bNjWdJkqBUatp6AoPj+JXYQFvRIhxlXMEKOLrUasbmxGUflro2jsk2NaNikSw8Svf71yaRHWPN1lJDzZZqoQjUDwJEBE3ArWEzdWtY/XwAzaoIKAKKgCIQg4ASQTGQqEARUAQUgcaHwGJcEdSn96FIBHVCIigTsrK3hQiy4w5PxHlyb/U1t+z03LbYKxw7HCtsH0srWI+outg/OoqV2la8esiCdNbS5qVWPL+gldvbeg/Xm9rxiSDRhj2kTptbSBkrifWhQfskWYSas1mFG0vwYhn9SkZrX1PLjVeTreSryU71ikB9IWBWBE0cC7NmTK2vEjSvIqAIKAKKgCIQQECJoAAc2lEEFAFFoHEiwETQcCSCugcPi467NYymz1UOyUF9O/22LZZbrW0RUsEeSejhib/EIBv3Ebkro3bYzurD60usJio/x6HfKK342irC66LET7w5HqHDNAx5skwOsRYPiR1+SyTxE6RtDexh7cIRRG8twr5kwVVF+7r2lphxpZJDqovq2/zcshmDWJCvxLY2JLURqGefsNzGszZ2hDZ22M+13lHtcA3h/i+tI168sDzcj5e3tnbx/OtCHq4h3JccYXm4v73sJK684+UVfeK3bg1LjI9qFQFFQBFQBHY8AkoE7XjMNaMioAgoAnWOQPD6+GxcEeTdGhaXCKJJNU+n6ZemOYEH908Z+kVm3AGlnc7HTo9iIgVi23CxWWM9g9ahEkKUAnvzmGIjhX2D1FKsPUnMgdi0LCYwAo4U62GJD25FWYSroL4dY1Ar/vH0QWvumaqxKb5i48awOmm5WvGIekv02ujEVnJE+SSSxatpW+MlyqU6RWB7I8Bbw8bi1rCp2zuVxlcEFAFFQBFQBGqFgBJBtYJJjRQBRUARaNgIMBE0GlcEtfe3huXjYdFVCYkgGhNTIlET7GoSxpmRi32sWjQWLyGcRGJ90NbLYbzE1Tfght+VABHv4BoXCRRhiCIb1UtuzII+rAlnjrVxM1hraTn2cs2aHFDkObp5uO1EDAsiwjrW2BQHeruPOJLM6mzL2oqlqxOZH96ax2kFz4SKY1Sj2M/rWLp1OWJtKgINGoFq/M//xAl6fXyD/khanCKgCCgCzQwBJYKa2QfX4SoCikDTRGBxbi6eEWS3hmXnZEN+ftT18Tx+O6GOJoKiJuFh5ChG0M5GdW1dIihg75EixiugEG8WRqrExHtzZqkoug5xsVGj7UQaXDVE3lZDbekF41IvTsUkjiCCxF/efl6yFQKJlOGw4QJ8piZG4ThbnW1J5uh3OG20lUh5o50ZagxCYpP4zb6xwyWv2tacOINqFYEdi4BuDduxeGs2RUARUAQUgZoRUCKoZozUQhFQBBSBBo8ArwgaCf364/XxWVkJD4uOnUwHp/rBnh26+EXrRWvtueUSA3ZyLzSKiYWu9mBitnejROdzLbgdW0GshCw5Hv2ynn7dHLYvNlHaqNiuHWWKesJ+vHpGMomHTwaJgN4mvOTw4vjhpBGOFA4gdjJ6V5+4LZlrstr2DLGRJaeNGWujEkWgoSOgRFBD/0JanyKgCCgCzQ8BJYKa3zfXESsCikATRICJoLG4NayNvzWsALeGVXpbw2ginXhSLVprJzDxJNydiodpClcnXvIOEkEiFRrCZiUN96RWN2rQzkZxW649y2MltYnjxuS26yXVkcaN79rERoiWsL8bUaJytFBMd3WQ5HZL8JNECn2t5AgIatEJVRPp4a7+YoOaa4kMpEJFoAkhQETQRLw+foZeH9+EvqoORRFQBBSBxo2AEkGN+/tp9YqAIqAIGARkRVBfXBGUbVYEZUNBgd0a5pINsVNzO8WXVqy99QrTQEFCJPxBJKJrJdE5prXgls3EsSyVFI7NMckr7MOWsVKxtTnJUqTstTW/lCE21tZEINvYOoM1eRnMy6k14BboJCyg9pY2THCMVk6xrM5dzbUtWWxcbSkCTQkBXRHUlL6mjkURUAQUgaaBgBJBTeM76igUAUWgmSOQm7sEevc+GFcE9fBWBOEZQUgEyWHRwQm7C5Y7eXcn9ZaekIk+x4gXKdHEXyJIzKAtaSWqu6LElbsVh9sSTeytXjQssVVYC9sSLW9ak57Vx7YkeqxtrERGzlGi9KSJjUjVxBJvsbWwRPxdPeWKlYtEKpF+2NMSUmLpWkRFJn1UtKCf9hSB5oSAEkHN6WvrWBUBRUARaBwIKBHUOL6TVqkIKAKKQEIE7K1h7ZAI4jOCEt8aFj2xT5ikRmVNBADnZKt4trF1xUqChUgkspM2W9heTTHYPjYCySlK7fw5SrS1rSVaL75bn4093fgSi95e5WZocjITS91MQW/2qe2Yg76UM1bClQjpGK0nG30UgaaIABFBE3Br2EzdGtYUP6+OSRFQBBSBRomAEkGN8rNp0YqAIqAIBBFYjCuC+vQehSuCOvtnBDERVImGMvGu7dQ+GDuRN+mCUdk6Vi5WlowIZpGe2Ek/HD+WmHFzSdtWwXFio9r4tkVW7Gll8VuSK9oinDEZzUQm77Cn5I7Wi5a8Yi1crYwidi2RoG/9w+QMa6w+WGPUmKMyB73CFbtYxFqqRBFoagiYFUETx8KsGVOb2tB0PIqAIqAIKAKNFAElghrph9OyFQFFQBFwEbDXx8vWsCzcGlbgbQ2jaX14Cl9T30bniT7buwQBy8OkhFANzpYmOuRYjL2G37VpIlqczc0ZYRQp4vg2C8egfqJoorN+weBh/3BfrEUeFU9kYuu+hSCJtnGrClq4GoGaLYJ2kkvq4754ky19vSB9FM+W5eJr/76sxEaXiEQ6hfXRNdl4oo/35jriaXeMPFxDuC9VhOXh/vayk7jyjpdX9DviHa4h3JcawvJwf3vZSVx5x8sr+sRv3RqWGB/VKgKKgCKgCOx4BJQI2vGYa0ZFQBFQBOocAd4aJiuCwlvDoumAmougyY8QCy6FwvGip0Z2qi/Tf5uH49k+t6KlpIuNEPZN3I8fOdYvjFHQ1+2FLWNjicS1lAiuLEx3MA1D3kEriUdviUNtXs0ja3rC2q3FjyLHz0vRg9lZ4v66tblyaruRE9mF/bSvCDR+BHhr2FjcGja18Q9GR6AIKAKKgCLQJBBQIqhJfEYdhCKgCDR3BJgIGoNbw+iMoEzIypYVQbQ1bFseO1mXlp3K21ZU5Fj7+FZiG2Uh5EHibEFPihe0T5zBege9wpRHTVHIOzZ3OKbNJq2wj9t322xPEvfh+JZ6c3WEQ835XY/YfK6W2+EKghaJtG4tieyCEYNfc2v8wnF+SZ9qj8odT/5LcqlvU0SgGldFTpyg18c3xW+rY1IEFAFFoLEioERQY/1yWrcioAgoAg4CixfjGUF9DkEiqKs5LDo7Jwvy8mlrWGIiSKa37jQ9POklm6BeEkdLoygR42GS4Q9tFXMm1lKDRJU3ERlBksOlNoJe7m1j5G8rC9pJ7Oi39bL1hWuI9hSpjWBbrJM63PU7bp3WSjzt+iCJHn6TZRAhsZAYgQxsLibmbauykVyUxVjspB/9jmeFif2C0CaeWXRQlIrzVjvGjagKRWBHIqBbw3Yk2ppLEVAEFAFFoDYIKBFUG5TURhFQBBSBBo4ArwgaYYkgXBGUZ84I+uVEUOzQXUZBJumxVjESM4/HH4cISjS1T0gEVaOn58wvtw6ujyWkdXVUlecbFsfY8QiiqRbWhX9tSNsK23hlO9lYkoQvWjlgH7G0kmCLbKOrs1FsyyQMhZSuRNp2Eogqk2jhKpH8kjKMSbRd0Mv2+O+A+lvnZyNoSxGoXwSUCKpf/DW7IqAIKAKKQCwCSgTFYqISRUARUAQaHQJMBI1FIqjNVmwNsySCzNMtaRIx6fZEwlWEV+FsHWg2PsexfYojhARLRSdSY8DpUCVa9vPFpmHGxSwHK8yv6yFii4BIot4WsSitRc9tRVlSBYIjIANkK7K0B/vFFO+FY7n1s9aegf+yEa21IMkS+o0ev/XwwyVoxLN2Y8eziReWK2WvrfWNF3Nr5VJ/OD9/g62NpvbNDwEigibi9fEz9Pr45vfxdcSKgCKgCDRQBJQIaqAfRstSBBQBRWBrEGAiaCT0698FsrL4sOgCXBFUabaGyQRWJrRuZNG5shomuOhCJMa2EUHBfDaGlQdJCqqLdSLnSklmxyPebuVWyx4cx7UQOb1deayna0ltl7pxdezJv1KT6G1U0YiEogXHQz4+BRKrkpAeMn7XQcTKuGVzBfvUk3qoHV07aYIP+1DdXCf5uXFc63BuV1ebdqLYtfFXG0WgfhHQFUH1i79mVwQUAUVAEYhFQImgWExUoggoAopAo0MgN3cJ9O59MK4IstfHFxTke9fHC90i59MwsyA9f7D+fNtv+Cpq0IRfCBCe2ksEjhcwjul4Nmb/k1X61IETQggf0UkWISnIW4gTf1UNC30qguuzeYItiRyU2l5i71jCI4yX+DNNYuMGR2B7Lqpcm0TwfXnfGHZFY8dgW1br+wUaQi15HvQyAMq19WRs0Q64xnTcrOwXxoVRkZxkE/aJCRpHwJHiKFWsCDR4BJQIavCfSAtUBBQBRaDZIaBEULP75DpgRUARaIoI8Iqg0UgE0a1hsiIoH1cEVeFwfdrEa8USBoay4GU+aO9OvKVNb3qIVDIMAu5owkk+ioXGiI3K9mwucdCqms8tSkpK8SKal/Pj5pI2q2Uk1DMaV41lcWVuKDQgEsWzt7U6No6W7RKRIV4FgURuERKXc0rPVEtmYbExoJhh+svzpNqrCC98J7mEjadnjelEVWGtvBaFq6oCisScHBVE/4S9Bamw3ItjXuQnT3hwMh6OzlauvfjV9BYiaVt8a4q9o/RhbHZUXs3TUBAgImgCbg2bqVvDGson0ToUAUVAEWj2CCgR1Oz/BBQARUARaAoIMBF0OBJBHQJEUJVHBNEY3emoO703hADyRVVJVUgJ8L8YE7FCmgIZn+LqCkhD4xQiJLCfjG83ZiyOocl7NSZB/6FD9oJWrVrBO+98bEgJw3H4zhRRolp6RNRcHeZOSTFWlRVIkvhppIH+lIvGgvmSU1OgqqIK7SWuRJM3yZ2HwgREXlx8yaHHdFa1a8Nd68R9+rWPdeE6uF8J5ZUViEMFVpsMqSmpkJychk6sLS4tg0MOHgKdOnSEl175N0qRpEomGif0SOpgyoBRtYfJ0KF7wbD9B0NaWgb866VX4fvvl0IK4skP1yaOlC/wSNfPI42gH4EjhKH5DiaO2AYi1kOH6pCB1EN6TdnsEGAiaCwSQVOb3dh1wIqAIqAIKAINEwElghrmd9GqFAFFQBHYKgQW5+ZCn97Do7eGeTds8dScJ+N2ks5T4j3a7wT7duwLby6dB6uL85nsMRXghBn/XQJVMLJLf9ir427QLrMlzN+4HF798TMox9UqSUggAa8x8fgLIQVksk05k6C0tAQ6d+oAd9w5C35atgwuvWwGZGSkIKFERBNacGkByoAj2DjGBNmY3/7mNCgvK4OHHv0H1ppiCB8meixsVUiwjBo1HIYNGwKvvzEXvv5mIfJDEsvacYvkFB3f3osaVBPVJo/Jz1Yict5sSGQVxXHcjA3JjZRSeIMtLi+A3l0GQduWnaC8ohx+WrcANhaugqy0VmibBAvnr4HZd1wKE8YfBVOuvwHefu9zyMnOdHJyM1FdUv+W4iKYdMLRcOZZZyBZmAFLf1wB9z/wIHwy72tIS031Y3KVPAYZi6+kBg3M1O+OUCoQS/5ro15Qw3HFyr7D8nDfWja8VrjWcP+XVhwvXlge7sfLW1u7eP51IQ/XEO5LjrA83N9edhJX3vHyij7xW7eGJcZHtYqAIqAIKAI7HgElgnY85ppREVAEFIE6R4BXBB2KRFCn0IogO1nnqTlNaHguz+8qIG7ktAGHwUkDj4CbPnwY3lnzHWSmpHurX6qhpKocjui6F5w7eAKUIFmxonA9fLt+CTz3w0dQZrZ5GWbAm/DHEiCGCkA2oqi4FM466zQ4+6zfwH33zYHb7ngEOrZvgWVgTRzCxLAVU4XySI4kIM7i7/+YA/l5m+GM3/4JUs0CJYxhhsbeSbhaqbS0FC65+Gw48cTxcPfdf4Vnnn0Ft8pRPM/GCx3MZ3uMFK16sjLxDdMbUqUMxKN8rJhaGIYJIIyI7dLKEhi554kwct/joUOr9lBcXga5K76Flz9+ANbkL4PUpFQoLCyBAw8cDDfNuh4Wff8D/N8ZF0Lrltm8S8xDnELTSiWzSok63kP1c+WETTUsXb0JHv/rTDhq3Bi4/Y774MUX34CCzYVm+6CQcOJBIXj8XjD3RUEjla6Q/9okHhMoUjkVAABAAElEQVRKbhBtKwLNBwFdEdR8vrWOVBFQBBSBxoKAEkGN5UtpnYqAIqAIJEDAnhHUHomgTMjKzgI+LJqpAJqQS4vD0KS92qzoKcUtQ/1a94BebbvA12sXw8bSLWaVjujzkQi6Zu8JcPLAQ+HaDx+B73A1UFl1OWwpK0U+QCb8FNWnHUK5ACoqKqBduzbw4F/vhBJcmXLhJdfDz2vX4VYo3OKFbnRuTSUyUrTVKyMzFVLSUqFoSwlkpOFqH28rFBFWZWUVuKqnCsYfcxiUlFbABx9+YtJSZjMiXKFUjPJWrbNh06bNcPVVf4LJJ0/AVUgPwNNPvwQVzplJUnGAvsBATIrE4mXiY7HmbZkTCmMfx59AsOSMl4UGi09ZZTHs0nkg/GbcdBx7Obz+6cPQHVdkHTb4VPjgy2fh+Q/vQnf0QfMtRaVw7z0zYL+hg+DaKbPgzX9/YHCxbAwl8kowaTgX/YqYYi1atQlefvxWOHDY/nD8Sb+BlSvXmC12nqf3Yg+OENRsXY/+MqhC/t06X7VWBJoWArS1duIEvT6+aX1VHY0ioAgoAo0bASWCGvf30+oVAUVAETAIMBE0AlcEdY2zIkim5TzRpyl6OW73Gty2J+zZsSeSMJVQUF4EH69eBBtKC81GrzKcxI/puid0a9URhnXrD/3b7wLPLXofCsuKYenmn+GjNQuh0pw7QyUIdSDx+cMQ70GcyRYkdU47bSJcctGf4M23/g2TJ10K/Qd0McriohI4ePhg2GuPPWHJ0qUwdPDeuGUsE776+lt47Y13oKSk1GxdatO6BRwxegS0aY3bpnBlz08rVqL+XUhBoojO16Fzdlq1agHjxx+JN6j1glWrVsMuO+8Ehx12MK6Aud+sCKrAs4KIHpEVKsFqAcdTCZsryiAfY5WjZQssvhjfVTiQbGy3S03HXjJswNU8qRhlC5JSZSjJIbIKbVLRplNKFhNpHiT8YkpE9pmt2rwEzhwzC0btfRy8+dkT8OS7M+HYYX+ESUdcDivW/wD3P38pbCpca7ArwXOCJk48Em6Yeg089rd/wMyb7obs7AzL8mB+L7qhXey3QIX/0Dazn+Hll++AA4YNgwnHnQZrf96E5xEREuTNSMhXDAR3YkTL2YAiyFh9F4qLbJi3O9GKG32LRhr+62n0gwoNoLZjbOh2oWFt1240Fro1bLuCrsEVAUVAEVAEtgEBJYK2ATR1UQQUAUWgoSHAZwSNgL79u0J2ltwaVuBdH0/V8qTVTl2R4ECyY3Lvg2BUz0HQPj0HMlIzYdYnT8CH6xZBJtIcpUhsnLPHGBjQvifs1rozdMhuDV+v+xHKcHvYVxuWwJML38eVLUiX0NzHowAkD0nkoZwb8grh9luvhWOPPhIefOhxuG7KHdC9Wzv0SkaCYiWeVXMdnHj8BLNVqRxXD6Wnp0E6rgp64u/Pwt33PILEUCqSOl3hoovPhR7du6FvN/jyy2/hjN9dBJmooxVFxSVlcMftU+HQUYfA+vXrcbVROrRs2QJjpcCtf74bnnnuVVxxxEQQwSHn9AgmpbgyZ7eWHeHA7gMhK5kIH3zkpi40qkbibP76pbAFt3Dt16UfD5lYLu+h7VlliOk7y7+CNUX5lADHJ9GxRU2zh6saiAi66oTHYc9eQ+CahycjEVcGF5/wV+jQtjuUlxfD/S9eBkvWfIOEEh50jUuh+vbdGR6acx+8/fa7MGXarWZVFMdnxLkKl86xeY0FDrscSa4H77sF9thjDzhm/GTIw29iHm/uakdCHrzyyRtawhePC00wpWx9Y3Cc2kw5Tk1usoTRVakINH4ElAhq/N9QR6AIKAKKQFNDQImgpvZFdTyKgCLQLBHgFUFjcUVQG2drmEsE8eTegEPzcZyI01S/VVom/pMDx/TaH8YhKXTjfx6G99YsgAy6uQpn+G0zWkAWHsrzmz1Gw5AuA2DKfx6FtUhylFTiqhlcGUSEQXzSgBkGSpefvwUee+QO2GefPWH69Fvh+Rdfx1UtdOgxblnCA5HnPDQFVwkdD998swiunzoL2rZtC1OnXIarfZJg2rRb4IOPPkeCKxN69OiKxE863HTTVNiwfhOemXOeWRFUWl4Bww8eCrfeMh2+/fZbmD37L9CpUye4+KJzoGfPXeCmm2fjiiAkgszWMEtyeFAYLPKQgDl658Fw5l5joEVqNo+LiB6Pv6BxvvXT/yC/eDOc2H8k1o4PYiQxiJipQLJl9ryn4ZN1uXzGknHnAIYDIhfgm87OPvpW6NNjIPzf7KFw8fh7oXunPrBk5ZcwdMBYeOz1afDF4rk4NrpBDJD86ghzHrwHVq9ZC1dddQOsWLnWW81j1PjjFWlamJShx/KqoXXrHGjfoS3sPnAAXHrJebBu3QY49dTfQ2k51mFYHIrBa6R4LDwmltJv/EfG7o3QpI1r7edCC8JVH0WgmSBARNBEvD5+xowpzWTEOkxFQBFQBBSBho6AEkEN/QtpfYqAIqAI1AIBJoJG4oqgLpErguzUXoLxRJzktO3ptP6Hwqm7j4apH8yB99cu8FbE8DS/FCrg6kEn4PawPeGid++FnzavM0SDu96FowodQLSETPT5RrE16wrguafug359d4PLr5gKH37yOWSm46obdKEVQXPmTIOTTz4Orr/+Fvj7Uy9Bi5xMOPv3v8LbwX4F02+chSTO65CZiQdY4wqi1LQk+PvjD+BByltg0mnnQVZ6Kqxctxluv/kyOOH4Y+DhR56EC86/GQmgNng20DQYfcRhMOum23FFEB4WLVvDeGimTo8zMaRJRloatMzAw5hxQLTrjbad0YqcarwZDdPCZjwXqYLIlYwcY1OBK5EIjBTUkU1VZTXklRZ5W+YIBVoJ5OHiJaqsrsCxZ8Dvj74FdurUG56YeyucOOIi3CL2KGSnt4BxB54JT7w1Ez6c/yKkJ2eYGJ07t4GHHryb4IILLrwKD45egkQQXvnOobEEi7htJcHa9XlwzlmT4De/+TV0aN8aKpAwm3XTXfDSy2+auun7U1lm7xa9vTh+WJKFHqPDH+NHOhq7eYmEewE3cTJCtBPTgFHj6VD5ZkiNp2SttB4RMIdFT8Dr42dOrccqNLUioAgoAoqAImARUCLIYqEtRUARUAQaLQK5uUvwXJyDQ9fHF3grYGhY7rTVm4WjiM5uIWLj/3wi6EF4L0QElSEjctXg4+GAbnsYImjp5vU8jzdhKK4XLyqHRxGs+LkAnn78dth//0GGiHjs8WehTascg/fC+cvhwQdvgMmTj8eza34LPy75EapwxcjEY8fiqqAr8Xyfu+HhR5/yrzhPR0bmiSeQCNpcDCedeg7k4NawhQs24vk3szH+fuaGsBmzH4GdOraE6667EI4/bjz8GbeGPf3cy5YIMpnpx27eIsImNTUNcnCLXBWSOvhvJH9waxau8qnCIeKx1bidrhwxrYSWqVlm2IQdjZ4WuFQjiUK8UBGuLKqkRggWWQRDxFJJZSGcP/4v0A+35a3LWwl5eObSAy9dCWOG/grG7H86PPDy1fD1kvfxRjS+1r1Htw7w6KMPQG7uUrjqmmmwft1GjI/nEtFDn8D7DKYOI6Tk1VBaVgYD+vWCgw4aAgNxRdDhh42EeZ99AedfeCWSQuJov6FFwwQJ/JCV/2CHM9DYyYtwsAMWW5GY+shZQPADaUMRaPoIKBHU9L+xjlARUAQUgcaGgBJBje2Lab2KgCKgCEQgwCuCDkciqEPgsOhKXKFiH7eNU3TskoQm8acOGIX/jIYb/vMQvI9bwzK9LUmkK8d/rtr3OFwRtAdc+N69sAyvj3cn/RxfpvySA/siQoO8/GKYdeOlcNxx4+DRx56CK6+5A3p0bW2yL5y/Ch6cMxUm4Yqgs/9wKXz0yX/NapdJJ4+HSy/+E57vMxsef+JFPCcozZAOeHQQPPH4g7AZVwSd+n/nQjrePLZy9WaY88AUOOKwQ+He+x+Gq2fdDz07tIAZ06+C8cceac4IevqZl71bw7hG+uUSaTRJsKmiyGwNO2PP0ZCJ2+XwSCDvkTFVwTtLv8DDsjfD+H6jDOnD+7/IjGzowOgKuOPzp2Eebg3j6OLr9FC0qvBHuPL4R2EQxtmEK6yenHsLLFz+OZww4jwYNnAc3PH0BbDs5/mIA15fj8TR7rvvBvffdxe88eZcuOGGWxEHzGWKlxFQDVwFt2yvsrICCreUwW67dYc7b58B3bt3hXFHTza3spmyac+a9zifTETmbS1cseQWbZS3p/NNomzcmNpWBJoeAoYImogrgmZMbXqD0xEpAoqAIqAINEoElAhqlJ9Ni1YEFAFFIIhAbYmgJLrBiVxxPo6LWaAYiQvc0AS/H3A4TB5wBEz78CF4fe18aItbkpJx9QZN2+l2sSv2PR6Gdd8dVwTd5xFBXhCOxgFJhA/5yLxfBCXFePPVhNF4nfsleODx+3DSSXhr2MAOaIy3WX23CreGMRH0wr9eh1NPuRL6794ZD36+Afbdey+YOm0mvPbau+aqczrjJz0tGa+Cf9isCDrupN/jNqtkKCwuhz/89kS49LIL4NVXX4NJk66AQUN2wRg3wtAh+8LMWX/G7WWv+WcEBavkikvwoOeBbXrAqJ33wRU/GR4PRCOhf5LMFrovfl4MJRWlcFD3PY2cEKJDpMkiGf9VjqTLyz9+6mNksWAsiVijWOs3r4TTDrsaRg89DX5c9Q1Mf/JX0KfzPvDbcTNwnAD3PHsx5G1Zb8rcUlwMp//qeLjyigtx29vf4eZb/wLZmRkmEhlQDnkoun14lQ7lrMbv/tPqTfDM32+D/fcbCsdOOA02bMhnb4cIIl+pOX5cNOJh4Bvzu4Y2udfyDOmPjR5jKw70DlZsbPRHEWhiCCgR1MQ+qA5HEVAEFIEmgIASQU3gI+oQFAFFQBFgImg4rgjqHlgRRGfWuFNtM/WmSTnO3rtktoaj8ZDoVmlZ0Lf9TrAnHlb87tL/wrriPJi35nv4z5r5higox61hU/abDAf32BPO+fedsKTgZ2fyLwSH/QY8vXcm+dgsx7NpsjKz8MDou3D7VSpcdNHVkLtkGWQgoUFbw+bMudFsDdtcWAQf4cHQdCD0KLz2/YP3PzaHRdMBx0cddTgccvAwTJQMIw8dDiXFpfDpvHlQiterv/32hzD33x/D8y88Ap06toWPPv4Mr5JvCfsPHQRZ2Vlww423wj+f+hfQ9fHB7U9unUnm+vh8vBWtELeDEW7ZuPKmFBuEQRZi1iYFVyXhvzZUl0NadTJsQSKNyIws3EJWibjSRfYdcWsZbdoyN2ihzN5ORhEpH27Hw8O2O7TqAr8/9mbo0KIzfIXbwNq17gK9uw2G1z99BF7+9K/moGj6VBvW5+NWuLtg7z33gIsvvQo+/uRrcxOaCWSq4Zb8ShZ6G6LFCPj6+FdeuQuvj8eDwY+ZDJ989D307tsRvwcyT94jX5N9qVrGR/pix+Mwgb0ROTh6RvSXx/4oMAHwh1gj7+/PxqJW2D/cD1rbXm3trEfdt8I1hPvxMv5Su7B/uP9L88bzrwt5uNZwX3KE5eH+9rKTuPKOl1f0id9EBI3HM4Jm6RlBiYFSrSKgCCgCisAOQ0CJoB0GtSZSBBQBRWD7IcBE0CjoO6AzrhaR6+PzzeHFktWfzHsTcSKCxvUaBi3TMvBQ4jRIRkKgHK9Gr8BZ++erf4CPcGUQmdKEfuxO+0Lvtj3g7wvfho1lW/wbsWii78f1EkVOmTDQ5sISmHzyMXjr1UXwt8efxINT74E2bVrg+T4rcWvYNLw17AQkPJ4x18Nn4hiWLV8GL774Gnz1NV5njwdFjxk9EgYP3tuQC0Qm0Zk+lbiKhw5Anjfvf/Dc828hWTQKxowZCR07dICCgkLYuHEjpOJV9K+/MRf+99/v0AdvyjIPVUmPW71TuRHL2IjQ8B6zesbvGXx8VUxDBPL2/LyUlUgmDdhpKOzbdxS0yGoP5bg1bc2GZfDhNy/AltIC5ExSYN3P+XDKKUfBlVdejETXO7il7mbIycIDpPE2NXpsJZKD3/Td7LY1ktGKoDyYc+8UmDDhGMT1Ffjiy/nw8UfzYM3a9WYLmsWColpchMwJUoocM15+0tJjomAxZtWQDekVXpM3x9BfRaCxI6Arghr7F9T6FQFFQBFoeggoEdT0vqmOSBFQBJohAkwEjcEVQW0jVgTxygyZh9P0G/8f1HjTVRJk4AoXj1Mwhx3T9iYiWMqrKqEM/5EnHfcrJePqGLo2nmb3ZmLv0RBMOohl9Jty0lk17du1heuuvxTKkHC64rLpuKmqEhYtWI9E0LWGCBp31KmQildwpeOKoDVr10EJrvahSRTlzMBDodPwVi96DCmBRSTR1V7477KKCigrwwYmamOuS28Hmws2I/lUZEgTWjVUWYHj8bkHbGBM74caoYcMyYYGaww9PbVJ573N1igijKhv7ciCnliJSL3YCH5GWja0zmwLxWWF+M8WqMADqTk33qiG2Nzzl0vN2UcXXnQVfP/Dj2ZFlcRlqorrIZlP2pgOE1hiW4GHXA/aew+47PLzoGfPnkiS5cOtt82Gt974CFdm4Q1uXrVM7okX1ctxg0QQj5B/2YZ/uRaRcE1eLDek+QNirdjW/ztYe/x6Grpd/MrrXtPQsahtfXWPjBuR/jtMr493EdG2IqAIKAKKQH0joERQfX8Bza8IKAKKQB0g4G8N649bw7J4RVB+QQGuCPJPPPbpC0tk4NTfTM55xQa3PTLBzJ9QSW/6kUm86Xti1Ig43hA8c7bzjNu1bQmZ2dmwetXPKK8yW8MeeXQWnDL5JBg+cjxsWEcrVJCOQPLJHIqMhRFvQPUFyQisgxKQwhhgh+xw0kVxidCglTPUozZXXVPFNBIT1Ly5Fd9H4tq6mHyhKPQEPSkaP0S2yZgM0eSftWNtyLISyZsunTuaLXTLf1qNPuTHNhRbrG0e2yKtUFScFQmz8nJccdUZ2rZtb3Bb9tMK3GJXYgOJYeRbsrEy2HMdSMN1mF/zfTBFsDS0iR/BjVY3bVtT4ngN3S5x9XWrbehY1La+ukUlcbTomui/k3RrWGLkVKsIKAKKgCKwYxFQImjH4q3ZFAFFQBHYLggszs2FPr3xjKAAEeRuDaMJCpMjPmmB5EPsXNydyPDMnekEKluIBSFWeLrveoQHRzp5KBr1K2hlDhIFKeZsmiRYu64AzjnrJDhk+MFw1dUzYEthIRp6noYt8RwNuRCOiLoYOdnY2impz0HENihA6HH9Hd+QFXWZCGIFh5a8ifwkPr3lYZn8Cn1FJFcVrsyi6+jT8Gp7AyCOhj3Fn3Ly2qB4WdkCtdiglUF06DY96Wm40gtvJqvdE5W1Jk/8azPJ7V+RN4iaHFWvCDQZBJQIajKfUgeiCCgCikCTQUCJoCbzKXUgioAi0JwR4BVBR3pbwzLNAcn5BUQE0YTfJQwEJZFJP/xm6oCthDqK9SEJWcZ7xENsrL1t0SQpOzsTsltkwsb1BYatII7KRpYoThbfXSKLzrP19Tx6a2VbrkesVLShNxkGyrFUEMcQksZWH4oQDhBSc+G2fIpKRA3HlRzkFCgjZEF687BDrLFJEE8pzvYtVUldkpsiSNtaU4v/Zqy9WNU+ZzBeTb2ouFEyiRO/crFouO9E45Kqt3V82zM21VZTXTXpZXwN8R2/dvrvON0a1hC/mdakCCgCikDzRUCJoOb77XXkioAi0IQQYCJoJK4I6uJvDStAIohXfoQn4TRwkTEIMmEPQyJWPD2MtfJXroQdQxnYn7KGNiv5cydaOZKE5xCRAM8pMvHol1uma36kIivhVshOVhKJmSndteE4idbRxI4Wg0kI425JIElDao4shtZFNDIZZjtXb705nsQI5rG0XDCCWLOv6KgncYMWbOf+so/r6Wpr3+Y8ZkeYCfbLI9Y+t1oqAg0PAV0R1PC+iVakCCgCikBzR0CJoOb+F6DjVwQUgSaBgH9GUOj6+Eq8Pt7nAXwWg4bsTs6ZKCBJFFUgcnm7gNWWCCIfji1RJBP16XH7vPWMJSJnK/4VH1fGdhLd1fBYSS/aILEiucOZxDoQS4xMCebHiepaBlfEsIbtOZ+0xUcCU190IvP61DVNlvP19GQv1BC15ZEY0pe3xJR+0C7YE5vavDmuH50aGCyMdG0iqY0i0NQQUCKoqX1RHY8ioAgoAo0fASWCGv831BEoAoqAIgBMBB2OW8M6xNwaRvCEpukokSm/TN2lL9Z2Cm99XRuyo8eb8XPHRJWInijOy7UKxxUdyaXthomwJ7Ow2HeJVVhJbHyRkI20/VAiMAEYI97GhulF5xtTI4qkIbmtIJiF5JIZA1JMOS/JLLGxWrYSW4oZfkRHb37MIdXURJF8YZPC00e/arIgvX2kx1ltbmuxPVpRNUbJJHcindjIe2tsxaemt8SUd032rl585O3qqC1yeYf1ifriI++wrcjlHdbX1Cc/euL9XfySuPFickb7n7Oa7MRe3lKTvEUefsfXExE0YeJYmDljathJ+4qAIqAIKAKKQL0goERQvcCuSRUBRUARqFsEXCIoG28Ny8R/CkK3hlFGmqrIb/zJmNWwfawPTaVsrJomVmIdnihJ9LC/yCmvPGEbkdM7bB9tK1WQB1uE/UgTHS3akqxtLtsiuX3YVyJIFa416dy+9TUnLYvKM5MIbCU9MXJ8A7h4ehMjNl+UtxupxrZHUlFKQ4zhm/mrXxy5xtRqoAg0dASUCGroX0jrUwQUAUWg+SGgRFDz++Y6YkVAEWiCCPi3hg3oAdlZeFh0VjbYw6LtgIkCsI9LCNCEnbXu1N0eUSw6q2WJG8NGZmKDLaxU+t5KGj+fnBskcvJA20BozmurlKgSk/q2NrF21+NQdNM3Lt6hzsYwHFXq4RxWa1tWwy36NaFMN2xHQ3HrFHTYw/UL23H0oC/L3F8bwZVuTdtEoDRxQ7k1RBvRaiPzeGpZcRSsg5RuLNGG5eG+2IXftbUL+9VlP1xDuC+5wvJwf3vZSVx5x8sr+h3xDtcQ7ksNYXm4v73sJK684+UVfeK3bg1LjI9qFQFFQBFQBHY8AkoE7XjMNaMioAgoAnWOgH9Y9ADvsOjAiqDgDN9Ow4NyKoqmO+4TtCW9tbA61yPcDltZf7GMnmJJbeIf6yf+Qiww0WOl8VsSM3a85GO1HCG6Pje6rc22rJ7jBaNyTLYWn6CF9Y+tyNVRWyKE5bXvx49AVZFWqotniRRWwMT9S6l9HQ3TUjCoy+q2R8xwfduaI/AhQ0G3NWYozHbrbo/6ahMzsY0SQdvtg2tgRUARUAQUgW1EQImgbQRO3RQBRUARaEgIMBE0Fs8IaoNnBPH18bQ1jG8N48OXeTJPq13iPzx9D65LiUcDxI8TTxNNIrjxA5V5c6u4+oAxk1TByl0DL5gv4hptRZwlWHltMpONtaNW+AnXxDaxlia3FOCrSSAdUYYziN6VkyyefdAu8V+Ea1ubdu3z1q4+zikjiRppbaqqnc32qb12uRurVW0xq+vx1TZvbe3qur5gPCKC9Pr4ICbaUwQUAUVAEahfBJQIql/8NbsioAgoAnWCABNBI5AI6uocFu0SQZQmOOXnKRITDdSWRybd0hedK088vXItJQq9JVJYFs+e7dgruF3LjUBtW49tJSYarJ3EF/tgNWLHVuG8tk9eNpKVY1Q5P8cRRq2XMXltGMfabYarC35T15LqCaIWJziK5TzqoL/Yyzuojd8TzOJbbJ0mSKVFo7x1EeNb17b2uraLX1H9aRr6GGtbX/0hKJl1RZAgoW9FQBFQBBSBhoKAEkEN5UtoHYqAIqAI/AIEmAg6BPr274ZnBGVBVnY2HhadD1WVVRhVJvI0oabJEz0kkydIS7gasYj3jp2KOd6S1jiLpeSPjhillTBRb4ki0aXPb6eWoCKmJ3ndHGLEdIr0Er8ljmvFRJBbYRBvsa19tezh5hJfkUlfYge/t5WaFhqbq+hDYvfvRkiuGJMdIggSQZRSxum2dkgpmkQR2AYElAjaBtDURRFQBBQBRWC7IqBE0HaFV4MrAoqAIrBjEGAi6EhcEdQ2sDWMiSCuIXY67dbGU2v6tSSCbdV+wu34UNOElWm7o3NTe9N6tgracI81YX3QUnIEAmPkxKMOWssoo2O5+cIWpAvLJLZbAdkIGRdl7+YQf/ctPpLPvmX7n2sd71sGbTimRCZdoipcu2Cc7dfjUcqvjJnz1Uc922+kGrlpIqBbw5rmd9VRKQKKgCLQmBFQIqgxfz2tXRFQBBQBD4HFuUugT+9R0K9/Z7wxzJ4R5BJBNMFnEiJ6oh9c+eLabM1kO8pPZG6cWBlpw1ImUayftCy5whL2FW8GhdfdsCyoif2zsXFJF71iJ9arNhIZgWzhokq42kRZ3Hrd2qQtmaPsSOfK2TZWYuQkNkHdauLYciBx8HvbvxFdj0jDmMSn5LZ/pZpBEYhCwKwImjgWZs2YGqVWmSKgCCgCioAisMMRUCJoh0OuCRUBRUARqHsEeEXQcOjfvztk4tXxWdmZeH18AW8NwxmznAEjk+cgVUBTaV5REl8vNZNFcOotPd9XGkbhdjgPRTLH5iQ5sZymZLLWIol4o58dm+QKVxgcbUQUf0RchowoyrL2Mq6Gf4VmsQQW1Rg/jx2JHYvI4ntxbWIXrDRaSjbx64jnU1MFwcy/vGfroFYguysQM/mD+OWJNYIiUCcI6NawOoFRgygCioAioAjUIQJKBNUhmBpKEVAEFIH6QoCJoMNxa1gH3BoWJoKQ5HEmxzxfllmzW7E7xRY9y+jX+lk72+I44mWjuhLPmgggXxwVwZV5tq6IqAAaj9xXjm+jJhEmNm1bgC/zUzo6aYoP2wSpEddP7MQv0TuK9BEZxeFaoyNG5XRlkjfam7Wx9rESsgyOViLTO8o+UUbXt27bhJtgRpFtzVhjuEznb71uq9BoisC2IUBE0ARcETRTVwRtG4DqpQgoAoqAIlDnCCgRVOeQakBFQBFQBHY8AvGJoEozUY4lgtwaZSYdO8kPS1xLaof14ag0YadJPB2YnJ6eCinJKVBZXQVlpeXeSh6KQP/wRD89PQ2b1VCBh1xXVmLtzqSeYqQkJ0N6BtrgxKoEY5gHiaVEdbBRLF8gcvu2UWzL+oVlbt/GsC3BisfmWjMdxBJXbn2pJf5iIX3XSnSuLMrO1YfbUTHCNvXfZ8ykDr/mwGBJSgJ8+wbioW9FoP4QUCKo/rDXzIqAIqAIKALRCCgRFI2LShUBRUARaFQILM7NxTOChuOKoO5mRVB2Thbk5/OtYcifuHyKGZedP9uWO3vetnm0Nwn3kKOpOxNBOD3HIn79q5Ng4MC+eJtZATz/wqvw5ZfzISMjA2tDsgjJobatW8C55/4OWrbMgdzcn+Cxvz0JpYbs4Yl9YVExHHnEIXDYYSMhLSUZPpn3P3gO46SnppqMluxyx2Q/Y7RU9HbEtsU68XPlwZFKDHmzR9CPvD2JOJt3/KhBf99bkvhvNwIJxc83iGhINWHfCNMGKHJG6DQDhToEYkCuHUWgHhDQrWH1ALqmVAQUAUVAEUiIgBJBCeFRpSKgCCgCjQMBXhE0Ag+L7oqHRdP18VmGcAkeFh0ci51DSytICwR7QV+mGxwLahLjZB6W2zUcSUCX2D9w761wyPBhsHHjRpg+4zZ44h8vQbs2LQ0RVFVVBTvv1AWe/PtfoV27tkgSfQtnnn0RbCksNhGrcdXPslWbYMZ1f4SzzjoDUpH8eeHFV+D8i6ZC25aZyGElsx3+yk1hTnVGRz9SIQuiLFw6TOzFizck2Z4fNtAQAoyFYWvsi0i8AmXYjpiJRPriJu+a9GLnvsXHlTWutodGFChKAjWuT9kMqlUiqBl8ZB2iIqAIKAKNDAElghrZB9NyFQFFQBGIQoCJoLG4IqgNrggiIigzRATR1D84a47qyQoeyvFLyQKfCMKJeTJu6br7jlkwYsQw2JS3CW68cTb846mXoU2rHJMJFwRB9+6d4J9PPgjt27eBL7+aD2eeeQFsKSrxKqmGNesK4Lqr/gBn/u503GaWBi+++DJcfOkMaJGDW8W8amnlETXj1c5jFiziWVl/thekYu1ZIvGwDHzIWu4IYwHqfVfUsgGrzK9V8xoqFkpW6om7yNyMYR17uxYskV+xr8lO7LfOLugVvxe/vvg+rsbD0RX5bYwdHKSvSdyobU0N3S7xKOtWq1jUBk8igiZOHAczZkypjbnaKAKKgCKgCCgC2x0BJYK2O8SaQBFQBBSB7Y8AE0EjkQjCFUGGCKIVQbw1zGaPnbQJsRAkichua+fSFIn9jLP3I/GJoDnxxKOhd69doai4CF57fS58++0PkJGezqlR3wpJodPPmGxIrJUrVsNTT78IZWUVftwtxaVw6CH7wcEjDoCUpBQki76GV155G9LTaGuYR72YMuKfGcT1SJ1SnVtxWEd9sRMd2wd7/8/el8DbVVXnr/cyvwQCCYQQQBQSSEAccAAEJAhKAyoJVevUVqvVOtdW1IoKQQmgdW5ta22d/9bS2jrWtraKiqKiyCRgE5B5DpCQOe+9/1p7ne/stfc559773rvv3Zewjs09e6/1rW+t/d0bft0rZ7AcMkaMDBlpwKIFbmMDSt3aOgNUfTqTT2CVLJ1JRhypJ87ED25g9Zyypz47y3GWu7gOi01Ym43s/pgTIb1dVGmzRsme195U0WTHNdU9HnZoIaLmetp8wFlb3bgOV8ddh+uUz+LKH4M1dnHcee1+RVAXZXcqV8AVcAVcga4o4I2grsjoJK6AK+AK9FYBfUbQclrKjaCZ3AgaKF8fL5sVbIh0hC1dtErtdqYI4NqvDNf+xFunbGyogC/5eeDBR+j+u7fS7L1n0H77DvCzfeRKHj1CG4GBN9y4nh8EPUjzF86kfeftwc6CSUj49rCtW7bTXfdtpm3bh+iA/WbSHnMGSojNWdAWJ/WgyjIgeLFuwYQkaWgFk7rrc0bOkJOn2hwpcoSrlqqRqA8eZcEszYtZ7kVm+PUsqE7Y0qj2M+ZEQr7qK60/r6w9WycISafMJrcEyu1goZaioEfV7WGivP7d60RDx0y8At4ImnjNPaMr4Aq4Aq5AawW8EdRaH/e6Aq6AK7BLKKBXBJ3GzwiaRwP8jCA0guTtW3LEVgA20nEPHxdYbKKLrXa6lRdfakGcRqk/RRRX6YQd+lB4hFBoiMgVMmIzm3VtI7CNfYLBLV42pzxpSDe84pfswiH/h6xoRdgoVJmvN8ZEhIyK2lKjmSGuPocAYxUmrBzm/JjjHHnDEkMcckYLUPCAXhEWB09UCZbOzuBCfcgIexMLcE3+sdqz/JhW0lYMY008CeOxeCmt2+udKO5u127r7j23NIJW8q1hF/qtYfJl+OEKuAKugCswCRTwRtAk+BK8BFfAFXAFxqrA2nU38VvDnsVXBO1b3hr2MN8aNsiNILQAtEGBWd4Yqaug7hardKOZb7eERZs6wMm5DiU2+IAFjufhqhllk085FKWYUFmBQbRgwBDxMtLD+sAGXxoZrXFks1SjIw5MaTaJEAZUr3hwptiqFRabJR0D0dyEit97Grlrzuw6Ze2pglB711ybV737KRCuCFq1gi5as3r3W5yvyBVwBVwBV2CXVMAbQbvk1+ZFuwKugCuQKqBXBD2TnxG0KDSCBvitYdIIGhqULXLcJsvItgSiJ21uqL3aCNIrcmJuG6/W3KJv89LmUIyrH+WxQGmbA80OXU91+59HVxH1fBonn00RYk99Mms60jow04jAUmlyARMZO8+Wx+Rc+LZhb1V55Br5SPjHi7upGm4H5Wl5PjHPKGqqye2uQFUBvzWsqolbXAFXwBVwBXqrgDeCequ/Z3cFXAFXoCsKaCPoVL41bJ/w6vjZAwP00MO4IqhIwfv0cMsVT9EeEE/eIsAcHru9R2sotRX84RSjk8ZA2LEXm/RQh43RugInf9jagAqsxa6fX0CmmdSoaXhsMyMOZ+EWfz13q0jLoONQJ8w158gWRzazvZqlJrxsp2h0nk2sqS2daRQyW59GWktd9tHaqnWNlql9nOYSHcNqKosdrzW2r6w3iIoAXSxjPLmlTP0uu1jwBFGNrG69NWwF3xq2eoLq8zSugCvgCrgCrkBrBbwR1Fof97oCroArsEsoUD4jaNk86p8yhaZO6aMdO+SNW3Kg/RFbELpV1k9s9RRrP9Vjt9XtG0GIz1jLfZNl05ZGQJZXydhq05ZHSSEphIYNZTOADXF1qEHPyKjxmEVMyB+nPMotGlONTILKSRqNGb4DrbkEhwGYBRtxdnURH/lgQ7TO1R9R6lVtLDeid90zVqorTFXYdVc12spFjfHSIP6aRltdc9yjg1sa8KtW+uvjm38H7nEFXAFXwBWYaAW8ETTRins+V8AVcAXGQYG1a/kZQUtOCq+P1wcy8waL37Klm0PeIMo0zCW5bBjTDRhm4o1HqxvB0k0nGg0xVkb1rJo/rSLGSUzKnc4sMvKjxZE3g2xsvhowKQvy4ixe8CuL5UJsuzPYcAY+rRPMmg9r0fzwpdWgMuGLiCp79EmjrA6LGD/v2gqM57c7nty7suqd6+K3hu3K37PX7gq4Aq7A7qmAN4J2z+/VV+UKuAKPMgX0iqCT6bCl+1E/3zsVbgErb8fiBkBfcT9V0dyIWxhtELSSKzYTgJJoOeBBWwNz9cbWBebRH0dVFNA4CzbWCyviYi111wTZPBpZtSgDeAQFDGw6hzVWMPIRGFF9ZBB28cY2EOYR0/lIvpHIKHHdqL7z/I50BVyBqIA3gqIWPnIFXAFXwBWYHAp4I2hyfA9ehSvgCrgCY1JAG0Er+IqguczDzQRuBj3u4EV8i9gUuu++9fTgQw8XDSL28ZVB2nKQlHkjCO2DWE61hRDbGdpgSBtBFm+RkbHzEa5jUZ78mp50HcqKWnSGWux6UTOqALfaxRrRMratGcSk56pmqT/ONBfm6Uyt7bMhut1ZlMD6wV2N6bT2XuGqFffe0istOs07kQp1WlOvcBOpRXMuaQSt4tfHr/HXxzeL5B5XwBVwBVyBCVXAG0ETKrcncwVcAVdgfBTQRtByviJoIfcuhvg5QdPorz9xIe23377025t/S5/57Jfpul/fyE2gKUUBsdmBdoRs1eTAXGexPYJ5FWEtaSOjjiu3RV47UlTcPgqv2KLFom2V1QYIkFihzCOPsqKBZPMgrtvnNKNlT9WzHjuOtVtrOq7Tyq7fojvhE3y3cbaG8R53u/Ze8Y23Tpa/V2vsdl67pvEe19fuVwSNt+7O7wq4Aq6AKzBSBbwRNFLFHO8KuAKuwCRUYN26m2jxYn1GkL5Tm+iFL3weHX30E+mUU55J//Zv36ILL/oo9YdGkDQJcKRXBNVtY8RWPdBoUA/aKNIssHibqcrRymIjlTE2ghBnMWLT6mHVmVo1wlYGDm1iRRXyKMsWY8Yy0kaVZIy3b4EvVQ/WkZ2l4qgV6heO+vWPjN3RroArMFIFvBE0UsUc7wq4Aq6AKzDeCngjaLwVdn5XwBVwBSZAAb0i6Nl8a9g+3OzRJsOmzVtpyaEH08UXnUf33HMfnf2Oc83r41GUNgdsu0A9sHTWmlA0YqTlkDYdogd5G84AJg+2btXCQEDkyy1CJTaWRT4jsBihiaWe6A8xoV1TXU+FZESGmFGzacWaL+ZPKTVGdM2jU5zOBBOYlBqLr4O6zRVwBcZZAWkEreRbwy70W8PGWWmndwVcAVfAFehUAW8EdaqU41wBV8AVmMQKrOUrgpbwFUFLl+1fNjyGePMxZ84s+ssPvo82bdpCf/629/Czg7TdIEvRloNeO1JdWtlBCLgYFZERgVvDYFGMbQalHuRWXOLDJDSCYlMkjmL+OJIgeRi2nNEmiV6sM1rSkaZEYvFp5WKR2LKpUigmiLEcsUIoFCtoqjXGxMyIjpY4CjUnS2qtYIz0kSvgCnRbgXBF0KoVdNGa1d2mdj5XwBVwBVwBV2BUCngjaFSyeZAr4Aq4ApNLAb0i6ARauvQgbvboVSP8qCCaNWsmfegvz6fBwUF629nvoe3bd4ZGke0RpG0ZXlfogISPsvWhs3TNKUc6U2RsVeRe25bIfRor1oiKIzaXLrHWRwtHCatMxJAeYEnypJCGWZKlAZOawxvdyrXxlTtFUmWqq0Crq9ZYh9VcgasakBbiM1fAFZgQBfzWsAmR2ZO4Aq6AK+AKjEABbwSNQCyHugKugCswWRXAw6KXLlvIjQVc5cNnviroQx86n55w1BH059wI+s/vXkZ7zx2gWTNnFkuRZkL1WTVxnbHZEEfqRZ+hVTMm5UFEzhRRTaM0YvQ8Tfwjt6MGRKYVwtrqjKt8JBJsytLMFa70CaTNmFY53ecKuAITr4DeGraCbw1bPfHJPaMr4Aq4Aq6AK1CjgDeCakRxkyvgCrgCu5oCeEbQUn5GkL3kZ9uO7fTiFz2f3vCGV9H996+nu/hZQd/77qX01a99m18tP61YZtqGsNfSSLtBvHVtB0RFRLQEYjsNBJEl57XQoqjyFKPUJFfUyFU0w8N8xVHuLKMmYlBXdV1BgkvtvASuHa2gWKuiUqx6i1y85qB3gNThIpePXAFXYHIoIP/NWrXSXx8/Ob4Nr8IVcAVcAVdAFPBGkP8OXAFXwBXYDRRYu24dPyOIbw1bdpB0GHhFclvWMG3duoNe+pLn0Vvf+jrasnUn3X33vfT1r3+TvvTlr9G0qVO1PVE8j6doMXCsNi5sm6HaylCUSidXFCm60toIgfDWC120OGqdtgYAkCP6eKWhORQtwEqVer2TKGJnYaIwuYKqbC6xKQCLJpN0bORo7DjZ6k1+ibNTZVEqaeYYn8qNVYnLOjUwWUWoKeMQGKdUahloXHPdhb8nJymuusZKKeUaeDnF96BfQwexFTI3uAK9U8BvDeud9p7ZFXAFXAFXoF4BbwTV6+JWV8AVcAV2KQX0iqCT6fClfGsYPyNIDmkEDQ0N0cUXn0tPf9rR9IEPfIy++vX/pmlT+rgJNCUgqo0C2zYJNOGjbuuOfboAsDUv2xns7HTTbnliRh2BN7drTKwVebFy4CN30awKBv4QIJy2EVQEBh74xRYMyl5AilMFpHbTCLIIxCpTypc0ewAssyhLX3lFUBbLbrGERlDRNNFQtqbQjHkST7EO+X74f3EZcTSJq/fSXIFSAW8ElVL4wBVwBVwBV2CSKOCNoEnyRXgZroAr4AqMRQFtBK3g18fvxQ0Y2Sjz1pmfFj0we4A+ePH5NMQPi37nu1bT5s3bgk8R0iPQTXXarJCZ2vWzvrIYUzRZNGsJBndpaBiApzmXeICyJNGmI8kYbYqUq314xFc9hXp0EuYl1DaCGCvyhdvOAkHBFzS1uVuMQ0iMC1ez2MVxt2YkdJKpbIQEWiazfAoIy1GzgGRUnHOs4MsDuNLQMOgBzjSC5EsMqwlrabmghvo7MfdgjaGsbuftZK3dwnS79l7xdUuPeh5pBK3i18ev8dfH1wvkVlfAFXAFXIEJV8AbQRMuuSd0BVwBV6D7CuBh0Yct3Z/6iyuChoYGafbAAH34Q+/nBhC/Pv7s93Li0EkIJ20YaS2FNfqDWTZlesSRzLXBYGO06aOb9SKkOKkntXVvlmdM69QZ6ix9Wj6W0WExCBoBXKCcFJGoozB3SDQKGJpd455oFLWNJMQKtquvZSTrduxup4BfEbTbfaW+IFfAFXAFdnkFvBG0y3+FvgBXwBVwBYjWrbuJFi8+nq8I4tfHyxUufEvYjbeup1NPeAJ9/GNr6OabbqE/fxs3gspuCK4ZMYYgZP3uO0flmqMhIzgwjG8LSCpA1rwaLLNd1dW43cIiX8DutvTdcU27xY/NF9GJAt4I6kQlx7gCroAr4ApMpALeCJpItT2XK+AKuALjpIBeEfQcbgTN5+cC8a65n+hv/+qDdMQRi2nq9On0+c9/hT71d5/l5wfJs4HQBEIxtmuANk70WS+s1XMap/v2ziKrXJ1aNGea2fZAxjt/izpVgBYAd7kCrsCjRQFpBK3kW8Mu9FvDHi1fua/TFXAFXIFJr4A3gib9V+QFugKugCvQXoG1fEXQksXL+a1h+9MQ3xo0xJeEvOPPX0v77DOfbrhxLX3xS/9CO3cOls+mkat1wvU0oWGhDRP5zK+xGflVPdXmzMg52q03b/3E26/SyF41grg+lMglYCi1RaXTSifHTCrtlWbdUKAb9Tdx4FvkDmvyjeZ1I150RAzOwOYaw5/bgccZ3JjjjHiZ5/XB1w3unGMk3FJbU7z45LC1d5vb5u6UW2oSrI2FTc446vypLVwRtGoFXbRmNYL87Aq4Aq6AK+AK9FQBbwT1VH5P7gq4Aq5AdxTQK4JO5EbQgbxv6eOrggbpNzfcw+Q7iWbPoiUHzaMp/bLR0gPNGWxzsDWCX87pVsZ62o21nSTx8THS7WJG4q+rti5+9CuoYxuRzTzo2D4sWivqYV0jWsQuAg4/h+KXPCZp+XfLNPogbyZKfmaYsL0pR4DIhwEEGn3j2Xhw6zfUqm5BFDWZsjSu+GyoWyNt7QVBwIOhO9wiWfhv0ki4Q4FFfpQj50Lz8vsDd8AD2KbuEpvxBwmi3uoNRvN7qXL7rWHQ3c+ugCvgCrgCk0UBbwRNlm/C63AFXAFXYAwKaCPoFFq6dF9+hThvqcLepNiglDsim0BbQbJlaToQ3eRvsoc2EPZPo3hDVhNvtLeqOqKSDbk1T8SYd/1BAhGx1ILH+sVMRAWPrhz4SYz2Rwu15HsLHPz3Qzo34QApz4uhfqV6VZ2a+DPBF7H8fYdGYGhzFNzydyLwKovmEG5YtX0qv6BOuMND3zl8mN+MJ8WDJdYuefiQWkpO/CwZXVc3swhWyIQxrkGICj5xYpxwI08H3FxvYAu1h3SSgI923AUm1G6wUmtwsXb6RdboYvBB83Q9Mgsqdsxd4KGF5Jej4A7sQ8S3hq3gW8NWq88/XQFXwBVwBVyBHivgjaAefwGe3hVwBVyBbiigjaDT+IqgecUGRFjjBke3PsU87HR0wwiEYgUVj3QW7e1GyhmShO1cO/zI/LHi9nGjXUF7Zkc8ihTQn3JYsBkmAiR2NBA6+fnh58zYhMOwa/OmIBslt9DV8afcRdIR1q3c2rwqGMpTmdOss3Q2DQKWP0Ijpb5uCS1rHxN3F+vWoqQrFI6yPqmVv7dVK/318aqMf7oCroAr4ApMBgW8ETQZvgWvwRVwBVyBMSqgzwh6Jj8sehH1hw2U/qu80upmR/dL+hn+lT/JKfZ0B5jOEnAPJtjtjTT15FrFSKt3/GRQIP/tVX9Tuum3tQpG/uSxFiPj6MdIW7QWB4+1VWuwXh2ncTLLuat1S2Qrbvj4Ehdz1HHbtUUo4qMlHYm/E26JStfXum5k4RgtFgY+V2uq6lLFGAIzjDVpGo3zW8OMRD50BVwBV8AVmBQKeCNoUnwNXoQr4Aq4AmNTQK8IOplvDVvIbwbT2yNkCxK2Jfgo/oVdMmFbE7ct1fzAVD3tLLoFaocamb9Vpa2YRr+KVqzuG40C4/G7GE0dI4yRq3DKn1E5SEiSxkG5zHpsGlj8rhmqYdWYbnBLzrprX0bNLdWW66zn1nUW6yux1fUleoRJzi3y18WNH7eWW9QhNZn/dlbrtZaiJjYph9Y9PDREZ/IVQRdduNqCfewKuAKugCvgCvRMAW8E9Ux6T+wKuAKuQPcU0EbQCr4iaG64Ikg2felhtyXRg21Ljo6IyTGSDWt+NNcMj645xlkOYKK3/cjG6+ZXYkbD1D6XIGy+TrIAL1iMbSbLIf58Llhrs7F1Y5ujVVwTLq+hLkedzfLB3yo/MHm+nAccmR3T4AYGnDgXoAQLn5wRB0DhwxTuEmdjR8ktFBI6Jm4hEQIUKvPiaMstOAYhtKwDBHKGEaDCh2lwA2PjZFyA5FQLgRFkRTymcJfBORHPK9iCI4mBzfBn3NIIWrXqDFqz5rwM7FNXwBVwBVwBV6A3CngjqDe6e1ZXwBVwBbqqgDaCTqLDl+4frghS8nI3wlPd0ajF2rtVRr6JaseLHVY7nPojWmpHm0vH6UYVNvBirZEBnobdY3Qno7r4BMAT5Mrto5k35UOOJn9zLolAdDNKPBaV52nla80aveBox21xdhyZ6kfA5t48X+7vZG65raLd4Jb84Ae3zNNbpTqpsh4jXPLH1mrH9VGdWVG3oFE7xp0xNKNs3d3mlqyo3XKLvRvaKLdfESR6+uEKuAKugCswmRTwRtBk+ja8FlfAFXAFRqnAunU30eLFx/MVQQfyXQzY2OAspNjU1N9kYdMCKbacwc41pgltGevGNq7OX7VphFbQXId4BJmfq3zp6ur81tZJvdWqLMPIxshn1wEG+DDv7AxVWqNbrcEyCK5bDYrWFY3OW7eO0elWzY/vRDzIMx7c4O8Wd85n1yG+sR7d5rP15Nz53GJHOs658vlI+SxeubQRdIbfGmal8bEr4Aq4Aq5ATxXwRlBP5ffkroAr4Ap0RwG9Iug52VvDqhuaujaQbjPlU735tlNYcChKZjlKbBYp81ZHXTzw4InZYnMHGHuucllLrgLYO6vXMtWvGpXk2qaRQHWWNaJlJEyx6tZVpJF2lrLkqlhkngvzlGG0ddhMnY1b1doJw1jjO8nhGFegWQF5WPRKvjXsQr81rFkk97gCroAr4ApMqALeCJpQuT2ZK+AKuALjo4C+NexZfEXQvuaKoGquvFkhm/nwRmqB8n65umUWix7wqSVtc9ibtYBvPqexKS7mqzYa4Evj8/aE8KWIpgzgQwTmYLTn1py2UaPvawNXmhuz1l6gWq0CmPE8S5XQQPJg3Ou6xnPNzu0KdF8BbQSdzo2g1d0nd0ZXwBVwBVwBV2AUCngjaBSieYgr4Aq4ApNNAW0EnchXBB3APQls4KtV5o2g4eKNSH3Dpn3B8brVx8ZfeZQVn7EZoCOxp+2Qdq0T64+ZlCc2HYQVuWJdMZs+RhpRFh2rifHCFWMFLYdYgAmG5EM98tmEy7MD11/wYh5pEQFW9aQ4faNTaosMIxshj64lxqKO1AI0rDbKRuR2O0dsXn8+HykOeJyb+ODv/rm6SvlbZX+HTTXl9nQeeVM72MWaHimu+fcZ/zZpjhiHUZU7zdRqJpzgqcPVc+cR+VyZ5G8rtFVLxKW80Z7j0poiTutO2YEFKuaABYjOzvhO5dawlSv91rDOVHOUK+AKuAKuwEQo4I2giVDZc7gCroArMM4K6K1hp/IVQftkVwTFrYw2Qexciio2sLxjwYZOGknYwNiym7dCQKfccVYyW7oytzXGGLHWb9LUo/Xa2OrYsqFGYVU7LDLDGBxVW44QpOVHZI5LMXbWCgmf4jFDjvSce1vlSCN1ZvHwo11Wr4x+M8DinNch9jpusVtsjrG+Vhzik2MkeMFKPsTkuYUPBzCYj+bcxD8SbluvrWE8uSVPN/htvXY8WbhbaVv3HY227iG+NWzVKn59vF8RZH8GPnYFXAFXwBXooQLeCOqh+J7aFXAFXIFuKVD/jCBhT7cu6Uw3xGrDtidFwGrrBCL1YZZ6ZSYeWO1IIxCnGXKctqlsVORrblYIXplS9pijzi5e1KtI+1kXATTOFi9jraHJmjMC3WSP7YvImGKb6oj4dqNOGCwGNQtvWotmsn7krsOJD9jcn9rjFSKpHew4R1xrbmHBrwyxfp44BVp/ixNXx/hmGuIrL8/iK4L8GUHjq7OzuwKugCvgCnSugDeCOtfKka6AK+AKTFoF9NawZ/KtYYt4R43NVdxcS+GywYZHbgnTt4vZbTe8ukzrUUurT4sWHjvXqZYVc2SIQB69mgsYtWMmPrHIn/HexEtOzcOD7Ghlz6AdTIVNDrtKmcMuYzlyv1rtZ3uERe8641yJusrxfdX5clvT9wcO6NiEy/nsHDF5zd3mou/3oQAAQABJREFUBp/krhtjDba2VmNbt+Wz/LCPlVs45fZJOewb6FCDejr7RIw9SyTmqFlsY6kb8cKLA9z2DJ+ewxVBK/mKoAtXpw6fuQKugCvgCrgCPVLAG0E9Et7TugKugCvQTQXWrltHSxYvp6VL9+e9lW5S7FbF5grbIDwbqNJmiMiRbpdiZP1I6sFWySLyPLbuOrzE5jGWr7vjukxYCTLZimEb/dmyt2KuqyxmbeW1GWJE90Zj4Udsq5V3r9J6plw71FSP7syK9eTcnUWnqLyeJu4cl7LUz+pi6vjrcPWM0VoXsztxy0rr1shtLn5G0Cp/RlD8KfjIFXAFXAFXoOcKeCOo51+BF+AKuAKuwNgV0FvDTucrgubyXgSbK+VNZ7EZI1vS3CcR2KrWbWnqbJql7hNM1leX0fqrNdWxpBHjOcuzt69/rNXkGmNuz5IjrQwzoMZaxVjiu1GDcEzmA3pLjeNRK/jbcQuuHSbXsVPuurh2ucAtse2wOX+7+Vi42+k0vtxyRdBZ/vr4dl+w+10BV8AVcAUmUAFvBE2g2J7KFXAFXIHxUmDt2ptoyRK5Imgh9fEVQXHbE0fV3Nj8yIYtv8VKn7KjiMihyCpTswU5gGi/OcxzIKKuwpwdWeI51h5tGDX5YK9jRzXgyM+Ize3pPF9j6pUZctfni/HAVRm6bQk5Y+IW9B2BOogXnvE6Ovue6rPnmmO9qHcs3JLR8neb2/J3kxtrtrVLrtFqAj7Ui1plLgfm4FdrZ5/jxY2aUAXyDPMVQfywaL81DML42RVwBVwBV2ASKOCNoEnwJXgJroAr4AqMVYF1626ixYtP4iuC+NawcEWQbsg63SYpOq0CD1zWJpEyWb66mJSh1awpOt9MCQeyxphYG7bN0YeIYUOlw/gAYbgCs+jFt8qlDK1qD1EFAFHW1iq2bjU5Hpy5Pc6RrT0yxnQywk/Haoe4kJM/Qs4ssfgyUwhrsoPTnpWfm5hlguJbDiT2+zHfPvsiXn76YhBWiy80L3zREzIWLYW8evVpfbnPVt1qDI48HnaJzX2t+KyviaPJbmPbjZs4muzt+Ky/FQd83dZE8k8Et+Spr91vDRNt/HAFXAFXwBWYTAp4I2gyfRteiyvgCrgCo1RAbw17NjeC5stuOGMxG+fEg01L3WvawRExIVTMMCVcnUwQmHO3i63Dsy0x59zCCRv4JcDaQACs9cNn8ZYHY+tHDHyjPVtO4bB1jZazwzgsIS8B4e38wI36bBNgbMmkMGOXYai1KLic2xiMc6fhCRAsuskOnk7OTRxN9k44LaaOp85mYzodN/FYO7TqlBM4yyE28Fg7bIjp9Gw5esFtc6Y1+61hqR4+cwVcAVfAFei9At4I6v134BW4Aq6AKzBmBbQRdAo3ghYU++R8U6Qp6qxx24VRHWqsJQq38OIMPsyRW+zIDxvmiKk7g6fOl9tGgpVY4HG2fGKTo5MaFdn5Z85t81sfxq2YoX0rzET72tU0Ek3rNLDx1o+81t9q7TYWOBvbzo+YunO72Nw/ktrzWMmf1w0+W5vFWLsd13GL38bWYazf8tlxXdxYuO0au81t65ax5UfePn1Y9Cp+a9ia1XmAz10BV8AVcAVcgZ4o4I2gnsjuSV0BV8AV6K4C+tawE2jpEQcxsd1sYTMS86lX7XHbApxYFKERsCM+n8OenzvFSVwrbCtfnjOf57H5HPjcns/HCwdenJvywp+f47eXeoQnP5qwOS6vIZ8Dn9vrcgJrc+dxwOT2VnyIkXMel/9+LRZ1dMptY9uNW+VFbF5rPhdcJzwj4QPWnm3ekeSzHPm4jqfOhjhbg9jyOXBytjwWZ+0WL2OLy32dzHPu0fBFDn1G0Ap/fXwn0jvGFXAFXAFXYEIU8EbQhMjsSVwBV8AVGF8F9Iogflg0PyNInpMiW5C6Q7Yz9tDnpVhr3LxYXNgGjWYvZEkax/U50w1gY3DhaOJoFzda/0Tnq6tTamg67HcKTCs8MJPlXFd/U232u8Aa6+Jb+Zq4O7XbGjqNqcPV8dTZ6mJb2ZrW3g1uydstnro1NHE32es4mmxNHE32Jp46e+QIzwjyK4LqRHKbK+AKuAKuQI8U8EZQj4T3tK6AK+AKdFMBPCPocH5GUHhgbklunw8kGxM57CY5PjpXffZTcSna+jsdxw2RRtg5asq5JGvus5XYVaQ4nVks/JYTfvHV2dN6LAJ1pTbw2Thwiw1jWwvsctZ4y2nHgqhyqLX1Z5UlXW/r6PbeOv72UZ0jcn47t+POGVNkE4fY5bDfF8bqqf/M+ewc45w7ZwIut+dzi8O4jhu+PD6fW5zlgR02ietEi5wfc/AIhx3DP5ZzzifzbuXJuVFnkx1+roCfaH7WyjNozZrzotFHroAr4Aq4Aq5ADxXwRlAPxffUroAr4Ap0SwFtBJ1Ihy07gPqTh0XrJqWv3LeZTVG4ckjmxlZTELY54gKNtdkQ+MUWMTJCw0kaUxgXKHntE8BlKRqDpoVGySxmwEhDEZgitA0Wr3tCDHi1grS+uMqiPpMTfOLJj8itHotVn1oiP/JGpK0e/FjZcCmSeGSW6RjqFE7lTc+RDV5YZC6HMEZOnakn/Yxqih1sqFIsatM1R3tkUU+co4JoiSP4EIN8gtCxIqCc4GxOi1dWxcdvQa34BKfl0zVBbSA0QuuKWeJI/FbRNANmOEfW+D3n3MDqOc+k32Dduix3ew5ULWf7XWMm9qhxXY3N+RALBruSOpv46+xik0Oz6xjITjQHpo5b2KI9jsSqszQrsqtfFLMx6h3m18evXOm3hkWtfOQKuAKugCvQawW8EdTrb8DzuwKugCvQBQX0GUEn0eFL9fXxw8NDgTU0hcy+RYayTcFRbm6leWScJoTxPAvvE9dTaCqBBDHFWU6hr8P+HIdXkoc+lQDlkLQ8Ru/KjoO/4K2Mg6H5w4Y1ozrzmFKrATZRMS5NNrA0MoUdy5TnWH81QStLTKCUGXGWynoxxrmaBR6cLSK3pfN0JnGppfzNlZT6YxKUYHWTrk7xpNFql0/YRT/5HSlLtEekjnSLXjR4OKZJc/BKlIxxgB9znIHPz/DnZ4srF8GgOn6LFX8+z7kxtzix1XEDi7ONsbngx7nEFYN23AKTw3KCQz3Vz/zvRCs8+PMcVVa1AI+6W3IX4KbfijDa+JwbNZSvj7/ofJj87Aq4Aq6AK+AK9FQBbwT1VH5P7gq4Aq5AdxQIVwQduYIOP3QuTZ02nWZMm5FsYiVL8u/Z2JV1mF42QmGTg52OEqbRrXwpUjeFFp/5kyl2bGJEzAjrT/i6PCm1yXjbloi1ZHG79LTtoru1uixRNm2bZaT4toTjAdgliuxs4RO2lAlLlK27dV65Nex5Z5xK559/ThbnU1fAFXAFXAFXoDcKeCOoN7p7VlfAFXAFuqrA2rU30ZIlJ9FhS/ejPffYi/bdZxENcddE/yWbNyncdOjjS3TC1ROSudy3yEAOAegoabbAVfyTuGxoimEBjifl5nYTeKKrdhT6IEkzBBMtTmZ6m5teQ6KNLKWKsYipTVEYlS+uuQmLwsGJucWrr6ylvOxJ7MDbsY0VlfXGEbHiuwgP9w60MQ5MASdY/mNtYlernOGp6iTe6gFc4ZEvrMwvNvDJODhk0HAAq7hAxUOcNchiZJxzqi1eC1SHiekVF+fCh9/ccHHlmnglCzJbtPw9wCH4Jpxi2FuUo7g6xpJNB2X5rbACtXXL3OLzqmRudVVsjgqg8MEehghKfmeKs/wRqd4qt0WkY2Gz+DALNv3QbDLG773+92BCymHn3BqiGoao8NG0RiRgUBAFc4sXgjiX2vuLKf7bhih7zqOiz3AxwfLlx9A73/HG6PaRK+AKuAKugCvQQwW8EdRD8T21K+AKuALdUmDdupto8WJ+RtDSRTRnzp60776LaHDnNtq2bZtsNzkNb2vQeeC5WvLNnFaDTVvYXPMuRzY6OLDhxtyeQd8KY/EyRkxuxxxcgpNxHR728iy8/AdrBJec29ngx7ltLANRm8U2jS3WjgVft7acp66uHNPpvI6rziZ8ud3OkzFPsK5w5lj8foDDOfAC3wYn2PwAT97YacLB3g4PnJ6LRkOxCPmbU65HCpCDDdGGkegAgMJQr8x0XN8IquKK+KyBVYeLtvbcytqe2+J0HLllnq8zx9fpHeuMaLWl3HJLKhRFDM4aGfH698f8t01qi/RhpP89USvqTvnSANQO7tSbz2wtwhoPeUbQc55zIp373rOj0UeugCvgCrgCrkAPFfBGUA/F99SugCvgCnRLAX1Y9Kn8sOj5NGf2nrRg3wNo2/YtdPc9t/NuCFsdnGUjhI0Ktkp2DpxUZ+3pnLc9bNDPVji9XCPLozurgt/67Fjy2bmMUQ981TmuQojYpjjLDT45gxNxo8AFaerigkOS8GHzYG5j7Fj8dt40boWDD3nt3PJZu4zlEH+72icSJ7nkkJrk2jcZtauvaY15HHDCLyuX37hgcDRoETpf9gojwfXzH8tnc0VVlRk45MrnERWzWD47zrkl1vLlY80p1npuzY1PxWEmZ8uHOdaR42C39WKMCuo0Fx7EphWkM+BsTRhbXWwUxsAhj+DzWlAr8shZ4uo4lE8fFn06XehvDROx/HAFXAFXwBWYBAp4I2gSfAlegivgCrgCY1VAG0Gn0OHL9qHZaARt20J3hUYQb0aKxkvc3hQZ2aBbFZnDqxsdzGxtsimKh+LCnJtN+q/thVdgJYHibKSggjs3Mn9oLZWxBZ85YcOfhDZOUqJw1VDCJZMip1kbNv6WNmUKYcrEDt0qRnVyLHiEFyhgxIexEsqnWIqoekDpNoOCqGCrxBk+JCoT18UUeMFarmDWdkH1S0TdOOeJWCmO14tlUkw6kzixyB85TC1qKD6tvWAQE8IstrQXuNJXBUeE5Y+0qbUkqg6QEwEgrqasxtZaQMBOcAIHV+DGBM6mcwMO3KW7HEQRaikNrtbPxgo32xAm57EctdxMisZzS34U0aIA4celhwKzIRXu6AwPi151Bl20ZnULcne5Aq6AK+AKuAITp4A3giZOa8/kCrgCrsC4KbCWbw1bsvgZfEXQQdwI2oP241vDtm3bylcE3cF7oMEyb9yrxE2K7p3iHDu9iEV42AXxPq7YbelUN3YMjvg4ksgCnYyUkXG8QUMDKeLUW2LCQFst1qN4/UTGlEOs8AMBC5jyFo7YY1zzqjReW0hFUwSUxdmyZK7GqY1JK66bpattJM0cNiqyZqByaisqjcUg+qBijoCW4hfl0cQTnK1DcGiRAakImckh6JgvmMJHnS16Ox8hj0Sg2mpGoPLa09U01ZTb8zmqze35fKQ44HFu4oN/Is55DfkcNeT2fD5eOPDi3JQX/tbn8q1hF65uDXSvK+AKuAKugCswQQp4I2iChPY0roAr4AqMpwJ6RdDJ3AhaUF4RtD1cEXQH71HlVfK6dZXtjBxN22r1yieQdqQc5SY43AqDCMXHT7SLxFJGADyGM1oJo+GMa9ICRsJh12HHWApsOItdxnI05QE2P2tUp20IoFudkaEVRnyd4sAjeDlkhU2xFhPAI/qwrGNjap/W5irQSCnTpq+xPbEjHuUKDPEzglatOt2vCHqU/w58+a6AK+AKTCYFvBE0mb4Nr8UVcAVcgVEqoI2g0/jWsL1DI2jfBfvT9q1yRZA8IwhNmU52snbnq8VYS2AqaXiAy3nKulO0tgfEWQaVyM4HwinxOXfOAFxux9zGw9aqrjp8XVwr3GjxiJNzvnbYLCYft9MC+MmMa9K11XeGdY3mPJm1kPV0Wt9o1j7amE5rmuy40a6/szhpBJ3Ft4b5M4I608tRroAr4Aq4AuOvgDeCxl9jz+AKuAKuwLgroI2gk7gRtJAbQXNpwQK+NWyrPCxabg3DFUG6GavfkolVDmyydQ6r+sTLfkBglHNJylfs8BitJ+OwaBNQBmZ+TPMKYK8rQnxNfDkPcE08yJPHwZ7HNeGAlzNi2mGBs7F1MXW4PKYdRvDC3SnO8mPcKnY0dYO3LlZ8rfIhNj93skabr12OTvikhm7j8nWN57zbtfeKbzw1yrnr1+i3huU6+dwVcAVcAVeg1wp4I6jX34DndwVcAVegCwqsXbeOnxF0Er8+fmF4ffyCBfzWMG4E3cWNIDLPCGpOJRsYObAB1nlujX4DlTDt/oR9L571optgccoBXp21/kTW1qjUy/w2ReVKpRRdP7MEQNha4Lc24LpxFn5wjyYXYrpRi9Rh+VBXN7iFw3Jbzk7ySKytz44tV7uxjUvzWo+yNNXbLof7XQF+r93QEK1ayQ+L9mcE+c/BFXAFXAFXYJIo4I2gSfJFeBmugCvgCoxFAb0iaAVfEbQnzZ7Dr4/fRx4WjUaQbGKxkcWGF3PNqo/qTSuoRwomjQWDnJtjFFX9FC5ERW/VUp81RNSVI446kpiiZmSJ6oLra60hGldTXWUxYbwWK9riKI/FinN7jBDdo1dGiLGYkY8tS+QfOQ8iLB9s7c4jyTsafuTP84yFC5z2bPl3VW5ZTzdrt5r0nttvDbO/Vx+7Aq6AK+AKTAYFvBE0Gb4Fr8EVcAVcgTEqsHYtvzVsiVwRtB9fERRvDdMrguytYUhU3XTJ1kmsuoXSzxSls+rjmm2E8qdxagO/zvAJZJUDCHsGurQlBkyUy/QvSnjrAeIFVXAkAfAXvmyaQNtMwA6KNvDEjVhrjDxxZP0jHSOHsmGmqnQnQ84Sc4y0VsXnfJ2wjCTnaPmb4iR3k6/T2pviu8EtNdTxQ7M6Xyd1C6apvrFyt4tvyjvWurEmOdfrEh4WvZIfFu1XBHUqtuNcAVfAFXAFxlkBbwSNs8BO7wq4Aq7ARCigjaAT+a1h+9Oc2XxFUHJrWN4Iqt+sYBsl9SrCWrAKvAI8ztE0kRhE1GWo34YBqe0lsLY6I6K65yo8o7otTDKWzDy2K6mvnIYLTDjZ2FbVg1cxlllfQl//KvrIKHnQihOrztVfrcHyK2a0n2ndo2VJNe6Exea1a5VYO6+uvT071GnKMRrO9lkd8ehTwG8Ne/R9575iV8AVcAUmuwLeCJrs35DX5wq4Aq5ABwrorWGnciNoX24E7UH61jC5NexO3i8PMoPd7AphdZObIxRVtVYtgmzellu8Zq3mlniLy2fixxGjZSRR0aKYlAlxzec8vgmJXOBHfsHnY7HleLE1H5YVKNgwb11p6kV2xOLcZIe/+dxpZI6TuRxpfWrr5BPxOXa0fDnPRPN3q26sw9a/q3LLWrpZu9Wk99z++nj8Vv3sCrgCroArMFkU8EbQZPkmvA5XwBVwBcaggF4RdErZCFrAr4/fxq+Pvyu8Pl6uCMo3RpIsbryiFyPx1V+dAkRsdFiu6BWrHmKLufAUG1hjBCwSFa3KYT/j46ittZOxzVDik1Q8GZZaY72KS0BlaBxYZtUu+uIosBTQZpTiW/nrfKKrrQLjkDN8lxIVvzV9LpTaNGPTpzKE2GIYorIi+goquVAqwHgexnIuqMUexoIJdnhiXWkVEtF5pTGTsJTZUsrSLv76I0ZKfcDFWqNN4q0dfIjBvOncLjb3d8or+fJY1AAO8cvY4uADFuccZ2OAkbONzzHWZ2PqxnlsO+7cX8cJWxM31pj787qBAx/OsCMe8z5+WPQwrVq1gi5asxpgP7sCroAr4Aq4Aj1VwBtBPZXfk7sCroAr0B0F1q7jZwQtPoEfFn0gvz5+j+L18ZvN6+Mlj2xMcGCTInPZuGCOc8TlFuDFrgdaO5hHTxxJFDZIikt9iM3PFmV9qNna7Bi5ivhw4o/Q5LE4XnluFmwNLuoXAAzCOeWrztrjFFHFiaXdEZXAmjUincXqxasxyKdnxcOGrPUVqGbIwJgyLLNFR0FYArUIpAlnxMokzWuikoi0RWTjAWuOBKLpnEa24k6Ree1N/FG0VtwS3Sl/jpPYOm6xNx3CUcdTh1fuKjpa4kjiZdZ0pEjUnX6/EpvjwJdzt8JJYxyHxdkx/HIWe5PP4jCuas4vDeO3hnEjyJ8RBJH87Aq4Aq6AK9BjBbwR1OMvwNO7Aq6AK9ANBfTWsOV0OL8+Prw1LFwRhLeGYeMjm5m6o7pxsajqZky9YseGrYlBM7a+giey2Kx2bOvOM1mfxOT+ggfPDUrcycQmbDNulROryTFtKBvcYIPbsubV4/qt3I5YnMGJs9jtGLh4tlnFWpOhloCNZajElBOlLmnKgdrLzwxf2psGTTx1+JFyC0fOLxy5DbnAL36M4ZOztddxdIO7jtfWUDdGrbY+i7P2Jv6m2ttx53nsHOOxcKP2kdYtuZEXa0A9OINb5lV+bwRBJz+7Aq6AK+AKTBYFvBE0Wb4Jr8MVcAVcgTEoEF8fP5evCNqT9luwiLZulUbQbbwvsRsTbGSszSbGhifamiKa7DEyboGbsgm2mtEyWES7jO2ZlLmbuHY1YS3AYd50blJK225plGLlE+wxun6NwEWetElXHyXoZk/kasCFpEXmWGAIK6y8dcYqLADeNEPdRjtHjG4u+ZC/Lrf1Ydwqk+WbSFyrXN329WqN3c7bbV1SPn99fKqHz1wBV8AVcAV6r4A3gnr/HXgFroAr4AqMWQFtBJ3Et4btHxpBC/ZdRNu22UaQbFxl8yR/5MBcZ/ETdpyjJx/pM16Kq43CFTcSI4fNo5bmfPDjjLxyxrhdzYjt9hn5ca7jZ5+4Zc3hITmoNcdajhCgMW0bD5ZPOWABS9TJ5rT5rD0fdwNnOTDGOc8nc/HJISvJx1id+C2HxYmv6bAxTRix1/E1xcKen3N++GHP57DnZ+Bwzv0yF58c0MzqpJ7qZys+i7bc1p6P6/jqbHVxYhtpza244cM5z4k5/DjD3nQeH5y+NcxfH9+kuttdAVfAFXAFJl4BbwRNvOae0RVwBVyBriuwdt06fkbQSXTYUn59/Bx+axg3grZv20r33HMH7RzaqVsw3B4l2WW/Yw/s0YK92GzySR8AXMwDXq8gARxEShet9rYxbF6RMvFxTcN8xZJGplenKHfBKbUHAvkQG9hkKr5ijnERZmFJHHAFG5/KI3nksuBAYlIq2PjympJ5jkMqs5ZQL39gHUm8VB45+rgm0SyvQS0pTvisPaQJgRE3zHz8/wygKKy2wp/oXKJrBkZb8YacZTr7HZfGgsTOm8ZgRL0d4pKaECNnHAVfBSf+mlwJDhzt+IDDGXXwvJbP4jCWM+qRseFoObZxeYz45BBe40tqMvaAxZzPYYiaYA+ggk/G7E/44MfZxJU4sckBbhnnuNwvczkMLhnDF0DFB/htjMWJ3/rsGLiCI9Re0IaT2of479dZq7gR5A+LtuL42BVwBVwBV6CHCngjqIfie2pXwBVwBbqlQHpr2B60z/yFoVmwc+cg77/qNzJoeEiTQd7uFFDYz0hhyX4HHOIoQAGAOfwSZA9gxSZXD/Xznxwjvnho60LiBItDi0FJylBg9NIkAPWMFDZ9iuhoJrrIEU4tuMSFlILHHGexNR4INPwYwtUY26EjrSOddUjREtaWURaCRbVkGn9nXms+RwVNdvgnw7mpxtyez1F7bs/nwHV6zuPzeac8neI64e8EI/lyXD5HTbk9n9fhpHF7yikn0DnvegvcfnYFXAFXwBVwBXqqgDeCeiq/J3cFXAFXoDsK6Ovj5dYwflg0vzVs333251cWDzJ5f/iHeMkyPCyNGG77yL9a8zEUtj7SDuoPjSCx6RVAMgpI/sTTW7SJMyzNnNAd4e1PHzdq5BTohI0H7CvoQyyyKJ9+DktNMuQDF6LgH9LFLhsr/BGMNKrCwWDBlbHBqBGwSZVyhHlZm9qEEzi1tP+UmFAMn7CuYCtCwafqFMYiRHzAAtdUQ9CBQU24xrisjlY4cOt3Er+DWHV1JHVh3VVvtOTrjx4doS6cxWrHdXONrH7Kb1B+s/khfHJgnTm/epu+E0GDQbnz+HwOvvxcj8MvM9Zdj8vZUp0kRo50jcqtfLH2Ki6EJrrnNeRzbd6ihva15/H5XCuwn6i9w98jh2LtwtLEr/Z6bhuTj4UT/Lkvtcfv0+IkHoe1D/F/e5918vH0F+98E9x+dgVcAVfAFXAFeqqAN4J6Kr8ndwVcAVegOwqUzwhauoimTp9KAzNn8yZJNiu6fdHNfB/JLQpyaHOlGDNEkGHjoiZFSGiBL83F1iu42G2P0saDGCqRGh1yBofcHqRHGWOIpFY0RkIjgn3SvJLaBY+1SAh4MKrwAZA5sqnWK4R8KL+O5VMoEjw4IyTEBDN/JFiZFEcYsl9wMYdBFz7g83OIN3zFV5PADFtiLycMCBQml42x4zKmZhBwgUidNs6Yq5Fh8YUZ4/xcjQq/h8AbEnFAFLD0CY098ppyP7ARp78v/VRvmVOmTACOGAOW+nPE2cgqNuLUl8+rEdbSmtsi243l7yhuJlQsuGUmVelRxcGjKBsFT/2agLTcUWfE1p0tnx1HbJVbfK1qj7HtRk3c9bUPDw3TGac/i85ffU47Yve7Aq6AK+AKuAITooA3giZEZk/iCrgCrsD4KqCNoFP59fHz+UIdvsIn6RLw1qfYZ6nZbnW1Lrut0bEGxA0WEIqP9uq6BFmkSzaVkRExaonMyMybUf4XdKk5rbTKIEwxPt+ExmhbE7LbWGW2nuo45hG0cOs58qhFfGlmQdTXLp70iFniqCkaq4pbd1QWVdG8NodaLLt6q5YYVWWJGSJKR1Vs1ZLH7CpzKD6Seq2uosRoOJrygSs/N+FHYo91gx2/w5Gw1GPBiBzd1GUycodnBK30h0XX/xrc6gq4Aq6AK9ALBbwR1AvVPacr4Aq4Al1WQG8NO6VoBE1hdm1UhIYQukAhJ7ZeARKaGfY5OGUDqdi7Y4NmQwMd0wSm8vk8jCzvKyuisCMrGyZgk7McOOtMCKUWse4c3Enh+Ub9fTR96jRubrE1JCyw6cRwlUnZhgBhxFjiMZezHPChtVPHEZEaZTmALzQv+STG4jCLeGWt4tRexUmFuFkvVi+4/KjidJ2oR/CRIR1XudSCemSmOdEOi9zRF2vV6Oa5rUkqqdaONSMvGJE3rgQ1Rk26yxczaaVSAXLZdVicVhs/pSLE5TjLkWoh8Wkc8sKuiMg4ei2iZlilrTfmjZl1TVJB9buKFYEtaiYROFKcWpFrNJopQ1yLqhezdfY7s/GxQtQFNl0Z1p7iOM/QEK30h0VHsXzkCrgCroAr0HMFvBHU86/AC3AFXAFXYOwK6FvDTqDDlh1I/dypCdsm7AML+nTrwjO+PGjr8E4a5KG0jqb2TeE/xXNAip1M6PMwX6Dij2BWI0/EygefwqhsOKldYTJWMmyONMTOxKIouWLpwYc20P4LF9DCRfNp82Z589kDzBB5FD2yT8mWb9JaMcRsMirWF0bpWtQbHAEVdMc0WMyEh9VVqz/mS/Gw45x6ZaYe65dxJ4dqkiMRrSuJsxQHe2pNZ+CvW7PE19lThuZZq3jUNjr+VszN9XTm2VW5ZXXjWXtn6k0u1Mj0kFvDzly5gi66cPXkWoZX4wq4Aq6AK/CoVcAbQY/ar94X7gq4AruTAnpr2Mn8+viFxHeG8SZbWyfNaxymaVOm0sLZ82lm31SawjEPbN1E92x+mBtJuLFJttLSPrFbatkAgT964ii4CwzHmv0SWMAQkcWIHVu2bKOTTz6WXvLiVXTooYfQuptvposv/iu67fY7uUa90kmz2+YMmJQZs6az1iH65JXoeiVOPKgXcznbA/66rJHJRqByscUojBAD3jTSRqjH4nKOPHYyzbHOvKZor7bTsL48RudWCSDyiMhpPfFXkDIJRlnTFqTGWgbki99tjJXvTP9mICKuMeIjg4xsBvu3WKup40ZUZ9xYMfSwjLES1D06bqytc+6YR9cZK4keVRPc1q6q6fpVv1QLqKqxaVVxFteMaK0FCPvfC80ifHooJv2t2Kzy4P6VK8/wRlChl59cAVfAFXAFeq+AN4J6/x14Ba6AK+AKjFkBbQSdTkuXzeXdiW5PsJ2JPQfe/LFRHyLdT0vnLqQ3PeUsmj99Txrk5s+lt1xJH7j2G7RX/1SuR68MChz80cf3bOlGiLc8+n9FGkHIEZA65LHO9M06iuB4fUBRUR/iJJSZ+V/MhXDuHrPpgve/i4455ql0yb98jX5xxZX08yuuog0bN7HbxBSZ6k+yLase2PrCk6Ma2cOlTXFbh/h4tpE5K1BFdoGWEGwtgdGzZbOeMswaG8dIhHMTsN6fa6XRWkHrOur5qtnrcGLTI45ggWzIXoeI2JGPhFdWrWcbX5dRbRFrq4lWsMTvOddVeZAXeF2r5VSP/h2EPeZR/npuWVV6aM56e46VSmKe6I0cYsMsjiJSc0s7TLW1tdTponwxp8VbXWJeZK/mVJb8O23P3ZQz2lG55oyZm2oZ4v++reJbwy5cc14k8ZEr4Aq4Aq6AK9BDBbwR1EPxPbUr4Aq4At1SQBtBy7kRtH9oqMhGJWxPeGeyY3iQb/8aounc4OnHrV+ceNaUabR4r/1prxlz6Jxn/CH96JYr6NU/+Rw9duos9habm6IJMsgcO5hDbh2TW8jqDnlFsuSaynmmhEYStkWCFr58CyY23iBKI0h8/H9HHLGEG0Hn0ODgIL3ylW+i+x94kGbwW9DI5ASTsNYfNq9F6Jo0vj2LoiUe2z7LZccRqda6/BZT5498Fhmtqp7OBdGaY7S4KrO12LGtLB93iquLy212rmtWdvlsOgSHGlrhbLzVcyQxrbHw6i9IWzU2EyrQNon1YA1A1J+F30blc4mqs9WzpdY8Lp83c6e1x7h0JPGoPXqaWcWjEYLWI42LNhnVcyum/jOtuw6DzJZbcJg3qT3Ezwha5Q+LrpPUba6AK+AKuAI9UsAbQT0S3tO6Aq6AK9BNBfQZQdwIWsqNIHmwMh+DfDvCvUPb6TGz5tL8GXvQNRvuoj14Wzizf5qm5t3LJvb3T51O/7PyAvrxbVfSay//PD1myqzi6hvdZt21fRMdNDCPDhyYSxsHt9J1D99L85hjBt+qJRtcaTI9OLSDDpjJeWbuQTduuJemcUNIbjmTq3hiGwXbKE2vn9Y2TE984jK64H3n0APr19PrXnc2PyNoC1fMb0Hj/+VXBMXNl+XDOPfGPLoq4Nqdc546fOS2W0LdFNbhW3NaNhvdOsoiRz+uNiRGz9W0KW5mbFo5IqBApzjE4dwuDrh2Z9Rhcd3iBqfk6DYnuMf7PJ6175rc3gga79+c87sCroAr4AqMVAFvBI1UMce7Aq6AKzAJFdArgp7DVwTtzfvHfpLXFS/kxs0rjziNHr/PITSNGzIbd26hz133XfrBXddRP++nZNO/kxs206fNpK8//1y67Lar6C2Xf4EWTJlRNF2GwxVEb3jSc+mYBUv5mUJTaCc3l25+6G76h2u/Q2s33h049pgxQG984hl0xLxDaAY/bGjH0E76wnX/Q9+9/WqSDZDuZ+0GTsZy5BvdPnrK0UfQ+84/h+6//3563evfTps2bTFQvaJCDeknGJutmivPaPHtOEYeK+xNUfXZUM/oohC9q56bVo31QLN2OMEDi1g5dxJn8a3Glr+bvMgp/OPBC/7xPI9n7bsmt/x38KxVZ/itYeP5s3NuV8AVcAVcgREp4I2gEcnlYFfAFXAFJqcCa9fdREsWP4sbQQtokIZo9rQBev8zXkGHzz+AGzzX0M18NdDpjz2OBmbMpvf+8B/omgdvCbd5DfIGZQq/nv2bZ57PVwRdRa+//LN0QHFF0J3bNtKfLDuV/vio0+nmh++i7952NS3g28gO3WshfeXG79FP77uZ5nAT6V1PezE9ZeES+uEtV3Nz6A76nYOfSgfsuZDO/dFn6bJ7bwhXBtVvzKOWjzyyjdav30ZveN1ZdO57306X/fhn9I53vo+2bdvOIN38tdsCir96wDqWTbVyCAPYqnlgsahWOVszNUW2jpIaRh8ZYyULeNKHHWOV6k+rkYjUEtGdjZCzM3RrVF5JN7kls+UfL+5u80IxqX08uSXPePBD8/HglprHj18eFr2KG0EXrVktifxwBVwBV8AVcAV6roA3gnr+FXgBroAr4AqMXYG1a7kRtOSZdPjSRbSddtILDzmeXv+UVdwEupZedtkneY/TR3962Mn0lqPPoktv/RW952dfptn8LJ/tvEGZxo2gb535Pm4E/Ype9ZPP0iFTB8IVQTdsup8+u/y19KzHPp3ezc2jz9/0I5ozfQ96hMs9nDHDfDXRCfsfQe867uV0KTeRzr/yX+iWrRvoRYueQBcc/4e0dv0dtOJ/P0ZLp87g/Pzw6WL/Jifdcsmojx7hV8S/4vdX0nHHPp0e85gDaa+99qSPfvRv6T/+8/v8EGnVRm8v00fOqiX9xBYutcpMPEXiqrOtJUbGDHHUNtwAIhNWr07YU1ZYDUG5TbW26jiPTHmreGvJY8XXqhFkY0eSx8bVjaUO4UM94Ma8Lia35fEjic257By1WBvGTTnyGOByu/CIL7dbW9M4rwEcneA7xeb1jYVbuJAXtdtzO27BAoMzbHKu4wYOZ+BHgm3HLX4cMY/eGuZvDYMyfnYFXAFXwBXovQLeCOr9d+AVuAKugCswZgX01rBn02HL5tMWbtCsfuqL6HlLjqcHN2+kR3Zs4j1TP03h28PmD+xNl91+Hb3j8s/RLH72zk55ADQ/I+ibz1/NTaNf0Zv5GUELp8xkfB89uGMLrXzMU+h1T3peuJLoxvt+Sz/i28q+d/u1tHH7Fn420E562eHL6fVPej49xK+e38h5+ovN+7yZe9JtG++l4//zYjp8ynS2ylvI0g2XtnX66KENm+mdZ7+WzjjjNNpvwTy6994H6LzzLqQrfnFtqFvjJLb51jAImGYQq8bBX39GlGzcJEvToTigm1AjsyNbygqr5UoR1tNqDKZOooFt4rMcOdb6muJHakcO4ca4E46mWkbKkeNzXvitHTZbp/WLHZiR2IGV2Lox8jVxIy9iLR62kXJ3ggc38jXVYf352Oaxvjq72OTI87ay51iJHwl3K7xy461hfkWQaOWHK+AKuAKuwGRQwBtBk+Fb8BpcAVfAFRijAtoIOo0OXzaPNg/voIuPewU986An0Ldu/gnd8vB94Vk/8vp2aQbdtvE+uvzeG8PVNoP8WuPp06fT1888l98axs8I+ukXuBHEV/DIRojxDKflBxxFzzvkOFo4ex7Nn7M3rbv/Vrr4ikvoNxvuoNceeTq94vGn0Xdu/hmtffgOjpINFDd9uBn18LZN9J98pZBsytSeb7gEq4e8GWxgYID+6JUvpZe97IX0zW/9B11wwUdo67bBUIOi0AjKecCi59beFJtGaD2xqnrsyPnreOqy1DMDWe/NuQUNJCKBgR1ze86xsV0hqFaRkUVQ4MkjYM/ZYAce88g6+tF4cI6+mpFFWi1HFtl79HjWvmtyyxsVz1rpzwjq/W/TK3AFXAFXwBWAAt4IghJ+dgVcAVdgF1ZAnxF0EjeCFnEjaDud/YQz6blLjqNPXPkN+si136BZU2fzA6OHaQOv8cCpM2nOFH6jFzds5I1ffEkQffv559GVd/0fvepHn6K53P2Z1S8PjOY3j7H/zu2baTNf7XPCPovpD/iZQSsPO4H+8Vffoo/++jv0ykNPpNc8+Xn0qau+RX99w3/zw6T7aDM/pWg2v1FsDv/ZW24LC1cDQVxszjEvNv7cdJLj2GOeSGvWnEu33HorvfFNfxEeFo32DyI6OedZOokRTPs2hDLn/IhTO2bCmCNjFosSqx71/PA2ny2bcNi5Romlyl6Pq8uTsypXHXIy2Oqqq651PCqNOo8Hu3PuigoMc8N95coVdNGFq3fF8r1mV8AVcAVcgd1QAW8E7YZfqi/JFXAFHn0K6BVBz+JnBC2k7X076cnzHkfnnfAKun/T+nD1zr1bH6a9pg/Qsr0OpPu2PERX339baPTI28Wov5/+/pQ38Cvm59KHr/hXfuDzPfQwP+vnHn5t/BkHHk1bhrby1T530za+yufMg59Gr3/ySvqHX32DPnn9f9ORey2i8/mh1A9u3Uh/w7bbtqynWXwr2FF7H0xbBjfTT+7+DV+FNKX4QnRzHjfKdmMuvj5+ffxSuoDfGvbAg+vp9W94Z9EI4gZGuDrJ4jv/jjVrZ/jOMoAxrkTYYU2bMNEaK2jV2lI8omw9sEUeO8qRcR5HipfnLYlN+FpVImjFyKj1YXGt62zN0x1vUwW5Et3J1gmr1QdZ8yrBY+2wSUyTHb4mrPjha+KwdosfLXcdh9jkQC0ylrx2Dpuc5YCvXX2KjniZIwYc1ibjbtg75fBGkCjuhyvgCrgCrsBkUsAbQZPp2/BaXAFXwBUYpQLaCDqd3xo2l/iiHL4Gp59ewFcE/e5hJ3HDp5/ufeR+2n/2fBrkps9nrv4mfeu3v+S3huk2RtoCpx30ZHo1vyZ+gJtF27Ztpff/9Iv05Zt/RP/wzDfScQcfTQ9suJN3Tv20cO5CuuGedfSRK/+NbmNOuaro9MccTX9w1HNo+pRpdOfG+2nBzLk0Y+YAffm6/6LP3XgpX13EjSBJxTuz4lSsUvPrRJz9tPSwx9IF7z+H+vmKpde97s9o/QMP044dg/Ks63DYiILEnFJ2bAQNoBimOOtP+Ztx2GZKDmmm6MOshSllAM7m0IjUks/AZ9ma1yPRQAKFuTKnM2BsnOLgAV7mOpZPeBUbHIUJnlQLxSEy50RMwdalUytWVNCUCpU2+WGPuNaMikNFOTba9fcTWZEH2us8tu9yexUPbniQuxM7sBJr8bCntli1tUusxdvfBeyWP9rs36XIYbHKrXmbcgKvjc6IiqNmbuVP1y42OVAneDAXH2wylgM+sUsjaNWq0/318UEZ/3AFXAFXwBWYDAp4I2gyfAtegyvgCrgCY1Rg7bp1/Pr45eGKoL7+PpIrfeSh0Yv44dBH7HUQP99nT3pg6yN0+4YH6JbN99LOQX72TtiqaMtBXs61HzdwpInTN6WffrvhHnpg2waOn0cHzt6HDtpjX5rKvHdsepCuXf9b2sC3i03vmxau1JFnEh00az4dtveBjN+LHmafNIlueeQe2rpzB7eKeHPHt6WVO6WiASVzu0EUCeTKn/POPZtWnnk6rVt3C111zTX0t3/zj3TPvev5wiVmkl2Vfgi85kg3kjWAwqQbydyPzVtubzWXkuqP6LG8sqG38/pYlUtWgyMsXbQMhmiHv3qOWeJIUDZWPPIHrSfxp0eMjTjYLFOMUpzMMQIemDQbUPDacx5pfTKur0Azw9+Ow3K2qqUZ15ShqXljmWQF+Lsodp3HM7CwY44zFKirIfdhjljE1HOnf5eARaycNU48ygwenCNGRhEnMzk0Kn5balVcziE+MMhZZvgd6TwYy496bnVbbrFgbmPsuCQtBs2+ak0By42gM/3WsFxGn7sCroAr4Ar0UAFvBPVQfE/tCrgCrkC3FFi37iZavFieEbSQrwDSZoFskuT18Du4ISRX28h7u+QB0DP5tfFTeI7tV2jGsH0bP1tox9BQiJ/FGIkY5P9t5ecESaNIGjr9zCmeaewPB8fJ/+S2sUEeh50aO+RmsBncKJJa8qM0FbspbKoEt337dlq29FB60YtW0cKFC/lZQb+lz33uK3TffdwICreY8dY6UOoaJSbPYPniFk+Q7Y+cK0YIa703zRcj4jY32sCgDYImxogPHGVqjdZ8+imW5vzCg4xmZL4nzRS11Hn1Eyw2V/vcyImoGAFLNVNqQcusGd/ksRUjL2xpjrHOwC48yFBXFXxjzVcXL/nGwt8qvpWvrpaR2KBTXe1jzduKeyQ11mFHwh2w3giqk9FtroAr4Aq4Aj1UwBtBPRTfU7sCroAr0C0F4lvD9i6bL3oVTr7FqrsexV6PgIqw1ZEzOLgNxE0hnfGnDMKEMQU8oMVWDgo+bj4EaPEpVv3X/Jhbw4ZpcFAaT33hCiRp+oBOYpRDRnrUrSb6ZFQUBmM45yxV3gReVJqjYl3VHNGnTNWMYq+3xtwFb9Ay4lW3iGo/Qh5RSxqA2ryL1sitXOl60IwRH74zxcVP4bJR4FYrvPGMNQBnY8GqPiDUCikiXn9XcY5oqIsml0bGCiIfIuDDXM7WZse5z8Y0jSXej0enAuEZQX5r2KPzy/dVuwKugCswSRXwRtAk/WK8LFfAFXAFRqKAvjWMbw1btl9sBIWtuW4/7SbWbkhlk1/dXEvmfGutUbKBj/EFa3GyW+0wxqU/cgUKR8U4XVmeQawFlQLkUxpIfEtaYKgEoJ0gcdrgQGCExhF8msXMKkmjT0Y2S+5J53UzzR/rk7XAFhWJVcooFlRVzdaT5gMW/OKNNh4HauWWW/Akjc2kbLGSyK4s6oFfIuOBGbzigS3+lmI2i4ssdhSxyqOfEhc9gm9m0ghFaAwsmgc89tyaUeNG+5lmHy2Lx+2KCkgj6MxV/NawNat3xfK9ZlfAFXAFXIHdUAFvBO2GX6ovyRVwBR59CqxdexMtWXICHbb0AH6Wjm45sUW2G1BspGFDWwdzu7FGfFs1peETmj2CFKYiMgzRishZtJL6HFJVcSWHNILK4spBSRaaHoEq+kLagABPniViQVS1wCOrQXwTCv4YozVEO1SIFsGyVYFlhlK7girESVCZOlZTQMxJs2gO/WZVgYK14CmUDXFpPYYqGSK5RWNFCdCsQ+0xl47k07Kk0XamwiCzEaAEKU89W4wr4Zw3tWqGOub8W1AMMlkW2GKW+pGNqUe4dXdWIDSC/BlBu/NX7GtzBVwBV2CXU8AbQbvcV+YFuwKugCtQVUBvDVtOh/EzgvrD8390w91qA6q+uEEvd+hFd0I2uWgtYMMrMRijCotTG7Iqsr5lYHkYZ0lj5wcpTNaULawyFICc2LQXhMJVNqlAF7Gw4Fznia2LOq9EonicgQuFBWprQS49wwMrOIo5poUmgo71IEbO4Kl+76KYWGM10MjGN40jb4pIvwfrK0u2xpA9GoCJlrQmZbco1BEj1Gsx6qsiEaO/dczsOY+JrDKCV8eYSXw9zjLr2MZUvW7Z3RXQW8NW8FvDVu/uS/X1uQKugCvgCuwiCngjaBf5orxMV8AVcAVaKaCNoOfwW8Pm0cxZs2jWrJn08MYNNMzP2+n0iJtVHcXWQdzuKpfdHOtmWCLkT7lV5klx91GRXtmKCWORowiSyGBSO3ByBtJe0WFRaXXpLFxyI1cVFSyWN0MWLrVavFqArs8sVuilY5tJxloDWFKv5VRs6peZYoBEriquPkOOizzWA6vYhMfOgQO/VQg+PQMhM2Wwloitt6ofmQWjY1jy+HqWKlq/gRidjnK8stZxp+tGfUAKD8Y2Q85vfT7e/RWQWzFXrTyd1qw5b/dfrK/QFXAFXAFXYJdQwBtBu8TX5EW6Aq6AK9BaAX19/DP51rD9uQk0QAOzZ9FDDz8cGkHYrAaGZJJyxi1u2rRJUdEX8QYRdrz8UVyFYzfAukGOljiSzTO2z9YqvLGJYxEYm8wBm85lpnyWtT4Wkeqt4sUivtyDeWtWCQ0XJyFNcgYHjJbL+tQuupcIDEoYG8QWriAKA54U53BiPRk7PKwsYopHSRJNlREiFIsIWHM4/NFeRVYtQMfoOIJPzvFXY631WEE3eWK0ICKrzvQ7R5W1v/pI4CNXoEYBvzWsRhQ3uQKugCvgCvRUAW8E9VR+T+4KuAKuQHcUwK1h8vr4WTP5iqCBAW4EPUTD/Dr45JD9bHGbkdqDIQxNeyG0DpK4ZBJjEnPBApvlU5tuxBEtZzmAU7vMoidu3+MmHl5EC0b/qCX91DhEp7FAihUIseUo8ckf0VLO7C9Ptl52yREuhRKAILWRJQ9q5t5LzRGNfQVIs8sn+8JJMMKEVoaJKXkRxSh+MG24PTDCQt5BsfPzo7SVoXhltmVlQdYVxhontVkkrBU4Gyyuzo/K1VePrreKItWjHis4/NJkLJHNSEHokWfQmE4iweBnV8AbQf4bcAVcAVfAFZhsCngjaLJ9I16PK+AKuAKjUEAbQSv4rWF7FY0gvjVsw8M0VN4aVr91rbNWN+b5ZriuwJRJZtq4iHa79Y6McYS9udS8k5sW06dNDSxoXKANEjbx0sxizPbBQZrS309TpvBbw5gq5tWtvt3swwebzuvWgpqAkLMesAi7vAlMbvmQTV5ouEl+rqNvCmOFIoYV0elJGkPhTV4lMA0KLaYh/tw5SH0zmFTcpskU2cQhcgzRwLQZtOfUAbp368PRHUbDtHDWPNq4cws9sn0z9wLlLWtyiLoar59q1U+7AKwcqNgIgiWyWUuTDODTZdmsrca2opFFanXyGdtHKZtWJLVbu10L7IrETDjrD8XV+6y12zjLPd7jbtfeK77x1Un+G7GKXx/vt4aNr87O7gq4Aq6AK9C5At4I6lwrR7oCroArMGkVwBVBS/mKoJkzB/iKoFm0gRtBg2UjqL502XbZw257YVeMtnWiTef5ti2d69UwEqO8OXuKltbCth076IlHHUaLFi2iH/7octq+fWe5LddGkHIM8ZVOc+fuQccc8xS666576Kqrr6epU6bEO6LK7b5WH5oqKD6cbSNDOBWnkLo61WMrlibO4JZBmn3wAO195F40c+/ptOHWR+i+n63nV94LpUVrvOZR/rQRZHMWcXwB0qyFs2jvpXPpgavW09b126l/Cto24MN5mAa5EfSSJSfSY/ZcQBf/8t+onyn14q8+ms4FvfnolfTwto30iav/g2b2S5NNDptXLbZGHcMi2KgTRpbBrhh+sLY7W54mbOTsBN3EUrdq/T0oq3zGTBFtbSmiOZN7XAH+BXEj6Ex/a5j/FFwBV8AVcAUmkQLeCJpEX4aX4gq4Aq7AaBVYt+4mWrz4BFq67EBuBM2igQG5ImhDYyNItrR2u9tqW43tbxMGXGntQCNaz2hjwKtXuMimWjx9dMOt99Hn/no1nXzyCfT7f/gGuvuue7mZwbGgCUn6aOvm7bT8pKfR2W9/M11z7fX0+jefSwvm7RFwCtUM2jySoDJjUWZCWNja4cCh9QxvG6IDnr2Qnviqx9OU6f20Y8MOuuNnd9PVH7+e+mdxJyik4BiuX64c0oxFXrmyRyzhkqDClnwjXPHWYTr0rIPoqNc8nn58wRV016V309QBbuCEq4KkVo5jGmnJbRvaQYv3PoA+fvLr6PLbr6Y/+umXaOmU2QIKsClcw2sffxo9d/Fx9I7v/T1d89Bt6qt8Si3yB2uVurW+eCWNINSGcKAxxzlFwVp/buKw6JSvkwgbnY1DODPy/4XvJ5DjFyoT8Ms5OEuCdFaa2w6qTG1DHLAbKOCNoN3gS/QluAKugCuwmyngjaDd7Av15bgCrsCjUwG8NWzpsnnhYdHy1rAN3Ajaaa4IwuYV21soZbe8sNlzU5xgYmzOqs0CtQ6TXMEzhe+Z4lGx6eZbqKRBUm62mYkbHJs2b6bfXbWClh25lD796S/Sxg2byj14QDOhbNo3bNpOK5/7LDrnnLfSddfdSL/3sj+lhftKIwgNF2zjs+rDg3qYJJjhw2pljnUUZ2lChaaNYrRhJWNeydZBOu59T6XHnfoYuurz19O9v7yPtj+wjTbeupko3B4msRKuXCFbuCMrjALhsNTLVwugWSQPcebH+ITnCUkjaOkfHEJPe+vR9D9nX0Z3/OcdNGU2N4IELwfjpJ5B5n+EdtKFx7yUjt7vcHrPZf9IP7/vJtpj6oywTH4yEIOHaPEe+9GFJ72KfnnvWrrop//EOUIxgSoUyiO+qEg+mVi+n2IcLl2ScXrEVeB7jBYgqxZ4qudiVaFmeGGTeZXLehHR+oyI2MgSizBHD0bRJpwxexyJ3Q9XoL0C0ghaybeGXbjmvPZgR7gCroAr4Aq4AhOggDeCJkBkT+EKuAKuwHgrsHbtTbRkycl8RdB+/D3ShHgAAEAASURBVIygmeHWML0iaJBT69Y138DGDW/r6hDXCR4tAWXkSA6axVcnPfOEp9Edd95Ni/ZfQIuXLKbf/Ob/6NIfXE7btvHtTnyb0rat2+jYY59ECxbMD32XLXzFz+U/u4J2bOf6Q8NiiHbs2ElHP/lIetrTnhLw27dvpde+9hXcCLqBXvjit9CihXMZm1cp1aPd1EebBreFW6ikEcAtqbD9l7Gg5HapGcUtU2HNoXzms5Qy5cbIfsfuTTP2mkXLXriYZi8YoOu+dANt3cBNoNs30/qrH2Y+jZt/9N40f+nefMXQFFp/44N03y/X0xDf7saLDhJN48bOgqfvQw9c8yDNWjST9lk6n/qn9tMdl99FD121gZa94lB62tueTP/zth/T7V+/g6buMZX2O34ezdhjBt1z+f20fdMO2j48SHvOmEX/9Nxz6dq7f0Mv+P5f0dLp8eoobY0N00ODO+mTJ76anrDgEHrZty+kh7bJs4JkpVLtEA3uHKKnP/2JtN9++9I9d99HV/zi6tAMis8T0l+SlSMsovwIqpUzGViLxGFuxzagyW4x6bi5mpgtRsRfAiqBDzzhm4OxPOfo0uEDV6ADBcIVQdzgvmjN6g7QDnEFXAFXwBVwBcZfAW8Ejb/GnsEVcAVcgXFXQK8IOpGWLj2Arwgq3hoWHhbd3AiSorD9bVUgNsGdYrHZlo34MF+R9NjHHUif/cwn6ZFHNvJta9O5BzKFpk2bRl/6f5fQZz77T/wcoB10w6/vpC996YP09GOOptkDs2nTI1vo1a95C919N98axni5SmbVqt+hV7/6lTQwa0ZoIAnH7Nmz6Cc/+Rmd9vw30tJD5hV7f1upVM8VsWka8zx1weLQ7BE+ufhFkHLxjqBu3bie1m64K8yk9yS2IXM1EE/1ihmW9BkXPJXmHTaP9lg0QFNm9IcGkHRNbrvsdvrF+VeHW8MWv+Sx9KRXH8EaSCDR1JlT6IZ/X0e/+vB11CeP6GHueU+cS6d+5CS685f30v5PWBDyTZ87ne658j765snfpSe853B6+tueRP979k/o5k/dSkd/4Cg68oWH8S1o99AVH/wVbX9kB3Fri47ZdzFdtPx19J2bfkSvu+yztGxgXlgzZ9GFcP7bd26ljx7zYjrtscfQWy/9G/rFveu08cWL3Tm4gxtBffS3f3cRHcuNtp/+9Jf0J298B82YKldxNRziEJHKI5kk1jqOOnSFsmRpGtQxA1vNALR65JeqTcDUrr+LajR4/ewKjEwBvzVsZHo52hVwBVwBV2D8FfBG0Phr7BlcAVfAFRh3BbQRdDIdvpSvCOJG0AA/LPqh0AjCK8+zPXtRkd0AY5wXiw2x9YvNzjVGLdhayyZ7aGiQDj3kYPr6175EGzZuoEsu+Xdat+5WetELz6QDD9yf3nvexfS/3/sRP8tomI4/7mi+GmUBrVx5Oh15xFJ6wYv+iO68424uvI+vFNqbPvSX7+fzQvrKVy6h9esfCrijn3wUfZ/jz/q9N9NBi/ZmrNRgK47j/WfsQR859Y00l89Dw/IQantrVD9deusv6cJffpUfshzujyp44iplXXqbVx/NewK/nW2fGXTkSw+nmfNm0XVfvJ62b9xJG+98hB669iHa8/A96NSPnhQeEnvVZ66jnZt3MvYw2uOAOfTtV32PNvz6YW4WTaV9njKXnvM3p4SLnm68ZC09cP2DNH2v6WEZV33gejryTUu4EfRk+u6bfshXFs2lJ73mKLrrivvoFx+5ijbevCk0lDby84FeedhJ9OqjzqBPX/tt+qvrvkP7TZ9TfEG8ftZEVvHIzh30x8uW0yuPWkGfvOob9Jkbvkfzps1iTz/rz42g4Sn0pc9/nI495li6/Kc/pZfzM5qm8wO4owIMxWGNkLjUXUFiFhjOCM3PZXju6GhuC0HA2BjB4mdXoFsK6K1hK/jWsNXdonQeV8AVcAVcAVdgTAp4I2hM8nmwK+AKuAKTQwE8I+hwvrVIng+UvjVMN8Z122Nso8WHsV1RXYz467Cw2hh5rfpjH/cY+revfoEu/f4P6fz3f4juvOt+euUf/i697W1/Sh/44Mfoc1+4hGbOmM6s/LDotevpki9dQCeddAK96MV/HB4WvZVvpTr+GUfTRz58Af3iF1fRO95xPt17/8P0Z295Jd8a9od8RdAV9KKX/ykt2je/NcxWwlcE8ZUvT93vsPB2MblcBo0gbZMQ3bvlIVr70F3cJJJLeNIjrpc5Ay03hfi17id+6Dias+9suuycy/WqIL7USG4u2v9ZC+jZH1lOP//YlXT9Z9bS0OYhWvTs/Wj5hcfTD9/3U7r1X2+l/jnSCNqLfudvTwm3gv149c9pG78ZrI+fpSRXDO18eAcd/vuH0lPeeBTde+0DtOCofcPDqH/xwatp8z38HCJ9iA9tHNpO5x39Ijr1sU+hNT/7Mn3ntl/RnCmiJ38jUngoeZi2cFPulAOPonfzs4T+4+af0esv5wdKz9xTcfw9bds2SOed+1Z66lOeSFf+6ho6730foWncCKrebifEIUw/CpnlFM2F0cDqhp2h6iJhixlhKb6gOPWRK9BjBaSBvIob3P76+B5/EZ7eFXAFXAFXoFTAG0GlFD5wBVwBV2DXVUAbQSfwFUEH6q1hfMvUhofx+ni9RkdWV7dtFjs25OLHWOx61FvFFz3aTrGx4hviW8MWLz6Y/vmfP8u3fn2F3v2eD/GtXdPplFOOow9+YA195Z//lT72sb8PzQZ5Fs0Nv76b/uWSD9Ly5c8MjaDb+YqgzZu30ZnPP5Uuuujd9NV//Ta9b81HaSPfOvbS3zuD3n72m+nqq35NK17wOlpy0Hz0RrgwrqQsRgZDocFz++Agbeaq9+RcG4b4WT2yAm68zOSLg+bzTWJz+BlBcp2QNFD0+Tk8kbmeipHw8VN1tg3T8o8/g2bvO4sufefltOnOzXzbm7SBhunwFx9Cx77zqfT9c35Et3zrLhrctJNmPWYOnfXVFXTt566naz56PdGc4dAIOv0fnkNX/v3VdN2nfqO3kRXZBrl5dPjvPy48LHoavy1sKt+CduNX19JPVl/JzxmSArkM/vMQN4LecsQKetmRJ9MnrvwGff6G/6V504s3hhUVS72P8FU/Lzn0eHrD0WfSF677b/rg1d+kA/jqKDnC+vhKqE1bttNt92+l/fhZRHvvxRysk4oRFQgB9kNcUkvt0egIaOtFBmurpUyMiLLGThgQV1zlVYTEvymWz8euwNgU8FvDxqafR7sCroAr4Ap0XwFvBHVfU2d0BVwBV2DCFdBG0EncCNq/eEYQN4JavD5et/5xwxxHeenYMIs9RVlP0UoogwUZbg3jxsuhhx7Mt4R9nr785X+ic877MD93Zhqd9uwT6OKL3se2f6aP/9WnQwNHG0H3MfZifjX8ifSil7ya7rzzntAIeu5zT6aLL3wvfe3r3wlXqjzCjaCXveR5dPafv5EbQdfRmS96Ax18wDzmkaq0Tjz/BUXty7dBvfsZf8APVh7gW7biLXN661Qf/fyOX9PfXP9f4QIYrV8idZV6WxiYlHloxzA982PHhiuCLv2Ln9CmO/jhy/wQaIlY8sKD6fhznkbffzc3gr7JVxnxrWED3Aha+a8r6OpPX0vXffIG6pvdT/P51jBpBF3xyV/R9Z9eq/kkOZc3uIUbSi9/HN8adjT939dvooEFs+iA4xbRzz/0C/rN//st9fFFP5JrK1/pc/KiI+m8E15BX7/xh/Rmvipo2cy5WrnoEZpiQ3Tv4Ha64EkvoDMOewad86PP0A/uvI5mFVcOCZO8jEzeMrdty06aOrWPZszkppg0gpSJz/VH1Ap+scgh1cmBuc7qLeqTiCo6xlVHyGE97RlwFZhmixzeCLI6+rhbCngjqFtKOo8r4Aq4Aq5AtxTwRlC3lHQeV8AVcAV6qIA2gk6jw5ftzW8Nk4dFzyJ5a5hckYNDtsdxywurnq1PrmlpPtJNdtxQRx4ZKQO/2pybFIfwrWH//tUv0k8uv5zOP/9DdPNv76Q/ec1L6c1vfj3fGvZR+sIXL+GHR/Orzpn6ht8+RJd87n18axg3gn7v1fyw6Ptp2/bt/NyaJ9JHP7KGrrnm1/S2t6+mu+9ZT+98+2voj175Mvrxj39OL3n5W2m/BXqbU1qJViNX+MzjBtBbn/IC2nMqN4LCMlQbufJHcv/0zhvoS7/5ARfPjR6xhXVI00cOuXIkDGKnYucwnfDhY0Ij6Ifn/JQe4TeGCY8ELFq+gE792HL61d9dQ9d88tc0vI3oMc8/gI5/z7H0g/deRrf8++00dc40mv/UubTiU6fQLz91NV3/92s5R0wyxK+PP/zlj6Wn/tkT6X/eehltfWArLb/4eJq11wz6rzf9gN809hDfQtZH2/nKpgNn7U2fPv1d9Ivbr6aX/vDvaGlxpY9WrCt5iK8I+sQzXklPXbSU/uDbF9O9WzeGdSpmOFyN9YUvXkzPOvlEuuba6+nccy/i39BG/TJVhEKAyBpGXLLqCbfmw8yiraeGMvxu6uwp19hn+N1qg48XUCT1RtDYtXWGqgLSCFrFr4/3W8Oq2rjFFXAFXAFXoDcKeCOoN7p7VlfAFXAFuqqANoKW02HLFtIAN4IGBgbMw6KludC8vYZHUfJpD/FaG9BitfYYEyO0EbT4kMfQ1/79i+HKnm//x3/T7bfdRmecsYLmzBngZsOF9L1Lf0KHMObkk55B0/lZQScvP54ef+QR9LnP/xNt2bKV1v32VrrqV1fzq5fP5dvMDqVvf/s79OBDG+i0055FRyxdzA+b/gE9d9Wb6HEHycOitY5YQ6xLnPfv3MRXvgyH5wVtk9vFeA3TQtAw3xY2nWbxH3Q+lEpXGRoE3O0YDlccCSfb+faskz7x/9n7DjCriqTtd3JiGGCGnCUrKKBixhxARTEg7q5pTbumFXNWDKCuYE6fOa7iuua4KoqAAV0BBQEBCQKSmZxnvqquU7f73HsHRiUM0Gf1nu7qSv2e8fuffv/qbt4alkVbw75ACRFB5nozgiW7UwYOvHtfpDVOxcyX5qC6jM77OaEbUrNS8O5p41E4rxCJaUnI270JjnzqMEx58FvMeHQubUvjqEzF1aK6pBo96fr4AZf2xyeXTcS8R+ej97U9sfuF/VG8qhQfnTeBzgoqR21iNQroXKOnD7wAeZlNcNmERzG/YCWyklI4UXpkJq3oPKA7Dzgba0oLcelnj9JdYyKnUKTCRNBsvP/Byzj8sEOJcPsB59OtYStXrTEe5CfQNx0lyKhD9kIEyTj/hv8ytKfkWqw3kYidG0Xlm/qtf8ueCNrUSG+f/n1F0Pb53f2sPQIeAY9AQ0bAE0EN+ev43DwCHgGPQD0RmDdvPpEkdEZQLzojKJ0Pi86kao78oCLIrPTr6clZ4CtREFrW6zJdl87WrRIAVoNvDatCV7o17N//fgbz5i+gq+EzzY1mpXQWzVNPPotX//MeVucXYshRB+Hcc05Hs6Y5xKXQYcm0I6mKDmNOSkrElG+n0zaye7DbbjvjbLo+vm2blqisqqLr6IuRRBUxU6Z8h4suHom8ZlQRZIia8HLepSFoAxTNSogWzVPpB3fmdlautdgpzVFLFUEDrt+Vbg/LwJQ7/4fSZWVCBLExnR/U9oiW6H/eLkjLplvAKFhVZY25XWz2c/Opkoe3kNH18b2zcdA/98O0p3/A3HELg7CcGWVaVoPOQztil7N748vRU7Dss+WoXlCDPf6vHzod0hE/jpuNH5+Yi4SUBDr/pwL70EHYd9IV8s99/y6uo/N/eqQ1MnOl/4ee4ifiyI67YsSAE3HLxGcwftkMicUMDv3DeNYS7s89eTf23nsAVVl9jVP/OgIpvDOMxuUviPOyD6NhHnqJGzuuqEUUTEPGrZaMur8Sx5X4tkdg60fAE0Fb/zf0M/AIeAQ8AtsaAp4I2ta+qJ+PR8AjsF0iIBVBhxER1CzO1rDfsryWJbxdsoeX9NHgRo/qIl+98NYwvj5+3MtP47nnXsDbb72P7t27YeHCxZj103w6C4fpkARk0eHWrVrScc1EkOh2NiaB2F9hURltBVtFREoV2rRpjh137IHysgosWLjIEEWl5eVY/utqOqhZ9Jm5cPOyObGUaCAS8BYsK5dZKX0ktuqBteK0DfNRg9S8VCTSzVrlKyuCc4fEF3tPqEpARsc0NG6fhcSUZBQuKkDxgjLUJAgZZTir1ARktk9H2YoKVBfR4dVuUjUJSKJzhNJbpaJ0KY2XyW1mqZlJyGibhvKCSpQvrzRzqqaDntOSU3HNbsOQRe8LJj5O1U2JkQqjdCJ5rt3zZGQmpeHqL55COZ3dZOdbi3X5xTj+2MNw9dUX0yHRufjnXffg8Sf+jWwisWxSMjedIb81XbfFckXMbbk6iqraW31Xiz3Zh3Wi7eyob3kEGi4CTAQdS1vDRo+6qeEm6TPzCHgEPAIege0KAU8EbVef20/WI+AR2FYRmEsVQd26HkhEUMugIij2jCCZuy6n60LCEiR22R2tq8t3Xua7/qytLOx1a1hHvPrqc3jqqWdw/oV3oXu3JuZsGr4mnT0wIWH88JE95FpsJSZHMsSN3OVFg1Qpwwc9M5lCVS4mE/NDpSuRVIzA+GaH3HN9as/V0mha82MSsQ5l2PxKEPEZBDSvoM06kSZpUb60yYsKlSg/ngj9q1VJrCimLNeILIk4oLb4MJNg3/Tw+UW1RBKZE6XZZ6BfTYvNLk1ao2N2Hj6jg6BJicw4HhFKpHcgXR2/rGgVvluzkLbGUdUV/U+eWvyyZB0uvfgvOOOM07Bk6RIcf/w5yMnJpMos1QlUIy/OUXIWkdWTEfmVMf11I6os9m09uWPqj+fDcbXv6sS3dDUEq7DEnUX0iO97BDYGAqYiaOgg2t46cmO48z48Ah4Bj4BHwCPwhxHwRNAfhtA78Ah4BDwCWx6BufPmERG0n7M1TIkgqiIxK+foRTL33cW09kVmtVWuc5QR4zLKA2vwAt31UE2kTft2rXD3mNF4/Y03cP+DzyMvlw91rtuDLMx5XD3ZZb9kE4yZLU88xgSD1VE760cyYxKGt0mFx7UnXlgzMgtWtUAEHTenaF9qEI0Z+4x+xA9LJXfbVy8SXH0FhJmeURRR1wRZkGgOji6n84Ky6awjQxjRVjiWMyFVVF2OZCJ20hPDZwcxAlyF1Si7EfLycrFy5WoUFxdRhRUTdfxEgplcrUTknL/kKn33l3XlkTxFV/9GdIzf7jxduURX6/CI9oL8+GWIMZVHvwPijcSBhVGQnKJ1f0tfc1eb6H58efR3Vy2LhUrq5y/WTu2j33X5i9bblP3oHKL7GjtaHt3/bXqxmNflT/3KO9YuPL6hnt8atiGE/LhHwCPgEfAIbG4EPBG0uRH38TwCHgGPwCZAQLaGHUREUAuqCMqkM4LSg1vDlAjioLzoWf/j0inxNcmH48bwKhHFgKxgaoYW5NxjAiIlNRmdO7bHiuUr6JBnuqmKtizJY1bu1NS3OtZlur5VHgkU2Lh91VEbHXPl3Lbj7ghrS1+lLIkmLFz7sJ5dhAdyV1VcaYCIX9EUGkIiKX7Wm2Nk7CLY22mwd3ri5cNyVrQxOI6Nq1+bdGirWy0RSOWVfC5TElKSlQRycxFf7DUWRxtfUpNf1pTo1oI17ShrqF+RWk/ReqwbnqlIgl/jxrUOjVLHEkE8ojloftHa21I/FnM7u/WNWS3f+iMI1BIhLlvDRv4RN97WI+AR8Ah4BDwCGw0BTwRtNCi9I4+AR8AjsOUQsGcE5QZnBDlEkElLl3v8rvtRakA06tY1i2ga5gIVPi7HPA4rZBbXJFeRbIdSAijQpxfHU2rCSrVll+oqkbfKXWkkCRJqW8fj6euYvGMs2ISE0XLWDntjDZXE0xYfjAMXqwgVwV74CYI4KLjeRMf9DfxHVwWJY0dR83FEcZux+dr4+lXCfxHWjY0Rj0hx5ylRwrGsNXsM91xN8/fjCgJ1FskB1SwIHnaznoqguv/SogOoQ//2CGwcBPhMsqHH+uvjNw6a3otHwCPgEfAIbAwEPBG0MVD0PjwCHgGPwBZGQM4I2p8qgloZIigzi66Pz4++NcyslONkKgvx2OVwrESNdemuC3IjV9aHOkZuCA5pq13su66c1IdroVFdmbaDXJVxIbFqGyojJozOTbS0FzFit0aoI4E3fRnCwXUaDIiRJmXe6oE7LkEiSq4Pkazvl31pJNZTEsaVhTXUgCyVQGJDzjMILfkp4aOe9B3oGhv9ccdExnmoVPxFzZRYG+WreFx13ZZ6F3vp6edk/Ygdderme1xr9ahvG1Ul8l6fTVjT9zwCvwcBvzXs96DmbTwCHgGPgEdgUyLgiaBNia737RHwCHgENhMCckbQAejRk4igjIyo6+M5CV7sBqcxh3Kyi+P4y+FoqehrvUjIVaijfsP23GOKgO25zfSBanJPxkOOnI7VdIRBM7BkFWUJiEWQqpFor9zXhwyMjSDEbXHh6ji61ORxfSRjV6ZzsFrh6FauPur/DntSvNRePEf7JxsVmSk5HWoaj4FbIWp43CiSmejql7KONKK8RZv/uvSvwmIg3rQvHlg/CEktieF6VH+uTLMy2vSjn9jVkXY8axnR+XBPtcSv9kTP/3oENjYCvDXsGKoIun30yI3t2vvzCHgEPAIeAY/A70LAE0G/CzZv5BHwCHgEGhYCsjVsMFUE5dR5a1iYdOHFsC6AzfJ6vROK1VDbsJmR8g+TMMEQx7FtXfqrRJS4Jx6j/aqecRp45Fe87FVXxiPKXFbiiNRSpPLrRg17sblz5m4WYT+RaKGG+nV9WgWVulri1aIXaBsV+iETbkZbslaMjQj5l4wSzbg7G0VcFPSXPbsRVK5vk4h2zFslNn48e9UKmVJHZ2LldWlyXqptdVSi9nZEJfIOZ6djOtv4WKiWf3sE/hgCXBE0lK6PHzXqpj/myFt7BDwCHgGPgEdgIyHgiaCNBKR34xHwCHgEtiQCQgTR9fE96fp4UxGkt4ZxFZA8uox2l851LZvVht+uvpXXbckjYmMrRHQZnkAlOubmc9LRfIxPa+SMiBezo0lLQIyIfqivGYgWewkTNXQRFsWqRTJpqm64lEQtnXGTOWWm5BF7pTYffi22YqMW6oFj15jr2ulK+yBX1eEx1YvkYbLlnKMfngNrWRtDUqghOZJRaydDEkHjsFbEC9tQTjInkhoDFhI5ZHLmaPw1alFJlQupiYwYjTMG4twGixVEJCZj5+CeSjp8mv+l48qRTtfVp9ItZOSRgxkb0xaJ4z9OyGBUvq503LQUKZG5I9at6rAkvkZ8qfXgWx6B34+A3xr2+7Hzlh4Bj4BHwCOwaRDwRNCmwdV79Qh4BDwCmxWBefPmo2vXfeNcHy9EkCxzZenNiXGrvktfa6VTqo+ltRJKQoiGWiIKKlCFFCIhuEZFsrD+mKxgQqWSxpgw4pF0JEXIFc2A3zxmo7DE9iprqtGjSVtkp6Zj+upF4K0ZRj8gaVjbIuBSDOyF/keuOOPqaiJHqogcSU2i69TlGna5EU1zFzdMVpWWViA5OQHJKXR1u/Fv89GWyN1MjSL9iEZ4ToKZ2rAmY2nyMy2WsJ1I7XxYLno8rjY1hEk1YcuyJCJmEonwEZILhgBqn9UMXZu0wVfL56KiusL4MPYRzKJnwXHkYRXGLEiHCKBa5KU3wsC2O6JZaiN8ufRHTF+3GGkUM7CgvORxSRqWqDwYDl4i1Qxi9dQL68mcWceVSj/sX/3J3yhr+McjsPER8ETQxsfUe/QIeAQ8Ah6BP4aAJ4L+GH7e2iPgEfAINAgE7K1hzYJbw8IVQbHL6PqnrYtlaxG7VFeJ6PKvLMjZRhbZCaiuqUKz9GwM6bYv/rd0Fqau/hnJ5ip5tRazjo1aYLcWXdC6UVOsLSvGxwunYlnpOinICWgCx4JDGP7BNKhVRYRHSkoaHtj/XMxaswj3Tn2XrKqIeGJyhB/NLezF9pg+SEBhQTFOO/V4dOjQDs89Nw6rVq815InQCzpHqjqqrkKbNq1xztmnYMaMWXjxpdeRliZkkGgpHaFYSBY2F85Jetyyj2YkRJXSG9bazkM1XVuJGuhQZU6T7NZoQmQPahNRULoGawqXkTrTcTUorq7E3i2749o9/oTXf5qEJ2Z9jAwii7S6SfxqjjaaZmDjcqsWvZu2x4gBJxEJlGW+3Ss/foI3F32L7KRUGhd760X9hr24PUHAlagXK4v1orhZXdWxsQV5TwRZHH1r4yPARJBcH3/TxnfuPXoEPAIeAY+AR+B3IOCJoN8BmjfxCHgEPAINDQEhgg6hiqC8CBFUUJBvKlrsoleXwdHZs4Y7Fu67I3ZJHe3D1bIRXa3S6nL0adoRdx5xBd6Y+ibG/Pg+8pLSHZUatMlohqv3+gt6NGuDxfkrsLRwNZ6b+V/MzF8KKrahh39k2a4Ro7MtIlLjyPb9cd6ux+Ger8fhk2XTkWEqUQI7NiQjrhLipqn0iZATQgKRGLNmzscnn7yCLl064/LLb8A3301HanKKpMBboIx1LUqoEmjgfgNw7z234cuvvsWfT70ErVo0Zhf0EBkRqEq+nEOQuSmjUeQDWZCHSiWY+NF5a4/f7sNh9LHeqAKruhTd2+yKQXuegS7tdiTmqhYLV8zDe5OfwIxfvkYqkTNczbOYvs+/Dvg7ulBV0PHv3I5aqgoKYxOdlY1oqS7J8qSu++CCXY/Hg1Nfw7jZE2l7mG5R5MysneTr0jyxY6xvSS2dobxZ287VttVvWJt76t/GFPttjQpykYlFYdNJtlTc9c2ovjnVV299seKPCRE0CKNHjYyv4KUeAY+AR8Aj4BHYzAh4ImgzA+7DeQQ8Ah6BTYGA3Bq2X9TWML4+Xpe5HFWXzNx2Fz3c5scdF4ldhNt+fG225RElOsS/eKQtVlSRsraqHG0zcjC0y36YtnwOpq1ZSFuF+NwYIiuoWojPkzmmXX/cuN/p+NfMT/D47PFYU1mENCJdkrk6JYigPiWj8ALenIOTlIgx+/8NpeUluPObccgvLyJbrm6hLVBV1Sgrq0RyEtUIVdEWqeQkqiCqJPIslWRE8sieMEPuLPp5NS6+eDhatmyBN976AKvXrDVbqjgRJpGKS8uJKEnCmnWFGHzYfnj44dvx1Vf/wwnDL0aHtk0pvWBbXrBvyqKrOfOM5HGJFJYo9aGYut9GrcM4iI14i1BNlGc1iipW48rhz6Nzqx3x2bT/oKy8GEfudSZ+WTkbj71zHdYWLkcSzWNFRRFO3mEfXDpgGP71w0d4gIi6JkTUad6crbYlLzeajPJ4In2vk3schLP6HYmLPrgL09cuRiJ9P/eJzp39SdYWk1h9VyLtcE42u/iaKhU9jWS2tJmOIqt69X2HsxBs4uWypfSi5xGdR/T45ujXN4dNrbex/cfHzm8Ni4+Ll3oEPAIeAY/AlkPAE0FbDnsf2SPgEfAIbDQEpCLoICKCWiCTDotOp38LCgqoIiggI4JIWu/Cy1ReAsljW9K3i1jbUl1+y4JZfVgSI9qPUTWGLTOboG/zHcBn6ZRUlmFO/hIsKVptiJVy2lq1S7MO6ErbiXo1bYOh3Qfi5Vnj8XP+chRVluDblfOwjsgL2aakMaIzEzn9P2rYqVl7jNz3DLz/8xQ8Ov1tIjnkXJqa6mo6R6kDdt99N/w0dy4G7N4fzZvn4quv/4eJE6dgHVVQpVDFD89uwIB+6NCxLaqJOCosKjbjpcXFXD6EGiKBqiursd/APU0lUFFRCZavWIlLLzkPX3z1NU7+y6Vo0zKH5h0/RwakhvIsJwKK34IfVw4RFRKwEqkUh8mvEtJRN/rtElmPckxPTCH86NwiVaCWpTJYpxZlRO7s1vMwnHzglfh17QKc//Re2LXVobj5tFdQVVuFcZ+MwZez3qMzm6TSqYriv3bk9VhKW8fO+uQ+NCK5zqIu5Hk+/PC4mQsRQX/qcSDO6ns0Rnw4BtPW/WLG7Y96Yol6d/8erTT4qsbUalpPtrX+0XjeY7Ow6Fm/vuUR+OMI+IqgP46h9+AR8Ah4BDwCGxcBTwRtXDy9N4+AR8AjsEUQsGcE5RIRlP4biSBOOXZZzNK6lteszWP6DtuzJT+iweRA39yORPDsh7yMxuiS1wUvTHsbY2a8h5bJ6civKsMJHffA3u37oFVmY/Rr3hXf0IHFa4n8WVW2Fv/5aSIWFBBplOjmKBHkl+MwIUIPERlDu+yFv/Y5Ak9Mfxevzv+Sa4GIZElESXEpzjprGEaMOB9M3qzL54qparRv3xYffDAet466F8VE9qSmJOOcc07B3nvtjk6dOqC0rAyXXHID/ve/7+kg6GSqBKrAlZedi1NPGYbVq9egvLwczXKbIbdZU7z33kc49awr0TIvOwZRpRm4+qlb41Y4pOOuaESHWRuUiDypJZZMKJ5EzFy1AIsLVxidRCZ7aIxHZY6grVbVmLh4OqasnG8OYNZDn6O/w8riJTjzkFsxsN8JeG3Cffhg2nO48oQnqDqoD5FQVZg4/Q28PumhgGSjA69pe9hjh/wDOXTA85UTHsOvpQUMqglrv7WkwekwsSejAblVW4msxDRc2PcYHN19X1zy4Vh8v25poKOagf0GX/q9eebaDhuJR9dvTJaOgetDbdgzYWu6/HfiqEdmFhK6Cr7tEagXAvzf59Bj/fXx9QLLK3kEPAIeAY/AZkHAE0GbBWYfxCPgEfAIbFoEhAg6gCqCWkUqgvKpIoirV3Q9K8tZXQBH5xO92BW9urTD1mFb7bkeeGtQFRntQoTQnQddgBenvYk7f3gXrVMyKb0apFDVTkpyGo5ouzMu3X0YRn/xAj5e/iPlT1eQU1UOn+bDC3Z59K2RuOpJiCCWnN17EJFO+2DUF8/js2U/mq1KTJCUlpTi7LNOwqWXXog5c+fjrLMvRWpaGsbedQN26NwZI28ejZde+RB5zbKQmZWFFavy8fLz91NlUHtc/I9r6YygH2grWSIaZ2Xj1Vefom1XFbjllrH4bOI3eOrxu3DAAfvhrbfewUmnXYEOrZs4+conUDKjuKoCB7fZEWftMphuNVPCiGbHV7nTNJLpZ8KS6Zi5YgHpHGWmzDNmMou3m/GikrfavTxzPF6aPxmZCXwAM8kNHBrFmGFZ0XxcMPg+7N37aDz0xqVom7sDDh1wKr6fNwH9ehyKH+ZNwgsfjTKx2aKc5nTdgOHYr20f3DTpGfxv1SLjW5FW5MU7/ZrEhKTKpGqqHXPa0CHfeRjW6yA6m6gaN33+BJaW5Rs11vptj0YVq3BPZOLR9etqmeRIUd9so23XRnzx34igx2PuuOvT6oZ1VB79ZlvXV/S49uurp/qb413fnBq63ubASmPEx8JvDVN8/Nsj4BHwCHgEGgoCnghqKF/C5+ER8Ah4BP4AAnJGkBJBmVQRRJU2cYggdzHOSxb7uD27cLUtqxnbcm1jR3khzH4KqkrosOjOeJQOi35x2lv458x30So5wyy+eZzJjSEddsdVe/0Jt056Fu/8MpXoHT7dh262ioTgBhM/dskuC22Ws5cEXEDVKEfssAdGTnyCrkKfEzmfpqSkHOcSEXQJEUFjxz6I+x98BkWl1fjbWcfj+uuuwv0PPIjr7ngSPds1NQTanFnf4pPx49Gt2w50VtC1+JaIoFq60/6gA3bHXXeNxuRJX+DoIadQzEwikc7HFZefj48//gzDT7kc7VpHbw0TkoGR4P+lJSWjeXoTpNC5RDxvfnjLF28VY8KrqKIUZUTK5NGZSjx5nl21GSM8aNtYNRFkq8sKaJtdubE1P2SvMAniCfi1eCEuGnwP9ux9FJ7+4CYcvde5WLxiDqbM+gBnDL4JsxZOwVPvjSQiiHOopbOaqnEBkU/HddsPt016HhOXzzLEk1bK6N+Doi2Yg7aZ1dK3bY/LB5yIDk3aoYq29I2d8h+8uvBrNKY5SjWTa80Za99tmZkE89DZuDJp62+sh7CN6kkEd8xaqnd3VGSuDo9yP1ZLdP2vR6BuBPhMsWOoIuj20SPrVvIjHgGPgEfAI+AR2IwIeCJoM4LtQ3kEPAIegU2FgFQEDaaKoBxk0PlAGUoEBWcEcVxZytrFbeySVjSic7QW0SPaV0+x9u7SuYS2He3ctBPuP/xSvEhn99xDW8NyU+jWMCfAENouddkewzGKSIj3qSomMshMhKPHkTWqkh4mGyIkzuh1KE7osT/u/uYV/PeX76SShsiUsrIKqggaRtU95xMZdB0+m/Albfsqp7OC+uLxx+6jK+JfxK23P4ImOWnkOwE/zliE8eP/ZW4Nu5RvDfv2eyKIgFP/cgyuvGIEVf+8j5NPvgLtOzXGoMMOwB13XI/Jk7/GaX+9HHm52TZ3k5hmazpIJyKoGW2/4kOak8zcEsx2LyaD+CmoLEU53dqVl8Z+mBqqFUKLqoYMbUTzXFtRgmIigiLQMFFEHcGDzRKwqmgJzj3iDuzT51gsX7sQiRT3uQ9uQbPGLXHaETdiyswP8NKndxG2Am4Z3bh2xz6nY6fcTrh+0tP4ce0SciRjoavk9XOwHf9DsbJSUtErpz360FlQJ9K2sPn5v+KWL57FSjqsW4kg9hRGggT0SIToMatpW6Lv/optfA+unm2rrpVYCk1lquNGZpnbV13/9gisHwGuCBo61G8NWz9KftQj4BHwCHgENicCngjanGj7WB4Bj4BHYBMhMJe2OnXrRhVBPVsiM5MrgjKoIojPwOHqGXlkCRuhCYywPstaXRKrn9h3PC82mo4WVZcREdQZDx5+GRFBb+GOH95Bm5QsQwIwfcHP0UQEXbHnyVSN8ize++V7Q4Hw4tsepBwbXWgEicK/h7bbBRfueiyen/ER/jVngqmgqSUiqKi0jKp/huPSERfihpGjaXvXeyivqMKgwwdizJhR+L//exy33P4YHfTM5AtfHz8Xn47/D7p0pYqgEVQR9D+qCCL5UUcegNtuuQkfffQxTjjhLOQ2b46z/joM119/GT77dBJOPGUEbQ3jiiCTOdkI5pphERFih7bZGef2PRJNiAwS8ocOoSaKh49B4jOBxi+ehhkrfsbf+g0xMuJ/CAWujKItYLSo5M1yL9D8nps3kc7kSTU4CYL6tQTR1XRG0KkH3oiDdh1OWVThv9+8gHGfjcVh/f+M0wZfj7cmPo53v3qCYvAh2bUoJGLppSOvpMwTMWL8Q9Qvozb5ZLeUmxImkRkZ8onyMgp8TlA1smm733l9h+DwzgNw8Qdj8UPBssA+YiXOAhvqOI+gpALpBcFV6Lx1tpEEI2N2xCQekXND/VkdiaOxrVzMVO7aykjD/dU5bq4Mt5V4m2YefmvY5vo79HE8Ah4Bj4BHoL4IeCKovkh5PY+AR8Aj0IARmDdvPt2Itb9zRlDmRiOCdNrRy2OVxy60deGs9ABQTNucfqGzYg5o3h1PDr4Oz059DTdNHYc26bloTLdfCdFDRFCH/rhyrz+brWFSERSQD3xYckwCroBjcrwEdG7cHHfsfy4+WzQV93/3hiGCeHtVUUkZbQ0bjssv+wcmffElDj7oYDOFt995B/vsvSduv+NePPzYy3TQcxZ+mr2GxhbQVq9P0K37DnTI9Ah8+MGnyGjWAn137Ih/v/wUVqxcgdPPGIFp332KV155lUihIVQl9B7+dNpltDVMzwhyKnRMtATaflWFjll52L/dTmiUzGck2XlwdQ/PZObqxVhWstboJNH5Sjp31uRx3hr2BZ1/9APdvpbK48Y3/bAiOyEtPnupnA7i7tGmP8488laqZqrC5U8dQpVR5bj2pKfRjbB+8p2bMXX+p3RGUyr5rEJmWjpePvxqOpx7BU4Zfy9y6fp4IU40QLw3x5O8+PwiJrKG061hZ/c9Cv/4kK6PX8O3hpGOqAWabBHJmjv0kIJO0PSixwMdfulQYGINdYCV9IkEVkHwDusqyWWdu+p1+XB1Gkhb/1g4HfO30EDy2o7T8ETQdvzx/dQ9Ah4Bj0ADRcATQQ30w/i0PAIeAY/Ab0FAtoYdSkRQbrA1jCuC6LBorQiidSzf8MRPiHgQUb1/w0tnNbOLZG5ZHYmUQYcID+60G92U1Y4OR87APp36Y9aKeViw7lcsLlqB1+jAY97ixAdKD+nYH1fs8SfcNvk5vPvLtIA74Iog8hrJX+OKgBfwGpMjVtJC+Na9TkXTtEa4Y8pLFGMVkukw6mIigv525kkYcekFhgyZM/snOsumGv3674zvpn6Pq6+5FYsXLUG/fr1x+ul/QuPsLPTq2R3ZOY0wc+YcFBcV4atvvsNVl9+Fca/cj0GDDsacOfPpBrJi9OzRDS1a5OLttz/EKWdcjlYt5IwgQUYRtzhRUQ/K6NYuPgSb8+UR3iJmzgGiuWYQFnp9vNZ0VZMN6yWbxT3p0Jz4/KTww0gEMvJTQ4FWl/6Kq45/Aj2p2mrxynnkowbtmnfDrEXf4NmPbkUFbTFLJE/Lygvxjz6H4/Teg3H/t3S+z89fIpNIOsVW4ghdojOysXVuNJfaRJzck4ggOmvo8o/uxder5iGRtsAJ0cOTsLrW3nrXeC6Fphaqr1yHIRBVGHpbLyFxqKM6IoyOEVI1HavBltwLexBZrN1mlig4GtakbXNXsX9vPgSYCDp26CCMHjVy8wX1kTwCHgGPgEfAI7AeBDwRtB5w/JBHwCPgEdhaEJCtYYcIEZROZwRlZqCAiKBqJYJoIrJwDS9d4y8PRTPe3GOtVRLtKZDTK5OuSD9zx0Pp7JjOZluTXIXOdEUyVZ4sxf10cHQ+XRXPRNBeLbri1N6H4enp7+PLVT9TzuqfyRKJoRJehrOMpUqWsKycKlv4drLbqCroSbqmftzcSURopNK177w1jK+Pvwivv/EOdtypBzKoAmbxol/w4MNPYfqMHw1ZtseAvjjzzL8gp3G2OTSaM2XigyN9N+17nHv21RiwZx9cfNHZhjRisuU7Oki6U+f2mEqE0ujbH0Z2dgZlZTNlwoK7EQk1jIwkLqnCMTiSVPawtsyZLaUX9KnCiZPlXsQntaUnOla/Fum0XWvo/uejXbNuFDcRi1fNwVuTH0VB8WoiaZLNgdXJRNg9fMDfTHXQMe//E7l0KDVX96h/QdoECbIJ0owidngL27G0Leyi3Y7H2/Mm49ulczBj7SIsK81HCvsLiCz9ixSP8ivzsbOSmdi+q6sWml94TKXiITzGPR0Pj4S1bVzVFklYK+xBevE11AvrxNeI5ysskwzCMrenMUhPCaFQqFDHNdzIbc5jY8Zy/Tlz3ChZb2rf9Cno/0B5ImijfCzvxCPgEfAIeAQ2EgKeCNpIQHo3HgGPgEdgSyIwl7aGdes6kIigNsgwRFBwa5hDBEl+7iJKiASR86JNx3QmupCz8njUg2rHLvzIjgiL2oRqzC4voaulSgMV8kvVMGadmJSFHqlZhpxgP6VE4iyk27A6pjemihfeMkaqJjz/xOZjZZI90yg1tOoqIaNb6Rr0dXRQ8aM/fGiIjsitYXRG0OlnnI/xn3+LNq2aY/p389Glex5dX59sYpSXV+DnebOozefjZNK/lLs+mZ3Rs1Nzs81qzqw16LRDc5RXVmDZ4nWksRBo3IVuHcsNtDlfi526sG9n3JmelYrQ9BmECOES14MVhlpszXVHwOriX5GT0YxoGtomV74O2bQtj8k4rrYqooOp923VA+f0GYSn6eyhz5bNQDoRRIIvf3VLySnS9q0BOQr/m4iWGY1x9s6D0b9FD1TSmUiPT38XLy34ArnJWapsviZrWz/SEwWLArfqety/4Lp04svdWPE1VGozcSWKDMuiNaQfm3d8PfVq3/XVsxbSCtspwegSePH0or1snn4411gMNYstpafx9R2dh8rr9/Zbw+qHk9fyCHgEPAIegc2HgCeCNh/WPpJHwCPgEdhkCMjWsIOJCGpORFAmVQTFEkGylNnwApj15Im/+Fm/B7UOa/GiVBektVzJYoZVV+Px240ZtOmVwISSIRlcXW2H/bBeDV3v1Sw9G1l0dflSIpaIHaKKoHKcd+7JuOyyi3DmWRfhi6++M1unkukmLbP1jGJH8qT8DAlFIQwRFYQwdIgZk0oZPleHx5OTeOsTPzwxUg5eRrRJfiQAp8Wtup9AjybD5/fwYc5cEcR2/D3MdfU0VkNnDmWnZaJlWmPMK16FBMLLeCZF/VZchCSPjRgmiHhUxqqJjMumG+E6ZzVHZnIq5hWuwGraesbVQuYJkVqSo9qKgv1156gp2AxYT6XWZsOtsIcN67sa9Y9Xf03X/x9tR+PJWajsj/r29r8HAb817Peg5m08Ah4Bj4BHYFMi4ImgTYmu9+0R8Ah4BDYTAqHr49PTY7aGCQnDi8G66RRJNXZpv74phJfTuuAMW0SKWcx2Jh5zF6WBB0cUIWOUiSELWVDXlbsut+PFV8cJqKysxFGDD8BJJ52AO++8B7PofB+uWGLn5qyZIBXNXpBgoQ64cUhKExMCiS3Ej6qyprEKTJhMUS+szY/OU72KzP7a+OzdUFAcxdgJpiyN9cse5NGIeo6Q9I2NcaB6PK66IlNKjEcMJ+QmaVRU3w5oLkL08bgdYxOTLYktocTSWD8sdR/xonoyEu6F41ifrCtjqmHtuMVSK1E0rYTt4z3qTcbCPZGJD/1q6kM9a1y11L7q1fWuj578N6Ke1RNHFpk7YqWqV793XXau3G2z1/rkviX16jfz36vFRJC/Pv73ouftPAIeAY+AR2BTIOCJoE2BqvfpEfAIeAQ2MwJCBNH18b1aBVvDwmcE2UWgbBOKn55drLnLRdGNlRi5NTFdXv7J47a0Gkh8KJ0jC29HPQihBIl60shqx3Iri9bSfpw3pZRA596kpqWisrzcVANF1qfqMDDjrjsDG1EUZInPGqoV5SDwEydRHTHvcBwdYrzkYe9WR6XR2UgWohdoq6FxYz0EboOXzV08U8VQEI8jmEogGhAtcSh6LLO26jN2TEd0Dqohcu6pFyPRTlgtgoVYaWTXOsogrjNXXzzZXwmsf48awY5Ht1hTqbLoMenbqawvrmtbXz3XJn6b848gwolQR14RaXzDGGl9c2roejET24SC+Fj4rWGbEHLv2iPgEfAIeAR+FwKeCPpdsHkjj4BHwCPQsBCYO28enRFERFDPVsjMzER6RnrosGi7BJQlqi5Uw7OwWiwP98KapucqRDmUxTRvR2LiSa4Ul8UzW8qylFvsIsqUxcETrRfW1MW4KGsy1kYWxKxFD4l5hGV8hhBfya67k0yuxjVpqhvjVH3p2widH2MU9NVQZdp31J2mjqp2NAo6ziZWh3sy4o6zlJ+QHndcJdMOEHMUXRXW5yonebjDraAfrajyQDv2pQZir9+KpSox+bkxQgmHPao3sZbqKPUkmqrBDm0U29bxsF/paUZiJ71YPes17Cvci7Xb3BJBIDwLkTW0TDc3MlsunieCthz2PrJHwCPgEfAIxEfAE0HxcfFSj4BHwCOwVSFgKoJ2HIyeXXOC6+OFCKoKDou2S0BZIMrCUKYoEqvhTjy+1NWgNiuJEzNgmkQo8DuDCCk+PLi8rIT6sd6iTI29/WEP0TZOoJgxtnRtpO16sKPhVtjOZiAt1uXHehKSSaSxv0I+qVX0uPXCUeNrhXVcD3bEtmQ81lf8PNjOjap+1D40roqq5KZSR1tUA7LG7Ilz83AcRgJJZLWLdWttNA2ViK7bi9Zw+26bLcN9zdL1FpuL2oRHoqXsI1oWtvC97QkBvzVse/rafq4eAY+AR2DrQMATQVvHd/JZegQ8Ah6B9SIg18cfgJ49W9L5QJnIzMhAfkF+6Pp4caDL/fBCVTdvxVsE/5YFrWtfSwcT5+TkmAVxfkFRnPzd5bJYqr0syqNN7KiMcGYqU123z17i6QS6QfWLXGeu9vHers9442GZRhTc5Nf1IBK2sd/C9RC2j56htXZtWEvtjNyo0eyDwPLiX0UknJG1lb+EmMzE1LjmMfFi/4Zcb+aTRNKkhmMb1QmmINHUZyCUV8RWGhG3jpLEll+bkW2JZcyMHA/ajIeNjuk7XgY6Fn7XXzNs53vbHgK+Imjb+6Z+Rh4Bj4BHYGtHwBNBW/sX9Pl7BDwCHgFCQIiggejZqzXS6dawzDi3hglQuui2sAmFIAtnXU7raL0Xs6xIxq4930SV06SxISgKDBG0IW92sR6XFDBJybJe8wu/NbqNo0t71tugZVwF9RmOJL1g0iHnJAsIJj6AWh/Xi0ojs40bN973UEv1qm/XQRDJHLSt8kBm1BURlVmk9S9D0zbbxNSFsQ06IZnmpP5YkdqRg8GDca4Qo6bpmdwCPeOXf9SPlWtmrudoTavNLfuoN7GN9mD1bEujcfZ16avX2G/DI9FWVttG8a3tEwFPBG2f393P2iPgEfAINGQEPBHUkL+Oz80j4BHwCNQTATks+lA6LDrXVANlUEVQQUEBYreG2YWuLlRlAasEQWxA1YsdcZe+ouVK+HygnJzGxkyIINcDa7qeXUtX7tpEt6Nton3GLtjVg43Ay37uKQ3CGnZU9eVdlw7FlX/kcnTDpGhu1ldY4tANPBBRczsRISm4cjcr8cpfz3iMqJEt/0N9Q+i4rmiAu2KpodlalIRDChyZV6AZMeAjpfUmMs6F7MxYoMBu+F9SY6+J5JBHpI5IdJgrM3FIT6qW2CCwpxY/lpqRfmBpRmTclUvbleto2KvVU10d13g2jqvL+cmjLWunI+EZqJ4d9a3tFQEmgo4dOgijR43cXiHw8/YIeAQ8Ah6BBoaAJ4Ia2Afx6XgEPAIegd+DgBBBhxARlBcigqqDM4LCPnUJ60r1AF5XJu34C1r2wSO6bI7VEiIo2+htmAiSWOIv7MtGUR1+x5sDy3U5z215NEPtS97cq8sHjyXQrWLVaNUij24aS8KSZcuRnKTkB2ekWZEP+ic5OQmtW7VAQWEB1q0rQiLdThb9cDSZmcSVX9Vy/Bkt0dRRm2tYbv1Rvvytq+hcprbpSMpMQtmKclSXVlNQtXEjqszmpERQbCyic4i5SUpPQkZeGkpXkt/yanJLPoxL8WX+ggzpk0DjZeh//HHIad4aE597CtWVFaSfiJqqajTv1g27H3McZnz8IRZPm0qcUhJN081NZp0QKSGSvmjwr8Zz5dxmOc/C+tKWjIi+6mlP3vbvJhzH9aCaYUu3p9oskyzd0S3R1ozqyiaMzJbIcHuI6Ymg7eEr+zl6BDwCHoGtCwFPBG1d38tn6xHwCHgE4iIwd958ujVsIBFBbYgISqdbw6QiyJADURZS+2EXgLxUTKAKD1sTostyGWHz2GWkLjDFufSsFrf4dq6cnLqIILGzWUh/fb9WNxw7bGMX9Jw1z8ls1TKpWQ9mRurGiKkTIUzE46yZv2DcK/chp3EjHH743021lWz3Ir+BOkeorKxCr15dcdWVF2PqtOkYNepBZGdnmBAmLFfPUNmLseF86NHQEolzltxMvqYpljLu/pI8gSty6KnhduCpshYZ7TLR78LeyG6VhdTsNEy5byqWfrIMiWlEtBif4ajihAc4Okd2a3Z4VPWJZCLip/XAluh3Tm9MffJ7LP3vCiQS2SQ6mn1gQwRObudOGD76Dqz55Rc8948LkZyaalRra6oMETTsljtQuHo1nj33TCSkplAKJkF2EDzcp4dSiPBNIon8ikbwjSNSno3kbbO3gyqzEmnJDKzUfAfbpVaQTyDTbNlfohlzLXS0Lit1bPVUIu9oeXRftaPl0X3W4wxZ7j7x9NzxzdGOziG6rzlEy6P7m0pP/eq7rrg6vv633xq2fnz8qEfAI+AR8AhsfgQ8EbT5MfcRPQIeAY/ARkdAKoIOpOvj6bBoIoEyM/mw6ILIYdG8jJFFYWxoWSrK8jn+ckcIAvUQz5cssGVEI0QqgogY4MOio3hVKahfAABAAElEQVQWoxY/nnoIv8O6EjGsoT1a1gfrX12eK3HDlS21lA9X7NRSxU8N9ROpIkVz43E922fWzJmYOOk9c+B1n97H0PlLeXZNbapVyFdNDUpKK7D/wAG4//5R+OqrbzHs5BFo26oJLcFrgiV4mJqw8xC87C9nGySuU3HeEUKEJ0OPoW+YYKJKoJrCBOzzz13R7dgd8OuU5Sj8pRhzXpuP1dPXICGZiSiKwnOnfJn0o/1armdqs0/6N5gXvzl/fjMetRXV6HhsBwwcuRcmj/oSs5/6GSlNicAxqTAxFVRAEUmVmJKKPYedhP1POwtv3DkaMz58l3ww2SMxColgO+nJB7HzYYPwyjWXYdaEz5FCZ1oFzgxmpq6H50n/mG9jfuzfoeoyCvwIbtozIkmNmu5MRVfG+deO2ZaOmqlpZwNvto6nH+t1A4788DaJgCeCtsnP6iflEfAIeAS2agQ8EbRVfz6fvEfAI+AREASECBpEVStNkJGeboig8BlBulSNt1xVFFlH9dyFrS7A2daOs5XrTUb0l6pIqCKoMZ0RxJLorWFhL+xpfU88bTey2gZ6OkRdlujD4qSkJGRxtVRhEfKaN0VWViaWLV2BsrJy2qHEFS7ypKYmm0qPHXboSPJETP/+R2NrXNNPdXWVIZDatm2F0tISdOzUAfffOxpTvv4Wp55xOfJyG1FsTSQOTnbIBJSu4iw5GAImyIdfMjs1lB7rJKQmIKk2CYPHHWy2bE26cQqKlhShuowIosoak0VCNekT+ZPRKs0QO6UrS41uIpNE/DABRM3k9ERUl5AdkUuZbTPNUOmyUiKCatBhSHsMvHkfTL5jMuY8vRDJ2YlISk2iLWMJqCqlOLQLjcmmpu3b4eTbx6KsuBj/vuEqFCxfTgOcrzxVhHWn/rvimBtvRtHKFXjgoMOR3aszqfDcDL1FrwSqaktHCuVXWlKOqqoqU1HFHgwnJK4iv2IZ6W6woSiyomRm81NjV0dlos9flr9V+Lu6Oqq3fo1oiw31NxRxQ/abYry+OTV0vU2BjfXJRNDQoYOpWvAmK/Qtj4BHwCPgEfAIbEEEPBG0BcH3oT0CHgGPwMZCQIig/YkIak1EUAZdIV/X1jCtndDIvECTx7bc5assh3l5zo+74I72pF7UjxBB2WZpn58frghiHfEli2qxXd+vWrg6dS3VWcdmLEv2BNrCVYEDD9iTFmRDsGDBQvTpsyNt+8rG7NlzaQvYG/hyyjQ0ItzS05JxzjmnoXPnDqiqrMbyFStxyVV3oE1zOfi6nPzkNW2C8/5+OnbqvSMqyiuw+JclGDzoEEya+AWG/vkSdG7ThHKQyJyNmykXunANTUVQMVRGhBlrpNEZOlU0yNRNZkIyUpiZCcC01AN744ecEHmT2jQZ/Wk7WGrjdLTdqxWqiiuxfPpqOtcoET88/yNWfb2KSB2g2Y7Z6PmnHshpT3MgwmXdwgLMfednrJywCkmNk1FdUIW2R7TGDod3xuKJS9Bq1zw03aGp0V1E/e9vnY2u53TCfjftjc9HT8Kc++aj5aHN0evP3VGxthwznp1NZweVmsza9umDsx59EpP/9QLeu+N2pDbKEhLHED3kkub584yvcONnPyA7txkePeVPqCgpQIIh4mjClHBei1z6BqejbdvWeO21t/HfDz81dgYO+2PiyY+LsCOmJo/IX0PdOqwhD79j9WIl7FGksWPWk/itSyMI2WBf8bGITbeh68VmvOkk8bHwFUGbDnHv2SPgEfAIeAR+HwKeCPp9uHkrj4BHwCPQoBCYO28enRF0AG0Na0VbwzKJCEo3t4bxYdG8NJEneuFqR1QjvIyxC1ilVVSP3/GIIF0esx85Iyj+rWEamSPEj6kaHGl9j+So2pZ6Ub8aIQFFRSU4/+9/wuWXXYwiqlaZMXM2qmmr1IDd+mLyF1NwzTWjsHLVGmQ3ysCFF5yFTkQE9e27Cwppi12nTgNoa1hnIjRqkF9YjjF3XYfj6P+Hf9asucjPL8SOO3WnKqBmePutD3DSaZehQ+smAVEQnh3PpLS6Ev2b0Rk6vQ5Ek9RGAY6kR7652oVv2fp6+Rw88v0HyEpJCyav30IQ5h7zR2nNUtDv7zvROx3t9m6NSiaCphK5Q5U0Pzw3C8snraQDpJNw2L8GolX/Flj25a90cHMNWu/eEmvnr8PnN3yFonklKJ9Tib537ojdzu+HslUlRid/QQGdNZSCYur/98hJ6H5pRyGCRk7Guh/WYZ9b90RWyyz88MJMzHllPmq4kqiyEjsfPQTHXnM9Pnr0EXx87z3Iymsm39iU8sj3WDHza1z07iS07tYTb91xK2Z//BGSqAKIv1p1TSV6dNsBDz98N9q1bY+HHn4M9973COHCW/j0Swew8Mu4VHwceTCkkjiWNBRfqjb6jvYebSX/LYSl4Z56ivcWTOKNbDyZzqD+WW282N6TJ4L834BHwCPgEfAINDQEPBHU0L6Iz8cj4BHwCPwOBKQiaDBVBOWYW8N4Ww1vDYs+LFqWg/VdFKoeL5djF5AuEeSOshXrm4qgxrQ1jAbjbQ2TadoYthUPADeCOy5WOuoSQWH/CSgrLcdZZw7DiBEXYMqU/+G8C6+i7V6puOP2q7HngAG448678cSzryKvSTbS6HDjOT+vwOefvIimTRqhS5dB6LljHqqqa9GuTWs88/RDWLNmFa68+mZM/+EnPHjfLVRpdBTeeus9nHHOVWjejCuh9AnPrIyIoAEtuuHE7vsjJ422X3GJUKDNmCbSFq7/rZiH+6e+iezkDMNzyLYp9cdv9U62hhxJwPHvDULBoiJ8esEkVNGhzKBKoPJVldjpb92w64V9sfzbFfj4nElIzk3GLuf2wk5/6Ymv7voGc55fgIrllehzVXfsdkF/lK0uwyeXfo78n4uQkpWElIxkFM4rRKchHTHwln3w7UNT0X5gG6Q3zcB3j0zHwreW0PY0TokOzi4uxZDrb0TfQUfjv488gMlPPI60JkIGSvacdwIKFy/FifeMxc4HH4EJLzyNCQ8/RFVNfLA4EYhEznXu1I7ItpvRq2cPjBn7EB5/8gUiguhY5mDaBjJBxtio3HTIvyAexl3GFDnFT6Xrf7ue4lm64+opnp6Ohd9sXX/tsG19e26GmzpWfXPafvT81rDt51v7mXoEPAIega0FAU8EbS1fyufpEfAIeATWg4DcGnYQEUEtguvj0+McFi3FE9ZNeAHKy0N3uSh6ViLLR7uIlBE7zvrqg998EHNjujWMSaGCgkJxFyx4xUvYNtwL1EMvG1vFEs/SP2EfMiq6CSguKcPfzjoJIy65EDfcMBpvvPU+ysqrcMhB++LusaPxf489jptGPUbVPExcJGLWzK/wzbdfERGUQ0TQwYRtG5SXVWLIkINx80iqePnoE5xwwnloTgdDn/aX43DDjZfi8wlf4KRTL0O7luwjnI3Jg0WUViWV85TwoTpEblTw3i1iMphLqSQbPrC6EVW/ZCTSAcv8MOsRYjrCfmuqyKYwEcO+GoyCBYX46OyJhoRLSE5AycwyDHx2N/Q6oTs+GTEJi/67FHwuUNvDW+HAUXvjpzfn4+vbpqFsXgV2vrEHdr+wH2a/MhdfXv0dkprpfVj0LenWsM50WPTB/xyIkhXFpgJpyr3T8N0VPyCtZwpNQ7Auo+98/G13oM/BhxERdC8mPf4k0pvlSP5B2vz3kE8HRp/w2Fj0O/woTHzxaaocup+IJSGCSNmcDdSlaxeqzsrEokWLsXz52gACdsL/ymY5hkcfzoAfCaM9kakN96JHVKOud5B2ZDiefbQOK8fTizgJNWQ+IdFG66hvoW0lp/pnttHS2M4dmYqgoYNw+6iR2zkSfvoeAY+AR8Aj0FAQ8ERQQ/kSPg+PgEfAI/AHEJg7l66P76bXx9MZQVQR5N4aFuvaXbqub2Fo9aKrgmTEjkfH4MqOHKoGsURQbByRsA9uyWI12o/th+3dXjxLjmsqRwLFwmIigs4ejssuvRDnX3gFJk362tz4tfuuu+ApusXq2Weex/U3P4BWLRqZfGbR9qVvvv0azWgOO3TZl7aGdUJRaSWRPsfimqsvx9tvv4uTTroIbTvkYvARB+HOO2/AZPJ5HN0a1rktbQ2jCplYdOisIqrW6dSoOfZu3RPpVJGkjyDAFFQCfi5cgY9+mYY0JoNowC7jFSu2Eu+1VPxTsxYY9u2RKFhIRNBfJ6I6gQ6zTklC8YxS7P/i7uh1bHe8c+onWDV1tak4an1gCxwweh8s/nwZJl33FUq/r0Cf23piwEX98dXt32D2s/OR1IgPkpasqpkIGtqRiKD9sHr2GqRmJaOSDoj+9LKJWDM1H0nZcpV8JZ2XNOCEE3HkJVfio/97GJ/cey8yWzTTKQbvBKwibM97cwLa7dQb74y5Hd+/+w5SM/lwavkrq6HDdWfP+pH6RWjUrCvataLzioIxi6m0gs9rfNsxV2qG6IdHo/+Kdazut/UpOnV5juchnm48vc0hk9lvjkg+RjQCfmtYNCK+7xHwCHgEPAJbGgFPBG3pL+DjewQ8Ah6BjYCAbA07lKpW8oKKIL4+Pj9yfXxsCHd5G2+5qstGqxe9hJYRO+7GYI/21rDYiiDR5UoeN3YsccKjNoLouhbsJ9ZKvBsSg43JgF9cEXQuEUGX0NawsXc/hCeffonOwqnFiScOxvXXXomHH3oUo8c8iTYtqJqHKnBmzfyBiKDxRATlEBF0CGHbEuVEiBx44J74J51r8/mkyRh6zPlokpeOS/7xVzp76AJ8/NGnOIVuDWvJB0ubw5FlBpKGZF5UXYaDW/fB+f2GoHl6TjC/IFFS4XNwPl08DVd/8SyapGQahMJzDCZk3JEdVwQVJ+KEyVQRtKgAH589mURV5sDosh/Lsc9T/dHrxB74chRtAyOCJzEzEe2PbIuDRu2DmS/OwTf/nIbyRbQ17Jru2P2i3fDlbV9j7riFSEin70MVS5ygIYKOpa1hN++FL+/6is4CAna/uB+WfbMSX9zwNSoKSUCqTP6133ln/PWhx6jS5wV8OPZO2l6WZXzoHPj6+l+p2urK8XQ4d14envnbWShY+StVKiUTZETe0bwy0lPw5z8PQ+eOHfDphEn4+OPP6ewgmhPn4zwGAqfPKJoP7siim9E20ePRffEZlkb7iKfjWkTru2Obq834h/9721yRfRzZGjaIbg0b6cHwCHgEPAIeAY9Ag0DAE0EN4jP4JDwCHgGPwB9DQCqCmKxQIkjOCKqiw6Jjn+hla7xlKuvIstEu4MN64iXal43GByvL9fG0FYgOVJbdTerDbucSCyuv22N4Gat6dW4SYoXALTdLiAg6h84IupQqgub9vAhXXXGjuaHrtluuRfv2bXHLLWPw4ri30b5tc7RulYu1a/PxyMN3oXF2Nl0JfwG9s7CErpqvIEzfePU5JNO18qNuvwdzf5pLW82uwAH774s333oXp595hRBBESgkEU6FW0yQZSanonlGE6QkJhsZz0FGWSsRRRVFWF5GN2nxBIy5+KBBeZxubRV94+IEnPDFkcin28A+OYeIINpulphE1UdrqtB5WHvsc+1uKFhahPEjJiKJqnn6ndsHHQ5oi0m3fIWfX/0Flb9Wo8+1RARd2B9fjCIi6KUFSKSr5OVwZjrvibbQdTqGDoseuRcmjZ6MeS8sxD5j9kTXozphxgs/4rv7Z9IV8/yXUoPczp1wyt0PYO2SJXj52itQnl9AOfO8BIFK+g4tu3fHSXeMQXlxCe7ddyBdH9/JDPN8eWq5udl4+MGxdLPbznh53Cu47baxqKioNOQWDdPDvuSRlgAif6t2TDR4zD7hvyIr31DL9bL+COIpWifav/izXkV/Q1bRXurb1zibyn9989g+9TwRtH1+dz9rj4BHwCPQkBHwRFBD/jo+N4+AR8AjUE8E5NYw3hrWth4VQbooVOfRi0Met8vluomgaD/qT5bpUhGUY5bs0dfHi6aSQRpf/K3Pa1iTvVhtJRFsFjymFrTJqKQU5wZE0Dq6zr66qtpUsLRsmYc33ngP1990F5E/BaZq6C9UjZJCFSpt6GBoPrx5ydJldFB0FV597V1cfcWtuGvMjfjrmX9GZXklSukQ6vSMNLRs0RwffPARjjnpH2ZrGOMm0W0Okdxoz1o1EUJcNUS7oLiYhtqsRx36J4HOCEqi6hcmVtw5BKyQKTbi2XG/NjgjaPjUo8E3ff331Amoov8lJtN2LTKvmFWBA9/aB50OaoviZWV0gDid3dQ6EwsnLMWXo79B5eoqlM+i275u74U9RvTHpJu/wpwXfkZyBtnz9jau0imrxg7Hd8T+o/bD57d+jlmP/IzGfRrhoLv3RdMuTTHx5i+JUFqMhJQEuv0rDfuffgYGHD8cr1x/FeZOnkQEDvmShLHux59w3H33YPfjTsK7Y2/HN/96CWlN9UwlikVVRW1aN8cjj4xB9+698Mxzz1MF1gNU3UZ74AgX55MaBHj/n5CM3BUiKRiQV9Rvnd8kSi+6G6QfE171dFz7wdc0+jzm/hVYXdsSO/vfnfrZeO/oLDaeZ+9p/Qj4rWHrx8ePegQ8Ah4Bj8DmR8ATQZsfcx/RI+AR8AhsdARkaxhfH9+aro6XM4L41jC+Pj7+4y5AY5eo7nKUDy/mlbarxT6VIAovcSUaL8cjRBCZ5xcUOYt1qxPPdv1+xdZmb1vqVd7uL+vQ9fFEBP3trOF0a9iFuOfeh4hsaImsRo0wb97PePudD+lA4tWGlNlzj10wYPf+SEpMpD7Z0j/mvB96T/3+R7z3/mdoTHbHDT0MfXr3QhURSjNmzkLz5rlYsGAR3n1vAtLSgoOeTRoSn5uKoUr4zQ/LpR1I6MXVODpulCI9+Rbmu/AAKfG5091P7YTydRVY/M4y2l5lHNAgXelOTFNyRgq6ntABOe3lzKb8xflY+P4SlCypQGIaXdlOW95y+zZB671bYclny1Awmyq46LBp9mO2a5GP7C6N0P7Atvhlwi/In1VM+8Wo+mfPZmi9R0tz9tDyyauJ0ZKZtOrZC8NuuRVLZ/+EFy67GGkZGVwKRblUoWn7Dhh26z+JkKrC46f/mbgdosGMncyynMi1/ffbHffeOwrp6Rm49rpReO3195CaIphaTKSlmIq1i5nVlDH+ZexUHra0Or+9pR6jLe13td+edUQ/nlXsf2fRPn1/60PAE0Fb3zfzGXsEPAIegW0dAU8Ebetf2M/PI+AR2C4QECLoCKoIakrnqxARlClbw5QICqiDyBJYQNGFqC6Iua9LV33rAlZ1LJzhxWz0OFeS2K1hdV8fb/3V3bK5qI5mrktqkUfnwFLVpBu0qHLnXCKC/vGP83De+Zdg2vSZRC6koqSsnKqDuDqH/iF1ri6Rf2lLFDeMG6nuYVKF/jFuiSdCWjof9lxLZwfRGTmsWkM0gwmpcdmYnoBMk47+sk/1TyqmKXYild8oT8bCJctYy/SZTGFlTtDJW0OAimmSUpOo+oi2jFXQeTvVSokEEdiGfVDFkPHDYJiHI/BD+dJ2My5kMuP8Y2xITrLaWt5Kxt+d/iWS6KDzzkej3BZ475+jUMX4EGC1VZVo07cvDj7r7/j61Zcx5/PPglhK0NDV8nQF/al/HkrbwW4yN7Ndc+0tKCwqMRkIyJpXIKKXZsgtwcZKrJZtycyjdbgf69taaSu+XrRlvP/mNKLoqkXYn9rZuapVWE+z2bRvzlHja6R4Mh6rS85j9c19S+lxjpvuka1hg+mMoJs2XRDv2SPgEfAIeAQ8Ar8BAU8E/QawvKpHwCPgEWioCAgRtD8RQW3iEkGcty4wdfkp5ISM2HnJqFmWm3Ud/dA/claM1eKWaBql8IDpCSGQk8NbfoAC2ool60mrL8vL2IWf+DVmkeWj5i5SN7ZK3Ld4Vi0ZSaQzgkpwNl8ff/EF+Nt5I/DNNz/EzM0lEUxFjpuMzphkggcP8jzppdOgt2KnXIybmdvWLEUmDsSXBJVxJWtcy3CbK3Z0rjVcwRMZDlomPyoZ4g1o1JZKI2qonUmUlciWyBwWW4/izHhiFXpqmfUJRYkiXwLjiuIiIn5qkJbD18Kz0CBDpFsFSlcUIKN5DhJTiUgzk6Zhg28CysrLqSJoAI477mi8+eZ7GD/hS6TSNr26HpNbMCizDxI1Mh0NkjIxRFlGdFyi68ytdH1R3ThqH62vcaPl3HftwxHDvXi2m0O2vtzd+A1dz811U7fjY+ErgjY17t6/R8Aj4BHwCPxWBDwR9FsR8/oeAY+AR6ABIiBnBMnWsEzaGpZO18e7W8PchaUsP/nXleqkZFSW7KwhRMdvI4KCCFSZwtfH8xOPCGJ5dAZiySPhJ1avLs2wnfTEms+Y6dO7Bx3qvA+d9fMWbQVbI4QOV7BEMmG/0dHYi43HuqzBlS9K9qiVvq0PtVOfiiz7dB/j0ZJKJqN4JJCNoNZcuCOcDjXETRBeY2oObFGHLMqtuglb0Hw5VhCY526w4HeEzAn8Mzakx7q1pGikrKOAGR88GuhH+qZBZFAFFsxbhradWiKb/p7Fk3oUHf1VDxJRpZEsVWDe1oNqi0R7qqw+tV+ft0asj67ouBbhiOFe/T16zYaJgCeCGuZ38Vl5BDwCHoHtGQFPBG3PX9/P3SPgEdhmEJCKoMOoIig3VBFUU8cZQVL5otOXxbD2ot+yKI1dmsoyVhezseN0IAyym2SbM2bM1jCjwvrxPaonN34cr5HhcHwWq3bYU0BDGLtquk2rmgiqlKQkIYGM1OZkLV1fKlWZMYr8yKj1EY++kRxohF0Y0sTqsyMjjnjkho2pUVViSZGQQdCxWiyQ6h8dojGTAA/wv4GueWkUY2RCGBIwMJUX0SWuvTMmngJ/5Fy86V+Z+uZxboue8R/4i1QpGZ9kb/aaKZJqz/Mh6wjpJAnoaJjM0Vwi4ciYKqLM44zF6Wv2gXLkS2hf42lf39FeVW7fqqEetG81uBUdPzy6uXuco+arsV2ZzoF13Lbq+jcjwETQsUMHY7TfGub/IDwCHgGPgEeggSDgiaAG8iF8Gh4Bj4BH4I8gMHfefHTreiARQS1DRBCfERS9jOM4umSLjSkjG1qMbsjexKAFe06wLchWBIUjurnV7dPa1Edfl6Siy16JUFDnSn4Yl+ubJduQkRvQpuG0Io5Jpm0eVkOWcRxDe/AAPSQTsXTNaNCU0civ2EaPBf2QD9VhoTsQoMFVOSTmEfvYnlgEObuph3xRNmpCOtq08cQL+xdklQjSiNZCdORXrVxtm4J6Uh/8jvYj2q6UfakPY2kGScJCagcvaTj+xEZ+YyMZT+bHaliZG99K19eKZyHzXZ+VH9v6EDAVQUMH4fZRI7e+5H3GHgGPgEfAI7BNIuCJoG3ys/pJeQQ8AtsbAnPnEhHUbT+6NawtMuiGptjDoqOXz3UhxIvT+i9G7VLWttQzL34a09YwXjSHiSDVjY2jI/HIBfXrLsKtvoy6Y6rPb6PHPw6TobpGHCi77YidGWNtHuV/63pURz2znng0I4ZYCnTUTbBVSi3DnkmJ/rHb8rRCJjCOEFXxrYPyo4DskK+qYe08xJblsV/D5h/JSx0YHDUuv6MfkinWgY1qi6YINaYh6lSfJm0qj8ggis6JDmL6Gl1Tc/1bA+Ms0o3kEokZGYrBIeyX9dysAsKQxZFtb2oRPzNG2j6qKxLFw4771raAgN8ati18RT8Hj4BHwCOwbSHgiaBt63v62XgEPALbKQKyNexQqgjKQyYRQdFnBP1WWNyl6vpsw8vYqB6t7htTRRAvbi0R5OpIFDeWjLo6Gt1q2ZalM1TLfauekAzuiF2KayTVZS3NgfPWcbGWmhWryy3W0Ld2VSNszT6YQoj2KpLATxxyh8fF1rzMj0YQeXQczUffaqdWpG/CaZ/GnabRjoAW5KUuftPbzcsNwG0ek3ElfbgfSMyY0WJBhGCJTZNGI4/Yym9EGGkE8YNhyYA6gTiiZhqKuGaoo1G+A3ORkqMQOaY2Ekl79h03cPx0rJFvbaUIyNawQbQ1bORWOgOftkfAI+AR8Ahsawh4Imhb+6J+Ph4Bj8B2iYAQQYcTEdRssxJBDLYshKMWySxXIojOgQmfEeR+Irvoju/LLqTDmhpXrepeWMdmJvGZgOAjauiSeDMH9W/piGjKRubEvATryt1Z7F1jO20WUTdCHdHZRLV0ZlJCUnJEWwiOqOyoy/6t1PXNecugw40Eumph9a1EZWwvVBS3+FEdm5TInb10Vsl1E6ht+MURJGfWFbzVisZMAjTO/9DfCx8sbZ7aajpiqhZJdJZTuP5GbWPfMhf55VH2JLYcpxaVlVVIpHdSSgpdoBbEiXWzYYkNIboSSAJu0Hr9cdc/ukHnXqGBIsD/t3Dosf76+Ab6eXxaHgGPgEdgu0TAE0Hb5Wf3k/YIeAS2NQTkjCC+Pr61OSMoMyuDqnDyUVXHYdHrn3+YLJDVet1L1Oh1sfqura3ZQEUQayr9olb6Fq+RhbwR2xy4VVdc9RB+q7Y9MLiGFmfltZXISCRiIPLYGCxiq4qaSlQSMZFChw2nJaQSWREHH1YMmTKJJHp8TlN6ZibSqTqqcMUK41OqYNxZaH4c1XmMWPSKSsqQlJiI9PQ0Gypma1MCKqsqkV9QiqY5mUjma9cD124EN7ITLWi6miwKTSxWnTVIheCsx8O+mHij7x4YRXJhe54P/ZOSlo7UrCwULv0ViempomsUY4NES1iNHyXhaui6+qqyCrTv1xfJqelYNX8uyktKg2mRNv9DyUf7EQWWmsDGp2nrRG0gGdN+oGmFsZ7FZ0jRdOK6iFXzkq0MAb81bCv7YD5dj4BHwCOwHSDgiaDt4CP7KXoEPALbPgJSESTXx/MZQZmZ6UQGFIBvDdOlLKMQb0kaiw4t040iV8yIRV10jdpqDPXPC9oaQwTRGUEkDFcE8Wi0X7VUj/ZtR+wy2bbUU3hpbW3Yj9tLDJb1tWiSmoHcjCZYVLASlUT2mAH5McGrifypoEV/x0a5pJeN/PIy/FK4kopJNH/NgvxzCOrqiLQIP8IgNSMTB51zLrLzmuOtf45GeWGhKJso/CP5WdvIQOBX0N+5Ty+6Vr0MP81baL6raLlzS6Ab0arRvm0b9OrZBdOnz8CKlWvosiyuQrJ6QaqBRHtOTEdXpDrPAKJAlckczT26ySrq2UYWP7W1VSguL0BpVYn528hOb4K0lExyIeNVlRXotPsA7PeXM/Ddu2/hh/ffJwInOeIvEjPQt/41vyAyfaea6kqkZWTh8BFXon3vnZBExNg7Y+/AvC8mIZGrjYKbxIR/cj3pt7RzD6Zt52wF62m59mH/mq1r7Gq7ct/euhHwRNDW/f189h4Bj4BHYFtEwBNB2+JX9XPyCHgEtjsEhAgaTBVBOchMt0QQV6PIw0tMWYja2oe6lp2iZzggUuGeLtIDZ3FeosUD2ooQQSQzRFDISmKwqK4seEy0bO4sk2w0ikjkN1pPpDaS1uiI/ISu++KY7gNxx+Rn8MO6Ja4jVBFZkZOajev3OAmdG7cB1xHNXL0Q13zxNFKpgqgm2BjGuEhUoUXsvi6KRfugqqk6p9t+++H462/GtA/ew4f3302eKCObFPXZhxWYghMHFB7Ny8vB2DGjsHbtGtx221gsX7GazFRJ7DmD4tJynHHqcTj3nDNx+5134603P6LDw9MlJv26jxORxOqLNXiEfMrEIpkxBjxi7TQuCc0TtmOR6op3ImYI19zsNjhwt+Ho3KIXKqorMHXOeHz+/eukyzNgnRo0ITLr1DF3Y+WihXjl+htQXVEe8cY67qMxWCYj+hdOeCxdgcOvuxZ7HD8cv875EZNfeBrL5sxCWX5BgJ/1ZW2tzI0jbZkjt8NaGtO1CGuE7a0f8RT8/bjmMW325842RmELCOqbU0PX27TQMRE0lK6PHzXqpk0byHv3CHgEPAIeAY9APRHwRFA9gfJqHgGPgEegISMQqQjq1YqIoExTEbQuTkUQLyTtUrKuhWq8mUYvv+PpiEyWuLW0oOfDouPdGqa2svBf3+JWl8uyTLaZq4fYrJhSiV1Ui6VmVk3miTiq0wAM7rIn7pvyCmblL5PtRwERMad0DUb1PwGn9D4M4xd+h/cXfou15UWYt+5XoxdGzsnLXe8yB0fnAp0w6k6032VHvDryZvw8aRKSMtJogAmPatrGVYNk2u5VXlFF1Ty1dL5TMhITk2iUHBF+7LmGfDRr0gi33no91qxdi3vvfxSrV+VTHkrN0Pa1qiqUllSgsLQS117+V1x+2Qhcfc3NGDfuTfpbyCAvTo5Oz6YrSOpfhxJTBkmjxPZcTaUaLn0R/XXUq8bkuZA5/aQmp+G0QSPRpU1vrFizBCnJqcht0hIffPk0Pp46jmdND/0SCzXosqux00EH4K07bsOM//4XySm0Lc+MS+ygGbimkQgxJlq8/W7lj1/jb6+PR6fe/fDOvXfimxdfRFaLXNJNInPWk4jqi3tiHTumOvoOW7LUYiM6rGE9iqyuX8ZfItelsfnl9c9dUdu8OdY3v82bVbxoviIoHipe5hHwCHgEPAJbEgFPBG1J9H1sj4BHwCOwkRCYN28+unYdaM4I4oqgDDqTJr8gH1oRxEsmeewyV/p2RDXsos4lB6RtdepuyRJaiKAcIoL4sbeGhe1+y+I3/jI5nJfqRC8Rtc+HttIJxEijf8tqiDyhxXcqVU2lEGnAREI1VaNkpWRgbmUxHtjtJByywwCc+v5Y/FK6EilEhFRRhU8i6cqWuTjYmT1GsqivJnImO681Lvz3y5jxyWd49aorkJJNW6CIwKmsprFGWWhC+Hz9v/k4etAeaJ7XDB99NBn5hQXIaZxlPgOTaVnZjZBOZ9tw7qWlpSgvKxMQg/CFhSXIy22GAw7YEz//vBADdu+LK6+8FNdcy0TQW/S34JwpFMAv38hSF8RB0aPoEbETKDDFI/yKVOoI2pbwMDOlvJKIvAo/4ktxZ0Kkqrocu/Y4FKccei1++HkyHn//GvRpvy/OHnI7VqxbhHte/jvKq2RuVRUV6LrvQJx4802YOf4zvHPXnailLWO0n8uEcZGX1Ok3mgiimL/O/BoXvjEB7Xr1xuu33YAfP/4IaY2zaarkQScZJK4+JfPwbOw8rFz1VRLPTsdUtz46auPf2w4Cngjadr6ln4lHwCPgEdhWEPBE0LbyJf08PAIege0aAakIOszcGpZhiCA6LJoqgsJEkF2GcksWp/IrS3tdrurYH4OUt/gYIoiC2TOCwj5/CxHElnYGrh8hXtysXT2em851WWUZrtp5EA7tsCsyUtJRSmfI3PHlC5i+ZrEhaFZQ1c+tewzHnq17o2l6OholZ2Fp0SoTeeaaRbjui2fN1jCiuUjGXpUaoRZ3mWhiOZEMVbRNq98xx+GoKy/D58+9hDcuvwzNduxO48CsmQvxyKO34shBh2L16tXIzculg50TUVhUjEceeQqvv/4hFa0koBERenfffQu67NDJbPH65ptpuPPOe/Dr8jUmYCLpHHfsIJx99mlITUlGFd2MVVZejh1I/6qrb8Er496mg6rTKSOLiLRsP4mqY/LSGyGJK4yMmOYUzIVPmGKMKujsocYpRCgFZ+qwHqvy5Vt8ZtKvZe7B5DwiuOgX4y+QX7IS5w0Zg75EWI775B5Mmf0ehh98Ffbe6WgUla3D8+/fiqkLJiA1Kd1sqWvVoxdOuPE28lWLZy84B0W0LY5vXQs/EsvKuG+fVTOn4IJ3J6F1953wxi3X4cdPP0ZaViNSMBMM3qLPEn7CHkSmY9L7Y7+uf/UbPYs/FsFbNzQEmAg6lraGjfZbwxrap/H5eAQ8Ah6B7RYBTwRtt5/eT9wj4BHYlhCQW8MOJiIoz5wRlEHbgfiw6A0TQYKCUiWKiS5Quf97F6kRIoh8/J6KoHhx3UU058Z5Wj2X7uBRfdQqgQiNKvRs3Bpdm7bFnrQ9ad/2fXDN+IfwzaqFRrmitgI75+6AJmlZOG6HvbFb2564c8q/UVpZitXlhZi+ckFwWDTHtpElgvwKdgkoXb4Sx48Zi94HHYLxjz+Cj28ei+wd25FhImbN+AbPv/AsTjrxGKxavRb/fvVNc9vbkCFHUH8NLjj/cixZtopiJOCoow5B584dMHz4cZgzey6uvfY2LFm6nKLXokOHtnj26QfJZy1ee+1d2laWgCFHH442bTri8iuuxcuvvE1kkmwNUxRcxPhGtN5N2uOqPYfTnBvR9jQhuJgI4gokLpz5eulMfE/zPmPnw4UsCmbOt38l1tJNaqlpuPbzJ/Hdivk0ThU7QaWNUEE26qqixbjqpOfQuXU33P3vEWjdrDOGH3Q5kUAFaJzZFO988QTenfIEstObopbIp0a5eTiaqpo67tIXD//pBKxZtsRsD9Ovqm/G2yIfEHSUe2VBMTJzc3H6o4+jUbNmeOb8s7Fy4QI6MJpuiROmS13QOzpbOyTf0/Z/S0tnrz60X5cP1atr3Mu3TgRMRdDQQbh91MitcwI+a4+AR8Aj4BHY5hDwRNA290n9hDwCHoHtEYG5c+ejWze7NSyTKknWxd0axotmu2y2WIVJFHdBavVdqbWsq8Vn2zRusr4zgtgymoKy3nTRXP+odg5so/Zuixf8fGV8Jf3/0P99p0E4pc/huPqTB4kIWmC4AaYDyokcmV+8GI8NvBiHd9kbB7x6NQoqCtEoKQ2ZiamoDnK2eTFC0tMM+KDo/B9/wUWfvIXcdh1oa9PtmPrqf5DRsjlllkAVQV/h+edfwInDhuDNN9/HiSeciMwmPfDo/Tdi2LBjceFFV+Dj8ZOQTBUwBXQVfKtWOfj3uKcNSXTVVTdjKRFB5RXVOGbIwbh77Gg888wLOOOMi8h3Y9oONgYnnjjMEEGvEBGUQRVB8R6eK9+M1oZImKM770EVUik0D0KthuZiyByyou5P65ZiSfEqDGy7M22Oi32S6JyfV36agMWFa2ScbIVWUYTkS6wsWYbrT36WCKB2uOc/V+LMI240lUALfp2JA/sOw4Tp/8HTH45EczpMupYIO2LtMPjam7Db0MNx73HDsWrxArpWPtVJQP2TyDBXPFSDitIydOjXD/ucchqatmmOJnkd8MMnH+CNm25AemPaqki6THBJ9Rbb6CNZc48zdryrwgbe8l+K2MlfgutDUKjbhatbt5Yf2RoR8FvDtsav5nP2CHgEPALbNgKeCNq2v6+fnUfAI7CdICBbw7giqLmpLOEDgmMrgniBG7scrWvhaqFziQ4r3VArdGtYfpEUYThGstiOzcdRicl2/Yvl8Kh4tr86T/V/es/DcHLvQ3DteCKCVi8IxKxVi1l0WPSTe5+JQ7ruiSGv34TiimIkUyWP7vximkD86eLfjU0yYhqKflyOs995EW167Ij3HxiLr59+HllETPAzi7YsMRHE20XuGvMAHnjoBaQRyXHpxX/FhRechTEke+iRF5CVRSQO+WrZIgdPPvkgXQe/GlfTli+uCMovKMfNN16I004bjvvu/z9ccfkDaNshC1ddcR7Op+qXK68cGZwRpLeGac5CdJjJMClC/1tTVY4SIoVSaI7MAVUSoZJCrE/a/7P3HXB6VUX7szVbkuxmE0hCCCWkgnSQJqEIaFD/JH4oovJRLFgQRcSCBQIaUAQLCIgUFUURURHFCoLwKYhSpEVIgpRAEhLCbnZTtv5nzty5c84t7/vuZjdbMpfk3nNmninnuW/8fme+c86FChx3BZ6PVIZnKnVBGxbRqMzjtpHhsxN75YifjNvs2LvyoYzQOyiD1W0vw7kn/ACmbLMjLF3xNEzHwtLVt30G9px2GBy+1//AXQ//FH5899dgfN22WAjqcqt/3vqZL8Hubz4CLn/HCbAGvyBW6QpB6C8u/BCbGMn1aTVQDxaCNsCOe+0Dh73vgzBx+s5QM7oJHvzFzfB7PGeoejRtC6N8nFXUcl3yEkm5X+zOI2WUjJVHSjIpCRbzonrxoRJrjRQGeGvYPNwatnCkDMnGYQwYA8aAMTDMGbBC0DB/gZa+MWAMGAPEABeC3hSfEVSHq0DojKDO6PPxMsn0J6/CHOvSKxhE39dnT7QiiGbbdEYQzdWTF+WTIUYYSaWokLTiPk/nGckSLmr4Ep70sy/ByPOUOUfDibu9EQtBV2Eh6FkUSyZYCMIza64/+FQ4Gr8q9rZfLcRCUGtc/GB7uksG1NYyiIuGKtqa9KZzzoHX4+qcu6+/Dn7/xQuhcdedHHbxk0/BjTd+F97+dpwcXvwtuPaGW/CMoEr45JmnYiHog3D5Fd+Db377OiwE4bYu9DVp4ji47torYNXq1XDuuRfCi8tXwavN6+GSRee4LWOX45fEPn3h1TB1fB186qwP4NayD8Fn6bDom3HLmVsRRLnK+LAZXfTlskm14+DNO+0HtZV0IDUVU+gSfBkse+1FeKntVTh4yu644kfkEQpdVuHB279a8jdY3roG33EYw3GBUGLnVSwEnX38tXhG0GHQiVvw7vonFn7+cjGc/pavwj4zjoBb770c7nr0ZjyseyxuDeuCcdtPheO+cD5M2HEnuPrkd0PLypdwWxetCKIc6ApjkYR/TbhlDb/ERgcYTZ45B4479zx3LtA1p7wL1q1Z7baGUT7662J/6i3K2j1USv6Dy1UFI0kKxgLJNLAr0Em5KYA11fBhgLZZLphvn48fPm/MMjUGjAFjYOQzYIWgkf+ObYTGgDGwFTDAZwQdhoWgSbgiqA5qsYDQ0qxfDXNzZjcrzZ6a8sQ4a2rdF/I4Bm0Na2hscA7ojKBEjQDlWpYKJ8B+T/ON0/fVUXokUmQkjAsFrOV7D7Ti16u6EPzR3d4CJ+1xDHzqj9+Ce1c/AzXlVVBTgefH4LV4wxq47pD3YyHoIFwR9CW3IqiirDL2yCuDJCInJKUFxyICuje1w1RcmXLKNVfA3266FX5+5v/C+F33x0Rxaxh+1vyHN94EJ+DWsF/96jdwwgnvAqjbBX5w1flw4onHw1lnfw5+9/u7HXbFqhaYM2MS3HzzD2DVqlfgjI99Fh7613MwflINnPzu4+DrX/8y3HD9jfC+95+GmW8PP/3JpXDCu2hr2LnujCAuJlGunCc23EWSTtwGN7NxCnxs7+OgsarefTXNZ5Iwj6x4Bh7Hg7LfPedILIZJwYtQvAWMPgH/tQdvhsdwe11FOR3mLLxEzejF04HQx8/9OLz1oA/AY8vuhatvPwd5XY/nBl0Hk8fvAt+65SPw8lryUQHdeOj1drvtASd+9SJY9d/n4ZbPfRY2tq3Dj4bRV8M8/9jjjDwpvRwXsxueffKf8Nnf3AdTZr0Ofnnh5+E/9/wFquvxi2wJLthaPKE6dfkx+ZfkICSOaY0biQxTznIF6iEXYophyIBtDRuGL81SNgaMAWNghDNghaAR/oJteMaAMbB1MMArgo6AWbMn4telsBAUrQiSw6KVBX9Cq9JCU2BFldriGN14Dk8DnhFEs+LsFUGMo3s4AdYeI0iPLfoTq6ghWtI7RCALvRK2DEbhqpvZjdtDQ3UdHIyHRR89bX/43iO/huXr1sAKXPXy9LqX3daoxRvXwnUHnQZHT6dC0Hm8NQy3SUl8ji7xOSmVYYsKQXjOTSV+9v2jN90ELatb4NaFn4eW5S9CeVU1bg37B9z4IywEnTAfXn55BZ7x81Ogc51OPHEBfh6+HT54+ifguRdWwNQpeKj1AXu7LWKnnXYSrFvXiodC3+74fPTfj8PGjR1Y+LkG2tZvgB//+BY35Hed8HbYeadd4FPnnAs/+/lvob7AiiDisJy+BIYraGjVgmsjU7Stj74GRlvA2vHgZhp4NRZhOlBO4yQdjZ7OlqbaTBlW1tpxdRHp9Ap7xMd246fDGW+/DNo2roP7Hr8NxtVvA4ftdTx+Tv5++MHvzkOf9J0yDIcxX//Od8MxZ3wM/nL9tfB/P7gBazuYpyvw8LofZl9/udzn4hTlQP1VyPMZt90DU3fbE37xlfPhP3/6PVQ30OfjCUFXmKP0SC1tB3M3lWoJ08epRexejUtqqYeS4AYaJgxYIWiYvChL0xgwBoyBrYgBKwRtRS/bhmoMGAMjlwEuBB2LK4Ia3BlByUKQ27KDE31/Akts0MRTp7cioefmX7Q1bEx0WPQ63BqWvrKmy8mpMGeXzDv0JcUAKk7I1qYQQT3aJTetYRv49L7/A1PHTMIizSj3qfKNHW1Y0OiGvy3/N1z88C8ccPGmFrjhwJPhUDws+p2/Ph/W4WflK5Atlw2myIUnyV9zlnIUAakk0YPFkQNPfA8cefr74Y7LvgX/uhW3gI2qxULQ01gIuhy3i7wVz/tZARMmTKBaDKxv2wBXXnUt/AwPee7s7IL3vHs+vO+0U6B+dD2+V9q6BXhI9AYX/6af/AQu+8YN8MEPnAinnvIeqKkdhZ+P78Li0HpoapoAXzz/IrgVC0G1KKcij8sSCymaLXMko6CMqc2lFK+g4gpELGcLujOWvZEVedUnxxA2JAJKsTh46B4L4E0HvAcLSDVQVVkBL6xaCj+7+xvw8uplkZceqKqrh/de8nUYNbYBC2jnwcqnF3ORyhWCKL5cOh4/I9G2PPkcvPemG2DWIXPhbz+9Ef76g+vciiM6g4hzDtmQjMWenyGGZMnfo4w3aZfEhXrpkTVzlI4kGHoqzpcq76E0u6fviPWFI2b76Is0L/ekr8HCJfPo3z4VghbgeWCLFp3fv47NmzFgDBgDxoAx0EcGrBDUR+LMzBgwBoyBocQAfzXscG9FEG4Ny/hqWNbEVKeC2sqaVPd2vFmfj9cI7I3ihLKwpyUEbrlpLN54ZY5iqSVYv8VRqFhAU3yMhatKtq1twCIEru7BAkcnfgOsEv8j+/WdG6F5U5uT96B8dHU9jK6qhVXrX3MyyZifyTvnEufhksEbFpi2mT4d3nnhxfDaqpVw8xc+DV3rN8JTbkUQHhZ93FvgS+d9Fda+9iqMx8+lP/iPh2DlylU45+ec6+vrYGwDf+mqq7PTFUPo/BsaS3PLOmjDFULUn7LdJNh7n91hNR4m/e8nF8No3P7U0twC6zEWLaRBOP91aWrxxBO6AREnruAQrbxxQmdMUh6jGxorIjVKWBVJuePuqPJXUdFvgt7B2Pom2GnirrB+QzO8uGYprMdCWzmV2vCk6q7ODrel7l1fvhie+r+74fZFi/BcH35H4osCSUg/H/exMykWoWIj8rPHW46FeWd9yhXgVuOB03/6zjfhhUcfxm1mtI2NLvFELd9bqHPQ+Mb/QqTLHoRoliY9Cbb4U4twxbGGGA4M2Iqg4fCWLEdjwBgwBrYuBqwQtHW9bxutMWAMjFAGli5dBtPxEF4+I6gWt4ZRIagFZGuYTHV5chpOUUXnUyMImfz7uuw2WbAnqiXQXDyrEES2frx0IShEcB5hsUhyCz2RnVyKYAlFpL8s76DVIFgx6MZEud6BOuzTV8HoP7FxhSL6khaeWePbR4DUg7gKIkcd8vO6N70JGiZOhn/cchN04tlB9NUw2ho2/7g3w2fwc/A33vw7mNBQ54o21Vj0cBfmQ9upOvDgZFdiQH+uToOHINNqq6qqCiwMYfEEhe1dWNKiYdE2rShudVVlvIUrmWz4DkjrSSiIqx6RnJyxwzKqwngw1kbB4pETC8wD5cHvF/tk5/yygy48m0jePeHL8XwmUffgWCbssgvMnns4PHnnn+DV5593RRuJFOdDCeAlcgnhkozyJO67Nm6CvY6b7z4pT5+lfxBXZa1eshT3t9F7JesI7FriDTvxpXoRuYJZJOZHBkbAvXpq/Dy/vXJn4CHBgBWChsRrsCSMAWPAGDAGPAasEOSRYU1jwBgwBoYrA/rVsHFQR4dF47kw6c/H6yQznP5mj1rRNFVOT3RDK/Uok38+LJrOYylzZ9qIi9CTH4U8ZmmTmDBy0kZLA4JTn1RsoIu/biV+I72rXIiMceSrrAdX4CBEChcaz8dqDI5PffqL+9EQRl/BoosOO6ay1uKnnoAbvn8l/L//Nw++9KVFcPtv7sQzgvAT7FiZcgURh2ZPUTOQhOMgG/KKsVwaEpvy4zfHmWq+DhZ45I7KedWRvEunpRiy2ia2VZ+RCB+RlzhJ0fi5kIxwaE+4ODA2nMtu6MatceUVWAKjPXMRgC3IBiWxDfmiiwpQZCwKAhEW5bgvsLOj3RXGKqrxQHAsdFE2hHZw16BO3KCOw7hG4sZxVJj17yP0JFiRSo4ip6foVBYy5tsI1pepXf+1KE4yhi/z8/Db/ZfBSPBEhaD5uDXsokXnj4Th2BiMAWPAGDAGRgADVggaAS/RhmAMGAPGAH817EhcEbRNdEaQrgjSaZxM1JQvmdLJUzXhtDRroutjg3Y0sefDorEQhBNJ+moYzSd56i0ZpfNhP6IPvGJHspRpeB4uaade8yKqhSDIN7bjbl4sBgg/Ald/1BIpFTSYg3bc/jRx4jYweeK2sHTZs3iuT7srbPRg0cKPJCN1hsENUY7nqFQgIdxyIN+DGAlA+vwkZFoT+XQQ0kb+sOkXX9iO76Efjc9yxsR+Ir8uCqqcVk1Y4MZBQN8zWUSMkFFkw9k6p3jjWFQW44v73E7KpB9Bg4fYhd4VonqVhS1BiJSiFZeFiLzo4tOew4MBtyJowTy4eNHC4ZGwZWkMGAPGgDEw4hmwQtCIf8U2QGPAGNgaGOAzguZiIWhyVAiqc2cEddMJyfGlk0xqycTUf8ZQbER1hmhiTqhSLvQcOecVQXi+DUaSz8dLXPZEvayreKw8yyxvcULxgLJRLBXPmAPhiSX+49SiVQ8syZqwR9YIlXJONC6nwC1L+Gq6oq1nskKJEXQnG35SLG1xTxCsIRld6Nilg1oCuEuycAoRxk+BhdqklPsiJeMQ72cau8YGs8Kj960ZQ1vanCWq5NUQirddeUKn9P2G7ZB7zozvfsww45BRHye+fXwYQRDFnr4HwrqxFTNyerGUuNTPyrEkZwYaAgzY1rAh8BIsBWPAGDAGjIGAASsEBXRYxxgwBoyB4ckAbw17Y7QiSD8fvzmFIGJCp6ClTkRl2o8blfAcG/p8PFk2N6/jiTB2ZHqbLCcw86XGybZmH9n3CtxmRKuUuMAT2ktUzl7s/UypZMEomaYrSlr6VD9kpT2d0CtPYSbkgyOJVVZcykT06pOE/MZk5U5cVCG37m26hrvJmFXCCPbtSzmCjDuZT7TByhkIJrSWnkb0W2xDn6VnqfiI/eJg3DgiN2JLOG6LhAHO3q8skZiFfH4Swl0BSjBslrhLFvI2EuoSuurBB4s0zJkRopNx+XZJPGGTMh/fX+1icYrp+yuP4e2Ht4bNw61hC4f3QCx7Y8AYMAaMgRHDgBWCRsyrtIEYA8bA1syAnhHUlP35eI8cLSCwMG86GU9L3VxPpumeo5ymzK/jw6LRvgU/H+/iBFuX4gieJz8bF9jThc0s6xAR9RBIn5UfN24cdOCWrPWt66OSiHqg0XFPZWRN2YjEZ0BkUYTIWnPP45jXzogVPX1Pak8aiS1SH0n6TFtXCEKVVIIcTm7qQXyKRqOFXkWveG2RN+6xX/UuVnlP8cEefB/kMemXvIhvxvr2pJU+tSOs+xFSL9SJd/ankQiZvrT4l9YVl0jOxZE+gq0ka81QJD7W2sOFASpkLphvn48fLu/L8jQGjAFjYGtgwApBW8NbtjEaA8bAiGeAt4Z5Xw3Dz463NDdnfDUsPT0Np5jSY5y74y15SHAphMaFIAS7rWFkJO5jB+l8FES6lEFsSY0s6wAgHfxSWGNTE3R0dEBr23r0WrKleEAbzSXfOq2J7VDV4xXC2Bvj6R4Xo7gTR2NEnIbXUA3bkkfaCkjPyImH9tni2HwPIGinXlUjSNFphFAjerUssYWGWrtin1zTIgWPiD1JZN+v5OCPMJmJYnzLYu2+WanXdGrKsQAAQABJREFUZBakyRqBWCR/l2Qf/34E1NenK45t7oj6GnzrtrOtYVv3+7fRGwPGgDEwFBmwQtBQfCuWkzFgDBgDvWSAVwQdAbNmT4TaWt0alv58fDg1lUmpTg+lpThp9XZCKlvDqLKgK4L8gYlnX0ZtySEpL62vXrFFHXRHn1tvGj8e2tvboQ1XBIUh1CI/AuckmUVuHZyt833EljgRp6+PSXCW+3bJMkB+NqwJbckfl3HYs1hLzy/xsIwtJB8po/hexUfWM/l78P1n4YvLJFNEuiQ4Ezk/ie0lZ/GmNnHe1AgOm46wAlATcRI8i6gDbG86Ep4Gx4U7tk7HU2QeojdxFSt+0xEVY62BYMAKQQPBqvk0BowBY8AY2BwGrBC0OeyZrTFgDBgDQ4QBLgQdi2cENRTcGpY3WU9O6nlYMnHkeXk2Jp8AKr6MxTOC6JLDokO0+g/lMlElvbRDRKFellcuBDVBO35C3BWCEg6SJRiOnJRqNpIZx8I7NxQQ+Q+zl56AE0m4bjomo8hG7ElS2Afb8J2skmj2lJSqla/Jsiek88GO2BCNfDv1Jq0EWMTu6etYQZLQX9jL+j0yQnDiIe07CJ3R6b1FhpMSRJTplopVQjoGGSAGqBC0AD8fv8g+Hz9ADJtbY8AYMAaMgd4yYIWg3jJmeGPAGDAGhiADvDXscG9FEH0+vrStYTwcno76k1IqGkm/L6eldNN2rMYG574ZPx/vfIlDJ5UJO3UChdP29eZ7FR89mMs4XBHUkVMIopKDZMD2WmbI8kd+Cc+6PERfRpUsBJFvzUxjUgb+pRiSak9aPpbbrMnO3ZcSzu+LJ7eVSxTUkTY20mySlYwtBoorp/M6rpmOS3Yi1fcjduxVMCLtzTO01Ui98TFUscyO/jKGap4jMy9bETQy36uNyhgwBoyB4cyAFYKG89uz3I0BY8AYiBhYunQZTJ8uZwT1bmsYuZCJO01+5ZLpPE8h0xNvwWU/8Xhm/P+C01fD6Gqhr4Y552EEtvVlLJG7TF+lT898NKN41VOICgpB65Jbw8Q7syDjFWmhJ0XheNkozUJaWSPybaVY4sukrZklvdDbYVmo4fzIXuJTW/yITC0VTzi+smS+TlHMBXsN8/F/PSFf4p2tXGpRU2JIvtTncdJ7SoGiUamVeFZJoZbwxn7T3gvZDoSO8unPLNCfc9mfPgdi3CPTpxWCRuZ7tVEZA8aAMTCcGbBC0HB+e5a7MWAMGAMRA/rVsHHR1jBaEdSSOiyap+ohbTQ1TBaCeFrMd0JnTbxDL+keHRY9tgE/H49u3BlBbg7qZqMRWP3nTXqz0OmpbBIlRQjNKSgE0RlBORdzkaXkXLkQwXrJgzV8T1oqRhjMxoUepfRBWPFAiDxb9q1abbFfuYsv0Yc5iVTQ8kxaiZyerBOEaNiT708iEcIvBIncyZwB+kq6C8atSm1x3DCe5JLPmiKoRdahx7AXoge+J6Ppryz629/AMzCSIlAhaD5uDbvItoaNpNdqYzEGjAFjYFgzYIWgYf36LHljwBgwBpiBJbgiaMZ0PCx6Dh4WXVMDtXVaCEpPJXkq7sv9NRYyZfSn0DJhL4VvnlLjiiA8HLkBC0F09WVFkOahUf2cVZpGSmFLMD09uDWsqdDWMEaS/7S34rosK82VC1PaT0bgqCRlDN8ZpVKJoX44L7FSr9piz4Kjpy/Rt6rFGV/PdoLSfEJ/nA/nmbxzzuKB7cSPL2UZ6tmBHyASsohsYmyE4vjCjuAiJT58vGBJ68uzAvtY9balWvRGfIY2N66MdnBHtbmjGK72bkXQgnlw8aKFw3UIlrcxYAwYA8bACGPACkEj7IXacIwBY2DrZGDJ0qVYCDoUC0HbR4Ug/Hw8nhHU3UWfE/cvnhDyJFMmhzQNzpogkp7k/pSU++RRtNT2L5H3RCuCSFf8sOh0fM3O9y5lj1DGPd+C/UkudF6RnBHUilvD9FPloR8dLftKepQ+e6eiBEnoL1uyN/Ui3gUhfbahHnsSv+or9Mh2jJJIXCZgz6E9o9mzRlIp23Bf4oQyXydtenIcwXKP9WFWilWttnw7llIWWowimfgTfd4zHyfjJ0uJmCXL8uzjsvShLJlDsi/opDzZFxw/heV8LpL2yX7ob2j1+pprnl2ePDnqJC7ZT+L7p29bw/qHR/NiDBgDxoAx0H8MWCGo/7g0T8aAMWAMDBoDvDXsSCwEbRtsDcsvBPH0UhNOrrSQqTMj+rI2gbaGNUYrgtxh0TTnCi4/RkoZT94Dk6iTRpPC9yc9RlIhiD4fn39YNDsmtE7AxQfr/LsfnwsYoYTY9SW+LXmVTN2n0emz8iEg0ZOs2KtD+0nGkdiLxPUh6l+04ftXfRha0SxnXGF00ib0WLiX57mwVVJLXigL8eaXzFRKVpKrIEkWXqwp9DZDvPWMgTQDvDVsHm4NW5hWmsQYMAaMAWPAGBgEBqwQNAikW0hjwBgwBvqbAS4EHYOFoPHB1rBSCkEy1U3npNPjvkyE4xVB6EbPCPKjqH+WyuSdp+dJrW8pE3iVpdG+pAeLLeOamgoWgtI+/aKBeGOUYFnKd5HpWDS7sBX5cl/awrKOuCZQ6EQFDpRUoqETlSOu253FxCu71KGufJIVN+GbVCQFD69kNNIyPs8q9B16K62X57k06yTKX8mmOiqkJTPNikvjF3kWF+pxuLVoVCNrREP9DdD//iyYb5+PH+rvyfIzBowBY2BrYsAKQVvT27axGgPGwIhlQLeGTeEVQfW4Naw5a2sYUcArUGQqyJNd6fkUyTSYZMmps4/LbtOKID0jCD8fnwrh+/d9MDDUSk+d8Bom7eu03W9x5pRL/xSCKE/lgrPiu2aiLX9U3PbHUYbnKHXDpo2b8FBvgOpRFVBVVen8x3ZIWldnB7Rv7IQuPHC2tq4aKioq3DSePFEmVCNq72jHIlcXVGDo6lFVUF5OxSEHcFhq+ln6GUpGhPEvHyNyxqqn0FZ5EXxvn6G/fOv83EKbbByXywCII39EauvbEcLvK2ogWz4TEh1lInYikWflwf/GSaNvRYxZmmVlsoFhwLaGDQyv5tUYMAaMAWOg7wxYIajv3JmlMWAMGANDhgEuBB0Os2ZPgtraWiwY8BlBXdEZQf6UUaeIfvo+wpfz5FEnk76ucLtwIciflGb58beq5WMLZ81+CdPdjYWgYGsY+Qytw57OudmLn0OSjWRhjfUixf9Di6HEu+cHZVWVFbDXXrtDPR7u3YxfeXvgH49AdVUVz/d7ymBTZzvsOHUKzJy5M1RgceeZZ56FF154GQs9XACCsnLo7OqA2bOmwdTtp2DRqAsef2IxrFr1ChaVqlzqXkQeilcaiARxfUH68qSs/fTZlxRSGKX+k7yIl9Kf6quwjbDpo3zbLD1jSUPnZinCt/P9STtdcBTNQD79rCRXlEVi/GlkvEXNx/83Hr6VyEf8e1Qbaw0cA1YIGjhuzbMxYAwYA8ZA3xiwQlDfeDMrY8AYMAaGFAO8NexY3BrWEJ0RVJP5+Xh/ghgOQCaboZR76a00WaikjApBY8fi5+PRNR0WTXNvjeJPdH1LQoiOpuDS9jHaVn8qy7LoxtU0TeP7Y2tYxgQcKyU0Mfcv7kZCSQi7JOEx4fQcOzNn7giXX34pnqU0Dl548UXYdc7RMHvX7RyK0IufXAnf/PY5cPJJ74WamlHwve99Hy775jVQg6t+2FkZrGtdD1dcvgiOOfpwaG3dAJdf8R342rduhGnbj4/iSWZRPtjVlrItKJYwQt5AqpgQeGBLGqbvV/31riV0kRX58/viKSuO4LJ0Ypf1FLssXW99ZfnomwyzosRcApKFyqg4R7rwvaDMQfDmdH5kFDijSGaFIJ+cAW9TIWgBfj5+kX0+fsC5tgDGgDFgDBgDpTFghaDSeDKUMWAMGANDmoElS/Dz8TNoRRB+Pr7AiqD8wopMNqO5J46W5ppyZ61inKrITc4IIis6LDqcnLL3Ii4yiwC+TVZGWZ5pIjauSCGI/Io/8kFt9aUtxijSaVTtjTPCiA67JIkLQdiePXsafP+Gq2DM2EYsBD0PO0zdB+bsukuMWfzkMrjq6ovgQ6efhuhyuO7678MFX/4m1NeOijErVrXArT+7Eo484ghcHdQOX/nKJXD+edegn4log/EovkslygctOQ/pO1h0k2SpS2UGyZb7EWjEPfxR+4PLYsjXD1wbM3LLfjgzyU/y0b5IJBPRSD/jycuJMhQmGigGbEXQQDFrfo0BY8AYMAb6yoAVgvrKnNkZA8aAMTCEGFi6dBlMn34YrgiirWF1+Le/VwTRYJOTzsIEUCFIzghKfzWshAkrui+GSmaUh6etYfLVsEKfj/cjymqLZPFM5Dp6RPiBMSnOKys7lhGcWpMnT4BPfvJjuDWsHlavXgUnfeBzsNN242LXL65cC6ef9g6Y96aj8eyfarj913fArb+6A0bJ9jFEtm1ohzM/eirsv//esHHDJrjl57+AX//mLzChaQyXcfzlSsmU4ncqGVFoGYwWgkiaHjdJR8YlI06OJkVXEjCQffdKODPJj/KRNoXOfic+wkswFqOXQR2Yl9NW0rRC0Fbyom2YxoAxYAwMIwasEDSMXpalagwYA8ZAHgO8NexNWAgah1vDsBBUl1UISpY0dD4YzxFTM0RZPZI36czLCCesWHxpaBzrZq7NLcnDojVinofiCLWUea1mqzpqlbI1jHB8Hgy1wgk3S/ieNflOM5vFl4yIsuVI1KLzfujcYnp0dND5NdGFcLIgfWUFH2xMB0YjrXjpSOmLROWV5XhQdFnkowu5J0PGUSx3kbOoSQ/JhpXZdx/HppGDbHg/Sr1k+9FrIVdZfGyp0WbmJdVFTELYkBwpL5YlM+RfoujJb4wQYyeMpdSza4AZoELQfNwadtGi8wc4krk3BowBY8AYMAZKY8AKQaXxZChjwBgwBoY0A7w17Cj3+fg6XBFU460I8qd8WQULHZiP5AlkjMdJZFmvzhVBS5z8NDSOQfdlbmtYaO7PSiUDiq9yaYVSwoYSzprvpBW7oIXCcU3joKujA8/RacMVPCgIEyLTyLN6cML4xnGzCkEEia2iCby619zElYOgWDXCdCgpC7bxUATVO1+BCDtUAcIDpLlKIBmFNn5PEJJX8llGLiMDelD5ybcnPPlIykje10uZ6E+vyWwka2aAy2pJjPYHMhONkmi51PAWBKcOyjjtSOcB8IdFKv63KqDIr9/VH2ciqHUHggEuBM3DQtDCgXBvPo0BY8AYMAaMgV4zYIWgXlNmBsaAMWAMDD0GluDWsBnT52IhaDtcEVSDK4Jq3WHR3fjVMDcx9FKOpoooCWaGiT7PMXXdiRQAoomo5y9sot4VWfDhVgQlC0HpbNhe/NKTLs2S+2G2IpOnZCfeeWTSo5S6sBA0HlfcdEAbFoJSpKAjiUw+pRgh/llLCFpqI9EkZ0WlW5pDUsc5stTNy1EgcdUztpBPKcKJDellECJzIqzcuOJNrKcGo50o7qkH9uNrfY80VuoTnrLjpzBAVowWOUkKXYzLRsjoleFsnC8t5M/HUdsfl+okfx1nUpdkUPXcSuaQ7As+KU/2M3Du3xLion9TgnBPV6HD7P2iDuHoItd4US9qesNHSSx0sEG4UQJRri56si8pJeXJ/kDhxK888+KKvvDTtoYV5se0xoAxYAwYA1ueASsEbXnOLaIxYAwYA/3OAG8NeyMWgraBumhrGH2OnApBesm0UCfcqive8if/+WiMEYWh7ViNqRVB/uTP9yITLXnGbmJQniUBpFwRg70Geezu7nJnBLV3tGMhaH2k9T0SKnn5etJpbjKTTlr5FkkdeycEajxXkcSpaaWSFn2o7KJeXBEmKgioNDGdjvwWesNsy3fO18+as0zfI8dxPr6N7zFp6Y8uqeO+nyt78nFpia9NtwvF83MOLdWqWDxFhh76u4dxJF1KyW/7oVw6AsBnVsEohRdBsbEKrtDTJVAIYDpkwFYE2c/AGDAGjAFjYKgxYIWgofZGLB9jwBgwBvrAgJ4R1ISFoBqowxVBr2EhqCsqBPGUT2aTOq8sFiqeYsYTzmIWqHdYnN7L1jBcudDcsi5YuFCCl9TcV7PPsi48qaWDq8fjV8M2tWcUgmSQcZFD/OdHlAJNPkJ88FOyE7z0Q1TyvRBaIjFS7JN2vj+/sJLEUd/3yP4KeRVdGEH9+nKub6mOWmQfYkK9YHxpMbyPLbUt4ygFXyh+KeMpJUYJGFnd40FTOwVJF68IwtxkmNEQXLYZKYclRi+ANQeEASrwLphvn48fEHLNqTFgDBgDxkCfGLBCUJ9oMyNjwBgwBoYWA1wIws/H41fD6mpq3dYwWhEUFoIoZ54pynyx2ChoPilYv4BQzI709KWuRjosGq/m5rxCkB/BQYObzmFp445kEkCijkzek/64TxOx+tH10N3ZBRs2boxW3bg1Np5X8ZH0nx2X+MjWJO21FJLES8SkXAsoomEkj8b3z3rVkiXJ+G2FnKXfoHjXt+z7lrZkKdHVSkcmWI2skvyW5JfOLN+m7xo/7zwvMsakPuIgXnEjnCRxyX6evyQu2cdcJd3IhetGYcPo0pM3r77IhrYKZtfixE7xYYv0kkSoCXtDHRdmO7C9bC5sa9jAsm7ejQFjwBgwBnrPgBWCes+ZWRgDxoAxMOQYWLJ0KZ4RdDjMnI2FoNpaXBFUh6twmhOFoHBSF/bSQ+JpohZgSp6sR5Pl3m0NS8cniT+HLZZvzmxXHVNeVLzBgdHYpIyjfnnEaiAepVzha6idxotUfbKNIFXOuTg5NmO5AGNJJBBArCe/KuS3REqSZRWBCE9X+BbZg/hhRP7dDy42viyPkXyP/hgKoTZfJ/n6noQvX5bTduYJvBs63kjn2jm2fRKjU0k5ChunkAonwdOFIBc6Nz+x61OCZtQLBqwQ1AuyDGoMGAPGgDGwRRiwQtAWodmCGAPGgDEwsAzwiqBjcUVQQ3BYdN6KIMkmMdd04nB6KIWgsIAg9plPN/HESWm0NYzW3bQ0y+fjJWKmZVFhceswe5oaswTHgcZl0UnK+KF17JA3v8ijttqilDQqt0SrG2wEIZrQSusEjFM04aXnGs5BLEGt75G8+pfgst6N6rhFd/aV9Ogz4HtPt5OW4jGNLF2ieZZu0xekxOmLLY7bmWf5iDhJU9OXQJ4NxpJw5Ju6XgxuegKxdMVO7KBKzEnlkIEgw1Z82LPfGaD/LVyAn49fZJ+P73duzaExYAwYA8ZA3xiwQlDfeDMrY8AYMAaGFAP8+XjcGjZ7otsWVoufkG9JrQiilP3ZYNYQZILI5QHqsUVWsSHLXmRoL2cE4TS0OS4Ekb5YDuIj/cyz1Kyl5ccRGRaDujqge1MXuDoQissqK6CsoiIKFI6RYrGlRBU/MgJGiFTx6bxFwp7UH9tKFJET2vfmRxBPgqFnmLcg6A2SpXr1owmK9IpQKXvN1vgo4SiUDb1esZEIx5S5jyU5/nUFFpJTP9K7pt+ndn9fmosfPfuNY+yMQlCclbrCYcTS/k7Y/GUwYCuCMkgxkTFgDBgDxsCgMmCFoEGl34IbA8aAMdA/DCzFz8dPn36YOyOIikC1tTW4NYy/GhZO+fzZYDK2j9RCEKF4Eurrk7bpPn0+fiyeEURWWggqFD/pQ+KpjbZCrCKlFendxJhkPe7Moim77w77zz8eNra1wpL774cl9/0VyqkY5MzYNhlDpdgifyyIygG5U3KXgPiKTCIblbKc7lKOER2Zi5W0fR3JpK9eSCqXFIKkz0/fJ0vo/KQgVGRQeGSh16HfE67yMvV5SWDdUhySCcbXk0z62BZIXpiS5eKTDVwPb/Q7pTaHyQqG2ghAD7pilAicMJY6jN0GlgErBA0sv+bdGDAGjAFjoPcMWCGo95yZhTFgDBgDQ44B3hp2NBaCxuMZQXVQ4xWCKFmd9vmzweQwFMWaJDapT9qHfSkEkZS2hnESslUrxIY9ikOxJZ7moa3QIuyJHbmJLFDU1dEFOx9wABz8zhNhwk7TUNUF93z/enj4l7+EqrpRkQsEknkiUOxRiiaodxCcmce6IAnSyrY69kefhWe3fPctWUIOtEU95YB74V2wYQbaIz31snHiSwpGaZR6EuzwfsoIs0aRN1a0Ccx8Pn0/KM9z4cNKaFNhLiz4RClQHlEM/7eTdskJe3CG+OOgAHZtMQaoEDR/wTy4aNHCLRbTAhkDxoAxYAwYA4UYsEJQIXZMZwwYA8bAMGGAt4YdlS4E4aocmsjqtM+fDcrgVCsSespKEdZmY3x8sp1fCBJfyVxITjLR+x5ZruuUkrZsxVK1971xwQOn0DgJnvGGQ+D4878CD/7yVvjdJZdAZV0NDdgPGOXCIn/izRN1KepkZyuOOF/qkQcen+TkPwXP42e8L2NLHRfrsj2EKLZUdkIt+ZFCkMajVhoX6odiT8ZaSm7+uy40VsQJ1INJPVAjodLTq7z3rcKFIAqiZ1Nle8/+XcTjIKNeF4I4bnY8kxZjwApBxRgyvTFgDBgDxsCWZsAKQVuacYtnDBgDxsAAMLAEt4bNmD4XC0Hb4YqgGlwRVMtbw1KFIAruTxQLzV4ZJwUUTrv0CaEUgsiCtob5E2WOKjNs9py+E0riaS6CEw312Z+swOGe2hJCxkyfte+AiTNnw/uuuh7+/cffwx1fW4RQPDw6mClzbuKJfVGvO0JRm/4mcSjyLtaqQC1Uxi0fSajkJXpfJ7IQ6yP8HBkVakkmhaDs8gLhs+Ik5ck+R8uOX4o/sS/2zItbzE5yIPu8SzCol2YEd4Ug104JEEwyMfB9Z8jVkQ9M2Ee/a3IZheNH1AksEeThVBXFdmlhO8tUwVugFeUTR0r2RZGUJ/sDhRO/8syLK/rCT9saVpgf0xoDxoAxYAxseQasELTlObeIxoAxYAz0OwO8NeyNWAjaBs8HqnV/W/CMoPRXwyi0mw16OdAkJ+sSXJ4+y0ZlVAhqwDOC6NIzgkQvvqWf9ZS4gpVVOHnbyxgvaPUYSnq6u2Di7Dlw6neuhcf/9Ef47SVfQUrIVnD0lDVAIiOtj5HcNIpk50u0zS315muSUokdZZRafuLn4fvhdpiZ9CSG9H27QjofN5LapYzZw7gm3uKVNCQgLukvtaWPzRIuKb7FP7nYb9KY/PKlLf11ii54Rr8XwfMbl/eOUlK4rsgCa+sMAAO2ImgASDWXxoAxYAwYA5vFgBWCNos+MzYGjAFjYGgwwIWgeVgIasz5fDxN+mRqKE8/d5kU+jhf39s2TnXdV8O4ENTS0hpH50h+DhJbZH4/bCuCWqQTCbWogBJPsb2EFUPCMixQjZ8+Hd535Q3w4lOPwa3nfQHWrVoJldVVuDBIviAm3kNbdio5SQjqd/PcWkTuyaUhQWd5UrhoGU13kfgtxoda9RHN751AolJHPfkItsv3xXq5l4oTfLFnf/srFq8UfTInj7eo6R5UtMFiC20xLHwl/Sk6/pWyQ1aUUUd8RgHFBLs9Lh7LGSVYAflPiiCXFhbdb4EURXMX22LP/DGGlqXiQqvN7w1W3DBz+t9C+3x8yIn1jAFjwBgwBgaXASsEDS7/Ft0YMAaMgX5hgAtB9NWwyVgIwhVBdbX4+Xj+algYQKeHLC80mQwte9srbUVQsYma6JN56ji0RRn6k96oBJJcUdODXzObvB0c94WFMGHqDvDQb26DtctfhOce/hesffEFKK+oSgzVXwuUzIOgnEFao4WgMMeE+0TX98N2pVv7tuxWJORD2omAJXfJvvRcSna7RYCl5u7jhDN8umFHOlcIwqTjwo3gsgbi+2O9c4UO418q/T7xkhBkkbpQKXrSKUZbKZtcAXkqxY4wLmquJ1aUiiviZgSrbWvYCH65NjRjwBgwBoYpA1YIGqYvztI2BowBY8BnYMnSpXhGEBaCZmMhiLaG1dVhIagZurvwsOjgypvYlTIxDBwV7ZRWCMpykzexlBz91Q5Z9jTNlY1ask6IcDL2Hiivqobd3ng0HPORj8Oo+rGwofk1+MN3vgEP/eJWqGngVUzqOa8QJP4YKdmpHUXk6X6I9BHZbfHFdqG16MiSNH4/35v4KI7O9rGVS2Oi40ZECPEp3PaGI/ET2eLD7U5EF5lvSGCRUjHa6k10w25ZBqwQtGX5tmjGgDFgDBgDxRmwQlBxjgxhDBgDxsCQZyBva9hgFILiKW50RhD16fPx2btRohluzDBNbFXGLX+yq7rYJNGQlRaCFGspH9FXmcZtNwXesejrMHbCNnD/zTfBsn8+gKuCnsOCEOZZTgdHy1UoA/VMaOmJpS+RXFRXvEX+2C60Tscp5qv3FsU8mp4Y8N+Lvi3/vefzRLaRTeQmuxCkGGeBXbFk36S3a6gzYFvDhvobsvyMAWPAGNj6GLBC0Nb3zm3ExoAxMAIZ4ELQ4TBz9iT8alhvVgTpRDKaj8r0NKOw0TviunELVkO0uiZdCJJoSZ/RxDcWc36K1lYMSTSkEETiNBpXCnV3wsRZs+HUK6/H7WAPwm1f/hIWgNYhGGNROA7JXqNtO1lFLFntkxWFY/uO2F1v7mQtxSvfrnde89DETJ7OjzYYbR458zoUc6S3IjkiPxGVtFNMfkLKWl7+wj8ZReg8KKk9jFhy3EJGmoW1BpcBWxE0uPxbdGPAGDAGjIE0A1YISnNiEmPAGDAGhh0DXAiaCzPp8/HujCDeGtabr4bJXNMffF+mmTJR7fa+GtbXQhBPuP2MwjZtAPPz5nz57svVigpB3TBp9kz8ath1/Pn4r+JXw8rRJjLgI2DU2i8sEUg1VEqhWL7E73EeGjtsZVuqjepD/4oI/WX3eofO9mFSYYBLQMwpvxX8hWJDVvMQTn4TpA9/O+wltpPfDgsSdbno7Sd0QZc6WRVKDmP3IcSAFYKG0MuwVIwBY8AYMAYcA1YIsh+CMWAMGAMjgAEuBB2Nh0WPxxVBdVBTW+MOiy6tEEQEyJk6Phk87cyazPqovDatCBqLK4JoSptXCHJz2UwH/mQ7E+CEPF2WPAWXYcsQB+hs3wTb77E7nHLF9+CRP/wW7vjaRVCOXwtjKy7khGyIhnSeIwlX0lN9ZMPFr+KoJVLXch18G+6Q4qQXsVOLRGUhYUA4sUmorJvLgM8aLxbDQhDSGLOODfmamI/NdhhZiXHwOqKOC4K/xuidBxDnNC3JjmXSwWSACkHzF8yDixYtHMw0LLYxYAwYA8aAMRAzYIWgmAprGAPGgDEwfBlYsmQZzJhxlCsEucOicXtYM301DFe/0CxVp4sy65SxqoYkoVZ7XAwirMrEQ96zxysENcdnBJG9+pEe+xC55hRGk57gyEqx7CPvjra0QgnPBtrlwANgu9m7wu5HvQnuvelGuOub34LqMfVsWEbnA1EcWYsUloQYRHo/B5Im+4zke16OMp4srO8vwuEjXAAifgv58X1be3MZEKb57fBvJJTJL1Len7yjrMhiiTpqpqAi8MuP7Fc0GUZZgTZTJtG8fDfT49ZmboWgre2N23iNAWPAGBj6DFghaOi/I8vQGDAGjIGiDCxZioWg6XOxEIRbw7AIRCuCCheCeEKpjstxLpqc6MnslJ88HUzaqYdkiwpBckYQFYLchSsbdIVRGE+iZU1uFSkokcgkNRk9CocPGVdXRwfMPOxwOPqDH4Gq+mpY/uQzcO8Pr4cVi5+Cikr8ZLxzRTfKkL62xr4lEve1xxGSfcmH5NSmpxSX2MLdnVnC1lV5xM7DOh+YE60OEfdxQ3yIIsve91WsLTkLLtnPk28pnMSXZ15c0ffv02ebDh13l7w3UeK7CQt2kkNWrpERPUgdv1ex4af7DSOG/MbQFDbLf+iHe6Xismz7S5bMIdmXOEl5sj9QOPErz7y4oi/8tK1hhfkxrTFgDBgDxsCWZ8AKQVuec4toDBgDxkC/M8Bbw47Az8dPxM/H1+Hn46NCUPT5eJrG8BVNPKUbP5MrX9I4LeDERgUbsiKIZq4tLVgIcknINDb0r9MszlT63OPJLwfz7UJsKhmEyoScyk89PZ1QO3YMNO2wCyq64bWXVkDry8uhfNSoyNSPynmKhADa9nNIRuWc/IyjgXvAhL104wDYEDeRFXcF6LmKm2JAGGnHymHVkFEOzVFwWVFyk1yTBJf2b8Wzjl+beE56VGwMHbD3rBGSWVi/bwxYIahvvJmVMWAMGAPGwMAxYIWggePWPBsDxoAxsMUY4ELQsbgiqCFeEdSCW8O64q1hNMGkCZ5OKP3kVCoTUZUojqe3ohGk6sNWcEYQFYK8i7MRT54ic3Irufs4bWseoVeV46hdKLrhSh+sDrmR4IqlHrcVjHwFaOdcR8t+CUMe6C6ZM0biqjSPZ+e40M13EeQk0dWzn7HmzxlyX5wVCihjK4QhXR4uGSPMKt9rnj+xEL+hPxmdoPr3mc4pmQW9+fh3gUqnR7OkJWOKZec58H5R/NrJczR215S+FKIiXbEQQ0afZCgvscHC5eXTP3IqBC1YcCwsWnR+/zg0L8aAMWAMGAPGwGYyYIWgzSTQzI0BY8AYGAoMcCHocCwETYJa99Ww2pzDomlCGV5pSajnXji1FRt/OioywpM8KATR1jAf7CbUNJFOTvwCkAvNEr/84sSeTvthEM5IyjYuknzeKQ5DmLgTOUJZtAeHNToybrE0aUXGSWTkMPshYOeIbiIQeFxyiATCVojVPLTFBiFOvKafjKPo4sFvp/HCqOJDjHgJpaX01HPkO/099gyWSvGc5iJ7jCEu/UZEojjxIxrOJvz3kp8hWjlD9RcvYyMjcRodFh134zeV73nzNV5OBZ0NdVzB5PtZmc2FrQjqZ5rNnTFgDBgDxsBmM2CFoM2m0BwYA8aAMTD4DCxZuhTPCDocZs6e5L4aRlvD3IqgYGuYTCPDfLOlPBEnnU5tkoUJ9qN6H4tz2PiMoDL8ati6aGavsXl9Dfc1B/JW2pWHFF+alxRQMAVXCMJyQ2zsjUkGG8++NQ9BiW8ajLgQM9WJXVoiGgohdjLxpyKI84kKrkNhL/pqlChIn/QqMmfrAmgrjldiQ3LyY4g3lWlLdGn3+Zo0NinhchB7CP1oZLUJESrvj5Yfj+P4papiEeg3Qh6KZOjOfkJM8K493y4JvEVu2GMRn565NQefASsEDf47sAyMAWPAGDAGQgasEBTyYT1jwBgwBoYlA8HWsJoaPCOIvxoWfj7ezShT48uW8rzT10npw5clndH0lPTuidvSxjTS5+PzCkFqXcinorRVaBqc9BViWasYGZXOw5OlFg+RKMKEnjU7ammEUB71nJpu6CPlxhdEGIdUn9rK8s72vpcsFMmSfsimuEwRSXwYU3qElzZFLeXKttHIoY/eeidr8UW20havWTLSZRcvxcp/hhmFPR9XYjubjhKNDTbYDNjWsMF+AxbfGDAGjAFjIMmAFYKSjFjfGDAGjIFhyAB/NexI3Bq2DdRFW8Poq2FhIYgG1psVDUkieDqbnNTKJFrk0i+0IkiwPAWnHs90xTY7sk7Y1T6JVExSIzbCgPT9IkUYP1qh4xUxxJZ9q4dkrMwseIj50NgdNhzWN+DMBMI9dUVyb92TUwhWUelW0k8awezwuDlKFsaX+XHFv5NRxzV8hG/pt2Nw9MvIZNQZlOLN9yxtjZDvW7D85NFIPO6FiHSP0WKT1ptka2DArQhaMA8uXrRwaxiujdEYMAaMAWNgGDBghaBh8JIsRWPAGDAGijGwZAl+Pn4GfT5+sjsjqC5aEdQdbQ1Te56+ljaJVStuFZ7OilZ89+CKoAZcEUSz/6ytYTL95hU3ZOWvtwhjk2/xSxqJFaK45+NUIlFY6+wFGH1aTDS6nYckVALRNUHkT4pBJOVDqFGYSohsCUHesC3bf6gdXew36tMDMbHvsgoUoFP804MHXLN7/y5IdsYawtLl41ifdddMsrRJGaHJb3EryUU8iKUzRSVTkUQJWp44vsiQORR59rOYN7GS7Amf1RacPuPsPYviTPg5l5qbxky0/BQSKusODwZsa9jweE+WpTFgDBgDWxMDVgjamt62jdUYMAZGLAO8NexoLASNd2cE1dTqGUE6EQ2LB6WTIdNm9ZS0zdLQYdENDVQIws/Hpw6LJilPxdlWPcgEnRB5l6LzEOKd9YIPGMBANGGP6iZxNlS6odzLsEAU12/QjcPiU32whPsSgeLJCDwZiaIDfwkRB3VYxOFXharwnVVWjwIqoG1swzOVXGLkQ/ypFXnw5dwXCcf1oos69XRpRVKNkoKhQJCFUWSZjOtzSPrSCkGE41juPXgcpCNkSShSeBXPPMRzL23ljy+tZSv5rWR57JXMD+AH7pUTAw82A1QImo8rgi6yFUGD/SosvjFgDBgDxkDEgBWC7KdgDBgDxsAIYIBXBB2FhaAJUFtbi3+5EJS1IsifW2YPnWacPqr4DDQL4ReCmrEQFC28iUL6/mkiTx6SccPsQovik/80Xks4pCuD8igi9VhCxYdx1fUwZXQTVJSX49a6Lnih7VVo7lgfZUhlIrr4Ti3KXWUyhixG1MbPvmP9Rph2yMGw/9vmw+SZc2DN8hfgtosvgA1rX/PiYBR3grTvg6LTpTJt9a0Uofacofa1xTHz7/7IxcrPht4C94WrPF9kzd70zRHWj8C2aQnL/bvk4suy7EJc2EvaSoZZKPadFcH3UqQtAWIY+cuKFgN62Rjq/no5nCEKt0LQEH0xlpYxYAwYA1sxA1YI2opfvg3dGDAGRg4DfEYQbQ3bLioE0efjmyFZCAon1KWOXyaLhSe1SW3hQhDFDie0frEgmVmITM2OHdyPLwi14xN04qgOIBG1NLGhqx3euP2e8K45h8PYqjpXsrjhsT/AHS/+E+rLq10Bg33y3XmIPkHWU9aN7iULF4CHKCLUciRCUfEIMWi75qmn4Ky77oUps2fDkvv/DssXPwH//OXPob1tPeoRE9lLtm6wA3TjUYXOKTz/brgVatM9HS6tqJL8fVsfkbbPk3AOpFV7H5st9RE0jvxL7BWjraSVYEkuKF+m+Gyp6ktoUYB+cFNCJIMMEAO2NWyAiDW3xoAxYAwYA31mwApBfabODI0BY8AYGDoM8NawI2AWfj6evhhWh6uCmqkQhNuM4plqlK5OqJP5y2zTn9pKm7CiT9pxP6mNC0HoorllXWJFUNpHsUJHft5JX5yJ5KMjwBZ1SIHPbvyPfFa4pUrlTtHV0wU71I+HOeOmwN4TZ8IxO78ernrkdrj+6bugsaKGAzmHeEM/NMZuLHZQ7hXldK6PXBIIgHzSVe7iEFLz6+ruglHVNXDKd29wW8MW7bUXVI6uh9Hbb4v+MScZhLj1BClVjOmfhhsmupI43Bdp8Rhkl40Wj+wj7OX7zfYV4kv1FVppT2NoS7VhS2L5SJEpMi1RXWkt+o3Kb6Y0C0MNNQasEDTU3ojlYwwYA8aAMWCFIPsNGAPGgDEwAhgIPh+PRSD/jCAanr+tKLugIhNWetLU1p/ekge6BMO95D2ppSLJWDwjiOQtWAgqdrF90gtZRatnMnPyvZKt5B1OnVmKd/zTgYWZiTVjYJuaRlecWrnhNVi9aX1UECK7Hljd3gbvnnEInLnXfLjmsd/Dj575K4ypqHb+HQKLP9VY+JmCRaMxo2phY2cnrFy/FtbiFjJX8KE40AWNlTUwsXYc1FRWw6sbmmHFRjn7hzPqxq1nYyaMh/dc+h2s+1TAJa8/CMbuuqPjLPs9yXjD8YlUnz4XKk23snGcnf/G+/IO0tFYQjE1LrVKvSSvLHxv/GTZs6ww64zh3P14lJffVxy3Ct/ZXx5GfRfG5dkPrLzUnIY6bmBZokLQggXHwqJF5w9sIPNuDBgDxoAxYAyUyIAVgkokymDGgDFgDAxlBrgQdDhuDcMVQdHn41syPh+fP82liZpcOvXUwgrpfIxg9ZnUxiuCEKJnBIlvepLH5AQx6cXPgG2cYe7N90fesY+HNMvBw63dHTB/6r5w4m5Hw6iKCiz+lENrZzv80G3/epiLPRhmRWcrnDTtUPj4Pgvge1gI+sHTd2NRZ5TLuBN91FaNgnP2PR72nTgDi0DtWDrqgbX4/ORfvgObOrEEhMWm3Rt3gI/uOx/G147BA6AxDfwS2B+ffQC+98QfoQLPJ3JnEHW0w7ipU+HEr30LB9oDF++/D4ybM5N2jOFF45UxMy96D+WE9i/W0ugF52uTbfIa4qTH8RSf//sRDFmIP/Hi66QdPpNxQq32kh5Vw1H9ftiWnFgqfjQuS3gDoWhDD2Ev5FYs1B+hqRfGDX34vXwc+Q79+nbDqZ0/xnAU/Y0LvQ9sLzt3WxE0sKybd2PAGDAGjIHeM2CFoN5zZhbGgDFgDAw5BpYsXQozpmMhiLaG0WHRuD2MCkHJM4Jowi+T1nAQ/lQzOfVM9kNL6vnWoqVtafz5+PxCUNo27Unz1ZbEkGd6+hUVgRyAxtwDHZjPIdvOgM8ccCKsaW+F3y97wB0X/bYZb3BH8Vz8wI/hoVf/C3XlVbASVwSdNP0NcObe86NC0F9xaxh+0Qu3k7X1dMAJuxwCZ+13Atz/4iNw53MPw8T6cbB94xS44uFfwHosCE2sGQ0XHHIqTG2YCL/+z73wfMsrcMSO+8Ju2+4CZ995OTz66nMAr7XB6pVPw5vP+hK85azPwKrnnoVvH3oUjNl1h2hY4XhdUSsaj4xbn8IbvyuxFKniirfElpBsrxJtZfnJiuZbZOnZT74mjON7CzWSK5e08vyRPevk34EgSy8BSVyxxB+FK9zFfQFk/quIldbYihiwQtBW9LJtqMaAMWAMDBMGrBA0TF6UpWkMGAPGQCEGeEXQMbgiqMmdD1SDxaCsFUFSCKJJazip5mlsWi6o9DTXz0fsFIUlE9wOMbYRt4ahi5aWVpmBR2biNyHOmDwzUvAaISs+ywirZRPBdeGIP77nW+Fds4+Ehfd9H+5bsRg3b3XBm3fYDz6CRZ0bHvklfOepP8CEyjpYjVu83j1dt4b9ELeGNWAhiCpGG3s64cOvmwfved2b4NsP/hS+9tSfYefqOqjBw6S5vFAGh03eFb562Afgp4v/Ajc88Qe3dWwaFoUuO/Jj8JMn/wgX3/kTeMuHPwi77HcgNE3dEUbV1cGfrrkSnrjjt1BRjVvQ/E/Ny5BxWDwq4UJGlnyyAbOQ1BXuJz2rJ7VLYlQjiZJEovto0if72dYizfIiuuTT9+5nIjiJLJvcRO6eqIw/yubOchJ0gAo6Ei8rFr0pu4wBYYAKQfNxa9hFtjVMKLGnMWAMGAPGwCAzYIWgQX4BFt4YMAaMgf5ggL8aJlvDagquCPLj6XQ3r8SgiFImtzw55tUVVAiSFUEt+Pn4cG6sftNT5lCiSMqceqHeH09mOzKpwTN4zj/oJJi7/e5uhU5XdFh0NW7ZGl3XCLc+eRdc9tivoaF8FLzS2Qbv3YVWBC3AM4J+Bz94BreGucOiy2A9fllsv22mwUWHfwQqe9rh3688C/96+Wn414r/wOJ1K2AUrig6ceZcOHu/4zHOKlwhtBHTLoNK/Bx9Y20D3PPCw/DRH3wVTvz6RbDv246BGjxnaAPyc9vXFsKyv98P5ZVV3jDC0YuCGRAeZHWLMCNyLbuoRDyEz6woapPWpiXkTy00chgnu6d22mJkdpxiXrL16ktbIdL/N0AYySYbT1oflfQV9odTL39UhUfRV7vCXkeC1q0IWjAPLl60cCQMx8ZgDBgDxoAxMAIYsELQCHiJNgRjwBgwBnhF0GG4ImhydEZQHa7CSX8+PjlB1ymuPwlO8qkTPJn8EsJvi4XIaNVF7wpBYimexWMyY5b7aEUmW1SQIiSflFOHZwJd9IbTYOqYiXDr03/lAg1q6dthHd2d8PTal+A/r73kzg1a0dEKp+xyKHwMt4Zd9e/fusOiGyprecx44E8nriR6w8Q58LZdDoSdGyfDlDET4Kk1L8BZf70a2to3wPt3fTOcvudb4KYn/gwvtq52idG3w7oxzrO4TeyxNc9C54Z22PhcKxz7tc/BwSf+Lzz3+ENw7f+cAKN3muzwdMtcveK0PDIB8nuU8bJU3i096ZtoeZfgknpiLpt9RpIdYwQlvcJ2bO3ffTuWiyQvN0IRRnLIL4WxP7qzL7LCA5vcJVGyehEEHxyDrfmuOmqFXkSXLRWtPbcuBmxr2Nb1vm20xoAxYAwMBwasEDQc3pLlaAwYA8ZAEQa4EHQUFoImBIdFZ50R5LvSiW2hiStPhckuDyV+SM9ob2sYytIrgsgbW7FPLnlQeUOiiU9C9uaSHMgmztc1yuCz+xwPh2z/OjjzzivgiZblcUmADm+uwoOjK8gC/6zt3ADH7bA/fGr/4+E3Sx+AC3Hb2EQsBLFHzAy3iG3CA6E7MNtpuJro1Ne9GY7ecT+46uHb4EdL7oW377Q/nH/IyfDl+26EW567H1E8vnJ8UpxKjNSDBaWu9g7YZsZ0eNdFl0IHfkHs6/sfAA27TssYrrCiKhmb8CR9QeTJRS9PwUlfnuwvWytSicl96ZEHQYi3rKePz9Lny3xLjUQt1tBd5IKVflqjcQSrEm1psUll0pJ4aq8twdhz62WAt4bNw61hC7deEmzkxoAxYAwYA0OKASsEDanXYckYA8aAMdA3BvSMoPFYCMrbGqZT4TAKF19CmfR8m3AViiCST5owu4lxdEYQzchLOSOII2VNoCWHLB1FJ73q5IgXsXJ6VG/q7oJ37HQQnLnffPjTcw/Br57+P9jQhYUY/JT8jg2T4Uk8wPnRtc9BdVklYjtgVsP28PVD3wcbOjfBpf/6OXTgJ+KfaV4J7bga6OBJs6C+ogr+i6t7NnVtglPwvKC5U/eCb/zzFvjFs/+APRq3g6/OPR1e3dQC1zxyO7yEn5avxzOGZo7fCZ7A1UCLX1vuPj/f1dUNY8bj5+O/cSWU49axi9xXw6bjcDBhd2hNkt3svo5e9TL+LJ2iuCVYX17oV+HjpJ3lQ3TZz1Iyy7bMlkoG+jslCUfxyzgkUY0WhkKvbMdY51ndB0DGBSLsZEuTKOuXykAO+aWaDzKOvlq4YL59Pn6QX4OFNwaMAWPAGPAYsEKQR4Y1jQFjwBgYrgzwGUG0NSz6fHw9bg1rzt8aJtMqGq+swskfe7jlKB/HGvJN02D/q2HpFUGagUyZ0xKJJBpBJuXS95+KdS28YV3KFXE+vutbYf6sg91n3td1bIQJdQ2wvmMDfPWBm+FPLz8OY+lQaCwUPNO+Hr697zvgzdMPhNFVdfBy22r4xN3XwOMtL8MF+On442cdCivbmt3ZP+NHjYZ7XloMH7n3u9BUVgWbsFj0jp0Ohg/sOQ+qq/ArZK1roQkxdfg1sU/edSX8beViLAzhodCYVM+mDvjATT/Ds4LqYeEe20PNmB2hfsq20X4uHYc/umSbUMSSPElfmiUhyVasuU/3QoUgeSOMUxtfrtKsVu+y80fjj1E9S2T263tnjejFQhAoJ5XrJm394hFixEVkKh7EY+zGCZJaQdlza2TAtoZtjW/dxmwMGAPGwNBmwApBQ/v9WHbGgDFgDJTEAK8IOgI/Hz8RPx9fh4dF18RfDUtOSRPTW5wDF5ryS0Eg6aV4Wt249amhYaybQDfjV8PcSp14Nq324lnm2f6kX2ffhE8j1UtWy1sZgv8febLvwtN9WnBL19sm7Q7b47k+tVWjoGXTeliG5wM90rwcenDVUJQobt3qQXwPHIgHQ0+qbXTnCN2DB0K34gqg7WvGwb4Tdobx9Tg+3Fb2KhaE/rzyCWjbtAEq8FBqul7BQ6IPH78zzGqaCg1YBKKVRSvXrYH7Vj8N7bi6qAy3iOH/EYbXnloGp936c5iB28JaccXRS08ugTu+cTFsbFnnRhzzQsOPOy5EfCM/boRIsg8RxmJg3PBRLExK/HBJPz62kE4HIKhClnFyXiON9/NSYIiTaKRnja8nqSB8OcuYQY7ia2NiI1PxIDH8vvonbX9efkZhxP6MYr76lwErBPUvn+bNGDAGjAFjYPMZsELQ5nNoHowBY8AYGHQGuBB0LMye04CFoFqoqdVCECVX5rYZ4SQS545U4NArOZmkvujlKdPaJFa9ZLWoEDQWC0Fk5baGOZD6zLKRSKojvJ+TatItH6dFIML5xS/2yFj6SjsfHYx4rlRFblHhIPikP5HOfdU9DoMrpbDNG+HYTFRcXKOooiWNXM6hs6WzhrAHDRMnwkHvOQkaJk+BdatWwV3XfAcLQfilNadFBAZyvgnsXXHZR+RRntIVqB+dZYLgjKWXxIs8ii5ql1XcwQZ7IYm2fH26LTh6ykXR/D7JOQORhj2xE5z4JC9iIR58rN9OxuQIFDaiksECc0+NE6GDUWtsxfkRN68tEcmLjnHzfG6Odalj3BxcQH6U7Ob425zx9s2WCkEL8PPxi+zz8X0j0KyMAWPAGDAG+p0BKwT1O6Xm0BgwBoyBLc8AF4IOx0LQZCwC1UIdFoKaW1qAzqChy58y+kUR1sXlBIcNb1LIYGSoK9zzC0HN+Hl0nVgnJ7N+PyuOPxEsHJNHSjn7I2Yb8sKeQn9UF+Pc0jZs2e38+UyQnNFc6tGI5JsueRKK4xHL3CK9t+6EhPg1sXXPr4JNG17Eg6QnQuPsqbjQCA+Uxv/yskq/NUVKdIokl2pJ4iM4R18iNv4zLgYREE0EL36lz8xoz/dBbdGwnVinNWwnaO7x3bdROUkF7SNEpshiGaLe/Sh8C26TL/YtEcJ/TenfnZ9V2h9J+B2Lv2yMjiypL2aXxPemXzx39jbUcb0Z8+Zis7mwFUGby6vZGwPGgDFgDPQ3A1YI6m9GzZ8xYAwYA4PAwNKly2D69LlYCNouKgTVYiGouaRCkExtw7R1+qxTTW2F2OxevDUM1c3NuM3JmacnStkS36fm4ksLtdMT8sRUWlxGOeE3ztAdWQUKlPl9BNOSoGhFlYtBXa1weXjfjjN13nGVVFRJcPHIPaVALqggEJZ9SEh4Hg2XHAgtLiSGSqjFUl9HUrJhW+6l9WkJIyVazA0BXb6sF6/59uIn75nlIUsm9qKTPj2jpKSJT/+1eFpCuEvyJW/SFoUbaxRGdBpVLOSp9iHHHKfwnd8qYfJtJYMsT5pVltZkQ4MBKwQNjfdgWRgDxoAxYAwoA1YIUi6sZQwYA8bAsGWAVwS9CQ+Lboo+H5/YGhaMjCeW6emlP6n0tNgMt00FznI78YogtG/B8260DqETaDKWqBxRp8NeBoiiniDJiq4QwTL/rr4YLXiJn+UvlEkvGd15ioTijWKk2xJT8vIQkYq55Q7f/agyainDECIcl3imZ+QyaIlevIrHGM0uPVuxkKdYqvekRvoBwvn1xiugkp8aNzlijuMCBN7iaJEq7uPo2AdbCgfSC5xgRyP7CD8LLctFoTyrpLe8flj4k5gh2o8fariXbZWFNNngMUCFoPm4Newi2xo2eC/BIhsDxoAxYAwEDFghKKDDOsaAMWAMDE8G+KthR2IhaNsin4+XiaOuRChlxP4UuBQ8YbK/Gpae2GpG4jmcsouUniFWfZEF91RGaME72wrE0MKf6FwekslF1mzJFr6dw6CA9O6MIGw4LMoIx3Zhm2xYJ1qSyBVZiYq6ePm+fAmrZXyskXtkKt04l1jgSRSrLZd9lId7+KrIiYgkXfXNOft9agc4t9QpkCThBfoSOR2HPdJdMYGjSEVaebOkVzS3sjITjMYQz/p7Up3KBFX4GSXmWNJ3ynmyZX5meZ4l4zy9yQebAbciaME8uHjRwsFOxeIbA8aAMWAMGAOOASsE2Q/BGDAGjIERwMCSJctgxoy5WAjaLrUiSKeJMgklCU9lw2lyPhHsQz3lI0WDm63w/wve0Ehf1cIVQbg1jPfqSA4klRyozZN01nKcUOtP4hkvnhSnLUaQDU+2e7ACNG5cI3pG+PMAAEAASURBVLR3dML61vWxs3BNBmcRjpKjcDEhmvSjqMeBJB51GCdx9SkYlcQtZxJFi86kkXxjbiJzWjWU9CRR3VY12T4WC+Mo6AotUc6R+K5a1NEfJ6ZbMgohs+UpT5GprB5zXbeVDr+O5jxTIPKHt6QxiXMvBDs7BJCdsyVf1BAFGev7oTa/s2QsqgSWO6yz7cHcnHvyk/alEmq5wO7OcpaxFNWJSy1ChS9nP6Ge/WVpQpz28jJQhLUGlwHbGja4/Ft0Y8AYMAaMgTQDVghKc2ISY8AYMAaGHQO8NeyNWAjaJioE1brPx3dHh0WnB8QTzazpJk0rk/Joip12U0DSjZ9ib2hscIgWPCw6mkd7FhzFn8bKGTmSgzzJyMdRP5kjydJSzrwbPxnf1NQEHR0dsG7d+njyzz6jKFhYoKKAXORfyi8SK0IKJOPpOYhKESpJW7vsZIWSAwpaIlIOLFOJcuHLMpLxRIwUX54ilSXrkp7D3DkjKbZwPs4Cb0EhyL0lRFPRBotC6iXp38+I2hwhfp8Cdy8o7iCO2oIlO7xQJNvt4i/kuWoPKfCvy0VtylyfzMgX/Q1ZIgnLJZL8KpzC3dSbytiT9qXF/qSXfmZ7L2SVFT3t1ySDxwBvDZuHW8MWDl4SFtkYMAaMAWPAGPAYsEKQR4Y1jQFjwBgYrgyEZwTVQG1dXiEoe0KZNWn1keHUuDSWerrx8/G0Iggduc/Hp+arHMEXi4Rk1PafeVHZhrTaSmLdiqCoENTW2hapObIfX+2yfWVL1apwS0YjKOxjEUjjSwkAo6gwKktwmcJZUhJY2HAQdiEOWU0rgOgijMNGPLqVQeGbVK9eQLbGe2Qc9CU1xrtINAbKQ3BJM5H7g1J0rPUblCX747vfZ/dRvlFhh3puLA6OPf6jPrrRH+VZHukQ55jHZ3dPJ5TjF9okPfLlX+SXWXPOnYpaIZO+ReG2eolDRnmyHcf3UYX9qZdiONMPFgP021sw3z4fP1j8W1xjwBgwBoyBNANWCEpzYhJjwBgwBoYdA3xG0GG4ImgSrwiqr8PtWM1QbEUQDVSmnOkJsNLAuiSC9TwpZk89blUNS4qtCCJvPMkmW/Ut+ZDUvxSh0jRWs2EUI3pwRdC4pvFuRRAXgtSbttJ+pRwhmnQ8sk5LBR88BRYHjBoop0IKXVqY4b5z7aosPdCybgOUV1ZAfe2ouPCi+cVOYVN7B6xZ2wbbThgNlRVVzpHg2L9iOQomQKti8NI8qE8J018qeZCHbu8toT42I4xcJOcoPe3d0LMRrTp6oHI0bsEaVeHlLZ75KXfxkn5yfg6H4bo39EDXpm6oGlcZQiUVqYChllZjVI2qhLrtsTj6HK5Mw777vWFxqHpMBdRMwoPVn2qFnsoeLAiVIxW4fYx+x260+kYkAwooYagtl68Xmf+Momba+jj2kxXBR/ntYpF9rLUHgwHbGjYYrFtMY8AYMAaMgUIMWCGoEDumMwaMAWNgmDDAK4KOgFmzsRBUqyuCunBrmE4TZSpKEplohhNdQdCwBUENLhCopzxa3KqLCEZnBDXiiiCsC2SuCJLihJuUew7juJ6MmsnoebjQjFHhiiA8IyjDn9j5fjVH1oqOc+G7MigeJFNBixyfIpKKi6fiZgSgB7lxzzIs6FTA0UfNhba29fD3B/6FBa3OyDJ26PodnZ2w+26z4aAD94O77vorLPvv81ExKJ1l5CAKQuEwILpzHmUImrDDxWIyJqwI3JNXzvRggYWKNGN2rIfJB06AyrpKWPXYGljz8Foor8KVN6lLxiAKcubJsEu/IS7MYBt/0xMPmABjp46BZ+94Abqp4CRweroiEPkiwzLoeLkDjvn5XMR1wd0fvh+gFj2hqntDF2zz+gmw/9l7wgv3LYd/X7YYKsbSyiDnhO0xD/pPYpPXvIuyLnRxjlGiETjfrwyokEfRFYssOHsOFgNWCBos5i2uMWAMGAPGQB4DVgjKY8bkxoAxYAwMIwa4EHQsrghqKOGrYeEkM+zRpDKUEA35E9aQJCk3EJ4+H9/YEB0W3YIrLhzUn1T7cXQy60vZO3lLS0mnUmmpH7bF3FHU09UFjeP5jCB3WLQoM57qSbgQSRKssWTcrviAMNaonbbURrPH0UWVDHe2DYFdMcI1HPcTJ46H7179LVi9ejV88YtfgZdeXiVBXDSXKcLb1m+CD59+Inz842fAeedfBD//+e1YGKyN/CP3UQEqzIfeSXShwuliAclJ4iI4kK8SP+4N4RjIfxnWUqb9zw6w/1n7Quf6DujY0AnP/PJZeOTSJ6GyEVcFOS/ilj2ITPwxJIqJD6JHzvLp3tANB1+8L+xw6Pbw63f/Djau6og8Mp58CLarpROmHjsFDr3gAFhyxzL4+2kPwajdqtEfR+zZ1AlHXXsY1E2ogz+d8VfYuHIjnyUdeeT3SqOjy3nGJ8chiaMTxaIhWfrSX0dSx57p100e5D2of8VzBhxJpZyL39/S7axcs3IY6risnPtPRoWgBfj5+EX2+fj+I9U8GQPGgDFgDGwWA1YI2iz6zNgYMAaMgaHBAH817HBeEYTnA9Hkv6WlGWhFEF0yjeRsadKpU0qdojGK+4wRoBQQXN+7iad4quxm7ByvG9tSCGp2Xw1jQ8FqBiTn2F5UL4rYsQUjWaZ4afljiOxQRKtIGsePg048LLqNvhpW4BJPBCklq/Q0X3MgH6onOfYwABUQOrA41dWBK7awcNKBT4pWVUl/K90WJQJSLu3tndDQUAef/fTHobllHdxww49h7WutXIRAPR7FhJh29NEDy5s3wKLPnQafOvsT8PkvfBlu+dntUFNXQyhHXlkZfTFLM8KOu/x3QjunuF7E4+CECeYzw3aOIMLjf1j3w0S6YdSkajh44eth270mwIPfeAheeWgtbFy7CdqbO/iMHgxAq3OgC60onwrkAFcKUdMVxCgs+uruxFsFdnDxEx7jQ0GgogZXG63vhkO+sR/sNHdHuO2dd8CGlZtc7B7iEH3Q9jkHRv7aF3fA2/5+JIzZfgzc9v/+DO3r2qGsGtWEwEF2buiAWe/dBfY7cx94/Kan4LHLn4LyauKIc3Ejxnw34e+Ghl9ZWY5/ebsdCdy/i+g9RWw5W74pX1TikXIP4UjDeLHidxLKPFeZTfWSqTbhkGHAVgQNmVdhiRgDxoAxYAxEDFghyH4KxoAxYAyMAAaWLl0G06fPhdn4+fgaLALV4d/mnEJQsgygU1F/GhqifI1Pl6DiQgLOTXtwNQvhu7FCkfXVMInH02HxxlKdOotcn4TQrWc+noKiUpZnuEk324m/HvyC2bjx47EQ1A5t+NWwaBYeOxdcLIgaGoVb6lWm9WKhemqxP88rNRUCmzZ2wE47TYUddpgMjz3xDBz1xkPwq2YNcNed97l+w5j6qChSBjNmTYOx9WNwW1gbrFn9KqxZ8xr6R4c0Xqy+bMBixp57zoS5cw+BF55/HqZM2Q4+8YkzsBB0AdyMhaA63CpIGWUV81yGWMjY1N3JPh2S8yY8pVyO/1WVU/GDKj3eJQOlJ9Vs8AygxuljYPTUeiys7AGb2jrhwUsfcUWf1uXrYf3LuNqGsBsAGnYfA5P22wY61nXAS39bhcUcPP+oJirAoK/apmqonVQLrz7aAqN3roMph06EttUb4eW/rYTOVZ1wyDf3hx3nToVfnfBb2PhSBxb6AMbt1oA5lMOax1/DIhOdIdQJU46YCId++SBY+ehq+OO8e6Bmt1GYq3CHrGA+NdtWwVtuOAZal7fCPef8HTa91s7cEiNEBeL32nc3qKupgeXLV8LSZ19wBSEaivzu9d8BCqNLilqE44HTM/KJQpIz/5Ga0iKI9zumrl3DnwErBA3/d2gjMAaMAWNgpDFghaCR9kZtPMaAMbBVMiBfDZs9p8kVguicoJaWlowVQTJl9Wni6ayblMbisEdimfTGENcQHE93fV3WV8NClNiqFZeQtO+3pPSiPqSVHpOfK0WhQlATFoI6qBAUrQhKR/ej6USd4kZTdAZQWBRpVMkjQHn60C+hFj+5GH5441Xw1re8Gdbhe6qtr8XiQhm049k61153A1x/w8/c6pnamjrcEvZVmDlzBtTX1cEDDzwEF15wCby0YiW6wbODcCXN+049Ed532sm4KggLLVi56MRzgrbddgJ85rMXwM9u+Q1+QY4LQZKdZOvGj7dRuAJp2phJUI3nEOFpOzxWBJVhAYQKIc3trfDf1tVQ7goU4VjEF50H1TBjDBz0uf3w7J56GNU0Clf0AGzClUDkcvEvn4FHLnkKqraphP3O2g1mvG2GW41TXlWOz274xzcfghf+8JJb0dPV1gUz3jsN9n7/7rBmyRqYuPs2LvXKulHw8NWPwRNX/wcO/uo+sONhWAg6/g4s3HTB694/C+a8fSY8devT8G9c1VNWgYWg1m7Y9Qxc7fPRveGhbz8Gj1+LZwDV4uHS0RBlJBV4kPWRl70BGvDMoT9/4l5Y+1QLr1xCYBeu2iovr4Tbf/1jmDRxW/jrvffA+0//AjSMqY4Ka8IAOeUrlhB5xCPd4ktxriXdCOIjnXFsZ43hzAAVgubj1rCLFp0/nIdhuRsDxoAxYAyMIAasEDSCXqYNxRgwBrZeBvirYUfiiqBtoxVBNbgiKKMQhDNNOY9G2JKJqsxJRc4zZu4JRnXckmKIrycZ9WlFkDssGqEtzfi1JowdTnT9iKzxJclYro+Ta61H0AoSssC/9Cdwzhk5fyinM4LGR4WgVvx8vF9wIjOHcwH0JvKEWwdweJrou4sRiiM5WwsiAkaPciwEPQw/+tH1cMIJ8+ElXGVy5TU3wOi6ejj55Hfhl9564IOnfxz++/zL6KUMD37eG6ZOnQJnfPQDsOzZ/8K55y6Cl15a6Yo0u0yfCj+96VpY8+pq3DJ2k/N/ysnvhmnTZsA555wLN//8t1hAigpBLq0w103dHbBH006w8OCToLF2DC6kwaU1DocM4cocaj7w0hNw4j3XwOyacTgsXhXkIKjjUSIOuajAL3M1zRrrVgTt89E9ob21HR6+6jHo2tgN655vg9f+0wzTjt8R5l54IKz+92p44sdPQw0WjPY+fXdox9VDd5z6Z+hY2wHd6zph+im7wBEXvgFaljfD07c/C68ufg2adm2EV/69Bl747Qo45NL9YEc8I+j2E/8IM47fGea8cxY8f/cLcN+nH4SyStxqhl//6sQC0d5f3BX2ft/ueP7PPfDSva+47Wf8EojZqLSIuR/4xX1gxoJd4Pcf+Qu88rfVvCUNgV1dnbChswf+ce8dMGnyePjDH+7Ed/QpGDeuLvLFfpxP+j1EPwLix70g7BNCL6fRLulQJP+OSJG1csszsOYwZMCtCFowDy5etHAYZm8pGwPGgDFgDIxEBqwQNBLfqo3JGDAGtjoG+IyguXhY9GS3LaymFj8fj1vDsj4f7086/WlqcorqZqgRk+FkVun1fcUTa1TT1Df+ahi2W/CMIC3gyMQ4jEg9iRNqUOFdYs2ibKRf6CEcrwhqcmcErcNCEBc7JFq+pzAW45JoiZWH5Qz9PKkQ9AAWgn4Mxx9/HG7f+iWc/L8nAdTPgOu+fS6cdNIJcM6nvwC3//YuPC+oCta1boDtJo+Dm39yPR4W/apb6fPSyythw8ZO+N/3HAcXXPB5+N73boAPfegMTG1b+OEPL0Yf70Ufn4dbbsHDovHMKDdgSiF6CZJrFxYvJtSMgTdM3hVqKiodLVTAoKIanVdN8BdaVsOfVz4B9e5wHRIyA3SnpoyMikHd67qhfno9HP2tubARzyv684fug/aWDqioK4dNT3fAUbfNhamHTIF7Pv9/8Ox3X8Djf7rhmF+8AXY8cge490v3w7O/fJ6O+YEZJ0+HQ887EB697nG4/0MPAzSh8NUeqN4FV/TgIqODv7kfTJ83DZ6760WYcuBk+O9dz8E/vvIobgfrwtVAXCDsXt8Frz9vT9j1PbPgNjxUuvkJ2hIo2eogCLfX2a+DfT6yJ/z+w3fCinvwIG46mwivblxJ1oG1sUUXnAMN4xrgnw8+DN+79mdYXMODhpwv+Q0pExyBfhUawznDm/x7ydIJxp4jjwHbGjby3qmNyBgwBoyB4c6AFYKG+xu0/I0BY8AYQAZ4a9gbsRC0DX41DA+Lxsm/bA0Lp6MyTU3SFqJYK5NmmvBn6wXhtFg9cKtysCBABYTeFYJSG7BcCuJfsk1nkUT4mSva3xrWSlvDMEceUjiypDf1IBkURwjSf0oBgGVcCLrxxh+7Lwl9edFl8P0f3goVuEXrkx87Bc4883S44srr4LJvfBdG1492qU7athGuv/47sOqV1fDZz13oVgS9+tp6uOxrn8Vi0ny4/PKr4TPnXQXbTxwNZ5/1fjjzYx9G3ELvsGjMm1LHAYUjpuN0emBp53rowqKHFIrc6cuIrMOyzFgsEDVW0gnLaTZ4PJFj0mNz9A61cMSlb8AtWxvhL2f/3Z0DRH43PLER5t11JIzbsRF+d+qdeC7QRuhY2QH7LtodXnfCbHjsxv/AP7/wKFSOL4dZJ8+A/c/YC+750t/hxT+9jOcHYdYUBn9gXa2dcDCuCJrzjlnQgwdk9+A5SU/e9B944IKHseAUbf1CbGdLF+z7pV1hz1N2h9+f/hdYdT+u9KEaUXDh7w6HfcC5e8Kcd82E333oTlj19zWI88aK23rakRsKX45jrKJCU6KgFpOLGH7XSZZRTg6iQlRaGyRlnRHGAG8Nm4dbwxaOsJHZcIwBY8AYMAaGKwNWCBqub87yNgaMAWPAY4ALQcdgIWi8+3x8nVcI8mDYpNmo3F0Tbzrp1ZZMaEXra8QuxDjP7D4uBDU04ufjUdaCX7uiMOEEOAJH7rgXIkjlo9JZ+Fo/L2ormooF45vo8/HtQIUgmqynI7G971E9iG9fK7IQRQiVKF5bdEbQE7gi6BpXCLr00ivhyqt/DJXVlXD2J07DLWAfhEsvQ9lVPwB6j3RNmtgE1117Bbyy+hX43Oe+DMtfWgEtrZvg/M+fCSefciJcccU1cM5534Gp29bDZ87+EHz4Ix90haCbb/k1rhDDA5KDK8oOH5249WlO41Q4c5/5MLqaPjNPW79IH61zwoLHIyuehg8/+FOYM2o0ilGHf+LCVjxYZBPbtCqoboc6ODIqBN39KSwE4SfcyWbjk1gIuvNIaJrWCH/80N3Q8mwbHvzcBQd8fQ+YuWAGPPrdJ+GRy56AivpymH3qdNjng3vAXz77f7Dy76vxq2LkgmN34xlCh1yyH+wyb2f457cfgW33mQhT9p8MD1z6T3jmJ8vwHCD6ahh+ia25C3b/xHTY7yP7wD8ueQgW/3AplONh0u5CV3KV11XAEV8/CMbPbII/nXkPrH28OSoE8eCoiDhp0ngYNQrP3VrXCmvX8hfbPBfoSt5uKJUY9Cz0m/Nx1h55DNC/iwXz7fPxI+/N2oiMAWPAGBi+DFghaPi+O8vcGDAGjIGYgSVLl8KM6Ye5rWG0Iqiuvg6am7O2hvGEVaatsQOeZruu6rRFiqzCiSsIIIzONYnROOmhBRPuAGEqBOFV7Iyg2DYjiuiyp9iidWHi2/9n70sA9CqK/GvOzEySyeQiJyGQBAIaDjlEznCsEFAguqIr3tciiqjgeqLExYCoeACirrpeeC3CX0XuG8IRDjkThCSEGxJCMpOQTOb8V3W9etXd771vvplMkkmmns573VW/Ovr3PnC7trsfSSlf0WYLQSkUUTyNl6do8uORVrwWIaVMwh593tiSCkG4NQxXBJ188klw9dXX4UGy70BnO8Mf//htLA4dD6d/9otw400L3Ofln3l6Bbxh1o7w1ysvg1dWrIBPnHoWPP7IkzB8zGj40HvfBj/64Xfw4Onf8/YyGAuXX/4TeOc73+G2hv0Jt4YNTYpJ8fgo+w78Lvsuw8fBJ954HIwYMtRthfJHRfk+snIpnP3I32Bq7TAu9jhihAMdnZNgHWnoVCwEfecQ3BqGK4I+twA61uGSG6y/tC5phdm/ORgLOFNgwbkLYfEFy4DW2Rx/3WyYuP94uPXMBfD8rS/hp+K7YbePTIf9T90Lbv7CAngZV/JU1FJhios4XWuxEPSDA/Cw6Mlw5YlXwdBJQ+GICw6Fdtwqd9XJ10MHnjFUWV+Jn5nvhClvmwiHfO0AeO6Ol+DmD98NdTvhyiZX69L8a0dVwRz8algbHmx9C8ZrXYGHbtOFP+LOTvwqG+4N++NvfuS+xrZw4QNw5he/hV8Qq8FDpIkIR4b3S+O+s/duFC1f44Gsud0yYFvDtttXawMzBowBY2CbZcAKQdvsq7PEjQFjwBhQBnhF0GzYbeZ4qMdPx/e0NYwsZSrMXqIJvRMqQrWMljuvcqCeTnNpCwwXgujz8bIiiFdRiB0/1T/1qZcXR1Aaga31LogiCRaE8ODqUaNHQZv31TBCi8+sB9WpV2nFaPEiehmLPlXDrcWLFrozgk4++URYs6YFrrv+FhhSVwtHHXkYvITn/3zyU2ficxXssftucPxxR+LKoDo49th/g/XrN8CdC+6GDfi87fa7Yfny5+D3l/0UDwivgxuuuwWTxi9gHXEIjBs3Ec7Cw6L/fDkWSvBrY/K2JdN4BCs7W2EVrgYahvZ0teFLrMOXOAT7tClsOC3JcWyhpThJGtJ12wKxyDIcVwTR1jAuBN0J7VQIwqsbz/aZcPgOqDsY1q/aAMuufRYa8DPxuxw7FVYvXQM3nHo7dOIXxDpxBdGuH90V9j31jViYuRNeuWcVVNYmK3kwZid+cv6Q72MhaPYU+H/vugrWPtkKMz82DQ78wn6w9OplcMen7oPqUfhLwty7mrvhbdccBUMah8CVh14D3cMxfzxImn6f9IPrbu+CKcdNgkO+/mZ46m/L4L7zHsbDpvlXSKs4OvB3sw4Psn7g3r/BxIkT4J6774N3v/dTUFdXhW4op8QRDdBd+b9g5jtPJ3b23J4ZsELQ9vx2bWzGgDFgDGybDFghaNt8b5a1MWAMGAMBA1wImoMrgpqwEFTnikFyRhABZbIuBQGS8eQ01CqO9DJ9DTHUk0sxasnFIf1qGGGbk6+GkVedDmsG4s/PVGWaq0bxtX5bMhIZW9D2npHR5+N9RLmZ+N7DXMKe+JYnFRXocgUIZGDxoiexEHQxnIjbRZYuexZ2nDQBKvGA4lWr1sCFF14C115/u/t62CmnnASnnfoRLDzUQhV+3p34oS9Z0XXZ7/8M3/neL+Dd7zoOTj/94+4Lbe3tHbDilVdh8uRJ8I1534Yrrrja/R4cg1ip4fjOPL1RZpIf/1BoLCglBRrg/6HgnvxexSwcr/jt7qqAYZPr4LDzDoSNLW1w+5dxaxh+xj05IAcPJAKYiV8Em/XB3aGyGgsyOObm5S1wz7cf4C1ZuOqnY30b7HrKNNj7Y2/EQ6XvgpX3rQb6zDwt5aFiE20Ne8v8fWHKYZPhqvddCxteakNhBez/1b1h56OnwD3feQCe/fsLUIHnCrWtaIfdT5sB+562Jzx22RPwz3mLoHZyDR/Vg746sMhz1M8OgaapI+C2/1oAq59oce+Bxk4Lh+iw6KF48Po//vEn2GHsSLjyr9fApz79NRg1aihSE3KgzIRy/zdThBG5PbdPBqgQNBc/Hz9//jnb5wBtVMaAMWAMGAPbHANWCNrmXpklbAwYA8ZAlgF/RRCdKyNfDaOtLXTp1JRm93q5uT5qQ6noVarFG9HJUzB+BC72yOfjCUmFoOy8WWzFFz3Vjy/tqa2etOX7ogn9qIJCkPNNZslXoCSWn4l61RbhfEzcEz95T7ci6Le/c4WgM886Gx5+dBFMmDAJrr1tIUxoaoAheF4QdFdC68Y2WP5iiyt0QBsWPADlQ7Eg9HoXTN5pOB4mPQTaENOCn2o/9OB94OVXVsDdC550mU2bOQoPNkYsFYBQ4oaYPqWHAu+igpDTuIHJ6MIxM1x02nPFJCeuhPZX2912sJqRGJ9qOOIC/XduwK+LrQMY+5Ym2PhaBzRj8aV2LI6LtlpRpQffA20Pa8fzg2rGVblikU801aU6cXtYd3MX1OxIPFGhqRJXHHVBx8ouqB5f7Vb1VOIPrgvBbYs74IT7j4I2XEl0/bvuhKqxvCKIVh+N2acJDp73Znjl4ZfhrrMehOqRaEtDSm6tG9rgZCy0nX32f8HatWvhv8/9Hvz5L9fADqMb0yEpA3GL+0EhyJFL7l0ABnh3pon/+RG7IqxnZs0BzoCtCBrgL8jSMwaMAWNgEDJghaBB+NJtyMaAMbD9MbB06TKY7s4IGo+HAze4rUL5K4Jo7DIrFx54UhpLUxwqilY/iAf/SX7IY14hiCbxWhDKRmRL31t5bfWkLd8XTcRGjh7Jh0WvfT3JwZuMk1lOIcj3ppmEUs9LAslKfFvi4InF9+GWrj/CCSfN4UOd/3w1DB1aB7W1ePYMmbt8sEBHYLwcZ9ikEgELkhYVTvAiHBWN6MDpavqqFfbdOyNDtBOXDoy3sPSX+HCOkpu+pMSE80g6+GAb6buDotMO5d2FCMSkMEqefLDM1W46u/mcHSoU+fEIJ+8C5TQ2vxhCbSejghHFpBiOB4rBrpgdp3V1ohHThsHwacPhRfw0fFdbB4bDbWO4LWwofuFs7D6j4YWbXoZOXGlUgauUoAJXHpEp+ly3vhW+/tXT8cylE+HvV10Dp5/xLZg4vlG5pfjeJcPlyJSalHMSkCqCMYkLQbshJUJ/7IKz57bFgBWCtq33ZdkaA8aAMTAYGLBC0GB4yzZGY8AY2O4Z4BVBx+DWsJHp5+ObW1pwe1G8IoiokNkotWXqylKdgPoYQimOrMq50kIQumpuwSUgzoU/qQ9jsM9ScWK8YlkT69EjitxB1lgIakoKQa9jIYhzUftyxqMYjZPvIV+a5ogFhicWPwsXX/INOOatR8H88y+EW27Bg4xx+5criKh7HoALHPtEEOGwUMKFEWrzu2Y44l1Bhd+cK2wQxCn9m196YL0fnrT67n0N+VAN9QjrEDg+joO9ICBpSSBCzx+JXDGHtViKSVFikxabgnGiFu1cwcy5Zd9cgEna+OjqwBVEHZV41hCFx9VDVFwiNf4uujZiG78m5o78oTRcbPKAbdSPG9cEY8aMhBdfXAmvrcatY5W0RS8Zi+OYMuRYqPAuGR/pkrYv8pA8xoQ/lItFvt/A0DrbAAP0OzoJt4adN/+cbSBbS9EYMAaMAWNgMDBghaDB8JZtjMaAMbDdM7AEVwTNmH4kFoJ2wBVBtDWsDosvWAjCw25pDhpOU2U2KrSwNpamk1eE0WQ7ciLGhc8uXPbRNIK30MhXw1wuaTLZiOwsBYS+cdJNFqTldBinXrSVGqKICkG0RWjkKFoR1A6vr8NCUM4lUSUGQXyPqlepyEJ3+VLG4BjIHG81uPpn6NB6PCx6LatSM2pIFhIrVTI2uAuehGIXABI5cZf1IxFSiyIXARuMZm9cNGEJGotDV2mRjuC5RJPGymlQjmFJRECSe+zTRzPGl6SckIpM6QVQbuIOzxfiIloCcA9WJhKXkRj40fH/iGJfkmLmSWj2xSrNzPV9tTj24YF1pGCHdt8GGHArgubOgfPnz9sGsrUUjQFjwBgwBgYDA1YIGgxv2cZoDBgD2z0DXAg6DAtBE5NCUD0WgvDz8YWFIJpU+jNPf/JNdPk6nq3yNNS382lljC+RQhDJ5IygcN4sMXwranOkWMoVFM7M1RgSnHrRlu+DvLkzgsaOgXbcPkWFoBDJY5eovBaFESLjXKgnE3lCaT/ExZkrzmnQNeVP76YT/6qTQ6B5iUqYmbwHLo6EftmrL0NbMscVL2KnWvGbVwpiuENQYu4lqWXWF+k4uru7mNRKrtQ+Kuk4cPw7EyP2xz3Pl4tDK51Y5qMIyxG8lVDswLsnXIhLocFDJKQlEgS4IMxTAE+CSw7iMnDlOoLAjuOGhGnDIaQvv6ZUnRZcNbJ443h+VNEkLjlx6ZR4xnYloFtMVZRTkTxOLMbFfcHH8rgvuPhZLi62475tDcvnxaTGgDFgDBgDW48BKwRtPe4tsjFgDBgD/cYAbw07yq0Icp+Px1VBLVgIokIDTTJpGsOXTjBFwk+aoPtX2CsoH/gGURu33CQrgij6pn41zDmnAoNcbkB5oyKMyqVFuQxvHA6duEWotbVVakrOG2HchBxN3TYyiRF4UqFk4TMWRlWstMSG+u74G9fgW1oMoK67/CJEGZ7T4gEaO45wRDLwxKP/yFOl1JZSxjp/UBSAq3NJKFQinkxS36SJhpN200aMcR5QmA3OklKFJQqYc4krSswVvnwMJULRUhCmLG2Ssp7emWJ8+7gtA5On6NlPfk+k/NRfg+QR6q038BngrWFzcGvYvIGfrGVoDBgDxoAxMCgYsELQoHjNNkhjwBjY3hnQM4JGuU/H0yfk6bBoOSNIxx9OQFVOc3R/oqk4X8p4kSjG9yPttBCEhYrmFtz+hGZsSfc8W/ErHqKnVBQcLMT63mKNK7SkK11whYgPxhCKF4VM98OCgGSTRWXqGwKNQ6VyjZlJx2H8IgPHoztaJY/UUVagqpyWH9dXC7Uqw0AE5uAs9o19OWlLYdm6n+7ZskhYSIuT5EIRW2mS2pK0hFgemPKvA401OS9DnGWefo5+hgTUCL6Zjyq36OTbW3sgMUBnUs09yT4fP5DeieViDBgDxsBgZ8AKQYP9F2DjNwaMge2CAd4adjiuCBqPhaAGVwyiFUHZQhANNzv1DCXhNNmfkjJZob6IwPSwaKwSNDf7haAii2ykACnVimDlSXY0ml04KufL2SKCfDk13tKwYpkKVOUlwl6zZTO1UjBjte+3BJ+HYV1SkHIAvMnqFTF0zsQ6EPph0nYhInEhnlID/3dCxgQQJzFY5M5YOjFIPfetJeUZPxFKS+MQgnsi8xL3zERKeXBblGono9AILPHl4ThUE8rVA8eT4hT3KANcPxebYD+OlwPplUjG2CsjA/cDA7Y1rB9INBfGgDFgDBgD/cqAFYL6lU5zZgwYA8bA1mFgydKleFj0bNhtJn4+voEOi27grWHJV8M4q7B44Wcq019fRu38qS1JYwuW+VL6hDgdFk1eqBAU1W/IfXTlR4tAmS4XAtiWp9gRJC2g4HSbDot+eQ1U1ldCXWMjVNXiZ6SkwORGG06W80YqY/ezDa00vs9HKJV8WRrjxLeTixLHEcjTd6C+BKqxuCV2sdz10ajILl9D3nIs0iBpIwmXg81NxHPrudCmtMQf9/1CkP5iQ4wLR6LEhe+J2oJ2uOTmY0QuMt9CbFVXCi06fRbFF0TWr2jsua0wYIWgbeVNWZ7GgDFgDAweBqwQNHjetY3UGDAGtmMGeGvYcbgiaASuBqpLVgS1QGfw+XiZsoZE5EsZ09tJqPgiO/pSVxOey9ONBYyWsgpBkle5UcMSgFjT05VM0I37THhS4OmiT4HPmAGz3nosbNywAZb/85/wwiMPMcZhyZJGQPH9kZBcLpGnNQWnUCnjZASxXLzoUxBa5BGdaCgVLaLleRaZWGr2JMlqFSfDTGN5Km7maXyPrJdaG9uwXlCMyPOTCZYrED/hSPzyX1Gmapnn2NeWk52PDxnWX00Yp5QFIwlRFDu0Dj1bb9tigApBc/Hz8fPt8/Hb1ouzbI0BY8AY2I4ZsELQdvxybWjGgDEweBhYsgQ/Hz9jNuw6cxwM9VYEhVvD8qec+VLlLjshzZu+skx8US89IwhLEUVbwwjP/sUn9zg6TfazBRLNjCbRElGl6kG2CVGMbmhv64DdjzgCDv/wx2H4KPyCWPtGuOUXP4GHrvx/UDO0wTngaOKT46tnlqt/1ZRqibc8TE++yFYx2srzJbI4XkmrBBzbiK/sU5DiNVllJl008NedkZgtxC7rMVdC8MSnutaW/+ZFqhFEkus5FfoozdGXplDXCDUaTVqsD1GhB+Eilub3S3vKtzHpwGTAVgQNzPdiWRkDxoAxMJgZsELQYH77NnZjwBjYbhhYunQZTJ+enBFUh2cENZR/WLRMZPPIKHcyypN/9UR2VAgakWwNoxVB2Ym9ToypAKNlF47qijzokr7kVXxxKSiLEB9qScjOje3QsbYV3vLR98Nxnz0L7rn8T3DNty+A2uFYCMIVTK4QRKZuKDoeabFXieZLpa3xpJWnEQ+CoSfh8uSMKdaIf0FIn+3Yp8gEIzoeJ/cEk+pyGmQfMp68eVSwjow4Ct8TKv1AOX5Z5FlIMolINOI7zoLkYlLovkcFedBIMZw0ISIvYrG9+POtxKfo5NmzF0Hac1tgwApB28JbshyNAWPAGBhcDFghaHC9bxutMWAMbKcM6FfDRkJ9XT0Wgujz8b37ahhR409ShapSk1IfH09q6YygRiwEVeBXw1qSr4aRT9+f2os1a7mHRQYCOBHLJSd+qnUopx7h2aabnDgoF3q6ujpgzLQZ8LGf/goeveE6uPq756He98XtvIjZOOVJfO9pXcsXohvXpRsG1tjaKooUuSmCeT4jCDoIfMQhU2XaCB14+SrCd6LS0DDuiQ3ixSQRiSa2oL5AfdbycL2TkVeNSi2Jo9JEkkJVUxRLfLC3cP2Zb9OzJx9t7YHOABWCTsKtYefZ1rCB/qosP2PAGDAGBg0DVggaNK/aBmoMGAPbMwO8NexoPCNoNDTgQdF1yefj6YygcFKpU9E8PlhLFrzSIw+jMl7FI32JIxGoEMQrggDPCFrnz6vFJHpyXAKmntFZWjiJRqJT88iN68YrRCQrlHd3wg677gofvuSX8PiN18M/vvMtdhAUgzRdtZQ48UhJLrkLJnyKDxlbqOWeYhglxYI8rC/z7Xx5XkaSuY+jOOwjXxtWQPKjhbF8P/l4jh9aKeOoFbPEVfw2yd7PWSKKGelj7ySjK5bHfUbl3yVOqKWoqinlz8+PfJTCqscwmvW2PQa4EDQHC0Hztr3kLWNjwBgwBoyB7ZIBKwRtl6/VBmUMGAODjQH+fPxhWAiaiIUg+mpYvDVMJqsyFY2noDztpJKAaFgiPWFU+3keCSXyoq1hOmmWXMR3+NT4vlfBFNvyuh/CcTmJ79x3RY+uThg3cyZ85OJfwvKHH4Ar5n0V1r/2GlRV10BFZZWzI5xffJAxkRe9lAuVUSuWSz9+hlaSr3hQnmJccd9nRaL5aJLFl29TnLtYCTr0rqW7OILgxT60Iymvi5HCTj6OvYa+uKfxpBWiymMxtpEs5Cm+pV/6Seg8jypnrWKEA/aruPy+RC8XJ3h5xnYi35LPOIe4L7kUyUUvzxgX93uLE7w8i/yJvvTTtoaV5se0xoAxYAwYA1ueASsEbXnOLaIxYAwYA/3OAG8NOwoLQWPdF8PqsRhEW8PK+WpYX5LRKWxoLdMleuYXgkgjV5EX0dNTpsiELc+WvSqWW7LqBd1gIWjEpMnwjnPmQ9P4cXDjpZfAcw8/DOtXvwZtG2jlEhWDJJrYcXz16iApKn/iL5j4SV507NoKcTLyUMo9scnmo3qJIhgeQdab+Mpq8iRZtMRhtEQT2yxeNPRkNN9LIfMQjPc9qGfxxVqVF7UEH+vLtY/tyunLL6vUey7Hj2EGPgO2ImjgvyPL0BgwBoyBwcaAFYIG2xu38RoDxsB2yYCeETQqWBEUFoL8oedNfWnaq3JqiSRvQqxI3y+3CZ8WgvCMoOaWdckWL7ESj9IXH7FcpsmSjeBiO5HLCMSPyHWlE2aGtZ5qmHXMcXDUx0+DYWOGQ3vrWrjqez+G+//we6gf2ZiwgLHRDX2Cnq7Yo3jefM/SEWNGJA9hhqylLbrYY6wXXPaZj8zGkPclsfPtyL8iNVoRmvMOtWGPETI+0UlfI0he4fsUvI+jdp59jOlLX1beqe3miqQRrLX1GKB/h8w9yT4fv/XegEU2BowBY8AYiBmwQlDMiPWNAWPAGNgGGeBC0GxcETQ+KQTlrQiSgRVNe0Wvk2WSlJqiFnkiGyoENY4Y4eyb8YwgPutHLHyvJPP7FJUvt60L1UVfDpNVFYImLyoTnxKTUe4Q6x3GwfFf+DLs+Ia94F933wkvLFoES+++A1577lncIladFoJcWs6cC0nsYdPvxSMm35J3URwdZR5SRsuFFmUjz7NgiyKF8hAtsWNpNm6IUJ/ZUhAheXSKohbHCv2EPcGESMkx32sYg/1xdL6rXv2ILEaIPI6Uj+NYWpzMMqH+whZZSjZ+O0T1Ty8/96zvgY7LZrz5JPlc2Nawzce4eTYGjAFjwBjoGwNWCOobb2ZlDBgDxsCAYmDJ0qUwYzoWgmaOd18Mq8cDo1tamqOtYX7KPBX1JdJ2C2BwPkNTmnKuIk98WPQI50ILQUUe8ydQhOY88ie9cZlDzwNSSyoNqXdEdHXBuN12hY9e+it47vGH4fKzvwirFi2BurGjoGpILRpyLLbh0XEO5JMu6YUjz8+QLeTuYygr8ST6/nhKVpx/6DGOJ9gQle2FvrQoFtuzf43C+hhF/mMkx4yREjf2SOgYSzLFaYvkRVeejxhbnqfYqnSfVoi41WZl/QYoS8kizljkpeOZdusyYIWgrcu/RTcGjAFjwBjIMmCFoCwnJjEGjAFjYJtjgFcEHYcrgkbg5+PrSnw+noYWTybD4YqWpph+O0SpVjC+nrRdONltbMTPx2MnLgSptW9Vqk1RigonXA4in3RxPtJjmZO6oHjDQtDYGdPhIz/+JTx6I30+/nxUdyVAthNrjsoqihJmEI7cx0pU/ylol0YysZc4Pq7cdlE8Xy4x1Wc4hqxekZynliBIw/hSVmzP49LjtsWXevdx4jfUCjcUTdo+Ms5CMeQn7IWetRf7UI22yvOk+PJa4aiyNuVkJlabJ0PxPjCewse2OVYqBM3Fz8fPt8/HD4yfk2VhDBgDxoAxAFYIsh+BMWAMGAPbAQNcCDoCVwSN48OiG4pWBMmEqudB+8hw+qU9xvhI8st6tyIIC0FUCWppXiviNDChfEu/rxEEQ8iwDJM6cl7YWv35HgTZDZ0dHbB++Ssw9aiD4OM//y08dN0/4JoL5uOZQXhANJ5lRBffOZ5Y0lMk8pTMBKNyD5woSaeX5qYt1W5Ky4/j+/bl5F90sVxii176hCOZ4rUlmOyT35cgxWdRX+wFJ/3wqdbhajAdk9/ybcky9i3efFxeO7bLw/SfrNysJOKWzU6i2rN8BmxFUPlcGdIYMAaMAWNgyzBghaAtw7NFMQaMAWNgszKwdOkymD6dPh8/AVcE1eOKoM1ZCKKh8OSTp6zxxJV17rBoWhGE6BY8LNqfhcvUNbYUkkRPfR/jywXrP1MsAdMON2hL2IiJk2C3w2bDhN12hz2PPhru/P3v4Lrzz4MhTbiFjQ8xcu54LUs2WuLJH0oannQV7saxqa4kHthOoCJVvWg29RnGYf+xjGJIBnk6X09twYiNL6M2X4KSPj3VQlqCon5eO7Sinn+Jhdr6WmpLHL8lGLFWTLEfsYmfvm2sK7tPiRQ6kizL9lbKWW+cGHYzMmCFoM1Irrk2BowBY8AY6BMDVgjqE21mZAwYA8bAwGKAVwT9GxaCRieFoKLDonmiWXIumgzNn5LmzVv5pBgfFXJCk5/GpqQQlBwWLWjyJ+3Qint58fJwKiNvbJX6dUG4R2tTOtvaYI9j3wpzPnMm1NYPh2ce+SfcdOmP4JUn/wWV1TWJK4kcbqHSONRKI6RiXyIeUmXS8DGSaxE2tu1NX+KIb+n7PkQnMh8T6wST91Q7bRGOfRSV09RTaMXy0vHVQlvqT221JVrCq5R7eT4En/dU+zxtmTIJmnHmK6Rdpk9vZOVa9D+Ocs4Mqv/DbIMe6d+FJ82dA+fNn7cNZm8pGwPGgDFgDGyPDFghaHt8qzYmY8AYGHQMcCHoaCwEjemhEETU6Kaa/GkbSXkiWmpql1cIEkt6duHkZwQWguiirWFyOK4TFN78CbBuBRNpfr7qTHBOIsnQWFzwLvwiWA3UDB2Gw+uGjrZ26NiwnqGoD2wTl/nx8pF8hhAzl29HOklKx6bZhy2KUuQnRIa9vOx8ROyT8WLFWh8TatiTyNRvKBEvIvX9iY3opB8/82z832WMZ7bEq2SgqFBDvfx3rhbZVn5OWVxJCYfOgUiGOapURBnEuH7JKo1gjf5nwApB/c+peTQGjAFjwBjYNAasELRp/Jm1MWAMGAMDgoEluDVsxvRDsRA0KTksOm9rmE4gpSChp7jkTTC5cBFOlxWn3sKCBX11jD733kVbsZqGIz95ZwTp5FU9EpW+19Bv3MsjPmtNEpHSSDqxQsWWLoMKPBsIG6RhTkinGTkMw5O7+ApxAiGtcipS8SdPXy5tfrJ3jdFzuSi093vqhaXZsQgzPlJR3AqZUf/hWLJ5y28nxPnc+lF9uWQb/u40MrXCUmboSe1jG47Co+bR5VmGVmGPrUJZXu7CbIhMuKCgzlHSx9GEl8hFGvdjuXOGwiKc4OVZLk7wm+MZ5xD3JWYsj/ubCyd+5VkUV/Sln7Y1rDQ/pjUGjAFjwBjY8gxYIWjLc24RjQFjwBjodwbKWxHEE05/2knTm75e4sf34cuoEMRbwypgLW4N47OYeQrvFzjiKRaXHjQr9a8t1WpLYqtEWr7G98Fyuvv5iJUifXvR9sfTL7JwNL8cpRE0E5XltXgkoomzLvKiRRWyDFFhTzyHT44TR1Nf5fgQj76X0nZh1mKvz+wbJd/sU6KUKjWpJy3qsHXpvHy7ctqSSznYUpj+zapUpHydsusX2vKxg09qK4IG3zu3ERsDxoAxMNAZsELQQH9Dlp8xYAwYA2UwwIUg+Xw8HRZdhwc0t0BnJy9/0WkiTzxl+snTZelRIEL6/dLBCam+Q8tuXBo0wn01jA6Lxq+GBZdnFTfRqT/N1xwVmJ8h6fM0vkx9KNaXaZIs9W1VF7aK4vooiZHjD0XuYOnkSVaMFhvfT7ntuJwmPmN7wiVLpFJVNq6+gxQUNeSNka2OMVuOYa34E6QfMU8WBUtjCDarz4ucRYX2Ye6K9lHktwinFtzqCSd+BUdPukTOPb0LTiXcErtYviX6RTnFsQc6Ls63f/tUCLLPx/cvp+bNGDAGjAFjYNMYsELQpvFn1saAMWAMDAgGuBA0G7eGjc89I4iTzE4wwylkuZO10JvvQyNwIWLIkFqoxIlza+vGqBLhW6E/7LovbiVsSlmBuoyM8CjXWImRexCONWIhvlgjUvEraLKRKb5uEiOZb+9H6n1bcxNbfww0/uQL9smYCaX5ig3LfEvVSIu1Ybw8T4InFkKPjA5tQn9qSy3fXnF55RiKowj1IrEkD+krItvyo4baOHJeRLYI84lxko14F78xTvTxsxTO910KF/uM+2S7Oa5ycxrouM3BTZHPfC5sa1gRXyY3BowBY8AY2FoMWCFoazFvcY0BY8AY6EcGlixdimcEYSFoJhaC6rOfj+dQ/sSTJZsyhRRv6oOKJnTRnSdEtCoI/4cGu5X4hzIHxj4DHZqE6oPtfbXqtMWoxDzn4SO1WMBSd260H8CzZ4QqtcUg369n5poxVvRqQy1FaUuQ+gxtVB63yIdiYy33JU4xjhGC8z0W28Sx1Fo0JMm+WdbGfhnrs+NnIR7znxzZj8/e4xj51vprJb3kIe0wI5LGIyJZXy4/X9+esi7S+Ti/Xe5IfRtrb0kGrBC0Jdm2WMaAMWAMGAPlMGCFoHJYMowxYAwYAwOcgXBrWB1uDQs/H6/ph5PMcArp90Kc2msrRIQTZC6+JP66cesRNd3+J3xim2pDdDEitGVN6J1liT/ulDldJj+hnfQ4QqiXUlYSojCG+BAcPfMyJnkprOhiW5aLlrxkL7EpQoleLItwkjnjFaUt8VDqGUdjr/6bzSJK+cvnrciCfUsEzrx3+atn8ZIv8UekiN63slHYB2VdpCuK0teRFvkzeX8zYFvD+ptR82cMGAPGgDGwqQxYIWhTGTR7Y8AYMAYGAAPp1rASK4J0k5Mm3NcpZHaqyp7En6zCcX1cglNXVQMdeBbN6vZWqMWCUE0lfq0LL99KbDU7v0URQ0Q2B8YLSnKI7URPaN9HuUUgsvN9xH6oH18+PozJSF9GEr/c4OvET54sjuljRCf20pcnY+muCG0JqtQzG029qacsqtinWhVjRBP77Y2t+KBn7MfXkZb99uRdRx7aS690FEGV/+wpn/I9GXLzMGArgjYPr+bVGDAGjAFjoO8MWCGo79yZpTFgDBgDA4aBJUvw8/EzDsMzgiZAQx1vDWtuaU4Pi6ZEiwpBOm2VCWXPE1UfQVbc5/IFt3VNUFPtUPjCfv8Ow4fUw6KVz8Dflt8Ly1tWumIQR/Tv4llyKaY4H0lSsZVSkF9W4VwVof65EEQaGYHqilp5fvKwgiOd5C04jSiS4kKQIrTl+1aptvx4pbCCK4VRr3FLrEM5SX3281GhjfR6m4f47q2dxKOn+PBlvfFHvzjFa8v3lx8lRJTfK4pRvgdDbn4GrBC0+Tm2CMaAMWAMGAO9Y8AKQb3jy9DGgDFgDAxIBnhF0NFYCBqTFILqodn7ahgnnZ3m9mYambXOo0I8Crob6qqHwNt3PgD2HLML7DfpDXDtkrvgi/f9ASbWNCQOklIBmtKZQnRJ8UC8sMyperhpAUqBkpNKyC9J5RlPzv24ahW2Yq/iK882DyveJA/p81MYiDMLUdSLfWcRLJH8ivSbJs8btXgMMyyFJAvhI7QSX8XP/hqfn19vc+EcijIRuR+heDzla3rLVPmeDdk/DFAh6KS5c+C8+fP6x6F5MQaMAWPAGDAGNpEBKwRtIoFmbgwYA8bAQGCAVwRRIWg0FoIa3Ofjs4UgyjR/EspTSZ326loaHl2xla9RL0Ek/BxWa2c7jKobBv977JfguTUvwMdv/Qk0VlTh+dF4iDRebEnZiT+ROHWAEYkgqS9otfcLCqJlS7FTqUh8vWhDHSP0LiiK5sdWBLd8nD9KxSmCZH7Pz4Dkfl/seyP3fYv9pj+zWamk7xH7btm3EWnOal9eDnmW4sP3UAon+FJP8kU+5ElY3z/17RpoDFghaKC9EcvHGDAGjAFjwApB9hswBowBY2A7YIALQYdiIWgSFoLosOgGXBHUDF2dXem0MSxA+BNJIiDuMym8LsUvcShOpp86tdVVLOSPrVjb0d0JLXhG0DXHfx1Wrn8NPnnbz6C6qxOqsBikl3qS6KrLn+7GFoqnLTqSg+TFfRmBPNlGt/RwizyHiLif5UaiZ+0osn9Jj5B0cV964VgFS7jYM8noypMXydii6B5bxX2xE26lT0/lkHt0J3v/0rfiS/NGEFuGeOkV5Sf63j+Jb/rjEmW59vxr55w5J/KhffJDPTw4Pb0Yl3bTRiyP+wIUOT3pkj73iu/l4oo9bLomziHuS4RYHvc3F078yrMoruhLP21rWGl+TGsMGAPGgDGw5RmwQtCW59wiGgPGgDHQ7wxkD4sOvxpG05iwELRpKbA/9cFlF+673V0EcHMnLmO0Y9Gno7ISrjzua64QdOptP8VCUAdU0qqgxA1/1p3xLHIORJviEoF7CFp8yOTbL13503HBS+nFnzz7PrLx/aiqpZb6zGJEIr6lLzYi5770JGNBawxCiK1qFZ+ny8P5sr62w1hhEYh8ql7H1dtYfbfsbaQQT7n3Jrb83nwb9RFLw1h975FfjdJ3P2a5uRmwQtDmZtj8GwPGgDFgDPSWASsE9ZYxwxsDxoAxMAAZ6Onz8bwOw1+NUDQImbTqNF6QMu2kvqCozUhfolJq0dWJ1aFO/FLYr476NAytboBv3P0buHvVMhhTVYfbw9iW7zKxlak1WXP2YVSJS3q5yEM273ytxBGteA/jFvnT0cpaH99fNgfFczxBiFz6koXICa060YYyHYHfIg9q6fvzUdwmXGkE69WftkJvYdRQF/bKQ/aUVehTsuqdlfooL6eYC7LnyNlG9FnKAABAAElEQVRiWJZXyVGj5vnztdouyq+v41XPm79VlHsceWvh4jz6t0+FoLlzj4P588/pX8fmzRgwBowBY8AY6CMDVgjqI3FmZgwYA8bAQGIgXRG0+/jcw6JlquiXOTR/0apEW3kT13B6ywj1wVM5sUs2UGEhaANui3n/rofBybvOhudbXoYlq1+E6599EJ5sfhlXBrG9eqHJtT+xZq+cl4+SSbhkzDgfQZlIX7IqPflWVBEuGZXzrJxmo0i+oqG+4lmbd2f/oUbHkbctK8RSj8tnPBY/fohUr6E8r0deyss+fit53sqVFefue4jfma+jNuceS7P9/sbFEfw8fV25cX0baZfHkKC33rPcMfY3bkuOOD93WxG0Jd+BxTIGjAFjwBgohwErBJXDkmGMAWPAGBjgDCxZuhRmTJ8Nu80cD/X1/Pn4Fu/z8TJVDKeh0hNt0SAFF+rFyi/XECKYCvF+L5R2w0bcHrbj8NHwjQNPgb3G7gIvv74afnD/FXDrC49CdWW1c+58Jg7CQpBTJzeJXFxuUASb5I/A95nXLrYqLgT5NpxFnItk5CPj6FzEYanYl8LH9nFffMRy7pPn0gjfblPy8P30pl06uzij0ujexO1/bJxrf0QYyOPtj/Ft+z6sELTtv0MbgTFgDBgD2xsDVgja3t6ojccYMAYGJQM9bw1jWsJpKPXKmUSGVkKwWPqFIJUJip9d3V3ujKALD/og7DVuBvz2sevg70vvgfV4TlAn6uhoXtlkJZUkf+2JFl7EL0fKzyw7qiKceCt+lrb0CzbFPsJ8wnGpVRhJmGQ99UK92vWlFXrvnYf+zKPcyMX5FmVTbFFuzM2DK8p3U6IN1LFuypi2L1vbGrZ9vU8bjTFgDBgD2wMDVgjaHt6ijcEYMAYGPQNLli7DFUFH4lfDxvKKIFwV1NLSAp341TB/mhhOQ6nna4toDK0EJZaslR57JBmv6GE5FXta8TNM/++YL8P6jvXw2Tt+Dmta10FNBa4EotOl3daw/DgUT7z7RRSSkpys5OljqS0Xey72rxHEInyG8aUXYkr1Qgs/D9UUjY38CkospU86kfm4WE59unw7lvTt7sfsiwfKo1wfxTmX40EiFXvpOX+K0xv7UnjRlZN7z5kxoig3iVGkL9e/4TaVAbciaO4cOH/+vE11ZfbGgDFgDBgDxkC/MGCFoH6h0ZwYA8aAMbB1GeDPxx+GhaAJ0IBFoLr6BiwENbtCEGUmU0GZGvYmW7Jlu9A69Ck93zPjqVzTjit/1lV0wz+OOxtWvv4anHr7z2AIeq3oRrvAVGIEQudUJILwI/XczrMijyIX7+KJ5CwTjSAF0Zun+GAb35NqtBDEBS7CCpJQ0mYfkl35crLTaOKl+Enx8vBxHsUe8jW+z3J9+TbqtRxrsiwaiXrq31apeKIrJ/dys8pnh61l/OX6MtzmYMC2hm0OVs2nMWAMGAPGwKYwYIWgTWHPbI0BY8AYGCAM8Nawf8NC0OhgRVAXrgiKL5mKkryc6ahMMxmrFqFcehyNp5+ITc4Ier2zDYbV1MMfjj8bnl/zAnz89kthOFThIdG4TCi4yH/oK1AHk/qesL6l5u1Lw3YYl8eg2ZTjgfz5dmLje+aCjxZ7JAfB+j5ElycTn7Gd2PhykYmN9H2M6PJkgqenr/fl5bbz4pBtT/LQf1+zkCiht9I9iVWObU9Y0ZeO2DttUV4Sq0jfuyiG7jsDVAg6CVcEnWcrgvpOolkaA8aAMWAM9CsDVgjqVzrNmTFgDBgDW4cBLgQdjYWgMUEhiLaG0VU0FZSpYqmsxZaxaiEbs1RCceSkH5LSXwUMxwLQB/Y4CvYcOw2mjpwE1y65Gz638HcwpWZoElYiUJdtEkX0kEiEL4WLzLDrZ5XVikTz0FZU+JDQYhI9nV2EycvUH4m4EJn0856Sl+8ztosxvl504tvXicx/+kPx2z6mL+3e+Ipz1nhx9qFX0frSbPlNvW3+lmS0KZH80YifYoYEYc+ty4AVgrYu/xbdGDAGjAFjIMuAFYKynJjEGDAGjIFtjgH+atihWAialBSCSm8Nk6ljOVNTwTIp/gYmkShdXAhSr4QeWTsMztzvnTBh6Ch4aMVT8KcnF8DL61dBdUUVatU7t8hWyjbqNxtJdGovkuzTjxIVdjJg9SctHU0GHAgEHwix49sXYcjGx8U+pJ9n79vl6cVWcD5GZIKJn4TtCRPblNOXHMR3qTiCDf2KZSj1e4KIfas/bfl2m7ctWfVnlK0xjv7Mf/v3ZVvDtv93bCM0BowBY2BbY8AKQdvaG7N8jQFjwBjIYYBXBPHn4xsa6IwgPSya4KWmiuVOTcUHFXekTb6L7RGVHATdjodFr+/uxG+DdUMDFoCoCBTbsk/ypi0HytziiIyne6whCWvzdBnHKBB0nq5nWZ61jqhn+2z+ahOPT2KxjUYROVnG/nxdnl6jcSvGiz72K/KiZ5GfGJ/nN982Dxl74z4hyUfWgj3n+498yYHm8ozU3JUIpTwKJtdBH4Wl4vXRpZn1KwNWCOpXOs2ZMWAMGAPGQD8wYIWgfiDRXBgDxoAxsLUZ4ELQHFwR1ISHRdflFIJoslj+JDREUo8nm0VTTsaHVhLRPemsIDwnw50NzQJHmWCYPynakFQvloa+w7HIhh9aSSQ4eeYVAcKoGimMy3Lxk6cTS/ZXCiHIUk+JVAqTrxNLzSBemeXbKSpkkTA8EkUrVjUSTVE9t9SPYEN/oi/yLXqx1meRRYjw7bmW40sES7Icf2RAl6jdF+5YxPfIhroOIwYlsE6Vh/NtpF2EI/nWuopyivMpFxfbFfXL9VcurihO/8ipEDR37nEwf/45/ePQvBgDxoAxYAwYA5vIgBWCNpFAMzcGjAFjYCAwwIWgw7EQJF8NC1cEcY5cHvDz5cJJnpwmUHLJRFcKLiLPPrUQw/NmOqGIPdFdfKo09JCNI5IQJz1fK75J58sFGz8J7+N4bCqRFnMjPRqDWpLUjxv34ph+P2vrcydIYVzjk0YzCMegVtzycSrhlh8/ixNP4ZNxYS4hgnvqj1o9XT37Yw+lfcl4Em9JV32H71F4zeamuac6KQSRIE0ibaBQ35yTYlD+GB716E+zwE5On2R5OJKXc7mo5QB7iSk3p4GO6+WwNwmez4WtCNokUs3YGDAGjAFjYDMwYIWgzUCquTQGjAFjYEszwGcEYSFoJhaC3Naw8IwgyqfkdDGZOPt5S5HIn8jqBDrHAI11yivWPDFitFiTzp8wZTMTie8vOwLVqs6XyahjmT9KaUtEGgNlx338H0nnOvRAOpGonUQTj6WfZB/acmSW+t5lnVS+P0H62tivr5OoYsfYOJuwn+2FHot7pTNRFos9qKYnX4rEN4gpuw/WJUKxpZHQq6tIVvW4PopEn8C9ByIERFIB5snEKjGRGCIOn76DUNP7niTVe0uz2DIMWCFoy/BsUYwBY8AYMAbKZ8AKQeVzZUhjwBgwBgYsA7wi6K24ImgUbg3r+Yygbpwpu+kj3tJJMy9jgAqao+KNp6qE8iet4aTT7/koIsq5oUZ0sUdB+x4IqFaME2OVMyosJdEMnT2JX7ELvYhUn3F89h7O+BXjigtEmLtUngjSOoH0i5++Dx6bK0Bhk16DXDQqQYosfOZpPQchOOlJaaknnBj7vwWRlffsKUJe9uyZNFnrrCQnD3k/+LLod05ufDvn2d18aY4fJ0IgYeUiE79PcpT5IoGwd76LOT8JLahQ07deXoy+eTKrzcMAFYJOwq1h59nWsM1DsHk1BowBY8AY6DUDVgjqNWVmYAwYA8bAwGNgydJlMGP6EVgIGldwRpCfszdtlaabS9LEOZmiumqQb+O3deKprXAy7KNpaQbhunAylCzEwF4QOIBTR/wKivEipR4VM7jvY1hCd5H67TgMo2PfMYr74pkiSxFK7Amh+TCe4vt6lvp3Ka+ILKDcmbK9jERw/MyXhphS8Tm/UgjxFebZk0U47lLo0iMQbdZDViKZylNtOXeyEJlg6O307CnPLnWV/phDhiQCe8+Lkc1FbPr+zIvTd29m2b8MuBVBc+fA+fPn9a9j82YMGAPGgDFgDPSRASsE9ZE4MzMGjAFjYCAxwCuC8PPxM/Hz8bg1rL4+3BoWThOx5yo+OCHl/3KBBpei0BTVYQmSznj9kWY8+cpciwAg/hOhTNTJK5dXOH42dppZ7C7qa0GGLPQKeyyXsYS+ORdHDXLRjWdcd+EfYnDiz187C/HsS2XsVfuagyLZe6zBPodJFOopiyT/PV1sL+NRtNiWLoUIKs6VrFSnXoUxX8IZ+BJt5/sgfZ5Gx6Ie8lvy25HR8asTqdrIOErlmMnFT02IxSeJXVcaST/5p0mDupbvJFL1qVt6BH1yaUb9yoBtDetXOs2ZMWAMGAPGQD8wYIWgfiDRXBgDxoAxsLUZ4ELQ0bgiaAzU12EhCItBzS0t0NVJBzPnTUdzJqOyJymZVzLCx8nUmkcrGn8aKjJGxPe8Ig1ZoF+crXdgwcVFwKUxlTStdisuOGaRX4pdpNPoMYJ8+4ZJDmrAPhHT2dUJw2obYERNPbR2tsPqjWsxt8o0prrhGNTPXqGUkX5O6oVsQ5ZlfJyj+vbtVcqtMF6slX4plHrXltoVFYIEwfRqL2xlPZJepCEXasnZlspZsLodjNFUAnJeKUTigB9JRwxzn2jk2aVp5mHRnQ9VSByHUP15xf7707f56g8GeGvYHNwaNq8/3JkPY8AYMAaMAWNgkxmwQtAmU2gOjAFjwBjY+gxwIegYd0ZQfV2dKwS1YCGoMykESYYyZeSpqKzBIa1oGBlOVcPprY8MNTKdF2sfSX5jOa/SoIn70Oo62HXkRKirrIH2rg544fXXYMWGZrTg4pHEYQ94d+cZsZSisJxz5x7HliKAaNIyC6pppUhsqTiAVszjsPF7wFFT94VpTePgsVeXwwX3/wVqK6sTOy3ZcHzhM86IcxHfgpU+cy8jUJ+iD/HiS/CCip9FOJJTDLWnlqDFi2pFQk+RKlpaohG0yKXvP2Ms60gaW/lI1vkI0foyzZG8ikaQfiTS5cVkjN5D28AkUpGN+1mqcdKSPESRYyiqPj1j/31yYkabkQH6d9zck+zz8ZuRYnNtDBgDxoAx0EsGrBDUS8IMbgwYA8bAQGSAvxqGW8N2x61huCKoYWgDrGluTlcElco5bxpZzlS1tB15QIRzhAUSaTqpV4bACVJrdwfsOmIivG/3I2FcQxNMGDoGrlq2EC574kbolFVCaMdlIx4JlTIofpeT+sUTP3NC+H3pVeCaHr5ESxlJWwpE/2pfD7fM+RJMHzkJ/t+/bofFq5+Dm557FCrQmDDkXe7sje5cuOKW3P38JAeJRhj2RC26wp7gSSM22XGRNrzEi9r4VrGWbPNkHFM0YQTfhtoSKZZTP758rOj8/ERGT1/uZyI+fBnhadKd/t4I5Dok498drT7jwcaWZJ13SSTUec08ZEoiKSV2AOzJQQAus1PuOMp0Z7B+Z8C2hvU7pebQGDAGjAFjYBMZsELQJhJo5saAMWAMDAQGeEXQbDwjaDyeD8Rbw/JWBPWUqz+l7GnKSlg3181xKras5x7Nvzu7O6EDrWorqqEyma3TKoq6qiqY2DAKpjdOhE/vOxdufe4huOihv7ntYql7coD/7QD0gQWiKqzI1LjVOSTWAgz1qIBEmJqKKozjl314hDJOzowGkrQSRQduCausqYU//NuZsLFzIxz0pw/BhBF7wIjqegTjdrvUUDxRliIkmbRJ7l95Ot+HFmR8K2qzxyK/MTrbp3IUWYfR2G+eTD2olZ99KZtYx77YT9EI8m3UUvLxc/Bl0mb/9HugS7zqGDQL0YllqaeXtdeMLdLdlYKh33jmEmVG0UdBXow+ujKzzcKAFYI2C63m1BgwBowBY2ATGLBC0CaQZ6bGgDFgDAwUBrgQNAdXBDXhiqC8rWE0Wex5AsprVxSnrexIZfrpY/JkVGRpxwLQ2LphsEvjBGgaMgyWNb8Ey9aucOft0FzdZYe4WaN2gq++5RS4+8XH4OKH/+EOaib/pKciUlN1A0wbOQF2qB8Bazta4enVL8HLretwZFycoefQmiEwY8QEGIFxnl+70sWhOg8VntgXl0SoTX98aYv6HRirtrYefnPUGbCufQPM/se5MLNuJGpCHNvKqLmXjxEdPUO8G7unDrWeApu6bklQefmQTexVZKphD7oWqtgjxRC0jk7w5JkuzkS5JVmMURy1wkuw8Yjy5CTzcYJxDCXp8m9ZYghakAlI1O4Ze3XeWCNw5yaLS92gykHw5iKVVQgSf84CXTkPqcvwjeXpPOgWb0ruPQUe6Lie8t80PRWC5uLn4+fb5+M3jUizNgaMAWPAGOg3BqwQ1G9UmiNjwBgwBrYeA+mKoN1xRVByWHS8IiicoudP0uMRxNNOX5+dthJap99syyt13rzDrvCf+7wdi0AN0N6Ja3pw6cQfnrgZ/vb0PbyVh6bNeEj0rBFT4OyDPgALXnwULnn4Kreyh2KSr/F1I+CsA94NM7AQtLF9I1RXVQOt3Pn0zZfAK+ubYSOuADpgzFT42N5vh50ad3CYmuoh8Len7oL/XXwDOuGMeH1QhdtWRr51isq5EmpjVxuMxHg/P/ozsBYPiD7879+E3RvGEDyZprMvJ3A3YYP8sS5G5GNVmtcSH+KdC0HSIwtC6AhCH4ITTKiVnvwufLTo+Mn2/GYloyxCJOKP+uJTdPTM9yAIX5tnLbjw6SO1WKaYCjm8J/kNhIn51moTZOqn5SBkkxG6M4KcGlXOa24hiBCxbezP7/tt5x1vIqPn5rokRk/+Bzqup/z7U5/Pha0I6k+OzZcxYAwYA8ZAfzBghaD+YNF8GAPGgDGwlRlYunQZTJ9+OK4IkkIQfz5evhom6eVNP0WX94zxPkamoIyhe1wEwk1UOPGeiOf+XDD7E+4g6L8+dSe8+PoqOGHaITAZizXfWfgHuPOVxbhVrAatu+GNTTtiIeh9cNeLj7sVQfwlMZ72fmyPY+Ck3Q6DW599AAtFi2E0rjB6w+gpcOkj18DK1rVYGKqBS448DSYPGwN/f2oBPNPyEhy381tgxuid4OIH/gLXP/8w1oLwy2SuKKAj81v0tbLn29dBy5pF8Lk3fQI+u//JsPDFRfCuWy6G3eubkum7TPbEkpkQPuJJvqCYO0VRP+wxIr6TPeGyBQ6WZr2IhXiK+yIPn5KL5it28sxGEg9qI7mKJmvjYxUlrVgrWYk+/+mjXK0Hi4oicx7xJn3nwRVoJFag8QKIHkVe0wNkmmm9CTX6T0MMK+WsjFycO8KRnyJ8HNP6W5MBKwRtTfYttjFgDBgDxkAeA1YIymPFZMaAMWAMbGMM6FfDRvJh0fj5eFoR1JHz+XiZhuZPIfO1IiVa8uxEL9NToW8jrtg5ecbB8Nn93gV/feIW+NXim6GqsgIOnfAG+NT+74FfP3I1/HrxtXjeT5XzO2vkFPjqgbg17KXH4SJZEYSz6xo85+eMfU+Co6bsAxcu/D+45YVHcQVQO54XVAlD0JYOnD5m0iz40sEfgvuefxS+i4WfelwxRIdQf+XgD8N1y+6Fnz36D9jQ3soj8AbBufO9GfWffuNx8G877QOj6htxC9vLcAmeVfQYHhRN5w25Kx2kP2pV+RN0QbBW7154FRa0sj58SZ4n0vdGXhA4U/1Qn9pi2zijuO9HEB35CNvS89HSjiOKnJ/5Wi2dueJfGg1tEgOOWFSySfKhh59sGDpJQDLwxyCyPAMfF+tjuzysJBRjY1/WHwgMUCHoJNwadp5tDRsIr8NyMAaMAWPAGEAGrBBkPwNjwBgwBrYDBpbgiqAZ04/CFUFj+LBoPDCat4Z14ujCia5OK3krVDB8XE5BevrCkn+JTSj1ETqpV2k3rOxsgwsOeA+cOO1gaGlbDxs72tykmlbeDKltgCv+dQf8HAtBnR14ODPGnDVyR/jage+Hu156DLeG/R0/JY8rePA/3ah78w4z4HQ8SHrSsNGwHM8YenjFErjluYfhsdeeh1V4js9Ze74d3vuGo6ENY2xopzi8KqQB49zwzAPwy0evhVW4zYsuZYRHRGcLkYzOHXrvrofD23beD6ZgEenlda/C97CodP/KZTAkOZia/HJNwWfDsYYe9CqHM0UXt8QPI6TnR/Jte9L72J7a4ktxypvKqOUjKTO/T/o4W9LHMrKK7ciWryxaNPTM1ya/ZQHGzlOjtCFIfHpgSdYTeUAMzvYUzf00sKsetRXY+P4DRR6+KDAZ5uEDh1uoIyRROL+9hcIP8DBuRdDcOXD+/HkDPFNLzxgwBowBY2CwMGCFoMHypm2cxoAxsF0zsGQJFoJm0OfjJ0IDfTUM/5pxRVBnzoqgIiJ4uol3/G9cCCqy8eViL8UC6r+IhZWLDnwfHDZ5T/jV49fDE6ufxYJKjTsEmr769cLrq+HF9avdFjLaGkYrgr72lve7M4J+jIdFd2AhiCa7VKhp6W6Dg8dOh6Mm7QVT8TDovcfPgOa2dfDJGy+BB9c8D+fs8w44CVcfXf7kHXAPriiqr6p1BSRax7NiQws8v+41PLS6w6UsOboO3twk3j0BWvArYS++/hL8117vhtPwvKH7Xn7CbQ3jw6LJwhHE813quotGy5NymZozHyJNYH14iB811Vgqo1aMlExCVPm92B+NhX06TUEahMhahjwI31lpUXalxyIxY5QrBWEysdxFSYVpIwqejMIbDDUVnbTwwRAPmDIVuUy7PjYVBt5VWoQlhGajeGsNNAZsa9hAeyOWjzFgDBgDxoAVguw3YAwYA8bAdsAAbw07AgtB4zKFoOKpIk0wfW084fR15ZHkJt7OK9u+3tUOH9j1cPjEnifApf+8As57/FqYVDsUaFNXbUUtDMVVNmnRCZdT7Iafj/8afjXsmeZXYN49v4MNXR2AH5qnT37hbLsCmrGw9Cr63Al9fHbW8fCe3Y+E8xb+CX69ZAG8e+p+eJj0f8BfFt0CZz3wW5gwpNFN0CtwS9dwjEOfkaeDouNR0sg4b8oZtfho7Wx3h0X/4sjPYLGpBY64+r/xq2F8WDThs5dyySPXONLP2pQnycu3tKXmUhpXSlsqajKiBEILYnw0af0+RcnngFCiYQtewxVbF3sorcE8ksOhJQqFI5HrU9tlkGrJnXcleeSl41Bol5gyRIFSMPOcYZP0EkuxIUb0vrQIS5g8vG9r7YHAAG8Nm4Nbw+YNhHQsB2PAGDAGjAFjwLaG2W/AGDAGjIHtgQEuBL0VC0Gjo8/H97Q1rJzRlzHZJAjOV6UQJPNTmohPHjoWfnjEqbCm7XX4C24FW4GHRTfip91njZ0Kt+F5Pg+8uoyLPehiOG7j+vqbT4E3jtkJfvnYdbASV/E8hdvAXl6/Bo7d5QAYhgdCL8ciER0ifcK0A+GwHfeEr9z+S7gNVwCR7aVYuGlAzJ+euAmebVkBw/ErZbuPngqP4Nauu1/5V1oY4FHLuGiinWbuVJ14tlFtTR38+ujPwfr29XDYVd+AXevHQGUllpKokkAmyZYg7ohH8cl9B+NmD/fSSNKWd5X207OPMiPRqcjJVUH7oeSFizB5+t7UwgcpQlu+3m/ne8iXkh16LHKaMYoFnqHX9LPhIZMdAeQpiNifyP1nnuMiu95g/Rhbqh1zUDSOLZXPwIpD/x6ce5J9Pn5gvRXLxhgwBoyBwc2ArQga3O/fRm8MGAPbCQN8RpD31bCheEZQczPQV8O6cdKe1iyS8fK0ktdf9ESBomSyRxYk5cme+Er9JDDSOhQGP3CHmXDaPifAmPrhsAbP6Wmsa8TPyLfBt+/9E9yOXwirpQILGrR0tMPbJu0NH9nrOJgwYgdYt2Ed/G7RDfDXpffA5/f/dzh25/1hDW7zquiuhBH1Q+Gmpx+EC//5F2hFu3YsDu23wy5w6l5zYZcRY2FN6+swdEgdFm+q4McPXAnX4DlBnVTESS7Nn7L0C0HY6+6E5/Ew6tvmnA3V1VWw95Vfg11wFVJtMmatMPAoxScNgllRhOhELn15amRG5OE0a7EKn2QT+yEEy0NscQ+jJO8ufRaDEet5xyb15O6bae76mxG95iySnp4cxUdlJZ7WBdcMUk36DwRnoG8tQfh2FCDHBY8Vla4QRnYIIlzqm2SlrthpyZGgo97iS8U23ZZkwLaGbUm2LZYxYAwYA8ZAOQxYIagclgxjDBgDxsAAZ4BXBM2G3Wbi5+PpjCD31bBmPCOIJt985U26RSfDc/NY6aR2kSDpyuQ59IseqdgijrFJ+vV4Ns/MoTvAdDyAeVT9MFjbtsGt9Hl2/Wt8jlGCJ2wLFojeMGw8TBw6AnsVsHztSli5cR2MxtU90xsnwPhhozCDCliBq4UeWPU0tOHqHY5ZgWcKdcIOdcPdWUMjGxpxNU+rWxm07PWVWHjqcP6yo/En2Jr4E63r4NeHfAQOxy+IrcODrm977p/wgwf/hl89q0YXdHYRXYoP+9yL74IWecgdSYVVQfBT3gvjYy9k5XmiQkQyJEL6o/O95usStAT0Dfx27BSdkT+9KCP/4l7e6FiToPOTShz5EQjv9xnizJPfn9Mmbv1MXNsVa3xl5MsrGGZs2UE2vNg4V5G/fCcmHSQMWCFokLxoG6YxYAwYA9sQA1YI2oZelqVqDBgDxkARA1wImoNbw5oKt4Z5pQLnxp+q6pSYpNorikdysffRNNHXflS2IEUwARcP4o0t3T2dVKNHZ0cYzsxZoZ6+JJZGwz6dNeTKXoh3XwFzsdiGvlJGrW53SpB48p/UpgtxBMXPPVOuo3EV0OFT9oJpI8a7T8n/GQ+ipk/dyyoQLmxQXJe18xDfODJLqe1fRVY+TjC+H98HjUt1YqmSEFtOTyL62NL+JCqTx/b5XgSp/lIcipyW3q0f2rXFThSCCOXUE+tUI1AxpWeiJBU3UzRK2EMqydijJl0FlKLQLAE6Ed3oL2OMsvjqb1zsf3P2/dyVzc0ZsXe+/fx6Z9mfaCoEzcXPx8+ff05/ujVfxoAxYAwYA8ZAnxmwQlCfqTNDY8AYMAYGDgOlVgTRZJRLJsWTUpou+Vcx0kcVtbk8Qlq/ROHQwVwxzouy4IkbnanBdRw/M9Y5P26CzXHEpcuZJuNJASgoz3im2vR9s9d44k7uXscVSrRKaTiePTS8ZghCxE7yF1t5SkYyGpFzX3txNF8TYt3YQnXSY42OicSSX65BJBTPsY3IIzh2SROjwz714nIM/xLYW4gWf/Jk73581viSkDn2p159JEnxz/0u2A/nwHfKMmNNvz1Ss8I1UlzqIlWyI7sbAyUYsBVBJcgxlTFgDBgDxsBWYcAKQVuFdgtqDBgDxkD/MrB06TKYPt07I8htDYs/Hy+T82zseFrrT6Wz6J4k7I3u7Ee8+QUi8SE66idZuEm76CUzwkmbdGznClyEdzp8ehBqMorwdMUSD8yA5C6+fXuMhHAXCr26FFNzbFA7DUaNVBl4po6vSU0cSnqM8HGkFq2DBniVcCu2jPWK0l9EkU02ap43tdYWWbr34zLn4grL6NttdLHvOIJ4kNy0Ly225rtYi4770gvZ9u00ehYrPhkjv7EU5xppL3RqPWMghwErBOWQYiJjwBgwBoyBrcqAFYK2Kv0W3BgwBoyB/mGAVwQdg1vDRuLWsHpoiApBHEWm1tmYedNanQ6HeMXKxiwfqVqZarv5frJKhzwRQi205UdRjLZ0Ui82NEUnZzgu1/A9ZNH52lDKPfHv6/wAOXo/zdQsKR5RPzHxvag4x58roaSOXIPfXm6gEMiseDLyH0eW9yCxs3p1IBiR5OWg9tpivJwpHVvF/TRFrrZJsOCZvHGhM9ClJKOUc4gzieCui2NL6RG8jjdtYcP9hB1WcHn+TGYMZBmgQtBJuDXsPNsaliXHJMaAMWAMGANbhQErBG0V2i2oMWAMGAP9ywB/NexILASNLVEIojlvOrUNEsib2hYhY2zok6fqbIsn8lTheTrY6e7Cr5cFEWWyLkKx4L6LgTcnFUMSSluqBth3W8CSWXoAQbjzI2Zii9gYx1HlLkDqx1jSibXn3TXxlpoKzoMnTXykV8hdKk6iap8cp659cdqWXFJB0kArqsSQmv5SJ2nDMyjyQRAfLziRSV9ccSCRCkq07CvmFbWBQWglKgWFGYW+CaUWqivVonhZG8d6kgpr8Z6FlXJsOmMA//3XDSfOnQPnz59nbBgDxoAxYAwYAwOCASsEDYjXYEkYA8aAMbBpDHAh6DCYuftEqHMrguqguaXFfT7e98xzWpn0JjNcBKRzW1GhTLXkgRD0h1t8+OG6slWKEO5y9uytGz/n3tg4HK0qoKVlnRdEwPz03KH3MGqIzOlhkaNUISi1QLd8mLRLMBX33JDsPKQTJX54qKp06Yc2FNetWkKUD3fQ3PHmlzGyePJLofEWF7cYnAYsNy4bJMb0SA0pBoUSAWMYQvk6ZTAaQdL31bjNaLmzDN+ec4U34inhigfGSDRP/VPLwUmIV/L2E/8s27Q7/wLZL3nCaElASi0tqgVBnMKTxH1PFTSLcLE87ouTWB73BRc/y8XFdv3Zj3OI+xIrlsf9zYUTv/Isiiv60k/bGlaaH9MaA8aAMWAMbHkGrBC05Tm3iMaAMWAM9DsDvDXsKLciqKGuAbeG1cGawkIQhY+n05wSTYNpyhNPuFnLdzchxqbzULCNh4ofXbgKqHFEo5ustzSvJZe5V+kpluTpG4tM3KkHncCzjjWEl1KF2LCMe35b9PJU327E6eAlB9T7EDHznr6a2nKxh/zYgpMoYsOsizaRJiBKTQtpgmH/fo+tEiNZLeTeJqP830Aa16nQxhWCElscuI8liGikuMN0+dGx7YD4O/NS0PCql9hszXeR+U9yU6z1keW0mUEpMDkLGRQFocJjn4P1b6bljMYwA4MB3ho2B7eGzRsYCVkWxoAxYAwYA4OeASsEDfqfgBFgDBgD2wMDckbQzN1HJVvD+lYI0qk8z3ZlDpzlKE/jZsrpnJ6+/CWFoGYsBMUT6Hg+HXhEJRUKAlmQRJGGCz4+VJB+PFfASBISvQTjIgB5QFRS6HJQcZAaEEaFDpsASUp/DKW74Hw5ih1Ccw6RYk84vtSnSPDpguCtEv+onRZ3PAw22VbzcFpnS0pqJIc4V+AaHuy6rBK4G5sUgZzM90O2fj9JAwuBFfRH2wNFTXziX2dnF1RVVachFcAtLjChEdm5d8AOKBJd4o57/XnnQlCxR31XxRjTGAMhA/TPz9yT7PPxISvWMwaMAWPAGNiaDFghaGuyb7GNAWPAGOgnBnhrGH81zK0IGloPa5qbC7aGUVCZUlPbn1bTRF1EvpxwMkmnlm9PfboEzzpaETSiaYSTtjTr1jBBsQ3ffW++3pf7+Gx8mqCTZWgR9kjvY0SLllgIcfWGMAi0trVDx8ZOGDYMPxtfQYUSshE7ApMtrozBsa5pWQ91tTUwpG5IUvQSnDz9kZEtyzlv6otE8CzTnJPcSZ0UsRjhBJhDJ3RtRB+t+NfeDdVjqjBltGF16p39kWVeHPbo0yRZEz+dazugcij6rSIusMiDxSMtnTCSJr1dnW0wYeYe0DhmLDx9373Q0YFYUnd1wNAxo2HKPvujfCG04m/UXd54/HVGpPNUmlacOnvphzuPht6Jjstzu0krgjw/1hxUDNjWsEH1um2wxoAxYAxsEwxYIWibeE2WpDFgDBgDpRngFUGzYbeZ46GhvpytYeSPKwQ0P+dWMvXlDkqlBEBYvgTLPZmNx7jEGxYmGqkQhF06I8if0Is/eYon6ud5E5w+fYvQSuxjBNuKlnqMCCb9pEYxFXdeXdUMnzrtFBiGX2D7znd/CU0jhyVjIDv6I3AFdOI4x48fA+8/5WRYvPhfcOVfr8eCUK03EMEi3F3Ulwujuy7eMCZr+O5nSmiVUgu1+OhmY6rJOD+j3zQKJhyIv4ExQ+DJK5bBa4+ugcpqXulDb9cvOolHTjTxqWmlakcJruAZMbMRph8/FZ66+hlofrwFKmrIUrNM88NCUGVlNbz3B+fhsx5+/alP4tCweITxOzs6YMr++8Pcr5wNi2+/DW744YXuQPF4QyL7SpKRHw4KuYkNWZ2UxvctNCcZTtlPzN25Dvw6BpwLf7xl+zTgoGfACkGD/idgBBgDxoAxMOAYsELQgHsllpAxYAwYA71nwBWC9pgDu01vwkJQPdTX12HxpcVtwYm96ZSZWv6kWTWxjeB4SlyE833hXN0dFo1nBKGYVgTJfD72HXsLvVA9QvNUHcpUjE3ViH+SxL5Zp1Ka2EuP8nOxuBIATyx6Ah588CYYPrwJZsw4Gg/i3kFoYDcuQAW0trbBQQe9CX70g/lwz70PwIc/eiaMHjUcMX4G1JYrjchZJ10q6sQWpFJLbnGOiS8U0Uqd7o3dMOM/psIBZ+0LHevboLO1G+753oPw/LUvQEVdJXKP4yRn+OdiuFiulYnAfCf+HRrPe9rYAVPfPgUO/vpb4J4L74Ul//s0VI3ArV3uIqeUCPdoldCsOcfCnM98Hhb+5f/gpksvgqqaWsctrRai1VPvueBC2GmvfeEnHzgZWlasgEpabaUukkHzFjW3f0xSpRDYdu8ticdR834BrAnv5Kj4yo6dsEEgx0iSYLEj0xgDHgNUCJqLn4+fP/8cT2pNY8AYMAaMAWNg6zFghaCtx71FNgaMAWOg3xjwVwTV44qgelzF0tKS3RpGAXlaSxNnnhSH09w4JdGWRjMqnGRTIWgEHhZNV28KQYRXTzw1pz7F0CwkL0LTpRaCZTlrBL0Rt3qtXL0ehtfXQPP6dqjFAkQ3nomzw6ihWKzAJS5UqMBC0Ko16+DVlx6Hb83/Ngwb3gDfPPcnMHZ0g9NRzaMLx7Zq9TrY2A6wbtVaeOfJR8FPL70AFi58AE7+j8/D5IkjMLxEDfMTORVM+KKY2EK/Moqkix7yfJAU359TYSFqcZvbpPWuh46FIU11sPA7D8JLN62A7lpcrdTeyQU4rKm0vdoO3W0A1dUor+2GmuHVuKpHIuKYNnZBR3MnVDdVQMcqNMDtX1Wkx3oPrf6ZOncKHHrOW+Cu+fe4QlBlYxV0rET/uGuuehRxhzgcSMPoUXD8F74Ik3ffA3712U/D6meecbxRwhRt7bMvw6GnnwpHfvyTsOiWG+HPHz0DGveYnGCYByoWEb+vtrTB1ImNboWVG3PCEPkRZqSUR30dDXZyr9IIdxaSg8Q4jRZrcsOY0BjwGLAVQR4Z1jQGjAFjwBgYEAxYIWhAvAZLwhgwBoyBTWNg6dJlMH36YfjVsAnJYdH1uZ+Plyj+pFmmuKLLe/Jkm+48mfftCa8+dJocFoLosGjVSQy1E4k+89EkpSzii9FyZ72g6InFj84OmPXGmXDEEYcCnan0pr1nwUjcuvbo44vh73+/EV5Z8SoMGVILtTVV7v97P2H8OFjfugFeXfEa/PI3l8PIxqEubgf6aW3tgFP+4wTYd989YePGNnjxxVfgPz/xAVhw10J4x3s/h8WLJowo8SlXLmGJpKO7E9Z0tEIHFjw2YPGkCxNvwMJQO5Z0CDOmeggMxb/ABcrdSNyNOlzM2e2UaVDTUAMzTtwFNja3w/KbnnXFm6eveg7WLG5G3iuhuqYSdn7nFBi9K+aF5wateuI1eAZXC214eSNUNVRBV2sXjNq7ESYdOhGevf4FGLN3E4zdcyx0d3bDc3e8CC/85SXY5YNT4dB5B8Gd8xfAkz9dDkOn1sG0d06FqiFVsPSKp6F1FVaZMP/xM2fCR3/yv/DI9TfA32kFBNW7aJUVV64Q0gXrl6+Cz9x6HdQ1NMCPjj8ezx2qwbyq3PiQDWhsHAZzT5yDW+7GwY033w733/cIrhoiH/yG0WNyCaPEMF2+XnUJONKrVFp5FqJL2E+7fqRUaA1jIIcBKwTlkGIiY8AYMAaMga3KgBWCtir9FtwYMAaMgf5hgFcEvRULQfTVMFoRFG4NiyetUkug6KUmv1k7RYsulqRynPAPb8KtYViwoDOCeI4ua3p43GpLLbFkXdijPBXNCLozKsRKjyzEbwVsWN8KH//Yu+Hzn/80rF27Ds/26XBb50aPGg1XXHkVzD//h3iGDR4MPbQOvvH1/4Ldd98Nxo0bAytWrMIi29F4/hJuDcOLDpD+5KmnwCf/88OwYUMrtHd2Qj0WkEY0NsK1190E7//of8G4MbQlDmOnKWshiM4U2nHoaDho8h7QUF3vfPI4sMiGqdNHu55qfhFuef5RqKUvawWXjAeFuN2kbmwtHHT2/lA3ph6apjVCF64Aan5mHa7kqYYHLnoIXrjpJeh8HeCQH74Jz/fZBTaswHxbO2H4hGHw7J0vwL3fegDasXjU9nw7zPraTNjn43vBmuWrYdS0JtiwshVqG2vgtSVr4KqDb4Lpn9sZDpuHK4LOvxuev/ol2P/sN8FOsyfjWURL4LGfL4a2lg53YPWMw2bDu8/7NtzyPz+Hm35wIQwZhcUneSWYNjVXLroPzrh+IYydMhWu+O+vwfJ7F2JBiVcVdeLvZtcZO8GPfvQd2HnqLvCjiy+FSy75BRaC6Otj3vgdLynBrueKjakobTid3rxkVOhaRRYRzHWLveShTTaYGaBC0Em4New8KozaZQwYA8aAMWAMDAAGrBA0AF6CpWAMGAPGwKYywF8NOwILQePciiApBHXhIb95F014ZSJbavIrGPHhF2NEx/bcExlVQLpw8jOiic/KacHPx3NALQSVikvxZBWSxM4rBZE3fyyEFb9pLtjAWpQrBH3so++GM888HZ5e/ix84j8/D7W19fDd734ddpw8Cc6Zdz78+fLrYAweCj1q1EhYuWoN/N+ffgbDh9XDtGlH4hlBE/HrV53QgF9ku/Ivv8UVLJ1w3nk/ggV33w8/ueR8OPTQg3Bl0T/gfR/5EkzYgc9G4mQ4Ixn5Ovyi1jGT9oTT9jkBGuuG8fCSQRCGzsu58/lH4AsL/hdG1gx1ei5p8WosJjIxqMTPsOM2t2o8nPntlx8Na59fB3d8cSF+nKsd2td3QesrG2HKCZPgsHPfDC0vrIMbPnQn1DRVw76fmwU7HTkF7vjmXbD8iueh49l2eOO8mbDf6XtD14Yu3P51P6x4cBXUjqiBWtxC9spdr8LO79jJbQ1bcO5dMGG/8bDTETvC43/8Fzx6yWJMCfNBnttf3wDHnvVFOOAd74Ibf3YJ3PHTn3AhiEfp7lSsWf340/D+3/4CZh4yG2791U/hzl/+AuqwkObGib/Z6dOnwMUXfRemUiHookvhYiwEVVXi0qL0pXoO/Sb65oVHzLmvCtulHfnWhPT74qe0B0HZ0xjA3w/+u/DEuXPg/PnzjA5jwBgwBowBY2BAMGCFoAHxGiwJY8AYMAY2jYElS5fCjOmHYiFoMhaC6pIzglpKfD4+jpc/3eXihU6DuaV98SJFDunTkz4f34Qrgmhyr5+PV2TWi28tW9AkL3kqhiRxZSD0KTYkpRVBG3FF0Mnw+TM/Az/84U/hgu/9DFY3d8CXzjoFvvzFs3AFysVw9nm/hJlTR0E7FnyWPvkg3Hf/Qhg9cgTsMu1QLARNhVY8FOjQQ/aB7194Adx9zz3w9re9E30Px7OEvoAFptPgxhtuhfe8/wsweQKdjSTZcB40cpLQOTR11TUwvmEk1GIBpwP7tGKmCvdQdeF/6GppWw+vbFiNMlwFg5d44hZ5Ql9Y8aDtd51YuOl4GuC9z54ALU+vhRtOuR06qzqxQFQFrU9shIN+sS/sfvJucP/3/gkPn7MYaneqhqnvmAJHnn8IPPKbRfDgdx+Djf/aCLPO3Q0OOGNfeOGul+CaY26FmqmV+NUvjEQFGNwitjPaHD7/YHjpvpdg7Kwx8NjvnoD7v/woVI9DDG49o9xaX2uGU350CUw/8GC45sffh4W/+jXUjWzSEVChBv/T8tQLMPf734L9TnwXLPjzZXDDd86HulGjUEOby7pwy14TfPSjp8DOO+0If/6/v8Gtt93FW8PIGkHy6+D3r+ygufNBz54vipZ/hR4ZU4wmjW8R9yVGLI/7mwsnfuVZFFf0W+IZ5xD3JYdYHvc3F078yrMoruhLP21rWGl+TGsMGAPGgDGw5RmwQtCW59wiGgPGgDHQ7wzw1jBZEZTdGtb7gDyxpUl7fFFhRy/WZ1F+IQgPCMYVQfJZbsH6XtSftHSqL5LwyZmRD/FH+nyfjPK3hp1xxlfg9jsW4hlAG+GA/feGX/z8Ivjdby+Db5x7MYwdRat0KvCrYffC/Q/cB6OwmLWLWxE0CTbgF8I+8L6T4EtfOhNX/1wD73nPGTB5p9Fw3LFHwgUXnA13LbgX3vuBs2A8rQjCbCQ/zkvYxMOYsYCzEf+IE2rTVYuFoE4sgnRiux63QdFB1kVjI0/sHe8duPrq9Up4193Hwdpn1sKNH7sTfXS4z7K3LtoIh1y2H+w+dwZc/aGbYeU/V2FxB2D87PFwxPkH4fk/L8OdX70XWp9qgzeePQMO+My+cP8PHobFP10CVcMpOmdAXw3b+cQd4agLD8PVRpRhBTxz83Nw0wfuhAo8XLoCD5amZVdtuFXu6E99Bg59/wfghp9eArf++FJowK1hdFEBx1046DWLl8EH//g7mPHmg+Gmn10Md//uNzBkeLI6CkHE11os3K3Hs5hGj6iHmio8P4gKZuQAb+KKuswttfjydSIr/cxayHsrbWdaY6A8Bnhr2BzcGjavPANDGQPGgDFgDBgDm5kBKwRtZoLNvTFgDBgDW4IBLgQd484Ios/H15X4fHx26uxnSJNinVpLT0oYjAxLQSQL9YyiL2s10VfD0F1zckZQPOXWSGzD93ypj+BCCEt69kn+ZEUQnhH0uU/D187+FvwND4imr4gdecRb4Ic/uAD+5+e/gPPO/xnsMBYLEliEeWLRv+D++290B0pPwzOCZu4+HlcEtcGJJxwN8845G27Ew5D//eRPw7gJTXDKe0+Eed/4IhaX7oZTPoyFoDG0JY4LFq4AJkUMzGMtbg1764RZ8J/7HA9NtVr8cAaYahUWVe588XH44l2/gdG19KUy0iDnycuQ8QpLVAiCDZXw73fOgeZnW+DmT9zlDsauqK6EDYta4dDf7g8z3zEDbj5jATx304u4eqcKJr91HBwx/yBYctUz7pygjc/gGUFf2Q32//Sb4O7598GSPz0DlXXIclK96W7rgp3eviPMRpuHfvkort6pg+lzdoZHf7MYHvr+Iqis56xowjv1gP3ggxdeBDfjdq8b8YygeloVRsm61UCuAS8tWghn3fgAjJo4Gf781S/AC489AlW1fEYQ4XAhEuyy82T31bnnnn8Jz2laTQx4vzMZvWMtuAk/gbDHTt+senRrAGMAGaAi5tyT7PPx9mMwBowBY8AYGDgMWCFo4LwLy8QYMAaMgT4zwFvDDsdCEH01DFcE4Tk2Lc3N7jDkfKf+RDpvEox6/C/VAQjJCL6zJd/Vd7YUlBaCENTczIdFx5HEtzzJX7bMpFG0FXuSHDlfxbGc/L+Oh0V/As8IOuusM+CWW++A4+bMcbA//9/lcMxbj4ZvX/B9uOjHf4TRTfWw/OlXUfc8fg6et4ZNm7Eb9ifAmPFNsOce0+Cyy/4HnnvuWfxU/GmwfOkD8LvLfg/v/Y9/h6uuuhZO+eBZMAmLQ66GktCkY6qAjV3tMKNxPLx9lwNgBJ4B5J/iRDY0sodWLoMrnr4X6iuxOOIu8RDyTO7diqA1ACfffzy00IqgjyyAzkpaEYSxlrTDXvN2g31O3QuevfU5+Ovc22Eo1MD+P5gFbzptL1j43Qdg0S+WQNtSLASduyu8GVcE3f2t++ApLARV4DnW7ktdtGUND5jeOfl8/O3fvANeunUlHPXjQ2HETiPgznPvheexwESZd+NB2ON2mwkfxjN9nrrzDrjy3LPxy2PJCKkahIPrxsO125s3wKeu/BtU4yHbFx54GNRN24H5cmOtgJGjhsNFP7oA9t1nb/jVr3+P5zhdgody45iwQBdeCcGJMPurCNGle5tmXdq3aQczA7Y1bDC/fRu7MWAMGAMDkwErBA3M92JZGQPGgDHQKwa4EDQbv2w1Hurry9ka5k+g8ybAqKd5O03cMZOw/ECpSWGC08zq6aNWyYoghEghiNAULY7OMUgb+iVJ/pXNOfYrdiJ3haCPUCHodGjDw5Tvv+8h6GjvgEMOfQs88cST8KUv/zc88si/4LjjZsMJJxyLBzBXwP64uqUWV6osuPted+DrP669GX56yeVw9dU/g9mzD4Z/PvQYfoFsLeyzz164kmgHuOofV8O7P3AmTBk/EsMnW5lci8ZNY+O825GbpR3raf8cwpLR054tLKTQcphJ+On4xqo6t5LAvYOEMH/UwmF3B24xa6mAk+97G7Q8i2cEffgO6KzohMpqROMX3WuGVsMxvz8SGic3wDO3vohbu7physHjYcPqdrjtKwtgzWPNsPHJDtjzvN3hgNNpRdC9sOSy5VCBn5XndCuwEIRbw7AQdNg3D4YF+Pn4Rd9bBrt8DM8MmncgtK1tgxtOvw1ansTPk6FJw8hRcNznvwBT9pwFv/rsp+G1ZcvRD2WOfzjWtU+9CEd+5Qw47EOfgMduug4u//gZ0PiGKTzWhKvRY0bA//zkB/jltj3g8sv/At/87+9BW1sbukGOnC8EuktY4J7PTwIo89F3yzIDGGwQM2CFoEH88m3oxoAxYAwMUAasEDRAX4ylZQwYA8ZAbxjgrWFzcEVQE+RtDZNpLk+bw8kzxaFCTp6O7EieLfSEBZs8fRdO+pvoS1DoRA+Lpmh8SU7Sp2fo1dfEbbWWVnZUbEN60rVuwMOiP3oyfBaLEzfcdDvMmrUb1FbXwgsvvgQXXfQ/cOf/Z+9N4OSqqvzx02u6O/u+AMqShIRFxtHRUdkUEMNPIREdHB0F/4IzoyKIAyogJCwJiDruuKIjuIIiiiMuo+KCIiKyKItJWAxrINCdkK23/zn3vFP33lfvVVd3qrqqq79PqHfv2e/3tX7mfucufPtX785+evspb6DjX3sstbXy1e0uOGPDxI1s7/gRE0HvPWMNHXbY8+mss97JMfZjgojojjv/Qs957u5MLt1G55z/ER63bPnSipIQrluokRu67UpHLHIbh/sWnMuTcKwMHheD9WLgbHjFzcCOJnrllYfSlke20s3n3+ZW5hCvCHKI7higmc+fTi884x9o4tyJXNUAPfvYVrr983+hR3/9BF/b3ko7N/XSwjfvSQeeuD/9+fI7aMOPH6Xmdj6Xh28lk2EM9A7Qbi+fTy941z/Qnz5/G234AW8x43N7lv77Ilp83N607ocP0J2X30PNcmg0/7P0iCPp/73nLPrzD39AP/74R3jb1wRhBl0sqfmESz9CC/bbnz7x+uV8rtCzzH3Jodgy5ia38ucfDtqPt+utpgXz9+Dteh+mK77ydWrlfCFKLliAi+Hn8AkszSRPbnof20tG3pJsVlGpKENXVcoburGBgBBBK/j6+NWrV46NglElEAACQAAINDwCIIIa/hNjgEAACIwHBJQIClcE8dawnu4St4aFE1AlDDxOfhKbP5X1q1vcBL5o0st0gxBBckYQP7IiSAgAeSyz9tLT5XKpoLiy9AgKiVhhOlkR9O8nyxlBp9JJb30H3wh2F82dM5t+f8ta2nvvqdTe1s7GfJAxH2z92IaHOARvZ6NZ/C8vq3Htqbw1bDbNnjmNtzf10gMPPE0LFy2gnb29dN9DG4m2bqZps2byQdHB4cjJYNNjDjFL68KRiS7dZxE/rHGOOsDBwX7adncvtU7lQ6bn83YyJmP0kW1ZbMMT0e339NLE/Tv5sOhm2nznFmpf1MpkD5NdiUn/1j7a/kAvde3Nx1Z36Gogyy+8KB9+HQAAQABJREFU00DvIO3423aasBfrOzVBf3c/bX94J7Ut4Gvm+ap5+8ZyoPQJl11K7V1T6KpT3+FwFV0fY7XXi/+Zjj3zHLr9hv+lX33lizq+ZJB8OhBt2bqNTnj9a+iyD11Ed99zN531vvPo3vvW8/XxQgSFT+JUECWYFGIVFIW/ubSHt8jXeJsqtRJSr0rREbYOEMCKoDr4CCgBCAABIAAEIgRABEVwoAMEgAAQGJsIrFu3nhYuPJhXBJW+Pl5G5/gDN0yb5qfH7OVZ02Pv7/2K7fKJIPHSGPobryYqhwgqzmaViCZdn9uSNagEwzv/40101pmn0Vvf9i666fd/cmcoTWjj1SrixdexewYl2cSVBHMHNSdJNJ6sXmnmLWZ9juSQW60stxAebm6f2Msrrsl6Og5dAcRGLn/gJKIiX683c19bYi0CF9rySCBu8z8DvI3MnbMjPA/byRX0rjgXjNuFM3g4gAxCA2lSUYuYbZ1vMipXP7cFMcFGjPqZ8Jm9z0KaxFfCb7jjds6rOAkhJdfJ73HgQfTgH/9AO/iWMYnp8zTxWU5b6fXHv5pOP+0d9PWvf4s+dflXaFJXF5tIPclTGKMJ5M16MXHxCi9nYJ6Jysmyf4a2yPaDFAjkIwAiKB8baIAAEAACQKA2CIAIqg3uyAoEgAAQqCgCuiLolUwEzdDDors6eEVQT9GKIEvq1/O4qbsTyxTY0zA6IfbTYj/Dtkm1OKmPc7f5t3b4t3BGEDvIrWE64Ve1xvCRfJ5QXwhVohF7pusxLkOIix1849exxx1Bb3rTG+jiiz9Md9+7LiEOYirKkQn8I9uUfJ1Wq+Xjd4HtEZ3ITRe2NIUfgMVJUrNPmF3w12gW03umW5JNoyUxE3LElcVK1RfomYK7Vpn4SIwCqZLIxNc1ucE6V1FCFCVUj0tsW9sksJhrJusJ6dTLf3+D1CrbwlxSseKDp/kcpH7+Fq28Ja2psMpHqxJDsepk3cyZU+mJjZvcdj31l9hSkyuOm+aT9EUdPKYNRGU2R+5ZnEBqKxXPai9lUxwVkrGFgBBBy3lr2BpsDRtbHw7VAgEgAAQaGAEQQQ38cTE0IAAExg8CSgQdyUTQrGEQQX7yGU9Hk8l/Cj4lLNJUkZIAYuqjqWOBCOJuuDVMtJrPspqvj+CziLXI1VZr0L73Fht9zNIiWRxHYDCBMIFvqZoydTJtevoZ6ufVMfpYJO8l6YRnME1iWPSyfDaCtIHXxxpFWPPpmCSXZMvziP3jXlKlc9eYqle5YSbxvdZyFee1CtRWf60204XIaPwgupBG/I/D3BWSeCUpw8x+HJrHIHAeBaLNsiY24uSDJB0Rml3O1yj4eDvxKn5EP5RNsdfoSMqtq1y7Slddq7ylxlHpmoYfT4mgZUwErSpVKHRAAAgAASAABEYNARBBowY1EgEBIAAEqofA2rXradGiQ5kIWkBdHR3U2dXJq3BKrQjSWsIpjbTlkfmyParXGbTqQ4lamb35m+/wiSDx1ChGPIQT8nR8y2v5zNvkvlLWuCEw/cInO/fJKhXZymUBnSEb8OqXgixBwWJJHWYuDTk42iS+VqvEBbRO4e3ptUKkQkzNY3KfteBc+Cpm4zXJ4FhgfmxTIFFUrKt31EesJIpZu1YitDGpNs7lTAJHHbfGDMQqiEYm2tDCZ1Z5VgyzTwpLotrLtNoP44URzZrfRWHiCN5S5MN98mKl46Tt0n2zT8vT/Tw7k4+Fd96Yhqo9zy9Pno6Xtkv30/aV6WNrWGVwRBQgAASAABCoHAIggiqHJSIBASAABGqGgK4IOoKJoNluRVAXbw3LI4J02lw0Mw5q97qQvJApkz3ewhMKoV7shAiamhwW3cMHMCvJ4q2MSIjJB80Qrl2xnN7TbEwTT/51fCH1YBKxtygm476IhDhJdOFqnTSD4L2VCHJR+MePzUYVxE8im8bysDh52FbMOYj8x9uZ3t7OiDv2Frm0NaK2kl5ABLldXZ7hKnh7e/HxcaSXrtFs3fgLeJmX2IuFKAwhkckjODUrvHIqtasjiqYerl4Ze9aTJbU8XmeSrAhOZqYlDUsqs0NLXHMr4G6CbBdIxxcCWBE0vr43RgsEgAAQGAsIgAgaC18JNQIBIAAEhkDAnxE0k4kgXRGUd0aQzYfzQ/qZrU5nfV988vzTU98CEcQOPakzgjSOj2S+KrGer7BYojqxF51FsrbZq9y0Pp5vqaX5iTz29ZaFJNJgI09gFXt4Msd0QRzXZG9RMXHgbKVERwT5sWhLjJyh83I/3BWJlOHzhGqndQY2crVP5PwyuXnFFIz5mzbvHUfRXuwrPSfnH8kxkIwxjMj/h4izsVVLcVSx9JJCvDAAt+OsKaV1fZgSDmVF4ogcLIqXrqzcOFYc3o2MgKwgXLEc18c38jfG2IAAEAACYw0BEEFj7YuhXiAABIBABgJKBPH18UvnlVgRpJPVcP5aHCrUhutTPO0RWoT+6anvwACvCJqm18dnrQiSmbTfYKWR8mLLzD0mK3Qenp5+6ww/P0pcr1Wcbx+uTHLWhRUfYSRrS4VxLCcRkXPmH3lLDGm4tvkmJqIuiMwxMEya+vKWhWDmogkL0cTeZ/A5VC79dCznEHkFa6BYzvbyTxJA6xFzacU9F9uFD4zZOekFLfaUw6mdQrzYhtvuyCHxlzYLZFKtj0bQ30TEPtmPWCW6JJbaxd4uidllByqECRpSWKEuqVnj2DuvprwE9SIPMCtZUr3blSy+wspsLLA1rMIwIxwQAAJAAAjsMgIggnYZQgQAAkAACNQegbXr1tGihUwELWEiqLOTukqcEeSnpdGMOBiEt7Bpjbxtcuu1gUvSVDvtFFYEcbenO741TC18JK1EaJPwUalJwtgmk3fsYxqxtsOgiz2dxJEO2d4S1VFPbBheld7MUomartSyRtFc+Srp3b7DXak+YXIX+za7COYjb0cesKlz4b7zcqQTFyAJmzWOZJbaredbJhWNxNdHpZ50EX97xNK8fBzz8yM0CtARM07NnomzvOSxOGm6TqIIcaNoJrmFNOFBaY9/5cp6seF/C6uC+vto5/bt1M5/y03NiZ791MeySl7x17ec/6R5pCJvIz3/sHFSu5cNs+VCJ/HtwyWVDTMSzMcJAiCCxsmHxjCBABAAAmMIARBBY+hjoVQgAASAQB4CuiLoGF4RNNURQZ2d+dfH502R49gheWDTap2I5/nrJN1HkRVB05IVQelbw9QqHcmTD16vUdOxfRarTWKlrSx+Wq7eLU0tNKGllbb37+TzjMw2eSdLXZqZpBCbNiYjdg70sW1fkkfsYozC/E7rYjLtw+n3O+qVNGXWXPrDtd+mfiaF5HEkSVKzVWhVuH6hJu4xFzJp8kS+jn2Atm7bTjKx9E/c7uiYQF0TO2kzb8fr7e3nDBLNbCxT2tv0Kg8pF9+28Xpba4VR02SM2MhQCjbu0KKErGKcm7jvbNhOiKABxnj2Pgtp34MPpbt/9QvadP+DyhX5kn0sGR0HdhFcMVatGDtBplcgHH7ThbXYnLwwsOGHgsf4QED++7qCr49fvXrl+BgwRgkEgAAQAAJ1jwCIoLr/RCgQCAABIDA0AoWtYbwiqKurizp4JUVPT7cjDkJvm76Gsrx2mpaxGW+pGOGcON4aVnpFkNRgNIOvRzL5iL6lFqXq8DG0Jb5qb60mOmT+/nTocw6iK++6gR7c8pQjFJwRmwhxsZM7b9rnJbRk5nNpSvskuu+Zh+lzd/6QWptbWROsaJEUjunQCsO6hNSYu3hfesPqS2nTw4/Qt895P/Vu3eaqcfgyiyH2WpW2fIUek8mTO+jM/3o3dXd305e+eCV1b37W+UlqbSh627f30rHHHkH/8i+vo899/sv069/cTBPaJ5gR5xECRitU+kUCmETrUCpFaSopTFZEqY34eZpH2hrJqZMvZX81qrGxuMgsGqB+rqCZDtjrZTR/xl7899lPd2+4hR7ceDeTbVrnQF8v7f6PL6B/XfMhuuc3N9L3Vl1Abe2MOWPsatNyNGmSNelEL6skrC4yGGnHDS0cOQdyK4NGGhB+jY4AVgQ1+hfG+IAAEAACYw8BEEFj75uhYiAABIBAEQLr1q2nhQsP5hVBu5c8LDo1fS2Ko5SArapIWxshkOEWiGyeHhNBfGuY2Pgf7sTxtWfe0rO2OPpHpKFnaOXlnrDwMoshumb6t6VH0Rv3P4I++IvP0C1PPuBWo1jsx3q30El7H0rv+afj6ZltW+jupx6kdd2P0lfu/hm1NLckFRhOXA8nMS5A8zFJIo2+fjr0lLfToSeeSNeuvpj+fN33qZ1vdHNjYwMxaeIlQ4O80ke2NjXLtfZu3AnJwgZiM3/uNPrCFz5NG598ks495yJ65NHHxZE1+ggxJZPNzZu303tOO4nOPPMMev/ZK+kb37jWEYMaJag3QrAQxJUlVpLTHjcuV4eh48xYHRJBYWzzTIpPPpDDg32OefHJdMhBy2lCa5cL9HTPY/SNn11Kax+7kzPzPjgeV0trK73hso/QzD32oKvPP5cevuNOanZbxDh28MEHE5LKiQK5VJDqWlG7/nbghAhZsqpl3PWaEaGmCIAIqin8SA4EgAAQAAIZCIAIygAFIiAABIDAWENAVwQdyUTQLCaCOqmTzwjSW8P6k6HoJDWcuutU2Sa0NomVvrbNQwOYXimFND6i9Z6qFSJoCm8NE50/I8jypSNov1jr84qF9UI7k8URfTUFW2NrWCDkxtzOqfScybPpzk0P0LY+2fIlD5NETETcve0Z+tJL3kJH7/NSOvOXn6ObNt5LbbyVrHWQiRpJKCROKrF1Ld8gbyWb0DGJTr3mGnps7Xr6zspzaOump6i5td35N7VyPCZ+du7YSRMndVJ7Sxuv9NlM/bxKRsgm+1bNLc3U3tZGL/jHA2nbtm101133ui1fLj/XMcAEUEtrM02dOoWeebqHTnzza+m97z2dzv3gRfSNb11HE/lvwY2rgJ70rEoZszxGP3FTVLxdywghT/GInfkGRBvX4A5xVrX7LWAhmPMjtQ4M9NMec5bQfx73IerZ8gRdf9NXaN6MPejYQ95Bax++jS7/3n8xDaQnMA309tI/HLuCXv1fp9Ovv/pVuvGKK5QIKozBFUltbfo9+vp4rRGTaXbGkOV3yav1k4zNhS8kLDSqlRVxxyACQgQtX7GM1qxeNQarR8lAAAgAASDQiAiACGrEr4oxAQEgMO4QKE0E6eS0ePKfhknsdIJtHmIRSqwvb7UxvX+LTnpCUPgzgoZeESQRtcYwskbz2bSl9ILpQq3m1t+A3GCBRN3K5MwrFhxAB87ak1qY8NnK5wPdsP739Ph2qa+JtnD/tXu+mPaeNp/+cc5CWjRzd/rB335HvYO9tGHLJrrmb7/mtUStHEkqUAKEG64n8Qt1cUPOAtr7JS+jN3/sQ/Srq66ma884jWbut9RZPbmxm974xlfTC17wfPr73x+m5z//QJoyZTLd9qc76XvXXU8P/f1RR3y0MTn0lpNeTwvmzefgg3Tf2nV0w49+TpuffZbjaMa99tydzx95DR24/xJ6YuNG6mfC5TWvPobOOedC+ta3v+9IQTZ21vLWR5BOKuZatw32y5qigo203dokxkisWnnUfbytq9eRH4Pcsyi62ayruZ09CqMvxFGJ/m7Z3k3/esT76eUHHU8/vOlL9KM/fpmOev6/0fLD3kVbtvfQZ649jTZs/BtvvWujPiaCFhxwEP3LRRfS5qeepm8yIbSVt8U1MUEmZwoJqSRb3s444x1MgrXQH//4J/rJT37FRUkuqdh+XbO6Py6lz1vdZIg+FhEAETQWvxpqBgJAAAg0NgIgghr7+2J0QAAIjBME9NawQ4KtYV3ujCBZJSGPTvl1Qq59I12cumBhPfFQi4QsKEyu037qEVvppDjeGjb0GUE+krY8saATe19bfkuq84/6GeUhy1Ke7NtO/77vy+mQPZ5Hu02ZQ3MmzaSzfvZJ+v3GtezWRE/3bqN3H3gMvWDuYtp94gzWz6A7nljvDpO+75kN9LHbrnNnBOlVXn6tjGa13JyXCZMdm3ro6LPeR/98wgl041e+Qv977jk0fb9FnKeZ7vnrzXQ1rxR63fHLaRufGbT+gQeohVcHLVq0kG644cdM4qzmQ6F3khBBH/rQOXwj3EJavO++9Kc/3Ubve99K2vDwY5xikKZPn0KXXbaSDn7Zy+gvf/krtfLKoD2eszt1dU6iM8/6IH3r6h8kK4Lsb8CjI60+JlSeO3kWvXnJK/gcpC7qc0QP19/Eq2sGeKMWL3/665PraW33I/QaPi+phTF0o3TLothmsJk62trpc3f8kO5jGzlcW1HXX8lq3/HxzQ/S2SdcRYv22I9Jn3PomS2P0qnHf5qmT5pFfX076Xu/uZz+7/Zv0KT2qXx2UC9N3W03WnHexbw9bHf63Ilvop7HHqOW9jauepB6+RyhCZ1ddOefmJjjLWPXXnctffCcS5mq4uqSz2AVxCOOezJcN5RYPMyeZEqSDsuzXL88u7Q83c8rply7PP9KyNM1pPuWIy1P96tlZ3HtnZfX9KXf2BpWGh9ogQAQAAJAYPQRABE0+pgjIxAAAkCg4gjoiqAjmAiaHW0NyyOChi7AiCCzlIlQ6cemSmYpRJBfETR8Ikgm136NSqncOgm3vGYZ+4oNkxLJzL+DD3x+45Ij6A37vYLO+cXl9Ec+I8gxGKyXM3vu5hUqV/zzW+ioff6Zlv/gAurZ8awjOZpZr5uXfBZ1lL7VIXmItqx/lE7+7jdptyX70w2f/Cjd/JWv0qQFs9muiYmg2+jKq75Cb/iX19Ktf/ozvfPUD9C8uUxKnfVuOuh5B9App7ybbr/zHleS3Ba22/w59PGPX0qPP/4EfeBsOSPoMV75M0gvfMHz6GtXfp5+8csb6aIL/5vPGGqm8887kw499FAmgs6hb199PZ8R1OHiaIVao9Xcyyuk9pu2B53+whU0qa2L67bRsR0zJDKOPz1+H92z6UHG6kgmgsRTyB2NJpi3tXbS6pu/Snc8+SCvIuK1Qu4WsNTX4FhPPfsIE0H/w9vD9qTVV51Crz30VNpnt4N4W9jt9Ly9X0a/uO1q+uaNH6JpnXM4L2+PY4LpNe87nw48+nD65OtOoE1/5/gT9EDpPt7K19rRQbf+7iduhdC3r76WLrnkUzYsV51WoJVGCqe1r+X1qYoTK7yAwK4jgBVBu44hIgABIAAEgEBlEQARVFk8EQ0IAAEgUBMElAhaxkTQtOiwaDlvRqfuMnnX1UHZBYbTYJkcS98myeIR6qWvT7ZUdSER1MNXmWtUiy02YXz1ifOERFDoZ7b69jVIS8eokb2mKBevZDlpv6PoDe6w6MvplqfuT4Kqzz28jemKl5xIRy18KR37vfNpy84tvJWMD4kWZoRJDYssXfcw+SGPyF2LV85s+/uTdDJfFz/nuXvSjz5+Gf35mu9Qx8wZzu6ev95HV175GV4R9Gr67Ge/Qpd++HM0YUIbnfneU5gEOonWrPlv+spXr6HOTt5yxUnmz51BX/zip9xh0WeffTGvCHqUnt3aS2ec/jZ61ztP4RhfpHPO/yTNmt5F55z9Ljr55JPofe9fyVvDfsAxPBFUqJGLdCt13HKYJprW3sEUjhI8ckuYiEUvW8R28qoh2W7W1Toh2RLmjnR21s1sKBhs2qm3mCku4Xdzw3U/z2x9gs5+w5dpzowF9Id7b6SDDzyOvvPLj9Pcac+lw55/PN14+3foaz9fQ9O75nByPjibybplZ55LLzj2SPrk699ATz34ADW38/lK8nBOOR/osENfzLU20/0PPkRr1z7kvo0UZN9HjbN+3ejZTsbsvphrZ1kWy+wrq1/8NxtaD9fOfCVu1ggsntnVw7vcmurdrrpYChGE6+OrizGiAwEgAASAwPAQABE0PLxgDQSAABCoSwSUCDqMiaD50YoguZ47nFTapLd4EDbxjCe3Nn2zd9rPvNJy6YdEUHf3ZleG2puXTXgtp0UxvZvvmzDz7S1DdThKsUjHl34LnSS3hh1wFJ3980/TH596IAmgNd2z9Sm64mVvoyN5O9Sx162krUYEBVZJk1/q4/ssYSJj62NP0/GXXUoHHfUq+sUVn6WfrrqUpu63F5vJiqCbmQj6mpscrlr1YbryG9/jbV2t9J5TT6TTTvtP+vRnrqDLPvJZmsSHSEv4eXxr2Jev+Aw98cST9P4PXEgPP/IYPb7pWfrURz9Ix7/2OPrkpy6n9533GdpjdhedfvrJ/O872G4VfZOJoEnusGitTir1LSFxeHvZhIn0otkL3TY0QYv/DwMnH+SVUc1MCm3YspGe3NFDz5u9j9ITzLW5Q5nZjnePuYOtf/voXfTkdl01JQXbF/CUWRM9seUhtyJoPz6DaZDJm7vW3UQfuuZEescxH6UXLnkVXf+7L9D1f/g8TemYyX87fTR59lw67pxVtPv+B9Dl//Z66nnkEWriQ7P1YTKqv4/uu/dW1508fTGvmpqW6NyfWqFt9vqdTGxIJEQQd/Wg6ay/F/MJ3uJupvLOfcwo1yBRlGs3VJxK6sutqd7tKonJULGyscDWsKFwgx4IAAEgAARGGwEQQaONOPIBASAABKqAgJ4RxETQEiaCOuXWMD0jKCaCbHqeVYBMYIqfbGmxXZYkPiOID2N2q09sBi0eNhmXtk2gijOGVmIZPlnWsb1ZqNSySL6QCLqVr4eXx3zv2fYkfemlb6Oj9n4pHff982lrL5Mcbj2MxjM7rYV7uoRGu+6Xz7F5dhsd9JrjaPm5H6BfXfkN+s6Zp9Dc/V7ktPf89Q901VVfp+OP/3/0xS9dRe85+2M0e0oHvf/Mt9M73nEyXXDRpXTlV79LHfwtpap5c6fTFUwEbXxioyOCZGvYFj5D6NT/PInOeM9/0qc+9Xk+OPnjtPtzJtLK899Db/v/TqKz3vdBJoK+z2cE8TXtmQ+fY8Rn8Tx/5t504cEn0fSOyW7lj/yVyLeS/www2XPThrvo1if/Ru98/go+NJrHz/8IgSQYCCYTeAvXe39+Od302N3U2sQWbmtYmFAwa6LurY/Tfxx7GR1ywHJ65Kn19OUfraTbH/oNnfnaz9OS576QvnT9uXTng7/ja+U7qJ/PAJq3ZAm9buVqGuwfpP859T/4xrWN1NSqRJAcRN7MZyqd9q63ym3zdPMtt9NNv7nFbeuTwtyfWlhCUVuq94/0PGnl5ZktHnv0yPAEFDxAIAcBEEE5wEAMBIAAEAACNUMARFDNoEdiIAAEgEDlEIi2hgkRxNuB9Pr4eDtYPhWUPZHNlpZX9yCfETQ5uD5e58qybceipibUhbCmV0F5VubsR6hR9NdN9N0PUUcL34HF7TfwGUGv5zOCVt34ebqDz8Dp43p3ukn+IJ8RJNfHn0Sv5BVBy3lFUI9bEcSMQ1J7WKHPKDVYPpbyOTaTZsym0753Df3157+j6y481zFN7gwi3hp21VWfphNOeC3fePVnOv2Ms2nG9Kl03gfPosWLF9K//8fpdNcd91F7RxtNnNhBs2ZOpY/x9rInNz5FF154GT21aRPfHLaVlixeRN/65hX0q1//li6++CPuprbzzzsrOSPoXPr2Nd/ng6OFCOIB8z+uOv4RKOSR2rv4Ovul03ajNr6tyyn4oGg1YENmVDZue5qe4UO0F05ZwHINoqPUQHJY812b/s4YPasEjARXA5dDO03U17+DDtrncDpx2bm0dsNt9O2ff4xmTp5Pbzr6HD78eTv997f+g7b3bXU+A739tPiww+n488+h23/8E7rhvz/OS8x4dZuwPlxcP2Pb2TWJfvfb/2VRP11zzfV06aWfYCJL/971b8xGGRXj4ts3M40v2SRJ6bkv9ojCl/Lz0XPDQdHQCGBrWEN/XgwOCAABIDAmEQARNCY/G4oGAkAACMQIKBF0OK8ImseHA3e5lSQ9Pd08d941IsiylJrmmk36PcDbo6ZOneLEPd0jOSzaR7Q5t5f4lq/Npvei81KzlFUsCyZOp7cd8CrajW+qmt01lW/Mmkt/2fQAbeNbq/746H10xd0/datd7tn+FP3Py05hIuildMy15/ozglwwT2UZG+Drs7wi4RN2GP7jzl1Jiw95GX3n/FW09pe/oFbeqnX3X2/lw6K/Sq973WtoO18z//TT3XwmThPNmzeHfvCDH9FFF32UNm/extfAH8VnBr2ZOjom0J7PfQ7frtVHjz72OL/7ebXPd+iaq3/It4p9kF7xisPo4Q2PuhU8c+fM4r+BKXxG0AfpG9+6rnBrmJI8hoZ/C2qyekvG4lbS8I9gpSPhNjea+UylgaZ+OQfahZHRCQpiI+0WXglkGpOxgB+Noromt/3sxKPPo/35cOgnex6jrraJvCWuhb574yfp5ntuUHspgv9Z8cGVtOjFL6FrL15F99z4S74mnomqJLgQQe0dE+n2P/2ccRvkQ7GvpQsu/Kgbh2ZUQ6lNH1+r1mJy01pfva2X+3aEYaJ1LmX65QaEopERwIqgRv66GBsQAAJAYGwiACJobH43VA0EgAAQiBBYu3Y9Xz1+CC1euoC6OrrcTVHdPT1FRJA4hXSJD1J6Ilta66OErXhrWLlEkETIzuYn9WGW0Fot9DeMoauQ+nnyPrdzMp2w8FCawySQ2A3w7VR65XkzE0L307fW/d6dkfNk/zZ603NfTAfNXUQfve1a2snn0cihxPb46GFO0ZpG5EywMGHx3H96Eb1xzYfpL3yz1/WXXUzEW53cGUFXfY2WH/dq+u53r6N58+dSO2+xWr/+fvrSl75Ojz3xFNdFdPDB/+TIoNa2ViaVhKxhQoMVctX8z/7vV3TdtT+jPfdaQCeddALttdeetPXZZznGg0wozWUS6Ht83fxdfKgykzRapi+PI+kTKowsYVly+LVjgcxU3lyTkkEyzsRXXo5BUvIo/vvydnziEK/Y2UnTJs6mw//hX/nQ6D3cyp67H/o9/fov3+MVSXIjGEfl84Fm7LkXnfixT9Fj6++jb551FmMmq4F8LNka1sKHSV944VmM0yD97ve30vXX/8xVJKf+2ONbNjaXwdTRW6Lr41smKX5zZAvu6iq2gAQIGAIgggwJvIEAEAACQKBeEAARVC9fAnUAASAABHYBAV0RdCQfFj0rdVh0vCJIZq82f43TpSe/sZVOo2OPoXq2Ikgid+/iiiDLFVfleAlWiVSyqDa28eMSudyCtZlX/+zg1g5eLTXAq0kmOIKniaY0t9BE2R7lniZ6lgmJLfzvnJb2Aglk0bIyJY7xiw2b29volaeeTtPmzOPVLStpx+ZnmQi6na668go6bvkx9N6zzqOrv/tT2o3PAdrwyFM0c8YkvpKdSSf23bGzlx596lnq507vFv6Wbc00aUILE0wDNGf2RN4COIF28ni6n97G5M90vklsO218+lnasqWfFu05nVfRyF1g+hhN4wvUUWhfrbxtsc7wtb8FZ2ErY5gMyfaV6F4jrf7BPtrOW826Jkxlgm27Oxi6g6+glxOHJEc/j3lvXgl0yFtOopuv+RbdLauo+CDtkAgy2/WPPkXUx7eqzZzksODlQRzCVjNJhZabm8kT/jfAtPFoTWoeWe/YI8ti+LLseocfBx71hoAQQctXLKM1q1fVW2moBwgAASAABMYpAiCCxumHx7CBABBoLARiIkhXBMkZQf2prWEy0Q6nsB6F9OQ3tvLTfO8xVEuIoGnJ1jC5NUwXToR5NIef/orO9+K2Zoursml+7JNl42RCVnAjCwHLLFl8NB21xtNVRaaPq7ExqaXq5DeJynnlwONBWX3EK3mExLjnr3fzGUGfpde8Zhmd+8GL6Hvf/xmf5TNBaQsBSsgMLlYiaj0SS/NoVJ/LHcLN9rKdy1KarYCuK2RsnYz344BFj2bQnPZrEcK+OGok+TUvL5WWPYqi2CcEDRNvcqua+Kl3UhO/ZCxu1DIeXgHlVmHJ0qhCjsSW+wqTkT7sl+Dm81orfmsE84t1Pk9anvSds6vQmdrYcqwhBgIOARBB+EMAAkAACACBekMARFC9fRHUAwSAABAYAQJ6a9ghvCJod14R1MEH6XZmHhat03FJoHSCT5U1mTeZTJqtnfbzEdKtmAjaUphjWyRfi3qq3GuVagj7amdUgPRUKxKzSybpzlRkobUTJj+mk5FZW1Tib315W1sx0Ogiix/PQVgtwdtCyMojIXj40J2NG7vpTf/2avqnF/4jn/XzXfrrPeupXVa9yONc47qlRi/xLUf0cFcl+mvfSkfiaxWt1Omegk/ST17OVwgoF8p5OARErWPPsHc6G6RzLBhZLQVBNAorxmsNbyN57LtqJWYX51BpVizzNj99Ky6hTHyt/lBeqh1WlPZN9y1OWp7uV8vO4to7L6/pR+OdriHdtxrS8nS/WnYW1955eU1f+o2tYaXxgRYIAAEgAARGHwEQQaOPOTICASAABCqOQOGw6KV8WHSHXB/fScVnBOkEOmsaHU+Ziy2KJ/RDD8GIICEVeno2JylkQmWPzxNKrRbRxnIjPNRfdT6GRbV3qEmTIhI5jK22+ity7xv3wsyWJ357TwmixItRDxIricegbN+xk7Zu7+Nbwdr5fKC2pB7xT+eUvj2q98ST5lDiRm1Ca5EEFTmDUnqf2XsJUtrzMs2kv+pjUdM2gdYVzf1kpVPCNnEQ85V44m99bjsfkeuj0aQd5jH7xMi9NE62Rqk+b51l5bVoAYFdRQBE0K4iCH8gAASAABCoNAIggiqNKOIBASAABGqAgBJBy3hF0DR3RlBXV0eKCLIptE6gdZocTqd1MqyT/nCSrYMZyVR5gNmJaVMnuwDdPbwiqOjRPHFs37MalRhQ27AytUxLfN+30onFU7RKBqmdtzZtmNdH8PWZzDBLNjWZOOetOSW/ZtRsFiPHicXm5y10BE7lhmPjUEuN60eloxFvk+lIiu28hfeRllXsKwhbGk0lhcoSA80Yj4DtHSHEOrmWrPBwX0tyEvPRCAUjboSS0D+2UU2WPvQXH7GRf9Ny0aWferdL11vNfr1jUW591cSI/6r4jKAVK46h1atXVjcRogMBIAAEgAAQKBMBEEFlAgUzIAAEgEA9IxCuCOpMVgTJGUFyfbxMbWU6pI+fzsdTJJtym5XZ69v7x/JSvcKKIDbazIdFR/N956iT7uLYKtFKra64Ws3rJ+3WUmvfs1ba3hAJV9YYCSAxQpRsPYz4xPE0qvilx1Bs5y0KrWRIZmtyn9viF6pNBOoofuYjCo2jtchv4eygTLvQ18eTOPKEcVWi2bJqU33aK7s6H9e3tG6f08ahEb2d5Qn1JvNv/xcQfy2JY5m8tW9ZnqHszKNWdpYfb/2LKfVN6wcjrAiqn2+BSoAAEAACQEARABGEvwQgAASAQAMgoGcEHU77LpnHtyf56+PtsGib5spQ8ybzOoVWMGx6FfoNFyYlgqa6jHJrWAa7kIQMiRSfUQ8/DqvKqiCmBby3t7WxqEQsTBJam8z7ha2witgy7oURzd8slJgRqUiyLNVD7c1LZGobesg3tH5oqRFMY54qLfWbjuEjiJfXWsv01tfYIg0lZqUxwp7ai7VKTZf+2wxx9z4az/oupQVgoTStikBcMM9ulG+Z7Q8pEMhHAERQPjbQAAEgAASAQG0QABFUG9yRFQgAASBQUQTCrWFdnZ3U0dnhDotOE0HpifZQRWRNxIfyMb0QOVOnTHa3OfXwrWHGXIRTbp2wp+kAjaC1DlWBRbCshTRewC21CjOr2iTFUSJ3K52F6qH2YuNb5mExrZ+2Un1sZVFEGtaSP3pvpT5qaXHC3HGmUBO3Q988H7NR/fD+mrJj6rodr4tj+vFLZm+llce28Wi0l/Ywm+JoeZbmUc13cTXVzIbYo48AtoaNPubICASAABAAAqURABFUGh9ogQAQAAJjAoG169bTooUv5zOC5uiKICaC5LBoI4LiQdh0PpYW9/w0vFg3tESuCJ/K18cLzSNEkGzDGs4jRJJbRVOGkx+RkUpKqIQpvUYDhjpLkUUtqJ231lyWUeTWLo6rmiwbk2lci2DSsHq1sArtbR7WNyuR+01RJhUr1Zi9r9rn9LrQz0u9T9gK9a6dTuSE9rcU1p2VJdRbZKsw3z7Ly2rJ/ruzWOYZY+alVkMV33LSd3aRVUyK0KOJgFsRtGIZXbJ61WimRS4gAASAABAAArkIgAjKhQYKIAAEgMDYQWDtWiaCFh1KS5bMp46uLiaD4hVBfiQ2xfWS/JZN3vMtSmkGBgZo2rSREUFCyLipuhwsZHP2Esn8qNJ0T+ikgZyt/CRxw/BKBInER1QUmChwE3aJ53UaJO4HWRLLOJ5E0JhBESJMHo1mMfO+genNy3JodpGmo4ceZm3eWW8fyWt9DN/y2ryWRgrj6fcNJcWo+miWK7b3+qyW+Ai5w38P2kyMsmO4DMn3FcSz6svKAhkQKAcBbA0rByXYAAEgAASAwGgiACJoNNFGLiAABIBAlRDQrWFH8YqgmdTFZwSlt4b5tDap9pK8Vh4FkWefltth0TIh7x7BiiBPDWRP3sN8Niq/riPUWlvjKNkjMu2no1us2EuqSWssRig31EJryaA22Rktk1lJ38e0iGolco4ipIU86eLzhUFEZ5TxYzlLV6lWZpsRpkhUPAIxSZduEdNyxaJYWpQmU8BRJbC4u3deHMseBsmzDW3QBgJDIyBE0HJeEbQGK4KGBgsWQAAIAAEgMCoIgAgaFZiRBAgAASBQXQR0RdCRQxBBeZPdtFxnzfH0ffj129YwmYUPlwjSFRmc00orsXVGTGzKXvALXQul67isW2rtkNlYXOlrKVaQWhhGodR8QipIrFVuNZiVxrHfMI5lNF22h8UrbWXaOL5Jw7dY2KhCuba9v7WsKuubT1iXj5dlJR7ZcpNaDos9zLcLIz8cx4UKa7NY+rUsk1pbz2zG0jtrjGOp/saqFURQY31PjAYIAAEg0AgIgAhqhK+IMQABIDDuEdAzgg6jJUvn8Wog2RrWyYdFd6fOCLKJ9dBw+Smwbw3tZRY6jU4TQaItweeYc/LmGGG5roziWsxENbIeaCAhXDSMVqKhir1t/ZBFUR+xU4knMJKiErm3FwvtyW9s7+Xmre+4jrAXU0euDg3rHJ1l0I+jSi+MVawNJVpbKPHtUlHiCr1PcYujBFutTJ/Oa7my5JLL9Oof9yxmWW9bQSUR02FYJzsQ5YlVcU8t8AsEho8AtoYNHzN4AAEgAASAQHURABFUXXwRHQgAASAwKgjo1jA+LHrJXN4a1kmdXZ0Zh0Wnp9v5pcVT4LiX76Uam8AP8AR7mhwWzbPsnp7sW8PyY3GtYbmuhOI6zEQ02tbf0DKPAzDfdA0+VhYxkE+FGA0kcaP80UA0m9f7lmjyoyd+6eAqTn7jWJEq6OSN20xKRRmyPgvCCJit4SKqdG7LZcOK9RUkggp1ZTTkD8QKMXWZZ1OZOd5AoBQCIIJKoQMdEAACQAAI1AIBEEG1QB05gQAQAAIVRkCJoGN4a9hU6uroyCSCZK5rk/P89H6NS2yTninH2rAnOcTaiCCZZQsRZJP8siMlDM4gLyPSdTdpTx2NSDV2Vqug5DJUr9HEw+JZZSyKniwKQ318ZnGwuJGz62iWbNQtuximKzBdWi62ppO2fxKpOSRj9fq4ZWZWe6iN45ulSLPHYb5qYb8axXDSXjhOs1NvyaISi6Z5zS9v1GY98jfn0X/cDXVSrztcWgLmLl+LK83PXSu7/Ioqr6nVGCudt/LIhBGFCFqx4hhavXplKEYbCAABIAAEgEDNEAARVDPokRgIAAEgUDkElAg6nImgeUwEZa8IkqnTUJN5sVA7aaWffE1o6SbTLBjgyY/cGiYR5Ywg93CIOIr0dNKvBuFvMkl3PrFXlo9GUTv95UpE6DrmXxCYgt8iC+RuhYjYBz5OLQSXyqTrHls5wgJHViUuBb0QC2zo+4lfiZdlNZO0r9aQloZJOIILUjqzRAhzaT8jblLIUCNRgi0rpo1kaBy0Yq0hrC2O6uOV3yqBReF7pxEpFb1EvMit0nZR8Cp3Kl17reJVGaYofPYYsSIoAgkdIAAEgAAQqAMEQATVwUdACUAACACBXUVg7bp1tGjhYbw1bL47H6iTr5APzwjyK2p0jcZQ+WQ6U/xkS0O7cCpt18eLvrt7S7LAIrQIPbPa+bZKNun2I6tKrPURSkKkAybwdExiNDDYz7omaha75mZue29xEm5gkG00Dls6G09HCCki18nLzWji2tLEMSQl/2uESDqmxC3nkTAjftwwXBHDDBGPP8s5z8LX61tZ/iLLjlEsLY5ULMnLATkQqDcEQATV2xdBPUAACAABIAAiCH8DQAAIAIEGQEBXBL2SVwTNoM5kRVBPTw8N9A/w5Fsn2kpQ+Em3TK19z4Mw9JTbLMTb2t5fF1gwSWIrgnjVTHfhjCC/qsZ75LeU8BG9kT6aL123El0yHqNuwpi+TufHBc6YMYXa21po+45eJsy2um1sBQ/Wt7e38vlGk53o2a3b6dlntwU7hTRbexvbTJvsEHiGVzzt2NGXEEGFSAV8pWr7Dl4bt9QmC9HYLu6FePDoZYWSPMlLO+X8phHN9klbebQ9xtmeKk37e9t8jQ7FD6i8TD4yWkCg1ggIEbSct4atwdawWn8K5AcCQAAIAIEEARBB+FMAAkAACDQAAnprmGwNm1tEBBkFIVNpm27btNr6IQSmC2Vxu7SFkTdGBAk50yNbwxK32Dt/Wm/Eidl7S6N9fFVm4yXplo50gBfw7Oztp69d+WmaNWsaPfDABvrQhz9BGzY8yg6yMqiJdu7cQUcfdQidcspJbnXVLbfcRm95yzm0dOkcNwYhP57dupWOO/ZoOu20/2SCqJk+/vHP0A+u/ykTSO3cN1SL6/RfIK5v6Ppje99TT4/VyCNpTKvdZwhbWdoooxiIgN9yttPInuIsnnAaWUR4AYFaIuBWBK1YRpesXlXLMpAbCAABIAAEgEABARBBBSjQAAJAAAiMXQR0RdDBTATt7m4N63DXx8uKINkCpfRD1rS8eMqtGGTZqkZ+S2tDImiqOyOImAjakril1+wYc+Cja8uoDXGTfGGlxQRL6YokovoLEdSzZSfd/qcf0uzZc2n9+vvp9DM+QPfd9xBv/5IoTbRt23Z6wwmvpnPPfi91dU2km377Ozrk0GNp6X5LJJB7enq20MlveyOtXHmO669atZr+56vfpo6OCZzJak1XZfKsMUkYG6e9XeghfgwLjV1MmCTVsLqpLGLG15iVOEtro5SVYPJIGtc2hUrd79A/WRnEq3hkQ8eCBRCoDwSwNaw+vgOqAAJAAAgAAY8AiCCPBVpAAAgAgTGLgBJBRzIRNCsigvoTImi4A4vm8JFzvkbNlHgQK1kRNIWJIGk7IkgMuKPUhcWRib+1xUAfI1NME9MDJjXr+C3adFSLJ0f6PPtsL936xx/TnDmz6f7719N7mAi6594HlChhFmMrbwV785uOo/ed9V6aOJGJoN/dRIccvIKJoL2TRHILGhNBJ7+JVp5/tpOtumANE0HfckSQCOT8IH2sVuuLNCQ1iitNHMt8xUSQxbbxhkHCrKE8bJufVR3qwnY8mlCj7VDvtdlRTao+2Z42Lh8LLSAwdhDQrWHLeGvYqrFTNCoFAkAACACBhkYARFBDf14MDggAgfGCgD8jaCYTQR1kK4L6B3hFUN7ceghwbIIem2VLQxvhQGR7lEx+hAiS/EKcuIfdYyIo9PRti1Fw4iDFwyhdS6wVf5ZwYDn4ed9996EJHe20fds2evCBh2j7TsOJq2O2aNq0SbTHHrtTM2/76tncTQ+s/7s7VNrIlAHGdfqM6bTXXns4AumB+x+ipzY949qS19caVmFSi+LHG7fMLpRmxRG9lytCcfY4grcN5XE7K7e3MG2YxUf1LfMwe49IsY3Z6tt7xPKhMIut0QMC9YSAEMMrluP6+Hr6JqgFCAABIDDeEQARNN7/AjB+IAAEGgIBvTXsEF4RtFuwIqib+mUvVN7cuoyRx9P2uJfl7lLxj2wPklvDoq1h4sAEkUYpHUvi2FoXT3boQPTXBZOf3EczaCQxCr17e3u5Po7M28HaW1u5IdZWEytY2ctnCYm8mW1aWlsKdVucwcRG+nLwdHNzC+fgngiSUEZfeJKGdV6tnaJfzaBiq6nIiAVBItezfuhvUUrFCWPHlVpEs7DIEi2tKww6MTZb87UvkLbzemuFnr5u3zI7vIHA2EAAW8PGxndClUAACACB8YQAiKDx9LUxViAABBoWAV0RJIdFz9Pr4/mMoG65NWwXVgQJWH7y7VulQDQiRImgQSWCeF5fWBHE9IHjXFyQoWIaIWB22jdpWF26JvNIy/kuMxZJBD0Y2rMZKnO18Yog6cnl8ppLyCuV+HhKmGgNks1sPEHiPTylZf559Zle35rdj9Mi2ju2jjNrTyw0V7kZNWd51nF+QcD8rHK1iHtWUeyd7pmPRSzPKx0FfSBQDwiACKqHr4AagAAQAAJAIEQARFCIBtpAAAgAgTGKgCOC9nsV7btwemprmKwIsim6kRf5g5SpfNpquFNx8RcfmfxMtjOCZGuYm9uzzgX0UfOr4Ujso4cci7P3UZogloneqveWYXT1EolvSU+spWYhh1jDXdnKIXklvz1xTFGoRE10zGIbuLBFaGORzNP3h9+yLHFVPo7IjdDKsxFr0VksGTdLCreeid4etTNLH9G3NI7vq638qq9GMn0osxz5b/PKt6iEptyaKm1Xbu3l5i03XiXsyq2p3u0qgUV+DPnfwhV8ffzq1SvzjaABAkAACAABIDCKCIAIGkWwkQoIAAEgUC0EdEWQbg3r7Oikzq4uXhHUXbg1zFMPNpXPrkSma/aYpcpCjVkUv23yL9ZyWHT21rDyYkn0uIY4n+lCqSNehMjQf5iCyLISD6khpLyMstFo6mW+Wq9VLVJri7VF0dVEYc2hlcYd2W9ch9WumSqRQ2L4EWtPiC2Tp982CpGnH4+OVa0W1svyScco7o/MK4xjYwhlWe16t8uquVqyesei3PqqhU9W3OyasCIoCyvIgAAQAAJAoJYIgAiqJfrIDQSAABCoEALr1q2nhQvl+vjdSImgZGtY4daw7AlKOr1Y2eOn9CYJtSaL3wVShMUxEbTZG3IYjTR0PO9U3DJqoVgTSryVEBtWn1hYdrMwKsjd+OWUJklRLuxgcexKdomRuLjVRNKz+GE1lW0XslY2bG40Q8poIx2lVmG6GOM41MgRGblnXAF6QKAWCIAIqgXqyAkEgAAQAAKlEAARVAod6IAAEAACYwQBf2vYjBETQenJdjHNIBbF0iKInAlTJXyY8pRpU51L8RlB6WxFUUoKjHYoaZSschGbsHLzNVmatDGSx+Rmb3HkXbgenpcB6UjcoEUVPdnSyKThOiF+4eA83l6aJfPa4pZiXSyHBAjUMwJCBC3nrWFrsDWsnj8TagMCQAAIjCsEQASNq8+NwQIBINCoCKzlFUGLFr6cVwTNZSKog7eGyYog2RomhyOX96Qn2fkkRtoyjm9+clD1VCGC+OnpTlYEsatRJ7HXyHqWS97pRwiJqFI2cit4Eif1sS1Qobd5WlQfRVqJO79NH/ry6BIj03rv0K4x2zrm7JGncRArk5lHKVTMtpQNdECgHhFwK4JWLKNLVq+qx/JQExAAAkAACIxDBEAEjcOPjiEDASDQeAgUnxGU3hpW3piVpIkpjuIJeLHEorsJfXLQshBB05gIEpkSQeLHxItjSsxj194hmSCR0oSCZrQcfGcYG2zt66UWPhe6na97b5Ur31kWjyibCIptJJdmi3N4mkv16pX2tYoa8W24hKhmjb/0t4qRyfKPLdADAvWLALaG1e+3QWVAAAgAgfGKAIig8frlMW4gAAQaCgElgmxFkBwWPTIiKA+UeCIe90IfndzrKpsBvoZ92tQpji7p6eZbw5xb1gqcMEJ57ZBESFcT6jSaSNiKGZ9J7Z207/TdaGv/Tlrf/Rht2bmN2lpaIwap+JSbMING93SPZQizin3cDyOoRyP/eiooJINkxCEOIUKl0Ah9StlBBwTqFQHdGraMt4atqtcSURcQAAJAAAiMMwRABI2zD47hAgEg0JgI+DOCZhauj+/u6eEDm/X6+EqMWifkQ0/LZYIvVoNMBE1lIkg4kW65Pp6FQ3sPVSmTDC5BHCnsGcEgMtfmW8Tk3dc/SC+du4je+fzX0KS2Lrq/+3H60l0/ojs2PUTtLS1sbFEsQlhLrEsTQZIpy0sjxNZmZxHDLI3StjHmjafwbfIMUvJGxio1VHQbEAE5U2zFclwf34CfFkMCAkAACIxZBEAEjdlPh8KBABAAAh6BtevW8RlByfXxnZ1MBnXSM3JGkCOCvN2IWzyz1x1dWVNym/bHugIRxEm75YygwsHKI64iRbfE+aRnleRlaGluppmdU+jVz30hnfS8Y+iqO35Ea+68nma1tA/pG8bUzJbfMueRQTERJHGkTvMO41aiLVUUZ6xE5OHHGOp7lBOxWjiVkxs2QKASCGBrWCVQRAwgAASAABCoJAIggiqJJmIBASAABGqEgK4IOpwPi57nSKAOJoJ6mAjqryARpMyFpxiUzAin+vGUvbA1jE3crWGJOrYaPmAx3ZIfLdRYleK7tb+XnjdtD/rC0WfQD9fdRBfcejVNbG5z5EkcO782jV2cwfLEnh4zk4udeNvb5I32zsZj+KMMkR6+NzyAQG0RABFUW/yRHQgAASAABIoRABFUjAkkQAAIAIExh4ASQcuYCJrmtoZ1uhVBsjWsP1kmY7TDUEPLsAtn88GqHiNN/CTdtyRLuCJIiCANM5LVKr4mi6GjsHwqFbolLDU9UosidS+cPJc+e9R76McP3EwX3/odmtDUnEEEmUc6kq3msfyiNzSKbc1aNLGH2oayLO+RyfJrH1m8cr3ivPnfI7YbKnp1MEpnLbemWtml662Hfq2wqHTe6mIpRNAKvj5+Na6Pry7QiA4EgAAQAAJlIwAiqGyoYAgEgAAQqF8ECiuClvCKID4oWoggOSOov5+JoF19wtl8dOOXV8i0zB6ROlKGVyNNncZnBPHTXTgsOiZDnLLMH83mcyZZEu+wAhWF1ExhTQ67DzBps3jqfPrMkafSL/9+O6285RvURnyNmKNpfPxSU83ibAnfljmWQvYirWTLilVkWKZAq9eoI4tb2Yq0njKLzzEb2ThygkEMBGqAAFYE1QB0pAQCQAAIAIGSCIAIKgkPlEAACACBsYHAunXraeHCg3lF0O7JiqAuJoJ4a1hABMmEekQT89CpsCIopFk8hWI55D3A/19wI4L8rWG7RnxkZVWiQH+Lv5Z4xBYDfHDr7pNm0CWHnEzNvBLozF9/jn7+9Abas6WTJvAZQnmR0rFDuxCitJ2MOLQN9eaXpw9tx2LbxrertTcqPruKC/zHBgIggsbGd0KVQAAIAIHxhACIoPH0tTFWIAAEGhYBXRH0SiaCZlBXh78+PiaCii9GLxsQmdG72bj8KLVSapIvVkIETeEVQXxpV3RGUJiznAl+Ok96FD6GbznGy3XVW389IdPKBNArdj+Q3nrAMpreOZWe2fY0ffK279MNj9xBk5r5Onk3WLGPqSerPcjkROkazU7faWuvFY345lt427HYKo1LeSNqVGzKGz2sGgEBIYKW89awNdga1gifE2MAAkAACDQEAiCCGuIzYhBAAAiMdwTW8oqgRQtfzkTQXCaCOtz2sGfc1jC+Pj4hbkKMhp6gG0UReklb5TY5NxIjiscd2UGmRNBk9mgivyLIPDSuxUlnCftRbEebhNowTnBGUFKDaNVffx21wyuCJrd30JuXHEGv2utF9PCWjXTPxgfpugd/T/c98xi1N/NV8u5R4iiLCrK64zi+R3QAAEAASURBVNoSt6KXWstv2t5kFq/ItSYCqbKcisqzS485a0iWLbQ1WZb92JWFmIXtrBEZGoaE2afl5mt662e9y7HJ8oNsVxBwK4JWLKNLVq/alTDwBQJAAAgAASBQMQRABFUMSgQCAkAACNQOAV0RpNfHy4qgrq4ud318f78RQTKZtElgFrWRrj2cfIY6i+NlRZIkjUx+3IogNu1x18ebj8Uun25QTwmc/WjEgAhKzKLaEvdB6qd9psyjTx95Gm3Y/ASdeuPn6M/dj9GeHZOog28PK6ZrsnOWL/XjzfMZ2iLPs/7l+V/Nf//kTyYiyhoZk/r/aqiwkghga1gl0UQsIAAEgAAQqAQCIIIqgSJiAAEgAARqjIASQa/gFUFz3NYwOTDarwiyCXc83Q4n6MWboGwaHvqEsqEHPMiHRU+ZNtWtLYmJIPHVWBaxVDRfp2+l7XXtTjGFI/Ed7TWYZHKvflo0eQFdftTp9NMH/kirbv02dTTxKiA1dh7p+NJPIkRkRZadyszahy2Ez3FSD8M7x2iMirO+nEcopCj9AA2v0M5r0QICYwcB3Rq2jLeGrRo7RaNSIAAEgAAQaGgEQAQ19OfF4IAAEBgvCNgZQUuWzqTOYGvYgDss2qbSfjruW4qQTrrNTmRqEW6OUrLI69Sz+NeiDDgiSM4IauKDqze77WLe2qgbTwJ4XdzytfpWbCFki2VNiB9nkEjZzTyFFNo+0Ev7Td2NvnD0GXTDer0+voO3g5mNt/bkTzqft01rrO/rEUncMxurNa5etBI/lHqParU0o4+e7nvNrrQMt6HwkBx5NruSv/a+1cG1/HHVOn/5lTaS5SBvR12xHNfHN9I3xViAABAAAmMdARBBY/0Lon4gAASAACOwdt06PiPoEFqydDcmgnhr2ETeGtbdTTER5KEq3hwm026bpoudTsPdb2ruGFppxGKJ+A0M8vXxU/318S6WhlW31FQ/UiUW9vIZfMt0MWFiI0tL2Zpd5fyfPSfPphfP25fe/vzl9M2//JhW/fk6mtXSzgZcgSsizpFfl21FM3uxlHbsEfdYHTxSrerNSv11FPEYArdRaEo9Nq5RSFeUotb5iwqqoKDWY6t1/gpCOUZCYWvYGPlQKBMIAAEgMI4QABE0jj42hgoEgEDjIqArgg5nImieEkHJ1rA8IkiRCCf62fdjKRXhaYnQI0azWDPoiKCpzCcM8oqgLUp4yCnS7jF766s07sWWWT2RxXSJr1V0lkVavXxm0Utm702n/dPraO7EGXTvpg30hT9fTzc/tZ463dYwy+69NL786mMay2l906ffFjEtl74ngUwr1hpRfi2HacfPW0c/9HjLtRs60uha1KruWuUdXXTrMRuIoHr8KqgJCAABIDC+EQARNL6/P0YPBIBAgyDgiKD9X0VL9plOnZ0d/G8nX9neQ/3R1rDUYJmgSdgZp1AKxdazMDFU0HtSQqaS2U+gkbDMaQyw/9Qpk12nmw+LVlKEf10jsE80Eldt4gyhpREloUWaMPHj0GjmL8OZPqGLDpq5p6vp/u7H6X6+MaylQE5ZVPOwvq9LNT5jsaX3kVbWeMxCiSCxsCjxCiOfxTzwBgJAYCwiIETQCr4+fjWujx+Lnw81AwEgAAQaEgEQQQ35WTEoIAAExhsCfkXQfEcEdXV28SqcbiaC5NawrEfpEiUqPF2h5ITYK0HhKYrQJjuekyYOBSJoKhNBHMsdFi0GBdLFIjsvZ+PU1g3esaX1fD2+ZZSK2UhmXemkb9YP9tNO/reJmh0B1NLUrE6FINLw/laGqk1XMM6wNA//9tZeFrY8IRRK0a4+AvY9q58JGcY3AlgRNL6/P0YPBIAAEKhHBEAE1eNXQU1AAAgAgWEisG7delq48DDeGqZEUGdCBA3kEkFCivD5NAVixhIyLcE8iImNElEyQ39FVjyFNkuNI3o5LHoq3xomAXt4a5hzErUFUNPkV2NLx7dUFUe2nrfyrZjCMblb2eQCCx3kt8D58euY1US8LIfm1zgWTWX2G1ua1L+zvVQvvpotfU6Q90cLCACBsY8AiKCx/w0xAiAABIBAoyEAIqjRvijGAwSAwLhEQFcEHc3Xx/PWMD4supPPCJKtYaWIoGygjNooRWF4T7NWCfeM3WCBEC1Tp/CtYRxKtoZ5hsfH9iuQJILKvVajyq/PYy1v5VuhnUUTmflIpLS19c0m7gtxVOoxrzwb8xZyTSpRgs2kKrPfMJfVHMrEDk+lEJAPEn6HUnHtK5drXyoWdOMRASGClvPWsDXYGjYePz/GDASAABCoSwRABNXlZ0FRQAAIAIHhIbCWVwQtWvgKJoJm7yIRNLy8Ym3TZPVMenxlvJAZXRM7ebrdRFu3bg0C24Q69rSJuWnNIW1lcnmnbUOdtUv5m41/q7XGLSe6jj+0zM+XtfLHrCWCtDUSiCD/RcpvefxK+4SYl7a0v275cuE3HsoLeiAQIuBWBK1YRpesXhWK0QYCQAAIAAEgUDMEQATVDHokBgJAAAhUDgElgg5lImgBE0F8WHRXF68Ikuvj884IMuLBakj38+TZdja1jrx4GYwjNHgZjHi5H2foejzHTrySrpEgZmqx0rFNnrYL5dr2tYYxLJ2SLdYzb7GUes3D9Co3K4tvhI30vaW0zN800lciyP/a+UVmI297rDqN6rNzi0m2QjIzr9g7r/Z0glrZ5dVheCteaau4Pxxb8ZRvUYoISmMRZ8vv5fml5em+RUzL032zq8f3SGvN88uTp8eetkv30/aV6WNrWGVwRBQgAASAABCoHAIggiqHJSIBASAABGqGgG4NO6JoRZAcFi1TnV19wqmztSVm/jRKrFjryCBuNfOhzEKOMC8l5xIpzRFGkmjyaLVWc5aF2pml9fRt9uYvUpOZpelCEsd0+hYL7+XtNaeRAt6idC0aU63DWJarIGOs4jObxMdTUlaT2puXRsEvEAAC9YuAbg1bxlvDVtVvkagMCAABIAAExhUCIILG1efGYIEAEGhUBJQIeiUTQTOTFUHBGUHKJ4x46CHhoUE0oFARxTqfxvRCuGzf1su/g9TS3ETtba2eGHLm2aSG+fuIvpXtofr0cPMJHx9vqFaYz49ZpaEuO052BdlrTCx6OqrJJUNal50VUiAABOoDATkvbcVyXB9fH18DVQABIAAEgIAgACIIfwdAAAgAgQZAYO26dXxG0CFMBO3mzgjqmsjXx3fr1jDZgaWHFI9soCEFEUYwOsL02ucVLC6hSKXdT1OnTqG3v+3N1NRCdNuf76Rf3fh72rmzj2vSVUKeJrGImkV6FjvMK+3YMtaaj/e3lmli+6F6Ppe28qJ4Ox/RbMN1PaWrV1/DxMgiidPkf3wCtDwC8ncnT+GP3QGmMveXZF8olCdqvIBAFRHA1rAqgovQQAAIAAEgMCIEQASNCDY4AQEgAATqCwElgg6jxUvmU1en3BoWnBGUOe9NJs2FYdgkuSBwDW/lW0JkpK1NK3JHBIk3dwYGB2je3Fl02aUX0HOes4Dn6C30ta9fTRdc/GmaP3cGG8kZRuKdjqgSi8sG0VNsHamLOprB6JUidZHAjSORaq7ijOnaii08kaU688iyjEsoVMoubrtYgeQQu6H942jjpBdiFEDtmvKTwKaEmmBSPo6B+zgBE8OsJAIggiqJJmIBASAABIBAJRAAEVQJFBEDCAABIFBjBHRr2DJeETStzK1hNlO2wosnxWmLNK1hHrGdHYVs02wmjXjhz7Spk2jxon3oootW0r333ksnv/0sam9nXWH1BtchAYNg2fEtLtsO+ynQK7Gn5LRkBY2QXVZMkbKgKZhnhWBZfoTQs7hd5FckKPYZ3xIGyDDKAELO1za9+5r+J8M6SyTBi/8OsiwhAwJpBIQIWsHXx6/G9fFpaNAHAkAACACBGiEAIqhGwCMtEAACQKCSCCgRdDivCJpX5RVBNqPWSbH1/FiUCPJ9aTVRX18vPd2zk37/m+/Txo0b6V3v/gB1861mrS2tkanjhUpM6CPjEXVyyKCcWH6UxQZhmWonNho/XDMFCqEYOy8xFD2CXjfMltuSyD4OcI5nfUfghF8hbA8zB8yBwAgQwIqgEYAGFyAABIAAEKgqAiCCqgovggMBIAAERgeBdevW08LCGUGp6+OL5r0iSD/ZE3Fv6VvqmW0vs3DTmIdsxenr66NtvYN04/9dSxufeJze4YigZ6itpY15omTSnpRk/ukKK9EfHg1kIy2uyMYmFqHWx/dS36rECBCjbATkI6XAzxCVHQ6GQGCkCIAIGily8AMCQAAIAIFqIQAiqFrIIi4QAAJAYBQR0BVBRyW3hskZQXm3hoUURlhgasacqLy1b3mv0MfrRep7Ohfv6+unvv4m+skN32TtAJ1z7mq6+ZY/8za2du6HHj6mb/mMu9rSusLqho4Yru4JrS2K1Vkc2zRFfEQYBm0gAAQaHAEhgpbz1rA12BrW4F8awwMCQAAIjB0EQASNnW+FSoEAEAACuQgoEXQEE0Gz3a1hRgT19/NhzMJQMCehtITRF56syaIrvJWktJ63tEJMYithtC+n67CPuPFqH/lP/0Af7ewl+txnLqYXv/hFdO2136df//omuvdv6+nJJ7v5HKGEbrFUlqBib6twuAHzaKDiOFp6OABDx7Av9lGJAypPWUF5urZdzWvx/DgrWGwq1GjmSqVGFwjsIgJKBC1jImjVLkaCOxAAAkAACACByiAAIqgyOCIKEAACQKCmCOitYQczEbQ7E0FdvCKog8/g6aGBfplA2yTaCAnpJ2RNoWohPNROf2Vy7/3UTGSxXHr6aEyfIeXLp/Vu2bqVFu75HPrs5z5MixfvwyuE+um881bTN7/9A+qYICuD9PExTTLSt69VaSDfjyOm5WE/JIJCuURIY2hRDQvre2xNkv1Ox8+2qq40XUO6b9nT8nS/WnYW1955eU0/Gu90Dem+1ZCWp/vVsrO49s7La/rReKdrSPethrQ83a+WncW1d15e05d+Y2tYaXygBQJAAAgAgdFHAETQ6GOOjEAACACBiiNgh0Xvu1QOi2YiqLODnnFEkFzP7h+dzqRIGq8u0RLP+CmWiN6TIGEWmQjt2DFIn/jE+XTYoS+j3/7mZvrlr35Nt9xyOz38yBPUzCuCwifuhZrht30dvjVUFJ/ft0Kf8iOpV3aUMCLaQAAINCoCWBHUqF8W4wICQAAIjF0EQASN3W+HyoEAEAACBQTCM4K6khVBxUTQcOmLQnhuxFRG3DM7jS+/qtc1RtKWw6L7qIV+8sNvMOkzSOeet4Z+d9Ot1NFpZwRZDH67257ClTiBzjV9hrQmq+9H7VtZdqHMj8+3TF8qilhn6YujWDR5i0dpizjqULZh7HQ7zGWV7mo8ybErMdI1ZvXTtabHUSp/6Jtul/KTOtJ5wtqG8g1t0R7PCAzy/6atWI7r48fz3wDGDgSAABCoNwRABNXbF0E9QAAIAIERIKBE0CG8NWw3MiJItoa5M4JcPLnNK28r09AJZcrr7wNT+/Q02E7hMXloL0TQ9p0D9Mv/+67eGvauD1DP5h5qa9Xr4xPux2URUsFi+Mq0At/Pa8V2Nu1X67AX21m07LymjekYkWZHyZYXx/Zxh9fKy5qOUu926Xqr2a93LMqtr5oYpWOXW1O926XHVc1+NhbYGlZNzBEbCAABIAAERoIAiKCRoAYfIAAEgECdIaBnBCVEUCffGsbbw7p7nmEiKCE/eH7C/4OfuVqlnKHo9EZ+/RP3hCTRXCYv9HhbWM/mrTTQ1Eq/vfF79Pjjj9C/v+Ns2r5tC7W0NHNA85DYSlj5LNktiR16ZVuliZsEizzjzJhxlqEjZAePo2TbQAoEgEBjIgAiqDG/K0YFBIAAEBjLCIAIGstfD7UDASAABBIE3Iqg/V9F++4z3Z0P1MlkUE9Pd0QEOZIlYDKCZlk42jod8wvJDZWZxsKxBYs6JrTRS1/2IndA9Bv/9V/oD3+4hd781vfSnFlT+FKx0McymH/4FjufUb3KJ400UpgrjO3bPoPJiiWiKRVJPNL67CiWY6i3RbPI5UQTH7NL+0s+ixW2zV5kePIRCLHNt4IGCBgCQgSt4OvjV+P6eIMEbyAABIAAEKgxAiCCavwBkB4IAAEgUAkEwsOiOzt4RVBXFhEk03+exBovwImDZqEMowPC6W64zcsMzU76Po5vuWw8AZq/YA597vKP0KxZs+nhhzfQFV/+Gl199f/S5CldgWc2CeSiyb4xeThhbCXysApnlfmjEZI4mRZ5kUrHz4poHuVXl1MQxEAACDQEAlgR1BCfEYMAAkAACDQUAiCCGupzYjBAAAiMVwTWrVtPCxceSouXzudbwzqpi4mgZ7rl+vj0rWH+IvXysdJDn0N7IzuMhzGuJqSE1L6JJrS30QEHLHbbwB55dCM9+ODD1NLczKuB7Mwio0ximkf8HdFibAuvHhIL64q22COsUtsWPR65j1IYS5FrviY09fFVWp5XGAFtIAAEGhkBEEGN/HUxNiAABIDA2EQARNDY/G6oGggAASAQIaArgo5kImiWEkFMBoW3hgk5YWf4mGO5hEXeaiBPpVjE8G1eTNyw4dat26l/YIDa2lt5q1i744/S9ai3r8paPo+2dCxiXQ4NFNYUty2/5Ym10svXFNuqRCocvldeNMiBABBoBASECFq+YhmtWb2qEYaDMQABIAAEgEADIAAiqAE+IoYABIAAEDAiaN+ls6mzo8NtDZNbwwaYfJElNEJOGPHh0TKyRiQhfaHWZhdahZaeoDFLy6P9MKJlV5mtBPJ+WR5prRuIE9q6oDhDsX05kqxRmF8l4lsse0vMUjmHa2f2pd7l5iwVAzogAARGigCIoJEiBz8gAASAABCoFgIggqqFLOICASAABEYRAb017GC+Pn4Pd1h010S+NYy3hvX39zPv4KkcT0EoOaBUh0jl9i6v9aV7C5UZCaO9LA+No3766yP7frZnSEh5W6vGj8NqMU32WyKEedJ98wrlYm99eYePyU2W7ps8/S7XLu1Xi3661nTfakrL0/1q2Vlce+flNf1ovNM1pPtWQ1qe7lfLzuLaOy+v6Ufjna4h3bca0vJ0v1p2FtfeeXlNX/qNrWGl8YEWCAABIAAERh8BEEGjjzkyAgEgAAQqjoCuCHo5E0HzqKW1hdpaWmhHbx/nYWIjOcBHpzLyK48SJNoz8kM12b/+bB6L4KOkPTS2SPkYIE7lfZ1Mfji/t3KC4EczZOVRmXmGFoH7LjUttgSpRvxdKm4EzjKeRhjHCIYOFyBQJwhgRVCdfAiUAQSAABAAAgUEQAQVoEADCAABIDB2EVAi6GgmgqYx+SKre2Tyr6SGnaSj1Iv2jIYppgi8X4xGsaXoQ9okts/WhFHMwh0a7ToFSYq6yKspzhiOOa2J+6XiWQ3iEVYbR4h7peLFlqPfs/HkjaXc2mtlN5qI1WqMlc4LzDwC5WLrParREiII18dXA1nEBAJAAAgAgZEiACJopMjBDwgAASBQRwgoEXSYuzWspanFrbcxCkDomiZelaO8hm6vEp1MkYZ6vF22tc+RjpSviadmTEyxYNBfO+YCGXnlo8ZeXp5u1couXUc1++WOUWrwXzC/onLjVdouv6LKa2pVe7l5Kz/ixo84drDF1rDG/2vECIEAEAACYw0BEEFj7YuhXiAABIBABgJ6RtDhtHjJXL6anYkgmSPxI4uDevmYoFbmA3TaxIdHO50aJGZqzL959I3ZCY1kbXHy9lmEg9cWEmQ0JKKtUJLiwvhmnhXddHgDASAABOoZARBB9fx1UBsQAAJAYHwiACJofH53jBoIAIEGQ8CtCFrKW8MWT+PVNS1MBjXTe587i6a0tdI9m7fSTzf10MadzAgFT5pwKUXbpG01TEjgWOAsS42shI/YhZmKpXGEuGdZ8AYCQAAIjBUEsDVsrHwp1AkEgAAQGD8IgAgaP98aIwUCQKCBEVi7dj0tWnQ47btkHq8GUnLlxAXT6cApnbT/lIl09YZN9IVHn6LWZL1NfHyzAhPSM2mosumY4RFB6ZhZ/ThP3MuyhwwIAAEgUO8IYEVQvX8h1AcEgAAQGH8IgAgaf98cIwYCQKABEVAi6FBasmQBbwdrooHBAbq3f4AOam+njy7ZjVcD9dLKtY/yyJVcCYmgUgRQCFUxLZMmgtIW5UYOs1iFIkvHi+3QKwcB+QbAsRykYAMEqoUAiKBqIYu4QAAIAAEgMFIEQASNFDn4AQEgAATqCAE9LPoIWrJ0Ds/7maDh+X9T0yC1cPuCRQtoJxNDF/ztUbcpS+mbZEtWYjvUULKphDQRJFHEMp8ACuPkWxl1EVoPVSH02QgYysAyGx9IgUD1ERAiaPmKZbRm9arqJ0MGIAAEgAAQAAJlIAAiqAyQYAIEgAAQqHcEjAhavGQ2NfOKoITxoTYmelYyEdQ3MECr1j3uVgrJWIQeEBpHqRy5bj58hDSIbxdT+9BG2llEUNpG7SxjSEcoRWFEhdqF+qxIKtP68vX1qrHRhWOu11pRFxAAApVCAERQpZBEHCAABIAAEKgUAiCCKoUk4gABIAAEaoiA3hp2MO27dA9eCWQHMMsWsUFauXgBPaerjT5w9wb61badtAcfJD1RrhPjR6mcPJpH6SCx8Y8nYfQGsVCndI/GjUkipUCMCPHR1FvzF2u93chavtbh+ef5peXpvmVJy9N9sxsL77za0/J038aWlqf7w7Uze3vnxTP9aLzTNaT7VkNanu5Xy87i2jsvr+lH452uId23GtLydL9adhbX3nl5TV/6ja1hpfGBFggAASAABEYfARBBo485MgIBIAAEKo6Argg6zJ0RRLIiKFnz08tE0IrZU+lNu8+gv2/bQY9t76Nf8g1if+SbxPhuMbYyIkfJmLAwi2Iy6fvHeuavmriXtjYfL0cLCAABINDoCIAIavQvjPEBASAABMYeAiCCxt43Q8VAAAgAgSIElAg6ms8ImsHLfJRwEVKmj4mgF0/upHfvNZdmd7bR03xo9Fc3PEX/u3EztToz+bEtYtx2vuzJ/6jGp3LmhW7cs7VA+URQ2l48zFpWMJV6RJvY6uFHOcaBXY7F8MTlxivHTuoXu+E+HqPheZZT0/Ailmddbt5y7crLWhmrcmuqd7vKoFFelHrHotz6yhvtSK2ECFqx4hhavXrlSEPADwgAASAABIBARREAEVRROBEMCAABIFAbBJQIOoSvj9/d3RqmVQxSPxMnH9h7Hr1kxmT69P2P0/WbtvAB0kRtzC/IFEkeJWQSosILVZn8mljt5TeU+J4ndxLH5CXWupUslo+oV5JT0UxDx6203dAZK2dR6dprFa9yiAwdqVZjrHTeoUdaOYtK116reJVDZOhI2WPEiqChkYMFEAACQAAIjC4CIIJGF29kAwJAAAhUBQE9I4iJoKW78aIeOf9HV5K0MGFzAZ8RJNOT8/72CO1gcYurQPVaTElmRU34V2LIo57WU5n1lAjSXuGUIHbQRUpmpT76ax5ZutAObSAABIDA2EQARNDY/G6oGggAASDQyAiACGrkr4uxAQEgMG4QsK1h++43nZqZstHNXkQTmIFZuWg+E0ADdOHfHqMBQYTbvGzIYVMga4aFVDFpUywJA4akU2iZJw99023zCeOkbeqxL3Wnaw5l6baMQexDucjKfUI/actj8fLa6fqcE36AABDYRQSwNWwXAYQ7EAACQAAIVBwBEEEVhxQBgQAQAAKjj8Dadetp0cLDeGvYPGpqYZKH5/6b+d/d2prpIl4R9OTOPrpg7aOuMKUD7GYxEYUEQN7mrnBMoX0o9+2hLbwtWkAACACBRkbArQhasYwuWb2qkYeJsQEBIAAEgMAYQgBE0Bj6WCgVCAABIJCHgK4I4uvj+YygAV7tM8hMzGm7TaclfFD0bl0ddN0jm+jLjz7NF4oZASRUTbhqxEcemgoqj+Ypz8rnRQsIAAEg0IgIYGtYI35VjAkIAAEgMLYRABE0tr8fqgcCQAAIOASUCDqSr4+fzUQQUzBMBp279xyaxSuC7tq8ja5+/Bnq6R/gtT9Kz+ivnc8jITxtMxQRZFRSCL152yakOKL0jHSynOYRRhmNttVRjVw2ehmbjFPHqL/VzFuNsSAmEAAClUJAiKDlvCJoDVYEVQpSxAECQAAIAIFdRABE0C4CCHcgAASAQD0goETQEXxY9By36ofnHbSTbwwb5POAWpgYam0SWoK3jAlJ5B4jLcLqTSeyYr3XSkv+9ZSR6Yq9JJY8SouY3ggp1VXy12cYSdShxzGSqPABAkBgPCMAImg8f32MHQgAASBQnwiACKrP74KqgAAQAALDQkBvDTuYFi/dg4kgOQhaVv/Iw8dBCyEkLSGBuJ1wOPp2NqV/jBzxVsUS0xkNo/24ZzbyzieC8n2yC86zz68xrANtIAAEgEC1EcDWsGojjPhAAAgAASAwXARABA0XMdgDASAABOoQAV0RdDgtXjKfd4XFm7ccVcKHBjXxqiBhhOT8ILktLJtYscEJkWKbm0wWvrOJFperYBb3wpQgggogoQEEgECDIwAiqME/MIYHBIAAEBiDCIAIGoMfDSUDASAABNIIKBF0DG8Nm+p3fyVGSvkosaMiI2g8mRO3ird8pfPlkUgW2dt7ic8h2riXZe9lYquj8DJr+fga0/p58cXP4lmMvHel7fLyVENe6dprFa8a2OTFrNUYK503b3zVkFe69lrFqwY2PqYQQStWHEOrV6/0QrSAABAAAkAACNQQARBBNQQfqYEAEAAClULAVgTtu5Svj0/OAbIplWwNE3ZIV+EoUSI6eYw20Z55aM9+zdb63tZL4jhenp9Bo4Y1qVdepKwqsmxtDFn2YV1mF8qy2uXaZflWS1ZuTZW2K3c85eYtN14l7Mqtqd7tKoFFuTHqHYty6yt3vJWwy64JK4IqgS1iAAEgAASAQCURABFUSTQRCwgAASBQIwT0jKDD+fp4JoJ4a5g8hSmJ8CUqcvJIV5CYVN7FBEvKnW1iSbGHxJHHa2w7WHJikdPGUcxepFa090/nDGO7YIWf4qgFFRpAAAgAgVFGAETQKAOOdEAACAABIDAkAiCChoQIBkAACACB+kdAVwS9kreGzSisCCpVtVEtpWxCXTG14iVG1ZjE+t5fSR2fU/uiN5/Y1nreQyWhdXEW8/Lv0N5L0QICQAAIjCYCQgQt561ha7A1bDRhRy4gAASAABAogQCIoBLgQAUEgAAQGCsIrF23nhYtlOvjZ5VJBOlx0X5VzVCkSXhwdLGtp3b8Wh7BzuT2NpnhGkcyckek1jbLct8WMcxYri/sgAAQAAKVR8CtCFqxjC5ZvarywRERCAABIAAEgMAIEAARNALQ4AIEgAAQqDcE1q5lImjRobw1bAFvDRuqOiNLzC6bNMmiY8rxDKMNRef4eENZWq3Defvow/GCLRAAAkCgkghga1gl0UQsIAAEgAAQqAQCIIIqgSJiAAEgAARqjIBuDTuKlrgVQVpMPrUycoIk7RmSPnkQWB3ia22z1Xhe6lu6bcz66bzmX/o9Mq/SMaEFAkAACAwPAd0atoy3hq0aniOsgQAQAAJAAAhUCQEQQVUCFmGBABAAAqOJgBJBRydnBIUkipEhRqlIVSYLKxR9ljy08e0sy6wIYVbv7VtxHH9tvbfQVmyX1ub1R+aVFw1yIAAEgMBIEJCbG1csx/XxI8EOPkAACAABIFAdBEAEVQdXRAUCQAAIjCoCekYQbw1bylvD3PXxIS2TpmOyCJLQPr908cy2tLvA9Owhy5DOnI5sdrZWKLS3XObjbU2S9Q69yvPIigIZEAACQKBSCGBrWKWQRBwgAASAABCoFAIggiqFJOIAASAABGqIgK4Iejkt5uvjm9318SFdE9IrVmRIkoT6UG62Q68V8mt5vL+0wsg+ml+x5K1FmxUl9CqnHWaNo5fjDRsgAASAQKURABFUaUQRDwgAASAABHYVARBBu4og/IEAEAACdYCAEkHH8Iqgqalbw9KEkJEjabkNIptIMS+zSr9HQuEUx1TayMgjX0kTb1ozaTpz2DeP4sihlaehYmlxz+IVa2JJuXaxV3V75dZU73bVRSmOXu9YlFtfPKrq9sqtqd7tqouSEEEr+Pr41bg+vrpAIzoQAAJAAAiUjQCIoLKhgiEQAAJAoH4RUCLocCaC5gdEUJo8SU/GpC9P2k6l4TogszRN+m0bw0K5+aSjm1xtpWcWxXSSaYQK8nZhlnQ7tIszxZahXayJe+XaxV7V7ZVbU73bVRelOHq9Y1FuffGoqtsrt6Z6t6suSnH0bCywIihGCT0gAASAABCoPQIggmr/DVABEAACQGCXEVi3bj0tXJg+I2hXwyr9Uk4UI2zKsRUbmS4VPxpFfm06lW1X7Jkt2TXv7JiQAgEgAASGhwCIoOHhBWsgAASAABCoPgIggqqPMTIAASAABKqOQHxrmBEgw6Vn4jLLp4GGf7qPVRhnLF4RFOuH28vOMtwo+fYJviFzJcaS1qCXg7v5xqCY+Sq3LgucriBLXkiYMs6yTZkUusONkRc7T15IFDQqmVPCprHNqyVLXqqWasZO1yy5suorJRdd+ik1nuHmlNhpn+HUWKqWasZO15zGKOznjSe0sXbeeEwfv4UIWs5bw9Zga1gMDHpAAAgAASBQMwRABNUMeiQGAkAACFQOAb017BW8NWxOcGvYyOMPhwSSLDotKp5IyTSs/CmTWY687uLJ6q7EKseXax5O2e5Gt3LiwgYIAIFGQcCtCFqxjC5ZvapRhoRxAAEgAASAwBhHAETQGP+AKB8IAAEgIAisXbueFi3irWFL+Pr4wq1hATZCVhgrU9b/k3x4VNBw1vLkpy83ig0kGF9mMz9TpvlIhLLaZzgPiKDhoAVbINAQCGBrWEN8RgwCCAABINBQCIAIaqjPicEAASAwXhHQrWGv4OvjZUVQM8MwTIIiBZxSKKWIlJBZyspmZI29hYcaKl6qiF3ulso3kuDxmIsihGobtpVgOudkSumk2yIT4/AJg5Qrr0QMyVVOHKtvuPa1ji31Wu3l1GL2w7EtFxOLnba3+tLyoezLqbGasfPqG0peTt1DxRB9OXGqOf449iATxsuXH0OXrFklxeEBAkAACAABIFBzBEAE1fwToAAgAASAwK4j8NBDf6eLVn+YJrS3JsFkIsTUC89HZBIiTxN3XFN+kpUpsgZHCJrQzk+Ok5bzEzsXxMeTPj+6joe1SS6NJ7nSkzG111xxbNFIfPXQmiye0yU1qJ2rxOWVXEPbaWwZ/0CEhWRLciW5XWT+sdKlVilKLJ0uspMxDrBEdKo1PF1NfC6I5LS2vL1dEi8Zl/NO2hrPhQ3GqLl0vB5bjVf4AppLQUy+t3a0umAcYa6wrWn9eNkxxKLQLrILauJ44lTAgm0L7TBeTgxXa1STSIJ4TjegWGbalYttaCffkdEsGS/EIhhvzjhEHMdTw8LfUoiFDtHjzqbeLsxVCougPk1VFEO8uSj+PPZ3USpell2IWRIjiqeJ49qzsE3bBbUX4mX9dzOoie307yypiUP6vEE8TRXoNMbwsNi1eFLhgQceQG8/5a1JNXgBASAABIAAEKgtAiCCaos/sgMBIAAEgAAQAAJAAAgAASAABIAAEAACQGDUEAARNGpQIxEQAAJAAAgAASAABIAAEAACQAAIAAEgAARqiwCIoNrij+xAAAgAASAABIAAEAACQAAIAAEgAASAABAYNQRABI0a1EgEBIAAEAACQAAIAAEgAASAABAAAkAACACB2iIAIqi2+CM7EAACQAAIAAEgAASAABAAAkAACAABIAAERg0BEEGjBjUSAQEgAASAABAAAkAACAABIAAEgAAQAAJAoLYIgAiqLf7IDgSAABAAAkAACAABIAAEgAAQAAJAAAgAgVFDAETQqEGNREAACAABIAAEgAAQAAJAAAgAASAABIAAEKgtAiCCaos/sgMBIAAEgAAQAAJAAAgAASAABIAAEAACQGDUEAARNGpQIxEQAAJAAAgAASAABIAAEAACQAAIAAEgAARqiwCIoNrij+xAAAgAASAABP5/9t5EsW4cWbaVB8myXUN3v/v/n3jvqSrPku0XKyITAKm9ZdVku06T0iaAnJEECTAJgocHDg8cHjg8cHjg8MDhgcMDhwcOD3w1D3zHgaDPixMeVR5Y5xf0kT08cHjgG3vg1Ll5CvaNzZT6U1eWP2tVavo163tX1x2IAJ91uTx1xbxDe8YBn+WtRyclnGE4wIcHDg8cHjg8cHjg8MDhgcMDhwcOD3z3HviOA0Hfve8OAw8PHB44PPC7PUAQprdTQZrG3Un/MOMdSQfg8MDhgcMDhwcODxweODxweODwwOGB/2IP/K8LBK33Sn1c9zdboeFJ93Z2QD87n/Q8DwfakJSRO2GUQkXu2L7kgcNXX/LQ94n//o7bXYvuQtqXYL60QfO5CJ24kKtE4EE2zWZqEbwrQKRQr9eYLjfMVxXvsCyZXfHi0aMBgchkTUnaaMsWYEdtlv2uaeBhezTsbkzgbW9KD9mvHGv+IbwHzeGBwwOHBw4PHB44PHB44PDA4YHDA1/LA99lIOiTas/v9Na3L2DrtYVxz0GmX2VY6bY3OJaruznfZNXNXod8wJlaO0tYxZiRHcCtzL4hC8nEzVzJC0Fxr9hCOLkrf8XO/EnjJnrY2PKafq+38Qtrar8Cvlp+tfKUZfcZ0ryTpiUkdNd4PNAYaAOfkL2Hpry7uebq9C7Fn4P8XrlNT7pun3VWbb2QYGgc8amCIFC0BHlB/0UlUS7odaO73gHSXK33DlURNHwEVIoZsZ8FbDj5i8+yi0qADEFSw7Lj9ae2sVSER2jK5l/ylD9Zj2qqwid+2vH7+PHTxa1+nz9S/uTyR8Epf6wyvLe3n0yPjdB9+vRR3hUdctD1kV3TKL/Z5OPH+qk+/B4/0e/RY/+AP3lyIZjKjx9fPH385OKJUtMJB90j4OIJ7eNB/wT848+iET9H+nG5TXYJlB+ZKsek8twjWY1jlMbv+EbtBZ8jzSn2SiiytYczqQpnNqsTrlPIOh9JZxgP8OGBwwOHBw4PHB44PHB44PDA4YHDA3+bB77bQBD3Uec2blT6ZjU3FdzB9A1G3dicY/6b4blh+puVHOL/gAf69vMPsP6TWXJqqAYJdaYq2/NlhSUaAAR/9a+hs9xnW3gfvv/SUSAwY1uTUZCmAxStQ2e+zShJToBNyaPKYumAsoMzQnx+lECTYj0KdDxyEIjgzS1BIAI9H/RTensrmIg+fvzo3y242wvBKZNXStCHABA4/Wyq5UhWBYioBgEiqXWcpYMnXCcI6DxRYOWJ8k8U2OH3SIEfgjxPKxD0hPRSwSDolH+iKJH2F08vRa+Iz6Vhon+qYA0yFF3hRzCIQJFUiJ6jpQARLgQuY+KteCrXUvL2ktL4yIEgMzxxAIiwzwgIKR9uRJ72fakjceWbqtMErII+9ocHDg8cHjg8cHjg8MDhgcMDhwcOD3w9D/yjAkHcQPjmo24ScVPfVIDJjU7fnjQxVCe2IewEbgU1Xacr7kw+96TTsjNkB/h3eYDjesqne/i+3EpO8QbXwQJKUG0p75MH7vR2mus0NBLQelfeXchd6pOcG8a1kLxv4wdj4zstHQo+cKpx808abMIKoah9y+l0g5wFzgufugsd2XXrU9uzUqSR2Tf7zUEfMdouIym0fduUY7seX+Q5gCOxit1cfNJMH03uURBHwZybTxcfFOS5VUrAh1k/t59uVYZGs3sKRnDohhlBBIQqgERAiFBbZgchW6X+2QI83k4k4DNnAD12wCYzgBLwUWAHvCI6T58+vXiiII/hTxUEUp5ZQk8vk14qIEQQiMCRg0nIIiAkf2SmEUEhAjgKEBEAUkpUiuDNI83+oYjHEtbB1wSBkuIreGWs8J5jhNPhFF2n0EaKMtTQmyFrQflwFYGSIxA0fXHkDg8cHjg8cHjg8MDhgcMDhwcOD3xND3y3gSA9fF9uLxaX+OaCGxrdVmxuEisQNG5FIBQNdx99QzIzd6Vv6BZ9zg4BG3mloYgRcHfzjdfpmgzirZwB/iYZblZzo/dN1N+jNMdgHPI6tDBsLe5jleNx17frcfKt+Z1W0Twr5ebA28rGNnUsMco2wdE0W+jSIBuhFNpVFqUuk7JtaXKsgsle+OGgFU6+ZTQcrzU8uJbeFNEHNGGClME65DrJ9rm7gvYUKe/oXLQpqtlIySSI4lNeJVses4xDWM8ISk1SW/IEgRqmuIwDNIrfOIDjWUAqfCLQo+AOgZ8PmhH04fb24ubm48UH/Qj23N7cehYQwR8HgRQo8qwhZgspOORZQcghWCTDCS6xdcCp65XjqdoAkA8doCFwIwCvgfn1MAd0lFfQh9k/vBZ2SSBI+Uv9CAw9Jb0CLtgzpY8FIxjkgBCziSoYpFfMmDVEgMjhO/RIN6+P9aygpNhbPzseS1XWP6+C+Vor3keZiuUKuApuQWnlcO83ZkGtGzz8Guxg1Upw5A8PHB44PHB44PDA4YHDA4cHDg8cHvgqHvhuA0F3Xg3ru4fhlg78cGPBjY1uMkyjUt0M90wG48QHmHw4hiDdnNTTeiFNA2q9Y7GGAu3siLzc3PSNP7CW0+SxJaXW1+lqifVyq6R/81C3tq+F2T5XxIp8o1ZCtnVfGVKnthG65gtP5HFjCNcdOgGNKbVtk1MqC085o2X7uJTtsJlKu7N0i5zB27Dib73oO7U1nrQsVjqMDstiAwDXC+P6wIVq2UuacLEpYMyq6sKoHwJOb1WFhV7UJl+PQamXCNehmUpk62q9PmYbe2cd+zyYdoEjJDKt5DjH5MDBna4FUOqOVdExzzy4HrCJbagr8jY9EmPKyJdv2kcEV4zTDmsJTnhdIOAKlLQvxhFQ5lPRog44snhdixjNR177YiYPM36Y5cOsIAJACvi8//Dx4v37m4ubD7cOBHlWkOhuNOPnlsCQ6G8UKDJfBYj82pjeG+M1NvR4JpB0isUbMKxwWhXpc4DUQSDN8pkBIYI5Cvboxwygy6tLBXMI/Cjgo9fEnim9vHx6cXWlQBA/wa70e/qEgBAzhHhlTAEgpV43SPmnBISkS66TrDqitiWzgh7jWZepAC09RxzL13WBMjsIKKElKpYg06xbgZXsN4sXsFMHpfZER/nwwOGBwwOHBw4PHB44PHB44PDA4YG/3QPfdSCobxjsBd9M5SYi2QIsSN/c9n2t7zz71rX9iETd4ChJ8KPhpKWtEm5sVjrfuPkGKTzguBv1DVMIwxC0pJlgwEZwZHPHFK1DBiKtl4CUb3lLWifYDo66S4PzD6GL3JaypjF9lUeerdKq29Qb7NxjR1HLrvN0uAJ/IfocXXy2pTtl+ym69kssgwIftW+3PkvdIqWPAfaJxbMeYBWn8pGDrLmtdSz3TKRyDVvpQoDeskxJfAFIudYbwrlvA8wKEzViyx60vBnbCxyYGQbdoEcEdObS3u3MgLGDHzPdzMgQeDGTMUIuM4IARWCq1nlkLFv7BNCab5KWTDm68D+iV4E6nhwXiCSEFL6c8uBUKHISx2EEy0wgre+jDDN8bpQS1GGtnw8K/Nz4lTAFgd6r/O7Dxft3CgIpEPSegBGBIgV8RsrMIc8aupE8ZGTWUC82zawg2y2jCGBhT5lU9YnRtCzaR6e8HkaZ1OsCaZbPk3r9i4BQgkAK/lQA6Orq6cUz5QkEXSlQdKUZQgSJrpqHYBAzhggGPWUWEDOHJJ/ZQTp8fh1NvnysH9dNgjIxFmvjY0DYh69nCp2JDUtOHFVJymTF4g04eacBFbdsEHxpSYU9ksMDhwcODxweODxweODwwOGBwwOHB/5uD3y3gaAOb/j+om4yuMXw/UWVc+9cMBDA+47EtynNaC77MqGBeSsDRcMaCuGEme3srtWaRwVumKJ1xQjuO6Pc3G71xOzsi2fLamuaauWVqgneWNj1tlVhGaCRwagt/5C30FgEdDvYRl8V+o7vFG6FnaUbBoS6fIZD0Q6W7ZQlTRqK3u/kCXxKdY7ZVuopOiwIfNKepkOP2sEDfPZn6fa2Y0/7q72w8Zhu+rPpvFG2Ay2rp9Y8t+rYSDBlHoEzt+9bxtJTbK22xaxlYCpv2XOuDDKQIcO5ZXfONWj61zTYa5gq+VEXiswCYgYQr3rx2pcCQkrf39x45s97ZgC9vbl49+7jxVvNCPrwQYEeZv98+Cy6yhMU4rUxXhFTcOhGZYJJXiyagBDHW4pJ8KtiQrasm4GvVOBUF+bR+FjRpvhTgTIBG2bgeGFoRUoIAj1RwIfXvjwLSLOBrp5dXTxTIOjqmYJBpAoGOb28VGAIuscOGDFTiNfKmB1EnlfGPEuo1hMiCOSgkGx1ah/jNcyWtTHQ9mmXdOxDNvZig724N/m1YBWiI3V9h4Ajc3jg8MDhgcMDhwcODxweODxweODwwNfywHcZCOJmIk/54wbfrNYdhm8k+m4DtADMEuCmdtyFBOwbjb4J801NF+BbNm5IGrWKbhLrpICS/DfKafDa5z93OKbj1q8DQ2GxLqAorButUwGDQtmu3JDlBnOjWIW+WUPGmj9F13pW2V+ia7/cpZs+2+Mou55VxftkQLu4YhwH4L1ZFoWWV/nGd7rWa+bFtGw+Vt7Fn1ufGTGouw4ApryBVgbZuYHvOq48KyX5+3ArfksXHZG1PcaxqQNOd22HJ+2iuG1k+yNpZECoX4tQPtgO+AghQlpzDlbBQxThvUdGwxd5je501rHOkXZg8bYIjOL8Nz2iSyZc5E2nHSk/rhvUOWUFgJT/JEICQH6li9k/WhOIGT/vFOz5cMMMIAWEVH6rmUDv3t2onN8HzwbiVbCsBURKEOmGIJFeJ/uooNBHZgUp4kNAyJ+OH7oxJoZ1EA2bMCw+r4rwrpYRgjNjRz9e5yIY5AWg+Xy8AjdPmd2jQA9Bn6tnlwr8XF1cV94BIWAEfhQ0un7GGkIVJFIgiFlCDghJhheZFszrBkm1RPsnlQ4GlVVuCrGTIxAo+8ZXVWaZOggJvg8lNL21LNe16GhFq7ymPdLDA4cHDg8cHjg8cHjg8MDhgcMDhwf+Xg/8IwJBuGAEMVLQPrcQfh3MeBBzW28w1vykOJPTHctG11myNTAzNXRgIXd3gnMHdOrOaJHre6jS25Lq3nChShb5bd8d5DcCPNSm++iod9fZPlx8NuB1zHOb3wGYxp6uvP1p4Vu6yDjN8zWg9/li1f+A5mPyh8kjoEK73foibQ6PJIfAyKPcWENHueFA/9wWW2zS1qylPZQGOaMDxB0EMot2DgCJLLEX6phXwzRRx4s23yhz8z4zgXj1693th4t3mvlD0Ifgz7v3HQQCzmtiWieIRaQV+PFC0swgckBIr4nxepnXCdK6QMwOkjK+OoZf0TvT2B0bpx9d3rwUlVALe443PxaO9lfAFLRhllDW/6nZP8wEutbsH80OeqbXwq4VFKLM62IEiggQXRII4lWxa9LHfo2M18qYLXRJoIhZQXplzAtLSyHrBjkYhH6ZjQ1slQRWZerYW+Mpr3kKK92Kh84/7UiP7fDA4YHDA4cHDg8cHjg8cHjg8MDhga/rgX9EIMg3C7r50K2Wbxz6BiM3E7kr8f7MDQou7RuO+26YF3bfCK93Mo2LzgRjuOHLDVNLl56+g7LSGbQxvIXsj7HY++a8JTVpbhEJkqBrkacahYb9lit0KJlSZn5RLja+BNRUoUHWhEBNlVgvp7dQbGlCt9oX6qba2L7UYysz/rNfIxDFZU08MeUZ1exKGzNBtti2ryGOiW/JQFLHiTuX+5p0q641v7dtxa35u3QEKjqUMrE5nilPn1GeczbiXbD9q+xdt8O43VYFW0xK4xhLmOWFYRw1imxKg06bJe+fdlwTCAJRO88C0mrRrAnEIs8Ea7zmD+v+aDbPOwWECPS8e/vh4o2CQu/fE/x5r3ICQnwtjK+H+XUyZgAx+0eBIGb/MLOIr4T1LCH8OQNBGNSvhGEP/ps2uuBdVQjjOWDeXHAOEOcLizvztTC/tqUdgR8vCq2AjheKVqDnmYJCzBQi2HN1yStjBH+YMRTctfDXChZBx++K4JECQQSDCDRd6fPznh1kXUswCBtkTVvXeVJ8jY2kbE3TeV8qCmeC2u3pCDytsJX2yB8eODxweODwwOGBwwOHBw4PHB44PPD3eeAfFQjCDdx85EaD20TuSADqt7kzUdCg71Jgqs03HTu6vpvZ37dwIwbOosXf+FZniICEJ7Cn6R3waIVLavhemPEtOcShW4M+trokTdqWFzNbMGSThtJdOqDZWpdr4epuee/Stbwv0zXvqbT1dnqKBljjOw3dPALN1/hOG95pwgYq9bEvEWsgqGlJB5kyHaBrfONCdxc/6SbunF2R4WbWbGfTltHpOcK27zRdz5tZ67jV360tR1glzUzhVIo8W6xdUy1WlE/H+bigVljbt6LJj8XR67ztYILtsDpsCRe2dE3AE9uizJo8itN4BhBBoRtm7nih508KAH24eEsQiNe+HAC60atggjkg9P7i7VsFgpgxpAAQawexgPStFouWiIvPEuqFpiWU2UCfNLuo1wbyl8IEx36CpejFUNulHbZR58BVBs3mClaFAgkSgqoqb435VTEJIH+pKTxP9HqXF4TmNS/N6mHmz6VSFovm62LPlF5rDSFmB11rNtDz66uL53pt7PlzBYmUAnvGGkOsGSTaqysFgjTriK+KEXCSWM8MwjLsTigrBo68bARHZUhPXGrDUDLSfkIHW2+KQ/UhbdCRHh74qh7QKe5AbtqlzrOlgS7Z2HQHkPbLKXt2uw/Z8laahp0V+N+LwDWrqzaeWBFf2Yen1K3mbOx8QMG8tfMYxQPMvlZKW/4fIOkESRk2dYSGOkQX5VHK9TnFSZjcoDO9L/J1fBAOjzayWvUuskvuKs5E4LvfF2DgFzklzuTrORr+oa6Lla5crvGUDYX8GhWnR4Jwb3RZ3CozatrMOkwlM7ixt/rYgDakpMqBmc6CjJHe6OH+ov4jSjTDWgkwt/lANG14rURcVQptpCz7xgJa+Cw4ZIj9I1sd0sn6R+S4bhKhFJcsZk255BY8akzHrnVWflfcyjMTwrI1bZfvS4c+iNoApb9Hxh35ewd2fYAvOlyJhyjq+jXtkCeA/hsdOxp5x6otYMu0scvme80WsYhuJcUExq1tylboUfraHvhuA0F88acbidNqRTk3KKzNSrQimueNL59F01JwLTyUx1wDgIuk4Az8Aztrmrt7JazWm+VeapAPs61r+EVxv5Pg98m9j/oUDhjb/Z44xRm+L+1bfmsYXemicyu9OXqoQCuZsC/pO4c/V78pe+U8Rx2aFdu2r+kqqfPg+9ewu2lLTqi1S6Tr7y7fn4GsM5V6UDh87kGRdA9TFJySMv+0I8iSWTmPLj4wY0dlAjcfCOzodS4+Bf/mJsGe95r18+bN+4vXzAJ6+04BIM0KEuytAkUfNDuI18G0NrQCPQoC8Vl5zSwimOP1f5R+EuzTZ80yknwWh04ACBs4hjLQsBjK9YgffaFYvSlb1yloC0iy5pcCVefHp+X9SXhFT1gv6DGLSCvP2j9PFcG5esaMIWYBJSDE62HXDgA9u7h+fnnx4vr64oWCQQSErp/zupheJdOsIGYWPdWsIF4R4wtjzNLxK2mt17bkBlmgHAJVivx262vuriomonLhWPn8KtpWyFE6PPBVPfBWJ6Ziv966fffr5m6rvvZU663G23SbU7Zwy6kboVwAdttdSOTnLJGgliU+sqfodyK/XnGx7espPaGpnDJ8MzI72nLnHv1Qv56jW92w0qx5dA66TWFno4qxr/ZqM+ToB2k+HoOoHVpWpypV0zQcbLhb1qKjEYBK5oIVKNzoQgjanZdC18eptY/J12wMAABAAElEQVQG6YcCAkG3EW/BnFBCcoEnV6wuLDvzaacuNX2slS4EZMULu0WQp/7qUJFpWJEnv+63eMgivuomnfmqZ3ios2m0A2LTlfHDCilzjMWkGXu41tgtWnpDo7SDbjwwMQap2kzLzt4dqW3KLsyuX9eT/pj6lnyJyX01s46pB2OdvJIeBHR8bMLWeM3B5DBg5ma2YZ1iJu1oluGkjr0hei03/FQKXdOSllmnSDcw3DEsGJnAWl6Du9yKWoddupEa/uZrVOsacoQYNAKuQTD7Xkjw0HdKYeVHtnFFRD5bU01Iw43xLlIHBQY2chgrwCCQhIZPi0ofiQgjcoEla7EbQWnnyJstbrKtvmgoqk9vdZ4ZmTxrdfZImNnuP2h8WpeI0yIO6FfzwHcbCOobJzzhNl8Nbr1JBNODttVjNOK1gfritQJaphHhRLwvoKabrXvmVg2n8nXCIWdBW+5SJntOpvli7I5jFoe8oltlwb+W4Zp1ioxTNMbs5K10tktE1r2zb6WLhofsRy1MHJu7+2ltd+V03e5Q7GxvzkkXTnyR9rNKmtoH/aCLJLcn177kGEzba3kAwCFhlQ1cW9mXwvk9Q4R1iz1t1cSdvkyvnGt+8mEbf7L6rE1rDTykKfV5NbBt2afStzByvTfFqrpM2rvCZZ3sGtqYIizFqHOxOx8GOJ3Hn1C4Jsp8dFAmwZsPyiv2o7V9mAXE17/0FTBmA71TIOgN6buL15r9M8vkFQjSzKAPWhvo9lazej4+0qtfCSh9+iS9CgYBY3PAx/pstKyWAbKTwezcaBeAk5rEyDrC4FyWzOKbl50CCBGNOgoaWdLW6DT56tdTLyCtsgAMVJ9eMpOHBaV5PYwviOX1MF4LYzbQixcEg64uXj5/5hlC10o7GOTAkXgu/YoYwSTkZu0gifSgtu0gzSB32kY1VryrVTtqsj1HmjIE1CfyVq4jf3jg63nAgSA1VFrm+ClDPu132sK1qrfONl3DN2mfygAXQssVaEWvfKf0gG/61t3llff35BeT7mX7XuhO2eHr5r3W/z4kvl9l7nV2udNz0vd4H9M68ODWrY8jaXSn7xmBIDNkFML1lMbEHzf/SdO8kAnWciho4+Z1ld8Fj4M2iOjewCXMH2KJJKT5eu4iu1amrIMqBkRft2FS/wYTMkMDuZ6pJBDUdqpvZeu+HhUEQ7xRX+X52qUhBbd8sRVVSNmvAMro8E/9sDLujdONb4+5+JDJfAVm4lKwqNrFwgjMzS358EAI2aRXwQzohKp0u0AeA1qiuVxHPhiBBSTUOcddeZEyVuLBFLx8qMIPoxCtzb6S0XA/UkdeEs0vDtOw42MUPtaGmDo46VqPJcDBFWGhe8AevuZ1+gD+QQKDCqO86CvUAomePe1wa0SdlLURQqENvoM4bUuTrTatehvvFAPvkQ8Nx3dPlHMSRDGTjMrOsVhhFxyyBiGFsWEjbZwTLe1CBZcF69TUwEWThjf43R4lY21TjWxY+6Htx76GMW79QePTcW4385F+Ew9814Eg2uPY3Mq9mxcw2uggIFOAkAVDa+/WF4rAt61dpT4hgm4ROVkWGAobCbjzW0PC8Cf3rWoV3ep+j+jBv/ii5bSOjbyFbgOvQvPkYhBJLa/pW6fhi7yGQ7flSam6XOPO0Q79d2S09plOGau25PsS2vIm15K7gwxgyi3apY5Ib/yqFdhCNpRMms6JEmK127TLKQ+mhWrIOJ/py/LKGQm0+FPbtD3Bl5jCvjH7dJHSKECnxU9iaKHRL1Y2Q4eFQtpk7pAAyYmhzKtYCQSxgLNeB9O7HrzWRSCHxaAJAvE62Ns37zTzR0EgBYTeKP+WvD8XL5xeEYMmr4MRWJJcZgKpk/z08Yl0YYF+2Nk/WwBNbKzxa9E2XfGYNjCoewDWvJZrROhFAdX09mPlVWcGhwR/CNQwQPSAWB00cM/qEdxfB/PaQQoGMSNI6wUxE+gFQSCnzAzSK2TKs1YQC017kWmtMcQXxfLaWdYNunwi2TRYbez5JXCz2FZwJcNe8mzlGqeRAlS5QjjABejYDg98Iw90IMj3e7KhmrvbqPOz4U4cdCfsBdZtfqANYBeOO/hBmIypSnglO4pt8UvyttR/voRND9H5d9C19UO/MiPfyF36EB/uWO4pom0rcav/Lr6pSfl1gGOvBE73La6RekMBLFuZluqxkYRwE+8ecLk27+VRDv8WQ9+TG7NQoMeWNTEG+m5UGshu2CmtkOq1FcvggQiyuh9stmlzQ5Ki7hOylIlNxWv7hPSNZ/UzVilJSvOzVPeBbQ7+CLToEGF6M7vPtIGCp85lOwV0VkBIaG/mJRf2yFIBuykYrF0sjE5Q4ME13ikqwJgZP/nxkalzLIxQGdHIys+zgSSJvh6J2NQyPlUAiBlBnh0kBOjwzkCQ+YSxBvlUUhCWsnPsAus0koKUW7I1SZeVApqWL4jKrrjov0uzl7GqWfN7zlX2XkbTuhl34b50FbajO28DTFtsnTbdzE76Z3CcMTptEyNiFEduA+vCELQzeCneU62FaieIRnaWsdqRucmP1rHI66xbfEQRtAQs282hAuPYH7V8Qdp28xzpt/LAdx8I6mbqC2Z5qfNc9HK2qWURuk9rK6pcODlvIiON0TwQmrZJafwBNNh0LjREtNLXuotTssPbnSbU8N6hW2DYY7pKW9Y+/T10zdvWNm/DOwXO1nZS7+YB3rb/Hrrmj8/Kz4uOU75ovW0nhzJ0rXnaYrtaXqXATm1DnpBt15rrfDrE6GoPNO9e7uw81UUWkf1XhO2zwLhAKidCj2UWK9o/Qto607viWJXut2HxRSSiJjrIpTM3vXjv921sKYvMi4T2ATlwbQ/l7aZzKi4SmCdI7aEBFHzNS3IVnSCajULnDRBIsHluMggUQfGC6KFS+xa2zzCxca6ziYVXSG81KNJHvLQw9Fzf58P7TxdvtAD0G7369Y7gj9LXb986CPTaQSECQrwupjWDNHOIWUF8Vt6zf7TI8+fPCgB5sWfy6OOH/tiwq04GwKr8gCufQXEdWSHImb/zTd9MnU4pov/szpKq44tHCgrRecoqWaJu9dEn573gs0zUWtAO7Fzq1bFn108c/GGdoH4t7LkCQi9fXGumkF4PU0Doiq+OKWD0TJ0ys4l4xYz1hnhVTCAHgqy7ao5eNh8KZeMRzpA2vlOTeeeWDRhiGohTBgPJTsojd3jg63rgbCBIZtBMs4tNbvMF6uYczGjS3bQNhp8+INeyavSiGGdIZVrukCnSpm75pMBObUNeIe/yDsk79pXznPSVJX0UkM5NXS2r5UDRdQ11U7TExk8Z1DFXyaYB0vhOwTkvgbm6Uspm7lIE1H0uKPcvgoAzs4HsaisgiQlCGuRdy43lwBVqb/WUgrTUAbEtqbRFvPbAMZFcklAWCITNsiwxW2LJo2cKtanGrrvUDY6+taI1BBEyRikW9XPztSsdCYzcbH08Bay+i/EH6+jRfzsgwU2f8tEZAfTb+RNfyXSLEJFI/cOk8IlXnbpvGRFSBuJq/5DEgxE6QcuNSIvVjhAIN5noyQMTfR0TJCD4lGUjtZVla2bYxIYEqLhlxQBgTtKWxMiDF/6sn7FI2VFk0QE6bLUX1v/senSDbGyKv6DHfuopY526TH0AsbMx5WuCQP4hU3LC6NQWYiewOpBoMQlyLK00N9B00WU0YmFHbXHA2f6ABurGkd63WU7x7Ok27bBokH3f1vqazuWxA0qhsSckCd1YszV1sWLTnW0vdqVpHEydJ2Vb6HxIVG4S45vOkRJDRCAi8xXzmgfRAlpgsUHGce/z0IdXQI+f3d4QG4WtlvIQ03I3hjUlwunTalP7K1GzjkUac733DDY42iaCnD9o5voxI6gd+W3Tf1AgyM3IjY7GxDabZuWWltxZMKEuhr5gCjjgCNMGrfkoFF2mSQLYbjnRgOUCHJP2EpFXnWfJa9u30la6siECF7LUsesFYuZXvalxXwhEVXTQbLxh2afpjMqulKz1XbBTBrkH17F9tto9pZ73WXwQypLxIL2YFl2pTniLdSievsC3uZAO5JJpv2NN50Hbw6uJ8rf1Lm5vdOqorllMQ5eQjc8gpXAlvHkGjXlBstHOVK8yousbHEA26s1e5eI1ptBuqQg3jlJrIjUWIcKrXHQlMHDBSpTLm4606YVZfRZGWyRmK3bKVNQ6ZKVL+mG0nKQMHjk/+ZIX63ywyDNf/LpVQOetXvN6QwDotYI/CvK8ekMQ6O3F69eCAdfPC0RrNhBfFHv7TsEk8TET6COBHwJBF09kK8NspmgrKrL6A1uAKKXOGcgaVE4gECQcyHKEA2kqA2pHJR9ZGwRs0ImX1FUn78EsXAhiyPpRthEcyo9Xx3i965Ivi2nNoOcK9vBa2HMFfF68UKq1gq5/UGDomV4PUwDo+gVrBWlhaWYPiYdZQg4GaXbQM14X86BXHpAR2IE3bJf02wUyw/YJPipFtpFuK/IDDvIWbphaZiGO5PDAV/eATvuL92qa3a7dbMuKNQ+IMq2XllytGPCAuVC7xuu0nINm4cY1DbpF2Mg2Y8lpsqV4Mrva1HnSdRs6CvgQupbRvOfk/V10qz7yq3vQST/XMHw97OBguaCd842JxK6Pj7FQ7jtVSL/ZEkPrfYMQ08zK5kYZG+gH1hQ6EVqtghMWEnqyqzjKbd3ab7ctkQtP6bDRCAGSrU1a+RvX2sChx0EPwpMqzOsy1+MEH9ynle1dhZZPhS0DXv28Np8ynzQT91bvR7OuHn3y8AXEWNm2ItAbcpQX3n2nHuI4QIUsBDvN7ebggJd/dRzqmZNHFgT+yEE89FiRHwIofADhEa86i4+ZrY/pO2ELa/hsn6yVPmYWc1/7kZ3yGfuDS13hg/mxIkwJMvFFT2miIxubBUp7lKwYBPFHpfs4dWrZQnkWkApRtaSrINnnoJtS+AkrMcuoqGUidVaLEw/yxgM8K7HLh7WITavNcQ1iVZbrV2q1uGxIUKbII2tF/Ln8sKIyHId1a32dglvzqWm8Au/AKcO1gq1FgmMzfBAG1vsGdzoFLrKbGFmDMEB0DfmADAhu5AVrm2ZuQnQwLbfbTXEXDJ19fkLHsV94IbZN8kkZZ7+YriUlBR3cY5+fZi3YWm+P02XxWtU13/qRRR4c5zvtMoGgrd6j9G088M8LBNlPu8bt5tUOpKnNbW2UE1rnwwqofJ0fu5NxJ7OKNO77NssSwbeg+5LO++z+vbi+aOTC1R5/qM9WJ3KBivYv2T/p4D+tE0mhmx2vbVxNK/V9AfVgsvFb04YWGoc1ateksfr+fVtqfhk2nwtJjpC+Zz4hgsFDurMTSIPaitXg0MLL1hTUP/KA3qVv2lyym4s0+XVoVl1sDEdakyNEmzu9ZM/sp/62qSH7FAGxKaIY9tChaPzo18Bu9CUvFoq+0ZpA7xQEeqdZPgR9Xr3mFTB+CgSpTN7rA2mGEOl7BYne6VUyFpa+ZZFoV0KjSgWCLh7pxwjTASHKfj6nJMN617/qjC22rwxn8EXWxYZhuvJp29NZKw0kbGBpu26/YkgeDaI2qx6bUnm1IgeDFBDiaS5PQwkEJSCkwI6m9fhVML0iRrDnZQeDnOaLYgSKmB30UrTXoiMQRECIL5ApHuRBtfVLm5+uyobMDEod+iZHaG3YF3gqmvw8nwsnStw4S2Y+docHvqoH1kAQirudOz921U6Xxrpkobqz9bXP53ZhgeX62QClTVAC+9Rpga0HMvJN3vhOV7qGnUr/DrpzNp3S/2dhGx/ootJ+aX+flt9UYGe+fd03K6d5BZ2EJnE/XZUmscQ2zA6eOqrlVCBIV+8+ACeURaT2ySjRn/InWdomy6lxk/mKechHgC605StiHAQNHKxBOGVo6TuwUnIJ/utOLQiUl0iSfpiRmSjqL9XxjQ8r6EEKX9OkDC19IhkShSaGG8lgR7b045jymWAQtiFTaY4LfR+BjUghzx/r1ynrHanjHtCBIxCkH/Xwhw/UdZPC6+CQePU/+O2b0jt0ywbs//TJ05PKVvHA5zX6NNNIs25tjw0Ao0082MIWiEcFLlMD63IpDliPLy43j+SRcrPM5jRZ86NkBIKUt58QxAav+fBZGWJpGBaSfSOcFjZBp8hzlbbXrRJD0pQ+J1QuKxaKP5Ztufdx23dF2Pl2w56v5WFf51ca3QhXcYvdllaOM/kWcwa9B3OohuoNcicIQyAsYrCrbS7XRfCUvFVat48EZjJOtfiNTEGsQPhibhlZNwy+ARmWNyTMlHINg4BzvnlomYw3afPH9u098F0HgnAP7YRfGmM1Mxp8t7i66IVg26qMKjp3wM2O4NoAnT8Zi8gGfIGuVUug9Yq1T6CSkuRb0nX9y4ZT9q2+WPObOqhwH860343PqHRVvOy2fb97VxX63Xwrw0NlPJRulf2l/OqHNY8ufr1NXwXeuKTBhqcv6s0JPZiW0JwDMAgzuBsDcDOtXJFsiBpa0/WABTizbRioKf6jz8RnJtAHpe/15S/WBnqjmT4OAr1KEMivg/G1MAJB/lKYgkD6cthbBY5uNJ3og4JBH/V62SfNBuK1MAeANAvosYJBj5Qy+GOYScN/ZBhZGY591DnGKq8/zSpiA2awC8mHDmj4PI1ddWlfhrP3okOFmDzgLGkEfDRUlrH6epnn0d+KRmVBn+ip5xNFWJ6yvo8Wjyb4c6WZPnw2niDPNa+J8Un5WjOIFJoXL1lD6OnFS+Gu9UpZPj2vRaTVU9NZ03FTXTyTmwYygdnaVCnAVDJ570XhC3D5wwwMcMW/UB3ZwwNf2wP7QFAPTN0uq332OezTXQaOMsZWu4e+4YC3BZ8yAYnwzukBfenqE+KOvJC0uiEeOraN7oAs6hS80AN/n4ymJT1l04rv/N9N5zqVE9t238hxgE46F9tDyZUTMrZJ2l5qqvjT4kxo8jM78S6EdZkrWinSP8GJHLgobm1TYCANbyvo9wxTGk7tSxS80G37jSnRDKOCU69vxgiy8Nc3ZqTqU+jfCDo88medS3bZYFtkjF8Dc5CEgE+CPszIZTYtH2i4USfqwBAyRYf9bUZ8k9pllop0iKBlkyLTPAoKEbByLWN+2SZ/6iSlPxyBEnfLsp0AkOBPZH8HgZ56hmy+sJkPLYhG/SN4r7FnWTHSgR/p79etUA8ss6ZoN/SCSs2voyo9HFseaPjAkLDhQ+hS2uzn8eoctY1P/HDFvJHRfe7Qa0ni45jo1zOnLAkxbFYa/7TcAEuP8PjcW+miHNC0ODVoOvmhspES+lK1FFpOEf+OBFnDLOfx37QHUV0aNjQDyC9uTbzXJLk4QL7wNp3jIlBQDTaZyCONffi8H/BAQ9MUFhfqkrfHp0w747wgRW9Tlcwu2xDJpLzmAVlV9paBVhcX2uHN2DX2kFAQY6sCAnsHgDy8tV6AELN1xtwlJLDNdUB8QDlPX2opgu5vLeLYfTMPfMeBIJ5QVKOSe9YTYjY6cuvlYtLPXHzbzbQ9veJXnE+cFdAMu/R7p9uZ+7cWv29fbC+o2PrfudGoOVvYJx8/eBTzBZfAFcf1gKDLk/G0Y+shxSRz7tQJ1rC2sDqf4jS2GpoHbALwpS6CNx+0QBCvhDET6L0Wh36j9PUrBX00G+i3N28uXvNaGLOBvCg0i0Zr3SAFgXgl7L3WEmKBaWYTORDEV8I+8xqYrj4OACkI5JRBcmB+5CczR1tigChbsNGdp3YeQGK7gSMBUlv7C0H8p9xQiMjjhQSBlMeZUuq5ZA4AMWiVA5T/rKDQY4JDYuJpqcbEmRV09Ugzey71qthjvR6WdYGeP+MVMQWEtFYQs4EIDv2oL4s91ywhZgy9VMDo+UsWmX7qJ6n2hnR7kG0DuTZns32V3yR1rIC51fkuAGdYgEkZQM+SQcfu8MBX9cAmEKTGONu1zDjROFfQmj9lNHh+viYsBPMaOoHzWjJhnbOMKpzT2WdWp5CTX7dTvCs9tCvPqneV8y3zsamsHMbqCrPksa9vpr5k63KZupf04fJ8dR6yuFpPvyun/2Gqc40FKtqJHDffhjWZqJokHEOV4MEspIWcENoiY2n/4FBjnHKkn/6FuzMBHS4CKZpbETIBlUANCxXTZ37S5zlv1ffySjbBoPdKb/TFho88VFEHiC7PPKoGb93tDelxoAU61BV97MJG9EiAcDzYoAbYhmkEguiLsDP2Aguehx/g+YLmU70afclPneGlbjopGy78E25CoSs+K8Av0tmvu2ELddYe7eVL6VJ/36+ESYRtMNLmAsBps3PrPlLSS0anU6qvOy1LeqkfG4nrSF4/bMIi77HPB7DlhUcEZVOXk7JvCDSRRC7QSGmKTotuFotzA7AIILOO1Pr0Fro9bmonB42Obsnb6WrWVjAENqAJdinOtP8Cx+eDY2SCO6NRyGCyh2lHuZODtB0FIG+Te8tEKZAer0uGba8TqfhJkL3lls+WOp4YdpkLeU3X9m3kJGrr9hf49ngCm8daBbYS5ES7mBFun8siAYbeIxBkj303u+82EEQj40LQWzfalNO4yCdHh0BBu2IZnEXaHKHbS0l57EvMcj4NFBmfkyVwzW+I/iDdXsZavk/X90632r7mV7vJr7g1fx/dHreWI4ODpZ866LVNrXT/u/NprJwT8+JdDbhPmMUBoVsApskZNbsiyitl8BuuVUXnTbAWlvw44Ol0wDS2U9gdANKAjU+687n4D/WZ+HcK9LzhlTBe+1IQ6JVm/7x6pUAQs4JYE4hAkINAeh0MHg1cP3zIGkMsNs1n5/25eC8OzatgzARiVhApQSBgtcAdtmpzB63UHRzelaHYh+Hba5ap4SCTrWRsYMJAYSrhSTPYXOCa9YOizw4GEQS6df7RhWYGSTFi4dG4N8EgFoHWej8MivmKWL4mxjpBCgh5JtDlxQ8vn1+8VJ4fQaGXWjvo5Q9PL55q0JvBMp23ZGK55WMDBQ95k+k9BiwXTw9nToxIjkBQO+xIv5UHOhCkFpvzpgyhnJOwAEsRHE3fNBN9J+fTe14wN3jAdfpY70bWUmhdMC/gjaxzhbax9TxERvOsMpv/nP7Grzz7/FqPPW4tN91e10kd4xqjK4wJQrW5/LRAKZkym265GRJTX68LO8zayBvQuxkf77tgQaZmo004e2Jgsd9YSsmUYuwJZMoBNiVg+7ahObDQXMXGddg88hsxFuqbukadtVhZuKHh8+SeIaO+MTOACALdevbPLYEfP4RJX3rzjkAQvxsFjiRfHaHiQwnyoBkYdrOTTeP1aSkC9kn0sUh7+PRHD8hzFgdf6M/cD1cgiBk56pTSP/IqmPAK+jxV5okefDzVq1uXevjBennP1Ik5KERAaAkK9ewhPBOb5BvswSXaqVRpA2SPbPdrV7YLCxkbiMxMdax8QON4KCykdARaxMLACyx0SxnEBq+ClKQrhV95JZlDBaU2G4GkVWJQp/blYSxDmvbRCbw34yKu5ItKQOxIiC5s8LeElb/lkCIGGvbYmHzKBtcuuGCbri0aPGQEZEZP41YZm/x6gDaIFOy2smlFn5IbWFsF9ZZqb99SyVW08oNSIiQjRiw0hRcu1yaVUdXgQRk/rsGd0LV88OTL8iFvCNhk2pvTpPC1tJWYWUKrTaVikMwqqZXmVHJdCAS90HiUc/fYvr0HvstAEG7ZNzpOhNHQfTKEghUy2DqCTn7ftqA0yy6FtnHkz22jMbcQEc6TpLgW3Ck5Td/pKRpgje/076Rbdaz5vc4Vt+b3dMPJdxBbQMvodIudpcZ3OjG7XPn+NF23JNrPytdwYBvESvS/JN913ae6MvsZ+H31b1xSOLJR5tcyg9+WRAFgTzZOKCQ1x6QzRAcrA9V0clD2xuCSJ44Ebvoz8V7wWbN8Xr/VukB6BewNgSAtFP3qlRaIFoxPx7/nc/J8JUxPLT/wY4FLprSTSt5HfSqe4BID1E96PUxDSk03V+iD18J4CsiaAJotlMUhNRBIlW2W2Cv10JFHoW2u05TCwBDCTlkFbKgj28Nx0XigC4fyWQdI0uRD1lRgNtDHmhXknpa1gjQ0hIep73xFzANkTZF/pgEwT0aZIeRXxFhEWotGv3h5dfHDDwSCri9++vFaeQWDCAhpUWm+HjZeD5PMJ5KJ2V6zgUphUx1PanVnEx40A8Y+7PaFZaRp3OE5AIcHvpIHNoEg6aRt045J2UjczF3KbgPrwoLvrEW4sUfe2v4LHNkmjC7zVrnldHoG3Oiz6d7+s4R/ANH12Nt2Cn7OjqZF/V7OKZNaTt8cwUM/k5sdOCLRsjiQOL5TaCGpLZSNbrrGrpQFAwTTIq+pnTZL4zsdyBrHqry5OQPfxrSFK6/zFjJ28DdLfCFU8bjvKDmAsqUvbdqsE9R2KEWYfsz3RPKtAARnbpkFpD7Sr33x8EQ/ZgKRMgOI9fVueLDCjFwFgQgOfRQ9QSRe3XY/jlzLluTKM5bIhJYA1gAMVgiqfoZgD9VK8OcxQR4D6I/V9zgYIzp1eI1jBhDBHj6WQHqprxPxerQDQspfqlPMDCHS8MEvLfrX8UE3ypeNOtiNGhuEFFrsI0k4BBZokmpvx0eQLDVmHosIpwyGrfOUewHqwWcKaCWvjIvk9pQIJqCoSUo6wuHrtOEmCY7jwTjHxwv4oIFP7OLttoNdaU2TzkEBFyOvPCYIhgnmrY0EYk8bM6GTMvTNpxJZE+b4NKbTUbdBFwl39u2Dla7khjYSaXP4rNOWY2zLADjy4Wu6Ue+2edQ4FFB3vdHRvg22MVOaqQfTyIhgqxfONmmlakkt2Vzo1d8jteuccWhZ5SXP3u1DafNDtuZXroiIRDdXCJXxNUdZrk9HIEhO+E62f1QgaPoszc9ty8BcLmn9a2Nc85P3r8uNk2Bk/jrZ/zRJ+PpbuOHLemcroXHMNrFa29D7YOdo4AHXvE137gju6bq80re8NQW/p21dwPe0p+hXfvJdzrARjv3WnUJ3+80RfdHvjqQ8O/GRxAC9rZz6Vi0rR3UcRrdsOqi5kSdoo7GmBqZa20dr/Pjz7wrwvCYQ5CCQgj/6RDyzgV6xYLSCQMwWYk2g96J3EEgD3BsvbhlZH/lamIJAfDWMz8UzM4gZQI+fXKoCBIL063WCGHRSK5sY+5hOTkfnzpxUdtpudkWXWsiT9NIGGhFw7ccVTCiwBLghT77KzAiSdA/YNBvIr4dVUCjdtZfSFq8CQVoriK+lsCYCr4ddMWVewS3WC+JVsOfXCgQpIPTjTy80K+iFAkHK/6gg0I8vLn7+4dozifjiCrOLGPQ+lcwMfl27YTv29bbmAxOEx4faVq5jRlC8c+y/nQfWQBDtmo3E2aVsRO0KvIJO5rn2bbYqNzg3XkUhoUPuyEzuE6CJ/K/M5RrLNTe9F9dDHBHvclXuPgvYuK7+IV/h/T5qEdBa9uIGlVhOH7OC3hVZok5z3ZU2+xjnSjHckdBy0n+sdkKPr3hW4T7E/ZVu0tQggdEumf3jhyMEdHjVSxEdB31uPijVrCAFgN7r1WrPClL6QQEg1ua70dMZgkIf9eEF+HnVilfJrEtyO8WdyUev6QTjoULfcLou6iToAwnUMCuXlAcb7heVT8rDDuUVAOKVLWYE8QoYwR4CQFeaFeRAkD6CcHXJa9IJEhEY4vdEndsj8a5y23t0vul/gWQ0JC3uDHvU5Fe2q3+Ln4tbSUYH1fp0QfBDk0YbXxydp66AmiZoFwFx7ILi4PnfFMp6y0MZxjASUTJAUL67LQTQ1G8oH2VlFtLYsACEZtu4gLKh2UHd5bucC+Gd7Mo5kbm2tkTgLfVLsCljkys2H6kShY6WuqG9t7BwkF3Nge/UgVhYLFo0ezbgW3FQ7BnNfe9uyhXvyl525RPzU4Rb20onFKTdtixvxS9ioyt1yYRF5XXSA4ffgaCp6sh9Qw/8gwJBaTyJmqaJ0ZKrWVXjqguuHOrGpnRto3+1n21Fm/JXCcfgh8j83un+Kn88RM69vsCZ5VBfvVLudkGp2RsGPRfAFV4SNtZM+g3YhZa7YlrGfXwr/en8KcmnKSc0mreclALXqGaS7nL4IXxJJ5papCYtZeKWnJBTugocA3c6zYUM8imPnMlaPmnoFL/xE0rWKGAa+js9jfRXwLQ20Os3ejVMASCvC8TrYPr9pjKLQ79/r8WhNYD1a2R+yik5Cib5c/HqnJgJxALUHz/yuUwCQljNwPNKqhMUIhBkOygrV9UXvdpL/4DLxtQmaWoBvdqU22BD0JGWZpfAq62xkPonCGkGxErtUJRI71gwmkevDKETBCJVTURbs4OUsnAmU+EvNUi+0lPR58+vFQxSQEjBoJ8U9PnBwZ/nFz/+/Pzi3z8oKKT0GVPsNavoqQxgIWoHlqTFM4Iwdlg7c21/0CrhjHKIBxZGpB4b2oIfyeGBr+WBfSCo2yPnG9soK08T7jI4bwCEMFzpejO0BoJMNs6B4oW1BVbqpGFFtitO5v/qHM7U1cQ+jWMzNuRYlKOH/855sOnAdx6m0/SGFulKDcd6XbMENaC2B3GMJvY80PnCPi7+UEUBtNZXe5KQbaWgg6t+b/QRqyJ6qtHGisgSxKceY9yYEawhcEMwyJ+Bp6ypt7cK+NwquMPDE4I8zPq5IfCjnwND+tQmX94kKHSjQFBmCWmWqvgzaxeZ9FP0kQR/8EtMdBmbvO5QbLGJqoOrITr3eeqz6Df95S/1fY89QzeBG68TpAoTBHIfRUpQh5lACvBcavYrQaBnmgnktfKYEcQMIcH5oia4pwSD1Kl6fSEHhNBPUCkpeZcxjvxI8WQdebLCQLffVgizdRHgLjyCLM9sKhdIeHIWanGUaEHGF0YeVVkQHFmbc7PYYKX4vREtZUF3FkOaTDDalotmWRCmo3y3fbUo0lCskL8mn4DXVha+7zpi7lndCx0StnzxzRyZQSFJCMPXyVAYm4/58O0AKxNZDRkuOyGjaazGhcptkyb7U2mqMttxfBZFs4W1itTB15CypTGkfW2xvyDdVrlcR0tV1teAXAfw2QuNQ30eIOjYvqkH/lGBoHkSqlnR4EbD7MaaaPqmY/wb3Xvy3P8b9f1p0Ruf3SPtW9HdY9IfR/kSNNhz4RoNZ8CXxrRtWgvFPptWF07yXFpbclL2dcFVLpfDOLddvKZ7+eEOdM2vdNE5sS0PmkCjdeUhH0v7sg8Xv+0Gf1Ns8U0767vlrBICtE3pBTBUeRBKsHDFgA4k3UQ/pYCGQBABoPcelGaB6Lfv+DKYAkH1Othvr98oIPT+4jfNBuKVsLdKe4D6wU86Gezy1JOBg3SpUTAj6NOnJxoMa/DjBaMxgllAmhHEsIzoiwNApKk/9uQaIGuVSbnqYoToTBp6DyLNW+XyuUvYIXnBJEfegwynDIoZBDMArpYmHcwG8voQnmPP0J5AEPwe5utVLgJBzOIRXKbr7TbzMzC+1mth16wTpJlBP+q1sB9/+kEzgp5f/Ivfv15qRtDLi2f6gpjGyxpUa0YRXyJjdhF2xFDpWW1VkQ3QbstXYgAORtVlLe0YjuLhga/gAQJBWibMrZJTs1tnneKjjCmNO2cW+LXpr4GgLUJ0RdgD6RZuHdp9Sdc5G/574HFgbghzzZ03h4XbOKM9uh6hDcGu0PQTrMvVOIx7Kfvy5gh2Y5qigt4w6Zpuug1QHLJjAVVNbYnB2k3YRqs4qw67qsCne7L0We5DiMUo6KMu41Yzf27Jq49NsKeCOzWT9kaBID9MqRlAnl0LrIJAzAzide2PEtaflR8LR/OwBcutO3bTbyZQVPXAOCzHZv0Y0fPwgjIpM37yQIRADf0ydOrf6NvUKene0jN86K+8RhCzf/hYgjqxZ88ICCkwRABIX8qkTMpDkUvJTvAIfbwSnn7W+uhvZYCDbKSoxD7bqrQ2QOu2KavgibyyFTZmEXF8TKMdqWWuaQkzblFmnsJ1YlPqolJJo0YaGhVRtLN9EDlTGkTn4wNsczHbUiOP43rKrg2lCE6JmXz2jFnuM89UZyo5ZcWe++RsbKtCHZEcD8NWiQAWiUu22KVUEk7ahhxbntTHYCdAJIZo1xjsSYsZGoRsbGDbkmBlsjUWEnXbLYCVd5u/w2D2lWajBywnRm3NDX3MVS3USKgLASHsea5A7cLSrEf6DTzwzw0E2Vk0N5qa0mrpQLgosf3djcwNvE2IyuwfClt5vkVedq7XFMz2yb052ydsBdvdK+ALvKDH9lD/nKIbQh6awcgYOgbdKkd0pyndp65xpGz7qgNbcWseHFvLSCn7U3QrvvO/h65ta32dtizSBAtS/1jWGkI1vdbwdJHA0znhg8aFZ903hvZFPuW2rAB1ArlzwKPFlCcU4WIA5kGripATCOLpI6+EMRWdV754DYxXwl4p/ZUgkNYF+k2/N3pS+UbrBOULYVnrQJOCeADpIJCfTkqpF61UEOijXgkDp3Gwg0EXfD1Mr4R5tEkQKKNOleM9GyR7sH8MrHFCn1ScJK6TPFaRk3ix4dDCoG34wgUNJkIDOz4gzRNKBsQqt45NIEiG++qXYJDCW+JlZhBHSj/PDiK90JNQBYI0KHYg6LkWi1bw50e9GvbjTy8v/q3fv34m/UF4vUb2TIEgxcMICHlWkPixpevpoXENbPHFqcEePqYemI1bSI9AEMf62L6lBwgE6Q1Rn6c+18oY8px0TpNNoWjHeVv0puX0a4ZFpk9VyqCF51oBGSD3ScXjRLsqQn5s93jAV+G+DnLdwaP2s3fmxN9sfd0B39efYLJvmDk5Br5eha+l9XEZZQEil+taoAO3HMXPBBJASAk3Qd5s2KQGBmjqyDUf+KgiBW0uSyA1ppCuoowxBddaCCOztbRw+jjs9U2+Ul6xps8jgMNrXazx44ctyrPmzwdeBesAEDD9mBlEoOjDrV4Ve/+56DR7yDOFWENIM44k21/ilAHKut1jU7vA/awQY8YQyLafVB2EHsW4r3HfRyDI5QSADBPZo3o4AcvTpwoKKRrk18NE71fCmB1EIIgA0AgC6WuZBIUIDikYxOyhKz0cIXgED7ODnijfXx+j/37CGEDpel9Rbi5Y248lYHrjSFWZflJkPj7a0WbXY2Uq7Vbu5H207SHKK35oQT3C1SC6PVo4ztfmvXfolQT9Q0e/bHzRuQAMUSJi7R+3tkHHhY5NUtp46GAwGP1k4EZ26Vjlmw5U8KmRmKwX5tgE74SFKXqwz0oADmoXKLecThvhdFi6sb/N38+qa1ZXVQXUdr3aAtzedtryNk9M9ozxkWTIYruh0OHnEuh0Z3tURHCRRaD3OwhGDFAVBozMsqkY0hxD7OuuLHYstBA2+5BXVW/4Qk4WGZavDHlm0HMOXXM+n+HZiTiKf7MH/hGBoDQiPOHckgKh2WbzNWWUtu21SP7ShEZ9cltOEJtzjm5lXnlW+D7/ULo93z3lNs9+vEc+dPwg8UVxZez8Xs8qb83/UbqV7z55g06GmU4XIVJvbWynDxLUzHfSh3J/XbovaaPuqT+Dq3m2pHrBAu/tbr7PvfZiU25SId1WBBzBCwhsXjpk9h6U+mzuNsZ5rY7JwZfA0MfTxXcabL7XQPRtrf3zG0EgBXx+06tgr/S5eBaIfqXZQW9YJFrpu3caoOpkZco7i0MT8KErQt6nzte6QHlVTPo8mCIQRJeV9YFitHBdITspddgvvGmLodPPAwvqLH2Ue+BlkJ0nGTU6rmJTmycBk/DRcfKGGu5TBfTj8/H6IkO9FqZhtf1MV/7oUWYIuabK080DRwYDXNYJYtDLq2EvtVg0s4J+0lpB//r5x4t/KxBEMOj5NesJ6WtjfIZeC2s+0awgvS221AlDyh6nWCbDqLt8zjUyWVu8UB6BIDnj2L6xB95rxMuMIDba6dpKKe+3E6CQwNv3R3smyouOvhE2WHx9Y9n90ym9p0T+98LKmXJA+pSUO59jtND4qpdjaxy7gSazAsId2PRwU913iKGmz+qbKIudIoy9O2aUvvyHstQjp7eVh3xMz37D1AxK17aMKPMVL32V+ys1RF4HIxDE7B1e+XqnV6y98DMzfvR7/5Y1+Ohveb2aoJB+BIt6hpCCRy5rBlGCQOprkat+lnZuXcp8ZsatGrgfvshB9mPbgV2KCnXdqDkPXVwH7R57fT76i5oNK5hfCVMAyLTyiPs29YvurUmV8efj/eoXr0NnBhAzYenzCP5caUasv6DJjCH1hdcdKCIopODRU80UeioYa+s9lQ1PeLW6gkG4OnaSy0bZ23poGgbScO2U73OesYDlCJ36QheasKaOI9+yIRbdOpaAjc0jkqYLaLOfs3M34G3B8gP65EDQip7Cp37ByqaVcuQXeffTiaPFn5FndNOIPB6cAPw5N+BA8COthG3Suli7HIfmFnVnVyK429GludGWX7jBOjJQtS0AfZRKwrRn5EyKvYGsslvfSGVo28Tx6PzAl4z4IdAct6HNFN3SJrQ9NyWR2+Crft13gV+rDK1/leF6AAQaB4KUHtu398B3GQiiqey3nF+N6XRtcmpeLm5hfcHdy/uryuOacEpgm9LmnqIB9nfQfUnnOVv+CBz7v6Tv76gjtn5JLzT1RINstnNM5+DN99elD3EZ2h7itofQtOXRSz1nXelk+Fs3LtUrxYqbVk2eSbulpNRUSdMJhF7dnDIEMfhErYMhJtLOvTBnr34Fg4dgDp9+96fg+Vz8WwWAXn+4ePXb24tfCQL99kZlfnkl7I1eGesvhPkLKA78MCAt2ZKvlW/00xfDHAxi8JrFoj1gYhDKrKAeutHRdo1sPJ6sgXU7IRX1YBXS4V3xelZR4SXUhwExrNEwOvCSAxkdtl1ReU+Lt8ww57UwBXwkJMGgfDWMmUAMtwkGYfFjlPiLYswS0iBZ1Wf9hOdMj7+qr4YpGPSTPiP/879+vPgPM4P+/eLiJV8We/5En5rnVTKetMKbp7SphuTqH5mzDQWDfqvFVBzhrdMM1mep0EdyeOAreuCd2i4zgmiHa1vsfJ97mGQYO9Gf2vxU+BQC2I6HomU7U0ySbfFWFNg96orpvzGJM+0mX2CqtxLYV+c+ECGzg+LmdmwhdsXp8fZpEVQC1yJyV6jjCU3fFS3s3Wdg7nqzBgnXzWahURRbG+EUPv820NOFbrMtB9k8J4DfgRldl5mFw8wgzwLyA5LM8Hn3Xg9OFAx6pwctzLb1T0Ggd5pd61fCRgCoZv8oEHSr4BDrB3lNIcqaDcTr1fRpfDXMM36oowzJD1tyi+uUuqkq2LdueMJ/zAyiu1Yf3A9B0i/KqzrWPKsh/CatCtSkzOvQmdHDLB/NDCKgU4GefD0swSACQs+urz0z9lp5Ho6Q+hWyfq1MfWRmGCkY5FkMBKfQybGMxZUYBoSqAOsqGe8CNkMRfB9vQASvvA1HZOYEsHgr6OZv4bmpn/4z7d6AsGpfypHZxg3ciYzIm4x0Y0eTt0jSJm7cPrXxInoQHcpDuBXd/GodJY92RFvZblNJfEb7m7CVFk5zl3Mj6by85rXOEklijlXFEHESaPv3Nq2U6PH1gkzZHvwQDAIshEl3daTYqKaDckdmKeM6hLhIu0NX4OWa1ZCZDl6B0IOF/hEUVh78tWa2d5NX8di+oQe+60CQG2s5J42WJqTNiGpO3cJFkEbcTXA25AkJ+1+5t0XdshG8z++VtTHfgs7GyqDVhlP2rXSdv49uj6OMjq7jfTKgbbrOky4bx9Umf4FuU6+uo4R35wUo4GnQzC0KnZ2YqIUzuaQQza6HYUhDWp8Bv2MXaZGT/aphaic3tUXBliuw1WIgLTO5lgAmnml6utS5Jd+QdLcpTXlQY1NT7bmDz8yj5lKnjEU6bz1g1KLHBIIay6iIbsJBGInNa1y81vXZQSAWiH6nGUEsEP0rQSCCP3wlTPlfXr9WqkAQA1k9xWT2kManfq3MXwRTg+LlqYtHmvFDV/SIdYASCMoXw+rJpbupoqEKvtZgd21cc5TNYHbajhfyKpiOKDz8K82gJflFhPnxAQNmb0rIebBZ1zdkop+O07KgQH+P8L0mUAV/BGd6gl8Nc03Jc3QICmWWEF8CY0B8daWZQQoEvdBsoBcKBP3MrKCff/CsoP/8iy+JKUikV8devNTMIKbYyx3+PC+LDWGL5Fpd2WUggLEln7qLGtNcmaSVHdRH5vDA1/SA3mqZawQtirtdjrYKTkDgo3WPTOE4/dg2RAF5T9tXBrZmbfngnRdB4wxbaCkfGx6Ih/AlEQ78lhtB+ZAy8Pu2JlgdPegbqbSz0ClP0iw+Vir4UqdCXweHGBj4Ny50XIHN39dHCoWHjh4k18kphVx4Shdl8ymDfUYqXTaJKp+EhGZJN0H3wgMX1gMiTxCIoE3W9dHMH/WVr/VRhbeaYUsA6A19rMrvVKYP5YGKX8tW4MdrACkoxJpCHyTnIz8J9bpAmqRKkIn+DD3MBnLdy1bs76PUdlH3dgtxvCJVFfWnsQB9oV8Fo26qj+toKij7xOMhRzgJqhA04iuZBG/4EQzygtF6qMHXw56p32OdIAd+nj/TAw99QVOzY59p3bxr9XkODF0xayivlhEM4hUy+s7um2NH2VR2KRn2zwzQbPYGjLWNoFIDiil0AMs3YnEbURXxy7ql/Qg/Ggds7cVJ6ZbaYNng7Dk6tEmNZUMj34a+5IFDzRA/cwPUBNT3BBq6oMDT/g3Z1qOEBSchyKmbAptl/BTevrG1kpc6T3zE0SLZah/hVQpF4ww0+zw/O3jTurBp+MkGRm5LWisfuonZ52JZdLWePc2wTbqYoY62U7TdLuLb2DTppo2unoqdRt6i1YjSWnR9X+bmIxg8+w228VOG853jwjj22REI2rvrm5W/20DQpnHRgNyc2k80rd5m86Nh0hxJ+HUDzJThhghvOmj/3GYbvySi1X5tui/p+yvx36qOD9DL+81cdNIa1kqvbWiF35+fXFyutw2phyORAOUWPyX3pX5CTufqAm/k1LyljQ669KYwpNQ3rHnA7WGNm2nrPWX/Clvzp+WGIhpzHKYWnz/aeWCqAWWmjQbPK2HMfvlcs3GQwO9WI8y3euWLIM+bt8z8eX/x+te3F//vNwV/FAj6Ta+I/apZQeQJAr3VV8UYwHpg6tlA9VqYLwL96lcFgjoYNAauWMxsIdXC9GttsFM+r44t14LUcxMEEhWdMeykEVVyRM5RIwCWQbMBCHZlmwepcBQ72JSlND5jWI0htEDyWKx0BINSlvdEwuwhPTnVIPlSO39SXjODXr54rtfDri9evnzmV8L+9aNmBf37pQJB+pS8viz2A5+a18wgviLGGkUMiG1HKm57pDYwV4+da2dcyPBD6g4hg/VZMuuxOzzwVT1AIIgfZ3q3RVJ+bs7KdHkQrBZWmwdU96Ar9nRePLAtp4KVrDqNP839XwkdvnHt43QfF11Y+MOjlJ0KZt+GzNC520qCri5hRRIpOeoTxNhySzclWn2TSqBvtJaDm960CHSJ5io9NtG1RjLr9bFpXA3t0D9sGExNlXRRawC8dAsEZPh5MWjNAOLz8PSLXgtIgR1eA+MVaj6y8EYfX3irmbaeDaS+1mvx8TqYXv/Ka2NaCFrrCDE717NsJYs6397y6rYU6kfaenuGVB7m5EzrmUEYiY3px6iUSgCWzXUSjNSvhLnuKFL5s/ozTjxSbzBrzFd9i2I3fqWLL2Z6HSAeZjDTR080LhXwYUYQD0Ku9Qr08xeaGeRgkNJrgkF8TIFZswoa8dl5vyamGUaK3GCHP1lPWseChKDOfvN1AfioV2foD2W+UHjFvCrkRj2temESHfXUv2jOrWPjs2A0kr0l2IC27dbWbKF7UlEVqy2DaSeKtjuDDHtpCzmVPmvjIvQOXVsKf8uPD8/rnYb63LLeME9NM0ed5hnZ8KGslY40FEVnsvO0trlFlvPWdYFaaF/Jukw6fTsELOi0FWPKZ/dYYb69PNNLwJ7vziFYtHYWHuhObeAsUzvOWNzPjxl712rwZ9hOiTpgf6MHvuNAkJvP7gQIbDbXpRktLdYnklAZmDVPvBiy8C0sd1zcOK6bQ4svIgWwWO1MeIe9lClp9Qjp/EJukSq3mKFrRwP+wduiC/lneYsOs+4Vv8i714b76Fbcmt8LXHFrXnTp+mOr63TCnxtx4kdEHH+K+BRsI+F3FfZ+pHxKg20aB/4EkQjGcWsBMHV+Y5UQxqkzILvQdNboJtvwrgWoelvz52B3aVZ9zTXTDsgF0tWnA2d9gI8KBME/O3QNKrxOQNbygZ4QB+sZOBDEAtEK+rzSukC/KujzP7/9dvHrLyrr9TDKrBfEYpbvPZWdIJOEK6iUpZMZJRLIeGIdzAji1bDMChKsA0EenjEjiLp6GKZ0exhsL7bJQHtEOw844KCR6t8zoWqE6HZbJ2QPqKibfcDgmQKbhZFUpkAc360loedJeM6QvAqGlXktjBr3mUMg6Ma6GChfseaBMlcaBL9kAKzPyRMM+pnPyGudIF4P+/GnK309TLOENDPoWq+JXSkQ9ExPWVljKJal3mu78wWn60FdvJWdVXdqwSD92A4PfEsP3BcIooHTamnnbutp8M43HALn2e02mvqd0wCaHS2vtowNnlE4Mnig3D78Qhkf0TPoYpZ7Wzk618W6Di98JraQ5iwBS3EcVDKDNnr6ON57XITMzZVks1lGZ3MVd1tQJ91XY5M1Xekcl8ewem+92plPO5dFTwq9WQ0M0yqDboFFmwm8eD0gvgbG4s8K5GSGj2bNsjYQD0348qYCQG/Ut75TMIjXwTwrSDODWD+IL4nR/96K1zN/1KlSJxaERr0DPYrHsM4e8A722DTpp49lFGCcrbblMVp7y6BkewPwMQWxqaQAdDjQ6eEGnXtei4aQXx6C0OXyKXmOCw8+vPAz6/0wO8gBIQV4CPIQDFLgh9lAfEHzuR6KPH9WQSGVHSjSwxLomFXk18T4KlnNDqIfy3FQ7VbnyxK2jekBjX34aMv6saNarkP2qQ/VpV5dEq2Femd470wV0iju/CC4ywNha2uyVGNCKSPKkFXmCXGmXWlaqNLGmQ2ZJ+k4ZhM3801cSlWc51zjpjK3nS5ykZWycO5pSx72QY9Cb53u6UPTxyRckb3Wp2X1GLGEDtHgW3LqMUqmGSXZ0+PCWd+W1lTtiyovPE1J2jbhi1Dm2uRrS1W3JU6/rxJO5IvvBKbajDASilzG4fiIpq6lJ+t4nOI8YF/TA/+AQFAaznCKW6eardMB3WRocL1x4rjlNcBNb6UoRMujUS9oZpRsb3Ja0Cr3zJnQ4Ja3k40kUE3WkvcmbmhWGWt+MJdAMQ2+put0pVX+d9M1f8vrtOH7FHxvKLtva9qmW2QPO+Ff4Ba3L5cODmtq2AILMZKGm3BAw7MU/0AWycPmVtNy7ti7JejpniZfe5fmH6kElemzsxBS4pBI90SHaJI4w/CwF+MCAR6OUGzpGha+DB9aRh+S1ti0bV7qB7Xld0ekNGsJ5KkiVR2Utpcugxea6Lg0mNUUdBaAZsDKl8FeOxD0+uL//c9vfjXs11ev/aoYn5JnwPteTz15cgkvs4sc8PGdF0Eggj8amPrVMG4rCPooWDQCQZSjf1uLtlFo6mFHa4e9+RdCm8q+/himnURpHzpSaXR9xepXw1qOUgf1TMPOXHUdyrHE980fCu4QhPMC0a6t8oTOEhjyK2F6avr5060648DySXgNjDUofsFaQCwarRlAP2vB6J/1xbD/6BWxn3585k/K//zDlXEMoAkGXRIIon62QxbKHKzEqkDJVdmI2C0m00HVT21NeOwOD3wDD5x6NSxnW7VnFbgCjK2RAjhLu1fGcdhBdE+m6UlF5rOEjH4kbJz7x7b1AC7BV9M1dT0ZYRVhdRD61SDTC8Sx2/IhN1etlpmDUPChYGRyfJGDIG2DL8XsqVxocAAAQABJREFUhfOVTcgiE99SgMrBEDLSvwiDD1IENy9U69bkemaSbaHdtE+wyKoNem6+mAh6o36wX+9ised3Nx80Y5bXp5U6CMSr1vrIAl/a1GvXb70WnwJCwt0wq5ZXyWRIvwbmBybUyQ3Wc1ClKOUEgeLn1BsrMUypKmt85e2nspfziIcq+EcjAzmkKqASvskeODlB1Jd98vQjZgRlDEGaNfIyM4h+htfKss6P+h1Fbp7plWhmw14y00e/Fy8UBKL/06yg5+Sfa4as1g4C/kxBInAEhBwMMp9mFGl44NfOVB+CQQRnfByrLlTXn4ynjKkNX1LoyysJBEGqunmrYBd1ZZv7ai8CZHyhsv1hsiY3bsgKSkJobUXXsCVFR45HtA2t2ClQed0cyKlDbzLqMvW1lkV4gZxY1oLbZfOF1inPfm2TitZuEWIGZISwDSGg9Q0rbJxKqgQ2buUNqqr7NCbnsBRDstM/qYS2/PsrVYfRbNPHWHlKcNsk/NB9im5agczoKDrxzfI87rHVZ4uYBRedzqjNhgT7SGnb3VXcEHahze1ypcixNdpZB6kE8hjxCATtnPUNi999IGj4xq1JJTe4TWGQfDnTrbX5V47GrTDyp2j3NF0+J6PxR/qXekDuxuM+QmcOUw8Mf99x/OusjG1njPvr1CySWtc9bXG5oo8vYC0S7mbvkWXiPR4b6HiUtjkldEMJWp1CgiDpoP2a1JCJBAaQc80B1jV4/UYBIP0IBPEK2C+/vbr4n/95rfT1xW/6/UqgSPgbrX/gz9gyblSdOxDEmgNZIJpgBrOEOuBDoEivgqnnZR4Nr4WlhWG1a6O0tlG1kTHCrlWO1LzJ1JpBDU9KB6t/d7TMCPL0+ILBDm5sVbBYMwpQRLTxDIYgYpBDAKhTgj6UGSTzuFbrO+iuIItpZgB7pVHscwWCeCrKQPhHfUr+X3w9TItG//zjtfL6CQaOT+zyFTFeK7MODJQdDBLXzYMbGztxLsofDJhlnQe+Obor55E/PPD1PHBfIAgr6vSliWfrNg1uafOckusG2Q4U9A6I/Aa1Lgg9+A/HsS8P9DFI2l7rWxiVCQQNb+r4iK95zjsxHC1tcmw5wQPJcVFuf8CtYPJwfetS33hBQlDGJvpgt1TBRGz6ZoJ42Wyfdh0Iekj7wEReOVaXmUCQZvKw1g/r/PiDCw4A6TXrt8z84Sub+ikQ9IoZQawNpNfC3mvh6Pd6DdvrCSng0jOB7AgbwRU8PQF9NcEhl4XDBwn4JK8rvnGkHClOLsMEDg63ph8k/oOPExRS32XHKRUn8JF6bcH0bZkVFBp6eI/9dJI6ECRdvNLcP6/3o4AO/RmvjLEukGcCMStWvxeaIfv8BR9L4PUwcASKeI1MgSG/VsasIoJBeVVaE4RKj6tI7ea1Q8fBx0+wmaEQGmjxTGb5NEmHB+Sppa11oCWBBPjhxn/Zo2chN7QlNpUpy6BhVwQU/T6BCgJL5whkE6ihYY+0IXOhg9c0/khH8xdXGZwRY3QF0wJIA18hzgscWvbR3PuNDPkJNbkfoD3KnhACjFjjtxrmWbyF70utyzr2yF051aV9NgJ71m0gDNziVrov5VfOrcxwSq/AjYG6OUhXeOfhaxry3lZkwyodMpXpPNck2vrxatjOWd+w+F0GgvBH3xRN36gZcebkbDZ4bX80srE1XQGgC37LwUU0J2UIJ92QdCaz0VY0q+x5bTkj4E+DH27rn1b13Qv4si/66KcD6NaQS93dYzk7pK76OQ3n4PCBm1t34FP3xP01uXO2bO1oXd32M6DEB82/0tN5n+qkzvks0jNwaXmtURrEFr0Lv7IeujFrRyfj+Iktn5rNOcr0dh78fdDXSd5oYWgCQL9qbSBeBfufX367+J9f+bFINAtHM6B97ynx8PD7xOwf/R5rgejPjAzrtbAEmhIIytR1AkEelolGeffW7ZPyhWxerxvrMTV5V7kKDXOtu9A0TlVv2dhe6bZSQydRoBDC+McDmMUAfMrA11sHgSgrnyE3wSAFgXh6yowg/fB61jj4rFe9HuUzugSCFBD64QcFgTwriPSlXhN7odlBBIJYSJMFN7XItGYGZbCdY9oe2jomNlEP2yc7qb4HV0p5Sounj+3wwLfywKlXw7CFdknr7dN1tO/K+FoGoTa38jT1MUQJhrZeckiLBlzDybvgzNRXxQo8dOlI5/UCZ/YPv5DXdW7xMX4dMzIg2W1N2mkORB3gcVBypJrG/RdyxsFs+lgQFVznwofMUNCPcf2GYvJA1rKNmaiIqj008DpAssHMgvtydDStMsyGdRDHr4PxKXjWzcun4N+wzp4+tvBar375dWsFgl6xRhBfDlMw6IOCQLxezatjngUkWQ7MEOzxHSTGpq9MvSrAI1y+yklK/aBbf5lN2ryst+N+NhVwRXk4REUYG/CQhIrHf8z8CSxBI2a5goMmOLjoqZn46z6K1D/1N8qwvB19jz8Lr77PgSC+oMkaQXxBjECQgj8JBj3PK2O8Pqb+74X6yGvREgQiiER/yPpDigfV2kGqqarq2kqHUw4IVleaUvZNK/Z6QBJ4H8uMpVS32npsgFwUpZ1FX+/dTpoBJ8oKt7NFv/mDMkWTu32vhkK4lou6D5Ur2MwbSWLTyWh2W9BEQKy9AJWXDpun4iMLb/qHp5ZUdYqwqrAR0ZM9VRJOhT47T2mxb0fdm/MuZdNZG2IhLdUb6oa3TA76QhdvNcdWn8kaRKFlNfnJdBFufAto4moXXVTqlraX3WyruKJ5yKFCJtdlUqqO7znnryWjRS8mHNlv4IHvNhCUi357pC4SakQ+ybTzQrJ9QhXZ2k6bc14B900O6gVGdiNgh2+BG7pmWOQgdVtszm+ebky/x5pvRXePSX8Rqo9Xi1vL+4MWHB1Ftb5mUrp6KPnQLSRLNs20dJmc/F4fDG2PcFw5u7jI2upuxCqrmVYYdPuyIAJB3Rwt7Xy6yli5gKccihJs2MQxKDdVs8oAd8hykAeMSn3eq0AQCJgXUdb4jgWltcKN1ir4qCCQZv6wKPQvSpX/Ren/++VXzQjKYtF8PYyvit1KRr4SpuGgPiHC7J9HjxX0cVCIQSs9cb0exkLSgven5BlCNg5/jONr21NL4NlmC1nP/VOkXfXm3KcMBqY0/IUvxeV/7eQUno5iQetiIIK13hh8VTCIrtdDc8qf5T1+eoL6ifcEdNPE4BVBTHHnKedzrRXE08+ffnp+8aO+IEYQiIDQf/6l9GetF6Qvil1r4OsBsOg90LY+vBMfYWjqqDQN34a2vUz5brsdiLLRx+7wwLfxwLlAEO2VzWkXKJNXAydxNo1dpSo7l3xl63yoErwlA4jZKdfW54aLRfeQwXbz/29Occd0VTu+b5JJPc9k44JJv/AWa3NOhlWDrsODmQxMA6Biw8Ld1jRNjuNCD6+I+pI4dVYO0iJfuZDbst0NYDSiGljsSXLN5QrMzBy+4kUQ6EZBINb3+aDfW74Kple93mpNoDe8DsZMoNcEhDSLloCQvxrG+kDv/RoZ/ASDWFwaQ+iX/foXCrmWq4fxbFv3QBjm0IuPhO0tJzIzKBWk09HPDpoBIfowQK6W9XgnOQkAqdNSnfkl8MPXz3KPQJ9WNASDolmy1A9qwEHP9ITUKpkRlCAQfY9nBxHE8ati9H2a/aNAzzNeA9PvBV/TVFAoAaDMCOIVamieq5/kVTF/UUwziwguseYegSbrlv7H9k9qLcO87Y+bzPBG3dtD9oE9gf3aBpPGRQyKBMSL8WH8ZjohBin43Ybc6Ag16NaVdMfg4opZ80JKjCGIs+IFX7ipaZXd0E7BrfmiXcSt3PfnJ1Mkrvv7OffYadG2be7pKEOLZh12pc5NWLwEmbZBqfy0Nbmp0aRQq2FMqoZ+IT3BUKehGa2lTrY9adMB31uzp20rVnjnV17DtOPsTPvMeam3M9OOW9CRfjMPfLeBoDszguoK5+jr2srKdesJ4xsQNXTIcjMSBu8X3sb5nOgWLJ67dAtMdE260kVWTlzgTVPmjZNqD2/8mp7iP4Vvuk73NJTRdwoPDnhvTUd5b+OeDppTMn8v3arnPnkrHbpPbaf4V7rGJz0tMS2G+qcDzgUdKXBlS25KqVZ2wmfrBVz6UCm2bidIM+8UPXBuy0uPbpKim/ziHvIYFkQBgxDXzrtoYb9u8GXDxlnLZhnopd7NkbQp2zOR4xotdptWpNTTOpXH/nGuFi1JpoBDUPkayGqMp6eaGg5qAER6oxlBrzUj6JdfmRH06uIX1gT6n1cX//fXXy9+A0aAiDUONJDVC1DSx0CV4I8GaHxPljwpOHdDM685MoJrFhBwgkUD75poh8PJTw8VoAmSFtqkG0z5emUf+AZOX8JvGTiohPnpKE7xloA49vCqV5uFBF4F6xlBjxUI4mnp588sFJ1ZQQSCWNcArU/lkitPj9daQRrk/qjFof16mBaNJgD0/+kVsZ8IBikQdOXP7WpWkAJBdqd0EZRPm0JaDKWmLtXxxlxo/O4/CG18fYWWe2yHB76VB04Fgqp59umUk6QMNI52rLIH/X3aCVCXs+BMGCbOiBVXp0iQTVcpdD6XKOdUMl0P0MP037m/e63gGtM9WGaBrE7Dhe3exZWG3TkmJpS0cvR2Jmj8PQ+Jcn1AS8NGfi6Gu4MED31bgacwG0nRmzKdb9JO4bXaBsAw5KRv8fUdOvWfmQmUAJC/DqbZQK95Day+ukng55Vmz77lVWoCQKwX5FlC7/xKGH3t7Qf6XX0ljGcH2nJVJ0evzjp7BD7SlwJxzyMf+vXqsnf4FH71x1n/hc6HPH1HYARF3F/jqBHoUb3IO9hDH6YyfRipg0LkCQTxsAPH6Cc5Dv5gjfLIlQr/ei0f+i76LYJABIdYJ4jPyTPT53kFg1gP6IWCQ6wP9Fxr5wF/SRCIBaWVguPByeWVZtSyblC9etb6CDaxsedX1jnvAnYteGylzMbRpOzxEoyGUHcXUicIxIEPgycJjDJbcCrZN6EzvDRBlTEjuNBxHLm4hcdg1ICtAtnKq462EXtto3CyZ6E0T2ueKTkL3dCmDZnF6kIR0lZZ2E0y5Cqz2u26hD30g1DFvZEtUTTmk0Ifg7ITVnhgs8uL3vUP4UY3bF0fq0VegBhZ3Dthkt6ojJOtDSLTN/+mjvi7mUqq6ZSnHZ3fItNNptWYZ8th2xfQXuJaXvORHkbg/mlnGtl7vBq2OPUbZ/8BgaA+MWg+at5qXTTyzYm4ONGNrMs+OTcQy2h0NUnJ4kQqqFv9Wgjcjdq44trQTZpx4V3FVf6+pETfR2Lcno5yb211l/9Iupd/TsZKt+b39Ctuzf9RupXvPnn3053zFBLZzuGD3e9pj/dfcJtDcks0TXMMChtdadpjEwp4xpxN8z7pjK7PoqCYnCyi6aKyoYwaTbWNKYJK2qjQAzRdg4uKm6WxrfnS4JspEXjmjxxCiENjOvuGQQ8PInka6YCQdixa6a+C/cKXwRQIYn2gXxQI0uthBIJ+0yLSrxQI4vPyHxmQMlitwA8zgi5qRlCCPdxaZGYQA9C8LqaoiANCSyCoHWD7u0C6qZDK2gTet4dQFW2xr5wBYevcyOeYVMuqxuKBYDccHUSsRmenJV7XtPW1MKQxkufpaV4NYxBN8Ih2wMygZ5rmzkyfFwr0/KCAz498OUwBoX8pAPSff/108W+lP2mdIF4Le/5cgaCnl3YlemcgSAVZjTafEV0h6cAn6OrrIzYfgSD8dWzf0gOn1gjCHjVPb7TZdeui027fIoCuxxBNs/Jt8sUHHVkPxBcC62wkcGQv+M6e0gPdCj/F1/z/lJT6rHWK3V0zegw2AkGdD/3eF6ELzpQiWI9b8LqicRPsPqA5prwB2R20tibHKv3ntDk52ke3kSFnydiWYiIZMskXr2ErwjgAue4SMGEmEIEbf+6dL31pRs97fR7+DV8BUxDotV4H47WwLAytYJACQqwR9J5PxYuWvpNFpW+ZSWRZtW6P6jxrluBNAvv0mVzx6ydz6KL40Zd3IGgGKfBxfqzXRy/mz8KnFxv15uaWoI9rRiqBqp2eaVRASPnMVEoZHxDUoD/iGNI3OftYfSEg7ZwK54cgIkA7XxZ7omjQFV8F01ORK8/2UR9HQMjBIAWECAbxapjWDPIXNhUMesFi0oI/u9TrZbX4tL9MJiXMRiIQ5MMpI7BjaZ4qZLN9yoJ2Xpm0EY4pm+pEVjvq3zjqwZYgWvLs8TV/4W4ZE/+lXHNgywwuNNQaNiJcL0NEg6H8C7hyNEOZXNSUVshdKrAbOZtC0+MDbJ3lNbfRckfgSrnNQ+qtKjjKAlrVCYUWf8aOklYiRXmCf8MqEssrRo6zWQCyLcTr/cKaD+HpfV7FREgLTHYRe5pR0D3NWibPD6mLZMO4HoA1Xu30GZcAQMf2zT3wnQeC3HKWCxINh4sqv9ngQjV9uS9PzJdz2xPpdDOta0Ody3R/bNrn3w29NYH7kj3h/zJdaRnymq91tZ6H6Gyec2nLbpnn6IA/RN9D5f1Zur0t5+VtazY7T2oU3F4WmNSWFJpQRAed31YmVJttvfirEdHRnrdP0quhdYd8jnZPF51NPS2IOPQiO9ZOi7edN9yNuysJmdi+rbPpmikko34UW2AGNilDzqD8s4I92OW8B4/Kq+foxSmd8tWT2xstCM1sIM0KUiDoN80KSiDoV70ypgWkvdglr4axKLLsw88Xlxrx0esouMMaQf14kCBRhotKOy9azwaClqEkv/KA63baGyIyGT7pbXo4MJcnuslGGtc17XBXrjM4h387qShVN1Mr5ZlsqwbmAbFg+WoYQSB4PnhgzathCQTp5kkHg0BQfw3suRaC/kGfz/2Bz8jzUwDo//ybQJBmBemVMdZHeP6MtREuM9AVbwbXsYU6pp1JIz1/1ResB6+NFPyyB8rDA0fm8MDX9cCpGUFYUM12tOUV1nnT5FQE9LBtoedUqNN6KkSK4K1/ZBa+vaKOSbQ9Xd7T/ZPLXIW3WzuEHoONlGBQNtKmaFihnKy42Qs3BX0GXOFs/uZpeFOv8JmHqq7PRWicdp02P2qAtZ6ZCQU4txNnZr0Gv5D+U5qZs5rFU6+DsSYQa/3kVTAFgrQGEOsA8dXNt14f6MPFK14VIwikANB7BY7g4RVsP4RRYMkfc7CbVR/3oaRo53VrBYHcT8Znac+1NpCu/9gzfOmGqSNpZgJJEiJeZuraUxE6qoVT8maA+i9FlDzzR/Yw+4d17ugLM1tI/Zm1lCb1af1wApGPeTUMe8mjUnj3j86rFkKyzs8TBYEuCQRpVhApfRwzfp6pv2PtvBf6pPzzl1pDzwtKKyikWUJeTJo18xwI0sLTvCbmNYPU32nMYd2p3azXkrNZ2CEYQ5TNpvrjVQ47DcB9P6mK7kupM/4Dz06I8oAL23sZiB66WWMRl/7BuuIELNutXH7l+AOLHTA1/YlzAbQNdw1cWndVpQJ16S5tY4YqZ0LH3rkUpzmTuCmiBzqZ3OSdKbCLrmOoT++7yqexhlp+Kyn6O2yFz2lRgaCS2awUm2+9DwDf8GKZSQTmWE1oHbcVED/s5ezLcDSMlB/6NzYKyCUkdGodRyBI3vh+tu82EOQGUxe/vhHGbTT2bvBdJl1pXNZuvRCuefDrdh5Xp1Mn0p2LQJozTb0bO+cW0C63/KbscuNX2j0NtKfogK+0TQP8IVvznuNrPLKa5hQM/AqnfG5Dzkq7llvHynuKtunArfmVr+WuKfhVHuUtP91VIHu6pm34lg9stoZ3OWlzbfVDe7cTmbRqsGEw4VZiS2oSV4yCZS4ymq1w0PX5supmIGRe15/OniFHb+QiO/suTfOgbI7JJ6AHe4Fwsbe9q+ImFuKRB4liEcxP9mQBsQOeanE+OxCk3qODQLwaxjoHfBmML4T9qtlAv2g20C+//Xbxf/X5eD4lz4yg35QymNWYURL1VTAPVlUTDThZAygjLqzv4E+mtnsmkGlrVpADQqmpPdC2l2/AbDapaH8BH+RNJGSGVY1Zqbf06/Gw+7xDANPkS4+KtF983MfCEqtMrXhK7oWibQ2DZ2YFEQi6FR84eeTJ5wu+HMYg9loDWhaF/uHlcwd+/q11gv7Pf36++Pd/flBg6LmnxF9rLaErBYQIID3W09QMrmWDlc+Kp64BgvM4l+NehGKtAW076EgPD3xdDzx0RpBbcZqyT1SydQm1wW7SXLsoCdm4PtNNpB2ncZ+rDSP1ZVOYoYfMnrkZTFT4Nd/4U+meTuUWv6I6P5CnZAFrwhbyR+iQ0fxrfpGFX6yq6OrSURQAE/xJD5ZbDaDmWeRQ5geuRO2wS9GUHKUcjy39VnJwfUSbMjRwN3Vj0EIbWMvWLMLRZk4wmaf5FuZkQdB38lUvzfvUK103HzJ79j1fBvPXwZgBpJ8XhH6b9YFUfsXsoHpVjCAQH2O4ea+f8nruIjsJumBQ/XQAvPYNB0J9ZQJD3KTGB53Sd6cv15Fx4xa9D2bL6kAQwST5yfIS1OjZCoAcCGKcoBmsvN6cYFDKPNAA/4gO0Ucr3qCfsTjxezKw0gR/Cm5atRv0kpfjvYi0IjGe0aMPIfCq9KVmvV4pyHOtr4W91M+vg2kG0Ev1gz9qYekf1E++1OxZPxjxAtKZVfvUwSCNJiSPIJPHWv8/e2+iMMeJLWHKtvbV7u3eef/nm7atfbM88UUQCZlVJcndtiXPLaRK4GwcTpLAf5IEdNhag+84AN9zdPUYCljphi2pOm5OINIi8KfgSHQdIhs4P9uStGgHRPFp2NMVDw8yyk+GNNDgSDVAl5CyTDFgpEkmDtD4wZEo3q9Jd66UyVC6CVlTE5tULE46qh9KN2Lg1vQQWRB2KmdhsccgLBbkIJzJcpQ2ti3dhIrVQqMzYlwuMIvgMmX53lWJcRPCH4lQVgYQcOv9jppT3najIBY4eDL7ULkrfk1DTX6RvMlyf1ACxXd161c6UNfwZSzw9TqC1HDd5bmV0RGORqNG78Yze6CD5dpUAz420gPxriG6ASO3D5iIy3/SYA90aHdRpaXQyltAJzqAay1OyhXOeq4CPiP9OTyXaKrzUZeP0Ze2vKhY2Geoe0Kyyrkk6xzNeR1Xyv9GqxM1F0DKWEvaGpMS6eRLXqpTXbbBwKSlK98SL+1xpeJpmXnJz38xBspV2ljQpAMCH5DxvJliXjyBmlmnzD+EWOIGiHyItslueUXDPC6TuujC48fAhSPHbzgV4yj6RcfBv9aSdRxBP2klkPcIYp8g/X78SSuCtNfBcx8tr7eb2gwz08ObivkkjAmHHD9+o+mpomoXRxC1zcognES8neQtZ1YPRc2hvye0QGZ9gh9XgVdM7FcKVapiCjrENteARSdlWubokzzYD0Juee9O+p4gMrWiRpHyzTd5o8ofTd0niE/FvvXnYrKCls7fwhGkN5l8Inb//i2tCJIjSM4g9gj6x9+f3PjbDw91hPx9HzN/jyX0nJYynEBMtr0cX+q6iuglhbh3o8quld9iRlHDvVn0SjDqfo2uFvizLPC5K4KOzZS8YfOxPqvy/plWbgDMr8s21bAwRCixpYdI8vBV2BE/yMxXmsLOxZV3DrfCPpdu5fnMtNVcdKVbIHupahU7ug9ly0wvTyA/05+SYxZd9uNjoe1BVylrunQZW/f9fOnSMzeHdtZYF8cV8RmxeeFzYjJscoTAUcIKnvdsDm0nkPb30XHxr3wsPE4gPgnLitkcEQ9sfirGuPpGq4DM70/CWIGjMm1S1cWGV8z4qLGUf/ay+GXJmA1AL/XgCy96CTfgHQ1qmXzWhDysJHkqI+U4aV6PWRLmlT9ShnoyBnrPII9ngo0NjCxFEwxURQ5D/lA7MJXSO+Y7zLiIDP1j/MJpg/OGfYNwCPGptD8Vk5Pn/j1WB/GJmMZGjYuP9ILksT4TY1UQG0jfEw14rwjSy5Tb4mdlUFYmDV1UWoPvJQ4ot0DGz6lraahPGgv1z08Jg6J/6mk6kzZVCYN9ZE+xk26muIOExskFsofZthtaONUn9Qp1Ub3vyefuT0muSUl3MfcqtQWM9udrALTyiFcqcxWpDPqteJF/NGy0TVhWNPsYY8k/RmMchNWv8YaY3LTn2Db2C0bM5l+sZLqdoClkTVXeCjuXlvyjtE/lVzGlRXfSjVH76ghaLfVl038JR5Cb+WhRPBA0ovTwp8bLA8NgwIPTZnhKZ/6P4QfLJ+VsohnEtsw18VVbYG0Xf/xN27rptdiDfWg7n26OFVCdyTeNwOAjK3CeFsofGJea1UCTb06fjd5dJtUOzLRthOqQuM+c+VxoSobYT+7Mmp86e+UPCf0cobOzcQZ5vwNdmKQyWfVKIB0V/7P2BcIJ9LPS/5Yj6AWbXvJpmN5wvtFyeCahOhtLdbenQgqw0kfuES2njuNnxnmvzKQUmjqCxLfZlxrpZ/1JO6G4FK6xMsCDmxQbEeQLp7PbZaVveptEAfBvYIg0e5wOmBIMFUTMPcqqn6z8uaFVQDiA2CeIt6tsJk18k/0M5NTRi9Abt/ThNptgPtQeCI8e3bnxw5NHN/5pR9BjOYXkCNJEmOXyd7VPEHv82JkzHEISvvWBewuB8VR/EijvPYKG2UxwvVwt8Cdb4JwjaG2SHdNXGCrm2dLj085z6H2k46l0fwx+9G8h3Z7s7ZnIXAOGUJxcLewEeh6ADOg/FT5FN/C2g+RdFFk5jS+Vu+Dp48k6bIklf6Ewk7qfhRYibJkxLvsENT1kLdFS/IDOgifOd/dMXaGY9FWv8SwmNJNy2E2EO1oIAChudXb4ITBjYWh3za3yRPBB/TgvTthDj8+6WNHzViuB2BCaz8FwAj3V+PjSL0sYI7VSSI6gF4p5afJKq4DeygH0TsuAOHKeMXlbyYMeagB2AvEpkn+cGokbg5crqUhW4GbsjjOoMnI//FJDpNyt/JOdLQsDIJdisL1iX5CFDFXM+kgvnH04g0hRCLjxgwfnD4Hj4a2WBfG8ilZGJoutPQey3RgTg/PqHVHygoMfx8rj2LmjjaQ77vHp9CN9Os2pmnYIsYk0+wXhDNK46M/KtGcQL1TYPPpbO4MoD2fQUAa1UVKKUCYZUNUXKvCmNiH20z9VtR2OcWIwj8BlwK60lzCPqESCI4dgfqeWSwsMyUYbigGEdzAHUnhzik+Er4CZDse4YcjVr9JmmYHIehvulC7UlbzKAFa4BVxijoh5FR3txO/hVoGTwinb4iIdjNwRR7kvSQI5DSHf7mUKL9lWC4kcMkfNek8nY3gu/e06q3OqW0vbxaNo+CZvKI75Hd+gpz2adxDzTGO362bRR2t9ufzX7wiiMdHhj0ADovkyCPWPzuLWuA9B4xV3Ll26xudogIFvaPmBWLGirvFXbYG2p3kv/0h1U5qu+k/zoWNsDC5azBS6nGtn0XFPt9Ju7XGUEfp5zYSN8vYymF8QgJ4LRyuR38PKmYmbdW/vv0g1j0kXbuVZ3s3nX/6nvEk0AgNHTBxBLHVn/wI5guT4YV+gn3RSWD4Pe2lHEJtEezPMxRH0YTiCfmXSqreXeyfQuiIIp49oWN7CkncmHp6Urs4grCPdt9lBJpZ7a0jhUT3XwyxbitxikRWeu2KCjWaxEzDI+0tHiMYuTx25kUyrSJGHu44goFlCnw2jcQjljyZOD2NVkNZLqaq3NYnlrSeno7BP0A9PHtoR9Hc5g77//r6cRDpdhY0z730nR853diD5SF7pg0oE7j8/t0crI6D1C85EulwdQbXENf5SFjjnCEKXPnm06aZXHbc/X9S+oaF5Q0dzvxwyZedJ92PhC3z7Z38+SIukIR/IuXLcJYGsAiLiD5k1uDh0BVjcOWFCUyeHhc5/GAloOQN9okzlNS7dMRbeNrtA12I3tkHHH8zpSkQBDMeAQzhiyaSxsknGdWpe6VgejlJNjkpD9Mn9GfTgWvpQBpDDOdu31NJssYqHvu1oR6eMyxgFeSos+tGdukr+JExegl81hr7THnpv3sgZJOcOn0ezSvbl8zfaO0/76mkPPVYGZY8gHEE4gd7feKEx9Q2fg2nFbfYFwrHUeqv2Gje9aodYY6I/DfMLExSP7dDZP+np1w7KoKOdNcNe4JEaHsbj2D9Ld1IeOP45Z9G6J1IGxw/jSTZMViF2BrlEF9Q75+eSIpCArC1N2YyB4WHs82dnshtONB+uAL0eJIb9m0rbIYRTRw6hu7c5Up7PxG7KEcTJmvd9suZDfSL2QCtou4n0HT4RYyWRHEg4kW5pZRDOqTimoo/tEGPEASTNVkcQmhJG9ZVS7URvh5j1t3VM4HqaYuMg4Xpz6TMbmNq6+OFJ8UMJkFsQXgQUEzosSzjQgjSshANv+Am1aKULLLpOSYH0+TN6uYAt7ZpeSJxccWsaJHlC4+SWawsoaDUYsAq0UQCUYS+xNtvoId1ondlfytBYjOeK9px06NCSc3OnahVsy0reaCIiy/0uvnH/Dqh6s089aNwqqmDGl638ClJ8hJFf2IKHH1piXUjznN6Vo7S0yl7DF7TAX8MR1FYkQ/Hc0NRo5F8mUC4KzQbvDE36S6kUBa7Xr9gC7v7SbIaWZP6TBvNb+CbtWnSfoZprzDFHq55aTe5SJl61Xod1l7EUZLqOSq7tkLjQuGxdOOmkg0QUwRHERFI/xX7DqTeWnGriT8LkCPoRRxBOIf34NIwVQX77iSNIp6Qwwfj1m1ua8mnYGU6dD44ZhnjDhCOIGvBjYspqIHlDNMn1IOr7szqCqJF+m/6dwtQiA7GRbITDqDPf6ZUERqZ1EB5RJlPCA7kJfLEpK0I4/xtF+z6IYGQ94XXNvA8QYpmey5hMoHWMvN8sekXQe01S2TA6ziAcQXf12RfHyD/U2042if7nP5/c+OcP32vjaDmC5CTi7Sf7JnzHEnreemIy6dNl8NWYdrbTWQhgDVdHUC1xjb+UBT62R5Cb6mivS7P1M7bl+zweKgB+j1pzM71Ohbeukpn5IZxCDgQje1ruf0d3nvuPgZ7V/QwwtpANlcBBkDBtiiuCcGQNX6hzZRw4F9ybLoiVc01T0j4PE3/QraEaAosj4pTHuAGGvSJoE+1DkdM09ATo3o8TtX5R/E6OHTaHfssqHx2Y8OyZVv+8kBPoxQsfpoATiBWzL7030C9eCfRGY+rbdxzEkLGWMdcd9fjsy5+C+UUKY+QYJxl/PJZKaaX90gZ9pNAH/dWIXtGdFBVrnaFXVpfJ71xokDtoO1ZkXiA5OLsklF/TjlOaueDxLyX4L06K88pZL6kZxSOLEzQtk73zwkfTgd8rXdU85Mu5wZ4/97RXEE6eB3IE8TnYk4fZR49PqB9ykpg2kX6glUI+el70dgZpk2nGR1bb5jNoCXPdpRBBZeIkin6jfGVWEldNl9QbnsxWzA/lkX5wx3aRtJMXxu26n4cAllK70Pyg3Alzi9yocRaoOpYQ6h3xoAOWOzwlB7YJ+p0S50pfRRtfJU7qvVJ+LH2plE3wx5j3OAxIGJHb5LDVDlEi6M4Vc0mlCFlY+qQNxAVxYKvaWtyaNs240PZoGQ4igs60qhB1Ik0PcU/zx0+oGhnX6x9uga/aEUSToeE4+Kloy1cDTk931kCXPKFH4s+lO/Id84tW20N8pLnm/29bwJOXL2yCPkqosT4/W6d9Rr+27TMogSKRwT9du/KjkNnBT5ipFoF5tjOJ/OAVQRbkSQ+TxDqBeKvoZetaEcTpJ2wQ/ZM+C/vpqX4/s2l0HEHsD5Tl7m9EjyOIWRanhI2VP8jE0eMJLKuDNBz52HiGJRw+od92mPQwBb2Upl6OZ80y3Y3O569Mh4ZBSjBELUOlMHPZc0ZGFzY40JFkyt36Q+X9T+CBUSzCURx/cHgTTAOwBDNc/fTHAp+I6d2vYfoQwHxsGM3KIJwzLH9nv4MHevP5d50Y9q9/fq99gr73fkGcnnLP+yXc1ikrfE6mSS5vPdFD9umnalGeflrFTaVHnRJdHUE7c1wzX8ACdQSlD5sK5JmiTQfmeDxbQACXZm3eR7pw8yzyHCOgD6i4N0ae5A0zCw3zdkXHRYUNfkysstb0Cd2qwhG55KnTpqrgl2T+IXToUZPNZGyv/mz2r0fLkD/C5j1DbP5sTSrX1uzYswMnEJcm+XNjZ6kpfsMLSPd7EioO/ECummP34b+wrKYhNZ0udQS919LZt1oFxDHwxHz29eKZPgnToQo/6pTNF1oxy6diL16wKuidVgP94t+b7ZMwjRI4cfirT2NkVsfSh6tfZ28gxd4cGqcQ2nocxVaxl50V6IUMx9ExrZvawaOI4MaSGDwy8n88iQtp2p4lSiAvMiKXNKl8MSCgPHCIRx7ioy+Q4aAQo1+GoJ3SHJrAaiA/lxjW90c05tWYqdirZJXwptF6QXJXDqGHcvo8tiPorlcGcbjCI5xBWkHLmMmnZHwaxmljN7VfEJ9/4RDyp3RRxzpZL8pCP2ISI78RUG3qiL6kXYcN6zqCb4gtIxOBiHRAuAU0r7iyB4goZFOeBaRgzZeAjxJE6BVK5o3NnNSFp7LlRlJzUJBe86hxCoPyPw176adSwKcmI2GSpc5lKWHzl+LNti35jCx4F3ml2DgE2O6WgL1VhYWu1I3PKPQRFNQpdxKh+gBaWPVyZr2IDtwRv+bReTMFtEO2+XTJ+BcZPOXXFUGrgb9s+it3BM3GM5+M8Wi4BU/jzYlWYMd8KS/Bi2/8W+jQjcZuzeYzVlHX+GqBrRPcjQa0GbXjdpCnZpq99MfpJid0hHMyaaMpnzgtllSD53/KhK7Q8zFTtyNdnkxBF4S1cTUmcF0Cy+DBL0vIk26JTBDAsVrIG1lqwvpSbzm9OfSPOIKejj2CXm4rgl690nJ4bZDJXgl2AnkJez7zwgmES8ROH01i7ShyXgo277egOIVQOvBMI8kreHRLEhucD4GfWkjw8bp45Uxa1lMRpF3SSFNe8S07mnCdKTg3ShmNNJPeUIhS5doZJAfQDfYIIvZEOpPpm9owup+IeQm83mpyKsoPcgT9jxxB/5Qj6MkPD7x30D0+DdOKITuCNIP9VquImOx6RRCvONcg5dEhdcgVNHUNz0p8TV8t8Oda4LWapBZD6NnYB9qsnx1fRrokgvEYg6J/clxc+UYstEKuTi5PwhAt/j65UAxokWHy1V37KmpSL1SSIF6TDdqyILLpMpj2APwj6GqkFnWuXHS6BK++jUMnae5PkVrJpSBO/7fHYO2jcZM/1hu+s/dm4b9UMmoxVNA+tgBsZCgrpYau+aOviNWwdf6QRkBlEDNu8gc5n1bjCOKUMBxBHAXPqh9eknCq5lM5gp5qZZA3jdYY+Yq9gd5qXJUnVO9XtBqIz8Joh31pQn/OU6Gf+vR82oQDKHle1DDCcMPQL6pxVX4bs5QdNInJE3IPPJT6En7bQ/J8f2ocyFcj2hh8zmWE6+6nx3mXPouUpLyYGMgR0S6Qmc/MNBZKZvchctnURjfQn0tLH50mb8cOTqA7Ghfv+7SwW/5EjE/DfMKm9wySM0g4Vg7dtSNIx9Ar9tH0EsaqINotJqF811RpsjiCSIAnuQvoat2JlUA9WxyqITOpyWYhqU2AACxk0jgV2MT4rh4oXaCKqh4VkdbqOi0cyEI/4shda0R65id+wip9jcGGdoWeps9JucxLXYfU0Q6nxAE3c0tWxnkuCn7InRhwQ/cX0+9BlUZsSUpEYq4bfGPrfXSLqcYb9iQhMZf+DkjZKWfjIxvEBjomMM8lEuDGlWCRRwvZcErgJAQCyXVFkIzwlYSv3hE0ekA1HTUg/W8DT486rbjBBaKzbP74B3HhcBYHrOlK/BQdDdkhveCuzKKu8dUCtQDjN3+MZwNGOsOEj7ez2aOWbm2n+3aLTCYFaZkrXcsCHyo/SmRHYlKs42HRjUuVfOrQIYol+kwSeU6ZPhobkv2jyrNJIfqPjpB4IjkYYp+pFvk6gtgE86WWvrNJ9I86Lp5Pw3x8vCa5P41Tw5jgvtRkmBVBWtitGRYrf/jF8cMKIE927fCRpkx2x6Q2mmdlUL6jpqbUhpgfQfmhayGBr9dUPLUDngmTKcxU/DYNsfjI03UIznApLpGnSHRJsN11ryHNFaIxMQNGg/OqH6XV7kLHPRKNN43OsvhvnMYJpA/oRMQnYne0YTSTXo7MxRH0v//6241/sUeQTg7jc7F7rAqyI0h7IIjpOzmC+KNh2wchKroeVsP6jDoYFz1v8gfGqGtZrvHVAn+mBc6tCFqb5OhO/fxseokAGn48cqbXhbbuNLgmBlP7Y+Dt9yBpf13O7Xmhm/FDHwEWV5krHNiah1wwQCs/4DVsbFtixc509FX+I3Stq8u7QFcaJJfOpGd0b+nVf9NhIOCLoelhXdOyKF4EDmhUKrwjVln2+VKBTdqlKbf1sk6DJ0Cz8hioC38jomMcFoGabhBTJ0tWfgyHzqdn3NPXAURcm0CBKH9SraWz7/mxQbRelLx6o0+kNQ6+0sofH6bw89MbP8kh9PT5S20WrfERGn0H9lpe0Pe/fHNDw6piZDG+ZLykAbvGjI/upxkXFZR3m/UNZSyNHujVNs44CtwSeDFgWjOnzs7HrtBBPMdKwZWn2CEEipGEEI5x351MPhSUqaBLpI80JA4j4Uh/ktqgcQJl/52M9pE1HEFaKXtLdci+P/psWicq+PSwB1oxqwMTfKiCThF7/FCniOkoeZxE9/X52B3vpXfTewXdFI83jpYzqA4f11E6YQp0Bd70qr9V1SW2Vb1jaNfG90F5nHJzJrHV3AK3/sV2AzdsYAlclN/uT8qxBM9VB5EB0G1Mmz4ut/iJdoqSel9pCykZYrcs5wODPMInHbBCk560yecK3zZTspTzdCs1ckcbGqJShaGDH15sISS2ccKZKKQHlmd2LXfT9Fh4RKaUgTOv0iUtSayS4oBx71w0aSQoHxrg5S7dzEPc+77SIWJSRZLzCJ8IyHYBE9AvnSM5wmKzSbzxDUL0IYn9r46gnZm/aOYv4Qii4dCA8jDQiNzSP2m4PEjHpnrKttKt6SPlilseo+0BiV5Hrmv+agHaL51z2jH2WNvS0T4Tpxa18JzSRe6n5K18yF4npytul+Zh66ODDgM5xkgNhIUQS09FGRzJ5QeLBwYSQwK4huKIpzOolKFCJp+NvdZqoLdays6+B94oGkeQJrg4gp4+y4qgZzoxjE/DXr3l1DA2gNTkVSuCfGS8JqfsFVRnD6uDWKqdqR/DUtLZIBo6YGgLfK1RZqhb9aPmco1dOgFKbqzMceUDgcFlGBb2Lal7NKkEVcZ5f8Y2bCua9De+DsePSpUx0TBOIe+O5DaUGjEsa8bvlUC8CWVT0HeSI2vouwX2CtIKdr3RZJ8gHSOvyezf/vbkxv/86/sb//OPv8kRpBVBWhbvFUFyBnHE7i02jNYnYtlTQczoRYQKQ2/SDls+k7CrI6iGucZfygJ1BNFGt3a6pA3ThZhnsDTETvvBVFoZ2rtDibYHd8CVb99eWYk3aRs/kE3cKnuIahFruWu6JZ6Lf2+6YxnVrfDWtXliw5Z6lad1XmlXfWe6lIpPOuPiVikznbLm9Tz1vg+ml22fHknhh/fIX3WOODgYe4nDHUnbVcCdLGX8hxQXhTqCSgOYFUG/aCUQDiEcQawEev0qjqAXz1/LAaQxktWz+jyM/fResBpIL1NeveZkMT4r4/etHUEfftV46U+p6cPlENLgm8+ZgGMBBTkdts1mfTNiJ3RD+/T5cRbR1hk/7dQRt1cTAbMgUYvHVlUM+/GPViQ2lM3FGGjLlJGSLctFtoDBvN5JM6QwCtSvm0aThmG4saQjYyGfhvFZ120+g9Z7JW8czR569/WZmOJHelnCiqAnj+/fePzonuD6PEww9tC7q0+o2Szam0ZrJRFH03vKoVIwgX+6NK4zCC2ANfiPZ9SzviiZfiT6TlrzxABhVXqVU3mnsStecZv83f2woP0TQOtMceGP3Mra06JJMKT8OupUjU3baM11lXzKEIpeW8K0CBznJUzZ4KsZyYnZy0HWDObQgw71buJ7vrjJSOpQxHh/J7ClbbQZK5KduD2Nxekm9F6Z50Kdd6px43aArdh9oroO8iNL8zTNYwBUcJruaBPKUPxdHKNHpmv+i1jg63cEqSmlAxz2UQuibY4e6Hc3WidqU3CfhAUyHqJ9V1eVrN0kvqauFpAFZkeZrvG0nZ2aCRpCO/lTikBCx3NyjmK23ykvdGfJQbUJXySASEjR8Qx4hQ8Q5dOxTwHR6SOChALrwdBkHc7zdAFiryA7gl4zgX3v1UCsCvr3zz/f+Pkn3nR2j6A3XvL+yiuC5PDgGPixIgjNMsUj/u7GB010s1llJ7krHh3yizGoT4cs4ijdWgqwBFdCFGucgS9EgY9OzFSljDxddd8HlViUVwZ9miZJL8j9zD8kQ8S9KKcmaTKquTRh8SSTiZv3RMA2XRLPZ2KsBsIRhHtMk11NWu/eZYPMOIL+919PtE/Q37U66MGNR3rryVG697Qnwneiu62ZLbE/82ItvULfelqVqiM4ugDrnb06grDWNXxJC7xR34IzKM/H1IS2SnDcTPMjNnhp39A7bPQ0dggKOBDznAvPM7zRlDSStmmOxQxYo5V0DBUprgTn4sGEvPJXq+Zh+5g86LZaKbPqtso4V/znwqrTkR69ZnnpSVjxGI1W6osSTISe1bWU5NPPVk764dwfcKUEH+4V0nQdQVDBs8Kje0pCRuxciimXlF0doPQjOjqCfpEwn/KFI0geHT6Hfq09gNgQ2sfE65QwfxbmgxU0Tj597hcpb9ToX+lFCV9PZyXQdzd+0eDNuGhHkMc6XpSgp/p09fHYwLaRwow7Qy2P/Ywy5LmMO2I+j0/Q2/sRPpFuYb2PqZsFbHgSubeDSdFk5yOTQe/CsVZCxkXSRgwoEdwtQ7EL5Sh6YIWHCkrU/k43k1Wyt7XXDw4hPhNjVdA99gvSiqD72k/vCZ+FPbp/43utCuLTsPusFtKK2Tt6WcLpYTf1YuX2zZv+RMyfTyObuozKtO9ZYQM12ro0lHpc0l8ofQihR2j058o//u8Dgo4BGPdwxa1po820o5Ft2hJKDb5FBlZMoFk1fnpnpkbQlfZTdOGCmlLClVRhoYgOe1joq10lWMZsmGH/I6+bUi55q8VapO+jAaGpzuUoLe2ecOnvhY1vR3eUUmmKixKjeZWvDJezkBq+IEk2W3OiV9JpI3fZW3KRcU1+OQt8tY6gmEQNR501/7ZWxcDiBppWeq7xt/1C2IcCuqaP5v4YjqeB8mjAidu891Kqxx56zV0tEAvQamiXl9ogVG2HjYEdQ9sZcmiPhHauKSGwSZd2O+mCd55LH5YquCFILOEsHUx9HkTg/0dCOv/SzEJ2ddQkNHsElXdKhYO3na/faqKrFUEsa+ezMD4F+7d+T9k4mn0QBGNF0Gvh82kYx6NrNdB3nASm4YY3nIp5y4m7g1VCXjEkWGwFbDiDVGimOOjTX4esoaNo9tqSQ9sE91lK+pO5SWiIKQSDep02hUxX45IzrSfppJiQE2YfyCQhsGjsyW0l2xGUdpLJpibL3RfIn4Rxb3KCmNdLyRmELycnh/F5GI6gxzf+n//5m/YJ+kHHx8sRpE0y73uPIDmCNDmOI4g9gjSoi7lt0mr2sthqtdB1j6Aa6Bp/KQu81iOhrVLoIfzcLU+dnyvnB/AsLmx+4rY6QOiGzkU/Pb8JPItzHmISIfIEDxoU2SYbg+0Q9Rlztyo2cw5hRC2tiZ24FrrI/Ji8hWw+20shW9c+CCkbeRu8ylwod0e30CzJvbxNoVDQz1Z/23rDrwloq0hSewi07UfDFzx/5sLXsoLjCnWgE0befBtiJBRlPODeT/qpkQUOxIRCC/m5GLcHY+Z7eYLeyZvzXl6dtxymoNVAz9kUWquBnskRhPOHmJM1n+uFyWs5gHACvWU8xRGkfYE+6MdnYVk5yzioMZPxkbpjXKUdbOjqF9vEPtETGvLoSzs3vwYe/vh3frJaXG2BbXjZ48rmYrwvFD9zI2OrhAEbkXXJJVSZMAGXs8L6GFVJgwmEmRkXIUiesZKADK8KwhHEqtfNEfTtWDV7Uy9FtCpIm0Q/5jh5OYR4UXJfK4Huc4qYxsnbwuMM4tOyHCUf4YyzLYdHnrIcjzTlNzB/spqOq2uxtrIFZHbA8yBh/MxEurSKSRtOeiBGPm00tGG3USIKtqB8DSuQ3DhSEz9TLi4cgyLtxqDlQi1oO5Me5NBvR7eWsyAW6nKhxZoOdXUbdhOQepcuNAZuSZCYyDSDcMtPKhGIyOJFBJ3tOhh2dOBWQNOD1rwSISNn7lxdF1ngYFMZvt8jXUlbLDqLEx0r+Zre8KnVqJygCG0xi47cZYfiRrYk0SVAp0Xn6gvkvAR4fyBl+rfLvasjaFjxy0dfrSMoD8DoAGlKbk0y2GjM7p0u2K9ttSwXyH538PZA/u6SrwL/6hZIp/h7tchzctrqz1nqPO6clHPcp7CVs2kNTCqGycwa8hzvIMrs9cE2sc+emwEaOBPeV5xsouXsL70iSHseyAn0/+r37Cc5gjTB9R5BehPKp2NeEaTJLm83fQIYE9nh5Pmgz6vmaiCOiWcCTLmDZjhdmBpkvyJ0XX/Kjj/sxlAsAAGa2oJU04qZIGxhpIcJXM6gDUhX/d9PTQyQhGGf9oEizOQPTlb7IFu/4QBKLHFCf8vbOy+7YiKZFUHojyOI/RG8IghHkGh5+8mnYewT9Hd9Gva/fBo2HEEPtEcQx+Xe1RvPW977gBVB0kM2zGaYmmwMddGqIfd35JTh33VFUK1zjb+UBXaOoKEE7ZfQ9tv8DrbggZ8PfR4raTz7Ip4pOIPP/GGPOS938gRfHuQ0feT8GK7yyts/PI4yZr41mpDfP1VtTiUvmNG3AqFPQa/qVqrmj3L28H1uSoGruCmRsiZ8SjbFvKhvLY9i/js77Fuxk32TuXFt41GINBRaBiuBOLL9nVYDsXfeO04L04+XIS+ev5Hz5+WNZzoy/tnPL7VC6JWOkZcjSJtHQ+OXKvJ+5rMwOYJUBp+F/crP4ySOIEYWjTeMjX4AGC2ksNL8y1rT6FSrW+dRv/KwqsjVNN9WvTCOKzayI8g23aGSYexawc7EEDUvxRa8kjaN06W02asRjLgwKDUjGuUjJ2N7XoywMkhDYj4Tk0Pojsa7OwLc0R+ynKD5SE6fR/os7JFWBT26x8lhcgTp9LD7cgrxMuUW46lWDt3ReMkYSUCm/ttMjcGADgVU1VmtTXq6LVEJ/0/sNRWDp3/ouwjZG6utsiLRUmcSAtedSImzDCEY057JSwmaMwzOihkCK2gIH1zzThZfcVhhDUf8ijtNz5rOEvYln/JMyKRMqboKlGc8VFCc0+gcbMqdKfNDPIu6IHHyhDwlzPpN/MdS5/TdFb0xn69BnxXIzLeQbXKOsIEg2mhID0AcQbTjtPurIwjrfh3hK3cE0YhGkxrx5mzx4HRqRPDwND6lCORT+PJ9Lh300F7D1QLnLJDmu3aPe6rf0s4mJ/Joc43B0PZ5bta2eKSBTjQFJ3t6Lb7xRrHWQ2kmi1RQBWcCtRHO53fohaqzXAQrz09vJVMXIA3sYTNWBLFHkBxBOHq6IghH0FM5gn5+LmeQVgQ9Z48g7YHwUpNdJsd+w2lnEJMMfQ7mqRdrX1j2DiwTXk96fbpY3o6ih6c3VhS9OkkZOqKww5x0RPcg1ut0ApVpxKk6VtEvMEDkff8sf5SXwnRFj04JgoM2n+aNBfrcBzt8JMkOIeFFoym+eKmVfl4NhDMI2sURNPYI4g0mjiCWv+MIyh5BrAi6f+OhVgTxpvOunEE3edOpyTBvSr3/gQryKS0qLzWKxlZJpRFcNya1Sl+Pj7dJrpcvaFbOmrsAAEAASURBVIGzjiD04bmqXiO95YN2W3ZDBtEGXx7HAe769gwEGznPw1aSHw49t4KZbC0QspMyLhY8HrRR/jlW5LkgEkPwIq69jO0AujpBrnBU7TzdYAxLrmL0H90HeSuJ0xQw1BrRQrJAREevthGXaaFusr148xRxpiYDtihgutAWWg2a30iUMG50eunpBClDY9G5fF/K1NEzwJIylncoYjzUqec33kv+L6wG0u+tNn5+9wYHzztvCP1Mq4GePtV4qL3znr3QCiGcQ1ol9JLTxHAa6Zi8t2+RIVmsBkL+rzdlRyzUlyaJ6cHdfnvH1WZYWZtAetV1wFGctiUc0TYvDumoeKI8DrISw5KF5upM6ckgJ4WOK5YV3IZRarCVZE9b6IyDFxMGdRg1GVnXTA2VFyj+PEzm4BMx9sNjdZDekYxPqHWogl6OsFE0n4exh95DfRL2UKuEsnG0jpLXOHpb4+kdMfF5GVaqI4iY5wE7AbdeukQ/VU8Vc93kKXN9GTurspi+pf4LfW3t2PcgtZvXMk/ITFEqynA90o08JMgdSphOzqBT+kqFD2smDhQha0i7ATpL3dOsuDWNlK2vchrIDJf1gmZKqk23UotSvP0NuoktMqZof7Yov1GS6J5am+xZyR0dGT9rwicmYQGOTog36HmBm4zBeIHKZXjKCN2s2uTS/TbvwG1ytgrN+2Y7Cl57mpaLAPTTtSUS72sDrrNNdJR8jf48C3z1jiBaIA+znTvYRS2H9scfcm1U58zVh4CYRrg13nPEFqumKbryXSAzvrhj+S6ryGt8tcBigbaVtq/GC8kuubal8u4InKFVZ7JFXLq88Vp66cFYme2keZC29Cq8rH1oyDe90Qkwnq0NNJ7N5MvAswuk+YXN/NEhNBSU+pDvc8/bT95iZrPo93b6/OgVQT/rjSefhtURlBVBLzUpZnJsB5UdPHECddPofBbG5OOSIwid+tYzk5RM06iHwqwKJhxhbyT3OBMpmvy5EoYpgNTMIWrUn6SJK4R+j/SSt70DzUx6SPOsmoltnEOY+egI8ikpxo89gjTJ5cM4fxqmZez39BaTZe9//9v3N/6lPYL+5+9yBP2gia42w7w/Tg3DEXTLS+b1XlL9MZ960cbwC1IptOkkad/O0h/bESSaa7ha4EtZAEeQ9wiSAn2y0MVP2gDw/Kw48O0VSBvpxq408Uk4APUwlDyCF+mHstwHHuT5mdppxLwFIsaAEHd8AQ6oGqS3UA7AKLY4OAdoxDAHW3mtbOh0LQPMCmQ9DjlhkC+U2yLX8sIw6c6lTB/BIkfOkDDKJhcI10PBm8BQZAVlgGEPfdPLnUnfBamMGO7Jt+YDndeOw2hl8/WmKF41pEzfj6jgXOUOt4TpWbHjailCFOPhey3n4aQwnDocGf9av1esCOKYeDmAOC0MhxCrgfy5mFbLvn7zRp+P6VNIjaXv5QXaPgtjdGClrFu1PB4eLdTC3ZFLQ9c/2mb1LGlqFxjKVe9NUeOUK4k5Rn6kG5l3c8jYakWNWBQp0lHK0tV2TXuoiQ+Mph+sRxQFKSBnEy96qFszjZ962HAEMS7eZA89mYcxz84gNo/WKp8HrAiSI4jPph9o4+iHOIUeDmeQDlxg4+g7nCQmWg5YwMLaujAxZlbeP13ar5An0JZyuhltJ2lW/lJfj7WD23Z2OnCS4M8GwJcMZgbZVHWez7wNNUWJPxCuKAKK+Q0aJjQuZaAQ1sJAkg9uva40gQdCGsnwpawJBzdUUTzLN3jhSH5Sjjw2JSnR9K/lx4Q2VYoss2IoAOZqUxcEf1DGHy97VHNhnhLJE4onbgFLcgPRZkPb/mfeP+SEm9jwHUC4MT0FvIUWKYC1UeNss2mXBC04q6GLxax8ZoQgjiBi9IPkvp6HaKzMNXxRC3zljiC1Iv+n8YxHQk+cG8+lTu4LmvNix/sFdboW/XVYoJ3z76MNvWt72/+kK508SPrPw3nu/aOZZzdlHOmjh6G6zFVMqRtL38G916lhb7SUnVU+PjVMk9wf//10OTXshfcLesGnYVoR9ILNouUI8sQWR5D3OMAt4ilY8szElPc+QV7+rrzirEziRSETTPTo1GzExyqIYlpTGYfovUeMkXYjjiCuU+SGlNCmG/MnTNMUopz7wgGlg/Rovo9x7iCKaT4lsUro13FkfNY96S8DBU4Ng+6mzHCXSascQQ/k8PnbD49u/OsfP+j4+Mc5Pp5l71r+zqdhNzUhZlJ8U7NkHEHeCBMhqCk1SKLWaYh9ro6gU8tcIX+uBeoIosn2hwakuRg2YkAEN/HRrjvh54+HBmDtNdL+F6SJyKt/EXKdM3RyDZbnlvjIaXa4LyDoDYry82cZQKhJAn9Iki3dgDqCalIGsxEKscftc0eJG5/E0HtVi9psA7iYYlOm7Vbxq6KGyTYLOcmVZOYgDmYh3+4NJaWIFgRkBvnGt9C3+gAW8IYn4RoKGXwT6Do4CjJtWY9lJ48LnwCrxyFlhxStAmJM1L5ArAaSI8gnhWmJzwut+MER5KPi9YLk2bNXN56/1r55gr/UuPhGNDow7MYv4mF/IBxKH7Q30C+yCGMlzqC4J3htoALHuEmDzyogdFKahmX9ck+V2fIzHRiUg1SJ1iCgmXMloVxCsFyRkadhZKAaNi1Vsi5tIYJwBrQthXUJ86rWrm34c2pq7k+mFatB3NSDd1OfhsUZxCbS3/kIeTt+tEr2wdgnKE4hPhPTKZscJa+NpTlljBVBN2U/HEp8ho0p21cQox8wYqvHCiA7yfxhjb7q1k0DoZ/7DhGPGYD54Gyfks2+RfsfBNsbJXaBggOc+CiTfOY4QAihnjyBVihx08E0P6FJWfYgaV3JrpKBr3SV+Lnx9ox+LsNCd1ITq13dF8KRDGbC9/l9blI1dYoHstW9xi/5Ep+ipqx2UQv5ljTfIK0MYtopgbTzujRtBHAABCXcOhR33Lt+GhbTfA3Xr9sRtLUi/jBLJ95ObmuFixXB9YFe0yUxLzIXuuIaV34ba+UV3/gcXWGlucZXC9QCx3ZEW3EfSbv+Te0xg23lboOnO+W0f4mbHfBGmF4bHMGPgeKjNCN1WWWs6eIT73VvPVqGStmTL7nYI8qgC4HYk0xi58enYXIEvdUbzHcce6sfR8bzeZg3i5ZT6Kn2PyD/XMvgX+n43Bf6fIy9EOwI4jhcJrlMtzyxJR7pboq55WU/Ns102dwf9Dv+QOp3CMOsAzUIbIgDsb+pLzO4uGMKcWxhLbeY/bvs9DVMf8bUyMbjbkrm5hBiw2xNNP1XIxYQ3kvMWQUEnfDfvBc+NCxTZ4LL3kBMWlnmjiPon/94cuOf+kTsCZ+GsRqI350cH8+qIL3k9Kdh6PTt2AOhWruGkt/2Rr4ZbxZdwmt8tcAXsMBFR5DaLM8OF2KnlzzJXXBDD2RHb1CQxzEgQoWzB0hcekg2J8nGRwKJfnIOaWEGO1RTy5Q3eVacCXU5yksN9ShvoX9cBVMdZm4jVKJ/iJQHXChXqiOkekJDb5sxEaqL45IpWx70LRkZCZGKlOBmKjqRH72m40gJrNJMs94MAArVuNYLtNdRBzo7UxJJovMLSLiMlcNG6jdLMiBQWILjUajVURpHEBtE57MwXo5oNZBefvAi5KkcQT9rhezPfDKtz8FesSJI4yErht54g2g+KasjSGMPm0XrhUjHSK19kaLJx0WBHoyXAlsnEuSjH9DQEQ8j7eJhKaE8HLpWoRwY2wju07BRCNU7U6rgShH7tfzN+psm5SoFMbzb6ppBADw03CNqRiwL4Axi1azSt77Tj9Wwijlhk1M07QjSiWGsmH34RKuDiPV5GI6g+3ppAg37BLFhNM4gVhitjqCWuw2fUo7y595AdQSN9oRuo9/w6aeDPvMCeEVQoTXSqOPZCNqFbiZHysqQ1n33fAIp5PUTr++O4dGzWGIC4uc1ikVapKx4k4ojsOADO3eNLK6zhSD5NITmFG4Iz+lA5dlMbqv2GbbSWYNFuOGDfq3jKgJyQuOkFiFBb9dao/I2xC4xsfQ51a8kqdHIUTHCYDm7Igj8UGk0te1LSrMtIpzXhbj9FOzt19AHXPtDdLs6gjDI1xG+bkcQTaeNi4aNzdSAGq8mPDb65kc73kgL3wAXEv8RnQrrA3tB7BX8f9QC22TwWP+PjTQL7W9pj7BdLC9PjyX72RjPlx80cAAPYQW5MxfevOnaF+pAAWSc2VMvhBunBwqR4ehFKldz6UL8QZNW/debT/ZC+KBl8HIEjU/B+DzsZ04M4y3oz9o4uo4gbYr5Vo6guEG094FHJk3pvGl0JrnfsCJI+W9wBqnc5lkRRLnel8G1pE7rD+QAEX8swOYwCD1R2jOR20MEMN9apihs0MKYoCZNf2NypLAJtAUyGSMhV5hG+DllV3rbH0h/agnn98FjUopJWAl0V2fk3tPKn4eaxP7wBEfQ4xv/kCOIPYLuc3T8XR0hrzec32k2i+OIDTC7UfTqCEIDglVvRnnfasVMhLeJL4TXcLXAn2wBbTvmT8Noo3miokD6r7TdDT5oPqUi9ITGydHul4dgAHlSva/W6P8Au7vy81vO3zOuVqe6qBs4hNBypZeZfyodyEb2El2lhJ8cZQca1qlLylmhxZWv3NEHbCkaR3ZyR72Rkl/kcU1IP1oZdNUTV5ozsYlEXEaTNCMdh1N+u/VGTXzLruQxy5W4lA5lPw2jXbCpMitk2SQ6Y6Jejrx6q5MydWz8izd6ScIJYc8UP9N+QS+Me61Ppd9ohexbPifTAlBvEq13Ach9zyog/XD2+MWJHUHJM8rEKaRID0R0AkY6sFqT7N5iB+sN5wFOlV1wdsLMxQWQM0rkv4GTcmmNqgfwiVvLriVFMMKKneoEytUwXXCROS8nkK0jmPcLuqlVQXbocJqYPnEZjiCcPg/lDGLT6Mc4gfSJGJ+NcZQ8n1Pf6YbRGvj0Vcw2/rmMoVusW01TcfcbakeuIy9yhpfUa4T9Equ6qw0nqViJka60s3FppvFMRhbUBCs1AIaVDwrZpXfj12/Uoe74nPUlTxhJmDcBh9T+OSz11ANIApQTPpQzakLJrphwnruKx+3owEu73x7eyWf7jj7bNdGFbM2OFH7Rcco07RRzqDuII8VCPJIfp9CdsB6hWnWfWqwyRaf/bVPFwF16x5Dpt3zBuVOVJslzAy39VJltIuC0ESOJM+bhCIL0Gr68Bf4SjiDM1AbtP4iV94O42G/NQ7vllab9NWxwAXYy3UpLtZf/OXS7MqeYa+ovYAHaxx/dIbV9tC1tZlHv2rKLo402Xbq22xX+uXSVQZw+WldVOulg85RUk8Do+Hd0A53nKddqb73KALs6/iltCFpKBOsyJYaYgQiJccAMmABMfJmwvtVadu+HoM/Dnj3TRPenlzf+/UyOIL359IogrQx6rpNSXr7Up2F6M5oVQWyEqf2BPDJpiiXHDxNeVgR940/GmHZln6BvR76OoPCg9/pTdqkVOQf3HbO2Be9JXbvxR95GoUQsv0FsQ3KSt6ZNMMvA7v43aHizmdPAsCHGpDy5whRnuqR4nBYWHJNcTXA1ccvbSb2pvK3TUeTYuaeJK5+FMYn94fHDG//4x/c3/v79wxvfP5EjSKek3NVqoLtyBN3U+nY+JeMTL5xCBNpkVKL8lOw82RGsmuqN8yhcxVzjqwX+XAu80qMi/7Lb7O5J5/FDlRE3PUAXlYTOtBcpeDSXh8G93igEHqHcXZl/pVulHuHH/BBkGcfLJTn5s/+UmieUPmT010cC5cElXKabNEgLHTzp/cI/eoshS/2+sISt7FZT5EggAFp70MLBVSvSpQGGvJQFpvWe8kzTskKyv0Jwgl8AxpNXYvyFZezuviMSwhmm7oHzRxdtATaPi0oDeydPDiuC3mhpEPvmvdCKoJev3/jI+B9ZIauj4n/i0zA5gtggmmPlGT9ZEfTLL994RdEHxbxgUe8voVijjiBejHDPgQmntPVSJ+6xc7OclElHb9rUJXrPeqmAJUwn0AEuyQ5lX3hUc+c6ZqycvqcyELAJX4Wch0b80iJF1hzckZi4ziDGSVtE+wRlzJMjR6a6JaeQVwRprMTZ80gvUB497lHy9zSG6oWKPhnzuKmxk4MYbskR5E/EZGZM2DEwZStPwhVWQjHj+dZnqAHkUzGNnbZ/uDo/zC1RbSqMmIApSE+TAE0oDbnSNT1A5gU2QsSoZYjX+tkZRH2Gw0qC1uL6xKWo1QHQwkNdupYTbHBT9fJAtVKQo1VMysppHPl73cCZI41MdYhMbH5ujg19aZDHPRIAsEJk71NGWC+ozBPQci3/AjqT/CTVqEP12drNkLW3zJBGNBBlX4vueEQVN0fQQZHKhd/0qzynl0/DVBht/t51s+jVzF80/dU7gtow26DzvOlR2h68y/a79BAfOVa6Nf2f0H2OXke51/z//y1wHFT+23aGxVYZa/pj1gzdoGhn/TGGFbd1/jBumW0QCShC56QvAkZRzpTbMF08ddCsVH+PeUwNXFMwPfw4ZBh83urb+F+0KohTT56z9J1VQJrwPtWJYZwc9qNg7InAxPeF3oyyXB7eX/S2k1NRPKFdPw2T48cnh+m0FJZWxzGkaYToM9iRhq/1PKZdlXmx0gv5xIxUCHzl1QlBkacNzQNzcaMs93GkoTcCCmXp/4CkH2RQpX39akcPGVvScY6MpyBK4pMwnEPgs1LoGzuCmJyOTTDvfKs3nFnSzrG4Pzx54JPDfpAj6Mnje17mfodVQd7vQM4jTYazIkg2HDqhKmnbcVTVMJVK8ERN8KsjKPa4Xr+cBXAErZtFq9k6pC0r6TY9nr6BLM0g3UXgPoaHuO3fDwnP7VmGPjg78ScZ+oAh8QR3GRCeI75/kBaObLQ4q16JRvw5NJE0JZ5q0TpXmi1lDaIJhZEqnJrnBya4pgqvzAl3v2lOOFYtUi7XtUs250oWUct1KcN0S95Uyu9AZFJWhDQ9a9kXIt0nj7x7bbUXHEFv5QjCCfRGDp7nevHBuPdcm0NnfyA5grQqiOPj2TMvTiBWBGk8fY8jKGMq42KOjaf8nqoZ508cQHUEoT79O/rlt+nPg+JA3N8AbVEqP1d/Js9dLLcNXhttQAlQfWcQh7KFEK/pSdfUKmjTeCBz5ytg5JYajPHROkpPNQivnlXM6WE4g7777oMcQfqDVmOhPwHz/nm3NU7KETRWBd1nRRCfjOnlCi9PWHGLI4hPxHh3gvl47qqp01udRw0djTavQZWs2/DoOLgvlZB05G5CR41/U4TIGnefjBjhrYfs4bkHn7wbRisdurpmkOc+b3qLMKIpJHVxwuldsQUf4vAFuKaBIDnSj3go/XfkkaUcm93D+fFrhOz6TN8H6hpcHdm9N8gDE3x1BLKmoToNpYrkU/wKcR1XwJJuSR17Vt0WMiehdRPbmELh7EERw3QhdjqkeXwNp52ApG3oWZKt7ukBsMkG7TX6chb4CziC8gi0cafPczfoBndoj/+1JfOH8tqUT0W2s61OpSi8+Wt8tUAtcGwrsyMuxWnc9nTCeyD97XRDgDvogzBl2znvxsXdg8bzIUAeTQsYkCFMuUHfIR88vzWYXYXYYaALMTTOjxhnDn4L9kNgSfxbHEFy/vysk1F8Ooo+DXuqt6A//4xT6JWPyX2upfKvNDFmX0VORvlFv50jiM1wxrHy35xxBFFvhvM4gtA4tZjrV0blQDkc8wCPtQ2kUyQoXH8ShP7lYVFjepHOLvgalJwUdA8ofN4KBtbPEOoIIm+nz3AM4QjynxPsDUQNWfKu33dyhN3SCSjse8D+QA+86SVvN+/aEfSD9gn64Qc5grQRJsvg72hF0B0dh+u3mhrMmdD20zDUY7U61ccCVGuzhOoWSwWC7qOmsF3D1QJ/ugW8IkjNkXa5tsX2gcTg3G59mSoeshtilbMBdwn1dc4jYXs6BsUx31KO8J1AZf47OrgrgVTS9BL0NcGd1wBsaJT4RAjtqbSUNnqMISOl5Vp9aq3ZixZPnPSsR/KFRmzrkhIrtzTRz31rQbCt6kXM4RrijMPSjf65PKUc8iYYQHOJPd4oCSZjEBs6p652DAnnDaJxAml1D58/c5rm81evtRJWR8Q/f3Pj314RpFM0WSGrsfGVPgt7pxVBb/QS5Z1PCtP4KkeQx0XGVq2YjdOHlT91AhFz71kd5JFCabVqq0l+aeGuaPQ/PEHiJbg2Zt3bZBjE+IgOJPThPV6xbSUGt1Ljugk2+kwte59XeaE1RkJag1DSniMZq/DSJB/LqQSNlz5GXs6g79gryCuC9Cm1VvvcH59T81mYHUE6QewBJ4npk7EHOmABGjuCtNn0bTmDvtMP51gtWi0Zs6sP9WFVL/V2GLr6iXMaSgvZ2px552UwElUIyDW9kHwkWY4ql3x6sl93jqBKtxJbWdSvdbT+woSi1+pVvsoJ3Vb+pmP5Jt1MQT3lkMbOeZqO5USybUwj3Yy9FaRES19lpoQdRJnWMZZBxtRlpS3dWsq5dHmmlHNUE/axvxcsQ3V0bVLhyXhIDT/jzhzmg65KDZ7IS003GuFchAC2O7EA/OPZvCtv6nQOD0HX6ItY4Ot3BGGW0YD6B2/jncWWBxj8pYdhxa3pnSxlPoZbaUtHfA1XC1yywOe0x0u8wNd2dkmWhyA1Q/ClP5WZdsq144DjU0JD+lg53mjo6g/t3QIHGIHKj+GGx9c/2EtGmsAkB8fPh23COxxDGoVYut4VQRyVq20RdHLYezl7XmrZuzbFlOOHfRCeahn8z1odhHPouSbDLzQp5g3pe8m0M0gTX4YeT3S9T5C04FMwwb755pZ+xJr8ut/QEEW5aD9Gwk5aTB+tfU1tSFIrAjUlNE8aWKYfwXINfrUPlAELZ09K88paL1PkIhFIAC7Nk5Ed/eclttdE0iXiABq/vE/WiiAmmfy0EggnEAMxThw7gbQa6I48Qg8fcAzu3RvfP7p347Hebv5d+wQ91v5A38sRdJcTUHwU7i2/DWVPIJ/+JbOhD4EINRwAoe+ADRLnmQyEI6TX69UCf7YF1s2iaY8NazttG91gIlqbd3kaQ1+ewtaYZ7P8wPsnyqQBi4SVCmxhjYGdC8U3PtIAb0hZ9EUrdK1B/mA5r9NaQv+wWWGtQ+pImZTTni/5akI8cefKg682STq5yCtmL48SZ5noBvVa18AqOxj9nX8+gBau/dkkKoNqwB/FLqHlghN86xQnVwSBlmCRVQpjIoHxi97c46DzceqwGoiTMb1JtF58vND+eM80HvbEMOLnGhvZH8irh3AEaX8gXop4nyFi7w30OY6g0VNLx1gu9rbO1jIWTJ0N0CV2TG5wGdQagmm6cagvw4XxOFeK6AH3/KO7MtaXDBS8v+fVL+9faHUzpJZIzaxhzAYyXqokHEEeO2WWW7c+3LinlyfsqXdfK374nPoxR8k/fuAXJxyucB9nkBxBD+7fvHFbq4dYRcQm0/ID6XCFaIIe1YGZivsa1dU1FK5Np/qu2lr70SDNB7LCSjjwaWfUbQnGLfkLSU+Hyir5SXLVEybF8mwq7zRCViWoy66HNT62hi60XCNRiRFyd4BCTUxYZU9IqKxRgGeuKQN9bF1RDKkVXZ4Tm1W6ShfO82zRet6z3aDZliKuNULoPn1agxZ8Of4kz3g+Nv2GnpV4WkXqcVBtEO+eqao+4jqJdnIlB/gqz2ng4775+RUQ/VgRdHUE1YJfNv6qHUFuPG65So3GmgdBDxsP6VcWvkadvjIT/Z9UJ03Y16+k/uPZOah0yF7W1bORS89fpDAgzGF2L6qDgodMPdg8237zSVqkTFQdSwjOGCbF4IGzN8ILnYbyTKehPHuqnya72TxasSbCL+Ukei7cS20YzRtQTlgRi+TFEcTwf0O7ItvxMxxBOF588gZ7CLlM6KN9nEFjIuxqoDV1b+1St0vWoCLYgpA+jJoVYLCygUFnzEC7P1kByLIQ6PTP9L74j4x1RZDTdQTZrcUmjhgCR5BWBOEIEoSJKG8mmZjm2HjtceA3mnICyfHzgya0/J5ofyDgdzSL5fQTHEYcH29+XThad6g9KjXq2yqO/to6D8LVqhvTNXG1wJ9ogU86gqRLpxqNUe/Y1leVwX0MTy/HE38u7HsVPUPniP4r2KrZXvp5vSf9TE0FCquk5ifFp1JwlPsTtCKjjzxSb331CfueMv027vwjfM/ovnVPsic4m5sMx8+i01vDJJpJNqSsFhtjDpSDlDGPP8gYAz3+fRire7Qa6I1OA3ulmE/DPCbKGfQjp4WxSpZPpTU2crgCm0Szvx7jIHsE+ZMwHEEeAeYJYbwoYZz0vnnosK0QYv8g6ekHAH2xoBTaHojAhBhhrRMg7lDGq9SmdKsxPicdvjrJymHdjMpzBdwvSFrMiPdaJcfQ23ZRcjC5E7EQlqIO+TSMlyeyjPYKwhl0syuC/HlYVgaxIuix9tZj/OSlil+saHUtjiI2jOazMMZbRXtHkHUZZUsJ69DxflTWUwVhZoi25NfbAXmoVtohZDJvVDvQhcyRO/MaoLK7bBE86ZVyLb+WnrBC0BYov0i8oMQOPOUEvJa7IzyTgbb80vm3sC7SeFpmkLz8N2itR0sq7ax3IZ8XH+WccKnQPAUnGANOq3le4qr7iSSxHOU4rwtPyhaUL5yUW4gMja1pq+wRdHUEbdb6oom/jiNomMkPkBrRpQeJP57sqR3xJeuW7hK+8M+lg95/uJXxGl8tsFigf8AvoF3yt7SzHeM2dE7oZ8nSKM4Q4I4a1l0msuis25FvdKYltzCU0CAmAhDtOABsodwAYpccaQucQSLOH96CkmaPn7DydpSjb3n7+ZxVQXIIPZfzB0cQE+CnWiVE/plWBr3QiqB3on0vZ1DeqjLJZWrFJFbDt5w+WpytmD2CiHkzCj6V5g0sgalgnUGBcA0N0WkYVENn8El+bHgWzZC19SHKc4cwLRfLsHGwibU0fPs0DAocPyP2fkGbI2g6geII4sh4VgVpIqpq39JozCqfe7e194+Wrz9iJZCWtT/R/kDfa68DPgl78v0DbXopR5A+CburHw6hLG1nUowjiJJnpVGV4Gq5fcjq1Il0/htnfEiv16sF/nQL1BHEhH5tizRTgiNdiAvb4CTOBNOegQPiGZlPSYkCmT0yEgI7pS0P8aRboafplY50woSmP5nlgy8dULSY1OHOtVRIaHrFtx5THtjKK+WxlpV0hE9pExPtyE/YqdxKJG66VM2npowOgpwKK/nOFPCGdDLMdlJYbaN8QUiDkLzjILCj2wikGhQ8BoqEk75I+7SwN+yVx8mY+gRazqDnHg9f67h4vQzRJ9L+bJo9gjQevpH3x5+E4QjSglBWBLFvnmUzDm7OH8bFoyMI/KBxpdBNOiudFiO0rYkV+BHWeE+VFx6pZ2gvpcFexsURlPsO5e6PTwBnQrWaqEC4ftPBdyANc/mZLVACzj1ZTj8+pR6OIOVxBPEChX2C7iu+/+CmPqlm7JQjSONoVtdqlZAcQfe0OoiXKLftCMo+QZi1K8Xwn0Sr2C01jB0MH3qGBmVJtW0tyg8pA3KIVruCmtIOhLusubgM8ibj9FFOygPzdXMKIaLyE++vxYUuNUHK/u4XO3tO+EIX+U2HsriVCkxCoY2BSvIqApBuDPfD9TriwI/AfKZ0TOK4nw17Nmq3h0zblOPT8SL+InH/1vjY3wFTE0lcTSGpJ88TJDAotp2ID6U3D97NlHgDQsx8njsIQr2KDHXdI+hgxC+Y/Us4gtp4sFP/kPhWb/Hb4M/Zrw9B43M0K6x0jVfcmgbfcCx/xZXmGl8tgAXaVtq+Gl+yztqWynuedg4+pbskuzLdQaszpilvnfUi3E08/XWgNHnlCY78CJwRIEY/q5PSqfUCniERSZ7uoID+/yKnhZNKxwEEmIlwJ8Mu1PsEeT8Eff6FIwinEJtG//ijjstlpZAmv8+0kfTz16/1GZn2FdIM+v34NCxukkxumfTaCeRYTiE8Ip4gj4HQajHtY4JMgE8s3lNBsdKBRy8jl4sHzpEPXekFHMxTRmCmE9D2ASl7ulTSgvr+ykjrfd5oBWeiGqWwJWl+SOWTMJxBsoDiX3WEfN5q8lmYHEG64Ai6r71/OPnkoTaFtgNIbzWfPJQjSHkmtY+06eWd25rI6kfszZ6lG7HrNCzCTMDtYFTc7Uml++2PMuSpl6ul+BquFvhSFqgjCDfwGmijbZ9N05xJE4pL7vRafGMo8jiMfq/PyhbvZfB88ESPR2jj3lOtOUqa1NFwzZd29r5AkiPl3piEQrQG5+fYkKN8E5q/NJPzcunhKmXKPKflpEuqupQjPNGpdgJWLYupHPKp7aznWvdibQf/JWOGXMK8mZc24G51IxklCx7S/ilFrrVTaXumjTsDsbISTN9ubrGhqVcEKc3LEF5o4Ah6wybRcgK90UlhL/Xp1zNtEs2KoKdyBHFa2M98Nq0XIy9eyhHEJ2Hq+n9hfyBtFI0cfxLmPhrHT50/tLh1w2juPbDAf3XDB0YlgSXEmjMfC2DZ1l0FjjBXSk3YtA9EK3zNA6889Cc92ybYcAI/Hy5hLMnMpUBzajV/407o9jAbGJ9Ua0XQd0pzcthdbQDNyWH35eThRQorgp5oFS37BLGK1quCNHZyEucdOYzYJ+iO9gliZRBmdbuQDlODWBUddjVTvUMPUzHUd1p7l4M4hgE8AgUBL0LpSwHUIIO84soZhUEAUS9gscmvfQL68WtJsfAKCUWvUSe1rwKTp5KjXKUWGhnRMBSVtpdXuo1GFYTLc+Rhm+Y939oII69XwARsE0dQCKOBUVu9LS8gOLbUxxIr1Z7/Mtc6P2x6R21lpa7rqRKi8kbS3gu4UaMyW7MBqLDW0WldzDvkDTO6eWBdHEFwESHrvvYIGqIs73r5chb4yh1BtBs3sdFoaUB69EdDXs0G7Njo/2zYOb1WHa/p/7sWoG3+Me0Rm+YZIXWuDOC74InUAhkd93FAWCiSXOjODbwmcs+OPunioUvKQ8CmaUX19cEvmvHylhI7ZcCIEwhuBhdLER5cHEE6JcVvQjUB1lL4f//EZtFyAmk1ECuEoGESzIogHawS2ZKFHDbBJNZUTGpqEusVQUxmx5tS1NcvZUMPLrWwa0Z6bPorVeu3no1TN7EqlMbxYJ4yRDBeuXBrLB0kfR1a0LcNIb/ylwF94qBzn0MamB1BxHEEbZtF2xEEH3BtFKGYFUHf6dMu3m4yIb3riSyOoDs3HuszsO9xAHFkvFYDMaH9XvsegLvNJJYVQdoXgbc6OJJYDURQ1vcPZbGd9Rxw8KGTBa27qwD4Gq4W+GIWwBH0No8TLXYLtGWCo9Fe88x9fruFd4ixLC48nfOa3KQiD0d6p9nHwtMQfOlmXDxxaY7pfX6WFI7oVV7iGdZc09V+UlXOhExpSa086d0C+Rw6aBgLCO5fNmvOPnjKr9wpGT5y/HoPnNSFVUAdq44rREpzjFfJLdeauIBC4EqJpvcqTUHSWZKw2NQnJcCZVag4PXAAyYmjik9HkI6Cf6PVQG/kCNKKoBdyBD31vnnaJPpn9s5jZRCOoFca+9gkWrw4g37J6trVEdQVQRnj6hiqW5S89NP4GP3QlRoCi97ksV7CChsgR7HFeUcQBKutjmlkAuOXdFfrlhJ98qy0fJEu4Tw0BODm/Q5lahipvp+SzixkOoLmiiBODsOxc6+rgrQxNCtqn/jzMK2iZb8gfx6WkzjvDkfQXTmCbupH2Tal7vGYArg20WToQEaV5XlpyLNDbsIQ5HnAoDctwj03KGdjwU2HYBIKNShp40goKL2J2NLhs/gxQ4Pd96GOobAOsWkjKanX1Ag+UoUqazlVB0z7QmhKH3yp4Jph0oV+YvaplBkaz9GNDvdRcjV0P5GKi7pUwtqO3LMhT3F4pqbgKkfJ3xyq70VGFVX9jn8PrzzV2jd3RSgNbuIPSLJDiZXGaV2I+3NiM0Yd3NJOjQlTZY+gT9bojAJX0O9tgb+WI0hthoft0oMEro1/TddohTUufI3BEdJYp7yVhvQ5usKOtNf8/20LpJP0dTNE28p/0s42IU7QXulYf0O7lSoM7LBsA/xe6A5Xul0NPNiLKcWHW4SdOAGAvjyQyQ0BeMcCCB2wQ5aONg8vjp9wkGYfHybFnAjGp18vtern+Yu32ixajiCWwj/V8nj2R+DEFL0hZT+h1ziD8H1IjI+Gd+md4OIQyn5BXiEkXHRJ2Vk+T406iaEWSuOt2cJIE1lX6jdgIw8p+ifWdaE1cDJKNPUEqkv+b7F1s8OM+w0Y2kHnPzD4M4lC4wjyp2JeFfReUOFGOo4g1VhvM9mw8rYcQnd1vC1L15m4Psbxozea33//KBNaT2zvabKrN5o4guQEuq3z5rEKzp06glJL7CUdZCNPSFyTpPtp2FCZ6l3D1QJf1AJdEURbXtsj6TyHQTRPzBO20ip7NiDzGPxsCBgZzeVZnrkj17E0uM+FagfumAZWvsirVJ7O0+DeRZj0XOQu6bfiSM9yptSWOGWc0lVO48mdVDgiKWNJIGiPlmstQgUf0JkjtVJG39C13GwUPXnAbiIkjnZBX3wMyKU/Th1bbgkH02BE+oYxD9JS5lYXETD++dADPnNWmjGNvYHYBPoVK4Jev/Xn0DiCcnACe+XJGaQxkEMT2B9I2wplf6AP2utH/XIcQax0pYXmpUjGPxxAXRWELhknGXehjb7AlbaqXPpTUmlyHS9nDcMZCo1D5iW3htI0Btd049CfcwSt1KG6UEyRiqOGdF7Etwa+lyp/xuxExN56vESRtdgjSGPqdzfjCLrrvfO+9Qlh7A3ECtrHrArCCSSn0CONqw/lJOqKIDuCNP7yYsl6qF3QNDJGCq4MzqeGTdcB8LhvvaXh1iDhGcYdkbNucwNgnqXCG73wK3iUs4FAK7MVZXEDW8fPgI0/+Q/i0htCsunoMsy0wYrbyhVNnqfQRcmmETDrPOnKDV3TKTc08AW3UQwb+enleXSe+s40XAloKbkDh4zQGURup/M+BxaO3xZ+K8en/q7ICj/pgHk2I0QnQLXadr8hEx1m2U1/wxJ6cPAOuk0IMDHWptFNjiBODRv81+jLWuAv4Qhyo1weTHeCB7sdH9hjvuSX4MU3/k/p4LuGqwVWC6ztd4WT/vx2Rmd6wj0AE9H21073yJE8PfVJ/7+h1g68/LOEAXEzL5Q47T6DROHlTtxBGMqNQgk7gFw5JqkMGNDHCcQwH9mKNUtiYsyRuHYGvdCqIE12n2rT6B818eWTMCbBrBB6rpij5t/odShH7TJ5RBbOoLkfEJNbZmOe0kkp4b2JJrZOuUz/qkPeHAZePOoBSUhqy8cYs66WReU2ijIqFhww/UfRTTYvElYEeSAVkUkHPRtAW4aNx+dfI/9BXrCxUohPwvJpmJa3668djcNxBOkzLz4JY3n7I09i7+vY+MdyBD3wXkGPHss5xPG3cgTd1SaXt7Ui6NY4+pOJKz+P6OMeqnDbDD090A/9M8lVJv+vkwCZ7Rq+rAV+kyMIVUfb/Rytz01y9/0yfYECzwfPNvGfFlJYdBx6jLKjzlSGVP702dOdUzV/5OzpIilSwSSV61HG55SDjPTJKSd5JKWklmGj1rgDBwU9+hoCG7ztswb9SrelId2LWKiFsKiVYDDQP/p/4thKskwvrUa81oMFoHUEvdPgh2PnrZw/r1kNxMsQnRj2XOMgn4U900rYnzQGPn2esZBDE97Ke/SBfYG8SbTGOxWiD5qkh8pjLLQjiHGQXxxBSaMMefTiE466JYDnlz8m28qtvHGken+Sji2m3ZUPQpQKZcU4/hm6pMMfaOYA1mvQpqzyfDpu0Sl23AWMz38XhcTjL46fGzpkoXsEscLp5lgRdFcvRu7o1E3GUZxAfE798BEnh+0dQawIYlXt3dtaEcRhCxTKeDmq7jFyKXutDZpmfodu0b542PcQcqvdBuUCMo//XokOoYiUjaxCBTDMeXNGfgzmNKN+zBg37SbDgofDy+kKXWuRNFf4Vl6oZ36mSmuRG8VKvaZDNa/BcSWkX+66wFkGRvW9CZmulCqg6t2+HBmexy801Tg6ri3U3Bvlb0lU10/xoNen/g6YNaQukjgBFk/2APJ8E1sYflDGsIEj3XyF0TYInr8qptldPw2zSb6Ky1/CEYSlaNw0JW+OqlZ0aIdbwy/tpQeh8NIR+yHeP+0n8j5Ftz580F7D12KBtJt0yOnz0Iy2dGxDf5TG24Chdtt0y/q89ijth7JrM2UisuaRWXnHclreFmvERmS65w26TywEOzrrMiBDLysy0jvaIRFYxCXVtGl5tpUg7dhAbAUsE4MagHdkr7VHwitNhl9xWorejj7V52BPNQH2kblKE7+QI+iVTkzhVJW3inkrymkpkY/jh4mvtPCnYXIG8fbTeCbLKTfOIya6aKsfs5xdmiz3pnDlAZlGCdMjKz9wDsBh2YIrvIkODppKGnhJ4r768zDxUi5Fu3jViLeIrAaK4wdnkPLjU7AJy4aXPvFE33Xd1vL021rh80CffT1gHwNtEs1qIDa7/EG/h4+01F2wh7fZ6JJJrDaL5sQTnfhAubwddf1dn6Hnpi76BY8u0TV1gtx5xddwtcCXssA5R5Cbsi6OUWyk85wt8BOlafjh4rrxD7piZ988IAuhnu5BDbBpQAvRCfwc3QobIncRz6F7wB10ZlJ+nlZKh/q8zNKAT3rStRalQf6kS7plpoazni0vZdOvrbikKQm6lDhhY9TY8VAOFJNq5qs5nX/wucJzEkDNKm7oTa4bSjTbkCQkmy7Zl4pXPitunBASxCbJewT50zCtCHqnb8NwBL22I+i1nUCcjsnqVzuCNOaxRxCnhTEePn/12nvkffAnYewPxNjGLjfnHUFZAYTzh/ERHZK2l191So2Gfq5jx0bM0Ts89ZcABbgIl2KhUt2P0MC7EfllEPne9d4xEX00TAmTrFoPD4bVRPLpj5VAGVsZ82761DBZyI4graiVc4ex8b5eljzhxYk+rWZFEPsFPeL4eD4R04ogbxaNI4gxVNMOHbNgZX6Vk6+W85iJDgJ43BzqGi9g6qGr70HsSrsKfNbNqQJrQmKzYL0hy3KgFrEFhclkFlJmZwZZsNbRApEnmFnjCIK6taoakdA6kCsmMa4YrJywaejsXh9A5SWV1oDkpFbsqLI5ig9lqJCsX+d3ur8E99MUAcoQLilzMxlo2axzQKSfhuq5r88p3cchlfIxqo4t0WfU4QwDWtYCrtJBbbIF+R4r03hMa3dSN3ol1rSJDBt3RbbiH3W5OoJ2Jvyima/SEeSGRIfEb4RjA29DL/5cDE35zuELW+nWdPGNV9yaLp4Y+DV8TRagDV1qB+u9mm0t2oMrrHSRFXhhs4Pc1bpoi4ictc2s6R2fMitupitwUqdjjk4zPfHnUpu8Y28uMdESayno0vwqpzAmK4Q8okm36ZPbiy+XWbbLBmVwUMZ8Yuxjn+Eiy+M9CkknVgS9YaUPTp7X7/1WlInwz8+0ebQmwtkjSCuCBGOzzJevdMyunEHv+URMMwzesMYhRC01CcMR5FVBLJHPxJYymDBTft6aQluDDD5bKXBsarziOGNGXlAC8nbBN2tAIG0wm2QID0ttHMugE1D9JDB4aPKLowcoU/hMWLv6J5+EsUJIK4LA697xSRifeN3is7A7t4YTiI0ttdGl3mR+LycQDqGHD/W52F3tDyQnEMvab2slEKee3JQTKWVjNZSg7Blch1Fx6AimN23yXk2U5PV6tcAXsUAdQTTR/lDE6aXdGtY8mU+E/Hm3J/IzK9A2J+GZ2T82LjggCjsi9/KS+1y6U144R5VOkRvuYxSnbL+FOn+IfLqOoaNPI0XvFp5yEid9LL0U6BncvC/lAJ6RxhQby0FWyBB0MUBCH8eq1vZ51cxMgnPv3Y1vGksj8bhYjUXIqK7A+OKXFUEcevBW8TuNfewNxGdhvAx5gSNI4573CNILkJ/4LJp98uQM4tMwjoz/oNVAXs+yOIJcY+d5CcJYxw/HD5+DNY+1xk8VqsU8TlpRcNFYiZFuvnEN6tqEzNfCB9sR5tJKQxx52G6FbnDzz8vkmDC0LS/QSNR1A6p16ZnMGC68jO8xSw4gYN9qNRCWZOz87lutqpW5vvvuV42H2jBaK4Juy7lzX84erwgan4Y9Pq4I4vh4OYLu6ceKXN/xMefBwi6PMXoqNfSMvplrKD0bmCu41TeNy7Awppaj0S11DclCqOSgJeJ+b3bJfGPlcNp0SplQNLJdWHolxuqb5JGeteN5TmgKXPiBJDVIHAGB51y80pH2jo6WV4mrAaL+KO+koAkgRRUx+YRWh2hjXX1PoADXazU9hZvoMy+R+HnE2/hygdx12FfmhBIaa0ydzTBJwr/PG6ZLSZs3leHcVRK6KqI+/jTst1RsFnlN/c4W+DodQW4sbkru8DJ4zjxP5LH9+A+PY4s9Y6zfg26VcUyfKfIK+sIWSAc0laAl/fHthz6PUtJuZ+n71Np+9hjpqHbeTt399gV5oYObicTkWeW5vivOsgaFVFy1dFkr85Je6WxEP3MDOox6QrNI3+Eql+cdYUKCR2RsN/LA9KNunHryTk6d12/ZMPODPhHTRFhvPn1svI6O94bROkWME8RYFcRk+KUmzm9Fy9HzbLjJXgt8AhYnkOzlI+Q1m9MSeK8Csm005dOeCpnIULHxQ03xeophJ5LSGEyQfa8ELMErjZpZYupECPuU73xQQoYq7YC0bGUHS+41tOxbEAs11oTVewZpbyA+EWM5FE4gTWQ9dRc9J4V5dY+cO3d1bPwDvbl8oGNv2cuAY+OfPNTvhwc+6eShHEV3NdvlbeZNbQh0i7eZ8uL4HxNnSV91Br4+c8WxmnOxij5PW/MScg1XC/zJFlgdQfmTJQqMp1ENW3n9HHFJNomPXFdZG5kfZT8ZAvXpV5IHZJm7rF1zSi4tCjS9SR2JFbemP0Y36nUkcf7zns3tWV+0OCvOFryk+8qx151cQ7kTjz5mIOn5EvYcU7/Ac18qIWVxdW2Xe7CTFbIhf4fZ7oalm06yndmRK5N+e/aLrQ3E+plHWrizjAAo4ghiw+exIkifOr/WmPfqjU4KU/zipTaL1v5AOH6eyxH0o8a+F4r5RJrNou0IksPHK2B14iX7AzGmeSTgBEz97NjBAaTx7Nctf3AGSS9r7D4cd8jQe/yhL6EKhTXdOpJv2rUCMEOqq/xCQ5Kxr/dkPCMpN6ylHsYzcMJCw3UTP0FO+Z6TWphSgwDiCGJ8i2PG66Q0dvbTMH9erWmDtgfSKWD6NGxzBPEyRSeGPdaBC+wNJKcQJ4c9Yh+++3EY3dVnZN/Jo+T22AdeheNwanlVt/oTM2amv1CmCKpgPgDSHfWNK8GACdyQGlaE7q0AYQlP8KTbXwlPG9gIV0mhhpKqzCdzLSVyIzFpKJsapVfowKTsUE09NiInInEPSw5M5afdtFaFYuvRrnnQoAZlsuq+ZIUrdLPFKGOTU9GboGJSC5H/5lCVNtEfkyAFsdSq3wl576N1PMEaYGtQ31Z4IWtzXUB6QhRcdqBmW3hpN2TnanY2i9YeQZ9Vqci8Xv84C3z1jqBWfe0SMlgWk3ht+Gt6T6XnfHsI9umP0R1xa77yiK/h67OA283okHyv6CT1L39Yn9d3vadHuuLgTJqEfpTRJqBOj+bQTnTlEdUufAy3Epau8Ypb08Vv8YpUun1zbDCQAha+kY+6bPVY6rPRbFzi5rkivzFMKuwCboiE6iRgK3Ta9ghCHoT6dZLPAISjBkfQW356O8pnYi9evfJeQc+1Kih7JWhlkJxCzzhJRW9H2UvBq4L0VpXTU7oyiBKZ/GZFEG9Db6os8pSdH29InRcsQVqOyXTy+USK5f3FQz/TqoLqQFUc1jQAkXKvbLfmR2wuRJkHCSRkSRsr9jQrFtqcQYJ3fyD2BNKUP3sDvVdRLG9XLTUBvaVPwu7ouHgcPBx5+5DPvzRpZUk7ext8/0hL2eUQ4pOwez4ljImuHEFaDXRL/BwZ73/oJ71ce6ddJYCu9wC5jk7rIsuaaO8YMsv1crXAn2qB1RFE+9zaK1q4rUadwyMa4IXrKmdH4g7NT7HAPM8jIHzggJybaIcSyfA1DvT0WnzjU4pAeILpR07pgMzA0/155Vaex4IpwGV0DEipR4oQF0fuqFctljg6F9YxolLCn1x1T53Ksb/XLsv9qqh8L/YWqKTeqsaFJ55jXPrzvW0Z25DtP4q82kSlShBjzTfqT1M85U5N6wh6r5cYfBb2hhcgOIDesOJVewTJEfR8bBT9TE4gPov2p2Ea93AEMd7JdaG7FydPxjfyjAbyYPBjZRA9uF+EcK9HXnHowVFD7i4BGqwKkHT0BZY0eX7T1jO9v1Nw7Nht+/ClhawyKiWyF2sfhIRuajVTLm+ldnmBlsox98d0iv2yAwuxWTSxLKo99lgNdFPxLR0+ekfeID6zfsCpYdpn74mcQI/5ebNoTt7UXkF372gPIT6vZhVuVtUyHi9VXsqUnVT14mkn1oeGRxhRMouNtiQJiEbcJBAVGGiEpA0OkW6PkdprRZpu3NPwgMnPNBKHZFpWeSKDclJWZSY/6mQpE09q5YcqchNPGftUpME56SN1tqTkdRVZTemy6gjaRIYgq/skb2knkNRmFYTP0sEFcAFQjdpSkw/hb79G90/wqVjbyvdR5Y545aqq6Mf/oWrikZ00g3OhsykOykAP3GPXYF5N1jaHZxs4c7+7mkumJxllXKMvZoG/kCMoNqL90bi/xvC16vU12urP0unYgV8ut21q9GIXCVe6po/EyAD3KVlHvv8sXy0ulVY80jeaJhwzcC5hZRjgHd6wFTLS4luhlciS4U+F/aAhOR5RMqCPL4wsm0+1OBL3rZb24BDiEzEfoStnUI7RlVNIk2AcQKwKYnLMRPm1jg5jWT08LLVnA03ukad7mgBnA2kmyEyOGfiZOEMzHEGuQnjyHq8TYMHkBMIRBBbe/JpWLN5t+nswBUVsvEq7SCQsNss0Bjkw8xvS4I1VDP+mn34Zr78AcAgx8Pr4eDa55O2lnEBaDXRTzp37rPTRW0qWs+eYW5xAmrze5y1mTg+7x95AetN5WxPXO1oNxJHzrAqSCJWdiU11pf9TESOMihIZKKttyODiTCr9Nb5a4M+3wEcdQVLHUw21W5pupx1bM76grmnP4UYntz3PCw2oym8fsKD/y2Q1Hs/kIu2SruVYSP20n0pYKWKnPWTmjmUh62PyjvRbH7rwlX/ipubhn9aemOi05kNbwStm0rasQI7w/LEHjbmHiFUS9/hX/yHkxEKofrNMbXCjEMa+9/qemU+b38oZtDmCXtcR9EYvPLQCls+itS/Qj8+e5VMxOYRwFPEptB1BGs9w/Njh4z+/7M5QnvGOMa55xrU4jlAQ+vCgUGsDPD+ge2fQpAkOizSQXvPKlhySbXxzBsCRGsSAwrgyZyQ0wcllHZeOyOizDLdelUMprSVpPgvDEsDYK+hb7RHEGMheQTh2OEVzWxFkR5CcP+wRpH33fGqYHEGPdODC/XtaEaS9hO6I5xZ/CFMFqWAtdEktZtmuoS7UgPT2N8aq8FqlrY9ZgYd0qoy0A2LNz/RFcnOPe7TpQ34+jbMA2lXDlD3vzISV6nIM7abVWbJKS4xVo2fhZsLeet6Msd2KnbLP1QReKCfVrEVMuseWPnHLIPfbwudyfs7fPKvu57QAP2lCseXPKGKcLrVX8+ZUJvYnoZQiurnrqWHnLP9lYH8ZR9DWAX7CTn6w1dIaXyL/FL58n0sHPbTX8HVZIJ386PB/Q7v41H0vvvGlWn8KX77PpSv9x+KjrLVVuoOG2Z2xBkF6ZQMisYObAABAAElEQVQzBdnkiql8oE2yIfe5SShrl2nQhlLXAS/6ICHUAgaOXtEn6nEXw8mnYSx3f29HEHsmsFeQlsfr8zBW/rASqJ+F+QQV9kzQXkEcucsk+q2W1rPfwnvkSxYrjHACZUWPJsD6TGybCJuGCQy/6BRFldfEGpinAHKMxJkTWGi4orN+FKTAIMl8KTmDjOZ+OYwIlQhYILTlaJy3lKECxk9GoQTebI2VQLi5Uuo714Cl6Ly15MSve3IC3ZPD575WA93VRtFPNHllo2hWBvnIWzmCHt5/4E0wOTL+lmast7WUN0fO49QZjh0Vjfqd0LYqKngXqJJxSnSigiMIi13D1QJfygIfcwStbZn0Ln9BYbfzC7h0A31ezxC5gPZ10H0qUNrH6IonJhxp6b3as9LfzB6nHIGGc8KmtAkjhfzGoVmvoU0p0FHTo0alhwqKNZBbIUlXzpE6mqTMlWtKDC550pSYvtqpXVnlgs798xDpfJGDw/05N3s0GGhA6exKy8+YCx4EF5XXtBPOgPSn0B/kzfHLD30e9lZj2GuNcy9e69MwPnt+8darYDk6/pmcPz/p5DBiHEPg2R+IvX9w5mRlEE6f1fEzHUHQ4BgiZsxLy4gjKNWtXsiM3rP1zJ489e/9CCd1sRHEOUNtMFBGBG9nwkZKgvIGrmoMHczmSxClDnzy7ek24Ra7idR9a+24Y6lFnD95bcSqWvbY0w9nkFYF4dC5J+cOp4Gx4ueRXqawz95cEaTVtXYE3dHJm1oRxEsVxlR5knAmuVpDHa+mUbqrkKyXLjQLTi0dTWpWbVRq9i2zlk3B0+YYOmFcnqWXzLKp/X6OEpqVfM4vsY7atElGBQRJirlHAk/TbCcTSgpcQuOR3UUrruXsCJYMtK7FkBx6rvxSXvrYaJU7nLlTyxGljGZbyRibzZZSkhS9i1N5NrIL6GVQp/yUXCu0nEHymdHncnV+Ne/TvoBqFOWFcx1OaTa64hXHJntacqUFT9r5AskL4X+DgPnjPT03n1snyriGP84CfxlHUE3gB06ZS428dI3/bLrq1/Kv8Ze2wMc68k/r9me3n2q0lpuBlF613WbTS09bxotxe/NBcGQd+UqmrI1kFLvlLWLNzbQnyZ15iG7BzILBC5N6DTB1G8REHnDQQRmOqc1AErcGW968Y78fxa+1IugNx8m/eWtHUFYFac8g9k2QE+gn9kzQpJjNNd+8kzNI9O/kSWKpPW9bt5PBcAYx+R2OoEyWKVewVApFpJlmbiNGT08s4MUZJFwmGtQJWn7UhZhhkKugXEZw3nPo0MISSghKSDzSwmeSmIlW3r5pwqo/FmQR+YFwCMUpxN5BrBKCntVAbPLM0e98DnZHy9RZBcRqoAd6U/lI+xg8YCUQMC1nvy/n0GPtGcR+QLdZRSSnDZ+FKbk5gZR0QH3fUnQbsGNk/AbMhOi6ImgzyDXxhSzwMUcQjZn27DZN2onLbZwqbPRn6pM+Tc/x/i+tHaWf8lHODvEfZY6CkA5s9CVKues5I/vICd8RVmln2E09+7FQlL8x0PQEU5/KWmnKjbxSnsbLeFUhipFzHhNcSVsef2z/d6GaVX7tFt1zfLJoIPNAEA3TttyTup1VCi8+7AgaK4I4AZOx7KUcQc+14ueVj45/rlPC8knYj3IEsXk0K2I5Pp498fJiQ589M5axAsifMeMc4ofjJ46hOH/iCIozCMvxi17WaTwEhQkpedRhbUlHG6ruZvYFhRRy57lBwQXmqwF7WuBbgGfLoDu5Y5kbgTHnKAqjuLaRjM2pdV6kQEWez8KoJR/LxRnkk8P0aRirezh4gc2iH+jFyiMdHe899vRp2CPtvef9gXAEaXxdHUGMq/5TWEXQOlI2eivtDOXGvI7J5T9ECvN5SL7X6YQpZMa0x1FbquaA9DUknzlo4CbdyEiE2Xor21yp95ptjEshgfXpCOIc3cLyiWS50WWppbmARccpBHp+sbKwW0OMJOoQHITgJ+8x1XrYZhcIq19pjzLO5aNfMOU/R7fBrOZlRfeYyxKh29OmhA12YDX9QDoN+UaMaWNNbOznTfzXFUGx6ddw/SodQW5DbjBpSesfxfTGhzZoO+5ohmX/bBjlXcPXZ4F6yH8Pzf7sNlWdUy45ngnaGZMFDej0qks4Bwu6bXOhN++AT7GbtFAKr/9Gj9gELRf2pgcnjhuDoRcOnaDhX0MfFQbaiggdzplQIjZOoHCV+xd91sXGz5ob+2j4d3LwcDrYSybImhw/e5G3pU9xBGli/EKfiL3SW1ROWXnNcfJ6q8qKomykqTI0HcvElhVB2sCOvBxCPTHMq5N2TiA0nj/tmKMsq4qoS+HoPCbH1CNVUOxKjdyIRGYbkFUitNCRit3Ag8Nu32jyabhW/3hKp0/APmybQscJxMqgb1jOzjJ28bAv0B05ge6xLxB7FeAAktOHY+PvK36oFUEP7+kTMU1c7wl2j0/G9GMvIZ8SJiE4k25SR8Wk9d+B2L8CBnyNrDd4qW4yXdDL6ZXwmr5a4E+0wCv6kNEmaYttj7RXQvO01S2zT0LmcCApeIt5nKGJ8zapMCrNI+2HgyeazII3ERdghImnvwl9MLmuvOUhPv0Dcf75Hp7KK9eUOnu2WXo0QYbVn8RbKlLRvKlZi8t1PEoL76p9xo1YEjlwHLlQAk5+K/ZYx9BMO9YZlFLNfPZSfOO1jDJUNnn35HbYV/tobNtAKE29slRpfw5NLOgvGqj4pPmdunbGrpfaI+hlPw2TI4hPw3jp8ZQVQc/1aRifRY9TwxgrGYdw9nzwRtDa0GY4gnBpfMM4t6wCYs+gjodoiU7kGWkSAgscyMwnPUDAXb3UMcmkayeesUky6DZAaSljTZNXoFiH2Iykh0tiw4dmpROw8KQGYoyxYZGsIcROIKXTsqg946kswXgqSYytrApijyDv+YMjiBVBGjMfP8ERNPYI8kpbvVTh9DCNs/d04AJ0d/g8TI4ja0GZLoscWubnPke5xumTShONT6/lP8XsIDa+ZIncc0aQsI5gtOc82NfrlBQLKVjIuCo9HFZhmwKOfdJea3IEyU7CqUhE/pRTqsIqp/FgP4mQXE2L5FlqeYapGM85lbH8ce9dr6WW5a+20JpP/FMvSZaBqn31C0+pUn7lVO6luDKK3+le4DEeOvmebvWZREfLTsxMQbOn2+PcDg405hlMa9oGFyCOIJhkC9q6KnPdLHra9UunvkpHkBvN0oh3jdoP23mzrXRrutSFNS58jcER+gcs8blwjq6wc/RX2Je1QO/jp+7970HXMhqfq3nbyue3M48zbpcZTpBK20x73Tpnd7Iajj7WbumY9Q/O0Cm1NfORkMCKQNfj9n+U7mAhpKLL7oUqAobo1RboupUbKR4YUp7K1SwYaf5xIQ0TPwXecnpVkOjeaXbMiiBW+rx+xykqWjav5fLP9DaUifGzn7VUXo6gl9pDCEfQG60c4hMx+N5JEBNlVgUxvWPC/I0cOt4vqJ+ICR89NP2zLqLxZ2Fogj7oxWqirCgyKBfLg8pBvKMq067FWQyX1rvE5HHsqIgWpViLjxTAyRAohSMIg/z6Tj8248vEVR/Qaem6nDaaSbI30L27HBPP6WBy9Ch+pMkpn4Pd18qfh4I9YCWQ4rs4i3SaGG8vOSYeJxIOIL8/lix0QQXrpOKHas6Tdj2VcNqZQVsexQQmuK7K/8femajHbStROrZly2vuvP9Lzk3iVV4y5z+nCgDZbNlJHEn3m4bdBFA7QBCAigCY7OV6qYE7r4GzK4Kq/bZBtHX/GnAQf68tp/+oB4KnpB8gQKQhINrJ3v4xIYKFghwhPFuchQU9KAKbGvKn6MxD3jLRmx4JWEOHwJFY6fbyV3mR0rr2tjYl41Lr3dLkD7nYwXjUdKRaasdtHNTh2GICi0WNz4go2JbUonx7SAnXt8qIzUUShBxjr9I4FlqXD4tGgBtCykgZWjbpOGkilPJiyjeNU3wcgVWwOIL4UuY7O4K0NUyfj//j7Vs5grQdzFvDfq8vaOpsPG0NY6zEueMRga+EjRVAeenhla891rk3PnIEMT5Sii5Jt3TK0LDCUxgHxa5HXwRRS+pkcxVp4JFE3XWbi5yCJzOvrUb6ey6Ffa2SGJJhDpxLvZN1mEalJGUjJYbesUufNKts8xl5VtmeOoJe6WVKVgTJEcR5e/q91jjLWMsYiyMIJxDnBLEiyDbKEFteRqcUOJ3A4oACTzplIgmsTZ9pBPSv6A/pLCrSXN7U4ak8ZEQOCm2e5TmVul4cQVX7getq0a1qibsskVLyW88hXShTtoWgklPepFssN1VsAZpUysU9jX7mTB08hy2H17ZumyLx+qyTdlVI3NRAjURH10bsis6ttNtzf5WD52i1r6UP2+p+2liEF4KoaeCZ5ScjHMgzxoAzf8nr9oQcGOmz3TeKCNvYGtY9iWkul3urgf8JR9C+dmhE+7Bv9Pt805+DN77jv0sH3yU8rBqgc+pR8+/e1y7Rnn+fP0fX8H18jv+UbhRhh+r25lK646anHpPRHXV68aJV51wpCUeOBkOiAJWood1tumlPBC4MYtb/wc5IgDxgA6jsLt8SIYXOv5LafDaPYUNEHIDJknlWBX3RuQl8EYyzfz7qHKD3cvawKojzgn7Xm9HfftcbUhxBWi30QdvFwH8Src8K+ix+zZS/yLHDG1jefDIx5i0pDiG/CdUn5G16O4Q4FBpHkA3CYkKcQHYiyUDu6Si48cguWusJsLkbFegyYHI/VAHWr1mGP7cuJvuqBPXkhQmMft++fVZeXwlzhQHTaiAfaJlDKXlr+ZLtYHpT+apX//BZW30mnsnrq1d82laOoOvncgJpksqWML3l5HBoLQpymfo0CV4oU1M4clBHOfhxqaZCbgTDKtdpYniQcwmXGrivGvhLjiCMrHZ7ZC/tmd+5kL7MT7NIOg41ueZNX7eVAm7LMfG34UJ1THHu2Ws7poaWssUcS91zpVz5g2stwTnurY611OrVlpA/slpi96+db8KUcQ+ddQ0dGscfhKrlXhHUMk7iQ9MPdNDJ6aZ3iTImT7pOtW7r8WqdaKQdMNaxNexGYxUvLtoRxIogHD3vtSLorVYE/dcrgt5rBSyHRbNNjK1hcQR5e7N6Wm8Ns9MHZ0+98CCWc4hBJVvEyGv1kKzOeMgd6BclXRLixlOK5Gcs0EkQXf6bOuiuAVCpp7WOPPJ5gEH+pM0g0wrW8RYa2bWQogzu6A5+ylJ+ofUf8habEgenLWGMw8oQ80XOOINUO3IKXYmJsfI5Dh59OcxfDWNrmMbWX3VO0Bu9aPn1V6205cWLXrTwguWaM4I0xvL1TcbQUumxMFkZxXjvwRK9+tXA2eSjUBhZ7awkLdFSOEEz11zQTqr+1NDQ0oFUOBsmPEnrgQosPERcQ91x+GveAs1JiNxNuz/UH0booqH1NLw1rgqi3eYuYO5gwyKl6ZAtyNJoGu82qTIHtdVtYQuIe2U6xC4htk/C2LAjWui/l/wup1S13cg6+jtga42IEDqBbu3w7kM3M5PuDDFMF2LaiuvCQADAZJWRxGnPLy9nBO2r+N7y/xOOoO4E3ajVGndt0JXXNGS6oXXaBHXZ0wFOB9mtNoR/lW7VWaou0QOpgb43a3xkWt/z2+iaBv6m6/Qqc08HDhg86XmBzAHeOeNWOqAEtflq9OEv6EZe5E46aLa6wjWvjOEMGg4e6SvdJJavTOtu+BrTwUNA5048aC08FozZDmSCm6zrIsLMC0tEmQ5r+HGxeWYMAY4bvvyFIwenjj+t+0HOIDmFskWMN6VyBOlt6bs/9IUVH66ZLWIfhzMoZwVx1hBvTr9KCbv27WnRZNjL6XEAoZ+fC4d+fv0nBu/U9cNB5Mk1XwIRvmlJuR6BISMYZ5wWHHEGlBNIufXrMl75IxgrgfjMsB1CGq5NbcNYBZRPxdtQaL0tLAdZeuJ5deUtYHza9rUmqW+0CugVMcvVtU2MJe1sG2NLGG8r/Zl4OY+eqFwar/11evmE3A6xI6WX0TLc6RTPpR5lq3IVyuBun2TW7WXNc4kvNXCXNXDWESQj1rZKuvNre15tHc/EClzSPV6kD88TL6mhGB2Dnn2DuDQNVKELZMLDv88j8vuwcnFH/+5aVllveviG7AiVVQ9Z14wBe83hPM8fiZFBGspVRuP5Y26GaCWPfW3FyjfvxwqFcmoglVzVMP2pA9AOgUFhXTQE04WzJTR1j/OeA4huSJKY2Fo63KD2dRaroQDtlx4a7OaKIJ2HN7aGZUXQ23c5C49Dov8vLz7kCPpdLz3eaYu0vosgU7GbrWFYwuqgOH5w+GSse6o2FwcQMGixSqOIi4QtHDSdcgJCDvgu2RrPWodyBhd+Zp2KhNRGrmjKLUhNdY1B3lqSSM51bFmNlV0tCpDSeW4lr+EWgHwBwI/YgkoaFQeOVsdqW2LcY3w9DIdQVgax4vZaH1+4liOIDzC8rBVB/0dj63/01bB8fEGOIH18wWMtDiBtDXuuMZZVurO2UhdMldre6QhqWNtXNS9aipRmRKpDFVzYyBIdZVGm61a5IqZVk07edKT1f9YXaGChgZE+DCsAZX5iaElJGovaKjg7jfDWusLgAu77QkYhGnOd8OT3Essi80PRVMhBD3lsjpxoGvpGYYUvxtlftwTihNnuJFO8dgSVhkHTiWFJSovOvxN+hCtji8pa96rze32pD+pCIRUzSIDF0oB8jwXouOunGVZaqhG8q7MRwKrWQeQ+/fLLSz03P1Km1nOJ/70a+J9wBG2Kr9b4bzaedJbdgjeaN5kjun74NoSXzIOogXMd4n0Zd9R+jmyZdN3qj9tm6I4kbOmHvAGuxMiXDD1no85KdU0/FiUZUhswLPQzKpxk7geNpiVuetJDPTzkHUcGeI9C2GQstmlaxk9zND4lf8NWL7aH6RPxH+QQsuOHA6M1KX6vL4m901Yxv0HVm9RPHBwtWq8M0ooiviIWZ5As8htZJsia7rEaSI4gvx31vDB6M9Jhi4IK6G1hj3tVkKZ2OIVcB8IpTphTPhEUbIkEomz8+6ZJpmP+CtAbSG/1smmSJzF2CImC4HskJ9CfvKk0hAmqLJaD7kpfNLnmDaScQM+1LYwzClgN9B8vV3/pbWCvX9dKoGdatq6JLPRP9IZTY7TPBvLE1CX0nw3WjZq1NE7viuRiY2LBGz2qQ6iLI4iavIT7rIFbHUEYRsPVz1E34gKD7lBknT2M6bN4yvNTxMMQoCL6CuEAQbYJ9DH0CLcHqFbu9Nd7LmhQc9qbG1EXqKbWSA4KeS1jQqI3cKAzFZqZn6lguK42Nn7qAU8ukNREc3ScEk2JLSWQSdUU4PObNWueQbpKUF3pXo3xsIUcxE23ckNGPqJbQccrJemZh4Kfz8PTAPVFvn5Wv3JG0Ae2hmnVKyuC/tAY9xvboHUe3vhqGI4gjXnwcKe9/dlbw3AIyRH0mPWd6rm9QqgdQeTlBDKd3R22J3ZQT/T0bSN5QtvbcGAeEUhUCKUzIznb32zZSvl5CFFfAa3jxlCpxBxfkd62qMzF03EZYnjSkT4AIpzcUCgPM4dEU1KNrziDfGi0ZwRsJmfVrc46kVOHbdTXz+QI0sraX+UEwhH06+IIesNXOHX23nPR8QEGVhD1iqDojf6MtxifMrsmScdo20Wyu47US5Vl0EANbA8fBEpMifN+CXbCIpjBK33SJqXPKl1Aw75eV52kt73U7FlW+eEBN9tGeFtDKI7oYkVraamxclrY0loH7W7aghb+rdyrxtP04DXLqmfK6NSgPRVzFtISW8ZZQiHmM3SOWnD9d/M+ENR3FFS3s5VsP6fP3RaFEqRxmToMRGyyXaaJ4+z5ZWtY19S9xw/eEdQDa9fUtuNvKA1Wnca5lj3JfgrdqmufXlRdkg+kBtIsll7pwK71Ph6gC6Qu3B3okaxtp3vUgZ7K1VBztt3udIkuE5N0tqusVUbSK5Z07B10G/OV2eSh3w3AlBmwQ5dTkDOF9Hk+ps0wfoas5KGtQ55hDyQ2S/ylGBmxi4jzeiIbX8lnnDlyBn2SM+jm41c5g3Sgppw9fC2MN6M4gD7oE/JMjJk8f/ykH5+T52whzZTZXsZXxHAGZXk88vlxKLVgiv02FZv44Z/xSEo9MaxnRRCTaHKsDvLbPPI2vEtIXGmitWzKUyI7gawnB0Bn+5cAwuMEQl62iMHMj7eTtCNqjQFWE1TR4WS50rmgz57q8/CafLLih8Ohc2ZB3lS+1kqgF694i6lVQZrEsrT9qd5YXulNZbaEUZaUsP5siB3SxIQV9USE3J9KJ9pkTKdL04O8OILWirqk76MGVkeQ/+gqI2in3Wb97C35tQ2vNg+eFbik05f1c7sgxlORBwqKGZDq3kZx0uCTWp8nIIQV25wrLJwpa3joS8fz28oFoHfrQG4fgIQKxzVh0iAfWEsIf/qSCYWnckU4xqigfMW2VVZQc4wKa3STnlZAWYLNBKbrMrZPfJV3Jbck6ibjkkWcvYgfOxf+rR0wlm6bIcKFwBjzLkAlednxVZcvGoi08NVj1ke+klmfj+dMvLd60fGbtoTNw6JzXhCfj8/XMTV++YUGTh6Na7+044cXHoxRdWA0A4xX/rAiCDpsYUtZaimOoH5KMvZRqhQE2ra9YyO3l1FBVRfCuti+9nifsd0YkEOcMlRyhe3YOqBOoGYhFSxaglxzwPWDfiEBwKqf6Qhi0GesZWtYnEM4gq403vLyhPP3+Plrm7U1jM/IszWML4ex9foN5+/hCNIfwNfP+Ronq4hpN+jST4Nq17phKqpL23EZD6xraFPGIExFYVY6ylfSgvcVCqBo7RDYBChvEPHpcwAftrY9LWVaOCGUdYY1DfQo31aB6/SUkNQWt+ZI8yNQG3sbgw88fzvmGeYhPqKPpFz3fUKXjfvRNV+U1ty86Ax+2ta4c3F4giV9W3AZVVW2w8/aAYfv43kp1DS/yDil2zuCoOBJgQk+guNdhmzO/1QNyKzLV8NcVQ/i8uAdQWst+eETwAOAG/nErg/mmp4USa24NX0b3R635lsG8SU8zBpIU6HzyUB2roNr6yfd6cDXOGjXdPMKquTUNekmxewuM/AcOzC7PaU3HbpsfMvqnrbzbROxtOyekUEltpZna8/QIX2oU+KsPARHoRbIbO1m0AtqV44ypqmTjQ4PKgKgbzUNmx00Yc3BmzVRlrPmszxCn29wBmmljz4F9Omz3pTi+GE1kJw/796/kzMoZwW958BobQ9jQs22Mr7IgiNJfiQvxeeNKBNnO4I0C2Y+yKHS5LEpOAwkr6JrIoWXJg6gxKQp+GPF1AFUCUz+OpBCgIUoRXmVlkL/k25iv40UKcXHwRRHEFMPVv4AxwGkn1YAcTD0E3lYOOSZswuutcqHt5Vs++KcgteanOaMID4Tz0ohVgI9q8/fZtm6+ZmUUgRZyNRffzI4bX3AQMTslIf8EjpLNZhOAJGHtuiQAfoSLjVwXzXQjiDaYbdZbOl2SXtn4guONKGiZJaraZb8Pkn7d4fhJ2HBwujZtSmSXNCdzB8b/RSFduJIWZBj+pJp6TYHZZ5J0bsDq3jwm2KUFzlI5hpJwXcdoSnagoeOFKFxzT1sQu9B6HEJ1Jo2KWYWDxra/QQocKw8CmDBpSduCmQ058SjpLU05Yzbpo6DOdXrtiIxbj8MaCIJFcCW13aR77qdVkEGq8/CwxGklxW87PjIiiDGtA98HZMzgvhKmD6KoDPx/vv777VCiBcfcgTpi2N8LYwXJj4jqB1BauF/ahUrW8XsCGIl0OjpO71bFeQ2miclNdQl6pZAvgvXsUAjhMt13knjyATQ4/qkEXwV5XQA3IOEjis7IuAbRQMz7/GCJ+lsEjh97FDxaiDBcAKpbdgRpDGa7decEcSZemypfsFZe1r180ZfDWNFkB1BbMGWE4ivcXpFkJxGrAi6vs7XOCkDd54xnIJyADVW4xQCzhjJMzdLiB36FT1jtLNFhww48lM0QsMlFbRCWppieH1vBXE6WBONi/gYzIt5tH/ZEXEl1PSnkEjEhgQohn6lkp+4InOUmqAOoFv1rFSRl7oKHdpaI2u5uk5aDnGgcEmu6qDprUWV4Rdxs1KGwlF+dAjP7YDHpDtdYTJ2WI+e1hX89hrqLYzcbTzgU47YxFzywPSyoSQRtTLiylYyAK4L3R5H3jBdlmGsm0rVDXNZCGWhfsxjLyuCqJCHEf6nHEGuMh66h1F3J1bQIVzCw6sB/3H98Mz6hxZ1z/w3xKw9eQ3sU8pEOlVNep2KjMGlm3uzKN/JHrI8ME3hTq1snQZhXl3gabMyMZxUecaSh74nyrw15Zygm09aFSTHzkdt/Xrf5wKxOkiT4vdaFfRBk2fOCOIsIX9KXnQcwtlfEWOr2RfenioWWJ/u9dilibRWDClPW/r2VXiU+ydb/NyXA0i9E86fdgylt2oa8Yh2rcsIiQiXO0IN/qo3jjkriLeQKTPRE83+7KRh0qiZImf3PNaJzlekdWgBbxqfPlWsSSdvIF9oVRBvKlkR9HJ8NexabyWF00qg5/pxmPSVeJ5qTxh/H7BaB6cSWknzxXrS6HesNGGkSVRYkiZwvoArjklu/wnRvJf4UgN3WQPnHEG003rkTuK2b23LwMzTyDNxxiI6jvPBk+nz6L/0zERTWUrGSSXKhDzhA1Falx7K9OEvKcMy8vzgLnHG7Z/pxvtvV1P8/cu+bla9i9WLgkmx2j/TKUGX4+/bOCWifJPDhALQ+3c6tTYpQ5Z8W038TQ4gv+jQytUbOXZwAvH1Sz6I8BZHEJ+Lfy9HkL4cxtYwVgb9rpgx7zOOIG1x9oogVV6cQawIkiPIK1izKogtYn1OUFYNxfVPnc4fJePu9gqStlUwG0x+lmebXkuEnAoe6DNLgHN+WS34rDZpYsUWHx09Ji7YJhjtcSGfZD25gMpmlW1EI69S8wZIALaDeULSjiDlfVaQVgPhCOJLYIyv/VVOfzXMjiAdFq1VQHyN09uy5SjqrWGMt4zTfrFjy6RHshgTPcYq9nNEPkYVlSchMVSEXnlsDBcZ7wJX2vAqm9OS5GwT7f+WQiMEE2+25h3wCfWdM/mqJ+kVMmVO3q321jnxM9W4rcSJP07BxQ+uLWfkpWVz1T9XTOs54kNCS1Nyk+Ze1V0SSfeprXPmt/JbCvEaaHVQTuqJPYJNLOWU1la8IjbpsnUDSwbW77Gf64OpwuZ1XBn6VOzy08QzpTxt/LIi6OAG3BPof8IRRHvqTv97DwJ0PNQdn6vX7+Gb70fpoIf2Eh5eDaztgXvEZOPsneIe0qN1O9oUBy5aY+71KtfAg8tp+5kyNuSt15ZFx8TvecgT9nSBnuoMfFxhkwhLVZq3XJG0kweBflQHgdU+nigDJ++fMvxXxgPEToQJfRHCZVQmI4P5S9QgQ1cPZoiijqfIUOc5i96v4PXDYcMb0884g+QIutFZQe/8yXi2gelzu0rzpRVPovVG1Y6gz6wI0ptWVgVpNRHpLzh8+EKLYk/ANQf8pgk4LwWtS+MY7QdHESObHUKuSU2iVD6cQLoqTba2huEYMg3FnCWmOiic/E6umkxJUmaEyxITMIASevh+LM8Mq4JwBnkFkDxBz57oU+9y4nAQ5bW+9sV5BZxD4AOgr/ksPAdBK2YFkN5QXmsZ+3Po5CTiDSU/PhP/RJNT/Y982cXElD8JsBNzyROIKpl0Z4ytSxPBW/gGOdalwCvXJX2pgTurgSNHULfRbpyj7S6NdUkOWwffgJwm6Ku2YcmjiP6sO4YtoXNbHeQW/oV+YkhZLFen+2JMEfLHCv8yXW8KxbIlEhZYJYEXu1P01C1lT23NXJDXZTypi8nlPr7wW6tVChsUzRnJQ3F+VA9+tdcirC7lS9+qa+mM9GlPpwyv+wRs0i0SlURM4yivMyXElJPcUqg7Qtd2crljrOb5oku2P+slh7Y1f+Qlh15w8IVMtob9zgHRdgJli9hvcg6910ohHEesZrUjiFhj07c/tWdYsR1BGqN8YLS3h7ESiN4eR49+rujdqiDBwRNmfVMYalCxDd8UzrS5dKkapLxBKT1cGe+B67cXM/JJ+DE5IULkIBzjzkZ/6RywNd+DrWrKJWLgx0jZ86i+yukz+FyTWhGkVbgv9MIFR9Dcfs0WbDmBtEXsjbZdv8IZpLP4XmnsfaaXL3w2fjiCGMcxRCb77ijGuUPZUj6Mk27lXTd2TqVq3Gb9RigmOi8620vkAL+BlXdRBqzbG8ieN7Kamepf+VKnJSfGQCDLRDjEm8nwtiHabZnhIR0MRUt0DtYywXd6YTtJNl2XLDyTc8VPqMvhbNmqMm77qUmLymGt60J5xe47hEBzqLlGX2BTRvjbxlkIKJpr6JjoqXeBrclZjsWmlcDptq8QMXFQjUegIcL3vJ92ceQI6pK53ZS8tCEJMZKRBWZZqIg2zufjj8rYai/x3dWA2vq4XXen9Tua0uF109oR0yj51w/eDr3P3jUd+i7hYdXAP2ni2/bT9/a0bW7pzpc/dOBPZaxcp/IyaITriPe8bVPuwqdkc7Qt9AQMER70C2mOQTgSFpnJwZROKvTQkSqbN12MKSCtyY1TugQOad8vIEnXIGvSsqGeM2hYDcRkWX4gOYC0ukeT388cGq3Jsh0+enuKM8iflb/hsGg5gG60PUw0n+QM4oyhzzorCOcRB0fbIcR2MTmXmIB/FT7OH8U4hKSIvc5+e6lR0c6gmjRz35goEz9mkkZQmol1LOdaZXCZKYHyBrnESnIPkrZYj8IW4zqL7JzjgyPoqQ6DxvmDI8iHQ2s7GKt8+syCa95WKv8S54/eSrJdDEfRM9E/k+OIVUCcCeQtYbKVL6FQDGRTAkzrXzuCBIotJBRsfpK7TIDII1hOpVt2MJfrpQbuvgbaEUSTrKfVRqztdLTdardt5S47npHGn8R6pP1cI3D0iTzn27CfaK/Y1cYVfj7dVlp5JvFKNrT50lOT29kjwuDg2OKAtJzGdD6YhiJ3KXKyvrq/HHURxJariDd1Fi2pp/SUoSp4scwoEqdt027KNuGh25kzxZxNTQmnJJKp/92GBl4A+nlCrtjUkEHlBOfUfZEDwF/I1FjE2XYftRrIK141ruEI+kNbw37TJ+T/0FfD+EDCb79ra5hefjCWsZLVq1g1UH1lm5idPZz61k6gipX3SiEdJs14hUWM8d4SrZhCOA9OlT/Hf8qfn8viy1GddEkpVqWXyuZOwuVxtfGQdnqIjK6TOsVW03OBRvIUJTUQpuo5hmWbCd2UFTbS+fkNEFJV/zhqfEaQ8rjHcAg9lSPoucZTtoS90tj6Uqt/ftUHGX799WXO4/OKIJxBwjH2Mk5r9a52Y//yVPMD/HCcLMhf2rjfMrbHZsbangekDMp5RYXsgYUiEVdFhIa8CzELPFLmMNOodngjblB1T4hc6JBW2pbYQF1kU4stSjDhaZoZh3QwTERKc5A/J2khPUyio+7pGWtWK1zPVZDWGAklfJOBYgJI9bPrOjMLugmTruUGGjh8HcC35NA0ZsaTesLW1GzXK/Q03dadYgJpW/b4UYbFkEGrxMCLsdsYzZH6xcFk+3i+xH9ZEbSv3fvLP0hHENVBg+lGnQ5pNrHu+NZq29OAu2vYkV2rjZf0PdRAdUJ7zfRjtKiO9/hz+b/fptTlS1m36Wimja6waD2vo63Ks3Cerp+VfekKrghMQmWM4hKMs8p5gMRIY5gcQl8TBIHBNJc7/pCaWhcHg3ieyQ280gIAi1TwTL8E0YTVtM6hq3KKSK3Pmadnctaw5B2HkJ1Acubg0GH5/Ac5fG50ePTHcS6QYDo/6EaT6XUl0GfNtr2SiFVCOJNYJSQZOIF84KZm06wWYnXQV8ExiRVDZa6sSojrJI6glGytIchVnqpPSWg2VwvgVI/kUinsx0qJTWc2XXAwaQGQY5w5V+UI4stgzzTRfK5Z5ku2fb18qVVBcggpj1OIVUDX+sLJs6f6RLzexuAA8mfitRKIRUs5G0i2Sy9vbLRIKHoT2SQMxEbbQtxpmQps2waKsWhMC4iEAuIr6fzlcqmBu64BHEE3tF0pXtsibbnzI12Ahh/ZCu4cnmcjzwjPNVT1/BuItMD/9B9y4MnPwB8Nkd389CbVZ06yDcx4KT6iW1iMR98RHeZ1CH6WcUE1yZlYlLJjLZH7cXcYkedSSZnnfouUHufQFX6lSjESW2bbBuuEtaCGwLqRVKImrDksZ7KtYKdb3zGnSHzD29QyuEugbBUdQv0EMEnTJes/nnTJiqDEfDUMZ9AHVgRpq/Mfb/V7p6+GyQn0XzmDftfn5H//7Z22jX3weKj3Iv7ogR1CcgRxBl5vA6Pj/9NfDkNvDpGOE4hVIXZNyDraWH4iUgDOT8F/PMMbu6ELAiQB+L4Sq7zAZyUoB1zXBYaEwR8Vhvgerg1zUC1Egq0ksSQ6LITkYoPxglmCV95QGlU+9qyOID2fuNM4l0+7qXU2EC9ZcASxBYyvhskJxGqg1y99LtBLVgOxYkiOoGf12fhnOIJ0jh9bu9l6jR04fvhhM6DYLhuUSZ1o3uG5kWY9EIgq8eQJJ4WgcCZSTLpDw/Zx4U2a+YtUocEIg5kk7O+N0Nw3qOaTuNWIgOCnvMBaNvy0J+Lv9WeR3fKQcxSCj/WrXXvaWBCdqWN4Vhtm3UValdc3ZZbZON+M0Ld94WkLplUpK9a01FjG8x6bZhxMro1bYZu07g8WdJ+5wS2Z3LEFsCThn6VeEAWvog8ENjuIacNXeZdadpkO+/RDxgu9caS9X8L918CDdATRUPJQpoL2jdoTiIO6W+nWdJM2rOOGrzE4QhqrOgTZchSO6Bp2RH+B3VMNcPt0S/s+fu/e/wy61tHxUcm7rVife9Yfb2ffk3deLx2wK0P1wWChy1A7EhEfMoiq/gArzbWeEWcE85LPYAI6uMLZGpJOzvMVcAJ23eMIyuBCPxC4qUVUL21il5A4guDLJFcTZRw2+n1me5ecOnxu99MXOYM0ceaT8Td29PDp+Bvha/WQlv5AD+4jTiCcRpo9fzFeTiY5hbxljJVByJbT6JvoeXPpF3Ts7VLpsLUN9GHRmkh3nbkNugYoA/RrSGmZZxmjySUp5wWgvnNANGCUyImjEfSKt4s60IdVQM/k5OHN5HPNMF8811fA9CaSper8XuhtpGlE/0yfDb5iFZAGYc4aYjsYejQn9S9bzjQ5RQvmK2CTm4FUO27zsQ2YqXIBj4UAu3kBK5D5k6MUW96GX+JLDdxVDawrgmim1VTTzsuI0X5p098xbJWxJ3X/MHrBRRkIPzh+cqp/SDoykJqpe/9hscoOFts6BVb9DLoWMY1N/53+qseDVV6nwdG3dvn3pUcev1xbUWsxYlxSxMgzx1LeJpr6QtcSG998hnORKpdRydDGmpW+yAYFuJVKf/IWeVNGUgEdDdvJuT5mObbSlMMmRISpMmbkMkMTWj13rvRXvgnJMrZ5/JFMXk5w/t1HOYH48XXMt39oRZAcQWwP++2/5QiSQwhHkLc+syVMLy1wCPmMIFb+4Ayil1cn39vBfnmULWNsH6N3xhHE+EoZaX1xEGEZPAV3ddVAsYFBdxS6fuFXwTqrRO6lAPlfzINgU9Vwz4aZ2iuGJVK9ihAsUsyjuNuMgVzs9KmnR1lL8030aybhKa0qTzi+FAaeQ52f6IUJ5/PhCGL7l88Deq0VQa/0tTBWBPG1MJ3J9/IleI3JOILYhi0mvYsRr17ocAtk02PJwj7GYtqk52ZuI9Jb4U+2qWEu9Sb9WAwJly5bSCnEClN+hC1ltA+kE3bKWM9Ca1uUd7zKq9o0aeBcya5Uq4bVMtLbuzMpm27ijyXeTtdlSH2Ne18WDjt1jyNd0lRGr7yiHE6f0bvgoLOAVgdvla3jfa1AOm1PudHUIjoGs6ZDefs1/TblOLZ9zEFbYZE1dccu1sioiKR3xgAyWBenhddiOQfyMBnupNLKU19sDeuew8SXy73VwP+EI2hfO37odsD9A7vPN/k5eOM7/rt08F3CA6uB6oiw6u/e1y7Rnn+fP0fX8H18jv+f0MF7OgC4Sy6xSsujwhA8w5oONE4X0alJMwCEgvy2jQNv3JSXVFMG39iZM17ZHoYRRBp9BMqRvcWkwRF6gPP7OsP47Dorgpg0s3WLA6C/alk8W8BudICQJ8RyCnl5vQ+IrrOBcOxocs3neW84QFoOI1YQfdKvD5BGDp+Y/8rWMbaKyUH0lVi8imwYh0kzP5ujJLVL6VRf2K1kbJ8x1CNAJJpHmphTvT6oWc4ef1JWAGBd7ThrnvR2ME0sn+mz79de9cOBlfpELecScBYQTiCtDLpmybqcRGwdY9XPM95E2hFUk1DrQzc/TQPJyzD0YC1X66bNkFEARXmMMyQZ8l1OgwUIb9FCoNB8lQ3wcr3UwB3XwOoIWieltNnRNitNvtv/OTNNcwaZPm3zdIgyz/360Jw7I+icbOD9LLbqhm3kNlIxeALl6b42kNNrytwcEw/kGCprdsV0mRZFHkPWfIuVsv3YtRNlynXczDjVKmNR82xz0962u+OJac42KLGhjdpU2pSwcmzL0IzUt+hV7gkpzQJHEomRMh1jjFejio9x7aPGIb4chiPow6eP+mqYfhwQjSPoNzmCOC9Iq4P+0GHRN3rJIRa/6PBZQVLMV8T0yTCdTccWMHSxTUzOHfYpqefP1jEcQdQpDiNoGGsTk25Yf20q1guvm5HSQXsU1pJX2pEH0tEWtzVUchaRVUVG4KACtUo2QkCNWPrHDKLGUBGFrucY5KS7GhHWp+BwxCbyOILQAYwtYfQVHBStoVcrfdgalq+DvdEHGdga9oYzgvhSmBxEL+wIYjzOit0n2k72rD7uwMsWZNkRJAU+Hwg95eix08cWy15PNrCfj0jAA11K6DKZR8yEKnsyxgZoWZSEkBIlPa84gpjOBL/ETBBQXGFNpfompGlui3EDHLUVrEJSW0kq+W2raLqtjlMoNbTlhCN0rYPydjryyjIBlyJPVWEfee6b6bZCJLN1N2LWERDwhAndlr25TKTLPt/wTYwh9YBs+6FQRdciieRiAMklO0WL7qiMTc8U1umSR54nyHBd4MUefrTbl5czgmbd3nPqQTuC1sF+rScPpgXo9FGDX/nX9CqL9G24FX8bHbhLeIA14M6J7mmG3Kt0TH3ffqT9IOFH6I5o4L2t/az42+j+iu3IxJZumtMuwTZVQkY/N2F6e3PW3IhpCrbTqQcXGyCOkOKGyiICLXGDSokRmqJpGERLiiIPwRUDtZNFcZZFKzYu2sFDz+ocJrz2x7CdS4kvrPTRj21iX+QMssOHlT5y+uDk8Wd5NbliZdBXHf7jT/N+0rYxVhHJWcSKIJ8XBI+3hIUXRxCf5cURlBVB0o3ysitTOwxjElBB9jEodsBmcNSiL7oh1CsOFpxBXgGkjJeOax8YDpzcNOqYN4ps8dIKH61Nv1aM84dPwV9rRdArpXn7yBYxO4LkEHquVUCcTcBh1v7qWOni7mKDnUBKoN9OKNIgZXe3H8pDGv0Ep5130Z13ebvQIYv8NW3u0lvpS3SpgfuogXYE9XPQNnSbd7OtNt4waKo5N/mIgZ/D0W8hY/bDzdYPDMxLnzF7DxOusknD1c9iciYbl7YDvVYMxplBkoQILe8MnXUUDZT0XJENZrF9iBW29VSl7csM/zrOeUwRz5G0qrQdLha43K6zcLZlmOLilE2xEx4ownvWdvO0JeEB1EUyusqFkpFsFsVdkmha+nrkbITJGhFZi2PyzWVNHtO8PVlZxjBeUnBgtLc+tyPo7Xt/Nv53HEHaGsYWsbfvWBGU1aycg/flS5w5fEUs5/7g5OGP8TiAfD6QnEKG1WrW4Kkpfv2UVM15fANGwGbB4xEwJLBKjqgrCYArKrHSrqVUjqlXSos3VFJTPZ0rNVVnzaRsLEp9+557mYKgraPirP4Iv51Ggs8vhimjQd4OIDOyEoivhWWs5OBnPsTwWmPua50PhPPnDSuC9NUwHEKsBmJLGD9v3dZ4/eQpW8o0huuFDNvLPObjXFLB/PIF/TVBS02nbvwMyRb+EcKXUtp6X8xsfN0lp+fFRJXtdMdNRX65l1S4xKZGKzlIoZM9FmGixjgGDLTjFTmfQ7BYG8rQzie5pUKf9HyGw7NKRRIS0LvSBWJEXZoulAAFcR/UWgbhLHQ3vk1nkPKtDTO2tuUlZ0QpYawEOFODpBLnMee4pDP/LWH0JbI3t5H71XYt0jsJr9KmaLLVqKJbUaT50WQ7TZF43GipsHAv7FykfsuWy9YwVcwDCQ/aEXSujrpxn8P/Eziy54NyXtIR3b9p13lLLpgfqYEfuac/Iuev01TPaUa6yYS0n84RT9wK3bazVdaWZ9I1zV5e8h4M6Ig3aDIFMEIy3KtnKNWUQ/jI9dDag2EZOrlbdyHORCtVp9HkUAOFZSrt7VfK+P5phGLlD2kGGEXmgpYVOgw0vDXjxdk3OXZYvZMl8aJXmtVA0ODEMU50TLC/yjGE4wjnEJNrDphmS9kXnyOE04dVQ6LxDzlKizbbwyQTp5D4sQub+AwYkSfbBigjCDYTg+vaNMgX1ayrmfN/NI2QF4YVPI81WSR+xCoe3QeqnjOCONuH84E4fPKZnD9xBMnx8zRvIK/lFHrJ18LYNnatSacmoHwW3pNNSaK9YAOW+lf3lCXvYKxHBJmQxlqoQ5Z8sRSsy1SFg4SCKjSd04bkMlvVArwkLzVwhzVw146g8VBQRh4M+ox6UPxUAdJzllAPkPOBNqYINlGe6OYRaknuCN03pZfaYDaZHlOIjwLQPWZ0d0cMC2zPt6BOkmsxYtNKEmyus0SuWpM1ZuWJ3bGB66phSzdzkr0rXOo7FC3BehlkFPq+hgJAUw2IEt2nOlUVWrVTEeI4jo4XIoxhrAjijKA4gnRYtL4O9l7nBL3V9rD/iwNIXw/7rw6PfivnEC83+JqmxzDtLPr6Ndu9/tQY9ZXVPnb4tDNIMauEysGTkQGaGII7hDGoW6Jj81MejyJVsDK8clUo5TICDjCJUSeM6wZs0Z1bRI7mqATgba2GkLqfLHWnetJTDEMfjgxRU4LcY/EaqRIrXlcDMR7yTuaptnJpSNaKoMd+8fJGTqBXWg3EiiCcQP4p/VJnBvGRhld6GcNHGviqpxbzaku3tnZ7PGZMLheb5MURJJ3D+Koz2cEMIjYLtozTqd+VJ5U2ZXQldk0N4S53+Gnf0DXuXJz6hjQ1Bo9yIofjpM2D3oXmq7uyw85s3ZEJOJP6Z3RldQpvDYGcUXYLGL7lxt1COVFdF2bVpe9Q59c4yAMKKw7cdeq2AudxgPK0Hw0tuI0GyV6qJjjrm7IHvRIrP4+bnyzDa/6LbfoxD31+2Ro2K/GeUw/eEdSTkbWejiYmR3QrT6d/Bt0qY59uPZf4AdVAdUS3WbTex/N06mbdMdLd3R4ir2nO00+6U5rVpknXMonh2dq04SkKKAmeGHiQYHKz9vDI0Y+em0BH7yTDOpn5J7vzNcOwDFHUVAVOBehvD1CcUkW3nSmSyeBDOo4gZaTEafSB04Xd83DlC15x+mBNnEXQ6ye+dtIkDUw04vPEmvpgK5liHxDt8xd0RpDOC8qB0XL64BTizCEcPhwijfOItBxEdkDpbSsyvWIJ24S3XVUOMkCwjUm1rrF8UwkaHDU5ZIDE6XOlVT6PNVP0Ac5XT+38YYUOdQ4dB0Rz8CSTSw5/5kBonD98LYytYC84K+iZVghd80l5bQfTFjKv8IFf2q1aZc69iD2+/QIAY3Lq+xsCc5BMI8qdh9CwBoNX2K0gD0tQg57ssKNwl+hSA3ddA7c5gvZtO+0/bZ4npvGrzcCO4NDocePKZRP4O9uTY6CkN1ikBXK0AaaxPJHpdXiukkbfxJdQ9S/oIoA7FzbjSPX3e1r40xNI88bozkwNqzzkTMxW6p6uJe2oahwGulJQboUT4aFZwUlzbdysP0QktGw5P5YCTt6mq1gIk4kt96L5W9wub/DsS8lS/gTBS57HKrGy6JQXFmP7ssaqT6wI0llA79geJofQb3IC/aFtYv/943d/TewTK4L09Ut/DEEx28O+fqUNKGZ72Fj5w6ognES1TUzpOISolzh/7BDK24rUmmSUC6OqEdv1cxHo3Wd5DVJ+QlLKAChh2pApmshMZFpmiSY76inoYEp+85cKIovyRZnGO2Y0VoKHkFmBYVy0QocJhAFfNR4iQ5vmNC7aEaQLjqBn149/ea0z+d68evHLa1YDaSvYaxxBXhmkFzGc1efVuZzhd2VHEOcC8SEGVt4ij3kXMl1M61ntpW4U1LD8jwYGjwK1bx4u5B0FR3H62TSdKVKaoi6IOdNudZ8ToAjVjAEJbzsip4grii1k4IwVhVqiicO61rEQjOSk/FGJg/UwscpbCQLv53vf/6yUa3pPh5RI0pXECIGO7K2JlTE1GIiu7liYl+luVnoqKtoFt7dvo3bQrfomBdL40Z6GqkJb045t0BvppyjUyhvkOMis6tfcUjIuXw2rSn0A0YN3BK111I2beB8aB3xN/wy6vYw137qObFrpLul7rIHqiNZ7NTvTU7vO0/XkLJ1a051K6DYYTDrU8OxpIwPoMb7pm252zk3Ps9Dp1ktHK3gN2sjIExM6JhQehOnlzU4MLnjoR7AYXTQJyKAAA3n/NxlTFeS13L0UOBpGmtBxclxDwX1haoY5cQLhPBFWkzJwULGl6ytLpA2DDucP+I5FDw9SPVld89An7+knMvRPIu3o4Q2qvyjWZwTxRrW3lynOeUFyANkRxPYw8bLSCOeSBZdeCfcUEyPQMGInWDTkGgObkMHxsWaJjz3JZGUQX/ViKbm2hmk5D6uDuK1sF7uSc4ivfj1/mnMH/Dl4v3HUNjE5hq4Fv9YqoKdyBLF8nRVETDa73olH2vUqu/QfBxDGosfBhMm4zSgZV1IEmE4sG/piJRrwgrVYskw713yRXKJLDdxZDZxzBI0/iWig1Ui7LZNVk2/wxtaFfAMn033ACULPHDirKV0nNAL4+ZPm7muPaDCsx6WOm65FYzuhxwg/pDEuiN3VdNDr17yQkB8wEFRQ9R17castnYZ3HxoHnDT9+jaEq1QVqmnyJ69NgR8sJlWdrX8QF+OIoA1fxrGBAFpjCLD+Qyx2TipSbUUJEkCDgAwdcw0ThCr3Ei5p1v1vecQduowep8Sm9w5+qcF2Lw6MvuGlxU19Rl6OoHfaCvY7jiCtBMIh9Pb9O68cytcwtc1ZY9UXOYFwKOEM+qZy+fBolw+nEFvFcAgRUw+LM6hW/gC3zcYnnRNzgOZfWijlCmWuXTuJO+e6qjNvqMGuK+O51IPY9UKdOL2R7Vs0bgD3O3JTk0uVBqDyDgLdI3T6rptNabc5VgNhObWPAwhT2L6lA6KV8Yoejasv9DKGg6DfcC6Q4tf6UufrN0+dxzHEFu1XcgDxxc44guJAgh9HEnp7lZFtQg/m6Wf9uuQRoFRUSGKTQAAh10ROc4HKdVVp6AwzPeUlX8H1QRql+q15k5Tw4QiCTmSbZ9OVNaiHbEP2l5KnGnX5fT3iCB3Xtp34XEiZumw/SpeaOe1npMV1ITkdL4rzvJYO4bNyDDtlbRcPEUvZ1vQiysngSE67h5gJ2vU/C2InsO3reIcuLWUrYlBW4mjxJ6HxivdaR77kMA1ueW4iynPfTCcA7eayIuikhu8V8D/lCOqa8kDQmQcUP1S7HlAV3Y8p1RH9c+V014R0at03ttzgTjvKxp+P95LOUx5hvsfddrVltl51MuCeUBx2/+m8RZk3f61dnAx+zmaq1LIXqU18GDO/Y2AYNpQmBonYp6sIenUPW77Ig2cVjg/P6RXHCwAAQABJREFU1IwWhxAwD+QVIzcB6W17bPYch4Hd8GhH7ld4NdNmkn3DVjCtAsLB48M25QzivCBWAPX5Qj4jSHm+ItaOIDurGAUllr8B4pxyaTA89kszrq412AotNYfPW8I0Q3zCslkcQT5T4Jkmn9oexj/ZDu5K5wZxxgCfi4fm6VV/kUQOoHIgXXGQtGg4G4ivjFFsF53SRx0qY6zyuQUjEXsAmyi6baMncIEXKnJhNUBxhZGtxMgLTxtY881ziS81cFc1cM4RRLscz0o3UsWdxL41vdpbf7uuIKfpp6qbEzMPy0EooS27qTp/wLEFNcMWejb3I3JNU5VBulVseBt4VtMpomVt5JySDX3HNT4V80fYzIl6V8fn7ktUrpxtRCzkZcJ5G7eYIYV77dBxy9zH4Z9SKjUBcUVIjB04Gl9u9BKCs+vYHnajrcz+jLwOhn6n32/aEvbu/fscHv3hQ84SYqszjiNWtYqfs4IkQvJw/qjO9MMhFIcOW8FwBjGuywjDlYWuemxK5D94Xd8TnlrKOOG0HAcuBpdNNdT4Jxijo1/9+K9H6DJe5ior7BXhXpb0GsdECVDyEY4NUWI+y1CeQdjYvjg7bSn96KS0nntUWgJtC7XCs8rKHZxAOIT8xU6Nqc80DnM+H6uB3vyqQ6J1NhCHQ+MA4rwgtopxkPQLHEH8+HS8+HAA2bGkmDbq9VaKKQGBR238DEnl5Yo9/m9M089CBex6aIEuzCCvRJC+6t4mEK8/oI3rNHWNtlgDdK+7WaBYuU06IMGc4kN111f3zf9AKeXg57v4NwuVtjxr1mLWai4d/8DMwRqxp4YC36kcPJ0wfsdqWDHW0x05wNZnmrRgtO/LiqCu0fuPH6QjiGrhDfs+/KijBbofebB/lG5vx235H7XxNhkX3M+tAToeAp3Pz2wXP9p+tnQYw5S0jMKwnxDol3sytBc3++weZIqSDhliTUJ467UNZZ+fpZ7sQVHSBG+5oYQGuQ09LWFjoD+dlJdtvllJZxVQO3okT5PFrLzBISMHjPoIHDheCSRc31vkt018xt0/JpDMwNDMmy1+DrJbhsHLih4Ojv7C9i9NtL99y9fGfNg0h0WjUzgfMs3qIBxB4vvqrWFfhuPHZwQhD9v4xyTaaexKeajH1EcmdmC89UuXbAnLiqCnrPxhmxgrglQGaB7jHFL+CiePHEKsGnqGY8grh/S2Ug6gKxGCZ2sZE1fzSSHPAHq55dFPGhsFpA71z7Wn6E8mp80ANfecvNLmbQFAujqVXoPJEVm0C4vbwJpf+S7pSw3cRQ180uPHT4/IJrg5d5tVTJJ+okCmXdMr85lHwX3AoLNAHowEnj8/K8p2DGZ5dEJ423WKu41q4Pws+8FvkI3qzIh5e0tYbVnTRkAAXcnrMmzEQ6OwHQ8LlmhzbbouFr1O+qfYE+JguWZUW0Qsf1w3FM6Vu+Ep3cytKY9qZUTbNPFbacNWgX1PdQHmqiGejFZ5Iq8JoStiRmZ1zR6f2Nr1hQ8bsC2MVav68eUwPiP/7oNWB+lT8r9rJRDnA+EYeq+PH3zS7wOHS2ts49BoDWFyCMkRpBhH0Ff9vn2rVUB2AunLYTR2DACnOPext4jhHnGpTNMOIn/xUr26RweVw+OuKAiUEyGpH676+b9g9TJn4AedyPQwwesfkuWjQn5g/aSlXWAj42vLHnHVoxDiFBT1FTxnEMCOII9/ORcovIHTN7QTiCkEDiAcOnYE6UXMrz4X6OUv/3n1qpxAOhNIMP/4aqecQM+0Mvc547JWEUWeZEqWV+C6hmNbOy+pLv9kJ/EInqvJrgISZd7ThapnxDzAAifbFJHVQqnNRiplgUAmfk9vctFNvVvJzQq0pRzJALbFh+rvXilJt7DbZOzpsMEvEvdMqVxBuyRQ7soKj+jWFUFmA74Lqenw5zrlkQoeDfwLzrd7kQP0e2FKXSm7DIEN/QsxsMBXvkovdIYov9I6zQW4YpKGQeznCngQLp0q6fL5eCrnYYQH6QiiwaSTOa2k2QF2R3RKs0KgPyfr36Br+1bZl/Q910B1TNOK0UVN0JnUafvZ94hhPKU7FuhB4gfUn5eH/oSZSt5zN5KW30PJOtjSBQ8CDzWzy9YEai/QtAyu64QLYAjnwBUpQBsG1VrMQ9EQrYHnvrmUHitqSOMYVmznjw931idUWeoOjyaSHA6tZHHLZlV0HECcjaPVNDhD8FZo9vWI2aSLxJtPAn1EJgNs+fKh0uggLWcPZwx5BRCOIOfZEiadesPq7WnC89WxTGhVUuzArjEASrZLFgMzWY1ma3flCKeY7V9P/JPjR5NGPhPPFjGfJ6AJ6COV5YnKgaPHdOSZmLJiyDA5goQ3feX99lGymWRyf/xHb9TFCFecjXYdKxX7ZTN1iF20R366CEscVq4NmpCkFpI9i6t/g98zX/KXGriDGvCKIHUtPCNrIGuQLm7fIEkXUceVHRHwc7gxD2F2z0NWgceP4MfLCWf/+mWReRvzOfum5VMQ/YXpFyaSk2KfiWb3FfR5G8JTq0z3HSKLoHKcaIFtUPJcPeItxvUf1atW0M054S1zQk5SY3BdMaeSwCLtFDN1DFyVyRhdgE8qJCkIiGuD4U/DicYZfqwKYjUQK4M+a5uYzrSTs+ednD1v5Qh6+55tYTiCbuQc+ij8J60M0kcQvCpIziCtBpL/SGMa9+dxVgcpJs3WMOoxq4JkD44gjDKeuQD2yCjBXd+kGSOqR7czSHmPF8K59VCwkyBJdd/bESRrBIuLySp1oYp8gLIS/qcHFXHRI3Lk2hYnPOYiIzlH4/ndmGFGTIBWGWzxeD3zKa2O1JbudgSxLYwt2Wy7vtbBz6z28YqgNy/1lTB9LUyrgV7xOXkdEv1Kq4L4WEM+6KCXNTqrz44gadTQ7PLgUnvkr5mRZ7ydNjuvwXrYTR0Mz0AKAI4XNmtwPRoAfOJmapFZ0q3DbZxUftjC/U/omFxqZugBtT7D8IVpXCFRLQoOZsqaqUF6pwnrl+1YtbVsWwbfl6VQ8DUPBqccuu4JQS4hdAAiYdW53pXI5gkT3VK3k38RuktG8g64ZGcxTqWtZVpYTpKjWSyYxUz3WaCsSxfPhw1QXYuQarqsCFoq756TD9IRlPaSBkOaQWVMoioPfA17GnB3DUPfJTy8Gljbzj+17i+1KbpB94TR+pd4114VdtqWYbONubm1igITNSvD9VTfqY4Rmqmcv/NI1mHFA0CipyQjHRh00xZlDvKBfu9qm4s7A4ZkI14FsQNFs+A4e+Jw4StfXp2jGGfRtz9ZvVODuc2KzXwm64nP3MGBwuQ0jpRMLDNZTQ0wCUYdjp04lZhse6UPjh8cQMr7S2TlGEIf28K8Skn6mURmBZDs1lzS8ii4EkmnPyOd4AIKrxyTPSaGmI3DCkeQnDt85v2RJp5PWPWjdFYEKe9yiE4w02mC5c/Mw6t/wGhrnEPAFL3E1p20CpsAPA2kbFEZbJ6M9Buychy53cKNnZLLP/1dULyKLajsR6ZCgZIhL0DD9vEguiQuNXDHNYAj6EaNnua8Btqo22kljK+GW9FKPtJFPvJrgv7FzxJ/1fth99OWdB484/NgIanwqxBbRa/V+G0vD4t1VEfjZ1XAGW+ELZnIazpKvymLMuQPg3QeWQrt4KG8sgm6PSx0KcdqO3DTi9d1J87oqXp0ZpEowerCHKyjFHUNtd6UDMIiVgpc59Y6WKEWvF74a6iFwt8ClF7AKwdUzgc/qQZvgyCrNGUCT8xZdoxHPvxZHp0bfUHsRlu/PnpVEF8QY4sYTqCsCGKV0AevCJIjyKuC5ESCh5coHB7dZwbVNrGcD1QOIfRqBRKtxxuYyLsEehqcxsDgeIJsrgcyjUJu38IyVhkD51K4TlpQBkzKSAuB0ikBENMvI8jANreLxR7LNS9szBUiASkOlgEV3OCICc00B2w7hsqRRMmgyQHRjKeqHY3HbPF6rtW3fIgBRw/nA9kJJGfQm9dyAOlcID4b/0rbxF7wsYZyGj3ji2G1oklDacqGI0g6ois2VdUx3Fb5217hlyQ1hWOoawxUly4w5AIDGpwTguZf0xfn6gjCCLFBFyniLFhkMM1Bv/Cqr9ZtHDCYFcKPhKYg5kcINqlpS/OaZFC1vKZL3DT7eNUWzj1F8rFEFP6vXM15AJw2o+DX0hRjSkf9VCCF3i731oZZ7tDAFEpSBNctUndG9Bllocp132cCPYKhI1b1ddobSTAGt7W3sAVcm8FKt5oKvH+uWyFNCxE/CXmheS5t/BLuvwYepCOIjnztzPeNmvxRWOnWdNM2rOOGrzE4giceSq92fI+ueVe6S/ph1EDuozpbOjo6ooMwcdDdfu9bxhFdw9Ldn9eFCekTj3WlJepKojrPmI7MOXA4F+ImcywmVCh0TP+bdMQpTdZvpJrGygAWXwT38m8BFUrZJr3CjDh7ackrgWEyquuOAmCjHUAyEqcLw6M/9W5HUDmDhpNIDhwE2gzN2HT/7Bx5ki9u2RGEM0WOITtSypkCAwO7Y+n72k4lKZcarwCyg0fC/Zl4TXriBGJFUtes7Ou/QLAbmbY/ZcAsO1YUU57UNIUTwM4Vm4sJXsmD3dQDDiHbqzyrmXAEMc9+rBVNSePowVEUx4+Kp1zBaMwKmrdSFVRdBl3pJB9sxxgSe9Ku0/96UmziyHedYoCY4UAGcQsjD7lxio1XxvqhUzBNks1WuUt0qYG7r4FbHUE0VoVuv267unTbD/b0qifkMPSYUU9NCZa0fmDUZ7SuCEAjvUkMab0dQ5Pehj6s6dLnjH5UNOGGOgGcOSVopQsl0knpH3SyCQGxITgTFBVywHbZWt5eJ+TBmdHlDE9smXjqYMo70VgVZLPKvEjcXcuAUS8wLCFius66FBDAcRpS/l09UzF/MZgjVbblLKWrmYD8MxDdGo/Uj7M1+UaDE84gzq7jM/KfbnD0yBHEF8S0JeztRxxBH355/05nCAnP1jG+Nvbxo7aUaSUr5wyxIqjPC/rG18Ts3af1siqI7WHol8XC5U99HESC2QEmOHivIhJQ9A6Mq/yjoIwVCr4a7qxlbKsOqQQG2dR/6kF5V1jJM41oBWM8J0BnGyuT8bXltW7xixy7CG7XThWdhZDm2ZE0K0+eWr/yp+LlxFGhtFNbB0TLCaSPMeDkweHDAdFZDaQvh70SXDC+FMbKIA6IfqZtZKweImalrv67frwtDF02K3qpN+ykFqAjpK0mkXTZLZBqyNdutcViKOVJyJwjcgukKLTUIzWonMkVo2Qs+QhVYDCFDn0tvfV0vfq5DtlUVin0WJe1YgPyp6SVYdKB/zl00+rb5GFR27TSkZ6hMd0rTExS2I+cjrf4KSupugci6hmi4cOMqg3ap+5B97dbmdyekvYdOlbMO6xVu4DArSjyoNsc8nu8HxsQIiQN3vS+AKtaxTbh2BpWKuG6hHusgQfvCDpq9N3Y/416O9K319P69w9jw/f0l/z918D+Xu0t8vg2eq1/v3ua+tLBtj3VZzrrrh9TRg9bHWsTV9ztLmRIaPtbWsX1tiN1EZgpN44ghK78pBnQEoOd+KZbYSb465c2MUNHFVkDB4OHfmwJY5L3RQcb2BmkiSyrd3g7+ifOG9EwgPr5ta04UTTx0ps7toKxesZf5PLWKtWsvSYZiFw2iiIbWh8rfXJ2Qpw8dv6gQ/Bvmoh721fZJibfIxeBJKXXDM0xYknw5o5Zm+raeSVNJL3c58yZY5d3rjFxxUb9cPTwj2JlEpnJpJ1Baki8JeJfYgt2Pfj8aeRLP8VrgywjZAbSFqFxu5BxmOkJNQZLP7KRkDpTW7CwISAJZJAibnxVwMgHHbpKw3IJlxq4rxrow6J7k2jbQRPet+fOQ9NNvOk7HnwNWGI/X84vD8bsDIxZnxW00PMSF8eQ1vobPvOCNLCoG7dRsNMLDrpmpR/1c190sWsjyeK4qGebjMofUx1DI6S1JjclBL5i3b/bplvkFQq+/FHfcjvOODHrlhFglecSNfHAWd6AUuSVZ0HckjzkWAq43hbAdMG2RjeAPM6ZL6zmqVVBnGV3o31e3iImZ9BbzgXS18NYEfT+I6uDyhH0IbEdQVoR9OmGDyHgDMKxhExkZyVQO4KsX519YnCy3s6iLnu1S39trEtGTG1mvIKl/9zrOs74IQT/Ia+AtGyndqE9HjUeG0yvKFuh6h7VYES9MXL1nYtl0igB+aELZTOPyDkYi5fKVYDX/Kp8xkpepjz1Cl22hWk7mBxBL3T2zws5el5e88l4HQ79RodFsy3s5TN/Kezlqyvh2D6mLWGcJ2RHEKt8XQzJTeuZxe/W5NqmqLaky99xIYzlQnnzj1E7sp0QlND1EUxgwefa98Sx0W2RDHXoPJlO+06VhlChaT6b5JbQbJaApvAjb0EtDCSDm/bv0CP7c+l+VNpQ/7cSXWruOenUVtLMCjtfqL75auTNeaQ27VvS1k7kmNDQEzIJ39w3UU3rImjFn6RXgMjJ9i+J3E3so0isCLqtPNF4ud5FDTxoR9DasayV0Q0eWKePGv/Kv6ZXWS3jiL/pmrfjhq8xuEt4iDWgzmfXQfW96g4Jq3P/t/eQjrkHopVnX8q1XazpUzr0NJRE9A2ttCETdKyskgMPa7Wz2B7MUdu1HdAjj7/yndwPJOmQ7ZAYXTYyNxorH3gkNc0swykPGv9CwMyVXHnXvey3k0dn8NjxoxhHEJ90zzaubOXCccFUaEwy9YYSR9CVzth5pK1V7QhiNRBpHBy9fB21KYnut/SlPqVXAnkD6xVBEgwcRxBVahqvCFryZT/lMI3zhScNsOp56BOEW5qfEpp5Oq3aeKQzgsbqICaOQiDbdivNH6+Gwc8/kE5VXkoA1e0HafUUHzNML5vMBgCjdGE+LKtJKY9cSwlDXq0KlxCdnQkJxYzMxBsaSyzeYrtElxq4rxr43oogt+NqzGs77vZ9ZHf/CbXH0Wcgw93AeIK2VFMHGvopg2ZqnKnm5VmtMBJ5Fg3leZRgo+p5X8gs2jIBlgGbPqNEpx+ozIjSv42sEqZLIZMWLH3qtky2aaELDZK6hG3llg+KhCpToWfdBuuxM4Ue5aLyRznwshDEn561Mu73jPGlrQi2bUu84ibHcao5D7EliOrocpAu34RZIKFMOIJ8bh3OIHlwvNVLZwV9+qwzgVgF9F6HRuv3jlVB2hrGlrD3cgS904ohvjT2Xvk4j1gZJKcSH0jQl8RyQHQ7g65UC9RvtxtWBdGyZYAH2bTyjB7tJMJMSrn+PKSlUMaAKxGiw9HioBinnUcdCk5rdQyWvEK3YWe6zaf9hbN4SmbGxIxfvSKIe+97jb6SEz2R47Qav0uqF0zcC7ZYP9NBz2wzv9ac4rk+Bc9qHxxBr1+8tPPnDQdGcyaQfnESPVGsL3hec6h0HEj+YhjjuPTmpY3SFK1vOMkqa6/cxsRuryYD0PWhVJwGlH5uz0q5oEJ41R1sDmAbZnedc66TBmMhFeY8af18L+AlEEc6uPncDgFTA3j9C0c4+9qSrNslhy46SjokI6y4NT0IKrHi1vSP0HXdzJJgbcoA/23yUq5who5019lWYmwJvuunpadXqroYz0A4Wlq3iYwpVWc7Wjhuo8tdidw285zFReX7irYuzSbuDMRFVD2smw+2mkQxTeriCOpavf/4QTuCzlVPN+5z+H8CR/bs2M5LOqL7N+06b8kF8yM1gKNgdspwrL3WXkI61j30Z+a7Mx2aFnOWpDtyd/YoL+IVP2wSbh0HWn7wk4MOeMirOujDCoesTQJJDIQE0oSO1/QKM9EPX7CbCRG2jaA8w4YHDxG00+ervuR16ghiOsSPfwjJj7NyHuMIqrOC+IoWq4NwrrC9irhDyqdcV6L1a6KlZmMcef2YmNtRQnMyrbEtZsQRE1yTVW7QlGTfY0x2/+EZqKYRzouU2JFsFi5oIKH3hGOhcT2CXM1SGg7siKw1LkI3DNVhDFcZU5uIMnUbxASxQENF6W+4degy9JEW0nAT5UL+Ei41cJ81YEeQnuWlK7A5o60q0f2S22s12ooOTa8n5ASXZ2s8NRFcz9sJsQCt42iucUgvQ/v5bXzL6Dzxj8ijX3F/dIt9q8x9+kivaZbi73lmHu7uMYqhBGIXPVNCgJ2b/OeqdlL6nipLTJ25rENA6CY19+JUL9B/EjbcpayrG5tI9x9rbQtlZ/zRUXXuo+MI0tfD5BBie5i/HiYn0AdWBvElMa0K4vPyH95//uUdn5r/oO1hXh3EFjGcSHImKeZLYv6UvF8v4NihFdcn5a2c8leectfKIOqg3CaiT4nYOsa/zq91lOEjWH/QAaqqCNewdWU0dwWYuUrfhNIYSNWR5nbkXXf2rEQm99T/iP1QKiYtfcCZdHjFmJjRSEDGLNFX9ws4gvgiJ86ga80ncPS81MqfVy9YDSTnj35vOBxaK4OAv3iRs4M4QwhHkFjFy1c+VVNSTu0Seq2tMwXDPuaq2OCAvUlhWIXguFITro12agpmXtGWhGZa4omJ9NJhcCvpXqzz+9haNzqoV7fbRZOTzaoMSbS1BUEtBAtv6JpyQeySP5tuJ97ZtvD71hxxz1vXcpqK5zs1mXsAPq25eEph2kX3Bs19GjfdKWYLgW51Mhsr5Wv5SK/2rriW1jDHnWmkYk+TyQuXtkFCciX44ghaKuqekw/eEXTUsIHtwxHdnob8z6BbZezTRzovsPutATqgbRe3t0cdrNqUyQbqtI2BOqUbDCNhTl2mPPTvwgBBPTIbon3bSjmKZDHPL+haGc/GibgCGEc6+SmiJh4GTGg0Jb8dgpqmYyjXdDi/d7UVbZpiJFR2sHrw0CRnOoK0IkirgvjEe28XM84TIQ2p3Tdo5scXPh7x1a1yBD3i4GjBc75OpmEhz71H97BBGduiyh0DGPYBN0KUikepk00+jJY2kvDqFwEaIM2IAEM9MCINMDbFSQU+0C4W+bJ84BDb0zbSDlP0yJModYYxBSF4hVG1HyBd3m5SJooh4sAg/QiKIiEgQ7nwK4SzlW82WAmAL+FSA/dZAx+1qu+T2irbP9Yw2q2A3W5NosuOdGVz+uRZLIr03/3ETDbkb5+1qePWMWCKkI3ppwCRPhFYtI2jDFgyrWkIuvVvZovzOFr1NgWs+4DMlH+P2eZXeXs5w1YhUsRJMXAStxZ/Tbcmr7YQQ3TFAeB6WWoD2pZp20duSFFi6m/oj8T0wYzZg7sViTntQOMYNJU3rIgNV1qLgrxa9QvnBGll0I2/CPbF28H4ZDwrgjgziK+G+XwgOX+yKuiznEHCc3B0fUXsk74+xnlBrH71V8MY83AIaazMGUBRnjkA5weVo8iVS4nV4j0Jga4MNU7pKgdUBGNN1nSBVU4ULrWvVEL+SG5GJCRABZ4XFljrK5VD5bZo2cB9zQra0oNdwMUeMuo6baCNNU5y7PYSLdvBnumU52ccEC2HzisfBC0nkBxBr17jCHqmc4KuvTLopRxCL7QNjDOBnj9na5hWJesFFKuCNP3w1jAKhw73Ey6ITaJECgFQpk2IsQPkmhFJ6gfaLgMkyov+uN0OEU6kJmKPAfv7WLUUgRI6zEqdwxMbzJ0LZNyLDovtrQ9UwAuy6R0DbxlrekOkzG24lfav093OcTt2ag4dV0LHyaWElHKtl5Q6T5urgDZb9Ulb3tRtC0LGglvTC4mTjSsNE42amXMqtgVo3AoQuLPE6y0PR/ADJ4Kk9feGbH2hN5sU7RLuvwYevCNoraJuwMT70Djga/pn0O1lrPnWdWTTSndJ31cNqPOh91EXNO/V2mkZuRinLtkd6lE7Ajd51/QiIJ09TVSikV5JA0baOF0kZP3jfJWzpqftcyDwmG0itHQIvukt31Y0npi6SDmIuwf35MOAldbQpQxrfh2+EJTQZSa3WkZ+LT/5EVIVk6HsY5LGwOezedgWxhYxzVj5nDtf8fqGkwgazhBCG3wSaodPrQi6siNI0y5vB+uDlut9nOgpBbweGGs0s926NMx5yc29Sh1g+6O+CRSMvK+0OXGoLn3ugCu7kJYJEXiu1kDOgftm6fBYGPcqUmMNeP0rGKXYh2KLvBbcRKg1LHoBW9RCh+0ulmCAJyq6cBv6DSuIVq/YyY4RrDBkNx2wkkg5LuFSA/dZAz4jSH9DsSJobY3+I61hhejHcKU7sh38Ec2cwM8nCn7kurs4ErbAuk/veEFtku4bqv/pvn0lGPZJMTZFHhRt1/bJbH0dr7LWdOs9okNil/8I33JahvNdMY1UPCzEdudS0w1fSDcipqjZ701p3APkICXxWXmUQ/9cHtPuNX4nvxO8U+vb5XoSHf0sNo1b6VysRMxXvU34onEPZ9Bnzv3Rduls/dL2MD4XX1vCOCvoQzuC5AR6h5OIlUEcHi1n0CfxftbKIBxBuCc4NDq15M3HsoOnAVsoOyOO8Bxk57winEY9DkKn9CimE6kxKAlIcrDItDXXgzHQiolCO1SM2IJMeOYF5EMuCstUpJh76q+WFa+f8SjK/RZTO4G8Mqg0WIQdQXwxjO3lcur40Od8Cp4DoV+/fOWtYK+1FYyvhL1mJZC3hemAaJ0J9PxaziBWAymNI4lVQfwRzxlBqMEWzvA7CtQeZo5ncxDB4NqxDLPLTrcXjHYllFDyVR5SMxihbOiofbMKEoyuvpfkAkmsdIyKqCW9WGVczx2cyWQjyeVKMpoTV6lMsb9AB77jPb7za0n+ijxKSRm6tDN9Xu9qy5puW4gjr6UHMp+MwHMXJjWptt38kwCU29BoF+MZMWpz6T624w2yMn7G2zwr61axpd6ZMJB7uM1Z5JE3TcfK4bilnfIsPL+cETTq8r4T/1OOICorA/Z9V9ux/ods27HF/z9A1a12b+TiOvOdgtObVc84KPf5gThJrJRD28aG7uyhzJRrCOkJ1SpkIElE4pCr/EyDP2VkzD4NcEU/FWQSXTKYTuo2Z5Wb+lyFVlpRrMnQ6GuIp8CNfX1vSu9SkJ5MrI4gHwqtiS8HRHNodK8SCk3eiCECXgaaR5qAXfHpdb2K4zPsTAofyynEc+pVQZRWaavVBQldqmmK8MoA9wDsVFOlWPmjcYUVt6KkzG055P3VFbQJnM+/Nl3pB45oXzrORChgajb/yHdocvI9yWzbm8axgI0n3/fY/GX6qM1RBmyEQvWxKBptK6iisJbDS1irVgbzIekFeKmBf70GemsYTXFp1mnHBehm6uyO7shA9wcHCP/BZng9ZEojk1ypMvboco7uR3j38vb2bWV8z5KtNHohSrCVsaVxDoIfDLGgSzyZDkXo5hzCJ9uSCmXkT67ki6wyE7uwb5IQbjg32KOMZeqycq3pHir70GL3waLPV7omY/pmOYIkia3LnzUWftZeMbZ4feRT8nL8vNc2MDuC5BDCCZTtYTo8Wum3HCBtx9CnXz7hLGJFkJxBX/Qpefr4HBpNCVj9ExgOICx3bCcQ+cAy701JWEHkEqowY+sJZdA//s9ARjz8l1ck3FybKHHgIYU3LS1X8iiBMtSyth7kPLOSi8dFocClsvWIF0ZVYp/J0/rsqJETg698XWk1UL4Spm1hcvC8eqXPxb/Sp+J1QPRrnQ306qW2iskZ9ILPxsthdC3nz3U5gliVfKXVQHw2ni1mZY4NxpW2D60/JZJxBjQUe7uk4ezSN3TzNDTbXsmSbxLfAQuZkChf8wvjkoQt+sOBTRtRhY2kKa9TiygXt2UBh2bNr7RrepX1I/Qr75r+WXKQube9ZXcMTdtKfEjfBIWH52eE6Nu0FhuwqBtqjmCNbJzjyhA1HLrRrymRdFYE4Qiit7iE+6+BB+kIolr4I28fMuDsoad5/3HXre8UPSA/SjcYfiDxozb+gKgLyU+pge58mCTQKa1d1LGCtAtw3WV3vKWPvC3sKOcOfqfXQ2WbIgJr6DxCMpNRYgVO6YHWgGsw5dRwLkFM3kbvK4Bl65JUyWvjneUCl4lC36paVNmTYliiKDoWf8lrJ4ExpapFOTYd2oQsPDIjqeUVSgjeIPCVLpaxsOqHfoEzgtgW9k3nBfksG6VFJaYSJDGeXGr2dfVYjiA5hPJmEAeQpl+ygQkeWmOO0uLN2QYpFqbxc50ogb28CY2O1KTQoSizK4IpzEQR4rxl9gUa/RiKAUGABtIMjqlHE8vGIRlC4YvZOS7BD6jYJkekN8Qv6GzUYF5opzXGijUWKFdikpf0UtCmOZ7grfDiBRi2APblmkyX1KUG7qYGvCJIzbH+fB1KPUFd2ngluwF38x/0nYBu0Daw4jn2pK/tJx/0Ec8RbCfyb2VdNiuVhl1fsBUYCzIezof4nF17uo2sye7+bNaFyo7AwhOV1iQWPn8taslH/u6PGYDDwBNis2T1x0rXLLojqzGh9rUv9PxbqUNZkxzHYmq+lQN1vgWNhE4Az34Vk/a4xE0rL4JxYsTRwvj3RU6gL8rwOXlW+nBo9Ec7gr7IAaStYGwR83YxOYjefvrlLVvD+L3LQdIftDLok/ZIil16VT4NPnwxk1rprWF/egkoMAxhDM3KoPThvYJIKNeP8pRHlziDXKjAqAVwoEXLuJ72iGwFQAxSxIFUYuQEjQCuM1XEqhffQrKkE+kaygITyR7BMFAxPOGzVV65Qx5HzlPNI148eybnTr4UxsHQcQTJIaQvhb3k62E+PPrql+d8Hay+FOZtYTiBdN+e6M3LWBFk7apJqV5LBTj52GP7DGiqtAfoujR5JoCHOa2zsM0Gw5mwnclApLth9mY+EwM2nXR3skkRMyycqQ1aJd3mzfSPLkiMNbeL+R5dF+17dK3lx+igIuTedNmTa0mNrXawR4qs+SbHUQrG2ymHFZC1HqU7uUo9gjW+cY65SEae+VA0nEfaa/YNuDiCuv4eSvwgHUEZ/NxiTuqp/3i4ddKxcN01Xdu3mHBJ3lsNpA15kHQXd9ymjs2jh9SP2cAPsJlsR4cEgwZ8JDwJIQcNoePkADRk8jSuB7tgcu0JF7lA4K/BR7IsrUQGU9JCbK4NvDPGZ3pRHJabdAlcK6hBwIbsADvruNG5OaItGpBO1wTDBWNCqbzS3g7GljDS+qqHUvoiu3C1z98TI4l6ogkcTh+fD2RHkIDeGlZnBLl+VS9Rq1GqE8OUWdyyDUComrZiReNN3+Ry+dcyMxi6eAbqAnsDF76pzoS2sTWGzIxO9vSCzLrKx7Jbz57SdVploQJ6Fmk6mNb7Td3GbtAhDb6q0Ej+TtjaaGEnl9B0uX6E40TEBXCpgZ9WA+ccQbTM7htGjFYhbmu15jtjnf+ws9w8U022lzfyJPKoNCncR8AF//1k/vD+Pt1RSYdtzX7GnM2866QMzYyGMwJMstWmWpuMSyp/ui+AkRT9VoQwom4xG1wDRbKBt7AVuKbB7/PhscRFbEtqDrgGuhIZxjS2QcQYBwU4NUJWtZpM6e6T6cq/iokXI5+1UpatXmwT+6iDo0nj5OFwaLaKvee8IH1V7A85g9ge1l8X84ohrQr6KocSbplvGgt594JDiLpNTAevH18sYNUPH1sQ7SPO+cB5wEPigYtSxT6P2ZYjuTLYY4djl4ISKpBOK/BzxoX/+mVMrbwpkw4PvAlIsBShO0ai5TkWFOXgIcAGKMmwrVyxUKa3emWulGAOwQujpzoX6MVzrfixI+haTqBXv/znP6wIIv0iq4F0HhB4VgI9farDoXU20DPmIPqtjqCeJ1gfOuunyIEal1GVqygFmXbbejgB9VMRHkPdwJUPSQnZR0FOkrxegoq6o2rmPQ2U62GQEMhjARQztU1PbS3nFBKzVwlNe1u8ymneI9htMsA1T8toetfJplyN2cZ7OuTRuiI37Swc3Om+d6d6TY8R3HvfjOZK/E+us2yxashC1cgksc/v0Bv6NjNObMlCfAmY7VQw9VVUyAs5WNVzXMIDqIEH6QiiXjJwpBVtJhXCHTlb9jTIuGvYkV3YcQl3XQNr96W0/6+w79nTHeQ+PumXLWjXVw9Y9Jau6iUTzSGghwh3mBKUiUDpbfUSYR2IYqYoOHIoUTpYnCKS6V9gELk9IpPJWwU0AEfPEE+PLV50AGtdxIGgK8OZa9EEXZ8Q2RKTrlC4bajosZfgT9RiJxY0EJydE1jVbxOjMySUTyuAPDvFCZTJmyCiV31Ytocfe0M8X9XlCUtPNfPic/FeEaTYW8Zkz6gbW6UylH2pmcoYx8UVkZxQo14gK9RCMWAtswdEytIsLvv4i4T6QG6kQBf7ck9b39SBleRyTzCs5ZKeQVAQBMpM3EYoto7GgyOUDfDFbgi4LwCAlY2GwqBQhjVrgKfXIrOKSD2luUAuNXCXNfBJ3QY/2m63T/Sv+TUN0Up3ZOvsbbfYbvM4rdd+hucw/uB+nvNsjv6xxPTzWr2FoOfpmrf1dIwo7D8tA5BhIWRLmNSUDaq/FMSA7d2HNG/KA7IgVl8Z91OLTaogasdlhpybgsTiIQbkPPiy0mTkhLANFU/C6A+vhU17LKcv6FvD7XkklQlbeUZsQYNOOOykj/X2MKU9rkuUtWksYyxnpOty+dBo6HAE6Xej7WE+80efhM8ZQFoRxOogOYP4lDyfl3/3Vj9tDyP//p22i2llECuHODCa8RlHEC9a/sQhJF0MzR6X7fiJAwiLHj3ii5wZWzXwlpWynTJKDlHGasZp5PETkHHbNKSRrYwKRJkYnymst26TdEGDMwIGwVMhZBBUbYssOF1M4jR5QnT6ubDuzB38AknGNL1t0AskvTvSaiBtLdcfqxwU/VyOILZ/cTD0f9gWJkfQf14r1q+/EvaCT8VrRdCVtoaJxV8Z04Ig+cw4ayhlk8hqeugUEN3QlA2xtYpBBpwIbDfkzH2aSLGL4rjkLDg6FbGIpjnIJcwUKsgFkiv30qYZDt4SQLYoCAiCAQLlW1vpVatAxp+yRnLzJ17uJYwKq9pOdxyKXFcYaULD9rqDnfimW3lW2Cl9sKvcWZpV7lZK6jrSuC/NHzj5SAmFeP2gtPa0IbeFCXL7mTD0SX+3mYWuk60z+dA7jTolgKw0azo8oSHd99w0uhCv6aZv+4j50eZf+OMtTXGJ77MGHqQjqBvLuYrJ4HAOO+G3PQyTavvQ3Maz4tb0Xtaav6TvugbcDZXSme6O6MesWTpHd4twrbCtFDq1XX+9JehcdYL0lB4C2rwS3YMEbctBUYYFCEOcoSNXaCgXnTFxvqJVsrFXcvyPGJlLHl6G3EejJ4fSJJrcBTtfnZI/HxZr4jwQKbII+QMnaeqIgc4+H9IBAFTa2j3x7Ukkk0ZoLJ+YiaRWAflMIEOpk+Ctj4mPbKeomp/qbZ4mqZqB8cbSW8GE5BDpURdhSrmt36bE2PVKvWHrEpK1AEPRaUFbMuOaNeVYhCwTtS1byV0jpXOHJn/ni2wiKjVktgHABeyyuB4GUSZgqyzuVRgUS0bySx0txC5/6XVUrCuItFmKb2Hfk13ylxq4kxroM4JomPnzJ2ppz90+u207X8DGHRkJ7gjPY4gs93s8TU5vaQdfI5EFULz9qDaN8zu69VE/sg1Yy3P6HFEjo8RUrfc2lkNcG75Dpv85g+warErrcqW44iljJrd6Q8GaLqrATopWn/IHjhinGKgcSvAuH7rCrXQn6UhZ7VjTltAqQhoDpD50Gc/d3wrgMRB6/agvtjx7yHYh8ocYzhYcRp81PuII+iwnED8cQjiBPn3OqiC2i71990E/rQyS8+dtrQp6pzznCd3oAww6bkjzCNnANjHZxHaxdgbxhGiNjAzJIGtHEIbVuXtsGYOCcQJej+MqAPKwFBtBdPsPEZVgYpEzNlNOjdOspnGmyo0e/YeAiFBDdjJ9n4vMeGOQTd3KgeKKiy04ubSpzrZgDyt1+FG/T/UHajuB2BbGp99fsiJITqCXz7Mt7Nf/vJAjSAdGv3nhg6Ff8Kn4Z6wEiiNILJKjNFWlpbo4giiOy4StNiu2rVvE+qwiG90X8SVQd50GyJica4OBmtyJhjbPccz8iBDeIaF0zfwht9CrlpmeqbJI7IFxB7EcyfvQuMChWOXsqTt/TAe0Ma35x+S13L8Wb23f8mIHIXHKf5tNTZebIEoaD1HEbK5GRZjIJHs2kg0dmSIreeQWiVFxnucEM+VZri7E4zFTuuGkkqYN87zpsGg9HOuYeyD+ArqjGnjwjqCjhu0JxL9UQUf69qpa//6Ba/ie/pK/6xpwlyOlHW+TGzim0ReK1BMLs3Tn2HETEf/NgHwrcWK1TAJ7UCx9ipLS0KKETfJVEirPPBV54Pj1KhkvmlHe/AwKCFDOkxzFtFH+tVSYs6UoUFCwuC0zoEDqkAQamdc554sgGICgnjxbJ1kQzRcS3oVDCspoSAxgAOPnuWJA5Qgi86cK5m1fbAbLLBMozAqkNLho8kU69mvy5RVBcfzkDWOvCOqJbGxj8iq2CsA6Q30FvH3WCwhlkTI3dmjWyhI1DXJ9p6vgQ3brg7f1VaJpum9pzYk7tyhbkrYZAdMATJi3adCKxmQgBRxlINMBXPIWt6IgWfOdHnJaxo6s6Sb6krrUwJ3WwOoIojl2k6Spd5oE6X4WMXDgyByE7g5WlB+H8SzmeWqnbNOdyJVSw8ZDN3u95nFcdOmn0HQiqcgC3+vdyCLThR169xKRQ197+x8elvudfsA0R5f1L32p61Lta6BHzxYxTd8rTj41kHGCUqWfbFphTdDaZrm3eiKldU6q/CHUQ+G43ZCXCsbiKT1wo+ovKNJ56aGSKuNqEI/HAApnfl0UvK5FRDhZOCfoRuMkX9T8fCPHkLZ68Yn4j1+yTezTR60IYgXQexxBxJ91XlCcQh/kFMKJ9MX8kqFtZnyUgVW4bDlj3M7LmjiCHnFgtF60MHbiEGLwzZ91tHwbqJEa42Xb4vzxihaDKVvwro2w+AUOjonHGrspr4vrWARkkF5wZ3SRlHFFdYd+FhzLBkphh5D0JobNkwyL5sXRE/1xakeQPDmsBHqqj01cy8nz8uVzHwb9Wp+Mf/NaZwO9eanfKx8WzXawF1oF9Ky/EqYJFV8L89Yy2cpLKe75YzWKPM3ElH1Y6j+IKZ7Nb/hSlmBo+dgOH0gSlCvB5LpYBxkDClm0nVvj6Qia1hk/9GwErayW2nra7EnQEPhnOrnt0zR5OtW2NF/D9/Ep3Wptp1sb9WeYxFZz2tVMrDvVEsj3rNnzdT52xNauC+4bEGwiTFsr3cqM0EU3vmnMsLukPNWf7XCdRaT7EcV5PhaJqGjCJT6CNbpxjnUh9s+Akud0ldLPnsohXZetYV2L9x8/aEcQDTaNdVtR3ZCBdvocXcPPyWoZTbfVlFzzdnyO5gh+gd1HDVQvdNKtCQ5q6fti3QpckR6uzTPa2S3FYYB2Z4y4kxAdXvItHO1taioGmEM2uQWCzhQk+CmHrg5ePi4A/hG624nSDMT02JVBKPFSLoSIgYkr6m1/Cx4xiDhoIr2EtjYr1LDW4IrRmGcmSjKRjD6I/YawjM0bOpmivF5wpnw1w8HJBSC6WREkIvMJIsP555eUAGsGzqSLLWHZGoYjjIml8tQDMzPXhwzlv2baMTl5iwkgdox6MLkvUc+1CBeakQQlEttrfZU2myUUaUoG7bgBlBEemxQdaYPNF+GjFRlsAeaBfYbAybdzLi2gDBR8yLERpuSiMHlJT7pgLSHmbUwPdrmWKqKhu/gWqkvyUgN3WgPfcwS5iVY75VkkfK/Zgj+iqUc6/cHJk2TReYb0yPlZr4d4PspIBRn5kcfTJJhpt1rb3qAKpwx/qm9Cs6Fow9RUIcjYgSr1AkXnvq3JKgbX8O5ZOw9Jq7MuGwdQ0E5bTlHZJgMoZYV9qu3biSiOiMASaiq2O73qK3UpVvq5ApUUVDckqdTkYqAoyBmiC7HDSKSYE1FyEMtYZ54QU1/+MfQN22Q1abwKFWBjHOUnn42cNjiAlMYZpJhVQTdaEfTpsxxCnBMkJxDnA/krYloV9IcdQnxOPs6iL3IG3XD4tFYU8Vl6O5VwBGGblKfMOINwcNQ5QY+eyiQcQ9RxcDBQksxNxIsAQbIyJ/aSDkwRRdKPtoNziRVB4wMPgvWLFrc78iJ3Q0asQ+5u56xd8nGUxfm0dQL1izMmG4hgnsAKHhw/V3ICXemgn2f6PdfBzxwS/erltVcDvZJD6LVWAf1HZwO9lkPotQ6LfvFUfJwJpBVBT5W+kiOIL4WxCpmVQI+Vl8m+bbbc7S6GMx5TFpeHOUsXYMAAGGs7U64migxqsZ9HUw+BTddxV1bHLbm1Rg9Qp0ymFMZPlhbmuNtmA7dklC33peOmI95p40753iPD5VyJTR8OcGXhCR0UjYvRrSUOM+ekgCJNC9AYurQGcDOstrfstm9Kh55cauCIpymIoeO+rSGyqC8klU1uK5Oq9fX9po/osqx9bHNs6SJ1S1cSK5o1kZKkNC0tMaQrnLTzunR6Nds4YQzTpW2+OIK29XqfuQftCDpXMd24z+H/CRzZ2wflWNoR3b9p17EVF+hxDaTrOe2umrrxnSceXewCTMc5cQvqILl2fi1tkkln/pdZosha5RLfyMmBSd3OGHC7fWE9Pwc61kp60qU07bfPcrFweCNsTDQEEp2vFkbKP12Qx2SMEOdSYMN+JTyvm5pLTfSaU5eWEcklo+RaVhnuesPmOpOg5odenu43oyLuO6GlQWi3bblICPvbrJR0Bkbo2wmE48eTS95a6vn28mw7hlLGtq8H+UW4kxlo91CVx4Y3XNwSF1jLDW5LB6wK3qxADNrC+353vJCP5Drh6HIMpBKxaULiJ6M+O1BT1JniAxuaasQWmNzG2inQyL1eA6VgJetP+w7Zl8SlBu64BvqwaNpl/zCB9uu2WnGn2zznO7OLwR3ht89Xnrl66IaEI748m5unbdCviZXuSA60hh8+nKukSg+6SDuVCeTArgPQKv1Uzort9CkVYodJpXeravJMOiwMVbCdrljAjQwTrZCW2XHb1/EKr1Gq2B3pkvseeuwirFwGeEiLpR6/ixleJweTdMCsH70pHHEEcWh0tnV5VY8yrOphi9gNvy9yBungaBxBH/RlMb4a9oEzguwY4vPynCP0RbQcNq1tYsRyCH0mlnNIouKrkvK4K2SAX6zIGYQTyGmdGcS4il2mk50qgOcQLggOJRmJ84XYtlMwpRiYJBKXhGXIiaL/SjOu5oWOsqaBzvVoQIDb+QhqJQ9VrEuys0c1Zb15kWQ7mGjYDj4uoa99XeH8iRPo+umVVvjwpbCnWgmk3/PrX1690M+fi9eqIDmE+Iz861fXvzzX6h8OiGYVEF8Ze8KZQCoIK4I4dBpb2R6GuYoUZIt0k9/8oAMtvONkiqqhFmAY86DOwQUTdT9jsAjZUFV+wnC9JUDLr65uaCNn+P5iKWExakolu801r9tH4RZWayYPF7+VrnmP4qaDd5XXtEfyvkfbvD8rbrs6njYd15LpIOpAI1Jbbf4G7+N1DNjjyLfISbdIRMUB0xGsyRrnWBdi/wwIVR6x9G2UgWfzsjWsa/BhxA/eETQb7KwwYPtwRLenIf8z6FYZ+/SRzgvsrmugu6NV79IzGbzPA6RdrW2LSc+aD/aIE+50eKRoZ7psCMkIqMjglXgQNhIJCiGvtDLIVGibIifyMrGqyZoo8q8FFOMU4BT8YB4z4POfDEDNC5iXtYmARhp4kSWVyQg1ZSYJQQ6Ds2Py+tf2oIKAPOtwRnZzJgFwzeg4sJKtYMDgi3JKXZrFaDk1eYSqHUBOK49uJmM8nzgcDh1BSIkgRZKOGOcxKijyaF3AzptADAMnQu5BymrsuPS9AWD5KFLINakCGd6X2CKJSvBv5Zg00Zv8amVTVLywZ1VQ44XQ/9iILtLgqo6LzOUUsu2cdCJAdoXQKaOExSgJ2vBmLphn+kpfwqUG7qsGVkdQ/zmELbRZ2jgJIoLzxPq5PQM8CMV2gunnKog8Hab1czXzK+N+frH2JZOO/m59NiVVytytR+wghW48xAN6kDihSw+0pez+YlGyJFfatRzAKfdhGHqPKehxbckoBgonrXMDV6il3+HOhbrqzHcy99Z1O0WVeSuA9FZfiIbEoIvKlLps1Au3SjT/QmAb4Cm7QCFnBDFTAq+EBaf81hH0i7d3fcURpLGU7V04hPjE/Cd9SYyvh33S7/+x9yZqkuPWkmbUknuV1P3+DzjTd3SlK6kq90yN/WbHCNDDoyp1l4zU144IEgdnBwiC8EOQfPNWXxTTKqA3ejSM/LVXBCkA9F7BIH9OngAQj5UJp8fL0JcvdeYF0ulc1ERBIG0cX1YDeRPsVpZzmR2oNq4IuS/qKR+Voh6pZY5tjg1dwUEg8eWmga7imGSbhJQlpZ/c8oJsV8GWtB85mAn8GHYrWhdzgx95BOzpE63sUfCHIJDy5woAPVMw6IU2VgLlHUFP7n7ipdF6ROwFQSHRnhII4jPxCgbxkujvFQjiJdEEgnD1B89TqNO47qiVW8jeM+7A6brJywWLYFqOOXBaCYiUEn2gyef3lH3ODWGxVEPz+DieiTuc3pulksoBKzZ6d9slha2lYZwMa+3bsbz4aimsi++q4UPtcgrorONgMmU/vxffgsqNR/Fq+Rba4l0QlNYoXPv+t+rIfDeW4EIj9lJv6695OgYdWYkAiuvh0nm3j7E7vHPZwknHVhPM7MyCL8sX5BM951pkLDfCcX3aSAX8x78Xt3cEXTbno5X/5QJB7rbqRA919LZk6c2LfygvX/Pf4ivt8oRE9pa+hRbo8NUcn3b40kdoHLv9+DEgg0l+KfF7ZQa/XZv5hUNn4VU6sDPeIzzSDJwGtZsc7m5VyGQQnIMoguiblNmZ14XoXP02lx+wQMcPIQIo+tN8SQAKBCunTtYVtXC4nBYiyi+CMJwHdt/5wNYIPcnuoG82TzKZp3nCqXposuS7irqLZ6bRnbuEEpJunvAi8YJF7rjFNr4zmcIPwRKIPyzTzt1KHhmD6PO1Mxn0C7YZ5FBM2uFgvMea/Rpcz/2jbaWfuuGT0xwPfCrKeJW9bN78wyvCsh+fqp/8sGEF13fhczU3N5d+fLAvWKqLJ1W04Uqw2AcBw2751BHi4o1e8Q2vy8BCwDat7IDjkrpBtxb4+i3wJYEghhn6fPt1vdy6fFHO08dPKBdyrmRcMsy41ZNp2E86XeBsydh7X2MxUuKxbfH5/MdrSGUjVyXQx78TlYpjg9jwG1/P/TMT6rbxCFtTHq8v2Y9y9OEE3tUZyC2fvIZwL0WqsuG3dFUcEuXsmJYyVXcV5yCcmkK0pRnGKQs5LWjc4S7AKPRKW/Eh77G6iiJx7Dety5iOxcFeHSPhSxU4+xY+Dl1f5kx8gdVAzAU+8QUwyREE4hLq9wcpsPOOL4jxiXkFe/hSWLZ3Dg691TuEoPFS6XdaNcRLpgkCvdUKIYJCXhUkXQSXqCP6M4hTkz4mxtlC2b1suhb9MpXRXn2efarsNvDOKIsuHMdLf2pXcNTbx8hY5K1N9Y0n1ZlcOJshAASgbewCow9dbLwbiMfAnj7T6h4Fg54+faYVQAoC6QXRzwkCEfTRi6KfC/eKl0aDFwzthVYMKW7kIJBXACkQ5NVA6lNMM/yIum3tfQ9fcGn54VbDHzzLv30zn1gpIAIxuUDJGxYtL5lesvBTt4PXkikLLfyipHXH9vCZfFKA1DnFdnDA0Vuepb+YcJQv9F1r5YPLfvlZ6tIGhO/RNG154cXiTn8td+p8zUfaBq7SYne3s3Tu0M5X2da1NV85kqvXLnwlXXsKHIOm6S8tXuYeU4Xs/LBj8c4XldEZPsEUTUi28wPvPhUuz17m9HKdpA+4anN6pkU5921X9XqpQBA8t/T4LfDNB4Ium8gdZz85LhkesdwT8RFduJk+WoAhanbMZEUAAEAASURBVIYpRqXL9OAIVEIvGQgWd6nk4fI1k3XHUriE2mE8Ljx1GWb+xbMuQq1RfBtWqciF6xO5B1oG5NwVc0Bl5kQekCVks3Yml0Mw3GlwLW0vdsKHt6st4m4tQwPW5ufgpUNC1jV6eNafxJ095+afeuz+ahbLiyk/fRBNRhz/AcfsVlNd9Fo3q3ukiq+BWSN44bjrZi89qR9YPuGLOJRzNzH1BM4Pp3jEvsneolN/JIuXeJG7Feb4XZBcNL2EMLd0ync9kckeHzKmpF1PQi7gY3hD28vxf8nsfAu75AZ3sGVitXPWqx13wJfmNnUHadrKbQvyIBxabsCtBb5qC7zR2Kj36ror7l3SXVM7zv8DvvDM+AscRfDXaJdjJ+MVfMcp94Cc0P+l9JA/X6501WZBV6T3ilwh76jf1HO19SpdI9XQK2fL5Wte/st8o18RLXe5kl9hNGHhfUQljLyPN/CMe/vxXxKbBYS4jpGrDZwJIjeMAgnyY8vXeeXAve5zzdQlNIEg8VL2aiGuo8LzeXjeGfROq3w+zMofvhbmTauFWClEAOjN2zd3r1kVpI33Cr0V7oM+J/b+AwGmWR0k/cwnPPfgespExel7+eors0pTBzs/5DSCj7CvzVyXIWkXNu2Hh4YAF83aD89SxyxHyfzwhrLvIaM5OpTrhDbsuUCCQD/oK2FPFQDi8a9nCgYR6Hmhx8Be6AthL8G9hMYXw5SL/lTwMz1C5k1fCWM1kF8MTRCIFUEywHyE/LvvP6s1Vov4iMlBPxqm3Dz4BK88xbtc81MLu28stVg1CX72CCpl1RQAW3SC/5I0KiLKHh8tiEekcrjgnelhXchFucBdL1Zr89gMLzg2+viXpspc8u96odUecGnFUS4O+n82oa96qrv579XJfBUeByr7n/UHubPKC40qnumxdA1XH0pzrl3rVbxtGjlnp85VB4LVP29fDWsrPn7+TQaCaBa/IPaifTxAMugzav5Ggq8X399g84D7JXy/peOSlkH8Ensrf/0WYCia4cgZ/WahTleCk3Orb3X6cL5snJivFmquRLrrzFWC2v0wAU8lZcEjmz58f0IYEfp4Bt7mnvTJgieAmvkx2WBJt+meDGJjjNAAgMoIqfgtOjjKRBTkOMPkIkM4OFLPrejJqaiJjuZ+bIhajQrkTPYSGJqJSdWMG6yGwUd/spaXXOInPus25ydHg2Ac/eiSDS+59p03dKI/tu0ps2jXgUmf3XF9EgjCP/hVWyavrqeyJkwhMz7SDoXLcpmnOacyQ7w2/lwbZ3Y+07E7qmhz6PFhHLo0bv+X7V1fWKuw+T0Fg7jQPyppzz2t82HHXldRDtrPbWRVuwYROAS3dGuBR2yB3woE+dxXN+XsuDYOgL+WHurW+1iBrM/xCwUP6bxg+6LirmuHD+Hj5DwwVwGC+B2/0MN2HhmCyPiDimMAOcBdCr51HRL7PYUgruAZ2+8ZDitKrpPAltK8Mlt5TC5KaTtBMMWShplRLccyvLkxkyra5bnmtg1rA0WM7+Br5TyGj77aER+/qeDHhcK6TWKcV+yA5xoKn3b52lf4uc7mnUG8SFovheZdQA4AEfTRo2JeLaRgEO8RIgCkYBGPjhEMeveOL4rpnUEKIvGYGZ+Zz6pd5XImVZS/wLJ994/vbdugkanLXk9gugJ1dp9wK8hXOc5cxppoQKfkLSHrOsba2Bey/OiNoHTrjFTB8wQBFJmT8F4ggkB8Iv4Zj4Q9e67HwRQAUrDnpR7/IhD0QkEgr/xRmdVCz0R/xsuhf9TjYzwSxnuAuClFzsfTpFtTE9kkEKTjyiYfu8VR1WLqx/FPMAgZwfYZHwWlUVuLqSmaJglIu6nNqKwq5hXQcLicLNwgDslRsDKoJPwx18EKJngzdDfqYDtigKX9hp2DRUBtFjcqjUfvJf1wqQKnvH7+NldEaikl12F01Wbs0xa/ry9tdp2v+lAfD6PxOnfqHNsSuMK06xuX/6nsnkpOwCI3cFda8o4rDM107QzjILCR4eq4B6orgui3t0BQ2udb2H+bgSD1ol7sLhvJJ4I6ER2pHeySZy9/bb4MzLsHN/hxWoABdyZY26A0w1Zccme69C7IkfTgnaH7KvOl8FHeB0IjGRwBpKZzlbhVQvLgooYJFgld3gSD60SPyRJ4TwAFGD/0T0ymNPGDx5M201nSndrYGV3BXas5nxywkQ33YfzEePcqcMEfiVAow6QK+X082vkz7ZrDMCFhUvM9zwGP/p4bnhzJT8qf+EIJASAml6qIA1cTGFKF7C+XYyZa6P7BX+PghY5MugbnXEcMX1Ix+TlH0BMxHN38IOwVxxFYST5RXatxnoLLi+sMIaO0+sgggj72vztWYUTHJmlZNLR2brOly0KHjXgeHbTt4ttYLsDwXSBVTB+tP6H3nLjPjcBV7EJO3ToZskBmvYvnBt1a4Cu3wP5oGF243djwINxNS9j8u4IydcQ2zoA5BRinem7OubpxRuecd4xR51Nw47wEOTvX2QX1If8uJVe5ErvR4sJ1Li3JSz/xZK40G9PuE5qw03xne8hK+eHdfaSMvWvaykdeGH7SVj5MBrcoB0H8C150K8pOF0jwpREQyg8f4RZyF9hgaUd9+UyJPQIeKIhu5QK84pfcW2jcN/kkonGWkT/0tcLKCeIQDOJrYLxAOsEgrfYh8OPHwPQo2Js3Wh2U1UCvCQ7pkTEeEfMLpEeWL4r1et37NZ/1sQfPL2wwvtnX8Z1rinvoNCPnFecCkRD9H63Lo16u0cjhvomURxZUeGgL9ICZpELUMj+Ya70QmS+oLBw3k/hK2I+K3jioo8e8shpIwR9WBREQ8kuiWQ3EY2KsFiIoJH7JsTEX+VGO76uBHGSinqpsPhdPYzgc577gA0yjqI64TP3JXX/t8he89qkQ7ILYDlzLVJSEnih0XjhE9pdXb7SN7MEUX8aQfcPVwZ6448v4dKlmFJRnkYGKvWY9jiyOw7EvAPAykstexYLJWRJceZDY7T3kYfA7Z3Wf83ix+Gpn54L6u3xVwYHkIEy6pq+0L82XNiQ2jZi6UEIZjks8bO7GAJPgMZ925e8cNHgIOQqc+88VgKXf39Ljt8A3GQjKxaRdSR3Roxp9aAazKe/NVx5w5ttkfosPGrLtsOW91Pd7fLvN6rjlj9kC9B8GngsfPKoN8oFBqFPqs+gDzBfqW9xlPY6DsG36Z0DKppmkAbJCwvtum/CaL6gK9E8mVlkxk1U+3PWbFTSeAEbGK2o0E/QkTflnBVc+aqbmdwco95026WHyRvJFXv1f0yMNyjKsTXMc5dpRFOjBGhopLDknkWPCIwbfCZOgPswlXO5Moc+BIE/GIuxPqVprKvuZieVMLr0yiMCQfOYLH5+1LGis5u6d9P3I51l1AeEzrdzRw3Ze0JhLayYD0d2JAXXqpJM7bXC63uxtQDuJeAygflNPZ2EwDq2mQWiKKYmPTfF3LNjHlB3u2LLjom6U2a9YYm+748elTHUh3/4Da/0Zt3K8YiT1FLzGvVWrMeN+t7cfyo+ppNgtIcfq8f2GGWPY0bb7nbYXkoNyS7cWeMQW+M+sCHJ/ls8+B674/lC39nks/pwLOXM8Eu2K5iT2OXycXNgS0/Cd5MWYcohLn4Q3+ftuwi8GZ6P4xDTCGUzEJx75do3TYxDmTGfUuG94H6fQY8O2vfFa+WahpHt8Gw8+b/oMth5iC6c8EqEj2GrbMaBrGEEKmMmM1c45aARIbQuBDj6UwcTK+gjYHmj7g67yFhiVEABDnvbNHZeMz9aRY4z/DpHIuK6OvoZTzqqfmR9IEdd2a9Kj0bo6j/9UjvlC3hvEO4T4Ghjbuw/v/aUwvhbGS6R5R9Cbdwr+vOal0lodJLwfD+OdQe/5CtmH3MDZ5heffSMHH2RGO9rbsHPKtIYSuVyhTX0lNqzyXJdhyLFSzRBiEw/1JAkcyEXD1g1BG3OVrP5lTqJjIb0EbPi8PfCPmjP8qEnKD5o0/KibVE94MbTeD/REq32ea0XQy+cvtApI7wryO4CeaGWQ8DwK9pQXSOuF0uL33AM7nvcQCOIl0djDB1peN8a0xyWF5lzmKOH5d0zo8Fo3u7JaSCJqix/oW9rcLhGMhgkuoqktkFwIZCiYXzvqq4yNHboOXkFmA7OQYoQ1FDymzVMUbvjAm49y0agRDO/R/qYP0wjTAy1bQVQJVRsphgcCvaIykVv7HQ92T/ZdCPTttQ4PmErHYupU3K4JeXjwBn+qrT6Gtzw7X+FwxJdyJ4+96Fz6Yi376jhwbtylpcfK50bQPn57GfQ1vmFXrZrGJhlI5YtWnuRDXnQh6MlHQhbc1hmsy7vQ4PVoJRwm+Xz8dL1DzQ14nBb4lwgE7U1DB/rS3uOBcDuRdj07vPPt8M4DvNN2eOcDf0vfQgswAmkj45CQOx1AisfhKiOsB3JkyK7hSo6BQ/MARxnZqlfeLrkP3ot3XIVPI63mC36BMit8CPLoRpxy7sYJliIeoWRyR5kXQn6EZr6srvnIp1/LK2EmaQRajvYYw56IEcDRnydQ9GNNbJhYUXeKLLHmS7Fe4TOTDgd+BBOU+VEzIl68yGogJllMuHxHjomYcNaLNutCcSajn7QEnaXp3KXkHQR5weX4SGOJ9aknbdIvO091Z+4H6X6q5/M9sdMkzI+LWW8mMn4Gn0rKWH6Y5dKd89O1dF1T/WkEqqqEfxh1NuX9WEEllaPTiWtjwqG5Bz2iX7yvL7anwjU/ljI8pv6Z+KT6D8vs/i64tZbn+k9TLB3U+ajTMjyMC4HfpypbqGdW/JzOtYRu0K0FvnILfGkgiBPB58KFf2ccnbzjzQWjiqFyXgDNeSp413HA2wmUc1PjoTihz9md8/CCzwEN8ax0aByUbIPCBadLerD4iN3LdMZsig59kdj5IO3lQ+fm+5LaFY2UzTyoRaJDE1/HHItYafWRB15m0/Ydv2Ev9yWcYwB2TzoSMlT3kAUGWT3+3Q9KCFYIrZ9PIPZ2qcTg3PbW5oAKah0EkiJ+bGXLDy8kdVn3dZ66EPz5jDziyvNxhPDw681f6GQewE0XXXPf6UXQBHc+sOpHj4rxzqA3/nKYXiKtgBCfmX9LWe8XMh/vF1JA6KNe6MdNHOYbfGr+k+YXXoWsMvMSAlL46f4u/1YN7Zbc05/88+ph3NX8wv6r0SIT2cixP2lw/eBzs4okcQVU9OOSoIwKrCL+TnMS5iXfM2/Ql8F4FIwgEJ+JJwj0o3K/KFqPhr149iKPhmn1D4Eg3gnkF0YTFOJxMAWCeIyMoA/N6xU/zH2AmStpc5PT7N6yEkitYN/zXqDAdAh6iesvauZU3IyTrCskpFM0id0JaSrsc1Mko2E52rKw2ewnGBKr0x5OVmK/rUuM5cafUM/SdfPgs0xL8C7Y/k6RtrMrypfeELG0SZ10oPF6Wlqw2RLQgiu5tBfzcF5pOH5fLr5f8u06oufMB/1SpkcA0tCm0S61odEHefhoZ587JlzfWeN+EMom5fc9KTF56eR1DQrnLf/un2E90U2bwZBrFH760bCrFRoFt+yrtcA3Hwi67NjuN3Ti/6F0ae+aGXhIlydc8ddkbriv2QIeps4GQTkdwHYVKm6Oq/l+r4+Vjuwmp2Ix5IUBgLNT5nKoxkOTHmDToGtUZULFah7el5Nn8/N8Pyt+PmjC9YmXOOrunid1mogxIWM1kYMpBIfEZ3nREmDRJI0Jm4wwUWsfpu/6j2APQRvK2o4ADmUFgbhz5btsE9hhgvUjgR7Nhn7gbpkmSj8QtNGki0AQq3eYfBEYqq5OmLDN10j8hRJNOJloftRy9U98oUR+y5RbFl+ePmPixrP8nbjxlQ/ZkG5e1ohuuWIZ35XzRYcaqSFRRMafdaqOAFBp8CPN1AGSkjkGPtgMHKUwwmt9qz1LCKf2IwJf2nwUl1CBi7x6L9BfXFz2HhapjfaFg1M+714uvoNjAWUUZprCdZ5qT/3XJM2CzHpv6dYCj9gCeyBIw8eR2ofJ20uLK1PxLaeTM8osmUXrKdAzQueCGDnnIhHOezqFMG4GqvB62n0aOeInCneL97RJGQzw7Yy57pxkxXJFenCLkjpgU/pG5aLuvuwwHLW/wztP4WqrcpXt+wV+PDOXSFBTxdohL4zutL+hrS3OHJRyzSDf2Jam6RTQ4pG+TrQrmV9G0C1vRgIcWI7MtH4Qg0uBilSfrtdC5rFwyaugq7j1YC/X8/DYV4+t8l0XRd/AQaHU5ZovX7Y5hR8X0w0Ygjw8EsbjYm/f8g4hBYFUfvf+ba7Rwvtl07o+f9TGC6c/OZCkIBBBJUWY/MgYj3rr+s1q5PiF51PnvW1cPdo38w3mBZC9GloNxN+5bS6FUyc/gqd+TcDLQSDVmRtFnncwP/lBN440d3DQR7BXFc9chc/EP5svhvGpeB4Pe6ncq4A0v3j2vC+FZp4hXaxErt9qY4H2kW7A/IO5ShIviVa9qXqOnI4VoTzNa1wN6jayghIImtz06qHfDKfwaQG1ilHBm3PKmKc97YZ28J/X2MQuXrlDBFj7ie5Yp7DIW3WjPugf7vhSn4bR/TsMHL8m64CFaJ3wnQPZTzMt3rFoO7uO6jrnOQZnaUp7z0kZH75E31Yj64m93UIw1/excp92HY/313yC+2i+NpLa1vj7yo1Z87Pf8XX09Rgc6qT8UvLS653ecQ51gTmuSwdwE6C/jisLCQTdVgS1bb6F/JsOBNGx7/04Uau1w9OAhR/iK/4hXdVRPsqXqbLNL+mU68c12g33GC3goWeNSnZhRqZjNN1GqsPF/QJyMIpa+CEZ4SHNoHjmVmloTOA88A97zfpir/7uAVU0Jmw8K++VPjw6pUkVy7g/sTyb5/sVzHk/S7vfa0Lml0AKz2NgCRoRBGI1EMGVmagZniAQOE1KGJyZrOE4ffgIAMllAj5+nl55VvooZ2JJmQmRcgI8Dvwo4JNl1txlY/m0cp6/ZwJGIMgBm8h7ZRCTPtl+7zuOb+9ev37n5elMQlkhRB2Yy7Jh54kmZQR+XjA5Y8LGixt1546JGRO0J2IUm/m5MJGau/FVRS664MRpeiZYBr1DDLpbw3yCp20Wly5koxhaYa6AyJ2S+IyDDz2UyZ2wRmo5peqzblBjK2DlU4+lK7Ldo+Mh2q7nt/jsh44P3u0+qVQzzltinlhXW0Volm/uqmpXxgqfNN4Ktxb4ei2wB4Loju2SdFGXi4C2wfXwjEpvz+hSjpUf54JPEJ8MBzF6GE8YGDOu9ByGxkhyyNcR9FhQO2RSOHTG4dAOkm2HxR5YhZVs57psmU94zdoPvbKbVQXgY3qpkyIrBI++FoApTjml7KmH0doZvsID51Xa6I3Xo0/ZPRVF0H6BUUdKHUsPjvqeXMVFs3AEECofSoYohUUD0GJHYmAEJyW53nS1y8Ehbo53ywCjDSS6HUxQcAUWkbg2ou9jFOv6rXnDXMctiQz9SHlW+gKiCz+yZX6ha7/mFZ80j+Bmkb8k5vcGseJH8wweEyP44xdHs2JIL432aiDdvCEQxKZ5BHJ+zEzzD883NM/gZhOrkXvzyfWf60nqJ9/wj+oqce3OdUa+u62YKKUdsoe+tZMFUyc3t0ACP35pM4EgbkSxeQ5C8GfmJBMAYv7wlDmK8U/zaNgTXhStuYUeBWOu8Uwrjp/7kTA+L888JyuPf5hgxrhu8w0CuZkh8LWLOY7/YAmW60KdOHZTl6kfMr7BJoC5SfX6gKkEW9qAl7ajygqkX5wTjHJDDpymGS2wSNpdSBIkaYyNMMY3G1iWra/+wY1JyZrNWtauMaL71NhBxsf68LtacvypNcGyYFMqx7KC/fjHebzgxRHqKtvuFHvuL+pD+i4t15/dO2p6yYfmehD/rvOU75p8vMPiUcc5BqEsCzlX5IfoO1y+5jtth6G3RuYd18GtWphydQdfa4CLhgWsfpbz2NqI/OifMQqAIl3v9o6gq037KMhvOhB0rUXcST2AXaP+13GcLB5of0fVNb6eaL8jeiN/lRaYIYmxh05D7lRg8o56B3VH7PAwXMnQZG3arcH/LNvxfA2a4h2k5XU1ZaLIJQQ8Y6Yf6VIQiMAPky3uwr3TnTs+68qEzJMxXvDI3Twv1eYu3vvIaYJHIIivg7Bcm7t1TNqYmB1365g8MmmTPWz6bpb6f5ZVE/AhuJJgkFfzMMFS+bjT5iCQAkBapcOEi8nT0x/1eVWCM9xp02SLQFCWYGviJXmvEJKO7/T+Ao7L29dv7n7V9vqX15p4EuTShFPvLOBOY1cQIZe7dbpT9+JHPbevpdt6PKwBIQeD0K0VSwwN1CMX0hys/UgA59IeaA7BcVSRbwqYC22OFRiO1vV0OSYcnAaOkoSrp/noi8FDeSc7B+IqcKHjgufSpwvyUbzKZ5fP+sN3iC3g0neVadui08707i2pf93SrQUeswUIBL1TPyV4Q29sjzQ8iANXYBy+KB7VQNdDqWM+dMYayksP8CpVh8ek4eOUZNttUCZxbq7h6b6egyi+3J21lHZo2PlTzrnOysxFu+YfWnyyGzhrGtSWLV2HPxv1t0HJIu7BpHpaeySLG9Ck0skD73uCC1Yn5PEzxja4hiSRPzTu8fDPSmod2nb3Ty9QJoEz3na8G7Ec/2Vt00e7W581+EcWP6b0gDd3iZyjk7lCAzu8HNoyVpNVuA4GEWjRn1idCC5xo8mPcjEnkAIe8/qgr4N9+MzqIG08KqaN+QarhfiKGAEiHiEjQOTAkYI9eYk0123J6tqd1cmsFuLmFTd1VHdtrG52v5sqxsW0mWHtyO0jvIZUUp12usvgXFcJ6NoPyGPiBGmyMpkgUN798yMrfjwXIbijOcq8E6jBoSfiY1UQXw57+pSXQotPcxgePX/qFcjSqS+CMb/oyp20olybBvXjYfhvRznWzD9FJPemzMEh0NCQ1bFHRn+5AYe86mKdVpR+k9oLnkAQsvrjWDsmBR3YIsEFBG9T2sXR3pAC7W5asjUehQVM/XafTBy9QxaKutC/imm+VCECD372HInP8Oz8US5O66O0U+FOxcDCFXqkgMtdCvzhXLTgfntfjXBV529L7H59iUzreKnVljeTHVd2jy5lKJfvGm3HwdcbwAdeyjeTRmNvx+0w406TQcaiDRFyNBhmHECb/rH/Qr8fblPANtjj5v8ygSA6jhOjV+ErbdcTofkVFqN+j165L+WDH95b+lZaYIYkj0CbTx6XQNKPNvwBrslSUBY4qDsQ1dvFT4jgyiVZ/ftOoGlhgCcTw+FnANWE0feQRGRyRhCIvEEg36HTpMpf9tAduvc8r6/J2BtWCDFZI1Ck4Infs0MQiImdg0HKWf2jQdjLtycQ5DKTLU1OuGOFj/Rfgj/8zMiEpxNJLlfcDaOcQND3vjvGahzeB6TJle+cMeHK3TWv2CmOiZc3AkbaZEdq/Gvmjb5QQhDotT5Z+053HD9oGTr1wifssbGa6KXu0vkFjgoCvXzJ8/x83lVBJ03wCATxDiHz0+SatVCL7JSrfVMDkKChznFet7OMZQeVY2QdpyMqSelmCHooHeOF6pjJjjjNfxbqWBHS0Gyw1uNn9TX/XbsPMQz+9/ScxHFrubOR0jIHCeAobGyDBtO28BDZprjNAs6NdSt99Rb4kkAQTrmLs9vSRfGgmPconYGeB8HmRKienhuMT/3BUhqDSMYtJHsCnXXHy8Gh7B7bxdh18MAofoxdDm7isQ/m3X3YbQePz3m/yT3Dixk9JLN8AR+MB9vIHgiVQeHzURdr33YVJocvWdu3ytdxgW+uDVe0REEI0Vifykx7IX8YsglcdNNqV9uRRIuS8EvTQGT6pR8OXb+tQ/MC3zSizPWbMhsq0EzER/Yly7XcgQWVeWTK1xzxoFD/kWU+gLzmCOj4xOpjbjwRCFLueQdzDYJCvvnEqiCCP8w7GgzKI2PwepWQaASF/Ji3Hl3PF0HxUf6qEq5/IgtuK34NuqpT7WkOPASM3wCqmuclcKtOXlnMcZe8V9Qwt/DqY63+0Y0ibkA9eaL5iHLPR/TYFy+FZq7CfOSpVwsJdllBIHDanmkukQAQH6fgYxRZCcScReoxHc+YYsZFXDg2QlisCKKp9XNbcxkxgppVQmKlwN66aArXxa2RtnCDzHFOz9AxdTmiDgLhCP8Yt6yzHPdDuQFR7YAK2LUHEE5pLAsn+vhnBtm9JzGI1AKu9GqjvROFvOaG0a5a6Xg8egZ1ZEUv/VV2sBxaLjExCrZagKupeppDu5ZKb36NB1zpzS/5iicn1Y+U7u9p6+G5wlot9+UW5uBR/1jjWv0I31ItfEkjuGhLZ6HSyE9dZBhydkuRgz6VCi9BSJ8bQlPLlzrP6MK39Pgt8M0Hgi5/uLjfqPdc4i+bsvTml/TLcvmaX9Jbht60TrJgdlp5bvljtYCHKo1AOl4cso5aO4xrPpzwmuC8gx3kpuDOlCVlJcegu3Oti3g01QolL+dmwNQ8QfMwl/0+H56xJ5DDZMx33HhmP3fmeJHjO6364eseBE54ybInZeZj1Y9W+EhnXxDtIJCDP9HJ0m1/Wh57MpxJpAq6+GhqpWZgiJaX/Ps862DNXavgeP6eiWWDNLy/h1VCBGoIzHDX7ZnurDHp8qodTcTyQkaCNpms+XPw0vf2zeu7X39hVZACQO/eqs58keSd2uQfntSxHJv3DrEK6MULVgQRCNKLHOcTr5nU5Tl+XhjNZC1+0sKuydRNxiYFO/XUwepxNJk6CjBOOwI/YI7uI2LhqIMOX/QfY4LKuSsYrmg970txbqPRdcJPAf3obn6NZ8eVr/lO22HoTYfvB6JAcvPKxXqJ6NEWo8ZZVZZR4uHL2XBo3WeFB/IG3Frg67XAHgjS0HEkurBPDQHtztupcuAOgQ3Y9WzonDc6J3LecC5zXpzPCZ3lWI6YM3biQW5XdpyFg6yzPSGt/CSQgujbaTnOQKrdUDNuBB/VrFQoT1Sxbx2qNGNE+aJr4z6Dh75rfJc69tqXhrrKbvQ02dgq/ZzTCq7XtEePwyUXre4phAih1U6McOXskYm+HEFw/HlMRYGkqW5WYkWTj4TAXWOsFINqAkHl5y6+rtmS8CperuH8TR3IdfWTDD7LOg6Ra5wdcDSJzT/U9MPM8w/l3HiSvOckrOTR9pEPNyj3Y+iaZ3zwzacJAnVu4uBPgkKei0imwSAeDfOKIIJBmoM4EOSbT/ipjYyq4iYZOyDwSj5G+E6Begg4Alu60BtWdZmH8K5C5iD5GlgCPf4sPDemCPDoxhFzE88XuHHFPMQBIOUEhLQ907IfB4+4+aSbS7z7kFVAP8gHz1eA8cH+yL+pxhQz71CBPvGdgj45HrwTSBjaVnniX5zzEaZ2CW5N3VDmRN8RoJ1HCPWhrBgfuz2g+ENbjBSAjzdl86CDgvTAJPih8UkC498wRgws0PUESVvmDtRKBlLJ4bf3A+cYHt7+ptoSrTFG0H2R4NotuIIHT3XYRWOrLZhr+qYOJx1YuMQfDAPEk6X3sgxbcUDVt+OiCq/Aurr0+wHpC8ZDu5J8voN3x1Eu2R6Xxc7xwXb5pLEGlN9vkSVpkSlGfviRA6Gdx0P0a1yp3vI2EAQvU7+XOrnEekvfQAt884GgyzZyv/lGe89xIl46fSs/Qgt4+NlGtinvnrgz3cfvmIypOwYFKbMPlNFs51owgzEyuQgWBpVAkO7Ifby7+6CJUpZUUxasSdcHEbj7RvDHG4+EaftVX/Hwc/t+lIpVQizd5s4bX+/guXyCPjOp08oaB4VUZpWQVxsx+ZMjic4Hjr+qx3Zu5WJF3XIhdC1nUsnkhfcEsRybSZRfxKjJ1nMeD9PduKe6E/ecQJACN8BeMeQ7buJloqWNu2CsCHrz5q2CQa8VBNKKID8W9t7+8e6fp7pYPFGg6eWL53evCAbp064vX+pdQVoNRHAIW0zenmti1y+HSbWqUb9zPeII+BjM3pdXz/CFOA4WQI6TAOEPgvHGebfjF/YSWpOOWi6HW3J37MJW+R45f7Ca4//unlA7Ni25GDIhOfN4NrBYbtCtBb56C+yBIPpv+zDDR2EA4G1oXLQLj813gWvRp9PaCX2e2B/2KrDlO23BPUEXZolcwzHMRAbq2fqShLKnzmuicde78Q24U3cdW2tu6E1+w4YXWrxcpGvaq+MaDcnS7+f7+Nx2ia1SKpNVORx/r+4dh6CGY7ddOHl+E3cOMNdafiQdOnQUjuLlEZEOGS2ZH1kcPrzLqiCu4LrWGxeF/lqYOyz2kdc/v7ookaFMBt0NtLM+5gParK0BIfGxSoj5RFYSKwBEUEjzjDwCpvkJj4op2PNuViXzDiHflAIvuQ/CJ5ikoIgCRNyEwh5Bob44Gb/slJzrajL7Ob4zz/BLoMXVr4H5BdgKfny/3ZD6niCQyk8mGOQVyJp/8Ig68wPmIHxowoEeBYCYr/DY+g/fi6Y5xo/SlxtbCQAxN/FKIOUiea5CThPyOJabkraknMzty7GkHfnRe9SRFUHg+JnsX8ojKJxUSU429Jd6p+zj4l0kA8oSAaGxyXug1rEdLzjW+UfR4Zu8GS+TwT0SQZgc/vRCIfQfvhPnSQ9MTKOqHU7bOomsfk3wjjo7nXiKXHnJ1b0o96HYXXX6Epn7Wr4Mc82v4vCgtuvT0lrKwgBVdqcatyM2kfJvqAE5fhUKV0uLd/WEndv0UVwZioUr37Jz7c5lxpAk+mtTYM6JnBuUOa9vj4a1hR4//yYDQTQLzxNfJgZMLtidmFzSWy5fyw/lX8r3kPw1/O/5dk3mhvufaIEZibYBySPuPkJhds3CTk5U7HKCeGLSSLnmdGvgrWx4wYfWMpdF9OZFzpoYMcHyxInJE1smWORv9RjYOz23/+aNVgEpSPL2tR4HE8xqoLe+Q8fKoJmc6ZxhWbcDTHIiwaBMvLg75bJyf9pVznCjyq5t7u1nnevhq7wg8bjplPuzqRL1lzkUrEkgiPcC6a4aL1vkzpuDQawOUlk433Ejd8BGky6dy9xpe0edFAx6r/cEfdD7gT7r/QR8Qp75jANB1vXD3auXz/142As9Fvby1Xo07JkDQQoYabLHxA25Y8ImuAkwG5c//VEvp+YUqHHLa/JiNigz/rR8zt1aRoUPMBdd7J1TylJ3XEjTuGeuln7bbrl+z7/Fdx3afZy6rCpdEdn5sX1mqajxKvQ8OrFxsG7p1gKP2AJfEgiiD7unbt11A0/eg3+IBuN+HnCOhDdny2/JIUv6Pf3h6v6s0eP3GnF0zvLOkZ6plSE/4zqnOWujNHyTmW/XZ5bybdJFVf4o7z4I9gBZX8R08BW380O8lsRrUmWUG2RsLm4dl2gAv1P5eTX67dOyk+vlbjvw3rbfz7WGtqZ5bLkTB9iDtM2Ml5s+gbatHJF65nz04QO0uObQguvsVUFojVJBQvtXOIrkhQSrx9Bc/Al2MUdgvoBrfq+gv0yquQnzlM5VWP3DDSs/MsZK5dyQ4hGxj8djYlpVpABQviamG1HodUDIr77OygHcmXkYNW9wgyYnbOAPVcyNo+/8NTCCMwRxRFXgh+s9H6zgBdFeEcScQe/98buBWO1DWRsrgFiZDC9fNHUAyHLcRGLeoDmJbHplMbZVpukSjHKT2la7ghtPdCfVoWCOJ8EujrQa1b9l3NJud4JEJPitXxDnTsrMDU22fAM/2PL7qHRshizhBIIsOcrQR3ejmF10pXMgyTb2xlCyC/s2kl4fyujZsviRPbZOZfjsBMBx9siNrgcCV0fhuZ8QH+33iVcw6KPFfy/d13sfg45LfXCRsHCGd7s7Be6WAz3kX7nqvcstoGZL5d1QA56P1BI/e7zwEnOH2TRJ+U7HVqXJD5qAvZw+pNpJn68pQ6f6nNoZ+1J7eG+BoK3NvwHwmwwEtdNcax+fBHTei3T5Y+myXPaH8KU3/8/yIXdL/1MtwNDzpe07vGTH8KUC4sZVVQvwJYFZbBq8zDJ8ixA1KoeO2gzEzKc8vloEgWwUR4snbnmfj1bxKNDDyxc/ekKlIJAmVzz+9Y6vdrAp6PNGAaHXb7Ii6DXvCPLqoLwTiBVBrCgiAPRR2+dPWTrO5BAcd/scBJJ1l+XHzPkEKTF446KS/WOyMZVKmQFcUP4ndsbnWpk0cQeNFT5Zas1LF3k+38uwuROnCVifxzfM6iFPvtRWsvvpk+ry9q3agMfBqAMTRL4Y9g8Hd/ziRul7xUogrQB6rkDQKz0a9mICTNAJLrHU+4l8od2/14uoXR3tuEQ3geMIMTHnHlxSjlm4Fm9lyK2rCAontlNhuKib2Exid9Jw8IC2uoNvSEhwTOYYWHopPJnf+Zb0fej3+ezJye7J0IXKtNo0xdTjgsXi9l2EU11ghECdbunWAo/YAgSC3us820cE3KFrtnc6n/LvdVl4K3eulkfQi7EDXtmWAKf6dblNyzA+bANeUavI48omr3K8gAFiNZ0Z8emEkd2qbL5pDTgC4TxJj51KQKuWSz54oD2Eh06qjku+6g3XsXeQYeP1uNo6LryhoR0tJZVGya+5qkjtspPrqGptvoUvT3/2tkodB/3YEIoxiljhMoK0TujRyzW6bIhRg/wVj4+5BuKrVUuW3DZQ48rIaysyBWpSiw4QZu7gdxWK17nmEXn3j4I/BHdmxdAHgj7MXVgJxIohB4ryviAeb2dVkN8TxPVdMp/nsSmCQmkPGcZ2qplzjz6nsj9coYAMOSuB8pgWXwcjCKSNYJDnEw0EaT5AkIh5CKuJPf9gjqBAEEEhcA4Y8Wi7+PyuQ+YM0iN7Uun5DddL7PtRMOXAJHjqJ2WKm+ugpm2z6om5zHocUEdlDmB6EzZ0xCY4gk0U+gbdqEE5+kl5WXQsJrhHQCzHGwc9h4OsVH9T6r6awsRpYUg7gn8jmjoN63AeNDRBCnmYqt7nWWy7m234Q/ceCLruZOxLtjJA1+xBv/BgpM7YxXfG7xZwdfFRup+WP9DwKfqCX9K7FVq1fPc1Lkxaf3Fa566orGqzBhKLWnm1yDspsPjaHWxntWKs65URJ6im8jcHP934EI2Z+q86i6H81qUyY6XxInAevdCcvbPw2rrlj9MC/xKBIA+Q04ncqa4MIOWhGelsLedCsxq3+PKRg/uv8u02l7Ub9OgtsI9GjF50IHKSOxNAmYBTMhuweRf9GGAZ3kFPXwQOV5T6omwD9C0uBiRyCzkw42XT+tqXvwDGKh9WACmow/bOj4MlAPSG1T+sBPL7gT5qRdA7v7yRx8Yy6dIS7nkcTPEgTzw8ERy7DgjJLHf2sB5fcEe+eiPL8nfTp8746pc7quz6EVSiGp6pMIHRgM7kjMmUJiRddp2l2AkA8UWxvpSRFzCyFJugUe+6ff6sdwLxXiDlTJq4yHE+8kJpgjx8Hezp8x/vftL7gV7o8TC/KJpAUB9D4zl/Jnd6TI1AED7RylTLzT91mWoyTZb/XDC5BJFTHtbUTniT0lCd3Ihvam/5EBdrIOmRIdoIezZdwpZ3DMqY8QCfFFAFJxShkDRKfRxUrK6QgrUPF8b/WT73+zFyT199E71+1Fm8PHA4pWTc5s9RL8+qw3Pb31rgMVrgrcY0DcHuoznFct65j05HLdx+21ORMn19z8tzvy4ZQ9e5lLOEO6MkSn4bjM8T+SD0OmVipXYhHKPDmVFaJFjGpQATZ30jh554Yha4Cjivvx0/lreBYCqEufh11jGKnLHzuGc/XTjwUVRt0CChVLi9LmbZ+cae+Za6wzGj+BlCCm+u61MeVTGnvWwdY71lkOKYqK1gGjuA4FcKbD7Y2DIRCEsMxAPDkme1iODD3qme0RdWwfoHhsU5WoszJj+rHCSIKFdUU7KLHT+iNHZ69GG3CHVDRgY8l1ApwSDlCuKwsofHy5Mz79BqZgWACAwRDMqLpnODCtirgZT766Sqqx8L0zmX9wXxoQpOPmxSoyT7IR9AMy8h0OPVOQrwHMEfB4GYd4BLUMfBHG5IQeOGk3K+HPYDN4oI/njewU0rzVk0X0DnscmWV/5Mjg+eS9iHaRvh4tMqL6/HeR8cznX6GzmfjofGTr1FdHSTyL1JKQGnhRHnKCaIFJBjso4l3cqra9Q+KPH5aWXoiaYAkmEOY7vIj/29X8q+zbOXPepIqg+mDi6U0sIbvvHydE2n/hseH45AED6jjV14UoqPpWw1se/gd/6U4UrbLG1LZ3nIw0cbLDs7vTzkaKi+aAdnp2cf7rRp8D1aoVSa0vI62KXd7btpZB4c4+LcqmEL3m3KBNKOPVbYcS2Hr+0ffPZVCYvrE8CVLa38o8Zu7DTsncoVEAGd2D3oAJSlrHja86VuFN8CQW24x83/JQJBexO5Y3ak2glX4Fy4j+54hSOonW+HLwV22g7vfOBv6RtsAXeD9oXJrx0qkdY1UoMXI94a0gxniOulIXUNVxUuGnfoevHgQq6ZgNUxyWJVj7/EoZwVP175M49+vdYKmbcK+LzVKiAeD3ujx794RMzvAzLv3HnjvUCemMkr6fQLH/WZ0awBZ0DO6iC+DGK6vKk/vvZwYfYspL6ntrljKX2aqLGKiAkCf6R/EFCSMro6ky/Nr6yDx78IyhzP32vy5eXXGvB5zCsvihY/d99kM4+Y4ReTJj4Xn2XZeoWc5VhB9EJBn+cK+rxUIOjlC8Eq866g5zyGppdTJwjEC6VlQ/KZvHFpRSeJPH47CCTKd/pCW+Yr1Dnbqj0yTZEzy4ClXMsPHf8NY0B17WY9trg/htrjcenLPjbt8D/N1+qrPp1QZAJ1qUmtWIdLUhlXi17yZVB+mjRu+Bt4a4Gv1AJvNTzoQ0lOHbXbl+m77r9CJA/f1cnrdPTJwnja62zlfOi5pEJh25XgLutToyc/hMKj08OA4OgI0vJ1fvj2rDLBXSjcGQ84hqM3SMMHfQNG3XX6dawHjbNTm8INpE7iw0Q11fuWTaFQgsVXoT+GQUcdtNAXF6iOqr1+wMXxb+9AA2ldYZZX4sJV0cyha+/yz6gxOTZcd8G+RO24YdtFpJhjbQPCx/v6OmXo+Anb8KCCOcA8iKUSN1uUaRfucAau3ZRwj0szwSTmAHmsK4+MOTikeYA/PkEQiHmIVvNm9Y8CRQr+JHCkuQLvHZIiPoBBEIm1AXl/zgSE8Ic5hl3hvBBIfflT7lUvOiH8UmgFfnh0jICQaZpH8LLkzCcI7gR2gEgwj4tx48nBIpeFc2AJXtnxnAFLzGWkl6YBthPrEgVuUMdly4dP+LbaAameqaPqlIPLQTAfvag9qfUj51F5EnWCtbp3mOPAgTdNfNQVp5CJsDWojC2zwj6pvTL16x4i+sb65JW5lqet7Ad9CJdsJEqsa9yJB9FRH+GnbTMfwPezDbSvXg1t1eDM+c+XMPUl2uAr75fw15P7vpdyzq/xBQffHCcMr8Y9w2d199iuVTL12Br70L1wR12n8qZod+Dt3RgXco2Qw2PGHD36QeXSP1T2uUCe6vCyaF/nLupzK379FvjmA0EZGNulcoLeGz3+G9vt0t411R3U+uOmPMW3fMu/oRZwF2o/mnyNgXFU6OMiNKNgB69tWMsAZ95grY0BszMwtAn2hZuL3pQZAAmwkDOR8nt+WO2jYA9fzXrzWo9/afvVL1AWngCQHw3LCiFWDvm9Qe90J85LrgkCMRnj0S/scIElZ5LElCZBKOz3/UDAnRJw5/A7gjhMfzSpwK8kdOiPiRsTNMNM2gQyIWRA10YTMenwZIpJlCZYflkjE64GfoC5M6cJWydknnBJmIuASKJrIqQCK4v8QkdN3Py51/lk/EsFfPhS2AteFq3VQZT9ZTI/Fia9mhwSaCIgRa3xi8tU8k++CwcW9/1YmOuZsJC51Q65M4+EiShQGvgSHeJpf5z7NCIX2Ssp6Ou0M7t0YFqs65i4OGxxaLw7i16U/rnxrMKj+cJA67j7VIlTlaeK5bO31tWeN1K3WUCb75Y/UgtoKNWKII+cOt1yXrE3LACY1L4f2KgHd6dzYeM65gs9F6Qc3LIRZsrFHcB2Lva8WtwIjAR8AlPK/hBFEJQRBzZqjv3gpc+sY6zqoxHmQODNUr5DDywQi1iSxTyc5/pTG5uSqDz833UuuL4e7X04UWdylefadagCyv+4xY/56DyqwfV9UnoMhYXDOf5qhdVAUDeOkabNysV1djZTd59wKXy+nqMfZ/RvLLCSf5AdRmIfOfOMGXhi0tz2csQ3H6uEXHrImE9IBFl/Zl6A5xsK/mBBmR/38ldOtWKIeYbnGsxLJJiPU/DeQungBpL9UC7B+CQM/9JrczAwp1DugI+wfjm0JgpesaM5goM9mle4LZg7qCLMKSinTgR5hCMABI65BfzibaDIusovw543KBe3bfvSRNk48NkATHM9tHPC6STq4Tljc9dY9VUZG2teIl9QRl1lxI/Aec6WtnC7o0M8ebSMZhJsUzk232vyZhGcPBLw+CP89ALjFsW1xNAhBQD2lERGNVwXFLk9ug9CuPqup/oQ8mhWIXVdpu36PQPUNJLL/5NnRwEvSNkXXYXoKFyd1zRWGv5YjvXqS54z54z7r5bqYfRge3ypSxB6fC+O12WtaTJrGNmz7noqjtG3xiA0rRof0AEsS7tbuEPZfQR4iLQT5y8tDap4nxtg9E89vSJos1EPb/nXb4FvOhBEh907K81DvzF+2upapx5S+KYXXtN1ja+4Pa9s851WuH60fMu/oRZgNDqSRyGVMiB5FCts7GJmUGXqQh/sAB1q9r0wMGFKN1uj2oRJJO0eiwUHUnJnjWftP2t1zzu/+JmVP3//JYGgX3/99e4XBYPe6ktaPAr2qx4Z8/P3vB9IMjze9Y4XSmt1kOZdnmCROwilqcURBNIM4TtNFP5BpEfGdcNOPExIXPSJhN/+9Cg8nd2kZcSXensSiH7+wDk4BEIbipW4EHyn9/IwyUpQB3W9M8fybCZgCfaIRTQmb2oZbTzO9SPvEmLT18GeE/zRSiBWAT3ncTCVHfwhEMT7gfSo2PPnvBOI9w9pJZCWfWt+55VB5BwnZUrk+MiRoN6edgmto8pmnuRZYi1YyLQjvgGnzjCX32Lbruc9vCTzIQw8ObTCJnzJbvRFawSsY/A4WJuX6uB7iAZv6c1H+6Zm/J3jbruiRmfqVuaponvCAe9ODyMTgyQBZTyrKsMtv7XAV2uBd/rh+sGBIPVvWV2B5Omm9NXpsj0PzIiHW/91l24f3/CrIiIOPedRmVEDzA9D5aOIkYkxtyNVhIOLYfwaHfwaNDyGlVluiqqeRXwOmm/klnMHZBExVnUJ4HPajrISyA91obnJDhwM48iRg0Of8DU08EkM/ope+F2+1T5WuPmChXKt3G2KWWj80yiQrX/sCQYVAg60DMzWlCNHKccreakuqV6WkML2n47NRz/AHm7oQNkV7W21fqjQa1R++dsgSAHI2MKd7gUdfuAD+q2velKCpMQYH/72P6uzBuhgORtyA8j+oUfXBM8lNA8g4IPPDgyJ5rkNq398Y2pozDm4jmjHXIqKwodvvqGEKelSUSahK3ObgQDmcXL5ga+aQzg4BF0w8w26vvmH5tYW3e/dsRjzkPA5+EOVpCyBo8haN1hsQ1aiLYCTp4V7/MyjXY+jBbRzHbxTXVVfHgOjQqqpidZHBZWY+3hGIiRnPnUxnkmZEqLRr+NgWGU5Y1j0tFHqhZP4iSbo0SB+29LexLELj/7WI4twhxYN7CcVbRbt+C+uPBvCpK1sFnwTgbraH3SAG3nnw2M/hhDfayR5SKEA3+dROytFO/bssnH77oy/1BIry8PqGP9Hkeu6K72A498FcoqxsFo9tsarOudcnBxQp2pMebpJmgw28Vhv2ZERovVwv3A/GJVVZ93CDc2Hz4pEqC6Vq78ouyW8W3yQdXXnoXVd1i7jkQH315daxa/T9pa+gRb4pgNB19rHHfsb7Ty9UFzz+4Z7xBboyGQXTgVhzuWUFg5oXfAXHjkGQdOtl93qmHrd8WjmAiK8GDX/8Tt9+Lw7j4QRAHrNp9Nfv7v75e/vlL+++/Xvb7I6SCuFflVAiM/E8+6g9wr8ePk1S7AVCMoqIM+hNCHDhu4OaTAn8KMwSyYWeh+P1tvYDyZt9kEYD81yyRNHAkaeaKFjTypLxpM20J7LZMKWlzwmwJIWoOaa2BBP0nwm6piMARP00VlrmLKsfJcwGfRnCuTwZTGCPs+10odVPy8J/vA+IK8AEqzPxb96oU/GExhyIIgvlPFpeVYasd3586+250qwo9XJ8/JpB4JU5GL1g6+iMq7kSYmcY+J4Tr2AoSPpS85va6WyD6Qv0YHo6nMPKPoa6FX1zdqVul1BbQIBZ5ZwYuWA3dKtBR6xBRoI0hDi0aI9sjlDdyfTOa0PCqSkAyhi0Cc8g0/Oa0+OdT6YTC4A+MvHBvRXecbWVR6ayNdOX36c8ldpuPujwrhD6ADggO1e2nWEpXwbxec9ZW3+32hoVOV3TDXY2BCMsx7aD8pwmY78rmGH4Ztx3DIjZxU7HkTSGnc3XpO49vUaMTbkO/zYh3v5IXo6iz2zh4dbB5Ax3mZyXUUX9Yvl7Ot/A0HuI9gd/XbtUAk+8m0ntPg6bkaXDPFYNglRVNV31wVkOwUgG9d/5V7RLFhTGPvaG1sJCjEfmVXEovPdB9UsAR/40eB5BAXNYeKkFbVHYhom53Isf9OcvsAL4zmLnfYjX0dARXT+mHj4TxMRVhpjlkfDaLujrhG3YoHG2zRwNwAJn8vgmqTYSRwcO2Dv0lgKlRlfHc6nzm5/idlPOZX5hw06cMJ0Df6s8BY47X+0kyvC3A0NcF5LxTaHJzaucRu3Hff73NdkpXtHXwaCNov2lGMwxskNa4eHOYtCLGzPKzBy4c65cqAAZHtnBSbItqccJWk96mkLG0s1NIckeIrhPsu0RmdsVV7Bju3dQriLqQxlPE6efTjLUSv1rxrAA9e3g89AuHr6LdouvbAVweYu4/LGVpp90y70dZzyugnqo98KMnULBG2N98jgv0wg6Jgk0bt+q79qoPHFefKH2hd966L/EJdMfSEfGg4fH1Z3ozxGC7TPHDnAtSS8+hYDmg67Jz0UFvcG6aJDCd798mPcDMHhpp/lskYgh69q+OsaWu3z+u2bu19+fXv3+lcFgn55HfiXN8LzhTAeE8uqIF6w+OHjdw4E+esbml8wEfOdN3T/g2CPJgW+PUZAiAnCj8r180Yw9plYaC4heII54z/1xP9OLFcfHr9TCU8EJZzrpycla5JjDdLnAJBssDoIvYTC0Megj3VwWaWDRT2yJayDP6wCIgDEe4Bevrj76dWLBIRU/onPxevdQC9e6fEwAkG8G+ipAj8KBD3jcTBd5/1YmYz4jh8HxInJJLD8lG3ayD+ChMqKoFwi7ZN95KdgEn55Uu5i9UlOfGwdN3Z4RO0DEtdo8ID/knRp4yF91fV79H+Wz/yuiKDVBKPmSh2uoA6bqJjj4upXXzpG2W75rQW+egvwWBhfWnRXVL9Ml+c8Z5zIeJeuPVNqF6azw/Ogxxn7DjK6VMj4G0OcE4xRnLs+L6wvduA9p3hmLccPidxsOI8pm1OYuXAw8mi+8E+Y/GSK5Zyu9aI5clxFpNQMu/KNZwPDiwj1wrfUD9ANoiyVB9iS+PCVdsFUx4/kNVCdkrM+lcknVSaG0v66TFlvWKpn6Tf1QBcgj2KOGInckBu4fDWuvPWEj+LkFkayItbF9UkI/8vPofVYcVm3Bl/fUe1eY5xZx2zrjktc702TsrZfWiBt6hrYx6mJfRw7ZPY4R5tAiwNAwnoegWJtn3UnyXMKIW0PW8CeH8CSMkGklLQXT48BfLFQZylPgml8oLlSf7Jpd05YtUtI08Y0AGhNRAgAQFvnVo9DcNFotYBO1lV1ipJ7AABAAElEQVTYBdqGDbXa26dhuMxwdxLnODI+12koDoBSdA08upizwKw1QeLQdtKDlNqQAyo+2u6zYHSzy2ruKWAG0ClQ+z8mIqT8YLKWsLM3HqWDOvgWajGnLeObsJWB4Uob2b70xfPNf7Fjpm1sRZLnj1S1LgnfMjTa1rl4i0dPCsGkB5jt3o7+St+LpZIjxz4BpGoeeosjRDaHzxKX2lq7imEMmRE//E552mXoyJRP4JWUVspYsDhrC207fF9BqVDSt6bxTqzWLFYOK91wurJ5oC3LVpOAMar3MWCYHAhCYpS80qr+2xTQTfnou28+EOSOvvU+yqRL/GVLlt78kn5ZLl/zS3rL0Js60LZMvtN3/A1+5BboiMXh2/rT4dXgDzaVE8FmsCsW7sDsDYmv6rgk5fKEMui+NJnOBZwvaPC4lx/70moggkB///tbPQqmIJBWAr3W42Hgfn377u4d7w3So2O8IJo7ax/5JLxyLmCKC3my9Vl3KNGb1UAKZHgCRCBI2wSC/HiYZLhI2T85y/P8lFi+7JdIi84gT9/tnUeVVINtc2VhdNWQDmBEaur3BQl/XBw9KWirtDWoBLIKBInOZ+BfvOJrYM/8ifhXr17e/aztlQJCvBfo51fC/0SgSC+J1vuBXrzgfUA8EqZNVxGCT4oHefk4Uym3vH1cgSAfP1XQP+zsNvXMhRIXmTieVgSpjVJN+Sy+nudpn1WeBnBWPnJSyy5su9I31FWwNkusvubFP5SXr/lv8ZV2sjnHuTRyV839LVjK7ftp+IUHKo0WCUwf2NJtFrA1xg18jBZ4z2Ms6pycJ4wPTe6n6fCerELyiHHqwMPNwOMBVExLRfq6+D0MwsKff4HwYxnGYYZHJXwgHblLkrFaeEM32jCGh98nYyjsccfqlY+VEI/CAKJXa/LBO2OH/cGdNUUf+4O+ULRV0MiiWRjXz61o2BRIVi8gDlhJLYI79AgAf/hjkchFq4huklGkNo6EhRa8WTB99/8E45q9RMEmxTU3iRGN45sfjgbESl0HN674B79E8JN2oA41lUOna/IoTV7Pg0QmN3p2vfFjxNIFD7/isbsZtWh/a2cUzjqpCbqRI6doHeO4S7GAX9ww8pxDBZe5HoDX1mBRGwep+b1ujakzclDin3lVLi5+hC5Tiy+gnAsNL+njvoTYfwpUISEV6GkzViRLuNvYrjpyNE4LmM002UnruhQUOiYB+thrj8qNBEZE9mgePWO3Op1LCLkcB4UtVA+zIY6dyWvLOPCRcma7W0PFJjwDjSKzsLMBS8HkhIcxmDJ+HJUao4dEbY0sGTSsWWzDF7Ss9HRuicqOtW0P85YR+hhsfeLSOGNnNZGLokPUxZED2WCgGQ4uqoZWcc8xqVbLHJXYxtYQDv6lz4R7vlbfVh2LgGfburDxeBpXNuehUNxRo7htYuGTEQphoj/nz1yjZlcW2P6ImvNvpy/b+GaKdhlPohNZyws/Tenc1x4Oj/HjrRjzeyptz3F/9eTJLRCUpnz0/TcfCFot5C43J5275SJ9I5AH0G/El5sbFy1A9zlSC/SjwiHug6xhjXCLIxB7XzydLxwzkcQZGAHD08kRAyXv+HmtQM8vv7L6R4+A6XGwv/2idwKp/Pe/KhBEcEgrgviCmD8ZryAQj4TxeBeTL+InBH/AsEzdm+8inYNALJPRA1MTDGKQJ0CVi02DP5+1NBufPmqm5uXcroYuHcjqYuiLKMttnJRLh88687GjXRjt0xK5cBMECo2BHh5mgoa/k9eCc0ccmBVBn/QuIAV6FOxhJdDLl8/vfv7p5d0ftL1UcOgnbX/Q6qCXP+Vl0S/1bqAXL/RYmAJBfvm0ZoG4yGTQub0ef+w3R4N64gv1V+4DRCn18bsE5Lxzy2QXGeRWund+H5OGnU963VBLbofu6diJGxz7G+IKiJnd8hWW/zzqQcUPVO4B9OHA6Eu/GOwtEHQ0zw14nBbgi4t5RCXjQftnu/P1czln3kPnMhPgJkBzewzS+aqcrZNis2qHncDxo/LkOXWQqzI4GWc6PiMfHEpgm9NN0JaENF6KYN95kF5pXd1iFM5yjx/xdvCiDRk9gFwGqkVFvMXJLQd5tnq/jKT0oMuVT9tpb3x0AtIWRjlQMdD4MNwWiVzqNDrlqHWXTi5dLVaXc2zsB/dAAkC0R8YGE6d8kyGg/Fw8rtLBPb6pvCCIU9KxTh1bV/VU64ziyjRfkvQ1aTGB3WwSG5eST/nwzvzlOLyQNHOJpSbaCA4tHGQMWoX4gQxrl+NohgOH9rYFdbTVmh6ZarOuKMaImKc9xM/lxF8Qkwa387RZYGySIlx7YHwuKsck9fM6O3RBPNJh9CAcbXXwCDAbO20DZ0xxYeOUpYkIoAerSWN12u+Qmn7nc8q+7Q5GpucIemL6kDam/Sd2sre+MG/o1ix6e0yiLbiNWaAog6ate4zPnKll5UzjIACg+GgLOII3j3Y99mEUzQfPPQFmJWqhv9FhlCubeliPDcVYxt7qRR8S4QKyNslHOjSrA9z4XASz2fXqrRKcW3lMqAx9WQpjOIJ11TYG192+xHJ4x2XEN9suRqUZaumoifS0ya/V4zpuKTz8FADcrRzk4VF+8MzRUznnmcqCOSduK4L2lntc+JsOBB3ngwDf0eCEWGfkvZY7DULu9O2WYS29+T0FQlQ/g9k/y1fZa3pvuEdugXYFOlXhk0senTRIDZE+N3DzsnNNLtuCUdw4Q2CGO35keP2NGN++/aRAz693f/3bL3d//9sbrQZ6nUCQVgL9jRVBWgn0RoEiPhHPSqA32ngXECt+CDLx+Bfeff6cR8ESCGJIDT2fANOE0SuDtOySl/ZIhlAMDmdCiD+8zJDybOLipZm+YOi8yYsLqQMbE5TWB0ZtJOUZ4pFjasj5MgSVM60Br823BcOj0JNcISCkz8VL5qke9fJn4fV42CsFgn7yaiACQnpE7OeXd3/8STjBL/WeoJdaDfT8Oe8GUiBIrz/ia2ReFYTvsv0jvsti7r7WrwSfmDX7ONr/1MfvLqLWyDFpxH0l18t8U1nRS1tjQjGRYB8aeeGRh6j0z4wP+LpsPazvki+W1r42/2m+/dbPUpc6qFrUjHrKTQEbw4DQSNDNx+Gnj5CRAMpkxG13a4Gv3wIOBKmvHys26Jfu1HRPFx50Kt1XPJvMwexO387PSZLE2Me4y/noc0Nofkj0PEXsGG0kxlhUVWiJR+wzLp98SOF0btpyzUvMVRu9eNSxEhhrJLSTuLJEgCuYUoWBlfbxaUTjX9SESZjwhT9yCWpY3fh86JOixR8VaSuPynIhbYchtxlKWBZ6kaoWfpLlWh9QanOT0BcG++5hqpUBTzK/RQZsQ4YsCs6Ir3nwfiGxQPwUxTz2S3xmj2snOIzwasNBM1bWWkITykl018P8cSH1gTp1E5RADW0LbgwLj9hoFS2QzcYBUdGSRC2ApcUI21Vd0mbhi+zIVHDkeywoQrIWATRbKhEkxVgAPUdEd8GoAzey8N+LiuHruSMBPxLGzSEHgOQtK33RlH/ntWtfsM0fyItEU+jf9NQ7TD4cAqepxAMXfPDTPrQOJeURGWpwKpySpbXDz7Yr4vjX8+BQgwUZplw/UBa7yPRIxB8q5q972ZHqq7b4HRvQkI9r7gcuCJn/w5fYH8azqsWDh24g5eaJLXs6JKOnjpkfuiJily8BYxvfYXYO0DMUGJok6iNsNSUtFhOix8UKLaKd9LW54HOCN0LWC7/bAo/8fyg/+G1EJey2/ULEhiCQzuXPMrTgUWmalLhfDi56so8fUqfishWFPn/GkYr69QcqrMATrRBXsi/nIDffYjH7sy3aVIwyCLthaQVGW3Ap0LaU58rhQgNBnMcEbV/p/aD3R24J3dJXb4FvOhB0ag2fUOnM29l+sHiAEk87ZMsHwwAP4f+7+HrCXuq7lR+5BRiVSO0gKV3sGdqGUXwMyi5Xdrh7uU1vy4AXUgdISoHz6BUBl+8U6Plw99e//P3uL//xt7v/+I9fFfz5VQEhrQbS6qC/ayUQn4vn3UDv9TLod/qu8Ud9DuyjFs/sj3n5JdR63w0rg/ryygSIHAKB2dv33ylSAjxDLVXIREo1Ur2oQy86riOTJ6/QoIFI6FHmq0xwnIJGul2gtSXIWQ0UEVrkaAmtBPrOfBJ2ntVA4f+sr4bx9S+9MJrPwmt10AutDvqDAj+vXry4+8MftP2RR8Wea9UQ7wjSo2H6ZPyzZ6wIYhWQJn1a991gEIEdloEzAccHbwo4JSdTTeVq777BxgTsOwXMsgpKPLAhK959rJiRpxUMYxpo4GbipB20c/se6ENDMQ/meO4apMHNd0/fSGMrdpB4OD0kfylx8OHE1RQ7tWa2Fq7y0xbycbpK+oUYkYFwS7cWeMQW4MX9vOiWfu8uKV84/9s9fQq2m7qzz4kxfbddeDtVXRu4LHYhy7m6b7bj8WqdDhXZm6XneKzjq7g81iPXsyqSoOE7+TRuJwgVzfg+aI2D1QytWGCduFLkAILRi1Y/a8de5FcJgkrlGL/sc/D4vJJg/aP5jB6etpnoWb2a41X1Dmbt6kZxPKW9Qez51GH0DruPy6nq54LZpuXDWweGUj1Uxn86rhxaHyv2wKcKRuIeznW5qBBFyeaH6lgSLjVZ15qj+UWYWipPgZs9tMW5tyyvCzl40MKW4ydziCOpnLYVBhP4CL35wQhu8ZYs1MjreI5ei6sAjXbjK6V+EbUuIH0RNT8uHRDimuJpijjV0ASA+EIYN3WYA/jmjnLsOQ2Q8w/M9AnbHgdAD58z+73RoE/Cx8Wswt4YovScCvuuozB5jG1eug1NOWyHx3p8cosK6hCnbakLCNfKuenmCR5/Dxdrd1RX1oLC0Qd8EIRIPcnXMYyB+3vUmc/Gh350yhjjeyH1NBwpAcuCh7UwtB+0ThxweLPFZ8GoJbPxFLsXRSl2m6+65vhXY49Bc/jvn5tDdWNuuscHmzt8HNMYUNpXC+U4xK8hWyqc5s75NAiswhd/5PdibLWE0xxcbW2tc9ByXg1u2sGlQ4FKPflMb1vtBoBpK3yI4OpHORpp/MhwuJnvwQm/JbRDBh8dCBLtleb9/EK5pcdvgX+JQJD7tHqRhoX7J+a04X7CcqK3vE76MBZPqTRwhUfdIf+lfLvN6rjl30gLMBIxvnlE+g2fPAaqnzFiMZjR5yxzFqREXwxNueXgjw2eo6+EA0Ea/HjHD0GeP//5r3d/+tNf7/5d+S+sCNL2dz8qxkqgj/qcvL4QpkAQP1A+6uXSBJC+/17P0v7wRH3yR9lgesGKIGwA4ygTNFb/jN35clgeHViTt3UZRe+cIz65OK8kzZ1V14WddB0VA6bO1BEgg7th4aDkviA6U/N8lQSYABEyrEuiPIEgRwY++6tfftRL7/zhk/Gv9LUwHhV7xWNhP7+6++P/0qZVQa9e8SJpvU+IT8crCqRXC+ndQASClCv6w3uCVLx7ooowVwLGLx5Bi88UKZNnHKHOuXuYiWOvh6kXjCQUTVsJhUwp01imw0nq+HIaU+yLd9YWzof36A93eGbkc2G1eWj4M1DqqYJ9XE6aXL8oVMfJR3Mt//NOiSDv8cmAzcrGYebwYxRtmf2h8w33wZqDtHHewFsLfN0W+KgXrn3SONuv7+Q8oVczpjFiJY9X09vdgbWbE41h0j+eNb74rqcHEqT1Jxoa3PcZf5T4ESukz0PoUNnZNjyDG3YR4wVsSUhks4xBdqCVD1j55kzA7YEQ/JUdOi57fEb8UBHPPasX7hg3kLU4uzDnhyN4GPnf282WzJv2xQZyXFfEaTXRdaqPCYjZuHkTyIIXvHIGeyUfg9FnByRiH82qXe3YM2jYHT3NjR6cYCcVUcV2pPFnx1YF/vNH8vtqfEkdadNMcZXgxQffmOjFx6yVHzlrW3I9dpiBAw31+qjT6C5v6WkUhAYjvgMWur5jrSzA9dV4uJBzWyIfbG1AqWfkxRdrsgu7tWDxt/J2zYGgCQLJoc86Xzl/3A/8izI68IcVQXxFlAAQq4XBeQ4UhYdZHMKO2wp4KnrUEX5t0RzvvTcfOpERD12PHETT6HIQ6GCEqHqpbHZKw1exTYNQ0y9qZ1okvEIOM/7l+Ia/OtVK8cuawux2tb56gDZo8YsSKTpSx2AC4/ue6v6qInppivA1P/Thq1igzvB4+J65ocXNQK2S8A2dKknY9TINOpsnFWE9ZFRExhzsgZOnIO2HWFrvmOcOX31H5QGPpkMzKqeuGXzMELcMTh02024zD1RiGx/MJZ7QlBvGsI/Yrunsy6a3bpQZUtrMGLd3vElLRJR91qFNYyF1thFx0O4X8XHarDTl4N1Oms+Zx/zBp5pi4N+Z5OEX4hYI2hrxkcF/iUBQ2ki9h45K4dTzQ722p3N2cLxGL27n2+HSm++0HS6dHPwtfYMtQPdpKnxxqPYhLiwZtCK2YAayDHCjwP2MsY7hDcmERVi+/EmBiHzq/R/+Otif/v0vd//2b3+5+/N//PXub3o8zO8I0guj+VLYOwWA3uol0Q4CSZbVQHd87YoVKwoEff/9U2nnk/DYyYuiMxuhn/PGHfjxiUkQK4IEu8yPDzxTWf/5sQKczVNpfpRrArX6b3T2IkmdSQQI/ENG2jKgC6erWgNAyW1JOFoprTItojI4bZbR42EyS/yJFT7PtNrHq4L4UpgeE/vjH17e/a8/vtIjYnpUTC+TJhDEI2LP9TjZE030nip49CMvjmaTPLocHBJAC6loOz4m+I/DzmkG7hhm8xJyy8BjIbMCJYFEGzrRnKYL7WIvnbZ7gaatr+Iv+GLe+wsKNjkm12k78863wzsP8E47waLtZs601P9S12Xl5OpJRwunNuCA3dKtBR6xBT5o1SVfXyTRZ9sjgYsbSNl27ple7vR1j6s6cTYun2ORnz0T/DJIXBMwEzivN23CURIN8nHeD4ed03hihwfnc+mswYqjRWN2Sr4OAE852I7RQkpFz3D0B65vS+jgiQLtl21QlnSdwJc2uf0PV8dmj2v3WINwPeUwPGy5fiEvzWJxdayanUriOdoMGD5lrCShvV2b+SVmKjzDZ2WWOO/cFuU5SDZ6iFKCD538xMKHrFaasV/lSERBeU5IM4Qr9T6MSd/olx/Uuyl1SDuAc1l0t6nL8sn1NaViKx90jvZ1HtfL9Y/hcG1OLG0HBPW6toPFQO0e2qadYlOBWoI/+lKGb6oRuFVd3Afo1BJyq6r/Ewh6ojtDXhF0BIJE3RsLn1SPHMr0Jwr4efiBTsm0/c7emlFKRB+HGw9KXZmZTYI+DRBwCqVv+agShv4jvhMrOCgk7fN/ZlGJQ2wxAcktoJ1Ky0B0lHQ1b5vF6kXznSUgpjFlIr7TdpizD+aOPtgcBBIu+eKoUtd0mOxyBawt88loRpZy031dpaTBpk7j62TSOg3Tg2khcPAvDYGOI6ti7TFnHj7yoge1ZyaxGz5Ab+rHBwygkjPx4Seu0aLJoauMDrczFGB2k0aHUYKPJjRZ/NBRAG2cn2zKS5nZxIdHzP177rh8mKOtJMN/A/OWGTn4KKsy/sOuUH40bJkabbfsMVrgmw8EXQ7I6bD/c73n0t61g9KT5/JCUfw1mRvukVtAgw+pF4CUMiAZVpfqQFUag1ZT5IRhQBPSm8m5SGsEFA1YARkP7Hnk4JOiOe+1ceeZl0L/6f/7693/+bd/98ogHgn7xV8N02qg91oJ9F6PhGkTqyY630uGfs5qFYJBT5U/kX7da9KKn0w3dGGyTfhjO4Eb4fl0vBP4OF2/8TVbLmIu+n1CCGBzbe7jEmTORfJk4xjQoxFu7taQc5XJpSnBIQJAxjuHfx7VUm6agkXkmrcpsKNPwj/Xp+KfalPQhwDQ//r5p7uflb9SIOgnfUXMq4L0iNhz8TznkTJtrApClsfEeDSMYNCslZJuX3rWAfPcIR7y2wnId26ZOOL/dJD93Ca4NWg4lFKjjEWDmQJ8Oz7Utb83vlwynw0twf8kdM+e9OD9HE5r7bi11/nEMLYX3yD2LE0y+kI4VWUKG1sid7uOG3xrga/cAgSC+KHZfnnkOi85NV0uktIB11GNMOrb2fhpEjgjTHgQ4XzrqZ5HcCmH4vNudERC3McJygi2UkfXY8JtJ6VHg5ld8260jBrkOf2ww3WCwZy/4XIWK8G13g5iiK9luytx86JQqeZiPSXqBTQsGxfVGgkRrcE+oSn6vBeLbR4GI+N2El84kaG9SZXdKdhX2ahNxriNDxZwThRWnQZ5HKczbXwSUyDVeau3cSrThh033SoiUMYm5fYF24qiAZdcvWvTlXdjd1uXb6vttAyHHCobUsNZgcHKsa0thLySWoejya7wHKjN1IE7AcNw4qPeeKkd0xsCP7qjhv9+NOzTRwWChBeux83tKyGvCNI8hlVB32lSwKPiKCNLvZVJD9VmnyYhT0OYrawIDR7U1lQUlaSXCQTp4LvPBbn2awdc0uKnvjYpgvkWSbahThKQ/rQfqzAnECTY/zlv49qmrHqWxgNToPpXudBv550nIV+PYznes//OK9rlmwlnv8zlHTWmnjo2nk/Ct2/4wZlf3LSZy9CSoj2etI+UJTTkZt4ni+DMV6LV2CEoUXp1Lxu/Qa465xp/Yc24FWUOsMg6dI6Xw3jKwbgl3J4pt89ZC9N8yQDnmKHPggCmofDoA4z9xseHSIJAPvzRM3zJLI8n3DChbxJU5vcA6tBtWe+ysh5NQh9ywCA6BllGqFsgiIb5NtI3GQiiB7nDqY3oi+5IwHQ2lcjpTHsKLsgd3nmAd9oO/xbfJW0vVwf5LX2jLbD1laPfGKfddtgY7EgcSvM5D65ylDIYpl+6PAPf3ed8NQz6R42U/MD4oCAQ23sFelj986d/+4+7/+ff/nT357/8zV8I+/XXd35J9Fu9GJp3An2QzEfp+cdnhUd0kdLDS57g3P3wTI6xGojRf3LsygEuFp7gk3v2RCUaCMJhbUq+dKpO7rOs/uFPleXiwg+LdOEM4h7lRe/k4qi/lHRARy9tFom0hApTxk4u1tDDk6BPPIFGUCg4HiXjJdBPnym480SBHr08+ic9EvYzG5+WdzAIWJ+R/+mZ6Fo9pGDQM/E/URDoOYEg1YNgUAJBjTO08jjLuCK/5oLIPI42yPsE0g6up1mR6yaQJH5Q/hFmRcZuOxhkV4rpAx0bNoYTWPqovUoDWb4TwxR22g5f8u60a/bKD58biSPmxiileepYNuclXckPPtHcJuVBDcRburXAI7YAwXfG3axknDFD/ZLzwF1UqwwydqmUf/dbnxsMD+LjU/CU+SucKiHJgAEb2hjNGWcEaMf4Q7Ik8syuKc1551ETlLH4Mw5YLjA4HkWGhC9jRjosNrpFVNmj8YzfsbFsQacNUMNYjJ4GMZJbvelWhoCMxKz3lrGPwxUPRDt8EWbgXkOywtQtYPYZmqVLmtRA/ptKITotGl7tDz1gaEMy9rbjnTELNbg2kOVSN3zBFCRqdJk4bGFojq5wdj+u2m94+TtujgBSnuQ6jgB4+z1kS/o6EmZ4U7uWqyV5jid9QeVpByhHC3AhR8dFvSN93tuW+HBtZ7/gEj36fpsPs+W7sH9WaD4cznEH0Lml+RABoASDBGsuRT9mhZDrBht6tPtB7xskCPQDK6h13rLhGzsy85MP4L7jghAlih6FAIjG550cSmiFY0Bcu6GDuIDqw6/6AfUsxnm1t9nIw+R0AK6fjyn6NJ263/cHNwawfz9tx2XaqjxuuRGJz7SEsNjbK1ABqNYRodVuknEheMOAW5N1DEYVM9kEPRibsdUcJWtzsEh+tA3cqOYXT/3z3BZjjOUohyF5WMCaErxhlUEN36FLKKfqoHBUxq1lMmJOoyNqpp1VOOhiwgfK9HHD2uUPDVDwZfxRHhDFg565uzFW7N0ygj5Q1ou2/iEfGKVcjyjxf5loX/unMdKPZuJhBhuxSkrCPt8IwgoPyWN7Fck+PugMtl+MpS81Z8/NhjLd8sdqgW8yEESnuzbIcAIkHUAR30R+fZD9Jlz7v9eJGRM9nqoVGGADtw+FoXvo6WcazNxqs1fGQEaJvsmcPfA0rUY+1riA41Gwvnvi3Qe+AKbPwb//4K+E/enf/3z3f/7fP9/9+1/+evfra30l7LVeEK2vhL3/QOCIi4FyBlLpynt/mMzw3NPT5A4EzYog313Bj/B3RRIXvI7m+ENyPidQYC6KGcCTY0d6KiCa5TJuBy8akzCU+fw08wj4YognSgcMIxLx0Dmfkecib1lolNVywv+oCxpBnadP9b4grwrSKiAFfX56QVAImEAQwSE9IqZ3Bv0k+LkeJ+Nl0y+8MohJ4KwIkiP44iMiN+ySynX5aCEx9cWSIuNisqMhBhH01G8Kp8w1t9GBTtR/ucK52hfuX9TwonjBnKL1Tf8oAzOPW7q1wCO2QAJBjEE6ddUdPRaSq5ANQvA5+c3pcYJrBcMEXdtzlpkkd+hgdKOHo9PD9uj0hFu7zBdGB0Je+TC6Dt2StY1owla0rqzvpLO/QmeMxrukXK/sxRGoMrXOo0oDoi1o53bQ7nu91dVfVrTfqoJo3kavHYM/NUx7WUsZ5npi/4tL/Si5zeSD86mvL10Qbd8e2SZWygc5dcQD3z9HGjRKk1E+7B6AaeVJQZot4t3Qk2F9T1wz8IPEDx2uoCulfSkX8vEVy6k8ApEUpSoGIIsnGy1KrdlgZUYXGW3j3PvqSCHVazsNQ7Ot2kU1381cZ/t9juq6mquyvS7v9P28YxWCHwvj3GBjhZDmFARcqWXblkPxI6uAFPz5YXJ/PRUb07bYSFtMb1GbWc20eDN0XSbkQDs/6AcwlEup++VK2I+NXN1BjTUf05GooBlOBWGijanWOg9gvLQyei1eHRuPQPp1KLN3Ftimj90md+AWsHQs2Ryrh455ZWeOoLEnktghCMQ4AQ+72vYo4HL6/97HE1RZ/YNAB/PmnI9n/+oj+Vj1TVgVTyl26TPrPN0ZDmtG1sudY4dLp15sBCY7xhFECV05xrat3TkobCaIc8+oFDDPRg/6SNghpaaStOqMZMyDja+BsHrvoI46WF7cvvmJdQtlNZDfMyoJ29RuzNnuUTchGUtvK4K2Bn5k8F8mEETkkE7FoN6L3rW2K735NR5wv0ev3JfyVWflbvk30gIdieSOB0GXteOKB05/DGQdpMBoBgxCVBGGZl7EBHhyAsS/hX2p0uQkd4eJnPPIAds7vSD69Xttr/WJ+L/96gAQj4b9h1YE/ar3AfFI2Bt9TcxBIOlDR9//kxVB8kWPhDFTJyCUT8UnEJRLJfVAphtDLLj4mv4buDhXWDz+k/9UwfotE9m2D3kvt64vSpRonpyHAki6UONBLA0Or3TxAM+EHVrWTPHDC35wCQRxO4sXPPLSZ1YGPXn2vd4XxDuB8jgYL4/+meCPvij2x59f3P38BwJB+qy8gkQv9M6g53qk7ImEuZvtz8jTbNIef5JRXxzfVwTBQRsddybkug+pq6AdFaWN5G8IglxXJO+njhfN73MEU3rz/y6+h/QU/3v2ykd1negOhQ/EBcn955IvPOzdv2YiwiE49PGr8pZuLfCILeAX83/I3Wb6qcdEDxwZF91Dff7XyYxmlDgtMg6SZ3JMNwdHIrM8P2rQMXo84XY5PB5HxZwfAvoBo2ExMHTZMw1c8Nbt6wQWas8jlMvZMeaW1h+8sWcCO9dXTOSqM/75kVrlDQBxKeQ0ZbUlFswDTmI+fV1B4IZF4u9YUhaGtgm+jdcC4uOR2yecwbfRJ19ynYE9bYyUr0m7UmRG39JfW1KHXdHHXfuQmxF4tOrWY1EcVJK9sTD+6T9GBKiAu9BUV+dmAQHr5CYYNRiI8IceigUsU/UwHzyw1tahJZz2G10qgpmWtVrD01bmHj2Zu4yAOdcOlpVGQAjLL8L42nZdfBuLwGqrV2nvnSeS7GkT9AXDeXCsCNIkycGgwfl4uv2i36uBCALN1lXOaeLwpBnGj7bJ0QSXtVsexpsci9Yi1Oi9bBmOmY9JaqR9dLNv3ZDf+SiTFo9gfJSJM19spl+ILh5kXB2KYwtoOK2XspVdlMCat1Wh1L5sWgnhBJU6ILVwZt12bnef/xkTkMlfNGysOeYy42Mqwnd8WmxSVgpl/ghqaitIOLOByV/bwlxuEHyXVTnjldzkyEz+j44z+InD1sdgKLAuNMd221rwP53Qj+IjqV1i0LYO3XMgvboUP+wjvEpWQSuqJH1+/HFgj1EjC8pHn7IF47tNT3lUWQ+yzIEbDDpcHJsOBLm5FYSlDZZaD/FcC3y+CcgN6TB4nJYO8+vChmlgbL3yfP1k6VZ4pBb49gNB6jC+YNNr1Vk7IDZ/qN1Kb/4QX/Hla178ZQ69yYN0C8p32oa+gY/dAh59Zjz0+ARiTyl3UF6UHOtyM4Axin1UoMcD2zD6EqULiW5WZTWQVve8471AHwjyvL/75fWbu19/fXv3V30u/s9//ou/GvZnfUae1UJv9EjYh/fc5WJoZxVQBlJ/Jt5fB+MRLy5MytX38sWwwXk6Hjlkc9lnir5S8FP3o+sC5FyCM5Pcva6C5QeYynu0nzJYtwXCgrPt8IZTgAfP+CPn4v3d3UfrTiDoo3SJh02us6JHi4L8Euhn+kLYy2d6F5AeA3vFaiC+JPYHvTdIn5XnRdI//1GfmX/+3Dy8U4iviXFBIpjEu4LwPzaVYxqM8uApBwaRQJCIqveefLojO2gyq5p9eXvud0x4aBy5xLfcvPou89KbX9Ivy+VrfklvGXpTjmlqd1E9s5iXJhoBRI9+sNTQTZ1KM98EgjY2Gn003bJbCzxOC7xXEMiPhqlX07/Td9Uv9e8xweD9fuoRjXPBW8ZDzotOko/a5FdHAi1Sio2sQJQJjVOWFzO5ZXWe+AeAcFxPGvwBB6xhUryCJ7fNOSPrU2xr5ENAqWNSSioLx+hOXbOkv3UXTj5Rb/2W9nUBnk7yvUJIP9Cge2Oc9TmMNlJ0EsgCplK2OYYXLAgBKi0ALu8xBsZZ4NFg/BEEQMKyo1jUaopeyot2BoUfko83ml3Gh5UMz7UgfPESfP0L/7YHnOsmmpBzUtYWOvBTd3N0HBR/rleRs1tLhe3iZfRChZjK9BijH8whazsqh034AWBUojRdVIWlz8Rjt+ENbuXNh4MdncNXe6nZ2Tb8Zz6EqJ+9EkSf53zIBzfI6QP5pLw6GWzqg8iw+YthvCNI8wBuKkFs0DU8sZ+2GM9UaNtYk1QNV4EpQ1XClDDUx7DarHWEcj/lePEbJnolho3d6AjR/H6sZjg5zvf50CcBKauf5bPKXW8M3XMJ35c3i7z0gYudqMMgaQyP5eDGZ46Z/7WjHuYVYAXIgclfNVW+ueslJebm8Ekn4vsrBtDDHBL/+aOkHrK105qb+3GyaJMOKZQB/qzDgirZ6OAyE7bW8IlVycdg6oY/+gdrfGDK19oUvSvVFibRb1kV0sbWiLHglXu8t3/SIRkP5ypbKzpGDwhwbONccvsaP4s/znXzTltLEeM5/vnaBI1khcqtx67Jh7Zvas5QX7Y80qn6CNF2at3su+uGP5IRz08zX8fULT1uC3yTgSCahJOvg2BOoN9uKHjKX86vjfsSP+vbLf9KLcB4NQNZLE6hoxdkeGDyAOaCyovBgzY0jWYMaJ6QeFCjzxHOUK4RkZVAHz/eaSWQXvysAA/BoF9+ea2XRL/RJ+L1hbC//6J3A/397t//9BcHhd7q/RT5IcLzt1youB+LJ8p57MsrgLgidgUQ9ASB4PGsvfyHv9EhtHinDvgOwokBf8HUk6LrqD2DeJl1WXD9yj2XDRfXuVbN8Ty8XB4Gzy8Xw0izQQuO1UJc1PV9NLEQMFL4S/Z/+EGrevSDgxdBP9PKIB4T++nnPBr2BwWE/vf/1ifl//DT/8/euzDWjePams7DdmwnqaSq5///wDldefoV5zHrWwsgqW07qT63Kk7f2bK3SAIgCEIUKEIkdfDq1enBczmCTpg1dHJ8cPz0qTqycgQpxMFFmZTvWiraoWElIvdtHj4Z9CgHdJLDofOLxVSaErtHMdoF/x+kf7btalFdLnXmslRb2MoCcOonCZ+/e8p1EAmqMl+daNr7Y6+BB9TAjT4djyOIg1u8b/OEthYGdrrtHs2Y/qDfftoC0D/ASAlhbEvNQU/fzi/7bptE05ehA2Y+0MvYqPtQ/8LS4LxQyJ4MBYM3P5lOw2Wo2UjXA2XymlPKJdpyZDBFASqsjtg7qsUAAKAcP347y0Cg4YrLm257DExUjK3jOGLQDa5pFJoL9Qg/zsiBHvh3Ehkc14kRCUwJGqwQ0LBBoIueQZ2XTUtJHow0U3I4UwIYkqxscEvKjCo+CBYTJJgHSaJGIAbuXDv4EJLFB3QjHWjjkBssR8Mch58BOqVCpjD3zgNev74eXNfwq7qAFj662fKnjLB1rVN3EbutqlLuZp2fEvVn3pUHpmROVli5HGtRsEYnXIiaXwoedDNiVrdOkx/yNboj0TOqgE/L+kUbRLvtc+3V5nlxZGdpZ6PxqQXyJdHH2icIJ5AdQdxv4tN6NFuduDdchgRAzxwEpnOEdOCt76Ez8lpl4M2F7KOMQVf8ip1pmm7yTjkuQ4QpcZCOyFZn91FxCVVX+JjEV7CFTWiNhi2aDifnMhC5gFGr1Du4fia0HFCmAAoMHWkhuUc5oJv2xgkBdJEgqz/oOEy7hC5c/DwrTFmRJzdmZpaTRMpwpTxS+hsytSUUJmIpImoXROjXnpZCCcMjUz83J0U5xotH+JvU5fjrdaAXXKjSZsln+VwPYvohjIyrU+hKf3ZOwUPAyjHq0dcixZAr7VbkpTNyKCVetPEc0QtxbFnrhKKJDyqyQZNMPsMDpz72x/dB4UWaQ/mtbQCimfIVwMwkk+smPH+g9CPwfSaAQTpRztneERTd/gLnX9IR5EbmVhQN+SZxL10NbcGtOuR+aFQb1y0eY8UNmHDFdbxvgv8NXedtXvvwF9AAlkfHbBeK64F6c5Rh27SLMmi2XDmZB1kzVbl4qGPxTCAh2BfoswYWLDe41nKva80Ieq9ZQB8/Xh680xfD3r/7cPD2/YWWhenT8XIQ8YWw7A1UziSbZjokfpj5p47HEWQ3icQGhzNIQqtspLBTyHnjZqFzMIJKEtcBXe4jJ+sUXDCTjvT4+Skg5OlKis4KLR0MlnkQSGkVXxxBkZQ8OILUsZsHdJoVpBEO+wTB/cmTr1repc2jj+QEkiPoWI6gs7N2BB3LESQnkBxBv8kRxCyhM20sfaINpXEYMZBhRhGh/vWDoyTvBqD62JQI6poEbZuAxtd7OHhqrZj+YWHVbqo9E2v7WeMqanOsuDW+IVJixa3xpmtYhw1fQ3Acf92eUa467L6UKzPFzU9VptZDFyliQ1nFLjpLpkEKwUhssu4Tew38NA3caPbmZ3leeuDhRq3SaZppw7cbaUyJbK1sPveBn1cc6kQIVNnaXgQKP3M1DlsTGj1gs6RYaTs6cO5IHvZBYbkwfVUcIKITHFlxhDAr1UuUFaccaIggCw/dSkUAR1qChMjh24+QQbOeypHMG+xqWS5La8Hj+GHPFZxWT9TXeJaQ0k9xGrEpv2hZmtCzLrI8LLwiQESgVGR0ISUP/DnsgFe8ba/JhKQeyuV8DDCp0xcpxPXGG8YhuuZTycCM7JP4VJkNcUhG8cwgKjSxbSGuWggvJYjOOGQW2rgRh1t04HIs0OTHlWhepqxygbr3Ju08cDErzjrQSMq1LorO7QaOJg6lVWV56roLnFqAJxZi+FAP9Nn4ZpPHa3DJ07oIXckyZILtFkYuDvrc5m0hi1/Xx0R1Qi9Gl+zQpL9GvpJZMb68+hXnD21f1557A29p1xbnJCV7IKtG+lQvhFgeRh1y3dYrkMKHLnQfIQP1b36msEw+CYlehFWyIGGiHNFT6aLogoRy0QeywEMwRUvPUBTMuoAfoNv8wg36O3BLubl+kdPUC86sAd46LJXrCIo6IdVaJgl4I5/bI1l2D5A6aF/mQQaUZiDXINfB5wKHYfgmexi3XUhW7nd4IRX4/hk7K+vKi8J04DhmqX5xSlYV1NctsiAMMVpv2Fl0QQKn6JTJYxFxOzbEx2W5vMTJb/lEztLL3FcBua6UE7YCFn+CcZCRf8pT6LZAmj1I+yADOiatk/65BZDeIMDOjkzEfQIqfHMOLfKZj8KeDYTJ2+ii84gGTpwdurAqMcIUNhTQcKua2joz2rpD3uf6KEw0Hvj+/HAa+C9xBElB1eiGgdnRGe3Q7b3g99OtN+wOkyV5X/6FxNFdOtL74xfSQCzXaBtcnTZmLeVsO5isvn6hTJsSvPjwvO03AkREghNGzxJ+q+x9gfQgf331+eCKDaLlDMIR9EE/loW9xwmkWUHsFXSu5WLXbBCtyTB+uBefduj4y182kU9l7oEjyxNMv8KisxMIsw5MP1WCeMQE1vVMPB2ewTun5G+qqqZoZKK7E3cZ4QcfaFovk5mhlUSSetjccQRFSrpTeghqRJw3fj0jSFrQbKBD/RhoZEbQU28WfSpnEF8Q+12OoJe/nR38/upMn5XHEYST6FiOI+0TpAEKAxiWh9HZpHYKXbG+jnTQkhGYafT4aQUgt/4cT17XutJUbraVBQjCWmmY+HA9utEYf/fpPrpd+G66ud0Hb3yH/zGd9dW5S1GdtJaSMFlXe+C3kehMlPofpABHYku/T+018LM04BlBstke8MsguEmOprltoPN25t5Wc9aJboBMfa+T5PDDr7kVQcCCwzP54uBQSkw+w4uBLi8TlP4qp4c/OmB4vibJAJjZS1/0soFZQQyQoWFQkhkSsW/tCGIJDYfQo15EsHEeOOjkGRPM7sHho3uSry7ayVOw2FPNtgDP3m04jrz0Rl9r5I0uPET71Pmxo9EZZ8dKIa0X5DHOp2ijsghRtlcCQ+8fceqq32ecAOhF8cnDpdguU/bkJXYuFPxaOhJwTDiDEcstMg9AheOvqRyDN2ySKGwoKLPpnWmSLUUXP4irfnZ6ADZKHEISFpXg2plJpScN9NGTMyxVJBoMDBcEqU2SxK7kAlmgDWFxCb+cg586WfOtFWk+eW6AisMUfr7oax1ZuBZxA4WHv1BU17yXhSVUPioDmZTS+5McelZwloelPYRPt0vK7oP8yI8EKb0xaxgcEGh8hOXI47zmA3ZHn6bdgYlqcjVH53MJo5CGT5QhbgDKPegooBNDCLelSRNeSLF5pKP+VqA4KN4ymU6pqqYFANdwc+siw3pIQDISFYEDQTqEi+Lm7evWOXwZdQJJ/VSeiZJ2q8Cr4MPMEoV+gZHFThrDaE3wpXwzI5WfAlku0CNsXDgWvfOaDCWpGvxRRnTmp1zikAiPZYouk8c6A+nySClq1o4p7UTw1EU8Ikd4UuOUpVAZoYZ/MencnVRYr4k3cisT+fQ370LKSUmWQEn3BUqURMYn0VImj88mgnhyTIZJ45hOyE/pflmBpvSPeHtHUDT2K5z/OxxBfQNIY9zUvhF2tDcNvZq7G1qa8y7tpKsb6h6e/ykd5ax5dsTbJx9KA7E7tmeKJiRCXE0kZr3SCmwqhzFOWzJV8QHlB21FbOC0fIv0Z+01wUwglnp90lfAzrU30MXl9cG7dxcH795/UPjx4J1mBp2fyyH04dKfjNeEoTiCzNOPo5Igjp5eAhZHEDiWh6ntDwcQMGg5gFfYaYdghMitYLoCL0GQMfWrUQeuX3WglJ0j4byvSEeKhMSXnztv0nRproHjcQAJLkcR7zqYESRtZICiz84zK4hBx9GhviCmvYLO+IQ8m0WfndgB9PK3Fwe/a58gloydnrB/EI6gp/7qGE6gwx7kiCvlyq8UPVBkeqY8c5Rtgco1U5pBUh8ZGHRKNPfYn6ZofIcNX8O2E20zpi4nVdMAabqOT6rI0+nmc1fZu/zIcy8dOvI9EM6+T5wuxbTOoOvCF501qEPnL50DG6Sropt4H+418BM1gPOeB1TuBbdLQiK095oNgji8maW1u70rTugUZk0op+uesa00zNTJq6h8Ns7jWT6iJcSBw4uFrzh3ZAu/4AjyDIhvBzfCEccBwssCO4HUxxAy8/RzDZDJQx80nUHwVlk6ISNiuU4R1XX1LB7df8z4YUYFznOcQcyqAMasH/ZceSr7S3iot7fY1MNDDbIfY2dr5kU5gzJziDzVk6j+aMwHauh06Rl5IlN0D13LiLxkyX4UpSPrQPrRsmteGkhYz0ZyHvPiuqUQnetAlircYcNnSC9KwU3XeYEQ79BcSsAEwU9OTR9+FAfdakfDLTnAMRDl+hiewgY72lBsNvjoo8tNqknNqBMUmJrulI103RZKMOfZ7QNSZpXAhajjNp0QhZ5UTT1DyyyCsTH1RDlGteHD7dX93Koz2vAXtXc7hOQA9HJIXX+3bLVx5+ektslXoXBOjs2i1UYgQPZcnaJPkRbffgTrTEKIbi07upi1G+pAVvGg2IlVYnN0iQBD6WtA1BUmzFFXrJOWFZjpqwRnE0XK65K34ZBv4d9tBpx5NCOXlpKbCyDTFc1GF8kdAYRvXLcLc6IMXYc+4GU6XdzQCZ8K1DVROgJ2Fi5BEcERYn5c7dgyC2jqshvFEKcqlGTvXElB3DJFDhqbdWuwTsrgZVqQmhZenSf8OkV9kMSFiDpjASUNJ6xf8UIWkw8GSled2xFMMhI50ziR17V2f0O5tM8uIxI6b+ncgQjaoQMj7JuLrvKjJQFh5KNqqiQkJVrhFACPgMYVmyJUyplM5hPXzLpVSAn+qR/jT4H1RRbWNJztZwRJC7/G8d/hCGpd3Wqljbg7bCN1N3ZCV7o1PikSW3FrfKUDvj9+IQ1giXRJht1LUmcQ22NCYnDBpsOTGRMSPMYMGGl3CMzUUeKG5WDa9Bln0JVmArE59IV+zAB6+/bjwZv3mgmk+OW5No++0mfjNWPoy2e5QPRMq2d68WiTLRNp5xImu5w/Nps81OSbWw2nYjbjanNlzhWK2aYN7rTHxomsj34zQrq/gpYuxACThRwTHz1Qf46kF2aUv/42jiDoGTn1TCCklhaZNfRVXjHF+eoXzpenj7U8TIMKHEHMCjo70fIwO4OOD17JCfRK+wS9fnEmR9CJPi+vPYSen4pOAxQtJ+MtdjuC0CryxBEk2dSp8kYRuV0bgdwJl5oMl45aTchy371u1svpr9ItWf6j6F/lv9Kt8d3CVtwaN91ySYPLiBddLajJsvTXAPTXbST8kmtDtjw0dr59uNfAz9QAjiAcKLTxMgoqfrbSjDPU5tV83YIJR5yH86KuLKZRJk/pLzrPIFWccmR67OzJTAc5f+Tg8TIwDW55McASGDt+eKGAowenjxw9X7SM+MZOImYH4Qz6bGeQZ8go7c9qw1/C4WBCsvRdEtBC+eR69kABZxCDZmb0UH9mA3l/FRxBcvQwC+iJZgEdHR56I/6nenA/Ao5TSPmOtAb3iZxCjxWHDnrvIaR+CnVSjnmXjlARcE62q51WuB5I6oGV6mJnkOrzBUeQZ0DhLNPHBiTrY20MjAlJfcTTXSgWioNz1dnp9TRx5IWMAGiO8BicwBVdiLrHTI7ORxhb2Xy2YdMBhTdpdUdJNbLS7sdDAQGMl5B+8/tH+vE8oUSm6IKSqTBtmGPFBRP47vlHdOT9oUyiaSlW/s5Huy0e3FM+BKAd2xGkG4d+m3uD0BVQHmsRBrRlPTe5/XppGDPcWB4mnE7I3wcx6p8ShW6hGmFCJzqLZcu12uqMSrcuB3FpwmUaSfnQdUGT8lZsw2+9zsizHINuy7OL2KGeGSEoZHI2pVILK9e1mS3wyahj5Kc9icg6LmIFIxvMhMMuoWvg6MaOkIo7KywtDi1AVIrHGcxLQsqo9lw48G3LfCM572DSzCqkpczn5CoIFsZPTYdmyF4UHVC0243qS5Whs0OfUIB87AW5o5NBUwwJWkzCSKWIH+8FWQkWnlKd+OvZFYbKVS0/vFCqfujUm0aIDT5Q6LCP6BZdYx8V1Q8eMISmQvEGZxBgHTybw5P6EsKn5Y3Dz4xNS761fXPJAwOt66ZLSh24nhwsKX7O7D2n9qeH1sAv7wiiAfqOI/zO4Qbrm+Q7REL9HXQrj93490vfYx9CA7vNYrYkTJVSBdilIx3Dq0j+3RRt8JXT9GqXfPHrRp+B9+bP158120fOng9Xdga9ffvh4M+37w/eaH8glohdCO9lY9pM+qs2iOaZhs7DHYjNOKYch4/eFor3t29yBtH29VCT9cIY5uDo/IxTBagJ9XBIfBxVOdKw6fsIwiY2LHQGiS9/7hwV78PpmajszQTEGq/O3EqquDlCE0cQHY3z0MF/40tiBVen9kSOII059IZPn5OXc+f05OnBiZaAsVfQay0Le/XqucMX2jyaJWMvz+QI0ifkjzU4OeSnOj11L+hqyxFEuem8U6ZqGFDppDo7UaGjqClvV+gIf3SsduB7tL8i3SrTiN+qcvRjNdI61CwSR2HbGm9wjRrECznXZ3/sNfCAGsC5wowgZrTQqLF62Lk81NLISziF3YQJAfNgO0aRlZc0eD/0QoN9VwKHP0uaWP5l5xMOHqVx8uDYYTYQ+xV9kUOcJV83ngHUjiCcRMonB8hnvWi4YZaQvjiZmUE9QI5DKwMnDUjE23YLGZeDe5Oqej8fJRhIsQSXNHEcOpkJ9NRwZlgcHh7JrjLbQktv2xEkG3ukNF9oYk8h4E/kmPGyMd3XzC6KU4jy4Nk2NaF7LMmCBYicERS984f+3M+SVsIzQ9DTlxvr07wZSIiBP6FMXRhVKMSGceTs6HfT0DlP6YpWMGCFs7zF8S7+pk8u5YDRWnoxFhQU+cFOqFL8jyxdGiFU/EKjiI6kV/qVW2gmxNTK7v6bxllHeCshELwatcabtsPGWdQ1jwgMK0I/NsC3Myqktyc9JUi5BlTh4AZekVz3tHFmvn3Rc8I33Ru+P8mDDDqp6akdaG+gMSMojiDw1vdUlmUgY7c1Pweo3FsHwpbAUy5iAOGLfJRP30i8iBtvKuhSVvIkE1xuHQtdcL5iCxn8wy/PpaTu4GQRaWOFU3AHlWHmaJsV3qZc6btRUJLkY0n9XYevB4jBxloJoPnRAFRqLhs2R/yos6Dcx4mbSAno2obp+usv1ysv8eDU9gXl82eY4lOIGWfGT65P6LoW5CPualXeWUXRhty8fRLSuq8QmXj+t73SKaGkFVNsP1oIfVi43C5cIexpNl1MHCxNELnQO7zMG0qhu2WQz7PWpS/0JzNrh4/3fXMau6ha6oeO++d8MKJ8gqXtIjMHNLMkyoG3+AhDWb5edJumKj7K69wCIi/5/VeX1ft7Cc61f6F7tZ2BUO6Ph9PAL+8IQjVukN3w1GrdAN16p+JW2BqfFImtuDX+Pbpd3JpuHoT749fUQDcVLhFxXyobKclbl61s1qhAOp3qwJWpyckAD4y7YcpPR/AJB4/2BrrGCXT5SU6fS20SfXXwbzmB3r55b2cQewR90mvfKy8jUwdBR6GZQMwGwrmD84dJk3b0qJyvfsXJhtF0VvnJhMpmQ9/piGw7rmiHoyKi46hqVmSt9BKXYqgbRJ0L+dbDaJ8CXaICdIpQPzOruHHpxHH4SIP+UQsvD+NNgZaEAdf223qzhyY0K+hQziDNCjrVJ+T5jDybQ7/6TRtFyxn0+uXZwYsXzzQbSJ+Vf36ijaWPRMtyMn1tjAdCd47SGL2fZOEhJvUjjojAVSyVJYROB4FhhIbktN7r48HHFDCblOQF3/QLi0208R2uyBW2xlca4itujX+Pbhe3ppsHLWDWkfpxdB15CKCOlG/VBX3HedAJZ500DaxA7o+9Bh5QAzhccNQ81vKnOOT7jwAAQABJREFUbo3d2h3qRDu33VhDyWy823Eq4OasdAYBDFw021MJ9vQZS7sYzMrhcy2HBrNHWd51oz4B5443Qy4HkGf/gHPeOIC8TMy0ch59vtFPvOCHgwTHj36ZdYSdo3/qQYgltZCIy0O4ZwOVA+iRPCjM3PFmuyKw80f285DlYXK2sOSW/df8I91OIX3V8am+0gTdkeiP5LVnVgYzh8JfM4aKL2Ww/Awl47hBNmQhxOYjL7L7j7pY4ehdcKDC+Wudqiswb3KtsrHVyI0tyWAntklMfVAGh+2Z+HR6Qi1SnUIHrlsD9FxXcibuHsvlCgg2ZqxUbP7IAjV1qABKDuMTzRmA+iHbXUopApenRPQAnyDQT2LOuOFoEk6lOwooEYnqiN4DDAR+a9pUVW73BaG8fW78CEXSUq3U4JGJAW1f18ZD7wNBPfgvcZxWy6B9u13z4kxtQM5QqscMMYsuBvB3e2NGEP0+M9TU2B5rthpE4LP0B6aR0WdkgsJCiy7oJjAtet+VuclCQAmUodDXkbAxW1aGhlCIybd1sGQrfpF9ZQgNbSu0wft+qGvbuOY5JSk5fYWApu5m5PoDibzwW2Xp8ldHUMppKWBHHskD/+LnUkxCXUPDNcyBY0J3mWjzfGaVQJTrYZvAne+737bNctVzYp7VlF/3/uBjnUoC7EGVG2HwVkyYH/sar5Cok0M3LWHrOWnTUE/JwPM/PzVPHXqiZWYpaSa3q468XEBeHPKeBcMHAVwWcPKkxGp8Eo+y0F8kdkRiA3G7l9B5EZz+BQ5QUi2OMoG5D5RgaS9OIb4GCUlmepaN1/T4Locw8mCFdehEOrJExkeib6fdDFGpclNwHVUj5VdMOK4czPwHUnFmxxLGEcSMoJm/+ezDn6+B/yJHEMpJo+FMu5pHpx62UfnGmELtY7+IBrA9dx1uR2uTMZ3MlkKizrdkDpt0asRt6HBbiIYNoi/lBLrS7/z8+uCt9gY61yfj//3nOzmBPhy8UfheS8VuWD6m3uPzFwx8jLtn/9gBtDiB+GKYHUFx+sSFghMIgaGTfEN2RfQf+TC78whJETqoOCSTQWdWXnflQkIX+SDte4+Y+S+FbEtsBKF+UWLlcrcG10rXDCBnYfov6c/qzOgo9JbeewVp42gNWHAEHR0/OXiu5WE4gV5po+jfFb7QkrAzbRT9m/YOOuET8hqc8Ha6HUG8JabTQZbpCFJtVGY/+PUl7n7N5MqRB0hF/v94+Jp0xTvRbafDwu8kO9cmLCVvSJeHiA3tPrHXwE/SgB1BKisPuCmU1t42gXGHW79OeqZ3wnhIqzG3vcCcEueBn4f34QTSIOFGzn+cNjhveBlwrX3k+KokS4o/azbpJ80EguaG0A4eHEQ3diKRZinYJ2YC4RxSHjuNyinkDabF2wMUlUvZGcx1X+Ya2O7G9qq+OGY0UmBZF3XHSUN3g1OHmT1eGibcoZaFMRvo6Eizgo41I0j2lZlAR3IIMRsI5w8b9DqUw+hYaeDAGHywiTSDkQzMozPLgO4sFgMqnDsoOoMmzwACXX2H66J4QuqpvgG5GfDbwcSAKANABpW+LFwL8fBRke7ZBtj8oxGuG2RNk3jSxNNVEis6ynNqnhqyCzdFLsEk3sSUozN1uMEnsbJA3u2h0m/BSsWGT2T02rnTRjr1fxpSyion/LrkFQ6saYHTFwcfeRqHw8eDabVvZhN81T3Acxcw998iRO8sFczSsHIGqV2wdJBC0o/DMYdjizAqcSMz+FDnDOlKsWQ1w22aVPIZac6kV6oVH6rb55X+NvYuSLe/xo32IFb3cRv1KoIE2wzdXprHrvSGc3/eKkWUzYqQX8/m4v7Rz3aXZzTpzPZTwuvqugqEcbooxKFCCWwjoAvvGYw8P9iZVHyIOye8xdSplpb7PPc6IYdvf+Ito6EkyV+JAQs9Zop2GMc08tVMT+yu6ub92ogLESc9Ni19BzDo0RN9CdJEtwJSJjaY8nSifOhw/ji/wsShraOEdB7pAhtuuy647Tgz6mUj7RTy/SA4DlJuC9GQDy1RDmfGI0RbpkglbQjMr2cZUYZVDw8jI09LxnXiaL6us+uOXoLRos1aGoYU++OhNfBf4wjijRINlIbXhuku5TW+w7togP0I3/n+Kl3z7Hz78NfQQNkdG7IYYV37e0WD2uZZbawMIuGg75yhobui02CfB/YFutRMIL4Udq5Pw797p0/GK/zz328P/v1Gy8PevD34KAeRXuTqYT+ziLIcjPaMYyefivdsH4y/pjnrcdf8KUNdX2Sj/StOpzkOjLGOSC8zTNoEgYwKKxlSeJChmDgBgKMZEyLbLizlhAG4NfNunPT8hXO0BpxNoiOJaNS5Oi3nD3OjmO76GEeQlogdyRHEPkHHGmicaj8gLw3TrCCcQS9fsGH0s4PfNCvoTI6gI+0TxBvsYwYJKjBOJUqOFunIXbMWlaL1s2zQ68c9b3qF0ZeSdfzIHjS+w863Gza+w118pxvfYcMJI2Ugd+FX2o7/FTrzbf2shZgJgBwDRWQkGjtDdMiDE0epNkiUvT/2GnhADbAhM03Tb6UXOWittn2yFx3HdsQeBkabx4S6FS9NmbfBXvYlWx/nD84efU1SDpxrOXeuWUasfuKT9v35RFqD20+fmOHDvj/64ezB6aMfjqp2ILFkzBtEA7eziJlGLJXJrAnK5SHcgyYJ6uekVWAqgrwMGnQjrrOAbPd0Qg9Pa/CAMwen+pGWhjELyHsFsXk0S8JYfmtHkBxEzBaSA8h4vtxo5xF44JmdgeOGPL03kHWqQT4fCcib9doEWvIyyLecdT3oIzyokGyo2TOYFO+laHFkCcdAqEZTuRxVYfIYUGnrBNtOnyAEYNOkN8LBAP2jvrjETSd6DzAhBq8fhKVjxXIYRrQY70RDVOcINkGDyRYEp4Vbyp8kkWOUCyLUPpvnZGyYZKZO1qvO1AW4ccV35CCyIhY84EFXcIKV3FVcATs5IkNnyv3YRY7ZQOUIwgFKu6aNuxARUo9bjiC1NxxB1NDXyEKEq+Xt5yPBcTb4+m6lTsrE1Ag6dEaxQ2IQt47oJJQg16oDnZjIvnJrWmj6CL9OdShoX++VYaGd33UGQEr60lFNNfE6d1ld9qCpSILGOpN1CsQOXEBKrPUA5PZV2bCdSICtgh/XBPHsCIJS93MU09oljGOYDMSxb7KoZM5zGvZA9yM/Px1rjGgj4DKJRwondb3T4lNbYA2HyvFkIBlRHAsOmb2XJ44s1QEb5f3dFOKIb2e8HZeiuTEeex25h3NedbCjmzqJp9syEY6IJqXMKC3TaJFQ/W+8PDYt2lXaeuRZWdn0wPtUbf6J7Kw3+JcDPnu5xTHP8twn2GPbTulN5VjtYWRZYJ1nNUrRTzhK4jma0LNGpWeunXUvHutRuZSTWNqcLxv11R+3LcbGjiBmdIrn/nh4Dfz6jiAaOncCLU9HD2Q6vE+Fje/wPrqGN12HDd8NwffhB61OKFxxC3gffUgNqOl0n9/2tsVZLqUunuggwH7ZZMng2oJheA0UvK89xpmuCUq5L0THbKALbQR9pY2g32t/oPdvzw/efvwoR9D7g/9XzqA3WiLGTKHP7Avk5WBMncxsH8yiFkGJGQ4eHJ7ir2nNX+QgokzKmM6fu2cDiTBSWkTkbVnB9AEnDs7w1NFVcyh4K8VKg6DpkgcImXZ1ORilACVhuP0F1VpDlhmn00hN+zPy0o5nBLFptPYJUufGbJ8TfUHsN/YH0rKwV69faCaQvhj2UsvFtHE0m0kfyhH0DEeQMsURlM4OWWxHJFLk0Hlcb1fK4lJ9P08AUqIHiH2v32cf+t7/K3RNkyJUa8lxH9+SbOB/RPef0rfc5Gu50M/2+nIdZYeNqPYApFSoaCs1Ueh0NA/T+V7akPWFCfH+vNfAA2jgk9ol7ZSByNruafEcow8QADo7g0RIm8Z6+dhp7zhvPNOHmTxeBsyHBNQ3yPFzrb3hPulDARe8OJDzBzhOoGscQZrh4+ViHkRodhCDiPHTQET8WB4VmEIJ5NkSEoQQmZE3b56dkswIvRFTidzLeYOsuAcE2Dq99VVdGEDzwI/jJl8IU8jMn3IQMdjgzTKzhuz80UyhZ/rhEDqumUPHR/mKo2FyCHkPIeW3x8Feh64LeyJp5hNOIb3t9/IvxVEwas2bf4XKy/5FT57ICeVBfs9oYlDI7A9CZfOJGna9c3F8TjTKgLc1MdSDVgzJOWTJUhmXoKKDHuoJS16rHf1zVOA4hJt0AJRv8C6jIp/ZRIkOl2O14wOsDC7e/CbTtvMrnbFiGXEVESD6AKh/0gpTSag7kaejFWK+laGpgNHjDzoQHNWJmEvBaL9N5wE3zkI5gGjvdgTR1hGUH/l1sLRzs1m0GrMdQaBpSzRyoq6Mo8uphTFBVU2UgMN+0lLsDiwaCAmoZKtr6VqDI51yQrMwqXJmjuYVykIb2LxJtM587SWs5dIpbYO8ye+MpuckDluw0QYJZZ7SF/q1xBRYh6NWSkOKHsqCh3VlcgAXQYWHJDN7JILiyL1xBLn0yqu4LITy9LWWLRTMTJSZe94222G7E1SO2wNSRJKEyEe6soNzEtk40i4TFQJZlcgvM3KQHef+F2Z2ermupCtnPc54HD7s38Zm5tjxbGzPTE79hPMSR+ybGMVmow/BYUxZChx1klPLCLx0SN1G/YgmTQgFMy+xzUc4gHDS6zmY52Fs9LE3/Bde8OznxkzN2Exsp9lSKnK45lMEl+6i4gga183ll0jUoX4KzIPnez/hN0/qrh9/j1Wnl0+PbIOh3x8Pq4Ff0hGESjAs07jMm+I+ddE4m75pfjaM8vbHr6UBjCsGqk+kZbr80EuMwyCduXpudw6DANeXFYPsNiVCZqjy6I37gv0frvSp+As5evhkPJ+HfydHEJ+N/58/P2h52NuDP/+UI+jySg8zrFvH8YMTqH+kcfok/VUOIPYHwkHUMHfLFiTuEwubE9XRofbvJzXidaRiQnUk8HQbxKnIpKUMinAI2J0n3UDHiXS6MxrJKYdlaRyhflL6fHAFFqePSwLnEit8JMUar32CNBvoMV8P09sINozma2AnWiLGRtGv9On416+fa2+gU80K0owgzRB6fqo31+r8GJQ8482HZOnBjYVzWYhEWTkY2PmQWMDs+JG++l7ua19USwA1dflnjp9tu7oWrrf0o/8oBL2oqtO2Uu9qJ119QD860HmrrEMGbftjr4EH1ACOID3f+75Pk+cxVYdOa2iLAazgxi3NFzi+TqwXgwU7dAhx/txkpujlpRw+WhJ2qWXE51dXWk587Q8HXGl/uWvNBMIZ1F8D87IynEBimlkRDCYoo9PIUgMJwagDMlk+ErrZ5j0rDMg+FOfW8wDATqCkMYDc/7w5xm4y2NJYwcuwvIm0gF7upYEGywNwyBzJOf9MM4aeeVnukTf0Pzl5FseQNve3g+gwIfSeQSD7yoDwmwZQ118+aUD1STOc9BVNwbwfhuDsS8Hg3Z+lV3nHGugfPWNmErM+DzPwZwCjn2c3OW7LFDvjuvqqda1HuFw2LBnVXo6Z3sLDG8KGtz1rm7kwcdQq54Jw0JeSsdNk7jj4lamykKv5uxuGRkf3C8YtPCxDSHIeOLWD6v+pWcsUzaDj2U5ahGZTkjs52CnVdJu8XR2Hlj75mlmFMw9cqgQFvus6ab1Ebg+g1S74UpzbPjOCaCdq+6mW+Oh/OILUVp5opoHbjQbFyO2ZCxQnQjRgpwDpof8qeK2kKcnrjJbUeS21M8Owa4AIPqhHlaKQZ7jiXfiZc0gz6ItEwW6elJyWGVxT7PKDxxa2cCu9mqAKw0a4jgrNs0MxMa6IbUsKthEPmGzUlEpSIoAOO9F5jhWSa4UjL7QqE5gI/bxlDShD59PzH8+AlGlHELwsF3Adoss1pSwZKtIBBk/cNw1yEfe/7bOipsVu4D7K9aWtwQFsDksgHsiOXaft2akjm45z38t4NZvzEzM4NcPTszm91Ddx0g3Dce+ZnGq7cWzGbttpj+7EP3VNiASWG9kr7hiKpb51ILvrgb1WHNvMLE7v64bjXj8+sPKMj6gwaxOnkJzy2fhfz8c4TzXDk2ugqHThwsKdovgp5RKJ29YC6+tWNBDpyNVB531UnH6qLoJnRimeGUGHtt1NvQ8fTgO/pCMoN8VsTm6gdVPQ+MHfday4Nd60Deuw4WsIjoMy/lO6zrvy28cfVgPdVNrQYsbSPWDZSjZH06bceZmCNhD8vK6VQW1EzyLuHFjeRQeBk4fPxeMI8rKw95oR9EYzgjQTiOVh/6MNo88vrsVQD8Ms+/LSL6wvDwtPZShZCkbbjvPnizY+xAS3s+ir22XKX+NrHaoG9CIleEcqPYKueAB5WwItJr9wqldi6Kthg8EoYhY2ywp10ukKEodT6F1TcU1a/YtLwMWV5WKEzAjS7CD92CuIDaP1UuPgRB3aq1endvy81tKw37Q07KU+Ic++QewfRId3ouVj7Qiig2v+44JGnOr4lNC/HwN0wRkAcaDuvu4dbm0CTEovzhH6tk3k6XihR7Di1vgg+Iv8Om+Hu/lJg+PYym7Q5nSLjqefccy6mk5JQ8Ta98hWDc5VxRpPPA630ZKiuiYa5ewjew38XA18khP/M/f9aIs05tn2uw/IW9nI5ltjafNQZ3YOThp9QZKBgmf/sK+P9o7TkuGLCzmDaunwhfqKy6tL9RXMCvok55B+GkzwY/DgL4sx+FXhlG/nSA0YssQgfQ84S6q+yGHVIXBD1qqkXiB1YHtTZwYT2ImeDaS0RmlUjz2EJlx2VHR854WBQwYPWrLL7MvjYw045PzRgONMTqCT09OktXn/Mw1E7BjSTCHeUtsR5GGBBkfU/cu1dCUHmAZVzAbCOcSeQQwrvOlvLS07URnP5AiC17HKYt+LlgOT3XFFLfu4nNR3uVYoKjiA1DNIOxWEcKqyOB6yjCuNjW6aDpDjfVYig10k0VH6zgVSGmIuAUI0ruOAhMoBYZEnOrIaXjDzUdtgkHbXYX6psNAu2OXOcmYubLv7ZCGzvLypakA36khZKtPsFJdxJxrNwM+I0rPqUHTugwYWulCaXCfI+sSgEvXY6aN7gVkXqyPIfeuivyxzjAPoSe1P1Z+Pd71ct/Acum+9p1BKr0NaWHQWrVg642eMZPSO9KHLcDiM0p6gT4zqNd1tLqEjZ3SxWy6YeZW5UqvGQ71ShD50nRfpJo/IgBrRUT+vAO04siTvgi+9V26TWBYcJ9yMwg9+wChRMLL5+RoBSKhMnM3w2cikBuPyHZZDqPPILpAP/hiu1quNWHFK21AR4outpnyXTSkuSPmU13YDPgCxaQpKLOfDCvHcknbHLE859jWjE9vOPm/+MaNTzn32c2OZr5f64hCyTZOTiFlCcgDZKeSZbWrLODL1Q844vu3h18tflaf+iMN9DBHq6PoiLXJ2WingtsXEcczLEcQSXbV/z9TEXspWntoZxB5v2Oo4hY5Z9quXq55F5Nnz4qsiUAdXJGUSBqZzbCwy6UBX4DgIdZl9pE1yzfmjjhNOv8YHYYAxunnhGUGVMWT78wNp4P8aR5AbYzU6dElDnsZsavc++KRI7H9LR7798etoYBgiRRKPkWoJc7n6msnquw1hxNaj8G5kiusfQy1Hv38sA7jUwz2bQ59faFmY9gd6ow2i376TI4j9gWrD6I/CHbAE7PGRfizxKmePnULMFFq/ICYcn4xXYcwWmg8mSo821nIhq2R2Esm30oPdHpWvyCZ1dTZNbIbQFn3DCck0ymvAGhaNCdFnSgknHD1kp6NJnNk7MI0zSDCcQBoQ4BhiiRhOIL1QliPoiR1Br15qedjvzAjCGaQZQXIK4Qg6UUd3oplBqyMovFueFrvlqdpXgGotCuHQM7Ll+N/ahc6/G97Hbxd+K92MJGPsXFXAcNegKQIZdBvwrcQoZ2Xna9c8E3YKMppJp28xFAA17h1Bd2lmD3toDVzLiLNPEAOSGLVIRHvGGvUTrky9j3FbKBKrJguljoVZoX7zq5cCn/ggAF+RZPaPBgSXmvlzecHHBDRjVA6hq0v1E8S1nNhfkZRD6JOWR2VZGM4kBgiZDdQDkVhGlSlc7iVZSguj+992at6BBm8FdW0ssTKbUqfYWgZVVM21rQd8BkVxiuMs8kO/Tpl5IzssVRmmXN4PCJvLYEPLwQhPTuUMksPmFKeQHEDPTvRFRw1KeAON8CwB4/dZX07LgIkZQXIEMdtDHwtg9gdL01h+xlegWM7wDNsOTw1scASx95CdQaLx23DJyaAodWl7lDqldsBQSvSEnaPOpHFUdD6ngQKDQqd+hkjOyVPo4raNwbfzjAhFNVnHCxQDqTwpAIksWaO7EMtTwMjbkFnvkaciLqoqF2pB/L8KkYI5OxbCRQZaOrgpl2mLb8/amGWHgXkZaGrxSJkND5UIhrIsmtO+30TOzB8vC1Pb8GbRul8zI0hI56P982zArIZsUJ6vhrEJOl+V0/WSnLnezV8ZXHjkKegUv+tpMurcdCHZppLb7IzeYledgYaOY0tV4uxAJ8/k4Xw/LBjK2x4zBzGcGlOmastCWJWcyg5CN6RU1PKKjvuix1WrXgyDCBrpu2nSqEnDDtdAeJij288qsUtRftGJNvydsTSTuOUnL79kMS1l8kxOPpwO8LDDBXsqutSWvaPULmQ37EQpPl5qZnkkoA5sKk6atuksB+M5/xq7Lof9p3IAYePbAcSSX+zZp54h5D3eskzMjkzJRltOm5aUOISqnrbrlhfpaXOSQSfPeLJS4/imzvRUrruDcojT/vVjNpD3dsMZpJeiOIGOn/EhFdlhpfnyLg4hcGyhYPraUyjO1PDD1vMuGpNNgYK6SGkcaRynD+Cw2ioe6XPP6E51JagL2qeOzA0CzMhn7wiyYn6J03+NIyjGhUa3GJlFhW6MTgc/DD9303I0HFAbq+ZJW27qlY47EnjTkbePpoOXTVrdEI3fhw+rgb78XL9cb9qHuyMLtrYbzBctgDx+KOvMNn2Q6+KSQf+w8EaeCpn+f3HBV8KutCH05cEHHEHvcASda6Po9/5iGF8Ou2BGkPY4OHiEIyhLxGxtZXEzI0i+ePYPsuMHU5kZQV/tEKryFdBJpVOjsUlYAmL00q6DUzqlRkUF0KSjWoasJ3cxRaXASiB3FbCSOk55HLthoHfDRzdn6ZCwBxvQU3t1lwm/3YgmziD1a3rjcaBO7LE+H3/qT8j/riViL7VXEDOCXr+WI0jLE071VpoZQSfq7OiocALx68NRq6xlDkaG0O0jl1dUlafv75Ef3d+vwGEj7rIVKw/ithnm15gOpSMEqaPpSHbZA2u61KXFAtTx5rHLD/idMiozvF1OqWjLT9j8i8j/vvoWowvbCZ2fy2rKoVo1b0raH3sNPJwGrjRy+KSHfO4FWuO8T2j8sZ/dYoH0fcVDLoMLHuTlt/CsUDty5Ai6qhk+mQGkr0jK0YPT58ozRtkriBlBmg2jfYKu2E+CN8glhwe7ulc0RsgggXuMn8vG9sT+2P4Tl9Dg6Ct8UIkAfA87KQbwSG1wxECJ3QXoGzNZRey3wUqhB2YA2cniUPFKw7MH2Mzy6b2BWLLFzB1+xziGZI+Txhl0bOcOa6qxLdKaB0U3N9o3T06gGzmFcAIxOEJ+BiDYezam5i33sWcDHXvGEY6ldgQ91RsC72fE9WNwJ+H8c42oc6vDlTY0VzoE4DtNPsomuOtg82gkn3jiM7Xmsc0WLs8awsziQ+bCgINIwVxLjqQUNo0Avn6VNm/TVgZkAscvF3fEXSwoc20x0te5LOXpLCnbxEQHL0soMF0zMhL1URH3FQ1bQj+OlMzO5ecuERSsWE7JVJB1IIGQiR+zw7w8zANoBvrMGqsBNATFy1/A0zMVsyJYGsYyRG8qjryi4U/U5pksoxYC1j0AAp5Fi2C7ekv10g6Ih3rGaB99dJlJz7YyKZqScEKTb6ZXqjVuOuuA7EpF4SvJIn/aLfZq9+h21teRtOlE6/qQpdWVCyR8dAatnRgwFf3uzLSQJXOredXaULmFSjuejkXZKl8DCdD1JANF8XNVaBM4fBTSLmQ/2Kfni+xwnOksK8RW06akI72A9dJBNirWc7W/mijDhhME62c3i4sIXzb5/yRe7PnjZb3X17bbfCX4SjOAsPU4gLzZv5xDzPzBMeR9gpBHnQMrBtAtctoeSG7svITy0651zL3Fr+rlqkkatxrXGdllFMUn14vQIitEbuwzy3px7sR5fshsTTniT49xnuMEkh1mlqZnBoHrPYQUKh+2nB/5eemKTmzzxTe2f2tfKZ9ySzzFOCyxztRNtUDv/Cl00nHZd4V7R1A09iuc/zscQWjKre2fV5kNG432B8dddLlBf5Bxj/6pGvCllLWKkW1jFBFmk8KcccRwdTytoHFAZZidTEfpDeI0COANAE6g8/OLg48ftUeQloUxI+idQj4f/0azgt6+/6ilAJ9kXQ9l0O9zBMGXZWKywjLuuEXonOIYSvmWYcgBTAcyWa5InLNrI0SnIKzjDlAwOKJgJWbwS2UV6QJcCIiQb8KGFcoBXQC5VxwdRdIOxVK1zcBC8OB4a82MICnXjqDP6uA0f0od05GWiL3GEcSG0doXiJlAv8kRxBfEnuuN8dkpXxaj83tqk0H/7j7ecuhE6OJ1ogEw8KgDZxAw19Inkkrr1w9JTXtX+Ffp7sr7T8H+qkxNV9W2OHeZwdBtpUWdqHI9SG/yl843ZHtH0KqyffwBNHClB/yrcgRhlmjfaaNLS3XjxpbFOtGueci1E0iz+m94W8xyAfUDDAzY84ePBrD0CwcQLwAuNRvUM4JwCOl3qQEF9J/Yc0I2CEcQpkhjA/1ib4inLNIoZ9pn7JH7B+4zyZX7EpkhNNBxW2DnZTACKIOSqknoZRDjFCKnidPzqN7tpM/bYnOGu+kY9LGfEEsR+Gw8Xxc75s0zG5MyM0iOoWd2ChE+y9tlOmKVwTDhy1fN/sERJCfQFw2gIjvly/aLN/u94fBhcEL+E30YgC9DstyMpQ/+xD2hlonxmfpeHobMyBh+M4RzY1IHANFV6KFdaaIL5xMdOk/f0XQJ13OuQ+kaRC7cFGUl3sRL8xIEWXzdqhbOjJzLsZP09V/QI+oabIgF8b8xopt80RvyNiZMZjqU0WDTdDgKXHKbkvp3+aULXmTBix+twWgauw6fSwbIaSfZeLdnUki3ulFwRCRHeHnQqoE9DiBmkTEriM2ieYLyPa1CQt+FUFofA1MA0VqoXXjTl5xOwnelW+NN3zoDFy02ZobJByV13tLdzhe6pSycHDusk+S8SFi6neUuMRReTJxDyQ6hIm5uDe+sqxiGLQBy+DmSUP8uw8w6905IXkpSSBbshUL985CokJiwQtv+4kxRe8CWeBmWnTZZYotNZVlWnERwlCNDdiLOEmYosvF87SeF4xCHivAuW/yZ5YlzP3ZaTh82+NfMf4c48a+v7ATy0l7tAwcdS8RuCNWv2Ilp2RC2arW0c0EB8q+TS1WYV6HAqCM1p/3maEWQErLAoHEF8YcjNA4c9gvipSj7ZubLjl5ay35BcgaxROwER5FCHPi2tdDJjtsZxH10yDM3jlXxl523XVVhuadUfInjsGSxXJGu9Nj14p4Fy0l8Rf/8ia5BVyIV3J8fSAO/pCOIhmKPtJTim4A7gtb2nSMdsOh+cPwddCsPxMoeKz8U8QeS7dH/lAZoPrSMHsyvrSQdi5A0LwhNqSAmS0HanduhoXmgoH1i7GX3PV30Qk6gDziCPlwffPDXwj5o0+iP/nT8W80Oev/+wl+KsSOoZwTpweXAy8JY+oUDKI6gPODHEcTyMdIRA1koX7+IhaCR3elZs44l7FTIRxUrWZVXKgMN7rX5wJsyl0IWJs2XsOODqSAFK9la5GgwDxXAGISkc3HNxEqa0JtADwjkDOp9gp7IGXR09OjgN80CYqPoPxS+0N5Ar7Vx9G/aN+jF6cnB2Zk2yCtH0BMPcFjSMGUiSt1sTnQN/XAvGJLaESTdGuc8vAmRtG4Xk8ddsdUm3IUfMDHvMgfsjshf5fd30K08iHMfRB/bqxq6CFtkldhWYINrVOlwuRRc+Mbuw70GHkQDF3LEXOlhP/YNEdImaeseGC9NtKyT7w2/ZdaTrcYaGgCwHEwOHzl+WCqAE4jZQB/ZE0gOoAv9zrV3HE6iS4dxGF1rJsxnOZGGA0hl+2FZxp2y7BCSRNmfaAriN9vC27p6KYdwvulCk4dt4mWBGUzVPd0zcpIbDvPnUosVRtI9kPj2ZsweDIh85BUtuDhrMoBgkMGAw84h3kLjFBKMpQg4bJCDmQsM8L7iCFIf+u2rNoy2vWdfOImkw5tTa0DPZtTwP5EjiOVhpyw70xI0Zhzx5ho8yxs8E0SZ7QyyTupqWS/WVBj7nMFMdAaubH6RIUL/IHdfa/vF00LofS6bBg3HsKOta4BNU7wBzXKdykly1tUqYGTq7LlOlXebrdhVHRYcUReLDnREekFW+YyJ7L42JhT9hgZdlnwMlhuHzFPA4kTANYbHAnKUGkYWko5x2hkcw18t1vl1h3lGBV8Ly5IaHEG0H6WhsWQ1UJXjx44gBrH6saE5ciOLr43pO4cSQ/ZdQSPj1F3jgSfOeejEfKMfzreP8At88rhNB2Sbf5sqnankaHINGze5Ih/8Njyc2ECUUXyGLpLfuUQ2civuSyq6kAYzajNY5tpPfnmmjK5KtkEr/ptyG0FhwdEScv0oX0A/jwUXBxDLctm3R44gzb4h9FItZubo1xs8ey8escU5eCiHNTMJmW34VC9m3VYUz/LgyEiTZPP6Ky31YiYQS30v5fxhf7crbfTPx2GY1Xklu48j6IbZQCwLw6arP+ElMbJLNLXbrlfV30G1lHqQ931RZHxVeOgFPEpWvQn6sPa5AWRSDYdMP/osfjhwsI/+WpiXgjETiBmbcgTJPuMAsmOIpbsNk43GVtsZpOVifF2MhQss0X3EFxvhj411GSnXZfLwrsNyVItJVXSmvYCUvUcNdt8qQr/xXI64vSMI5Tz88Us6grgJxo0gHfkWcEOPwbJRp1Utxwpb4wuJoytujX+Pbhe3ppsHN0SOEWnAPnxADXQzWbutGN20n34wiVkOLA+qElqXMg/dVCDXFbsMlTf01NsH3ujyhpeNot9/OPeMoA9yALEU7L2WhhEyM+jDhwt3JN88I+hYFnVZGsZm0XIKeUGUZgTxCXks/HACqey8oSoZRhq56gBV0/y3dwYpap+8Te5KjETjqnOCtpjUIzPM60emLiG8gaSMUAWrs7I0Z/CO+1RxXRx07cc1hfpXnA4DvHpQBgdaHvZY8SeP2Svomza4e6QvhuEIOjtgadgLxX+XI+iV0i94Y3yqddHaI+hUAwQ6LTqvdgS1LJSDPP4vGdxLWULJw82sX+7pzgXofvsDxz6+Twc/C/Bdfs0Dnmu8y+hwxa3xxnf4XVwRIRV01gV1rQf05pEw+ljIqjZbqk4NOgFyXQsDG5D7Y6+BB9TAhZw4l3YEIUS3R+79ihPKYGCR3JfoxG3hTwLrQZyJLLwJ9swfOX6uNUDg0/B8NMA/HEHaE4gvhTF48J4SGjRc6g0Cb5vjBJLTx/xVhsqLC4byUib3JeUj3+yPkJd+QjDL2rIDz2F5FbW1oaA8hgtAPFwLCxffjg7BiZ2Xf4lSZnT8lNSBtDpUQBww+qKXNyll8KClXF6SUEu6gGuAcYzDRoPzUY51iq3Hxkc2qoGzCZpDDUTIx2CEpWf+8o2cP97zQmXgCALnr+SIluVh/HqggnjwcaVQBMwVZsxi6U0BHUfbvRECMyYa6njzGWEruWgduDyKk05RM4dCovBpEOBRSKQ1qE8lciXzBAO9n4cLCg0yu6wkKLhZOEz76Hzis4Nv4nvrWAQtP0mXUOUOXTSjCmt8iICRqYKtdMgFQ8nV9RB3dz0ibEcQg307f9SOaS5sLO5spQ23Gwb5dgJlr6B2BFH+uJ+VKfUsYWACYNEJ8nG/mS7EUOkwoWO7dVhx5L59hFFa9134zhHcpJtlNkXCFqzxHU6qppj0sWOp65TB7aPqn0tVbU0ZR1sRuXG6MORMaZMHZYQFdiTtEZjuaP1EbYfHzAnGuYtFyml+lJE4IeUyQ5CQE+XYsSNZcNbgAPLSLNlTQhxBXrLF7BxmXMrZ3DOD2B/o8FCzE8sR9JgZQZ4V5EKKv+wxvNXmWALGPm7XV9oHFLu9OIJY1msnkPcJ0qb3Ko+ybiQPLwqQk3p1W64KlO6oDEeHrT/qF/1FA8jVbbFoCcQX/qVFh+gHW+1QTlA2gj7G2SU7yQzK7A8kR4/saRxCsaMncv7wYw8hzw6SY8ibTctm85EWHPjeVNozg2Jr7QzSlx19dZHPoiGQZOeaWaJFRt20ebGhm1eCP9EY5zl6X+qvLPvjgTTwyzqC3NBLKW5jaWl3qsn4wuTmabI0x079OGxOWy65WXdht7mtnc1t7B7yszXgK1aXbb16o3MrkwWOK48J4yH39tHtQhSK0nF+1ZtcHuRvbvTpeE3zZ2kYjqDznhGk5WDvtBzsTzmD3r/TbKGPOII0atDMn0ePjzUDVI4gO3yY8XMonnKB8CZAcT4dH2n4khgwym0ZMP3btIVHaD91pqazvl2fNs+Tj+tJp+PICq+4EXmcq64oWXzmBEGXlA47pZAfeIeJCWAZd0uEKrVkJlDz5GlP+qp9gp7462Ff1KnhCNLn47UU7A82i1YcR9DrP+QI0tviOILkDOo9ghgglBgEjnpQhDBdlsVy2vcwuvY/HRw56uhoV7nh3wvJs6H/HpP7cbfYLGV+D7eQ/aVoSwBxHjR2s60UhbsDtJurmW1IeWrZH3sNPKAGzuUIusAR5Hs07TFnWaSledpS6ETfwV4PftMs84RDiM++s+yXlwF8It5fkOSNMTOBmB3Em2M2idabYz4lf60BhJcRKO9XfSrGNr5tvl4IDJuPXmyfp8VkkBArBrLiZaOw0XkEB1eHHsBdNcmd+9kpIRlIu1YK49jp6vZtmTAccVWRbkmSX3v4qGwvR9DAA8fMkd4ce18fb1bK0i4NvDSgYIbQU94qi4lI/WtefCpeL53juIdGBpvlCQxYGJDgWMogRoMZwY6PtFn0sRxLvYyBgYrkaB7Y7NRlq43uyUozCrrGig0drtDWVcFmcrJAh7vwYTgbl3LonvtT7rtZYHgXrAtacRG1eM4qjDp0nsGz6kba5EO+lfLHcWTgB49VnvtyQtfiNX2Hu3nGLCBxRk9+apGczALy3i8alH9hFpD3e8EZVO1aJVAGjqAnerZiwJo9YLRERvdU9+e5vpRebeOWIBOQuyFpU3clFqEn9QL8oVbuYLRmd/xuzluyv8Jn6n7NOy/9tpy7Uti6AXckqft4jHJENvL56iDvIvNAFj9d7MTyrJpSCycMzdeOIHjIflB+O4LYD4j9eHrzZjuBZI+vPmnprZ63jRM+2zjACwezliQ9PVab0SwgOQ97GaGNUsnCS7BPcuowEwh77a86Ysex53rex6GP4+dasGs5iPjq4Y2Xp7FMTW2zuhTrSvKmL4ke1vt3Y6GgUx1dP+L6RW1xcJMbEIdDESSM3nipCo3tNKqqWUHYUn9JrOzoWCqmZ2TvGSSn+on3c8tMIfYTsvOemUQ48tlMmp/vrbb3sbfcV36+puAh3Yy7DobrXpZOW15e/r6Qk2rvCEJXD3/8so6gOVjPveC1m2pVNLwVt6vC4JVHDTN0tNC+fSZ1003I3bHw2cXd5gcFPPfHr6WBzZUioUvkqZplRA0aJ9pXyV8ZKzAwcT1OigijxlcEPuEIYj8IDQLe2REkh9DH7BHEXkFxBGlGkDaRvtKnJ/lU/KNHcvyoA2LGjx6lDcP5kwGAHEH2s5POLCELrTJj5mnNElL/kYf40mEDrQ4BiokhRuX4zSMDC9IN71C5bw06KFGPRoOEdKRYwzxqNVw5oE9W5W5410Y4HTykP9JDHwMO09hZE0cQvapnBGnwcqheByfQKy0N+5f2CHqpWUG/a88gZgi9eM4eEvqE8VkcQZTrgQe866eLr9j6I9myCO6HR4ipBXUdlTUTYD+2P+KnfKbruovlejS+wxW3xhvf4Ypb4z/CN+1foRsio6aRGBw6MlHQ3KIbZL7+urQ60IuDIHli2R97DTygBtoRlNlvPJ72UXZebZaHWX70G9DhCMqbZjmB1AcwELg416bQ2guIL0N6PyC+IqklBCwXY5YQzqBPcgKxvIBByQ2zSVk+gJ3RADYfDcjLAawRt17b36QlmZ1CyNdS2kIpOdMtfYc2d6ZnhJOKpE+gBLICIxS++g1Kh2PCojM1N3GnRaObmluYt7s4eZiBwWDhqAbjDEAya0cDETmDslwhg5pDZYReEzj0Y3ChAYUSOPp7yZcdQd7HQp+e10DEm5yyD5GcQYdyBEFPWQx4xowg6xNhZx3QT2uIWnGkfg2dsIkzmU9gU+uue9l2wdGRoUGZ0txIK4LKQzC5CDKODBBFlP8BvxWpa0yfYv7jmquYADxY3hGmijaBWY6YBesUQuboWGMCTS0b17SElG1WxKc2TAKP8FF+nlGKwS4dlYdu8FejTXfB4DEDa2Zn4Aji89q5D3XvoAsJ4HLUftj8N3sEMcsjM4IFtRDQpRyFVRCB4RYs1xEql62Qw7x1zou4rZxD3pDqPCG7dYRPuLlUp/pEyakFHJoHOTo+cyZPuDV0W9YsaeYPL9OprrOmk7/59sWsa9XPOaaap4hAeiui4eGYM+K7ZubbMic757SHqjFKd7mtgVwFF4Ndkr2w/HiRBexlYTgJcf5cy/FzI+cMTvlemoszyA4c2Wg25IcWW8tsoCd8ulx7deIs9BfE1Hb8bCQxRGaHI3sEYdtv9NzeS8SutKeZN4d2WdkX6NozgbLM1Rub036t59QfRfUVxuZFb6WPfsZW0rbaOiNfIqk/tKo9enQ2YUFUGQmjr5QkHKMI22XZZBxCsp84ddggGuc6S8MO5RA7VXjKRvxyBrG/m2dcMjvopPcPIo8cQ7Ll2PZ2COF4fYruVB0c8L69KBa5feQ69jUGzpgpKI10lOGlZgStPW6Q+/NDaOC/wxHUN0C3MWnKN21a2Xf0xs3D/bJkHNS+o5QCt9Jt4YPcEd3OYng3PwjCZ5tnn/pZGsiVnKWNq871X65bXz/wdgqV8eKhtq8+XEb+yVIwiDR7R51EHEF8JUabyMnR87YdQdor6I2+FsYMoTdvNSNIy8I+ftDyAG0oaucPjiC/+W1H0DojiGVisqwykdAwS8hxZKAOhJLBYQurkDTJ4NUpFA7KmGRwnQEeRdsdUUCDJrx0zpOqsOKkZHNwSMetPw7P5DFwStK0xpuqaB1vXoHh9jIPh5KfVyqe/p0vhz1+9FmdhxxBukiv2Bfo9cuDP16fxhGkGUG/yzH0Up+R56thdgQp5N6nk0KDlsVqkcT2SlCufvqPNomks02HW3HLWjXpCpH1bz6+b1dmYT+DblTzzno2dsrk2D3gQVU2eEPGDbc/9hp4QA3gCDrXQGHjCJKhw1bS/P3TiX7C0/1lQ3jjywCDr0Vea20Ys334LPxHnD/qB7wvkJxA54KxJ5D3mNAAgk1GPYtUIw0+WQ8/+hKNSvRAzdck6Q+w+9wXlK+4SAg5Epddcjz2KdYN05V7CXlD0fTk5ACTw8uxlE4Nyds9Q8J5V5JWhckrezw1EulIW0plYEnWU/2e6AXHYzmFWKLD3hIMzOPcYcYQs4KA4ewhZHDC4F04DVCAEfIGOrOBGIworR/pYw9m2G+IQcl0LLUTiDC2O/IhtzWHXe/KO7R7QLGGbvFA+xd9rpmjxzJnQky9zmjBFMDHWnU/CnyhH6kJmzEh7zjA97UO2hDrvwqbuVJ4tactGDHCq+AkBCEwXCHZ18MkBVjjTddh4zpNloZ1uPIlTt9PS+NwHEK1y/4SVH9221+DUv/NTCGhRZNSbs0I0gjVS8PA6+fl3l2zEoIg0bT7ZjevrQh0pISqzQho+cVINM3HGXyCEL4Jm0tThq5xM9f9sRTcJXbODjvftiU39SwNQbsprvJDMakT6+dk4wyaFCFO6ZTZLWehUDbhDYBOVLoOt3maOyfb4CkT7SHc7KyWnXS713XFVsMny8LYn012lRlA5Zxhma73YpMNxjHkGZjXmcUJj8eanfhYjqBHsrfMCPLXuHydtAmBisRhgW3+4g8AxGZfa3YQfG6Y1Ul57AtE2svS4gTK18tqRhAVoto6UQtfF+rAH21SoQ8hbe8JC4C/hB/tMKRqy2ueorOudB+4vebUHOg5XA42EXvLvYBd9bJdZlLiDJJNZT9Nb+rPXkHaUuHkmfZisyMInBxAchR5DyHRey847LW2Z7BTX3Z7bL+QLqqETl2oT643lSHOHZ42QE/3kuvQerDk+9NDaUA21y3oocq/t1zEGqLpJqjb5l76GJlG0wS5ibaG5zYMrrlhRlkQ6Zj84PUjulmeM+9PD6KBXIUUnSvWYgTTLd3XWgT22gtFGjsrm1ntjLZHyyDEcDcfCEirw8ARxHpgzQiiYziXs+f9ufYIeq8ZQQrfvPmYr4exNAxH0Ee9KdY003YEMeuHODOEemnYt2/9GXm1W5VB2vQqPvsGIRPxEmjIBQwZwRIh1DFgqU/DCutkMoYRjy2TB2ilxTi8Q2PoiDYnwsQH6lYabrs0ETFwlc518EVSh8HAg46DpWFsGK09gp54RtDBwW+aCfT76xfaI4hNopkRpH2C7AjS54XlADplRtAzBleZtopMfmiEP5LiZLIo8C8kOOk1b2y6s1Y+XYdxOPNI/aORH9uuFP+P0MGaJ5Gq77YMgOgFPToauoreG0CsPHl4FxV5ueH2x14DD6iBc+0hgTPIny2320DWSG3TloJQPy+iYnCgWTwMPgix5Xw++PqSTUQ1G0i/jx8vNTPo3MvEWD5waUeQBgzaQJRlYR44KK+dQOLLywQcQY80Rf5AHxBgYBI7j0XE7nCTia70wx4LOYCXXRr2qXHOZTJlHceME4sdNHNK0k257VJEo4rbLjtj7HGG6oL3vVx8uI3zEy8NOHDssFTLjh0cPoI90SAEOHsFPdEAQOMKDSzk4FEcR4+/WuOlCHlbbacPA5GaEXQs51KWKeAEqjKY9eGZIOpFbbslSP5Vn1lP7BdHniJdIafmICQYE+mEZqFaLH+jHPJcwD9ciZPbYbMG2QdRyte1Mz0DN3CGETM3IIa3rHlGidxtaMMqpeaCwVdQBRkowqXymMw5qjwwBkJkeVyidSM6SH10pGkrHPJuqUhBMY/ONyG7MdrzmsdSqh5dMrHMhlOL00wgz/4gFM1XPXPx6W0cQX4wESN0lr4+bcP7vqjNrY4gaEaZLrDvcxIpGRo/C4qypQmMGuQ6E4vuW1pDAOoXXpwprSFNER5d2swBZZc3aWcs+PCk9Yx6TBKXHPgWu3I2RsKlrNARp44wqJYproHZJoLTgXOkotaRgTo94vmw6X0Nix9Q26vwnnrsnAqjqOgcspItTnm48qcyfC/rbuSG9HOxCacjSHYYx0wc7jiB5Jj3fmxyBMk5xGweNvTny45+nvdsMc2+lBMIrUpi2X/VUfJ6NpDq6qW/anOx2Un3F8RuNLMTm/5ZLwGY2ckXD+MEUvukjaqPgCeHw7LRbhFSIvcq9fKRqigaGO3PeuAm8SE+OKtQvv/hW1fKtNwHcINRhcRNq9ED94HidgjhCPIszcwOygwhloPxVUY2jdaPjaS9VIzZQXIMyTkEnBlEbCKN/caZxAzN7BvEbEzxVxlcrX6kQ5JUAbkQj3pFK+gIzb9gj6BuVErvj4fTwC/pCMrNQOupQ42F5s8Rg7LgAr6Fu4uuYR0uWUcUHIcbreKEdx130TXsLvo97Odo4O6rlevZEuCZxmhhqOgAfMXrYdhxX/MyyJUJeIy62qKml9IucASxNIw3EufaA4h9gN7LEfQBR9Cf2iyamUGaEfTx/aWXhl1o8OBPw/sT8e0IwtnDcrC8Df5qxxC4FZ+yUz4CSRoLKniFpFP3GNsS21JT13Eo6pTz0fksDAZRR7rDSjjuQjpmZUvO5t1hw+ER2DZ3lQiqeShuSfxwJzyhNJIvhxF+UgeD6+yz3jLjCMLxgyNIn44fjqCzg5fP5QjSZtFnZ7zlkCNI/NUPHmgLCsuSsYH4SS7f34BpBVxvV0Y1VCY7f8hrW6Dhwn9gE9pekKfjLsYnClH9Ftwan3SJrbg13nQN67DhawiOw/Vdyl1piN9JZ71tKU0nOChY+1ZJERvCKjaqhQ7HEnmaikgTNWwf7jXwkzWQGUHYgLRlWycPbmizsg7aw4dBEJ945wtXLOvyBwJY5nUhR5AGGuea6cNMoAvZfy8N01LhKzmILvRGmr0rmD3E7FFvJAof8WPQgZVjafAjPRCzXNgfEGCgM1wQxHUgm4LcQr5xAgA3HEEkGCmtB7Rt/eFgbibo+9DcZCAd2kCCxgbrXHaYm9iDQhzo2OXikzf1KRVbCw9sJjY3s4M0QNAIgeVhOIO8TEwDCJYnPLWDR3tUyBHE4IO30QxM/PaZQUf9cBKxL1D2GZrOpZSjstQfuKdU4R6ElBwxLdSLH5JxYNt1Rg1le4ILflKZ2LkaFkhrU1AbvgkNH5Ulvrb7KoMirMImq9D4QvqKdJ5dOqUpv+kHb+Dk90GdUm6nE7qSjo4XR0MXwk30Qu7SBsqyCeuSaAMqM9c+ZQefPIGIuPmiH9M3e3I2x8DWM20t16XqK1LuDzaFznKbOH9wDOGMRf/cD7R45HI/r+cyNsZ9ov0XaXfZI0gUEm7qK+3Bl0/50sYjics3v6aXxK5GXVPhUk8YVp6KULdo5+46dvu4jd1CQhc1poS7z9uyotvOu+ZorQ+cKw7/7dXotoU0tKciU9jyRQdSWeEaXqUp6TKMpz02XeBxConW2YRE2cSLsKWJTaT8XGNIWFbIM1mywph2wfVnVhB7deLokXNGz+G8lPUm/XLMX2OHvZ+PnPay0TiCoGfmpZ3uktj2XaAv8JJ5A+/ZQLLzbDCNQ8jLgOW0z75w4iPHD7h+KUAe9IRM/kN08ebngzrqSBuUllwF1ccVihqok/MA5F9h0LRjZVADn21YzMC3cXG5aI4cZFZcWSg19wW2kVk8zA6SE11O9Tjp5diRPc5+QMz8icPHXxfj64z6PT/RDCE55P2VMYUnnpmZfd+yCTX3m8pyH8KLV2Sg7ty/yMJ/tBFxwaiHU/+XGUFK7o8H18D/NY6gNmSt0d30j+DBc+vQUOtm9Z0KjCPwhIFw3i1nc7NOsn3sJ2qAKzUPDCapDhWTRQKEgfJBUJfZj+edVtgdoUEypr6+vLkVgLcBn2WEr+XgOdfSr48Xmv2DI4g9gpgRxNIwzQj6yGyhD9ovohxB374yt0VPsHqriwMos4KyHxCfkY8TKDg9SqsshJNxpUPgUOhOppLI5o7VaTBEDNU5IUHFRgifHAr9L76TfOA6H4DUvyDO3lhLVHnMDolVaHgHMWncX8DPCMGlx9ArdI/BgKNmBckBhBOIr4cd6hPy0xGUTaOZEfT6d305TF8NO+WrYXxCnhlBYs5bX8rwYwSV049yuK5j9hF6AKcwnS559Ecv6twW0qfc73S6kXxitrHQtW7ATXquCSmw1idl7x4tU8Fv8wMhGUv0cAtx806Z4X13/mIeTjuZla/E2kqXeuRcJJ2Y7DYxy6jryzFIiYDYH3sNPKAGPsqRzyfkuf9xzuQNNwON5VezP/ks8Cch+IqMPxWvt858IYxlCOeaDfRB+8SdX2gvOL2R9ldrtFyh9wTyIEJ5b8SL2x0LwLJfliZouowMVDmDfE/IWvkCsrwAAEAASURBVDnEHuseMb3ksZ2wlRQs905uIewUx3o/JT7sv7C5A7FdsYuEcSIkJ7zs3AGhstz30VfKAWRHru0yS8SgFx/TE8SeEWI/wWN39eK9fgw8WC6WN8o4gtiQ9NgDiyxHOJHz3g4hliuA49fLEgQ7OtKbZwYyepGPk4kZIC63ZGBWEzY+siEgcf0AKOZg5zxgikABfY6mpx59NA7VkHOmoQAyocHRxzm2koooegXXCJXsawun5WhaQKVXclDOchLKkKrrQBIZByKnVuIwiw2bQdUR+K3S3Q0vIcSjGW7pOpUQKv7CG9iMCV4sqMqYESKhv5QjCIcQM4EYrTOjjgzuw8WHurmv133EjIdsAMxMiBo8o7vS0Vpu69xyuHzka9UCrUTJFsCs9VQ4tUr9mqZD8+6Ewt10o+6G34behsBzLRuKtZxofNCgOlOkrjvEqb9YtG46NJ1OXKeU0HwGJhHjV3kiH4z7GpuHmTQnZa0HQtMoblunBA4a2yRdP993yCZy7DMxlg7yQhZ7yz5A7NeDI4hZml4exsxM2WgcQWzsjLMHjdidrRn37ANkJ78dPTh71M70u8ERpL5hOIbU9uIgEl44b2KudtgOKeRGLsvvuAStNkd5caJSX65EwtSA88yLvvulmXmBVDu2vXM7BkA5FOISJQsQKURK4araNgti204oI+b7Q/l5pLWT3rY0zvljGWpm+Xg2kGwuy8HOtETslOfpsxPtuykbLVv9TBtKM0vIXxfzzCDt0SZHPfxcRpelMJJJGsvmU4sr+Xiuf3Twm4z5tK/KtD8eTAO/riPIDZqbP7qpe8qJOahRA+Se0rFrsICBm/nhVcxALsekK2YLjmjjO0wz3xKtHc0Ws0/9vRrgGvZ1Wq/nLkzXW5S55jsGqeDGORsnugYjODsv502bUQPgzYTfToh5OhG9kdBmciwLwAHEYOCDNonGEfRBjqA/3ynsGUF6S4zj56scQcxvyVfC4gjqmUBzZhCPteD0NC3JeMx1vSXk1iFU0lr4GScZ7dQ5QcHApTtKpUVN4x55ZqTpnJ36Q1Sd9gjhmuwDRLJA5NgcxpU8uUpCqxMjjnPGHZk6XkLcYdK03iBoeZj3CMIRxJ5A7BEkR5A2iX6t9B8KX56po2LDu+EIQmuxEeZr3qp7lRUNlSClEWYE0atxP897eluTqErSwq/1dodtSeevUr5LR3n6b30o7qjyEK4lU1QXQ7lpm03R9VCeQTi0K04LXlzh3jzqqoqmD9HCox62HBdqW15oXTpyKUkWjpYxqZyNsgjFG3DRr3T7+F4DP1MD53rYP9cXIN2CFTDY+FazgPS87+Vb3txZSwu8R4QGG1ea4XOlQQWbQ/O1MH8dDLuPY0j9QJaByUHEnhVy/rPRLS8MeGBnMNEuC5aBPeLrkTiDNCPokcN2Z8Te+15CLkm4cQT55uEG6puoQ7SXO5o8ybk9d2rYxsrK/RuLQYGVm8E3PwbkSCE4NGwunWzEgdlYuJeyCRWEEMcNgxit5vKMIJw7LPc68RKEvIU+ZYAh281AxJ8ybicRb6jlBOKLYSxFsBMI55L4UbbLKZkjD/DqRyKc0shHoq2cMzidmaKKghv00KXuM0fSUA7bpgzQkYa/s7fOKj8BfFGfDye4/uEXexqUITo1rm1z5Rz5B5xqmF/K0Ln+LYlxK/+UP7lFYkqFPvKs2Bkn1nQdn5BgUuaETjpiKSMx6LfcSAHsmrdESvueYQCugTeOIPXbmRGkPKbvvNxCDHLVTrxcMPtHsXk0KuKUa5SS6zJZ16497PRXl8ficG/gYJqyT3z0TjrD2Ck51NGF+cHD3EpOx3PapQO68mnSv4MuErmmKsSt1qV13XbLtZZ0T6Mn68pZSbRUa9j8BOMhRv/9LANV2qtjvp7EWv++NmhMEevaN4riCuPSQAak059l4MQT4ZSFNsHsHL7CiBOHzZ0v5RRieRg2GlvsLzrKVntWkGiww8wEutGPPJ71o/CLXgjg5G8YNhtHUOjJI9lJG4YzKCrJTKCqI4HbALKiG35pJ/NaujJFB714kkcMxXLoB/4cbrt+Jg1Hs6dsKzJ5kqltMgaH9hkGlP6YGTvcB4ozgweHEHYUZ70355cD1cu+vPwLp49mA53KCSRn0Ils8/NT2Wj9TvTLMrLM1LQjSMY9DiZkFW/9XA+k6Ivd8bqeep/rGUGMbvbHw2vgl3UE8dBDo6Xp9xHD0am/EpI3N8P3qZsOo0Tj/R51kDFwW0Jg++Mf1ADqnpfqOwWVcRRFX8thNMUDLH/NChyXjjRmmzzAphGr69wOAllPaHiop7O4+fTNTp+PF1oexh5B7y8O3r5/f/BOn41/8xaHkGYEsX+EviATR5A2hKZDc6ccR5A+jC6evAnWk7NwdB6ZLYSpTIcyJKwHYaSKU0gxhFeucbhywB2xvJPCtQyp8aKpthsOZmZ2zbHQTTbLM2lTTTCZ3f2leJVVPCvW2QJVfunRD/FcC9Wc/P4qjeM34sWb6C8HR0++eo+g11oa9i99Pp6No/lq2O/aOPpFzQg6wxHkpWHIpQfE0kucTJSVDpOO0tdeVBy+fyWQH/AsWB5Scq+HJufwXSHEoWu7BcXfcUQ/zalTK/fbMCCRMDjaaj+kNacRiqQfUJIrGPI0ZyBd4m1dFKeVWCDai8stdCc2ZDwx7I+9Bh5QAxd6Q3wpZxAtnAdvO4IUfpFzyG+Ase8MEDTQsGNHIZ8UPtfXwdggmuVffDHynH0pBGNp2LXePn/Wp4SvrvRmmT0kcKLIpKm7cJgvhMk6aqCqDYJkwwn56X7Qz5bTfUPuj/RFfQ92X4DSwPev04TrQU/XBxUkXhBltY0uNMVzOOArYtzATa8KSCPClu1UKuQQEK+fmNiVBW/9vDeFBgoMOA4V9mCjlxvgEDrj08XsRaG9KfiKzTMcQWxcqviJNjE9fpavjsURJKdSq8qyLmWXDAKXKlvGtnASyP86QcMflaYKQRH46JBEdAQkzwYmcKbEOIc+5cznBiGsv0m38ugr0yoOVUPXPMRVwiJUrlWV6oC6FN1OVtfxtiA7VJ13ClxquUUXrU66RawdWjDhwnnWrHOEB1rrWdowoJ/2E4BC4N4g2jOCuCe5l9I2wy/1po7MBHrKviMa6fKbjiBz5WQVTskFUAI+lghEiWaZWkwy7hzgo9e1Xluizh623CGbkrfElXK5fxMd5a8lOm3AX5FENJU5wcpph3HXpAsQadd9jeEwQaC1/eAkXmlaMmbcsCTMThbaQRVJmCMlwNLLuuTIycdb5AzCESSH0JXs9JWc8x/5ouMlm/hrQ2ktD/MLXBljf7lRjh/nEy2fi2fWp7d8wDGE077ssGcjOS753S5Tjzisug6RKU++iuvf+Upi9Jm6F50r03mKtupKltYFFnUqlEz5cZ8kDnWRwE6/1QbbNuP1xtFmXD//yj6TlleGvYS8XEz3zVMZ2jjnTw6eywl0piVip88VV3imL/PyqXmcQXyYJZ+aZ3lY7DJOpimrikREy0scfUbmvSMIvfw6xy/rCPL0OBl3Orc0JrUvxWcrcwsbmsQop5FB1Lg1HtJJN7IukUk/6VzoQqNbeZSFTF1u4gvhLxFtTdwlTNesaTp9F+1PhSHQrjAtZAsC/i4YeMEHisG+eoukMURkjPkeNMK6Y2+qMlhttBJyfXmDqweMcgTRoeDMoSNh+dcH7Qn0QZtC4wB6+06OIDmBekbQdATJweMZQZoVhOPHs36e6hG7l4R12DjCtq6lFNzvqab15HoYRWxVjNLcNNTHOaZeOm288oxZRkUHn+Y2QHDvchQSJzm5hzI5G5dQBQw2xPwbQiiia0Q+O4GU9PRRXSfcYcTXGUF8Ov619gf6l5xBvyn8/eXJwe+/MyOIqaxP1XHxtiJLw1JWCrIjiM5Q5eRtMHDuZwcqVBH9uKcdWk7p329WIOqDfGsacknfhqrIUmrnSXgX3ZbiNl2X1Pxoq30ld/k1bfNskXbpGk/YuOTlKnRJs5auseuYnKioee+oAtVNXBc0iCdPjxQbvw/3GngADVzKt3EVk2AnDY5cmqoHC94fguUCbAj9RY4gfQFMIXtPfMAJpGVg7A0UJ5BmA2mgweygK94qq2/4LAcSb6vtBOLRvAYScfq08wd7r3tu2HTZdtNxN3Iv6tAJmRy3jgoufGyBaCEvHEFok6nvZ8N8H66cRFzZBwvzAVyKMUN4YYtjpxmqc5/3YMQhaf00D0O3tuiJy9B682gNFA61vOuY/YL0Y/kXb5NxBJ2yDAFHkJb1Jq04SxFEw54U0JLXjiCWhjH7w2JTD3rHhBE7PSplc3ipb6ICBdizPbB7Nu1gELy0RqyyOyf5WuNWn6GUuT2gumX4dsnEwF2h4EHVedD11YouZ3kqywpPmZGvzgymya/AMmzFcsrVg2BzjEKVHVynqW0gkK+5+uoHnjxVNKCFduUH5/BcKQItHujFHHSqOP0pjiBmbLA8Mct0piPIdKoY1ef68cn4JziCdC/hCPLzWioOQXN36FJV4Ci1Cg/ZokUBmiaSilVVGP7JVoBZg6WsLrcK2Oh5IZtS7QJvpSMdUg2JRBMZup2SCTqomq7rvNTavLv/d2I57T7PWIejQYpvV2nJQ3TwK3w0gFMnhDh30HOuT+R0VYzGstAEeLbOczr0xBToSA3hSX7S4Ji1yQxOnsVxBDETiJmbF9rHDQfQB72kxV7jpPe+njX7hy/+ssdnQpaWyREkZxF87IRyGVzntCfK4zccWKoIT6k5goMkOkdW/dyOu63UlVJdQkNdyRd4+M+UiPhXXVvfXM84cjoHBNGnOOpm4H7oJbrkI21a6ROd+jlYz9TcWzRmP2OLJjOEuI8e2Ql/enZ68FwOIBxBLwif84ytUF/n5XPzttVyCh3KUe8Zn+w9JFUgi0skRHidElAHYpSpvT65V0HvjwfXgNrEfbfzw8mGSDEW1aIQhSgtrBq9WtRGwGF8RGiygR+t0fSTDna5STeMKvE93ErfdIRu7RT3ix1oaquFCLjC7otD+VOqtL2ctwUM5O7zKrwoVlbdvMsUCamY6dMrQRv9tOkuNxFvQKGlHRG6XWEk2dxTRp2HdcGZYvpJDyof2SwaZ5Bm/rzTvkBv3n04eKvlYX8qZGnYe+HPmRHEvkBsFu0vgmGC6SCYIdQOIH1KPqZZIbOFMJn80qm4ndFuuSr6n3VVjHrlpBAMPwMVC2XOS76FV9MqUx3J64SiI+UnoUoPYHOGOrosJkouD1YCkqWzFSvtEZTrEQcQeDo7fnICVfhES8OePP6qWUD5atj/oxlBv+nLYZsZQSdP/Pn4Z1rD7AfEVoGulbtiX0/4SkaLHA07qgzcxupNhR8SlrDgcs2hgc29RxMU/Y/omu9KR/l9zXbxHsjU9Vxxm7iYrTK2SGsZd8aLsHmhha5q4tFL83N4J6MAjed5wzpb2gVsAO6PvQYeUAOXapvX+tHGv9mxb5+0ZwT5U+8aEFxdZTkYe05c8pUwzf5h5g/LwD5qcPGxvg52KQfRpT4hz2bSHrQyq8iDmOovfE/LlmvmAptDeyCpe8BfiDQOOfRo7BuO+7+iivjOcYiyfPMQ0RG71HFndaKsx8iTiPs051K6br/1LmTgMNNwI03oaMUblkEFPSaHpdLJPRZ8iOvHwIJBxuGhvvyl6TxH+kpNlofljfJwBMn5c4oDiLfNWuL7jJ8c+rx5ZrChVQv+4QjypqQycC5zaGqmKdu4sjGx59QDfQ1pK7+gHvCRH2T4QMVhPolGExhWmLSBLfUUiYNpPxc6YXxVduiTnNcLBsAsZ9OWEIZbIqhyeDmzENC7TbVcjUcExVO3Ajpo5sEDajqkgT4heYObOSYMHEeHSa3n0YLMb8UQN28x5rmMQar/dN9QDTuAPMjXIFbPCJldB4Lns2RG12z+m81w9QJIjiCcj+sefwyMIeewnErgQCDu1ltI67BqYjrn65zO7hO68TV2atVVdNaU0WHrBj6RA97U874j+SbfXTrwcAi/pksquM6B2wD4Qo8DQEeXPtsqhJKv2g/y8Yy00kXpoSPuOhTBLAHuWz3AhDEdYdugfNWNIsnZR+SFrx0xkp5HQ66Vs9dFdxbsqLIBkuk2Hgd8HDt8RUwzN2Wrzz/GEXSuF7Xs38ZWDjiJsnRXTiDB+LKYPwvPEmDh2BCa8mhXdm3zTGi7jSax37JyCKFfx2PHlclSCacoL1gzVuh6V13BdZVdB6wmBzwXjOk6La3UtencFkGc3KokE/YWJxXtnXjwYiuH/De9lMh+b2yUFEcQaeKUiQTQy7xqps9TLwc7e36q52nNtmdGkBxBL/RjltAps4T04vVMDqHjE+0TpExHsu920iu/VaAgB1dTepLsOKK4d/lamB1B0O6PB9fAr+sIqga/NRJpNdu20zfJP6nLLvH7ZdnYNuk/Kc4O7+9LtUP8NyX/lmr+w4JjfjjGWZGGTTW0EBWq3fHnT0D6YSRp6DH4zAbyBp8ybNWv+Y3VezaL1h5B7zQj6D0zgd6+tyPo32/19TDNEPKMIA0UsjQMl0Q7fOho4gjKUjDh1PmMuH3m0jZPSeOtseIypJG4roQDIES2deqUEMZs0lh+DnoNIWywC9TWfCRNKKINgBz3H43rkMzEk07nE6GQKs6avGOJs4avhcU5BO5GHc03O4L++OP5wb9+e2lH0B9aGvbq9cnBS3dOj2swgbuELpyjaqxra6116NqCpzMn1KFIJEwykoKk3k3UuITFfQv8QWqyKt530JvvD5hHoh25vptnIvt5Y0JS2ylKt7GC7BQz6O6DDwJF2p6vMNrc/thr4AE1sHEEMeCQUceus1EoAwIGE4TsM3GuJQa8XeZT8SwH+6ivQ36QQ+jyXG+dGUgIfimnEbOAeN7FCfRZLwoyE4h7Cbsv68K+QLLx9CO2cjIGsaLBcxc67RuT/LFgfbc0v9gQoI1BkeTN4b7OeRvCbVjMoFK2mXOmA+s8kcwSLPdwSgEHP1WWw7Y6Nh0e/Taa5QLeJ4hPwGtmz6EGDYfsRcGMIC8Bw/HDkgM5fbDhOIS0F8Wx9wzSoENOID5xfHjEYANnUDmCoqXUwbJFni67bey054oJmfqZynGnhQg8+I67Wpz6sFqiG4pc6ZpkDdvGrjBfFwBh04EBBVrJEx8Fjcig6Wfk2R4GakTgO+s+wJsI7X63Ql0aIejb8qV9TO1tWI7E3XkH2oxbL14mVk0qjh85gJhZJ4WzVxB7/OVylzTqRxiwexPxXhqmZyUcQZZLwrsedY17MO3cYhQuqQF06AkYEB671mMmFSt+UJckK2nKFGTmCRrau+jXzJ0nYd9tkYrcjSdPQ7ukFQceJwqH4Y7elrfzhHLKx+Cdo+FNZ+ACDw1U0aPxzlT5FRDz7PyC027R9chRzE0naq6Tr3mF2FNg1N55ZVjYEJyZQHlajM29lg22M4gviGkG0Ed9qMUvassRxF5BF8wYUohzny8/jmW/zAaqZWYWE6cPT5G0JbUxtymcQLLlvu+ogCiEsNPHtREslshacFvFZnNU1WecvJufUeMUGXT2daDugSzcRBuYZwGpbIYJ4DuMjsMDxw/3EHu+sdTX91o5g5jFCS21O9KLVC8PY28gbxjNMjHtG/TyufcOeo7dZgY+y3plp6HHRh/xWfl6roPXPCKjr58dQQcHrzXFE6fT/nh4DfyajiDphYF4GlK3lBgN7oeGRH1pYKsqfYPaYKzQ23HoulO4A1sg+HeJt8vazeeyd4H/YPpHEiH5j2gQ739L15r5j6v4V4T6q0yH8LeZTohi43pP6CwinYxZqe3Q/vLgQRdDnE5HRhZHkH4Yfg4MPF8Y8NIwbQ79Vo6gD3L+/M+b994f6M8/tURMm0e/02whBhO88f3KvhCaFYSbAodPQpaH1ebQMqRxBGVGEHsHcYXStmqwIEH9OPDdCzDrSaw7kQF13nRatv4qAxwds/VAXHU22Xo/3fV063w67RwtXsKcoznKyY97EJbdfWY/IHVIGljks/G5Bo8ffZYjSHsEqTP61+8vDv549eLglWYEvX6pWUGaHfRSy8KeMSNIA4rjY10v8U+fJOYqg8p1GYiZ2hKLHDE4Vd8B7RqIhvZjDRnpk1jeOkxHeXccYlHHiCgN7ZpuGsLgVju18h/Sigzet4rdFcPFRN8ppctdCRs25RjY26hIeAec3EOmEmxDVg8Ms5R9bK+Bn6sBHEEsDaN948+wI0jhZ83mYaBwpVmc7DdhR5Bs+0ctNciSMJYCa2+gc5aE8Yl4bVj6GWdQbWorHl/UN9A/MGu0Z/34MVsOIBxB6k08eAAX2zMdQWghg4fcMeP+c6QtF7j1jup44+nT4JQjtkuAAVPEWQaguLlnGbmaqzOaYfDc2y4JWMPdN4DHdsNCjiGFns2pftP7UOjNsVaHZdNRzfbh0/EsNcAZdHqmL9ZoJtAZAxA5hbxvEDTsE3T8SEvLNEiRIyifK6Yc/XXZljiyeWla1WDKL1rLDAIouUsFxItwwqDLAQwtmcTlpcqVpahm0DZ6dpXNITRT40pXgutjewmoCboAIxooORoeiczUsGaQYsaZnKBS4+azlUlNto5mHl02tMPOnXRdg0ZuwuYDcLS+DUUnqK+lIaIbEacUOdgomsErS388gGXWnuA8m7WTgo6el3RPdV/xbOYlYhoFP/bMbXCRw6HytfxAzUcR7jXS1pFOwHEjCezDuP+PvTfhsuNGsjQpiltwz6rqPt3Tp3rm//+2zBT3ndTc715cB9zjBcnMlBRUnucRzwEYDAaDucMAmGMhDI5+duVJNPBJlwSB49v7G5qyLjd73PmcJqUVc3ISXrgnTeis93AGHVFYiewDgwFR5hkEe5Q1tNekFil4GzAyCJGWcuQpJKOCroJXUi7ZKF55NznBQhrMYQzCqM47IAJw49lEzPrCAKOHhK7lYr8gjPfs48Zyrzf60Td/Rb9chiCf8GhjPrqapbzS8dLvNvizTEyz+8nHyxF5SNbN9NGTVwxCvHC8HcrT8ZSJ/AkLCk/yU4Zc0x+8lM/4RiAtNAn4pju0Un4IAY3cILoR3vl5An3X0bl+lwcu+tD8YPRRDVJlUgwPA2PQgAnBH16V9o7WeDEj0/sA3dU+QehmzQp65I+tF9LNLBmT3pYhCD2NMYh9Oe+p720DFAzDg/9wU5a+W1LhN56yTBi883XtEvhBDUG8tHntx9s0XieHUg94gVwf1kqR+BXSBvlbkr6Md+oNTUXry3yKZiviqbjfEraWcaML8BTbG8Jv6+kj+IeyPMn49/B1THjM9VR8YLxJ5RUISmm9CjOU904/GhxPTR1hkvBsOZnipqa3+zmr8aH9oeGgscHY81J7Rjx/9jqGIBmB/vaLlojJMPRcG0hzpOWvSp9lYYshaGwY+lnGoQwYaEhiBPL6Y+8lRANDKRRnV365KUlLt5YKf2Lrc40CVRcxW1pgpknMQNg7ROQqyRFf8NFt9HTja8NQfEoKJ3SY/VPjxOlgahbFicxhGIO0HMwDC4Vv3fqipWDaF8inhj3W7KALGYL0wxDE3kD3NCNI7l19eYZGByMprBo6Pc+l5Mlb95S/PBrATVdLkNAxWHGM2DiVpfPaxUwxHwnt0ZYQ7+MSFO0+deCExoNckFKcpaj7uDVE8Vb6a9zw76IP4mhci3wieUCjELvkeThXJjlHnCXwe0vgjfrCc4+g1DWpcw0kvnggwWajfFV+I73+QksMGFAwG+illoSxDJhj472/hAao7CvBAEKTF1ylOhuIWsqw0rr8OLAgTkYhK4YxgEiZqSn6qYJRxwiJLbmt/SNesFyEuRJfndG0DoPigVRrrRMAtKeUCZSaIxSfsPBG0iwhW+HE6Scljq4GcXPR4YJBg9k8HCWPQYjlu3dtAJKhhy/LGID46iwjUE6p0QBjzBDyIIMZQXe1bEG6vYYgD8fIlsZj0zHKW5lRHvMd5uWPp25LSTtBgoFmt5hEbYWWj2wIu6h4v3JZJ4LvNJcRrwAb8WTcyuCCMHVvEUZeRJTRgT9xwQGfiPHOHBjdU2sohJbsF3lBc72aBph7HydyyDPKo4tc0/8iB+qS+mLaeL1GANYAMYvB5oAywRjaM4Iw/sQQhKFg28dFHGZpWBNMHvd9+eCVW96tYV9wGZFTJZb3Zbxf4I3yrSVe/TPH+MpJ3eCG+kw3fcf0/2g40lcqMkw2I3CaktGEN6rUaaQNuhHcIPsHPXIXUcsbd8OcvjC2RRgHTYLO8hHvMl5wShi6lGfrpX96zlAAJjReD80GwhDECWJaGiZjDzqaGfsYgjyTE0OQfiwJo0/OjCCWimEM4qMuBwWQH4Sh6xlBVBx0Nx9u5e/psuGZ9wQZ8MMBL+HIL/XLcQOOP29P04nmrnIKbnKVD/ykpKETCqWChPt2NjcnH2SsH8FBSHKpQ35rdRqvBWedTVzgt2VkY8bmPVnec3KjjEEsCZNByJv7a5YQxqCH0tmcKha4DEfS3eh361TXSxWjRVG+yVujG8H+Q8t+z4YgnuP1Xz+oIUivo176qI9ZQfxiIzPXjfniu0Lxsh20FhVrr+h5KX8/2L4i/74Pt+rh983lauo8i/KwPZer0WcMidbEjfkqrDkV+XvdpFvvVqbKy+8BrChyixccPYmlvMdEeuokvKEj9e4wnZ+frf7AVRgbgtTIvGR5mAYJz5690d5Az2/88vdXMgTJlTHoF+0bxKky0xCkpWHu+NOwYPRheVhmBBXu7jNfky0wMy2/GiYamjY2g4eUAW56tVQofdhsOPHF3zaJHnSauoTSuGzQnad5Qb/0QEDx2wVITyr/ZgG5m2ZhuBb6KKUbKE4Io7GQBIYhCJsBs4HuaeopR8b/x5NHN/6DGUFP7mmGkDaLlnGITaIvLm7q64QGGMKDD9saxIelKEbNm/MIj3CeeitkXfbDN4hroRw5QWAbbbhEb5fLqPiRb+Emp4TJCWh9pbRizjg/P6MDi5wIhn6e0IYjuFmHpDMEU9ck5/hdHPHFBS/ZpBNc1tZ4UIRDno4ubeicupxWN+H5NRz0h5XuVIoz7CyBP0QCGIKYFcSLnFNicLXpqAYSzPRhIMG+P2808+fFWGKAjmdJmL80y0CEIQijD+l81DW0MN7zoUA/G4AYqPBjJhCVh66y9b/q79Dn6OLoxqRbBZD63YqGO3+uXgPZ/gFY4aWVeArca8XCD93qmBmXnBUeKMk98ejWDCZGvBvLGO87sNDnEld3jS+0dABdzj4U7AeUDaK9RExGoQfsRcHeExpQ+LQaliXoy/N9LTu4p1meGILuySDkr87iFP2OTrFIzVx4R9rmMYwrlCEdHEf+LiYhnkSegdMD4ecnIV+K3Dth+pk8j7QVykeZH/uZ4HFF34miM9bNrmPkH4HB45oHMbmITE+YcPNKeYMh4JpUSMZ0qqQOXkrUZxucxIR+81xLTnp3fUQ03BYr6UkJjrMMsZN3UiX96oY73xVpLiQT9/0Jq532zGxVLAbmDjPSB06efgh6B/wSyAAkOWTD6NSt7EGj58vzAd9M5llVjutzA4/LDtnI42cM0M8q5WyfCeykAIE8yGUPU0LD5o0Ue9g+hPQLmdRn+n/FJw5L2nmsvKx+5YEQNuQRZxj5b0QOeIM3o5de5G1BiZ4endOb9CZowQftPpdmRU4kYWkgS269PEzEbmJJkN7EGASOdas8GPGZncmGzxh23miD/1feozMzgjAKMZMTHc7sIO8PpM39MQZxOiSGIK0M83tnXt3Pju52n9s8pw/ud4PwgKkJECMNE+iTxM8VWODTHzj34g1Up5k3G9aNYiGOCBXawkQKXA2P3ITvJFJEcBOOwIGGfjwTzwhSoZ02epsN11nmxV5u7NOGMYij4zHWe+YmhnvrZhnuWSImY9AjzxK6p2W/tHF6IqqGNtjBg/Mni3xkvyWZ/af66bjn6/ol8IMagjQl1C83AlqU8ZDXNPisL5Fecr1UKHag9Y8kdgpzBd7orxhOaIDpDHoHDAdNQ74Vr7BT+L81jGpPOVv9f2v6/wi99Slcma4Mg1D/VcyX4IZ3BaKViOK+hSeE7XFLIeFvFnpRBsu8PzxPfupoyPU+QQYEJc93LA2DAgn0jyHotQYLrEXmS8MzLQX75e8y/mgm0N9lCPqb/H/XUjH2luhJYdkcWr1hN2RaIsbm0cwMsjEIdc2yMX1HHYMJMkoHhDxjiArvCituK0ZLQzn0F+HUn8gJFZzkFGJxHD6CFgSioNHLsl0BisCQRsMWMuHPYTFq44xw3H8zsTRI1Gsau3y10EI5GhNvEC0zGV+QtSyAJV9/4dQwDEEyCD3RwOHJ43s3nsoY5I1GNSPowpuRJm/I09HnwSLV/Azo4yMgvzBdhnBsYLkfcT49TH7LdVdeF8I0LAsFDTHODnGINvgzjwNOIuBKvzxF+01cPoFDWmWkXPB0igQwSHDVL9egI37xgr0lWYLhRnigOrk85As/x6swx4PvBMh/XHiKVNjZPUvgD5bAaw0sMQTxfn6RH33L/kB8UWbzZ46Jf4vhB0MQJ4RpUMGX5VfS8xj+30jv57h4aS7R8ObQ1tlDd0v7eJBiLST/MAS5JoxBBvWAGQ3oYmst14vUlNQbhNI67oozpBS/q9aATPwAEhda0KDzvw5+RzLD8RdzwvGVSt3EBrd1WnEMLswAePgZXNQgJOO+Nixl/MYyBTb25Sh5joi/68EFy780mNDGpMwK4uszhqAH/vKsmUIYjWQAunehn5aIeZCB3JSD97awD16UN+2G4Ct/aNHqeFziWhr8+SXF6oeOSjJoyUMq/lXOYAObV3TxoMxz5KXYLp5z8rU7CK9pFtSBSF6CKh35pTXAl8t0HE+Z1rySfGDZsQwGlZHcTuiCX7nEB5z5F/NKvs198nJaFnA8cct7qJXSKJrlGXqqg6qH/H1RvwqjEEvCsndXjvSmf0A6cDQk13sFtXFkvF6MLg1D2B6IUh/LiDAjqZRr1gXhwprwzLXShh+FSbsnAJqvfQmNOGKOTlNsJT4ijHDxroj+l8EtN4TkV+GaYzlLFpR+xgV7j0FyXyIQGkNmRhNk0wXCEqzo8Yi+4wdaUELOApcXEnJ4B5gJxGzLT97bRnzpmbN5szuRenIxHsYYz7HxH2XcwWWzaPYHyowg9c9ZJoaBXzM6mR2EEf+9cD2LiFMelQ/GJL+DvDdiIm8ZuhsDECXVm4K7+eHVb49xzfiQCP5ZO0Zax5Fe6Ra8vV8hx6Mk5gW1KUmeUTgEA3nyh9xjeIFAdCHZdFaQ44debpqfhq7+lf2DlAZD/W3NrsOoww89zfIvb+Avl838o5sx1MsYpGPlH6K7pcOZSYSx31vhqRDV1WgTaHMxE+g/tSH1eUaQxXHttx/SEISap2Ln4g3mX7cNwss+Q/XhpqKskMv+SWmNG1CcjTbeJTDQowwUt/GYiMJXqr+X/zJX386JNKfL/u20X8NYRXaK/i7fbzFeAiueOnaXHsOGt1JfE60c6yk6qk+zqnkQkWOlO7KxclQC0lRRJv9+CUDpo+mSx2d1TDAAeb8IGXs4Pv4XbRj9TCeG/fVvWh72t2c5PUwNDwMNDDzdLBo1zR5ADBB+VcMWuN5i4X2WcYhuNHmTLl54GIYgNKzSuxS1rrTYC/8Q6LsKmV74LRYAFYC9RTq6fX/gL1TsbH558A+ndRYqcOm/0TAYNuRnYh5EYATKsrAbmrLqvYL07G9rJtAdNRoXbGAn1zOCHms2kA1BOtpSx1k+1c/rmTVQuNCXjFvah4I8kjcuTKWBRPpcaRiLV2xHEWtP67TLK14sv1WIGzrv1JCLkpK6MopQBmLINtXmFtw3MxHhkztVYLuMrJtgO/iGMDxOKP+aVsHmtYMDHHgb+oYYeg7qtsXPJHl9grb5Z/mLuC9dOnEj0dk5S+AaJPAKQxCz4/VS0/nXWOPGFzaK9iACnS7jjwYOXgbmWZ+a+YmuZ5mYwq/fciQxA1WbsL00wbOAfCokWic6Oi7GHlUgDxpOuAxuqJ3WxXJ1ucbsKhw0iVvijTluwp3owTlEK5i2rfov8U0FAfykLSwY1qHxDg4UP9ASJ7oeSIAkQUqXqxWTH5pyNbuT4+RpqvjdxbDD4EJfm70fhb44e7kBhiAGFgwwbBSSbtdJYlqVIHwZkPRj9gczOBhwoAPd/Mlj6QimbMIDji6LNN6EhWlZD9hoRUccxaJgtFsuiUMqXVxoD+UWLDDJI6G0tSCJivlw9BYPM22PE7PcV4ILGO9CKjHCPYVu9kbaxHOH+/3VtLj0M1Ys/EBItaarXPbQ0E36+glBI6n3w9ng5D7ytTxFVW4G9TUEyRikmSD+MKd3iFl7fPxgjBAelYcffgxB7BPEqa6dDcQz4dmebLLFAPmFw9OGoJSCgmw+s528C6uEjuGUECnkKl7hdRvf8O/lXpX/mh+8fA/emqb+mdaP0+CFFl7kKNfjOyMpkH/naklU1nLzfLJUi9mWfHQ1GSsRPVn1gdGtGAr1anhvN/YHQn+/lYEHQ/5rGeyfv5S+1h6er6TPX8k45H3epMeZCeT93YTLvnCcOgYdWHOX1G8wWkCWEfgyb8oX3niu1eWU1f3xAd+euUtEwUGIO4xGCiS8ua2DBuRGFvZRan69Gh41y+8xTBcuPCUkRy5owBkKiZYKWg7bqCq/0n3xMrHo68za1L5s8txWv9uzgmQMuuf92tDF7OMmvaxZQJwellPFpLfvXchQzyliylsfbzH41xB000oZ/jAEdUaQg+fbNUvghzUEsad5Xt9IKJUhTRNwXlxeZddNofD+G4eb3zXHbg2AwUIKndDkvs+l4WDNJoIqY6LkaH/d8mj9MMn+7r5w09zL9yj6Fbk3zRp9lMca96/4LW8R2NyvZU5cGTmJN4A7vBOIKw0/EOHwnvBy7K68B36DnIZbEyND0iQBDYwfffHYuLnK38nolNxQw8LX46w5fq4vEM/+ro2jX7668de/yhD092eeHcQXZZYQMGjw0gHP/klDhhGImT7MFEJ9k+1ncL0/UE0XYkV5s5TA3ZYaguBnsu+yuMyj7LP8lIxoIatBGEXc3JVGiluiuLzxvdK8VEZAN3+JAnMblXR02tspo+NGO+6BiBK6YfKa7BiC0vXGXPbJAweODb6vTeg4VvjB/ds+NewvTx9oVtCDG499rOUdzQxi2uotTWWVIUgNlw4k8GDARYVx8nFechEX/lEgHOQZTiOXxEnuwssFwyJDYKQjAJ5hRSN6gdkPjRA0qYmKr7niyzXjCSfkAcVC2B0RogZy05LCaKN/QPh4mZVmUvcSkkhDVPGmPfynOtROqvgWcRNZaZaGwwRMcRAu0tk9S+CPl8ArGYHeaADAO8tAgMHGZ8EYIHA0/GsZ7zktjL3fciAAX5Q1+1N7BfFVGeM/x8wzkPYsTtFBZ6PDvTQMHY/CYSABjHffCggY9QBlhBv9WH8qnarfrsIJb4TRmknvKir/comHXPAEHnUOiMKunBtC0Lb7Ard3CUNjXFA1v9xRTI7ihl+KR3nQKrqP9utHwYFJn2t250/a783SUZk5brjLwxhg+JQaDSpsAJJe98wgGYceMiuIvSg0E+hCRqB7Wv5L08dgghkfHhMqR4sWnjTggC2X3ooTv7mzG41rgQiaC+3PVW2MC4RfVKkL6bDLuyg5dDHXbCsc9M0xSSr+FOJn+UwcfM5NvIJ6Fb3GNZ/gDeILOSDbczftlH1BSX7JzdhHKvAdCZCWvxmCzorfmEBdYqfgOXOFnz29pgll4QjJpZdnGoIwACmsvsFuRhCYQ4ZezqUs9Ubc+JmBJx/JNHhlaUvlzbPdqtGQfzgLcy1d5T4ep1HKp0goV8pGaYAmZKTtlrJvwc2zSqvAq3Ab/3u5Ky/lYYU135aV8OpvfN3TcabIzdGSlYTKLPHtkjfPMHJEf/IS9BnwvEo5feHsD8SMHeojp4WlE9m+suqpIjjxCyMQxiBOAnut5V8vvTRMMzm1Tyd63EfKS6+/fafZnOBqJhA6/KMsQH7P1O8WK/rlKQ+NJYbgaLxXcEeZ0GZ2E3Y5FQcmJUjpCOVHuGUMDDxqStMQ1mUC3JBZ5Ubq+kFqnIxjBicc/TByFgmTEna4ISRByShvWi4oElUtcB9cjZ+M9zayYwTi1yVitzV7kyW6WhaGvmaD/ywL03JeGYUePWI2pwxBWkpGGu8FR13UtB9EF4NQ+MeQ/1+ipSp7vn4ACaitziv0A/CysTAOthsvcF/j9YUWKu+TXiZiJ8ZGYuf5VvwO+U8USJX6PoaLW5dUlUvd76N0GYv0K93LGMoLhO9BJPElPBTVJaBAgn3z9SVjOg4Q5qon8MAgBX2uYXRRdBoqgbYkU7HToXcSJaOzQqPzhhMINCPohQYJv2hW0EtNQ/3r337xxtHPtDQMQ9AHf3WQqv3MfBfygqZcDEG4P2nvIDVAqOkYguAjeHAHro+wlKuF0kMqYsKDjZYhvNMy0slJOjvxF62uoC2iCSo8o9pACbLIaPOSllabRnMKeRAkLXFJit96fzEEZbCgAYINbmmEUnpNT70pCalTzwajGID4CvHw4W1tDv1Qx8Y/0Cyg+zYE3X84DEGaLXRXgwv2EcIQxEU5kF78kXaeovyjEOayBdoEkRS+Kw7wLoqIwwWJVQSHaAcR1T92Dd6XzCnFRmaBQ/cSHMBVOCRo3Am8SQzEgbplUBgExM8BvsrCpIdgdmiM4M7XWQLXKIGXmv2j09+tu2wIQj9rYMCSAc/40cwfZnu+fKWvyTLwP8cwJP3+SgYg7x2kgQYDiXSrGZRI27iSo3X4Ra9j5M/SXgz+0oKqIGjDwKg/1AX9XCXqwteAV0ZbODoJcKtwUTZXEYkzUdNONTydYq2zTnkJDcCkCL4pG5Q4twH+AqA2m8zGV2YbhjzAkKQ8M0iGII0C2IOim5JyepiNP5r68/Bhlog9EuyhjEIxBOkgAPZ/06xPBhnZEyZtiqUNP+powBftTGY8IY3ybG4B+IoW5b7TqAonBiRS1FDQttTkVLY9NZNUnqI2dF0gi4wUt6YJV8WC33I6Yatvjy9aw8i14uAHr7jJjxIWsmITO+FwVzxiknZiFHPFW6nt/aOtNzBUm36PRz588CJWP7kMxrf9geT/oplAHqOOGUGmBqpS+FuY0md2GAagOSPIewcRp9/X2l2ydnl14/nxl4ehnPAqH37kx4+rsBkD5KqLVKVwxPlauiPubxFuCX4LWkca+zLOajDrimXdOsKzLpLcDYuKwL8rBDjqLyue2UDeK8rRekrqP2BkJ96zeeRhw36W6r7R0fHvdIx89nYbS8OYEcTm/9LdXvLLRtHsJyT9jQHJs0H9vomuHrx/1uFok+hsGHMd9/MMrDo8TPOOIAd+M00kBR1dlMvXwFlCSSeAUfKsgt3nxlioF7Dxc3RmSyHS9KGDF3EawfjhgnCNQRiSFFb9whjEYS0qvWbzqB+tGT02BqGntUSMvYLuaGbQffXHmc3JUrAH0tUcMf/YMzmZESScodvZ0gEaLBOjjlJf0VsCDUNQShdOz/frksAPawj65BecF9pqeZNPX5tCCRe2IS2er8UtaH9Kb6v29zBf3LqkqWzqfg+dr+FAZ6W/4jqPRn4L8YhHhwdFtV6jkUgpDnErnjhKx2zFwb+GRcUMchtqckRbP4I7OuBW8gR5L0FVEhoiBg5v1fC8ZU2yvhg/4+uDpqT+VbOB/q7NotkrqIYgBg6fZPPgS0MHEAwYaNTYNJoBBDRZDoYxSF7zleVhypevXm5kwoBF4afpQpjXHLPZsgOHilyj4CpsP7QT2zvhRIGXUMJ8DRHIBR9YRNhQJffYRoFi5tMZI0gjyvjfHXbCSkRHT/O/hTuWEmggwUbRLCO49fOv/nL8kA1DtW/EQ/2eaibQE/3+QgP06K4aIA0W9OWYE8PY4O5CxiCmpIZnnmgkiCR3vzx0wZDpwA4qrOoKvKC6idvfSX58RfcYii9Dx4jvCGMw2y5ntoWGjBUufaHauyRZsHdejyVALm5p7LBmwGjCuVTeJd0xzuTFv12R2lDPhqAp2LPvWiTw8sONG68+RE9iAPKAQoOCtzo2/uUbLf2S0ccbQ8sA9OrFKxn5cyDAW2aAerNRLUPQ0hUqdwYlw/BjrSOd7rcd3Y4BSG8+ruK2DaIVmvrUgZ0ckl7pWvEcSw1quzCr7i7hCKxqoxWwVX3Dh63i424IyGXGBWWLVBr0o8Jb3Y4/ihCZEGaQEQO/vzTLGEQaqv5tDRL4enxLX4a7V5C/MGv2j/cKeqRZQfKj81mGwBHyD/xFWoMT6XmNU27cFiG3KWKOjwbZVw7ZkMcJnZNCjPKm1BgKUkrwA8u9ZSVU//AOwQYvRD1AHPBAjrIjrBRDQS4UB4GmOk1vxYfEvNYAJd9xu6GlbInfgJsHGsmheHuqQZwSJTzTJHZCoJE3NDhr16C4cROP2PzU5LrvwoBcfQL6QSw3xGDAINXHySthSxE5KC95bjH7QIagnBiW5WHkkbZ98OoElFNhaILAJY/xBLbhCJj8R1mAP1IHQSEoG9nuVbfmFOyVylUpfh94+Vipw9Mp+IqD/xTeCmvZRG3Ug0Ambb8VlTtu/crfSfx8hEXX1snoIUpz8Oz5SZ/MfrCoy7LAci72D2JGD5tEv9UG0W8xBqkvzoxNdDazgZipj1GfTaL9E84HDEEy/ue0sHRh4SMfX+k58laOjv5wY/gZMBgdPG+ldJh4ruJVNnL1H00TzZN0ic/7pmQOElOq0029gvYhXkHeaGQaanVhsf7QSZ+YBBmBYHj1ptFe2svsTXGupopZlxhzslfQMAZp6RcfZZnN2VlBzAR6yGbRci8uMBIJV19jb7HHkNJruyHP2ENHw0tmBMlIdO4D8iCv/fphDUEf9UKvVaN+XnUqS8KpL/VXmoTzuo/61Ih/M5cyrmX9WvHArUyKR9qj7Br3e7iq/7nK9DHzxjfz4pnzY2SR5KJ40d51tyjS6Od/FFDRYjk3WlofxyWZkMhXBhiA0o/JHVKOEIxOPyErMVSvDEFsTqfNJt6+V+OjBugFR8ljCNJMoF+evbjxd20czYZ1H4T3lmmoGnx8/jIMPmosYgRCPTN4AM5XMpaQwTT58UujgiEI3jCvGA4OPI/LDZn8LpqaUbM+4lxzEETbqSaym7IkwVLLyGvQp7PUjhUu+cMf0Vs+ePwrjcTDJHScSi7ct/HhiFiVVj8aJqamygikrwgaJ+ir8c8aGLAsTIYgzf75y1MdFa/ZQE81ULBxSEYi4u/oWGEMQfdGw+O8xAcuwphSDKeGK8plUqzLU+EJDqf8j5sdokkX2RJ3+drina9whxtal/FPQZpkjZvGIGJzuSTwpKDzBTzCxjACkYEbtty2OlnYJF3I5pYUtFrGuleVrfEmuwoNAJHn6yyBa5TACwxB7zXoHIOJT9LNDA7e6ovyS31B9l5AMgh5w1ENJF56NhDLCrQMWIOKd9pU+gOGIGszabSxN1AMPuhydHV0Ou+7dTfK1wMIXAq/1INDnbB+Jd6Vr3hxExfhEQ10q6MBG3CEHcPNvtRbTd0aWHmVGHnM1NGnzbVwdLr8GPcZZMifU2lkDCKsr81eIiaSDBDYf+KOBgvsK7HtE6SlBwwqPBNIev0RX5ql4xlgMCPo4oI0MfbfURucr8waaHiAIR7FElzNwU/C5ZDSpKxpl3pHWPETv2KTIhfxHWjVbdzqti3BVYIlqjmQGzwOesMdjBm/cQTwd/YEJFOC0k1pgHLx3EzbfoMG2ZZqTVf/Hi+Y0E38KUOOZaH4ulAoJ3ULoRey4iW35T4Yzkwg8S8LkA0/dMBsBMjskRqHyheyiFFVg1aNXJnF4JNdFXETS6FzBQeOJld4s1RJGROHI4x0pQaenXA9IJZGJBLeZzeqGIH/2Pe1BHAK74Wt/mMp1ri1vKSd4bzuoRdoaYceMAx85NnlYsHgvZW8/ayCGyz6wRiC6DPrPQiWVC69xRiCmNHzEWOQlnq9YZN/7wOkj7Iy4NPvfiFDEB9iMQC9sQH/g/rsSiP9jQHJewNtfX3qJL3T0b9GX/snzqW34THlRX8Dg8u8CcTtZgiBaatWcKY65b0qHeJMBI+vioDScrXm5jmt8nSOwhBMxJEg8g9+8EJZePIUXnrpU1Jw0mn6heoam7FXpzKbR2p6GHSiq32SmI1AWiom96FmcDIjyBtGX2AIYlNpjp3XVg3a1wGj/W1tGvSzDnuBLsOmaQhy8c63a5bAn8wQFGm1ChHiJc+Lnrj1fhV8xfkz+1d1cFU5ilP3iHe1/JriW1IE71s4yXUddDbV9+USZTjzWVOTd6kcSwe8cdBYcQuv2zIM10pVfkUXAwXbqfqGSaOV4ntN8Xn//kuWiPEVgj0l5P7t+YsbzzEG6SQxjEN8qWDm0Hu+PoslGkTNeJW6Z7CQhg0+2UOoywyi6smJxkY/f1WWV37ifA1+zWM5ht/E5j5QaWj50SgNUEjonk78Dl2BIGL48p9bKfnQ6KaAC28KbRkKRgZbeGAKIalSWgYM2QwyhiCVWg0FDZE2iFYLxClhHAVPI/NAg4LHanCePM2SsCcyBmEI4kSZRw80HVV4DBDuaNkADRj5wEA4G5KSgGgE0yiKJyMVNylGskYqWEqO+a7bUuwm38heRYBcSLdwsQtP2U4KzcdxTShgvRuBIs6k9hnvirhJJImKhszq38iNDLd8t4iBa7kvQLx+fw6wc/AsgT9QAs9lBIohSMsL/HVYSwtkrH8z9gFinyDvD6R9Jlgqhv+llou9k8H/nXS5T6jRQMLtQg0+1tHM7KQ2rIYgZgIlbH3Z9z9KaJZ6VC47W2WD1lq74g/qCg+ZY/2kXei1eAvakx4Ia5oizpyC1HAHGNT2bYCtgQXx2UAaw5DC3psis4L8pVk6nsECep6vzD2i+IEMQez/RhjjPzM+vTxMX5ofeC84DU6k5+9Iz3vJgRQg434FF90OjzyFctlStLiB57t84opZd6YgzRg+IZhFOKdwm+5y3ORmiLmoewb20Bm6TFBxK1DyF4TfCl0JBIPYkxxs6Y7p1x7UqdTFr7vPk9yuzs8iJV4ez/6R+6s6RviZIeQTxBSPIQjbkGkpI6Tp/ozq0s8acDMbiFPDsncU9Q0kPplxTc7CCfn5f4tNnyh4a7UcEOON3Ie/VCftLeKkh5y/F/cUgXA+Y/5RWsf0k9I/5lvz3ZeJZ1mpBut0nulr8nyb86CzE3wMoOkn590wPoSFx2dDZvSwxw8fWdkb6LX2/unhLRiCXmqbhmfMCpIh30fGM3ufvYH0I61sSMPIJK55AUSz2zHEGMTbw09wG3XAGX1veDW/A4azPV8CvJ+FkT4wQxIxRBV4RYER25cdmb5MI33XvWyDZpgFD6JqRx4CHBthc0U3/rRO1DfCGGDRz+hqwrBG8+SZPCr6LXmir1kexk+Ge/W773p5GEvFhuFeRqEHgt2Tzr7PrCHh3cYYJD1/S/t3oqMRITON/ksz+KXCz9cPIIEf1BCkTtlmCZ0NZyoYL25gvEOn3iNgvP6n4n4Amf9mLAxV8U16NJoTt9Ih2ZQQvhma2Ct0n9GKs4+ZacCZVKvbwD6mnlhHWmAWe8UqTNForaH49qnXtPJb8co17pL+En2pwmhe4SZPOkFrMr+FmLd18YWCrxGsN37H12R9cXihr8VvNGD4RcsJ+L14/tpTU98p7qU2lX6nL840QhiBYhBKw2KDkFjLjKAYh8gLOfJ1ma8L/srsnJP/bLzgZVxr8QoTGXeJcEPSMaFP+WgQACUxfuMJ4hqnQL4UkK8gg0i+eJSgXCVMDY3M+tzbuLmhUR4043yB+PJZ8/+YEaQ9gVxKloVpSRjLBtj8mZPCHupUMGYEsSnE+RwGAABAAElEQVT0YzaJ5qQCNqfTWmUanAwONKhgcDAMQZQEXjzecsGU53AtOeSgP66WxQFuiGCJd1DldfIN6bSnr86p2Ej2VMyEhaONhcHhiB98jUc0E8nH4xjFm2lOMeznpgQjzvldxdjAnRqE5x7e9pknBK2SMl1wBYjcdynS0ziAzsGzBP5ICTx/9+uNl54RpI1GGRxoQPHuQzb9Z5NRG380gHjFQMLG/Xc2Er3RYOKdTgt7/+GTBxExBGVZr3u6XuKrl/8nNiuTHqfSSHerKxwXg1ANQcJonZmewlyLpkj0gWBW7hUnOnejM5Bab/dqAC3bCx+8hWzxtnh7wsOeEyKCVThUnR9EPLBgmCYdbz+GIAxAcseMz5uy2nj/CX0txhDErKALzQbii/J9Nh/1PkEsC2NWkAxB/DT4QNff1cbR3r9Cut7LwySWW2poPNAYZVlnBLW0q1vd77ZgyIt4ytMyrfhADVf5OtBq/BGf9gScDW6leXxGojjwSmcm2CD2bHgbwcYD4DmsEX3qe2hT8FTyrAq57EKtFPOU97kUtqZc06xwKI03Yw8eIdKZnm6eETTk676BRv2GYRQSljeOdryoDgaRDYafLAvDr9kHCnO8OCUFL6gjwch3loE8gkPbTS11nBKSYp9qJHaJ1rjTWMGeOTX13iUtOF+jQYpTdL43Lem5TtFITO6lt8JO+Vdey/t475YsgrUAIDWCepr2I/uAuCuFE0361BoMguwTJJu7cIsN/Cftu0nfW7pbP/boZMkum/wzexPjDzPzMQS9kj5/q9lCGPrff9DsIbn6z4dYsWLaokf/OvocP9pBfW7r3WH8IeyXL5ojeh1c/Xa8pwy8nynXwCEkkPvilCWJnF5RxOonOB1nF7W9egcUpxoc0iAbeyBOh+RO3DTmLvii6zjJ031yPwCeRfrjmZmv9CqeDUHKi83X2avzFh9bNVWfWfrsF3RhYxA6mb3c1B9ndhD6W/1zZgXRN2cGEbM+mRnkvYJEF539n9L5Inm+fgAJ/KCGIFlpUwOslI9ysnIXkHfo+B4R7qt/jDvS+XcIt6xXlyUYxat7Cn/Kc5UimJXkPjXPoUp50jumJfXAk8KLcprPaKabucS35lV/+VhTNW6FDb8tEEs8HdIde0vcmtyNQfJCZfJCgWls3foFIzNkmMOSNcY0Lv6x0SjHVmp66nPWJuv0MBoj1igDZ4BBg/RBnRuNO7wvhQ0/ettx0ctMeaXx6UyhMC4N6i/KNED8Ik3rcacb0jWj4R+Jh3O5+U9SwwsY5RMhG4NaYKXcLqOKmlogTmzobCDCadBA0I+rz1l8zPwTRU4MDgbnKigzglTbdbqMNxGl5DIC3dYggT0hLtg/wo0M+wNpRpAamyeP73vfCI6Nv6/N6i7Ak7HIpxVofKU2Sg1YciY/mm+LRM/fr4R5sDiGTFKucEgZhK0yiMlxbZ59eGuoA3YeeI1+THPASfDk/XJKv4Uiu4/huV++4B0WwK2f96nFEdxCkAuBgYZz6hLG4SL9AURwwE5FGfsUs8tA+ATFM+gsgd9dAhiCXrxF7+mkMM3wYUDxTntMsLcEg4kX48hhjo/HKIRBCL0O3nstK3ivQQhHWsdIj+F+6ugY7GUcokfNT3FZGqbaySAV3alf2pPw4Do7Sk3dS31KmxAwkH0tcx0dIJy1zkZzAIwObMri1A1t7sHY0jlCMCFO/bOmqp8U+JWPC0SYwYVmcCjvn1gqJgMQYVx0PYYgjhnGGMSXY06ksSFIuvzCe04wGyjGfvYJ4mSa+2oDHjK4YOanPhLcxhUdVIn3lEPU8lOKfHiAD0KXr8IZ+Peqb3Vbwk0CyJIXZrmKv4A27xpHnvuUG5r5/DUN1AQefSa2UgQBihPGkw4kuR3zSxj8xqx+6A35xeu2Y3i/6kBlcnEZlTboq5fYwejjPgh+PhLxk6y7JCyGIo6TUU7Cof5wMVDNbKC4Pj6e+oZBSPH+WHV4Zi090gKHsOnFs5UlOSjy0mVEQeteQjgAwDteV1M/YiZ8igYxpfMtXhp/FZ0110plhdXf/BqedCvmiXHIS0G/oUIs7oCEmBOiLyNZdGdmhnVpWNAos76lynivzf3R3dLbfGD1LKCX0t0Y7sfeQCwP4/CW95rJ+V6J2Evoow4KID3qipN89aqJL/rcytuGH+pq9HZ6kfHrJRFcTFqnw+z4me8RN6DBgV/wW/fx8+8EREoO9dcF2hpTV4z6Unig4awpxJj+fRu4TQPH+POuxx1hO+Qh3SwhuH8uoi6JiuyZltbTMrYqwF5cbBx9W5tC49pwL6OPjUDM3NTsIJbz3tcsfvT5PRuNYgjCkKSm7zwjaDydH8X5QQ1BzAjKi5uXfL7q9dF413+VML8Vf1W6PxO81Zyy1g//CRcSN/dIBX8kWBxSrUplpZg0aw6TfuOaZ+j0vlIxTNmRr5VVkeSmAd7zguJ0nm4tmg8uSqvhkWagblw4mriFJoag3bXEGa80SSU/xpUNnw47IcEKFH/uyAvm9clqVWiUOEHMa5HVONEAvVKD9IIlBpqiijHohWYIMXXVa5Q1kKBBYi8gjEB8laBhIv8v3m9CWpPGyIMJBg8JR52HXxoRGi9C4U18hVXzW1khzqQbYKcAxo+M+R8dMSBE6KoU0umizFLmooVrmAgLAiAJxE/SJUhodpxjBApNDDPDEPTlozpyGjqJcb5C3L6jI+PV0LAvxAMt+6JheajZQI80QHisBgYjEEvF7t+9qwEEm9dpIKFBAV+JtVWQBxlyxJJK7IJQHkqvQtmlXFwqz+gs2i8IxTbPRA8se/Hn38GIZ2BCFu/uEm2F9/QmQtInfEw6w8UKrZk6PscWxSDKyDvaJ53I8BGqfg8MVvhYJ5ox8XlhhjsyGc940gAvvHCv18knWPkIQmQjcHmJztdZAtcoAQxBz9/xJVSGIJ36+PYDG41m/58XMti/kBHfBqFxlDx7BTleaThtBmM+hiBm/mTZF2bn6mzNBpK+dtj6G42EztTPhiBFqyJ14Jv2RSiqK1SP/TXrf2p2KxNY4CssWis0MXsqK2HyuJwPMKjw65WwIbsEDci1l9toY61XgGP8EUWF+by3GYI06MBYz6CAr8N8Mb6rpQIXd9mElNPDmBWUDUi9WbTC2SuIwYW+NMtodLeGIOl9JMuA5ZbaEGaHoOcvzwiiBJQubsqYtPGn5JQ6JcZHil4jJbqMMo1n5zYQ2BUXS9e22HoGC+WEpPY3Y+GV/o7soAXa6avvDnmmPDPL5EY4PtzgrbQqn+bBEz2Nh2xCPfHuqaykdv7yseYPgnkRMPGiqA5Q6gSGIJYD6d2hnknGNRTxNtHgShyWE3uOeJNodR7s8g6M/kmeTzJymcyAyu0M874GpDQh6/K2/LtCbAEn3kIpxRL83bxrvuWwsIa/lXnxi0e6fwR2zOd02vRBlveQOtOcWl+UtO8QIL/z8IK+HNevepaoE56/jTZOS58XGAe1yCjPMl32BtLvlWYCsUH0C056lCHoGTODtDUDxvx3MuBzIECXhIWeaNUQND60/sohLeYBjmPgt1GofMklnPc+76Hjy7NLmvLk3YBe5SY3/wNbcSpH8AbIDsD8IiMjKWlcXtaFYlIoKrGKG2iF4AYfl8j8fGKv4yRR621GE/TTxZH1abpqzLKjb74tEZPOZjNoZgVhDGJJ2AMZfu5rSS8fbHO0vGYGqX/upWTMAJIhCBHyofd8fLwf2Q9x+0ENQZkRNFVBXt/1pacCNnyVJL8Vf1W6PxN8q+sb00fIMRxEoHupFg/o6t8IL3DSokqCt/pXbPyJCzQpTj8V40kbOtYKszyQFv+abg2HC8c2yYZawOJG4w6a0Oaa9IoZuDgeo3vgTWoc3Wgo0nDpy4Q6Kh/UKmHU+aAvxT5FjA1H1QhxVCVfJ5gVhBHo7881Q0gDivcsL7AhSPsFKe0nGiNl4tlB2kh6a3hYXoD23AYbDDQoZH7hMao9fMPsbKQKSztEmpQS/uOjbJKjv74Rmy9xA8s4FSnPSYXWGD4/wsiAWUJmJ7ftPrkaOVmIaYiI+0kzgT57RtAndeS/ZGM6Fe+uZvlkiqk2o/PSgPsy/jAriGMqObIy+0Zw2kw2r9NGdqxDFns0MnwdprzJY3T2zAKNXd4zJASG8XTzs6xAKjQwFAfb4OXaPCPMOxgYyfFNMhPXvhmx4CTNIDFobqRneE94wjdfiPNMwlG4ssg3HHgDY/C5RQ7cBS8YwqTgxZtC2GGWd+j6wrOUdUs/ou2oY3G+zhK4Tgl4RpCMOgw42fyZk2bYH+iNlu+irzk2/oV09Wvpb36vXiue2ZzS8xj/P2hQQd/5V+tmGYOwbKgbHeOP/Nax6G/edXSkYHrvbwqfASuVZDsu2/VF9XDUNYL89rWEus1VaLEKU7zzClZqteIcDAxMLut8PFudLm0DicCjK5pyC1cXJFJ30d1g0a/JEDi6dxiDGLpJ3zOY51OfT/iSaKSuvYeEZwTxlVnGoAvp/AeeBaSvy94fjo8A6H2WH2gWqKz9GIJYonD7VmaQIk6NMyTXcMqsI/iItpN3u6IhwcrfFrGVeJYc01ZDfoJuo1veGQONhvZyLjS5TG7AIm5z94hBX+7G257VErHzJm9KTRuQq7nsEB2IJIK39+9xibPxZaTaDH5beMp5FuNyvjyR4+V89f6kzRJlvR+uU4J4byCHU0djINKHM8qmf8SRY6nTB2F/oJ/G7CDoeuaykMqT3SEWnHLovMELWdGdaY78JjyIbBSaw2ns3w56zHelXB5asjXu6C+dI7zhSqbhus2jYdzSmmmiDgZcAZ4Tz44LyXbWoN/RJk+kqCUP97vAFZz3xkYbeXgP6fOykQAnidGPfuvtFmIIev6CmZzajsHGn3eelc/+nPTD2dwfo0/62/AUIxB5ek9O62jpb/WfY9iBl8zMN+d6t8Jf4HvdKRgF3S7887czBBGjqCGSLUXwG6TkEY7efvtDjR466eFEf5tBSLISwdJsOwK1yDm0oHHDe7URIRr8nM9wZbwPTWk+0aabBq8c4PLzz/TRMeigd9XnZnaQdDZLwO7d1/5tMgoxi/PRo4c3HsmQ/0AHulxoKS/99Hsy3t+S3vaHXhH9L/Xxz6eG8TCu//phDUFj8qck5NfWkqrv0C05KcXinoz8NwOmerdQUQ6u+EOJNOafcyvJfS4rLSv2b+QFDlf4wle6+OdVaPEm7VAIFykjqYpXCk2fcHle3GrJI79KuEWVGOrxKkMQeWP8oBzSkp/USuUYS01VZa8glg3IEPTGywy0blmN0HO+UsgQ9DdtHk0j9YYvz3zN0EJlvlJw+IzGFlmzDH0aKX2NuKmFtT/9pC33ld9PNgoxM4jeLvzFNR80Yi4XbiBIal6BU9Ch8rcoyk4j0qnY/vKm3POVmsgS1HNQvmkY4IcOGHnxN+hDdWSrtkOXb8KFTnOmew1cNV37A2mHDk8X9deGO7+qk6+jgrUU4LGMP94kmi8MzATSAIHTZNgbyAMCNy76KoGrRkkfh/1l2MdUkpfybIPYr4lwCXuUQQ/RZZizggQr83h31yiU4+HdBAZG4lLSlriJRzpFJs/C/xE3z6wpyknDcZt7oZRfmDxcXdz9CAbfK8fTPynzpFZZmBZ0jtkIZtQ6JbHi2b8ChMyLc77OErhGCWRpWPTcW5aDsb8Ey7/k5wsyAwpvEK1lYm8wBikeI5G/KEvns8eE2wjpZxuBZOiRGVpqZc4EyoACo5Ded+ltL61lwDr0ZQY46nyrYtHBZxRMzbBf7lpLUoNWCAn212iyDOzga2I0rfSJEHe4zqnxuOh0ruEX/6n7pHVEbg4Mzuwv52gQDdsYWEj7wMtPv35wGL3vpcBSSF4qIF1wTzOC7jEjiL0nMARJ//PzaTQyAnFQAH72n8AQxMDCxqAxE9QGJS0r5ss1F/TTxiQcZnNvudJuzZiUdOIQhgouJaTtcJs4BBA6Auqa+nEVTqQXDPxKcdDJSRv6xbvKhTLJ91SP2LQVkx55BnLEW8MtY3jflWvQaqkmPXOzErGckrYUmipohFZIchVM8uSPVN4cOhVhGoKI18c2ZO9+it8rpdYDgYb3BFKApSveK4jpYbTt7pw0l/BgzsTE5IOclzgoSsgtwYg6OKQoxuo/oJ0Mfgu/3JxMfMj3Khzg8HdVXt/K4yq6LfMaX1rJz6FRP4xl/8DB4RlW4k06yG1B5K8frwFvBSTwe4YYrp4ts4GYjYkh6I1mbHJi7ysZ85/L6PN87MuZzf3pd8uIrz74R+3F4A+uMgbpdRJNGXb80ZW8hvFnGIHyNgLLjCBXPFc+tAA/lXcTh/Ds57YBhz+woyFIkYdrTUeUK4EllZYhHBFj+QmdFFF3eV9dj4YQt34v2IJFFyYSv8OGJx8kEA4sadEFB3+NQbj5WJtTwNTfVj27p82g78kQxExNZuZjAHr85JEM+DLiax/PC8/cz55CnOx7U19rOe3xf8hwpG+35+sHkMAPaQjC4muL/3gtKydeUl73uIVedvMyX4b/O0Oo3i13lGwq/NVlXlMUq7C40LuKCk9hU+ZNvrhN+zW8yW+e61BvC5V4L+OdxizengAlaCnkRiMGBtiJUJSXU7shAj7wSJqf8GkQ3Cjg3lSjgiFIDZPwWR72fmw8yjKCN2qg3qiB4mvyM50i9vdfnnuJGMaht285wYCNo7XUgKVlclmrDB0MQ6jin2QIunnzjrKT1txmBWGEIQzf8cPm1kApENkPnokS88GhTKTTpfTtwNLJ+qzWsZ0yDCd0ynxR8AjLnavR11bDQC7Qgk5Qh5MAd6ctd7g0OGls4mbJAJvR3VHnntO/LjQYeCgj0CN9UeBoeBoUppo+fqivD6w/1pIwNpFm5hDLwe6qMWK/CW8YKgZsCFJObfxsAHFZRmMqNgbXKo9gkmXKAcO9jiVZw/inTOLvvTHJAalDOxJQvvWs2RTWLBouDrmNLI0y/DPHBdFxlGnEHmgl/xE/4kxzIdHE5nw8WN6LSG1FnKA+f6NN8ER2XroV8VKmE/XsO0vgj5AAhqBXOu0RQ/wbGXvQyZz2+IrZQJwSxpdlG4ZkBJIB6PXbt5o1pA2ihY/RHp1vY/xNDPUY6DH4sC+QXNd5errAxiBDLpvZ/qQOtHWCKnUM1BjEU1+zjOpYs4917+rK42rmaNq1q6VIXJqBSStGK9IUBpfyq86GX+JWuhAJjHsGHwDyi9ZD11tQ4ke63oZ/jEM6St5LD2K8uccsH2YEab+ge5r1w4wgDgh4/ECnQ3JIgJccsCT4QvtOxBCE8ShLgvkAwMbRKgHNIrwMRRseWp7E9S40Wk8cX6O08kdnEwP3o5DGpZ3sIIuYtp/Rj0DmNSkXT5DloVh0QofG1joP/XiKHs8BeFBW6jNPeIX70ltj6vezNNbVeKHOHS4TOkWzXEysUG9e0yWvcrekEhC6Tj/8m8FHnaFtryD1sYAjP83rEEei4f8agjg+Hr/qFz/+3EVqXqDjdybOD95aJj8DZDuw+lzBWa9QMLcC43LNPBL+Z++l1/Sl2/yAH3GAXYVXODi9VlrArqJXODSOaUrrcnoe0Y4mMhUJzwKS33/gWNbjmaiuInn+t+S866Y0zBEKxADEe0AfWT/1V9kgmq0YvCxMupuPrM+kt58/i+72Rv/M6pT+ZianZ9zL2IOLviPfnNCLwWfAbAyi3NHbGIMoBP3EmEuG1nBFDJ+jUioN6Xrhz2+va1ecFbd+XEbClSRaiFCo+Z0VifbDgdLSIPtNbxjdmBsMvQW3Zrv+kYvhIw/yxuiDi/TBxzBkacjlxEY+2nq/IG/2z/KwnCTGEt4nTx7rI65O91X/nU3+OWHsnvr0HAqQTf5v3vifZ0MQj/OHuH5wQxAymhUG36akvyK+meIrSP9GUVEPLVBDdQuvewpeiR3jCm/a0y7PJOrmdHyh/ypeuSmXp+gVp3lWecYlpX7+LxUw8V9OCQy1ueE7Xcpq+GgEwFM3V40SXxm0X5AsOB8+6Gux3HcYejQzCGMPewU9UyPFcfLP2YTUX5f1JYO9KfSloiePMSj5JCWtFVPJH+OPOjaZGUSD1AEHLj0dmgA1TOInBq1RFjd0inIHCidlhndjCJ80DF4I04DQ9NDxAjMDFEMsA4GU1UgrP0Ygyu47fjxcI5/4kUyj0pilWaeBgRMaHL4O/2oDEPv9MBuIZWGPHmP4ydIAZgGxNxAnyNzXD0MRX4TvqGHhaMrb6v1rtqqXGdA45msw+bok7rjzjtIZSb5q1JCbZBRDEOVAjuvVAhV2DBdel3hk2GvFn34V9R++1iSTEmTWmJINzI+H0vp5TLx9+qSxnITi93qQQSKD0oQo8fp4m6NQ+fdFGvIGr7AQCrWBtvZiNtDZc5bAHymBF5wapk3+P+nHkjC+IL/WRqMcEe89JqSnmcn5moGElo3xVZljhzECYbCXzV/vOTqYGUH6WR/n9DC6zejlGoXsl55C396UIeim3C4L62xF11VXk7VFXetNa1Tdy9JySqKVbKUyMaMT5hfqxlDfSVja1YfSBKrQ1gflbWPJmSQzyBge3U4wuh4gWlFtiQ4HYHkYewVpHq1kwCBDwyyM+dLj/hAg3Z5lwRiCmAWqjwDaE44BxuPHmhUkQ9CF9D76nwMFMATpO8DBEATP5EmJhjDsztLVtzcEKYmuVQJQcdlFC4kwCEW5FUeh7UobbCEYdsQJLxu6PWBbX+7BJ0OlXN1+Ekl8msURWd5P407oEW8vtYmHNC6/V6s8WspSmGmR5fFyvpInf6TYzQhSJXMd4VAJCZ59YhjMfqbHpXrX/silGUHqLyFU5MQzWS+HJKDKkrhZnvCe931NdfQ3NdTw7/OY2Ee8xhzxr8Ir/tFtviv8CGu47or7r/ih1yuSCyT5bIaIIWH6O8bS8/Ofnl87EaRwKj+jUIEyRhOeAXUNdGsP+rM8ewXURXYchiA+tr7WjKA3GINkCHqu5by/PHt547m2YPDenFoqxgbSLP1l+wb0tWf/2NhT4w95yW9YjEPW637XCed9ou8dLQDj8z0XOw6nNAS4Wh5c8e7gCjPScmtcQemHR3rk4FwSaVT6sASTLtyofK5klDOoTq9A+53o5JkTdW78BsIW9qgGIkgrepq4n6VXbXCVzvaenOp437stI48+zN5jRpD66U8fyRD09KH772zw70MA6NtLZ+fD7c83/ueD84ygPqHrds+GoOt+Av9w/lTMWY1JvtX34SMWhXv6OgUvvWNc4KF3mtoKRRFdle8ad4peObDC3JVj5jBxgF3Oax/fdC3TdPcD48DTgWua0G/IU+gJgKpM3Dg5UgE1BlWlH2noNM2Uxin7BXGKWJaIvf30SY2UBhjP3ug4+SwNwxDk02n0lZmZQe85ulgGI2YUeamZWjvWM7PmPYOMGITy1XkYg6SiiddNHOVnObjRFMvimfJi3Olmex6sqCDu7NCYKX2MIpQtXTXcyCnhUfSW2nLg1i8Slh8CAtE3Z+ynRENCJ66SyvpkwvoiLGa9SbS+5HqPCL4caHkAS7+YDfRUR8Vnk2gNBFgW4OPitUG0vgh7fwjNHmKzURoX9gdiRrjzUaOGHDIQkYsMzF/4omknFuOPx25C/rYhiLKtFzlcviyCDRyccEMeuvYIG+bOA+KCV++lHI3X2FK4HNZj9rtwKb2SJE5p9B9/sfbuWkdKzzkOtGJTSN47rg3m8AAmSg9+iy3k7J4l8IdK4IWOjn+l3yfp3tcy/jAL6BVLC2wM0gaj7DPhvYGYLcQeQtrk3/tM5Is0ywx+1QDCMzV/Zgmv/GMpwTQEyUrhmqD3HWVjo770jvybfpZhxHpagG3gpFSpMa031Bf8rTcNC7RcxlbUvp1bEJQenFJNTGBpQ1b6+Edb0XRmeknvyh5qTkkZoG4dnJysh92maAD/heVhDEZ0UqTwbmpvOJqw21oux8ait6XbOY6YGUEXGII0I4gNo2kPnugr8wN9FGA2KMYglgTf0cECzCZF92ePIPS6eBj5t5xAuVq6+pRMF/dq6cbEDTRUwKL994OTM2nJL6UYmStiuVYcwG5/LbMglb8dMQU2vbzQKi5x+9wXJJUjrfYKK34pJG7yhsQaBzT+xu8hxZzQUg/V05yVet1y4JwFhCoU3fdQAQizVJ2PNzEOMUMZxBiEFG2ZIwtODUP+7BGUzaLVP1L74j+ILleCukMLZ4lrAFqHZANrxQaj4VPYa9xVfsieihvZnXSaV9OlKEZVVGJXHPwT9zLJb8U3xR7PFE+RNVoitn4XJOgf84cFnUt46DrQ/bLLF2NJwn7UxAnO7B+ePXXPs+9FAj97c3o2kAz0XrarPvYvGIBevFQ/mw2idTgL+3QK/kZ9bLZv0LxxmzZi6KEnGMMQepuZ+DCB6YN8gzOMReh2vRfR60RHc7i4CrYMeHO5ZPLitmzxD4QTTtMQFTnBMf64uUPSlIyeNLkj3zztTRdZkEnnNKZHXStMfutKIgInLoZ06Wmlp68eA5JKb0MQdU59bomA/YKYkX+XTaOlux9KRz99IiPQ4wfS1w+kr6XHpc85CMD7BGHwl6L+Xw8xBIVrs3S+XZsE/jSGoL4uUe2pBFdJrbhXxf87wYdaUJGmj/In1Iqe8ISflkDiqfRVEafxCi29uoXv3X2DSkOQpzf5LX7oJPfG7kPF7POHz7WMzaupwa+/riBoygN8HeSSqmGkAWpTt3FKWPnTC9EF3kcGBfphcOFEmY9qdD5q7cAHDTIwCGH4eaUvzMwKeuljifWlQg0Yy8Zev9UXi3cxGnE0sdPrszMDDX9B9hcuOjdsPKpOj79QZDBBA2ZjkBuvNGCWG3xjEHJnSp0o8YlBRgTNM3ToNNExzeyeNK50kCJX3SmwLsJOpRu0IVEyIQbSSDfSuCFzqxQ4TwtYZuswC0iNyGhMfr79qw1AbDrn4yh1Uhid/idqTGoIwuWEgvv6ZYpppqZmc2hopTvPFNY8EXguT3BJo0YUjZni3OtQ+UnnwriEIOpa/YF8/X7EJ1zZpeSR3AYOuaLVvSqTxte9hJe8AlbZwPOlvP2+N7wvWfH8nDcSSZysuI8wyMIpmoNboFgbOmL25Tx4747XuRNwlMg5/AdL4KVmbr7SrKAP2uzn9Ss2iNbm0DIEveT4YfYHIswsIQ0kmBH0Vj9OqfHyAinUT/qCjP7NjCBcDEHMCKKe4J9fk61BUTbCYUZQDALSStQNGYI6yE1tQ2/Nurb6lpomjFzgkg533hMyaLmFbnIpOJiFUSb8/eFF61MWYYbAyEtxreiCJEWoWd8OZGtA6SH2C/pVJ0RqOCf9+0n4fAxQOn0IwBCEgf82+8Np4HCfPYL0NZkZQA9lBGKPoCePmR0kQ5AMRjYEySB0m02jPSNINKRTcmoYLM92Hu57rT63ZeaxZW0Zgo0kgKREGQJSXPTlpBPc9BladovMormMJ6qbzOQdihTDRdvbyB66kx7PwPHFxz1xjRZP/ME36cGbdI6pKhukteIRKm4phCZYpdeYiTvTzbhIa6YCh6t5Uy7+TF/vCXWCvxqCiP+sGdaGgeufapfbEIyAPPexP5D8XhpGVVN9g4vwXZ6T87Gs5acSI93lqzTW2Alr2i3dDjAD07dh7jyNx+VacyNs+IgkDrnkGphyZpoZB15kPlOQzjQ2enlveJ+PMjIKtzVqvI+8n7zXiRrEFDYNXP1j0OvSV/AwupDMdYeUBpK/LvmtGuXFpV9NV4aPpJ4VhF+ed+/oR4+luzYEvfTysF+0TxBHxjMbKEZ8bcGgPrnmD4p+DEDWz3pHMhMos328ZMwzg+CDuqp4MbnNCgIm3qINyjLxZh7OdV32Z6xQeN1gz3ufDpDUSHx5WpaKeInrV5/IkRcUN3GTwoEA8Q6pDuxwW1j7zNA0Z+hk95f1vOzKgI8+1QMAlx/9balsG4I49dHHxEsv+wPuo0faJ2gYgrS8l32CLu7JxdDPFhDS8//70R3ToATn63ol8CczBM1v9ldVI8T5tbjrFfdvn3tUQunuQ6iP67l4As37qqdB/DGuaeC6NI4ucVzHtIH2XgU3+SAG+lWQa14rL6K7kRaVgVYXKm2cqvhB+aKG4ouWhgHL3hGagSO9GYMOxiDtQfGeJQecHsZXCgYXnELDAEM/fY1+90F7CTEzSEvJPJNIBiRm0mc6Kg3mmAXkLxMZZGDF8IDCgw4Yl3bWL0XQXczRINCxgu8sD0u5aHLofLJnRS74HwU2IH7uLpnIuZkiC2DJJAE1FvnSM+Vrav76AwUaNRoRaFAoSUp+L+vS9hp0/u/ri8F9NRo+OvhC64zV6WeNMUvCHqohuc/RlNqQDiPQbWYCqSW6LSI3PRtIjRN8KSsPLOQlfwNw8bpBA8RTytOLTCkLJRsFA9WXE8lHQfHvCmyM/e10fHICU/EluU94MrS9c0p2mvKSbHQOCgn+zIzn6mcnhK2DAMwPkWfWlOQ1cttgzR23/olvn8CN2ZJtKILov/EbePZkNtDZc5bAHymBl2NG0EcZgl691HKwYQh6wYwgNhztqWH6qvyGTUllCHqvGUHUFwYMVxuComEYPMQgRAWhBkjHoMf17tt4T9UQMRtI5Idw60m/AU95gNCrWA1Tw3utfmBrDKE1bfzBiMZMTY0uRBdYc1g/jnghG79kN+VRynUZRFDsDGh+shEIYxBLw7Iv3E0tD+Nr9A0Zgu5oVMEAgYGCdX0NQcwKUlvAhwG3B3LZlPRCbcaF9gjK8mC+UNcQlBKY7ij7ptPgB5H4iq+t5QotTl2KWhrWn6PMjW/aU26kNjFLp7ihLZFMlEbt3Iq7QL9OS2kK5+lfwlXkKRhpmm3j665xxQHWeNwVvuLj7wVe0xR2yRUCXEPPfRT6K/jpAMnv2cwnDEHIgNnMmQWEQYgZQenTuG3jvd2YnJIPP3uuQAskeFuyE8yuKZ0OwEbgkJL3W/82dqwJSSJUv0pym8ooBR7ydllCbolZiZbKEp3MRT94ob/Gf9u/8fkNVHJfchmFE0TlsZ4b/UHzwgsvwul7CUJiFzBliP5Tv1pp239lQg/GHMhgEOJD6zv233zLHkBypaefSW+zLOwZH1s1Iyh9bIz4GILIAkM9+7ih4/JDR+fUMJs99M51lhCcYjSCv9Hntp/nFR0pj+OB5Ko7gnaAjTJueI1f8fuOIsW1HissNGRiSrv+XtL7HlE7Ja+Q65Rln7wojS85LtVIlH45dUkRgoGHJGz4SU1UMoz30unCYVZQjEGqczbsZFYQM34w1D/VB9zHj2UM0ulh7rt3eZgs9re0pxB7vP3vJ3c8gzMMne/XKYEf1BDEK5fOg99KSYj3lSoS9+si87v9dZR/m1iqdcu7qg1UABcS28MNvnSDRlJcitoBQg9QsY8py80u2e8cOPJAuctf3fCcQfCQiBTaTp+aS5VQ5Ia6HY208CEzSGFQgYK/EhhmlemGhDg3UnI9hZX9ftRwfZJ1iH2CXqvBesUgYxiA2JSUrxmcdsDMIC89GIagd5ohxBePPGG+Wog3G226TlkN0WjMvCRha9hooiiEpAA/+nWgIa/5c3nAIo0b49GojUL2KToauSCU/CdLYLoouWUB4bG8AenwmzOvaEBAHl8X8CJ7/e7qS8IdfSXAxQjEEcFeDqDZPxxD+fSJjqLU3hDeG4ippw+Er6/pGIH4CshaZW8QTTFElyuNGE8wfBjIpyTiDEoc77IlZREgsVIws8LG7dW4hk+5p3GybxP4I75kFcxzGDFEN27zIieeY3gCJe+nPMuVQc+BgOMXglu4MPC5eFbxzXvjyrVzFuqET1z5BCbmSAZeIQ79SynpfZyvswSuUQI1BDEjCL1sQ9Dzt94XKIYgGe1luGcJ71sdG89+FBiCMAJJq0mXojyYpZllu50RhM7FnyUGDCB419FIwpcO9+DHlYPqATIGEjm6ucrg3WoTEd++gnX5DqXkH9+kNutfYNGc5XVzxefUjUojZPMqqvBonWr2wjteU4qyjd+JKKNGYv5hDGKgoeVhtAtjRtAd9vuRkZ+ZoZ4RpKXAD7U0jMMCnjzRjCC1CY9lFIohSPsEXYxZRDpCPkvDMAIMzkf+4WeW9egT+rgSw33iNC4ucvDzGgK4Ek8R4EVuEthOaSeV74Ln+X8lU7Je8cKKSZ7mNEaUgfZVBx7CzUTjXcj7ENgpnGKDd0zfONzQ4W1YKa4YSY84aVpYhlJDECk+yxDUpWEcFS5hJd6uqpIe3s/qpLg+jRlB+H2MvFz8MDjf3+RHr6WXn6nClAM+nEZ+wqcul2UmP6CsqU4hJX7FKoEVG7bNSyNPuMYXHjLjyn1Sxuf6SaQIbu8ZQccRsb8o+z+DB5XmvHJDHbd8qet6fvQJS9/4enbMxs5sGYjM2ggdcOm54coOaIOg+9OK/MSsexmCWBqWPrRmBsnw4304MQS9euX93rLJPyf0flA/nHdC+loz62PgQVnQt56GIMM1Ez+6m3c3S8OMh/6mpDwgu3J8YVbpFUnMsDFVxlm2QIqP2zeUtE0Z13eTxKef8x5+eBiXUwqc10FPHhTw5Yl/5DoCKYnildBdMbupB2SBBDD80Da5tUNv248RCOlIUiLCxtF8yL0rvc1G/5wW9kRLw5481PIwuRiCLtS39xHz6sNz4hgzg/6fJ5odpP77+bp+CfyghiANnPWS5RVpBeHFjP9rr87X4q5f3L8/B26knI21wC5DZAP0lIxW7OKtifdpUI7p5KzpVvzVz1ObfK0x9Z/K8Rh3CmfP1SwdvK2cNXfB/E9cf8lnUhq+oejBqhZFf0IJF/XIRU72e2CgsAIMAj5rOik2HDovH31Swa+a8aNGS4MJNo1mYMGyME6pYWYQhiGWHdCgvX+XzaW7X1AMCSmRBxJsauenyFcNuEClY8hgwDEGGYCRAjz7FxmYd4VJiD/PZnSkRIuoNAKk16UWgiy2DpUizYk67r4gIgnQWaPJ3ow/bkDAYLjEFUnZACQ/DQiNDxvMcVwwM30yG4jZP2wIyp5Amg2kTUJZEkYj8kCnEnBKGEYg7wkkApScAcA6Cyj50XglXxfUJRMPKnc6TvDDn8qu9JlCnhRO1rRbYI0bZXfcCl/9SWjxDE4CEU5FN2ivqYhaw0UOfIlZvJDx62p6K2+rn8iG07mCxHiaimqciTimvriRlrk7ooIgYmXpUvRCuzim6d5HqJ/vZwlchwRecmqYNor+KN3MTE2OHn6BIUjHDXNi2HO+KPsn/SxDPjqavdzQ8XxB5hdDkHSvBhfoE39tls8DCg8i0Mu6PBCgokRHU2mpD2lTqDX6LZUnGpuE0VUzDGx/JdnxPolRBdERQPi13gejtdKa3bFgqEBx7G/9V3oaHSVc+YkxaFIzRQH5rs7lVhImNBPI2sfLwxQnQ5BaSiFoNo8GCHc0MGCpF8sIODGMgwFoD3wCjQxBT7XnBHtPXKgd8BHyMgTxdfnWsjSM9sB5ftMQBF95BiNF0uleiQS+v0N2N5jdRy+hPLcFsMst0pqx+6VhE17fhu8HKdoRcqOHGyyknmec8FXlCZz7Rt10CPFb061+I11xK6U1fWGnkjR3ZGq/kL2cXcgYgqhsn9Wf8DIx4WAo8qukm2f/SBAYE7zX4ZgR5JPDlN6z7kSU3lEveFnfXeCONcOpA4RnCjAWCZH5eAaJ2d8j96aACoTXa33nVviKmTRXYzbdkXbh010xzM1XeJ+p5PsGXkvmviEyOVx5nnkTt9niGIMw6OkiXX82BBEeUk+/Nn1p6PDM+bCKMegjfWp/XGULhs833rwbM+vVh375Wpv7v5Ax6DnuK/e133Byrw5koT/9SbP2bQhiVhAzfDACoeekp73cl2Vg4gEdF92usHV84LxJfktr1BkvCTD++tQoH/5KBTTKmjec2JEQ77ha9oa31EKFDim6LGyLm8jJn+fg/1FHHAgMVOc6ntVQk+IL3sSZPHTJ2keH25RWQqc/b0OQZnAqPYZ7dDuzMPkYi/He+3ZKh9to7/2BHmom5wN/3MWoz/Hy7P95m4++MgT9n7/IKHQ2BC1P8Pq8P6whiNXAXKkcuaO8Ueh+mU/I7Cr4CdQ/ISjyCOOnSrrGz+Ihs2IXo+GhFgZyYoszKQR7pgEjoSPu+mTS0M7nljRJUYrFmXnhW6nOXCfOzH/Cjj5wVjqRgTtvImnXylA4Iws4RSFyEVWlHZ0ZWvhRxKTH7+ZMibKmmEaLBoRubmA+WUbhTzIEeZmYWjFOCHvzIScY2Bikrxk+rYalYvbz1fmd1j1/8lKEj9oviA2jaZiYFosBh3aUPNyAkadqhQsypq7qG0vCuhsOr6TVZb6VpuKpkSnpxxNUtEVBwzBaC8uGG/9uNEKPBsJfeUQwDTzLJsgM6SB3M23ZmiuldeOBEUeNAJuDXsjIc4/NQdXx53QwDEAPZAh66CUBMgpxSpjwOCGGAQBHVmIIog2h4fLP7CCJkZ9dgDRgKbyyFvO6SRhKlp8HZdBYZUY6rqSLnzup9jCHdBuSMwrZ+f3ZUEnXK35T2uIbN11ETXTw4jNsSZNnMvFmanwgLsiOnGGeDs9m1AzHGl0ZuiwTdSkL3KSk+MDf0JwuZEbUCCgHv6PJaWLIx4M7X2cJXKMEbAjS8fEMKF5qAIHh57k29H8mPwYgn+7oo+RloB9Lwz7oKzQ62YYgjO8eUDArSDpE/hqCqCv5mowhKHUnAw/VISvWAZOOioraatOQyDFcQRWOGw2bGOoz1xqvkILGVHaNmVipg/vaOZR+25EqGpeh5JXv0KvQinEdXy7rYZGWlEbJyVk/Hx2PTv6gH272CWJW0C0tDcPIf8eGoLFMWLqftoBNRx9jCGJGkL4y31c7cI/Nov0hQbNCTxqC9tIpb9FglQShQGY86S5fwEhFE9Kyn8STvL4aD3XLNHTI3U/uQIw2pJfpLWkKX3EKi1ZH8i1jy7OWs3HkUX8oENp4GrFJuX9LSnVNT9qVWqmv9JJL7i0hlOlHGI8+hdpo/viQlhlB1DlBRJx+Bu0XMsEAFCMQrno+eocwDq2GoMpo5rVyAL8tlzBEE6MEuMUHeysTHiLWdx8EpXGU4SOlnL4HRhl4OCFYYoaMW2Fyed5bPs50RRztsngn7w1vh5I4QMQPeuSwXpWPYcVTAJqjJEEn/bi2uJH3jBFCA9Dip8vPts/VsPCdvKUtlMZ+86gEUkFOLmJZFoYRiO0WZPxRR5gTdu1qJucb9gji4+rY7B8D0HMZgp69fOllYSwJe6OPq+ztxp6b2ohA+jczgqyz3WvM7E33tSkTfW7g6Paljz372ulppqzQHM+fsgIERPHl+CK8ebbABguk8L3ckU2T8q7Oa/ULSlC/QnlG9keQjjQpyz8sEqYb5kerWz7Qkv/4CTfGfIxBWc5rnS4l2BMfpbJv3JIx3rP71Td/qJMdn2hZGEt5H3lpmA55Yc+3cYz8bSlrDEH//R8yCtE0nq9rl8CfxBBUOelFlbf1otDV/Vrcivfn9FOtryphq39LVpWRRm2qh8Z/n3s6R3hAVSTPy4385BFfOVv9yf009e/j7GtY5SscmlOBym8bp42ClNrkeECHxT86M/SiT9O9UnvkchFDasL8AMYQxNcsGW8ExIDzZTReNEbvtPTrnRqmN9obiD0nPDNIDRkGIdY5M4WVOIxB3pgUGur58HHMLvQI00CNn1vOUTt4HnOGSzr18J4uuVh0WsqZOLfCBLkkCNcyud6IkbAaCMuHG2FKTG9Ybjog42ud5vEh25jCiCetHRttMAAxc8cdNU0nxRB0cZevvuwLxIwgjD8y/GAIwiiEIehR4tkPgmUAnDZwW40NjRcNkBsx8nE2ma7qQYkzFg8UlocCz25RccNbDWagMjAbpRz4UAyePFdeg5TiISJnlwQAV93h3+EY4Ss3kJMePmd+BYfYrjNnasBPZZRaUKrBWO7yktuUxcjHKNG+fvTOY7kpUbgcsJVXp6WWHC4e3vk6S+AaJRBDkDaLlsH9hYw/LA17/owj47NHkGcEYQiSbmamJvqa5Qjo9mgberKqF14eJv9iCMrSYRmGGExsbz/vPPjj57JTQVxJHJq3FXb0Q4e6HLdpUrsJTfwMCIQH6i4m4Ubsa2jqunkFzfo0WiEfD5TDyGLjZMuSwcNIMlodtxckGIMJu8wE0ldm9g4inq/D3vdNgwoOBaANYEkwS4WfakDx5OlYGqZNSGMIyoeB2zo17I7aE2YCqXnwYAZOv71ZtAs2WsHKkZRIYggLFF+J951ytvBL7PDuHGhVvkSEau6buJoi4IZOup45MfL2IzHWMWHy5F1IHsTPp7vHTlwyiz/mGCBND98Tb/Un3byT3yzXTDMxLvsscZUpuSkNy8FEhJ4Ws4D6AxhjkPpD5KJ/jyPVdmdmEIYgLRlX3frJfQS5sKBbeI7804MLH0RDxxfI5oMk+1KCB0/BWwIjDXDT2hCcrdIkEfTqN8ru1pR6RsYjspntELfA1+ltaN/0kEtz3yEv5drBD4GVj9VvNJc95YghSE8Ywx4yFtj7UypztAXZ+WbPyEQw+ruIAoOg+9LqC38U8IMMQSwN4yCW18MQ9Ir9N3Xq4wsdxoIBn72CXkuPv0Vvqy/NISw5fIXZQNqY0n386G90OW/TPDWM54Wxx5ps75pHwc07jI9r8D5rGnDi82YHi/CSRv7qiPj2qWsEgvRKJbQGLCK2nJK6Mk8K+mvz3Rv09QAoGazw3FxKiqRw+tTEg6sHoD70WAgtOhjuCSueJb0Y8JXgtpb03lKHHGMQhvunHB3/QLOBOPVX+tsnQPLBV787OmGMA1/++z9vewZoy3J2r08CfwJDEK9oXmwqClWI31XX1+KuSvPvA69GoET4v0caa5rvl8SqrqrAvpZfOfnncvt+voLZXKoW0/D0PXIrtJIUcyi/KEs41Q9nXG7PJE+3STYsYIQJNXKqn68JXIRpUMD3l2MZgTAGsU8Qgwg2gn73McsMcvRlTg1jjyCWhtFwvdOAg/2C+ArtNdFq/PgigjHIYWWCMYgvFzYIwTDM0HLQMVJYXSKB9HTU4OHmFxyXCXxdngIbL9gpPg4NA39qHWhSspGcIuTPj3saii9qIH6FOeI2SwGzf8RHWHJH/Wd10Dybhw6b2uCLe8z+SYf/AYYgGYHuc1ywBgDdOPqhviYw7ZRNou+osWFGELyY9sY30qYpH7y5bOUz7rosDEz4MrtuAJEYgONlQkegw1sWCiEjrrwr9upWenURTZr9YhxdMPc5NjRpbGkMSnzzT1zT4B4ppnNQjNJKOHce3ylZeD8p+Hd8Uw53YW+IYi8LJVpQksgP4EDnHDxL4A+UgA1Bb2UI+vTJM4IYPDzTjKAXmg3EgALjEF+b2cPtnQz03nBUOpwBhQcP1AeMQHSlGVzwSZXjiV3PiZOS89KCaKbUU+ljVxJ0q9Cpdpcqh2DbBQK/XqnDDdUNxmVc2qPQjyd4M8N9uHBcfpRHLl7K4YuALiVMrR4tiwmFmjGdjEGDkjs9bUU+Fmib7fglxZ+8TxCn0MjIj6FfP46H7x5BuE9sCNKecdqIlI2jiWdjUmaJMoMop4ZpQMLHBumV5Emb0NIBmdcMZRA0Y1LUvf5LCKm7GJAcin7SoYwJzUEXtEDeU7MwwpaznRwqh5XgytTwL8kiU3N0AnFQRgLk3jcG8qezGLzvMNfckscsSSkWHnemmDJLzOl76dk4YFkhWtHWS8vfF/o86nN5FhB9HvzKxLFy/XZK7hiAPDuIGUHquPCRyR/DFOc8VDxKOPkb/BjAc82TCpS08RE9vBMwqPR5910IQu9NJdr2iv54Z/acFE/Q4Z14pVUXBOgF8Wq84IN3CudSmUr+X3T3+SmXIVuTpbMqgA1BROgffIxBfukpksslj/1hBuMPYsMAiD8bRNOPzo+DVd7qoymHr7AptA1BNgKhu5kRxCmPwqFPrSXAeXc0I2gsDcPcwVukRU5iSXqZvLatFwR3hfRbZp0dfBgUTE40ArzDr28UbfPbO24b7gbkrSMNKZK2IVDGY24UoFxGSi48X/sSHHmHIoUx5cYNlzc9v0FO9FxCioRfP+Kz3YJwPeZBT2O4l95GZzseAz7LwzACSQdLb9/R0jD6809sCGJG0CNv8xCDPieHyRAkZf1AS8T++7/OhqDxRK/d+aENQakkU0atNtSDq66vxV2V5t8PPmr8rmBIpvBKqeEd4sArzjFuhpP6NF5zqztT/TG+cDW+PmxZplGRRjPEA1403ykmB5jmy2JT2ErXylWdE8D2Z+KkvG6sQMf4Y5OIYP5yrMaENc4aP9iYQyPG14k0UAwuZAjyTKAsE+OrszeNVuPF5qSfPuc4eb6AZONpDUT4IkLjiG4WbfWX5JF6lpvZQOoMeVPSNGJYdVJqwoFRn+CbZiEi4G6onGA7drRIOKPZEZ5LqPR8vUtnjQaC66efiIsBiPXDPyshBiAbgdhYTh3+n9VgsMHcBSeBqeF4rK+/9zECMSNIfjaXY03xA8VzLCWnwzBAYA+JuSwsfMMVvzyJsg4s/MgzitOmmIKMAYC87pCIx1IDf16Vw4TUh+yGaCQDZHe4HGmhTdpXk3NikhhFt4mKT9wRN4Cbf3tOiZ8cgDgpFF5IXXCmP1iUhJ8vIl0OuZRxlHOLJ3pEFQ2CO5oKTHwCIzSBonC+zhL44yWwGYL0ZfkFSwpk/PlFywrYINqGIDaKZpDxhgFFDEEYjbYZQXw19owfvmrrWGJbzxlU8OMFR+9mkOFa4HeeNNTXVIDq3606bB7k0ZpUt5AZDtYarj+udbxpUtdb3wH0BwWuZkw3P3HRibQXXCu+/NYFC8UqJ2OKglCSItSsj1dDkNsQBhif1FbJECTk2/pazNdlHw/PjCDNFH1wf5wYpoMDnrLcQEsP+KJ8YUOQ2gR9ILhFemYU2RA08rVujAzgo+WL3wDxlxLOULAoVeKQReU5KBBcykpBoUk7fLx2eY3nPcmttE3BGWzvxQl65EU+plt6x0yXsHpASyj8h6eRXzjfcJYWM3koJuUfZTzQg2IpgZlQ02xk7Vnj6geTK30pUXKZM+h3v8KGAPUvZEjIEqEM5o03MqZ/kf2B6POoXWefoNUQtMiJfPs8yZlXpGHk7uxFF9K91+fskL3xwnfwjOzbfHZKPSJNc5SzmBMv9ICffH8GET/zxV86dUsvcpkZh8tiwVPeHyBNc1W+ha9pJqX4ro4bOafwfq4YFLK/U97f5m8XvsqT2CcZYXjAn5n19Hcx2scIxGygDzIEMXv+hWb+vNJsoM4I4sSwF880I0h96Xfqa/s0XuFnc/8YgugTZyYQLsb88VFVOr1wON22T/BsT2Q7+tIW8+QbfucV+BrGv+LkKeUevMUv7xbaPMIacMvFBPP29lmVjvPRzbFyNxIjohqdiIwGhCMPr5gNQKoYgcfok5mbaAcGHOz7KQndRHezpDcbRt+S3ubkR2YEcXT8Yzb5f6x9grTHG7M7L/RB9570NnuCPlDf/v/+l4xCqqfn6/ol8OMagoYC2ZSFZZXm41uvzrfir1/svzcHru0jk0pjhf3r+UeVhw45fA/178X717mDAhylEaG15wtTL5QmCs+SwaMr92IMV8DxGhoQ5Ytq9TwYxdHNauPB16rgu+HCr1SftTkdXxVoyPgoYoOQ3I86Tey9No5mZtA7b2anQYZcNpO2EQhjkPzMCGqDx/IFjqP/IOvP53FiAuukP6thJE/PECJT/TAG3VTDxSljaWjlupQMWCjtCFv7U7zRNDg9JeQaxKT0e6X0NQKR7/iBO6b4Q4qvCnzh5VhXDEBsEMfU/1taH4yfIyRv68vAfXX0H16oodAR8Xz5va9j4/l6wBRSlgfcv5vj4u9wKgGzgTRaYEkY7YeLoWxxYwRa3ko/w6N8VAAAQABJREFUOBqqch43y8PyvPmC6Esu3piGDgmGJPZUVNQjmhCQ6x4e+r4PA8rIcEeO+BPkdjgr3bINQjYPTD483vj21Po0jb+jmgDxpOMpD0qWW/3BIsQblDLu42bCDS7PynMDWzxEeUnO11kC1yiBl5oN9PKddKpmadYQxP5AnQ3EzCCWHHhGkPUzS8OYnZnJ8tY8+oKcfYKGK23ELKF0pRk0oFuBDR1LLbPC6fsf/dPaZ2ijNs1wdS1ObW+dr4tQm2bvrhjJk8w6cJF/Y0Ae9Jb5Fo9OSKQRyCDxI58uuQUMNWOS3GVwSym/tIzaDNoKzW11GEl6hqdk4qW/MvizF1xnBD3Qx4EnHEc8NotmRtDu1DAvS1CbIPFOQxAyRaMdSzt5N3/iYELgfJZujcFfKaY4oXsprduAmWfj6+5zCHfEbSn2iEFf7sUz2rwtGHtvdTq8l3TdPSYhns4s1y4vxc7wIovLRDbITmYb9LLHeG4sxKUyyYel4WIAEvCL+jzEZUYQSOGHstgQJLljDPIs4Z9v2SDUfbhc1ZytsPXvrLbSKEK0Gt2n77ZcEZWV3eIt6GnziSjmiNyIJjzxGn/a/a3xTufy+0An74ug5HX9l9BjrKDuz/xJE9Ehv/oTj+pBVfD8bQhSHxrXhiD1f9+r74zeZmsFjEDo6RdeGqY93rTR/wsZ9G0IUh+aLRa8nMybRWMIiq6uwWfOCBK3NgQp3oyhv+ENtgOzPoc3Q4WfaIU2zyX/UmSn2t+arm6SbyF57N8AynuT5/APguXJwTXTza/SyB96lCy1lLCHAvKwTDezgeiigT+NQcwIQkv4x9Iw6XEbgujbSw+ztJeZ/TYEaT+3Rzo57KH69nzQ9cddZgRpf6CHcjEE3cX6f76uXQJ/IkMQL0xf3Kvldn6trpZNVddljMh2wr8txU2vzESb70hti/hNPUcOTuca4w3dIV0k8U/dHTVANgQo2Vpa2iW+AMQFXQny78QhATQNG2GWgZFPDEFxvTRAkRozGG4jEMYgaAmf2Tw0YjRmGHswArFvEFNY2SPonb5ycELN2/fvg6cvHx/UmH3EcORGjVlBWvMsgxIbnWYKtejTzmpGkmcFYYjBGCQNzxRczcvZ4JvfpWeAMqQg/pAWnXr+6Hhxyac73UQKQKdMrsLpmMUYhMz4shs3S7h+5gsvhiC1FqwN5ovvXRl1bsm9oy+6Ph1GXxAe6KsBfm8qp68H/nIgHGYDsSzsNj9mE6nBocMPxzHkqHkmQ/OHftBl3sI/7ILri+cqD+jGU4xd3bJZNFiUq9fqL2y6zmYG5ct7YxIQJrnynNf0uwNExIksNtDmmRQ2n0dldLGEpAJZBI4UZJeO+KTagTdCjdvHIqkM7FoQ3BIi9nAJAAwqdYvhsNnwbZb5bAiqiM7uNUng+RudGiZDEDqUpQTPnzOIqCGImUEsOYghyMsLZJxneS+nz3hjUemQDCZyfLxnBzHIQOvIZdDQAQSwLgmjlriuuA5QQwQZldgh3Yjv3d4BAZa4QCeOoCY6Y93+WVcUa02bfIfWFG/RhwkTZy1g1+Ux2db9pAWHPKE6DUGTAjHRtxlMpNT5ouyj46WsYggae03o4wDGfvYA8kcBHT3M1+RuFv0XfWl+pAHGhU6QzNKw0S54s+hhCBJLyVOMjbJXUua+ClJIlLglcVlGacGf8KaesH5Yog1KW7jgkDlX85F3T2uESGu5xXWaPWJAzQO6osljMFrzMVZgfkQjjEO4sJKOW2iQV7xA5h38YpdGw7NkeeYTPtPHt1KZcRMqn8uWmT+Izn0LDEH0m7TsnFnP9EMsexAov/7bF/BMINUnjo73bGj1fyxZ4ZEPdzfHpB0XcId0I45wrhkAtntvJoZ9SZN7KE/6A3XvjOe4B14OTcPK5Tggja97xApHRrRskdfxXV3TlE7dNW71fz1+lh08d3ipg4Dp/MoTDN4XxU8mF39yM7ZuvAfo2k96FzAEsUcQ2yt80EbR9IlfvdXeQMwIkiHo+avM5mSJ7ytmc7Kkl9n32iMIQxAs/HrjjkShzaGrp4eOZosE62u9CBiG8mbxBsEkcVzwTFyKZJk61NtWIKeLrBKX9MU7uqRrWuVY7/Bs4SajTgyOeKbppTeyiQlLyiPjPvuWJLEjV1Ui4OSDAR039Sot2DTeS3fzwVc5gseJj/THc3JYDEH3ZfTh+HhmAnmzaPb8ZLNoGYjuaSbQHc0IeqTw//s/7mpGUHk+u9cpgR/fEMSLPN5r3ud9w31adGs1OI3xLSg5QaXut/D/TPFDK2ws/3PSqmRWdyN58Kw5HHM/oH5nsLmCXop9XnsSbnakKKsEQbdf6HTi3FgpCe/Y0j/YwoElj9mERQkTR+cBWwnfNzHGcLEfEA0OP88EomGhE2MYjbHihIch6JP3DJJxh68b77V3EGuZ/QUDw5AaMQxEhNWYfZBrQ5BgTInl9BoGMJ/UIGIQckPpxo4mlj8MQWLQ5ZS2pqOvRixGD2qSVb1cLvnFl/6HRJFZDD6edSKmU344L1YMQMChgbGJ6fnIlBk/P4/ZPzYCqYPPlNC7cvkicEuzgu4pfI8vBfrR6b/nzj1T/tVYYCgSzl2Wg8lg5K+98t9WHurfuZkGplxVHooID/DFRUHCI47RBN1mA4k/y0Vx8/lHCk6+0gng5N1ZjBjEbClAUzCHYcEhOfYnv6Zzjtx4icY10BLaBQZCO1VbYsHln6hbRgLKb/qFrXih52h5gzHwTFLPcQYH8sho8Eva7VKgtCi7L6WHhOGmlfdkxFIBN+/Zc5bAdUhAk39uvHwrQ5D06QsNInzajI6Of64vyi9ZIsaMIO09wa9LedG5HzUwyckyGTRg7GH2JfsDoWdjBFJY/s0QZH+1FO9+3v/MXKD0CuefQKPlaUWMGz1sjMtxqvNb/VMsX9NbkSeVlUJ4QKvzwWBmuvppS0aPHXIw6V/8tKelWGPQxEidz8ABP1yMgQT7BKkljCkNQ5D0OzpeX5VZCswm0cwOxRD0lGOItTTsL7haHsaMoW2PIIlczYVngdzWcgV/GHCxGKy01OVbWY6Lckw+ASY84+MDGiq5mzRynaSNOAd9A282As6ndP1+WGaDvvCQYdqiSrJ5x4Vi6Mu3PuBEf/W+smnej4yP8gVvxb5MdkoIPkOtWEipqY8yQ7aXBCZI8dI/I3WW/0Dzi/of3hxaHanP9EX0kuYIefodkUdlRnvu2cd8/JIhiA2j6ShEVMqF17nMQbzX8hwAZc8aaIM+S+uSrumFACxwIuwbWRwkEyb8jJ2HwikvoVxOfQUecfusR14IQdf2Xjg0b5bNwDEU+hHcpfxnqknvFN3yUpd0xau7cpvqN7gvL3VTjGQ9yr7xAasK+JkLnySf1L/2HkFymR3/XnqYPvErfTR96ZMdZcDnpEcp9ecy6rO8l2Pj36t/3BlBMd6zUXR1My8GOpp93XD1U/8GnY1fiP7Zb/2t4AbDC+567QplVL8NlGG8Iyv29K/pTDbZjDTurwp5q2GqE/kTTLQZa+zJl57qxZ5BEpjShoFHP5dWD+yUIYhZet4jaNPdCssfQxDGV3SwDEEs61VfnhlBj1gaptMe2ffzIX38GoJuswrgzo3/D0OQ9Pb5un4J/CkMQX3B875O5XyV+PqCXxV/hlcz/POSKoXrleUpLoC1XFKVBK2E5RnoKDUUOMrVjeVXCrE22Em+KmC6sVHExGGIIY8agMj7E3kLAIz1yeDRIDCIAJ99f1hq8OGTDDtq2Ng7iMbtvb500Hi9+yCjj2YF2SAkP/HMIHqvWUMYgz6Br5lBGIU+e3YQx807EzeilsVWTuqO1L17RW3k4B+oVHrYBxAC4jTfGhJm/E+zZ6MLLXzFLGw633yJ+1mNgY1ANuRg/cegIyOQl3hhDNKyL/lvYxTC4CMXww9LwThxgH2Dbsvwc1s83vGUf74yQDMzjPhajP2ATh9tSDcGVXCwI77MvyLhFdZ11fVzF6a1iMtDISjTUhiFc9UdwYOzZbODm/IOUs5Cds2HmIRP5vQtoHjeULYCHrLeggNT2W1ptrjLMLiaJBeehxGIpIXa1Q26hRHPteVlD2/+cp0NQYswzt7rkAAzglge9glDEJuM+tjhzAh6yWbRNgRlRhCnhjFz84N0tmd6Socz85Ovx1lqgBKSVvIgYwwk/PVY/qFzUx+sRVNc9M7QPasO2qmjWYvkg4J+IaS09RzdkEdH/eolUjMJmKGT2pgwbBCmXeCaftoH4ENrOi4po71sbBKUtjJwUsefMH5+MczYxb8tDYsxiOPjfRiA9D5Lv+4PIxCnzjzWvkCPNaj4y9MMLjACYSy6K1dNiGaKikNldutnDeFGW2DtaAbIu5yJ0XEF4haxILl7zDUVfmQVkUuCCqzxCxF717jpnz5oXbpm9BYF6BKu8U4gb6mmZ6bNM/n/2XsXLTtuI8pSL5IiKVGyPd3z6J615v9/rm2Lb+oxe5+DQOIWS7J7tduU7ULVTQCBQCAQCQSQkQDySmnI6cK4e8uaxMO/D+/vhR1kdrBjKZwSsD314XatDmJCkzOCSNZ3YuN8qn2E+0XBtt0vNQLxyzzE/kZ40nZB+45VKuf8TpyrD5bu5LNud29CYOZZNBfFyfKRP/iTUPyJ/eP9u+WdJfzvLfuiPn0lZecmn5pnOFqc3mFYKmaxPeTHrfcla+a771gVxLzXefErtoapp3/gl61hWdXJ1jAMQp63mfM4eenqPDtz8BzufxmCavBZhiCMQd7R2Spm2F9xqljai6Jd2iTaEKcywd8R0iINLvGTcKeiG9nAyIJSFl3bV4YP+YgMQVsykSgm0g1vnqE/uq3xcHIxIZH8ksolNeeGZWsYaRbv1jCfWn7LEOTcW0NQDoxmfu7WMM8GeuHWsBiC1sdf8rK38/7nzPf/P88IAv/BfXoJ/E4NQXb4Dk+jZO0fTkV0f6vp/K30Ty/2f20ObnTJPVVR/r+NY+o/+i5NieOXsUtxTly/5as0+8bw5FiYerYDlgp3KtNn4NI31ZATEn3xf1I5O2kBkK8cLN+5S34WjfOhIPlYRuSKoRwAzcPFew+FZi+85wB94KHDLyJo9Mnn5t9zXtCbwt5qGAKuIegdxqEPGoMI5001xiQP1JuvbJR962dPYiig7PhWZv3saSOnTPrNlPrVJ4M5uXRwkFoPY6ysNKjlUGhXALntiz36jzD41PjjnmDf3nLmD7+v2f71BP8xbwU0ErlKKMYfjEX5BKV7jRlZNP5kgGGQcQ+yBib3/3fA8Y2fBiHKghnxfAjwvsXJdu6KLMPziuZ+W3sQB9Wa6XbexEKA0PgB3lyOIm7g0tu0QBLPeLbrbcwpcwb2xY3eWeSEb+AFhq7oU1juzdBrQdZyteJV8sqb4m6I3hQrMuSsycp3esBK5koFNNQmx0Jp3Y3MfThJPRiCTmk8hD+BBFj889lfX2uQ/8BbZA6LzkPEK94wr+1hPFz4CWIfNt5gmHe7rjoau/s+/61vi32w8LcMQU6fl0ForxKil0TldsrdbpQOY09bnWjJYLr1JZL2KK8+pkRFS2E6XKhdOEkiTUh0Ox26qQs25ZjOD5tWeQhvJkrYRwPzG268kNEMwnAwk78WdrEEVPy8OCCsNpL3nOfm9oO8Udb35+fjuyLIsyY8G65fmnnCV2fYaoAhyHOCvud8IM8IcsuwXwzTIMRQcmMIcjwoxx2vZHFxajCu8et6hS5cebpikw9M69sb8BHdYpErSn8kbpyUyLEYkyLe0DLFFSkTL09kE34HL1RCNKG/eZm7HzYWtuHOsv9m9l9FuHg8Kd+i21qm/NuU21hkgly76qcyznzKORU/RR4frv1AaR6OkcGM56chqF8Ooz/iLjENt2e5605IPMkLR7oHWsILdcBX+hXq3SpGKTST1xNraAxJ0yZs2r33ezId/m/J9qY8hbDa7FnOQSrBv7tc6E07PWm0HlcJziOumhFZPJx5tmQ2w20vmbmBX0MQ957sht3i5er5rvR5zxlBnA2Evn6pIQg/n473sGh0+WtX2DM3fv+eLWW+LI2iYxsvxvufaUBj4Nkrgm4MQW0/8rc0GT5MWvd1N61eRLvirdeuyKqasrqkcG/db4HJl/ZjcZbhXOmkseQi7DQEScZ7aK75C8zLdhISR72rr55sjVIzD9xPDYFFl+LvFUHV2+psS/5SHQ6OxncNQc7hc0bQMgS9wP+GLWL5AAx6vF8Nc2sYh0X/6cEQtG/JJw78Tg1Bfai2idbRXFekjXvgl9/mPNgX/CH0v1cCqJTtrvu1QTtwF++Mb6T/6cBdKnfjEERhBrqSOqltJBOI1bCu9jOIqkeUHdEMeCDY9mYuJ9WFmcHJ8E9MVmoM6uDF80ImLjHMlBpXVS1aE0IZHPA1Evlp+KwQYpBzD7T7n8fA4z7oN1kN5OogBjYPkdYI5HayWUGUwa5nCP3EgOdXxfR/0ShEHS2rn/+1fGvLz7pVRCu98QC9iOZgoYz6n4GhX+ZAGgxOMc7Mah1m5F995bYu3tJi7HHVj6t/NPzkzB+MQF+vFUHZ+kXaI4xHbhPLJygZTbADEaZsByP3IMe3HA+KBrwMRfkKGaNUD6QGDi9IFSfTu4YErWGh1R1c9z0XlzS83AvzBdQ8SxpJc6AU2mRlOM6yrviiuGkO1s48DaiUktdyLi6nBuS8CllkPgKUeVFv+N6lEjCBfFNJk4K74E09igKOMCorke9xBxshBUrI6y/0A6UcHPchKCJW2CvHg/cggX++BP7KiqC/ckbQjzxY/OVlDxj9H6wEqiHIt8wcOOrWsPX5eL8+k+24PFC4gnLOCZpVQNsQhG5ylZDGoP15eXtCHj7aS+xl7bf2tnHqpwnrT08aHSGs41L7tXFDA7ty5NmB7D1QvvmHWvMk6+60TVOLDgP6PBJsndWXCJeeXXj0bR9GfOibh8Mvdn9PLVOnHkDqdgIQYwjSKGTcMWYMQZ4fx4ogtg/nXAnPj2Nl0AuMPxqBskUMo5DpT/hamF8OY8i5NQQhwG18ijDl4XINX5DWx1pXj0+Kvr9TZlIJni8qrYaAw134Yo2b8i+IKUO35a7YgbJYD6K3wHFr5FvKAqeMv+V7/0VuORMaHm5zT+ot9G5satiWdeW5QmeOj6F3IVO3yxCkAYiWfRiCfMGVB1/g17wETiDmCyhXBGUuYP8jLE7kuAU1cibDtNHl72YO7jlHGMntG7aqJf9zAyyjZKR/pohzSWgxM8jHPWkesceV5v13aHDkyfvQPr6hi4MrntBi8i7Fu+3qbnyo/Bp80vVTi8iCUsLbUVoENHH9s84NJzU3rDKTBj1Nb7UFVwRpBOKXVfM1BL1ka5hbe7s1jJWdGvL/wlfDMAQ5l373jheuGoK4yRp9oq+jeez9aqRra5hK2a1haqfyaHy0BLjh75bf8A123ZEWgHExpPOxa2rzTGpiXigr3h1DkH1lfpHPasPmb9t1PkzOyT+yNl8KGZ1kREhrKIfRnTEMIQFJqJ/Fic5bh0Wrv9mumTk6uH0RrB7+PIYfzwj6zhVBGoJma5iGIL8cxjOAX33sV8PCzMPlE0vgd20IUjarL6yQ8XYMm65pd919sLs4D3Ek8GsCHOH8rfTB+1/wLWKc9+2MD/x+/z7M+2AnURWn1CgpAdpRFDqQaTQJgOerB5zLIZtn0RZvkBcZJw8q5C5dXauCiGvYyaogJy6Gqd0cIN2BJ8RaaWj6UKGejTEIA9IHtoppzKlBiIcPt4Rh7MnbDbeFsXroPQfg9atiXSnk+UHvWSo7K4M8TPonX53BpPylLobZolaHeqdqgVtfRxTdqmOUP2xq8c8WLMLMr1D6TLYIfIkBKIYgDDlZBcSqnq9Y5aNxxy1frvp54uci3fZFPD+3gfHz62FPmMxr4HE/f2hC20GojDjgdFDK8lR4+oLx+ytGJj897zaCr2I4kgf4YQAa3WDtlG5d6z2p2Wvt7bQuvcQHK/cx+eRBtwRjWmQhTISVbFQ3Q6pJnSzpJ+HAhcKCzUBd+gLvc0OAfPAxbdWwLqlzn4gIbUq5mXsqymZ34QcgAR2J+7YbP+ATDA3wjEtr4JYx4SEtCZ1445JvGAI4eTLLGKQH/0ECn0ACf2Vb2EvOCMpXw/zSDNsJxhD0AyuEfLBwRdAPnEOR89tcqclDiAYgFm5+9jPH3Phm+fNsN1BR+pDBG2dXA3lmkIagPHhEuRG+tFP7iL1h9YjdMU5BFOvqT4bmN3jVXdVTCwYtyeVhAX02+W9yRs9tzbYyXvxRAWBSUQfV9+HC0MANZfyzBMdNEj9f4+dQjuHHhwnKm60GOYcCWA1B6CzO9nmMPvc8Od8qf83WsG0I4uFhG4I8ewKDkAYgDUGuDGLY6YpR6GdrmDz4C5vqQ2udmBBCV9h4HpgCr2Qn9a4vbimRsuYGg2PapmxyRGGqOc7SEwXQnNHt4XDhmTcoS+cnYg1WnLQZA4J2V/Eu8rfepniAC5Ozm3ZzYExQTGX2Md6qAynFuHg3b1MN6Zq/4fuvU68YfhCg868xBGkAMt2dAj0smrK43/kQRsi7kpgXSswLnA9cRiA55w9cW4IcGq/VsnHpWgPnBg0Fgzy3NchNXWU1XLyCits6NGz2qVNwJq/+KvOulOQvbWM88G7cEe1co/UJ33fYTXTqIB3rd5cexM96mj7xu7gDl59JGx6Gx7Igk4bgSnqTmLKPCgg/owsxIPTNUjmKKveFJhCN4coeD4t+60dV+L3iMH8P9VdX1xDEyk5WBP0lhiDnza6Y/3VDUHXyGIJkAp3NyqC/xxCUKoyMd3V2jW+qJ27l0tBZ9UKufME0umg7/5aYebzX84zS1VKlpJxE733SEOSYU/1WmPejJR2BUJRq9bT3Sz2tvjZ7paPh3mV4ruLUMJQvBCuhzzUGaYRVB+t/gSHo68++//7bfD4+ZwVhEHJF0LMxBD2qIej/zefjFz8P3ieVwL+EIUgJVXnXp939pvtb6b+Z+d8xcTr/31M3hfe38P8BAr5bxFnsGS7LYl+FqpzGqRTvuguTqpAcegSCmQuQ/Bfz4wGuuE4+6o5S0I5LN4felK5CzoogJsFOYDzULoagvIUQNsYixWvhKGlHumjbhqXlz3zZLuYWBPJ/wKDjwOd5QB98u+HAptEHuIec5kwhfR9SMCCJ41aHrij6cZUN7fBGCf0HfpWbgpeM5UEniz1Hqb7GmqwCwu9h0Aw2GmTWqp5HTMQ0/jzi54HQbg97rCGIESJnOWAAeuzPc4PcPsYMXiOT8o9MvUfIzoHmFz5TyYUf7/+AUeRnX/KAID/59HzKpWwNSZT7BQjhFb5BTXV6dxsxrA7JvYbshGdCA4A8M2kZCZBJSkwiw4vR33DWQZavcuXkdDsF4Bn+FZyTDVGmgI2+JkvET1TRuNVxlmK9LE/4jSMeEZu6wpM+qPG9nAUcYUWTqoAzeUKDiLS3S3jku6DOIB7cgwQ+oQTy+XiMQRqCfvihB4z+D84J8rwJzwj6C/5rPx+/DEGuCPKNtEPDBz8h76pP9PjnX3gAKfrxc3z0UQ1BGoUIYwjKGUEqVDqaRpWOLFf7P7tKxGHSArb/Ch2sW18dlodiMGZsnH4d3UU+cyRXHogvSlK9nIX6G7018fJsmg8XQovXUMqXB3/8uaDTcHP7IGFtiVO2+j4rgMTj4UJDUL4+Q1qNQJwNxxYDXxY8Y/vAMx4qfIjoGUEcFs2B0S/YIvY1X5F0TJmtYQwHWU16GYIqo8pjcVwPvlbgpha3sKnh+OZp7ah5Se86ilMnDX9T9gLrAbqZZyycA6NZA1gFEA6vEw1574WcLPSPlPoNxU2tOc600cWnNIbqFFj8Qi/5tOTBPWlOePJXFhMbf7Au3zq1R0TGrGLuizPGfsCzIqgv2wBYf9FpaLbIiIBL5gGMKRqEujKo7XjkHm5WNeYeemNaMuR2FZGJRClH59WfLSDSCiFTgAWv4V43kfDlvdKJN+HiBXoFVyikV57bxKFbTspCad/iEVuok2PSfwV7km/8qf/fynPWqyJbOfBa/kFh1wvYRrvCYaAVW0YgNMW+B5W+2WIIYo779q0fV3m3t4aps9XXbgn7M7rcg//dGpZzNd8zJ+cMzWtFkG2jxh41VI0/Gu01h6Cff0GhrLA1EV7WCGvkx008AeKrSivljN+GRzLSuPIMvcNXoBLnX0PQiC/9n4h/zvP2vF50kGM00ie/f/aF7UaeFrz6XLmw1gHia+BpXBZS++DWEGS+GoRqFHIFf+bnXDzSYRuCvnmer4e5Iuhb9LhnvvlhGF8IP+e54L//iWcBtwA8uE8ugd+tIWgUgNo0Snu1F70oY9vsr7ShXwF/cmF/EgaU0z/a/S8K+JaluZGF5t7Cr0rurruv2OJN3mRc7QLsTQKshE8KDUfX2pCMBqe4Tkw64dpENjvZr7tipjppkb4TFs/80SAUwxATmp9clWM6s428zQKv0wmVM7X14UEln4cEYDwkTLnm8UsJGnvexSCEMYi30T6IjFHIQTFGIQ+VXkahrB7KtrEahX4Gx8EigwaM+hATeayqCXfQq+vEqgMAUFfawJ9LPzW2dPXNMgTFGKPyf5QVPW4Fc2tYJvRZEYRhiDe2rg7yHCD9JxiNTP/S7WP8LkasN5JUdrx5+IVX7X415BeftDIw+cbBQc3lp7419u0fK4rclsbAXEMQ/KkvVk30JtaJ37rnkXtTe88NI4QkK4wlmBPWBiTivS4TJ1L65tyB0ojXlmk0TsEO+YHdxdlwAsG9pUFrImFg+Cv4EVnpmHaiC7vjwiawu1WcEkRPeGgdBW2cFdh4d+kljy3/cNfM+wA+BB8k8M+TgIagl29/iaG9K4Aw/vjVGc+ccIUQDxYv1xlBrgjyE/I+WLiC00/IxxBkq55VQPpZFeRXxDAEaRT6TCOROOg7fHtBdHF6g2PH1Svat6k/oKs/Tof72B/8pnTybr6omUXgZmuYdH9VvMNHxyaZmJVAYYh43jKHvcFd4xVlzbjly+MpZYw+nichT87787Z5PWzkUUyDEAr6NAS5kvT5MwxBHDzqF2c0BH3n1jAMQflqmGcEPe1XJd1K5htp1ckjDot2jCh3rWnDA5P5PSoEb9LlWjfx8YX5GOg4H9gSYOS8pQlNK4hTDrozf+MLgjc4QTwvQZk7tPic8g6Cg2Epq9iTyg63fdjmrhxNvHRxyQ7xWzxjk9J8xvwN3m1qcc5r8QZ7/AujtNp2pMofncO5QM4JtZ+5qhmY2+5/cW4gEXFyn52vVAY1BHWOMquCLMd7N5W45RYaux5iLnkbilAt6JBdoyKWnOUauqlU6xOkdbnbLq60W27mOadt4y6dC3fu96+2IQsIutxdzF2hi4N/ZEi+5Cmcr8LKNZHVJ9JYDfMTxXvNNWz0SnAqGHpc9L0AF90V9m8x3Htm27tlCPoBne3WsD+PIShfDcMQlHM1/ehKDUHOzNz2FcMPLUNTtC0kMIw/blS1QOPqvhqJKDs4eKYzV/+IV5NwhYs/4QT35Uwf4KYFwJw7Tn0rCv3SNNVnC0VY33B/xXGOXH77EtcXpIqu+ZV3VsflGUBFbR31lfD8KqXUkkYZcszFb74aRv/83NVAkWBXYvqs4Bcfv/3GFUEv2Br2nK+H8Rn5bz0jCEPQ8xqC/FpwDEF/5PgIb8WD++QS+P0agmiU6RY08ihbG3MhC74jBG5dm/wt7D8utrXJqrlCGdgSkMrkdAHfxbuDs/EXjR3/jYAkBr3kes19Jc0h4QzfJTV5W4GJmWsxhzfQBAQPwDC/glR1JJnGLCLlGkkcWBzQ4Hsx7PRvuWbcSlWoc/gaVg4DUIwZK44RJucGQcuVPvmDYNu0WwhqYMkDQ4xBLUseHPR+1pjE04ZfPMjnMz1DCOOS28biYxhyS5jhnBf0wa1hXSU028mk4WChkaqTrNJOReHEsrbAlIXcyRcjQL7CoU885/Iwy86WMIwx+l+gyXPODzPxr75cZ/4A11Dj2T+u+nGC75lAgfnWALiGID8Fn0GMEpXLzwzwP1PXX/B/+vl94vmEPUahGHgYg322cuXRV3xm2O1hX1omNF2ZlO1qi/fUAp5bHXwqmYca6yXUOvsLgtgGAfB2I5OZwB10TekAaUgHiTjltgfY42FuElu6NVOezZMyV7DeJCxgolxyU/Q/hhd05oPzI2pZvac3BV3FiSuR8Rea0wHdDdg6DlB/4oZxpgWfy6/VcYq6rcvKKJEH9yCBTySBHzACvcIYpKH9B7YS/PUV50zwUPFnVgO98pwJth28xH+dw6I/7AcLDUFZEcRb5jwsuDXM1T/ops9YFeSKoM/RTdkyFgOQD6w+ROR9a7rCtVXYbkUv8T+dKbFDIvYg0qZT29eSAzxg6e9mjjv9lW+lGZP+YKwMh2fhOn3HR5ENq8kssf7Egw0xuXVsi5PH8Am2vJlLyxDhbvGFMjrWlUC+4bOULzUEIZp+MYyVPm4N42HBN8huJ/jmGW+Uv/06B0Z/79YwHiyesqr0a78a5jjA70sMQG4b/kraYdmyWxM5lPMAYG90dhkuOHUJvqHWZWprrOnW7cplNVs/YYRS70kfukNZ5BUOmU0oGSrfwg60lDcUwgSJU07OKlHGk2EXLR1zlZ7XK1SkyAPopl3wR9cpa8oo5TPXGf4oO4CWbIqltWWv+7FTbcfFNH0MQbNFTF/Yj8zFnGxFzM5l7E6O5txw+ctKIPzMWfDbF23J0F4ych4jAf/CzcXeuj3WZ/F3VC1lkhIHvElzPRCT5j06bvdNZilU+oaCZwAnT5dbONA7sy/2L7SE4DcJpXGGZVTuhrJpH93Ts4BF+S7e5LlTcGR2T/bNtGW3cDlYNVQ2hLfOmBRR5G/FIwHrlbj3s7rzR+aI3RrmqiC27XJYtIagH/zqo6uCWBH0l3NFUFbRM3/+wBw4xFyh6eofrRCWpzmDMIYg77zGIVfLVzvZespDjUeG+W2+JGh8VXPhWgcIBN7LjgRssi54Dd5eLQZaFpOcjB1yalh5/+xzCb73Jb+d25VxZl4vRVGsxtImzJtJXqgQTmSohr4IWbmJFCwvP/S0/mes0lc6sy3MVUFd1akhCOlgCPqK37esAPr+Dy8++54vhr347tvo61kR9MwVQTwLPGO773//g3P3zfhD4BNK4PdrCDq0SxVCu1P6B80xPULBFXAjwntAN+n/1hH7+HJHcED/k/5QuF+i90Mtwnyjtq648FC8LiZuF3p3iObeb4wJDF8T12+Zl3+mHUR3VmAbvAOhM4NejUDNsLOB4YDUiuCRIP58MWz8GHBQtBpxxnIf3xLAt15OVNzL/oU+hozsa8/gJO/KqjJ0m9hPGH4cOKUbgxBPIj9yTlC2jmkocjtYto5hBCLcr4j5qWPeXnOYRVbayCx63BVLHRDkZSQcld8BQ9FQR4eQLzT2EO72K41BGl9qDNIg8wWrcvJpdw1B/Dy/J2f44GugybYxaLh3WLwakfAZX4UpwL7pw6d+rqDSGPTzTx/y5icrgzAE+RCho/iUb1n5xfhEmfKiLOWZ0dOBL7/kaj0bBA6pz/OAsxL1bAIpI4krwTLnt0AfeWfbmUTy3NCXhm5k3VivZ/4z3NTJuXNI2vsYhoUiw/BNXuu8Ecv5Eb2CJ9KChuSEbXsWMYVL2r+V78y+wztQImd0k0lgYuBlwrIKffAeJPAJJKAh6CWfItZo7oNEthf4dpnPiSWuIYhfvhrG+Wuv3/AWmjfRnuX2EyuCPmgIyhMpiimrgdDjMQph5I8hiP6J9TpbxTQE0V97eH+m1owNa5ykW6jxp4us0JKIZTS9gBkZjE1/0ueXjrfCQb4oBbo6puH7HfwksbyG4DFGtIA+XOz84Du2mdG/6leJlM9uIyAnCTUG+bDRB408SADXgOMLhUdu+WIBlV+X9KFBY9C3GIKeY/z5/gVvmrchyDOCXGXqilTPjmNcQJ+4NWzUiuWNHhpfnhMeZbbiTT+v4lkH9J7ejev9OGlOsrDmGkj9u7gfkVzok18mLXfTWgTOfBNuVaaEgZ7ltw3cTTFHc91NIQHQCRXPeP3mIhqI5Xfl2GCslKCdVEojbQSUSWl7M65cr98YgnLOYQxB5HDuYkZ+DrGz6oHBPy+LXAnkmYO2AedTNLTw3Gv5aslTurArPA/Mg5l42vWGLJkZt5DlT/LypzwpB+VMv4o7oADvhR8oUJLWzdRiMt1X0EcFn7R+O3yXlY9JnZBb7PAYUOHtP4b5FZTwGILmnoej1S+LDSXjKUoDnz3yC+a9HhaNEcjDovmK43w1zAP+/8yB//3y4w/Z2vvydVfFv/WrYZ7tpr5VP0Mnxh/1MXPuvTIIXZ7wNgS1zPC2DUfEwmc1RNMmPL5QebcmqUDQdvUTO8Sx4gdq8llMKUirSHkOAGo/yDPKQdQWkmcKfTLHSJqwrMBbcDXmNK8U7XdNGOPP6GeLHB1umquCNB5pTutqIFcFucpT/csUnDm/hiBWbmII+oNfDnNFkIdGf1Nd/tRPyGP90RD0375/MAQp/9+D+90bgtKwlZSN2gYbt3rEip3er6ecWP/G4RGRVUQYdvwokxN+p/qTPv6VfJtJxdKH0AvDu1J34N7gLTheFM6+hxeNCW1aQ5IEefqNLH8j8Q4hWdn0jjTA21mgb5tQ4NcA1Tp0Wsk1E3rTbZGuGuGqEcOHgxhZMNLgZ4WLafxqBDK87gkFagjSeKERSAOLlf0i2wpa776RgB2MP5CvIUhDDgNhynMFDeV8WAahHCztaiEGSQ1DPty4lewn0jWyZGK1/OEj9c7MovKIMSpChxeU+p5YGean0arGIOIadmLgeRSD0SO3ahHXKJN8Tsx9Wxs8aZVGcCjYgaNyq3HKAS5b6VKvDxzEKu/vkVne45DDST7DN7LSiPSIt8Ly84gHrny1ANlpCsokkDpkIMS3rt54/wx3YrLqa5Iu0ZVYBIBmnJ9I674IipuAmRtOaMAL666Xdt7iVzYjAxD7CBOUnCy1PU5JJ9aa8IJrzuvx58KVqs5bG3qETzYrowUj0jbbiXbyJW8LsIxxO7wDTZloyjLi/MESDU/B3qgH9yCBTyiBH3hAeMVXw1xN6RfCfuDQUX9+hjjnBOG/4o3z61fvOG8CQxC/d3xhLFt2OcashiDHgWXsQR/FEKQuyrlBGvi7WogA7Z8fncA+WiMQ3YH+YJewz+nSzxvK9YJNxwm2mCt94kSjuy74hET0y8mT54JffbDFT3zxuccG4U2rJq0xKCWvCsi3q4AsY68GSscnTlYfsfq2mQcKDodWCtHn8OybZD844AHQnjPhWRLfrK0E3/r5YVYE+dWwFxiEZkXQE4xAGoJ4JxBjkEYBw1UrShiOy3LKIhpX/iemv+qlB/vJ1yA8/4oDz5pa1ty3UrFMa6YM6g+FSZ94mZPQLd6kb/xUQsbA600alPAwdSzncnXXFRYVfCRZatxNljMiB2c9FkcXY6vIAhb2UUJYPuLlYKowJdVXYo47laf1dC4lIHMqfOdUvsEqjkXzxw3I9nqEEAMQceceaZ2EXSWcOhxcNDilGyMM8x32DRRjeYkYNkf9SdGfcND2pXgTldPLnWkJh5XFT1lZyAcmwbbo+0tUdnFmueOmPaa9Kbw7biDiRbj3tLNmKfG0t8m0ad0BEC05249IXMZfebLSSzA8ydbgp5674xEwzH1MX8PXEOSZbm/R2TUEabh3a9gbVgJpCPKrYR78/5KVnGzlZS78TmM/L1OZSkOLFUEx6tQI1Hm2xiCUB3eqB0Uri64Qmnm4aTLT+7B6T4QB2Hz5GdZVVlY61d54TZ1raQXjyjKJ0uh/aEgiPcg5PDgxvuNHJwwJ2zuIrqA38xiC7A+99epd8kBD3nThMImmVS/XUC8FOVwwDUBuEYshSGnJRQ1BvtPNqkzm+q4I+uMfvkNf8+UwDo02/i0rOz3vLSuC2Nb7FGv/f3NFkPf2wX1yCfwuDUE2TFcJpCnboG2veKc6TWP+FfGZ9p/s0r3bxyOGiJD4iPIj2SzckWnxDwIfZSggA8zS4J0AFn7lXCG8nFMgbv9BXGkHbcvPYLRg8pEbv+KXd+SNAhMN5AkHMZlnVriKW7BN6Iif+VMnyVWJR9EeqCPIGHfA6YDWlTqG3duuEch8sd4vOrtYWnKWKPvQkImMRpcOQjEMyUsrnyx5YEDnQjFV9HP00lWZy0PKZHB0u9iPHBbtxMmzg/K5TOA/89Y6PIrPT6OQjqyVC77z/elf3oP5aQxyMtVtYvLpz7duGHvQ4l0ttL7+BTxn9TgRg0aMMwwMDuA1IjFRd3JGVRWnP1kYnpSZRi4/e+85QT+zEkgjloPPZhQplHZXHH0O/a940Mo2McqVr8jQ2ijDzB7IbtwJNwVa7pavDIxjxuKDypq5AG3YwTCyAiLJtIfJs/zADXOfbDXFa2LK23hXzKKNqen8a2whxpvBu7CUSybppx4Wgpu8u+8AL11x73HNloThc+oXn0sn39YXWuAr86FvXKcfPhIJKJdJt/DUUTznDMRW1iJ7bx7cgwQ+oQRiCOLTwh64//KV5wF5SLRfoGElkGH9GIIwBmEEesMb6DcagtS1qCXs7Ogp+4FKbW0P00fJffHFY/oIU3AMQm5FyAsEdJS9oCsoagzqSwKEsPu2PVy3rgtuXIgT8wkXp3gJ96lr5yXQXKurDWbhXnW3egbmgcFnkNUlZlYL6ggTGJgp1UsAi4D6NJBaUud5iFCHyLfv2xEafPaLMzySsRrIccEtw35J0sOiNQS5LewZK4M0BH3LaqDveKB4wRlBHkb6lK+K+WbZL4yxkChvpLsiyHLCZXmQQdxw33C5jwITMHynMpPXhIZHn1UeC77yNNZIxnSCc/fUlzNWLDZKcZWz8y48udp5SbwpwpxUTHpDd9KHv9K7ex2sUp6YWOFJgOMeXtLOSp7IZgArPA4yvm36zBI0Ma2TkZP+Qpw6Dq4o1iu+4cxPnNsw43G+EuNP5y6Zx2SQpXDG87y4ok9ZXr4+mrmHbaAw4bJ7VNAShMT1/lSmg9daFiswMJMj/AsptHUcjNL7+Eq6/1eRQakoBJofP0FKDl4uTVsyyz0G1TuV8KCEmpfKL2kDIzLtL8WsuHx70z4isfKlXpa78IZcffndXCdsf7tBD+GDOonJ5QVwSua+ylvmsfjmrzMA35YtfnzmjBSSe4mvQceXnq4IeoPefoXe/gtbeV0R5GHRfi0sXw3jnKBXGILeo6Q13uszxYSmZ7ahNFKA+rirgzRtGLZdBbZ8w6sG5Gl7Dx4xaSQNPluFxfdOS+C6WKe4Bna1zb3TFkbiQxcY8cgJ1DwLAKrcAEiIdCTV9kEo8oIvyfS6M+w+loxb+NXV0dHR2+azdtXbrgbq6s7Ox+eg/2yyU4fzbOA8/8W3HOr/x+9YEfQtK4P4fLz6G539/Bu/FvYkZ4U+xRj0/zwYgrghvw/3uzUEbQVG09OpD2yWf4/7+7D+Hkr/ejjqg6WRwvyl7lUMv1afpUV2RhFDqRmO4EVh8hRS0kW80Anlf8GXwpnB96J10YjCUnktZscvxqI8dCAeNdlidh5xizl0VsXjcVlkSvP+6yhYUSvDRUN0gyTUCONg5sqcviXOah0yn0aiu+322hLmhLv3ZZYxJ+7IuuD65UG+CUPbh+pNX1YsLzwwaXI1jXG2g3nY8jYAmW/xrF7PmxFrBsxfCsGbRpI3CkymtPQLm4lVDAKHcUi+xflcGHznB35g5lsDOMPTwnMolaYXnTyXD40PNQZpRDPMNjENQhqCnAz6xMVDhG0iK44o04lotqRpmKJQDUJfaAwCx7/UB7ldBa6CrXPSuRrOhUCy1C+GiQ67p7uNNaWwyHKj3uKFC/hKIVyv1HC64ZMumUwyFtHeK+WAvMR2sjt1UKry7kWXcswfCoVxPcvcwBUwLbQpqIYgqEkzP9tAf1PEmX8Vd4Juw6sOi7umpZ3foj3EHiTwz5RADUGuCPKBYm0Dw/jj54hdEfQa2A+cD5StYa/dGvYuh5R+QL/6FZoP9EUPjVY3z5vmrgjSyP8kerMPHsZ5iEhHcepsX+QBhED69ap0u4njh4BiGYq+X72X1Ju0Ey+G7IUnPGQksDreji8KgjOKpuCONTl8pVp665PiSGYRGj+smAoc4lIoD3lkgroPDz6Yl+fPP9OwD8yDRnOej4agbinwTDnP/nnMC4av2TrwPF+a+fqzFxw+6gGkvln2i2G+Xfbz8p4P9HgdFO1DCOKNQWi2EUdvWeGwvHgkqssYlFBrNHIRNehJuy4bNogracMjxQv/bqh4F/aVXoJ3yIaHEzb69YSFxn0kN/ETu23hhORub8AOkJvwRMe/KYxChyE5DQ+/yUhpek07K9Fp91PEgq7+YMx5zRqLaEdMBegHzg1CiEvHo6yoXoYfjYGZq5AWFrnMvU4+7xOsXny0VGHTtpNvxyhGR+bmNyKy9T18wXFTCyIEb+evhZ14W84FrrlI8aa88lMEqomz7Luu5d5NWtBk2eG7Wf/u+H3lNvNNSgq6SjtfEottirC8kOT+9p4Wf+r8i/NG8TCoqzOdKmgINeBqTA1B797++Nnr+Xw8OrtfeeSw/z/zc0UQv1fo7DlPyFVEvlRFS6Di0Mf4CqaGHlcJdVtYcZZuBtZ08jn/Br/pMiStwggkrb7gSRcyTtiEL39LKmkrduBFXiuj4fyWoILNJT44ZmubI8R/58GrrMEzb/KPL6oURndrrLem1HzNi2sEQnerz9Xf0es9I8hX2F/kjDZ0OUb87zgb6I9//D5bw9wiphHoG/T3c1d4YgjyjCC/9vh/uzVsOudi8cH7NBL41zAE0ammX+jbZO9zZ9rg34f37w6zj6uHnDzGRX6GP5ZKZVa8hm+lExh0VC5DrhgXPcvSObga7iB7wZq44EwIh85FobiLTMrKHQfw0UC6iF0DOQAJpuDtAUzm+OG9kH0lcOvCTJgvPEGBJZ8QsDhl4QDGT+OED83uY3eAqkHGh3XreUzEm5HJKgMQM98xrMhnBjh96YZtw2YorANQYmn78hJ+uKQEfKfZ8tO3aIX/kgcUYMFfSHoocoEEe68I5J7glxf8zDjKR3iRr/BKJtKoRgeZ8BsQE/ogZVmq7Dsxv+ph/saFzS2TCdupb2oqy8pUHvv1MAZvVglpGMrKIH0HIYjHMEGZrgDqVjRXBgGHuU4GMZTIUwZ/PDmw0uGkvrzUEGQc2sQFBC7IDMLw/TMoibR1wbiQNI1A7oewIAaTWCQXOglBvHjGzINfIsRwASo80oYE4LQ3eKyxxnqLSF7ohdL2hwipouDaX0KY8nQrIeHGLEu8axvhTMC8ly0nE4vJim+eRAc29FY86eLRNnUbzcAwl5SHy4ME/vkS0BD0krfFbqN9zcqfV3wm/tXLd8so5Aohzw16H7irgV6/ecOXwzyslFWXvGH2/DZXBuWcoEyfebjIyiB8tjphmiaOzufXpZAzxXb63Om0HVKt1h7SQLRNAF7U64GsfnxhzxfB2q8WhSiTTc1itjugC6Yukv+mtB7pnGSrv5LAWqUkg9kJqOjwlgZC7xoe/anvQwOwKAL45sDRbi/g3Ld1nk8NQWwJ83PwGIKe8HvO2UAagvxpBHrOW+Xv8P0U8Qu+QPMYXLeGuSII+1HGHun48+El84ZUaYXlSZaX04QeN0BxCSu6AX2sk1dmEQ48oTsPgdG1qrfKrsQHJ1SMCA6hIiaaxNJLLoiEHiit04wwKzvpvYOb4BBelPQuyoYaW/KAQfN7Db+mCotXml29RkZ5CF7usIQTL72mByjiER2OKxdiK4OtOBHQhQUcJhjjTMOZ15dbWRU0cy7glpBSnIvwy3awjPvWg3HLzEVIr5wypZgirCMonR0tegCsX/5XOPk2c4vH5KMFSchfKOHFTW2JUImhtxI3AWVhztNNe0uZJJg+ckgxwqY4A5O4iSxACK/c4E37MXPvwc5wBUJv8kv7DvFV3ol2ZW7ItLab8pmyhuRdZOjnJSX6w3neuBQLkUgnBCWKFFEs0S1cxhD0Fr3tVxxfvWZbGIZ7D/X/S84IYmsY28I0BL3GEJSVQ+BpvHeemcP8ncBmBVD1sFvF/GkCyQp8+JKHGoHQ2TuOrlbnmebZcLh88Tchb4n8GinfDa/44Rk8XUhuAFTInv6xYVdAkc7tSXnG+aVYri1+fODhvZh5JgTZmqWlErYWxsM9sp9aj+6Wk45U+p4T5PawFSbfl+TJJ+Q1BqGA/bpjDEHfcWB0toZVh2eF55PH+VpwDEHf8ZXhipCyH9ynlMC/hiEICUXp6vOzyd517QR3of+Z8XTp0RSIoLITeiulS5aVqKpjhrGdBh3zzwDyW1RUXoNXyUMtzBir4jE9ZVwJQQ1nDOjykLA+Sj+xAiQOrpOEFBSaROoGZ0VT1ygZLjIWN7SNDKwpuUJLclWKlywsVkUdOci9itV6+FvGH41BPRfIc20wXECnA11GnkXXh/d+8jyrVqwfA5Il5R6FJQtqWSuQvIFJ1AAuwRUwnPLkx3AihV33g1I0XJkG1vZhr2HhOGS1pURRMQA0YYlRoJNtmQx64A7S/nQrafvBa1LzTBheKkd8J3nAlZmViCFor2j6kXQNa8sgBMIqijKcIKwVQjEI9ZwiDVnb2EY4gkkucu5Rt1Tk1xlMYvgJE8k9CVcHw8ouUZn3v7GCJmVg40u/ci3NoQeYP0suJxc8QiY692bamwANNZMjq4LC//QdGR+6q/x4bbOWMFylzFS+5VoXq58Dz7P9EDq51/jSXMY38wmXUFu3gNK472reB0PQfZJ5gH1qCfzAW+IcFu1WA1b+vOZzxK9e8/vh3Wcv33SrmF8Me8VniDUCeVaQDx9+vtjPyH/wEFJVVg7g0cjjg0W/SqNB6Isv2R6WDimsX4m0b/vw4EOGzil1+6R99Oqf0S0A8nf47XXm4EeHbd6lvySY1Tf6RnTi7cjWKU1T165Q/NFH6szhi6A6k/SgeBlGDUPbmlhPVETCjgUtUr6soQiebfeBvHx9xocJVgS5I/orED3w+fHXrgjijTGrfZ6z6ue5n473bbLnAnFgtGcGeT7QtxqCYgRaK4Ios2fE4VPQLhveUm64XhqzgMBlXb51Ix6rJUp+IvyGW6SCcYYni7C7JD7GE6OYgzv55HjurigZO9IOWsLGD/MTM+0Mn/FSa+pqL0S8PxcWABFyf6/7nzFzCale7rZMicxP/3JCTleSLUd4ycMPCVPH5ik9ycqhL4B+dN7C+B9jaAmtEuEBxPlQhFvD5Me8l6GPeF4ipVRLNrCchYdg8nSFrSTKw64T+R0Xww85Df8cHPBOP7SG9vJNt4iJ4hdtAVee0jfRslfJCR6ZgfvSK65oDd+9HmlbtsLCi/QOhJ13lZMkLh/VBZhpt+zs3AaSVR+cjZbAamvez+QQwXuq7umLLcFF5QqfxQNIZ/Z+5FlAFohvQxCK9y1bv16hp93Cm0/I89XHP3NG0F80BGVrmGe6YchfW8Q0BLlVN7/oag1Alqc+nlVBmDt+Ni5P9I7cE+LRh8YLj040XyruZf3kk9/o9yoZ00TRn9qJGCiQgYkjxhEvSq6reTS8ImIqo/hkluR5D0JJeW+SBqhZE4ofGhbt0QuVSM9zE0n9bR/QrxEoK/PR4RqBxNcQ5ApPPx/v1rA//emPn/2RL4Z9x2fkXdH5/Fu//sgZQV+zCgicr9kC/H+94MXtKGBKeXCfTgL/GoYgFcGSkf5uz4fcJv0A/UcGt2wMIBQfIDuwneIo1iXVKp5LhqqhkbRYQ5XQ0iZRdAGvgiyumiUFiday8VG+VWxexTdxweQveaUtDcsDsu75Le/rTZEkQgDMi83yZrwU8SRotA/JZgtt/DNMNK5kh8cSrlw3710AADL6SURBVLJPCplDrD7MZoUGfley8LYBA1BXBXVLVldWGDa/96Fbl7569LhbmBjUUz/oOnmtJCRPJEUt30j+L/kvljffHVQXn1CKzImek/zKnnKA+9PpLYk3bLlxDZSVli849wfgsBg+AzctbO7c0242jaQUx3LDgvIzzCXGDmI1WHmvbTjEYxCqTBFm4uaeeymnnl2kfD0jKNvVZntYJk69/ylZZhxA0ybIODzTVqSTi+HETYSHGye/5X74b7IVIESWtHuCxWvqtOPIrzODhV4ppeyirusFSZ+D55RsMYslq6LQI4dGEraM5CF53+cwW9ITDE9EUtL0FcshU+xxZiaxPOM7z/YvMIk7RahL8St81zNtDEGmpTwDNvoH9yCBTyiBH1jZ44qgHzlk34NH377DGMT2r1euAsIA9JIHC/0Yh9wWtg6N9m30W86n+ED+rArKg4N9R8O+q4B8QPDz8XQaHzyib4Bj+K+m8UEDfPH00yvMX2Esj/h6AAbDDtd+ba+LNkjOhNOVSklS0x+jg+50s9IGuAqZMaLdXeT+5o23eOFPPz91MAFe5AfTcYxcNch3LLNrR08kp7rcjK4GwqgfQxAGIUTjah4NQX5UwO1ez56MIQjDj4eM8rWZGoIwDBGOcchPELMi6BFHffjJ+awEorxsD7Nca+CFIvVOd6YFDiA4y68MFg3y3zgRF02Tku9AmLhlD50hMWkbfQCDENJHRPqpRHMo78RDu6X3KmJxwpwMxgk0PHGBDV/XtrowazLppjkU5P7qC+FfUx8ezjmLSDPu+CRnymZi4RXSFkl7lQx1KI36XpVTfpMdP8FcXJ3R1dZ5UTS4ymKVl7Ge9udLH19ajaGEZ9JbNxMiOU0FJCaKl8phr7AVLFweNg5BQfzmq38RhLRCzzz232RI+CxGUqmSaLiRrwW4YkO/+XNtzLHYAk2J79jbX4G5lvAUG+QFT7h07YHhc/NXRO/B2c6Gj1A4cCPvowKTZB0NH0mRkYDghK8idGVKSzAxcwKSlEWMLZuQ3EqRUm1e1jkKhSDxMQS91xAUA/1bznTDaO8B/5wX9GfOCPorZwbpu8rzNXr6DYagt+/fMUen/fJxkS9yTpC6Wn1s2+bHnFH9XbMHbS/NFh+sGIEyN6qObh7rIoM64PH04V3B+DMl85yGTbpxiXPh/pq/Kyel2/jgmjZtAawBm2OFxxdCWLkmRTkbmJjh9kdD5vK3xEuYGrmKE34yMqXd2UbXKiCIaRTiLpBeo9GX4DvNVg97Xtv3336DIegPn/0RI9B3bA2LISiGffQ3hiCPcnBF0P/5LR99GfFB8cF9Ogn8axiCkE/71NXY7xPZb6fel+PfD9buTr1UtLt6qIYb4VwpKoibJLPufKVzRcHkP/paBbEQpS3s1rX8QTOmkumkU2TzX5nkQWV7DnTCZDxpKWzhr3yj8KrKRJYqOKksITJmkCshUhO48Qci5UxUDp4AnSyW1qZhWc1jndy+FEMQo4eHHbtVbAxE2eK02PLLKI8fYwhCY/pwkIONh89IaN2rwFqVsjScylXDp9wjXZT0fmsmGhn7AI7kKT/8KiHlPFRSzirzqNvt5EBih4t8G88YZ1A6el706235TXx8edHVl6fK0gef1tcUByzjx0+58lqHHMnttUOvdehEsCuBODNoydclwK1PJ7EOcU5sN69hCgnqZ2BqOOCPDEFTNri4a+JnBcxx8ZWKAAodr/5PoYE2RTq9I4YuVzk0HoOk6DQ2S7iolm4oLNoz4RLLfFlllbAQ3brfq3jLbtaRpjj0VVYF9U2ciLYwcm7+xWlt9e+AEz/5jyzEE3ncbjwDePAfJPDPlcBLHiZeZZuXZ068++ytK4Iw+Lil4DUrg16+fsUqoJ4R5KHRbg/z3AnPm3jNGRXvNSLljDOOzvSTw9kugHUj28NcHcRvGYKiXPyCGL2g02yn2mo5lU5610rDw0WTjE5vDPpC0YHrr7mIzYTdjAB3fwz86qcm66TSS1RKHt4Ck7fosYOnJERL5BHAJ6RsMVA/8NdVo/iU2x9Q3xBbBoVYW2upAchtYb/8/B5YVwT5uWG/EvaIp4gnnAvkZ4Xze8Yh0bxZzlYw3ygT7yoh/KeP2BqG8YinD88IyrYgyvahwvKtO/+7foYzD1nwyKaVFeuSVWJcSJvaD+iuH/p3gCmn2e+kLH4+gg6gzBwsTcKNH765tA1UqkGgcuXnpCDE+MAuf0YQ05JvKerkIBxMYf2P7322Fv71nKuMoBHeUJ5a7jhZfJguDxfdphMn0NHUuiwoeSJ7nywpq4YgX7KtuY3jn/nwIwHwvqRP2QY9cDxzKmQkt+MyZpFpS4z8V3rhxsXLeDcZ5crCTkd06t+GRk77S26OiMXf4/UqS/pJafKmmHIpYxcDraJEcOBZF+k3y2UQStKmcxNYuMJKC/oHbHCTtuGWoiymfDOfzG7EyR7fag9aRGCWyZaElU9vweNx6Yp58zdxo09+Cdq24xO0SfCzLXhGkIagnBOEXv4rZ7m9clUQhnu/GqYhyHOCPPftFSs436Cz33DYfwxOWZmpsZ45uAQh7ErO6mRWBcGPOjkmjzHwKxf4+im8EnYFKHwVF75CA18JCpfnRMVLYMXr5To4CmahV0jkX3+DbbHMyIhuxCMsH5NypaftkjDiDK0VHyx9f9HbhqNLkITGHZWmMC3+SoSxKL66Wxg4syJI3etXwx6jx1/wtbA//en7z/70/fc5LNrVnM9YzfkMHZ4VQRr9OSz6v37zYAhSvr8H9/s3BNlZbORpubn8qtx+O/VXs/1bJdh1lYNKYCsAAPfLRsU/bmNf+Uy6wAmu27HhV/47yJN15VeRqV5VWHFXYMWHkoq0IAelMLgUkpn9m2jSoRdSK0/0dPJZEEAnBy2w8RW7JNJ0sSOzzdeiG/xhGtxhDriTmPwzKXHC8gFjkINbJi056NiwRiEw+Zn1q6+YxC5DkEuanXj4JlXjRXiQV/DEzcQEv5MRAtudYdEdOFDRyif86wewaK64+SMgEimgSj8FpSxhZshkAzxJVDpmxN0W+xHsTD7DIVTsfZV2nbw5kAmxfeD7k4A32sHHh6HMKJWjBjcHJGQemFQ6QDqoe2C0PH/BIB+5MlHM+RzIt/cuqdAWD8dlfGkqgk4+TDQuXzp9eN3hQsJrYHfj8k5C6O/A4sEMlW9D4WC3a2EtR79pG2BaEtedyX1cOIMrbXDE2KvWFkG95DSf1JOVcPymWr5uamvY5OYzdrlFduUvXpBFmUSDZXonhYKzjwf3IIFPKAENQa/ztUW2euWrYG+z0ueNBqGXbzko2s/He26QXw9jtRDGIVcF+eWwN3y15j2/GoI0+uQdKc3ehwzj9T/37TMtPw8a6B2NQ32AaK+yj2dLGVHhdqbpOhrB7UgMMV7Tj37ROB24ZMXc2KFkJ9t6S5pg1B2hBJto0O4ZUC7ybm+vjmy/F1dDl3jq7DWmwa8fFshYBVw/DxVLbzpKhjIGoCwh4kuQv/zyPuG8QdYAxEOBW72eshroKUaeZ5wP9MwvzXgmEMagF6wMev4cOCuBnvt75mqg6nofPNRdoYVv2cbjUpclogXTm+QJ7LiZzAPAFSUbPoFFTzTdgBtr2YaXqhtw/BtcIze0Wuipb28yT4R8V1bbSPNlvArODdGVa2Djez903sOpwxoHiN/MHybLqmnaAW2iUTmZn/QMX9VKO05cIpa1ahe/hFfLuNIkAJm0Jcdt/vxQhCtA/GS4H5JwjuNXUH/SoAiZWQkUAxCNQONgzwuyHZa/cgZuyVtK3MCVpKiWZ/cM3EvYLK+WpVseSKUmrxtWFK6lLLyhlbAQQ5rwpIXCSovcprChtxDjUV6qZdoQGLyBLVqpMGGj12qSE7lhVc44ZRB3w8OBMIj3+OErha3E4ePElR/IeSyQVNVr3kiLWy0EACkmokPCj4QNK3J+zv/8Ku57DPh+DewNW3ZfYvDxS4+v/GoY5wT98BdWBuGrtzXcawTyRzOCFvrN1T8a6fn1HrotrPExCv2MvosxKALiLsGjBiLvkZzVl2+ZXX6E0HRvlNVrcnEWqtC6gMEKbik1z0gjsdBR/48r1Nii20wtbxKVKxjyrVuYK1ygsMyTTSfiU0jPWKMsZN6VdV0F1G1hNQKp18XLtjB8D4l2dacrNL/DEPRHjEB/4sthL777JiuCnmrIx8jv1x4fsSLIg/7/68OKoNyL38Pl928ImuZLIx0lZeOdtj5CFPbglMtdydwnlUsJDP59Mk1OUUncymSHVzlncUm7ANI0cyFeGxIuPSerQ1fMcfs+q5n8P/HI18nxwlpkr8GsecyXXy8hLbmUR8DkIjRkbB5WI5NF8CP+JBLnQNA8s8+5h0bXaPFzVgXx+fMYgaynypRHA9azP3rM8kiWp+aT6gxImfhcZBd96w1w4Be0Iet9uIhhMdtJFxmJp04DB19y4ob28oV1+SqBO05JDf7I5w5KoiPRXcCBtOUObMrf7BNQ3rN9bgm1iCm500UNaT0jSFwnhMAzGVx1hLK090HRyDWHSDN7+IJVWHVOLp3picvf3Esypo4gyYvtrQ9S45sbjGmH4qcCuTRzKLQU6fdBTY4WzkoKk70E4tAv1uWueMsgdZOB1ibXB6Akpj5QSH1AHjQnLMy05r6tZiAiqCU6bczYApUVyewMpi7wCoqbZOPyNPCFt0GTLhoZFlrxU+DK8OA9SOATSOAVRiANQT/z8+yfd+9YFcQWAg0+ecPM22YfMjQKvQSWrQavrgNIXRmkIegzthTMA4QPGW4P65lATq3VP2MkylQbHaZenV9h7YAKATgdxT5kJ/MvD0yBoAfjuyooCCIlpF+9RTQdDfjucIMTkgvfrJRFkqnpz8nQh/BtCKKfVm+iO+VnIetbA53FxAhjmMjUzIeXntHSL8589ssH8vMwwXlBbuX6igcID4d+zM+3xc88R+LZoxwO/e03zzhbgq+GeWg028Kefs0B0hiKnj7FcBRDEA8fPPjn+ZAyNQYZ3mol/AHQySBx0+9zJuvu5h14U5ueei96Gz6B5Utn8CqnFJ/UG5qhI6Peh8pySKmbB5bwknXTlbD46nIhhhu/SjrpTXiNp+a8KW+l4w1lyRU6HLddFMM2MfD6Q848ybfz22ZKSZ4NGkt4+XiQtb74NCQfTp2TuJrah/6f8ulvt3D2xZpGodAUjzFe+bgt/EseMj2w1pXXfrzCNF3bZjmWW9DjrKvhwISbp0lcm3bWyyT7bVyINHzdH+NW8KJS5JVFj+RiQWmHCy/W7TXlk6EUrdNK1/enzAY24fFXsumCip+QsbiuXils134qHYwSt9wBn+FFZrNgnUJtFXOGxZVGfoZtjs6r9E9HWdVlcMQ90ZDlrbRcz4zy4GcP63/nJ+Q54N/Vmm7f7YHRHPDPiqBsF2OrWM94qyHoxw+2uurbMfyoFdwWplGoBiL1tYYh26MmDw2S4KgrSXHVZ/zAZTqtKP6uBsym3jBsiXEyf9cFRDr+wgqGzac5C/VaOldcxLkf4zfzonUSTEKKSchi8+Nir27/sJ9Qa3/WOj7h3ByloBFo+Ws1kGeyuRLIPqcRSL2cFUHff8dqoBf5cpgrOV0NpCHo6yduDUPnP8EQ9PxhRdC6LZ/c+9cxBKXh0upsvfe4XwHfg/nvCDqVwz29/6bKfyv9QhZTBVP1J3xCAO8lo7n9dN6V/rxuhTiZ76XR/Mml4jSwHRQmz2JhxxfOzpJ8V/7JFjQjIDp474EPkFNsdF+5Wxnu0h8ZDNyJrgtTNGT0yxY1BP3Eg0XPtmncSYsD2uOvHsci/iWnY3bFioagTlRWFeIJS8ICGlU96xoeRgOCCxxlLLYDjBGg4OY8EyFiGfIkxZSXXMfltog9WEim9TfvktcQWGWEdlKLcBY9YP3AIRaDWYmGuPmtjQNP8ZCn28IYlMTVCKRBKDVW/iBlmpoHAwY2D+Wmbl8yIXRFUO6zE8b8OaOQavkv/XW1AcQ5EBLYceHza8jklL+yjAiSPZfb+yFoSpRfs8nPVLuAQCi7mOHbjLgUs8pKzjK4cMFcefa9ATeTZQDCmr+DfghOGYupFU2SyJsvIffhFLNX0hdK4pZ1E4fYGQ9S2l6zP1wfJPApJPCaB8o3/HzL7OHP79n65Wqg1z5cuCIII9BLVgW9TtgtYmwPyxtmzhTi7AnfSP/k1rBl7PEhow8Urgby5wNBDUH2gNk+9gtbDqID8sCIlvMpJ3nbkXw4S39dHdG+qLZrLzbn0n0La/Vuyls4o7fS6UqJTHG7Xwdc/ZNcK46GgFOv8gRJ9Sb8ZNVqENUN1SkqCbuxxag/ynWl4PgXvvTdFpYtBsLGEMRqIB4cHnNw6Nd8TeYpDwoeCP3sqYdE8+DAaiA/Ge/n4p+6Soj050/BxRCkESn63RUglCsPclseyg/RS30Tbm2Efuzkf9yE9Sc8aad/N+1u/C5uxPurND8eK878Ce8CGjhzWO/dBm4yTqn4CU67sW5H2uTZjWORC2GJ99fxqOHkXiR8SNYZDQkCEy6fRTQc+MI1UhhXygoVfW6oRWvwcRtQtt1/4EwuV1gzt8qqa+cB5Jh53BecPO5qA1ddu/W+L4I0EHHnpQd2jEP4tvBwSD9RDmnfQATblsYtrERbA3Lt9JY9uFOvxkmbDIOAv7OSNvI/8W5pgHOHxozxIZnETTHET/RJCYxI006MMnbVx3jldA3+Q6W4v3XdmGcRhBNdML0x+sS4vdrb2ews42p2UKWeO86tdK79gTbwnt8HVwR5lttbdDNbeN3G+8PLHzgsen1JLFt73eLrIf/vsp1MQ45bxEZXx1+GIFoL91cjfldxKo+fY+RXgOpv61OjkPzbemyF4rWKaU0rnCxJE+NWzoEkzfYf9S+OYF2E2b4iVHjT6L8EBq8vxYlvwJUmkvCQWpetI+1j0Jx4ND6AnA9k+dQqRqHULiMVt6GrgbptzG1h4GM7o8uxGkjj/Of56uO3fj6e84G+94wgzgvqll6M/Hw63rOBXLX/YAiKkH83l38ZQ1AUYFp1WnQa8bR9pVno70au/wRGrL21PqUwiuMs/kw/4Wf4wjlDO3xqsKVx6okxkje8cyziHShn8Ap3d1HEhEToLdriV602vyiTLQ+2xhduEk4WRF6D/o18Iio4sCz0WspwdBkNbL5xFJYqB9lI/lPU1Ft/JsNdnVJjhoNUtmjFWGFYxR3kTG4eMUF55GogrOc5ywZe8/YrIx14i0/9quoyJTgu6Q3O3b7hiaRKD1o4ByqFa+ycVOSeOMCazr88xMXjItGBjQCKkeuiaAEHnvmM8xs/2L2E9EoKz+CIpnxi1DmBIPeNUIfZkbEPFD/GILRkm9F4cQO//vkAE0MQg9OX/hytksawt3Bk0rLHWXSpIBMFNbycWKcAkzHcD4nLd3Sl7UivNKVticK91O3gDogxUgqrOy50aPT+islf7hGpq17Gw7pFkOHsJ5M/OclyxacsM5mn/kYYQMogbbE4PAVbFla2jzwTvE+6M9M5427qw/VBAv9UCbxBT7+lbWrMeY9h5z1fmHnrV2Y4J+hlzpzgTXNWBGEQwhikIeg1B4/my2Hg+2linlVp364I8qFAXaMCqCEo+tcHCx4mbPwagqKyWBF0s80g/bg49uWMP0sS0SF0H9+Ktz/blxzE+NE38wcoXSs6akXSgSUift0VWr0VgN1beLo5BcfwAzU1qVjG/TMS1SiplaFU1Jnip7ZJzONRCGq892wg0z6AF2Ex3mAe48HB1UB+QcbfUwxA+WQ8K4K++eY5xiCMQHxt5jlbxDQCPeVt8tOv2ULG1jC3o/lQ77kw8qXklHp4rFceJ7z8iFne77jW4wJGNYF3F27+VAtU0yZ+4gVmGvnPok6clDSAjZQ7vZMGHJ2+ZFtYM17Y5SMZ92Vyn76yb9x2lLEjlTlwEl/0AXtXU8nUNhIObO605GalhNjSDQlLMm3KCyJxyZGWOVKLCUY5sMACjRvUAKQh6MN7jEA8+P9I2IPdPzAHcM6wSIjNAymrrTUGuepaYxBtq3MBDUBtM9a5Z1q1vXSO0dI7HKXGKXs4aV80Zm3w08f0i3uuqJm6y48uqAn1UugKp5LiLClZfZIEW45h+1TcJARakMFu+TKwYOOJv5xy9M4n68VgUk89U4R1DT0vB6HkACJolXcWuzFP4CKRtkA4xY8vmcVPvEUgnhcLyj/SmA5JE/QjLK7CfM8WQVdkvvUQf7eHYbx/zWrNl35Cfh0e7WrOl4YxELk17D2riH5U9yKQfg1MvW27Vm/zQz97pptG/KwYQkDqdKuR1UHEvTddFaRf2aZdiGNamW4eYHXAUzFxxk2IHKu+GxLcHTto2b+mveg3LtWK0vJxXlb29uGWGYOPVYRHa2250fL6NNZZ62TmbQjiGSb6PCuCfgLu18JqVHWxfVb4ZLsXOhxDzxiCXrzgq2EY85+xovOZW35Z0ek2YLeRPWEr8H9xRVDqWd4erp9OAr9/QxAt9VSmHRSjHyI12/p/Zluamq/ejhxUCjrl0dCVdkKu9KDfXEpDjAwdSevbHmjdJbcIeX9MmvI3QdLH2FEYGJtsFdYNL2oy7/cmYHRipV5lV7XbMOUmXxhoXmlMPrJPHt+F6RIn/TLAWGbLSSlq7JVP+D57J/XUaKERAkzjGnqIy0NglpEEPMOmhRyU0LyPsmS5Xw/rpMSiWh7X5cgn/wg2MKJhaBCkfzqLMY2yzLDv18IR2yyWYzhX8CIjABljjYsfuQHsv5CVz5w6eQpmMwQ8cSpo3OiR31x33VRBP3JjZC6ssuztIzEyUOak29A4Y8IVQTEMxScthYLqPUXYn3v2EoN33w76ZnA9YIVzmesDDtRWzvqdlFi/SWnY+zf8ypfhLFVelRJ7xBYQfF55TD2dmRvX670iEMInXkEj64iUDIkbaQivdUnjCnSnlOeUtQpc5E+akIkL/ZWeuuZyQm0nlczOQ/LIRZjYcRM4iwXR6CQFrzPv5nm4PkjgE0hAQ9A79KYPF+9dEcSvnxvW6MOb5h8w/Lg6SGOQb5t9oAD+5g0rgvIZ+Q88nNq2eVig07uFwIeKrvDpSqA8VABXm3jYaN5G+yDitHvB/XqYeik9RF0QhdJou2I7U3uRVwdSdSbwIAgTp3j6CaXDFTYpVy9cvdG+SWJ0kYqLsnNGTnhSW/CXsDnRASCripc2wE+W+Ja79WdWBMFn3iTLr4Yg0jEG2fXdRvC1K3zYKu22L1f9aAhyVdA3bgvL5+PXQaO8TXZL2FM+Mf+YbQVjCMp2MFmGn1EnSjFuVVj+dOGzwV4FHDiKPKJcuJvOgZeMK66n2/6CL5KKcdM78ZJhkFZCZBrJnQkmXs7xzbsaH7R1h8PA8LArtLMNPf3+ko+g/Mmg92TGPlHEygCmHyci48y0B/kQL4grLN6CSctmFCdsBRsiwfKA2SIiI8PmMc6fYcd0DSQfOGvxgwZaDEHveIj/oFEIQ9BsESNLyCoTt4M9oS094ausj1hx7Wpgv1DkthXnAFlBBl4Mm9zczhcolfZo+/G+WL6sy9e4JK146iWeCIE1QZy61sPKlBL+lRiU0E76IjE57+IBb7tYCC2wEYtFPoIm2/i7XOooiq7zlaP9kGvwixGCBFduE6eMs9wi317Bs44bfVKnbOOEjc78X76Gt6IvZKlA7JIvqYL4ybFNcAxB7BLMtkG/9qgh6LUrOTEE/YDhx7PdfuArYhrxjUd/sypotvJiU8zHMDR9xHgf3WuPV2fz0xhEwRrwax6p7gYITDleq4pSr9ReHv2Zjq9A4ho3mFpu+HUPAvJ+NcN1TUKhoQlGchHJs4c0fQaJMEHGH7nKp3LzGUyXJsvFGaO6UtLRmcBaIznXdTU8PbFh6FP74Hzu18LoLx4O/QVWHPXvY/pYdLgGfQw+L9Ddf/iOr4W9eB6jkGe+qd+fkf5YQxDTcbeR/RcPi26BKfXh8ukk8Ds1BNme2+xtrlVN7QyJPzSeo8WMXJBZoFe8kCt+qR1T7PQK8lJSUQBAzDEiVhXoqlxIWdrt7uCW9AGmSOgSd992iG02KDnEuUhuiKulRoMBcyAQNJxceAttcBfd5QXdbDNxtQTTvCZExHgMVCCKN+pPeRQ7GbgUtwxrjOBHuRoh/KKSA1KWZkJwr2ix8BQgKy2sg5qmCX5ozq5SsTQUMpo4dZULKpyBHxryIalxd8TTNPHWDRJfkZx4kbO84EZcZa6UxTXkOOGXps68yRMeLpmkHiYczjqsIoCW7vjGkrYCF14JDE/WuQdAL/xUamGnTVk3f24x8E0g8uYtc77KNvVOSQ5yDHNM+ry/XzEZ/CpfFMnQF/58s5QHnrJQucCfvET20uOvsnAANHbrEndsNbArOTjgC9uJC0dICyGPPIgjrP6+LnoZoKnLuEi5hMNbGZSUOGbyTuh6H3fx0udnfXSWJ23BCxT4vpBgWhATbrts+qJNxpu2sgjdS28IW7AIIb6AreRgPPgPEvinS+CthiDaprpdI5BnT7znnKDXPFz8wMOFZwPl62GcNZFzgniwMK4RyFVBOTA6TxX2aR8OHE808DiN9qFCI7QPEysdP9sLsiKIDmGejEH2Y3Fxdq4o5UZ7bf+pNrIT8UNPwnl4b8cydTpY/X21qBCqnrhGl5WwdEuQFgm1qbyAwW9d0/GJR3FBJX5Tq65i3gp/PVtCnY3C5i1yth340E0hfmY4B0Tz4P41q33mYcFtBK4Ees6WsG/ypTDPB3qaLQdfsxrILQUakFwJ5Njpm2lZihaURXg3HEdY0HbiWbeFt+GCDtiEo56WLE7cCZsetXbknbQb33RcvKF3k6fAXBeu9ch8wECTQ6BzhNIzwaSOy4M0/hQw8d7x5nDSMOnWwTi/QV3k+yBpu7UM2mbaiPm433imm5ZsXOhC4ShNYtE4ywnIfFLAt1Sd80Rhzq0sY7aJG33PdjD741tXBHkmzPv3rA6yn/pCiLYVIvLj1+cwBLHF8AlGwyeuCHL856nTbWJf8eCZVUG2G/lfvtzYHq3VzapoCk87AG5anDfc+UgS4Bm/sm+ydWjtuAbNSpb20Oj9O8ozk4n4zb9JDBh/UoaKOANTBA1n1YxB+MpcA/TwB+7cuoNCcgV91bB3gTpBIi5l7NhUe3MjjqJIXY0sliIeo96blW5abpVzZmu0cCeP2eMsbv30DGeKE0a5QNx5t23Eg5/fu6WXT8i/YWtYt/RyttsPGO4xBP0VQ5ArgV6iu7MqCAO+hqAPriRCZ2dLr22YFjG/6uBHyEvd3WeDGvkblqHo9p3P+pMms6mjDFvXBfBeWI84w1cseSZJ3yy68Qd3xy2id1v5jQEoNAEUVhIjX9v0NgSpzvnr4f629/IizNq1aH3bfgogxbu2toQR5tuYyeeqnpzxhlp4Qj9za6/bdp9ymP+3rOb8PodEawh6GthTjUQcFK3R6Au/Lob/fzxTj6fYh8snlsDv1BBko56GWWW746NlPrHgfj/FV07ycyqZqqF26kk504d/lcBJYXD7CWpSIOStiNhVfheyqHUtbEjGN8nfVkJCl0LzXhrsxMGEcUPIey6s934KsmxpRp2GEbkHZtgMC2bYMbsFUmvinews3CSZd+jrpzTot4TQXbRTLhcnKn7BQqNP96iDQNh4nXnLSgbgmXRAXPoq4OxbX1+2aJniN721k4L1KT/ysd0wtQCp7iQ6mSJbZRsJkaK/YOQduReusqdcsZbfEi295Sez9O+UKygVGm9GNOhcPJnpciSVP0ATNnV4Vrb91H35lAt/+ULOIBL/mRVBytuviWgIUvK5/+LAqO3Nr4ZoDPINoT/fBlpmK2JAZi4vExaiF53ez/2mNJkr32RdecliLkCV964X5JVp5G1R4E8Lyfi67lVzh4TBw819AUTe8n4lr9QlR2P8LaSmtabJIS/yu+gYHEfSR25P3tI50xJCe3hIOSujsH2/LeekNpEpcCMuJNOH6JnvIfwggX+iBDQEvV8d9F1WGxDnofMt2whyAKlfCPMQUh4sss2A+OtsReAtNGcF+fni97ya/hEdmAOgf8KPMcj+rwFojED1M5WmT/j5YTVHHqZjCDLdmfqaGdM39ssM+kq6UTqywkGbBOCbYAzjhum49tbqsCTeyQNO+mS0RcIZhdXdQTfv0h6LfGPyseCrh8tGSCE3/YT1l6KxZvLYhwjC8Pj557xJxgqTzw1TRd8Kuy0sW77WaiAPif6GTw27FczVQK4OeuZBo1/j8xDhdoJHX3POHoYgH3B8Gx1DUMoenuqXPxJMq6d0P3Ykmm4TiDqaugG7wQ/Syr7CerrxEyYyqk56E76L10KF4oJXWRpVeuNmHBFnh4lUM4O1mO4dMueZe6gMbPy2FUjC3+TpTW8bap2SlPYodaUREwqJPDaSmPZC+RpRxZXSTIWMhzULwSWML451icO3PMfG0nOO5YoPxnWspdL13K63795mld47+qV90xVCH378wFlBnjvlIyyyo4BHrAR6whdZfRh9PCuDMALNyqBHPHV2/mVtWpe21bIkW5k3Qqv8hlvCrR9ZChcxP/EMrHol1FhkpwwWTAkOZkOLdqDe+3ISck1qzoRPgFRWnEKUXR08ynciXBsIf7nHgg7iQ1E0abT88iFs8tTCEYQWm0Ti97itnkizqD2vEpcCc5/TViRS2PBZAFeSktfgCpsW1olLIy9joeOKILeI5aw29PVbDIZvMOCP8cfz3boiyFVBrzjn7U0NihiB3vHFMbcapu2io9XZrtTU4Bkj0C8YggjXbG3b10hvu1erwQi++tr2CqItcP2MW13ioDXM1SyN7uukb8DCT/wMC9i0esd7/qft0jYADOLtQxuRLPzZZm3rgGsUso6rnUvWcApsDSACTE4lWB3eGtcI5CH/9hG3g9mfNMh7APRzjK/P+LKjX3V84dlufDnsxfNnfPURI77nv4GjEb99sKtB//SU/NB6cJ9eAv8ShqBTTKN4T9h/btghsDpCP503/hkWp1bknfQrgVAroY1x0Vet3JaQwkGYLJM+cYl4v5pTPP/AGoTlR7EG2QIEVlmJWppDWSScGhS8ZF/4QQykpaWdqLGSjmc2/1ZcimpB//omCEiAJky5Zm8eB4wfmaBoAMon4TUI8WahVnnfeq5ckkH7+kWwz5fBp28uNU6s6dQ64NLyooTDRwjIUsqcdj4sFR7WwmaxvSZDBx0H2SCK17QTb+puqmhmVecbTtyE5ZqPSMhUppOmb02CY94kOBCeTuhKwVtiD8KJVzgtlAFd+Mjbgv1zG8HAnQb4FRHvRT4fy/1IriFOJSJ73uZE5pwZ4BvBMQRV+rJQviabfssw7RoIxRI+LuFdlytl45Vs0DeMWGhncAXqPVowEQdveBHW9mjoch2sr7ih3pXKfVZ0bSiEpT0u95eCpzzhZ7rx8MAlfviUU2XK1b6kM+yfDJEsRlzgE7ngK1eIl9qF/1Gju7I/hB4k8E+RQAxBtHXbomeR/MjDQVYe8GDhOUFuD9Pg49lA+fGG+TXx16RrIHLlkOdOMBzkQYIFDDHyZPsX41TOl9iGoTWl1kBEmmOh03rfKLt9zKl2epedy2A6mz3o7Fz2ov6q+zCME91vh9uJV7+srkn/XbkyHkcZLLrCM54mwIV4nzUItfeOngExkPT/IMqHWKMzm6O1mocIcDAC+XCRc4E0Bvk2md/XPrQ/+4qHBw0+rArCIPQNq388U6KrgThclAcLw4956/z4CQ8fj/m5qnaJiKG0+kk+yk7HtHBWfgRbE0U64ZW8H9ayUmiA+COdSuBIOIJn2hkeFGGLpYDuwynugQVSbiG+cr4Zs9HBSYPqkYP6D+UTel94YPrSwF/txbHVoOUpx2LInQYThVxBO87IWdoMvphtf1DQuFkGzVgHb7u5BQLlxW5Ige/378zmA35We2SOxXlAPOlr9HnDKiBXe7yjz70nnJ+GIJeEyCsF+ALoMeP9E4yGthm3ojzGMPQYQ1DOC5pVQc4HwPcz861J2bRtCAgsviHaLAkj7amHolA+EYuR5QILQzeARJSpFFNGEQsXlEIJbHgwix+snRAcZYWkg75n9qIsOumvIZrMhZtM+lAa9PpDLTlXucW0rIsAwYIFfuxI+6gqYCWL+tGw84rRN6mmqQSSsWElKspUYcsHuDRsb12Vz0YlABoI37lNV2MQX3zUEOQ23lesBPqrvme9YQjy0/KvwVO/v+Xn6iDb23zB8WcO8M/Xw1z9Fm2hTq4Bv3HnvJcxqH1AGMzCsH7aNLl1asDU3WQB1vFwwqxPp1ZWOFhARCR84I8svOti1RBknzW++q7EjkzR9UJoo5ZhH5FOysOXfH/hnFgow8byw52rgRyZwMEIZNhVPDECedD/0uPfZEsvuvopKzlfPMv2sFnZqaHIQ6If83159b5bNP3UvIYgDfkP7tNL4PdpCLJx2xjTWB1I2loySE2P+PSy+6QcVB0oFzvtuAmPL1zM/t3FmtzjmythZN8hYeUQmBSoDWnuQ+4R8eSbewTmoDR3MgfYW0qcgDib1spz5fOeq3jMu5SpOZKpVOcqRugkUKhjc/gn/yIRXi/6k1u/dY3SlDUgktJFag4UAGcVkJOT/JiE5BOmGiN442lGFW7ooOh8C+WB0K5O0RjR/ekwRpoDnWWUzxREPrR4GLTehYmUMNGp5PAmSCqR5JKnkJCYgJkF8NtwQvKYviQjuI4/GUWC1zKoi+nJqDQJSC+JxTDSewSaycUKzdBd8Y0jEDxJhjUDOPMaTHuK7/TGvzXQWT+RhLAlTENQDEILlrosvnoPGHCc6HEPPJPJ8rMyTT88SW/q4KQC0lLH79sQIibz00sx+gu80IkVlsDEkoHI+HdxgJ/0rrx3Q6vtCpavM3kYAJh7d4cxJ7qBc//Mp6x1CeNP+cJT56Q27CorYfkRjvFSRP6l2wna4k28ldd0/uM2bMWFz70dnCRlRrKQHrwHCXwCCbyjjb+PAsAQtIz8GoN8uNDQ84rzgd7kcGiNQhqDeiCpDxRuEfO8oLd8ucaVoj+yGugDvi8I+obZcUPNeRp+1L1Mp2Mc8iHDviGOX6ppPNtwVp9Lv9oDBcjpQHa86kQfo/NW2xVBC2av7F8Fmixc9H05sUJDDHRSQnL13AwFKywJ05dLiLgPBsnEVe6Lsfxs/xpDEA8RKqMveIAAiefxvCTxoSCfg8cA5NfC5vPCGoJ6YLRfCusD/TNXePAgwRnAPIDUiKTq8HFtRNMxrCyVl6uWQLczTdGGffypZYbCiYhz/AjWrXxH9klZ9V9o0oXW4A2S8e0mIuKJueCByujiyfs546gGk3FBT4UGcqXtzEMkKE13bJXJFGHbyVgLQmintFWH1V7ycOwIT1wSCF7UtLMYgDqO9gG1NQpxGAx7U5e5UcQt0643vmH73o/MrX7kQd15lge3+3nwHPRL+L2rg9wupiEIHM/WkvTn9KdHWWH2jIdOjEGcS+LqoCc0msdsE/O8QF8I9cwg5mNkGnnKie3J2nXeadi2HGCMQWFfCynV70sRmE3BpUMsLpJbEfMrj3FBh67UI/tJELLKD4iwaCk/+Aci8FBE+G6ps+8v9NbHiA6CDXKVuPcbX/5unWnFLf6ZOtikmO1EGDLCDK94ijpIpDkZJ72rvg0uesKHZnzbYwFDJ346J/mgkaIWLbfY/oTOdfugn5F3a6+rgjQEvWblpvraVZ2eFeRB/y9fvUpbehs8zoLDENQV/tBBp6mr1SrVw4Z7YHSMP2FURrp6qHVIywHWOvVOtOVMFcPvVEbEVs+QdyT1MTngIy0IXhZsz0uTp/fdfnOj88P/upcQtWxjqny/nKdsz7IaLte1/huuXpC71qR6fA6O7vlA6uBu0dXQ6kHQ33z7NSuAPNyfs930WdH5TF3OCs+vORfI1Z+PMBzNiqAvsbz/ia2+D4Ygb/Knd79vQxDyabOuoGy4u2cUdHu9ryPdYvzbxOzkdVeoauWEq8j7d0InfPkIbsgowxWOl4H7FCzqwYQDb8KCq1ZKYBSjGQJB2eaBkMgeHwkX+/L3PfaGT2KYnULLj4pqDzRJJyd50k5A2c+ZDt5JvyG2clye+cQL3+FZhQuvXPqmysnJ2p/OJEV4H5ythG+P2IJEoRp/vthvoNwXW4OQy5KlP+WYp2H5uniL0kdWUcRlaDNpdDsiU/9QuCWz0QwM9SG36STQO3WTAbi3Xnmad+OLJNNCAlxh4dslgditPzyIZsrErzr0fubtdtIdlJwsCrcN+cZwtoYt2TuaAs8EIqww8GV/N8O4xjh+yj3GEQdD0ddjjPnSHoGWF/OSPL8Ftb4zoVNSw7eU5N1472PjwnUnXiG9Dnz84DrHALCLXhnKb+E3uW8yL2S85Ocy906+JH26k+bI3vSZVEQmtntknzYLkdQv/so9RIYwcUHDlvibdoBNucm2O+gQefAfJPDPlcAYguzXrvbMqk8MOR5M+55zJ15nu4FGIVYBYfR5lVVAwDmXwkOkX7k9jLDnTni+0AfGBLcc+G4AcvzQFzT9HgrNVgLKyWqh9EofNCYOHnrLhw47Ww3ayKIdbz18Glc+IRidBMeqSOirJ9eDwe6FwWwWclUHlvYilPKD3u4JHWlbxAJMcfjRKSuuF00IUJaut8jEA1MxawSixkz6fZB4RH/3S2E+EORcCQw8z10J5FlAHibKA0Q+G++n4jUQ5YBoDUVu9dGI5EsVaGABUizqNVR69dvB7vApaPTN+ICq40lkiC0C4ejIk8ZKOvOZ93Rn2oT1DzIn+ublBDqm6Sb/BIZG6BkJQvXwaQiazKQQnFyBrouwE36FbTPeq64m834512n6jItLypTvCYe6PlDWeAnlkO9DNK2v7Qca2abkTUqWcmfwKt1wHmUzvlsny04f5EHd7ZbZoqkhiNVAfhnqbT7/jfGVVUE/YqjNGS9wlS4C7UdsB3vGigR/T574YyVCjEE9PDrzAeYCPd9k6kPVYNP7b//IygcimU8oH9qstbANp70XsbIwjTzWo72gdSLSPPjKcUlhVf6KW65u0uMDnHjTjB1SI6icddKO/CdOTldC11UuCV+gk9LC0xuEgozJhbXakAkuyPZEJs1k+91dt5KDYNPqPArag9vCyIZcCEfui8jIR/ZEnyzmVQYabn4iknPdcpaUZ0hpvHcFpwdHe1YQPw+Lxhj0V7eGacxnlZlnTbml7AMH/duOXFlUA73mZQ3yMqPPL0b73PjAo6MjH2A+08DowGTdvM3fcGBTGeu7nD0uOghf8KCs5OVR2ZXH9LQ1QO2fnRtXmG0LV+txrgv3PnOQT7lev1Ue6XUSzMgEqert1mBqUmP+l7MtTB2MIf9rfo8x8DxHX2vA/45Dor9xW9gzjUF+7VG9ru7mGQidbx4N+eocDbF/YIWnLwce3KeXwP8PAAD//9fZEwgAAEAASURBVOy962IVuZKta8DGGENV93n/h+xVgLExhvN9YygycxpX794/9jK9lmVn6haSQspQKBS6zFc/MWe/mRGlQevVq1dnZ4MhTv4xfesyKj5fm8eYf23TJpmGmboe/bppx7x/DED842mT+eZJUl/6d/Pz1JuIDXQHO/thOmGJND4wE08mCQONH3H4fV8tN7AhQf1EnpRnOsJMozk4T8CMS/GvziSX0AyON7hfv544wnXH/ATcHCbjpmlrNVj3A8j+5Hl4+MHz8+z7w8PZ/ff7s+/feL4/gPeP4g7elvvmzRvKOz97Q6FvLrDPX5+d4397gZ/CXxNuqVOutQu+qb+4iBeG1yu92voxwh3QbVjeq02NnvZLtSZlgbaajmPyjr3KXfnlmwO3NReFTzLDfgYZgVcZsZY7YeOeGu1+MzKvY5JUP/n6+lk6ACp8APvHD9saO+7v+H8Aw/PdEJNAWMmEXMWNCrx6/ers9Zv1LV6/wW/b8w34Dsa1/LzJo2m0iDrQSRGVBsQ4ZeGi+Lh9WWxjVxjxYwwfc+puCt8JFw3LrXXizjdYiY2PiaOBOsXhVT5ao1+HeAgj0loLkyTa49C98k0qMokXO/0QjyH2pddJhG3jjDk4J/MtCIc4xYzd3BO0wR3zW+Av1ksL/DNb4A7e/i20T/+mn3+X3/Pc3z+c3d19O/t6++3s9u4em+fm9uzr17uzL18Jx31z8+3sy80d8d8D+8003xgrYBD3944bZ2cP5p1xj/zpow/0yJ8/39D57Jlv4Gn6Gfuo9E/dr87pc/R6+txmx0uvsb/Yn+y44Fg+uI9D9tuOpasvrz7X/mZ+ZlseiMuMCjF9VDttgU3+Gt8WN3ZciZJr6qgdN2lfAWxt9MuHXr3+yXho2NnZ+fmrs8u3r88u3p6fXV68OXv//vLs/fXl2QfsD++vzq6u32K/I/wicVdXb8/eXV6cvXt3Tpo3jKWOsfJ1bOsibr60g9OpO/EEBfdG5Z0kx0hCn+KzA5KCDvlPVslneQLLiyb4W7PB61hw/VLy6AYc2Hgq92pl2BZs1vN1p6DIO/E8VbhhU8qkWGVJdRLngvgJ3VrcjLuOo7bMT2mmrR36seUHH+EllR8/aEHH5JV+wylEZ1Vae6JjMn6vtJb7QFr7yvf7Hzzfz77d39OXHuhbd/Q1+tjX27O7e9z0x2/3t2f39LkHOxi45A8c376TVqCfd+/PrqCjy8tL6A0aurg4e4t9jjxw/gZZDALSDkqgFTrFA1mliwVl+5y1JH73CwzSIZaOhz83whN6fRlA5rsRhOEVu/mXSMS6eQuh29dkZ5krtHF6MSnB7Ggrs2x/b3hxFTkz4r+ZmCzmhLYIMb3F7NQXsCdexTURlr0gguIRekVYrHQx8QnmVf5EOxlgfAtfHv0rBdakXQgmTfIhYZLz8usri31HDpT3yrO/ffsOf5Y33y3+fHv26fOXsy+fb87++vIVfv01PPzrLTR1K51Bb6RHnD/7Tp4/fy7+LN2H9kMVhIvRG8KG9g3X3fZuf8C/KtW2LmzwNvykUpQ1H5ucYo4gE4Y9zSJxpO7hze1rtMBqG5E3UQuxLd/wRJ7NeGI+y7/lndzwgX3oSX5AQ9AXOyJJr+XjrylHPu586hxGfHl+Dm9mbgNfvoaH//Hh/dl//PHx7OPHq/Dya3j51dXl2TU8/OKc/gbfl/+fg5BjhLj959vO0TZ0XhzP1gJ8/3TLZ0PgqYLD5BZai6wDVkYB8UJEJfpDagHTEbCb6BD5r+mULZSJT8XHtr51C+MUeg+JM682me/VYCSZHBK3grf4lVQYn+Fjzd0AmSgx2/ehbDhi4HkZJeOu4GAcj7zHv4TrauaxzSdMz7SGNC/R2oYmYfxPYBldYvGjl8nkVWYoAytTLGxzs7CjoXwijFPIdkKgUP/ACPEdoeT+AQUQAso3FEEPD9+TULycKL9GsDhHwFAZ9EaBA0H3DQzwAvcFDFDlkEoIcxfVGtx4ZMLDeA2feG2ZZsO2GuNd2JNOV3Pc264JSN2k8VqvE9OCyR/cjZxCF1C8SeTgRttN4mQ7wGMTqVNA8mtZE6edyNiP0SBwN4CFJhbiFRYd6DrYKSz+RCn04IMQ+IPvY9zYP3+UPtoitBffQ+WbzxsUcq/xqxBSEIxyw0GJ0m0D6TSKDgIA2ZpDfPPwsl6SY+xFr0IarzE87u1lnDg2PjC+rGesxqd8yyQ8+BAXWhUWo9s8tDXL2mAt46gIMr6wiimLbggMKWknF17kubkTFmwbTlzyFamVR8h3UqyERlsXzeQ1/oQZT1sFjkoMTBzJO0lfXi8t8CwtEEWQfERaDM9/xYSgyv87JpvfUAI5Kb3BvvnyDYWPSqFvZ5+/MqH4zIQDpdANyiFhb6M0+s444eSik1qc8Cv6AHn+gMc8yCPPzukzDE6Ol/TQH04+6BB5wOPVK+PgVeIETBTXuunUw22SnwpycC6ftMNaBoVtZu+JiwuEx5l//ZMbCQQlfWydMrplDDaiQxFthX9UV1H6rHgasLUQVdzho7jPnUBgOw6+u3LycMEk/TzKnuv3b5ksXJ1df1ABhEKISYWKIJVAVyiBLlECXWJfXDCJUAlEPvIhFUEam0Wcl7dhB/8WvuCSZnsFvGl4W7/Jb0C29AO68jnCDeyAxA9c2mnZE/dUfjvcAXESdFxa7U7F+x2sLnAHEzqJ3/CF4IovpG+pa0z9kQtxJj8yn3G2E3ZgrSS0aZ6hl1S6/uSEX5x+OO5qm4e0HvxaWkQCgFtiUhUu5RFOxL1jOJNxF9vuv9N/UATdRc5qn7q7RQGrQuj2a/rjPfKXfXJTBFkI7aOy5+rq3dkVyqCrq/dMVN+eveW5IvzCR7ksi3OM/8hrqQl1kKZ8VDK+ksZAM9N85UfCrbY0N9WPTVgaVDvGvmQtCWgjxJ8+Y3BMHUNnURYl/eqNuPW2rZfb7Lb0KxusyEkrQneKxepijRk1z2Toy3xW3uJ5zHJBJnPdp7EG7xAWNd/UTMj21LSQxSsalbJ4RXYKfTTcNrVye3seyjlmLO6WhS2NaXybn4+KnPBclDoqD79KL/Dir4s3//Xp5uzzpy9nn1EGfUJJpGJRhdDtN5SK337yPIRfy6fl0eXHSxGU9jintPq1rXUUQ1RA/t1WEGH9eolflRp/K1m822i2s6ZvXZX194rrMjZZBQC/jY+yJt8QOjM+39/4mLZh5iQkNK3K/9rNe0pu7uTgN4Bfd0GVeU2+SflF+8YDMrN8nLzoExd43qnMp0+9e/fm7AO8+88P12d//vkRG15+fVUe/o6+pxLfhXAS27/ekKFYMBSc/QlPHz6+kH+xnqkF/pcogmydIWKddf/SZn8T/Avcv1DAKdMeplJ74vZQXeOzsXTLyDA4J6YBqzGfaFNZRGCTZlIJ2PDwarzaEUuFW0w2K6RwXJUsHRiwHRyMx9HcwFyGhzGP5LcyNT6fn1f+Bj8ZjGH4ZYIOMjIdhVEVQgnHHb9peCrYmgtl+BAQAZuyIsC7CygrDaw+IaB0pQoh5dvt2Q/ikodZwc1kjudvLnjQfqMtd9WziqCuPqkJH0UQSVLmKjXuCNRGYMRHUzTFrKb+8e12v0dahnpgJ8nkUrg0354kmae9DAN0UcFWtsFDPwsEuH0IKXbG7KZDB5mdlL/j0fgVvZL5XTRJsrmkDUtfNsShP88PvgPPDz6QCiAVci4MRoiVfhZdmaMTnlEEvXKnFt/F7+OurddOhvgm0oj1imIoNl4aY8e6wobtJ01Ip9KwtCvdiriWRJoxGj//DSSTCmwtw4ifro5LZycl7GXaHkGJLHTP7iWc8fNedv3bdyPvwFg2pvAVU5JmwhLb+OVclsgFc0vYovZyCRKhMQfnAXxif7WT9aNaW9EX89ICz9gCt3Tqb3ZsaHuRP/28yiAnpPdMLr6p5GF30C0T0Ft2Bt1mV5CTCScV3RmkMuj25jsT1lvgVFSTLrZjSceTTjIoRwZzdkGZThyceMhXHQHYDWRnMl6lNXZ49NKoyAtq2lfdKZkxE6Y041YnSsYv0JUkCmHd5NlxaPJatvA2wCRd7pXLFlcAx2mUPmmwsZMB2Kv0aT9/Da9z7FXopzqMia9R7jh5YOfGFbt+UPa4A+gDk4f3TB5cPVYxdHVNHJOMS2DeksZdQbBu+HZ538Yf5dMWu8zUyDqMe+yBGfvIyiYMdGMmjfa4B+ZoH+OO7iPM0f0UzIwEWzWeBlqI2LpHU9+pIihf6AhEGuF23msRfr8MUkQZX/rxW3Z8O1O5Y5+wBaDPygra0m7xMC45b4ogx0TzMs/djDuwxgFQ+Y8xnH7yHVlKv7usVQTd0c/u14Kbitdbdm/c3H3trjsVs9/uogiS9qEKM4Ss35y9O79kJxC7gFAsXl2iDOKR1i7cGcSuoAvlsnNoif6kIqhyYRU/0oOyQHecOUH9iaxgVZUhlQ9a1Kr+ThiGn9S29TMo7X5ojPaXRJgorWdr6rJ8H01kEWxCffkfX6yURbuT76b0xW3b5hslExM1z6ZZKacAvMKfmtNSnoqbsJSFJykOGSX84B/42IQrM6U5Amh6HEU1IMW/qQTx0dQ+vhtmflLAgzt6Qj9V6riTU359A692Z9CXz1/P/vr8uYqgL7fh2zdfboiXjsqn71UGkdn30LI8WQW9GPqFfFQGWWN59ppQpAZWwPiB1Q9+QyirzetvFlMvfFsdpy0Me2yMs21M53c3VSxqPzOm8EFeUrNYdufnskNkhGIbl1LzIVTcmycVzxiiEsixpOGdK9m36At0APuDfeGtCn0VrPDnK5Q9Kn7+/OMDO4I+nP3nH++zy9Pw9yrxURS9ydxo9aN0JHcEoQgiH7vYi3n+FviNFUF28bDCtJIdIcbZ1nI24PD+u/ADyL+as0PAsVbDZsowbMUxjWm7Pm5Emy4pbN/N4N68OmRG/ulmIFqZBwcSb+HG4jfaAX4m5ypPFBQ6ga+dOAAT5kQ+aUkTx8pHd2KKTIURgqCJYKUNg8lAArOLNhw//GcN7AelUISAJIUZ7elVArVWCCQIJyoXstLAVtPsAlqCSbYsszqlAKMwYQ7aF+wAurxwG6SrTgiwrHiqeLiUCcrwKFfYsnNxt0qW2XJVDtRtZYtJ6pa6E7RCDYvBkTZa/WJrItMKNOkGzqCENdJxSpdG3jzuhvz6nuwa83fQqzYCi9cqr2n3Qra8xnGSXaiJgRT6AGHpw79MevBnhxYf5zsKoB/Yrgo66Yo7O4WExQ9sBkO/t3/ZpXUBPbgzyB1bF9juDuKb0BgOVpsAdsCndGmeCjJLgHX1EtwNcwyVah2gpzo409b6ScmrX33i+8mEXwVhhWaxjeu3qfAZ2EXTugPHKOof6McmGFoWf/012voBiUm+uBIPIslXv8hqtFdi8/bfsgI3IIU8wFm/ZVba8R5tPuWC85sQM4lE8MW8tMAztsAt/OOOzhxBW7oMfb45u5eHwHu+MTF1R6jHCLKCnInpfSYTKoJufJxcfGXS4ZEEjh94VOyeNO4cvZdXZKIr/3CSLS9gLOAI2BmPRVZBZBe0oxvujkXd3RUUfpCXvblcozyxQnuVQXAamFEUQat/TVezz+kO1yAfd0U2BMuYBb8xLYMNNHzi4tfTY7qBJexVjhIUsNyfSQQdPmvl9O8LeBWslbHQHRtvmDhcMjlnkuBOIJ4PKoBcRWYnx3uOE7gb6JoJhjDvgL+4fMPRanfXyotUMoU74Z467TUJ1gtf66sZu74GyI9+jaC5V1phw5rwm/4EfAJWFuJxYJ0mDW5Jw0t7zFO47HAL8hcgcbBliaewTgQnx5YlxI5lx6FjuY1ryGTf0YcwJ37+UYmOtUyCpf0UIfT+pOWlnxVWGMdB8dJW3iudW+YRh+RvcQBmHFXGokNEAeTxHPz39MVv7tJAAfsduevOnUEqgVh4c1fQNyb3KolUBHkM6IEFIfFzOmn/ffsWRRBKH3cEveOo4SWKoPdMWC95LpDN3KV94WIdOxQu6Fsd+0ufdgnlwexacHx9w2QVIpB2VWIqK/it2xf3Zpla7nW1T7TF07ZGJNLA9RBkWFqW1/TG5M/LcMsTXHcBm0avpu1ZAN2apCRBbd5maPiy53sV58YFoFBC8mdexjXPRMVfl++B0J2+pGOZ4LyyTi4AW7zh0odmoZvEC7XVtgIumJUm8ARah/A20wfLAlYOU2aXlpDZoSUV9ioQfW6gm09/feV4mLuCeG6+nP0Fz1Y5JP++h86iDHL3Z/KQ/lkIiNypmkJE+EJuhYnawq/Ft5EQxCMVACYczyBh9a+4BWfYqtpm5zsBGUNDGi/UwJnLZvRsDQcM7sJp2yKm5U05wTD0WhoQnc6PzIJUSavSlwaLFx6A7JydRobR+80vfYLM7AcqgdyRqf2WeY2KfJX6Kl7/UBHEbqD/7z8+YF+j4LcfopjlkX+rcI18ar8SFwq1Nf/AL64v5vlb4DdXBEGMdKQTRhdip+Gk1Okx046G/ZuZYQet9mmDlEU07DTm2EiHRouQWqY7O3JsZ9OW1axSYCQKteUpOmQd+sushZdpZ7UHwAgWMFkVQQ74ORMepmscYYFxYFf4Nh/CNyaV7FdZi3XCTbK9fXERB4kM1GF+TIiHeYWB7VsSO8jDlJZiJpN/YPgPg5IrKQx1dQrBhFWCewQThY87bFemPCpwy6qvdcvukih6EFYjcKAAQuB4e47w4UoU9wS9hQvKPEfoCCO0gWJ0dFiTOfolFHY1M8B2+DiGre8VS8QDboq0UXxb/hOpDTVYuIb2jYuKa8e9ovyWOju4lLrqNyFmyyOeBO2vYhsYaGFyafyKw2MW0s5TpruymlIQacfGUNkjXXxnwPKuph/u0HKShlvb1USPjCkg3uOWfqxdaQOlHALgW5Q/2bXFQPZmud2yKu14bEw6mNpDBqGF0GdoV8F1TeqyAlVB1vhMwGwx05CD9K2DZDGK2ubbOq9A2yD4yeOInW+BHT+NpK1wqsOBVBpS6dh7DsA5dIUNTI8eNh+7RfMwfWuEJQaWGJyGvjZCA7nGmLbQsYReEbF4tR4EH9wDE/AFv8EROHVfUYIVybpe3i8t8CwtcIvCxuNh0m9oM6838JnVv+nEPXbg/T/wfu4AmntLbr54XxDKH3YDfc39ExwZw75z1xBjh4sG3+RL3Bf0HX7kaWKL+pGORS9lcqHyZ9spJAYogqIkwp17zejkUdwEP1/Tg+U34qjyxzFVvuJ4SgFjwoPxpE5a8hTLWJP5sKIVSQf1b46YGproZhsfozs2T2AN6n0SFE7OPuUhriDLgzxKkMlDjgawiswun/coeK5QAKnw+aDyJ0cJ2BXEzqCr9xwXYxLxnrHTo2PuBvJYgQokx+7kb/1BTvwW5rgOJkjX/2Q8UaaPGXulEedWunnHbxBwG7/Eb3r52SQ3me7kS/igMHDGj0kaXwtIq3C0HQ6/wd8a41MwGWhjTNvStZt6cmiL6ZtHmN34zbIbgCDzNcuMe7oNC6i5WEgfF00St2grMp8kQcKZjEMO+5hIePOFNiEXVUwqRisbVvnTMRtlEJPyezpJ/SpTuSNI+StKII5fRgmEosh7GulbHiebukPZ7Dhj54/05U4gnre4L9/i5iiiO7SrBIKWUAh5X1AVPUxu+dDKghf0C5VE7nq4WEdZOsY67tpttBf9pTloCb/JgTj8gusfmhE/DIoFA6n11k+bZPUX2tbx+jjuZvxOm5PU5AcTpUjaNV8i3w4mEQjpId8Lh3ko8/lXyGbSr6v7UcZNucLFzXT+PYZrPpNDYv3IOizIlLin+oYkH2lGuPh54TVJcDZqAhqVrEb5k3a2zqY1QXI0u845EAMzj1BO9JiuCp67e46HIa9/Qunz+ZO7gnpXkDz6k/waBb7KIulNfn0Pb2c4gI7l05QFby4n07ZM1Re2M/7goNvvX7v9F3figN/qZFr9wFoBvTE6WqcVEP/T7glt+5FspdTfsHxpysifaPhnsUGvZQXWtOmk8u+V3uNmuJG0SVVFUORM6P0CQrJf6PfkwyX8eHZ0yq//+Pie5+PZf6IE+vjH9dmH8G/ug4OHe6Q3i+BpltKkOIjSn+Sl/WKevwV+S0WQhBJCDX1LyTy4JeUY/Y/NE0GPQf4V/WkiG2czu7vD0EDUbjN1YJBRaIYVlfWvOMFp5zWUpYTJWd7hI7M0QlunsMqhCqOZtGc11IHf3RsK1L3XpcofBeMKBSqHTJOdHdidXIsZeTub5j+oRjPfsh00QwZwOSfRXbHpzo6c8yZMpmW4O3JcmcwdPrp5XB06R0PjmfEM8BYBn7deCiBeOvfgAMHuHy8L9bJC74C40397E4WEmnGVQZ4/v2SlUyFEYffywjPqKoJQPlCGjBQ0gudOprZ2Hyuoe5hi6gUe/RIgBlJRENkOhM53w2FSgjZI/Ad3AEyDSdrlMB1PcWk71m38Y7MlJGJBYdlOvxrKjoC4MAROuugAeZrGOppH6kpGyc/skZBCVgRITxVQoQ0CZ+LjbqA5qqci6J4B3wmXSjtX8L1DQJpKS/FRFV7P+S4XF24R766tC7aRuysodzrxjRysigx1oCxp0ElbaZTJHJ5cKqgiSsUTflf7pdtRBtmPrEdwJq3urQ7WhWfetS2zxVYZpRuspZfNVpkDjUq/KrCkN4RYJ0hu182F5EshZB0g+Y2eddukgG22GBg29CCumqGalK/f8gOoG3DA9BZ6DzPtFrHgEzavSRA46Fj/ZCRiL+alBZ6xBW4ZX6IIWjhI9xH06dfob9q34QUqmr2Q1ONerjjnDorsCMKNIshdQZ85bvCZMI+PuRvIC6bDl9zhwAzjnrIyCYYpqAySV8r1XYHOpdF0jNwPpPInO4bcheD4th7SBL2Fq303/AehvoqgjrurS0+NYrenmb5PO2FBZh5rfk/1SFLQb+2461nun5k8MHGgfOMzgYDnwKryeDTMnbKOsyp1LpmQv187fz5EGeR9Er0o2t1AV0wg+rCYohJoFEHkEfYMBtb/MY76xUwjahMfezwCLMBjGybRes2OoMQDH/Y0GR8BB578Ut7K9wmQBg0O+KatB5dmfyjkCAsiw5+3vBNmVUwD8AHeVnC80fjNRq6b1iloQ3XX5ZjlA+2YxgyItF7FKl+fQD4sJjAL3U7C20cyVjpG3lce/Cmtk1nHRuQ+/cp49iv6kQs6yoMqf9zF8Y2xNH2Msd2Fnl7g27Hcoz4uxEWxiv2AEshjm+Y17cOImWNfF1ECMc5Da94Z5HjvIt0FfunQo2E9xq+sCF05rjJQumin8ifyGnCZBK/JbxYOVQQBmyNlNgVPxlWJJg3bFk0j2XJpI+3ljl98bcR+pSQjIAqgZVcZRN5rbLT1mz/WoQi/nX0+2eYNZACEE1D/7k6xFr1SiKffckHqXbnEmZgddqO2Da5QlsJTJCaoeOKzmuKQb4RDWrAQwYOiqUmcPGjPtE0jVl5Dz5RPZOObXgDzMUxb+fD7A3ewQRPuKFOGVxmkzK7S5/MndgZpf+GuIJVA7BByN+dXlEY5fqjiHjfJoE2VQZZnvfuxVdbLi8+46D99wHD9qUNqUNjgb1zbLBXd6rTgtJYR99agdax7RR7iGi60ia207qbebYv1mxbXTbwyiaj6QdKO2rOzE3eMCiDqTPuZX3cBrf5gP0jfsE/Bx9nlk6NfHOVVEfQRRdCfH7kjiGNhH7HfR4nvDj3TlXdLzzZDse8h6I/Kqwa8mGdvgd9SETQDU2mkBOQEN0Zqmv7wuPn+DYlqWEEbZRqkob4nfmfqhZFZGDZ2OUXDJhe7bfLgZfPLe+TlMnTdUQCFt3Sirj9hKpphKD3z3V/Y8gJO03nnjpPoTqwJU3hw4CeubgWSwkoHrgiEf4lUaKAfOcKxTM8ngnOVPmFgMJ0M2A7wPK9V/MDIdPvrIyqFcpTLVaK12mg6J+CjsPruAIEwn9VfLwH1iTKoZ4t/sC1ZRY8ChOfPvaTwHY835b9zi7KKIVeimLzP9vhhhFYlbuqWFncwpJLWbJ7A5Pvo0tDgsaxznaslGqOH9jHHo2mq07D6gJyMSDA7kY5pp8hkuYpP/MoO9IOLtpibX9zBozlNMuP4xDEmn/CGNJ8MSbRF4MgIMpLYItyG5nCVbqQpBnsEQhVBTrpybwBhrsarEFLIFKfi5WREYfAywqHfx63iCor5lTfoQqHQPiD+joVRSiIMpJyDkGp4hNAIGxVsQ/N8v9AqGdReAnEq2jAK2L+VHg3tYttEGemkD5TjxzErlsaJXwRZd5kptEK7b6MQqiDryrsCq/cfUJ2mVUGU/Nu7ySZCm62iybdaX8Lvzz+m9MW7co5BmEkTB3CCimvzCMgBaHcGjugp6yQfM3gxLy3wjC3gHUE5GrZwCA+DLqMMho/Zl70TTIVwJrDYKoQ8KuZRsPyimDuDmFR89rgBl0jfcEws91SwgzQrzfCpO9L4S2LduQhvoGPI53z8FTEVQfYaN83PJfceHcvY5tg0CiHgpts4ec9uRPCLIkhbbmme1icvHfbmZZs4noZ1BxDpSLsZnQFbMHqbAS4iMyCbBh7r8ZxXEQJI4gRC3hNOymTaRZfeE6H9zomDvwzG0Z38qoz3A/HkmBgKodwdxJjpJMOV5Ci7XbhhImL5vXuoOC0U9yoWs63OG7qEp76H6rVuRixj3hOPPWn9IpsxcGCWe+CEGbd4TlMe3ZPPwJngFO5Q8CpK2NDjAbDfaXI5HcNFMGPoFLbsQksbY44u3PlvmEVNGQ1pKzQPd6+RB57QrnIefuW57AZC2ckQHBmqyhwUQPw9MLvW7+Q8CiD6gotsTtijKF2Tdnf6FFa6dgwGngxv6T+O8e4CcpHHe4TsRz94REccpIrs6nGMj/LHHcCO+YyTKn+Uwxg3s/iDvOhxw9cuqkhf2hlXneg6vtZvOMmyaJjLboFlY3HH6ozJlGvzMHBCoTYL+IiRpn0qY2oadQ+jhYwO4qYpbesn/xVgboZrYi13fakwc/mW5tit0kJaKTw2eDWPhqU4Qnb8tgxTgrATp7sIJjvC95DTmGJTuSHZTILgbrVTz4Q2d/HczdCvdkMtK8UvoDaTacGPDEvf4goYoIYZVxmMloVwpKne7anS3l8Pu6sSiMv9VdbfoAT6hxf9s9Pslgulb+HXOU7mrqAcU6z8l2O8264gEVQJpIxo+frFde0WSgXS6IRrNzw4Bk54TXEft7Y1sD41izbGOzEBGLjp5WMXuBhBidIkpt9/z1m6czyTf9tuHuu1ReOm3RjtIEHmISDtImOUoipU6Q+X9g8UPO7y8aJoFfbvw7vfnX3kaNgfH9+d/cFuoI8cycyRMH4RTMWqY8CmBLK5ghmjHW34B/EL1YS+vJ6vBX5bRVCEjUU2JelppNWhljWhG+gW8O/h2Lv57hrua8geOkxkMYitSx7bqUwqaXgl/bJnkuv0WqHYybKKmyh/FAAUShUIcHd3jyugnr910Hbi7iQeuMDoRrBO+u62yC9zsUpk3mH4KgBk8pYTRMSzA5JCs8wuwjFMy9XTrNYwwOeoD2EKBW4nz3bfMCSUQCqDUPycw9QuYVQqcLp9Ua21yqPW30uhHUjy88GsRn1V4Hf7P79akZ+d5BcHbE2VSVl18qwsip/311yUhiLoCmFEJunZ9DBT8q2iyRpotmEE3KmgTLmtnXhhAke9Cy9zNlleZrCMfh6tvOJYcWZ78Ou0HbG30GYa+C1sgGKvrCbt8m4JzG/PLe4EGYZjcxNTt/BPG4c0i+HrF5YEcfPxtTf6gy6inFEgZCeQtKWQmC3kfKvcI+B2X4TIjHm0gQPbBYqgt2wTjzIol0c62ehRMS/1VtGSrcDSKGUqkLqC7+TPlcgIFgigCrBZbcKtLR17JM2jJNL90K61JLh+PSBjHUPLaWxbpPQ8O9o6YBImLfqAd46CodxxN5C0pBLIX925cNUFf35VB7rP/QcqgqC1PqV/qhVlkJMzcbAtFFpt5F0kwRN/hVwgAxc0k67fxnCx1iQOe/xbQGLXa0UKW6d0vnxaob9jghf3Swv8c1sgiiDGLLpb2Guo08GFviLN/mCVWZc8wZ0Lrjg7lqkI+gq/yW5Rf0YeZdBndgXd3NycfWGC4Y4hdwZ5x4nHE3oJbpVJjpndHQTPoD/kUlL4FFdyUiI9hNlmFzrcGeTYpHKIGPjA4hjpO9HHyB0de8kzGKej4ZLvEVJjrY4u4/YwwUybEJnmirN7Ghaegae7UomnLFqiDC7q+kyPgGSlFzxtS1CFD3nRs0prj+h08vCRX5i5vvY+IB7GSi+I9p4J7wvyCI9w8rTcC8Sk2920juPmuS9WiFXN1HDzHwJ2qIndbfNL8yygbTK7QAyeZ09V1yTVt5Jv9hH2CDfhA39AM2nz7VbkFucHkGeT2DC/g+NLS5WXH3NtKseYp03TzXdOeQsw7tBfitvKs5wUJ1xwafnSa370AzqI/JdxsrLfd5SdI8tlzEQGnONeylUZO5et4sed1/eO246nyl3klfGeiqjkdKyfO4OEMc/sBs7OIWkf3FJplJDQWxbnvBDaCaz3AUFPHeeV/7y/0YVAFT3dDawsGDnOcRXFkfTn+JrHCTC7GvKjH1ECMQ7Pr9c5ruKWzm2/yHfzaVa7pv8ZJpJ5hBVZ/LSdtp8wz/qW9nCyTGDkW92YX781pZrFMrqlD412nStTPILa523bmgVL2IQkPIBC7qGLM6x0p5a5PO47JE4VTyDJrjnulBd8VzHHvg15xRg1ddQOXSz4AAA3Xpuzz6vQWBSPym7Ihh7f/cIuIH/18TOy+2d5NDuE/OXHG3j3Hbw6uzehxTt4fBSWKDTlr3Onm61XeVoFT5U8tsu42+BpDT8Aj1/x4Nfdj7LqZhvv+Nsq7dsGTq0SuoAmzOS2xPj3r9McW2oHtBU3HyhtRKWWAijlZMEUuAVjjUTd0xPe0fbWX24MX+4l//4KmJf3+6uO18xx5N0feOTrH/jlMI/72mfogvS3mffIv8GOCltHqdDB9k8GC5dfX8zzt8BvqQiSQLdOQRuVwYWK9LXVlrU14WP/FvGv6xhWsNfwNGSxkY3hTOwwDNtywspYlp9AmapxfofeYaCtINzV0Sh1otypYJzVmyUkO5hn268DPkJwt2rCYFX0qADKBNrVHvJ0pcjJdPIiTEFAwVYceMkTfcSmjLgfOmefGYW9Q2p+HUpB2Qn97KToio4DOUJAhAS2OmbAR0BV2FwMzQm1QoRCgsyqE38HBVZ9Wem9QwnkYPKVI2G3CvmsKtg4bz0LK5N0uzurnB9UBCnUvvPnE90hhLKBPN2lEQEZBiv2eYbxYof5pn51h2mm4q13a1x3PsphQDG3rX8EsNBbcpPF7F99CwF0+tkem8aeJG38cHCDDnHWokURvjnWt1p+wK3RIbrxA574lSth5u5j04ziR7/umeiobKlAWEWQu4IcyLNzi2N7dXOxJDSWn5MnA+t2rpB3cYWAx24gFEEq8LIrKAKhF0gzuDM4hXZdFSK9K/+5J8qVSGghftwRTBVacWdieFAGtb9Qj+A89Vj+VT+/zXxCm0LFj7Rb5Y9xHTj15/hXaLMC6qVKLOi2P98J7UmDrJhHgFXIldYZgHP80YEWmpP27RPzKXZFkO099Ndyxcv2qi1yhi/Ex2pW+Vh+n80IN8aIlW5PXugNLLOxSfBiv7TAP78F+qthdsiSq7TvRCTkyytKmkwIuojRxQx4w50LBTxMZp1E3GZXEAohJhr5BTHukfOn5VUClSetoy2ZnKhMZhyEb5A1vM0RwMmFYjEddv2qoR1vFELyJ4X77Y+48HheOdYDrvbl/os9Rms540/qulrb5U4yXztw++gS4Fcm7a4K8cKpCAr24Q/drePPDDveyreclHuMAF7Fo+0qci6H9oJodgBFCeQuIY+JuWjiJNzJN8qj3knh7iInExTHoLCzi42DpALKCn6/GKuxnAk6hK/g8jM8awhO2oBNQuJS5CQYWyBglpXQyX5sA+WXNtERLsCHV3gq/h3OBE0xaCSf9c0yThMQiPWdJo+W1Mxnoi9k6OFQ5pZvYhp7hKt81QQZt0lgUY6/Gj93/DT4LJZAxvviSCbRXTxRWdoddNK6CiDlPpU9a2HlO7t2HV8dV13UQbHjrrkuplDOKk95sbtyPQ7ecTl3ATI+KzsOXtq5BgD5T+WOdwDl2L5KIeU75TAf5UMIKnKZ42XgqxjK2KoSKDKi4y1KIhdcoGPp0isGsuDivSdMdHO9AEpKCUllpd9ButH4bQyxwfwSqVEYi32mfTbB+IxP6vV9pfPxE407OW10a1hbKNkvn3m09NIFqZrJyl2wI000z4YO1sFsK28opDC+d1OKKJ7SRIqaaDyTe0jHeALSTw8wOgdugg0wjfn7GG+LaaJ8XHEN4S08Vh5e8mt3nc08w/mH/PiLl46z8+cryvpPyO+5LBq34SrsPd6bI4jQsHLed+4LYioCPcqXzbfKHweHjAnBzDCeVMK2nXYkwAovZVCOlAm/4IJzExV/CyB1rUUbBiXE0MTUToM2bkL1JWsdmKP7JC1xP39+5yGlWi7zxRbexyFGo30ZPozyJ7ImMmd+Iaxy53sv/NePMv8at8d8r5jvZHeniqBLxgAuW3dBcpM75d8pxXripug/7Jct8uX9zC3wv0MR9Ii0H3HEvQlPe8Ae/i/qOjKCvYoN9X2MP7qnmSasw1Dh5Q9Jy0t+wTgcxuGEV0bcHT0qcfDLMJcix4G+R8EIg5mqGMrlfgrCTqRdzXHC7O4h4rJF3kGex19oUcCOMgl/tuUzaqh06ijQFajc3k+9FI6lgdkRxLaJKIRyoSSDfAQCRtP8TChxb50cu9XcgT+rPN5oD/OCmcnQvEQ4O4UQClTC3LtS5UoWgr7HwXIHBD9festKgiu8X7BtJX+hwon4OzXjKH8+ohFXwH2vIsiVTlY8FSY8npMJfdEu4+UjjIBQQSCt3jjrmKdCxMb71xcdwSBAi73aHoWbr9vvSDbJad4qGfb8EslrKGD82ErJovTfma2ozfEEDnucWT0uewb5EWADs+CEdaqkHVR4ZWt5Vgb5Pv6MrLuB3LWVAd1fF3EFHkUQ23wVXCN8MOh2VZrvcd5dQfr7SyIqgRiQeByqTKdw6rfXvmOVKAogaQJadkVSRaHnz+8f1k43CpKmR3nl8UJxjtJUxPFYTwfg/llLjW3TlcxRBEUJBJ1EGegkaClxFGLfQaf5NR2PHUK/KoMyyYKm30Jv76RtaF2h1UlYHoVXBV/yisIppZaCIAUwKI1FmQiuFQYar0BaGtusOKzbL+b0M7dmv8A1YANNAb/k9BLw0gL/tBaIImjNUKT60CavFRQ8fjIZyPhHoAppFThVEHO/kBNZ+IFHh79yBMHJhosG+cnr3EEhf4InEe7x1fyaDZNYlUjmo+LZCfiI49kVlMUNL40GEdyOZ5lMaINRwotpxkqRjHCvzZ//mq2fHvvhTEAKst6mKVCtutse44Y7pnEiEJCutgqgKIFiMzmW/yjgM956dFXlzzv5FT8j7KTBnUDXH95zPIyds04mVAJ5tx5wOUoQvgX/Ct9iagsvzFw7ZRcXkZaf1iQC59iH4AEn6lHsiRJo2mtgtOdZuTVg8jsUMfFbWhxbu0/k/9RemTwqhtS/hpil5DFmIMaecG2/4+m4EypZIJNiRgL9VsIJLxYN7dgcm4DIcEtWU5Gjcqa7ebAdEx0fv7lYqMxHXyG+iiDDjWfMVLbCre2iSu8HcoHFxZuW3/5A2WtsreyInMhuhoclX9on09Z5UUvQzkLgUvb4C6HerxfljzQJMSkDviJeWnuNW0WQ9OoOoe7u1lb5UwWQdwt5V5VjqpeWx208ac4vlSu6mzwTWhBw7m8/SdcFr9jrG3QnhPSxvoaNuoyf0id9jCbIj0QYZmC+YKzA5OvR36y2Rzttsbaa8EnQFCttEvESZsypxNc0E6d9GnLqO8JNpkKk6GMhAh78m1Pg5VnoCrkZoxINnHWMXGUYntDkQBqvexUcSYYAeeos0N1zxcNX+K8/Fa/C/uvauanC/osKIXYEGS+/zg7OzFek1c5VlAmj+KGdyRYcaMd5xCktpTIoLSAyPBABJpdI61VGN17iMD5hC3ejNea9eKp13jmcngQM0PKtsABPvOmO5hhuX8Ef+GxVB42dCrIbPbxWmRNFEHTvKYfOcTpXujJMpY+/ygfPzhwHvt0wdggRnmO9nLYwD7pYTlmkus4nVnniIZ3/af88ovvifrYW+C0VQWGUIVj6jB0IGlp9h4bCtXtOG+7vwk+h/qV8x65+rNgaaragX+HaWMdwJ876twmtl1gS5oTWgbhKH5Q4M1l20EeodXDPll8HfQb2bTdFVktZCYUBR/FDZt0SrwKpSqAerZHp1h/BmPLcFSQJhPHnVxemKmrhMUzcI6guBptffVpKoJOdFE6k1wrlW2wH8Lf5aUMZWX/K1hVItwK7nVjBU4VUjwD1srkMFCiC7hDw73I87JbyUQRxKbS7frwbyB1AH6+5KI0dQe85J9vVTs+rVwBRM07xa6ItXUvGMGIYJJ2w7tZskTcH4PxE6wMJmy4xYWSgMyZ9xEEGgztJeCXYMAauApNm5bcCjNzz2Qtb5W7A5nJqtsInuAHttsu9ouzDDd+qkxhzT1/XTohwGQ4br5uICD4LJivyP5YiBiXQN46HZdKVb9MB/YYtvwqVTtpKywiALOGp/HnLN/OYmEfE3iDsvfZYobTEh//JMZAIpgoD0LiTPGnd42ZdycTOJK4KIQXc0Iq0DG3v5bVccW+f8tsNLg7xq839LD6UrcBqOykrxCZcZdDscFOBeeGqugrMbZWGiVYUQq64e3ZbIdbt7ByvoG7vskXXiZlbdS2D/BycLSctvMq3LPyGpvy4d1pdoKWnfiiB56M1ejI0Le753olcafyOAxY7BN7kL++XFniOFvCy6G8Qq/Q4tBk87Ls6sKOo0U+Hzn1B2Fn0cEIKkL9S01+VlBd9izLoJqvMN1EOZdXZyYYr0iqp4S83Top5GGrySzX56Xgw+OkvFmB3kYNtB/bVPPAHB6fE7Zimn63+JboiLL52wC14ObY+uUUUfiVL2tS3NRebRGmP2/zrrv3G8QsoBX/HOJXPUURzDEeelMmExwlUAjFWXrNY8tGFk0wolqIIfubkWx6VH3WA17n7IvzP/LdByy+y170+sCFI3lUTj2jGhBfhmtiGrjitA9zmJlj4LY2OkzY7xBGlCTyvrY0b/OT7F/54hDKP5X8abmIp00JjdDR8YseeeP2FGro4hdi/qnD9whm7aOSMY/QTZTN357gI6GKffeAOfxZL6AMqekYZ5H1YWTBhHP7Ogol3rygndvdP5UTHzuwKIg93kLsAF9qlYrOLomWrAGL8dKcHZSszqiCyPzquarTtJ5EDocOx3+RopWNZ5bDuGmIchD5VDGWXkGMr46UXS1+uY4wqeLxfKHej4I4SCLlROr284sk4C91m9wNKJXc/0MBdwNFeNGG7Wx++T5/i2s/ltwB/cTdeuFUPv1WHR13NC6C66Q+tN2lMvnI22jxsEayVH/6ka3hby2xWZjoz+u8xCVqvQu3vJ6EIFMIyW/iyzYO4kzQDQ5Twfr6kE3YZ4RcLS3x+STZ0CA0IY55W3LxWPVrH5ucl5fJVlUHSVy6Dlh8jx2WXpsqgHAvzuBi7gtgN5K6h7txU9lN5X1pzJ1B3bHYuZPny/FEGWb7tv9mpTP0J07/CXIhLW+M3KOmsixVePLeXthuQwGUttzBx1j+0Y2pzbmS/85aeBGm1NFwQD40FnDYM9iAjavnhHNzaKoLeX13Bp73vVOUPvBv+7dzGOU/kUJX8KvPh7VEcwcezm9N7tdZ4kCELDIaHD84uafxpHwwiL6/nboHfXxFUii2ht/cM1Z+2XXvCadi/iW/YxFT3wCbsggQbMrZQZRbHECEy8MOVMvjykgFWs+7KjgO1A3cHe+9GcFDvwM9ATpwDf46BOXl2cAcmdygAZ3xWd9aOoCiA4Kjmn7uF4FFTVibvDvLimcGeGoBg6yLWPNBCdjhkAt2JvMKDd6pEgISz9eJcmBwjtNsUVQTlfiBWK91J4S+YuKUxx7uYULszyJ0hYJQJvkKPW/9zEbErBt9uz747mKAU8m6fK3+aVKZIHtcwzRwNc8UTf+4KIi67kGCsku65OGMrMGi7QhSbgQuvteXd8Nq6NTJSYmmEsScm0YFykkBoEjTVY5jkzgssAmebCq/d1o1Dz24qaTQrooWY3DegKTORxbPfakGkkKY18ZSX0lae0pzIWEedeRKHP3EGsu0XjyuJvTiS75PVHC/+cyB3suWRDHcEoawJTYmJgqErfG8Z5Nwq/i62PymvoPiT0UoYPjfpnMwp3PLdQ+PSO/Tr6iX+CLSLnu0LP1yhREDu7h9wdayVfuw/4m99pGFDV92yoZ04m0VapWjwk0bEQnfDVMpIj5lgMdPKLiAGX+2stDNYO0BnlxD0rIIoCiAFVXcPqRxyZVMBVkUQhCftmZ8KIREI1Vh+irSdCG9UwuJOyOkrgteqg9+H5Ot1Chcf8QKEfle01U4DLP+L9dICz9ECd/TNbzyh3yCgS4JFMZzFCHmStNvQ9us1Kc6ElLFRpXEmwYx32FUGdXfQDSvPX9kZdJNdi+wuzQp0L5mWxzC8wC062e7qM6JxkLEjyhP6GDaT4/TadKCF6uIrKyGB7Wu14w3+vtIVrUwMmS53Y6zk1L8VpvblEcHSElZbMS50NxD8BKYiz3J87X0r8CEnyvApfya+kwYmFEwiPjA+9oJodwoJ5yKMD/wJHuU4PUdZ5UltdcuklZa/yoKFehlJouSenRAGsDXEmTobNNVODNkd/MkmgA1fbHBB7pZwNtkGP3ntIHEd4bbmfgSj9ym41mGPO8JNJVL+ll8r1+roFupQOduFsIFqTEN8L6o4pACeCMcyJ739AY0qfqT17opV3oPWoWEVof7iUuRBJ94ZH/WTJm4XAJEHicuxbmRBd/VkpxAz9tkJrvznGOFqROmer46/CyzKp1X+RAG0+p64t31bn4yj1LSyoT2lbpU+tpm2MC6waKsgcvdZdoW7IJgj5IsmVWoypoY+iStNQ9fZSY4SU6URSk533PqDJBlXoV/HcWnXsba7cNtnoiTKx7GOOBQQtlZXBiycdGy0JukF05hfHHIMjbUnBIIJPbQJEiMONVBynEMD2gfARApwKCQJx6+n+ByhAvLoZXyKOiSN8+A/JgnsJAJGsAHVNgrRCgcY01buzFE2z/dO4gJNGhtBmo38Lt0gaFVek069PoDdQVHQs6s/CiDvCOpRXuVHL47OvaAo7OXjLlY7J8rDIqHlR84jrIqg9Qlba3Dfv2CurgjnJCz0DML+K9SJe/DPK/XJN7Fi+ceXSlqz0oTxgSa8bmGINsGYxBkkZNMebZWMmqQHZMOWdpOH97ikiqC1wJhL/d3lw5FelfdrvqQ/i5HInvmlZPuIC48qVOXjDGE9FqZcSZk8rant1e9n36giaGKC2svrmVrgf40iyPYpySzCeYp+ngp7pob9ZxV7YAMpcvX1jQ38HR7HdLpN57jkwOvgL+PNCpB2Bm0GcSbCrgA54HsUx8FfLXqVJRUIvBAw2+UVjIFVKHZynVUkV5QczCkgiiDKi2BNGU6moxAiLMwcPMRHE6YbO14IwQ/N4+AeYdmBsCs+ximUOigTzWDcCbDbFCNk4nDF0smzW9PfX17BzNi5g9DqapDCaCbvaYeuZuWSQ7b1exSpP1X+LSgo4MoIVQR5HOwjW95z34GadDXlKIIsy3IVAsQLNCoggL7Dmv4y5A4j1JaQMk7Da2rv5L3qv2KdMKQ9TLcBTdgGtGDanhvYFt1yx7vHO2xM7hNbe4epf7CsbRg12DxA89/cjvDiU9ozTrdJmmyGKuvV1pA+FShzSbRKSRRBrrJ7fC8XejvpcneQNOcg7q+Y0BTn7Px5g3Lk/DW7vnJ/gLu/uJSVNYniyG4glJSmu7tRgdk7PVxN2lY5I9SqEHVVVMFWQQFBVvqNcJAarDo0TBoOPeEYRVDq5qtNks8nbUQRlLBGRKEZ2pF23cnG5AkaVfmTS6JVBBnG4yCdezhcyXGCxcB8lUmYaRRmK/CGFskztijQNgqb6UkU24FblKTXfqf/6Vva63dbKZ7wABITm3JfzEsLPGcLqAS6DwNYtAjNDtl2HCCcgPRjEDWsuxM6dlUJDE9g0usuxBwTc+cP46OTji9MLtyhKG/K8QR4U3563l+sUbEMzxn+MSvPZZTlfZHpwyjcAavxPY/+dtJRaoh7+dmyGUxhTUvpAX+yIo/MpG1w47dxBHjdwWYJ9K7uyhvsvrlbD4dHwVx4kT/1V2OYGMCXuuvWY2DuCHK3rHfpEY7tPRROsnOnGfwpd7CYF/ypf46DqRGo7fbWJkEYCCdauE1zGtca+Tbe12ldDaxJPM7ms+An8gl74CfqsX/C/69tMvr1Cx1zOY098tzTmElTzI5xjlKaCZM+NNJNw4qEuyqyc5uB1CPTUQAh17kA0l9ZUv5T7uNhEdDjjtkFpOwXeGVA+gaDo/Kf/cOLnpX95ioB7/vpDg77QSeJ7nzreF/cMxEHuZHLsrACXUZJNMQO/oVuWttl9YyOq8TnB0WIiJJp0VkXWrozyAVB6bEXSLuTljHThRRos/dBrh1sHHO84tfvVGQqA/YCafNAIYRSKD83T/6z8NdFHvqKOIqYuEzrp16GlNaNLc3vsA0TBhMCni/XoEX05L385FD3SpnwyXWHGZclnhr9xzLsWf9nMzCxj8lxH73/p5zsw4HnVdrst5d1QSFEloKF2/v8MQ3xJMxVFtIdj7svQ6fIcDfMSVT6RBnkDn+P9PpkURE6hlfLu3P9BcUp21m2/eGH/JryJyy0CCLSotjMV4wvHwEqlD+NWy9u4TTCzbtVM4RHhUmieBcUH4iknMSsZMLaHtomGGA9CTAQ0xJnZ07kPqjQeZHyX+50U0ZENnbR0DlRLoOGZ197ykElEE8WuFF+KmtGHs28SR7exUZ5uIuMyrPKmM5xLDOYrDqpDHJ3+n9kIXbwLZYv7+dpgd9eEWSzSCrTecIH/452/i78edr2n1LqsatbYPiEto/tgUMeNOGErE5ZO+lXvAMuPBMexMAN56sCqCtBToZvFQCYIPencde9LEzCsyIEgx3bAd9VoQcn6rhveVTyyCzNN1vs9YfB7kw7jFb8grz4wTBxy0bUxg+PyzZ6KwU3iwiIu5p2/Lj7qHDpZPY1x7gULt2uGIanMohBX0E1Kz4XnoXVzUXChJt1tNgQW1ahXLVi6fYBrYIXFKqEUADOTgwYpcqga5kkwoEC7hUKhyt3ZSAoyCC7A0McK5zAI61ZJ90UpVCQCfiquHGr9tg1opTgpNVjSEKx4byaDUhQw3aYugqfFdUFb3+ytCnT4BpaH3BRGntijnbTNo+Eh+gO/gMOK+pAix0gzMNyslMGd7FsHjNg1lcst3sHoC93BEmP+SlQBnjPgN8yuDvJyg6z0BsthKLwFbuCvDdAtcfrVxeZQNgWQ4eZzKHclMajwIRucxcQtJx7DCgv9xvEXkfPSKyQ0HPkxVL/0K4Vi5+XgqzDu3XVpJ685G1Z+TNAf2Lb/lUQSbdrtR36dCUyW9ldwXSlkgnVe2g3O4BQPr5jVdNfd/DujazeKLASLtwbHo9f5Odw14CNN+XbXxy0HchxZbLntw+BBKcnXqsuQ4KPIbbkq9Kt26qjHeHFvLTAM7YA+pszDqT8SuMERXE7/MyxanWE9u+OSbqzo4GBM8fFsHMEzMkGSuWv/FLNF9yuPKsM6o8OdOLhUYXclULf2JRBokKZTmS6ALL6SDqi/I8RD5j2oBVdLyl3AABAAElEQVSnH2e6mGkXnvUHOGH1C9cw+/j0QO1xW9NOXCwnnIy4ZQMkT8pCy1r59TjYObzVozTeW/bWo2AqoplMvGN87EIJY6P8yJ+P97gBE4gqp0lL+uYx/EcbfMQzqIZrigzG2hkogBgvG3fGshW2YqjJqWm+j8IO3kmXbNpMh9gWuZov7SWcftONSRieGVsn/LH9d3CL5DbwwSXhU/gWS/lb4Tp+RdovpzmNsRX9qsU/dty4yFC69sc8orBhHMyOH8fbyHkslEDTOXLDOOtYmwUUdtLOrqBMvlECqTzKhBq3Ye0rhFGPn8iALgxK+06o/VapnuMSf5VhdryNyxgqfGArU+IMzrFprP75TYTqt2kLOCHHz6OM6GTYHxtRQdSd2ywUQbfes/eGnQ3+yliOOTJm+uMiOXKdO61UZELLLLS4IzzXDXgMm8UalUE5duYYu+RNy3CMVXlqv4rMCm7FibpQsfbp4hy8xTPY9zVpCrjXa5PlFuz027GTixXGNNUCPMl9StIWauwddiD2kLqOeQ5M7CeyybedDA72lgcJH8O0/y7aWDSyKUnIo7S/Sl7pR8ayhVXUZyeashyPdJpfe1RW9Ncec48bi4fIjbkLVL69Hucu7oZTjnRuIs26y0h6VmaUbvmPW7wzPoBQ+5U4QWO0vThG+Wi7SgRQQJr40AY6nRcllzQCbmz/zCmvhOsRTgsk4mx/2IIbS5LC9fPrJi9R4DULjzkeCXF68fnb0Dt8nD4QhU8u9OeEA5f7e1dQwpQvpXn4vIuN7vp0FyhJo/hxnjW0np13/ioy5RZNWih1Kg7/QaJZ3lgov1jP1AL/axRBe8+xc9FaPqXzvenSY3bvv4Pr2AQbnzg0TcJoF+G2JsOjf9IKIw9Syy3Tk8m5EpQJNys7ruooAMhAc44WRumui6+savqrWu4U+qqiiHgFge+4Zbqmd/UHK0J1V1IX84TBWu7wMssXThPWtxiqSK7ghgfCmshgy2gXpw3TlckZp6DqX7XT3rfTCW4UQjDjXKALI5IBzh1Bl9wbo4Io24ZlaCRWWEhLiWxG6yKdc7QwwewsYqLtWdmseEbA9ddSuto5l/065xU1sevK0KhqOmxUEWRNzX8xf92YjaEvXzLpi3fr2wExAOsl4o2b0PrI7VdgIC2r5RX+6J4cDjG2x9Gk3QkgOBfnreh+S4OnFmtQMK0Ikc/EzXc2akzqZ1WSb8AzuEuTOYbISmR2ArECr2DqYP4V2nTFcu6hkp6TD8qgDMLi8qM7gaKc5HLLXELNDp8oPCPQKtRCxwqz2QFkeSoEeVQMQrDZog7+bur2GTpcFUs72ww+GeOta+q7AlelpDEVQdJXv2f7hkqZ0Ay2dP0WevTXTzqBUqGjsAr9OiijyJTmvFDauziky9zJAT2+9wJzBVdpk8E7E68IrF0JiqKU8iMgZCC3PFrKF6bvOLeXYesTx7ULnhvIqYN6j5n8UucJfLFfWuAZWoCrTNaOoBYeMl2kap8t2dIf4SEG5/EFEdtboyzC66Q5fIFxzV0SmWzAg26ZbLja7M4gf3XS1WfduVAa+45fT/Int901kQlHfkGs/EK+ZYEWd7bCi4+F24umJwlAEIDyD3FqgGnLS4xsKG/cTSn0uEgbZ6GGD8mbnJIXqnyoR60dQ+Ef8JELjtt6V4qryTk2Az863S2L4kclEBNnf3EmCiHGy+FDvRha5bz8znHR0sTbcmuvmrReQcYXT9ykAPkOax0sgj9VMdrw1qr+JGlOTb7cWsbNcwh+0vnf5fNkgv9h4BPD80lKv5NtMyauIzKJaEC/+rgnRW3ThcaxkwevjGm0gBNf7+3x1/EykZaWo/RxN0UVP1+h34RJx8RL9y4WZvFEuQ8ta8ZP3Y6ZPOkndKZOsK2HypzBAweVDx36ivA1coM445aOBRNnX9p5HRtAdyAYUu1E9QsvdUhjGovIOIfDnTwqgZwMZ/GOcTWXl0PfWWwZ5SYLfN5vdc0Rx4yzKDY9MpadQ6ZxfE2/YNGFtOkf1GOUp9JpcRCDvV9NpcRp1ShYF6b4dzgu7kIF7kgsJE5yYjoeL18zbTsZx98xF0OeMoUTcs/3KTjDHuew+U8LOv1mJ5mVUhvU1CZt8sZVMe/3r/+YdZvBdMpjWq0jpLfmMtAe8punE/JLjtDrXB7tImIWElUGqSDyQY505xv/pFfW61wmfJq+4bwmNAwuRGfOJH1VziveKn/6hWk/P7rtmI9I+PomhsZYp9SrdituDVctqZpke1JzC1zRcWzxK1n4abO3OFunMp6L5FWAOtcpD69ix/lPTjkoP8qrpXOO9MbtKQeU+NkNhJzZ+7OUJ6tYesOCe/oTL7oTbnl6B7HgDb45GgYeLzuC+l1+l/fvqQiidTLQSegaGdxG8Hol6UfmiaBHEP9y3kOTbHWbJjNg3MJtsLRT/LxsRmFklmVmCrNMitHIyPCqQVehw64eJtq9h6VCbBRBbqfMpFstuoIAk3EYrDsrnFiHgcqQeRRqLbeT5zLpTbgWB55hmh2urYGMtDjq061RVNyNH55HS0aLLbPzL1pvQs0lQqY7g9buIBnRKGk8E5sdQjA/LxTOT4SqJGJgl0m+llkyuJufQrBpzxn8Xf10p0V2FiEgXMtE3aXBrfkKB6bNymlwEy9xlAmKaWuh3SeqhFTW+4f8Lm2lpmlaEjaCVKlt3rr812i1nSJurLDmtECAoUWmMVeaSbXbJj0AJaf9tU02tqDJvQHzLYULTmtgLt5boiBMVPDRPiky3/EQtOCk15NdQa7g5IiYiiBpsUohf/lNwVOaqxBBO3jvFcQmPfoLdlkxQtHjaqV3GoTmUQTlLizCVPz0KFjdOdJIfuKQPEHYL5kdadY0I750KLL+W6nWgSTU08DVrlo+fCrhTaL5yb1DgkRgtf3WgC4tuaMtdKWwiT/HKqDT0C9KTS+U9mjYe+jQ3WkqgD6+58giyqCsYELf7gyqwGpe0PPJdl4Hb2gUnFzRFD/xqhCCHQx56RD3LeDEOVC7nXpYN+BW2hdF0N48L67naQGu6FmXNUvMGCxpVKNQX7f8GN6hn/CQ/Oqs+oVzNTgTA3iLCmR3x6qMnl1BXW3ueJmjCC6mZOGExRMm2/1lTRdhVEo7Xi6eFbt49BiCmBWXIHPsgCKD0Qqfib27rcDUTagoXKiHvGdGVOuWquEYnpTJA/7yBZXR8B7HQcfN8B6UQI59jolOEFA4e3TVX9KMAsidQR4HUxkEnIsmMz7Kf3onUGqVsh2tej8TvI/KZBKRty45qjjLNI2Lr0iDeP2JWnUVVriC1GHD8GCIikmdJ4zAyJtGrrAFtllGJV/ij2k3gKNjCjFs8vubvI3+Bd9jMtL1G05GTdA2wr1qZPaON1PMAXpDwTBpVzt58tLv7lZ/1KP3/kDL0Kpy3RypycIL9J0j2I69KoCgeek+iyeZQDNGMn7mF2IZbHPsn0n1HAOTPlP2lC8OPBq/oU8GxoT40j+m7sCv9FJBcpjG09dKrbrRGpMgyzbCrx/kIC1XBXGUy/EQuc0dDiqGHBcZBN0VpIJThZDHHnNUJru/e2wm91xF7qM/AKsyKTJklEIqSsmLfLJDgk5k7exLYuCzEBPhzX2MSz8UedMEZsBSITOooe5JF4I0SNq3tJqx65seb+iUNu6GtK8Jbfy8N2y3sMm3UKdwCVsAJ6gntS8iV7GJD76T44oiLLIWBOOfvFZYXVNa6aWZGppwCpfGMqdxPiM9QpvuCuouNu4MQl7M4qHzG+cyzGmiIIKuR+7LyYgoMaVnntB1bWVIcVMedKFbvPRH+2y72QDgP0qhTeYhTANkjLzdkMyPEmqM+SyIlVWhV62NPsJY/gJYxcbXnWikgeh6R5ayHTKg8xOU973gH56N7Ji5DDw6O4BQAs1xMGXK/MoyvNwrNEyT++C8E4gCOtdyJ6njhOXUdt4lTrRQ3p0HztEwruJY7bDQfrGeqQV+T0UQBG1nCo1IRXaCzBJ1wtggrrK8R60m9dtx/g2IazXL1vFtCas+Js7l17I71i5E2tFwAmdben8Vwp0PTogVZBEAEFJzJExGmW2UMs5esCYTVTjwV5XCWFkxcvdEj9HwDcOR/V5VPGTgt3jKVAy17OAUxGX2Dk5+xA5SxumOXU/4a/3C+SwTZ/2+1UYbzZ3/+6AL3ZRBUbrRYYweu3EA5xgXjNDt7VndQZhVKHAHhf5uf1Q5pNArE+Wc+GKO3ZGBn/C3nB/vpZkIuEgXbkJprUrPtrt+BQFthRGHamqfxuiwrV/TNPNhHdTHbeXaJQqZuKRoq9i+uhprXgtuhTmxOWY35Qqpadxq9GOx61utmJM8UsR8p9iW2Zxrz0Bl2NRx0QAFRnAzyWRuhvoPRpJywpWjixCUq4/uSMuka53xdsUytGscx/jyi3Uqf3jQDSEYqNwhjyUcqOjMVnYUQd0Cfxzs3QWEH8Wm24Jz4TnlqsSUnoufSHr0rG1uo1QxRDDtLNjUtkICAXslcUMTWz0j5hAmPVhbzfqa5JvVeGzILzTsQHqB4tGfwZ3dQW7dzc6g9ygnObKo4PqB7b1e3DqX+3lvkMKqE7lM7CKsKvi6xdfB3X6xNz/FhPS0B/WEBb8D3PJv322+Zex6UlVeQ7OT5MV+aYF/dgvMjqADWW/dTh6l4hjOAe0DQX/UkoNHSZvOUIyjWIa8td05eJ/7UpYyiPHxhp1Bc6T6s/cDuVOIMXWOW3uR6T38y3vKkpc8B16bCTOMJjsQnW3IT9bAGVac4sVNbMcuTokHJ2PWP2Dm4biiGqhKX3mPfdG/8CFeVi0ruvCA7JYgTD5hqoyHTHZznMsxDx7iBPktO4PkL73LrDt/nFi8Qxmt/d4diZkwA+tRmlEoWYY4BG1rYbsXz+LuO67gJa5CxZalONbHbwZ+G73KE4ZaVwKEO5iWFbCtbZIuaUz3yBgweeCOougAMvApasEd3QfQOI9xR3eSrsy0Js7wcTev00K6wyBQiV6xBV1vwyZc2/ZJmPZ6DIP0MjZm97cTZRQ9/qpSd7e5e8Jdt11wcXeF961UCeQ4yuIL8mN3UShbuhMI+iVfJ82h59BqRjj8qSUb3mrb8qHvwZxgv7WI+h37TVcYMKaK2dIbRh7SD4lIhrvlpv82hEBifPAre5ncyWwVNo6n0LpjovIe9J2jMNDupfcDQc8fGGPff3ChBSWnStDEoSzSTZ+4VEmKOwsuSY9yyQkzBfVhTF/Ib3UILqlNcEuVgm9apXUNzrZFUF/4S+U1TdO+YUj7yorEKtxeYmP0bxk+cgthv2/aI5QxR3PMZeBir6bWPSYtn7qIe75wogyftK3k4AyU35Fv5Zd1Z4mQhmj2HPyahomx4fJTFv8oy0W8/ogNcqF3WiEfKidGXoSG/XVH73bzjqAsbHvP1SiAlBGB70kHaRweDz459ki+subuEKJMyioNUxM/CP/i518nHtgHkzqQXlu6tWZDmzqTrtmYA1AacoyD19grxtim4R3eyveTf/PkUnOEu8qLa26j4h5afYuCx+sFVALlSK/XXai8J0zlvbvMVQLNvW7u6PQJPafMYqZMOvOeXRHkV+HJN2QeBsx/nPPLvamRGL+Y52yB31YRlM6xU33JhUHDfiW5hdCx607A8mwhz9mu/8/L3gZL20hDtcMMrX69621LOoCmRdOOgoQx0Zj0y23Q9qiNzLGrmQoACKsO+AiydfMT6u68YKKdn8clPKtFMFV/TWLfRSHTpQwLipCmMqeD1eBouZoRBBIfRdCqRzBdMLV4n+axBSechmAnTZtD6iitZJBfKR14+1AaO4McJIXPMTEYoTuDogiKMsjBX6bXwb0X7irIer8QjNIz4zy5mDdHctZxMLZJojfqZNpVUxixWFtuyqPelhm/4TytMbinwfBiWtPdLowpl8G5mpCA5IjVEH1VHQjbOvo21rjEZ0aDK0nyVQRexsDmlQDBDl7D+t0eBQZ4UlKe0emwU5ukJOdgsKALb1hkQhJZjbgDAeyhfLPsqrlCprt0vODZO3xcuVxHMlyhdKVSuuVxO7ADuFvVPVI2O960c6mlAoIDuzuCjEdqrVKU/ImLAoiC59ikfSlyJriId8SkgxIo9xFZHxH3WfiLe/qtjoOxiWrMi8Ey8YoD8/A1871UZFIaCdwN5GRU+orSxhUaHCovHdSdmDmou309v9LzgdVL3F7aGkWRkzLo3O29Cro5nhHBl7y13XGksqno5zOGJMRthU29xH2qMLZhY/I98bSerbxw9ocX89ICz9kCKoJUuGjy5rW8+KefG7hBhJDtqSF6FSo4s4PFnTzAZTeFiynwpPvZGeSEGb7k8VUXT6IE4n49JyH6v7rogi0/Ml13K1YhnZVmJwjyIgrIqrEThw3vQz8KnkV3ixd2od8wOBNIy0dUCGWnK5WJQC9fkcfkkZ/Ia4CBH1xwuae/CuPdKTnWxeLJW8Y7f6HQxRB3BeXePJQ+LqxkBZkxNCvNsZ14cMQV3tQ7geRd8DaZDCajhHVS4RabL6A7sZVfhMo4Cu4N9zv4NWpPvEnknjPmJovD69BiW+iEaR/dLWcDi2PiJ/Sx33DDnko7aY72EY7qxGjvtEjQLxmuNklbdLQZzI/5jftom6/tYwHSrP7vFKDSRuWn4+RtfniBo4zukvjsQuDd2acbjzdCt9KwtMukWQVnxk9o3kUTj5Q5+XaMDO26gGIZ/GnnSWWoUHB30u53FSH7EHBpOYAxU7OpW2BX+v27GxphK5VJSvKZnWVW0Em2OY87dhQKtsQarUEi95yQVY9AQv8umEDfXqKrIvPaI45rt+21E2QUQ5dMnnMc0kUYYDoGtx/0OLbjbCfgigrKfSpVs9s3teyrn3jhkiBrMv62h3iPEb5pGjJj6tirUQecnDRp6C3saf9ehvGPUxwSb86BOcGHWL/3Ek0DKx1YhZRg3JYDDj08WxUTyYsMMoeRDya9uQiXXJoXCVWIGmT9xy3EyGxR4EQGdK7jgmF5snOe7A5SOb/mNV5z4Q+CZGEcnlwaXz8ewjzJBcbIk/DlHHmkopJTdwa1jqmMuFgt7KlfHbbBwj9kaZpF+5KkcUmy2h+3PLpJmi6VXW2wSmka00XxKI+Vx2Mr10HLUQLBn3MUEl6tcqf0Cu2y4yd3uyEf5hdpVRJ53ym8PTvflBlRdPqjOsqi9hXZN/8xfmf9XjdgmNM/67T9pQ+qCDpDEfTuRRG02u25rd9TEbTIRvKXiIbIprHSydMPZKQNDdx4BvB/m736dtC2Xvgb5HsFNDggfe2Jhl0vvtBWXJ4MdemQ5sMTplRNebY6OqGGGXYLsJpxL1JjsOeSS+8zyBlaBYJMrgvXW/irJVdz7q4Kf6XJXRcy/345mZA/yd5yxcPdXVEkEJRLoAd2m/2XpQXHxO3VDX80r1VtreUMUEuxEF1i0Ad+FAx8yYQ6ADdljojNCmdsBm0G864CdStkf6kJNwP/e35WsUfCmGzDJN8hHL9TwFVIdneGTBdumIk0tqgwp97KH1wGt9oqggAS36Au3v49qqFxgbIOTZnBwNwTtwCSti3QfNpOE5tszYg8EkYxGajMewjI+FX+9mlERwSlpeA7OSbCyPxPSj0dBAzBDJhwq5x8IcKTXV7AGS28dj+6IlwCHWilr9gMwvmJUBQ50m5WKLNKqZB6e/YZwTUrm4TlIktoVGVntv06yGfi5Qpm88lOI1cw81AGhYpmf9GHmuAXj6AuHYNgBNCMhop2Ee+KuMjnCdqtDyGbWfXP994CzXtoITUmh7FLG1H+EBpFEEV0kJfmUEAycXNlPr/8wICd+wxYyckdQTnv3V+D8BdPXKXPAL92vHW7r8ICAi+dxLxSLXALjrSDNdJs9nJYlQkrBH7CjiY6YeEENA77cZoj/Iv7pQX+X7cAw16PhkGI4Xv4Jc15pe9PP13B0mxomNcm3EPJCyyLIHfwmVwezeKKK8lZYNHOnWbeTzELLE6wnVRz5x78y92N2aGYicZSCCE8544VJxrkK29SGWSBwxnEKeXnJV/Cn8i4pjqEGiH+xT3KH5Zvo/hRqKceuUgetxMI7yTLQokTCNzu9nFCm4tF4S8XTCjcIeuiiT+44GTBRRLD/bEEL7Z3IiyfyU5aeIt8JRfWL0WzKJUngDBI9ztgW99W6pRRyEiosJy3CTuidjw0yNZINVNv66TxbWus6HqMwBTi1B64oFCw7T3wSYsneZv5IzNwx3Kfys9kkzzlJsM9s4nbKmHUgjmJ25Ns+Rk/j9GWP35pxHH0gbEMMoO2oC8VQe4IR8nzyZ/Z/nzDg60s+MkxlR3ha1fQHQuFyoSzuOLPwn/np7Y18wMKkmHKtIyU3XEzFaCys5N2vt8+kRZLcSU/nVj55tBlxtwE4U5xpWfhx0g7fUpX/sKniPzEdmdv4pBbCSFcLM2f0hj0IiNmdyzjquOh9A+dR9aDrnPvHosq7pjITlsvRXcS7W4KFEM5Ejk7LaB/FUjZYU7fmcUcqXbG19ZNfING8Cg+vm2Bjv2iWqAFiGX9pw2SBo/yWtrKgGVMYVOlPROWhluxj61CGvo4nyPkDtW8j3FNu9DVMyj7DeJvQN87LrZ/+r1Brdjy2z4qw5XBcMsf8te8t3xINzuyQ3uptTKc8pz3sbHY58IgihxlRnlydpVHbnTnpgoiFZzObXiQEaMYhSfnXkrihcnCt/Eqluw3DCZZLASR8udWeX29KkVSpb2uxRl4FbBpF2VbQucRHnqXVmwL6WUzwEgXGsHTVMuukn8paqQ5CFoFfnaoSY9L+RMbvl0FEDz7krmNSiDDUGrK748yYu6nZJ4TmdN+gtApDpav8U4u8RDP4Cq+RPaPb6ebJzuC3ng0LDVL2pfX87XA76sIGsqibYb2F62ltZ5iTiMAtDmFnpQNef73jlMY3QGhDIKHOhuV+u4vQhzs7FcJPKTeoFdY653OB6x/6YDEJmk6n3m9yhEbt6PnKBjMzR1AX3L0i5++RQj46+bL2c2nHgVz6+SdDPKrdxrIJGGQMtUct3EXRZmtDFdFj2yqR7TYIoPbNZAOQsO+RMh/KyXqMIX4dRMGflPVY43XmJ0ke7iu3dccGmZryJS0bb/N1oGRKbn9vRcGVuB1Z5CKoGyHzCqP24BVADH4Y1+7s+Ldawb8asiRDaIIcoUUfhtBIgM+woRCxZQfG7SCw6rBtEwrPy0krrgFPFSrNQra6wVA/wE7Zap7HzGTmslu333UuB12wRFcOmu8NLQb3WIt0A6/xzf4mMI4/Y/DEi6tEDHf2rAxHcgbFxgiGDMxpWkFSwfibNllwP56y6NCCNr9/OUmiiCPNOZ+A+jb3UFOylQAOUFzgtVjYvQBFZkIseavAsi8s7JpXS0zNC0uDH74E0Rcjlm4F3YNaiOk5sPYRnkKL+abOVQ439mIIW6neMS39Vdr6yEs913hHJp6AzKv33R3kHcdOMly0ubOnuxYU3npHVZsY7/+eIXdLb/e5eFgn8kbtK6QoDJTJZLKIH89RbxSTsoGj2WfUpqIY2wQjCDCTfXin4jEF1D+Y9yLeWmB52oBFUFRKINAqHLRsL7QL355X/q7NA1caFbaHfqV2Cc9tuOfY6qTguxElMfk0l0UQZl89CiNY6j3meUiaY9du/s246oTDibUuLuCzfFW/E5kc+cZd4ipDAqLsLzpaGUZwTHVEHfjDBdBjJiKbnYC4ejOH1Z4nWwwBnaVV2VNJ65eBNoJBDsNowySR6DUwZ1fWHLBBF6THUGEZedEFEPCc5cEcVEWBV6etJRAMJDwMXEJZuK6KuB9Ek7ywL1VK/aDtyv9uldtUqFyyglflbT+5D75r2I2y/DkM42zYhK+QR0Cn4AbsCnD/OZzTNz/xD5m3brtqZLfFLAHb66kPcQf8xJI/8mDxzx9HOPsA5kkE6BCx7HvG/fkOW7+9fkLSqCvm0LoM0qhz4ytHhdTUdSdt/xCJ5+sCsrmOfKf7U/2lIXsl/KW3BCccIP3K4/Q+5XW+Ck95+iPCVZ6HHEHhgZy+mncIgRcuus1BupKBY87gqgZw2sVjFb4B6uWVSxQYbG0OF7SpRNdn1eMpZBwxlJlO+neY2A5QpPjMu58W4qfXCDt2Fo5MeEqg1SEYmcnHJnl+DWZky3o00LUR5yDd9AoNYtNW08XOPmmTQLLK307bWQ+SZ1M9rZYYUltHpo9rP6/C9tjC1EcdaeZTqPj+7ucRdFn4us3FzlrrKTXPW0RuqBO2prCqf7B7bfjT/46ps2wgLXSHnHEnXS8pO3wZRxRBsFj3U3uIqH8OLuAkA1V9ESBz9UCKoQSj90jY/DvLJq7E24pi5YyyP6TnZvWjfLy9IuJ1El9irM1AJaOKH8XzyiRkn7/1j2mGwowwWaGPlpTvtFqAvm4bZlfiYXmPPKYI71LAXQhXWaXj7uAuhNIZVBPOKgYYjExSnwXFU3rvIhxAd7u/EaZMGMHdilj59XpP5QtLqLjVw4v55vVRlaFmv88RxEUiK06L45naoHfWBFki5RJ2mGGoOpqaxk2LjtEBIQJwj9xW9Bv5AgTHwzpMUf/Ec0yi9bFjv0r3NRzMqOFaJgwIF7teA7OMhqegNlJHelQBCFQej+Bk+NcCI0w6kDvduBPX76cffp0g81KkEqgHAuDKSqgwvB8vpHeFSR3AilUxI1dFkY3Z4KcifKZyiBgRM6veUC7x4zE2XDjaw4gBU9j7G0Q8CPQqt2eWhYlJsNQ9Vv3tpHhmjBMBuasBCGoZvuk2nMG/Az6a7tkbs9nsHci7aT6yvPiCAOXa0eQdu9rQXCgbTtBx6apVQZZM1m5tk9aKeHuAFm4nOAbkEAmwaGumzMZmetTpqX8GkMbrPLEpFA77JHOtnK2TAxZsENo4w/M4xS0MuCh2yTr/pZMrFZOoiLt+DkcNLb8G5QQ4wMXGjaj0rJ0LR12FV1FEALsElI/ffpy9o/Pn7OimRV4z4MzwGcnmzTsLiLSKsA6weok63UUQXyRlPfwM+JacbMiQ7/WGb/iWVpQOgerk/iEzLexXhjzSI3iW6/GJb0h+Tal24FtLjaCRVhq02RAhu6zFXcJsB63yIQLwTWXtzKou3PNXUEeE1MZ5CWAnv1WWPVejxzfYLBXwPX4hys/CgB0h1RJ3OYRxTQDKJzUrihtcYFbr6lb7fbHbHM2/sW8tMAztcB+NKx8cMbL8EeIfB9vD2OnuELI7fc74pJ/JsA4MjGmH7vD4p4tsq4Wd8x0BVkFNOMokwf50devPZagsjpK6jUJ8R6LpukuoQf4lQs2ufMMvuV4Xn5pwf7Lj5Zbf/jIbstz7Zzy9+wEYgLeC2xVADlBVQGEO0J/78PL5bdREOt3xXgpjIHJ/XkZLxn3UBxHkUz6KI5UKqlQ5jJSdxgZlrvIHBfziEywFWOR9NUHhVBwz6AgytQsDMdGF3/AltKgnKRchcD8ByBZL/+hGFJuRt5psaY2z9Vc9W9QRvI8kYfBYx7nMeFP2ceyJv6X7MlQ3jo4ZdgY4Mc2sIFbCB3zGvfYNmmaFTu6RGxIKe65CNcd3Y6RLqT8xRj66a+vcX9BDvzMLyt9Idx7gVRczi5yN9s4Zjq+ubso8mUajl/nxM7uIFwpP3BtUvFSEWRtld9j8v11lZ6F6UfQEiY9D2fdiZ1w7DiFIqG9tgpG20h5WGUCj5NuFQn0z4efVHg1tCVaXuQ1jy3i6L152Iiw+YEGxkdlvfQNj2CzKBiZkLH0vXfxXbnYwg80MN46yXaHRY6LsbsiOzKorse4pT/Lq128rZGorJ6MrREj4oNjKT5hBidu+kfADEz7t32aNqCJeerVUp6Kadhqbzz/XT5P5TL1sVXDf6wZRDD59JuIr3lbNxz5hsBNvzeOBOVnQPENo7xfihMjJz/bKU+sutsOSkzOVfzk0qE7g9y9Rl7yVXiyPwySO4RwdycQu8eXvNh7UpEfPRXhbk53b8KfG++OONOYn0r6hZ8dzDYT97j60p1HvHmkSF7wc134bZ/EEUiUP1aTe3cOtC3N1Nhi5laebl1TaxWN0G4VOOs+yOzSVCHZUw65v425zWV2si1lJvOZtyotXQh3tydlq/zJDk4VQfB7FaSh2YVPSwTXYFH6nOO+wrUu1mz9UTel5hdFUJrst3jBW/gqv6EJyTzCTCIX3bEH7RB+4sJDJvi3tv+u2aduY/frTENM77cN2sEGLpWd6K3mi6nQAYf5ORAnsWIGwpSMsL/6wG4gVoCyFVglEBPof7gS9BfCAAqh3hXULZRZrZShuupJfg8/ZLIM9jIzB/Tt0ufzCJoMqymLl4XnGeVPUF3VS10PEk9ZipSAITKuxDfM9za4CBVPMwudUFaaxAHFPxlXbFFo7sXGOIIMC/NE6HUSjPDqPUAZzN0CfPX+7MOHqzzevfKey3jdaZGLMpl0X+XuA3/mmxrDATMhV5CQcfq02JSl2zqlXHFncAuuwliv/AXIl4HAjN1BssEGJje9MQXr8DBhp7a5Y1K+36vlncIsHwg+1ecaOxjjw0k2qc+G58pCfHyOpjg2jUOIwkFqbiYxe97GGx36Il5szbHhTLAgwpzlzo4g6NjJFcrMf6gI+senKDS9y+Arisw5JpZtvUjA3yIIIAxn8K7A6qXl5m27unbRytHGQSlfkXDs0OJq+0Vb6ysDKvCKM6uj+aWKBKx6h4QD21qaS1VnzaBfNWqqBDjQimV6Xu7IArPcuwHm7FLzl+1yNAMh9Ipf7vnABZfZEYRCKLvbshoEnUvPbgd2K7DCrTvd6AOQb2lUm1KmRrrz+BL9WumCBsXgkL6N3Gy8BuUxPoAvr5cWeJ4WqCKodNhRZeizfO+IVVhUiNnQEq/9XNofk37Ay+7s431B7m5QuM8EAc7iPSpOQp00eB/FjbsYUWDPpbv7LzBxtxkLNPceXXDFWZtJywO2R3DML7xTnsgT/A5lD07tf3tP8w4zV5ij+EHQf51dCk54Vdh05TerxyiALtjVM8cD5CU5BubYuBTF/dVCd02YTmWSE2bypsNr74om3U5W4CE2XRgdyIKcuG8NRkj4nWHhDthpalpaHmtiY1QEEbVzwgAlrukCtWUxWS2AeI/u4U8py6L/xggnahv8gitWe6IjXKqyR524Bs5yBy55A2WeE5b4k5SpfkMCuCLN5wA3bm0fx9Cx0d1k7JRuMoZCq+48c2FEmdDdQJ/+YgxlHP2MHOjOoM/IiB4V857I3LHCWKuy0nyrnOO78G340gyP59j0D+jNeliOj/1s/BnlhLfSPPEDMybOVT/jdPbVkaghpp1gSy5Q8oziZ8VBLfbDM3+Vk78f9L/sCopg0RE/iCkHmh9Dv2Np7rHCn/tQssiCWwUobndJuJNCufCan9nufXzvsK/zE/P+3LbHxLwovbss6Dv0EZVIjq22hvjOokiC8NdeNaaxJlz4cJxFGIYrCaWuuAOY9q97tVg81rkAhwYmZC9NV3NM8PbaWjk5bMFPOEx9zCFum3zVwZL5JDGGWQ3b2nBhNaWFFRZmQaB5+Jc0fEfs8FUzSGRz8F0DzvmI0pYhpQrjIW9oHFokPEe5oInw5m1XTxcJ/dXZLhyyYKjik8XwXpGxdsRld7nKIPi0u4ZU8EeppCJIRaNlgXPqe8qfKZ0/ME99nEcJlxpCk6OolA6tR3m1dpvDsLabrdZe0/rqk27nWG+V9e7i7IKfSp53Hl9UQans54Ig9OvPw3uUV1qeXwVzp6cLA16aHtqnUOcxmSeBe9oVPIZ2/Q62cppbWL9LPPPdVv0Id9n0D4+GFYBUL+Y5W+C3VgRNw6Svj+cJO7RmeChz0d4TcL9TkAzgf2ICtcFOTQ19Iv1Er4yPTDOKIMIzENJO7bIIqkjCOTbjeXDOfjvQf+Y8+D8iBFQJ9Nc/GPjdOsmgfwujy8AvI4WTPmTSXFaU7b+ZOIuAO4DcQwgjkT3AxHZmvBBdVWj1CMO/VdUsMAFZganPCksOCW8m85bVjJnmiC1jWvQRm8CJ3+ARADLwqwWPIsit7u6mcHL8NgP9NSs+H+d4TY7buDXYyTaKoKwSuZWeY2IKxDDk/MwuBakMsnhbQaPd8mdgryJocBq4X78zbQ2QcANrfkdzFBsM31tkh9oVDoZl+NgjD66n0v5dyU/hZfpjHke3xfgJDcsZaQN+MdIW8bykYeF1W9aE51gYkyKPLDqJ8qiFSs1//IUy878+nf3jy2cEVwZv77eCzre7ghj4H6D/rIhCz94PZL/oYyPrVhFUQTZfjY/IlyVs/0Lb1wx9KQT36/SXDo9fSYxP/f1WiAREdcWP1OvjtObHkqx4ar1KIA3KH7H0J+fpjeSv8EANeM5f/2DChkIHuvQi12tWKK/5GXl/6STHHLUZ+KXfXCAdgdVjZNA7Yf4qHjJAlDsVAMBFPCkl+OI4qY2oLbO1zgFG2JPnEDfpXuyXFvhntgD3w+ey6I1e/w+FS+I+0vHhpS/hY3fsJQxgJx6Ou1nvJUBB30mME26Po/YXmNgVxBjrhMJdQSqAevyaHUVMOFx9fvAYwlqt7hjciW14Ighpb3KFSIoggXGulwog75zwbobuAmJcZKtDdrA65kWhA7/wWJeKoCh8HP94HN88FmYYjEHYrBBnUqxf5Y+TBfN2oiwKHXfDP8Cn7aYtQvIt7OBYWyVQ2V8QJnlSxNY5EyNr1xzIicHA8WA34xl7j9H1OHT82uM+TfGr7zHcY/+vKf4vQo6Z2SwmPYbhbevs9knua/wwUduosdKhzV1adDd4ab+7zrqYcudEFmWQY+UXlD//lR1Bn9kZ7vUAPN4VxM6gHGt08sviizsplF740tBWx8tXKIEi86kECh6MUpadx++umxdxMVHwFd+G8ObftEMtG2zSrHRxO+bhJw9D8/BapNP8LcoeKALkmmM49EEVQVEMOfYvfMTCHxPRKMPlCCNjaY+8VEZ8jV846d9rAq5YWNmUQe4Kur7uL3W6wOJ1Ahln7UPKluwoinxJi1HMQn3DPQW3+JRhfaxVWwcneNY9QLUD105CvEnaHjpNX6iVpoF5t41tlXEdIpezOEz4qc+U5q95HJNAXra7cTbxPKZLk5vumBC3MnowNXyLbOLwUXioVCct+7dlBPgGL6/zL3SRjJKnu9OkvfQH4qtUKl92QVBZNPe7Oefhye4f+LEX+0cZpCyZExLE0U+6M0glkEfG1u4ieTy4qWiyjpah7ShAcPw6bJfGlSYTJw5qj0xgdehT57RBrq+gGvJvqta6WUfi/LML9WLo1ln+nF/5gnfnmBfKoOwCYi5z6W613Aek/Icb2c85To+LuYPI3T8oK+Xn5KsS38/QssBr8ZiUnIh+3xlH7Su70d32bWjp7EURtLfQc7t+W0WQOvshqtUfQoSboHNoOeizRoLEbP6G/pbvp+oR3KnD47jH/sLJJ/bOZp2PwlCZY2EidC6GE0ZDC8Gf0sI5EgZjc8fEX1nt+coOIBRArAS5O+gfnz5HMaRmfJRAvSFfZubgzu4JGatfKwi4m4LREwEAcTC2x8P6UQamyG7Ypx5T7/nq1lIcgSK+VZWNbqlOBMbANQnvHcp2iZBAu/YPtVSK0HcwTKhNZ5gXZHZlU0UQE2l2SLid0ksBr9kVtCmCctRGRZCC8uuz6/yiGCuj7MQgSYSECxioKz8VKNoMwYlyWn7VDtbUSjYMxhvU9rrGmxcQ/BsjrE/ZaiITfkzV+MbN27BmMrnoa+jAbLZ9yvZfAc3vCdhD0MBueRwcT8U55qnk0fwa34wdtAOjzSOcSbKSuVZibpnVeYTRy1f9ZZP/cjfbP/5CEXSDstNfvHMgZzs7k6pu4/VImTuKyEscIiCIBXRtP4wCSIFWHPgiaYvStFBpw2UPbc2Xa4suYUZk/zuT+HTIftPVltOke48A0I5AgyVnBtvYNiC0Sw3wYxOucugNNJ1jjkzgLhnY36H4cTdQLpCGdnM8LEJAlUQqPBUKrhEKrq+NVxFEzUBEXKRjbfHJOL/8eGsO9RQuj69ltrDlV8A4RA/Yi/3SAv+0FvgGE/FngPc+1qI7ESlBl0btUYvACZiYsAQ9Q8i49eaRb8bfQPmXPCb5EOEo1bsmmFBnfPXyetxMxN0F1F8x7LGwHj1AGYTi6AF+53GGbTKzygmfLhtJucGpRadSnRbZj6mt4xKDUnYG4fb4gU8UQUxSe7ePCxxMCNZOIJVB7mTwuEDHR8e2KoPi97iL+ZK9x6IzAaOBHH/DM8SCdpJXVnYROR6MrRF3eNnioqY1fuWRSchyN71tGQ6I3TZPZhaSp768DWpRJzHz2Z5IcUi8Oyk+aE66ifnFv+C2+Ef+vwtP/hO57NUEg/4vdgIGAW3raXm1MjKYleNmJqeOc4vuVQJ9czEE+/aOR6UjNOiCiUfC/mJnuLuCXCBUYekvhzm25m4UxtsokdheYQ/alUCu8zNOMoHtSKScWLp3lPITUzxx7R/bt7LyPprYug1LSN1xEkB+4xyHZK2R2hxb9qS2RCfksR3sKTsKWc+0sYgiUvanlYy0pMHTBTzCs+DCuOvYilu/5Z1zN5+Ta3cDvefagKscC1tKIWRFlUBZfGFczSW83r2CnNj7tuxHKSbl4jwxwSVoiP9uSvX6i2+OUOE2J/uITWNaPLXzbvsb/Ng0pbmZn414Wp7wzekYnhJWVnv4lLgiYglZBRw2oPPEMYAruw3v5TfnIYWUQmL/nNP0fqf6dwxWhs0o7THHFDMPILqKoFBC6NAUoUfyDo9GEaNCKDxXfsxzB8/N/UAe40XGvL33qowujsu7VQblOBnCpNcUqBx9oF9kcZHMxdc+5yPvUhEZ/6I73cJ3N5B1K5wt71jknMQf3Sl//f/ZexMtO25c21bu1TeuOvf/v/CU1VqSZfvNuRYRwb0z5eNzxx0l1RvJzB0kQRAEEewCgSBt373XUdAsnFHwZ69TFDhR5qu495MwfirxfcGXU49VCmkRRHuMAgjFv0pKn3d8aei6UQvRtn94mL51cZ+UnJK3f7SNhK/Ega+b4h0y0s8yqTsxR4gn395ZBCGKr8J9lYogG45NZ7W5Q1CrXSXugCNWcfbrgf5VB7qQGRblf6/dwOvPomfFtsTJM/5JR0h+SyP9iQGHsY0BycUovgMPn3Q5mOXbbx6U36wTITQJfsUi4DUP0O4X9MY3QA6IZPzEoiFmlUsJ9CkTsvdiJnunsy4AnFRcHEQR5AQDbu+bzJXXcA5P3u3ezbMOwXFAzNARzNTJmjXWXBNujaU9EPyMUC1XylmUOoNnIAUgjJ8U/W7ckINrBtxsDsgAyqtNN/rLt+D5HhwzYE2BeVh+zOT/2IfpB9/x1oc9g8D7iXyMpyymGYx5evbNqAOqgyn/ceElISWn4+bAt3Bd2D7qEUjgc4n4EhmKk3Ltjywu4XmQP0CVzxHdArfn3hAOjjcYlbgt32dhJMwEeeIckghhJw/Xb223kVYm73wSxoZ+vpn0k7CcwKNCE2VQLIJevWYR+yuL3HUsqG95Yr7LJE17dj8E1sDpG31IU55VbEYRlNWl8olqhTTSFyyMBbt5vqFP6MT2ev6oVf8DFadOoHir1ut+54FnYaRn7ShrEWueKH3SN1g40J+rCEIZ5EMY8Pk0Md+Jowxy0s/RoCqB+PTLPQy0DNIk2NNPPBb3ARZDmrc/pm0/4sj5HB1N+bKQPa8ItbaFJYE0YanFqgrRJJlPZ3vWVUJNc7E+6U29u95J4N8rgQ8u+Ok70w7bTunBW9s03K65AcMmvXMyLrZt/vuw3Xjnqy7uGbsA8lgAHlYZPDB8zAM1nx8wx/opWSwcgWezaCZblT5+uuCDST4H8wEjDw3QhVZ+KRea8iHMGk1kwfRiEcT4pdVORjU6oQ8Q6ef4UegwZ7vnj2EPTcjePyqAsBDyBLHjcwEE4xtjrX8ytxmXHuOPvixUjpZk1GuYOn2Yz59g1ihiKJ3B7VgoTX+mNWyocu4omLi5SyB4wj7ngrZYmSzjfy7PwG/Duw2WqppgOQthan8F3lEu2tTgTdnJD7B1P/1Jr4be8kaCLV78rvs619nm3SNFZaOnvX7ws2rnT14KfsDy4S17AXlU/Busal/xSZhKoF/f+eDrvkAoi/KwCy1o+PD8J22KlQ7Fdv3nHOn82ZtGP6Gtu7rSBX8JQm/qEiHlPq8ZhrD9Sxk4c9hOJmbtFomkipNpOdi0xwAGWxSxVfhAJT/nTPvg+SuOqPQe51BoxLIhvm3SNRrzK2Y8huWA53Merln78aKwL1bw2Sxay3H3CfLTsMe8UHkUJRE+lkH57NotB+hT7kE086n+zI8El0tPXrUqqFWb2uNnvidNhvtvpKI/clZ6UqjsJr+QcVLe4Y17DeGkNXY73qQlw3FRToj5oGHYUi6fgVpCMqUNJJQ8VkvXGnjliQAisaSRkvdT/3BtN5WBtYWA/xCyHepy98xqBHg+zYKOyyvD2TfScXbtJani05/7qcaiPMofrTdde1YR5GeVKvAd06Mg5Xki+7nRPzJuQzzKR8d0wvksjf7zB23KDaYTty6DR1jeVdjX0odxO8oZFUKO4Y67Kmv42f+ioEcxT1xrnozbGb+14lQBpDIIhY9KIZX67oEqnHH9J/Z/Sx7iKv5j6YlcMp47piOm9sWR8+nP/bHP5VOviLjtNneGeixJI2/7m+28iiD9O/flJfCVKoJcLKl5T/P7G1JqQ/0biF8NyjkI7h1hOtdNNk/8Pa34DnDtaA50dLSgrDBydOGZgYcw40213T4Ec0LSB94AqfDR+udNTofAigJz4FeYAvsA7Wdivh16z2LBz8jUppsvlkB0YgfUeyqFnKjje1S83VueHLDQiGR6Mz4/gnlohjF4Kr/CTB9nuCmtu+HiLuggLryDypEv2bMiWCUjJ/8cPGVlWo0lObGUFx+kSePiZ2LdJR/lDgNrjsNF6eOnNU70T53s2XPl6UzyD10QaI75DT+URyqCVAgx2fMPvUtJWAEkKSs4JJkqEBdA2LQ6GSoscRCa0pwLqUk72gqfWbdcQxrCI4edzoQPNABT2g4Tz3jThq8DYPJRi0TW5eQpYo+Ch6YQQtPW26670DfNdqzZrgqbtGfa4Zz44AT9jgWqbyw97e41C9k3WLNp2u6xty5eY+KL0sjPMVz8qgTqW3ro8pmjSs2q5ShztWUXuHPXzoWt7btLtsrO2owcCUPnhFupuoRyWbUnfMhhJYYSl+Zv2zjGQXCywSVtpa5t1gWu/X7ebN7z7SWNyfamEtJTHmzH7vGRTaFZmObUExVBKIC0EIolkO0a5c9jfk8fsR/W0/tZVESJSYHSdJnvfdHXpa3qJ8Yl9WgksNTlTBdfyYkWWkW9u95J4ItIQEWQD8TTfm2f+a0eGKYApD+SluYtggPSypReani1/eVNNCQyfi1IxjdoqNT2qG3fPv/Gw4TWPiqCovQBljfE+J98aEAJlIcF4eA5BGSRbbEQt/87N8tXrC9kIvwVZsTRzb6bfXbAzXzk+CYM33jeAtPhfeDIKTEMIG74rPLHuBtJfy8u6TH0hbwPC/kAKKSgCyzzZye01F9W/OnO8X14DzS8cwlecZWxI60E/fci/c4zyjSyzrgNjcADMSLRegtEcjOMbzJpwdxgAQgHZhnXLvjmXekH3Q1xz3ekb/SSd8OfYGiLt+gb2FnwFg9AeMoJrGsj6Uhb+addLDnQXGhvPuR6yibtTh8Fo9Zn7/mcJSdtss5757zp518ohNwL6HWOjueTMJRAOTnJPGmr89AsDzLAaE6DSEuYhuEcmso7n573TF7aZq0KealE6xjGw/tYcUi6951McZalK72GezXFeuvqQTX5A+Jip3F9zJ/CoFwfwvWF6wxq7aNTGVQlS8eHb74Rt1a32r1LR0WQBys4j/qi5T5KoPu8SFExFOtb9mDR92Xhg/Xi0JOZnHPdW3Isbp0T/cn31m1WLVuJpFNma7h8kBMXZdU9gNQjkiPH6SZcGlQnScYmZXALK4XCzjwnrunb3VvUiinWEfL+K1zcEnXDAQCXd9LTboG1Ks2dJViyisdd3O7hPO3Ivyjhp9lW7CC2aFtO/rmD8DQyA02+pJd1ZsZmle8obVwrorxxfzatfVT+tO+4fxuwxFXi97OwnEYWZVA39s/+kyqVHNv5FLFKH5X70lYpNIogw3BA2X0xajt0nGbcVbETRQ+b7vMw4Qv2KIgYh7/jGUtFkNacwmLVGYWOyqBaBP3IIjD7BQGvct/PewtzL9Ts76ZSX4U/ccvIuhNxKaJNTPCkU9q2Pf3K3eelQ/SBNu6dmXvPLJWUb2kPWgRlbA/k7vIlJfBVKoI6xDow2qzavesrqoHtYmvzu2yEe/rXF56OMfWZQVx4BsM1aDo4tqu1DnuaE9Wg7TUMPsKIZpmINH0DlAUlvg/CWQwwCPmN60tOBuvDcyf+ly/9PIx9VZj8tQbS0kIlEGvQPITXtLKPcX+sU5V8RPyTcKfMTlm5gy4OXBjkvnmHZMz03lEnvQwUpgzjFzXe6p908aeFLECozUXYBk+jcHrgj7C+mnQjJnXiUY5rAM5bIzjPoMbDNAOiFhVqyO9nw7Xup6LVxNMnj47PxJ742RgTvZtHP0AR9GB9JobinYGZd2UUloW3Za4fHm18KuUC43SVEPEIhRTRRMB38ir3Z00n71bzk9gWWiQgC6YC0bsoucjTzqQ34Y3MEcwnVNBK+dBLeySS+IF18inINOn60zkB20bzC08rZdXTqYNmG9pZzHJp++XTivWWxn2u3Az67RsWsiiDPPHEU8Oy0TkWQrZzTXc9vlnlT04JY13nwth2HQWnq064Uxlku07dCLvIrYKo7XoWqcX2Dja0WhSMjkSFny61WlUDKf9H6oKHEpfJKTfK33hM1+Hd9q8wimOcisSaTbjSQsOF04Q9m1rS/Wy/Llh9A5TNoFEG+UbITc/vu/8VFm5aCD3VIujpg3vPHj+69/TZg7T9KIDysCdN2ywSwW/5+Abgf+KWPRFhprdvr/BKrjRFvnN3EvgyEnA8UBFke21b5Urfsp3bcPVM6TwRkE098SQluXlOdVJTgkfQHqkzfg1DLx1FkMof5+UodOjjPnzMXkJVALXfqwj3IUHY8UZcuhKWW3wxdYXhAz7nVvpvKid265XxhcnJfuriPw+ndHAfLmIZ5IOIn4ipIPLzBMYT8fJW2jx2ZMqc/Po6/XlQSHr4kzd+pGWoCqag8qw/+U0KqdDpvQivIAQdXxlclmIuIOIYsCjLEhaAQHPUmZbEIKzwStOb5NBY+Y88K31DDz6353AJbnERpHnQM85vqn9kFAl3kbbhHXSB2R4ORPPxq7RsCZWDKDZzmnvWgc5/furvnia/7i9R3B/Slyes+1wTvtW6lpeCbg2gFZoWuL+5LxC0VAD5wqQF+sLP9kTjYH+gpTqhfF8QniKWj85cKkLJQgwQOAuJNueDP60LWlYkjUsAzovYOus14UK85uF1osnT0goS30LJ6QM34b5AAWZ9Fr0pKoqfwKTRfNo19WfcFy4ogrQWx9oiP+ZSFUG1vkUhhCLosXsGMc8+yYsW1olRDonHQzr9bEmwfSqFUztYHT7k3XDwSDCsaOS4YWThp2qJCTc9CIGd9bIWlzITS4h5d7wJh9aWvuOZtjInaFrpyddOr3hHG886i6wC4FPFx5Ev9zzkjjos4mYIIXlrv/f+SaP4ph4O2JLOkhWAFgKKvJV1pXqGW4Tx9hXHYMK8McyLR8ZkrTLtB1qg59Qx5w/60WEFxBpTZZGnQooXpZC++YBX+QOdle4Y/4m1m2VkTCeecd+CUyHGYRXwKIK+8wTGpQByHPbkueO0RxVAKnD46X/v/m7g+6mXlj75sf6rVadKIMPSrPWQdLopNGM+MA/Np2RP2AAAQABJREFUUVzti4eID6kRSHjusv0uilOg5pP5sw2lIrlXA6WEe0++uVMERVRfweWrVASloyMcG0vdNCQa2YBuEV6b7i0JXyEoA9jiawbtHTYs77CbeJXL4I6fcZZL3xJ20PzNAYZFZJVAfhfewektb4Fers/AXqEQUvGj75Hx79724fodJ5rM0dpa+2g90QfjUfz0QVlFkINvBmAXCFECeRfXVJdJnRsI2wfnW9jJT/jlLZ7hZOU4PAMzrFjzyVl402wvtgoo5r/hKoLMA9iZnCxdgG+Lg0z41JJ0FTkudmtK6VsfTl/CCuiJD8tPPEmMh2ceoJ8+VhHEQ7WfiPGb08Sc7FlHh4aS0I1facnATOTyv9Kzci1/AeZCDkBWSE/shho2ZQ81fl6PPJlUQyiJQ8PI3s7KzZn/IoRs9/bZiVcCxTqp387R0PaBIG95BjCFpLMrIfJzydKLBax7enRvA4/39MQdNrZ0nysWrR5vm03PWczahlUICc8mq8zuTt7My1GMqjuJIkjatFctjZS8Ss2+wSQeJaYw71jvlovd1q0w4ZVf/a5NxPAn9/UNJXpAznjhwQi2VS8FaK6OEUrU3QVsBJJMQo1vVkHAXay6mmS+T7tjzs8GgD7YVSHEt+GeDJZ22vbs0bdPUAg9QwH07Onje8+fPYrSiHVIaGRD2cU7Tfrks9VLnOQm6MPaWQ8B0BEXpzfSC+DucieBLyABH4Z9k9uhxv7bUSwPlGnj7dk3HkhXOw7LR7hjlbD25FboCBNIGHx9x4mMRYxpvzMIVsHjWERfpp9H0ZOuzrxkv3cOJ1PC8e35odhrJ/4FOZmYMsvmqk97bx+cSWg/dayxj2rxY5/nIYRfPkfwoYNfPmUJvkohcVu+tEODi2Ev8VtTIXXwXfgAKoclmaZB8vKhYqMJfSmsYhMuB0PBvGfJZynDT8sLBhf9NbwG1bj0kh7IdpmCNtCOZ7HeU3/j9vDAFnuN7gSEXMcHJG3CIW0ZhlckZQ6eKdyzSsy5wvakBa0v8r6hre9rwD/YO697nvzKKWFvVQIxX756yb6Qrv9iIYRFOPNnPlu0nZLfuVeaFAQTKn4cyfVRBtEo/EQ6CiC4oFjCvWfO3yMP+UqYQNpw8Kw+dBf/2RR3F5ZFmvH0Gtmv4IimCzo3d+LJHO0jcknhzpnhpMws2tO42puZR1cN5NTfpSLoz35a4wsWP7Fxw12tg1QI+ck1lkGe1PnAE8V4+eJa0ZcuT7QiZ/6NlR085hMc6qokx1l14/rWoWHqs+LCdMankga9NU1LTJQbbqpaTGODO/7QMOsJuwyTtLKK4b1rizPP6UwLU5F1YsS9ByfOhIaT9GFQe38IpMNPHkvpnwykKeIfLkWscsI72SMkMEATfz4Rk+tYBQknue3VvuL4S5zb75jr+jR9iAH7DxVC+D5LRYFP2D4xYU90VAGkcidKIRVAEPqNePcOWr55VAKpONJ3bYqfchnrrbtsHwogFTpLERTLTeOOy4Gr8OHzrqUQ0sIniiEV+CiG0s6WtZDWnYcCiIVcPjvL+E77oy/H4oiC6YYpX0krJyUk7HTC/RXvImnBTS9G75eyTDuG3uM7RVBk9zVcvlpFUJteG5+CSmPi0v58wm2gjTngXzbFr0HAn+Ohnbz8drC7xMzAxeDjYFkLEHwjF8741NlBowPxoInv9652Pi2C1DhrCuxbUDc4c3J3E8BXbqrrvkAqglwMEH6HZYVWFloD+bBNFhj47t7vuQF9WI4VkAsAuvZ8KibMkbZ3gy7PwFILinZ/2U+a9TISt0LL6/08aybKBW4ihcxC+MQYzOUv8dg2lI/RKH/0iVQRVBlVceYi3Al/LQDypiVrk7z5UYvuN7Z+8/306cN7z548xiqIDaRRAj3TmsJJ/z4bR2v+i3WQn4nl8zBGUMbalg91pVHXJVOlAoR7Fb7wT7cqISDyV76loV/MDUc0fjqH39Z6BuTCM68aVAZeIDKT5d7OSlu8RTH8tfw0zpILD5bk/ziD/k4eJ6X+0LaqNYUdyEqPIKAAODiAmSfzWZdvJnOiA0cvx4w9SiA/CfMNJvsasJCtMpO3mWzuF7NdbmsndJZymeBVBHWxeloEqcxcCk3b8SxybePUpjJqW65c506OL7Q8pz9uVUpwv69bWoTUS2SWqocSSEQixxFCFqTAQ0si3FsE8+efn8Az3vabDS5dZGay1zKNRQWWQT+wh5UbA7opoIogPxfTfF0l0BPa9PNn/J4/uffi2VPSWUjQdnv6HYolwi4GrG18wvLmJb5hWVhu0sSddP1K8IQN/p1/J4F/pwRiFcHkVquWtkf7nu3V2DFnAMw8bKL9zrgYRouJ35A5Den0JxzAdpEMUzJjn5YanYNMdvw1rePwCjN/D9xE5/S48cxTCL6ZVyReKrOFiMv4coZm7FcOGcFIVxnk8FfF0FIKmZ7JA0XQmkROSqb1J+kdfjJUxlImOIktXotPZIsH5gXY8FjKxbacWG4qj61OgXNZWS94OXOSvsoSP24SN3IR9TXehj7FHrdkxzUMzbC38tzGzNC44GHwly+pkOYyvuufo1zSbZVpsxC0HZiWNkbgE4ogP4nunlRYBGEJ9BYr2hyuwL5AzpcvUQK9zObQfg7GlgBuDg3Ox/c+pNpW15qSEZyZgQL7OVhemNhY8uKE+dN5VX6jFCq/WR+sOshX67DWXsAF5B7Du23O+9n4whUnbr+rZ/0rw1A9sMw5t9QwpcGTOOu3FEOlK1gm+OE3n3hdC+q316sMWgohkNxD0k9w3Fj92JRXCyE/wWZPoMeeGuY2AlgEPXErgSfMtXx6/Yh0nt07p1LdrA+tc/7KkeOQL110nUO9v0S4YLSOb1BeDYhVoMGVLJDwHgvob1zMoyu1kF/hSWn6KvqWMsKXsoRfFdiHM3hEoZwiAFg54atjBiUFz/1wDSe93MmbJQKn6S3alJ68ltqA9EIz8d7N4aNttpLK+AqiLwptIkJNj8JeOIO2Cp9uUaDiiDB9SwVRFDoqd6IIEq6iqHH3e/vE52ET99S6KFZVANHB/IxsXgLYBq31KHuyYTTKnHy6pUIoCiCVQVX65NOwpQCqFRDKIa2ISI/iyHWgSiAakhY/Wcvh25PTthQRAV8AGIzEaJe6Ix6gEKU498T7IexITKiS7LX4I0faNNiP7hRBCu2rcF+tIsgxw8Y5zmY3bgMPKH6b7wXoq410MjrZy0BJx79wiU5HaspNPJEuJVIqTrAOmgxEvsVhoMoJS3mAZr8U3gC9x4zRzaCd+P29XpZAr9krSMsK91R5jwnkRx62f1/WPtwVuj8TfR6QNQEexU/hTo8dieXJydyRxG4/Tvglv5OSSTiR1mDgYl9AElEuI5trDBGaNm8WLbNmww5aa4Ig28w7XWT7potJH5n1WFFrIz70+GkyqSIo34Q7qaMAevYUqyCOCn3KfirPmOR94+Px2495K/TwoW+IVATxy8DbNq00KoHWSsnJbz7XSjiSA4ZL1TphNQ/hBNraS8GwOI014+3X0iBtQz1gW5ZpZxvalnoGL9IR5h7f6Q58/JNCxJ12ujf/qaMCMI9p+synUUz6mZdHxb9jE0tN2G3HKn/eoMR8QztWsfnWRSwKol/ZwC+TMwTaJ1jGxarNiV5FkD/DbcNtr4X/uSyCeiNsz0raNm3t/E2Y4IIJkd/eE+F18n9UZsGahzuXxKFQX3LSWrGVI6vxFdYjI5lr4u4nYVl+47toxX7PY28hQvNjUYDSkcbngvN797HSfBglkJtXerpJrNxozy+ePb734oW/p3zi6NukKoL8vGxM2aXn+DxjNMHUHq9OwKpT60A6sPwWytkPFuDOu5PAv1kCVQSpQKVt0lA7ktKuV1h2Ol+IkYhIp7NBX7nV7Gn+a+y6St+j9nsfsPUzBuDbg9Otg2hCcywviIYvS27MeSz5V16nLjGDL6+TMfAg9UI8U/UC2a/7wsS3wI4ba/6MXFq2ZaXUFp2clzNA8Y4KbMXNmLAoHSgbqdCeuHOb9yYDSHiflCWvob3uh6nXVRwU/ckd/zOIg2NyRHcK9iA1OAeAQO7jAuzhwTmaDJmP/EegfB84k0m662cg4bnXKy5UMrbXIexDrC8DfRHIUq6bQ/Mw6glheRGo1Q+/t1h/v3It+PJ1FUHMocL9pPoDc6yfYu/KnXaWbg79bebIHwB5Vx3VmUeZU6M2wS/fraBhXWEGuk7teliovPtgyt1OI2w+89zmruWb0o4sRykrK3EFktIFmb7Fjd5Ig53kEa+/1qhKIQ9Q8EWL+69oaeHD+H33ZcFCyFOZHmgVtPYJcj9JPw/z1FlfIqog+pGXMjyr81DuXNo+lnGIOmhL7610rrVKStcTwiIb0+HX9IgML0j61856ife/chHGlqMEep208Ytm/7SfjitrK4dgfgcVblx6dGDNdeYEF8SuQZV53ZluKBmbEKJnyQm1oJVz0LrSNlZaq20uwrYlg/klfBKR12lrUYiGf1oE/WqUQf1st8oclT9/qAgCWcuhT3wDfCiJWLuqJMoecEtBlDSfO1Ak5aVo+OiYl8+//OzL5w8ay5zuGAUPip8qeUhbn4W5l1CsgaIAMt3PeelT9Ce7aBQ9VM32RGPKPUk7InrWWPkrCf3+IhhhxpuURniR56Aw92N8KTWsko2RA0XQD2njpty5LyuBr1cRhFzSUNOw2uq87o3uUnRt0JewrzfWia/8nQPeLbVbA46YxZseeHvdkkrPlb6/mgR3cMpnYSiC3rufykceklEGvX7NsfEcEfrLSxRB2SwaiwoWBD5Ee5KEiwX0QXlYZgqiUK0lGFAxA7Y7OzgcFhRJs2zr4Q/8LBLktbC/vIfwO654C3tGqSP9Ei95rO9kXqHkdrZMAhfohIsZzMCPTAHmfjgQ8/tTZVAsLFQKmZnJiDzuxu+k/WNOYOJTMPYIeoYy6Onj9Xv2E1ZBfBeOZdATPhHzuPn77BWkBYaf5sxR8lZn3qY6NcmTC5T48jQwAeF9UkQkdcmjyV4HcfCSSeQL19qvtOVNjh1xb2e3Uyp28Ahe41zHhztzXacJG2ugtpti5Jp6VknjbfCnaXuPXPaEExVB7geEJRCbWr5yc8vsC+RpJ29zCopvM93f4HjYok2OpP0crJtC+zDWhSt3afHonfHnG891R/KkZG3sBy7TxhlvLLitQmA36j5p8b2AgXcxHggFbF5lrOvVUJANAAsRQLbZLlLTdq2hwmJzyzzE0QdcU+fULxVD33GsvEodFqu+xfQ0iRxxG6sgrYEe3/sZJdDPP6sIwmqI9h5l5lIEZXNYqhwpcAmf4SdsFbBYm7TyS5KZcMLvFEERxd3lC0rAlyGegJT+xsV2Oc+f+mmp8dvf0x/TJ9d8s/Ks5n7UpHF76MxLR289cVYmu6pWQUND/1hoD/YkEk9w4vAWfPEySDpbyfAg7CFrc0vaQk01uYhTeRA27vjhA0MQLEgc3ZQ8ZV36G3qw57KGgInWHx7MNOFLjMTKP0gyY31xx6NiCizGcLKRC+5cgkpk/MAn00I60gikqKt00Q4cwwtvsRUqeziAhXcddskkrSlil7W4A9cfmj5QFU7bCnDFyBy+8GNlQBq6HywTsAJiTfeeF3sfeDmitbd76r3FGij76qH8+YVTwtwzUstwT9rMEdmsE6sIsmzXflp5My+ySW18wt/yUJfBPes9P6127iyveYi2EsqHS7k86yHv/RVHQVbZsSaX1kYKy029u3YaepOaurd0C8RxKZBy7B3mn1zDUWELbeWzZYknDi9ZkqXzrHi+aOmcqiIIhQ4P3H6Sk0158bsfn6dx8jkYyiDXhU+1ulURxNrxMXvyOf+iN8rDfJRBVDkjDZPjd5HDmicpkGiciiFd+tGCB7TgZXT1g6nmkda8+9WkQTvh1xDpVWqXpK7xpFCKxeM6KPhH71ywb/KscJYqcpKo7KnSLr1incTmMWNa1JkiCxM7aZ+QjkBHnEBeEJqNn+GsRVOBXLrmFk40fY2A1kLpg/pksm350t2Xjtnnx08peY7o/kG8TNf6h7TsORRfZZGKH2G1JPL5o88g5cH1kptB9/PcKnjmpDBh2bdNxY9h3vJlfyDCUf7wvJJ93ID7AtC5yzY0vmM6peR/XQ5hRaatemSS4BavfNv/itBEJQvFw627ueKNmS7eI9ba+yr6yHQX+LdL4D9AEbTLxMZmMxr/TOsAc8a/9lAn7uHy7Dp2Uuf0+l0dnqmDP+l0xOAXY8eThoNUjkJkoKlWup+G/YqS5917lD0ohPIpmEeEahXEA7Unh3l06HveAnlS2MePflPuWxu6bp7iVARpEqwiyId0u/Q8NOvLv/dHuD4/R5+ELwcJgLjF9c58E3o1+8pbAIgXuOfgYim3DTzCk6kBqCHXCcObmm9JdjGizDXPZIB28neFntRaVqhR9zMZjwVVAaQi6DHHhD73MzE+qXnqN+Ck5Vtw3wg94EHbh22tKijTyV5xRDqUm4cO6XvD8GXLQXixR0y3xbzfKy50wkEDXlhzVEy9DpWTEiHK3NtPaVxdLe8zeLnNC92Sp6Txh9LwNPE93TDizuSXmkcOwBRSEHO3giOee139yr4G71m8vuOTL4+7ffO6nzNmbysVQbEK4pMwTsRTCfQBE3jv4pivZykI81X02IYtX0WQd6WKzrTp1J22rlTlJ9LVd/rKHVwwPCCRwVE58S5dkrzw0xsM67UqayDO4kz3/ugSTsjM5j7zmzcKoMAlpgITn/blEfJK0Db+naehuIH0shL6PotQPxOrIujB+jzMz8J+fsHv52cogtjrAEXQT7GEW5ZttP/sHQJTeVuJf/IX1o5L6gG7qxrB8yL+nSLoENNd4AtJQEVQ9gii/LTVtE3fnK4RDaB/OvtiQsyDxzzTJFPTp4O4LvbS+ZXayrUNnKsrp0s7X+sypjeY+AKGv8E/EwxtyARDXhC/DC0Eh83UwEgIybOI5Y5AEAen8nCOAMzFue/SmXdcuF70zvJMtbgpf7Av/IMMJTNOhSURwueJKV+lw1WmljCmjk0sxuQy1hqeZPe0i/AV4lA62AM59ABM8YMzdPRla9It/CL/VYYsqUQAfhveuk0Xaava3FvHfUvkPvogyp9WNBbhzzvSzWjdG89TwtbcOZtDuy+Qp4S9Yx2o7xrQlynsGekm0Z4k9hEcXwi6R6Rldc70ARNFkBYH36oA8mWJa0JGdJ84/+zaUNaSR2HErT5iXdNIy6UP0M7BOr00s6Ox2SaSdHFZ6CvHGRMp6ICUh5G08hFa2pf48zOHTmlVhk0rND0I/sr58o1Dp6td+IXhWs3yMK4iCBloOe6Llp88kIHPw7S4zelhKoFQBj1lD75HvCw8Tpd1CwEmU8UXBRD3UbrKwjWiP+XAf8rWdxWS9hMYF6vUixAjp7uKngnFTNYdeNAZYNtVZArIshvudSdvWHqBhTChvQDCpoXiDge2IzarV/Mv3xEt5JpRCynj0stlQ+8wWzyTdU3u3TRD23TJT3+aplLCUh78BMtKaHXt6DrL5ZbjN48NVQLRoLUUalwlj0ofYVoCqSjCt8+S0WcNQFESJQ6dcB1mbQf0t6Xc0eLMvtf1l595TRo+DaR4KoK4Q8a9UzMmREjUhnaWGlUYS+QdvxcKtdPBB4CBjT9KIFMyXgeXMI019zTxRbZUAlm1Sth2/fBOERRZfA2Xr1QRlCa4GlW6xN+S1d4I/1aGL4j0OUXQ2e1kjrr3/7Oc3iadTrKaBKNpdkBilPGtp5O5O9zn4ZnPaN5yKphHx3tKxCssgjQNzvGhLAL8fvzDxy4gZqPoPLo52fP2B7UIrPFzs0DulGFhCUc51GGjw8uw76jC72D6CAA6w4Otfyy+DyB4/T8ge6BU5qrfcHEIM2hvw/oxkR7pcsIAnsUJg7UP2LGu8OEa1hmHGXD/5Ptv9wTiO+9lDaRC6Bmb67phtBsBunm0E72fh2EdzEKhVhUqgzqxw4fhFFyedr6U7ulAPBxYZsR5LdbEdwpBOS5iFGvR3ckfWBO4TDxjpVAsJ4k9PtChsfvizR2+zCNt+0L1bec7oJNbF7Pi2J4xcacN+zmYVm0uVlVktg2/YQFLW6Y9v3GzS5SdftroG0037PNNJsciUBqS5QbMgjblL2ugtl8VQfIInjObn0RmtSVseK+VUOOFdeyB7iGswbXmy63AibPg1E8sk89ccEDkGNMIt60U16tOfNunkjuVQVLSGqgLVm9T9gsC1pNQUAbxmZgm7Zqze7KEJ4a5UH2IEvMFSqAXz5/d+8fPT3Ks/EM/H1MZpHUbCw83nPZB2YWq7VkeyiuBzU19hu/w6gU3bX/SCr273kng3yuBQxFEu4wVAsWnbRK373WISyTxcEc0YwT+bW51cx9Z4hpfyEQ6vpyJjgfp/fEXfLxkbvkg+R+3jyHy2Hgp30Yr40grM5RLbOog4Qmv4lzkm0VwfDv5wUHhHbUMNzQY+uImb8JzacrE4q86nilTywssIrkjobmnjJybIJXJv4f3HJHGARgsqvt5Z9ogbliCrt1+b/aweNe34KIytxEb4qQNrWHz8sEV+ZPQ+9X6iecaMFsCMG960tE7X6AwJ+aT6iiAPnK4Ai8FYw3OvnrMp684NdYXhb5A8VCRHiwiNUdrmcTXSiEPoXwSFmsg50QXR86xzqFzF9ZKBsaEJW/queIw3RdwyxcjlRApiPFW6LizQ/30pa1rKQ3vcdtiZdS04fBoPcnpSxOwgnvSUAEEXkivz0iJl0NftvhysFYZrg+1CnIbAZVCWgW5VsznYX4ipiKI/YKes158yDrREzx/YhKN1W2sipAe/Uyazq9jFTQiscxIlEAUQVnPAiRuWt0ZGsiWeID+OnAtx51m+7X5lZVuUvWFTHwlA1yQIYvf3mzuazdIA9/jlAipKbdKiYV3FGq8GHvOk6nyKCHTbQH2HcNti8lOHIII3mcQnfxONYyFdpIIm9+2DDGVmloIxVooz1+GVQqpAOovezpCIfsBBddnNddwwCSGs/wq4FX8uObSsmcphhJX8cNv8Zj+SNh2Y9vQL3vGSjOEAVY6icm4gIlc+Y2eCp+VHMKrHSz51MLI9CQuklPS1KnF+dT48O7TsCXML+/9hymCbGBHi70hPTvqf4qbzl5+zzrZ8dc4sJKmI13VLHjX+VZnWwNcvl11IGIQ+o1PvDwC/gNvQFUEzT5Ar7GeeKUi6BULAI4J/RUri7csEMT7gDUQz9D81Hw7qth9+wYoiqCLfYOcKk2Xf+/DDAZn2BqcHBvTCRFHf6UmumAObKQczoHyiDRwDs7Ej8QGxC6lSahCx5zCvaxxjMjKg7xUBN1j811NO7+plgJEH6wZZHmI1kLCU8O0BsoGgLzlefFcRZBKIDfedUNA3wRxnHz2CeIB2gdvROR62l9UDviW60Cd4OKhdR6emyKmDN9UwJi+4wTxuFymQpP/trOhf6CuwAa/amctB6leFZdJ8mbug/BQPPF6HwN38lTc+ZssSgQYShqacCZV35rkrSZKnrdatGnBxk8LIDe51CLI8GvebLqB9Hs2OXcvIb/Tjjk7by/duNyfe/9E6lSk9Ff7NU1ZxmdRm3a/FrPAO8bYznWVbGGgbkIpbNUhuFQiFaoX0LpkLDBtuRGtvpO8SSlpJezpabuZyF3AIqD88Py8kfYq5youO5EDw8pNZZCKoZoV30tbfsAbywcogx6iDHruHkEogf75/GnM2bUKeug+Qmww/b2fOaI8Sl4aadoxDOkPXxQSZ73yNooKmJbfIOErxYu+3Wx31zsJ/Nsk0OPj7Q9nG01bBnC2V8Jpt0Vqf1y9noSb41Y7s9eGrE7x9oFz5vnDF+3IYIFGVjkEjR3JhHXDY9K4JBcEi+dViG75eMEp8LiWu6nzImSuhW+mI5xcLaEE9rCQM35b/74oP0VZutCDy4TL6NAy/cQpDfNtLskXkC3xDC6JnoAV+juKoJS7FVGOrkiZToL3de7tYLQdTYGVqZU4SCqwIzK58IfegIgfe4kEn3uOPw+tg9aXgFp18zmYJ2wyd752awD2/nmTfYGYS1n3aRH0SoUQ1kDOqc6frhc/On9iGe5nZRmtmRf79gxFEHuSfMu38lEE+ejpqWH5MeuIPnMpzHdqNO8psbIt3525Wu9AgydmyCTLxKTrf+trS08y4EtXOsFbCc2z6IeyuRethIroSqAlD644zp3E4VU6xen8pdIm+7RQNxVBVQrpaw3OCbLMrfe1CGIefcyasZ+GoRRCIeTJYa4l77NG/IkXLB7mID0/3Y4ySJrWV9GF1/qd0513gfbGJ701uOV6u5BuQRzQWfeBtOTC24eUX+Uv+T3Hjb40iTaECYfw1fr+KGyQxp8E45R6kCG+6hZRBM37N467tZiZ55IRWSkVzzVg12Fti22/1hLioW/7FnfVvJFWhXDwSZWmlzw+wMasXSspaM8eQGQwj2Wm/asEAtm/01Egk1GUQaxXo/ShUYziR/h3NIy0DVHJKFthTd/4SSyh1pFyTVhMD6yo5SfJ8BJaJFQOG2+LsH1BJKOu9YqxEi0iRM96JU65cH2nCIpsvo7Lf5giaBdam+oJaWM84193KJ3vgsV2mdXlVgcCwYHiAu8y0mF4YGJ2UDLkBtFjGuypD5rAf8gCAPNfvg2fh+g8QLMYeK0iiLdAHsetNdBHFgJkgY40tYxACRT1hQ/RHh2foYew/hwlL+4MBGu63CswSeS4cIHviBepGWgGMnf+duyBFqvSAxY5msYvE4OS42/xsw+eykz8HOPLA3Wtgpxc+JHXSfhHNDo5BvQBp0HwOdhTJvgXfB7mptFaBOUEpmwczQM0b31+dHNeJnf3CbKsb/lkxwfho3xol5Xy78OIPNQtJhNBvmRK8orvODtm4baIK3fQvYIf0Sm3gMvYgt0gOpnhL8G53oYozDtj+wCPf0Wuk7XmNAwt4H6LXcs2LYL+YJFq231XRRCm7J5yl00ubcMogd4As227+I0lXGjzxpIjOnKCnTOn0keOlpH3ffpL6dOZ1buTO4Q/7Vy+++sdsK7E096F7654U5e5l9eiv5iERSbbUDr8db8nLZQHNtJSUISjwAxMYlX42F6ryqoiSJxvv/m0LTq/zVG3Llj9rPGpG0ajBPqZT8QePxGGtRBWQQ8xbXMvBDcj1CrIid/FqgtV2+vwO1KwbvNgUglu9QNZ6Qq/c3cS+FIS+MgEp6KY7pT2axtOm0x8zQ8rTQzxdOnXqw9OH7/2xVrDGjmaMf1fIhnoOlaacowLQ8RCth7VeWwVnrTLi5Qyo638egcpsjX9gmQJiHRFtlwVbtLI5hpvK6G0cj1KDXpJXxeyx8FIlksmLmOLiZP0Ku8K64jeQNz4m+oeyCsN6d3Cx0XGW8heUwk+eDvqcW9JnPYTvJVZT/w0iwkvv3RyZ0+aZEjzARJ/FZa5hLRjbgLuprQftQR3byD3BPJTal+kvPOgEJQ/OWChL1ScM1UO5bMwLIfc2+QDn5P1VCPbl1Y/9A7mz29V+KgEwkrhm++cV7UE8td5VS7sSX1pJVPGw1x9rrItz/kZ898KBb21EMc5xNyZZvGDCLKoDSeLkc0lMfFp++ObsVm55qa3l6aM5FDe/nC8MFksEQGPSCzlUnhxZv5zywAf0t1HMvtJIgrXiR4n76bRzq9PPEYea6C8KOSli9ZCD1gf3vf0Tj8nY35Fv9aXLYhaK4vQXxNl+KIM53NFKo/5Gfmf3ELRO6XzuUxifB7TFH+u32St9JpH+Lo5BIitwryLS2xiLPc5RZDJZlyZF/YBC9l1H01LoQbWiDvxJRcxU7YoQS6C1FXWxIFwfJ47MPJnDaNvvmTz0vzehEGVhmUcv5Q5deZZTPpR9rRNuQ4rrp+Mke9CEQShKHlavutWrYIsL4og2gVBmi8+7cx7kDhlljsT5ajOYPqW/K2w8RMl0PDTHMaXk5S/M9pIAAMtjkhbTnIoHyAC6Wvp64SjCOLFrP6d+/IS+GoVQavlLAldNq3bxJZOelvCVwpLp7zBW3oL0PENErZz6d/oNB2EHKjsbHapDCzLd2OynBYWs2C+99bKh8XAKz+fYZNoTwkz/Iq3QK/e8akYD9LvNAfmzdF7FgC+EXIT3T/+8LMvlT+d6LElIJypiJKEZxgCp/4MRdYjg6ys727v+119kSpwDeIHbmp0ESteCV6TLeJlngxCJHRJbmzKkMvKNIMc2Y4FB/B+HqYSyKmOPH7sm7zm+Z2H4e95QPZECCZ2JvSnT7EI8gQx/Md+NuZkzzGhj/k0LG99UATd5+E5R3E7nvNTWj5AO0B6MoVBOQ0/qdxWQ4GJyqh4XtYgu8ID1S9uQqF3DUt8ZL8Vc5Gx2besKXix0Uz2u5HxoJtSuNSU8+RrSvGaT4i/iHeF09RVzDg5kqgSSPN2fc3UPdb2zTs3tFShuTa4/IVNLlUAYd0mTGuhmMRD5Hcs2o4Fq6brCt/fasO235S14plZnV0XTvFAX/VYy7DGDyVQ6yiWzjoN/oocfTNJqWRCZ5RMOxVvuU4vbYLQ6TfcdKRM+0kBFIakjvAcIX9YBGHlpqWbbTn7B2HdpoWPexnE8gdFUI6QZw+Dn7EKsm27aH1sW3e/ID4Pyz4ILFoVj6brZE87Lm9ytFyFcPBvYOqiTLLAHdw7/04CX0ACOdUlWuiOU6PQXF3vGDtt20YCN0L7tes7iqxmfuF3TOwM03ArJwXnxNBhDLDUFTsIZWgQPISDYyaAB2zRE3QNO7JuCQtPnvnfXHHysBPipnYuMuTb9NR6ZZq840/dJr4RNvdlNLGBKbnyLn3/TElZwvkbmlnfkGh8cud+UJmJk7TEs0OG3nBZGi3rFrxDkC15L0/6F4Wt8oTltuzkCE9UkqHDpVRD6bZbmYRjOpEAGcL5EAMwZQkXPCzPA1YQzApi5s0oc1B2uqbzJSBz5ys+//LTsNfMkfkUzBcoKIVyyiYKIF+0vGcfvhx/zUEhnmjUUzarCOoG0TzCefa5n3doBpM9glwfcl8zb1pb7g/htq11r3LjRhLUgUr42YyBPoS34rOvSYXWekNwudR81d3wbe6Et7TGK89J099/pXPgE5Dr3sP2CfPTg5sPT9xR1Dh2KAp9f7EAd25lD76fsPx5gELoEYqgJ2wn8IR14hMUQ36SLfzBAyyHsLj1IJIcLhKrW+hBiPeHKUjR8f4wtzjKKOJJIkFfN/7UsNB1ncQrvAHveYS1rs1rWkuJBI9yzlRDM5qVom3SPPEBad092rzQW3zPMjS0WnBoSa8w8xlecX3ynmv5oiVufp3plj9ZC10CMu8glqq1shlK2jFPpUxqLFras/fUtm1e6c7zT2JDvWyWDISa1r5J2Dh0+0xh8oKBb3Epn7JtaSlWqlYgfcggfYn2kFTAUaLoO0aLJj7OkbV9LtFerE74wbOS8pEUCz5CIhV/pUlTWtIfF9pbPPAgVKoZwwo8ykgRyjV1Lo8Pv/2xdRjCd/4Xk8BXqwiymbY7KJu9cX5eVif+53G+mpSLztfOZie5dMT7fwneYu1gl71SKsJVBLm57ieUOiqAPuY4+N96KgTKn+ytwr4qWge9zp5BbBDIAuAjVhceL6o10O8qgaIIUvnjhK/lz1gDOeSoCHJw7MDYoSDDB2kuep3kCe6OASGjlvAL1h2JdQJvZAps0Jt6jTep0qjrcOe1o3zzTTnlVkyV7R1woQHPzYeP/HJf/vwN37o4TCMBTCEeZXLXYgJFEEofT4J4zjfgj3lwfsanYo+BPeMB+kGUQb7xcY+VWlHM97ueOqGTZrknTsAJPzJwgO1NFgBfxB2YEwsGIWOFLHIrtd7gGjvIHRgtv9EzbJ4zdhm2qD3tILUCe9oebvJw0wnV9FRP0a5w7pSLfCYrJ0gf1jyNIYod3my+QWn5GkWQewL5aeNL9rfytJOcGIZy088ef9W0HUK+gbENqwj6loXrt1EEnUoey9QSyHKq0HSy92dbX+FMrUSXnI827owXgVqnqZd4m3zOWTH1bLvaEYrdNmb4vLcJyeAqxgnYyT8TMazxvxwcIcQuMMxgZZQibYoG4aLUE8SMq8S8x95XaW+k+ZmYiqAfowjCTJ19DJ5h1fbMtoxFkNZtxh+7AToKzvssVn/wx5tOzdh9OzWKoPCzi0FWcCle33oMAN8F7lkHE+7cnQT+vRL46NiSvtK2mPYJC/q21zj86ZUHjAStHWy/q8cVN/E2/NX8L9Il6VijO8dqxrpBNsEwtAeWMrdxRJTDDY8ADE6edjSxdsIrDmLHsKaZ7yRTmA9D9u2DIMHinJg3aU95g7OXbVj4wAzzS3SFzb65kXluxKqYY1/XSebZ3BEtffMeYy1oR3LCTSvsxDvvh3T3HCvq8HnlrrCSS1anlsf9WPlyL3cai8DgT7FHHAKGmw9k8QEInXY0ZSiXNpMSNZ5Pu7Du1hpIRdArN4WO4se99Vj3MVe+RDGkEujt299iDf4ryqBsEB1rOcpxyvAhfs2JsapF8fNtvnPXKgiLINeGV3Pm7K03vKfNbQKQb+vhGiv7qRAW4r0bRdBYPyuyyWqeuFR8hQvJdYmI8HXadb5J159fCUWCFSPckHaU1Tz5VCw3QIudzrHyp1IoyiDymP2HOWjBffaYYx9pLc52Ao9dJ3q6LPOqn2Q/UikUy/HuwacFeZRATJLOk9LOj+Kl2/UjZQR+9JQy/7mrGf9XrnXds1yTOHuPfC38yTY+BNJv7T/HmulM7Bpgp7zSBEXucnDiH2HSj7YQDHH4hdRJb3IWbMxQuU2OhRBFZMIb1WSKysVKJF+8RUOvd5ok3MoeP1QWbQGxCBKbduPzyJp2iks9Ix5ikyWzSwtrfyAcduITJtL48okNLPCyFKY6ZhoMIy2DMl03dli7HNxmLZe1JnSG7jG+TNlyMIXFP7kfeZxySK1zS1UpP7izCJo79MX9/x8pgv7mYPjFRV4GpmMas5PpjjGvUSHzH8h0ysk7Xa7o0xuNMU1BLBZBKoOwovBzr0/sE/SOxYDHg2ZzaCwntAbKt+I8XPv9+Hs3iOYJWougP7CkqCJoFD8ofZjsf5+9gXI6xKYAWlpyB7CaBLuAsA5yunHLxLnFjvqD3ar8X193GZRIS/I6tA2Xn/odx9xoTflWxsMvGIzWbuwmb39qSZENpLu/ygM3AsTk11MhtJroUfK86eHB+fkogp44ya9No1kUfE8ZeXBmpJXbaPPxLxRBiQNLdeDlEBaTV4DX9exEJdrlYhaAsHqHnG9rZ8JmUo8cyDPyMfvBwqJ1TAg3Uj6X7+BiUagnXcvupIgUiORzLWCGRwHkJtFat32kDavA1Oqnih83On9z719RBAGLtZubXX4AX7pKlkWqJy2MIghlUBez1tg2PtJ3+pNPb86uCCpe00jvjYG4uAs/PlHcIasjAGyF423wYlvnPWPIhH5L4CpLevSxKoPAAWB6aNhO/Vl6iNneT0VQWeZTMfYI+tM2jEJIBZGKIDeNdsNoT7d7YFvmbaUnm3iMvHtePfftJXEXrffBy6bRPAS4j4FtReVmaSHNxdOMUZVP2A+n8lGeNZ8/4Um8u9xJ4N8sgbwoYY6yXdr7dbbPaaPC4/AnOHEhA2v39SrkHDn3bp3UNRA47sQJ1OEPbGg2YWLjF3p9HT7PfgfGjSzhYLE4BRM98AaGf8D24Mp/XbjM3+qu4RIVVkkrihbjdSswscaHwqQ6vrSOAwFZfKLiOlKftCa3GKczZ3P3etytC/SmmSsh0i6SBy7CciNH67Vuc1ImPOnS22l1Lh0qpy9O8WwZ5FosDSzpXBLXXwJNO1rxD8yX7/l9RBH0DksfT4Z1CwBPCsspm7wEfOUR8iiE3nFCWI6XRxGUDaJ9iej+kNkj0sLlgheCVCSnhvlSJXsEaQm0LG0zUTl3ylfXVPJtuPwnknQvmbHWy7bOXv0ATVzvdcrU010vbiLY1L7p29Usx3016x6fG4JvEcUbOtMLzTSwCTqnCuNnXkL6s4Yby4ysiElUQaQy50c3hMaC9ic/r0bp89i5lP2B4qsEQjnk8fKP2U/SzaNVAqlA6p6SKproMUt8lplf4pbtHFyY7Jimk8vU7axC4AdCY395bV/6K5RL4u3VimSDr6B8dcuIcB+5SXnFVkiI7iJTQQO7Sg+m9ykB76QU11rf4HIZNwgX4yzV5GE37U86AEJOWqGxxnllfcBM4gXiVkbzpJCVf7GVQlq2CV2npRhTgqTMzvyEvHk6fdqQf6YXPGnywN8ZJXaLS5mlnnuTspBUFFKWW6mFvmUsTr7xmWgBLSO0z0DiXQKbMvy3nIUdZoS0fuA04shw7wGflOrfuS8vga9WEaRo9uZ1KaqmFDYNycY84UvsrzFmx+hE105yzWPSnCBJsLNlIEgvKuZld5tBovUXzX1Vfvukee+pCPrARO8mgS9/cV+V1/d+eenDdE2C/T5cJZH7An1ECZT9gXxA/gMlUCyCnN5UCH3v4yVM9VOx8OckD6xvgDr5ezeSFkWKIZ119tpwPC7hOpc1gE/C/8oPgYscljOD2tJFA1iDEWkZutRAwFROQeLhX/7KIbkzwYgPTvYK4n78jq00D9Mqju5nk79+UuMG0c94YNYSyM0AtaLoMaG+9dEkGKURk/xfKoIsO+2iLAzvxHCrfjBY+TWedrGlZ+De0IMMarCbMdhzyf0AngkieJVP8HNZmIST3UuEFOkNmcM3S1vtAVr8Gh+C40vDsIvN4mfCoMG7N6VvCmm+sQhyw8pPKDR9UxlFENZAr2jD7g/0ivasRdCr154Wxj4HKIlUfJo3p9qp1GHR+t33mKL6AX/eXrqSomzK+p2fx8fLR99A2p5Nd3GrvIdfpV3Fp7h1zbfHIycTJ2BdFnb8FZ876T0f+EKrF2DLcTyQXT/FiomwEZwKGLVoowSq/Kh4uKanUg05diEZa6BYBPHJI23YKlDzfCKmRdB8IqbS56mnm6QdP7z3IifiYc7u28u82XQvA+XpQlflJnwpYouQP6hvQ5XA8plQUCy6C1z8O3cngS8lgU8shn1pYoN07Lzo7ekfac3pe73IqSOCLfjS2Yvtsqbo+9Ot3n3EDZz9Y9EZZPs1iXlwGVioFG+/Hqv0VUq8G+HysYaLrVz4DDEKSaCFlT5ho8dksqc1qWUV+yx3DzWPELEuKGSs3RMcawepNC8oL5kUCbwyZyBuko/6TMLmn/fr+t6Vs1y9jCzgsfSAOY9stAzK37A8eOc9BX9lMC3LB3zDyXNFTHEEb+Ai4aQxIO+FdRDPd2tJD05bV8rjMvj6voRzvvQAkA9Yg6sI8rOwHAzCiz+3BzCePYNQBv2qIsg5FqvwbBBNvfmyDDr+LJtBfqxls1bihUo+C/MTMUf/Woc3bCVWxWQ6NR+fKJzKo/NVTlQK79axbhQcpSFspYkwlQyNRiZfcm/piW+JWeccBKA5uMExcvLQgoCt/MUl7g0NjcmvmmsphMDtGOJLF+dFFEGsE/3k6/4PWoerCNIqSIsglUFY2rJ2VCn06AHpWNv6ouVHNo/+Eatz888+fNN+wgcX5/TIaXUfy2saAZxtwhqNSzVyuYSbvsCDuvnKo2PgBlz4Ujen8hCrrjKeSHMnkYYeZdBiyhZTR04ZDznCMn7BuVgrk4WYrj+okyaaDlrFXvfywDPNjDr8CYKcIsVTOWI8FIqTZzEJkjdhaUwGwCll0QpY2IFjepAWzYmsV9NJLM5FueayPP5adH1zZy1oAGex4Smxm5fFVtldzLmutsTcp2FORNLTfiTDJwktnXDq3bIMZzEX9uBpCjhqp+TMueotLdzUzTL93SmCKpev5foVK4LS/G1Cf1tWbX5/G/2LIl4MlnDScWPqPKwR738A6XMgpjMNyuG3Rzoo2Ok+cXGz3Gz451shj9zmrdBbFgWv/sWD82sVQW6s6/GhLAI8MeJXFUcsHnzo5jlRayCP3f70h5+FORBpDSTMCd+3Pvo6FwgOCnZvZyUeL/k5qMz33x0erE5z3LivIFvC/71r/W9QOEY2UyydPwW0XI7clmt5Z8btIEs65OSnuH3QVlsRawosg3wA/8mNAJm0/VzmMUfJ5/MwJvmnfAP+7BnKIB+m/czmEYqgx0zybAgYRRB5ncTlON96U44LCfkTpkuaMAN4prZ9K+fB0NeZUukd1W3CxTU0uWzVTzj1DObJQ+ilnJIwZXfnBLBDG1bG487QQDaigJK+Fghp1wBcLKjI9NS7PKi5x5Wnl6Ck9A3nyzcofDwqHv9VFEB8GvaLSiHeatKW3UjaPRCmzarQyadh31URVGugtahFbpalIqjSh78sar1BtvGJ7/y7hPGnm/pMvNC+/WrYSvIfp1/5n+39uB+DtHCPTMbpT/au6LQSCuggHEXQLFCjGEpJVMUFo+1LGiwAVGiiDNIyqJyTxj5BP2KeM2bsnmbiZ41PWaS679ULThGzPXvs7SMs4H6iHbu4zeaY9Bn2t8xiFRblUFap42VlAm81Vrl3FkHekjv3ZSUwiiD7gj8v7cln+7U9XzjitudLV/z2uqbMOHhSmr7f/jFz9QWdIF/SzkI//anwG/wsAuJlvk18L3VKWHPEIn+WAm4iXprPUOeFqcXQ2P0Tf4d+Pgz+jYmjJZ15jNdNSI4a5mpgk8VCPdk/+D9ntR3noHMMriEYlH3Iunl/W2zzgz6BJeaJCg8d4CupxZ+VGHYu0oNL3qFjfPIfWSdxUQgOhdW33PNeGVYR5OEfHhDyHiXQa38qgvx8Op+F+XlY9wpyHaiy6CMW4TkohLWj1rS+JHGPPeezzJtMQK79Eo5lbdeBzpldE1oJwzJLTwrPMze6vpp6lddYjKNIqCXwqjGLmMx1sYRYlQ0dwwtngW/EL5IvIivHwK79IVj4FOeLkuPpeLJEM9j5209rdLa21DoLMGDMs+bNnpDMlX5KfZ+J8j6KID+3djuBsQpyXvXloQc1ZONo1pRaBrlX0HfMzZ4+FquglJPisv7M3I5Alan8zq8Yp2TkcO5AuJ3KDeJf+q3fjpLsAU8asgiwhC/mflAuisuBHEBomxfwo4ChCSCNxfjAxjftyJDwRZmTtBbDk8ssDj/t24XmusYkWBIjuddtvawX/ESO+MUKKpcys8P28GANzSrOLIl8C7FpW/8tyYN2yljo8l95D+UDeXEy8Cu/FaQv51U+44MKIR1MnMyF9lDM89AZKfoqPN6FLE7+F9XiQzvV5GI/1/n8dP/u07DK5yu4frWKIJtOO6xSauP5K3mduH+F9fWk7QNXF4S31REpHGAmGjsdcUEHOFXqwGBaaAFzgesnYVoFuT/Qr5gC//qREyFYALinyr88bjsP0x/ypuid6b4J4qnYUyZUBGkJ9Pv6/EsF0B8ogA5roAW3vDw4O/kfD9DwJ1xew3BtRBw+15BwowZOthleHXTOSqd2t19ErLuWhdBV/BWGmMNDZRtrH2BRFMB/rS0kID/7QMliJR/Ls3EiVkE+XPvG5scfUQjxucxj3vCoCHJy13/+7BHWQSiBnqsI4q0Pn4h5BLfWE/00TPrl03rPT4anZrX2uKydqZHtgTU5mivX6yyi4KR3iPYGzsilPAVxGCHvjp6wAr7hinXiSrM984Q1n3FDStiTDyIBgJko8P1cyzasRdvvKCY/RBHkXlefeItp22VvICyAXvP7hc8bX6IIymdhUQT9ymaX7OmUpdmcFLb2CMIcVS0cSyx+tGmu8lC5lI+05+SFwyiDZJKwi2H+Gp5FrrXwN0stgjrq0Ds84QLtDynLLLh4wSWCvy5NSVxYnSzkE0aiFf+SrQSX8qcKITMW5j3/hsWk+G4czbKe8nnN6xHy0gFPpeRsUPkTby5zuglt1z0MXtCOVQT944UnnbBYXXsdaOZO88cqCEUQhWTj6ZTRxVLqWbZTSsuSB8usu/s07BDQXeALScAXJrM4zaKX+LRPWWo/a0fMXHcrn01vkmPe2YubUlh620L9/PxP6eAMD/X3K8mTuPEinxlXyHyEr/ASXXhXSWeB4T6crtBWyAquKiQmnT1+E1tIx83yt2OYe+cEPJhXNif0DO05g7AVHKxcBBpo4io5sZuUTrzUeDFYvBM7WFtZ4WPPukqcYg9UAnv4yHdwF8iJIx3omufIZ5iI8LjxhfvjUt95Zf0WvIqgKoHeYiH7BkXQGxRBL90jiJcmWoO7Z9A7rIG0CtIa6DfWfr+5PQDrwKz/mBxVAvXR0TlvLGphhLnRo+N7t5z/TB8lkOnCdOMLawXsERE3zDPDpw/aD82tHJ2hXbCInRy5pKahuKArDNykG+5WIFgDH9+Me7iE1mqgRQ2z4gW1a8P9kI/WUrg/XrSQx8/DfshLE+ZJFDsPs2F0T+d8kkNGHteC3A2kUQZ5GMNPvFjU94ROTxDzhYufmEk/8rB8Ar7AGcsp4SNlgnUAZdu0azdjSNJSnxMj5E0I3DsFjSByiRzEnUzjC1tlLbSlh2nCcYXLLYtrvxOwJ0xZwNIpTFvpZrnFNfUzOMOT+ZJ/4RGdl3atWus7RYl1Me7Di9kHdlIpd40Xx1ZwsE5Yl3FfwcwNKJirrc3cpTB0Jjnx3IRWvtdiy8vEzS6u8fGl0XawEmmfqWteHJqKK2DRkR64KW882p9EgUk3bsUNC1uSS9KM4IGlWDHIy5q+oXt8GsY+Y6vEZLq7fDEJfLWKoDatad42nXF7E7+ED/Zgfs3+5UKwnHYgsq/RYVbHHJgYdkTj1jo1t6MCyDBgsKGkubfKB7Q57qnim55f16dfb5n0f+Eh+l//8lMaHqY1CcZ6QgsKvw//jS+f8LJR9GwS7Wa6UQTx8JxNA/1O3G/C7cSj/JG5PDTr+ys/3Wy5k+bimnwTJ6jLjLGGjgySBVtHyHzWzWAzCOIGdpEpkipK4AxEh2z7EODQptmzn4fVWoRyma865JnfgRPfh21P0PjzI/eib3t+1PT3/rf3Ht93s2gfnmsJ9PwZVkEqhJ6z14qmwE+7GeCcGtZB1YUP5VDnKUsWwyaXhi237PcKpvItVkAdtpOrojR4kUe0LhoSWuJfzUcQzgxrkCa/i5iQWEVFZMHj0qISOIu5DjXe65FhUWhthZoenFiaeT8K+OTbyCiCfs8+Qe9RAPlpmJ82vvJzMDeIjiKINoxCU4sgFUFvUWh6/K3KT63Z/mSh6s30eFs3i9aPOftqq2vp6a1Nvdp+5M87Mz84TRWUNGnQFZDbkAQTg3D4rTdR4SNLYi46mrYwzLbux8hYDFNDcV8wAHBsyORpGKTgSMIK5PPFtu9p4+5bEIsgFJf+tXSUQSiCureB7YLezKLzB617WIA+YrH60EUqlmzPsQj6x88cJf8Ui7dnvMlkDyH3CXJvrGxmaT4acxRB8JE+lCpR1tQrPAPEKbPh+U4RVJncXb+cBPwsTItA22Ta5uqWjkpd7OsfvfEzjE6m+l4n1J7ceBXeDdvXLbN4l2T7cFR+TLF48dJvuBxhYJNf3o1MeVlDEEsekk4HZOEGlrpN6lBbZS3wlHemDn79z8FLZXFwhdQ1DhiMzR2rLjltbIMZ/AtGgsllcrS4juKX3O6xE7v4jOkZhAde3EO2qw7GZ6weasnhZeHEW2FxtmCYnPzCJ9s1XdOOfCCFD/EJmz/pXBxmlaG/zgOm0a5JcG33Hivwdxye8Jq5UUXQL56s+XpZA/mJGNZAfVGIBbkvYLC89ZMwX8b465wlA857rv3wnVNdAzKPtpVpYUsyacYDC468mLB+qxJpp9ZBnv1b/AcbVKnbRkQ3p/D+8AIoFAImfsZ9Lm2Hn2F5WlQPv4QHLm7lHLyUrTQ60XVuRWXmOtFPr7mqCFKRMyeI+Wm1lj/ZK4g51vn1yWMOZuCFiy9a3D7goUfJ74og5tc5ml52wRgAAEAASURBVB6S4S3rR+Z0PxX3RY5ueY14lcmzemf8Gn7maGjSx78gIspOVIZARBbydHE7djRyZTwFNoqXFGb2CcS/vuxEVrgZimh4R9nTjiTuX/C8Q6vAGVSTt3fOJKMdj0q+zJF5lRPyIbbSx6PyqfuShWBprWzBSlETsi8tYbV1TRu7yge9ot3SOhdbI8HwZrmroImnSC+TQHvNPciacbjC53+qlrxEhkbgkxiCpPC/L0/lsNQmlyT5W0V0jEoxZEUp6v5iB3N3gS8pga9WEWQDOpvhakl/KakT+y/RvpLEi8HmczytHtTaVx4jifGPQSA9qoOGaX5Wk+PiUQJpReHDsScpaRLsQ/R/8+CcY+NREP3KJ2E+QH9k9gedRQCLAb8NjwUQE/9SBPWzMCf6ZQ7spJ/JnkEt3zo5FckIfMTDhw+nei644boTZ1a4ghe8C+4ADtjEjqwBQPdI4L6v0eQoYgDBWZgLR1CGKwbBc/EBx9Sjb1dUGoDEJaUwYfg+LPfLCf53NtqN2e8nHn77tuYnvud+hCJIa6AnfE5TS6CHOUHsxXN9HqpRBHmMfBYE0I+4pG0YjiyrLI4fKS5YKrIuYB71a44myC/xUzB7ppt0LvAmEsks3Eu5DcYl0bkPl6mN7Vdz7byWTSGtOak0GJVANnl9FY61CPLzxn4SVkUQFm0sZF/9gjKTzxvfYOb+C79aBP1aRRDt/CNt3naqwtJN71zAfuMbCDe4ZPGqBZhLOGWZbmbZkevUCe5oE+JErgf/cm37H2d7Mc9l/Y76Sneh6ic8M2PgIjSvaa5Pxm3BA57FlKwdf2Jz3+xntk9o91dbJ9cbVc5gAZRi5LSKoHBNgVEEYarm5s+eCOZby4ecYvLk0Q+xCIoiSMUmiqDHKII87tbj5lXkfL+dhCf9LN7DknxMTZYEkz4w8pJ/l+SZche6k8C/RwIqgcYiKA8yFOtocTr6S/rNCbkZmoY+fvt5R9NezTMPQGJN35D0mauR6+LCz4bocDE8hhbZxtmnh94l34tqBhjDJTij3eSRzsIckge9A/A3Ah03V9124hfUp6T6E5P8zGXWVbgkZH0NlaLccDO+TYL5OvIM5IKRAda/NWnj6Nb0Zt2wDpoH+hG4vM+Ct6Qj3x64xjnu5ypw8tuGves9ZbNrLecAP/NXEfQryp63vEDJcfEogFQEqRBSMeSJsb4cdH/I9x9VHjHvui2AiiDWfc7DbbcW2s/CnBerANJ3jnQUL1OZUxM2brp+0xonGge/8s1FRVD26SLsn1m0180/kWmj1xIbqkORrEM5JRoVZ4GbuDKdNAXLSJN7JdLir/KLJ2J/naudT/1tyqC8kEGI+I4pmSdZdvzA/PrgJ46K5/eIlyx9aciBDMytz1EGeZJYT6LFIiifX58WQa4bXYmMk27khOiFGp4xYXD+3/nWd3d7vOGTM0MrfUcDWhyvC3tDO+5H5LvKCpqXQVz+yr6w6g3abWknheCezxltg/ITylfF2BZXhuH4iBs4iiJgC9CtHAkXY8oAdGRo8hHl5p3VLoWdzox5U8YNQhthaZoXHeEU38JyXbwcbRjghLcCpWF7GpcggB12PIMsvMmuP2HzJ74q13VpYfbqWgRNKXf+l5TAf4AiaG9Wtro9fim6y0n/Mu2ri9E5bqtJHipXx5FnO8+46ZuBZPBwaOgg5sQQ810Spewi4GMsgtgAELPgtzwc54hQTIB/+YVTltxThU12X0dB9GltFOi+Qi4CRhHk25/uC5STwlwUENdCyAfrLgjkivimCCrH5dZpfmpq7tSnM2g4Ny2YuRgzdylc1/scADtwzsB0DpCTYyhIlNRBPOhSCtoGjw2e8vtJWB/6ozeQq/DUB+pIFXxXSH+wR5AP01EE+YkYRiePHrjxH8qgxz9mL5UXzx9hIfTg3s9YBL3gAVrl0AM2A+S5eZn6Sh7eFJ0+nBoPvwlPbS9lEayjPmEQbKGlEBKBXF5OzKFLSRekW/aZq4ke45oBPAmrjLXoscghYcqEew9LaWCNnVwIb6y1NrNSkKdu3sz9YTHazc79NOw3TNbbTqMI8vNGFEGvsW77RVP3X16lLcciaFMEZX8grX+0DOLGahHkxtER/CH54VVeFo/2ryytuEEBKeHhuAqi5vIKzuQ7/DN1ZBA/9SxkqFnzuRf6Ay/WSScPQimpGCl1tYV84pgFKNRspzgVlo4LEvz224wOBC0AwXoP/VQMojndREUQDVpz9CxWXahy2p3t+J8/P7n3D04Qe65FEAoi0+/7mSMMqAjyraTtOs8HFkd5qQ8X5VkWvMJHPZBYGJPHOty5Owl8KQnYU3wI3Zplw6sRt3vZE8W47pE715NWf/XA5DlS1or+jO/5W8JtRVjy8GcOyUy/kVbpDVUxih+O94xSob8PKOlF/x+vl9RBFzCEjtwCFjAZWsKZt+mBLmCwHTjWwJcxDmCsooJ4EC/plW8ejia1dM6SBl45JRXQzfTiOe+cchnY0Ig//EJKVnc31HeYYjjwxBcJP3zrN3rAL/KuiG1osqY5Ej/KWiIT1XKykmFNo0VQXqhwce6MtTeKoFeu/1D4eNrmL1jUvn6FZS2KIPfUc9+g92wL8IEXLlEEeUqYv6z1lM2sUEbpQ+Ew5EuWKvwMiy93lXj9wrv+6vx53re8Hsw0pCLIOcs6xBIZMqciyBpabyXxF+4QtjiDe0jrRsaD3qBc5C+N9v2rrIOXOXbajFLo3YpCKC8/WUhzOqc0YjnLEoTzFbKNQF60YFH7CAsg9+B7+vQplkFY3PIi8SEvX7QIeuDhDWw7YB7nZQ9yiGRXgzAc2i5lVjjpRqb6G+viDusb+EbwdrxrgtQbxD6bjAxukLrgQ7ZOt2IhOyl7GXv4zHVBULDM7s7ojaw3AG2aA9Ynn31mBLTLKSuYo5xV3qVXWWxFD2lAdQDS3rKWHmDZj5Xo4jv5HACXa+hoqUfVLiU+q9LJdfphM2VXLFIKTS+p5CrhCC9G4HZVMbIxkj4cPCPk53fIyTiu1Bru/Ge47cSS6Ra48mAJd4og5fF1uP8wRdAuNFvf3vRscqtF7mhfa5hedHJv6DbewQnSmT61bt7myZVLBggSfMOZo7bR6nz0TQ+fybz51Y10MQ3GAuhfKIJ+4dMwj9x2z6D373/P0aGg3uPAsJzglGND//T7b6Y4LIM+uUE0iiCVQLGy0CTYBZx85wmw4dEUl78RfoaBRDoYDffAj4FP2PzOfIZay1W/FRN2PSBOLv2L8heB5llYjEpVBDWuNZAzRHyCM/bH54F6eNstgrSkUBnkw7MPyJ7+8IQHaC2DtATyFLF/vPAEMU9euo+VBYog3uywbyAP5kh2lTPLLGXjT2e5Fc1eE3lciQ0ZWSFbv4PukXARGCyBCS+8E31Cm79lOmUtsMN8MDeckfpQsKy2cpGE6l9kSDz4XFJ7/CiCmJk1bf+ENkiLII/A/c1PHGnL7nGVfa5evcqm0b/wSZgWQVq9vX3Hhuj4H8Hl63zkuNopiqAch6ki6IBxIzZ+Kv3y1/qu9i3ONIhVh1ncNv91vc546kYpurOehbakJggZ3IGPBYF5dcLn/uZuy9MC1Bwdga1CYuXAYlVFz/4TIUv2fLdFetohPZrG+B3t2E2j72sR5EKVtvyCt5X/9fOz7BFke35MO3ch+xNH3fZNJ6eGkTdtGsppt1yySOQeuvBvy0wVSDfWuth36A537k4CX0wCjmR5CMCfdrk6UXhKeyal45ig6aVJ3i4Dr98RsvhHXjqD4dVFt7wreD4lB2DZ098FtKuQ+6LPTLliNNweVrTOr2eGjlvi/j0nrXAc3qS/lzccDf3xQQvzxRba8fTMvvMIdHMbjQ2aOu9Fm7ahNggC5R7yXihT1kluCJ0E+lB7YtzIM1mCskduYJ5EVmjHnvDc14mLut9v4fM7uCRgeIn2uBPSulAEOe7y+43fr+6Z9+5d9gDSCugVewO5NcAblEKjCHrDGvAtR8bHEij7QvpJWNd9ronmU2jXgqpoZm70paBrwNbFWaXzpf2pUiEus/lvWsPiepesIbzLry8x1p917HrMuSsoXMQ1Yun1EwqJ0hnMS7/4g3GQC71LzJZxwi7LBj43LUHp9ic8c2zi9HwPFeFloRZB4riXT16WML/e5+1HX6R4epifXj9hffgYhRAHMvDi8AGfjT16wByMIsiTxn7gZM4fyOO6UaVB+OcSaeJ7aFtETNg0fxdOwFR+JYhvVa5xb6AegCsCEByIKKU3kK30BTrLOQiaa0MkGNxrGtfxlSVZr/JfUvtMDHq3ZJvb2jEAhAVo6cTN409AGkXbVAtZPE428eK482kXRJJPYBNzDZ0g3nopVeXsnT7lLdzSL91lfGIX/mKzzIRIL1P5g+CBSClQGCKk73PH8Ge2yaHvb6rW3gHvAJOW5alKX8miCGIDLGt35768BP5DFEHTGtPMkJr+pVtD5CXwa47RG27Wogzb4TootROt7nXWZqVb50saxpuniiA+C8MaaBRBbzkh4i1vh6II4tOaX9gw2gXB+w9VBGkN9NunZQ2UBcEogrpptGbCUQS5zwpdOINUrCy8P10gzFr2rF15cqly3je4jpZjcT/hDEp7jc7ltEPH5e8Uxwk/YTuVEzohZVRbJQUorrLMQ6qjGIDotlaz0/qnjrdWmqksi6BYU4DsJzWPtaDISRA/LEUQm0Wj/HGT3eecuPSCSf4hOD4wz54qMaKijCiEoKOkFIV0w4OXw8lfIwP1HpzOxCIkfZAWwsq6MABepJ93a0+Qj6ClvUnImJTwQ/AkMin7HRPPvynbTGfshJbKaltSB6CYXSDm9Dv2BsoeQSh33qkIQoH5crXf134axhvOX7AI0uLNva7esimme2MhaX600ygsVQS5alIRBB9aCfneUeGHLzxc+ZNPXdO67Jq44GWknSr0HnTCFmfcfm8isSZAuLRLPcAFuwFP4uJlEvUVEHXwT1ffCRbFTvqQZay46VH6UDPuZ362Z/C+wUrIBmc7jCII0RyKIBaittcXtN3/+geKICyDbMeedqKl0E+z8XksiVAEQaOfV+rDM/Q7hhFf7dSakBR+5fxOEeTdu3NfUgKOVz646tJj26XaP2yscdP5GmuP9HrCxTzjZ6jQFXdydEw1Gz+9KWJ1W4AtU/gBC95ZVvIld/vTJR7IQ9zxoeQEJpg9eUgXXDqtTRC2S7KF3yIu1leu4UUsfvyHYghCb5IPekEAT3wc6cdDxY486QvnwOXGBC30F5kVDs5cUsxZeEubUfI6w3W88hjmJ9fBQ4j13pnWOzolDAPLF3yycSQKugZbr9zyq4QD14ANEz/iGdr4e5Z8HgYxD7SoYkWLcCyCmBNd873i5cgbPgt7iUWQh4TkGHlg797y4kRFEPPlp9+8d6x2eNkXC3Bf+vECkJLDRC1+XOfJkDYw9VOHyMT8rgt1zSPTvdfjm3KtCKpaSNn7Z842hbbvkLMQE5YzeAWapCtfbppxy77h7FIUfMb35liwaW0Dg+cLGN25hnPhohKIOv3BPgv0d/fPc45VmeNG0O4T5B57vjx8zkEMz7G2/Zk9gp4+epTj5R/xkuXhA/YJQhHE1+xRBLkXpfzHUm6V1/WjtGeGlZPlQA7+iqZWws7qDeaSdaORqRlxhusa2OVxzO3gTji45gV9kUh4h5ce1wtiQqewRSAgCe1xgbg9byEtcEgMbPcPhhZwI2vRZ3+e+zuZrzJO9OAhmUEur5eyAFmeVp7Jmrhwk0JnUhYQ+GSzdRlu3H5zumJP3sKNze/EJFTkBJKjBIETSNqBcGQ7qmhg8I/UCVRyFmrPtf7neGZJTbeYmWd9SWnKQxRB+nfuy0vgP0QR9PcE1aHy7+F+cSx7Bm663zw8XfI1g9LNXjj5jlFGWgH6yVM/qfnI5zRumvseU5+3WATlsxneCP03D82//MJJS74ZYhHwQYsgLS7cKJo5TLPgfh/+fSb7PzkhLIsD3xIx+WsR5HHrecDOQ7YLAhdHrc/4R+3S2weKP7NRfOEzvBnGpSIrXMhWTYhlpJHo9TAiH3VXuQeMX5k6POVvIU7bOQZmSEdHQI55kGaFBWuzR5CS4EcdVO485lhQNwHUf4IC6Ocnbq6LRZCfhmEd9OLJo1hYcIBEFgTf8aZH7n0Y7sN543mQZ+DVj0RBuq7lCTHlTHURN/XY639iWBZZRty5Y8RxJ35Dlp3QynymL/zAr6ErLZ5pi0bBXIeTa7/l19rMyaSThvtcqRD6DTO19yiB/DRMRY+KIE8Nc5+rN5wYpiLo5UvCSxH0DkXQB3c8R9HjL28t3SPIhSuKoKPdqhgKTLnIkwtfPFy94XPalfH1S1LDzWueSQ+J4gav8UrklEKgAJtvyixusqWdG2/fkrnw5+LTe2078Zd0b+pqM0UiOiUKRx6rv3U5obWQvy5SfWPJXtoxQX+AokfT9IcP2SMIK6D/+ufze//0U0ctglAOPSLdN5bfsTjNBtMog8zftlye5OUPB6JwP/KzTwkTR+UTd2eTT5DvLncS+DdKoL2m7bTFzkPVDtsZ+hxcnDPNR9vTrZj9mT53WPqBMljxJ0JG+4fd1+7hb0sCYKyQwQMAXsdb8XWTNylEjjiZLuglZeVJwqKgR/zAPQLFXTnOSLKdtB2XJrNKhCQfYxrZAji8k46hray9jpPnEhkayiT1mowSvx6RV4HJLN5lPMPlIry4XTHoyISWKwsy3iXeQG/3zbvnT3gBdri5jcvPwC1eN76sT5p4tjCnhT+wQqkiiLUcp3+9+9UDFGoJ9JpPqF9qPbsUQR4n/46Not/4iRhrQC1wsy9k1nmj8HGPSAvXQqi+iqBRAs2cGSUR6fpZh4RR51sklDB5E041Nt5bST+3b33WHAYd76mweblx1rg05ipXhyOD8aOZrbjp5aOYodvgVu4CjDeE94ZB+GgFgZffKoIsW0WQaw/qoyIIbOdFLcBd+82Llvt88/UIC/JnKIB+fl6roOe+LIxFEPv0oQz6ydPGnJOXRVBnUeRCuSp/rKifdfsyS1ZnvTrs608VEt4jO9IWFmVko19n6IwN1AaqTEf54W1WJLcWM4SPzJ8J3MDbyjWYtvSZvNfg4G75b9DeMiy0A/sIiHPWqO3qIjFEhKS93kxK+lzSBhcfeof8ruqlTE1zftJJ9iR9jrGFlb/QG2T8QL2cGRMJHrS1GgyOCFc4I+eL7Fc8WtRk9L5LYsgc/W/BoiAanIUci6AyIKE79wUlwJjiXfm6XBuUjXRayd9j8cT/uupzKzdL7NYsgwP+rbcCvDUdXuCdEhkZScD/pQhiMnLD3A8cGR+LIM2DffPjp2E8NL/8l5vsahHEJtEfWDDsiiCtgWL94yLAPYFQCOUtkYuBLhD8zCZWFQ4oPkx7ryh/tL7L3qY8h0U55ncsYg0DylDXtNZkhr6iTxMQ43QtqwTM1Zx7+gxxJ8wQ0oFQSohcCytO6TjWlVqvnVzNUR7//JPJHQXFN5665LKHhT3PwbGUeMSDs5/UPHuKIoiH5qdM8v/k0zBPEPONzyOURPk0jAw/rCO95xMx1Wu6ljoSbZlJuLiIVczdv00RdIE1kYNsy5R0Q712vG+rm2JPzEI+NyGetHofr/Pt/J7hkW4Xm94jdQh/jCKIFeoHjoOPIggLN03bYxH0ij2CXtKO32oR5KdhbIyJougdOB+wcrMNxHxdhVAsgJSyVkC2VxVEwt03SK9t2LLj8BtUaI4sIpkizcICCKw0ki/pxWt8C4M75M+0BUOgkxaSQVihNat6R7IhaCZwOILxmtDDiahhvrUurYa7nNBcXag/F6ou2VVkkneZrmvZ9pOfh7EHkPsUeLJJFUFaBLlHEIog2vijB/fZLJpFqoogtDnm+x4GrhVBv/tkQSm1n2pdUl6g7n1w++KV5Dt3J4F/iwQ6sp89z1bantj2WiZWerwV3pMvOG36tSJIFLvfOaqWwMyXIbFILy+gYHkJsCMPDA4gOLkMbGVOlAT9CYs3FpAdKwLxcuFSSjJxkZ4/44v2TtFx5xgzp6QDaCZ/4tRPZLsE6uWgvSXeEuw414SdZBRBC9/P00uy8rq9ZJHPlNyXjYczBawUCjoVPevawlpSwwe5jc5KiTfg8E1k4hM44mAbzm8BhwXLcKaSxuBPu6oiCKtlGhVTJwd//M6c6LoPiyB+7g/k/noveXnyinWg60HXf86X79gj6NPvnR97SAiDc/YAsiDD9grT+bEWrCIIGIw4k8hQeTa9c1M49V7IfCqgD6kr57w0n4aZJKXcj0UxXvKs9n+VP/dghLHSLEZQirso9hYGLugNoR2vsFmfh7fVEOTVXySTLOBGAcQc6+dhzLXOeXlhyI37CaVOP/tCEYTSR0XQi2dP+fFpGFsIuIbUIugR2wj8yOmc7hHksfP7HkHWN9ZAUO/83TZhNcLCsI6f5bYJuAE3dnnN7YF1a2r4op1L5AJgXjEv3dC4hE5s4YeJGwUM0ukHz3JPUENJOIGfo3eBJhEAwqxH/EFYaQdF4KvM5e0pZG/LLPDEsD/GDf0V3b2gHEJKq03fSLta+W+IGQKOZ2llK/GiFxzsLnoWCFvDjv6ETWpicUcRNOnWpuEVSoSyh6mF2PJX3fFSd/1SP69b/qQFB/kZAfvu0zDl8HW4r1QRlJaChFbLSxPbBSb8xGnM5jn4O+5XGrY3ZFCgJtPRNlYzeQLPwEO1UjfjG84ZdKCoE9/NLz+hCPK0MI+O1yLoDebBTvzvWABoSZF9Vtwj6I1WQ6ci6DcWA24WfaEIYkEwiiAVQ1FTZCaaBUIHKplwYXtsVivf1jG3ZS2NfYMh4q4QYinh/TxrYV2mRq3XeV1YF8k8amqSnHLEvKQkBJEsih2ILgazMCjBRQCvD6+FqOyJ5DX3dXLH982PR8i7DKoiqJYSWlE8ZWNoH6CfoQD6B9ZAP2NN4c89V1QE9QcHLAx2RVBLL2fW4KzFVHYqqD9hawdmUOR6wScZ+GpmIwAzVCAJFNH6zWL6oj0uOrP3UwoaWCS6yQ16Jnk3W1j57jVALmIsAgE13NpaDyRKBn9aBPly7belCPIUPC2CPPXEU8P++6V7BHEKihZBWgdhEXQogrAImraa9uonYZSrNVvKH2sg2rEy+0YthumL2fFTReDJgyAj3+m3A9cfIQcHdFzvZMPN3/ClPNZtocDip4SVaZW7YvLk6WAu9MMJZc1x7VP8IfcUAp5PB6mE/lIGZe8C4pCPMlOFDCtKTzVx4/OfcrLJ97H+8fPG//NfKIJcqB6KII6Q1yJIfH7fsVHBD7zxdFPMeTtp8VoEtQbI2AAucS76ivzOIihiubt8IQl0rGrhZw80vhosoWM8TN+2E5qcDibilSvca+aMI9W5w4avL7BjzQUVIrOotvQzraHhaMbpkF7A0mx/CzwlpJTWZHXAfXQZvKNzWgz0pCK94J5MNA5K0IYZImepA5TyClNueNvomKoTI7QME2kdTPlrd5RiHlBbNUKLyLkOaBnF9zpMDIUp3bJXGjSSSnxXAA1HweIiVnMbWfSSOJi3+KCJMkXt6FJwzosTj/ARBzhFjB8mF564Zs1LglgENb9WsW95SfJaKyAVQVgC+WmYiqDXxqMI4tMwFEG/YhHkvpB/ajHrS5L184XJvXzuZX273uvLQOP8EHZ+Sx4Nz3ivD5P8KsuGW8m5orSKQOwXqcVKMEzv1OtlwU8Paklad+xMWCHTLV4SwQ38DC20v+lBJf9SMzC9eymBgNbalbJQBLECB8cXLhwqwljhOs91otY9nrh5HyXPA14a+lnYC/YJevECq6DHD2OF6xrySRRBHt7A/kD8fNmSuRJ5j0WQNcky3HXkqpbD0rQROb2u7R4/8ES8ckmzmvy1rV+OlIMe+a78O+1JP3wZu6WcIz0BKAyRtIXL1MYWwmeZXzREs7yhZ+Y9fmsY5OFxfPOlkkMAFNLm7u+hlLX43uViTl1Y2fg5xpcmHzRF6Vwh9br0NeErfvIEbMlipiTztxhz7W56yqKCd+DudUwmLsc9sK+XaNC2puBYe4ydoIg3bWbQMh6TIJ4wxykLfsCbQFfdd+7LS+CrVQTZoKbZtnn9z8I68f9n3C+OQaewjuMcFPYOVbi957L29lHz7XmHhkCm01hTuMmu+6S81yIIa4q8GfJ7cT8NY2+gV5wY9pIH6LevUQRhQvyeRQM6o54apvUPCqE//ugJYW66qyLIDaNrSQEXy8KCKSi8tIPLF3/p8OUwD4YyLRYjlX8ZVjJqiXP5C2pgK49e3J5CuOSblJGGIWUNiE0qvggpgcvId89aAl5P+pNTcobl17+Z2KMI8mGaRb3KICd4TwR7yCczj5ncnz7h22/e7qgI+qeKoBdLEcQbIB+cfeB2E0AXDlEG4c+A2CG3MmrZJ4c3Q5PLOtL6Rw43EVftUpnLFebCTf0SrnSO9igTuGuZpagO66SefJy4nQau842cT98c1nr9IGwe32hqEaT/MYogNj5HEeT+P37SqEXQv7AIeoVF0CsWur/8S0XQexRBnCyWDdKpEYTOZVoXtwq80o72g6LZIDwKDOtA2RZuPv3DLSFEisjZm6YDLN8JDCzxpILXuqxY0BpOERO0uKNtFmmNZORvqHylPpFJZevnmeE9eBvDYddKFC/KoFRIRRCwLFBRYqYP0oapjtY9OTXsR6yC2MPgAXsZuJmlG1hWEcSeVyqCONXkEdoi9zioIoh9DFysUqZ05Ml+k+KOJ5nKK5Lioq/TIuhOEVRZ3F2/jATsIefYd9H7DoZm3hAwbffziiCx2hedi0/ng665Lc2+2Y5wdBFTdvQzY/rppKX89NsNYQXTnw8aHZkGy3zJ64hiBw1vhZSvHdMwaYOWpOIOVv1r2B4/w5f0/5rCZerN2MG6SRQx1Q18xSpax95jdF6EhqfJVSL7/V1kF369jMFm5SYk55a94/OGHrwtvgUn27UfFIADN27Yez6w1g8g9FOLXHY8eKMxd48gPkwi4yctgt684TRNPgljL71XvED5hfXfKxRBKobcG+iN+0ayUbT7RF4qglQIMVeuF38NW2jnz8Kn/MJty5U44z0MV26jFALnqARkNscsTwz+qfA5Y1rzEKy/4U8wIhANd+M+BHZbkcmVPP+7i/yZYxWIsBtFMq7jSBl/LILYbRNo14gqar738zDWflr6+GnYfdaMnsr5wlPDXqAQUhHEy0IVQY/Zg+9HrG5/ZJL8kTn5Oy1vLd6awojzvi5WuM67iZl6utvEvaefmH8VWvWdel+gNu2gaWDQb8E7QJ/FOyiV0B41c8bPg8oZsKK5OSvDdb7BjEBg8FYerzIdOFdwMpt0PWac8SPjlLr8Db6RPNbZOzaoaVHBszwC8N4qNvNGbc/ZsR1Esea3I0jDttJyr3BKekdPuanwSku5W+ENViaTUdigmJL4AqSn3ymCRlRfjf8foAiaJmVLNDz+TRneNhncxPpKIPTIqdk1R+fgAAaD34lpHGxEMBrjPa+DkX3MNyy/8cDog/OHD54KxgM0JsKv3UOFhcC/UAS99AGat0OaBv/mt+S8EZqNon/z6FCVQH/UEmhMhfOJmIXnoXc9UPP0lwUIjPnQLnvlgwio86C6UqiOgxRYKFAGdulPjcTzXpsq1XFWfgtPUCXVmvmO5JW/CxSg/ucH7QOXUo7wEAvrkxt/KLqY9yQIflQ6PvXwYdYNAPOQjDLo6WMVQZ4Wdp9Pw5jg3QyQCf8JcB9+f4oiSNnAh5M4MulEbp110nbAnnKFNaWVn7B+w7aHyouFF9Aj54oUaxJIvUAC34cLhbM5xRKI/p4UuID5zTKkmdfQn8ie7eCVlLm3zaGEz59kvWe1COrpJx883hZlpfv/xLydk+9sx/8fe2+ipceNK+t6kDVPdnfv8/4veNrWYI2Wb3wRCCbzryrbvc5uSb6rWPUnSRAAQSSnRDLJF5wgpkntsVl0jph/p0nwccrdTF55ozl1F+3aciGD5neqw/wsgyvIUd6UpbpFaQqbRyRHj7l74keyXQPyGxQ8Dyjgb0DgKutRx6NLMJoPuVMjMOL8plU2rAqCMStwanihDvMgeOLsm0adBZ66ixEo9LshSO96xxDEZpZMQB9qsvrosd5Yaq+r//nXU60IkiFIddpL13VqGMfHe8m6ZqJMUmULkg6RRz9Jh4S+iW5bkmoqwsxfKY2NT7eGIBR1676UBjIKTeWUEG3phzxqb0fy0b7oL290SZtHRdP0QYF27lRd8IMZRs2HNrzDJzWerl0R5P55ENPuL6mmPxhEcCjf4RpOSgQtbLAaXQ9hBZC+hd3OBUIEgcmZB9bgoMPgbhRFFc5/7syHi37OhbACS0yzTPmNu8tqChDoheSqeOOMzoArHlpHcjEBWe263dIJlmhwm9poffAcHsASQwSAiOO3Cy0fs588DjxJpMr8m/p3hge+yv2o+d8rvezziiA+B5NR6GdvFq1NozVmvnqpF4M+XAFD0CetInevLB3Sg2ful1VAfLKtsVMCM9blXgLLWIUmGK8zY5FgFjhpnlut8ZI08OJMkpIKQB0JZEYP52bMgocOKQB142TA19wpi7FlZyxwD3dOjbaP1MtQ5BthpGyHJHMNQLRLl5rtAzxP5Ph4acdzRGlSiV4NrjH2vsbMB/r0ms2if9KJYc+1Ioixls+xH/GyRS9a7t3TiiBNGH/w6WEaZy1QSso4iprz8uVQa5shqC1dYai3MOvvooDgGa4LftwRKpR7uqBiusVKFJ/Mek8haOZnrCNWAeBuXDNI+kob9CVAyYULzhU3iGVVPvTf587ignJ4XckndS/FUuLwS91gNNlcs4AH4k0SHBoudrgmtqsM/uBGVDS9aztMKyJ4FKu8Ea19MLCFd8KB/5Za4sUFpgdxeQiygMAMJ0OcIpTHf0Ob/iEtG11RCj4NW9mF8vb6hTTw1RqCoo9Wk736Tc3aa+coLxX6C2nyP8z2GPTUzqYBFeaOdvUGaVBmr96AWF00UVhiNDiGqI+aCbDB7jutjuATsR4hyoogTg3jrdBLVgTp07D373/PN+LaG0hzB32KgyEog78NQhqCbAxiryAmAgx37mUY9pgE8MsSX8rQML1SDUG+k/RSll8d5jIEtfNsWkt3lNOQK522OTr/YEYW5Dk5ZBMAuYynC/K5rljvBz7p/Fw0YxRCqcODTQAzyB/GII3p/lTGA7g+q3nKsaDz4Iwh6CedHPYP+Rwvz1HzmgfozRCDuOQQbQxB5GVtkpPzi2SBn8t1yBw4cf0sbpZjH/Uk8Awf4JVfSDpQB5qr6yP6Ah0Hi40s8HTmyQft7A7ozrFpZXiOhxNpSElm3KPcJ/aZ+agV1u+0uo19fzAEZY+gl1rZlj2C2ADzZ9XpX71ZNEfMYzRis8ZMVlNn4T37AikP7wkk5WsdTOoon4qpYEjtujIFTpuUTFNXUm9ibEspIjfpaRctG8UQNsnjorcdkIS0AHIeXg5Bmzi5uQ5rht+9k6Ak/Y5mgqARLrXJxC73QJQ2HBGXInWiSU4XUxgCWrRuH8fQsiKIPQzuaRL6kM2iH93xyrb/+efTb/5JfdbG5+xd8Ehp9+RnZRvL1rMiKHVZRjUxdg0YHfLZZp1lUyp5+/Q8y9DUW/9WA59XA25X1Ee5tCC32iWE2z+Vdu8AjeyeauGdA+HHlXbAsFejkOPAjUK7Di7ehBYr2nTpk5ZrDUELUQFEZNJfHhF58DdE9xPuiAbTiCC0jU6DxEuHRUA/AJO2/ORbI48QAnBAl8nCni6lbjJ+5Nwh14cv8cxrGFq6Mt9gFQBQk8/c09/33nL/ud/GncsKT1mqioW3cwa5eGR0ESepsrY6GX3Dc1wXfHAcH148/BMHHYdOXH8Hj08uMAT9hq+E3/QyxIYg7w2kFeDsD0T4F1YEZY8gn7SplyxvZAjydgA2N7B6NnM9PgvTqy7FqR/oJ4agxOfliVKoA9GchEKwzmAcDa2AyyG3Gwa+lWEIEf9cTi5ycIsLgDj55e4lNW23eKGxGAt0cFmgjfMBuwyNEPLSuhonBlCaUAOHu40++N9gCJoXhdIeq8b7eZjHTMZYGXhYRW5DkE8OiyGIfSQfPdZLGL1s4RMy9gbiGHk2mU4L5WULeUXOGoJOrXfSjDPiGqQLqp7ky4KuOHTFC/kwWRjnePidYQvV91MxkkHk5/st/zpJwmySFPkTtuG3ETUI+90VXn7EG97xYLjkU4KVcUIQmf5WH0oYPP2GLnPGM82VzIo/cqQuXcHi4cDMqWHGwZdMQOvaTQM1XPKkNYb5wp3yBu9IY0ywLvD9jAZnwpPD0I3XbOUDQR6FTjojTmplzgIFCBnvgvr77alhKOQrcV+tIShV7KIm/onSzs3jT5C/cPKp4dwoi1uTmxvNam9rJUFPe4tdhiAFemIY/qtf9WaIN0B6E/RvfVrzUnur2BCkpcHv38kQpIdsjEAfxxhkw49XBWUikFVBTA5o3B3Yj4mA89WFPzd2xnWh9QE1HYykdUdDYn+In1Kk6+CaSUHvvjAEPGIZdgEWht9fh0TSRWYVSuYExDuwjKSiAWCPcuFSOkLuF2HLoI6M4uE9gvQw3RVBmm5lRZBWSHDSw2MdCeoVQfosjFMg/vHTY/3YNPqJBvh8GubPw7RBL4P4tzIG0dejZfKuFNEsUoyAI13KCRzB6igfj98UZIc3vdikpZRHSkt9QBbOxmqnShZA+rvQ+YIvThvzjamhlBgYJcZRltCxFw6rgj5onTurgVgVhCHo33N8/P998eKbl/rEkY0wf5ZRiEktq9/e6PcWQ5AnseLr5e3KQ5YKv+HEYqGVQF67opvgDZfZQ2jyJX+EONoo8km7Fh3axIHFyTfPvbzAejdDkvINCd6WH+2Gf/i43jm/5IN8JGHP+aS6yCdzkzKyiwxSflzs4CjNpjADEgPvccXeBXBE8xiCpBUtzeF3j2XrWhHE/gUYNn/S52D/+ucznxrGp44sWX+oCaw3i9bKNoxHTG71bxWsFUpwTxbJe65LPAVuDUEn1dxGvoAG0utSUeOon6mj03ZdiZu6+9N+jga3JyrsFu1WRhskH1ygCZOYSXTiR38z6ZtHNk1PlpUZadODgp5Y0+oDb6mO8m3sFWwfHrykTdhsCr/0J9MTs+IMUPSHJAfiBZb7rhvVbbJyEeWaMyhoRrpfglEKsPgBTh+YnKLD4iut7IRnN/FgCzJ8t6wEvCRa2OHxB1coS13/OnTkMu744KSMgz1Z4hmvPuPlbgjSG5RX7KfHHnqcGKbDQfhEbG0W/UrzQR2w8FovTt6+1TjrgT2fUGMIsjY7hjI+K4xSbAzyeE0bkRSiixxI1PFRwRVm7ASHAsm3QiM9sMBbEuhwqdNHvZ10y5h0ZIHL7js6l6QlfYeDf4KelEsKeeEiIxWlkKRJNsMiO7MKa0Lo9ueTMK8YV8u3Rr/DICStejU4L1y0Kkjj7HOdGMZG0awaZ0WQT+V8dM8bRmMIuis87xOEIUhyIhF8XGZFYoQqHJkP12JB85+6M8259OGV+3PwPXAOGCHB96TFuMABLPhOfUNa64AbcHGuZbAz+4PwBa35Fp173HoGTHfY9140UwdW+QIy3IWG7Sq+uJyymcQhbt9erZCTw6ahhsmJQdKHkbwJkWpH1SjsjB0oV0qzNUG3zSEfJvGSF2GoGiv3QqZtGBycYqZVQH/VEIQu7uvtY0cdsG7dl9PAV2oIouq0+bVaoaS9Ql5V2t5cr6Z+XZA2/EupPGDuHZE7G2ENbGmDTmHHc5PKhBMc9gh6IwPQezaL1maAvBnipIjXmhD8W5ODVy8xBOloUX0a9k4rgt74G3HoOEKelRTf6yFcQ1hXBom/dsPRRKOTAboZ7kd+frS0cJVBEZLG5S1mBk5O3IqTyUhlON5wwsBMJv3EQrAyPHdxC9lvs4hVLtQWXOtKrJPDkV7adlqkxDBQLpnC+92tHsJ5kP5dhqCuCmI3Y4w5rKDoZzMYgvLtt/YIwhD0zycyBD3SIH/PS4OzT5D4qzf2Q7iLlYd7DGSRLtqNxJWyvgkUiW9M3Rd37kYJh2Ljl2KFAKz6M535iSD3ofVxvysZh6PJyIcRapc09xl2O13YH5JElsZtkjBKxcqbzRiC3qt+vtWElaNwWf3DEfI2BOntJvX6ZAhiYivDUSaoWs3mtsFgqrr7XY6RXyeGWXImr6nX6ASj5lSUiKyrjUYWVZf64ocu+ITLIJNNuxiA783cmJMuFHF8fFap4Ww4Vdv2H6Ol8sBgSKboA+EwtPZeJxuuU3/ML7wCUxr1DBbkOJudL3y0o/rrPYL0pjLH22YjSz4N+4eMP6wI+ocMmT8+u5+VQnpbeVeninWpu/cvUB7eLJrsLH+ym1ynsJSijk3Tb/cIqjZu/S+jAVoK7QlH3SSUtpJY+yLSnbgqMJhqh26jTp1LeBFh5Eg7VX8oOng1tXmZyykTKJt3sYHVkWfDwlzhDfcKv+CD2p8hEC+ylDoY5R/mXPMGvNSGFGl89VgCr6yvQynFH6UVR/6J3yHogSE+sMolflmnWLm2rzwQDxankNHpsc11JZ3izqBKO+MtAgIVpEDF9/u/OAy8aKWjq68uqUceA0oEe92uMz/VMSHm0zAMQtqhRm/2Xns1kPYGwiCk3881BGn+x4sTHx2veeJbNov2C5MaguJnRdCYOjTHYCRZBiEJi7Y8uiCvhZdgVgtaC91WSYXDHzQtDH6g5bB0QTkn4pxKsmChCM7pLhnjgJQL4IaP1MAifZLBcYGWlGnNkRRZUH7KkNJYPpHZ795A7gH0olCY3/OyhZVBNgRxKINOD9Nee6wc/wkj0I/aJ0iHjPCy8DGrgrSf5H0+DcMQpCXkPjVMDcIadYVGfsUVRlr/nD9ljGvf1LTWp5WOPkn8U3eh+OKXITwa3jSW+x49luTIb+NpGW4QhE7g5Ca+ka9CnDuMobqkFxjQTr/Hd7jRBBhY2TvqSXDrgBis8of/ERU29wuvDE4iiQcMJ63PdICiq9Qoh4VjuNPOeaY2m5GyC1ZreKlcYxaDsEemyooIN7uuXj2QUv9FMSDnjlgCHdnAP3NWeHusVSL53tcE8uBG6q37Uhr4mxmCUBNV56hmVVwq1N+nWrXBH2U5ZE/jnDKqEa3S7uEpeIYBMEKfowb1eZcMFBiAfGqYNgN8yaaBLAeWIYhJQVYE/arPafh07JOXBmMEYsPA9x956GSPIA1hGvxtFNIQhBFIw5lgdC35ka/DEqFyetJr+YBUfoU9gIXy6qdhEDDlwZVTYu3QGgvGoa+UnfjlT5wsK76S9Qvn0sY/clM8/+6XSc0PDGRTN8anNf68hlVBOh3Cq4U+aU8VVlBoRZAekp9qJVANQf+yIeixDEJPtPmuVgTx8KvfXa2+iCGIBw/pRJME8kLCXbuVOJIcklYyk0BlQ5C7eoMOzHDNlaSGwCAczN4lMOyMljQGCBtHJon7i4wHjZEnNV7SDimaSx+zglU6fDiiCTld8PcVQUxUWdnGZtHZ40qfhnF8vE5CeaE3mxiCqMtsjI7BiD2C0Ojv+pwxZhAmqFI8K39s9CFMZsAJyNctJt/WFVdXUEjDdaS0Lxr4FBYE0QMnMmVR2GVyenTmuDMSkHTlux5GxY/27z/LyWQPPMqiAZVquPcDCpPskxjkw8y6n0y82kwXi0lGllCGTDs0pJ8K6qPgNTllwvlQq37Yv4BT7tjs/F98GqZPGzk+/pH2wrovI9A94bC/T1cSsSIIwxCbwx9mQUoR5/6OimNHfeeztltD0Cjk1vtCGkiLoA3RvmiObjgjDfVVrWkHOYX6S+BKglNXpd9QwHTrP5pAcA1X2tVMnA5NWhEcIg+ckn/joG6yCJxnD8E28PRi5lMxoIxranPbMJy10s3rgBNCuiQc8GG4vKZsoiyqhTSJ5laCy8QVV/mEY7GADX50MmIq1el7pkXc+Kzghrdnf/RgYBZpx1gc/jBQSvyG/4jg0hBE2U65TqS8PDYowsbLnNZI/P2HTzIEsRpIewMxTmII0tYAHLbwgtXhJ0OQXrhoj6Bvvv1BRkvmfNSHmf95LOP+81JFOO7HPWtxWZgTEnBdEK3bkqgZCbhRkRE/Idoc2InhEwrUxUrC1PGUu2NxuZnFaKQjTmD7FUlGUQtM/IAltF8XogJIRhoC5VepXRrGU+mZ8ZN47hEvOXn1lLE1K8YxAumnVUGMl94wWtMQTue0IUjGoBiCtCLoiQ4c0bjLynJW3WIwYpUuJ4fxgjKzCvJLyZKvwhLP+U/RCOP28rckho+OBynFc+SaS5UvbkdnuDOApnEE2aKX7EYugxdfaEmYxB2nsMVnS3SWWxwcCl5RFs01Acgu8ba4g1u8yAuufDKngbegJFiUyjPEvhEJu86ALmcxB8XEA1gg4ayweVCrdDcVTh085+P7vI0hnjMi0AZzHSHvxdiShGfxzFYIC4f8pmgj05YEA7s1tVv3K1hp1WKnTIGgM2elcjy4NQSN9r6895UaglrVqZWX1bCws/L2Du+c8nXG3IlYtNWsFHMrXA0zWhA4LcgeJMtQRKcw5NUKDZIOgz2CbATCGKR9gjAE8cDMGyImBV4RxAohTQbea7NoTlt6LyOQFhJpAgF9DEDZIygTA5k+lJ8erJWJJwSOK0N3TpHdd0vBiEUJ8ofcNf4Y03sErW5CiS6k0c5hsMM71/I+4NFB4tlgd08rVTq0481msrKcukReF8UJdKTmYnJSGfTHENTPa3RMKAM934A/0KD+2Kc9yBD0VCuC+O5bA/y/+CzsH4/0ediTbBatuZFXQmhCwFsi8slbnUwmkARJIy2ikLeFICK3JFUYuPTrXpgWkD8mami9Lm1j46H7lYoTevMo8uYbTXyob12xQpbhzBU5w5dYucEi+R8yXKZDl9TSM/HEESc/aVw39pPqMZ+GsWINQ5D3CPJm56wIknGTE8RevfWm0dTlN+yLxSkoWhFEHY0hSDxtBNKE1cfGaxbGJNW/5Oc8yVn5tk15wmvdkqAJLgqxfIknqbCU0BoRHrH+FIwTqgdCx8hocOSnJUTXXpGmfLLSCE2JkGzkfB8uGSPwCJ2NoIvQ+iXZmZMviVgtxYQVJy2pHvozLVlz2CPoAYYgff7F5ub/0L5A//rX82/+pRVtnBqGIegBS9Y1ScXw45/rcow63jDawiJDhHY9qkiSofpl40z937pbDXwxDdDuaFOrGqqyOkwlnYp7jNURM/0AFRo3eA4W5oRWf0dg165kUt1mydnwaRTI44k6Qpjd8KyAiiLWJCYP0w4eSUo3+U4PHWm6uqc1ggFzMdTpxXTCCI1Wwg7CcLIM1ZegS2UmBDABoZuCuALGI2nCgzXlUqx4k5DbUGYi25g5bTI67guyDpMyt45MKHqlO57EsCvNyNq87YfOhdj4lPUVH/RD3D3osCUblk3c0BeOs1JC603KPbxLL794vAKgLvM5NbAPMgS98udgWQn0QuMlm0XzadgvWhHEnnprRZBeBtoQpBNiMQT5pQyGH2Xel3+Mlw3jxzEXpO4g0PazsNBW0KokMqa8LTV+w+HK1dTiczy47txCQb3kz/go6sjOCKkTSrVhy0x7KdWKKyB3VY4DllE6cWqY4lI0WXpG7OxjCHLcpf8YI5AK4RNmNfXgpQurwu+xIogTZTEEPdMeQU9ZNT6GIK0e9zjL/kAyBHmPIHQhnnnIn3wlbotsOeZimC6UnzD1PfVeMCrHojoHXXwTQ6Rf3eqUBmAeCoe5AoNc2tKBB8x4BY5vuNIXryLueMDq9rBgzvICRr0bUUp18kEnvX4TEWPCZkHkAs/plVUM2odUt7k74WL20tmqf/AeXZyzDn4zd1tvvhYB7JFNgqUvEEICTktZgI2qRTJ3/VCtMI92NOHRVeoFmeLic3XOChAOlPTAqX2GwRQ+k2dgQho9Eed3tBzmf6m797VPlvNQ+q37shr4mxiC/pqSUvn/Gu6Xxkon4mYjUSS5WsTqYwRJyqTj6ZfJzS75NMYFStyGIE0GvFG0DEEcp/1aR4jGEMQbIj4J08MzkwGdGsFm0n7Q/qBNprUaSNurxBDEZ2H6/aYJwUd1PEwOslfQTAIMC3zNXinHkicye8AE5p4ohWEFzIE5BRQlHUPKWS7tKurDSI6OUP/RWdOYkGRCm7dRIJK2pxcGbfRV+ap/U5jkGHR97DbdGZ+Caemv9wqSQYgBn4fZdWqYVgQ9wxDESRD6rOZfMgJxfLw3i9Ym0qwEYiLAqiAMQDEEIWFMa5S+kwgkjasuGo+fgUdhJftdkcqTkqZcO3bhO2wPR/MHhLdcyXW/Jh3VO9PBOPR70JN+SH01BItAkVUxRyJltCFVyxrEChhOyoohiD2C3s+x8Rwfz6bn7IGguq3Tw15pYovx841wMATlM8ZMZr0nkIxBv8sQpDugLBTuBFZ5Z9+DkR9ZljzItP+IJh497A9QW5mmKGazsSVomCpb/MQTky5ER32iHmcSl7yGnWhExb+JIxnYB+xoV57OSzzzEYPWdoxAXsnmOidGelN5R2X6/q5WqmmZDvsD8XvKp2E67e5/MATJoMmpYQ8Fv38/k1NWAfHJAm85Wd1DuHsEoTLZl5aclnHJmdrmdtCCLYTbwK0GPp8GaC02rm5ZqhrbpY1NQ9vSj6DS3K4EuRzAD6QVom3v3Kj65M0q3jYU0oND6mAr+C2WceGde3ZgoB14KyzwpfPwK2DLN8SCwIQfbguLLZw9ljqLHcfI/08XuI3ki88JNtlFPcXcRqoWCGp0A+LGsWpZzFvGMBywaDbEydJpR/gIHbz+WqhSF7vx+jvnworLwQCIBtxFq29AsAjaCdEvT4TNHnLQffj4SftCsvpHJ2zywkTjpfcK8kEh73SKbOZ/r9lTjz0iNdfLuJixkb31WBmExvM52BiIqEFKS96UYHCAK+pZiAT2+CqkGemCj5wLguQtAX7DwM2qd2z54ERnh+acnxpXIfgHVqFT6yd6xi4OuZ5lOMfpLXCVlZIpJ5XJczbqo8ZWwmrZnt+xTxCa4qUfc9472huSwxUY+zD0eLNorbp9/vyp5ozsEaRPwzAGaUUuYzAbRXdF0PfS6WjV9cEP1CMucErhNqDACiPuOJfysnhNNMWKhMEJ9xQ5EF1monv6FnbF3eKltDBD53Y896fp46dUiRz3bEOiUyUPKrzdYrwhbUlFA7SHB6WgxW7g9pwYDNK36Og7MEritE0Uq6G8gItBOBVYf4OLaMlBESHb7lPZ78VHR9SFuPjpJgdbIEL88gxaLkOxMkyc6zWgJIrUOZShuSppK2xaCK0hjMB3vRXNrSEIpXwd7is1BFHBqPKtpKnQf6ayYJfmz7C/bHoMC5EhE5irDc6l3lshABUvDZhgmvyhnQxBaO4jhiBvmitDz1u9+XnDKWE6Pl6rgDAEvdKboVcyBv3K6gl9TpMNdjltTCc0YQiSQeiTVgXxMP0bxiAGe4W99R2TADd2Oir9toaPfEgWmdINpGsDol96JfUVdA0DG2zikB/4ioQhgXFzf8m3IOMA338ZWI4juBU3aXEmdzHJg4ByPRhKvshCLvyYvjDg6zBz+ZJ9jEAe3MWaB2R/283x8U/u60jQrKDgAfpHnRrG8fF8GsYDs1dCjCGIh37Ux1RqJFLY3ebEKeQmmMNHGUglhgXBn4cRtsQOTGzCRlSYwfPCpbQB9nae7oOBO2SX6YLZRA+MlicJB5w4co/vZLQtpwsTW04/wSDEqSYfMFiqLntSK8PPz3wa1o0wtWk0+wexLxafiL3X/gjUXQyXLhsrgniLOSuCvrUhCD1Qr8kvd0Ah520a6Sl9EHgjl/wMdPEtK7BxicOieE2ZwVDRlBddCjv/QYKN9Bxu8r3MvvSTB/iTyUBZHegSAABAAElEQVSMkNtTZuSunxC6v1BWuEHIQ4IMQa7PvL0ETxNVqSYbWX7rusyKIE6/o97+z7+e2RDEG8uHWgl0r4Yg1WX4LkOQMvQpgdPUpqkveVuSVm+9ELpdEbSUchv4Ehrw+3s1KNqSW14b1xJmGpviexJtpo521hh8rnWDUzxwyI+HYlzh+Kd8iIuW7NoDpfsuBdSknftYcBfGBJCNPC3jSjS5U0jFJX/hqWAOux8kJen1G7vCCtS/6HYeDZ9INz0bDlIVtKeZGJnBCqfOk0znS+CNjybELiXYU/fwMFW+pbzBL9HgXYcOzPXlmsQdRJjxD9F8/7csnc3kdaIR8m/6QcOc5qMmcy+9J9BLj5leEcSm0T5KPi8HfXw8n1LrxckH7Q3pvfM0TrLKJ4YgjyQKq1P3LIXxVGHiloFxFRzGT15eEA1NRUYe/1CgAtTUyM2VH0QNKziOHOtS3LTTwkYAUesvjWIlBT/X4BFu/QDtMm2RTiASJtLwLmPD8T0n1CyZdojxh/IwN2Quh2b4LMwrgmQI8j5BGjjvyxD0o1YC/YghiD34njzSKlwZgbRX0GPNI1mZ6w2jtZ8An4jVEOSSWCRdxnf/oITq3zguoi+rtMG/LGvjwW3squ/MLsCFyV8VuzChWqA9fkEuwUmtlMWsJOc73ha785haQgWzK+WOo3DBRSN5D2/RxQqcug2X/sJybYhmrwvSpCoCgYg6F79xd+blC5Z5bxkMJfgpf+qte/iLchwaob5N4qVczUtZgGEahdvvNRn/EnYWrWVCspHODCff5l/fOMGz7NZb8gCFzaI3VLK/dV9IA1+pIcjVzxU23cSldo4KOdXaTQaso2Fc0nxdcRpcO4g0vosyqYW08WCAoGRtpCedTEsKCjiZXH7U66T3HLntzaL5Hlz7A2nFxGsZgl6ygkITAo6S/1VGoNf6LOwdK4d0cgR0epbWEfKsxNCEQN+Nf9TP7zX8sDwTgXmQToeA7Pzi5e4lSjfAd+v4du4UCWvAdDjgpJcSv+HhW/4LnckGrul0wZWjviB8EyM4naTrhvWldBGjR/Tmt7ITbr7mgHwy+lhOKxhjkB8dxEuDvgZ6vv3GsPOQz8I0ePN7YkPQQ31Ko82i9WkNy39/5MQlvenh4Rd8r6QQrR/QlRmTBfK0XnLnFU4JD98IugSzdb31gslZYcWBQ6qIaFyG8qgPL3TZgSThI8+RYepZ4a6bZlEZww9uOyThYicV2IGX+xjRKkuY+A0ndU8BJqpsGM3qNh+D+zMr216qHrP3gfa90tvO1zJ0vlE9ZnLLJuk1YjIV86dh+FL8t99q6YuHbPKL5n9nf4TRa/ShOCO6R3XqEMng4BJ32UAZuqqXOH+fjB8NBKfhcAEfjkAzeWgWgdaICTb8TF1VDixpXOOm17CoUMQAlPqWOysjEMusXMdUl1W/bcy5gzFIK3tkqeT0O+rzU72VZDVbDEGPtSLooT4NwxDExFSfhmHIETHGzHwmJikVd54pasVKIRUD3GZvGop662418IU0QEtg5QUVk/ZxVMepwAJg97dr4jT01R2Q2DSCJLQzMGEuaeNphQVPLm4XgZE++PIOtjt0EBYm8Qg5PVUR7EO5+ChC+1txhxqL75wsbOLHQ7baNyAjnLIIqPCyA6UwwqWd9KppRwctLoQpTwmVIuF7O8zcxLrwvwl3Ur9Z0YNG931g6rhpFpMrOCt/81PMzAqf5N1r0so0ALIltFQweEVb8B2vONAJga6aWwF4kszPoglw5iXdaKyEL3MaPg17yadhfBLWFUGa8/2sAxZe6pCQV6wI0ouVrghif8hP7KHnNSwyYbAinLAYYuTJWFnDT/yMhUrzt8eVUnoe3UW+Y54lJuKZkkdSMxc0MNJx5ZQY8SO9eggeSigFeFdShxse86PdEesPOOHmUx/4ZZj4+WfjjzsKjEH0IxiEYvwhbiMQewRpZRAvXJgHsoqclyucGPajDEGcGvaUPYJkCHqiPYJ4GbNODrvLuKqfso0uFLAI+MpPoqeGy9cAPOpHeMF7hUBuTTaIKDU3KWFAl26pZegv0y0IwKbXF0iy0M6Qh2wsiy+Drfac8hBvCVIzKnmya1pihe2xZC/mw9/xPbwjbyIaXJmA63dKJu3odJxmlRkJqeV08TMGtAD4KbzfB9CCp6RTBk6xnhaREIZzZHEmTh02A5A3oTCZa2Bkgt7k9vyQS4A1roE1grJAgWdIpw9dSHOX4EXcd8gTueFPgnko4HAQTat4+qXpu1U2UG9XBElHX4n7ag1BVKBUsVTDv6KvqZJ/BfWL43QScrRQWs9V59Jf9BqhBb8dJnQ0VMFEQJ/1UROC9zw8s2eKLDusCMIY9PpXhbWK4qUNQawUej+rgjQheCNDkOlEq1VBv+kNkVYX67MwPSzPprtMCPTIqOwwCG35kv9WhKQhX/5SzkDzJLgbgoBPWstKJwOIHsO+M5uLJh5Fn4dyyp9JSYWIvwxBSk9nJ/jwtGRkozzXqqDJIQN5MiEcx4OzBnr9OGmJT7u8wkcXTgx7opMe8J8+uu+3PM+0afRPP2rjaL4B57MapdUQlIfgdIiIU0NQ8iLfKaD9hpGCcKbHxJYzihitUSgpf9Ymwjm6WrxO0WDkOjzJRsEDdoQWjwns03ZA12F6VdlKIPPRgmAYIjEEvZdlkr2s+IzxZ01u+RSMY3FZEfTil1/9xpPPHdlMms/H2DDaJ6eIHh62vPFJGIag+TQsekx+nI4XEdBttUY9Szxpg0tEwQzUiSS95UtbNIybK8ymK3KEBQQOCjXMqCDIrbqamBCHw2pk8A0euWVfoTCM4VjSSXSwbJhRADw+aexnYcmdtX6a+s/eBXc04fQ+QDICPWZFkPa6+j//0mbn/9CnjjIEPdTbyfsPdIrJD90sWrTifUeZYNz0iqCIZr4Vu4V2Kbjod7siSHq4dV9UAxiB1L3I+eL2QjsxJJ7DuVwBnNpsEfd2XJh9JZRDe4TGSU94Gs8QHulHqJhBOVMlB3gUf+Onxuierf7kkV4iJOnTkpBeUPRm1T5xEZ0DwgFtz/mMcI4tqVbgnH7IfwPcdJNjechv0CJDugJNueR30DTlZkwwrktdmZhFpOq97p2+SnumMqnrE3D6TfzUzaSdrhdigAtoPXAJwKdhrAJ6oZWz/9a+kKfj4zEE6aXgr3px8kovA99pbLUhiLFRPTcvT3g5chiCavjB59cZC3NBcsaXNy/egCFT+n/GncyxOu+tVvDBG0RCdmaVlBWPRsiG1GTnwIyJhW8pgxv8q4agcmEErLM0jWx+4S7VwBm1E0dTrLTF90tCwz96DMQIxFzx++/0OhVDkMZKNoz28fEaXxljn2sPyR9tCNKnYTIEPeXUMD4N48XLrAhi5uKxHN76ZxWS9UlYABsiJJHnDioyt+XQyYiMuFfc6MfwPXyJeB3xDmu4vuhdDxTf2BI0hmGENnwjNg5CCfewwMvtJRROSVf6DYE/wEudvaAbfDwMKDu5JRwxbwovbkIYVIN2PjBtuyDReS3CBFLVw2HPi9TyrV/SFd8YGjYFva68gS1K17Mdj82fLaHbvvJ2E9LlIHH2waLtE01rhw/z1Xt6brpAN83t5fNr4Cs1BKXKpIm7BkkzVBnC9a9T1t4pXJf+uWC7zNfn2QHxulQ68qab094CTUA6gTWMKoxeAmd4wqDzUQ/PPkHpvYw9evPDoM9x8a9fsyLotXw2C9RnYVopxCdir5XOxrzvtVdQJgbzWZh6n+y3Micw2QjEZCB5prNHGoajw0U63v8A3VL8WRhSxmVlkNKNUrz65LG7yVOgTFoVd+9YuPzpnKDS0Ok4NWM9XJMuBbpzku8/snd45FJHR2lSIvk2n4OUwf479lVhQNdT8D19x/2QlUD69ItjP5/Lf65Pap7pe28v+9Wx208ea6Pdh3dsOOoeQdYg+UicTquYSMTFb/5ogxJGK4QSW7CZDEFLWTGugEL6EDmFqN0Qhp8g1smWZhB8CEhDBIQTeYJXSRNbnBJ1prnzA1geWt2EsqzOZzCYhDpdZWCzSj5XZFLLUve3+tzxJRue++Q7TgzThtFaFfRvGYNevdYpeK81sZUxiH2CftNElr2tMASR57e87fSnYdSJTHgVUF5MesmcfD3dIiKaxB1xGGB04tJKHy0h8WgAftVS8oU++MG4LD/p1n/Vgr/cFplM8AJlYphQDUHNmRwJUwJQmEBa1mUIYmUbnKJtDEHU5R90ZO1Dval89Egr3FR/f9JpYf+SEYgNz3lj+UAT0/tass6KIIygTAC8v5AqM3nUEES+dS71VllclZR4uyKoGrr1v5QGaAVeIOe2EClcd11p27ornYGNuF25IZ7BSVdb0P8Km737i4BCAv+FpQRaLCnAynQPFwaPPXzEd25ADyd89V2kew5PwmKtgNm19wiXwTaLRePY+dI0sY+7FE1QQE2eLmsVYdEN+fLKGEAZ7AOFGMLTSe7gFGomCnQOtQEPgslkoSue8sLNkSVfAHNF+EExxALsAGhzH1lJxf08UvfcwIOVUjcFlH3mIkra0shv8bpgRRoOOuY18I0hiJWzL7QKiE2iu2m0Pg/joBDN//xp2BiCPmhFEC/5GDX8SbXytkHIK8HJkJbBT4PFMvgwixk4wheO/pWSHwJJIsu2IIEJAwgOPy8xrJqBkEK8WE3L3Wpqq0qwSIuC6nNPEr5UHPH+jjzCd782Df8yjPGH8mkpvSYSNgSJpWYfhtusJgG/1ZzxrgxBXnkrtfGZ9fPnT7JZNJ+GaXXQ0yf3vnmiF4lPZAzi07AfePGCIUi/at/5S4TDEJRexKUgX98HldYy+HIUhPZj8cHeHXF+JO7+jmPCi/RtYDdtcYJ2bVZlaRFCv1E1VVJUJkBG3vyiBb5fU76mX/ggNrP6Fyio6IoTrKRuX4ODjuEXXSsYQRy/wkMA40/CZTYrXwW6b1z57bx8t5WPs2p+gzBRxyrbom2GlR0fAvlphdF4+83IMxyNAycwg01s3RZVzn0rg8lCmKHwXaZchqRu3tMEcJfX/G4vX0QDX6khaFWXUUqrFdGpucuv3lKJG/ty/i5rpdire9JXoxdKO4fCbjIEGU5D4l8/StymlZwUF5xGxyoKJgLv+f77ozbZ1Wqgtz51SSeI6RtxHpqzZ1BWBb3WvioYirzqQkuKbQjiGHlNAjhNIt+IyxDEQL9OXlK5JDwdk7K1I//cPYs58l0OFsRdgPgKHxoqp/C7clXPuA17k8wQWQ5nfxl/lM7DMtyr3xiCRk4N4JpCJWIsMFuyDPQe5JlxyBj0vb71vqslDRzr+UCGoEcP9BZHy3of6eH52aMH3lj3mQZ1jtx+rk/FeKh+8FADujo/PwCjRuXA/BVD2FGq6CqSVheRRehySUm4pU6Zj/ojEU16Q0frNDIuHmzDIzmQprs42Vdf4MclgeugnEIHzk2pmL3QaZxPJhnUeC2jfN5K6p6/tyHod9dh9rd6oR9Hx79UPWbD6H+zWbQmt+x79Vqr3zBsYtQUmTebXsfGywDkVWIyZuqjJt/haFEyefVPDUFIxx0aHAs2d8UVh7KjJ9qbEqmXU6C9fk6xXNCwAGmHKioQA/+QD25iC0YWpODrxhhbidwbcI6f0qijOOGxcq1pllR1N1avLmEPvvcg8EkmHB+PIQjDZla2/Y8MQT+x15VWBDEprSHIq+JUgftpmA1BiuytMYJYlPUA2nqFIQmaW3ergS+lAVYEsa8KjpbU1XSNe0ygbzTKtCtjp+05gTrspBUwxtalDgVe+gnQyc99x5aaYBrFtHKB9lF+lyHhYJdJ0yvLkUrv1c+84Eh49e3I1Q5MYf83LpYuS1k3K/zJpl1O48vfcS/CZX9IeIEQpQ4Q6RFE/+1ASCnxKJvy2AnV+IktTkUfsLxAcr2ugKAo1UmTDvJ1qBvc+hUdogaVKwjoXDDDk1JAZQeahzHGvoOe+kLaEkfhS1djC3icHMvYmOPjs1n0z3xK/Yv2iWScxBikFyavZQh6y4ogzfdydHwOWFgGIeeL7Bh99NPYmbFRMIeBUS516AhnfIU32dFHPjsBTJirnO+lWxmxKSFwYr4IdoSBJQ6MkQYaOaMeKeiKFIvTMHgTdnDFCz+g51DkOGCZoyVOWD/GVfYG8huljq3xvT+QBGSnzTuyofkF4g/6NEZzR/YG+gcrgi4MQewnyTiMAYgxF4MQpe0LQ5dXeUZ90YlLnGKr3JQ9WoicupI2ehrtKw7QmrKfyyAu/EDJJTXwAjVMw9sdAfd3cOCRLOZeDC+lk3V7wKKXc0hyf4GdSzIMN3jp27+Vzx/6JbpEQraBkRNh5zhwyuZ0X5KWepYyQVoJgQ8aYLuFW8CJX+7MiUYE1uepkxWxeDufyQy+DlYuIjujm8IjR2nXvRviVV64O1EyEiwd8inue2QmkY3kfGnhFqIY9SJUyHqPPYJAunVfXAN/A0NQq9uf6+rcWfw5/v82Riv5X+e7l+3mJnEt3yEth/h0GHTVHCH6rR6EdSQ8n9Ro49w3rJKQUYjPZviE5qVWBb3S9+KvOFKe5cGzv0pWEH2aiQGTZPHhsxkekv0QrUFfhiAtcFUxkZn8+KVjsD+NHTn6F52csEQaqdulhEswfW3yAkVH27A3KVDW6FE9yhc9gyFs9k6KR1V3btOp5bkeSRnUmyk+xoqB8aZH+N7kWuE72lPl/g98w83bmjv6LCybRPPw/FSrf57J+GOD0HM9UD/kDY8MQdpkd+0PJHXyAI10dIoM8LjkR4h4NUuZKtceAo+0zR1ok3IMphuWmUSPA11sVsAJZWfoOUnp6GyXp9iX/o5zDid3aUEkvh0GWCsKyZf6mU79pjnWB01s2SeIFUGpv5rU+lQUTXJ/5lQ8jJt8AokhiA2jWe4eOk8aWQWEAYhvmDSh9Qohad75WFtMcPlRUOoTchEXDvIphEOmhJGTwa5xRUQavY6yVhxKOddHqEiXXzQqQSMK1YG5nPOVzqmH4IrGhsRBCAfqq5wJVWf5hNERASS4N6lno3O1PZarky31EIMmP5ahP+SzMK0IeiKD5o9aEfTPn7TZuTY8f656/UArhu56RdAdG5nIv6eH9dOwaDRiWJSjmJbLoulyawg6dHQb+jIaYDP6wxDUFugaeo1AhdPS3Px0BUa8aQri1LDSOySaa+gIa3heJCdKoxx4oeu1mPULr38TvOUSHjN2Oa7Npf2u+61JN5JxhDVsi9+03V9D+Q68Idwsyq/+VfSby7OEElH4hYu7UWBltgIF7P5Gs/AUuFmgnfgivBgs+A5puP5CcuDIsPMVBkNwPbYQOFBCuupXa1k5axajID8MQS9lCGLV7L9tAJrj47VH0AvNAV+xIkgvAG0I0krbfBr2vV/+kWE+C2P0YPNoHGMmvXtXBBHWz8Yg1XcGgK4Iks84xVgZh1yUiTFIEP0cdhAAMmesmmRJoBrppKRvd3VTxzbvM2MpKv/iiHxEo7z6he9phJPLpaID5dqUtYJdFJmbU6YYgbKHZMKsBKIA3/vTMD4P+03zP2lPAy6fhrG69rn2B+LTMPYIYkXQk64I4sWhDEHMLz3HFCGl6d22MZTxHP3qQr1HPuoPjpW5uMYdmUu1ecAGdwESRyF7Ww0daQeHYCbO3SV73wZ4ESAeJHNv0BSOqAxOue7Se5u0vUc97mXphvMwS0zXJUzx8Ad3BzV8szDGgB0oZUv9HJWHwwXrFUWU5rH5CzYBdFjeoAG2/hRY+Wy8uL9uJ5RpMhtv5XKKrwxXcgIXcMq1P3dWpqLhOwxzZyA55FfGplMe/tMNuHTmi9zeLPpCjNvol9HA38QQRE1ztZO/h6s018S2gwI/q783mj3jdsSkEz7jUaaRfVrQJZ5L3VYI9s5jCwcFXhrARUT8vZ6CP8gQ9I5PxGTseauNf9hM96WOjc+m0Tppic/D9HupX/ZW0ffi/qRMD9FsGq1e6DedHuaVQPMgvd4C8YhpuTM8kS8Oj3Iw0BNrJzApxZAv5+KHsINcEnQdfolHT8WEEEgMXyn3MBM08VCAU0aBRIfhSibdHyiyAJfEpkHyGIDAY2oUQw2GIAZyjtm+ozc2d755LMOPDUBa1ss33qwCIv70mZb6ssLiiXB5s6MBmgVV/qxG3N2JS4fhnbwz+ZnwRBDHqhYYSbju5d3LCp7xYWFH6mCMl3Tq45EUXK5CEpPqzXX4zHBQ3c1bkgCqZ2IN1z8ge6rpPNJRb9tGgEZm31+lq0rqMy82wPyYVT8y9vyiFUF85shGmC/YPFqrgzBuYhB6rc8dP3ASil75v9fKoExYZzUQN0ATVe8VtKZXxDH68CNvpl369dSxKQZqSJ1TMmF5qeUK1DEJbhhe+vc9S5FWyhHIIOobDCF4wzs5BDN5S+foafhyrztdyueL4EIMTuovENczS8VbRJasq6SoQTjfawLPm0eMQPfYI0iTUG9YKUPQc+1x9U995vjjTzJuyqD5QPsD8VkYuJ73i8Md1VGeBTjVJLAUgXyXo1z86iQABqhM1Qu89W818Hk1UEMQ7YHq6U8saWiK0cbSwx0ygRenUDtqE++VG4ykr77TRAc1LdNpyoFxc41JB4opuLitW5LKBPXhnL2iJUXmS1iwBXVeg4vIHiwEVthUwOymTyJuPKGutNCvqNg6bfCMP1yG1eJB2nT33vh2RzsJvZivQFCR10wEt+ykI8CJk9NMScEW3o4TrS46I5MO/o434dHTSg/zq4h7XuLjqjRYkOzx5rPXsxhaZlw1QcZh60wErY9kExdY6o9SlUHy0ViptyfsBblO1tRLE06MZdz8RS/+WEHrz8NkDHrD8fGcFKsXfqwCj0kGPy/8Mv/r+IgvZwOQevDN+GNdz9hpFQnt95kYRi6ODlH9VcSSWt6W6pCf4mX8IiM5MyvH4xblLlYZ9Zue0SXQcNzDsD3ikwVAO1KO/AgFV/5U9kN+ysNPr6zG12zFd4sy8LvDytwxBPkT7B84nTPHx7NZ9E8+Pl4ny2pVOZ+GPdUYzB5B9/TiJXsEsXqCu4ITLyoSutMvcsWnHlA39tXvJuEiOGTXuVTbtgnq3CUWuVAG0i4TE4cHhMTCT2FgF854gqUXy/UCxVFe2OLMz6V31OUN2+Oa0nOt7KKKWIGJtLKbygKWe/hGWBGttIHv3vBMn62EiVsowlt5Xc7Cxi96/bKuTnfVosmUa+MrQvOdfFIWsj0ADg0e/CalWdmvbMjh4hLAjQ+Ny2hYnuGAVT6jCdD7i25x5mXE3t/UB/hi8vOfbjl4D/RFRahMenv5ghr4mxiCdg1NLdtBRzM4QT9nxAOwWsm0B2V9ruJtKG3wkY3mdMYD3g6LsBtcWx+Aa5xxDGeoYDBXo1NjYwUF+6pwesQbvgPXp2EYg1hRwQlLrJ546TdDfCqmN0NaRWE8DEZ6iP7IigqtBuJN0e/fzWqKb7WmlQ7an4fRUeehFxnIF1Hd2BXgj9gnBk6XIXEBDV/+UsHq9laS1dPextAg8zCOCwXh/oBu4Rt0ZzkRxzLio3cukTEb6s5bnikHXZknJ3rDc1eWnAc6/vOBTnd4xGlhWvHz7PFDf07DqWE/yhj02CuD7mr/ID61kcFo9lXhYbmrgXhz893ohrtnOZZMiTtfZFN0aYi4XQZLB8WnnwXBcqE40YULia5w9qC5INXEJZXigK4yNGXv8sGmMgOJtJc4ybsUqbMWyBTAI6uphex9glSfmWO91+TWk1f2udIPg+bPv+j0MH0ahoHzFzaPlmGIjdExaGZFHEOQzCXbirZMuM8r2/ymk7eezp96rQmvJ8WCSA7kjmEqYUZBw0bu1Pfcj1TP0aU8lyXFmvKplJMswGVkcOAut/JWDq4rKEMyHeyXxtLOVF6TMjElZyKjQJavIw1vKVX3+Ezxji6ebMqgSZ1mMuq6iyETQ5D2uHquI26f6PPHB/f1NhODkTaLZv5Pab2yh5VH1GXJtIqptLrqz3GJg+zgRlvFuvVvNfB5NbAMQVRa6uXK3g1oxQiQdvSZJ2SlXIcvCo8nUOPCHcwTthpD2quRBq2SHJhA0scM3nCJTGe8YlzxjQbFlo1gyT9yeK5ysLOw7msEq1SR5SjHKmYT9oxLNDzdNwoGTZN29HN4F+ScsuV+JCyG9Hr0ffF3BKNw2Vk7vgEWn4PyCG14B/CaUPCKvXwFeh8jy5aZFG2ZQRY488qyVlk21AMKMA9r/vyKmG4oLwJ5UcLLkV/kYxT6hZ8MQawIesGKIOZ/nCark2NZPctJYTYEMe51vKSjx+iDzwrZWQF0GItSbzwXrICa5Hgei/5bcEqtSNaAZIzK52JoI0gNcdcYgUt6FLuQ3FuUxF/cVX9PPXDAU4pZlQYN3uwOScAJZsZiwpKFyQnzQ68MyssW4tl2m9VAvHD5lH349BLxLi9cZOjpHkE/2hCkUzk1j3zCZ9m7IUjzRuabSMoq3mRPXgKQ98jjUs3FJ4eRvoq3AoMNLS7aS/kOnI1w1bn9uQQmB7YFEa/4wAktnR2Igh5ubTgsUDmQGvrjzpVfKSNxJS80/lIP0Z2pkyPIcd0QCNLJGeSIKa5chmShFQFSAcuCPJJPEc4+teXkTC+NDf+mOXrJaOJ47peNHOANqGFH4sa/0fpBGhwBlywT2EgPVIgpaf4Xe3CDnzLRLVAfvJm9+FGXHt4agpYev3Tgb2AI2lVE1aLmXa2S7Rx27M8VdodnkSJXOsxz7m2wq3Gdk0+xvcMN23N5m15NTK7ikUGXOPmwGsKGIG2Uwia7WemjU8PYS4V9gjQB8Gc2vDXiKFE2jRYeewm9eRcj0ketBmKPIBt+PDGQIchvgBjo9SCtsF+CKE8PhcrYkxeavQt7aQhqUVumkd6FKaw4+EowGITLO88QCDxpx2PlxIdd5BhijRQes1uH5sag03CHiB+l4aEZn1xcOvl0YjIEaTkFn9BgBMJnM+jnT9jsL/sBPdeJYT79QZ/YYARiGTArgnj49UoI5lbiTLZMeDyYT+WIKCN804RnyJpFh75YYmUXOYWrBPif3fHYDV3r6ZnHQWWdGBEuCqzwztW1fyUldk4fyQ1sXuSSMKHmWV9pzg55U6fZ64c7wL4/b9/oTSZ1V3WWeswR8j/rZJSXLzZDkCa+1HkMnxiDmJJ9a2Pmd7oHKJ83nfi83cxbT90ZhfnRm+iusBpoDEEt/mEIity+ZQpS7qM8KVvqJuxSLm5d6plguIEnfBEv0EzF2yMpPhNoNCEZzRZZJ6dU7KTzdhKJDBsD5oTRJLRsWsmqIPa6usfKNn3qyP4/GDKfyKjJ/lZPn2rDaPnPWNkmoycbo3OSyQ+isSEHPvp1vyD45q65AOty3QQNIxS4t+5WA19KAzEEkXva0Gqf7kDdCVk0Ves4VXD+0uIBpdUPaIuHwtegg+w2nxWoidKOTZtEcQu/tOrigAI8jIJRvEMSsCtbccPBUiQYMuMZGpZOc1+2pYdAV2DCow1vnE7JjgztStgDO6HwOozJfny4HQeo03ShU7Fwg3otntKKBzpRo+eKXg8yQgfGlnDkcyCby2K1y7Hz2MNmX/74yQ0ZSu7yN2L83Dk61Uina0jlEVZE5esmsmbKBeRxoHz6xFgnpwjzPX8mrXGST8BeaN7nsdIvTd7JOJRVQeyr90qGIMbW33jx5+8WNTAw39MLP16gfLde/DE+MmPhJUpqZV88Zq8/wVQ4bkXmjIeArdsUDGOQxzEROxypBUuhu+qGouDg4ts7YWB1mEisnpEn8xZRQGR2I48JFNaYbuebkGCvyb2x+tt9m1JTZjO3vNN3qAzMv3wy5ydWBPEihlM5eemifSUlz10bgXjxon0l79/Np2GsuNWm0cwfeaH4VIYgDh1hzujPteXfFXEPhGBMRxfUFAxQFJPGiW+9O9EIiZO+FSyYAONME2rrH/3l3m5ERh2eCq/7JLrMR5BH+BZmGCseDsCVMOzGMy53vw74QU6IEsbVJ4ZsjR8hEs75NL9kPPwom/BCtyRZslmCMh+BUrecMdkfbB07AK1OJY9ei3TGc7Vp0hAEVp1FF0vCMl0lVxkKw5/MCRa+5G4+pIE6ceNNpDAnbZEVNLPobokw+YOTOhPmxEvnsGjxaefco+hfm6Vrw6zKquRb9wU18DcxBLVa/ZGmTl3CHyH+r6e1U2z1d6MYkRP+c/lvwjMljfAGd6SkVfpBVbiQ8Ibno1ZQ8K34Wy399UbQrAhiJYXeEvFJmPdV0UoKNt0lbkMQn5GBLwbw4DSJ7/YVQf7+IyuE6Fo8AVCeWHtp7D3CFNmiG3cBxPTDXfrpoM5pia0ZY6P2pwda4cb7WKn4ZIEekMFdUY6Hkbx0otQX4eXfti1gxA8DkAap9fCMUYgBRD+92WHH+4d8QqNVEg+1lJc9gRjIn8gAxCc0TzkpzOk/KF1GIK20uKflwF0FwUMwMpIdPJlaxU0Hj+DkheGqSYMxQh6dr+DwCd4xsLoo5lIKEAXVoFGWlOhwW9h4STGUp4AbXO/uOXnHj0w75MCFu34eyIIRmQJvffZkU5i/ffzGm5q/9THxeavJ8fG/sOzd+yHI/+WV34K+YQWcNo1mjyw0jNEHwyUGTIw/2dySGxGjkOHrUzDuiDZHRy79MvFTUNiWyfIiIwNcXMt3+EoPiugSdjsf/KSBIAr0jTMa4cRTDcoxbcw5AjKu5tugTn3xpEyTTy/HV93t3gXU6dQxTGIilgp+UH3kxK+7d2MAuq+J6SMZgh4/lQFIhqBnmqA+1xHy1OVn8h9pZRufkLGx9B39mJhae8ihgLsFhYEh0u5afVwSXfC9kmhHug3fauAza6CGIPfrV/J2bTW0s4s2U4AeU0pDwrTBgvB3fFoFD/KdL5DWfiU0AkzDIb/mDuiUl5GTGryG066GRVheXtVvda4BXnHNAflJN01ThBP2C/cKSwCDc5n2R3G6UPqKP3YwRpYKobD1fB3VX8EDZ9wWDGQrxJW08t5wKlP52S/eCTiYmhuN7NzPFiN1JBl+Ryc6zjnpkhxTH3LvioF/yNN5F3kQ/qDJ2696acIJmq81XjK/+0UvTn5hLz1emtgwBFzzQY2T73Ra7G+a6/nFn8ZBG4C+/0H9vMIcK4kBxS9R5gWJsrbpA1/TIzZep0zMBz2XmlVBq5a5iKnJtIFPvGiTnMwfGLdESshXjDsu21Q+dDSQ1EPDU1eDcugtmM5MPHBHnQ5UuGQDJ/kOErVrrLlNuvG4REIXdIMhn/fcU2k+YQTy6ZzxWfHNKbMcHa/dBPS5V14MPtSLFU6X/ccz7Q+kz8Oea8zFEMSKIFaR3xfeXY3RGI1+0GC5dILWyU/5t5foSnDraZRF+2rJWzp8kY2jjLsTN4Ny/9JPBcelNj/hHwzMv4agcOpsaPiaHEHFu3SCOYxvZlvCJs6l7Ga10pMaGLzLIwjtsxa6Ja22fOePJEKLfHJZ8TOaUa9JM0iklbH+JTV4J3IQBThgEzKDSTQTZD+wuMVx3CvRKwnQTkH6QgvySr/Em+TllZ/rg7M98k4dCaqhzUQR5rkHZsrl/gh0MeU+/c5zmIR+pFXlnruuXG8DX0oD/z8xBKUmtj5+bmVedkLN34OhIjel/xU8Nypapdx1/I5GR+mjAdCBs8Eux8h3k102z8UY9Er7q7BMmFUVfF7zkg13vdFujpF/q6Pm/c04n4ZpKYY8sebBGePPD/L1FIklgzgNX5nZTKK8MhnIhIeOK2UnVEfoiBUKLF37RZqjvbN7J3PAkAGXx9LhKDqT4qvjqSzI+kmTDgZVls9Cil79WYvix5soaFQqG4L0YM3gq6vT9aDN6h4MQBiCWAXECoofn8kQJP8pg7k+DWNAZ9Pd+6wG4ltvvQ2i02bKkof3hM3XYiOxTQb285YHHTrxmsvITyHk0DW8QAdSsqQOUB6jfXMBs3ihij4M3zI2j6ujK9xMHx4HpyP3M4YJhiZyZQJng0sT7UfGGoLIBO6sDGJD8zfUUb3l5Kj4F5rUetNobYr5i4xC/9anYtRp9sXyptEyfmZ6onprI5Dy5E2nV/9Qj7PKDcMPdYiTynKCGAYgJsD6XzpDv4dsFZnBEfn4Gd8Jo9tJQ6dUAHBc9uMygHrghQ8B39G5Fxh4UhdTl9wnkNZ7P8Yf13lm5/xcj6nD2biSt5bfaWLpDaLVjjEEPdDEM58wsj8Qq4E0MX2musym0U+14s2fQOo423tsdClDkOhsCJKcWREkSRF7RM9dtRLWhepT/QCkC7kObxHcBm418F/WQA1BZJM+2TX0Sq40R+r2pQv2JY3i0w6KT58BefqhQGm7GR/D2FyGrhxDRbsJJJgH1+KR4cZlEM7YpQIzY4VDBrerNxcxNSWXho+Myib+4Di5OHu2hZ2pJoPlXabeHHd+NzEtmZBOeLtAxZEP2HgbbPQcBQy8N95Kat71N1pno0uVuSX1DsYQpLsp8sVBJCbVlXlIYyt9Ahl3TkxPkU+e5zC/YWVP5m5vZAh6q20BfsUQJIMQYyV76rElAPsEvbQxiNXhHK6A8QhDkNjK6GNDkMbG72QE+vY7xkhqBy9ReJWgnwY9UJln8mynocl5p45Dr5/fDpzEnHIzR9Qcix9/kpk/YriMCwrPnINbEB0Nr4kUltOiiC3IIOK5VkfhvpfiPjpd/obtoNORKC7Ric/Ns8QKO1dd0Aa/3xlzZQj65pM22dTYy1bb33+vn3DYg4+9fx7KIvRQq8mf/1hDkFYEaVU5p8uygpyV5vcxAOmFC6eLsY+fq4YkyoElGXeRznNS6SlyWJAIDaDO4QOQck2ciPRygg0d85pLt0AhKKbzz3x51CvEoKCp3AP4QW8VXs/I/OaOXWadPAZanPI7ISvjxV4JeXai3yt8TxUw/yNl5NsxTrwVgY/dDUg3gK2Pki76yfsksBMPzGOsSLbcL1zzATP143rZwQPH+EO0yiB43ZHjgRu8pKSWCVv1beEii5Aa71y+8auGIKWI5NYQVK1/ef9vYAhqdUJZ1GDi9YHFtVNo/HP6nlTRkV7Xsjb5+CYWOf8Qb/i04bv01/BtujsEN0J0Mp25iCDxJFejMiuCMADZIPT+dxmAtPpHRiBvGO19VV5pgsAmu2+8mbQ3jWZPIc0I2F/oo0Z5bXenrPhpIqDVQSwZzkN1hz/lqbw4gYWG7wHewueOaVTUnbt631DPSD2aOns9DnJYTWeDFuEVF62m/K4bRVYyenDefoBGFskhmLF1QY8YhDxfoUPzP1MZ5BUiA7rC+Wn6o3vIMaB8QvP4IQO3Vk/IEPSUjXX1VocNotnsD0OQB/WHGshlNHqgCQAnRTTfvM0hf/0kUHVQXxkKRr7jVr1omZogHyY4+CiY+hi8SUm6rwMxP8mjKBM4cju7xFvP8MVxfmdMkzvl8g4nJZShRQMZMprf4Uu1zgHu1ojivF1MnKs0IpW805tOJrdvedMpY88LGTVfsO/BSxmCXuiEFCa6MgT96s8d33tvrJwMxHJ23nRKBuoy003qc39eASSYDEFsmsl0lD0T8FOPcndSKibD+93atZMyRhuROzQUgbwpSeD2dNknXE07hlYQgum3KUTEB0NMtRPdSTkSNEvTFdabSSalXhWkOkt98ltLVT4MOXzedZfJpvYG4u2kP3NUPX6mOvxMdflHGYKeaL+gRzIGPZahCGOmjUdMTDEESaQac9J+LJZlIm13yIkOdx+aS7yd5jZ8q4H/tgZ2QxCtNi0q7YsK634PH0HmomanpGmQRERH7NwbDP7CU01XeOvRhRCXPraRQq/zk9fRh4IzcmzoSQcQ/D0EhM7GvpNT1mzoO/AWDVyc8OhfrsnKyX/1giqsrmFlGS6JLdMl8Jr4tXgj+I7um7UDFL4u4/Kzj6AXNI3u6b23Tdv9nR9w4SJd50YGcbFyRy+6LydDkGRvPePOZJw+RGtpU3+Yd2Vuw2qg3xTmJR6Hg/jFnsZLjD0vNc/jCHlOiX2h3y96Kcj4+Vp7RHKEvBbPap6InLwq4439HfXxevnnDh4jEOMnHzuxKgg8/fziRPNMysiwIxj4bjteQSRlWJ9RCmXyn/B42ZY48y1crg5yUcXb21Xrj9PNUyHymz/DkxnEieIbJ9xjzJqki+woUxyBRIDl7g1o4oYpXPl6oiyfg/3++4dvvvXnYawE0rjLmKsu4IHGW166PLiXz794echnYT/y4kWryT3WPoyR6L5WAzHe8jkZ4/VRGo3lijB2RsfRkXVzIKUYXGm8wL2iC78lc6J55B5Ii65zwAen/MTALJI0sogRzl7C1YXrLbySONegm0TMyu/AavrK1DjFayp+MXLfJYKybzuwpOtGFrPUKaMFKuPBpQQ8b3guBr+S3OAb/4a0BW4eA4CG3+7MR3jlt/vGqyBK4B6jU993EG9wrgvF/wO87SZYhzewizLgM3KG5TAG5qACKzzlFPyqIUgGUz1z8WlYjVc35nub8Fk08JUbgv4THaSj+k8o/rdw3emZGa1BLeE6t4On/RxN/zqCA5ZO9YhfF8oKhjbI+Dw0s4KCt0Mf9LbHBiH5vB1iM10MQSwX9hsijheVIYglxK95i6Slwm+1YfR7n7wkQxCDiIw/v3+T1RP+ZtxGIU0K6Dnd4NPoWYNAPIPLKqzFTozpcBTSgcOJ11xqCAL/zAlkhkJnpdSEDYC3e3MSyQsc/XkmNVzUU9KZwtVzFfVIlshswKEU0OYBGg7gMpizzJe3Ok8fZXNojEHPn2r1hI7/fCYD0CMbgthThZUWbKyr1UD6ac8/50EWKX18yyDeuMD3sMHr0gF2AQiU2Rr8UuY96cAHubr8o9zK9KA0Pz8RHDAkjbTAjtAZ44g1FO6RgzCUvhuUQf/cKoIpiTAUZtRg0vlBlfrdB+1jpX0NWPbO0nZWuL3QRtEYgnwqysuXmuyyLJ7PHd/rrWWmJJ8+aSLLpJb6YgMQYVa6YeyJoegbGX+8N5Dq/PLXnYnkmAPjUgbEw8XH5HKEneCRWTCTh4fhc8nGiQcEXUyNFE/9mbEuTPaFFiOQMCgGEOvNmELhbaQkYHn6yRAUQ+b338scponlPRmAmGje04qgh9qTgNPCHuqzxh+fPv7mifYF+kn1+eFjJqUYglSPvTdQJqfkzxtOfpTGzwmEJw6M3+5adVwUJXgl0Y5wG77VwGfWAIYgmgqVlfrasXya66rDrsvGMZal7BBzFrm1+wxtjNSrGOYelC1Ymj/24XbIdB2TM0v1GVcFcPdxwhPOKf7HQlzL809I/kvJe+GuLwH37foURBr6GxF2/nv4muKceGCoSf3qZ1CLQpUtqBiCOq4ktTnYh17gsoWKuNPkYwgi8psyYnXQB/3e+YWJxkB9Jv0rhiBe/DFWeiUQn4fl0BD2EHqjlbYf9KKQvYI+Ygjyiz8ZguYFYD6pZozUuOgGwlHzlCnjNO3Ii2GQkYHJT3mS9qIxIW/GNOaVmVfltRspuPqJEe+nT5Q+4Wqh+MT1M2nSqlUwCkl4cAeasZUUEZs++QeOpElanhOCE5KUwXJqzGVvIJ/M+ZuMQWpsjLfMG/UljE+Yfai54wN9hs3LQuaNP+rTsJ/0idjTJ/o0TEagRw/0YkYvGjnAQe9q8in26DIzC17opEyoNj/JaT0ji5wbOYkjJzCnpM44OuUnHCxTOim8ghW6husnHygO/RDa8yM2+oNMyOdUgLgdeshASmI7LLiF4Lsd7CwUvkoHt7jUC2EMDRIeairnYv8Fv6xWvjfTkOVka6QVnmxX7itw8CruNUkH0oT+Co7vR5nuHIb4aBdKHBgeJCZTJHd36tTGq0F4MKYyxNJR+JlQHQXPXg9vPw1DK1+F+xsZgloF61/V397xX03970E6eUzzuJSPeJwt1jShthIHjvQT3qkVNqV83KbS8RfPbNUg4a3fepBWC2Q1hFf2yLbBQzRGntfv2Bz6g08OYwNBPq1hnxUMRHw69kZpb/R66B17BWlVkAd8vQXyw3M/q/GkhYlBykBj73Jh5DhEG6HOxTjFdq01bK6Qjms+q1cyHCxjjj/hjW7I5SFU0DoceuwUSQbTYaFuK12XBlwMQholWEnxvTaJ9icxUsMDPUQ/0QOzVwNpBYX3VMEYpM/C2OjvCQ/WGtR5eL6vWQBvddiYV/92ldjTPsvEYEQJScG/tgAHtAyG3+GJVmn9bvuMRiwQZylE3ydDL3OUJGYkXpD0ZipcDvD4EymFcbjQJd6w5TCIKY5i/KsiMcBgYPRKHsFstENe/TBqvv+ouvk2K4PYByF1WMveZQD6RUfJc2Tua+179UI/No1mvwT40S588okNQdL+GINYKcTdwdDkI+NtBBK+VwS1joOPbOiKEkwpxNdsJ43iOG68og3upJl2gcAOjQO6HBOTgcDb9yC4kKITG4SETN0pBwxB/nmvIBmFbAxVPRYOE0vq4Q8yBNkIpEkpBiGMlxiBfHQt9Vj7Xf2kZesPVYcf3OfTRgxBvJnMp2E2BEkd1F9k8QJBBSwXcf0uHWk4RDWNLtfhBev2equB/74GliFoZUXLpu9TD6T62XbovlA4rcMOEFFl9kOT21+ZpI02JmbCS4x+7SJ10MgMPOV/oCtNEburVDelBM4VmmIpdJVFkgV3myxJqa7BL7drko6cqotzQVyKv3ypLPX/jPA/wEP2luMq2ynZTQgokYqRRxsxSsa7PgZ0wTozCn+yO/oxjcgz5lPnYHfuEYvDrSTs+ySfXHHUVotDuhL5YVzxp2GatGEIYlXQu/ea83HAwsv3+iRML054ecKKIBmEMA69lEHI2wHw8o+V4DokpCvAOVyB1UEYgvwCRIYhhPVLEvXgzPtsBEIGj90C+A2FC4SYcpEYWdMCIufRGgjlL/iU9nCHIchZC7McFRjUtNzSJL/oNjCHjTtpvlHorTTwGokMazgIAwIpBAvguyAohefzMPYG0mdh+mbO80api5eAbPh8HyMPq4G0ApcV494kWquBftK+fE8wBCmdffoeaNzVsOwfc05+yZa9/tBB+gnuQ2xEI6uQUiwJl2LGR1Z078qShIAyd3GBIFS6UVLCi2sZAqbMEcnQUSJSFH4EBBXQ6rrg+MfR4+4tniJwfkNI2GkUVwGLQZg/AY5nMyFaAOpyJQkny1yQuW3MCa60gV96YZO8lTbRSywnwOpU34iDOUQug6LcY9rVTbwWXIFLfk3DX6KfIkeeBoNUIgWvuI0WXfkODz6xnRhZXH/wYYQv4JrbK0ycct6uCLqi6S8G+BsYgrZaaDU1Xr+6OzqNQj6XT+Wfar/5ClLb3RLwiV50TGkqgmfwb6d1Fc/kpidUPPivMAnqORiGkIcBOcuQMQTxlohPvLRCiFOX9GDM5IDNdH20qJcLv9KE4LWMQDIE+UhRnbqkN0T83nGUvHmTiUY0JgaeDMzEQFCfHkbehBlMQJU76yaw/+jqXuXqvd47n4Sj4+Q7+p7erSnptLgPCKaffCR1OmHPlElQYWX0YeaMeSJGIHXOMubweRcP0w95o6MVE/4UTHuqPNNAzmogTlziExt/ZiPfq4H88CzNacT2mxxlURmcZUSxUEueSGWYLpvbO+IUwrxUjug6vGMImrKV2gXNhMKUrj9RhaiKJdlGCpDk4H+qZyjP6OANUlCDfw203Otv6ORgCssvdvheTopvyw0oDOySXQzYk6D7XlE/+QTM9dZvO19phZvqsva8wjj0UvsGUdc/ar07S94xLHWVTwoBTya26IU3nHrbebEiCDxPgBmehRetCjY6SFnAQZcpYcJJ2WHwyhNZ8cCErhcCccHYIsjuqDgqEWMMnxKAx+kh6M3mtDEAdTVQj7RlebqNkSoDm1DeHSPQPfmPZQR6pE8d2ez8KXVZhiD2L2CT8wcyAN2TMQgaDEHUY04ckzbykwDIs34Dt6hzQcblJCflUFMaba2U28CtBj6rBnZDEHWUepm6ShtLaLyjrgYsXLCp92r7bnxKwB+4Ey8uphg0+Bp94Qxj4gqeZzTJa6H+SQDaSwqPNc2iicQVTt+xMb3AG7RDBxvqykhI5Arr5A9jEMtsJ/pr4Y6PZ2wzPYMu86jAF1in6IgVeZsyvJ02CIsXafqtG6eyDgrUrSfrnm7k0GUzZ1ikr3ZOwnE9EyPvWdhylBmM5cqz92nL1iqOZJKH1UCKsCKIOR8nwL7z6ZmsoJUR6CWfhGEIYiUQK4I4Xp7wO+HqxYp/OUqeuR7GH/889mniI1iMQfT+CXu+R576MdYh4++8GbCjgBOUR+1Q8vwSbgysQgIDgoMCPqVMlBQnObBlovjRdo7Q6WZZKKQ90615DmWAOVnKLXiiQwUGOCDha4LBi0PdX529JnG1P5DUxHzRhiANeOwL9FD7Sj7WSbNsDP3jE30SxqEMGm+faex9+FDjrVbpPpRB6Ae2E4BWL1y/03jrF4Xi7foiXdQY5P5H+afKjMDoiqJN1GIrblFdf9s/tPzxU5+5t9HNcT/M4dqLKV0xufeaN4/bs+62GCQFvqeWYvfPd4Y8SlGJwQbLfc4kOqbwjrNiCyeUXONUykVzUILefOuDf2CEmit6s24PkEPFrX9oZxCh22ja7Ju36cDZkMprI0tQCdz2ovo2l1GBYF7wWwUCPkyvy4O0pg/aKY6MxoG4iMCU4HK338NqLJxHOqWW56Fb9+U18JUaglBMa9JfU9K52/hrNP9bWGmklVe+WmA+Q5ocVNnXZFGgyErHTccJ3X/eGpobOZTaKx40GrtBCoH2RpgdfjJIxxDEmx8mB++04oej5H/WCgpOknihB+hXb/jEhm/G2S9ID9m8TdLKC/YJ+k29nT8Do4dhJQV7qrAqSA/PLvOShK6ZB+aWbR9OBAO8cAkXj3BdSuVvdgVqGZNKjDx2F9gOIYyodQ0Dco65cRNjgBWUn7st3R89OH8ngxCDOQMxqyjusLpHozNHxvuB+XFOCnvC52E6Xcn7AmkjQG++qzc/nLDEmyBOjLAhSNwraUWzT9YKOHsAvonycTsCcCFSb476c+DAI8ViIKcsuPi5wswM7QPLUNpaKcByxSuflbDmVORxNTV4UJOaHCNzOVRO5yqEtgPXU9U1ymBjEPUZHjYCCVuErNrhAc6bn2MI0oog9rV6acMPR8lrzyuHszKIOv5B1iMveZdFk0GJlT6Wm7qLcVP1lX0P+HQshiLkZaJLGg48Cep6L1oKNSVL+Q1AUhKUhEmmmo/v+zLJxtnSKfClHhcqyrITjplQL2MEyoogZQc9E1EmYh5w57Mw1up7UhojkKrjN9+rDv/AvlUyZnIK2AMMl5xwhyFIq4GeaIXbcz5z1ATVm5yrvjMpzV4F5CseGsEZxEcrGdAVQdL9F7lzbSmIUQw1C9PvOLfhWw18Tg3shiDnqzrpPliRNjvX26nbVFw/sKv++i3nCNu+uHXcrRkGbtRbyxaMNpx+InBowduw3M8FqxxpV8HgCjQ+0J2StMKOPn3hn1FH+ni0SZf5Rhz64UOeZDtEEF9xzZ+0kWBHc7mhD2Hy3yIGb7TlYW6UUfxXvqKbLMJtrsPupKINL/34lKu8lO5c5770zlzqOXLvBdpybr4biLqTl3OhSXlBCLJ1q3HuUHHgzlckoVK5Rdj7kPlCaop5M+mTw+PE2Hc6KOEdL/P4NExzOfYF4vdSq4N4CfhCL09eyCCEYcibSs/ckJXjNvjM2JjPwmgEjIcxAPlloGTnPjAgMjck7Lh69ugWuVsOwtVXHg5HE5bZl2l8wBlBQ6tYWBx4i09A5rPQzsjUwkB2+B4uWzHgv8wUiLzCNQy8FRDPSYfABpDZRkCfhzG7+F5HxnOf+CSMg0KYN/oloVeL65h4vTh8pnH2J425z1gNxB5BrCDXSxc2iua0sDsaZDEGwYcXkiOc87YhSLfEEind81civMQ8uUhaEDqNRoDDSzsepgAAQABJREFUWxfunYsZvZDee+Vk8PQ3ucmvg8gSCDAUg1Z6kzm1NJe+pbgEOg6rXRZiOGCRXpEje6dxYW6S1U8DKo6i5eCURIQrrRhHAGAKr2INiyF0GtNRnG99gotuxyPsLOC34zUMD/2Mo4vD+ApwX+xvuL5XxEu04W1oi9YwmA/+ZZm45yTnstCMbtqLC2xwZdk4sOqisGnKbsXM60Hw2KfOifr8UBWb+eut+/Ia+EoNQelCjo6n1Q6F7eGzAoN/hn2OWBpXqz+V/cjVjZmoYFR+GgLuqP9pCKThOsgXz8DtUh5wabjJsOatk988eVUDetSfWDMxgAb/nZZG5Dj533zqkvdVYUKgzXa9koK3RPqOnIfrX99pgqC9WPi0xquCxIxG7c2iM9zZGGTde5KQcghDHQBhcu3VweOSJGOBn9+R7PjgnNOOPMBeKMOpHKpTwDv3UHOfoOyPwXN+6sG4HbsR6A6b6soQdFcbnN3T0l0enFkB9Eyfh7FH0DO93Xn8VCuC9LbngdIfaiXFfRmE7vLQrW/E/eAOT+VC/gxU+GSPjyiOK9wOlOQFNIIw7CNzJHdAl5Q19xvZXbRVtmJRLnKhHobGHbSlKk781K2BQZIsJ5F8AsxwjN5wl4gHNCli4nwrX9JNp0Ijk98uqpIil99uKuyySGHeP0HyYwgiR1a38ekim0a/UT3F4IMx6BetAnrJ5tHsGcSm0arPb2X0zKogrYyTbSRGJvSAZOxzFQMQe2FhAGKya6OnJ8LEhWfdEWZC3GkI9ITxHfQFtiqC5VxppFMu/HWFDiA6HfCZFdCT461bDEE5SYS6ZOMPewOZVzaIXqeXyEDEJLGfdWHM5HMw9rm6J2OQ9wBi43MZgJiMsln0U20UzeSUE+/04kY/TWRV/1lRxD23cVT5kjVqsQwK1zdc8UsHfIpu3JlPXaLdxm818Fk0cGkIchNPA01zlxSus7rYRyqPD4yDQUxfOUSWWmGQAcFw8Jw0F2NPZiSf+ltwJjPaWvM1OyWZto1o+IUkmOlJSrUhKOixpUlnhoeYSgdvFz3hU08GN/0EU2LH0iN8zje4wq8uzLDjRsoECJe8FEhBDVsXZ5l8wzMpLtfS1GDvaBsvd/mLoQKDh9dMgxPde7QboRp2eZOpqXox2kSafaKilAzQVQWlwUdvFsRe8o08jC1Di6AesAVTMKMOJgduVngzv6MMzAFZEWQjkOZ7+BiCvPqH1UCcIubxkuPk2S9ScbYDkMHoLSeIaWz1+OcXJfTSHfdk1fDWAMig1UJjFMqqIGTBIbnCKhPxwPAt6ZQ/hiCjQoI76XM4US5zIA6/cjOFLgM5gck/rq1nh5ByGbeUw6N1OfepwGG48g/cfPzihRcuvIjRKiCVA6MNewUx5jJu8sIFw84jjbePNW98rBNl2WPymYxAP854+0RzynyCzdxRq3A1TjPOyvNYSRWxRiWY6xlxdOa6M2nW4cjcgioKxDROgstowEDSeq+A89t4GBLdW5/XVWDhhKXuhzMThx1PLMvxKneIrzrwhqlYJ/9ikeZ056UQvvUiPIVDW2zSIACvJT9jUIvWfqRBM82hs40XpOJ36kcGtmFFCOcb/JU2WVck81Li3IqF1oDRd/4K+zYXYfPhceJHJptb0fLD5ye30hL1tWgFXYdTGHonbL0MEDltoAbudhKf+/lQc9Db+V81+2X9r9wQdJ1yLqsmOOkmVo2+juy/CDt1eGoKNIirTnLTKtpAiqCWO+2wSemUr2dSqvir54BpJqafNLNlMuC3MxsPGiddIPMIVlL0RDA2CfxZBiAMP7/owRlDEKuD+DzstfYKYrXFa+0p9MGbRjO5yFzEb4tmlYasQso+n9focd2y0Ks1+ynyKt9RiKPsU6DoZ5ULaLWjoGc/xFPeUxqocpfdPAPcxkHhQxovY5WQWc6KlVoDOci6T+D5pAcN5He8aR+nf2lA1yqf+wzmWhHEgzKfh7EaiKPi+SzsidJ4o/NAD9l3tSb4nlcDMZBLDvEe7Sx/hI6MI9our9O5CNh6FvWA1bKAkDBphI+UI1ScKaTl4R75XkJ2xY0ke1YKw/EA7fxhcI4PB3M2zSIuXjnJFygGmqT95rrMzFa6k4XBk2UsDZqcenWaKjOr2rz0XcfJew8E3m6qHmME4iSxX15rryDVZ952vhfuB+0thAHpg76XtNFUdYpPwfI2c1YDMbnVsfGpS9yx+XTMpSaun+p861r1uTZY1E2gBPw8L1fowAWulBSxnu+tsUhCJXJ8ze6b5NgQQK2bhhpiDEIvSvPKH3xyrCGI/YGYnMpwpBVpvFnkqHhWqd2TkfL+fW0O7Q3Ns3HlI33imFPw+DxMk1RNWO/KEHTnzhiRVJf5FK2fSipni4e4yINL/atG4iclV6NJTHxw0eatu9XAl9LApSEIOaYqn+tygWp/eWhyK47YVOQOdqsgU8nl/ZEjOXsMbViTV7yVseW6yo6ehf7mSEl84zfBg9MAIAF4kCbBsAG2M3JKYJck4Xt9WhlGRUdGZx6RHph1a12Gq4UrWUEWOpwj1kqAw5ZwffGMAFr5lsKkBV6HAGL1rX52y2qn2uFlbUqVy+MB5SuBEehHh5k60vyRcMjAvM0kGVBMRXJFKJgxDccY8VFjHGPee1b5aGXQG+0TxMofTghjL0g2jPYhC14dhGEoL1R+fc2JsR81JmqM4/5jDKpBqHM9jEGMgfPSBElYPYt2+CUe6SobxfYqOpfFoRSgZRd6KCBPOeILCrG8oUqccuqH69woMVDLKT7XA1asa3wYKq/USEWODDbkBRRPwpKKQxm++aDxTFpQZoyRrDG+o5VA9/Umhf31HsyhDHwS9kSfYnPS7FO9PGScZaPoJ5pT8uKFTaLv6YRO5o5efSv94HuM1ViOfG5PTEPIf+kvmkee3VlayYQOInnuUfQBNI60pheGbwwSDgaFyg+FApuT9gbfqb2XwnDcfYpCR9YbbYNJ5NqQaScZGHMH5zN+8cgl4UHG24gv60Fkaj6hKd9zmYffMG8ZDRWM+JV8h2R5pV2ABHw/L2CXUUj5Ie+f5dP0rdiLXWHmU6bDt0jNp/H6pb2MG64Lfm8vOJ6aSjFJnxe8qsPcg1tDULX45f2v1hBEh+8HIetor9bXV9HLxv05VetqTiOY3uOYzESK9NPqfCd9l61pZ9glLs2oOlCovYZAi6XCfgvkh2c6YngMV/AmyKTzvSYIH3RUKG99WE3xs5YIv9IDs5cNs08Qn4mxKkg/PhH7VQ/ZrLxgw2lo2HOIbtjdrfjBk0mBP9/hzVFXBpEm3OvKDYvzPTvKl7JucfcsU4ClB9I3nCZf+OjqyCcDhKSSrhgseWCmpiENcXVQemDGZQUFKyFYEaFVPTLqcGQ8n9PwWdgjDeTso/JEv6dPeKDW4K43Oo9kAMIQxDLge1oTrPHcA7jE8ASBrBjA+OEogXXBJVkbuI2dyzAA/glHxLnHQ6y46YAvxJ3IHHQhV12HvloxcLukHoN4ACtiIU0iP8LnfINV/TsdgRVIuFyIU1dErZ8nvfi6R9Rpk9gAoTomn7rNUAL8/Rwlz2aYvzKh1eSWOsvnjawG8io3xd/pDehb4TAh9mdiqv+crpITVjCQ8sYTntRrGYFYIURd9p3a6rRwvDQer3fSYdE6TWDqPbwUzO9SK6QpRQUgHecTXxKcKwYXM574YG4TQe4PBh5jGY5WVJ8V/vRJJ5bMSiDqNW8pf9CrRa8C0kQTIxCfhPEJozeu1FtJDEE2aLJxpVYIPdXqID4Jy75AKjX3gPrMb0k5WhhRRxXVhP29FJE1xExsy2fY3Xq3GvisGrhiCFKdTH3F2Kp2qmZH/GiKam9E9E+L9NimODj7OOc4lzoY4aBtWFG3XuMFmR6h/aXx53JitSUATy+SUPByPeAlCO9gQpeyOVCU5Ufe4l6AE002knfKtjhe5Kz+2sUuk6KL/tDZyAZMeM63eIo3OFlGj8bjHpViiJvPQg6DxQM0RVbyBPAi+TAIYEMEXi4nzCGoWFcJwYYSWVfeZSW46xS+OtcRh5h+cT40QXEXVZeVog40uekqfqyidT66clLme1YDeQNoVoFzhDxjJCfDanWQjEHsE8RYafgrGYKUzmdj77WCyG2DPH9jVMn4qBFAGc6+Qd/pBYnijJfpyRkzJYPSLSe0/iFT7hOzLf41QqVgXBkIlD7NKgVaEyDRurApZfjCDz6+ijZzBljtDllNOsA/a1dBE08y0b8v9icc4KCR4FmI8lCpbAjKJ2GMj3ckv1fz6HLf++xpzNXLl8d8FsYKIA5m0IvD57w81EENz2ZvyXvMG1kNxMtH6cWrgZTTMc6Tr7Spgv2udPJ2xFJF25kEGhBdmiJBMBbwFDZXcQZj19oQA9vB1QuwCYf2yCzg3KPV/gUETrxuyBsdPwj7/IAaUzL8VUVKaUbFOHArn3NWxqYDbTK2PJsQK7gCzWDzh349mky2rp8THm8RlZ110QipQlz6IT5pl3jE6/ayX+KtWzV89qygX3HxI7zzXWnNiPShqb/jXAkLgE7STkPoPg9+zOcNUh0T3q0hCKV8He5vYgj6a8q60oH9NbL/Nywq/jStTEjCruEO8DdlQuOiJYJXmhtxgzyt98CiMZLEQ/NH9kBRK8TO4bc5SiCNxsnLHL+nUDoGHb300QaCekv0qzbV7dshnbL0Sp/XeHKg1UCvtLKCY+bf6gH6Pd+bzwM02+F1UKkhiAyOjQUnXcJ58LcUSIIw8c5X0vY7OLimA7NxBdv7bj2Yy3qB1mi4NlM6IR6WxYbjtemaWDVhKcFRyfQmJoYgGXR4Q6PBOXupaBD3w7OW9fIGRz9OC2Op79ogms9tNJDbEKQZAQ/RDGaIypsiyk7ccx+y4yd36tgBDJxiExxSUg63AwkbswwbP9AJ7SSN9D6eMBH4QgbSkR1wk4AlduI8MCeuy6mM19V3MaUNeCmpwjYCzeDhFVWeJKudSHk1dPLJIm89MVS+ecum0fPGU/X4ZxmC2AD9pSa6vyrtnZbGe/NM4X/UyqD3On2MVUe8Of3dn5sxodUnYZ70MvFN3DfPJSeOU92mMeFcB8GV7A4TknzE5wdkDzfuSqgU0tR0g+T7CMbUkQR1FQKVFqf6Sw5k55858EYSHOqzGrb0xiQRvO++ky/l31c9vk/9vYcRiH2sMGpyup0+a1RdxrjJkbZP+MxRbysfP75r4xHtwRs7Kz/2AJ2Si3fC9rnIjRe1SJzGm7bHaQPlZeLby60GPrMGrhiCnD9GoAoyY5LiB2jaoVDSd6YdnsfvA6ec7NNgpx2fJswHd7WdldOJ9LoImFdzugrZaSlR5yzA9355x3Mjpj+xg2flui7XP85zmETYYbO4tF8r+8nH0ck23HW1/oYbUZeFOKHFwAihGdzNW/maaktw0EwbOhLXfSvX+gcKody6sxzAwa4RhHqyyjYym05Av1C7KAf0jA9WE77ipxx0A1t2cIwrLBaJenzUGPnunTaOfq9P/fVC5LVW/PCZ2EtWBckQxOrvFxy4oL2D+ESMleB8Ssa4yDzyowxBmV+pt/a4txmCHGdc1E9h5pf06kiEnP7pUt+1SUL6kRAgBVdjq96ih5SGMav19PBFgybhqUvhzBkOV+3EjznImQklPin8dmjow0cPRkk0RnIxvvOhdJREvuPMH2fMlcwYgGwEEsF9VvfwApF9IzktTC8LWQnEOOvPsFlJLkOQT5vVmMxqXT7DZqz2oQzi4XFyGilzV+eLooAhFM7+yG6ZC5tiFO804gJcCVNSmAEVPOwCGDyDtmwNPuElsq5lL3kN4wJr9Na0ySHeAUwtCCfShtRopEX3woef1HJQbpwO8gB1PeFdk27QNfDFQIE9eT2OAN+Yg7NFFzm3bqdfCZcBiK9B3HkSNr/Bu4HkxLks60NvHZ6wro+UhtRTeIsQXFEFuNeutepMDNfNYoy7PTXseh1/CehXawii601H8FeqNqoL9udXoiSdmt9ByZ3cCGKpVAT8pu8yAs8qh9V09mSH0cCMlFfSdgD5sskfD9D86JXg6s0tkcEDrg63FJy3RZyixAliXkXxluNEc6ToC60O8kaCrAiSIYh0Tl7CGMQEgROb+JSGxu0lw7wCUTmYABwrgwLLsAUiziVJ0LIpbuVZS2giacsvTeGT7PRLGGnkicsNcdnLi0gkVrIKrs7Iq4E8sNJB6U+fhqGx7/XUy+DLKqB7Or3h7p1s/txNdVkt8ZjVE7zZ0e+xBvK87dFqoAdaQaRZAHsD3ZOP8YeOFmkPLR2SAh9xK2niwElMIZbvYpB2nSMf/TLIFrP+sCs/6J05F5X6QBseTnQuulV2wUzY9z7BK1fKmS5/z0y5TCbl3Cwbh5GNP8ID1fW4NELyijNNNqnHrlfCwZDzDqPOW010VUffvNObTk1mf9WPo+N/efHSK4RYCs8+Qm9ZGaQ3ozYe+XNHGYLcZtCbrBz6xDErgZjY6s0mq9usAErFD5wxcqJAC5+06gk8ytafglsYKHH5UzYg54ksbPUH72YBDW0azTKQit7JvgjOjJ0VbkpnspgJIyuB+CxM03Tp7J7eTObUEq1g06q2GIF0bLzq7yMmpxiFtLrtsfa5esSqtyd3sxpIgrgOKy8bMxGLfJUtJSdIe8HZm7QBBZbkyDxht40J33q3GvgSGtgNQdRpmqTbViuvhPK4pHhBxlPlTwus1LTNugmXYcEXPlj88hII7oo5n+Z0QfD/sXcu2nLbWJItW2/ZPf//m1NdtvV0TeyIEyDIzCurq6Yl1VqJe5MAzhsgQICHIPg/yo4NNzyj5wKvRttT3Fyfmr0bL3FP6QvX/aooT+PURfU8rT7XowOPEVMCibIjTTFS7+ltWc1S1QImWUCtiLxjTld844NuyZPSpWPQUPcaT9pj4TFg5Hoqrn1FEDKqxdM5XeJdZiGu8iknwXgxeXzUAUfQex6W6AHeO0347AjyxxVYQcurYloVpHleflothGNIDwb/0JYBfJ2T16jjEML5g+KMgX7gpxVBjMleEcTWAPMABWdQ7K675GKXjPSfKwKRyOWc9ueiADKdy+VUZhURzjx3w7Siwrodp2IEIdUzc0A30knS/9EcPcQVvseaKwqeEnJi8ho2+efaSI+99PhIyEvtg8Ln4F/jBGLM5XUwPsrglUDZMJr9gv6Lz8rrwYwdQd5YOhtFe2Yhh0/sxRbG/gTqhwzNKDYnL0hoTOCZRgpvNiQ2jIBmFVOlQDPnIZGwHrRO3jRFXmJwo/UqRsJlaeuTdCr5IgEbgiOOPGQexVg+MCE9hd9wCLN9PV0AtmBc83doal5JiNtVbcsYsugqcOKTnfDugpouz56/SygCKx1CpccnWM6UFTLhatMpLdxJ9EZnW5EPbEn8cmKnO6UnU5hjHTjf6arMXcmrDcvAhyPoy/X8LbE/sCMo7f9cGbRYmlfjA9sL/AH5dikaOhatgb69UbDaRcPnoud4x9Njt1B84iBMcSbbOI4kYrkx9t4qVJMClpHkZhCZ3LExqfisFRBs/oxz53cN+qyY8JckdAPNK2H+6RWx/9bNNE+I+PToH9pnhRvt3jh/kiDk2FbXAEoYaKTH5You7Io5FKIFURygYa0nZZw/xb0K77wnulDfHkeBIw766SKUG2peCwPG5CLx37RygjnJMzlweI2GLyu90nvd7An0mldoePXLqybYS4UlvXodTA6hX3Tj/FoD/ls5gXgt7DmOIE0CXkgY8qZGbD2lJ88Pta4m1JMmfirACF7xk2TCIc/tUTHtMWf/IrR0FhcKt+CTYBERkJdoxWCA7eSFETddPHlTC1D7gAROqkG2yGY7MSWd17Y8yIuQNuY2QoV68hnpbIjpya5eW2RT8/d66vmHJri/8cTTK9u0wk1t+A85gnjFkT2v3vlVx+yDwEqiT+ozmezqrNgRxNlhmXvydnb6RAXOajifQReAA3VoI6cgR512ooO1ptGRYLiTSql9Z4obnMvp8iqPWHMMl2bnPb9IoTpStwy1TEZzztmoEqk8VdTDRTt0WA3EJ+Jpx2/fapm62i1OoDeK/bU7TUh/Uf6t2vsvv8T5iVPTRZd04uhTOqYt23xaoNFvD/CYVjHdrXhieKjJR3jUwPeqARxB/NouscO92Y1efYk43fLUD+i/9DSjjR+iElsQvBtz00OTnip9EpIbLdGKxf3/ToVgI9Ia3yExKHhbJ/radUuNninFHTrxl7WFbHwSVSIBx7B7ZCeY6Ta+zUboIihKMkYnnSMEe70DDSx4Hdd8QRihfN2ZuNTEuwUWIVjoTxhIV1h4VK7zudBJiAj0HpDomQYJ+GSjacZAO0ME/ipHkETAtsKkC7J4ycJRwgO/9xozPnzIfnqsAOcDIKwI+l17AjHnwwnEwz5vB8BrYtov8jeNkZ+0YjaOIB78Zczj1Wlp109f/GGfIDmDjgcncQYln/ptG3exZQ/tTZHrgHko4SfNlfoAkVVA7nMtzDRC8zN305+lKMprcEAk83Quymzx6wC0ZybpopRDr7JjktIoSK5paxKMcTWUOINwzLAiiMLl1bDneoDI/NEPA7Wi/O0rjamaP/Lw5dc3WkWOE0ivXTN/ZL+gX+Uo+tVbCuihI69i4wjCmSSDPG+3ztgTW6TO9aRINFjpaXfrgIIAm4PxpqP0ljDYIYRuwqqFrQ9FLOdoVYnH7qorb2Ok5jwpVmbXgi38DEWe/mJToEYNvnzExRKT92+AjnRgPoFNtXOajqXtB/ed4V2Ch+BUpipGLjp9iA5XT2VAR2icXEDw7nRKW8yVlnzpNv5rcpVto7+KgmeVUcilf4Sd1JQZuquyLQ8Z+J1mT0NqPUPo+nF/B06fFzX/9GHl6eMPRxC19mOE/zBH0F9VGpeUbxvWREWN2x3D7d2pzZBcRDfAStrivzT6af4lSAk6op1AGmE9yNomCUe+Ol58NAwEcQIxUXgnbxA3xkwQGPx/J9ak4DfdMGeCoBtp3USzkoKbaL4qwWoK9LDhLk+NkIduvuaU12Nye2eY7aI+MKJwG6S8gq4Y5MA35ezAwkfZQhXcni51YcTo40fEoC3bZAyDtgceYEaTI6HBXKb9hCNIo0mcQM+9P9Ab7f2DI4iBnJtkXgP7L30y3u9662aalRS/6FUbVlvwxYeXWtaLI0k+JL9O4w19pQGr+utUioFlWY0ZtknxF4JJynSlO8F3gRfBO53aBecp52anO4j2qgfKb5cOd0PO8BVPC1EY8eEfmBkrUSSi6T4HZDpZDJlaCCMcNo84nEY4Jj+oPX7QCh9WrNlhqYkuk1tWtv3+Ts5MpXka+rsdQnEGuS3zyqOcSOx/hWM0TzV5oskrYpRGJ9K/ddaUz6fmsSOBGJuwLRDStRHINW0qV/y0QAgmVCxltTgApv2s5f4ipP0yERS9PylrzUxGawWTU5yaf7ot+8slapBv1U5/YUm6fuwLhEPojRybODJ5WvmGVUC8OsakVE8oiXEk1R5vnyWl6DVMpjhtvUnbBoBbKA2gHeWJ7kb3SD5q4FvXAE6grCw4NLu9dtYvcNts+0EA6bcH157aOvMOXunge404NIhASqoPcij3PLCvC39lw1NSZNVd1rvAi5CvobmwOHvw5XWglljwffAxLbjiK+sK2/CH6BIf7OA20hDcYzhYc0bIP03nV+VFsYuG2rOPGdDWF4oQpeBxbeKmjZgDbJ62jNrVFge/52dY8V6OPEz5qFU97KXHa/18NOEPHpZofGTT6H/oIeB/60Gf99VjvJRTiI8s/KGxkvHUH1bQwJgvdWpc1ByPkjE+4gjiwQmrg7IqiLFyVgaNQYyhuRGU/diun+uB8vAnOu+FpwkYN/Ju/xOvO+9Q5mgh3EwiT/z8EDuxkndDrOaYEBXJ0+NiLpIIidMPlCZhhVjO3JFJLzRMGD4JTU+OM4i9fF7py7L+IIOewPAgkZU+vH6dfSRZTc5G0awonwcyGmf9EFFjtL/QKe8PziC52TAmYzwnH7usC93AdQhJYqUz63DCNCf8zL8XcShOx0gWyIkId3Lrh1V5YrzJjKQLcaCx0kqEH8pNwvkauCFWklNS0T4VwjRvog2/mCZxpdvxtuXWoEVSVGMjdoF7enHdlnHvrxvZVyerpvE9RnAnO4foBlYhim9wF8FXfPOOm6le5QG1/3MB41w9VgRdKvUHyP4HOYLarBvfq72/voDc4/q3YGrZbv8rpvEbsomVzflfMAZ7OogtpkgOSXCRAHeFDeDJCJa+WsME1wG7PGLIBMTrwFoB8NBzA/2Hbp7Z/+fdOy0d5mmRVv9ww/yPPzRBmEkCmwv+IUcRr9h8+PgxN99aasxrYsixPMm0M8iKc7H3U6kWxZdqCpdyulKcTj676cTsHA86dBxh4C7Q1KGQi8QJDio75Vcyr8oo4YsRsVZT2Gshfo0qbBLNhITXwl7IqeONnvmykgZwnEEM5rzXjeOHz2z7U584iBjEFfP0h/2E7ERSfXOTK1HWsbsQsHzlsWsK5UmH8gYQPxXKMPgT6eCI0gZRoJwnEZvA0oESCStfwrPRABm6XceAjrreWMDt+PIV5tgnA73BnnusjREuHFCEjlSlkEzb4szjNPqoH6t6+DqKJ7yKWRn0myazv2mpO+32H3/gDJJTaFa/2akpfF8TY9L7UU9A/Rl5Jq/Wd3YGYWvgOIKwR2dyzKIOE0LjIqQ4pml3hIbSrDA0TPQJbtITWzQAT0QFpO2qQ/2Tz8TTZoVqW2JSOprVjnFoamouJJ+Kf6mv3b3WxuV+FUxtl8/D49S0EwgHkNovTqC2ZZya7InFJpc4RrHDdslG23TJGw9Mv4bCnFemuF0Ol6XWWvke8aMGvmUNMG7ddwSlzc/lyia57ZJyY6YzKCECP/yY61lspzNDtHXqSS6YcL5+mcECLQ+y/399Yim1ltvD6K2dWHRiUabFaHwrJBDjN/ov0t1DwotyrmKEXGknqai2EjdNUmkzbLAdbwH/kwPnU2JjxIUx9hl5Ot8XstVQznDOOG3Nlp7k6xo75scJdOQrgfGBYbxqS1+8862CoYPHjiAl7AzSGMcKcMY8viDGPI89gbwSXK+DsRKIVUF//79xBGWTaT0g0TyvD/y8JQAfVFAl/cxr0/r99PMLmYEzSLFhtGDmFDqXMixfsGWEUg3IFoouEzO2C+8tC6Bf81ROgAhaHmj75wqYHLKU549/ZDrhOIdNREROxVk8uvlTBpsaMo4mRxodjnH2yAn0z21rAT1SFYyHLpKgrQWeSRhjLWMoDiFvJ6C541setvBBEX2Nk9W3bCvwqx7C/MorYZo3Mta+fq1aVB2wCpdXuXMdwLLMNnA4NW2Lbb9Ast/lJHYCW8Z+APqHNa/xDaIEIZtjcGhxmIQjne9yNkHeJg35EQ2jdHNumF8fc34srx7BlRxqQ4/00max1nUoUBdEysiJmOM8IgTYlWn4l+Qq2+QaVHgJJ09U1GIRTer8Dm4RnRPIKM8ZA0I/EYA/1S16QI0BpAlPldE4HZa9Zga6wchU0KQXPfk74Yq/yQOQTNupNFn6T/o794oB0tXfqH9Ml7+j6QH6ljXw4zqC1F7SWbYW7JppvvFeXb007LD/5bQaNo2dQwekgag/cCGcIHM9yLuHHEDK2CdA7iRwCRayxe3KoMRfCu5jOjABQFby4iijlWlyoIu6FxYIhSOH12O4gfZ75J4kxOnDUyE7gxTjGMIJxJLi93o9DMeRv0ih1UE4n1iCnC8waaiyfsXq/fwoRZe++/Lt5cWUZAxj5kW5Aa1pcHIdNFqRzadtRHYot7pGDIUn6me1nR/DlGZw8rJb0bAayJs6MwDziXi95sU73S9x9GjVz1t9Yckrf/RazVsN4nzykxUUftqjG2bfOM9y4H5hyc/FJPe6IogJA+Oin4KpbmK7Yq6U9wIEKUqwZdhoXX3QGEfZVBdT/jBHgOtMSdNDLnrI4sQ4FJkf+WFb9ID2cHBEtdUPAazDvs5obare2hMWG+LkbnpkjCYrUNlGkduXCNR01eZ4RUxPPnHosEJI7ZL2ywbSrHL7XU5N9j74fVa72dkpGPsK2Rmkp6V8UYwl8H76KSX+fK6tz8SWtnnUVRaIT6XLJmiwFhpSGHm0ycCOOkkqBeF6Ad4FE4ie4LoBNmVFG09SmHR6ImoNTMZD8kyOH7dp2rVg7FHwTN5INp18pfbMV0hYDYQzk43O39KWPUHltcdZBQQdTiDx8FrkS01o8xncKLEpNjQ6nR8T76WX+bDDRwxwAo7SPV/4I37UwLeqgasjqO2R/reuUxiz2qoa8hBNV9gIhVgXr5ZgURUQftFxkxzyESgKr5AYSqBw8zsoBnmJSnsG3+ruKDtXnCE/07mvonUXuqd3JQt+lnHiNf0Vn2vjYl8yoSstsSxONGCuswQ4CZd4XTiFqhjT3TmUtXTOJ1OV5loDbAl3WTtMAkbmiV/k5Dsn8zlY7eS41nceSMOradXkORWX/03dXtQ9bV3QitkrZpXm1ecPGhff6aEf4x0rZ3/XPO43Pq6gcdLOIL6yqRW0f//7b57v4Qji9xFHkB+SxKnzyauC4uhhRdDffnohk49VQaxl8TipMRG7sN2PKYgpgPNE6QM/m466oy6m5ET9iX6oU4fhdJo7TBxMVIzJIN1CpIFJPTuvAzUcHGrbpqDTTwhK57SlCjbOH82epQqHkLTyQAYD9OPhC+MvWwKwXyRzwzfMIzV39GvX8vL8qjH3V+27x4cY/osHijxk1HjLSvLXfGBEPx7ePNPEFIcSVfEzY75siBMIO2Jjq0lI0yUvHhdlZh8A9Q8HiczrKPcxLzHqdKDc4OGCGZ0QAL8fiokeaI6UOUqwGe25IOJvqa0JGwmVRK7pIGhhEQA81IoBjVCrK9MmwLQcqNpLMOsINF35RWfchR7FS55w1b+xmeOUL89V1jVvwQgNguugy3aAjFhkJKAJ+bJlsiu6sXEJOHghHnGLj0RlF7jnaxuMPQfgOdduxZqwm15tmvP71vPLSnrE37MGflBHUBqPO/JX1s5+cWu7hrUNtY16jyMaikIDybFSit9xlQpsLppzUVkXOEY/oyPHF2F6h7Ie7Jwks8sKS49wgnXHFWMkFZvY+AGRZtzwRZzOJwCwVoLxEoapngA71gSBlT1sCsjyYX6aKLzjRtqvis0yYt00cxPNPissK+Zz3e/GIcRqDPZp+aTHRjw5smwp+1MJO4Jqh0ugUjCx6t38KlVK5yXTLjD5wI4yUNdDl8gkHbQATRUrRZrKEHYGU2XGAm6W9dMdMw6Z5/pSwzM5crx6QhenlwzkehWMT22zGohXZ9jkzysq2NhP73jzBQhuslltwY0zDiA/yVHMWIx8lvayWBodFCk37digP9nl2IZuzYDCuiAgttDyAip+YtdPSU03FW689ChOHQ2RaZKOuqyAMXkbi9EHoXUc2Z35mKgD3U5AalvlDrVQh6XtD9SBBbS/jIxQojD9i1TlIQZ82recmprkMsFkvyC+hsfeBh/ZQJp2rHbKvlY4hbLflZxBODRxbHqVG6+J0ZZ55VHOUPGmDTPppTg+g2muPAl1aehDlCoVEltjU9KxO5eEKb1AyYvNIbzNmQ+QEpwvh8ZwQuD2jBRn1J7UbnH4qGGp2dmxufKCvVC7xgn0RvtW4dj8L55Aqu36C2E4f/SjjYPDUUQ7ZiUQe1y9cJvOcn36CTr7OkPNIm562Y39O5y0AL7MEc8PesS2bYjsER418M1rwGPV1jHd96YNt3GrmbpRB6d+QMPV9WZjS8Perm8uCIx01RwMysU4V2OjBO18wHkpgW2xhstHYAToim9sxBwOWK6YO/1Ot9uFVqw6eKXjWh6Yh6C238gDrzCR09RU8wcfkEKXWEFSq6HLMbaVJnyupW0OQbJ01l9FkDdta0btHVhBYWmuTIlntmeZtgHhYjhTk0MKcOp00gIzBh6QUiV2O4DTDc3sPrhG4FXCenyAbmgab3BOHT94eVDCfo68Au2V33LuMNZ5KwA5gngo8g+Nj/9gT0g5g/6vYj62wIdE+lGFz7weZjkdbzv+MT7K8TPOoDiGmPnMWKkxCrP4MZ6usihNTeD4zP5AxCpIC9UyORbdtEV41n5AqpCAgUmBtRCfwxJZmUKT9HlRYgMLqrNzgiEYY1V4rwhCJyfio36kP2scy4ogzwH1dOOV5o9vNaYyR+T1aj9EfC3Hj+aOrCbHAYQziFfF3mq8ffWKT8wzXufrnIznrl3mqNiDBSqo256dULJHRnqljXDgbbPqmjokLQ4j2pYoRUbbFs5EhkbCJBWFVnjpJB1xlWvwIrauEoXRDOZUHrzP0SIMq0kHVjZin5NDq4nRXxoAzCUM24C20dQ5nPIbnfk3upMq0bl97vgvpZGLolG2ygqPYEVbfRrqtHfhDYRQgQLlAraYEOnLG6iRdJorCV59l6qNSPRXh4WhB6FGN0oGOAGeRGe8YBUFGaGidvjSJzxw43RInxVgHEH0H87zW80x/UBcqEf4vjXwgzqC1HTUgNzAVT80mnQGKqtN8LbiQhf4ParCjthN9VbQgpRypwNGmJiGTkdmbKDp87/1iPOFOPJSLqXzP+Ioo4J44TnLABF9kUD+tiaq1rzIoONZaOSRzmCs6ZYEkeYm+pM6Jk+MPGH49NFOIV7/Yhkx+6jg+OFG+jc5g7zKgvSsDOIm+6NoeWrEyiJuxlkZ9EnKuKHGGeRxMybbapfEN9VTCttIOpe6DAbB9TWhlh/oCqkSZUnwG0FLHu1oBm6doKnh3ICqftjUD+fPC90s48RxzObQs0LiFU4gDdqvcQQJxkDuDf/0ZIfNoRm4eRXMr5NJHjfhvbmlJHYhCL4cQYKBp3wuqeyk6RAcL7sDC2LSxa0yHzRGFe4TvtWFyUQximROIRiy2kfrvti0weSga9UO5Ih2nNM1tCQh2Ns0NqRdDk0zxMYx6eFswXuE2qQm5kGSmDYMG21eTdh76DDp9RNQOYE+yaHJSp9/6Kt4v/NFFO1z9Rsrgy7OIDZDxwGa1UTw5/XJtBqs4Smn9GCXRmhP0QAYlkPtNY0xexlUFspW+sG3iK4Tw0w2VIcC33a6nqLF7YwVbPrp347IZ2qLzyUIBxBfLnkhJ6X3+9GE9BdNSPnSHY6g/6PPwvMqGBPW1y/lDNJklC/kPYNPgpHjyajSbq+SqQVHvi71jBA3jdlO2770XsrjAF/xWwzQzqvShfpxfNTAN62BLzmC2ifdRHVo7KuAkL0h3a9ty3gzT+N3v1gYJ+jZvgnWFQF0r20oSQ866K0Xev1IE98Lpdtx4TlmT4cUqL4scdfFlWzXu6cPulybdlxsAXLGxdaDs6U+a4mNcB91QqrX1UiJDtJHvqlbW0QWgWb7Et1uS/QHEv3HVT76e2xNgW8anNKphqhX+2DMoplE9j4CCzKGmUUHYs8xnSCTUDoJGSLFlilyxkdlaeP8GNuyykdjouZrPCTBCZS5XT4n/9/aH+jv+lIsK4VYLcuYyCpbnEif9ZDF80UevGgCmRWzUuavbTLb0X5Bs2+QZz9enkIdyJ0hQz1WY6bLQ0HA6QidBhr6gGtDcxXXlXIJPROJfbQMFWrJqtxKFefUQ3Q4W4GOW3eNd2QsgZMfevQ7vRrGK2FxDnn8nTGYvYFYEeTtBF6yB5DmjVoNZGeQXsdmPslr2TyU4VXsPIDRAxscQapCvxIm5cw27OiZiohTFjtqE7HKJL3YSqD+4OEcGKaDryvBOs1reqW1KLISta4/QY4eorZOs4luoZbeYCLHQEwbucQ9e4tBQmJ9CBd+CUqimpc4JRDLj9A2UpuMQ9/QhcjHHCDYcUARvgXOshUUPjwbyZEsTnGTIKeKD5iRFQjFMCxa4fQfGXnUCFU42kdUG5BBJ8KWEZ49gIMR3tLt+KaLgw57LUcH8wEbQuCuE8WkCx+0o8J6PiAkbTixMqT/1AUpsFz3HptF77X4fdM/qCOIRqTG4rrJcZqVINd8K7Dw5Mm50RU98RleHSCv1GdKs0Oy1CjhvDrqtHrH4DXiMfC5N5hRbLpi22FkNVywkdVL3UU7ONNVIR0H4DkssgG3Iya2AHW+xDZRyX72nUmshzbhHSvv17s0Y7Ajh2XEOIK4kWYDXt1As9kur9pkpZCcQdpvhU9yf/zIhIEVGHxaPq/mfFKnZ6UQm9vyXnjMkMUxRxZTduoO4wufix55h4l1pVpsCwdb6TbqBVLCHihKJ25dRTVmmhs2O4C0+ocbYF4F6xfCXuim2askWLbLigmcQLwaxua5GrzZT+UXrxZ6nmW8vhHXjbLnMtFhPdLxTGpxAu2OIHSzpJWLupIpDYXj1zC4Zm9iGBtIiz7sw5hMKUJgng0xMgLB4nthiEbHPYoTzHSbDiNVVsFznoMjf9hMWnB+J35lTAiw/Acp7ZfeW1ZypNumcUKyt8EHJYj5Kh77Xf3OUnj2vcKpORtI/8YGmrwmxqT3vSbL8OlVMRxLvEYZZyQT2bTDOIJqy8B8BrA1+cTnvAtyOohWVU8/4P8IUmx5QJQm6wNptTFNmu2w0YTvOZuT49BUO/SKNDs2tUpt2jWOoLRZJqM4M7UiSJtU0rZfaeaJM4hVQGxwzoT2Zx3SRpUmr4ydQTZQaVmSUt0xWTbCS9sutsVqDL8D/XFkF/SIHzXwrWuAG2RumPfQ9n3qlrRriNSAiTO5NaSAXUTSdAYuSunAJ/wJaoGgI+/eWF+su9ZJ0qjfYEhZIg3n6lhY0iX3GKyr5mgueMVn6gW+kzg03CJPpb1FXyCx5QAe1hfWq1DzLfFRinv1VOovxUg4eI/UzpM6u+Ku+XCcoMo4z4G2QeSkJKqdBFJoSGg+5XHaGWiOMKI2QOTSrO0MEg/xBw1o7KPHqm8+jvCHHuD5IZ8f9MkRxGbRXhWk/YI01/Nr0/oS5wftt4cD6ZN44OMjIUytPssBxCohW87eQDiCHCvNvkHjCCLG7D6K63iKwdyEUhdZFZQ0xEe7U8pEcxMJk0LxnnNvkKMOQ0WlottqTBfevc7ob+TdVUdW6OGkgLGeEuQz8bwSllfE9LhIY+/fNPbmoSCrapkvZl9JOYI8X8yefF6R+0YrgfQghlVBr/XQkTGYMfyVvhbG+Ot9JTU2+rETAykn0UG15wYAwDOfFIq0jM01g3moMuNUowxGmp8yZg7j7MASiZIiJhO5yrc+LEc44pIVZpYCnbkQAqt4TwwiBRbs4Txe2WE5hwiAkx8B2yIp+cLJ7WljLwq+hF/VHbFnJYXt8QgjgtftaMMDTwldYmOgQXBLTuzzp0JFXAzOEbpAM3dSeitPMBZ7OhS+kZ7wZE64Mox4ssU33mG7MPCm2QjdVCESLGMlad0PTh5Zb3XflTJB+AjfswZ+SEcQFdJOQodpA6RBpcMMvggYlM5rRWT+OoQ1LbfpiiO+CZDeELQZy1pdBfIKUrg9QA2PbY6BcwET3yjJRYHyHOKXnosRpb2AV9al8SEgd0YdsKWTXTuCyIvEP/CkdeCHEyhPfXDkaNKgm+L3GvzfvZcDCMcPe654lRCv1/C1MU0Y9GTJr+H4VbGsqngvB9Jn8ftz9nIqSY30UGj9SHtwj52uCu6ITVM6cFNJSgk1B0WqiGC4SB5UjIG+Eity2lm00m64gZZzRgcGyxesmPAgzB4quhnmhli/V3YEsQpIXwJjj6BZHdTXaN6Ihi+IPRcvA7dvoG0PTqD8OE84e35WoeMEykSD976pCLEZryg3zdhNQVzIKc+kodmqwdl1KF8BKuNiU+JAAw0AGAjOB3VFlGBMM449UJtO2YPwRLNsW3QlrLzps+WS4vQHbBh7gRnvodSpSMlSZ9sqQCXj3IQSiJ0yRpAGwkbOcTyynJ2VatlAOm3XG55r2Tvt2HteycHJq2HsfeWviGk/oT/kAGUJvZq9HUH0Bzue/BRU/URw+ortdzy2TClyho8B3Ob5rNtol8N14EKnLXvOHLTLdVAuBW7a0D3TIavZspKHdsx+Pl4JZAcQcJ5MZq8rVrh5Qqqnk7/4SyUsW1d7N19WxeEAol/QNt1+PWkjLZh+ZA1XGTG7v5RNeSd6HmM99BTWZfVJhC/8PlPocx2E/nF81MC3roGrI4g262ardntqmwMHCU3bvZs1DIRp48mUYSgXDuJc98C4N5RfucDGhgjycZGY+0BE2jnfXGRFf45gohu9pA5rbnVCfdCFHtgRIvWw4UgdNKTqaAo0dpE+y4SbUHxtvkrNVQpKMKmzWNo0uGCTisymb+UVc6a75srXOjm4SB1WF1/7F50BsREam6924WvspX0YKZIlQ2MOJM4voCSPGMuqGYB1IWeMYqyCnFjDYR5y2KGTjaPff9KHQRjztDKI8RBn0H9rjyDSjIPvWTGk8ZH5HV/n5MtjOIR40MJcEpl8SQzXBbMavxpGmlUp+rR8DMzMSKQ2BnvS8jBeP4xXZ/M4AY1CiqijCp00sKYuNIsDWYSD7spTXChFp4RNMB/lCC+puq88uQfuFUBxAmXPIH0uXq+F4QTK+Ps3rxR/oxVAvPblLQU0V/xVXwj7RQ4g9pX0lzpZIaS55Rt9Np5tCV7K+6OhWPNJOYOoCvb7U7nZXsAn0UWi9qg1KtyAsVsZl8EtSnWumB9/agSpXpVG54r6pU0kpAZIA0cyEDfDQXFPQzBWNMYHNDZMZsk0cSkSC8dU33ZwLkc2yM7jW+eIQUvz0ERndM+UBHCrwHjyyMf2TTzgI9hGCCBMZCGGH2QucoWAI32hMfXQLL0ReyLtq3uV7ntCMbi+XbkVrngKZ7HUE0yiNc/MHbmn8L+QPk+QhGzlYRsy233PdNMMXeVEUDD3eICh64q7yQ+h5SpNljK4XlVm4OSR9XAEqRJ+kPAf4QhyXanx0KieDjStCUpuuUK3eJe0p+839r2X5ZIkUSgYVkfYV1HE+jnLlYKwIiUKA6wsdI7NAPEXwsh5kmJkOJJBnQjkos4NLYNBOiY066cEndU4JfKqWFb1sKSY/YD47ChLhfnUfD4lr/fLNXF494EbazYgFF431LxWxus4xPB+1hMo9Kd+uNVMwLZVMUplMGIgOODmMYCC37YBzocnEK5WpUXrPRxUoV7RoEFV46J/bJ77XIPjzxp4uQnmVTC+DvaCm2I299Pg/ELLeXma80qDtfdWEYzPx/u9b+21wuohNtTlRhy53dQPqxm4sZJgJxCx7OCPUkNPIOrNNYU1OIU23jTNg2zaiEVyTpgOQn6WuPDUmmsV8DS0ijws3niUzGC1RChxK/cwDN5K3Hn2dGg2LUaai5OcxrGZDmWpFY96txnR0nQySC6USZi+wcaTStqy96/Svj98RQxn5jt/FS/t9Z33wGKFm16DxBGkmLbN645uy57w5hWxz2rAuWmUTC2R9+CGQ8q2H+ZPTY/tKUNqhjOgfEAuOxkGd7dXOIwLNaUi61gJoG5Dctiw+ue5P1HLSjba8AuvaMtkVI5N73PFRpU4NmmvrGJTXpNSHJq/6oklX7jjE7U4kX5WR2ESyjmPee6Yaa9SjF1uw4ptr+0CluDYZsd28i5LsotuyFc+Ogt9xI8a+PY1cHUEYYHb75jidjzAtvdO2ttD0zuHYUXT+Ff+nDBWwnMNW5Ldcbacmch/WVpk39JduZrnWpQ+vlsVvTlmzICuPDtl01/ClaZx9dVK4qah2dN/JbdzCHgIldX0WZpJdNillrO4c7xTBsP178t1UTr0hP9Wimj2O2HJtB06eN5CZmNaSSVIe4hcwOjjuNroAXIFQJo5VpxApFkVlIcjfFBBziDN595pbGTMY1+gv+u1ab60yWvUeeDHnI7xUQ9GeDiCM4gtACSnHwf5qNeoWdoaZ5A8Gnih+Kw8jiCvEKLu8sNE0m0NoVU9uBCuDZcixaQmW5suknJ7OHIrZRGTc1T+RWELYgWydnhlBxYru46JGKv1uXjH2htIq4GY8+VT8dlXj9esf+FDDH71iwcwrAbSV8LIa175RiuBvDeQxmT278v2ArwSljnhc97B1ol+1rlkT/rE0X3Y7K/hqsw/aQyn6K56O4CUp05dH8aA3UprhGE6TChsakVqCmlcyhUfpoR4zw8RoLRRzgXnGvkh9PEi3PTmlb0qN73kQhLJERF5St+lgXLoTOB6hHaoi4vEo11OngjKC9l9ZSeRO8eW3owE6rowOjSgd32Fem7mzElJSyGuhENeIU/EEjNVMYyKzqINB7T6aihPx9g39dOMKJzUwev4HAumCxBwr2xT/Ngs+lSV3zXzQzqCaCy5ZFA3XAzOnSPNjCYKJUHpZom/EPZL4cFfBjdTZbj4cFS+KjYFvsAWrp7Hq08YaRCH4mqXbdoMo7c2KPmlztvOahbROr+xV4xjzFACtOmUOW5UqUc6IrGmgSOjpgYOv258Rcekgde7mCj7SZA23/XNsWJvrquVQKwIyubR7zVZ0GaDWjHEDTdpJhgfWSkkZxBfq/BnQ0dZbMuZYAKUC40MwijbFeMgbzkomGsYlB8xKBZxJhAaRPGyqJK6AudnOX2oM5w/PGXxQOsbaDmBvCcKn8jWTTKrgZR//erV315ooOYrD6/kDHqtpzXcQLMqiJtpvgDhV8hYRSF6NgYkEJGiNCmR0rKVqSr6HTsde6D1kD00ylIwy2i6MbQrKOO6UHyCLwIJWYH0TkV+8BsYiwmNndGBOk1rDp7jtO6SJKaANuoMvskNXaSdsbZqZETPbjmQ1qqSIoYUW/aYYixeTYiMEx3d0u1XB55o0jY/yGlJ2+SVxvea3NKWcXB6rwTvi/Dxb7/h9BSciTJPP/2aIw5NOZfYA8v9QjKZDKddqwWrTa4WIL222+VWiljBYJdHeebK/AkHOjQk/J+YhuQgySLCYUO7e64J9nO3XTaCfqmnirTjtmXaLY4hOX5Yes5GlHIAveIVMbfjbIL+Qo4g941xArn7oM52rR6ZGxRskH52HsjZiGPTpoHyr7Ym7/aiOqFsDU5CNjA7gop8xI8a+A41wPWBsc6Bdqn2STRN9Gi/AiyY+ghjaK6bR7uPkB5H0BNowEXRt7lmOShdPQP5N6IKrQjy9ODCkz8sARs8NpSq3Oc42L+iq4bQNZc4Eo56PeQXs0MKQ1Lq6Bi3Agv1bf2V85BWyqOMSGiAPte5QFonxT8V16qjfit/l946PpfbJRhDT/ZOOwNGG1lDwhixX1+vdsHTFSAaqszv18PmwyDs/cPK7fd6tZ/VsH5tWg/2/o4TSJ+RxwHEl8XAvdcDEl6bZj/Id14VJGcQewZ5PJR74k/GXe9so6EQJ0S+JKanbrICJ1FrlDhp7EsHY9BR7W3VZJzQe11Cnhotoah2JtBCmccCDu44XAHOTxGpOFZg3AOYOoBmZY7zrAYiz6ciWA3EvPJPzwf9mrXmhXnogrOHr4TpYSKrgN5obz59fbZOoDfal88PHXkYyQbRKj6vhDEesp45OmSbTjg1FWdI7HbNUfx1wQjNcgRRj2sunHp1pYiFGCkJCOGno3hSP8kbqIPb22RMrcNSu4iauBenhothawzrqhWcOwm0TWfVYgFA2QZxGB5xkx8Rpaqq2/jCv9Rd4AiijFfwVSAOt5u6sNCjzZmHDlthd4sSZMsBD2T8ysbKqZ/rQDbhhlSygdNem3Z5xTcurvJX5cFfokv8NfDqhtX0OnDHueqTB6dgBKB8D0fQpZK/Y/bHdQRtraoXgzZgWtGGTvXRshSearBB98IC1U7Z9MSKli5LPR9izyiUMb6IiscX7IqyBtFARmSBkx9x9Wfk0gGhAtFhhtNL5JCYTGngi3wRhb/1Q7zS6oiQmW9kcXE2q65sTBpKz3DUpz69Ef70mSdCmjwwadAKindeNqybaKdxDPGajVZb6CabV3Cy0SA31B80UcgFATq4CXoAAEAASURBVGXoIRDFNgpDPZ4HKttiPgwzSw4uNAf9dPXjD8cPziCWw3pA9Q2u0hph2cSZ5bfsC+SluLpJzt5As3pCN9Mv5QjCGcRNNM4fVgi98ka6PLXJ6iFevYHP+7JwPhW4+GpM8HmIpzuG5vkMkwYGdxHlP3TmFF/LpDjSBlE4WdLD6zMledEztNcIQVSc29tV0EY89kPXcxDq6SM16CIvNAZuwpQcfWnnh15SO8wTgShcZaZcMVlynQiPXx+UAJe72pyfPjcYm4p+2yB6A9LO/mSyKh6cOkxi7ZjEIeRNpDWp9d5WxJ/VZmnLrHLL5Ped6fkCmSa98Cvmy3p1BOEg9QZ4spkVbwTKS4twET1bGHuMwEYTuOHQbvOvlNsuENW/y2IS5UOTL4TRnnntK44fv8qoNoqjki9/vVScdqtYbfaVnJleMSSnDw7P7AmUJ5f+Up508mojZmKWuonj1ncmooFhBzSYRvB2lCqT8y5bSh4sNZC6WHgSrpTRoWxWy5XjET9q4NvXAI4gftOsbQBp591G02zb7kGkKdNL1CeUz03U1fbpHNPmh3AR0VvaY3JNHBTyo33RNiGUeRoXfo2LT1wtpWrvbr5x6MpbaOL70KMEUEGzh6termx/rWPXtKcPXdFDHR0agPUXG8p7jXcL9/SZjjP7VDhTNlfq5LHsLOGwFcr7Z7hOnnCPDEWLl/TKSIpUkHXbLPxikOd0oiFmiMpn5LWqVQ4cHEHs+YMjiLGOPSB//wcP9HhtWg9MZiNpO4LkFGIlkPfRY7U3ewZp/OOXPqT5I6+IjTOI1UA/8Wl5Jl8eWeLioOwxdVwwNp5+lFlUi5GS0WIGsqpzEqdImVaetIVjjq4wSVHWspSITPCcZ3ChFWBgiYvH8fO3nz6bNk4grdiRA4iHgM+fyxGksdYPCjV3ZNuAN7wGxr6SWglE+lfNJ/niLE4h9g/iQyOM1/moA6vUVUMqfuaKetqkwLzS468dCdJvEzmDaZtCJxgvWs13vVG06jNOIVEwp9CfsMKpviXjmH9R3wn79YfT0eogrh5L2XCLbq+6JVAJw4scKfCP/SaVEOeJOS8HmdmdBUgCUY3NHJhpJl+bGpdsxWMO+Bt5gyOa5GI7JUbhriNpcW3GUC7CUd9kDBLmVoNlGC1cKyLkPs5dBAJtIKJ6niDYVIceMkya2MDtAD2taendBNxaF8YrfM8vW5AjhIsgBW6xnFtEqAEuR5AUv523KiL9cfyeNaBrzTqF39OOk26aTa1K+9xa6VDujbDMNL578IPbL45cJOwck3a0w8+Cc3Gt1nQ4K76wcxHwHdHqpmNJDbLYQ49vfJdY4FwgtzKZ3uAlEfJ2dkvioJ9VwOtOGMZ1qsXgrnmMBRYKKx0X/k4gvGeQgPtKCAZ/3in3smJibqAV/8Z+K3xmno2l7Qia1UE4grhpRgH22C4sJME/9lzKGtQ4kKY9DC0mUsLcwEqORuRneiddL33Z8eOb5tlI184bvoaki46X8MoZxGsxbA79Qq/U+Ob5Oask9LqMbqpfshKIm2vheP3GTiBeo9GExk4lDa7PcTKxAlr2UPeUhOD5AgVyaTL4ugkA2ukgVjB/kq6HyrGIwh0j4AiL7gBN6kx3Rl9xNSjSMr04c6RgZ76D7gwv59O2leKIqRvKiqS2TfMfh4VfXKPWbYgKVHA/W2cBCHDaFO1WbUft16+IaTUbS9s/ygn0UV/H82bSitkIk6+i/KGNMXEGeX8ET3yFkwOIp6YfoZEj9JMnwJoIS5b3DtIsOF+mQ1ucqakDnoIq6KK0No4XIpaFgmsDKRyFngirQiiLyyOE9+yhkvRjDx8mnPkaGG1ZbRbnpV//ygbnrAp62SXnODtp52r3fJmEPYNesCGl+gp9wntlIZrJouyoEwiTsYkrwVQvZsYmGe/0UIROuBTUCadBCIbp+zUt9CZGYlbxKfUIjxr4XjXATexaETRG0E7dVnVIm01bBk3eH30gBvCX4T4V0AOjPjj5XMv+UuhXEBzSr8TBXPHX/JXrX8/fSi6ktfuU7K+hqwzipp+S97Xw6L0v7QqtjV8rG7qrDIFOYqr/mBP1Wvw/0eIxckQzr2P+xQM5DXXe8JkHG4yFPCR5zxfEWCmrlUD/0IMQ5m/eLJq5HSu99cCk2wIwXvqhilYG8ZoZr0p7vyC9IvZPXhHTgJLxjFVBDHKMhawWypjYoq65BPT6uzd/9zx1Cs144kFl8okEbHU6jnTK7kpVglXxmV8Uh7MFyeTnFxRMFpf9XfI6mF/51wmwE0g6WAXE/I+9gZ7piyCvWZGrhzB8BYxtBPJxEb0Kpr2B2GOSj4uwGojPyXvFuVfwZj7KQ0scSoryywzcNUVx4i6rncrPEydwKTc2ktajGSU8f8ApBBShDozaeyi8Mbg9rexWH3ewgBIudPB53De2yEM251hnOtgDPMLuRU8Qoece+R0YdLXE9kFzAu4EzHwmfJWCkXylVXtJG6wwxdOJly0bKoUZzCI4hC5HUEu9aA4hB/UB+1JqiSjjxAt+Yb4HL8xxM+JrnrrMPahiDbTtl6h6rAi6VPB3zP7AjiCaUiZIjtVyaES5uZiGtlUc1G58opv2vDp/8sb6RiZsyR+SJu+IQ/LW11eWDDpsqnrbJVwaeQZv+HNTJyoJ8aCHIRG7jEyWIalWV2oGbnJlMUZkFsNh0qYpEbF+XHMMklG1y/xGS9uMDRkwQssY04tgOrDgyNHvM3LEy8QZRxBfCPPEgMmDnUF5ivQ7jiA2GuTrYiwpfq8lxlpezEoKXxDQMTItG6P2PEoC8vGfWs4RWwaR0gvHBINKUEoD389y/HjjZhw2Gllx/LB6AucPTp83rOrR76VelbHzh31U9HtB7BtonEJyBL04O4H6RSb2ZfGNudTy9GZU56zJtFiCVdiZNsI4DLw4bCXs+a4KAkZ9WC4iyDwRzL8LWaQrcYfzipOAFgJqpQ+V0KoMKifnjHaciZQJdUhbIEfwzUuNP4QEuR0rp7F5dUCb9RggvegDyj/IpwL2174TDdzg/G8Z3rRcDY8NzHEGeWWPGjN5nEAflWYfBG8m/XuejuLopI0zSfYndOX8+SDHERtJ81n6TziDcHBKkV8PtQ3p62xcjwPI9k8ZTkURDvNdz06n3JSdX51AjlnVZsem2qscOnZUssKHVxnt+OHrdvrNK2FMSt2uafs4feD3irjIZV8rdZnolw1MRG2KrXEhdP3gvAcO7vgppYK4lBBAZyzxqYQWBO74WmLklAqH7SM8auB71sCTjiAavMJE01cD64TeQDVmbm7S0Qe/omnp7icFBsZxsFKSHpRe5ZHtwJXtX4qXhhN3oe23vZq7GHc0tw7KdxI2mfCe+3fprnxfIw/ev6aDIlS+zlThF2Kor/aUfMdF6pWy5+mQsdMd1LWGK+IBjZ5w7DpJe5wosDxuWAuYxFXcBe2sVOwrVGHhxzyq7f2TFGbcY/zTT2MaK7nzOXmtiNXDvO6d51fE9GqYx0M9IOF16jiDNAf0gxHGU8nXPJnVt3xWHgcQr4fhFEqL9ohhHO29Nvn8uf3jQJp6BTk3zUodgX7Ej0AyqUmTg7EyMm+kIjzvJAbnDZ+jPXOa8JhxDna+6KkeVtp67zOJI4iVQP/U3DJOIOJXPFjU/JGVQDiEcAT9wl5AbBCth4tv9bDmjV4LY3x+o/wbrzTPXPSFx3UeX3q2cnIEHXsEefabMnqsje172V0w1yFzYWH0w3FQR5CdcZ6PiNfFLfc5dtVCAhi1WyglIOgshoMTIbw5ZaaDYOeGNr3C+iprqDZxECoML5GRkXeja0iBV0bLA2rBhm4BdiR0IrSqzS5I7odj7tOHA6Ybk6sT7bkiBHLAN6mXAqUcI0hknM/kzsJNN6CDOnLJL12nTPDG7UxKL/qQnI5X3J53vY0O92O1H+N18PwYW3AEIZF2rJPzWBF0qt7vmvmPcQRda8kXq60lruvW3rDFNG1TcYhz67ExrqYfWPrjNNgqtbJkjqnAoWhdPFgaC9kcaOxcNU3pq9KgShKRYwFMtTK24H/iqoz8CBkGohRkwa0Duu1ndgASwJP5XozW/dfYVNk4aBrsrJlMHTdkqWeeADFx4BPb3mdF+678rsnBH/osdz4t/0430GzMy5OkLDvmhpmb5TydsUmy6cjHzCnr6DVeF4++VubhQ4VyWaYQfr3EK3TGESRnD6sdsqEuqyGykuKFX5tR2isk+FpDVkvgJPKeQX51hv1WtL+KRnjjcTCNYwmZnB0G2jmtrmvXu+wlPqWVKaxwirWnydcR5PO24dHhicp2TqAn9LQlN0fT3SE2+im4lEhYsFhGamIr+QKfaK9YOAm0+0yykn/qeNDRPqPZsI3BbV+4XVv1YCo2uJ/Bg81mOASUH0fmn5oF8yoXq9v4BG6/kMfyeJw6fB2PL6XwdTxeGbOjk3YuZxAriNzmcQYJ9pHVQXIGWSZGSH4n30whsdHTOwzIv9u/bYzVNtK207ZkO+azQoeWRvP2qh15HY/2jNNS7VYr1l6ovb5W7FfCaLuabGbTaBycmWj69S8mm3p6iSPIS89nfoguLiHowdYG0rSIwqAhkLddZCivQmkO+kEE/cQxkyLzY8SS8gT5A/yogf/FGuiN8a5itUol3MZ92FrqTNrT2gdJp6A9+4JTaU/0B1iEAmsK8fEHK30p14joM77i/kfx05wH5kjFkpalinZ8Yfdj25/S3BB8ScpUxQ3PFXCm2+1sOvGZ7iJlN6RsF5I9+zQJgg4s52wXvcs4pw+eE7zMoEmvNnTQF3VApu2Ud7EFwJFz0nmxxQpGe8dZw3j4QZm83oUjiK+IsRKIFd68Fqb5nMY9nEHveU1s0sA+2BnEFgF6xVoP+JgH+hUxrQhirvhZE1c+I6/HcTLiGR9gtzVYxMqh2EKdaWBiPsX8esaCjDxbfbqvURIFD0akoB+WYMBOQDpViAz9+eZThSavAToze9JjhQft1FLnEa01j4/Sj/NnOYFYCcTqWjuA9FBGY+1rrbzlIYy/KMtefDiCtC+QHUJ+VSwPbOIEytzS+0yqzN7GwIXD6RQ9fgijMmBHaou0aibFVln2swrh1I9iTSWUZ6RWQnWb4IqbNHjBqfMV9vQAU43O3MEuTpmFcZKX34lWdRcxAy2doGYb8MmUQ/KkThINMySCb6gLuHLt5FeceUSw0+zppXQBSSBlAZSdMlHVG9i8HETeObHRJ3Yg+tkw0hJiIvhgBNJzaKIDD92AJhLg64PVlFFx1d6TcMXteXenjYk8+PTDSbsvGuhivdV91mqiG+8j+e1r4Id0BFENbUBc4mine6MznsMWcJrsdNCTT7i9sTkkurmWUP2ZiyzCGCwKVl5p98m5as2l17TWpREQLXG4YH/kdHAxc8X1YiGxDJwOUna5vK8y23orEeUUihu4smKXwQD0I918Lz7oaHmgdxjG0MyQI1zpLJ+DYHbgRGocQayi+JCnSu/ZeJelxbwO5p+eGn18rydNcQTxtQlWXiBX1wLLIvalE5hUGKc06mqAaVSPriPj5pzIbte/7KJ+syII5w+vzjz/2zOtgvCmunL62NGjPA4gf6GBV2bAM5hrECd+qZttXgN7rtVArKR4ZjlZWXR8USl1ys00YarOaeeB6Yf9xFzgiPk1kC6+MU1BTe2AK20VPQnDTHldX9AvQUqUDoFIAVnYogNlAogmgCRI7pG0iMCN6kHxzh/BQHYVyt4NnKO9HUK0YCMAvMvVGlM+vZZzLk1VL/r0qbQFF9fyOlDemmB2NSJ/dl5PBHHeyC9ph5DmsWqbymt27C+iqF0zKfYmmmq3LIn3a2E4gLSPAsvpP6itf1aer+F9cruOLehZdqoc3qgdEG0exBSjRdktZXVMnIzjsMEBpB8r3fi61zO1azaFpo17BZvStFWWmseZiQNITkyvAFJ71iaUXgnEXFuN0XsBUXdS6skisetyt2LwUwhoaXXT22YqIqgK4AHcBQ4PQOg5a4VEcqCkSZl5KP2kcqBGPQ6PGvjGNcCNMeNMg69BasJuqzqkzQpLmgw4xd5bb104Bzb9obLMTHdwnxiomdNH6CtG+8Yt15Bc26xu5zJzbQn33pNGdlUoHsnLhDHdFNZ5SQ3roj9srtZSxDZy6Aj2GJsOKlLBhq5yDsuAtCxQn9OHlR37EkOJ5MpuXQVycwpGSWU7Fqx0zUdqjpG8Q450tca6WpG6OKhuU6UMX20WXQGLZQBEbnCKdpopzxTf/Ebr0DJVFPk2UWj4ZcUOYx/7BPGamMY+rQjiFS/GPD+80/iWD4JkFRCvS/MZ+TqDeOj3QfsF+SFgH5Loy5zeU9LjKWMxDzee2ZVBmi5me2QQdnhOB0xlPMYBDVacG1O0plMNlIPqcD0GZQBJdylXiDgRbgnSqDQrgJg/5DdzSVxTGpStZ3CwV5b1qNLJM87xOXdNH+enV6s1Z2SfH3+ggY8x6CEjTp43OIG0IghH0C/zCfmXA+PDDfxe6MEkq9U9/9RA7NfOKIOscVr22A7lM/a2jykGMbRJFWeg8KodVX1WACkh4zPPGsYwmdj1qBTV5WsOdbXwqQvXpVjNLWTqeZhKO3yLBjgZhHEPVabCdi3goNlZBPN8b2CKVqp0MFgsgAkVT+ywA0S3kaY8VzrlXV7g4l3p0gU8OM5FFEBn2bSXMSo44MIYhhByU9aRXxPBWdzg22mRUztw7EEPoVQZbtFVQQx+QnHOVlHjEinu9aF0EmM5xHvY87uYHV5b4Wua+E/OseLziiC9GqZ+0PupXdcj/e1r4Id0BLnDuCXlcnXqEGqFblSXuvJcDtwFTjYX1DRw0kdounFokYKOdKarULoBuIHDSlojW/fDGNNtjOlKC6voYeHnUJjFRS890QPXdO2oCKHrAkay/BR6C7xAYkBllCij/w681Cj162D+LW9c2NbF2IJCDhcXDi2GyI0zziBNAHyjzORBK4P4bPx7OYFw/njlhG6YP3LTrO95/8ksRIFJCPa4nnD0eMyWVV5RBY1sEhI8cTbkNescZAT/tk2XSG6auaj4phlHTm6EXyj2vioauLUNkAZwDcB2CjEY6wed3/POa2TP5AzCCWRHEJNzj6WqMenxTbO0Y51vgg9LJpWIKuXHgfPiNPlLKJ3xCCU4ZnAHGqDLqCTnzOfelfa03FQq/O4RSD2Hnk+Ljx4P1meq6LINlF/6rRd6wmpByyZjQhiSy/Fku+javkyG7NHh9ql8Sh8huVHT0zHbntq5Vqyh7gjYS5tWkJzKs1NG+TiDErMyiBtBJsQ4a7zSTW0UpxBfC/PTTpbNs2m090XQKiAxsD+CN5BWR/hzc3BSjFU3tGnlj3Yce5JP+UyvclN32Ow/Joduz0wSM2F8prb6Qg04q9xou4Kzkk2OHzaH5hVHYEwyw6sVQJq4PsMZhFw1WCaZKIoWFNpY685BMMD6UY1gmb6XnjjtGQrY0x5dSEPgoL4n+Fyl/CRTN5F/kHyph5TqET9q4H+vBr7kCJomHOVqw0fbTttfY6rbd/rTshRiGv3e+BcyiV53IWNCzDUq17gzYfWiwWLP6JXb6QK82HSh5CoUeffpdl30//NVubYg48AlvWsvHvqzxENeLK8tu7xl8iQigytN5B3jSGWPrERPnoJFXTrLi1xUDZikwzUP8LbWWtZgbstR6yPTVDrsTaTlqX2+UaqiAsO+jr22NgbRtFnFx7wNGKtzaLe0e74Oy3jG17+YozHe8UDv3czdeCUMZ49hfGFTK4beabUsq4aY5717hzNINHxNTGOhP6ZghxAWaE8g9guSYmrFOqU3c73Ygj1+pYa5lioh9QWv4JyB/ItGCWV4oCGW1UcMhhg6Zc5jKwOwnD1Skh9OILgPx5DnSBYobaOfOR9jJjKZ5/2s/YA0/OoBi9Y3MVcUMCtxtQpI+0nyJU4cQsSsCjJMTiDvB8Sr2+wbpHE6H2vgYU4e8Hh/IY/LFICxNiuCGJc9KspW71NELHz2LIpNFCN5jCdNfar2FNsJRIwUyU+9IKF0TpKbX/Kha9pVl8xQQe2qUsLVWGxMcC5aSgi9OUo5sWAY6pD0cR0Y8EQ+JyJxn3AhlRbrTF2GKufOqoCMfWi40b7hXHwIGtDT9NCRrZyiNqqpW1kPkQmx5aBczLa9pWwcutJTRs5p7yEHO8JyHukfGDmqNkWBAbC8KcuoPdE1Y5wyLrMFwhzsqocBkedXslDtNRHIahciJO2fOAvnPo6Qss4eQWF9HL9zDfwHOILSOtsIVwPeWmuTdMim93rNUJSGHGmlusai4Y5QoRecc8eEHgl0SZSpkY9BXJwszVcFJBCiLfTJtlNAa/qIcXpdIgaGhAUbWahz4CJPYvIbeASHzL0QpIuVgdR2m3dbtYAoGxT2ynM9kJlyQuLVFLpZ/qgJBDfMmQBww6zfpw+6UWb/FPZi4YZZT4rYR4VBWTxcBDClr4mhjZtzAkeROGQCAR2OovAFw1EGyR4GCpLZHwgHDo4dbp65EeaGWYO3V/3oKY7zPNUBDp2WJTLoMxhrgPcrYHIkcSPtVUASzM0zaYeoSjoWrPSeEJlPSc/TcO8kTkNHMH7K7HMlWHEmmANn+2gL92gkBDkwuw16Dcohy+eviio5mnpDUuiKjaaeJbINNwpMUmmRItBdHYAzSTvJLfNIsnzbfZQSWNSWGE3bwL/Zd8impmLRobdyKEccj0wK3QYljzb3p5a1f1Q78wRZADXfmSDTjtWumehq8ovziBVB3vxcTiAGt7TPWGBLVQ5KwaWkZWiM3g6I4Vi153py22PCqRkpDsmf7QTCwSNHD85OVrOpjdqpqTbMqiCeUAK348eOJNo/5432m8kjp+Y2YC0IxauZq36BDgoweZ5HBc4RmYldSEM4wAvctbCgBVVbENhniQfdI/WogW9cA1dHEOrbKt2SdSAmtLkaTv8uxoC2+WDDIFjBBpwPoCxDgnGskreu9q0zuXH3xG0azZH8LeVOV+xTtBfVd3Xv8kKf/l/ZjaG7DTMXOUptEq4fqQmySHiKe+iqZLgdbQeqMmPIBlTyxvaLmkt2MQMPbxSvNmCKkzGi28sCgW/PXSqTc5gi1kaPg6YEGSuesgWKBvj9A7CZ0SRDK786ZKD1gxAl2B8Ph5A/nqC5m7+oyVxOq378EASHj1bGvtO419VB5H/369M4g/SKmF4PY/7HCln5lDTGqez69aFfxr2Mr07bXp1pGcVXrnjqhhMGI2sz5cZmD06KO98ziD5D/XCCDRCtyuI/5pqeUyIfRcw/cQDFMSRA4GjSQAd7HpYw92Peh2OKMZT5ol4N01iqoVfjrJw+mk/yZVnvCaRXwt7i/NFDGb40y0cZ8oqY0s7jJNLr235ggxOIMVzjtMr5HD0qN6XVLDzOJ2x1nhILziAsWIoIbi9u8obNAToTS+JaZWVgCRQvNhODCM+CB3TQCT0g4itZAcAxN7TOGRD6SkhuxAm/9Y+SLCSyBNxYnBTIc5ONbieChzYAdwOpTYzBBzZUrvoh3GlveYU1sw5iMq3zkjNtKRLnuAmI3OE5EYUXebvdR5pSuTYEQq/I9KtaRG1qyC5cdBp09+D+tTOP+B0EYy3b5e0ww4tEhtL+idPFQoYS9H0AFMObRd+16gH81jXw4zqC1IA8bE7j2hsm6bhrlKDRKervXgXmYptLBOkjJB3Zk+buTYEc8JI7r47OUw7DR/EhIR2Vdu53ng/WSBJT9CA0oWWAB0VES95c7YITIkoPGYyd/AEfwaSdXUySF4GmtYYOtILjKsjgKT4xljZGjDCJj1DF4kHculmWg4dB/6MGf78mozyv13z+rFdndIPsVRO+uZ46FcwXA91ke5yu/pGLfuTXJjJiOeVN4EK27Io1qOLcYUNdHEA4dvw6DTClefrCwGucYl6fYfLhd79nMOZGm4HZN+KqDCYdInW9UX5UErCvaQO2A3B+0MBLKKxpcE07FmBIzWh6HaiDe0GWDRjGSe+0TS+yJ+gqBzrJMRvlVnbprgyTJAMuN/Ab3VhEBK78X0sHXyZppI4QObTRwmydMkctMHkEX11QLuzUT7jS1sFDzwSVQIQe9i5g3wTatprwLHOXQ0hOIFYKZYJL245jKCvcNLnEuYlM2qlTZJC5/axLWNEwEKLvoJ1Knrq3U9ITUbVXO33iDIqTM+3TK4LUzlkp5P2D7AQirTarts4PqWrOaofqc1bRWsHaI6jkpvVBdCYVifmHjLrlqudVQE4NHfhh8OXKacEo/CUgQ8WPjDkvYS7TheGRfdTAN6gB+jtjzB5okW2Ve1MlDbx9lxlKA7g2+6TpA7f9gGttCUNxyEA66L8KJal08k2HNzl6fFIc0/8P289aoCzXLuNM1fIDPcu70oXygMaCa/7Quks+qJJKOWLdFYfRqVJRqSJ6Dq50rVfwe/pEJ/7WLfDbej3wsbz2l+ueja2n8O4ynR4WX6NJ60e6OGJ+KxgxuUmbrbzEE1oXyPNPcNAZ8zTGiSBjnsY0zcky1mneJmeQH354xXcefLDq+50cQ+wBydfE+gUxVg/li2JyGimd/fY0H2SOJ0cQDiZWgrPq9k+tCmfcpc/R2qDBHorxk+ZuOjjjugAqpM/9nDDGEEgMhWkLPMxacwgNtLnhRJE12TFEqnOKjEYSIDmI96vZmgN6DNU4yryR8bOrgNjTh9XlOILeaN8fvhDGj1fBcAy9Yp8gOXxwCvmjIziFvFo3e05mGwI9pBy5Lxir9YsdWQWU8lIj5CfGOBwMKgfJhiazMihlSGFIg6Wi4F2UYSXvhiHdg3PW2NKaM2Syw3SY80Qwv/G5Ii4pqNr0Ua49OAcxNMQuI4kx0QlnjwOkYhzTl40HAaIsDMsvdAdVtBx5UlMtNvNkacTZ+sPUUKwyiD82BU6dpa0dOpZMybviQrUopgQtCXHSlK7VyBmG41wXhz5SLec9OmsrwcSmGxHG30lDCu4GP4A1nCrvPocMpf1mh9NpU4/NoqmMHyP8uI6g6ZVpn22tR6VdGyH5DCAHTVLHxSkX1+IroTFwaJU/gwwvV7qW7KH3rRYPVvkxwBpHRi+25Qd8I77sQhy4IwW6uchTrr0fU4TnB1XioTcTsEJdunXT6ioWT8gYJMMHPTDz6WoDN3l3apWZFRTeV4W0HT/cKDPg86oNA71ukDW7jqMpEw9zSwGTEAY1yyAztnlVBRoGRITS02ogIQ3HMsqPYfplFYVuhDVL4ObYT1s04Howd7yt/BFDbrYlQ2XzcEkZJY9XcfJEiJKjo9ZhTAKnfNTavtgzMBATNMY7LNoiJjZazGf8nL8KFS123Rs0Rrzrw2mMpfJcgSjZhIxOR6VLxsd7K4Io/+lLCLuMld6tWMB/IcEk8WLvOtfTJuZ8UBfYRoCjFiSRc2bkFw6pItqSyii9jgWMMyjdmnbMK2K0b09iaeeC+etjbutaDWQe6vxQZtnOa4qH41OolC1ltD6I9L8X2W2NiaEnh2rHbrebcwcnJ45KTVY9mQSvdmtnEE4hfm7MyKDdqFRUjn7uza6o1iX25uJlU8kOLe2HJKII1DV/Fq28v/Sl65zFmSISkl/S7rZbsHWjm7UGOvM4PGrg29cAN6VfcgTR0N22G9Nx09iVmsQy+2j/AV3zizC8av8313bBrlLJPy3pkBm6r6PkqncTfPE6oB577tEdJHdSV+tFclUlkivojiCBbmW5jBc779Kh4yuUcAladKNuopNJwBAX3CH4SIWcs3eu23Bez6rliBnaazta9oy+6Iz8p44eS5A3v9LZPgkg7o8rP2nGpeUMEuMnHDVy/uTBBzEOIRxD+TiIHT56FYwPKtgZpDRf2sxXxHh9Wh8PwRkkp1FfOUN+N6Q2TB3Oc0VZ8KcesGSOwafPZST9oYZT0/QF4Eqf4oAWJQnLoS8z6/RTGcleMWOvvE7wjQYnPUZKtgY35o48YPG2AJpDMkfsRxqyMTROIK3w0Z5AbAr9y1t9Jl6vfr2VIwgnEXsG+SMN3jsIp5Fon7PnJCt4WaXO69sa/zS+8+qZHU42IhtCM756biPYz24AtAr9TIPpW82M3YAOR5AITcuBNC0uMaUeZJI3eTMObkvbDsCWNPhLJBtsGQdYxdO5A3njLiwulOj4MxN8m9qQR2dMqPBiEvdIHd1cR4vcBIfuvoyrnavoIt9x+3lA0nHPGaqer2g5jruMQKf0YjhsF5X+j6pQirLpwLyLueP1TBy0kbqX8dB+m4JuzT8rZOLdVkDkd1il7TCnB5CZZZh8lgV3LIWho10/vhrWevwR4h/YEZTq4RWEdJS21sCnza069OB2JhkcnYf+1LgsldAYuGh8gTKDPc9c2M5B0hg5AJ9Qgq+rwhgytueCLPTQm+zEexUX5IVEFwNKQScKPXECiZSvkOgqrISJuaD4FlWGIO98Ez40FoTlskKK/IQFLcrmhliTCJw9qng2CQSfvCQqnXMGrxe5+hygKzfesT9PbSKTsvq3VQ66bKftHeXYJZmZGNg014udPrIzAzgxzh/hdeDJTpb+ckHlZlb14htuwfkTDHn9oYK6Tk2QmyB7sJFAfMWTRxbBMonnB6zBJCPIdBU60hceJJWgCGtcpyDLuwQOXfMlgE5lvWusaREGOrEzOlAPS1eBd+JFdwe3g/6Kbj3JM9MUbsxexrsskepPs08ZQz08riXRqDg+e+dibSaBmDICVXnpA76GKMv8UdNGD5Ru65oMe9KMYwgnELT6cW44PT5FEmkryCNz4C6b89Cmv7huSwdOgTqy3XJGkqT92Smptusnh2qvWbFG29ZEUj9PKgXPU8a0X5/viLK8XMRI0vswawyz1kBQGOywGNe09GCYfvSdn7oi0tfA8INNIC8a5FEpd0JohwMZj/Coge9YA7sjiH7nvix7VptWwunG6imMHbTvae2b9ec2H3lnWIlnHZ6zoUOLaJWxvhL+y/F9vSnNUziVySYcFkDJ1WFq4Y41Y/dgDskjQwJ9bQN/IDc5AVoDFUHY6LZkcDrmarXrHb6hSH0ms6eXgCY2EYvuJMpWXc7HPYtG11jWXOLSH4Kr1jEVPjUc+rRB0q4OoQ9OkzpvtmFAgvNKuF0yrwOoisxDngjJqpnMqWi9zN0YxhjrGP36JU0/0OtDEK+GZVUQez/G6fNBr4rxVU1eFXvPZ+W1kTQOIBxEcQRlZZAfomjZT7YJ0KojbxkQRxD7E+EIYsxN+2CSlpYWyym3Sk7h1d9SB8knN2lFlCXFTewxl4Ixe1QhPedkzmd5pnTdUr9xzMhZg8NGnppn7MUnRxBbCWRvvnxUhJVAdfh4NRBfBnvL5+F5JUx7UeIIgm9WDr2ULD5Q4j0nBcep5Ic7Uso46rFdVv+kFbsEgVJGnbiuEgps7DUVh/QncAQ7gpRZfcxlpKSZI1+5w2XOlcx4vbJKVPpwe06ODmkPaCdOGrhthz2Ew3aizbmb82U9Yqw6KCdtm3wON3lG78RLVdlEcTaQXM57bW87OEjNUbYRf5SzCNFvAbJiruW0iLOZ5mypNzFLhudpagq311oJcn3ClbaDbXfEt9ot/kvnqjjbvwtSumXabSR9D36Ftc7SosNEmYGbVucz951pw48VQdda/n75H9IRRHXgJNjbKE2/DRhEG12rjs5IY7s2TiDI6RCTNFyruZKZIFoJRga6Vgsm3aAboSMIUYU2jnyJJ262TJCUR7DRZmDAWGoLjIVihRkQLdLpnW7I4V4sSswN23Gxl3x1SOsQmnouufmaodasiFgdGDrNGLTWZ90M1wEEi1cAqex27tRgWHHaMPJBg0CUKOIab1VmDoP1m25oSWvCELtMvWzNwIdcSWWQ5YZZN8ecBjuAyEsvaR5RMBiQJ4jMwQMEKeUtj9jZhTEdh9rm9IKGvtnwKrfkJI0JWI9PhtC2SAp7Cw9VaYYnWXMByXmUoCVrEWyMQro9QjSEGxmQ1R5MtyGbpDAK1dc40B4hivziG5eCeIft6fJm8io6Sxt7FU1qF+U0Z2e12kVe6u3cUbfVv6QAGZsWTLqGnatC7eEzuLRnfjiE7EAVnSewiqGLkYojFskCKVPcOH8gdN8wX9Ahg1r0/tfB/Rr70n7jxGRSt8NmMslkVQRMBkNnMSmVYFI1gbQEGKK0bEqAolSKIVGYaBxAcqQOBDv7Z4NgsdhNxvAX0vO94iUdQpirTclHeNTAN64BHEH89la4t0o30bEpzfW4BtPG065rdFt98tD7GkE2mRLO7GO0KlrObdENdNE2Adw6Jy78Gt+nq22VnutOrgzb9fQqTHksqt5cLirrQizbc9UJPVi0VWN4B3ARseTX+GHmMnoTAFKfJ4Hnuim2ZMR3ZVX4DR4dIFP6Ux1Yr5HmjokcDxipQlInkObcFgeEYLsErH2WpwM2Mx91UAa+PRTluW8GLstgZWuFwZ0pqVIaW2iPzOEI8H8WkpgbNM/tJMcPOnjgoV8f+vFVMW8izb5BWvHzAUeQVgPxeXk7hH7P5+bf60MKbC7tL5FpE2n2G2JF0AftF8l+Q5/48bq1xlO2E8BW9yFKpzLaMh2Iga9CU3jmkhge0iTIK0DPbSYJ9zkcQAbi5mK+qfa+ja1Oa9DkIcrzn2f/Hr3axZc5WdmDA4fXubzKR/FrO4KyKTSvhr3RhtCsCuJLYaz6MZ0cPv46LQ4hnD+Mz7LZWxJIT1cCYQ9TUa/alcEuGkCnXQCqIueecVwgamZqx5TUjf/0RTOwTPN9ogduAYInUMfUCzo53+EZ5OBCG3wxSJ5A/Zk3+cpLTvIQqTBSlDpavRHGpKzJQzy2IKwCQFZpZe50A4OMUNKVAW9xF0ITRAvlWEFkpnSdBOr+IiBkqbPEi0eJ4gzjZIrwqtFmYIzOIWkMcxslt+kbpGDQVAoc4QKakHkfJCf9RW9x8Y031EpaU1U0FrYWLMKvhLlIksO9HeLIUyuJBRDi4Qjaa/XHSf+wjqA2oK19rlq711B1z+bQLreI1RDpfpVDHI/7QXGkaLmH9PAc+dAJmpHVkgxbJJGebhBuukLy4ea4yCd15K80wmzsdOqGOleaN6EFhadJX7xX6UPNQLCKuaWpc2w9aktSPVHQpACxGlw/6cc73179I14mDdWVahkjZexPGvzYyDkD8GE89P7p0AuVZUxhnF51HPnchNs2XVQJkTZHRZQzDqC5SUY/DiANlEpZD9drOCmhJcAXkA1yWjie1jKuNqy6EoD5VkPoR8YAdxjp6LrQCHHQYQ8h5XJyO5RuAx3JYbmlqazGBwupA1rOxkN3yZ65/3/n0hYzOansw0LqPubUqGlvJSUWTTgOvqLNRVtASjJGrclAxQ4D+vi5vYtBzd1pJpLBMTkOTbWde8whEEszKRV95qjOG4aeGqk4G2a2rIkzWSUtmbRl0XWlEG07acGFcFuHwEI5VPo5Rrd6MJYdNPArx4+QdI9Td4IWclDuKTiri/Q5hPsMs+FL6wX3yD5q4BvUgO5R3cfb9lGZdp7EgiuRtNr4AJ9u7bvht1RA9jEWgYg05dKzy/j309dxKDZ8jdwprEhn6B2mlmtZDsXC/elr7pVH6LIN5Yp2MQuYxJnlnAtF9HId/HeDmoPC9exsUqXjrIYc19SvCbfzilyHWy2RbNgUE4ihOuzXUKO3quAVLM6Pp0mKPVaNSSYTsrr8OWfhmNt5NZBijwjwC44Mv9asDOMWr1XxOlc2j8a5I6fQfDHMn5nXJ+TtENLqoA/aP+gPOX9wAPnLsWwoLccQX9r8oK+O2TGkdPaQ1FiKoS6hC2i7MTs2C+OT6hoAnHyz+wn3uCYuxRlfJ4ZHP8M0AYwDhlXirKrNBxi8Gogve73Qq1za3PmFX/PCmcPHGPRKmPKvtQcQX+hkM2heB/P+QHIE8VUwnEAvNfnE+WPHEh8pGScQYzgPaTxWyxB/xdMFaRvrq1TkXWrFaU3Ou0gbzmVPBVA3VEGryO3DHiHgBw3qrM11Ta4hNM2lpsgN74FwvR5Z8LF1wSZ7lbjwX5t4QsBZ2x37TvLP1AcK+CggGjL3iwvLJXuIWKnNUCX/mn4xPpG4Stjk12ZxAt0xTwj7avC6zYJjBF8tAfVXMON1IF60ztMPB45z1sLUGqXrrfoX/fERvn8N/LCOID89V/3s7YSLmy/ogq/G5pTgIgTGBQ+e4H35Uw45gRA3bcTlwFfDKju6zzmTr94zSpeMug/CucBOHLBKtJWABbBIxXsZW4qwwxUZdCLTLUtNIXIENZ0kr5OkzoZXaEg8wJd4RPeCmOXEqiXkMRFQzMSAp0PEPOHx0l7l1/mwjNytYhtf38pKHd63pl5sdItgI2FpwDpPYgoQEnxpHGPPFjLwAWDg04CKCkxAv9K+yCAYOBGHp4JEg0bFTuc88I2v6au4cz5tDxj2ILh4sisNfuUqeVM2ydKXonkMW2nTloLMno6gQtx+mjFqpJyF2fae40iA4MQYbhXqTBfq6zF6z/zlI07dcz6psqEjMiDSCnd7rQLRnPKFO44817TLp4P/E0MCGDX5Wp2FqY03r5hJsfKZMENJOM4c9sX2QxYNOrZyxSGNjMKURgQHzIDf2dpEe0Gmj8GAUqN2u4ZNbZ00evtDD0JTddbgvA4Tg1NhuGvgX7wEItJ+wDiQPH8FExtCmjwkyZt4DtV3wHq+kWCFi0u0M2k9qB+pRw182xp40hE0jXu1cSWSpt3GRlp72/fTVk+foIOmU04PZUwcnAQicuVG/tMyvxZTictk6zjE57r0V9KWdYe4FsXXnfKnLpq7jc2uw15WqK75cnJtmyoLaCPcTCm5Y6r53wu7ZNK7QIzHqENDsxvISK6djBWbyUqvmowAxoKhOPhvx1Fk2AodHOto+mEi4oEcOOqLn+dSIAREC39hjtMKFL+8FkYamfoxZg0usnjoJx5e8fqgvYK0h5B/cu6808qg93+wMkjOH68QwhHEiiB9Zt4rh7I6iD2D3r/HCcQPp9AHf2320598hTN2SKX1ckB/Don2MSploChUhks89LJ7GG2/M8FXWMZOVukwP40T6JmcODh8XuIEYtNnOYJe6lPwfJWTPYHs5MERhMOHWM6gN3w2fpxAr8X3CgeQPi/fDaD5wpj38vO0l/FaPxUJc4lJ5Ay21imPrd5i6kCwFEpYF45Sp9gjx3MBATNPkHSUiIp8SBID67zJcB3SYky2HUofKovROWodlvDmumfzkJiSIAXQLq28R1xsynbAxSvU09hgit/5amfr64xLznUz9gJxPUw9n+hV+HOdUbZbW10XO6MMuxXnFrtxt3b2OEJ2Xpex53J03C/3bsDXpV2SqzBsv8P+VzDjOYifa89ML11fLo8OfpiKbKWps8erYXcq+juBfkhHEO2JDnhto7k0uMnpQkGLTZq6M8+dRhwZhyy6cmG3dS6crkAZKqUNwqUjXLlzQht5/Q4TRvLAN+HDOTxBlM3F2IVsfAflUNugjeAQLKBoIKvgxotnBgeVpyiK5vQAFoZiDcwrftSzeYLjjXK7rFfvibEyKDwe3lxhvArG0xb25vGSWDmiGBS5DB6mjOFcLRxQeJgOqLbUDihLnbrvMdA+ceGUYA1v8HmQRBhhVCZz5ygxkFRfKcpmeDNFKr4DGkOnnYkAmtJNTU1dUCuDM4GMaCHTMIQtQMktVN7RHiOHels40w+/gKTAHRI3ytNsa1P0LyRvJglPyDjoZFGN2kw62Gr1IE0enpwvTbnhN9m0nDvy0AdR4tDnDAyvIuTgDMIgBjQ+gYsopxWTadusHMRa8izThsx0MS7ZkWkQApHiWEkzx5KCzORDbOFoQto1KQH6g/9nr3yrZSaWfPKVSJwfsJRqBC0aNCxNIidHPnEwbcHRcT1Cc+g8Yyt7Qed8rPwj8aiBb1wD3IjyS9uO8rR4pZUwfJATCZg+1Z61uCAwcMfs6VDScbmeHGFJPnQKucQdhE6dWC84GwDBiNwkm/IpmWO42MIRETpa2S5FvfsGNuoWmRKkTXcYuK53hucqgb5cj0o3Qs5RkU/GVcUlZekptWAjrpAvxJUUknUtG7AvWYv7jlT0S9v+YZJKTNwSSwgA/YrHSGxfeWV2DWu8EdR2rTFbOZj0c7tCBr+xE1rTz5H7+rLmxamMBoXBx7hHQE4fBHrTZ39JjM2j//zbO1b5yOHDqqAPbBqtVUDvSbNXkBxDH+wsGmeQVg3xpTGcQZ/0mtjHz+wjlNfGUGJb0TU6swJ84MauEeva2FxQ5uyVQf8k7ZFKJ6x16IeTyj+T44eVOy+1CuiF9gR6JofPC7745Y2f2fy5DiBWA/HqF5+B109OI1YGvZJT6JWcQTiB3shpxOofnD/9YANz0X59DIeg96SsEcpPzSru+e14nNglbWEai9ZJeCQgMiSL8pFxzDw76UShKjUwBxjcYIJfKopXbCuH3VGUW/4kj3QFKIaWrHl0oEROI3vpdYbDCuGxoCnnQv1FohpLFhl7bulfkgXJ/4IciXKmHOTMr4OrbNCGTTr9ZiCrjMnnrA1uMw3Ilh1JByytPee2ekdKzvfi+NcTS38FI0rpBd9Efw2sdtKHr44gz0VFgBzS3Js9VgRtFfydkz+kI4g6yU3MdMJLJbVR0n6brmOITllYLkFuenMx4rKEzFJcBAuuCjEWuvSKmSUi2J0cOPyRRM7BIA099IZFV8oLLdmKaGzAyCpadNRDxEH4RBBBBw8cNwQGiJiCMqxV7GTywEy5iaXkzcJL4D1xTwT0ROijviLB+93ZFJr3yzX4Sp8v9tLHzvasAvKmtnpKQswXE7w3DzbqR2GIRrtNctUaEts4oh66TGycs3U2S4fIEFEKRcKwwhtvaNM8eUCwfpaPrCbKENOaO8W1F2DTxD0nIXYtbTb6jCQvAtS1XYLhvEca3LQBSRRsh/occ9VdVovDZDF+pV0Zge3UsQAI1ios4SsR+L95TFs89CNuh03RasVWmpZnWTiWUFP6o474n8bq2jEocErlQVpZ6xOx96ya4iIV3a7vkcxZqlzaXlYIEesHTc9BGG0zSQa2TaylIacwy0U/cgBamMk2xuRdHIig0c+nGJRAsKpbrTSzXZviBhuhEa+0bR0Y4lgJRLARgsN4MgSx+QsZeMi4rjlFjoRZbWdyOlLCyEsrNeJ0CH4DWX/kbdBH8lED36wGcALpGccR1BzpHm6VpAezmmq7jdr7iU0E6e+5NpkNZvcbHyIJQeo42Sw60i1bJKYSCCi/jSu8wAoMa+gMC4Ij1zoTKhqyJ+VFcPou/dZ9tNcJI5FgqTomDs+AFUUfiYVxptlYJpAS1NEqg8nPMi1rE0NRVnGULq/rDJEjnNh0VjICzBsrfG6GqfI2NUuHjTQidsFXmCE69PrNvKZyQ1291yvd2OD6o/zJ13jaERBktBKj1wAjvCkwZwfjyyC0eQSzHdjGz2xyNnoAI6/2ZpXCqHEP2HQdZmBhDo34HAwQTZyW/1RHYVsAPg//WZs88yUxvijm18RY7SNHEI6f99oXiFfB2DSafYS8EmgcQh9wBK3Xw3AEySmk1848v5ThfuiomDll+iQlwXr+nGz1KOMCAXXYz1NhrivKJABjGJs/s1UBDpu89iWnD84gHD986n1WBPE5eL765U/G4wDyBtDk2RSaVUPsGRQenEPeBFptQVNdzQM0D9Zwyd5Aa04wpq7rikrDVaLjcPpPvh5GebkHSZh4Fb51MavfKZnPWdqFx2nD4E65jQd2CYFwbqVPMlDpNiQ61NmmiU0rYGAQHsIoSRsd1p01RTa2YIfpFiuwySCvMoGt8g++vENvUtMNXsxld2LJXdAS2oxzRsSQWd7QV98ibEJEkE+dGYqt8HJwPbbM52LsRbon3iIkxRaQQaziJR4VCoYRQ8g8cKNTboXyGjDyUsbRsSiVFx4SDogt+UYSuwaw46FvwFbylrGlqS/juF8Erwymv1Xfob88wvevgR/SEdTG0iaSC5Wb1+ocW9d3LYJ1RzyaogCGGh9ZXKiuFyuj5xA8GRquL6WHiJ1w0pK642eANy8oZFgxF9pcrM1wbfzQ8LOsYYJ5Ca8gExhMismByZQmMVhyClacWEnKkgt54SY6H4SqvHTeDM4M0J/lCGKg//RZ73drJVBeFYN+LnzizVeN9N4nq4HGEeSnJNxIalS0fpXdkxrXAc6jzdSxphZSnpSJsmFc8uCBN16Z4Se6VvGGup8cZdG3yd6oq8/xZE56yoz+GuUKjbWwEDxBcD0kX7nJpX1yvmBFvsVOPVPfhMpyanBpA5wP8QATXag3hhps2eiA4pCmjMPR57ChdMUe8Y7b0wdFUjtuT5cO2FHgsbq2lugSxwkpWv5nkGm1F0ZlHHUmHfr3J9BJbvJtk+/sNDlCj2WqnU+fXpUpvujARtqyI038Eut4P8RM88Izp3HR0kUI3IMhOYc6QpUfvdVn8tGZYoiRfIMVBBZ5mmiOXtVIqHZ6QwCAU78k37JTTkGMGQMQn/Po2oLaIRJogxHudqik6U8GinxkDesjetTAN6+BqyPITdJte5onjV5hmrNjj0YCcBMVXK8xafPuQzCk0Sc2ZQ5cmeE8+kjkW9yIqKSNLeJG7A5v2iqVSZ9DwthXgonpzeCmaGPNIj8SLUMhZVA+YawUXW5euR5SNkq4rjImdVWInzqzfYK2/iCAHnUOyCOx4oAN0qFmuBSTSVTMVrfLluCiQsckrNMYHdDfsaKx6wkcWo0Pq3ls1sXuCJGsUWCaqethsuyhs1No9C6ZJDZdI0KwqZfR66iHkUcWRw/mup0pw6fV/2R8pO51YR90OMXnK7j02WbkrGCgGdjLJ3tD8jl4PQSUE4gNpD/KKcQKIFb6vNfqoE/+cpicQTiBhH+v18bYKwiH0Lv37+0I8mbSEtLNo/0hBhkVR1B2sGNfIreVKUxqUGfBxlKm1MVxnmQ0BePgIgiviRbOOsZ7P5TUptDsC/QzK4LkyGG/H1YCsQKoK4JevXqlPF8CY0+gn/162AvF7BXklUGK7STSEiB/Gh5e68i8jrmd9wGaakw0PZ5BXvVomOIYPHmdI/oQq4dAUS63uZZJ5XKSWImsAJYk/0uuhdIW9TC259DxtBnTmYhKWsG6yCGcDKH8JPnbeN2/bZspwzJ8WIgGzht0KkrsGzvCAWzsGHVTMCFSNgsxb+kiL0aOFKHCjk5qCl5BYDECyKQFqy3mxrZFODqMmAP2TZk2gRvFKCkdWaUXC7JRPmJKtgTYsOTAEWz60Aei4+CILAOEeM0z8gHtoTijr/JOhMqICLrqaWzY0KKbPL+mB3UDowymLWHzQhiXzqt02slb9R3PMyvwEX+3GvihHUHUCh7D42JPC0uYbt6s4zRCjkfDdUbNs5xcMpoOjuPRcn1JiQhdPG4pzbPINzyN/kLfC3Lsh1aXH7PosLE6Td56R3kyVjcI8QwOXcWXXPlOAGCCNOZIpx5RRC36Se3KR8VEFYfNdF4cPt4XiBVBeprziVVBSjNo9wmOn0RIIa+A/azPXz/XYMmrYSyZ5QlMVgpFt/VrtLQVqYwvmbOMW3YBaeacXLTI7ql4qqSI2HEWqQNlNnyQjoCTgEjxoJoVcAL4CSGHUQALTZlB06ZzGjKIDctEaZ/hD8/R/s+UsSMDkOXNhTZKd9qR5qtu6HfsUaJAz+32TJlcKgK6p2y7x/UULOUYbExN3bpQT3C5ammj2KISM+E1l3Ou8qCCN5HOHCKx23+WL/zkzakTbQ4d9viQPZKQ05agdFfpwEQRCOZPMnlwIBXvONOXCTRIaDailRbdIiXtjNrMFttW8yrlC8EItBXboTyWmBJSJqhdP5YBkXPuAy7zdK7QIW+vndpy2zbMC3mDjbYRhTziRw180xrYHUF7S0x/uvQ1W6ZWvxG6izxp8dNYrvJZxXyHeZNNp7/TAABAAElEQVQPluzq/9uVg/60NIgodAtiwEWUpZnu4IyUxUYiwtZ15FAerun/6b69ud00mX2zDZH6OapDYmDCHAE+CfV1wmks2eSKkhys1xDYYCyDOrtQjqhKXOPcLnF4Fqfy/PkyutNhQBvJSsragW0om8r55iS6TicNAnXoGrVDC2wsUHTcLC3LTddDdTa/y2P1NsJx5PgPM9A/ov45czFNSoa9uO38CYN9zC8Rh0OIzaM/Kf6sFUL+KhhfEsMx5D2AurH0fF0MmF8NiyPI+wTZEcSG0h/tOMgXyphrZsuBrDbnO2b6m7G9dUT5HJRwPQOo+YNyOxLS+1TiCNLclH1/nvkXxw+rgbwSiFU++gw8G0WzGsh7A+kmFUdQHEB8FUw8OH/0gNMOIKWfa9DfXwcTymFVJfZhmm2jDcXyRJwFnwkdocuDGmCUN0FxCw1cQNp021ZWicHLObMmRXEEHW1ia5MILXMUWB6CUYP8HG2wSC155I8eUQRr4vDZRp8ps9NXbBIkI+PQGztNaIWR5uQqa/hgtwIn5rCUi4M08zXH9hBOLQEv0yRWualzMdSuko3BlPnmurFomhB/5buwwDlzE7b5UQgxUGERJMsRs/ZiHxiQ+okHmkkaTfqcmOwgnpRXtqFzM1Maswya9JA5Al6zGxd/kx9AzkQYqUvAjidNgZH7Vn1q9ZUKfcTfpQZ+XEeQqsON86ZVc2FjUJpWR4Yweb6IcA1Q+AI78XBcyZRXA5WcJUEdOpfC6jLJ8EnKIhy48r7M2CbJ2fEuTWyfkiXCGOhs1M7QtOIajMDMSJbsrgoC1Z+tII9YnkLIHp6O5MIuYdiHTAiuQXDbLRx1zL5APE36zDvdGrQ/6v1uXg9jsGKSkYGJV8L0JASH0zM9bdEgSjq/fDUMnV4GuNmCDa5fGxpDMGsPVxNXXomVLsMw38CLn7g6oNvTFegqFoJqcl3A9//YexctuW0k2FaW9bJn7v//6Dlj62H7xo7IAEB2teyZNWPprNXoJpDIN5IACKJYLMG8AuahbpSU0BIZJV9URsm6sBIDjGBjeeCqZVCHwuvG4ijGKZPV0wbuwmFfijLRJpde8+WxzG0laqKCi1/5xueQV75siXHL3Borbvjc5oFR0Dpw0xO+FehwoLl2KnMvE1tHikx2po3StTwDP3MCG7vWqcxxd/xjKJhE0K4gJ9IT3yf8VgSD6lzM4ut4CM/Q2iy7MHhI93SRFwNy5rsxtwo/6pxJ+Tr3Rm7hRAKpSo6MqugAi0ipFled1ZXpLmeFayY4FleUSJo4pY8ERsWZ2n+WQRNvuk6BF/glAn9DBM6NIMy1Z7tU5nE5g6RjrrNLx4zlRFxzRQbOeH9yYUBKNVb2iK3FsFfU5WhwITXVBI35xPaorFQOIeDxH3JYC1/gJXAAVuj6oUV18CcbM40QssvHS5CiuX6EVpzp7EVEk9YNWU90Xqxmzw8RnXkk84/bajtoaEJ764L1n9if+PJSMjdRjL+ICLHO18EKn9eWns8KR4ct4jjyQhFTb+ZNPWrG7zKNbrdXfLYpFbv9iiZ18XGcafEchJ5H+NacKqfxp6m6rHc23vzCZ2A7jsOJhb+2f96NEQArIz7RuMxLmJvHfgiITr4m9jvvjWRzSE8JfeEdQoL9lJCeBPqsdwnxS2F8Lcw/Lc+mEB8o8qtieorIrxtgjcnGEptA6h9sMmVDiOu5/lhn8ifb8Qng8G3gtn89BaTNH97Vw+LUv+Kl9wG90W7NG61RuwHE0z/vtAm0N4VE03uA2Ozxr4DxImlk9DLovAtIsHXw1a+sabHHE8ENncMnZ1ribDeB7CP+rjWJKsjq3ISfdrZx6bfU+YNuSHWfcV/3Y8hPOQtrihzxuh8SupEE53rOu0AnTFn/2JzCcpaUnNft4o4GyoTfpeV1fkbwbKf7Jw4gSznNMsJaAhltPdENqayRtoYxrAgQu6WvnImRNYJCRxuTqklkdinZ+BV9Vll1h3/Bh5Axx9hYjEsvRhfa9iHBdzQIFI2y+NDAHWn5Ib7pJpEZHsQP99IeoZbt4TvZbMmCVz7PBwfj6ZFdPHSd9RMuS+1Ds17P+zovQrSPWU4I4vjysuhG7tuX3+lGED3nUXCmJ9PRHjFo1CB20jJ5VVkm063lbiOTcTu0+XiW9qEtUentZcblmllqZV0dPpNGrQZnlqKoGD4V3GHqg1uF2iMYu7Q5g00XatTBw4wynxDkKRQ2hfgfw1OIcyWrdsYFXxtAutizEdQLNy/680ujWdBxgRYvF0La6QuiLrKvtdPLI7jdDMp7g3AHg+Jlw2jZFpB/03AE0rhA1ek8pyvOKLkwpuLTUkHsCYZC+dUkJvjmtverrM8R7YGzcPSCUdu4TBzSfl+2n6jiUm2f1RA3celbwMhUa6q5CJ88wMPTQgrr00iNrl2Y9Qnfpv+vIezb8/F5teFmeLVDzOn7HgVqcdq4+kxDcsQAnvRHEdVW/pwU87IDWIdPwjYOHZQlzlLw6pubPVCVIlR4wIsecKVT6qAoT0lCCZl+EqIoZjJllIgu3Ho3kEmHhoOf9jueTwyByIgISTkD7JZWrKdxxHadn4N3xRkcu8cv6SUC3zAC940gXKF3p6/HsfZ2xpJH4yCOkRRG5xBLaXmQFw/DiDFypz2tY5cb8J00rmcM7nHHhjRz18EouXF1iwoyzoZLRUaSFj3kwT5xsDJR6fEsuyc28yp0YaWuavHNawZtFNjPpVt86LCSlLlGjt5TuXTiE6iVu90g4jv5df4JPvpFPFJ8FT3/ooQ3LMRk16/zaGyM02LffluWJtlL3IoO54IPlZaj7oiMKYrwBOG13OhCK8n5NMiFMkrHxGKOsOKNPTZZVLJmY8qFMQr0pAz1md+1josURDOoVDrme3/aL/0ZN+hW39T5xI7fHamNoS9+h5A2hLRBxDuFeCn0Jz0xxAeJPA3kn5bXOrJfDfN7J/VBI68f8C/SakOI9xHxXiI/GYRX+M+fDE7z4pv9o91J+VoWmxc8BaRNG55I13qTzZsfZyOIr4G9fauvgPFEEIde+Ownfng3kL4SxlfA3vHEz/wMPCXvAXqtg5dMs64lhmz+GJTp80q2IienEkm8o986enNy43HpRN6tEMIf7JkcXDgHHrzPteDY0llXO9vf8C39OqXHElSESFNEFTHFpcQWMvWwRsb3Doc84si6tFzuAyILhV7IH3rUMvyZOjwmdJ0F+yTsXtJhCJB0dEVqxkWffDBDsaqdCqXA7YcJ2JJGBgoixKGWxWVtrXk32EtuB5YikcYYrjxhHA9U5IPckTvFR2ah7iyqgzp1c97OZl/MHhX4NKQaiFCQPXgAT/0n7YThc32Q08uN7Hht/wqf+occeNkIInLfR/pON4IUnOnNnkge9Gxf8G7dtp/8Pw1tu21eUvWUXszsyovd411ZF1zp6jMsPNqAJ+GfTTDZg3OWSTBgJiGzIzeyh4rs3JjhyEZ4tXPqZzwEG6uST7I8uKUhLGy4qA09ZsOGSRmZmq8VDFcXcF4KnQsEj/zyoj82gljM+dCFjQHer375+9faAHrjX2WQbV0leXTWj6vatpTal24E4cmRoIGRTuIPLe0Iz+lniA/wyFyVgtER5EmqvoUrImof5zD/Fb6ynQ0QDnGfD5W5kV/Wx0N4tr+ImyOrQcdDYZrzNPGjPnZeq3zoHkokWD6q2wq1ne5jrrybI9Cd705v3f4+dKocN+Jy7OgfKLkni9E/aYsztyltNNGNPCNijVKF7yTyfnpmBNl0oNGQQC1igPseBnqu/W4ElpKRE+OcqmGYQnh0OCFTPjMvyjBAj2Kfg7R6KYD7EgPYq6+8aBq14Qdx8rkGxodZrWTwW6E17iBxkX+mjaNtG45uNL6klwh8iwicG0H02+nMGRru9NNbBaeq/s0N83Pz7NkI67tNAIueWZ51zFfHi3RkfpN9w5kc+sl7tXs+g1E8wNCfzkdZNbWNdaVzJ41f+giFK8WUuyWNi08Y7bxqXChQDaEULcSsG0G+bcOA1ScGNJAPk8C5PbSD9lQPOiAiB8Gp5VQjbFtg+sR0qDZWxthYtdAyb2JzXLM94NzeLGsGpmY/pw2Hr0v1ANWduAbJGbFlIVc4hFiegndNaybrbjTmXE6MHCXWWPVbCojx3gjKjV+ephBRjHDrm/yKAxsbEuyhjjPax/Pw2o1ixMtaU0vBrDldYo/3B73SOjGbOnlKSE8I8UqB4+tg+QoZT5jrySA2jLQJ9JtKnjznp+rZFPptnhDCLl9to6SQRYHES064XqfYDGVtmY2bH7U5kjUp61C+1pV3AvHqAmB+Gv6tfxUsL4x+740hNoKyCeSNJG0g0SdVeL+MjR82gOh++veBC2sjCB8HP5doM+E3T/1EAA5FGJwg2pMUfNooDNWhpQ+AQ4i2Q6s8OJ1LL0oo41lKSGyMhW6SrCZ+aEgc0zeBc4h7dI6s6qznl2FElcKf+wBoaY9yO5jxbCz2xU9u28NJGyPzRLX1k8Vn0V0Jf+Y2MLHV8QkLjNhIStlYDDI2q5jaOOHNdJRPvfxbl/yXSkTdxJPBpq6Ci0+0eHIKbPjku9s+NfoUn4oO4tIhtSe8rQQq7Ym7tOnOfNTvtCf1QbQ3o8z9QzocK/V/01ThfLxsBB3B/cbgd7wRRJc5ejk1dR5PWC3P4DGrdoQan6kADfRPph/SqbE0E5wxZaEnjFxouhE0qGG9aYnqyEm4k1CnPst29NmDkb+oGSXla2lnaqCeqs7/MROZQ1dmosbFOEkesLBS4lMREjG0X4dty1qlPEcn/yq9sTSbPnx687teFJ1Pb3iRXyf/6OZiyfew2fhhIwh73gQSngsyzbZt7NM2DsfiuHjYw2t2nlL85CAd7rtSfPtAeDfXtDrCk1cGLsN3xCIcYgeOJvQUgG6yGvPdFW6/ITsE05LKI2FalV3KUqPX/VPMjtGKDAJXvq2io2BsP8e2BXyu6As+Z9PYWD+YBEIvn/vQQS4NVGL2VEPjaDE3KBG5xki4oA/tAqUOmx13EF2/KL2KWI2UU3YjaPv5yMhVnlq5GsbFIUKb8BUXJhaJyZI9AbfpRDyFEx/w8qIO+fyfvPWQ8kwIELVxWLWcn10eSsVVAyoPxpzv6m4ZO5tt+ofRo8ermvC95C8R+BYRYCOIoz0bH4BbX+NLCOO0zug84VElhut8BxcUHe38ql1g6tLGuIsOcQM8k0qqOlso0hoiGP8wJUgK7eeiR8A8NWY+PBWNucZ4+My1vaFadNSYZjuBLNG67S4+fIn4eqpD65TACFcxVvXn9YpKbOqgaIrb+BmMeQyGf5msgMqEopSWB0OdOw2ZfOPF6PC4sHF4VAvCUjsGahkiotlfZXCffcXzrhmChw8eEiVqLQvCa7r0mZpzvODrLgS6LITAtufNGSnyh4Ssk0mU4s/ewcSb+3xoWveGy5bMGsUQr6k+w5+vjGX9ycuk2RDyE+W8WJpNHpfa/Pmdr4zxrkk9BaT3Cvl9Q9DlKO+f7AYQm0FdZzoOrDkxr2zFhepUiAdt6tNArDt5ioeS9/nwC7ZveNKHdaqe+mFDiCeC/AtivNLAG0Q8OcTTRNnEeKungVg686SRxNapTmRwJom6ccriq+oJoigAIfiVADCo+oNPCPJdsMOX/k1baFc2MjmXo2yUT839C4OsYcBRxg/yfOCKLtbn9E1iEyuNm6V87uJnc7s4+pCN7jQOOP7xlFO/Smk/ZNWS8slPAmGXb1XYKWyaIb4OElK8GCbXwkeeMUUEyglyFG02QVdc+4Xx8rfaLSJW2rQkqCiZq7BK/DXfOG5+ssOV7LhX08XKyIp/yI+5os8myiDUJUktmj1e4Rn79m0YB+VafBabkI35sK3CphAiTfmc+Uf4O852xp436tStOQfmo9Thnq6Sc/qyEZTQfw/5d7oRpK6jzrJ65y1SuwNuqCONjp+UDriqRf9pSe+FCUl69WMNmTDEUhdaImqYrLIqC0K/VowJ+pQBU6WUhQ/IMTrMqQ6K55pMEuyJQ3UGXmKDL9sZ5lM0e8ACa2InEb38dDzvB+LTHR7d7ac1/UQGSTaCuNDoAqrjtS6ifAeb0nVfgHRBorQfmL/6cImHVUbvNM824uWA0xZfGHZThrg5oyUMZ36xd2gPv6gCClfpAzMlqVXXRD3yuXRdqaktmTkXlen17ThFD8Sv3hGn6rtSHojeUJW7of9StbL/rs2tXJL5V5kLRXWl/bGQhQBtVJ1zj4IaH2W+6Fd4cF1ETtVFdRUXndRGb7hKXiX+XPvjExfMaxfI8O/iz1RufiNk1OWEZxxDuykJyopH0Vk80D0CzxTyKf+HXoEXvw+ljv1ZD2/YK9TysclIj45MTo8ZX7AvEfgbInA+EVRz7eEulV1KBscwPO7pEEtpWc275MoQ6ijbpGehpW0BsG4ttjzqXFxUc0WX65rEivacOdZ8/Yfj0J0pqdxhjN477qgL3DPp4L2xEN32ApCDG2H9O2Fs2O9ztOlmzTWCOq1wBGvvKM2/shpAQWJg0oKhYzh8nYZbnq1Z/lV3mVoWfysb512KQSaJQW/g7Rsu1kh11M/8/nsEobm9lI22ShYPqmOnLWI1x5KOm/XfzJ52sshh+mV5aw0IqE7CLz9tA06pH4ZODRNi8H9QVFE7NvQgjzcWsOtfmNVXx/yuSb7yxXuEVPJuoTz1w+YPT5hnIyhfLxOMLm0M5WszMcbZwwx3k+mvVNJ+26fGWlS4tSZVI3/wps58PYynhFirakOIDaK8B0h1NoJYs7Jx5B856bo2G0CEhnZL/ZP0AJU1pJ2FPYD5xlH/VpvRZaLsoXP4YJMonMoNJBaxPR7oJZa0PjERzn0jJX7zwy1iMJ28/fEOU086+xYwukRpsA2ywegTYn0eu6KHRRJyJz4tt+1++KDpTzrTtnJOXX2Uc3m9BQvnODjtaS20Kwe+ie52l0/llUk23Dr7kw4O7wi5GN1SR3Lt0LHVb8jiB08kj/xGu+s8OOOJY3li5eFh7qbuyvioJtmLzL1+k7nwinbWDQ+ConXDdAb9Zz2OUvVT2fqZBwYO/6G8pG8Tge9yI4hQZEMig9OdSjgmj30BYNJT3V2uHB0Yu/4fhfUUv85Co+7ae6lZRNnyGITZrryDDP+wbHMoCH900kYSrQzGVaSn6vZbAbGBNZOwQVXz3oB4he6BooZcjJ7bVBJzx9c6MsH7fUB8MqNPcXyxnou2FdigXNFo/lEzfn82Pk8EcTFmA0h47M6I5xy6ic7Qsj2KOjzHl5Su3DJfRMBJB1z9lIPGnFIbThRtCaSqoQWPKuomKeOUPzIPdzdqkFlNoKK0taUejRCuCstH6fNaBKxx7InuatxlGB/5uXmehzqWDtMPmcv3kHgg/zpfYzsNVdT8dQEaomNhFTP7NkF2TQhKn/+b44/6S/vyVno4LHAsAKRGaQduyiEr3U5jxEJauc/H0rNaIzpw6wLFs2M2bdpaVv/jMxT83JGJu2iKlygaOIilZXMeKMDlRoBrddXMtmJ0GLGZ2hL76duh3EafxqyCIrNKfEkvEfiGEbhvBLV3tjy6fcZbJ+j6rA5+zj3XsbrHkoXX5MAo4ZN56MuS4PIHd7DXWkqxDYfr1QIuN/Yz8g/VsSVOZK1YzBYQynVxFG+tEfb0KzDNBtB1XcWF1fyQsgbx/FwdMTkcKnCWwiU2Uy/ek+yg6h80z+WWy+aAn+LEOTnG+kJAREEtlVu52z/VrHNgKj0ClkNe+qxS6LRlZkHT4ntsgIjsVROCOtS41T5X7cXVLHOghPloLUL3/oSgVY2usTx260Bj/oM2A7yJIyE0+kNBbSo4fvVVJVeU2TOoipqXlOxx/wy/smm98TBdpm2McDjFJm1GnieRurb0r4IRDw04Nng87rRhxPqS8+GXTov++x88BcQmEGtO/CYyYwC9y5I8aQUcRvFt+oJWnbP+1NqUG2h9B45+4g8nvdHDZhCbI3ztS31aONaogcU37ezPtKPW4XMW2OZPH+RBr2hGu/F2jsw+RlxU+1s9cFeRhAy2H5y0RkK4YcevUaU20lAMzRkT0X1ZdZ5wMh4BJYuPDiNWBv0khB+NhkY+duWPzlE2gvDjvEtBx8hE0hZid3Qq3uWo+fZj+yC2RY/IU9+OTgBvInT6j0jjhhX5KF2OmdjMad0x0LkETnzZmgLRr0muDdF80/ZQl7OpTl67UJfeBYTp1AuTnwgzSa2TIMdK1Ffl0Hngvgba1qlA8M2di/iddtYNk6FDZeuUnnvAMZaHQMxengi6hPebVr7LjSAP23aY6equTqgMMwEMzxnBDJST+6T+RXiJ06t16N/JeCoARYZ0z8114a9UJhcGhDVUHTqPUb60G8hEtljT8DFpI1buaXiuI73wMzlahXVnqjx9R5rJDQud1D2QlXGD7gvybAT50xrBDOjVGilnwyefwOTRW3+iInt5vFaXVBxQ5hJ/VLdXdizNcCsyS8xkIQtTh2NfIMLvuuW3XiiJVLxLZMRkwBZt122OGvsVOQsbO3N94jbotXhWPZrMurK0aVUFxDqY1czxlwYmFqEuTgHl3XR4nkuRXPJiq3wloBVX+Air2UqvzF8tkTttV64Xxtav5fLCfuELGN4LsHSBVADQ478Gw44Wf9dKTMVgJekFbufR2KVfTOi1A6PGssFeGlWZ59p68WKYUyS3ndWyYXA7tqR5tCgqm70fv88xsCXqdJtAW8ZeY1XmMdnqWULy0qk8smnwkX8Nls+JXN0mpaWjDu1VBvw0RfUYuNxRPOV9wbxE4H8dgb+0EURfryPHjQeo3duHyYiNPTmqgrktH/hXq/gPEeaDVltuBzLUPcxNZOx5FPsmtMsVa1ZW+YxQ1ZhXjjkxsKig4QaYOSRzl/JpWqMwZDcH24jZHphhdhuEPyhUzOvCNGeDjjVXJitL3GU9Uh9jxrp1PnzzJrv2waUkXYn+zqG0z+siKxQ8GyS+zkTYlrWcsYFcf1Aloumndzf4Ro/P4Uk7HN3V/lAq1POd85gdmEPW50V1KcqTOpI7zpPd84aN0FUZcUS8sRJ+VUr3eRKVOuoUR/uJHHUqOqyPbIiJiSJSnrAjZTm3YHjdL9HBoXNHnyf++QGSxN+vH+Bl06bxZBD4fN1Iuc+RdR8NE2uSdMoN6XQuSKX/2azER0qhKAF0UAavNasekMm6FRK0KRULVXNgSRVVk8CD0tF+RW3Rh83xkqPmU8V0O07WY8Cj7rPggIXrwmvdVhJo9OHr8qq+GklbRVGWMQKfNryEi+8WBOk06lpFcLXL0NSRam/Juc1mHbBTAatHcuzoPFUOPvzC72EDbM1qzFOJmXfrY2Spje6R8FNxSxqgTh1Q/btQqyexwqvrO2dVd2OjvH6kduQObuvi0n98jf1aKccqt5uyEyxNrg/V4ZCN0AmDOlQMx/MFfvQ+x1wgdHxNx5121g0PgqJ1znfD5l//GwLmXn4+3pH/LrLvciOIyGSiYgKdHjpddPoa3Wto7mqe3GCB/ngyQE9T4WijNlN1GWyVyh88kqsRhz8MxvgFATz/ZOnsHqx2QGRKHVNFVXCGqOiQqPdUZpLrYIfFgxy6+ICtz0IWizh2oyalfOLCGl75qj90Snyl2jAPBCnwAsslO7bI69AFmU0gP9L7R54G+mM+pfEUIlm7rZs5vjvNZlCfBOKTCR6xxZafCoKXRkhAGH+ihFf4kLbJ3viCP/jNH5svqjohBwy/cn96E/DaPngqw+IiyZz6+feUeME/6eQHaYsjV3g/NoqEPQeIsFWhLLE2Xpn9HP+hNu71rq7Ve+rTDTBR96ruKM15qx9VS1b7ib/Ce/G0baUVV75r7c85rvyp0fY1bubsbFxo50YQvI1Z4qaaP+wCm74WfamDS7yBknruUBR9c26D2I0We58o29qWkgxzhfOM+opukYNYeLiHZv8NF5d+suMxPSAd24ZDowX6Y5AsZQOqDW2v9eM4fNMAx5arPEyWBwwRne7BsJuMDfE64RvWgmPxSD+JLEwiRo1xuw1LgXhOOFp3Pm0t4mUjqJF4Kb9RBJ5sBNG/1YWnm68uz7jo2PD4mkGT3p7xyHionPGj6zImBjc/SyGSxhi6MTpCFLbl+g4MOBKmCy8TIUH12PS103rB8BdBQ/ZTmCBn/DPmUWwVcFtPytw8Gp4WWifyiMgZwLg3uQuy1FcpG4kSOfPLwTK6hIkvlK5QD5ANg8iuGGjtgQfbDxTBr4I1kqpYdY596lp4eSMoFsRXkXyohTz6UEN5T2gD25jFv3DBDn2nxCcuDGUxhVZ96QzERZgxa1BVeJiPXb+7tPSN1YOOLuvvAgNeH+ykwIit8CD9++C8NpUT9iWtFZWYRLlVIOrq6IFDCHwkUQBXBzr52pd9UunzyVfH4FPF61ctSO0NcvxVmewCjnWIKFbWxE180D538sM+6joDPjiVrE31l40f8KwBhJmvVtFnsOGDTCljVDjMwWvjypE1x87qknntb3w0Hn0G6K+0LXba3tyeB4/G3fbqj67WLqV0r76q9/LQLvs6eKq2JsAtlPFc302IW/AOG010zILxmtvNDvvKuUdw/6Ex0x6IeFp/ErHQE92TBrz5HR5rRwOHcnzFsU00Hr0DSAn/+oOHEy7ZZTdcR75jjBzKHS+ByIyyg39AmoBLYqHkiKXYEiZE2MsIWOwCJOdGoyAWzY4cSWhYm5fnXDLZ39F3kZVUVVtFHKyjRpFFv0rRYXGbUiwa6Kbyt0554uoDOOOVBU6svRFkofSAn/VOLm9QgntJ3zQC3+VGkLqNxpC7kvpmBskZJdMXInytPt0EKuVednTQ/zOtXzg866gjn+o1ulq3V1Q6gg5hUNyHeRSowkYLsFVVX+vwKa2BK36rNDJ4D3hz7Wx7HIVeWI5z9RFNT2WX9qUsG0G6COuCzJ/fDSSknwDioszjutoE0nO9vlDTErdfqvIeoHz/mg2gfMIiqq6y/mBtPoHBj/jyyKe4knMeH7xR4YZQx9r4LSW+OFlf4NUQAQ0vS4lUkNOhKjp2bIQalZVpuURPxQtenhhjFb7iRz/I4JbAcQ6gxIp9MTxOCD41Bwvvljk0bvACrRZcsNfKtnfGotjyEuOOweLi+51zUx9BV++3f0u/UHvxJw1yqlw5z7F3h2trxUxstMfchqPl3oYd95wv9y76VCSrdpVo4fRG26CPygIBMH5H2KFc+LaJxSSZp5bx2VFYJXolI96VDJKN/EnyuQsnVLdxCQKUmXPsmSo47EHCFml887miahT6plI+6sbuduHqhWye5W1q56pm6C/FSwT+zgjcN4Kw7X5eJ1RJvy+CcViYXp9K+vvu/yfHhgWJ3TfzBjPHWkpZpRmzh4nIjBLwHL12pRLi3PtAzWhcSlAOrkYEqW7yDFJd9We8rhZp+OORfGGczlwQS1Y3/MXEL9cIhtJuxXLEWBPJViMGY7aD1z6HNm4ilPUUaLHCnWvDIFwPDd6ktHfDXHP0BKrWOyhILGgnHCn5mlCvObQj5xe6o4jQaEc3+Phhh1wny1w55Nt1BK2Wsp5wq75iUtlIw2s7oteexYtHW5lwtlXRcx6lBzpJ9LQ15tySxZ929UM0av4wNJKTZ8PFqi54TKAohsjbdl/TXJE+lb/TDl160M8VqB9gmk/G/ScG6qSIoh39wSEYS2YJbTDenDnOITIOizJv+kjEN6Go41Cd840lIj9gFDuXTfMSO/ioAht8moXdbTSLMl9rx3Vs8LfFhyBN64l7ocBm/RF6c3xobJbx5ReY8XOM+4mozSjZVK46jrYtx9LGtHkhlyYAnuKyQpGv+sKGW9IyNCpmNLGxDCck+Fqj/e4kLtFt/YtBepSsg/PrNhthOB8emiUZ7KObPpdKfLFdYWBBj3OKW7L4NBf55Qp84M2fvLasFZT0WSWZ6rZz4pBd4390YUAxIWSkKRbgurL6QX2sm/9r2eKrUvR8ReBOO+u1jzh405QZFtGlb4SFU52x95Pe0+Vr1ldsvpD+ngh8/xtBDAL3qh2Q6VYbISgsM4Gb8rUh0Z5/UaEK+OtEtDmeypydf/MJGn+ZbDx59UV/wkPKZAY8jDPKvZPdES8+JqejuhVnXaY66WjnqAs+JCYbtaiWZHS3I74IJUe5EPsn4flURqvjwCn9JJAv15KAWSp+nIssT1Pwkr0+BZQngPA7vuO/D7uKLyTpccPGYdRuDzOpGWfmWyYdsu0JBBujMfrCitbGuMLeWJoK9Eahn8yiBx/iR6nx6+RHxaaOwoWTliHGL/ojSxJ7tLiiAEbwTVsrnpDath2b8pxy4bTARV8wZ37Rd1MBDTu24LhODJfO+lsfOIWSeXYQJE43M6c7gkXVf9YRfOUQlLwAwIcJZvqS7I4LxVfZWY93268du3KntM5pcxRveluIOwumQlK5QAA7La7FKFzhmbimGUZj1z4Nz8O5zTHAjgywERuzypPie/xI22twcQhI39vnNTz13UqHHRxUPpHdk20dhDYw887ILMDtBxkvzetG7XNgXhsZTcCkl42gxOEl/2YRODeCPAboluONS2WUHcMZV+ntsM2MORLt2FN18RQHBjn+uO6SKDIX1Lpw2OYQzbA5lW2WYozzze/whsAMUv2UEPXPNVzJ6w1D4eKmq/yYyFiOMa7ziUHGNZ/uNqUJI2H2yNj39bwE815cp9zrEPGGfYqpVDmsjdHgWkcgUYQQPUhbgzLbU/BsD5bqUft/0+E2KLDoi68IS+M6qO46fLXtkjp6b8k4OyHCYigC5itsFmXBbhp9pBbcB1Bnf8u7mrTNoGxUeDOLKsI+VHF924C3Xz9BtPZ8TYbZbQaA1gwhYkZsgsNWDeea1NqgLTzXejTRDpfKBWMPC+lVbABRhUMYYAzxlAt2IPqoDWyTVBqMb1SoImpfzTP1wYMqzTaHx0ZtH4Sdsx5qewOpT92AnSTWcSEIRDlA2kB0gUsEpg7LCIZNMehJF81ps8YjGC1TBspBSFnOA0JznsyGVZL4DKRmVRatvKXsU9awpw4rcGZfpSjlburikLrc14y5+icZzs62uyQExAfO/hpzYuyPf8Tj8MPpdorOE1722ic8+MTAaGcdv3DiUXXhC8nnACTznTEPshGqLldln7aQzvKuY9UFYDPnPCU0ouI0ulJRPugpVn3RYanoXfZkGj5Ylp+VlfxdFJXFtRTK6axXFwTwpikzLKLLWctimGH808vLoh3H7yH7PjeC3HFmMlcPmv694pUJkq5F509XpSPS3dbA39wL2kA1RpYBOdpSpnKwZ8KyIwfN4FFHAD+cVAJ7oVd4r52uC0BblS/jFgXNctOU0aa0bXTDyEVCJbxAS4WF53FVYCXrsQ+RiUD9m5LNH+Ku0l8H05WZizC/7MDCcX1KIZ28fM8LQ43mN7x4Twb8JJC+I8YAD82W5R96MvBxJC7Jj/EtC0K3wL7mPOITDvMfmtmRX8foXzrFoX9zR9Sy1RPiPs+xH92+IcWeUryNbq1PunKxuH2AzQA8TSDoeyLhz6gt3zpLMDiNklaNW1wjViUjUmVTxc9IgNi8o1m4KH+Ob4mMT9OdglYstmvR3XFFmDa87Y5bpvUC+ed8WeShhSfOfAqUWV4OqFctW9Y1Tq12myetNx3ZaflTz+JhfVqMRkfxGYOeqvgUWXLXYZeB1bdcjw5zDpgFeJiX3ekcYTlk7Hu8Rm9iqNLjkDqaRxdlHVQJduk/HRDlsCCXVUcU30dbFuAWcuPiwU2fZLz+tp3hRbGY40b8rUvxXbRaF2/tjfShBEUv6SUC3yYC50ZQPaBHplcypwfyuFldVf19mDtTQs8YzZjoGEjPr2YUs5ZAHmUDM45g4YID0MEypWnQAWAZRO1VhvsgX7OGFSv2FEY7mBvsdS1Hv41FwNd56xYBEf6Qy79gW3A7rQO9MoiauEQ+h2UqrwpMpDpYvhLaKOHR0LTEHDPslSJLZhwbQruJog966MOPC0uHVja6KemvHVUnlt1eNZNyHdtk2o4AuuYIGauO9nLAfoC1Y6CDSRMCOwchHqkbDjTdkhDBadPkWEfaFVgXPTrwnXXy3rRQj+P0TRqX3MbpJaLEQtTTPl2XwXINMqlG2rK0CV2OG0BbCY4a2ZGqBpTtGhHLHjMxVKoJ7jLii6pGcSt9goFRQvFreWQB+ynLyy3bX7VpJ/JnVGa0ig127BHLtTm6xWODfNrVfaytOMSIJK6GyabtWA6ssrpHn4vFVzJ8ViCxtLuUJQ+wdAGkElWjHNeGL3FSTXp9FG+eZEtKADB+z/IGQTOlCKfbJax/hU7kyhNPkuULW6fmK3fGbAIRE76lUEnkODNjyiUw8xT3I7TNT0KBXDGrL8vFqLML0iVyvLEi+xVM5Oqr/UVn9QLSIqOmHGlwTeiOv+jnPxrpT0BhFQEA5tFv2ByjKeJhg0+prMAWnZK61d346hbjqzGs2cpQNpW/ui94EUun7Jh1iKDonznXPEK+PBHU6H0f5fe5EaTu4ouCYtSBssPV6cRdaqORgd+dfXr8op68pbVcTJIHp0MdNbBr1ntqsETYxs/RJSYPxim5yULOA0DzV+AgzScMuKRMCIGFlUomYHa5zzblk5fQKklpDywTfmTARTZ+Ya2P+dZPZP3yPjmLn0y0bAbxuKcXS/q1MGZjhjDrCOwziL0BJJh3Afll0cLxkuhcONDa9tBCRdO+CYdTSi6n8Y1Bzzn0xIdyqG6PM8nmkxhskWoJuLrWBpxta4Kd+MN9Tdd65XtmKBeHiPV/zXSjrO8Rghl/kPEEO/Tq8AQpnOMkfa2HrS2p9eg6a7uFo3jp70WkLbjST/1u04Wt3sEVgmPb2D9S9d/CyQZ/+YUQbKs2vu3zO/FS8C9ui9uer34wTsFXJc/46XNkOUcjXFV+hmNsVJ1ZVl/avqKg/jaGVipd456dXfDohcc6bQBfQKA3zlAGDK09JMpgjrMrl4HtvmDV0bHoiBxpzNhH4NgPw/JVwpanjKJTw8BIxufwKk/V+IKnd1H2ROGh+wV8icD/NgLPbQRh1bPNjKdLv193O3Txe/9tT6/f9/qWQdZjTixoWWOxopQi2LZ41vVEOK7FvXYghzx8sdYcIf5Tx4BhGwrtoA7f8I4+j1d8UL0fmHjDCB160lkztw5bD49ybz5QW7GDjseyVldcIYPP/1SU4CW1BJbX+C5Zt3EA4MwnzHPwTQJvWhGUeDqlNza6EYRP0wIJoYY1judy6sWphE9q5sO9fVNjtc2kAG3ocVMlh5dP0jp5m2IvEMJOPJF9n7HoMkm6RbZuSgO7Xm0267Uj5wN10p5TEN3CmWc0x505n6M/MVfcsCFDNqWM1gG7VVKSMvheAxu3EC0pCZK5VQbnmFJzQ8bGURcozkaUWuUD0yQ8KLbtoF4cnOFKOVUXdsOu1J+gyXu+l1dSaJ3K9pPvYiweGapRtcp1jgpYZJgsMYK0U/H0+AJvlvKJx2zBh29odsrEalPlcEpwzjUaaFcFpi4pIpzuETlzcG4R1D/Px6w1LjoiuvoidViPJpojdssdIc7WiunouRRll76uCzPn5L4EXtsbIftIC+wvSDaCVNj33Teot5+NaIrac+3gp+FKYFBVNlQD2wc1mHkFTj9RrRIKcbjEwsKWMC/6UGqfYZTSZdm80W91Y/8pHMyZW6/kR8VJusK057ATf57KuV0jedd51g0f+kpLHOZsM+86Ni8bQdeT8e1r3/lG0Cwq2mNXh6SbtauBFDwd2x3aQ22Yny3o4jsxEaJxXWSl6GZ2M48tTyoYdG//QY8bC+QmkVLclLx3h00JLqb+ZMU3kRoOiw/L4e9UkN1hCUg3O9q+gIvJbWMS1z+LLWxwkbcLggWaP5tH0ctEA599YcZyZQrBmWhVl4N+J9CXLJB4eS+fmPGkxg8qGcDY4VMQfmqTn9lkE8i/ECaD/fl4eDhiIT4BL7wALiq5SpoRZid8xFH+kvC98KAk65jEiJBXHeXqRhB161gXv/LjwxZnEo4tkJnckeXE4UEukqJgVz6GSwSI6jRgwDUzTB2G5SubabIjIvTw92eEzRxcpIaj7Yf7EYzc12jQmzZfFyuljDeqcq7E55OxqY8g962v8NXaV/l8vmXu+LQgrSRfl0aBjS9aqzleoR+Mk4C6BDZnr8Rduo1TNd8Z2s3mU2d9pYtm0OMYOIS0MYJYtY8Eub4JtueqV2abEd/EYZXYwXAbY+a2siXqR9+s8n1eIdsFtQyYcQ9gnBUlo96EKeCRXayqgyKzLmAlq7OAM2EoZxSIF997Vs72Wlc0SklHFRpf0ksE/v4InBtB7t/tznLF41hI+mz7fnHt0xnV9RthuKMkc0LGRTlS6qkOAF0IPDQBD7aMkeE8KzNc7A9kAbEUXuczX/hyBwxPlauK336aJ+JDm4qK8ErI/2qdSlJKXaukKzdl4mUOHBsWgG/+LK/xPdHz5grOwq5/Zy6BlTpXYM7XWGNDQQje1Q7Bnb9tD+fqJ7RjWmF9tGyigz9da/I1E2g0AqGoQC8yNBjYdqjbgSnwx0oHKTIJXjAkywWitvDA1BB3UmnQGYaUdPJW1UDQJq1MbNCGMbFRfXyVK/JBdTWG9rChxK+4ImM8otA5rMReUXHN6ydgxyo4hO39+BQ9VmCdaTMc2FA+usOB5iMNDcw6B0PmHEGmtDPghYjmqVAbxV5PgrYUJQ4OccrWLqSqV7scS0SbQPi/pQj2YUrHVZZRPHhx7qSKbQ5ynR+wNnbhltwIjF3q4Tj4BLrGRX6hF2DbaYct+xxES+rbuV2PPqkbNVDWefQ4apxV0pGu5pbc1o0CHdVXUy7bf0RUnfPpuJRH6KoPTeMUxULyTi+M+Sk+I0HjWyTS35CSXvnp0n0wygHbxqi8nvNwxfUMDLhI0WWKmNrnDKDQ5uOD9V/qBz7KlKdvW+8owzb3bKd/i10AbYsm7p2MUAamXoOMb9Vxb284JpdY+YypGvAXxmvlTjvrpz7wiyaAucR17idNVAxk6+eXr4ZdA/wNa9/9RhAdZsZ6wkSnpdcJmc7nniVkBgv8wGYDfJjMtDureXpDPpOTcQ/4gnJPp3vjg4e2Su2hGK9rp5+ygQbMwUaLBwQvZBbBNDISdRWZgH3tBum6JzUmNtmlVV7kuJI2It70Wr96wJM6/hUvkKojRbIP+ANqNjeykAvtdznP9sd6P5A3gvR1Hb8kWkKKN76wCfPjj2+8EeSngtgM0iwmtDeI8mkgbcEQiehMW6jK97jfQBpJ5pgEcG2DhpQRA4uhg4pwY4Zqwwk7G1wkclwZNonImyiJPujGDbMKuLvhgxx6V3NM3xlnjj90OE2xOXI+a78bePW39fJv8UCRq/TJtXH236TikC1cmWnn8Fl77haGoZYlJ+dCRwfnLjcAWxN48YhvL3AQS70l/I88Kb0xIMBdmNvGeSLtSHyzVz4RqZt3MnQ+TA3DjYwu++E4DfEBb9UyVpzEWvcoA088xOC+IB70E97RbEAY2QzPaDM9ZiUJoKx1eMBZB5kJSyPkmSysNPWVO1qShbaVRPdiss51HooWE3xu+81c4wFr5KqRMnDOheD8J85uHG05FD4ygOKX9BKBvykCl42gm832dfdYZem5HcNh9swvgrs3DB4CGQdb3a0uxbwsl4TcDI2RrZ1IryGCColYSnMgeGw/SVIW1s4jcAxsGgbnaRZR0AB/0oZWXQzwJBav5bd0+UBN4MyjkQBmiubHIpD0eAcXLUsWbqmxcpcBk7tZni2Xc/GMnIMFVW7Cscfaw7bw1c5St+oAk0daPmsx5HaM/yETU3mJDtY5hq3YeHjcXsqJQRoQaZpnWVfHCXTAPziXyoybGJoUBvGJAqxjuod5zVMdizd85i2OUirQb1+c/eH3OYL0KUEPDCQzBhwVUsef7OMfpWLld/g45vD6VlSikXALpWeX2AaRttMYm3MWk+BqD9hp7KXSi21oaEA8KpSjf2rdBFo0K6CGbH0wcmfbZPhUN6rlcHK+k7a/Ni29bKqlneHYFpfIeKD6qCGm8XPzLOKgGDzwObVc7PVHiAUKkNLIRHvFaL3ZllEA+BGIUooBjXCbJAeLD5QHCJ260ilTeyZMFq6IGlXEyXSDV7vBY0Cx4BSAtz02EnxOVG8XsXvxVwJylcPC1Hy/5KhjX2jEdNM7/rcMDRa7aWOGVgwIgb92J57ZjQEC6SJysWutAlfNLIMw99iRkOMdRtm++gNrz4cn1bTIPkVNfByVwR9m4nOpuzyX/TZdNSqpN4E+6+DP+gmbVgR6BLtKqYpPF3MJOlTHt5eNIKL2faTvdCMonYUQ5bJzDxYDxl1qCB7ewjAJgHJ2FzLendOU8CBjnDonnbekgq5r5JyDBxx0fMhEJViPSfOzmOyb5KtWmnQEs+BwKUZ/kqbST+FQR4ftCpI7TFwk2gDkUlfvTG6qs+LRTbk3XRSYTnDmlcRr/Yz7GzZm3mRxlAUSfNiJL0yg3hSyH8F7YeSvgzHRZvPHL4j2r4Uh/Zts8rilNpnQ/frNq7d64/tb/fwfL42OP9AT/5wDiSklujwK6Ir10DiqO59zYNwtw3EzR2JRb9XqMr0xVUXNPFL928Knr5tVdP27jn3+N7Emll6dUcN5h4JA1OPDgNRBkZvUuoy7btrO7rh7fXNeoX+XD/408qqHmmlP0f8bzJwvxgLpFuplE58YC8/RF+MDwO3Z2XCkP6DQY8n6HwgPatk1/+ar32DO/lSOjZOVZwLrT0UtwDxRySklk55S/CMlz+NKidrWpOtup+q/Ul79PxUAE0XOz4nfyq5tEN672pv+Ar1E4O+OwLkRVNsdIS6VXUqYfKMR7s4blS32a3VkPFq0qED3ukYFKf17pKzxZifqC/NIdCxXRGfeyMizonGho7HzSjhk9eC9enuvLR/w+5irvY5QPTcrSMXJ1NUGC3KrXnh8tI7xdfnsZguJDh20J5ndcSuQA0sTtP6pXuxgqn4aRot1WHxlUqE29GXRrgwj7QCMr17LjB9LWEDaD9D4QcVThPk3lByFK/Wc47zSmG67qBtN+wdGX9LgqCNXAnyQtPygXU6qw0Zye7o+09rQdeExYR5lFlNAK05pWAq9qvFGEDhhTah2LChZl7PEzrjEYEAVFhTr8hzSYKFFd7wIb6jB7IiObRfdkNpUK13Z8Lo+/lR1LIZzArev4enjEEPasQHXvkZL6CNpP5SniX5qkysbn5YfC5AwMHTZs+GTNrqF39gNQTVlUNMkYXcMTm76wfTYpY8IeeipRMpjV4BL6r3fEFyhU2dxyF7wIJRcDAxDhkbbI4KFpj4NoAgavPqjEcZEabRaufWZJP+xo4xifegLPFIUZ4x77uueSw+SYPg83fde0mgMGUZGYUb2Vu76+AJTmjPMKqaV9T6qhgwV9RMC0WwsSyWdBM99QoFtbvDfyJYpyRQeM6t+V1e+E3/H1Wd4oJmujPh2LnEp3Gs15OVl0Wc0vy38XW4EERIGfYbAESAPvnSxDKbA6XIMv5kEZphsyQwb6tFK5zxg8GcvNh90/iRrXqRlY2B3bjZThGBB5M0fPe3z5Yu+YsUGkDdaRNNK0y9fhk9X7GwKseGS3e20IDn6aaLbrSybL7ngeJPBF3U2XsBlY8Yyg9fDQNqo0de23kK3u7lY4bpM4CcpPqlt+MjG1Zff5L8OngLSO4H+0IF/XuzIb3xC71s9avSjjjd6pI+NoDfvtBkk21wk8DXvCIoNZBwsV/lKlDydhnH9TCvPEroIE18DkCdZRDCRcpsXHLkn58+MaiMnjLgihmG3Z7TFSSjXZHxQyKcPxLLVLW76Q/DM0MSgHmbCS+zccLHZKpnbWbkqG59chXbWy/PvltUxtg7xRET4pyRzVTLnhBh8PcG3YvEV1jsfdnqBrw0+sSFG6LP96qtTixGEKl7BBEm+Tt+Fr0rM0QrcA6dcsoOtCqrQerpbDpuKcsbn+L6piy4dK/aLTD+lsnUAjUcBiugCpfWlAwAHR9DlrT4ss+Kjtk0eoO2SjS7X4b0kiGeifsfJozmPcM4o3EIeL7v6Ar1E4O+OwJ9uBMkhzwkaBBkH6uPngHD/3l5DejpbIiPKTBpcT/LEaVCgLTfXZyrUnR7ATAFcae6J0bdtpwYP0MqXD7jE0x4xHnvL6vBPncJyzMmZr5HzU8SiZY4G0D/qJmD5YMQMIvHEtdRozeH2oyj/wj5NfY+JzSIoZsKDXWvQtZyvp2MUewMKxj7YHCqceomwD6zD2MiRLzRozUsjy3V86UST/iOnHBin3A5kSfhgwHLocwwQWkmx858QUeZYuj20ybwjYDqCo7Slq4Nzg6Lc7KvvYD16EousBVkPMt0iXV9RgU8gI3GU+MQfpXSHTj72Y9q60l4QouV/lyNRn0ZMBbpyWDcBQLUDUdrmDmRL4qMfwpw4z1bjyIKftBp61G2MOq1zsezaNDilwDm/9kb2Gs9wSIxOd5gr/iwZD8vOECpSvGMD0oh6Ruy3+vE2TItPdMllTBzyqyGxZHYzjgMUjs0o8kkLL/3WkDJHW/X8qpwRhwLsXqr2H1Q0HbQnCGh34aDazupu24wfkQeSttkxR9ewSbcxflSmZZyPNXhP/Ui07Zw/14nL6PPce+gGjL8zH2DfcmiJguin31LvEbAcYe2cEi40cK686ehW4ce0z3W7929lsTci47ubA/yMpkf4Z3EiQDNdGet713VPWZyeV3j14eXn45+J9t+P/i43gtxp1HnUV57p6uLQSPOg0mKG3rWHzx6wO5xoCg/j0PqpA1A6Y8gGMWhTsvhgAOfXGpDxho4WAiwi2TzRPooXRV+E+KyvWLHxA8xmypcvXxbsr13x1TDLshDBdtuB1fjudmtkMjj9yZSu4oGz2fKaDZnZCHqtr2nxVM6PPwinp3XesFHDRhBPBunwtUpZIiA/Zf83HezzsJBjE+jzl886Pr36/PmL2qRNIHay+E6ufPNFVnreafPn/fu3Kt/qSSB9NUxPA71T+ZYXRGtmxE82gprchoknelJ3E+VTvWmJ1HHeFIqei5Ojulvu81RMuYdiPdA2pyfzYbtxV4nLJQETehYCgP4w7Ddd0HYq08E7+sylOJRj0Ev0z+qL8auAreDtslN2KOkV9D+wV4uhlzsRvHJs2h0ixrnghfKc3OKTA/d43nX+pTr9yoqes/hIC7w7ra45qD2vbJ5HUOYiBXJO6NbTMxyp/ZXJagnduX0/+fFNx6xGoOScxedwBgYfelzISDz5au8ou2JikgvrQdzgU9LpI3y7Du/T3hZd8T2w85eNoCMYL+C3iADXcK6FZ+qoaX9tNy0+Y6U9PVjLG7XHwtZ54sSkySGLY8GQhpzhrxmnkwesW4nXAEWc+LJEVW1da8vIMhd6ZAVbYbVCC2yITImCeS77JyoVPNPrLzKCmXvSBkGmEcnMT/7wiXlHevxnWOcAA2ibOQk7nntV3/OJIIKkg5twbFD+iE3sxDyFYfywrH0AiWh08FRQv2YS70REcHShe69TkIwuzBtGTzy0H5lv+awpTqDznoibk2WlTwj7YwKwqGMA3XvDPh6ufrF0bxtnH25zHR9dO7yGFCtPjCNRukPva8uO8XYx0PkhTS5D4l1mOUP6U90oASnTTGDXacuRpicI0xtDt3Y4WHc+SqPbJDTYsmxr/Sn1mxrtsXuFEb144sqBAZSybDINfpGl0UpT2h5141IsVgxNMvkR4RHO3kmCGwMlRwUF5V19I1Ry0qAHFrP/2w6hx8cFuAOgVK1oZxietosS2jrXS4fN2CVriBrPCekf4y74JsnaDDqEz1kJHyi3Vo2wiLKWQMBn+6qSEtqoNAQcQwBKFjR2NA06ROf4EjZbDc6KI5c4YId4pITJY8uARXYGj+Td3q3S6tZRVwAAQABJREFUzrrdMsaYYh2IXbNsYOs5IGzxwX9Szku9O9j+Mni6hRD1np87rUof4R/iBknhQ1nmcpWapExWezVtvzwR1OB+B+X/GxtB7qX0LHUjRoBKTxGu09P0zwCEaPqUsDrIIHWYT8ORUgfd0jD6BfRTKIuMrPnE6wUjMrpo+wkfnv4R0oc2U75oU+UTmz5srLA59FlP2AjHRlA3hig5shHE0zdSiJ0U8V2uMEg60fTJH2/8eHNnnsrRpgsbQjyd81pXeDaAftQO6zu+sqWDJ3g4mD/YIMrkm7bj19oE+vz51a+fPr76+OtH+fxZ/mUjiIUSfvhl0LL14f27Vx9+ev/qw9t3r96904YQTwOxEYRu88kmG1ZukVuVhtGWaSB8PuCCse0W6E8PqRfvq4oqBw9az2TS8Fds0UfO/URI7JIcCemeapDNh8mLTTGs8yJ6YU/gSPsKKSNWVGaV/gd5Oh4vOKdRBF2JqjJqHPWV2oZDa37F/xlftCavBgwnxfK4VBzM+E7T6m/L4TlbZh4kFCDgxKmtGk7rdBZj6BsZA64syI76HAgVndti7ZX7r/CVB5mrvvRdDPb8+ipe5atMpOwFri/mxQDyqAi+xIz6QV68ux9a2npHj2M0cZJoxYdqZbnlql76QlLPaylbGszuM+arUJllCFtnE+3KjS+nUL1x4XdbqmqVUuj+sVoxlN5hL8YX4CUCf28EfF0/N4LUV72BoH7dfp++PnWNMtcH6Uv4kzGTsZCvJcwA6UChzwv+vWPhRj4GsQPBWCyw/CiOEoY1Bgf0tbNj87xiRBDMEjNAm2qJ+YGApH6o9ljPu9yim/VLZp3KEiO1T9XMQcqlFx3gmQMIw+/82phk+XP8hEt4tjbqyDkZpoYERTY3pHQ2OWKH88aaxfbHpb4vxGJk6ECf1jf8OEbYkbcYjg6s0soslKx+tLQ34hMVeTak0NINJKppF1ZHxQC+JonIh4Xc97dufjJUkbCFT/wphkGXKLqNKG7wIeZzHzzRhMxGEOtANoJIlfYSRpWcidDI2zt8vqBKse1jQJDpkuuTu5VYfWgMpEBm24Q3RwfdnFOwCkRiVh4kk7CPFvKLRt4VxZ8JsXha23BobouZRzH63D5xCu8m2kbaPMaspnbdL6igUketqrbYgU98CW4fxDYf+JIm+nLE9zQxag78rAWbl4Hl7wDmEZFq5DlfkTrPj32bOKQQZhymTkyRS2ApqVxTm0CXA4alLlpWGlKGOOaiyszEGx79WZlKCbSdbQNW4w/a6khLqCTqcKWtYGwco+j0n0ozCKGykYEGOsMdHdGDVCEY4OkYbCyRsVdDx+eznciQwMPIzwMA0mDjgJ2ENQ9zojncH0Fii3s82/RJjS8rx4eeAHSNL+iLplhoXpxDMzz2GT1lupXgaUvpLW9saRN6xGAeSlVyejm/RFuxVv6z7h/R+ZK+fQS+640gwsPF/WlKh6KrpVuFgw6WATrdazokXZg/6D6U0THbWdkAYiCxKOlEAR/Mfu+PCF908KnLb/7qFxtAfA2Mp2k4vvj4yBM1n37ThhBPCenpINXh+cLGkMo8EaR38MiYN5MYFNLZiYNRgef2XkguON0I8idfx8bPW20AefNHmzT5qpaeDGIDyE/qsCGUp4eyQaSLJeszb4CorfL9szaqvsi/T2wE/fLrq48ffxXuk3zUV8Pkr7a7vID4URMQGz4fPrx/9fPPH1799P69NoHYDHqTJ4L8ziCdJ/nKRlCcT+zSFoKqg7a5oZl0CW8n2bY3wQ/FwQd8JnF+ck6BnkmY9hkPvXFueZcKr70xKZq3fpqRtHlcX+264cveEnJ1jMwjkoO1PD+Fyp3y2v7n+K4+pYYTjX50tW2JTS6ATNpbekNXL/7d2gRAhnLBr/0G5qqPPnPylfrEm4MP2iNtlqGBbqzKyliA+iMpKVsytf6IT7hHfOj2uXwkI5Hl6dBPNsvutuR8D7KurBJ8jvCFAOZUudh9/kUt8RlG0FY7fNcmBmketzGstoHqdiqrCJdpzZiUXtJLBL5hBJ5sBMmX9tRVDkDfJ3kMBJxeD+7xPBW2DrIIMyw8w7pcijItTbW2qJ7wWZ97gtsYzlVsX/c2NKqtzxswg7jrB12Pz9LjGZ/VAK9h0hBxKzD+J0CafVwwCwGQpmSdJRk2gvxElHS1tB0vvtAfKfKa4PaJjQdI5Gy2EPMsOShlQwfzKbk3PlAg9KEOjG26/b57sseRY300evG4G0H4QB278U2IKhXBvJZjvSrbGFH9UXJ7aAcqXLLwjE8g2SCjDdCTRo/bJu3YscUr3exLZpuH3w9qSw0bQbgF27WMIJbTyrEsJ+TaijtYOGljIDBJ2CFNEZh2gHONjNqjY9pv2ubL2Rx5xJzQVo1jk4hP3zGLeBe7eO3bRkRN86qCb3B4XdtlS4kuQcm2G1emVas+EMD7nA7Lcz7V+0Mg8oknraNN3TCwtmmz+5T60FI9eHzuOcKZwvaRsXMk2g7dOkpqKb7qbokorlK3y8rqenkcMtExZd/h1n+fyqPf0x4f1jN04ViW6V8HnhFL+VaFqieFZthMN4ddVcb/kFKglXjGt2g322TVPpQRRjbyxCqVczVztSE6DSDNnLNKMdL2tpDzR/DAtEXEK08EgWEOQBFpPFBhtSrB1JRZHmR3um0hiJ4bP2jmgDPdeUrD9SZAV5V5zofgfknfyPz9k+5Xb12v4i/l3xyB734jiEHgTx4ugUlnoqt54oNGj3Wa4cMAU92dMZdmxpcSeG1zuIMymdJRc/CeH/68OFHvRzc4vkrFEz/e/NHK0bDeqfNJGylsqHzW5g9fr/rkTSA2V9j4Ee3TZ28I8fWrbASp5BMgjVomQL+HB8eX726ABrrawKGqN1iAeeJHq5u8o4cnft56A+iNBpM3gvQC5zfv9B4fNoi0GfT2Le8K4utb4tNoYxHQzRo/mSSf2MD69OtnPQ30i58I+vT5o9rOO4Jo/G/Sy8/E66kfbQT99OGnV//8pzaCPnzQV8TevXrLU0HoZ8NJM6A3rey3nHacmWCFp3kcaZpjWnjOFFymug4zKScr8JJOFfVXNmOG917YmUEGruyFU2ZjcvNv6MIpPnnaRizSE8SmiJl+1fai93nuJWYA3qf8aGuKJvKN29DGYz2ayM+LFppCAUra/t0p5fgPyqVqAQ46tecSY5CUeG+vnvAvvvBE6tRcWXCcv0SDk35y3fVi1/TFJKCqFvMiHoEEh+6D9oR/006vy5YR1Nq9rBMtS++ZBX+nlYdSMbB58ZRtu7MYS7LEWREC9oni4p9ombKRDxSbWF835wv0EoG/MwLnRtC1e898LaTxUxbGR3r1Ht+mDBbqma79f66MYRDJ1CvLFh67GyF/auqCnMrMZ9uvzYQJ5j0ssu6hZa1TG08WtF1Cm0Z650rWLoK9Tho9vcFztPB5Js7Onz+wCSTNv2kTCONZf8UPqYo/LfHEevHocUu4mvlfBmzbbcGuDj05rcKJ+kx0tp8stFxb5jwL5ZsS69ONCixWtsR9w5abojiX2SuWYF2bUyOKiqZpjkNM2xxLAflVLkrhfFK88Jz2txXyQc79oK//E9fdOrRvnmVL+jsv54PEsN03guDP/gneRU+jHX8VG3w1Y1hqbUtAHGkRSx+pEJ2n10d/n4dDCxj9EYvhA7f1mMXtbp+IFTgSiZxH1d2YSlKPxnpl7hv6Gks74Iw436W3U5KyubG/xZ5AeMNBsr4qXQiTnKGTlPYYct3ZCKdJ2CdmJ1kV/oVkXBqW5aWLfmMDMeLPYMBJRfCUhz7DygaHKY6yADfRR/yFPpU+l3ThIfZrdn5SbXxAh/nwVX0eH/MDO6prQmbT1xtDtJG3VPgPhUjG5zjbNkHb4/jqpWTczvFcg9bj1CUkdJhFmmeDWSU6+mQf2pEOdoBBjjdmyJwRXvjRO2wByDXQOT3xN6Vzj30IiYeNqdZztjaCzrbYK6STai/6i31Qjl/lc7vscNp4SoDOnHdir3zw2HO3K3xpSQjuk6C59wUlw8y1Ly+LJijfR/p+N4IUHzrYjJqBjSBjuLhDQQDmP/iUSK9d47lAtHMy5jiYpAozIXkyUif1IkcrlbxPJxtAfvJHkxRf/8rmT572+cjmjzaEeBroExs/eqrm0yfB4mMDiKeD2HhhY4iNH76W1SeCMkmrATRU/nQgU/rnV1UyGb3WTotfHM3TP2wI6fDLmtkMmo2aH/maFoc2gvg1LzZpKN++B8cGEp8MIas2q61+dxEbVB8/6Wmgj69+/fVXteOj5imeWGL2/cM23uvJH3T+9PPPr/75j59e/eOnn/V0kDaCpJevob3T+4jwl8ei8ZmmINs22X/a49MiPMnFOXHTdjj2hasT7EhU3YoRJu4pNqLe6sSw+SIwZqAcjAMLd1ErZs6R9R4Kb8sU+147tCPnNd5FLJ6RX/SH5U9zZNq2MmdKTZQe6Sw/LWosK0tZ+okrvvqe47nL/DfqZ8xOffd4njTg0lve6eFxnnM55/Fuz20+zjH6OFt3vjNwOS+NVsrWNMrsSntUY9m6icMRS8E85SsG+ubknMZWbkciXZ7K3MtwRQsaBNXhrWAxWVr0FZbhqdYZQMLelVDfXJsOrrzAJ4+qL+klAn9zBM6NIEyfPdI99EAwDu5jIeNZI0m0fQ0Abj9H6wGLERIYNkf+k2QfELWS0XCYgOCq1QtWxfMjIsNX9rSnfgRLXvpZuk1CdAOIOnTPJFJRXZ47qY9rhsQIrz9j0tTIRpDXWVqL/K444BdrMXjI6mcqzHW6jkmhfeDO05+M72vz8kEUb5poWoQ/PoHUQZKo9QBjEIRb4MLrLT85fcpyY4uP/gOuutjXkip2VHq9g7HipBYTXA2Yqd0u6jq8CcTNkZDeCOIDQgAlPvz0Db0rWbsRV28GDW5WW9SUZNDKbS4o1Z9sBA0rGwFmRwwHj0TN8S4ORAMpIWKd1PhTA2dGQY1UuMIdXrXKfMVRN7dE40f1o6tm0z+MMNpn2+bS5EgmBmjWEXGLxLft3SBTiNVxdW36GbDbS7xdCbWwkOmHRpjWrJiab70lfI27fRTjEdpRU+mWB1qotnawibwMsHmCX+7WcCFufjjVFu+Ywqiax8d+usT+0S792R/DtWARxGLrKI3Dhg733LG/baOTNiqXkTzBN/rElHepSpZ7JDnOPZL/VLdOJgwl67N11EiR/PPXUK07dM875nZrzEO1bfNL4L2hGj/4YJyD9q77FPkZ/pGT29Acc8/XQtCgSbWJDg4ShXW6dmYISr/nkhnnkNU4ZLgvTDtz7uZsSBebUswe+ErrbSEyYFWNnCq3BCcJy/CR4D2agNoc0Da4eC68ojeVt/TTB0JlOyamPX1HEH2UrviyEdRIfvvy+90Iml41fdc9dMHqov4TD/2MAURqh3SvpiNOD3fpwQdvJqYsRPiESjok7klISD8BpDovTO77f3gB9Ed9hcpP0Hz8zZs+nz9rU0g4NoI+q2QT6OOnvHDZm0Vssmgy+42NI20M+atg/DIXF34mPAZ9ZuxM0PI/AzUD3W1l0DNZqexm0Btt9nhDhw0fvgo2T+2wQcTLm6HztI43g/zUDk8J7SeE4GOBgV+/y8c8FaR3BH3+xe3wRhBfC5M/yH3QV8H8RJA2gP4/PRH0j5/+oXcFjX5tRHUjiIHtiZYTcZyJNcEK60mz52omiExqFoIjZ0ztndNfwsOSeD3ic+xOCfPJoMrQdk852dKbTsyfwbWUPnXnNnVnIp92K3uX+mt1NN21nfVHWrD4V63+Vb5Hdv5t3IzjP5Oj/3gB9ieMf8ZH24jVne9p/AZjgcBrc3lFf0tFb+ai7WLpLaEAn/Vw16/UnsvhagJuvWVpjOBQOw+WknJGHldsEsVTl5b28tytbKG78DMKrahZfGztpXyJwN8dgftGEPbpuZnRBeU/4wACNJXt7S1DaX7HXuuMRzC+Ht6ni6o4yvt1jvrDBN6mYo880OZ2vfOo+E9V5d1lFLqu7NwA4ivzHNzfZX5JzlMZ3NyvEh9QAB86tObR0sq3/3zQxhKIez2vy+CB1fX4nGsyjkKUDRrvAISZOvdIrD/AU0J+rXVRNnRA6F8Ey5otsNDhFQMwmW8MDcoW01PMquDaAxNuxPbULPuj/LMPRqI/1OapHkh0SY/fEcRakFjISm4Gx2iFZYENnTf+oE1v15BjbmeeWxLXoRcZO4qBiYfbLv/YsQJLoQOOlIaoKa2Ih36SRI0o53jOt2XuWYSw41ghhDaj6fAA+zDkdaEZYHbq+cKqVQibczB1+loQouzzo7M4tqKH/PR2aRulrgt2dTLbxp3jRLo9cJkHrdF09Rr8TmVtCWW57MrmrerdphtDAhWBBDchlaDPGor17w3WwynbswPyF7n8M0QVKWRPO/vMstaBBoaS1kYcBTrAiRAeahnPjrWMMrY9bkTBp9zLoIe+Lhz3Rzr4hoW/paAfquGD8vyCMZuisqvJwa/nQL3T2BWM7qwg4tcwzEZL/NJH4BorNJQxIz6NBT4MdwmsV1vwYzveMIXug+Z1jEkr8qgA5KDBk0wCqVTYPMpchjT5RMrygVc/ozW01wfswKPDPsW6+aU5JpNbOeqwqQO5P00nX9WAuwlCAnfHw3bH1W75iZNHO22Cn3tfC6qtUvyz7mEV+pf0HUTg+9wIogNNr6Kf+Dh6ON3KA4Zu5f92MKrtWXwmT7dj2sl08YdeLEfiDTgsPPo4IotBfvadzSDeAeQNoNkIYrOHjZxfvdGTr3/l6R++AqYngT5CF54ngvj1LZV9WTTvBvIvcanMoodJTl7RPlY/4x8+xWsmKAYzfsZ3Br6fBmJiEtoviNZmzmtt0rzRL4axscNXuLwhpJKnd97zdTDR/VJnngjS5pCfEOKJIsHE73dtSvHCaDaDvMn1+V+ejF/pq2HYZoCysfRh3gf0k54G+uc/fn71D31F7MNPeiLIXwuTbk2q+MXp8YS72pIzEd9DdxvnvNK6aaLaLgp4GHRe0HXSRmToCF5T+a1TJOpLxqxMPPSDTEAluj6Rj8b4QN9gEXnV8dRm+Gor+sPlFtlfIOw8TjT0P0tofE7rU41w9qL5lPoc5j/37jmNz+Dl3hkjn6uvBX/U/FU+2NuWROJB7OgfYxPe058L9/SjuJAzUN257IVCXh2hh7fYLVP8lrtC5aw2qMU9V8LDHHImeMsPRHuL2fhTAvhCmcoF96QX3tsD94k763cf79Zf6i8R+N9G4NmNIHVT3+RQygWuKSsNDI6bHTM8O18xyMS4hkAAro5b5YbgM/viP0THgYsvy6kbgEn5hBq0V11KrkmxEyn4gqNe3tBSp3m/sWGhkg+w+Jp8bjglK9hzp2nEhJs48Zk/NPvCTd2ssZBlLfSbrrUshfyBnGQwThGvz9JI02i/I0apCjBrEKY839QJw42dvkmlmz7WTa1zDRRe9dyUqo585YB1sPbxOizWbDjtFigaexa9VmyH9eMa5ieSSatE3/xZQQno4U/K815G9BJjB8/a4MhNaZ4K4sadhrm9tMWm0L6T9aFD2K7N3C5ihHiZVWIfXGJOboz1WYWgaA+HrRwgddRarnqtjUr0QaV32S/fGoJh/UsCn5U63NZlPbKqMiqbp4zDwEhwHVNpkBeSgylf0GISHn2RgbWaE08jDqmRdxGY/oLy/UHQ1hHpx/loOnSHL7GNF/FHeLcB+u5Dbpsw6KmM+WCrcrWNs+btFelgPMEbvaPUCg4Z9Am3btaRp33oVcYZIY6cpeoifP6wV4PDsUQeJ1AyqZ4j4zlA+JThEbf4GfMa+5p8+SDar87QfZPfufqb7qG495oNIT8hNPMLTw4mqcQAtkk1X/I0LL5ljHs+0GDg2xTMB5RsCHH/xC8fM0/kB3nYABLeG8n0GVlhvhCAenJw9FO6BIm5ov3W/EYu70YOJI4S58DRJxjApHlZugLmM2G+2g53+nAMO8eg+dB5TdAX6VIJ36jfQs+rWjxL32DudQwSfs756lvTgfq0I21j3P2se1Tcf0nfPgLf6UZQFhmExwNw9bbpZepIf6iX9QKQTSMPnRlkTIyZHBllgelxmoBGBe/96SKQ9/ZoD0eTUnan8wJovvLFxo82ePQenV+02fOLnvj5yKYPG0NsALERNJtA3gCCXxsrvzGp8fSPDOR9Qjx9E3/x1eNiSnvV0aCSP/5pjTdWXOWCzqHhw2TmIxtBP+pdQPx0vN/nw3uB2BhSydM8vB/oPV/h4utdPCWkTSI2d0i5wCjOejn0l98++gkhngZilcMEJxPa7HmbXwuT7E/6xbB//vOnVz9/0BNBH2ZzSTb8k/XiJ+EjnvNPI2lLJli3imYpzQIAngfJGojD0KNR+Y3fIRNuocPoOuBKgw+n9L5GaEktNjtndPoR+mFruRmvEP0z2lQeejNhX3mpEYmxoBqSdfDiNaxfTUjG7tbwNYFqb/lXeL/G89+knXF7pNcxpj/NyWi777xnzKuz0YW3sGOALqXycaJZkG4uk3P+iXSNRizEQV5QXkJVS4Sg55wHnxrjgxSqwWcytLTf1I1qYglyaghfsemb9cJ8rsAV625gG7cWWnFkOJZXE7JVD7A92vXTI7D3ejiDv1sp7aV8icD/PgJdA2Dp7InAnm4Gf+n7vjiFm3Gd+WkYj2vAnjREO5Pnmujv0Lta38z16aK2yM2WEQa+w9Hro11dQxDbhxwg9RMHXFWled2ixRPrJ2/mKHBs5HDT6U/ztXbKV52ydgPnDaF5CpongTh4ktr8gvnQzRtCs6lEG21P8xBw5ih54htPnCTWcY7SB1WzzA0fP17hGzzd0PEBmddK4LgB1LqIUofXJd0kkoLXyKn0RtLoZjkz95SyYvNZP6rNXDf6RDdrJl9H8A/HJ5oSd/J1SRXfRGtdxdfozWZqGs0Hc4j5ZgkVXEd8OeI6kc0sngTfN+LEx7ef4l2zueDosz9tx7B5fUawkKCgj7hCBuwalaRNLCaWaHsx0uHhsDGmxIz4zCvuFZfaCS562HYgcSPM9Ux/djOlW8fd9qTYawxhrD/iQVGUqbCkq4FQMPwyYBPLFrRKLLYAYbToqJZU/Gkd6efSIb5Y9vkPyjxlHCIe+jyaRZZqrMyti544U9JpFEn3UURgqmKVgDrMP/LZuM347P0JJYzkgB4HqmcDVeeIMcZJ0iDpH3qbkGOu4OgZtz5wwvA0EB+w+12qul/iGxV+z6o2hvwDO8J9Zo7xNyv03lI5gS4yWjghqrmpwxAn8Im0N1EZQ9yv6NA8wP3Sa16dMd+seKfyjei8b9UfrPMB98wPjF/D0sd9EfMC+g1jA5yNhVYv0odpf72if6mmf2i0gTJRFpPaFq8VMYRWSoRdlUD6XnnBRsr2or6sNz1GL9vLRMStZuFGK/XiYCscTdf68lmMhsWcc5VeeD4RRLz8q2G1XYUv5TeJwHe5EcQkxKIhgyRxyeQfOJ1TufjocJloDLijBreGlzoj3Y46O9FMJHkaKAsafbqlh2DYsOElz2wG8dUub/D8yobPx1e/aNPnF71L59dfdVDyXh0mKiawjzosp/cD8VUrP0mkXW7ZYRLzp14silTP5JuJzAsGj1xGgiZWitug4JJIWz0NiMaExEKFzaA3mqR4gfQbJmS9J8jvABLOO9x6bw9PBPlJoPl1r/fv+cl3vtL11pOarSqoXDh+1y48G0K8mS2THpP861fvtIn0k58Ieqd3BL3zE0E/62XRH4T3V9Bkz3alp5tWdphMJ4EFD4sv+4z/q4lplaqTqN+SUPA395XoxtLqA+mSVG6qF2Sq755xsBkM75a40/9aHQs+n9OC7UNa9Ne0fJ0XH+9+InHHYQv817XB9TT9JzJPtfx1DOPjz9K9jV/zEW3lL9/XLDCPPE2PcFH8tB/Be/IXfq48rZXnxNVrcNB33X1s1YtvCT/weGjVT2lwnakc8YRZ52lKv77jT99P+M53r8fHO/al/hKBvysCujT7pum0137vUpl76SAp+BCqiXGYtHGlPcbDVxk4TvhRHdwkz49PZ52lYQHwxx+vjWbQBhOmenspVXEdFtkC5tDShaqT1zVC5GlnPVUMrHXOHzxJzU2c1j/+AIzNHT5gY7NIawt/8CYcH7LxtXQ/gS2ludETrw2N/URcNjMH+QlpNgi0jFtf95r2cU33jZh81nJE64358Mk3dFrH6EOx9fS01kRvftQ6iA0iP02tEnpluPmzntzkrVMj3xwS4kEs5Cz325S5cWYjiI0bkOoRJhIuwRJkveY/la7jM0ATPNoIsm5unYgF+qCjD2l8tLxgx4LYVI9K/IoAQoKlR4n2YNTrOi+Dt12/t0Xk9ufc7CM9aQEjs+qJhzmXTWSGwexlVklb7Fz7FG0CT32XSBAW/1S2YbfQONbIJOj0i50Co5E1IjoerxUrg06xbAWjf2PLOYTNOYTIUgmC/NS3BTa0uYPLuQocLVfeRGpwp/JTcOzDBXqiSc11nGJcnSKOk/k5J9Dpw2zqTtl7Ferm257YT2+iZiOVtX0OUaaPwSO1yx/bV50y90CxyVfAeNfqJ3247g/c9W7VfrvC91XcX3355Lnli+YP37fhK2NMyrDhknNOxQNApQk7omzg/MAGqnxlk+dH5oUZ+9T5YJxvN7zRfREwH557U4g5QvOFxDzuGH9sMPuehh/ekYm1aSyz8JEy3gLTV1eyX/SY6fuL4NAZ38AhRuwtfrQnc0b6L9RT/aHu3wIP9TY4bl50PMKVoTSXrYjY+tmHvBE0BHxnI8jxqrKX8ptF4LvcCOKiwQYKnaXd/ez0nvCmQ8FLZ6PngcqkME/+BC0ck8Xr+UoYjzhroCljMfKZBcxnSm366PDTPjzlw1NA2vD5v//S+3PYCNLLlP/1CxtAeSroo94VxNfB+Al2L4p0Iafk+sukxWPUfhEaCyLB3mGfCxg+46sXMbSSGcMNTCuTi6HJ5GCzoMiiwBOTJjpvtswixp+GaVLyBhETHRtBvCvIm0A/vnqv0ps3nsRrmuDxJJA2dDxhZhL8IF6+GsbLod9/eKv3A+mJoJ/41TAWVHPoASMmN5lPE+QzdRIe51MswYveS5FZctKGdzC3YnqAVSYGZYiV6PZ5L+FSwsXBIg/PWsePU1/w5hT6qu/k28ppU/nQ5X5pS8KLVv2lHZICsfRcqr2WT/li63n6KQHXX+M8pf4zmauGv1bLIvHKy9h4hL9y/Xdr6RvV+fT8XM63A3rnaZ2yMPoK38uTBozS8lBvOvE9k2d5wpVJ2a/D7rP5HG/xV/k7lhg8TafPJwwnAndcNUB7qLAML+VLBP6nEXhuI2j1SgHupQshdwxPn77NU5CuvX3z5WJB3RpvnNtArxtiOHRJzqrCN1qfjJ6yrCudN61OGSinXsFCGCfA8jYc2DdwQnrDQ2sbr2v4wMtP9lBm3eNP8YX3E9AK6hfh/am/bvi8OaT3f0DLU9f9NdWsw3yTx7ppLqb5Go9u4Gid4puSm6zZDPJNlz3GU4dzbQbxST9rGG7upvSn/rrp8IdXvukTrJtDbvryK6ysl/Q1Bd3gsW7y18lkV+Jeo+VmJXEjnOuGlrWd1qn5MI1NMT3VwxpQ7fAHfbgoHfidzSZ0v5HOc0PILdAJUADgV+a/qefMZb33I3qIhxzKkw5ip+EUkiVOrDW7CWQ8IeSP0qxGWCZ0G/V6xWccRlLQgSWfzZUgHYkL/awAt06pdoGhPZNsx3VDBw3+rNPywyP4nQP8SjSkJkCKZ20OeY0dXvKwjSxtW3ILcCzjE1ayPr1+BSyWLTGqgqFyQQT9IC9Xy+nq5ixui8kHt2l83K6K5YY72oROmu91k9nUKnBIURomVsJrYHtM6zz4lRjqx96UVR9m3EJzPx+L1qkBkQ91GV98oKwNIezTx0XDZ8JBf2PeIKYZ2yoFa2h4k5gnAdkQ/qivYXzUPRbvWPXrNfxtC917UZ/XbPj+SrwZc6MvDbENGkz7nMl2KiDiC+PF90f4zj2LN4ModagN/paE3nXqV12wKcS3Jyi9ISQZvobJnKB55Y3fJ4SemSc0DhnX/Cqzw4B9Haq6xIszgU5KHwMe7xHzwUlyeyYnluFrXozRom6twfz13HYkTjjxH1WxfdXxCFeO0lySjT4UFce5B14bQTJI3F5+Pr5R/Pbld7kRlEmIx0TVzeml7uzuVu5dZwdLCNXRZkKAHToll57Ampikg8kpm0B6AkiT0mdtALGA4Yke3pPj9wDpCZ9ff+UpoE/a+Pn46l//5xfBKnkaSAd4NobYBGIyy3uF8uSPf/2CAYVtbHmy1TDwNR4f421ytc2jj0FYeEbjyeYWHAixsERicvMutWS9M+3JWHr01ads6GgTRptEfPrFhMdPvmdDSIsgTWh+DFJ0/fvwZCedPBbJjjgT4gfJsQn04Se9d+i9XhT94f3o0SQqQQ42laRCE2aeCurEltmFSZHJVAw6UuxJkFbt8+uasgZJpeNDH8j5FGQqeprKbeVda5wMSx/29SdlMy1ZxWaNpurD9PV8xeIVjz74kIqmBdn3evnvlvWq5SN5WvGYDhY/0s5p9yMVf4J7rP1PhP4DcuLH6db5fRT0m87/CR/Rutgmuolk+4TdmODm9CbKPffbzXbESraEozKFe7a29NZ3ypWvIyzeMRtcE3xJlp73oqWjgi+95YkL2eMNtNLJ5fodYa7TzxM28SsZyh4q/IrMC+klAv+9CNw3gtob3TOV0ZsLY9Wjj3nKlNDnIvDUKSYJzykZEx0Z6DtT2IL1vOKb2c29pC/4+nbXJs2gdOBjtcQe66Ak8J3uvAEjhGlCQvMaDJwO5kUObsaymcMGEBs7/FqqYJWf/aMZPO2TzaG850OwApwngGaTaD4464dw2GF95Bs9fHYbcV/zmtYOftKYFQ+B0b83QnwNh1eCOvyUkG4stHzxmog1CTdzPA3EEwD+avxbrX8Kv+MrIXqPIptC/OCF1jx8YOUbPdZGXtdk/YRdr7NkHBg/ufnkBbZ/cMPMhpeepP5Nd7l5spr2cOszH2TKZ24UecrlNU9uyyc/zW19rOHULNpGW3xOiL4OoSb4cIoPH7sRlHhwwwkt8sihRpblGzpQIaJLeLIRBA5CEqZJE3ag0BdCDEKhkevCTvBBA3kSwIOa9iAw56jX15TQ6VcwkIhZzi8KvNlFiY1xHLVRToFtIwbX6yBriBN/54WWmLSJ0StfTIF+pNXo0RvmYaASBPkye4of+Iou3hEofouFQNsdq0PxeH6zJQ3TaFgBLSKgsaDusaZGe+NS4WcTkw/c2aj1oc0XntwznnGr8xaZ0Sd/GH8/0o/ZUNGmJl+p5JfsGCM5V5ThZ6xwD4Q99H5iU1gH37jgq2D+OpjvpbQhxGs1+LEd4dn8yWYQc0Z884fp07f9RN6cvLaP2OVUJZq0l1i538s/f/gtf7knessTQfLfm0Fs/Lzhnac8DZSDTSE2gt6+4xUbjNeR41sWvp+auUJdjrHNJpHvwWTT41nRp/+SKHwuXFOmis7A1MQXtvhKY9rfhmXIpk+LomQwVXsvK1dLdzr10ijtx+Er8qWX944rfpUj4FOT4a9zz0yYtOYm4TgvP2se7qgdlpfiG0Xgu94IIibuKJ1Z6JqeyJi0+Uu6lKJ7B1K9jw6puUMTGptAcwiheUgTn3alP2lS4n0/LGg0AXmjR5s9/9JGkDeB/vXLq//zr1/1NJA2hvRk0L9++XVeGs1ExUYQkyUTXSZZP8KsmYAJiMswgyAT4QwqHGU0uZhJioonDerIbt62CxZSJhA44M3gzSIl8I+8/8bDLk/3+I34s2HDRhDvCWJR5J+EZ6HEDrkWTfwMPBtA3gkX/F7vAHqvhdNPPBGkXwjzE0E8GaT6e20Mwe93AzEJ6vDGkywzKa40g9078nOm7LMY9kQomODRakSPBluTMuK48KabYjOLZ1QsPlPPjMWiepL47htBcC1/RjUuucvZ3qkn+IW52F1aQkbBkwTugVLzbVr6D8hHOsy8smp7xPk1WrWXZykc4JG+O89/s85Y8UJilHoBJBhc4dr7d/lO+cf65qzIh8bjPJvg1tkpoDI8pZaAl+B6UCdRJxV/8hdvhsmgNyFz1gtTnvCVL2OH3gTb5vOQE/Yai/LBLC/HXVtQtupVE7bJh9m1E4b5rF+EVLH2O/Kl/hKBvy0CTzaC1CXda9VtuZzRe6mTuRTG808qvlFibFG9zDPUPeam/58DqDB6JQlb7FhLsRd9IBnPtRNbyILZybpG31heROo+ZB9duAHkEpwO1k6//c5aKfiUuiHkJoy1Exs/umnjq/Ef9aHZR96hKJxv6oB1M0kdPn9VTE8O5SkgNku4CeRmUGsn4XujyBMI3jfQNTo+s+pjXcENJk/qpN201c2FjH+6if1dTxrpe1U+V1ziuQHzzZ7XN/pQS+udd3qamZs9vur+Tusaf02emzpuAsXHUwE8LeSnqLUmestm0GzYsL7yGkfrKD7Uwmc2q7hZ9pMTulHN19s+6T2QfAVOdfkEnx4csCx+sdFkO/pqmt/rqDb5wzwRs+5bZ0dSSmpfks6UQG5kueHGF/68ITYwfO0F3HQTS/cPIY1X5pWl+BejI41OONQm34CiGWXYFmSXeiWkXp9ChtXJ+NLOMnB9iXxuC9N/octfCqm3JftTPyj1B854ivELbqsPLRJWIj9VOk1pmTEyFBsEhqXtOuEQFpsB8VmL9AWiIx4sqDImmdXd6kf1yizCeDssmWccp1F6pSMgDL5PTGoblA+8AVBSdxSoMazYeCOIjR5vAjE+2cxlA0ZjVk8E5Ze7Mk41XC2Hc/R7xkOfqsnXK7UZpLHhJ7h87yNj9DPZ4t4IXXw9FFu/ssGjjR/mDb9SQxs/fPj+2V8P01fERPPXxTw/jD/ztdI8eaeoM/5kgmZREhNgzjkF2eoLqsavjDXud157E4j7HcY9mzx5KqhzAU8J+j6JzSDNEe/YNGZugF+499SlB7zfJeQP1TU+2QySM54rmIcEu3eoxEeS/VNJ3dsj+Gp6mNKbhHSD6PcRokDK9UBLGTTwFoGNpLplUBXMkxz8kjNz5L7KL5bSWy7FVhg/PPzoN/DLMZcw6txxfcFZTP6s+TdthviSvmUEvtONIC7wuWB4UKvvtK/S4eladK4zUffihtKTgniEZAL8ojrqDPM1MDaBVLJ4+VWTH0/4sAnEUz//4ukfbf5w/F9t/LAR9BG8NoJ4MohJjE+7+KSLCdQ2cEQ2fmBC9GqEJQB+YHMudMbER7dJo9BtUskjvxnlT4dFB04bHBlaqj90CIE4kt7k4D0/+hKcLGuSE14+MUm9ZyJjEmMzh8nOu94sjHhEWj8TP5tE3ijST8b/9O69XwrNL4TxdbL/n71z4ZIjxbV1+e0ql+3uOY97/v//u3fGb5cf7bu/vSUgIjPL7h637bNWUpUBCCGEAhSgAOKSAZQMRA911lAUaAZIKL+xAslcpaLmU8FhCIJPJ+UuVe13hiDuohyVQniVwzDCTg6VwI5fV4y0FCChjYw6TO6kVNomhdRyW4IGGjTgCYSKkglsEYrQt3nIZnL0bXm+N9ao2vcmfIJeBoaHibTxTlvDe8w1bQ3/ObxxB5Vt6pjRHBdiub0rfrclkIBHf80sjdt+402MGVpxGrrekQ7jd/gQz2/XzTxpK17yrfWKzJpG/M4RvPAE3qFb+V3Dh5hbSPjYws6xswR+nAT0GPfYYC1xtMpq6vZ0SVTtu+Dk6dZ+m86ZWMqw6XRNIATXruVngJ+BFBfDDOXhWh8Sdn+ECZMoxspjQN78DV8BNBsGnkwKoZfJoT/TrHS/3GLCBQ6TOFYJaByDIYcDXnlbjwHovSd0jJ0YR2XLPOnZ6sHkMisAMlYiv9K0pZ7J5icMQaar8mtsRiXgM1XB8MI2KhmCMHj0ahgSqZfGORiBfLahxjvGERxfNpdh4GGC91AvwRjzPNKKZlZFYwh6cI9xDeOhTPQYD2XrmF6IicAjDEG88NJ5iz5ctia7MbIwrpNMWK2gyW1WPLHdLasZqCdGojYiKatXH7ESgZVI/Dxx1OAJA9HcBkXt88swpeKqFwfzZlURrSETcsZWG0d78T/3DlJcJBPwQC3P4VyS5jKNWTYU8qkUGpCcnyMJ+ZpLcGY4Zc04FPoZWmlhqqCCKc5fxq0zf/gNw1wxqOYP6uEpfnDMq8EdDxcDl/pP8p146JO9ZOa+0/IT2EmuERFiLtX+elmLCdaamrDhK2KhADerVe7IWbhbesS2RBhZ45xdSZk7JG5JK6ENvBgseRmOUdYvtf1iO+2X/uoX5bRt4aEHoElpPnSdNqz+MrZaYWDxJEQNnQron37CS3A+Bf9ROoRymGN5rqUjNt59uLExKFvCZATSisJ8vVg8YJhS2dluCg8i5vLFR4LmBX5S4+gNgLSTuEpBf2h2hNGVfoYxCL4xyt6zERiDTuLArC/k38dI7LlS5kgYg3iBjjGZD/A8Io91Bi/QWUWovuz+zAtxdFeYK3GYpXAE11Sn75ajvoDLPA7n7G4Ms45TO5I31IIneiFraAVD6JbrwAspFzRglY8kiXzj9jgkdvkO1wU8t52Kj7mwkOnjV5Kf5UT62f1UCfyyhiAUiJV/PYwspWqBVmotNjdidwdBNGDSlXSMPjROG39QeLzlUuIHrQJigMJKoHcMZjSIYZ8qq4DevOMsIBmC3mglkLaFcS7Q6zc3VmDvpbhQYrzJ+mBFqYGMCqAMuiWsZdAShWNeFh7cQ4vnPNTEszoDHcJvd6SwVgc9iKauClARE6ETJeT8QBWX7pFDaWME+iyanNuTvZhs+7LSKkt2lkBmAMQgyVvANDjiy2CP9fbskVb/XOosoEsZfi51ODSriR6p0z7SUkkMRVjCs9xZyg/+vR1NChAW4AVfTkH/HG+edYeA41ZlVpUVEGjqSt1rZGJsZ6rcTQP52w36FS9vSi3qLPk6UyFJTk2vc+8wGmy8ppnajSQHtvn2VLe4t8fI++/kP04dilse/45Sjpd9Gpp+dDo9KfSVdRJ0DN+DSCU0XkvwoM50Ghx9OCGHHexMirgtnirXo/XOHDmGFtf94zN40FtKnJknFwvsVLAZxO/wxO2Bu1MYoJmpibdv7RGF0oUHFunxm2Zq1bJt6PSTnjjhLmuFT+wZak0xIefQWQI/UgLMMRgX4LrN47sFdzNWnAGrW7Zhaddfa93QjDuBScfzAywF+ZpgCuvs8vOcKzrymmLyZBwBesM9kV3zDxpoJo2PNJ6ApscwGhv57b0AwG3kkO+JnASEMYg39Rg42ArGOYnv35YxyJM7jZH0Uo1zFZlU5pdzQJgAetLpF3C9eoYJKHThAZ3e9ROTHshQq5q8aVzkSaZkhbbwM0B6l3HO5z/4yAUv2j55DIRBAZHG8MLWL4w5rGh+5AkeL7T48TLr4QOMQoyBtFJIeJ4E4jM5ZBKoMc5DDDfCeahxjyeQ4g35ml+NKTEA9RY5vryKEexznW0CT4zrfN6Q8rGKOiuRNJnU+MqrKbwNTW/FlU694D0/1cMFzTEL6dkahogWfPHTjklyWoDkI9nGhe5o0G4wlSQg5dnZ14XKDYCffiYJNKi6wlyjgQsoknHO7QXE/hHssHg0Yq4euzpOKaMkQ1LAfFaI6wl3eObZVUipK25lK685mZXZPhmbw22upicoz1Y8XyeVjhfYHrCuqdM7siD53oM37kElFu6g64AuxhvQQZ9cJJHNtBSgz/fWHJqGt3BiyLTRhW1Z9N30X+Y43u5J31U6NNCR0MAYeU9GVW8NU5iD2P2yWdvF0nfDs8vA4Csa/JhzZceF5leaV91onvWBc1aVFn2BEUoGIPUhePLKOoxVIoSeoELr3XHdVFSMlchAdxqg74Zao/j17ZFQ6TteWah+w0t39yPx7j6N0Yf5DEYg6wEMtdIL9HsbhMpwjCFIv8fSHfnwTlYF+UU68yL1Ze/AwFc5nh+JLXRR9+1wZyYJTie8vov4PGs6TqgNvmP3hHA6vX2IdV92swBwwjUeYsXxrDAdXfbcAQe2hzsfl3JrmcbVxfnspydly6wyqGB4OB8W3dL7+f6vawiSEsDR0d1IF1m5gTmRix6KuraSII3noX1dGOjZ8i1yWJZvPnzxqh7eYL3jTRarfWQAQjm9fqsBTq0CeuPtYFkJhBEIw5G/FKaDzqCjcUCUrXuVFI/8NgTBk7WQcKyA4Sb/1Vk1yBHXURDTXydwQrejC7mTUSnXKnAq3dZUP45kjOFhzACEr39luTSKW3hKY2CDwmZZIwrPh0dL4TFIusIApJU/T2T8eSzDD8afK20JuxLsknOB2jLOnlnl9flAKty0VW+UnQ1PYg1xmB/qJ364eoBG2uC/7xaw6TocP1fLoTWVIsgMl4dl5QCJoH6GdHzgKWBeyi8aFuwg4ZJAGI6khdSAb2FFYKQmT6KHaQvaQXCW15I6zN84B5n/JOCQ8p8k8J3RDwY/RZ/7fSoNlE5vf8+W67m0mW37qfaiTL6nHmyEAu0WaFqFrovAuvms7acQlwZIz1c0BJb8TTPlcF1L6mJGuQOtUwJI7yJv4MavsiKLTut806c7DX6Lvuk0r4YJqbMQdx4hjMpXRnubjILs4ysu4WYgmmKfeo6fJfCjJMD4oA1BXWY3e5o6P56/bvYVB7Bv4av+WcOhuWA3wS5s+Cm19U6juaQlO30LHLQLusVlGVmEzGQRhO8Ktu9xkSI29FAHT7KIM07C8JOXWz358pd9mCgymdN4DGOQt4TVuOn9jbbN6yXaB4xBgr1jhRBbw3xeEBM7DD7KJ7rZbsLYidXYrA6ifPHIDwbNu/QBYlBY0zX5mmRqnOFV1iS1/OUzUeRMHowxbHXxSt9Kv3vvi4xA2cLiSZ1XA3EOCGccaiKnCZ3PS2Tyh9GHFdIa42AMwjjkF16Fk5UArJrWCgJNHtshOybJHz7KAMS2GibS2uLySePKj4J9kdHM28E0XvJWNRmk/NLN5bOiQoYqjb+8zaZXBol/15dCLBQJhrrpz1vCJIAczot8kEday+CJbG7M5OPfgrVIER74w3UYEGjlkoNICZP742jlNV+FDGcFtk9aP/AGXhMnTfm6XiYRDlNCh5s2uPrvApwdnkjXxWNCR+BCIMLAK91JJR+lDXaAG6e9iiS3gMSLlyYHSTmokRI/cV9dVuhVsPDtdXGJmI4uFNO8yHdQsJk/fPme1T1oUYRgExhkHXAuEaFfURc88OE7hlf6ufqP+7X6pvrRB89vNBdSX35P31VbZqXbR7Vlr94Tvg2u4g6j7J3aTsU2KwxCXq2nNkw/9dhCRdK/mSfxwRzo3Kif+mgNdluwIki7K5hT5TwgGXxqBRALAPxFQeX7w8Zi6tL3I+OFyAGh1f1tqdV5iL4/JZ60eXjTTxOWrAyiH6Vf0keBoSfcH9XPmd/0CkGvAsJYLD3BsRh8MdlHZQjvMTskbChmTqUjNpgbYdzVXIv+ygqhrORTubArudzRNta+y/A076JCQnC/NmbOXBVy8s1cgAasI9AebdzAXIDjXE5FGg+YHfBOm0HnCULydxjUzjv8DqxpglGWt53Kz/Y+ENSHVPfziqCW6M/3f01DkB5mfDKQ1lldv9vptgGqMYFjTz5KiEGN/t0AMdag9HLAobaCfbzIWUBa2cMKoHcoJBmB3tgYxEogbQcDrt9bGYXwifPWh6+E9ZJJL51WYTwS4sQlVhGWIbrHpzuTZn665ykOb94TLqYxB+HyxisGLUiCwyXZpAQ7v7V7lSmPkLkgbDDDQxmBeEOGr4fyXSkekmytVoAlyRiFGAwxCHqsbWBXV5cyAj26ePLkUp+J17awx1eCaYWQjEA+IFqDpMfGZ2VRlJyXQlJtFTwMQa5TeKIKlOufLh2mYoRdQfvzEviKm7RUf03tPAWzUIFZcvIbt2GBR+0Cmxj94Jp5k378ihLPPZvpa1kTug19C842x6EU9ul/Pf5XuPnrpX1bztHGvw39u2Ehi2416XAeRpygX5hDgCPnDh94p7VfKMpbw5pNK534O1JHo4MBpVZ4FNNp+Gu4CdELClleY8zQhCSH4j24B+DkPQ70BgPJ9k3XfV/6pkxnpLMEvpsEGB947ryjuDbzTdiRP9vWv4Y/+9MMTT2RrrWjoaghefB7YD37sCojQjyt2oHLcxTfW8A0MPG2r6q/VwRp4oUxiEkcYyZP4jSBu9HkjDM9MHZkS1i+ovr+3Tuvpn73TiurNdljdbVXFjCZVH6vkNGYJR/UwHBSk1AJnbEZzPjZ7odw1ZyJZv38Mk0GoayYFr5QUl0mmTIC+Swetohp3IPBxHpKYx7Za3jbz4TML71kdGEVjo0+8r01XmOfHB6NYUhGGSZ0xssqocc2CMVg5HMRlfZAA52Ujw5VvVjB8Om96injj8aUPuxWxrHPWh30ScYhl6+VEhh72FLCSzdWWz/WCmuXpQlmVi1plQUTUvHtsaCrqvEbN5AxnDxk4B+GMf4si5KZ0luYPEfzLMVPuifBZrzw7a15uQ/ckHadtvc7nVuhtE4WOMGmwRhP4QhLqcD7B43grVfDBOhnU9pqYpQFLmW4XEhYKimV+nWqkwrTngHJn7TdFXkZlCscQK1jayhlVP4qs/lpDkh13rBWyNMzeBKfCRWa2RY+Cj9pE2OfeZLl3itVP3v2abGCY6BRu7KBFgOvDELsiLiRYQaj7nu1Y3Y+YNDEqOsVb86j/CLKy27a831trfRqINot/RWDkGSS+Y7KVZ/8KL3xUfM4dMKN+shb7a54w9eXZQx6px0XbCGlfBuZ0Q1+yY6BV2HxnH5NXbrO9D9J3PpBMP0Loormx7XDDo5Lp5M3+TG4YgByn/LKvPRBr+bRHKeNwum77JooQ1Dtmri0IUi6Q8agB/qKMttMWRUUw7MMQg/QQcyV6icWvG0Vv8ZTbjvEzWfud/cbYP7Bb4VBS4xQ56swNzqEAviGK1k2eZTfsF3eY7BG6TS3twIapgs+cHqYw9bTAapa5xVBLcRfwP/fZQii9cj5YeegHpHyu0Px9qAbH4rEb5+kXDigkIOhGZywCuidlBLn/7x9g0GIFUEYhVgRhDJUuoxAb1GOpFkpsoRR+XkLJSXqMqSCsncZHz5gJPtR+8HEcxzFGJfOMDtylBBx47tu0HDFugeprlBJtOlUjYMqpUKtnc298aNiGH80kPBP+WUMknHaikhjIw2IpOhkzectF9btJ09k+MHogyFIiu7qSnGtBmKlEP5jzgsqy7f0vQdE2RObAQqGILahsRwSHUcNzBMMU248+5Fe0knGkY6Ycvfw+ZsuSibQlmJShUV5eCbAJfkTIlYwMUa7iZyAl3NBE3sFTyihjpFhx9+G26aw913QHljx0Jt1o6yUcyLDXwbfxsVfJvpvZrRkc5NNif6wHZgeL+Bb8Y7n3kK5A12m+6P56XteuAivb81oDzucQRb4mlbhcQM60DiN3/BB6DAAqtF2uCbVMPz+jQydUawZeaHd+QAtYYIH2Zd0U2ha7Rv4DRfo7Gl9Q7YzylkC30kCxwxBbpX7ZllxPz+q7DxLpLUJHPSnhUGn8xyHyLE+EuLWQaOzgVe4x2gryfqPYgZTogMrwMp1GB8yHpNgBFKECZeGNfrp7bsEwcpp3uIzThpGIFYMaAxkAw9jI70YY7L4DiOQxkcOK86KIFZXs2rI20pE54MmeZ+1Kihl3bFBqCeKIhuRiadM9MK73qFbnkwubQDyC7ZM9/x8tKg0pZUR6FOdEcTXuxjzxGAtwwnjEd7OazziSau2tXvVj1YyMOZh2wfnhLThh5UAMcwwJlJY2+MfaszDuYqXXhWd1TysDvJQT0L21jRNpGMEwhCkFQ5aCfThw3sZzPTTBBi6XqIuuQEAAEAASURBVFEgOhiAHrHa+kqrrb01jVUGrEQSP0rHEMQKAtfZddRYxfedMQstg1UMueckc8v9nCLsu4ocM8bxPSesv+BKIBhOTCd5TZo2YQwFDhw5c4/yPFTEoMCdaBBxlSjPIQh7TNoY8T35rbEskBxOvcWhQcC761FJHYY2MnDFa/oflKS4dILmAp86ywnWdQ3g2NUSc7muyw6FVOTtqlGG0cUNwFHekknggbbDAG6XIjs2/E73WFj0PSYxA6JZ5R4gF60m6TxC6noD908AVgX1ah2MPN6aZQMQW7Yw5nJos34+B0yGIFa7oRs0j6IfU7O7aq/3ZeBkRZDP5sIYpE6HHoQ2f14NxKojVhapn6AnMAJhAOKFO8anD9I7xvMKQeZWyocuUlfmx0ogu2EolsxVTsv9jsr1LTBXCJ0WIn+sDEq9czdI598YpqFo6RiMQvppIuND41WGDUKs4sNgjGHIL831clwvzh9xfAbHZTy+p3j6uM9P9RyptpRa52CIluHMBiHpJPdvs1n9vPs2tUxd3f9dn0DEFmz7F+7BjTNcF7dL5wcyHbGSYAiMSHCIWn7QWHGTvLkad4ezI5eyBOR20dWdRxcbIMmrm+o8Yph6nj8fvxHxT438koYglgey9JfWS0egZeHZqQFZ0eELilKInhAGDZCGp0bIGy7pICuxG72h4mwgfj4MGqPP66z2QTG98RfBWhFqCSMKESs2AxzehGEEspJCGdLA98YfyubBc2/wBDMMcNL0af7w627g8FRIKDZAviiwONWB+pCN1LgCFIRsyQqc3ldbw1wyq6okR76qUWVjtGFFUBQUA50HF08w/DzR5+EvL2P8wTCkARCrhLxtTEYiLOAsmcaIlK9rsBIoq4HaEOTzgmBSPHV5qVUpX/MAn6DEd6QuwBp/hVsGVV97bg9Focggg34ABhVKyKakXoMQy6qLDspSVCcsIAeBd1oNMDYoqV9jbJIOIgeFCoP8s3UkPvG+RrcxG494h0dAwMY7YOknA8xrbvJRTtzHld7+USQBM0BI6mgLJ5Abt/F8B4qHjZwYfQ1hUoh+zWsjkj7Chdxxl69+mYxkXnCdmMuKP8obgcq/4Hew84FqdAD1c1ohrKQ6b/ugWImugA7LLxJRNAt8BJt4+52h4wNxFzjWl3Yo5+hZAn+jBDTH8XhhXwQt2K1YF3y6h+Nr/+1Meqh40p6H0EZdNEp1zkRNUMHCN2W6SgqYWax79GSwvpl9yWjGJ6SfvfkMmpizVDQQjOEz4fIqHRuCetKWSRiHOPOGHiMO2zn8Nl/joPe1neO9DB7v/eIMI1AMQVlRwKppJo15acZWMx8IjXwZBynO66kvOo+I+jA2Mp9fGDPBG7qAulSdaiLvWglkHPySBYYYn4eo57o4hzJEioRGaOwsEy1P6JiIsfVDE9ccFisDkQxBnvhpLITxxwYjDDccBCsDDYYhr+DRhO9KW+av+FqqJoP9zk+Fa9IqA5nk8vGjVgVxvsqHd9omp9VAMgT9oTRWIdnYo7ETE0jGWY9rTEUZrMbudAxT+eIQY17koOdd1RWJ4PxREfmZHEoylRBDUMaKUz5ChAYZLcsaZ1rGBuaC2JoQkBZ0UkkcIYfBNY4vlvEcuQQGwiBZuI6PfLpnhdDU3SYoCfIMskUj6NzZevYrEfxcCRAKxLgOu3TDgVGMxQhakBSYLmV0AvJKuRO9ITNPSlR7pfymP5NhcOM62r4Tu8gVUwjGUZp9LmLe/CtoGFfDl4yKN078mSf1S9UJ54B2VvyxEkfzmlqtw1YtPohDn8bAy8HNfEmZ/m/cMg5TOB/Fob3OHRAwpC/qqQAMyhh3+KFDvFVUegRaGIvtS0988BawbP9iqmf9IObRGw5bYYlvtwXqHP3Qq4Ho216JBD/cBLdxfFoEPjKJVqgISRGE+0X6GVljfJXhWUWgBzhTle2cGGdtxFGcM7688gcDEDpC+sG+t4exs4JfGX7lc5aQdQiGXhmIspMiq4TQTV4ywHEeYgAeYI0fbvgK2AQun0fBmraGgx9atPFCPd42ldHi4l4VEeLk6XyAcdBtGP4+Ds7qaHvg8ayEuPPogoY2fDEEUeYVsgb37H66BH5JQ9AfKAkpEtpId+q0l1xtXHFrVhxf//1gocFlkHMhQxAKj0PKGNB81kqgzzL6vPMSxZd8FayWKPpT8VJO76QA3wmXgQ1fE7PFnJVEGjD5rRaDGhQOSgmtQTdV2CuCvKcdGDyigPQTM+uDGd5wxoDtpQ7AktJ+YZuGYPKDQ3oiI7uzgC+ZMUDC+CODUAZKMQb1smnyPJBiu29Ldb4WdnV9FaOPtoU90TaxbBXT4Oeqzw1iRZAGMzYERakpu5RkjEFWoNJUKDffDpUxDUHcQaQB//BICD8wBcrNeHABExIm6BUmlLDSnBCcwI2YoK9KswZNeZ3gHGs2J+zzNnb7pB9kqsSG34bTdBq3400V6aw8gHeIO3N9JWRSTS903F6+ku1nJDeXdaP/JAvk/jfktJZWjNxODaTB8Zr7RFi4G4I1wjEQOiQ27ASJk2DyFi/2iHdhe/8kkUpo/BWvYGuSwysA/OJh+CuN28LQ2dO6Df+cdpbA95WAhgeaeBy2Qp7kw1UTHfrT8bT5Y62XFODdK0JnGxu0R/vfU2r8TOpCr2GiT7AZ8kMXiqExsSYPaBgel/jeeqFAvualMZLGWmzDyNkcmryxFZ7Jm8ZEjIXYQs8Zirw0e8+KIIxCmtgRvlG4PyX/QatgPmm8pPmf3+x/+jz5yaRMcc8SGBv1WCk+6eE/owTwxohAfPNs9J/DrgU1kgjAUly+JxWcweFiSSOssY7GaH7jz5lDGrNwniHjNsYt/tKPJrX3mfRhFNI4BwMOH83ASHPNtnlW8cjny6ouTzQpkxeWNzfvNE7E+KPVQNoW5pVBGII0/mLM9PjRY58NxErrJ5caZ3n7vVYGidYjDEFMLBW2gcr8ZCUUUkmNqQ51nJNCJoe55cBJAVN1RTa6yfy1syiIeLU6cEEG0AmGBbQmVNjyXuHkEQmBZilAOtY+sC4qsElF8ZG/oH5RNznPmA+q/IGTmhrb8m/qlZ9o4REiz5YToIfOOF3YyNH8ThqTs9CAG9rQUSeWuuzmDt/hTjiasXBGWiGPPJHBSF4CRtElfrgd1RJek8hLcrVdG3aY42RrGH3ZZ/jUPOiDt3qqr+scnxzgzDxIcwrqzVyH2qTDWa9AHwMOK4I+aKUeX1RmVRF532MkFV0frVF6xquShI+hR+rHRiS3XeImFp5TBySnVq8O67YvP/L3jEOslPEUnsjryubeOb8uBpHkgC4KhAakax6j7Bh/MHRhZLKty2noCc13tAoq+kFzIIzGMgL5/FQMP/RnvVS/lN7ACMSxGoRZUcgqRF6i32f+5DOEVJ54zrlfoo0yV9l4OGqLs5EoQV/T2tIbGqtxXR865Tc6F7WiK1zFH6Wwpq3hRjZMF/w1DF+6vQayuyVpqoPKOxuCWno/3/9FDUF5K+U3I2o5bt8eKEhgakDSFQG68ao7VyP2GyclYQjyoEbK7oPecGEI4lOnrPR5rS+CsQLoxSsZhGQUwvjD9i98cLJ6SAMg5fUSaSnKzxrQoJxCX92RB4AerNMijtmDbsqvHx7ylQdWrXzoBPR0/YtdOZSZ3Lw4MvoyGe1UP7KBB0yRvH1csiqNUj0gqjdkeWOmbWIarGSbGN2RDshnTaWQpOzYn44i443XY/2uZfjha2EYg574rKBHgmkQpEGMvyYm688lA6VHnR8lhvJksKUaqfqWAGEzC1/8NX8KGA6vQLuS8YHggt8xYTl5xskbUbSslUfJwWuaKRuZJ83qyOFkTlnzOvNN2F8Lwem+hntKqX8ksJVFYHspuL5FZOU0ZQWbZGTgNiR/Pv66NCFMMRa1n+913RgI8PCL8fSQrxhOqePfgEebSgMaBVtUZq6FRgSH32EDDi99Y5xSuE1mpAEnQtvsxKYrf+CZyO7S+IAV7mwDq9Ld+At4G70NHvSatyUvQTeugg2vC2/fiLp0/FjBwPid3VkCP08CGIKYiPDcsqumOlpnN9Hy0/yn/jGegNYdjatmP7aLuV91P+hCKt59SdF+pjXG7DvgirD6I38ml0fZ6IutF9Od5jOxS8X3TxfGTmwFw1jThqBMCjEEZcxzo+0gXuWDsccf0mDMxAu09xpLaaxkuMIyEn1gbFXbPz7qRRvyZOzFBI8yvRaZgMc7qocnz0wkM1aibh47McBxNTVy8GAHmZKPqouWfv78sHhnaoEcPAzT852PYTgOMsaf1HaE++kXHMWgy0U0mcve0SDmoYxA/qw0kzYOdLah5uHFtQw311rJgwGHz8+Dzxt9uGbVz0cZgD7KAMRKoI8yAH3QmUGftDKI84uuZOhhbEU+xlUYla6ePIlhiTGVt4tlVVAMQZocehIqHlUO9Qirqp9iHg8L0hNEYMaznCSXElh8AyEy8JGzW4dp+0Kq/xoPHzJ5DjcO0OmcRlSIwYPPpTySzGflMT1dJkrGs5VMKX0EAhWa9QhCZ6Ms05A3wpYMeFDhhxc85wNEAFARcrLC7Xd5INIqXZeFRurYnU4IdtwjybOKjMxmGS5PeJVs36xUueapKLVn3M4AHgny9e8Ad2o4gkkIInj1I4GklaegaoZAv1SC+76W4ny2IUj9nDOCOC6DeZB+rBCy0VeGHA51zhavbN/qvou0LBXRo1diUGarGcaf3lqGUZmtYR9qHsXqQPOmurD6B17gE71EGEMVFQIG3mwoyBqZcyaRjDUO627hWy4yBoHPlTKc3xSki6AFoMpzGNz0K9NSx+ZejnODTB/KtHHVVGTv3v9io46NxpoLYeh9aGNQVgAxR8qXlzH6aiWh507MnzAqa67FvEn5MAb5zFbR5GV6+HatYcl8EENFcZvhCx9nfkZMcaq4pHcS9e88zri7lLqluIHo8A4PGsD5reGgNbRiihoXRMtccWROMmncCIKCoUcxBFG3s/v5EvglDUE5sJBPoKuV0KLVBNNe1A0Mij9akWB/CDdKTgMRrNIajGCVvnmvlUAavLxhb6reXr1+/f7ilQY1r1691uAGhaeBjB7i/uIFq394EyaFxsFlDJag9Qefnld3RW3kPCApIjRDrwLyaELxUnJp7ooSMCzKLrebjtG1CaSvgbZaCDSwxtjCrCB8EcW79DypYzSpl03Les8eer4gZkbw9VPpvB3zah4pJCzcl3prdamlzwxasGLjXz/R4EWKjMOieSN2KSPRFWlaAul99jol3woNS7l4QBwYheAenv2rOLAoHktQMdKtHhyGp+ThGjihgUXmxdVwJkQX+EFQ2aKGoFl0TWotu3M1bFtWp/5ZHypNMXn3dBPvlr3Fbtz2T5TeBTQa8TV8ItsG3Pgb4I+N+B6J9wy4ulKnefjueAhNxabkWb7vzYwuDC3Alh+gDo87v+CN3KdgI/PATOAYfqOcykP6bWmdf/VP4QveSfY7suZtHttf006FoXOM1in8M/wsge8vgTYEdUvc+BWx12GPzhXxuKRaMM/g0efDIz0B/cGg99DtYFg0dqAJSELoVDkLbp6rNR5yQYNRx0Dlx0QL5xdaGt/0iy5elDFJ4+Dj/pQzB0JzTghjIr8oYzWQV1BrnKSxEttFxsHQTPx4+6+PaWRLP2OmnthRIk/6jJ384qxHCLxIo/97ZRAjhIwcemzUYylPJMS7D47VKAyf0ZhFLgOQJ2giwxAMcoarxDxTauwDXH8RgfKWLDwhUbGU7G3uMsL0V8Z6ZRBjoGtWSmvbPG/6jadzEm3P0rjqs1ZOfP6sn3w+k81B0cQZc2FQuuJcIFYKaExlGrxkU5gDozE2PdS465EOjeYjHqwSYCsKz7dsV1Et1H6qavbFquvYMOJOYIzntuaaB1hxI1gG1BRXUrawQmmOQ6BPuwW+un08fICRtllCVRzMcJEQOC6q+oIx9+QM1MVlh1au5J4u2XQdpJsQPnD9XE7d706eJGZIBbS00ji69QGdGfd8dIp3AxS11A96DYgPrvFdVkcEa0R8ECpuOoriDG68gBauCrB4VNv9pfPKH9lJE0G2Znr1n/qRD4SXAuQgZxt9eCFOWIe/NywvyNn+KJh+bPlClzDN4IV7tnGxqgfjMjTrAOoyAPlLy2w3VTp56G3RB6GBpOExaQqLSWQ/2p8rIMYlGJlP5DH6ZzVfw9JfXLnSIS23NDfRpGtUOd5qJoDbrBBo68i8jXoO2zhTfRCLRuVW17QBJ18G43Botn1hNL6rF+Ws/qvVQGz/9FEbzKPo/6wwrK1jWj304GH6uekwFzMPaXHw7pvs/ges+JMcLBlwq30bt9DhG2dxJWgyFZye8CKXgCim6RBY83emhoHX4fY3OAIiLeMpDE50QzJyD3zOlBjg9nHcSGukpnP2f44EfllD0B9sHC3nzqrmlUblYYBam+JLwyUsPWSF8kEBPlHKdjBW+dgIxIn1Mvy8fP1OxqA38t8Izn5YKT8pQFYC+UsXGil5axnKQ0Q//8HeV7pgzv/xsIF1gjYEqRkzIvCoAP5o/PjgYyMPg+4QUprdKdaO2HVsXznspt8hV1k041tBuBehKFyqUlBw2hqmVUD+OcxyTirDoIhtY6wIoiPy9olPHrIvXgMVlkNLgV1qgGKjEIMflJkGLqwUyvJo+XrL5c8mcmg0n2gVDQ+OxEsN7TIog0/96PCwmVowgCNkydjXxWmBNh7pyUH6eCgUbmO5AVTGzg/+xvmJK6mZHNLDtd/hNd7ltu8Mf/qy5k5tU6dZku/gjm6nIjHCK5Udakc7S8c7yx7e6avfuCvsp4TpG6ptP83EQ/cVYB1u1v4s3pr/KD0Iu8860MXYvyN4Arr2Lbm1A4PfwuexeMT1KLDRjqBsbn3jraTJs79/xhNwg1fxtYzmH3l3mEw7+UPGtIzX5Rm6UlO4GWy/k8HdwzoN3wpsBZzDZwn8UAlomKBnfD9v4rsbqNlmsh92RtegSetXniK07zytGOhuHeOAct3P3JdWvQAxqIVmo7mAEWnKoWZ9VoThuFPNBQ9c+JFHdtL59+RNcXwmbGx95/xDvgbGG39W9fhMEG3jYBUQY6L3mhR6fKQVAoyhmCx6u5gMQaz+8dt/bRfx1nm2hIm4zwbSGCpllwEIBvxjhECf16/GTCMOzLoW3OK7QtBiNRDC9KogjWGoH1WFjH3C6NUaCykkfPiIhBy12J1iymyjhxBbxXy+oWhhiMlnpDXR0+HSbPfAmJNVQnwBTNvH9NPOkLCusZZakMqqn8Pw90UrgTIx5HwhG35qZRDbRjhjhC+JcTjtQx1KyxgqL+gysUUK1DFjO91PqkbcnKcOCYOof+qKgMoHV8HhiLdTNSstE2rGV247RqDtgLx97ua52RTwJdciuj5fgR86cFPmbK2FVQmdTl1WvsGCotn3ZQG4qAbi69c8KYYr6Iarpmd5CaP5Bzcw8dttEWC5gbcyS5Eut5HCr2MhaB6axojAhPkfKUlayuOeryhL0sy0hCy3Et4g3/Kg48vRBTC6YJhhJaCNuPRl9XX6MzqAF+feHcERGRiE/aKcrY/g0d+ZK+mnfP4CocrMYfMYgzAqi676RVYICUfleG5GfdRhMz+iLyte/HqO5TAtRDUNu/JordTc5h95CuuXP6W5fh0PHrjkL9L2uxz7TVs6hTbc7Zj+578UYb2QPmWpuahsJcVwXEdkcHaQd1ZoJaEOj2ZVEB/X8Yd2rh9fPNULde+osDEoq4geCw8j0PjCmMq9pzkZL9IpH+bNe/HJXM06bkjCNRQauBEBUnO8eAfu7EWjyFbGgepyLEITSp7OEgKhDyycgRg38Jb4RuaCt9zNni7E+VN1L660CCE1KAJn76dJ4Jc0BGGxzmHRkUs6B2E3dzUjWjuNVnG0pXw/jnXhDZ9XAklJ8cl3Pov4SkuaX79iJZDCr99evNHvX6/eeO+797drEOO3YsobYxLGJsjeEx8xAKGQrJRQZF4bDA+MBsDLgIdOADdWdG7wRApWirg7BnhGbk+A1C5dg46PKy9hR3Th30pLfqL2vQeTs4FUVgxChGX88YBHNWIw5UEUxqAMoO7LmOMvZkihMVjh5zdYGrxcyzD0VAdHP5FCY4vYtXzeaj251OBFVm0fOi2DkA1B4gOV3Xy34rIaHzz6jqUuXXnFlLxx5GkXOU2MhLiqVZRsG/eoLyTrS+qubMlvqkLH7/A+d8rYQ/+dOBTdduXPNn2M4jeUDdshGAJr+BjJhu3xiP8CrvvFnpUMQHOP1vB3x6uH6r49DEPQpsC1zawCVdj6qJEbr/2Gr/6ptPXGFM5aVLXkldIIH+B1GU2z6SneT+5T9MiCkiGLs/syitrKq8tZkm8Nrj39VsRz4lkCf4sE2hBEG+f523rZzb5LVKRbPWoieJU40sDYtv/RtZoOvvtS9z/5Q+84cWKa3KTXy+pBaF3pMYpQbCQR3BMa+ME60hyrPGoFLvwwvsFwk60fbAHLlnm/DPMEUBM/TQr9dTAZhPJVsEwIOSSaM0VYRcQYy0YfTyizLYRzPxjmUEbsNlRC4XpFlBUUGjNZiKTl7f54hWTZTOlGfsQhkgkE4xe/2BKuxxgikzN/qDeY648XY8kLCZLWeN6sSzoi5NGd8ntcpPFdDEJ3ciis3vrz5v8hq6B98CurAdgen7EPw0HGQBiH7t0TPfHU2+5zFpDGSx5bZdXAQ60Awgjkw2jZKqIXiz6g1nXikFoRcL1yF6Gn//wU4f6XVFytXKionCsoXMY9BnEhD4ma+DqGsQlAraYQ1H+mDV56gbMQJa6MaXdr2IknLzPPSsm3YZMneCuIe33KUYO9a1j7pCuc/x1yKA/6IyC+kEnHIUW44wuVjEMCcJ8z0lp2ITcNomuY+DG6R9BA7ay+ZQBOOJOs+++wWVLfLxjZ6J8+vB1jjiKsCsrWL8VlDOYlOh/K8YpA6QMMwW+1/dErhjAIsRWU7WPg6JdVQKz2KSMw+gC6vFAXLR8crbjA1kG0uVUfwRPtKi3aDLut5uVv4uRBCv6jsVScRtl/kRJp+qmOVLlzp/rdH4SCKwGR32M2eS4Fmp1RSNY1NYdCrWIswmCDcYY+zhYv5kH+2qAMG35Jrr59KePxE71Av/b5q+y20LzqipWB+unFO18ie1hfGcO4LNVhfQLd1u8RhNir8u5KbxKGPX7bKxIEN30dvGPO1e6EPY7i43FUiNDZ5Km8e1g3MZ4zdkIAp+tCuuenJCqC5r+U7kPVnd3Pl8CvawjymxZ300jJLS+Pv34Mjkar1iq9U9vBtC1MA5V5LpAMQK/eahWQtoPpfKCXMgDZfylDkN56sZ+dZdI+vEzNk0MM3ahl5NEH0dXj9TlTYKSp8eL7cUqHc1wt2b0OhUvjT8+BXdMBplGRw0rFR73gOt2RcYFCegdXk8Z32FdHYghaFIMf/JSjn2SXLWG9Mojuiaks3fSu9tH7TZN8ljvyBgzrNCfjs1yZT8pfaSkjluynGIG0r/3Z9eXFb8+uL54+xRDUigwlqMGPmIROFGl4JYyjo3tcA+uqN1KMs6QqnDxEqoYDbuxxo0k3oeFHrgP9MMDTU1mskBRObqj27zBLIHtOTuH9Wfiswemc4XJK5QimBVPwRl9hR7JU5VP1Tu+8Hf9Jfj8wbis+D7nbK5mBWd3vk8TSb0ge5S5tLAJCMGovwBPcUYOPNaHxBAdst+e10vbggTsyVv7v4KFAvtZJuljzRWRhsBUQlRrhKaGJu+QZAlhhx+rSWuJY2hl2lsDfLwEbgqp/RDOnzRJ2t+i+IVZmPM+Rji8oG4a/1u1McNNFVkqVIJDp8FwvHognJl8zrOiw6DTUFQedmtvqr7yFZ2zCBO2ztobw5p8DoP0pZ73xZ8v8O1YBaaL3llUAMvg43VtFMin0eUCsHpIByJ+ZN61M8HzGEDyVIajPAdF+fTSofx4diA8bqWo7GGkaOYjV1gPUv3gnn2jqf6iYjJsEYfxCDamr0RlXADC2M1AqGf2nYQ8yMkx8T5qCUYjoeDykHHkrjwz106AG4w8TtYyP5GMQ4mwQnffxiImcJ4GkZ2Lnr7EylmKlNRNDJn3aDsKkj1VAD7T655Hi/mKZLEjeEsaqapVFnfgyGIYg6sazzNUjrLS0yFnHIRilxSmN+mxc1xc4MmhaixFI5eS5qVRnB2t1HVd+GJMbz80VbQkHT2UXP+QqTswDxZhSkd6znVrkClno7XGAx0GkCDUIn3qt8VRuC5lFCL5EqKcKnGVOSiNUspj5RsqmjMFEk29/i+UqmMIufVBd4QDXeNFqGSfavQBUIesfPYCRxQYc6Y4+S5Uvg3mVj3yvDlRfxwjsFUF8UUznB73Troq36AjpB68QlN5g9c9n6SYf56F+htEJQ3C2jaFzZCRCL5hd+OlfOIzBB9YCLzbFo3t0IeGpwr6f7hEOt66Yd7lClovwU2WL3yDIdBphpQzZKjBKrHlC5m1shWMelTkTRl90g/WEAuyIYHUP/Zj5EwZe+j3nArGt9Cm7KrxNrI1BGIT0wh39YFzmXtDAEKSVhmE77+yZaYpf2j4GInjV5rjBc4eAu2KF4+j+EqQBFWpEMSCKg9N4LjeJQ3YLLsEV7rAu+JswfaiQhyFIjZDyr9ga1uWBc3Y/TQK/piEIxSJtcthGpCw8McOXcikEDmbkCxUMUvhS2PubPzywYVDzVnvbX8roYwOQzgV6wWHRGIQEe69VQ1lBhMKnwT5QR5A6YMBy96H8MgRJRdgApLK7oUdxtXEIRmJ5N9wtnw6hAMrRg48BFJTBm7JM0Ow9UKpe2p3E1IH5n5iCGgEFVHB5UVzT+OOVQAwgpcjyBogCkStquRSbNA1GHAYftmrrE6t9Gv71E1m0dVDicxl/nj59cvGP508vnj9/opVC7ItFkUmBSWTscWW3HFZtHBz2jzoQdl2sYJEQDp9QhBDYzGcUX0jJ/W7FF6zCUHaLa/FnXrJyF6oMtKodfv8KdNTL/V6TJg8r9LZw6tklN++TzpTBpLJKY0I3oUlwAx4ibRKkHsNd07cUfkqM/pfBXphdw3uGbktbcRuv/Zm2lbljKnaLVwIC3hk7W2cYKTsB0yAR+sBrAu0X/oZep5XfaUTX8A5tk3YrnhKtdPYEdvF9Z4LmqKcirlvnoR5d6E4GjTLSG68TiPM7u7MEfp4EVkNQc+GWWe28u7LTlJA0xehLjqPJT7f9ngiPh1QXsvrd5wrWTwyomjLPTT2ye8WC4SrfY5b2hetnimjxkijM8dyERg5mxRCkBT35NLQmc0zq2C7/VkYgvqD61hM+vf3XBO8tq4P0xt9nAfHVH1ZYa1uIzwKpMQ279/0FMhXCBJOXcZovuTwYZ6wWE830M93KZI6DX+ET/oCX2UNhnIXrEHWAHi7PcuUgGbmUb3TijWhkxjnwwUU/j8MKYHq6aHxEIrRYWeTJoGgWh94+nzNBLjI+0oztobZ/MNnjbBCMPLzZfmCjT3yn8Xlptopossfk0GMr8mrcxCeqMfzwAo5xF6sMbNASJxiCetWBzy0xb9RV91XsirVySCL1M7Dro9TgAFBIlY/MCFI/Izotn9+GXIxCyadyVBYyO3wedl7yLOncBAs58P01yblXfs4bgdKKx86gCqbckNuW1kiUm/TBn2Q2KU1eSE8Z5F3D+zj1hcKREl3Y8SSolLAHUy07px25mAsV03XYo5CO6/RNFY6wN6pV1QtKI87eAIQ6urVr4tRn+rA6MOehMnfiSI0yBKnvs0WMraB8MRBD0BvtrIgRSP4bjMf6Sh4v0UXcq39En+7UegCdgD7AOIQuwHhND8W5p7qS4ZGUnBeWF+pGGhfy9I+e6V46YNZ7jatytvFOwG8aCgpvOAVnrugC0pCXz1nVWavhT2k+VkMctEFIhiD6a7Z66cwgbxOTTtAc6UrHa3BGEMdq8PEdrwryIdI6XkM6wSuDNIeybmC1IQYl627KDbdsoYJT6wiFMuOc3CIJoSglV9eK+ugn9p2XlOGMULFdGHxnGMgT71jShq5QyY9YTafjihivLl6dhlFN9Tx/NWwv6J8X/zUNQWooKKq1naaFAinFoYaFUuOHkmGZ441Gdd7vLkMQp99jtX6jw6FfaBXQyxcvL15oS9irN9kaxqqgmw9SgqLDgAYL9J27smqoh9/VKiAMQXdkFGH715cv92ZZKFHfrz47SHmbJ3O8KF8aPx2hBiCtTPLQcZIp5SJk8OW63u7uejhGsemqhDzcCOunzjRwFUgnk9Jy52NbGKo3UorxB1o4akBYvkaYGQDJDKbBCUufOQyaE/E58OxZWbWfPX928Z+/XV88e66flNrjxzogjVPwZS97IF68d7a1kqhTDj/0Gj/HXZfmIbCuNOntjOtIoK1Ykk6dC7vk1fmO+gwuLNhGVvnOHjjBTjmafwGCl5IZtP5V19zfRqHTyp8Fp9BvZfhrLHYxX8P7zulHq7O9yd+5xK+RkyBG+bcIpeTeGIke3gynN9IoesVbwwPhILDHOiDpHHssgD0sSNvufGB22Fl1Se6mkdSBk47SqMo8UiZshJrGAHxDoAd034B6RjlL4G+QgHaFX3xS39+3RFp6nrWz0G7+eamSvmS8iXIyNNTLSYz0xfl03PYnVq7wJAcrz/cQ8jNeqDzNemxAP4UvRlAMzDUMsMHmg8YhbAXBoPNO2+Rfv+elGB/S0CQPwxDGIMHZ/uFDYzUJ5OwQxkneQsJhyKJhA5DKzLiJcQeTP/EHH/pRqL1aSR1JIWHg+Bge5pQGeBuFgisU14BaxIUe4UjBchcpG4MozWML4pUhHCCY4kk+DOKYZVXQATE9xkeiw3gIZ45Fj8kZZ3iwjZ7PRHt1jw+GzVYQT+owBPGCTFvAHiqNFdUYhPiqkI1BWjFwXwYjDpi1AYhtJaLr8z/su0jFGYfm5RpV8UoABbpa+PwY2bUsFCg3KrW057SXICjddU2Me8AfQkuLyXWW1nT/jN+cTl5G2Q4ETklus4YlvC9lT4H0pt64weFOyZF4JNNsE8Y6uLjNGrrPvJS2TyoqjUE/O+6U4LxN4DjiMSiwznos/Vh5XYrTKhMw/9T+8Vmxxw6IfD1Qc61PGHI484sP7HBMBltBOSdMq4KYQ8lg/Fo64q12VfDF5dfoCYWZY/lMoNIvNgBpSkEPzZk/1UJVqHVR8+F7r0jdzbGCEDgM2nWN5RtGnJ97ZaGQBgwXf7uVP/QwnzRW4yVPX7vddzxzBK8Eklx4kf6HdlkMvS8WPGei6zgcHeGVg3wdTH2bXRVP6nwgjEAJZ7tYvsLM9jH9pE8eccwGRmXlu49FuJhVSMHSmGrEd5mjOl51dXouXT/7HenqrP6ShljXvjFEKfi4DZV3xWtyK47DukQ7hwCw8awqBMeZ24vgeUVQS/Ln+7+kIYiVLTYEufWp5aoR9QCJLsvzPMqGAYjC+n1ggCPFxaDlvfa0vsEQJCPQa1mx//ny1cWrF1oNpAOiX8sYxGdQOTeI1UN/1GCFr4LdlSHoDkYgHRJ4Vz/CF4J5mFDKk7L4IDuDq+YJRSaQHBcHpJ/kF29ot6hgXQWrCgXm3megwaSGQh6VNvgAlHOYgUKFpY0K18kx/LCMUdLp1UEusJZSh2PKsvQU1RNAYT4pf0cy11hFFm2s2tkbjyX76TWrgh5f/P7b84vftSLodxmDWO54KSPQpd54PXqc1UAPWN6sOvuWwZ+K8VBPALEpeEsr8HCcuqa+yqB/wnDomiUhgIKRhnP6jLi8jg4iRozszRf3wXyAmTsyiIO7c6HXTMxEyg70MG1ikTt4gy8nnsoLrcIcGnlHfxc1uS1xg5pMFRfYsesxesfw/maYqzBHAG7nPDCmWxmdcPpDP2hWjJlvGwJn5l7TdE+UkBYhrNOIRwjsKNLQaGNrvx5FBZdyQCNGubgdlQCX63GWZq6m5ywDTK52AbbMbq2jsyVvt1ZToZDhOjwKU8oabkTwVvgaZ6hzdmcJ/DwJYBz5KF1DO5+tkZjarNp7t3JzqIjjfoYQDl6w1ja+r89KJQadFTIGz6YOHaVu9F/oTZ0Yows08rZdGgV8AejfDpDmdJHSy6uPGrD4AxpaNc2BsBh9Xmvlz2u9EHvN5I63/UzwtDqIlUGcDcJk0OeHKM8naMhn2wdfUaU4j8Ok5+CBsmJfgfcYefzEV7p91w0J68fsaUgbfhP3+GYDFynochmyoY6GWoc6TL2DSaIokA7dSBa+hjwVNjmY7zwqYORnPOQCk85KZ9dAZcqO49U9fPYdY9AVZ374QGi9NGM7PVs+2BKiD2pc6hzFR4Jl5bTCrBpiBZAGWWwfW1cCud60KdpbtTkfXq2SGTthDDKv+BGGmKIironSSI2UHakLUnXKgodMQi6tF0MQZUZegTnbN17IO9tlZyp+Oyqf+9RsRNZcJ/9NZ60i2SdGcdgiWIqY7WsW6DqpwB6Hk+K2shIMcMhx5SeUKKRDqWfzN1MaQ/4O2PWdCRQ+kRzisucJkoLTzJ3jWDo4R9wGtYpiKAIv6Z+8/Faf1cTJZ/lIL/T2sD4zKAdGcw4QW0gxArFyUMZiXqSjN5hXKcy2MXQK/V/HC6mM3Fmv/lF5f9DPq/+7C4pf+Gseu9dRDfLEQQOGK6pwgtBCLJXuEKDOlzTnMi3gnUauOOcv2oZ1QmOPcuGJs49kBPr8UeV0f5NhRqyky6h0hTES00fRFWwRvS/DzhVngnlFEMds8OVl+Zo3+YvM0hv51Px9fXyHrwaypYy5lHSEXsZDG7ZEWj6GIPSAOBdvhEkbP5AsF/lywHMpv+uqBLdHI4BUrvITQ2yUPdutSQ9+jFPZ9uGOu50p0m038epZimS8nrt2XhG0CPMnB39ZQ1AOQKRlqqV257RSicRskBGcvaiyAdmow7lAfP3r7Y2UVlmuWfnzr3++vPiXDEH+UhiGIBSb9ruSj4MMdVKOGrsezjb+sBKoDUGsEGJYcTdvwlgNpDx++1V6IU26lRM9gI4K39AWnMZvlukEBCqWHuKUcUkSWe3w6Zi4oqp4FEWXM3AlJs4LyNc1yhBk5SVG4UQ9M7RUiIxFfipcxBDkAZNg6ASWJ7Iq6JEUGkoLSzYn3//27NnF778/9fawp091bhBLG5/wtquWRWrEwqogBi44FAFSESgwy4WUA4nN+iKTrrBlEaU3BhqVBu1IBGpxJEW+BWhPyFlSX5jC02NQ+eZ9ARW2m1ZnDZxakAJGHKFwNmGd9jU/+cA6kddMKK3lAOoxxoC3a1LgNau35Wn89pvOT/AHm0dvXhjqe9uDxVNsevBXiaPNnEBuXONJr6SMFt7M5Nsg8ORzpiVUKSYgSBpn7p+SuvU07qxD8o267TD3pXSc9tMtN1wNzgolN5UrKVv8pjJ9YxeyKRHuzA4Urhg17oAlNiWz8jEIdGb5a3qBrXE6fPbPEvjxElgNQTTtPLeaj+gFt/Rq7p5MKplogeSnly0dpwksfmN3r03vJEZo2zuCO3MofdWPCjsPvn8pBnzPgcQP6YxTWAXTRhx/6UszNj4J/YptYRoDvX6LIUgGIBmCXnubGCuCWAWUg6T5mIbPBBJBVg7wAoyJpEjrKYqPISj8eyJIwS4fbpKeOKMLYHXKhRVrw9pH3oRxlT+RzdW1U/Lw/aLLBTub4eSHSRxeBztMWixESqzBnHFBIE7tMJpk7OQVAAr3Vi/e5PssxSuNgzD+sAJAE79L/Zj4cUYIXxxjBdFjTfIw/nCe0D35HETNpNGTO1WXyZ3/FU5bypgpE8AaP4mrPLMyfqJCaRNpC11BpDYcZKmnRUG9CCo/QRfpEswHAOdNAzLuscvmuQkZ3cdN2wwVwZO704g3v+ktxc/AI4CsV3qV6HLIH5qN53tquoZ04uLTnpLP/Dj/ILLgdVBpYYOKQTRFkAU4rrJbWoXbdyAIR67FRLKGp6azYjevwFxc0T/AbfiaucLFnmOeOgkAjD5KAK9X7mSb594QpJ0VGIo5K4xtYXxtGWOQdlL4nFUMQppT8dGdG+kJby3TdMIGYnQBnKMf1Kppqf6pYglHUu56YcdwBZ3HXgva7ZB8OEtj6zsBeKeBh5N8K2/HC5qormT1/Vv8kUiqGASH1ZY+g42tYbaisYPCBXtaylmrxNERHPbuIzKYP7FyUKsC8/VlPr7D6qBeDSSf7WEyFHv7GIdIe1VQPkFvI7HklZfnvFyH1zIGFb/eLlbV7trj0342jnjYnWDB1nZm8QnmvnUs/8xp1D25NQ4NSSROYd8GEFqe+A5TH50RpNWRBzxX9rP3YyXwaxqC1Hpj0KC7dsudbZoGhzLzT30zb7v0JYx3GuSgvLQiCMs1e1pfaQkjhiCvCtK5QCixdz47CCUGbV75tCGojUAPvSKIg6IxELECyIdJyxDUh6ChF2j08CICNQDzk1x5Zo9yP+h76k5AJB3CWEIwiYFDIGoKMuA0PYdJc0J806i8KC24ymoglJniGH3kPKBxKDjm3gahMgbVwMeGIA49kzGIT8ljCMKa/ftTrQb6h4xB2hr29Km+JHZ1T2cFSZmNTyGyx1UDHBhShfDyNqvqUMBRB9eax0My4OdNnJn0ZQwKoEedEZR9BybiGgq5iaZMNgQ5M4hR3EgnVOKTgsIFtnUQJC0PpcSCIXVdaWEt0NPXSWOlAn4xjU+Dch13OI2ykj8GI70r0entd17iq9vH17S/MdxsugjVO/d4ew9GG1j4MIxKLgRO4qWDjtwTryudNtgD1tlwKh1vKSe86tpwBgZrepPtEhcy3FujOm9n6gyKO6hLycIYC/+Dd+enAGRWxQ88tbKQKB6DPPEm78FrPsJwBo/kaQe9te13Wug2VrjYw2ZqQp3efvrPHuscP0vgR0mgt4Z1eX6RUV2C/hGXPpX+GX2RHlFdVkiezG8UATm7nYdKroFRRP/AS5FVcAoqjJmX5wfOg+nWJfjK5u7f/IpxziXssYrP/mAlkMY9bAtjkscqII+PZAjC56wgYKwAiBFIZ4UIl68IjUOgNZT4xCRP5WU7RxuB1I8HDA7BgZmuVyaFSCmSap2yGoXAJ71k7boCQyapmOtIrCZjgCkh9ymGGyUPl1yKQgIPf/wqwEAOIIkOE2XclHGSV0u7DG0N0wLxHAjLZ6D5wqpWAnkVUK0IYlLHm399ZIPJ3mN+WjF0pR/b7dk6wmoBf5FMAyX0aiZ8qocCrgstSXDCMQSlftSFeiZl8k5IzPoaHww5Lg2Gf35OEBA4ZegeeXuY71Vo+6q08TyEltx49iR6Ekb+5mNhoHINhgbWQu5IMHWZI4KmHdQZS4hapl7JUZKYLDlbeDheH3E8WaxCkFPTS/rxvEhYmclfjG3xBnCKpUADPyUevy50jyOI7I6e8QSjTrRmeMMg1L91e9inj/rIjrZB+Ywg5lBaEeh5ko1AMgTJ8PNGusLzKq0IIsz5QRiCpFqiD+g2qgwtd/xs2HXrE4x5XWRJdRBU/PBokHVAxzudiuHKd6bQ7BSnFTz3PSnb68RuWivERYhG7qOu0gF+ua5VQXyAJ1Kc/Sg2a47W0E/92eekqh/zQpxzVi+lI/hSmA+Olk64RjdgKNYLdraOcgYrOoKzgjAGsYqQM4bYHuYVgxiLVer8pWbRpuKcf/Hrfu26J+w66dJtmTjJ4BWaYuWMXHAKgl68xghgxhxqnKZnXxe3M2HAF+GBx32vHwS4e5eaZ2a1I5Cz+5kS+DUNQWowvSKIpm+FipTUuGhw/LB5aGeXFRHKiMMMOdzsrbaHcbAZS51ZDeQVQf9PK4J8TtArp7H8mbdefEGDA6G9HUxGn3v3YgC6K5/zge7cyYogBj0csuiBlfwsj4YP+IQxmjteBjIJ+zo6n9EAuXeOWCoT1HGFWlGk9qYBxDAe4E5vjJHNtFnrQhdsZSZuFe/ykua4FRtp2QPLeUFW31JkbPNiSTOfkvdBZ08eXDx/psOinz3XYdEcHs0n5e9fPOVNmH7+lLwU2EPedEkEcGZJKOCBjuLmVhd8lACDHHjkaiePPHbWbAoJBj4KpGqfZEMVdKJBIww1wO0sBwFsFBPQypBBnjSV0xqRNP0mrImv1BLu68rTQmYEmwL1sGtNrIRt3lAcmjvIuhoxebtiTSvQee3CvgWvc3Ue4sVCJ/1lv8v/RgKuTuXxfZaMcr8PCaAHcN0e8O12vG/wil4Q+5oMnW0tdyuHPZ7EJJDnCH1Tm4hIc0+7/WwHgat4xTM0iveBB52qzuCyeVeak0tOxmuYIuapZWHiZsb0LAulOavyDDQK4Y1WOWiANPEoACAOv1tswoUpODlw7SeWPA0jT4fX9KbfsLN/lsCPlYDmP7U1LOWufdiDVDqM+kG31HSJfh7h45KrcZrSYR9JCtfuDVBgchRC6qc1KR+YIgp1aJPHvgL0aPdlX6Izu7+iW3IwNJM0vfHn/I8bDDs6BLpegvVqoFdvOTNREz3GTho38bWw9xpDMSHka6psG2GcRDGZQLbxB78MPE5D94kvLuaSOL9+qntEUGkKa6zEixTXzEKlol3T5G06TTG1DtQzDZdIXIWWcUhcJU4m81K5my98KXDzqYvzyvf4wGkZL+WlFPoRnJwPonmLV/U84BBo3uLXga9M4Hjrz4QPw5BXAOjz0d46pgkg28b40pgneToLJF8a0qYs1dfjI10QQWrNHSactDF+cv2AmknxHx9cuA88ObniQonAmu4kS944uofcBYf7Poj0eC4FfXP1M0WQzXNzg5HIcTz4bke54k3lHjwPjZL0YEdGx/HAGLWFeZgLBdMmOMvt8to34u5ywHsG+jusyAnSvgcuFxRiqwvvwduwN5AG99sqh20TC01agIGjjp0zraAJruU366gWf7hGAX/uXTJitR/bu3w2kCw6H9Tvc2i89IB0QVYDMZ+qlYMYkPWSnTPG3mnl0EcpUHSW50TiLf0dn50WalnMibgHcK1LDMipC9fwKcZK79FS7ZQwwgAcT5II+i95gSXPXAlUNJwUiSUt+QNJeL1Wk7EqsREI/qQ7vSrIL81VolcBUSLtll9WDXq7mKrK/AdD70P1c1YF8Tl56waMQLVS8EpHbXBw9DVGIhmGbCx+WKsIdeRGG4qh5S2iqk4kK50gFtAJyGOpJSKOCsTXz3UBYQppk8E4XfkVv2HtQ6PcnhzgJm9fF3xuJWJy2Eh1v8WUQ/KhdSnLuusCztn9VAn8koYgW431sM4y4aUlIiq1Lh7P/FBiPuBM/g0HREtxYcVGeXEGEAdDs4Txn//U+UAvXnlrGPvg+RIGByIyZLiQseeOjEF3MfzcfSxjEAYhGYDYJsZqIfVwlBx7YPGzMgiF1o27lBy8mZ4DCsI39P3vTrF0DSOBQWepRFDt7CshJBgu4aS+nDDVWEKmYBLu/A4hne50CacMcPlh/AHOj/XeGugofpe9rigy9U5OsreCwloty/VzfTXsdx0Y/fzZ1cVv+orY9fUDWbRl5dbgxwed+ZA0LXtWfjo3PMMvYbNdcXlJC0NKC//GkQZpXPBMgErpf4XvsIx66pK7JBI96RUhBosQjdEstMNFrqFFicTXkqlVlFh4WNOSq6/OvZLb0AlWBhtLObmBRQL4afpdzoG/KfMgdQL2pPfxifntoWNlf4XurHJk3feLQk9lRfY9IBzMHUE2nu+hsI7xVoV0e1rLDt0dUdEAcogX7PW6KbsS0n+Tu+nEX3MSDkdrOeSdspr4gZ+q3IKn4MTqUPtH8Mws3OHih6stbKUa3L4e0u6U+NBpWtuUc+wsgR8lgTYEdXlri5wT5E5Vix0I/RwgDeA0eXTLH71YgJkvtNKXeQZJlyn7Vp+NQkw6dLqUbf4xgaJQEa2nvccpvOH/pIkdEz3OTcwbfo2PWP2j8dFLfziDLWE6KJovhgnOCzLOBbrRmIpVAhiSqJl/YpSJHDxvjUDRAnMVUD/VPQoQY9QHGoQS9kpsxwRpXbPxydtukYepRPYZvzSOR3OKKA2jCYXJtew67mEPCdwA34Sd3+MipbkWxO/4VEhN0HIGCKul+RLYZR0GzYqfrPzJCqErjYs4J4g3/pwLciljkM8V0paR+xonsXL6rraQ6F/jLQw+qV/X0j5pYtMSVDgruuGIOywn/vJ8iCwId/4gjJorc9UlCUMmYPDndj7GXoKIn217rIw7bzWW7JIOosdpwnHdqMqBKHxbDihsAafw9uVYtHSwjVOZRWBb+gbJkUFvQdxTS64FSvvDyZvZkt5YDe94Mhy/rvci9dnjQWW2gKaNIKu1OIN1A0ZdBThJ1HMZ4WAE4kiNj/T7MgRxtAZGYc4HYlsYhmNWA+VAeRmJpCcwBLFdlH7feiEtTR/YkSGIMHM4fpzVxMt76oJhCh77B3Pdeu27AahOhWfmDy4tudUnT+JcU75utf7sRG+mdpgU93RlqALt5ZgNH8ZvHbi8UBcRcqAJsXjcuasfMM2fbLxRn6aPYwQaxiAZg7OFlB0WMvrody1j0JNhPNaWMeGwyhCDsT8prxfynk+Fe0s0fZXy44ZPgPrBR6VZFIINR5p+Fi/ARlTQaEt85CGNfIW+kgOn47StVrvG5153ugoExr3Pgf252+cVQUjw13C/pCEIlRbll+ZH48W5Aat10egYnnDYs3SXrNhRZFZcvPHCEKQtYV4RpE/G/98X+mrYv15fvHr52mnv/XYMQ5C6lQ6DvsuqoDof6L79fD3Mh0VLidkQxKBIGgzl6fJp1PBSvWS8yTOvuvg/fncW6uDk0UUSA26QU8EGrs6iio+BQMPsJz1Kb1JHbuQLBBx6o37qofNhAkxKDea9bYzaxBCEcrt/D2UWRWRrtg9D1IogGYJYFcTn459dP/KqoOunDy+eytL9SFZsH3imARIKzIYgFS89Zm57SLcqqQwLVbQcnPonlvDhHx+tlXsexWEQl6Q6NDIkdnBtaVgGUBZh3vDhSKOsdi5TEaDhoyEGCqaaGDQeLZ1152/TXQdhUP8RDqEAO/dIBBHgUj7BldlO3sOAH4MBb9e0FvKdtPFXvNtofg1vLQc6Fe/qdpl+kDhNSF3eKjQQFfc4q/BaT4wB2yA28WbbJzttqolDj39ftnDTcSGhSFDZ6CNr9iTOa9OH5rZ1CceMy6NOppM+vuGnSAUnEdI7vsc1KaNNvoDteZx4ncbgpWSxqSYReG9n6TieUNI7Nf7E3sI7diq9NUPjnf2zBH6sBFZDkLtBdRRWhLgPC9gtnkc9rlstPn3cg3OnpJ0XmiAzBGYcsNlXyc/C5DxxSAJPJVffDDvpeckfSt2/TU1ZTF3jE3hkBQ9fBGIb2Ee9wWKC5xdkfP3ntcZHmtDl0Fe+oqrJneBsCXujF2Q3ekGGAemDjUCsBgpvPbGj9nmrrye4CqNcT+y8Oii8uy5O66e8mHJ9OPMCqelXPvxHtwUHvDJ/yMcR3zuXqhT8/U85AOEUWFcCZDaSJMvZRh/FLczQYeLHeKhhvCDzhE8wJnk+60cTNcY8bPd6pPHRpc4A8iHRCrMlzOcEeaKnSd/1pbfXxxCkcZXOVPQ5IDIq5cDovPWndNdHcrMIVO0+CwQJYAhKO6PFCFt8JY8ShwwIx80WA720rr0kwckfZat0EXQ8jU5xSih6vn8zDvTUM6mygKFf8nR7nmnbUKe3v01NbC3vFF7jkGN9bhYbRZaKTml0Pbc0w/ugRzUkDzzDFtmstIKQOrv+lYfwKFLJplPcHHhTbEnqcveIEIQP+6HZJRMzi0oDNr5crPaVPk3fZk6jl9wYgGQI4jwwtoX2FlGvBpJByOcD1Xli73yOmF6ka3Uh+T7peA1alw+QVwt1S9N8il6jlq1uTqulv1NueOIlunUG9aEK8NcBYDgBuy5pvR0jsTIRJMyvZED7nQ64/lUVymj0AABAAElEQVRnHNdtemBj7laF0h78J13AzpQvNgShCMUJyiVkRQ1doV8ZgvIlsRiD+AIYuyoe1hlh2QImwxBnibF1VMbi3kL6xNvHODuIQ+azw4ItqHx9DOMzGhPWPK9SEBZaOK4TlxIPYuDXzm2AyALrNFRwp5eIOmnrV168Ksbp+7DjukDr6IogySrlSY7Cu9Rig/ns3BZ5jv1YCfyvMAStIqEh0UlRJqzSuZEx6EaDFvarsiLojd5svXnzwW+7bAiS8edfL99evPhXrQjStjC2kWH5ZkDD9i++FsYKoHv6ZPz4WtgFK4S0vFEDHMYG2Qurr2a4fAZb8DB7l8CjUw1+1SPZ+909c2KjQKaLcoICbmIB91+BZkowN73SoKLRiB4sqCzJKoqXDggOAx2MIYThJYage+qdsWhrwCMF/vBSVu0HHHaobWBaBfQMY5B+z55dyn9k/xrrttIfXaK4YgjyXlnxAK3Sm65VKyjY6yGigkmjpuZNqSA4TKqc2ASEi9+xwG670lZMjnoq4HivDlJseydKfiY4y7DiNZWCcV9vLfR4KvXXLTEfGUycItL52xcerIWRJdOSDnRlf8G6NbgjMXCB/xV6g8BXAgt9F7Pe7yXrOjhD6ty/uPYTC16FjVeCLuz2jtLby2DEFVAxvm+jOAVGuKhWXVbaXd7wTXOfkdRjsJFrE7iVvjFDa+V3DW+IKeK06gFVBaD1A9tM+5peBGRNB6fdvh7gbXtXY07fQ5wZPYfOEvjBElgNQRSdFh8mHKaTNFzBTHtmeuO3biLesGDpqq7hwbEBpEqL0V1EOyuLp1YbuZeOS7/fuOpqJgE16JfPWIWV0t4O5gmeDDua3GEIYhXQa03oXnLeB6ulvTJIn4FmSxifitZb/ht9Iv4Dn4s3HcY59GLVTqujvzCp+8KYCCnQd+GfsuPDRCaAkQIv2uIS56nP6gAP/m0IQmrQVrrriN9Tg72kKa3lRG1VkioeiK5+sAYn4oLyLJ0U+PMPsIVWAHsYVhTwjWF8pIo7TI0Ia+u8nr98SIPDYDECsTLoscY8D2UAGoe/YhjSmOiJzgG51mogxk3XTy69MoDtIhpOyRjEgdE5S4ThIYfMwoIKUVnIwBE/7j18VNTSKLj1quuePORr1yhr7WNEaoz2UxZ3I9lTbk+aG6v99dmzhjv9mP/98Khf1+xYSadhzkWD8L3c4k2pBb7yu4Y71xh+behRQlMqHu0Vzy634E3oz/pHeI88plyaA0gT7p5BxPH2ZSR2nxVNDpPnfB9WAL6XjrjRC3I+Ff8GfYABSL4/uqMzgV7VWWK8bEdXYDTCEITR2XMkCpHhh/6L/0U+xuO7mkOZV8nMBijKpTv5jzyEIx+TCMhMd7z9Qm4M+eTrX4Nny++0lj4pHW5sFV40FBBv+g8II5BSOBuoz15NgoCqYvoj66qkH2o+IfUoHSGtpyp7/iRDzkO/JMcAlC1ij7WK8LG2iLFa0J+Vxygk4xCrCH2gNNvJ+IIYv36xDoty3hYm33UQn10XmmPzRrjhrhoZcQ1MLNeCGe9YOtkE39BZ8yvcafZ1wR+wCkRPo7XKECQhU9x5RdBOmD8xqntE0/+1nJVEc7VriYBhGSUmW06MQFoR9EHKyV/AkKLijdcLrwjSYOfVWxmC3mhr2GsfeMaB0jfgK/MXGXn4PLyVFUYg/e4pzkqgL2wLU4//zMBHmtN77eXbqq2UKNP0nubJvtKAEnaq+KcKOBuFHFKqYAWWL7Wy3Aarq2R2GvkbNyHlz3+oUZgdSikhPGhGliCj5KzalFKDG58TRObEbQhSRhQZB5Zhkeazho816EFJPdW2sGca1Dx/rt+zxxe/aXXQtQxCV7J6ZyCkAxE9wCG/6IgWD094UdQBwvM3GHe6ByEkFpi6WC6OR4mTfKsDt2kUMjKgjsAJR0bAkuIyOk8RbzlyW0KGK/eSe9Uw+ZUPatOBB+8TMkLAlJbaED6CaBwIk1Y5EWSHAZHccdNoPOAjoYC3eNDZu5X2Pm2Nfyc83wWxfGzwtRbX4Smyw3rOtOOinTRmn6Nfrv3PsjVi7tIQp2FdZlOKf5z3QwGFv5XGGp4013oYCgC3MiPQKOE4mUJImx9Zi1QIgsIdgEAmYZNUI8ZHGqSNttsEhq/UIGzl6TyF1CQdtVYYuc+BswR+tARWQ1D19smC+tzaXLsLAktPqOfazGH8mYfQ7E0L2ujGfiILbeqfmdv4VgQqT+BB7QhJj0dELAfAaqW0Jmrv37OyRxM8vb1nOxhne7BVnq+nshLI5wSx7UNpN8J7x4oAbbGPIUm0VA4jg57ceYsHkzz/BLdBSDjC69VBjKnCHtxWuIw+1AA9qZMEUyHFYxSiDGQtfUA2/HJMGQxqgKjneVFjGSm1vM6h1PzI4Tym2RkFKxTnZzjgZ6pihk96vBjDENTb53tVEAYhDEEc+szWj0zyclbQI03wrvQVVX9G3quC9OJMq4GeyRCEMYjPzLNKAGMQYyqdQOAXZWwLY6w03awt7K2GICqVdgevyNxXhz2+0f1IvcELHeTregorej7onWr5i846rvF9Hg+LIl/e+pxbw1usSS/3ZJ8647fRmFgKwbDru4EeRPb0Us8DNDVdpZyo44q90jOtlYeVxia8UGgLcOe7rR6n0jrvEIA5WQrZisYrDDuP6kiQqtrUqUSHiSuAwZevg934fFXpCF6ms0rQqwZ5qZ6zgTAgYzzmSA2MQMyf2HIaQ7EaMPWkX/PTBIL5k9ewYTw2XL1HigJdwSqb6m1m3Py5NqfrBU6q75AijYvfYZBmuPsAMG6P+z9h0No1OcXRwTZReCVQuPL5QGLaKcyfyAwt8mvLKFKNIShjLMJ8Tp7jNR7ojbjPT9UB8RwczepBXqpjFGL1z/XVpc8UcxjDkPTFtXEwJguPracyOls9iB38nlMhi67Hxh+8UR+YLNdIHW+/4L59DessSjONU3mF10XYrwj31qrV6ZKL4CSdDUESwi/qflFDUJRU2t9shTTW7qwYZGII0mBHn/7g3B8MQXwWlU8evtCA57UOiH4hQ9ALDorWqqBXsmzf+CBEHY7GFi8ZeXjTdefOQ/UwVgBhDJKvpY1YtLF0f/6DN2AoPBmBpPVQZL0qiK5IA4cn4HbuOVG+VjnquVZCXOTKU6gzzPoFVopKiMGtfM5c2SprDx4dpVyhgr2WYXmhwJTsAROvDF02fsIMcO6oAigwzgliSxcn3z/Qkp5HWsrM2y+/4bqWMUjK6jd9Pv75b9cXv//GdrGcgM+SRva78mUNH4bI8wB+wlZkoBJxKDTz6Zi5T1w3GHg76uFqmfdAm17j2CcTZNrfJOpekEkIfqhTZ+iC43tFADlMl4e/0MRAZAxhXN0TZyZdIIc7nWgSm0ZyLUUVYNS0KwkcV7TNpAFVSOO177T1chve5G/kKPSNwEfiEljxBm9Legdvw+s0cNdw5bXMRHvKexvuIto/KYJGKL/ptb9LHlGnS/AbPPPJZbouNz7COFIZoEKg3cSf+efNjSAnHu1jK9yUkfIdXskQdsdIK3JOl1clNKlmX3EmAw3ek1rjXSMoT/wi5NHC5GlBWIKdK37qoRKUjTdDdgAtu6Ib6Pl6lsAPl8BqCOrC3SrdRtOHu3EbJKS03rRlT6QLoVt10wlm9QP3p4SrF7iz+skjwkzKbnP0R8oHi3L26Iw//MJKK6R9diJv+WuVz3tWA2lixwTvhbbKs9XjpbaIvX6nSZ7GSZwN9B4jkAZUbAljcscn4rPyR6VpLJRt8hoX8fRWmiSTn/HCec4NIpwnfJ6qCaPr4Bzf5p+KW36EKUN/riS+f7O+UMXlWYEUoK6YxzbE+SXd5ODOZUGrncJmCmE1TAFn1QWhWrAYgnjbT1xhXpipHFYE3eecH+3XwBjEJA8DkLdxaALHWUH+ShBv+DXJwwDEhzWe6QMbV3rTjxHpMeMpvrSqsRX0+jPyOScIRsIvQxbXQ1GHYVeBaOUwP58ZHWfMZSmaDpSIQQcXjzJwwVtXAHVO7gVy6OdT8A+vnd7+IUYgnd7+KTwzivxh2PfhOGbTaX/FWmFrGJwSQ9BVRstvj3eSHgktvhWJcPPc/kjflBomoAH4FC3ydjr+aKv7DEosPD+alYwPVsRH/4CYSCiQXxCYBqAzPupiIxAv0N9LR2AI0jwJ3eBto9IZr7Q66I1erKND/CXBMgKhZ9gSxso/WmYMQcyf1H54mS74XZbJYAiynsgLfG+3glflcR9z3ugTeE0N+lp+1SPpfaXMxVkIS1x0uR20cfsjaclXAoI8qgRHu5g/mc8ko/CJ0Sc4nO3FjVGNXAufFVQ1uqutYhiC7vMpeekJdAYv09EVNhjLUMxB8k84PLq2iD1lRZCO3PAOC+mPnk89Ut785VYzN1MxkVjJZKnNrKeAVK3QEwjrmyu3inZglV30NghE1gKWxBXdYV3c/iqhROtxHyAbIkvIkLyU8uvz0Ray5+BPkMAvaQiiP7pxlkC6MVf7cicl/YPM2T4fSJZpljWzFewlAx4prRd664Xx5yUrg2QMevlKn0lV2o2MRhxw5k/H08L5Mph/GIJycLRsuioDxSRDkPbAokC9FayXCyrfeANWvSSKohSI+YZbDXm2GmgokspWNQSzazlAzp9VRKFlDSC87mBRCEmjfCjkEs88eUAjuHj3g88DJ8LRelG/0MhCblYFcegZb6kYrHDmD1ZpDjy8Zr+7zgT6TVvD/vEfzy7+63dtE2OQo69mAOckfJZNsyWMHzT0b4dv/orPhGe6JSA2Gr+yxYNVZXAeWF1c01lAFQyiZdAZB7KhLSpxJFz9J5kAIf0g0XlGuAGHJR5AyHPU5W4P2o2zwZ/ljKIbT/4x2JL8J4IqZxb1J/J9X9QelH1fqt9AjbqfFOa3CGZz076hQFD+Sh5lW9nZ8+z4X6RrrvcEDUyhJtuFt9/pq39b+aWfVnRGIWd3lsBPlMBJQxA86TmwtvZ+lPPMbHesBZNnzde4q9/nUuQpnOcR6ZPyir3lwymNWAV5zGQjUI2JOAeRA13rzMRsl9eYSIYgxkK8NHvrr6rexAjEYbGMpzQ2YpLo45E9BlIBrI7WxM6+V/ykhjH8ZDwCO4xLbChS7TPJm099JMKEm0kiteGvn7PentTPXPtd96qcog7p0s9qv9TyWAYItW+BRM8wTwPS98zlQ8WMFn0PMsmvPOZddFwJxkd5248RqD8dzbYPDED3MARJJFkVpC0cirDKh68D+UBYfW2VCd21zlT8TYagp/IZGz3mgxps+XisySFnKTJZFB1/GWg0JNfU/Jh3ouItbU58Ks7UG5dnZleICrRcGUdZwsKSH5IKbR3x1RBkXKPsMbf5fkyMejUfa/jHlH6qFHMEO7e65vtWpD+ZyP0lSxe+LcPQAtGE00JoIymm5zDoHZo9xg2pC60I+qJtYHw8ByMQZ4hxeDxbSNk+KiOQXqpzntg7xV975aDOBZLS9KpBFYLBOH2eBqzGzNyJPq6X6Tbucv6qGmDKzTwq/MFY9EQ4DPPpve7lo6rBDBZX4n1NcCsLw2j3CTjUYYO6QxApAdlbwjEEoQeEwtxJf72yjn6bMJwgaQxEHZY0FKZf+0cf10pAzgV7bD2hLWIYjGtVEHqBLzOzjZSzV+fZQTIUaU71WMZjdJn7vYpojUp9uk7tCxQ8AnJwNNyK1MAFZtwl3ij2T8GV2GXY16XbHQmdxgtAwmNFkGKI67w1bCPlnxr5X2EIsoTUGN0pFaGTosymIYhBD6uAdCA0e+A10HmlAc+LlxiGFJZB6M0rrNwfpMSkvLyvVXtWNahBYXFOkB7x6kWsBJIvOGYRW7vVsvtTi/g4dXs15Xpywxc8kQCDFQsmQDnhjL7UAfsdWdI7g+lI4aAFds7F7GAeyAg1Oi5Ky+MkcStNZmUGfx482AgEnHJLwSmNMAs6UWqsDGKwkiWOsmh7xY8UlQxCv/3++OK//uP5xX/957OL32UMsiEIi7YUF3lsCEIRip+94jpU/Rm0UB3qUFLd1q6EaUksgj2UzDYbFbQKKrkQRj6R30Joydbp0DaeC/FlwYqcSW/8TWJHOr3v/iTYEG5AOQUGwYatPogLz7cWvOAuwZWay9VD3EKfTGxQgiPQKRqN3entN3zvk95uqQog36eCeeBgPAF2eAGE0HiTZ1mMR1CVsBa2gA7oJS1l7hMPaUTse7yiL695an+mrCHaoXqCyNDX3CzW5CU88cCvcukow8EjfZeetcIbYQsLdqd9zRc2bcR08XHtw7tSNuQ3kaCTw3hK0z/1CT0Sjvb2ke8cOEvg75bAUUNQtdE5lQgXgGm99DQc6bTgxHIFvm/Vx/pmG4LAH5rL/SRlpC+nJMo8cPQlAa3C5TM0+YwhR8t5OPiVz8W/5cs/Mga91e81L8T0cuxf+noq531wICwvxvx1IOFiBGJSyOGvecuv8RE1rS3yNgI5LFhN/mwIqmpTB7RQ0uAMKeCH+/T7xAn3H8rBhiDwLPfgQ2vGI6GkUI4KrTFMdGJKV2FyzpmshJ0JswicFIXiGTJWYKZVdBEk46XaHjYMQTIM5atAedN/r16SPZJx574meY/0xp8vBF2xpYPJnd72P9VLsmfXWjX9Gy/L9OUwVgVoNdAlK4L0wmyMlbRqgskjLvyKU0UJO+4k6kIgP9fEypd4y6drmXY5agwdiO+c74Fpd0JLaMUGYY03bvw8nzZEtggV+yoeFVV9vobX6e0fLUzATm//a3in0hvedDaP3k488FUXRPZ1sZzG2+QXIcd3BIHhBE5KAIRrquLm7dahNu1tXIrQZ/zhG82DPqnfc+7PG1YHsv3LRiDpCraEoTNkDMIgBPyd5lg3nB+mlUDKpjIwKmGWpK9rwO/nOX5+/uqy4BwYHUOQ8vAyfeGXJhzeRSKNP/EB7PQGtGA2GZW5XQslIgN60KqFAqzHU6ZcjBA2HPLWA/J9hEboe4GT+LSP3vD5QM4liswMYxQifRiCJA5WA+YAaM7+qU/Ka07VhiAMQM+ZQ8kgxNlBTzl7VdvFOC+IM8QQDVLmh8OnpsBhnTCOeId5NkzhkgjG4ogXDjUgXNEFacIBGm9J7fjwFSAMT3Hq0w2TbNIuBVPieUVQy+jn+7+sIYjtV3ZLy2wQHZV0vhrGkmYOf8ai/S8Zf17JCMS2MFYDvWA7GAqtVgO91flAnz+pq/7BUkZt+fLAhiWMMQKxVaxMIWUskjGI5Y8qj4aNHye4exzMqdE3j+DxZ7zGVaKf6snZV7Kn2zVk9YugkHgA7d1gY5fQJM2aOh14LMP0wMZLnMmQuuDfYXAj307IUWQxcRFmq9g9jEEijAX7klU/smb/poHN//z3s4v//u/fL/7jP57YOPSEPa76saUM4xHGIJShdOBUWgp3efDaNcswLWIadq9iC5yur/F3YlXyCdcEuCPklBzkeFY1PQPqgswC73ySPcEBX7FnuPO176Kax/ZHTWedJ4V9yLUUUD7BwawjC/ISd+EkTd5nvjWL8hilGVN8jGygV66DC1qTbhT74DUOgI63b6TdZaW9weM+yY3ONBP90FZqIGA1kZV20oPUrWxN34bdr0SqWwdP0OJgQVQ5+Zc4U+4pw0330+ApG/TGvZskRzspUMfbn5jbUNOzH0ltETaxltUGqIglvAGukpypBTVgYsw6hsQhz6Ew8TqukgnqMqgROBuCNvfiHPnxElgNQeiZtY/QvqcCoE87Jqxq107tXMCSn0H67Q7dIIyluDydZi4nu/zANqokxSeheGKAzaSOszuyJeyTvwbGBzTe6uMZr98yHmJFkIxB+nFANF9XfauPZ7zX+OmTJoWsesbvLWGsjKbSfkkGs+qvjA5yDhB1IByJwRK9u41Ec8rSlZCcHOTlGzLLn4VQccL8uXpBNs0YQKZsKHSsCDI20nOu4ZPH1Mo3bWj6+dJozTxjpH7+VNjjJraFafLyh94eetKnkaJIsDgqn3fOiukHfEFMHxt5pDGSt4dpgsfqIL6y+uzZtbaH6YwgTfA8huKMECZ3MgSNT0SzdFr895t/8yrWYNdi8M1v3clEE0HCO3Wun/ijvl1Pt0Hh8Xg33HmUXHIC0ylkH2kKEjcd/EN3qNu55860Qf478NZyoE+8/U3hS6TT21+SjgYbr/09UuQDlJsjz1VP/blGrpWOhxt4iR7EC3wAd75VttVGBYePLm973lH6D2nui5JR5i/y1b/RExiEbMzR3AbjMV8NfMMLc1YDeRWQVv+wWlDxV1oJhEGZ3xsZlHWUmOjwEp25RYzFPcr3S3RG+9ITecGe0X/OFkO/kIdf97VujfhTci0OfOphR6BRjrS3wioviFxnqMNbTGLmRrx12OSLR7c5wvQvqqY/5jVzpwYZ4R4GYwRCY+aIDeZCks59XqpLR7AiiJ+MQpfe/pUzxXi5jiHoqc5ffcqqIM2znklfXOsoDs4TeiDFgG6wflApIul6pW6KdFyAFTaG0kEZiXR317GR9/EFH5S+B2u4UTrNvi4lxtA3kmQjOOmHK4Luu05N6+z/PAn88oYgGtDaAN2g3DH5bDxf/9K2MC1/Zhn0P1/py2AYgGQIesEn4+st2OtXDHg0IJIh6I/PWe2DwQcFhhGIgQ5+fgyEBFcv8hJpxgIUKkfncRCGpBXc6MdDlBQQUBp41SVIB580+4TLdV6yjrQREEhh4QStlCcw8PlNzzjkTHFBIAdLnKPMMAyxMsjZfGHJXiswCHIoopAY6hluQw6Hn0lUHFzGAWbsdf/99+uL//mfZxf/pw1BWtqIEehaygxDkFcESVuhuHpVEAVS1l6JmWel2Bfb+MMRqbpaBlVnYBu8kUGB1jqddzy5S34nMypr51npEa48iAecvYJtkkjNuDQABYA7CA25DC6KhmVvoNOMaOIFGxmLuj3RDekwsS8ASqYBEmH9Br4hh5ciH+TD5O8CaT4gNspTuNgcZfjeLQjIQPVZB4DBFU7f58q8VnvQo7DCG1QL0few85agJjsDe4hw0lRoJitSuWbmA9QTSRu8GYE4OdpfUpCF/pJC+6o2PVEcCnvHaGxzhE7jhUjTNg8lpDkoDmUwj8v76zVF54zMKJizO0vgJ0pgNQTBxmzhRKLDmz3aPOnBST8EY+2V4M5WDea+TyQ3eBsVL7Tl0ZxClqwEKR9y7kIJBqYwkysOb/UZHmwLk4EnX/3J4a9M7DAAsTWMc4LeejsIW8c4TJoXa5ncfZLvCWQbfTRGskGIWheM8REMwkeMRlVLbxsjDKP9pFfQDphkZY8VK+P1j+kCJj2/hKeOjoyBRuOVECwxIEhnSo/7QTmMP3DkzrUAjkOD1PI1yItxiThjJcV5efaZ8dOnhFUGL7lQW/f50KwC/oIYL8tYEcTPL8t446+vqmpb2HN9UIOvrTLRu9QW+kt9WAODEYdGQ4PzFB9ocgi/Xo3txiOOVT6TTXOMoKmTa5EwnKcCGIDgGXhaHpKdZ2+IRlW7qDlfQJVAVrlOP3ZY9HwGBNf4Inz4bJ7p/24I7oq1byb1rXz+ZTwxRN7Ie2HLQlYiDDfjB3hK6DSydrj9hdwMUqBi3F/LO+38MIsg5iE5mbOQhR+6wQc0C+izv2TF4WwgDMcfMRzLIPySrV+v2QaWD+xwdthLbxPjSA3pCc5efbt8Ll6dMy3PrVZjYllHPadSG6wX7JaTdIZmBBYLPPVPQcsK32FXEiDOLVq+UpKYMEkbNxIL2lLBh0p+jtRlpjQ0NDJHSnEdBiMv0hWoeQR9n3q5b/qecD+gQf+M7y+HCZ8+6Jfp7K6w4VdfHNTkiK8rX/pLyzo8Wi/W+WoYXxZ8xhmsXhUkQ5BWEj7VWaxePSg9gN7h9tq0Jt9146IifdtTDYcBgwB7FTRn4BVoJiTka+vbIjtTTHBGCTVO07OvC/4aTlZ0KXB8Xy3O89awrUx/ZuyXNQSxf7UbmxuwW1camRWb+h+GoHcyUX/QSfZvtbTxn1r63G+9bBDS2UBZEs3bLx2eqMHRJ1YD1cofrwj6ktVAKDKvBvJqIRmHPCBiDy0tuxVTblW6vGClHICGPa76dQ8Munsqzb8r5I7bac6x7Wk8kINPKAog9JdMDq75MLIo7v/wwcDGHa8MPDZOOVz5ik8MP/Dtr2PUKiE4iNKTf++LBzCX+kT8pQxB//jHk4v/+T/PL/77v1gRdJX98LJks5SRJdMcaBZjkCSqyqK8KEK6TOy19OCBMH8E4xMe8ulKyzeO8bhUvIGN55RNRIiVue/JIG5kXZTeWaB3LNyolQwJyLn4zjMihQydRhwgIzlmNlwWhAQ3fzO9slRaxaiL84zUGTANoiu9mTxCKx41GG14VzbRLmsND0IVWNPW8Ck8cHBNOzHxUbD2Cw5aZxmBIas9kco06ljxU14T9s1UZNBdMjTOAiK4lgwK/ZUW3P4exxmCmPa9qdge+0ShKxGyDIE4UpeVs7WQDq/pa76uBVSph5wvW146dpzapO1BICQkU8IZXCGjctwjfmd3lsBPlEAbgmiJadPdhwVQ+1xbqJss4KUfZurduVMRYsl5vJeYQieRpcP4Fa1gyi/yDEWsrpXY6SDAF6t5/GJMB0T7yz4y8ORz8bzl1wRPE72XejnGqmm+GsZ2sfdsH9OYSIuqPd6BPpM7DD+MJmSm0Mrnfi3kKYj6cSZ+4PLMZmKXXk0fJ5zRBHWcLlJKXJIRw0ySIqOqwEAmX+dtn8Sucfsa2xhGnJ85Sk5ICuLtVlXOKEvwQYqAyeniioiiK4HP2On/s/cuT5YtS3rXrndlVWY9z7mXGULcOaYBMADMxBANxD+gxoQBcxrrkbpRSwNgJP6AFsja0EwYmgBDMZAxhWbSZmqBdZuJSZvoe+r9fvH9vs89VqyVO6vOu/Lcs1fmXhHh7uHh4SsiVoSveLA/kPhqRpCNQoJxoqr7N+o28pXfB2N4hpAGeDLysP/HNS8Ru7K7zWBOhiA2jGa5B/sH3ZCh6LrocpS8jEA1QJSa3T9CZg80S65sSouceSKdzyUTlW/6db4wsqFj9wYDsQ4aO5VvtOR0Cic6X/TF5LcuAlndwXGt2vYVRQLfhK7TWtLtNykCJr0FJ4jlI50pA0l2Jfscp9ADvw+30MRHcRh0+JugXXQBEdfsD2S5G1dBmJwWO8iBc8IL74qQMj/JpFgjWXnMtsYr6DTjGB2BLgTjppwqqNUUGju9Za9UVX42iX6ssZL3UsUQpC012CPokQxB7CP2nBPF2FBe7UX2DqM+qx2g/lNw+Yhu40/gNkh2GOHkz+ykyICMqMyylkannoGwjStCQ/qWWIRaF42JiwKVZAJuZ8bzmeBQRNWWJiRmTd2PfKRAKxNBqZfoWvHKKJM0kFw01TC79dPHc8Y6+GMIkrY0hmIp6FV9Vcfwy15htBWcIsbyMPYUOznWWMozg7RfkE5ovq09WWkzOIDnkhgyuwgNh/eSR7Jq2ZDfMspFbAQENl8R2vSAjRaMPFshMy0gcAXbx65xkPQzHekKabZC4EabtCt6ywhwpIyRl8P1+TVwLg1BbNPH1MN9BS8dDzVo8nDKBWvgX2GtfvXKx8Q/9Ewgffl6pI6PpzfKGMSMIDVmHB2PIShfuOjYqIZ5JhCdm6v64br6xpUMy0kYyEMnKYU7RRsJW8oU9X2NU8OoxFA5irmNAB7BqSSFdrhupypLpwveHOUiXfyZFk2qbQiSX1+1Ymxp+kQ1a9dgGjTlzoYg3GrMtAM+nZ/rqrQ3bsQQxL5Av/zF7TEj6IYaMRqwm8faI0i0vBu8PEy1nfWtdJzIGJUeGZ2mJY7UCUv+ynvjTYi4Ba/sEWmBybu+On8V6ZRCK/I60kdC8BsSfYTuLNQStyVbIGfFafiWcubQ/qZtt+OchW86XNH6BVb+GTX7Yfk12e2l+67xZ1nwb/nN4bP8Wx5zmDhc2zw2PNif2H2bmW8mvmP7jV7xJnatlgFqwBlJuDNBw61rkAIcgTMiHsAHDfzAGoghiERGaR7Fkk7/fHWRbeh4324aJPCh4b7wXXjlPT/CZ5FVbDjQTHcH26/rglkmvW/5YPVaOz1jBKJPxBf8J/rC/0w/nxKGIUhLw3pW0EvRvaT/9IJlYZgXePvTNyJXvMTpC/FxrGC1d6IHdKYRvQTy29wuPBC4tUKgNRFtAAFmI5AED1T3lZ7pJZ11RZfpT+Hf/hLP/QwpzKmbN2l1ehvepUyczARKnwk/fZb37zUbSKeLfJDr3Eo8jDXMktYEIH0s05IPGXOYCX1VBh42d2U/RY6RP5EB6I4MQR7gyQB0ZGMQy0GuxRDEUfLaSJbNZOkn0c9CZhuDSszkgUB6pslzwviRCdE9mxtXf55tVTolPvo1H3mh5TLcBSrhwAwVubg24YJ2nBnc9aONOBPpKW94zrFPkRiwnw65Om78a7rIDQOyPcuzpnMSvoVuCe/3Sb+zLiTCktIcA9ki1wLdT7nQnYXvfMKpeQbGk0bu5crT5+lWKRhacsnQO9fjJT3Md5Rl/TgtGWMQ22l4RtDbd5rxQ9ugH4fqyEj8xHsDaX9VwdhDjOPlaVdsCNKyMIw+7P+TI+LTVtgwRLvh8ZOMkG4/qMsaT1lotSPKBs/GslY7oWB07Ewlc53bzic0ufCMwAQrr51FQfh4fmhniYZmotmOZa6dSJE6OIyrRUmbgoFVDHBplOHtFL1XEHwZNxWcdoKfZgTRTlxmiZi+kHtWEO0Es4IwGmuG4LE3jGZWkAxCmgnELMI7+jGL0Hu1SrUYnRlLzYYgp+186ibZLVuJ6+pN5qYLsTurBhcDkzWziX6mAb1hN8KG64Y70nVYusBV5BjXoQnshsaULhrb9A7hH10D59oQ1NrowkIBpjHBSst615evaaBYFoah59XugYxAjx7nVAw3bJreSKPGTvgYgpgR9KZm/KSTQyMWYxAGoayFpzGjE8SPQkuFpwqklhimG7Lg7/spl9Lf6EQNC8AOz8CQcgdaMR3yi0jQNPRNkbi50+jIx78BxJ5+fNUiXB0bo2DDxWbZctxQ0o3zMamiLmMQcK91lTX7mg1Bmtqsxuvu3Ru7X36pzaK1NOwLzQi6oUbsRGtab9zEel2NldRIo4UVm+VhKCyGoKRnkNPmVREpkK18dn2bsiJyX00Dynm2RwFcX+1x1zSgLkRFsddpGreULeGGsmkAN51JO03BBw2wqeUF7jhE1kW83PAsuEFnghXJOv4Z/CzKJE+4L3fYGi2Py2mls1DE13TtbvGEOyr8zqJrGuibrv24fW3pgO/juaXr8DbL++Af47eN3+m3fD9Zd1Z6Z6Jh20xHadtiOod5JqZS1BE70Zr5ynVxpuGua5DSIByugwY+owZmQ1BKo+54VOB5984XQSANVcuu8p/3L7DlHe1Wf1A2jhrgYclSFQQxMC73xiXSgDcYvKtSA0RHNXoreV/VbKAX2v+HY6C954cOyXj87Jk+hvGFX7Om2Sxa/aJ8RNOm0jpFFR3Qz/FPAzi+5HzgJFX1gTwjSLOAMBLRm4emDUBtFEImtMDFGzdtRcKLtpawq72Umfc+sQonJ3zco4GtL7CLboE2Jv48g4KJePQz5O9nCEf4+OroU9gDVAS3cukLiav6THxAe6fB8u6DNkeJFqQe9R6lDoxB3kNRCdLv8fHwLP3SMnqOhz6+qa/62ieIY+SP9dWf2UA5LvqqZw9x0hhGpCsyCNFXwphk2fVO5rXcxQ+5rfnuU1rLCJ+n1roxHeWWPw0882pPvqfcE7FUqBgkkgfmeKTUOvuUQWUfXZifvkM789tSNL7dLZ7wnN6ajpxzKe+tNELoosLbtBeyRa41TzNU/PB12S/dGlg6M1XTSHety8AJh89CF1jKLFBHljsRAiJoVD9dnusEJqQHjBgNFxoN4Nily09x1mjJH84ZM2VJmLbUUL1/K0Pwa42h2DyeWUCP2FqjZgZx6M5TtRu4L1+KXsagl2oo2B+ITbIu1CmCWRLGGCof0OPSMi4GoMhUhqAI5+y1Cp1zKzqyJ5cQ6rL85Uksg7/ODZbN0a6fX2I2PJxLxxZEENIU7VxmMGQQJ8s1qV/UE4WJUxtG9wd46qr9QrpVlYsRhxUWfCjHEHSVDebVTrBfGMvDTurksBMtD7ulmUC3ZEC+o1lBzBZi1mDirWcFWR6SxyM57HZYLlfr2AHopgv6KctiMCEnb9NVMhNmEx+MGFJEzFh++LPMdfz5HSL9CHFDOjgjSTgdrh9RAz8JQ9CsDxo2DEHMCIohiJMx1Gip8/PooQxBTH2m08OJYVi35T7TenimNr7QF7McB6/ayNcuW605Ph7rthq33RVxLpyqsGq7whRV/EhBA6fCrYKNdV1I/utSq2saGoGAjONW4XI6woKYICNyReIFHhkmIjNCFpgv6TWFZ/YMyVjfDhldNDnyw5PL68iN082GItHICBS/JoF6iqM0IpV4XSsnYrBHkDaL/uWXOj6ePYK0TMy73LM0TJ0fOkQ0eG701FiqDfMyMRoD9IJGucjVIglwIWltgeII7UYWAH7cjis6hwM0ZkIPujTQCjbD4pII28iBfj/35O2jvJDJD+ajVJHddMgL3zOub8xv4mP9TGG8n0huQ/3jBM+SCTjXrPaPqCrEup/FbxAUzRz+mfhXRXPS5baYDZX3Myj9bOm6rK/IGPUcroMGPqMGZkNQizFKpTz9rjRMt/QAQtnvMBqeEaeYEN7CQKVeBXOqjlTcU47I6/Xt+PDgR3wGHHhsCPISDvV1bAjSjCDNhMbo436QDsygX/TgsQxBmin96hX7CbG5tPYKgZmmozBs8YcxlsnzgawMQjkdLP0QBoIZ3shVNBG52cUoxAXI8q1yHxy0XO65SOYsDzNo3DJ7oOkHuDxOUP4auJVBBAnA4EYnemrSi5+dWOWZEdrwbXbEpuvDn0fO/tToUTQnBr3TjKD0i95JZs2ZEhsMNyyZd39H/is+IvqCDTycIMbG0McyAN1muYdmBvGx7JiZQhroMYP6SINADEEsESEufSw+mF20MSiCKVgSD407bSTlSj8PX8qfi0Lj9Ow6//AJp8UXsoQHVhnrZ+ooZ9z2GUz2kX49OkkpMeZB9z5eaxhyj4cnf+djhq1jfNPQInvxLOdUGbIoe9I9JaIAIpspI3XuYMB1ui7TjVrFSk7QGeCZX+sB2LIUC78MQXqw77TM8Y12bOaQndcyGr/WsrBXGIJkIOZgnUePH/uwHZaI0UYAf6aP58wezH6syKhyZUOQxk1y+aCu2qD2glomVx+XkSMlFn9mFOL6khzI52o2npsAzlBIck/OUh6BOFdFEFwF1k7prFXXLvHtV1RcJOyrfXa5lYx2FaZpG3zUZ7lI3TKMG3xFJDdjG+ZUKljw7BeUj+NeJkY9V0W/qvaW5aE2BqldyElhaiM0G4g2g5mEd2VEZq+xK1dFL3VrEpHHVZhP2HrD6VQm8HO1Ggk7P4bWrYmKzrotmGknfEeD30zX8HbnNPpZMdI0KyHj1zO3TnEjF21V9ghqTgf3c2rgXBuCulB3AaPQtSEI6/aL1zIC0VBpWRiGoIcPnmYNfBmCnnhzRM0GkhHIO9/bEKQaxBcuOjsYffgCJgMQhqEdX8FsHErjlo5QXqg00BRvZHBh1o3uQv6rkSHQNUB0BAim6TPC0L4lf4GHbkPTCmjXEaHRL/+GIFNiWjr5camCumpGEBXPazNNSWRk018yE8WSI2+MqLh6aVy4kI7PyhAk6/U9HYv6C2YEfXlHhiB1cjQb6KS+dmniUL6UiT/GIPVx3MEhC8i1NgQhYHRkFwMPGZGsOPycFbKjy/my38xsKDJN0H4ujj8iFaOKHYZzjIoIAnAXtAbvc0ngB6FruSqzpD3S+oR8g26Os0f4mW5GA58vgpMYM2rlP490s0yzfyW4AjNu9u+j28J+w8N+9PPzL/+q+EhnXQ1QX+pWFLOia11NTE0P/GAIau0c3M+kgdkQxJso7+oqzirIXVZxu5lcYKkYHd5mgZ7D3JCmCuyhXpOt2RSu6xr9H9rmDjM7BcEY6PkYaC3leKH+zlN95X9chqDsm8jAjsGevvprhjQzAl4zINS6sHf+ys8ogwEdyz70QQxDEF/91Ud6502gpRvc8s8DORuBkMn6i/iW095tftUvIAuSmeq/xZIXspgr+qefRX5nTPDqp5hHxZDfTQouBODET45+3HVBKlgU2K4x7gPZIKG+D707aDAEvdWG0Z4tvXsrvjEEsfyDpVyeFaTEcDkNyF/6r7BP0CV/5T/RPkE3Zfzhx5HQbBqNn32CrusY+WuKw4wglpkhlmcOWOZFm+SlB5bQkAk71cezXiQrsMCVW3nQdOiJgwaCtQ4A+aoYELYa5Uff+642VICb/Vvaj+Fm2v10yLQWYD/dzCn+75/OxSDytH46WeusgehvLXOT+SFQlgSIfAOz8gRX/XYwfjSnec7Jrhg4kFri0qD+NB/MKcscF8/R7a8xBGlzeGb4YAhi7MRJyw+1kuIhRmIZhGwEEow9xjgpDKMxhiANn5RFjJZXZS++orxgMFZbIVjGTYylWDJKL9+l1jjqeuQhLxkBoKpTOSNjfXnA0iptSo+4RJFwUzcWfTWsXdhFIzNOMfTfNLZhB5TUS7g8sU5N9JIvcbT4kqzaQkTO9JO8yWWkwUgLzO2E8Oz11RtHexmpphReUaNBe8HyL4zDtA8nzAZiWdgd/W4fy3iMIYgZRBpLaWnZFQ2qMELvMwRZtsrU3jJSuGSy7gWz1Bv8iscG1zyS24S6+I8SXDpljDn+gOmHrg57BLUWP797fg1B0k2XvS5gqIu9gyhoNGov1Ii9YO8fDEHPZQh6SGOmI+TV4Xn4sL+EabNE1rlqqvQLNjurzaK9DMxGIDo/dHhiBHLjZgMRxiEaOF6mksQWb4Up1C7gOgzPrm+ilUvjVRdRPn51o1L5HBFURUjOVQUOMKLa5ApZYE7TeLBuakWaDkxwgedzovCDCRzppiY/yZAaWelUk8wVhIe3hVNDJmOQGr1rmpvI0i+Wht2RIeiXv7iz++KL2944minPNGJMe8YQxDTGnhF02dOokxukpiNF6skRkFxALijjC8TadzaApfHAUxHkNK11MsEXIqVloqJsHePOhcpEE4PGt7skKX5T3Nk/0+CfcbP/TLqScc6gaVt2BayEKf0tL8KdVrv7aLZ0Z9GYTjdUgxiTikCtrsa3OyNn2OyfafDPuNn/MbqP4ZpHu1vaDje+3YbjAvuZXqOK7Hvu0ksXx4+pp4uhVasIQ514QB6ugwY+owZmQxBijBJZZdNOlX/8LrYlb7/Dlrdzc0gEhkLLJapRoUYqQRM0bgNfIhsNV8j8dZVOgvws3cb6wdd+Nn5+JiPPM30UY0lHlsdrv0TtDcQysUccHf/oqftDr3XCGAM7je9sCPJSD6wZ9IXqg9juIn0ilnTwQ371laSBzDZY5qPQYbFc4JBxI3eCS97oeSA+A5rTV3iAWvHZcA4u7QmHdqBrPytFzB5EFQZef9u0nLwYmVcUq4D6dwyePQVLroxA72UMes+sIAZ3bJfbAzwlisrc15F7RYM79vy5KqsOG0cf6wPZTR8HfSQDUI6D5lhoZgWxUSx9KY6Hvqrj573/h54lm8K2XhhEcqERhs8ufwMGhr5U8P6gJ78NYUQTMX8ug1ZMKJPZWfHxr+4K8IxjmIgMxN5ejW93i+9w49tteFxSrnxKTsr2fjqoQ9v4dsMn9xk2+2ca/Kgk9fHj+QxdxUa2LaMON2G7DZ/cPJY5jxPS3kXXZlPPnKK5XApYiEjCPej2wR+YwnIZL1GWMQT1jKDXGge9es2WGpo9KENPDEGP1TbIECQDEOMn7y+mMRWrKV5raSTtBZtLv9fhOpevXJWNWMagC1clC21Cfip9TjeGoBiEImxkRSZqkCv/kHvJWfI1hcUteUkOnSEytbryTGhPksqCXFINbBXmWaouJbYwE1t7hXc7q6jUIxPoofiPh5P/chWDNsHciN1clVtVQMY8tEnU64vac5W6fkU/NpunzeAAHsZOJ2wardlAbBR9V0agu3dOfIqYTybU0oqrUjdHyffG0UjFj2u45XH5QSzhnJ+ZCH9dpsMvwkHXyNntBCbYlp5nZT6NkIs39cymdQIOk+7RpcNm0ZM6P6v33BqCeO36JUZBQkVVuGIIinWbo0+fs3ZVu96vDEHu8NTSMCzbLAvDEKQpjjYEsSwMY4+Xh5FKjEDuAHlmUDpEbHhmQ5DfrnwpU1WncKthxRA1DEHI1y9lSrguXt5cJbYrpGuJobmFVPSjkuEZARM5zfjMy9iRRvjkTkr9w5DT/pZArmAdP8vCFDJYcOeHeCwNk/ZxladLF2UIkor4csUO9jRaGIIyI0iGIG0cfaQlYaxxZbNor5nXlGmfFia9XcEQpEYPfZB2DEGR2LIIyh8XhiC7vot2Er1AizFLAKgTo7HtThFXBAqswk3/OV0EQt52t7JsBe68bem+Q7jK03fgcH6itrq+LzU1v/OTwx9FEjcfndJHdOmS+3V0ZB7V/jRft6sdOLgHDfz4GjjTEIQoahe7aNvVDXeBpWJ0ONITChyDR/uJlTrlGhPS6d5DkgW0cCVe10c61fQ7KgkPNPjijrGCZRzPWM6hARwDuUfsmcigToYgTgDi6PiH8nOKGMtD3mgm0BvNCGJwh0XjIl98bPyp/pD8LPnogZ0NQXztr/d0+j+Rc5Yx5glysuQBeRGbiwE6F9V/ojCM25S9BdYKaC6OmPYEcejFZf8O3PAlHf8qleVpViLmjmARrgcsKJg+no1B6he984bR9I20YbQNMRiDlKbSIQ8+EUh+Nn2+wrIPLfu6phkTPiWM2UD6qs8eiiwR44jomxwLzZ5BzArSSazXZBDi4xk/vvQz0Ov+EukwfBY4aSqci7y3puVXHhqFC4fkvekD7VAPxq0poRI390EzOC6Qz+tDvi5F30WSLY853+A6PPmr/DXm7NTP5r3FLOnAbcbKf2ZCM66JiEsJWS7XTVWMuDLiaOD0VmWa+s6yMGYCsb/qMx0dz/YZLBl9LEMQm8l7X1W1EVlJ8dobTDNz8O0bpSdD8SUZgS5duabyjyEoY6kYg2jjaC/ITdoNskXJREL2JM3STwIFw9tXZyfUFYeWkYt7/wzwzVHKOLpA4+sUmu063HUHbFEkIUceRiDBqrkrqtQrAs2XQQm8/TM8cvZYx5tFgxcu9RtjUNoK9gvyyWHMFmRGkNqJWxiB/JMhSIbk65o1JNubxmBlQKKdELO5/WxZ0l4k/1N2AuDehA2psGm3uKbZF69wnYbdCrQfN36eYf0ZiK4Op4aVCs+F85MwBKGpLlyLIYilYcwGkiFIX8BsCNLXrgfMCOJ0DO+Cr/2C2CNIHSSmS489gjD22IrN1y/suExzrE4QnSJ1iJgZhKHogjpI6bgITw9DL4QcX09HYSrceimPF69KORWyKwHyUwNdz3ybwvY2cHYnf3vnWixZ3LC1YkSTjgwNHJ0WI+SPGxZpjEoSNdVE0r9abjoVGLdyTCqzgtTZ0e+iDEE0XkxhZA+gI52GcefO0e5LzQbyjCCtZb1xrK9fbsh0ahibH7pDI8u1DEDsmu/pzkoIGUgnsuDPhTwxAgXiu+TCbX+R0uaOa+BNJHDjILJfN+NCACjP0sjBZ+XhwVkp5a6QU2BFB9zci2Di33QIMp7JxKe9LjCKd2ayILiKd9MHuLlPTCbvisjwQlrGFTaBjtvuHhKDGt/u90V3Fp+Gfyq9pvs67lm8gP8Mr1VRnYrzrIqhMjwjMFPE76JK71CXi1rzoydzuA4a+Iwa2GsIqkLa70nEc0nVDbdLbfx5r3WRBlYlvYZmhAJd1SlBF07qRyxkq3dUG13c1xANZB/Yp0cR3N+whUCDPC35YFDHjCCWdjzWJtEs83jIBrCPtAeIDET0ix5o2Tx9pbfiwZIwDELe50P9nwtM5+0ZQeoDsezjgtxe6uFlYQzmEL0HmXgJk8eWr1yDh7Yqjwr36yYDGXTDFS74eoiGnyt6o2+SMLTmoajEjj8DIwCZEUQ68xOE1tSJJF49iyb9JoTOL7qWX8Y1+kbv1Q9iiVhmTGemNPsn0h1UF0d9JA2D1ecZgzt1gK5Kl0eaKX1TBh+MPsc3jnSUvH43WfqhY6E1A4DTgm5oZhDLyOg3aXKAjUEsI0HLkVZDannsJ63qALnvZ51JTlzJPujQiZ5P9ExMLspMXMPlRz+Z+WMC07QvbsddQ+dQeJjxDD7l/xRd49s9xaAAjW/329Et8oZPc2n46XwD6TpXiuxIYCZ/eR0B/8KrqQaq0Y2oqN3J5TnxzJoDNXUEivMSFapQAksZbkOQ2gf17flR39lUnjETB+xgFGZPoK8ePdKMoOyx+lRtxzONl3JimE5a1qCL2YNvFZeP5ZevXJcx6JrGBT0jiIN2KJEpte99MnPKlqsUcqnd4HKpptw5VLfO4AwTRcorcZyjws7+yvE8KBg8uv4ASGqdTFJvCYA2ZpBWjEUGuIRKd8lP3U+yzVuuAPDOD7KMdQhTN6m7NgrJZSkoM4I4XZmP7BiBaCdu0T5ob6B7d295RhCG42syMGMEuqaZgyL1eMqGIMkA3y4jls83pNWFf84m/hkPDY9FcJMJN0cB7avg+JtdEEt4wOXRa8ULU9KOSx+CgdcIM89UAlO2DzOCWouf3/3pGIK6MFHQVKx8ahgNmo1BLA3TOleWhskY9LimQD/SZtE2BGl6I0vDnntpGNWyZwQxpZEazTIwDD3pCNkI1H4bhSjZfDGTjVel+319LZITv58jlT4vXwr5UuFK8AVgal5AyxU/8WlGcrWr0IohVA0LZaoZ6egX7ZQ/eLhyJR5ueNCIGEVUjEB0duj0eJ8gOjzsERRDEJua+QuXOi53dGrYF/cxBN3yfkE3NBOItaw3b7LDfTVUYh7rd8LVfZS20zhaIMsUWb6WIUhyTlqxfw43z5EpAJ3pRu6P0Ng9LhGivz3ICTQz3kfffLbuxGLlbboGNv/m3fh2m+5buGbd/L9F/G2UryvS56Lbyvux8Peolo8lc95w3QmzXF3kPibkp/RUDFdkboA+xvSAO2jgh9XAXkNQJ6n3rsurbnRmCRA2rNy8W4Hw5o7b73D62LkylBmvEd7no07NuAYmBfgFkgEJVYhwG4JIDlb8WPaBgYcZQZ79ow9gHJzx0AdoaG8gZkarP2RXS+X5oPb2rTaZfsenGRmB2A9IU/VzGpBe4oTpA2EQ8gBPLkYgfx6XXIpPDwG9zIabISOC+kpeOjS7GFH2XUQtu3Ghgejnf/xKVwmTby76Uq2H1ak+xpmCG7HKjR5ZRR8f+g1/Z0wJuT/kfp76ml4exixpaJRrR2Rwx4xpM9bgjA9e7Puhfo9mBbE0jH2AmC19XR/POB76tpZ8nGg2NacCsRSE/tRNzbC+rj1CcjJQPpoxI4jZ0M4j2ievSUZ9UGTmApaf/cimq+nsQiy4Yxvd+Y8bjaA7OEHQ8OaEex4v5Iy8PHc/u0+IGYNPdJS42wjJe/htcSSXuIuGZn013zneQgl0H8VMvfhFOUXtFmDwmHBLHHyNiMvHap67N3dXOWZD+Lda4sXx8RiCnstYzKbyT7QP0GN9PH+gcdPDh0/UdmjGoGYTPtd4CUPQK46Z1+IAlpDSVugzr2cDMSMohqArqv+0CzEC0VaQpg1DjI8Ug3GSLQ4oVxezgvC5XMotsCHkI2VR91Jah0VQV8o96MzQW+DtwyWNWX9JHUyNQRpQbRqYflDwdrolg3G6JQp1piGhdDqC4YKy6zDLwtKCIitt3iUtD8PY65MGBWApKQbhG545qA3mmQ1098TGoNsyBF2X9efaNQxBbNGhPYKISxvBkLXkI02ulovwRnTjKwPxc++I8pp+MAtJzQAAQABJREFUCi9Ea7oZ3mnY1Q2XH3LFL32Uf2UIEs1hj6BZk5/Xfy4NQbQbLEyar24U6CBgCNIHMDVSMgS9yawgDEGP2Cya6Y1PZQzSHkGPcH1qmIxANgTl1DAMP6ma6ui4JmQqdE7KUGfIRiDVMmYKMSOIP3/+Aab01Zhm9gxNhf5odNXaQeOKqJK/1CeqwxJq/wwBxh9XNyMOdDyjOkZRdo2vWF3t0npizCHdXN04ESKaO05C2y8XUhuB1OHJlGgW5mkaND/lBSs20xc55pAvXLfVmfniC/YIOtndl1GIL1831Nk5OVn2CKJT5EZPKnOjpZwhY14XnRdyiz/5p8HwJddQbvgrjJzOtty+Rr+oAXbnCLBo7RZRklxiNL9Kb0FM9HihawE6DnBgQzjoSL+Y7aWDhoi6ZrpOv/mFInfTydu8id/0gxmwkoVYjW8X2L6ree/DfR+wfemfd1jnGzl/hhfFrC8/qikMfC5mLmdNfJbrcqt4c7lVZ+ZwHTTwOTVwyhA0t0sq5PP7xaVVty61uB4A6I6fvgCQ+POxNXlL5UnTrRjyQBX65VWQtt2MxEZcpjpHXGK5r6G+T1LSHTpVKjaFZXPXp2wILWOQZwExwPPHMX0Qk5EIHDOl6Qu9eVcftRi0eTZ0DD8XLmEESn/IH8hsCKLf0wahyIUs2TdoyaHFtZwFMyD+7R2xF92SB1GQybqSX/LZMKVo3qVlolTecd2U0P9SfIxBhEGHcVzj8OoyV+kxzw/GlZITlp8whiB/IJs/kjnn4lADPL76izGDMvbvYHDnPUA0KygbQl/1ptAcqHFbm8De1iawGILYO4gZ1jfVb2K/IJZ+MCPIJ4jJTT8p7jILSOFSGnJjiCIjLk9RTvIv6dzHI6O6yBLXKM0Kgwfsfq09ofAdXMUhzGX6DXAfLNQ/9J0nOYQuf9LcJ9NpmJ7hyGNnVDXSsA7P/EiCwqJ0pQPoYt1Ajrrai46MF7zoQ4G+O91Ko/HEnXXbvKAv9nYUWEu3QY4ghpiwjCFIxhwB2PR8MQTVptDMHmSmoIxA/NhH7NmzGi9phqGPmVfcd2ovmEXIRvKXtTTs4pUjzYK7KqOOZgMx28f7h7lWKCtKn3ZFwpKtGHUpuMoAefZ/XAcsd3K25E91rgJVM8mRKbml9CosNgt8xgNvuvgT5j4bgmiJw22wF5vmFHcCFCuSbVxmFvJslZ7qIdzAt9+GXMGp0/hjDFJ9x86uNsMnMqsdiCHomsZXMQLdu3NLJ4gdaeylfYSuawkZp4fJKORlpLRxUmnSGcJYBkTsomV/oQ0DMF8w0OW8lB8neTMqt8I1pGlmuuV5hcGCoy2FJ7Mr8aMLDEGaXIFQh+uza+CnZwiSyrI0a6fZQO9sCHotgxDr4r/SOldODnuk6dBMc2RNPF/A0rDJYKTN0ZgO3VOhVSXlp0hiEGIHfIxAeqO3IcizgNQp0sUU6byF1cDJCtUF2h0GcXFJV6Huch2XquAaAAfY6IrbIcIN6WZ/NEwdB4I9U307LSoYueLPLyg6MPZXaorvxqkSTXepJRFvtdQYgjBusQ6+l4dxahinklxW48U6dqYuMuvnzq2j3X3NCLp//1YZgq7tjjESaWbQsjQsluusn0+O8iqIrGiCa+Se/OmvLyXpyxD52wXYVOQfMr6eGVhxoAkmvjyURiqSmE+hIpJjhoXBzxusE1uoTtO5BYRw5kpcwYybI5e/hSfOd6KrdL8LP+Jurz3Z2ZI4/H3RNZ929yUGjgs1/xh0nZ4T/XndXJJ9K31X9l1UBPcjkH5G0d+op4sUeBdNeqaKNVSKp4k2cQ/BgwZ+LA20ISjvT5oVCmYVfJVP3llcXd5dbAMy5SjPHadK+EzX/E69Ciop+hBJNu+l+T1ohOg8mBIZpMwICs/Ew/DB0jCWhD2VoYflX08ecVR8ZgThPsU4xAbSwrFBLF/3GdxRIzEEyXyh1Rs1I0jGIE4P22kzWPo99jNj2tpwT0JyMNDDH1FcvQnlH2iubbjAErk0JcDcDpSSFl1FJ9zJNQp4r/c3g3F4wIXohPtDnJdtFBwkeF+SpfsV8lqpQYl7JyjXKSI3RiAvC8MQhJ/+ET9i5yOZv/Yjj9TDBy//MAbpd127uzKTmr4TfaM7J8e7W3eONTNIS8T0pf+G+lLMCGJmkGdSKw6bTTPLaK8hSBlZjEL0XKN1ZCKPiEV+uswm35VDcBqwNx2UNmgQIyQVhk9wQyfoaroSD7o8h++DrnnAu/1TkvbOuNn/Temig32ylyLEcJVHKSgy8dxzlVYhlCImoMvGwqfpQ+eSZd5bfk0HOy4o7W/ewPawNfHqFiJMHYgyG4K8WbQsQmwS/fSZDtOhvVB7wAE7jx480/jpsfcVw1Ds/YO0PIwZQZwa+I6Zg1rydYH9gS5pf6DL12WQiCHonYxACJcSCR1p0z4w8BcKHMIrQ+g1eSSM4JGXHHMt2XVEhxdMfImGP+HZbW5mJvw63KmlvoRG8ugvlOJXLP0YpxSszAqbpwggVS59d7sgiMc4roDyi5DZg6nLhGMEsksd10+TMH0QD1tu8DH9pmYO3pUh6O49rbbQZtG3tYz0hvYRO9LyUQxCtA+edSjmHpIqda4uK3YRMMKN/JjIhPHlGRRa9M6vMzYo7YGucc12TREeVptudiGQp1ontTvR1WFG0FZz5yd8Lg1BzAbi1+VyLmT42xD0SjvZv9QRiK/VWD17oQ3PsGgzxVGGoEeaEeT18UyJ7o3PNCWSxixft8oIpDc4hqE2Dg0jEB0f1TRmB7nxotbRwyB9DCcUbhfwuMCRDZmxBC9X+WkIfbXb+VvC0yt80C7Y+NxoNVC1lEYm/1S26qS0IUhcaOIQaoqiOM1LBEKzBl6Z0j/xa6NoT4FmaVjNCGI9uwxBrG1ns+gv1FDdvyfLtb5wHalDY0OQZgR57atM3nwVYwpjjkxcmloaTlIf8pBT6Saw6BcQctGQ4lYW4lew48rry3F1o8FacERcLr+HHBTF6vksNGsfnNY8gt/Ct+Hmcha88eWOlnYDPxU8g98pMACufbIHs9wrsqN0vAU7fEU2wmd5zqLbwrfh5ncWvPHt/hB08N6qjHR+phd1aVyzv4CtGqM6MCKsPS7i1StwewSaOCAO10EDn1EDbQjqyj+XyB4MtnhdZJvGYTca+OZKwnt5vhbcUq9mWPy8wfnHsLOwS2o2BKnSpN9RyQVlegxBjzH2MKjT4I6Nor3cQwO8NgRxeuoj7f/x4pUMQQzW3nM0vFw+fNHfYVSCnxlB7u/0zCDg6ifx5V911gM7eh4Rd7jkGJHcd1syCnhkB7x/rvvkOO9+Ew0qIpRO7ECnPyIWfB5Qwiph8VKgDUHIGhzxEhUW44pSiycp6PKNfhB+9ai8XD59Iw7U0NyrovdQW/wZ3IlYX6PS39GsHhl0OA3oimcF6VQwfcU/Vr/pFgM87anIjGo2gWV52E0ZgRgAXtXXfu0vrb2FdDS11OzeaeWXGQQIRLEgP/hxYgiSX8lH+mhzXfbQt6jJD4rBxQvMzMwQgKG+C755fIBPXV0/YtQ4hV4BoP16dBKxZFwx2ATO4reFb8PNZoF3YqWHJih3oSuAhDuDUjoUL7Pj1lS4VbYmaKe6xCn+HU1xeDytM56Xy78TGLG3kYhlCoihcpdeMnspqMryazV4NgR5maj2C1Nb8YAlpA80I0hjpycYjdlKQ1tusFk0M4I0CtCKSLUV4s0G0RiBLsoYhEGIMmVDkNsDSiTPGWMQ4wrSj6mEZ4ou+ZGxNHEjs5WP5KvvnfcgW4fRS2DVqUiOO+fFiycQTg1IasBmHmh2oqwodnSrYOI0nQuo5CmizAhqWVQvmSWoGMkqMwdTV4ExniKcvYJwZQhixs9V9gyTMYjxlQzG97Ttxn1mBGmD+RvXZQjS74ZmBV3VUfLDECRG8IKvb5KHNEcQ/5KBwGkCZljRG1R+E25vwu1hN9g7vm7NmjTil67xKzLPkx/jd9qow9KwrZI/X/jcGoJYnERh6YuCxUWDxoUx5hXrXbV49bV+rHf96iEWbQxB+RL2iE6RvpAxZfrZCy0jY0aQLNv52sWrVp0fUlHnh3XyuDYI2U/qgtkYxMsZukiU2UBLwabIIxZ3Kks3KyWqYSYorEg2F7GIF/5BNizuAqPZMnFA467U2gDEVyzDkSe+5uLGGA4CB0bFpHPTX730pcvht2pkavqzxDrSOvYbRzrxQg3VbW0W/cVdjo5Xg2VDkDaLviZr9q0Ygtg4kcaKaY80dHRkkCOypBFIGAn4ISP5kokq4pZ8xImUhg+Znbm9NxrGynLh82xoIDuVYhnCbjm7gIWo4s7M5HewBSwSHDMv+OyfSOzd4lZpmZFuAm7pVnwQgqvpym/YfNvQmaxknMnIFMVuluUMMkf7OnQdfxJhlSQBcDNd+7eEM90WR7jx7Z5FA/zryL6l28fvZwTramH17nlGwHkGVq0DCivQqu4oRjmgG+Wbq+kTOtwPGvjRNdCGIIpiv7+HEAIydZ064KKqG6795bY/cSjgeWOt4WBd+E22d4CjRBhKDP5+B0AeTsgAh66P7YLmnc6yjywJY78PzYbW7yv1f9gj6IGWyz/1sfLMCNJpQTIEMXjjTeyNoD9oBpD7N8yKps9Th2ewhN5WlRiIoCd3uH6bt0yTXEiLnM7LEFKA6XIe0atg9htHaL54GoGZjW6YYaDnQj3c7OilT1i9Db/L4gcmIDixWXROwCzK7XAB7QDDIwlkYGNWUD6SASdM7sCzXxBJSCM2BNHXkRTq8Ggi0O6yDEA58YfNYK/rkA0ZgtRfYq+gWzo5jEEfe4Mc6wPbFe0Bwkc07xWkZ1Eadv4usQRMF30b92+sB+kiGROm5RVeIZE572QBv++I66s+AyK4LmZROasOBjZiFQ26sy5Fv+jR0X2bcbN/oYgvOOkUuSq978qv438q3Zmu/ZGqFZMyZF1sBa+wZW5lORNnEC4sRxIpuY3o/PPkwETvwTZN3HoEpTOoG79Ou6G4PfsGQwxiUlzfyeMZQWrwmO3DfmGcIMiyMNxHWkL61aPH3jj6uQ7Xea6xEisomBGkM65U/mkrtJRHs4A4NezCRRmC5HLQDjMDGSv16YLzsjBVn5K46iNiK1P9vAimMMw5QG7nxPKbprh8UJnvUmrOXQcEnLVotq2rYt14jz0UQGz2Jm2OkEHjpHFhUnHxri63A0AU2xFSR2n54JF6Kj9tg2jcvMql1aVJBXZZbQbPl82imT14gz3DtFn0HS0hvXf/jlZbaEaQ2oqb/gjP6WJsGq0TxJhxqAQuMsZSWl1GxHL4nQ9w+nFDRPsVofMHKsBkE7mMM6Fwc94bVlEatXIrgCYgj50d7aJj/ZXOFkPQYWmY1HQurp+cIchWZheqC9rJ/v3ulX5v1Nq80KyfX6vD88gbnmlpGGvjZQRi4+gn+grmhk0NHF/CbOBRh4eGLUYhDEBlGLIxiOpVP9davTxFf4HarAtrN7UkFSodFAWBGA42tYgw1UBpymvXuKlWOcyt6KDlciuVCpV7w4MelT9BIuhHJyUuwrU8xKThXVKNz10x6FxBZTxSxtoIRCdndHBEfnTzsk++ONaU5lua2syMoLt3tanZnewRdMy0Rk1/zowgaZg9gqQuVMYXM1JEe2gL/9L8yu8XCbDWgQmLTtRkSRdt/nyhgzwD4i7X4o8GQLJ8rJIZjeUSY/KRRjNof7sT2Xf3KhGE77TOYjhnch8NsnG5QHQgoL33ffwcd0ONXF+D3SbWDxv8ujJ9X3SfejY/bG4/K/euVxZiKgf7io9pNrrq4thR3V9TgVqRpbf0WfN5SPznrYEYgrqUbppjFVaGzWBdbnXDtd/upjyXKqe3WEFwOo24rl+pFLy1/W5zB1qI1SCJqKpMnF5FysTLwAuEUTLiaJmXdnT1TCAZfx6wPF7uVzICsUQ+hiCdKKYZQU+0hP6lZkbbEORZPuoH0ffBANQuBiCPXHpGkHDMFJI2GNZYDssSbSDZqr0QVQ+LhBoXsXNFg0OX8gTSePokUCbMWzz5TzivK+lCHseTa3HVV7Pu4IcfbDodi4At6HCXdOxTEn7ihc8eQdk7kZEN/avMCsogz7R6jnzsYhIVLv0ezwriBFXPCtIX/1rycU99pswI0kc1nbZ6k49rzAiSIYg9QzwzSJlBVwwiKSIYgsSWYuCfcdYN+qif5DINdGTEV/TDPdnB11iYQSRh7QqjBNJ/bJpw2d4Xui1mHf6+6dbcv2sIvfV1Or9nyR7KKS4PpcpKc7NrkjXd0G0lt00Vap552E1xYVjGQPOebqGiTC5idA8bMnhldg6GoA8+NYylX0/1sdwGY4zFGiP10jBOFnz+Mh/NX6idYEYQhiBmA2ZTeZaGaVYQy8NkMP6g5aNOD7x+jKP4SE9z1XuI4eeyTqmr6Kwve7kVUbnRVWDjjocK0Zf9lPsJ1jjBZriTKTLGNRiUxCzU8qb2jMgWp7mOOmNAdB0CIgJUazfKANrgj/lTujAQiztdHddn+RkPIYPHRYJzIvMRS0T1syFIm0Xf07YbX9y5HUOQloZ5VpBcZgQxa3BlCGqxncaiopa/0XYtFLKUyLggCr6iBTzRbXEzf/sLkPeYteIoGMpAeaQsz8EQtNXk5w+fW0PQG+mGrklfXc/cwAhImI3PXssIxJGIL9S4MSPIBiDPCJIxCIu3pj4+8dcwrXsVzTtZtjPFOY0WqXjDM7ukiFGI3gM/1QIMQTYGsY6eOFzUGhVuBSjiLuazP02j4KHk7uM8Ha/jmwUBE7VlejQ60OY/BNAlcXyuoPbMNww6NEoQ2m+hBi1Suw1uiwgVVHRU2cUIxMRuGjEZgpR1N1hSyw1tCM269pv6sUcQxxsyG+gOM4K06SGnhnGMPEvCvJkZ1mo1gp4VJHmctjOw+IH5kpjJNxoW1A+7IYRFZZqixyEy8L4Udtve4UaajjQhCHJ5Dxm5AJ1uMZh5D57y0KLPdDNu9o8WVEB4dVIlQ4tnxFrwhcvMY/ZD0TxN3cwV2NIZX7ezcMDna2I3g0/5zyPdLNPs3wo/42b/TAf8Z3qtinjVhbn4zP6uV6jKKtujTxfXFVMRHwxBP9PSdX6yPRuC8sapwo6IKsd+H7W4ClO0+XEx0DgVx/BATTRuzTeu71UfMuihSRdUMAZMIxW8FZXUiJJoGea4Hqp/giGIE1M5KeyrMgSxUfQDZgSpX/SUDWDZG8SGoDd642sQV30fjEDZySIfw7xUzH0e0WhwN05Rpf9TstFHyhKxZNCzEBZRDXR+grbOFr0F6LBuY8DWBNYD+ayM0z8pjh2TcL/TEQmd2fijNiXwenK0MR6Jojhi49pTfmBB8RoGE3kSiCGo+kj0lcSr+0xQQ+uv/orchiD6TcyIZnYPmz9z/PORjodm89e7zArSARvH2jwaQ9CxDEHMBLimZR/0m65yjLwsSf4IZ1lpJnk65JG0KJNJN9KWvO7H4XfuyZKu8ju7jWklW2mmEZn4KiemW/vBIUZfpiu5EmdCNpHcj+Emsu9A1/mA2yLDD5tu0hkp8zAWpS1+i7TIxPjBZZnnp79gmtcSntnNebKfwrm6EqZk+lIQiH/q3webh7cYgtgw+oP3U+2ZQI9pMzROYlnYA80I4nAdZgK9wBj06o2XksUQFGPxBRl/elkYG0d/0C+GoGwaTQ49RpNgWb2RZWLI6PzpNgxB8rcuI+/IjXMSGPWN2Fx4KuCIpLaBN17g1gIU0HVauB9sWKvYZtlYqIUfaUrGQllaIdyuCY97gXpnYiIgKzLVz/XVLaXr7+4ii+w0Y1Dwrt9uK2gfOD1QpwzSLnBq2P17OpVZYyzPCLqh/Vm9NEynh7E0DEOQ2gjGWojWkiMnfiTBv+RBgL6aeBPuIWHHB32KxybuUJFo7dcNt+Gd/mwIomygI9qzw2bRUsI5uc61Icgvv1JUFyovy1IJxbqt2dBeHsb0w+f62vWVZwTRGcpXsIfaMJGO0RPNDHrmDdByaliqKa9TfYKhkWbq4zAEpTOUKqWST4fIP/YLShWPSPJLqBR6GgAqAH80Zl0VilJBTBwdz65q3rpeEepffNCRpq+uqQn5HlSlZafSV+PkF093DqaEkgJ3RbD8RMQQJOOPGzXkzz5BbHhIg0UH5aY6MifauOxYa9v5onW3DEH42USaPYLY/JAZQWw10Ovl2+qddEkL7aczI0cXMOnC+YuWDLF+gJvI+g2PhOc7ekicGVoRC2Tt+/kp/WHMgeOaLuQNL7dJCDYKQifcyMQc9xVOkSggxO2ro0G3V4YmLLf5tbtBj2Dj2x2IjafxuB+7Wrx2z6JtfLsz3Qyb/TMN/hk3+z9Gt8XN4ebR7oyb/Y1vt3GEf4YXRdOq6DKKDmY/eBG4SOOCr+uUyppO+HSSJsJPlb1menAPGviBNDAbgkhilF+/eOi0855PGae4gm8aegN07FP+l1rAm71p1mJDM1HDGEi54PhL3OYQd3CXh4FXoigdoTHO2BDELGgMQV/RB9KeH+wPhDFIgzzvk6iB3TMtk3/BWdB88PJADjfLPmIMqn4RIxR/FNPLn9nQ0LoPowSVnk0TekdHrshTuVG8lp3cJNQQXPt1wzVeujZM4eVSXGUy+YQKXklh4Z/8+7n4pqcxDEH1FICbieLimkX88fruZPPUljD0fjYaSWfWdPpV9JNiDJLcll2a8UcvjDZ86Wd2Ty314qQfDdzoP3Ek9F195b+jWdR8VDueDUG9R5AGhfSd/BGu5M6gMbzReKcZnaG75IdwL2UkQ86PsxPK5C9+6xDdmHCCEU9wkk5/t/RRznhQjRdh6JsgLOd749udcbO/8e3OuNkfPPLVox3lomWPLB/jM+MWPnMq8c90VXhWRK0564VkAchNWQ22aYAlLT0JAZd6v2LpAM/Yj9+R6ZPP16JrfB3y2IgwfWnLEKRHJLoxZuL0sGdsGq+DdR5ofPRYG0SzcsKnhmn89ER7qrYhiBlBfGRnfJSxUtqBC94fSMvDNoYgNpVGARiAkIFxGReGKOdXOFx06ktu+RQkHcqyRa87kFzWhUMFkX64Eh9/h3GbS2DQcY0RmNAZp1Vsky2cHLthAYvjLKuYSaA8PwiVQSwcuL5iuCXMUMOtJa5kpnV13RaOesv46IoGWD6VWYYgLyHVrME2BN1izKUP7d4wWjOCOEre+4+VIagSFC/4dyh+dAbIWWnURAOIoS10FBmH46zjzIjC48x87S8+flbiV8NPjeHyHNVqKi3aTtopGYK0h8gwCk58D94fXwPn1hA07xE0ChmFj8Iml8YGQ9BreZgZ9FLrXr9iWRjrXb02nqVh2jxRHSOmTD/XUYisfX0nxlUFxUedHKpm7RGU6dHLjCDSoerScOWrWNeWuMZzo4Bzdw1IYQe6XGlGHKtrnKvoTIEfivqJIRXbcVa0gZBm+5qLOyeuaEhDo2SpRFeUcvDRhlpi19SS1/sCwbNmBIlI/Rc3Wkxz5rSwk2N1YLS2HSv1PW0UzcaHtzAEYQRi00M1WDEEqSOkuEyR7k5MJGh5kGMtvzUk3ZjO8kGjEDCiIXec4VZwIJpuwCv/CSt2/v081zRL6NM+pCiBPk3806CY3yA/DYl/eCm7sP3wKZ27FFy6pyJOs9alfq9aCrgXp9yFXxiuaOgNHK6DBj6jBs40BFFq1S5i6OGi9Pb72ANvwvWTs7r8LltBCKT8L658BvH+xXManxSIu9QTqPJVValUu40h6C0zgtT/4QQgG4I00PPSMMFsCNLGrwzwnvlIaO37oUEcx8Nno2iWfVxmDrCkIHf0geR6OVjNBJiXhik9ZgrNb3CPgyY5kZqLfoa6Ab5mfZXozlrrk+id09bJWkfFqJ1iguOPO6OvBmDzFGBUek7fR0zCvLmNtAeAKNIMs4IY1Vgm8yEMF2ZOR2NtCEIWdZf88czGIBl4mB3EZtF3OVzjtgxBd2+uDUHqP11jaZgeCTOIvOxDSvOHOQnDp0n3o8gWvVe7SRdZLUPlhVz31f52ocwVSJ4fzBZ44+02mEAXz32wRPoNv7cC9mVzwaFZl69B9m0VBs9wwoCQq93B3L18QpRNsCzLos/MoDslA2S1GSq2bKPBOOiRjD5fyUjM5vI5XRCD8ROFn3vp6IuX73avmBHUe6q6TdBsIJ0suGN/IJ0syKmC2V9Vef4wzQiiekgPnNDMVUXTfhoDdJQit9XNrDnk55e7fWYk34iWEtx0rgcDl5hpR8xmaYdEQ0qQStSKnohJUSAnI5j/F3n9dAeRM2oGNixJ92gehjHWEh1jEPCCya/mQDCMRWkbmdkTQ5A+qMtgfEeGIFZc3K8ZQcfaluOmx1iaEaTTw65oYJVZQZGZLODratzQFhP8uBrZAIVNJxdUo4Gdit/Iijvj7dfN+mx8ESyGoBiBeCa8Ew4zgkpR58D5aRiCqkB1pSWIIQjrNhbrt6qALPuiQXuMlZsZQeoI5eQwzQjSaRqsj3/2THsEYaZWJyadHr9iVeDzVQxDUIxDGIho3Ki+tFw0BILJlEsjZnGqUlDFaTWAdUch1ubp6WLMgMccETThrr0Vhj/X4hIAxo/USN8R7Wu4kNVhActrgHxWAwTSbIsrrZy5yKWTY37QYyKTIUjHxtN40SHBmEPnhCmLx1oSxnGG7GTvxorTL/R160YdLc+G0t4jSD0h4tkQJN4tfdLpcNzAkEt/6AlZ5S6XYIioaw0NDCDqqSxt6MjndPEMkKd1DkNHnGjwDoYFN92GxgkLNpKQZxuvoxhOoIjNr5m228R73Jmk05hhe6J8EtR8mpDwvuvrpvO56PbJ/H3BzlDJ98X+vPJxKR3lel1FzlRJIU7hBUjdDMMV/mAIOq9F4Gcj15mGoFGelxJrn96LdGK5COeNtS3bibPENLlu0C0VK68eva/nl9eET6zmEtdvd/dLCCt1OXB8qxNUWRpGv+erX/OVXx/CNCMo+yY+zYxofeGnH8S+it7zRwM5joyn/8P+HvzSL8oQBUNQ+j/0hzg9tQ1F0QC4fUvCohmk3+pFXShBx6tGsjtX6RjFT7SK13pBPfZDXOoLj2KgQIe9PMxgyQZ8sEPP1ddpfuAmGYva0Eahb2ZLxxAEOf0kGMArP9LwYE+ZS78JQxCzguRqrMzAjQEeeyqyPIy+063jo93Nkyu7E5aGaZDH3kBXRMvSD76SM2sghqD6XKlE/FTUGSK9C970MMrwYHPoRT0c+Z1v7uigcIF2Lt0TMl7s6kqsjt3QldskAAffFcWpAP2tPMtTqBXg29O1UGuBvj2/lVgVWPNeKIAv6aNdysxytb5R1wxfKLY+c9RDQ2eLEQiq0/GB+CdanrtPNDYMaD1j4Ma1Iei1D9VhFpCPjtc+YrQdXz3QZtEaK7Ek7MWLdzo5TDOCZAiywZiaK8MxS8MwAMUQlKVhtAHvZQjq9sB7Aylt208lBReyuSyiqq6XIGAvp+s6IK5FV4vPcBiN9gKmFR9XcHK8vqJHqNL2wK+eiRKG2lXZKSIJF3GIoT/kY9qMrvlJJkOiFKHTFBPGXBaNAYvgdG/yBDDeEraGyt+GINVvtQ9H2hD6GkvD1B7c0Qf2e1oadl/7sN7ReOsmhiA+tGt8hSHomugv60s7MweRL7KV21lI8PR9xiOffpQNCcq/f9bJNibIzQVdX/brxtAN3XXYeAC6+vh4dIZuDjOCrJZzcTvXhqBRMFOO/NqlhPn1K9eGIFmEsO2wE352wWe6Yxo2u9od//EjGYJ8cthrNWwqpKJ/yxctd6h4vWIAKmOQXObLUWWpFqnmVDimRdIY4l+aG/AUe0SkgLtBw50ebzcgUOaSTwRLGGioiLfAA+v4MyZ8uCcl4mTancLVKAUXCcOzpHUUydhGICqqOxh0MvS7mCmNV1RbWeaFIeiWdrNnk+gT7Wh/57Z2tuf0C61lPdGR8Te8q73WuKqhcueHeJekVSVKuv1reVqzSN/XWBqW7ChXlWtEg0PB4dWX/TOgaIKfA8VNtJCb34g30zXn2YVwH00zaNy+8Izb55/TKf9IbngitNHbNM4QbQ/bT4L6jfJJwq9BMIn+UerPRfdRoQrZsrXKv06c3xAal9QuruRp8re31TLCBdjCh0poY3Q13gF6A4froIHPqIHXKpZvVDa7JLaLSPbTLuIRXcKhtR8a4EWSN1b1BRy/3mFzBZr87iuILhUs9YPejY0r8BQo0E6tKXlLIw/w4N6oY2NDkAZ3v/6KAV4tDdNXfuDPdCT0C46E1owgjpDOEfF80b+ye8cHMBuC9BnIAzrS6T4RfZ0YgkjLp4jh1o+ef0vespCjvhbJhVUgQ6r4TSMYHOBBrwu3w+BbR+0CC5EjmpHTcJAbM7ghQjslmwXkJq3hVP/M6RgHPTFy9+Db+TKx+kly6TQSz3EZ8BWM/pIHeCSdAQ6nAXE8NIM1jDsYgrw0jFNWdYT8Hc0Muo0hSIdrYCDiaz9HSJtWewRx6hi64PkjME8ig0ilQT9NgoIHF5mFRx5fIxfOfwELA65/eMsvN/pN3NDMMTf+JuskN+gfJxghyMLIunWyP/XvbhRaZ9aqE2gNXYciSbcB0BZ+LbTJ1jE7pOebQrDJVPC0AbnC2UvDNB7qzeQHF3m8gbOevfdTffnKe6mybNQGY1ZRqN1gaw2WhnG68kvNCHqpE8M00TCzB2kD1FZc9EwgZgZlRqFnFqqu7DME8VxsZKDMIYwc6003Pz3fphxYYN8MJFfdFjQ0buALj64HRIOiqc0mYYGgt+pxCenf1FbkeDqpC9BDrB+G2aY3x2bvgpe0ezVGp+/6i/x6fmkf0k6QH+ZfUo+h4XdJH86ZEXRNX9GPj/ShXWMrTmW+rw2jb2sVBoagExmCrrM0THsFXdWSKvYIcjtBpnSVg7ifvpqmXGdH/gZ39k4xagIhtjSEUUfDrRro5bHf3pTYw2bRUsY5u34ShiBKFwWs6qtdGhgMQPoQ5g3QaLzYG4jNoZneyLHxiyGII+RlDFIn6I06Qdg/3vo4xFRLXrWpljR2WLbp+PSrtow/CtPJoLp4GrJqXBd6nil+Ogz+U2DuuHQj4jbdlSM1qutVu0tVXHyOSwK+CBV1166SAqg7Ke6wYJ22EG6EkC4NBDATipYOJ+kAUMOEVqtxokOjdkYdGDU4MgRhhWY3+9syBN3CEKRZQBiDbstyzVr3I61bPaKhUiPFdGiOj6cz1OO8aNIZIHFrtvMcuZEjkLz38FdeJWTPFEJS5wNPXc2nw+1CtzwDx3TcSiWP0sTCNT+Q7W9GuFEeDBcBZrokNsc4g04kxJvT6QzNcMGQPbIW25mOYuj0RYFMdZnFLEuh507QimdHHEA8C78I2kTfwd2yhdVPBTZ08x3y/xOMOhUrPyra2/mRrYrZ19GR6454uJBKIbjdQPwE9XMQ+TdDAzYEqXC7eVWWtkU58AXqjr2oDKGNlgd/3lZ+61oxKeZgNu34wNJ0Q6XLzPT+dSTdqq7YyGISE5gObqC9abPpE54NQQ/0ZT9f+DUzWkvjH2qzaGZDs+eHDUEaLObL/jXVQQZ0l/xhzLOC9HZmrw+nw8cxC1dGIfvTBwoekZN7smFxHCZU2Yp30VNnpVxzmP2mJ4Phx33oSbihMsfRrR7c6jm5nwZKeITyBUN4EYi/fMa2CO0aaIJK3/0lIlfc6me576RGzVrRDYONjUA2BGlWUC8N49QwG4L0lV9uDEHXZAjSaavaY5GlYVf0KDgViGX1pJO+EP2opUfKkhJkTDkMHXgMUz07K3jdEdc6qPJqJRWcqISlq+iXWLk886sVPWDpkzQN7tyvmOE/jh95ncEpOelBeZrLC8jvF3ZWGpJGBpHIRKqhs4gEdbW0elIBTPcFsvjycXYisjd47u3rGG0IUsplgBER+lD7xphH/zYEPX/xMhtEa1P5hxiKNWYax8dr3PTiFcvCMiPIhqCdrJkaE7nN0AzCC5oRdJGlomo73n9g7zClIfwHTmEWZWYEqVy5SQOnC93wP37AW3Ly0v52icM/UudaMPFR7tdXh7eu0i0Q6Te24y5FnTpUacmFlvoxG4KqJgkhAogdoSJZVmlDGSd3xKe+QotmWG1hbVBf7c+oE6POdWb6YAhSO5HNou/svrwvg7FWX9y8yUwhnSqmD+3QeVmYpgxiCLKIuuH2Zf8EcJ4rPxa3ceVaevkJ4m+3+a2YF7BzTLD9VkeHGyisvUI6beHfSz80cYelYVLCObnOtSHI70MpKgUpLn4GJf6pjr2WJzOC3sqaLePPMx0Xr72B2BfoCUenyjjEdEcs3UyLfi3LEY0b+wt5lZjNEmwWrdT4MkaVlasqLD9VotzRuYCOnyqh0MgzKoACNrAINkp91SI4cbW79QV+GruFh4tik/DqInEBtNcPL0I3oArn2yGukRD4RyMtLSpELutHJ0M/jECM0djk8Iq+VF27qgbq5g0bfpi2eIsZQdorCJfNo4/UgF3XzvbX1YnxsfErQ1A6SaQWGeKOnCJOXRf9IiVAAydqy6/QgIfQccE1E3jgb9dkBLiUNxGiE1x+MLZbeDvcCmc2JmseoUAmX0XXfvNWwNQdeQiDbnNF1zMP4IowpVukw4m8ULXMJZOcdVIIvNCtO0OmNM/mZ+LOh0VqKSETvXElBihY7Ltm3Ozf0s642d90DWu34bMLjquz8zGZvitdp+UEf5436t+45Lfa6/m033V0j64oPlyNz/HPqaZGgG8iAw63gwZ+fA3Qf8iMoCqwKpP9nup22e2+0akQej2m6CoIbb9LeCf3qwpy1xFRDH7OXngsOVXL7vcRdQVc/5qiYouxY0o+yJbhkSuSPnAtM4IefMXSsGc+PYylYuwd9Ez7gnBsvPtA6vtc0FRffYeWq4GcjrviOHn29KAfxGxpls/TF6ohjfwa+NFHSo4VRp6SSe5y9a5KUJp8YLu6t/03YedKhHDjqrAd3nqBWTWFdd+H59QMR/LtwW1/4jdb8xezThXsTDn7wUGfGUE8p9J6DfQ80mUpvS73Et1vwnCjHZfUB2JvxcucHKZ+lJeGTYYgZlgfqw/FRrCnDEGKe4EOLnwtEEeZJA0bggRrzYPv2UDdD+kcudwq3tAThdP8fDPHpiW10JHP6HbdhxBBXc0v5ZbyOGuzqZqfNCg8cT5G17gfg67TaHeRePGB41rLfjqfXQTXdBOfLl2lckqeW4TS2ZojenIJFc1SwxdufkpI5fKbuIG5J68yQ1ll2wwTkKYYepxjuPxCPtfJgeylyp6qGIz5eM5py1+preBgnZecGKbZQK/YLFrFmyPiqf/eJ7UMQXwwxzDETCDKEPj3kyHIpZU2y3WNdsOi+EaZXWQ3SrcZEn8w8psHFGRowcXIAlXDF9yIaw88AoHS5AkaPsfisTisW55tzQiqB93loqP3Pl5ukR2RFpNxVOor3C5phYVhfqbKhdsRYFmI24agqxpHnej05dt3jndf3MMQdFurMDhhkMN49LFdY6xrtVn05TYEYVSyrM5ZySzpkEWgKmYLPGSggsfVRdOw7yIfzpaQkLS/XeK0v3XnsAOUi4F1WYBLt6M31EgeNotGg5//OreGoPn4+BSsFDjKF00k70l+THWk4XuhhgtD0NNnMgA9eVkzgzRDSA0bFu+nTwXXbvmvme7IvkJq4FhaRuOSDk+WhmH5HgYhF32Kv2hsCKIJ99teoDRuPEIln588PdMG+Hyt61m/okORFPCHak17GorETnBOAD8NjBSUimZhACK9XUgcsWhMJz+Nm6cqqgPDpocxBOm0iyvZnAxLNQ0SUxZv3+LHMrHrXhbG3kHsZH9NDRUzh3xamFS4zAiqr2VOObIs+Y04SMXlBs06EEW1TKadWymyAm0rSeHJW0hgybM1DbvO5zZyYlgH0KyvbbhTWlMRArOlPk21QPxC4TkUaMQfHvKY9BaqJf7sC6/kcYZv/c3PZaSQQ49Di0Ik2W+WoW1iP0R40s2Z7L+u7PvoGnYm858XYlUdKKilnxW8VLKUI8ptgCu6CqxU3CPCn5daD7k9Rxp4pVfm2xrgz2/lLsMtqsttFV4G/VwE5/LsHoEAvOX62tKcblTh1W8B3MzU7fhj5k+9A0PdbzeokhYfuB6xRxCnhNkQpIEdfsHYGPYpm0XXjKBX6v+wxOPChRiCdhrcxRDEPkHaeFo8+brPDKHkhcUMbYpI2gxz6If1ezpiRBb02Lq0HgUORi5+3Ry2RWfJOzBmtvTVswECol8DZnAKL8Jh5miJHZr5fW6IGCzck0ooT/sDETX/Tpj0NXiDR4WjCUbK9HGkKWZBqxBgPmNWkE/30Yc0Pqjd0AAvm0WzNEwf025omb36UDkRaJ4RxObSMqZVGgiQ2QQZWNJkkm5mBPXT6VxFXiiG/slIP4RkSvfuu5L70oAdnnvzKviIE0/3IRa6DUEFfwi6T6W5X5JvB0X+0+m1bsIzeexyucZ1qv0c8gym8ld6Tqzc/ZgUkXR5vss1+wMFMnGr8ZCgqpS9TxBpkkyWhWVf1Xeq+y+0MoIDdbyZPIYgloYRVjuBUegls4G0NIyxEgfxqCTrJ4OPZwEtM4Les7+YZgRRhnLEvGqB2g9MHC5j+J3PKm8CZw+vzk+7S17mPC05RScdWuK4Hkgykgt+wYUa1AyLv8smNNtn3Ol0fDhA75+BeaL97FNLxNcR3SIqRm0iL2ROTca8TrtOfcVwTDsCrVpawXqzaH9wZ48gDEH37+1+oaVhd7QPK4fw3NT2G95HiCWkah8wHl30ElJnP5JFGPmnPLe3+lqQ9NDVIlcck434ZmfGgJpFQe0sMHx5zuTOzw+QfgsNfgBAKJPR08EQZFWei5vKoZ/OuRCmhaCqcGqYC20BkdLFh/IkGMYfpjuyNAyDENbrJ1r+xTGpz+Q+1gygR/o91qygRxiBZBBieRidobeyALFOnk2m8wr3q1v+GIEWY1CaGhpUOjzulCGVZwSxFh0JIw/yocr8FdDY3EKJ/7QPSKDzPfH6bozSqCSTaCPbpYJJKelAWaDiK3+0JzdwKmPvEQRPXjx0XujI6EOWpx1eV6NzWRuYXdfPR8bf0pRmbXR4whHyfM26ranN16/urtLZkTGIH40ahiR+nrJs2dJEREz86dTQpHbpAwZGIghGCJ1Uo0tY/9DaWNR+U023kJFD0xOBJ9hXGvSgUCQYv2/hN9GZnrTk6dit9w43DXxMVxkhDfNaEZpapOS3GAe0ulsfij3oVtiFBz74QNd+e1a34JKD2V9EJV/zMHQP2Qq+4j8F4EXc2QV9Fj9w89VxZ9j36d/y34Y7rZa3wz8rtwrEeGjJfBdXY3XrTgRYcMaX3qo4GrZVnUkGk46wpTqEDxr4cTXwSn2AN/rNVb/LMTC3xSWS4QLSeTcO/MCp9R6Vod4BhathUIVwXBEq3H7c5WefbrikwnuKVAnnPdFvdN4pmhmtT/ePatPXBw84PbWMQhrkPXzE0rDXu+ccH69Z0S/Vabp4ieUdkyFIQ5V37zH+ZBiTr/sZ2KWPhKkDk0TJQX+o5Fm0IF8pyXmWPzokD7oU6C/AkEV37tUF3xxJRH47fSeTAYSR01nSKwZ2IIOcW0dJWjNV/A1vd6GomPCwVzc6nL7kTwLKQw/spA36T2J0SYdt0P+5otlAV9n7Q1++j2wI0h5BtzAE6ZCNmhF04qOhZQi6zsyhLA3jlLE86WiYPhFheNOnQrcMt3XzbKDQIhhytc6FVNBE8trFIRI/HPer5DHdcCDSJT7QyP1UX6P7EJ+Dzv0ppKzy0GEk78t1OA8x9bn8jW93pmvY7NJHJurZdHpeyMFjUMRS38yiVC06EfhQLTEs0aucQQ4MDvPVHHFTrpuiXYonM4KQMVRIoNGAAhiDKKlsjfGyDEEPHmps5KWjGIJywA7jJsZSLA97LRdD0HsMQcz88WwgDEJZGsZG8ywNI6cxBFFWMHmQLv78ShNymF1jlG59tfRznhoGTfnl4Esod1qk/DWv0FvvM0j+Lcw6Dxth4QKFAPm3m+eSmP2MbMgSLeGFJ4zy87IwPTvl3M9QVVl5pt3g4fAEZCRyO4IRyA/Me4n5Y/o1LQFjCendk92X9+7ufvHlnd0dfYC/SRshQ9D16xpn6eP8VTUu3h9Iw1XmX1qWkpt0fVm4RUILq+AEWQWIRd/OVaOJDAy7+V4pDBB1Dtj4NUG78IPGzKUJubRhR9p3pOxTg9fB83k0cK4NQRSWvihDVCfKlBs2wvoxqweDDl/EnmtD6OfaCI2ZP085Ol7hJ1oa9tAniTFTSMfIay+hV7J029qteGms1KSwNxBLw7B6U0xZHlYGH9MgiNLLsjECXVtc5QUHLQJdVd7tb7qmLqCdhuXV3ZSLO2jFNpW9PBZkYIckzAhq4461ZXHQWpo5XK4YgbSEjNMwdLmDoSyzt49n9AjAtOar7GSv3smRGqgTWabvaqNDTr5gg2iMQWx2yEaHV9nAjE6PGikaqDR+6bjkhZb0yS86isaUJ7JjWGnCYWmDF4jg6MW6KTqBhm6JgU5a18UhOXQg6RAnL2eFq9UhXi48pPR9XAuvyP5xnmd3JhKveXyKrlP5FN3H+Fk/zejg/ow10KVkqVetDOrZO62n/ZM/+RO1o08M/tWvfrX74ov7roNLnUqMuW4aUhV1VD2Ah15AlHW4fzYNvFKnHENQLr9t7O3y7PJahdZYd/Lz3oJw7qO0n75ArvAbQQM7rabpcLt5X/d7LX0ecRAairxJicvgi/4LdfWC+jPv9fFLA7qaFfRYA7wHLI3XV36MQk+evtL+QK/1Mezl7oWMRjYEXbxulz4NM36YFZSTw/jCi9FHgzpnhsEduYshyAM9y9PDjs5h6U/K8wIx3EKhz37P2O8ccCMP5IwcJYc46UsFPsIOwijUvsMsAIK+rBNF8sBYAVM4TvCzHEAahVui4HPASfbNjAnkxxPgl76W0lFeGPRZczos46oMQNfch7qkr/p86ddpQDpg47b2WDzRTOqb6kfd0rKPG/qYxmbRV7UVi/cIKkMQ/SP0o26Z0uFp5If8hEk7+hUhtIbhJEf1NBRZYWBm2KUUeuhCSwi6fhYruJHLjfQXugW+9X3fdFv+P2R4v+xW8irZU3STOpswIJ7YEn/R3wIzvZ4RlMs1+4EmHKr2C1pktjHITzC/PCuWhPHxnI/gz2UM9snKng2kfYI0Lnoko9BDtRmMkXx0vMZI7LtqQ5D3AWI8hBGoDEHMBpI/S8NoJ1TqawZhZhIiU5c1acBlViWSsmiFIB2XXMKVgcjcOBOYpvMXzIJftBqYWZeOOvbsBj9qxoJS9Pn5gCBNxOVZOV7nQZXOf2Emf8tDe5An01ts9FjoA1t2+PTBt+InYxBGoQ+ee+mxFh/br8lofKJ24p5OC/vyi3u7X96XIUhGIYxARzICHWnFRbcPlySLVvSmnZWsJYrlbV0mc8KAbAKAgOZwCFc05AgayGjhthf4pQzLr7DpShX9vDqedVtA61OMbQhqgoP7WTXwkzAEuWzphuufbvTd+PXMIAxBTHl8rrXwT+WyJ9BTdX4eazaQv5Spk4RxiM0SX+jL2Cs1cuo7mUc2OaNWqTHDGMSr14YgNkijgaPQJ21eqD0zaG6yq6mwfOsnmhqX+xpDKE1KKlxjV7RKl4tOxsKphQFTDQ8+W5hV5VzhiKiGqSpfSW9606lReq8fPJkFdFGNWzZ55mjCrGu/qoaHBupImxnelmX67p3bmhGkTaLZyf5E61ZrA7OrOlWMr19XNCMIHjT//nIl5m5MkAvNVMa64cyXruRABEUiIndkpBkiE42s6CK6vcBGwKj1rSMQx3TSEa5u5azpjZJUQ1en0AOwffGfJcaWbjDYeD5GN+Nm/4aFsrXIPvs/RjfjiHO4DhqYNdD1rGEUkX/2z/757q/9tf9g92d/9qcG/4P//h/sfuu3/iOdeqFKP13QrquSuJlhiEZp61HiFPfgPWjgx9TAK22g8dqjp7wbnLYKKGXUEL93CwAMr268V8H3u4x4PezB7wu69tudKsGpMDh+Mu+YjPe4gmKAa+88sBKKzzjvNa0AQ9ArrXV/qlnPfPhiOdjjpxrg6fh4TgViycdT7ZPIYRmPn+pjmb702xB0SYagi7I+aFDHBtF81fciBvGLOYO+DzkERx3HFIE8ybXlcmUXENeX8oyO9OdNVtEB4XZn0opBns2Ym5iSBi7A0+9kMxcOvuEduYiLDNGbl8PwYcyUUOdnj+Mqlgd0QvSVhCvpddrEh7d/ks1hf+Gnb5MBIB/iLmo/EPcgVRiuam/Fa/pQxgCOU8NONKP6LkvrbQjSbGoZgo61EezNNgRp726Oj+e0VpTW5Ym0YgTi6VhIG4Aa3nJN3Z6AllyLAxd848udwAII5+j1tN4rhuRqHPpv/8wV/8dwM+2n6ZDq4zJ9M36h/nS6Z9FJHuWbx2AeVcLQYvQ3S1M8jGtsu+D2+YHtg0OfKxShaUq7vlUdFSnlIeMkGYHk4YP5G42TPCPIH8i1fyqufo/0sRyDsZeG1R5B7BXE1huU6D4VjJPDcny8DEHeFwhX7QOzCfWc2nDsNsL1MeUrJy5HZ13krLO50EpeZ0F3XFoZXDcF5W8KayLE9nLjseQSYoUjoDJkYJ4UocAqSjsCk56p5KF897iKBC7IsqNFWX72JhLPcIIB4wxpgy02BMQI5A2dBfvw7s3u/Tutc5Hx5/17DED80naw1AtjD8fH39Jpghwd/4sv7+9++cUdbxzNsjAO4rmuDeX52E53yysuxGHkeRHa8iwIpCM/ypQblClOUMYTqYp1wo6zUaNgrVbcUfcVMXoqvJBN18zyJB3JtKR1MAS1dj6/+9MyBKnwUN656LulkYsxKMcivvZGZ891lDwbRD99+lodnxfqEOmrmBq7p7J280WMKdIvtGb+jTqBNFzp/GDBoLOj17it3vLLGGRDENXIDULS93cZCr8lyS3+hm1qmEgWSHzQ4+sfXJqmXWCdyGIIMlS0lXophLAbLKK4Y0sjo4bJZEXrOPLTQVJjZDoRYF3OJs/a3FCtFz+WeWEEuq7ZQEeamshpYViq2SeIxupIHZgTdrHXkYZX6LxgPFIjZaOSUk6DKJVKPvzOiDPWDae0WB1K59z5iDb6KHletIgMFHRFV2i5HAMy8OXOhEk7cc1PUeMi0/7LL3jLLa1aroWucUDMR/jmZPkEJ7yl2/IhPtdMF8j+e9O1u59q4bePbivfUNpZzA7wn7UG5qKP/3/9X/7n3V//D//60Mkvv/xXdv/8//m/NTvwWAVvgFeerpOgqQODDA/Iw3XQwGfUgA1B7KZaRTEOnWV8dOrzph1FVWAbOFyWq1NdFaUH7p2d8BgheXgnz1e/OdrNOxt2/b4Aw8+zZC1SySYg1BiC6MO80iyfp3z8eiYDEAYhlnowO8gzhB7bEPRUMwEY9D3XAO/ipWuy/8QQRH8n25Yy4MuAzn0cDfTiLoYgpLfZg/T1QzaUl7yW9tARCpXS0FXsGoT9v7iOq5s/FBGgDwPTcI0O4m/SjqwUBdJPSmdoZiqimo36dVIOp9PMl2NInn6Wpw1BYUA/ymKIW/pPpGHOwyUlloWlz4VfaSmel9fTp5Jc169d1Q9jEIdtaPaP+lC3WV5/S0YhzQg65mOa4DdkLPKyD2YE8VFNkZHRP3KppMlheqqRw3oF51IALrLLEyV4EE7uAXDBUE6iJ7wKLnTdd2hXZLoan5AhAqEn5KxHJvBIYBA2n3YHYuNpfLsb9Ag2vt2BkGeGzf6ZBv+Mm/0foxu4lSoS4E7O2x20Kw/PCbrWUbtN1GHc9oOb/Qk1j+ZIyoaJNDCF7aeNwBCk2UD6av5GBmMMQZ49qHaCmYPeQsNLw566DWH7DDaWf6F2gg/sme2DkVg8OUJexoLgabcAAEAASURBVOM+Pp5ZhJ4JhCHZP9KlJUSe/BCf+tqnLcs7ru6fO4e+Tfkb4SW3U0EbPNozsW2Q3GIi34IfrcdEN3uLUgqkDfJpbKrblBN+tBvyOAI5zFW5VWWltfReYVIDKyz4KI4RiDHXexmE9CRUp/WTwYj47CVGG3Ck9uJEY6v7dzUj6BcyBH15TzMIb6oNYf9VHdqjWUHXRNsGJotRqS96iTxdJ5E32hfc3uQ9/mSDclJkIzfJXTOPC+dwT5j2H8iA20OO5BED+PY1niBA/Wi/rsuiRUk5XJ9fA+fWEMRMHxdO6cjlifIz+XnFU6baGMTa11dvqgHTrKAn2hn/mWYEMSWaUzOYIv1Es4PoLGEo6hPE6ES90w+r9wWmO8qVR2nLj0GIKS7glViVYdGosAvGzCBgrlTI0wI3QO66Qi0honUlaCqqENdCpYBADqsxmeGhDX1o6IgQ1s+yWkOKQxiuhXM4s4EwBCEyDRvr0i+p9brEzB7lmQ6JjUA+Fp4ZQTe1LOzW7p4MQcfaLPqGjEBetyr3ijowPtJQxiB4+TXgBrE7MEpbYa6oiLwobekweUeP4BUSzC6EjtIuscHoCit87mQ6qkN1m4j6RQMGlk7R+DnCt/PPbCL9SrRvx/QHjLXIu/ii0B8w0W/EurX4jSIdiH8ADWzr1BN1GP+z//Q/2f2j//EfyUh8VUtRXjvVf/pP//fdv/fv/jurdnCvOMVwKnmpvHuJD8CDBn4cDSwzgpJeyqfu+ff7ov3dVnYHvN9exOQ9u5RttWP0IQZkemHNL6/hb3ze2VSVVJelkw2vwJZUocYQROqvNL3Z/RudmspSeH/pH/sEPfZJquyhyCmqL9Q/uqA9gi5cOvLMIPo59Hv6g5iNP3pZZpNYTBC80TNbKDnNe86zkYRJNpN751le5148PHCyiMbU+7+iEFep5qJ/lT4WKac/UKitA9+G8VIfIce08myk0q01WyJM6SNbM1mn67SRpeQgNTgPHpY5D8mGGD8Y+lI1E0Dq0nhNxp2rWc6hAR4n/9w6Odbmrze1tF4HbXASkAxBzBS6qT4Wg7wsr5fL90jrLjmj6zQbgpDDxkjnfNvPqzw5451BS16IhiXY+lmUscZXpD0OdB274gxQw/dE+00BDTUNzydyNuuk/e0SdfYTXuoFofmCcinZiTliy+OY1Y+mLFMXmA3E8rA3Whr2ku0z1A5kH9XsD+STw1hCKuNQDEHv1E7IECTDkY3DnvGjQq224sLFaxorMDMom0i/e582wkvChjEIiak1tB0SgL5+N5zUpB4PgB3lhjidH+LEz13eCthHqC7CeQaklmvDsCmKNNRLrIq0iSuqqQ1w4ytQG4NcP50eTNNWeJYoNJjVlT8MNv4wLj/Lwj68fyM2GI/fuLVlbAIdYy2Wh968rhmCNgRpjyAZgn5x/67ai2ONwzQW08f2a5oR5JMIPVajDSAt3bic9eQfvm6SgLutTPtlUoeBG7VoDDbFytG41ZUcJpAU4s9G4OXHKcKmaTcohaxP6NSWSg4fHw/ycH12DZxbQ5BmJFKGXTaXQu0y5ELezSSNnBs6ReCr2CttcsYeQE+eayq0Nkh8+uSVv4o90oaJHClPg/eCNbKaKcQ0STozbEzNZmhsfsYrl5lAafAUZmmYpMCqjhzdyGJAUrB+XYNKYAT3NaqpQoFxJx5XY4F1jMDT6TCdbmEXWPDck3riyW8l4SKhXM8KCl1Lms4VNDRKfPXK0jDvQM+MHrVaXhZGo6ONvK5pNtANLQu7ro7KbW1yyCZmd2UIOjnRjCAZgI70tYsvWRyRSjzzkfrcQNHIKXl3YOS3TLrnmSYcow8yKheA8NULrELRUeGM59a0ITLPUUZMNEdY9EbnyehZ2cVjdmikPtoRLeLvl66FmmSfhfpOfniHb2nAKneZKZ18J/aHyL9xGljVJ+XuT//0z3a/+tVfdj7/4A/+u93f/tu/t/vzP//z3d/4G7+1+4f/8H9w8dpXcileaZrm8lfqoqE4XAcNfEYNrA1B/UZW80jZVJF1CdWtXUTlvUa5htrNp+n6PQNllfVV29rw4ODTdGs3/Yx+/yhUpJZAftz4SZG+D2H2PHyiZV9PZAhyP4eNo7WX1wOWh8nl9FQGfg802OND2EVt+LpjadhlHQ1tQ1Bm/dTCJvHUm7sMRE6vjUV+PyNxtkke4ilsqZRn9EOcHjS5mgtpCuGdAzUKyRsZyC9tTufXTArX/vCAIqqNHoCaqRA9qER/8DNt0UNNmiWpQ7rFdeLEyQwA96OiXOGTDv0ofDb4EK0G6zYGye8TV0XAbCA+jLE0zEs+ZAi6Oc0IOtGMIPYGOj6+rr2DLtsQ5AM3+Ph2Vf0od6CUlpOVPiUTtiHSi0GIPlbp0TlMn8/dLOKQaX5I6zA3flztJmSyptuDDxV3MyqXcGsW//YK14Z+235Sp7hN69vya3kWN3lAz378C+LTvqHG4flEnFkn7W+XqLO/WVVfvoPlQhlq7pRK/nJ1uSdEaeWDNQN2loZpi7/MCHqVrTMYAz1hSdhTlobRTjAjSPuoygCEMYjfGxmYvWxUxp737Jkqg/AFzSa8pPbjAoYgtQvgWSb2ARqniT5JF8VGP5a0yiwF+4MLa2Se79DlWXSOCCd/cFqgxEpohic1cMuFhrjWuKUVCGVj2600HTUzBM3DdRNmis/MQbcBIrLQEKN1tJC6mg/jfPTWmEsjTWYFaVt+txVojbaC5V4Ygo5lFD7WJvL3mBGkpWEcH4/h2EYgjcmuX5f2GWdJhsw0Sp4ssfQciZEBiAWXmJjk40dmUKbnhl+/6Fv+NNxCFI19zSlucXKA9Agbppv5iOFsICoWRYO+iIAB7DAjqHVzHtxzaQjCMPNOhcUdCZcbio+L7yh4QCh4/kGvRo518nwZwxj0TEu/WBP/TJ0jNk189DCGoBwxr+PlNTuIDRbfyRDEtElmIGH88St3GIL0+pXlmzQo3PQL3DegkZM8RPGPxo4a5av8ChskXCgbX2SVH0JT/WsOw6Wqdcyu0AkndWAtCQ2mJSpjEBZqC9+SGo9FWhXSGUkuLqvTclkGL1yfdKFPUjROTG3GEMSJF0xpvntPX7TkYrW+wXHx/imu1HTJhqA0UOQbGWno3HGhF6O0sVQDH/IjrkKE7e0w+hyY+EKQuE0fKpPuucHMDMejgT6d0z3kZ4DS6QAZXqfJSlY5Vu8ggL4lXOLu78Q03YhsmfM8F1j4Na/TcRYZGzfTLv4FyzPp0JzOwf9z18Aoyyo2dBB+92/93u6//m/+K6vlX/7Lv9j9/b//B7vf/d2/tftL/+pf2v2Tf/K/7f7yv/6vufh1KTMhgS5exbCDxtOpOlwHDXxGDcxLw/ImijBpFjFq6O2aoyxHWznGAsLl/VvFnOI8VQDvi2F2E3AQzLBWALAMOoytOhNnriv4662vdyUzkd/IEMSy9yca1DHA47TUrx4/XgxB2gz2ybPn2i9Is6I1W5qlYfRtdvq6z0xoLw1z/0eVXa7e6OLLi5s+kdIDpvAY4FnSZDdvdQF0oZFIKldyZWYQCMKhIJal7/xZJ6WPgkHZF9HAhm9DcQUZwHiKi+nnF3LSTlwokwzU+tEfEoA/D2JA+kMZA3GScAzf3VdzPyaDGZ4XP77s07+xEcgf1JhRzYcyfjEE3dGMoBPNrL6lgzZOOGhDhqCbnlnNgRv6+OYDNzLQg1fnjeLnfpTks4sulWpgSFi9UdGNa/ihnH+DwnDI4OYh3awkkyXfiTH7A1nu4OZrJD4Dfxj/x8SaUtzf70ru59K1n64ZNb10Jl1ZZ9Zt4z/mtk7ahfYsf/NJ+VvTBVdDageaC67LsQu3a5jHK9RZG4LU53/LqWEyBLFn2CP9MPywbQbtxQMtD2NW4fPnb3Yv1Z68ePVaJ4flYzlLwHr56CWWlV6g3WBWEBtG007EkPyB8ZQEIc384regelYuKXWz3JTzeOzkcUazxOl6apJmstKbgeYLjVkHpPvSMq3hS5wlxj6KZoSMwlPHSQFS/bIslE2f037gkqbWf7kt6DYBl4/vvUH0JWYOajP5yypD7EZy3QZjGYG0UfSxlozGEHRv94X2CmJv1p4NpOGYZwT1gT7mbxERpmWVhEqvxw79DnJpoH7nf+RBwRHV1b/xzW7CAyIZJ0WW/LfEJ/sDD3Exx3F5dRsLA9rKw4wgVHRernNpCGITxDcUFv8p4IJNuUrj5oJYGqShIEzj80ozfFjT+lqGIBoznyAmQ9AjfQ179FhLw5gR5OVhdJieq0F8rymT+mnZJjOQ/DWM9a3uAKkL444SDR1WTuH1078bJ6TrWUERhRoU+WgU8XONhoOIBhXcYfnlBgI1XDsMQa5iJVxgocefDkDC1QhR2aw78PqpdtogJFoE13cB4zNjiA4M0w3Z5Jmpyfqp85LTLrRmlbXrMgQdyxTNqWEYgW5rquINr2sXXa1bvaxGjU3RsFS7kyyeyIy8TI/kWFXSpoFyR6ryhWPZERXqchuWRoy4wtoJf8JnXcQll6S/NIahpvEBH9nM9iw2E9zSKJxE93UUApuiQGsBgC3Cnh0Xmk6nokYZg+mSxkKbPA4Sp3UaBh7eLceUTssJyeH6zdTA9rF38Wl3lesAHcXlL/X4L/6/v9j91X//r+7++I//ePd7v/tf7n7/939/90d/9Ee7f+vf/jd3t27d2v2dv/N3d7/92/+5OW2K7cIdhIoe9XgUx4MhaNHPwfdZNLCeEUTRTPtIO0qI95XHAKPgykQipPEq05D5NxrexKe97U740vbCk8s1bHINHOG8t3iPB95uRBN/16G4SMCP2c1P1dd5KmPPI/VtmAH0UMvhH7Asnk1g+fKv/s8D9YOe6lRVrQEXc40sarPoD7L25qAMZkFjCKIPgxEIAxAmB8LZD4ReBzIgRjSAnOmddO7RI++suKDxha5jDibFKdktxoniGPBuvk5RvIZOiqXDC5E5cjNIt4VDpeIIpLU1Aimsjp4HeaKBmv4TGoDZMAKWf/TBpJ5LKiP+Wk9fSB/VbrC/opfW61hoDfBYGnZyckOHbXDiqn70o7THIvuDsOyDE4H4EKeo1p3z6vR5GnnSbjKRRzoQmURKfvg0WV40ksvCS1AKMMIrji/yjr/wKW/ggz7dT5H2ICfedC10FXHg1nQD/CN4FpmWxH4QWJQ3EkED21yvYTP2LD/sZpyHzxsYNFDNdIlliG7g+lG14bYNQe/01fuFDsp5xmwgLRVlTOQZQYyRvMm8Vkxo6Sj7Az3jQB0ZhN6/w5CEoSfGnktqOy5euO7xETOCbDRmsVPvL6ai6LGRyh1yMG6yuCpELilVXCxvoUbhm/LWeRBok1sgfcGlNF1eQkmpcQt36gs1qevNgml+JVwHi/sICr1UI2b5kFnakPxsDMLow/4/yoBdtQvMAvJYTO4lbShPO9Gze9gD6MYRRqAj7796545mBMkIdF+bRbMfK0vDbCRmaZgMRzEEdVtEGsqNxCglWz7rFgWq7e42wwjkh5Rb+U1LUA0KftqVfRc4kuFViN/lzK45pt10WLe64MWHiswSIiJlMzIfacxJO3a4Pr8GzqUhCKPMWwrLRj8u8IJRCH2JgLJOWHVR61nZFV+boanxeolVW5tCP9FXsl4LywaKj9X4PQOmho8N0Vgz+/o1O+pj+fakPfFU8VWnx3sG0fmhAVQivSae9JBuMQS1pHSmgktDBDy4lPfyW3iYEIZbU9mrGy/69gsnf4JFS5iGx3EtiYlplJhGbAnadUMFpRREa+F4iuvWWaYvdVqYxnxFjXumJ2eDaL5kHd1gjbsaKM0IOpZlmn2C+KJ1w3CmPmcTs0s2BKWBsqwSAdfdRzVcblgIqwVZGuDkz/lCZDy138G2EXc2Q658O4bJScPZLNziRE/ogQu6uPVUDOhEwTS9yb7xLR2NORr5JNE8ocbMHZKUZaVbSYdelCVrd7rmOAPpSIrh/Ccfsz/pnc6TWYtw1llgLeHB/XwaOP28lueUQpFnPEuYZ79AwoMy0+WK8jTKkgJziQxdpTsxnyWB5g//8A93v/M7v7P79a9/vfs//4//a/dX/sq/sfsX/+L/3f32f/Hbu3/8j/+n3d/8m//x7u/9t39PX7LubtJdJOtCl5wUfG7kJtKD96CBH0sDW0MQbazLqG740pHFr1qhugCOqmIXfwnqDu1SYQ1dDEEE51o1+9e4rqvQh51oGVCZY26Bj5QlD4YglsPzpV/7A+n3UMvAcpT8ExuEfET0/8/eu8Xctlx1fuucfb+dY3MRIR0M7g5XycE2KLRDWiAeYokHeKARUoeGAG4aaCJZCBRwuEggobRAMjKkwQQag0BAw4NRGgnBQ8CAIYZgiRcQMWmQ6A4YfGzvs++X4/x///8YNWuutb5v7+1z9jkfOau+b82qGjVq1Kgxq8asGrOqpsY/H9JkD7ys8JGhx9s7tOqZwyxs9AEW40++EJTxjyd7vCATL379VAwR5m/mL5whO4WQmQHtV32dgUs0kjVTEXFYKckHPiXELTDlnArtYPvJ5azJCyuKtkwjZyZvvQqIsREy5xe4cZJBdVFNRSArnPR8J+zxDHwmzmqg014NxBaOU36Rxorqi/rYxmVN9K7osGi21T/Fp+NlCGIcxYqgCxprncUQpFXVNgRpTOZ6qjJeGSS+NcQS/4wLU0P44I615O07KemkLDcFzHZIYXIqqKqosqDS+Scc0YKf4FFX8sx4WzSLxi7eTHMJPyzekuPRQg9Lf8abw0tp1BlZtJzSLmdJLLi+A1vSbMz2G3uROpJcYozxcTN+h4Pp5Alj3pbTq3I8d9H9Yo7EB3JuY+SRwZi5UT6qI73QhiDBr2vudPPmHRuMeFmuBS6aH/V857RWsZyRDLQ1DCOy50ecFSTdINnYGCQWaR5+gU63av6kOywx2lIz3mnEC9hp7U9JBBdHIRYDl8U5Rj+pZFKAcd/2u33wwFYpihB3X4C49IT2lHjyOQxBCEswa0nmW8KzMUiYNgbJZ77ElwHP6MU7B8PT/y+dv+AVgk/r/LCnn35q83GvLEOQdIUPima14FgRhO7Jz/xQithJWYSBGmBmu5W6/kqCf2O0T1Y514sEuVljEC9qngd3PLCltVJdYI0LXpFTrUnw1bqD5+XhsGiEcjLcyTcEuXXmQYvIFEpD6xamVkfDQwHe0bJHFB3LGTEC3eRQaP34bLxXAqH4pPCwhF8VjPQ7MgRxptA9vVHzNjEZgTg8Ok04g6IYfBgKLOVb0ZkXugydQb7y9YHR8BlrZyk/KFKXdu4tU1wUlljC5sKX6lAKB4dKo3BMRDAeGMqjjhbpEKYgxWFUuHnDlRqkQ5JDb7Ckv8/qPKDTZ7SEWW+wzuFj6MEQpMHLJf0uShld0TJmvnpxURbqS7zB0tsrPi9/hj3tosHXMrBU48yyLhiCGBxRbX5Jbp7xwZSLFhOvhTxglW4k8MixwEwv5BrDSAYVTRJa7Mjfue2vM5K2HtxMJB9DcLu8jrcfvlPXha9W6bu8gx+8xjGEy3DQxjW9IctFpAP3EHgpJJD7OjXd583E3J6OIrbdLhoP+N07dzdf//Vfv3nHT79j88b/7o2bf/tTP7X5z/+zT/RI4Sd+4ic3/+JfvMno73rXb2/+yT/5b91Hu4uu6pFO2UkpovRFl3fwDxJ4sSVwS+OFu8yQ5NCHNNM8IxL30x2AfvGExcS/8PDbgTu7j8oQBIHqOK2nYSqagbQOLxy4n+olGFu+rvOWn1VBGt/wOegP6oupfEIeAxBbQYiDp/fSqhDnA8nQw8ofjDz9GxM8DEKCM7bx6qBal6JxDpPL8ITMxpME7uUilTYEGVLPnqQmJ/UDPOppSmDgwFGiBzIL/YlMi6n4IEfokrt1mjmBDMBy/poYMtZ4iXFjxkb467ApCo3xVe5tXtB5ZXPR5C16G4SY1PHF1VO1xZ6vALHq5wKrqrXt42mNn9gOhiHIq6p1PpC/FqSx1jnt1GOFgL++Kjo2ALk6aWuUj6R9ByQE6rP8gpPqlwzKM5aFTOU7B+HFgdrymmW4YCyhgYf8jnGPA29pJ8cU/AIlwf+Dyht1fOgyW2bx0TrcERxyn8OBRi+RujjC3R/Wd4sU/wZ6+inqDZA/Ia9pw21t+bqhudB16QF0AauCOB/o6oeyOogP6dy0MUhzKB2fcU868v49tnzRAvWlpzIE2VDMrglvH8VgnBbKC/UYn1SuCrauME/FN7JVrq793JQCd+K4kL4PHoSW2hybqZNOfOkziYFfad0/HNUl/yAMBwq6CA/nEqQ/vMOCg6AJ62drm78KxlwMwdfqIK8QCoz5Un9l+RzngklPsBqIVYNPa371Cj7KU7+npC+Ya2lq5o/yWL9IGaCVaX/mpwRIeKdFSEEFS7xTCf7jSedTk8X1iiAg0XdLGvVNneOPuEsUIfHQ93FJc3Em4paMfJTotq4sh8/HL/J9qUMn2BCUBuvWVQ0XYbmRuVeW6ADIcabQPVb3SNHd4dBoWb1t+b6jc4KeZYAkhacfb8yuabDEJxNvaQnkLW0ju6OvjdkYpLycrB/rd60EUs9B+TEUsIXd8XQ3m10wXgAzH+r69CbF09lgPB1RIcC5FC6xdk5SSfEDHQNOgJWwqFAxWz0PhWMl5PxRxxiBSMc4xIAHhRUlBW0s1hEciuWcPhN/RvtUz8vkzH72vMWSry1hF3SAmQ1B7F/V2ywMQReEyxcuzmhF0BkpKBuBpJk4GI1xG3W3FBRB6bXiyW1b6kiVzAUXRySrqtOoMElkDLstBkFnDEWaBgmiAZnQV3kE5KI4g5jDQElL4r6HPmkNfxS8VKEYTtFVvlmbIOtgl9f+OrViYhfFDl9W8Io3jwt+2h/xgUe4hbggjvpPoEPwBEhg956m/Tb8uDZyXBpV6/T291W329bvvfv3Nv/sv/9nm7/4i7/Y/Oi/+dHNN3zDN6hNiYY6+O/89u9oNdD/sPnz/+fPNz/wAz+4+eZ/9a+0lfS807dpupelY8BAksvbxj3EDxJ4sSTQK4KsU6tQmmWaKC9z0JsAgKXBxhxiEGA70sazCzz0s8cChWAvzx4/m2fwCOeZkW6SZ1iyLZrbfc+lpuT0YX3wQrMtzkT09jB9KMOroPkktLaGsUXsQ5yTqLNAnlGcs4Ry3scZjRIwBOkhzoogG4Iw/nAILFONNhDxNCden5YvQ1AkgHyGFMwuFyDRIQufI9EB1bX0QY9FLOkW0YwsEqZnUhVR3so+nmuRXjL6fhAs9IXcsgIokzfRYabaY6hBt/mLD48Uz4HQ9j3QUVyR/jGWYkvYKYmQF2oX/BUgtn9ponfhglZTZ7LHp+M9rtJqIL4YxtaQs7xQUz6250PHzhVMeS5TPFBWJN4rgiIbw2YBgKV4KHHtH/lxxEFYxjgD7ABJye0xxBSu5OG9lHjz8xCGOj6YUyB9JMKZwzMO4ePS5vSBVzKZ6URilmxLeEpe3yBG5abr69LHkxtgMGh7YPYdJNblgJU0zwKIOm6MmunbKKMLPvObW3q5c1PbQ2+gL2QI4iX5h9ktwU9hPrJzgzNWZRDivFV2WnhepC1iMRjz5TDpDOkJVgYxGOgvDMJlrwrCAOWyVS485kKguU+NnDYlp18DDV6nkzOu83d87Xdq+53aceulBg5eBmAEcnuFrQD3IKhwU/dKRh7WBDGvii5hjsXcKiuC/LJec68nnkicO8ScjjkRq/84TP6cz2LVmWHSCU+xWlDzq6cxAukIjo/9mKdkHDpXR3YoD0Zm5fXWMFXGumCwJZ5gDb1kV77uTRYkgBgJ+MolUWM7KDVvEiEwJ+fWKZEmBU7Kit/3K36lQ5AioaVy6Zeeo0JDf7ByMAQhnJPhTrAhaFF21X4tMTdCh9SSqrEB480OhqB7UnRe5aNtX3dkDLp1966UHRbuKL1nZRS6wYogvR27oT2wGIPYM8sXx8jHodMcqGZjkArgk4g2+ORdDE04P1m9Xa55wUjEYMnqQl7SntTgyo/r9AaFcbku/hIiJamiXGiG1aAjuSMBG3jMCdAYhaTqlQ0OywgE3G87pajkhzulgSPCUUqyNut1FF8I4w0W28H8KUMZfi7qd0G/SyxhJi6r9UUZhs77MGkNXGSpRqmx6of98RhXkAK8u+aCkcbgyWDXCf62nRRtUJS2pLZicd4J3hjUYYVTNKJqlKtpVoa0I8oXIP/bjLzAcQoeTCm0hPNoSdzsdZIjSy7zOlUStEKZeBWWgBPalLYEnZfMW0R40L0obqvcF6XMvzeFzMKph/oDee+b2Yh9H6EVNwatDTjC38ETieekM9gW9iat+vmUT/mUzU9qBdAXfdEXSc8ysGEr7t3NV3/1V29+SZ+Ux/2H//AXm0/+5E92eKc9VuNsDo2UWY2Dh8tBAi+FBNoQlLKtbTuoNp7nmedTarjddmm2hPtHhjkcAgB5Cm47+mb3T1I7HLyeyPpZMZL05OaFjqi1jh9f16yCmajd0CroGILY8qGzEXVANG/6P/RBVgOxKiiGIbaG6SW/Rgecf6ixireCYRDSw9znA7UBqHyPfZSGIUg8LS/E4DkMhNXUtq8tTT9fAiRDnCvSYwH03ahsY/iZ5ojyQsuPKV0glYkXqfVUhS8jCxcE8hRugeNJjtk+w5go5Y5JSo2bjFj8QdTjKQVMlnGOAozw7BPmp3HOGQxBnPGjQY8NQZrE8ZafT8Rf0fjpss4HuiJD+WVtCzvPCzZ9bCNfFeOFWq0GkiGJMVVVUgHKrbLlM74yH/JpXYRxi19SGPJAbkrtRmzslpQjohoHtaO+5lQo9naeFXPiFH6h8SbSjz34MLxbruKk5fdgphZMWu0SI+ccS5jxfFziyz0OlHaB45oQfkMTdjrbutTWoYaeuH1HXwRjTqTtX9f5lLyMP1dlJP6QDEHXrmaVEKuFMAixs+K2DEHMiTRFEn21QAxA1hP4Mgj1llKMyvDCnEntjakH+oKX9OZvtMGuCdx16yZfapHWTlo74ThtzldptO09Duh2SseXXrMnI6CwIQLS//q5CF/6fmD4ATFnBMWQ3Lsu7m2e8PYwS1u0FiPQk6wKkv44rfkQ+oEdFedkDMJgzI4LVgNduXxp84pX6KM8+kLzx8rnS2LoBr2r9yoiz7fMV+rXdQpDYRzYaF1+BlEHpbkOlU9Ivh1Uo5xQ5TpnpNT069ZYNJQiCSSXvNwv5VtATksUOtAEMTiE0ZnnZfl2kaF0uL6EEjj5hiA13qi2bpJTI5Tgqu354X4fQ5A0z10pLVYFsW/+tqzf19nvqh9Gn3xWnpVBOjFfVm+2h3Gw9G0MR+Bri1gMSuyLRZGVIQjFxqCpuEHZUTaKMR0ALoHlUc3hi2iiU4wQBOuHRroh13Q0cCo0alP9laSCqdRR/SijbAMjTAcrn+4pxQPHgcdARHreVkpJKYXDm6PgMmg5rzOALmg10DkGJ3p7hdGHAUuvBiLuMOkyGHGgdJYxq356i2VjDzXnXvmnQuQwDvFyy6Iw/1EZBK0YRs2d6DyWRWsUKk1Y/y2j5FW8kpxpzyVtJvlSFmHuGHnn8pL5QfSClbz9wGrYPp8yZjzzkOJX6NSr+VslKLKicQyDrs+QmTJOGn6m3fRalirazvkTfHxX6r6I/fGV8/8jynP7OapafU+XdIS829B28aYcU1ud8d7/N+/ffPEXf/Hmj977R5vXfvZrN//yX36DtjpcGu36jJYD/tQ7fmrzG7/x6yb2737x322+7J/+U02KaPsL/Q65/81wRgMHd5DASyiB2RA09CL8qGkSZ7LtybHjYdTPOaPkeQZ0e0BrdedBePIs1+4A7ZOy9Nm5z9OHWu0HTr9SmWNCVVnVj9j2cUPjG7/JZ+Uzq561DT4HRWMI4vDoa1oRpDOCZCTqsc1dj2NkefBDGmNQVgQR95YxT+56XIMhCF7pyfCCpkmcWqQeHVrg4/kCCOeqm0LF+ym1lgl1x+E7iF9A5BCZVF7iwU5VnE8ZuDEkwKwC5tkXxkqhEZ8xU4+TFBQuZVJT0hdjkMLmA19v6DXGYWvYKcnrtCZrHhfxll+6kRdpnAX0lF6g+QthMgoxriLMIdIXNY5iC/45tthrhQCTPM4NYTxF2ebXfFA+v1QnfAXHpkb4GaKrwDQGSLsGST8nD+RERXfofaNxbxccOGk38ASYw53e/nFpjYP/9wlPorFryZj3gpEGvP0CT15y5U4SbiqN0unE5zTaZRxtAEcZ4WXJ0znSSoLZvGC4xSjDLcUIyu4H9ARfD2P+w7mpV7V99FntkuDIDHZOXL+p3zWl8+Uwzaf4GjMf1eF1slcO2gCkBi998YR/nBOEAQhdUfMmnyuk1UQqM82JvPqNtgmH/MLbug4AnSQvOIltX5W2JxnQDE4fCMGVnp+xzCS5Ck+8co/5gWZjiuvSZhCMP6ob8y3mYJp7IaGsBGKuxfxrMQTlrKD7PhfonM9XVd/XvOuSdmGwWvAVly/q4xv8ZAzSiqBXvkJbSTXn8u4LdIsmU5wtxLCJn/4X7iPgiXcVTTWMx/xTETLoQnXs2ieidNdTtZnlPVAiEqeSbgnUfcSzvigcyBHk1/kjI9olbYGyDiuCkNNJcSfSECSjtb4aRiNKU3IjJqbGQ8Ny53OAxgaOup8GQn0yPquC+IrGXb25vquljSgzr/qRJZxtYtek5DgkDWXIqqAbWMfZSiYFyTYxDpG+hxUcOurLMQZRLhz1TwrPcXiAmRh8kl6vdNTaWRVEq/eQ0j64gCqPPFLjQmmE6ZyuX+roLG30wbjjtCihdLAonVY+YlD/UU5PGl+4oglPLC9kQMtkjj3s52WVvohBSKt/LnNwGXvbGcAIdqmWMvvcIBmC2BLGoIfBi41A1EE/KycFqA2PgzUs8aqova41EkCKsyQMEWikRASO5xJKi7Lj/gQWWsnbys3kzdO61M4Br9zPkVeAKKz4jbftQ3/B69Tcx8EOVBfmGumBfmiDNlPaky3Mj4TOl9oseZvXIPJACEb8kf0QOBESyH3bbjbb93Cb1W7B3SQ6vuCFbsN97w1qTVp9TgW/5z3v2fzjN7xhyfqA0Otf/zmbP1CeJ1EKct3yKGtECHQCCuPgDhJ4CSVwrCFInYjxs583aqrdWudBODD6kF+0EKm27WxHGoIasTtCBLD09Rme58tI6yQ/q8IbK3H9NSCNda7zcqtWP3+IsxF506+tYWz5+JCMQM98+Kpgtz22uVdfAvLZH94WhkGIVUAYg3i737+8DNOJ0gWjnuJLEvHEcEiGekRKGQVU2F4gYBjFFUpldp+NwIWf7PadGzkbxrjGI56Sd3RX6AjH27cKF3wXo4vz4DFmKhqkGZ5xFLXC4RPivrYRSIiGwwPpGIA8vBOejTis5tGY6Ize9nPeos/94Ewg/VhN7RdsvExjbKVzFlkNxCfm+UiHD4/FGKRfaJoxc0EVXJ6u+JaBkoHrkya6ysFTWFdEAd0fUgAlRKxwFUrKlF7ID1oR1GOFvmfr55EJ+/I48LpMCuhy219KXoc6vf116m6s8drfxhgyTUN08hD7NvIUB4cW1XcgrWtCmNJDL7jB6xICg8Zyd5Om1mtoYl2KgTHO0MZtmGEl7z0Zje/o6Aw+E3/Hhh8MQWwnxRjEqkJemHOO0DXh8SVmz430UR0bgq0rYgTSLEDMsFVMOkOtMeepqpVKV7DqkBVB/OBLLMiF+/jEgbtVVyS4iSz1WCSXFK6kDnihLjkiocZe4HPJM1Rw82kmTT3zGYwoVVjMHyoUMwh4MSab4zYCCbY2BN0zXl7O6yW9VgWdlZ44i45gW6iW+lzS3OspDEGsGOTLzNoi9rSMQK+UQYh5GCsNNU2zjukvMzPCWnGPnhJg3UfgDDnoj8o5A+EE+xEiUqOOqVco50oqdIOT1ZTRiBYBkkjBdX/B5n4CV6jEabq0QcUJk+WiVgTNZZDz4F4aCZxYQ5DsMHLVitRqaDBzw6VF+c9+GipbGVA6HG52V0YcDoC+51VBWRl0S2GUG4afZ2XtviGDEIeisWIIxWhjkIxAN7UckkOn78owdJvtYvRtKbYoObhSR9CnEu3Em9McYQAlToXbr6WeeJLBFfzz7iZKxbUBD0j5PHgU5d8/SqEXVZdTFFUvGD3JyqZ8YFZIMMnvnlBkjXaPi0UaOigiymKQwRt7jEF82YJ9qnwZDGMQ275Ypojh5xLbwFjGjBHIBqK8wTqrAQyfOeVcIAYtfHWM4WHqQTnh3+cFVTj1yD2kmtw3HHVdXw0oeMKFWpHGhsaC22FDl4jpuAzLEfyUWFm3Yg19Pn7TX5gAQmxW0M+nhBcmb/Epj5DbXhGG1+b5YcpqXHxc5++wgUdc9uV5mHxHkFuBm68VcIp0evud1Dx1/GXp6ybe1x5ZtoS94x3vkMH39OYf/aP/crv7RDTC5Wtif/t3f+v47//e728+7x9/3hCb2wOXHSegdd9OwgFwkMCLJoHZEEQDH/1fAWKOc3E8bO0YgpK84pnB8ci/Tpli3TFcyt5nhB/3lSPPEOEyyAln8VQWL8E4z4MXXoxrOC/oql56XeUgWN72sy1MWz+e+dCHPfa5w/hIw4X7fBraEzGNEIYRiEkdT3JWCDG567FPxjo85cN5JnA92Vi4Sn0swerjhoz+LkwTCBXGNIxuOpeCdo3O82nkV6CmGabRz9UeU5gH4wjPmVJGb63zJK7KJi8/j5XylFa5wacMO9J5s09c4yvDPZ5iHJVxD+WcPq1TUjSeYkXPOfnntM2eL4RdYbuHzwaql2oYgnjxxjZ8bQ07r/EXhiDNDT2eYtLHKqNUGB7CR/PT4yt4o3qMKl1NZCRehxtBUve1xJbYyFFlTvFD8HgJpIEdj1OpSHvcEsPWMUDbGH3vPd6vPLmbjvjie6+c9J84WkqgI67+bZWhIvF5SX5TOyVusdpH8x2MPhwYze+qjEDXMQhxiDRfELuu3RJ6gc6XmO9Iv/BBnZwfxryGH2cEZVUQrdFnjpU+WRmCVK5r7ElcapG44MjRcHBm3qnBg+KF43thiq52cs1Xg6e0yldgY5rHhQPrHfoVnc5OdwidoVrS13K/WvJ6Ce/+RxzjT829mItZf3DKrKQjQxBbR8/yMR50gAzGrBB8WisHn75y2auBbAhiVZBWB128dHasFmRFkP4110LSkox8VEXkONVdwFYFY2wPrn7IM37incswiCm9YWAPV8DUuQ1BiTVOl0kc9FBLqjGRmYCEKe9wRlBkcxKuarvz7TsJLLkLeZDSTYhWU23YjYvWlD81KrOPZRYlh8WawQ2rg+Lzefh7UmScAcRWMb4idktvza7psDTensUYpPODalk1A6lbwgGfgdVtrRBiZRCDJWhjVc9AaPHdeVhKbWVWChKOZWyJMqnHtRSnH8lURvgYTzCjuH7y6OC48hSiQP3GQIQwdUXZqDO1cUiwDHC0VPG5u0mvzxiSF6Xht0wizMt6tqud1vJlv7mSRZoDXvlC2EWWK+uAaAxBHBDNaiDeannrGG+vvIz5jLeD8QYMxQRd6OP8hqzvleKupi5RLcs9BHdRN6lt0yBt5ajy5IIdQCehVCwm5IUrD1zScPYWRMO4K7Sjbcc9e5husYtHKbv0oL+iJzTixZpSlxC4a9f0ZtqNT1qH17mOi5GLtrdQ1r0pMg3r/HOpDdvnv1R4+3h5vrBHl+jzLfHk5f9P//E/bf7Bf/EPzNjXfu3Xbn787T8uo686/ZajHb/rt9+1+cIv/EKnvPnNb9689a1v3cJSdLthgXEQ9K6cDpAXVQJrQxBNMo0y+nB6ZgnczXVlCAKxdHnlFP8e6ubZv7c2dAaeMTzHm+qMOHWWCuLlGacA2dqRHV0uBLa33+BFFi+19OMcRM5E/PA1DoLV9jBtFfvAMzIE3bzpFc8a3mhsgxGJsZPGLxqf9CqgfCcrcV6A2Vgkn6dWxj88sQnjzET5BviC9DIRUfqoZgeUc1SIYCgtuV2t0KmZGKLOfYmEye/nt7NOT/IuThMbSmucrASCpCAub+aBWiWNa9csPCLwTOzMg/JbCsqARmQFEgc9n9blrMZVPveDFUFP8aUwJncXvRqIz8Vz/qJfuCndZy1y3iIriXjjr4bFKmvGUfAYFx/TG6FIHf7ikHH4rjgMUrfO7niwqzVX0kBQxgo30aK9eCQ0/gJdQsen746Tlpxz6KTgdW1aHEfWvBvjXIkjwty9XTpryD6MyB28/Gga4Ys4/YsC2xDUHLdPisLVb3NmT3Y6YNzxKh8ZgzAas3X0OvrCOkNnBWl+5C1i3i3BnEhnqUq3YDiOjqBFaj7j1YO8AGdlEHOgwOkxzzFXUjxbwwjDK67D0EqrTE8l3m4JpzZLnci/6wTLv1NDp1t8sLdzEV/hLQw6A7KlTVqHIXj9+3BoAsallgq7XIzE+UVXKM2rhO4OOKuB+IQ8hqDzeqF+QbrAXxbECMQh0doO9jTbw/wJecVlDGLloHdeMN+SfmD+Rn8fc0UVb2e/W1ADw7/TXRdC3WYcdPuhKt2OCCy5nXO5KCHtGMkhgi4vKBYJhAoOnWAmn+FFg/nH4fPxkdtJuJ5MQ5AaS1YElYhGI07jA+oG6QbXy/MYzCQdf1F6WMBlBGKPq4xCLIX0XnpOzZchCMPQDVm/CXNm0C2vDoq1/BaWcClAVgfl04sYg2jaKDs6zOxTtocFSpevhs5XQzgryIpEuE/IaoJq4tFtBUO88Dy4pBOVgzqKJQOWUjjuaQxIVEFGg/Y7DO5d8SdrtCrPqiAUElZqBhZttLFV2QMWDVrwZZW+eBFjjyzTNgYpXquBOCT6gtL8Bos9rRc0aOGrFt7HXkoJlkVfYyEbF8Q0/7ZUd3UcF8wJ+HLAcCMt0d0r1SvXeTqOPyXvxEhv+tt5t+Pg7nPcn32D1G3cI/EoSPfAt8qZuLNwvXAeXhaOlhQyrGNAFkxiszs6pbFmavAMNV91IY3wjNP5HsV/WBovFd5RdZn5Ifxydv/6f/nXm2//jm+3CN7zf75n87mf+7mlx3al8td//deb13z2azZ/97d/t/mCL/iCzc/8zM9sXvWqVy2IRzUoBlfHtOaFwCF0kMDjkcBRhiAG27ypts9FDp3QzzRAjpMglzRd3darXQsZ+D7nZ4AfCkdhkIs00cq/fHIJxnCgnfoQepwxzx29tb/pMQvjFm350Nt9tntc0xt/vgr0YVYFsU1Mb/7vaIxwV4MsHwSryV1WO2v1D2MaVgBpDOPxlH22eWREQs2ylQMjEeMZ2OOqH54uXes8XyQpw4cH0nDj2eo65slIYiiGZuhUfmQqsKe+MCNO7RFUiHCKQy7EBLOHX2HjhWsSXZYzFQwaijO686fmoVpbP3I2Y2gzrvIbeg1+WBl9hpdqGhudlc9LNVYCMZl7Sts9+Hw85wL5rEWNuc5rawhnA7G62oYgtoV5ogdtxlKpC/ybNXiQc7sDQFQ+50V1W+yaO9mVTs7lmhB0Zhd5AkkZc1rCprgLHpCZ7lE0BvILGqBtjDZ0DOVHwUuDWYjtq5Hp9U1YUPeGyN93cxdhl7pxG2zRKpJGrFve7WGhZHzhdb8jZcFSgtqI17LZMCNDkGihK/g4zh3pixusCNJOCYxAfFUZAzJnqMYQxNwIIxDG5buaQ2lWgbHHBh+Y02qgU1oVJD2RlUL0Gow/4MB2dBMwDEN2wBXIi3Ng+uF5bkUatQwoclbErvJ3dILRhuNGwCTX0CUN+CpmhooNSlciZUMX3wEZ7m0IUsUwKke/kESYfoiPcmZuphfwNvQzD7vnNOZj4PBlQesC7b7AGMSXwZ7WtjDOBWI1EIfJv0J6gxWFF/VlQXZesGLwFGWIFXQD91pBhLU4h2kNC9C8g1FVSMDXuiQ7VbSTv+QuGN6graDuU+O4tI5M6H5EQVNp8ZBZIeKrwAtaTYDuOriXXgJ/DwxBavS0UremNKA80BkAYPBYmn2HaW7DGKQAK3pQYPh3WPFj5ZdT820A0qDppqziHI7mg6WxfmMUQvlpmxiHSHNmEAYhlBlK9Tl/NQymopozeCLej2X54tuHRgvmx7WNQoBZDZT0Ng5hIZVdyNXMBWUiclImo4ZjMAIELuiU5kYh8DASqaLPZXsYBOC2VwHhc77PWW04xQjEwIVtX6z4uajByaWLF3UuEG+tpKC0fPkCK4PwOUhay5fPntdedraD8QZLGgmF5FVNYgReVJxrjz5XUcOR5nRd7Hd8y6e67RrPsEpoWOMABuZkBwqxESrdvNCGSgHhb9MiSytN2hFh/G3XOMC5C/3wNe5gZqFlPO6hmW2aodu0EtM1gZ2yGy+0CkmRHR5Vvus1KWrytGv2Ol/o5mHXOLM/8AVcSp0xqrxKb/w1xoIDHDrmscLA2jWceJe3j+aj4jUt6D4svcbFf7m5a/rS0D//qn++eec737l59ae8evOHf/iHm4/5mI9Zi6FvgoR75+6dzdve9rbNt33btxnnl3/5lzdf9mVftsafb0KnNI2OH/yDBF5kCew1BKld8syIdldEzzkUB83VzxKefeZTGLRr4+synhdB7meDUbcufqZ3n+DZJMe1nzmto53gMnh2VDrxyuusymhDkM5E9CpmTfL8YQzO+NBBsNf09Z+rWhX0rA6L/oBWBl1T+KZejHki6HGNRhMa1zB5Y8s75RDOZG4Z7zDSgD/k4omfpJAw8fzyTFGsGWPM40oAS2CISdFZDq5fVczUikbnszxTDJwkr/ERTNFyES0ccWx+lSYBLfRBDuegQz/3M2HiKV++xlOsNPdZix5rZYU15TPp89hK7/04cxED0FmNrzgfiAlcH/r6lCZ4bQjySgCNtVg1xI+DYHm5xiQPWnxRyOW7YbkyrqsrqATzyc3GmW+e393SBKCSSeqg44zTUitHRxrgjF1Ir7zGLbjRKWOOh0bf626zge6/gnscXqe3v4/KXN5ReI1Dfsrr+HbZDW881108TmIPC/CtkKWDbBVA2m63wajUEdkJeFJMe3FKrp0/Ei+5TzldlJjJ7Uw6PGJIGC5MFXOh22mU5nvWLdv9O/eAFTo+/xRDkLeT8kVlbQ3jbCAZjq8q7O1hGIO0IqhflLMiiIOjPQdiHsQ4k/6t84FYGaTXzfrxMvyUdAcy0k/seoZCXD+cDQmuUvOMv/xaGp1KUraPkRtoyyPx4NWVSq/Sc7/AnB1YwdQVcibZ96gw1dloJ/4JIX+aA0p+MQShN5RH94li8dn65Ti19nwtczJWAtkQpHSO5MAA5LmV5lc2/Fy5pBVB+rGV9AorhC7qK4MyHmsXhreLer6l8lQO8y6MZvCPw087IUYanMK3KxUM80e6HHWK5zoR860BR/DOBepwAJVIOf5VQs4MIo2c5HXJwiEUZ92TZBP3ykzhX6AyB3ciJPD3xBC0lhVNDLNHG0nS6Gh2+eU5ySMTZYUBSF2QgxFlyGE55N1a6cNB0V5GLf+6B00YhzAGcX4Qe+3ZRnZbb83YH5vDp7OM+glZ1UvRyUfRpSepDAxERLtnoYjdScIPRiCWUPrT8nRI/diqFYUz+hMERn2WMA8B/Uaa3t+5s2OQAT9bw+7LEJQ3WFJMGlhQMlZojDgcUuYBiAcjeoOlN1UXZejhM/FsB7ugrWEsRyR+XoOZS/q0POcInTsnqzRvuxi0eEuY6KqONl7Rnykeh684oP4BJpzL8PoRRcrKGXeCuLpTvIOIdZ3WTDRGyrIiWiM2Kwvio4Zgsorj3g21t8uC06K7eRiCsAdpLn+3YnPqEj4Sb1uCS5Y5BN+49ldpijyAyxn9kcOT+I7NO+PN4e1Mc9oc/mjxOh+0Xm7u3//v/37zjd/4jZu/+o9/tfmxH/2xzdd93ddJf3BuiNw+4Qr267/+65s3vvGNRvmWb/mWzfd8z/doIvSU477sa0wvR+EuEjmEToAE9hqCxBdjVJqsntR6niukOM3Vz2mldNM1zPgKTc+YDMQz4FbyjsvzYslDVtP0JeXkWREaY8AtvNUjRPiocUYGnInIwa5ZyazVQRrHXNNq52t6wcWqID4ff5XPybMiiDGQtr1jEPLWd03s7jFZZILH5E2GIOKMn3JGED7Prxr3mFtWAeQll9DkMp5Z/NQDKc51Myr1UMBySGaDRz0Vcy5Vrh5TUIe0c/UkZzx3TQ3Z5N4EzuQL4uJZFTHtIbxMWCAH/fYJeKRmnxc4vPxjtMmYipEe4ynyarW1cLzCWnNf3tqf0ws2bwvT+On8pTP+CtArXvmU3vJf8dfD2FrPSoDzZ87aYITRiNVAPdETCY2nRFvtrXmKaCIpJZtPqkQYh0wa1/IJ2PC0nwIYknAkEgrkTRmiRdjy6TyL7zFOpc3hBWM3tB8vpc/Y+/FmjOcXfjD9kkgEkMIq3LKZOTC9RWpz0t4wfTNuTW2O9f1svIh6xqDN8StqZHAylzl35wFGDpxatPqt3oPr/tKP1ZpFhnkRuyU4M/WGtotyLtB1jEHXpSdkQL6uT8lfBcbciC1i0id6L857ZukJ/UTTZdcZQT5knpVBKs8rC+kpwoNjryBy+eLJOgS+lB8YIc4bcj2gaYh8HHFB7CVsQF0Cma4Tytwf5jyEQWtUz9WqbQeP8pWuNkAzaB++WBGEPrAucBrpqrHD+Po9iZ7gJwHLGIT/5BPooszXOEfMK4E4koOP8+jcsKdlCOKz8Rwsf+XK2XxpUIYgDpTHyMzKQ3iJ7lHYf2ZTIRUTlgkpPCLO4/tfOoUcXZ8FK7QjYzC2XCHa04X72W7W18BMY8LnrNi/+X//evO+9/3fg0lWWcLDOR01wNyMIwf4Gu0nfuInSj9qd4rmoPuOIegyD/4LL4ETawjKV8NUYTqAFcNSeR7y7pJu8DR8HtRpnnQW2qE7N3veBWYA4wGNBjYMlDgEGkv4LR2YxplAKDgOjcb6zRc3bmAIuoWvFUFeGSRLuAZOnBvEQdQcv+MBk+iiXD08ICz6qGp6Q5Zad9v30EIVYWhDt9XVnUBxMczPhqFUV6kiqo7rioi6LaiuPgME104xlIrQUDyUSnij84FqwELF2ZNKOoYb9q5jDDorazRfCGNL2DneXHn1zwUbfrxnlX2rUkx5c5WBzTm+bHFWhiC9sjqtN1iUi8Iz78VzsQcT5hsPR82BOV5AvP6RPLtCcdVHmCpvOeqLm3QeMcNySdirrwCsEY3S9IlwX2hVL7xbqIaF5kvl7eFpVb4Z1OWR8chIOXMNiRa8qsn9w7XvCPEKPEgaRa2zPXb/Ycp7FN5h+GHq+NgrdoIKuKelk295y1s2P/ADP2Cu3v2779684b/Rl8Pm5rQtNAn9b97/N5uv+9qv2/zqr/6q8/3Jn/zJ5jM+4zOWmm3nIaVv1oJ1CB0k8KJKYDYELc0xzzie5TFGqPFW4vLMS4P2800ck5xnO12FP2g0ZH+VTEG6fVu997ONXE1hNeBmHjKRhCfi9zW7Y6uXxyoa4+Sllra/a0zDl4DY8nFVW8M+LKNQvpKql10aA93V14CYGMom5LENq6cZvwDz2Ygubxnf1IjD3HkFQBmDzJLrLGkMv+pg+QW+Yl6MW15VIZ6JBKl3ssh3wNQDrTEQ8GBXZqM0oR4TJp5xIBVJHIm5jKKNx7gG5zTGNxpj8TVaTZmVLZ+KBpazPjL+yufe+aqPDolma5jP/jit7fZnM7l75dM+9JUDYTESnffqaq0cEj55zrQhSJVRUHVVueLDvrlJHXOHBa/qFavCS6hHlnA/XOMKtLSxJd2hihYZ4VWmQYRA54G3hNdou3kWvN20mfSj4O3nbab20YbhsetYIeqpSu5wv6BVYTuAHSbU6kx9D7Ut3LR55jKR71w67RE6gmlcbzcnT4DkFbbrQEJau+cp7ss5Q5X+7YOjOQ5DOoGvKV/X6sFrOiT6WVYScoC0dYfCzIt9v6CrAABAAElEQVRkFLrVhmPpCPKjC9CS/bVB9EEMPRiEMO5El8C2eozTEhZ/0jGpFSwuLRh+8yO3nOpBVYHGJU64YfaXy4BXhpXnfj9B0A3UZHEJIz6+yEgbRZejg9EDGDKYk1Gc2JYvKbjfqsaecwkmww+6Qlo0+gLjkDKcks9h8uzCYJXgxYvaQir/KZ0l9sqn+WqYPtZDXNvFLmmLGCuC/IVnlcWzJhJLOxn8+obDJ00WHknpOphLZSxJOxrckV8B8or4SgqkA4bSoMncloRyyARuum1rCrwicl5zzx/+kf918+b/8Zs7ywP9t7/97Zuv+ZqvkX7UuVMH96JI4MQagjSeGa3QjXQ0USWosXVnpNVZcU7N002eBtlKSY2XTFix78qizYCJ7V53+GS83oqNlUEy+mAYYkUQg6hbMgZxbhAn63MQIyfnc/h0nxl0f7KMM17Qh3bqbRr8pfP47RqswLSdFB5Kj5+W07BFrLeHDYx62scAJKJKSJpq5nB8G2S84keK6pRgT/KWiqXLyYOBCCUivaNOpcOhFTgrow5frDh/tgYmUkgcFH1Bn4y/cJEDoWMgwj+vPJxuf04DFoxI/fbL5VKjZrhq1oOUitoDJTwTSIphSzTAY67OhjzBUYS4FZMDgZHKfnmcjYIq1DFlwk+KkxXpNBITbkWWQoLHA6DhlWPEi5I90oxncgpHazqt8xfFSnNNZhIVhsti+CiUUZPCIz6VtxCdaqx62BkPwol7EKZg161z7Ct6TpvDS3kJkdb5HwWv8xxHbzuNeJfX/lE4wJeaLzzO+DO/HZ7TXw7h27dvb/78z/9cuu+W9MWZzav/4au1dPlyqr7vJk2Cet/73qel5c9qG+29zad/+qfvrgjqmzTleTnI9FDHkyuBNgQtTTONk0G3zwiqZ4BVqC74POfQ+DjwcFl1mjAriFC1frYHtHN1biG16m7f5ZSmmrtJSgsZnnOdHwWIHsfswXiEr6XysssrfrSq+abOROSjGBiCclaQviAmg9BNDYRY9XxLY6CbGtdwfiJbxe5p4OIxkkb0jJeyKkhPVCYALheu+o1/whaKYHq3a4Xs5wmyCnNJNuuMe1Krrq81sSpDvOtkvyQW/Mi9QEVPWCYF9pyj4/KZsFWmbA1ThZaCxbFSzWeQsqoasuRiusP9YVDH5E++YKQx2QPXhzvrxdgpLeXhwxsXZOjxlnpt98AQ9Aq2ejwdQxCfjMdQdE6GIIxAvJg7o7xneLmmAZXf+vekUvHwEO4zhtD4RhxYpp65lkx0T5BR3Qlj6GK8eRwCLC7yz9XVd22T1tIqzOleVXAWX9ELDSKUt9zfNS3SZxpzmLTZzTTm8IxDmLSu46PgdZ6ZnuWhS9JaOjPGUp7LtdT2461zpSUtsMgld4w07mtiuc9gLnqhLX/WOVwo1/d/oehQ895id7y1E7nSoviiMZN35iUYeenznCfmF+Ca7zD3uaYD5p+9oQOjpSdYTXhVK4Kuyzh0Tb4PmOaFOIYguoZo2BqibWH4bA/jfDFFFMYYxD2CmdIfhK0/5DkMHjRaChU3BV1IG16FF4BCJU8ndXpL14h1Aa/T4WBxvuctN+uH3Anu85MyZNigr6xA70sXeOGB9CTGWrNeqwPRCcsPw0+MQdYXzNOYh+l3The+yMwXwVgNdFnnsLItzJ+P1yogzgziQz2kn9eRHJRviYr+IqXUBnaRWIxq1KnbTvOGzg3v+GDbX0SxiisXVbIj3G7pM9xP6MXhL2mBOw0iCmAI+lF9aOS7/+e3bJ7R12Uf1n3/93+/jxoYK9EfNuMB76OSwMk0BKkqNgRVldIw6XjVANUS2/iTw/xQp9U0CycdRopJyontWm7eQpEO04BJgyUMOqwMYq88K4O05hFl6IER4T43SIdK38IQxIogDEfyGWzxZTJ/kUw0OT+IOEsGoxxRGBiGgEXpFnfisrqyVivFIFSKhu1h0wCAzp085OCXKmCEIcx+cn8GXpEnNRA5LUNQLytG/bI88ZSQ2HaGYZVlyH77JCPQBQYkGIO0Guictn5d4MeSZQ1g2OeOoegcy5b7hxFIZbANDEnaN0MwtTg/p5aoQ6BFAU0+sPoZ6aO6RJItpZBoKSs2BT1IUJtpJerCK715+6hYeIhMKM2ZmRF3Y56YPIrW88bbulFER92T1oOomYUJbQa/pOGH5emFxIuEXtJqv/SFz820hdt+c/cgQc00HjZP4x38gwQekwTaENTkeUa0Qy86zgC8wIHl+YUirZGFQno+KmM/j4zOS54mtuUHT092jRl4Jrh7VB+ZyyJbP7+ahJ8hc3+iEP089tDBzz77h7M/NK65rbEMZx2yKggDEF8HYoLHqmcOluZMxNu3tDqaF2P8RITxDYYhVjjL02SPyWMmchREXal5firYlaSumq64zsAiO/NeFWK8U8jyu9IKEXSloFtw0+waExE9w1LxEaYoctmHLOnB0QgsUdI1DqMM/pqMfeIJmA60kh+fymME0qwXQ5B+oHpspYEYW+17VY9XWmsM5S+DaRLHRO7pV17RaqArmuTpM9CK98c5OBeIsxrPiIZfrokBfMZ2SNX8KBxe4CYSV9FtFyCYeujabTKtrSpDPVoMItjy7dQl/yDkcudJHTj7HOU9f7zBXJW7r6SXDnZcHeHc96juwMNyqdYk1/Xen4v741Y60BIAvrRNR4RI2kBMiDZuA4uShstdtxlT6LQmdYcy9G4055GekD64oZdA7IKIIUhGILaH6XcVwxArhPRDn9yUbkE/MP9BN/iFOy3X/ZvZB2cFRT+4RYsft2D8Cm84i8z8gScngfLXteywUXShSoEBSX2SFirb92PBDRbpS5uNribFuREGYcvTQWIuxqtx1DGfUP9EV99HX2tyx/zTBjmvAMpqHxuBJA7X3MZi4Jwjpp/naJqHiQ47MTD2cBTHZemFy3wx7LKMxvq64FNaBYSuYD7GaiFewpsHqiz++okyJADrYd+Mu57ouuJ/1NRGJKEoo/UF8nAO4h2I33BiJLVcXJQunY64Opyc1pgdtH9O9f2xt/9vm+/6jv9p88EPfnDzG7/1rs1/9dmfDdXNhdoaBiIvEH/xF39x8yM/8iPaRvY+5/3jP/7jzWte8xqHD5fHK4ETawjSWGTtaMBqPIDdzG0VpzvqIV3QbpYonbRumrF6ppQVXQg09WMpMJQgb76kzLxVLG/QOBn/lhTdbbaL9U+K8RargzRo8mFpGIKkABk0Oa+MQPdE9J4GUN5rzyBKv/tSFv6MvcLoGdj1g4AwNasBkzulTMVwGkMQAf3oZVRD2DmVXmHHpXYVwFKNkQcFo/6kgQTGIQ0oZJjHUEMYAxDbuVgJdEZvqzAEYeDprWHnbQTC6iyFI8WDUYgzhM4L9zRGIJYu86MsylH5DFasOFwJ8VmujUCzLhXq4Hkom4J1PvzO63oTnxOnMPAUOxfuu22shvIQcEsRIC0GvgXV08QPeGTqHMGcilBwLr0prjESa27a38Uhd6iJC55kcDNIjsCa3C6ZjwIy89ThcBIJKow8ijIy2eeAdh22uF2hPy68VSF7Il3unqRHBh1Hi7SXpTvqps8C6QbyIAHtozXTeVD+Q/pBAo9BAkcZgnjO8ZSwllwZgtDp0Z2oTY3zpUrzlAkUJuv54mf8fqb7uTQOMXb/SCexPq5+FdWcjjK6i9KCDgMqqxJstLmnDeIaj/BFMCZ4dzAEaYkPK5uZ1DGZ4/wPvgCEMeiacLwiQGcgBhdDUMZFHCrLizOeXbzl95kgfo55VCIeNK4axh14QQK8u+YlF/VWnIAjhIFpcNKuHoZ+Niqc2lfdGmflNw1hQpY06i8PtkI5VLySRxRTBH5+RlRtlI1coQEZlw4w+RmtaRQnj3MXNWCscxc97qqxVlb25NxEnw2kMRTb6jECXdKXVzks+pVPX9G2D6221goAf6RD46nTvRpI4zHGaYznFDQvTCYtMthrXmBW/65vyYxUpG1fGQj5AyTNvlOSL6DgBl9XgAJZDk5aZXTuh7l4PDXxdFSeXbwuL3x1evsPovNC46W8Esqewrs8Y4jlYIb3PegrELj81m4XMqfTXrvM9LZO7XzyKZ6o/eo/tHNQG42wEIjS2pMWoy5zLF5Y83Lb5//o5fcNzYFYPXjdn49nFZB0BoYgbRtjq9gN4filuIxBMQSJpvWDdAEGY/dCGXk4lsNGInplcLJCCD4Eq7T0QFUgykG46cWplKJ2pE/hDpbvpJHeqA2gxoRdc+cg1rI1WElOdTtuvPRDXqbzwp25mVdSadvHc7UqyMZhHgDcK6348Xms9F9qSFxhjMaEPffyfEoHRWtXxkUZfK5IJ7AN7DIHRcsodEWGoKe1hfSC5mN+Ma/dGBzJgX4wz/Bd7HXtXDNPLF21utAG4F+p1tfUtzOKVhEzjQpHABQwS2qiGQFlLlvgsp8NpNS85RdwDEE/vvkuHTfwwWc+sPmd9/zB5nWv/xwV8py/GmYeBoXN5gd/8AfHR0d++Id/ePPN3/zwW8omMofgI0rg5BuCaClqW92QaWZRdVx546MfCPVzuFu6u0+UDpZRKyyhDmMQRhsNmO5ixJFBCCNQzgJi9Y8GSDfZNiZDkN6s8Vl5Bla8OburdZG32SvrN2gYhQjfkwFIAyj9vEIIA5FGZliQGVA9p7LMJXrD3Mq2q7rRWf2mzOF6p0NYKhVn1d14qoqNQMqDgecUy5IFY3kx5wBxmLMNP5znwwCDN08aYZyRpeicPlPKMrtz2sN+VoqGvernWRnE1i9WAGEMYtkyRiMNVDAE+Uwh6JYRKPzC09qhY6iTXQfkg8c40flIBEA69cEr3M4CAJVH2tEu2I0TqS6kO591uiKUDY6NQKIssdh1mU0npTa1pnK833mh1eHO0fSHZBoAosLmbztTknZoNc1tfya5nWY5A6zynB5hKMhDohMjo7kGzdZMv8OmU5eZdFMDbxs+53kUvM7X9Npv+LZPert9/HYafuM23lG0G2/O+7IJt3C6wvuE0Te8cY7yt2mBt4/eUfkP8IMEHoMEZkNQdECeBG6aUpJ+IvOgqrbaelOPNTsnOQ2dqoDbOc8xuWEI2u0k/aRpQxCTjDwUyMe//kTP5UFMA3qHTT+oLsPl5AINb1cXDpM0Vjez9Z2Vz57gscVDK4OuaqJ3Q1s//cVUVgIwxtHYhjf9d7SVjHOGeMl1T5MeDEFZSZBJHmGGJp7MiSH7wJjAmUEkw9gGP3VIhToMr+2Ub6p23jx7xNMIFsmqnhUBq2UDyGGIEbGMalxIWHJh7OfJtZhH1C7FeEaoTKIJLgYgv2gUrsZvH/mIzxMQTj79zIs3xlVs78rKabbba7sHH9vgK0BsDVP4KQ6A5fPx+moYq679Qs5jK8ZpjOEyyWPshvmMsQnjXNdH8QyQzKTrIAnaJwnnOD750lIVJrcc2Qi6PuQDGFcYjjjvBMg9aMxdv+k3HvEOr7AHHyl4F2+Bk497gtvF2w9vvPaDtXvt9PZ3MdaQgWfZrdMmES5yXqMcGduZq+/BXIqkvea+EnBbHfgkVGQITXEzR9tVwP9cGjEUmtITMsKohTsLXwDL4fK8ANcqQc2BrtcB8/Hra2KGyRDkeZC2kvFCXHMgjMOwkRWDNccSgxiO0QMf0cqfMAwP0Q3xA1/0h3jtNqP8/KUnKHu5JHedGtip8ZdUQmJscktaAUlGTKUTXJFKoiz64inNh2wIAqCK8iEeDEE2BkmK6AsrRFGx0Udo9GNWAdkYJCOQD5LnxTrzL/SEjuC4KAPQFR2MfFGrgS5LV7wCQ5A+IX9F/nmO7/C8LC/jrRdUTM9d4JnnznBuXAD55364FbiNgmfU0ivIOHULXFmcb9BSoJsNac7bNIg7HABp/EywynQcGE4EzotptoZ9j7aGfUBbw373PX+4ee3rXy86z20uSrYuP9i+crbkZ33WZzn8fd/3fZvv/M7vnFIPwcclgZNvCHLNpRKqxZSqE5QQ6iwP/DT9TgU5GVA0T0gZ0UGyMkjdVh3nnpZQYw1nZRArd7xHnr3yNu7oM6wYfzQ48rJpBkmKs0rojizm3n8vRcgy6j58GmMSB1B78OTVQSrDgykMQywvzgAEoxC9x3tr6XGqmDu4/XRSqowatJPutPrE11If3vqc0qqfnNmDAahW/GDwwQAkY9BZGX0C1wogrfDBsOPDolFGHFior1ZwADTGn7MYfxQ+q/NAWA10BoORjUzyVSZ08K2MxBCrjZo180d9GqT6ROoBEG6lQ8JII8OWM+4gHMzcc+h3zpLJwGsiDU/csTXICQzqmxIA0x94nTIAIXYEOIlbV3DJ3v5W8m50F7Gzr3BhtOS8giuywj8GbzvfIlPkAB8Hd5wEXpYS2uoKQz4tjE7v+EDYE2jcOelh8s34h/BBAi+wBGZDEKT7CeGmKb3oOKNpAQLL87ibrpM6UrwRdc4VvCPpCH1FrWf7uAKl41HHUcmiogev6RlQNMgcAsVUCiY7YxrNU2TA0Yogr17OuOQWZwVpEsebfg5+dVgHxDLZuy5DEIdH58wgrX72yyxWBWV1EGMVxlH8/Hl5ijfMnBluqQwexbMNQawMCk4qBBxep3qYFrCqEmJwTDiqUFcT0JyVCLSA2WeiYwRyKJ9lGWIjaGIS0PCDa0Ah+RBYj9UYr/GiUQdFy7pGmDf8ftmmF2N+Yabx1EWPoZ70ljC+AOTVQJrkcTj0U9ru8ZTe8l/RQbAX9LKNrWSnWBUgSxIrt72iW/NhxiXQhn2PBV0nM+m66GJZ4NHe2tEyWp6EccibkKtjSGREnaesTkle0rdTKuNj9yjX3Jb/2At85AKOk8zDjJvI3zS6pg/DRLdfhtsLhe2wE+vSpUxtnzvLHGPCsCGXvix0WpzPCdK8hy1ft2tHxHUOjdYKwhvWFTIEaUUQB82PLaV8SIeX5sx36E6cJyaCbQxifhV9waogyudHTVgpFD8NN8aipAvHDbL5Tc3DOnUjPbHVdcAI0PZbDmAt4aXfFK1KJmZZi3/+qvc4b++6eFJzIIxB6Ib7XhHUh8dHRyBMdm1QHquCvDJIhiDZORTmRfqT3mXBS/gL5zkfiLOBOAsIQ5C2g2EUYoUQRiGtEsrcjDyZiyEWpGHxmGGFl6pVNQXgplIDITqoWJzgomFRKc10Ot4oTqyIwivyU8SrgCoeiTWB5JlQncBWuLfLEPRdb/kOrQh6ZvO7fyBD0OtiCLqAkW3J7tC73/3uzed//uc7/NM//dObr/qqr9rCOEQfhwROpCGILqWXUpNTpBpuGh+J/DACqTM63CnA07wI+U0aS5VlBfejtgYwNGjODsIQ1G/Ssr1LhhspOH8yXm/ToiB1UDSHRfvHCiEZgVgiqYESyyS9t55BF2cOyfjDiiEOpGZ1EGmE+XR9lCXlMVgL136TU52zHyx0VPolPgMD++pQ/Hl1DkYfbSdjibFX7cjKjG+DDyt5NNjgQDLO+8knSmPgYQBi2HkZizD8MIiRj7HoVBmLMPxkPysGKg1QJMqcPaTSxbcHKuLJsrVWQMhyAsCz3fTwaf6BU4+1g8oM5PGxdvvzzDjmZAaEt12waFNWyiRkea81ZuhQ6AzfjndpO/Cm3wjlPyzeVraHj1LucW5bGMKfeOp2t4/CETXaQf1o8Y7Ktw3fju8w8JgBlP+yc9vN5jgBPEhA+2g9KM9x5R3SDhJ4ASSwbQiCJM9Znn3jSaEHX+IM+JOepsvzitDcuMntkYZ17NLE13jB0Bigxh/+RHk9T12GLosPTeJFTcWtS0ycRxYk+GgFBqGc+RODDpM8PooRYxBGIU322BomYxCfj2aL2A19MejmHX0i2uMfXmAxhslkL2MqtkdoMideKAd/TPQ8wcsYC17NPD5jL6fBf6SLBPOrelRl7JkueHIjTAp5Wq6JmorlJLgiWTUObjJ6hEUUB8hJQCUcIk4jnLjx9cY/E0OMQBKkjUHxGQud1uSMyQ0v1Px2n5XU+rDGRX0u/oLOXOTwVw6GvnQp2zwu85b/4iW94T9TRqBMDJkgMrnkJRv16HESrywzy0tdzZMrQB0EM8/UVXgOR66Wuemk3ZKFKlrm4FU+4O0oF4L2G/gYfHiNYWNNHJZSdkKu0h4+17lS9330HgfekeyI2ePkRr4j824zuhXPPRd13UB61JrSFlWY4EavXOKAR5Lx0ja8favnQcrH6j/mMd4RIQMPqwVtCEI/aBUhZ4vxWfkPP6stpbdJ09fDOGheL8d9zIZeqnvblMrz15QxDImudYPLYVRPQ5f10z4GIP1KZyBJDuaPA85/4uM+l7xHTRu9MIEPUFFavKXfWJZjfhJJo4OlOSOrqT9kG+gpvwynv/Iy//79u14NxOHxH/G2UddU/Uw6RPfK5wGpqqwa5IU6xiBezLP7Av1wntVA2jrKl8Iua8soB0Vf4sBoVhDKMIQh2VtONac77x0eqVfXrf1Vs7BQdPHNBmNglQiUJpCh+Mi64gSdvTCHN8Ehazz5GPk6h4POIGRRgY5FOxHksOgfkyHoO8sQ9Pt/9F4Zgl7rXOd8zYWVqFevXt286U1v2vzKr/yKge9///s3H//xHz9hHYKPSwIn0hBE15oNQVZftDU3NWJpgusVQaQuTdOPNzdIVIp6o1sorTuqNSty9IZLWazE5Hsblww0vAljldBdDEFYy6Uk+WpYvhyGAkyYQ6b9RTHFGXSBhxHo3l22m2EQygojVgn5s/NSOFEmKhNDkPjjjR880Tm92oZqCuS+Ovk+TJCBA4YaaRmWJp/2ah/OAsIQhJGHpcpZ6cPKnjN67eRDopVufIxFpDuffCknbyvjLZXyQ8MDFHVeBinj8DMxMwYgdRvo9lbgvie5NeaZoGSdRw40qj7xdJ0DiS7XaBCy2FGuy0k0qZQbpQN04CqcdHwU/5yS/NTBSnArH6UsuYO7D7SU1iUV7j4PYYyn8D6EFxo21XdVnW1eZ7zw2DJ5vhytihUxSp5KG+S38UbCCQ7sq8cJZvcI1rbvyNw2tmo4kuY8U7jRG6/jc8mdNsPm8L48c/ohfJDAY5ZAG4Joikvr1nOLuHR4fJ4bYQQffelniUCAgY0JS8MMr0wKb7vuGmw9Yh6S7Qa8r9efCwldjCg+304E+LqomYRYEyjCHu8IyLgGI5BXBenC9g1WLnscI2PQdQw+GH741USPj2Rcqy8GcXYQRqPbGr/4pZbGNfB3r8ZPfQ4IMJ7zYaN9pEWd8SuMrGrSR7WS3nKpfKad8RAYqQv0Qz1XUnD1skgkLCb7yEy5PEgpbHs1TiDccQ8Ra9JnA5AAKoe3/YvRR+kIEEMQrMr311hVJSZ1vcWDj2r4UFe94b+gg10vaaLHJ58vck7QRRmAFOasoCv6sfq6t+8zxjrl+yopiWefASLaw4xGnUqyKpxKj5jnm4aENa4ZQzpkmVRyWFd2KFg0nTD5uSfkTTkk0f5a9hPqTrDHDQ+Lu4tn4U50xQMMLaxMaevgUTxuw7fjTeUoeKe3bwPF1jguMoP3/Y4m1jVrvzG34/vgQ05CftJlzwLZE56ILvcx9xDsGGu7JMTLyj4ySU+IU+Ym0Q/MYzgLVSt+pBNu3Mg2sWd1wPx1fT2MA6Ovs6VUxiFvEdPk/Y7mOjY8q6vwxUH0AvonBiHKEh8yDNlkjuHnyTO6xfL5oRfQExYocfGkMH8O6+q2QDqgrjrhR3CWybiHIlJ1N7nJCNxyjx4RZ5oHMddiXsQ8hvR7Wh2YM4LklzEoUlQNtRrI/VlVYb7GnIvjOc5rvsVXmC/1GWKEtWLwslYMclj0JRmO/fUwGY85Hygv73WoNEd/SERUt6uMlCwHmG95AFMkhjdHIlKgjYN+JAn5FsGmOYgna65K7KwdgJZ/lCUsfulHyQLUsEQd3v5q2I/95L/dfOqnfqoJnVXluDd8ofbP/uzPNm9729s2f/qnf7p5wxvesPnWb/3WzZd+6Zda/kXu4D1GCZxIQxBGIH5usGooNDY7Oq0bKI2eJsdqIH6EaZhLyIhu4WQo5TJWBdEOaYLQNUGbkHqJtlcJSaNhzLmPgUcHKfbqHxuFNJjyodIYiXQQ410MQDYY3fU2MAZR9zX4wjjkN2pK64OkszJIgz/RTyetjgkfdFbV1Z2dKGH5NspoEOiVOhiArJzKEIQBCCMOq4A4/0eGHq/0kfLpwwz5QgUrhtjv6r3tMhD1CiKUnM8TIl1GprUByCxZtr60Ao24Cx4pchWrq5+r1LlJlHPd5FfUsCVShPEGwlyYZIaMJhBovuvGt8pP1gh3EOLNLaAmu/gKdSTc+ArIxQw6gAux8Z2miJGLODLqdFMqQo22UBFeZAbaVKWQA1jO5BVufx+8YbO/3JnQH2ypMJdHv+r6dWJXg/KUTj+zPxMmXPmcremA33hzQHDfM5DJB76DhUS6IYbqopjwSE3ZCjWq0ixiJwqh+ICmeYUEDhK6UG5lBVSknSI4dSugvJRI3GgJjGt4gh/cjKvIcC2zEKacxYVV8VTAyLbSm0bXo5GUvOA1NZAJzz54ghql07ZghQ49nOuisLv1nLfSB3mVtdR3vlcms3MJ9UmGxY7JKmwWQWplsEPhADhI4MWRQBuCujS3bjVQjenVL6qt02bdaOMT5nniVDXqThv9SsScWnmadnyA9Kb0EvoEq4Ge02yK/O4WLld4lENYP3+KnoL0cznJ7rTOB33GNXrHJJoaQ2lG5hdSmqzd4VxDvczic/JM4pjoPasfb/69PYwzQAT3l1P9AkwGIY1fMAZhE/EETwzxlSCPnVQG20qAwyeMR9vRqfkBjJQiIME0+bPMSKMuctYJikMmNAjImV5LSbhBTIKiyAUyTLxQ8oyTiqSzL/jJYnpFU4NeJWPsYcouH9oKa5oXH7i2hKl2oqk0pbDGgfHXeVZOa0LHBzY4y4NJ3AVN7Hw2kLaGXWA1EIfA6g0/aQ7rrf9ZveHPSmuN2+BbDJtnUacu1ENg18G+wisnHqOvEQU8UX3aBgGHnN8JTkvImEHvpClBWblVSq874LTIsdpZ5RrPtYrjmR/5zc+UtBPcl39GokxXi/rs41fgubyj6DUOtOErce43kMVt45Gyj+bQAUoPPWNy2XFdRPvbVSHernE6vu27LAPBTNsITsXxJoJNb7mPShQw2PBe+EbsdUbcY74MiLH4I9YPGIJus1UUo7F1g3SEDD+sDPLWMKVd1ypCDpdGj9yR3kLH8PJbKsbGIOZP92T8cTdSeRidMCBjyH5Cq4Ke4DvqOkx6bQgSv9YP1LIrhh+NgR9tAF5Vxj74uMozbjRxV1bo8Ykn2HFBxCv9HNrL/YUchp3MiThblf6arWHa7cGqQSlFzz0xFvNTbBiBhMtcCqMxH9s5zxZS6QQ+B++towpfxhCEjpDRmAPmL+u8IIxBPquVF/M2JKEn9BN1nH2x7r6C6qpqEGc+aSdZp/9WMmCLptqQEh0VMeNVthZfiCSPkzq/fGTHogkcxceZmssgZcgwaBsOi37Uz8d/5Vd+5eaHfuiHNh/7sR/bhRz8xyyBk20IoqWrnVVTo5lZHO60DhOiQ9IwuSZ9yZEu1KtDUHqZ9TCAaarVzSqaxp4GzaodrN35xDxGHyznvDGLIeguYQxAMvpgDMJI5EOj/TYNBckKIXytMFJeLOZ8uv6+BiFZEQS/zYdCBOmo8umu+LxFsjLwSiCWFceoc1bK9Awre1A2WtkD3Gf+aKDCqiDO/fEnSoVzmkOjn5QvGmwts1FIygaDkt9SSXExIIjBCYUTOc56tvq/5ddSFrfmHjzEafYbRqTCCS3pI144jhNWIQvt4sGJght3SZ3zOHzMJfcfRZgCu9jt+DEkHpBk5o/BUXr+rUxzn0FvDrazzvTm8KPgkW+/Q1k/yO0bFD0oz6OkPyx9ZDXYPUYUa7w505qrVbmrTFt4iiIlilzlWaOtYg+Lt8o0RSir3b47dAy7nc3+zMccbqSmTVoLdx9ed8Y5jRbb+ZsePqRwndaku06G141MqxeEmdDBHSTwEkpgryFI/NA0abOYLvxsq6ZKuyaY36S//RBMRUZ6d4rRK0gnNb2Ea7aG8eKoDBJMQISTt+bwQXj5JT90lFuMUQTdKhQZqCeel1ra9qHErFDW1g+NVTjfkEnes9oG5sNgeet/k7OCWCWUN/23dV7QLSaEwuNT9B63MLHTeIhtJYydKCe+ns2Ku3zKdv0YU1FPfP0sB/HqcKcpyTjxyT/0vOksY4EB7xwiDXXGLJDOaqCCQc7OHC2CAQZdCwueJW9mqowcPWPFEKdfhb0KiNqYvmot/8yZj2hSp/MVebOvl25s8eBz8YQvaZLHljAmfEzuls/IYyQ675XYvN3PaiBJRbxDU/8uo6Xi8gQFHpd6LGMxJqTAGidtwPGiRz7ylwSIpmE4sFAm6ltjeIWLdkALhdUzQJleyDHETLtYeUG9ffS7ZitpIAzV39I9oo6RV7X34rJpbTO9cw+EcBxu52/Z5p53jnnqXZP6zlB0eyULLcO5KqttBk3GeUivn/ou8xG+MsgLbM9jWBGouQ1nhmEMuqazgm5IR1zTqqCbfD0MIxBGZIXZEcFXCu/wspv8Mgb5zFUpCE17EKeMFCnrCRl/ntQ8xEYgfymZDsxWU1q/nITbrVoRAP5xxbkKHSkfr6s2wg2oXDb8VlgdXKHCFB6yhjv/KS26FqKCe86V+RZGoTYEPceB0egPG4BED52hpXr0Z2XxaiDOZGX+xeffWTl4Gf3A2WGsAJIRyCsFrSf4VLwMRFpNeJGtY8zbmKNhCJK+mHXE0AHi2zWgnl1XBI1rISg6t5K026SD0iIniBvpCjfJJAQAef8qFSkmGOlR7pyPO0q8zwj6zu/4dn8+/r/+vM/T19Ge0m3AcEa5yi/CN9W+/uqv/mrzl3/5l5uP+7iP23zmZ37m5ud+7uc2n/RJn0RJB/eYJXBiDUF31DiwGdNQlibWTS0dV11RaVyzLih44OfH1WEpu/jCIKxsNgSZdqkegbsz0OBx6AwGQSg4jEB3ebOm/fN+u8a2MK8GwggEDIMPlnH98LGU640a5w7FGMT+Uq0yktLwG0D8rk6KC9diAj7MsXpK3hzJwqwwVuZs4SpfmgIDkA0+8r0CiO1eKBMUkY1GwsEAhEFJArURqAxAPg+IMlQYCsz1F098CpHyrTMtXUUki7AraVueMO2hna5gJ79VvqMNdZIxmn4g03VoOJXRSH0ThNbvLyDCvabcvBVQcMKbKI6gjUDwQ70MXV+JzZz2Q3gQeIQArK/YUdylKQHf8oMP4K7ng4g7d+c8Bnkbr+NHZBGT5uWI5ObtQbJoJX4EmRcAvOYy5R1PNmLNw+U4zOPqOKd12De2xbpmy8X07dy+/yROzTs3v+Tf5AafWwAXs6esFKjrUWmDYNrZw9xHsuzgbdFvWazq2GVNvLcsZv5SF67d2xRG8RzcQQIvoQRmQ1C3TNihadJa2xDUcdo2rTa/OYdbOFmVxvMziMFY0oygS0MwPDCpyKogQZXNq10UwFiAQYh+5/JcuEIjc6gRRcfwWGZcQzzb3TV2Aa5nt88L0tiEFT58BOOaD4TNW/1n/eafT8vr/A/OPtREkMOjGd9gFLpr/lg5oEkei2ZEs78WhC5gBRK+/s0Etc8IAukxJYB7/NQnYdfIPAOPTkndoBNa+A6BEicU61N8/bgvMQRJRkpYybsVlUhAxTIqRtnWEaaZ0OmnyZ3f7BNGYEpn3MHCBe7DaY2JGE9xIHSMQLzh14ogjEH+ShgTOk3yNLm7pG1ihC9qAnie1UNKZzymIZhoMQEKl76/8KXfePlGDQDIlWfmR1hQV0uAUIm/kyckxhXdTj7odD4Sh/GgCZh4ZwN7Lf/lGbCGd472HxaPyrqUVbmh4ntWBKG383zqwh7Cb1rbXFN2uzltb3lCnnFakmtYqO1Kbjtvl7r2Iwsoi6oJN3X8hPtWpSEs+cmb+0kIxz1PD3KfJLszKx1loX9W+HF2ab54nF0Q9HmvCpLRmC8Kcmg054mxEshnB1lPYAzKyiFefvOlQVYTsYOC1UX8mO/QldgqZvO2DUHqUJwVpMavGY3C0Q/uuaNi8KafMLqvpxbUKW6gUgm7lpPKGvOTkpdlQFg/gwJHfmlTbJAjWfEiB322XJ7SpOkUuyXEL4af+1oJ5PPcWIBgPaHKiSiripij+UU7RhwZgPhMPF9k9rYw6Y0rrBDUKqCLnAsk/XD5MsYhGX9kXM4n4/mSs17Yu1wMSpqXibp5gT+cWLdMCFY1XCciO6JogHPmQr3qB6BJLPJcYDM9z1WRT+Vxvs4sRHZqIAUcbbdLZkXQv9EZQd+tr4Y9o6+G/R+//bub17yWM4I+4s/Hc/eR54c//OENXwz7rd/6rc33fu/3Qmbz5V/+5Ztf+IVfUFMB6+AepwROrCFINhc/5Gkw3agiCCUIQCfEBEQ6fwm3qMiRIYiRnaEoKQv914YgU5ayEbrLSDYToV+ZssYLbQzCwu3tXhh+vO1LgyR8G3zEgxC8P1YKkK1gGIP8Rk1ADEQoRt6qQY/GT09ODcJ3OqP4hg9daP70Afaq8mDy+UCyFrO9K1+ekIGHZYQoKg00/GUwjEO9dUzWHZYjQwNFBh0MS70FDMWVZcmRtR8iYsi6GZYsq+KxeDXDSrK8pDUjO/n8SRlYhKlIcARAloZDc3YmIgAIYIC7kgjI0CS904TW+QruRC7Ai8+gmKDBwSmoPBuI4MugwLfJOdG8QRscZQgrIbfvCl7nIb1Ik6/LWvszkUYGdlRBjUP6HD6KzgyvsLJGzpAoGuL52MHbo+BV/Y+jNw+05vDCbeq/Tuv6Lljm37yvxT5hPHQdza9oteQpbQ5Dk/jCRccaCwxc51wwA+9r5+v44juHLiXCJUGhWRZzeIW0hbedRrzztn8UToy/2+0ideI6agG/phtKzXswizoRDwgXepMgC+ngHSTw4kpgNgRRMs8x+9V4eT6Otq1whqQ8k4IbtGrT1fCtKkfnAG/X9aQWvw1BPrRUmU3bRHj+81wtY5AJNYfSUyoDEH4VrS4WfimewTsrg1jVfE8Id7TFnRdRtzknSMae61oVxPaOazfu+eyP6zoD5CZbPzAGaQsZX0llbOOxDi+31H89hrEPzfT7lJPyEBZlW0KebHoUIwh8L+YO9HakCb+RqpBSj0EDSGCLVhYlVdq5lY0xh0RkP/DkMRO6tFyWyVOIe/zFkbZCGOcBMTB0RqRIOJMxJmTeUq9JGtvAWO3jFUAYgOqtvn3iGH7A0Sohbx9ju74+Bc1bfvj0CzfR5b41v0iAOM5wXUkbvBc8CCQEs/U3sgA/0Pirq6s8yQL8Qna+KqhIKIVQtWlCSqcsXLdbR+qySmtGmvnCCcu548fRazyXBg3RMzdb9EyWsho+8GbOqAW8z2iSducRqsuRT7ktzzWFJTZeUpZ8ltwLznbI9dkGVtz12k4bGcT3FIZD6mFOV/CFAL2rk0I7Ma7KXYj43eLi+8vJkgnzGz5wcwsjsHc+sJWUrwlqJZCMPvgYhLK1VKsHpR9uSn94l4SMQD5TlbmQ8tzW2WQKaj7EvEi/zGY0t9C5MFoV5BWPNgTBDx25dcDCJ6E2zBTz9iKHgsw3t0CWQgtCsKwIEsD3nQQoq38THzDCyENwJYPxhOZLmStxZIaUgPTDfb4iaNXAJT9yMcfyPEx58NmVwQHxGI3RGWz7YiWQVwWhM6w7pCdsKJKu0M4NjvbgIz9sC0NXjF9xLM+uzwmDR7MPlGrxK0eQ9FwKz4ACtbgL3zJVuunhA5/pES6i6PuUG4RcBRs3Bp0cIloUtdoa9u4/+L/q8/EyBKmexVJxsdEX6p7dfNM3fdPmZ3/2Zzevec1rvCoI/+AerwROrCGoVwStqj80I129/2if/TfavRoYFmcc1zRKN1gasfpvljMXRjXg7gzOVhcaPSMqn/Gj1s/haN7+JcMOyynvY+TR4AqjDwMuOgj0OXj6ntKxtHv/rHwrXMHx/RZq5stcKrNY6gcr/GTwoNoogGW0lQ3n/bC6xxZolIfSDUOJaLSB3mIpMj75oMk5Azb8gKsf9JFA/DwqCBsoj3rz0EQE9q0o84ACDcTkTYd25zfNpKJryJsLgT3O+MaqxCWMlTkOv8NAFE4RSR5pM04nwZuQpyzhNiRCZkVMGbfj0NpDey9e4zaNJV/uq0rvJDgbYfK9UG6XaCvuBw12XigOnh+dRWb76VC/43B267+fzhrqlj6R/eiorGkeFTuqBgOuAO00vS9Uut3uownbLzi/kyyWMveUMoE6uM2PSaURul6mtzUYWco4hA4SeHEk8EBDkNhgMuJnmX1d8u/+puBwCafTOFzKfcbZ1jF5rjImYFAySOm5wLNh/qnYflhMeAw4qls5e5dug5AixDX08M8rlvUiytvDNIG7wSTPW8PY7sEh0mwN00RPB0ffkqGIj2JkZVBWO5PPtFRgzjqUMchjHplNVIZ/lFnlpjZIjp+nS/Yjv8CcFmGZWRuFBo3UjToswlFM+Iw1EEdW2PjueKwUWVeOYoSYdWczBv8eBCJzytBh0JrgRZBMuBkLMZnJ5+JZac0bfj6ugcEnXwbLeR+c+8HkDsMPXw6L8Sdv+H3oq8ZmfA4aY5KNVmIVvqE/hjeU5ToCx5nbgiQWeNKS0JDgdtNo6MhcgbSzJSe5kieyIuWovEXiob2F4m6W7TKOw93NvR8CzdDZpj7jP/+Sdo0S3MXH4EQU48XiOqz2unK003a1qqWj5XdO7m7C5Ggzp1uh5yu8SG/DDcdd+KB4jsAQ0AYfGYI4F5UwW0hvta4ARzB/XfmutpIqL18TY8sYNGw8lu95lFYEsT1sOSOIzkDLF0/tm2+3ToMX/qtCoFbQ2Qq/+y7RdPOltgBs4LFMm6J8ggUDA2nwh7OUmCOxe0K+V6UIl+UHfSaQpadOnLlZjtywEYg+jzGYLaQYfBT2gdB8Lv48KwkD9yogvjrILg5ZTFgNdJY5nQofBmOFS0Lmi/B2NYrl1MdYy6Vra0gJzh6EJtf6wyKZ4QpTnuFFrOeEoBXIOdI/oI4s+ZNxTHqPr4Z913fo8/EffGbzO+/5g83rXv85ovncWBHkzHWB9i/90i9tvuIrvmLz6le/2mcFfcmXfMmMcgg/BgmcWEMQe9tpSG60VLwC6ahLhyUU9agmyZPVrTGtPNeJimi6Uef5L6JTb1BP6M5AWV0uPsusOx8W82wBk+VbdMYbMowm0AXf9FGsMgSVsQiDEXQwFuUNIPj6UUD1Jsp39ym2HBfMikYGnqzqUeeSYecUgxNW/CiMMYitt8aTIvEgSTSioEJTuky6NoQJi9xSR4VdQXmRQRiiU8Jbd3yizSwrauAdcxvZPWgdvvgBVQnOE3JAtlwluHwl6f4tqJ0mX0T63jQGWeIk7I5Q2AiT2hF41F+TLPicWsQqi1IKl0DqraTOIGSSU/OEkn9kGuRclG9yQKahC6QGDUVmiMtU+oKzkFtCnbpAltCclnDXYcFJKaQe7WY6u1hzzZfUmeIeeRTiqPsqTmSb6ppGx+ZS5lydu/FIW3CXEPBFAsLezlDx7RzJd9RVmZxBl5neHvSBNuEBm6Iz43soNMiURlsBGkinf5R+MUK77D4XSvup065XvFexYBs+NcBBAUV0cAcJvIQS2DUEiRk1S55rPii2nimt+/Nci7YGxjPFbX+rfbtlKyEtnGt6B8/SNuj4uaq4XwrZB6sG0VUg8yOoxCiEoELRz0TFPMECTH48p4OTyR5x4B6r1HiFFT5M3vjd0Pav8Ul5tojpCy63ZCTiy0GsDsIYdItJnd7w39Zbf7a654WWxl2ix0swHyYtRsaXghRmYskBsUjSPIul1BuLiGB+JhqYdF0tIR9GnYjI2wFPPRVSFtQGQxl5CZsMMgoMShKHfqo9ATvKJOAEjb3E4TAGUVLe4tkIxPYtQZiM+SthmqAxWeP8jiv65DNnAfG1H3y2imEEuiSfLWBsB+FT0Tb+2ICkT81rNhSec44IPMB785NXluHTcCV1ywHN955kNzQguGA2XtcdaCgZabmoirQQlywk2m3U74LdZYf2Al+I7AmZpwV3CS245mkLb0lNyG2DNvy88KYauFDVufyUsstdl0v6bmpyOQ2+Ir0JL31rwXq40MzSHB65xQjwFFRcccNor7MTbMEjzzo9ObnCpzHXYQlHx/eIbOkKyf/uc09ml4MUBp+VZxXhrTsyDGMUltGHFUDWF9paekvnBI0VhN5OWgYh6QsMyX1uUH8oRy1O/2UISg8TP1rHZNZ0sYzpeWnVgXc7LXFU2qhO3w1XNjUWAfd/53D/B157RoY+EIzwSE9eeggOVtw2FGCO5T6IrNATogXLqDHmWHz1j+1jNhbzkR6vHJSxp43GfCqeg6E5MNrbRktP2AjEakHt5pAhCIPzaeZzpX88j1M5SATnMuUPlgHCbli230GSqENX12GActAZxIgoE+m4Jlfg4AJXgn+NY8K0q6XEcR8FIz8/VjfZEPSWt2w++MwHhiGItnxB9a5ihRnHSs2f//mf33Bg9Kd92qf5S2JvfOMbO/ngPyYJnFhDENun0kJT86XBqPFJAS5/YAlXCDQ/47ltJkwHZvUPcOfhgah0L8M21BnT6YXjDiEQPnm4ZMCWCIYfDEFeCYRRh0GPCIITltM5MjjSQAlDkBLYh0u5HV5OeYevxc3lAh2DHhRO/TjvB+V0SsafWKtVP8FQSuxr9c91AEZdBOOh4Qopj+i6HAomIMacxKUeNuEpPJtxJVHP2cnk5KhXAonCUFjSkB0myyrXKqLsPOAmjLAoWToQvhhWBk5xRWAA5offNnHwVXf+nIQcKqw48HbATbpALtlyWSu7xl98MuwrNxiQ3XHwMODhaQfnSECXNRgNptmotIX4HtbqnpLLefYV1MxN9Lbu/SKs5gd6ykf0SLoqm+Kov+hlogXgAa7ptT/Qp7IHTEjc7B5FDPjRAbiC9OIUo74uz5claV/lnNkX4YWntK19/BVKo2+VvBRUsjJAyCWzfcUveZaQb0VXYQHvCYXTJMxlUn2VKxqjFvAgxG4KXQZ5CR/pKpMnZiAVsj97fGSmQ8JBAo9fArMhaG7CbqK6BKZnm3QK/cDPNQEbFz/PDjqbYuochsH6yE+kHVSSG4p+GeROhmEifcv0hGI6+GHG5EOlEMlfeo5SUXt5Gjqnn5qURhrTIFbzcHAr4xcmdbd13iFv+Xv7BwdGX2dlECuCZATiy0AcLO1J4E0ZgzTBu6uzdDKxYxUT9LLymXENYx4MTravqDKtVTmHxLMmMy8+vTIg28QYNcBjy8R6W3VKPSIPpl7UiJr09fQp19jjI8STFUJgFTXKb4FGkk7L8EY4YTJ+Te6gzX3uyR2reM5rW5cPiGaLhww9l/Q2n8/B92fifV4Qkzu99WcriN/qa1LHmUAYkTQ/zIs6jcuerD0djCjCZnjNWKn4rprCy9L6KuT2Jbh8qtYYlk0ENOWOzEJVqNwXefxAXfJIjm5fBTPhIhYmhb24xrVsAZuXUcpANI/Ewmjx/DzxihZkIwPRs0xmus07WFMy4Cm/E/ddit6gP+GshxS+i5bjXPocdpHKv+Yo8RlvKiL0KtH5Bs8A81tXmftXSS4LnADogcku30iCm7YpC08Hv1dW9AjzF01v3L/p4xiC+LHahzNQs0KQlYPSEzIi30RP1AHz12U49kd0pD8wAqE37igfR2Jwvir0YNRbwj5SB0RrnhCTK/zo1zwqFqGBT0SOgHlPtAb0JOhHgn6qbMcIG31cgoPx1yS5qMLGWuEILql0uZlPUXz6IKikcff9oR0pCXyMOflITxl50BUyFmM4vsAh0awKEuy8DMYxFmMA4gwhrRYsIxJGE/37jCB0glSG7lDKhV0cvu+peSZSP3ntcs/DZ4edpszkH6KciHawyXX9m771sRKTLqkRkMMjr+FNhAQ5dLJUoLeGfY/OCPqAzgj63doahv69iKV9y129etWfjf/N3/zNzete97rNO9/5zs2rXvWqLaxD9IWWwAk1BGn5MlpJjrYVletoFJqAScVEUKHRCBPIl6LSGMEYgyQ6vwAxBEkRJXs6t7Nm0Eep7vCjLBERrg05Umoe+Chug5V8jYfCicL6N10bg0jDYKSGH2OReAbHSih41Iw83fmKDdcdZWDLsxJRSr3KJ18TS3xs8zJy0THzUSZQtyITLbpe6MMExKM84cFcSCDjIa98lk8JybJWNlSTaUDNNGcINJMOjm8jlWsHkKJDeNQ5ySqP9OHIuPyc5MtMEBVFrbolmCXnalLhrmNK18CUWCALXKBy27C5vMbB38ab0zqcvMh/5SyjgsnbSo18RvKcCr3Ec1sW3uYy6patilxHkm/JndS5pAWfHjhhEtxGbNiE1lUevChPkuc2NxGa8i5lE5oSmqhhlVfJDTYmF5KmbM1vlzYnzXhL22+alYMCRkXgSW6OT+m0t5W8gu2r0ZwXFmcmF86at/l+uoKdUPSmIrt6ThlozXrhtzfSBWgOuvRZFo0fPxjcuS53J+8+IhRmxKXURvMKxY6sCzvEDhJ4USQwG4IosJsjbdxx+7rk37B6zCbd1yWf8+jSfbfIFFZ76QtRHzxj8yMVWJdNgPzEHWpizuihufGHNnVfk+7RQxQY0aQlzMsozkZkpTJv61nhw0ofJm03NGnD+OOtYvhM8upMEN763ywDER/GYMyTLfGip4EPXzyDZr6ymjJiZ6FcMyVOeEYzXsCnTkpzRdEnNSHk4e+6MSlNZV0HXRZ9qhqVsQYaTDZ8P0pGI5fozHJ1oQWL0CIdJiSw8YSMNORlK/1pGapYZc25Puf9NR9W+7D1K2d9XObQ18syBmH8uaBzg5jg2WCkyR1fceVrrkzulJ+5DsYgxm2MUeA9zpVyECmF76Q4TP39r/qN1MbCr7ZBlgavg6QsjuKWIpPF9LfLbmLN50JiHQJvuSvrtJcyNvPfYfh5UH0ezHM1yRViS+9hqM/crIjMkYnQaCqDdxLT7ztLaE6ZnJA4d8fdCZhAgTYX8tXw0+oVViIGV+w1zE0w8vorYDoK4662MfA1MbaJ8WXkPhsInXBdeuKmDo++oRVCN3yukHQF28gUx8DMh3LygR3G52rp7uMUx+to+j38oBP0a9YUc6e0zwXXifhi0sKpsNMBpYYkp+4ukaicKlTJCYAEIEC35IqjD+JIUwTWgCXoMP05BhwMOWzrYguoVghiDMbog/Hn8kUdCK3Pwks/XPDZYVlVmC1g6Bj9vGpQvmhzlMdiCIpea0MQ/CAl3MS22e9qIxLXBl5TLeOPC3C5rh/+jNZxYMbpRPmUQdRyxe80w3QpN/cRiuOrYVkRpK1hzzyz+Y3ffFcOixaBi1KMWUSw8WHR733ve234+Ymf+AlTe/Ob37x561vf2qQP/mOUwMk1BEkj8YDEZVCVcAYPaojViN2B1WrtOwd4/fBMeHQQ0rFMqBHGQLH0BBo+DZcekLDKrzBgHNQY4NgIJAKo5KzsCaek49xJFGEw4xVDQhzby5RIuvmgeCNXPjgQs+ZDIPzmIZZpqU8NmhhLeUUQurSceYZftIUzxoNeDySQWSuTka8kRzxSNOOKpTbmzzxCNDBTJkh54w9oCs79Mpbl1VWER1zIiYDJcZFMlOY6mK7RjJhzgoJTUOOTJ+UsBqAlPaEqzoxgGAx/inrJevjjuuApNDM7+GvKYAoIoys8IzbSlF74nVICALsVIEnhYItG54E7kzkqvRFBwoHX5TYs8Mi9YM0/SfvcXMd96Q3rohwnQq/Y41b0VpmC3Ont7yFhuQtOm/S9n+rg9tMiggZuiKITAu5rdEn6Y/pIwp1eVHI/AVLeMfzBU/fnOdz0+r7MaUs9Rmliu/gVvQVKqCsUis1KNVx7IwAAQABJREFU+0s5S8j0yVkya/5CC7wqASVJGDzwG16sgOn04smiHyw1X4vf5Vpmyuny8ZsydDSYGniQP7iDBF5kCbQhaDTlbv0A3NarAyheIPm0W6VXX6ENp30DDF7yVtjQvqw6lPO5L9EznNTPteB3mSkw9KwfeLEklM6z8aob8sALPpMs+htP9fDl8YpibE/nqz63NbFjdRD+LU3obmjFDwdJe+tHnQHCqqBrWgHQ5wexiuiu3myxMppftoaxXV5f0ylYxkfwBi+uQfHQ/V2jEA9U5CE7+EOgfkFT/CsfE1NXhepQKVcjtcGIzGpnzj0kq39VCpmi59iOr1pbEAAhJGfYiIiGaIpAr54+z1t+GXTOyqDDOR8+A0iTucue3GmSp0Nfr2jbBwagczIA5UBYnfOB8YiJnSY4/jqr9JtXGIk/Jo5tbIoOXO737pgsVaWmrl+xmnZGBWiB5SfgOGgVderqokS3CyEMUSq8tA6FESJOCCmrZRQwV2MUnmWscGS94HTINIi4wCq4E2cfemZOwDk84xBepcHJLn/OMvCEM9Am3Eo/qo6DBoHiPXV1ii9Nre8EfWsUtaANGGnt9uHNaYRdrAPjAnT8mndoYVxs8S04GFOLS3kOueCFE/ck9R/7osOcyDpCfY95i196Sz/ckVWIvu6vCGp1z41b97Nt1IZjGY1ZOagvivkQaa0MwoAM7La2kd2ysVkGIRmcvVoQ2tRCW9CQWFYEuRK6VE/g/tjJ72BBZg/VAgUjua6upYjjIxP5da89W3Mypcsx3iHduMT1m9PBCcAhLqgsfozd+XCPjb1sA2XrqIw/fCnwggzH/kS8Vw1e2DxVqwc5O4zVP+dZKcgWMOXnIGqf88pKILaDoS9Ujnd+iCFFFY8ASjKJTWzDYleBcItuqvrgn8yhVniKuMoAK6/j1LVgxGcxOS7kUWayhrASuSdLKR8ZhqDv1hlBH/rQBzef8upXa6XkWdVV22VVQbbo4nihcO3aNX9Cnvhr9WWxX/u1X9t8wid8AtGDe8wSOJmGICmhO2oYUbLdNOWrdUZ5ubWN7UPDOISwCj0YJT1g3bAJqxUzFgBofVDJ3YnApbNnO9WKkjsACpb2i6KBy+4UKVq0Ia0e4XRFSM8vyol014MAShx8uQ5RInzBBzw5Dj8eTLSCEI9oikpv3CBDLY4HBs7XBBXrEgEQThzpWnkS9z8X0gqP4HDAegtYCK9jdfeaxMingMmltjwWRgloO5dRBZks4by3CAnF+6YFkCyGJV9KFpryAwl34rYGy1kt1lCUb/ItmGTK/asiikbn6TtFDkut0YZvOLJTls61JJKn4M1kJ/rGK9IsDbio+F40ID60F1SVugZMyEvCwB+gwU2YUjkDZ6KwCg6+H4CpMlzMCm0UvEtyhbdKHpEeBA2AAlCcHWT24YFj1qdyIjNR2JLvPi730Ux7mwjuLXvGWnDdP80snC0spOwkmL9tpoNedazIMd46+1J+Z2mIuVzJYXRKoabdkgc8P/QL1zwmYW6QQJwraF0K90AUSp8Z6XA5SOAlkMBtWUUy1qDZpo/Kk1OsGrWjuuC73UpHt5puGM/Nyp3sxncv2VMrnrMTWOFZ446+NFBCGbizefCSHHleC1HPtpB0wYrzZDV3hjtNBChXczqt4pEhiMmdJnls3+DtvQ+IZqsYEzx9Mvoab/tlELpOWF8Uu643/2wpuy+Z+XPTmtl5dRA+k0UZg5js+SBprDhyHic5FF40zXFMgpSvemn1TYed4HETaXnqw+8sG2Jk9aRJ+oOJku9JpK5c1JRMjCvw60fMSdwnAjhJSMT+P/bebd1yI9nOa557t7Zk6V5PoOfSvX3j5/Bnv6N1r1Pv3c0i2+MfI0ZmAmuuYrHJZtH6gKqJjIwYccjAYSFjApi51mMyxgRPjzOouPOdHtvgXR9/0uSOwg9FoH/+57wX6J/1eMc/i+dv/zW5+06TvD9+N8Uf6fsXwohNOwlnT2KNT/zh14HMPtCpMLxM9zJ6Z4fR+R9a6KawhizY8mlZqpte1h43pFx05OCwt3MRbPjsJ0WG33XtWyoDbt/BVsdtgr+wXnZuOOzX5xUPd8e4qKVP3kZ5CReRwX9i3CsXRyCHpXFyRoPjLGeUh3rFb9vDsPGrD5F9F+rcbvFR4D5aPDytKiEhHTKWkllZorohlK0LDJ6iEKcZzhX8EI4Obf1KoIrGvAxavyrGe8P+RXcA8Z4g/7y83i3mcwUvoef9QRSHdP7g8TDOLdwVlOJxzgn+kty+8K1t5cF65TgShfr6TzwOdxF01Qkc6YAYA0YBijJ+hKsTWRQw4IkgoFmqlDbbPjo+56h4Q0sh51u/D0zFYB3/f9L7fygEUTTmfUB/+jd/UrH4j3/4d//uT+Zxx5DfA0SR+Juca1zwmfME47d9ReF3DokhkYa4j3Si5TPDyxAabvlqvVh3sAcvW1oMDLG0TW+2wzZdf01fMtHcROkIIdeEh9HvdHr/v/7v/+cP/+f/8b+Ph483/+k//Sf/bPx//s//+Q//8T/+x4+DH+mvloHfZSHoe52F/qIriHXDi88SPsS147JrchrIqbC/LJWdMXs1SP8Z0N7bHbj7u3FacZLzTo419S3XijYFoHzTRKbt0kT0ODn4Dh8EmQ3ZD7b5YKM0BDG0Og+NLMvEhxkxq0ebgNL4glRM+EyeOJh9kjCQ0ea07oLYKCdmOnhrK5KeMaH3Whcpjo2VLNLhr8FNdwePEbK8/2HL0cWQaExpzeC8iIapxVGbnRMx44G3lsElMT1ZzzgXbveBH9o2ExNaa8DeY/jG0UCtBG7k0UQAFtVYqj2Gs5Yjec47MvEc++C8vVCwDJNLoOEsJoqzLcJbFxwOYnRiHmteViplEz/pR3/5xZ/3y6UFegyoWWOEiG6Ru6393W4ZVHTrk9gTz467+GLoF1c6/mVt5TXxNBd3m+5LeUYz+Yx+dZAuc6I3/4qzJwG99dS5bGcCPJZB3caYWAtzTDhmo7St8GxX8Gc8MLE3i7ouWGJOrDUedXKcMMYaGp3uHOLfx1yztjZumtt6ZYwse5+iFz+xN/7cDC0wu9r0UFiLw7NxYdSS53Ph1w3fjOEEPPSTgX9wBloIqpscUuzNnM/C9XGhkwOteeJn+mJYeN21BUBtVFeLJSDwL8cmAi0c0zYxoOpHil5899hc33ALCI/obGCCpu87cuB7ydGHef6s8+gH11kUbvh1H34u+l/9uJge7eBRMD3e4RfB8u0+7wT5H/yy2L/ojiHeEZS7iPLLqJokapaY9ybqV8V4J6KM0t9fliVGX0747xKxTAYdnlZMRn2tgAxm4iZePnMCGW5ykQJOVPe2GiznJVuytpi5bgLnLeRW21EXmV/r4sOTMh7V0PMZfHvPO374pj8vfOUlrzzqoaIQL4bWI2J+HEyPhfGzz99qtuNv+TXB8w94qBhEEYgJXfx5AKL3dQyc+TZS8RAjUWXMlpWDDRjKjc+Vazjqz4n3qlVbyRuy7DMQ+hQsmtjmok/EFvWcfNlPBT77UUjk24G5r1ev9E8kwRBo25ERMovDRsbyUZwwA+t499/1WsPIidOe4i6KwaycHTiPf3BztGLIS/bZ9s4We1JS0/BP6RsanJijtfR27pH46Pb+UP49HjAeg+C+lrfBbMOMTbQD6jwnfewBZeF4ZZ7EOeIHETz+yd2DfnG8CkH8OphfOE+xmPPFX1QA8qNiKgZxZ5DOI/41MT16yh1B6FI4/lEGf/iBu45kHx9acWSUrv+2TUj6WXs3cZQL5Z6Dx5CWNJWP3hqdEcbZvvjk3csipqsdiHNN3gmkQhB3/qlQ/Ef99DuPg9H+sXcDcZ7gozuB/i2PkPJyed0lxAvjv+VRUc4RuiMoxR7mcRzX3Z78XRFG/vsLz0TQeTBnzIbvsTEkfZyLGSa57PGLbNhoesdaQxPRHJ76JAEMenxMY2c6zWnl9jEKHENealgd7rL8L//vf/nDf/1v/zU2MaAP4/4jRTW1jZm4ySd3Af1Rv672LL9dBn7XhSD2q35IiXc+djLvrdqBRHECYWEn9D7mjllegc7OG96iRSBj4QDDFm157JQcoBwt8QQJlRNs7TgmISzZf3FkqYsPX59Q0QHPOm3s9YgMdyLwycFQx8XZgaEjzYkjdhgcB9L0kgf3YofI0Hu9RC8y6OTEcfiq7dQ6sD7iHYkAatXPKPHG0lak1JqrBtLRG+pMcGIAOj4wYqX6VGvDR9/KaJQH46QbCflSdDYxvIm/CGJH84ja/doGF2zCepNP9osZZHFSebsUJ5D3JemcEaMQX+wXs4hR22XR2k9lGk/3TfMN3NSpZ01iHbGbNwOqxoqiDLfl1kOPQfrlXRSmgx5ytyZiqfaAvdEfhej2/jGQezHksMd2DL7blXa2rnDZVHtrGy2FnXWMZWFXiT1ZcKBeVXxtyePsB1fB39fbm2XiofmI+3rpvpX+Xan9jL/mzG2nhtwiITMIpdN9+IKxSNJjUcfF9p4TbfvqgFujsfcsTwY+Vwb+1XcE6efDCWD2xZ49umuudjBgzw8d6w8f2Ob0vLG5OQr2sfD2lCEdGaSQU7ubkB0OLDFsAYCIWuM8Z9kIe7d08Z5wafWDbPP4xwc5p2jjYpAnebwLJEWgvDD6gx4V49t+vvnPHUE8GvaBd4d80Df9KgK58MO7g/QLZPyqKu8W+SBb+UGN/Frqvouacc1nYu2Vl0PmhNvYkbs7eWBoXgBQbGGtf/oDbpGSRpuPMFxDDSYTrin4uFAjLEUgFX54TOFrGeExjW9VBPI39zzuwTf9muT9Sd/w84tgfkxsfhLa7wbSIw7ffsuvgukxET1K5pfGziNrXKMRn/1OzL6e9OA8QIVHO3RGYCSrjCQt48motO7OeGAstp1qRY9zdnW7j+Gtp2SbAiAmDf3gElPsZn2e90u/RZ0aMehrF+zeRPTxyULU9yshx1FAYHt9N7Yly+hWTU7u9qsCbsfSHtJrTKfLa7zRQX5iYkGySXxztjcfeveF7SW/MnRKc22NfQkk3NttPI6Pq3+hgVsnfpAX0z3DB4/F8ky4xsQ7pxnOFxR8KRxzrvDPyrtwnJdH+y5Cij96XxCF43/9K78mpgKRf3WQl0xzNxAvptfjYXpM7Hv9CtlfuHPwA3YV3XxMy6EfSXPgLQ5NxGoSGwGGt/JwG2PlHtYacXpz1nSC0V95Lk1rw8KLpkjsR1DV+p0+KgD5RfAqAPP+sO/0cSFI7Z/8LiAVfriLkJdC+5fC8gipfxZehaA8MprHv3z+sl8Gp/OFXHK+6HmDJywaI7Iet2tIkxOZmCGTl+w/It4sHZYFKHU5afFyzatWdG1Pyk/XokEEF2oUmlUzi6KwKMax7f6kwnv3bRt6Vp8tA7/7QtCbzHhv1u6plpNUd0Ba909GlWfnczPyHhTs6JfPCOD5QsItK5Y5UeM1/5d/GPuiq0EQpiyt7joNxZwtIuzBiw0dGmJ9oWdGc+BH2WGx4r9ZWs2BxR9bWB6/A7dhAfNHEKUe3LYjcZcdUfxgExMtPticV8hj3NehQ2PYXiSONzB8Yo9YrTmOx7rkXYiaRZKYT1f9WGCdT07Q7QdWlHvkY7mOsegMLVByNzFbqVFnGyx1yXq3GTnyOAgQ9ywxGXrWFo38HEv2gQiqnsGWRwzHSApawYhhuozETDBA42viFyQ53kGhtZfaEOcQJE8blQEffZENq1yn486UsGYdW8FHG36lMdDeGtPgI03nPX+HaZHy3p1uRQIiUXEc7J6ocQA7F+YWr9Ut01sB/ELRUc+2zohPADSyi9Yd8KafbXtnv7Jxtz19oB8LyaZjr4cPrK2yfXkvfW8jCIaO/7CPCueJvV/H4rami6GnEOTsP6vPlwEKQX9RYcMX2tq3OaN6H9Wq+2rPjScf2h8zo2PSQ9mXtzmnINH+75MPx0GOBV8vcNB9bFFM1v4JXM5r+RuQow4vaCYq6HrOBC9YvqH3BI9v6TXB+14FnL+ooMPHPzHPi6Q1sfufPOqhgtBfdUeQJ4M/UATiZbAq+vDohwpB32uy9+GD7gKQndwlxHuDercQk0llV0GoSesLlsZJEhQrA3HIxAvdMzC46wKsRaBR8WkYjRR+dD7SpuCLIH6W+UsVfSjW+N0cpvXrXuJ/xa986SdY+ab+a9H8ShiPhf3xG33Tz4ROnz/5xdC8CDZ3DOUn4qXn9wJpgic9kb6ziJj85hW3CnK23S4EMQ5Gk7uwGbDjh63l2kvfY+XcO8tbzJaUoo2G1vfknSBwgoAlVLfvKPRY6B3uDanmrY8BWYF3XNW8Z/Gj/mq3UWH5XOzqZHwC/Wk6xL+92yxdKc/mFD2WhnHi2Xfrp7CLOQs3Jhso/Vx7n7L5u1oD1lUwDq8xpt8e8RIPoZW345tjy4OSnwmw5xCz0RcDfe4G4pjlMTG/aH6Ob+4MciHoryoAqQj0PY+B6Xzxr9P3r42Jx2Nkf9U5gsfK+OXBv+hOIr90Xga5u9DFH51H+EUvik48WtqCELnovsZA9hgUYC7mGy4R78F6NnSIJCNta1uo4y+Iy3N/F2F8XhGYOwU5b7hITBGIc4MKxN+p/Y4XQ+sOFgo/3MHCS6K5++dP+mVB+v+kIjK/FAaOl8/LjM89ueNH8cgndwDlPKDYtdBPgUitaKRsH/9FEYQ+w1wLtD5lYdO7YxkR73EffXDgY1TtfbGz2LNNyWuWdm+L+YJ28DFDR6j8N6vbEl3G828oBF10DHtWnyED//8qBHmvJUvzR0Z9dlB2LJa5rsjOZ2ZOhBZ6T957HdS5E7JjcsAZoRV/yDERjXrorg+3dCnxCrMUezZqG+u0HlVDA992fF5jTGCklYvInMCMNR8Zizg6cXJwIfNBFq46Aua/LpSSq3UmGD4WMNc3C9DHpmOwM68CinCt/5bfdgceX1gSvca4kBB4YYkUFf/hYXtsZ+LaQFqtbZsEctW6Tvhoa6lJg9R3wpqnwWyQFayiFaZ6hxAh+AS7sGPYfgFiG3t8ZjHvhhvRgTrGWMV3WvuqLBZOO8v3xETunDqrnMiJaY2lNo+2cEGLXoQZ5QZ49qCrflhcZOVtlwCi+TStlXNKh2Ws0txxS95IzJjtMvTHGtsTYFp2k3MMWPWu5aDZ903A1Sf7k4itI3Z3xdgpZvZtMXGFPkelSbrbgnu1v3FzjEq6dCBWsNMZYZry1Cqfhvq4X0pja5TG89lEZzjLHPsXsSemsE8b91hHETMnjL6387awcyKenPNoGBO1Z3ky8Lky8C/cEaSiBvtuiwfEkl1Zx4EP6NmHzcyxDrmvH/axHr1YMPI41+W4Og5rYCwxH/rvWHMc56iPdwpMvpvIbWRE2OOdkOYeKB+iLgZpAuZv6fVtfX5NjGJQ3hvEhO7P/Cy0vvnnJ6QzieMRET6a1AkHlkIQvyr0ve4oAsMLpLlriMfFVmFIzl0I0p91Hg0hKOLaGSQBxEr2NB4PKeNCci4prGxIUVzz8KMabE/e+fOVZmDc9fMVhZ4v+UUvJnb09biGZPzKF4Uc/5yzCkC8zJRf9vlaE74/6j0gfNPPr4ZBc5fQd8J/p8fBuAtIZqSLXXxqwqbzmU5r/juQUShiEu6hMOAs/Roz+0jGQB4yhhkJTVQ9lmrSDsLUpuHjFUvnEkR9nZLSFxvqOGS3d1vVUGulaNbnid50Ynod22FvxY7NaG8bV1zkwV3/hGzdBPiehZ2nM66Mpt7xue2Rk9eLRi+Y5doHQhQZfdYsufYVBJzGiG8iuS9wzQ6Mnj9hFi09y9tKalPZGiFny5zmfJ0z3oljYvExaFr2UUZHre/mc8v7gigG8egnPy/Pr4hxtw/FX84LKgLpXOFfGOPRMMkoAFEg4o6gf9F7gozXC+npc07gbkKKQX7HmM7F3EWYd4ypdWFI5wudKDwWr0XR6YUY4z8WzrFZdltIh8Z4ockx53C2iT/q0P+SovHXnN/nhc4qEn/tIhDnAApBeS8QtItAej8Q9D9RLHbBmPOHsDqP8GthvEvIRSDOEZwb8MsX/RpIYyNm5nvsy46FFpzb0J67qM+CnkfIaobaESP3AqPAw9EFN3wKX7UT5azBLvzYI8Xe3w3R/jV9usQ8MLVQwiIP5QIfNI+7+Y6gIy5DntVnycDvshDET8fzax49CJqZ7Hzac/Q/h1F3MLXwvMNpR2RP08otpOwh/4IrEHZVjnYtNKa0WtVXS7RCEEPBrF25AGwJYgMDFaY+7Qe7OqlwcPB/vI/T2NknrsjDZR3D5x+J8SiZKDniRMlJ2jZox4N1e9bDTM56brB8LhZLj4umtShPq7eIShVRr2cUBi99ywQaeUa4RzKyse081Iyx1ZE/+8kIF2QR4/AlZo96wRfxJnhJiIK4FsgEYzhGHeHydwNfVT+5l2i0voR1dl75ecvjIj9LZe2He82zeIUhJtHspKPivIueTRQDswb26y/bKBnfPTxde+/6ZggawPm3C02GNMOyKry1TQ9BsMMYlz7OTIex4CO3wVlV1jbsa++Tx3IavtFvt4l8jBvCuu/DN/Wf3d37VVXPMZ10RueL2kIvbZO2sm+puV5lD+Wb+acQdEnc0/mNM/BnFYH+8uMH//3z9caxm+f40746vDTsu9qvxaTv84bbBL7U5xCgfzmPuhPh9eiI/l4vS5t1o4gLS6edWHaEksTG+nM9fzfA6F4Ux0U4fNPvawn93fcET8WbPAKiiZwmeEzm8ggIEzvuGhKPO300EfzeE0H1mezxaJhkFI64S4g+j4ilCNSJn/xRdJKvftOf804Gk/gZKKWSxN/tAPftwuizjdh+XNTprOI7gL7So17+dVUe3fpK7/3RbMx37jCp46WtKvZ883XeB/QtdwRp8ubHwvQNPncE8V4P3/nDZE59ij/fYUeFn+94FIyXvuocRjGIF7367gGFzC8AJeZM7LyRvM9kdPvLt24ZAs/+FCz94SQF/lvX7RnZ6zXccxl1sZwliy77oyWnxnv03jKbinV2K6jwr3/TNxa7O5qgr32wV/zbPlb2Ev0eg5v/PuV95CJuDHfPAS3uPWmHjXMrLrzktVwo/dnKa94QXhFnG+3Txqa3lzX2ic8S0bT9YHVvlbvH9NclpbrEWA+YhXYxSMcs5wqKQT6mNUf7q45vF3tU/PVjYFMYhhc+5wF99Etj/CKhHzd1YYhzQ84lKRbrbiAZ5lcIebSUF9KfBaE9Bo3E40uErInWudFB55JRRKh4I3TEHZXP3cJ6zsP5QrTfvQrtaxKOZQrDOsZ1cHOOoHD8re7++U4FH4rEFHz4pbA/fsdL41MAcpGYu4T4JTHdTfgN5wzdQWg79pPzRH+l0PE43wpY//33J1F7G7gv/o4/28aQc4xRH001p2xzYwisk3UI2ncsB39MneZKn232rWb3rT5/aMCz9rZTn/PjP+s8/PZ4vOo/vd8mA7/PQpDONhSC2FmysBtlR4Plb7zcItXBb6b4gq0dFJodEEaZwEUD961/opYPMTkBfrkvP4SdUzw6KLEj02jhYGoPFJMy98+jTEpciOwZm43IFp4Yj5WWnTmdEQgSq52Hl31yEKHB2Pjnajl24EVvDYqgx5d+MouwMe0V/gcNxwekT4w62duP2oDTBI+GvGOqn+nbLobiBmqWjLWTxoUbA4XD7zd85dmAOh6vErxiFu0YtrHxG01wQgx+xxHqVBLH9pGUr7akzQHA1l5gNx8YWHnfkI13/m3okHL6Ky+W2VRsUxQ9tiVHjdGwbK/exm+SYKhxoFmwe5pKVsY3O60xAh1AVPaSXiNY/DFBE8TGYSq9oge8uFgb34sK1twkwEaCu3sZ624iM66/Crf8YPOIRIHV7+JLPWaQZDuvVFi2fRtI14uwAKe/vUCFeWgWdkYTM5gQcOtHe/XlI7SiWwaX1Da8GR10TM4ONJ1bY3sx5P3tJk53jvFxswpE7m/fWOHDwrmj+QtH6w7MoOx5lo1Snpff9pbeQzwZ+I0y8OcfVMCgEKSjjEMof5tpE0BbeqE5HnNMAgksO/Sie1DExKDF5Hjw/zE+8n0U3fkF3PnTV3O6Cs1xSFxpoTjyUnTZPK5ZwOnyIa1iS59v+zUZcwFHNAUfF3U0sWPSp2/y/W6gefkr9Pd+GSyTPd0ZoIkgdwJ8cEFIk0Mmhprg5Q4h3R2k6zq/UJpCkK7ziGPFphgykWN886HJgHzdV4m4c5rLeYWJ3NfoqD0f5+AdQBSBUvThpc4UfMT7hgIQxR4KO/nwrp9vKQap5dGPb1XsQea7f7hrSLbgcd7yL4P5biMKP0wm5XoKQFzy8Re++44LP4xBA4GXMRA3CxkAzULpK9L0s65084JKv3SsbUylZ1uM9gPFQ29Se1dzv2iHbmRgjXyBBDBmFIIfnkD1E+3ruli4mw61+1uW0SLJvhyXG7l9gQwO7VL4yWVPNLuN4MfKgd1mZxC1Eswp3vpQwPEeRDyFb4mc5m5/9a7Cgqy/OraX+N7CuzXkbQLK3CDaZi0letn2kSJQv3LiEmfMGAKNXWymGPSFCrkUg3SOEM/Hv88VOg9QCFZh/QPnBB3/LipzZ6DkuSNIdwzxUmk9ZsrjYi4og5vzBeedH7DFO8hk4wPnBwpCPkkxvo5RAShQxzmxm/bKYc8oJMz/aQPgXOHzvFsVe8CI5s5B/6y7ij4c6/64IKyijs4T3+lc4PcB6Q5B7grKXUA8EsY5gvOGCj89n6iIzLuEvqYIzWNhyi3nCLa3P5NdaAZimdpuAdh8LBs6/SAQrUV6bKNzsVmvhjv0BQavGNpZHJNosNi99AdTO8YM72y2PBR2cl+XtqnO/Yz3KQSdGfu89O+0EKSfM9VJoQfM2qlEwKP/o4jweyLjJDFCyX165MRBfrUaag60HHiIcrSp8dkQLY6InFzZcTPJoh8eCrFF9Zk/4jbvdq+wQaxq9YlF6ZtQXy1xZSJpamxazSvHPV1iwKfxaPKfIhV2aN2nEcGCn1kYVvR2iygQ8NFxPwF6zJx0ozfGxrRvabXrK3/cydzwbTc05TU85oJGhgppvKudwMZXYgO8GOPm7M/WeAUD7THFRC+zYF8urjBn/QbGPqXt5BwgS47R62KVwqdDzlhY1/7CRbS3hRx2P7KSV4C6Fc8BjbJ1NnrlWlD2Mtzb6hFX9j50oJCyxE9Sk/06typbKNReYrNbYGtvRKntCc624aAKUnu3kX4A0Iz/zkNKnLU642gXqVQyHmUewiYGoD5ds8EtS8gN1FqtjFgjLMlY4reZG3hEBo+Pi47ENZ2DN/0yF1YgBw2jCjF9X9uLYVfc5aJ2rFhXMHLS8WHfNizMaoXhQW3p5mf07Z/tNQrsvbV/95J+1sTtX9p5bgk60/LQv3EG/kWFoH91IUiO/ThRAuiXElywsvgw9RGU/bx7O+JAOE/MEdIDxecOAdQ3a63466C/KdaEKU3LRHqJRa/3askuULgJzj4iU3TSi83YxhtLowTH9RIt3/mw8E68fvPPt/5+SSzf2jMp4zGw3gWkazPfDeSiUB4P+xc9CvJXFYT+6sc+eFQkfN8ZAF+TRO4uyjuD9AiIAqIoxOVLi1HcuU1AhJNP4lu51qD4h2wB1SNFX2g2x+NfnE5ylw6FG4o2udPHj33pEY1M6L7VBE2TNoo94n3tSV6KRPxSGAUiF4uguRNAEzrb5C4iTew0R9SdA1xDyqdb+VQQnsz5kY+hve1EE7GDnljFj4itAo9eR1nZqEjSawmRXkBXq5ytHU7XsXz2GkoiQNJ9HPq6H8LpkqyzY81QIpAZdEa6g5d08cbET/UD23/9r/iMekypSfzhJq9bdlLVm1ZGowkm1O5HzxHsA2iMXaNZeWCU+T+4NrWK3t5rof2F65FoI/dK+OrU1m6B8QGRpVhFbWZakwJuN+LYR3nTEXNdsgPI/2WfXNQGxymH6Acp9GkEflreBRydI7iLJwUceND66DzggpAeC/MvjKlYTCHIRWPdGeQ7Dl0MojCUx8qw+YN4brHJycID0ozLJyudpxw+zI6j+di5QWI1ndfYAjmnq9UOzzbgnJECDcXj3P3zFcc+RSC1nBdcKNYdQd/pPEHhh7uA/omXRPfXwrh7ED53AHGu0DmDQvHXnDNo8cElqWLJ42CJlzW7GNdBxOX+HkL6yJTrhfOGUB+cBxYFk6wsOGT07zhUBjfm3K8q4rUc+jajfs3R9oO97iNLd+SgkAXrvWkVgv7tc0fQma7PSv8uC0HcDfSvXIncFv5ssnQHzOT1ugtzSL3ZKWdHRNOHkVSq1cuKWIaL9VnsSCvwczKpNDt3T1DhYtsx1bjMhMQANsFBhAvHf28gbIIDJUsQ6W/0CGkUwEZX/cqD64IGZxstcwoZ9+KNM25VvPoNejFXzKASfx6lSj5PXftYG2B5XHpY3gua/cjuGuhhcdmS+CJnyxXXdlteFH/lRpxGaxPL2EAVqzeG+BZFjpf8AUk9e9ldRMZY22612rHqjw5YzHZjYzoKB3AYbFfjMrq9fyaeoGKg2a2phCRZczZ+eNYZf2bHzMQ3R5SDFWD8YicmrpbN10CIzFt+X0HEtbjsSx4sa/sfu0Yg08eQCc7JhtH8Qv9giFVuK4dvw2iw7AylK4TkKexKBo0160BLjgstZs0hrEsLJP5YNhjA8MkH7V7MFS/5qAy1SIonBjFtL0bZD1o0dDzb6FBjxXrRsT4Bq+u4QUJbYzlI7+hmL4ItT+UfbXcVKwa2Qs3II8k2Tx5O7BmBHbwyGIuTlxmbjPxRF0783PKzPBn4XBnoHUEckxwWPkf7b/3s2eIzkTd/gjRupuehEbBfH0eMjoMeb/5SBekcuzlEOAegpwUjLO3TWrkCSxuQO/0bsc8lnIfQy8r02PHcSfZ6ziFKfLsVfP4KOEDsthjEHTt5d0ce1eBRDr6l/57ikCZoffzjg77R504gCkHcDeRHwzSRW+8OcVFIE0XJuGPAhSXZofDDC6RFmiZyYiXO5sZf7jEm8T1WQKYhwoPvogytJnn5Zp+7gHR+0WSDSZkf9WJCpwkeEzffEeRv8Sn0cHcQmEwAwdKn+PMtdwe4+CO7mthR+MkdRxRQck5lgsdZjOuE/q3P30F4FHJmABpUznYagHi6t4LGy+x9orsPjSDi0ZuOMfu8GWT6V63gw2PNJ7nFn6MIyzmtdWJGxmrR8LpRDBTDvK1ae9YdY9BnH7Zxl/bknHg58MJf5yyMMjS5jHyjCIkef632wiVJPGxZcRvFWPcxEr60VgKgxbUhEX5aAEZYEUKz/cJPjlGwkrjYUNTKoxFaBWkzL1a1E9FrLLZnMZkInAG7nTFZOVjyl9giY9wshnsNwNGaDwsZxyJXaT8ofo5TzhMu/qhCxLt+/CiX7uahwNu7e/ILg9wVpF8L49Ewfl2M84TeN8bdhRSCuOOQwpFpFYN+0CePk8oWBSZXiuWZIOQfv6HoeaTuh5k+IwpKhDr0+bgIp7G1ELTOFxzn+nzF+YLiMecEtdwh6DsHuRtIhePveBSM94Tp843OI35klIIyBSQXjyg+UyxWq50V+75DEOeOqHsHsYRFC5lu9o30tVaiK3NRaMwwNu+aHWRb5MfyHs5wGSZb2fx4yZL+kT+xT/O+nrWu+Iegx2jttPX53DaybX7UPoI3CkEU4p7l82fg91kI0knlzzpB5A//3tOy8+bklYowO1F2pKDOnWrTPYEUS9qz/x1a515sVXZayQMZLxKMzAIZtli8vEAZ4djkUSxjWZk4WpEsOqIKGW9RL1y24oGTAf/4Mxj7608jMeBKOpUsdbRXB6k67ufkMpxls39YxVgL8HhXu4zBtSHj1qnNiQ7/7Rpo4o/fjKxRI61F1xLU73h2nAGBCy9UcDBL1Xt8GCXRyhO0IcUh0yj0RxoL4YrwIo4DSs4wsv9wzsixV+xo0Xh7zWAcg1ZkgIV+9m93wxB5XmzFppmJS0orxR5A/IPDHkv+yDe34RnFY1Na6h86x1DuvEOjMeGM/hqT+2jAJ4j4+5ts7v0OHhr5OB6tkJv2enJoS0EWH6PRZd/upoQT/dhC1ZMguAi18jgMYjV7sQ9w+has7Z3e5M05jE8s+eoGDQ9q8iHxGoNouwSjT2mR4+XgHiTyLugkhnLetlYVMGHUS7ROXeOsrvE0YYc573pZJditsM8Ly2CE9qZVvWKutCdpYx+e4ztGblNaWYYcbJWhuxCrlCkEfafPszwZ+FwZoBDEHUEc8+yvuUENWhNNnSz9d0F89nXvz1rn346Y6RFL5FA5d3g3F53WbIS2aWIU1qG7jpXTkpA4t3XsHhO0gS2ecdi3da96WWOWzncR5e9cYW3BWlfnDL559/xLDP+Sj14O6zuEeEksEz09GsYjHP61MH+Tr4mdXx6dd4L4/SAUfpAx2eMjPR4Z++DHR5hEUmDiPYc69coZhacWgXyuIbAJzn+NfL7O2Ly9uINLE65MtijYaCLHxKuPeGgD8s0+xZy8C0iPafDNvQtATPB4bEMthR+KQEzefBcAL5lmYphJHb/445fHyh6FoNxRkG2Mb7Ka/UP7jLdJW8mUv269XEfwF6oZZyyRZ1TnPtRxTqvGnkY1mxpO5dm2i4Nfgdie/ntmfbDhRUtrALEMMGzHHHuNNFcDyG1w4dABUxyC9We15oJemGJjTT0U5joJmr//DKw4d8bG5kYb9nlNE1hl5B+NHT22DulIlnETyLNUj5xNNBJWH0ytRUrsUGsrLGxx1lHO2C8Y9tvYQWRBJ9bKiW96iWFiMk50uwNPtzGpZyVn19jueeCy6U1tJ7bD2CFoo0uXYhB9flGMO3c4X/hXvzR3o8BLodgvglYB6AeKxyr0UCD+njuDVACiSEzRmCIQ54QUgURztyF3ILoYpDsIKSxJlzuQGB/5xX9iMYPgFs8dM4JDwtgYwxc6fqHJa84ZFHcpHHP880JntToffEshyOcEikLT93mBQpD6KgqlWJxHS6EpInE+yZ2Dssm5gu2sDexCj/0SeRfF4O0ROVymjERtfKFqs4+wzwhUfs2ob9bYcqf0tKdKc1Ed52WOvZwnkGhbo8tSW+nZfHVhQbt/tPC7eHsJ4JY1tLYlV+n/VjlzYa7gp/1sGfhdFoK+10XBX3RC8I6pPYedx4t2WO9IdLSDrglhpGEuOpNiDiKra3XZp93JH0ur9IqJDrLx1b0clvmrlUGuViRofDmxZs0JoHxA/GNxLKay4sBkKZa2ByTV933b4FGkECanZE7OEzh2xhbHNQeeu4zDFJ7FgRknEPrAsoKIOdkYY9HIbclIM6w2vIrEq1rHWk77jsP48WdjxH9o2jYCCE5IG9tcLTgwLadKw7GgZo0pipzF9rZTLUUqMlKyMjm0sdwxUqlZWjG2oNha6e/xmKHV+DvssY2jKzGIhtAdwcystjb9LIHnT0R5tGBz9VMtM1jZX3vBqVd/5PnF4lw5KZEXnjE26CqCCc4SrdY2k2ShIQQbi6PcHnkuPaJpRs25+mIVoSga4UggPpdLK/ixhojtVFi3bH0hZWzG4V4f/S1XCx9GdEV4ib32rlLQWU490WcyAFgsS02qSOx6XR5xmRPJSK3bYcNjMQJwFMw7SPe7go8COqYruPVXGOL7TDPg6EtbgJyvMDD0Mlji6gUuHF9wZeaE8rM8GfjNM/DnH/UNtQoTTAw4EDjm2TdZ5/gfWufH8pGFLrYXD8Eg47jx4Q7BDs/Sg2mK8mVHFtj5N8F8r+YcxrkMpdt5hOOvZ4nA01/uYNpZ7EBu3/Aymh7f/ZubYkyQnAN9944mdR++19UHxSC1fs+Himm0TOh438dfde3Go2R/0YSOQpAnd7RgNKnzXQQUhSgEqbDEN/zYd/FJ9l14klvP/Wj5ELH+u6Nwv1Rl5kseB1PxJt++q9U2zOMY+ZUwHtX46kvuDKLgk2/6XRiaApAf/0DGrwIx8aMQxIdv9IXxYx0UgXj8C1/ym4ld8uW+/252u6uNyBmFPD+ZyYnpgcgm5NBQowr5ho7sRAgzztbeSH7WEqz/hMlaoOKRTFuffC78SRw4yIowybZafdllu8HGJvJZwCzcwTvloa+o9M61ULUL2zEc7ZCFqGu/Zx/euSxZXS/GoE5+6buB8ke33cDWFjm1MoxuM+UNVf8Nvyp7jMiwQrZfWYvbLV3HBzatu/O/zWdbsQ17xnIMwl8Wx1grSOpNJW+RHKv8652DvKeUghDnB9M6rtPqWNa5gEIQLS+gp0D8l3/lvJA7gFwE4jwhPr/eyEvmrfNB5xQ/FpZCUHyqWEw4WjECIlyvxoCvpXlQiKEdujsahjKpPh8KEJzzKfT4DqDe/cN5wHcIqkBMkZi7fHROgKbQjCyFnxSNUkzW+YJzjX5p7Gv90eA8xLHpIjG+1t+UxpGMEgc7AMcoJBGzrH66lvkOd/XRYWy9XO94rehVzvE2vZkXasyON4mOcxgOJiyHY2yCs3N8nwv7L38nGjukz0veR4LMlgoivwCnYpdE/065pH2Wz5+B32UhiLR0f+s+WJ530hd5+3txL0yZdbf3U7jGC+4e48+x5QNJNk57d9+13/Yub7/ytuXf28rbvpKX1/jap32Phwyb7y3Vu7d3PHKWxne3+Uq/vGhmXV7tnTLo2n0PV/ldr/3a/Slc8ff25+ifMX7MX23i68RVvzG8h6u87YmDV5u1Vzn8k67+vX0PU3t3/Kt+sfe22Du//crbwj+Xjq286t1xyDvee3uX1db/Km1zwnialzNvp/zEQJ84+s/yZOC3zoALQb0jSDukL0xnx+SSmMl+q/TIKJLAz7/s8bn0JvL2j317rpyZyKwDhKtnWQgaPS0Vmym5r/gj8tqzhgks8FxwHxDrFCI7vgCvXetEaBdjI3FsfmWYXTQ2gGgMfhzkA78aRBEnPyVN0YefjP+gPPqxsSkK+fEO3hHigs9M7FQI+qDCkb7o1yMgmTxSAPKdQW3xo8kj/lMkotVoVg75Fl8TLt21w/s8cgcQEzEVb1S1+ZJvmg8539bnbh89/uUJ2zfBUuxhsic9ikYUe1oI8iNgFH/4GWlt+H67T17YFKTDm4SYiNT7hRsgWrSfgEOkln0HHedxMuv9CRxwL+xV2MtCr8LasSRGLDMG0PjxLFF9FwcdG7D8A9bF72NS54LDLo4EdBTWD80YEpm2SyDGYY9uZGUViTROLxjYa8l2js9aUYsa3bNVh2jwf8a9TJmIv3pFg4VjAcq9yV8kuKlfOKXRqjZ0UTn+06tcemOsqOhe7QUywPFDr1bwwuL9ilahbHvFEd9YUuNjwhsncZ/RGwcGo0qabalDLrwNIxl7oJxd5zY0iqLsTl9EoyuaL6f3uUW0jlvf0YcPjmEdq34RPHwKvT6uVTT2eULFIJ0D8jgYxWPOHfPhTiCdQ/yiep1DftAJhneKcceRC0H4x4c+MqlFK/Na1oKVMWSsBq1tz0AYSn7JWa2Lvir0UBim+MMjX7pj8Fs+uusnL4vWeUEng5xDKCZzDtHdQ2pdaNY5IucdzkmyLx+rAESuCJP3hvWYnZybb3kKQcDg5ZNxYctJV9fHf6wBDV99cpFjIez7WuLb4syIZ+dqT4BksLPBtx5BASOczVVes4zIslNeqPcVSb3NvO2mEKQccw58ls+fgd9tIejzp+aJ4MnAk4EnA08Gngw8GfhfNQMUgv6syYdvUZ9rYxd/NGDPiXX1mzuDuBr2tTCX36HDWvxeJsPmTgmWTEhMrotprqAzneRCfrTmChq+zXcSQN8X5gBsOcYg1b9cs8OznRCQYxawfaZ9j9+RGR536C0jTOo0GfNHdwSpWPOj7gr6gcc3VNTxC2JV5OE9Qn6f0Lr7R5M5aO4cUPsjE8G5g0Cpzx1AFH7gqcCEO3x4wmc+E0DxFQixECXFHwpBLghR3NGdP3nMIwUifzOv5DDZy4QthSIXj3QXEC0Fo2/dpgDERA4bTBR9FxAFoPVxwq/5Zoo5ySEmEGfmmfyxL/EYBNsJzJoPTopposeoobo2+baPHRkJcqPvmiDY9zyBXDsJKPnB1VqEW2OARqDVQA2z8dhDjO1sJaQSWh6z+FwLfmUwnAG5CUebeBYxc7CpX7yjH7nwJA419AmyYxLdyaRNj0aazUG9x2SMXIDuJKuJbRyJ3/6JdxDD2D6ahwi2NTKX7XE1h2a0a2PGRXP4rXQcWss85Sx73Za8okB1u/YYyqiQHCP0NsByYk9w2ztc3xGEVgLwnT/YZH/AB8dw7+zLeYJzBoVeCr+cBzhPcO6gGKSPij/+2XjxXBQS/0fuDFJLIch3B8mQC5fyiX3HPiv4PBnSfdhsJ4F4dq95ocjhQods9dyx7ghSMSiPkuYOQd4XxHmCgpBOIy4yUwDqY6M5T3Au0XkDu2rZF1tIWX9H5CvvGSWKvcVIoTON3HTavT+L66SiLzrVL/FAX5cOFT8ZN7pAzxyQJ+mhzucU0TnNllZbFQBVaQjtEw30znkNwNf2R+r/6unvBX9vuSPoeUcQmfv8y1MI+vzb4IngycCTgScDTwaeDDwZ+I0z8D9VuPiXv6kaoQvTdeGuGBY9V7x+dIzYdDGbS1xd0M+17r7kPS6V58rcl85cIaM6bS6I0dKHi2P4S3i+QWYwgMDicEj34QJxi6cIDZM9el02nYv53QcBL/prmoKrztRnMoFFF2hcpEkBhwkej4nxzb0fAZmiEHcIfPAEkEfAKAJJV+0HJnXcIUQRSXb8qIBb9RWUP8bKJpNK8TzZ63jUclfAV9z544LNFH64A0gDZ1JmvuR+X5AwfGOfO4h4FCR3B6UQNI9/jU5+eYwiE+8dUuuUQytDnZ01oUcRiI2yiwzJrDeLVqh9pdx2O1ndQlEaRwWn+YppY40t1KVUWvSYTGKKJdxiYHqqObJsY6TFW2CYuGLWn/nHavEDiyMNqnaQXyedh/KQy4axw6w9eGYVNW27A3/ZCMOYWNGeKuaPEnz6m7epgbgp92U2TuOgLxt2W1ls47EYxb01wkFymauPicbQ1mx10mfdQC6IaB8sxrC2Edu321htD29sYS0xj/IMYMUrtmm16Pm0OPasLwPYEMs+fE5ArsqRj30dyB90dxDHPncNUhjm0dAUj1ME4pFRn0eENU3ReApLidCe5IdIiFNcnGrxnUKmso+bHBk4BodGC0F9d88q9HBe0KNfFHkoMvMoKecNChY+p0ghWLU6t+RcIZlOErkDSPado/FBAOr7CwaCZCMTAIv7OTc4JkSyYwg6AzT8UCPxjEKsLB786pUr89JEeZKTc/qBU6AjqiHhkY+ePYjm5FI17B1L2W7HXrbR2LlgtT3FJnIXDLV9Obc+haAjSZ+ZfApBn3kDPO6fDDwZeDLwZODJwJOB3z4D/4N3BOm2FE8QcK+LWi5Y2+/1b/tAfH08V8L7HX5IhmlyaF1x24a6TKBKh6ue+Lmery4X6csrlrIwy/CiFlKYk4WoahkBHhhJ7VpZvdiBazPoRaT44Iy8THWZI9iOaGL1Rxfz+r8meXzrD4PWj4eoysPjXxR6/G4hTei4I8jf7BtKkUd4F4SYKGAME4oZG8iUMGOkax6xig/tYo0SkJ+MZ0KWyZl/BprCDnFTKNJHczzJmciBEw+5aWWIiRx9Wm1YT+rIgmgygRwiuVaM6jgXZI1Y9I8ikL8JV9slhUOyzWSSiQ/WstTC5mBfsaA/OGR8ajHbsvrw4YDJP2Jhwc4RhjEWjMwxA63hCo/WohVLgIW3dXQKwH3aJXBGxkGjZsRaPDZt1/Hl/fXQs9x+sZs9ddtFX586hMAegGUjPnZRbimMx5igQ54adDKJxy3fCuRzYlnMEtv+MmfirR2QXS7hMigxIt+ShZe97BdoJ9LaubQNwIqsOhr2zSB97IjPMeVlWnrmjG62ljrYzH8h5Ft9cHallkm9FzO14v98XJjhOJaei77i+31g4rlPUZi7gPThvOAXTqtQlMfKcvfg3zg3+NjHrgzomKRJmF7bJwyxExeEej0+Et/0GYvHpLFwfPucwDFPcUcfnShov9bLfnI+4PyRYg96HMM5X4Tfc4WPbdvGdfYVosPVWhbdd41GwulFSI8JMqcbzhnqdKxAGBd9jJIEFrWmpjvMiGwRwRx7w/UfCfvEnOTjJ7qGhwfDAxjjHczg4cZ6WqMaj3U3gOPd28+sbFOK408hiET9PhadY7pX/T4CeqJ4MvBk4MnAk4EnA08Gngz8ozNAIWg9GqaL03W9OxfLNOsb2oM3pK+CTfsb1T1hdJdL5V5eQXYwFDugW7nhArqzY1+Zx3owAImrHtWKTo+1dHFmHhrFbfNY6GKbgwkdyXKvF1nP9EKCbSukJFJCz60LNhRrUrDhUvJvKuakaAM2YAo6aBnnYpDw6nhCKkz0Ywcd30FwTACN9WwCv7HriRqzuRZ5uENI4X4pXmQzuVJeXADyI19M+sTXR3BhodNq/mc+edZ/D5cWwlsLehZFkASQqUlGeJNF8WJj2x9TMVxDamu2LSJpbUH7TmWnt0GzNhYlFmLB8aqwbJzlBgSHBHgGAJHF42kHBOZYafEY3bprX7Yx3ZM2K2ojTYOty51Do7R1Q/XQuCirs3EO7SLeBaAL2zlCb4eTPNK/8q96b3uTgTOItyBxtqdS9YXsnJrnPHEa2chwL1v4sDw6gr/1sTUbqo8btiDHD+Jp6VXffO/0tS3f029rCT5HCZ18tLYBMXQYcCS4GKQNmeNe/RZ9112EKQLB9y8IEpOqx5w/opNSE32CrPn43rGV79iIx1jJ2ZmJtSN0zOpxrAvnlzpTqNW5gPH5TkK3nB9in18Sxp9Ucn7g3GH9ymOrvu3CHeU1gbp3PhoGA9zCilAI6ovQf/uSrvsajHEZircf+izsRwxxZUZYxj7MlS8QH1t81honCVkd9w8ts+xRdrd85X7tT1sHE8TTwiP7GnheJM4Y/7dvv30eDdvp+qzUUwj6rOl/nD8ZeDLwZODJwJOBJwOfIwMtBHEB7EvvXrzva12FpYt/rbmopWURuT654ubCPxe6BnCFrv/gemFu0itxuWivzFfHdMQzMzTrLPHGBXjjND/GN2TQBpnGmEDgRNa0f211RMCmfIF1+y8O2dKHnIv9IW3Pw/Rq3hsjmm/8jWECp6UYJqEzaE/06KYgRAtO+aPlM4rElruMdAeRbfG4iKLShOwrij5fUADKJI5h+tt5bSQXg+ivgg8YjdAy6aNDatQnp57cJblOVydxsLwwYyco+sRJxy19R6YGQBZgxEjrglPtjJzGmPbVuUOifQAgHUOQXa9iAoMhqbUkcsVvxdHHDsvE/6qAghU+XUrT1qYxK+hMEotfMciKxyHcacM9joGlj6ayapynmsYn7RfQcsE2sG2PebEXMZszfQe+RIuH5Z3nRnjD3brZ2ugJf1OJPRQ8kJ2Gm42OaG07y+HeDB7R1UR10yf60VmCEKsroIsywiVVaclfF9OzYbu9HLyMtN9jgqHtJTZYm9IKH9jjcDCtjdrjm7sE85iojmcBuOuH455zBodRXzrtEpD4/FuL/OreupyLh0ko2VtEWI4Am0l9tRMya41Gx2XPEww5x2fu2lsy+JwUMGqMGrrqrEJQuoh9TOALmqVteqwTUPgr4uDEhM8XDo5LO4Uo87p/WG/GZJseWDyGZJ1PrMeTsawkai6Nt0DUgomAXn2RosF2u1evrU0cK/iRbUR4m+tzvDYOqXwKQUfyPjP5FII+8wZ43D8ZeDLwZODJwJOBJwO/fQYoBP3P49EwXwfrCjgt18Wi+M+1rJnEqCLEXPLCMlsXtykEqefrXs1qfBWci+DwMmXD1hJBFzJ8XgZs1roSx+dMSiQ571AK7hIaYC8Nt9OOYkHDc7/go33DryFhlkyE6Wk9yYBBn5mydNY3wfAYJONSyz8Xc4xVz79j31IAAEAASURBVHKris8dBMMDpI/t0MpsbJJrbRlvp7kDCIdKEY8c0HpyJ5p3eZBGtk2KPaI9EYSn6RZ3A8guK3AmaeXay+LDiID4DVDjn6+mPzFHiezGGL78bb+Mo8USCVS2jPuLWU3kLEuQrvLDXQr2b+kpH1qOQp0y1CcCN6ETPzj1Rzw9dYcha6bGXLm2aN4IYGg55bUVidZiID8xbP+ToyF++vLGQWx1Ar3Sx8Y9nC75xdPsV8Kxr1lXtEd/6J4ycvTKVodA24n0xRXBzECT3WpcUcC6XyJhyycwE7PawV19zXZLgtexZzTuMX6zQEjsgfiMLRNOhVcrmK0Lv9uQFgkthy9LikDhmd+ijzou/kwxyOcGKdkGMj6y7RUtC+FktfJif5EaEP9a+z8rK43eFHI0Do9RIA9JKx2qtl/DzoMSbraFomPKTPhe5KI2uq9EJgGLO6Fni/gczp1xwXlEtoEb+5XByNSvGfwMN6wRTBNnyhmYg7fJULsfDa+Xk31u2QE0khMvWoZqizbndhGGVyeIyrFAsY/z87//7rkjiHz8HpanEPR72ApPDE8Gngw8GXgy8GTgycBvmoH/Po+GMenh0tUX9I5AfTHMdXsNC2wmZSJ0lbv15sLXsyAmibnY9+TGl83SZGIimHmGa3owfQSw8NsL+m17eZS/xOugrYFSeMRWG8hDz+TWMjF14Y/9sSJuFrBvFgzO8lIuGW++8J0lAhjDqnrmyR+thcLTzofGfSaBUmJyaJxWnVys1kBMS8vjld9J0M4JPDDTMuGiz6RObb919x1C1pX8nP98SQAEz4eGCKFdAlA7gUN1QJK5ACZe5GjgnwlmWovQHXPOl5jjxeKOJR1ce6TTTUyNhsHondYCiU+rZRr7JjQ0DFG7YAgULNn2Qhe5PtZh3ygP5kgHDSPpuUQelGVGZIWOU6m2Ka6dtkbaT3TOtfMLELkG0JiTFTFrROTiYYABzELhDDvOrdjYYNuwmBYvvShAw4+FU4IALp7y2BLb0Pt+VL0+NU76gBy4eGO9Y1Jn4jff45bcAe3YT3ts27Wok27WmMpotFbHpiUyLSVvn9nPasfHhg3m0SgbtBCb5LLesk28nSyZo8THsnxxKAHFvj/xn2NcPOG4Q4iFO4ScWwcMnX6ciZbTFGtmXKBNtj9js6Z43sdpxcgA3Pj4ty42ke2F0cSJeJIhNoRzSChxzvFvPjhcRcdOR1lxoeMPLFtcOXRfrMbFcdJx2dZpCpnzOI7ssEkWT0vg8eGNK9LbBxbbH//ZmYzvCp/cLZr4wMVWYhkUNib++BmcOulfdewSOxYOVreFMdZ//913z6Nhk9bP3eg8lk30uQN5/D8ZeDLwZODJwJOBJwNPBn6rDJyFIHymmMBVqy6HfQXMRb+vfufyNzTYL+fS6XKhDK8Xvcx20DKufO54yaW2L9LF9vwH8dIbH8tv+1zCM03oIn7+y1TvFCr2wBDQsHHhiYDFc8k/smqs9gXfIS7AndhO3sNxuWmZVhmv2pnH0EfG5NAY+J3jeFJnqfSIW75wBw2pVbzPWmnyxGtkhTulTOqqw8xSjs33cOzZlC2p0sIvH7F84apL5Y1FAkgmVp3IipFJn53Ydid5RJXBnXaIfW8VMMRjs5bg3dwQ2HA/6+4Rnmija+lHVnqRkzOMA32gTx1vF8aT/zEkQHKk/bdg5GaKuCzCLJuMzKN2W0dbQ953Z5ycjBh2vFZ+KwPRkIwG4u0fSXQPjPaZOEXrtT3b0Yo0sGDD5PQZVfoZa+zI3uSNbYI8W6P5rT/a6wInqslXpNGzbORLq6bwcDenvs9ZjFPC5JdzkWJWUB6LW4yqLxobK0rRCOD1uLK94a+koKxlMqGWQwAfUC3siFA35zzFQhjS8zEufotDwSDLAqzu4Ni/QOTTfLavCHNsPwU5C6WcMl1jAagPCtE2meMZ61lsuRu8THvIeKIaOlsJe4rAdk36bkMnVF2kyPnwD51ij0hWWNzpWVNo+rhW3G5hIJe5yYD6sTvRFeFWogQwQhrQ3T9JhxcEPgcmUhm3qgtuA4khdaoDf2jbZaXF9k1rpCOniTixkhv2Ecb0H/74x6cQROJ+B8tTCPodbIQnhCcDTwaeDDwZeDLwZOC3zcB/12NhPBrmx6101errV11tc0G+L7iJaXoSGCNOC0GJeK6G6TAZ0mQEXN5sAw+BeGpN0s7VMvMmFzwgLgsW8rFPghLknCxYfuHPZMOgsXcowOHjUdjoxWE6dpMcwBgrlmXiL/1hLlvRzNoyTwvcx42seU2LxHkQjtqJJWpRs93hezIHHwFaplkJ59kRmui3dTfbjjHrPzDUg1ALAX8xhgeI5QvPVBc+RC2olcH4Ix71rceoMq7dx5YNsgppQkjpgH61rLC6zQwTd9kSg/2mQNtOp3lAhbsG9jI0jWPWi1pYRNs8vNsyEnOXtDHJjn0co6C/cKabkxhGtQUCOMae9qrsGBciysW7xa7Gt3ALMkxANTZAdxPPCnLpS3jAbQ3ZuVS+8oWQwkM2w5mrUy3DG0eyAdVCAD5rFh2jZD/obsnRFReKcWGzxx4sltqcPXD61ghAa3wRZx6tHN+2Fxxye8H+bBfa0gkAof/b7hm/C0B4cY7kKYeRt7lY4hMdvjKW8AzXSSBHw2mvtP07JrvcYSi2nfetjx4vnyYO12VRk34LULEilpLIY5tZhOd8TX/G3nEUQdLJ+36cq5bEJE/tTgvWPtTPoTjnlRvWpzFh0F+5nr4a810goqPFb0iSbVkl2269xoAZIbDlHJddHQeKbnKGLk7MnrVZYsRDJBtjjRkTssZhMkKBcUcqc8wLE6j7aDDupxA06fodNE8h6HewEZ4Qngw8GXgy8GTgycCTgd82A/9NRaA/61Z1rlO5cGXl7/jV9iL9epm8L/r5fa0smeLQywRHlEUzGwJ0zErAnBfpvEMD/NKdq24mEyllyCexOcqu6QJQlMYNRjxHaLwFwrljRTiDWPZEjAzpHp/tSzVco2zLVrXigj4X9ejsbGVsRkmp7Z6swTJXKxeCpECfKF0cm1kDPx29MmjATG4meR6W+Jlwob3HOZR4wx0HRjVUGXB/geut0aAsGh/oDC7pDJ9QOqUSaTow1nCyGDfbYbk75C5EHv06mwhr5tZ2W8mi/2uVWadD3d4Rj1fPiFVicw6V987QsHwoZItc3Y3pCS0+z4xV3Z5WZ/za/LYK15BFHP2rW+FiLJauvUD3Hn1RRY2gcyDFAd0TZEzsN2nOFSA+EtUjO2PuNIE/OmPLmMGjuHRGvna5qglrE+jgSHweMzOdrt3Drz1DxUjLkZ89r8cByFY4ffOa5PwjFuLOpk5kCaN3EeICq7FMESFdHdVmjcdpIowFmY1txe5/6iPJi9PllwNcjrHJXUHZz8HI2GDJqxcOMngYwJc/0mnyxOBY92IMHjM+Cp+YgeNHzbAj38uW1ToAu1m24EYsSjouyDBwn5zwhjEjHJKLxRKT23OxnrErdBPeSoobC+g4tzaXbel8WzaruDIOD6DI21fm7wzGYqxmBIw39pMXeR4/kcuYF20Xh+7IbJ1jhNTuQldGJ3NErBgkJ3DbAzemyho+cnx3O+EfXcQZd/ZF3rH3H/74T88dQZPGz918lkLQHFPeMUo7Ed2ZXmQFkcU3zD6oRqk76A2nfTMWyld/QcsTgp09u+3YuzTSGGyOiSq2xUAUajv2LkaWjZNrXMvop+BGB1d/ZzDQePVhGK0GMTb2NzLB1cqI/+7GuXjXWOMit4rugrt0lv+3uCUScQwqwziEtXdg2GDGVVZ4MSe/vGJoczJdEgUXenEMLtfWrqLT2KKBnJ6hl5o660Q7Gid2GYGoYAzS5Q/isgXmXAb3rhwsRgZ3N3Tq+Y/DaXvAxpzAmmusF52f7nSfYb8gtppZLkSAsRxzg/tJy5+I+/j+uL28h7ueT1bUUexgtpk1voN1I29KNnm1uxCfMEYgjv3m5WrxJvxIF99/r+5HzC7RGtsi9ibHLxe9acPP5Q77TSJr/xrlaUza7pb3ajRc7HBuGMwFcumsuD+FiNv6TRhfy8+6Fv4UIw/mkzPAQzJc77NwDHxs+QnxVl2bbxFbdqNAdBea3TOIxsSejGP6nxzAfSxVbDw5AsqNw9frN26lZCs1daid9rb4aqG9LT8MQFYwQB+zno1U88BUlUmMdSeCBlKV2ix/6V1T279nwF6pvmem5j5fuwe2qY9F05HsdIP2RPBjap9m/GMWLrJG0XxfhEen2wJWdSp+L6TqtAVfbHl3W3ebr3DWmdWyV2ANrHYLPEYd6FwnWn2Ua8Mqhatdu/zYqmiZtsHVC2Fj2yLbk0H7/LLZo4TwXK4A966sE/wuXavr72KRHpA6Avhao8DK3d4c0gV3Y19URrzGKOKlaZQOgc2+Z3cJD4W704/035odOxac0py3iJ1riY8tH5feNB3/Zbg3wKd3fyKsTzZ0ZpJ9g/F8pQrfzxrXJ3t7gD83A795IcgHrHd87ajH3uF9Vyu3M4rStGBpXy2fvjNti690LvZ9BFw4ci0tdmLJfKKbC5D7qacHj79FmhNg/TUCxuHxS5AiQn0FsXGyrk5sYqU4W1j5Mle+6htplq1jf2KueC067VXn1m4TN8Gte8HVLq0ElrFKN8NAFlxvkw/OqF9ntWLq1w7vmCVxbKvb4lOWQqwkv9gRUE7e2XrDuTSnqYxSHIgaO2gXbxYomg2n7LYXu2I69ApluxObbu+Fx+/gIBvGkpc4cGW1fasjg9iUoH/MjKFfpYiNa5gWXToDuvPGhtlavd2/B9AGp+/YKMRtcbc4Lxg6xb0R3BiDw/Ub92tDjLMTcNKHSbY9ahcxPlhOZnmRRPyC9944KDB0PztMGP7KzIl5SxPYz9d6a+fjnB7JvtA9obh2bnKGczTOfRIG9+dEt4+rt1o+6g92H5nZ4RzCzfxJKuE33sC/UUMx6FmeDDwZeDLwZODJwJOBJwNPBp4M/FoZ+M0LQVyor3nRjGJfcEfWS95cDg9oOhfemQWUJDx1Swc2mqczo19YtOIYPH2UrgozEcNoRTAw6waYa/cWZ2qvbY39PW0DaHvaOAIwu/1gvVactAl5bHwsrI4Nex/FST7mgN466k8steEWhZmiXXQbH3aOZTAX6GnvgEKOR1HSqL/iL1iYg65cLS+KHK5aUf4fzheqaKe6XcRhsDnLTvBmny/Sk/DLYCpJ9s5JOjA83eGX40kA+ujxugOwdzweCKvfGL0EANJyDuO6e2ubjWFiShEoRVLrrZVk0A1CneotHnIWgAdu0RYeqxN3sN+QxbW9AWbTbL/v4JZa5W2X4EZI7jG2lfhN0QKVww7DdrfjVx/SXQSlQ651tw8Mj2cM1cxyIWLlHawVWF2XcRX5VXTp1W7bi/Af3HHRFB8dZP2tYCSYxOSbSYAH+JK0q6imaFPwPzkn3fM6QKzjfAUwQPofXxpVke13B+LM+I3i/frjZh7pk4EnA08Gngw8GXgy8GTgycCTgZ+Vgd+8EOTJyLravcV649+6l2v5m+YndN9Ys86bC/Ay3rF4ilNOATi2D6EnZTOd2vTrGN5x9RF27bR9BW0wbTfWcU93j+GVjZ/De+vn52ivHK64Rlv9HfnPswg6uT/1TmvXmJnMlbM0phKzJAK0IDQUXhY8RH1c+eG+4t3UpwveOqNSqyf6nNyf/PMOo95BccpL/z2PnHBzc47jY0RKNLn255aP64jj2ZprQBAnagka5t/Rnvau6m/3iav81+xdRnLpvO8F2Ak9abTu/Vr6lHGh28ycRUZslN+2dn9v7Rr/Iu4RbsHLQtAF3tFunS2urBz6g1OyL9IXyX9lcWe5Nq+tdWZV/acQdM3R03sy8GTgycCTgScDTwaeDDwZ+OUZ+M0LQVxHn5NXrp+54OUbc1/4erUHdusOWPI3grnEPq7Olx9fvx8KxnAhL55lr/352p5Vn7XZsKFqc+zArT3pjZvBVjDdBWz/Yy1+7vrg6/89XXTe6p2FINswRNlY0Nd2nY8BbQRK1wVc7pTBeuUdQ7FXC+6lujAAjISk2ehTv7bLSwu3eCO0Mu8UXFXcw73HuMAxRJfFW9QdUaqgBC9GnQW2+4OFnTwUGIvtWR8zi2FnF7MWRc1eIN/dLSVE1jt+int1V4rHi7vxbxfQCcG+6Nh/eglUwZqnlryQD+6gQB9boapwtBdbWMhjlonD3gNusBfHhx3IM+iXuMOe8Xh7Z0Hw0sYNf+AOcoEakh29Yy/7zbh7hZFhs1nhZBbvH5UtpggwfF7ZMvMwcuA4lA/Jha75j7WHqXfd135DO3VO23dcZNWiF8QbfUFAef8xUc3pOGlTuFx/ZAbvjRXbPl81iBc50x/KFQPUZZGdpToCMG9wlnUEd41tkTD9Mkf5xMa3sv/VFj/Uk4EnA08Gngw8GXgy8GTgycCTgV+cgc9eCHozgtvVM933L5lv2i+AvezOrP2GP7pcys/U9uBeSZvf1RIVThps2+A9KWnUL2K6Wn2vd7U5050B32Xv2Sj/DEK6+V/h23YlLaJ4q8/D1uAOzuvJz4GrlZfjsZBVUVIseXDfBvxxzopviMPkobhQ5rVHyxyw/RDaW/wCqJt6DbcdcbqxkHeJiOP9aOvfVJYA2F127xtwxx2g3hG0dtdlHUKFnOyw5m6SyfMatXEGyG7usoCVCTYo9Pj5zS9FdFoMz/PnNyOwJQnRPI86+lnWrw4sa/CPQQ1u0LuJQyNj7bR5UfrddTq6tjvyjBx+c2r61QhQ8g4rQiCym3PbKzAGwcfuO4iX7FG7yOC9Woj1p5ZXuueekUCvVl7pXBHxTAacB+cjiOvZPpaa24tdOrNPoQmmyx7XwRzhloVB/0RFfnJqVa2EkWfNHUFPIejIz0M+GXgy8GTgycCTgScDTwaeDPziDPz+CkEMietfrpFzHXylkXcRxpfSJ64y2tpwe4COb4YNP2cACzbK9JcdEWtSMEBkLDP58oTD9sSrzIAaoXPSFs6qzk8e9Hv8O+4T+zKHRU9qhq5mU0F/RbmIok5heRRHjvSYLeOfpAtYmSMoR+boomqe2GO7MRdJey6NIe/3YAp4LsQDR59TcPjYqR6AgjLFCnU/S0WnnyFrQ93uCyVpvcj37CYzVllxPHMHj0AusoAblbaN1zlyQImA/sKg4ziHg20BXHyaatCJjRmNr/aMx0ZQ10IQprON7FOr3CWR4fITny4ETaAdV8Yzg6GR6fgjFzu32PQ2G0xG1ZISvpUjZFr4ZU9HiL6MRTe8VaQSzjGgCFgtpIdoZXVmOXkHPD4KurWfikMNLEvdVjfc27pCtYyri8nhOd4RAMcw550O0ibUdWscHZTd2asNWDxgsbf1y1sgETY3LuEf5AlbdOX3dgFuREPruOi/txRbDD66VNb+jvREIX2LDA/Zxr5CVTeoRnHVo1ccVPdl6O0nPdbntv9GJp9C0M7NQz0ZeDLwZODJwJOBJwNPBp4M/PIM/OaFIF/gznX1mkxoHIve19x7dLer715qAzVdxok7aVsqWu1ytl0syjJ6P4WTfO5oWM+1odspUWO6TAMrX94OgvheLe/xX2E/kXeaVEiddLxKS3ltX3nYk/6PpTaTdvKDv5f2JCA0ZBApFLj8YLcN+57axP8qt7kX4vS1ig0vBhJc7LTAQLDLX2MzcDqlG2GDdH/HrlF5bGaLXvYxo08e34pyPCYXFHFsBVECtAm6RnfYyEbi7ZGkiLP9lmVTVkaB7WJFr9C1htrikYeWEnojcNHFdrJtv1SFhn97yRjOfkZ7XSNvPuKrwW1bptpFLPos+tRibLEm1MTlmGFIpybcxQaEVnuMMK6LZbA+BWeDn2DvE3AOdnC+M+ygCYexwEq7KWTmikXsLGyynxrje7gxYTt3e95vD0DJtveobORYgQPDctLhbMkpO+ng9roy2tK1gqc5mlYbTZAsQXYf9v4j5H0vBhmNrCN/jQNbdOwnKjztuOrfYK8sG8BTCNp5eagnA08Gngw8GXgy8GTgycCTgV8nA5+1EHQOIZfH4uyr430lfwCLO1tflZdxYDf5E0Y38KA+ZvC0d9CdJdUKJmbKENaBLWa1d9m9v4C/nKjpGeLqqt/J/+nkYxPIjdP06R39jRlP43fzD6pFBrEMs0r0zjjZT+hvU1AsRUlm4Y3vbnnReLuO3FM7mSva9uj0kSkYa9ATEOIVGPJYP6IadyMYQIa9p5zgoyOeCBcCRvMycN2ihNyxSeBJ7HYbGzGUYtPQNERgj9aXLrfawF/66ZtnCavEw91ANpXApcP2H32jhPwx9z0EFwNGYB/8jL1NIwryXDcOW4qf+l8wMJGX1Xjap50QT1bcwrmqXzHtvXVTybV9gbuz6DP2SeHSN+4GflUIsrpifj9sJPbicd/9LIcHQX5OXLQPwJDGiTb2AB3kUoL3fowL5kj/sbjmoy2+7/SOJ7l7G1HHWAn7MUdD+6eFK701OVI/hrdsAE8h6JrFp/dk4MnAk4EnA08Gngw8GXgy8Msz8JsXgnr1e042OgxPGHp13GvmCodvtoFzCX/HFU9rXBXVeeXUuI/IbM+GrvbMZ3UEzGNDdNds0x1AsxTbfts7/94v7ldoz5DGzentnAie9N3zVUZ+rkvlbSM9PSVNTIjYLuDIne/ygD4fZVqPKu3UdlNGbUoKdGQEme25a6b5dPPYk5/vSnfEtWfmrMZceoOrta0sDkzfGtHiyBhQ4/EtXU3+SMEKbluL/0wo0f4Rhv6ThuYEW2hggse2aqZ8hMjhL5n9gddHxqJLC5UlE9nofPnlsrZtCYbdrKITg9jdduI01ogfGQUjxmB9Y4l73iXUIJH2cU0Dx4f1mhMEh6+BYCsxvJUTif+PCG1c8nEoY2M14FgEbGj/WJwjcowd2sf8viwGvU3JbXzjIyPzuuNv+xaxx99c2M3hq6RtjO2m73B1IZFX7yK4dV7hXvFuau42hvrZenDaK2pbiKRam18qe3VssEeyvEXHLseW9z1j3qKIA+4rCXYRWDbhfiPWV29DNvRZPRl4MvBk4MnAk4EnA08Gngw8Gfh7MvDbF4IUJRNSlk5E0jvW714hHxj0z+6lcwo+0VhV3g0KwGGrE9fq0aJ7Lu6yOvRO+aLv8nsf4M32svkp9pejd0N55fHQ+ijpyfia7Q+0Bo/wkloJylMLyWL4XnmiHlgMZR1s1x/dVAPCRnCxZvZ9O9Xg0WYiJ683x1WFfbnjRBUDQ7Vq6SKqnTZiHKuKg/8ylCIDce3xw2uRhtoS9NSYbH/bhKo9tTEb25Lwzh6W2IPYdApC8RIr2XjEZDuylZc+S2/swmdJg7HQtQ9/vQhb5v4mJy0GzUCjL0Nf6i4m7NGOQcvWiqDOpb6ldCk8CTMio6vW3Fp2AN70xcg2OJ29pYn118S99fBpHMbXMaJx0m8tIM32/JTYSSQ45+gwdu8Xd0BCDrB42o/Ht9U+FVefP4V/6/uu0ShfW3yrv2M9NTj6eqzCv3sp9o4L9jwvFLnbbDMKqV/84WsF9BSCdm4e6snAk4EnA08Gngw8GXgy8GTgl2fgNy8EdVLiS3FdOb+8eH7JfDvYy+V878a5wF4Zeu8y/6KoGZFwDdaiw9ZZBDrNodPlIMt63R52L4D3+K8Mv4e9GEznhJ6x36DATk+nmjfaCN1wq8KxVLetRYKsiabpWFwpUzecWQuM1fR2WzenrLxXLTg+l2Wc1rblWnVzB89aiILUM//cz3ynUnEzKRS+u4fv4FiOY9VW5D89rYnlcEMMLXbg+gcxGpfvEkpU0tcjYbWCjTFlEsUpBK1YiEP8FJVik0lsbTsi6ViNu4JkSOUahyfTbjGBEfpZRBHv7pn+m5xwH9CPPyDUB5ja2kkh6EsXq+Ildz4ZAB6D4yST6OVNxEUYAS4GDwPECaPLckCMNw6+BM2DgcfqY7ID9lEbfw8uuTg1M6TGjAT6/fhOZLbjvnPqavfiiyRJdeVqzNiXdo7myTAzD1tiwq/uqB6AK2kbV9bLnnGnsTq4oU970Hx+1GBOPlz6WU6jcNhvd/zZ907OxodbS/GwpbGe9S4WbXtIvvCdcidy0TJ02noeDVuZeYgnA08Gngw8GXgy8GTgycCTgV8pA795IYi4mUz0Etq0OmfbiXDHGKzWEFwhTxv+oJjB9uq5uMUYjJsllB3RndmcEOg3shpvOwrF0d6XF6wr5GbrInxP9sroe9iLwXTu0PYxW/qF2mK9xE1ML2VLcxPGydkNb/faHgljgqERzvuHyOHGFvpaasbtBRAZAEMHyLtw3tvstiec/WmbUtToYhvdz6iwjD3ksScecFWAXARaTqwpfFuXW6SuPj62C9Ps/zajFRNZT2XFg3appvhlT5Zq+tYSG2pWmXYNyQUbFMagSL8mSGPz3UGS0CKngeLTtclZGYUZEX9TAciFIMbhu//i2PoaukpAtsfLpX1X0Ng483Dahu747vxX/RnNEqFb28SwlulU3nbJb0TlbW/i1a287RLciMrbnuILjziTwm4pQ53uc2ymO/pRGqOrCHEWbYFomWbR5Kr+a20JTUTHOG3P5hZD2OoH3Ys+ujDqUDR+1jLgNzoLIAL8aeOQVQRrm91GGXr4G5m8eCc9LJUUzoNsP+3VPRZ0DE9QacHF0zUDwW18pB1vooqecxrGH55C0DX/T+/JwJOBJwNPBp4MPBl4MvBk4Jdn4DcvBPVb6V4mc6GdC+Fy1O+V8Yyvkj3czTG1uxti6maoUtjv6hR0b2ur7SFfthbxCfZr571gKj/8XIKu3ivcqXOjT/hJ32A/r3uM+2cpXgPIdp/9YXaCE9G7W+6TK6elQEKBnpBo+llz4Mvs81XAo4zo2BnN7eqL2+RRPh25nJzjIJSEI0X55V9isyFJxRG/bsDOO5Zt70fJWxhF5goTvKpjYWg3rObjvpUSA4FcYiNWbM6Sd0XDISaYlKBKL7PL36hdGuyneEY29M+MgYj2I2cyTlv6akA9fCeMi+gf1vFYD+tnUmDf5Qf0Y+Rd7W62uh1u+6/abDelRWCOA5Yf7w7C1noA7zk8BpSS5FJ8Q2DiNMN+0VgWeOJoOG0rP/XLOzEnjbz4ttV5056KBYt3srHmfuXYH8DGncLSW3r6jVTrHCASvcaVv/CHkXo4WIvkeCcBJ+YpBK30PMSTgScDTwaeDDwZeDLwZODJwK+Ugc9UCNrR5zJ6T3Yr6eSXC+p1qQ1xXiEbzEQV5kKFNG7AL/VQkeCc1byHs586bmtmVofrxV0ThcUZ4q5P/5WBOw71Vzj4r7Dwb8sddvbvY7/3a+rOXzk8Y8Pw2a/y2Z7Ohy+WiymsLW6L/O74sPVGtBnee9SF41CtRk/9824Gc8y97haKY5VLooam//+BYtAMI43WEJqhMw7I8GPXhqWL3zUbra0pyhivFXftQPtzyoa3QhnTbmxXoRFeAWMHOcuZ15kiR6B1VczABsS0p+zOJ8a1G4h2Ich+yU/ywCEaPR4GA/9OIUgyltPeq75Bv+bKwRH0OdJxMKx7TO+5B3ds+AUjT+fyCnfHFH+eppzIAdIQcqPe+m8LIMuW0OCrU/57LTYXVoR9aLX4SxgLdBdLxLoD7XBwwcCHYcMbRPdk2aZWZy6W2hDO6cWc8nAawXyV1ELWS7+kaN8ir4JKH2NLcSDrDDF9mqtOtsY1kGsvqj0miaXybzSorw7LD/lk4MnAk4EnA08Gngw8GXgy8GTgl2bgNy8EEfAu8kz4veJd3SuDS2o4+4J+LsTNPC+5e/F96ld7jL/b/BTutHnQdnnTNe90dDIOXUPon/Lq3XHwT1zl5bVf/XfaO+zef0ft4+zGUBRG77zKzra4CcINq7wgWTunt/tIT0Vjwvi4H0s1kUpbE+qN2jRr0jXI7G/jhTguBqp0i25NUCdgj0Qrd2eGmketlgFN4odusWfwLgTpuSp+HQz9FoY8gto8BzFmarlmwcPDRscGj35WldGamyKSlMDf7RFNxiD9CrF1LPaXAYg7NkeODKvcffTmZdGBHpaupO1eWb9ir87xwkJfkbZr3l4VvTnvpuOEvE/P4F7ZJZTyX+XAhaADAGmcdshr+OlV/n4wnyapHfyznL5OGhlYPiyVtQ339bo6tFPnXEC2zTrmyn1jlBxcgRfI/FHx3yQbxFC93o2imf0f/N43coy+1WqBCEl0sXjiNr2jahGoWl/L0VMIInPP8mTgycCTgScDTwaeDDwZeDLwa2Xg91EIYjS9It7VnlUwWo/OCOPL5XXNnIkak4FMTkXUDjY7Uygema/ei0Mg+sRV/8QZduCwXUfgKocN/XIx6KXkNbOBIH3PKJjKTnx1bjx3X8Rxwio+zZ40plkWTkT1T9ySC1u5FUcXGr5xA3DDSiWJoddEDzedfFkWZyax1WX8sht5ggxf24hJJL7Ae2IILibCtHwA0JelXiSvng2xGpmaUNsGscPj491Jysaow68BlTZmVujw0BmPhy1aRDHwWDpxdd9hEdgIRTnMvPDHdCWNAxvXJQjW6LLwDp/StqcV+os3/dqO1qxhEpw+8Sm9AdLPr5Ll+KUP1PKha6synHqbSiDSK499gAs3fTfoiLBtEWdruRnCSNn5FDN2Elfxxt5WM5QL13FdOD+v41ilgm3igLCfadcY68jA8WHg0NYVowZh26Da6pztqLm52/4YDhmLDq5JZbZNuG/WhdfFG8AvZchwt1njSUDybOdJiP9WmOUHseLVuhSNBgpXOcs+B5/jlX2ZfSUqA1GTEtXBNh55eDl+awM+S/Fp8Zyl+yJ8uF/J51MImuQ8zZOBJwNPBp4MPBl4MvBk4MnAr5KB37wQlIvcufT1RXUud0V6MtaL4MvoJOzdIeD24xu5eM5a/BiJ6kmbU1Qvr882KoEJ5/C0utgwc4AjqxqmL7ODCs7WoJPxK9NnfHfTx9j99f1gac6wTvqViZcupHTqnfTHbFxwMrxsQ0whSDk1mxV4evq/Hq0Sr/JzWCBZsq+EaCEo2xQeiC7TobFBtSd9MuGP7G+aJXp6WB3MKZCqEhO7xRaDZgEjoQLs/s6dPxSAeLlyfnY9BaEfdGcNNsoLnddG25TsbJuiMOt/0EQy/bwAyJKA0EJOy6frcK06+vx62NSUrHHSQAa2rG1jmVgzgEy+04fGs+NMiCuURoQNF4ZMsFoQR1u18g3oaoyQKxy5rWyY7FmNG1Em+MlDoQAwVRyq0Bd7C3yV/VKcQ6+vV+M4/DrIW+gRN+ABM5iXOPFP2UmP6mpOWWlaLdNcOxP7P8RvPF3XjQmuaI6vHdfOR7bPTSYVhyuh04Qu9MsNrn0DI2uJ4wz35AcAP2ez4ODaR8Txq0iNw18CtJQ7gnz+GuzTPBl4MvBk4MnAk4EnA08Gngw8GfilGfjNC0G+HJ4LbIK/X2Tf+8f1sMe6L7FLpW3vbULek3DJjey8HIcl3ssLfywP1iZF18Qx1bhi0PlHL7f437i7jf+EL3oRb7Q/zhjbND9lomG8h1s5F0A0+4Ghe3WEoklYZ0Zjb5oDk63r7Snf3IFz2Uwg2dZdIF8ZqbztqBjaF0Zju0sE7kGeH5h76ikd62WcPEnFXUB9BOxHEf7AJxeS9afk0SBdyO9j8h0MGpcj0qq/ysWklTt89pihqy47s2BbbOVXNmBbhUJQJsWkjNQjr7mmEb21LJN6QbSzECnr9WmcSymyo3sh8cO4Xy2xLskQxbVdOmLUROxlkB3DwmGK+G72TvlJ/xzcm5hOQ0M3Rnenc+G90HnJIv6fUCR2Qz4Bh49X8Xf8jmFy9lN+u60+CfcTsdnvWr0Hfs1vKXWpazD3v0NL5qBzLLCzeW86j38Bdy6aCLSz390j2P1gQd3z+xSCdvYf6snAk4EnA08Gngw8GXgy8GTg18nAZykEEfqrC20uoJnbciXcF0DnohgmF8pp9+U1EzV6+tyvnsX1FXmvtKsO30ttqmNZgSO+NKP8BiedXvUjYznMhnEyP+Zjo38e9VM2J3YbbZDqoOaclTd27vD3zBtX3Y9EfNp7nZxRHkfEpE/3D7d1A0T0lDneOj1xAXqc2URWjE5xxcB9b5z1MjpXGPfwYFefJYC+TvoQWSzcDM8tur3TJ0Wgv/3hh7kjqEWgH/RT7D/yriAXhnR8yBayv/n9QbFr2/WvliKZcyR/3EuQIhCh5njxMWMZvBR+1gBmnGhip4+HpfAT/a+wrw+PrOi/kDbtVqQXm6ktYppt39ZFJCGrS14GPhbGjpjI3lvQQdwWXGlia77hbyR8tGqY0WpBwcsuer3yXRiykx7l1Zyyk16AIZDVj2nx218hgiU8+XTUQ8N+s9xkdM9ljfqwd8cU7/pp7VVRwoNcOUgSb8IaqgMUT7rytqfspCtvi6xBiHa3/SVg+3DMRcDflJVX21kKGcPuLmlDaO5puXvv5SJf2ZM4poLJmeCK3kccDnNEBIfOESM7g5av9dHh9ixPBp4MPBl4MvBk4MnAk4EnA08GfrUMfJZCUC/MffV9XJmvi2ZfkN+uyteQe4kNoxfkJ28BP5GoDeDy6ZnYe74PfnGn+t3jkqHXzmHjjv/Z/b/D1tyFYldWJ66/w44NzJi4deRi4m5z+nf2sjHKblhpT0hF8DZxs0K0yP9LexvTnGc+NfiK0V3bpMyPtVbQTTyXgVphc4KB2X0c2fmT0O5TzYGvbQHug6pAP/xAgedHF0J/UDWIwtDfpg//gwtC3CUkrIzQcuhQSHLy1UAxphRHTfmOoFXEsUyFIe72UVKcF6dFmm6tPrLcQQSOws+XWrkwpD7FoXxuxSD7l6mxtbMRAZPZ8sCc/VF1U8zJg0YnYxwJnRdgWD2t+JxyKIUfxlkkiMXJyZjfOcHxO86KpbXjk/EefQRkyHUQd+l10Feb96jWOXRg3heajCbL7qR5OHqTi0tI6gzW22BoGvovF/iH/ZcYMWPvDnzPaHHvyfFSzCuPV1lKMG9xb3JhyB5QCzlnLraV2z500y1uTgG3aIlI/65h/uG5I6hZe9onA08Gngw8GXgy8GTgycCTgV8rA795ISgTZF3p6iq6k+XLYHwVvK+Erxflusi+gHfvC2ap9yvoC3Y6L6/et501kXiJm7hO2al69/eubI/vrvLz+n+HnbvKWRh6zznjuOu9wn4Md8pO2naGYR9aaTt232h7d0cx45Ni0h7D5oqbEuqZnP6rsS3oIuzOm57Jmg2cilcc/vqLX90tebMPd/wwJr8TSC0FIAo//viuH4pAeSyM9wN9EIA+xR/T0nFRiEIQuDH+o+zIqKorjJdYWsj5UqzwvvwydwdxrABJYceHIuGSKVbGo/PlV1/94auvYucrij9ff+mi0BcnD37UUL18bBTeCzkbj0IQfrIodkjtj5mgH3wDpq/GsNG6N5HJFv+xTU70v4tJ5+x9KwlJcv6r030QDZbDnGMxr77UqeXika/lwC2eiNis5um3vLNFczSO+E57iwYm1WR1OpcRnL4GR35wd1tsY2TJywAa2iUmZInxZmZ1z9wu5gvil+ASWu/QSTzZBNfY1hDkv7l6Z1MFIQUfx0nuMVQJ8t8jqa0Ma3tpIQj+RGUIZ5b5HxWtn0LQSsVDPBl4MvBk4MnAk4EnA08Gngz8Shn4LIUgX+weA+jFsi/4NcHdy+1i3ZMeXWf7Cn2jfOl+XrWf9AnrFXrNrutyGHS6qP+ujSoPtmrLVm2orexgXcmbravwE3ufamMCPOFnEehV/I3glK3cn4YG+CZnp2KNqS174Ychk6vEoglnCiaaxBW/3MA4lsrLvoSmvav8Em1tAqcFjM3ao8Jx2DJp1jBRlQqfWNBatstzvWEw7Na844fCjemj2PPDhx+mGJQCEHcI/agCEAWfH/QI2IcPFJDUmq+iEYUh7hriLiHZ9aNjOOO//KvMoyJPij68I8iFIBdsVHpBrkIORSDSsD/kKR/u9vlK+l+7EJRi0Ndff+W7gCgMff01NlIU8qNj2KSqI/80trlykj4y+JabTimNfhcf/wKSS9NOYKToDrV2n3Jetew7taFRzaQdpJyzJobp+TFUxzRFn3GGnkHW2DHV7rDfNJW3fQO42XNijrEiPnVP+m7rlJ30iXPqGNP4SAaCsGzA8J0rtoHwLYDV1gVre8TZXMbqaRu92mlbW/e28rZ3efvIs/GmrUDtKvyJXrgl35HFRPrQs0ssJFnwYheHXvlW2XyS4P3JrfjSi4WxsyzTR094/1uCCSF8y9cO+jcVgnQMb+hDPRl4MvBk4MnAk4EnA08Gngw8GfjFGfjtC0G+5M1F9G3uk8FcmMfFtqW9vH51gd2L73tO7ti7nH4uzl/MCF6Az5h6YT82PKs4VD7F9QEPedp/I3yH8VM6RyAn9KTfsfybsrXtCYnpkGod2hzcQ6P+Eec9xZ5xtZCjYR4jXZOzyxgMOFEX6bVzOGsIjkidRLrhLl9owoYKWHZjf0SnWEPxRnf1qENR54fvp6XgQ6Hnhw8q/Pz4h+8/UPDhLiEKQdwdNAWhKQZRAPqewhE2kMsP2PjCuabymjX67h9NH/MIl8svKu5IxqTSRSFF7P7gpVosul+p8EMh6JtvVAj6+us/fAMNT0Ugy/Tiki+hwcp8M0ohAbofF4iOPnxeHm25VoksePLW5Ui9c1r+9rQ571FnIeOkFx6HioHcae2YTmeJgUhfLy42eGtHPubcic3XeuWeYyzvU/SKvbeNtHls/477lH5tnNhPtfdK97TzS+iPxbD9zrbMFpW7LYnva/+tzXDux3j27CsXJNbqEfuvtut9vz2/7kDn1GdfTYRZ546gt1H+f+y9iaLcNrJsu23Nk33u///ju93WuDW+WBGZBMhibcm2WvLpC0hFJHJGglUiUgCI3CorAisCKwIrAisCKwIrAisCKwJ/JQI/NRE0HOZ/VPvhPI/ENPtxGD5PMPWEHb6abG7t0Dd9PIm3ugjv283YT/G7NoL90D0raSbqK/gWm1nPcDP9FD7T34rOaKdKzpGz+Ayj/q52a/tWvub/E7XNa+Ax4S1PdU+QGNqXjkVha+Z1wO5FaBXfJeGA6T42v9p9P6YmVPJJSZeetHmrGhkY33t4nGSWclnuC4c+f1Dixit7lOn59OGT2kryfPh480HJng/v9VFy56PwJIA6UfRRvNCNUwIIGRJAThLBhy7JcZ5QwpXVOtzDv/5yT+6wWkffF2VkXJOskptZJSQuJ4XCwyqf++K7r8QPK4BIAj14+EAJoPs3D13fu3kIjuQQK4IeKBGkFULeNiY7HX/qDh2JHkaP2h9diA4wfCSKfj0OL3gp2L7/pbj1R1JMXynjNwVGaTuxY4oJ8n8YKFiI/BVbE1HSMNKUWXHTYi86wZ3JWfgg3zhJlFrGcN+XwdPWwFzyzXZnH4f8fxpqn47W27O233xzf5o218034/Zw4jz42vJssXHETNJuDlzGd7r/9gYuWrNk3xvzvTQERu/2iSBspXDP803q3xWwa2tYBWdVKwIrAisCKwIrAisCKwIrAt8tAj88EcRTN3+OxZie+RRxTH7GJDOkuT0ergcE175VKr+hwpNZ9ugrtMZ1PamdRUHv2i17rCf5DTzqbpmN4a8Brbbrv6alpL6TT6Wth59pURJBhO/rjsIxzpq5o0Pns7PrAuYfffS2ruJmexZl844Ei/hJuNCPbAHjrWA5A4jzfT6wCogkDskfkjofP96850ObRBA4JYI+kRgiIaTkjlcIsVpItI+WV63kjz+iJylEcohDgrCvD77wxz71CiCSLtrepe5siSDDJI6gKbGjVT/37ykJ9EArgPR5/PChEkBKAj16kM8DtYU37gGrhrJNjKQQhUjxmcfCOF3g8EfwL8r8APPWsV4dpKbLPN4ZLgSaVkzFObeAYdvGYyMmIdOUvsc28gmw2YW2mS8nTvhb9+bojgeP7pKdmffeu7VHSZV0TZ24pjmJkOu6mzISJo3531N/3fcE7xAydXAf1MRQOICNNN+J+5ic2d3ESgk8l2Xg4B8ywEXT2O5pa2vYZRwXZkVgRWBFYEVgRWBFYEVgReDvRuAfkwiiIyR++lGZ3T55c1TmPuOpuTlU62HbLeSY3cK0PV1DD2ooVdsChVflMvO1/MYnxEYXME3CNlkA8DNfO2I9vpj98tIGLyl3Y75VbrYtmRajblLjMNizpp7IuF/FOPd945NMy8/6gG1DwCzXNqhbd9U93gjqxqy3bXFPSNFmD30IT0UozKUATfyNdl1cVGbTxXUpbBuzDDj9xQu/th0Y3/TBbUo8JPGCsl/qrV6c65OzfEjWkOghGUQS6L0SO+9ZEfT+482tPh8+fLh5L7qTQkr4cD6Qt3+R8NFKIpJAXk1EMqhWAzkxpETQ52p/Fi2dwV8lWEgC6SuRs4JIUgHXyh81shLIrOa7p9VD93UYNKt/vBpIK4AePnp48+jhIyWEHtw8ekRCSG3VDx6IplVB4c/KIL6J9J8IZOURiZ7oty+C+YYqd2RfTDOv2oyXh4xIiqECi89mhmjNRTcs1FRgreGYsICM05C3tR645pyEM4bNX+ZP7LVo6tkyyv5KmXUgH6dmV/Ft9xspLq+cmpmQLL7WYW0bjlbK4GvMLPvncUMCaArqnnAH5cD4J5seO4VxTqWMPsofu6R7aHqNPCjfOe2u62C59xLavh+QzRjMrgUXu9y/2LcvM5Ph0jvh0cxdTfEdIIOuq40+5WmdwIVnlRWBFYEVgRWBFYEVgRWBFYEVge8RgR+eCGJ6f61kwhaqH4f7ibgExmN0Q6kvrwgIC+Ggo1Ttq6/ylRJNAnqSOpSfGICvDRvem0vrKDfJnLFf4I7yFwwTAt0UybRY1yGM666PA30BfTOfJK/ZmpVK35YIkru/KImyTYmuyGeydYV40D3GTYSzMZkn00UfmpncpRtd14Igr/gxzsysACIRpMSPV/ZoO5gSPU7afFSyhySQ2tS3t59Vv3eC6Pbje+E/eIUQq358SDQJICWE2FL2kYQSSSBqJX96W1gOkVbiSAkjvlX5/rAaiD7qr1b5MJH0yiBlYUgaEDMniDQZJmFDoebjFUFsCdPKIFb9PNAKoEePSf7oo+TPY9UPH4rmZBBbxJQcEv89v01snEfkt4tJ9X1lezBh/arvyTAJIHDUtu0rU2EH0DX0LvOEegxbM3Tt7m63Gdhx72yQsJmkb4xtxDX2oy92LuEd+yYzY2N5xvxdeH9byv+ESWo7XpcWiFn/ls7wkXOmzfAlH/dWsJd8I26z3CXfTB3wt/INiW+BtiBpRCsWHk7hD+5mlAd/wiqs/k5YGQ0n98aIbceF+4oyJOjX9TJoSHQiCH50txbXunhr2BC5rnZRVgRWBFYEVgRWBFYEVgRWBFYEvjEC/6xEkB6BSQZ4csDj8PZELKAerMfzMNDcmmF6P9rfGIuwIeZZz1G+nUF10cwHvnlnnuazQjWOZeI9kz+yn7ZnHacMQrZv0MU/i8zwNfFvwV/r4ixrN3TpGeVMK3gjiY1E0FfLnXZbHmXSNCvfKZ74puD4DUSImRcFgWkn6ZI2bqJa+ZlsAxP8Udu92AZGEugDCaD3729unfz5oAQQyaAkhW7BkwByciirgNjy5WSPFPNWMK8qEuwtYDow2q+SV5vvCbTPMszkUaAdShV/8ZnvUn+Ig7dtkQTiHlYNJyC7u0gEkdThjCDOBnpEoodkkLeGaWUQtXCsCnqkhNB9EkK1gugeW8qUeGKVECuKSAL5YGnVftMY5w/JGnZYGWS7rssHRdrT6fapfFfl4ok1jqqvhptgTd1AB5oJBZGoOqBaJAUqEU02jOL7ohkii5l4SGyCA7MvLdPY5jvim941fF/jmXnLxUZtdXTE6qyv/QhjhWyTOgO6iw7FjgG9Q991vgh1zzpmnTDZqZwa0VeJmgl/BNH3VV0SShTmWGzIDWgf24bvuW64FkcxWRNw411H/973MPU9VwJX7p1N4aYNjU5YqWYMrIe6/FiJoI7oqlcEVgRWBFYEVgRWBFYEVgS+VwR+YiIoD9TbU3f1aP/Ar0dis+0fnvuhGWwmHYJanfXUdLAepAt14CmDVDOf0SgDeSiapO7tzHT4e5IpuHVeqJn4Nhs752elX4G/RW52oPxv387Em4ZlZjuXs8P4NPOdelkMFzrAz2XvRMxpQr9H25fcGy3PpP9YhAG58/lCUYR2fG4clGWKGGlgfcRG2x9dGG0SMJjLYdBJzrAa6P0tq3ze37zTqp/bd0oAKeGTWjjRaL8Hz7YwtokpUYQcW8BI9nQSCP1J/sQWeOxh27UY0lYtpGFd+EMwfGYQkMYhuQ9W6ag3dNk8CZlX6Thpo4SNkj19QPRDnRNEQohkEJ8H1H12kBI+3iLGNjHx3PeWMcmyosiHSisZpLOE7iv784A3jMkB5Ym2RBD2Scd4BZCcT5TjD+NovIfGztZ3Hb/Tn0B9NWM3ttpxSCi6u+k1uF0pJqmxNQcoOuvXZMedRiumdaFQOGsq2gzDP5fYCab5Bt2addlxuTHbHDD+pjVws+70Bxp83DQGh0GahXNVscj3b888J31meKcMfbnhdH9y76WP+9/6SMy0GT7T1/J7PvwbhR7ynaCYz9+NartqohoGR/xnTcjSzhjMlOjytXgwSBf5zbBS/XBUl4sZfNE8BsWWyjGCse2xes6JW5CrrAisCKwIrAisCKwIrAisCKwIfIcI/KRE0PmDdB7YQ+uH/PSRh2aVqvyK7IYL+Yv/+zRsMG7kRn1rjeDmXgOztsL5Sb/pQzmY3UO/G5d8k5Eh/KehM72zktlv8OKfRWZ4FrsKt76D4JVYXKgx3wU2iJ6tqcVQ6sYcjIZ7cjvQM9SeXR34Wd8seAVmEtceUHe7604CkajheJ5s/8ohziR13t3e3rzl81aJHtW3Sv68Ue3kkHFaGaTVQl4h1Ekg1Wz/QidJnT5wGtfbHrDb1PhY7S9fOKQ6DctvK1/q26DYkwjyN4P4VqYtK2RYIaTJpugkbTj8mVVBvCnsIckdDozW51G3H9N+qK1h2h4GH4kjfeC5D6/gx+a/pyQSW8jQp2QQuuWHKpx3hIlyDowWQgU/GEs+XAxvDQHIGQnNBDhVIh+JYIIVvknNRZysHH0TEQGrjOb+SaE1W4It5SBrriOueb9P3e6e+3Npw1+5b3Dp2/m456Jw9uFowomTdvbSrcLoDpbhr7JdlT8jHDyZlM/+Dsnmpy4OByMcTR38A0pyaM+xb8Gre8d9pK8tuwFGtIusQiS2Hd/WlRVBe5nWtOoVgRWBFYEVgRWBFYEVgRWBFYG/EoF/WCIoj77z/+L74dnofhDuurvbkzTh+8mZh+8mH2sIG9+ROLU3PphnbWrjVD+9z7DFix+R4XzpmA3PsAX/wuUuHbPPrVr8R5Fju1ndZ4jSs/URnVcFJr5NyQA2HaiUno6fOaTzVO3eXnkTnbOOWRbVw6q8V2tDbMDEEVeYrIXREpsIEvlAB9Y5QKq9LUsEEjec1eM3frHtS1khVvu8ffdeSaDbm3fv3iohRMLnnWCtBGI1kPi8Ekj494KR9WvitRqIlUXYcxLIyZ3yR1kJzh/6UtkJ+0Rmp7MVVXs1UW0Xw9+MHyGP/64EppVtZaSZaOu8aL8K/h5bvEgGkeBxQqjODFIiiPajSvawOogE0APOFVLCiLOEnDBi65hoT/UhIfTwYRJBXj0k3U462TOnspQIin0SQt6uJiftD9f83fwH0WPVfcoI0VcKkkSHGkgwzSqMHQhTjS8DxUMYsQ49ITWnVy5ZdHeZFEdiRz1vWPM5aYe9g6/NigVP6WO8HAr4ehlX4z4oB8iMR2n1XfgR34NMN+1HOzPCjFwnM5r1rD7nQ9/en3O+S4109cLu9DuzaQU4+B7ExhHl7keBJbJxCIinAoDLzogcQ3LyAABAAElEQVRGqyAWAy7DQmyaQqcpPu7Po66VCEr81nVFYEVgRWBFYEVgRWBFYEXg+0XgH5QI0nTGD8x5aubqR+V6QIbmiYmf9ovYk2A3xRjRtEpOjb9ZNqWlR4o33XtatzYXAVw2iloNd10sf7m6pqdtz4rFe2Sf21u/zmRbz8bUiCv1rEMwK1B6RmSJo57yzcGbnYr6GWPNR/GDF/CbRRevLrICXdS2G7PCyZ9tSi0/mJCFjcl2T9JIBGXVjvI2egNYDm9+f6vtYGz/0nk/b9/d3rx5SyLorWolgJQEuhXurbaCkQzKGUFJAnF49AfOAqokEIkgJ3zkKDUlG6jig9bMqA8sqemECDy8VyhD65VESgT57CDh/J2BSBxcJwYdBM4aknXTWCXkV8lLv8/1YTUPZ/840VPJIK/wyVvDshJIPCSISAQ9fuRtY0+0YujxYyWCnpAI4pBp+H/xSiFWBvVr471FjSSQHNMb5eVDEkI444Ot7Tb9bP/dg2ALNE0Yxicort2q8aNZ2NyCleSo+9HcKBJwTAQl0RJ5RiDa20bwXAdf30GXPK0l/rVs8x3rpnd9Tgd7Wc6xl3zfEbN9VxSLGpROaFxL5ISvf/cj1zKzZ0f5bnc9886x9Zh4y2G+O/0NGvzTjdF74QbRkO+NurMOpL7hNjS8pbFwde9KvvuV0OzHx7dh2YePFX3c9C2zEkFbiBewIrAisCKwIrAisCKwIrAi8J0i8FMTQUzT5kfizMv2j9LdT2iZOIAZUj0Bo85T+KC17CWmKTtVx6d4M9VUsiyi6Yp/k8p2z3bvNH6uK6qu25pNBb6mZzZePEfWY7udvzQyYb7Ft9n2JLoDZ+MH/2pi3uwzZ+M8sTPfGbW5qs7N5UZPsJpjIhVqTBnRzF2aWiuBBGRF0I3P8/nAK971udXqHid6tOLHiaDXb29ekwh6p0TQ62wTY7vYOyWD/IYwbQPL2UD9FrBaZaRZ4GetBOIej13iyIcEEBNLkj6qydhsNW8H03oasX0hqfRZbxIjGQQHMvYeWSEMp2Zb2OcvnzTh9DoE0WuLmPg4W8gHP+u8H2/r0kogHyKtbV7eEqaVPiSIeGuYt4Wx+ufxYyeDnmhl0NMn92+ePFH99NHNE28v+9Wrg9gm5u1hssHKoHuyOXpaMI4WXZUKvtMPQK7pS2ggp1JIuP3d1XgxdqCpkwADSIlFrhRd81eV5IMMIwRpyLVQU3XE0+bTxbbVOPJBb772ojzd8K3jWLfcEZ/23dRzmb+HPX6v/p62vyPd0UaHYP3d/u3YfdkPfHdG/BDPQ7MsbU6jOfcgtoPOv2HAe+E5bsixGhCe/rfnvhKz64wg4rbKisCKwIrAisCKwIrAisCKwPeKwE9IBOlAFRUedTsR1I/FPBD7YZl5MAzHAqPxADxm87AcyKw1c+PB28/SRoq+yQURGQsWRzOqhmgbVAVAlg7+0zYrBkAI3maKEx8E7HVl+OBAyLoe5IwvYcNn9E34DuAod9B5JNN20CaVu7YZQtx1RfgLvtYx22zcWX3qzAljjzUkyWxi7cOGOJFt/viUiRdyjGHUlVbL9n0VHAkBrVVxgoYkkF4PL5iVQN4O5oOeP3n71xttBWNL2DvVr968uXn9+o1XBJEYIgF060SQVgRJhlfDf3yvV8hrW5hyNjoTiBVAveqI836qi5X4yU2lL4YSQJoWVq1kkLZakRRyIkhBkVrJopO7V6mFeRg2OJ0mAeQk0JePqoMjGYQME89f2SLmt4Dx5i/O+BlvFXNyqJJCJIg4QPqxXjX/+Mljv2b+6ZNHN8+e3lciSG1WCCkZxEqhRySUOJRaXeDgaB+E65QV9lkdRPRjHz/8YSA0Gbb7RacRTogU5CkmhJem0Kzgat550l1kqirispHonr/rMKAjvwkwtb0SnarmK1WmNPcseZ0vXFyjY5ZC3cBa+T/kcoytPVVAz/BHlxn3P8d3jElrTKSd+NE9TWtESy3f580LERztwTlRJzBaNsShaRUbsYFiUoXZvrfao+bqfqMjeuS3gPZ+JYI6UqteEVgRWBFYEVgRWBFYEVgR+F4R+AmJIKZl+dOd2J6peXDfPTA3x7GWBE/Vnfjxo35pyZP0JgD2gNpoBlAFMDEOfpwRbSBk9jhhEQNOd7GyNPLg34iJp3lnxRsO4C6ZHeMdjaM9dBZuJs3wZhe17cMdJq6SJlvmObbPBHeOnDC0P9SsYFGxyF1yE80gE/nyhXutlQhoTuptbijefo0828BIAvlMIK26ea9XxL+vV8QDv1by57VW/rxjW5g+r1+/1uftzSutCnqrbWK3nA/Em8K0Fewjh0NrFdEHZW18JpCTQNxXvTmKpJD89L2mbAk+kwhRUugXrw5QTfKn20rY+IAfcWZrWd48Rq9yD1ZP6Xr31N+1rAZyQoiYsjJIPD6zRzVnBf2qjM09rw5SYohzg5S5uU9CiFqrgUgQcfbPQ28LU7JHCaAnSgZRP3My6KHgbA97pu1i5kWPD4/mTWIkfYgsIxM/WSkUP5KY6eQQnfEEn24YHt1xX0tD+iwWxXAaZVR6tQU1pcc8rVw3/TRLEQmbc+7IfI9rJ4Wiqz10GKpXTekohfY9bH8vHZ3Q+F767taTeyM/vRnJjNIY1R41R3NDb0Cpp924Efc7bW9sG7Bp2Mv1/RdfB23IgSNu9KP+OUt706jv2VoRNEK3oBWBFYEVgRWBFYEVgRWBFYHvEoEfnghiw0omZ3n45pGYh2DPufJU745B3R6XGzBSDYuCzBTNNQqML31oAVU4mrSR6mIauCOfGGoOaNae4FwmgdAXje6T/UNWEwB3atDb5ubkhjg46E60lzNtE/gTwBX5C3QHQPUWjPLBVcFY7oBufOXOX+FrXahAftcumxtu8qEHOkKRO/qz8ZR/8KrfTgRJZ1JJahmXO9KJg/LDkzJfMvHmFJ2PnOMjwfdabZNXxOdMoHd6TfyrN0oAKRHEmUBvdEC0k0Cv36l+o+SQzghSAogP28I+KoH0ScuA2FJGYok3fpF+GWcBdUIo+PQ8iR+vAFICiNU6v/76QF4lWUNSiFCxrYStYTn7J8OZCMhOxSjTUxI/igO804qgGydlkPjiZA85JhJBrBC6p+SN20rk/Cqc3wJWCSGfD8SKIG0LIxH0TFvCnmk10HPhnjzLaqFn0HmbGCuJSATpo7/STc8zIvQVN/GR71G2xASGAI4PHK7UB27nho13O1GDtn1/gasNNXLFRwMM+gEpVorGFPPbM99Fhd1XSONR13tqWm3hLr7mQaLtH3Vdwx/5fkxbvXEML61tv4eXJGOa3vUZW8Y8Yxm+mSuGwxOYMXeb+wO42TcAhHjNrgv4zsRA2vGF1UjzHejFO/c/PqJX90OqEsLgXjn+wdPm+34Vo8u9lQjqUKx6RWBFYEVgRWBFYEVgRWBF4DtF4KcngroffjSuJ2kelfkwDT4vcPcHaMB5sI8U87hSeV0Nmia+GR5CeAMfD/WX8OALtPFJ7zyhG3zRkfYMD47vB53o31BTx20Qh8F9g/VdoCRzTeib+VBxYhjVLpONHVs1XO0IlgLjMdjurfQvaQcRhYfH1514DDOFBM3ZQB94O5hWzXzQli7OBHqnlT15Q9gHJXze3bxS0odzgZwEekMt3Ks34mU7GEkgrQSqbWGs3PFqIN4MpokedzvJnM/c9ZWAcnKI5TGs/AEvviR+0v7l1/tK0mh7GHhqlWxhU2/Yb0ZJN1TTC/pOnQ8rgT5/ZltYnRNUNNJk3MO/6ARnVuwwhCSASP4oB6Qae7xqPquGWDnEiqDH/ijxo5U/JIGePX3qhBCHRj/1eUE6M0iJoscPtYJIySC/Wl4GsGP33PucU4SrMmE/cICEED5R7FtAt4Oja4wWfMa4mxndsBlN1zPaRrppjKPjzm6q2550znosqEvzoQMY22d8zf/n6miPXiTbytDS9gfmZ0LqeQXTfil2/Vt5l1fbb+VdTBe0EZsev8SnGQc2q8K2b3szuM43G1D8jPUQ2/GNhngwDR/8ySKbfBSlXznrJ7ybmLm5gEkhTo6dUOg5xm0lgjpSq14RWBFYEVgRWBFYEVgRWBH4XhH4iYkgHoXHtMmPxTWT6Idq4/yA3pjuNpTxMZ9JBR3ZW2yuh9CMzXyg5GeW4aks45PK8YF9r6gf9WctM0c7Sd08jZv5Zvhb+WaZE3g20zOQHVvb2SGvN07HaGaXvlZp27MDM5/gOWHRMgcWN/Eb+lFV3UPwQGp1NJoVlqwRGJNX8xdD84HDCNNIkja3WsHD+T7vVbMF7N37T94SxmviOROIpM8bJYBevnnvZNAb4d5opRDJog9sB9NKoI/aRsYqIP1VEoYOkMBhVc99mSL1wcogvhn53NRqIXfWdFYGsSIniSMSQL+qDY6bF70590eqKB3Drh0f+s2Hw6IrEeTEET3XR7zkn1LLEwXR+SahSAZB0+4wzPnD9jDOCOLNYT4nSAdHszXsuZJBnBH09OnDG1YDPdPKICeEoD+651fMszqIHlO8Kgjd9F5G7IKMuKaNQfjAFcz3MliTNrxbVhvdtH0vhO2G0CPnOBQORKyF5s4VBjt3l5a8mw+u9vluTqxF5+jfLBHsoN3t3X+WKr/y12Z6bBTcO6PWfL4lzUlv5j7Ga/j2v7XHXl/KbHo20gZMoQjuqE1h38QnZoETJ18CyqT22FvuzLpNCUWVoWPQ6o4QKd/LveKVCOrYrXpFYEVgRWBFYEVgRWBFYEXge0XgpySCeHo+PjRnwl5Py1T9vLw9QE9dZtJrPA/aTJZ6osCT9ODjQbsfwGfYHK0fUxPfkB4u4AsP6JcTkonbfgy+iXIF7E5ST06fcrez34uvjJTp1kocd8G4FhjEdzT8ay2lG12UjdT0DVFEmEQr9t2ETziji+bBbPj0P/ljo8cczbrBpZ3Jd7kiwAtudLEt6TM/eASqGKc+No7VQF4BpJVAt0ro+BwgtoOR9NHWr5dKAr1+9dpbxF6zPUyrg0gQcXbQB84FUqKF1UC8Jp6tYPFJCRylPX5REojVPSRz+CSZ0zzlgzMXJI0IQBJGTgI5ISQcstDw2U5zyXcjWR0FLARV+f6xaijbwqhFR5weW13ud1tKjklqwIlMjXa3lSQS/f6D+zcP7z28eaitXw95dbwSPyR92CLGm8NICL0gIfRMq4U4Owi6+B4pIcScmu+/69Lt7WIy1smCfWJIeByzD7rgsnT0vYNvwoRQEJiUxLN5wX3BuAo6HFvihG2ahwIneKj5DWuu6JjZ4Qm1paBe8qGtfw+HxJ6vbbZ87A997UW8m70YPEdsePd29jxDa/DNO+PByX/FrMdKjU1NQ02b+TIGrSt6MgYSrzFA0TxWUazeS6zNtMwlX3za4VvIito79QA31IwXsTLiI2K7acNN77ruhHIEe/63ouIyhIu/+HIn1thLBtv2yH7kO0Z7JYI6zqteEVgRWBFYEVgRWBFYEVgR+F4R+CmJoH7krWdv9yVzMT8Gf6VvSG1P5ZHd2mqeqmh+2CeGDb0B+wmGteMt9ElOLT/o7yYVZt5dtskP/DvKXld0X9rYiXzHxs5tu7L37uum9r5+SyzQGT6gY//3OGuvyTmUY9l5O3fGanN3YaK97LNxwDGWm/UCUBEwE9qMd6yCJ3HEap68Hv7jzTtt9Xr5Ruf/KAn00lvAtBpIiZ+XOiPopQ6Jfiuc3yCmQ6JZOcSWMN405m1bTmApi6HED0mHX359qLN3SALpo2U221k/2HXuJqt84KWdLFZlZpwNQYf6qiU73j5GkoiOa7KZPzaDoBU6Ok6CJfnjbWGsBnK83VtEo5NaDW8PU5bGiSBHSny/8HoyO6REkLaQyf49XiWvpNYjXiXPVjCfF6SkkBJAz5898Qqh354/uHn+vGAliB7pTWKkvEgCyXPbw/8kgtTGfRGp8cW+cXHblWmBuGr0RN9P/osqsUg6GoIzCafXc2meMCeK/ZvVfPGoW6lbX2Oth1A14q66mPa+dCvETZ8AKHd8RTZLxIKMSWvaCMjr0/him8mncPPZl0l+ZjbtDrs7XvHNX2E8ukv3kG2uxnRPut31se9Dvzn2xo2K6zXCZeZM+8V91iajpWIrPe7jmQYxol8ke9l8bg9l94TPWVkDt6AVgRWBFYEVgRWBFYEVgRWBFYG/E4Efngji/BFNQfX8Ox7Qt0f6k4fyrXN+OleraxNackzL/NCtSfdxUrbpKYAVBrvJ4pGhpiM2d5dfBzk8YnpM7UnYgT6mXjNhniQgeVd7lvsa3PFBX+k9TDJILDhmcJg2297rD1/Rr7ONic+xK3t1o2W+Vti1ogBen/2EN/fOJgx7HN9NKFkJpFSFSx8CzVu53AePpxRLtqeFoNpyhluviRfGO6ZU32o1D9u8SAK90efVK7aDvbv5w6+J13awlzoPiK1gPieIN4fpLWFKBL19f3vzSUmkfv08ukn63JD4YVvXvQf+sCII2Ikg3UBeFSTvsW/f8I+AqPYbxTyeSp2QoAEmEcTNysogfwSrcPW9LsE+N8j9VBJnnA3ENxLHmj+CTECtWhWrfqzLdzfO6BX1RLiSQf0msXtKZj3S6qBHWu3z2FvEWPlzX0kgJX9ePL75TfVvL7RtTMmg/3nO28UeWjfJoJ7wYpasEAkokkNcwDEZ9rYwcCr0qwcdkVHcQzczlqEgm18duOFJbe5GGVsNKvNQ5zeFurWPXx3oXfLLgxyc3JpW0+Su6ZBvBiE2he1fM0EY+ja+JpuaxrivJ+IJeJ2vnUDo6HHTwMcncxHPCnBTjtLmnvigH0v71PWR3u2mJ3TXfJT3bY9af3ztLlhZfetRoe8XK9vm+yT2RCwTsddefEuNIFaPpXQWAS6vRqu2/10ElsHtfCE177Pq76hqtVcEVgRWBFYEVgRWBFYEVgRWBP5GBH5KIigP53lID5xn7v3D+O5pubo4PUj76Rx0HpGPD8qePklhT6OiwE/ZAlUfBHryMB76kYSz/IgCTzIK3CZB3e66raDzsuz1hX7ExfKl7BFzxndmc5KTqa1PNtv86q9AxiBajz5NOgpMbDvG1/kdySgdtmd1lcgxqpM6xF0OOYTbjUGKDf8mWwarR2RPVLj64yaG+ajQ9ioat3Qh/ZBCkoVJLW0SLaj6xOvdhfuobWHv9WYwVvlkS5gSQXo1/EttAfMB0bwt7JY3hGlLWK0Cesur4t/xVjG9JUxnCtEFJ3d85o+2eHlLVxJBTgLxBjAlc5z1gJde4pMw5Fo6CaS1M9ZVU1nHiDOCEiv1k4OliZv/SNjd56K+OY7UNOlrEjlA/OktX9aFqNSSa2EMqNGRgkOMBTXpNlb0KEmjzA2vlCcpxEHQDx9q65eSQGwBe6aEz/NneouYVgb9riTQi98fKxHEYdIP/Up6by+TnN8iJmO8np4kkFchqR0f7IRhoLQEdAHhzlG5k7im0nJ1PxEbo8aY0+6kxqZOOOJIIT4U2g233sab4cCnxVL7EkMTF2QxFR5zEdlGOPIkASlUpTN3RyOpRxm/Z+njoOyhxKEslt5wlD03IOxjBrptmMUcBUWp/Eykjnwbf/F13O/iax5kw0dtE6Vu57xxDpnRl7QtiPLxpKfRORPKClXcDnH2a7BAy+jYVwjlwt7nPYFoWR+8YmzdnSCFe5UVgRWBFYEVgRWBFYEVgRWBFYHvEYGfkgiaHfczLw/AIOtheaafw/2E3jWP3SfF+jKV26tWK0/zEtpTjpp4nA8HFo68JzbNxeSCidPRq2+Tv9T6NbnZDrxz+6BN5O5RutO8XcP/NXsHnX+5WXYwbbA9U2OLHQTimbpNuQ1Kspk8SbaSAP7fdNEgj2VF/K969dG6STNEayZd6MnbwUi6+Eyfj590vo8+qt/dct4PiR7VWgn0SvBLVv8oCcQZQZwH9E6Jn7fi40Bp3iqG3Hu9YYxzgVjZw+HQmXaSYdGHpI3PBlJCSFvEspInr4HfkkBOHEWWDgXf/aAHgvlLn6YkEFkc905o4pBY+CoYRSCTzGGVnktUBcS9aicZZIHohEOylrOOfrsYh1hzXpBeC6+kzoP7v9w8VGLosd4aRsKHc4FIBv3227Ob/3nx9Oa335/q3CBtD3uoV8nzSnmtJJLYjcSUVJIO6vJDVfVRtT0UDTcK7pqVYNU5d695LUVDnSJpNbT0PYei1mI2EFtBYlA3dAHbnXUk3HQiCNn2xXrcCCY+T6IkQ4/8B+OtK1KlR40DW8hfvZ5JzRagz+1LhdeoZ5ovpf8s5mjt0soeM7eQpT3jaO7bl7/d7WPLzz7sZYnVSC/NfK0D/sYP2b4TcWXIszKOe7ZlV70isCKwIrAisCKwIrAisCKwIvD3I/DTE0HdhZ7E0+aZdzweN4fweiCen9eZfs2P1M0ZPp6m6+n5TKFwzAejr60Vfyva1c2zQ15pxKtMJgKH8Vt0nDl7ZuYv8smFnnCMICuSu9iKaQvO3s4W23Zp42vEt9WbD1vIKzY9wDt8J4LQXVOk5qtOcP/kw5iiXX9YguMVQCjLm7aQ9rS98D3hoiZZo5yNV+580CoeVvNwvg+vfn/7/q1W/3A4NEkgrQbqJFCdB/ROK4Zub8N7KyVOICkhhB5yJZ9IAskdVvrgC0kf3o/1C5kOrQb61dvFSN6QCIIHfu5vwZKjTpfBdQkfK3nQhYyTHIwJ42m2wd3NJH6EN4mEDkD4cMta0SFcVGG92uZTh0qWHuEY9wW8TFh/vffF21mc1NGF18VzQPSTpzofSPXvvz+/+V3JoP+jRBBbxB7zBjEOmVZCiNfK35OS+6wIki5WCKE7/cKP+Oe+nFwYe3re2+DCkjHHP5y0ryWbXh8V0Z8jjjbIc4nEp2XCxwiHXe1e9SawIm1tbl2oFAKRVieIfo2CtX0b2sV3cwhsumapQT7DDuuD7zp0xo3Wu3yatf05vlkS+MR/DeAcM/zLfd6ykRl21dZfxj3fv8DhjnTDc6u1HevcqUcskpQTf0NwSpbRnbeGrURQBWdVKwIrAisCKwIrAisCKwIrAt8tAv/IRNDo3ckjt5/UmyMP1p5wHR/8aWuOetTgdsRKyfxQPgiZEMAy09vuqMdEYuCQyUS/ceho3Xfri4S9bOE76mt8R1sTn8y3B8Z2wx729BJky2SKG7Zmbv1715Bujj1ltKK5OGvlQ6gt2bWwNqNYetUJzYkGXJPjtkrih8nfZ/ghWzFKuEPUj1ox49leJ4IqWUiChiSQD3XWbqf3arDC572SQe8+6M1gJHxea0uYzgF6+TaviScxxCohVgPBxzlCvFr+I4kgVhP5dfHagCVH/Kp42ZKJ+CNf8MOHRHNekH0Dpw8JIPyiThDcl3R3jj1weLhf6adHa7d8IFGAami7VAKoyRqL6LBbsKM62qsOCoGOL3X1CHn/yVvESAL5jB/Vj/RaebaHPdXKoCdaGfQ/L57f/M//6PObVgTp3KDHoj1RIogDpllFpEVEqkncVTII+xrCdEs4HKnRxccGg+ZukI8+3ElEB4048xGHa4BRoFFG4qBjAb4DZJbTi2N+QZGfZfuENKGk/2iivhftpckHnqbl92dSdwV0H+XPQU1xDyyh6ETIFVVGJ2Z833ybnLK21vjYrVNWDyNjM8bgnA/spY/RPdsxJoySoDXGMugr/kxjljOosHh32cwUW76zPUKTbKN2pneN/D4Yxfc6tJUImmK4wBWBFYEVgRWBFYEVgRWBFYHvEoGfkAji4Xb/8Ls9exu47FcmHUxQRNs9dY8pWPMgzWTCbfHb0mYg4rbgCU/8mCcQmTBMfKUPmT2ftcTOxBO++Bqf7HSYfY3NCXEC9qSlZw6wtJ6zulXM/I2b+YPrMI/+EIzqOSAi2xjVBLrUmCQqlloP3CDAdWnaPOkit2E5XVOjLRpnPnQwDWIVBX/YOuN2K690SolaB3wcyIxT/G962x9JIIwrw6CMQsYFD5QvQESpBbz49Ilk0JebDyRylMR5x3k/SvC85YBozgPiDWFaDcT5QJwH9OqdkkNaIcTB0KwAYkvYeyUgOFOI7WCsBvr4sWzYNXqBXaUynIgiu6FVQTccEi2/6k1i5ulkkLjTFkAUcNQ6qJIS2TAEUbT0D2yKRdzDgdhWBQkPHVFHxJe04d7w9sKdEDYyTgI5ESQPS55kTb9d7NdfP+vMIK3yUWbnCYdH68ygp8+1IkirgH7//YUSQTo4WjArhThYmtfNs02M1UAkgnxOkBSyReyeE1V1L05+4Ql3E25nSyCY8pOO8YHHDooPBxVbmvS4KhqKrZkNQ6dl+gYNuplMDVf4onHzyfqaEokoj96NPNmNVVxMwgvtrc+yxRut9AMoPrT/4Bq2jC7zPdE093HrUvWj3J1d2vNFY9vd8bWxqlv1mT8zq3WhaOd392u2p56KL/qC91iXMvAt5T7iOKWSlWkIZ3R5V1X6GG3Ow6HLfK1jxxhVdbX/mHEw8ks1+whbq7KWUlXiSG4gEB96wv2ML50Q3ZgWsCKwIrAisCKwIrAisCKwIrAi8Dcj8HMSQX7oj+cTuD0O16P31j7vY7ia94wnD+PjIfuMB9zMN8NH/pk2w823+SLA/eqJSD3azw/8LTNqpDMFGLhrUPdps3iNsfDNP3lQqEHBaWIhESNpIH5uY2MrDvp7WorRU6yhfGItQfM1rNogk3yAfOLJaNtZDPtvJ4K00kVtEjwpSfyQ7GEbFuPmRExrtiyTrqwI+qDlOyRwPuhsH5/7wyHRegU8bwxjO5iTP7Ut7JXOBnqjVUGcC+QVQJJlRZDOmPaqoNQkpuQiiR1sOglB4omJJgmhJIF4i5i3ijm5A1+SPPBV9y0zxiPRqE5Wv9QiodRI1VsYjJunzSDmdjat9S2bOtKNQyKrs0qrV92wIqj4BJFnyaHTHCD9WZPYL0rusD1MSR4Ojlay54WSP/9HW8N++/3ZzQudGfT0GW8W40BpzgvS2UIPWRWkM4OUAbqnMUPnfc2ImRBTbAOgOkplUAPPZBxvsjVMULnqcVdHqM1bMPdKygZsesFsultRs7u2pnZDNeMWPUMOxvDtRHeNybbxak8i9sMXIYfDxTIxls75t2mGdybVYFxb3f7NWei0QYvMOmb4qG9uf28+dOMRpXvc7WCvXN3JrFo75ZCyTd8IxjB2EEq/ggTeArjj032A0nZwhnd8c0PM0pf7Nx4Br0TQHKMFrwisCKwIrAisCKwIrAisCHyvCPzwRBAPzvzxAzW96Ifvaz26eNjOQ/I19r+L/5YJjCcAdj1P+v2c3565f0wv8P0fVTJJjkvxeoS/fVWfMicRW+Pu7sQ2RIQjaj0HMmgVGW+0xF7iFq3tU3Bm1yUx9Luz7A8XTZMiQp3/tm+FmUDpf/7R78mUa4ZASRc56PUV1E60kICJNvjzljAdEK1VPO+1mucDbwnTeT8kgt4oyeO3hQl+yWqgV6+dFGJlEIdGQ3vLiiAlgbQbzDo+KvPzye2sMIpPuI5NEjx81El8qUSQXynPOT/2jygwKXQ0ClblZJJq0fjj4iow4wAteGqCkCbXjnTgcQWKFOOEgFqqSxNkwqUSfYDZfgfPPhFED/2x/Gclcr7oEOhflODR9i+dBfT4kRJBWgn0uw6L5pygvElM5wc9UyLosepH931wNGcF3Vci6IESQhyjxPYYtprhBx8iaDepVfApfa0VYU5SVV+gpwOuHTtlk5AZHCigJErgU8KVfu1xZTW2dTWnBpuaj8s2hmniBvdD00Nua12LVwwbD14e9URdGE960eT+PRu6mtK1/K3Y4Nddpdjs/918sdZJsWu8w+41w9GTmIWH6/W+YOk6te9/OPKbAP+kt90IA8SMg/AOv40zvrkbbGkXtLbdWiVeqB1bNO+vLSobW9oKu+LyFsum76VWa0VgRWBFYEVgRWBFYEVgRWBF4C9F4McnguymHsl5wt1KGnlo1kO2mucP0PDpMVzEnmQMmZ1Ca97zDZnNbAEz35FGu+ldX+Mx3s5b6oztp+MSwTFRaajHo/vIvKNhYn4sxL1ljrRtJASY78jQE1clC3pyNlhamgnXp80yUy/+xBPx7IyLUm22h22wZ2+5X0gd+GBmachKHFYBRY3WrThx00mgD2zx0lu/XvPRdjASPWwL43ygN9oW9krbwl6zSohEkbaM8Yaw91LmRJC3hSkRpMOhlVfS+UAkpwgWtpIA+rIlo3qrGq98Z0UQiSF4wteTfyISHT0OVSu4jSF+jJfLhmyAmOACms5K4cUeCZID4SPuwc6ygq2wk0DQJKMKucgoqkKw9kkvA7t5pAQPB0I/0aogDoj+7YVeIa/XxwO/0LlBT5898pvFSAQ95NXzfotY3ibG4dEkgjg42quBys/Yine4231k/OfDoh0XZDQOgUtB+ZuejiunJ0UrOGAK917Hm7qj2bjiU8XopchOoTue3W6OkHWtDvg7V7BxMJqJMZHVTR+6acC8L+ErxiI1lyWsp/Xt+VqT46QGsZzhpnc902a46V3PtBlueteDpp6V0/19bk9Bw9cHKg+ZmQONhz5K0P1vY+aYGgYl4z9HPFZbusa+zJWbU5zEWsger522FpA8fbSa0gVfJ4JgA809P+4pOFZZEVgRWBFYEVgRWBFYEVgRWBH4exH44YmgPBhPT71/z/9/lPR4vqd/mk70TOYf5GUifx7/00lLevLNPTjXfBTvSTTKjxLdJomwTzREC3RN1pot0yhPWJ0AgEk0w/D0ChvVvRoo40KaCT5NvEjY6EMi6JZDnm/1xjAld3hFPIdBv1MiiAOiX71Sm0SQVwFxUPTtza0SQm/1ZrGPWgHEVjBWBrEaKIkg9RP9SkBkdQ93iOAt2VNvDfPZRTow2mkTtnYlEWT34ZeUk0IG+i4DKdhN7jXao8xtxHoybQ4hrIrGCKR1lDqztcrU1mK8hX2zZHzQ3Tx4C8xKIVYXsT2MM38ePdabwZQReqxEz3OdE/T86WMlgh7fPCMZpFfIP9OWsWdaFfREr5h/rIQRZwU91AqiHB7N9jCtDtKM2C9ZQ7+M+GNb2EufMu50EN/m0jHK9/LadzO93KIzKzB8NiFP38NKOGUhDcdoUAe0V5tvw2TzgnGitW6pCNsF8175f1HrGIW5fdnNjku4jt8H+M/kkWrJGQp3UyLp4bXhbcTd4nKmeyN+BUB2ll+JoK8EbJFXBFYEVgRWBFYEVgRWBFYE/nQEfngiCA/nSSkTMk8kq77Wg+t8PDL3A7qgP6nvmr3Gt75uX6snD/QQXxPjefZxTfAH4zPBmKcZOID3iWOPzehPO5iJ9Jj8NL4lx+SFbsPXWgfnBMFjm7MvDafWOh0JoCUJh0hDq0QQJEo55cOiaRautaAjSSBW4CAUQbSTBGJlEEmcd6wE0qHPHPz8lkRQbf1iCxiJoLckgd5odRDJH68GulWSSB8ljzgUOtvBklRSLsiHV3cSyHcEDpEEciJIvpDw8blFOR9orAbqdEN1sFY2dey72+7HaKjJhFRGjMvkFJMUj6sa3Q4kRmUuwPWtWqIVIYsGtiCX0uBKQWunhLes2n1GEMkgVgXxFrCHOvuHVT4cGv1UZwE907lAnA/E1rBnnBOkN4o9V2KIFUO8QeyR+B55ZZBWAmkpEGcGZWVQrZCQMSbIlI5WXFR/aovg5qt4iAYHReMu32k+1woRqV5esFhPUdEAX2uiDr3CMilpnlaIecvq0ta6RuHGT2aJvyAQcMHKvuR3ao+jtROjLcP9Hb/k/nOYb9X1Nb6md33VCwdNUVKtf7hGOA4C6Bkx436EYUSCMCaUA9cqzFrc0yg0eZMEsd360t1yzbgNVSO6nkxOYFNd27+J6C2RO47VWBFYEVgRWBFYEVgRWBFYEVgR+HsR+OGJoJ6E+GFdvs/thucuHScH3a45wcy6gwfft018jvz9YH98oIevy9HfpiBjeOJtmZ9dj/4MaPbJkxucF7n7M9MHLGrxdd39vtA8sXqChhImuC5d00gygSkeqrNmB+gkEQT7Zjg6GA/8r5bpY3UNTlQiiBU6opIAchJI6lnRQyLo9t1HJYM+6m1h72/+0Bawt0r8vOaV8bwpTAmhV28/eDuYE0FaKXTr18t/uvmk1UQ6J1rJIHltvZxVo1SIcO6NcOkViaB726og+6e3hfn18e4PaQ14VHUCiAYOH4qnn/BRunYjloy0nkQ0E9ehyOrNP+Rh79t2VrkNF45EkTjVOQK4OReY6PrDYdGCWRH0UCt8OCvosV4l/0RJn6dsB9NKoOfaEuZkUMFP6w1iTgbpTWKW45XynBmkpNkD6ePQaHxjdZD9xWd9KL4H8Gfnlwjq1PjuKupq4+1ZIVqDtmk2K5ZnyaYyai4VVPAdpuaBTmzBm761iRda9acCbZkSBG+6BN2H4RwqqxRzt+hf8R9/p1qCesQksZtpR7h5Wx/thmfe/wQfdrZuyy6JIIJ5Zj/RpXP8RapjEw1cA81eA8OXWO/lmq+lWl/HjNHZl41TBMOFkMse/z23Wq1AfPpWbfcJfGtFEFFYZUVgRWBFYEVgRWBFYEVgReB7RuCnJYK+Zye+VRfP2v2A/q0yf4ZvepbPcz1P/f+wMvo/oLhYE60JHe+7DxAanjt115RpUtayUjFN6aRo5kEvbSZjWGNKRKEO3s2iB0Zh+JgbWnepTFWJFTR6JU5rZhKpBJASBp+VufmojM3bWxJBH27eaTUQr4x/pTeFvX7FVjDOBSIRlNVAvEGMRBCrhm5rFRFJoI8+FyhvCctKIPIRsUNv0iuSUaRHeqsaba0IYnLrtS341z7TQ9rpvQFd0oO0drfY1qgAmMUphEQSX/RnK1FdihpLkmTiEdpsG0oaCByFuAuePdzGTHi/NUy6WBGU18hrZRDbw7RNjLeHsTqIN4W9eKGVQToriC1iTgQJ/0RJILaJJRGkbWKcGSRFrI7gVfIkcjoRFGeGn/QxK0bwsx1XHLb4DBjfmyN6EqE9ri3sYw82fWfEBM1CE9w8rWVuD7aC1Df7b+XF2fFuBZvVDVHArPlI+9/dHnFKP+jpEXfWw23IJyJyd8kminxv5nIpYYwve95LzlnP1+H8mg2+lQgasVjQisCKwIrAisCKwIrAisCKwPeJwE9NBM3/o+xJWk14jg/SR75tInqIwV18fqhnVoAN1XfpQC30Wd/B1DapvMbn/hyF/gHtxPYY4W5fxsUTKYesafvpkbvUsy3UQLa6TKiblK5HlsN4mTqdT7kRTnJhSypcJIIwMxmrMWUVSKRjzc6wQgMndFAzZ/NkTEktZbXOJxJB+nA+EAkgzgNiNdAbJX1eakWQD4bmoGheGf/m483Ldzo8+u17HSCtLWHaQnarz/sPvDJe5wLJFquBOB+IHuCWVKum3/ihjAi10gY+J8jBYVuYVgQx29sSQQ0LZX7q82JOXeh3l33Mg4WOP13sUjdUDxmSJGFEt8skFyWFYLlTM/B9Aa6kXLaH1YHRyms91PYuzvvxqiBt/XrMFjAlhJ4oIfQbZwU9e1q13hwm/GMliJ7olfOPdU7QQ71OngQSiSCSPw9IBLEqSAZJmXWJffqi+4DO8qEvuOsOpkf5bo7JO+RRjvcQlMihyAmfKdpQ+mOolIHreLc0mijdtltqR6QFqxbW9yoCxFZCrQ8Upe/l1K0ttL6mr8iWvqOSYtzzXdqa9dnDP6WP70L3qzWl7j7IYvXnnG8OwOjvXpcjoEBtphQzvonHiGeEoZzb6jujxylWznijHTW+vYrlyOmxQ0nxbf5F8biWQX4z5vFeiaARogWtCKwIrAisCKwIrAisCKwIfJ8I/JRE0P4B28/Hd08Cpr5emwTMD86wH/k2m2K8NimZzJQ8mONj/cx1aaep2P8nlv3k57Jvx9jQi8Ty2JupfxuY2H6t6yRh9nGNH8ECM+GmdKKBOnijdakpWJqQJOwJVlQF75sCrXAzhWclTiSTCOJgZyVxJHOrJT3vSPDo8+69VgK9+XDz8qUOiNa2sD9ekQTSFjElf1gNBJzVQGwj0yvnP5AEUgJIunPmEDYEK9uCT+kNflQiit55dRIJIR0Y7TZvDgtP/AWmqK4+NSZ4SBCEzV+jt9jPzCVfVakjppVGKd4W2XS0CWvWpRUwLsCcxQMtnZQbtDK6MJDIIynDCp4HPueHmlfJ6+BozgFSkueJtog916ffIvaMVUHaOvZEZwjxOvnHnC1kfur7XgmkxUFeGUQIqwd4QRj8yT2M7bhmoi/p4fhuVrsY+rvhrg2hHTQngiKNTf1xICbWqd18pk54t4/EUmTfN3XE8tgXiLJbnRxqk8YY7SjJd7j0BHV6jTqkd45d8Fof2KtZjSHStgfmHPrbfHI+Y1++V9DQy/04Cnfm3B6UGfK4foXP3Zf+4/ifau+QnhKHZb6ZGYfgViJoxGZBKwIrAisCKwIrAisCKwIrAt8nAj88EYTbPKz3o3k//Hd91q08yJfc9rA/c0qfH/bzpG39bkemOU0tPLhMGnjo7glEcwbXrW/hax5kWl/73Xr+CXVPdocvl7OSXV882YQbviQ2aHUfgdVw1ZeMbfiDm+GkCgYPHON+iJ2zRFD4Qo/WyEUTvjEpRROYLtFMuiBJoPSBJI380IUtYazmcSJIq4HecEA0CZ/Xtzf/ZluYtoS9VCKIg6M5H+it6tckizhPSG8Xe68E0geSSZq9+Uwg7JAAkkVeF4/94YN8q21h9odMRq0SSjIIP/G+PwJ3vaHdRVqro12bV7jRDi++2KGtsneFQgCGETfkjbIgNJWG67sLgkOZTWI2rL/paSWJhPhFZwSB+/VXJYDUtXva1/XgvmAdAv2YFUKPfr15yvYwrQj6TQdGv/jtqV4hr9VBT+4bx7axx9oiRuKIt43xWvl7WhLkrWZykNVBFPtadWD8sUOm7+5ndU4UyQwpoPl7AZ1Pl9BjJ3KhbhrUbH3ErsIS8VLUvEZ2A1rDJliRIGLXROIstuIDHkVWm4BU0XbfzWLe84XxnC8CrYvWOV/iuBktO2fVmfzM1/SuZ1rD0CiM5VW+Yywk0qNNTEcJNpiKhTkHT6z1qNpyiUMpSVXmk11jh7gJuxgiLSazzHylda4ge8Ve8a1E0BydBa8IrAisCKwIrAisCKwIrAh8jwj8tEQQzl99oD/tGU/Ffuw+pZ4hW/8sdfYM3nzomOGjzpk2wxd88tMpiWlicuT5We0x4T2LBF5l4jn7N8ev8el/WnTzmraMWY9d4rLnTQsbgZJcyBwfzPGDzSGTWfKGEnD0Vprk4JcvOqDZM7FMAkkEsS3so14X5kSQEjtvOQj6Vqt/lAR69VrnAb167dVAr5QQeqmVQG90LtBrkkFKGL3128WUQNJqoA9KJnklkPR/3pI/JIFICuGvfKoVQEkE4QN+5WOc5PJmM/yf+xB/ZwwatxgUgfFwSeAMgtnGu/aCpc0Vx+BIMoE201ljSpVZrMmKAjERp6naXSPrEUBYJX5q9ovXHPbtLWIS0EKgm1/vkRDSmUEPbvJGMGV0Hivp80Kvk3/x7MnNbySCVL/QdrGnwnGg9JNHbBHTCiLx8fp53hymfJDrPiOo3aUOLN922Zi4znWLU3oqjJ0fDIXZY9EazBTesqV+VWyNgG0WFtz+7YzMjY2hmUtJ40ufv2cFD/FmAhN4/m2a4SETaKbNcKhtqO6PHld/l5p21Djal/oGbYYGX/cD3cBftzHraXhIRQf6RwmV6+ALlTu/7mghhn2gWcNMA3Ziyir2XHMiZ1IXY9euZfbo30oEXQvYwq8IrAisCKwIrAisCKwIrAj81Qj8tERQP6DP/1t/1okxUTijfn/csNcP9scpgx7/a3Jxt+9MoL6/f39XY3pznGq01qK6Cgwl6YHmuawds0v0AZPUwJhsNXnYCYYEQpINma41nbo/noKpTYCTOILUnCPsgpyAYcLWiSD4siKIN4V9+qxXv2s5z622eL19y2ofrfwh6aPkz7+1EuiPl6wIIhH0VlvHPooums4Seq/VQCSB3kv2g84XctJHyR3OCYr3scerrv3f+076EMlaJdTnBfkmYVtYnx9EFOjB6MWAu4fwAHOPdQ1OZc5UlCeW0sX1tikmchFCT2zaqi7bmFcCwHyIbKuAwJRS8wDDQDIIeWASQXKJt4cJeU9JIL0F3omc+zoaifOCOBT6xQsdFK23h/3+4qngp9oqplVBSgKxUujJIw6VZhvZA20TUyKIN4hZhyKpeu5uehC/kgjCh7nQT9q+TPXMkzgNyeYF43VbqkMNRbiQoqRux9bo4enGtdqKyuImoPZsepIFXdyCmqkZ9u3xe9b0y7pjkpjttc/6v+13TxJR6CTJpbWBGXwDdwahbvzWHv3bS4y44MeeNrfgm3lnGvc+92/ThxowbT933tVEUClsH+av0WyrYfeRhkxgpW2DWokgorDKisCKwIrAisCKwIrAisCKwPeMwE9LBM2d6MlK1zNthpve9Z6mh+d6ej6jN+9Mm+Gmdx0arfmRvKlTnVmKJz9tPzI96Zx4/wFgesOVT09q2jFNfkYnGumJSA48zuTqhMW8FYptBtYWICa9E5U9zUp9mDlXYmdM8COZaVm8j7ZoHd0QTQ60/5m8cU3iRWtIMpIkasTHVi7OB2JF0Ae2hmmFz1sdDv36HQdEa+WPDof+t84IeqlVQf/6d14d/1ZvFHvD9jG/Yl7bwvSq+I/aGvbxU84HIhnEiiCST17x4+kk9ugN90P7orpwvVIIP7nn4MtHlUt6Erj7P0hNtajQ1P0K8uLyUG/xkwrDCKKubEaeERGhlFYVPpQh60SQKL4JuF/A+2L6L184KSnMXhEkkGTVr7988n1EMui+VgaRzGGLF28Qe85B0b9pe9hzJYK0KojXyb9QYojVQX7VvJJBbA17pM8Dv0aeZFISQdja/DSs+0X+jD7KNxioOkhqBtmSxdCMYXd4zFr4ik5Qoka6EkGNtZ0oMJ3QXCtl3mPmGIqxEkEbTjyw5b6OgNulc/xOpX+t5mgy+o7YY1t9knJ0zHqPXLRDh3985/46H9+Ntpv6UlcC2fa6nvkaB2fD6L0siWBpzL0yMUGNWPgGyZpHU1DHKvfX8D26d6zf0Mfil1lWLNZttxJB+zCu1orAisCKwIrAisCKwIrAisB3iMAPTwT1RP3c9376Paf+WGymAnq0t9l+sG8syMa1X55Eb9yZ3DTtn1KP3szeD3iMD7jR20CjPfenpZtqyZogMiGjJN0DvNe7jyK0aJsTQcEFv4ONGjLYSVKiPVHdCRcSQWLN9J2VOzko+qOyOMoB6bwfbfliRZDfEHbrVUD/fvlKB0ZrZdAfr70SiEOi371n+5iSR7esBtIr45VE4tXx2RKmRJDXqMiW+03CB19IDlEHJgnUiSJPWC1DIogONB/wXUWpDrEmTtO9Zh0tN2KTUPkq26mbixjZ6ubjRjFglRIhfh1/w7QNKAATTC6D43i9OoieGjFWBd1nZZBWCd2vg6OdAHrBa+SVEGJ1kF4lz1ax56wIYpuYXiPPWUIcGu1EkJJAD6dEEF4pHUHlYvhsa5j6R1/CmUCN72zwwUbPuGejf+ZtvvQ1/ChG90wryt2VBdp/an1aySbZdBAzcYbPaMg1z6wD3mNpevMf6f872t0L+u174Up34Bu8o2+wXxEZTAVx+4d3L3Gm90L4CuLo11oRdCVQC70isCKwIrAisCKwIrAisCLwlyPwwxNBeDqSDXqI1uSMNnPQ4PcP1N2zwRf+xh/rP8t3lD+2W98Rf62N90wgmYJ4Xn2N8afgOwXQ05Su2xnRN9QGmEi/EguatJqe8aIVaAYEFxZ6S4C91NEc4epEEBO59hoNuUeSzonG0lqO0xoTdnstDFuvWK2DNu6f2hqmg31IBLG165YEDwdB6y1hL1kRpATQ//fy5c2rl++0Mui1VwmxGohVQe9qW5jeGi95tpehlxVH95IIYuCdgEoCKMmhTgbRs/jlJJH52k9qStfqYXVvfy8JKZb0tbjVjlSAJGKizREUc1RxDdQSSVT96pUHkTheFTViZ2cYA+i6WJUvgnFImbFyGPvxgURQHRqtrIm3iXmL2GcfIP1Qq3xyPpCSPxwarUTQC1YG6fNM5wT5LWJaEcSbxB7yKnm/fUyJICWRxhlBuUPS//aHOsV43Jvi2n3v+4w2EtHBNTrBBFfKqmrcnAhqi9DAW+FezC1ohGsUIdQOKoJNz++iaBLqe6E5Ix+p8d0cWvd84BUBKcw4znx/Df5WXV/ja3rX17xpetfX+NRJdZWYTvXpYDDmPc7RRjT5MArUKY3p9qhtZmsOCeSPWoyaVE2gSX3xHTARlfOcfGmuVa8IrAisCKwIrAisCKwIrAisCPz1CPzwRFBPQniYp4x2w8HnIbqf5RtHOxOZrq91veldX+NrfPN1nUfvPM43j+vy2/CYme1YehKBrp9b2n/8CDymPU3Dwz3s1owSx5gmjz7RPYfAY3LUk7FLHCO9Vzl8QjKlp+pd9yjoZc+eGWeNRq8vwu/eBsR0ve+l6EI/yThqzt/JtixW4nQi6JMSQR90SDTn/LxXIojtYK9YEfTqnVcE/euPlzcv/3h38y+tDCIh9JYDpW8/Khmkc4U+3uiQaB0Ure1hnz4nuaOjkL0CiBU/Ofi5VwTRfxJF8sX+xDeSRYlJJ4fwHNooPZ8NZkSQ7V8zp9MVzj60hom3rGyYum+zWokokgRKrKKTa3GrcssyNW02SRf+AutD0ucL+1mEgD9JICWA5BPrpHKWEYmgT/Xq949KBCmho1U+WyJI28N+f/7s5rmSQb+xIujFYyWAlAxiRZBWBvG6eb+GXskgHRfkM4ew1HdXfK87vB2zL1xChT+l68Y4ggcanRt8ECvE5oOyUQVMJoN3XMy6kwumrlI4rLQAGJVSDgfuoz/1ZjV8zTgJ5Xes+YvNlhpOPf9G8f1pzeXBjrl5+3sWG5ec/wm+tolDbbfrnZNqtEfc046delV3xZHV7f5VyR2AdPhHkvAo1lFiTEbMkOvS9jxe4MupHsfm2+oWFR+s/rkrmbUiaIvSAlYEVgRWBFYEVgRWBFYEVgS+UwR+eCIIv+eH+vN+8ATMk3E9CU8P2Of8Y3Iw088mCj8C17M2bP2c0nE7Ws+0qKYaE7H5qcWjWUh7DsbdMItb5vF5N8b1KEmGWXKzmItRRJPTBK51KZwtGR5eIdyJBDhTTLdqaPjXU7fwC9GsqsfkD9jWdBAz6Qgng9Q3tGhHlxI4SuR8+KhXwCsZpATPG60G+kMfEkGvdFD0v/79StvCOCsoK4LekQTiLCESQUoAcbbQR60G+sS5QDqM+uaX+7bj839Y6eNEDz7MMC7FL+PtLz5RwJ8V8NVXILPNE9DIbNJbtmKKi2I0tUpHSzB2wIxW4w7eWL40ON7S52EQTm2vGOLVabISTY6yHeM18iSDoJEI4j7x1rBfPysRxFvDdCYQ28F+ezISQawK0nYxEkF8nisRxOqhhzpgmi1lrAjivCFs+f6wpb1/RTSFmKdno39FEH7cM3se9O35t9AWZaY6LFHqxI9pXKRmlrPKcjXEEqKykGI1Ke6RY4hiYxCDG20E8xs36WRMxHj83f07OOK5/97N9n4UTL+3QG59nEfNfbQ7fa/QGHLwRkNw/Eo0jvuiv5lIpQxZ2owHNpKQhlZF4Ih38HPIWkvXLUbd9hu3EkEdiVWvCKwIrAisCKwIrAisCKwIfK8I/PBE0Hg4zgN0ty8nJXk8z8QmD9x0+pJvhGKmzXBzNK7rxs81NAp+/VU+PEc2mqzuB1/sAb0ouz3d6Cll49utblOLx00u6oNEvRpHNZOdlNbHtEmFpkomRbFaKGEDRXNiUpqxZLlxSbu0otHSlrVtIE2SzQYNDfgbueBbG3bxn08lgpSwYZMSeCeCdHmvRNAHHRh9yyHQOiPoD60K7DoOygAAQABJREFUes1bwvT5v/9+6UTQH6wI0uvk3+lsoHedCFIC6L3kP33ivCE81hIV3vzVb//6NTbdA5JC2JUv7ot9KJzh7mnXcOFn1wJLEnSwXLsMGKmRdSj8VgXYuMMslQD6yM/ArbdrSXSMQfVYeEsc28Ewqb75hNuOMDXJHxUngogEK4M4NJq0nGolch7pXfC8NcyJINW/68OKIFYGkSB6qkOjn2tF0JOnj3xgtLeG6fXzOSNIOt0HelS9km89eefeMNlOyA0zV1+NC5UrnMjt+z+kwyMdyNFfVYMKMoXQzDyNv5oIgtlC5b8FJgMCvfpLfA676bFMdyzKfWXx4GNz8ND+tt8zvkvy3/qsMKqma2L4rfq+ja+/v1+z+618n9UJ934EiAhUL6AApwaaKcHmDupr6hLfVbayxXZTZJ1tg3iWDaHapZ2abkSdx8DJ3nJsJYI6QKteEVgRWBFYEVgRWBFYEVgR+F4R+KmJoLkTd00CvoXvKH9st45r+KZ3/df4Mo3g+Z1tCT+v1AxicwBfMikPZabPMDzFUWjisJVm3XATbWM6AzKVinim2q1q5oYrk/fw9AQsvGwPE/e8GiiIyIiGrCd4FpBv8tOanJxhGxYfadUsS9p80HMSQXoV/FttDXvHaiC2hvG2sGlF0B9KBPmgaLaGkTTSuULaDqadYt4W9pmEyC8PZE7JH68EqoSQ46SEj2rbxbY7DL1jhz/HKKS9R9OaMQ03PkmMxAB58PQ+fI3vGpo/hIkVSy6JWeTaPwjSUU6CDSicc3JcZHtLBGGPNv6Q+KGdFUGOQiWCSAjxKvlHegtYHw79wlvDSARxaLQ+WiXkRBAHRmtV0GO9Rp5E0L1KBHlFUDlEv7qv2LcPum5FfqSfCMx9S2/hC3ZPa2zr6YTOpRZxYLaL4NbUMkbMPPDCVLHdEngWHIzum++hVj7q/E51uy3Sbjh6vu33TDGSWNxp+dZ9qMUIRydnoGLJuKo33FAKaivN34hv81E2iEXHrIVVN36LnH1sKxsWTn1GG2i01KhSPdxF8hgVu1H9C3+Ez/TtFLWRk9r+TC6uRNBJkBZqRWBFYEVgRWBFYEVgRWBF4G9F4IcngvC2H+J54PU5DnqavjJXcOd4wKcg1w/7RkyX5pn5Gp7YLN/t9uNM51EfMjMfvuNV80VXP73H33Ib0R9YegoSHzLFAf5aIsjTj/KzddBsPYFCyVumZq4SvFJlig7/tckSVjpJAdyfDVsJIPN49qV7QTU6t61ixsdfUUWRHm3X8nk9bNMiWaFBYQcTi1d44xdnBH3QgT+32vb1+q0SQa+TCPpDr47P1jBeH/9KtPd5xbwOin73TodMS78WE0nXPa0Iki0lgnwWUa8KclIovfB9I358sl++MQo2TqIJzqgLpaqKe9oN1fS9cdSUrtMa7YEfEDzt34AVLOMzTsBwScqxdbNA4RRE0B4HgOJJ4oMVQfQ2ySDXhtkahk4lgvQa+Ud6hzwrf57rTKAXv2slEK+O/+2ZD42+mgjS4qtHdVh07uuMdscjduXPFg8blM30Lf1Oz3Kd78qBB2reRCIY1LamwqQqvGMCAy6odCLITXhKGe2AB8Y2UAwbnxR5dZ616kKOsTQA9W/Q/DvVrP07Rfv8dy9WNt9qLG1ByMhsnqgP5XnxQaFM3R6weLlHjmXThq4O2gmf9U72zvrXPG3F/mLXRhvbHgRLi3smfnDXDL5wtHz44A8mfHbZyNI3xB2IuSvm0MUsMx/yhwKZn5R2ZyWCDgFazRWBFYEVgRWBFYEVgRWBFYG/HYEfngjKg3CehK890P/tXpWCb9U/883w0Y+ZNsNHPpINTHzg+fHl2ixj4MeEZ+B6Qr2fyuH9WR96Bcmf6x3T9Rr5C8F5GgY8T7giRCKrPGeG5VlWamOrK658kd/+24kgtGZFEEkgPp9qaxhnBN3qbWBv3mVL2KtXr/XaeBJBSgJpWxj1m7e3SgR9Vi3eDzkbiBVBX5wIUmaCWfmvnBFUq4G8NUy98D1ADGs1Er3YcGpsMaF3XeAfrWBpz7i0WdmUsQvX4AVq/q4HZjeu25Yw7IrCqiaPQNpGdrxpSJ1jThBRLZon3uaRpM1ltEn4sCrIr5AnmceKoEoOkQhihQ/bwp4pAfS7zgj6TW8Ke/GiE0FsDVOC6Cmvj3988/gJK4J0NpDC3WcExRk7EcfikGyWo6oM1ndxt/opFBgZPZXub9czrqjuW/NbKDmMZk2+dWgqfsgVngjVNSumGlX92MyXsNpAdCE6NobuXGtzPf82zXBb6XqmBcb5SXcxDrv4oO/RWUfEO3V1x3epsT3A3NapCfnXwdkHtODv3rO9bvgd2x1XZNrvpqc96xuxyO/V0D0nchzSo2ODdUClum6hDb8SQVsoFrAisCKwIrAisCKwIrAisCLwnSLw4xNBPYn4xgnAXROPOQY/km+2NcPtjycO6l9PJBr/Y+vjzGO0K51S7oCfP2deHnvydxNB2CAx0wkf2sM/rPUnFNF832R7GJL5qymTwaYXG0IuvL2LxAw1UkpBaIbGRAt1rAi61bIeXiF/+y4rgjgb6LW2hf2hZBAJoH+TCPqXDotWIui93hf/9q3eMKZEkF8drwOjP2uL2OcvnA+UT94YpoQQK5DUi9hVb2zfnss690b8AKbIHZUZph0sUAqrbBoLjQiSCmq+4Ip540x7cA16j2P8jP3AJEzwcSuOf+wAYtfZNJtUv4xM8qfdIeEDZyeDvCpIiaAkhvLWr0cPf/FqoKdP8+r433RAtLeFaWvYC50P9ExvDnvBm8MePb55RCJIh0Xf47BoJZHuaWhzXyQOwPysYJNC8gkoOHrX/Umiy0yFDaXpUBruujBRXYmjmIcDGx0iS+iio5F2BfrGt6dMfsOEQn2sMHCZnaRsRXLEvvg3n5stPLRi91JLaDV+jFXraxVbvaeF71xfGdskrwF36piEvpUPu8ckFf2+LA6u0fQgveC6Z+Z+kUbfxVCOVN9dErMNZ36s0pfo7LgHD18P6eAsCOUSmm+Z9mglgi6itRArAisCKwIrAisCKwIrAisCfzMCPzwRhL+XD+t5gP/aA3/Tu577PuNmeOYBnmkzfBffkbZr19P9rKsf4MH93NKeUFNS71MC4Hr60Xxmni6jH5mvcB24ifFOkJQAFoaOZt/bbe3Dgjz2DAo+ffjb7ZpZ0Ta/6qRL0K0Jv8aAZJDHpxJBaGE71ydeG89bw7S05/ZWK4K0Neyl3hjmRFCtBCIR9H+VEHqtM4I+fNSKIG0Le3+rFUFS4oOifWAyK4E6EZQVQT4rqOzjQ/e941be0hWV7rEb4yL0PFaJHjiNFzHgIjg112iLgoKJzxZIcFaqCm20OxlEsgzJSgG5MSVMrI6LpBxz1dw2RulStbUKTo/RV+cDkRXpQ6OVoLl3jy1j2uKl18c/10HQz1j5o9VAv2mb2G+/1RlBSgQ93RJBOiNIiaAHUyLo/j2lluwE8cAZiiJb/fUqJFDVr0ILkT4nMZQrbMUYMEI72PL0TUBWPaXbDVsDcYDRMUgNnuKwBfQVPR4eKx7+Wx4cK66o4ZvkBsi9XXcFrMoY7G1Ysdlj61zL0Efs0NH1oMSDWd/Md13vXt/X+RzcfSdmJzb/TvnKdwRmu1Ew26YfaXMd0OgfMrkz9t8S8HOxq7LLPZAYhxqdM+egz3x7jvolbveq1m3u2+DIu9orAisCKwIrAisCKwIrAisCKwJ/NQI/PBGUSeRfdfd/hxyTAJ7hmYz83DJPRwJnWtNeNX2ahDbpoj72pdtbby8kLhG9CmamoKd1xB8w/YGzEw/w8ScTb0HMwvyXRACcKkEJKA1eDUSSo7eFxQdWBm2JoPdKBunDGUF/sCJI28J4Zfwff2RFUBJBWhGk7I9XBL3X28Jk52O9MeyLVwRxULSSQTc6KwhbHnv5YLu4leRD3NQk2rPB4bZnkT3dw/W5zJkGafryRXvSVHI2EhqjNWsXTDAuWIFNFwLVLF4g3ZNFDGBi0Ckg+eU/9n9aFWRlXBJ3N5Wo8ESYi4ZAvbKlHh/aXgUkIquAeI28XyUvc7wx7J5eH//owf2bZ1oN9JzVPxeJIG0NY6sYiaLHj24ecVg0iSDl2vTWedUkI7w/z5axR3FvdLFN2o0wLf3DM6PrilxKsKVFqG4XFBNb+sxNXczVdWsqXprNV6RDRWyauZV0u1hlYI9pv7oWXzp60P1nmrOFSa8tz+0/o/PH8+56scVkxg6fwJ5TBg89v9Z73/+m7zm+pnNo30Nn/qwVQfsYrdaKwIrAisCKwIrAisCKwIrA34/AD08E4fKcDJr/53bGH7v2Z/k8Keqn9KMytVvfCWmHusZ3fNBnGgBurpH9Z5QxvchUvb3qXnQiCPzgDdfchxluHV1Du4sOX6bqbXXwD0y4Qmlt9hkWIZz8SPaBRtyVz9agC/NpJvluq/7l16xq8Thq+k4CCFqvCLpVAqgPi2ZF0B8vlQjSK+T/rSQQiaA+I+j1m8tE0CdtC+NDIshnA+mMIA6n9llETgDFNgkXfKKkbv/AFGwyF/lsfmjushI2QxpcEj50lHFTq+Ph1UGg0ntolI5fY61VNuZkUNIj2AaS30WPB1ZiXb7ILrp4XXyScWIWrKvb1OmVI6FWtoNlS5hwUk8i6AEfvTWMRNAzEkGcE+QVQVodpK1hwCSCns9vDVMiSMcv3eit8/7kfsAfJZlwyn5XXXFLnyAxHpR4h58WcN2XYM/wLU14Z3jTIvxOGn+qGJza8CUhB8Ph++eOMG6lD2Zw/ruzgLBKxi3qrTnok6u/B9v9ccJwgbqub9Z1xjX8V7xlE54zvtbT9YULhWh612d8tukvkO5N38vtRaKzl+E+8Ldjj55a8fnM6zARSqh9DTZj1/Bcl2tGXdPK3TDzrRVBcwQXvCKwIrAisCKwIrAisCKwIvA9IvDDE0Gd7OFhnjK3G547dnzo73bX1qFLtE2S/STd9UQ6A1tf12c8TCXm18Kf+YtcTz3Q9XPLPPkJvJ/2ND0JhUSxcV3Tg7kfAwaKvsZRN4zcsYQ2T3vPOIYGkhLlsWZchjyJrcTIhpv8KLfh4A8rclzpAo7UBKt5ekXQnAh6/ZZDonNG0L91RtC//83KoBwWTSLog1YEcVj0e68I0loXnRH06cs96arVQH5DmWAdGJ0VQVmFhEv50LN84mZ6mglrkQgIPncJi6Vy6EzFQRo7KcZ9SCJkxAehRDk4lBkqrfGBRt6olrYjpnsWnGMHXX8q2mrJa3ekdPm2EQyObUxNt1xHW3idC0Qig2iQACIRxORWLwy7efhQiSCdAfREq36eK+mTt4axNUxbxDhE2okgbRFTMujxkwc3WkDkM4JIIiE/J4Iw757owlePjyMJHIo8owhR19E3cPQhtK47DvBTzCU29MKdS7XLPmhrMQOtwYtPjlfQ9rHHKpkseIdXZiMJZMO0rHmrbQKlLl2LSzjui64nBcVbEpssfk0O77j2vM03dB+YW2fro93wxNq/j1/Td7TT7a4nlT0c7fAWLaJ6VsAOSu72RD/XEdFZemAdY5MGjmbrdNcnRIdi5jZvI9Tga9V8iK4VQURhlRWBFYEVgRWBFYEVgRWBFYHvGYEfngjC+X7w/64d0ZPzUe88UeA5mwfuGdf2vwXXsscJzZlsP8VD+7nFU4xyIfB8DQGMZ/STq+A6YqCB9+20mCyRKOjSfN3eS6VVEx2xzFaGnmgbqYhKOmgi2RNkT/4xUZPLVK0NvPxodxkDjwOIJGbIWcwrgj5++njzVm8Ne3tYEcSqILaIcWj0ax8WzdYwEkGflfdQQsmJoBwW/cWHUveKoE4EJYlClPAuPcMPSmgN28dBEprtbKH6arjHSZFwp5X8IS6s0KGu5E9bQg6siLEdB2oYUMhHdphpCmbFTMDAQhrHNaLoAkstm+VOXAmHOQVmS1hJektYEkG/KIFzz59fbu7L2INHOiz6iVb+cCD084c+I8irgV5wZpC2jAlHoujZs4c3jx7pjCAdFH1fHxJBbBG7KxHkpFPfC+pbnHdqiE5UcWC7obrbXUMiHlNR/6yNfopA/2sRj/nMK9xWQKjtOIGsdtPZLmcG36vFaCWgUV6cLdf3MzaEc8rCcBgvf5PkP3ybA9F3ySd1YvyrfOXlz6vSydyi5cX+N5ggUTqQjrr5M8JJ/jQXfH3nR24vS8vDY7uDw5DjPXAemQnXHnQ9OLe04Ib6fyURRNz5JmRUcu0RcPxMy+gFHCM1Y4ttqppv1pJ/uyYm3xW04W6Jprck+Bme6Ufa1/jaxswH3Hq6xkbus1hruOWNnRutMOz76/wbwH1LaYVp5XoHX6u3yXaya+vTpZlmnQfaBcuJH/CgutW3W+16U/IvXGmE+Y5yJG+6ZcX32+xYMbuycX4R1Mpf1QKGM2W1FKiaVUHsf44sgg59YKKi9L8jwI0Dahh9+ZWaGUJtntlq+NEWbPvTLnd70up/AyIhmcEQ1DYArZEay2HcfGi+YjvhiFQJbP8ZBr9Km219jgvODEWC1bhwMPJ7xsZ1vVMyWWs69Wa5kLNM8zUP7dnjhme+I65pX6+RbOuzxaPkbKH5WvbI+2faraPrPyP7T+ftPnWNvzPc/oPjQ+nYpjXw3e66x4z2DDc9+GjL88c5Hxz9+9ZPsbO+M78aN9tqOBbT+l586Jl9altndfpzbn/+Sh/9bP3909Jf/Q0vle1HfljLRjGe8k0ObrKTnhk3sW5g07s2oR0HSWnDBbqpi9l8yfh2v0IIDpixh3ZPD4K8Zfk/UX54ImieZMyTjhk+dnSmzfCf5WvZro/ytKFR8POv8jG288ohK/wpl7rLbLvh8VDBLZbSCQb6nkfxIkxV39WgGo58P6yHmRsVeusONtfIQbmkts5MslsqmsI9EkBQtRWoviD9dOXERAvig1XKO41pNCgRJPizurglgnRY9Ad9+rBozgh6o7eG/YskUCeC/nilRJAOi67Xx2dFEIkgeaHXx3Mccs4HUiLIr42vVUFxwDbT59HHjiHpERfuu7r3UhVv4cYTIuND4kcVyRjHgB8K4B67GuMwWX1/7xDzPzpWn7HyCiDZ2VYCAYdLzDWeDmB6YePS/UUHZQfGFTPglP8mEVSPrBwSrSc5unJPNQmce1Lrs35YEaRXwz/lFfFK+vzOodG8Rr4TQUoAPSNR9PTBzWPOCPLr4+9IBMmG7dBrwU5sAdPYPkbUBfxcRpsY0KuOReB02VzdZXiKqGOPUmCY6BsoAP88NMhZEUKKZyl1hRaUIgiCj5U0tWrT5OeOJt4q3+P3rHVR/yf09b35td/bb+EjoM0H3NGa+zDg+p4IQfwYi/C3VAffwR1iG1T3h4Tj+0aYh956TSl129gX+6a9zPJVwpfm+38lEfRJUfu4Re76GNQvZkVv8PX3tb4opjc1tER63BVNrcTucTyqfVYhuY3bCUPTuz5h2e5N9FzwNbJpahslxr4v0NlsXe9wICkoF2fH4Pp32IzlS9/T0Zz7MTrQ1sXUyaf5vm0etJ7xgcw3qFy0sJg3gcKXvPvdNLPp+4viKnhnO2r7t5RGIZqv2VHDr26bjCKwJWuoLhLil8KywFLGdmz/+xdsFMm+3UOpSlVbJ0wDz0c3sX0qh6pCbCvBYQv7kQtOGgKUT2GIj7HdSvCh73co/JZQ+wPMv8vSsrUF8c9O4gIQGH3wUHL/9MiBwRk+FVMAmhbQk0A9mLnZAwHPVlpzdOc5b8KVZf/GlmL7F5N7LWWz/YkTB0bri4P04kiNwrYfvqFvM1fAn+U7yg9rbQnMDJ9JYLX9nuEj75HWMs3XdGpK2+062MsoHvXAd9QB7lv4jjx3+YTOLn+Xr/Uc69YLvvtE3TD42ecZD+3uMkvCeZc0vPP37FLzXdKX3D8fQ486AvjOz0F/l91bIY3D1WYEnkt32gIi7PimBoopE1+esYPu0N+prlipvpUP3u1nTkINdw2d4imTkF/qH6tuMz/Fac/u1MB9/r0hCfTbY51B+x8oPzURNPcnD9LTIM7ECb7Gd8Qf263iGr7pXf9dPuR/fpnj2fD4hw8P08pENP4OvuE/j97gz/oULYNG+4wPbcGjqa2AbXzgPGy2Bur2OBM80SXcuCiKb/MXreX9KyO73qrlpy9t59I3rhNBeWuYEkHv9dawNzks+o0Oi/4XB0YrAZQVQS+VCPpQr4/PiqCcD6QvKK+PZytYvTXspl5X78SQ/SQlQqm++1ev2+VXx8tZCz2ItfMG6jEOnDteCR90HxJBPIYRiURXEXKjIlXBCR1limMleZIIYjUQ44z9skmSqnwoxaLKBrpQpNrPeDQbJ/ov+mHrRJC49NCbpB3du8dr36m1P+zeff246a1hrPhh69dTJX1+0zlBJIJ+5/XxbAvTh4OiOxE0rwjirCA/NHYSTH74QVf66YmPh+JHVn9TANI33E9pInVjg4MTTEejBDwMhovd3AVroVLKrE6YRg+g2Cxch3+Xom38ialxpdPVjBAs5k33JfMm+Hd/zzZFBfTvW76TR+q+/b1t+54kNofSdpqyxfGOuMDbvyWM8xzNBL+1HYxV00OEoQPboRkPmk3EeRRnXmA+VlkE7mnu5//20omgOR5znLr/icWRkn83hmygIxeRH7hLaMi3tX3dEl/jQwre78GHHj8k3qFwtuP7XogZd+kPVMVCOo9fpTMzMx/0Lhc2rugjGLOd9hEv0bfptKHWnnq2YViX8M+UfHNnDNL8k5l/f/e6uj8bPwqrsZssgDY+/576twKdGhAe4Gn3b0D/JrZ3c1fsL5c4Hme4olu4jg1NflP7l8j2sAXvVmS3BBq/1eX80R5x2H5H1OA71C8owE//uwlOfO6HcNGB5nxnuj/9Der+zt5tsB2iJS3dOftfHMbZgrG21ja33obe9uJQ2Hv8quXK3BA2edDHtlndo83XoK5cz+RtqfgTnyvCfxo9a/5W4TMPz2S/le9M9ms4D3cxnfVhpsPWPEf8mZ1v4W2eM/m/gmt91D3Cjfsr+pZMIjCPd8OOcTc6UMd24/9mvfttPxnQHu+vmfkqn/x3F8Q4//w1DA0486b6t8v/yHfiX79O/t1XLUZIDzVx+k3HY/wnyg9PBNGJ/kcMuB/euwZ3LNs/OArINb7mQRb93Z5tQWt88zXua3wMnGUZvam0vlm+/5Fq2sT+E8D2d9QN1WOMfALTWFycH53625J/jpvr8ovQfMhfm7aEp3V0zT8Lpji20dyxc0v48ObKwwr+UkWuvA/ZtKIwaAGV5EjmNYkgzgj6uL0+vlcEva3DolkNpETQv0kE6YwgVgTp9fG3Hz7fvHvLK+c/+ZDobA0jiodEkLz6TEJIdTZE4Z89FW6ONL4RKz+W2U/3Wz6b2w81IaedHwV1xFqIwJfPmj6pqc1qUkwiKDSqwKpBGZ/reLBCqzaE4YNsUZvmGhJ00aZfz1/I/NhOVHolUOuHTzR63WME4EOiwYmc1UDZFnZP5/2QCHqqLDeHRT/RqqDfWBH04tHN/5AIUu1DpJ0oYkWQzgh6qLOF9NTKYdF5PTyJKe7Xcc86ou6PHKNv+EdX0ruKhBEgT0rTUhMTIHdTNcmexDSi6WtgbG/2WgDUBDushQuM7xotGSF2LjYteDZs2ASxqN5AAEm2qJTOv0fo6+8TcNPANQye0nwz/lv5ouHyeiY/czW965nWMDSK/QLuzjaDastXG77IEJQO1AyHke9IY/ejDL3lgCu4xjUMnlKSM/rgIpq2r5H4zjSjCRV8uot0k9Vzvq9g+C8upEM/1vfYMVCUUnMdhdgl4gNXUZsRgvdy/Lq13kHLSHh8rDcqwje4ZsXHsev2bK1xLde0ttP4r/GZLmHLF/Pu1hdubpsFHAbaaBubavg2nXfxtX7V8+/cpGq7me3HpnjH4ft5w2APfaqQoebjsgGi+Se9GOyjiBO9RbYA6EvjZ+mDqlYD2qalY47Z9r0shl0bGdsOkf+t9W+GblbQeVEDNP79bOcOfjZaXOi2D8CIle7A0lxtk9QYPED5vQ6P2mUvFFTVv3UIi+ZET7mCC7C7FiffhPy7DIF2fmPyn0K2KkzJlB14rMQV30Bj4iM0QZacOmFMqQs8dDfUeqwCWyVfZoUOh3/Pi+zf+smOZeGbfEV/PNw8i4npClhe20rDZjnwwZnSWgfXGQRXS0CfpRo/41pHy3V9lKXdfZvrWb7hmT7razr17EPzgwc+K7OeGW7eo77Gz/WZ3Ez/s3Dr6/qafNO7br5uU/OhzLFoXCjr+j0isN1fG1D33Bz4pvUAteHjgHyNr3RubJJvGJWzuhnf5rpuN7pu/LGG7n+LirF19k8WNTjXRfSzrRCeDaieE0Ew3/9vTQRd+zHfD9ExxHe3K6a7gb1bYk8992nPQ+tuPrwY/yhdSv8oTEcDew3P/9SB8203OQRu8O6/ItzVdxXoM89x+hKaH8wmNeMBs2Sp+IZMpbXiffPnmaH4ulINr1X0k4TqevRJLR4e4D7o1OgP7z8pwfNh2xr28tW7m1ccFD0ngjgjyG8N+5wzgrSV7JO2hI1EEGcPKavB6+OdAGKVEDbpf9bGxKNENo9d1SNVWZmjzIb89IOM63SCLpiz2NGQlUCESB3R69Nd6ak5S7CJEPiiS41AGlw92n5gLaU8YuXhL7Y5Jyh+ZOygR0E5YGPTyiOy1qjWx+NSDXqfoihwRpAa/h9JdZNEDq9+54ftsRJBj7Xty1vDtCrIiaDfdGj0cx0Y/RtvE9PWMK8Iuq9E0MOb+0qIa3eYEkHyVBkZ/2x6xhB7ecB1L9QNIo3l9A1/2quKKqhDqX4au0kPOSkgMhXOcLVSiW5bww5aZ/6QEMIvWvkOjrfDhWO70Wn2TW/rRqQzks/9VDiqqdz9OzUY/+l8w9OvQz0ccPr7dCoSLq6Betxzz3BngWnsqQpRnXA64dr7sL9fWlfr3niFmCeyfc/oVvd3p+X+W+teEZT+8U2e7mwFiUl0x2zU45fmelwyphnplkTXDPd9cF3L96ZgfRv7E+Wm6cJvxMa3ARGYm1/T1yasbxZswqH+Vr6D2HB2hNcsh+Ym1niP+DELs3EV4E7GeV+rH4nRuBdo87N6fNa46Lb4+ns2m5rdMN18+temtkOD47s/f193vzV2YIRi1s1tZ9lC+t9x4Pq3lPveNmUj+qsXtM3XgtTilS26yxfEsBr8/0cX/CIyjo5okPJvo/5dhtdtElmBqSmWA2cmdOhPt81AanUU++w7dUQ5fav+mBb+rc9qot+y6B7qoNgeKGwfiDvONOA/+GTC8OdE6BSFPcbheomnXI9c+z4MDc13jQ7nrO8uvqH161Dbbf1Hia/Rj/y0Z5mmz7437qz+p/HdFWdod9HP+rdw3xaB/F4MXt9TZzfWYAn0F2+g+Tc9imRMuvJd34/zmRt/ymwpaD1b3XjVgPnPjoaNyWxAP/z+TwYxWEQXzkb9r9salh9/d/E4zLv2t/L5XwzdWa2xB416Lt+q7y6+mTbDsRMPwO9vrdmLHwV3NLDX8BwjYPDz4xLtwZs+nEWzedBN6f7OEW9cOHIlMTIXfQ37KQA0DwX6ElhSk98zKyAJ7/wwYY1H5tJH9UUPCPnhyUMHbw77/9l7EwW7bSRpV7b2xe7/vv9DzrS1L3bf+CIQIMjDU1WyJblHMqRDJHJDIkECqCRIJhCkR74+/X7vnV4WzefjX+odQa/0jiA+Hf9vBYD+9+XLvCx6BILevdXuoUMgiHcE6b69jFKUQoGg7AJiIdGfQweqnzKJwBFtUCPGeeJgEIuYgSNfyPYHh21hJQ1q0B/6NDuDiYNDHjTQLL+B8z8Xe5AK2WDnCiU7vOjz4ikLqJ/8PBV1ZyUJv42FvSkPsQadW6ShyB73pRsZO9wGWUmOan4PdWBn0ANtdXj8+Od7TxXgec47gng0TJ+Sf6Evhv2L9wTpq2HPnmu3EIGg5woEPX7owZAAkr46b116yG+cPxkwsboLWoDGT/CnzRptsB/anl2Oc5rgoizJZRahDlLOJ3WhyJVqfeHIsXyU8MOxnOtvWFcFVlhctbUWa0Kb/2/6Vnplkrv9G+OOuNJWeMekwkpb4Zv4jrS1fJOOv8I3vGYV+NtOcglKfbRxAW0lM/qQ3j+jQNb5NPxJO84UILn29wq7gvWAWRJYx8ao51HK/JG2sn+P8BYIGs5wIwP7shBYSq7Llm72RrjcG1NjJKKl0nfTVu6vm/ess02b6buxY1og+npuMXRfudR3fMhfa/Oqb9azAsPAY1300yCFmwqU0LcnGL0dZLBZXfGGvoSqnfF8qUzCaIgOScGmX68nS/kgvJmSr34Ctp0iwQP7Ksf1zhyeBTq0BII6HyA89Y12zDl46KoJVIE+dEWvEHPxL5oNH3SiQfBGiGPsGpA954ppGGsImT+MMjzaa1iS5oJn+XFzyDuElAemPOZS06RaSn9mTrUG6CiwETmszgLPemTgmsMY2JDryE5tykmoHZDphkFKMLTJUMbBgmWzNxba5vUNsoGR03HDL2I3UOOD8CJbbWeWlXbUfCyvfGd6jvwtl/fYhuq7zb7b6K1nzY91rbTWe8St5dYJboWP5dL29RW7avxzcH13lAZ/jQbvWRuPOj6H70z2e8dt48C4/trJX8K5q44BL0v44VrGkzGPqEdhO6aadMRfK89qh2DlZ77gab99QC6FfrpCOX/JeE7QuO9/ZszN71++v0fDpsvU9AGPwf6qk+k0OeX0D4hVdoUPylbZFT6wnddxZFK5OrYqR09zYs0zawInGr4mCluomyXE8LFPuZx4uQSg1WafjiqTgsuRiwSIvBeOmZYD+tvO5pD3E3P1RdsQN3KVCT4+3fd3/04ekspCzxUV22otPGgVh++UORATA/SOIAJBf/hF0R9HIOi1AkGvGgjiHUEEgvRY2P/+m3cEvZ/vCOKT83nHkBaFukPol0UvgSBqzXuDsADvDx+wiFQZe0gJ/gDrp0WW72axgjMMfftFQEed/9vgAKRhQ8GYLCg9hIiJBZja7FHGIhYf1Q54LBipy4QGhdLHtsGcsVUVjSRgDEwgqNtJ6ASBoJsCVW1IWIwq/IJoNY/3AxHMua8INzuC+n4gcoI/v/Dp+H4+XoGgZwoU8Y6gxw4EEUDKy6Ljn3H+ZuVsTwff80LtmU3AsP35KKttfg7A8BSX3EdmEf6LjD6qC34Tt2a3fcONLtgQC2Q9+Mi4+HHuClqVT3i1HaR++t86MhYtFSxgx6kFdQqWr/kpk5ClN4dv9dyUcyPTFz0fJ20Bqqf5QtqBpTdficXZDtXb+sBvqVa2ozJmtLTx4Vrk2j8rZYPte+mf58VQdKpv9NVwyaZkgXwWlE94rusHEnDAduH7HsEEgo4ti//pC0a79GSOK2f7yheEOVfqBu/5gr/UFjx9uzt1NjUTaj+jozDE6iyuZWjFAZOOssHqeMJYm3zelfHA5/MLvVK84yv/Z+a1r7nFWyfIkUwHvzIO+sI26Tuc9V0K0+ckqwzo8mnjzKSD/q/qOg4k4CDK6NRVHUrtK3QA62d1LgUDHR0OBBm1WlfGKEDe/NI06wdnPLhc237XEAt+ITqPs0igDO0Pza+mue4xRwCjaTHQZc1tXkOMerzrR23N2MEYTNNTBvaNGQHBh+bdhyKg2kEfCTf4Q4DoZ03iGU+Ryw/mrMtk05JsEw1VmkcDG98gDylqxc7kAznKKZnEQSrgi2/hV5l/Zhi8yuKXlK8f9/VNPsx0XVQ2sQO4QBwZbihb8XW6yMfxvn5q89BAshVT3QRC1HHHN7F7AB78ZP8NmTOPHFucsxFdrSX2oKd+3yw6Soe39E0D+s5Sxv4936XO6jvTcMQd23jUZroUlq++R09xR5jytAFZMbrvlK8y8N01VV/zVW7FrTA8a3kHq9C21Kbmq+4jXB3kTch9bqo/ltMmPloUta4FdQqWr/mRaYeXsZS3tJTkkGNbbvPJIr2pLCTi2ve02fwjn3DLIMQUPnLeY5t8IP2y6Bf6YM7XSN/8HUGe1GiJPVHg0AWHIlwnKNDfLGHu3W0I9zah3V3ybg2q827TW77m0d5BOp3AcL7SO7wHR4latpo2KNrWcuHm4egf32jMcF780OCqjjJbnZu9Q078R+5jeauBSUk/MfgiA1DiIuMdQZ/03p/3+vzX+92OoLfaGaQg0P8qCMSLo39TIOj1e71Q+vd7bxUE+vCOQJB08Pl4XhbtIAE7grhItSNIwRU+L5+2JvhDnebDFgpuAQsX/XHvX+Cf/LJpoQjQyNT+LDIk8YcXQAwUBH1kDLmDQaap11QJ2PE/4stg53PT+gkuUDcDIbbwn5wyqblAG966TRSOhaq4oBEdMQ9SRjhnQUryLiApzYui+Qz8zwoEaUfQ+Dz8s2d6JEwvi36hR8L+pU/H/6IvhxEAevaYQJC+GKZA0EO9YJpA0AM9FoZv8p6g1O2eBkfdyvVfiSM9IaztoCzO2qniBoeG1Co9y1EbjQM2rbqQGjD46jXP6aGCPkPNMQNBlKqsuWvGRtJqa91eWjiuH8u3GHud+c4UtFXznYXuzHhN87ENnAW1JOdISsgfeYO5xG5GIXVjzUN4uUosfJPOTfslNEdfK0hLfrRAUH2OC675vp67jQ7fsS8ic4PkUaCVfan8UPWsTgAkygeW2YbyZkhoKYatpWvy4WwtKX2dI1fEWUvStr0Fw/K1AXc2ivlIOqXQ4j6k9VXXHJWB9/RUBWWP38up5LUD12SuyyiLTHSkxeCRzU+8DvSMwI+MxV7mb8/Xov2udYiDQJrH+ZAFu3sJABEIkpTo4AgKJZ96A7imfAyDFsQejiwr/Bg4ucrciIGDHYY4jEAQj2l3B5DfFSgcnykmEPGz5lnWIT+LER7wP/mXYJDXDOiyVqs0TDW10TRsN0XY/HcpXJKDeUlD5YIRKGRqAub/LAVxOKq445gF17XKHipH8LPTqu9M+FgH5b0MpWIvr5vKb1w6E1zRXguoYsLByqIJaCsVyxmWdI1ezlrRvHjy0z5bGQyndo7oaH2tf2M/q2Gjfh7U2s6kYg+U4zl4jbt2l95yc/CFm6+8wKtFtQDe/8vppv53225oIONh0woXl3xhmoR6byKuAuf26RqZKgKc852rPVqEhombwMANIui20bBO/lkWAKzZwMgtEIQOfVhH4/AvT7+nQFBbLqc0WLKgTr0OH44z/w3M5hO9+amyocd/TF9jKJ4z44b6yrbPbelo257y10vRfbue8pGTmgdOiWOHYeD+wsMRKucwvx6TR8OGO9ItMA55FKoSq4z1mpAaVinXq0PvwDFYV8ccuEW/kAQxdea8YaFEf+euIIEgBYG0wPqk3UAfP+kl0O95NEyPhb3m8bD3fk/Q/2gn0L//rZ1BejyMQNC79w0EfbwnEf9+/z3vA9LySSsn3hFEWx/oR33U3Tw24c+0Qcsl2USAYgsE3Rdq4JVz6tEUcgNI+j/tQAuLSRaRwN0VxJBBcCa8IiCipLrQ05y6qQsMMBWM3NcYZWgjDyxd1rvqHvDAHx8NwztRrZ1BWkTyfp/7WpHyjqCHD3+691hvfX6mx8F4F9ALvhg2dgT9PwWCnuuLYQ0EPX16X4GgBwoEZfF6X4Eg3hGElQSDqJ666GcezmNmTwuwPImy+2NSFg6BbSt9Ig2TC2nTUBsldiow9Q4He7G9ChIXu5asxjYClTF2W6YnOGQSZc9aEwHSJB9sTIqYhL3kpIXLhZw7od1+RLqa9ty3jbHmtsNQER1n2qqneeTWutKCyjbfzEpfzxoGQ/5uE83iPgylbU98lFJxYVm5c17s6XAZM9A9d3A2sr4comp/NDGoBZw880yQXq5kkv9YmxzfL8COIF4YjV/WlHM5vjjSyhc8/bnxBRr9ZMZeFedazM9BZF9+yj2uDFz1tc50dPjH6T1JO6D6hLzQsTJilhhGFsoQcGbComHHuCoCXogLuOMyfiEuYPl8TWJULqaiL/JrfKhMGpAyV7M0AyeXb/XjNnTAbKkqW/KB9/i3KS2UfJwXagNsni5HjeWzwqHKTZ01MLdA2BIylRsiIQppvA5cx5yLzM1smmXMZWFP3QkMEfRRkOeTcLoRRSCIG1PM5/CAg+6AkG/0oIcyNWBTdAKTOHoMooH579xBHoI3ojvAI9g7fITwrlzxa6Uxgj4Eewj6CAMfNN2sua87OPQveHbx3vfaBF8SJHLF1p9642PbKBI5ddteWxlLjTA9to8GRM/AJ2NHcRqEngEqV6mDpWDTLBAIe70WUb7Od1BXbgvGhTJhm0cGaup1oE2NiHxbRYXgttrB1Czg0bqA48iNMfyRhHT0FjNz8Uw215ESTTcEQIpzA4OCCM/I8VV8EVpZIrAcFz5kh/aFIWB9YRsWqvnRMXCbN4OAUtrQtEjvwb0PoSF5rHEvk9JtfKXvLelyqxqna0Ecql0lrU0Hsyx8xkt08gowbvDYv4O4iPm0Li3n7dRQ03a5de4wS2EhLuDCMGxaiAs4+W7yxWQSAN8859ZGrUzAoxLzLrRjuazkXJvnCWVJZ7ZP2sY26yfgAtpy47x1Gyp0S16LWm+rmHgAIWlXceRtp3E6eEgHL4Dznn7n5xsFFdYJyis1fvmudgTVE2r0Z6fZYfsB/poeL1BUXzvrNr5r9OKrr+XznC7WoMdZ9cVTdN+u1qfZYCtcL/SyAu9T78BX/pyYnMb7Qbx6WAXA23Y2H+oWPBqqtfxdbIdwlC0XNUiyws5b2Hg82bVa59WXvK3soo5AkNZa+XLYJ+3y0U6f12/0omh9Qv7V63f3Xr98e+9/fvvNgaD/Vc7Lot9pJ9A77QrifUKKH2VXkHYEaf0m81gsKQTBN80dSHA4Qhez6pe5fl0yuX6xnvODoA8+JOgTeMuhj/b5nN/6rAsbeocFJJeTlojKKcNng0Y9OIPeo6r4gjpyblIHlUAvLbllALGf/zF6GE8d6tFUY5gq3cOyJxraSv60UwsJ3IjA3cg1EPTk0YN7LxQIeqZHwX7RjqBf9LWwFwoK/UvlFwoGPdMzsU8f6YthzxQI0mNk/lqYXJU7meNllzLOi6mYKm8qYRyZj9shVpnjhBp/RGaTBPrp8MfA4lwrt3+kfJOartoqv4CGjxbn3rojiP6atqy1HWCKUp8acryoXgj6veeT6UNux1vVqCl94K5pLttOzxBfcVW54u4G01dq3xjbMezUFvt2GLtTvNVcuXKFUuxOaBQGp+qeWjBmTUP8Ji1ldzsoiDlnLePEJvmj7AjS6OVAUP1yLT94+hrbxG+eDCryl1rMd2SWiIYup3nZpXhxvuV8HMQbsp63+9ECgbXyjMg9DUzhXF5Zbqjj7yZ5XFmMTWuOVmVdsV3DXM/lKUA/Db5FX7mu5Uhbgw7VFF7PFAb3+L2m7B6tju1cqUzzKvJIoIuX69ezoxi4hrOLR7AX+Tq/2f2jKRHa7wR7CARpzvxdX/8kEAQ9wSDlwNC6IwhZHKSfM58PlPV/msi4mAJHAjWU/dMEzA0YdgZRdqDHu2sT0GHnzwP9CBQR9HHOTRutabwrSAEh5u4+HrbtEnKoxiMwMp4j6nUMdcLvHd2EiNmDVp7080BOWgJB+zPITUSMtu0EttIGha8n11Gi4vSbfTdtLgV5/VRfdKotAmCjHNzGSyuL83kxym2lFW3shuB3c5bciAPfZB74VQaU6x3II23SxXTWxNKRM1yjRrlZ6ebRAV/MJYw7JnasfOE997yvFzHYdhiVjrLBfsnjWtti/x2qsKQMpKn4cbv2NmF4aMOaQ/U5YwWUkm5saxWUmfwMt9K/OFwLD4bfVs8Ndq7nn9l0MK5VTd0Log6fNIATm67UO07NKc16o38JENxErDxLrZP/CBx5LqpdGAqSt+2GNVjMsgDPH8MR2R0qLv9XIEhj76/fSyBoRrmGVy+6sT0BfXiIwdkT4EJzp8Ey9HhCEH/L5KeDunQQHS8f4lN/df0pnGu0BszEdNe/6Pwy4FbPdX1r68q14tp+cD71BlN5thzooo/MXSx5uY7wUCsNTPHVyiW3wlPeyOrdJmX3PbXIqbOGVjnqBm9JB1ZEdCek/tZNn0TDWIhpccauoI8EgvQC6DcK9rx2IOiDdwb9jx4N++0l7wn6t3YEvbv3RoEg3g/0lkfDtIDTV+S1aOPxMPSyI4jHwggEcb7el2fTTt4j5KbpkECQPOBzWRb7UTAFkbC+AZqRwxJ82hE/qe9ohw7+0X8D9u4gwywYh4zrkR50Gc4Cz5pZtFED9bUK52aeuNAGhxT37xH3Rxs2bPCCYGyFGa2XfnbvUBPvO5GbHtzP413aEfREW3x4BIygz6/P+WT8U+0OepidQewIUiDIXxbTjqCHigLlLqa8rQUsXyNjIPejZ5gsX/gOKL2MIeDwVbyflskH8Y0l02rz6UAavqA0XTg84baZyaqjZzCW1nywOXN9e4VRLlx8lJocBIrBqcAytGNYIttsFP3Y1Yf7dDHWjV5rD2wNQ02pVhNnFHWacw6uPjxl+pJI2u00gRRX++2buGkwu7BrzkF88t0ZWCus0KpUdP23H8c5snLVlmppV6UPy5m8vIzGJMr4nGvWoeW12rB8d0fazq6gNa3n3ujyg6dX7kt4r21P99g6UOVzHS2YxowRTtD8ct0MwZEV124qHwK5dkIJHzqjC3GPy4Nv4s24MaEPIV+JKsz6BDAO71KJiEgGcvMd3yggDR1bb+KD3XwASq32mszktV4qQMa1GTadclHkA17bhH8817gtYXCdOGTqE95I6Em+lqa+4Ia09QG7jFwUhglTRWCIbRshwNs0YTEAM+V1bYtMd++wmFcsx7QEdRT8Yd3g4I/eUajFg4M/Qn7SNuM/FCVKAIgdQwkWUc7OIv50cauU65/qSe3DqhqF/fKNmwUkwPEZTcLrriDv8NEc7DlTPOwG4kYLO4AI+HDj6IHWJw/uP9BOIK1RCCIJ/tmBIdGYgyXDBybua47KY2OpK46rQcO+kWF3XFx68+DNJgYHYMNII9yOdoibONoGP1covsi1VCFrMs7nT4pwoc4JW5rA9TzzNIydgxG21mlACHoCnPEcFl0qhWDcRsg51XKEartl1oPYorvnv0qb88zZZYAL+Gio7nlxTTd40uazwEbqMK+5A1/8Vfuxb/jdfBgMJu2CK1D40J1agbbEWb3y1faNo9RV36W7N/6VL/attMCxZNqzNWnaMmUGbZwK6YIheHn+qO6FtsLVB87dOHLwK9+OXiHyuNeYvW+jb7JKLybUtp2+pZ27zhi6rbd2ldfKpJCcrGXBRxYzLIchYr7KIXO0rSL1C+XwpIbtfFzqtHKuDTEPm9rW6iM3DkB8LKFjDHl0myQm+IaaixyeY9qkNznzrIRFqPNJq4WNeWOWhci1wCgvSPMEu0g5g/m78ZHG5O9mR1AnS3eKRq06If5KT9AZa+pJkJ5Sh+2FJmv5cDDwQTlI48o3BRfAcipnQuAkQ9tluplvTEg+nS5lvz7GHlA1q+2F12EWHNNZaViWhcYeB55eWfnAkcDzayofV9yWiL0iXeomE1lPYCbqwEVp7kiwaKIv2xeRqAYLpb+tv1Tqjhb00Y2xPjguLLZeezu27sa9U1Tn7dsPejxMu4L0GNgrfUaeR8L+rcfD/kcvjH6j3UJv3n249+7tR+8K+qCdQQSRfleQR5l0jx1BGmn88uiBYweSPUwTZFEeF4ttPh89Mj0QqAUV5yehDdrvskGYnXIqxg/xRX2SnPbETwwjkqVCLeb45/9a1HmxBmbgU+fqs9SFgM1BD8IjNYiKbtWaRjW3k8EpBEbdOvz0k77qJXHOBjyvteO9+wR0hOBziI+1I+jXX/WpeH0u/l+/PtcXwxQU0nuBfiUI9Fy/J7wk+r74FAjST+tR6ZMe2X9/CQT9JNj1SD+5/itRv45xnDFQ+MfwCtF8shU/wFZ/RH49Wt3QN+ChsZnbTMFtH/kgTpTrGEgyqrB9EODipwTe1dOThXtNTWJoYTyBB+obZ7TApitf4ZpxhivtL+VDMRkVu+//ksK7C9Ne13sistJWeGWtLHlP1+ZcMzrdv/vEIkhrn5FybTLG5HpVXictvhhXsLg2vmpojlh1gFvh8ERL+PB/KsqY4JHCbKue0qxvjB3RxVEyxm35RtugjDWpr/ylxoKWhn2y68gHR8+p0MBc8qGP0cN654Wh0rATqSZuC3hsN18sub3eS752096+Sz6MMq8ZoTMWh29YnEz0UIel5rekEZhLimTa4HsSyAlZlQfNEZIwbRzCxnnkRecQqBzEwujkvGWl7l0/BtnhIw8qsJPHvQjoJOBDsMePQGqu/qibSjyWzmNgBHvY+RM+HgsD/iSbI5fAEXWMOX4YQT/VFlAuyGbMpm+5aLzzRzDv/FHMxrt+WF94DtUfF9nlk0AQu3/u8ziY+PP4toJACgwR/CEg9EAT8P0HCgZJl3cKKZ+7iDRIeReR6sD7sWK1bsDKAsW54S3Gltv+QDREiaa4f8o3cGlpWYxcz9VFxDwccv3Hur3zZBf1lDNVBbHAkx5l4YYOoXzBbscKccIMoy76DhXQpCRsuQ6Oaj0Olm/IbNfLViXQ3hfRt+dI6Ta++nHlW/W4ebZp6JMzDmem3ZORbJUEjtPGSmcSN944d9W3+qR8oYey0qsw/R5dxZHb9pigkgA3Nhxtd0ohF15pK1x68zNacc3hXWHLLo1YaSvcOpqbNgoWn+0KsrLk6/nasbF6mpef8grHaeXa03Z8G4uh0lzf0j6Iy6Wx1NX+2vJcI3v+QzWzrqNeytiwSypXu+3bEbdCzS0v+Vky3yDO9g7G+hkyMNetn1LBBuYQ+MA509yg8Z4SPN/djiA3tB6h4buEG5WUDSjlgeQPWOSP6ThAHcvlv4Yvvflf42Mh1j/aqvFL5bT90jN77Zf+kdMGS06yFDjtjsMvfP3BBbzWdyyXRs4POmltf4JAYCstD1FUag4UuJgO3Hn8iYsBaf1GFfBZRgD9tWkYLPOq3l7Y3HpRkUAQ0Va9LNqBoI8KBr13MOilgkH/Hl8M+00vi3755q0eCVOg6I0CQdo99F7vC/qkXUEOBukiTSBI7VSwhaCXrmlfvOwYos4+Ika9sUF42aelk8zmnj+BpLSBsDUwpRwAkjJwWLn0oI16gFhQ4qMMHH6XgHSwqM+duui3XvCsCFHu/67JZeuUWpuCcuSdm3VA1C/Q1QPHkp/SaBHYC0VSS7VqcZBGJc4Car2vb7/zaJfWlfee6gXQBIB+/dfze//fL7/40/EEgp4rCPTixSPvGHr85Oc8FqYAEu8G4g/jn5RjI3ciGRJ4TxB1Aeu/af7L8WSs2M5Nc3qhLBGZi/DaXvpu6IOsgv3idiOgNGA0FZ5CKx90mE5xIPWT/ckHr7IIYJcL40ChiOaQVniw/oBZXYw3Cn9NN9y1nvbOmU3G6bCOxj11Od85r7/3lEAQnhiNFegxUTkjzIULDgiu3IxEe0+hcU1nfBe4UTdja6tBj2ENApmLopUxoX21q+fAt9KAq5dBZdV35KPssUcngfkODUJPUeSM+aQzX5iwO6zSkat0dIT5wj9Cg0uanlnKA1RWras9O30Sp+ecYNZFkNJyHORSMlciIU01wwpywF8VKYzvwBUPEFg65NvauV6DaFv5B5Nx4Ak0+eamML8rqEMdnwjiOMBDIIigDo+ACRaeL5TyXkICPR+1juBjFb+DUyDIvOZP8MfvCNJu5T8kP+I/+cPADdq8SXGfRqs8aOT8xV9++bNwDvyo/HDs8nnADiCtB5iTE+hRWYPOz+wG0pz7UIEfdgQ9FF+CQQSCxK/dvOhKQClrCj9exnJGyX0qp+6658JWOIe9ytsLkY/svn/LK9qe4HpA+Q8rFCjBs15bnmL3Fi0aK+EAAEAASURBVIXxM46HaiNJI0/bVsWDWOHduXgumLZU/nr+V/kq3ya0fKzxGh4+9/E40Ifb2XnUkjKsZ3x4YtohmPI6ntVTrkq0puIrXfqqr7zD2lnMOUFx0xKitNSYcovlJj+U7Zgjc0yX1+2R4y+UW9+hSSz7L+oV78Qd+K9aUP1nDNDO9Bzx4rlgA3fko4674k7sYRi8qOfAR50eLoW/jbeip3wLEpOxG9/avwMGbZxzXSn+u0M4zQmee0REzX/YHQrs33cbCLI7OMyUgZ2Gc7FpmNBvDuL2Kn494sHgtKUHhsZVfoVnhSd8Rxrlyja/xgMeO3LRD4PPmP8SjnbepvvSF6kSfGg5ctqdLXvKZ89GdB6huRekKfAk7eziT/7S0eNlwag99vcIFyk9edI6+dSRUfoY5tHXHVzpF/8DwW9U6yWQeMlbh3GwCIHOTwJYcH0YO4LYFfTu/Xu/MPrfL185GPSbAkK/6ZGxdwoSvYb+7ne/UwiZjwSD7EKtfjTKEhAi3OE6dYHr2g5sXFppG2wTVic8ouVZbBeGQBBecHMsIpy0pNlcE6rQetMq9wPt1K8vjzYOLfhGdnXHkcvgRh1Ut0upZIfCSlJZm08bkBm/7BiKx+N1HtcieIMWeUZNY+GptaUe9dJXw/RFsH8pCPTrCwWC/t8LPRr2+N5zvSvo2TNeHv1YgaCftSNIW9b1dTEWrvfvS7f0YQO67G3lDOJrIIigSuqXbTU47hotAYlN+aeC5IMzPIUoDRVD3mzC0eSqxhdOxxyG4gbLReaVCEz6AVemyptbsIVjDrE4M/6wh9XdeGQt/1mnVE/zMz1H2rFcGfAk7CqPbdSho7FpOpD/OIGg8WgYjT6kjPHyhy+61YMHRnk0PrUHTZxjoUpVHS7K4dtkhj6hXQsXO3WSxoXftYbpQlvDkU8yQ8qiPVAvlMoWT149WUOIr/UOWnmjAyRjYUa5aWOZlE8+NA/bXcvBsJ0vxBfbwlRWD1G2nQqib7bBdrZE29JGmzL0rW0xXgfbrgnSkhyW9sJTmQ1da6BG3vlc6dcGriPpFbuvJ+Wstf3+vKECOh5KksXYiY/gE3I2CdnB1axl5vbqZw1BsKe51xYEf7RG+MT7f8TsD1MQ/IFPvw8K8phOPgJB3FxiJ5CDQMJ5txA3d2SQdxx5QaEzyHY1x7JaRZuAaU/wZG6f/KS4zQwEeeeP5lSCP+zkeUBwx7/s+Mk8rQ80aNduA0APFfx5+OChdSA3A0fMweiwrng2N4GwIWcEkFNNpVPGiLfZCq/w6rxMhSpJdzoE6XG+CHJ/gSLRf+Sj4wxTNCnXUuilwHxIkGrbgUQRG1HvfNAvtBXhigeTdR4Up8HSp/agV6w934dUMtGs0vqGcrdxxzWYUKLf0I2xLq6s1CWkNS18K4th0WwbBRuIpjWhAdvDF42jtmEm3PRKxrvkq4bC6bnYGgk0b/WlplJCG70tFStnNJbWesHuucoRfqraXLHVS40k01b0ELstQ2RxxWQ/deek7oG7VNs6jrzFrxqv1j2Y57khoaO+VQ9wfQZjeWedAqauRfdOB0KDr/r40+aYzLbqOzLcpSx5tx3eUa/FFsNtJnxDX9nK0nyQp5rikaPNTdVDGXRJ8OSXs7KBa0oM7zDymoj/cAPAYz+4BIK+r0fD3NrRMavn1N6Z1GurI40/w02BLwNsA9vN+m7mozdl/7xSbtb1ZanUfZaC3w+I4LjyVpleiSuu+sAde4UyvyOtfMkTHEFP8UeYsr2G5wRVX4JAXDmeKJfzxf4VG262jM+PTT8anIQfp5y0ho4aXhjNheYFmhje8tiXfm8/8HjYh3u//VuBoNcv7/322+t7L1+/9cukeY8Q7wh6//GjdwV91B09gkn/8efiFd1wMIhaR0BIFzB2pF5wpASKsBp8gkCsdvgpNRBkeGuPabZ/9OLwxbqAAPY/5/gm3sRJDQZF7fBDlMpA2sBv1LJUi4aeyxtajG6YD15ku5VWoMfBhGYhiEJeFO27hl6IZrFIYOeJFo1Pnj7So2HP7/2/X9gVpECQXhT9XDgeC3vu9wNpACQIRDCIO5cKBOECh8+knwDTbjeQaELZSzGw53Pa1fahIb5x64YXtnaC6J2oQYy6WViAmBSE4F06lnfEUcBPcWbyOvmYm/2IbBniCpv5hzzcxeVfwjF4+6yu9kJp1/hWG8q7jVOb8h8lEOSvKg2ncG2u4xro+jUs8Zj5hszN2V164aChnbKgQe3tGIgT3kUs54kEp6z4d20chG182qR3fBt6D9kwlAgwvCevJUY85ojb0p3qXZTMti24I7j2qfmHufAd5TfezktiZnAfpmOf5UbuQrXApqGfduJTRDq/EQQiKAQrvvB8Kx3oQ6N5Rx6dORYPAzAquGNLgMaBHO3sMexdP3rsyzeKdLOIYJB2+vhdhB8IDhEM0s0kBXw+8oi5yjwW9okAkAJEBJFYl/hxMen2Z4SpUe3JHWICQzGw7bOFGD9T2k2xqwze8eP3+mh94Rc9jzK7ffwSaQI7nmNV9tfCftInix9q7n3kXUHeHfTokW7MPNTuIXYK8bg2gaG8QJrdQQkOxY90i4NBDvjEDuzB9Nz2Bxh4zYFj9eM+SFPoE3MvOPhJWTmNgstQdi4YJeOixpLVMAqnGTI7EZWrG3xh2njkO1NovrOLe2G2HvQZ2LTSqtY32UFsLBP99YC1sqM10IRLIw2u3IXbikvTN442KhrDWWrbdqw9vXHkKveWUz/XC2mcVkAu55Bai1gpxX3pvC67VW9ccSubm3O7K2ZX3apQ9XI+XvVFCXvXXaoV/Vaz7sIjzXf12W0uQ88uqXxErXTsv6kNnFP8tWG948+Oo4zLQ4nnI3R6fMx4bTE5PHxLIEh8vCPohcber5F0A/+W0ekL15qdHavS4ZUVBSxvulPWXi9u4UX6gm+hH8G7Lm5u4ltpK7zVpUnKZ8NNp9XG/XnQbPEtYqtf9/BWAuI3zlprLI5COWlHYTMdDm1nc8iFm/tD4gt+5QEm0ecZrlvikuCcYTTyqWoz8K84DEc/C5ucM60vGmDpDwzLh+QjECTdn/T7qIUYXwN7r99b7QhyIEgBoN/eKBCkgJA/K68g0Bs9IvbmDV8P+6BAEAEhFnksLRWa+EmBoBEAioHskaa+LMUcvnCQZx8IGmENcbEUCn/kY2vOJZGuJvSTdByXcy9r8JVvHt3DL1powut/9TEyqNMRGbs2pYiOOszCIStuAeon+srWjBc5C0bei0Q1j/cJ8Bn4R9py/lhfA2Pnz6/6Uti/fnmhQJBeFj12Az3VC6Ofa9B7rCDQo8cKBmln0EMtXHmKzi+3lF4/FqbaeEwMzzkgpGBTveFGYJvtUQakgB3JvpBzyHlpJk0i8MOYjIa036z7Aw49JC8qEIBWOmWqPqbyDXzqq6ByV9x8MBlXRS0cc+jFlffHzNsF11qPl27jQfYmviOt5Wv5bfqgcw72lLF9w8gfLRDENdnxqzsD8M8xTT4GmCW11D7O1cwIt+/TlS9wjnAC9bhJbVzRtlR6AmYkqhUnDEJV46k+GxxLIi34OPZWLT4obYVLb12DVl8s5Amm2tSLSqA17WSv1LXyGxaf55naKGRAtKWO9FPqNVI9UJ7IBkuVHr/rPRto5K7fWhWyngU01znAAsEDrwWjC6WSzuPa7Y3QUWoRcUjFaAePlrObGJ3KHfzROkI3iNglTNDnvQI8Hz6orGAP9PeCuYHkx8PI9Xh5gkTZJQQe/XxNLPVsO4EwABvAGyBzQblgJwMp+fpBZlDjM81x+mOCgFACQXk/UB7vSjCIoE6CQQSNFBhSoOfxo8d6DOyh5uwEgR4RCNL8/Ujz9yNt7X3EjiHk/COYlMfFWqfzzUAZPPwqU0tL3tVZmuMe0UG94j7qu0GHtJkmPCZhnxc4alAn3Z040MqobzpO7D4dNrFJW9mmtJDWeyAibjyMEwjs/gBUxYVh2yWMmrbvKPvC/xE++8N+oF2jCZQnPICW11buOmgVWpmAQ+O4OT0K92rFcerbSE7pqLO29WCXg0CpeI5s1bLWWdyqp3D55tqxhEM+qttcJnplD6zTprXeysNL8+3WoyA0/Sp3hE/YdyjkkElapYtLvvJNriG4yYt3ME6evRqXJm0C15nWendcIpRG/jlptbdw+9Kn2UCSlY5+aC0zDphuBOM7rzAJg0du/e2SPHyP9LfNCwXlv0b6mwJBdUWa1MGx+cBenLSmi8gkVifC6070lRq9ez3R1uNKW+HSm99EKw95+ZqHhh1a1ly76sL0J4/RfbtwfdxTHQlwOakiT7l/KJcvPBsdqDS3aqchfO6BwRdMZIBLI/gRC7JArq49P9yVCCR7dcH4nTe+itCSVD78bF+TT3KordNonyOyQSRU+RPyClyg+4MWYXwa/oMCQQR5+JT8b3ph9MtXCgS9enXv5W9v7718qyDQyzd6X5AeD2NRB68WdGz19o4g3g9EoEf5Tw4KYQNl2k4AgiUN+VoOT5ZA4dn8gyx0rGcHi7KZKOjH/x3BQ0dEJu9aRC7ngK8htT3bD8lR1z9FgVMH6hNoc3UDO7RICC7Oowbs0GFJdYZb7J1ACeSwWOQF0U81oD1+ot0/Cvj8+izvCCIg9EKBoSfPHuTxMAJGj3RXkh1B3L3kCyfShZf8lZLR2djnstoFHH+4MTIyL5Gmv+cCFHNlmdsn/hkIkvDwtDmOB9pkBgHoc7lMCJLIIaxl8GcJPfqHzVaIUJVuJ/KGs47JMDS2THGFB/kHzI5dcHQBXmr3HGlr+XP56v1ruquv+VqXZXTo1efzS4zkP1ogaPVLdhUKw0Xii3ilDli0jFWMN1zD5Od9vNHDt9eGFKl6ckWd92exqYlSpa3Ch9WKFd44Al3SbtYn2y980doZT0KfuSpBn4ZO563NPuuJZkqsactSol2bBH3gP5pLnPlW/85rVSZydrOECtxU7Yzr6edSZC/1mbe5rEHAfR4+2pkxvzakna2h9ymYn5jz/8O79PTPj0dbRAeeM0bAysnRHX1ZL2h+FIY5kvUCi3a/2Fmwcwd6+ProR68l+JjEh/daI2idQDCIr5K+1w0mvjr6UXjeD8SaI7uG2AnEzqBDAEh1pf2jPR0cMA2/OI+VhoPmOOSSD2m3jZ25tJHmzke5NCfnPT/a4cN7+AgAMd/qJX7csHn05Il2/uimjIJBTx4/0bytXULGaw4nUKQAEe8RYm5/xPv/hFMsyK6kOurBk3UtZtv4YTQ8MND/XJUUSZXZlaGjSO0fmWUiIRn341CsdZeDRy5Wy9ArAZ9W1BN1VbHLbXOdiwz8cGzqKE0dO10Lz+gt206f9tq08NmhiprveDbFJTff2BajhZz1XTIOWiRD3stW59Sx6itxzXd1bLauLHiy+pLvqfF0cJPPPX3FNlhnvcexvTZEduoTv/vC1ZQndVbdVgrU2i+5j5xfvkzd1Nu8NRzLxX+JfG1v6zni1npin/rWfbVSNtvBth17jn3bWo8bfGREh5T0Gj4hzzrgq9Pgp5jDXmphu1blXmCUjna6rEP/zmz91A3NdB13ZRXwGn+L/SRB/TXlc9P8MIrKy6JfaBz+Gum/IhB03rAx4a9Ee3RFnMPu6HPSN8LScV2cfKMqT6vJKRdS4E5IG7uXN1vRp+ml3MJwAtbjzWEBXssNBK24S3j9+7cVZTEkm3zl1LYsLqjC0Mhzigy9iEhJf7WnZT8axkJOC8QP2tXDDh8Wb+/0e/NOQSC+Hqb3BP32Ro+GKSiUT8vrEbFXb/R4mL4ypoARu4jY4p2dJvfv/S4DYhkXLG1WqYEggkRe7uwDQSwqGyjqcqi2MtpFnxu4wOhNO0dm94jBCVd5OqSxIxX0OaACft3ukDIwjbNDObpR1ZdopgR+WiaqlLADyHJWKJiyeBoEUs5CkMe6HuorYdw9fKI7ik8f8x4gdgTls/HPFQT6VZ+Qf6FHw3hcjK+FPdXjYHwxTOvPBIIkjz0P8Cp2jLZ6J1A9yAk0CLSflFYN5qCGm+rD0O6yI+js/BzVuK4dnMqDPzkyUWBd7VyimGrDMBSWYTrgVliRhZuH80c9Lp775i6gB/5s/bqMdoGgGv9DB4Lu4k1d71/mzK8eRoy7aNx6GuguEu3T2/Kb9HGeXKbWHmJLO74zZHWd0XbCKtzIu/puL8hQhiheZY4hJQtM1fib2fGY4Jvzv7XIzx788XeM9lzVyWDpBVY2rYcADh+dYK73SzmpekQg0OIAArLSE6tARr/Hac1reuDZ05sf1ZLyPM7F+34U9NHNoPcEfj5+8PsD+SIY64m3+sDEe60TPmmH0DveLyj4g2i8H4j8Ez8FhdhBhB7qTntj+3BX8NiXSUNcSjFPuBTtKxWQZ1pmNKHNBgeTpuH4T9QEe3TTSgNMgkJ5zIvdPv5KGMEevQ/oiW7YEOx5pPyJgkJP9XgYgaDHumnziBs6BIkcCCIopJs2+rpnvlCmuV8+RLddrTprMtYVxs3A/LJigjpwZVK5gaLSyElLj822lWLxrUMHv7KJC+d6RKbkpXqzgDfuSFgVrPDgi75qXRn+DLxWPi06KPqcuo76Dqr+UnHVvSo62nfkO9JX2UsY6XriUrK6LyloKnWFWsMYAlqc58VebpLvANTKO7AeWFbrN5u3dh/Yv0hxrXNV2Pov6cWUY5X6fLjj3+dLpjexppas8ET+GcUnMqudhTPuhnmbAzu2ME4HDj/lBIA4yVTKOJ6ClCgQpCD3d/ZoGF2yJSbyLhAWbOdhXOF+M9+Ai1tzZOn0c31Q9+mufHupa6VaAj0w+v/eVJvI+e2PLS1/fkwOMw+ZtgccLdo02dsTE2q5yEn1wU2BoPKIO8ojOo5zIdgrZ+C9EJQofq4l86QZPCwASclSD7AXS8r9Ukbp/aDt2HwJzIs03cF7w+Nh2hX0SkGf316/cfDn1au39169Ffxan5L3ok4LPb1AGhkvErVU6VfCsqTh0TB28ozAjwNB2NBAkHYQ6eKPPeShZbEnGH63TRzsMpJkyuAB1e55jpkqHeLNyDJz60eWJBplAbmzqWWifaRDKVCjm2qil2oCkUcDw5a/UmYF1Kuhz5WpJepI+LgzyPsH2EL+QL8n2t3jIJACPk+f6stgTxX8URCIr4T9+vypHxV78vSB8H0sjK3q+sy8gkA/6/fAerEvP2ytN717YGcn1LQK24dpIOkVl+M/qKOlgAM2SHEkl9P0opKvOGAYV9woVp61vGHzwTiYyyDMvBCMA9FURHPwhZuX98fMr3TBV3fG6v1D999at/l16OJhHep02s8/pG5V9H+YYX1HUK+J1afXmgZPxtjbvc5Vvo5zuXYqlzEAfcU0v6nuTd/R2k36WO91zms1XcP/eU1Hm441hF4P0ZZjXZEoljwt3jwCh/EieB4fHJmXM+NFTuN71C1H9RXzleYVT2ljNe0xm7lvcOZmxSzNHUu5hiSvi4rriqAIj4H7K6SS9tSZg4xEXj/P0VWcOaK7lwgg/S4Sj4P5a6EK/rAbOB+NYP1A0GfsANKOoHfv3vum0jvhP3FzSWuKdwSBFAzyTiAHhdDx0Y+D8Yg5frHdONLOzLoJ61wcpsWrwq5I+8rhHyvxaxikkJs9JPtJImyaIRGsYRcPX/ryjiB2BrEbyLkCOnoxNF8Ge6rgD8GeRwoAPX36VMGgx6Y9fQJNwSDRHupuDfxP2MXLO4e0exedBIH8oQi+8lmfq27a458OuL4wjcc8l5UDADd1rt+aTbDJzR06Fm715bYjSBogafI9rjGpH5+TFukgrh0XRqu9A19ZNtuvYzjHOfcv01KxiKvt8MaWym2853wn2iWy1kuf7a/myBztS72X+q57tDYeZa5rOnIey7apF4TVb+0Pb+o82s75lpForxGf/ZM2D6w9VtesODjv2nt35dtq/++G1kvVPtGBdX7xbi84NcN0HaHNsgqUNTvJh9kZxHVI2Uw6/LMjaDkHuIidjiPbwrOCx4t+pa3wNb7jCXuNb9WVnlN31tY98RuWOM2aAq/HUMB4mTQYK9O88mf5hXcG0+gjlwozoQBncRXGjUb5OEGDsxXjIplXlZB1rTWoQJkLaEuBkfcFBQCTFeaC4yJjkecXPGq7+Hvu0jkYxAujP/gF0W/fsjPojd8R9IodQvp62Gu9OPqN8Lxc+q0Wftz54+4gi7jfdfWPB6OkPYEgwg750XZuyWGlljT/YW8L9sUn3Rnk88a4BJHS1rzgkTZ4ySwdPNIUXWm/dakr8wl5SMLwH4IAZ8sRvxwHG5umQ32ZulNNzme0oBScPOhFOmVXnIWXiPYCNmqV9lA/v09Adwr9XiAtJJ8rEPTs2VM9CqZA0Avyx3pRtO42siNIi8ln2gn05IkWpd4NxPsH9LI0LVgdYCIYZENjFwtBFohur22XidAxdU0xe2BGC62oytrq0d5VtvDQOSSK3dd1rHfjOoFg1s+rkRPlFxdFa26OymvwSXU/AOrofrxzxP0VN1zTt/bCn6kPGcYQjM2fbrHyRwkEeSxeegpXrD6NN/ZH6O6PDgh78tXSptfSQ8tlfdhAap7Sdtz0bDig8J9JZYzJiH8ps0rA2VF7z3mt1nKtWopb87Z5jzti11pKO9MMbeWtVmxf+TvfGD8IDG+WVf+Vd9XH3JX5DCCaM2TLOwIqO3MAz8HRyzllHRJmnu+OIKuyvASoOwoknD8LjUOPhKNDsoIJ1vgFz7z8WS9+5h2BPPblR720FmCHMO/6yaNglPU4mHcKaYeQ1wraETT4P6rsD1Xwkmjp/V3vFspOHtopg2JkYFBurQ2dML4wmw72NtMw/4bdBIP8uIEkCPyQkOFHmRdF89n47ODJe34I4LBDyo96OQD0OPO3JuGnunHzRDdwEvRhruZxMQWBtNOX3b4OGPnLYgSVCCYRaGpQaNQ7bVC9wxZbBiy76QGSz4g2rmW1zTLpebVjnAPQFxhp0jZ9UkaZ+K0TalI4l8KBXr41x3/HdCp2V76p7FTLpLZdC+JWEFvRiily7wBuEhtMU+AmXvRRwTW7UTKS9QFf44VW/pt4Vj5gkvglGukcL00617nKTF0Au/MpFI5oicyG+5Ggcy9uHlh9cxsvUvDfhW+r4eYe+Hx9m+Y/A03bB+BMB/KB8pjTcnCdl8QzTtQt568n/rZiBNStBwkgo6H0+35H0Op8BnMcQj5TB5orFyZ8lZsyC249MW7iO8oyuKXTQrm7LJ0bm1ad3x5eredkWssrzH2utXyE2xcrvl5tTuvKR178EcdJDtXT/C6300Qb18Xs01wg0uPzgow/+knSYiB5QPhMcg7IH1fzIkNMiYVfF3mK4TiQwzP7LNLYrk2Ah90/fE6eHUAvFQB6o91B3iWkF0a/1g/aWy3yuOOndaE/Eav1nPTSNs5jljT5JTgEDC259soY5oL3H37kGOaGsGcFQDkBEOUJ/ICFh0WU9JiX9seftJMFr/+UJKPtOSQXKilnA/z5N1QNKn5FdXL0D4IzlCYIlDxlWyDQd+G02mLBJtN1h1DvDhiLxCdaWD5XwIffCwJB5C/YGaTdQNoV9FSPhT3Re4EIAj1R4OiB/grWLnQ/VsaCNfYwPmAcXsYz1OWGTjvDJ56gY7zaCgMZCRV+Oahx1W2RtN1cy2HIgbHsQqIecE4L32RcBXYwzEPAK9RBLI9xQ+9WgxAwkCZjihM/ij9gdub+1Q312Io7g8/4bsJBW9NqB/gz2ckvZvgZByw3ypQe6PzkWvreE+3mcd01cX32guVaxx/kGb3iU2PFt7oIXeFetW0wI3A4ggt/pYI7UlZs4bXO4sjRlFRLW1usLzX5ZilysSK4TQ/yG65aNslN48a1+WDjA1pTali9F0xr3ihgqqc5mlaNhSsNvR7Y5qZgrEOMzF1TH/2t9PMYpJMxp80rY4z948aIeP2uH9WCHhJnSOpXPZ6LhZOiBEXApWzLJcOUm8R8ii2Rh++Tzsc/tOuHKdUflVDgp4EdHiN/28AP7wQiEMRLoMVDYAg+AkEO/LC2oOzdQDwOJh7hPmrhQDDIXwhjzR/DZY5sEMzslhS7hNC0xwGjY7jtR04/POt/gnc7gsQ63/PnZkqfcN79w/rCQSECNtrNo8GGefu+Djwm9liPcfez8QR9HutxsEfaAfRYASG+IGacgj/c7HmiXULeIeRHwfVomeRdh+bx6qVexrPsFEpTVLQ9rB2ASW65ygQCnewcPkIR280jGz020m9yBLue8EMOKgviSP9TMEbq7L6ykZNgtqxLOYAjgS+9uIGCbJoymzj4qOOoDj7zDJFVLWpOJHbVbvoWIyJYE3ZabMrKOhQU5frNtNaca2m4bOgb15eQmw34Mb4dKsQrD7sQPvserHBttxVKi6/XU31wbLW0DrCtp3rBJW384Sp+zVee4NG3QQe6jN7o5fvSOXV+/Vq+htXXdLY1B2/Ovlvl4L0L3yazcV+eA9v5sfH/Ndyq5xo8LRJwhFt2O6ELyHXAuBb+XlH8PcpyPzOdsH8wD/G3eWTyaBgfJPrySeNuzPryqs815kV7cQ9396/Xrm7uGdVRhAuz8In63YAhvoumDdmV76jG+oXMxDF77cgWOwbfpb4xyN35AscfbexFVX8REV9HSU+5PS40TjhsgFY6eXHNhbpIq+03wdA4uamhfM2HB0Rk8ZauSv3pRwipuBLpq/ja8KCv5oFiEcdFRmpfGS+FvtsnvNZic2HG8/1s4X77TgEfBXvY/cPLo/ly2BvtBnqlINArvTj6rXBveGG0Fni8I4BdQd42riuZtYuDP/+5r5wlTdrO8iaBoOCAHQjyCLH6Bh+woyg5i122cLusox8V80pJdMo6uoVqrFo1gkFCp9nK8i+cE22GnOtQSGgjsDK0KhtgqPZjvOfFuRrKQg1uB4Cg67r2Qk8HxYAUxNGCcCwYn2h7OYGf5woC/fpcj4TxXiDtBnqu31P9WEjy+Bi/R/5kPNvXiYZrIanFquvBs8Oo7ASK1QSDsIy6wQDbJopKF2ON+DgP/U/64CeljsDHI7SjHlfnyiAOCTMu0iu9NHLDDP2kIQyONPlWpSGFWLjMa77Sfix49VbhemC6VIgjrTxrXv4VV7jdhJ7Cpa26SyvfSis/xmjed3IcV1DPsx9yR5Cvb13P8kvG9lOvzXEKx2V0CF+5m0PL6BC+MTrY3xzgO+JCDCXw5x1TH50aK3oebFqKaR2RaKl8GaPQEmjTW47km7ZqSL0bVzg2vlLQS/tjaaWDqfXlvcwrB2WF95yyWp3Z9R/dm3k9vWa5IKUk4Y9YH7l8ISuWcf+DmjpHwY88OKugNPRz/jgYRN3+MV8JBwuHTBajh2grcy4ywFoTKADFJ909t/P4OGsDvfeHj0UQ5Hn7hvcC9Z0/PBaWT8LzDqB8JSzvAvqdAJJkCAZ91KNgH9lZJByPllk/tnHhj3rdALdKNjrnqBAIdmMgwMBTJLHmwGYSvp3+VhvAs5ywz+Q/pLsLiJ074PVuaAVSeFl0+MD562DjsS+/IFrz9xMFgggMOQik3b2PFCh6zGNhvP9P7/YDn8ARO4A190ux3zsk/X5UXPWzlEG/c2yjXfxkN7Y5GVY77BfagHPG2kLMcGb3Ev2fvpcG6ZEG8RlW2Y4hHxgDIsNmxxhxOIQ99DO+Qa8O1yacq11VrXxUF7Mu+YaMTQcWY9eqg7TLQgtqNnfHcUn7S3z2wR1sotqd7cMBi23HNu6dEX762m280FdFm170OY16OV2KAo/LSWl/SvXtxldoaaMEio2G/87jbN83NK91tkr8VNzqs+Ku8Z3Twa5aKk2+p631rlzA1VCJY13l/xJ8vq6l0HWgsGOxYGgEeKCZ7pzxecVRDsIj3aAxdiHD76Emvhd6WuJrpL8hEKSG0SqlXJhAAwE4ki9BObEXrNHf4MLc1VdjDjk8pCxkDsTZlk5M0MN/5Pw25frW0+hS5YYPkj9GiwNTuHm4zo+0Dz7yta0r7NWb6XBquNWxKTAXC/LOxknSc8X6TYgMfeBuEC7dcT5oI8LE0EBQpH2dqp4sDqmDXUEftSj7xJZv5b7bp2f6ea7/jRZ8b7Qr6BUBoQaD/GhYcNkOLhkvGNFBQEh1ENHVYpPFZdpLO1PWMmjAapmCRdATAa5fRPeKl3bye6CFk1ZoaWx2CRkuv/TQUNqk3AtBN1Y40ByGLCVLcdgcPJCDjTphxLKtChUZ4VLPf/7gFuZWhpdEy1jgEbghgPNAi8Gn+gQtj3zxToFnehTs12f6VPwvTx0AIhDETiA+JQ/PE71f4JFkHmpnELuB+Bwt2yK1RrX3qAfrsAtvUh+JvLbGEpVH+2Kx2TwoS9C7gTCZ5vlPj9HOkYX57Fjlg2Z+cAAH2pn4HodAhZSfVZ4LQ3xH4lq+Bu9r+95L9eTXbme9vdZXHHUXX1zLN9kFD5dwL8nK/GiBIM/B+LCOEFw/rv7zuAgCvjlebRz1X6U7A6KL30YPnBEvI/Wm5Rp0tKjajpqvyYO/5EULv41Sq6ofOdKx/soc+cK9Ha/p2zSm/o1v03hZ56b3Nkje18ntGy/u19XP0Vvt7ALzmC5EFsdjTpNn0ueySHPj7H/5AphEXns751NdrqtRPxyal807Kq01zCaW1yHvAmI+V7BGcx0BGx4H97t+tCZ4ra+HcjPIXwLjsXIFdhzoIejDDiAeASP4ozLBHz92zo4h1hmi8ygY6428G4gFA3ZSO0Ztv+x4ahubw6G2io2Alu2nWcO3+HrztxTDx883UwTIwSwvuLnkR8S40aJfXxyNb9ixQ5DokYI8fD6eeZggD49/ZadQgkJ+Z5Bwj/Ucd+ZwAkHZBczuoMe6EUQQiZ1BvO+PeZyAkNcIqrN9TZ6WuAdyIfCHEG3Sj5ybTlKhtVCCYrYfu2kg8rJ5QClztE8ABt9QHwzHk4SShe+EA3X26VR/xjR4IK18l6pzbm985UhrNtUbn0/jjXAB0d9rnWiq1jKf4Uq7nh+1hNPX045EYW8/NjmZb2UuYZBVBJPzObizI3U6rQ091Gl61YtvrRVaSOOobNY5zyVr+K86tA1YDRzrv62Jd7GhPHe3rBJ/rUWVrrZr9X8pPtfDQQoLc0oaBi1Ao7BTcR7PJi7MGePg5TwFt52v7Ah6rmD710h/SyCIhuyu2+GuXtQ4wwO6LsR5UUqmdPLii2t5ddLKd9OIveNbFRRmsBk2ndUDGywk2hW4p1jwf9+xp51sq58nZIuHaT1Ni0OONqz4wWoKU/amO7zQ2+7mlfF0bzqLlmqHP3D47V8hrNkHiXjRptpGOf5Fi2T8P7I5jvooiN8LQAHka2IB5UWpkOR+PEwLM+7U+T0AWsS99csfRyCInUHsBOIRMQWFeDTs1XvtCNJCkEfE/LlYLepY9H2SHhaRKspmHvfqLiCM6nJnxWGDVkf4gkWdHylT69yg4H/++YGDP5YXnnN2O/cl6vZlwYyj8l4FKxBxpFFMhhLh+YnfdZlgrwY/yN65B4xeKnLwR0PVGgha1Gld5iAQj3Kx+OPuIJ+Lf8qXwBTweaGdQL++eK6dQewGeqLFI1vK9RUxvSD6KQEgvReIr4V5N5GCQtkNxOJPRqh+nzGC2SJunOzi7mLiJWnLrrsxGVmQyuFb6WkxBP+nkhuT6xGvqh19JPbzy2TTI36nteIdAgIKlfFb+SwLokoE7uBjeeWD9mOlo+taPrr1c7xS2eZH2Wseb90rf3lLs04VOIXAGV+iyvnDZ9XwfcJpP2NXGr+4YLvOQGZg9HhUX3Ys3K4LMw5HedSalxQyXPO5ZMc8Ily4MkIPQXNuF2Nrq2XuucF6Dd40BVr5jjTK0T1GuTMG275xpsW1fRUYI7nbtcIrD/BKW+FLvmDi2fjivN62otriW+YknvBK/w6/M6dY1cbruS2GzXMhu09VM+O8aeSB88UvITkvRteQGdQBGzmlfH713DJetHEuoRK7CAR5zSA6cziPbyVow40eXgCtR8Y197MriB3CrAOyy0d0Hg8TT2USCMqOIN9gIiDEjSbxEVTihtEn1fmHcNy8oV4nHGK7aCkBj6ydco6rzYNWD0SMtgfyuoa2SH/WAcJDkhoHglBLFSr7RdEEh6TTgSDwgk0T/DOBIN4j5JdI66tiCuiwu9eBIB4FGzuE+HpYHg1TMIgXSGvOf8RNHeHZLUSZ4BBfDmXnETeJHAxSHbPemChbMxJgs8gu++tntMmBIM37mvjXYJB9IRz8OSJI2s4z4/FdLvyQdXQdo4Sb1vJkAoAAQ9MJo1kG3pkOHc8ithKFoc/cnwMetXMmusLBbl8M2CJRth0XvtmA1dZywle8YBdbHjyuWQTqIWHehFXe2OXbQdxw8b51WDiy1UF9W4pUdESv6a5v0zj5Vz+t8GQIgD5Ly2jrFrr2b6zimDoWcGUUfW/vJv0PtHlg7amb/LXyVRr+4le49M/Nb9JR26hvhdc6il9xwLXxiF/L5tFhzYGrk/GdUw4cp1nmm2jI9S6cmMCzO9zjBnyj9u8uENSB0RfseuEtXs2gvSAE9qLeY79s6Sab1ppu5qOr1YIONKvgN4expamn1B4XamfHMxocK776mvdUbw5+hSknAALE1LzXJl8NBFbQ91wQTntGEcfEDp/9m/Kxtghrzpc8KryQXJjGUiNrAjHwYNwfWjh91M+fk+eunp7n50sg7/RJWL4S9oYXRSvw81qPhb15w4ujeXTsnbeI+70BWuC99x2/8VURKWUxmZ0+DfywGsMX+WXHkGwkAMS2dNsbms3la2FMSNoRhFzaLEqIaSYybP+2LIcMJr60RpvDPs5JxHtuAkeLj6CnX4d/bZQMy8BEAEje04KMRRl55QnKIMLCzruBuAOo57pYCLITyLt++FLYCwWC9IUwB4J4QbTfC8QCMlvJHxL80YLRi0a55BGLPr464oWe2idb8NCsVzDlYto0MD7jETlLME6a/K9zi+DSndKUC/eUOuCt7ojbVQBxMEwlMCxCxlNeGVYY/pabg/sx0+K5r+oAPN26jl4vHgNWvmsG+doVo69ZCSNfHT/ajiD7TNdm1wnXfAaeADBOm+PZFWZmCcaCtZ/q36NIxpLh/zMmKVn1HOXvXkbLWsEGx95o8ny44zuv4e/iO7fmEkt/5vEneuKyL7Dffp3+TZ/RdH/5iotD/R2ecBv2vGBIlSZ3gEdzr2YpG9J1wHZ9eWaYgSC4bJ/nYdYC2iHMTSEFdj7oYxCsH9j580YBIII/zP8vX7++9/rlGz8a5q+Hid+Peymw43cAERjixpBeBk3QxzuBdIPJASDWGQ4AsUYYj3LLiHhFbXA7acuY8znPBfvcBDZ9zIJpottJc9MOtc9rArcsXkBMIlaFOv3YlUOwB28CGxf1k/5Qc/gDEQm+8G4gvhD2wO/8431ABHvYJcSjYgr66DGxpyrzLsDHnvP1tbEGg7QeeEQwSDt9efzsPkEhVZlH01yd14EJ0bohaZM7j/c0cf784RtEBP/8jiE1pu9OlHlqGw2Lrnl0pwc7zp79ZWeJ/cFqVr+WHPUt2Zc+LYvpXSKVI54jdt02nsFp+4e9++prublmva32S+b7ei8144Lw7K/hGHXJf8Tg2zSxNa1OLS6+uM1n6N58Wz2bjrXujW/FbjBS1WCsDN2VN9YfFKpfN68UU4dslGCO9PJ9ixxb7lL/GV/bscqf8bUd5hvMvrHBn0aH+jsMVSfntnkqoBI8Hr9RXLoryefjv6sdQXe6uLkE+d8B9JaLcvLZadcPX4Jv1bHCW60aIBnt/iuGkZ52WJcTL6dorS3dp6R5Stnn5Vux4GhnU+HmxZOPVYZRvRtLIbzMn6lBZS4AFTachXIQGYn4l4loyC8sK8gagsXgDCyF3QEg6uuPwBCBIN2o82KVRSBBoA8K7PBsP9vB2Qb+Wp+LJxD02kEhtofrXUHaJs4Lo3lX0Lt32g6uVeSnjwkq/cGXQLy4ZCHAKgw/8GMJxEJXASIZYTu8K4iyWiU+FrNa5qStCgS5reO8cvslSPs4+B+KlBpVlqDLHMyPPn6YoX+mk5nOAU7QANGFhcC+s2hDCQT97rINZ8VsvWqRcu7yEQjyOwK4E6gVH4vFZzz+xa4fPf71i3YEEQT6hWCQAkF+r4DeCfRMO4ZYKPJOIH6PdNeQXUX3FQTyIs92uzqdG7UvZrsNth3/jQUK7RAcThGd2kgVStiaa5cMxpuzyg6uUZXPWaNGNVMJ/MWt8DRiUTj5hCs8FRU4ElpuXr4fK1+8eLXheOiv8h11HL1e/SvfCq/GldeXF7YVMez80QJBjD9dI6zw6jNgj2XDWRmzjhwtM4ptTi209llw6SFGZ1L5JjAEPHZOZHjPj9d6/MhdvlmjtGfkzQBQfPmO8pRX2gofeW+irbxfmo/zWr3gP+rTGxyppSl+1dF/TIONDb4ukOWmAwOt8O1vy2tyiOyQGeeP65tw+tOni9xpCyS83cbwNKqgDPM/v9zMyccjtItHAZz3eryLdwa+fsX8z9dEX9979eq1vx7Gu4T88mctIH5nVxCPhQkmZ4cwL53+9JHAUt435MfBJOO6ZBRBDm4Dp6fbPs7EAasdvDPQ7QSmqaVZKLJd57jtqjPl+JtW+2XKdqEO8jPBHV9H/ry7UK5SfOhUPQRsHARiLlbhIT8Fgvja2EN2BGnXz2N+mut5ZOwJXxVzIEiPjWmH77PxQmloPCL2iHf/aY7XBmfp0upm7AxKUMcmqVWsxmSAO0sZTlKZ9Zl3BMnG2J3P3js8Jlu9RlDe5GbiqaFH2v0POrSJrsCSb1oWJOBCoCp0uEorhMGOAxgpDBnP5FcxX6s3NOTP0lZx6z3jWnH8fTkvpZVwgK/pW1tiq0Yb29SerXt1nE/XfbvSAq+1oCntX32xwvu68D3+jMzWOS1v3Hu+DX+EdnzSTVv/SfVA/RqvrCXga7468lXbtRw9lbnGcxt+1bHCR7mVdoThrR0r7ajDfAujT0eVQRW98hTnMXoo6/iA7IbP33fhJxB0X4+G8Xfjl09/y6Nh24U7GjRGh/Ui9CWI9ycLA0wu+ualka+4FV55/izfUcdabl3Nj7S1/PfBnEo4s/fHeipi0RE+luGJbCCG/5UHbBN8S6cV7TyLGkCWf2igj6uJCQvYZeZ9UhFDJadJU+AM1IbhhW4F4bK4DpxvXmiJju1IjaXGYE+Z9YYf6ZKMHw/juX8/489XxPSIGI+BveNxsBEM4r1B3B3UI2LvFCDibiEBIe4KfmDxp4Wfbvp5cdnHvQj8OBBEoMcGJ/CTHUFaHRlHQ/Rzw8SvfFsEDj+OyY/FEXdLfU25rXEebUcH5yW/qANGnYJLrJqEZEHFakGUxKcsh7TK0uuVhBwT/SwsEwTK3UbRxYNOv1hSUSAWjXkcTAs/3z3U17+0MMxuIB4D0+Ng+joYASBeFv1MASFeDP1Yi8OnWlA+FHxf9jzSriDvKsI8lblbaVPlNepqezF3eMR2YLdEnOY1iYHyV/w9iGuGwGgy6MqvLHeC4/RNvnqvCZfuHOGjglG+kD+zENwZ/kL4u0fgNbv0Sktvoq0in8O3ygGf9Vz1NV9lvABY5CiP0/bHeTRMje4MVd8cr+HiyfGjk4Dslbi559Mnmb8YJQJFRfsLndHLMfo83wmkP5JKo7TCgzyzlbbCk2EApdUK0OvNkrDV4ubX6i69+ajkIiu9+QXDQJTe/M/w0cIEO/CmPWr3ZgUAtUm1sBhwGj2kIuM9/7CBlHnNICWXj+O7b5KIyrVE4twqbBR6pS4zpuriRpBw86MPCtrw8mfWADzSxWNhfDjilQJBvC/w1W+v7/2mx8MIAPH593wtLDDrB3YTeReQYNYVBIH82Ljq4VP0eRxMNjGNYouNSlvct24qu3UUABrztedufABNJ2T8IduRtfzQ57YCj7kb3yGjyRI2w8hL71AlJDyigpDC3jDyY1wEbcTP3O6XP+tODZ+M58cOoHwVVMEffeyBGz7sAgL/XPM9n5v3jiEFi/gS6CPtCHqg+Z13DjGvewex7OAmUtpAeE71j0b1xlby3yWDHPYQCBrXPm2R3fGPHSFYCJoTx9Jq/xNg3vrMbBGB5IQLJgBtIgYe1IoDRuGZsikSetjos0Olkw81oTcPyZUMrlTe6povKnbgpK8qqkm4mjL5RLto9uBzzdZzZn/6ofp2RhwKrcuqJu1SZ33QfLIegNKbH8izWHrzSTgA0P9Jf90D7dG1n1f4WMNNtCPvXcrV17wylI9pPedr95GneppDN+8QmLAYmIN6nveaGGy+/iccDRqvNWZLzvOdBKBH7gfZEbQ6Fcd6egI50v/Fi/K/x+aebjgzJ9c4dYd3V3qWRSGAP/bMyjvEnbWzmq80YGZ5aEzxa+3h79rPtcMwFkeCLMVhHZcDZ9Ixz4lZroeLSYCDJeiSHuOHHchqyWEcizOuQq3TspVbW7o/KhjEAo8Az2t9RYzF4FstAv2OIH1e/vVrBYBUdoCIL4npZZI8HvaBHUHcAWTRp2AQ7bd6+wFf6KeFjD8Xbxo2BJ+ctuGbsSNo0Npu62LQEPAHwRlgWpHGZTeRfcZBWsYjZvajF1FGug4WfEnwogMlSgSYrJN61AgvKgkEqfzHJ1HUSapX6rQg0x1C3iXAnUItEv1eAN0FfMR7gfQegadaDLIofM5LoRUI+kVfDXuul0bztbAnCgJxp5BgEI+F8W4g7kTybqAEgFig0nf9pouNc1uFpkn6ecSAyWU49gmupNG6Fvc59ewxdy9ZMWcT6XO01CLlF2KlrWYcmdbyCq8yPwZ85q1v0fKj1z/XDl/PMhS5Xn61W3+D+Rpr+XvN/eiHPXC3Fm7D1rVrfq/Hvl1Q9Nmxn8Cd9WX5zuiLyr8IthbU7ANBZ7aeVfal+VrHXfWWvzlyJLeMecMnN3mRJvuQuW0r76+DyHT+g2vjp5b+FnmBmp5mH6NPWiZDduqmyI0YPg+fIFDmbT/upcfC+OQ7O4McCPJOIO0I0tdDX758rV1Bb0zze4RUGXM+QSF+/ty85/+hV7DXA8zVnjo1g2ITPwGd73MC0h7mfp3lnqATiEmjM9uFPx7G/tESK+Ra6h8TcbbQXWSJ0b6zy6RrXEjWMHlYiWUu6zt8HLDR5Mw8z+Nc/hooc7yCPnwx7KGCPH4XoOZ4HhN78kS7fv0hCD4zD043hfSOoae8/09z/QMeDdM8z2fqHWySAQ5EueOpWz/7J+uMtCe7wmITwSN8kTUSbeqa220RhXwNBOEjsJwF5gE4SaZxmIwLk4lD3gwLzaCEBs8peaiNVBmPOm4r3yZ3Znh1ro0+01P6RgMqNlr2pWreGr5h9lDlNt2bvsta9rLfuKQT6mjlN7bg/3x17e3/tobc1K9/xmYPWaORlteBfOoaZViK83g2ZMB6HmDcLgyvy5H6bt8RVB948J4NDtaX4NJb8PDDMbelL813W33X6G7XNeI3xe99xom2nY41ZOVhEUBaccFkqL7EhzqX5inujtCQTqduFozBdqg0noNMaFdbahO11vp20oa8ieMQXWktz5bnUSuHL8yRgIvqkRIveyTQBdUnFnVs7fbz/Xr3jx77ItjzTjuDeDk0u4NesTuIr4j5cTHBfmTsrXh5nwBfB4kOxUzGgk+Ley/Ysrhz/SrzefnsCMIsdgqx/a+LG1o4FoTi5V+byt0+/ZdsAkEsKlXSj2uFjLw/itKrMsEVeLIbKHwqjoS8fjjfHSD/EWjSiszBHyrEl/GYF1nU9UhfDMmXQfQ4mAJBfkmkgkC8K4A7gs/07oBn2gX0TIvE5yrzkmgHgvQeAb8TSAEg7hSyXZw/fFkk5pGYBJocENpabhNrMnl+Ovo/pTUdyxZfGTY4KrbyKYSP9olla9bQgnDIJctegBJmmW8w28wzwSPOjIu+lpsvpB8MrKfwROGbXHBXvpt0QDt6/i51rzp9uQnBOGSbpKA6fqRHwzTSuP3+A/Do1MVhx5nGfzBPjy2MBxDtmREOhENRV/NVPszaTHNvHaTPinfl22Q5Fz4n4RPOmZ4312Q1m2zn2a3ctHWz/SbdR58hVUnL2bhoyDpOPaFieRg2z5KnoRNC1wAhtbaUej0tVaql29wJl4MlzpnS8m5A79oB9u6d3/XBCD3urZ8/G89jYbwXcLwr8LffFAzS42F+/MvBn+z6YZcu57EfCQNmvpQhfCXso3QzT2feVl8JxsatG2gH6wN5kx9rAUVqAqeNCQ7VWfR6ztXOP9bJTRzrliVU4IOyITZ9HSF3QnC2ZhjEXJb5Fxo3fFgzcLMngSDN2Q/0YujuCNKOXl4YTTCIR8bY/ftMj4ox3/O+ID8ypvcAPiVoxE0fvysoO4P4mlgeD2svsTpyA2K77PcaTu3yjiB8Y3vkGweDCAfhttFAe0UYyVmPyo542Q8bD1UgAnrB7uB9wYrRNtD1FwhgtCw4lZwOpD1HZTYLaMfNf+uEN7bvtbXK5CtfKUf+1i/rT+vd6NV5vd7U15pOfbERvyjkvldHHlt3rOS8jUeu+OIS+w/mcz1w1h/HM+pzdf5V/uNZuuo7s3eln9nuYWUwWZ6DGBnrSQwNnc8Hyte3YSvMXNhrPnPTwFnDPzuC4khGniV9zsVc5y7iF+CX1Uf3ZiK/qOhvQfTUyym8DZXFY9QRbrl5Dd/K0Qa+fdO8vGueBUt5oyULM0sNtWS+qHTVOCf4oavI3b+o96C/1ExNC5miW0Tf84+FHro9gZkmbv1n2UHiIm3dBGW4EP2ySK3YWMjxGdi3PPqlF0f7nUA8CsbOIAWDskOIl0cTFFKwSAvHD3pX0PsP4x0BWgyy8NNaUXrVZtrm5QmTfXYFJRBUfxAIwr4RuMkSR7bXh1iM3VpkSrff38OI4wb6IDKLSNjQMzLjVGBFJyQkJ+fIJdlTGGmFWlBivD3E4lIeo2P4SQ6L2Z7tl0hyd5Dt3w+1OORFkTwSpm3iuUuoXHcHn6nMrqCnPB423ifwSH/pEgx69BBducvHopBPx7fvWcwZHjZiG4PraImxo8XGHRo3pQpsrS0m+fTJHn0oXZE+oGvPQfikOBoyfHrCIFSVt9NWrnhhxfyocL20th/vnOFXns+Bj/rOeuRz6/MlhZ0SrGzzHyUQ5MdluK7v0BmMhGvqfLDizmB8Wr+e0c9wuY6RYrSL9NHGlDmea490tV/nK0draX3F35TXpjMLWiN54b1Nl5qrZ5WBy/hbhDefLR4ZJ3f06kj5DqlVbdxYlHZEPOXOBpm/Uu+8njSwt7rqYY7npynUNIJA+eKXbrAI/qAfXw5lNzA3dt7pc/HM9y/1aBhfEGVH0Cu9LPqDdg1z48iPlSvYg06XHQTSzMnaQ3UwV4P3VO21QOqtPbF/3PRRL9mHDgIlEMSagfM8vj2uBfAEDYlHyBo4qT9CFGW6azBHZOChF59rkeVC5kV9ul0FHg/jBdMP9SMolN1A4/EwzfmUuRHELuAX2vX7lBdIc1OIANHT+7oplN1D7ADmhdHo4zEx3geYOZ71BuaMsQCHKXlXtxZRBKPwA4+q+UXX2Kd/9kgMNb/9JNEEgtpoSAvcplpioywcRlKerF6Pcj4FM3nttx3n0HrIJJbrupLND3xfqYjvavv1KvCx7BxtvM4n99xJ3935bqrrm9LUrm/bM9+0dd+0snntnNSKj2+in4jciLqLvtv69bPs8fW8meRgu4YwdFQPQ0NGtQ3HtVU6WC4148Y1B4xMeP6jG+M/33uhsfNrJP1NN2r9GtpPdGZy2pp/ZOmgQt60wsUd88qt+G+Ho9atTRlAGfhqzQSK+Mb5ZtsltGHWNuxP29oPb/gZIlfJNKh8x+aBX3/Qw1uNmbTHxQCZC0nEzq0sRrjA0qfkMCUBziIASpUj7wtLCJ93oCUIGQUsM3zRGoF+AkCV01dDVCC+xV2/AABAAElEQVQIxN09vyxSW8Q/6p0BbBV/w+4gdgbxBTG9SPqNtoq/4rPyelyMT8tyJ3EGg7SgZKs471X2YpGXQlKPlijEWAgO5RPzsovvBrpxWtawCNRWGJY4BIG81BEt/5DXT4tLhWfSTiEQpY2k5hJIghbCQEQHkSl0kebQ40BQAj82NrVJXl5jISReqmExxuLwMXcFFQBisUcQiB1A/lIYdwId8GEnkPDsDlL+BFi8vGiSr4M9eKD3Ain3Vm/pfYhe5T/xEkvVxyLvZ58MWEk77EDbkHNEXhmN8LkkjtnXEYmcdMb3G5J2gCMB357qrQPnAR07DjynxUXQ9S/lU/4VuRq8wivPjwMfPYdH7oI789BdZa95/XPq7TnZvPag40cJBLHoIRjka3s4wNdwL06POv5zWNS9d+dYN+SuZUhVsv3bHJkVXnW0j1m6dXwBbmJ03jQXu+HgjI4NV66zWuOFTT+613qrBdxqBzpX3MYX6yhHz9aSzY6tPqCWIrOVwVcvsitMeU2lzTrVl7M7qYFCB90jXEUdoJtT45QRkysJjkesmoDwGkfm2FWcqhwEYo5XgemOGz/+tLue5eZrXzwS/mYEgvhymHcB805A3g2o38s3CQQRPOKjEOhxAAjd0uebNOSqA7P4cTOIurELHuAt0YbxQmgHO8YNIfFiu2ZEsSYA5PO9PnAb0WXFVirVLgfXM8TYXXXigjM4XOVUo0LrfA8fc7Af49K6xAEcDU7sBvbn4QkCaT5nLcD7AfkwBF8IZc53cMjzPzeH+nJpHiNXq3gcXMGg+1rjcFNJnnF7aWnMwVGC1D6CQfaA/aOAlHwgMfHnDPMXxGx+2sCawL6SbMYINRKS/SmQThn+w52z5I6J320DFDH4qjEjnFsCRTLd+qppzxcu8Q39S42DNBS5VB2V+jo5fok9m/7/Fpy9ceLvzdJ4cC1/MVj1rr3xxfT+gIp6Jh+vBvx7F9xdXXZXfXfp16NdV20YjOV3rkPLAPydySW//r3p8czth8jYDE+kGEfyug/NG8BC82jYDxMIsjd08CW49FYG8VLvlp8NZmeSK98KH3lX2gpf8qXT4fnvSDm5sOUS2jCxlXJ/e4nQc6R/OFlbisy19nZKh77ybLAXbDrbcx1ItyZo4PyR7zl4Ec0EjTQWVEvz2JRjLiIeoRq2iom6XBr947KFgzdNB+4U5k4fz/uzOMz7grw7yI+I8YJofTFMQSFvGWeB+Eq7ghQYIkjE42T+ipgWiflkLC+SJLiUhaKDQAr8/O5AkOxivcNI4aDPyFnw8LJIJiVWPFoG+Y8Ow8jgp/hKRzvDHJZzS6aDRkk6AnH0biJj0JHBSIAwwCMQZI+piEnAyrGEBaG3imv15pdD6l1Aj9kBxG4g5ez+ecrLIkdQ6Dl4LQ69RZy7g7pzSCDIdwXv/5FFoZTLdAeCvAClLp0E1OuzCHhrCCb5xyF4/AUWQvKWEQPjdps+CsAjDZEWl3ypdMHuQWnPCVwLRB627BkPpepWfhf2nfQqsMI7ph+mUE+2wXjkiCvtz+SrviN81Nd6V74jT8ucNlz6HqYqKCLgjxgI8nU4/ECGD5tW2AQ57vp1u0p5hPyi58OmPb28s03EduWGD1/kGNXCUXr5aXFp4T16oRpWPvmBE2koq8SaI8X8Ud2tN0JwJgVi5NxwY3Yoy2fn1iV1Y4iUvKzYCtf1TZcJWOGdBATVMPTZauZVamkTYKE8dPgFzrrgHKhR7h1ButHjT8YruPOOXcDM4/4IBF8M0zzPzZ7xxdBXr175xdEEkLwLiJsyzOeqlOuY6ZTcsIxgPeEbPhglG+BlZrNBtk3+5hlo4br7t7l7TSd51pTimSc88rRKiTpVD8WsCUYv2wEL7LqGwBRW2ROopaMMlmgzJIux1sEa5n2CNo+0bXe+F5CbOtoRzO6fR4Kf6x1Bz3kcnPcA+j2BvDyaNQC7hvMYmTYR631B6EBffj9z80dm2BOd7GkDbZNTOX+9HhI/gR+7QoYJa/tm82Qr8E/u8MCjIYNCW5VGhk68ZBkdC4dpsFEPfLLFfIbVoRQOCWuOOsJCOw7MQ0F1R2GZLpVvfEc9X6Z8V/0r3wofrVhpK/xn+Va5m/T9Gb4po7699Pyk/gN8hgd6Jh9F8O812pH3LuVV3wofZW/r1zObruoT88pveOCK7/W+0pCi7L/5bGD/3grF84fGXOYP0ncbCLrpAvYlKM+vPMBfO631XaurdnTRcclHz6kF38Dey7rPMONMGhNTOIprXrmWyVe49LP8tn5hSic1TymTHZOlkq+U6AH0nZqa4PNAPLMafMu5EbGikyMEZK2esAkG7R8NU51mHpJDmRdskiS5aiEUw/GOIHb0sAWcnF1B77UryO8LYpGo3xstDl/q91ZfFPM7hITj8/PvdCeRu4ledOpFkh+1GuTdAdSFTQSI/vikF1WOMgvK+MnLILVRucxkN5DvfvG4GCxufBYkFPEfSxPOudEcsCOZQzJ4QomDfi5JbmDlEyEXBnrGvaP6Cc6gNzuAlDsIxEse89JIfzqW3T8K+jwh4KP8mV4ITVAIHJ+GZ6v44/HeoMda/PH+IN4VcF+LQXb/8Ml4Hglj8yP1OAAkm1wvtoiHdtomgRRJavHIsRFYP7dj4I0zy3aahZRyJMyA2MIeoUlZigtIl00Z12sLFo5R2YLZgV3sGjkU7BiuFY56j+Vrct83/nM8+Gc9UU+3rpar74hvufRjDt0/HTif1sS1wfX3vac8GpZryc31tR6/nDa/F538VfDcR5UeC69zJmHhOzj/Ku8l4Ti7oanajrRIM5KFoxZW6ybL6FauUs/yjIKZBURvxVIMiP7U0Zqis2NnNFYImVJW3KY2/BzRt/EE33LrCtZ8IkVCgNnO21ata66rwIoQ22uWh6xLKlE7GAJ3thjVLYL+spfmYOZ0PuhAAIjdvv761we9H8iBoMzn3gXM+4G42cMcz80f3fThBhCBIP335829u3cEeJjfwWOOHwlXnsfOsQlDtjxmMdfjd5U4oQlyEPbQnJ8mieITHT6JK1kPRJJyr0nV8NRJbmToZaqwuQbJ+iwVxCyDq458zYsgED1BzIp3+/gDD/dHYEcvjPan5JnvNff7AxEKBPkGkXYKPX/OxyJ0w4gXS3v+166gsSMIXTwiRoApN39SRzoUM9RaOhUf6Zc1CeuimNwjtq2J6RUW+3UlXIGtDj9T38KDJzbEsGP14cK7ghc6INKOXaJNIDiXj7Tgy55zABWXfKgoFr4znur50vld6rvJ9tWem/jqT9p5E99d9R35ps9U0V3PmVXHP/ClB3pOXlK+DqbnSLUf6z/Sy9f8Gv8RX35yX45S7FyM8E7+tTyQY3QWE9aklHNPsHj4u4wy9w1Q9N0FgtwuewtoS+tFDcxvXpRiK33FF7fyVeOR74wH3pWvsmteevOVVngdxAuX9t+Sj/PPcxmn3XKaXoF9Cg7zK51iTt1BcnbTpdWpuXnlkIlctdNHPjWoeiDx5/S9CvFvdGzSDNkIBJNG9kIi8KLLSmTrgSgl5ia3QjwSHARoYAjg/C52HhHLp1/5ooh2B/kFku/1hTACPtk2/lovjuYdAnxdhPcJ+EWTWkyyg4gXR7Pd/BM/BX8cGHJQSItIvUw6O4Zkp+ph70uWILQl1wF57oTVh8LLSN+jG/biEUtEzC1IK6AMvwI60Tr+sUhNa+tXxFHkhZZAfRDMd+t4KeP8wgdbw1XmbiA/XgZJ8IcgkO/+cRfw8RPvDMq7grRd3EEh5eLnc7MPeTcAi0Av/rLlnNY90B3BtFVekB30Cvj0zrY4W+Mn9KvtNh9QfhtWKNJgosXHRF1n+GvY4FG4+bb1uZqpUFqBb0zHmo9lhFNX1Bz1Hcs3VvbdEo9eO3rsSL+rI6qn+VHuzPvX6irvSucSpOzfShDuRwkEMeQ7KO32xwn11bx8QLfgcUtlXRdF5RoBZ0YApegqFEo0MwL2ms1oaIF5gFb8NT40hdZ6wESqmI7asa/YzbK2c6Ozm2XjmwbNulpDa97z1qZgtzZuNQY6q5e6tjbva0YftGu+gJrWR676p3UCghs1tA9XoVnlkJ60UZ701sF8qX9aULN+0JSdpPJcwUi0aIhU63cAOoij+Vi5b/Bo7v6g+Z33/PnHC6LZ9atdvm9f5ZGw1+z4FY75/o2CQt4R5KCP6tdiIUsX5aqDerjJw2KfjIAQHoiXsHs5M/xOQKjg9BOrYe/+xeMgkN9k0OkkwDAV6jcy4cZZBGImdChZf/rBagfaq4p5QfFgvBK3p/WjdZCYb7lpo/iP5nECQg+yDvDXP/N4mB//1q4gHhHzzSHdCHr+jF1BCgwpCMRjZDxS9sjvB1RAR+sA1gP5Wqg8o2ayDhgtu6dlgSqmffKE0HjB8z4sIEhqp4sp+Wi/GY8cvhfP6o7yomPx09BY6qhDsmazMaKlXKbKuI6BPK2qNBGxibRUPai7Zo0m6pw5Ydx0YE/1Xdbs9g/5z+E7qxMjV33T6AUovflCmqBplGQXcMBz20v7JnwyxeeOLfrn8Fc9cNmj+JcrKGmFP7euyjavPGUSdZzBJl45HGWObKs+aG6HDms+dQjwXDQM9N+5QyY7guBk3M4PY4H528wUHb67QFAHlRsHBzw7BgVAUi/+lL7O8Sab1hpv5qPrMumsMn83jFVJhZqDXeG13KXUkR5NOfaSWHEr3IULfCtvYA3/8+rReW+euSNoqPGpILY5MAMjbv7YRnFLIXIZ5YJiQRQOuBP8UdkoLQ6Ul44O7DCfjorXOECjV/04Z9H4SdEbgkEfdAfx/Se2kH/0OwR4Z9BbFo1aUL4nIOSAkd43oLuNvGfog+40+sskBIOk0F8mI+dT88oJCM36vXiMzewMGpAb3u3OHjiGvciN5sxLJ8MIbRkNgskplAw0qTNf39ACS0oS9BGs1RgLMgeAeI5fBQdwCN7o26/9UphfBEkwSI+D5aWQeixMj39x149dQLw3ADw7gfjsrD813yCQAk3eaq56OVMIBNG3LJNpEEu3LPpEVMcIlXbG7MDGhdJxIucKuCsJEm5ZWIbK6b9NEkqpG7YQ50vqFbCyzTFsqaRCI6d9x9R+K/5SesWscCV+zPzSk1/PD3i99R17oHhqX/muWePxBkb+3lLWH/w/yqNhflcLDVbispnrBMrGnhzsXB3y/4ShKDPu/FrKbTmSpO2aLCZ2baXwrcfOnp0BVxrS1Xmmo7KMY+Xby+9L6Oh5B3ymMxLhZCQ9H3uiJ+PnNitWd/N97ZSuU1Ze28UJj8TISzdWnY+/diTh0p7LVpWPPDtuBMCmnTn4MDdMADYL4f2ogI+/7KW5mRszH9gB5Dl9BIE0f/PxB+Z0XhhN0OelAkNv9cXQ95rj+YQ88zw7i9j9w2Nffu+PvSrPamLhse9pn/A82JQyBiYQlGCPyqA4jMAP9MwptB0itJHDqpQ2BzZdCP/DkcCjXI5djiola+xhREioBhRqfiIIxL5o55qLBw0e1gYP7utFplobdF5nfcCXxJ6xHlAQiPcFEgjiETF/NVQfiuCRcAJB7ApmdxCfk2d3sL8Wqsg3j4l53THqwhKvAWQJKfbJKwKwMwesDbgGekx3QwabuXJgVYUPm9CbJADCLBdPvgls/Ct9z0MJNZvUsQBHEu1Z7Sl+zd1i8+00rix/Cxzbb7fpjK9uXqXP+I4Ns9zwxZmOzes5V47jzVHfriyF4+zaof8pfJ4H1j79PMm/xs35cK3univXajiTu0kfenrd+u8JDZnomHoEFJ58wpjHAhmr4eo5Ss5frdxQIH23gaA07/zojtIFvg4GwNfSyneNB/yX4Ft1rPBab0y9bu/K+63gnojLKXmoeuPYeLochXWlH0R3MyZT9ipHmYQ/4pNo0qQHZlwIcLDs9IUgcTh90QhgQF67f4UjhfSaLG1dXG6NrJbjjyoY+X+0+GrrIpnWUn8uRu4aRjq7g9jZo/cJaIfPh49aMGon0BstEt/pa2IsGt/y6BjBIAJB78WjRSZfIUkgKIGhD/qqGNvR2Yrel1MSCGK9hb1ZVFInFjXFl56gZFttxlETFtTmIZVPv4sK0g4Nb/gFgxONcznP/m+5XwzJzh0FffwjGEQgx3fy2NWTYNAj3gHAe38aCCIIxEshlbMo9IJPMsgpFqTHwfgELUEfdKdvWWD6ZZSyuVvDeU8QrdwWgfnDxB7ReTPOoOEhyvFV8p53/z97Z8Iot6ls68yD45z3///lvSeOMzp561uLBUit3oMzXtvs3aKoiaJAiK5GEl4YKWS71K4QGpR9ISA+CbKskYwdgdcRT7ZO/IgPbxO44DnOOm4YFwWdey8d5VojCqL3RtV7ilgevO+Aoy/fjm/XcdUDtePMV/y5VvCMR9ObD6b3ZUdQA0HrmipHyIFMAVc+XvOZzodLhnp59QL+5XNO4Vh8pVutJwpB+kd2nZuxyzwVOOQ5h5G5mInMGVui8SCqQq6gaL+y+MxNuXz+emuGW9uWPrjPdOaczLAWnzp2vv3KHg1oat1L7giteqNL/Fcii+0obkuXFZlnZevQ4XMHlUOt8bo+YOvkQeNQwfX1Z37QYYcuP8Dow5vBfvD1W4Eg/4ijYI92/vhNoMJzSxgPjJ7XeG4J14fXxefWr0/8oxHtwoesKYy3jYwFPr2Ssd7AGD7CMYA9iKFntKznA8Vonxfin6Nlb6ukpJB/NzjtH1eQOgCaUq6pgWtCfmiStGyIKcmjUSsgb2/Cm9q+PC1Xa2SqlgQK3HBNH8EgFdjxm11A+QGIH4ReKAD0Qs8MesmOINYEWh/4eYHsDuYHInYDjWAQt5wTAPdzCGmVYLzCyF7244nhNuWkeEqetSN23BHuHGP5IRWO47F8CyvdErp/rd8qHkLUUeyEi1iKh14QcN1Psek+vZRl+67vWLE9OI1adlZHMJFvuxftGlr13tJ32g6fOXfaDj/Ed6bt5Yd03OVTszPCdo4P8HM8sI+2bZg9qOKpfA8p2XXs8C4D/l7a7Ybnno5d3tOsGDP3hoIe69KhOhcf16+MML6j8WMCXP4+BiRGZlyuVaR3KxCkRnkSVcPaYLfydOgEtZ/AwC03P4nNYunNJ+EOUL7md9hcf2kP2Q8Puv5tKUOqQxLrdvhcLm1f+hWXSTJLjXM7uWSXb6cBpxyNgfeLti2QqMe+8sGRPAVYfNGkmPpjy6rTLD5A9wnFuAOTq5l+LBzKRs4OoVoc6VXGFoJBLIb8HAtybhVTZOjNCOTwPKDXeoA0C0ieCUT5B/Jx6xjbzh3s0Y6gn7VXnO3k3DL2s3YC/Syabx/jGUJakLaO3/ilUXX7hziMcsBstFIOHC0yAjj2Nx++G80M9tjCvcRYzS4gtntn588nWokx+fhhkA7crADQp3qgzxcNAin/UoGgLxwEYuE3Aj/sBNICkEUhbxHxYo+gkvQScCIA5F/+qFvNyy+ALLTGcll5ls9pS5oCTmUZH6nRTrDWo3w0TLXgtdtkBUKLrz6QqMfccLFlwK0EZ7kX9ohbAkBHbjCxeMeXr/lZqmUk6cPkqR+Ztj6YD8eHffvn+Cd+jy7q28utoXaUtpcLl5ecOYax13nPSsGJ9r4Eghz8xhnyA36bD69nN6Qdc/K1T9A4LueqpdBwSuBJnQVv+XLFyjkWr6uuTgZL3P2xdzikdVbf6qX/SFWxSlBa35kersx0taaaQjseOy+UJ1YhWWjxx5JeJZbtiyNasC0WRoJjKBwDQW8bFqb1FrM0p53RtFFH3y6+1LjKgjxBr3zNzeFl7HCdRBWavb5gR5CraWtrWRbXPPPPO3EdDMrDob9XsIdrODt9fvyJ5wH98NFP+oGHgA+wA0H+cUc7gR04+kVrgej1biTVjdd/1+6grCmU2wb8RBhDz/ux8dg9gkGznJdCuK32bP2QPL2JzEgDmO5TGZTXpcAmqO2Dr2LN7VJUC/D1H8s513QyGE0uwOcht6zbwXj2DS3Mh2u1eLQE8LWcN4B9rnvJv1BAp7eCf/U1bxX73AGgBIK4ZZxAkNYHX49gkBTw0OgvdWsZu4N827kmPq8VbIy8pZzrPueqXSY4a+y9gfHSbCMtkQPKDx51e0o5I9lE1LnhO1fgFYTaaXv94I/l1lesa2phU9O20F/w1JYza/gYY6FQLryps2/2vneT0C3Vxde2yqGx9Oalrfxoke2x0dd2VC52p3Rlb/nIy1u+h9oI/5/JF11xlPuBCj6kt/LAcaQcVTD2PN5GfqS+fWkf04/Vf1XLlUx1llbbK9/ziXPWPL4ACaYsBPPWQM1zj6sS14wkfkxAEpw1OM8mhHC8k4Gg6Y3hhnM2J8KN0MlhQ/2rwX+bvRledVlLzYsnL655h3B5wHcAl79l8sLw8eHSTQrtqC28nChJXEyGBUPcHDqEE64wFz/NFbB4ysXploVfpIQX09QwlGjNqBTp8oHJVZGTmBNUclpZ8rwhPztoBIJ4s9gvWhzylpFftLgk/5GAjwJB7ABiwfizAka8dexX8AR8tPj8abyFjECQebWzCDy/MDrIJL0EnLyYZacQ9etAzsH44mysCVjqUg5qU5rVzO3nIu8FiZgMa5XFA6D5Jc6/xilnt092/zSI87kXaV8QBOIXPOWf+1k/BHsS9PHOH+791+dLB4C+8K98DQKhj0WeXxOvYcFzBjCML7v0yqd+PlD6kUUX6Iye4sSeZtp+pNbFuhA6BZvPil0HvCR7Z6CDWcc5LwuFaYN7y8E17X4ujvxK+VNx1XPW3fKuZ4eva6229yWvl/7u9h57Yo2ap9rhc3sw7zCo9y4QZD/c9iQ+PvsZ1udcZ9F61ly9oYXqesqoiQAw1yjNrpkYqNr2XNkELfqAOocF3o+zih0peJeFRB3hrQTY29Q5vZS2reXm1fKY7Q/JV0d0HkutZ89bV/Lw+1o2WrZ4y7kwgYI/9vfQJnX8ooo+Xxcl4MBMKjDeNZqdANHvui7z40yCQdy2zXX7e3b8/Pirdwaxy5eHQ7MDiNu9X4nG7iB2+fKjjq/rusZnN5B0+la0jBV6nCsWwZ95FVOExT9xePw4nDFotE5lR2AIFpE4Hj/Yb10CyBmPbtPMXVJbxxpBVII368sGekdKJTl3ZA8muTZdm3OLOD/WxPLff9dtYWxPpjbrAx7PCxIOPs9RagIvfOCZf/x4lLeCJuDzhW7/4jXyvE7+JbeGsUOI18rzBjHWDt5hrEDQl7lNLA+OVlBJPxzxjEK85UDQsJNy02jKKMbmSYtLXCxfc0jALU+ZAgdKFG2n/uQavfBAeSMZVI2bXbfUWhQLb1lLP0telW95wVTnmaoRc6Xk/yAuvjsafoU7cpxLx7nmTP1QfswD/+bRdB77bcvb2DwuM1Xh6w0FdFmfDtWbPHP0mpvfs0AQFylHvjbPcbI1ClZPsqDZ52F4rvjKv+d/Nt+u+zkwdvybUgdibGqp+dlS8KUtmBYVe5ZY5f0yvbB0qB9uvKPcyfFTFtrS3wqai//oyRAmzkaVWQuB8W3emZRBIZCDEuB8VHD/QNf4G8qOywhYsDlC1iRhL7S0MGLnTt44wg4f3eKlhSXBnbx9RItFFprs/iEgpIUji86flBPsYUu6F6IEkMARLBI/On7ll0r9zPhGASPOFQJNv+n19Q5CyRgWZV7g2bAugNOqts/NGW0io33krDVpI033BywLLW3HyS4dLcS0+CLI86l+xeNhjr6dS0Gd7AAiwENgCHwWcJ+J1ws53hii3UAEhT4H1gMkcwsZC8PxMEjlDgKpWr8FTTZxGxhWfaq3mrHYI8W29IaX0uDFRtbR1X4O1lK0DEBJEGpnOaBRJzRF0pl2/PVvUsO8uEd5ZcNUI2rPeVxBhHbWurTcpyyetjWYY2lxvU9Qvbb3wd/R/rPva8dT62bOQ4YPuvplFvkPgSC8EL+c/VxKrrWPe53zMFeESq4ZItKh+rylU0hMRvQKOyJIKrcm+FYp5B7hKV++3rdUjkU/t2vUtBgndKtjkiawtA3LJ6VAtDxsO7zRxPFYL6WF2bUtLPJ7OtoSvl5Td77A4j5VW2vJYxgMSXQVsQpfm0GpnOfzpJ79fGIMsB5wQEfXZt+azQ84uha/IvCjnFvEeCMYgaHXDgRpV5AeDu1n/+la7RdGeDcv1+Wsa6gj4wu7EvTJvD+CQX77FzTsD0/4dtyAxZM1ySqDSGvSPmAP0SKpX+y5jQvPgoAnY1rsI4kp/zJjArkeq8wawW/tJChkfQp2af3hyjgHrI8Rys4gP/XIMvyI4+eZSZ4fiPxDELnXBHqtvHYGfatbw7759oWfF8gzhLg9DDw7hnl+4BcKBPHMIG4JIwjkQJCDU7+xTMk6xa2IZwFlov1CDpQcOJSuKz1uzAktybxuZJ1Ij0RucSwarLdp0UM7l4PdRd1vU9FOGbyqaH0nOtNXmf5bfFPhCRj9OMxa0iD2knxnfYi3DaW3HNVPqze8f9fxaNOxbbEhuCPffevg+5D+mAeOo2bpwrP3aIvrr4Xu9e5jdt2zfT+nDYuR3Pqa7+0WkR8RGGacw3NHEM1W2ThJ99r1uebEl/pu9Vck7bjG1L8v0dhUuXnmonpP2ltPnU/K55zMT2nin62PJp1tvmjm345anb2gMVRPtpS+54XDeu+E4FJ6nVjIbIuZeRHKJbozg2sZVbWOXsyjN8RIZclcOkuebdg4iONlkJC1PhIqj4kevGlcBA+GS2i7GFi3GBLIJM9DIrVZR4Ed7fJRMIggkB8CraAPu4B+YieQdgFxyxe7gdiK7p1A3iEkmracsxvoR+0GcuDIi0zp0mLUwSDp5BYxdHp3kFa71M/bxdg5xNgGZuIgYZNbg+nG6MhCSrDHo7qGJvXB0B/r5zbKnyr3TqDu9FGg5zNNOt7540UaAR4Fg/T5/HPdBuYgUZ4PxM6h4NkpJLoCQ8ixxZvgkXcBaRJjxxG/FmILB4I+3PIRF79xmZHTnUBh82+oYl/9igbr4MhECiMpUa5TAGhSXefs3w0d4TEGrGdgNKjCNqVE2GH49vKt0loXrpR2CRslLUge8WfdlEm3dQSf48PUnfPdhc9+vPbt27f/St/Z72cbHquN0xcZyw24MjptfG60/K7mntdm46Y3hIl3OZ79HHZh8z+l7wG71vKsq5WvFEG7I9If3C5jCnMV1O2a4GIkIOgzBAX1OgMZimUpbHyL24R5AM9ssVk0aXsdG/ICzHyz6g1L60wdS2znK88VrhLlOWpNCbkjnfYEs3SmdVzDFk5yKhDk55xwoozPISi5VQZTBgdvL4P12RsFaLr28zV7WkDf/O6dPwR98hIH7QLi9i8Fgn7wrWDsBsqtYK/F44dGq8zz/3wtV2W//sLOXa6/sZVWEBZxS7kWuT7yERQyTmWPn+Z4hXakLb3aCbG8pbbtPKaBwz/OAQb/XhaD2x+y9QEu1+ma3LGsnB+CPtYPQqD6Yw1+/007guLc7gxiZMuDOFzPDHILdK1MoCa7ellL8EBoB3e0ZvhMH14dzxvDvv3mhW8b88Okv9azBHV7GMEieHnjKLuCPmdXEesHfbDLu46UY1e9gWfTmtFAl45jyRweUJIbDnP7ISjtsMtCdC1phsFx/FEolHVc9Y9eWKQBtZ7JOQEYSr0RO0k/hfdKR/VTKQ1UJpB+Gxjje95UA33f86e4q/w+XxuZ+u/zXWn9o7jVsudriiznweN983zt75NER8BDbR7D8SGWSXtKrz5VX0blVG3gKfYeJVbJ08soFkZfde442A5zs4heLyjveUjuNZEV/D6eEfTOBILYzWAv4Atf4HHIeZLIObi6CvrO78J2KB1U9RXe2FxPy53kznVDP+srrjLVUb4dX32llfffkHdQZnjWv+6Qk3mLkwtG+DtEMz0ujoqirzqLIy8ueeSK2+hnhZRh2/GGF4LFACcLw8P9jjqRx3DJycWVXQjovm/fOYxVzQJOCR7yU8pyuryph0FMvfoft4ll544XhgSEHLxRMEgLRb8xjJ1B7PxpQEi5f4lUYMiLS9F+1iLTD5UmYPQm28/fCP9Gv8aRE2j6TQ+oZv31BlihYoJAuX0sT7KmBW2DPayDgz7KvQNHbfxEC1Iv+gjKUGY3kGB27/h18Ary5CHQBHRGAIiHPhufYE8CPTwLYMgo4IMMr4Z34IfnC+kXPR4w/algP/TRu47SN15s6YCN2Eye303pK/+7F9gtpB8albKXzOtAMchqy/jLAXS1Iw0XreVwrI4T2/gusSpoXZLxGKAqjCGzIiAbAKB0Dw4VUVcvJYtzKBQuUI70FJjFFx3PPVZ76n2u9LvJv/vi7N/6i5afabs3rvjAXcnsvNVx5ivPGQ8/80jxOwztfQkE0X52WSZlThgnjP2D/+rDwZRM537wHCt/4BiFzo7Ng84XypMkaqTO5myV+llFzDUmt74yrLoDFQ/3aI8tPfKVAletyLyw+Foj9IXd54+9xtgV6vJZ5ZpT215P/bCsCD02cY3cJeFKaiszn7UFpS6e0sGkB6RvqCRbeiITmiwswRyUsXoizeZzRkq4NkalatNk7106Ipouta5Hoowz3vTJ84AaCPqeB0LrFfGveTU8O4Fev1YuWPgfuU1MOHYRsUM3ASDVR4yk9TgINK5ispGHRevs1Se4PPxZZdVPwMdBHzdDcC9MNFZGYucaF/CCwGsmhC47zEcjR+o61GsdoVuGcXGF2X6tL5Un4DLWAwRfuD4LzxojO5ATEPr99zwwmh1ywFk9qTW6UKelb3Td/3j8cJQ1AreG89awb17q9rAXX2knkB4crTeK8ZbRFzw0WruD2EG0Hh6dW9O/0DODCCpxe5gy31aO9Xab8n3M1mOltcWsFWSoG40HPXYW06Lhi8FnZmrZ+SwLBeTuzWv4zFVJ8kM1x0pEjSS+P/IhSYpRoaePZj+HYRzVnrBaD/BR3/BJmSSFHvSSzjqL32m1wQLjsPhy3oF+iG+v56l8e307vORp22j8xlB68400wboDX8H3Ib29B/YzY/fk3js7DzWV74xvufRaRXmnFd7pwDv+rKO8O09xV/leZ+nHc4vzJ3Wik68S/X6x+Ji/pYl5VDy9VtVSzgsolhOcZwS9K4EgX1Ro9sPJHbWdhG9zQj50su+173w7vPMA77QdvscHz78tLc8vKDY+VN5pGc77iRC4GC7NhdEMTEqek6C4hb8R2asUvEuggwu6WawwzPN7u+vT6VQh94NOqZZFpxVI+SM8arxDiLzyyucvZgNHxtZr/1kIWRacBGUICvHh2T4jSENASIRfFAjiVbV+SDS/JCoQlGcFKVjk28K4dSxvGfuV3UFsWddr6f0MA95OpuDQG21f944jyoIdAJJOfrFLUIppY1lPwAfHzW3eI+BDYIjgD7+ufUKgxsEa3QqmIA63ebGLh9u7PlOQJ7uAvnTAh9u//KwffuHzrh+CQpLRKo2dPtlRpOCPquWB09TBm0D6HCB2BBHIoRvoEmwFxvlZoGF7cOZRyfxCs9hkZCWhBwUjGCRkJlSo4yvC3tmpxaK3aOq0FdIxQfPm9DVWZfLCJt+U0XLkiN5w73Dld9wOl/78/M/R8vx6/20S7Qf8UfjPsHHXV/iez1tv+R6qP/OPbEVoCFb+fQkEMSczj5EIwu5fEIzruW2OcRjOffxaG8bNvVMLlNVHcKg0nO/5NBNB+AeeAnPTSksDONpyTnBkdqqSXMVa2vnhW3P5UXf4kKq2wEv+qBEuNCybrvTFB0cd8K101LrwgR6mToeKOVpjjTewnlWdyp7qNXG7j1XwLcHTNPmJqvVxNnN5j1u2RDzgh27wfiC0AkG82IFdu6/0/J9X3xEI4nYwwdod9Fq7gL5XmVvF/KBo7fTVbzC69qo+KfZuILXIi3gHfWSYbGxIhF2qumFKtWKwrmC6JgKFzvWZEh9wtAXZlsEKNi710RYxOHF+YEN8uNpZBq9ToG8yVg1Cal0LuWzEDHKZ5+u2fygSkjHu29Jd1wgEEf3SaPpduVY66g+VRf/4kzw36BPd4s3bQHkDGLeIf6E1AM8V/OprvTHsP9oRpEAQL5f4mtvEeK28bg3jgdLsCvraO4JYg7AbSD8uaa3xuX5I0l11tkUoG9+1AG3gk3TjOaM9+u2EnfcI20u0l3ZKyt0iwBpHBatnQAyFqXgrn/FhuJE4sK0WUC99utKi7biHvn/sfIFRWAt2fanI/jFj2g74NP1HvodkdtqCDw21jYt21G3z7hx2mbBUb9ra1t/y3VFodKSQ+ZDe3gPtiXsaOirv0c/49uUZ3/Kub4dLb36vVx+y9yF96J3nrRh7XUOfdepQ3eFLiR8RemX2NcTE0GYgyMV3OBD00InpjtJJWJ6/64RsfXTsvVRbzgvVM3/5zvh/sjyG2IUJoRwJO26HWcTRQ3xy8e0lODi0wO9epKAUeP7ydaCFY2Oz+C49zyKYbMq4aHHizDOwxO3ShpJe1QVbVF8yaIFh5YZRs+EEKsWC2Y+jHve7+WWDlbiSyKvsd2pIqYNBKrNzx4tH7Q7SHWG+jexXBYfy3CBuF+PZQMoVEOIBlD/x0GieL6RX0JP/4rKCQgoMEQj6VfwEghIMUoCIYFNXprLCJunYgE9/6cuunOzQ+UTP7yHw4yAQQZxPWHCNhRfP+HHQh0APO4EICOW5PzwXKM8KIldASEEknv+ToJIWa/7lji3c/IKnftDHi0v5PF2hY9wq/8ZS5/JRFlrgwstib/wWahF/8RIZcQe5/EufCiThRw2ii8Mdg0ZoqbPVeu4FXwSwknk3XNcAaKlXB1e5LdcDotZRxKGCKH4KzxR/JrCZ/kzJd4/92A9/fvvqa+opvNey13/b5ztnYPgto3mjss3fl0AQuzRoc8+7dX2NJzivD76ejEvm1rNgKhX99evOC0f7ybmYwqdj69kEqrGovYxcZEtdeb/ABnOcWRbXEapdR+xtafGda4/fgi185rnSF/tuKTumtT6mD3pmaEu7r4UDXRVTbRHkTbJb/eBr8ZzAh59ddYIx5faiWueSc1WS2kWVLCVCGNkRxK3Z2hmkH17Y8fPqOwV+9GDo13o+0KtX+vB8IO0K+mHsCOKHmTfcDiY9fjg0uXVSM28EI9BDHbpyOQikNnzcQJDwDgRl3wy3YXU91GsXa5HYSM4/PogfuKT5sgbJNB/0pUM5uIEHMp8O4UM+PNUFv5NI/GDjGuTfXLMVEBLReAdkFfThV2v08WvXb7+OMgEgPuOV8goAERQiiNtn/LCjxwEhrS1eKAD0jR4U/VIBIHYAvVBgiFvFviEQpDIvmPBzg7z+2AJBWlOwjvCOIOBhuNcD2Okyh46vtDV84lf/NPlUHq6YWADjKi8ZGGmvBSJNr+5pyhu503a4sqOKKpgsS0uqUi102kyLXlR2lmHezlfqymlDeKqj/JQLL3+CW9joWZJHmVXLxjfrO1NX2X6lHld0r7bhf4k9pY1ov+VDd6w3JNDn5ZN9NqS3/qeeD+l5Hjj38POk3557jdv7Osqzc9yzt7z36Ojw0BqMhsUMPx9NifOHGGjMkb42eW7yrK/y8bzgbASX6f0dDATFabjnmDJJ5KQGXhMZjmtXBO6JX3zLu8az/BUP/DvfLl+49ObF7zk0EnUU3un/FpjBlYWKrR1m3fZFCDt+h+UzMRwxQ9XMVn8VxeJoCYleFhTt8BAoyq4VT06g5OnLceJAsIoocklCLBBo79SNQn049zj1Oh4Q13LGCdmhTv0IinHoownoM12HwHhTJI4WoFYuwFmE8uWGH9AcCBIFmJ1B/gjJM4N405iDQQR5HADKTiB2COWtJgoA8SwDBYK4neyNbxtjQZogkF8zr8UZk0aNxzYWSg7EaAXFou4TB2zYvfOpAja8mjWBoE/Y0aOf2j4l+PMZO4G4zUuBHgeA2AWkD7/MkUv2cwWR/Ep55L3zR4s06ePHOl4573rlCxZu2V6OHXhlT12k4Dl7XZOle8V88PLxg6RF9lKV9gCTu3cFIOoUn0OBh9RF9uF8REQf+2mIpwctwiaj6BQNthRaSfPizXD3EHk0tIa09CwANZorceZ4WvmPST+tjv8rXO0pfHIFP7cd1dN8l3/I7637ih9a9fnUVdn8Ouy09yUQxNnvJVF/TptO0xnkuVW5HTM8bmfBpDNodkKBehD6ZDQUSviYJ/ezk/pNUYeYj4IA5ozyRR69SZTzVToycBYqDzmqogOJWBXIRR+gH2XLYasmD8Cw0HqPMqEe7Y38LV9tir7UnxZEC8ekpW/5bOmrnWlnS3ut3HYTvI4MeKX9OAqpzMdRoztXsPJgli8sr86JnqnWi+csrEWDKBFyQD7QXn//s2/L9sOhtSvo9fc/ffSdgj/ZDaSAkHYE8cr4H7Q7iNfH541h3X37iXSgW4Eg9wB6fQVUXdhHIEiBIedZE+QiCNwyfNiejwrW59wwEEEj8Bz8L5jCVj4Egjaa+FwaqIjEdzlfUn+u1xrBKnq9gJ/1ya4g1angj9e1Oi95BuHvIxDkABDn6m+/xBh7IoEgbhPjOT8EglhP8GMRt4Z9+/JrBYMUCNLunxcvBOsZQbw9jNvDvtDr5P3cIN2G7p3H7ChScIiHpLIOIEil355y/eeEVLLNo33Aw0OjR/AuaR81YCIQmhl8gIuAF3hzmaELgsUXiF5LSr452ehzeTCPzGsTVzLaIHy7tZyugX6QvdC6jjnz0Z6dtuBbG6BFfpe5xxd8ZG55sPMh2k6/5qt+c57aeFsfOkge/wMejTF+HkzDZ9IhEUuN8TJ5BOz6drg8lms9RX7In+UBejG91rPuKA6tPb3DR67HS5Vt/pgEfFfpbG/5auOVTHHm0aE5+F2OaZrhxLDsHO7vbEMBuAZ9IvuOB4LqhOvJIV5xB2wnYU/U4bO/LHvIpr3Sp/D9XTbvdj0Gdyl25NuH6055Cj4X2XBe8fdUQi+/gFX/ji9u5JDEd8NRWSmZoM4cl8zfi7MudhKew8fAUCg8i/0hZXv85QOMF3VHW1gkLUtGrdRvG1TOv2zVgmH+GsjFNr9QcmJ7R5AqYdMOHweGROAB0Nwi5odLa3HFc4W840e7g/zmsHErWN5qomAQO4EU/PmNnUE8M0j03wkGeXE2HiK92eZbwtR2gj6Mxc8I0nAbGEEbBXocxNFi7dOxO4hbvHjQM7d9QfMzg7yIC54AELd/maY8gSXplJO8I0guYKcO7sZt5J9qAecyOJBKLK1I+D1Bm/ziCGawOGcB1DL9kNvB0J32uJPVXjD0g3NLUE5/gk1CBkh45wKFUMn6TCmeglL5U6ICV5LiAQ4K8Z1jMCrbFe/w4li277jnwfc0P0/Lu8F93Q9/btva31d+f279DGPGpYcz8Gbq+xIIctB8tNzXTjsDR8Qb+Jlz/5AoynGdWw60Q8GM1rT7dmeZmjcGg5mkdlbNW6tOz2OipoZae2CfBXhaDzMP88/9FFr0Vuo+N755WF9kH+NbNkYfVsSG+3U/3OrRRg/ywLZzoJE1OMvDztGp2Mt46CfDAtuUdIiYykOerFU5D1twA+aa/P1rBYL0PCCe0Ueg53sFhr7Tm8H6kGhuDXvFA6PZFeRA0A/aPaTrLd7QLwbuP40D1+ermmDuYaIf+OWDW8KUc16zW8he1A8yvs8JFLQhDy12mxnqoFGX/21/8EGE34yWhWv6ABYV4FmpupOf/Yq7+eQanuu6b9FkjYUuB320l0q7frgtjAdFe4fQ7woEiU5IDBx2EAjK20dZd3CL2McK+nz10YtvvlYwSIEg7fp5oTzBIO0KUhDoS94gpp1BX49AEK+c59b0Bn9YY/g2MTUK97oVgrueoJ3Y63y0fLV08Ju6DqGvcqF7+NJpY5L8ZLjlxbF4dtzAZlAcCSoxxvv9qBYfbRktOvDdqHkEsWu8svsR8Tvko+13mIT+u/jasrb2qfXWcstxQnxIf8gD7Yc/pOQthOm5x+o+9+5D/A/ps9wQ9qnNVDjqr859bsYy8PNcFxGR8Ijif3B8r0OT3sSoa8dLBdD/iqQfaWreX6H+VmcePDdatpHPTnYHbSchOxow9bGTufTmWxUH8DF6mZ/KV/49R/bfmM7Lg2XjuV+O5XMfLblC4T/yLR+sHUELV8lzDoe5dDiMUFXRIetcxFmrGdkJk0UVCqIji8jfWSQI4V8KpR9JRHzOCsi5awmbg1660Asmcxttg1wrdbtOmOBidaKPBK1XM4J2kWuxRJ1aJqlA2R+to/ywZwV/8kBoor8KBIn4q58BxO1e2fHT28HeaMcQzwUiGEQQ6DeXWZSJVwszn1v8esfMQaUyxUEgAj9aoPJAaG4F+0Q7efJMH4JCCQStAFF2DPm2MS3g8vwfftEbAST4tSCLLmDVoTJjHc85FyC0YBaHguOe+BGEk2wkYSqex17lkCMXur9wGTf0W0vq0TGdRychTtoWWP7VzUjR+YeNjlFeNDmaQemREocEvw00dtiLwf4YeXNABI7mR4ZWkPqOfKUhscNHDfdKz5e4p+ndwB99e90mfAbfY+kxvivf39N7TxfDEhk+TFMdztim0/AtRgSS/7fSIRC0mc552NN2nTmr73z+uhPuebfKuC7wd81nFWUdOfW6phPRdQ49zFporlaLbHpCDXZXc+a7nTV2jl1yU34XrDV3GQbhlg/MwgZqG3aLrjVfcYDjWoleHfi3Y8GT6BNnHGfK+ml4WoOg5TBIE0LC9xqcvgrVi2fRc613rebn8sCl5lcxvyYQxIOgFQgi0PO9dgT99xUPiO5uoASCflCwiGAQzwjidm4Hb0YgiCsdPY8p7AjyriDqcECIN3KKjgvnbiGujNpNJNy6NSzy9o4UoYtD10sugwJnvA9wucwh80XwOpqAXfsvzl6fWDsGyZ9ct2Uc51eu3+C4XoPrB530HR/VovWKb6zTeuN3BYAcFOJ2MGj6fNy3iEmPlgwK3LAjKC+O+Lo7ghQA4nlAL0dgiNvDeE7Q18LxrKCvv+CZhASBEgj6VN99CKP5djN+1KJ9OrCuoDlkxpHPQJAKlJPNI94P1l4SPUpynGwTWPJnjsjDGJ1AC0cp6QqHS6P5rLW2ObxEW/QPD3bmLIkcuukj+qV56rs9lt78liOY0ps/xnePXvxjesr3lPzJuhjM8kkT0PLY8pmcduArf3Pqsx7yD+kPe2D1SPqj/bLjH6ukMmc+eug5enb5q959G12WGYJkfNDdIA6IQR7Ds9e8zN3slmUOz9ANJ+f2DARJ2eeanN+tQJBcMuY4Nf06+UQUqRPAVTkLgPvyT5kkK73X85jeytznyzCozeX/N+Rj+G2mdHhuqAk+RHvKCR0/THUsmg4qL05DoYwVn3MOgqeYYZY4uQi6D6S0dMuMCj2H5yBlg0OrhyyOIoM8lJyEgq0/CqjDfSilXarBYIkhB56TPQ9m5gaBLAvA8eGCzy4g9BqnFSDPDqIMnmBQt157pxB6RfBbx0RLcIhdQ7oVjFvH2BHkABGBH9ElRHCIZwQlIMRCzYrdLgKoLKDICeakzG6ecVuXAzvAommicYDHMsgR6GkuHSp79w/65Fc//BlX2T/KhMMfuJx+8Ed+jx/rU/jSn9BtZLwv2L1gOQiWs57UZ4L8Ft2WHvJDIRQWWHNyYYyMelXpAMMiHmpoPXCNtVkEdMTOJCT3T/F7Xu3BRTPwVGL4jF/lW14wz0l7Tc+Re9d4jz2RHthx9dOOe44PKo8MOvZy9VR3aXu5cHmdC8n40yk9lXbs6nTLl56DwLtXaCAo100ckjZSZp7Gl/WnKT5B47icq1CHUETHsVKc83Bc8y1sdSzMQd20ZPERDsA6aii2Mqv2HRP+a3vhy/wXqHL388y8O/e17U/hQxK+aEtrIhd4t3zhQ0sPnT0MTX5hQHtQh7fPtnFFF4dYoBq4LvlbP/CY01Flfw9BnTzWOppNNb6aCLA15g8Oxl90ovnWMAeCFOTRrdffv/rpo//57ys/LPp73SL2nXYE8dwgbg1zoEgwu3N/m0Eg9sBwvY/HHAgikuLByIj4TOd0dsgSyuD5QIQNdDXLtUa0eGr42jaOdgx4vyjFfRlfdaNx0iJ2OYQWZwyCME4YsF6VcN+zq9JBNuYv5mKTyzYfP4MHgyI00X9aW7Bo4QlL/OCkQBDrDz8niH1S1M9bxHRtpbvYyePgjQJBvAqe3T7fvvzGbw7j1jB2CH3zrW4P49lBugXsK90m9pV2Bn315Zd6mLTeQKoPt6Rbj2r1TmavPWSbTMLVmEeT5gdgtNzgoIUDb/O3p/TAwkAN3/CqSUeZaIOAZ3Lum21gCpPDcZsaCNopHtsghkjnPVCpfxw9vsTmfhFN5cLwNp3xLTcv3zkvvfmZfi6Xr/mZ3jL0pit7SyMvb/nu6T7zSRDHbKqAhXPVi/aQvllnDNl0fQDfxgN7b+zydMk92s73XNhdPYQe03/mvWdT+e7pm0NOjObhoA9rOmjMV8ycJPPy/QP6FBR9wOAC8k3uXd0R5EbKA9Mj4xwNZh23SQNkT/jF8G+GaB8X0w6ff4Ot8fnwvAxa0LV19+hX+Ke0k4uVvXKq+VoW7E6ZtQownDPFpvtkCnI2xZf7XQl9wdmoxDGfcTKqVHU5Gb38MS9iviXJglNKCiQzcG6XViUEWfwLoM5+9BCSYW3k3UBihs/PFTCOIA645FgUGmX95qYPC2UeTOndPlL0hlfHuzx2AGlR5gAQCzT9Kuegkui8RcyLbGyXTSyivWuHQA6/0Pn2sO4GIsgiuwn6kI/ATwJGjGHJ6Oe46MEXBIbwpWC1356yj3BGFll2NUW1SaSZFt7iPqRL8BT6aMegWTbSLFFJaEdH4HFEkORcxJaHjIvqj8oxMe/sKW3HQQfTXxdDRVM/waxjp/iFWRAKq7R5qecy+DPuXK7sbf50zlvZdwkzh8Df1Kgrvz/bBgl0FHEO7PLvWyCIbrvy6V2cTu4r2lX349fdt1c8we1caD+WWx991cqNU5k5phLFgdhYp7by3drR+qxh6rvlO2Lu63ucLzUd7dz1Fa5lR43X2PDkWkmgINdqyuIfIvahGJlv/QV5GEKWOZ/5m2trMEs01+nID6FhVM+hubiWpuLIub6+0g6gH37QbiA9HJq3gr0iEPTd93o4NDuB8nwgnhHEs4N++ll05T/rBxkHddwjeU4QVy6HDmTjb2Pnjx8UbZsdWbEMkYvcMgaOxvUKqsJwR1wiW8dk4CvouGj5C8LWPhqkZjjFnz6qjLL4JmriG/vSFePrlCjOIBAw+HE+2d1oxxjVpdoEorGBIG4Nkz/YHSSafuIyjX4m5uVbupRzO9cMBH2rQNB4RhABoJfaHcTDo18QAFKgyDuCZiCIl1TwIxXrjqxhtFFIMEZhK7YrqbnxJLjhEOjQtjRafMKHf/ECLR2buMHFtyjBHdc6D+nYJX0+LISgqxrAXuMPom9VQO/99r6Vyn9IqK04egpszgXMOtIeNjTj/zkSD+t7X6ntl39b+6969m1t5ZrSBOiiDhO9weHNXD3Pf+Zy5Cpo2DPumOPfsYdF+2K2e00N5sIzHULZJ+50oTDhOfOZcHH4s/kuqngSCjv+HWn5MtAq37fvmocWdblxlL1uK12d/ij3me9cDh/YnTKtGcA8YVpep5wVWLYKRoHuCHtOOhgZd1ks9SS0uNsIL0EId6MK49RNPsawx61o+dVSyxEzM54J+iSgQ6UJBuWCZNqoNPDQrFUdMiyGu1OoAaFZNj27gFiUscWd3HQHgIggs2DDKMxhAZUgFTt8EuBRuxTdSYAoO34oh1dC8CPHr28EkaxDflDzHDTCn7Cpbpqbz3Gpsi69smMki3FQMjzy9opzdJZBojMIpEqyxjvWA6vTWCzfwEaMSgWXzVYttLl2Vtq07CqM1GoP2PX1PSWOiB65wPQDR1L4OJ7TDGdiggAAQABJREFUGXcun/lTfhrXtey7hMX3+OLYB1sLy7Ch/gh45fe7dd+rSAJeCIiOvn7Bg/19CQR5vhy9duXTqz7lCyAey7X2ca8zu2S2teDdQ+qqvltr5vdNJgrm4LJIZIIFYqH5qNAiyqsd3P0E16boPuNbUNC7rGgtxVAOx+Ir7aqycF1xBOcgAr7Sp/UaXuoHHgR96qP7dgaCJBptucZix/4V3KqHv3yJhVl6wAOSc24lEKSdPgr6vNYr5F/r9q/vX/340f9+x61h2gmkZwN5RxAPjxbtJ73B85XovxD06POBCACp4/tcIK5WCfQUp6jFCPY453YxN4q3i2E5h4QwbBsoJ8nLeHAwOjfceUGY/Ls9kHTFh7lCoGybb0FTPdhJbZwnhpzDlTPI6xxHWHS9By1mmzj1or+BoOS/a/cPH3YEJad+7QgSn5YaDuA41wTGLWIEeV5qR9B/vs2tYewIeqkdQS+5NYxnA7EjSDwvdGvYl9oJ9OVX2hGkyM+n4wUUrEUeCwRx+39ahP20ALuT3P7RqrRt4cvzWL7kll5k6KVF27Uc+XbK9HAG7UaS5eqfnCdBpy0bywAZThFvPddWnPXdahr1nOr9o3z35Bceu69tXjxPg/6KNj6t5g9cD3mgI3Pnocev8DvPXw2fR91T7bmyfT+F0cMHvn0dB9I0DqP1Pcd9XRKOPOd0rpOHHUGaTF/q2Wp/RfrbnxFEw9v45zSIk5x072Q/48/l1nUPX3rzP8qH/L8jMerW0PUYHIPw2r5whFa4bWn5WjL1DMlL1qHHI706qrvlpWW6ULqmugE428++Ic7YmnKtp1WQG6cxKNCfoSNqcyKiyq8NRReFykvCf8jscoIJlmSxqsWeEmS+2OSTsrVpEekvfKLBg/KeDyz+EgiSnGYQ8PwAB86pOOMT8JFm/SCnhZlwbN12EAh+eNGuoM4ewFlBIQI90HK7GOM1H1wU2LuIHAgqTsvG4QsWPviFIi41GrlYqqPotduWDB7zh6+81oUcCGSiGoxQS+cOm9hDVtXmLipVtoZgpTZ1CG14Mg9gZxecIpzl3uEK05u3CdnRA4PI8jTYxZ223S4hU/PiK3SL3zXeUiv3fuXtrd03uwf4Ej+HzE44wffkT2xjnByxtWHHtn+uaAwxzwsIDIbyvS+BIG4NyzmzeU1OY35gWuBsqU/KkS99Y45gIrrhKGdy5HO+BepZfpQ819Ke27jn3CaFqtd2D7Fy59XTQRp3UtsxmHadiNNs8LGOr+jxT3FhCnUKbH466jzz4Srm+vrcNi41hpbM4ivLohVzrK/YSnJt+nhcw4jqZ3cJXKvm7AoKztchWiPyx+MhzNXpoIvHSzHRsbqFWpWGkarWZejgeRYfbwjLG8EIBCnQ8x2BoO8VBFJwSJ/vXiso5EDQzw4Efa/XyPMWT0INjCFybhNjh1AujOAwljUAQR/RHAgauWAHiozDMEIm9H5g7OLj8YCdGiCUm2z73uYhQMYXhpmGE2Ij2FWH6/J5AnadPeYQ3r62PchFM9CE+dGJnT92KAEgHhRNzi1i2KBAkHJ2BGnjsT7ykiawLxTM+VpvB/vmmxcKBH2t3T+f6dYwBYG0I8i3hvm2MAWDXvCsIAWKFPH58ks9I0i3jKGHdQfrEWCpcoqtMY2WGK3D7jW3d3gxYsEMFVFU2Vm6D5zlwpkZBR/d0nN2gcebR3pKWf+Vo3WrPFDhAr+gcrlOoUeX3+EJt8/1xThUpM+7BgV5zXcfPxTN7Fqe1pNWG9a5fsSbbTugj7TbuJGPILxz/KfO9PiZTaPkxhe3PEfMc0q3vf0c6XeJd+/5wrSPXt3LbfM9PHRmGOh8npru6bun48qmc11nnftQQp6P15kbvM3QHqPmG5UxSzAemVbrFXD9/gjunXpYNI1NW+OB60kDZxxTJ4N7/KUjRR0tn0/24stHfqXzuXx7PdW366CefybhZ4ZthmG8fmXJmXIuM53SdzkF5olQNhDQKE/iQJVHBE/Kg3f5TNhdBlVKY/63zqliAvRzeKxnuwBEOkejrWtM/FtFVuXxmJbVbvT6wdLoB2l7c6ICx+5RHpXlFzV20mix5wXcdhJLYRai2GBltt0NpC7pcJ34l7IOLJKBHQQyLhVBiy6Q8MLHL3SBASRtOSYim08Txo4fbgFbO3yyQ4gytrDYwjzGLX8OBEnXIEcXCklS7sUXRAxx2mEbN7EBwKEmdQXiiDwVK1P7Rua86t0PcBkB3/gocwIPbqap1fjhdteBX62cjMqUdtzATNpSfJjKw7YvwAdmZa2IHG/xSfvgCRU7hxEgDwk8n+o5EF04St7nu5V8tzG7J4D3tPvsTHuMr71xj2/Hn3W33jMeGcZf8TsM7X0JBHF28QZEEr7yOe9JLP4x4XTgjBqcyq56ZxfgXMPLzXda4MwzZ672HJKkVXbHHSaR6Anbic+iwtHBkGKKgMWXmsEcbVwciE3BO3zhri7bcjrEbyBjCBpJez3FtL57+jo366oztEVyHYd2rk1qe3arCufyVuMAaXnmyvhh7gYa1mV9JS5UiDPXYwG+BoBBkT76p0SCN9fN4H7R2zq5/esHgj68NUy3iH3nQJCeEaRbwNgVlDeIia7bxn4Sz/fKf9Zz+uYbwzSf+w1izOsjuOOrogJBBK4S9MEW6NijKIbgHZ+5n7bAl7Rsbi/JZ0Iu/MYHHp9OKnzjGqq68iMS11v059P+ElY4rVwwzX9hYS1Dip70WWqnMtYX14EgwloEhLhFjPo+/Uze0NvD+tp3B3x4axiBIJ4R5FvD2BFEMIjnA7EraASCFARiV9AXyv2wfJn6KToJLtne2GpLZRa5P5ov2j5sTrvSEvOWT/k5lV78uYz/XYdy4KbwBZOxW0ryW94Tnc7dEjYbg+JBWrZskPxB39N/8xywhUtZ10vhC/+iBkI+LYueXWbnLR5c68u5eLI/CuESXzQsG6kMpFo5FqjVFRy6I9Nj6118pVzkqcgEep0Uj42j6A/5wvzTfos/85C2XQu1YbHlmufdxJ69spfrlba8I6TlPd/lit/5d3inA+/1PNQDO191nPOrejpuLQ8D38lar4DqDZ9KQwllxirjEp6Oc3JmWz78v1uBIDwaTwDNRrtw59CJ4A75Lvpqkrpi3vl2+My703b4Hh88/2xiWDUF3jEeXSXf5EfORc7o9XGwbHOv2FabIe9+ClxN4etx17HD5T7km2kbKJZjaVnChfVIw85bHBrC55NzKCAbWAFw6M8LolgFnYs3u21YtJJ8UkuIhZh1gaNOy1N3U/jH9x3j+eWUHUF89mQ9HJSiRwB8tgf+fInCNldqw7jgYp3+FOlhgbbyBIX89hCUih8R55ahoCIdMmilB2+yDrTMmIEYdlAa9oYeMi22TuVddIZCNWrf1mzzqZwWLC5D5uOAcfqMulwWdlNTQZPMthtU6gGXBdZRCxp3recyinb6rhjlhwpK3PBX9B0HfKU/qhbngrZK3htw99DDHnu+S3Z9he95u3aU78HaxMxtHB6bgiuLzPsWCMJfh9PZjsMjt54meE30uvOJWQ/eCyayzNxH35bKnHRLay+seg0VXWHyW4Nd5c4aLTp2nkJGte48Vcl8d6TAFQ3lQXbxQYu+0nfuvQ5k0tqlsfRdpvqgdYavXOs45rWx2kqNBtpNEIhrV/QQOIBn2VOJBChi6TEQFN4GTrhELndWj3L3R7S1NvjML/Ivv/KMoLwdzK+HJxDkW8O+d+CHXUEEgl4L94PfKsZbwxIIItiDzjw0Glijh/r4Ecg02a0fXnTmDvzIVWaXWNYIBISwN9d/bjcjxVbwJOVCcIUntc3hAQGOEgm/BkqJ46bHJPxJPcHbr4bjZ/B9xg4cq5/ofZSzrmBHkHIcyavivROIwJA+tpOcQJBaT9DmEwUHVeANYjwLiB1Bfn28nglEIIig0DcKDvGMIG4NayDoq/HGsD0Q5B1BMh/d2OeW2NBVJp62POkWmRdrYSWRFzZiHMCVr/BOB64sfCsdS2gJ3z0tS7LQ7Mdt3JZGjqYeDYlvyphWG8Jp1LMOlYse5tOj/mtlO98Ohxtdw3JlHZ9uYgsHtYy/ne/5NhzUnQq39p0YTkVsqf0n0lZcbdyQH8CTBzo6T2iPjnu0M+9TynRZ9e3wWbY0d/EgVu7M23JlWj7nHdLMQYalcOrc4PCFUpgSMNf8nnfkevLrPG/eqUAQzqOBnJSFDfxLDk+ZLJ5qe/n+uaZlsKX+MfBujNl5duIZf3HKDJZ98j5PnLAgWV9k4FPPrm/BY1jMwQ9nE1y1qhJLX7mO+a5vly08uaWwJyC4M50TFCw8hl1xsJD8C5gDQWPJJJx5zQ+M1tBo+lpYgzXCuNjAs4W03LIMcgC0OAu2UevQv9XjLdrhXjUgJ0nbFjtZKTkAoxWVfrBTYQ/H5Nx0f0XUNVvHPKAwFoE6wJRHmwEnLBw6eU2ic5FoDXAWbuY2/4DMATxakDE0fSKC4BaHqeFWIf5G+pgmPwJnvqVEtPZQJdADvJfBZZEOdJ3gR3FaCXTW0BauPJoqmdJuXDA9ZlS09CG/9e/JJ0fHnohPK7Y37qmqDTvfY5oZSR63Eq48Mu9LIChvDdNZIKfht30+xg/n1HmkzqK80u7B4jNznimlUiscO33pC2Tehxh2gaF4Z2/A33NPWrlLXMA5u6+t2jVnDj/zYQIfOPl4zlXuXxldXnQVnWL2MN4YtI5r1413qh3G1GCRA9+w07tcpauDXDy2d5CRSx/WAvJcI4zn+oEdkicoghpErQ5hkkQ8DKBbTXSVj2sqH17CsD8DiFfJ8wyg/32lQJCCQNwG9p0CRa/56GHSP/J6+S0QhA6CPuyBciDIFWuO54cg/xiUHUG+uo3duOGDnnZlhxDylHN9sJ0upzFpW64x+xVpb7PhHBCyT5Kn7dXNq+Kp2z4UA7blowLRlVQuXPsFBCmtDAMjp4Eggj4JBvmNYfrawo9Qn7AriLXFpzwrSFZLNYEgHhL9kh1BvD5egZ+XL7I7CPw3BIHYEaSdQS/0sGgeEs2OoM+1I0gvHLPdmNjbxATKzpiN3YZVponxpApK4PfUMnnhnV4YGq0/81yV66XKRjKl2gIPXkX+rANOuq/z1+28R49tSYWeBxv2BkTfra4btoFoDbetuSdxxj+lPg99CR7tat1L431fLB6gv4KvtlX3scYPpbfxwNuPqrepbZ1jj9V7Hnn3+Mt3j46VnoLFCE+n453fuEFD35hlBYwZVwz+G4zAftTHkHnnAkFq12kiAHM8qXsS9qQMR47Qin8OX2V2XcC7vjNtpz/Et9tR+ErX34vbhyE1H8sMtPsJWobrOq0Wtykd2RMNNqm+YuKHrZeyarXOdVVQMbI3KqWubDZ3VHHJJ962qJY8xtcr7M636yhMYMC7gGDUJ74Dh4EsPrTw0UqlO4LypU6LJtZMcUmOnPQ2Tgf/uzA4Mq4Zp94VNOqCER/Yp0MGHqom58tFLBr2ICc+Z6iXMBnJelzx0mnm6TlzeV1ofAVFBxzNjQisSvt4L4/tMpW2wB474YXHdsgR/rMQmkkUVrLuQYoktPBQh0HTj3JLw4BO5NZ24IOHj/Tya21+9905gc9lNOw4yueUdoK95Uyr4t3ImYe2HXwhPpw2NCBFn0c6ch+O8cDu4+WxbajAdnbvI86rnuY7O7h7abelPOXfTaC7PWfAJLg08vclEET7PVd67NPyJPtcB+d2jCDlnlPMAvGhM2HpArIKa0N4P4c4o0KHQup5VthIuGDcE+em7RZywJnndybBRqrONOFE3OsTw00lYW9LYy0y15zlCxUukqRke83ly3U0DNrwC75d+LP+RasNSDfFnjgotQ4uXQx9W5h1Fyep+g0FGOYk6wWnDYH3hiLSYBB4q7Do1p+SpzTUjeftaYxJ1oEgPf/HzwPSLV/fEwhS0Od///s6bxAbt4YRFHr9048KBOk5QlsgSHEk18/r4x3gcQBI9Y2cq8fxWUG0ZASH3EZCBN0VhI371YZy7HY75woiYYV4llZhRNpjgEKwOo52K+cvFOF8zUVP6LaTUv0OoypN76iAAR83EISUgj1oY2HDbiCVgbktDJiAkINCviVMnpEszwgiEPRCD4Vm9093BDkQ9FK7gXhz2Lw17AsFhfSwaD0jiGAQzwj6zIM0t5jpNys862Fib6gZrH/aIk4v48VDopV8xOJcmVPxLd/L4WuqHso7vvQdH//Fu4GXDfdkPYZ3ZdRTZjdgWAB8Su0/r7uGkNdHF3zF7zKrRVEObecrfFLncXOPRoujJ+24bV96Zbcjpq+69/qOfHHMVd3hk9elfpfZdQHvtB0uX3Etf8j/mAcuhu08N9E8RvdbVVLZ5ldKMmLWeQjPPZtK22XOOs8069KBfIfNJwQzJOObcdlxm3OC+TlSuYNEsPn47gQlY/mdCgTVAWenvovlf3YiYWDtaZWvoJ0zMFwd6rdUY6xoaXuIn8XIMZ3Loe7aHtJ31HXLedBzKJwlj+WddYc5tX3LlfPCOeE5jQkANRBEvzeQ463wo4osStVuN10eGbnJW2W5NUyL5swS4RNzx1N8mckBC+bkIUVW08Pu4lTmukHvuqxv5x32HrIzXeXYgS44zwwywv8cxnJcILzI5S/wLn3UspdaW7ilajQW4OF0+YXsSmRWZ+2jXYHDDryXq+QKj7LyAk/lFdry0Nvfi3ArYy+Almq059kPS+IDtLz+d/nitpfewgZ1JgsFj5jRt7X/fQkEzR1BbjieYN6Id+2XOkS5g98iQe05sfoB6CwR4WgNzDHyq3ymL8oRov7H0pPnnccU/UF6/YLJ9UxxzR+q4uwTywh5v31n54yygwe6btE3LHDPbJsRfIXeryvsMIn1yiKu655wwFOukEaESFzjCOiDdV0CCALx+UWRnFcOBGnHj24L+17PBWKHEG8N401i2RHEG8T0QGntBsozgn72w6IJ/uj9DEqEPwjgUJnCD/oQFMrtX9ibZwUFj03wKCIiHvNXzr3iVcRoC7J80rasOyglxEF7kmSEfEA5uEUJXTShOD9CkU7xN/iTOijrL852oM79gwSOGto/JhgkcT8HCBx9SeBHHvBzgWYZGjuC1HoFg3iKEnfJJRDErWEK+ij485V2BH3LM4J4axjBoLEj6AVvDVMg6AseFs2OIH3yjCCF0VT/HgjCQyNGJDuGd2yjCluCRmqeUlvf0nV+loHrCncrje9ik4fKYED2Sv7euXDFO1T9H8jwwUMteIz+zzax58Q/a8X//dpzJvw728Ho/LPs6zmMPuvUobpZL3QeCF8ogZmfuR5qLkV2KCIHU9w7FQhiOLShwL5Yq8HNwV2l0ptf8YArvfljfPfoxT+mp3xXObL/XGL47MM8Aw97Aq3ytY07PXpYMExsAa/I0bDaSrPHWJ6qkT2mc7l27VwsUFRuXRtYaUiGT3xomWICbBO8giceplMqH0w7n09Hn6jjhN1OVlQQBPIzgnyb1QoE4YipxytnGTqMp4hf7BtgM+q2MMnwvB+fJ+YRwY4YS7gBM7XMt4vZnlET+gW6ulY22gnJuqw3SFswZAabszzDoJjqtgbrCCTpqavjg8o3uVFk0YZM2uml/sm6yOS8ifY1kOwlqW0+9M96Wt+WV8VAuW83fsDBEo5RIONh4SluArNRO24of2aWqd+et9YxnFxna0alfTEMhedgb+nPrPt9YB8umz1202Z8KWfC91jyeHiA6dwnsN7Te1eXBFgoIMf50UUDuvxFCOAdT3lrWBuZs8D+0mGdHzlzygXdM4KANTOAJbUXWl6YzsqRD/eSaB1Qq2PxFFpnb/jcZ2KftQmI9MN6qm+TfLDeaN11zhqXKntj1V4Cfsx1J5jls3Ik7zxLCS3RlCO4vXbKC7N4guc4cA4WCJYRxnDgw4A3QqCvbeDiXXLPey4PNtj1SZyCvmrtgmRY2oQK0fQhud1DpoEg3hrGa+J5CPTr7/WMIAWC/qtAEM8M8sOiXwlWUOgHlX/6+RfvGvr5za+qV/ti9EAv6gX2LiBXrB0/9ozqZB1ABOR3fWzDCP6MQJDbSeCIiMnUYVN1wOZhd1HKqY/kvAWV+SHoXgpvdFknnT907wEhfBY19I2khsOAaWmu3cIrIGQLzAzMrWH7jiBq/DUBG/WrH6etJh52BPWtYbo17D8KArFL6BveFqZgEAGhBII+0VvDPtfr40cgSGbn9nuZIJhPQnDp7dkq42WCErhzOuOm3JlxlM/8oK9wd8TN6z6YDDmzMnomcvh+lV2HGskasDYe9eCDQR/5kj5Cz+OLb9d3NGo/1lx9x1puSw/zVefTvPmwrlX34kP/re5JZwBlwA/hIz98afetjlXbB+ipHmhvw797dsc/VdefxXfu2Y6Ap9jUNuy2WE6HynsK3Rg63JrDGd5o83c5cIMBanYEMVTfwbeG4ZucaGlgy3XA5rs52RXXE7l58ee89OZn+rlcvuZnesvQm67sLY18593xfw/c4djaUvZxjUQTy5nh2FLlkrvVbbpZdAFWGVXNjxKrFJcN4SF7JQTJ/kd02GgfWmbpM1nq0DibQvnEZ31mFt8Qv2frGX8uOzaryhK1zcnq/rcRWyBIC7vIjpPYq9VRORkLP6yBiaL/Yl8wLOx4PhB1wFDLh4jkgkrFCQQlbuwFoWWs2toLzbyVQLUNqqe2rKos6zenGUI6/Q0UuGUpjCkoTIe4zKFpLGaGfuqlDdpDJYZDpUNA9IpO/wR3xV3WVt9y8yuZM6/LFSCXgbFhly7cfBd4HG6b5PHBTJtYzC3XxZm3unxemC+Lv3KkD1v6kOOB5d3jeVW8GdoZb+GyXRSde7nqWldpe7lweZ0LuT8sGqU+/0V873YE9dyTT/Cf5yfmw4PDOGd89sBgSs6FctXzJyH0+VM+5qEm1zbo4FJOLYt/5w5X+Pg6zDPQZhJ6K21oncOmRG4SDgC1JkXHlaaFG57YNPA1f9FLYFYDu2hHG6BValkQ6b0HmL9xe+qtxF7L0lPINdOP9tHyQKVaS7RiVz+CiESMahDnw6XVsSUEmcBh8JgInPEALF6oQ4bbuvr6eAJBr3TLVwJBujVMgaAfGwji+UAEgrQj6GcHgvKw6DdqODp4NwN97oc4c13vxzUqAEQgaOIYZfAEh22ESWJ3aRbUAZtjt6o5pJad62Bf1DEbZ/mMGueHdZpQ/conbQhLYXSvfvpEnY378a/dbA413s5ft4Zx21g8otwytJCgELd15dawl3pYNK+MzzOCvtKr5FUWzq+N1y4hbh978RW3hikQpN1BXygQhCz1+6ODc9WELYxDu3g02Dho+jQB8xksBxo8eP+cHuKvPmSq8yy/l3dd4CN/lKQfMybEQAE+GjP6A5mmjGtIi69wecitb/Ds5aF2Y921IxcTmm+MAzza3nqa3/KDYexQD5+OLfDnFFvMKt62657u6LzyxW6jZqkofEAf/PCl7VhVG84Wfig/zwN7T+ySGQkdETvlj8EZQdFxr26oO19rvMdf3tJre+XIoZXOBYdrzj6eeiqSR57zAEmVNJHlO1+0gM+3uvBwHrxTO4J6YtP8dz118vln2ukRtlWtAUUpI2/iB3aWHwJ6MoTnWHpIbtHG0nJMyuusCUctrmbKhXcI7vJG8pHjn8RMgGYGgrQK3X3HQvVT/wrI04K4gI1gzikQBC3b3GvzuYU8KV6a1U/7ueIJhcXVWLawAGKi4TXzsQufsDgTsv4dVdw031We7LfVCNSeSCVIJJutkzxKs8293JUZFR4yWly+EFh4YT/YSOaIRQtz1nkuR9fdI+yj4TftvyskgquJRNq6S9+DH1K4aLSZNo5OamVbDu9VO69w4W1/UPqQ4oG9l8BsQ+EvcdFV75xteLRiCXA+k5iid/n3KxDEHJDUfBQPPikuX+LCeTwXztKViG9zHj69niV9LUNtXvhtHXf+jn3WQRm5TWRjOVKOpY3N0lCTdr79Cy5jCv90jMG3pCpd37QcbeU72EmhhLLP/MA5sPSr8F5/DO+f2TJBWnFUtwKuO4EtjiYB14EgzBrSlglMu5F1AEd5AkGv/Qp5XiP//Wvt+OEZQYdAkJ4PxK1hP/2iW8MULPrhF90a9sa3hVWPbxEjEsF1Wdf/BIVknwI+vRUs+eAZgaBcSIWbNkKXrb4wBj67Zy8DD1feGT/pS7EtwArwR3wS2goJVhHrCcOjAgIvH/s2L0R7fkqZ+BL8WQEgbhXzs4PEx/OBoBMIIpj0jYI8KxCkZwHp1rD/x44g7RDyTqDxsOivFQjirWEEi75UIEhudTCIMTCDQpiCXaM1bdGOc/vGoXSKO7yXr/D1+U7b4cqXj/K9hFz5OBd2PXb1PUHhd94H2N6S9FztbcVD1T2F5578c+25p+c+nhoes9Cn5vT8zv3X23ff8v97lN1z/ybrr3rxbW31+SuF5NbRfDS4OIrrXNd1cPIVNqdmUcrjM2Q+10T8Ursk/4qkH7CWWX9FBWede3VM7C3v8Flmp+3wc/kq2/wsTxkaCbv+CF/1WNk/csiAWlVrUFEYh1JpbkYA7S2MDwbHAO2WClkphKThMuu577O1mBtSyqTwoPOosH0QA1d9g2teHbF/2T6pE2Bt1Wru88k/IvYX3XM77D1V5EWStM3o7fBPbg3T4k8nK3/YzqevdK8xHhfUIzn4ZqqBQmQSIOhEw8JhfsEsNI0abZqBINmUOvFFhFAJxJIDFOqmMB4J0TkkFrOg+HBAn2vTQtesJkASACylQU0ClR1SJKOvVbot/kZylLd5qLJpQ6e1jVoGrXxuz6G2FNx8McGHgQ/xlXb0D4I5H4YWEErWOPJhjD0QfDFmvTh0HPKKXvct/rZ9SDYtOGMw+PN4BNt+ruSHPB5oL131h3FisN+f4LArHRVbPVXMymsDmPKBu6tPRHYEMcQ8JgV3bL5PgSDPs/hpnMT44Grsi8VpPiOrfJy3JW7QRA2AOfYqMSNNGzZ48UZ7v8ihJbNkpEwFKfvB7PqWjkCd/W750LLsu8+HHldmhakrOGAsOCcwsYqxWNkjH/THbNr1pqbqwHZP7jvLqFU8HdRg3LmDTaTR5c7DFl9j6ZrrYjXP3rsMBEmdeSWKXRz5h3f/8Iyg7xT04VlABIJ4EHRvDeuOoASIfvjoRwWCfvyRQNCvH/38668rECSFBIQcBBoBnvhOFfpNYQR6xg4g2wHcXULY1uDPyFEFWgd+8jknVyXk5kLDxd/w74QJuwKxqg7jUsY3Hg7WP0aAkFyr6Be4+gYwgUrwjKCPcsPa/TNxkiMQxG1inwKrOQR7vtXunxd6TtBXgnkotHcEaVeQ3xrG84EULOLtYV/p2UDcGvaFcgd/JM+5jh48g076Gbv6EThtBde0w+DO5eLOeMq4Zc/hJZW3+c4XjuNx54s8/lvJXaGD5zrQ6hzD5IttQqWB2OHJMICdtsPP5ats87M8ZWh4jHEFmDXOLeeRj3Ho1m+MaXH4oqf11he7xBXfpmyC1QHCMqPeXVdp2FS9U8EH4K08sPs3IyRqdriKr3ClPTffx8qV3tLPend7d1r5oV/pA+8hJSK7RZ0EG7crFZyirmFoIvBumTJx9UVufM80/JF2BL2jgaB4Ksf9JN3xZ/ge3xl/LlfPPXzpzf8oH/L/bOqgwgpsYXAtXAasygtVNpsd6yPhtux85rg9HH122/6J4Rc0G3Cr44CRD12teNMCqLn4l69m5cJT7Ckvk6WPTV6co60DQS1Hf0mJFh+cnPOzOY8A0CdqFx+MpXneFTRXWELmf15g7NfBK9UzUW/q6GwiUemHZbUentSRYBALXaaVVY8VdhzWl9YBRcLUrT9lPkY3pS1ZnQ8bUmD1GluZNqJlVzEuuhGH4i9vo2ks6tZSB+reyqUn0jmWa8ddweWrVVc84ODjY5MAsEH5GH0gRqqm2BhbS0OwdHDn8uBj0p/pHAhy5aKqJwTSv03Hc6tYcR76YeHfZ6huu9MD93rmxmX35M+M7bUdXxt2XPmuaAwdRoZpg6F872MgaPpNTnM/2BmGJgmA+SMcPRfqtYU3y+lAIGjvj7Pm0PZrQDFRRC3dS4GmzqOtBjrzKfjH0jVfLWp+X9/iSE3oi7Wr7tgz5rhhEDzh3PmuJBE413KFGYpPbY5GaBrhrkqHg7pY5/l2xxuOl2eLxFoV/jGmk3YrUV7eXF+FkABsXIr5ELzxjiACQQoC+dYwB4J+0DOCFPgZt4bNQJDeGPbj2BH0kwJBvylii443UsaC30Eb5mFd+9kJ5FHpdQBrAc3x3CJGg0XzTiFGreftyExYXOazcwL3tm2TdPCOo1WwL2TKTNLoZNxOEJyiOHYYbpXxS643WVekkHOku4EIwriZo9YZ9HGZtccxEAT944+Dm7eG8YDoly8++kpBHgJC//HDogkEcUsYuBEIEp2HRX+pt4Y5+IOrVDl6sEHLIdWnW85UiE1pR2GRZ9rhIs84ymccvODit0V/jA+5pl0eXMvYvutxqRW1RhqjTtn5qnfPuzYo31SzM1G39LGuPKcz/lwu/z186cllr+s5Ym+9CF/HXC0/y6SMPtKV7eFYx6fZeN8XSxM8lB62bef/AF97oCMOTxaG81yu9D186c/N7+kDf5V2G6/oZ9x+SiFreR2qp+Vdzt/v4N2IfrSHpMCBhoe7QwbTu3VrGM7YT+ieuM2hn9M+EdzjKw+y6G95rwta8eUr7o/y7fK1ca+Lev6ZxJBap8JcmMpH3gEzhiuDDzafHMB7Kl448ylvGxdbLsgtX/FZtysJNJUhtK4KVmFz7lwAutDbzTyJ14zkYiydmne5yQBeTLZMxgNNX5mJE7SYAdPI0RT4/bBoKtI/JPMjQ0FpjoewpIx87yEYfNTiSWGXgw/70IsyWydOrUgJOL1xEMgE09mJc1xESha7xDJzs0db2qFVlhOtoUKlkaUweFWoyVYG0aRFD798AoDdQZgPP3wiBfglNYoqXKTxvbBu+7B5VwtrdSkfLkqbLvhAlQcxt33jo2y8cOZ12ZB5Q+VYoeaLskOIh2NBOz1vWkmlv/9OIAg/lCN4WpixAiF+6tjpWIpE/FPpD/nyQDyX8nTvINfLFM+0weLsig/cWWbn2+Xv8Z3xlhHS32nRL3jneV8CQbR/n8c8a9i5zHG7Zxc85w+h1jkD/V6vRJY5tn25q3adYgl9zUmRyrE81dS6kCE5qO/qlw2Txvk8+MwbCR851JbYRinQXudR/lpfaq626I2mBVNf9IaP45KAGjr1wbfXG2qOtS186DhrCV8DZ+Wwxo3VNo9fRGyraJBTt+CxG9Yi4/oBzmNDAp5LJRF7dETJmOAbBCJww41Mv/6qHUF6WDTPAHqlN4ZlZ9C6NeyV8botjNvGftJbw7wjSLeINRAkhdwWRm35cMVVCx0tydvC8ip5AkGMUn0cHGqgKHIZszbULUUXLeZ/wsKsBB1fLEzBUDZ8HHXgXbKDG/8NfVmvqGopdx+OShoAchBGvLxBDOuYsfyqeM9cvEFse4uY+jGBIN0aJjo62O3z8mveFJZAELeGfUsgyDuCEgj6WoEgnhFEoOjL8fp47wiSuXNHELBq50R7OBCEDWev2LVI36RyNodhhytwxu3l9kVlz2Xw4NL7lJLi6mjiaP8zdkcf7HVUxuNd9JlXVxmUZ3yhZvFtZIPloVC+wmYYhzMf6NZ95DPFqK5XtqZMVnab2RObk671pfXVNRVcAFfyO1vpzXdaYWik3RelfcjfzgPp6SW7l7fuN0PH+hm/pJ8HXekDN06tKBPiis/E3RAxIefxDHGjTVCAptWp3/hBXHCukp5pS5PijPHQ+A7oHztGPZ9rIvxW8+Jfkf72W8PcptEDD52Mf0Zjn6p/59vhsw07bYfv8cHz9yVG017fGF0nA4LV0UAG3M5ik69Fd7bDBcAnxUFm2bH7KfDRyql0VyL43qRfzb7EqzCG0lRzBSAzmntFLvUOrWhOUvSgKXBLLDaogwXf6vNxEmvBWNfYdh3gqU1ttmloNgH9+Ux9yFkq9qDVfwSCfnujpRY5RqAfnsFNwQiQYzEAeUvhF2LyRRq7h6qNGzAtiieOpFzbsWy10Yu2aPIRCstm66lzBr3a2taOGfBDoiyHHNM7Fh7iQ0n5qgB+J2gGhlFezA7aoJSj2D2/rfcWM9VMwVm7MIWbTyYD8QW2lZ7+PHJ9KM0hNTy1l/+od/B89Z3hs+7ynfFXZc8tUuixKcFd9n0MBO3nc/3VUd8yuacsT5oD3okTvpJkjjr6ebLfAJ2NIKRn9v4BC0c05uw80+Eh7XwPcf4RPlp71WLq5xLReq94jnbDccRY/NHDlQxXBGoObR4nawH45EkCFJ2oPRgiTYcHTy7r3CDR+B80cj5JyWFzMEiAdwSpkNfH51XxfX38f7/73m8JayDo9SsCQT8qEPSLXzP/0y/cGqYQxwgEEZJgLwyRjhUIYh3wma7lyuctYoTB1m1h6d8VGktoI1fK2p1rKN4YyU1JeyautIu87pt+hAefKaN+F/Ezf1YoGCeZCZxgmsZ1UPkMBFkDfPaqWMgVBNJr44PTLiBfO4VTfggEvcjDogn08Cr5/2h30Mv/jFvDxsOiuTWMnUBffaFbw7ojSJpz+z23hdms6JZheM3djbmmZawJ7dS2tkwutssE/iFahe7xlE4OD166Snh911H/R+hWaudFX8f6le4/A/dU/TtfYMbC2VrslS9Gs5YMiCPvoj29jbvMQ21/Kt+uw9Z5cO3YD/BzPbCPaHza8g4/V+dj/A/pNm2OxzU2zzprp/EPKRSDR/4QIKtsxn1KPurQcyG3qGf+XXx8d4RHubRERg+LdiDoi7OJf0r5HwsEcVKSDhepiya9zcl7oebJqKfU9xzbn1zxX8LIEIqfo95DagzCwKvac3lRHoNufbbXifRamO39feZyPXNcULq1CRmwYdPl1GfPpSYUzNbfaDogWOhUl8UuD+OUPI5Z6Yk9WpBg1LAfJ/tEtn2tDD9Etevbakm7wmeRtt2E2Lexm2r9+lmSDYTeos7BFUQPdbQeL4ZauSTA15iAWZqED7/F0Ng1albBmmMgTJ6wll3S26YuZOpSGV18OLJ4Q41NOshgM8TwcUwKvlUXe84tK+RjfMOQUc/QonpjCobxvxsGvJePNaddO8dsRBiHKPYt2yoFS+Hmu/4rXOiTMoFd7v2E7/fS8AcMf9BfFW9dLdfjxVOGtpfLM3MRiWXA47FBeRLzxYdbId71lNfHj7nJPhA8Go0/bueWMVcMnp77t36qljOFhdae4KvnjzKr1KvALnfs4/JW05FzlZhjr7UtHjQvqx7SWL74rDbsmoDrUeq94lk1LH1nHcfysm7hl5aFoyXUGZqP8wCgz5gYsQ0w/qkGf92Po91pQxNinlQHn+Bch6MyAiNkIV4ukXx+UQUNBPF8oO95fbweDP2//3390Q/sBuJWMb0+nlvHfvgxbw77QbuCfvxZt4a9UehD8n51vK5k++vj+0Cbjz9agSCdwQ4UeUeQ+HPtTXsc8LLptAfcSA5upofwmpvoYvuljOHJsTh70/5Uk1ceUNpCt3/x38C7h9Qu1hXeFaQc6s2OIJ810OgIwmAKAHlHMoGgBIM+/pjdQdLTQJAq/Ua7fV5+8818a5gfFs1uID0w+qWeGeTXx7/Q84F4a5h2AzkQpNvDuLNOe6zsvu4Oog3RHd94fpRJ+Mn1QtcHK+MxAVuCdk7WeUZu5V2mMDl1PJau+Gonsrja48LwrcbWB+9DfNCb4Mv6uJi/Nr+tr+1Y1tM/JA+t0TvBHI/PaWP0ta6jnpaeo68+m1bX6Cr7kD/bA3vv4FfKzZ+t7M8QGAa1azMel+LdXrD3+JZEZkOvUdQw9FVH4FHaMsZZ6uWKB7+O/IPnT7ADQWHyrWHfKkj+V6R/LBC0N6YTSPOdtsOlN7+igbuil3en7XDpzR+ilYe8fM3PtL38z8MMN6WMN9mewQaKgd4JkPKe9rYFhjp0bYzRAYJTnNQ8pR6nviVQ0pbnAo9Nk19UNJ5rrk2zDV5EDVWbCa2u+arsqLH1sYDghJxpgD1JZ33ioxrk7EgEOJkV1rX82WjzRWvrSikV5Lzf6rVaHeoLVrJKcLxRIOg3dgQZlfiy6xaNarHHdm0w1Q/qtLko3nACvMvA3TQsnGOFsnEIYR+MYQJyqq6pV/RtyVs2ycbf5IVrp78pT32uasrdA2gnvmz+GJ8qHaYjxP/ekMJpY0vRScsWpiXnCz2qB5tUuzIGJtZAcBE+0su3dBwxH0p4rX1w5Y2HaDv/c/h2OeCbbheu+ppPGTHD3w90fhHq+HhvdgTpZB0z2HRgfBVn3AaC4kH85G+Jnn/GHAJuS2Nmch9EeTRnJt0YB5j5p31Gz6y05iboqY/5rFzRDH8g8OUDe04P0cKbxlTbY/zQM3ctS/Y62+YzH3a2DTt/61t5qGfe1lY7jzootRcCMzfPGlVIWE75ULw8i0Y+ZMppgE8OSZs3rYUFG2defvEgwjnVQNCvEpyBIL0NLA+L/uGj/yEQNF4p/189Q+jV67xO/rVeIf/6hx/1rCDe0omuEQhireFbvjQCPvlMEYu8LczPBdIzgT72M4MIBImvASEbiM3glM82jDJ2jzVMPdZrKCQ3cGZusbFWF8huRa9XINbvgspbmnXUjyO3oFcuUslzfmK+A0LWgFX2qJSRsxto5ANPICh7pQgkJZjEc4C+ffmNAj+8Pp4HR+vWMN4axodnBDkYpOcEffWVHhKtt4ZpN1AfFo1ncOGnmgw51+wpXD7wIPolbHlWRKXNQ0EYN8EJWOcsBQBXnwE37TC48jUv3zkvPfLt3fg9uNTX67/7PQPkrMrlne/e+h3Gh/hK2/ms/HR4Oh/tufLaUaFPYbHteo8cKZXe/IoHXOnN/ww+n5wYuifadsbt9A/wwQP7SNhhmFreBXqO7Lg/Alef81khwKlfhTFW6Dl871RcaatrAV4jMitmfDOPdo5f1zb4YOWxHhZz2bO16+b84Y9rDZzYw46g/7wrgaA1Qbj97/SBCenflzzibJbHmIdjrQympXOeC2qlntK2p/DstcBfG3Z453kGjKon9UHrfIJusZrbJ+rix1oHUQRMOjycwSBgaNptOjTTksMDgXcR/G8sOpX8bCDtc/9NW7J/4xYxLcI8gYRJHJKIYeZnPKZqtqyD4uHWsPErpVlGG8o3kSEeLEvbqCrWSM2wazDPrOeBq8ImUYbmyXMGwgV2cLqSx6TOWp5Zlvq2xZBWlqvGUpo/Qfe0GWBpOkru+B0u1xHX2ottfld91bxHeX10t8kPdcddoYcJsx8G26M2bOp62iBjOR12+fcpELQ/HJEvdndi+hruR493jpluFXnnyJm8n8/hxM+7r6f8DYC2+5yeTuEQC3MpnJW4L3VTyQXi2IoLhiOK+sHosEsemVZLdh7sfIqtyPDJEnbX/LB0v6BHIovb1DhgLYqjQceeFOtr/1ZRrR7Xw9nS4NdYKF9sTSBIV0kZzvN9eEsYu38c5HmtHUF6ZtD//vd77wL6TjuC/muY18dz+9jP+ugWsZ+5DVvm6cBemIworp9sW1Hgh+0rXFydj0CQbhMzztdZRorkfMEd9muQ026abH0e9IPmVo92KJtXzzZNenrtNuvg77rD6wFwcayBgj6/3IbUjXY3bPie9QXV0BxyduMksesHS7MbKHzZDRSruSVMzwWiUuW84etTLTT8+njdCkbw5yt9oXmpQNBLdgMp5/lBL4R7oR1BPCPo889XICgBqItAkIyi7TaLHCNlOzjA2AKUco+rbNI8zOZNzBGIpl3Lkf68Unohdsb/1d/+QR9t6alQ+r16St/lz7zwPEQ/8799ubXUqsc0lb98uxy0vVye+3m5z1rvScD/VN6zDte1Dmfye12+59Pn9s+f5cSeS/Q2cynfmpw85wLXsiO45ML+2LFXMviQnX4AdiHXPPON8gr6xCrm7omThgSCvnys6rei/+M7grho0+Dm91pRevM/i++enuIfq698Vzmy/8qUkTgHZy82yVnmXCe3Z6zM5wl0wbp89ie0v8Zd1BPUqINstOvIasK4oooyiotnnqJG1fbmi29AO7vg+sG+YWoZ5mAKC6SYhFArVj54bnTPHikllUVnhIwZB15Nz+fNyPOq+kwwVjXqiW24gLrZOo85LFp3HMgEhOALqxhsOrTYQntrQ2CVIHvYCA6bEEroCeS6rRO6kCxCI0h+EDLvAWWWaqKQhD77F9Kuogzn/AE+SG6CgLAxJ+1KA5d2Vt1y6EOHRNbFBspKtR1fr2G78wQuH0aV75ZLenfkqua9hegtXLL34HTGIN6lT8YAT+E7u/+yXqnbdRU2rw7kfBh2+yz8GedpTHmnj9waxpsPz6l+At/zAR9xKcqXN+YrFczI/Jdyz9/6FYZ+OdyWgBdjpDU2P1vUcunsUqCW25S6y3dLP2KeyneUmiUqkwq00NI7o3+yBzjyLV+Vbfls11ep1epbTDU0x+dwJdFPlR7XSZWD0dE0uKtXAZaZqiV0ZDyPQnffA5QHOJb71kOe7yPdBIJevdItYXpD2A/e7aPXyH/3k28NYxfQK+0K+q8CQ99ze5h2AsH3WoGjBILQgYkJhXiPCs8CImLhHUEEg/QhGORdQOT8+MJoHR9lyGNn3ZBFP2U8RTq2IeWM7UnSuPf12vzrgM580ISvN9oAfQ6ZGl7Q+TFn9APnogQJ4thS2Yxl3IqOzk/0AxQ/PiUkNm4Ja1kBIIJBCWL/9tFn0uGHRWtHEK+M/0rPA/pGD47mYdE8K+jlN9wapodEKxj0jT6fsyNIt4V9zuvjVTmn93xYNHaCGzkeJdm9o6GscaDnWG8GU6zJ41AdO66w6xkF4M2VZXmLvDaNL6Qq1joohmmkamvfld7Ksna4/e4UqXJJl/Q87zuWu30pOEHVd0KfirUL9NmiE+uNR88tXW04S57LtS0jFuoYy4Nx0odPzvItvw3fGhm39lfv+5rv58zunR3/V/tmP488Kjz/7hbslqk3Ke7kGnjGn8uIWRXnbs69qrENLuS87ugk57lzsXHR8rDoSL9TgaBe/DnRSHu5sAnj0BOyuJabF3/OS29+pp/L5Wt+prcMvenK3tDouHHBLvO/JpdtGVfNZFkQbTtNPLZttZlmUHILzcckDT+Uc4rc03wW2fLO+qW8cOuFEz6XW+9WsG2i+iTDOJIMNDT0IQ19UG8asXxB/VGRVqOLfx8GQUtBrTywM3qpLl9o5u1hUFRZ21eN5FO9kSnFbAm4qEOMdx2RJQDE5KFg0Js3MxgEO1+m8tDHLJ76EGu/2l50bMUY41W2TZT58Cc6i1vYSPn1Mna5TOspYp6BUTZxLQDQMv5njkrXZ3krEGbpNmNUb8f0AbI7p4qHBJ36YILvHi8s9u/gU9Hl9BglJa2SrW9qibahPjyTlmpBrjrFuY2HIeC2dzyDwxe4kN1kC5+aK+M86lIR/IPodhwYPxTaB/gIuPmf5Zn6Hn3Vf9ZdG874e7YwBlg8jNPJRhuWgvdlR1ADQZkfPMPafYzx+gLE8j8zTHrAMsO5nocl1HODMzn90XM6cu2j5kdsa0IG+XLZpNMhHBwzIhYZqbPk0nfLvyRpZ1q65Be08wUOL7UBVfaq9vBX19EGsKFc6asMGiKXPSM7Htq5DA6J+j9lXyOthutHryHI6jPKzIvRl6/qmS9r25a3s8uflXgq0pG5nTGky6VghTIUt8hr439WIEhBnh9+ViDox4/+l9vBuDVMQSAHgr5X+QfeLiYe7Qj68Sd2BKkl0h+dsoux1h1ACgTpCqt/7QIiOORfWxoQ4ocW2pGwYbyErrRQBF2346XQwJC2dgrO9XngRfLYN986oDNujF9vxu+oADwgQSESbSIY5KLXMOPNXODdBVDEpSBQPIDEghMak3O5fioQ9Jns49Ywbuki8MOr42cgaLw1jEDQN7otLLeGKSCkHUFfaEcQt4d9rtvDHAiXv/1MIC1KMIPuxiteyiinYNxo+MSbKwzwN+0wOMo7Dni45AZffvLyAN9Lu67FE8n0dsYAgS6wcx1Ag8CMSmpf+7t8OScGkyRa3w0e/fLPGX9suUpSgBubx+alP+V1rL7mi78WBwO9qba3nEZOy103dpXvaMuSqs7FN9o4ei1Wp81InfmWpkDHNuCDez47SsYOalttPHK8v6WrkVMv1WNXPH+2xzw1TKWq0XNKxvpEdwgKsdt0M/42vikLMPCdVzuGq2tMTxZhLHIt8nwrOQJBPd1LcyBoGP4Ft4YpWP5XpH98R9Cf1aiesLu+vw9Hre1qJo8MrkwOu0X/FjgDDkMzMe62g+PiTmJU0x5wQGc+yuGB2nbf8kFd6dgvrQOfLf3WPO1LLa3tnFdz8LJVfyyEx3Km5EO+rF7WnnHoIBW/67WtVAidhjv3kYbYX5zEhqENBlgPtoVgvulAcNadFlnUizAts7SShYxuB4H0fKBfeU6QVrbG8SvdjCzHNgI+fuOGVnLY2g+vu0eZg1gCCBTFnPD4oZs1RQTTRtltG/AcL8MfQosXAST0y2H9M/oFetu6dI6LtSbn9Js1bHwdg8haQ3yUKoLD3YNENqp1XwDTHTM/8UGrXoPw2176Mlqrz5XUBgohbxprzmjDoCy2Gg2hio557YSjcjtucheA8UOyB+ovCrhnL9/Dgb9KZ/lzGZl7XfCcehli8FtmwNYt5Zz6DcqCe1eTv6DzTX1L+7WicM4q5vgmYebJOXAilj79KhK4HV8N9vugFS6tOXLQWv/Ol5lzxyCVuWzHIpv5DXpScbXtXEfl93rPtmCYpk6n8o3iqOWxrBrHGNzYb/UUw0J2+XMTEZjZM1rTAlq+JIGZW5EKnYWwX01OW/hYoVojIQIv/qIMUoi0MdqmVjMi1FoW6DW2ZMlJv+pWal4bTwCIHUF82BH0PwoEfa8g0CsFgAgEvdKOoFd6iPRr7QjiFfM/6u1hPByaYZoWqC7vruVaqsAP90+xG0i3g/U2sT4nKK+Tx7a1f8w6ZJPN4tpnGM/sCZm2CX/EJ+A87mn3xtHxxTKOa1d8TB1D61G5JBePFVXAvUufDG+rmgR/FOjhxybRW07fMhrA8dYw5iwCQMk/UyDoBYEfbg3TjqCv9UYw7wTi9fHCf6Nbwr7++nPvCvr6yy/8tjAHghQQskulmf5Hr3WrTKsdHAKhNtkG3D9gkUXHdih4Ggn4jon59V4681Lmc3Zh+cCX3nznri3gCjN30E/hr6YocklKq1fYPz3ho9nlo2VHHFVmHHQtVCPCd/TGETda5Toe4otGd+WRzX14rrf138vb19DnuL/HbHzsfJBlIx7bGMKOm71Ig97jtHfl7uHdKzsPrtr5/kzXMcar2+OM6WpLh7EnRvOPfGM7gJVpXiKzY5PPrVExsKv1xTrPm8tcmnOcSi2p3N/npCRzeG4N+3/vSiBoP6EPJ448udPqRPI/i696mu91FIZGwpa354tsdEZf4H/HcfdzBnAG7WpvB7Gm03EiLNpte44+ywmUli49lIdrGespGDhyls+SOrT+cB2P1eczZ9iZ0+lo47L9OJaO2o60yOwcMsZGDZvGirIXc194VK1r1mFu8xMfYrYV+jC6OS5IG4fyvUrLSahMKhN0gZNgDzt/3rAbqDuCFBB6o8BQ6eilHn6FJOjziVZUDsoI50WVcNg9dwjBSwsGHVMEauGrw2i8IVXgOnT08sU2CfLkBsVaXHdKw46hI0qhbGlUEb+L5oo3OjoHT93h4QPuXqqaU1WwIzb1Dbj6aEaWjsqFtE1M35f6lnKbPIru3wEvDmqm3s51aRNfcpKaj5KK/eJy0F3uI/vAfsjqb9xTuF4xTkj7s8gH8isdZa/7qaNwaXu9pZVvp01+IXyJB+gAAEAASURBVBkGHoOD0bAY3qcdQVyb3Pwx4D3D1C/DWfVn5yp6ecJ1mpjKhxgcY7YKr3UFs/cHMsMCc1C6TtQYWmrnuKdq6kwSGtjFFw2tb4UHlp7UEIlYe9RnE4bCWtQ5K1qu7K8F0ICveJDeaVdwccnRsnyRIBFamrAczqb5A4IQWZOohVLiFnocLG5dpWyO51W0wOd5FG3wxZeBoYFHrw6CmUeB+QDzyY4gBXd++kWvjFcg6NXPei7Qa7097MePvlNA6DsFg3huELeK/aDbw9gl9MNPv0qhAkHoIqCja2u2yiQIRPAH/MfaEcQPMAqFyJbsEqLMk3NqIzlfCmyj9akNI6d9+k+ijply7aZoX7ihHv1LLUQJ+8caKYxOkKB3xcH4OCsDgAcHKqgz/EhG0KWBnxiN9dwSxo4gaIPOg6IJAkmIQBCBnM+0NeiFvsSwI8iBIN0a9vJFHhTNM4K+URDoqxcKBH35pT4KBH3xiR8W/dlnCQSpEq1TEgxaHsTTsouDTB6m2ubYgs3FQg+8MGilN2/TzgNc9+x4pFqGHr5yQl1p8S3OUglcNXlMU5v8v+BQq4OSaR44R7h6mt/n27WhI+OEVuCynI/lUb8KCa559e85NNKfxxftNPM59bazNrdi1UHHw/poQ+p+mC8+gfPIh3D6j3qBj+kKd+R4l0rDlW4Snthbv9Ng2OlnmhX8gYP1DaU3PVK8CO37c/0e3ie+M4/Nsw7PtG4R50P5PJaFJQfn84zvT5LJb2A5Dy2jczLXrf/P3pko2G3r2DaJxyS3b///Z77uTuIxeXvtDZCQjk5VOfO1Q/uIICaCIEWxIEoSXcw8GvZZBoLstDocT6RJOcL3+M74c7m13MM3vfPfxpdJ53YCaO1/be7BJxPSRmxxjFLlfRJMC4MX8ZF09FkP/SlUOqqiK46cHi0jfq6S14zNpPzMd9/Wo41DxRm0jRM57ZhweFyjZLpmfMxCLI9oYV/8jVrS4hy+gNRNLTbzRjZ940AQ1RME0izhINCHD1+9Z0eQfl0vUWYCQHn8S4tHrcgcDNLWAvBs1TYdm9evLCsjwbfdTERGu+mxMqPGCNvtP0BisY6RdUtrpZM2UR9MmfAW+wlIP01kpMEgjz2Ppkd4WmPnnq6rzbaPuk4Vpe6peMLbom1ja7dH0Lj6GO5jOwevQKpe2iFVYXMhj5Z/0vRA+2y4bJIZme3KA/7XFq66oG2YOpvvitZ9bVoxNN8XFQhSz+xTTtfR6q32Bf7Ej8Fv72auEsXC4taJ0f6GC3l+WzYaJz4YuDvBfcRGJ+dw8OGgrl1uaaxvPnCRTX6rFe6dmk6eOsiP+pq76bvcbW8tTbnKI223lQGVnZhT9zUtrNQWe1Nv175lTm3svrI4/S4JByCQ5NdeTdvNZm8ASasnP7RXDZLtOsNbJCE74JIAjrQLx46g7Ab6oADPe+0EevPV//y/BILYCfQ/ekTsf3lMTEGhN/qE/P/xcul32hHk9wxRV76L5UCPn0ViJ5ACP35MTOEFYO8OSiAoEQtCKUd7sdlNX/ZLtxtSfIdJnr7VP3DV/t4tiOssIdnozLnU6z2rNM1QuShw6uv+CY7gAKmsUE4Z67MjKLUQGGOtkdEeT39U8OcXvxeog0C4xF8N024gXhD9nQI+/9LXw777XoEhvSj6++9f+EtiCQTxWFi+GPZCASHftFLdaTKBMII3ZStttpkOv2GuhlAFiATHdWlBH5Ob1W2bZbCzDBwvHPGR3kd8E77mDm3Kg6HvmrM1x/6KZi2V9LE4hjp0nfVZpxi7j5f4BZC1RitEEwm/HuXP9YZPdZ/4Gn/O/wg+6nh6G88WWfqA3Dbij/bFgcWFp/pi69s6kH1I9+b8vKE54hp+qMVXY/wh/qfSzueS5cqgK7sa1/bcGyVnPvT2jiDPUqq4eRrAFnC55omuSYBrEyc8OIM6BpedQy+0EPxvPV77RyT9nYNJf26aVfYJ1PmVJdBIyN3ja57J1zB5pzMf+Cudn8ZH53UN6HN/Wu/G/p2g2Lsnqgy+Ls/+YSIr9+Mp9wEtufQZE2qdLa0jvsA5RYgwx6XLKMoASlmAwN8Yo1f/p7R97LpSUZMqT53druhLG7pN3W+bB72q1/pO6jz+RMv/GDDahU+6lejIHTnpoBkieEyRW20DKRk1jWkYglnC50AQASAFeng07IOCQPyyM4jIccIz1I01bFVnIZUdQdxd64CQ8A4KoTd93GP+Oq9G2FCaxL/VtPQNNNu9dZpdRVvPQULRH+ndzNhg/sOhx5w1hFKg1aFGyd1VcDDCURbTrmPzLW0CDIvXLXKBw1RWirzgbXu6ls7DT9vo++ShWaVqaS2uUUjsav7JaThCBm2/yqAsE2a3ucB/spMHtq+PPQnbcO2hl08qXGze9G71wYmxeU7oG93N17omP/0K3r8Tw5cSCGLmYl6L45gP4giP/5M3cxc/Hs18IuE1CSC35xP7NBgdQ6tKMjd0x4hqOXNl/swsZ0LGjcW3QF8rgoHY6YxBH4ljtKbcOHJS6whfcH2MxNEm5pWWCZ9abiAzTsvey6/qgbfxO2dxS6n1H23N4rfrPFoUbc2PjoYPfZuCaLmG5ZygHyVhhcoFR56jalu02OWFM+pJYTSYBXVsRJU2zuoF0PpsvH5v2RGknT4EgvyOID0CRiCIIBA7g37QzqAf3wr3P3pZtK613LnVk2UaOwR+aDGjUZWxZaW/ECb4a14UXYEge85bV3YgqHeCpp3oa++RD+MNp5yxXj7AF9zscQtjQvwUdzE282sGcnA+biQIJ/KqWUq3NaGmTN8Q/CHVbiDfdEIOeYJC7AjKLiBeNP1Cr0t6rhf98FWw//qvf+nxsNd6D5BeCu0dQXlRNF8Ue/mKT8e//Oo1n43XI2G8H+ildgTRxcwF6KT9CQKpPtuog81fveAxIey2H0a3K1iOpMwhgeex6ZEKpWHyhluGcozYZyIQlpZx5gifwZhcR+OJ4hUXkOUFpGtCa45itC/2WqP7sLUUl9uOHvylHlp9nVpoTbFYoPkobN7oytgL3LSjzqfwtZ3Umxa1LqSP+sLLGCdNPiMuDpE/E7rOrX/Xs2kNtV2q0L6ZfdCaF48Q2NXlaWOad+611vBl5fh2emKW2+/tkTNf439rvoa+FDGXcE1QvDpnYVU6ea7sok8nDzad+YwzH5Scc4tHgOGVMx/ryoo9vuxRpg5mAP0NiX7owr3UQvDfn2MgaJ+MuO73T0/VP/kmfLZo0iZ8y0fnZdI50/4OZQZYRuO0RjiltGvir+HZ/gmfueeJw8V8L3YmZ0+ksWFSngpbEqc75ayu68xQEfxA3AWjD3LLCFP6PdmvusIDF37gh3OZZDZfLQlE89LAPOFD+iZBX/o3HzYxNbCI/UXvAmJH0IcP7796p7uUBIJ+FsG7kMwon2q2+2YEfp491xc4XNYSVblfOqm6YjdWUFeONoFy/ounKWbRwaPIwwhTTV42hzcy4e8/6kxZBAy9TlEV23JxBR5uOYkdaBgTZ524ZGfpgCVsRxtyVxUKKbSM22ByPMo0pXV3mVxWl6dap/Llp8bBOGAVr2owyyRI5ChFjf+kk4tufQnDr3QcYq3/DJ89f8V35ukyQ4Kx56EB3ATlX2IgKL6dXhgOEZg/DeAKZ+bd5kEO/Eg3J2frPvKBDUXzp/8NHQfwJG8zGteMR93Gmq/oNza1nPI7fK5hqN3zC7JbCGiwQRxp8w3kBXiPrzUf2+t17GHkTpXFqywBA+VSk6lw6gHWb6GY91lMp87ArRc8cGgzKNQcnce2qk/sHxXJ+fHNhwoEffDjYTwq9v/+n74U5sfAeBQsu4L4xPwPfElMgaK37xUIki1c353TGtumEUkg6CteEo09/diYysC2EVzz08Sy380V3O2w0WlT2gaMHyDoEACMYeeWOR76D4h2pm9MwSKntXuzFqTUGNfgKrDISdXkfKMsT/5CICgw7wMCNq/Wd+wW8mNhEmDXMY+E8eOxhu/1LiC/LNqfj2cXkB4N0+Ng//pej4MpEPRagaBv+b3W18IUBHqh4NGL9WhYvENMgP1VnYgV99oCGJeQyApsd6kczM43T8uQk+Ac6ozrQ9MoB4aT323Cv6n1ltYY01ucvm24GZbdC6E2MXZuGDfDb4Seqn/yTfhc/aRN+GE+ueIJTbzWd/a6+uHCZ+tMeKQeZB9LV/qReYrsY7r/U+mPuHWdZ398+7CEcyY10ZsH+MKAs+33RsCZL6oSxAGG7roELN6C+29E8NxogRFexqX/lgMq3Gf1suievO6dNDhupr8j37RpwtvuTDq5TGzs3wXqyY8LYvdHhuj1UL9u421rHuI70MaE7D+ypeq65ts6noKhfX2xj2ZOM9JjtcB35OkFqP3DCYkan6mtU7nFdGdOorQTDWHJSQwGnH1g9T5UVYJhdip8laII9a5AWE0OChvn/UPaDaTgD7uBCAQREPrIN3HNHZu4k/KNF1Ls/tFuIAWCntcjYs+0aOWdQbbNC9euFLNkxzDF10CjGln2isCC2Aswal7t2LqA1kKtxa3ryJMSDNEdVS0AdcCbLWLjiK2Wxa6GBx0Q8dBS1+KDMOuRLZMPalLkuhQxKmtMQOOFMtoHeVYKj35qLjEOFcumrXJBliixIb3oXzowuuHSFfjsMZ5LwYE86zj3w5X+s8xQZ3sYt5brvBi+mECQHODFkNrd58i988B/mPJXoWTgOZ+35bpkVoJva/4+EquUa7bnjvTCgetQuDAKE85jAEzXaXuPSlQqe6TvPIdSBbN55osIglt8N5WByKjz/J2G2CiXTbsRiuLDER3NF31NRiU2hN601EupMRNq2UiJo+0ahKVBALbiFVcjnthe0lVBVMDBXNqKkLtOHQSCWvEk330lEPSTdwR91GfhP2gHEI+G6cXQ+ooYXw773//lpdEKDCkQ9KNeGM2jYbxPiDu31PXxl9qbIqUEePSxdJlET3cQCBt5ZxA/EjbGbh6w6nb0GqNb3TyRED8iJVcdoBqip8vF5GL0yosCXO9ykkqCc45BRy+4ysmUrFkHAjqQaGUxCcr7gHj0PCN0B4K4zn+jL4Uhx25jAjnPFAd76UDQMz0G9srvB/q3AkCv9GhYPhuvR8T0WNjrEQh6/Uq7gbSNiE/Io8PjXjVHf26mqXKn1esy0W4SNp5xo8JjQo8k8sDND9OG8f4uWcGdA1zx0K4rmOgINr1ZPTE0dx3RwvsVaefqqsHZ1i0JMfb8eMc0o+dao2qxxWeZyXemzfLvwTd1THjWA+y/C+xA/HK/vZvW3jlrSjm+PfYTlIVpII4alKFXYI+NXW/0P3SE90tON659xBmrCx7h+zQyVvScuyVdVxmYMbJpbTcYd+FEbLY9hsyog/mYZ5M4p5eoAPCMoxWoZ8ePcPwtBWf4yWt2UU5Q6LMKBLmptHSkPqk6H6QD2PTOJ3HiJjx5gCdtwg/xnWmz3Do6D432ZQKbvH8HeHu+LlbVF0f7j5YeaOezZbAe+AZ+gvbM4azaFk2+PwZmkZa+GafmRVU9FSXv4cqJyTtjfHqW2X1Bpu38/DJmahG9aVSApr6I+M6lkcG0flCdjl5BGRNFJoRftPMn7wdKEOj9u3e6U/lOa7Na4lmYRZns0UqM9wM9dxBIgSAFhggKJVcwCLu1aMO+zHZtQXLaROXJwaHc3BRcykGwG6IJrhHFYRXAElt/EAneWswYhpL18qn0NXVJIOg2bsoNJDr1oqLz5sHCmUJPG13vIjZfcnzgMWC9hbtoxaqwKjZn/wWydGMX+rZ95iunlKi5JwzCLM039P0DxgP4EffYnxdOeYh2wf4oqrriwHdVd9fb+RRgHCDTcnNcfGmBINrentjnXM699lnmke155jA7b50s7clIcF57Xmp6563wnDe980Fvm4wadAdpunNtWgpY4hk2DYsm07fJu+NnRe2FErE6aVKdxzl2yAjMbJ5amw/RmuXjphKJl46+glTmFZcrLuwtLxh+R67JN2ZVD+wSUEbChUnw0bakoBfRyLgQHONB9Zp5y5TozsTqmxXKzUsuKrif3nzUTztq9Un4t+8+6qXQekn0//Cp+Hd5NxDBIP34bPyPCgLx9bAOBPlNOdqZix4eSvL4UhAIGNt4KXSu83l5dDyAnfr5E/Gxw+2oq2J4oo8GuL/KOek7Ga//OQgDrENo4JPiE2CtB2jsQjAa9K/KycwQQVsXEN1eLblK/nQpvg4AWRh8AkEEgCzjQJDCXxJOIIeAEL/n/mrYv/VYGLuCXuvF0d4hpK+Fff89XwzTI2H6khi7gV69JgBEMIh1CmsUbIp3gNNeyhBkg0wzC2zFlzIcTWwvHfP0CNy0L2nrCu+mNMfEBEaG1Rf6+HfUiBy+ip2RmDOY6jmtD/AlLuZmXnXXaKNV5VCMhzlpkBsMHX3Re2ivkD0mHtIzaRPuOjp/iNY85M23bZpUvIQXJ9+204R1aL72GWN8EW8BV6j+6HYX7xYJhH3qymUn5qzR07LS3u24rSi0K/yXhsOj9FL7eMJnXzxEO/M+vZya97hQLW1MKXF3C0eeoMzWjk2dHuJrHpRn92dmgz6/qNMzgc/3mp/hlWGY41wAJeDw8nffzwoEPft8Ph+/HLI99tlBtJHJwRPJ36x1DLZ1BqTwCRYiME+JTxDdtVpFtHyyAZ9W4Q33r7O9Jw+PXRdkdyGbxuTgHTgASm4ZbED6v8cC4wLUyRYLxGCDEbMm6qBu/9MM9eEj7yn4+av32qb+TgGgd+/eajHLnUoFiIgua/aA3wEgB4HYDfTCi6tnz144CPTixQtt3eal0ewWyl237ttqQoxZx9i9igWk/Uxa1AkydtJm4G5m2szisvTYX4JhG8lilBcwOSY8hK7AJb+JSINmsXY3XVZx5t/l2xZca+67vlDbjuWcEllaL23Yek0unkdYt9AXBC0/3mszDL+z487qHrXhZJvPH+Esp8OU/5IDQSc3reL8Mwpk5qzZC9ODOeOZmf7I5EDQrEDmdI0P0RCx5c1cOk5/GxrLfGPeobvYDxk8Ux1lJMHxi//ikfBtifCK6TJBJUWq9YFBe7CUZho1GDxe/a6vN+UTt7brpFYpsA7VJgflvNn0WSswrGZvghre5xqBoDc/fXAQ6J0CQT/oE/H/q/cAsUuIL4fxkuj+dDw7gviC2Nv32nmr2McHKdGGXN2Q6Tpo/TMt3NuzFRzybqB4lOCRvYTdtosyfBQEyxGxNTrsATunrzLw8D+8iE2YslM12H9GCE4RAO8thFhVwhnF0TlV6r/MYR0LueTE97UeSadEqOsrwbwnyPzidYv10g0/GqZJ66V29rzU2oLPwLMb+b/0dTAeA/v3f33/1evXeRzsXx0I0lfEeEH0d86/+er5Cz0WxnuCWJ/IhtSpABP2jGSPqQkT3T0Am/F1CE/7stpYuqb8hEdVJxC/zTT9yJ9vWJw6zpxTKtbouE72c+2Ug+PYus5cU+cRnpwtfeT4+5Xac0+1bLYRmcfbmd4p/cV+wI2q14hRNQse9CtwtmCv/a84/2649t3Zp7/Oztb2FGlq/BT+p+icGq17VaDaFnytaZLveWPyHLVUEIc2iWnxCWB2CEIjDgJ/sylL8Ck4xmIeDQsd+IX+lvtsvhqmZtXFB0gnlq4iXIw6D/b22PTObzmCaXrnj/Hdozf+MT3N13n46WpNGWv0LKDZ/rJ8DUiGXhVWGzHYI/PavCMZ4et2bb7mSe7qJLKlMvUyuXIq9PLnuvbfit21PqwJvml3uHPCYq9o00eCM34V4tCKJf9KxtnQZwcIuQdGGDk2DZAqTNFkoLuHTB3UAa6/EvZBQZ/32s5OEOitdgS908ss33/Ip+SR5vExAkGvdJftme6uPXcgiOfuX+pOnXIFgtglxHP8z8TnnUxnF9lOjAnh0uwYGvtkox5ca+OVy2aJWrryfvGf7/JJITrxGUIsLwO5WAcwnSYcN+Ir0pEiROGhAUKnruY/MJjOklKcZiwhZUlRZuusg3Jw0LfVIiIPaVRmzrXQC8saAyK2ps7RaT2lBgaLFwNVLDrwP+nGA7iqu+Ie8S79JPAUPvfJkDv05cDf1SWB/PkguwtuMd0U7+5u1GeZf/T8UZ5zFnj6rE+rXCvidc4NAsx2nEWmBK7KH9iei6DLwXseuHClKqEPPpUv1kjOsvu85ly3PlSusz10m1smeB6xYbF/TBnFsTPm6ym7KQ2dfRB8sDxeE2nmXM97LaYcHn5Jreech+pFrUHouxTqPFJTJftHh9EA+nWmXdxQNLSc1gy+NsYPcA11VkX5jGsjsPTt25/1NTBupvys6+jPfl/Q/xDs4VPy2hVEEOgHlX/UF8N+0BfGeEyMHUEfdBPmwwd+uimj6yyPivlRL7yqTku9+aIYE7femqPahBccPszD4vxiY3iMszN8y0Tiubog0TR7xDyGjG8Irt1m1g1gSLEq5UK6EDy62/KoDg/BIGQdMModJpXxnn4KBH2tHUDQuZ7nkTA9IKcCO3le8niXgjkEggjysPPnv/RY2L//9S89GvZCO4LYFaTdQPq9VgDotXh4LIxPxyP/XEEg7whi/pOtPuftD0YsYxgbVL0S7Z8+CEFtKuSmbX9an6RCK0U3etC+aZRuU9PJ80cdvmxs+HepPQ0eOHNDrOlWYJPPfx+i4SgnTjXO/UIjd0eHeRybr/NBOoBN7/xAHIXH6M36VL7mfyh/WNfoXbti+/qsc+up/inWY2/J0z1wpl+lmz6YqfV1PmkNL12N+ILyq57Ag+A7/2PdsS3orky9Oop0ZUPbdu+0mnhrv1CS01ajSkD/7UY72wbq9pirHJjriLGNE7NnE3L9PssdQX1yeCJT8++dSGd8lzvHdVep6Z1f8Uxc83U+aROG3qlt7/LOGR5MJo1ZQCP+shzLVlqFAD3AH/NBy9/nb8V1hiirGiQ6FoStyNh9Uv6x3rqnvWxdNnU5efe1c85mfiupTdrCi9/m+IB8YPOYYGLYbbWKUtUaqSNyWjgahsIdSP0UAGJH0Mf3CgR90CNhCgDx1ZO3Wqy+4z1BfNVE7DwmRiCIgE9++gLHKwWBCAgJ9+qlYC3UeESsdwV5VrRBJ/uqo7tt04Oxk0lMNmtF07bHX90iKZUQcnkULYsef7VsVAq3dS+xwrj+Ii6ayiNZzodCii+2baZJ9gKXSbpHpuqwzZNJolRNSnsElC+MLFng9g2VeoRLLnf5qoZa0JmvDINvqjs07WyH6jDdeqmQWleWwj9He6D9iIuAO/+93FOuv6lr6p82gJ/lhic/DPxd66EBQ8MCv5QdQR0Iav/2+bvfF7E9VjOI5xywLq+TCQ3Dy3USo89np8rrfC6VrrPkW5Jzdc0JjSz+mZ35TrUPVv6M4FohFLYoxybXLVTLrapMiEz7opWdA0HhgtrSra0lkjcfdoTzms+BtaPoqRRp5rDWlLqj71qrZPQfmv/6RcWJ0b451bQ1Yr3OEztD0HrECmylAtPXjRy56FTL7927X3QThSAQN1V+8afheSH0TwoE/ehg0FsFgBQI0u8HvUja7wp6807XXb7SyTv6fnEQybuCpPBnvTPIXxOTbl4kbd8oPwaJgt8WYbBGrxvOKFbZ8MQDk9R+g+dyqAyo7VIg0s7dT4eB1LTik9plHY96CZ1zj4CLpP0Dz04gcPrpmk9Q0YEgdgOpCYrf+LGwl15nKLCjFz+/YrePAj7/1mNh/9In5F/zjiA/HqYA0LesR+B5pmBQAkfPeMm0Jj52BNFm20JbCgbAVmyChufOCTzC3SZaa1wdp0zw0XXWcyzHZ+Fv74Ljr7jQckRqQ/B36Qx/rXGThkHRrxhpN39I7pTx32uNNYeFcbMVtOalQ9kusd9uBA588oX0rjruMQvffJ3fY4Xe6TG9zdt893RvvmiOK9rTXVtspNT6buByuvUN8VVvmZ4e2Hobar7OG19DTdlu+6J9IcBw56HFeATaOT8w/ebCrv0w9VGrSMxb4BdXlbFppvMp1uUl18wIWl9dFcXImFt1r7oaD0LhHsnxdxuMsSfBH2sRIjuCvvl8Px/f/vut+c0JKIV/Hc796fp/a7v+CHnGG4O1B/OsY+H6THiQz5qWOP7uk2Aj4wsTCmk+w0f5JTOA5mhzIF3hhsgCm28hBGw9G3pcI+3ilORir+STVdqrgm4PecN2rlmbLzSLFAoLSoVPflSnHk0KvtvJpEC9Wkxqhcm/jwoCfVAQ6IN2/7z7wCNheoGlFqc/EQjSY2LgCQbB55dDEwhS8IdPs756+Uq/l1891y4hFmLPFQh6ybuD/N6gLP2ZkLCLrgSyfeAE7HcJYee23TZzZ9QWxmakfdGV/fYJ+iSTT9Z30Ix6uERSD60sJgtzUPKYwgobZNRka4nFFg4fW8LSHJQ2XyGMy6RsBtZkm2RUDldIcG0BXF1jJCgbo4PcsPTGhiOvNRUKXngQtlypa9tNghaWov6TtQfwSyd8NcvgGcuH9XUzX+Rn+XPZ+i7kQN3Ue4FrUfoZfssUDI0+Z0iyYPncE/MHwSCnyjLmU2CuYE7xnFH0PuWO7pklGCknzyyaKsA3Z8104spc1BydN6fnKRuFxjaCfirb0Kj/bScszYcORDmpZz3ejWlVOpSelbcByqmj9X3j99Fkzmz9zUo9jXN1yOrX1gLzq6uZoE0H36nbnDLSkxpt6ACbUtcQCY5NSy66/kdLASWy+ELcCqyje0eFqJAOdt+oyK/8sioLW2ySPuuucePqhEDu7fsEgggCsbvnJ91Q+YEXQuu9Qbwn6EddV3lH0E8Eg/iSmHYFvXn77qs3Chzl+qsAknYTfdRNl4+6XvsrYnIIPsnun8o12bi/lfvRsXIauLxcmtyjQ5L5JL2NbjiFHGlrlQUgtsoOGqhd8ZZGgGnQqbATHuAHkZyUPKoU6DGQEQrI+ylwGF8qdfAnLTTMjiCCRewCIgjE7kW9glBrDgI7WnMoCMRun1evvtF7gF599d96LOz77xUIEu57fQr5W301jM/FsxuIT8a/Ft8LPRbGTiB2K7/Qry/J2GXT2l6bnXNg4wPRJqDI5HzY4/ksE72hR649s7W1x4506iHRs8ApTWnf0zdP+GJLENHulZc7i5Z2jS3iVqiAzt2Cpn56jr7o6hwfea1mZaH3fLbtUbvFuPlS85+Pwz5S+zilbWfTup1NP+eM52pTkbptPc+2f6akaUJ8UrvL1t+j96Yt/0nwHHHT7qteusJNmafDPUaikTm/ddMXs2ydIoIjdYAo50Zwl8eSueVbfy2ukWrd0u/ZuOrhhdEwMHMEv+cQbq77byoJMjY/yx1BOHWeTBM+O3zSJvypfC3b+VmeMjTSmiR6ZBi7D0e+PYA2B7ooRd/E/9XwbNIcwFha4zMmqmCcDpNvEeu0ii8yWGHMhDrbXVobhd6Gq85RjHodfWKsUtlyKo/iJUjN6C4LHumN5mSaCLzk7bRo6QuGZw01BE5+HLxsiagK8UWkWmMxkikxD5A46UlU46nARclLl2mic7eR3UDveU+BFqDv3/NY2FsHgX7S+w3evnujO5sKBomHdwY902LKu4EcCHrtL3W81q6gV/69Fo3HxLJriF1Buw2Y7pL7KaDK/HcWmg22zZqwyj/eGYS3l7/g3YsIAkG8rNoapMx/BIkD72QKLH6U2wcAJOEtJBB82CDcpsGHvTZFXI2OAAu6YKiX9q5+PWmc4/s+X+uIcPhGE9yW1Hewadh3qta+tu3iOcgUI7h/0q0Hetjgnoabq3GdN/5e/hAfNFIPx5RynPWe+SatZehn/y1ADoOEetx+STuCOBfT/DjAsP7QbF/EX/nj0LBOAmYOOwxHt/PCuI7xLXxKVm3mlIMKLH09D9w/1zkfH+bj3MSUyUfFsVU06oQJqPhOjTTP5EvTpEOA9cbidZzzGXWllkU2APbMF49HAvpOs2RLNsnW5RqdVkFvHvJzYr4VxyIF2DWo7EJbN+WbKzQkrUedSm7Yvtwy7u/iS1+gP3qQJ71TIOidAkDvlfN7+/aD3hOkR8P07qAff1QwSNdUXiDN+4G4vvJ5+Z8UHHrHI9m6GfNWX+t868exdf3TdZwfu4O4lBOUYd8MtnFThzrZS8OLokm/9C4Q/uJQUI/Gu3Vf83F0QeAdAoEeGUZ9t0vESqF1iYrAuE9NouZODXfeqmsR4hUX1+T0lSPmKFTD8oUbcrdKCoX3o2Gy0u8F4hFzBW7UFHbzsNvYAZ7XevRLN6Fe6JEvAj//pU/Hf6/ft1qD8LLoV98+F0wQSIEjCb/0i6LZVTQCQWoHfsQNNCneLKTswHsz7VGCDBJpb2Q3Z/OBn2nyIUl90TC5AscWauBrauSTc0t3HcFMjbJCnRpbcoPMGnTAdMZRZFtD1Svir5unZhtiDVbveUr1ud6mdX3YMvmmnuaJjX8sX/vBXnLF6WPsa5+17ZunGMNUpvuP8PLu7Lf2hWuKMyRR9SpbPXLog5z3bR1VLD3w4ePWUfV/CdmpB+yBxuGrhtsXjeu88b8tp5Y657pC59UjVRkoz68CQJ3TGgoiNNzqDrzWV4GdqpexSeqchoNKIEijQzJcP8By/kCbQSBwn9U7gmjQVeqT5oo2cff4zvhzuXXcwze980/n63btIcRguR5SXctfk3cX1HhdRri8Shu4wbtdTe92q3xYsE+mwcMQl2PO1FlGMxJbqiHyyDYG3ns+RueRz8w3h1u+Yx22rZ0mjecxbLoOzrtlnilm/aGmci7i2wy+AMbq0Sgdahnmcm7GQWM3EO8l4C4kO360I0g7gN5oJxC7gcjf6K4lgSHeGfRWC1c+D/9Sd+V4Pv+ldgC9fvWdvsihhZle1Pj61es8Kvbilbdu854gTGbcc4cPD6TcONpXLfQite2PbbSHSY2m0Lj4KAsHc1aDWeA+45ahVOk1kM5bUwr2glHtsa63fWbXiq3pW/4INd/Ecjk4J9pMOvfrmc89I9a2A3okOeKHndBpXxiF0Kb9KigVHdpcZv8qdZ+zULs6vXLb0nv4W86nYaprDsxtw0Q23xWNMQXePx2mjV9OIKjPojgDX8Rnp3NL2OUfAflDUMDVCe8OiDz6SHfZQl7Hu3yr8rA+lW8pPgHIa8q9TKDbDywWmw8PeNEq+skceyfqbpXCy2+nWiALsfEb2ny3uqCB3ZQNbbmpt68LRT1NpLZNB//xezChC8kjVtdQVZlamye6wfG76ZsRNdD9lASC/L4f7RAiAKRHwgjw8DWxHxX44V1Bvr7qevoD7wzy5+Z5wbSCQP5ph67u0PgrngSBFCfx42EykpxrAMEhvEC7stjH5/kRBPJoxlCN5K8JBAn2u/QKhzTyu4UFrcZtyjxTgKHYD2gwGyWS8lXmOg0+/ODLOmGEV8DHgSACQglvia5GqY3o5FEwLR+0G0hXdAWBXtYXv1hrfKffa74EpjWI3wmkR8N4T9BrBYe+5ZPxovndQKKzE+iF3w/EGkH6NPE913qB5QbW7a7DJozMY2kQVbpJ4HJtpwPCsXVQRmtTgLo8aXgieDg6gU2C2n4G179Qt6bwdy+2NLUb55O5rYutdi4+LtunpYFLpzrh8bWLtEhtzp0yvbIz/lxu7uC35Y0/5/j8cXvUgk/go45t+30btu3b82f7UlavDTXpw+u+hj89Ukcq2RWJKkUuR/M8rjYisvpxcnze8HCxW/9QuT2Blydf4z89p1fb74EPivsCOhTPejkbI39rT2zMqGnxo90jEKSB1np7zHEtcKgHAnQVfTMdWDgkOId86fDfVQSCPuNHw/pE6bydOnNoJBxzj695Jl/D5J3OfOCvdD6dL5r3xMeEm4lw6uj6/xa5BxonyE4MPpd1aJgxCvLMZzR8Ahiwi76A9FXEbyfX6k7IF8m1ltaGYQscgV0REBQm2ckRvttj899SogN82jT10UZpd0XSIAfxj/+2xIet0Ys4k8Ojo1KPi83HZMC4wd/rF1bvFAqu6byYUlvQFQj6oEe/vDVdQZ83+vF5W4JAb95y55KgkPAKDLFd+4UeBeNZfd4P9N3rbxUI0t04fb2DYBCPib3U42I8NkYgiIsZfcOPxZcfBZNt3LD0WBaBydHNFYNzLcZo3y8EsrxQpC1pMQ7KOQtDdCPkHUHUhS4qIx9HwNYQvIk3SNMmY8PFjlJ4ljmCXd1EGJdatt2tYOfWU/IRT2ULb1aV9B86rWPZHAuUDduqyQe7LD4OzQOqzaUfojuMk2eI/gPiM/3oG9JwvcuNv6JNOejN2zooNwyd1Dwp7eM9vjMeCfoVvH8nhi8lEMSih68e2p/4oByb8b+dAprzyxgVai+jkCr0ySKeTvAxS+3r8iWb2Xs+6nnrQt2asxBoPiya84dnt7K/+cjbBmCS2yE+WM3eFVZbYnv4wiBG8VhPy8cTKpWOYszM3DiT12GbBrR9uxhWZRtzpS+S1Rf4WL972kyhffxWtZvbvhc+tuVIabECFbtr8mIadYW0z7a9g91VQqnNNwnQ6Otf79kRxFfAlPOuoJ/8FbH3fik07wr6QYGgH7QLiGvqTwSC/Bg2n5wnWMT1l5syeWk0j4h90M4iPyImm1T018X8RJWM+ejrvYyQudjvlmKzdwFxkdV/wR0Ect58GG/YwAlebgnR7mifVF5i1NrRRPeaEdTL2Zf+SzWxMHessxPoZ3YAue9C4w8ZdviuoE29b5BHvLwjSI9/fcdn4bX2IODDDqDvtfb4L3YCac3x2o+FiVYvlH7xkp1EeixMOrmJhV52GNkvycoDtIlxUXOFSk5C5/zdbaZ51UTlLVf8Kwt/RhrILi8GA60Vk5qn/9wzSnIZ/Vt+ng1Tf3sQ65i/8odhGou91lBzwJbbLaG+Ilfe1sWSHCVZIpmnqGfSi6vVqth8UM68rSu0aqOQ6/yDoJQ+CLxobWzQPjbf4mnZUTG1bL4WvmhEkeCNvuXFFnK+6eqZoebQT9VQ9OB7aHFROcrZ2beo77qB+Kk/S8SVp1dNSfnLObofR3NneXSDOdplZ/wQfyLYvdpn0O5zjwNHWUqVKrVNOnS9bcesbA7j1n7qZLFLgxj7XXaMo9bJmDPsaxf2MLdKRH9Hcb2AGh5yZhcElOv32QaC9kmJA/4e6ak2HfnctWrAcejA87dMjC0Ma7MvjMTyB8iREJN5lHuBsGQ0cd40Hc5MlPFLylF0e8w5Oi3IZLwxObl3GR1t9U3lsvM0mbvKh2xoHanh3L40fNQudtdei9OUVIlYdi3RaUsE+oRXAIW7hg4KidO4scBFlgmBO47OeT8Q7wlSQIgvhr3R7p8fCP44GPSj717+JJiXR+NndgK98DuBdAfuW92J+/Y73YlLIOg1L4zmvUFaqH2jx8hsHR3Hf+UEg/xyZ9nA9m86dT3KZR470Y2M3bERLLbqCKhUueTRyz/r4W6o9JDImjuoIpiag3maSagsyJoBQsmYsfG3OZd2ErbETpeMixUnBYOvaijeVaPK0mm+gDD0iAP+XZIqv6n/jPhdKvrPVjKGyB/aEFzfdZ27ofEYMPnuGeTzR4xZDBy5vpRAEO8H4l8S5+ZeqbFhgWkRX/JbR08gyARrkg+tJ33kry8x0YptUzb3XwHN+We362gJHrjkY645sqpp4Aa2wOUZAOHsC/narisdsC6+pbcEDjzNFeUcA7XQsdTYVMz1AYiZ0cAmC4o9XCNAdx6YYyxkXKTk/FZN+Mp0WIvd7fMTWYXTZVTXUf00LhII6q+IfUggSF8UY4fQT/ogA18L4/1B7BL6ifcEEQgSzHuEftZJy66f995ZpF2x0ucPO6hij2ntEqIfObf9EmkG8hrMvGqZ6yE3YvjD0VdHLrwCExhIu6XALdh5twsMibKbXePcSLTbnykdYWhYpqQTLGxoKVg0bvB89Ys+QIF2tjvREjHC6/WBTCRw4698KRD0XDeU+OoXu5C52fStAkHegUxQiECQdgH9S8GgVyp/q99r3lPIe4G0/vAn48nlim/8aFjeOURdsY18PgqGrZvmwk05bWn55kdy4oJvTGSiHUpSqO0ncIxkuPrXuNhJ6SpFb9dFfwv2mo/10B7fLWu6Cy0j/Ysv2jhuakvuHFo4N+73huY8ddY963+Ib8r9vnzqt+UzamEuOnotfTktCLz9uiE7+yh+KyjM7rtNzjk4dG3SZwv90WPvnuMOfXo2wucc55JGg2hn8lmn++3AhPaLfrS+XfPUvWDrSYAnFefvwIxLHx38CVuetHipyPi/9Z61PyLp71ufDX+E7kudXd3ve5L3xHjopcv6f496p44Moq73OCjg+9slmZrBtU+AKxuxfLXKA/uKSzxuYp0Oai/9m23Gt/x3Thsxztoit+/1tBWXp1zZaCOs51ZTTqpba46Y2wl7aGqHISK0x3DhWJjshMzGrDNLLH6hZTF2XfmjjzuF0Ul5yZjXlbkGLy7F91G3LzsQ9F6BoJ/4abHKewze6P0GP77RriAWqz/+qDtqLMq060d33wj2fPedAkGvX+tO3Le6U5cdQd4VJBoLObeEMct/F1iQyVr9CBMRDGJMF4vxstiWetrTorHP74FFXY2LyFKBA0zWBZVfjlVtYYy+OURf+COJlLHKlJcSg61waQnC52Y5e5/PpWPxoi5jGlTXMsgGbQOVsWhW6iqvRuy1TRY7HO7xgZ/pXJ60LxVu/99r/20vX3M+xHemnbpljYHJN+FzjdjMcPSfZacGfEmBIP7gXOec/JDzIGcSbsGH8bWOIu7ZNtjt1+1E5l5fl/BxMVxyp7Kt4g607LtDb/RjfG0DfO78Fpx52W67BXNDwnLIVEqbKM/WNbyZIlJ1bXEzwH0MrE99pcNZC7Z+esCzfzE1fsqkzwjsJUYBj36Hi11dG2hj1bOb2HUqR8wc6dODilVl85sdkWhEfJMUrMmOIHICOe/4nDzv/9HOoHd67OtHvTOIdwPxifmfuOFC4Ec3Xbje8q6gn/zY2FsFkbQTSA3j5gyPiVEmgILODzKQDz2wnuEankCQ7JAhbilvWPbOAa6wMo7rLblvkjQNo/nREuXVCErT8yqGxUAdUFVcaLDqurjj3/VImInRGDZmIq7nynk0jH8Eghh/Mgt21gP+VLxszvsIFdThsTC9d9A7fhQE+k7BIB4P82NiCgT9S18OIxjEDmUCQXwpLI+SoSM7gtDpnwJCzH22E6OUaMsep+lZmx6y6JQ2Hv5zmvzQzuUzf8o96praevGTPVV60iOpt3naJvJDb7iMzR4Pcmx2BqFx2tUWJs+8Ep5ua5dmPueftmDSG558jbvKfwtf1z91TPhc36RN+CG+M22WGUNZm8aH7rHunsUohPkWwkDG1AknhdbX6o7kVcrTAceKsCVpAY34LPNuPa1t+KGGPpXvIR2h9bkYjTfXCuZgkRrvafWOge6zGxr60T2Sq2K+3wn9Fq0cOGxcD6TBeGBkpLPKjC9Qfd3MjqDPJBDkptLSkfpE73yQDmDTO5/EiZvw5AGetAk/xHemzXIGkjpTwG4W7csgC+9psEwFfzZcrreFOlTxcEKcTZony4Th45PhK9VJAM91GrzXDBn4RfMFUpioy0nRYq5q8aUdhCseqiE0jlN6aryFuWyTchEx5MrqFO2Kk5sTsCxGNOICgiM32vjATABeb2kAZTJo3oib34tI0flsvFaufleQFqd+NMzb1hP8ISCUl1v+pDUld+bY9aM7b1qUff8dX+vQbiDdkePFjf6EvOgs2nhZNIPAZlU7tB4TKrt2HBCiFWIjiMMLJWnSoa+ZuGKsNRgEgk8E+hN+/jBzIMhlISoBdf33YFgz5OBEI8eWMpVDUilxnWbZfJA69Tyw+ZpyzDefanRDB32rVhuhi6ZKzmxDIr4Q31W9ZbrZJx14pnN50r5UuFx/GBXTF9O3E/9r4VOXWM0YDktt19v5IghgnCDD+d/93fmXFAjqWd7+80KNOaWvr8bWOb+97nPAzpID7dzwtX8PpXIq5+geIF3rQBXf6oxWRhVNAyf4Zi4o3p4vwkZ9sYRjW+986ivZc8bCsut1PhgO7YNP/2hR54O15k4z+dB8tLxtmvyBoUyrwXY5vts2XNUb3fjJC1+LI7elPJOrmrSRXEw6xCbao6LGQyRMtK6hAq0jFS+sCOm6Ve6XorARAFLspnbwfK2viCkA9JZ37+lDDCKw6+dHvROIr3IaVs5j2OwC4stivDOIoBByPyvq80E/Pi3P7iK/M0gBoLw7SC2l7epEHg/LF0AxQ9fWDgRhII1WOb7QEVq/VLqM3h4b7REy+PKzmldNTEO7oByvOPhjmGs5zK21aNYGLj8HgPSnTca5HgcTP1b6sTCCNewE0u+VvhT2wo956Z1AegH0t1pj8GiYbzr5fUH6ZLwCQd99rzUHXxTj/YW8F8i/3lXEZ+P1riHZxc9PrFd7bCr2u3VpKzaC7xSeWB58t+3ok7PM5mpNyVlRNm3LxC/haGrnYPvPwIkDf2vdwvmvUPW3eJDy+BeQOQRZ0rYg5abTb5LzAD/zxIamd97y1rpkt75Jb3jKTrjpnT9IK6a0MTY/xA970zvves550zs/0uMHfIhvvdNttPuS14y7/skz65jw5El3Uxm1HvW4l9D/hST3t9q6e2HDZxfgleY7055enhpaY58jpcXn3NAoNk4hd3uJdw9RbNiAGOn3/L02dBjUXA9/6WOG8rkpJejfMwrrvcxf0KnDuWGuGTWT+AbKZ/zVsLP7/rPLdONMmXAmZgylI/rPLHkgVoVnk3+FHYdAkOTv7QaK6nOFnFpHHKUjBslbDNjbhL51ut6ShZkn4ZHhjtyquiTrJI1Ji5gTvxUKzUmck1t6N5s4YqMnByYIzRjweVcQjJ5BUMTkEdGeJCDzjiACQT8r90uhWazyomjfndQ7DFiceoGqHUGK2vBlsJdajPGc/nfffq+AkBZnuiPHixq9EGPHkLZxOxBEnVRNxZrF7BEtRlmesCZl4iNeBM1Bot4hVA2MvTE6j9JZGxp9EzMt19HADgaZ4RMPtjRmxk6qssFU9riyZr3L2ToeZZz1HZlbxd06HiK08FElrj+kc/lA/EIL7bo/s/mnbnnKEDyY1+e6/4Q4NeBLCgT5ERQ8Yx+cHCE0fo6vy+PKCkLqbsK/pOP50pK39cAL9kxBoqXu8YC/SmfZK57GUS/8XX/nS8doSNNa9sG8FM82TP57+MlztGxTUH3v+sp87euYV877Wrql6ZteM5UVlM3QVlW+AkKq74HG713F6N6+RCViDszohCMwo008CuLoPUHaFURAx7uCFOD5UY+BvdU1li+KEQDyl8O0S4gXSLND6I0+Kf+Oa7JksyOIXUG1M0h6fma3UDbS6BrPY2MEg/AFtnOThVAD7aZturjqwuoAUZWDr+sydtvJ+zrt5vtQNAVp4iVyLtoSCkJ1WNi+AIcW628ZcObxLISHqNF89J0XK8L5i15STc53HwgCveTLpHrM67k/Aa8y6w12A/EYutYf3Gz6nmCQAkHclPJXwuoF0S9faF3Bu4E00fHlMQJBzHm4wHk1Q5ltUabU7QSXH9jtKUpJ0Gc6lyeNOo6pnLuQ8WGKk3aFn3QkrmquVmlMu78ueVqu82XMP8Cv8ABeXGvqS/nut9/P3z6dXdfWuSERXDhgLi37T0S2N/9c229r9dw5jTgFgm4lNnP3TPMwzwRuyuZlbuoZ1Fxi9GzlPGPP3Cqz3oldM0eKmybIcQD+zL4ahgN8USm/cSGi3Hmhb7Kmd37D8Cv13dPT+Mfqa76H8qtJ4CH+P4xWA3HpV5mEfQarHOztcfFBal1eOGzeLCQ4OR5SBq1PoM0H5IG/1T0JiqZeArTe+6Kzjl17a2l5UTZxWZaxCy0nKvnWF1mLcYDN4zu2gILD7wPyJ2UZ+1oYmlF4zR5eJw8TkETOk4IOP2t7NgvOn7XYfKfHxHhfgV9ayaJU7zJIEEhfDlMw6GvdTmMBxq6g13oU7NvX+WoYgaDv9L4gAkBs436pu3cs6rDFj6e5xizCeVaffufXwSDeCZA1q+4MigCNhJ3LP40wlgI6oqjYo3ONg/DMI3zovJeiJwvCyXsaklvcTNEY2RxhwDTa79R5FdvE5m4NlPdcFuryhZjOalod+eSbeMND0HzTNsGpKVKt50bHF47AhfhpuPJXe+QpemafUNG9eqeuCTP2kLFcwW2wnto89HnjP7c8fyCr8XJM+4E27kWXC97AEH/rmP+bYMFIzV4I+qQYtqvESeXJYCm74kqnDBbWlXPuWWXhb9KQWzT4hK+s0CfhKnouLY4rVWctq44TkFleVzDqle5TbeIGQw0zbyWz5tATfOllcPORi1eVMF/atVWeHN0m22RDuLYYKLZcL6eVeZRm2rc1XgWCuhVIsCOI62o/ds37ghIISlCHYBCBoDcKAr3nnUB8Xl67bx0AUiDo/3SN5TPzH/Q4mYNH7NYF5hpNcEnXaR4TYxHfASCervpQAaHcHUkgwBdUtZVdNg13YGCHPOzC8l/a2R9o6HGzW6+GSZ/dVz702gwcTDpQ1j1tAfD6v/29g0F4iaRcNnMe8uOmEWsBcgI3L70TiE/As9bgnT/sCOLGk4I/PI6utcdrdiXzniAFgXgkjF1AfC2MINIL6XhGMEj6VMyLoss+LzV08CsKZQlNaTuwTkUncvAzNa1xKXO8z3eUOfJFD7jGX+WNg7t5W2vn0ZRj9z80/folVpQoWl3zTN3Q1WIxdG75pXrzNr3zxXICmt75ibyKj9Gb8al8zf9Q/lRdD/PFV+mWrNq3l6h9l+J3odQH9EwfDY4D9ZH2OnAQB2i+sK5qZjFqGjMEPxNwezaepExrJ/7c1MfoZ/5j+VZz96nPK1+ijv6+kbhjAPJMmblpReG2IY4xqUJf70wWk2SoY40Vl8GAzzXCEHjNt1B8lB6uIS/199xn844gN7p7hMKvTA+f8FvpX8W3LbgP1SVZDHQ5oykpU1SXoGzaxmbwTLmWNm6KlL/ji6FB1cJG7fxIlBtnxOnQfJtxYaraUT7JXhebX4O9GUAN+zHfF3mvspvQeQuRg7vCT574rWtdDb8j50mjmHMC18kto3ySE71x6mWI6hc/fWgxHXRjUNrbrtio729IXnyiOchjOFLNa3npJvdXuQSxBZ1Px/vRMC00/dJKbU9n+/pbAkF+p0FeFk0QhyAQizI/Euavhmkh5kCQPh+vlzr6Kx0KBj1TQ70slD1up/+KkSXCexmigx8LU87z+348THCCQ/G67fRYo42xPq3WMcARL92g+yfwE9LWv1SjSfXvstRRsE1RDUifYp7zoOtYhKgeuGaCcNB+wdO87YFdfhJ0qPtW/dnmc/lJdXzmTGcXVq/+bq2+0nceFWcbHquccYmM5QpuGe6K++/DRnymOYEgFj7Lv3YGjV2AWw49/mZ+MurRQ/t1Mk6tq87SHVpzNHWWp6aCMcZzDXyCuV55Rdj0IWO+LqP/KgV/oKqAdh7NKSecvHOlJ7jW0604c0JvnjNtlyfHUROlxnA1aXjJqs0duIDqy/kiBkh/dr+qrqqurxRxW5AHF7ae1QfY0tdkqZFI29P52hHEDRYh2bnz/p128LC7p3YFEQR6o+/M88jYGwWF/k/XV14g/Ub5D3oPH2VkeGSbnUFvKyiUXUEftYMXXbpxowpYB3iMK0hE3fofw9xIrrIJsLh3udgufLdDXpOdbnc1ov9gcANWC8ttujjbU3VwgEdqG9dav/bOXoyJOQkEbe91sMi7fNVp/ky8dLP7h7XAK+8EUhCIdxEKfq1A0EsFflhnEAj6lk/IVyDotYJDwHxZ7KXWHX5JtN4N9Oy5Wi1d7ArK2oKcG03BEwjyByuw0ZZmngi8cSbV4UiblCMMX/NuSjl4IwoC37SrvHGwN+9Z+yx3P5cVIxBUFSqD56kD+qR0AABAAElEQVQJPdOGW7m/6m+ip9Z7a/Hvi7GHavI4eqpKynIdEmf+ywCkfn2y9D5oPtLZJxtAUWvPe7++hr+35NHPR1vbB0fsby3d1lhdHsUmu0NWRbcSi3QC0m8gI3PUAz5hHCDxiMl8lefvSAjFV4ZxDQIZfm4guKQD16fP+KthNPv3Sn1iTX1/L5z7c5r3F8C3A9ZG1AAF7nVUJiYhRDMOogcqgJJUUUQjizrSkjkwmnTn0AqjoUs+E6xQByHBp55S4xUkGH4zJWjRpylas4DcPG2reTjjZHROTDhZGpUVValrsANEgV/0nxW9YWFHFMcnbxtXDjCbOPOlLyTEIHbIP9eF3jtvBMPrl0hKkf8VH/yIkbyDiKmFxaP4PupzJ++1qiQYRCCI9xW8dSBIO4R0x/KdAkLvdMeSz7S/ZBGmL4e94utgPCKm7dr8vtW7ghII0idatSAjoOPFadXBHzDtPS/MnmlRplWZf9y1k20OCLGwFFxNj8E6lukuN0yTOhmHXCOUs9y54Rn0IxjOlk8/29OlpDXVBVeMvvBiaHeQFLrXhVv9jkXQT3yuW+jW2g3OHJN2QMOexUMZhJJxOrhYVYSyj8Ua81ZB9ILbpNaJ5IS3pi8bOvhfrphlPIM7zzjwV+nMey4jU91zI36u454sfAw552gpGJD+5bzgfPvcE+3n8RnyPsxreMPMR/MkuT0H2tOdL3XDhTrjJch57zlgVRoZika1hNCmgATgMOYRd5TnDSjSqvnTZsI6FHUbogyiUjdA8pse/VTl6szHSEBddmLGlDl3mSz+blfKbfIwI4RxpI7mA73qHDwBJ+WokdLGRFu4sVg0/KOLjEshV6VVOzgNdMsAK+EPI1zuKwQctFHJfdDttSTYlX6WPNhic+5+kTDX7lzzdE0XF493+VPwfswrO3oI7LAT6J0CPcC8IyifkFdA6Ac9iq2XR7Mzl8/GszOIl02zC+ijrs/vuU7zaJiu07nG83Ux6mD9oLFuy7ot2Kl/HgvC2W4eG0vylU0F2pF1CI3CfrwJEj4Obq31WDMTBwAUQMOMs8BIZBcSNagkFxMI6vnGfMLP3DuBtGB4wW4efuwC6t1ABHwUEOLndwTxaBg3ouDhK2H1gmgCQLxPiBeg8ngZ7wLKC6IJADEMWGsIV3a6Gca7KbZVxW5akNi5MHaI8fCROp9we8wMh8OWP6BdR8ZzOV3k5iVvGKkuX9UMvVtQdK8NGwe9U877LkVu1jP1N9ekN+46X3POIP++OMa1vOGTcFfyUB3dIloRWTDVJiO3noehIXdglJJWbrz6dNmnM2HBJSQ12GttkgMmTb5uz6RZQqzwRSKyGUHWJi34p6lW+9kdusvIZ2oPPIab9KfDXVtqoUu7Pvrl3MUQG+fuKKMbd6w3utPn9OmWhe8cCAJnCR0I6nQyXATfGBAtVG4WcF1CMH9nflYvi746cWhrn0TA5zRpE/5Uvpbt/CxPGRrJJ67gaa8JdfgtfJlOpjbBqTajxYNqXtQGb9HAPNaOtj18Qwdgj0XrC+08mDGJH6xn2uFGJ0xL4RmmfC+1ETXgYeMksb4CBY/zJoqK7oWLVi62D9EWDJePsPqkLxynGfq65gBwtZZibI4aDxQjq1Ock1UrOU5i+8EGSod5Y5zrkIx5kLU846kmIJU/6vEwf1FERL9Aktx6MafslDov9lxmEak7j1pg8u4BXmiZL5zwuVu+dsK7DHSHUsGhd3qRtHcEOQjEl8MU/AHWouwVz+mTsyOovtTxtRZkzDqpS/XXHUkvwHTwokw5X7llO7h/LNS4gwd7N7/dV3m8cUSCU/PpmNVjV3zFsngoJyEdLS3XOXTD5b+wubaqNAyOJULkv391rhcrejrdnmeuweSwp4weVes0YRBbIvXBB+uZD95SkeF00gd/pwk37p98+E/OaF+2X3Cf/X5Ba56ZN//ENdxd0foaTz7rPfNN2pIRUtOBBT2GBPdY4o8hzsPPPXmulOe6/ThRpcN5gA+2KwTlf1xzdTK100TzC0KtIOc6/SBIv60xu3hKSOg+Ty1m/qYVsQjwoQWd0YtmbE9dJXXIfP5yPTjxoedoUykVXyYMGMSBCdaYmlsGfcuaBs2XwyXfgR7ppcO0bt1gXGBoVJVrcKwKuTBCGduBC4jc2ZjJDRIXbZv4BetKU7SMEXEJMLx44hf4qM/9ErbUD654oTkQpJydOjwqxk6gn/mkPLCuse8U2GGnDz+CPOwEyifl+Zy8Hg3TlzrhgZdAEQGjDzwipjLv8+MLn8gS/LFu5Q4EUZ8N+UZrhG4tOb88ju3H3sp2TMZeXNbrOl+vwdEi6+IQf8dP+EI/LtAkXfQoJ7crgwbln+TFw3yDGVzzIRCgsR7hWCbkHUEEcng5tB4DU/CHnMfC2O3D4+heZ+iRsO/HV8PYffyKgJCCR8+17iD3+oEbS/zQz3pCOfWR81isyzIlN6Jsmr2UHqYJ1WZAJYnFHcppgl1TeGiPp5aA8x7ctK6txzL8LUPe9FnztrxWTyiTGP0On+gSBSSPjikPDZ7oD58ZYT6kWz7IF7y7sqF7q2o9nW/KhqCRsOvIBz62Qi+2wQf21qatb/oC3k6R2XzneuGb9bbPWp9o+W++aBPN/u9S16W8ugA6abZxwiaaIZBpmvc6Wbp1tTNMBAlfEVvgPzjfrU4juoWUJtxNbFznjf9tefv0dK2oedddIBa4jBJA/ecEjb8b4IvxQBecQvlvRCvImLOMD8hvOxIIEkEyXItM1fkDK0cPGweCPrN3BK2LmJ20D5cn0iYv6B7fGX8ut4J7+KZ3/kfwobvb35OJce52xoIGDTBjqwbNwsF4Sk+3kXoRHoO29Fd20pxicw9zNp+Qkd0asqV4swTa9EP9JjZtBoLQG3zPkbF96wUfDo5c6qfL2upg8eist09Qy/uE40SF98gHBndN7fsPlQRsli4pyNe1JLBWXTqxpRKe5KrHizhO+Cw6OfG5A0kQ6KMKXiwqN10vE+A9QMaLPzK1iGSxqQWldwURGGIBqvcZvNcW9g+6O0nOuwpYwL3UTqDnLNII/FQgyEEgntNna7aezefFjb7LjvnYrIazQOGz8V70aXGGz3mhowNCWsTxniAHg9jWjRw/Oy1wgc7wb9OMUCF+VWnKTaEBI3uQN43+2vhJdxvoE9EZS+YMu2B99cSSHLLg5jwKrwVChX8odRuqnPak4FoGX4Svj1dsZZYFGoaPX5fP2vB1pwk37kvOp8/u+fAeHll+0IeLH3XnFe+0oxU03xWNilkHmFYMzcf5xR9Dn3vyXCkP1HrI3ugz87b5YMpDdf4+eNLAvfzKVcG12KXU0fUsnaW9+8CMxU2W60qwbUnnYJFDZ1/Lwnl9vOWLNbPN3GEEu3C0Wb/U01bHWrgMdYNLaNq3LbnFgimRzWZM9A/kAYTas24IYGJz25NgXLzCcVHFEG6LRPxwjFVtbezLsZu52MsvlLNGav/sHOhnbsRQL9dX5XwCnusxASFdXnWN1S4fXWsd3BHirW60/MSn5BUMeqMbLf+n3UFvtCOIF0nztTCuwW+9Oyi7gJB7T9nX8QSHfE0nSOQ68QCBIG5mrdZj9WoKHnL7sFMAawZyJ+kATBENhRfR1zj5wdd1FayxL3wqUBZ5X7f17BVkgi0sYfAb1/qvCcwI4NFx1gMEZp7rOS6/z4cgkH58lZQg0KtnCgDp0S8eBQPHTiDeCcQOZH+9VMK8GJrHwnghtB8Dk85verex6mdnUAJQglWnA0+2k7aUfeSjrfJA2qO/1PrjJWJxIscvu9wQeBPNxwF/JG0/4tuvv95zhUpiafpVDq5/aJv8lPFyczD68y9IaPDHkIxddKU8aeavA3avMTEJhltW0uYD2XYX8yZEQuUrfduekrvMVAlV9hiNRhDFfWVPkW6yyFAv6aBy8bbeIG5thL7rDJfGg1DWJzIcGSPMm0PfAC1XarrPDrxRfHFE6KxoWNQ6q40XCk6oErDOhk8sf6PibPnZE+dym30P3/Sn5dc1zzHEVyi7LrinxP064EKK1BJdDpbj3hGUc8mcxc64aTt8ngnPvMU1CAI4gzoGlx1BLzUX/1sf+/kjkh77bZP+CPX3dV5Vu094TtQ49x5f4z+Fr2XOVt1OHkeOpnd+pKY07ZjwmXfSNgwXXd/DUtBo/4Sfpg9dx4SO7mlUGxbbmbNpWNLwUVNKpgnMkG3cWduV5BkXGfQAXfZRhkKMNTzryYW1LJgeNIqpfZ24Al2PjO937uAHTsL2hxvdEuKzH0oTj4RhX3b5AFewR1pZcH2t21peeImfWv2DR+sITuhfvMAk6FMLTm9Fz51Hv/eH4E7dReQO4wctLsFnuzpBIMmJzlfDcrex+LXgzHuDuDNJkCh0Hg17oRc5EujJYu3VVy8cEGLrdu7meXs2z/prxeVxxkJQtrMgfEZ7NPnwTgDu4H1Ti7dnBIIIAFVOm1mk4etI22FxO+3GEQMldrvZPWe5pt7m2EIib9gIH1i8Uutc6IngOlOpxxOCMmKNrTIoW+KxXQz0H7n6ZptrwVQ81oK+a8CCTnr2uak6YL+TUE2i6sm26wp9EW8INjHyUoA+65rKSsWXnJ3dhnsaN+ErH8HHb84oZ757Oq66oeu90gGu6dapAkPMOB3Iu64vJRBE+1lA+TxtRwhH4jxPEmGeTEZy7ha5czuwCnW+V7b8jgRne8/ZIbhig1aheteMoErQuOaRUWWfj6lWMl1Z8XTWfF0+59RAffN6HZ62JuOCBptHRNtki2+1tWwsT3tvueIn12tt6IwfjrzlT9Mm3LX0uN0+K4/ZH2jEL6aq4HnUZQi8iSa0Y53VXtvVdcLRMH5ArzAC4vdhPTj0ur6mi1/yvib72qzrquhcX9Hj6yx4Xa/f6Vr63jdeftHNlY9f/aQA0Bs+Ga/3Bf2vAkIEhd4rQETA6K1uwBAIynuGIpd3BtX1mpdI+7quz0Nw40eVseuHHUGqVjlH2pW2UcIvoGlDbiTJS5wokLAXyaKDa68A4Ht8QgDHNE1sQKx3uGlnrPmEFI15Bl7vvEFOCF/zheNT9gRmnitSwwueWS+wruC9QM4JCrEbyB+mSCDoO4JBCgS9ciDohW5ISUa6nisglC+E5cYSgR/e/4M9DvxQnwqc888IUJme63Rss2div2TwniRwgxpH29JOinZOgOKnliS7uwvKkcOfcKDLuYAOBB3wSw7O/oFsmJzUUimlHIvhwO78k+TFo2H0HwlbAzMepm6TbXv4aH/LXPO1/C1fdHGENvka3hyBJl9hlHW9W8+RL/a1rjK32hjssb7wP85XFoz25+a0wobLZ11rxglrc6cyafHtJrh/fJ6Jp302mtgKRRRoOR2wYejYTNsnjYu5MWDDCC+FzZrcpPAfCX/v0pU7Zgsn/Ntbcl2bsRoL7kcK+jVn5wfPVuFm+EiK85aE3OruUsK1rBOcJI8toT1rF/mAKx7jJML1wNcE51/pZdGfWSCoT7bj5GBf/eWHp9r0+/JpSKnj2y8POeG2XgbZHnQPyYaWQYnIU6SK+1btUiMtNiET6y3jNabr3hdeyQvpxaD0ceKFh2OgVCQijEoxQauElYJZRbPhW/Aq5P/6YyN1yII60ZYcVfBPuZYqXZFPSnj7lxMVKdmu1co3upVG8KUTC00v9gQ4GKTc7yHwYpA7jgrkEMRRQIjgDncVvehk4dnbzBU9Ml6L0I/cSYSHrebAWli+JzikXOJauBI8YkcR7yTQzhc1gGf3n/mrHnpfkD4l/4LPxTsIxHZucASLtLDjVws+gj/faMHnZ/aF96JNQR8/1y9aAkC8LFKLQ36S82JNDXdvuBt82F1XTgHLTyIGyAFJnaeU48TNnm6eWv4t6SyxVC3OV8o5tfuscehlUcnYWLu5TOSghPiqXACDpdCDsJjMykFsWruvVKhVbuAevukP5R7OxTDhh2S+JFp66tNbjBy/q3F2pW324ehys04bJt+Vnhbg0TAPMwlPeZ1yOV/uCn8eBM+najltt8/qnKN1d/uEE0ACj54HYsi8Dfv0buqivpn674OJu4KRs606kkfuqP9KDtzttXxyRuuRL3WYIoek1DIpzbaZr53ZbMrhnHyDtEBkW+NCPgI85rM9F0dRyg2Ty9hyHfZhAyl5HQ8dfeQIdx1rXKCuVDp3eQVXRNMYY7ctlwvv0lEjgHO95iYN7wriJgw7cHk8jF1B7/Qevvd+RIyvivFI9kdds6GBf/9R+Xuu7dyo4TFtrs+UFTDiGi09P3MNr7oY5v7DQbkfB8NmwwSoBJAoiz/rCQTDY0I4fKQtJK7Hvq7VNc44B4CQEI9c52snLpSvcBfrBT+aJRkCQM8UsPHNIJVzjU/w54XWFASE/MVRAkB+PxBBIB4J0+PnvAdIaw6/D0i7g3hMzC+GllG+gYR+1cX199kzvZ3JtsgM2SyU6kpuHsqsR8CJ+FzM8NOKHp8uglICdrtcujpM7qaXj7uonDri+tCud7unPo0eSbQOYFKXU2rO5LEwLaAVVAZf3YyrwGAkp70bztzRus91Nf4qR8eRf5Z2DVeyE9ecW/rh+axl03/Tt01J3vZNvubYdTWG/H69zR9bN1/j5YkCL+dDaN3MWeFAb01CHgphct+eZA9FxjNGZFAfSLOwbd9YcH/3dHbJH2/vrLHH0q61+9sYnWf2oA7gpyT09m7j95wAdWEpHJMEHcAR1lw6dA5j4Iw4XwNNzzUIKjh40GFIOTCP0v5bQfU/Iv3pO4L64n81sK8a+HfkmzZN+Gz/pE34Ib4zbZYf0vF0Pl2ENMp6DgHu1AO/y50fT4DCFnOGbJS0zpZLDu1WM9hIceGErsuiEPwR3WMEdOBw5rQV0oEgrQxOaS8dN+EwEaNfJP/UcNqO/ly605K2FYtcA9VhiP7nxJSMFluRk2xf99V4AkGsZLwWk16tH83rR75YTOo3PytLEId3CXj7uIM/eidBBYO81Vw4chaZuStJAAgcQSCCPtTPIlW2aVUJ7hflCQSxcNI27ucEghTMIeDDgo3dQc7zaBiPjUFjq7YXZhLKyyDVng4CaTu37+J1MAi8t3krWEQgSLDr0sGLNy3c8B/j1c7GffJ1jw9oLPhwF4kMHL+rNPFnvtDo0UBWSd9SMWggyvWzflbU1V/YxNZ35NEgtqSl0tjS1VnqCuNhhLnKkrBJCy61dol0n/FFThCp6I075+1H8BM+832p5e7Ce+2/53v4Wxaeh9JZx5m/9UwdZ5lJA2besFznxfClBYI4Ozhf/eeSnMY52f7t3K5ZDtU5eCCU4zqrkw7f5vz27NBU6/ZZXHwQmsP9YY5xdal6oQGmWPMHwiU91Bl773DNlwZtmuqn0EnwKAkbK0KecAt0/hCtedDGv/ZCtG/qhLa+vhROasPhYh4Wxk6NbvqZlD4GV31feBPXQTahSCm+qEJQYAOV06Y9qHPVh1zXap10vq4LTyCo3+HHdZ2dOgRu2HnDY2MEhHgB9I8EgnSjhg80APtxMF2XCQK91Y4hf9FTO4PeCvdWXxtjZ69lCQRx7abMzRvXl+AO9WVdoSbI2LYX+/ylNSEECg+t6dCKPy2X0QLkBlyAj7i22SscBt4wqMJzPfb1n2t5rwXY/SOYNQJ6eFnp89phzDWfm0yvHQRiTcENJq0veEl04XhXUB5JF926EtBhPot9CfowzvhRPwEXLscdBAKWqHYjEZRibYJwzsUyvVvu3Hpx3koTRoKUcRS2SQ91HtHXY9S+8igKB9rUAz4GA0wHkKbecAYPnBZ3mTMNdgJ3jD/+bWn4OwWmX9umplzlfzxf27atxY579cIFTcbb3Ht8W4fZJIPIsY5Q7te1/X/PZ9Hnow7pR2mF/bqqrnLnj/GlavfnFrqG7JdrUrU/xOUz1d07/e6I/S3Q00VPde1T+W4beK+2rfEwjDjXRFq4CUv5ljoXqLnrgmukEnJgX2hzFWvDLZl5PCVg5nck/E+w6WA0yTsQpInws/58fA/uznHHVWp655Nn4iY8eYAnbcIP8Z1ps9w6Op+0CTe980nb8B6YD01+8LeezreOI9T0znMJlTwDbYx06ltj+6jicLKsE8fjv6fPDGbrOMgGf0B5mG/M5pBlKvRl1JDq4CQ92qaVAYyne8RgjhfQrgMsKUf00e4O5vgEbNaVw5tHBoDsO2PqJJWRnLTY6oWaclIvT6DtR7ryrh8WgCwQ/d4AweT8PvrOoWDdNUxQSHcQxfdBdxez86fwivY48KOFqReTBH4I+rBIJVelLCxZGXriEM7P9vt5/gSCCAqxrfu53w2kQJDgl7VbiODPNxXQ8d1ALbrY8eOt4FqI8aJHdgKxi8hbvLU47N1E1inZZyz2xMeCzj91XBZ38b7DLekG9waLPMYcKNNSNCI9EJrQ1XtA554/0txfTKrVGwwgVeH+dmWUGARK2cXFOccYkSQDTT40lUMbYW7KWYDBaxWWa+LIW67al9qKTjWNEHxVTWtqWpfPOdVg8mN8Z7nPvdz+IL9K+O0e7Yr/MRz6zulKf9fb+UFGAswnyEHnVHbfKv/SAkGcW+tPoT7nOpeHfK4enCcMf8lasPIDPQXInvsF9PUQCv6+0dlqWq3rjZ7uGKlberBqvreEvjzUUfaXhkM2+TYBq2ZKu33Nlq7YPOkbhsos1vmmHKGmdz6pXTttjHei76EzB4579VKHF8fD93MhLEHLUhdwIhzRZxPKCmA1XwcmV3M7n1AhfD4ZVp18Ic5BH1dUQZV+RxB0MeqSqZQbOQRYaA83WXydFU929fzy1Rvt8mF3Dx9oeMsOIB4HI+ijaza/vKePa3o9Ksb1XMqDT1CIYBA3bggAscuHhX4+GMG5L3v8ww3Q9CgZ/tG1vmn2JTYWn9s5DviIYArnRa5v5SHhKeNm8zC5kJT5nX9aB3Add9BFawNuIhHEIfDjL4R5B3GtHVgDeIexdhoL34EhAkB8ScyPpfMpeWRYX7Au0MWvqlfO+/rwMvWz3sJmgepabIRXYrJHNolAUAq7aBezpTNJ82l5eovkYRFQBdBdMBmOAKZBp64H2ExDR+yOcB9b98zDG47GNz95xu2yo8tm5SzBMBwQmxIcaj3Qjqnnjs6P1C4xBlofeesLvWXBAt/67MhHKTLBS6AB563vgLwotI5jla1rtzV0/K/54GT7VNv05JzMrcMtW6xTH+cT5mcGWCwGjvoyTm2dDk2DccJHDaE17nE+a0dji9zkrcPj5D7bjdxfhegWUT/mdnnCZ9seop15b8tdw6S0xuP44R1BnSylIsNhjsfmmFqb3nnrmLn//hBDxmvlUoIe62rYkXxwzPnUn5HoMnM/SjsQpLn4s9sRNJ32D/zbPdBD/XFNGpQw+bBPzh7wj8sXhwUyaFHm+rnIHyYxKqmKSuwhjHWIwSfByZBtnyAX+oI6awgXdRz4ratoZc464fiLSzX6hBt1ws2CgwVk1wSPuXWyekEmhNZmlvXCUTD83q2jwE5eNsmXRFgo9mJRue4msm3cL5fUItELSS0oPzgoxMJSi0jwDv5oAalFJAGjPBqmXKtH3mPgXUBMErLDdy4FY4eDUwLYoeTdQFrIZXdPFnYJ3LATaC/ucrctizUvvLRAfFZBH74shkwWgnwtRHcDvcDTYo+vgEg/iz6+HMLdQz86hrx+LP5YxMkt9iO5e4Jyw/IbPja++2AUBmgeyhOHyCx7hNMX+qchWZOxSoKrB6MHu/gno/hnqnmKzZirg3jF54vyFblxwyirrbIzIcZ1qCU+OcefnQbYqC8y7y78Mxt/9v2n2JBx2SNTVjM2hvH8rca58rkn5lT9yevGcw5PH3TbccOtK3ION8/Re8FaV1TfaI7O1tq1nstb+zUEf8vmmnDN91Rs19/86KadKTP3/JmJ2h6rsVvf+ZV98w85X4OL2fM0V1KVjapDnxvRtduPI/zPRt1aFnGHS6xQmn2d5EqPTq6Z/W4e7GAZ4DKCmpidKU/wSDSut2LyzRrtCmK3D9dxX9vZscsOIQWHKAfHdZ7HwsAT9OE6z+5f3fyRvL8spuu69bPgl0GGyxbfzBHOgSDVHV+FhwbE3uBtrFyAF7CbhG86EOSdysbpUBdaeB0k0k0e8wq/bgD5pk6tFSrwkx3BuXH0Uo+AcYPIXwsjEKQyawMeBzNOgSA/ji49BIH4OaCDjVQc76pegj9YTO9AU9m5gj8C4PU7ClXAthfsCHIgCJslJ4ZqjuX7sB/jorL2SIPp2+Ylt0kTMeFF1FrCtk5i6yZvOG0JV+OmDBaTUNw/gWbNmKaTtuQyoPiVfXKaOrbmT1bzuwtMu1r572Ufeq70H+thLki6vt40deWLf2EeBmQCvfpYynnxON/U82dfA2bdT4U/1V1P1Xuf7+Ead39Lg+Z3PD4lJkwd3SNnPLRINkcwfawrj4vUaflS0jA58zxERl/ImdspcT0w1vO9Hg3TnPvZ7AiaC4F2mv+gOvRQU475X8V3tOKPLaWN5zoYIscBx8SBL8E2NQPpKHvrs9Ij5inniWgqaMVHdb44u6vGRZHByuXV+kr9FptKU+cRMy+caROyR57ZelXgOvqCmprCf7zIx5Q2iFz0GmdeWHVZwnXKmYc/utImAfrvO1CSa0vnIkybe7yAnIs43zlkhw8BHS0G2U7uT8sSAPLikbuHgrWY5IsjH+CFT+UEjbJjKC+AZscPj4RxZ5LdRUwQlLUolN3U5TuKunPJpOJ5RbbyD7u/+YZt3QrksFNHX/XwXTVNKL7TR3CHRZoWWX4fAIEbYPETxGHxx3uB/OiY+Px4mWR4VpVn/v3cP4EgBX/8MmrtOOLzsN5Ork/TW0cFg7CFP2gZZ/1HLbD+G8dwWj1a+PRp8YiPMvwkywVc5S6GxoQqCf332Kx+NwKYOhQoM68M6Yv1Ymtl5FRsAWAKxU0kBwVCAa5UONNAqlxiZlmsjS/BFqvizkTAPzZjY6136RJ+wpPtS4PTQxf++gMdcfb9TV89UjfjA5m2nXO7E+dNnzON+xxzzlcWUPgCf/Y1ZbjCzQ5te8DBeiaTxbiAxeTzTyXO074GQEQMuT7/lxLrG3qKD5mZqBt9c+5Cn68VQ+Z8HT6rnzoDI7wh5nMSejpNuHGfktPm1vuQXPN1zZ3fykTf8NoNCzTkff1VIfWrBvmQoEcw4qAMZ3fcSRNtz68J06pqlWRRSSnr7eyaBaZ/fDNHA8I7beyL1kE79JOsx6R1qCxlvrnD9VjXYW7U+OXQum57t+8bBX74gpiuz+8ICnFN53qvnEe6eey7HyPjmo4M6wZ0YoMfFSMghN3OgfXTNR5/GQevZPCU/1DASH72avsia5YEgnJzx2Sap1/7zYEgTSweu/Knd/T68e8EgfJuwVzfs17YL4SG14+C6Zr/WmuDvgnEY+f5ZR3h9QNrCtYBthp75X3bTC/g6cq1Q4jhzQ+bchNJQSEFmLymkB4CUG6XdVVbaLb0ITcTZVfTSOi4ymkzTzTwLE9WRoUvxFGgYysjv4IX4wC63q6pyqh2qwuvLLYXfWk4lunLOZ8ttgWEf/ui7VwMA8CHGfcDeQk+Xu+l2EDudgBh1cZkfA/mJ4FPtSm+qDrwu9s8/3RvW+hx+WS4bIDLpsfqhU46881yWIqvNF/VVSRnzMvDaZP0t4Kv2kFLr/C/3fCHtR7mA839ZzvO0umRo60ZP1gKd3McLffo8rjKOKNe16XcdejgEVcFXw+tYl+zzoEgdmb+97efyTuCaOvDE9fRofdK8yS6xwP+r+J7yKbfRpsDz6OIVpbKWW541FYD08NfZDhacnBlwE7EFczsKOGcWFzMKaMNraTOU5pHLvtJ8PDbcj02LF1osp1UMoJDKLumlOFtreFpXp2UxUzuU9R5WbuFXJ2bozpaa/6IEK+EtU7zws2BIBZzQrCQY4HIAo8Az1teFOn3BlQASLh3H/TVEb03gIXi+7d5PxA7gvKpWQWEJMudQwI8eZyMiUE/4dHPuwp4DxD1sCB0EEi2ZPFos20fNMa+H/ki4KGFWLZ7sygj+KPAjgNAyrnT5ruA8Gs5osVggjgsvARXQCiPhbHo07P/DgQp8ONFIIvCvCPgNXcIwRFkchAJ3QoqyYneGs7CU7B7pHOZTbn/2I3f0xaO0Gbqcv/xRfnUddEvv3Ufu7fp9Eo9XqMLmwINlmatwdG1Vk3KWtZam7ylmsEYxs4hUd7mhHeWB3P7A3KLndXBfoUbar4Y8OzG9tnv5YArfWffn214rG7GHTKWK7hldAquc6Nxn2PuuQwP2Am0cAE3ze1z31w6QVK+7gVrsdpbfWCQmpI3XBBvkNQc9JbdjHfYLcMVxfOSS/cPW1vzSHJX5vm9KX9mjgnDjEPVu92xPm2FJS0OXUf+6weXLlXxiBcGxTfg+D76XJlArm3+GUG5gPatlKNXYZR0kmAHWhSEYJxRN9dMvtTkuITlokQkJfrIVwnLY06+8MU1PrrYmevrta7NvAeI3T/8+uMO2R30LruAaqcQj44hw3rhg+7u8KEI9HC9JtDDNd83lVz+UEGg0D/KUF/z4cce8fgXg+0DgzrkWsvLhxMIcsvss/JV+ZAdN/iOoBCPkvvjEuzs1fWb9wER4OFl0N4RzMug9YPH7wryDaEqc6NI6wm/K0h4B3GkO18H0xrCHURf0AbuWuFPvJrHw8j5i9t28qUw2/qL1yY8su6XUzsQpBtb2EsPdS54pcKt8g0ghnZSajtwMF5XGqBx6D58St6KRCKfMNxdBu7UCmdesNm5MUW5xp3Fmveso8ufnt//m4i6tt33+Y51/jq+tJTautbd0m3Dho51/vpSz73SfFCeeaf12pZBJ+DvSWPY27wzty9QPGTBPZbCEr4Dtwj9N9FZhz14YD5z/PXl4Ya7xtCEp/DdVXAgMId0Hx8ILjDvr3RalE8SPGfXnumx+swV7Ywm+C2jw5Jt2Ll4IDAnNq/yDgD5+gRFTMD/BILknMfS1WT0d8c91qb79B58DB9Sl4EbB3yRYHVEQ3mLTRHTjzTPgaiCr2TCFkENU6i6SLYwpamU8k5N4VRpPS3Zk56rEnKeuMYhAbNSTvgqFMbZ4QA9PAddUuZ5gHwRjhPImpwljgaszeKRLeO8ODI7cvaLJLXAYxeQfj9pwUcQ6J2+KvKGO4VaIL5REIg7hr5rWLuF3tZ28bw/QAtB4R3wUaCHO4HUR9BH/2s3kLymelnkelEoOnAvHrsp0FiMYTgLPC/MvK1aCz4tqHg/jrd7ExRiEUjARz8vIGsBB70DRM95YbR4CQYR5PGXx7ww5I6h7gpqJ9ArPhXLQlFbw19pAelt4SwoJYtu7zRSXa6v/pKzibIfX5e5thl/k8hFdk6ZBK5/RlS5YfLQcZL+4xRvqWlN5Enmky9ciVHCmL/yzdoiypGKSgy/ZDEDTPpVtc3XKFhmitZi78JkuICnrieKXGj5vFDtZ1o1/dOtvMI17Zyfec9l+MFdpWkH9CvZlmPIwc+PoZoZVbCEOFU4Nz73RJv5YzcnVP1RyPlVk1pfz8n9pZVyiHcEHXpheroWW+LNcqv6y45uvs7FhMO7M4wWI3n3HhcNeJS4fnVq22DzdUk6cj1E3TVf41EXGWmr+pe+rsCk1AuqdecauPWbJm1tGxJQO4d+lZre+RUPuKaT85uJerYlsax56E1salcsm7h4qc32BbC5VF4wNUgLgigrQdrfurmWhV7txvdi5Nrp80gyvm5LJztsuL6ix1mtheDLiEMr0kmNowzMGLKcDgSE/Ni2b9ro+s41X9f57P7hhk4HhrhxowCRywSCtAtYF3S/RJprvHSxniAg5F0/5KLzkmjv/hUPQRPWAdDbjwkCxbIYvDySayk+km/wlP2lSURxIQ8xoWpdwFoAXNYFzwnysMOXa30Fgrjx449KCOfre+3Q8Q0hgka8c1DXee8S0jsJ2TlMlzBneQ0hgEfA/A/75TgszWMRBIXKu0x8+smUsp9HxLQGkXzeVcQ6RvW47+GrdkkXMk6do7IStB4+oBo2S/HhIxsFA4bbpqEXPMlstITUlcRmowbuWKYk4VUJMImaNd5cn5xmetNCD466Jh7a75PwY89FrfGPwLVP4zX6TrWp0F6k7uDav7HmypZQft3R2qtS6y4j2gcP1dc0clLLADeNbtp9Gjz0Ts3XOXgCnx1sWr1MHTUO20dThjqclkCKf8cj9mNmt6NtfCqu+R/Lo+9+LeXO9JUvDkOjhDddeKkZXXBjOwzpjyMf+KnaOquhwLau8owfz4xVd64v1sF1AfM093P9+qwCQZcnjto6Bzhtn2nSJjx5gCdtws3XuM4bP3NoJOz8u/K1D7d9sbmGmO3ftKNfVPKAa4l5ds5BbyU61PiVL/BJIUz0cBakQVzKpNmUHCY80AIZ3NHbPMn72HVSBmbhNZMnTSEyEaZWT7yTSXDk0ZA2m1wNsQ1i2CdsuPsSFN3CeTxUy+QAfPBRDc4WcQVuZJy3hftlz3nki7t+b/T1kLc/KddXRd4oGOT3ByggxGKR7eJ8Rt5bxxX4eS9ZvzjadxaZCLhLmIUmbWfB5yixXm5J/b/wkkvahy0sFpWbDxoRI6fkbj2BH7WDBZ+DQoITGCLX4oqAD4s1Flz89N4Af0Je/N6SrcfK/NlXgjoK6DgYxJ1CvgrColFBoNf6rOF32hX0Wp+LTVlBIXjYOWT52l0kW1yXDLM95LKXtZeXQRQKTjv2ERKpc/hnanzn0AxrIsVX8o5/s2/DK4x84vOq3Qd7J//xJ8KitRR9IDjF5ItHwo0X2OcIKhlSsad4pgwMleB7SoINFU9kf4rK/2iedmf7ZTamcZ1P2hX8EB800pXv2wboZ75Jg+4kJJ+PryHq8dJjRKecz4/i/Gwz79TABWo4Y5+/Cz0fC25fdOPXuY/P5GGf04cTK5z2NcKi+dFZc+swFVoHdUXTpkGwBuXnJJr/uI1MXzcWV9my5pVFCBCtqZirC/bzJ35ZcOBGByTqADRP5ZPRPhAitsBwbbtFF/UhPixrHbd8YKIrNDhpSertXMWVMsfCh63uj1av3CAXMiXGQP5RMMqAr2NVtF+AY4SxJR0rpIMxxThyUEhwB13gUzHz8se6nnJBgiB9/qPMGnukpZcchhGPYjOsz72z52ft4uHdQX4cTMEe7/hR4IebO+/egdfOHl2nCQBlxxCPlrH7R0Ej5fywi/cIcd13sMcwQRPWA3pzFnTz5Xpv/0gn9tpPyrv/Mds7aggCqS28I8h+U8H587jMu4V0PebCy44gbvQ806PkL/T+v9z04TEvbuToml6BIa73fucPZa7rXP955NxrCB4lF79+DgShUz9f521otQ3H+YfxOxBES3gPDzb3z+sRgj/sKmbt8HV009bwaN3iftKBcYAf6D/lJFA7Qdil5h8YgeI5CpU+2UVbzEwlAFEGSB9Qb3CPVQKjmSsvWIuEjGnODXEI7fUFalcKL3xerwg/4cVWwKRN+JaPuoK94mtc52d5ytBImb+3fUbqUK1Mw8wXsOuFr3niz4f1wU9KvZKwz27rDVf4el6hJ9t/0KlvOaBo1L7a24YtCwetKzjlLdt+OZFdNK06e/FfMxo7fdujMYquhP4euBpWy8wu49KG29LGdd74p+dTY3cauGicY60X5TnPypZRcUt33V7Dt3rztd7mqFy0jDPKY5yJvcWxAzhj0KPP1yjGYGjgmPthTP5ZBYJwTQ/mhsnvpT457tF/b/xT6vPJq4rTifct+Cv5rm0bQ7tH5H3zF8VjvktW0cKV18DnXNOwb86R68LvExEq/0i3fE2hCiRmMrcPXCzRBtesjfIxpdZaJozqtq62wgp9AkZDM6cuSv6xGNNsQPDHj4DVIu6dFoE8CkaQh6+IvOH309uvfvxJQSACQm/e5lEx6ASBWCw6AKSFoXJeEu3HwNBHYIdAj+pgrdQvsGRBsH9Z2LoMT/mUdWE7jv7nH37y4kqLGFZkWWgl6MNisQNA3gkkHhZwDgQJZo3onTx8vpU7flrs5XEvto5/oyBPFoW8H4hA0Lffvv7qWwWDvn2tHUH8ameQg0FeILJwJBCkAJN+qZOFqtei+WM3XZHeFTwTRfqBvElSs9LENzIjRFIIkphYlWrNUvpaG3STx6FoJQchY08AvFK0RGBdBTPqkOSLiEDqHaqavHPRq8aNeyL0a+WeqP4/hm12wR9ldPu66+py19d4ytBmuXlWDlFMnL6MjQRAtsyXFAjawRDNXXIGrrnnO+YNHAc988CxF85yV4GgyTP7aeKpZaZjLZtykLnHBHsxMo/Q3hvWmpyMXxNVhFIUxf+nZGaleGzbdAvlmrD50IHuqQupp+lrLqQZv9HT+qqhxuugIpie/5KXJRI2BE/1u5mDjbCOXlOJjh+ou90jkpP1F9R6fCMFnfo5IKRrK2nxEmyRrvRE+cEDikooJzddINddVKBPl2zv3uFmkF8GreAO7w7yNZ4bO9z40fqA9QLvCCJI5Hf/6WLNziH/xE/wxzt+BM+dQcDeGaTFQIJYagdrA9oDze0qE7HTv1xb3Tfc6FEbCMaQuPFDQCOwrve62PuxMKEcCGIXkN/zxzVesAI95M+108cfidDuIH9BVBfxF+z+4bpOgEbBGgeDBKPHOrVueM5kpkSNsReHYTcdTjuUu4/pAfFRlgw2g/DcJ5tdD2sS6e9+h8Vti3ZJn5M5g4wZZ4ZRpvZKqdrjw93faBdKUbE784F2kDI2A6ec1p9hhEoJudTm7wU9qnhja/NFKwE8a7tlDEMd0cf4+LPSY/W5FeXQ2DXbJTu9SMIR+g+s/xohMp/f5N0tygu8u53XPHDHt9LEmENf/peiWaAu+E41LtULKNnHs677itM0V7b1bqgkDj7bWjZfQRuxmf4mULz6ZxjTNbUzupxR5KHUZmiMlWvXObe50/9dhs+wDtFMKWuPVue8qs04C2WdghJBih/JPEZklLMWwBD4PUoFgAsfO4K+0TuCXkf4dz5qrbXM/J1VP6zuqto+YaBN+KwJWst/Cl/LPKTvTKPc9XV+jwf8U2z/q/i6p9cp0iNyNMgDvvA+SQp2ZsEg4ot98Wv6xakh7c3HAM+PKpcdFJxyQgCmlmD3EYmWOuebK1Du8jVc6xEXf3bDBFYl1iSYIjDXoAVbQi3gpFSA5r0I/5+9M1GT40aSNHVToqTe93/J/aYlUTe19pu5AYioLErqkbpnyEVVBhx+wwOBQHrG4SSQFndcFp5n+7x58dq3gCnpo/p7JYBeK/lDIujH73/IFUF6LlCeGaRfClk0zsOkVwLI+rDBIkknevyRI3z4msPjSFmIpp06bz1hokAGH3E+vlOnDOBOsjBSH6XeWqm9UGRBxSIxE+NHukf/g4+4tQwcr3BlcZdFWB4sza+BXOmjxZ8eCs2vhC8/UxLo1edKBH2mpNDHKyHk28S4bUy8XCHkRaR+kbQ+6e8VSPijdePyjTaluyqtbBdNTam4lJNWAmOSDzEqPVEBX8zY0lCFdplXJoTWJ3YvUtg3wIgP/WRD7UFKQ4geg/WtOm3zgtwN9PzR8md4/6jO/218534498EJ/9k+Vbb1XR78vZx+nLTynvvcx6+YjNOGGbO23pdEEH1mnvU5VJFo/xOnRM1nCB10tHp7WG4NEwJsJwz0qJl45ti3rLk8GyBgW1bmhlGXDep6zBp+hm/vrbhQmYsyNfC8flQVfWnpGgOcI3AShXP3tMlf9CF76qRNSZRCCwwXEWmJ8m6hnTLlan3SNgyVsxNbPleH27JdMUwOxrxsuiazLAkOq2vthvtcPjDoJA7VbQBdQtROJKMfWf9Jv8/j7Bz+U9kpxl58j+IVZ58s2WfqYZC2g3X6gg4eeUNyhyt/eOYP6wKe/9PEj2/31vndVwqR/JHAeui0zvdvSP5IhtvA8jIInc/VJgmE3iSCuAqp64P0wzFxzBp1IpI9wzjyx+f0jcs5nr7kfM+PO3mOkJI5gv1gZpI9wB/Na+HnRyAnhJwc4sohJYJYF0BTvV44oXjxjEFCxe1gjAuHTXFSR9Q/1iq8G1A+K3isdxRB09gDfNwLbajxNz9WzRrE/SG5Fb30zPzipaAhBWx0eFsCaMOhm2k2PvZMPGSHzX1YfFFh0lIjpdNfO7T01DDCwAjU62KC69oOzpbY1fhD/TgBfC+bpvgtvqeM7uMo+DN85/F32j71nfjCpbcuPnGgxf5W/+WTo1Pf3Sp3+rFjce/j2gkWGBUTM1DEb8fC0KUdHzxPHPhaj4ZpyRT6s69Ojit86S8y9HHKhVYc9TgOfTk//mzvI2AeQOjtcEj/Y7fpA9sjFnRhPAZbeFB/uoqO57Qwj2Nkxo8a990NfXt3NOA9mgJXMV5C1O2AZrlNn+8KNjZo7DLiOYcwL1IMiuCxKn37XJX5/p1LBPWgfHRAOCL/wc0f9el/Ot/zIZxh7iPgMuRvIgxLpi/qGd9P2EPjBFh15bdQhra3FfVgD/HJ9rA0Vu8sLCta0FitxbUOPocXOPVjXAXe+GgoCanCrZEGZr2iNZneCJIHNvt2MF3N86Mf/Pzri+9IAH33g5M/JIG+JwH0Wskg3RrGVUJcNu43hwkm+cOCkbeL8PavXwQzF/gXQc8M9HM+/vVnt3PJutpKFplV3jHpcGLyPpCz1PE/gGFtvG8IhDoKzPnjvBQ7iyst3kQgAfTBh3qQo2sWdSSCwGdB5l/olNzhF8NPdPsXzw549YWSQS9f6llBXBn0yYsvdFXQSz03KImiT1587iuItIBU8ohbzT6RPq0jvdCjm9gnGRT/XD08x7GfKNT9GPFsmwg4CokRfFUCLFKoi03AUfiGsTlmLCmCo6SqYLOuIkbKTW2WjUO1wbfQkLX8XeaZ9p/hfUbF/3r0s3H+i3tGrGvrHvfiMXnyPXRhmLk1jGOXMXTOUe9LIsgPi55F0e+dX4lpYs5xyGyZ1j2+hFazo9Fdd9A4YRP/xk2sPzXA9OE5+ynp6I3OXWJsHyDQU3BAbHeh9Zy1kwt9jLCr9OYIlLP/7+uDmzMUX6fuZfkNXaqy8A3Wfbd6tVUPaE1PYCvOAj4JQHHI3GnRMsNHxUIaxexraDR9hU3x4IijrtbpXE7t0Fo5BrCQq2hCo6f4ypUb8Zs+kcgh4cNtYjwIOq+ZV8JHiR8SQzybCDptzvlJ8lCTBOJKYxJBJH50NgcnmLafE6jaySAZEkl2fcYXXYbP3ahmfCcu+tO5mnHiq2vks29tgoFkis+7SqiIx7eBO4Gjt4Vx5Y+vACK5k2f/fKzzNlcOcaVPrgDKw6CdCBLOL6TQJNVnDMZE7DLSvL/kG84SBx976idR5NYwx5GOucwoIvQKNc8DIuLr9vXpD3sFOlTgR2XjBbHP4ZzKcIV84g65KOLHqKEvLQsegGqR10OkrzKWNR+cDoLqSlEPzI9/tKTUrrplzII2IO/MV32b8hiSjcsi5zHXfxf7R3162sfT8u4T8fVYaYzMtnbgEvpzdjMXIBzdo2aCfsEtCxvAp5QFFPF8Paxb4oBmPxqz+ALsSHRcnJirOcbrJUxX8n+wlf21PU/773NoW0pAdtvQbmoAJM4ZZ/HoJJ8+wtn1ffHP8bIffO4ZRvjWMa2G5abuj/hgfW5U7flQMujwaFUN/E4lgnqg/dmDt8F/rv536jttnfDdt5N2wk/5joFyJx7tpzo8PA+OgE/5nrB4NF75clCE00NVoKcXH0/ebydLhrN49sSaSbKy1IFva5WYeHYbfUjee8fiK7jxy61HiuIodt2HcSl+cIJPgYuP+aJ4KPU8NQcxSaCfWIiJOc8D+FVX+uSe/+91u9fr19+/+PY7rgT64cX3SgB9/5qk0I++GugHJYZ+8nOAeD5QFop+yKQSQPwC6AQQBzuOeLbRJTP0VSudD1ixGU7N6meWRu4HyyZPGO5U+m1QG08o0pvJRGq8GFCLDqnXXqQ5CFl8kRQi8UP50K9yZVGjj5NBTRDJuhaSLP764OiPlAz6XM8G+lyvNvycK4OU/AH+gppbxXiItJ478FLJoZdOGnEVkZJBeog0ySAvIOUHX3hxh4QQwFS4Iz9cBR6wqCyhgiyOVvF7j6dv0BhBxCX82jpOUFQ2mxqhGb82p5WOxYhBmfOLuZeqETlPOlV38hfXun1o+/fq07Pf430X6Sveb+mc99Fb6CW9je9Ou8e9fpx8J1wbrmEWkcPSc5RqUHwo71si6DxGmH/y6yvRuEbZ8wSxkkDODA7XZdMYei5cEdWxry+kKafO7qHY8rx50VaJzB0PSBdU55gL8kGjVh+Q0mO7qI3rVL09ZFASBWpvT/iutbT08U7d7Wquzk25QulluCuzOYhhNBDznuOCY7yXGpheCIPAMlsOdHa+Fp/PZbVz8kATp5RYj/WF7ufsOBFxtWstEqqfbbs3SpTglePNgCP7oFJPOGdjx8/0U6LHCSEhXSvZ4dvCJrGTH33yyniuAuKqoCSEFBf5hYyvChKcpBDx4oogydAfElaqkcEoQ5h2yxl9HxHqU5JBxERUuc7zgpwIokkSiOfvaIL5wDXJHn6kISFEDU04nd+T5IGuq4VEI6nEuRsduaoXHTmXEy/WCLmGmZaiJTfVm/hLsgu/6YM7kSuEaMNFwV27bD+Jv/QN3NvBpkvmxwqy7mdUoGVoJl3bJ61cKLxzgTrUmGXaCy3A49HS+GBgbezT9GsrK5Nq+8taj5AUX/GnbfSd+72c0a2YDiKxoCUd3gHoKvWQelbf5gF63u4f54uO8Ke/V9n6d9o64Tv322gn78nXCJwxLKyRNrEt16lF8OwO861Y7n20oUNuyRT3lAv/Vt/HSD04fT/hasMpa3yqdrP8xyB6kbk1PUz7be7sSLyN6znaipoYrrBbRSGuY+0yBmX4mE4vBsp3hvhUtZjHeVL1LUBLrxqhZJxl/gPHOSAUxmJkWpNAf8cSQQSnB10D1cHduvh7XXrrk37iTvjkAT5pJ/w2vjvtbFdH65N2wqW3PmknXHrrk3bCodOfp/E8+dZI70guUSPttFF9HaZl8xRz2LCaDNOyuGbgetFOndTKovfUlMUT23NiWGwGNi96tjdIRb78LDQocLVU4vQgdB9jcX4pLnelqd2PqX0wzkJPa7wXP2kBw4KPpM4PSv7wXCCSQD/8+OOL77798cU3PBfou+99NdDr1zwkmiuDlATSVUB+JpB+DfRDobWgcwJIul3LEP45EcRXGiV/stDnXnt9dJ0MCzZWck0M+Sc9YaTKTnvyAE4zNXrpEROLPzLoHlIJz+JgEkLAhMdRLvzB/EqnpBCXeUP3FUIC+jwAFoifauH4mRJBL5X84UPy5wtdGUQC6KWuCupDpD//nGSQPkoG8ZaxT18qIaQrg7Su9MLRtTxiv7D+puZDcT2NC060jAOzXfivfPR1l+sIhBO6sMTlZD1hjx8hXKNrTsCAKrBa0xiGLVn+IcB0ljH7ZO0nnlGx6lPsObgyz9HfB3x314T2YZdP2gnfmd9GO3nhu5f6ceKrr/WiDXOPTtNnMDEc359EUBY8Ky4CLoccwbgdERz7TgT1mKwAeP8JcGEOFCB61x9uD/VeVU1qBHfZ583bQnKxhH/zifBkp5dZvDbS9lkjpDIVAOfYp0mgsHWbWYmzJbPc1ffyUNelx3zL6PA9py9awh3LtVHL9cHPvQOpT368KEWoMiMsGEr3QfSdDPjO0XIvmyf6pGUUM8f6diu1mwQsd3qGTTGlI1KcE5BjzY8vZCFwyE6Fz/wSIJdB4sY/7qjm4pa2jSPBI7uh79u/SPz4SiDo/Mg0iSFfCSQ9fraRar82Hr+thzYBUtsnF/ojd7VhvLnQxmc1Lw+K1kmVPBB8/jiJk9u6ksRRwofzsZI/SRDlCiDavj1cwiSHSP4gT9KI83RfQX9JmMpWLQAAQABJREFU1Eyo8Mhe4R97FZ/pG11wYmhmPYKoIg7zw0trhz1jy0mg7BpWQ5bpNt2nhfLwo6V0o6cVLFQgcYTJ7bU50MTNxbiDmbZxeC7fcdj7YmmJGGhB8QqUEDVqN3ZvzBc1jjOx2gW5XdgPjPHW0Ykn1wK9JfrCYS/GFvStp9xbzu7WGfQVLmvrt9HaZ9uKis1+9bot+37pI4YWtVaX70/7sFgCyOCaF9ArbL+EVy3awXc8RnCQNLbTA4fjiJYRi02E9iOc3VZCI0NzOyV27/S027fWgz2iOnJVq2b7MpR/Q1WL6Q8Gg/EMYPscB5t6dena/yvt91uPtFbj3u/Wc194b7aHQ3uFVABDf+3bB06RCOo+Yn2SuVqMkrOHU69zkRT6D7zh1J4VNTe+c4mgHoAPYvf/Uf/OCHg0ngY7zK+EHLCZLE2ZyWqGsxVEQlupyOFdHYuSwS9urIBNXZtW4815CqsWCFwNdLbRgHS2FvUGnnhbXGw8/aWl9GtdG9ajDesTrb/0AEglgJQN4sHQTgQpCfRaD4L+gecB6e1g3ynh862SQN/pyiCeEfQ9bwrjiiDdIuYkkJRwS1h+IeQKIym37vT4tze61Yt7QzRpJBFEAuhjLSxYdJEYUiJIH/+iZ5jJhYmN/uqP2h9tVNhasya7LLhAhIk2Jftq4HUFEC5EmquComlqL8B4ZpAWf/qwaGQRyCXjL/VsoE/7oGjXXA3EA6RJBn2sxJDavlWMxBDPENLnc711TLeJoY9bxPwrokKgpvctX4ABqChMvIXdBjcf2pTSzzr9DL3b0BMrcIbavNcwIFD8YaV2YIF8Z3siAuODcj8nwYKuU/8DsSeoP8v/RMH/csSK97+5H/e4/yk/hjlHosaR2qf8+5MI0sw0XwjZfcQ0s+Ojndn5H8ZE/zwXEL/EcLautjZi/GcK7N3Hz8EXfWLGRvoA5RmDm+EurnYt0kVmMRaXsHGO8PYic29s6Tslmh979FgqfX4ssbUf+0RIuBvxnHq0f5UIKOwajiiPmtOECSc6Z3b6fl1HxlI42cKXeMFHj8g/ACcBdf5eK7xji9yMIAcXWJL6Yk8yyPEX3jjoKp6zdUsP1vmBiCt2uC0Mt0nqOBkEXh9fMaSrgLjVy7wa5yR0fAuYanxjTeCrf6SjiaEkhUJHJ18c2nd00df1JRanVOyjzqJOonCitt85v5KooC/UPt+q5tlArC2ouQWMpI+v9BHOD5B2Ygh+ztW5Agi16OVqIE7YVITFiZrW6hO28JEtt4jJWwVHH2LEsS6cGyAoq+2G5KKDNclKlUiZ9YmFOiXQuTV+1BrGF7db43MkTh0oNba+07ixle7ErAXYFymnysBbmLajMX5lXyG3emclp9tPjMfMbVsbxPhtpXyb58Q8lQ6V7VPa1lEofG09V7fX1VgP3u55tZW77bfVaAx/PDst9DhCfsEeo7VwcsdK9mfx5QvtvjXvdmDIW2ZDWzK4B5QY3owXCH5G1cipqoewPdB2kX5b49Rz8sXiiQHOcX7HhnLH/ne8uut6W3v3wNBuyt3tQ/tzku9ay/02nsqcZxj4Pa1BVMPyUz+5Isj4HBeMycztqd+pt4atA64RU+2T7IrUQbiB/ym+mxu3Jru1Q+RG+tPNnPT+QCj+ezHzSLw6l9iCG2KPjAvb+Gee8JXNQ1eN6y914WE7Wpe2yt1j11PD5ocT+fvX+eA7+VVf5EKrbvIafidFmZYXT4HaZR8Ac0uY8jdKAimJo0TQj3og5A96LhDPBOLV8K91O9h3Svx8972SQHpGEMkgvzWM18YL71fE/8JzgbLQ43Jw1kFeEEv/Gz8YWn1TIsg2vehk4fmJPizmeNcr7dynT3+9JBIuzwVikpCvEj7HTfzfuEw46Z0kEJD1fLLYkhkFitvAwCfauX1MnocXOjTzKRHEAlAJHH4Z/PTTz/I6eW7/4m1hSga91HODfHXQF7ky6JUSQa+4XeyV8IK/nCQR61DrYXGqfcQXX857wBRgwJ4LBx0ceJim3GHLuZ/lSF0+xhuwI+NNG0cdkThQHuM6+sIAybqqsEaEP845YWZbvmfokA8VSLy1/Bnetyr6X0zs7jlC+9be/FG+tyoR8R77+vF7cqaLGf5+0MUx3fK+JIJY9HD1wy5HEDZyoB1xf4XSZME8X4nWMPucYhpYyclO6amja2ssW7nQci8jo0lpr2newg+759zqMaKNZ+qZXw7HjFE7c+FBkIZr6xmVFzTarj7vVrWBAQ6l2IuaaVRbeMLP1h9irkHtc5VPP+wD/RGT/C8tATjPnCGjPbFecaz2EXUFrkUybvqMZ1uWwBf/lW88dlDVCzWxFYCMB4kRmsDgI8fvNpnYteqRTl8JhF76p47+Ak51+50kUBI5JEFI8tB/J30cG375zdqgCSBuIbM8puDnaiMHDL35pfjpuMJF+qEPx4VhKRDsRI1+wIFGYscfTTD8wPShzuPUuVVMVweJx1cAwWtd8IRXKp26ICSOTULiNQFxI1K4KRBWtbPmYLx17eOeqP/ujng8Fn38g8NH4UQ3HiVipAKfMvvXfKaU4BqMNB1lki1GjhJXA4sz0G4zgGxPqG0XRqIaAfCsiVqC99aoi9xY2LzlG99KF5rQpJTnaXsdE5VTj5dY2VXj7UlJ+2AYEF/3fFY5iFet224E8fDKEfzz27NPz0mCP/m2todYOX/6vrnbj7FTvhVgRUYkU4171Jv4cu7npZ+DYOlaWAP2c5xNbA/PDzMHdnoczHic/TdMm7fQ5sIoWGOe8Nulh5tqeEgUcul8wnCVDF9wMT9OjFy593i80p+o/28hai1KLrtIpEfHwFXiajx9k5yAi64rm1vM0T1G0Am/5VVX1ni+DJqueXFqGDPnMy7N5fPLp/q1/Gu9nfnvKJpn69bfof6xzr/CZIP82MLG/v187Ch28dvKH+FBHj7K7+kL15/drljUzFJwtbdPGeBPZsFmDe48IDKMRV4nxFMuGk/MMr2AWo3NsSDq9i2HTAWKb108dXGtOfg2fHKesG1qQ82HhR1JIJI3JIJ+1Ktgv/ftYD/rmUC8HYyrgPKGsO/0TKBcEcRzgXh9PM8D0lVBP/+kV8jq2QFS6IdCSmf1//bbR4LVbz8ldieCSP70KqAPPhyYxyh7IaeTDlcE0Uf1yQtQKWTiyHFFHCl7a7wO89CF1z4qDDeRYV9637HwcZtFKhFnSvMUJfhXvFUtHhaAlnmjRJCeF/CpHgqtxA+vlOcB0twCRjKIV8hzNdArJYN4ZtArXQX05SvBX3z24utXL82n51N6AelnDrDQlAV094ND4PCRMtWCoVFOfNvggk88wFPSD7YHPl0Lw4EOottthRPJWRABY9GDdG+fMgsW/91kVbRevG8B/gzvW9T8ryXdY0hHiMkj/L/ayUf67nH/U/bEDH8/AMAtOiR8LLT9rtZ+WLTnmunhsTR5FPNwMV/oOPYOyBF5xg4ejvGqMm0xQNkyz+3D8zw3nrnqfHTigGvruhcProvC5czJMHPdvdfy9XCS8/mj8hj7iLNz46Ztb6plY+AqdkvcoXCwddy1pbDe5ZyTq1oCQ6c4XlczYGWs1rSXBCauwi9egNUwzN6M3sG7GktSQDO/so5kTZxjyHaliROQ8B9xsgPXj7AU63JEZJO+CYGPfLDhxbwTPDCT5Byc6Tm/Jgnk4ORcPnEK71wZNOf2JIdi2X0gMYRqDN+KXcU3AYwT/2ki4Y8fXcDliqDc6sWVPblKSG2uAILOuVh9J1lkHVKHjLC25jWAIMIk9thQLYD/9fG5e9YQ4IkP6wqcZ4/kSiFwRkiO2KRP7M0UjlTK2Q5mocQxmIqk1tZ2F5XWyYlcxs3SUPJaD11FkmALjr63ZA2F9kGq2uQNlX9zTkwP7vSY/p5yJ7y1bJ6M8ZNi+FSDw47vI12Nr3pQtqWgWpErnyyLMbtr48p5qS8KT9uP5MCdPBdNq1GOpxquPkbX5sLfHSka09osy0YBc9SgkIBpHsgyHzXdvpeMt419ynLDOHbiH3SqkwfHN3Z1Q6iTa1vc0OLdqD8B3aV3e9sNtCmn+hwpj2kn378CX7VmjI4eEs0CrxxP26fVR/wn/YQzM299y7YMFqbOFdAgPSOayDhTU3yq+eiP+f5Tzctf69Ebf0f5X5sIugcjkxHh2+Xfg9s7tpa3XfzJQZA5cLfL6xGwhmR4N+1vgDqa5UrnFo+6i586SXf2uvDRoA8jG9ADlX5EBCSfGoIbDH8Ti6AuXFBkVSVyY8WY4KL1ii//sLmKjcoYpYM+ciffU5gDE74cgCSAPvB9/Xq0jxJBeh7QD1wNpNvAuBJIiZ/vXv/y4ls9HBr4229V88p4HhQtPp4L9JOSQD8rceQHQrLu0YeEkK/k0WJq3e6Gf1xi7m8zLLyU/HHiRwuyqVl8faBf7HyZuhdi3EpGzBRXLQS53PwsbqGPiYR9Rk0H00O34Wfh4v0mVsOi0yaKv/32S/h9i5gtqc1eypVC/SWQRdGneu7PJ3rtLDWvlf9ESaGXqj9VYog3iL1S4ucLJYFIBn316rMXX3718sXXesvYF0oQic2LU7+yVrrQy5df/OBqIUp9ah1s/ByWouw7/lOoAyc+OQHTh12MW7ERt1mpI7M5gaKNyq+RP4iLe1hMAhbBu/bgvYOlD3ut2JV7/+6ylQF/mr7zvQ/ttQ+ms2ds2v9HuNLO+hHfc7hTrvAf9oXxIaF+PF5GCeOd/c+XrXe9MMP01jDOFvz3XEpsiAU4AOayLOUY8yI4PglS2cD1sK4+xCluo5ADzwJDMczmLLHlY9QKZfHOUh2ux255ahSV5TvVn3wD0+/r/KO2acGvL6JReGoTBsbTKOTBXea08tGfiSnqL9qOBgTRkaI85lOsTJA+n3foRpMkqjX15vwqJvBRtZVV/9iCjD2fu86YSHe6wr5B1zAicHiGrBfloscdGMMB5M/YRBM71rFXnQQHR9/GxRs0pHDapQ/4Yl0aJKnjX/oKjjb9N1XjXPwEw7LEJx/WCGcSiR9jHDMJCBo5a5M+9UxENNqvAG5lvtAKg/3K2oGuCUnfSO64VpurgHzlMUkfPvz5yiB4kRlZ7wXh0DH2fJUQ2uEDyz9E+WFZgTmP01mcc4RNtwpvNi1N+CgKhMHILXj0mDgnzdqEEz9oLzZUjU8B1LYcyKNYCe1oMUUscFmf8PQ3iLCRCKNkDaU4uZE26zknhYoLdfuGnD5ExLEberClIUzBUP1qbcJtE40g4UrJ2AtCWPWhfppudcYcQuGrkmp1iERCZPlTonGDd8DCVTS3X2anBGOLp+wiF7nlx+Oqcl2u1odWgcheKW1bqzbRni2t4IdgUTAAlOFTH9Y5QCT7pYEWLiPG9MhBo980PSCtrK6oFmGrHrbIwI7sKYYgx5+FoOsPcasZPWPhikPkD5TRHD/ewl++sNRwsW2HGtPZJlJ3+oxPW90yV663OPNWUrXUNzELBTb7LsLlwoXGuzFt/ZyZRT9MwNtZGbg6sWN4DDLqcj4AnzGY85wY9O/zgGWY90kEfahE0EtU/uXlP5oIykCfqPzlXeMA4UD6ff0n3wnfXTppG67+DHZkNu0K3/Wd7TWgZpietBM+dZ/4Pw1f4nL6nkH5rL6s8o6JRv33AUQcgDsxVkPiM9Qip952QYTT6lgGqJSeeuso/uSxwMhc6dZb5WU7a9Gqm7DwYfH1s+7B55k+3NblB0P/oCuClNzhaqDvdCXQN741TAmg73Rl0A/f51lBonE7GFcPcSvZL78oEcTr4aX0DYtEf5T0YYfrBO91oeokgnCK5wGxqOBKIC0AdPUPizF48+BocJJFj7D5RTE+5woedNAf9MMRmqdbN9nk4yhpg7p8kNJHDRYxvSKIaW1/Ks+r5SUnfi4l/0iXLfK2kU9Uf6I3gvmNYsrwvBTMG8O+0CWNviJIt4V9pSuB/qFE0FevPk8iSNkeifrKItdSvJ4XJBteTMoDh0E1xb4HNEyEWkqj7qexCI/6oH/6zARLsUzAxRLAlICXLaeS0E4xs9xEvHi4yD5tvI2n6lo/ld6YP8Kzud896L4viMcd99/p9anvDt/11u7Jd+dxexh9YSCwPpWFzvj3+s/M7+6GpVBuDVPvdXCeVzqc8Tgj0GPQRzAH9K14PhfO+2CO9ce6tizHIvOJ+dioPdUc8TcjNLE9+mmeC0Ha13L1oLpjZPygkr7Ma4MDNeDCmy/az7XBlqjlbZOYZdlZWutDSqD5jj7BtTgK3OjVRN21l2Mh85yrfJWMYJ9bXG+/tqyNryamwpVz0zrPGclmdJgxcBIg4+/4uDjFF9/GDoSOHWrD6j36JmlCbBdPLUoO1VmwE9OUmKO99YODTtz91zYxEdGyqq2LhvHELF8CHDv44NG6pLF1LZyLKzstV8Mrp9eXx5UI0om0MG8HY33hK300yfjqH9XuLrIOgrXYeyBWJ+lNzJaXYGTtAFfmLPzoubkRgtYicpjdh/ZjaiIFnuzYwFw9pKhgamKmuuyjStUuptVf1T2A7CFsMIxH6sjyrSJy3n43DsIPuPpqDcguNQUqi4xwh5+nH+HWNv9iO3hN9Gb3SXT0dQwcBIOYOWNyNVzuQ2dBCXZ/48LF34r94TqdtS/IHP03iC3Q+pizDpdoGYhQ62DUQDpLYgHGmk6S4aquMY42OLOtWGTDe9ejtlw4RsfVxtBWX67Uw/sQdm9oq4VR61AT02bIeIo/lWgNT+Bi0pcrTlyPi9jgvPfyMfOJrUStQivuDk83TvF07CJBTE/fL+x/SSP+eXu46n15tAu2fmT6HrN7G5nKd2y5rc2qDbtlZM4dCCKhv2Gk4vjmw/kT/Kf6nvTO3RrWCee5yYygUt424YXjr93+EXtX32enziDf3nBSpMWEvbGPoPCxszu0LPiE9Wr3CXkhfrcP9uf3nBp1i22Ai2vFZcAysmd+GmFO4LtPcItzaFTQovCORbK0XSNvK6Zmc3FoZDbOJ1UYo/CJ9eiLJhap7CtwXAWUN33xbKA3uhKIN4TleUBc9fPd659ffMtVQSSB9EDo175KSIki3S7mJBCvh+WWMC3avIiTTj8QmvHA0sg/J3Frl2A+4G0b3/OgaHjWFUBarCHn5NCKm744cauZ5PK8AYxYlTYBDVxa6OcWr0TVuSZgXhFPzUZKqAGvySAZo3j1dr5JTImgJoO0sOQBk1zdwwOgP9Er4nlmEA+NfjW3iH315SSCvvr8xZe6UogJjsTRx3p2ge4qsy5uN2MxL3VOABGhR4kgu4tL81Fl+KyJHMVjL+DwdNwi494mfMNjIfAE44KDAv9Z4FMbvhtprT1hLw/wlAu9yAd11bZ+wHI3/YjlncY92U1/U2+7D2qv7Zq749su/VJDlAJ/3QHW5+R/XxJBfm4KUVAs3P8VizMaR6QFeiFpMviDdgTYM5smSR//B36Dp6y4rOaxri3zCDr9hH5vb5lS8KlzT6hpLc5MyFJVieE63EOia4Mr31UGdzyVGX2joba2DHKeUhm7NXf1jvME560zspIa1cYWFg9XxvqKF86z/LmGPTHARlwQVIP4MMUoJzhA1M4YKJNqdFS8NVzlBHe2zQ3SxpPmMGhFSY5YwMoSF3z2v4ar4bFIT/igq7GqLdu3jjgQOcGW3UkgQu7Fvy4fJra+2pcYg1fMUNHnAzn2EMaabeCYubhytwZBpS8kfcgskwzq7V9NDKXLwiOJqI2pPkuMyImx4zqsjAfkOF8ji6nVdmRQ2KIfkWxktxNU2uimzw6w+4s92lmTJB42LdbxJMBIowVr4960BovAveArEgjhlwQv7kHyhw3wxGjYQZqO3hEkEZYf9MZJaERnOSVFKthFBLcWqRSzhM+8XjvC97QTlofJJMaigAd8Vu0NllXG3xtojkebhypRVJfG3TaXDuHNJoKXkYsQwP6ixv7U9/FReKsd3YHTaCxOe+hvGy50t13+WO1WdDOcnDE6JtlL40RlntblnZ5GwbZM59IPi4Z7yQyw2lh0LJhnn9raNjCzpcr5FBM2VN31PRAfNY8Mozn4QE95HtmOv5Wsl60fS5T6r9Xxi639rJuuNc8JCXjGoiwXe+K7yF+I14b1SatH2yzuA8cO9Ohizse+N5GAGIx8EoWP/uDLFUHv0K1h7uoZeRAqGfAEi8mTUAU2cGyg9UD+M3yVOVQZPPXdabRLb/2YJ1i61QMqXfSeRYsZTn83zFeAlrfxVVd5/0zMGFTSbf+wkUG2NQXCd8dJdWDw4T95F5+QDFQrVs2JL6X1li3XMEgrtEhioxKhX2nBwXviwaZ9ygQ3fKP0wXCrStfOzLLmUIxYbPGWsJ91Nc/PP+ktYSSCdIUPbwb7Tkkg3hL2zSSBaH8r/Pe6Wug1bwvjiiBlkX6VbF4dO9Fx7ElJyC+fyFkqfaQFn9qigffVQm5zEz90TgIsyfQsIQK+5HCZq4jYh1kc9hdDB9FdR6//0aiiBioAvVFaSTqtFvzgMNE98QFvEoPfSvXQSv19wMKMxQ04Gf/gQxJC8kZZGt464jeOkAySHid29C2WN4N9wWvl9fawL1V/9fXnL/6PkkBff/XFiy+/1EOmlQT6hCQQtRJHJH+cEJpfKnl1/ccyEt/skDf4xkeeeOEZX4OD4WzjL/EyjrhZCi4KGlI8Jmk6IPQXKlLVdoCLT7SquLH5gaKoLr584FoeiD/DZomqqvhZv4128r2r8D1uxKO4E/6z/a9s67s8+Eeltk9aeUujnQXB+CoCtNp6bxJB/vqrecb9nygZbjQmLnNswufj1cGFX5+DZrpQ1Clbz+V4zOR38EkGVXcpEKNrqRTq5IMBnzxXbnZjoi60wEudOdHDV/Bgr3zw13ztgQOL+yfuhLcFsHitz1RIG4bkDwTsl1fgUZjnTYPNsGoFlyaKXLPRJy1tHXzVOm1wjvUPFpxnyzH8qw8C1j4dG/WGiFQzFrvD1roM2e5sCR2aVpfQ4LKVTlvcOOGOAQOi5MCrWfX2RJtctQYzBawFp4q8KZAIrUp1mDUob4kPKrIOSLxyBVXw/UEpfMJp0iApwpXGVSrsoZZWivGy3yt/WFewPz/oG8HopxFbfnpin5YeKWJf3kt5qXGHrlqdYMLoUQVxipeJ0Nqmpr2CQ8KHbuE5sBtOCHnstA2Zz4MSmVoQA3wsIqzqFCqMA4oDDBabcTC6EaVknOg4h8f8AkRDEhy1/81gEeEPG0YhrIKpAbMODJpt9EQn3d3fFzZPIR8DaiReKGRs0IaDtOLTYv04qlLdgUHoUyHB0QN1ykFbfKVRR63r+H7VYXJ1iL1D2LEQPvNDGOInqqI0+0ew/tPfmKs69wUdp+GlE+fCyTYQOHQQs2KGKoccW+Gxbh8YQiLHG4t6g+8V9/6mU1OqNeO7PSl1dM1ACBUDKISnelqDCQy5bNv2oXdE7G/FJWS1m80mSt7oJ1xYG7ItD/x0QihXGNo6ZeID/WBfUwJH4q/bRnctWK+C0fhhOrSnFsHPLln7Fa72phKWH6THz+g89Xo8C+HvE+L1vtKm675BuF2PfL4Mo338RN+H3slnBPUAa0D/J9R/1Kc/zjc7/dnOZbhsffdhdhXcfMIj+nb2Q/jG7AF2kBfIaF2NE7wdMPHbTnhgI5fJsgM5WmqXQ2Ef9KeRaioO89WEjtDjFFPOnjAOR2F0AedTxW5LwXPdtW5tOOjg4QobFqt+XbyuAvpZV/X8qM9rJYJe69Xwr7//RUkgJYR4Q9gkgV7r6iA/RFoJIZ4h5NvJpMhXA6HX2QBWEXzkPQ971sfx0MmCK33wgwWWfiNbfFld+Lc58Y+8uucriqQHIW5h4zTP20SYhNBDuUcGvHHeR7KsGY5Jbn1GyG20rH2Z5wHZmLWzB/BcNTzz4YGaH3AVj1Z/PHiSq3c+ljLldvS8oA/9qnheGf+F3hb2j69fvfj665dKBpEIeqkHTOtZQrp86KWfM8SbSqRDulyTYJIukkF+oCXdxr3p4FTuGxGinDjDs3JFbvcjfMSs/FBdrD9YbzEm3ANOsVdadcFRQ2WTD/AHixdBZ/v3YNQ9p/I5/O/pfFfojve/qTPEuvbucS8eV06+h66JGf4uhmmc8u9NIkiTmL7WJkQKGoukBuKMR0d/5gHNnyIyn1FyjBs8NtDethdyvrjYiLpDx9s1bMaLlo1WRy4U+3z4+4y9KHib740G9T4zHoYD2vh4oFj1+TQmHrYTRhCcH4jtddZbrBeXpm/izTkoNR12G7z+fI716YPW02LdsnmxcbAtuuZz9nN1gOfT711u0xEYVNGmlL81Z1ZOJGaDf9rhhwYqcdjja3yXUBbxis8YzlUM205ksZw+1T8wZ6k/4HI+D5U+JumT2rEk+SPyiqsDEQ1OSsnnJmrgcR+8Ccz5mauA8C1vCPPJVbB8FC3n8/jb8ykGbUEbH5Pah27j5gCEr/wTtmVaag23Tu8iy7IG/kvBbxX2TBJdjEFgfQiQaq95PFWIN//jyzgkef/waU00AMbSsDiSi32AYaE/Dh9tGq2IU2HXxCyIVLuNvfJSd3x4xHXMCE+ZLksXnJhM7Ub9TsPbHJsHYkB6EV3uneH9HKkVgeGOr73K3BbH7MW8uB1q1SavmI0aVUaJ2H5AQccDVus4+cw7jFQBtcUYjI0JjC12JI3EIlLGHCBtHwdhle5JioknEQoBkcwEwyjqdR/gxmG0bJbrpsiJlv24yYxvYE2ZIC24KqaeEWTb6Uf0jZrFVTGo6YuAsJZ0rct4xe6W6Fv8am0zFXo7fesp/64jeXKc8Ob7c1D9QVdhxsGhxXCiS0dP2sl2SGQYHsQDXGyZczWSOEcNtrrd1ibt4/iE0/iOSNVqe+4XoX+8Pv6duTWsB+WTg3eF8gr8T+Q7fdqw9pwPHWoKk2wGhFuGy2OG2WTCaVzOw+/kuuq4U67t7dMVf2nJFc9BM0pz+M1AvB2L4at0+0c7E14w9OMRT3GM9dH/7BSTA7MWaAGnfYWjdRkcI7TDd9J7II6i4U1l/doQ/77Vi0uxf+EtYUrq/PiTHhCtz2slefh8q6uBeEPYN7olzFcD6QogrhCC93uSRqp/5blC0uH7+xUiljHuGckSEj1OBLFswtc8J8i/AmpxkMULqyPofFQKO8D0MXhPFtYvG6pZI3UfrD5bHkvoaY1H4YV/wxNpEA4W+xScFNMLKfUDQPF6rhaCTpIGLSwkk6wBl+QNt4dxSeNnn3+kh0brwdF6YPQ/vvzixT/+8bmeE/SFHxz9UlcK8ZDpl3qwtB80/QnPC9LtYkoIsWjlKqOPuXVNSu2rrLWcbda3Lca7O/RVfg+cfsElhHF4DjyNsWBVCk4sQoPrMFBDroVv4C94qb2L0K6p4X3Cc9PxqImau2r4HuEeyb/LuFt4n3T1wS54wgPibXx32j3u9eHkO+HTIEOP4cOXLMu1Hqb3KxGkB9QrFsSE4y3nsrRXzCY+mh0cL8fMRObPxWWgTWJvxQICG5HAD0hV/gP1L4DVgqWe80aNHCz1kWL7djjI/LMlRt+hwKxqn9PPnDUOOdHFs/Xg1qGkjtS4auJOyTYMmQsDd2vvRjfn0PygQqJGSJ2QqLBr25yjBN1L9WLSdssyxk8fKmv3yickIMkMA6oto011V86ETrgwIch5ubwCPO5mO4SIiyf9iVyTNIvIrx9isFsoBKLKJmxt2m5Qyx0LzjwgmKbnBIyq+FY6gf2CAM0nfSrsqm1OESyinT7eIG43iK9fD6+aeH2oS3c5r+bNYKwswIe92+hSi/WFKtuRIbPZYDhN04ban8LU+nRchntkhoeWwKOkM5PyWUb3uJqewYYPFY7YBOKqDqTXVyNgDYJH05V59PU4gEgfMEQNPs20SbDFDdoaQV6zDT8dZydbDhgnLQ2D93F8O22Evu1vfnDugqWzwXZLaPQV3W98a6HjJkT19YHhyOSqcwDgsbPNuV/mYzOGqE6bkCjgrMKAUd6UF1oTlYs6xFTlRFfgibTZ7dbhW/zdMjA1NsZOw/2Hxp9w/lhjNul2jnxPlqCFrKnYPdpCoDpyVYTF0xfB/gc3moacFvrmLwaqyPXikZF0Q7yGG5mYi8pyjymaY9YUwWP65rNNPd5IsJbQYT0HJ7Tg2Fb7wfBA5ko9pbb2aNvtu8zb2/Xj9Ilj8whAFegYZK4jtkixLxPnMFQTLe/nEyHctSkLRvidh6Yu+gD1wXzY0nwKzmRtMibD3PHJ+RIYznc2EaTeTScJNIM9QQDfcse33bp897r01nf6vV2+1nd629Bbrv7efc9B6109pD3QQGw9UckOR/M+2GundW3X7nO+3vFtb/vSaFvjmK3GCl/0L3wnzSyV0ZUi68CZwaw2h1yU7/6lr5HbC8HQqy3WmRTHNSNOnq15x+60gUB4Nl26dLBbZw1dDXjMeUElOokgbufi4dA8H+gH3e71o98WRrJHzwfiCiA9C+hbrgb6Fpi3hOlqISWIfvxR/NxKpvoXJYJ4O5h/vHI8MMp+nWf/sGD0sgh/85wg9j3wSgTZ68g50E4g0ce5vQw6NpBzLVkZRAL+jpE0jY2akA2P9uDZ7/YVBmA8lmLBNqRVYJJCasZTt7l9rAu8JGroJ7hJFonIa+FfvvzIV/680hVBX+u2sH/8Q8mgJoI+16vmlQji7WJ+/byeE/QJVweRBJrbzbgqiA/FvqVLuy0IMmPSBdfxv3WRxtGnKT7o1F/kFpoxI4SQwVdpel7RXcMLO3EPFrnsU7W3eOBlJzSasJxoNR+W8lGfasv8CFfa+1I3jmesivsrYnDGuHpPHDbu+LNduL54zEgB+LVIpj2M71ci6GcP7MyFiZCWa/NlKe3GhYMzfJx/dKwqXqSsCeSELgLacmzmmO5xA4eC7B3njXnvckvBc8CoscGlRshD0QFKy9nCf3scN7SFWpeijm3XBnVizrcRNZL5pgXQfZVcpLVdAyv2E8OBK4icWJEBIGYpLJiBq2/QqrCTqzZ0ZncCAl/zgGNfiSBDnIvAWfnyKLr9BVpuxKYVWmctpB8RxeftUXg3JpDnYAsvSRMiR5/UC3dbQP7HaPlJhUyZ/qdSHw5ZdPgcK6TVIQIjwkUIznoneEgp0iXGUR+ZTVriteGo2x5vHUW2thtnhEnCFc/aI5Ydd5OlRd96+PgB0TKOfT8jqLeH2afDL+RUvAQImC12jrFXUvvnWpuz5guX22WeGvxZdpNe0p8xJCcMul+hsSOBiNMGkErzAiwRyRhGVsCCzS0fozOt3QfaPR46l3j/NY5efMCfHoQXmJhryziZzrmqP+MDPsVy4089AuhYcP3guIsSaEkOxuv2j/HAg8V/faMrLWMAd6zLV4Dhu3WTCMWGO2keNNEeE6M4Fdtx34gFI6/dRT8BV7k0hJWAZVpftEHTWB0Z7BvEPwtJfmiptDVwECVEy7573ISWZCm0tBO/kZMOLy9Vg3E8pla1/AmPDdYNUSUByoLa2HCtIA1uRtawen/OgIit7Au4p0OjX4q9IzYeM1ZJvQp8apgYkSf7b3hhozzZT0HvrRijbpSKUlmY6CHtEweeNhJ3PLRrSUzo+bZQjt+XLue1vlvemg216XrGvOCil/NSunCCPR4vCNHPtrgz3tKnnPO2Zx4N4rfIyJmndkyDED3QmPn6FlXw72QiaIfoXYFm767udCCDL1xieYu/t8v3TP1I5TOsG12hsVWTZsgBDThz0xbL0D3a5oJzPqraDUgn/kowDUs+KMwL90V4sMWljquZLAqHsXxpbV2H3HSIg7Tc0YGMPOHEBaQ6t3RNIki3hf3w40++1eu1ar8pTFcC/VO3gb1WIugbXyHE1UE8IFp8Shz9pATQz7p6iAdNo48Duf7yZcaJoA976xdXBtEnTuL5+CRqL/H0+PgKomn7lybJHf16o1vPVkxlkL7Grk1IV2sW9PUoOE9wpoOvlLxysOxd8GrLy8UD+UNdpUMv7Zn54QkfvPllTM/30ZU9n+vtYTwr6JWSPl99ydVA+vyfV3pY9Ke+XewzXRVEIoirgvLWsfDzbKAPdVkRzxzqrWFjanyMTVwxXpv4IEfwxbPz7ts6+burHTFuhH+267S8A3Sb+K3d3BGpV1e8NZ+kLbEh0bcHG/026Ozjyfd7pk7edxX+s7H8K+Jwj/uf8cFDVE5U5jx+8e29SwSpz8w0e4G45zvi0fjsozcHEPHzZ23gTkEXVz24iJ0Z0zOFd1z2HpiWEy5u1Tdi9x/0zsRovLJdW/CC6Q8j8Sx+eMoJCFuK5vvOXdWN3ZMN2LJjKtdLVUFsZfrPIrPPY4neERaD5z4p8peVtGYur67xBTsyijxf2H/j4cb6e6Mvnn62jXrnhAR8olsfelGjTewA++yoGsbZR/CoDHcabps9wXNnF8l6YwMcklvXZrX1EQIWX4n4pDgHC2l455yJENFxd7TpfudcDM6C1FPA5ccTFKthRKkQB259kLZuIrptarHgY6NjQUsOO+J9IJB4wwAXoLmrTP1ZVwHhktpcHdRXyPPgZhJz90L3i60qeE64MuWjDbxCeGtDp0A/ZYwzxRH1uHJTG0fC/e+RL5za5gw7TKsUv5JJQsRnZGigNLB5i7NDOQ6sbBzsOKEG5b5pQ8wMg0MAeojGhCbK6DFLDLp/+LT8EnN2gS0sPbWN7EVREO7KgOkWcZJSxkNeWMIzHiWpDfu7PpL25K1x7hMuriuZzHLVOz5f7EwDb0u2HWyVcYgnHZL7bGQpEfC+sYzwBI+q2tQEBfksifH4IH40ruSXWhwfTgQBKzZWsB2Ivj1dOGFKbPLfeuyOsbBjSTphsb565SOvDdNyzAaV/sxW+rTX9WmvUneLhVguXU0hgy883GVRXRDpP1RuAnTT++IivKwuLGI30QttNR4AW9sjDY9wD5RcUFvjiQaLtrWLzPZY/+I9FYw8qEf0c9/6nC4mmyCCAazNoBCpw7NwAhpv9PFqnjV3ifLOJ4KYhM5AOmIPNv8pvgeu3FDelTfc40EWppPfw1Po8P/hPorfMXubGbReYovd2osnHopCc9B7dKre3m2o3J4cyHK4nILlQOapXKmVbDuc104UF+wsgqy1fE/rbbG08aKLtM1Q02LIyYFZnNvBSOT4ti7dFvaTEjrf6+qf7+eNYN/pFjDeDvatXhufZwNB+1UPkFYSSJ8f9csLbxf7SQ+X5jzDh1g7YaOgeaJXEign2iSBTNeJ982b+cXbCzfk5qofd4UTRE7c3nckhbwPh0d2sOHM8fRxKhStwjhA3ahUrYXOscBNlGGfPXR5UDS8obkaWXDWA0nFNM96nsLU5kHSvzgRxC1fL3mDmG4N+0oPiP6HXiH/ta4K4lXyXygJ9FJXCkH7QsmgT5000nODdFVQnhWkRJAWrDxzyL+uxdy2KYiTMvbtg/2atv1JRDLHOGDibJRoj5ywKCBS+aNtjSZwsl9iAg+pAYcXGdS2eRez7IgffEXf67tZ2qP6wvoId2F4DxoKp8s9Zs91/Y/yPSdf/D329aP0t9UeomI4ZTJ/ROp9TAQRi/11b+bRCSLxyiyV2Sfx0x4QsM4vE1T0eJrz3JHldg4eU0bjnv1iN+hRoUY8gdaS58JUx0nZOKBSPD7UWDicKhGlMOjTLx9Fef5pX7ZDc35ZYms+sB2EVXavxpTlY9QgzACHXhQZrS3nDH/JRRfxg916EdnOAyfhw6I3CSGdBZMM8sPrvDR2EoL+6N8lVxnJRhGDxwODy2Da3sps2eOpfEHn+GWcGmDwuTw2SHvctsQogssSBqLLKGvFYFoWlQz97RKoP8jAMaqXaNtjJrEbXTAZr3b5LAh9EFQOs4DAIdi+nOILra8Qwh94XAPn80aJOe8HiNLgfSijPMsP237Bg5IC1OwDbsGGwAqj5y6Bq/g4wo4wdhPgVsC3YIPixMbBWzzkwmacDeN22TAkwvTBeOvShn/jZ6Qb3rj4GD5Uo7WxsT6TtIE2+t2QIE27742xjh9Q4hi6x5g6ceSY3ak99hJrc1dXTEoTDmQNij32lkefFjk9NghQxDQzVD6SuKICcil0O31MIoQ39b3heZU2wAJzjmknfyLPmrS3BtbaGI0+2M6COZndVoPAxFncHJ/DEWr6OpxLqBxV0jp8+GXfRp97fcBwOebS53OAxLO/VTMnCc8fRO56cJIIIeET1xwf0WNl1gfNlmvLtT1B2J/KI9tyjif3BDv6wx4l6qwZxz3vRyuUUM244Ct+1JjlnIPiy0ieaoJ6uoUnLpl2Hvf90Rgyvjl+J/NoOz27qTPH29zYph9xPcKN0WerrfERy4qb2RLxU+KEka8H4IFdD3DyZn8jkbVJ247ZwRj7iSTKIJUH2lA8Xt/pRBCBapCA/9XCQf9H9Pz9fMdeftKZDqM74ZRZw+vO9C+0q+uxKF/814FglvhhLwHrrurnYwujPtL11Fr0mf7AhZG8UCoR5JzQ3TgPUlIKcCYHfzoKNn4czgu39GpmM7wQVu4NffQvSqKRCHIih7d9KZnzI6+L/0Gvh+fKHyV/Xus2sH/qSiBeHf+NbxPT1UC6dewHPR/oB90+9uNPuq2MW8qUTOIKn8R5/NWJluUVb+hgytdNU/aay7R/06vjuZHKv97Jh5wu8Fn98axOv7JAQ9aZkNbum+gylpMfculoo0GLSPYEAf5MACVS4mKVZ6fRhFT274KtMM/+MU1t9Iyn01+JWVavp/dKmfpXJXP0nCBdDaRHBOlV8h8pEfTSH64K4q1hvFaeN4p9rlfJ8xyhz/goCUQi6GNdCsSr6D/iqiB1gkVlw2KXZJGaz0Tb9EVTn1ZU6J/7mP5JRKU4dESKOSM6JTlwOKvVgrM5cAanrQorKQdPUWc95M1/Eq9wNbUulTbyd3zp70v9KIaNzV8Vg0f67nF/5Mdz9j0kRUTG+1Cbwsi8P4mgXzSP6dYwjeL0v1GlFg7k0PhC05kK2CRRk0CblgSKj6Y9D+ZIGb2SQ3e50WG5kYfL5wrVQ3G1rQYtN4yvTSai8JQzlM4zIrp0PsOnzM350pd5qNqivK0JRhRoy5d3yqhM3UZIdOLSz+rYvzpWg+ZMZPVxYsryy2u34sd4Y73oVrsw0dRJzV/CkBDeSXwrnugTH7fRo4/9nXNj9oBt5bwnss2NH8iayuaAMGJd0MHr4//hQc47CjlIxRemjaFFYXit4jEicroagt1aHAcg8qJVh+qCAIaLaI0KCa7xTRNF2viqBsHWO23zaUP8ewVWYX4gmtWB4+er4kj+yJYfFq1zqxNC3hfBZ5/gxOFrmmN4VQHw2w7BxOomHSFuPSdXHDq7vNEFbjnAosTtjqs9BlT5WYWLI6S1X8A7WMG3iR1fLSX58LJ1IzWM0zY0ztx9Wr6LEJhaPZ6Opj/qnYFTydZkSD7yBd5eGO7ejqx1jo6LLvuZzUTkdHuomW8YAySCflUiyCHBjjhyBRN2YJ/jjYb/jZyYACMze0s8kUHuWtDbhMTyy7iDL+oGEb1pWPpgBCQeY5cmtqemagnOrrtvy4RUurfUjq9aDgJ45iU0eLO26Vv6CJyPLQiO/fIgnbk6OqoL/IIn3kGFL3MYUdc+wkZ65bbnOdoYGbXQiX9K67SmO2pc8aYK9QAbwbdsz7mOCeOqo319qgC+jLpNi+9v9wON5duSha7Wi317HY0rgMPc0dYxmvgm+mevTvhu55E35tcmch5xhtdY2zspPCNQOUb5DMY1JqEhDy0Jy2jn7oh/fP4Sib+8aE49PP3L1T9WeJrkAKPd+pFEJsIE5zm+8iBffYVPnXc+aI90/nE+dlL6EOjUt4dO9Z19l6fj2uazPz6kShuWo6q/rQ/SAk97m+/UScwXe1yZ9tVH+gMfw1JJDHcytwNl2B86Vn9gqq0szOC1qPrWcnI5hsMBPYfo1uIDxietUNkyTaGtemtxT1/sFzilT3X4UhsvRBZWXAnEFUG6vUtJoF9I7Oihz9/yljBdCeQ3hOlqIJ4RxEOieWD0a10t9Pp1HiTNQ6J/+VmJIN2iRTII3fZBq4P4ommfxQLPCPKKQV4T1KH/NlcEWQ4n1avIDR/9vMjuq4LgpmPZI+JvxxoZ77xG09zSlWgmEqPAiRuMQ6PWh8onAwG4IoTdFprShFK9RYCxw0WNv+n2AJJASrHpah4SQXqNvGreDPblq0+dCPr61WdJBCkB9MXnukVMHx4m/dKJoCSDPtGtYjw4+mNdJfSxf7m0K/4Fzu7hh31RHTdXO/6DV59g1gZfM76FcKzAqxgmzuKfmHltZ1hkanhcR+S6dYAsC1veEGKjYsNDYOpdLdSQyn2yAB+SCx5NkF1uFop+7+rGqjUBOONK+4zdnQa95RFf41we6pPvxN91l++O7zAEz6dzVfnel0SQZlD9cr0TQUQ2x5Eix5emCUjiNLOOcaE1Xv7RQMxuV8j7iT2QT3XABIyMWWnr4wWYdkTEjRAMZ0vgYtBa3uAyl5T7Xvt5LRJqkoN5hS/nYDhFMGO7CSyFbhlBv8aq8fivNyqOrOcsa7lajEzk7KdA46g990+7YjBJZ7zoDwAimn8xFVh9L4PYUGwbbHGPqdNuWnFw/TJkPB1dRZZpWgZqJScubgdXkc7btLHVb+fGW3Vksx6KFLCxVqXNuHDRPA187WkStuzv6LGYNu4P/CAuSo628PavbCdf4eoSTxOTPreCJ66rzhcGn+KE9HrGPyhpTcPVIJOMkxrZ1BjjqmSPL2611g8s/DglhN/0CZ14jHPZN0c3pk9P+w1hl45B+t/uBApfxnn4a6t81FdttDxSR1f63ttwzKtNfKIlDTSmshXT+fIPfYrhiSNrnhEl2IDdj/ErXlntER/C5B/DFD+PI+YLGbaMaYqETWqDnE2zHbtCr6tVzMfai/hLn4Lk2u0996EnOsUrGJ/8sW5UIxeWjpOMAwzYiOjwiImP9Hd/5IiMIjxhsDtm+AOn+M3L5U+2KwnRMt/CMXbhhW7MrmnCVbzJqzFA1JjLLg6/XbW0peL/6k9wQ4nPyHkfgyXeo18Ve8kt4QLdbMsHHweyF7v0HT273ZhVG2cjCltg/sRtHEjbkT0wxpsxHN4fxhLncFQ+bo+U1Y0lmxNXD1K3Yw4VZp1mqxtLmA6kwSWo1sDxqYyp2S7WgRsD8OEKx8lXX3adaKVd/tS1uHn/KHSXRB+4iZcg+kRsl28CLKWN6wem4GUcZJ+E4Srjvbzke/zZLuyj2FzAY7O3KnoOHyZscFzl2IrgO5sI4gBYB2ji+pdu/6j+k++E786ctA3P3hXz7FtPUh0s94Fz19n21lfM4/qP8j2Wrq+tw8UAX8UD/aT7lLDIHr2+beh+AJcP2VMe7TlAkC2EwnDV+qb5IIVhHabQTv5T5sRb6ConQXtwuNR9ozWSxx+/mv2iX024Iuhn3hTGg5/1XCAeCs3zgHJL2PdKBP2kV8crMaQrgnh49A/fv3nxva8GkqxyHm9+/fCF8kDyVRZ1pQ+zxrr6xyd3lkjKhjAo4PH4Fw9XBRE+ySaK0CnwIJNap2G3PQbWb8DQ+aSkb+1s9NhcGVyPMcN4q72CIDMRTtDWX6OeWqalCF1e/MAHbB3UyEeOX4Jf/PaLmnoDkHr0kR7g86meE/Sxrgj6XAmhV0r2fKlbwr58pdvElBR6pYTQF59//uIrEkFf6IHRuj2M18x/qoTQp7qCiATQJ8omfaTby/KFZ6LKYkkW7h9cCo6+CHaVPqYv+BcaVfsHlFhJWgDptulw4kPbBe0PSoRFkMYLSxvYLIz8bl/5T674uTFZTCB9LVvvhq4c70Or0WpficUdV9q/Up/6Cj8X79ot33P2ZihmzDA0bz6/f4kgBYAvGA4g0csncUq0manAO1Zz8Jhdmy6e3IZtgFTMGVxNJFkU5j+2ZlpA3ryiezE3TF6wmQIVGspV7BKbIEIddMnwuYiPaVxzRZ7HMnMYc5k+/solFnuJSvBjJ9PLaFfFTDAR8LxIiwJfIDfXxr1Czp9EsDFwf1af0v/owF70xVbk09XQzAAoASRPuYNjnaXWL+lmDHfA+BTd+fKJMmLlL7g2EX6MJR67bcMySBxyjkRgRWVJmzaOWfeiCLCSbmlPwQXJWIz6gOEwXrVdHRGq+DiIulo67X7Kgp3SqdWYqMhmxpzHt8cq7Un+6Bjoj1pvJhHEuoZEkJOa0kMkfAsQSxD9nYkgvmQQi3xCFwv/q/TLvQ839Omjf228XXwA7reDofFwKhkaKKOxKRgNNzZhKFAyHtOMrR4Tpi3zic/FHTXok68GsgLUTUxoS9ba0WFHaEXGxwY8U9ZYmQ5RcQwjmlu5Ron7hI7+cJoYLL9gm96SCEqyLkaimqu0ui90HAiJbPxJlODzeJDY0js6TRs8S7GMAxu1ke5jry27M+E3tbXGFROwzWnDvxTnOY2sN3EIARJBu7l8WcCwSXYl7+I8UtdiW9gRGtNaN9rEcCXGNIiNqyi33ME5YPff2r8SCa56d41WikWlL4kgIQRbPTV9CCr2EXBBT36KbXvVh1vuE3rA6VN9WAi8E0GjNv4sq9EKf4vDTPO0o6bjU6apx+wTbWXT79inZjfoMb2rxcLoKq7nhbarr/XGb6g0tHQvbI2lPuIv7W11g4F84PgrS7NWcL+ErIVKtH6kvbylLV4BgffWcy7W5zgwJeT0d+AcO1su7PiZdUyO8dA/0Q/i78zr4wkinWPgFzbwzAa+BvMZlr8U/UfsXX2fPfrECw7uIGcsDAf81yF11fdE0UK8nW/rfXsf6m/ruzeY66EJz+aDQp9KpU2hNxt3lym108VVX1qlVRs1BdnI+4BZbXCU3B5QOHXwJ47486lU90fusdcBp6tXuHz2R13Z89MPv/j5QNwK9s23ugro29e+Mui7775XEugX3ypGQojnA/GA6B/56CqiXzWL/vrmI/2izQGMJacRBM2JXNAH60HR8ocEjyYl+k8iCJ98SxmRZEZ2YQwJPpJBnHgzDlqjo/FTbYUIL0Dy6NvjMR5CF14CQGcSqLI5cUeP3YAPd6j4MkNNW5/EVLwkk7wQ/Vk4Zce4NUxnPl4F/8nHb/Qg6A9efKnbwL7QQ6JfvdKDo5X8+ZJE0CsSQS9fvBL+Mz1Q+vOXuk2MZJCyRx/rtrJPlAQiGYQtvhgvf+JSfDn8oVOJjOp0UP4AyD/VyFN7LhKYwt4S2sr/fCLIx50VyTL6By60mvbDDgg1+27xhzvN7BvgGzlMl+3m2NCF4b1peHf/zb1tjLFV+G7ypL3Npw4HeIAZr4ZH4XuTCFLi+A0JZA5CRXVf5bgjnCUpX3qFA80cynEMqE+/9NIilsUDsAikjQznE9OHwbcII185f4GGSRhkndyWzkwmEDJlotE7bPwxRZtVNO8anvmXeVPzS+Yv1esLHzjNP9rZ/jt4UBB+KQKQQ9bJBvOqP5wfZzBVXvOAUKEP9nkabmuT2KUOSfDwWDnwFKKH3JgVdvxYdAETH3jox/yHw00U2IJx8FlhOLQVzeRDVvsrfQKnz9Ctf8mVPzzQPLfD7wLmgM1Gj4Td7ohzc41gxd33xFEu4OZFzqLmLf4wvVQtHBjcmQ/VdAvKKrYzLY9Zn1uzH7zvxODEj/0hKaQ3RHGK0x8JBt4YhWLa9Owjnbj9jCDBviVMaxISQh53cg7/4IufOR8uZwDG0fQxATjh8nqNMrxJ/NFIiY2owtCmlONe107GG32hdL8NdQKoKogoUWM1gUvkuBaHacMzs0PkTFuSC5foqCmnnawxn5oO2BwPXq8N7PXaci3c9iMqPd+wT0m6qDga1p1b9nxVkHTXb9yH82xbcKIIDV+sTZskBcsvxZdgayygS3+Ytwxb5kd8FPINQcYf9pM+641zGDVf5dJGl/WIBgV/T5s1336IGrIII6GaRsYrOmQ2XNSQVtOQWhixVXwH88kAAEAASURBVCirZA8Ur7qggAUKcuiraqQ1Lbtg2+M3Tti2SaWbC0sZT0Fv7SGn7XerRG36oG37lj3BFg17/5nd40nQUjvGR9dCT/tKHaQq5g/KdMX7xrzanDqWvADgM1qbs/uHbySntE082Syd1rjJV8nNFY57e8s9D1VjZdNmC8bjEWGju97f+ErDcikStsaDweC0U2mr4HJs/l4iqL54n4xT+5jIcUPc0ZU/3r78jiWCCHAnsjPYmUxDO+GTB9gT3QTvz/A9snnXd7d10k+7my9D4HJwTWNc3KzapSExWNIPiI/8cr/EwyD4vT6aK2NQ2jIQD6MG67unGDuRQQbRA7wCHqE+PdnXex8QtU/Y0R+ta1nTzYX2iJNJDx1MKfUiOutR8aelTjuhbf5Moe6/HYqOxpYvAdWKy3jpE53OAlw6y2vjf+JqIF4Z/5Nu/dIVP998o+TPt9/pdrDXuj0st4Rxe9hr3hT2gxJAJI9+5JYyDnwlgn77yDX90cX67lP2nSwrcJzU45/o4k/kmgSSP+AU8B2P8DBdkZygro6VHFp9rb7sn8Sz++bo+3iQ/UZExON4OCKCkeEzv3CIhYg3dj1Bpj90C9rosaLI84WOq4K4NYxE0Mcf8dT7N75N7JVeJc/DoV99oYSQrg7iFrFXX/LgaD0zyLeHcWUQbxP7VA+O1lvEdFUQSSCSQcQTH/qRAVtnXGK+/hGN5TNMavNlLnzqawc27iIIN3q1cct2dkzhopRmZnTUcAhhQgfGTavQkGq3zbE7TVqrZGzb0uDxp/2CE6MtW3JDpb0/9RkRen1G6YT/bEQq2/qUB/dcufsDX/lPGsOCtnED19b7kgjSLKz5U4kgB4neZ+ZxW1HzPKPGGx1XPOfFZyliNR+iBy9ffljcN6Ze6KvROT94HUswS4Lvyv6I0X+W55d6lKAvc6Oft0KiO1zry5uZjJ3ZY+aEHP7yVV3xklN1f9XmTT37V39RNXHxNh9fsaHThL8SMJnxLwW0KZxCaE/Ldew0bZb5AXrOPfGWfqS7bBMfU4KcGNI3yInjBAiElPnfoHm8sfD4MnT4xj/XlcMhlcyosjPtYE246d4su7fgEJxPOj44NFWp6vwv3vgkJC6bpo1L67ZMfOIL1AnVkxp9pUXLbFE1NFXbvZJBDo91jG9Dts6M0WD2eNf+Ea/XCh2r005SiP2n8as1Ta8KQoPHlkbSSjZqfH3sV8drdMlRj0E54uSk+Bt3ZCfUa0j02IGWEp+WDAkEF2Hc+dFHf+Ud9lIYr4UrUQq8jS36MrYzSgODtalh3PGKP9GEKO34lJoYUbQNEHhth2Zvh0c2sGXXgfGu/RSybY5WivmW7tNMkgZNuMQ34eCVEHOE94UeB/Ch9k/9RiewpyWY8x/3bSf+YN9N88fudN3zR2Dx4qcqq5JS/qrbiaBJlsHkqxU1Z3FFkB8qbmc0N8qQezPrWabH+rt9GEjVwhlqy5HDW7Talp2iO8aAFM80XE2jMYdx8eKvGvSLcUa/POZGl61ov8EP7LXa0Fp5XTvyNsUupW2JsbUMWksYbGtAVWWhzqgQ4ILnyFH3bzjwGRLFNdLVBPKEj2ZlYLkVHzMSQ3LphmdUuY+lDR/kcSDgbPE7nlc8hkfVhZfGU3ww0VFt8CUiW8FTyU17BNWr+HNybIp0KgDYcieGUInyIWt4XPBYOhQuPgHAHkOGFZuprUMwseWYAI/Z4M3kNYk1QFPheIrGHFduI6u/dy4R1InCB6mjQ+f/Z5Q/6tPmmz34jPubrwwzEtqc+infjeFP8j2WzuQHjYHVcvUog66DsTytPagP2eLDX52tNxUo0/GmbQjqngT2hLA9gzf88IHP5Hnq2L075MwgmSuj25zseLUmD4r2rWE8/Fmf73/i+T96OPR/vVYSSImgb75bt4XxzKDXfkA0VwL9piuCkGXZxa0MOmk7AcTlsllcuWeTAOIsnEWM/JM/jocQLOH9CzcnUfo5NM8gDrj4VXvyou/5NuAY7NNL+nx2M9rsAVoBVHZ009a2X24cpOXhkHk+RGRxBSuMVWCKa+A4DaCPlgZvkgji6xrPJPr4w3w++eQ3PyeI5wB9oQQPt4KRCHIS6KtXgl9OkojnBQnmaqDPSAhNIogvTLKg70x2Zio16lt84mqmjqj8EpN+4WYeZA2fxnrQlreO6Rh9XG1gCZ4RXAIGoIhnZDwOI35le9DKIuQRc61F6OQ44avKjJAr7v1qXaP29/ad/TB7/qGh+lK+h0zoGEbGgmG1l6wArb9XgvM5He8C/lddzeBnBCkOPBONY4o4dF7PQ36ZUYwcWmLmBZM2mUv5AqxZSBvkyePkFozd5o1KxLofnhHHFxqSxUkk9Ys0X47AMafp4x2ktnnRPsUgjqftqcD9CMq/7GtHguco9dUYmsSYZ/ylz1+ydnIIeq7S0L7nKiHN+Z53pR4TmYOFZwKkrU++/h0+yUfwKfSXDqvVSv0K94mEO7FBlqjsEm7rDLj1C0neCkT8xDf1beZEy5gsfW2guF+kxxNQLbBhhnhFRDX6hAwGbCiREVx75qP/EyBzbhg+S6v2Lo2C0bu0ba/EbD7ZdtePekSXUPWNK5tcV3ETLDWAG6N3cy/btid8v0xo79gX/5AlY4zGjHHBHqMZp+utYRwM4vuARKOM+fXh1HypV6KB18ZnX4luGJfGqcOfDTZmy7PEZhgs6X5JCyyHqkGbs3xvtyXWCSizAepOlYG1BZhiEJnBpZotsRAhmgS5GVrlqdkvjreRj+n4bT7zH5204X1gljLdEHf2H2x+RhDt8cuR59j3saN9cySC8MdXGiAnLUlUpyfRLUvsP3weo/3BcfcAAnwwxUf05u1z9sx6M9bwE2OMGcbL+CWfKLapDfUMseDVsBj9MiO86PE/ktM21TL1l4Zds5vqC3JGqJK7oRF3QcV7EuRYL0I9U/z445w62MgsA+jd82Ov9CwvQp0tMGUly5U8mgBVKfSDz32ujEfVWX0oK27PZxyZC2u1dJ3iH7HtRNrZlvfECQY9clDqGfBZUGf9z6iBF5LXyNZSxq28GKyY9zRwg8sb6d3TrQ2BclX43i7+ufqq7QwGFLQ1pnRpeSEC+Lv03cqK2Umw0Izz0Z913B77sHv8U9OoJTUyYgTw7w9e5rzsNYbZ0fUOJ4LoIwcCQWoN7lEpvfUjHnClt/49vufoxf+enrVTK5DhppaH3cJ62M2AO5APwWVTA8Nqblylt76RV7N0akYZf9srlKdAFmlcDl8Q5YFB5FSG4dqlcGsoJxxOMHcsbQ7ITTuMmNLTfz2CHp5OP7RO+eoT2gfXPeWfA06Hm05Yv+inYBZRv+hB0T/qAdBOBPF8oG91a9g/v9ObwpQM+ue3fmX8dzwkmiuCvtcbxeZV8bqbzIkgXfcir0gEfeJALd98kpKH+ic5tHx3IBj7ws+tYcjQpjOZIOgZH6rKqnfeEWqbVJ7EMMzZZjrJdDdahhzv3LBBIO1RwSwqe8KKentlUzVni/KBkwRl647zLG6cYeHBr4J5hTyJmw8//NUPjP70U54V9JHeDPZhnhekt4d9/YXeJPb1K79OntvDXvm18p/5DWKffvrJCx4azRVB6KH/Os9LX2zbn/qhBpE4r2IiqP6SBCP9bZ9bTyfSHSuQjsTtMuitmT4OTbWUzQdYRZVv+zN+cKFct+pDjqHyoKeluLZjM7b2AqVUajwq14l/3+DG4KyfiwFRhu/3yu/x3fdW9T3S/UhXhyH8ljkFBWfMV+u7WzsRNG8NY77rmO5s9QHzJHOfAra+eCg+moJ9LDHtgOfLEnN6ftUGzoc4k/Rnfvr1Zx0vQvgLELwS6BU/vypJBB8/EjgJpCs/TVMbmXyA2RfeDKym3PPsQM3O1oaaLyYA+aKdX9V5cK+/8Gki+/ijjz2f+Rkc4uULIG918hcavoRZT2psVA8mKKM+jY7qOCj2+Owo4S59UMWXrDQVgJZ0Ry1T5DIWgoS79nLb8KbBZpo2vvIEf+kH2EWceXjpw+gySMMFPd33wLXruI4H4GXJuo03Y7AmzUYRW034SKhN7y+azITPdUeqgNuEXthhPWkl1JJ9UePmDmG44LAx5zBEq+bZGpsau9DxIYlJxj+JIPVKyNQZp04EsY/5UUZSH2qMEQMngmTbyUiNPd8uxpd8OZhxhTdwjrsAdQqQbzpGDdIBEa/k8cHFLNHh3WQZ0YRnrMIGi2NiKGLn1iqqbwj5koUOKci/KeYVVPsipRhgQ3yo2PsCEAh6gDTV2G3oLuGvz6DtO7R0wIj6YE3s2MrXlnVpgw+i4SvinlvYf/qzXs8LHPtcgZMrguCtDIo1Fbl4DKzOcKTziX8wWGN9NMEWMo9ajrlP40aczJ+24/aMKcFZb2ls9A1zOiHZjvRlzpQc/rhPrqyTNnTjtbFuO0VPw6ztKhktYOQj/7g6DOs4MT50mOBJ9wT7UM8YBslYjh7VaFV7UKrlgWWhjT/mgtH/HqfYRybF0RSYeqHt5ATAjO1VegSfbbcvtM3HJvMTZztjqXp8DX+PN3TsMrD5B1tyzaNqPogyfi5FOIscNNonm0dT+cTNSGmxrBv3eJRj1+HdEqWcts6ohP6Uv3KP66u2kwcK2oiBY6FuZO+EC/oTaQuEftme+BHyeB4dRmnjUVX6EfygzJBdDSg6LIlk1hbwebaHpr936oogOkzpoD7bhc0wG/hOfNutT94TLr31SXsEl6/1Ix5w0Fu2X+lT8B0lmXTATZctu2WqZess7TkfavuP8NUmVuwyiMvJc/u8emTX4cPnfcDX0911hiVsnRja562zMmed4XxiOvCD4yCIL55+jIyN6M0W50LPlwKxybHNN7Ke5UWjlmA9a1yos9jXr9A62TkRxEOg9WwgvxHsu59f/PObb178l5JA33zz+sU/9byg7/0AaW4L01vFeLC08hw/aY3FA6Lf/KYnIfvNXtRM7rJLwHJ2cnunWNRU8a818s/PBrKP4peco2s8XOigHp1ppG390Cg5oax+Brm2oaKoHCFlQQ/cvZNbDEINHo/imdr2RZXqgKIBOLAAGjceO5rC3vAGIK4IUiJIY+8jrgzSLWLK67z4TM8K+kxvAuOh0F/94/MX//hCzwj6+gu9RUy1kkJf6E1ir/R5qQdHfyaBT/VcoU/0vCCti3yCd2JJ2ilYtS/aOEqyFZ8dVFH5AtICLv0xpgOiZEnC61vxrFgbxpDjk/F5RhCKi4GDT0HJUQJ1cZnV8gsVmT1+zXKTQWLzoflalrKbpSvXu956FJUdub+u9zvab9dZ23DVt+zFq1wPHXgMl3lqfR/IovQq9s619hVBipLmzWPWUV+FUzyMU+2z09R8kaH4+WwiOBE0yRxIvQUYPid5RHuj5E5+BefLM3O4kkO/kPjRUasfBewLiR+SQfqsNzBJB+dfaRIvc51Nt3LD86H8da2Nv2Dr22+uCpokkCawj3W76/qVnWehCee3N/kL4MB8+ZIObsfgdjLmCU4prAX8x4DS55II8iASemov3ge2g/jPR39OBpk2HUkPrNudG/3nnDMoc2J7zaeC6TN+rqtMYNC/UC5Zu7AXwZ82Q18Bnab5BIvbpe1ocAQuVKtlg2oLnZKBuw1ZPRs3vL46XPJ5QGqMEnOXRG6PUL+kjXOuIlD72/d2ghihg9r+2pHAqLV+NVs8hqdxSQSJ2VewSYKxzAGQMc0XCdEY53Laazm1fTsi44aTp2z69fFcETRj04lH6CbHqbpWXxojjpOW7NO2UqdvkoZNVeQiw1ht8RdgjYNQDkLEJB+Kz8eFxb30IRIWqzR4tCEu/yQ/2iyz2TaEsxwvVmv0SCyWAooTTId9t+3FfQPTuCkfonv7hX9nPNecMAkhnPb+xH940aVBwbggPlNN33Kk2uLFITCiLafjk5Be/1ZXrpQcf6TYc5xkOH6duJ4rg+iFTMcvpkFg1/Gr/qLfYxanx8Mk6Unfg8kf8PYoLVwNTuu3+g0OpCc84JkDqE2jZs7VyDp4mxSiRpwxDuBxpHUp/teBRDAmcNm8Ihtm45L9iK7sEZDupDG0bMc1a0paKfZ4mvEGXs53qq1+PLC67jMRvA4dHXTuVvJsveoI0fulfMumEIc6VLn/5Zs6JnCIj70++goTMeAPOhzXQjuU0jZHNbL3N9+mR9O9fdX/tHW1Fnpx02pTsUR7+w7apO1MBGZbviIvbQnmKFQtQsY+nIO1YsGuoyGo0IfsOSB6w8vuLwc638lEUMLx/7d/bwTuo/oYiRn2Tw7eDr09qeEhctKVf7dPTaGXj/pxqUzq0SnWtPG1BXi3TdeBsPlCyxRSeWQjt/lGnw764MQ7AHUTQHw5+OUXXeWjRND3P/IMoFwR9F/ffvvin0oE/VNXBvHaeOjfwsMDpZXjIBH0sxTx2ng9ykvm9fvnh0kIeYbx9D8+MUHgzuELzTd6dTyLyZwnDz9hnRhksRk9jQs0PjkRojZtdKa0x203OrTRNdoXG0AbZ11LEauHaLgv5KF10clDovXUJKlU/cGvOjFzZRBvEPv1xWc8ONqJIF0ZpFvEvvrqixf/4Kog1V99+erFl0oAffmlkkG6KujzT/TwaJ4VpNvIPtPzgpII4ioJTvY4cfaLBcO0SQZ15S6enGbN7o1FOxhWv6Mwp4jweqEg0AuohmWrmUiKYNFKTpSi7uAWKNxTNY8YEQN/5QazuTcEN+UpJvh3fXuN0r+vt2+L9x/1qcOw/G2z65kXlAtYY/3f17N/vyWSL7/oGUEcc7mqbkfXs5XiQYySQKfmwxcPHtSvL8H60Kb2r9x8MebLMPM7dJI60FXzTDgnd6BblitDJ+mjhJDp4vnNV4xK/lc9vwiYL9aDuySCcKzHqtzOl26huK1Ls89Hqrnywlf4qOaKn4+V/PmItyEyl5EEAlYNj5/dAo9i4SsDuELI4yBXCuRKopn/J6Ei1hQCk0jJtmKiQcTZ00mfwcNoNtozT1Y+t6Exl+hvnnBa1cgB77Y7nraQptEfOWsfwVzGb/jRQ7EeNqBVs/9aQJs+CPsDBqTZ5ttcBRY3cYHtKh3BK/bOFVXhqbTPzTUpuytu4DT2HhVj76RpN852T7jVHkWNwKoNaJxijzGeznss40uSPRn3TgwJSRydxGS8ev/GuBOKJIJUMq6StPMXZ/YZYxQi7PhmAMTTcsYBl+qvLc3m3jerNS36OF/72H6qPpZj5EatpRN9KDWaGJQeYI+txKfUe735Qrm2l9IthumFBrj7slnxycckIjT0SaVaf8RjXymYZyJaQkxOUjO3jQzzlmWRXPZrS4r0n7GLP3y0Z71DgFM8puxGdKxE0Iwz37ImGeYvZD1PyUnsiSVzixqB0aG1LLKaS+lffQa2jH0VTeZzNZOhurNrbNFarjISNVLsf7sBTn/iYSxzBSJ/nndIoOMzbY1p/RsfurDDC14s0nz8AKo2kpOaccNm1QF82KVw6yS30BZ5ttDGLwuCQ7dqg6EZCaKqVBMv2l17pu8go+MSC2E7T5mKXMS1PUpETaRP8Dxfpr+WOQRHoJhq8f4yDcpVM72kXLFGHZutMch7+2B9CD6vHYpGoDbjicD6hKrQHyp9gqxXyLigtnCseN9hz/uwbEcj/KEbFg1y8BxfWotIDh0Q+HvnE0Fe+B1BWjG9Af8pvpsbf2vzr+tjhqsP9hVbhhn4DLfW5lxoDz3xdFqZ7opuPktXfmhLX9uP6tjOKeBKz7R54rBUa+Ol+5AT0KJ5doYVPH5XZssbN+5SNRTUTQT9opPWz7q0x4mgH/TKeD0M+ju9Nv7/cjXQf/1Ttd4cpvZ3uiXsB90+9p0eFP0LVwNp3vfVQCRzXuiyayWBPviQhJCmRPvGdC9fDE+yh7Zsj0s6UeI7uNLxMf6XJ3VwUuY+ZIoOjsVDEkGNIfhKF5c6k3WmQGk6uMTv4GxrjqhUwVd9J4xGLJWWWlKKp5NATgRxHZQCRRJICSGuCvr0Yx4e/UJXBn3g5wRxJRAJoP/DFUFKBvGcIK4GeqWHR3+u5wN9rkQQt4a91Effh3RC5/uV+kCYpwfxIydvLvsFX9wJ423x6S8YCn0MZZ0gvN+CNVXtjh+LeCPKUgioBm32of6jdeJcEmR94KVOQYhCvbHBwBdc9l/5qHepho15v6Azak+j+DgWf5TvsXSwz8X99Odt8ueYqkwPRWbh9ykR9LOSLF2k5wyU6DouOqaIS74IAScJ7y8c+vJB8kbivirCyR8lefgi84sSO7w96Weu9DnaXAHkpBHz+K8/O2HElxj08My4X/XjwK9akPlKIRJB3CImPf6yPV90cqzyNYLCnMOkhLf5QsIU4qSIEzz5skKbJNAnulWHxI+f09IrgiYR9JGS3h/riwwPi+X2EPicFHLiSDJcLaAJMKeamesIlT4kwImJnLVPTB3xUHwLNqvoKZ7q5DrzS65ckm58h+wvTfQpZX0BcROF0rtsCyl+5me+NPoMiE88VmRUmBf/AtxmvMOO9WuWBIUOc4LEWIrjLZAIbPqmXnFx8pTecGWoI1Va9q27aSZcTyGemcMXSoTGov2FF5y7gdIqBnSA4XharFMb5n7O74x7bVIrKG6q7T81SGzmSpHwrERQx4HGnb/Pa+ywD51wBNb+cgJIOCcwcdA+qn34enqYGNjDoOvskkXFCHuBwh5aai3jtgwgWlpjDYOlY8j8awPhCd7ci8XAha9xGpZTfviKctNsiXGVpotHn0twD1ZjAdW3EAZGXhUe0Q/tQhUjHG/PFyQ2NDiAKcxLfOBjznJCULJgnHgx/HQsIpujWGtUh4h9Ch8fbEbGIDZA4dPY6Hp9jRHmJO1P+MIj/sqpzq22qkmi82XW/VM9PCTbJSB8xiq26AMFyC6mqQGwRlDmOVEzHlUrLJ6nhGN4cZWbx7TaTrpr3vSViYrfh/rh0VcCdW5FL2Nec5LnsjHqSBRGp+3JJ8NxKldZbo+DbVsnkimJMDpUdKmO50GS6t4J1my99Hpm2aFFxvM37Nlp4olj6T9KpcOo1OdxAxW1aL6UqLiihNMueFDS701AGMbNvNUFRx/fVq7UGX9L4EoN+hFuCTwAtm93YjzXVvG0VrMOLObQ71KP2447qko+4dFFUL1PjvENu/fryDHeib31wFdYDvqYsi6QcPatYXquxt9QNK4x/+8rz5nz5PQHXHmO746/t9vD5/Clt/47+NCd/jMUnw/7X2N727jq23Z9n6yaPjBwziKhzzAG6wmHXdNJqBwmehPMoUlYcEvz9DYDensWDeEMbzRVbmohLekaPzSJhHE5hW+dNPfppNMxtHiEI4jqPOQTKl8Omgjy83+4NUzPAfpOzwj6v0oCffPNtysRxEOiv+dh0j/ypeKDF3pzvE586GGV20TQ3BrGyldOZapp3/CHsv3HV/zOx0TxlA+/t2yolYVn9MNCTGBg41jQOAo8CkIvsY1W6AjEqwWPImKaUo9yulloE++ytMWnAOeKIGT5lZ8rgnJl0Cd+g5gy3DpB86ygr3U1UK4KUkJIzwniiqCvdYsYiaAv9Baxzz7lFrGP/bwgTt4f6yTuBa0cSdInXuJX2rkJjzbj5r7W9heL1WcxDYPx7s70UHbCu/dHLGEIaPoeQ4sE4P0ufKNj3NEO3koOOdoZMaFfpM0XV8HfZaPmMTa092XbqBGLwmffn8OfPMB/hu8ue2/Xj0c6TRuGzmutOeLel0TQL35YtCZVTzwkxonWREzx6Rzu7xKaF7nKJ784k8jhS4iSNiR/lJ2nTQKILyB+EYC+mDgRRPKHOZ9kjx70jwxfTrg12MkfyZHsgednEj8kg9zmiqDw+suNYL7o+GoLfGOHa7JgMcyU7S9xzP+C/cVkvnwD+wuKEjqfkghS/aGTQFwhRNKHxFASRR9pxztJpDYwVwmZLl1ODqn290Tb6xxdZ/ANWIkrO+eN2sLrjy9PdhlZA5oGpYxzq39R1/kL3Z6NXEu/90vk2KIDG8hnhgRSm3lTvLlqUzqRt5GxBT+yU6tS8Rne863xQcY3w1EQPfZKtqmDx4vCEVXLJGbMaDR9wgB82rFn8Ls/V00565kU1RK0Gm0sIqz7YySNsC1i6aods9Jha2wqC65FfN6F2YELdkJIHmhoe/zjDbhe8dYv4NdEkNzy/tV41B8nUPYP4ygPkcY39v//Y+9d2PW2cS1Nx/HdSar//4+bZ6af6Tndc6arcvM1l5r1roVFUdrfdlKJq08fl7m3RBIEQBCkIBEfRWnSqsZUzxZl5K0mLSonbo7ORM7imtbwIXQUjYdvGhhwcDhzQDYsR42tIDTrvOouVficO0LYLg7XqJG0gCUz+/Tw1KT6B8eVKedOaM3km24swFSvy+scWs9qVYozD1BazMxP7SFGS7n+4gSiz1RiO7Y7XGz3RMAYgDB5J1OB6oUfxQwyxyhfR3qC3kzg2oljaWiGH7Yz9uKQy2NI48aOHZW7DrU5DnUcPMBkc7GPsps4fPw6LnZHeLahpLHTxJYxUjpToYjRHSI7SgK7kkNw7Ax/iuM8w7mDs1OH9OZ91jzOYz+/UBrdukzPnrFTWGwqkBacUHJioTpNNjA0NcEicxrZHdP5gTE8oTF+HUGLeOoUdkG0qc/6WKb0K+yKobQCeAmS30mdBlRJjKdMJBl0A4O64xksevohFMTUk3g/o2tJtkCgcDA+CUernD2dyg7M0BxxEIsBnHqKeWLzG5nIcRcp/Fyqk2tyfNRZysY7j8pbWKl2XOtvEA0XIJpinIey1zy6BQYYWPC3NHD/4XAVl8HJiqBP1BGUC4CLUJ1VjVXjE3fwU34fXnEgKV7Tw8bRFQ/gLZ7/KN4u+4f4Be86tHYJb8sTjNCVf+Mz9WDOlVxdHPLNqBQak+YVSMKeAdiRmSEqvIXlRFFnCJ8LwyR9eaG7IK5s+AXZVaskudzAkH3JL4TYxmBYB3Dy1VWWlKWcGJ4+JoGR5WAy4MkBsZb3vJETiFfAXr/W6h8df/vuB28Y/Td9PYwVQW/1efk377RySCuCcAL9/ItcHRKGz8bzeKUneonxWIfqPjmCkEtyWJGMc/KM08jJ7QWYjxRNWuXA3Ya2BwTwm+eGkHTwYI4BmUqGn7mApgODXWpXYO2AODSlNVKXzFKmQ/+q3XwozhigbIJomcLp0UD3VmLKftbDJXy0R5DiR/pl5EucQYqf61Py7Av0jT4f/xetBvoKpxD7BPEpeb0exsbRzx4/0+thWhEkZ9Aj3cB1r7czyBMM8UeOtKea6kQncu3D3HhuH2XobhFH1I2bSsAIgiK31WShHQLQVjiuyfCOBKu4gt4BBM/SuSyp5lvfkA3T6faFf8Ha6vj0k9e27/lR11JCtQrgWraQlLiFB2yn2XF22g+ld3rwerk5PSdwOky9Cu6PVASv/0SBV8N+0qEndEmNlVGjrYTYR00bPJkAJJOtSUUnIEw8NBHGUaMCJh4/4eRhVY9g7+X8+Ul7uv0sox2Hz892AtlBpHK/KqYlnjh/PHERvvF+khNIPH7WZrtxHmU1kCfaTHIsDzaOcPQqNoCDSYdjJtfqxLxWwSRFr37pXvFYTvD1epicQHYE4RTSJIb90L70gQNINg84BxMdTd7BffQFG0x3RKp+JjNSjloecZhoYYs7kCylR1atmmJklL2Uyr/0htXKi6dXHDFxwsjwn0gcoEhbqdkV0V7XkXwmWGk/4kFrOZXItDZc4HsnVI3QTRp62BNzWmTeFINCuCimoGklkT333MBNp/tmbfTi5AKfXBHwyYlQ48znYS3+rkIn5HNa5Tu+GtlhGwQVrnIS5AfAbdzpMpq6HKnsUGsmh/SuJ+jErl8Q9bOfaeYaYAIOHZNuHJ1cKK5Dp6z8kYDu4/Sz+1tlXYHi8XtIvGgrDKJG/AhNXUvOaddEol0pUeVe7faXh4qBglVMuKaO8HdGsAhCoeBL+aVKsZ2DA/Izo9JBHV7wuQT0t4f1rClg2lVadH1g0hcj6ACTXzjIsfCT8HkhwP/Mk+slDjmuHz2p6QCH5zl1Y9LqZ8YAf46pFhgQKkhVio4rNSCUMaObftHgS48Ez6SqBB6ukzqU4XA/SjhkY3x7DKrcdlj2Fjub/alwouN8H6e54B6H5LFH5O1sF00qQniLTHuQLrIiRPpU53S9x+iMFWRBPzQJkWQXPbZxlut4jFMdmMe5bKU/NKLxDoxnSB3YB9tojJ+CeRHruVRsqZamOiZPOQEJEb2SWlDLrkK3BfrhYXzklNymgU/qgxchdQ1zAO7nKbH+oUEHxUmcbHRzHsGijfpgcjcMGyKhRQAndlTJv/DcYslJfNQ0xYvo3KoFXonUBxdCKwzv0YJLrhgG/q4TPFPLGZ06qFFn/YPhtkzauAKCMygG3TpZJ0Yc/EFiPDQ02TlY66Wca2kPlOkymnozrij3dUeZkc1B86WHD77R2xH/jPAfsiJoV8YxuD9+8+C913VfDTvenr7i72V7+o/ihS4D90P8Dv4avgzYy2A6yvdU+O4Qj7YFyIAE6wiCmT+QGZ1H4TIMlNziXtQ4KcL/hHiHaKt9L3Mjy+24KHyRDlv4WmeuAIE2Xobl4kdSSnyxTUyaG5h/FZ5fh9/bwfP+wQ+v38weQW8efCtH0PfaKPpb7RH0ozeL1ibRvELGq2F6HUzzBTuB/NUv3TK+0IP5Ax8yiTL2NjaSK3/IWMeW0oL6ViFZ+B2ZZrlbdfJtxHCA/NOCHOARQivObrZOSvhXHZcZZTvFkJhDUM0tCOVIjrQrTNEy+jFH1Whq3+kGfT2c4fRBQk1IzIN0nEEPlc+n5Hk97O/a9+dLfyns629wBmnTaDmDXr6UU0jpOIK0GkgrgvjMPBtH6/6um3kdQfC1JnRTR/TI6fTAkQyxEtq2AKw7TwwoBWYNTQxMYfovGgwouE3fE8PqTrgJDJaF2cuVXnIPI4pvwC5Ud2r9VwW0R68qu6WPXbV72rhloAKXKU+czMbNQOWvFZtow5tk2ZJ1WngM4WXeBQSuZ1c9sA7RJxyxIugnTRRs1LCVKMKTFXSiK1BZJiDYb5ntScvpI0MM3K964fTRRANHDg4gnDw4gXD0/yTHjp1Bmii/V5oVP3YWadUP+OD6UPonvQoGLquB2B/ITiKvDmKiBJ4cQRzrgpQdsq2gg2KXmWRkssEv0jiCWGVDrD3SHmkDfBw9guHUecyrYOS1gT4TnMePZetkGx8JHyfQY60I8uRGeNCwmsj44oVubD4sCzrLhIuYPyuuZSMvNhIaZBaLxBpo8GayZLkU+97F7Yy2CY9wHoqqb/EGh/5TG6Bk3JoutKE/+JSfmeqkkuFVCHGghYwIhqbmkXEQDkdAMPczvAhw7B3R3BlYCpwPjKZzBwPOuCMM+mQSUY5TxwwGzyVhGL6TnkpWBVc9hGPO+7NeVlsgBxXoOvC1wHWRA7hfDyPvMo1PXU/lQfVMmGkzq4BYGcFY695VdUAgD/2NgP6z3NUZcmXMRWPkb4eQhU8wcq9ue12ugtR18NAIspJbfpQ0dVfBxaUPkPkIxSUOvPrYcVp6lKHTYCi1mhpQ8nu6vA76qS5Iymz8ytgjzk01OdLlOhsHBdeR+if9Pv0t5ZDPXj7YvRkP9DliEVOb9q1EF6Q9biuHlB996yFK6YCrF4uR+sTXvISQ8Ra94jREjR5749DBZv+KjVRFPFPHNhIjc+yq8VcaGI6gjs1IUd3xKXc/swns/pwBg9yxW9ILMOfRlcaw7CPjN6soZRu1jQB2FdgT7KjS2XdN91McQRr/2OFcA9FJdG+2sFYzY1c9nmzLDm0hq1AUgOXIc+aRBx6cXHPYTfI4gsBKWlhkBpNUWRrukzAlUHqr/Qco3InXfQg8rv2dn5lup7Cbeje4K07enMFzrRZwEI905SmH832h0I2fkgd14R2DZ/yW3oLeD4M7kreWI21IdWks1Sug27lRlFKgm6H4FC5cATWUT4HxsYPW2F5Ec50Kz6AVwzcwX/fUg6AKjzVev3n21OmPffoPcwR1EPuCmgH9sRv3R/ghVxV/H/0u+304wP95eHBfI4rMKdzfhp3mSO+DO8PwxE4ZDOKO1aF6xjNHPw2d4c6V/Kh2Q2qhQE1ueOkP1QlMB5HRkEnHSptjGNQEUw6dD8iV5ubJ3j48/DOJ4BWC93LwvHrDiiB9HUyvhn3/6s2D775jo+jXD/6mr4e90kqh93ol7LUmFO/e6SanG61edFLMjRrDzqfj81oYjh30pVvUPDWTRlYEBA9BagAFRCbDznK6DSpbbQmDycPMDCcmLeNjJR18TF006piQG6olEWSLnZSufTMmM2Vj6ej5SK4WDd9Ewlv5rP4Jnlpm2rwa5lVB2jCar4ixMujJU3m5+WKYVv+wMugvWhnEnkHfyBn01ddP9FUxbRot4/dSr4g9e352BH3JLze5+1omfpVGJsNIS/oRSTIg+TRnmrTkNxGFg+2nD/LwCpdlE8iOjoNAHjznRltTQUA6TyF50zdBvAcKi9t4L7+dbt2U/n6q27w+Nei1J35P+9Ch6ejXSTcu/dL5b1XQDrkHb4GFx7Dy0CKmoinUM6sn1a37U41/kp1YjiBdg34QQhd6sOX6y35ssnGebDDpUJrJh+y4V/dgywWzPWf1jw4mTO/lCHovu23nj50+OHl+su33aiAmKObBxArbLlqcSDh+iFkVpD2EwMUJ5F+1FTPJSZiOUoYUY8OP/pqg+D4gY2uHjh1BmYTEuRMnD6tvcPLg7MEB45VDrP7xBIaVQZThDAqeaYWH84hJEIOUOjmQD7uLjJZG+Ug1dht7Ca7+uAcwtrpZNRMlv35GrE36KfMv5mLsajSBIVBPgniqX/hznTN4c+9TDdBTlw/uF9SqQN6nyQ+3+yLTQFY6IyqznjUWBlg5fP8qHIJbaWRSkcYQwX3nVOQqhe/NyliVgxiKEJkO5BII4GTz5rmVD9x1U3bFG3zXgX6nMmtamYgLnDGYPNdHXnMEroNrQz++2DFEFSMTK0zYv4kO9SRYnexXZdRZ7h8VJUYHEawxWcoiD/VEZyOuohGU+vTX2+jqdxdrrIRtyJTZswDLPwhzhigFYJyKUtuGdyk98FMTejuHMz/f60EV2OlBbroxgptyxk95rvICbvBK0bneSBd9+BUsdKMD9vCk+fQ3aRwptEMmSeUZB/AEh0WVSWMzVQYtdO4VatHBxWndE+e6jjTpX+rAhKTe1JlywcTBK4BkF7OifuylhMF+/oTdxY7aTiuWQD/LBmMv89otsOBa4Ggxskdyn3uyXqwLrKoCaZw4ktt2yWMZWxr7iZPdjh/ZTxw+j2Qnn+Bol5PdqzGxrXYMya6Jj1cSnVYIAYvuFUk9WWHuNDq0ELF90YZkkr6wgXseUQmiYCZgOpMqb2iiRQJsVBG9S//BHy7OtP7IRwnBphCZSdPfyza6+HQaNq3qVLYEENTONsWMnCMc6Txv7xIeWLdSlm0KkDScOB88znT3wc9YR+6Q7eB5wHzVK2uuo5/0JToLlwM7+SWBEpTNdMOFO67TGwB+6G0DuV8gdF1iZBydQnvguv+Mhw0PATif5KthNHYPGDxCbjTcLFYXGL7jF6f4xHu5CXS64t3CAXfHK+0et7zxXtY0ZQTq2NMtb7yX7emWN97L9nTKqSv628v2dPk0pqztj6g81Jab+O0j/DR8y+FufAxdyhjYxw0lEmIcD1lVSfDMihLqNSgxcMmEWIBXGYLq4IZW9BZL2cIrr6TdMDcyNzVwfTOdmLTuYf5F2K8NvP9VK31+0mfj344j6L0+F/9an45/ZUfQt9on6NWr98L55cFbvXLwTg6hOoBwBv2qPYJo+696HcwGeGSSKbW87hdktEw4jWiFjmlr42mm219DDiyKwHCmnYDCo2XECbQt/No7AKhPFKuPk+9DugkgIqjC/lmPhlNbyimLJMIVGzhxuB8UeQSoHr8WRn2kMWd6GMB1hgPIXw8jrRvs0ydfaE8gVgE98etgrASyI+hr7RX0Ul8P06fkcQI9f65VQf6EPF/U0YogHX4IMH/qVV2MDwuTOp1ENBdJchl/yn0dIFqQdaNHr4TVMqUEMa4LcgrSBpj6rLOCxbgBfGepdzS4FQeNelqRHxeodrRdRgvTcMpPSAYEdKb4nLtqAPWPuq5Fh86FdAcHQInv9OGwKlHxAN/CvYHHde6jNBsdz+mM9U891BHEdcpzGhMaJj7Ma9jvhF+cPbkgthM/vzZ7ZafKcfg4jeNHKzztzLGd1yb/yhvGqiA5eH7CIcRkxRMW7HkcSJ5U6eYAfx84iciL/3IAcb2LzhPKra+P+2vsieda2BZd31/qVTD2B+oKDCYiOHTyyzQOGE1SmKyoo3G+eKIyE5aHwsXpwy/bj+Wk4RWuJ/rFm8kNvy7nVSwpSWOEh3MGHRM/KW+GjGwuTnMGI8ZQAdufV9WQSfaUOnAujVx9DY2JEvLb1nI/w1YpLGd76xMsE2zqEE2i0ImkdPklv/nRUzgKCBNncrrkp2qVRQZiN4cC6tMf5OEKC/CCW9rcV4UBiWR36V4nEP7NDx5SYyJfnyTdZAiF07T5DB5R65vqjTusl4PEZSUcfmax81YlHVeISZo6dXaaCTddDdyvNXpcqkxC/6ov8MV5mfsUesHBwB57dIwnwIpxRK6PL0hw+qoHysiQgZZ6qJuQBOn2rfUpZGsVxCnrhNKEqjrjkEYqgEd9ya2zqafMwOHX2g2DyIgjb7MuvJ5ATC33OoJU7GpWXeRdgZlVJmK3lZiSDcdZoBS4ujKl5BrMScC9/ZFxv07yQRH6mz7FQaPDjiDZQaWxRXktK/Xin6YXsAE4gmy/wHP1PJOiryP2Fxo9YKlbT1KqA1o7kAQhD4/QUz/PzrxKy2pMnECyp6yexGmuym1bVQaNnfZ2BJFO3vsHWS7Vw0CdUAkbRy+SiX+PEdmhjk2e/5yOvfQXF7FdsqOPHz+1HYsNxakd+/kYx7rt7OzPxtiX8+gRDiQcQSqDp1dycn3UdrN4Sq1PfUr7Xhxdp6MFUzvSc2iJsuRIk4JErJXmpAM0Co7IqYw31Sb9Ozimv4IcPYAqmGT0oAhj9xM0XK+VZqpwda5a5Yyj1g0+Iezpj2RCNzIY4zihC/CmWheAabUcaCvlNit39CvVVzJzMy6wHeeOkIvjfQkL75puYYy1twJav9u7IZcDIKclJpKCt5dtJIaDZF6DF+DRGmjdtzDTkG9Jr7HwExR6TsLztR5Cs/vkHEEZ7Lsqk+5gb8k1fx9d8f/zxB5a/0Rxf4s/o4uQ2BcFJCu0vIBrvpQtT9yLZdieC0+5U2WnkmZOGGK8+p40SELoxSwL7bRpYtEsoC/8yUODTeXQM72P97qJvdfN6Sc5dnjli02i/WqYPhH/3Q9v/On47/Rq2Pc/vvLrYvpg2IN3uuEx4fjlly91Q9QLT94fiFVBysv4wz/7EvDQPIIiHfLq9AXOIoT3IVwEg4aIeKVNnPLiw8Q3GBlMp8MffmAfPHg4RE9AEg4nEHIMTFHehx68EcaUloObtLlOjNTkuc2kTcrkBkDbBua6eEVAuNMKxXEEZe1UVgfhCOLLYS+f4+zhk/HZJNqOIH1F7Bv2CBLspd6LpbyOID49r3t6Jhmqw04gyxX5kDETFaRTWErNjYabrHH0AEyIIwhJ0zoDfdoUdQC31OjNde9pcRrSIIvvejDYyEc71Bvx91vpiYExqWFBV+IA7qC9ls/pP6CBducfIP1HSE7VKIP9cGisDM97eficsk80eq+H6fcYZ41yLlkmHPya7FgTC5w8WQ0km40dxlkjRKeV/1k2PPsD6VdplfE6GDTvNFHBCfRezn6+DsmrXtSDowh+nkxL8X5dwflx/ogHZUxWvGG05PEGqJLQK24Ez8pJrrx0GJf5YQdnUiGYJxmyVJ5MeAKiSQkTF/VtXm14HMeMnDHLESQ8720h2JO+KibnD86aZ0x2ZASZ+GBrmNvb5ii2LIgj3awVO5odxC5j+4TP6xGSxxMhOXuYIPl1NPF+pLryqhrywhdnAc6sVAL9YSlVEX0lXUgpvu+o2LIYj/uC6wZGmmPSliRpaAjQNNCEPV+49auMW7LsajEpbVCKymC0wlEKyLkpP+oLD8qAlZwx6aC4ySOhko011d4JhRFP+ibeELoOVdpnH/J2QKpS0n7W0Ph0LLyTIwgnqlYEMb5Lb90z9tSPTCIPR5BgEiTlE1sw9EA+7cV1QLAcOjPhTNrg7QR0miji0gWBcZhUz9S7h+g5PHZ40lc4Ek5wYuUKVVwaytAnRZySJ5cQvOoL2K00WMBb044TPjnfB99xSJuPdFB+hgVoVOSlTj/T6TLzBHE5grCR2DDapb5WbHvJuMB2Ce8n7XHm1ZPIbF1jM46DDsEa0FNIw+jKn+qiPq/kyfjCAYVNxdnDiklW1LOa/iftseaVmbxyi13Wc7VXZIqBHVGisdNd8v3iPddGPsnEHy3kvIeOC49LCrA/jma8yi7y7Ibz3KvacOrgzH7yRDaMFUCP7Wx/JpuGPXvMiiCcQqyu9KtjwYc29k56gBcOIdlEO+6lmziGVL3qRn0+yFRidxDjQcpSyHVBHnkzTrgGoEvY04URCwF9WB2jD7GET2iJyYGaNEnCMoNKp/7DQhvhelqy7AXnHoj+z7DV5iHb2VglO7tLGtxo41Jwb3bnfi/SVnCVdStS0notaFdYYeBsaZJXCSgv7Ipb/obPiShtpl9DkSj9Wxh1cV0TgJHKHM4A559oXH79qe8RRON74aMMwjUfaBTV9DWGprx2Jd+Hd4Vf8+V3hf+R/O/ldRsvlxH1trzxfbK0vPEZL4POwzRW5lx855IoPhSEQx5yhuniWlgkziig3QyHfLnEwmUMnih8YfgiEdNeLIJDR+CBplenqVzvyMJ4AEkwbmq6j9kJxC8p73QTYxLBwUbQr+Tp+fEVG0NrryDF3+m1sO9+0KogOYPe6Ith797/XSuCoOFmhtPnkXjGAfQLr4fB25UhT2QjTkt0tpj8vECZDuGCDt00SzwBHHrsL5iCiqSmFnrSIIOrg6RCePEwkEnKQB15IqBURfNzPTy4cxAcYYRqpLixAeTITY4SasyNLXRMbpCD/vABrni2FXHK4Ajia146HupT8rMi6IkmJLz69UKOnpdaAfS1vhj2X/7LV3pVTK+H+XUxVgWpXDjPn+tXcOHHEcSNGi3kVQgJoJA6qQeJHKQQ68aKGQzpxhIjNw9EPBhPG9xUgeEUep0naRRlaXXbllz00BLT+VR4IF0xR8683M9UFo55EFOhsjj62i2mHhlupfcil38+/eMaOHfVmR4Ff6j8jP3h3A1eZj38PRSobqsPf2XH3oeZ/+cufY9N9sQ1D0FKahKRSY4nGThmsNk4eeTMeaeVnHm9V5MPpZmQ4NwB9g6cyeMA4njrfBxIP2vCwj0AfjiTmOAcEx6lNalq3cDzBabYEltHd9DWSdsASV/p+vVtILaFybftrfo/K3GYiMXpY0eQHvKYgDAp8SSdiQhOGQ4mN5q8dBUQk5gncgQ9faoJj/D1bxvF0MIuxZYhm9JqB4PHNlsIdvwjg2B59QxnFHU9FD85gLzaSPETXq/AWYDc2NqkzQt6DzXVwT/6ox7Z3eoG9dRGIhcEalLaRux7GQn/mxtSrlC4GNESStIuJQAEkmjy7hLBDy6mAnkLKT1wVGR6UICmNjXZ9pdci8P/LhwE8IsHGzjdCQUSg7PnF/FBFV0OX5dHz34eElocA7o+0JEO9wDXC3nGtvqEVRjLmKhCxta+Iojx5n2rJAv93Hth7uMRlDSycM8nIAr5TjiTowTdp5Rc0DN+DDaQ8enEnERzyg9/teECDv4OHJ0t0MYIGMWJB3FqRE8Umq6ILqNVKjB6aCSGkYlIU0X1b/ogDEnaFtCJsbnfOllk+PIMMgjD0vUAsgiKeTaso8eOH8Gy1476mue99jmrcZS3M4YfOlkFSV4NhxfXHs/McQZxc2k6MTjIkEOOH2ixvV7tA1+c6tha7G7s6ns9PwN3XcBxzKtunFTea832FluOHOJlu05eGm+DNaCOtAXlJDkzRtARY9Cxxy1jVzDZpkeyW+sVWjmA/CoYNkxwbNnTWUHplY5yBvVVMfI41p/wOhmOIttcYuxxxi51ZDVn5OAHTIFWn1tR00tZkSmxlW9/EoOfayp63YYqyAkoXgEdfOFnQwE8WIFWD4rhPPxcQgUKRQ3tyJci1Z16J2t60iZdZZHZYgjmMhNQI9DYAYPa3mTmbMkG91RwygQL0M7vhOIy6h+VXAvvyX8YO2NrGgaqdLzXcYca1A24kjsRkky+w9h4nAQnCjxje5WpxOntzLVAQM7QxabDgPwTjcevP8U9gtZF7+ZLb5er45r3BSKcPR7SOxG0vwevhL8Xf5fpKn95NS5u8VpHyxsfeEC4mUT2lifWoGXAKYQfeQZXYPv5St98Y+o4BfEd1hv4gvM7SpDFVBdSX24nWc+1kSsJMpZJsdxeLobVWGEbL7pC+uNGWqqJ0SUsRcJ1djiCHvgX5fe6YbF/xFvd0F5pJdArbRb9o/YD+uFHbRbNq2F6RQxH0KvX77US6IEdQbzv/DOOoF8fiV9eB4sjCOcQ4lM3s4DolduXQZLF7SNvQJrqZ2h0YFriyN64uOXpOAxFRVAb+Rt6GxQxBZag2Cyrr82RYzi3kGDnF+TmgM3TQ28z6kdW4ETCsE2b6APyKhEO5UiVNHLgfMERxCfk9SoCm0frhson5F9oQvNCzp6XOr6WM+gvf9FqIG0a/fVL7Rv0tQ7Bv8IRpFfDWEGEM4ivh7HZ9LEaqHWmLWjYraBqHR46OqFTxhGyeTJCn0igtgE8Dy23QLRkzIC0/5PX2XDKHIgHdyApB5aAI8i8SyJ8U1Bnx4TLDprSOhbY+GQGxeKdkD5n/pQGbnXjleHePasvr0iTL27xVgde8FVuOzVPc0YfGib6XFufengn48xKHWyo/DP+5ZmJrL9Co4nEe//qnNcRWMHJnj9MMnAMMTEBxgSIlUD+lZpyyhQbX+VsEB0HkSYqqigTKybNSWMb4JGJSuBCEgzbwWWXONc2PQLw3KnpcmwKRWNfhEfaEwKdvCpDDh66Nb84M6mRfVCZnS+KvRpIExO/CsYv2ZrMPFWelTtP9av3Y9lNXueCPpOjuTNQ7wwWy6A04wdHTMeSf0XnF3Tzz0bUdQTZ0YSTCUfQyOe9i6hnGJc/7c9KBO4T0pvrnYGLwhTA5VmljgaxUV6c0Y3/BskR2EOvlINVbM2LBPsNT+EZDfwEV2eeKldZ7wgtP2JTL15HffCEKjw5U4Vvx/BzfeGyp6mLYLwhQm8WsUzCEijNDm5Q0mnwKC5ICtJs4lamLM4doIxTxqwdAIqPPHD1gsYr5TgDluDoRgNgXxEURxCOAY07dQwTbQZp74eIZJ0rdtnIaNksXgCRNE2w0ICnQdbn0BlahSVjLJJ7OPgP1EprLcCG4cBTnc87m8GC7hDAXHZW1XMpF8L0gPKkAJuL9LrzO/QLA8anGZBxqFSFNk8h4kMzkeM6eVwrRPRvIlcl8+S+pRrbKuXzihi2i9U49DsOG2wjdhJHOE4XjR1VmL7l+sPe0Ks6lMb+0MvwtT1UTL2/6odPHO9xBMWxhD1lFRCOII537967vvd2AtUhFOfRcuKLcZzr2HTJx1jG2J/Clo9yJCM2UUiWnYixeozZ2qY4eVj5I7so5w/Oc5zoT5R+YnuJYyirK3GqsycbK4f4euNTcMDHvup4pOdMO+zHIcSKSUwWctipj6roFx0RLcpyP057sFjN0xRInJ92mFZpjxeabRbTfj8MDCMigWm3D7jwD1NCsgDcX/SfE4I7baTjZCrKBgSb4A1kEIg4MvpIw79UiYNzwJs/uB/1nlOMmSiUAABAAElEQVRwi7ypg9LyJg30Hw2l32kD87nFZSsdW4WCU+TiQ5hiOT50NOAL3tKz4NU//KDLEM9YgdrlsoHEy1YYD0DK0bOvwSEgn1fDPrHNomnfNayBPQXX/FLalfBfLs8obGC4XQPlt+A73lYu9J3jgQXObV6mnuKVplZndm4GDJ/7uB11lBIjkTAXUK4at8rOChWDwRjxo5vzA0xJyF0ebXBBHo4gTTp0M8IR5E/H68bGZtE4gn5gRZAcQd4jSCuCvlP69XIECV+bRf8sI+LXwbQiiH2CcDD8qo2jkYUWj6slhgZ5+McqLNmStnZ0cvOQmDQ8uBGssNEZrF9xFGpIixYeurmKR7zLSggfI2L8YcnNzMn1QMbje3DAdRlMTAkEGDjQBc/4oucxwpI40q1OsZMqqyaCoRVBxue1MK0IcvoXOXYearUPm0Jr1Y/3CZIjSJtFfy1H0Dc4gng1DAeRNo3mU/NPnvAVCJxAtxxBkTP1Ia0kTzMsLu3AKseOcBPg5o7AoVj6dB76SxAa7OJ8oqzMmyYmAN8D/UmeR4E9UK+CyjLep/zU90HxedCtYADKF7RhfU7+GQ1cu+7P8PpHaFVvq2asZLyIgdJeEXQdOv8I7/8kuDiC3somY/t+0kSGCQQTB1b5MNnwJMNpOXbY7Fmv9Hr1D04iDuF4RZBs+jvl+VWaX7Lj7GfiwsQojiFPlmTS+jqFVwfZNsR2/t2vpI2tUGdkckYPjU2xTpUfW7Ffh033+cWxgNmnh0mMrnYdnniL3iuAyJPGJhlX9s2OGr3eoEmI97fQhMW/Xnty88QrgvgF2yuKRM+v1tQFb5HaNncSJVBW5LCyR2U8VD4UEvyY8DxR/FQOJjuGNDlitVFWKKWJTKAsN+1uAz1i0QcTOiszEOnLKDgkBv8h9wMmm24jcsIXrKQdi1o5k4hrSX0NeAJrSDGVudrJkBxYqm9dRwOFmpDzAMkMLUnLMHkiDuBujTJqimHACeQJuzjm71PKThWmEhdYBYNyNxotTCMyOc8EAdyVZ4LPGOXQGI4jCIeq0rp+1nOzKutYsQNA+rEjaOB2BgnGbWr1jeohTRMFPoURS7AoIOf2YPQRml0R8BuFwU28z6Wpov0/rA001Zk0yLBpSvzuho1oCg/ZAVTPO2Vogkd5cMx9iCsPVS4dW5K79ZkzxHuR8r5eR3rzph7h1N4svsB0QO5LDVultB1A8vV5VaNgcQDFCfQzK3hkJ+M0z54+vsKsI/2Aie4t06wKorM0iF0/dSnh51rZZe/7I8dSNuWXLcUBhCNIK4G84lL2ljLbamyz0r/KKYTDx3CNQzsxJbydQcDNf2wEjYkCIpOyK0hIbJZ1ZYElt+0lNhW7KZvJ8yCre2Sn2OjeDh5eDxPsCfYSxzZ5HD8+WP3I8yT5rIbk9VteHeNLtl4taZ5K60dHO4WUpz47gpDVutNHTxQ7OaDIzbMxgYYhe66fXFcGrVPYBK+OMV8V0wdBTDupyGXE1sWUKg8HDsrpQ9L3BvO54gyFyqYWxfdxCdyoWyXkr7CteEsWqzFFe107fCP7YHKnPyO65Fqssd5aKLoWnzkkB/4tPPTdwIjuPQJYiriWJuUo129AgXN9g52+iz0PCNxP2BHEQK5yUME+sG/lwb3SgNewl+3pa/mtsh2H9O+p6z8Krzr7rXbcj5eBh/zw6FDNJXyUUX6EMzwDNJSwOPLNHJRNuS4hgmsaE6W0UvDIEbxCVM6DJn2vY5F4LAwjR+D3UJK0/rnxJc6NFEcQN1Ldh8YRxE3t1wdvdGN7/fadVgO99Yqg75cjSK+HaVXQ69f5VYWJyvv5ahj7Ax17A/HQiemnBdSLiyTyWMVJIpig4OkQonE5QTf5pCEg7LHSzuqBmsQwtpkwI/jlV9k6gkKtQuNGt5EQ1kMUKVxbJOJ8LcsNu60DOTc6+KilI9eIRGt8wMlpOX+IcaLgBPryC7nP2CNIkxE7gl7gDNKG0Xod7Bt9St6vhskR9PJrrQZi/6BZEcTm0qwiYlXQl3dWBLUOpEMgm+T86tSxQ+xD8vuhN/3kawkat8MnmDjkGolO0EraBGp1FJiLDGu+fIRpwmgMHZH3+CAT1YpoNNoZBeSpMKwPds5H5yn6fP5IGkDf1XPTex/s1XxMvKmLceGDemZc/Ks4gt7qlQYO7/vDREFOG1b88BoXjp+s8sF5r9WZssHEfBr+3Xs28seOZwUQ+1bgBMLRDw/vX+FXegXXL9veUFo3AuxAJ1XUyetfBPwXnWTHVoCre5BL9xO2rYPAlDoFAi7OFwfFThILSBqb4tUZmoN5IgMdZSqEjnR+hcZZw2sKTGJYDaTJC5MbTWb4FduOGzlx7LSxUygTFW+KKh755VxOICY1OH9UgZ1HyvM5WuiB81oEX9vJr+HUo/oE74SLFUSVnQbQhoToxXvX1PHDAHbAHgeZWw1t9oROZbap6MCMws3pwQuL8kl9QQ1uvS7BGxh1Dgn8neRhnzSIYWCcvS6TqdyyispoTIgPdoEpz8N6ytflCblhro/C4hjRxUOUtOtWGfikOdKOwd0icOxkM2wmB0Lmr2PUKz1U7lVsKvO9X30B3DiZYVgP6QONA/pTFeeVP/IdK7oHSR7GieVTBjxCzpNQIVeLHyGMCUbDYIouKSHDAx2bF5wVVnmy+9n9tQFCAQ/XuvE7kHY5pwbXn3Qh4NM28pFJTNOCE0rKMm5UrrJQTKLVCs0BhGkjeGEICIQpM/zAI3vIPHjGnj5Umv6jcnehGcfp48/DC267Jbh8LkKDDpuXvuc1LVYDvZOzBvuZ1Tjg0ccRnB7KiM+zB1VQZexifjhlXOFAZ28121PsLit/sLmys151pDyrN22vwcUJJCY+lG/aY1W2l3Zl02hVRoXuKWSSzVBkCIbQ8IjrnE7ozA5lyptX2s5qOXe8B5Ds15eP5My2s5tXxQL362O8OqbXYMHHnj7FliqPU+gRznZWB8keYmdte8XDG0pjP3Xd2DEkddWWoTk7yd0OZRQsq1NqicWMY6j97aJB5Bqi76CpCS1pWdJPh/0FM3pI7HOGnJkcKi09GAQX6zTdb9Vb3yg8HFyYGgY/vRHa4kx84OWaIp+r3j148DR1yuZqM56rpeoJ0F8pW/b74p1jpaPGjPRdH7xC5zCV+u5fAVZllcZqsXCu4YJn2FZ166GKpLEhablJSVOmSi2d0lxzzg0e8MM59Ak7gpauJ3G9SK55XywatcTXAO4Ov+aLfx+85Y3/GXjw3mVsXdf4z9XNMGuInu7yKxx5ZoCX5Dfj0PqcpC9cLnGOG11zh+NckjfgB8jc2qcwnSOXkvCUx4A5KBHjI/CWtjzGyXoWrjMuLJ73ObIiiJuZJhPaTPTVOzmC5AD6gU2j5RD6/ttXD77l62FaFfT6Na8X8NUwTUAU89UwH7qp+vPxvCJG++fQrUG1jVQjqKOcENTlKA81dgWQ06Lrg66KFJTHaTFBt0HfDA2nTBysHjOT8QAiAMdUbRyzgQckDq0t8QEjhaaBE4jhCiktnODEUQe/+LYw0loSg76Q4wdaO4LkAJILTQ+h+mVaN9mvnj1+8BxHkL4OxpfC/qLVQHxKnhVBL7/SRtGsCHr6TJ+P56bNr+Pzepjf1T7akF6OfEtGZJcYbouS1Yv1yy9JUhA3cesfZZmw1I1przhYmakvOtl0oXLadzuIzxTlcSB6ZIz4Wlhk7eOpd8GHa8XZ4iZv1/sZ+oc0UL2j3KY/xOgfwYPPlafyBXEdL5RJ+4GzQyPFn+T5jZxAr+WUZ3LjlT58+YuJhiYxb2Wbsb+e3LzFFmdvCsq8GugtMRMQnD3CA1cxv0h7EmP4TEx0E2CyzKogFN/XvjLpkmqxnUTcLPSPqyNdDPQcjuvvPAiwKxC7XKfGpbaDBaj61ZMIFRg2swvmOHba+FfoTETstNHkBAdN9gsax40MqZ1ETGSU5mtk4OAwd15pr6CUrbVjiDKX82t39sLwpGgcSq7HjiBZJ8nHRsKWUXFXc6z2eMCiJ+47iXvfeOiytBh821rNemgnITpq2iA5FoyZjPUXboMF1SAqMu5CnQTR4BDTD3e7LbiCF/PAmz4Tb8hKajxlnB9g7ohhZUZFLmgxF0DplW2CuOlJnlmMJjUOgXNmTEbTitG5x6jiSa+NgzWr8L1O5XUUxfnDeNscQfIyJ5/JdSbY3JcI9DcRd6nkHVka5AEqyfJ/YJgIOXlSaRhcReEluPG2fFEdQ6/g00QLAIfw63VmkiWncx88DVsxTuoYqlMv1BR5lp7kVpJClbctaGjxhLYF56ThkRyksN9xgeH4qy2iTvezmCMjdeQZJnmeY8nLR3OMAQFx+sQR9P7BG+19yR4+rLJk/DDhDK+OccXlz7hXuvYR24jjnJWVXgHEyh8dWSEUW2vnD6+O4eAxnuwuNOwFJHntIMLxLpj5TvvcNvGnTQ3oxvppwnll/C9M4MCcH7gy2Cev1JFds/3jK422Z7GNj/pJecFwCtnJoxj4YzmBnutHSPYRwhH+9Kkc7XomxUmEcxynEKuG6gyK013XkKrnSnmoHyQfXcZyR8OALfaYvTYhDZkzzaIfq4w4hwAGlPYqzzMrNG66T3MZmYPQU7NzBzsozgGECfA+9wLsNwQL1byYboHrO5CcoSvmhqZk4Ec9zZ+xyEF/ruUuzv2QW5TlqNrXPUP1b6gkt+yJfalPwD0jQtNuiIx796FgdCsYiZuOJtrl3Fd1KRg5MMo3mIo+6a+GYch9AaihMeooKuGaB2/HLx7xjls84OVNmnDFK+zP4u30t2RsvTueBbpxukW/o7W88V620tKrx+W9OosuF36GsrLbaD4KLylG7NFvyeWWX0PUUW8j4wsBvujfkU8MeIJBvmqSN0xAt0BI6MyrgUxAHowQV6/hCycTTrFkVIEPlXChcYHqfuTj5AjSze219gj64dUbfSr+3YPvdXz33Y92An2rPYJev8rE450dQfrVRXsExRmESwP3Bu0npv6J3QJagQxIJgEQ0cZIOa8gYowKllLHKw06+OZL7IwnCEmbaeDCRU/5S1pniCZMuiSnMrRCZfwfNPA6eKyeHX6iEf560ENQ/+skuvAJPekvZkVQVgOJThtGP2KPIN1sX2rZ7ovnuhnLEfSNXgVjNRArg7xHEK+G4SDSiqBn2iz6mW/I2jjNkxxVqXFj/lMnziAksDA+qxV+2JAsUXTyyubVMN1YRbD/SmaAeRx8YEprqIs4IymplKTMRZYleqgcjo0ueP5dp8dG2SxtQrsFynfQpDM2NrzPyY+jgfbHrvMd1nRrK94OB7bnwb2FB1x4O6qH6Qb7l3EEaS+KH/S6lycxmlAweeleP6zW5JdtfoUmfqs9KTjYr817AMlpxOtg+ZSxXmUgjSNIk46f5WDy125kB/zLNLrVxMS/psuM5Ve5dMT+MGYIndF+AzDhCvK9anrRZXcuTmygiH3CZulvmPgepjT5/Oqr8m1iw6M/K3qYgDAZyesPmrTol2smLuS9GaomNPnFm9cdtI+QbKtpxunjr+YwORIPVvh49Q8TJ/Nm0jP42Fal1+RKcpFGD/r3cYzY2F/Ukmeb2kfBPZCjGdpo2mFguy2Y1QtMpQdv8rqz9EKgwBeIEYPbpKiMZmhlmELXSRocM1BaOdeL3HuYnPCcEjp3RaSCcuEqY04+BWfn4rSQeZxRNTMiwmDxoHxlduZnTlOFZV+6lXw4duAMDIeBV6sJmbHNRNuwmWwLoLFvF53GlEaSnUBqlQTAAZQ9gpJmZdrh8KOK4FnWU2OQmQZQN1H7XGBlOed5BzRB2pAUHbp0Pv1O8hpM5gqGhQDOimX4qq8sB5Sp5MhfuZ3zu0h2+FIsXuiUP/qPfPmSRc+E1p0qGS/gBbd8TcpJ/BwZY/gNHF4EoHtY/SpgVtoxzqprWJKeHzd5tlGlMnV0tW1nVgVlVQ6rJV/LSf5O2x6wUnKNEeHCx75w0WP3/JykZ1KcP1k9FLuJIz2v2MoBJIcPttVOdhw74onT/pe/yxEkO5uvk+GIYhyqnEN48PSKNWKU5DYoHSW6+X6WtOLcA9ILiFvYFGU0Y5iZ+8Tjd2xZ7BfOGtk6rw7C3slBjr3kwNHtdFYDPdXz57OnT+X00TOm7OgzOYMe4RDSCqFnvDKLDTUNjibssJxIOOklE7aa1ZWIV5mdHnkd6UQZ0uJYT9PSPpc7SYr+nPEHBZ2rQEnGtlIZnIKR1j9CGK+1H+xDbRbnU0gjUZSp8jO2UByohxLyaUHgnA+cwJIP5oHVsmKnpubOtbaeK/U/kr/FEXp0Gz7okst5SboJc6Xea3bXCWHRwfUGgUGchOjxPnVjLUIgWdSP0HLYEWTzMjDjc30QAvukHUFu55yuRvya70Wx03wo7c6+1UsXoh1vT1/QfCFWho+Bt/P/EL8/ggfNjMOdfEtTmrDsgLP7EC/GObah4kIa3XqlhNkxyPmHR0IvnOZvxWAfeJH6MGkqVD13HUGhubYSPRIqA/FyBCnNRekbpmI2JvXS1m1FECuB+Iz8d4rtCPqOPYK0WbQcQ2/1OgJfGnv3VjdZbsS/6KbJiiBP4B+pHvbuUf2WgdvaoQdkIuvWibZOIANUYDmLdCdum2ChP2fFf9q66pGe6BO6RWdxydnsOMnwVKKTfjE3vrmABJ3CIBL1AYiyRaeCxau0Sx7KMGGRwa27OIJYDfRIdPzawq8xX73QzVc3Y1YBfa3VQD60Esifj5cjCGcR5c/YH4gVQbrx6l6sG3FkOhxC8zpC2xFlWBaSyBTHkGTUQwIhMQ1K/tCrS0GAyrht0xEDTpl1NaabtG/Ui0q5sliam/p4anYgr/TCG3Cjom1xk0X5HH8kDXyoD1qG8pu+VruX7ekrHnnxgA2XDw8npDNWKRRcw0Jztk8+vMYRpNU+TFj4lZkvOfq1L2L9qv2OCY0cQXYC4RjSqiBWZzIxwQmEgwg6JiHsJ+Rfspl44PRRLHM/E5LomPsAevaeKtIuuqcE/RMcNxOQYXTnZurIzRV9IIOzQjMisi30OTxcZMOgMmV8/1Ke/iZN3Mn6Iw0E9vXBdvKVGzY59b4YmqDwmph/0dakxZs983oDBxMWT3gykcHxjjPJExs7gZjQwFOHbHEcR3E6US9fyLFctBGZ1Kg0p20lzn0lEJ07eBWvYYvsIdRgrs3O3STg6nBi7pGuS2cQwtw6KXtBFUrnpG17JaQkkhlxnazjYRIcZF7Fad8CRQ5KD/sdXEh2WcxiZEVnDU5u+VQwUu5wCLa8+akC39PNLGk7ggbufVdI6y8r3DLphsYrgRj/g7ucQOpXqnnI6jGlcf50lZD7i7wxpB0hui2c1n0K6tSJjLf0go7B2oNZCHAquYFXmqP9gTSPQOXtvnSxSz0+Sv+hWCoJVRPkxBdducenfLA8PAqiHe34ykGr7gTjma2K0GU5HJhUb36ARhZkIMShlz6PtgECR/uS1X0bMtsylcXmyX7K2PmVWhxBcgK9le0kz4pJO8dVzhhhzOAQjy1UrJVFdubAG2eO4vdy9PBa2Hs9J8fR3tfM4vDxfkDC4zkxtHL+iCG0ONwZo3G6k0betMbNJK3D/WgVjtJQwA2VAia0aDihXevxC61w85jmB0OPbWI5tSdvW2gbOHYR24nzR9sPeLN82c9nWg3kgxWWwl221Y4knj/HaQ5/P4fiCNIVM/3LNYStJCCnx5MSlZlVdx5Q9KLStD95kxzXk3U1PCBRHibmIzr+/O8BtBUqObeyne0wn8j8oJmweCTvOpSkDnTsupQnvYfiASPttjq1YzW9YwcG5MyxuH8mLscrd8ZaZHC7ijZVXbK/WwD3ywW7vI6upW6Qpr+tycCCO5oVUmi4VoAlD+Un6QiqAfdFQCsVCkvubn7HLc7HjteF+QHGlfO35Pn4eAjVgXS/gL+nDfvlZxtgdtcL53Yd53aLRgwCU3oe4O5QgiZgLoZzKfCGJcuOqPThCAJTl44QDSPtUC6NAUou4eEaAIsDA8mn35kUsJkeNzdeDeO1gldvtD/Q69kjqCuCvmdVEJ+UH0fQOI1wAP1SRxAre754pENP6Mjlu0DqRAYO6uZsaTFGonEaqBORG5NLsKFynJypVQTcwYoiXfy0j35Y/SO+9jqHYjC5+QxgJJjbhoAiUNkqbrlucIHBEFpOTgg5ZZZrCFNyaJ12rxVBUpVcZp7MMMngV+vnuvG+9Kthj/X5+DiCvmKvIL0m9kIHK4LiCOKmzS873SOIX1eofw47myr/yNdxNHppllYEkxv4pNfduxqgVUo7K8Nsmq3tA0EXpTh0M7Qqq0Qkc22akU7ztLAesJVf6eJscSsh1tHshvE5+bE0MMPnXnZV/sfAEw/YMA5vOYL0vLkeLO+V5xMo+FGO9u/lCPKGpFoZ9MarfnACyfZqj7Z3fj1MjiD2BFL67RsmJ+wFxOqh7BPkVUA4kmTg+ZU8DqA4gZiEeHJCjM6ZqBBLd9E7qeQdt9BXWsqMoI6yXSBj+9PBYCqAc20ODcVO6rRQlch/QEovR8mkMUf82kv/e1Kjer16RwBiNjf1qh5NWJ7q1+zn+gWbX63Z/+KJNkx9KrvKBtC8fovTiFVC/OLNV3ZwwH+pCRK29EsOwfiF25uuir9fg6Du2n5kvch7aIqmcG9T+6QzoTkkpuHRB/ma2GCE50qbsroL96wqKgb4c780c+GG/RKFLkNOnQ1DGmcFAZXQ/J3nMyO0dHAFK9i8AQ+j3A+SL5XNt8qRYdBs5XezHvnCH2GgLe4SzjBB+XcEhu5BmmDn/i5ta/wuR5CQGOtC0KR7JuEe30qrDBo7glT5F5q04uj5wo6gcQIBU5kdQDccQUuPFR6e04QVFUCbaBSNKYxcYYsAmIEbJEmxn4DsSgrP7SYjkvbqQW+kqaO0d+ODb8pczQBJN2++hhtieFIHTz93IP6pcy2e8CshoreNVw6pL6WiUDGssFGUONKpjiE0PkWO0YdXBgn9V3/FljGA3ePQ6hy9NvtGtvKNnmnf8OOl8vniLa95CWd9YUx04/TBbnoFj/Nx7OA8wrEOX1b/mM+Uq7rUKcFwBNmpZB7iafkkM21AcOclKypU2m2xZgFET9aFTmiAfm6g7Q6OJj0wcqHL+M3GzhrJjGmNZTu6cRAp/6VeGZM/x87xOoVwmD9nFZB+kHyELSWvFeheZSm7yatitp1PZS/FAyf8E6XL245122TakKc6Yh8IPe0g6gGYsMbZNCmo6FKFbaaogK92mhBegobAdeVk1YZ06Dc2UBZNdUzJ8HDhdrItUD66p7bgL37wMj7nQEndZXcXYrJ/yqnStc7mqQy9DnziXpqoYse8I5rIoKzKruUnuBHDzzzNe/o0UpjcdeoU/Vo6ExluWaFJKXw+SUfQrkguBhp8GMyUXvNAi7cG8TAqD7J7eopXtJft6YUwiQ+V7bjFa7yX7emWN97L9nTLG+9lxyWmYcEIHp1ddXGiuRcv+o4+oZjReyJu5lzmoekRTnkdGtz6lPagL93teERqE24YDnht/UhzeVJXONpaAfZ2MI58LekEB910qEwHHLnx1D3Bp4O56XlDUf0SzSsIrzThuK4I+pYVQdojiE2k373XZqb6BfodX6sR/1/5Yhg3YLVbl6lU8aWqIs0TfGQ5HgeQpjfByIaMacURB8utT9LnPR9cmqVK8jd10XL4WQdmHO6jEXM6npzJ9lE2cXgK7KfrQXd08KmkAQveiQKAaZbLzFvlHqPWvlQiB5CcP7oP60ZMmsmMXg3ThOXF7BH0NZ+KxwnEqiDBno8jiE/MP9erYbwS5ne2FWsuk4mKZeDXZ+ShhxOQFV2QZ9xELwKsQIlud3QZmlQipQeHpRNhUIaGzReSgRAfFFf+5KfURcUUlPoEy3WjBA7F4oJmfPCUJF3SiXfZVPo5fCwNjN6t76Y/xLt91fg+3JY3Lp7qWNVMOuMicBwBGaMl+DTj7zVZ+U4OHyYrrO5hEvOWlUBvlJaTHscQK4PYAPWNvuLoFUH6lZtVQd0U1ZOVOoA0WfHEyOYNex3rxcMg9sATEet7bIPU6n6YziDyla17DzTH9aYrlj50SGdyvz56cRgMxhEddiIw5c1Hp/w7D8xwxdRD/3N8qUGQ1Rty3MwEx5ug6tPIT3l1VjaSvYOIn7LnBasoNcHBWeQ9hTTBYXLjX7RxAsn2svGpVwEp5tdt70sk3p40YVcliNtqGythopHDFjk/7XVx2gik1pS25j447RXP9AQlCSkvVmHEWNzQw57QfHKcI5cLFjDYrkkEt5+nFmXk80UnOipQcD2Tbr6xuauM4g2FYofUruRKbPCBOWo58TDSUFsh9y0VCOYxW0cQzh6BmXwbRxkcQAD9+o/aEjhOobnPaxChB1ZMML5wBPX1MK+ioF84KJcEpI9A2ZFzCsAyVLkzAj/Qkgof2gD+XR7FJ24xbAm0oSFJ5cUH3EO+4tyQcYiLsSpAFICcxM+6gqey0qipUsdhG4bVilxOTgnSKwF5AE44qdMOBuZatvYJZGi7yzKpvCsDIICGbnastMcA3e6uj8PFq3xw2sg24jR/hSNITnO+9FWnjl+dFZEdQziFeL1L9pJnYlb4KGunz+H8YRwJroNV9HEuUm/rPMajUFy+46Rlklvt4UBu656C6c/E6UOKo0On1N7EiUxsPmgCXgwtbpJE3ecKW8kzVr76xbiv80ZOIdk7HEQ4cXAE4QTiFTBsIumn+vER2/lEm/TboS5nUFZc6hkUZ7vsaDbxP14ZEyvbSrGVHPqbfK4MScY/stK2LWALwUcvDeARCjpoykRx2x3UTnmUgxvETjritNuVFApzxr7jYEIZeqdyCscwbF8cMh2IxWtcamLw4UDY04Ec5w+VHVj3pVrDXn5wrD4vygiy0ChnLC28nY2lV8taXrbkx8Se0AX3OBcw/Bj7StO58wzSPo9Op5xi/eW6h1YA5eMI+vz5+FHISdWfM/dqoKP0PgQGV4KNaDO/K86AzRXRRzr4YcyoVyHZpD9wHuzB2GTaaXQhxDBSHpxeQM0f6MiQHObVFBg7pblWkQ9XATcwNs/zoXeb2XvilSYZP+jz8dkjSK+GaTXQt2wYjSPoxzqCeAUBR5BeCeuKIJwJX8gRpBVBX7AqiKd21ZFDEix5EEAH9VdPSqf4iMEw7dAdvCCPITKR2pW+ixaPdotcmUW+pcAMfLdc4to7jxDCDRnKIfHUQsFI3XIg17S1bTi8rXUxYAWP3uzSREPOIB2s8GGTaB9sCi3HD44grwxiRRAOIg5Ndp7p8/HZIygOpMdyKuUXa/WoeGbikXTEGZkcHUYWaY9A34lY/3BI7Izy1W+w+4BILiMKHdM2Qtvf2ECdhtcJDCyHavB4TTy4pRmsW6TGLHqr+hx/HA2cFP5xWH6Qi+pbVU661zJ0mBMeKj/18J0mK3/7UV+kYR8gOXte2wGkV8C0d9urt0xm9CqYnEEcb/SqwxutCmI15098RQwaHdmLgtWeTIj1IMakxKYuVyyXlh1BUmYf0lB+rWHvX+0P7jN7XxyX3NiGA+DuSTbUsQxJ17rT0yWJ7Z5yAbFDlAWuflfGi00F8x1FJ8eCYzsf4bDRxOUptlErKbPhKb9u69fs59pcn8kN6flVmwmPv46jiQz7CnkDVFYG+Zdy8dLkiLqZQMknpEMn2WvkSEB2MopPMEqPdgRXpEostL3d4JpBSg+cUt6Nz7wo36kmvXfUKm8ZNMi4093IpRkgn8MGJ9lsb5vky/mULnDKnd1gi+hUW7j3zDhNmsk441Gjdcblr3//JY4B5VnhAeJyBGngHyuIJJ90nhVmTJhx/MUR5BUUMjKeQLvPKdWf5PS90bIlfxLzNzNpqLt6eFxJDv7nknYl91c3vpEKep1IRMsIpfWjim6ZSZf5dNSxsuLXax9pmXzdDVcY9QTGY1y0dVCxCh1eHITikC5sJB7bcubPSi8Ctid9T59bE8ZHRFsRxTyTkDeeTsdrsLKL/GgpRxCr2d/IhnpjfTvN8zUxVvf4da+B4QRiRaVft2XljxxErA7KfmuML+TJmMJB1BWV6I8yhiW21vJZ3ovMFChYVqdyan86toLSizw3hgI80vozgJhEYl8TYeUBEQdMnutkJu3slA9I4x1HOs+ecgBp7GM/H8rm+QuMrJ6UfcRpzuqg53pVzCsrZVtfyF4+1TPoE5xBpFX+1K+JsVJIjiRWDIkOvthKtjywE0qZjH33Vvpe5bUZx1hAeLXnaGxwt7yb17wJD+rj+jLW+DiO8kCnbGXKbAAX9Et2Ud1KBBd+SXE+059zt3h8PNilXcO40mXcSB6jneW6TblLVi47TKwEpg88JIelMXUiZrxPhal28inTmYSIO7/otY4tOmg/cUdQbwQ0vmnURrjmi5PODM71DM0/gnelv+bL7wr/I/nfy+s38WbU/RZey9cgXUJ7JK4c5b8vgOhRO4Oe0c8jbvmhezgBT0TuQwGOh3zlYw4mcxvyJH/HGUQlrduS2bmSipED80vOse4OwLhp4QjixsXnK3/SaiDi93w1TI4gXg17pT2Cvpfj57vvf3jAiiAO4G/f/qqvhuEI0g1Tdf3qL4fxtTDdXeQI+kLLTh9yx+FOtCkVGeIBVhSBgOgiR2rkIySduOnInty0RchhPTjK7OXcZNzezUVdHbmaVVtqbZl5IPaU77hn6UIXqUk3H4ozHG1zXaNxYq0CUkU4groi6JkmMs+1989Xmsi80D5BOIJe8sUw5V++jBPoKzmKno8jqKuB/Eu45H248ebhLA4tyWSxRrYtbcOqPO0FzPha/UV/8A9s+saIbZppwlMYzmVENU1MSXDIJQzM4FAeq4GkGeCuL2Xh0XN5DFehpP8FL/qB8jn1MTRwT/d9DNY3h4fqW1VO2mNiKvxXcQT9TRvy//UHnDvaH4hfsrW3xTt9rfGtVmr++PqVXgXL5tHsFfRaTqA3Ks/XbFilqY2k+WXbkxkmJroydbHb5vfamms9jqCUoffo2hZq0ukPP2e4Y2Ilj8utqbG9zQ5uqDMJOJsRELCJuXTJUavMWC5q8TmnwchBFR4HTDLIqC1sUOqJiH/RliNIzh9WAeEIei6nOnmcQ/7Fm1+3mcAw4RknEHsJ+ZdxMeS1MF43s4p0YpIUB35EU40KkRWJGMeIQaANzUBfJbotxshp8XPWVCXbsO5P4r64BldX+H7RDCwUqmvKYtuvXJoXthvX/BYXPiKsqiLAXbribSwAGTxlLirQ/FtJiJJjgqDgCjsRV4nymQBrbHqc86qPMFXkrzWp3K+OCba3fU1QJclDrXbo3kDdKyj3Q8b1jAVidSTXjPs2ot0+05bVhGmkIvrN18GU7WiuT2SFEfsebUYZI247TW6tQvLX5XQthD7XJ1x8bRRPsWkWYQpWVgnkOq5z8FfpxgXOyEJZJM11Gv4d56uNUhR/lKIznZ0DcoSjnoyl5H3mRP8RcdC//BlWnYQbuoL+sHVZEfaLtzvIfmp8BOW1VlD6NVo952b1JJ+Ax+kTBzpl2E6c6d4MWs+52FavEMKuIoMOfwFMFaY+yUIaGTL0BE8LI29kQ74eKAJ9cI4uiXCsCYbeqtijVCmwwc8RPVBhnVNG8claEQ/4MdYJxCQ79u341mM6qyoZ97w2+0zPl3Xo4Oh5YSd6XhV7yf5BWi3EHpVdWYmzqCst2bQfZ7vrUd1eqY4M4k//Exin+t1yQhJTdMB6jaldHtcoTSE/TihdehOGmjPg1gO+1Wt9WkULVHIA0eeBmnxH7cBNyQm4a9koV6ETKfeVPvm9/NzSJbNQdpl2ij+evssRCAdDyypFWTNOr/WcqCvoFanNgQfpDW/RD4z6gKUrq/UAFlyFvo4EBjs0B4wy4J/kiiAGehro1vsUo35/HvzSNT6wz6mWNz6X3s0Vr/FdjEAob7jKX3jj4hbvPt4fB+8Yjdd6mm+cobld7EeTKvpvxBms5idMG3bGKmnHZ4bO6ZSy4PV8YA4D6RdnxnCVkBon3GmoZeChVQbiYXoq8sUUzrwahnw4XXiM5WYNOw6WwGbycLwaxq8nTEBwBH3LV8O0Kuhviv1qmBxAvBr2lhVBcgLxqsEv+hA6Gz9/8fCJDP8jxewVhMkhRAbLphyxJyFjhNKs4ICNnDW5pQXedOjJC9PjULQmhyr8XcnkWi8lVhMKNDAloaHWCUqYbfNBVjn1tQJwds40pnxBSuN0izYdZWgJehw1rODhRun3tBXjCGLFD5+IJ2Yl0FdfsyLoefYNkjOIV8ee85rDM/0aw5JefoUhFv3aI0h818bRqifDYuTsGCFrUBtKJmmkjE6B0A7gSYdLzipQaJq4hwu2Mng0CEf/hkgRESdjxP0NC0p5EFCqPbJzKCcjtKDxKvyc+GgacJ/cw23X+4fwIC9u8cg3XfbkB68P1BTNsJVdUXGGSyk+yfivP7x/8D+/18pLTUJes+pHk5e3msS8kSPo1avXTvMlMZzxr+WY5zUx23DZZOw4DiQmLrbvfj0By4UeFduA8ftwrvMxwdbxwjFuToZBnX/hzVUpPu3SdppZQ+CCIVB2QyQnXqnVQ0Cnkc62EdvjLjZcZeYVHFkzs+InhyCRl70TgX/NZiKiycoL2UxWB+H8eYETSBvvP8M5pF+xmdg8kf30aw84gjRxwamOLcUB1D2BqBc5OnlCASMKDXCavJurxCqT/XV6lBE4WMFZeFvePIzx26dqfeez1R4G9FEVJwjXz8KPKOeK7OEA1EJhN3nGPMNhOnhFb7zq2+hPZUIYFQUDgo3fYuxSel4QG4Kku8IHWCbjcfR4o17Nwrk1s6ojz0xZHXQ8f1J3Xi/0eJsVQXll5oAjoP+QFeGQjxQxJ8sDTAFYGxjEgIXna8ZEd8mMBLnKdxak/Vw0CJWdikiHZ+hAOb6Y5FxkHFoii8Zpq4SrED0R+is8bTKa8Gjehh7E7UyTKPfZ4z7YPFumwrQpSJMONkQTIsBJlaIn3zaDkTYPW8uIHsIiz4+0QTBl+GNM+HVYvebFyko21scJ9EpOdK+0ZMN9raDkNVu/UqvXxXCi8+VFf0UXh7rSPmRTvbm0HEJZncCrYzh+6AeEU91SJlf+ktOyjXMIBSgPLuD0K0rSQURQzJe9rCXSGLXkXGx2TsEjPyi6nb5RYuvT5kFJBHsf4mr+1EE6ziEue2weNq6b5vN1MGwpr3s9YRUQHybB2SPb+vwpdlTPn9hZVgV59RCv3opGD7LeTFow77Em3jjo4a9/VZym0j5a1oBcKIbYulQWEKFj02lB0QFt5t9kFFyC+ZkZBSAKc0OGxwquaPpQQJ7nT+UiNMrELQ20ucXNieCTZDQc9JZluJ0pDvE2Ma8ofyBPSw5pUv929kUqlIl3lVHZcQ2TO4LxdiXdJ/TAQa0k9O/xvHEw6TXDc8GBg365njS2lcrYIP5EHUGHio/UfhMHes2jjPsCuNfy/91hHq/3N+m+pt6A3x34N5AuIIyQ6Kh/tzxLqPI0wtBW2MQMWEK6hfSRJ2VKJa7dRrUE4E5P3P6bYstnmBkNQShHZAo45my8VOgLWlefHUHKcAthNsXFZaeQcPtqmN97lnOHz2z+oAmGvxpmRxArgnR8z6thbBbNL9XcXPXriVcDwS8rguIIeqybi5xBNEqHHU+VT/V5QoKspH3jZsxafJ1odY+BgTc6BQLqge8qQrJwoAdpj5JZ5ntnUEShm1Iny244kNbemPJhvmiVF88DSoqjjiB2UIoZZPXO4QiKIwdHkDeK1qSF+OuXz+UI0itimsB4RZBuwHUEPdVE5glfDONmLUeQVuLqkGyqH7n6mpgqtwh0Q8ZPpEvT3VJLmJSY6J++oe3HLUx9YwRObd0wJnIovGYeILBwNkpPyOhSzvthoMgEo35iWPSpoVUItNiWHNjn8PE1UJ2j56Zby8eGwVd1rGomnbGaSv9VHEH/3/fvHvz7t29kY/Xal5w8vM7wRqsz3+i1sFevBFf+nX7RpvytnPVvvApItnicQLzm4F+u0aEPbDAH+tVkgxkAFxGGYVml4DJJsRUrLTkIFWKrlYDUkJ6wGU0rLoFrBJ5C2FgKygd/0SkP6Atec1XMlIhgUcUnmyWPIGNH2yKc4Lwa5j2BtAroBa8z2AmktPYMevn1i9n0dBzpfJlRkxs2mWbi8/RJHEFMYPj6mPwCkUUiMImxjJI5EiEV6SPHTZicYd2bDTpQTyFtK6jlxNFNSxLD77ijpMbilbaYOyVlTAyLE7mUs/pGh2YEbPIb9oLRcOOJ4U5Wkr1S0hs7JW8Gm/e9BMTWcYLvFSIm4xBYxiNpO4OAMFmYFT/Af8EBpD+uB9MpjWOAiYaD2sVEG73gQGEzXVZEsIFuVgYJRtsFe0idSud5gKT6JCCxmla2DTBvGnoQJ5TeCDNezEflYB3lpQh52QGtU8tch5jyyEeiAXmbTrwkWYkRT7IAqqjRcWoVF5UNATownsaVmScuXeubomlT6g6fpqOi5FSvEouv0pt4SyZwI1ecKMVx3/rhBCn7I6fSEor+tjNIDvGfeF6VjXzFa7Syl6z+YVUlTiCc6+/HCeT92HAMyZH+Xpv1v5ejiNVCvHbLV8J+1tYJdv5Ingyn1GsdSA7kIg0OYX/OpZWxrZRsnWO9Kq//OGlk/YAZPrgb+nBORaqRv26U7jE+thFKh6HFjpLUcNewDn/bWRk47qvsi+aVlTJ+XRXJ6qBneha140erJrGl7BmEQwhHEDaUr4yxOggnEHngrFIPP5zrPJvSJOqklzZHEAKpQSOiMx5boz9wQSBu/wsQGvFz61HI8EFvvsaJBwwMFNpKwAIEvZpM7MI5IUN6NnxOZaYGAsYhe7lV5tBsbV10rT8YPUemK7+W/tH42ra0RyNG4qOR6tbJVAKKjukCq7QNLSyI57NVX4VcqiVbkGOdEkeHPPMzhuk76qDM5dgmAeRzdcDOk/8kVwTRQg+8Tcu+GNJ2n695lHGl2dBPZbfwCmu80zZNGeH31PVR8eaC8YW4DUgLs5122Y90ZJ5hZOyj7K6er3hHHj4z+rY6kzyXoZ9cLVBM/QI5Xatz4WHVGiekZrFwjnp3o+JaXdTyxhDmgvZVNIhFjQi9CS1XhOfb3Jg4sjGebnZ6mOImyb4TrPz50a+GvZMT6Ht/Qv6vWhXkPYK0IuiN8NYeQWwU/XccQdobiBVBX9YRNE/U0gvy+NCJ9nIQckNNmbQhiHB9c6d09ElSoTTmExBQHcLLf9Klo54THvkYl4KPGoJJ3jcebl5FMoz84MhA7ZydFgjeRxrNk9dP8pYNF5zdcLoZ/+IbclYEsTLooW62WhGEs0f7An3FHkGKX+r1MFYG4Riyk2hWBOEIeionEJv/4QjiRs772MhnR5Bj6p8gxdEWxmp1mNYYOsOXdUqCenACz+hLIXk3QzhgERrv6dQZrkZKvUn6bNVN3qsT3Ncz0mG55VOjYDvDoTUsYi20Fn2OP4IGPBiGT9N7l+9VtB/+LJ7ozUL8GKceqwOjoG+b7lV/iun/99u3D/77317ZDvuXbK3OfK1Nonk17BWb9csRlC+GaUIjGPa6G6O+06ogJsD8Qp3fjdFQru24IaRcXeNyHwucjssq0SjaVmt0j/79e5xi9O97HezIb8FXL2wFc1E7rjinGx5AsKAKI4prdpgMETJpkIjIuPgJ32lLqWRiHmn5RZuVPv7VWjbTnz3WJOUrTV6+evFSExpNYjRJYTLznMmNbCkTHK8kespGp5q0yJbymkNXASEKvCNSbOg0EBFXAIfGRw/SepoA6E6wjd6gt3GqocZnLNdnHmd42bafKI0sSkXliaP2ok9sbJVfCy917HwouqKX6162p/dy0i0jbto4O+O5syKbDkp2xw8TG6/I8EQhr0WiAzuETKLyoTVrKWU5gpS2I8jOoNkrSDfUOEu5RCSUZXPvjpCJfK+smOBRx41w3FPbHyBdcankCOR4NlqBdk8+cTJUSyc7HuTUtyidMHbpRQOZ+QjGLbdjhtjwwQljkKiG0ct1APWEwWud0B94G1KTiqEuB4vkEwgkKHFljioLIOomD1byJJK302VwPBY0JjAP2e5Ar8zKsfNKziBWV3o1pRzsfBTFr4rhENLBhvw43ilnT6HsEaRVQTiDZFdZBRQpYrnGIkgm5VPkdGXCrvr5FsC02HjNu9+iieiPfmRsRkPVaeOqBj1Mq12vnVOMff5sE6mgOEl7xZiS2FUC4zoHNpYjPyjaDqp6yvwBEzvV2RSabQnkSOdVWxxC3oBf2xhov8onzse2PpONZaNpXjvjx0p/jVH8aUMO8bYEx8mtjRok/4wfYqHkmELl0R856wC9u53llbapIo1ptU9ZNiS2/qYIzDs0xouMvg6mukQ9p65j7J+5VIJgU5nqH0lTNkwHkVxF2tNT/BGicodV645c0d1UYTS1KkVLJmxCM6WGwmhTNqQLbzge0QWPvjvbNEhTMfbcnau87+oCU8Yfecpqm3BYfqMx988I+sEJMf/XhTTqdpXrwh9xrnlogd0S+Qq/5tvC++Atb/zPwIP3LdldZ8aFk2oi/T9hKyho4tsydvguBjd0lrLWQ/yPBQanKDzgMRQEMu2bDzM02VBA6XA0uIAYMnLXMozcap5kGTn49dRgnbg9AO4rYfmyl1gJyEXJwRJaVgXhEOLXEG6OP+qXE76y8L1WAP3t2x8efK99gv6nHEGvftCEZHME+fPx8i7/vY4g7RFkR5Deu/eG0dSu/0ihyjDe1ElSB8E3SwsPDGk5Eg6nUJBrSJKbdhqVGw2UB637AU3kX1jTXwdgaiEKx8UDXsOKaOeaX4DBD43ZuTGBBTqGTU6f0DJBiCPoywc/+8bLK13sEfRYN2E7gnSTzefi5QzyyiA2jY4jyJtI4wjS4c36cARxo9V8Dvr1OpgGRKZ9Np+Wkfp9vY24ylpyrps2gcb6OrK0UCQP7rn11Xl6NOVp98Es0JtnoUYfYKce1yXkqHDqhmU64zbbkraSMm3+c/znNLCNFXfTnr+PM33we/HgccVVfoGabix0HlizQgTiTzf897+9efBv//NH2VlWAskpr437X2uC8laOeb8aJsePX2NgxRCrg/TLtX6w9kTlZ14L8y/X1SX3JT+JK6aDokQeU7m+0Hev5P7aja3gWszB9Z58LlAT/A7lw1mB6M61mTLbH2pXuVFsg5I2TEDmRJYyxkEi+DHRjLNBMLxkQ+XA4ZdpO3m0KojVQfxa/ZJXbF+80Oti+hVbtvMlK4VwCumVB7/KoNcgeNXWXw7Tdw5YZcmkCHlct+tXZsJ5ZVKA4BEcYX+VuNlsYx1Wb9EYfusER/CnAuVInXN36fpsVTzTk4naFZNQ/y8APAbb998ibvChd7tavPMEteEK5yHFNFMH0R2cErsGZVpJcDMm4cNo1DODnlkKA8Jmvhmn7PFiDF8HxtFTEJsHi0qHgoyIN4uWDhiD/py8Jq5snGsHkcdhxoHRGYwS2BGCU07BhGtTDniuL+OabKcCC5n2cJST6rMOGLQDGF3nQ7TkI1plA1PhIh8g1zTV0cV0Cdpo10SrqQf8m4EKHbb2wwuYBaECclMRcAWwdyjp5mkL6YTSN5+2Nkdp+riQsV9T3dKNEnGOxBHED5Y4glg5yUb77K3GyqBXsp08477TPmtvBXttp7r2ZpuVmLwm5tfI9KMnq4LYO4hx4x+wrOM8adFcP6fyHIyM5NWqxM0jc1saucnWDqM+68lGR2PP6NGb7aT1C1AagLFTnA4np1cGHSVgOufaMPfk1OHEwGDJRvj58VAWQRNsVvBwn1XStjAO82wY/Qwnuuwq9pNXa58/Y+812VEd2FvbVZzr+lETBxC29PETHEzUwaF6daJtyNBwSpOJ2E6ggb08TacN6LctLKe0CXIwqsMO8sW26ODBcCpAPvNv3nh77aA2f627bFoL9YN75FvtDgV2zRfvz8Wtd+dy1BQ9qmzmXZQQStU40LatuQPvgNyfKi/i1kva0ihB2o4gs8i1S9LXsMYrNDmw+bxy+FCOoCfG/tin/3BHEBdIL3Bf+NPCPd1Gg7fjF0684xcPeHmTJlzxCvuzeDv9LRlb7463Rl/HqUdl22hxb5/myr1Vzwyz1U7qO+Mx/BIqU3KFV5hi7fGUMSpXv9ncanDbxLpJxhp2y8gMG/IrCGev1aP9Dl4uCMA7aT38i5cS1i0MdfjRx/xHLsurIjHh5lVH0Hvd5HAIxRHEMlqtCJITiM/I//Vb7RGkV8P+qoMvib3TZtGv9TUbbrC8GvarboA4hPggOl8N+0Irgr7cHEHWey57V4xo1Gsn8GjKDz2Sx5MW6RVDTkuXuYVIwY97Tq+SFMBHbTsM9aEluolgCqUbAwvWIJAXIDx0HhaBgU0ILpIlbYbhydmypyw4WhEkuFcDcRMWzzqCcOA8evSLNyR9pkkMTp8XOr7RcawI0usNwDVxecFSXBlAf/1GTiBWBGn+c3YE8WqFRWy9qn0pgJZLAOXTJ22rYh5AVGb9K4Wghy4N4KRg7ekc7LQN+Dxkk/ytYPmoYupUnrospsaA6zWOyn03H4ap9OAOecOeLuxz/Oc0MP2kDjnCDmu6pcXb4cD2PLi38EC74GU8HOT/Ko6gf/vr6wf/97//4F+n38je4pR/y2fjmbjo1TCcQu/5xZrXwwTzhEXOH28QrZgvhdlfMteXLm4uNl/bXN9Z7QDsfPWiby+/nr5InuudAsqUUtzuoysTxlLAT+VGnpLbka1QimwTQ4+4Flkl1HHkYWoBFGFnkIODPWBi57wiiNcT/OoCG0NrosIKS71i+9L7rOEI4vVa7CmrguIIevaYTyTLEcTkZRxBmbhEBo85t0nVOkh21W05uYkSOrkS0Dbfecp2wiNNCWatkOEiSELyV+jopEiODxxS5UcR+mmptRuBg+ROUikwp6FonjRB3Nq+ABZ/86UyEnulg7cqJg//VY8pQ2dC6izRHl+YCsf3MPFyv5utJgx1BvFUoHRXRtxxBDFZ5mFjCTvOPhxC9JnufXzcgi/IMQv2irBOXukpy9gx6h42HRLnHmaBFnfgCWkcZ6sf4EqQubRTFYELlNjPRIoJaX/SILhrpA+zow1OlB/5wT1IkhqUYrYy8q6j+O43gEcbwUk1Blpa9UikVn17lcAL2eGkmwdjYZG4EQo2nk57L0K8yieBzkjyGh3jgR83WSX5/u0vcgTJGSR7aaePHEA4gn6UPX1vB5D2EGJFkF8dm5VBcgh5/yA513+Sp12sRs8aNzwv9VcJd4aefxW77sbIseRqy9v6ycOG/qPtxByMPdvp1gcSCIQ00PoVzI5xxjclqsxwZaL/VO45gunHJtg+JV0nkO8OGur8oPhQD5BIwHNm9wPylxbl+PH2BKwAwhGEo11bF7AKCKc7m/LzgyVfwGVvoCd65bZOIa+sk4xpW5qTsXQ0zSJOO4k6BAckyBaiBgHU0tGxdaQMfAHlmlCGf+sm8I2L6UGwvqjIhMEga+JkhXVAot8zP0rHOpjNhxxBw9J4TX/8uIop58ivK0OCty2SWGjJCU8JU+m0U6/yjZXpwN8RW954yotSXOcHSOTnjinkuiXYnkuK2EH6OVrnDYpP1hHkls8pAziZPV2c3WAX9qEYHr+HZsfb01fee9me/qN4pqPvNWh8EXe0uODOEAz0d7UJ2hltptpPd+HUfYQP0Q4Wcgqt4q5XXQQO94Nh2nVwXymhyPY6cG2Spq8OyilcBK5y5SLzGcd9DR9hWTanYX5eqkot3EDYT+K93qs/HEGsCMqvJT/o6zU4gLxZtFYG8crYW26qfTXMm0VnjyAcQrwa9sVDXg3j1zU9WM3DVgSO0fENUzKtm/bI6Vb7Jhq80FSXadDVEUQboytSzQAZDQ6YSQFJLAAAQABJREFUIpsR2CzYSlDsYErpyX9Rrsck1Kd6poczMRGpmOaPtG/LShCzCki0szKIm6zcZv7VmNU8j77k12ytCNIs5KuvtCJIk5RvvD+Q9gjSLy9f63ih18RwBPHFsOeatPiLDdqLG0eQXy/zgyzy5cjvUiOD2zvtPBru9hmqRvkGR1v7YDMPIUuHasWRjjHOmQlNdUh9vzOURDUffJUW/AuNoSNQfiMU3BiUPX2D5DPoD2pg9dWFHn23bE9f0NwvvwcPdoMHu9z8Dxhs/1UcQf/t33988F//x3eetLzWxIRXdN9o4sJrYD/YEa99Lfz6Al+60Yqgt7xHzy/WemjSoTfDrD/uzVyh8szP9cHVKpivc6wFgXiuaduw6Jwy+mO/l7SDKFsPxVThTiaeACFwEBuUD3gHThqRjKzpD3RMVAxTknztKfJd0jgAsLF+FUH7+3i1pCYpvPLFJ+N5vdaOIDaL1iSFX635+g0b8vv1BuHihPcrDTNxYRJDvdKa5UF9a0BKTosoUBpJfMhr6NzQwUs4tzn0R+mRKv4Z0tw1Puov3RajbAXawf22tAa6TJAUGhRe0OyYk97Eb7LPLEOcSEC04zJIR4YTDpm9CmfE1R1NYWsgTUiec8ZixyrOnYwHIMsp5OtAY0Kx3ugxDWPm7AiSjDh85tkkjiAmr/NqGHAm45LJB22qfBMn70ZayvtOopy2MUbacFpzlxbcvfW7+nodUg/waHpYw1dHuYOzqiKjsPhOgmjBjBC9kiSkLGO9eAd/6qMOnRBm6i6NC8PmJBMgeJTfoATnFnBg+5NF0aob+PU5En6YA3CwCywCsyNIz6usoHytVUGvtcISR1BWBOnVMG2+/46Vl6wI0jPvGznccbLjNGJlEBv2Z/N9VpqJscaGx43Gx9+xo55QI8XhCKKVlsHPsioiv9LKQgMJp4kzTpSF5wIL2nLH0NA+cyehfz8RKzFQDw7S4BAmNs/0p+2rn01Tzl0Bu5sfK1kNFP48X7ICCFvqfYBmTyCc6U9ZdYk9fS5HOzZUq4Kea9UldpXV7f6Kox5w2UiatO/dqo72pS7S1O/mWg17n6ZkyjY8S7YGRNtYbOLCDiIg5FoC1grYLBcUSyUH6crk2rR1G9Jw23mG7OBD/nxVDun/sqjSHa2PdG0z0qlsQ2sfFHQVNW08SK7lt/LwKr8T/wG6XAX+Ix6kW44g+LMi6GuNy39G+A9bEYQxbcPbsOOGoYHUq6WFiq/4W5HxP1Re3Fv1tmyPP4S3l+3pnZ70XranF94MCG6+h+wAO+wWphM3eZxRfideKl78VF1q5DxCnfie4YesQYqZBaftCDdKm/J9o6yH3SorXsstQ4wPF0n4iPeCiy9WtVeXEIylPLYcewnvTKqQCRj0BEr4jlUcQXx1xp+R19NTXg3TqiBNQH7QZtF8Lex7vhqmlUE//KjNSnWTfK2D5bZ8NeyX2SNoOYLYI+gLrQ7yAzVP0dR1TPDrCIo8kZlWuhleWSR0BfAcj8Rg5qZ/GIu0JVjGDokAU99CGA0qTz3VoivYTjaNuubCq3G1tZiJYhgpZhxkLFC+H/QAGk9MV/kXFzmFWHrbV8PY54dfXlgFZEeQPhv/1dc4grJfEA4iluCuL4bp5sqGftkoWuuwJG9+xbEWeSRJ+5g0IZvbh7xObCcEUtv8YAIP+gDNqN22O9bC4JMOA1rTNHUlfYe5sY7T0A8a7NO/wDkUXEbB6L3wlJ7PG5kLhsUZ6XPuo2ngvu5F7y27L40QHyobIX1dCo/YLInLWzj/Ko6g//o/fnjwf/4/33pS8koTk9ev9Gs1kxQ5gn6U/WXi4s8b65fud1qZya/WOICwjfxqzYSFq55Xc5msEPMLsxLGyfVLh0TPqJgjv8TFXljtQUxfgLxCaH3WyVer7cVCSMJMSCbh8+rQKXR+ISKiA7aSmxhWnMmOeaiBTOptuz1INAmyjdMLz96TQnZRSyRZ3fOUDUz1sGinjxzr7A2U1xd41VZ5T1jkAMLR7tcbNLkRHb9g8yVHfs1lvHmRyMjiZkhUZy0lctN6dA007dhX6QIFnhIsa8LBg3ygLQvPcmvpECpCJwkHRSGRSLTSzyES9lwYEYJCZTZaCg0DTLrcNhxAgqcoZ5fOPbolptz5ldUeL7YrcRLnxIsaxc/3V8noey2iaKLviYL6H+3ecQRpzPClJ5oFzdURdLwCJt3gFNJx0xEkPdGc7hnkjOrPc9c06p720jo/W25ok7wRbbqY0nYJ2bSfVHRBm8s/XTl9HJSASE+gx9xrqFPpubTdnFUPiRHDuNAaJt5OtnyQBCN1aiN4OvZwYO/QS3ojQp1lUvA1htqwKUBMjwQ9i5L2F8NkL7zdAZtFjxOIL+Gy0b6/IKZV769ZEcQrY7Kp2Ni37CHEq2FsFK0fO1khz+u2bJ3AeLMdZbzIrtoRZHextSCJFKvuXuXEOSJst2Ww7L1u3FgghNHzKCxF9OsA3GL1PFnJQtOpwQ0GNL3qseI8OGBFItgY33Q6GR+pKMeupjzPq3qu1DOmN3+WQeTVWWwqX19kJRAbSbPikq8y8votX2h8ri/begsDPcvi/PErY7x269dt4c09PM+Z1J76SCU4rxP9R2h5m0/erWHwOjOIIK8gGAQUgaOwJZ0OdNBGj4U5HjrS9Ah/0WPyxS2sefBSWyG5jxy5pO5iXTE+Rr662WtL2iUtpnUrPdILbYFWYmQfdgXv3E9SbwXgFr996zwn6qIPph+ahhdpX9PCQ9fkCZ+kI8gtm5MNqhp7XPgMVjR6N6CU4u+lO2xP7zik97I9/SG8a9meL4/Ge9mebnnjvWxPt7zxXranW954L/MomyHY8sYH3qZHqToXM6UZdAceqW10g6E+mLGsuP1ELMNxIW/pDjZMJ+IrfqoH+5AP2YGHRww3xNA7UFxGIR1c4LkFMFlQ5zsPP341YUWQnUB6uOLG51cQ9AsJjqAf9QWbv/0gR5CcQX/l8/Har4L3rPl1BYdQ9qbg9bAvcyuRA+jhw6d6UOpXw/QrG79y8FSNpKMznt8ti2B5zI9M4FSXR1PSYloOjW94jkk3kIquCkl9R8641H8QLfqlQ6EzBnjwA2adK+G08tEvEqY+eGUyYlMFhg7w0yppVzziDIKHVwTJESTfj1cD+dUuveb1nFfDcADp+IscQl/rSzesCGLz6Je+4eZXF35h4Rds7w2kGzVqZaNo4miOyROyqP4IZ9nXuLB0PaVhHvMaC3EEwQhJCY2T63laPqXogrZ+KMCnvJAL3AOWaxIIY0BFGquWqWhRaUj2dFkWj/hz+OdooHr/EHf6A7y9X27R7Xhb+Z3rUmXAiu4xjhn5xMP/9W/fP/g//ttfvX8FX25kHwtsMhuZvvpRn4/n1TBPTv7uX7r5xLE3JpUDCPuYlRK6grQiE2vAFxxtIKQ3l6NXdCjl8kMAOfKnqxhA8UCZTnXXcpKNSL9gJ/Vnm2GuxvZpZYOZWgSUEEkLyzitOQQ4UlwP1fCeq509cGRiTx48Hbpf2fYqbTvIPi84gtibQr9ie/8KXl/Qq7VscMqKSiYtL756YZvKV27YU8gboD7BUSTnkSYu2GQ77XVymjFnmSQVCsRWRnCEUkAegX12sVJHG0iv9hoe3QW/VAc9KaBwJW5aSYfjEjhoW1aa2vtg0D/CaCFtWOHCA0TjTbzwlFhkShhvAAu+Ize9Vwxd4ZcYhcPH6DtDaLgvSIPud6GR3hxBmqIrn3LGA69IMlZwAnjo6BRHUOvMvc73ePG+6QgCvg7uifRnYOaisobcv1QuWGWkbGEM7nGNuJEl3+JQ7KXl1yEPMmnGU+QRYHS3ibQqrwxo1Fr1adKCQTNqnXQQBk0YCVyTxRuII/NvJZYIyQgL6FwhQClvfOYZeCnNRxnjDMEul9M6NVZvq9v1NKLu/3nGAavC3ssR5GdW2dBXcvRkdRCfk+crjFoBpGfdt++z6T52FkcQm/HzOti79zgUtRpITkWelb2iBX3rOVcDRy2xy5gCyRnJIw+2t09GNGLknPYritwrDi3whoyXA++YsbsGocGUWkgVhk0MB0fqt3AIHiWSdMG4TshzPORi8R0hefYP4vUubOkTGcJHeui0Qx2nuhw/L9gXSE7257Kv3nuN51S2MZAdZQURe69hY3HM41BHTm9QLf2R9sFpwv/P3ruwW3Iz13nD29xnqP//e+LoSSxLj2zJimXrZltJFMfRR879Rma9a9VCY++zz5khOfw+aTg4ZzeAQqFQKAAFdDUa3Xj5Lw6IC02sMybWWNs78JJBsGkOQpGDQiXcAu1HcqVnf+iESq7NQmubnq7xm7LxKBA1JP3Q1ad453mP1I8Ruo56pSHe3NbIJG0Bs4iyM7EFR/IChC8o4CgB/ArhuhLBhdZMmx6bhunS5kCSuT/FT4HRe9zvAYOv+BTIK7yP1K9+DfcH2xF0qTJVAKTt4eJ2cmj8s/9LJHB04auyPtIuldB2CFaHSPxOCs3XMVOsnXJhxbXfUdKRNokzbAUdC/4+GoWj4bIGJ1mYBxhaVu1OqiKj1ExU71QWiyfOCeIGgxuNZzL0PNVE+fTp61u/wxCkT8ezI4h3qp9jCHrB4XuvNUF+oQWXfrwiZrWXL4dhCGKiRKa6FVFZ3RnUyfFgE95cGymlTEHibauG6yQKaAQrhEk81VHK4EkPxEjUV9MEhgvRilaxI4vTASDh+SMcUkORHLjQiZZEQeVnOGn6RcoK+5UwJCNuRYvPvH/FjiCJxAdFy5iTV8Ok3B49uPX40d1b3+r3UOFHuolhh9B9PWnJbiA+z6knLXqNTHOzjUl+Yi2aGJYiPXwWAyqPivYnnuAqvLVSziSomHFdZ9pqpY1PfU9d5N56ulpCOG2N0xxDF9mQzRd4GD7qO00yZwYvDPRL7izr+9AvkfgM+wAJ0CaVtdtn8gDb4+ekLuU5xzmLd1wCbthFTDm/FUPQ3/3jP9/6z//tMARhDOIVMXYE2RCEAV6Hl7KLk6fbHGTKfMNii5+HvhrtS6++pY/ZEaTF94ETjcoiC31KnrjAaVc0YMBtyPhWDegKMthXqD6glW+IypvQNOpocIBtZDAcNpAE/eQgrbDPd0C3g+OF4uCZ8eg5blR5Pcznpqmu7O64o8UiNy68WntX51uw6xKjEAZ3XmPA+O7zgjjbwjuGOGMoupW3mjGMs2tTKloRyjZLsOUfLMbBzwHbdwQd6cMz9Vl/pAKvfoQG1A93GjvKD8Z5aqgdct14OqN74JzT2ON7+ODJLOfi65p2N5SLwWvIGfemNJVi6bn9J7wZgoBc3BF0bgiCiMvR2KDPSqGwPokhCKMfr7QPDPjZDz7XGnHomPdrLkeVFOJ/5YGRS+7IQerJ8EAGzSafOruvbHRDP3SZQqFWimRtdjAWLSJ2e2phh3+kNjQFCKVlHNiELkNPcErqBHjwfWlFsWdxWBf86RpWD6gFdgTxlS9+7Fzn4P2nz/RwUzssc1i0viLGIfwYgtgBJMMPr4zZyK4dli91gDT6ld2WHL7P+tjqxn0iH0Kh3zB2u346xnF0sW9uhV9Zl8/Khvo17UQE14qOVhcVp0NtcyLUbMZx0uBHQkpPnvjmRljyrVO1ApfPOwKcvQZjX0v/xQAkY5AMQXxFjF2WnAW0DEEY2DH+yEe3PmCtKoMQu4c4zsCH8muxyrlrPifI61/GVngXePFd/lurxvEbJq33XcVzHY6IQsVW3RRsrQ+UQBo/jW3ZHSytYHvMTcZDzk2bBHvJd2j2Pe3XDl+p0UmBTi2KFMVewwU+yXFEigtew6Q234F5hEhr+t7f9zBjy6sRAQNPLsMFYNWAI+225vmHn6ohqBMMnbxhKr6HieOKc3VAJJ0r+X4K3pHzcqj0Lqf+NOj7adHoURbpFJfpl079y1gfIouWR9dOOP5OEXjd4AlUVQu/cfFtCLqJnJD3ZOctAB7yL5y9XEvFqAwNl8TEVJyVX9ktuAA8RYhMchQ7PgONGwLvCNKkyacyeSrCjh8+r8mB0f+s18K+/+75rd/x+XhNnH6fWu9aP9fk+ubNF8rDxKvJj225PCXhwGhuPtZTE9qSBVaMQZZZXwETi5aVarH4m7pT0aP9o3aTNzLhmpAxVW5jlgzAOBsWCCr3rC4WpgKEVw7LM+rb74Ir1f3LOGDqZxqEFRCDyM+0x0eyuRlA8qpVdwSJv681EZLGjh4MQXxiE8V2R5/hfKxdQBwU/e23D/zVsId6Ws0ZF+wM4gs3PLG+Lbw72qrL6wvfiNcvdcaQjUCi6dfOxEqs6vCJtODxtNKrPtTVFafNaB8q6YsCuCWVROcaatQZDOq4uylr3dxENqEFczsu5YvCTEaIj95s3NVmO/4WbqHn/obyOfgRJLC3F7Le47+E/AVaHj5D08VMWYX7yLG29y8p+1943r+REeiv/+F/+kaF3UDcrPirYTLQ86rua72ywFNqjPaE5UVPasxEnyMkjSQJjN1AueFlD4vM0XMDHQNQdGr01yGUNjF+xiNpEbzjWr2vmPX9ET+oTGgasm1IBwKUs30IVV8YkZTtR6lb3ETy5LprH3RddJDgUmP0EZ5kf6UAupIbFF5l4NdXxTAE+Um2zrm4r0Oj7/OqmNK7Q4hXbqGFsZ5PymMICh8jD+naGKcUF3tWmbBpPOJE6so/8YSX7JSh4WIXcsQh29IDFVs3usoGpNAPjZNMR4PA8CRtvtMbP8mpyNRP/Y3WdHVB3Wk6i4Al4Sy6rLI2msCcH9jQ3pIDVUlDH/9kRxBx9QPD5a8dQevVsNkRRH6Xz6s9CtboI58b1a8uGoKYF5mzI3XmKzt7Ez7j1WWorCNVIf4N4HK5jkbaaMGunXzXrWnUVwQjNgiDmPhCcVmNXfUX7SYNvrkLubBpOAwIUWHXauoGCNjmJXIN5FK1TUP4qw9tFAiaPOUeLJhM85Wm9Z5UCbshvZ5V22MEeqMdPexcZzclHz55rjPWXkpnYlz3LksMQ4q/nDPXeE2Mw/d9GL9ovGEnkOjwVTrKkLlZfSc7LdGv6Rdwqf7hBo5AvJZFH7s+gbUOlX3rULhRF/5KPcAq/VgWXU1PKaDTP+p2/aqwChtuhECaMFkfGq6FPDC2VMlH77H7Ih8l4ZXbL2VMly7lVVoZhPiKGK/esqOSrzI+RI/O62F3tXPdOtVGoXxOnjGHcR29ioMPYLjxEtmue3pxVu1WJRWw7JtyYJq2opV5dOnK6JKIAacTGo+g/qAWSrkO1spj2Rkj9IpVvCPuLL/Hy2n9LhVMPRHZj7pn2/lszvrOC8IAikvU+kcBwk7e8JyPi2BON4LC45N0hJE7Y0WJAvrPiYxlQNzFHBk5LPqTMwTlRuiopGXnTk0IYVf0iXNFMM1X/0g9DTW9/mnq1Vjx6l/FCIT0Ovi5yRW3eNfRvop32lncq1TQVTwN2hMewhssHuD3y2yr0lQHOud1O+KlbeUM2OgpO8aNIbN5O0+lnhwpaXKrVIVUQONDXHEgGSyEjnfVh68krxLLI/m9ZBZaMEPZKl9IfnqiCeG1Jr4X2j77nMPzNCk+1cT53ffP9Xvmc4KYTLvNFmPRa020r9+QX88SmE80If6IAchfEBO3Ywwy315MMYnWMXmWH14vmwXv8HjwSX1x8p2WGBD4t/NKovDUrUkt0KnmMSmHbCLd8Mh1/jCOGFU5FaCIGFmIKiIt5b9ZhIYb2oy+Rkb8MQQpc3YDja8bFtl2bt3RrMj71PfuyRCkG5THOhuI18Ie+Ws3t2UQyjkXPM2+zW4gGYN4X9tfHJNRyTcs8Kay8gl5+IQrKspXdah15GZ+XBF4Ux3FZNa3zMzvMwRRIWhV5gm7robedJlWShYjZhxA04KCG5Gf+MJL2kXKTTr3LyJ/Bv5sCay2uEChsifpJjzSi1s84g2TLsd4zLhR+MI4pa/P/VgyfKLX//Jf/+etv/q7/6Edmc9948LNSwxBGOZlCOK1MH4yBr1B92rhzoLOmsc6NOEv9eVGblpywyKFI+H67AzrBxZZ0V9WEVtjuFl0afO46dww6Ig0JOOVdMfVKF7bK8mwZqR9FN6jNDJ/1iJOSGrmb8KJh7roOx5Y0qildCqdRWl8MrlhbtjdR8RHXmvQa7S6WfFB+3qKfUevhnFYNAb3e7pp4XWxh7qRwdjup9o87dYrD1/rk8fsBMKoxG5N9Cp8U3PXd8KKyqHj4ZNU4TlcvgUCvOrQegRMnuRbuYM+V3ImhdDhIJnSoDd5LY8Dx0w5Fd6D36uxwDdvurhNwTPAucLzxJVkR9ThAbivkQ9SAyuiZZZsuU5mkxy6Lbdoi/4KNEXU9QfP+lHSsQOIeAw9pDM/nxuCYPBdDs6aOmeuO3b/qP/qhvcbPV3xnCi+Tn1E5B5uOExlh91iDwQxNTJo9cBT2GtB+a7utNyWc4KTaUugunbya6wFy/WUTxi+kED4Nfa6rOZckOQxWV3wT0o9iSQ9WZ1jo3IEGRVHNnOyZWm+A2PlbJK5ABoc3/gP+qo/ceGvLIl6PeaVheQuWw1dw22vJawM5TLisGvytXYEaccPRxpwviXrWXYHYQTii4sYiNj1452VehUMo9G7dzKuS7eyE4jXCzk6wcYgC1RKwbwyGcEY/SJ6MWcGAWM0pL/Ac/gWzONFABz8xkv/mFhwJwE8ubTjpGg9GZf4iGpg9UiDB3CLh68fE6vhRCcdwbELCDi+DUGsUzEEYSDVal4RXrv12UB65eu2DEL+CiMPKzEGcVYQHzlhBzvGH+3YiJFIh03LIP+1FDM7gvxFRtGzdMQ80lShlmX0uAG+UIOOQ0QeyYJOXeoS5rqgKoe8RYNG5pfmgZKwB28N2+KRvBy00sfTu4/Eo+QgFyucTh0Xnd9noJI4eN2kEzkVRZ0LLFXd8trAe5bFPHiHoK/mOxAPPHpZ6ZNOu5gMhblc5vH2V6XTIJo/aDOC9qdQ8uaMoM+vhlkwCPSSu9rpaYTzgfAvC3apHr8MRjfDTU/be24STq6Vjzu5U5qfCDTq3H0VGZjJV7Z05JaX/O7wCtYneXKaYMu7kg7ALtMsC1zjAOuIMitRlKdUk3PhaaSZLw+0eUJhPoZHeSymmTyZ7F5pAvRh0HoV4bnereaJ9Pffvbj1u2fPbj39TsYgjEQ+t4Iv2egdamWsMQhDEAdGowJ/4AZExqBMkkyU+TFBHk9RxNsIASOVKud6UgMUA/WqJIA1vKCpguGBFWMSyIQbMDLshBARH3h5HSnjovyR2icSBx2IQJMLEydPikw4ccGQeKrFjYni7AISzK8ZSDxfsYtHM6ANQTICcUDpfX0NjCfVnA/0+LF2BPkpCzcummj5KsMYgsBliy6GIG56OHMIKfuppcqAXytS8aXli8Nek/uS/gLn9HnkjR9eebolkGD+rTCwU4diRwiD6bBB11xoGy9XKfjElYIL88JulQ91MzoZ9rxBT0LD9U/of478IglU5si24RL82DCK2MtQmOgO+60Ygv76H/7fW3/5N/+PblqexRDEDQs3Ln41TF8Me8tTa92koHv148b3R72ai7Me5aZVevhL3djWEISyYMnHDRP63gssSdg30R5rhC3xkTkaizgjEtoZYNUbiUJRcP8LJyjOsy4iYbD8U/rQRn82U7RK8iUMB7i+VuvI6N2kqd7WvZk9oM+rDZTEDQdfu+HJNTcmGHh4nYFXwh75jKAcesqZQQ+1CxPD0H3Shfu1dmn6BgjdqrC/okM5ric1phz4Dn/o3jqnKQkIGNHLpMJddGZzA6VV6kKT2BEqnUCLKX+KtM5WOHiULuoCdp4rdbfb5FlUSFywwYRgB53DC3vDnUzymt03cXveZisDWz0dXBmLeOY3H3hTHz+EEW+ACPssLMVs/OHmVWnu2yxE1Le4kUcOxhU+3S1saGyof2Tuw48h6Gvd8R7GoaQTp0Sfc2hWNjkpjlvyFmMNW97m1AiDZ++GSys9coWesFPf1MWZvZabXrTYUYD/Zth88uMKSkzXJhQAgtyOF5T2WCRxOHppIWAg/9TbwaQqQ76mu+ekkOBDLcUib0VAC4Aku+bck2lKHO1L0/tGUkqNQ5195qUMQBjLX+mHweepdv6wq/2JDEK8/uXXw+SzE4hdlaxh38gg9FrrXxvLZTh8I2VpA5DosuM9hhxxYfnTL3CYMtJXcjB/4dGZ4AQPWU3FxK/YNlxk/dYp9bEsg6wYDgiA1pzg1NwgpY0cQ1lXr/MmS/NTmOBwQMGMB4etfwNjHRt9rF1BP6BDVTP0H4YgjRWMQrx6ix5ldzp6FcM6Dyq945LdQRiCWLfKGMQDy0cY25VOmLGFLoQWY0//Zh32M8IUb13hUX/UybvyBxeMSjf1hkvxLSLWd+OTD6eaKWnSDCEFupREatwRStx5Bq849clbZ/pDJ2E4TAngED6wgfw+XGsTTk5L3GBCC8+CTZbmhGm6DI72cfepH/DknYgRT/G2FKvdxkUmtBWwvdEJ6G3ahHsWOY23tA9w8JNimOJ5NewT+2oY9T7trIkDr6tybRzBnOdpGv6etoeLU1j9wnefNNyHlPWHwvPgV+Hvq8dVvNRNtYN1u9a38VMfPHfh5TtnR4ugxkDprFGEAjqlsihMIHmQ8Y43kUk0p7S3UXZEI6juSrDyH6Lj7RThHWwmskwjUWfArP41CTB5MjBf6saCrbO+8dAW2acy/HwnQ9CTp89uPfmeJyr66XwgPrvpc4J46vLqB0+47Cr6Yc4KYmBr+lDRespm7gkzGSjGHZ3T5HHTIme+4M9VLL9O8kVJ4zYVvmCR/5ZSZCdEUrrqP/SV7GgJiCeQ4BT++JlnQ0wrHCVfJk0IHDci9DEmWCZqJjkcE9+XxDWhAvPEyqsG88n4O7wWJkMQr3yxIyiHROucoMfaEaQJlU/F+3PHmnT9fjYH98kQlB1B0NeTGmi3LAr1hD6++RseheuxDM7Uz7vJuBWZNnCtwVPdkXdlQJY4AZXqtlIImQCJswAdDFauBpinI94QcvbT0pY0bQBl/0qyGVJQrsBwg5rI5+uvIgHa4VIbtLC2xS/FU36TED10ovXiwCjqt2II+k9//z9u/cXf/JN0rnSsdgDls8Y6v4IviOn3WjqXG5a33okp/asbXpbTOLSBltseUV+wI0ivhvnmlu0tSuMQVUZwFmHynQfdRV7o4MdZ/gqiZ6IQRiNYX6TRGcNxSmtwIHQaaHTxTlnWs6OjjtJWicKw9tkpZIibtvBE0DuCxDn8Zm7XjYvgK2wa2m2JIUhPr+/rfCCeUPOJY14Je3zvfl4Nk459yJNs7cLkNQZ2CPEU+xuMP9LRvBbGjiBekUBm1C/mtPLrGppXszc8guegU8ANPvrSYSF8ORPaIT8j62It7Ag0VkkKEF90GwDhBNFZ1yVzmaI0BIwtggslAdIgtPBKeMMryDR0sY+nNpnwwhY94DgoX2HS5QGeMo2yhYlvDkqeXxWA7nU7gjAEcbAvKwpu4jsvYzSibp3XTg0+zIGZV0/h3LTmNvXUECQ++T/j3fxNbWFdKHbgYQxJ+wONXCZ5vGIfUJrCbuqc/i2IKhHZMuZCz/6QWJQmUDJD7Wd5lJcaQG2VcIXW6j5qgvBosTuHc5pfYKKHXPBNUdf8x58iKoO9DuUAn3S/Dqby0Gmc6/NO+tC7JmUMwsjDEQfPtBvod7OGxaDOuUEvtFMIgxCvgrGzkn6D8Ycdlu9kDGETGT8e9PlGVXLn+AOaD67pZV7HmnGY71pKunjqSWVspp7xDs+4GJWgS8SgJa/EjNSkgGaQuS0UtoimnIgrxAiHJJgtQL6DQOAoOtMCtD4mnfOBlDa7g9B7NQIx9xLGmM6uoNsyBmXdKv2J7tQDS14P8/lrGN6lb3lVjN2WGIK+1FNQG5NEAzpmG/ri1eOiCmQ1uPgRks/yEg7GIyrsc4aQBngQmbkEEI4qCurx5robLf2M9CvOjAiz5EpgEOnzuPb9xoGBGjdtoUjxwgXzxe/DlemDo9NSU4dKZ+kRkNQvm0pgiR/gNeQsdqWB0vBF1KHBOCkezZxxE/rNZ/U8KT7434VnfLjHirG25ydrCJJITlyU+wE6j+9K9MBKqMq18PP4++BNr39d/qbX/yl45DnpjCVy5v8UmtfTozfGtTMSSx9NN6Qc97sDdXLsHrh7LkWdPfBQGnzR84A6ATb3QTPUrsZRjB2QYSnq5crIHH6tSA8yW2gQBEFVQTMsBU6YHxNdXhnQFxYwBPG5TT0pecGOIG2jfaJXw57IAPT9kxeeUJ/pScozPmmsc4I4L+gVNyZjDGJHkCdJjXYmz5wXhDpkCY0xKJOlzwtC7vzkzJ98nzMEV/pfCgMEow0uXoKH7yzcHNQVQXEDI9PKNUUEp6owPk8dmMih05REUWLpZ+LMwsTIwo+oJlFPsKqJ8toIJAhn+OST8Zr0tHWHJyw669lPnO/yzrUmybt39dUwTZ7fakcQn43/Vk+sOR/ong7b82sNmlTz9QZ2A2kHkbYTMS96chZTmSO5BaSuIzXzpTqzOhJO+EYQqpP5Uy0VMESLYBwthOsurbaNgdslJZBzn+gSP9BCK315eJrEpOy5zRAdRkBS+5sM56QBl8i5P1k+ex9BApV7Zdz4daTBex8Oea+jp7wre8P1le23Ygj6q7/9p1v//j//061nT7QLcw6K5iDTF+zSxBC0DjDlCTg7g5Ba9G6MQOhYCVlPYb+yISjnBDGe+TAA+h59wJ/DDlX2ETgU+cUdepDGq+E44eAZw+3fxk0zHzSOEHqUXOM5nHKUJ0rWPpSioYQpuClPptyGUQ9uaND76GEl6gYOGLYqzrfwgdHSrT5jTTcv96VjH92XfpVBiE/HP7ynmxXBfNCpDEE83ebcNnT1V+hqDEFRrGYxNybRZ6lp9L0KdYUXzwo4PZWdOlKH5KqupU1wuTY0WK6rk9elePWd8QJeMxRvrXGacMUfzOHH9dm4OkGnrjh504pXDEFz32u0o29MJuiWMWP0AhDa9QeuqPsrSVPmD25n2j8/x5WIgQgDEKuHZQgSjH7uG12o+4GUfJ/xkpvNLxX+inlVSsZtLDkgM8tN5TssvgqjBLjE1a8sDsikDkJEW2zSdirED1cRF+K+LXzDqYoT4IdAfQeTAnzIB9fgK5eiXUm4AAidUtvroaIGbHoKF4sAckFuqcNVwqUU2SqdzAOErmkWCWqGRTPQrixxvBtdAYzkHKSPjiTMa2F8/daGoO+faA373HoU4w/rXAxBfoWMnURa+0Y/Znc8dFnDMtp/+CEmC8O0TmGp4uWK14rIHyWhH5OUOQ6MEU91DFMg4fiVWegY6UxGR4+KTJQbOVgWUJ7gks3EBU9OSkNe8QNVmDWiYTL6zO4fvxJmWPQp44edkLxi6/WrqoUBB316WwvYnll5V/oSQ5DPYpPR59EDfZFR567x6Xh07EPpWl4N40uOiIgDqL+UXmWM9SiDvnorZs1v/Fy9c081pW+wzqXS9hRPHXbso9bg2IGHQwR2CayoYA0vkpNFpU6eelchbSFavXSCfeQldMRK62P5lAr109IvU6cvCm9H3Q1BQ6XUdjToFd4wft05buGMHdyet/0eOGF4im+IuqchRKy36b9dpwD7pD8fvytKwrs7jyOYHf863OKRHmVwYO40m3aJ5k/FKy1KuonejndwdRq6lH/HaHr9Pa1h0nB0tDXQFR+ouiBKxig/6dIBtdNkMEAqW2Hp4ES4nJbduBN0gbe60BuAPOJRP4oYPGnOAIL/g2hC5qDkxs9k5KcQW2VbLiYMvwctAE+XvZ2WLy1oWy3nBPFk+umTV/H1hNqfldeXw7xrCGOQJtVXGIJ4v1pPTJjY3unMIE9wXnRpVT0TJcagHNLIxCr1CbviyRMmk654gK+lChxOJc26q6cLmhenoFDilJFbghAtUL4RTCg8OUllTkbkGxk3RHmFDbUwZWKU8gXKysYWlWjFlQkUtvwqmAIOf6UJlTDbbDWTceAehiCs2nd15s89TZL3eGqtmxWepjx6fPfWH8n3AXwcdsqTF02q3wiX18G8G0h5WW8wMfLUBp/lCvcc3Eh5bI0hiEoS/1HwvoPNoiWyVB3dLlNv0YGSxTJhIOcOGZMMHnWMG2FODIpAuPpJKXcG8OD0ye2yBXCaYOkwQx1EqKQc5wtBEg5HghMP0OfQR5JAm3SX7w5ruMUVb4cD2+PgXsIDLrwd1eNzg/1WDEF/qd1Af/6f/k8Z4GUI4mthvBam3yt9yeaZ4hyAivGHmxIbgqR6GEOZdzQipXO9g8GvunBzy+H9URi+cZJM2TmhkRl9O1KP7BmvNMboAae1wQJHeQQyVyuTScOT23MEsl1Nn9JTYvGbBx1G2BxseoveYUMR+WASvXZLrzKMHsaPgUCGIBHgSze+YZH+5An1N1K8GHx4Uv1QNyyca/FQr+I+1JPsB47nvAuM7V9hDJLIbAjyjUt4ZT2RHU7VZQM3b6kjupgKtD4JgAf/qZn1pkCNJyfXytborkfaA9xUOxQmxyok+ECh2fVVkkWzeGbDBVMULG1OgCK2UKdumcHf8lGOmmhcExPNAwXkBF3BjDfILae+yytS/SGraOa0+FBgTtl3Bbk/K4H2B94fZItHGg6jDzLq3MfNJl+ZsyGIuVHxGoOYaOHG+ONTmbLdAHPr1Mw+eQ4XfNLT3kcKIXhc+K4reIGb5lzapqzjkk5/ggJxAkZ0fJuYDQ7egZFcXA+35V6USN0pEw9e2tVTNkA5syKfdDcnwHGJJ2fopd8AST5d+S+jwBW/Qmdxllw8wARHGyRt+HtlI7le65KR54XCPND0Z+K1I+j/42Mn+gKuH15qrYqRKEb1GJCWbtTwjcEna9jsBtKol9yxMarr2WAUnRttkDVVjEVZ61IBRjlaLLwqsri3JHzZYNAGadyBM9Ahs3SgBISMoqlJDB7x9sb4ulqQ+GJ+wvjZ/aN8GNTdmFpHmo6+bKuB7RrgqyDi3/DgEl2q147vsTZl949+GILYvY7xJ6+GfeNzLjEM8eUwjkLAAMRNvF8NE2HP5+Lar/5OnSut9C5dZyyS7F1B8qkp9baDdaptJZSaT4rwLJz4rbNiyMRZhIiPs18RlnZyBkFX07O/5wuFZokfCZYcBAgXh/jHceV+p7zDGm5pwaseAUqdLJpBQa7Opct57lJxPkgJYbzrcaF3TmviR7m0R+791AmDP4m5H8x9XfnmPuiR+tuv4aTHD7Z+jQLOaVJci0T5NQzergwvxXfcc7ofK37O03V0/6XjXeWbrnsMyqiEQJZyMUYuVwfDBtmCxl7tSO/PQEppp7Q82M7zbmU2c/oBQ2Sc8zRjfaWJYFRPEa/6UMl21OAXQ9OC+x5PVN5pknijyeCVbjSYUPm91GTJIXtPdMgeB5U+02HRHLjHk2leDWNXkJ9WawvuK02+3KBgDPJrYtzgS837wGimFC+qWITxU9w/4VhuUUgnhiBVcdUeoQWVCoiW6iyvUljtqHFFnZYrggAe4Yw7J6Y8aPXvCCvkSRzELPyTl7ios/jgj9UCisuLESbQTKJkzSSnCZRXDBTXOtO7gDgYmh+vhd3FCMTrX5oo2QHETckj/b7VZMq71ff15QXOBroj3/lEiBsTDu5jMSAR+IthcJX9AISonX4wzGLZjI9EvIAUHDnSHvhi9qgr+XFGiO/4cRnqAhCi3N0FdtobBWPxMU6U5U5zuTzAoFK2nXyTa3zA8mh3u/pE9nBSP18/hgTSpDdTQvYfA8/tn/b1ehWyG12PqfOuczNn/ypT/+K//N+3/uyv/lE3LTUE6YBTGd455JTPyPMaww/sBNLaHT2Lvs3YQTj6Wa+y4GYnEIag7AhiUY3Bnz+efPOHJsv/IWirjpFcoMfgckgDMBCue9jRyRnvyJn43p47YuYvjX5lCFVSiaMRxIXhcKOfGUz4xx/f6h4APScdY1+PNXTDT74cbsrrYbph4XUGGYJ8syJDOzuBeJrNjQtnWbBTCGP8Ax8qzVdupFvZwSldzU3Mzhd617zhwY9ceDzCxZ9EpR86kDY6unFqC35q7hybcSXx/eocXFLcnnQl7DXEudDJ5/KC7haEgffRcz7w1HOMO9qaOKSaX7RarpcAFGf6RTCAHB/kkosyFZqyfD6Q2huYb+A9FzM1sxOIXWGCe16+6vdrYX4NTIIgzu6vr/0qpeZD1inUAV8cEuZGGOd6JHClTlvtjNtL5OtMuoAVWk1fPuCNCNWty5o/gB0etpB3MYfEDlDSWfRAfk/ojKWdPecsi8ZrRH6DIDk8F+RnY8RKqDREQf+MC6NeYHh6GTnlKFFrVrUx9r23EgprT39FkZ1AWovma2EYgnKkwT//8xOtZZ9pp7t2scsQ9IazgfSwEx3KeULoQnake1e6mMAukh3q3KTyMROtsgQ0nP5F5/Y6kdGf/pIawBtpcJiRztrbAEVdP5IM61g64CsdHJzKAmaRlCxw0RqqDrg4G0RCgT5z9JuEE0cXoS+VzcbR6E4DlAd9SphjDb4SXvq/ilMcQ9Ad6VEfVaBXb9GhPltNr4z5GAPvAtIXGKVzH2hn+2PFOVTaZw2xdmWHpcYaNFkXex2rslw3+CFM3eyQqX4Kk+7dQQTE34GjqHhebiVYIwsv+V23oQl2JJpcI91F4iDevPAFPbey8Y4SE1rFCgfsuPqBHLFJ/lnezjkUz8u/RPTAM3ayBLEKWjGLdtJ2lHOKxdvrcxFfCG2ak3RFiB9pqpP40IhKWzrdGB5rtK/vC6euPFB//KkZgqIYqfThgNXt4cJOOn6B41+id4bi6MfA22ns4WMgUBSDXI08rb6HT/nSUHPHOZXFKU5iB43K6XKeA++UyjG8gSsvZBgQJbeh75SNttKUsieGklOtNLbkkjX6TmQPL7oTANmL4MjFUMuwhE0tyFNAyxkKJx4q7JIhCCqZ2GII4kbhJYYg3pfW5MjrYU/8CWNNpk916J5eEXuu18KeyRD0TDcnGINe6hwhJteX2kXEZJyn1CzIVCaGBr8SJuMPf9yU8EUxVtMyCLnR6R+CspZjQV9+WoHW1JOCxwZ0lD9zrNstu3IQWRQlU9lyJSCA5zh8J4qOAwchDCT0G2RpY4k5Ez6yNy5MorDyg9nchOTmwwYZkWMHEKz6S2HcVIhfH0LKYc+aDL0bSEYgXv/iMOhHD3UTwkHRbK99dNc3JXd1KDRPX/xa2BiAcmYF9ddELR41p8ol7KD5Te38upqDKFomdHCnXgrHCAQwCaRnwVJQ4OQJDqq6jhYjHb8u5ZZeoIKN0A88cZ6sQjmgyHenloXSlt5iBDK0SfWb/tn/OBLYm7Phc8rIvml7+OfgQUc0djXn8ND6rRiC/sNf/1+3/vSv/ru+0vjcn5DPa2HaFfRaela6ll2brN/Rk9qMab2J4HgtzAJkINvYLp3rXUHVt7qZ0QDmlodG8wILf+SL13m6sPg0bFw1o2O+CSrmllJFI6SVE71MJJcQC5EJZ+SbdXEEKjcJOGqWuZCYwtYnrYME0dfDRhf/+IOMQ8qLwfzObW5WZEz/mh1BX+u1Be3+kdEHg4/PBOJmRTAMQTzZfiQ4B/L7k8m8woshSLo7fIkNdPvwBzfmR1dg3nXZNFXAfBtHYdcFoGhRnYETYr6Ji8+V3973B8Fe01ckYtpRkmQiAV9HD7ifKJTGlufImZCvg9d+Ql3oQYBdRfxVH6W5gIHtFQKOMHeYC+hlY0T0KM9ltjy3dQw+kMAgACcYSN+p/cHdDUE2fGIgElZ3++TVMMUxBO2vhtkQhCGVGRbekyfhVaGjDUGAP2OfXZRWuZhQJHWGpOhUl7pccmt946IOJIomc3wHeznhZ6WD/jPcsHdC84SMWIKrhXewGLRJXP2m9RCcPM6HrBzm6kD8uWKoOVxwOCjfu9nVH7ybXcYdDn3GEMTOH85Xey6diQHod7/7/tb3OtvSh0UL5y07grTO9cNL+o3I5+uL9J3sU4lRKCPeB0arHLiIHoWH9BFWYO73rGntUhM0Wdc0+MEZlHqU27D8huuPNIKhiOP4W5hcaePmks8YINesv9YYMlQpdDbS9DOX7sMKO07N3mmspAZebiuMDuUQfnam43N4tM8H0jqV12qrX1m3cswBO4Lu6YxLDOq8bssrYpy9Rh0wBEE3xqBUj3gF4PqlUq7bsSMIvoOfayJcyW5k6qK8yLs74YObrMEdveJMTSX/EYZT/tLqhOJaVkd94aHeWP2QPGIH/fNQanIKJd9e3jlOOTzN1VhzE099mxJf6SKImOkOdnt4QMvbyBGsa9bGS6/6f6UT0M/eAJGhu6L9JEau6r9ElcjYd/+V/42IP9Y8/Wu4P+iOoFaITktlrSwGuIeLh188/N2VBrA9vOOcp30o3jmNPV4a8feUhsPnKd4p78XEvxmvXTCyOpXF1lM3guWLDirhUcKRqiAgpw30Emc75ZA5xWrMikORIx6ija+CS7D+SjgCJO18obTT5Cho3EGVOtpdoMeAos7kwoiyRCA4kwWTKYPtrXy+SIMhiAmSpyoctsduICZUn1mhXUHPCMsY9OKZdgXxtFoTLofv+esLys/nN/10hYnSNwzZBeTdQDIEeZEluCdQ+BFf5kHl057wC8tm26yLb9UvBiQmEOVVPV1nTVDGJS+4ui6DhuOCOEEX/oOkFASVH22WcJ7WJh6ISTgfl0ycMbIoTFQ3In4KPcYZG4FUbW+t1Y4gbiS8LVa7gO7oJuOOzwbSa198EUzvUz/QltoHD/VZY+0C4vOb7Ay676fYmmx9OLR2AvHEUpOoSEh2yGKMP4pHeq3J0S/WNmDheIy4jqoDefRjqidgg5fpRDakISL3FVc+l8BSDmHKrTO5FU+Ma8Zm+uwBTSh5J2yC0y+HqGvSmUSw8DyJeMrq3Du5Lflz8BdKoM2LfBu+iWTx6l+H2/T6xVMZq5gJM1bdH+X/VgxBf/5//OOtf/eX/+3WdzrP4rl0LYebclPz+k3OZNNXjv0aBPqVXUHclGTUa6XtQTE6VzoSwzufxs7gyYj1DgrlQNa9Ekk8jUE4bh/lB4ymq44sHn50x2lK0s1Zgxd90+QiTlQDZVCtJl4usrMGZuEQvULlZYRX2Df+spD94LjMYtKZdzCmS5eyG4in2BxoakPQHYztfOFGuzAfPPChpn5VTMYgXicTuj4jz+5N6V3dsUjlhifreOp3LiHF4Vdw86woN1FguS7UR6lWuZP1WJO4kkqNP5ip4ooQiDvoKU6WoTfJJ55YMA5fxYSbjqVTJFCOspOm+DFRnqCv4hwQVXz9FgUFFr1DGGqulVMIC3uKozwFAS+0CQiXeYQfDp+29lNiYBOnfjEEYSBiTTMGIRkLsmsIo6GKYP7kT+1Kcbwq5i+G2WiqtnZ7YwhSqsredwOBv9zUAVqWLT4F726r59HeB8LCXoEjjVDpnZINstc+IMEm/u6QWeMkKmKcM8RJMuYebtb652mLdhHkwyPVLa/1NxSwzJfpCcEtUMTyrERkap6NqCBR8DZ5woPP9VG/0nLVa87X3unDA0zOBsrxBTy4/F5fYLQh6Cmvhr3WOUI6Q4gvhXFItOj6FTPReCcDEHQxBOH/yC4gjx0eVOonXJ8bJB84JpS0v+SNvrUxXhkXn7QDlcDJ3+CBHVfX8YiuUHM7O9ABQIqgSRbJAx4kcy+e5SM3Q+SPwTS6Ezj6Exx91IQ86FNzrLYBpl1A1MBDQf5tGdZvax36tXSpXw9jnYoxSMZ2DD4+a22MPw/0QPOxfnfv6MBoGY00FdkQhHEdnpnPbQxSiamHJAVLm1vGH2Xw/A+meYTLQd7zVA6i4aAy2RBEgUsO5I0Dx2Gl0788RlcqNDaCWwwokiqlHQto+kR4gD7p5zjgnTtwd96SXgqrxifZ2gNPgBcjWx1HFh5YLvHgbolKoKJdIgceWfF31V5c+rPd4CGAabp0OVeUceTeaNRD35HIWDvSSePH64Wf3I6gSOr0uhT8gM/jFdZprn8Jsbb8JV7c6pcSPiKsPe46kuEPxXY6LCc+7H8wp/TgzR0xEVKE+JA01pG+ZXpPkPwecPLd7i5zKLV8Ie3lnPaXcAEqE1JyBtthESctrwqwxZYJVcYgDEF6ROInKzLyPOFmRDclz5/pVTFeC9PrYT6ET2kv/YpYdgX5vWudF0R+nlpnEvxKvrT+vBLmXUGaNLMqnpsUlYsRKgu2UQCWjfijfhaCVB6LOP28OAPmH+Xob7SR62V5HXJJ/QcohPBFOrLg2uW1lyaKEQduysqAEiI/ldINiCZUcSkNlomUVxRAwAjkryJwwyAS/lS8+OUJs18JwxCkV704G8ivKcgI9OhRJtBHNgTFGHSf97A1Yd7R2UDciNgQJDpMnK6/ygrncKmfZtDdFzNixwzD9PxSIyNObsvVshXKiUtfgWZdqZw8YRECcOO5vGCvPrhgWTIZkwzLHSUEvMcJT1yJV9g8UK+mLfqfAz9bAift9LOpfHhGlbeKnPDqPorrfk19/8PJ/WvF/DPtBvqT//APt77X2Wy8ipszLTAEafeljO4Y6FE73JBwA5Sn1jyJ1oCwgEZQGjDe9YDgnEaeyBjtxR/u8B2NnjNcF8bYWtEl/RhsxwCMHp10ZTpSCgupxpZ+KEBcOI8uyau4Ar45MSy8ZiWpSpt3dDEVki9dnFd0MQLkqfZXumHhYH2+WoMhyDuCxvjjw011A+NPHvM5eelhnmhzXtA94dsARP7Rv+hyup6N8MProY/gHf7yC//hHdEdXVZyOTIpjyumfNS5uRzNZapcCBgXsJp82ReNK3kK2OgXBBHAR7yhILuVLPPiMVeTKenUo3VMVWf+hvDuTLa0SdjDOyLlTNmrXPq/oMzDSiJ97f7xjiAezHBzn75AGg+ZeFhjJ8bgMW0pXwYgPo391bwaxkOq/Hh4Jc78g7+tlXZ2w55Jb8GUtdWrcpmEi96IMbQUoZirNJMVvupWWLDgb4mDtHCa6SP4lOWSFCif9U3+JBKcticZPG7sQ6XIG+/D9MpTeQqFfseOINqXNSufjueVL4xBPJx8xsNJrVc5I+h7dgR9px1BMq5jWH8rC3p2A2H8oa/Qj8WNjD20QV4Fww8MTikvRqBwSry7MKMB2vdjRPLIPxM66+DW9LQ/CKp/kYwUEm1tLUqTAq5fXWFB4AoFnJmLb91YWHWnJEr/8p05uAlj2jJc+akFP9qoBqFvZAi6gyGIHeraZZmPnWAM0mu37AoaQzu7Lf3a7WPttJQhyA80yaM1LWewaSnr8cczCofFHnWJISh1cDWViJwIt96sP41BQylEjfDJa5kKGa6td53XlIx1foFSSttSWtCAmhuaDZNEPmRzvTuwCR2x63NA7SrFq5CdwofSJo8p7eTO5/adcPHPYDfV4ybSe9k0nZtPHOGzHsHR9wqH290QxAAk3YdFa67+NdwffEdQlYIH4XlHPIsXB/86B72fgncdncJLr/HL/od1EapzA+uL9PVlppzQ0fB0XZsNmZDOsOUa5XDI4oYBCV8rtwLXOJc+oo8aSUkDUq5TQq3vkX4D4TMk8ro8ZXF7S3BGsQCDXGWEcYRxHfwJjKDzVCNSqUJrUfgskcDB9+c3FWBifaM7DYw+L/RawnO+vuDXwvRqGK+LPdVOIT1d4XWF5891k6LwSz21xjDE+UI2LumJyi1+elLyYw1BfW1hblrg2TyoTPL4aR+zrGpiJT9CwOtOIhbm3PRgFLG4hR7jTGgpFYR4yCD/io+iUaEzvQTH04YmD3gS7kwjQ0FciIb/bN1SSIuPPI0mLQtOAfOEQ5Mbr2xByp+KV9zGHN1U3NUNyZ27X+ZcIO0C4sbj4WN2AzFxcuDeN94ZxATbmxG20/rVMgnAB1HL/3La1WJwTTMOvvATHKpEhcPzSCH1hDEWbkIAAEAASURBVC8A7ijCUb09flzTY8xEdqQnNylQw+UJiwKJDmojRllj0q9EKOmEhx3VzIRWwDsmZSZeNCZ74+1o8HQWDxefr79IAns7QQgZn8N+bgGXaLVtodlwfYEY81YZP7fMfyX5/vQ//vdb//uf/51uXl5It2II0kHRvPKgm5cXesrN15D6GkMMQVmy00DsuGT5zrhmUGB0z+CQwBW3WlVqFl4Z0WsRdjpKRaeNfT64Gq+PYKNNCRFeTsHG6hv3iCxUAh7xTkOfTd7RdXSK6LfoYx1wIRAdRBOHfswb1Aw9TBo6E+OPz1jTTQuvMvhQU+lZnl771TAOj36kHUHaCYQhiB1D7Mbk9d1vbvOVnNktInEi1a/UCWdWiliHZxWqULSkAnbVXtFNyEd/rTd1sw4GtbJrYvKv66r/yAMiG8x4ZG1zrYyBXaR6Df4V3L2soY+39LD48B+iBy58fg7Pxa9oGDKXVcgEBv+c/6M6CbnKCv6otqWJaW/KjlFIIQHZEeQDo4Wcs4J4RR1jATcVrG5wmj/VN5jrKZrXJ79Wu36lQ6FqAHK6DKipz2m9lqAPBkNWV0C0JnzFrcpGLjuYpMYJbmHyvm99r2IozG0BIWd3cb44OZcUEyiUf7mDlsvTpeEz9lchrleRBrrkQyJrMaeXApFx7Rut3wbnJvGtFCBGnLesVXU2EK/R+pPxHFugteoLrUU5xoCD93/35Ptb38l/xY6gMQQpmx94ojpi9ImPnvSOH/GGcchp4uEdO4TEg9kmTVoho748j+6FT/Wv1EhpTgafIJGpM2GnOYOXZcYZ2JZk/TNZncd0NBB3nGkVEWM8QFMVM7OGCFdAJ+ArrXHBksYDTchj4krtoOFltsYdB/Czs9KGIBlP70qP3tHHTrwriI+e3JX+5IzLZQjSq2EyuLMzk3UsHz25gyHIa2Q92MRnqoJVOerkr4gBgSfEOantChivXLWph8PFHyRqA890rO7og1LyEYpLXnqjkM0EkLRQ0hprjuv8YpNuQieIZuUEcjkClZ3SgXUZ2vTQ53oznkU2mRAVfft9DoqXsJx/Kw4cetRy0Nevc4VRBx8+ykt86WgyKlJfkeh2ga3pJ5MNQZ/aGUFLaFugE2lB5/F9giBtjzfPuf/7wdu7Cy1+Hoerwpo+PcPwhs+5/5A4dJt/D5/nHQVcVCcTUR797+CbqOyIHRBrchtCQ/WEgZ3+ScI1EWgvNwPB5Xj0HNSMpktUWHI476DgRdVb3SsWwoFnAJPOBMikyKHR2j3rCZMttq84qFQTKE9Xnup1ML5eww4hDENPlfbS23A5J2h2BnFekGi8QxOMIShancNL+aqNZgAmUWv68MRrDhxY3fe+4R+l4L6rCL53BCkPCzWq0Pf8GQP+U4bWSVldS+IBHi3kxYcnciQGofHFU0KCOSOZudGAhpQVkycTEULCb1yLTCYodgNxw+qdQaoiYd1/aOLUkxBeCdNrB/fucUi03qHWuRR8Mv6RDEGPdHPyQOcEcXD0Yz6/qRsQ3qVG6ZH3a/HnCVOTsfkzP56+4N6yyfvcroogSA63KmG8wHQVPTuqniWGMA/clZiAUmgjXY1yjgfSOSz0fZ2ZYEo8Q11QU6BuoVR44/B5OLqNh8CAWp0D43PoF0vgvEnPCdIg78M5z3NTXLQWuYY3n8VjFoY3EfnXn8ZrYf8bhiC/GqaDomWE50n3W70T9vJlDuTPk7IvvSPoR7/GoMawTh29ymhB+RimUaVoXmWIjCNna0xpChpSbry2Qf0kHtdonSNDsl1zPSU9RAaoGHwdsSR3d2NKiOZJCmG4zQ29da96zBfajenl4xgGMAphMOfmHt3JjqBvOCNINzHcsGB859ygezpA+qEMQfe1I+ihjEAYgvhaI0Yhnlqjr2NEYs4Kn9bv5jnSCe+EDz7Ni0C5eSIfWLn65sSVmVqbFgAV8IEu1I6xQjzc6JqAihNUCpK0BftA+skQfk+ymLZnAdOGbkCCWRmrNJVLmRRv5zjwieM5vMGIm9CGVNrO5sToe8MlYcrTP/MxZdsoJN87gXQWEPC3PjSauRpDQR7WQIlSMPh4PSH+CPPVMHY41AAUg9CBE1wzc3qBoB0yaPjcn3otoWzyGVRn5aJfyCTCNbk32QArLYFXigLJO7CVMIUMreIc0Kshst6Ed0JaiJdw3wujvUDSr7P8aZ6jFPMzUeOo/rQ7DyrZuY5hh8/GYwh6pYeRHF3gsyz1gJLDor/Xwfvf64th3+nLYexaJx8/DEEY1lnGEY7BR1oG449/rKQIexUYQWk9a50pRjhOue2e0cY47lgWj9NOyxjadhNNjxTHFXLddNE/S2bcQt3CwCbLCQ4w1YLs9pFpnAJUTo50MyuGI+8jzbtshMca1qcjKQkzVkxZ8rWGY03rB5rSjTUE3ebLYdoJ5F8NQTragNfBHnEQ/+P7Pu8SIzyvlN3WQ1A+lOKjEqQMvRMPcVGvePbhF9f60t+jYwQUj8fufzEKnq8JWT6CJG+MvUmm1uCn9oFJFojhIBDwul6bsDBK5wCc5mms/oF3OQSHqVXTT2P0m9Sj6Xv9dxglllpKb19dWNdXfKFswj1gl0IUpWIoA9m35KLuZRNu3GHXSLVShHzOrbBvswRRiydNsNvS148+1R1BFRb+UvIDPI+vAbFn2nDP008G0a+KtzOkAebBu8Omg6SlTxNW7MbEhXU1kI6e7kf4ejrBLL5Qiy6/uQrayzmBDaI7vMPtwJCLoqGDs/DDbxmTLWQhiNvSi5eEyGuVASH9aF8Wy6udBQYHZ48IgcLJ5qj4IlxkkPSP8Qd9QBpLbKzEPHHGMPNaCygfVipDz0s9lbYhiEnWr4NhCOILYhiJZBjirCAMQno/O+9ha6K1otF08mNuUHxGEAdGs7L2+9QYg1K2J3SN/BqChBAFLX79ZSv5WPf9br9hYj/WEcvC8kA+5OOyOccLk89NEZX3n2hFYDUCwRuTZRhDDZkgE6oI+ckiNx2Osw2diZa0Y0cQEydfm+HGwYdEjyHIh+vd044gGX74Wg0T5bfsCJJB6P4DDo7+ShOonkjrJgQRcfPyjQKEaXM4wzF5mSeHmajFrdhlzOVGygigOu6AQOC4ZvWdYKxBIR9OCJsrNZdrCW+JNwRLxSMihcNAHIkNOzjY4OGUZh1CkHBArgPJu2uWHfY5/Asl0Lap4HdyHxsGbZXXIhte41gJvxlD0F/811t//Gd/a0MQOy/5BDJGoDzxfiv9mPHg1xS4WUEr+Ek12oGdl9US6FaFPTgyJ1nXI2u1H7JGW6IPAERzkpi03TfQF3DrjrB1ZcGmN8UKZqxBPcWbNHAOUqaSeDnzbZfgcJyblfA6etmGesKevYKjK1+q4cthNeb4c/K8DiZjD0+yfaApXwxjV5C+IubPIPOKmM5uu4sRSK/xsjPTB52KIXhahiCFK2X0PgrZEhSL5k0d1zpZfJDPetlh0TEOEf7zpxjR5agpkKN1Ej9axghXLsFXPpVveiFkHhzkQoJ8+Frja6dUIHUefEB7DwFeYwxZPfeaIHSPmnjHrtINcV80diALbQVIXI6iF58E6bwBuTyffyIE7/bxfKzaqy36ulCNPz/oDj+fGddcrfxIHINPP5ZAmNfCvl47gphv+WlHkBo5O4fCo6smIuFkmFFS1x6WErIXovsBDNtRHoGDTnghbzC4Jkw9HBN68CkRvu0EKyl8L2XkF0Ze1p24jbTTG3c+pQ/atXgmcobXPE4rwR1xgxGkCvC0gV0wsC/UpqlrCmkblxyIpYFMuSF0Zl1pd3ZJYsh5I6XIp+DZ7cPuSQxBz/RxEx5Y8nrtE71m+zsZgvh8vPWpzgbiS2PoUpZv9kXahiD5rH9/0MdOomOF5/Lghdrzk7kEVrxvhpYufMc5wmkj+kB+ZKUVPVYQkP+h4QTFEx7PcUPAI4BfXMuEiBm66qOf7JRuYYMHx4nXTyPxQFNp/JSOjst6MmtbdvDcttEU4+kXWqdqN5CMQbd9VhBHHrCrUruAOHhfZwU9lJGdszDRo7xSxqu26FYMQbe1dsefJbzrZZmUV3Fg0aiiHl+Fu8+kDtZBIwvLUmD7zatxDM1LDgo4UmnBI26JDNwoFy/J1/wXURZwzRULcn0gNQtdsMpZagFv5TQ0Ur/AjpoW78Bd+QU6eD/qWo6cY7Ihf4L2Cyvi7oOn9NIlCfTJ4oDDG4Jvm4ThnqZEm3tEBDrNzRij7XdDEPdDjz9FQxAd9+joCB1pxe3hwsA9z9M0/D1tDxensPqF7z5puA8p6xTP2eZCi4ZOq0QjO+zG3nHT9AfeT63jTusgvtex4fTFM/UgYHMlfaeXWjTdKRciy8DinkybtvYHbfJSR6M4ossJLYDjhAcv/LgwSbqfOPNkciHGkNJWuoj3tSGy+UmEcDzYlObJSz45gOGYJkzN6dnFA1nOC3qtiZZ3rtlim4Oj2W4r4w+7gjD+8LoYP3YI2UDEq2GKs/1Wh/F5YvUT61NDEJYNcap6wXX4siGIRVsXbuZu6iDewPUTu5k5vJgTHU+y4hXZoDKojKc++XHKO2HqhfO2fNcfGeTnhaGJwdtIxxmQu1URqwMKUlvEENSDojGhsTjmK2H+ZLxIsqNHc1+envig6HwlzOcDyRD07cMHt779IwxB92491u++jEAP+Jz8/Wy/9SdttaL7hlWdiHcygVsq5EWTyiSu+VQBDGVdBIp/YMO/+76ikcxQEgIooWCKjo+IFhwMYEjE0wY0TZuUq45yi2I8UCaLCTXLTkOZzKrTwlX5gkbTFu2iCB/YZ/crSoDGR8b4l1zl/0vxlN8kRI/2dpvXV7m/FUPQn/zFP9z64z/dDEE8wdb2TJ5gv9ZOyxqCGI8+3HQZgaQLCc8uIBoNHOYAy1J+bwloRmQtO5JcGpB4XcLo0zZuU3a/adUjpBWm0BE0fEUVaDhzwF5KSoZzu6X3hONKUAN+pMvnJsc3Otnx4XzCo2ymCX/dRjceGIS4geFTxjYE+XUFGd4xAun3QAahGOm1K1Nntd3VE+y7fuWBVxrYwRrdik7WlGP+KYt64EdShOCndRg86svcJVw70Wr9B3CkDUrhqecJ8L0Rr9kGy+s48QNLyGRYu56GEeBub5MN3URIVk2ppnyvSYriuql+4DEhqqYOr3QCpA2g9AasYpdzcIsvw5PyZgcQxad8jAKeoxW3AUjzcz8jn0+MZ5dQOJIu0UQZQ1DWFMeOoInbCKSw2tt/rldYK0uwjqvxwvwFJKCqqHSXZ8QgN0/RnL8X8C3UpFquZDOt5Gd5Ql+0k+cyypCApPCL4cFY116MV/xrsMDBuR6b77xbuas9d0TnPC4kwddJXrdbcNSCLkhN6HoBJdwI+Rj5doogb84G4rWwt7w6qx1BL17JcI4hiCMMnujoAh9nIEOQvhb2nYxAT/QlRnZYYjxivWkjkMpgWUfYhh/YEKM8xHQ6cP0wDqFfw882+pew0b/AR0FQUzUQVVBg5aPNMiYC8y73CtCJgy4Y0eT1NUFgHlvynA5OSlm+BVf4nuZKDD46FRxVDvwMaBnnFBeMV8Mgj97ilVx/Tl5K1buCWNuyY10PdX0Yvww9t6Uv78koZF2qXZcYgh5pXcsOyzvaNcQrYRjl72idi0Gdcy85xw097SWuKpOywo6rOfXDSy3US8Sfx70gfhiLnJTOJSHkFtnV2EvyTQ7aoX8Z6yj/cnqh8I/0zh35zeN5woV4+cBPuYE0f9On1kjB9aanneYBo5DWT1RW30jhpdO+5OSraItT8OAFPML4lHLuPCxIIP0cr3BnYi6BUyRHm+pqJjLHUCvGenX958OiR9IIhI4eYQ1wvHP4ebzY18GbXv/j4mVwQpt2pjPtjr5BR6j7uGXTaU9ltsp3b1bJA8A7uCg3Z/5CWIEtjygITMoq40NonhVBFNb4md4MBguvpSFI8Pjpcm4ISsqhnPJsNe2A+iC3KTgvk11kRBrzwmtNshiA3uhznHwe/jmfiWcHEO9gcyAfZ1jox66gpxiD/G42u4Re+eYlT1z09ETGID9e0/SSw6JlGKJMc85mVL3m4G2+mtRZHKjcqIVUjLZjlshTOoUVZzGH73pTj8qndRL/1E1YuVpDp86r0krlj8Zn0gAbfOdRZnwUERL0eMMgxETJzYdWD9kdxPMixqSkqwy6b9DTkhwanafSmvQ4b4IzKnglQTt+Hurzh99+KyOQfn+kH5/aZDfQPZ8fxKsMPCnRZKtZkonS/FCGSnL7w9LUF47hPItbhYW0WlYzffLCYeujAND8Ezlx4MU1Z/I6fxNJarjoZwDnPsOb9cvKsZcTckeZMHiW/ci3owmK3D+7X0ECbnTRvdLWF8q6trHOcNtW5zQVX6AJW70N8LdiCPq3/+HvvSOI8yyeasclh0Tz6kMMQXoCjvpBPtJZXkBxcyIN8IN2Xe6HlzJ+GP350QbCr+wVAz4qUSHCEfSImwwDcfDsshFS3rj6lFQnyo5kLBs6iXjRXCqxNzaUqIRBEYb3O8mHK3Fso4/C2+6f3AwgFBnjhUOcMqs/v5EhyAYhGYLu+ok1rzLkSzYxBEn3sjMI3ayn13xN7B6HovK0+z4HTevBAIYc0USfH4Yg+Ix0M0eIRfhU45R/WobIV8oPbF9jkJSaRgqJf4SrB81GJyI1ACle6+BbTGa37FEH45v5yUkVXU9wQzGekKbCtDbygiCUuLquwHDpFAn3upchmClPNuhn/k3ZNvyQTwm0dwxBwXmreRkYX5Syz1qCuZobSGXJ7ln6JT8MPzEUYgzyTiBNuMylvL5+Ygga3qfKlH44EfZr4wck7U3dnU8X/gnDxO4GFrrtSQKqDkYln/4I40PDYeCEz+gJBOIHOdDOsl/Md45n8pcyniNu1EBvFqMZQPsF6ehTWyaCRnbHcoJ1lqK0+WsZyGlndkxyLuVLjingi2EcWaAv3dp//lx6FEPQcxmCnnjHEGtMDEg/vGXtqXVvnu8pLDmLH3Qs5wGxKx7jnP5VHvq2DIkBNyZ6FwZpH3hU2OvIwRNOqqd2y4AwDlm9pgUXUv4jiYQh48AW3kiDUp1JMM6SEf8tUb4RE4+gCecXwzooykfFyOef4up7zCawTK3Iwyu5/nIta1Idd+AdQQrc4Uw1rW0x9tzTg0w+IX9X69vHetDJTve7OuqAM9nQozwEvS2jEXkV9W5L1ss1bnoNC3tysG4RTzhg9RKPY/oNv9QZfHKEDmMk44Q3D5DtJTfFrKTzOAnkvARfmbZAS7kO3/UZ/OI0z0ZGrUFquPaYmMTiNm/zFJP08zRwjnyDcYZ0Lp/K3F2hhWx+6Zm2ItfhUQw/pvaG8YngmxuHacfArPMGBsTd0n7bW6+Gqf89+pTOCLJMkMCZSydGITEx72IP4hroStvDpDa+k4RG4Xt4xyF8U9qefhMeaTjKO8KGGN4LaK65LgfeZVk49wm9X4a3y8L8uA0ko03UBFvKHi7/ZX6XRfHDL9djiLm+QthxFq09UB4GkXwFkdnmEY0Ocau/gzPicVmAEVv5jB1dD47zkei2CiVTFGzNB+CJyBs9BnnJ02j9XskQxFkVL3hFTLuBeA3sGa+IveQz8nlFjJ1B/aIY72Lz1OXdW00hbC/68WvJmFkP5cwrDJRtRjwBY19hQvaB0aCH2fgjCOTN4u1HFmqZoVxReA0+ahSaXCOVtoKbuY1c31jCUzx48cNVRJTJBnlTCJOmymCRxs0I/jsdsOca6DbMRiBuQrhpyLvUvOZ1WzuCeBXhgZ42c7PxUK8gPMYQpFfD2A30SJ/ZvK+nJXcxGOnJCk9LmBTZDcRk7KaamjGBuy6WT2rr6U5IWeQCE6/L6mLJDA2xTFIIHnSoMHnsHEla8ZpSFOKTNgRdZvgCKTSg6TaT73KRO0k7HWjZpb0InvB3kAqW4s0OC6mPkz5fPqYEKuSdZtuu/p7WMGm4024Q2H49xxN++k/ykn31J/Wb35Ih6N9oR9ATnWvBq7c9KBpD+RsO4fdNCzcljBfdiNi6I52ITmVHkEciwiW9PwSvPJZ5x5lvATIekTUocvXPw068ckk5ARMmdxRzShntq6Tq1+glYuCOvwxBgOC6bq+B8L3wz41KygImgazXw9AzMQRZf8qgztkUfO3mGxuCMMbr1TAtJDH0PNANC2e18fUwDonmS4739QSb1x14em1DEDuCuKEQU9bL8q1nxX/4ZGbgFhJHnfhVChGvP0IAgpKiNoNtROXsVBbs5E/afh0CO+i68BpIQqDMMzxK2NW3kQbvSFMuK9fJTALO/jEHgeI8pIkoUvGcRamQMBgO+ClGwRMU4NTt8AmbvstImUjbMC1WspaT9D0nB87XwjDK/FBDkNIc1929qyNhx7CnvideeLj0ldrYr/2NMYgw5w/GIJS2N6MbT2Ucmq6XKwoPIKWKrqfzUJbBy3e+gSGWrkvBCs0SwWecuBRd5RRp1pOEAa60YF+5km62r6R8OMBlXEekDDR9CiRaUAOuay6ptxAWjtkJMcRq2Q5tdnq91i6gdzWSa136ooYgG4HYCcSrYS/0wPKFvhqGYV2vhnldKo3BbiIbd9Rm+KgVFcIaGN3KT11Iz/sOQxB9WyBddz3L6KXf48/PPOqyNXrWNGHeayKlecc+bev2BV+FOw9h/0cCwPh3ukGB63rIymaslYhOOlywrHNH1uSM2qXCk5c0dKyEgIks4xhjPIXLOIQBRz+vb9kR5Ne9OAwafcmO96+1tpXxRzr2EYYgPeBEn5KGvmWN+42MQRiUGGPo5oy71DVymDDMjwzkTT01hmfcq9UW1B2nMlSmyrc+pM7dlnslUQ6OtD1s4HbZ0xommXy7I620ireXS285d3v6OUXaIfU+TSltuCYdbVG8llHeeBtC/yIgPPnGbeLGDGnuQUojjHPXkT9Rw4g4++A1bN+Zho7C7ZHOLwR3NziFJ6XabgCeC8q4ox6/iR1BdNZ9EiB+7s5xztM/dvxDy/tQvBP+2kOmmrS5g7p8KL334+0ybIEnXKxIMGdwLOh7Ah0Rg0YJO08uUZ37Ehc071n2i4W1G5QGfST9JPWpRd+jVGkMeAZ48JsrpMmRVwTMqYGoCxx5CBtHcQarw7owEfLUhafRbLmOMejdrec6kO8Vu3/0OtgLdgrxqU7tDOLGxZ+X10TMk5k3eprNZP1Gr4kx2cKlb1jsqyA9dTPnVgSZfD0PiZ8qjfClq/nEUwDWWaxtvMI1f3YkgInhiQrixl+yN6pwXLawJ512zJ9gbgQhOhNSirLyK2HcgLDI/IEvLbwVLucCzSQp368j+CsJ2g6rSZBJ0udR6AkJT6IxADFRPv72ng8rvavdQJpP9S51vspA2Xz5xpOxquClBzxTHU2GeFOzhIVfXCVNt+gNilgEyNUB2iIQqujqldii6gzJJST+TjvuyjCIpRe8xA4eQcrCY9DPvPbHEzDM7Q6iZyAqlHbaET+HP4oE2og3EaM9PgaeaLh56T4a/PRJj2gD1f8ZC13V3MTPv/K0f/vv//7W//Knf6PzLGQI0qsNPOm2/uWGh9dtJZvctLBU1zi23pIFWoagGoeysEKX0zjR7fiOI1/D8fU7i+/iI/2yg+7uUs4p5MDp+NyHc1KtVZRNJUk5GLaUBHFzOLOFwlZU6GBmh/xsGCc8+riGe25a0J88if5G5/34U/Iy8GDs4bD+2xwWLUPQAwxBeortL4cpDd+GIN2w+LUH6W4bBsScDfP0w/IqLjJTVVLx203hn7pXEg6lkpuoRG+LfVgQIi3zQg7LSXAEPnMFWDfksGxL1SxeIGvQ0G4RSCAuPv3JT+ZVWKbUqX/RQN47guOhsDNoXqGhpJQlbUBcF98oTNg3EL05FMyvgql/eJcQuz4mjE9edglY3mpH/jDy8TU4jEHeESQ48S91t5u2E5Zxh8fhhyqUn6ZkfUZsKisvNKiF6OwyEIT8unLRNQSJBWKwL8l3ZD6nc2Am9L70c/zr4pR4zssJrhLN1XsRJ9fgmaYqH/klzesREvgJL2mmboQmkQgufYAzgt5KP7LGZOckZ1OyK4iHkU9k/MmuIH3gRGEOisa4jiHIX17U8o2dP93pQ3k2ALlwXrslPX52nEV/RreKBwu5Ixc/a1s/qDTHXMaIaFzaX/Xxv/CpmuKByQd9LoOuqJEHTCL6BES5+o5UOrvvhLkMHM8CVoAKQy+Cdtw7Lh3XTCE/tXs360p9iVFl2oiDbtXvG+nW2xiBpCMx9tyV7vRh/DK0P3r0UDuC7moHPPpUaRiKeEVMxiAOidaRXH7t1mtc0Tp0amRB9SwuBVpV2t078vFXmDrhkCX+jFtFaIslL5I2NxLZINcHIWvRXY9yYwr5W4cbEZWYml3GKh1mPijS4+quq0+kae0S7COLaZQv5OQuUYLyT1ALF96aoq/DAQ5hpuUNJ3wPYIi7vkK2HhcD4SG5YgCCDPD8PslXw1ACx+QhAclZMSS4rpfwVuIW+H3i7WXt4bDTBk19Vh3pHBd7V3JB533ualkhSodXqSv7KV7pHulFzABQelGacOanlAGazEHrCCl9RlQW6AdZ4+xE9nDJlgf5DDaiRnPmKcUjZcKTL9mAZdgn02QeKh5Ugw9eKWRnzgxYEYI8A9a+Aq/Z1aNZkR8TL+9gv3mlz8rLf+lzgjAE8WM3kLbf6rPHT/3KWA7ue62J95W+duOvM2jAM/GKpFjQzYt+VmcYbDzt7Dc3WWLDmRWBMKJIyrlSUnEzG6iuKH/4VzvQFDH0ULEiEwZBPxxKSISTGvmZAos/AZsrZy9ZMuFINx4/ygj0o7eijyFI1WCS46nJ17r5uMfkqMnvLq99zSTpz8Q/4oBovUf9WD/fiGjrrHYD6QHJvBamhagKZpIsD/BRxU+4P+oRnMFVAnE3oBMJTmWXNwHhNUk5zhxEoLUIWlZnSCtqbHCvJ3hMICAbz7kWjeFq4qqP5AGa0RfW1YDrexX8GfJLJNDGuEn4e9oePimXBlTi++gpvSgEaHf0ebvJb8oQpMOin+jG5fkYgvxVHClNDkSVurEOZVRg2PFrt9adNxuCWAp6OeimoLGQr2CraUinBZKWdK7XudAL9pGn2IVoFJtk/avpgRjfK8xo+eFOiXl8gN/fqSGI2UF5rI+52ZeA1GnYncmuTJ46+5UGvb6APuYT8ZwHxBdvuGl5KD38QOdZ3BeMg029YwidzY4gGYy+0R0LtDBu+AZGOum4ackCm4cAcAd/SrZ/1J/qu/bRyyep6PRiOqMvYJfiAf0JITEDT7KPZB1BEQqHx1M6SVI9nDiIpyhHzIPxiLphFU2LpY6rkkYrbM+jMMXsOoHkrejy2eLwbfwRWm8InKaL1wZUlDA/Hs7YF8gPaugTpIGZdkQSflVHPLBby4Yg9ZUagmwcUqPDYnbZwtypE8nl1vy62pLEq3WH3rlbZBRYYYeCvI9Ji0xIl+jsdN+XvuP+3HD6i3LDZhnfw+eEz9KQ3+rjhAWwTA0nnvqXjNNdVOC0J0ZyXg3jYWUMQdqpLkMQDyQxAvFKGMZ01qTf6bWw75680MPJ7FTPGUEaZyqP7hMjenRajUPeoe5Do+GHft41KXiMdH7ww09h1p76VQt4AaMU0llHZS1FHFxWviQpbwIOAoKcew9p+Q9Y4QTGlwcnGbzykaGTopcSCyQ5AndljJk8xGMQQneiS0WSpw0KzIpc44ADo2MAwucMTBvZeS2MHZTaUXlPP9a39/QZ+YePtCOInZYyrqNHOXz/vvBuz44gG5LQzRp3fOXR+pVFL2WHWcESRgy4jLNoG49nGg436ciscrO8ZdAtrSCeXkPpFPZrxODhJj72Mmmz1IocU79BoJvsPLvbmPLVlk4WpNG+MPQWyXDEFfnSv3F7eKEm6SQtuc85LOLQEwFolM7ivUCXK965B+MPHW3kIEStj46nb+v3yb0alk49gpNHxwVmZXGAr4R2vOtokKl4VwicpX0o3iU6hZVG/DZmUw//FK/d40gnlEFPN4ss0jFOcRrb6a3e5pzQDg38du76zY9vLC4/wTnP6rBHRzeJKWSfyMrJSREF1lfiPvcB7u+ojUIjENJcsvkIcmC5OnkuZRU/agF8+lvKDNzULMbGmQsw2rxVwJ9258mLn0z/qNfEtDNIRp6XMgJxaDTnAz3VNtynNgQ9z24hwdgZxNcc3morridW0YSuJ0vu9PUkOxNm/Zg74KF6niVc4lEG5S8ccwVOnfLzdKBZZKZaAeWsbEY2BwHPeyYOTDmcS7Khb3HzOSQh4HDK5HBo+jlfIsEYVEMQEyVPoHlSohsJ3VDw9JmJ8IG2y/pg0of4+uFjCOJMCp0bJHRPfFi8mQCZGDEMUWh5SHT4gEfxTBpO0eCNHz6DYL0iHLe3K+sspyEIIQMIjSzA8hgzzcD3MbfrH7KHA2dOPhUYfNKG7yQDiHN5Aso/6A01Klx8im+4eTcSZ6DP0V8qgcqapmj4JprFq79wyQxwXNPrFy60FtN2pj8U5l0Z9IdP3LEj6N/w1bAxBHGT0/PT0LncpPhVBsuLMSN96VfCeDVMcS3d4+fGBdkjQ+8GmjBjLXDS9HPzHLJue1X2R/MJcYD2lNGk3CYmktZRMLHozwAFW/BCFvoWgFOoH7/h0jDC3fWj2SnloIe1KxMjgC1l0s2c77IMQTy9Zlem9LF3BHFYtMLsBHqomxUMQehnvtx4X+cHsRPIXw2T3ub8C6nk7ByBJmEpXurnOvqOOLwaRh2JEkH/OTjziCIkkehkhS7fqjQ1uM1F7IOcC0lJFIjcGVP1r9CYAYeuDqIwJntxifpmEUDZEz5ZJXUDAfOjIOObnqOh58SFBGbcopfiXbQvwwbNGooqTyEtDEiOflCYuBjhRx/w6+UwJThhnytCPv15lwA+usQ37mpT+V+qYWMIwsfohyEoa4BLaueER4SQmouHqa8gOERgN3VccQFNY5LtDZlFbkPYgqG5yeycEGW0WOjueffiSgLYHj7JMLTOabiP78R2IifEzpFSmOtIkgKEHSc86GouC89w0etcsHSb2nadn+ZdQVqPvpIhSGtNG4J4KKkvhj2zQSiGoO91ThDHG3BG0Dv3GXUR9Kn0WLWODULSp+wGEop3BsFL1qwxBDGqvX61BYfKujOJX9awjcM/hqGMBUW8pqN6eUiJ7gQXtPgODz5phk5ScMVQ40bmUokhMFzje3hgEbJxIINMwfe4dgVFg8oaLh2KAEi3vvvBa9SvxBe7Lfnd0aIVvcqrYTzsZJ3rV8O0I+jhIx19MF8Nw0jEriAMRewcwgjE1xgx0BOmbuhYaLs8Xak9USAVT/hNPd0fxCe+sxmzeZKR1zvJz2+XiqLLFY5/Ex4Zml5/ETkLNL0+yZd0yFk283iMgCM3sqhc2srkBcoPl5ZKeL8eOPRDei79vbUO5RN8ZSAZmbr598QtTJuYTP0tjaBL4DJ0dnrONyzgEYe3zOvEDXX5hK255RPmow+PNH//Gk7jwCX/GrQv0vw9F3eRh18PSIP9Mup0GmhUUb6f2tUOHRa4ith17oak67IYflK/o6Q9D5MLrkWcZNkRGxbiOc5V5VEMlHcz4hO5KgNjwICSwUANxKEMFJoyUS5EcQ2TDsyToSI8ZcsrYhzOx9NpHSSt18L4XDyGHr4a9pRDozXZPpdB6LlgvDL2klfI5PNUu19rwM/hpvAhJjRhZmIdY5AKplwUQCZg8OFHEPuoDeJccRPKjKB+kwnYE+xCSd3JP5lTtgB+bU1wMPoEkL5HOMYgJqpIz0sBJkavVCQt34Ro+6zSvRvI22VjCGIXEJMjT5v5Ws0DPX3mqfND/R5oVxDvTz/U1lm+pOB3r6c875NS46NswxN1zGRCHJVOa1Nd4jjDJxIPmbSyQlBwpGT86iAw6qB3SlGxVYgSd+QpeYosCfs7jCx73AiLztVUQ6aoPd/KclJSiO9458mf4++XwNVWUJ5rBf5+eqcYF6mfohATWov0GHdEPXaA3NjrAd8n7/Jq2N/6U8foTc4I8o4gdC5nYvihbfQ3pzfc0iHRflKtm5cYu3dDUASGCMGt/s/U1LhozQCq/CNktMy4kwG2Rxqu37FOzrP5SCgH1qWw8hghS92Uvofhnh9zQGap3pZlF5AMQRIOaejmGIJyjgXGdQ6M5vPGnNHGjiAMQewI4nygB/rk8QN0sgz06GoM+LzugO7GoB9DEHMANy7o5UgyqpG6VlLxj246eFPz6FdkGklwPZMSib/cmY3hqazdSFVIg7e3UbMsEhqMpDN3ADNcnYcx2rox96Z6wsx/ycQXYnFBgIZpblihW/rxO1+xDmG+9urAfBCf9YHana7xVpe8zjNpWkSwfiAPOSnfB8maFwyG3Nhq1tWEa2OQ6sBcztzHT8kVj/ND6ZxHIMiB2kB/d3t8Dwf/wCQ7P2dvAZOcV5EScfpZGYNmj6Tz5I5xEEgzeSKXnBIXjgIrvOGerj+3hA8MpnoHF7SbJOjcyCWyGf01goo2SBqGnJsNQXooyflAMgTxmth33z299T2fj9crZHk1jPZid7pqh9FHtaT0GtJjCBJchdJ3YjCCPcHUiBiM3NAWLHwS56gDKQi7pGeHUFA71msIchZddkNQOo/G2XQUfPhKnFBowelpONKJqMhfGRaP+k548kbIwgXuAZLdlI6rQborKNKRIUi7gBgL6FYezNzmlVvpR14Py1cW2RHEjkt2BGEIeuCvMPq1Me+OR7dyJhdGIHRz1r2oDIyxMTi5WojGckGaI4rhn7sC8cs/nWY5ckROlrOivQ9YKNcEkAq/X8u5LheIt8xwHh6oW+Nk2XGA7zUmfad9qR7n6YvglLKXBb3dtewd1vCe7xKeYbrgN32HFeh0XXJvRx+lBF/UJdNn3eIT/uR2BFWg9T3gI4WCLvp/KLyLzFwBpgE/oBpXcu6A1jHKb085DS88g7euqWA4CT4pe/yUyk+Pmd4iOHU+IxMZVPW/v3zmk5KEPi7TYMLHdVcUk0OeZVXBl4AzJUIS2JHZUZbLVUIVDPEsmgLLYNSEqdw25GgrLpPwW92U8ITan5fXdlwMQs+f5pOdz/1qGJ+VlyGIM4QU5ybmLU+3ZQR6o9fMPAmLTibYTKQxBmWSpVz4YEG3KwLCOK4NxR8ZaNZg4rDkZ2to24IMFRHETUMVzyJEeQTQdGQZIScWg3CWMwN6A8Bi0TmFidSyhOAwWx+exwSnCdLvRc8XEzACsTPovm40uOHAv8+ZQTyFln9bk6NvMCR873wQZZ48dwJcSwtgKrrwSO2It9mnxVW/8CkmLazER24SBKmXnPuSeBDllNUCA5EMWWi0tI0CvDVK4LoCioN/jnceL+4Gh6yjuuB/dr+CBD6k7T5ysR2bh68+OnwwvjDKfuqOr4b9r3NYNK/b8vpDvxqGztV9jMYVOhI9LQ2gnzWQfHSqb2Q0KgjbUERYWaKlkB/x6Dh80jyjKGlELQhuZO1xfsh9xzmgybFfk6brdsdonTxIzRt/qDpCGL16cNdwOCddNyxy1r9S3twQYAz64d0b6fJ8PYz+gt7ms8Wc18YTaBt50LkcFj2GoOzSlCFIBiDr5c0QhM5Gp6OTOTMGH6MQ+hix8EpDJRiGMoNVkFRnV5OpK5ii5wwJJ0jqLt09LRjH9RzvSCHEmAlfRJJ2ru33tjDSRpI0oqWxJYk2Y7K/kE951JWcCCY1xdgCRuDho2mgUYKv8qCBa1krXrh8gsCzIyiAdVC0UlkzZKcH6wr6BL5604RZSSB51Ag3325DXfzKn9rZfIpnvyrG2oH6DB6Fw204Hj5hQb+4Q1cVEmRLxBmbd6WvnAosOgT5iyPPHoYn4tSh8EFdHnlWWTuBhXEhcEZvG7YnyFIroX1d4SfYN0cuyk5A6yOyqgz0HCXSljhrBnSb2hRDEMcV8On4NzKYP2cdygNI7QTiXCBeCWPtif877bBkl+VrDOsiikHdZwKhM/2juBjHKd9plCsjUT8lT9nWvfBhYyTCrUGdvp5fWEZQSVdC+lICbjfaEUFmvBiBqGF4OOMkFFJGABBZ8KXaho22Lkk/0rb4yDG9iAxKE+wLBC09argXxApb+JTBTKKvhrEeFg+ymfrYAp+9JoP5bb0+y0dOMLDn1TB2BD3Qx1BkCJIO9Y4gDEE6l41dQbxWxo8D/DEuQTMG2NXyGaPihrRVbdfbraCQ/tI5Vq0rr8hUV8av22ChfFAAaVViewb4uATfcQif45X/+qV/jhc6V0twfZx46IWDk6SW1nnuY55RjvNEcVqeUvbVK1nOcQxrgUq/QnYjQxrlFqfxAhp3e3oeJ7OhHvvW4Yp3zvmkdgS5qldbBfBPcnTyKsibMv5+8Wi0m7j58LQPHcSrO5/12EtsbP33wxm5hDnE0TNtg/PymGDo1GHrUL3n5Mhn1EkIfqaVHTc6bSvFwcSbZ8dPODzkJqGpwXa5Ap37UbUH3HOEVgY+PM/nBTERsitIh0HLGMRTa14ByzlB2porA9ALnxmUT8vznrZxtYuIL5Dx6U5/UUyTuSdkfV6eKQghIC0PfjGVhV18KwLxiiEo8rbqML5ro4v7C0Li6YKrqp6hQNoBv5UloHJUB9415ssQ/qSOPGiwfvU5AZqFsijMYtE3BKJHOjcE3AjgWAowOdoQJCMQrxXckYGHz2bmcFJNgNr982CMQX4qrRuRe5o82VKbm42UDd/efpsKrImwE6LBuuCj5NMnEld06ps4C9848en/8JsroMAHyd4x5pAdpUhOUx4xZIi/CiY8zvASL/Cn+C7ghgwu4Ib0z0k/SwI02RXRvq8d39dWP4OTzhv4JzpV8d/MjqD5fPz3+nz8C+lR3+yMznz9ml2ZCIcbFWSEvpxXwjAIOT43M+BEQzDK9SOO225cNL6TJvAoks5WRu3FnWPvIXu4SPQh6EFxZuRB27H3MFwdrlwCCafheOCaf8otfsLC46ZF+Lye++4HGYKYUJSOjmanhw/f11NrDEHo43syytcQ9IgdQTqj7WHPCOKrjuzi1FkWdzHmS1/zatjXko11PjdC6H5VwjwojDQVGZf6oJcj8YBXsqIJj3ySvKArugJgQ3OnkDIWylnAY4i2dEC4Rr85j6mrv1x1R74VEl0/nJHvOZkm0B+5sxsosoJnhZiKw/30r8zRlHRa3qIvsFkHRUDgxDv/O0yTg6dBYLiAPjNEeBiC/BCJtDEA+QBpBgwjQvkyx6eV8hqY2lXGQubd7gryw5+JD+tmplxDLcwRIAiTAQVC/cGSP5nwAwlGr67TlhkcXOR65C8t0sO90SKLI3gm2UmAfhkoaPw2/TL+wG+ZOMdV3PUh/Qa8s2yXo1OGZQeG4glLcitNcBldWMt4Fw5oKpeHkqwp+WrYK+lHvqj44nUOi+Z8oCd6NYxjClh7Pn36fBmCvCNIxvSsLTV6KdMCoAxMvB7RLgvDkI1ASgePLtSfNZDz0RIIgjVsw9QDWOD2dDnWViQd7Qo8TQM+aclpnEBytQ4sYASkkuxW4+3pxQEWjvDj5I+QqXmNPt0NhF5NOPqUR8HoU8aPjeEKsKOHw585eL+v0j5aO4Lu3fpWxiB2wGN09xlC6FUZgtgNlHPbanSdHUGznkYWSMK78+Vbh8C2+Q3/GW8GgqFfnCRZ4Ul8GHOb8tP8Uv5pua5it3j8cB4cwk1rOOk71kGPOxrufajfGi+m6Bobcc8ZaCCrnB3B5TflKGcPnaGvpNalua/DIwOqGjaL4/E2sHqBqVaey8ElA3ocj5FPWn6f1GHRFgC1/8gOhYLAdveHgMHD+8ptev2d54ZJ+xB3DP6r2KfSSHo78lXsnwYxHRWAT2e1g+etDQi6HgpUFQcv6M6mLF7bbFmh6R+XjfTkOoBTFmiHa6b6YWnICA1lEr4olzBk7CuL10wDl+eJlzQmT74kxnjl0NJuzcUYxI/Pd3LAKecFPXv5Zs4O0s4gPZ15JSNQjEHC5amMbmhqDOIJzQ86lM/2GPggrjJY1LGIqzKAF3izUiACx624VuhWkoof/abLJQGpADnGJ5CFZAxB1kFKY6HPApZ3oDEGfS0Lz9f2BeNJhmZBDnG2UUi47A5iS2vfmeYwvNt6SnLfhh7dVHBzwdNm/fhyWF9NYMdQJlG9tuCnkeGPxShcM+FSN7zceAgGCgA50h1XeEDiY6ufsTSZCxidkIpbdhti0oaIUCw7p0uaJqyLAikDGhSyFepydCFvwx/LhyBl4T468ZD9fL3cnCdybxtUWHu7fAzY0KgOouyMU3SmnC6/FUPQn/zFP9z6Y+0I+v6ZDEEY0HWmRb8a9kqGIHRmXmXVwtBy4maGmxd0+mYMUpqO95TwgLOIjM7PHEQDSntoXJMH53sah+aSwa/03ZHv6kA0ZF1SQvDIXfxQSozrTrnh+ju3maHIgZ4lX2vjJ+LocV7PxRA0O4IUcX/hhuMO+luGIL8aJkMQ+phzgLg5eaTXwvz5eH3ymDOCHjzk1QXpaevmHPTPeW/+so3kkYNNRx+LIV4R822jK4WOhNfdRbp7bV2PJRNwnXnzqWNhlUfxmrbDSTt1HTuGCjVyU1srgWYl/ZgXKC3l4c+IG4JTHjIWpPNIHqAozjxNGpOnCMucYkOK6YkkZfgVLOXNvALZKQ1GNgf9OodJFllL1GXQ8uEd/vnhKDuvgbE2SDhnBMGXflo/sHvEu4KE79e94dUTrOLM5ZrTv9Avc3rm/S/VbxgZNgwNq3hHuYrIBUZZieNVtpPNZSZ1rs4E3kaPJNMoLUVIH3mZli6mLdTO/UPRuBapGSitler00C8M+i7AYNMniYD5KN6Hw1r+zsJex1JcMFhwcQkgQ9Vel4Mv8qDnEHA1C3isGV9z8LMWpOwIYk35gjOC9EDSB0U/z4dL2B3EWUG/47Do2RGErVjPIkUXHcq4oBQkml81TNJiDAKFNal98jnsHuJ8wMNfWobwMfqVSE0toAkCkovxNHKwMAI2LrJZeQhS+hWDj0eIc1muxiN6wMM1HNaFUww9QKEbQ5BiGkM2ACm/Xw2TbgV264t3Hh/RhRiDjlfD7sgQxGti97QDPq+G3dZXw7QjiFfDMKZzRpD8e8LjFTL0KIZ5PwBlx6UqirGdsUb9qDd1cWtYZoGtQQYCvNNwDoNPOwrP+ICje1rjn+KbrOgiwSF/JXvh9UG4FAZWF1kndh4uTmhQ34aSEjq0VHPig4eUTqGu+8DIvfJSsYmbCrIKyHCnARLM6QLsfpGgV3hhxHfXuH1dVpwAZUi4O8zmnik4tWF8po13Q9An+2pYOzBC3MO7UM/Tfgle89Y/L6dl4XdgecBdQITGVTwa7xR5L8t97yy92DsesNJvev0dr+HrcFtUO29p/GK/hEuIzk2Yyk8lK4dIiXQNUfCad5jyLtOhMyDjEbbeV4DwVQfFcVu5BZ37DCwoIauGgWTK8DTjOdgTr+Blk3mZgsiDwca+EnnVK18T02QsQ9BL3bQ8lwHIZwbJf+7zgTQ5a3cQh/jxetgrHTDN1294zWF9mn5eN+OVsUzSlDOvoIkZ+t/6lS8JJopajKk+yDU+AVx818EVCNQiIAhNPJXThSQTIEo021Q12X31tbavjjGIp8oK+ys0LBoxCtlApOme1w80m/mVMD1BjpFHvp4uY/i5z0To3T+cB6QJUdtk7/CkmS+FiRbbY3EuW0HmQy8x6pOmsP6dhp/6yp9GArY7p6uGxxgeU6XwU/Ngk528GePqA4pgUrMsl79THnxAU3ZTzYOJDWQPF6n+nraHm16/afif3e9fAquDXFN02+Uj4B1jE11DefTflPtbeTXs32EI+rO/0+sMz7zDksNN2UWJnn0pY/oyBGnSQEbHYdHo7xqCFOZm5YohKDgZwcrLo1aPfjzSxrlN27AF4gMrHGzC41vhhEKwoFeKxSNHw00TiYVXGIagwKkF6ZBfYcfHWKQVpQ1BOizahiAmEN28+IwgnlijvzEEcaPiHZo5p82vht2TAUg7gjAI+TVddLVuWO7ySWR0tH58GSc7Qc8NQZK2eMIYhEu9OpMapMs2Pw+IegW3OKlpY4cPvPI4oAldl8Z4KT1kpjDzHINowueUiAcvpW3NaNSMv/IRWp0vY2AJzO3jJ/CqnSL+k4AcdqJLSlkuJCxRxY1l15jSWouuRbxeAZd+DVMO+/bBCxjqWAOQ1ye6U7cRSHAbgeSD48dFiy9uaGW+4qEPc7rgnvsVrsFoNwSlBvCgED+84cNhQ3Ip/1Qk0tgStyB4i5xpik/SfYlX8SGnzM0jn2YG3+UQGHeWFqabKB+ilLHjbckOkob7CXgmO7wn83FtkdA7wbNAg+es0yHczkLMzq5JJ03/6MFXbzXmpRu9jpTPuZR8NcyfjR9DEA8hMQR9990zfznMDy8xBOln/end6NCWLrQ+RCdGj0bDKIyuFRQ2gUkVTx8Al3AN8YgqMJgkvDQAFbYL/44JVn8SLZegBX9lWwhQ3AVMeI9HtsCAhsqkbx13h0PPa990ZmWltqqpBSRBufZ8NexHr3O9Y9074PPQk8OiOd6Ata6PQdBDz0dzRpA/lqI4O4HQv6yD/TD1m7waxvqXh6iyB6Xu0qceL2IwLSE/zLoy1mVTMfrF7qzHAAg/Y06jReFT6ew5LocRw3m+0oCVhs9z72kN199xL8GupkcOow32ZIehgYMX6rrjkdYypodNuqDuA+RMvoRSX8JOPkUriv3VH1OwGbgkD4ap5QjN0t18gG098HI3p+uGTFvz5wP/Cev3Se4Iasd157aYL1/Aex/O5Zw/D/oh5V3POw12lHsTXrprcC/hFXZQo8PSxelkWyGKF+7E7XKKtSX80uA5YXf8U2BZDMdqwwQyMqZ8D5hzXoRnVJH7/9l7EzY9cuU6k7cXLr3o//+lecbjsT1jeZuRNZJsSbake3tjk+zlznnPiZNAZn1VZHezdWU2UfUlAhGBQGBPRCKRnVZANHrYM1AgjFRjxb8KmjARxeSJdaRUU6QcHdLtLOpBP3gmYePUU/GZDLkhzCHQnP/DmUH5pOdLb9HV62LPMQxpl5AmZ4xDr2Qc4vPzLG6YkDEM5Yk3ix0ZkyTUn/eUYYgxnhs4nvyQnm8+BaEvzrrId3M4bkChMCjGpfwT5yi7qQTfQorRu4JIw4WQMuUpITeC7AbygXZ6B5onyo85a0Iw21pZIPirB/WFw7jDIaM+HI/dP9oKy1MSvyOtnUI8OXnqxQVyWKDImCTdNRc6H7ThTn7kix9hdHdYfidFfPB1OwyBcLdaliclCX5KSEwGGybO6MBNkScRy0LaxbWQNzQ62kHj13Cwv+z6NmX9Mk3e/dituxt1fMp86+Rt8E2aNMX+aERtmr+VHUH/x7//q0f/27/9rzrY9Ll2BL3wGUHf+SwMja1a7Hz/HQtbFbzKiVd1WRhnIbKMQHQ8zg9i0aKlrlgZ4xkXCc/ixHV3XqikGkXI//CbcfrywDSCMB99vBT7DQwfrDsKGG3A9wrrEhrNCc8SzAJWLoT3ggE+DEHMF9/p4QEHRnOIEof354mzzwjyjiB2abJQefroKZ801tjsr4ZpweJDo2W0/8SvNcgQxBjOq2Eaqxn/GZ+9c0SDboxCpC+V/ItxIbq2UJyZyfNdXG/Od67Ab3qlBM9yG7P95dgFhEGkRPnE3F3H7GVA2qmCRyD1hM2LewjmD9AsxHomD4LJ1wey2EamQiov5jTfmw39gIVHxpE+Sa3krMQe9r0LCDnuDWBGD4OavwnHECSdMA76/oGFBbvFaCPRu/r4tRPpxOfjP9CDmBiCqGvyMD90B94LLUkdD2Cql9CHAyYKP2DPpLsM4e64kQs+snIl3Kg+ZLhhIYuHB9eyTOi+65J7V8IlDgns7Beyg6MHdfmQq27lO+lO3RyRQ8m9qpIXIXHS5mCjrVJrTevQAABAAElEQVTXGIJ4Jcy7gnRfeRiCOCOIV8M4LLpnBH0lQ9AfdEaQdhFx71pDkM+odElyE8Z4mDswjzw6iN/jq9JkdxA71sHbn3EXDDf24MOL/hO2XGBQyddxbYGYu5cLX5iFJLqlGHbYkHDmOdOsU+njR9Tia/jwEeWCVuHQefRzkjKwW576kW6HbbBhh7z+vasHo/ljjaXsbmfXz24I+jO+GoZhnQeh7LQcgxCGn4/98JT74I6pjLFW1vfeFA/B5aOp2gB93a7+BF3K4vE/vICSELBMr/VdBBFjXpWERVxTuwoimdfxXOO8efgsubm79hq4ztnV+OVEEt/XsyjRGZ+jSfrZwPFO151vT+ci0kqAO+QpUJ7iWs74/lG3tDvHC3fHbGawzjvv1I4g53bL8F6op1qZkqdAro4JrXhPrpu8nffK1zg7D/DOd6Xt9If4QmPSjTzi3Zde0yDOfXzQmt7r+Crv6u8lR0p7+Mr7k8LOYxox8fZOSRoMokwOTpE8ug6TV/jrNIek44w88C6/YRiyefbYwHuanm6cMBFF3YVYF/B0tqE7BEY/2hK+ldGkd5HMumO5dfPFDRkGGn+FQf53fv1Lxhz5L7V44eC+l9qqy6tiTNLsBnqpz82/eKEdQhiEMAbhs4tIPgf+vdL2fj8B9y6h3GjOfZ70a45HIcrVPzwG/Qx8zgsKw7/l13lQ2KiDLh4lkDGIGstNIE89sltH7zSzMJCRh0Px+FwmRiD7hjEQYSiScYjwPEl+poWED9DTRPiECXDiYSj6UDQWF34NTJOrJzxSVnYCT7YmzI2o/vMDkCsOmHhkCp/8DsvU4uTWNMGUSSMQjQiwQNeFuL6KJ30uOKGXE79FCOP0JsEjbeT9VDcyHK1w5TT8U2W+5/95JdByv8amHqDVv9IJt67KB+6WvCufeNoU4Qcm4nhaqKnNn1Zk0N8996///K8f/e//9i8ffTk7gl5p/OyXw15iLNfPixcNyssQRElhCOpCRuYRL04Spl/3/IosdCh8jfmMmeMyJgTfOsz8lYry2GLeveJWdUPKuDECd4/61F9j1k/tUsOu5c1nESKcVyI1Comsw1FjupIs0WIMYgcQ8wRGIM4I0sJFYQyHPrMNAz5jt34sSD7Va2A8uX6ixQufjv9cixXOCvLZQcJzmKl3BHn81oMAnlir3ZF/fBYx+rdu5Dc7glpSyUfzJy65lW+ojnqUBPSdG7hlsePhw5WW0H3X9h3TE7jDmrFdNXCI3NMrHN2pJ/Pp4jlYceIz98fIYnkzZ2RukPlD5RWDC+WXtgYfIPLCt3KFKlXHvi7Vj3sAAsGr7bJWdXgtErxAF2PPCfKra7pJ+VHvsWf3AG2GXQKpT3z0+1DGPsYXXmNjkYvermv4nIdVfKTvezt0O2BrhT1AOukyxdeHc04PXsQMLZFBOFsRlpCukVH5jeNwqGVxDC5S9Sz7oAwAPWqGr/CFDzkt8wvpFNzFnQiXAHz3udJ8D+IMkHYUc30TUUEwaW/ynfDv9GW4+WqYDEG8OptXw/Sg0TuC9MUwdgT5q2HAvBr2zaMv//CVH0CK3fesjJEItK92cRiBBsbATmExAgH7flcYdPtRO4noHdENHZHlf3HMuAsRHmeUi4HDOwChhzJAEAcOMXIJS6jHxeCCtxZHXLRy0r4GWnyJV1n48EekeCl/da7DKMQZEBp3f8cuSxXHx/QH+ocGQV7vesrDT3ZQ6kEn4ytGdfzPdVA0Y2sMQXkAmh1BOixaYyoPQRmfvSPI/c2KOI/0SdJAB48f6Ge1ZxwanZ1hCOg87QeYHCu2cfEd+WddYppoVGRHk1yLv+1btSGVH90ecomzc12lVIO0NaiLozW/Y0InTUulMZosOSqzAU8qmQ8CToGtaIObKyzQXEZEmjhUB66oPQzs/oBfPmBWnYOI37rOjqAagt6xHUHNJAWpylMBtNxThLlOOe2ofxa4Or0usdt8K2+NT2NpJRd3tJqFOMoCVBrfrVJZZbZFddw9XPhXK8OTYAWmHp3uRttAkZQfsrQh6RQEm9OSnH8FwDuKLrmVEuJwtJt0fq673INFgIev0c/yN1YkgMuvWqRzF+96Hh7LVXuFRufMK1xMjDm/grN/vuMsCxmDXujpC+9sY+jhtTEbhRQG51fFZBzi8L5vef1BPJwxlKffCrMrSI9t/CqEJqbsCELP6JjJFW1UApo07Fxo6U+MKdFyeERbuQs7mfANB7wCyCc8OSBaTzzYEcQTDxYSWjjwmUyMOx8z+cmgwwTonUIy/nxkHLuCOEBv6M+ydZYzJ3iH2k9B5kkIKltt+aSZCU+wdCEb/eVWJLzwgXc7gGeygXcHFqIDKnSJXVcKx25KiCD8xmkSxueGecKUy+Ea9UAMMHiz3sdzjXMNb8lcSacM3iG+R7zVEqD+qIs3qce3yaf0SNJ912mrfY4Ov5UdQf/nf/jrR//q32EIeiEDunYEjSGIcdGHonpHEOMtixnGOsqLMRxDUHb/5EtilCXhLljCw+iRMV8Vpz4eJ5r7HhfwwWZhNByDO4hB+3oaWQ++MqQC93ucsHR0ht7fxGGVby3la0AsP/ChPXgbhriBzGL/xx9eqVwwBOXMtscYgzx21xDEV21k9NF5QPsZQZ/JOMSrYc90ftCnOjso470WOXzNUYsV7xKREjESMBZLD5TSjxKuo4SjK9fi68MFPX+Nc6s8Q4ukxbfL2eUvjqQpLcxK+QimobzGmW8Xv/OPLDxS9aHMEkl9Mic3PBnXAi6GFri9mJMOuxEIvtN8ojCyr8knD6QTZTJPp83k8/GjgxjgQZ/Tq2GyCvWVsPjLYEX61B8+vxiCBNsQhL8MQclDdLgqad2cOPpT+3suKC3qgJIAxs/PgcvFMdfF1ENaolvOgBZ0wMjdAxfZ1+CV1/m4Mo3Mm7RdTwlDnrvsKf+7QHimPVrgKHtk8AAsgTrBmGcnzy1Y8YgatM7tUbt+pR1B/qKixkR2mPPg8Xg1TDuCvpIx6BsdGI0h6Is/fPPo9zon6DteJ+N8Sgm1sXxuJH1QtMfDMeL4U/DoiaGIsZV+hR8drAs1qrDH3+Knljt2pl2Qk8hynib7O9w2YrZcpk4l2K5+63qFrcRwUXYrXJ74tFDcSh7tCAuvf3LuQrbpSwRVKkcnaKYRz+wIkvwagj7W0QYYgnxfrHtedgR9xhdy9eCTz8d/ziH8Mq6DZ3fls8faickZQdwDayeQz98cQzsPRmPYT3ty/1TK9a2nLm8ynvmhhrKSsli5lTg5EcatsrrylIO2Rxmlbx8xAU7lDH9k3C9pybwfOlK4n2Uo1Yn09jSRsEvJXfzCuPsd0o9W5+yUtrgPxgMg2+Vruld+Wkz51O2tT3ka9/DFC6zRWn6YO472AwDUATR+79SOIGfYhcXgkiJqoR4lPgBUN1gKYnDwGj/+oM1XecXd8pH3S/l2GTuMZpMlJ50GMXncGlH0Wrk+y0hDOjfxiXHSvfEzydzKK7iW2330n4XfhK6aGUlD21hCcGEMz1Rid9usnIiuAB0IRxR3Jvm9bQ/FbJR2+PDvJFghIgFuLGUN2lOBE2vd0ZnLYyUm0WzZpf3EOGNjkCdVnsTNllvdhNkgxKtf2gWEsQfDD5+bfzGvh9lApK/ivNQBfxiAbCjyRM7OILbvxgj0vYxBveHMPW1ybN10YdCnjFIa1Tntu2XD9m/otDHz4jdKCsA3MuCYeNjezlMLf94SQ5C3vrIbSBMa8AmniZAvJ+ipYncFPXmiuDYM6SmJdwppQSH/wz4BkWz0oF7tj/akf/zQg8DgjgkR/CCHnLyYMxfnsQUkJvq6g722ksXucQBB5iP+xRDksmpKRIDnhpu83EvfoyDuPjlbUs5Y+Xb8Lus9/PZLoGWO5NfVVXl/KR9yJIOm2R+NpE31t7Ij6N/8x7959K/05TDOteCg0+4IYqHDDsrvtOjxaw0z1mYRwvjNmPehbqc0OmjR4qfYvrWHxoihRYwrk60Pw288lSy6JxwvBUwH6wo5+t0BhDTXGUnNWkI50zTaQMaXF/o+JkGbnys8MFqCZ4FgunxyYu2BeVLtG0jdKmq+YEcQr4jRaHhizXluGHXYvcmOzmfP9JWwT7VQYecPr4nJKPSZnlp/JjwLlk/0NJtD/r3jU+M1Bn2/uqDy8rkYGoR5Ku5dQMqE5wr0G+1TqufcJ19ikFv08gRr4ulCLsnz1d3CLR6o1kUAZda+Q7qFF3egdd+lmI4cD+qR2gCZR5Cln1bEmfu5gYc78ZlrkYmLEaXh0CGZDovizTp8pRX0kbhF62IDAPwTiTS5R0EH9IGOz+5kLyDsoyf3KfkRuzuHPZ+OLhiCmO+9wHUds+MhS6juBlNSh05klfSrNLDDVybkmym9xHk3z8SdMgCFuDhB4JW5A0dYLhoFniJOQNdTeJN7MGzAzrv03hiu4C15EzHtJypbb+HRe1QeSbnvCgWaSuXIHMxbQMq5fYHSDz7XvQJpA5Qor2fpgaPGwxiCdJ8p+LkPi9ZXa7/Sl8JkBOL1sG+0K+grnROEIYhPyMPHV2J5wPiDDDveweWkvCVMsmMI0unh0otcaNzkHCF4FcIg5HZH/dg4hJrJj1XmQthyUgpGGTelshWOk4BhrxRE4MS3WCPFaJfXCpt3Lul7pS2/ctBsuYxd0PyjsPVLCdOvstvSN8fK9Uc6I4g+4o+lqHi4p/UDUd8Xa4yVkceGIBnVMQT92cUQxOfjbQjiPliGIB+7QL9T4jYgf8B4nvtPisNjvXzrAwL9XNoDrowsSGzOovjTNkHsLvk/l9OVB374UteBey2pCcELJ1pSdrdkhef11+gWvrP8a1yaAGMn6e11WgnRfqdFP2frEBZtj6IFL5ku5oNnASe+sJrYNMvpMR394Bm/PJVtf9Jyf6f0QPo/3LcMQe/sjqAWnhsmBXEp7RRJuSCrQsVXf1EWDdwtenl32g6XXv8hWnnwyxe/NwY7B7lQo3PF77oLccMhB1e5N1hO9PLfxwc+GsR/iO+NaUfFBFhX6qdSyMcRELzld0h0GjhKKXfDFIXHffyKPfxyMwDJVdBSwJzlCj3tJ1bzDF3Qw8NgEQfdHVOS3bGLl2zE2xA0MIYaJmafVQFNcXhiw3kWPvtH23df6PeSHUJ6NYyn3BiG+gTnJa+KEeYsoXkCzplD32ubP1vPuZH7QZMSNwLeGURa/hulyLyUPHQnCEkXD3W+ec8NKW0lP26rNOlQwBMRMYSzI4inwezo0a4f7/Dh9S4+hZnXCh6zYNAunx4M3S/TfKwtr979owmO8ykI5wlI3qtmgWLDlHVDyTinLZBJ0XrLRzUcvifFCyz17cZLAJ4DUil1JBYy9bl82ChHO9NJCwA/5QNt71/Ipv7NNlEdf4lx8MDdt6YplwUqsPtXGmHo790/Xwm0blsvr0u5fPXv4y+9fvmUXpOkfeForwZ1+a3sCPp3/+lvHv3rf//XOiz6hc+3eDljIguYFzKQy/MTbRuDdJ/OmOgFiTqIDUEerLmB1Z36LEYYj//Iwka+8eq8WQDthh8qZMZQ0fnLuA/Uugms4HIQ5cZL4HTtCDOVCu9UajBcLz9vL7DWkaQI1kw+GgMz/twyBPH1MBJgAf+Y3ZmzG5On1hiCPsMQ9DRPrf3peD4fP4Ygvx7W3ZtaqBC/O4I0bGdnkMazPLnGTwlHSfQSztfAzXno0MD3Sr0hg1i7i5SWOLEWDF9KLTGutGDN1U6kAOWNLiS1oRczPKRTceMfKQ2ADOK7XwrA73wcYcyxEjPGoMyzhBFogulNiji0sSMdwkZecE4zROjRITw2RqGHeUYf7ke00I9+MRJybhARPfdKH8+zStvzvV8NiyGor4bx8Qe0riGINOvAOzi40jYWxw2/sEqPOP7pQn3UHaAA4EO2GQ5qKMQdnjJStKTvInacYZiopZeEf+C2xA7cxgi5btfEmm6I8lHPlPlKoLHjr+QChR8acc681CeNw20NUHKLg9lfDfPuHr0axgPH3RD0tQxBnBEkAxCGIH6//+KrR1/q9TC/Zjvtozt9SD7j4xiBsPay18/5Ec5GoOz9I3s2Bqk5oU8NSTUQWRZZcZxkyqOBwIwKEMeZzIXMhrck+0IFCx23fOrrljvjw8+IU7fkZSQCnxFJvtjK+zt2A2EI0k4gZ1RG948YV9UvdGtrn/ta7wjSvbB3/PjVMHb9sCPo00d/prE1n4/Pa7mcmclrY8jgtTC/GiY/hiDpI+WA0ZEaIAxc/QTKMY4Jo4pwDqiQW048bV/ICRvSzvzQksoZDxZMsfFHkCOVAufVNR0LF3HxptQrrXzX+GhErhdf4XI2Jnyts9JWaslZ8JFn7qPMol+1NJ8CkFeZVerZL73+TsVQiqtcJ6eAlnGHqwromj6e8RoGwrjDyE9JCMfvsd7S+Fzt69dwehWxav0a4u/K/GdO7q4CvyqGCvvlCdCJf4p7iP8tqHNXlUNoAK5oTN4P0tZJgzvnCVxvhBj4cCtu5BHDcnUpbMYTJzTFdORdwsY5aLxyBEZqcechxf126sFxdKmVljbMb3VWpHAjyE8LDkXmqYt/TNTaDcQkbGMPRiHtBsIQxCc/v5Uh6OV8WQw6u4gwIv2g1Q4TPLuDvCtIM6/TVw48Ccv3n5VzKR3lb5Q08kApfTDuoNeH2lrA1u/oKdzGA69vDDXT8e4yB0P73B92AfmgZ71SoIkMnHcGeQKMoYgdQH7vWbLZGcSXwPwq2Ed6ciIYuZ7kdGldkwdcJuCZBMFJKfPIt34NQ7rAxMfBV7fgJDClVLLr7Qi4BFv/YEe/ewxBYr/tmpfb1Ndjl9K3eV9Hvx3rPfbnlsB99fxz5b0u3qSH5zHUYcaYRPyt7Aj6v//Lf3/0r//8b/RKAwuZvOrgg/Y1Dn4rIzmLHp/Jpvtz737QOEsZ9XwgFi304RqCeP0r5pO+xITPOM9PvBpQUsR0sPm5r+nCYHO4DTYeCbsjJElnZHCwOZGkRJAw41II+L1LLI5wtIyvoAbKGIL05FjpkEvvFmLR4jlCZwTJh8ZiZb2myy6fj7Uo0SfiZQjiU/EsVj7VGUGf6qn157zOwI4gGYSeaXenFyh67YH4zAOM25QqT8JVnKOHPOPRF13qE5LeBEU3WouoBhld26ZhuHvfQqS6wpEd7A6X766/0hD/CtxhZM67unMKxE82wsfcTr/UHOw7+9VHKZDMHiknYJfXpEFeKTMc9z2ApLWnt8Pw4VC/WTC/mkZah/C+J1C4Ookx/UJ6CccDKdpEDyJtmVOnKJCHPqpj1TVGH37sTqD9uH7RmTrH7coBK771Qj8zrAvi7QQURt4BQ7xEIsgPngvJ7U/om65lepP4Rkil5gSbcrT0KCHhhy5TCYSbjzcSf+FPv78t4UhLcUiOX+4xk5JxKg1eHePVsB++X4agfD5eO4K+eXnsBLIhSGPpP33x9aOvdWC0z6LUw0Ub0i1b+XNjZGRhF1AqXt/HEkzeFZ6dlh6N2vYcF/0SX7e6LifLGphRQ6Bcy3DPs3B7MNlb16HFi5SkEJbsklzsB+TG0LG5PtTKOGvTpo2GTcs+7DIEYQzCKIRByDuBVCz0EX8ll4eeGlc5b63nYX7GK7Y1BPk1XL6UW0MQr4nx1bCJzy569TV2GX2g3UaoLtB6AKPHKrnqB3blRYF7XGIvImHcm8RVumJ3ESTSG8Ya5ge9a/rRa9cOGK7dv08kPI17H0/xTvmUPOP12Z3IZ9IbhYh/kqFAw/YbHiRe+jSGxwQyPswaT0hmT8Zy/h6rvXyuNvZruH8RhiAmyTcxEP2p+N684FVdU8l340Bo09vhM+fdm6MzPaHcVLTM3KQremNvKveqtPEWZNjrAEk8fg2Xx0gHGKC4JS2nlBCYYQtcO/Rd5Rxj0KUmhqP5QrrQeuNUvmhVvhkkHbkS6sMzwykoCcBrHSE3cIfckUx7JKpch2DKOjeAogiOUQaQLiqnSdQGFint7bQqSG7KvsMgNMYgvyrGAdHs/GEnkM4H8gSuV8aggcP4wxfEvCuIG3xNRJxFZGMQkzhhbvI8UWXAsJ5Ra/IDRiopH+iUyYZDIXujN7pCU6GEL5McxiJuENm66rOBvCtoJjw/Mc4XwT7SogEDkQ0/unPkvCC2lOur8/5xsB5fWvA2c5UzrxQ4naiGdi5sXZcPXygGbCTacQMTxfEGOOAD39pDNPDUEfxyDpml1OCR6rZOXU4Ke38knbSX8m++5O16bJQQlkqLZIETvC/yffgl5T30tkvgVl297TR2eUqvSbp9Oay2OcjfiiHoz//fv330b/7Tf3v09Zc62wLjuF+f1Q5Kvz7LGWoaF9kVxNiqgy7wKSPG6JxhQe+dRQ049WONQOp/vDbGmA4Nn061Fip00OJM4nL0uwNIjXGXLHfBamwD21oExiXs67oYj97MnPCsUQim/NDoCmc3DgYgxqkagjgMmN/3KgPOs9AyTk+aeYWXJ9C8HsbOoE8x9nym18OecqCpXgMTzEHRfl2M1xm0U4iv34jduzh5tcw7gjBAeR5RmsqjzwgiFZ9RhI6rLFIEKUkTnIeWAnwpNcqquwkyvgafOLtE4KQR2g4Hc/M6bC7XdqJhrA434wl5TkGh/IuiPEuW2xs4w2du5loaArnhzJ04hY1AQlwXwY4t5ElFmEYseFoI4ZRZ2v6QVecxFPgcILH5vgCc7jm8k9iGIPoJuxu01B/9vOBUOjH6aN7Ww5sYgjTviYjq8PBKoHVHh4vbdd5h2PY4wMhqnkxrBkamg+QRNi5y9i58oQwR/uE98BcA+q4b7CeRDqi9ipH6PJwYbcw8EFdg45WmUWPHXflXeHEl1qKgW6jtG6hUQ1BxjGPU82EI0jiYw6K5n9RXajVmcjYQn473l8ME/+MfvvQuIY4m+F5twuOn2w7lQzvVWOhdk7RfxsX4xrM7iJ8qhjKiPXqXELohQ2GpE7ppukhHjwLEAeeCp1D5QT87F/0Ffw5ayhHptiGIfJCqeJ0McXa3xqXRzqo0nWg6misqY2kMqBlT1UV8/4yRlIeej/WAMw9INcZq1/ynGmsxtntHEIfwyxD0ib7GyMdSnnGYNDuCMASpMzCu+tUwdTS6JP3jOHtNKreYMj+kyKrnniNg8M1peZLThsqFv7vEutP2Nxa/OaFw2uWe0sZ0B7zyNVwtG6H4h8K0QGrmGjdxkMDvTZwlnMS0396NfWK7S3b97MNFWYi3x0U3+gaO8YS+gmtc8+tC/jz+iNC87psMeMUX/Du1IygFMaXjYvl5l4ca8C7xn5vvNKEcilzz+3DzRef73U6LXDfpHb1Fvqa8kW6CO39hRJ/EDwE1yW8a73CYdu686FdZJMrEsIeJuYfhGWnxJ4BX/IqhYe8UuYH6cCqW/tsBkY+Do1xXnwnXTpl0PF3cOdWz82oY1OAQnptlBnZNpMogAwATLucFfS+AT33yRTG+CvbiO+38kcHHT7sxDPnHriGeeuuMIE3uxOGJHjuDeAL0PUYhbvD4U5gbPRuJ0Icw+gFH61GdmzoMQfl9KMuMd+doNvtAE3926tTPziEbgmYRwE6fnDExRiEZez7mizIYhHiiwU6g4bWxia2zH+cGUkn6CzPZgZSJDqV8U+iyVatQOa36TN1mEuykLboYJNYVFZqzdo63qmoqVKVAQQjfEqmf2CsEW0IR8gGNUwkdmhFsanvhRvwqb9EiISn85Ot9kY2fzPxkoe8j/KwSuNTzHRnUyet47kR6ACFZFcdYk18x6jM24D4Q/x0h/Ye/+LtH/+4//e2jr/UU+5vnL/NJZO2c5BUxjEKvOIxfYyPrWj6b7HFQ/ZXRogeacuvop9v0Ro1xhj/4KGVqGrfXVKAGKLmMNIThJy4O2EBgBRw8cKUt/87iBF4qso56HelAoUEHG994wclR8Jk5g4POK2H4pGdtZQ3QYwEbgjTjeC5kTH6qHZuM03y6mF2cNgTxepgOh+YT8pwRxO4gPnnMbiD8p49lEFBbYxcnZwX5jCCl5sWKtGLsjjEKnyLKrW2LhS/dFLYiZOvkDqqwFqBrcVe/Ea9CruHybf6wuFz3OnCqTWfjP8DUxBEEcPsKxv1SpY0Bhjq4q4lyQ2HJcQ9HGflSxkmaKQZXdGHQ4OonPajCDzOLb0DTBPSehFZAv6BP+Mthum/wPYHvHzAEMffy4EfydUE/7gs4tJbz+/KwiIc33BuIVzy5p4k+FjAXqzL6FF+c81yk5JAX/wbeSMmIEMTlB1/FAtftcHGHv0c6kDeAm3xKrQkeUWiRd5AHtRGWuLbgh+Js0QWG826u2qJi9FFIDcX1S4Rph/QwjiJ4pftI7wjSriDuJXmgyAPG59pJ+Y1eDXuuw/YxBDGW/pO+GPbV11+LJ8cNsCOIe1M1Ff1oERpJNFZ6reRRRcZzGoBf8YLO+JneblWAJz5hvuSILJzplKDDxI3zq2bCt9ziN9QyWW2tlPiRWll3xloTSGvGSUUi3pJ6DkPLGGsm86bO1SccjbKnz9DRsyuIXUB5sDnjo/oMh+53R9AnMgRxRpB3W3JYNGeuyfiecVhGIhmLHusDKt5tr7L1zkt1NOxvuQ+3+S1jhlKNjkqfcUSFmfyg3HLFUTqFQ23uLtgJpm4qxxXVwNlX2qG2ZZ7JewjRD0h6LXWXFTjK7jmg9eyaQNvpSwbYu9qc8522uOLcirFTXw/TWuqsgVTYtSB9hweJFxy5gliffilYP5uAvK77o3eQ/dm7siOIgrptLGkR/jzfgxilurk/BY683U33jAudPq6GfdEZ9cE/7EpPfu/jh3rmfFjqLSqNGxmVUx7CNFb8VerTVZuwffLYWPF1/txxn9j4ZHnnA1+cB2fo4Oa3UhVW/+CXa4L1O6CJQzfQTocIibrpH6kddtOxk6K7qGY/d04EYIhBxAjLzhsZVZhAjc/k6FcZ1JF9XpAmbwxDTOJ8weElXwyTz0T+yrCe7mhi52wgDgJk989xUzeGIQxEPAG2kUg6wGse6eGFkVO3Brnx02TDk0Dv1uGmT9sLPjh8bvq4CcTnRn/4FGYxEWMPxh9eE5MxyK99BZ+vionfN5EygM29g28oVWT4nbC7eECrlKZ8pQcMJlBKvXSToKKXaqhctAl+dqoGw+NTFQkLwKkqXD1Tw9ShE5Xn+gyXceED5BkEcnRFDOkZM8wjGp4BN8DswzgejHWNcIpcovydtzw7bmN9D/6KJfBQPbVe9uR/CU5y2vacrAYV/H1e+K0Ygv7zX/6PR//Xf/47L16e6xWHrzEAYQjSTslvtUPolXYEfafxkzE1OyYzlmfBxBjP2MtoocEIuIYg+1RSRhIMQeEVynEoc9FZIdsBA/gSv6DpoVBPB/r0NEIUCK5QIgRoPScsnBHQ8mNMykzlmeWIV1yXYviMqeTGixVbJrRwYUeQ0mXcZpHyTL/HT7TTR2M3hh6MQBiAnupJ9afPdD7QZzICCc+5QRiFONetOzr5QgmHU2IUQKZLTsDvODRVC8GM7dGbrOIoguhKCNrmkOPFbLmRW7g+/DtMGDngKq9++RqGi9QVHtQqT+FV1k4OmoC9fwljXYpDL6oGfop2xMVXgHB5iUvKdc1TDULGj/rKviNbHrJH1pG9EWP5FWjm4d3gLOJp/5r3kaOfzxT0biBwuTfg8/HZMaR6U4bY5YO+PLDKayrZyXsyBH2oHiQ+8kKXcL1W99Fhz9cJHj5wTmr8pJqSQsTGZt3hvzp4YDy61h5pYzafwpY7PCc1hXN9Dg9R3X7FBL4u9b61E/Ntbcqyoa/207SPzDqhJjh+E8CnUMTjdEeOUUcrgxyp8GBwwaEmowMGIe398wNFPibCg0UeMr54JUPQK+0IsiFIRiDtCHqu31faEfT7P3yhM4Ke+5w1n1/JrkrLxmgomapkxszsoCRtjZ8eC4MnTNo2tgsC5ut1yEBX6xkFRWNEiv772ApeQn3txflpwH64TqgDv2jUyFlU9o00HuWZEgvG/AaXFrDkd9YiM4D4MASpD7EUJ/7H9AmMQTKSYxR6rKMT/ErYHJfwiQ+LfiacxlmdD/Q5Y6u+GGZjkR6YYih68lR9Tv2OcZX7ZnbNU8zHPbNLb2UtbTT6teQ8vjgb4NGMshDVDSr5tQTqNNl1PnOBHmRoSB2mRt387ggC5XQ2WvVp7CWpmDCjIy6tZsGhomnycJdvj7H4Gg+fOKtuS1maFOPyQAOpVmrLcdfW7UYIcAef4J1nybwLwVdexqxTWDTGaQR7uh5edKKNuT504Q+3DEGi0w5Fe8cOiyZjyew+oDr3b/nypvJ3vh2+qrPTdnjnI2+3adMKpqLT1PaYCyb+fe6WbHeiG1FSyvdJej2e+NV6F1+c8yqeXadULdzTkQVOdR8J9mthB8IyFl/HtaZpvwHxHoO1BUgb/S8y2uHqCxTROgzTAW8sM9861kLn1hsB5LU/Oikz9NFtNRlmSzW7a5hESTBpMpF7Rw/GHRYw7BDyKw4Yg/Q0xz++iCMjkQ1EeR3MT23E78/CElcTU774gKGIJzu5wcsrElkcYZpy3nxBDW72MvmwmPxQd/k2AnGDP6+AYQT6UDpzk+gzIXyDyJPCGIPYAcTTCw4PZSDKtlYmxExmvYEkyzH+uLhdBC7ueYLtKlE5Unfg4Qeyd1w76S0sfITgtB+S4UigtkQTfrI9NOHBuUaXbyJRoGxxgmk6Tc2ikQ45znEbOOM2rgvDGwTvi3wHn/y+gcT3LL+kBG7VM/Koj9J2+JrWTtvhKx/iKm/g9OGVTF4NuxHxHUP9l7/8h0d//hd/n8NOdWD0cwxAep2BnZP+ipiM5v4iIzuDNFbmPJTcCmqIVLVodOHO+o+cccETbgxCLHCykGF0yY8FDj9cZhMqloVQqkpXgFziFwX65KbyzL9V5EhfrIvGPBLXWQefH3j9TB7YCxG484Sap8MwsEjQUkw/wlmsMFsxjvNalw1BvO6lVxN4Ks2OoM8/0SeNdU7QE14F46cFi88HkrGI1xg+9kJHz/9VbIz5hF1iyhtyySLjrMdwr85HF+FxoKIPIWhX50IapOQh7OScwoapDPBJayPeAJW6ysdSdXE5H2W92El2oSu7dMe2jD1FsL6Rl2+tlgBhEofCcZ7MMDji6YezPNNWOFCu5SMUNq7kKfRBzgKc+xF0wugjWJH70AnjT84Ioo/kPoG4dI08+EFPdtoyn/NgaO4T8Hlw1AWqeMgPvHWu9gZe4zfe4d/Dj/gjjw/wbGrcw3UbTfqVXxku0iIn2n4fq1yryKYtncTS99LOQMM3NesQuMOVdCAC7Gkb3umKwz0jqjX1qhlfZwSJwr0juyL9IJFzJ2U054Mk7AR6rgOjn7/IjiAMQV98+eWjL774RjuCXmWnOfeVbS8Yctyj1TiUr/18NXo6Y6LPDBIf7dOGH7dJPooCgjYomjOCrOhOyRz6u4xa8qIf5RKco7oMFuTg6bJojrXEmcspD66k+I1XP+MXikPvr720cXI2EBnU2KqC//AjlYLGw97/fixDEIdEswOIr4Fh6PlUOysZbxlX/0wG9yef6HUwdmXKEMRrYbx661fDuI/WT7fUWTeo3Cll2mnSH73obErbOnJZWTj4knkR3TjSVwFb9qbrguy6sBIHzCa0DPZVokccesLrHBxEuI+z9Kuch+KkdxGj/TGxE4crv7Pb5S3Yed4Y1zidsqH9VtZ9OXgwe5JNa7GTIJY9yKmspu+wLgtP2eovlWYYGcsQFBr0d9IQ5IqgoJTBVV0Uwdntg+6Z8uuE3iS9XferFq7QQT7Et8e78jW88wAXv6ex46/8bWxX/E8J07ipn3YS4rZR05xN0yhTnRatjT0xGYjaCdxhdoEIHdcBCzmWLTwD0s5efCSK0byWPlIKmyAc+g0JeQs8IL4wFR5PKRvPhBXJeRSTzwcycyQBekeQdtqw+OCm2XkUnkmdTu2n2BiCmITn1S8bhDSbfseTHeG4aWPHj9/vJ45wyP4RvATJMy0wN3l5PaKvkaFf6iF5QQ8MP79j4pGBJzuBuNnLhMTTCe8GwteEzxMP4mDkYaLCiOQtrMRnYcDTEPkYlZgUbUSST7vkybEAl6dhJlBCM5kZNJXaECX/MADGHbhEg4JI6MCRe7AaPzEPb1SgsixEVzuVyMEzDcYY2HClOk2npnp32mA2V8YNBXjw3EO/sN8fPAQ9JHRnul/Ue8ovKIHX1WOr4C3wualKHn5/aN62+VsxBP0/f/WPj/7jf/2fMQTptYZv9ToYxiDOtuDci1da6NgQpMP0/WqExj8WwIw0frLtXsgtNXfYH2nskEHIixkMQb7Vls94jimjn5RvReKzGJLH5RhICMuVLaG5gtwagFfJW9hct8Kda6D1x4xRGLDh+Bh90iDIQXDkILkXTqpwzoSGau8IYtfP0yc6AFoLExYg7Aj6XGdXfMKOIJ5ai/6ZYAxDfBESPn/lUfE1HdgI5DFeKXgMpkgKyyet6AseOL7RDgUHCJSn2xK8FWTvZ+CJI4WHnCWJYcneudtfnBZauTPBcZs/VTx8h6ClwxHLeQ9fRZLn0MvPXJXf4lHtHEKoKWmiML/dgccZfaGFEhrxvEBQOj5AlLAS8CMp+b4/0CX3Bbo3UBvioZF3BAlGP8/5oyevivNjTu9DoM7p2aWgnjKTLjVn1+yOnnhFDUfYNiRxr/dwO+8VJp9SMW7ScRj8lfknhPe4tA3CI/6OFMoq91K30kyso/26r94RMQil0sxMxRO7su/EEvsyBEGN1sRJW+Jz7o98z4gh6HsZx7sjiPHxuXZOPv9Gr4c9z66gr796rk/H66thOiya3ZU+f1LjJm1Ft6K634wGfHURPWVKFkI/rIbeCZQwY2raL/rE8EN8Kja7g5aeRvuVM9G3GvPYK7beUcG3V0B7FOirQzr0pKLAAPF01cpbd4hHtLkDP8IqPcPlIB59uG7nB9/xNvgfde+r0vBuoDwQ5cHoU+344Rw2G4I0hnIAP4YgXgv7TGewmc6uTPWvZzIEPebVWw2uGNj9oPUwBLnEj/vb5MmlZfVoPsaNuqFX8+V3FyJtayupxTBQ2+25DSJ1lYdLZ9IF+1DdRLtw3UnMmneEu09zYl1p5OCKi3TwzeFtjl1e8uRrwEkr8i9d0wkcbEnO11t8G9mgcymFrJOE0N0ra7p+wiDFZLrGDoLhJYJDGecFemwXjrp65wxBlNq5EYJR2UxpQ9vhUNcVWuP/FL7GWZIC7fKuNMKl13+I53W6TzORiPvzSDr8cK+TVz4zXy40qTqk7eHiX+c3TrQ5c1u3M0r6FjHD7I2ETx1GdOJMdrf4keN0t8QB8yOhIzELWTIoW+KrGx0snQIiwGgu5hOQ/5kuqruIZsQTTjM0eWbnTWblIYpGPdjoogk0A1VkMGk7DlE06TIRe2cPN242/sjHyMOkji88WwH5RCg3fTEiBY9BqDd5NhhhVLKcyPBN4ihMvm0I0sST9/7H0NMwxiAbc6QtOOnPJAXMFxK4OezTQd8oahK0HMVhsQEtN4sYaMi/8qlfbvgpVikAzhVA6Z0dZYQjXuFyQCk/si1KfmtwpwNTC2YDAPZ10hcVmodbM0IUkH/HHYz4IpnFTGEYT/qZ5gSUt6Tr+IIRlrRDJwDaeACIRgws746LCuGzsEaA800E3JH4HvFTS2Av8sZt0dcvfvddX0K0rqHdJ6u0keduMjiiNIz/WzEE/cXf/P7Rf9auoK+e64wgGX6eazHzjYxBL2QM+vqbF3p9VuenafzkrCC+xOjXZFU+PJGOISjGnixkPlYfzs4g+g3Gn5imgWMYclh9PlXA6NIKlG+w4Y1EHd1xU8lmH/jg2cPAK5z5k9mQ29rSPDuGzwtMxh9w+rlRYBBS0Ln5QbkSXarjs2a3Icivhcm4w5fAZPB5oifXnF/Bq2E2BGkH0Oef6vBoDEHaGYQRiEULB6L69Qd8/Q5DkFJjaCR7Tkvpe6gcnTPTudSjI4xtwIKTN8VhgjhGcWQQrtvh4upDa7nVL+3sJ9kLj4NcIifpMs+e44be9iDi6Jc5LLxEiSRdnZ94zE7JQUoDHpx9WAcmTf8Ib3h4R/BoaUwuw5f5XfIVEXma+vUGy9wjcL/AT/cIGEs544S+giFIdwf6ZaGsxzh+kMPc/ZEeWn2oHUH+qcI938tn/tdtgOoaf3IVLzpuqgGaBYVwG1+r9/CHXFbYd5gwiPJDq7gySq3lIFaAYAcbXlyWR1lDRzZwHlANPLy0i72uK4J4OFoGJR8ZSSi0PdFyT4KkupMlJ7UXWShz5Fd8sKKff8Q1MTIYCaCrWjX2Zfz7no+LyJrzUmcEccQAh0Xzetjzb3U+kMbPr2QI+v0XX8qXIUg7gnjg6Ndrp72oqdiQk50+jJGUsCvfPuMmP40IbkHVC/8H2r9+wIzBhsXpEQw8+tdNPk440Yj75k7Mm0jiHUEBgXtFcH9wJuXyk6u4aqSwlSEsWIalDxxWbnRDyKthfDKer+nyShcHRPPqbb8K9qnOYrMhSD6v3H6usdbGHwxB6lPPdD7Q4yf0sRhdvbtoitn36VJmulqqXGm7yPBFq97o7PA0Gs8hJqYeoOOaq71xJQp8zXt4fRURMSZNYjtX2iyc2/h4aFXO+pZ44wJ9hA+1fWoxh37mggrmLB/M4rtLJ1adY9JGzUbZTsyzyKQwJDeDsl34LHfjQy4sLVrWEA4LBxt4wuYDnoL2a1+mmzr4jOlgPOaLF/53zhDUhnjfwEt5/ancm+p0H1/zVv3v4yu9/s4HfJ/b+cpzH3+aVrl+nl8ZJ42KTNN2p3K+hT9ISu4YjJq0e8RMFOBOQldcsk8/OcgCCsfP1BSxk6I7VhMC5+nIiMoLdU0BK8VQiLV+lxRFSN2mU9KBd+cdQRhS/CRFw5sStSzpxQ2bf4rC0x6f6WNYt2g25mAc0k0bvDY2wTfx7IsP4xA3fTYWcYOHcUjxNJNznhBy/fWYyQO6MbEcO36kG1vA/RPBZwXJzytiCktfG4IUBz9GH+KP4QdDkPlzc/iR+L0oaDEpPco5jpxTXlxRrNNIW4QY/Z9JBa46MBWD37D9IQTfGHd960HiYhxN5AcK94SgC71TlDunT3qdNw6Ndsa7yTreSdgmG5336IQdAaQDIOTuwHussOS6M+749/BbKYH7in0XThW8Bb70E1W95DGsINLtcmT/VgxBf/nff//ov/zVP2kBI0MQZwTNriDOB/pGhiDOCspCRsYgGYJ8cLTKiEUMB5ZqeJRTIdoAxI4gFjBUkg47xfijAvbCRvg8AaesofPbYWQEZwKXU5DAVM7hl6d4Ij0El4avSjcvsH7+B0djCM2ziWFw8PFaGIYgqaZLxm/G7t/5TLdPZNz5ROcAcWApi5XPfCYQO4CE0+LkUxmCMBR9ot1BPNHmFQdeVaCtydZvOXoWsBYnpMNPF6fprT/oAT6+jg+SCwx0hoNJ7Mi6U8bnQm6EG/6exiJnfkbL6Nl7knDcjpP7qXD0Gs7IOeK6c2beWJJ2HvKkMG1sGMrXsGtyqg+af2WacHU40p3SDW+YaeceH+bewB+REKIPifyASU+d2C2SXUEyBKntMK8zn5NnvxamV8X52icPsLwDeNsZlLlevYV853+pRrY3vQnyuzqi2snfQKMSnYygT/Iz3He8xoXgZrdzbLJ39H1wZJFuOMa7w16d9rSPSOZOTN97t4LvSNkQFSgUMdNWN3pB8XHv1xLr+OT6Jp5+Nnzr/g9DOHXM7shXOh/IhiCNkc+fc2i0dgRhDNJh0V988dWjP/D5eO0Igtc7iZQGftoS98T0YqXtA6LJNb2ccXIM6B4z4QsvKsb4Q15o8xkFLEe0yJMIO+RV5qDkkZe4lPIRPoDSByG2cBa/xd4IC6ygaN1YyemEXHfwVePA3mGkPuPcSyA7gj5SX+GhKF9vwhDE67T4vCLmT8drJxCv4fprjHo9jNfBeDWMM9ue6nwgf1lXxZrdQOwKUgnTH5U6zcPj6qgVHaN/6S0wwve5jmfk5iEXvpZPee+GM2cW/5BEaFe+a/gav+ku/BlzDpWLEmMkhnqXoxjSBl46rG5annBsXXPjbmrL3/kWdkGM7zj4uvwkdf9GDXtzCZ68ZINB9DM267lQPFZAYyfZn2m+/jWcXn9cxfNrJHCV2eTODfHKtcL/Evl2nXYYrZs/4J22w9B2d6URvuWufOW5j9/trUw/06dxo81Jo0PwAYx0NeqtOYU6MfGEME4wPr9BT/wzgmKwZRWmIQXsIGBpIfpaqQqYpKdginCo5MggRpiAXQJ8azpIShEOLF4z45sxypkhN1csPBikUn1MmCNf7Ey4TJ5soyW65nCFRRcyRh8MP4GRT7nD90cxspvF/OKt4ciH/mEIEsE4PSFCfhwRBWmS8c2fFOLpXm4EdeN3wBh1eAKorIgHfAxH8h3ORCX7kRcG/gIYExn8Ek8+j1I6gKqAAuhxlELCxFuxHGoQHXIGRvzwSRdxwdN4UjP5A39x6OS8cxE8NVBkSKWhnijpV0sQ6TitLU1TYb7HkewIXxw3+Isy/+K0rg7uhJtCb0Xace/hX1wCeyUVvgqlbkrb4Z/BRz+n3bq/j8wd/q0Ygv6/v/3i0V/8t9/L6KNDTmX4wccI5FfEZBx6/oLXIDgn6Ec93Zavs9YY8/ixI4gx0mWpr4RpxFKh6sfo4XFZT7RZzDAoeFGDYYiRhcqjKsEDJ7z8qeaizd3LVFaDd1ap0PkReectjM9IjwPef54BhBqewwg0mawhSKIZv3y+D7s1NVjzJRt2+vhgaHb7aHHyGa8reAdQXhf7ROdX+NPxHCYtOsYgG4KQpwGW3UGMsx5rpRk58A+cYHTlFhZkxmzw6IrDhxt/cG7gYC1FeDmXd8B1hf6QaxqVv3hdVApmPE/apJd7ksZb/ItPXBLHYhZ3h3OSisQVf0GJ1zy7LS3impclYN7EcbVaX+H29AIvDNCUNApalnFqBnlwlHuG3AfEOMDXpPLAiN0iOk9QYdKibXgu14X5/SMNLI+1wmVHEIvcvCrGApXX21Wfuuj2INVEosI1p84eAfCHY8EtZ6Zwcr1ZzRPHIkaGvZO8iLKM4X+d5y59kXGKo4KITm0XJ6oDiV4+F/uJye0ejJUf0g6fuO/ypd7XnUnZKyLpox8UYacAGefcFuSzI4j7RnYD8SESvjjLq2EvtDOIXZTZFaQdQV9/8+gPf/jm0ZfffOWvijFmvvAYyj0lP8ZQjOhJr4agP/JFF/docsuexBU2j3TIF8coH+KGD5Wrt+GUdvKhawrNwFyI9/Mc5WWncTfjypIE7So5/NkXSkkm4cYhPGOucutD8fHJuSJ+/DgHRGPEwaDDLsvsCMqh/P76osZYxlJ2X7Lb8hkHRI8h6MkTxferYYzR9LHcd2NwRRH6Y0qwJeSaTvbE0qmlY1byYvJxiSSCRCAUrtQH+MkrtIWEcHJHiVz5Iu7ESyCaU3ZXV0nBE/2MufIngXNd3pPo5K1ldpXUvCdFcd3Jb1LZs1j4lo6l3U1nYSiB8rW+kGV5uuww6qNT+g0+xiBkhct9XQjWfuHTK99qM59rPv813J/UEORsTwW1gV8zecU3XP/K33Dp9Yu/zy9f/Yf4SrvbuFK5pV9lXcMP8ZW2+8THNd3KK37nNd8giEXzqj/oN/LcNifuKYIIafQasARUp6lOsyZudG7i4Iwf9AGH+aTkZPdIdqJIFIMkrI0Ey204PGvSzaC1eIGgIs+wJrRIHuklCItrPidkD5mtg/iRYX7HiWzv2lEiTLyU09HZBdDhdRFuOr7imS6cpvwxGIWeJ3+ZyHkayG4hbgr8h2D920kNHw6pgqQs2QXEq19MOuu1MMGi2cgDn+b7xJkbQWTMDaGNQ/DOxIvMw4GfpHNGQ8sKZUYhvIlr8Ch1BC0MIYtWOZGGrgkTENtBF9/uHIcLoiZNn/2kUNsKJP/2kRr2kQ1nbmYnzaQMh13zeIQFIM9pO92hVI8dB6n4YTs8Cxg6yIYnH0F4qjmibEwb7j34VkrgWm+70KNuhHyIjzjlLR/hwtDlCLo5isYYAQKc2XT5rRiC/urvvnz0X7UrCMPPVzICsQvoWz3Z5pwgYH9CXgejsgji3CDvCNLKmjHzexmCKDuXHwU2r3+5N7Oa1a4gBjdu7THY/+gzMcYwJCouhiEgjwAAdp4SGjh8107ounp8aV0fWIDFZ/jgyZyz6PDxawNgNAeW4ws2wIztmBLGKNRFCgt8v87LAoOnz1qMsNvnUy1MeDWMBQtfCftEryxwoOknelL9TIYivmrzTIagp/ogwBMtWiTGcwFrE78WJj/jL/mLo2TAoSt5Niy9QKEPAG15vyeAF47ImKsiFkeulgt9hW9BK0YhpNmQQ/r6yzxdqmS47M6youPCEWbOvePQVfFLCt+E7I3OFAZg03K8FYQV+WU50jJCRPlEtchRAtg/8CISx7qoqA9DEDs7dB/gswJlGPBr43/EYAocnzg5J5D5PjuD/FVQXnX5WHV/GINEVwPIT7Wdf+tG9uo2sCjVOZrGca9gB0rgmX/xpa7IU9jNOM1+MEYRf2c5whfZTfaIK/ohW8gaLUu/+pGb9tz0dh7ohyZh3sm34ROf2hHhqfykEanojq7FJQyN1h2HZox32RGOIYhdQRh39NqXXg3jy2Hf6NWwb7/NeUFfffO1vxj2h6++1o4gjZl8oZaf49UQJHVmR+WPMgBR/HlgSdqEO06iR8ZQDEUxqdA3wOOjo0eBIw+peUYNXPJZvuD269B31AaHOogjkLpabG5Rk9K5ITXKoaH15aKflRoYI7vbvXKoSB/oNTGMOBhNGV/zVUZ2UWr81MLc4+u8ZmsjvHZhcvbaJ3pNjLGVOE+eyrjOjjvkaqB9rLG699UuVaWDfktHl6gRHk9XBttArDdj0XJIIg/BpSQWdUGhw7vHdjEIgQQ7yYbO2DEIQhU/uHrlCXnpsfDlrG/ZDRx+NMoV5A6d84cuUBdHheyYpH9kwSyhO+6wHnSFD7jiSAM84cmOw5esubWNPA6LNnvliRd2R+FivErJ+FB870JlCwlMGdYQhCxeDftcberXcH8yQ9CvkZl/CTLdaaYxWB/q+ORA7Awnommnvn0lH+HKUGe4J8KdpI+4bwE49ZbkyeltiW6gEqy+vqVNRxg1qv4+bHfquGrKUBBJkb749tR2GAm75KvE43bbhMQkFdySDuIkdfLvoVIKRacAGT7XoJXO7v7t+R9tPNHiI9SCI92GIHd/xReqv+N1MiF8WLWExAik4UI3FX5fW34H7bRDTTYqXMqXNsKCAb87e7jhR+8Yh+SzVdXhFc/GH+OYEMOvYGQC2EX3E4xgu9BaGuQVElinLcg1OvycI9Go8b2UG27iFGvhdy6hLn3uNQRZg0Zviq1xyukuzZgl+tCzqO0+OJFLqKj7/CMtMRSuf+i5Cyux/n2C3+N/VgnsRf2zBLx5pCOpA1h93mODRP1WDEF//fdfPfrLv/vi0bfsBtLXb9gRxOGnNgx99W2+jKOn2X4CrvOCXgnOTkktijX2aW2j8Y/xjoVLzgFyh2Jg0+/Am85Il/H9uJ10d+Ky4aeLMUrddeCghLbGjLucHvG2AaIzDJwZGyXDdGnjikembgMN43vWEA2jEL/QGZ/9lBojEGdYaDeHD39mBxALEhmCnnJ+xXw6/lMWLryyIDqviOWpNa86fKy4yrnkkfvuCGI8miKwD81h6do84LfE0Dn8YNB5d1B2aQQbhm+HCV9dynnHFuMy9Ap7qBAsbjjamfbIR3or3co7sSlQPD6iGj7zjRx7gc03zJQGVUzQJTP48U6iwBmvi2tePm0B+wFtnebgRYJWDH7FRx3hex2izqtCP/BamIylfHyChT/nBKG024oMP3zsgYc9/roci1sbrMCjQwAAQABJREFUgmQQwhiklWpeH+fBUeZBqsg/9EdLwqOfwxt+tDY9vOEoH9HjnDuxq+YGBL/DLWTipq0tKadmY3ouV7ywh3udEai6HxEMRLmV8pn6ZqEtg4rg0GT0pnQSM6F77JL7pBVDUIx9GIIY97QjyEYePiMvI5DOAnqu8IvnL2VQ59UwnRGkM9deaMx8oZ1DfHGRsTOvluX+N/eX7KpEvtqWDD2omF4uow/15FoYo5AM6ah4GIGgjc5ur1Y2NdfxdFqPKCnNVddvWrriG9a9RFIu69pSW5iOScE0JyjssduKDOxc6ZVbDV/kEOPmB3rnFUPQE/UV+sjH2uXDjiB2UT7joH2NsdkRpN2V4vmEsfVTxlaF4ddg+pgdRey6Q65upNnBSRoxODHuXktpH1+lt3TsvW9y4cI+slhasCkkYjzsdvrIE+os+R4Je9SbMc5SCJ2i3CM2XNe83o1JfivzTG26YAu7+LYUE8PXM9sWY2N/Q9Bj+vB2nK8GbesOzwWP9R6OsT0g4YzzGe/dQs0TQ9DHht/25V+EIYjFaW54Hs7en4rvYa3O1DfJxznGOUQe38TtZeFB4EY0mtSlnb+J6NfzuO2mAYc5cNBRZKeGZynoDqPgwTOkIzwacCt5dZ0A6i+ePfYOV0Img7uUTITlgt5BJgMpyi3dna6YuOeEzx0esnC36q5PKenk/pmV9s4goMDJdTJq2STt8GLsEZ0bQEX0AgiZghlMPKBYnuIQobLVnryDxz6JcRMonGYhTz6skcCqIHlCgWOColxpisEYHb4TronArB/pjiNulTA4Ybzmm7LkHeRDV9Gg5xc8L9vZDR9ljhus4f0S/NKjhqDwLHwGbcJnSU5Pad1KBVyzuMc6pFS8GWFOqvdey7cLK0zDOrk9XCYYdvgU4X3gl5TAXty/RM4bxG2bgrWw/dEBQxD98113/+1/fvPor/7+C+/8yVdvtBtIO4J4PYyFzEudfcEnkl9pMcPrDS/1ehhPzvp0XBsgPDbmlTBe/VLBeaBW4VGAPTxacEwXGoE8rox/9CWPfkfXuo7Kqx5WI4Fn3xFxs/NvXZWxL8ntt+sZlRJX8Bh/PEPRIDACGYchiOg69wUDkAZsXldgkZKn1Xki/Smvh+mHISgHQ7NLKMYf7wiS8ecxi5qnGI9kIND47zlBSUmU4aTj5Kyul4EuM/Qmz9DGEOTJsIsuypDy4VcHM/ORsK6XFgj+zgd/eHd8uM58DaGLZRL1cKK2Qx24HWj6TWWFdy5g0klaSkkym+4CxJDCgP2A9+T32iXeISMxHE4ehyYG4rjq5XvXr3A2BOHrJoB5/we97gMcwyi7PWIE+p7zYNRfOEDa7VOzul9JUeVS1yxon36UNsAhuB8p7J1AMgbh88CobcIlowv+KZvobuLhgTlqr4FhcTAXck9ZytPPofFTZmBCg8/pDmKln7YE2vKHqWmlnTmSLtPPRmyxxovxdN+OgIPvvnjiOeboprikLugQdKDcerbsOTY6HBzJscvB+JGfm07fP/nVMDHwURGMQdQ9Bh52BGHsea6x8YXOWftGr9T+/ouvH/H1sG9lCHqptsH4ySu2fq1M1nMfLSBZjKWMob63rCFIadKjp7ePr7FVeOPw0Wt48N1mXSPB526SzE0+xk8938Wvwi9/eKjP0ABWaTVU7pZkfWKZX1HMo3ozTWPpqvcY291POChajBpa1Qf0SMGGIO3qYfccuyfVTxg3n/HVMBvZZWzX62CfYvjROOxXbmWEh0aYr+1iSMIghMwYgjC4MsaiC+OufNKTpmlW5E84FRI4XP2Ebl13DnK4h5G3hwOTZuphlSfViQ6uVsVyzGHMOIIo4q84CmzuLt5t/uAgx5a6Yc7S0uPR+FyLKw+Jjxb8lmvau37IEL6kieEsEPHAR8oluEQPtMrsTCJe47oOpULXOm3rps+F+uGhPs5zykFkPRecjf3Gd0fQO2IIaqad+19w2Q0hD4n55+ZbA8tDWj1Mu2VQOMfYG3k6y6U3mH3azznq2whNq57udU6ro8ekEx1WVyVsnFDV7xLlpCExV+yQMjSsYe7uE4BK3kUdKe9Iw3S2uiW1GG5o66LJrk8HrHJ4VDWDtJTYlWrC8Cc9BoHEqrxltAjG5MqQ78FB4waDGjA3hsHJl6iMKaJmbEnBiY+Bi1+MHPJ5LCFX4w/lyQ3f4jM55U7cBA8/QQmWQ5dOXJ5AQowsaBO2dx1BIXaEPHPqiw0t2fhIiqz9ugtfegbroV8ayEVBAdEZVIsIeHcPGYLMNyJO+aqAJT6Ya7h8u39LkHGKTHlZ56ugPdIO74Lfw7+oBK5Fvgtrtey4XwAfXUAyCu++Nnm4r/6CJP6XiPq3//j80d/8w1faEcRXb/SKg3YF2RD0XGcG6en2CxmFXtoAJGMQixrBeR2GBQzno8RA7lGnxh4OPxX8O+8K4vUwVR6wRsO+CgZ/qjsGoMy/nVVC4bqNKFOebSQzmtzpiqVPgzE9sLu2JB5RjjGqhhTFPQZyDD9ZqOD/zniN2YrDooJdHB/rSTRGoMc6H+gJnzJmYaIn07wK5lcYFP6c18T04xPH7AR6Jt6n+rR8FjcxDrAAohS0dsnwI7g64qeEBEh3Ssg08wp2o6UsKG/ackoNblxGcMdwKPNFy94s26V8oEgHvpbnxraBTi4JO05IlONi2sBBNp36Qad+Ok+Ipv9Wh2/axdY+uqSH7xRuQCS3klHg0EPAASNz+PH5neZ8CehDHx8QrTafV8LYCZTdP5wT890P+roexqEf1T94lVK7hJDmA8Ux+Gj+Z1fQYxmBeI3wsXYE2RDEjgUNNh+p8n0/oMbALmLahP5Vp6oHF8zMxYSFtxM+PNG7eN9pKNBw2etThm0mrl/CcMsnDmVQuDJqcEWV3iub5nAln33T71ZYhG+s8DnNwaVb7hgIm3IWPMwnr3F2iYFLObE7kDIk/4uHOIRXQtC+k3GvDwS/07iHEQjDDuPii+/1e/HDo295pVaHRf/hy2+0u/K5dw29FN+3MgRxuDTnBb3Uz2MobUntC8OSRMt4SFvLPSqvjUUn/PR6h+nAhOWXXl2jL3gc40Hy4eDkZVVH81YfLmCkbLgBN4zFhTMxWntN2QzIWYlZbviknRVcYyttkEP4c54mRhtpr+niib749Vh9BwOQjUAcsC9DD+MoB/J7F5D6EjuFCGNof6rxldfBbAj6ODs2kUc/pA8CUyzuX2po9BXy4nYtfa3L6N0810++zlfKKuUNPnVy5thDD0kK31qP7PG2FEYEmIDuwQOPjE2j1k24L3V7ipW4M6IciSd/TS0+6TYnwZTeaITjVhNIDDfJg1hgabkwbw55jBc74wYpN3X7gwsyZeUH+PC5vkkHpluGoHw17J15NcxZXTXiAYJCYKDIjQMcZ9fB/iG+8hCzfIV3aVc+aLfS/ql81X2lrYqeVlBZ5dn12eHqUf6dtsM730pv51gwKtDsR5VF+CVQ64tHCO2FB+DhK+2ZtI96DaPnjtGnOhV3n0rEPJIxzCCS2KFVUiVcwy2D3GRfqTUEIQtah6jwMTzX7UNTNNr1WvUrrP47LHIDmTwyaUYqaVxdKd0lk7DiCgC2DzwEBpHQMnD4ThMcvBUuwCmh0ox87Ahi1iE3NgaJFx8+SMOWeOAqy37SJAUPdrqQFuatlM4WH1nEqS9wla1iWUlf4HLEpCVJyodfE5My5NFkC2oqkX3QFPGAETwJo100RAL4MB4DdgRzdXyXjSJXDxO2Swd4UOHZiOgJcvQ9/I3FYCOW/xa9PKbt2l4jnRivkt6Hf24JXIu5dbrXb2W3CsoD/k35xEq73ZNrO67fLeRN7l31/+733z767//wdc63kOGH18L8apgMQH5NjE8k62thr/Tqw7f6Ao4NQT7rgtdi9FM3YWGcUZ4RSYMaA6+NQJyPFgNQwixoKPeM76m6GQRdeZ1hUtrncQRcYiw/fHcHhcHDjyoEfUn8hLmmjx+paqDJa2DwMWpCH5gJRfQP+Wk14S/ZaDHPGRb8fC6QDD58KcwLEhYrelqNccivhbGAmUWLDUG8uqB4NgJJFXxKynOB/DrGPmjRh2zM4kWMWZxT8uhoJvNZ5wNFWYt2hI/cCo/ruAsD9YMfaYWNOMlHnPj0n3sNOFa88IsjLA6ue5JQOy/SVhITWaG1VhKSZMsZecMcLxG4eoohwgC9v6nsysK3vI0AyD2CfeUTQ5DneQFUO7uCvSPIRqAxfgrGEPRKBoCXMgTwSph/ei2MV8MwBCGTw6Ex+HyoxSl55lUVvhb3RG0nr4XJEIQxiN3CMhRhDMrr5Fm8UuzUUNoAdS5lk23DAw5s5vCILb1MwMUd+Rf7NGu39MoCJ0qSEVKaT1g+/wTl7E24OGSbRz75H9bgHIIhNPy9XYS3MeBDCFxyoC3coYQHXN6JeWMMvtRDrpOaUhLsXTZkZBitPbBQFAnGG9oFxhsMN+wC4xXBV+wE4tBoGclf2BD08tGXX2tHkAxCjJsvZBhkx1AOjK7xkPaDIUgGRcnCsGRD4/GaGGnSN/jRL+O7v7jPhJaCGNrBT9an3wvnbE4FrSI0tlQiyAV3ghZqo5r5dEEDu6OQo/FiKoGSpC/j6yefnsdYZgO71HYf0CYMdvZ4J9D4TzH2zDj6TOOqjUEaZxlHOYjfBnePyYzP2n1nQ5D6kzRHNkYgdUO3X+456VMUCyXlSp/2im5k2z8udc2CwqvdDoMEkWPuofFxe1TCpgtJHUDbxEG2OwxBKIY+yE2HLMtJcORcJTVMioWBgNubK2bah6UDlw/6mg8gk1avBucSPLKjTdCDJbN2klxySINNORxsw717jXYSvzHQknDmU3JNEQDY4UEmjBHSUdyfE7hlCOJ17d+9O4dFk+UuhlcDTkG87eubyt/5dviqz07b4Z3PE447zNTuTnxDGNn3uVvpuvvciPLzNbgv9cF3UBhDEOpyw2JnP13eKh1lsRR0Z1HwiLJII+TsZXAsLkMbKRANGYXLsSQHU75MoYurEDIyMBUThaLfSt3lPCzAu9pt05VQ31pORv0pxoHRuoMGvJVlsgLIc1xg5qgRaAtyb1iHB84OJjBWlwNX+U5El/x7YPdNHeH9B7/Cdk24AcJO16kScHqUkm94Fc8lhjz9xbDiyKcL8qPfKQHxpFxJnoWHFemd9NDKswtc8qDiIpeoaHo4iNZfnuGDkngSlD/wkXRiu4hasQcqvf4dhgsiSZyRxknAQauwanIQFG+Hz2Leh35BCbTIryJaBeB3+KfybXHpByTnNjyDQvpGhP5WDEH/44sXj/7uH7+R8UeLmDECcVD0tzIK8UT7Wy1yWOjy+1a7hXg1jAURB6ayAGJR5MWRFjOMnT7kVJ2cm8hHfEnM1m5GJxbC22GnrkgqRD/9X6u+YfzCO7RgURGzcQWRuSq0JAMX9V0XOPEze7LkojHwS8rgY75CqmBZa/yqj3ZxeCeQnkbbEMTZFTL8fPbJJ49YpHCAKWcDsVh56gOi5WtBw7lAPOlmocJuIi9QlBq6UEon/aRCcJMX6NZHALAGa/ije3DV2+obZQkhOu9n7sbaGAzCRQngiFaYcOc64NwbGXtmbGeSgkjKDAMfjvnLyggWpaBplH4QyParWY4yN+rDg0LUTMuA5CIncSt+19tiTvET8FigC3+0X+/6xQikyHzhKQt/LR4Es3jP+UDaEaJzgLwbSIv8730uEDT1DwxBosHP+VEcXMtHIzD2fKRFawxBHBjNKywchosRSPWk/qGgFsIK60f+XXuQnC0tZgFc7ynR4o3WJeHwEyWlMfm85p0ybAHVh8dwEJGxpFzlk0BxFY/vGIfwnQK8Euu9tfm5zFicGIvPNyZbMDJIPJy+kh7hE5/CwlG3J3cKIoeI/BqddqsfIoXmXhujTb74xS6eGIY4O+2VDD1+fVb17q8t6ry1LzV2fq1Xa1/JkI7xnNfG+MqYD5oefmBeL9SGIY+pyGRXUNobGkcfvYw6uqQvWy9riukYfYNntCBbyZrNyqa7pUwlrSqZ9uM0SIdYSc+RBr7W7eJIia5wYlFgkba0NMVKccnYSk/zTfaEvdNShlB/UIV+IEMQ4yqfiX+sHZRPgTH2eEeQDuQXzFlsn8gQhMEoRiJ2ZtKH6FfsCtJCXgZ35nLuib0rSK+ckSfCjB0tOeezum+GoMnVqWTAwZ+yXCWQnJvqaIvioEr4igl+v2IIQvg+xrZGD76TGBfsQQpQHGV9ddQ7bbu1vYSlRSz+8gUDX+XucSpnpwVebQ0JklYR8g02yiYZzpNrnBPyHHAukUmTEukQK+AIDxLv1qthWfuJX0rbNDn879QZQRQbGWTQLWzgnksH53vIbx39Juk9pPveaR7i2xW/8jW88wAXv9JIyyz+yj/t54p+O2H3rKRA+jZQIPlINPUbFD1jkhW9Q0JvkKAd0YZt9xggd8ctPuL4ES/T5M4BHIlbssI15UWvYkwlS4fGgm9PPXiuHpjOowvMF5fJe0eShgdhD7BDSSYOtkMPJWSNhSAp0oVG/R88xgYPE3z8cDEaBeZKPWVATf+bLqh7PjTCaANT0jENWUf0CUzamTxJD12co1Ncp2VZkoxcBN90K4WS0QXneSgQobR/sZdu0uXSPLksJkn0Wy4wbc/QThJTy2g0OLQ+2A4APTZHANpGT0WcuLYIG3hlcViC6m+sAfcIO3yH8T3i55bAXo+3ZLTYfypf4yFz4rq/Dt6w8CYNnZtHbBjvuvufX7x89Pe/x8AjQw+7fmQAYjHjBY3OumAXUJ508+oDXxFjMcPuB73ioDgshBjzvIhRB/crC/iM4TIEcS6ax3MXZmCKeJ0TRAlPRchzXUCfijIvLHZTOQ0OzxEUcIxFguHmZt+uSZz88xjKyP+7Dxj9wXeRINhxFJb6fmqtRTuLFB9g6tfCOMhUnzDmq2GfaZHyRJ801usMn/A6GIYg+SxkWKT4izY688KvLmjBwvhPqZAGZ1k4qWi8jcXRAZr1GiZgG0KGf3mdcxPjIjXlIuaWZtNseMkJxNjf+gCz7oUIJXbKWXxUYCvR5EonIEcQFgcIXujCW/vpfHkgE5ksxol3Fq/So471n3qKvN7nwI+bZBPQ1fhEszxkd973DiC17xg4Nevy+iNhMbELjleEMALxifiXGH3UD2wAUphPx3+vXUJ+NUzKe/fYh9r9I4MQOx34WhhGwida0NJ+WGhgCPrwQw6UVltQI+PHF+nWV0XzcMc5E967GSZTlB/59tlCxRE2c/LdTJ/KggD5LxKmHVbQIoTDrzzYCptu4vBCHHd+LajY8p0TIkQLc0Kp/Imw820waVpxa7CE38pAMyjWtGEDCJh4ClsMF3oiXO5ZZgmXtYsRSG3An36X/53aBGcF+SBo1fsrXgFTW3iuA/e//orD9/VqmMZUdlSC5/WwfEGMc4XCb8MicvT7XrJ8dhDtTeWwDouObn0dLKWF0ccjx/hkB77R3TB9IziooQcqLxKWg7fu2jMbbrmV7xKe8o4k0QCMKx8VDMxyW7Bo1lh92Of5SKEPMOLIgPNkzgbyziAZfDC0+4th3v3D+UB6zRJDEIYijb/P/ApZjEDqTmOwr3EpO+wk2kWScVclOFl2MaHn6LPaF/otx73q7ni4gcs9eWnx9ytCXAIthl3IBvsemYguMwM7NbDKaqfs8KQycUhsTxBOfsu1Vos5hxcv+DUH7PirRCQlTV/35CXDGkx0Z3ESPrENrkW98w3p5GkTXXJFcxKlsuzrUh8mZJ0MQVD9r9zh63IyBGks/lxt7NdwGiNfl7VfI9lk8iq5DRuVdvgWX9X+KXyN85C8K40waVSn18koH/Hu44WGO+s+LVItAfxO2+HEXNfSFmZBakuHQ/oePgg/E7C82RGEiEP2AOmsIrjszokw/NohRD+iHPFDOV3PEwRRGHxWjIT3KKHdzXNSBo87JuMtZBCl7Pr81WoG1bu64Th71JvkLtVOcIgrrxlCzxIIOYmR0bJyXsAh30xNhNx3WNTAoZsCP+EUGSxVhKOdoBf6IcvtBlgInoBAgLaXNXx1acvIhleydYPAMMVAhWDz6lL5kReZ0GgPYYpE+NDT9QiDxOzO/FFLtEOCWAKXFzmOW38ILhVwOPloe/ARmmAYcqUsEgW/kXcOwdUJcIMtW+G7joQkq+Ju8ZRGZGB+CMdRN4czAeT8oAG/d2+1BPYir+C96G/R4WtV7NVyi7eyGkc8rW5QbZtUPdFZ9P8WDEH/+PWrR//wBz211hPrb/XVG864wNjjsy7YASSjEF/HefEd5wcBa2HDOUFa9LAI/o7zUDT+eQGjcU9gylLnBLGyzTlBGtM9aKRQKV9GvfjAqhzRCXvMwB8i4eIO5ETBi9skue9S2cgEjx8uqyBp8YM2rEeJ+GhQn7jAGGcYnz1mc4ipEB9rlfH4Yy3ktQB5pp1AnBXEIuU4H0iLfAxBXrT4cFMtWMTzWAubx35dIYYgjAB8HcdjoFR1OlHVV5GsAwEW1ixarB+FI/2iF1TyeXXgFMGuPoErfCsuEuGrjDNP5iPRpQe6t14OrlYeMhCDq38wBW0CMkgLmmA8Z1GR3J6EIUy6oZkxUQ/BxPW/AYb/4VI4sIEJRE7wyP4e2fL5decH6fFVMIxA/vG1KBmBXmEIkrHnB/WB7AjidZ+8HgbehiCFkYWR5yO1Fww7nAX0MWcEyTDIGUEYgvxVI7UD02gP+mE8ZfdCvjSqewXiuoyUP9HcFoeenQ3inbzzgMk4utqR4TOAXhRO6YSJb/ywmjY85YM0ajiu8Uqn8SdqZEMcgXsbMfFgRODiMxrdgnRwB89xzRjeKtgMXGWKy7czbhEEiDbx5z7HaSpzJinRGGHEZye8CD/IsIeBhnsodgZ9x7innw+AxkCo9sAXxL5hN+VXvGKrr4bxSpjGVl4Z4wtivE7GDiLaDXh2jn2nNpbXzGJozOtitPX2BTLEXkXGTMFKP/0Tn1GAyg7P5Co404J3NnwhjIt/3HU5WFro5aDATvdmTmSlZHHi2d1ovqGg8+PmOH54Ema+ZRcPr1CyW5L+8OypDO4aSzlUPwYf7bLkHCCfu/aRDO/A2VkHzzPtHmKXneNLHkZYfylMGUkf2l8No9QypqJk2v/sRaSy9WtpHH4bv/mDbZNDRmOEktx5bIeGSJjuc5OI+ZqgeSX1oEmCYIIZCW8J21MpXJ+20viJe6pX0/bEdzj8jYWfHOJHo50D2GWzlFc+Rl7VmQgONikFDrYLn9k3PicrJHMEzloIJmhYfnQACAxtNwRBMI/xlKpWWELAx+VjNcx3yhDkyVt5ozIKk9d/Ce5NdbqP75qf+/iueQ1fsG18Vx7Ct+SBu+XcgG4RHsARp/GQelsyTOLiJ4bq5HiNLMIBVgrChARvmsL4qM8QfJ8jGj9cfIa5DHWWM3B5RnoiOE46VRCJcRANNKa1OZF8zsRgMl+TsvgsZuIJPtVBxRGvrEcOhlc8DBq9BxjWiJ30jiQOGZFX3mFTHP2RpgR64LC/5COHHyyUtSdcA9uNmsLkwcOzeRTHCiiSYysN9DASAxNE26unGRzMebKBfDkng+zJ/6BNMMcUQLgdRYkMtzMVHJqlnVnqMMoj2dG3SNKwekaIwcKXfqAdEv7aX7lxJYKjOH7gI/YBmHhcnA0ilW6/gSXtiHAF9oZwsE/8I9xIRdQv/r3/1kqgVfeQQIr/LfF5KJW89LEkWtHcmB795iF9/hen/f7r7x7905cYd370zp/nL2P8wRj0tV4V43UxFi0YhL7hvAstaDD+8ET7Oy1ybAziFQdNJj/wRFuDgP98YDSvhqkv+6BojeEuUF5zaBWq8Cl/l2HGeOLiPOThD0y1l9NQEMbmwg0zSGIMUf3bI0kHCqGPHTTARITHeC+nwJgHowttgLE5bSGHjfKqD0+rvbODRYq+/sWiHqPPJ9oR9Ew/PhHPrg8+dexFC7tAbAjSQsRxteBRAh/xKoQHf6vhtNDJeuErK5QKrnj0Jwr46r2Xi5lPl0or8hou/j4//K0X1DlgRTlJk0L72A58mqObBJGSrasEo6k1HHXHnIIcLxenURxpKL3Ur5iRp2iEO4U1CaLtOsOFDMsVMwt8Ht44TDtWoj+oTf+oRu2zW0Rkt8b3Ogg6i34MQdoVhyGUnSDqB94R5F1C4GQUEp6vi3ZR+5F2x7EYpa08efzEh99ydhCH4H6oha8Pk1ZD8+tjWgxn4YpRqAeKq9aVv5wfhHFINP6E9g4hyabN+n6CnUVuHJQiuPi+TqEYNfAU68FU2h6tsH1ddpnF7fIp8VOcM/FIKxU3QYRS4Sd3DYuI4BvoM1IMG1/YaUU3nAoxZUAEehYufssGX8/h0k5E+xFDkJA22mAEkt4Yx/31MI2Vz7/Wlxefy3CusZOdli/YWcnuILULzgziNbF8fl6vlgnnV8zUpjh3KruEWhQx/vyR8dQ6VUf0A+5oED+Zjv6m+WlG+cCXA/7CA+2VKtnEquPsyNsupeWyHzCcBEoT6DGYsHuyfZcwhnDxMR56RxBGILVtXpt9+iw7fZ5oHPWrtjqIP18N03grY9Cn+j3Rbo3Hs3sIYxCGdvqIhmnLZIxNv8CoL5qU83iuNPFbatFV+k2FY3hPCSQP19wfpSMCfW4128UZnoSRQvGOeIXuOngOPpOTdrDlF24lUeTFbzzQO3w34pGPTcIZlzjgzmMoEYI9SyWUNO/mdUney2LXcFPD4M53pTVMi4KPpoWsyqPJtbUVCW03BHUucTwpXDo+lZVXwx4TeuvuT7IjqBkmNy1cGvCOv+a09PpXesOl1y/+6r+OXv435Sv/Q3koD/5duWm04GnUqwnBfXaNW59CJMYtRyN6WFpiufHdEiBchukLkZ7F75Jw0qMuxS9aWOi4ywE33DGdbNNRbrmmv5LKgF1epC8a2EovHDpY+G4NInDe5yJNMaWsYV9U5g6HimRXnYWINwm5nquPywQCtM21rCx84kGu5JbLRkr57jKcK92k6g7BG11180j8bOGNJLeSSZ45mfbjiUkXP7kzDYPLJO4wN6kkRKkJUIAwh8bFWq0AChIH/uFdZZF0CJOef0gC5i8RSEAuEx74JIVQXHgL1ycqycHVdhRarlbFYCB0SC4qd3GYpiDlkfQiI9ILT1or2kE4srGLNl+ZSziibIB4DjIwgYlnGNbKAYZ+RADx3r3NEtiLGrlbdfziZCqr1TdppY9FOqi2bWDfLJb/FyvwL1fAF8+/f/RPX/HKwh+9OHnOokXGn5cvdK6FfD4jz+eRX8ow5LODWMzoSTavQfi1By1oeBLOIimLZY2BgllY59PxjG3MJnqi7fLMzDLPXl3PlHcr3OOuEMFl7DgC5hvK1E35HNzqizHvcAMaJTjGIJAa+xiTYVTl93PCHpeF9UJBRBbdGGx4vYfzJzDm5HwgGXvG6OPdP09lBNJT6hwKzSsLnHGh18M4G4jXwR5rwa9FPq+F2RCkgc+GIGUCHfbfqCRccliadTTvpvu5gIh6ccTG1U/o7nWnn0pWKUwYDzZ3HgOHmMYooqwN3/SplCMiueWveigsWp/Q9j7Puli4+LZ6JhazVuelikUGcQkbxz2EwpYrBIt6G3/gEc27f7S49yKfhbka9w8y8nwvPu/cUHv3OUCa770jSIt9DEI/6MthWcizW2i+GuZFaXYFYdzhTKAn2k1G+2EnGb/DEGTDoNqX4/Da4BiE2FmnuL53UFtk1xB/tF1k2hAETgUQOPgUzczvoiXvqQXPnRSYkOA79lH0oE0Pa8ID40HPBWCFnZ46sNPR1XylmzOU1NnAVM7uLsHRThx3CBuOlK70PUzd72ESJFwNCdFyamxJO0DPtrkfZYg52ovaiF8TlO5pIzIUyhLAmT9+xVZGn+f6jDyGoFdqB99qNyXjKMYgxspvMaxrdxA7hPxaGYfxq53ZICQZGJQ4jJoxVTVp3XJPKXgaN/r2B4/MHFt+lC9XRn3o5LX5TV6FODDAblAGHDhoaQuNezAI2MtU8ATDSWCjWwhhRvj4HofBK4KGyIytsyvosfwn2u3DLh8b1TWO8ol4G9Zl/OFsoE84i010xuOnGI74GqMNQekTGINiCPKocBiC3G+kQ/qLkqesdG9tvaY9YghCTUo5Tjz5VxB5C71zmckk8Vhu+CplSCmCEWFhYvDcBw7hpIUORxusBHCNeJ9fXug7HL3vYpaciL4mcA2jQkomrXDFL+Q09oRM2MqtjPLdpIeXlO5EE874+4hbnKa7+4XFZtmso8BlThCU/wkz34QO/p06I6iDWW+O9nBhCqkOvh3fcP3yXf3S61/p13D56l/pDUOv2/U648LjvjedGfp9sq/4PY3KbXz8pnvEU0JLqxWDBoZrm60f7LrCV16wVz6G7pPrAKWBM+MDdYSMkWJvaeSJTcGtKMw584hhl5US2fUgRM6WJHQLR330YnpabofLX82gXXNHzD2FJWmHeAqCcx6axDERSgcyYDH3yRr8hWxRlbcnWD7RdrIH6EEUn7JTHnUDwI0kNwk8QeTJQCZtxRKcRVCePjCoZ4EZw4+f6inNlHfKvKVmYw2y+EtFa6LQAGWxkW3ViY9S6I7vRPElD9r8mPQwBLnMTIUHl/rOaJw2hSi0gRI3vuUXt5FKNn3wUcaBtrnmECT5cp8jb4oPbUneOUUTy0n0JOF8D3x4Nxmd4MFi4FRoE2kpYJ1ygXaHcJb1PvR2SuBW3VXyXgUP8cFf3vIRLlx5oIQrCXLbGXD66cb8joJffvvDoz8818JDOx7Y5eOn1zb2cNYF51rkdTCMPt9qcYOhiMUNuyB4lewVixutWnyAtBbPGIO4kcoTUs0S2g1Eb+az8R5PGJAUxhC0V0nhxMv4AEPGw1JbCYRbcwOWZPmiQpZLcuENTtqook3W9GJfFy+V8MGxuBaFMZOn0znnhV1ALDTy+gGvfmEEwtDDAdD5rLG+ZMO5QAr78/DgtQPEB57yShiLHC32ublkkUJaXb6hR3/Wm/CWbY/G6Nc8iZbXi8kLMWHeIii0HHRc/YTe7No4qYkk0XSSru+JJMwcJd0n3OJ0ofPhLMLIQ/vUzuDEdjUEeT42N3UZPkRRNjjmOUvXBX+mz/i0TaZPIT1ny8+5LDFmMp/zOpg/7w0sAw8wr4TZ1yKdBXsOhOagYBbz+iy4FvJ8NexHaOwIEkwabT+0IeqbXUCcJ8Wrhbwy5i+HqV2Zj/YhprxOxk4g4oATnZ0NhOlPtB/B5D2GIOGNU3Gq0WpNLDy/lI2vWzkR3tvW0XwoPLmW6H3+ld9xyqwCd1LKu/v91GrIrhWnkctUEBHEf9tt+C2NI74BCBsf8IGiVQ7tFL/YIhEE7J6WtuJwVOurYj4GAB6JZBcQbchGRMF+xWuMPM+/ifGn5wYxjmII8qfmOS9IY6gNQxpzOYOtB05jGMIQhPFRd43W3Gl3R5DSpWRjEFr6xojDaBJ68+KxwRXikcP5gydjxrn/rB4ER3kiKWGjfZkSPRd7y/moS7j0cxHHZ+TH5GHN/RqjcqMABhvGzLXj8nd+hZJXbzlPizHV563pVVvGWAzuHBBt44/aPjuBOFNIw+30CfqG5NIPyK18ZiIlYzy1TPdw0UijlLTKXPUIDWdDjCGyALP/D4x5hCNGHAwgEj+xQtuNHeYFXbaJTcmYGxHGUTbUdRnx9RP9RvSRgrfzE7bUDQ8muXS+YDm58HNd6VRGGIunVS1MoKVvafWTWsv8yNYIq9YHt/DGDeFUtGUav3yeGypv4pUGK/CxI4iw6pvBEF3Qm36lRwBHuu+kIYiCeBddGh61X+eqb+ANfDXQPfobxCDCrShvmjJ89/Ei947so9c0VjiYLpagdm5QdySYzbGHBOzONfmt5NW5QzgGxkmI6MVtiY8UPCRVWnhXaGO7oeOiKoUafUaci6BWBeEY3F0L1AVKCXdOZ5BOB4rC+t8HqjP/lrr5VrhPsBe/IAYOseSJoow0mrzN5wElNBt0lGgmHekpAGPM8YQPhZqPaKjrSiUaSJYGLP9tsqEp9rgVBz7KxWUiKhMijOZVoc39ocPEMt7lWmnyXdiE+xOIczLlC2oXtLQYZkSVbYtmnNKIjmg7eph3MRoSc2WMqHBdkdfwzrzDFVrc6a54kEuFcsnfkTu8sbwHf14JvGnd/Tzpd2K5eW/YPYwq9Jn2n43tnQO/fvnHR1/KECT7zaPv9GSaVxY454IzgzgPiEOjOROI3UD+qph8XnfgfIuXz/ExDLHw1TkXGIhkDLJh3OMVhdiCjPEnt9yZPXzTNiXq6tcF37Cu1AljWcYiGAXT7WA4ut8A8gzZV6ypPLwYCzLWGDYu/GbTBY2IrzXFMTb362DeBaQVhXdzsDCxQUgLkyc8oeapNAafnGPxTE+qMRBxmHReB+NVoOwi4rwKP6VmDlA6jMPynC56cCZQHbrwa5atpwLlh2idNXZl9Nwiu4B2SYXxLXVHvDHsFLi0s1xE7Rq8sdDRx2I33ZpE8E2ybWKTTsHk/0DmQUz0pHSQxQK+hh/m/7ZRFtyev+V7Qc+ODME2+ihOdv7o1R0ZdnhdjFfCwHFYNK/wvMIIpHCNPxwgjRHIXw1TPfA6ygcy5PjVL8F+NXBeCftI5wX5jCDRMQjFCCQDkA1DMQTFcIghKGFes/xIjSeGJWTHWPSB6dlh5nNW1LhcNLQP2rQCmeYoLEo1rWavwh3ey7T4xD+K+QBKB7Fg0lgu+MEdpAPYGMXZyj+148WSTrHzIX2XtcFSer/fs4KSn94uPliJrvJJsjIJDLoic0+n8Qu8foxbPKTkXKngaC/TZmTEYacPX1v0a7UYehxmRyWGII2tGmOBeY2MsfUFZ7ANbCOjXtNF3g/ck5Ge/CxQ40c/wSgOjYqGl4ruCAFO9Ix9Ao2HbuyEC4OEH5d44UyoV6ikbVbKAoRdofFhkkspC65ox5DWPZNNA54NNaKzKw5DO+MmhtDHHKyvsZRXwWwIMiyDEDuBMLbzKXl2BPGqrfifMD5jbNevcze+PlfgMgAmTx5zlZ4fS+Bbt4yrbid+0prWgbpHsSiuWbkGQPLA6UsT6+AUEB4AOUqFqCkdMGcH3jTLv8U9HJP+OfYKUe44WsjdFIurkOoeTke0lnu42EgkROxKSGrhuTdzlUmkU4SJdz96MdyApqkdlN5TOIlJ5wynz6IE9Z015toh5D6HIWjiPtYNwWeaz38N98/+algHwr0OGCCKfyiTfyq+h3RatOTI+aDHTuVtwGI9oL0UglyDZZnu8kA5lwUDTPmXjwq3Yy+eQoe6Rey+iJVveW2ZzmRSOOIfAAKiVFCBKxac8YNuNBddA8PcmNEhxDUMZOCs3NvlnS6JnA5M4Q9mwUvKGZoJuXrJZ4z2cH6UhXKrkdwWf1GYC+MCON3GF2pFC7KkxjpGtgsCvvKeZAiZGwGegOunydt84IE8OXPiUfT5/9k7F0a7bSNJ+6nn7E7+/9+cWJKtxN76qroAkPdcycqMJxs5vJcE0C80Gk2Q6AOSDQJx8Tlf+kh14LrZ5rsigRmw2KFgINMlx+Llg4tOeNqoP+NEiRykfsvPIMAN45dEZcgnccqAGP8eIHVBqDSXipFgHYZmSUh5oSp4RICl61ov5Wh61WGz3XISvGSHOTWfQFgon7ChvSRLNIRT4LuT3Ra+gKat4E5wL5f+3+kXWeBz/fZFwj5PvE4bkdYTCqPcm8nPS/rXplAs55v/UjDILz/VBCTv/klAiBdGf9QkBRiTmf96p5efEhjS5NeTGyYzEpB3XCjVJOhnJsKaILM6kqvEjDoyUgJB2DZdrUdwbLpOORi5xDODOGNcNsa9nnvAPJIoVV7gjE05Bz2+zCDjoat4p6IlnXE2X20c/sFTH6spGJt5LKcrM3iPC8Eg3u/CO4H81SfneWdFHvMhEOT3V2ii8vKFXiTtX6lFK/iP/qQxjwdpwqOxuJMfVesRyHopfwkEoVOauGjS5Fg0bVceId4G3uLAUlxEKp75C/EUwNf2gK5ld0v7ZkSd1CPkdyfVOjIi8JRHVZR7r9qq2wwmwfEBqHJPa3rziE/uosuy+OVbftwmgcp8BUw4r/bRb8DyWZ8DKvsT35SdV1DHq3zk17rY89UnvxCaYI98/aPOi7wgWpMH4fMIWd4ZhK48AuiXP+NTyvuz1kx4FQz6Xg5BUOcHPSbGO1G+13uEXJZvJdCj4I+chVUSKWuyq6ARQZ+8Y4rgEMFFYFkl5JVDylMPvoG7r0AQFqJsS3FM2Zk51J+CDdD0OuCPbO4DgFO+0C74gWxWjGRNogP3LoMyDNnXrdgrdFV8B6/yyUcdR3mUrT+BypCjfhKOsQafpI1up2Q6b9jARUhwxn4lfIOMDgYpEO5VP/KTD3q81u8CYjWl8l5dKX9hDH2vLzC+17iasZUgu/LiyaOGvDNIo6FvS1JPViTxeJp0ELw60WSvnHe7GEvJaFenc2d8DBCCb3zOmuJTMmsOc0SSBYtX2zJjYQsgHSbvxHe/YRjS/CBKgzJmUZbbyt+xuwJCGiNfOkAq39b5QfD8lYJA+Vw8465WAb3JC/gTINJjuPp6GF9i5Nx44UBSAu25dtN6rkBKpYN/fHUqGKk08bmhulGRstvAODFtwT9tGQhmsz2mvPMluKYpFXYz3zZdRTsF7E/IK73ON6OnfXmLNA/FLS45jngyxxObskDaNiYCczwxgWDDLd+s5i19uz6Y5473Op7S3es4KXJunpDkz7rxrtJZ1ghccpXBfpxDzrmvkcOdBwnBV6jDQfarDATR1nOr0U7Yo/zVITfFHb7LdZHQpp4Yd3M/zW3+p7gTsunaocIme5Id+dZ9Et11vOPKUzHBt25z07DbVq5T2o1k3OwKPem3VEkbp93UqaH1GL4KF06hdhk6OzuZK/iqj3BGj8w0MYU9HHwuELRqorbpmqWkYVsJTs0otI/JcSKyU3Kel0xMmRTTMMD3cacR7Bp7KQIm0n10IZBCXZsPQFZmDcSlJl36OB+Mf2nk8TCiQqrNuirnin2hiUxdrySdG0LhuFCjOxdCaOeAvUMNMPJ9RCiyOQY8eKgDyAVskAJTtjxkcjcoul4QVTjqUc+abdcMHgMHYgEI1P9Jc+bNYaFLPYGon+3CpnI52/stm3hhpySBp0ygpr8D7+US3uFnZeqfJf2Ew/vs9ojwEexZAf9G3C3QPsKMzd9pzvKX0MF3l6nyCeo5VVhuJs8Kv868XhH0zTsmHZrV/KLJ70dNTj7+Qp6viCnoo53VQfxq/ZNeJM27LngchslM3iekR8OY0GgSw6TGv2izekITIj+i8yvjDteeXD2wb8f7BIJUHqMzFjToA2xuz1ZHgc82N8XygZx1GqeSWQMOkwtfq8WQMRUa7R6DRex0kUcrgZlgMyb7S09M1Gf1Br9W+1EwBXn4hdrBIL8PKF8IezUvh/aLTeERLzz8Us0kx6s0Rj4TINSNZbiG7XybeOLJs5GmnWk/Fi0My4SuNoJjQymF4g4L5vcduU6Ivx02ip01Wg7wJ8AHNagxkFXcvQUWoYNpOE5+GirO8NOvIC1HNP5xBohuQ3h3SwI04FlpkYAPgUoCP3wOPo99aUWH/J+8A0EKajoQ5DLBH8oT/JGvs/LHgSA/Jqb8r3osTPL/rvTvfjTsb9aDa77vUfArOR9+8IKviCklgEOA0S8fJxBE2Y+L/Wi/W4+FETSSIHbwDfrkEUN4wAHHb0NHPcrK31klxHUc/1d5diZ3/k8ia7loW0ODb2J3Zb2Rjpldbp8ZLgi0bNdyocXRX/JZMVd2KOLHoUKIoFfWhdoZEaCQFaHWMgA/isBNVrxwi00w/8PQDR21l3zwlBOMSU3cATNWcZ9JgNA+JyK/84eXQfPeH4JBSvniIuMlYyirKb0aSF8WY5Xlz/If4HkX2wTTxYdfuk7d80Y2AaHoFV2ij7Xm5kq+xV0395fZuL+cgcYA2ogXJDwCSB5iW5AHkw52DpC3lMY+sgG5mmZIpCeQgTpPacq+v4KPMhrKcvZLgu74q2pgZZD8lfE1L9NnrGWFJYGfV0n5UpjKrAbiPUFvKCtQ1JdD88JonQI6D7TLL3CN9CTnHW0N7HuuC9LCsNIYL82kO2OEDS+a/MgafhW92R4IGHmFBoJtaacJLkfwYxGyz27QNBDUOmJfWIxV22jZc9KCAdv2w5mNtqcnCknqVl1BT7QHDV3qpoVt5dYPyOi1zk342KaOMIVM+XLgNs2H/sHxRjCutgjj/7WSwKeJxAs9unI+sfnHAedzp2FcEGBN/1UFgtK2bZX2UdLCsfLecnGN4ex4d6uLtDRwYcSW76TU0205TZUoQmn5AZXuUd2lSz3psDtd6yxNqjk9Kfnwte7aItTnsfJ3CvZo2EGMlLOmoiq9aeGnlOabQrNtweC/uXNqtbbSCarGh26kTOIT4BB8dsGSUvxU0yIX5w4hwJpXrW3GpJyO2aBjWOrgUXjTcKaG1t8L2WomiIki+NcaNWKGFEsnwOFA0FI00uFfdWtwt5hVMRi2HEndr5XsBo5AyxGFFWqrA4QbSb/phpKbTdtXdPlzBZLIFo29Iog8F0IdqDP70E7ZesG4jAAeqSfMBAJIp6HbNWOjsLsu+CSbi99kheeGYfdV5ISgfmGtqRS9zIlM5dJ86167VK5xIRkOsVOV5axk4aZV1mYBIRc9toGPumE/N2DeiiA9CAuGprS+XypdmYtsRYZTX2xIsbmQlqHpMPw7+SILXLoBznbYaVbBTCeY/eGsoXTlAwfsLJ/0t/zl1DrkA0fEnyUQ9EHRGAeCNHgxSebrNUxCSAns8G4gVv/wmNg7TVg+/Kxfr1t+9yHvDOKxBh4VI9Ukhsdo4MeQjInsnEOeSMnA2Le7RyAKs/X6ln7IiJaJ471zxWQf8MHcDBceMwYUNGMs6KQEeVxiDA44KRQCeMKtzvdqoB+0skcT9LzThc8Z61EwTUx4MTQv+32lMo8qOCikMr9O8+6gPDqWVRmszMikn5VAGYNRgboYFqNb8qjDNnOnNSZaT+Dw2XKZyBguQwHHhKE7jHlAkVuK5HsMV0uRRAn4KWvo6BjBnQBS5b3+qJStIu/sZxn5aih+YVnmbOuOmsVjNmQ2P9UscdIBNBNydPlVk2dk4kt+xIagj4I8/FDjQA+BHAI32n8hACrfTZBHvq9nJL3qhy+AyY9Z5ebHwZT/Fb/WJN9BIOW9QkgpK4bA+etj5AkGsZxD9fuaT1PRUe31Sp4J7BC44QXkeQm53islX2MiTPDxh++0Kgjf0eyWIM/35iHoo11wVgX5fSrwsKvMu4Tsa+aDhmBQAkH1eweFpI+DUxgN3bAnKQYlLYzi5ItLZ7hpcHkrDQXsLhbzkWdzrwI0RkDgCykEk+9upmthaAsbecZaoaG7wy/lVOdj6xRbSDiO8Nahsw4yMCZfeVoR/zRejc6XvOJn0OMP+JkD6hpYPxBYlz/wLjW+tkjKmMp72N5r7HTwR76Yr4qB511BoSGgxLiZMVOpTMQKJGDU79VI4+dug25uCLa7GXQIOe4xDfFIM/niwOivtJc83PCcm2hVbzfbBlHOYK8oFpISJoUszNJQQhj/uL76i3q8ON/ngcZNrbb042EaR/25eI2lb/UVxpesDHJQiBdE8+jtPBrGI7heEYQszhsC+fHttC5jI3r7B1CpsT8IoDxqSRf0I4STa4+IMTabssXnXpT2YDPg9L9LHFx2RkD8BJkuO7Wo5EAg5sFWsO9TRedxlcrGxmF0peZOLbb8Ia1SAJGPJiVoqZikhYa6EuIDLYHLOQB1OIqLLVIHrYdgKKq7yti7HFUNKmAlU/bJVhoLHgFOlrCwFOYpyiOcyLCW55HupwRbqZwet719HrlkoXw+/qt5NAxL113SmaeVmk/HxaT/+LEnyJZQ+Rvi3OHg4XlMd8EdPJEWF8GJnqBo9Ok5t+rvRep5brvoMESmfsDzuBXYX9tzyDTjqD66c9pdNtGtE+bkWUCo1cPSC6eeU9IiZmiLOCu/bebaRvagQmdpzSJNO7IteUt/VJPVu+h1ETZCr7VZok5E8+5q1WjaI33VCNrhC5Jtjz4TCBI8VitjzS3tdPWMJTvYYyUuqoFSSP9TSexHhcHS3sPuzgdjOEEgaEvfdKth2dRHHdkJAk1ZlTtQMnizgXMm9Tg72iSfo21B1mSjr+u3ZiayHNeb+txW6jT24hVqA0M1dLQXGtkX2xvaA0hTmVDVPdzEtjbkhUkZ8mMjWLkAA6tGW5yAonvus6WWCTHyrs0Y/QR/sC29XGfqDhn2o4zAixZTAt6t2lI+4cX/O33OAtuyDyx3II/s+GMkxjcf8LbCMl670VhQd3DLZaOzyf9ZAkFa/PMNwSDGyD7a8FGTYfK/6NdsJsW8v4LVQR/0K7YnNQSH3r83zL9qEzACz7uFRMtKCSZD65dynZ/cWzOJYXNYXnlPZgLKKcy4MAPKkEqxGddEd+aGLf2ZAcZ5Jhk5h0nHTzys6ZylrINTwTI5KJGvPg4EebLNBF0TET4TTwCIx8P4NPzbN69d/pFAkCYibxQYAsdXwQgAvVLKaiAmJv2CjV+EKsUY5z0JokoaqJSNJCOviz4Ag8bpQcdovGkRwjVwCOCx925LMVLFlqHZlK7mEwcow7mIVLTkA5x3MU0NYpnuWywrc4pDX/drdQtVeqB5pUMH5JTbMbxq0EZ8ingCQSCu64r9KC8/lh/ij6SUHdjR173wa97jwztaeDkv77bq+4B4POdnv6eFoE9WAxHs4Rz5SLCU4I92B38IAAnWdw4RYPqNnR+faKN2ms6B5jCueCWPMg7U4GeexCr4o1nsj98zuWVlBCuE+OR8PjHP+4T8DiEeH5NP5lPbCRR51Rp43pEinhcEiSSTIJQDQ64rQUh8hWBQVyKjE/o1tZ4tDw71t89RuvYHZWR0qzz6bMPbW0PlIgSigNDbLd3MV/RQi3HlVoaT5gZOkeOBoNph2udIAPRYVCKlD1EkgRbg5euZSBke+50GNVaU/YL/8CVG+dbPH7X6R19d9Mui5W+sCPrpPYGgDwq0a4UQgSGtGCLo7l3BRn+FbsZM/Jqx0l+nVdpx1Y+4jH6e3KKH7uEyHtBjahn2TU7HwqY3hQseC/DHCqEanfKx9aQ7QGRjJ2wURMdvRvlYxVSSq7IEcr/HONjVQF4tKf/kXVkEgvLCZ42hvBNIAZ+3b3khdN6/9paXQwuelUIKwmtVEF9gdABIwSS5/fZrVZbWSjHpTrNoD+MveQJCtoJS4N45Z1G3gSDgaphtA4Gpwm8h02ZbatntoBt6IENK9tkNGnZEyaIP6IbCujxAG/SI70p7Z48F2rpNe/OAQQR6ldHzRSQg6gxb1LJhtTPZgX/AEuyd8LF401bGWOmQvu2Pf/q8yRXD1wyU1ggutcUpX9Gdy2rC17UiiPapkR0U4mSY69qdtRx0+4Qu9I9Lf099S/f2ttWJl5y6PqZ7qvudruU7ZeFnHdDkvH9sPyx73wx7hLgT3gaAew0VgV5Lp2WTGzU0yNeB1PkbiasvzASGXMCB4D/jMVyZnmxlhi5Dy9LPtOBbUZjThgoCd8VXbw/kYnczSWGxOA1KUsorgkYM1yvuBdhIuHkjV13w/ZiLKHD0hLYbN0+0MyJiv5oXGSPaBJYl+ZFduUKJCBm0pylNYwjlIsglypMCgOz6dxsozha+VZtpikt6EKOVdSPVdUz9Y80XSfSgfv9LeFC5WFe4L423KqFM+5Yw1UH+KFto2m3dDlTE6SjYeS4VXkl+10PrLv+4WYu7/aM/CHjcQUNVGZv4mhPZqruCx2Hcuzb8IyElRlzyteK1gn+XnrPAaVUseC8DOGF3OTknprtBDnF7hqLzB6CwR3KRZ9fZopBqGGnQCpgAAEAASURBVBO21mfgV3ogEKQ5ixqryfPfv9UEVxMNJs9MeIXM543zOAMTF14OzaNi/Jr9k4JBvBDVgSIFgfxuDP3ynVUUrLrQ4wwaH5kok+eywWRpjZeq1qe4dHD/6EA5eR9TbicZE7i7Y64z63yczmecqR/Qh+7HwmZ8N43HYrih5rr1qyfSTKZ/8Lt98r6f17wQWpPzN28SCGJVECt/gPPrNAGjlwr+EAjyhEbvlsxqjky6mfT4l2rV5/cATb1paLS3vm5UDi1rbrM2YIzr6OvxHQG2QakhxT6HjUSZrWnqG+DzSUWYjYMA/h9EkxFrG6PV6qtnRIsekmEXUVoS6ghzC6EDeJNnfwnxyBge0XmCLAK5m/xX1/eZlPcrTKzk+UWPbeGvHxXsYdUaASH89mde/qxJOnCCQ6R+NIwVQQR+NDlkpU8+7S1/lmxWDLHyJ0EgJhLa5e+ZSNDI+Hp6jNbgY/EJ7MWk1AHDWdXDo2EveIE075hiVZDgBIPsi/JHv1NIgaIfoWGFEDTyOa8qkg+GTmWCRcj0iiAemWG1xAR/VCcBpb0yKP6APmuXptYPjYFT1s5G6i5xxqB1OGkAtky7L9sqrozQz+VH0IleSlykTuEgHB0D4bhzvRdxqnMobhaN6S/K3lu9Aiz2PeCCcYfljQRjqcytJjEEAkEE0+VaXkHm96k52KN3rhFUl7/9108KBH14r7ICQxpXs9pSwXQeFxOeMZTApWUiG7+2z3GXgu+pbB2hkb4ibMAqbUMnDTTiXW11GRgqB4cTJmRiytCHgKMam7alcBwNTxl9YkDnhggrsY2lZqz2fa9EErRhNQ8BTa9sU5pAUAKfBEMJ/OyXReuxMFYDaXUQYy6fjydAr1PBwVWvLlJb/CVF1zutcidig7GG0uZDyzlJ22mDGDmHxYNVaJOOIF12PoXAXQ/ygjGhYS0P85G4juF7lCxzq+5acNNZQRepsnhqax6ulrcWYFOindOqLXbl3OqjRLZ84Twhi3AqD/cjvbcU849idE23I1vQsvETnAB32F3WE/xIRbsEUJn96Y/7EuEYtZHB+aYzK+6s3FcXCKJ56yK9PcQnAWbACB1AFh1Ms4ErvI7fcmlI73SPaO50J3/zldO08DMtjjrIsz1XX/k2nakLNr8dWWI+J68yFvODDP6ERvarwTt/Asq3CNOOsw1pVQipd61iKS9pz4KxAXSREW6q9K6DSVrfCC97RRqsg8lgnHyemQXANqmJKaK7Ecr7krlplANnDh1Ch45D76QXKIsyxKtRRFO+0KuXDGM4QzFWBG05vT6ZZuj86JZJ8HNOf22Twgpk9SkiLa8DhMpDk0HCBctBUORFbimdSojFiJxy5fuXOHDGk6Z9pKEjVU7l8vPMcPmt/CAWXvTuby5iMLttaSc0kV2ZWxcu0lO7WEwJd3SSIMu3QIPBbGUNN0XgQ7Lsr/Jireh04IZTFs5tm7zFlHGEhT11XfoKYmiHHnL63VvTKa5ENEZZHLmxrRhjsUX5JCPLTVXRpeWUnpD/G3BY4FF3YLcL/FLAL4QXrOkhbmVte9HAWj8fN1s0ZNpHruIouGzmIadOnRZ/lkCQ3hWtzyBjIN0KKRDEBMOP02iw4zEZftn+qEkMq31+8nsuFPBR/r1+4X5HEIhftf3yaB5xYBLDV5T4FVwrg2biwgSdd7V0YuNJC+aW3T0CKfW55zR92ef36cvglEkOztWfKaRD3f/KMp64/0yY8Q4c71wh0D/gIxgPhHGAGz9NvjW7YFL9UpMNPvPNxINfov/j7RtNRl67zGSdFUEveUxMk26+dsN7KggEeSWQ6hFYdSrVpY1qrZOaQT3Rwqo4T5kWFk7KPuoaufmkK/gZM8sTCUJYEilbsUlbCu5TRyjR6NhUbD9l7KMxT6g2Qys7Grb7c4+lyLJkNzBMuX/Zomhxry0eFCjDxUFbVwH5HUDyu5XKF/k0NyuCCOjk0Rt8OKvd8jhjHml0wEizdybrvyho5JdHe+VQVwAhVwEjnRe8Yyh16pqrABG+7T8ptHxXEH+q2Dp2QqxJsNrZH4IclNFKnhcK8BAUYuUPj8f86M/LE8xhVZqCPoLlnUL4GTAFiwgSsYpIK4TwV4JCXhEkx/NjZvASEJJcvsrkuggIycT4Jbu/aqYypgfeLkC/XksFdg9hZ/C1OXC26YJNIxh0CwHRWagAwBDKdhuvfAUbzwG8gK0I0FkovfHDf9CurBvUEuMCUspsoTkIQeDFOOvKfTfE+Jz+hK8UM6jMmAMpgXTGOq+qJBDEWMh7gjRmsnrSQUeNkT/pMbGf9PJ9Ao7veV8QY+wEgfxZefkdYzHjI+ZBpleeOajeMRrfQyPRiLA6JsiDZmlbgz4JjUx71cG5p1RqOuCxifmGbJ90I201nPt1FYZugam21gF9OJHi4fI5WVUp/shKSr76RaCST8fzKfisrlSQnfGV1T9+FGzeDaSXRfsdbMLlXUH4f/zZgU7yqhv56O1eQjHrMa0HR/3QkZV+SaHD2iTufOeHXHTJcYQhZQlGvgFJTTVE+EPoRGY6aJ7ZzDPihiQ9WxkqWSD64msIv8qiivLcMSdp80nhyNyrPPGHu/hylQq8YNPGNV6jRPvceVFhO+n7KVvssV3sU5VthoxuVUGwEe30TgfO+ElHTQNpLatGKfgPWRJgHvL86asClimqryoQ5E6ipd1q0JZtBgpPEIviv5ep3FOJ/57EcE+Httes/6fqCP21ndDjrF+mT+ifZ3qkxVLz2aqecj2q4SnVswKNyC1I2vlpyuew51DR2pteremzbWqttLRh02foLZYUigct7eggVnNP9NYcMqYHFnUEfyUFx8ZYBI8HdVLRWwbBkmSIAUO6t1OFMy+KBuAsx5UxYISfm0A2ih3sktavSCev1JdeX4jN5sPyJ9VL3tWPDsFRRxADNt+oMLpMiwyMThE+8pAQYa4jFpqj23RKHp5DzDYXdN2p4eCb7Mm2LgyirL6tG27wYTvkAB8hG7pz1EnJNt+kZ1VIeLJtP5FwiztTS3zCswGtvymY5E/Ipv937uIHnzPHhfhSqCs8tfaV7HM1LDnPEVZcJ0XP0X0t8I86gfQEg9xYYyiTCe3+tVljmic0TJj1qzW/UjOZ8Quk/fJo8jwqxqSaR2zy7qAPPErjyTQTZk2CmFB74jyTGdVHdRlHSTNmMb56NYWQwTnjMdrjbJim/1TQCZcRwKrTgIybwNkn6OMJrwDAeFkv71ZRVqnGYdGA96RXMIh4zCbvasmEnJdA99GwN29ff/P21etvXuhRBb4gxkuj+eXaKzKYzEwQiM/EIz+rgCRzdHo2EIRCs61b8jTR0AVTyZMb5BnTcTPlwiIKg7Edwo/yHWrU5w4WyXVvCEdIi8+zlyITGErVvJitp4Qu5ZLpmN167UFihNeXXhE42KgAkFfv8JgXq9rkewn8EJjUxFowHvvi5bykrA7Ko2H4bFb72Hc12ebRL68Ikj/7/UHCExjqCrffVBc+66+Foph2vjiTwE98HFhWCWEZ/Dytxt86vvgxRBUcfJTDUPYn5RUQ+s6rgRTUcUBHwSDTxfe8+ke0rPzxo2SldRBIgSHkkCdoZBgBTk26WSVk34yPCjXvrprzp7pJY6y/uoIm3LYTZ78c/AkH5DI2umz3MsgTduQ5eS/bgbvAKTzFbYhynaSilcUesifbH8WqMik9lzT51BSbRYVYgNVo8UWNdxr3/Iit3v3jsVPjJ/7GCiFWUv5E4Iexk3FU5fcKQP4MjfDACWYyJrKaEjn4JCvTvGJTsvFH46WYg+zEL9xY6eXGTYO41/Rkn3IwHvQo2ba1ASMNeVIEVU7yAsx2yilMqWRVEhmkkJoanyP/PV8L01gpx2NFEDuBSh4J+0GB9VcaQ/nSIu9lIxDPY2CsviRlfwGNxtwE53VOONjOCiPGc+1UNtt3MUZKagIo8O4ppWmtetYwyJQfHpL06LYc41AtAvV2SyG8NZ2iaxyMUXd86ZJ2nDuhM5IMiH5ga0pu54M7j5/CnXRn/uhDga+l0NGKtGTnjFF11eds6VXGiYm8Hh9pC/Udfi8v/kFgx0WjTPPoRr/6/mLk5l6Dng6hzqSFh5jHeP+PfO6P2ORr421/hPRHMrGEqqxB+FUMFZ4aGYrdUQwSppv0kWhgX0r3nJzCK6/l59O0YJvzaYtO3sg9IcnnhP48LzbEPlhy8zyVV0jtvcoLoAwCmhj+qD9i29RbKduxN+ToAwFXNQcBsNQQ4Nla8mzls2otCA6eVu9tdBWiA+fGkQst0XZM5l8FbLtSpcahGv8RDnCrcRq6wqi176yhDxJQCY8fLTrYGwhCgfoHPM5LNnkfwbvBuoqigKsUbm4YqNN8oIe/TbEEX3zJ7Q0/QxCpB0FS74L6ogyalUzQwZt6zcVB56erJy+8ycieG0AUwcajW2TNMcBwiLRVRbdT0OShmStRWpOWXyJsuRJOBb7Mixkl2Wc7sgXH1sHXdlMapkdtRJDgNFF/0Qnyo4LRd0E20ch9kIjYbLYdePo/KaKXLLMi8IQ0nzR6YafCzfT/6aHGeaRr21kamvCI7suadkorJ1IfwY0/EHHvAA5wxTi13Yv8hOCSwNTTpoIW7sbvSXuJvuL0DATRMUxkGDo4PZjQ8IgCKyVYJcEjNeyspuiqICY4/Kr9sz+VzESHdwT9qhelsuICPvETHJLgPCKWYBPjcFZU6PxTnj8HgwaeCTSwKIM+piOjzd6pwSGpPEEd6z7TjRt+kbImvYyzGk8d+NGswRMGfU2nX14i9ZgsQdBlVQUTaN5XQbCHR3X0CJgmIwSE3uhxhbzDhQl2HtHJIzya3DDZ8QtLU59Ee/LuCYflR2980OM77XAD3KRMnNq2gHwsCek5EoK8wmKbws+UfKiTy7G2lBSyregkOfMmD4+74UYfzMmQPCta6UbIoSmdUx2mF0UAxQhVskbWASGtvF6Bo4JfDC34R4JA+BwTaPldHk9kJQaBH750F7hfdE4wiJVrrHYjIDRBoo+aWHvCTZlVPpLJpJvgEr6bSTkTcO1xyIvPctIkoJnJBGVBpLRS6NlkC7lZsuTlJKzqSUBI/kpwUkFGgjj4ox8Nk99+x6NgSh2kFA3vqPJLpuVwrBYiMOmgD+8UckBScAcnFcjUxJoJNBNwJtmsxvA7WoSnfpFnZZBSugD9anJS52v4qB79V77n4eZbvWo+DkgZIUrm0m3bLDGTWatIikCpZb8CP5WmnqkthG4EWUGrimGDmHzu9rSuBGbV61R5Ao5pZehBs7P1ngpauYzHUILpXsVD8NHBdK38IeijsZBVaH55tFZY+mXSrAbiHUETGPJn5GccpV4CQg5syi+7ss0vMJdsP5aIn+KT2tGputfI0Zhjxsa0g3EPWLBOKWsvFFne6CwKpgfiEVZHjWxhMcS8lPmTE/kdbKQaFzUMx59Vltt5BdBLrQB6qaC6H2+UE/r9avg1KysdbGdlkHye4A+rhRQYcpCI80N4xmcCQcgmtd9KRxW1SWEbQvjVEOVV8BgqPXlM1zoqzwb8Pp8BZbQOtMt02EGyUzJoDhCBS3GZa/GZOwKhqQDYVAY0rBeaNXZYLH28qCxil6b/h64yXQ11IH+IYwNDplZTicYYS2h7XZgDMCjdNssS/VZgkUZaisMxlZ+YRe7MXYwpdQBeLo2m2do4l4LtmII+p6zqB8zWE4D8ekRs6MM347XlKhCksfarCwTVSf15OjX+8Fk3uwcGiNP5Wm5auntafNPg24WUMP/eStd0Y6458N1Ovca1D13lcnKQdjw8V9nIiQ6BQ7HLlO7bqnuE5vTX0Sptve58Z7mtjoiWoov7QB5seSIotvz3GtqeBC9O3R9zGrp0R+pdYvwAzJDNRBlINjguA0cJhbgPnHDENukH13Y5aTmVM1RblZ69ZowO8KQKc08XTX70p11c1qY4NCtBWgqr7dNzYsSH1gRDFt+WI6ddYqFJKdj6HRfmCCbJeUIVUINj0OMGzn+6QjmdMoZJIIiU9pAiTzmnCBdM5nHxCQ6667kZdThmiExzKXe3mr7gYdTWK4JPblTdbtt1jEICSEv9oSieITgoVz55pC9yMViIvQhC75Cvdh9sA4w80XCBz690I9SykL8qsEgXi4P00YYsy6NyCEiHSWVAWwQ52tmtuaaBnxSl/DOn235fZoX67tW60x/qXOCWHcL0zFHZyi7C1I9fDIsBpbvDz3JuKr9M/39F6nsgKMEYxhgm2fyqrSCOJtj+kpgmJ0xSmLzwWWQeA+OLS6yi+FmrK1g1xCTH72LRZNxfHYPeKzT4NVsyNalhEq/EYzD1MW728Zr8yq2JtMYzT3LAKYrADq2JZWj6yucmqfb01wR9NID6hbhMoJlYa7Kbx2oIBDGhli+J4Qd9ZckrhJSH379Ua6LsrzYxGSEQxM7qH6Wv/bJSvTiaSTmTcd7RsibsqoPHbjQkJtjE5Fp6SbDmKFys7L9Wl7yawkY7DEvReVDAzq3ljLaURoBTzo2Um5ZXVS3KjlPQlDtyK71cz6RmEu+VWW1IrZZa3IigeMJ9uZ/rJiRcO7Y2TE6BCkaHsElAYKvo+tGBSbdW8X/zNxWYLH+UT3YF0MdZ7eNPdesxRlZa8LgiAaBf9Hb0n/UYI49/eSUQE23tffTGj36x8sITbAWEVM9lwi2lEoiS5vJj3ytIh9wz0BrgYnKK76K7CLU5yOGm5X4E/2Ay6ke/5Dw/aPeqHXzTu/xUPuavhuFLDgaNj8rBsoKIoKR8koCPUh4Ve0nKzuM37H6EUalofuTdLJLJ4zTs/OqtqhIMkm74MCqyo/qo636gLe4PUuFMoczKC4LPZcMS2nwY0LVgILyQILf3WaUugeVzKPGF4F4Y5c56kX3WbYHUmUzXvFe8e0f8FuEUjB4RGyn7jKPuykB/AkD4i/zT/pMXR3tV5YydjIsftILy5/cKDDF2yjdZpcYXGb1SjYCRaQlAEliSfxLcFPyjHk30qjWC7PLf+KzGU3zYAykjZzbef4JuOT+Vd2MCQ+WOB9GfTt89Zwk+Wcn5Tm/Y4RfEtJEN1oEf+aftKZt63JMjOcW/hMO3Faf0OOn3qbHKR0EefJEVQS8U7GlQyI/aGi5/JQgkvyVwlOA7tOITj0RbNp+g73hrDX3S0XFudHRGb+cCY1ye7nfKmJyRrG0s/aaDPdgjlZD4LRbd4xl0lPn37lK5C1N6ZOkyKJbaKiTfdsSLQwMs8HM+WBhy8YXdq9Cac45Q7A1MttKpVKDSb3kRuUEDTNUbsuwQKaYSTaiHB+rlV6HrsX7rssjdN1OHNRKM8y8Sg8A2+B5gIIZSdCaSmzVe54jPYRVyjkci9B2/PXSPtB81MP5fBR3/iO2ftCKIptQkadYqyXB/7NYKVo3/Q9UhVyfG2ev/oOReEJ5jbwvA0wqXO4o8x3TA4fmUnnf5ZT3hhZFahygx4Me2fWqa5ySe0nvbknaG4y4/NzLhuuO2rAysu3xY7wQGPJAt7dRVeSO42CirfMx/0gxJBYjIdEtuED7hhXAwSLjdL7nMl90TDyYh0PA3CNMrT9EcGjm4IWTzpYALH3+68OXCSEl66uLJBdF/vegCDuu0x0KcN5yDaabyoW0S3cFVv9LtYdWBXwnJMC5xv9dv22Aqq1inUWpaIqQ1TQCxsgOCU1t1a0mQJVsCp313vSyi9ZpVkFU/gFTSSVWkf/oIe65DqVeaAYntLW5fyFMZrcx25gbkZMNP6J8n7y75H2ru9ovfI1CWPyo/sg+ZP4lXJ9PPdUvuLerKD4V9JUACQXrKII1VSvs7cmhRhCYXmmxoZtNHZ3jchkCQv4bD55AV/OFX6j5q40CRJjFMWvglnBdLM+H2r9ozMcpjDIy/TGCUavxksvPrr3x6WyMqZcZdw5SKLoGg5DE9fcV4wc6NLsMpkxOCPg7+KM+E2p/Z9kS6QSAeRVB+8JmciA5ZGq/9PhZNoF+8TKDnld5HwQSEdwIxmX6j1UEr+CMnYWLOmO6JNGM7eXYp6PevaLZhXauj7ItfAeOwcJSPzfiH5Vx3N57OG3kZHCUT7Ak3iaHJ7WPkbGkb8yAnkR4vI7qVLsKCF0CZ+JIZR6NqFl+DdvOlPyvYk5tRrS2ClmstN+vsnijLh/L+H/kaj93I9/zCXaU8vvhO72Bh5ZoDlXqU0RNv/FJ+SqDTqzfkw/ZD/FEVaL4d/8Qf5X+0Q1nRTmBI5waKWB9laQXnDvbBhymAC1yyJw+cK0xWJqil+K8ax4ug8dsEMJVq1VreJRQfc3BI/vz9twk++p1ADuTINwkOKfjT9wURwCQg5HcJadUak+1XerG5X2ROYJOVQgoO5VP1Wp2huvn6EvWpCukxPZAGWHN3w1kWwLBppds0eTOswzDh9BjoyXaDPUMHeG3k2W+sC+9M61XK/8HvKkq8BDOKnBvvBUoVXGPWy6FFoqK2LTBnpIHLT+wrEuBxTyvPGnTMi6R5YTQrgPTCcsZTVgixKkg+SiAzYyfvWCNAKRkS5kCQfJUXmuPHrGRjHPbjYvJVxmgHhUY7f9revlZjVefqnbS6J8UGtG7TNJdwArjc0yYQJEqNo4w3HnO53yXILpt63FWZsdCPJOLbwtnPHGTnnUAE2BXoaZCH1T/C5bHG+GheKD004OcRSPu2ynQfOz5r31UhOktX/E17W7Tg0E8rOz8x7oALbbkceo9BPd2OrEGU7+5dejTCJti1MPJlrH6Uyd+3wIqZccXFE0O+NFsC445ruitnEsahTZscMqzxslsp0gZhdTJtNnK7BC2lSIEWiLYLT0B3voyRxamPJMjspCPwfn9o8DTi5G+1rprqnYntMq/jnF4Ineu576eezAsjgRVBX1EgSI3ySKgUo9UplLdh6bnbRqfX6Gf+RmYHf0qHwBiyvE3v/JTBsSHnH6Fr/RaiwynjzBff9MSRf24rnSnGLj4Vnmd5IgqTxyLXNl5ki8s06LJswSXoQUXoIfoeU2FquFYeCuTJMFVieA/KVhGhpusgMhJMbH3FzUqg6n5ImSw4NyH6IfuRalCD6xn8hA5AWFMXJVmDttOe2UK1aVMXOkY0KdScAujtgUAQZFTODBG++MPIZMS/RMM3dJQ9QRHMvwiaprShQyU++Zr3UeSG7lsmC1wwuRhqxoD+uYCKGJ1QXQo6RYA2kSdNsy442wydQiJebkinkQsqo47cCII6totIHYtvuuSRgV42HKKuyFG1RnH5R8BvvhFQjj4RBEVph2uDRFt+HQ2slcamQtI52ofUcuCxLVwXvkR/GzpCUjesox7Iaz6QfYRF9B4Glbc8+ll/rguklaAdCGbvFivkCCz1bxozrjaU68+SXi21rdL2Yx1omhb+KMUv7uf3hS6dJYH4TPyici96DNCwElwETaE4UnadNvBwE8L+tW8OBLnh0z9qvG2mQyYz/NKscU+/QDPp+KhJDRMOP1KjicgHTagzGfl1JiiUlWfC4kfI8uiNH21gsqJ+66M1fvRGtB5LBeerTIyvXn2hOhMI0tlInjHOO9ql71G752QCQJmAePLBBESThu8I+mjcZWLiR2k0CfbqII3DrKAgEPStJsFelQGt9kxE+CVaE2reYwGdJ9R6gakm0kzKRZbJj/IJIkkX+abEaQWQtJo85egperIc1AS7lvVXWRvg+3a635lHQMZf95Rldcw66cizQ9X84zpOrjvFUVYfucZUO4048GqzKaZCkYee4/A44aZbVeba2/G2OsR2SHUgiHSqaIpfdvcjh4pkrhdBa3KdF+9qoiwffKdHbvx1O7/LShNuHr9hdYUn06x004Rb8hyI1MvSmfzjj97tk1lxxDWM+4Zc99FJf6NQ7idGTwHrp6BjD1M/aAn9TvBF/SlHwU+ZSDuQSF4mcUBIed6jgk9B46/a4af4slI+P8/XxvBlPi3Pagu/t+pHfe1Ov2bzPhW+cEfwh09x5/Ea0Wgi7feziN8TdIJBnDfjjL6+Yvtp57iufQng9tnjGipafLy2KbPHavHQyxE3QpE/W89lrHW/BmCLtZGvoKYL2QyK3PKtUvBmIUJ2ykeL8FGxl477n+rk0UdM8V/RbAGmjw/FRxPE0XjpgA9BR/IJpPvxRMFZVckji37EVi/e92ogAj1aSQk9wfJfNPb6xeby6b+JJsEgBc6Fpw4CTASDsHt3lHHeZqCRNchO0yMQBJb2Fl8o+NomdvhWgUrsQWc7CMQ4yLiI32rHb/2eNMEIAPlxRPkqqVeqyUff+J0/BHka9FFevA4GESzCxwkM4ePyVV6oTtDSASWV8X108LVa6uG31kkNpwW57zxbhM+qPM0jWa2iLe69wPDFtA9w/BH8sAqImPC4+8fWRswBrMWOrOgWpPN0johODcFWXvP4U2oKJXCPK6TI8CbIyIM2HJvOjmD4edi8hvo+HIsAt/Y+BleJ0/8aM7OltlWy8pUgWmyHlFZF1vlYDz6KRVcOKf26XHYI5OEnSfLUqX9QVmvVETQc5tKBH8W5dkQHbAaj+ALIGK+vhrWarzQQRJNtEjLaYj1DYo+Ab0ecdjvcRt7huxy5pQyc0ll3sTvd/Bv2KLfpRv/pxEe0T2H3hkYnZP6uzY7+hTwSbA4Oqua0pes99I9k6Ewo8mF6oJwxF70X94Xa4g+90+9DQrNPtprhhC1pV6CXOQt0F3EKtLhFAH8LxkSyxZ64StiDBXyhgK8ylmILVlMukZCo0IFz5xkEBq6M6U3FxTQ3fWDzqALlmaTMxCWTFWTMREZw+LjU+JdoJhraeeEjS7w9+SCvCQm/RnOxZALBtrrwMAmDoJtpCmW5yE1+J2kDw198atphQmChrOUScJFNjwqdHbpQT6HMB86gNTLTN6rIApqXhMoGNYpaK8mpKMsBqUw05kIb4qSbk5tRpJd3XxlkwwWciixz8s8loqEJthcpkn0BVMFlGBF8CndLrUfEivDYgg3HFXMQfcXZq6WulmuzsctJV/g9NU197448yzgKxNorm2LzJb2XC3+SQtgNmSr7HCzsK015UbR+UN7bYcTLZJgJCb9AM+Fg8qF8Xqyb1T6sGPJqIAJFutPyaiAexfHEJTS8NBpebsR4B4sfndD46XdbqM9T3w4EZXUQ41h3qSlmzl+6q+MYfZWVOBp7PRFhMp2AjldSeBxmssvEN2Oxx2PlPQHXRNiBICYyxhP4UZ5HaDSx9kt3BScgxFdtCBRRH/XDjy74irL2m4ztKDg+xPgipTumW9+xM7zdlb1swLNBDF0hmZC0tM+s0oVr4+91hC4y9zFcOm60CpUioBT3uFl8UYtxWAXnFA7ZHIfHiQ6kvpT4EAFDonpmemb5CBt6Xy8UrJH/9CtKDjDKieN/BHr0dSZWW/hxMH2ZiZfyvtOX7WYl0HsFivL4jQJA8s1f5M/Is77ShTyTgr+TV+rAj+rHV9nyAxH0wkXtdRwKlzOx0NUFwWoAqa81OtIsoPkxDbMmALNWU8hRHFy0f5GPX+d9V/JL+Z8/DW9/nYky/kqwU37KRDs+y8q2l3m3lVazORCklMdwXisgxAohHsV5yeTcE27pIZk/4ttuxRykLGX8FsWnd1wwzK2hRdDlWu5mX4RERqhyNMODA7XHVhuZekaByp3ipppc4aUzmN4CINn3G4dFN3jo1f9oyR59kgeFBdAnfQt8BIjYPDokGMR9I++vyooeP8olf/NLzP1ImMZQAkE8BuYVagR/5JN+/IuVlh03E0jySiGNu7yfzY+JafUQQXcexbVsr1bD19Bt6+K+oD3TJtth8MsmbcNK1UzRZEv7Mt4qr8YzvmacSyCIe90GMp2XP2aMDW3eVwUNgSBWWPLyZ/kqj4N5bBVMPBmj8WmCR4y/wXslkOgShBdOcOrLecI4qztvViNVZTea1qURnGvOof6cudB6V3uaJ0OeI+O18+5s5YMYrKRRltxJzEO1rROG8jf1uSM4ugALvKZ2SdC9uWorDs1khHZexcLg3Fj4p7QQJ/aO49woHg2bD/Nut2zC2S/0tvRTnandvgJdaSsS6Ud+GfWEWcDdNgCpui0eLV391sZiBCOtSOpr2SknpVpgWTOIM14b57Rzp9/8oYf/1Jj5R2zyyYsp/og6rjJtiXSqEcu7AtP48GTLSS8Dipf8I5VLA3Ppmk83xuDbkUIHzSOZd3mfo4sV1YFLR9fOIY6oNHofbc+pZ5rQDY2UfGCGgy42qN6m/hTD4kwGS0iZtHtw1dvapTGDUTJGs7ua+bHN+DUCfstYrp8SwhDrUjvB9QDRtslSLox06tyI5mbgU9EiR++RWCKz931NoVuoa6Y63Opr0XKrOwobobR8laYRx9Fd8JCh1wghP9lSqxx80mDJ+4+JhwaL/jrNxCYvj+QzsbqQezLDyyRn0sLF2DS6GEnX77UiyBcpBX78bP+k3KDxa54vlLrhWquC0Ir2kDR1YZeLM35obHq3LQM5rYi/42dqiZuVtuXcSp66GNItxvmprGh41f6w09+pMPLkkcIBQ3/npJRpUI5/7aYNW8rH4EttbMhnkuc2pbbJG40oEyE7ugAYoUrXs8aAIGgK2X0bNuvFtYyMYG6l2mu0FIkYoAOTnOZisREkePSyBVxbVbhX/a9XpiVsu60pPz623U2hqoRynJLuuNKUb/VCHE4+IcjkF61g6rb4WYGq5CSLXwkJXWkepSgHAfyD5ybQN22P6L8iGEEgPh+/bEXbxh6cstidlTsZ9xIYz6/UrKbQrkkIY58nN57UJMDDr9m8sJdJjh/B0RjJJ5WhZfxg7PSYadmEsvXn8ZTJk/LUq2CRA+10qnb7gHDox7lXVdGdsYhAjIM0zufdKn4/EBMIB4Y0wVAwx8EfJs0zoc4qjATw+QWaCQw0/iVavExOGM+ZSDMxYTKDT3oMVZ2ejGA2q6RrgGhrT6kiP5L+o7Phzhs0rUjeDaojKk0b65EnTVpOdZ3UbN7kQnHwoMKMc5XI+WM6BJF3OgUUFay0IK07fQH50D86N7mfNJUOQ714UqZ4aD7AuSdXPeignQQ6+Qsk3vmCksoJRhIASnDSgSBNkN9rZcW7n/QomAI+vCj6p/cfVP7gr9vlkTAFgkTDIzjwEITsC6exuFzQbfOkWY2Uu7repPhg8Gh2Pi6EnqjrpiiDxvgr/suWNDADoESY2so445eZK58A4/gUJgCv3ZNs1eAAkPyLMn7qwA0BIfwSn9an6HmvhVcE6X6jq4HevH6VyTePhs0XmHgBOo+VvVBgyHwEgwgEaQJOW9gZB71FVTewOBdohwCM2SGt34IVjm1kFB/q4LDaohviIXepuPjEIe9KZNphj1oUoCkdtvam+sbxqzFgyEwhW5MBR9nsyvwK3OXAN0Og5lXW/jGpXMv+0/ExQRvGyvgt77Fi3HTwR77owLqCk34PkOA8lttgUHD4LXTybVYMeTWmVhkpuMQqIwJC5wujcT30sm46WDfSBaNNtCdtayrgMDmnA70kGtsAHyUAozFyVgV1PE1wBj8kiEMAR/e68qfuBCcTHNJqHwWAXhIIEuyF/C4BH8Zc6PPuqtISBPKKovF7r/6EDj3QjHFN6qkYPa32NJS+1n9bqTPaWLccntXEnIcIMs6tFp9PAEGQ0QETHEQ1qGUYYJCL0HjX0Rn0zI6wUKeuxV5G+ESLeOohZUsyZwSF+jF0Bx7aUkfRKVegiyM0xhl6C10Vtm5SNrTtn2u0CEGsa+UJQxl6XQi+4yTwhhDa1DLFEXyAQptj0WLz5uuK+EveFLxJpl7HM4Qs3umU3fvIcFmp36OV+w/DDCcQhMQGgr6qr4bFmGPSVbCRxtAL+N/I1IHv9dxF0skYnu3uSIHmeOLO/GCV0KkMBDlxdvefUpq/NzSdbR7x37GLy7JH2QJFTRu+ZEt773Ketr8UEU8pubP9Z946LGVKv6VcTr4QR23EbrKdD/bx0TxhilaQMbC1NAJVdL9YSnEV2UopD67OIMhT7Mk/edp753G5flA5OelTug1Eo44orEbsxKVCkxRPSpL+Xe+u6OSHdwR410WXyQoXZSZJfnRCF26WzDLZYGmsn+P//oUviHmmX7/KaQk3N1pc5Pilz82neprF3vzoRlKye952cpuli5AuczRsSsp/+y1YltEGFpPHjq12LoNUYbrcREDfwRK5FMNnZaV0+l3tUd4oNwQps4kcju2D6YNooqPFWXBscPIFvASFVPyCuF4qtPAyfT6FBX4fncc+Z9nI4HWMeBO4zuZKlXR7/xX+r1iKdbbmafEuP8094rjDnnI9hth1p4dKcRnr6KwQCU0+VGjpvv1HK57K7MPK/9kCQZjS58FhP8ycXSOiJ8y884JxL++kcBCIcVDjH19t+pt+yWaFBRMeXi7NezF+0Qt8PYlhsuKgD5Nr4TUkEegh+EMdwLILLwBjrINAgpNaLStDlyfgTpdxI2+9ddCcwysu/WiNJyCZeHznR14UxJmJSAPxfim0Jh35qo34O+EQb99xwQQ7k2/GcybJCiRhKypnRFC9vKiUyXo3w8AC0s6v1YzhOKhBA59kZIUbmK9HZk0pmNbZUtKMhsXZSosAboxLrYtOQMzIZnyyRxlKxkSlpTt4hNqb4Ndzc8suEeRujzLJU6+COxKOZufmm3QAY8tqjR74HPR+rHBWlvl9KxOMJOjIO1bevcsXmHg3EIEggkB/ffdegSDeZ5VHavxYzaxuY7VGfkCi3eiVNiQQNDpT/yiHLvqfvfqHt0GTjiFpt460FUZxOQ/3lJnM2YfFbL9V29N8lbGvzQGO4GICQj8IkUAQ9xEEbxL46eM3vPw5q4O+06OM+tKdgkCv3+gxMQWB/OltBYJe82lu3n0lf2Z1xkuCQDpPEvzkeq6KqTvJ6LHL6FwrkFv3FmqbddbR7TeStj/e0sfBn/k7NW1PfUqq1J2I8kH2HN1vvh9C2miqxN1heREe3FQg9fqCBmtqEg5tl0O9JkaOoTrgMnIvTTYZE3Wv6HtH3SvKf//mACb3lRofNU7yKC15Aj15QT+P2CovAdx7+nEy+W9fGk1QMwH20AMnEORxWvV2JVuDQfg1jVz6UVT7M+FVgfxpMDci8BzVXvsmoRd8UTvjJAEh+WNesK/xVve2XY2ZQFDGz/qVA5ai4VEvvzDaqy4nEEQASTIdSLIP4+eRad/0PXPOBQdGXf+MEqgnp+QWtz1Di7IlTS8V5uaYFnq4cDH8jLL3yRsNLIQuWrZE4eMeA23gUGJjk+qAtfwPQAKwN3Io5hCe+PfOI6PbkRVozioD1xlWUuOPwq08klBgVVDppOxoFk8wmUpsYZHtfQ2Dxi0YHO1yduCRA8V3a1AHf9AtHbYcKNYmsDEcRjat5U5gVQWx5CAqCopU+eKbogJ+QdnqqK/IczwDQTAz98t4TQ//+s0L+fb/1SPif8T2T1gR5FaPZSdvUyh/GK6NtWPvni34C1N6h63dkdKj4++prydKLqiVQh3qzKXrOAXQBYM2dOSyQXflrfxSNC084s627LpK+6nUnPvwKdLfhUOv3caxtZOrjgh7pPtZiU2x6E7MNb/pUsfUuohWefQ461241ReCADzVnT47QQgP75ZQSCvGDuUZEdPm7QebYks0v2/QyUmCq+BXayYrBHyYsDBZ6S8zuVgT9PGvPCzh5YaSX3H4hUYX9/w6opU/eqTAXzj4kSXZen7/Bz47nOXZP2rFEL+AeHntTZ3VEMFrb0gYyehzqzimIKFdDFg15GrnOXkS4cmHOABIM3zSlCLRIypyNTjapjawcIy0ZifN7puCpZtgITHdeaA9iKnfps8MmbYGstqdopmsmxXJLWjrtsCzks/kR31bDT2pYt3EysatcjBLGk2qta4N3NBF/FVkbJnf3ZJtt9/N8pDQcvZhmToulWP9x8ij4pUVGZQdCx5WBDDi0tU3Ik/Cir/hvqbi+bJon3fLiLuV2NsTd26glGdS4wmHUiYv/Uyy31OhcsbG3zQR1yRHY+TfmNyI1o/xKEUGYyzvtmCC7RVCOvMSCJoAkOtIkMkdKZ70Owqy57zzmEh/szOR0MTBqys0aUheEwt+SVaZybFXZBL80WSkj9cw+cg73Vhxwbgsek2KSdfLpEXD+wIcwF+mQQfpgp+Mryx9oBGM0croGb8Zfyh7FJs8pN3APXRIQYMzwRMa9LjioZduV/WGGfuBL0fK1xqKC1Tmf7ptkhm/uVacY2haYtaBt/3+dK8knnJ9k04tdsQIR55cRXRZrRO/Gb/TdZhAI0EgHkv0C3f1YuiftL/nc9ysCPrp/Td//ZAVQb+wSkiPiDFpZlJuf1XAMit7sFZ7C72ZFKCgUnw01rS+6Ow2OboXK3pltlS21usCBp3+OG/4MyM5JLOTHx+BVzuTavuy4GzIw6e86oxUfuj3+Chv38RHDSOQQ74Tb4KW3+nz23o/0Cs9HuYVQfrynQJAb17zzqC8K4gg0GtWBnlVUCbkLzgfZApalvpHj1HIMOuG9raEdEy6ytCuTTiYSkLe2wlMfbFI8dcUu3hrekU/Ld3pXKaGKEKfYluXRrfLWSRY+x06NrqQ7JBPnZKxlAsNBNPdGR9VYOUO46ZX+ThgnvtJr6zEH8cvs0qIsY970I6b+SIjAaO/ya/7eBhjLQEhj8MTCKLeBoI8bl/KUbm60S/5jHbsn36iPSqvRtoZbCuiLfJCB2y6MojVP/gl97sEbPI+NvLyxa4K0kq1BN7xsfgmvvdCcIJH9l0C9oy/Gp9/kD0JCJHPe6vwe/xSMJ8jygs3Wtv/2kfTo9M3JDQkZ3fycGWDx1JoL6CWlT2D+6E2OlkMqM3druxe+Rc5yFpPayDbhK5hCUl9FqO6o0fHkVzrAvM4NB63ugT5o0MkPDqWuik0R95KtqyzAj2FHy2XwKWnCOj7+MUDKvNLwoiE4h4Iov6g00u28dBXk4tkFWgnuODJkxsqJbYt9oVOaeXQAPK2nzK0z7JiUMvZgSAIoN2BIM6dF/K5/9TKtT9i++cFgtwaTKNtDEcawwZ8P2JkjNf0xJ+wM59OmnpcVWTA22pPOc1fZRT6NC1d0+dOiOKbbknjRAakbc5+SjkRVE7StGXL/HzubsfKe8R54s78E9q7zjg7J4nMD4qUnghZ+/GJlAWouKYLsTLpV3TixDt1o7pu9anWOwoYHbqTWmCKkkfm3p9nHSG0GBj0j/+yNa+aA/ANZOA+hkby0clyRgXrCEgbentCokAKF1kvt/UvMlnGm/dk6MIrHIGfX/TiPr768LN+gSQY9K0fQeAdE3zekpc16msdLMdWQOjFy1e6CALnvRNc1Filk3rXEeUPhRaa0Vdwt1ZA2x+k+wCtabR2/5MHNSOeeCvWCB1Sb2zRgR3plWG6MaQTHVKsRlCTT+o+UtFBIZhLZkEPDsK7n51eHw872RFDvW4vM4JMoZSCgVnJl2xlcTrMMcCICwzpp2jKaS+VUcpWGLQbWuyfJ237T5udrb/b88Td8/Ez2bP9chAsnKwdv1A/4eaq4Kx79QVwIXxKnQSHTGdHQW4WEMREiGnh175dAkFqrNssG/hUsy00xssemLjBIFImKExwEzTPJCe/XjNxzy/Yv/AIhCYnDQJ5lSUBHhkYvvI6ECSZjAd7AjO/buvdbMK4E339pLOnM0kYF/jrRNmTBU1WEgzKhIWJCXgHgzxpmfxMLPyolyYXfYTBExwZwr86i8+yoEUGEyHRsuWIU0kD9EAfSpcUfGBFmqYwY0NjOpct0e2mbVfPhmDTh9xXfFO2TOp6IJVdPT6ThsDH5ncNaYPIZitFirRr4wS7osvkm/HzpLMKg4W/5eqEVoWRId8NenxRl2A9qj0r0Tw5ll8RANLjMT/rERunvB9IQaC/6r1A7/VIGI+A5R1BWiWkT3P7PUE8TiOBfixMQv2CXbx+gjrWZSnwnf2eIFR/AOE8qIJ8KME9xWChNtgcHMYXlLMZfpMP50s0APB+4EhSTv+Mc34kTBBsHF9GInRZbVb/8kRYRAlexh89UdZEnMkzE3ICOX40R37/Sqt/3vg9Qbr/0Eplvnz3lkCQVwSpzCohVgj5xyo9VqaAUB7hYcq39XETp9xhmV7zLkV9biq1zgMX+d7A+V6NBpduo88clvQ5fwCxy9qaJ8WYIC+OOZRP6CqV9EQuyRIjOKh0jcUHW3qVVF+qFYwM2yjo4vCCs+9KN78YXwXGN4I2jJ8EI/mRkR1/ZAWQAzqUNW7+Xe++4vUEHiuF591W3GPyfiCCnNx/cq/KCiKC7PBQZnO9OqAPrka9wGomxnCc29c7UnPtNqYPMMXA1D7u7eSchjkgqRWU3MM6YO4ApAI/rIYXzGOtAj2MnQQqHRRySrBSu34IzXupwDFOQ8eYi7zkG/TJ+Mz4y9grFWYsxuRL4yO/YGpTPYmwrtsyzgtNypERWRFCqGu6UznhVah/x1CxVq1WGPKwq89VGzryIguZSLNAHaAjqKI+Klj8SC6ZM8gR3uLMpcOxRa/qlxR0ZNTP4xNUU82nSkMYkVLznBUHoW2EQG3JD6RKB2VsspFsKilNShvvmwOnNA1Fn6Iv5BkxIcvfBSn5/q/RlFocYpUhT+vp9uQlQ4CUdU7ALEKfG6IDAh/XGmz7Un75n1/P5+PHClcLpvT5fnjE9f8VjA77sk2NXtvwyoFO6EI/mxH9FzBExS/V89nKP4uYU/qzdJvgy3W7N/8sR9ops/lQnbRbh5x8Z/mSv/RzJFQqPlA0JzVnPcMGW9MpahTQAEx/I6KdOApB63dUcJNIIMhBIC7KuvBycdZNp5/h5sLNDahuPj/8rJtOvkaiizNyf2AVEL+OKBDEL3GvdCPGEu0X+noHq4Je8NlMLpi6INbrrCk6R+XoPXluyBh3ST2CKR/dTWZY1IcBO4SRQY02j5jV1OFyeZq99OhNXkSMHBIATknQOn+nIh7UpZhtSyUV3gqPdPUJQlup8VRysIIuxErZCkMhTMhN8XsP2NJsrdcVSKNJH8vJjXFwJhyy5E/IY/6vG/oPdMMXGGRLd67F281IwQhuf5yw31NhbxqY+Mx8//ew/cvSrEAQLZDRzt++sN1pvzV50LiiH6s9zjBB9+SGCQuP7DDh0LjJxIf3BPlFqeD4FL2IGVtNI8GenMxg7WAQsjzOiA45ovFjYeJxfwrnIUAFyuzuI405pB6RlPFEWjdwrPrhEd1vnTJ5ZoICLJMKJikqmg4e/9IsfsYvzUeSHteKTNCpJVtz1kUH3NGf/C6B9YsFDdJBotd25gEeKNPcy2E8e2Tnz5vt8pH6trhkSo0rQQTe6r0hb9hhSXInPZCtsiDKa1fmis/qm8LsAkNvH5Bf4Gdci+1HXHs9MdbjYPohhi8uEQj6wBfCdB1mFdAHBYO8Ikiwd3pZNI+K8XUmruG8q4r3tPBYIkFKAn+2jDvkeIDkN+W1q+roe4w39kjR+1qnT7qvvpRNnOeAffBZ/tyoTGkcFHK5LZYPzmVN7pbrvFjZ6D/E+NqqtJNjfFGe7Yk2/ixXtu/Zv+XDnmzLuQn8vNb9xmulBIJeKX3D/lr3IBMEciCIexPNxB0I8r0JQSa9cwt9pJybo7J1QTFth6VUSlvsbxCN3uS8GdbCbvcDZIlWanuukjILUDkIb34IV32D67XelPQIcGm72BbDCMgEchWg3YXkpIfHALNuftPpQIp8xjL6nwAk41uCQoyXjJNZdZ4X6es1A/i4xk+vNFce+rUqSD4PnK/heQW68tByLvCDpcdXxl7x6D8pdeo9KHgefyJDK+PRifNLxdlik5aWJwrs/idIg91U+IHx1V/HFUx+lpU//ACqspzGKy8ZdzXm/sg4a5oZf/0IIjwDByef9Qogj9/yZY+/E9CfMVk1+jxJ0DSj73KF3Qg0TL+4IW1cepx7TfCh4PwNPuexEdNGKLIH2nzlAVV+FSV35YfD/pF8z98YMrLQpHUcQ8swkwgbZQ27iz8IJwsFDI8oGcUebXgFXI/x1S9yo4y1PqtwfuMiL3Vhk2Ke6hXM1uospwK04toW7aIrcmw7kbuucQInOgALd/KREZjPg2kqUrIiSOcMSqgTyLFCjq+bcpK8+P63b/6isfGP2NQuavlf3GyZo0pbjIZiKKWn/Q+1cF5UbXqgLtnim16QR+Fz+JL+XroorhYcTauMnW7kltsGB2e4GArdvDtX3qZQ24yb5LM5bnZbSeU0vTMvXahkGrhbcqeW2OmrYOCGOlIe8ZUi9DeKo85I2FTlM/ygg+IJrQCozmD7qI9CryMZqwDdTZdUPUfhZsS03xoaHyULK9xJU8hFEWhqgw5bORrPnc6U4bRokTIY5NdrbhRz09nntnnBXx4H08XaN6G64dSvjwSD3unmk19IWPXDLyMvWRH06rUDQQSEXr/ihY36ageBIP1awkXSGlQ1Kz83IKhPGf2SOIPJKXPD6F8Sle/LD0PWUQ4LSUCvTsoOxmTI0PV18uPLInCXLsKxssvKjz4YChvaYNiyGk6+v24iHD0XX6rzEZ3hyxBPXnTQ+7gJraLrpT7ghkw6+TCGyTTJPjwOi5tU20wa13os4Gjl1I0m0RaRpwoP6/0TAGuDxxaMAWqr58xRGcG3VInjHXWoGQugtTuKrByVX86W7T5PgAjQPvygmQCNqyzWrzFDIIgvh7FhQzXbW87vOf8HBhmm9xitaxkpN1aae2SiLpjHTdF40k5gSBNtfzL+SM2j4A6XQyZJtjt5ZbhGIpcAUMZuCRduzVjIT8eQML4AYCxzoGYmDf7VmAmIJrH5BTnBH5eZxLCrwZ7kSobzgmVcnYkvkhnTMIxrIb+qH5gTq2Re6OBRalU9zoUGYESBF3ZkHU1ahNeRBYpHW+DUJYMtApen9N3AQzPAk2Bxne06Cc58iGkDYsEcp+AhKdlTI/Wo/mgVfRvbjDQnGY+FFxI+dX9SFTQf1p7gDQEcPzqjyTCPxLzXe1MIBLH6h/cA8XLon/76TiuC9I4gTZC5JvOeIB4J47r9i4KTDSjZN/E/dZx7zJ2LQ02IQ40jEIQ+bLlKxR7Jw8qqXvGYd84ffLAXV3G5C3TAn5Hi4CYnzdokDXp8hWTg32IEbZS9CwnevqpM/Ddlzaldxr9ZQeFVFcq/9MqfCQRpkv5K9x5v3rz65j/0ziC/LJogkR4Te0MgSPcsP2rnMbEXEij2BJeY0GOW6pFsrTQl7IhV+KMPa6FhGioS2gJ+b0CebqET/UIrs/LQx56x+ilPKNMNMdd39iGpdl3llZpH9qIRORW7z0IBinPbJCkYQT8crTWsNKQOBMnPOlZmvFTwBhh7gzgEfhRh98pKPxZGUKjjJ6uGVJZP94uNWa2ulULm16og3asi22MnqXg9Riu/gkPA9dfHwWwLlLTCVt3Zjnk2pNrnMU2pV+WQ2s9Y8aNzxwH2rAbKY4p5PJFVaX7sSzS8fy2PjSW448APASP5qoNETlWb/Ax7ZiyND7pufFqdKlR0oYP1v7fdALr6jp5eM7l7iwq0+f1tlmPBuf6N3HF503EImHNUEvCLwzeMV/kcD9VS86Lz2sjTPtiNyDlzbQskENJ/izNddBcWsih3Ei+2EWC6Q9jqcMEWLnlpZ27ETQuWNDL+SMvUC61tYQpKsXQkIJq/bNR+/mDhikHSJvnqKh/t4JyJrWyNXZd48F02PINKbBod/GJ3y4gucnmYTb383TDOvyBJvPJTZ+kZCNJr1LQi6Kt5R9BYAUvZdjGgLSeDnc6LWZajU9DWctNAnx6Lb/qU4gopXdMrdpfAd9tOBwT49WS5y3KTD8eqnDsdtmgtYx2XW3dEHIOAsIdaFftsWv47QfVoesffy6VresenHN3ajgfNv7DZRkCG8JQdm8TOwEMWySedETqYcmyZvjptVqpJXTF29+k5fqbf2gTPQHujVxGOPCGwAABAAElEQVR4OajMKkuds62eXIi2F79gRWz9oVSeCwtlXRAMJqu/DBT8csKSXX7RJhDE0tv5NUZ5B4PmRvRn3j/w7p1uOAkEffAN2I8/5nGwF3ov0Os3b755pRuvNwoCvdKz+jwqxs0Yq4K4aNJWVe0NzbSKPG1pm0bd0uQCidog3AwVsLH4DPLIZtw+VzScLrxROohYTF0BYC1E5KFz6kRgRdYuZvPlAY1EX/uZh3J2arHOPlByda624010hnF2aOlfIajPF3QY2dwAMhWo1ALAAddm/mQ/dbyKErNFqkZEinFafREBHOvsbeev8E3xZ8rduyB2/O9aoFKRs+29/LqOdK9mKi93/OxONCJLpKLFqQxIMQGGiK9+cyBIQwY24uDz31nlM6AsG/S88Y2ZeDImKBVdJiyd9GjiLiNmhVAmM4xKTLy9kkh5VkUgvmM0Nk9eUicK0GCQe4RxAaKjT0ZlgfSnAnsmEYZoLObXaiYT4AkIJTDUvNCCC+drgXgJBFEFh6nIWYps1H/CpdA5Wcivy9QVLmxJ9hC3CtvOyOy2+QKhTKWuuEROM0KSxS5DdyRgKNKH2Nn5JWcIIVpb2r6KZFCeqqc9F1wLIml7Iaau1FatUxdgoyb1fX+E76EdLUXUCTP00HWlmVfyaDLMRJiX6vKozHtW5Gpn9c87VgRp9c87rQh6r8/H82UlAkHv9bgY7w8icMRkGh/MJDzyra8vrNiAwA+rgNC7O2qRVypbux22ieg1IfY10D6WvvcPISE3bcYrc2lyPgFOJulTi6VLHiK3qcffp2aLAy8dVFX8fFJ8/nv5Yh9bbFAIf2bVDz88sSqIz8U7EKQfpd6+ycqgl3rswauEWCGkH7D8+JhgfjmvKmLlB4/uEBSyP+toHdWclNMud1wsNG3AYmhrQmP2oTyBxOahxyr3bdmEzIjcNE/pjePE9HmhlIGDfUhJqM3nhWTGZ811kR+6DT99G1WQwbb9f7XY8Mql//Fjr3oUl4M1AuQdaZQJTirVPSZj4K+6Cfw7QR+PkaRaTanVQ3mkjNVCBIW0Gkgp58bf9DETv3fIgSSfPa7D9UiGZcrnHZCSfPRx29DJGdoyG/5tM9M2+toFGklJfiB/156xNWlWWrLyJyuAHIxUEIjVQX3XGjR9BKzBygTl5cvyM7lZfAw/U132N6p2vZM2j6qTpyXoRavp4qMlFITJ5hR5wKbQH1IBqlVGkir2GzpYl2ECuwBkvPY9NhUn0hcTfk0RyAJPIXx3bx97Qz8VNxi51Jh2j1RT+uBqRUXjTIxuQVNL+dGRvMmdKwZY8tiznM7JISxW6JaL7zkWjhyxBBvHLcsgHcCeWoimwg2HRxWNWr+qU3m3O/cPdHDG0oWGWP8+WI7rlj+ddBaFSNXjewxORm0cuzgDOG3x2aN0rQjSPOsvWkH5R2z//BVBt1bh+DX8DfXJIh1Yg5fwnwGjD5cvWZF09KlL802r70olIO67IJ/OfCm9pHmw+F+2WSyBfdQ6GSqnYbp7QNPOBzZbFsAylbSAX5g5+R9Z+sTvfPp26NHfKLVCynPypj9jW4YEbHwGggLTMGK7S2WJck/PlYaLkCGtQiWW23qJri7IfpGfn7/mYsuNJL8q8py2VgZpfz+BoJ/4Mol2vhrmdwDp8bCX+pXt9eu3DgS99QsbXxv2Sr/SgePFjlwrMO1Ur4EpliZdm3VOiT7LPauHPDP2ghYZ9DECKSnnG86BGTJ1+aqJJXy5svBMKAaPraHB3iSUncfm0lt5+5Q1n3NHdQILfFQQO6qYNwqmrsIkCP35i8apzxcDU6Y8WrneCIR6BKLXfRsUenpbwiWibKrXulK/FLTJROxmDmPZaGFFUm/hyD4xrutPeKiZY4/Y8DTDaa8T/nz+sUT7kZhqc8r1r6aVaQmqGDf2pvxJX7qmkLEjh9PSQ0ORX2mqecQ3vzBjUKPpIyzgc9iGEtjQwGLGgaiQMSFQ7q24ecrkoxPtlDV8GtcxBNE+4wX3PZkqziRl4MI7gNGxSwyMB/BBwUbZ+qqTrC8wdRo38gnwJ+XLNlltwTiH7koZdoGTF4/56XM6nFSyqMXylXpLtbnmDKh4p3Uy2RF+NuD4kjeloZuyEuqPc5KbCjZaubZ147Hb2mKQVWzGdYqM+hzMM4sOwKxEKW8pdCJYeppvM2BHg0wnWqXY7qKTuA07CMky5tL/bOelzThDawFeCq3bcSHY/8bNuRyIR7h4ubMfCZsfYVgJ5JU/fjybT8frMTB9Op5Hw97xlTCuzwSCtJPPF5k0obbf4U/cQ9A+7RMI4itIv6oH12qRMVjL8TroUTq+xfXQPib7NBAZvE2Uhqgxbqsn99SdNsaG/PglWaoLF2Tz9QikbUat6RfUgaQrGYDCg8+Bc1DIX7HLah5WJ7Pyh0fE+Ez3KwV5+FHqjVYE8YhYPi//g94bxI9UomVFkINGCQLxwmlP4FVJ7jOiB0rQY/YvFGKrrsapiP6iignTBiCnByAtMCN8gD6cGzZCDoCyZ73ky9S0BKZjindu7fsTduTRwXKknzMRH4pWrFTKou/ZDrCwur45eJxTHpt0rGuKvxOYJEiYFZWspJS+Pg9Er/q9eo0fJU2TVUF++b74OD98z0pgSLTUxX1fziN8jTqx+pxX6KYycN9nuoPApT20kfZkw8dppiAEgPB5AbzKEp/Q3sCPXxoNjcZWP5pIAJHgkM4Pv9hceQd9nMo/K1dV4cPUyK2l87UrqRVROnmb1DD8Lzal2H46DO++AWcpriMVIdPnqpGSMzhSg5Ikz/2hctQFXmazLmR8nRpa80KoLXIQCkNgNET/1014pGfMLCEkQ5kGjkDRAkYBNvM6I2rJ4Dh1XOuhhvDsGppLCj0yshUWKMdVJQRC29ZuW+o1TKj0Fn0FoUkHBq4QZ3QQRIQeQwCNPLfRF4mb3tRLRVYo7NEU1qlTaYL4CEz9Ts07Iw/nmnFKXY/gFqTzBLgUoMyPVV/hO4LGemNsjIPh06EuXQ6nY575C5FFjIxbvnTlbVr4mYJjQ5cvozNX2jBuBsQTS3dsHGQNECCP7VKXdIgWIYC95ZNu5W/0h9iH2VOHJYM62gcHV2FND9TKgmN73mbg1Qrkh3DqonDfxu6Ajz64WsRIywjZ5/uqbf5cO0J3ysuJSj30Qm42Dj8TlIHJ7TIN9MEbPkhHf8GrzOCAyWiTB8sJBKFbLBk4ktDH77jQF294jptPIfvrN/5FUTeUSn1jqRvRD+910/n+p2/+quXn7/76ky4uugj6PUDcZPHp1rd+T9Cbt7MqaG64HAjShbL1o7J7rO2aC3V0UxvIcOEl8QU0INrUR8P4LRObqCU7J8GBNo3dIg7K3DCS4ZqzN1nUvIH0xsF01gIJ6KMUG9aOk68c9PM2actur2CkDvlbk6FFF9MrU53otOaP+s1RODzNj6iVHDhfaEBwgW89Sp0dhtw2IizQaelgU4pdoTg5h+RPltTsWKL5mqCwpoV/Or1LKXVs7XFPdrf/FHWmU5l78Kj4idSDjm6sPP9CCe4r3z7qjiePhmX85ZxgHOB8piN7viaNQThiR4/bymAzxtrcRCmlrIInME5D78nO4k3GgeoR2OsAaW/Qco5RiejZJkUfXxcYE6OWdSZr3Uk9XgqvzgTGZJmJiPEqw6cR2HhwlaMsnjVHriwpAaoasRGlQuBHCvTZnE7BspVfMNiwtSEumAn8dVzBqsPn3D7EXtQrnlEDftdBWiA48kKgN8XSXKpfiGDX0ZkpKalYiTkEKTsGdFXKkyKSbadq3aJL/koHn3zIk+BvFQhSENHXX1YC6QtJfCVMO5+K58cYXg7NqqD3ehk0K4NYEfRXw/ROIAWD/Bl5Hh/7wA85CSjVLxPcoV1zQdWFFS/R2pqlc3oTmrSflLa459SOy4ogfEs7k+RubrcOXuVmPrWZk0MS7OfCYQ78wLyLMbXoLLLxqsGYzr68YKMRYxZ4qaA9L5n+UV9t4jF0gkG8oJdPxr/VRyu4H3mtexG/P0irgfihisfI/BJprSLi8TBWBf2oVR0v9YlvzhtkRlOO0S+6o7TKRtf/KWQrjdHmjE8X3zRtS//Xl4pbqRvNQZsdjdSlfRh0TgyBzQNdruoFmC1KRYTzIkbuZWNyqXZJQfcZfYU4lVOVjv6PVYwb/tah3rae4UeV9D9jpVc+skoNv2eX0xO0MU4HUoJGfrG0fMcrgdSWFTQSLCvYoQ39b6wwUp76e+/m80owXIr2gMvh1Jg8G+eCNtpIhuCPIB5TVc6qIMoEewgakmoFJmX5ir++KB6/9wdenVJ+ZBFh9iVEE0RCuGyrFBQWdCCfU5JqtS9dnD8OtE+NcEvIaEt+01j3KVreABwEcn3oIQLt0+KU4TGCCtL3riykYI2m2vjqQXfQbMJhAGAmUv+7bsBro95pDzCReV94lLXOspnoPJ6Kh7xVFiHoHOGmhGWSl2eAXBs2Dz14tvKEL14t66AT/yULsX2ZrDTI0fo7u0VRREel3kZRDymG3oSKyOdKGahUeXz63FwVKDLQKrU9BCulUx1oNfu3nEtKbRGQ4IbINJoU+NxRXS/k23/5ul4WjZXcWqXdMNodVtxOMTKdct/u8Hu59M/Bi2/6j9JFt3EEhD3QtXU8m6qNWOhz29ZR9L+HQQLHx5zb/FMTQh7o+4Su5JOSIPc5uoMs9a96Hil99G0bJZ2ulC3daB/oftZN/vfoGLqYIgOWIRyyrXp2/Tu3bQyZB4tJe7JD6x5WM3zD7tSHOSvIi0gC4OcXGH59IQiU9wOxOoggECuBJhCkG1ECQT/p0TCvCPrpnWTrOXvfSOkXOAWE3rIiSEux+QXurW6+CA75lzf9GsdyWV8HXe80ExUYjdAKpWeDxBMaUlTlVz/lczMiQgi8lUktVztaShXr8i9Kbuzi8/QPw8ASgRzx+gKADOG8S9q3evFgNlL4lfp/yr6ZNmqRgYmyAXHcboaGVE4lB97ZATiRlIWnQsqWvOEUF82WdcmVDTrVWdHkY40IyC2jiWje4JCUErluQP7sW6wW6zR/2gQbPYKfNE/zjznwxfS/EuUfUwXevjHLocNdH/CnHCZVPjefKvVVQX6RMT/y7IE2Tin2TGOxUMo7DcwIHXzdFX9tx80UJfqHua5T8uyi983WQY8caNjM24JgnSiBu/ywA+DYPI500oD+Pj9HbyE9tjkNjj6FBr7suf0nhkPrUIf0khsdQTYbGug8WLseSsjshh0tEyclX5yBU09hQ2s6DmtrjQswmRm7QI88EGS7W1vw2FX/jOcUu226DQlemPwLAVU2JmvtIkOM2vhFJ2Oe157CSbEWdbgeGcR5CU29UOQm3C8cF/Dvut7ksWzeyadA0AeCQLwcOit+eAzsJz4Vr0AQAaG/KhBEMIgA0Ee9HNpBIlYQid7XcjlafI167AmqksbKD9Db3v/90idl9GJLW/2Ls3l0rmiGmxUSwsk+vqYyeHQjq3bkHjXpDgTFDpDYT+UwPPGN6NwDIIQTqTUnJSzBVv/qdZrzxCuFxGCZIuAF0F4JpMfVWd3zQpMavhr2H3zEQsGfV8qzGohPyrNa6LWCQqwS4h7lR33am8fJXioolNUb1M9f9ZEeKqAvjoEeZLNJ6dmMNw6KDS/+nvp+cegXbgsWaAqutxSHXKMvDGbZdQd3cFxkMvZkq4zoXejCDdoWKenodqFVweU5eeoLXhEkzFoZJMHJA1N+zguCOgSJsmLIRDov8oUw6BNAUuBIeWTHv1kl5G5Z56LbxSRXBLm/kSzrvZRP0wQEksA2IJXVifQjMAcsVXBwEF9XnoAO7/pxIKg4zgft8JmWvGWJBZwQ1OOLuBLK+DS3daRs4MebXfYBY9Y2SqEgcbCVopnIKOsKnFVehfxLfnSzdPQwPMQnT2QgfGQk8dHUYXGVZMM7wE01XCOEkkis82BcEHD7aBFJpzVWo+0AAzw2S521n3UxK7lwc6z8UAcO9F426zpAJwqNxabD9mVFJg43EiKHsXQxG+V+h0YIWtnNbfF9/IYZJ9KeJ9FaeIRSd5lLSNr6RPOpQJC5OS/E4tkQGe0+Z5TxaiClviVSXXw+/i9f1+fjD/ONQdNDd8NiVWwey9IZ5HenBH/SkC9d86TdKoty5TyS+aV0ldW6VwtH57O+6nJPlx5qY33pTkO5dGf6iO4R7JFe0Fn/w87AqsOnbFEa6H1yOPNp25ZOplkbVaccoOXS11Bgw0VJhtJmdm4EXOk202mr9tXGTm7a7/qoW/XuWkJzyl9yBNz5TUF7Gggij6zSeYCwSNEPi1upeisBei/D9a8sGhB4HtuPhPFcdgJBfjRMN5msCuIdQQSB3ikg9EGrgrhQEghiSfZLlmC/1MuieUeQ9tfsPDKmz7XybD5fWmjz09Ic0bsberEzwHPxpOCEO0Zgp4A1Kz4EyALLoggCNRMTLoYI9A2kct6Et0wpEXEC2JCSAq8G7eoXOnQa+5HOPtIQb31hPTdqBpe+seBBb0rTAPWFghQc0O7KOq+kqM0Ocm8jzCJ8tRRKDTS5cRnnIigXiWGxjO0h5Dam1e6K/ry50xb3btgWq4V/j50qBe7myxeJe8Kk7hSovmmqVirWO3elLLoh0D2C/V7Xf/vthe4rLPCOoF+YMWizZ/v8JZ8Nm7IlLTQwW3Vsi9090hzl3FylT6ihS7Y7HrWvbPrhQ/KaiPkG09gqkIphlCo9Dz0OUa6uUBmf1K/k17neMxcy85iuUkK7HAWiqbp6Qt4xyCjXMURCXeo/yxlIh4DJ+txPUQd0s7uwDlvuAlWhDYiO1jX0I9IUlgtYDUgbDpnKmnYYsE6HRWMGvqyj8rIZIqcGYL2+jsRF5+uw6XQYOqszMK7HLqPfyCPx+3sE8SRXk49feEGudgeC+DqYrrsEePhiJy+C5gXR71mdy2ogPQb2k2EKBHF91jX7ZwWPCAR99DWdGghUpW5PdnXNBuYO5NmU6ZHqhG3YUp6UCbH0z2NhWvlgERwkSZPj2Ao+Jt0kbqlTy5FvBxLp2NmTYwEldgJBmycaIEh4yUL7wtwSv1wwgSDqJKSFHN7T8pIAkFLuN/ha6du3LxUIUhBIP0a9VjCIH6ko+8XSgr19w+Nk+vKpHiV7qRVEBIhY2UGFtJB62amHwZJ6yBseRwtB88JCEx5ooaZtQ+bcHEw3lCQhuzIjiXOqEk0zhOWJUosE6RVFPlt8oCWnw3+KLB5Y/D1ie1viEnze6MlrXVuWci4oGduQOMgDD2jtDgKJcEgSEFIhj4+F16uIBEvwJzAHjgQjYOSxF3n1M+DIHqVdPzYSzJ1KyjZ2s/8Orf3cqEzw7acEcrS7X+UUzlNW3vQSTXCoeWzSwI95Oh6D0IbVksWfBrgMrH5C+fy7HfgPIA7+00mWVXdtiMWug1SZblJG/w46JUvB7hS9INxbdIlvo0OwW0eXxegz3/JO/jOPzOhmvYVaPmAUrYB+9EcZCJWWLhJGphJbDDIhajM8OhTKbdJT8uDhmrqepLumUKQSZPt+yw2IetERUVTWmielDbO5f12UrZRa7uDdjpFZelIsYkr47rbYokVl62OqJTfirHGkTFMtUQdseg8Ewc15A5zzBH96IWX/opWSf8SmcfxBq/+ImiqzrXfZVi3GfQf6f2rDIX9P8066M3/X48Sd+ZOO+hbu1ryT7pN56X341oV0yb5C7XgX0DOFZd+j2xdMPKf853RANHTxdAraJe+UAw3bKQ8lr/2xa7A4C9BhFyIEOSt3ZA55VmFQd9qLDhdJ0fiud/nv8KNmtzeiTmq1r0URY2LvWMb/bb+I9B9bhIEbtOhpUldF/Xzm05/i1FLyvCyam1ACQvyqqF8kdVPZdwR90C+P7z7wckp9mUTBoO+0IugHBYEcCOIdQS95WTQ3XfNMvt8PlBc45mXRKEHVHNj3QF6t+klidM2FFr3heZrCEzejJWxNlTXPLo8IDaOSq0LLJfNSUPFzs+ENf6uxlaKPxaPXKGQdkXQIG5RFmAVxxVtZANUrSIJTGzTEpiVf5jNv8Ztnivek6hvum0mxWJzqU1rJ+cWMvuCPrRjS5s+cif6Uh/ZcG4917rDifn96Sjgl9nzG9uS3xOVbAzpQi8g9p8PJB/JCq8KfKRD0s3869v2wLMG5HNv2vE1qyxkXY8Zij+wIDCxnD1eonY+hw5kbLstyxx1SLYBOEizVhjEkPgJeYw4QAYClNnApg6oM8/hknzEWgJBuX5UCNJvbJvhCHZlmY5swjDgXXJdyWID6p9rMuTwJWmpNbZURmxWInLYJiz3ZLgPaVab1WZ0hPVA6ApeYGQJdJjCTm3RzGmYb00jkPGgs+F5Tdz7iqa6bgz6SYdjiaYtspeBEwITWQSDl+VGGQNBHvRyaa7DfC6RHvngMzI+CeUUQq4HyaBgrgvzhBgWDeKluvijGl5XyaBiurrULqsszVhWY3DKRUBudKoTidkYn4PaWaUz0hxz68HzPqiAMh0j+vCJI5bQoJpi2AwXMJGNEmgcw9vNDacjtNTfEoplNTEuzRsHpG+X9oXvDPCtWX/7mR3Z+1KoeVvbwhaYXP/DVMFYn635E9yfcl7wlEPSWr5tqRZACQW/0IunXWiGUz80rEKQfruClZge8nBs9xpdpVCimVRTQazZfz1s4UpMtms1ivxu4XVyEp69uEUhgO+tVETB2rApHNkh4ZgNXusIm3XAR6X/JI0tZG31uXA4nSQg4Wv5UoqTndLs5Y6XgwgFzueQCBl4AdDlHgHinPLBUF9olX0V49E/GddTGPm/NpJZ4Tq3UTXIBjH2z7YTGePkEFKxOxxZr1ZjyDvooxSSmoUWrP6QEuLGXydBLGdcBig03JtGhegMg70n8NI7AF/Z0+4BZtiWsrFfKqf5UOwEtFXDr2GHaDIEaQ7LUJW9xkt28ALaOUqihdW5SSk83nbk9Z4W0vqQm1DH/1idB9A2bCg6RkuW6BFrtqp4hi07kQ8gxudaI/OTDgaiWcz67xEGMVl30sECG/mubrH0JvFl0HN1s4+opbGxe/th7yXKGHmUbYc6fh+JTE/WYVrLhq2Q4uGpWV+D2J1GgfrvD547p4Oel7eF5Kaf+yr4aRqe4pTocm2Cn0cCkM+/Qg2ey/5t0Z11nHlXaybQPl2r5Tne24AluOWmoaD3mekJXITf6gp9LbU2fQRi8Tnylpr5uu16guy9OGtNKj7a3vE0rw21ZdE8k+ByqjPI8pdq22Nq0pqTlqYyNLYaWbO6ndJvjkpPu2Ixtc5dCuC1+0dAe0+oAK3nv5KFXOteRil40PCqRT8zyhQa9J0g3oHx2lhVBP/OeIP3SyM3pL7r5/FnvKnivQBABoZ/1pRJuCH/QzRaBoBda/fP6tQJBvISRX+DYleeZfXYHgnyDpSY4RcM2h5vyDJg0n4vv/2PvXBjsto1krdiW9bDkZP//n9w4lmzJsnXrq+oCwDNnJDs3ublrLWcOAfQbDRAEmyAJie+8QAOMepDCxOZ8Bjhg29eRG4ZAwXOsONWOO4jdei7wkCqw+0ZTiEwaCRQ9kQYmEG1qqaABbTLnpxmPPIz9mWVwOREZss54Q2qrwfhUbJK1s22rlAw2DPwUFTHyBxXGdgNCiJfqqWiZCpnKnrHsQm80flHFey6/dcDRBLeoR8r3pEYKfcj9So3Z/nQKAddj/mhSZ6Fbkg+jgC248lzLzaEJy5924/1ArAiir/swWMcvx5/+8JG2pjk4A7u3z+VtmDKZDVX8G3jy0mhd+F0QP3IKTPQqZgziOIM6fD7qliHH+GBY7LW2kE+29wtdXDuC35EtEPyomW3Erb5llDtaeMbEsUpMoy9JBJE3nXZraFcencVV307x+GHIQhTGlHbkkrkMZgD2Vh0du/cBcdBULMTKM3azJRCyy238fb4WXUhNT+HEtQ0tdAhRZXXi67EJXc7TEdZjmeAPL+rUfRi/q+8d517OuX43UL4Oxmoggjw/ajXuj/pcPF8F8+fj9X6gt3y4QSuDeG/KOx4L+5lVb/my0m+/qeUV5f04QR/X2Ve2jPLY4bU0ztc9XckmoLfUg7bSpiCQ3wkkGVxEcR5HvpEV0IqpHBCZ6dkWgqz4gH65z2ojQLTBp5xjI/0ozsTCXyOBi2JWB+nyh77ES3p52fO3+jHf4J0/3714oXcEcZMqXxN7+fLFk9evtFrZgSEFiggSKRDEF8de6LGI53pcjK+H8ZdAUOxxX6XOmD51BJaa4JyxH5iA8K9jDvQjW7rhSFFSKSUHAyxp6GxLCUg56MrYfn3iJw8K00+rL2RVVMILMroB9Zi5oG8KcVGM4sMc+ILSmocix7bQeslTS/NpVxzHzLo5JxPOLzCGODKjCTmjawA55uI/jyGSkeMAZ0ix21S90PRqNaqpPDQ+VEjb1+mMLsPHP22d9mb+6s1y8PCMxT72TO5mol+wmWzyBsgxbhfq68FCNOMH3qHERTxl6syffaK81UaYlJBBb+rRevpo51jFZuz3H3k0U8qGWgdHRl7hEFSGxC858N7b0GFbbakoRh7tzA3XWKl9/i8ioLndPMbAJHUk3kb1aYHrRyUuWzlIk4cOV1K2j5SmHJjJTB5bUw6v8zLEvjNIwvIvZuDjT6cZ32oSmJEIi7eRann1mRFDUPyQxwcSaP2Wt2plEuYh5aFO7kPC+Bgb55bGeCxShj6n16M9+eufc0XQuK+tpjTOXm69ZNqBm57IE3bmTxryJ+7Mf4ruFneWK6Ppsp+O0hZXtvimp4wzb/xBX1w75iqPrxZ9Eb8zpbPByxGGmaddW1dyQ2bJO5/KlfZWxqfM8Ilq9Ct5ZIt9+LN2XgmjOXpzQN3WwTgxVcWSM767ytulymm6McodvLJMgEofKuFPnxirXQ7mnCiz1C8nSJbeEpnnxAEtKTDah5+/4qCLI7+bgMCP8tyN9PuBtDTdq4McFOLdBHpppX9MVN/LVk24WBH0NSuDFPB5ri+FKehDUIil2LyE8VveISTc15qcUV/mLA76KJNnrFlqrrzeIcRqoK/00kbmlizPZqJpGDw+E9k94wjlxzfx0vSz8ZhEZfBTGdZht++wIZs8fLjXcjxTYXAUxcIJ4/wegN0KApOiCzSpFexkwZFnOofll2BRZnNdajRyIDHZIfQUXhFNR84lGbvKRmVDPn1+Siiac4T9tCpB3fRXFVjypW+nL5q/9Umb7xb+ePmepHjb4736Rfup++WNoHKnf7WfpRWLu7BINN289F9KIOi9Ks0Lo3sOZTzCB2xMntlcVnbAhgXO3AEC/capPWYoH0fW5DluQsh+WBAVOc7N7kQCqo7DiJWdTI9MUmunLtKy6EY0CXWrLQsswn45EZjxnrFiDHW9GrXkToYEX0HXPHKQ5Aqow9Y2g292I+YGequTC/2DkuyId6qidY8U2wwe2/V/cIYPupFB26/GN1h1trBw5dwMg7aAkr8UgrBJytZlNkHUpGwu3+IFhJ5zMedc/M/HGggEvdMqIFYFvWUFLu8E8ougeTm03hGk1T+8LJrHw/hcPF8O+1mPbCcQxLlbPx7vlqyP+kSuA0GsvZF+nVm1Y0VPzobTcrIwPQcbbCu72Zz1QSEqrwZChlrWVSe14JRFvH0QIe55ZPlBCq//UCCLXCYXegRYNFjoBY5MwZ1R0EfnUOeVZtVBgkFPVS++3PRMcwi+7sTKoJe6IeUXRWulD4+A8e7CV68UHOLdQLroeaUVQgSKnmueQhCIFUE8WobBfblvvYVh2NRKOggwZqeCwonPdrtGQVLfeBb8dXOcoH1cspe4KxkOEhIDSLePoHcbLHoItCm5LyueTv/GVpXj4PCVuaXR50pJoGXaCSEYbS7YFuVM4x15bI0OQITssJ981fodJaZUYBLgwZv5K+Jp66DCKxmWkxQdbMYp7+sP+YsU23v+NJlJBWMi6P/wVkZKSBMEPD93gpbRAjz1ALWMNpxi6g0GechIX0cYUJL4haKnnUoxl3Ymdf2UUgevBCIVmqhQcC6pbkigLdmjhzzzbZWU+gFQ1RW8tSpDlnJywMVLGYT0JDXYO8+ZD3oHgeEWDFvvb+hXXWay0fZKJWLryYeY+6IwSjiSUeh+u6inFuBHQPo1Z+gAzLqQBQv3oAKC8Q/KqXZs5JUA61bfoYvNZWXPdFCChSItSMnSlK5ZhGnUsqY0tchsh6kErzA0IIKdwFzvtg2Qv/uSSuBFSmpNyoAPPMjwJBD0/Z8nEJTK4ci14bTZhP0fvXEwrY16HcUF/1xGHedwiUWc5QfsN/QP8HcAHoDvwAFV1x4qr4S7Wn+8cg85qu2q43TcQ4pAtqydu5Vyr7ztv4f9PIwDlgFnD2MdA2YAOERgGX2Cg7tBHp7D1g1Cl3nWmglmcDxPDY6ygj/wiNB3EBX88aNhWmL+4b1eVNnVQJpUvvvlvR4Tm9VBulPp9wcJzsSSZ/H5agK/Z5po5VExgkAEhPQyaU3GeHEjEzQCPw365OsLCv4oiPSUIJDwPOOfVBM58RAM4q6cX74np2oemr6j/NlCyY/TVu/Cf3gntDlZS4bKOxA0ckxGb9Qm/+jf3YMB0hQR43yPnAeDf42YtMXFKu70uRHu8t6V/sHx7LMfdFAsqjBehQd27BcrMLNPjc6zmVBA+aNu0VA9ZrLEQlz4gnefcfldz5Tn0z4sVUWEuuM9LeN+WfSkt1w36LtFeE6+LyUQlHcEqaePI+3hObeR7zGN04IjxzbHRZokIO07hbMvx6FJ2Ku9FuXV34U3PchW9lTVfOkpF1YGjvXLSsdbghJOutAVKjhjwMXogwf68GwGuc59kvR2uwu7Ibqybbkl23ePC6kNKZ/8btOKoH31fzPMbSGTy8VSwWovBC7DT+nQqHwB7eOxaotv+UzJ94c0TPQ5W1fBPJ7N+Znz73ude1lt68/BKwBEsId3BPFomL/WyefidRPGj42Bm5dHv9c5nk/GO5ikvD/JrUAQ510uBr2S1VezXBb6LCh7MurbHmyan8sqrHKrrvO33xOEH+wLHReTwrM3OO9vHEk+msQI61pJobxFAYNVDkL2akOLbABIOG6oaDVQHjsjvPCbVzYyZ3iqEzyPdz3VjagEgXg59KwI0oujX71WIIh3BikYRBDotd5nyLsMn+siiMfI+IJY5gnYqBUWNQJ7sLNl4Vy2bdoNPp6jEp/f6j+LQNgnNwhMaap4UNkL33Xc2eI2UXTt8qb5HTkZXAuQcEoBvnCT8ZgCXGVAHjMnDwyocc5HWmjCyTHCsd0xG7LFV17ErA37YNI/4mBgU375q+U4X+3pHleyEq++ZzJ2MS90895KF6xsFMU4gVPmnOK+pP7IV3bZcp7BQJcM6/jl1VOCO/gjDPAEggwUS3whKN3fW8yyMB88Okp1yHP8gGkgSHo98RVubKpPjmqh0TLxHfDiLIryZHZq8t+1o105dNDgNiIdsxHQbNPqAMfmflFDTD3WlSFkxx5EfrsujxKb9rQH47AX3vWetYPd/UnIWIFfUZ3UOEpBrr4ERbYtyG1q4IaFMeUDOspGo4SDK96pdrfHGHVCB8cSxOBTH+WE7M0WXXI9+RMFguoZaq2tLeFCu58Llx2djoOu6QV5FIpveqAu2c/hS/x76UrfAbHlx9LH5Boupumfd9nLe6Z3CT8BxM5pAfvUHY62oFdqQz+n0dMON9UwldfES1JKtSulh/srb3TEmlMbfDNYjk2RtGkqx6eWi+1zsjlUY9NtHQ/0yi7bD3lFVh/VxTfYTGqfKbGObV7ZfID7pZM85qWj/QOpf9wd1ADAih9NMv0ImAJArALiLiRfY/BnZhXU4X1AflGlAkEOAlH+kADQB70vaL3EElp4hacV8zUwXs5IMEhBIQWGnk4AiPLXwB3UUepAkE5MplW5gSJS8xE00o8vdxAY0pc8WKZN0IiLVc6oEuETq92AT+ShbvaPy/VdMJQ4ObJxKvb5UGkgw+9E3p6+kEQUzowWnR3dR8UZXmQol/8IR4m24mtdmptSfyZbu9If1YmUClgSR9l9MUueMwjVb6qQvBTsiWzJPa0Qaf4KTS1iWfYb86XmcDu+ONPHfFG6NuGnfVgqpG3KjPdTHpJz6Di5HthRA24QnSCX18HWG5o/YzGBIGrGHdXjcB33tvdnEuwjO27A4fyPw0jsw2C9B3a626RDD8KTs6FfdMqsY/OQZbx4ST+11R7TLWKO73KtjAEp+Ywyw0wNFHplyYQSD/QsDsQl7Eph8VBcXGEVpFKcXeVFt8GDA5OtuY7FF3iRBa5UCP1bPU6Fjt8jm1tXJ4K0MoSq3bRzWFKRnK9HiEBLpDLx5I2Cg6a0pP35whYW+PXze4F0buauOY9p8/Wvtz/NS6IJ9PST8QoK8VjYelk0wSAFgX4WnM/Le0WvAkGc+1kNhFweCfOXvhzA4RExXiW+Hwejzr4oGPtsE7Zp6/wNu93gqhe+SECJAgi2lUnx3LtznzRcDEcOB1DkpQ3aZ5HGOZqNfH44SwXLI6/Aj5eKfBCI2z3U4levfGB+4M97K2VlTz4Zz8uhFejRe4Fe61Exf9GU9wXxFTG9I+i7V7zTUDQvhNdqIeYceMrdQQcZ/T75sWcOvJxHZeHUc9lqY2WSNmq3vlZsZxo4OCciQsMf3dBm1hvGwCuP1BADlNO/YdYJJpTUr83lvPlkF8BNFijlT2yWqF0ki1AykmeFThhptZzbRu9BAwVkqCYIwg8AsG6VGOg2cI9uZ1h8fKCK2fTaT0W1GXogAHdsDcNohWbssH7nsYqfkNjpPOnoJBWKFWpf63i0Su2QHyoRiI8yvTiBIOoMnH/lVbAP1OcN7oogiABos1wVslJHQHQN3O/dZKxDr3/QxwblFq8F3eygqxJnB19ZJT9xhSUVvyuLnEhq29ldAMVMNRjve5601go1Et7aAv1UHJk7izRqNHghjFSKGcayi6SIF9RkCOE322QxPXxDN+jCkg7N2GvflK4ElUPZsgPI8WCA6+95RXkgBWUe1ZhUOox2qpz+IyNiobe0Sc8yCwRA2scS5gAjAXVL/M1PdrzWC/P/HZva1lX5d8i+L7M1p1HqESg5A9kPBi5eGu00seWmi/AmU3zTG/SDYumaPiAYAPhup13AUp1t/62s23LlPIC7ExW70+qu3vIVvik/nauFafmW6MPxteUJGcx07EMkHgCHK7DFafvrQdfsaV9tr+zSjMTIAmgCZFvJJnNOdrYZDrrKzoEzGqiT5R3lVPwi84GaayVNi4ToiEyLQb59ED9hV1nxIDRe9aPRgKCO5pQJ7hD8UTk/reJhpY9grP7hriGBoQSIWE7OUnICPwSGtCxdQZ4Pet9A4OD0I5BkfFYTfWQ5kWxjYshkkzuPBIWYhHHCS1AIOKt6Ethh9Q9BIYJFa7WQHx3T42W8S0h37Zi8eRURL3zUlzy4OyexlsMh7EAQKbrlLyZjZ/vjSE6G9qOP+Qz6nVyS0hbwsjVt3r6mRacNncjpSPGf9DlPY2mrbmSmnLSCR4x1pm1hHGblygfdsmWhBWlePt35EVHcqLyXtB9bj8QxlbgNBLkfDXMDZiliUfx8T/aXCDtdjncoN33MH+WB7tPbQ8r0meEs+hQiVPuYwY8ZM3BE8KM/wId87p5zqPzZNwJBjl/bA1R+aqy6X3v5lDx+pH1p5PrMXPjvEw7rhPYxkvLOaCJZ/EVXbQlNoDkjFPNpqR1TaNKMfsjdGuEGd262Y0gWroJEuPgrxsJbEH7Rljs4k1nRLlPc8OTDFR/Ys2vgErFYVx8fxpO/8kq0AkjbvKIg1SY/WmFTQDlDPHz/SSwzm3Y5D9ik5dHinIq8aqmC89gvJH2CeTd56kMs44N2vkGj8ykBHD4V/5OCPD/9TEBIgSA9+vXW7wjSo2FaDcQLolkNZDrBeWzsZz26zY0Zn+s5//smD1oUztDJ0490Ub/LV8LSorWFlI3xYOUpA1y+wl/IATjbmQc0zJXh8vSN9l5S+ovLSjnMpjTwyKZFKj5tKu8j2E7UmYwlEbqZ5ctH5Vm5Q1DbgW0xclOJlUC8A4hPyTO/eEXwRy+LJvDDY2MEgl4pEJQXR1N+rvmH5jAeE1kNhGU5Z1INfhhhOwwXgMYE58QGpgJ39uYHjhxI2RCRXAoDM2gjFoUztmtAlsVul3u8NAWDzy2OnchTs+SNZ8d20qXoKhq1dmQebku+NEWNdNKnjMgx4PoODLDPcYOfZNkJH5IqLSX0ljI2cDGMb4HaHdo3r9libiQeUjxeqa/ELnkC5tl2roBItEaPSwnM2KoARaiM/w2Y/hGbsIhAkPuSRC1dKB+juxLo9BV5X+Qrdf4IBGH4+uKkzRy9lo9GMtF1BoIgzbs3TQGJ6WJHZMSLINhGjiq37AYquw+XmTKkVqqyZFXc1BGigpoWdrgCkLd1DjV/OLLHaryfdAkdPicmZNcfNm8CoLxGvxv6I06Z/Ks4PhqyrpypHGPNiJT6IzzIQl1K2JqyjTBPhJ777R3kaZOAcCovQsvwoAhi9MkYxIEzrTKsj0R3Aj2CgxTEgSDDtYNOPx97oMX1VNdprxUw/3ds/4FAkKqxGmeqhFdms09a+AMpHX87LYz/L2C740QnNnxOb/FNH1RTdTlc8gCdLounQtWOf4fwLujiY9l7lrGJ9kEycHKAOIle6Ux2kT+sAwv13TpaeE8EcxCK+bZbeJ5/C7xIv6h3oXbjwWvNHtJeIfH55qmkUMUX8QPvEnWgVqg1GCpv3ylFBsEfnxxkP0vLf/W7ATSpnOAPS8VXcEeTxAZ7uFMI/FcHd1g9lADPL7xgkuCPgz0EjsgzSVWqQNEHTbh+c1mBIO42Si9t95Umlyx5TTBIZU54BH64A8Jkise+lHo1kCZmfoyMlEfHJgjEZ16f6rEyXtj4VG8sS5CItIEg5BAQSlCIQNNXCijRfvzwC39ffx2b7FHcq40+o73vgxLkoNWADdoQPEoZbv7bR+kw6R7CzgQg0sLtfRSMnuqzGEi9QRI56bMb2/LQkRS0mGAGEZ1OyZaOtKjCildakGmAewZ7ZaLehaSXim6E7jKw/92WP8cVdfXpmXuwE//78+l/6Y9qSylvtyDlRH7qKg75zTcFhu239nMcfymBIGLXeR8LjhhPyEE9UyTH+IBX9+bSCRune0gQ2dkGcN36eEsK9h4eGD9k5Zjbd0fhSoC2FKEDvrdKlQQd4zk7IS3yUi5/xrvTUlO2QrIkPqjMrQUjoc2I0bzS0z8HebPx8aYvnBR5lqk2sR2DXNoF7PlwUEkEDx/+YZvjBUZ+RgJXcdo7bay9x0FSIbMz/Zrs68TiOuKTyhFd5cQ2EGxj6UHX8/ZgTKHT5up2BIEcCOKGjQM3Ol9rRc/PevSadwDxKNjbt3oc7EcFg/woGJ+K1zuCtEKI4I8fHRP8vc7ZBIZ8E0gd3HMBxUdY9Utw66uvnjoQRD5BHFYGUQ+sborfpr7UeozO+gNVz9WUr+STtDOAqSy+m8ZJ6yHYDDjGOlqym8XnPxX4s11II0saqMu+5kGINjeXU4wjAISdOFGrgjxLUiBIOd8oEgVBIeYPvLOQIM+3CgIRFHqlR8F4gbQfGVMg6DUrgnhP0Culz5/70TBuRDmgZPM0d7Bt+LPzDSmwk+Io+0S2kOJF46gMDHUmJdlEMX4AzyYO0zoresm4spkKUZGXpEXUZasQiSgI2Yd+IxajiCZffdsu12JJaaYakL9pi0261cWKlEeeCu5TQlHnvN8pOIIa9h5KRAd3ZVkSHSD/S2GoRi/0CxNLuweuqaM3J5N3wwo6rTb4Iod+ipUdmwjQ5Gcq2+Xd8nc9iGw2Vrinf9CHBEOgkuI/+gMCUHKjNXjqRxdPGwqOXzAA3QhgHh4x2pNLuyBe/+6spMzP9S/9GSUTkKJfy0rJ2m050mBCj+0Ef6VzPYwTHbo4QEo/FiFib7s0VooqtpcG9hhNGnmAQh+qMw+kLOZVOb4cuSbWbvqNJYwZiGf7asYtF9Qh8SpSesyknSR1jEsVY5t5TA2HbJFQVPmdbCLER1/VsEWMDhs2VVXJdUVv4CUdAttvvaWw7eykb/Jwmlu7HFeU9Ue5P9EwX4TQiRB+l2wBqvufLBBETd008pVSPMFmryU/kMCN2o3fTr+QR+bEnfmSFNa08DMFx5YDcOs9acg/RtdOWvpT15kvvukFJxtiRbE7DV3Kdd+YvIk+k9v+Jdc6puO2OdIdQdNGl+Z5IL364a1ND4hGFHDrWE2/rcEWtsqr3luflsO+gEECL/47ZYBfdUR26xu40d2hfgnfeUAFZ6CIDB+44AaZg3xwmuhl0peJJEGcvPA5AaB3BH8U1OG9AXnhcwI6wFjdQ+DnV00cuRvp9/84IISs8HlyOjRePcQjYTKo7yFy/8WfOrnwx8nAQSHSufvhAJHOwH43kE6EBIR4LMyrfvjamB4Be6rgzzM9q0/wJwEhAkRdFaTn/Aka8YiYgkusDmJFEfKAWa5kohvZ/kMfbS+/pbW5q0ceuNrRf7nTt70eWnDAVt3G8U48pgitjTaO7OoAaJTTSIEuMPjJp5/tlh6O0AGu0SU5hRqHkHIN+VFemDFgmUyZHxtnqyUkhD5pGKmT4yKE5WQcgi88Od0d710dUljTK/aPlNCEFLWWOhBtMd3RsMtulNm2TykWDhmVnPHlCwoEqcIf5IDlR/tK3vDBWY8C9Bw6fppjnWNkj+2hKQdpjjW3gOWho1T191UGXDZgH41lADU4Zx/khR1V4JELSdgPxECgiKZIY6/etLB7NNuWbsrKJY2UC1XVjf7iTv7k79mw+yL2rHa5YXbzMFfWlnE0+dt9fCIL9G9La9sijP0+li4wQUCx08/tBJ78IePIgkSN8LtfwG6YWYNX1jCONU/3lSKTMjdleJTLQRzOu3o/EEGdN3pJNO/+eaugzxutAOJz8Xw5LJ+LJ6/39onuZ3/RM/nKItj0Qef1HNucI7/RQiD9eCxM52paoYEgbFs2YacME7t/4FIip82uycVsy07Ziam08C8B5MVYf3P8bEh8zdzBMIr680WqANAmEDQ8bghbL/myGls1B/r4UV8v9QUdmvV4mDqBz/dKNVXwe3+YX/AyaFYFvVLg57XeE8THLF7oxdGv+Zy8viLGqiFWA7FaiBVBT/2eoAks2VTaGcOwzRVWNm3vBp1KByY6zIH+cMhUX0Bt4NgQ1byAYVkA66TWbBvq4nV3ES6Z7gC7D5rYtkhKBTltYcADQ5xdjt7Jm50ywrxLUjhg52mbAxhYsDmMi0+vMe3oGNdGzghcokYnwu2zhSBzGFRfHGBEdSs6/TF8ruMQpBWmbtUpXOZvSbEZ8cAIyhhnGOsxQGgvnPMSTr9mvopsx2NE67xlmBSu8JFQR1I5jFak7OCucfR7sKTCjzOiS2VndA6bOTEHEnW9BILEzRZacNIFwAKxLPpHlPlDEVpxHrDKQSAyrrgKd33Q4S0asCt1jVxXK5aodtUejlhkzfb5CBrDl3WWF86rxlHruo2hl/PoElS9MkZWLZrYFs7q9rUGbbAcGM3eL/OVYVzCHNOpJnT0y4YQAEpNV2RrPWVE2WcqK20elvQJ2Kf11A5dFYRw8C6blnMQMJ8BlOpaS9do3/95VgSNI8dvK6EBpoPhtMe2dsxb/C38tlz6x+DFN/3DdGN+G7ty/qmUDvRJxmBx2er8n2a4SLv6l9JmjrwrhdEMHhcpDwunPQ+xG7LpbvSIJNagSYcLhEpbx0p4yCWMaDm8Ht2EMnYrh+lR8tQ5aPj6A+6BEXn8hPM53Qc15QRjHMBREMcreFixwwoeBX94v887BX/4sZLn3bzc+Wev9tFjX5R5N5BoObEQ8EnQB5jygvHIGPKRzfJrJqoMGNjBnQvsIk/t4kPSmdAJmhPenIi8Yoi8gjc8OsbdNgV3eCcQL5Qm+POtVgMxSesLpvnaR4JBBHyYlLEySDAeF0MGASHeP2Sc5Cn4g9vXXRef+OJbD8Cyibb7BiKM1sYAHotTZm8UlSNDcnSM3rEZ9tD6LDB8F8SWabmD28euhM9Ge25W5awzkE0FfPQs6iWgoh6k6jLZznSdgLZ0cj3dnIEgNPL3+Abnp/CPc/5PxWyvpeZnuXXCIyf8n/fSjE0eU26EVtmk1XHqvSGJTUNINwvtlxMIYmn0GQiyK/DDNJh7Mr5mU7IOlQViFInXHmt9y4K+ZBYmasGsb8rN35CV+qGAwZzJmLVAta3wyk65pUXuI7e0hZ7lHPv3+aAvBh7yJy94oAte4iCO/SMIOmjb4pakim7hKntMH/geb7e6zLt2GR2up2VWsPAztp8qMlLPMSkS49ywB5VEUEJmLnpTBsY5U6dX40n9Pj+v3NX5lnO38nwNjK+D8f6fNz/+/OQfP+lRMH7A9agYK4J4j5A/Fe9zPY+JfbBcbtJ8UAfk3M0kn3PyX776VjdJOCHyfDXe+epil8/p8jX2+acdpfZ0YG4G1cvn+jnPi0CEYEMPs0uWNX40IPn4eOddxkvY6D9cTnlSyW1QCB3Yw78l9OIFJ2pF0JOP+lm7Xhg9l48Ea1ghzCogHgl7xvxC8wdW/7wiEOTATx4FIxBkuHDfffed5h66aSV3EUhiZVDsIsAUA2zL2JT+REtTCyrMvDJ2UmLb9Cnf7nNRSM1cO/OHF5mWaJjF3zK7cW6B4qFhtXmvXfuuAWNgpEMVg6N9l8mxQT5NHcC530yu56jd9OiGhn4xxlDusVm56YFXRSa3riopFR4N1rYd9niFBeUyD2slpKqUIEh/gxy4DVW6aANcoioS3/pPANuhSvSxG0ME97mjDBKdPqS+jnzt0MGPdoHW7QP98DhZ5bmAZw4OgZ0szcbDmTyyvaGDP1J08WM8K1xEwNgmcT4A7dso4AdT+tJ4vi/D0bO2kd+y210FeLfITe86Ct8UW6BztUZIcUumBSHDni94WdF+BQLbCNMl7yR5V1q2L+FkDpkDd52lj0BbrUZ985Wb+g0TtEOw59LB4Y/F68zw2CryU4ZOv7Mui9J83pkrK5DGGyIqnWutAl2lLmPYJE8fcqqd6Ull3J8sEIQ/XT07ajwaz46bDmycOS2H4y+NGgkXGgqla94Es3PnmXwb8p7MP0q3G+yhjZVVfac9Z37ZofrurnRSJF+6pvjwU/QPJchHC0juyn3aacz43yzDCOhsRkScUop7QIe2IbzoEXCXQ/CQLrZu29HpYdZCyT2+jfxl0GntcBUUNat+FJdk0WAnB2ajt57viEDzu1mRowkfK350F5EA0Pu5M8gycU8QNTF0XjQsITdeuNIR+MljXwkC/abAEKuDHBAir9FDojWZ0IlHP05yDCK1x8vI2wDTKh70ZLurKB9Q5pd5Iyt2dO+PII5+fiG0Hwdj5Q8TNE3UHBDKu4L82XlN2qBjUsYqoKcK9rB6KO8P4uXSws/KIb8ckhVHovHKIE56bJyolKfI88CcjOd8GLTgTI33JnoVVm+njm0YRvHJQwNh+4/hBgomgcs1I7h0gSNkCV25YfdJp9hhj7LusYNNCW1SvgBnD66AgwDWPBIBEgqQnAwWtSBlINc89P+7nR6o94Bt74Xi9Not7pTx6TxjwLQvHUj5s18ZNMJJ2sdM94jgIY8sRI7AL+XRMA1pXtWIA+wLH9PK0WAB4MnlzJ5X1yMdwp3HygM3p5nSFquxRv7vPJYYgTCnG/o2xEYKtSl2LpaFlnxpkXRKCPw67p0So7kckX/KCv7e/pbWLgD4Cfb6lip5Yi7akLOPxPbt5VKDtTvkGiSCpMOKrMUErLVSXpRFuZ0r04YM/+hHT1X53Aw722nCEGSMzVwAEBa1mzEp51zecynnc26+8G4+Hukm/UnBHr4Oxkui/6HVQD/qcbB3vCtIgSB/Tv6N3gs0NglKlgAAQABJREFU53s+FU+eG0DIZTUQOiRGSmTcX1gNxKPbvCdILU4wSD7wxYFtE5noWydlXc/lM+qHGMHdBuwoeCMTDuQ5j6xmlQkFPg8T84acmoGkB2IX2FAFnwCQ8iIuL2E10y0FceRHzVfsUH9CKY+HJQSnoJBUvNCXwHJTSY+GaU7xQo+Gfa+Az/NnfFae9wO9ePJaK4Ne6mthpASCnk0gyCuY5TLk0Dmx3fYzr3A9c2ymDyvvDixSIyGYbTqa3Wf7Bz50PlZBjrOdJe9Naf5TvOWnfKtQMOZq3Zx10cRDT54+4X+EXMVIZuw4yEfgljw4wekztJUDFSLoaOQOJh3mAS46B76GJn1t6miiKEmfwqz2gCJF222yxTQ12saPXmk3aaprNLSGaZdxHj2VMIJHT8fSuJRjmxujBGdaTxFSL/j1j+/7zp8R4X6DBnT5ZwR6VDYbeWWwyuWxGRBl/9TnVSAcZOUqWS80ZPWjD6Y+lgTAc+DqXDipad7M7DChGwq7QWvhAFqH5E0C3pnuoFFeO8Sgp9KaQtk8tND56bgBFleJEAPDx5LuNDgQ1Q7FmS838GzwelPinOspfI0AOUWyOaRFCQwAe/NMKYnx0AzFiNPYVjztIr6aGvgwlFHppkeSe5RlmmRk2QPOa6d/zFmSJrOPQx2TdBvo1GGhdS9SpseYtcgwXt/xJ1oRZP9ldzZu3BmHNP8fSH1Q0Bqf2R7Q0fBiy8C5mR/QbdQld6GTXxB3b7vQDQGwP7Lt2jW3+XfVizslh25T1wCl9N5uDwiCAHyVei0tdtXn1o9Xzgw0V1/M4FYhl7TyHjFsaLHGNmpnyw7zyLaYA3Mmdj54/6LJHUtDdfDqRxCH9wn8os+68wjYe301xAEggj0KArFknAkiy8cJCLFK6D15zRDJ+6XPyNNM1CuLJvjjOZWMIDDEIMEJzQEh24Zu2Xi0Q9qyXk/dmcSxsW+OOWjeGzQreRzY4cXRBIImGKSgjt8N1K+HaTJmnGgD5/P0fJFMdLrDx/P/SSVHsHyZjEfPMrH0vJeJpOyxfhmjOd1M5A77Bl6baQNanxMA9jN59QYC2BQDDM1UNKADD+3u7+VAEFsH+Qjtc8irE4Ro9hHqo9YCU7akihNlVRvUwpEC72TVlRnpwGtNvAcijI+PFMP8BSaHy/9Nta+GjiuoUXsUfKPVXfQR3IUUERzDq7WD/WICQYxn9hMeOH17Oo9+P71eB3CO4RN/8eguwCb5tEUkFBVYS9cUyuv2mKarzCvPPSmRA2ZLfEgXOffgwApvetX6WGnrezj2XXlufZtJ92nxlX6VbJB2W5VRjKEXW4W/nudP7DWPLW5sG0W+2pJBVWVFbfoPPWltIvWcAdoBup9RFsDnVB1/STmf651V3NBhJa4KfJmTF0S/0dfB+GT8Dz/q5dDKszqI8/kb3hukINEvPqdnxa7P7UcgCD0ffusonkCQHwvzCZFAEPo5w3FBGUOpF/a6bFCtV4EgiDZ7Ad+QccFg+wSd1Jh6eSNt/mQQLJ/QrgACP7WVY67n6jmHS59zqJWSqB7BczHu1UCemBD9SiCImmhdlB4T+4u+FjbzBs01/L4gHg1TIIhPx/PuID4l/72CQawQeq2VQa9eKRCkm1KahuS9hKo/q4P8WLkMIBCE99hIsSmb8i5sXDFnGndaSDqFkHtFkApL4FBKaPy7UWiArGn67QAEpDXNE1NchtiwGGkJyWaME0Ayxy7y6AWobZmU4oJPUUkpJ4cuYPlf+B4/8AXvPWTerGcKfSwIGHYCvs7HYRFi4SiHzsCRSp+eLjzkUeC9BdNu+ABN2XYuZWj9a0Oo0Juig8lFNnSiWWRllJHYgIrkYiH76CKNlbAgk7+uRIk85Ebgb6vvh9bGmW/vWh9Xy/WLfuuc8qYGRz+L9sB3vjYCt1zqsWQEW1kCQxU6Gya5AwNTqU3tABUoF3bm4SkCnzzc8EttEP5CsguHCRZRDgo97yzZIMW6aEaMywtOHUU2PnNLQje0kaXRSzTxySArFPgiHqYLTBJUDqYjc6TyNUjbF8GmqVqn2jUQxJjcZiX1DzOV8VwImP7oa9xQ/xO9LNo1i/frgfgv++XcKcqZbcyT7Db/8EC5pfjXyTt1rTyNrvrUVpVWHVVjH3jFxZK9XzIGRPnedktnuSJ8jP6eDGDwZY+elCTFUMq3zbL1liaki3tE2OyKG2lNxj0uLr4lbjNtXbc+2zQRctsvcuBTDw6cXS0pcYWWspp0SRmrIKvrl7YRtcrioh09WZQSgjNM3Aj+5LPwBIE0adRk8J2+8MX7A1jp4xdI6uWRTBiZRPJuoAaIWDr+TgEgvwtIk0beJeRl6VLCqh8HmGQAKbqx0wOIxh8NEbIovijcBNBRQ3aQUMGpHEXgFP0TQd7jk1VBT2dlkIM3/oJYHv3K+4D0gkcCRCtIpCAPwR/ReYk3j5MRCNKzrH60jHcMCYasbxRM2quCpFuztgSBkmrBkCdymJcJnWiUx8Y1WQAHgerH4E6d2R6cLIAZc2Z2HpmL98jHXfgugnM3EUE+ldhvlPY2cLRZ4NI6Ejbl3VzJSZ1Hb6yossOaww+H3ruCv1xgWu7x+uPmz9Hc5z7bZY8z6UuSek8o4MIfKBaiOHX4RafMObX4kgJB1NtjGs6Qv7bLdBTgK49jORp7DMeFdWRb7iiXcFA72BoAF3o9msr9z6bYe7sVdlh0S/KgXJ6m8DYPMfmzDOz+dtUKjyHa3cq88C/C9kQBTlGTt2vLuEhgHvvu8Czy6fAmWfKO42CErLZBWWvNSYC+MFtk9LhBRuw2yVIgek4s2sAiFxNA82PiDRtxC1bZEgjKO/x0btc5mce5OZ8T/OH9QASC3vz4xiuBOIfz9TDgDhyxEpgbO5oLwOv5ArI/fiXZOZvl0bBvfBOG1UCsvhFJzu22Cfvo9zkbYSNbU1xhlwTsE2PaI3U0l/q2H0OibvAeFW6v74Wa/WGvkNMf8uVA5mT8YSPuc3BIKedr/a8fZshi/6KMR9epNEEgpVoV5JVDgv3lL+T1aJjmBjx2zk2nZ5pHvPA7giYQ9PKpVgC9fPK9gkEveWRMgaDv/WiYHjuX8m++0eNlCgjxOfkGgrDJNbVt8RR62Jo6z4QhYOOycy1XOf6jWH5JmDojLXNVK7SvcC06PH4tKYLhtNHlRLvVDpYuAO4RtwndqMi3lkg68qc8sxy6mrUVEmA9BSptGTuw99C6cMBc90UDnbYxrwWKF/0GAPTBiZiFtypQYqhs8lwQnx+SX2Mzto9w+0EC6vu0UiVCF4sr211cO1M4hVnlspAJyBoWvPrGTj9eZYupUY5ZRCA5VR2HBBiMRbMiCSEUnDlSgdOwkUEekMSv3ieQ80G5n9n+YW0bmnF2kMJjH5kvQQ6g82/KqAustQBvIlPY6uQEn2GUmvg3JLta5gUa/47XQwwOxpXKvvrjkAa6Ypy2UPi4UMXZXFPlRSgc5GYZuoO9DLGDsWxJIJO+M80h3zH6jiwLrYdGibHlKiGiw2e0FaSvjKRVU8xr9RsIohx4fFP/+PpOSMbm0ObRsD9fIGicGufJe3hELbI60omffIMETU+SE3bmTxryJ+7Mf4ruFneWK4P2T4OyTxcgV/iio47tDaYMDVnT50i92Dlkl+SUd0F8phDrIDqto+wePPA0B1C26oIG8y7mb4HGWWzYHt1Xhn1jtYeQg6t6Q/+QppDQxU7qdbHP8qZuVihA00PXOpSHdKGkBD3VBdyTOinhrgOTRsVwfMfQn4f95aMnjO+0CshfE+FFklrtw1dG/NMXR/pJWVYHZaUQASLuPOaF0gSU/Ol45DvQlDrRbxiabAsj/uSP4UqGstRQfBgaQnLadsUyTIXf3U103J3zkm/NpPqZeSLQTND8RTFN0nhGlQDQ13oHECt/CO4weQPGF8X85Q+9T+i5gkDPdUfvGcu+9eP5f2gIDPFOIb1Xej0i5kCQDEI3d/awCvuYO2FbrJwy1dAGjLo1PWHk11Z+pXJdNjPFNZO1IPDocxrHaQ8Tv1D6NCKC9stToCmsQzkKw7b0hvi6j9joBbOEVKcFmoeTAfqHxcTJb4gJ/3dX1zu95w48tj17j+IxWLnq8xyXa/xBbkluRABe/Uvs6xwgRHCRyXFLzsvXxQDdlxYISievj+UMO+jw2XEUCLvwc/BOG8B0bim7DQQmHUJPnjt1OznmgLyCLqUx7AJL4bD+DvYe6NbeaL/KUWlUXuFXeeBqWcawK95Iga798YZmFbn3Tz+EPv09/IcFk+1xsJrH56ijXpON76VgoZJh0lubUG8621EFRysdF9eQICHnx5Swt+KBYxuQj/BRH8i0lY7RlXM553Ze7OxUZW7ocGOHGz3crHmrz8X/+OOPDvr8/R8/PvnHD28UHGKFr1b6arXQW60Oyk0hVveKT7/IQ6Z0+Ec9qKsu1vSyaF4Y7cfCZBt6sS1p/N02BM4PXm8S41yqpEL8x12Rji/2iSppPunO+1PEHUEO9FQmYq5nmVz+amYwbXEEgkTbR8N8rlYZr+anAjMQV0KpHwuTFqUf9b6gv1DWFeZXejvrt5pTfPtsbiYpqvOS4I+CPi91M4nPx7/UaiAHgvQ5+dffszropecT6NYT6w4C+cMUMh77v/5K+ibvoAINrA1caueM6hPfBi7YygSf/TiJwsInwzER4JSVpJ+RBmYZym7ayHPb4A4XYwftESxiaUcEOhs9loPEaeMaVFWkSwDZowCX5O0+kUBFKeoLWALDpmLROXnblLzlIXdkH1TSRf+GLsaFY1tU0XYTBdseKuSgxvzKOGu8MewGZoxKlZ5ypSC2dSD1jPiA2fkcDyaEtvKigRLa6dFrVdxBYhtHmemQ4y2+q+6YV39WgGjPuqErlbYEU9mA0gOufJN4V5Wlb2rLE1mKL61uZA0Rib+kJb1IdhmYJWdX+ZT8obQilZI98UDwoYMpQ1fy0RyhFw0Bnfhp8YWIXUgKVWkJUDPOOQUrktQkrKXHLvPYGOVq1Eg0Vsw+syBP8HSF4RtmJ95J8qRWann0rwiGK3gRQQcYHyslazopcHAHiIYr8B4qKZLRljI+FQ9+1R/XX691PfXv2LSi89qc/w4lF5nU03VNhS84UHXyLeJ/SPlf4c5zUPg91f6j9PH8rf/ba6Pxfq+4Ng4H3ynliv0dli+GU0r5gJXgHv6qe9OWv2llTHmKtxKpLyjDlTnxpy84bjlIHQTShJFHswjc5Atgmfjx4mfeJfATL5HUJJG7iAkE/ZygkAJD737h0bCsCmLJuR8lkzDk+I6kdqToti0ePKTcgwqWZsjMEKayBn7oiOCzckgW2sZWBI7NQ06Tu4A8CNKHHIxRyp1JXuycX97r47tughMc8suhFdABRjCIoNAzAkB8/YOfBiuWdT/XRO7FC703iJdB8miZAkF88QO5BIOYzCUoxARuJpYyirt6DMzYR6CIbU82U7btqiZ9sFthLaeCkXPCDpYFvmZKQdr8nKJavDBYs0mTm/LQ3GUpf0mVMvblZPaQw+cL8ZScXPIbUpFfevrQe/8qj1RyfO7SGhwEK/qOuoeogSgh1zGcduagP+n9LowvoJmZHLF2oD0bV3Q7/YFzLuUSHdA0y0nVfFINc7MNXOUFOnKl+nyK1cj6jJSaYYGXwqEi8PuSBqpk4w/Wx7IWeUffHdB9ESK0U+9p3bD249BGUlVsqoELccLWoSQ07QNf8EPlRmseGZNXDlrmXj1ykHXmTa2TyEzv1zzTk2zTZkVQzr26bJzzqB/d9k0a3eDROZv3A71hJdBPPz35+9//8eSHf+jRMM7nWu37k74WxtfE8mVP5gK8K5AAUH6/MsB/5G14nJ3VX7wKqIEgnb9VP+zDdnham1UPKsG2O2/yEutrb6WcU60GLcwhUIYs/5zFMf6RLEfYl/Th+DT7nIWZKRguJZyHuTDOaTlzhnVuBieRmOcvhTnwIy0O/CTlK2Lc3cIeVvHw9S9eEp3HzL9+8lJzh+/0nqAXupn0UsEfvhT2/fcvnfoxsde8I+ip5h1PzKuPm3oOwjwB7xFcWva0othEXVVuage4ZC+UwFS3O/OEUahmSPuDY+QYWhrg8pyLWIcPgClR/VeejPobKH5DDqcK4XceRm05xkbHJMGc+8hHXrbk1l6ZXIweeMHa1xYUO+9s6zgXznnsvKVLxQOl/ymXX/rjLXmwgloQlFti9RVyauuje9vUUKHR+tA9S1ug4THGHg/K6J9jjrQakY6M/DxXbv3GAKqW7ACsKTWSJFWFH//JU+wGB0VzjqDkbylaDn1KEeT+s9CyVgLSDsilsOf3Sw/0VdTUMBeWtGamCraVnecmQrYql9VCAlLX4iLjWqrc4k6tycfvJ/zC40ANIwxXPQeV1HjcMTE6D1ytFngFjUThcdH02yV+2Tw1GHZ8mi2wlg32rvWd9EZ1+ywyQPFjw6/xpfjGqeB6vQcN9tHGGb9RphuCujB6reuqf8f2HwgEUcNUzBWydzfMqDs1xanu/NCfR9UNbema3qBX8XP4Ev5eOuhpTA8yZf5EWrnuT8MLOfDPbYt3fPJ7eE6Z2BlrCz11DjZJCZRmsKGSoMxx5k9KId1EED2QcxCSPa/kF2ozrea2UgRy5Fi7qQ1W+exSS4wzQ6ukUpte6SL1xJ15Dkgfs0qpG8vG/f4eB4E++s6hAzsK6nCHkODPT2+1VPxnXizJV0VYEURAKJ+V/cWBIN4RlOBP30Hg1TySbR3aJRqMpdSD2Q5DnhKnU2ZCKT9Cy1fFeEEjXxzrIGOGcFmKh1vNlpDYodePablMUEbDrBzvQI3ouoKHAA1LsoE7IKRPxH8tJKt8HPz5lpc8EgjSRE4TuJcKAj3X75nKrA7iE7ENInnipmAQXxhjJZADQZLvYJSqhS422n9N7MgHbNudVVuYVCnbsKUw+wtMhSG90JhxyZiB3ZSl9nTADVOIjXNHR1T8uTvi1rrorxpTGrKOe76Led9C979pffOGdeu5J/5LheFzPPMp338O/znfpemrQdImu8asEVCKq7yBKiHXMbyjWyYq6YdPOQa/gGbmwqTvnjh9ddtOPeROv9o9NEGRZopzQ8fjKEg1kTTpotDlQ5PKV9C1dFCu7EOKDdm5RT6nO7e4gGcNZsw5QcNWu6la86SMOFfyXUI3pdpgXkEWxWRKhypk1n2Uzw1Nj+FCF03txyFe2sU94+Mh1PYdQm1S7TKrdswP9uAobmAVMnlIBMpLYoOjphF12G3nwTPzBfMoP/w6lXsy7hW5yrOSx4EgvuSpFUG82++Nbu78qOAPL4n+QYGgvzsQpBW9ovlZq305v/+iyQGPg+kU74888JiZ5w96NxCXMFRgB4I4dyuaYZuoWFcFJXhEHRK+IIcrUmcoXWsu+CIS1pV37dErxT4mlHc9I9B5M1hs9CIxrRTpyRP0aTm5vkCac7RXEcMnu6BinMo5SvpYzswkxBMR1cKBqfdyMuFejsfc5f5W84h+eZRAEI+BORCkucRLPQ72VwV/WCX0nVYH/fVvr5680DuCsipZN6PkOt9ckiy7RoEg7MKWHGyCq44uu0dAF19C8nu2yAol9TS3+ySYSHZbLGHyBeDxrfMtwiE4gb5ufnQPfEHizXEU2ezx+SKQQHs76IpZKe1dFCLR71Rwzi8uKV91gVDakGULmUMYFMgzyBUL0lkELWLl6AxmUJITmovWLDio7JNzEagcBCSSgZJL/yIbeLHQ0yYql0ltQzabZLmuOrsUSIb/OTYwzbim1sU5F2XSK3meb6sJuL5zXbO7nJcjX0K0rfOQFGUeHniQ3mfXalie1dkWirEbsuG9JGlJG4N9kGGfc9ibX7ndf5BDlbwbxSRWBuXeUDVuClAANBZmvAC2goI2zt+3ttqcwZuI3VK9EYBSSg0yhoQj5GUKntJA0gZiphoPD20hEBzhFghf2spFj1cczu5FFoowcADDbNkmV/k0boSbCrbFVxGSClKbaSYzrTc4+qj8J2BpfbiokFQyZAfzwFd/nkDQeORsGZwzm31mZxZyP/WAXK/dJzH0/zXdHgA+YdQNiupOX1k235Bcirf07q/puRe6xwpb185NtxULg7A0HL5Nc+zBNeUcI6eEx/Q9sPckvBy5v0caNLXg9NuGneKTj+0c7Pe2z2kFz4HrOY0KHJxMsLwEXF/zYhXPu5/zaVlWA7H6hyDQj7pryFLyn9/mruFbgkG6Y5ivgykIpKARP99B5M6hZHriptHEgwLGyuh8cpNRngDN/Dzqs6yGAZ8JWiaQfKnMXxlTuppQ9lL1DGbKjYxM6yxCOCQk2MFEiXO4BzWlnuzNnTbmI1oE5GAQK3uIUvvRMK36IQD0Ur/nz58rEKRl3XrB4wutCuJFj35/kCZ5ftEjASTd0XNQyZ+aTyCIYBCPqKFX4NiLfqo+9ivrfA8YwwtTSp2BndtZfqwPnPTpVRK0tuR7R2GBlUH2powPt+NDuadlJ+fka5xS5OxJ6pYKZUshf7gfaV98Uj+djsBb9+AnzefzlRDfQ5/j64A3ewi7A7piTSCZI5aif+vg5Xj7UgJBGWcPBz3IHm55gDtbOX7vXqmzKY+HM75dpEwj7OYQdmAXuhRuMbflsgCv5kVTQIlEca1bRg2TmUk7DV4ek8emMx8xV6GXU+uph/yVdGF3fYegdAhr/qBOlnHv3FQC8Cg9tNQHgivRxQcjxrSqLJScp/bWPHMWxGnv/ykDC8B4nOf5ILqHlUe1QHJ+57yeGzx5PxA3evzINit3dY53IEgrgnhHECuDfvj7DwoE5dEwXhD9kwJBDhzxSJgCR3zpkxXDv/hkrvf/6N1AaVXeBYQNvBcoj4XZgzrRYQPV4KJqXQiOrfBSfUyGPqkMp14+MYpCU4JuBMZ2IChyLRPG2TincQMJiNRaLntvpmO+wbwgOGOkjLLnBYMDy5wh8LBnJRBXN1yEUzECQx+UKEKm8l/0Tg5WFDsQJGZuKLGS+DuvJP5GeQWF9KUwAkGv/MUwBYL++t2TF6wIEi2rib75lhVF2Kg5i5RzV58VlFLgH72GEn47YZT2lpoHL0LTCovNFA7QCAocBCQPtvgzcirsIBJPeqhg5EdG07OPx+6RNyKCP40SomosL3WGPKJHwVkeZcsOcDUAxmPb3AXSduRjg+05iJa1pRHlKTvHJPuL9hG+LT7YravlJX84krSlITWlMpKlOvmPlPr5f1ISuieUPvCGRfWyDsnxn9IEhdLPqDcalhrlH2z1pfWC3XUF9UneEWa7UbTsDiK+xGrktK4ujN1YyJE4MI4H/ye18dmVBELnu/P1TlSM/zADH485ymSManmCjK43vr4RObIsH5sHj1bqYO1miiUua+fUoihAJ8ahB+cfKP9NWfCo0z6ZnQow2kSVDRndIj9M9q1Pou3vI0yw8gSy2zbzd1uFmd6gvpihAv7FKPpfcoK5LwoJMTRk+5MMvsz8SmPiv2P7D6wISiVTGWp83XzuuYJ+V4lGOwcbmP4TMGz4nN7im95WEPhj24mp9z5Ff09O+YKjdEoFKtgQGYs9AyqlQXRiAEM7ZCkP3LDBI5mtdBweOaTqszkqQhY687LDBqUwj8LwDrETcLdbDsJlwg0JxWrdkq8y4IXGgRoGQyZ3muhx54+vinx4//HJW73vJ+8E0mogVgIp4PPjG71UUncO/ZjYewWEBGNVEJPLD7/yfiCt3PHdw74QOgGg31RHBl1vvFjSAzknIU+zVNRsj2COZj6GkRcPA0deNM3nbpl0rVpbVPpJ7vA5LxXiNC9lNO5feLtckgHOOKWY46ANq3lU8LuC9GhYHgcj1d07BYOe624egSE+A8uKoGesDlLAiLt/TxUMYjUQEzqCQkziuOBFLk1MTbmzt8rAxz5wrRp5NmyiW9hq7UzLDrDK4M1DGvDao6/y0ifDn+H9oJ42sVh2MInZiaUZY90uwjp4AyvKvKaIosn67kNEGBKyMFlU6SQsrXWFLvQXnKmL64LT1Z+CFffp9Orv9JmMXe5Ug55u4ePxlHfpZzqO2m/Ocaxj0Xku+1ICQbhPo1a2acjTZ0WRXltCgGnowHPkBkY+wvp4QEcAZD+2Zawsti3EhXzzV+aWmjItZjLects1Y9dUrlj1BUx0m8MAumlNEGDbRD4IxoFNHjnQtf9UjMmDHgWTnARb0NJ6m4laCNlUKk/LTgfXpAcE5bKSZ2MmPJXhYsy2Gx5Co4qf1GOf86IZ/T4Wlc9Ki7Q5MIZsSNhxviRvn6Uios/4TftgimI3WrCSAA6ralnpw/t/uNnDSt+3Wt37D1YEvdWjYf/NiqAfvdr3vVcE6SbPBI384QgCQZKZjz1wfqb1ZYN0fJxzuCM3zU9bYmMeDcNs+ptsdD2Uw+7xg8D2FydLV4cyKpTaA1LUVQ/uD+gVOhcfcJgwqfYg7dtIUzk0wMiB5TyasvayA1MUzlom0eNzLsZuVR5lmjB5pbzyvx2BIMJhngMwH9CUhjkB84NXCgQ94yaS3h30yo+GKRCk1UCv9OLov/3tpeA8GsYcIvOIb/UFUweBdLOKeYpXNKemsmHmBq4ctddGPTnmyLjjGErFslGpgTtINvC4PfVe8zM4Rqz55TOzC+zjtbhINoy2iPhB2s9D4CQKa44FWgkQeKSj5DEqpaJdL0DSs+qyDaFv+KaW6DPmBLddobJBo4VzVfU5VUkoW2GdU+chqp/MZB34YnQN/6lLvWLwpbsoy9zNoOghax2HjfQ2LAoOez3Sqqi8/S1i/q0YmFB0zYBDM+UYw5waiejkpz5mFdHhwJCVhcbCKR9bdVnvwNGHhFuPlg2zTlkez8RkuI6f1DN12vWNTRjsPicJ2AsPZVbwb9wYML4ioY7d5AIANiG+Eb3+HZTQi4KQyY+yxygRGadyV2RiLbzXDcDocbZaowxf52w5LQlActi7TtSNsuFcG4AJLjTKS+6Ag2OPIQCVxnryC1SLTJ+CPbDkoI96RRuM+rGttCoGLkrTojP/UwurNSsmRcv2k2EAyYjRZXZDy3jDO4Je6ZHZf8f2HwgEUTlcpXQayBWbvKB2oGGzS2MYc+U5iWBzo4XuzJessKaFnyk4Ng7i30t31iMH/5Z4yjjzmyK5E0f+3mbo+Mn41lfpfY57Uuz5QeArOJsCHj+TM1h4pVV70TOk4KBZ5ANH2rld6ISgGzBZyOEOZRhNR8ltoDRHDQTZxoislBHIiklzqEO07RRMBepimHYVt2kOETBrA8eARo59viKiQe9XffhUI987XiDJBFF3/wjoeJLIo18/8ViY3iWgJeRv9S4BAkGsBOIdAn5JtNJf9EWRD9w1nCAQkWCGGgJNfo+AdaJ3ZnV2iIZJZjsMly7z4kbx+AQla5UyMP/KnUhOGH4OX+Te6B+pbeuMDF+okMI+lD5n4HeXqXlO0jmtBE5wiGW/BGkcwFFA51u9ufG5JmhM4Hg87KUCQawI8nuC9CUQPzL2Ql8I8YqhfCHkqSZ6TOb8/iFNBPU+aQeDHHSSUdiCji71Rhd28cMTbdPCSNeG6dqoG3TOa+fsEBoHQsBNN8SmTD7HJnSqtwgjr3QIiEBkWFatrGKVT2qjCxhbgKW/xZZKLYzy3kafBW3o/+bs/uWGaY5VJlNY0wvynyjQjJ0TtJHbtE3bL1xeiotFKcAkdBljnKb1v5xHw2ZsVt3tM7kEf5Bfh5IdtXeLLu5rE5gB7+FZzjDJkCOvn/7PLWOqIMaJy/9ws23iQkgD3WPrWG+O4psaqJ35Pf9pQanN8W7IIjl1zvgchLhVYUy0vVN5yxxOknXejBhj2kcBxSblXFDp1rmCW6bkdz5jVYvuRiPIynKewp2tuNLOiT3n47QVtmSMHRni4Y+tj0WRN6zywOl3uWhShTEX++GLTzhrSBYylaCXCXZ4ySsQxLmYc6hSzs/v33F+1+NeBIL0LiDeD/TDD/psPI+G/fDDk//++xsHgj7oObCf5uMPDQJ9IBAkmQ4E+SIK/Zyn0ZmbOT6BehkPOGwCF9t9oQUQY3EBdpMJqYrABVll0SgYYlrLkBzNKZYfhNL/7MTbfjipZZsgAuFjG62Ta+AHgwZnu6Qac/Tn+YF5VRu3sQqkCgj99tsH/bIi6CvB9JSX3xnolcVa3cMXRr/THIGA0AvNFwgE/e3710++e62vhn3Ho2Hf+eaSVwP53YS6QOJmkiYJeT/QNRBETfJVQGxLhZhf+OIOFwiUmrgyVCkbvg15CKAzYanrdJGbrvAloBmna3yRQouu7OGFm2ylOC/CHn8Vhn+LWxxlHiL3dcvrsURPj/1t01u5DCrFneJqD4blGEoaQxd2yadPxT7oDL7ZCTgKbIOKv83Xmko4aPtpaRig+6jyTk8GGyjdM9C53XWg6Aj0eJlgBXW0dUnpkioDYQ5OJnhxy/eoJKBC2/mHVnT7lww0bKFGUrZIPXyAnqKdqlxmswCkvTGDPPT5gQaWckoZ67BlhDjFJv1rohx751rB1Wl9QuNKIApd4l22Dci2YYttkH9oV+Xjx1wPcUhTxpauZsFO/wt5kWm50UWWjWsINxcmtR7AJUCWUxX/2DG2UILMdVPGdE6Vp46hcMouZQGl1mUM7qb6IIc6JSgsBJW+6Ysmt5EI4ce2BErswEZZyvhdZOz0fwaNTa2d5yRIGnZSj9WFUb74nCcv/nSBINX2wYbn4hXa47GtjXeLv4Xflkv/GLz4pr+Xzo09rWnr27IV9E+k6L631UMrnY6MDfc57knBy/FzDqXk3WNN3jIdEYAkj8IHOjapOYfMeXZF3/ItOh1gHDjss5Ujafwg/HH8mo56L+lAxG+hvgcVkkAtu1KrhTfgs7VMvjTk2SwuWU8YmcwRZGGhDauB/D4gVgFpRdA7XhLJO4EUBHqrIBArgPiM7BtWBGkVEGWWjPMlsfeaMHrZuGaIfGUsA2gHWurFfbKMavGL8toaFGJSqxDMWCYrVWZmiK96svPljuTXs8iZ86NolbeM1D77aLIoKGgXOyRewSJweBd6Uk/6dMLhVMvz+t/y/h8mZgSDNJnLC6N51l+TOgJCShMYUsCIwJAi28bxSXlPBlUrUinwO4moFZM7pcAMjytig0zDFrbYtMsyacGTA1Dqa/ZCaD7VXZXfh/EImyTy7u9pGTanOAwhi11l0LdywmI+nA46vob0ljhkZ12qs5gvPT09ds/d+Ocx+D/lO7Wxj0kUH8qbbVq9pCes8MJI/dsd8Mt5NEwVb/3xizc1ltsLxCe2jl0XsjlxcBwZzuT0ogEo0kk4ki7cAp09JXRDHR7YVu5hZnNXrnQ0e5B79D3a29PEyyRodDvJIJg70ve0M35F+EVk9TptATqIbUGYvBfswgwZsIPkQXaUOpl8GS5FFZBteawF0DY22f+Gn4pglt9U7ZwDwcle7gocZGR/o72VCYVa2npUHhg6fUYhlVh0Z7LdfAJBvpkipFf96iYPK4Le6eYNN3lYEUQgiFVBf9ejYf+tFUF8FdTnd6U/+8ufkuf5QgJBBIbw8Ec/GqYzJhP8BoIUBFrndmhcO+yZPjtl247R/pGkDobPeVHs9hMe8J8q3gs5ZUbvEqiMGKQQr5B6c79TWRt+yyYK6KQTVdCHQqlhpJZmOLh944hgELL4XLwCQVoJ/ZteGP1REx+CRF4ZrMkEN3xYHeTHwTRfYB7BOwZ5TOyvf33lINBr3hekQBDzBoI/PBLGaiAeLWOukBtT8iYXwqoQ5/HkOm+hEvyT2ijXEdvjAGcoHNuGha5l0ubJKi+R2Q54QUrNYSG0xUbEm8Oz4Ml4L9SWuHNLAqA1XpyyKyypyYbJxwZ5AQ0/DRr4kK7EVbSoyDv3i8i8khjkBXwWoi79020hZFnGRbvOGKht9TqXBxiUsalMpARLS6vM8SaF/cHiPMMFcMrOw0WJVNoIVOiPl6MDcknGGYuRZEzJbgqGsIsdzll3EdiT/KZoOThsgojUAR9KzmNk2EMTvqUb22wXK+Rspcs2zceE6F2n1CX6OU403rhwrQOemSFVKdcXRyBIpvA+UuzpO9AcyBAoRoY2FooIuE1ip7JdymgCODa4IB3xc1y68rK7q7Cwl+qlXXSMC8Bx7jHRsuCy4KRkJRe9hoMbeoMNBw+2IRposuV6ALwrZ6BhIt/ylNeYwmGINX6SYvrNGQiqG6xFIqM/6cNAUOD4HRleEaQx8d+x/QdWBE016oFLreJZOqA7tHCXDj+04Ar/I3TluaikQGvSzo9s1df0JGv3AeeDVvX6lE0n72N0wE/cmT/5yRd3C79fxrNUN923pYe0cYYPmxu/1FWWcIO7lWNdIqSpo/FKYRgHT20i735xFWx7BYKS+oIN1ylP0oxL34AO+Yi7SrMyMwKvXQ9oTBFecIrZ6BEwBYCU4XGw9wr++HEwTQ55EbSDQEwU52thbzRhfKNgEI+DESTi0/D83jNR1MRwfVIWC6TAE8Mn33ggYfpi39s66jvTKp/sa3HDMvCmv9h12I1I2Ru/lz6IXZqcK44ESxERgGqPEAY4qKvRqQBZGSTLNeB9oyBQg0FPdZvvOV8J02SO9wN5hRDP/SsAxFfEvvuOz8JmxRATPyaABH/yUmoFfXR+8Auk1Yae3ElXA0GxI+0ms2hy19PnPcpU03UiM3RKC4KereWUZl9mOS7HlYbrEjYV6Xn89PhLvy1mUuQx8mvz3WxnXNw70bQOGJ8TyUl4KF5cyKfF0i4B2/hF8aVmbr2FVwo78/8K/9j/Fr59T588Qc4PbFMd2gfYfjbdRf0ufRC4j4e7zIecP0GWuuOv+qJVtg9VoGzcBZGK97hOadog0uxLy9CgmPOGSyJFYyb58HFEXTcdZdVl/FFweVP3eARiOzdKuS3XEnbRVL4xIdh+B1kI6oeIgrP2dFJMOTbdm9sEg892X4oDx4BJTLcGoWh7uI/+h41wUlYjKRVSUtDoiitSYK/LhVwHmqVE2By5rp529u/UFa7Uewu3LO0IBlUK9U5ee52jErAdn+mEgY7cPElPoMx5Ph+BUH7O9e90w+dnPu6gc77fDfSDHg3Tu4H4ath//zCBIN0hMp1WDynWkVVAumnERRLn+7wfiBs4skODPCP3Ry4UfM6Po7DVcGWwfI0FQru+1B9f3JSJiXnjPO18LsL8qAaSLBih08M9j4BDgia/+p7LpYORTTqVzeUWFvJnbu99gYZN+kN9atc0sxcCQVkRRDDoveYnanfdfee9F14RLEZWBBP84YbRM60q9lxBc4bveUcQn5HXe4L+9l/f+abStyLms/N+nIxHwyxHOmUoj4bhQc9PlMNYLgRdKwwXbmroPBBgdmyrHKD2ZnBpvfw4pQtu0bWPImd8ch6bldbjFrVRIcwD3VYU/IXuDuHSK5xpp90tYtOjP6XSqbzR0kUh7WvWGryMu5GL2VcBZptKXW0/9ISlNiiV3opZKkdS/Uix1STjupx92fjYX1Zq6+MN4fwr9R9kyhPLYHPReXLpxbSR+zRKyY/OHIupGrygKZGiZorWBYZtjUWIp2yNzuyycEY7jZ2/jYEOwjgvOHZKSYI3EcgeV6A7r4vgHJEbxBjmoBYG8m+DJx9rA1c+0rAXs6RL6RojuW5RmYDFB/0c7JYtpNQPut+0ipIyjL4ethjqEnmjyLLx7bnZPPEZqp1bYWxmfk+2QbmUE/DicPeHbVRfAkL8Mya5vVzEF5ILDEMEQ1hGiVhAzduP0ZO6wxN8cDDPb+BWh5MsExxBIBWsb67jKMIpdLpr/OoyLMaBxNfBkfcohZ/J65dA0J/mq2GutStvL1PD201euwO9pfqXlNtFEUbHpLO0Q3xSAY1No5kvlLb5pj6/S547ytYLz2PbKQ+qpf9Rnu3JnTul39M1ndFk9/Du50vIuGKVb/VUQulq98NWFqeQpa/As42osMsIq8DDTh88KmdIQ5JySs5msX2wV8Gd1DTo0o+pJcu8f9bdQYJA737+1UGgt3op9M96HxBfCnmrSaIDP/pKGKuBwPH5eFYEvffjYASQ8igZq4v486Aq4wgCKeQhO5msMngzgORkhPGM+8se5bO0PHULQnj7gooM3Fn3aHLZLGTXOzK11399Ee6lUXzyqO/miUZIrGUCF/8y4MpSmfq1Ijf9DOw3TNIUCHqm4NBTBXp4ZOyllnhzJ+/FK70DQHkmdt+9fiGYPinP3TytBmJCyOoigkDAHOmXbK2IHD0ZvG3r1AUvdWOQt/3sBm/aEhwpfYL6lC6oYXKheeqr2jIaayvUBe0u8kXnE83AoY0K7c14ob4yF+X01HLmH2oNW5mL/5+efrrOn6rdPc5P0f9x3KlBflcRyG2zmarNQgrAwIN+lA/YQqabXc5BX8o7gqg7Iw+bj80pcKw+tq1j+IYmPtXe/xzDTFwbCKqWU2obaXQLxejJdns+vlE1VKt5zWM+7WLHAgkgbtnSd3TEPgivlLs444/QV3tUcuVHNniVL3MXi9xyOzbZ4BscUjao9cYsoPdrvNXf4seme+BRkvq54Db3+cTAwGoPdUKM64ZCfqK71F14uLDVE+nxOmXDEcbGuZXzquUMjyBcWNEjUM8Fjh/pEoD37fmdP5zzf+GGjs7xPBr2I18Oe6OXRf+oQJAeDdO5npdJ8wVQP0amx755tIx3DfHOIV4YnXN2zutZGUStON8THHLtZSupfuKx7a648p4TQJb6JyEfmNB7cyNnfmEZCNNG3gzrYKrwiHG/RODCw2NWq4lLfZkVtUKlNDQ2h3O24OajJq6h9uQTCPqVFUF6NOyjHJRAkM713AhSHXjf4MsXX2tOwNdHcxPpOwWCsiJIn5H3V8NYEaRVx8wttPL422c8ls6cQQEl8eMLAudszE262cMuM3dhi33Jp2ywd4JOPwssVIDwVl00PfPis/Fm2I59j82RJExsWzJKe9Fb4J30U3QSnZ5fvu2HQpqmX6gUw5oM+hN8HDGHnnhTIDqNbFv1vCdXMAczxge1hZRHu87uvHGBMo7vbbyHPIOHc3QuOoLDw5agBRh5aA56UP6ZBmbk6g8f+z/5wECT21vtXeyDMo0UV77BKphu9igwxQHHVmCxFSMZP3L+oiIEl81DEwDnL0ItnCw/riMcOJm5dB4Zk7X65+at60H9qCdAp8pqk1jLTprAtp+K0HHbQJBfho9+nVcJnvsRWF0r+PpGBkHXQBZ5LLWZs4uN0WeFBLpKIJMc7BHazaAd4wNBXoI+fg8YefkvZb0qQ3eS84goNPnBQxBM/4bJFa4vNbYLxO9q0xkAWv/YtArxN3QE17MdhMAFRIc3EdJP8X2ogiiH206E9S08tGN+Gw4eAbQt+Twa9qcJBFErVTBexQfXbZxaIIdcHVfYvbQD7T3cCftX0F1kHPWgWm6xUXjSnfnTHvK3OMr3tgd0IrIrS3yXz1aZrmQtRJ6gD9TRKRf12HeUyR74Yg5XBF0ayX+gwkwliEDTSAgHhKsyAlf7Dzll94sHQgOArJIdVOn4InThtZnUug4AdYeOcYlBlmP/He8C0p1BJnu8A+gNj4ApCMRKoLxEUo+G+fEwpbNC6Ce/HPpnTxLzOVk+K5vBkOEEuZz2fvuoyaAnhCprMNMwJ6wGZm0r5GKbOBYUGHIldmUoZvmhYHaBhken9fsULFH5s3Gdt/BiJ7VU08Kd1UAyWAVbofQrJnekGgG/0cD7tSI23yiK45c4Egzq0m0CQLrL5xdH8zWxV3reXyuCCAS91ETvW83cWBX0TPS8BPKbCQoRAGIQ/0YzS68MUh6vYM9uSHmw/gCHfS7Hg6uvwHOzLR+JJy4ZRtNNHmH8n6hbOZQxqiyx8KCyxSY6yA68US5bhIjCUaVNy1J5pdvlUnyp6a2n7vkBb/0euoe8J1fGKWgIWK7+MSTrYkHKirPeU8QoMGiMyuiwZUPypQWCOC7xGS5hV/9RPLfSAeuxXHzcvJ3NOcWjudPkS5uUMVPwKpPeXujeno+xq5LJcy6KtUDJd2u5aeBRcYVV78adkiqzqXCu8FGWXp8nEXu7CTbYMfweUVCRyH7LvhW3y0x4obsv7yKijVX/wuV8eHe9W1YqHuTbEp1fvI1Zvut9yMpkGQ/AF9mWL/51Z5qZP8iRS76TbJ+PxboDQQSE5pyvQNDPeuyLR8Te6mthf/+H3hGkgNA/vCLorW4M6f1/rBImYMT7hHRRpCfJHAjyxZOUEAjiPJ/zPZWYoNCc0VJrWokf9usHmWzVCVYZ8vzUK3XCs1cG72qZwJziHz+QSlDdlDlCxIQ8/kAMSpFZGoMQp826nE9b5LigJuYMjbJgI1H2gZLiVdZMhhVB10DQr7rhcwSCxMN7gfylUQWCnj3Xy6JZBfT9K80ZXjz5G6uC/I6gp5pbEATihhOPhnEjSrMmflLMu4LYOJ5j4dhDJWdGhX34JZ4NvZkuOxGZMEDXSRLdLtq575sVOtEsMRT2Vl1ANk+I41FjRtcSsgXc5i66bpGYIRn6R1eOgU/INF18kQpUuKUE1IpJXmvmfjV6Ip0zl7CVp3z95dqlsIyNHDjDDaKrpqvDDeTCgtzwt44H/siW2D6QmhwLZJJvGgvKSKofx1lyKLEvQ5F6pR3Tvxg7skVSS6T2GMeg68mxPTTjD5fYqa/6ODXttrWBFAeCNK5gvB/F0sUJ1yd5H6h8D59+Hs9MJUotNcVOAhJfedU97xVVvXTX1XXTBBu8f4J4HhOLfZM61z6SKz0EpXh5vlc3EuDmB0xR7l8U3HUgSHkHg/R+Uq8MEg1fMPZ1DvaqnqwEjAfYx2ZS/u2dXBTZPna2X5QEWb7SRYDromsMB4V8vUHA52tdd3Dt8Y1TgkBf6yLia8NVZwJh+jGPgo/rFdzPNQWp/uMDCm4EAdYmGzHO29g85QWP+bYfQ5HH5lVYyV05EDOqqq7tR9kwpfgLRvtN6Z9wRRDeoZaztQGULucWd6R0ChzW9EBdYPfwpT1xZ774pp/ClYY0y41jkyccbdmTSPnKa3pB03PGHeDZ7tIZk53p0FWfHDhEVWTl0D1vfRtciEsfMaJczZODpmWbV1zT0Q2udAu1MrHJpKMs9CKgLOsmSZlCBSrvSOzIWiLNoB0pQNMnu2iotwpIH/QSK461FYccTvSMRx5wZ8Bjksc7ArhDyGqfH3nky18H470BvB+IO4YEgfQ4mFYM+RPy3CnUxJD3BzBAMrH8QERdWmuTg0CajmSZOAEhBmcmibRXfrbJeeoBLwZS6bTpggHRQAScP1IqC5Xz2jvrFBhFpRFgjhLA7bwMtRWiIRgEzBMsiw6OeaoHZg/QGpB1h85fBtNAnU/Lf61HxHKHj0nedw4EEQx6qc/DKhCk1UPQPecOn3i/0cSO1UFeIaT6MGATDGIAZ+LG9Vq2ZCjH3tnbZghVd/IC4+9sGH7NFp+0hE2hzfGwjiXL23jEUYrPUQtBUsari0KVul30CmiJYkXelJzLztgphwLK5g7CLzpbL+GX5m8dcuLO/C3dw/JVYvvUbm/p5BBhk+ArddoV2NI5BEkyOievI879NumXFgiy/8ZP9uPFacVeU44l/MoGeX3aRtje5VgWlkGjDbg4N7eGYJHo+EKwtso+8+jIiAT085tt8u5Ki+yYMuMMZf1OnZTaz4wwwZWidQ0nY7Pw0yFXVa26RtC/hrqiirISE48PIETeIrhYGDchBPyklYmYxTt4ycnYGHkRm3MjY7apxLP8zzkReSPzbiBIXMgMN6ZKimTsVMyWEbk+e0kounxm084XN1rGk4DQvAtQ528e6SYYxKrfv+vRMFYG/V0rgvh8PO8I4qMRvCyadwBynufxsFwofdTn47FL9lvX15rYcxbDU5znfUZTGjt2OZ603+iLststmo4Zv7guxUmgT4ypPz5kPpo/K0OlNgtKqnz+VHQzgIttUBqECylAaUDKwHQJpn2A2BfYxoPbtZNzHwkErUfDdI5//lKBoGf64qiCQLxX8BUrghQI+o5A0OuXCgQpKKSbSn00LCuCEgjyygAZQiCo9sS+lrHVLe0LS7oHLRDnph4iODYqpd+gyLp2gBgbxjOFmQ4ad9RDzJmVEM9XLAPBW4rllNZ6H7PtsLX2lX740+crjPrnOGhazG31I0byrYLe060yYqX7FQ7URnJSLlPI5F/61RMqbMHCa3rkjD+FdsmJdrGZdOiLuElNB71+VUXOZmoctJ0xVvmxnWNTgqOTfX7I4ocgsDQpGMJD5P34z8jAsASbt1aR5nixDB2LFoS8oUEYEknpkuoUNk07KLDPK1iHHvkEW8A2uPJBQRbDxPCbji2vIJKsvCuNG8xpMwc+FCRxIOUrpZqw+z1amq+T2rEOYMQem4RM+cZjiMcvjWcEenwNQ55rmXz9mHepfVDkm6AQ9vhl+0p/JSBEAIgfcOpAYEiyo8NKqK3riyPcLtIHEaZhG7TMgbjJzEqmv0yA56mCPV/pusEBH15NoTJPI3wluqe6iHhK/VQmSESQmGsSbjjTF7mm+JpxgmuKVBtltmWnKnqTPW4VW7pgtAV1aV8CQXO73wiH3+F0vySjDZ75T7lwCSLL6RqZ7HADQLtD2Xw17M+2IkgVu7dRd7fHPeT/hzDbOyafHeL/xtR0pM9LaLeF8sxfON2rIIDi3tbOesWlLg95LpBW/sqajnwDO4tbRgSkfCMM4BDm4MmBgpwbyjnwtgbw6+eTsgQJkPPzCL0mZq5cXMYB6cmgBzy+EqaJIAEfTfbeKOjz9ke9DJqVQXoX0FuvDOpKIFYN6XEx0f1CKj4GSpaJM7CyVNy2aMLlL4RxgvR0iYkueYJBtAkG5kd+2Ma3nr5EDuKwVyl86TsMnpIl9jS7yu0HyGreQkeyBUTrSMuJbDQ7oGKLRDgTzjx3qwFPehyFV8YRd42yXdXjgJAGae70fasl3XnuX4+Fff/iyetX3+p9Qc/zaXkFg57r1t5znvvXYM2Azc+PiKkqynrwRlcqS/Vkr+2m8pgFki1AvMlWaEoHZBDXQ6MCm0KvvncWLegK2DrwfbfmbtPij1QkSLzaAv7UY4phisxKHuAXn5ze+tc6445kgXaL3G+JW66TyrjpWORTZjrUDeqPX9yKoNb+j6R4qsfO6cvICGSmWgZlDLTHj+MV1HBLYI7kc6IYadmnbTK6kP/UNvioW4S7qFwLTRfg3niCiE/olIxiLW52ewyrklItk+7I3TT170nd/F3cZi2ZUunOv1LG1dpCkZ7fclJfQNISDPzIG0WnVxCOHF8mWXZ4gZFLKbzmQ4Z+Pq/qnAHemueczyMPDQRx44dz/3td6Hjl76wIeqPHw/7+A5+Pf6ubQxMI0nwAOn8MgvcEcRPJN5K4CNI5m5/Oy3lfEHax8teVMjy2ctYCNuOA6y2rDYrdoH2eB+Zfa0l9k0/dlR8fIC+urufCTMkQ2BC2zqGUwwMlcGzgQtib6Ht+pQyUX2F2sWwpzDeRViBI7wjSvOovf/lVF2m56fPVV/qUvOrqj0joZhEpj4e90vsE/6p3BBEI+uv33+nz8QoEKTjkR8O4+POjYQSCuLiVfinUdaLtcMuqnBbGSiqZNdbYxTYzqRQe7EuV9OG8HHh/J3P5Hod9muIW68Y5hd3Jb570gZPklr+0A79F46cFu0qD0yj3q+ltANK5otTiR4cS/OY+NibVj0MRFx6tFLLORYfJfm7+E+kSumnyWBJ1kr1Oknc1AdQ+Ou3oiY0Xq1ffDn38IG5vmYe2NEAp8Bjj4g3uYuf0UOzjT6Qezwhe6ZoBTsoOskBDAEhj1a+/6n1kovHNZQdbRGNejTcOwIiPoAeBEAWAfIw4+KOnDggICd7HxVxfrj84gKxP8iXDK3ik09cujIsOBGkVkFJ//VjBH76o+Ov6CI4e/WTME+yDvxAoXlYMrWCQDKdORDeoK84qJl4AAEAASURBVHkqzOiNPuwXDCtsijK0Asc2QZ4c59xg1islFOz52quAEgB66g/WsCqIpxGE143lb77+Vr+5liAoxFMHEsz5xAFo5HPJJX0Zs9rm2LS32ChbDSKdHO0xeRL3dQljGI08GJC+N9dx8QeOfMMlI6n8hoqWlf3zB4LwGLVPvTOKp2hYdzg5DVLI/fRfTXdfy4YuU6cOG/PHc+6G135zVwh09sVnaB/zxbJ5OikHXWG7GkC2AnKrqUoMsHksVf4sAuq2JRUSyit8uK0MuhxmrqtlF7+5qOO5QeGf6cEQsZfHVv8hb7AJXafyQM4gKycQ/X6vO31M5t7xImit+OlKoDdvtCpIZZaKv32jVI+D/ax3CPh9Ab6DyEog3i1ApFx1YPyTaI2bso2Bn/bT6KbBN8Meo1Hy2Okvh5UOm5RnC1+Mt2dkpwcM8aYe9AzkTh1Vunhn3CdB+pc08a9jSvmtBWUhxlo2vvJhzSPwK03k0Enf8WodFQgEGabqaDzW4K2l2/o908SOpdzPX+pl0XpH0Gs9FvYdj4fpUTFeIv3s2Te+0/ecyZ0G66da8s2nZf3+IAZtVUnnLcvewR/sj2cw1bbZ0od5wKnFEIja1fAucoMpVdNwWr4qFl+duHCNGBWov0rjO1viMnSbyqWSDRipkF43QwW61RnCB+RX5i+uVC/hl+Y/5YTfS/e4NElwWz/eEg/tOCBz4QWkUOdX/6Hf67h6XPynqvc/Csc4xhj5qa1u0VCwNvy1DrEFPTP1rKS7uexhO/zq1tD5c9ML4dFUbKfGysbah/DYuASY2Md1zSj7mYrJ6KG5ckO4IYxcj4ky1SCx7hwr67uqPc+HhV3T6HwwJtnZpwUn3QG/rfSBora9GEDnHldLlNRjqepLyuT9weMjYzDUvntdPyJ/8pXI1UTl4U+fS62cfsfYzifjc8HlAI4ufN7rhs47ncevgSC9I+htHg0jEMQj47w7kHcIsXLIMjR3cEAJeZKbR8OYh+gcz3lf+nKOJ59wBEGingDWmTi3xd3NaHe7nh0uJ7HrdQFFPaizpPZ83pdFD2pohlFA58YWaJDvKxgzBL/6zMyfFrcUtucDYyNdMBXi4cDIP/nIBSEXih8UCPog2xX8YY6gny/0xPyClUArEKTPx+tGESuCXrEaSCuI//Zf+pS8Vhd3juBHw3hZtPQRAGKcJLWLsEEVsG7sU9lfL9NBoWw24fNoLx4sfGHd3+z30m9OQaDrbxEM7CyT3zIflgRJA4Lylnbd4JP7JD3hw6mEfrC3yKJ/n1DwN2UV46NI8NghBScf+sylnfvaIcZ0YxA+g87HrWGHF6VkyOBerjF9IN5bD2jraikyD7KVRVdsqPTwrGCD6m8I8vAFZSXbRo4/xI2t44z26eWcq/XWjxw8wh8iXDRmdiOXknEqI76F0tcujmivCNJJEZxX5xBM0Ryc1TV5TIsVOASFGGsUeJERueGssscdgq0ESDV/YPWMDoyvFPXIu3QYC/WI2JpUyxhXNLVl7FC8KWOq5DMe8giYg9zST/CHd5464OOgECshOb5lA7Z4dVBWBHEdlVVBrAaiXtQovvcYpbLdw6omO1JlpcD8KBumUQcNEqz+8RcCHQgiMMQ7RhXwIfDzVKuAFBzy6ykUFOJjNTxpQADlG+jn1RMOjE1waD9OqjZnXKBvumGmK8gGto4hHmkxTFVIa0/L1W6sBo/NIwf+bqGevU7Qwz1+GSoB0/7COy9xSv9kgSBV1rX3rv5ZqaE48jPbOuj/P6G71IZC62DEBXtYfBCRHbJ2xIPwbvZguYu/BZ4+i6ro9z7ZxQK+8jMQ7yqZSATFX5imMFVJaQhvVAh3obqWUVoGHQ0eIEW/OYRcBxr5g3wkcRBBzyQs25FOthCTiDiTSU3SNDjx3P8H/QjoEOR5o5c//8jqnze/PPmHJoJvFATy18H8pTBNAjUh5AtiHiS5MyhelowzODKsMf799hsTQexiIE7gh3x/qQjDDpbdS6nJNr4Dxa4fJ7MMZq0bp7Vs4z+cwkaKALY6y8CUqx3C1RzKGC6RTL6YzHHqUFU88KHLS7NV5hzjT8sTDFIQ6BlfEtPLIF8q+MNKoASCWBXEO4T07iCCQn48TIEjAkEEhHhMTCeABoLQxZYa2ZHJK9taNi1tOM59KLzXrmkoxh/1w6R105XmlFn9V9mm8GSb3MZZS4omoStbxwEzYtmREv5NK14Jr6XQfon7tt5Zd3xzD37S/CvyHCNrmwZZIGVWfhHtzDr8BCpd+xx9n2Ptz755fJxKuo/XEZ+peFyjff6X/7YnK4DQSLctvEdUMZYXoRI5mU1esjvp0BpThsBOzHkePoXQf8q1cpyY5jxXGabxDss7qhySBDQa3HQil+8PMAdjs2i6Sn44J7ni4Vyn44q5pLUIycrzz90RNtkVWGlcMsqTac5nOdlMZVermMYitPO5e5zIfME/S47n/Ml5jESe/ni8J+tDxCzV8OsaRhdRuctOnke737EqSBc9e0XQBIL0xbAf9L4gvztQ53rmCO98Z5ybSBNQIrVsvS5Z+nrzhznJR94N6P418wBOojaVMqixdVKQkIRGJrsepFw2iMN+VP7wZ+ZNCHMVtY8vKNsL9K+BtWzdEHQTDX9smrUkJ6L0vhBVyjJPAGButrEtd8O0ikErGQgEPVmBIOYMmS8QCOJ9gs/9kQkCQXw+/vUKBP2XAkGsLmZuwKogAkHPvg1/bkZpJtVAkPTaLnbaaG21sn6BA6tXmk/twKd2pMMukuaUkl2+KzxSNl3LpDc04+QNfeD1K895crklpX/M5tzeFXykB/ORxQ+uzsDcb5CrvrQ2ZRGdvpbUeQgOMtMfNjB22ItjZzx6w2B6OKMz2NJEq+WaYhEHZLmlRULxYx3Hg9A+LlofYBgNm+3j2BOnWMM9mRFrmeUVi8GDC5iCJIqN89hQRJbt21YZ6AO5/XAfSbUp1yEEUAT5P+ydC4PdNq6kM0n8jpPZ/f+/ce8kfsSOk5mtrwpFUerTbWeS3NlrL7uPSIIACEIUREEkpQrsCMIBhIPGjhlslBwvMlLYqw/y2vyq5xSXCSdLuGTddAF+7dlAOFBwmOoK1sVmp5AuFLdV+TjYyeXH8jNmRVoOxTh22BCf5yHq5BnHeZ5zVPbhV13XLhON7adwBbcza5xSwK0rHdLOxIVZC2T06/5elcjXtuTHEeSvFJOWc+cbZvl4VpAcPjh/Hj2Wc4iXyXIC+Vkis4W8p5jsBSsUKOe55JHyrDroagZskHWjSnk2qwx0issZmk5ytKMdIqc6/YhesN833WaABLXR/YTmQpxmC6yaKAMqGDjEXP5fjCNIbR0Fk/h4oJOYRvGtwdV/F4wzxe3FJ52z5oupkh0yqmAFZN/xDuxAF+IDib39D6CdikpjKem1SiR9Qrud2YQ0HxMKdYND6I4La8FdxVYOXawPiJM2IvkiQghtLopcQFwYgRct08bLr4aWSzaMbcS4hJUFkootwSEDWQr168UHvjeK1OAPJ9CH96z/l/NH+wKwRwA/0uwN9GqWhjEYzGbSmiZuQ4nXPIPBGHWmhMcZxHDqGNFpzwBk9E9whwwMSeIgshIHJzCOKEjlkhXxEwSQNTs0ERzuTkodeFbgEJr4YIKOwc25cO1mjYRwsITD1g+nslAYTDZp5FxT09ca4AGLYZUB00DtCR55xY810GNqtx0/GuR9p/2CcAAx4Hsu59BzBoAM8tg3SIO8x6J5pIo8vRNVSIr+LCMZieZlYSO75QIPgShE9OIpQbbBMlPmkLYf5fSkI5fk5MtPsdU5HMwKXaw8qflB2oIp37hbf3s+KAcEroc8YcTxwLjDfmr5cqJdF7T6qp/7YB/TUPk03vGBPRSuMl1x23+Ii9sYXL/l/lglV6b/A/PcQ3lodhilrHsAeS7WPRYi1o7Qh2LnQBOsV8tggAXq0jE2ahAPoDF0CFEiqh64OYwc4VYC4lRcMWuHKrIxQLkQFn+XxTAUMkSuH6CCZ9IoHT7AxFD/1AfMg/qpxBRK/1P4f9MDBBYuXMzqOLhCbHn5IuSBGVt6oLvCloM26AfFRr5YyXq5TbFinB8eFAg+V5Kz9HrcMZR6eQgAwzOCQHbaiVWv2w2/aTf3Mf90jqne90DfkMSHtlIj9+OpERxE8XIujb7zIJUl4b6333AEvdYXw37SjCAv/56XRTiCftF9318e48HNjiCcQAzqqZM7qMYCrn93BHFz44FMxcjEgyJy8t+0MiM6LNIu0Gk3+pJy3Wal3erRgVGMRQoZHOkjC/QFKuTHMUwDMyiHCOU0ZcJy5ZyzBnPhXAkWrilDXloJgZd+/PMXPSh+kDI0O0ByPvpGS19l4GgiL45wBD33jCBtFq1ZwowL/v53zQJio2jNCvpff//eXxZjiQcfl7Aj6AkPtvR/+grjDvHTeUc+t0wHy6e8zrBlsYNRWWRoQNaMf0xlMH0lS86hDRdHbt7kBQjt4rRo4ZcQnvAzTOBVX2HIh66DKrLRpfKw2es4cxWqySB0IlX6CKzYAJoW3EkdBmWuFFMZU5VaXhAHLXAfh5MKcqmeWC8mEtp/bmPa0Ap33m0csF7Di4c45LwMJU1cykB4wiEg9a2gZGcEwWOdD6eFBS/+bDzJKA252CWQECyRQbZhg8N5id5KQI9qOrw5OpgxVSRvewYD+Atm+aDVf55dxEuGw7NzsEmyJ54RNI4glmZ5JpDgpHnu8ObNwLE9+mE7MgNITh85TOj3dg7JGYJTiMZaDurvlUr9OIJUN/bZ+5syw0eOHmb98FzznpgZQXJG4RziR94zlOqIksx2BIkHMXsHWVc00Yo7rj3anI5ElH6HRBLLusm1zfWNM0gvhmUs4gCKowfnDjOCvpUzCHgcP9lUHofPIzafJ9bMIc8YkgPoW+X5wI23s1DsvYTEmzptH7AlZBQ8Q8nJnO2cUoTm/Fh4p4Oe8wkdeOHgnA/RAf1E1KOC6AO9wBFNCEtlUoXzf9PG3+x59J1WVvwVQe1brfgr+N/l2ZZSgtZavdIW5ao1o03ZlYb8HjZ+vbDOxeFzq6x4vUgjyyZfESbe8T7Gz91E7Tzw2kiVkCSi/SQHb+nlRr09ZepudJcLRrJHXcO7eqaOjQK8h8KIVzHPxBBaduROuoh7HeXv06PMMqoLqcRgklatslHVgTc/c9G0dkSm/e5D5MXcfJXk2iLtC0040nxuBsWnXL/cc1NfnDVz4YnQn4OVc8ezfDTIe8tsID4bK+fPay0Fe/OGL4ck/1YzgvpWkI2hf/G08BhQ2T/JodpUmfcEcsOQGyPMECkOIsuITMIjbQUQMbRyXDi0wH1wG8kZCZ0J7r91XoPHcXUBmmw9K8LSEBrNiTSVYEetQfCYXOh29IS1TwFwqgSUQZhmCs2mjbJfMsQywhrwPdGg7akcQSwRY9NHHEF8QYwp4HxS3p+GlRPoOTOIoNFgDzqmj7N3EOfVdVjmaKrnHdFRgWUEL8JMw8kkAAe3KlpoLlbBFrvHmUBAE00cpDlCIy78Oyk5YWpW5u5yUg6rTDmlrXIKFoKxtgPt5ExUtiCXTeNAN7IvMHnW0JyCTQ/VVeOt6GbyITzKfk9w1xDBLmPp2wdcpgPXfGFfiiOIh2XafFxzpKbvq8BwK4yrATxdMrrQSPtcKJ37p5F8OMqUNY8gg2dzeaA6FVt7AGtbDsikxDhLm5S3fUAagiWZI1kQVaYoJUbyIfdetYpiZK8tTumBbx4AjTh42IOAiBzAI4ShImHw2wMsKgxFkBSl6YkTjX6VKdpiVzzK4Et+YAvnksjYJZy8LKDyOQae8917vh/QxNTOIJ724a+w2kBGZPQND54XH25t0S003Fc7Iyh6p6+oAL2LnrEC9+o4gniI0lII3ce9vJtxgB583rI8XPf9H3XfZybwK80I4oUQ+wGC56VhfDVMD0pZQsHDG5tFSzI5gP6pBy2+5MNd6uoIshweDyATDyOSC4XqZ3lJE4j6a14xev2nll7RFpY/9VqJvkFERwngEPLKSMxmxmrOdNj3GExwCsHZo+B8GK3rCFnNd0TUxUMdKedcSC72NdHyMJaJkdezmO7tnN84vJ8+596vF0V6IcSegmwW/ff//b3HB14ahiNIG0o/5o0+M4z1cQnGFKHnHONQirC+QizTjBOsAR6OVS4lWMd9EnND0x6SlNEWDLH1b6UBMZQShZSZO6QUmcWOA174uUgHOK/ZcCkeOulIpK659V0MUKswD1dGCiJzn7RrHM6lKCx4KdzSSrbPwC5q2coNRGcqW2DOKZwWIGx1jG4TBygpIZ4yYvpm8A76UyrKMB74hHBIatiFr3hBmzo2LiRdFlkP+8M5UCE/MbKDhM5D2BvpviIQeAo+DpmgIsdyFE7pPOArBRvLa360f4CCNl2dLL7UoP/YLhwoyqoOZgRlnx3Fum5sY+R0jiMoThmcM7/aGRPHEDNwkMCzf/Qilk2WcaS4rXhdEcIDdz1jyC5ZBrcAOygbiBNbAlC3Vzfg/JFDyM4g1cMy2F/ZOoN6Z4YQsuG86sbR3NPsBKIN4oe+0bvr0sG1Tsa6Ugkw+rTjioklkayWXzF7HrHcC6dONoPGJijvfYGYMfitXiY/lo1QWvDHcipT/kgxZf6iMfh6sGCpGI5lZkzBC1tCPaiHLqF/B+fVH9pvc9ew8INBlD4dAo60A+iOh61WXv/rlu90YIZDof5PuWl1fh5/Xo4gawX9JMwFRobr/hquhuJa/mn5G4xNKC1fwifVpx7hk7vJfmHj7LrIjVcZpk56FWdcIdyU+138THrzcLsNXFZcYAmr5gJucroBvKpMeZpi0afsigIXVzN4d6ssBRLmQnLnh1CMk9ZRaMGci806nHMx58QkHBSMryvZ8VRhp4zKKk8YYrylH1112E4MH0bObwKZ7i3nzmuWgckBRPwaJxCxBoPEP7/V9HEbRpaRYRThU17z1tGdm1rHsnmHMmYDIV80gtnOG0MlkBDZR9CcueChcJ9jYUFzDjFblAebI41XXP0ppjcQesM3LuUBU2Ic7oPho1gZcxcAmB0ugnEagKe/hw7J+MwtG0rjCPqGt3cevMkRJK8207txBuEAYokYzqCXL/Q5eZaJ4QjSIJDlYd0LgFlBOISoi7r9s2yqb2RG9shBObgCgAPRFpwbEDTnYgEcJhYPt0iIqSe98eBYvNTJkXCUk+pPKSUPHRv1OBxEB+yUqmxnRHK3S07EX0SmevirG9szQH1NP1Tnx+Rq+YwrfT4L+5IcQQx8UKivkSpDoPO9MT0+13jva7ENQ57rbM7Nuv43fjlX1XByx4kMT6C1LYPhyFT3XsQ7ZnpG+8eSY1DOtqANP+hp3zJOqi+WSLKN2JGj+OAqPYVHWZBphx8Lxtjdalc5EQftqGvIdpSkXScEqf4uwgE5zqFaYrFwkEQ+x6fzM3LnJuNBOkIhByXRDbyVy7/aB9/QEfPn/mQRoBWxB/gITdsQOm2Eivu+9wbi3l1HkJeF5b7OV0GZBfyTnD/c+18xIwhHkDaJ/oAjyLOC4wjyV3UYByxH0NzrxxHEPcVfDOVJA7n8Nn7SyKSfHnt8k139BjyLPrGoaKOP6ICHMOkwjiDA0UGIgimoguiFb1Y6/k2OILhYpyldxwMqEEjCYvYvYyXoCdxzCRWPU7aXUS6KyPfPD9KznED6seegnsX08OXT4nECY4Pn+lrYM/2YEcQegt4XSOOD7BEkR5BmDeEA8gMds4z1Y4xhpxNjjqVGC2xZIg+jPI+yLGvarGPQJOVKqFwUykJXvTQnUILOHeXpcqlhCgZhcvASZvumoasq9NKMatKFubLDxZx39sAPEmMtGVWXqxuoo3sPZUIbjvQVPUUpx24cmMm0Z1llAiFqZ2hWZ+3DiY1hRuzHtjF01RUl9eS445zaJ1ZpO3hLC0gQXhyVREbbBIMNcLkL0Rl/8KLzhjSRhSkgdOQCTsurn0PuwbdsQrbA8M+Piq0HygWLHYJpq8Z5EmcB1xlyk8e54uVaM1vngxxBmaEjm6MZOcxG9MwcZgbhnBE+Vyn7AjFjhueJ1ItdIZ0rl3OFDMivalyf9x1iFpDrlWNJ6Q96tvGn4lnxIHvXjfGpJ84g7f81+NkXiBlK8JRNwhmuMtqiZo9u0QQhMKtJOUnC2ZB8iiUX+FzTOLDsoNE58j4/LBPDmUMsBGYEMRPIs4O0V9BjOZQfzwbSj+UQeoKTSJ5ntqkA94lwcRKxj2mdQXYMdZaQ6rEtc73UH1k5IhRnfzQ4+cAXmhMuEiahR5SMXhIbqrSzzqh48jkf0PL88/VXLzWz6a8I/4EZQdMMWn0N9AQUrDJ3WJWTvoZcPIH/Hrzw4uxA21gp1Xurntbb8hUPh5YT73Ls6QMn9e1le/rAS2ov29P/Lp4EVBtpNQdk+WPBrbl6IXJKzNgOjC3f2vaalxgSzKi+Ce5Ekx64L5xwH32Lm40bZ/QwsnDDfNBe3/bD3OnRgLlUFs69LzgZLQaCGD8+F/9eAzw+GcuysLc4fV5lKdibN2/XbKA37A+kfYPe++1h3gwy+IMH3vxOB7fJUIM9uCOWcf6XNmtDNKTF40PcXwRM/pBZdLZGwNOLwXPz4OEfEAJ1KKKwMWAyKKbBjCbvdAsSM0SEHEbE8KQVlow0xjoINorpq3arCZ5ZQRhx2WWvy/XG0TiBNJjzoE8DPTaOfv5SA73n2kAaRxCDQRk8cNZeQXoaZkYQhpfabaBHbMs1siMPw7PIidxHGDFdNqRL9mK5J6KfKm90la6+qKxD86P9JV6pQNCFNWV2ge2qLxlVGT6MiFpTcYh3eNNDsk7zjv8lpq96q56u+vu9uimfxjt9z8EOa/oqD/DiU7bSypA3/qTBJdDXMwhJ/nM9co1l4DP3g7lY0Ikus3VN2MaozHDK9ON+4+uNaw5cgMBMC2bDUFEAhy1rrOsFCp4LFPtCVdY4BcJHYcNb1/MqoNgVWsaDoRHmoHLxtb2A3dR1YIxODAB35Fh4Z3lOOWWoHd1GCmWsIOrZgILBLmoph0VhYuwvJYbqsNKCfSxEtVCoLYq4h3jmDyDDYmUnZx0gDzr5m72hGXZTObAEEfs/+skYIfyBDGsR0DbV2Ich3SU6HjSO+PkDEXPvtkNI93T2B3qnhx4eut5qPPAzDiDtC/SKGcJyBP3oGUFaDiY8fz5eYwXvnaGxhDeL9kshpaXbbBSN3LmrMkOINGMBw3gwc4NpYNLt04anwbqRqhyhHdHW/vTWXc4gP3C57SCldSVtzINftIk81vC6xqpbRGk/MZ1ZDZ3SByUsch+Ep/suseq27TJM/Y8lYTircAQpZlaQ3g95XMAsHvbteKYHtxeaKfxE9//HGge8nD2CGCO85BPy//ulxwaP5AhiWVhiySQ+tZOko8a0Pe1AiDiCkKtqcf9XEQF4KEhbuWZEOylY1yQMjRgNUAzFEaXc9cK1TIMxR3Cor+MHziFFZ2T3WWD6j16LVzbrbK3y4CFPeY1sQ3KNkMHBUdKut+QUrkbs9Ut2mrEu2yFww0eupo0oPs7vjEfKxT+yuk9Puy0bpOiY+oTb9CozWWjdfleBa1hBhyu/JQH1iiwzgpSonEsn1Q4OVqOOKjK+JxPeRzvGtEZiWFKBWYd/8joqG44kqMcAgHaoID1f14O/ndRyAGHfsuyLZxRmBOHwyeycd8zQYUWCX0Zn2Rg9Hn2kT3Bh6EfjBbMDyLWpPuGJfRw/Qug+P3zunfr8tTDsoJxAnv3DkjCns1cRM5EoQ87K65lAPAehI8OpjMotwGotrfZPh6StIWWikaNMDhvJnRlNrBLQcje1B+fQN7Kh3TAaR9BjeZftBML5I2cP9uTJ4yeeRcg+QY/1spkZQ8Q4f8CBhhlFXi5m55Bsk3jbrujgmYbIGiEt7NdqG0Kvcz55oa32kU6g3bQeHZFInKSOYkJZ/tO/wEu5nn8ky/d6Qf5XhP+MI8haUHPQaNPoBQU/EFDIR1AeoP7EootM91HFUCLRw+E/hXdIFY1V0pVbilyJg+RTUu6dQbTK1s3gIC5Ka6hqmbmb7r3hMiB1l1ckxE65BKNGnNijFYDqCTZuwrWO6RmtyOXhhimxx12kiLgbW9Dc/XSAN95rvvjBjB7Wu/Kmj6+AvdGbwJ/5Spg2iGY5GJtEv5IDiBlCfDHsZ+G8Y42sB4wyjLoz/sZUS/HzVsqeeo18Y4hltOKNz4gFI8xA0LODEGqFvKkj6zcsKJD2THnb0vwdBWwKWaoZZEccDmKpVpktTzWRLBVSPbUb5vMVhMCLS6mYeNClgeA3v9mQfoMjSONdT8lkg0cN8jwzSFO/2Tj6OzmCWBr20o4gLRuTwXsunDqCHuPJxxGEDGLPDYHA0T+qnLRxKCQIWPkCeOhoMyzxdyXAmEH8DoPtXe27IqMhSUJuaUe+8CNGwCP3e1N/gPT3VvU/Av98lv5akdE99d13DipL8e6VZhC5//lhXfnSQuOByH2V3Mv0f14BD+K0P0FXGG8SJ2+rR3qu+1ypsf3YrdjHUOIINtk2kMy1uZhPHRMNnm9h4nVL1a4adCUOLkfqZi/Yin2vgl7Mwx/ZxWvD2RgvHEicoY2kN3zf13bAxvCKS53VrVXomSliXTsO76nVSfHN2CU5l0bwAVCDAPk/kB5I+f7tcrXFAuYhJ2kABloptJb6LasqYdo+F4KbKEylpibR+D99J1oKn4wbzA5uIhdNvQTQQzcMoWA2sF/iSCBe5HgvDM/y0cOVHrj8Qkj3+1faF4gPR7z68XAEMWuY8QKOoziCxEPjiMwO5v4xm0XjftEm0Rku8Za+ziDd60kjD+ME5FPa/QYDoLzBSuHMsvA6d8idhyycQMrrwS3t9mNk8HQ0P8c5wNFBckkz1AZjDvpFu+2zOT8poqS0I6UKQgHcv0FgNnBx6Gd8LcxLwnjixGGlH0vDcAB9O46gp3o4Yw/Bp5oN9FRfB/tOL4p+0CfjGRswI+iH/6XPx+uh7ttxBGVmkFqgz8/HTpKWHJKhfXvEEUBfOXVP4MRb7IWTHMcU0EraXR1wmYRPuRHrN/2npeFTnOQog8/R/1c1qk2MXWXOpdmp4iuHcmq8+CHChr3khdG9YS9LvUYdOUK24wiydYKVPPGPzJU81Do6QS+YJHIB25is5CSWGMI76cxcjoNZ03q3FarhfaBYt9HxtNPMQSDRTHiYn681ilomusqlmP6D4yvX2MRTXvsavpElssFf0uWAxGScBxZy5LMECBdHkAq4trmucZh7hg9pwXDMYJNwBDFL5xc5oPNxm8MRxL5B4Q3H8OaKpB5kpUbi8I8TCOeN7SAOIC376gwf70H0QTZxHEDYOOqOk0jPPnp4YpmaHUHDl/v5v/Q8RbC+bF+ckQSRxyoRCD2geqD+OS1JdxhUyndvoziAcNRwzTOrJ04fHDtZ/sUSsDh6vJG0ZwjJyczqBGYPaWYQXxazE8hLybTBNDOK5ARilhAbSfNVMV6+e8mY9jPDWiMDTdG/fzkoM1bd51Q6uBsKk16UjE6CdUoDEgKq8/kjFsiOILXhrwj/AUfQtMrapHlHaD85ICj9YkD3wi3934m317WnN3Gc3Mv29EN417I9/xCPAy8XCx0rAdPVwIVF9yUU2nygn3yEvCxKtOWbbMytwGkdbGubGVpPER2GGCgHiZYLhDwXz8Ap5GaPUVN70iYhX5oSbPbgMXkv03KHuS802PJjXT+DPwwaA7qf9QUwnDzMBnqr5V8sCcuXwn7+6ie+HqZp4m+IhcdeQt0bwJusqTOz7tZOHhxBlhMtyAmk9a2M6jgVMczAOU85V8RtTK4JwaFPM4zn3LSXQWYCDU2aVENLU0ZjW6LYI9INw8o6ypGRUmPoEElTbjeVRkgtK55jw9V6HEIYUBHKTtu42rkjI/z0GcvE9AbQjqBnWib23I6gF9oD4JkGgc+fPvnqqTeMZmNIGW3W8SKDftLiIZfEQS7axctSQmSYFkMT8DpSXjUc6UDSzaZUhe6DizKJnC3SB14G0sDgmJDUUfvda1gYB3rJDKp8C3gjUVJwm76B9sWAPlVnfxQPXZfHfXrvOQke10mu7zsnYxDpd1yOPHSEJpj0aa7Dzz0w8LH5UVtt60cJ3A9kRnQEkAGjzRY5dGPFSLvQKZ2rDZs5ltQFw8y450MfGI2di/+MsOfEJpw48qP2xIb7EIJYqElHyAg8VCkBJFmpd6OFr0mG/SpyggM05TAMC7jFT7ihgpKQvlixDJpDYTt7iAoPmrSFXgGeEHdO5/Rx/+a+C2EcQTBIWc4XacsKe9Xhani6l+GHygeArjeVQ2PXx5JleIJm2Njg8RIEjUJzFCscNdkbKA9CWSbW5eE8CP2s/X/evOZDEXIEaUzw0ys5gjQ7iFnDvIV/pz2E2C+IcQQOIP/UqeHHbKDuEZj9glSvZgTTWz0jyE4gyVgnEI2kvYgHzDE05DlER8R5Cx9HkDJuB9qgLIEYmhtHF0mG0YOxBHMV0CgNigNwJTgLiZsPDOBoOeWTxyFEb2M5mJeteWmYnEG6qLv/n1Z3aMbvV35z/0L3fV4UMQZ4wWbRfD6escH3z776X3IEPdVbez4kwcPa40f/0sMbswI0xlA9/roQAwTV1+s6baEFhyMoLWjLGoOTdMrTLiBpr44LVWlXMy2mklUGHwoTcn3v6QPR/b7XrVGOsiG/EbniBfd1OLl13pQ4JFioxVoAehGY7U3nNiw0J4xl8YLNfYpKEhUmwKqYRH6HXAK1iRQpnWufdApanCy5QVRqD0CnowrDuaN4IynX6LooB9/KtuyZUSqFxGJqv8I6VxIM2bhX0Wrgh6xGzYFzYObEiOqDZU26V4vQVRQeIYW3HbuyH3UEeb8e2Sj24GHpKTbpN7ak0Ew7703ql9GyP56dk9g8xNIrE8SUVlGPl3wpbyuhGN4fxBPHk2ccqcAxdkxOIZxQ/giO6gD3V9lC9kJl5hCzhrw8THB4d/YP93NXQJ36pXISNDdnDD1YLcB03XLp8tO/fqaatPJT5oSKoMUp5C+hKe3lYVp64KVhnunDUrE4gp6wREy/J3reYDkYeS8Xk8OZmUQ4hngxzVfHWD7GZ+dxNudT8yxfVT0aiHXfU+RDMMs/cjovcLQMAiFtSDp5lwt89Juca1MCHzKdCuGoEikV2GfmCFKL2lIaTOAM04MUn5WY4nXc8Iy/CnJCfKGanfjA70bgxLVsT19RHyrbcYvXeC/b0y1vvJft6ZY33sv2dMsb72XShrLTfre3KgaWCy/lxSP+tFCKsl9xyVXFrvnU2ELFAsx9eoDBriNocilzZeoRvhDoGaSnyGOjhxxBMSOg21GiBM4UdzPBgJuvDB6xbJodQHzunYHce+0N9FYOHjaJZumXPx2v2UBsFukNo3EAMStIDiIcQflUvKZryjBy31ifibcTCGFjsOII4s0fMBqYsgyVRuYZlOV6EAoB2ZNQ2gmAbk/gadNWWKQVm6zIhVoRzWCcBViDQgZTKSPaf0DBXYMtSufBizJ/OczlcgQxSJMzCF5+kycD+y1r+2WEn+lz8i+0JMybRuurIGwO+WJ+bCbNVPGnT5gGrqmcMtB2KImRH44tw7RYYiPfcRNRHoAC8SQDuBwpO9Ry9LFC6XcEq4Uk/NRnekZc6ENqyTWZetNf0etdCU54B5PFqTKd5TsjluuIdS78AnOfqrPiPaSih/S+0/Uc7DDS5zroL+kxN/kKGXyZDvcU95tB7OAInp9zYODI4CdhrkPZFHTBQwd22rruhQ2iALm6pFvsj8rADWI4maZnA9qtfL+GATu03BWDv0qKcYBgfrd46jho194T4Cuc7EF53OAz6EcVS7bwacFBqtS00bA7sofj0hHoYgWu1erE8L5EF9VdSj+ejSg5hznPnFt6PJXOeU5qmAnXBl3aUuWkiR0q9ChA1H5gglNPjtOTta2ljPuuwvQkAOZA3g9cEoyxAM6VzgjibTvjgp+1FIMvhr7+KY6gH9dXw5gJlM/LMyvIM4K0Cipvy7XcHH66efA+mRc/pDMjmBdCjAWIkUs/HFX0DrVvLWOj7QAMQ/YjcE34YY8HRB7E9ONPCeEdmEMuHsNkWIjzkRp0YNC67GCxKi6FRjTGhAW4wC29EsQA0ppo+1//ytKwOoR4TeavgfKAJTXIvyNHkD4goVlALAtjVjBLxfl8/Eu/LNJXw+QIeqZxg2cR8WZfNIcjSFWqQvv6Uv3ogPrVEM9SRqzI0wYh90lXyL5C2rWyoq7TLDC31G2liuiXErg2NC1a6Z8+n1gE/ucamAAPyzfnwPgCloXRjkxTPq2gGaCDEi0bzhOdoT3PbT9UTluOkCwKGQ1fw1NPr+czf6qmjYXSRxQiWIGJKRi88E3G4/LBjM2PBLAo20AGSRnktuxzD0AA4y7+pQxNSuGSvmDxRsbwFr4SyGV5knDavCdvZ4d47DJTA7L4P4ytk1zDIyl2zdcP2KpDeLBM4JqODQKGg5rCOKp1datSZgixL+lvmqHjvXvkFCLvl9k4adi03uXCh4do7EhSBnvxK3ZCaWyUHUtK47gGRpn3OGOWj8px8hBTjx0+wOQQwkFkfOXhLzLJia1LP1FytBs4bYtu1XK1N3+CkUZd+uHMAef4wYV8r1mlueRsQ6GDy/ATLbOC4I0jx/v/yMmDY6eOH5w/dgRpD7KnsjVPOyOIF9NPnxqWvcfiCPLn5pmxyE8PH7Yv1Dn9DJmpP4MOEtcQ+a0YF6Xn0V/Sj1KSPOmUK/J5OzmCtKTiez0P/RXhPzQjCNVVQUezDLFWD9ifk7pd35/D+2Nc2iBat6c/RvfnlB+XD/yiczrvOdwBnItv5OjHpgrLYEx6B51JVaILeG6dKiqmOv8wK8TMMYAKGBZw/ec0nllKdPCVOPKvhiUfqpkGacaYk4Rch/Fge0mCZi3j8WZvoF+1xIu3fDh8vEGkloW9YVmYloS9ZZPon5klJCeQYu8hpMEfA0VmAmGIWRaGI4jAwA9NsWM/gz+//SNt2RkEIisDRHAr93FTMFw4oFX20IhAQXbbIRH0CysFPobvAmBEmym64wOe8rlJLh5HvqpmMEcAv7DkmaoNU8Wato0BtTNITWZdP/v9MIB7giOIQR6/2TT6O74mpgHgi+dPNeh7pJ8MOdM48e7rBooTSC8Dl9OH+mg2mvb9lVi/JY/wyX9aUC+rTsSUJEc00xx83F6XXrlSQmicdHKFnXOurkWm/fRDyeDR9KdTf36Y69T9NzXtIZ3/LlmEDL5pJt0m0Kfbrwv7HGOZz7EmKCK2BvuGTnC8NlTn3DNIe4Ct4tpIrtV17ZcoXFbOiTJSGclVw0qc0fecB2474JI+7EUKyLsSZ1fF99Z7YAzjCnhLtoHdKjqJtSHc4X9CvGSEDOktG9pzsJRXOS8syLr6nkdl9OgQrD09jKxf3T+8nIuTqd8dR5CpzVXdBbsdCw3nQFspkP1RQvnpIIjThzD6mh+OHGe/wPcaC/ih6pd/avn3rxkH/KQ9gjQe+OnHt5oR9CYzguQIeqsl5O/0Aok35b9qGQUPR54VpGYejiDu9SiJscAjpXm4YGwwziDLJVmtc8roObR/2gApP+Tmj1jjJNruBzAezsiv+7IQCGYhTtPuBUyRj75dm3lKffQAwxK4LmAVIbEZC5ZxHewldeyV09E8jimvlddyML5uRpqHKd5yZxlGZgvzYMYG0U8ZF2gcwIyg779/ofGAlo0zI0izg3ASMQ5g30HGEjiT2L+DMaF/ESmCRkuWWY/Dji89QS0aHU3baGNDWn7kYCr2ww843DgHtFoxdXNQQNfuxws7cBdudbq3WoTIYT5B2o6HjHvtIOxc1/md83yb106x871b3d7+dVXR6faws1twgKW+ibAwzY3D8C37Vd/CTOLETRlqObr1qdQ8zRrSMr7wQ4GmGtJEW+2i8x+xf2Ll64KrTNxHdqKG83nItUHnDBx5qSU1cZyrxOSpC1sUkbmu8wzEswWOHZw6si08q8gB5C95yTbhgPZsIMHf4wjyXkE8l2Q/H2b8eK8fZvAwm8izfcRTdH52EU8cPp7xw3OM6MDBJnpvoOUYUt1K25nEKk90YhuHnYs60pBcG3s7q5e0Pv0DVQB3zLmUNjx+N5Mcdk6mtf50vVt5oSXzjWwpswKZFYTjhmVfLPWKc4cvhrHFhJadPtGsQzmUHzP7ULOAsDssRX0im8PeQk+ZMcTzhl5YY1/Yj4zlyXxqnpfRyIut4RQiAqEvxekHFsvQ9oojJmW7gN7AkdJSynk2kZXotPJCc8BWvvzsloZNex1xUquUanUvnzTKj2EN4FD2GfmKd5wWulNuw1DcxSufW5wfEKxkPp238Hp2i3iLf8rul6m0vw+Pq6sdyb1LVd+V8C7kXNvdnHnSLPgRb03ckmdCE41RdEkxdVYsQi8ICsu0RhCIYPQTLkL6DFh2roz8A2sLw+GuI8i1mhUGFc+4poXLAMaLroEfy8JkSPlEPLOBvDcQXwfjq2FdDsaSMf/yNjAGVs4kDQA96NPAwPXYpGWEkrWtGvRpaRjnGQeQMorpl8EPTc5Z0rSx7aPFCs0rWcMBbvFBaRjKZlUTvFsLpfqVUDHlBBs18sPAcOe368fUEJvQlEEHJuePjqzL914BNp7aHlNN/oYpl99qEKg0g7oX+oQ8Tp/vNOhjQ0jSPwDTL44gOYP4UgjefAZ9YgstN4ERTyLMzUMCA/Ov5YrR8qeE6qa49Dla0zYmEx1w1hKIqZGg+pWMijdYCl3epPuASGfcWPCdGC6t6VrYGq7wLzVfPe0629N/VC/Vd+uBX2FX3jvOteyan0vSj2/w63UNHo5P+vrnHmizxpMO6IPrqzrMwClltUnkohZdcyioSgS+I5kMTsKBoEyBm0GuZx9dpoMvYhAIO8FApi7frylfKNcKqAKbsLcGyEYSlieAxSr8Gq+6jgKLc5Pp4KgsD/qTh8fH8MteeJtqQzfkRXH74ddQ/uRHXkdi5GqVydlVYsmxzn64zDn0qRAOOjyd161B9A//pZKRQpAxrrvFNsqIRZqy3LNpp8YEAvIQhiMHRxAPV7/oRQ8vifq1MOKffnz9FbOCfvbeQHUEZckEDiA9R91wBHEnmp+cPxkL4ASKg4iXRYgcGblpCp0xgmFkKENP05/cboCMZWjvOIKU74MJNNBTq4DO5Bg4FeYPhMP2gJMHXpNTxQmvPHxeVAJ/rkPHpIWAlP7xpChHDHsYSSuWk3EBG77yQogXQziE+Gw8L4FYGvZC6ed6QfTDD8/lENIeQXIE/fDyO5exTEMv/UUXB5LHGOJnR1DUZVnpE7UqkYVxSbSrAoX0jOAh7znkSi3srKdiw6F4SR/4pNBbws59AcEYHGDgiIuUdx6L7PitTdhCX/yV9vVhNntd5RopcuQsgbjzBQ/tHLBV06ok9Vk+8EB1VRxCd9TcVGOhbOGoRUAyBgTq6ri+k92oJjksE0lKElbGXVQUFMsy/E5Kgy6kO6X5+pBzYw6WRSluVPqPE8jAda7IQdZ+4PNBffp1CVPOK06hvcnpldCjf9pPPVE79om0nDMtU549fOrkwTHDqgRiHELMUPwF26XnGJw+OIb8lS/ZMjZ2zufemeGjmT6ii5MIG8IMIRxFcQh5CRm2jLrrJJJcfBXM9kaKtcyKZ8Q/Mh86yMlBL+gBLed8OWnbEzD6aOBpiGxBSQtGZQ4bD/JCgL+XiuFwU/f2Xj+yL/mimJw6zBLiGUIPD2wUjfOHGYiP5RTiK4XP5AR6+iwOIuB8ZeyJViOwTxD7CXl5mHjjCMoSVNXpeiPREnZlfSYnR5S8m8CJ5ZwCddJQp30FKks3A4FywiO15eXnMyNILXLDpnVuYg6G9Mxv8FvJu4YyWJDvnIN3cED1t6q4j99BSSqdLxdyuVDbrfROWYmK17LCm//z41NrXf0JslV4lW0ruiTbMXdFHxdo9A+3pRllTONDBgph2fbrrIxshRxnEXOrPxUQ84+6bWCpYxwpNiI5hDVl/h2OoLbccB389s+OIN7cMfVRxlJOIDZ+ZLkXM4C8J5C+EsaUcPYHwPnDvkBvPRsoG0h6fyCMo8Y4ssHie5gx3BN/Y3QiOfmMIwM+ZgVZfssODFlFI5mWjE6nDTQImbE6NJE06iI2/BRTcjecoFZ2KDGe4ZZj8DiqXP9WqXBKH+yhdVSHz1FnbmkZcP1NjiBC1tYyO0gt1eCN/YJwCuFpjyPoiQaAfEI+n5P/3l8T04wgGeYXMsrMCnqsgaGc+HowlmEWbRxBkQVZM920rZnyEZxhz6cEd9GlVVTgXifSqcdMcpbSTgAMM1pDNQU86eO4l6W057vnFaqHAhx2Sc4cH6L8Msp23bTFV50V/u/GV363zsEtOR6qL9f+nFsR7/RfoiPopKtclCfQNXM9B70/XPHu5EWIrrmWsXI3q1onfD8rJlonKjyCCI+SEK8QY3oUroIkSlcwFuVSY4seKNiKKsRQ3Xqu2uXb00dFSqnAclz5nZC2TBlV+MZCOR5ux666rAiXWALnPimGw/M4r8JtPcM31Ge+5ZhGaCwA7ohK7J8OvlupkLuVH7p4OaR7epZ58UDFbOF/aTbwWzmA3mhG0LuvfvzHq69+0jIxxgI8aL1luTizg3nTrvFE9ggSTx6SWCI+S8M8JuDerxlBcQRlXOCnFzUqctHmNLD3COAE2sCxcDotf8j9Tzla0o8EYWOtCbCif5tlDr5/AuDz8dZEK4CB/3WQDgAjCb4ceBA4D6T8I60fXID6jbnTlVDatSMIRcgJZC1nvx4+98zefziDeNB67n2BeCiTE0gPZjiCvtfLIWYLf//DCzmCnvvLP95vUBWyRMwvhVCnOEsMO4MiJZITZoyilM6q8Qz2oTIW9yhpWwshf8BSQ8poeYM10oxj1Jmw0yzglF3zgG/BBv0UHXydQgkPBNoQzuAdfaQkob7w2ERJjyi2dAqqykE5qEhNQaHNDunGMsQGBNqzMqiK7kKOMmQ4at7hTR92J1UVbiofNv7k+W3y9BoLSBqQ2qIHHXWCI3W5EmvcP1nLpuv9kDH9qDQrVoI03OgzOKPbdwyjbHDyhUNeYmd2jj8tL8eQl3DJA81eZe9n7zKea35+r9UN7GX6yy9yBOVryDznZBPoOJCY/YNDKDOE4I0McQJ5FpIc49hE/ctZlJ7j/YDU0sjVFtNw0mmzrxnOz5yj5IOR50ZwpX8ziQZgmPE9vBLA2p8zXQVsKRYZcZxtSiuTPX1kl/T8ZYfzzAyKM4gtJ3D0zAwgPW98py0qnj17KphmA+GE1u8pew3JNj1+mpmL3jNIzJkZpMlHGfVbgNSJKEegLYQjtvMQCG2duM72AMDO+YdsqUSFn9mMILfebb4erBpsavV2RXggz0VW5RatsBo+FHzAisUJvJ/2wPoz8HZuTf8bjS3pJ8U1+iDT/vuI7i24Q0DnhI8HBsQ0YWBGJg+7Paa8gFZFuWE6Mxasl0ngrkOV1ND6/JWt8cXRvHTQhdk/s3VtocBwYebn2kuNgtnbLcNXY/pBxtNfCpOx/FmDu9dy/Lx5q0/F4xDSZ2LfajNIPhfPsrCf3/FZ2Rhcf65RRpMBH44gpgkz2EuLJZUHfXEEMQXcjiHKBw6ehyqVkzY6jR6QXRm3R0farX8gDaTREapYPp6VKRZx+FBkJuu43bRcdhzADX5gTSdWnVXqFCSSIlQX59ubRbtetZAlYrLkX2u/IH4sFWPvn+cywkwBZ38gBnzMCuL3vdJ8QeQ5XxLBW8+sIAaNOtd8ZYQpm7l5HLLRB7iBEOx/SzIGe9K3IutzV6pl5mZXYGOoo+v051vcRgAVIQ+BOBzI8yN34O1JFSwM0g2lap5447CDv+j0fqbu09mO85CyrvTXPLT3nYNrHbdoW3evd9PoUFoudyxJ+3TxP8eYNjMbA4Xe0WkVkuI7zY8ZCpJt5B2MG4CtkrI/DTRvkBgEXQmKY1jsgot80FUvuGWbuprfWQAj7PdTw651BC11l0HjlhELtsxyyyeGZdsI6E7Y8S+Fpt3LK19hF3xnKeN2QKywD3qjC5iYs+IihnFmtIgQRNMLPnxWDFOFUOhO6QSH3DXDOvd+Q7lB6n+NKQTk3ksZ4wBmECGFH3bkDPJScd3UvXegHrTYK/AfmgnEeOC/fvrpqx+1PIzl47yB56XRcgR1aZjGAyJTQPAsBWNskGXiOIL4kY8ziB6U8UpoInPal7EOslOWFpL2uFfs2SOITuTqRiPcf123yv2iRDGSZBwROeCVPgEzZcQ0D3iMvZIHTtq0HHVOyocU9inxpMUQnq5fhKNVCZmvhelKlxa4//Mwxtd++CCEZgTpPv/dd7rfzxv6F/qa6A8aCzzHEcS44PvnoomzyS+TVLHIcq+3DBYNoeeHXElbXjuhgBEOHHLA6DVpGWWhCC655MENbfMHRsp6LFzYsFPwjK8olZx+KfD1DsKDYZjcwmmRqzzqvYUaWHHS3pBzTCt32ZLeOXGdDJfVBDQ31AsGztRzgo32BCufUUOYhlHSDx1NLCb5P2O2volbD9mKtKqhozrDgVDi5oGlfU7BzP/Vw+Dt6D2hvk7EzxVzJyfZsaCz0YHwbJPEgyvW17TTwMErc+UlL2fNM3IEXjN1PEMoe5wyy+ednmPeYZvknH7z9o2+fvzuq/d6dnkvh9C7cQy9l1OIrTCYIeT9hRTb+SMjxPOM68ABRPuV93PKxNipSEaLaCON5McFKTtA7GeclHvWrjGsDEgTbPDEjAr8I00LE8yVA4qgjpaRBTw/sLEpBOBf6RkBO4fTBvvCzKBvvHcQW02MA0j25ok2in6uVQjPNeuQ1QiU8QL62Xy0hv2GnjzRc4eeVZgdZD4y16Q5o9Ro++dKqTghcuUYOTfItMHnfBRJacRPv6K5tKR94TN2BPU00uCE3uya/yMxncBKLO8Ls3XepnNRfKXZSfayPb3jXHmc8Vqjz/CJ7Ix3KjplPhXvIJIOVoYLY2UuiXsLLni+Dns9puNCSiVT0YnTlGFr11W7OJZAHd/ngMfunVmYYva4KIhXED5/7i9ulOhW4ypB2g7P2BozcV3wqyOIKeD2jMuDjgOIt3vs//P6lRxAcgTxlZC32ieIzaP5ZDw4P2svgHcymu/lEOKtofcFssccY6lp3rxlszwxhh74Cc5+AMBtQkg78Ol4yazG0EL/IupqM7CEaec00YakRb8jRgJOSNgQNzVMyKrSYB2Mg5V8yhB0pHNh8/Q1GXZappOfgaDK/JlXlojFGYRx5S0gb/6WI0gGmTd/bCDNfgEvZJS/Y3O3Z2z0xhtAfiwt00A2HWMJiGEW1ILvxhn4NSBub2bXsuT19kMJ5N/tCIoJv2l3iWF4AgHgxzFx8wYW1qIA7z2CdmKv/CeS3svzcyu4pZ8r7GNtrp6vuoVPy+DR9BWv/Ftv8Qq/FbsbC5EYW0m/a1B390Cj+c81Rl/MyCDc1Cm6SfFxBADhzUDBUNyDZ9Kd6a06bvK+AO/hf8HC9C9zuTrQjjRt2fGWeFO20FfBQK7lC/HhxJXNwqbgFs8S3CpbxFvihJcMR7Ofe4dvlafKhNF6HBs7TAsnN+nFTyBsdWz2cR31mnJ1oiHm909VnGUXkwcGX92LWTKeL+XkTTtOIWYE//jjK39B9L8UMzvorZeGMSMoXxc1TZeFEXvwwR2Q5V/c/7lDEWtjUxxBOIH0kOIylVC/5UMO5WlbfmksaSBuC6lJpB2MkszBWNGruECqH/dEQl5OARLA/9yjxUuy8ixGhXDyvx/OAtNReDiQJqXYb+ENpxlumXKRmNgPZ+wJpDRL11KBHEG6ifL23W/dZeT4eMSL5/lkPEvEnuol0Es5gngp9Jxl494n6K4jSM945oXWpvZHAABAAElEQVRI/nGyHRJXgyn32RVeYtBSnlTkBsYf9FARGjfdsuvI4oonjkI9xu0zElgymvk6QB2pjxqbL1Jr8Pkp8E5crDsFHwGEbqR8EDfton2R8GjjQVZuVl8bIuCQHIhTBv6GttIHYlLhe4X+m/l1LjYhYEW2ZRK4ciG80zoExrGwaXEvPPjMxWedupiDehdkJBXbRMBDaXPD/lDk/FybRR/enrEzsmQ1AkvF9GMpq/Y0ey9HkPctk/PntRxBr/Us8042CsfQ2/dyWnuGkGYwyoltm6Xnng/i102jsX+1jdRFYAltZO1Vk7bYwSm53Dzsw/ack+ZjMyjH9gVrNVYt9T5nqsTqptFoQXEwjxgZhjqKsraAEoqX9CC4Xu8XhJnVMgT2DWKjaDuDZH94BnmhjaJfvMQR9Mxwvlj87IVskJ5NsE98UYwlZTiUPBNIzx52MPnyp15JpfScGkuTA20hpE3JcZ4jK/pNUwfPaOlLSw1QC44j6HvJ/VcEPUe5ur+C922ebqiKejb36gUbdSzaW8ZlFf6hBAKkNo4V51PqA4dwW3UH39t4relo6W08V3E6/B68Q7ZesJb4RkdtFZUr+UO6aOlUuhX6wlVhO+0Jr6yFbziHoSUKrjp9ja2LdYGooPJjDvnjTV0uDy45GApJv7BL+mgcnPmlujhZIiP4/smw4QhiozV2zccQ/qJB3jvv+yMDqmner/TW742Whb3ShpBeDqYy9gxiMPhOXvRsJKm9A+oA8nRy6mHog4VArjF8GuwxRZHBX4wG5cGJSVBb3MaLnNPCtFMkE8ijgg5nnG/ZFblE1By1GIIclmXBkys6qBv63bTrQXolJh1a38KU3GKd46+9TExnAzUo/c03+mqIjCtvAZn5w94ALA+LE0gzghj4yRnEjCE2kMZhxBRy2WXPBuJNoOWbur+W/tItplUqRMMgOY5wp+NufihY+WEslm5f+yM4R69zxYBcBySLvsBIuI5cwweOKzHmICT9O44bh99B9Xmjbmfl32qoz6Mor7rd+bYMWNN7ZcVtWfM7zp6m3D8dMIe9rsH5UhxBtJkfOuMXe69EA9d302irxgzFPRTWTeoukkkPpmvcfxfzz4cg/mELhv+lLW1iOsdFhpEbEuybeW1tOWHvfMHZ8veRbCfixOqU2fic4Lcyxr2PQHDLNfEuYHlRfhV2y585q/cIQB/ijw18ezdSlv9cY0owGO/DDbGvPfD14y159svQGEH3fL4U+uNrzQjS7J9//JgZQcwi5qGKscErPXT5wxGi+4UlYvpQFvRfaUzAOETzWFUzNy7SvBh6rPuibmg4iXyys1cQsvNajAYDtkzQT4egTQ3cm8g1TuumXGV2/lh3GWmUjjjsVBgGq4j683YekPgD0H+uQB7qaAXtwD6ZOUml12jG47osCQvt13bzUi6nkF4SMUOpjiDvE6SxgB1BzATGEeTZQdosWvlnGgd89zyfkH+sPQZ5GGMJGm/lvTSMyhXQatpEru11YyxfekHwiu+GkdkC7YSXG2j4VKB0oGnzUV7iA69lgdCfRp4dpWQTUwT/ayjJLtHeB874rfE2rzPuNdeaDh5XjObdR5yJxBnbnKVPfymF4mF/0E7ZmWydwzt4g14py++m0rZqb+NNpYuZ8u48uzBNJ648x7WmSlyUs3HS2tERhSRNOA/GqrCkvr7LE3bU85vsDwhEK6E02YUjw9DZOv5QjZ5F2ESaj9j8IuODvcL585qvHb/RfmZKU/ZWM4LeqewX2a332kOIPdC8JEzOGJxJzAby18kwXdQ48rheyhBJcPJ+qa1BPY83GtEb6g3wbR9k4/wnRAyRGyOICP2VT3HACRQFUBkF4eprGZuhbGb60PIGcBTARa/2XhsQBVkyyilm1qCkZFaQ0t/qAeIRn4vXSgOWgLFUjBmIOIGYGZQ9g5glpJlBmhXErEVWJLCM1V8r1MtozwwaZxDNtBtfcdqoWCId0iIr/WNknjizLgWj2cLgyP1HE5kE0iHApZJHkv17Oaj+ivCfcQS5zdPKvVWcUM5pT+49eLvBycU1NMKHa0/AFS/9CwWDkfqNT70upMK7oXwa38VQnfBQuC166qI8aOriAg1J2kvhFg5+3NjK++BT1L1sT7ccbZhqmrymWxuh/AZniA5otXRwIxVplBCiL+it2GPuPU+6DDc4hoTrhkI7gtxGjEKQD4l0SYipB2oqaxtNCjV0ox+/5Zr2YnwwYHDLRZU07PlRv7/qIUeQYxnP9zh6NH3yZy0Be60ZQBhOnECvcQjJaOIMYjYQbwD5Msh77SfkKeNiZsNpD7oGfIptGnACWTZuAuwLxE+t7g9NKn04gKJZy2w5YzpyBLepAw/cKDgwZ+dAWcoDiL5RF9pJQJ4td8CnnGjhXmFmjiYVnG5twEgTz0+y/02bRNIGn5lZJvZI07zZqC2zgnjr98SOIKaAv3zxXINADHRmBYHD9HEGggz+MO4O9AtX7VuS2oc91oFYCM4HM+gcKyrJLW2EOawbsJEPJL9tFg6zkeDtcBRTEpgjHVQWcTjXIJYoMWUlb0nzYXSwKxy8ndOO96WnqyP0UD1d05+io56L4lbf5V/eV7ziExd3hxX/VKZMrxqIKCv/L8kRdNLJpr3Y8+PKsT7XhVuNjtIorAKbPgEewoNgCz0JAq3r9CykkV028D29cbqDdy3b8652q2e1sLABPCjTMCyJs6dMEOCxym6lp/gUgQevG/xWx4Vg50d+nTPKVLjyYmSeE58YT2Xl1Rh+k0aMwVKKHGMJ7jT5o//E2aMy/+fB3Jg6+NozvrhMnn19vGRCD0k8xLPcwp+PZ89AOYJwCP30D301jHGDxgJv9JAFnBdEfIWHhyutvPAYQQKJB86gby1XpNVNzPsE4QiSrHYI5ZFHGcQQePLSFXdONxI4YRqcUYHEFoHTSkDr87pIgJgjlApNK3bR5FHSBLjkC1jgFC5NivH6U9r3WtEgDrdk4mieN/xIDW3cYDhi/NMNm4cpf53HD2Zff/VUN/XnvAzSDCCWgT/jBZB+3+tN/TM9lLFs43uNC/ylMPnTPPtIN3g2jW69yNxhgSqduqnf2nPdOxxZE8AhB16gR1lTiaMJ8Dy6GPpGxSXftCgymFigrcT15YyVxxEX74AgYcORCl40XZqjtPhtYfJt57nucjrzOjgkVd5gd8x+xSHv8S/IRjxkn6xJ3E+FQxdzWtDV3YyRQ2mId9VuKHeTRha4Al8xXN6KQdoRb6dp7y5fsNJvostcHYtadUTXVEZGvyl0pEPsD+BwgD8cY7MQGoyO4MljT3juAD/LxOIIkiNHs4HYriIzgni5/U7PMdriQrOC3mlpK/sHvdXzDEtaP+AI0gyiOLxZ2cDSMj1viS+zgPxDJj3TUA/yWqaRLyciberzF15aro08W0hmn9T2NiRP2rNh4DgzDv8GY9cgDCkAbSXvypwOzAUjzYYztCmFVwLVezmXDIMdOOxLxnIvHEGaEfSt8sxAfPEMO6MvFnZpmFYgPGPjes1U9FfFWMKK88cb28/sIJqnn/cM8lK01llJaQfpuvVpUf5QJH+c2kqLTu53BGlGkJ6V/orwn3EE9SryBVEVSBnV3R9oKYoNm/uYFe5T4JoKIYPh8oVO2qW3D8W7W3qmShOPNoYrefAO+P38zjV8Kl6pqo3m4whq7pDgindg3E25hSO6H8AFWKf0aJIJ84A+PKbJoPCLaVF6IZUpCGWUNe85W4VZAtElti5JOu+EueugayzGqw/gsOWHzWHKdjdGY8D33ku+8ll4NobGEfQjBvS1HEKzJMx7CMmj/h5jq4Eeb/1wAnlpGAM9/VyHjTaGW/LYASRzsDmCPIigryHfDEBDh9SRL/1QLZ9mLz212dO2UHCkIMjcNq4Bg2TtIBIp1V8YuIGFag0ADV+lKVww1TXCEdGao3ZSLPQgRhp+eRP4FW8DBf8bM4JkWHnzZ0cQs4L05o89AX744btMBZcjiE3cXoIjI83+ACwPk5M+S8O4U6VS8c+tkoGpNY+RBuZ2urjiJqNjdbsAp4TaEzUJb9doKszZ2wiozEVNEI9enUouScpS7vyWTPmnHf9Nsk9j/j8Ua+sOf6gFV91e+VIO7IrXSotfvMLvxEI0rhA7FiotuF+SI4irDH3dpzPgURYaUu4+RPD28Kl4O81fnJZZut/+bB2geDThoVC8e3HK8wFdtA5rt3gF3mJcnrfKbsHuGNyNwRoHQLjBywc57pHp7tiRe5H+fG9Nr+r9FKN+jAu4M4GXc9FxkJ02wmN8wAaqHi/IwcMDFF8R5U07G0X/JKfQO70gYlzwmjK9NPIXejQ+4AuicQaF3kstNPMntyzuUDiCeDl0zBJCLu5iHhcozj0ZXMnHCeafmECkNJHthtux6W3Q9FTlxunRzrjZpwnt5N75LwQaWkhyL4d9eAs0IXmWgrlyIYNvKS2THmIsOejIoVb4vpm0yzwTSPWJkCXeT/QVHH/GWW/r+UqPHUGaFcySDO8XJOfPy+/Zr0P3fy3fsCPIn3RWvRKjyzNIE3xPRiiHns2RRbCceQqBEYqTHEfI01JwFrOVDsVVNyp2uIsPGGw0/mcFarnLb6/799Z0pb3m7+G3Xc/7s8lBfaQWB4E2sgV2YlMRXeoW3h2OADa6M8Mtd8LbCBbDga08tMACj76V9v8JyRhnyJ5r+hy7Hwlku2Q7BWv9pQrHXJnugwMjJ1SXydcsH4quYsVcwjhz/ilbZZvDB2/YI0g2id9Peobx0jClWRrGDEb2DPrAzCEcQXJys9fQr9rDy188FD/bKWLqHPm8NEwAVanQ9uji42TZLmDPmlavt10ALz+OppXQbqfsAzBnFMWy0LC5ypbtam1Hzb6iklU9kzD3PR3RcAQhCo6gb7W5GJ+IZ7+gJ3IC8Wn5JziCZGuYGfSI5aqaefPCzxzkWbUgO8Un5eW0ZnmYf3op/a2eRXA8s9oDG8TXC91kAWkXDq1pIK1USC9yykZb5dWndIGu7QiCbkL09NktDVMDaSPa2hrrNgNq6yfejcul6JQtXtV8r5lWvak2NVmUE6dV8V35VhE8Qt9677LgIigLOsdBc8Yt3rXlZyxy9/M44+54uZymnE46yZHe3TIwLqtPC+uaE3rT0JrPhUnLXdgyIfoaUI1c+MvBsSQQonCiPwwdZxXijQGFCwIyuSFyJu1Ji2MsKee0wQUjhyMIBxBecE+n9LIvbQbtAd17LQ3LjKCfmBWkN30M9t6yN5CMqOyspoprsCdfhx1BMpR8LQxDiTmjXloXhw+tlKHAUErueM6F4zZAo+JNNmTEICBsZjM54xsAKYdpr0kL22Lg1zKuCcioNnIRBwbpsHRqnbfCUzpHcIc7wipwjqBH4gRifpQ3HUcQgzE6ztcaFPIpeYysP98oY/zyJVPBn3319+/lCPpOM4I0Q4ivin2n3fzZQ4ivjDEj6F5HkISIttUuG+Ochekuc+2reuEh+oi/4uKlDTpKznPfa0nOMLmlt1HJQIqocmM4z7mP7ShMcZOLIqDFboNfkzdIryhfXP5jekNnH8NBabtur/g7jx1vVzY0V7wrH+MPovsjAOUBFfdLdARZL9eDFHLoGu1Uuwf0SuK80Yp/xdjgwus5wIbcG1rtvQgpqI35CNqyRSc8xFI48VC9u10ORo473p5eOMNv5X9vgjbfCMizafAGxhUEwV1hfNsUp3VfuZKR32XY0yoK/U6kOydA/Xv/CXBUb2vOfVp3IsG4O8HAlt5x3AXQe88NjxF0n9eDFrN/Xv8sRxD7bmhM8FqOID4ewcPVa70swknEvoHvNTDg881dPs544xgnpJX/+pccQLofeI8gOYNyB6VhlPNjvGA3i2LakvFD9KC7CiAOiO+fWlDdBuyy9Af0bpeXIloMrvJKWy+ig4R0SOdlyvCnnsJJ9cEL6Qg5jpiWIRL5QYjWaCn4tMgPTLDlhQ5v4nkoYx8O79XBLCDd858wS5i0HsZeaEyAg+i5PvP8nb8axsOc7vNSx3IEjQDwtXYbWzNImHZ57KEcI7T2BeODsgW30XkYgzkVeHSxIZ6SxSkw+XIo9M+Nj1b8u3xpa7nc5XFt044hqiE8YXES2g+X3oZuisD35Xmw2BmbqixO7A42OSt7VScO5ww8KuuRuCG88QQndhgcpdNjuG7o/8ofRYO7R8PA/MrsiCGdK27i8AtL6qC8sNgpV2q6wHEEZYP72BbPCJKNwqnDl4zffWCLCzmCNPPnJzmo37JHkDePzvYWzAhiaRiOIH/lEEeQbJ1fkLMiy/wjB/YSeXlWsWycPMsyd8qZzWh7ZGUDH3tFQ0S7bJUZwycaNWfjVO28KCYE6N4pW0WoBp1ZmksOfCjAWT1aNyjE8ZOYngeYRej9gmRz2GOUZWKP5RhiiWpnA7FXWV5Oy+5obyBmDeGIfsKXi5lNpJmLfEmMTaS/ZZmqeHapGDaJm7S2MMXHf0deJJ2muv0o1FILyL3IpY6KpZYY57N2BG1qcefpqQSeQOdZNzeBmm9cvCOOAlN+4B/lpNqdqmxAgnEitvqKtdPCt2GXq7A9Lm7xbsscowLdw3hpy6filZeNzYicKBdLjnDjNpAQwwTE3XGgR1T4PhhtuppsvlSnfBkM/16sJ0dQhenlorwHK7lcwtZMVeBzFVCcKiFGz/EzL2bmxumljcSMhU6OIK//l4GUE4gp3m81qPvx1Ws7g376CW86XwSRl12zhphaKXRNwZQjyXy4iDMbiDga5IEfGcnrp9iDJluKwUF+QX39j1zo0cO0ufgpzN+ck2kSHI4ZUcqQT7SO5HcYpOjGf45BzflX9hQCFfXc9FpouMDr3CFdFCuUvUZasf/gIO8ZZ0bnzwMyxd8++sobsTEd8zkbRWo/gB80FfzvL1/ky2HaLJKBId76l8sRJGeQxtCPNZD0nckKQyZpWoqJdmWgBaC9zk/7ouvgWloOBMVN7rrgdNLWNSMIPkYstofrYTh8iHKtkyJEm06pH+TajEDGM8ZRP3jX0Gobtzxcmvv/MRromamuGv9e7ey6LU94XOF7fq8DmpaVnnzTC3cQiRho1bz1svqSHEF3dLPrEN1YaYPlwWm0yDXLr9d3oINnoq3QiGC0NiOYeXUOxHYAFDJFVfJOaHnjOwgXQPEaX4qdrejCObcpyJDuobIuMTc647aATNM7g2v6imcmV6SD153iK/0iVeU0qCdrEXKXI3QUsgiOBLhXvkO/mqR82GOz4ZY/mGB3wfNvHmS43nz7AEgQnDx3ER60WHrRPYJ+0wxgPrnMm3TesvNVUd6yM2ZgNnEcQVl68YuWioHLEo13StsJpNsf9bH3B2MDXhzlaYE7VH7eMNptEs42dqDhXmqhmxr3fVDyFZ5RgPJtHfK7nEPWGaTtyxHEUx7/egNdRxCCqTzcjntnlnfkZcpwdWT+U4+ioSOl84fiFbhvejk4aRGQx5bp+cv3Zt6m8+b9mR60ntgRxGbRcvZoFjCOoMwIwhGkPYJwDOn3ncYGLCfnwYu373UEuf+rHhw8TnuMgRSVJfVzttPGwMHY82iWEmCBHxikcp6SyvHA2qF7WTFaI/mm79L8dZDW2/h2TZQWg/ihoFboP2Md9C7d6dxfqWirOeqQsuA1d6sG3wNVUD1xTqdbndB9riOG4Q/jlZtQqaDZCBW+he+wQez15XH9SYojg1mJ7DuDphO72hkHojMuPQJR2jg1YSdcErvh8sGDxj8dgOuL8Z4R5E/Aaz0qXzF8p5gN7HFe+4M3slX5GE6ec34WnKVh+TKinEEyeL9qqwxtGWQHk2f/iLnrxV6NPNQbHWCzFHwSSNP42DHAtmU+420/GKRleWACZyIdgPYHhJmE7QMub6YOISHv+LBBW7HdsQPwAckSSUaeO5i5g+1gFtC3OHI8u0eOID1QPJkX0k7LQf2MGUJyRDNrCFv0REvFMoMIh1Ac1948Ws8i2Yw6dilyqU12BJFzIy1O02m2zl2UaUEPR5BKV2dXq+YcP5JuP6OlYTRydLJH6EvhVlFK/pwj/KeqxbB1XuEL4X9oIu1Kq3ytjnYxHW3z0bS2vnFKyF1xe02C0TQGEMTmKSNc84H2GM6mdS1T0xKBvGTVRXGSWUyDiVmZgZsbWELiGNHwDty2B7iIcQR1kMd0SG8EqRlBDPL4Mtjb1+/kRZcjSM6g/5IjCOfQOxxBevP3Trvr//qrNpJkI0jzYXCnq55p3+JP3TZFXlcbw+hZQDZGYzAtHzKSJ8RB4BYjn9us1o9BWO1PU0StxKRjiJvhDV843jpy4yFkgOeU2Qw4ZT724JqaOcpVB9Lb8FKh66wZBo30/gOBAag0xOAUTek88gnYp/LA2whrQMhXQr7X1PC//50ZQXII6ecZQRogvtSPGUFPmBGkH1PLfTcUu/YznD/WuBLWtBqWWFUqnHQjOiQxjARh1Fh97E7K9DpuWiCV4Bj4DKnZLEaTa/6sTSha4UL85MS5vk8m++wRe2b+aEN3/T7Ec8fb63yIZsdrVwLfNEPYq4luTr/+3EOtRZtKjCqcH51EB7oSt7xt2kLctVSkctzLmj4ISe0UD1GV+i+NK8w9lXxMvhO5Mid8MicEZTfYwl2Je4QYsNEu/O5SzH1bJ69sc39GthFwP7F3GVwaAT9xKjPhHyLk6skRaPpMUsqpYqd1gAecUjXp/OwI0i2LJRM4TNhLkAest+yzoTEAL4uYFfT2nWYI6e36m1fk9QZeZe+ZOSwnEOMKYh7U+DFeYBzCUnJ/WTR3K7WB+79gfnqgjEbRNt295uUR/ZxPzeMIykslMI7G0548TAypW40G3FLxpI2ZAcR9OJ+b56FMOKDowL3S/KmaX2pyPa4LGYxMvdFUpKj+BPP5NUPh/hp8obNRK44bfbjHziDeqD/F4cOsIMYAehj7Tl8I5Qui/lqPxgN+CaSlYf6cvPKU4QjyfV7Cfgs/xZGVWPVb7tQ/DZMMSFmZ0isEmkA7GsCJVnfdAsuveI0PykKO+KGyA+v/rdRV5j1fnW4S3wBRCtU9RRtxk3sdhd1lkDMj+EKfGhwdvfKo+SzFInMVm3SngoELhk3Yi8iXd65PM3KfI3WU6uokU+IauQHAdWoJg8kXlucd4QgAjF+eXc75tY+PyuMAYn8fZgSxP5CeVbTsi42i38lJ/RNOIGYE8SJb9omlrZ4RpDT43gtN9s37pbJHkG1FnFS0lfort9N+4Y21UCPt/El50jS8P3Aid68nt0tMrM9p5HCSLqM0HLpWQJ4wZIWqCbPzAS2GZ84MpGAl5NxZChVQRh3+4pdsRhxAcgQxK4hZPvqS2GPN/uHrYGwOzUbS7A/0TI5qZgSxdQX7BD3h62FyWuMQ4vetndKzVAzntGwbM4RwUmOjrJ6RqHItGW12OUh+N3faSBpSDgot+1a2/4fP56th08q0MUef/LR62r6XrjSdpA/GC3grwQl4iFFpqBctDzonzkqfctj89eEs7Ke28eN4I/20iRtkw1yCzTrmgjowPtJyIRbDsfKl3aoJ3xacamsmhbGTpIusePWJXOQxjrq4p4IDm4FRSG0WTJe2xBQMV5/rgYt4OYJmOiTTKfnqB44gBnhv5Aj6x+tXX7368fVX/+CrYV4aFuP6MwO7D3IEMUjUdfyrl4TJzDCIkzDUmyGVTc+kERKDhElL+nACCT9KUFna6ymfbpZw3UdTkllAbql1ZEMoFDhu2lPu4hAaHOMjyRhv6Gp8lVxh4Rmyc6dENa3rhrSQRsYwAMBgqzFpgkbCOn9x3eEI0oDw238dm7GxNEwzgr6XM+jv2iOIJWJ8Oey7l/qMrAaAbBz5RIbXG0brbSJTy/3aVtUgIfVxDXsaKLEq4A+YuwUYiNSgNHmDJp5u4psGaHUEoX36Hv0wujEVGOVmGSwHB4OdMz7Z5krgvlDyFLboFC92J2gyD5DdwP4yQNV1Vfvvtrq6/Rif4u31fIxmx20XgoYrxfyU6VXzxTiCtjajH/RwS7dcXKfrGDxfuFARVC7Cu7QDvOfkAG4R7K73MrO+HqikREpesgvb4m14q+ChxEfwqavhNupdaGiGssIq9riq+TKdeOl2K4cz2T047yqn3jZ66ALVceMTHoNvZlt6wzvqOcrnlrmKaqsDON4Ouz8MGXYcCx67rwoEp8jXmtNUyh0uONrmZ76ek2XkLCH3cgscQXqY4uuibzVm4MGK/YH4yARLx/1Gnr0EPTMom0fzsBZnEM4X8dXLI+rhjpWxAPUe44P1UKUHAN9z7BQSvmLf2eYebhbiQpt010+C2E0hDygOoMQdG0gIEXmpmGLQOWQJu5Kkw0RHl84RRFDD2xWQRwDD0GbSbtFcSOzTwSwg/+QN4q06s4FYdsHDlx1BvIXXDGA2kX6q8cALZgOT5w09Pz2cPdJbfF4meTYQMvLwpRoRDin5IlmkBYgcA3eK/F5OWXBcPLhADx6UmHNRtviMtRUMzRnyn8sh597Oaz6S0WqukLT3fmlDLbzF8ja/+zm0BLqEa63uThTREY+KgnzjWHNT1INzkSNs7BnpEd4PfmccbMmVfrAH0RZj0u59Tp9x9p7lK9Y1GoeD2zVSKF9ac548MPTgWOk6hIh5kc2XvTzbUGlvVSGnDjN8cD7bDmGj9PLaM4K05ylLw1ym2UDMFPpFb7NxBLHs9VcZJa0M8wxI6rSzmv7g+onTnswMSptrv47rAxxpzrZpNDh0xjEv2kRifqhCv7FyVotrUnFqHCK0AGvoTEM+dQTPQJehscJA8U8l3j9U5/trHEDYIg2uvuWT8rJFOHoea/kXn4l/LIcLewQ99ctnNpKO/cFZ5D3N5ATCWYRTyDxYKqafndLwl3pkpixvBEJmZOXsEusqczPSFs5jYNGo9QNEYH60GafVD6rzrwjiH3H+CuY3eaa9KiKxhZzPK3RDOCe5mG+LHiUX+368YkxMT9lUgXQj0gnxk/mZ6ixLGJXrpf2nWv6MTOsZXiuLmY+pv1tLkHIzuFtayD5AbrqtaR7cPV3acxyqDOhIl4tizodD5W1ZDPCBDV7NhSSnXwgSMxAepcwZpd/EoOJs8adeZ0YQmz6+5s2enEA4fv7rp1dfvWZGkL4M8oa3gMwIwsuut3xMn9QsShlP8dPvNw9FNL1FceomxjFEI5BJPw/ggBN2K4FMh6xusZAsp7EpC1Vi5Y0emkwRByU4HH2G25+DJpLBH91e8yJzGKwtPQUTuXzVhaAUEDuRtIGcK4amHXhJYeoUdgRN/A1LwzQI9DpcGVmWhmWPID4V+/yrH+QA4hPyfFYeR9DTJ9kjiFlB/mqY6uRrA/zRHmTjzSNNdAxMaeD8KiEJ0hZ5gC3jHgYuoX3T+sSIt0Bl4de2GT3FYhRJDuTkgxPKliVGxtZfrFsx2DteudzC/ZJhVx3t+d+jl6u+ob0PtvP9XfUNMpGTOqy0YF+yIwid3urj3Puv5+GExwW1rpSmTxiwvhOqdzAXdsnvYF8AfyWeO8alvgez9xCsRkF8S+BbsE0XojLnYQ/2lYI8ARTufEkLuhtbQ1VuwzfMBnOKzpErOfCcmgHGDk36kMiOkeEUG175554sAmj6s8R+cEkruL//hhNFpp8lYh/0pMSXd37BEaQHLH9F1A9bcQQxdvhZs4bsCNLXR/lUM2/nTeuHLMYNefPOjCDuHnnAor5xAvkhquMDwXmqUJnvIXoY0GNGWuQbW+4sXA20b80IIrf0nbTbPw4ht3jKvTxB+NaIrxvxpFod+K0zLL2Qg5bYujaPwKwk8oZx7w8eewGSfqTpv+ytwaxelmWQfyEnEPtx8BD2TM4f9gXyjCDNEGLvwOzVMeVamkGeF0CohL4jdpIxP4SyfB5bWDoEU4gcLovUwkOmBNrdEJz9SAn5wIqXPHRX+IHxcNmO9/9eOq2in7nX3BBwdGYV0IcPHe7I1Y65kFloLdmx9/RD5YvJTnCkp/heDi7Y+rDP4YXn2JadxwXjqO9mKpQ3RoerB5asfKtC90wBga/f5DXUNawvsmtLiLFNzEBkfzKeU9i3zF809NKwN3rBrRlBOKnl/OHLyO9ZFqYNpb0vkOj92Xi/GOds8SWyXBm1T90niEFxr6jG2KfDmU3b80N+Czxx8IGNrVKMVTFH7I2KMDmmtkLMYXgIlyyFw9R4zg940nG4rNrEc9Iqx27gvMEZxOxEZgc9whmEM9pLvpRX+gWzE2WfHo1deqbnE2Ywevbi2KtsHo1TWzbNTqGxRziYsFE0yGHasWVzZQXO9cM9Br0YsmJAQGQ/ZfQ+M0fQphTOOg0lVmO3kqrMJ44MCvGDvhWzip3wgGJAxSMbJU6BoiueSyrDoLkvCYYslmrqu1V3+e31eA23adIm2B54U8nNlqbsVj2lIm55Y8MGYenP+hSwjXD5KtVtkAvDLZ2SIh4wCs65u+zGXo6iRFA2itdFe1R7Zjh69cO2VQXiEEp+1y2cDCOmDKGEkz+xE1IN1XbZW4y8XRO6cGJqoNV5FSt+Np4yfL/Jo8PGju/4bLwGeZ7ujSPoH5oRJEfQ//mRGUHaBJJ1t3YE8bUwmRoJZmNpR5AsynwFxEZRg7xVP3X6ZzMXgZS3YDpmirgSpPtDRtLVEYWGOCFSa0eRBoZOJm9tiQZaHwx2roQllQSj4+E1CJYsGtshOw+Vtg4qWZWRmDyDP+Pk7AHPbC42jA7e3/QJeY0H42GXsX2mT8a+kMPne20S/fcf5AjSzKAflI4jKEvDnsgRhBPIjiAZcr/N1N0ReWhNBoXE+bVpqKFqEmpkJt6Tyi/8qNNE0SZHtcUIQ+iGNx3+B5lS7tipYzTtTGxB+mGuYYFLGPRTlhpafNR2wIbk/0ebBu7TGSjVJeldn+T3cAsP2JVmx9vp78O7wssQuMt0WGnBvhRHEG1moNtgvVa5G9zXrzTUc8ElCYxrbCxfznFpi2lEcS9hK9rj0lDffXjFga54TRM3CK+okVkFt3gWCbppZ9u0sVp1LV6wE+01bzYn4PDd6lliXBm4whHC/LFTygsPs50SjlSc5uyNvMlu51O5RG5xwlAYMDNEaYLyLQNcOoqUT1FwioaAK61UzC+Q2NpVqnoYdKcHhRdpMKkmPHRvIa1flnPxoMSLI71x1wPWe40DfmaJxbxZZ3bQKzaOfqUXRowTXKaYpRjCZ6zx3jOCmFmUsUcfrOjz6fczPmBmMY9E1gfxNm7QDc73EspcjnL4IXfGSgjtP4Sf0ZPLGbAoeL87itxSKyJpscHNxD/3T+5NcM4RfAXRRVPJ0DecnzrNEuXqXADnhyMIUb/V1B3eoLMU3GnFz/URCPbhyOeZcQQ99T5BvBx6asePHsbkEGK2UPL62o904B9ySjXYxz5wUc8un2W2HMAjc2MaQxpVEDcNVvOkfS6SmCOlt0I53Fd+i+aPwFrfQzxy9lbfv4m6yxudVFtFd3eRVqrbg58g7mfFEAUnwVrVMWCDmoanaxy8wCPD/kwDVvCoTamNwVH/RVJ4gke9ENsIKGNGK3Lf7tgaefaQ8enBYi+b5pTdtHLHID2VKS5+tVrMu/mIDSm2YNFNuk1ymWDEcQKxwXOcOF4WxnOMDEycQHpxvZzUWRqGvfpF5f76sdIf5BRiluMHMawjSO/FZTXkKtVzjS2GdNhZQDkPbRcxP+xTHUFp/9E+lfccxCCv02j9T8N83epgToo5B7Yt9o6IJwrhNzWSNC4D/gYlkYYy4oTEzBIkgO1nBB1wBGE/Mksxmz8zGwjHEA4eNo9mVhCOH9Jenro7glTWPYOwa49wcMsYYY/4xPzXmrXIMrFIWHksxjrBkTZtdffWYcWDysmGmhffn6kjCBVtClJ2y1UN/3Z8Nip32bQu8No7QwNuSw+6nd+ePjAmdYffXV5Xmgf5bcifiseFmA64Ex9y5LI48klBc1C19IAMLxXQRFTE4JBk+CUfLEkwF/5QBbFMDUzGaOZHfhhS7ornwvAgT2W+SsAa2lDoiCEygeOUYg7UIoGTx3SEBWziCMpnE72pox1BrPPXDvt2BL3WJ2JfaWbQWzuC2B8oa2+Z6i2PuX41lv/0F0AygMsmaQwkKw+yIYPq99papIhsyJElQsgZ82XcWAMbZEsPcLUjbTIX0WDYoqzoiuEgRtQsIgIIDgsVvQiS/BmpuWAM4SWKIwihFKhrNGxZleYW4lKEYCDoHBBmBemfgaIMJYaTjSK9U78Ge3w+nhlBP/zwwkvE+JQ8MC8N07TwfWnYYyyujCSyIAP6881hdJI8lfmfyAH0JEy2JAcmUofG6ZvoUj3cZSUGjfSRL38QfXZXxx7SJQG0YPc3ScAPBLCP2oJ41PkA4RdUdEs/V9gfUcd+Dq7pK9/Wu+NdcZwfRLoLAzxsKiB+pBms5Bq/Sf3ZAN1mDoQHlFa9BJGjIXevDXg0PMCvKH8o/lT+O96evlSO/Vl2qnjVzYZ7wtvg0ckAoN/D/UTC2ip5EG9DHf701YeDEIwziOJv2/0w0fSFM/P9jgO57z8SgDtP7jWpynB3poXlHNdZHmCKB5X+5p7NeIIAHg4gHoT4uMQHvQFirPBBjh47e/RWHWfPW791lyNIM4L8sghnkMYL/bgEM49/YfkFD21ab+ZlHbql8HEJWsb4yXXj0UAGxWmLlo9xHiy1YhkDq9uwvAQCj2A+6jRpx0D8ILXdi8EDBrJrHc5z8lwT54WqiIc32KYxHYf5lU87KzE/8fM9XnVkvwzewjPzVw4dvfRhRpCXf/E1MDaMxtGjpV84gpgVxFd8WIKBE4iNWllGRh5nUDaKzht37CK/OoJ81uahzzLPwfdjpy2wU+kn1d6OvbQtoDVyLtx1cioB9/MI+3mvxmhd0oWkrX5gJ6n+4of7gE9H+lO7iNV3ZrFwj+cbERwVTvlGRF0DBe1gPsBGOxIsm2/5rXhDMu8Lzm0WtzADA//cyw7ZYZ3ySSiyLZgC0k4KCTzbLSVkkrwszF8KU4ZZPbzM9rIwOXdsg2SXfv6ZVQzsX6YZQXrJzSflP/iFdpaGMYsIR5A/nOOX4uKrSlLPPLdQ13qxrTbJPqU9iQ9HEJIWljTHPvP0/DvWwX2FRinQP/zyVvS+nnUOeMkbTQkJvME1ATT6yZdzBAGAgWY650sYTVKeulTfOIKuzqBv5AjC8cwy1BfsEWSnkOzQ82fZ2F7Lx9jk3vuYPdLXx+wEkm1jzyCtcGAm0Nd6NuElN7ypM2FrgJLJkTjbbJtsw0Tl4pQ/FrPPyxFEL+vI1r1CDZa2opjR2USHYTjD/6xc6+zJ+pT6wCHcZ/Qq238nHhKttmAklSHfdlmmzcDlsihFaYPdm8BReuZjLJ83YU4lZFdde1mVQQzCztRl6uRh6BwIPgcgDjwkHHNB2BDpZm+4Dinn0o9VCGalAa60s1MuBLpgNovGEcSbvkyX5OtgLAtjivf/kRPoJ/YIYkaQ4N4oemYEsVk0s4H8swD6/Ks3elQdXvsvI+qGcS4iC/Ix+8foFqgyIldki5zIHL3gTJuWmi4UlM8lJAD6ag2oPhu95XaSuka1E7VfQtP0KGj4BDE8QRpCojL0OSajn/9XwSCn/khKCzivFKXnff0NRh7PuWYEyeiyNhfvOk4fnD9xBLFHkJxAmhn0UvF3dgQdm0U/xobwllOy0A/hR3tYEpabCXrRnwooQ84GxIemoMaRET5TNoznXeuZialLOXWsCuKUKtT1qwz5rLpIJMh+3kv86XH5fjrF5495nJG/rq3Ve+tqvjVe4c23/BRTKAZE2CUSBnFQYDDR22Ugn+eR68JNrjKn/bda2/sOZbmeDqwrWa/pA+OeFPVeie9B/VPAH60v90LqurZx1T/yto0LzwpaWCvhO4X7mu+KAw8T06oMsdIBE8/tK7gX/dhOGnYU3FP10B94AHYVuF7BwGj6zgkZcvbwODj5bqn8zu0opZ4ypI2UpFS22A0gnzsVqCnL/Zt7KeME6NiL4xeWX7C5qh6eWFrBfoE4gnjTzj4cvERixhCzghgvsIcQM4nZg5CHtl9YesFHJuQYygMXb9un/hkbRBbawr2B+xdjhslbMRz0k+z8Rd7I/ZsEtWYQWCFjVNL95f57dChxNk/YuXfI1uT+Gf7mYnKjiU2Wn4X/eiE09flrP9MBvCRMREjPDJ5HemiyM+fxE+/D0U/E90279wHSUgwcQ2zK6uVicg49keOIhy2WXzzRrOGrIwi5aIPbQbzaOrJTPj8aQvoI0UtgLQEnFIUc+KRuQ++Hn6n/eK715xzczw+8j+HcT932tLZgunfdS5TSULhvXERwiQ6HVNE0dEnBmtLwKF5ye7Ut2XkN6Y426VXvkLmv3IPXFt6t8yA4agd2CzOw4rXXJX+UkQJWPBJcoYXNZeXLFVieW3INsufY+uy77AmO6V9kXPhKYWci4qBmX6DXr9/IKSQn9TiymdGI/WJvIBzUH2SX2KeG4TR2yV82dLt0NfEMItuUEXvytDnPW0ecKx0peWapTmTDRO/2+EYi7cbgCZ4nGzA59xnn7FeehNnsiplUM6lmrvuhESOfV5VRnOCaleQM6AfOxMzYIe/NnfUc4s/JayZPbJWc0Cxb1Q8HDzaKJatersqyMNkl9g/CZj2Rx+exbBTOaxxBOIHkt/7qa/Yg2ob3lanXSF6CICwQ/RlBsTtAsDnmq2H/+urzcwRNg+dMJaI36BTxz82rD6i3nC15mIqidjyz2JnqLJd+pykKHFqtzsO9obSNbyGe5HBvTDuuuH8VnnvRVi/dnlCdUERnS+BSyN8ApoQLirK7oXwoWWkQL8gZAwAUp0R3cOCxB2/yaKYi4GpoBSseyV2mtNHqHil7kH2Jm/U/3eBpi/lwyFUJPbYILzjLwvgqCG/5fn77wQ6fLg3DEfTjP+QI6lfDNGPoLQZUnnfPCNIm0TiCYtd05f+tewSxCeRhAPGIW2YPicYyuJGROXIpLZmBSLSlVtJ3gpCA05e8saNy7puKvRkkxjzWxKSjvbCZOsj4bFOha+2RfMJWVNAmWKS0fDTujsS2ZgNvOXLqJxU0xqP/hA3a8LpjcOXs+eElS8P0+Xh9KvZ7fUr2e+0bdHIEycDy1bDOCEK59DXk5bSfHUFo1y1ZbTiJu0kOrVHhpXQegNAeANpAUF1JBL7lBzyRzg0kZkQEnfqj2bSicMq5C9nB+8yNHGWVouni38X+ciHVERqonq7p36ud8mm80z90DnZZSnPl4bwQwfVvJ1L6i3EETftXJ6/CiKtk6YNkdVaUqsxwAcn7eibdwiK3rMjEhBt4dzrQp+Ld4gVt6yR9K7S88X04wFWH26j4Wp2bJJtja4V9rDJOmDvVQxUeQhzjqcBu6dYlsBZLy1FyYPcR3Ft9ZFzmd+Q3K9hRjdsZPEYFpzpb98bfL2SUtyjD2DDjhhoueQDi7bj0yEsfVZGv8mjMoIcn9tpgz42+fX+NI4iv9AiGM+it3sa/0dKMX97FefT+l84KIs9DF7x58Mp4hPqOhysepgjIA5yYsYNin8uOIwCNzOL1mzCjYvQhbTAO8FhAWQfBaTOR8v7JSeNYB/oJ7MjjECrJMFVU/uBEQvN3pdRJX7P2hg8PVtgwzfzVw5S/xqONWHEKMbvH+3BoL44n+nkzaM8KYjmGymf2ELOIvLcQ+wvp940YMobQMGCTG9mRNppyA5Wr5ighpEXIOUHJ3cm+lRhh03IpFI9eNsht2AnhT8i03quUV9Z/HM9jFbENJ46pE4tyf3io7H6q1vIQRus/4zxQ3zgf2oXPdHdzZ42Fb2G3sIPB8T6swOcKWXihoFeeWxR+AULTci4rp3UA7mcNJVi+5dk8skN+fpFzmv2A3hMzW1HPKnZEszk0jqBXWho2topZjd4fSM6jLAuLE4g9T5dNQn+jQ8/L0TMM9scOHpex+mGuLik5Vx1tTrpXofUjfOuBhijYsdFGEYsMSmwEk/ydFxxOtTuOY9gMJ8m5zfmN88aZwdk0SI3zU4Sksk/MUkQq10G9GqyzXxCzgbAvjxU/k01i1g8b2bN8FccQS8Seyn49ky17wcxFl8sJpGcXYN/quST7DinGqcRjIdUqIEUDTiDguZ9GvojOvYaCYMbectV9lo6gaSVncZ04Jauxaut3xPeR7sq/n51PiYv9ULbJdB/N/2t4bf/eXrpa4bSDzpQLJ/2MfEO6ZXM71dGRT6U9hcRCr8oYY4S76j7YhxS2VxggI7ZAsYUkhizwHHOFUJcvjY3fPpADt+1x3EZ7IKUyITCo403fb3rDV0Pqr4Zp4Pb6taZSsjTsx5+++vGnmRGkPIbVm7DJ4/6BZWH6/WojJ3PSpWGzvh8PehQzhtGdO28ZaRVGNUGx5zWrMZKzZwy5c36OeAjM1lxpl2lgh74ZWOazsDYgiyAaWVnrNXVVNQYthCSC1hJ4TOjJVmyo8y1vXGtGXr85x3BjSVicQZ0RJEeQXOjsC/BCjqDuEfS9HEEvyU+czaLlABpH0BNGmDMjKE4Xq8YefWt/0yd5SzbiECGLYYoJ1UXTvnlJ7v1Bw4iLKsyqoZ2f2+lzHmjPa+hdSo3zO6KU3z2Gy234XeiXDdnP6V+piZ4T4mvYZSjeFYc8eLWT7i4CMNhzmPiLcQSpvW0612LNzGjjFFVnxStdka754rV8v9YL+2+PH+oYmzBX2VN0bSH2S3eFUdp9rI9236XfqnwgGbqHzs0hH1IkOGXSqfeTqj9ww0l5K0PXiHrKwSIpjwmwqWOzN4SKIRCcuHsSwY9+5rvpoLem4PFgBC7t5aVPNn3WiyDeouuF0PsPOIKYESTHj2YRv37Lp+T1QMZYQTOF3uiBDEcRM4COmUGZHcQXxLjWvdeHFaoxhV4uEfJGnbk0XBORycIyblgnMbgtR1LGNZaYeJxAaQFcBzNN8wgEVs4Sm29mA9U54nuf2p3rDccSfAjiSsYA+FKQe/7iJ4htl8T8Wgl/Kv4Je/5oRhAPV5r5w8MUS8Eo8wOWnEM8UDFziJlAvGX313jkAOJrYY/0oMVnlLn1Iy5ytj63j4wC8iBRzqxBp0PQ0hjSSYGfkmFj+hAWcmIzmYfKbuE/BINXpfkz8B7i8VDZ0aamItVDsj1U9ml13cYq38a3sXYomNbkkKRv7xiky686bxzaK3by0UYpb+MAPfCCmyuk+L1mjxpHGiGfRs7NK/a1DTOeW4TELMNf5b2xbWFmIjOCeKGNPfLXDLVkVc8z7+UIeqWZijiEmAkEHXgsJeMZCIcSTiDPfBRf168DW1xgh+wu6fML7ZIM/5ovHtKOfcZiLEHaHh0EH55Vdx1B2A+bYND1w/HMS1zSAekalnzWnG0NTBRqc2L+XD+00Dg44VYkL3xbA+rzslE9LfqBARwtXdWRanHaMJvHM3pwBMkOZbNo2SQ5gtjYnqWrnrnIV8U0Q4i0ndayVXYEeSN89h2CH5vic8IIS7rkBLaNmragHCdVSpujq8DyLPc5O4KsHxREw6WW3G2sqOshg5ycsA527uAA0Bl1B5v4itP8PmgqLHFPWE7g/Xhnqk/JfSqvj+G1vPHtutWOUS2dnE7GxUCaQDotJOdLTUd3TeeBNgxJs2Zb4p4yeBnvYDqDh0V2f0JMDmnEoBUqjuwtzcXdtqyqfK5DZLOhZL3ZYTaMJEEGWLalNny//V/23kTdbhtpsvWs2e73f8fuKluzPNSNFZEBgtz7SLKr6u/7yY1zNgEkckICBMkkAGpQ/F3OhMwI4saNr4axT9D7OIK0NOwf+rFZtKd7ayB9xxs+9gjyW0INlPRdHEG6Qflas4IYQLsEjKVdfdNHbH2ATSW/5tunGYrkHJnysdQaGJqHGpQJtD8/gIHLTnYEUX/eDdLOHJalYg6BIglaNCEGkSBc6efsgqVkuB38xDecyx9a/UwHzMO/suC5ZSLPN3EMxgyWcuzoppCbPfYKYvlXHEHZI8iOIPYJEvyFvfGawqnB9pGcQY80WPtOWnX2uC6J2IGLiS8o5KOMK0Bf3U1h5aeOsZ/RrD9ga+8OrpQAQW2dyadeoRo7KjMsFUs/zLDBWupzVxj8WSeIwB05Q0Z2hcIK2MsK+7vHY8Kasua/a5aa/G7hBvwcvGtbXNuq7K68jLchN6+hJeOnAH3oKY8vNeY82ExxVHMzGueph5gr4sAX0TVPAXwIQ3s651OSI3jgNN7L9nTLG+9le/pT5cX9XDzj5wpyNcNipcr1Pmlnmzo/RFXqT5WnDYpNbL4i8yXuDjk6YFM7Fii/gwPKOUAgCJHx11XEwLIgLt/CzgIC7Zi7cMQ0VrR2Vil5MxQLru2K5kfan1jWkgyWUvw2S8TYgwNnT782+o48jiA9jOEI4iti3mSaT8n7QUxLOvhSqfjYCSTG3PzDP5uyUj10yhv4XkVjjOiqQgWl9eLJOqOj6uPZS9RESnsJF8q7ZibwwS9ilNLVKSzUeHClDbERcOyZIO7SpX3JrHNQsTQzfzCFJxquiQRuZbzETMyyX4YcQfr08RNtDv1ED1N9y87LHfI8RHkGEEsuvAlr3rKzRIyvhHnzVQ2E3Cd8J+aMiUsOCSpgHSYmUqg+yeVoVCdbx5Anl/pTDB49JOmDyoCBHun/dCrSP861Oh31uI//KbyW35fZ0kihjbHqPZn3YPc1+mvQj/GnLK1drOki1jQ9/Cq1mK1347T9FTv5WONKeeDWWoGAF9wDHtiRP2iDyzgADjHBaZ1qdgT5lIsjiGcW9gby/mMai3AAMdMHxzRLUl/LIY0z+v07HNTv5aDW7ETGH20WjRPoN35i7q+GwVfOIE5n5Hjs00jAC/Y8M8m2XtmA3vPMk5FCeVnaFwDsz3jUAE3qSa/JmMZYh5y48s1NBB53lOEZyKscVA43P8tbHxERTuMNALWsaHBGMeYkSJqVkAwS/kcojmw0EW8jwJM9zJAvzamGfjyX8Dl5loM9sqMHR9D3Xz1nY3s9h7A/EGU4ghifwHmi5a44izw+sU+QxixmMbJPkBWg/hPEPqBlKeqJetJHaCdHEKjqCPz9oGfFn+Rw+m8ETSJYlv1v8L/l6RoLTMdxY/ngfKprWyw6X7yXijR4jHaGL/SVaHniBb6TQD58pY7kHPiBHwTRk/KGT5muuMUL76lvmRCLJ1w/hfe5/CpnamaDmrflRH5tXTWKm9KjjlavSBO7NIhzwqUpd7jpBmeRu5IrtxKnGUE7E6XbLmHFiZv2R3C7khnpDM7AyfABFjF2VWyeBxx8aJkJxE75/mlQ9eDZGUEaOP/3P37x5+P/j5aHsfY/n4/n7R/TMbkpZLp4B8XvpSsu5SwPy4wgBEkus4NmEE09UGh+0ptBjPzXOJLQ12UCKQRfR/i4HoGmSsJ2f4QWZFpVg50MwcwgcsZW5NoPfaI5FjaxCT56QJfwjTxLGIpItCzLBi+/yA+db0QlL46gr8YRxB5B32RjaDl+/tcLOYJ+zNKwF1oa9py9gliz+wjHEY4gOYTkCGJPApxAfEIeW2AOrOjBXdKxp2VH9KG6y0a9oVPOwSZVyrXBLu5rCOG/VgUVpmEMWkPTujQVtGJuM0cVwUY7WEN0FCz8glysw45G+v+FswVqL6C1VeMz5l/P7XavvB0G5yt8zzdtvGaGgU8twTjXe5oxXfq4yfnrev//nZL62hw15tiGc6O2oA46HR16zkzWDb7jBSu2XA0CEP4i6nlevMJv8oO/4NdEyxtfy6/54jU+la/aWMGF4oqRI2w4AXjs88g0RVebHTSMkbf0YfMQfIR8IoLa+q7EQRDNVXARseoH6qUs1KqV4KvmS8CBHrILsfCOvkCZAMNkYTqfq1VkKX3qFMoLmfsK30/oev8HDhwBeHvOfYNnBbEPh2YGZUm5nEC6h2CZxhs9hHkj6dkjCGfR+3fdL0gPYcwqEh/25sAhZDm6xXGM3G61qwAAQABJREFUObjmS+n8mkfTuV9YuoKTOs4dEgqLHhg/hal7qhyYr04CAIOVuRphYCYM7rIlicWXsoMX/cpXNDHKtV1XQA1ezOD5Tk9Zj5gN5M2g9SZdM32eKv9E13VmBZFnU1ZvwMrDlzYPZO/A77VsnK/4wIcHNTaZ1jOW+aMeYyMViNp6bFUitkAX4ByTjqbFFfgmjP5F3MqXqTfYLScIo8kJ7bMz0fVzeBTzPutW4Ip1zZca+BGKVduVW9v6wGyq9T4wW/JwXJqHMW5Lyr+0zYOZNP0fvdOS4UDuCOe6HvDiltPHWuHM8czj4I/USk6cMtLRMTKKAx9OrZzDGQcWTHANNx4bcPAyZtgR5FmJcuzIEfTGjiCWhelFtpaosj/QWz3H4Bzia8heOqYx6HccQSwLY48hMfXKCDuBVK+Rgf28QbQeLHAGeSyaB5C86MYGHjGkIrFwfJIAp576SefUbWDNA/QYongw/OQjEt/niI8pikPsX/HLVXnheo8fivIQZOlkE+Y5SF4urZWQAbEugRgnELE01kMEPhtiZgsxXj1myaoeNNYXxJghpPEKOMvGcGKzZIwZQZ49pHJw2feUmYtsd8HMoBWkNibCOqm2DeG0U+MIopFtQ9XZf85/cTOCVGVqfScYbCvdKVwgEMBsvAo+I3GP+c6r5Q8o+BkS/j2UQ361+vf43Zra56qYwj8XzEjIyVtp1WM6bcFb3HvJxqs5NtOtso3ubtKI1WYYSAV0XVCDcxJrGBQbdExp6sKAdei9vNjADGZogSI43GwxE4i1/r6hYyB9zbp+zQjSIPqKzaL/oc2itTTMn5HX0jDvBaCBFEeQHPG6iWNcwQGFDHlq5QjKjKAMnjzNIY/9BbJzvtIzmNoODFymgR6PtoaiNhAI0hGdCY2TmyNk+um4yLDNHwwopsU+UDoD2tQ+9CEN0OmAg78w70j2AB1424eBuulYGmbgxLPvpzeEqK1tFcTqLo4bRpaE4dR5JAfPM/YEeqY9gn4cRxAzgV6wUbQcQRp0H2uzSPYHyufjRS+58ONSRECEp5g6l4PrhglaFXRVGtvZkUa88HdbCm+I7YA2zmKiXNObHMO4kIUnjp9zaAnQc9n1fDlzP3Mht3eV29L/exD0Puz5P6vHp2z239LmWt/P1WP1yVEMOmA7PQ87fytH0CcaybbeDbTZ7iHSO+g3qP/d8wkN0LzxLv5ztNvx76U/h8eBkxcI8Dlg97j+Wdi1P+/0vi58Styl3NeUCwyevdZY3pjVMHeOSK21dx3MasPpIBoRKmgnmJjrNfcKtB2yuLTmK2Jy3uipiYepD34A0/ILLQt79Sr7A/mtPA4hbyStJRuaeczMIPYO8rIMPYyxTxAzjPJCKg933rAVOfp1HEB87htyj8PdT/oS+vIPHKy9YmnZvlw0RdB1TQO32Lp+Ct4rUdIRHqwcrYz4uw2nz1iaaeeaSVo/eLDXBg6cOIHY4DlLwXD88ADFsq/H+goPy8Se6W07S8D8RTE2idZ+G9/L4YMTyJ9z1gDIrZL3/lOaGBlckyMPvaY+rlvv/lzNmwO4u7l4keTrvGKX3VAMAJmnMvoE9ku8itBvZf5M4q9R3UqYNrstuAO5LzPtHPRPc/sYxkNl9+XeUXBA5UPc9IF9D4oE4O3bBzYlf0I+3hGHoTPpQ/QHfNfJGnNQsccsJVafozMrFAdppDPuBJ9ZOow9xLzotROZMUQvsP0peJ5f9NzCjENmAXmvMjmB3nS2Io4gwT9o+Rj7onqvMz3DsOE0DiYC/P0sgyz04+U1M3zcv8mjPMvF0E9WJW/nCxbO2HTciYNDvVo3aJVVSP0pGYBgweR5gHMn9/QppsLzA1BiaESUl7+MgM4YCK8Igz80cQZ5lYTbMjDVfvDkENIgYLkaPzTie1nXE794Zn8yzQ7SswdOoGeP5cTmOUSzgV5o7ALOrCAcQ+x3xoyg73+Q85vnmUeMgdEmdZm+CAjVfECXZtFTQe3rGAzSqjN/X9hm0a01NZ5Ai7qyyo/dWrTHGXAHQod4APdmYB6SoNPcMfNDeLvM/+n05+p0i0ft2n0OrYvXkhlzjFkY2Ht6N+zVxJUy9xJpAhGXvnB47mnyd8MJKR0ePOupMvM1f1JtuUpjwFJayLlRom0TeitQL3WgnOJTLkQcJp4NpNGVr4a90U0cb/FeawB99eqtHUE/e2nYS88Iequ3ef5k7Ns4gphWiSOIJWJf4wjSbCBmBWkYkZDRSWUMjtYPhxBwVPAAKjp5QjIjSI4gTf2jvRqMp4Nj05FKrjjGHxJIPWioXoclY7XDMqH0rcrYfhNZtlt8lpcCwbYBORgcx7oGcNBPHvclG3n6cckQUHXXTZ1MxTRxNmd7pNk+zzTY4gj6CUeQ9gb6SY4hfz6eWUG6YXwiR5D2mdTNogZc0fpNggZ3TMDlKPXSTaPTOkiWxaKFxOanBHDaQITcTBJih8kYopvzsVHwOUJLCI+kzWaSXEgIOfoCNekgAM8PXcuNRCiCxbFladcD3pTxr0Qt/JvFtVWrjVmusJb9p+N7TfC5srfTKPqK0PdbG4P/5wg6t9g6JXewGmG35V7UNCa9OZf2jqL0assdXgb34s/CY6xh/Dka9bPI7sl7EHbwBuWW/7ncOCCdwo7TwsKav8e7TG5xj5KMw+StW1EXYDAHTnS0lXIb/pHUaHxkRHGMzsMtTHZiCqRAyJRAyB6cBz44Ohnz4AMg1xAe0KD37CDdBLxnvw05g1g+zr2DH8z0QMa9xFsty2AmsZeeM3NIy8p5e/+bnELv9SDn+w/v0aF7EF23/2DfIAmkXtmfSNcg5FpH4urrq+jAufIp+J5CmJc6lSJI4JGa6/DGMS9pWiw+UuKw6CEZ8t4/ECMO0XXK8BDE7B2cPzh0vtOndNjkmTfpz548yZIKZgfpWv+UZRUswWBPIO8LlM8w82KIpRU4grx3h3XmQRGHEAogN4+dx8OjVJ7BYbcUtSVAhh3NKgZ12uaaPHgPBhGax2fg7vdxD/I7FVgrQSxBx7b5x4UF7x7OGXaLV3knJUZu2xoe9/WIljvtWd5R8hD8wEhq53jVrTyImz7T34OW45XbIY8UlHP+pGAdS48jwcGA4HKNxqa3IbDqs2IS6mi9BjjrO+HBcCcUjrIZb5RUmlJinDV2BinDuMC+QHYo41zmi8dyML+WI9pLVP0yW5tDa3uLfNwG55D2OBUcx5GXtYqZ9wVSjE48x0RWYrlGNO7oLnqeU3h+QQ/urOkdcRhhCwzB3TZ0yRtNkPSi2qj0qZOKbT2frrAg33jKjInQ/qwhlJHAOea/IfQ+QYYFJ7YOvWoooLYBEa+v2TbDbXo4gpDxzTc8R+inZxbGlCdP5azWwwazEHH+PNMXjXFc82M5KzMZn2m52GOc18J9StqOIJxBvNhm7Jv6E0mVc0+jHvzaL9QWc3HpuGtHEFjSL46gL21pmCp3DrEUZnGvmMKebDFMcsY5Dsa/udFSh+hJVzlukhv4NJSRzNSpK7/yuMZ/Bg/aq05Xfs7Tsen8nwifieYzrHJjZUA5NQ4RspcNj9zDJqRKc+AKNuo1XlSb2uay5Xf6U7pMiFtvCw4xuk9qThuhDQNKko7+TtPGKu+tTusTmLXyzRZv9JgRxODgT77qq2GvPIhujiB9OewfWhr2Uo4hr/tnR35mBOnz8TiC8tUwnd5yBH1tR9D3GkpmQEQgA6wHUcn1DV37MDSCaRBl0Ek8jiDo2gZKO6ujL0fO6JBqiAW04BMolD3wIgs7dC6YQ1q4ENOSOdG3lLgcGrdMeYMOeFqBfGFJ1/WWGTWS77YmlliZgCnfjz1garDFEaTB9cVzzQjSl8O8NEw3iy80AD/ny2E4gp7gCBINUy81yHL5gSVViNWPNBcG93u1r6so+3MRtY2ASQFs4BvJ9sHDGMaLo1FJrC9celZqGD6ULPNNCflgduAPBNxgN9++IPDUwShFEyyyLHqdGi027iF8kf7dEstGqnjT2ADTHPmmNpuD9JFwpn8Y8V4TVNpOVby9TN1wBSd14DzfZf+tHUEXozm72WwZT4kHwAvlU+UYveJMROYe0RV+zVfigt9jUiTFDIZ7R9iKTsmP4h0yQCOcWVK+FHI5h+LeVvSMu498i3hZ65B9yBglbMDhtaEZsuUPnkoJflsUYOHQe2wuIGQnNs5Ujb0EmCpu+0w5kVmNQXxfNwzA4wrin9PB5drCjCC+1MPm0G/0IIYjiLfy+WqPXizJEfRWXw9jjyDe1L/TUg5mC/3GfkH61DMPdN7wVU96XqZhRxCzelU/ZElGHUHWz3q6AtbHKo5uuT6ppVQHMDqOTBVdP6dD3hqFhUuncqRtHIpUa7KiKR+Tkx+Yr6Hf8iDFtbSzgPL1Hb4O9oPWR3ynlz3sofFcjqCnur6zr8Yz9trQNd17AukewJtDa6aQN4SW9+c7PUzxILX2+5MicTZJNi/QpJuv+dxKSVPSKMufdSR/J1BmCtXRNHdwFgjkGh4glV62WVhRYMObbjQIkXhgF3GHN934huVB7lR5gHfclVyQtmxsslO1EIkHPHJTBvTg/bCcnbpcG3+srDjX+LDBWbPyaly65Dmm5xdOHOgOSXqv9cO9wLU/tffgWsVdz0qA4o7WAgJvSHqjV4cxbPBI+1aVWCeynUCO1btVwIwenlne63nEXwqTk5kP3DC2vHqrcUhO6TdyBPnjNh+yRIz0b3IEMQvR+5Np7MoYgwzk7z85fPy8gjMIPeUY0rMMAbzDETQOIDuMfDcujNSLOqTPkJ/6KUXw7fZefcHgfoCijc81G0aVd+wDLDL+SDf/KcZJHF0V02b+5wCNrhZ63tPTnmQnDQznkMcLHEN+aS3nkJTDkc1XDJml+L3GLzaz5yW1HUEsEdPzCHsEseE9eI80e+i5HESPmcWoMesHzQYKLc8oCWhx1A8YEH6jIpq4QQoFjq7GsCPoR96C/xfC/4U9glSL/cSikzmPiWIUrDUpN3KtB5qxdnqMIuA+AORCACasZwA0DpDAXcZBYangXCRzcWvww6Tx1JkvsoMniRsc2JY1m/Lb8cr/iJEpfRVd6Q8cyqPHfbzoTRnBfBY/6kbBWHeQkmvZ5YQdCkUOxWrGJzSZkWfWZS8wSXSguChKXjKDMJV2+xk5jMgfKVPPAToGpTBMFP1nGLGg9ADg5mxacJnizVfDftPJ9qs862+1NMybRbM07KWWhv3z569+/vm14pceWP1lEN7k4QjSuPGrBs/f+XqYplB+/Q17BGlGkJxB+4wgqvQvBlAGVe6mxgrcgrhPjCMIOjZJ635BVRI9zWO0H0MADS+xdDOOce0u8pjJIEcA7zYYfWjODXPFtwZ32Ax8WLeFMrgCnBZwB6HPAuPWPXpRd5xCvO3Dc84bwB80iDJtnJlAdgQp/lHLwvh0vPcJ0o0kjiDdUw7NXDxkIG4LEcGPm0Wme1umyjjnUMMaE3NlBVcI3Lj6AuJSdIrmzqLtpj/0qV9Szs4BuYTE1D39kPyhWTCS56IZfHPToTzAugkqNL6Rp/SjBDccvmiAbbPVcM/vJgOlZrvCN3Ini0emuMCaNpIOO15hxA/h7XCnB1B4xrOD09/GEYTNagSlff7WjgNftt7w3ABbfkseRrykgsNxcYy8Zlt0j1lx4Fm8pokbhNdRoNf9Xrs3lKOiAn4OXnHgcfCLolZt9NvxKq8xKL4aKrFXoeXXOPhXaPIjbuvvt5BSmo8EZmymvi1JbF1GoZZ5TBas9Wm+ihcO09JQK9tmtBqWZ4Vpe5hVXZee84zkIMDXs4thpEDEecqLBZZ98UWwN7x1ny/zMAuIF0csNecrYl3CwT0EyzM+8CAn55Df0DMziPsQPZjZGSSe3jdoYmQhH3mkvQk0Oil9KE+e6xdXl+jsyDjH4aiqqH1tc+2EGua5jodzuftBxmaBv/A5CMg8AG/uqgsuH33gWvotP5w4epJiNs/3LPOaX74S9lRv1rNHEF8J89t0ZgVpaRj4P5gGBxB84Ms9UXSk9twzKGu51NPXeupsHApSHktRA2gTWis2v174FLXTAGxaYPgv4oWGDvQHuIWzj2PLRX4mFS4h+Ekf9MlTtpcnV52Dcz6CXS7H/dcVJ1g+1wf/jIGcQ274HVwpDe9iRaMcq2NzV87kP1Z2i9863ZaU1z1+wEp51CU8dvxr2S6lj+vhZGl6Llhdwvfug0/7i1UtsnMBisRdo2qQvkGOe79Co5OXX5WRiigFnztVzntmBDHW2EGs5xZm9byXYxlH0DscPeMIYtbPm3daovpSjqAZe3AO5UuGGodEx5YYjDV5dpEm4wSKXug/Dh0vA6tzJ44hFKM8YxE2m59nBUFbG5/TsWrLamMbUQxjyZ7brr0qb0vioMEYPlh4iJX0WKcBAK5+dmIwcAAPItWNyDHPHswEkqMHngPDEUTaO2XL2v2q2LdybOOYZs+fHzQj6BGOID2b8BUxNor2Xmd6PmE5mL8ihpNbs4Qey3nNHqY8yzzy0lY9X6DCKaDUpqvlW6OoptLMWCJOHcD+XvX7Qh1BVM8tpfgcet7RzG5MBtr7qGfCLecL1qJZCWMkl8bwyT50FjOD+sbKyeMCkE64bkAuip3xaMw2+pXjkQ8NHfTjYed9D5N6XaWda36mctkiIOHTz0gLfCY5+Iu4pjrJOGVE3Pww5MQ4g5SjjE6/SgZWiLIuW0odHGq1f9krHd4zjIgpBKnTAQtO3sDh1GHTRxxB7LTPbKB3X73GEaTPx/9Ts4H+oVlBDKxMu3yjG753WhrG/kC/ywH0mxxBbt9vftAgImeOHUGqodocDXECeeNo8vQDfq5DHEF8NYw2De2+NAxcMVDIoGtu4hEgkdlwoAPz82UjA0fSUDdkiB6WQW/RHpvpYDkqReMik+cHQVuN/GqNWJ3N2BbPo9yDvjrPtxrcPHByE6gBlMH2J5aEsVn0i8dOMyPIs4LscWfKpfYIEu733HxaA1YyW4hvDEl58EUcOspYPq+d5GJKgS5nXET0y5RScGkHYlOtWkFsu7us9QsexyFxjCWabwljGH8JxJe01ClkkO5HIKH6Hj6LcCf4zPQu53NlXGn2/D2xn8v3Hu0G+5iYeybbSP908pMqVxkhbsmVvgqc09lghgZOn2sP+1s6gsYWNsxm9JUc45KvnY27HW7hQDIun6mGq6LFf+PzWckbRW6lw+d0/Waw2TvAJujP4TGlHd4d+8+129gm6cHwDO24t6Ax1cr+xxPi7+rvnV0Aj7NX09lMAg5NvoZ11ohRd7fZnu7VCYrUE2slYzz3IMEGvOJJZERHt9i16pHnUoIj6Fc9kPmrPfpKGEu+vDxMziA+3fzeG7oyk1hluofgpRM072aPD38GWo4gvuajfz2gZZ8/9gFhtpDlSJAfBq2ErmG+n1Csahx1b/1cOyo4icZLc8MLJcNZAbfCiJ3Wgeujr5FzrfxGzpnO/onjB4eNlnDJCRQHjmIcOnqTbueO4+yj8VT7ArGcgockf3FHDqDHPHDJWZSNVjPTF0dSnEtTJz+8oem89KEhRy/5jKys9cUaSYDsOpCl5vxI85LoboCwZSW6g2g02kHc0s/2TnwQ7Hoc0HsphOX3EbEnwlu8jyneshOLj2Zyv3KmO+dCHj3ulZT9x8qKc8S39TrKjlbcYU1/jhy4PxQo28rNbtrVF+aOIoNn1OCf2znXF8h7r45Es9OBOH0G6AQx4Bw2jkDE/ulAjPOHc5+VCyznipNY44e+DPYeJ5AcPewP1A2i37x+4+cXVjfgmP7VjqLMHuJT8fDjJTgOINKRTV3i9IkjiJNK21x4nyAlWRLmMYf7E5WRtr3ACy5Y0GJH9OZE5M9p47ZXKbbp4uQ1aqmMrIPOd5LM3tFsFVAUzjF3/zzywZXnCJgaQ/iwd4uZllk15PS3HEHAcAKNI8gx4x8ceEH9h8YkHEGMaVqtoDHKs4E0C4gviPlT8trCAkcQM4LYT+i5ypgRBA3jm2cHaRxEGayS0Dpc8gJbP7WHA9dyjfmeISQAZSwN+/IcQekJquHVMLGDobStOxyw+3jBvn+kMzikVzS34nQXt1O4I0+lx0PfQl0J89x0p4EeCpzwhH1A2KQtsvt4q3glDrGV2ROr+aDub64WsfWMPgdsUgKHQ8tzOjR3xTd8E0n+1IxbWWlNowN4Trdg2a9EIIDYM6IWbixCGJSJ0IMpnWsgFRYGIpwP7slzM+nd8rnZ0mjImv037A3kaZVyCGkp2P/WZtE//6ylYdon6Bc5gnjr9xaH0Vu9vdMg+pv6pr3qcvZ8/W1nBH0vWTrtVcZNYgZPDbDKe8NoYut+cQTJIfSNZxSBN8aZQTiDGDDZYOxSe4eVjyk/iMtkmcoJ8x4BJRvMc1QlgJLe84XtsbUToDeTwueBg5awHMVu/PISnsq5eawjyJtFa/bPT1oW9pO+HPYTXw1jVhAbRrNHkJxEj+UE+l7TLj2LiDeG4u3ZP5LkSxB5/SwaFdAbufqnzW1LGgZ8bmznZ4AO61wUAVimFz/TiSn5UFN2DuQpPeBNcZ7mXA1V4dArrT6xQ85cP5H7y4R3+LZid4oMqqz/NN5D8j4Flx6cTp8d0Psz8EFpFYt+zT8ocxCr15V+pzPqxpiku6oO0GUM05dyNK7Rp7/0MKep24jqkr8XbAqVdbh/CK+0HnKFv7NLupAYt/xK9+/H5X+fUx4IPo0D9fke4pYfYyk13G1hG15QU8czHih/te6RUUmNL0LvZS/VJmvqVONEsUZclWGz3REUNrtcjaWuDHVMaY5h2XrSJ3iL7PPUfCtSBfo/aOCdewaAwFvmtA4ss2C/Dpw83rdDXxDznkC8NGIGkGKcPW9ZxqGHtg9aFpZPOKtMcO8ZxH1I07y19wwhxaTtBNJsIX2xjIdAPyCobowPXMo6TqQG0fdIx67kq/fUZmpVaCm4fscG4aSxRxdZbMrsnG/Z80fXbK6b3+p+hbw3hvYsoPlkspw6PDw90u97lnqxNEwwloA9f6ylYdpPgy/qPNKMIO8LpAcuf3ZZSyq8p5CenSQq12Ypkfan3nk4ZCzsDAAcVHEEzTXU1+jUhWPqcNQd2NkRBMZtuHdugrlbK/1MsHWveo/PLewWUi1Th13GLe5te4KTWmCp1ofUfrdyj9NDsEOD8ADvgN1S3Su7Bwvl1Y63/B6CwPMhvg/By+uwSyGJCyene3IdzUmH9DsAQGpPMGRXRTtl+wJQn4+iOWmkzH6etp1WP2MsEmeCpYHvROg0PGgckINY57+GBsUaT1hqKkcPXwvzl8L0ApvNod+8fP3VS5xBmqHoMYkX2JoxxF5mLCnD+eOf+dNH+HH3jA78qBlxHEGuL3lVGp2M41h4ax+hvIoNn1grFoKXfjPwuj+R5W9e3JMmxHKtdJ4jOLcORxBYlCeYh9QmZjaiZay2EqbbAEUZN8I3zh8ZwE6gOIhiYRxCyKSVcATJ8YIzW04dnNxPNEaxIfRT7Vn6pJ+P1zPJ02dP9On477RMjOVicgrJsc1zjB1B2juIvdFiAWyMitEfnU89RIY9PnWPCsrPTzmnvzxHUGrG8RzcH3SgMek4ik7GGuwYMZnpXzYUELNQbKM7F8OnpOnQrqOYIDJhJRaznqyWNcVER1Pq1BlFqjq8Dp7hzPHA22k2mYO61ys0dIyDj3lRP1c4ZaXxAHJGDd7Q7zYKWvlQkg4b/rVj4sIaL30ueo3xrW9lQdOwYE2UkU4SktEgTGl/6uVbAEClKTPFDFy+obMBlBaep1oKCH05hmROdSHxydasz9dUSxw8OIJmnX+Who0j6J+/fPWLHEPvdQPnGUGzNMyfj/99ZvxoaRizgfL5+MN7zj1CBk4NVCjJrw3lZWGZBcQXwzI7iAE5wZ+dVzIDGjGDlGL+aOTNFmW5w/CGbCimhdohhlayGAMfrIUX5MGL/IViXqVrXBzlv2ZwbQDODzwNuNZNltFNn6dfatB9pB8bsv3ETCDtI/DTj9onSA6hHz0jSGtydSP59HE2YmOAZYNpHEGMrViNmmAHpqw7GK6D/v/gYpCrmO1JnhvbvtkE35bgLnNsIzLjRmcQqj/YBFP4CO41UMqPVsgfGIU2LYj76XALyyUSrBXCLNmmi18kFLnCBKp+d4qOwvIgLuLOr0x2vM9Nf4xfyz6D10mFybTvn8qGF6xpTgJ493BSenv8E2qFuMw3woJuuR8Q4+hA7HP7QmRnZyt5kH1xqdTfRritm2xqswqp5m277sirTMCaDLyLSU1S2I4H8/IwEpmFaMiRn+xdPNOU8EB0H7wF3yD0nuMo2FKbwse9AUzDeOkvPF//BN5IhtGmhAgYP4GUdisd/HPZp/Du0S9GU3iyBQyBT1nb1vpbKxW0rIwGFFuljqUrCjFkljVA20Lych85d5OukIGDRYT9opjjTT6XcQIPVLxdZ2kXyzSY7cNbd5w77MnxQbOBcArxmXmWaLzXxq5eGvaBWcg4guIk+qAHO3DAzUygwH9nDyFVig9b4EzyCyw5h7iWkea+6LjfQ6lRzNpx2PKj/zKkSwFOgffIEIUMFCoc0HL46DrrWUE4e3DuKPYyMGb64OThOiw4MY4cP0CBh4MHR5A/BiFHkBw/bAzNJqx8IMJfChM9+N9pzz/kfO+ZQOqP6KAL+zf6cf/D7h6uqFSl/Xrdtn7qvNnvj/qe73moB79WnZodD5apJVSE/ZyrBQ7KyHX/QgEnQlccyxiW3JMgmGxlF5t40FwWWbnvu+Ke8cLhinPLr3i3mMjKOYUO4d58qHKs3of8W16Ve4++tS6fnfe9dPEOefew0OGqxzUP3c5lT195tqwUOD8mKNF62WIuEL5JBFGebgAgcQnBTuh9OznwF3zpN4hEdxxB0HBuc9fJswo/ZrN7SwuNAf4i4Xs+Ey/nj2YHvXr1xnuSvdH+QC9fvvGzzAccQBpjPPZ4bAkPz/SxTtwto3PuoHH+5AwCRpmeb6bcM4i4V7VeVB6a/VcYdYUe2yRWUkHjCQmnGVNEO8YDC4eNKW0s0mQVK69SMiF1Gjb6g4W4Mh6QCqbwXDfl4GULEpOPwyeyNHpaJjUEXlzNeeK5hPGIsUk3YMxY9HIwvaj2DCA5g3g57WWuzBbSmEZ6OYKeaAbkfHremumAKue+grwJrrNwGO9UZWrSF9dgUPyD6vjlzAiiZ+/1ryEKPFtqlX5OwoPv5yDewemF/U7RgDalF5Jg7tQL8D+WiNgKj27u82hQcLW5p/qUHfXOSQu4p9vOBhb8gPFbspRu05F0GHm72J1X0cxHGQ8CYnLgk+qP1FECbXMMMg1dYxvdA8+wAkYkBPdwBPXmCm97HEGZXunPx3uPIDmANBvo/8gR9FLe9ne6yXvNF0De6w2gxg2NxXpLN2tnWRomRxA/BsfMBJJcKZufRhbN8NkdQb558dIwDWQMkfrKRr3k6Op6zsCLDY4bopRRK+OourXEONlb1cBbGOgwXhIGWqsW6Rq3nHhPF6/WbhnOHmAEYCmPxr9jISnN7vzZ76fOIL4QhvMnm0VreZgG1x/lhX8hz7unYy5HkG44ceSIj98QqqZUM2lkir0MD8xvEiXOF2aNPziBfJPpN5tcRKTEhHSp1IHjkVLm5NhaFE3cxIfZSXG5IiTtpNNKRciUBSPlDx1LIF4jpJCD10Y7OBvklDzGzcXlVO7MMVDclp0gH+FxV48CL3QFD++OSXdFXXBPOGQurE/lO+0FbxtewmPHPTGZDPQbTthdmO4IQt/HUTBznsNvzm2lMiNoY0zxFxg8Tn6kXicLXM36Ebq96CEy4Iv/SuyUfyX9kLS/wutjNJWTeFe/Jfepj9JTX7+P/BHo50v82LmINh2LSOcmfhMr4KExCM3v8g/8Ey5goRlzKts6J871AzSGuo434RH+p3M1IDuC8pDGnj/M9GGvn8zu8bINPXxlxo+cPLp/YFbxB5aEMfvHTiPS2SMIp9E7HEN+YBMvpeH5m/DzAAh/3uizvEPXMR4OFR8jhe5trPDomyqr0q4QVZs+DlKtc6RtB66ZSsCBexFmA32Hs8cOGqV1j0L+W93n2AnE/n48LNnxgyMoy7wMswMoTh/22fDn4fUyhz01cBh5D46hZVNoy9AFnMtxPueMvqqdH9byAOsZAugFnpTk2v2dEmlDdE7dj7q2zgckjqADj5JrOHoDJbXVFav5e+XAJOPjYoLj43EPAtd7HIE/HP48xcO87pV8iv+1/Jq/x/PPwuB55XvN/1meewNtbSC2HX8swSf/hjv9LNHR/2jw4hPzg4rzsuNHx5alKQjq06ZT0nS6XfYd89DZ+SMmnO9Oa1zA+fPuVzmCtCyM1QzMAmIp2Gs5hHhx/Vp5L01lrNGYwheP+TgOpxPLu/J5eOosBfzggA51BE0seJahamzRyofolmconmfAZ5yoI4lnH8Kqow3kCkrGnFWKPaNPdGusEUX27pwK+5zHZlK2hhMOAW4JGSt0FD/q0Cei8Ej7hYYaJ39xBKFpnUO2OFbnJbHuu+wIYkZQXjrzVTC+dMj4xX5nz5XmIzY4uJ/x1TA7gjS+adxjL9PHcgbxXMM4RaAaqHmE6jaFzvJ8AgbjnhL6BStLw15ofP1vBI2Jy8r/Df43PPtWPgVUEcukqgfyWGszGh0Gk+Tt+Ya/JYNefo0PrkkV3jjQsmlDHXnJdQsKUuBieQPYe+nCagLeD1mbU8T180kpCrPm0M5dLtOZUnTWSTCTDWrlnWt68GkqthUWiCuUao9X4bkeu1BQROLzuKRDRvZeSA13JqSbnxMCyGKwEjmx3DfgQmjM6Rw84KQ4xUH6A8N4UNVbNt244dDxVG7NBmKd7WsNsFkahiPoF28W/Yod+Nn8UZ9/9VfDvDSMt4EaBHHwsFk0jiBNl+YLYHYEocuMY8YBz5rQ2tJBeoSGATEOCT4/D05rT4I0fZBB0XUwT3oLQcewci6ecSWBAaGuNyGUw0CllnBgkS39lAYHFIb4ym4+caBoCI7i3tVb68DDh7R27xfed5qnzQ0ibwX5ehhedW8WrRlBLzQjiI2if/pRA672FmBX/idPNChrNlA3mP5GNuEtA7Wkqlg4lyJXwTIYc2K/42ZSaBrguZDU7tJGNr6aqzV1nZdjK7VA6lgSdiugy5hwYNbOY1f4k29Q+sRkL0udPPyAJryUhmA/H4AbeuJVGRsQBWC0hfAU4AwOxmK8EazkVrglVzGJh+A7knTCzh6HaAOV3VUFPAoGYUvetNvO/i4z2JhPJB3yACZ0Gm+tXni0O/AOOKmD05E6MNJnWkJ9mg4lOUCdJgyll2IcLL7YlOt9r3bT3kROHiY7sFXAcLOKhuZASHu7fMfbESbtfqG0z6/F8B7iBrvBOwD0655zhaIewXkdyLf/kymekeZw0EwpgAtiR6RNpKkvaIaZn4yG3VvnEfUZ0S484y8yciaXvBpPXoKqV+NiJkaX0LQc3VrJpEshPJV1tIjtWpY4Y/emg5LkUtdkkuc4BTbGwRf+lWtVjKcDskVGWd/U46Tpps/e74elYLq/AO5ZQswYYibQOIKAeRbRfDmMGULeQ0j4H1SW2UF8WUyOIt2khLdiO4IUaxbRH/PlHxSKLaYHoN9SGKVT96Q4HoV7n/FMXTtW1Ka6kH6rA5s9M9uZmUA4eJjp8w1pHEQq84wePhVPGscQjiL9vPyL2DQsAZdTiD2B/CUe9tKAnqVdmU3kpWeWiX5z9+aTmoejPMgxXnp2khqRdmRvEO55fI6JKm077Qwbt7gTTt84geAzpmh/md5QIsfr3HTusF2QrvmNtMw3PeAfikqSDlv5Rm28YkFzLw1+dN8pSRd7hz+sa3SIlPv84EMvO5/lB/czb6QHcoYf+J9KHRySgs+V155vfQs76A9JxQEC3p7vnWOKcv7IEmaHRTZcJW2vidvvgAZ9LDiqwMNLWoV44lMtONkI4ocjoD+T63b5NwG8pykvoJmBqHOfvYCY6cMSML4Q9lLPL2+0tcUbOYF+efXaDiKWjuF0fidHEGMNjiB42wnEs4rvlqkE8vnl+eMP53EG4eTBCcQPiyWNXrEL+PyEhxE8fivGDpxbJIcHKWzWZx2ftyozjpj9S+Oa78ElhQen3BtZqChtCVg4FWuFf2cWWfxSjERo0xJKm5IYePODN88onRmEI3o5p/WcwJfAcASxhylLXL3xvZaDPdNLa2Y5PhWM5WHsFcQn558+1XIy7SXEeGhrcECU7aHYQdYERhDcvhGQ9K/aO8YG/EHGZtFfmCNIlaxhbIWxBJV3OKzFZq7pEFOi1k4eyxX/Yt/FAzlnvDuCy3j4CX9aB+5oEik5ki8wHQzAFoyQvDu4klVzV6V8HevACUK9TD6F4U8JOqhMCWuhg6GDt6QfhUaMxsIVYXgrXsiXhHDMzjF4V0xKj+A67fIv6Hv2THnwcApE7G0CHYq8TlQVTZkjiDAE/xp0Yhdi/YArjBV9ag9gnVjQwo8TzW/ZxhHE2zd71jWovmZQ1Yygf8gJ9Iv2CPo/+rGB9FvNBnqj6d5v3+jmTt5xjakakDUw4uDx/j7sE8TgqR8D6QyKrgoKs57WFUTP6MugGOfPeLWZkwiO6wIN/xx05KdSjk6TMWRIiOamKOSRwxHUxCaSARLDqz1MKCuEcnFHW/+VB3lC6BsDY4AlryuW2jV4WFtl43nvzvzfyLHyrQZc7xOgm0RuEPGwe0bQj8++esGm0ZoJ9FwzgvhUI46gR/rE/GM+zchNqAZorWK2/tZXBy4Q/JDHJQx11NKxl+qM3XSweX0hkr17I0nRdCFz0CFGgw+2d33gtwckBxbbpKzplHLEys0lDiasVYJgFBicljVOMQoKUgUWm5UYHkIbdj33y+cqIeUj1fKLeSfeZVNcIVfUGzwhVOcd1/QUpdC63EHc6+DeOnVbtlC+IokTUtPVppv84iK5+MaWPu4fivPAIE5FKNs/EUeDMFh1FP2mSpocnkIDjr7RIWmAvPH2Gy/wvuDguqt+0y1sEFuPw8loMQJ4wFtkumaC4uPn4q1OBJUFH7w3djfJpd5KgHJWZNiFdhRlPHC/OKNu/EVVwp6bzV/422gWm/H3IVsc/E6qSM5ZiYpZYsXwjDH0inY4dNA4DsrlGOzq1zhI6B7JkTvnowuTjs0EEJtDbrUV9ZHMuRTGOarQtRAOKZAZW6rwSps9ZXOZVEyYS2ZUHOFojK56hskXeeSwYS+PzvL5FSePHD88yDETiFlAnjGEc4iHMy0V+13OnF/1YOf9gnR/8RvLx3iL7xlBmT3kJezae8jOIGT4x0ssZgjhKKFmKIWdopNV9AHtj6Dh5BSoJz+um/QDl8+Y863uZ3DweFYQ11ylnR9HkD8Pz6wefnb6ZAYQS7yyYTSzhOIM8qar3jdD+wrpZU746o27ZHl5GbN7rAzX9IzNaYO5fnMjpbrSTv5JYfYYRF+3ndS37lM7qkk7py8FCAzoEfb08BEB/DwOH4inVF4ShDYzlk7Fk5HxheJTC34DhSrpkW00W/+s2j2WGwwe5qBEry9H1Yb3hk+y0IX/YDm4YN/BxKApcnwk7+CaP5qCmvhIkT+oYxXygRIfFNWkEEr3UHjoD8riFN78vZhXiqHMPXt4pv9QtzOP9jlD6ahbMGVVgqfSjB9+JFAe22akDhGzWsqeOSvu6ily+led55l52BlBf+iF9VstP81Xwl7r+eSVHEN8uZClYT//oj2C3mhGkBxFOIJYHoaTmbHGYwUvnOels8/8zRGU5yjunusIku589EbOIKpkh5AqQn2id/BSAewQ51HUj12+1nkKLfjY7Rvfd/MSFkuIL04gMfaMP1XePSmNYOOlDSIhfJXG5v4Xh7F/pEHikTl8LNmai1SxyqYmwwpcXkwT6+cx8I88X0g/Xhh/p/HKm0Xry2AsDePLYTh+cAThFPLXxPTS+qmcP4/l7H7K0jCleWHdroFu1c+JVmq0cCdRmnEHTchnDErMCogvzxHUyj8Yu4UfLHXbTuky7sPYKQGR/vCpcGEYkivhNS+mF7qPiwn9VaVr/sqj5TeiLupcshc2F2pnC1Pc5InqAF77r9EuAi/ZxcknxVaYpI5OHAU8iM3poHjgRFajOjIAwDq6MVAbU0B0ZKAirBMrWeNQhAfWb9WEzA0WewTx1TBvGl1H0C/6apiWh73U4PrmjZxAmhX0+i03Z1/7y2G/eu0u+rA/0DiCPKju3naVcwXAYbScQSgDHRcAbgJI9zd5wQRxWW9KbAspX5sQu5qqQwbYDIoMvB0czaI0tqsgqnt5REIkJT2qlNAxB5hYWtKRPGnKZyAlqYEVx0mGfwZ2lckRhIOIARdn0NdaaoWX+5EGzKePMuWSdbc/apPon14812wgzQjS5+OZgvlCAzCD7CM7gbi51I8ZPeIYzSUL1TChbaE0FwP/VFPKOEw1ifrpW5xxFLhoygWYYEKYFrBqvZhtmAtJiYOVpS0Iko4y1Npz5zL47ZJ3TBmQ4iMY8cA+CsID7JZeKPfq7WT/ZnqX+DCr6nQyygU9OFetB0ngxUOgHcsaqHCHnVmn/5R+4TlR6JninCvFLe4ZAt4ZUj656FNKOWPXcW7SZZkRxJv5Lz1Q191CtazrvRfYSmdrXIrPhX8lN8LRaYWTQgt6KH1TfhDfFEF+FG/MPje5Ed8w38o+lx148FmkJIbxDX+QHwiL/oHyQ8ADCJwNuX47Bn94Opqx8tQu1dMcD2UZHnsP0KKjVNyFsIZQFRxlRwo65O4/85qD4VKZt/0s2cgSrjiDskQss3lw3NTZw72GZw/pAY19gTqTKLOANGtIjiAe3ryHkJxFwH/7nWViOIl4IPzNjiD4/M7sIMlFD+pq83DZQ2cOhMbJnersUYXq6kc6SyyIdf+ga/N3ngX0gxw1bAqd2UAs8/ISMTl/+LIODh07dchr6UI2fsbREwcRy8DYcLV7B/Fw5E2m9eGH7yTnW71t92wgKYFTqEvK525O+jMTmkpRwYzXtFteovFQCU2um23PtqChl/rvLX1Ox0ClHXM9EF2ZXvMi6z2Di1BYsAua79Fa3h54B+8e7QOKRcSIewjn0/CLohAAEt/ei4bH4Nnwd2iMtMObppI5189G+dh1DtrSR/qfO0KL3IfCIZvbxojKvWPOsNLVuPS74diOZ5Rcw9vg7rHqtz4/h0XvWlGHPppaRTfSiI/DJWp4NpAY8Nl3xhl/pfCdXk7LEeTnFmYBvX6tfYNwCL3zjCBWNTALCIcyG0Uzw5BxKKsVVFc9r0RHpX1/QXw4dbpMzG4TzwiKnnYISUkcShk1cCphu+gPLDIEonayTZxLOleVzrjLmJDZ+ODgCKLCduDwwlj2ss0557EO0YRIie0tU4Cc/5SkvaB3uuOG4NFecJ49zDA4SeN+4yfL2xGk2qtKGro8vjCmeZNovYx+7E/Ia08gloaxWTSbSGtGENta4BBiadgzOYLAY8zT7ZsD2kX35E+VAmSdiVLZY48g6jRLwzTe/jeCnplG6n+D+x2eWRqWivosWuLTiAeJ8mM1dyTjpfGdNIvwWcblZHyQnzi3vPEhjMIj56R4Dx5S6CyRBpo61V6G2IN657TYolZIdswUA6GMQHPwtiEPhwFOURCMpANAfmXXmHKCyixzpS3FRSGcJIi+YM0JW0XNWDChhTKnS+Xt4sYUZlj8cnf3MoLEUEioKorDZ7g56hCZzm90dBoymJiNdHYbJJeBxmjSU7F/0CgBeP1GQaNqtOdNHThM1+YLH8wKeqsN1hhM/zmfj//l59caZPG2ywnEsjFtGs2XPz5oNpDGWA3MEmAPexxBHkztbedt1czwscuCEUGDpAbVZQwUWYMoyk0ZxrLBZhRRyR5aL2rWutJvkMe6fpY8+bwZC0P7L24YJQ+njC90W9lY1TSViDYJXJYItpo0NBPzOeCU8esVNPHXeGcsjGmfMpbKM/3yN+n6Lzt02DzyKV8Tefa99wL6UcvCfvzxuWcG8dn4Z3ICPWc6pgdWnECaqqk6ElMh60nFFOIEUtpXUWSBQhn9xRiSS4x5cxMJkm8qUdXnQsrXkb52ZMSJv4ajBAjwMwQoFi00cW1c+Ki/8MBCX24eCKSpR6gLyzkJzGiDi3bFSx8Y2jAJ8eXo9i+Tj+BFEYiF/B/Diz2v/bUqopbLpn672JMPrQQT2wZDA4h8aYmpwt6S4BBc5tRG7PznH0rZdoAS2IhdjJBPa9nxrUL6gfur0yoT4Hsty/D050X1ZSZS99RttYHs0TajZLenMXeDjv3CIccWn3jcwSvNQ3htz3t4q1ERtgXOzvTbjXpLUkancA9Qmvof4cjc4lG2lU8ueOImtqgC33sBsQTk7fUN9Di2zOhhmMKhPzCT+lx+aHbiLXI0DYzrRmzhUWwzSs4TCdd/wOC1jmelbAss4DoOHgImNBk8gGmrlu+xJSATLPgZO+nCKPxd1zkejvgK6R964MpSDhw3WX7+O86dDZ4ZP+DK0SMHzweWhLHBNDOB9NAWhxBpOYA8YwhnkB7mxgFEjBPJewZJPrbg12sGl93ouvcE3Re4JirjuqZ7D2yBHfj1Qc33EMrj8GHD52/ZG0gPNY88uyczhNhAlY2gv5t9gbKnBs4iHD3MCFK5HEDdRJo9gHiBw9t1ZgRlKRjOH6V1QfwWHXS/gM78xuIZG9FZFXJ7W19wRue5oPuaSpnrFy7mc7pIGMGsqS/tSTBeksmrjIC84A2ioS5xijKY3ccTTYrNw1SABENuJOToXE/csh9aC+Kw50u2Ci+J4n4K70K2TLeqO4mltBjaaAuB2pvLmCx1A23xVorM6FKVPPuDe1931NwRHTQLfZFGzo6xBPwHEmgVzehXOHRbAZYlHqF4KleFTaHD0ZfCo/jtX8Toj/aGDWH6z4iyjZNGvFUwHc8YmRHE+c4+Qb/q+ePte5aC6XlFXw9jP6CXWg7GHkEv2SNIXw57q2cYPi2Pw5j9gXhmyZ5i6C17yxGUs01pP4MQx6njV7h6cU25ZxDhCFL/RPfq5ZUQtgDPOLQfP/RfI4zSjDcKtpXk2ivCfffuCBINTiCcQdia8xwhPEsQ+TCGIAtH/ycG0plFXn43NB4r3HYGCEvPA+tensExmlo/O4HqCNJ2EyrzmIQjSAgsYX2ml9VPtDTskZeGfe/PxXtpmBxBfMjmhT5y80Qxm0Q/1R5BOIi8NMzjrFWW2pGGzgnRDaj13WJZIcZWTL2+l+2+mM2i08Cubezw4FGmudpszOjTaQZ3dznj6eAzrAwBtgMAG2YGX+HkpxwaJ424cdhpjvScuwhYYeN0hqlDnFQU9+BmgCiyeY6I8mpZYqD6ER2qbOkduFP2BAWWLmlMC1F+52cYLMEbuo3tltwF3EmDOcy20kV/TWAg/+ckpfhks+oKkhUmKv/oaRbw0M9eVfHgpHKNhQpPAhciT6sWxIMlmzm+lYf9g74epqmW//jHL1/9rN332TD6Zw20b+UIevnqg/YJ+lUDqxxBH7JpNF76f7E0TD+86Aym/gLYpDPIoqPKZp8g6+CGjp7YKL0BPA2ojMnYTTgetFcdd/1Tkw5+oDDQfqMR7FuWmKm/tbL0MM692CYDbAcebIEU0ztFPiExcvJrD0qbiI9D9MDK+QHMoNplYF/JCXTMBmJWUPYIwsH9zJuwydnDzJ/n2iCavYEUv9ByMAZXBtlneN3ZZFI3kHEEaQ0vI3UUWfHXcyHBERTdPZw6nfphI2ora2MvHdJ/gCmEyLUl2wBuAgmsGYsM1Q3+IE+UC+RiLiGFAGsVDhrJ4F9Catk9s1QRAboXxzejgpHfcVxOPW8FWaRxy8SUK+Py+4dK+AxcjPeA7PKGS+pyyy+QygNRv0E73eMfYKV8Ci088uVwVqfycvYt5kWG8BSKvwNvkdNDomiOh/ydA2eMqQUEDp1j2ct4iv9OjqBUOrbALraN7aKDQvPJ/XtH23dYlO8Ou8t9EOnOV9zyoIQ0Y8O04l1WAQpLyLenx5V7+B1St/JFHw1ylGQGkFOg5Ay7yg7tiUgZQW9Jr0jJ39zjjN6iP11vPLgdTKsV14v1NlnAnpVV23kjD+11ABhFQ6ejKrhsKxKoHKj4MCXVcdTgYKxjdWtMQdu/vCmjfjy/+GFL1yE9u/kN/O/AdT3y0g49yOHU8aavOH5078Bbej4d//u8uc8m0XH64Pj5leViOIL8UKeZRXpgihNI8cwO6n0O13h0QC87l1XH6LaqYyPQN6nr7gjCCbTvmcdLJWbrsJdPvubFfkA/fPW9HnQyU4hlYLom29kjB5A+m4zzx7OHeFEjh5DLdJ1mViMOoW90/WZJNw9u+RoZb915YYYuvMiKnm6TNjp3b1RIP/5cVjx0VjrZ1EmIZkId7RWd0nIG1zMUsFGAczznAGIjRJcnqYTIIG0qDgvUhOJFHyoYGr8ogN2HgbpkEP9idI/FPdgun/IH81vBYYxRbiszZPLiF5sNmvmn3QZicbCLXOh0npK/EwqennwH4z8BQspxV+bnVPc55q/rRF5VBU+/Ucp1WPkBCqP1r8nah3xuoq5RD3x3tDJVsd7ZrvMY0cwuxInM7D+cQSwrfafZQK+1J9BbPbMw++elZgS9ZUbQSy0Ne/3KKxu6NOwdDmZmBLke9EGeD3TzjYJyAuGwyUMHe5yiF0vBxhEkbPRnJhD0Hp8xiRusdMRc72LDjOFTP/Pj3OSXF9RxBDFbZvA1pi1nEOe7DcXdkRU+DCoZy2rmi7rDG2wpmuuMYvEINX1PKY0viV071HXeMNr4q9+UV2w8tq0Qnj4jz1jFmMUSMPYHYh8gln095wX17BHE3kG7I8gzgoT7SGPnVF/jFPKX9pIX+U7oYG1RmDoYqHjqQ4wj6MWXNSOIWqaqtpIqafsMyDbAZLXZJIpm6pIvPkCHwIhNh5uP5VdQ5QF3ejEVqwM56w0BqcvA20Gx8u1YAVMWvRfasElEww65BKYDLm4tsOjiHVqsYkREX3Q0IvGOiW6Db3nRK5AWQNJuOTBHOuzC4Yucsqv+GxpFG4oxq9bJrlWq7MSroijixE1RhNTWiYdI0aScoE1cdyIkw5NoBircAMioebjZaF6pDBZCxuPuwVKzgZhu+UrLxH7+pz4fLy/7z/98rY3Y9DnG15oxpEH3lWYFvX2vmzg5guQ78nRND4B2BDGwsUwMT3icQnEEAe8vOltfYTlGR2GkdsLDQy80uwwUk3drkVYIrmJXhpoo0J768YatacDGhv8Y25EOpjG90YWX/jAiTBntwBzHSojC0xRwBxgcYpwx3iOItAfWDLLhoS+G6a1f9wfi7eBzDaLP+CqYHEE/ejkYS8L4dLycQHIGxQnE0jHdiPJGUebV+Oo3iJZrnVBBCWTrpptLGPIcgKtSWD9vBGTLvnmk/7jCPRdCUlhYqx7gXQJlpSKd/DkOc2hLD0XShaDeOQijhSpwuwkAyG/RjKxc/m3l0KslxOs8ToE2iEIKjwhEBrxdXuXLe1cqiBHBESYczCa8gHw0lH/RzWMoxL969DylTxXFGk7GaiF6+BS/uLsOCza4zVOdFcxICNc6grDjmaDKL+otsSMH74pdjCucsyW2TIRK1N94yvxdHEEYk3F7BRnMNjvM46Laccer/RbsXgJCG3UrJH/DMOWx/5TfwXPfK78L74wzB+OOvevcXPyKMwAa36ALQ6skWPsr+Rs8AdSPPWYLb3qQKR+q47Xbm+WODKC6Eg/XSJmxI0Q5h8WwdY3gHJGzgvmZ2QKRiBjOhiB75iwF8OTBwDXzaAU0QY1QebFtCxSbDQfRmjx8V52dlTQBdtTFIegrCwt+BJrBTZGs4ehB9wWO48dXQb0PwUnDCyf288nePsl76ZjuO3AKsYlr9vJgQ2ktJ/OSsewPxPKOOIK0x4ccQrKDnV8AAEAASURBVDiK4AUsS0b4rHx14uVWlEDX2E3x6Nko9Y3jB1jeqMsho4ceL5mWTbwkVU9DLG9gRtC3cvJ0KZiXes0yMJaFMbuHa3mWiCmtF1E4eLz3jx6iPPsHBxAwyUDONywJk+W5XfEti/RIW3D9bnAtpD8VBJY8eASOJJvLebdKSCio1LRJu+8OPXLSfwQFTXjhpbQSSUN87Wf7QKVSaMGCQYPvNUO7QCP30IcSiJEUacVd8ZnFAht9E3cUKFVWjcvjHn5xYFC8pokbTngg6lejFYdY9Y4CajWS5KCdNInjmi5g/hfeQjMknEgecGx/YjiYicDbVT0V3s0UO73OnNHJJxV3kZK3hAfX/c9JHfyAL1olW9+ICS4wagw/s5Ex3OemL8CrcNMpE4cLY0c4efmogJzvvLRmrzHvDyTHzzueSewIeqVlYuxxKkfQKzmCtNcpzzSME6x2YGkpY02eJeoIQm/qrTtmP2PM/qaqTBxBfD6eeiXvsUwsMsZQv9B6FYQNQF2mPuT7UxInEB2B2c04gBhDDkeQRkvpmWdtpW177D4GECes2GjZX/zYK3ZMaT3DA3ujOQE6xvnQg4tTprBoSx5HkAZst+c4gpT2+OQZQdr/54mWgrGCAUeQ0s/1ovqxXlLzDEOaJWJP9HDC5+SzR9A4giQ/Y6zETMA6CdHLeiqZKrvHOOP+owPbaDz/Yh1BtcWduIN9OhPGGtPVbjc0Gw5loD+IuxEvPCG7R52J5vRVkbrMdEw6j7GO1hxhdMpFcVHhzNd90adZdB3WS7ET60KXrktcSk4Xkl0OaX6csIRybRyo4YCo/0mR4vXkHvwNzSYrG8WVSErW0DH6JB32FjW4LobfjHpWQSdxqIZxM8KDluD+AXxTgCzqO2Y6o1KFHReothE3ZMIQAd72Dx8+eGr2OzmD2In/l5eaCfTL66/+ycZrmiH0Em/7ay0bk5PoLVO532lGELOCpPcf3vuHTy0ywDLgsUkYmo4zqAOmY+D51dQePEfvtJV09N0RVgO9vEJJvn0xPKglNNQtvE0H7Z1QWoxVvOoSaogyEO8tnzSyEo4ylGfg5cdeQOA0H097BlltwiYnjK4B9rj/oLeIz9mNXxtDP5fThy+GPdfvhb4Shof9qeJnbMbG/kDMCNLNpHxBdgRRzerucwkVWF+sNvXMoLS8FZVZZBcuBmS5GJFP2gjknZjDZNyDTwVFKrDx9LkWr3g/7w7cFE/bLnNey8FahdaXdisWJalDuFED2hDYat9kwBykgz7nz/D3+GGOZXbEiweg8jmKl47DyiUnmhZcaJ2lB6UcazgleCAP0EkAJcXdNdnTsLeIsml+kFbftdGulBuRiyYPwxaZ+ZWOfBB2tKZ37KZpK84U3sgzBq4xCwSV/R0dQbuZayfiG5PvhR9LP8SwDXOnvEX32LpsU2YnX/3qHiE1oL85bAxWp2rRFa80lG/p03kmmtxUDJMND8iII7qUBH8DGnXnTdp0Qcq52rGEGm/E4Xb3OCqYU/Upojn4GgK/wRSQcWrd0yud+4SWHxxNMmqgLm+we/N/qvwIdJUg9/l/8FmplRCO0qCtplOac7YBsSPatqjzB3xmBFEt7x+kisQplJlA/uKYHvD0DOTl6TyseaPpcQTxSXl/gUz3JywVY+YQeWYE8YCHAwmnELI9K0iychuVOLaKZtXP1VLl/cetCvckyvOqKTOCNBbp3oP9e77xixfN8mGmjy7afB7+EY4fw2cPoC4Fczk4chz5QY+HPfHCEaQLLrOL/ACotB+KkOmXQhaPidd1mbT1xMpS/Ohf7RlTGyEFN9i06ZFXXwENYCuv0sFUQcMBAd85SBbNSrgQDtUH3Ize5XXEfg5wf5Zlh595jwYLc927p/TQdWEciUE5AB9J/RncKxtot2qfig2fQuMpTXyiqYVCea7/zvosZG+qE7uTAs1Ae6anBKfFcd4X92Pxfo8GA3hwaHyuy3QCISKHiAPR9DfndAgL58xOB4GMb/7ChzT9LDzKC0cQ53HKGBvysjobzfO1QTmCtAzsLfsE8UzC0rDuESSnEI6g11oa9p7N6f/QSgc7gnAgowHnO88lbGVB3ckfjiDyTBKwI0grGDQ0KWQMZhxm3GMVl+mgLf222sH1pF+bP7jUFTkzxigdR1DqzTiVr4bxPJZnsszuNyfRT9wstgvbeaxDDxVKJrblOcCoPv+QjmhBUMk/xhH9/ExAu5Bn6wpVTPTffCNH0ODhNGJp2BNtAv1EzyqP5fTByfNCaZ5V2ETa+wXpxTVLw556aZg2i9bMIc8IQjVZTaOpZZMjoFPCMaa1vYG7Hh7bqU9mBH1hjiCaCCu7ttT5JsRIOk777mZLC9+Q/BsAd5kRYaVOvOgke6Aj0encaFF0Lxaf1K9FZ+ojl7cHyg9iO0HpzkyVa0FZ+ERThtiBePgVx7qTcW9UXFwILuk9S7FDgY0twSUVEXsMuiIGigPLGR1CXxpDlQGKuU5w23YgW3RoAN0xiCK/9MTYMT9Odcq2tiqymekSPjdRbLr4HkeQBkzewL19/+6rn9kbSGttf9aaW74ixkbSL+Vlf62B9y2fkdfNmNA0Kwj+OJ3kTWddLQOiB0AJ8YCL7esYIp22WNMnGbysZ/TOwAotSjJ4JK0hVHlArr3kQHUJwk0VwYmdLhjOUkYIizFY8wKmPFpVO8cViUrSunzc6h50fZkQX8XOa3D1xtDMKNJsIG0QzQ0hzhyWebGxGkvCWGvLRtFsEP1MDqAX/loYgyyfamSnfm2+poGYG0veKH7PlM1DOI2cfiSZuYBwpTr0U6sIAdsE5msSdRkeixXGIxTQPPQLeEIg4wAGv3OYNjMtTPkVK0I8XJhoLztzSS50oaJXp613TGOUvQpyc3IAQntQHHmlTv3pKFnYtcVVSBGWGNFCvvi14MJzGrD9mB5LrWinUsAasYsVbOdnGSDcCZV02DZIC+7sSNmZA18Co0sod43ACdTKOF0AcNfCCIVeqIc4EfXXMytnuh9ylyNIcOi/+xvtEYQ9VzfbjIYdyNaetlwzG57hDx0+F+8O/ZX0mg9JoFXrDpszaPWzM/icEzcYnvroPekXvNIQ78FoOsBvor246bSB+uNJ7sGMFFo49gW8kOHQLPEWjmzkb0Xm13zECtuJUOUBj3OkWFs811BDKsRmqpaDO2WOdCA2muLV7wa1hQsuxOKiV9QIwwV3lvuB4PpcplA/HqJwCmWpmGI9/Hhpui6R/iS0ZwXJ2aP7Cj4tj8PHn5CX88dLQ3AECWbHEA4gZgfIGcQ9DAIRkzf2yNfPYKAK1mHSqhAp6uWlGtxP6ILoDaIF9GwdxWzobIeQnD8s9zo+Hy84ef3yZbDsA+TlX7pAexaQHUHiP04g3ocx3K+vfEm+Jgf5xVUuA3OtBgd9bUfFGBJtySuNzgngT30KEoTi/HKkz5wDOBfY4JygZIZ9LHvmQqHvy0DbO6SZ7HpVZ86l8IickWYY6fntpFeRe37IrWPTezlp4PB7qBychl1u8Uu7lxV/xRQOAo1zrqRKzsSL9Q4ucMNdtjUMhJ1gCZ8E/f0KO/L3nULoOoIX+/SM8ApPp9W+N+zdEYUPrdmsxCF4GgAePh+Jp5TeS7DJkkrGafAZKzifw97LwXAG+ce48LufR97JEcTHbbxHkJxB7zQj6JVnBOnz8YrZG4jxhC+GsdeYhgw7eXQHrhNSs1V4pvDNMI4h7lWJ6au81CbmuWZi9BG9/jV7RzA/j1APNpgmhj71ynmn9BjI5UrTrp0RtL4cJjQ/h7E8TBU/HEFJi6mChDsay5lvpLmv0JaIxqGEnmjJsx0ZBVcRLakufzwH+EchPKnV5gjSMwvv4Vm5AB4zGnkZjSOIvX+yNEyzgLSf6VNWNOjXbSz4+rG/GvYRRxCqNmDJ5qW9q4rW1p06ANOPD4Z8gY6gmkExjUqDNQbkYh2nwTfsJLHNHiAobE/vOKT3spUeQvINmy4wLusWVxgdiwYL+jBQx0k4GFYUjU6pS/YLiGCGH4gVMbzuRO38FnfIOhGanxCsIDw2vAUD7IqciiNR+Cc86psSHzd2hXJKnXSYnFVRmhNxsUC1G/RVesg6QGDvtZB6Bz+si375Be7BBRoJ4pSzHiM0gw6DrqZb6kbLHnQNmG80wP4sz/orzQjCEfRKDqBXDLIadJkV9EZfDnuvWUFvtQHbe80K+l1Pch48NRh6YvN4x60pI4oGyzXozoDp0x9d0cgDLmkC+IKh8MSh9RBm0GoT18OHEE8SLiaHnvTETpdmGtIyueIs20kjlYUyrUmZB04YCBXeh9VJC88XTBwwDMDJf/X1ry6DVla2A4f1tnjYWeaFx/yFvhLGEjA+G/9cewM912cZ2Y0fmKdaPtJO/BpcH8kRpHvPr76X8G/lCIoOKKQQ9a1D7oAzgNMRirf0F3phVJI0piBewWkKF0TJ1LhxSjYElWMtIFgvMVikitcYCPw2TM5nV0RlUmZdxARuuuVwReFq5LwOmGFYFOS4dSRzaHCWbV2MCBJCTXr/UIaNwbqH3/KjauE3SpQkF3MVWWysEsTLkXIR2VS3RTcq6IXzKSAWlYI4hTC8F4x3KQN2DYbtBeh/G8zuAjae5LffQPiHzpWlkvIss/i7bBZ9NdzqPjIeNtmtfDJljdt4L7SRB9Dyxjvenm55Y5UtNhvMCq2CnQHKCrENuad3NBiY3z0mF0GMoaDt4I3XAg+/iESPAymj0uRLMPjR4EBexUrY9mJ4jEMHz6ZaxSW3BYoNIwa2REQiIMJetUPeFIjI2HPie+SzbYfZsCoPM+SwdF+Q24Rw0K8KDKtbvIHYBsL3JbMwxzAKwPyUhJd/OkBnRw0wnfA8lMUZlLxn++Ds8RKQOILyVTGWhGnWsh7+MhNIjzl2AukNP44g8faDlGRXRmcCYSLrS0w56ikR/bAif7rfEMBLwpRmyRZOaS/j8vKuzPDBGeTPxHspGB9rwDmUmUJZ+qW3/Lqu4+zJPkBiK9b5EpjuaiLGsjETYz56JM21KOHkGIrGKO1fcQZ1ItdK9K7JwMx11bf44CAx11clRYoO7m9FmhiuLYv8C4KzI3vG7ODD9B5u+EUphBYnOp0uaoCswMRFvcaVU/xrefMtb1z4Nf5Y+V620qsSByeXHfAjVZRAatvGd20mXhWVGFrGAsUXxuUDHiHFFyTBe6+RPkB5KaBSEMhUxB5juB6f8UwrVGLLvfCgn5kH7HBO+E9yzIc7ZMLIVWSdJg8daDiCiPl5k3k7gcZJrBlBvJhmWRgfuOFT8t4jyE4hfUFMW1rgCHo7jiBWObDk1LMHdVJ+7RfUOIJw/Og3zyicsJyJOHlw5uSZRve1owfOIfudVQHvh2qd87xy5NG5NoXXnG/E+vtGzuLMCMy4Yvtho90RJKrcxyt2GAUmZ+7YXvlIipzS4GA/HLQZWxiLPNQRicjPA2aADNxdPLvIGSSDs1fdtzqnmRmEfmy5yifjn7I0TI6gx/oq2I99RuFFtvYL+nGWhj3WvmlPn2qz6DqCrCAWlb7ooGBQkqikgA6CSjZ/BK4VdmbZFnkB/uxLWRrWHpWqur6nw24gG4ZWuIaHiK94n50fhkvWWUAb5oYdN2bTaCmTrlYX+B29T7jCocPdcgirjx2tHpT3ZEBohAc4lKZx0SZ/BS8Z54Il4Qy25Ku9Fq5FQTCQNVioJmekA4dUyxqbT2t/ViAo0kAJrOtYafv0p33bzNETpwXnGxs3yhHkzRnZiE1LwTQLqN51O4G0R9BLD7paOqZPyb9jne4HlpThqdcw4kESZ8AMpsqvWxMPvBpNGA6sQEal6DlDgfDRP552Kil6Vc950yTvGntEUcr/hsROJGHiw1a+4VEaHYJmO4yhuOCRzGy1Xq5mIJ1GQkQ0RR8Lk7hx/ngwJc0UywysGXBZc/t7ZvNoORj7DvzAdEst92LDNaZW/qR9gfCw411/Ol8Le/aDplfKCcRsIDat/FZOpO8lk+tWbhipDfpKHKpwh03dFdMS6EreOgspsYGrbBIGGr8dZEcOyRxdsKVJmnJJnMIFb/kRHxhNmcOYc8EEzE1IIQdC1WxJY9uhmQfiaHspNGv67DXcQq4Yzh+q3S1ewI1dSACkbUaFyS2KSVA658jG44rVPCjpxwdyU4nFzwJ9KNmdeCtfRi9MnMp0UWZkWdmPJOCS8y+q0I/Tg5UYEX9nR9DVdDemviLs+bHfDvpPpT/FevWvBwX+qZqcuER2jve5nLVzToj3cU+s/3LmLPEOGwvfdL5RZjtnhLZhTmYItnuG42Fjk/eAIoDXqbuhO3mjC8g70sH0GFurb8ajYuczyWdZpfbDlGrmhxQRZPNV8qqiCj3DR2/v/Yl4PRQRszE0s4Ps+PEsITmBBOdLRtlviGutdOGHzk1LKBqiL7H/iSh35UDm+q0f9ynEvqDq0VBpP6gpzwxc9gaqo4flYf7qly7A+Sw8Th8e6ISrFzuZQSR6sQ8P3/E4zTIwFLFpdaA9nAaWhB+KJhnclem9yEEjhEvo3RZEi1B1PtKq5VamNCr9qXAlaL6xmDGIfyRYm00n6+r8oedHyD+/aGdXlXbY53P6CGYZ7yhn2Dm3491JP6jfmcs5d+GjPk47n0y8oTwowjgqFfPeczkm76ty7tOLRuz76Om8Z77JrXPQ56UpfYj+M3YINffjBwfGiuUIEgVjQJeFETO7542+FsbysN0R9M4vquMUYjsL9gj6VRvKnxxBWEezgb7iAzfM5vGziWAT+3mDGUHYUFtdcOZ5jFKeMcvjGGOWX26jM7aWbYSbemG/wF2mcrcHR+F5U3o5mDszyIOFBPD1QzPgWYIE9/IMYA6CuF0nO1Gk6Kg2ADPtJXop2TaEqfFoPo1BpP11ROVN5fOV2h6OIFYxfOfZQIKLgOcOHEFP5PB5wtfD9LKaj9k81yqGp4LxzLIcQXLWPH1WRxArQRgDCYyrTqyD9bLmC+QEult/2QVbYAZWQnzBjiBMkQahIZMzSJmYybkWuLWn/N+OYLaFythA7mB0SHRZnRIE0Rp/5yHAPR7Dj04HdlEOj2UQCjfCznbojwhM/T6Ko8IyXHgFNB6OtTN4Nxex4iomObzKsoNtsYCnbD+OnJtIVELb2N4X70Enqp25MrwIHjZb8/DmDWGyuOLQTAwBYYRmgOdmTHv9yBHEtOtfdaP1XgMsTqCXWmf7iqVhdgBpaZg2imaz6Nf6gtgbnEF6S8fG0azxF2ne8lmmBlALth/YAhl0cS17rSxDgxWnV6BnnEeoljwpKTl18y1O28lgBlUqQSZ2sLg5ZBAcHOODNsNR6RAhacadgadvEjEcmhGsofrFSLPagU+ZMtgxs6uYXslA3EFVXwfzPgAaWDWLhxk9fFLxsX7M8GFTNZaF/ahB9ZmcQAyufCY+g6sGXmYC4QwSPhtRsmEaN5SeVm6Noheqps66jKE7FxP35bPeqdPQTP3QPpUCrlpyF9tgWyVTK28ci6X4GOHVXSeUzzWmGFjkZVQIiTFjdgMO8QXmonALrwzsEF53jxEb3lcE6FSeIWD4IcgMJ3+luebvyoZ2K9hYWaRkYAMeSGwLRJpvEYfWuqhslV+FH/lSxhFUeFqQXMsjSfxHRDFPMcjYYNkCBgeHhXsB7e1aHLNqRjE4Fl0ViIWUczKIf3dH0G4zm90G24xIckdq0T28ln1ODD1874SPsT5I7jG4B7sj4COgyo6c8it0J2wZfSqj1l56VO7AO5crV4P7Ql+8O7KER09m7LjX72NHlbuddMggs4nbqJYYkGdMsHzll2jgqcH11NyYBh82DaQXjwHu5Tte0xtBxtYwSFp67PSqoOsIreDBCSOuC5zXwOBAer1hF8COIF03f9dSc15MefaPHvr8tTHlvVGsZgLhFPLMIj0p8sl6bjpivShCW/sZCik8EKEH8vxkGbuhET2CMbdprmFZFibVdQ3kdoH9ffjkO7ODeHCzI0jpOIb0cDLOH+/94z2AWPbNNTp2gHtnBNHmkRaJZHbplHPp3bvGMYuXGoCf2JntED6SaRjHpByLBKp7ZcAWx5Uwk9OBNnVbotwNHoALcK/EidPosXcap9EkGl7Q/3p2ZzfqIWoHn5i34FKVEw6Z4pGOUUiQO4cxmqtnvBbD4B7+gH0CncvDamDKHOwOvGvTjPgIlchd7bMGe8mhWq7B9DgeQJADnn4TmbGE5D6cnHCszs6Pc9yYPmedmjxnQ9F7Lw8lMMYKnfIWS5rlXV4eOs7gX+0IYo+gOILeeEaQloZphtBLNo5+oxlB4wj6oAcTHEFZGgZ36sAmxjyL8KVjnkX46eYco5FWo/3uVQ1xCFEHnDtLJ6VxEuWMg4a6sDWG2ItfYmpz+Y29GD/4KqHHGUhEkOcHGJBGYO0OAmlFsLOMJCkBFPnoFBrGOtoNNowpDlSRTiIdgOGUcQu449QRxHOLeHhGkF7p4wwSBEeQPw0vp8/TcQTxzMJL7D6rZGmYZgt5s2i9vNazDc8tPK/YTrLZ1REkFbYwFRMk9Y9NqAT1YOntUz0D/TeC7pMR8T8Y0qPcGBWdduJ4UcWdUvAL2Pk76Hdr8Um8MhfidBJbHWbQKlRPMN1w6EXjgH9SDh6m0KEXpQMj7CqvfNVJUnBGPNBgmACeRU73GT1avOLiFeCOXiGNW0hcmGInd+Fb2UZijBZd4JyCDUkFkeO5RJADYBJwikdMcA8Fb3BLcqp+iUwR1PZsYqfLvIzFsLpyY/VrHUF43OXoef1WS8Lk7HmlL4a9lqf9FzmF2B/opTzxzAh6zdfD2CtIM4N+1c0bjiCmcHqZmGphrzgDJgOr8hr6FDEAK9ZJbW88eMDRcWhaPw1HAkpZ/WcomYvOdBi82gzaazAGXQOh+yv8TBoakxgXpsPH+EiTFbhhzN2jskhGYgIa9obMIgVeMWnoZT9ZQKRs0oYOMoY2qdNdqnD/8JIwnDe8TXz8SN5yOYHYff8Ja2v1Y0B9wWwgedqZemlnkDaJfqKB9AdmAzENnc/R+gZTFsOUUe+4cVRVrKs0aB0o9GlqnalTfiaFgetaRtQLoH7DvHlqlzAFRmiaEik0IZeO5oiLR0vZWlthyjhWBulSAD2HPb+nz1jhQDnyFO6gusT1nUI6TINA6GpFSktx0+BB6xPLGQ4pN7wZ4gshWcLgrXor737U8iCNmAoWDmJd9tAhpbY2+m36pAWGbulebrvgwh6QASo/o3EY2o3FAW0NN5JhC05Y6CjadQoaSJmcp3Iesz78Sw9uDswwdh0TTPun9pt5b405dMYsMZkdfi9vgjt4n4K3vPFVTuHX+M/gQbvX5cqr+cXzE8gL74awgMQ0wo67p3fME1yy5xw+gcEH4Iow/hJ07MDskk1vJZMLptE9NpHnB8JWZoSAnWzR8BmKYq14wYtvAJIH4Kh5nYkkrRkj5zAHRJ0nLitndcpC4zIBiKHjviBxyinAlcND1m868A4FR5CdPfN5ee8lpHJgOIzMFzo2RVVsjqNvdbA8FSaGp+TxEyAwUZGgvgPjHsUPZwKxHMIzffQQ5Lf4uujyQOR9frj+8uVOnEKqf25pUoYjqN0ns4EtAUGTsFBlGgMmrUfJKg+uAlk/vDkd/AsKaMbrcagGnjqiz6rrYN/rQ/BGSmMz2Q9zL20djOSWHKoN0Qijr9ORP0llVmrSylvJjcdDSUjD+iGMwIWXOp/REPPR8Ln87zGp8VZZ+h9Zrr38R6ePV6A6FuuqUrkaXiRkLP5LAbcleISinvFaavWM02c+09w1IiWh4x4x94niXsZKVhbkzOA7sTHe4MCGPL8JnOd8NREav1LdloX5q2Haz5QvGL+Vg4c9gnZHUF5gszRMG0lrz1NebuMwwhHkcUQOnq/9QhqngtJyyPgZBUcQ97GczOqf3McyWtshRB2U9hjC+OUfuOis2LbIjKA8z0T3wF1B4cD2GEvYIygzhCjAYSYJ2AnL8e98WhptTkHl5xDb5dkH24WPeajIfW/UZTzBqU01qaGvQ8jysjCsDYylYXUEaVySk9vPJewPpD1Lnz7XjCA5hXhm4RmGfU7XHkFyBD15KkeQVjs80rPOyRF0mMIWi9atCdZGI1de9VdKemEb+sGX6Qhq3a8xHUuVzk3CWI0IWMOebhkx4V4Z8AfxRGCZxCBOAOZBP3l3sOItHDWc0NagYfrVlMI6GB4piEdJGlh/57Iy32IQTKJE41Us2M7A5TosmoWoxIYLHsoTdp6GcdKVATT6GcfYOdwBUQBa6xSS6dih0tGnpHMuH76xY5CQzK8qcBK4KcBF7tCA8mAQHoMW4YQPPUDFkZk2oA09wGoaJevu2VzNAywzg5gBpD2C/NWwV2wYLeeQPfEMxmwsrenbTOP+VbOKFHszSKZWsgOshPn0ZuDhN7OC8Ib7TsuVPJaSgYu+/BjKGJB1THDbyH6K/Sf2VMKOJeEhyXVlMIUIfOMSDR0wy1TkIBo7gZAFfQaeYe0hPugMnlKZhoCtaMlHOWiR+ZtvTJ3m8vXHr0LUACuaH5jFo+sOs4GY5cP6WX7PtBk0G0N7s2ivudV+QNqUzetxtTfQD8L9Qc6jH5gNxE1nHUGjAyqggy/EqkesdcQUGkdolLn61DGKK1ba9Zh6cXUg7HYyg3JJcS5+TVPW8mqQMtpJnHVMDBRMYEcobXQNfvEO/a9Ut/mD486/8tMpDhyq6H5ygGxH6ycidE64i3gQL0bgbcycLA8zXOx2vBPJxgLKU9mwpqsXfugIJLI4ox0Uud/nZFil7isg8FKiwcJMsDNPKfStY2NKIi7xxspEm47DxOhBszCDS+aLPhABDONcDKEdQX+HPYJc/Rmzp+rL7M7LbAw/myGLdht/Lh6UR3Oc01euxSMmtIFIt2xPfwoPXMKOVz6Ng5HjZ+FJqWsftS1EfD3ZT7wpH12u9TrhbZnibbq21xrEgcpJrsc/Nd4xClJUBuCV0skxrRlMdeAgal/nNpOJxV7dU5vANixSt7A+44OjAFpYS8eovGLKD+1I7QgQb3kYERqTmDS19QMe5a56CuDI+c/sIOCZJUQ+ziEvJROch5s8iEUHmtPX6yUD3WdEhJVlJHZS/P9gFpGv99gSuYOnGHk4nLkEWjPFvt7KOwNfXr5kWUdshFMI3Cwly/0ATiSBHOBhJw46VRkzRpvIJuZnMPiTxqSGNd5wwh86AljGdC6HMwyLVHzxM47scJWIzHiQD3vDYEp+xLi44l0QexgEg+JJSLNFh19IlGiahDsfgDuheI3voBjU8sYAlT7qHkKKHZpoXCUpBEZ+L9vT4BCKd6Ut/BQfSGYtYxhiIwlRMf2MfnkIVipFggnezA5Dj0soGvplmFHCwRIPbBAdFkPl0GEuRELnbLOiwg3f6OH7CNghY5XBTPVQe7pJyak+3psHXIKZJHZdTa9ejQqA9ePWBDMQ88vSMJaJsg0Fn4/Xs4eeQfzVML+U1iwg7Qv0jucVr2KQI0gOIhxFH1gapjQfw2F2IWebl4bhiOF5xA4gYnTg7OqyMNonDiEqw7jE+NtYo0W0FY3PMdFlfA6e1E75GCIWlUTVn32C7ATSoOIX2qosNretkcoYxb254NjDvNom5ltgMu43gBACHzuCQg8D9Gv1XCs/R0RrdxDReGkYL7CRZicQH7dBvmoq/K5UwBH0zI4gbWWh/U35ahifj3/xo+A4f5gRpD2EHunZ5tEj1e+BGUHUi19CrIVstKLevlZQIRXRT3CwP/3S9ghy7XpC2BKYpMYAoLytNHGLgNHY9wJlxftY+QlvIwC+dBKctDvjLVsXWRwPuiqHDeg+HDzdqMab4l1BEw2uaTfEOXnguEYU0uCV/dKVgks44amsNBe01JFyEbhSRSQeGHpeZBkLGVvg1N1O2xuRV1uYhw8bE5Li64v9lNVMjm9RLQdVrqzIl8YxSCedM+wA46TDAcTnWL0uX1/qeCPnz9v32pHfXnfNBNLysHw1bFsihjf+rQZm0f7KfkH+wod4Sbhv2vDqS4A98NRKdvzm6x+UZ4CIRz527+CLzgykxAyF0pH0rrgHXqoC31SotsUI0CkKBbiS6YsWMOeVUKgMEgzCaNqlYTaVDgzSND1ikOFBTQMbwTd31k0HZgLp7eQfDKQMxPauwzOedcYvZgMxVdJOHi//kiNIMetsn+trYV4WphlBj9hsjR36+Vw8s4A0/TwbU7IXQSyBfgT3kyQnbWtbbq2GNXIjOnp7AEm6FwFXEjwqK2r/uHKQUjZSh6blLuVQLaIVWMFfCJNIObxta0MLK+8lfWgCT4um7OB60CCRX44HBpKgAp4boq3MotVWvCFS2tx0sA2cgbL6ic5Mht5piAZxsR1Gi2ELpgYq3jgiTCzCw1bh5rkke2x+KjF7OATvxMvQlEFK6rbOgkqe68gd1t0gHLOh3Ik7WOhCGWHSmy324TtY1TdtEbrWwipZFCywB3r7nkaI3/tmqX2slF9ebPOp3gSbFltMejOtW8RIRtRhaE5NVVgR7zWjmRfhT8b3+O0sWt54L2uaMkL1uOqc0nTBP4tn2jK8KNF+a6O2rMpUqOK9qOmteCX3MqXdf1VY6cXzKTzQpiElcHYsNiIMbUvBgLFGCBG2Lxh/F7KnSwrMiOUJryPcmGIkHxiTGn55WBFMfNGj9E1Yv5JYV2oWxXKMMlwZj5AxwZh+AEoJzwDsO4gc/3gmIK1i4mvgcsW4w7V64QweQx33IwwqLGN3ObL4KZM2w77C0QEHDnXhq1++xRi+yGDWDsu9W8Y9CHMBfI8gGhxGK8AblgZY6hQl3b4CkGu05ZOOhsa1fGDI9V85EhPC/YgLG+yiDR7Q3O9SbzO4OVgPlVFM+hYvhL6OqJA7tfTNYViVzPmoF3wifxc5+qAXSWQOmx3rlIb/p3Ag+BQe5YQdb0+n9DjeK6se5eUKHCRJbUjDA6udbLZIwui2DcrjXPWdR9NVZbFUomXA0qZ3FaVU7c3JosD5h6HdAYYrkX7RT63p9JSZ6Dhw3qFtnK+H/mZAydDKEuFn7IhDpE9Zxd0f6Fc9X+AUiiNIM37YJ8hbVWj7Cm1hEUeQPnKjL4hlRpBmAumEfyecD3IEsRG9tfhmPh0vJ5BnBPnZZBw7PpvRWs8kujdEN49XIvTm0R6TqC/4Otl94jP4KA8OdYBubAY9FeWpgHovR9Dc18QR5CcdlSvY9hbmMcEgP1NQBo89+GwyIHKhEy/Xk3tb+hhS9ScV/VO+Y5UJcfqYP88unRGEEygbRtMKzIKMIyiOn6fMANKL6xc/5qthPzI7yJtFxxH0+LG2v9Dzyw9yBHlrjLHBPjZiQX4E4rTM1C+Vkf3VA6iS4swI0oPUfyFo7L2x7H9BzMaSC09qRtOQcqhBNkxZR1CfbYLuiHOOLtwSF2cV3EnE4lvBRlQ+lDqdsgctxGhtfsPDNAYM/yN9dFeKhO9/6HyaDD6RaKjztY4bRpLw/jdDbWs28OM3ddlZg3cBO3tSgVadQfM+l+F4Iho7pKglxE5zkCDbvzGogrdN1gXTBBSeA3joSuzqbnhZ2tdyButsyih/jgfb95p6ydraN5pSyVKwbBiNM0j7BHmGUGYKsWnbO+0T9EFOJGYSedAWj07nZhD0Rmz2wDMgafNFlohpiozh2M1eeQ2WdhyhbzzCXEjQ33bY41hI9PQs+FOo4CsL9Kkv5RjMeE0bEZb6E2KdPwyg8HIMX8Z44XbfgGPdf/ssysGDAVe2wwsvRxDpbLzGoMqyMD3Mypnzw6Ovv3osj9AzLQnLZtAsCcMJpJ8+y/hMaZaLseu+9wbSkjD2IID2O80G+sFvJt38aOmQ2g1sgPTCwLFcdAx20ixVo44LVmYCuP5jp8Ooi/HQbAS20IBHK6TCKfFRBtUBQxK54u0loaEEaEqOciDRAFjqsZeGej/mjU0g4QnvvRZ90+46i5ntIILEcK/2E9PP7oYrPPgf16+MpsZXFlNsHi478yQXSC0WAsM5EVy6wWhfwEspJZwOl9Xuq/zEIox6XNeAAkIUdsNv5JNDUNkS87PWSjjN+QT+5HVGyRHEV8NCDYcvNbipMMIWWusdXNiGdpssAchNF+tTMPDBeQgPPjvP4gPfwz36vbzpfxfvSn/N/yfkwPMzw/Tgk4ky2slQc8HOKdgx6DgnMOwai9wAFSoFPOaMIopgRZ8BQhM4rEQBR7wXleYaL04UKFhPEYYW3ZQyEemUByAg//8fe+fCKLdtJekkfsiyM7M7//9P7iaWZcmPbH1VpwCQ3VdSnDiZtQf3NgGcNw5ANHkaBMUQ2kApt3WGiwa+zNoIKp34hAivjvyrvfn1fWSonusC+IZSWb7jKUSYpwr4EU7ORwrRSTCoASHwTwNBukvy4xMSyGWJxeqA3N5IkScgFLhvrCSv1wgqJtmIyCBYslOuNdJmxoFwokWOC9O+6M63ENciq622aoSrTClU8JPMaRjtT7Jw47IfXeG1b9dbgmOxPwWCxf723RBVlatbQvpGyA1SWXUQHt9BXdirlxzEyXvizvLfQ/eptKf8s3y350XjNxPeor9uXhuCq4DWTtq7yi05jSnPhl9LW/NJ2bI0WYEO+b90iqnckZHpc8Idc9VRG33O6kKeH5uH45LRqf1BF4TPEpGS+/pf5a4E4s2C7PnznicWvtdbwRwIyjYWDQS9YUWQ9ghis+i3uofhh+23+sF6B4LUAu4/dF3hE5qAju8/fIYZ5hfeOAg0TyvQV7JjBYJyZyALZbsdAG/2CEoQSFUl8+Ab5jLV8R3XMl4RpJPdj2ipfv5Qa2fP9Tlw93uU0x3XdAAo4muU/s37pjGnqE5zhSNwTb/7R3h9efieyNKYi7jSylYWuTfIY2Gf6a1hJG8W7ScYZk8gBYJ4s7EDQVoR5P2CFAj6Rj9g82P3q694wY2eZvCKoOjHB/dAELJtm3K3sy3EbmCarD1u5I8Egr40/J99+PcEgqaRbownwHQWPsApgXOcWk86fAOInM+ZFqOApQN/0p00CyeC0wbg1TH0HlyARdcyZNB56auKD026KIb0VB6jwsOESALGyTTJJOWh0vIUF96Fcu0ccr5YUHJv312W8cg1U0xZkgQD3FR1iC5slXqy85UfbE69RShRWxgU9VtVQ1kvFAbN+lgUAAqShh0qVyr5fJ8aaD5ITS8Q9NSHIScfMNk8J90Piib/qGfzs8Ganq/VK13faMLNK+R5VIwN2XhELHsGefd+Tcx+lbxyovaKCflXgJ8lx195miwd9FH+J03Cf1JE3s/IzmScJZqsCsqFGnYzAXDRhoS0A+OpnakeTYNaM4UaS3t98YTeANN2CWEsJ4hDTvyflPEIny/qBGFvnxUM8mQaunxLsSdQ5HiDSx4Hc1Q9wSB42SCaZ2XZ5+eVJkpeD89bwnjG9s//oVfGk7NCiB34tS/Ql3qrGHsIfaEVRDwOlg0pFUxautOEtFi2ydjYDnzb7y8XB31oViZ02udfe1wXrXjxJ+0lOeOLjVIqLoO/+h3qaqVMiiz3t2qP9BYoquRmWXRX6k2Bd8cUlVIDknZWS7k3baWXF4l8IuFuOTcd1llHrJNIXPiIAeh0WG+awgc9me2QH+G78l/pqG08/hPnbvwmHrsiL61OS9KmsGxGcNRsrW3fFOhDjRO5jZ080EcbSr9VhPKso6d1VJTHlLLEN8GxGqVYeJK4bZjhoRqf83RpVgRVcA38bebrNKV5arJbLSedfnrwBICT4Cyfbipd8xP3rPwSHfCm6rrTnjTQ/jPpKgu5H9J70kF7pvI1P3FnuXjyl1JxS995ZobJpy940Xrkm6cMyXPkC0+zE3QGcN5wLiAHIPXI9BF4GBfPEg15SadA5tNfhSVmFdA5hAcMKYYijzszcuiGFIBtEtxzi+1l3oNuPtih8x+WqnAZXnA6VnfFZk5O2w3TgdzTw6kfRQiZbPFDK7r5qlMeHcum+UrsDSqWWBRy0gWRSV0IcacObnQ18JMADtcJwUm1k8hWg6f1QdR5dgaYNM5yfP1gVdEnzRJrG6onWFpqq+wYbAq8+elbYCSI+GFkJ5sARsCWjVX9w3TjadOl3D6E3/IsaOiAVeIGGZrg52hDHniqNmjgql6S8YI0vyCnUtYl70YEvjSgatdd5klz0t3LdzrwpEOeVXAjPooffBaCxWeRgmX8bBNDMGQiSt9FkXlOgilnLsm1QZs6Eg5qMDlX4g9pDqhSMv4z4AxzMaoPObGV9vHhh+EQz2oZ0dtOX28KQ0WfkkHN9T+nKU8qsO3EjwoC/aA3G3vrCgWCuEd56x+pswqIwM9bnlxwIEirgnTfwqNkBIK+/4FHw/ixlpNUF+W991CZx7wI6OQugHOKe5EEgThfGJ++XvFKH2wSv3loRO5tLNd+030gxuvjbK4l40OCPwRExEMgSH+eW2g7BB4XU7aACMoYGYERHXqVraTZ+NqPhmniQywEjLUGtTtHZU4Dj4f55Edsl8XL0we8Ph7+z7VH0Nd6/Oubr1kRpPsU/Xj9n/yIrTcdf6NA0H+wx6kCQTzFQCCIFUG+51HZ+iQFOymTsMu26ZAZbMbbasyMdtnB/M09C4+nffPFbygQRIfTvR9M4yVnF0JBPso8DDB/kHaQPotLeDIFtjCi66RF5xn+aODxBZBOLv+lGVQGYRFVOyfNA+0HAU+MQPiDYtE9kJ6wKT/wPVEu0isZwZ+ka++iEEqfiiqVC7gwUz27IBcDRm8a0WVCDdwSsYELQ4H6MRY4BeMNiR7VoSPVzbUHem/ophmMx7qIwL9jCaY2W3unN4N9pwCPAz9aYuk3iLFptCLuf9Gk67eHiY5XNfLh8TAv4ZQgv+FDF2CNmhMMykZtXyjKzORLUIiIPJbpUTHlWUYqG+VQJlTsqm9pCLa63RQm0Z5cPJwXOejlIyIma1pPmbQmTNnoTpivAAlZF118X4jUQSCVDadzZA9wNqok6NMAGuW/sUG0gi+suvlMstREBXS0EkirfPIa+M//8M1/sKxSEymTKJOpnqVlqSUTKo+NEQD6cgJHfmOJGoANBJQc3FG73SbZYPvAq0CZBI5N51IXMe1zG/kSFIEPsh0C6tArS84XUyuc5eM/RBgOrgmPnIlA6APREDyDnzAMuacNo9fXnJMRIOKNv5bpaXCn/Mr2KHiC58u+NM2HVmKKwzeUjXmgD181X9FPbBFovSlsVDLO4//a0DzSzj64yi/dNXcPmTD6a0XflmNq8CA+RWDpyFu2kOPAiahGMF9BwqHfG4vKiKi0Wh18DssB+Lcw/EEA9PewIgjfMOeRXnJtcSZ66WDnvYT8leAfMvhU+e+iO234JeUZr5/Gmu+o501V51iWzo0ls9/Ce9xHTwgy51Cez/QvNYqhCkfnKddu46DV8kET2eHN8RC+wIJxPuuurDJyPrcGWlL1Ic9cLduELoVbqAoSOhfwXcFfaSxjtWa3Kt8pFTb0i2kZKf3+X/LQ7x+SYFXF9tzmlxBvYTSDL/p817tJVuD5HmLriCJfp1HsR5Tc0xpOWR+3GwnYwEEf4AZwnBM+PlFLMdJ0yI1W5PvqhdwGwt8Uaa1FKzXgwdnHiHUqfPgWHH3YM1RHeRhXdqETdF/5QiIBI5qs4u88CwGLGC6BIMPCOaIM2Yfn0I3/O0vPxJ3Gf0xcG/mMrrKVL9+Kbo2LG4/Pn5H3zISgziPee0a5umFp8Di4On7hLtQydDVpCra9bRFXhmEAjHmXbmbAU0uzz9cIE3PlWQ50KuT8Z45IgvoaCMobjXmpTVYE6fXwDgTxJuMEgnhrmDeO5gU3enuYf8zWfQz7Bb3TNhc8Goa13IP0KYSuDlqBHJ1t3AX4Q+N0YZI6duc6MfsDIYNrE66ZoedNyXKC6ZW5HH2UaZf7SiQEghwQsvP29Xv8CI8+dqD6gmrrlAwwMGDQSoYPT+7l4klkosb3CPazWuMce0aXZ2YCQdnagl5gNVCCOGqZmPMUAy+04V6FR8FYEfSN9gViewu9+Vj111oJxJ6mDQR90UDQmHsPBMmMJOE7PxqgdqSZWRFE21gR9Jt7fXy6YLwzvnjI3FnylD1SLPWW/4G8HfCSMPCH3lMlv+wWRUeeuNQEzP90rqsHnYWblBK0RnLifCiVbtHc6aduOll1NSxca+SputhXYUm+FCzvAonoCxtfhwD2xRIcNSEiPA2MoJyCi0BQaGyemFrGz1wrWI4q9Xu+OKEHg9zJYVQ6XWkZgg3qgkNyaf3rmcQw+fL2LwI6/mgz6Lfvf/zDX95q4nUgSEEgPRrmTdn0ivk3b3/UMk1F5lmmqU3c3os2waBE8L1pdC6tZG8CQXl14g4GMZHuyRQvyjKapE+fL3aVA+A2KtU52mtuaDydOj7dF5kRWn5ylyVBc6X9mclPk7O/4LRWidwyQkNYireD+flV9gZS8Mf28oYw/5qnSVQ3r3qiy5F0JkMCPK8UJf9aQR9W/xAEIprObvvUXwv/jSLuX3kl0B+9Koj9gdi8kgk8G1LmIpK+p7/dOnC2e1pMI9QmMjuvTrS/1FYcyoDyOTwXpWocsji45EFImwFIk1jAd5xAGk+klKMlyA/JT8yJH0mDfkY7HWyKlm21JI/9tMGfSivdiF3Sn8kPspjkfImT4ge7zLXAOJJwx8ZRCfzhuAhzVj7gB5CLvi3EpRp2Ch95J35zbelYH09t2J9scIVOP4P2GBq6Z8K2iOeligRLuTIGjh0uLl+EzmQcPG+FLayMM/GogsmG6ZAVQb+PQJDbrTZzvrvbcNNRVtVpXHz1e5F2XCv/k//TPLCcfpMI/ObzVK/Afa4PfMa/pVm24Pwf8nq2hgNEP9P1I2pZ9Kw+8u6opbeIpVcAyrfkeUWDsuQNYJxk3NzAC43FqbDoVeL89o8uamQkgdXca306aLDnDwEGRlIFqmZ5Faq6U0lVoQiaH5DI+Tr2dY3KKOrG01SvCc2af2yGyvSPygRgIglqkPpPtr4C/V1bePPFhRWt2KLU0xIdBdP3Mf7knLcNvpoQmRQ5AKQiPwSB1WWF89jkCoAjFRZ6I+JgFTcu4/FgwzTQ5KQhNaiwYNYRP+TaCb9l/raA4S1h6KYmnIf+IdPfFLbxxigW/PGYHukeaU4IMobnZC0Y2DM1pwjKL9F9hHe5HxGSkbn99NmpKAZuuqverWrORREmeHjKeDSVG/9e514pqUVn4Re6UWibDzLsK59jGWWePG0Mic97nZALVtolT7atLz2VgUsvquHh2prtJt5rJdD5aJg3ip5AEMGfv2hfIAJBXhH0hs2i82jYewJBgrN/0N4smnmHV8dz3usM5sdofohWjavnvRoIY4CJFlsIBKnOyQ8/3Dxq5atwVhZhtxqQMS0wddPvVUJ8wTsIJD5rpw/jUI8PikjgRizzAzJVB8YHlOrmUd7+ch4DhM8diVksTzKVr3sZSVp6TMQ2FtzTnIEgeYJVQeKjifxo/c1r7QnEPYtXBOlphv9QIEj3L3tFkF5wQyBI22DwYpwVCJJ01NhVKnrOBHZJu30Qpylqhwq093MNtG/0g/qvkXQOoe5fmPwN5e7dSukRzGhejMAMifOIuRkABu8DZG3JWd4Uu+SZWESmK9NGu3TDbSqVqIAnw2YXdBhXmnbANcriRJJ8jkvUEFeuc8FW1wzeiiwdxWPH5MYdB5PpUJ82x4KKWzKWYYeAowj9qJ0GpFo5g4SEU7cpJY4lTM7JX+hq4o0KymWyiH0Bg2CVGwioXOhIzVPZJp86LriaJaHYAx0foto/EgjS5OuN2bQk83ut+Hmj1T5vFAxiv6C/KiDEZmysCnrzvT4EgxR1ZxJmJdE7XinvQNIPjjtkcuT0Z+LMKiCCQnlELHsF5TnhL2zLupCzTTMp8GVCA4Dx57nOENWuTsi4tBet00zohtkpMlnNA4jpHRGd9OBn0mKq7uTFFQz8vmjTmwh+JhikR8G0hkow8dsg+kH7mmgVEI91faHHvHhWlkAPm62xB1BXA7FBNI+Jfc0r4xUAYkUQyynNO/sCeTWS5GEHk7j7Uv2TUzit1qpN6fTXmduAjaudsolx4SX9KttM2qqRinc49BxubvcIkfEUaVEYTI73qTwa0YxcNMTbKZ2cLQdvE0wfOPSF3cvudWGBnzjowZE4umWupbRtMvA47FZA6VFgEW67BW5ZB1s0WBUSotl4X8UclHa+8Mpf+qqxmOKt7pB3iALKL1C7jVe6Yk68vscnpR14wt7wQAIl+KIp7cHyEm5YTVnFVhEGH6PS+ugd9J45iis+84OsEQBYfBV63hbBhdNvPeXcvraSZgNvDvapJwAuZ15lXGqla35BPqk8ozthZ/nOfuLO8ofo7rizXhnNT9xZLr75iTvLxTc/cWcZPOkT6HZftTOGVR2YMa3zQHL2yB+5dB7/hw7m43WaWjlIPsd4uKox7jIODnmPpEBCYF1L7qZMSUcXcvamovI2zmLyY06wPtftDNWVU/Sv1BEocVPgLtJ6OdczN7mVOOlov30mluFyDp1TmpCmCIAufyjr6z2X3ADTB+CeJauUOf61HAJMGMKTBTqS6ScH73p5THNwoRSYlQeeam72EliTB5ZxeAOZ4xNVKPODUAQpi0AKk8A1HXTrO6l4yZScqkJky1fHSoNMPVpR4UcOljEd30aWYFV1UFIEvwQuwel7Mw1s0Z30i/EQXrxy2mA+FFW2SamgI3jTQdNUGdTPcvHN77iI3boOuuVPYLahyJ3HV7HJN/YvEu62bblt4CmvfZC8jYn6bfzuq5Nuy9nntXisMHmHkeVhfC5Cfc28uVNado4IX2fTByAs4OQQQN/x0at8UOSQc5lPIIh7EW87oa0q+LH5O9138Faw7/UDNPcif9HjYFkRxD6mbBb9Xd4aZloFghQMaiCIi3oHdzxgdHVNECc/qWqaAJcPxnJtQpDI9gjuVT/gJxCELF99E1iy8bSBhsNLG+CfdoGf6xnmy8w1nI+i0MHmmDtzJq3PtTszpoXZKYhB6uorUMYD9E/VpgNEPyGXIDLTLXyedambAA/by7qfmbKeaPh8VgRx/8MP2/xQzT3La+9pmhVAvO2YH7K9X9A8GuYnIHiiwfcyaqN1xmLur0hkU3S9MOZB4G6tnEf7/JFdnytY99sOBC1XTOHioUslzsOnD268C3lWF2NG2iAt6AnhI3xDVKIyZkVcbQyVjwal/kRBBAy63JZ7IV6YC/RaKU1zsBK8VK+C4ENzkl78cZX8oVonxUMRp6xZOJ5ar3KqfPIbIdB+Kiwng2RasE93o7hEMLUySiQ3ZyqnaPOGhPlpJ5e3TC6aWD2pGIcfD2NlEHsGvfteEy+BIH14Dvfbb7VRtCbhN1oRxOogJmUCRASDWI75TtH79woGvdOkTRDL8RbJZWllVgPxnBOThDaO1ofgEJMxDfgbu+87lx3miblc1C1fTB+3DoVIldI4JtWLM0CJADA8JpbA+JTJz/Oz7OCmU2UdoPVE7S+80JjRASCCQDxznIDQCBSPvk4UmflSq3le6cPr31nlwysWeeUi+X8yebKsUhPqf/xZq4B4Q9iX4LTLvgJHPALGvkAsx6x+29RepgNluI620YGg2g9QyRO8HUJbKTChXtuLgLiJkbQYpwQSSXwhKtkHLlGZjxEcnKIlciinBGqXTOjDMxiI6ngsZ5TS7tJc8z2K4T3lr9Yd8oPnmBJfjSpVpGpJwbZ25pYK/SJ5xn9yPBEv9EWl/XzloQZN2ieto2/z7dKdE3Ehvx1P+Mvsd3HX+thxAY79Fml8fL9V7BJ8rulAzgd2lz1mA/R5wAnwG080mQ8txQcvtfgluN0D4y9NzxTXkH9E7i+158J3N+DuhTv+ZL7TnrhPLH+iiG3FLl01DPzpeQ4uijb3qZjyWVd1E17VUBucszmc5JV0uR4N+knRAABAAElEQVR4lLIE+XtzhKZcJTMn6fvc368CM0d7PI+8lIFd4dwMNfk7e9qXsuR0shuifguX58zPtlHO9QKFuMIbQ1M9Cesk6fVqG+mryuanjktZDoQGP5a2uTXWwTVgdOGbonzLZnsE4/uZGzLVK4fZs/LxhW8eL0aclUoFZq4gVwcXf+BO9k8tj72LvJP2AtCAVXlaOIf/XMmIbpheZEfxVbBNqQahwBa2y4VswquUwn9hLvF3DTczf6HgzfYg/0Hj2SKozzpyIuEOfaQbSp8koh7Fd/0enyPM2V2wGE4er8q7nnjTuDCuIWpLPXus+YNACj9M71fIaw9SgjvvZo+gt7ov8QbRrAjiPkSbRWtF0Ld6NOzd3Iv4R2o9GsYeQdxTcF3Lqh9HRuwrBXG4//D1Ljbx2FeuCTM+CQTJLtniDexFpxnDNJ7DODcdLIKXj66brSd1ipGjgvBc1/v+x3xA9Ic5oJ3EIQH8YXCu3SMl9y/gS0ueuunHz51LwPr+wSoJcUVPniaJTFYD7ZfcCOZAUFYQsVjqcz/VMPsA6R7G+5pqs2juY/gB+xu/NUz3Mnp1PPcwr77S9hbKeaqBNvnyTYVexgHjs9NpP1DaPJ/xAdeBf9Z90q+R/husCKo76JBJBR2u4kuATmbAMECud/NlfCFf8g68YaPzUO0BdZD1ytwn6oVuOtJyoiD2cTzSoRvb9wUENEEuEhipLAGquF6KQXASkZZRxQccXMsPQqu23lz1q+7yH7ltSd2WWG3au0yWVE8YPh5NMdvNTtu/m1wsecsSo3lAh1FAUGUnTx+5aFgMOdlMJdjMCYsF+JIAjyrn28Ooa39nXUT9cQWCfpTO93qV2FuCQHqD2FsmXkXh/SYxlmJq8uWNYkzA32llEBH67+cRsXfK2XiaNvykj5+cIvLui8ZE4vc+QUwazIaM9X6wMWXnGI8cbLTxHFOniueccVwXmds5KUm6+WlnL7zQKN9psmF4wcqk5TJie5GGXPH8/LcfHAT6G4EgNcp0ov+TriY/14fgDzvnv9LE9VqPg33DBtGz2dqfFfyh/J9aYvmVPl9rg7XXmkC/mudrWfnzuVYUKUw2crMiCB1O8of3AEKf/tiLCFR+LUzr8U7I0774ikAQDXdt2ewLTKjzL0n0QeU0EASay/DpCxvCIVoooTFcwChtHPhr8mywrNy46gWSNgCJxcCwIRBq0RSeatu0hUTXpm+JPDI87pA26sng3pxQBr2kzvkbTKGprSNg00XwUdwk6lhuAdqaZcSiGIhklaqo3dZCklutinRjLMsRLG1CkG+yEMjnhQTK4+4lmi12JIhwDVSY9Tl5o3oBGY5N7m3G5sD6XfGZToheQJT2t5jTbj53ly4XTuGKH2cZp8NUP9k/V2FhK+xDssCVzlwCMLgvsLEC2CnLvEM/JM5K59xEJ/aFcunIz3QKO+H/QLkiTxHASA/qr4DWODVcdkcX+ihin3OdU6GpsqN82NTzJtJ0vIrP2LJ+IdpXx4DrjFzpYa+QnecXbmYeYDEAXtvMYWxCdOen2uYbwtOw9R0NGzJgTkp55BVYM6gP6QkqGZdJ1p+vv2iEcIhP2yNq7E8lv55PO9CTtqymWTWm1o2Qug6/lFDPAYWuGb5wdQgzP7bqxmsZObhyxaXxTf2zcOhZ8l2Z+kExbd504IK3zQtf/sf8Od2Nkeq6bkCGAZNTT6INndtjh2yxqNosuhZHhav4BUOULFlF5zp0nG1Gk7kKB3jGV/QKEjFDdM0+gBqFB711H/WjuHwWAw7MFoP9qSm70D2zwq21C3xPYN7jYH630sClf5HsMZ4hFp8UHVtaUz6AGY4HQqaiS5/pjgvOFfGe8pCRPkjOicO5UyKXzch8l94MD/c/2jdUr4Bnfx82i/bqIAWC3r7T0wcK9LAtxff6Afqv2heIx8W+01MKrAh6o8fF3ulxMPYt9QttdJ/yo+QkkMM1Vx7lyvUugR5d9/reBBtY8UM9H+oOHOmHbM8t7iz2NOUanetlGpNHy8ITB+WapmUa2D4QH/cagJQo5fFPyjgr3nMwRwo9b/qH3OAYx/BClVOOUniS7TqmuUUxg5a5nr5jBQ60XIH2sTC1VkvJuY/Ri519T8MP22wU/Y1+vGazaK8O4gdtv+CGpx3mR23dxxD8+ep1trf4XI+J+T7KOtTGafCYIr00dbfFbZA5HgFud/BAPpfBf9Z91a+R/uWBIH6V8PJPNWpPhjSN3qJDxlPTWk+adqIAQncSXUPokWU4KyB8V2BqOd1SjlaENZ1lqzZiWbcKoT9lVUIGGh0ZCCwpthQhpdNYPAjBBT/QVC0sQxc+qssXi1CFq/mpIg648pEQjqo58Hf+EOa4RIs+7Q4j8IU7yqd9D30OB7Og/plySK7SMBWQ7EmfqmCWr7wXBKywwQ/Lh6J3GcYjIa6JYqur38Rk+UIw0TFZEgDy5Kucnfr9injl3zEJKxjE5PqGXBMxK4N4bAwYj5GxXxCbs1H+QZM3SzJ/UkDIwSA3UMEg8ploWRFEmT/NQi7HRmjSW87Ngy8EVkquVqiwYbtcx9hH9mb46h4vu5Q3fMPsiVllcgGsGYQFc8GWIEReFa+VTmoXj4cxiXrVAqt41Ky8HYzHvV7pUTBW+jBRsqxSQR9NmNlsLW8OY4XQaz9LqzeFKRD0hZb38IXA4zD0MZ7w94yssc22ZewVgF8WpNLNdCDI9tJG7K/t9Q9OS9l48aPDHzS5zWhUQo5z6uJxhaO94loOtsrwSC8q8GJHmumQd0+0LtATV0j6olbg7y2j9M0rufqRG/5ikhefFmUcjrUSFWnj82GcoWckGNt8Vytan5uWMVLk433eS4d9u2W3Xfuxr1q6hbeUtoydIitvOc7cVGKUehFuHmsGrj+nCjfdFZTaPi4ppV2ATVOxhhRfHQKm+Gi5e0nI+Gr7LCuCDvm/0SKnNh/6a53Gait1n/b4ErzbP4TKDDRsDsDc6QfQsKP+rDjyF+peX4hboXTNb+iHaumaPxA8AbidT+AFPZX1pNFP6SrkyP8huurdRu/TrzCNf3Sszj10X4oQKZmueeUbsw4eO9SGxWNm1FXrIh46N9NIzr7MlaWJaUIuGyHMDJQBSX2UUeIr5Kgj0b9kw2Ud4VZV9QFAj3wlz5tIWCIpHHWqVVl2gXrZqOJKRuuwrp2EcQDr4Cux1flrj9u9KMeGlEK1zFXVcA5DsOhUwINOKp8zXH278CaCVh8Jjz92PUJ0lCHI1+UAx8M31A2pxjFnvr+DFow+sJi031QWNhTXjHZv+ivurO0+gh67ScidR85dd+uWzSWjnzfP8OlksEyJWv3JeFqVETjkT+fHg8Rk4q9OLLn44kZ7tsDNAEBzlF+8lSYun1tPyEx3mnvhG3GVmR9+4H6eto8iJf1SK+NXOENHaXAhBHCxe9MZZV9v3wRmHqkr3Pm0l2xrR68AfJpUpk+sHpjKJz2yUh9o7RzB9Vt5Ss8cyY/SrORhVZADQeR6gQ17/vAmMN9z8JSCAj9vBeORsL9qNdAbHg0T/Hv9eO1HxlTmXgRdP/+cwE7aSGgk9x+ZxBIk8jXhrBKi7JCJ+WSTJ28GKC+8IddnVgTNGas6MM54GsmHpNzOI9tw5h2f4yWzYTMv6Idnzhf/9R6EmzQ768gRHSU5isfagRsnWnVSA0G2RnVoWBGUD94RTIEgXlRDIIcf4ni6gVU/7BPEm41zH6PgjwJDr3UPw7YX3N98xeNgvB35q3sgCG3yyBi47HQbgnOT5+x3e4XzXkfQ6MPbY/8s+b9G+pcHgnyXnRanH9sqe4beokDLlUNn+IUocIMy5Ew9dCM62Io4cIiHfp3tKhomxqg2duu1cIvzIdihMW8EMkibIq+15M9gVTLmjWGrJrROpPpA4q0B9FIVWttddQu36U6QyUbFyQeNwRf5I/SALVmCnV9mhk8jQ8PphU+VG7A4DQMIxKKPL8GclNEbXtFBqE8mA8qWPEOjFwec0PpDoD42JWJ8jA0BcKqPqbkYRd4Y42i5ygRsNN/q87M+Wh2kijeBfq/HxAgGsTpIj37xWsY3yr8jCMSKIPYOUmCIQBBReqL2ea5XciSDt5GRPN2o3X4ul2AWRjMZuwE62an6wmcaNJMqUEwlMRnbi/iGP3IJ3ssoERdnWIplj48sACEWZH1WqSrR8Gg1UhC+tiOcqDmriH5mJZA+BIUQwusVmTQ/16T55Rd6FGwmSCLnr70SSG8HIxCkD7vse/L8RnRsIq1A0JdaAfQl0XQ1nVVFBHjslTEq9sR3Lqux/prRzMrU6LcQKJ8GuZSxJMv50qAF/hLhSzB1f3+hRxOs3yLFl4K/0ITHVx4UkWiBPvA1MujptZBtQz0SITrToMN8l1lCRoE9ne4XmKXzTRvn0VNRRvPLSVVWCh6spowvSHOeuA3mdNerlC/tAbnpkXNxxeKgb8Y9m4A21ogKuuQiqB8u8DDZpge8fxMy20uiy13W0lE3zAC1Rn3KWOAtF3jZfX4St9xczLSxcm0yFfAn8F4uvnLMeOXxdZTx6W8mopLveQ5GjW91jWPDlfMbzjW1ONWlx2koV8hD+ge2nPWiL0RUujq2Qstz1k+alktHXlpwZ7k0wO64j9FVz0l3yikeHc9kF3bSQVsZddZLdNA2lad54fe8+Obgz7LpD4MWTuOb/lsdpzLno/4gIdlM0wceqABrEJRSGMNC0aO1+lDIzj1XWbZgzTd6VAzz4KP2FEh51ztTZ24N3N+3J5XAewZX2V/QsSE2oYxPmmRZUuzZoN9Fy0NDKpmXJhwVrLAlgkWVrDQgiNzAqTyJWZ9E39BeNCNupUN2YAgLBeZlfoR3uEw/Ct3f4RrsiA3e/rNxlFzTKS6cPhY3qsyLT0bHzcKxOBrOY70EzGpsNzVkFTYmAf3FsLS/18NokHQLLMwgHfwdFGMGFGpsejHFXS+izRp16funlBXyqOdj7V4cFCSmkqaJ8e3CHWMBwoFfdUxfA5QQ3FH8o39uPhPLSf+0qag96GIwuh6TTRhwuwXYXCJuhmm05QraG3rzjGjLmrbAiIzSz6WnoGqvgSMQQqWfBUNMVzohl2sE5yp0RRA5PyoT0CG4806BIII/vEL+jVYEfacfn79zEEhvEWOFEIEgrwjiyYW8yIY5iE2fudfJ3DSBIM83OqllC6uFuP+cn1lldfb/8SbSsi2risBDH1rePqbKfJhZIss0NIZ281mptBozSGoVfBquDEfk/sNzZzvGuQlNs6ROgfmzsMylWCNZmpNyBQ++dbwwH0Ve+yM4mz5/ocALb3d+9YXedsyP2rqv+eYrBYN0T/O1Hg37D8G+4okH7nGE+8qPg/HWY93PvNL9kIJJtMu26IBXztQpkmYmpUd8DSggeYP5BIL+Q1tt/Brp3xIIWpMjg2J5AG/hDbssbZ3inhxysiyWm0eWOEQJZ7qjXHzyep68ZfGZcQS3TO606ZaZgEbwgY3+QcGKLZW96SLYx+rqCVPjYZ4UVRq+44D4JW0tzT0f0js4tgCVXstTjrw47SAvTPkpK1P50IFTETntq91GaGgc8p1d6AwZYtAm2YSG9MYdJbZB44Sc0QApp7SfOUWY6pFBZRI0Ob8MaNFqJQiXI5A6H+YYT5IiTEBIE6/iHT95535WB2kXfiZiVv5odRCPgvGomJ/DJSAkOIEh4G8VGPrhvSLxBJK06TS7/rMhGUEm2uBd+DUpu22eOPfkS+P6nG7aR+NoTKYT/G2bJ2cuI9jBxJFNzyCncfKPZiOK9pmOfBn5dLMAJj+RWRo8mSQ3LBH5PNdGOR8HgvTGMHqfZ2i/1GTpvYG8MTTLKJksNXHyDG3LEwT66nU2kOaRMHbW//JLbS5NMEkfAju2OQZQtA7ba0zeaQBUMSM/EkY716Nhtj++4MJS/2OzGytrDVA7NUnzHSa/1095bnhP14QhYoa+PmCXvvvFtIE2GSQFJZhaDmSOAgoXFEfRuUbeFCyQBoJKFVpGMH3EMTJmJFSA8shLf28NpS4+LQ3t+TVlC3xiHCKROrYTROGvc9GVKnTA9pwA850KOmimHarUM6GstWndI3epbK0rqGiNcseMlQuBBpL7G8ISGzr1kNguyLFvj4ghBK4PpOBpx4vJRIMdOo8lQKrfy63Xt7+rFUH4ZPxpt01Z2Uq+WWytA6hMdt44efq6pCu34FVLobDmQFsmJ2HECatRhZnodvhU3KfQnfqXmgPYQVi7Fs2tgC7Sp9B9jAY5D7Y/ZzJUJyQ58+ylq045q08LJCdJ0dnGm17Lr+rBWZcOBUfOPi5xMiazTygDv3LZcoy+2FdZ4kbniVPdN4NigbfSfF6Lzu2H3TzkFBHCfCP81Et3ijaRDgsG2yggO3W1XOCSBz9KB1HdtsAHoT6Y5DPbuzSYekmc7+GIKHTTMtdTi1+hmpoMPNVz7QMmqviu7ncfPHtmpi0n32odDbajoBfFlJ0h+EmyLsEX60fp4ovO2VeR9RPySmdLTBbRRxuB1uQX9Lqhg2s7qF7bP4DKENLtQf4tVUbbHYeL6OAtDBlNLV70Hm0sXfJQJxACRPUqvhK6xtiKrvisQZI76Un3TN4aFUf7P6DWTrRezIuVVz+MAZVB3vLdNtdHb3H2Qg9m5DqPNirXhGGUNOdHaer60yXfj9w36MYBGvYLIsjzXoEgnjzgR+m8Lj7bVHz7V60I8gohXjHPaiHRKP9B9yvceyiW5LkJHUw0Do949Q8XxDSIzaM517gapx8+50p4bIIXmM494T0H8EuVaLIyiHMSGeS9th5YnQDeH1o7PQSL/+IBB4DUEb7fsF98g6O68IZvOptc2ZWjeQIVzjxnyMduBXyUJ/+jtragPsvvyJlzvtRbjgkAfU4giH1O+RGbH7YVCHIwSHueui4cP3bz4hvuf7w30Cs9GaFAEKuKmK90RKXKNdImAKUpTs50yE+ftJt+514LmFYoycf/+Vt6NOzS6vggx+WkKax6iTRMBKvjcKyTYJC2CuxCZyIOdH66f1PDtTmvKgsvtPUl8FHxgboUbyIqaU6BkBpYQnK6v/WLNFXCCUspKhPKZzDgTVe8OAXIhBopxZt+nHnKP8vQcGtan5+4li/yLHQOJYBfH6qlbV95HxtwIDlIUb5wx3uqoxtO5ypT5VPxHTOe9ywHHH8koq4uuM6NPu3h12kHVYTrI2I//vhHvU1Mr5XX0sy3CgaxiTT7BhGdfzOPhHUZJnsH8QjZD8K/V1T+/Y8//OGd+JjICSohk1fLe1LFCpXdPJ3wGalMwppAaRTReXLZUr9gMWZjYyZH1QkEMWnqlwMHe8QD3/qIvoEP+6cNjySrQq5VGmYN4lHOt5Fn1ZTRpa8UR7y/IAikSeoVG6W90uofllBqKSWbqmV1EK+N1ySqoBCbQ7OJ9GtNmq/8TC2Rcy191Kog3/S6hTZUpSZ5RO2k7gtBmeCvGRlKkIuJe0XeazfOrG8ESwALeYLrQyCI7ypWiLi9HJyQ3OSvaFWKS46Ena440wK6Em3yo8R4vKdq2KMz7Y642J4zLpylay15pJzH6skMuBX7S8rI3W5rvNi/6SH1eFKOi3e60hQO9EJWxOTB6Sj/x7YSBEMP7PRMx5JwkYg0hmuS+KbM45f4zCuCiiZftBsIiM/2zIGrKcq3HuEP+DOZlVB1zYF7yLqAXv0JybPqXGf91pPbju/U5tOFtPsc4xdf2ymnBwHcuU30eBhdJm/5pHoJBs1d5cl3lu90Ne0OP3k+VH7gE+Bup+tVVGHQCfbAX/yR3+UdqEvxRbqbEpvCdxs2FDc9atwh9UHmnhGC4qxWurUFqXyWeGiOVK1t/4Ma0QYmShWso8SWE3uZF/n+OVHWy9xF+5zCTbGQlFPjeMLBJYXPR8+FtN0aF3qpKItyu+Ii8NBzwPEc1YK2lUv8KqwmiuikgzdWkRdTiaHdNZWGP7rB8IEvVLmOS9kwKT5XatK2pPJF4NYdOw5sGZSHqtKDkPX60t1yD/InxW3pE+QB+lQ6WF6ibRsq9kN0J03L5Bmlh3faeIS9kF7ScydfdCOzotuX0EfNo7LQ6riZRExfbMAjVy1Iey7XASI+6SsFSp8zGyCdmxKwawUNXcmr0Tk0IG7Ijp1luugq7sKvygsiLmTtM1/HDwbZXJqT4yM+bFVB3Y+J6f6BANB73Vu8e68fobUX6Xd6aY1/lP7+O28UzQ/TvMTme32+1dYVPELGUw08YuatKtChS3hmGK5weDqBkIUvigkAEeSZut5ZZd29X+HK3z9IT6AoQZ8GgnAIFyxcV3N1ST0BJRXgTFK/pO2pHhgBaDhBEDtg8tTNM3AbBa0S/WIZOvhewPUAEwAilAUtjabVlJWvVUCUkaH1ULrmeqX7lK/0uvbP9aiXX3jD6+P5kZt7GIJB7Huq1UCv/OO2ysodBOJHbX1esSLIe51KjwQzp0r8TqifRHMwJ/6gnx8/BIL+l/ZT/TWSvrtswq8h+7lMj263OXg8gwnKbcnylBw3Zd8Am4yOUvctB1IIUWkRanHDG9oybJmh83C2HtzgAevBUbHDt9kPw5GgVHtLU9iqT2HRtYCoaLQcH4RbfBmmG7dL8UG4Ia/ExQpsgPVF/LBlUCoNZQbgxc/AJNA0OlRPo/K1AV6Sddv8KPZAFvxOB9YUYjAPzEroqY2RkCNAyzIx5dDnSH8OtbP403PQSabysDtHBvXaSNuHQiUCM6k6aq46j5MQReftYYwYxX+0JFOvcmQ1kAIu3ysYxPJLAj/fUXZAiCDQROFV/14BoPdsIK23iLE6iEAQK4uYkH/+SUs/sWn02GdxvNrHhMqkTAMzqabJaTdtaSCI9rBqhy+K+ozHrDIh4xsmZiZJyuG3Q+1U8Q7IF7ryxJ+Yuewb1KvlwkPCaHAwRt6wPJn46hWPeLFBNI996VXwrATyfkCvHS1nQ2hgr5R7I2kFgdhUjeWXvGVMcSQ/VsZjME6yacwZwOhV7U/guGCU8gaAWA2UsYDdIrIvGC+yW+VUYy+BMmZlAkBeEWR/xDdt4VKKKFfGmlSusGVobECXjR/aU9YpykLE+0i2287Y5K8qKIej+ZYe3FU1fKuvN6lLlbnxdX4xdp396jYNv+lFQu4xt+RuvgVSgTFy8p84t0UEluN+qDeSp5Vn+9u+U1daHsjm42wx9eqQ2LzaG+ExB1JO7ibVI2nyqWRslUh5lNaojSi8kNYrVPCjuMsC1gzmA2h+L4EgXMVcVle5t6ZyjjOD7Lzx4NMBtqUg18l0rSh/QrJgiAY/Kg6uK1/pIDhp77ylO+GlP+04YS0ju3wnbMEPoGWNwAVeBTiS6ovKLfyeF9/8jqc+6tx+vjOeqGMS6PzkXDwReSOuLLBz3vKDBv0f1Jy92N9UlYB68oA7SJ7aZH70PyM87ZqydGZ87rZc1YwcbFtzzpazS4dKAblPNfmAI0VHTzbjJ9FZrGhKu3SHIU0scmxYVbXRemAiIY+cw2HYgoGDWYDAdKwwcCR0kO7wU2AVmHQUOdtKKVmUCzoU5Tr6ByAixNgsyibteABRk8Yu5OoTCSNjIMUYbfmbJ7AcT2glnPiWS3fSADvrJ+0zePHNX+Iv/lPzj8mp7cjDrtbvNhZeuvQD/bEprWv6yXRG6dwVzHQlhVAwFF74Szd2WKdhkIY5MKQ/SZZZJYNfVXHCTN1C0P0oAxGk4poDG9Qa+qYrXMjSQmdTIJi0cENXM9Z5KQAw/JEP34eqcwmrv94jELxh02fvP6og0Dv9wMzLad4QCCIg9FaPhenxMG9VoVVA32t7im//quCQ7ke4Z2Gzae83xL2HdWKQAkEK1jgQ5OBPAkENBv2sx774wTo/jmMXLeR6cT52WgNBgqneIJKvyxtUGg/iC7dxfEPD43f4jNRRDZVC9/vyiXBAMBw6fZgHONjnKjJvEtbam08L7S8GVv5Ai0Ph434APuWYjCwRcK/DFhdf6V6GlUBf6Ffqr7/WiiB+1NZG0a91r8Pep39WEIgX4fieRvnX/BCuR8kI/nzBo2Hk+qRdkis7rR8bsHkyw1R3k+YLDP/kMeKOBVYE/VGBoN/IW8MY1PxyYj/IA+ckYM8sT6kw5QUyQQ9A402O83VgJNIfeUJb7msenHks9kO0w7nVX0VdapJD71/EceKc6bDUdK2vr7+TWINqJtRbCz/JnIuks4KdZ33KA/Y5f6AvzQEu3gaITs/f6Wo7LnmexA2OM4czQWmyyTkpDPbBE0xKq+5m6NCTD4RZdCivhqDKQCOP8bJoYFCDOClNp7IDNKpr/jScCfkHvUWM/X4ICL3TXjnvCP44CJRlmn5zmCbkt3qL2Lv33/tV8gSL3nl1EG8A+HHtHcTE/JMUIpe9l5cP3QjPULKJccOt7QQsplPEYusdDGLS1An2M4EgyfOKIHEghomO19bTtrUCBmZa7n+0AkirVbFGDvBzTWo5yhN0YaLlo0COJruv9HYwJs6veAMYyyR5HEy76r9W8Odrrfp5rWWVTJ5faRL9UkstCQJ9odVDfhRMZhEAYvL1tW/twjynjAvsW5+xz3sDybBsFE3r8uHbM32s9iBP/d25xrkGmh8Nk/2stvD8QRvT6pFiQ2LB/SSwJaDQmKzje8GC+ejxQbQNhm3G5aoHNg0a7LbxmaJzXjzxsTrYaYHQcoRTID1fzva0jc/lBopFjJXNP2IfMlGKMHRpB3xJqXPcJTCLIGQ+nhQpQ2XKi3MFEbq2P6wIEg/c4bPgywEcn3oJQupN5mvlzEEswSfiyg+Gead+63jl1ynG6G890d66qePsWZt3/4j6xYF26w3THdJu6APz7y3igHvq+AH+gD8AL/riJnDRfcQJp96biMcqdnyYIZbqOGrd26sciVM9xOdbCYA1yPZ9/bCpI3uIIAa1gACaTqCITHfCntEJPz4rJTmga2Jmwd5SXbHUem6fmEVtcx6EivQKW/Qf0HPKr039hi9OX9+X5CbeZF41h/xD52YoInjT3RRdtKoixVy3nLp2r5/QzQg0mCsenS+nD+FOrk+lO3l+f+U9lqd/PYDufY0vPwX2zH/PeJ/QPdX7SJfz4BEOZLflirfld/MPEo+UF4ZLwbDfhyUww8lVSF0zBwXDFAbppSvX8oI5CEMuIn7s5SkDAjvv9OPy99p+4o22oXgn2Hd6JOxbbRRNIIjHwd7waNhf3yhAxAoi3XcoiPSDt6mQHEmOSi4wWPXDmacy9woK/vxRv5TiN1YEcYbaBjGwpUWuhAi5qKUWQiAIPtV1w5EfsZO3LCYlzve0I3Udzc81ebxGkMbXA8rjFIJWIrKD8NMuY3H9yx0S//yIjSybYiVYn/1MI0N15hyvBhqTJYf7D+5r2CD669evdT/DW8B0/+LATzaGZk8gAkB/5l5H+Zeiec29j+5zCARxP/SF4jU8HUEgiNTvrNSmGcbYXF/3xQfpdPeL26h7Q+XETL6UTf9L90+/RvqXrwhyJM+djksYhK6kbfUSNZfTkfgj1AMO9eWY504PWYONyA1nYCAvaRVczZfPaKrCkq5cPPRqWbnDdrmA5mI4ZawyBT5NKhdn1o0/JJl4f7FSPWVUVvJrG6+4s7bppOkFcQaLsDcl8Meuq3UavpJx0sWbgJ2qDFUtbyeGRDikdkzkRo2THjRIZLoicyO/EwcUPvFdoOJ/aosfOXAnH7mq2Hbg/EW88kwbSAHEDdpPgpEzGfKK+R+0yodgECt7vtfjYkTomYj5vGUVkDaNfvf9Wy3XJGJPQIiJmjyPiv3A6iDx85iZHxVTFMg3gtLpiLv00Y4cmOKo9BN7MU7/sjcT5d8kL0EhQWHH1yp0jyBW0LjOuCXZGRcPGExgxvyaGVlU5JU3lFUBxyqez7Rk8jPdoXbpJI98vSZirknxG70i/mtet2iYJkmWWSpinqWW8GQlUPc/kSg/Fmad9INNmsapTi+4J2gW7VKd4M89EMREji+YOGmbe1eyKLmtbjJCkEMQKDJoNP61e0A6iU6JsbDTWUZu6h6Pnu2v+M03JdARe0UJPtoMD5mtvjFAxUdflDqWTsUnCWxoQrdJNiZf58HMmJg24cKkoZZzMg/IZ6sMRaVnhFLdvCPiWWa6pcQU8X+J2356+0pXiuTB1Yp60tacbOpHaDgnsO+lQBCqoFvNipLrcSszHWrMc6W61p4QXMxThXp91/yzzwi4PmG+Sv//vsbYsh8ZBNP49NXppfGzCQ9nhfPmg/GZB9Up4wVfAj7JbtJW9V9NV5vuepcPlmUplM7XKVRIJU7Nx9JROcsHyccH9UlMucaexdjg3u0VseiY4nu+w8XHw1yFLaX2F7bP4YNInIOHcbXlUjHN5XCMMxvS8WeiWqBc8lq78N+c82ysln6ZJJ6tNvZFdsqlJ9/ytg+Cv1kzAivhht0iTberLVm6DRzObWBJVr5tOu0remuGjnM4kA0v5WpRyASGxkbgoalvan65nymhQN2uVsrQS2chi8iFZ9DhuRLeas/4biS/86p79nLeyCH0fc+Ye9efY+ss3/144s7ySefu0YGBcag8SVb5qQwLWCRn4RxrqwmrsNUh9oNJ+JdIetVtfsnmbEEFun09PwGg1AcuPD8YEwji/sNPGCi4w15Bb3y/QSBIK4De5vXxbFPBkwn/99s3WjGkH6QbCJp7Fz89IAsJ/vztb300DKMVANKbjLNH0GfazFo4BX+kNrZNIIjrX79W3o3Q9WP3CZJjGvzxXKCnGlIXCbS+l/KFeeoCXfx0BICk0DrhzPWnJahKcCfl9gMyKHN7w9Vs4d3fFGuRFz62tVAZesnJPU6+gz7Xo11fv/7aK4J4ExgrgQgM8XYwVgC91v5BPPHAvQ4vuSFgRCAoG0Rrr1Px+zExxcZIbptMdR4QZtAkwzwW1B5aRICKq3sH5SjrKQ++Nnk07H9L36+R/g2BIBqPB+wDeYFOoRvoEOXgTm+5kwogH/pThqB05oCmHB3lHG2jI9o3z50WgYJZYCVEd+Ugwang5oXf8+Kdc+DTNGXMsFGq1xclueXx1ZDH/BtFqmmjCFy4kmxQ8c8FrX6Bfdm1aT2ILzhX1FO3NABPDPJtbJNIKPXPSUAB15OcTdk1TR6pcozPkLXkUACTLCQiZWwNowMtCSjlZKOPkdabEMrgSZ1mGAaMOoIzCQaxgocVQmwcrWWWigrxenn2DHqvlULvFQRiwmUCJlr/zhO0gkAKAPEYGTv9/0DQiAlcQaP35tMqIclgySb+jE20F1v0sU1tGJMEFuZ8gaZfHDTQe+GQi5zJ1xI0iTA5cq7lD34lC3IDqRhEB3hSVHAmq3QIACV48zkrebSihyAQr0lko+cvHAlnclSkXM/OfvW1IuSKlPt5Wq0EeqVJkk3XvB+Q9gZCZlY5EIRRQIdJW0aiM30nO+QEP9YVT8ik2FYPMMkniJSgRFYEqUwLTIsA/icH5n8O+tg36P8sN9mA5mYb/ziZPkWO+5yLv4NBECKTGwa7eC2G/KV0o3MXm7GsGZthr6A1Og4VwWH35kB4EqXgqJeWMlD+Una2DrSxlS0rlqWvGHP1SRsM5WJTmf60uyevRPLQjT0irLxiNg2QoVt8YM+UM6W6l8UFiNQtVT3zD7xDddC4CE3RBw7Qs7S6HqYP0deoG11Z8NMupwTs97giaPl5BlDGWXwSN9ZT+Fzlh4Emqvp7F4ZOPLc+WPpaKL554eQn7CyfNL+U7i6jdZpbXc2Leyn/GN3H8JVbuuaF3/Pinbd/qCStc1igfN/Sb8V6ync3chKY23IOgkUqGP+ZMA1dslWD16zOVTrHh6mfHIYsjFe7xpoweZwhuAynLLSS4L/qBeM2nQ0WhHHtX7qHvrOxxQiGn0jn+DcAmD5gq8rwAl25HUYeJB9KlZecefU5R+liSOy0PXfhtWkxhDZk+c6yBvDjpWZ3Ua4PGeS5VvS3flAWdOd6ChyiWny16S7hf+qPHsBj22sppWtyLMe1Fmh6pH2v2oxzsMdpPSNv03Ge3+Xtekr5nt+W1Y6Vdxw6t8aF2gV0KnEYBasOCNikimv9zE1W2pFz4pFpueTyAVd3JBZM+AfdQebHXWhiF08Q8OawvJGY+w72KtVjYPMDtINB+hH6O+0N1EDQt3/93j9G/8gep7rXeK8fjXkawSuPeDTMewRhpO4VdI1PIEhXH2qsPsYpICT9BKJqR4IVuTeCDxlxDuek5HDTYQfSKypLPF1Ns/wdoMLuXQGV7C6PB6hCZ44DFgnhRSB94M94kxXUlqO6PaaACjw7EERdd3i+1+F6Vh//yC0rlX+hLSu8KbQCP9zfsC8QL735yj90615HwSFvdaH7mS/ZEkP3Qa9ZPaQAkFcEIYMnHuqONGVsok20TGly6oxtV1Xxz7zAKGO7PgSC/uu3Ewhyi91LbnTcsT1CPT1ozHnCXRBy0JXf5E8OmTieIAa0pWy1PR1f5ootm/dDlFccWram4KZuccXNRHRlPmqlO0BPittCSrev9IuI+PMCelHeVequrXE9A/1j0nTyiRn+au8NfyeJbcJzWb6pW0Rqn68MyANEDsnH6orCTLQ+5aT9gQ7/91MbJxik4cGJymSsOdWBICZUdvJ/r2WX799RVsBnIvTfayXQ+wkAvdPyze8JCOlZXq8eIiA0ewe912qhH8UnsZZNvkaivwA0u2Hn2E8GjX2lgsFMGlOm3fjHH2Y6lQm2QGj/wNv1puYGDlL9IlV+Jby+ENg1/zM2g9aESPCHiY8yH56h5XXxX33FJMnjX0ySqrMSSJHzbLimYBH7AWmytCxPjgRhYgdReduFibZD3mUV57Tey0SpKqVHGMeKupufNimwpPblDy8ozcSPvwwhnz42gDbiGxnBqiIS/MmdXQ6RGnwQj+VcwEB54raYK+ZaM5XYxtphilYqG86IAL4h4JOuMrcVu1RKcqBq/QmacnrBagy589dTIU+t9txpT/G7PUCvtU3nsbmr09rNUQ3k8cRB7GIkm+5QkjrHw/4D/0yKpxMjzH0nSbD1AfoRwE1UTeipTaPWWJUoLkxmiH5E8P/faM9j04T00m5PfVTIjFBXO/I2bjs4fLue0q6X53eR3534oUa/5KJnMqC9w5/AQsJRH+F3PYYEcyhuMYRPrN3n8SbZpScMD6BSn+ZW7QPxPwq4C67yp3Jr0Z3pThwhFfUSdfF37jv9SXfH3Xn/ufVqftTK+f0IRbvmRRuxsdfvjg3/sK3ohvZlGz7M//vE4q14rH775X44e+ofl5be/LuseaLUIB86Nk4rX5JeQZv2OiavfFCXw99jqviaXxcDK/gDi+vQ6psPGl3jN5DDPQNvI+Y+g+0nsk8QK4G0Z6lWBrGJ9P/567d+XIwtLAgCeUsL0fIjtgNByNXZlI/mVR4JcyBIwSDKusb242Fc5Ep/AkFaITTlyAg/3vcbxXxOMUf7Qt12c+OB/Zahg8uq1lv3q1GuCbAsqTm1LBsIn2jsZO4jDvqWLUNe7T2B5BEQQq4DQcpZcf3ZPKXwucrc5/BiG14Tz/2O33ysFUC+n9F9zdcKyPC0A3VoWQ3EqqDPFfz5TDe13Ds5KKRrN1Lva/uN1Xa3adQd/IFYFbuIXDYbp/xL2fXbCQSphedFLgODUWGYinEb3lC61EMXxD7ipDNFnOTdyC9yxQDdxY4ZWKcsl29yHvDL4pshj4RpnMkQelo0ZXA2LPVFehPNoDepDjfU0mp8xZ5EA5ss9FQYdJLbAbsEHQXr9eB0j8lUTuoI9/xQPYUr3wbaokPaFM0TxjFjeBgTJ3lOoQtIPkTFciVI1QHdk/l0QKY/Ivibow1MuKg0xcLBn0mRPBI9fYgMen9Ek2WaBIOyMshLNlV2xJ1lmJ6Y87iYHw0jIMRO/gr++PEx4QkY8YpHL/PUCiLFkhII0uzqN4DZFmzopK0S5uqzfjlw/AcALQGJH+IM8j9yM0nupqQ9JlNDsvIGjvBpvvGjXzy+9flneUbWm6Yp4JMNoXnrV/b74XEvl70SSBOk4CydZBXQV1o++VoRc+8HpGdlkfcnLf9xAKi6YiiW+mMb9Nzu33BsDJy20htJtAF6Vik515cUQR0HkwTPGC4/LlHZDkMi5fATBGIM57E5fYkVjlCVdwKghGIN9Po39aA4cg5AYhWTb+wWCWqlG93fduRBJDHiPPJ1EfiGXuQtwS08xxb6YiBo2h/zSh2ZHlcqnud/PArTlRYOu238T31EU0wSAX3Wx3vtw+KcnzJpf6w6SZBZuZtakAEumL8YkCCIDRsph32VC2u6Y/Sd9OhDRIk/NT8ZRmztrgnkhg2AFUH+Ye1Tdfx/Ssd5Wl/gmuUq+b3fM4FC1VmZUrjcPSOA/q2sSmK8Gjh+f9FNS7HVPJKV/54/UgZSupfwhX8qXek/lL8kC6cUR04qLLXr8aStQ0/6s1zOE1ae4nRCtb+COgn8e20o1VfLvJ4YlbFyUYx9yU5Zi+hSyLi40rXG8OigeUaHIFtFG4Zps+wS84rnjYPORkBySaHznF8jFr7ylPMPXnn1LrIaDKB6VayPN91qmukQ5Lac/EM8qhZrLVmAo4DPahPynum14TRg2viU7oNtrEIJaBtHb64AwMdKn+Ml/7vze8v/bgG/Kwa8lS7N8bHx6ZNH+C+BfKqsj9EV33xsObo+55rwHth890BzEJzmI4a0aFLNeEyZc2SGZ5Erhy3nTM8d9Amqfz+zQPwi1eTA+ejAS2d+1nUyPzr/pCcT/IOznjbgVfJ+UY3eEvbtd9obSI+K/eUv3/7hL99994d3gnlrCj9WpvsT7lskg8ANM0Kusgnc6Mz6jDdUERDil1vwrAjiKjzXyqwOIiDE0xEuD39WCUHXhlNCNm1RPucwV3JpFIWTOvXAxGUfwz1Fl2TpTD758R9cWhBNEFNHi3gJAh2BoD+xN1DpJZ8fknmc6zO1tYEgVvyw3yn3PKwI8kbRetrBbxDz6iDKeVPYK4JBCgqxUTSBIK7ZkMeLPrC899WZc9PsacbKvDKUmjocHl8PTSCIMoGg/617q18jyT6G2b8ueVDfVNJsnyw3M57BbiSrGpFPmqJO9om9KVdpF9Av3gd2AIzC5kd1wXt76mG9RX6whEw+TVNe+lNnGL+cHnFAPmrFjY1q1OZotE8wNJ+YbbH7EPTygSumhiPDeMO2yl0K1sQ9WB387gopSdmjQ/1z56Wu0+rauYIllTwtEEwF+waAPj7JBEkeXetUAI8YT345kemLsLIqiOBRczZ5zmT40w9ZJcSbwFiy+Z4NoTUp89gYq3/8GNi7RO49Iev1j2+J4rMqyMEgovXvPcn/qCAQq3X8ykjmMCl3UAi/OHiDjZT1ZWB7JqAF4SR84IlH+QqSBGJeGuQ2u2E5B+Fh3x+CI370a1b8fKnNoJkQeUU8m6V5c2jlr1gJRPBHAR/vsq9IORum8flSq4BYMsnkmDcf8XgZfuWrhKQCRlBqP9p+2m6j3HDaCTo9YHK1R3bKWH6pcBBIAHIk2lnwuBj55jp8Q52VQHDkkTCoqUFvKXOEkhT8IS3gwYAG9wxfQsZfJIcQX69EWczPAkHQ4IMkciS1PuCH7C689fClRnDwnoK5uqq8oR0v3xih+ZhNm+VjlI/ne3nhfM7t81vYacEmE6DDy1J0voYmx8CGcTX8hjMRB+DqDWWMsEf/LcLnhUNsCdqaqiZfc5GIfu8rguof/DXer+smPymKOh19lpFxrZfjJfDCV02MWOBVeAm+CKbwz6a7y/931O9tutfHd5nfUuHIHNhxj9nAhvTaHQcdopOuSsx3BZXwn54/G0Oxu9Y/qlymrcKtFTf4o4SB2GmM449o/ER5V7LaD7QJWKlOePGPeaiutPs77JH+l0HynZ8BNBqvKn+Z2P/h+iQP9Pu2xNv1HUPBtMY5888fA9X+z80v1wsWLdvPiWrNTm1d9D9vo6DbOSX0RFd/VIq/+03B9bywvtAIFXEMw9AthvcEgrhH0KqePO7FW4m511AgSD80f6+nD75V8OeNVgX95f+++cNftE8Qm0ezJyn3HO+5R9FTDGxH8befaR8BHvpIVzVcG7MiaIJABIRU0XUPNOwXxP0PPMplF8Gg2C48/Vw59tM03rA0P7SZRSnTIKi2m1IHsGBTIMPK5OHlAi/oBHiAcp0MXQzrRtHab0eE3D/IcprpugM3uk/hDWGfqY1sEP1nAkHa2uIrlXljGEGhrxXsYZ9Tnnb4Sk8/+Idw/cDNvc6X+uE7L7+Z+x25zD/i2ZbaFyttluBpewrrfMJk/oTcK4IIBPFoGDdQ//z0bw0E0f+4hYuDcc9AmhVuKgE/ns6TdV9kVE46fTmf3tBn08Uea7FK8E0CPDPjZF6k9OSN2HQlIAfPp0llqytMQ5gisOYldV46oVWspW3bM7NgA4486FxWHt4cuQHDhzlFweGfYRArVOEN3cU20XkwE8VED8QHr2oA/OEErQ3OBWWSM1uVDA08BCaYXGofkoCvAEIAW131DzwiOcqsOeM01aLB7fVJB8/CQSmd+IOPG5QmOSIvPhatYLsnQ+W8Bp5gTV7NOK+IJxjEY2MTEOreQH7dvAJBP0wAqM/3sgHcT9o0msfMWF3E42d/YxNp2YWtyM+kq7IKP4NzRD8BIVpoD3Owf5LDbdAcbbhQPl9oxNDiXwJBLJP0nj489kXU+xUBHz0KpsDPa33ymnjKRMQTCGIi9ONiTIp6xtb7CRFlZxWQlDMpZvWN1cUS61bjYhyDwO30I2vYjH+dYyMfW+p+Zoz2kbBsGj1iRKYe81ihneZ/GId8GeQ2npVBTQST4g5Jd6EYDIyRyMtAC8g2ChXrSp/8VFs8UqKjEg8eCYMumtL2QIAVl0F6pdsyLH9kpNfxVNLJA2Q8MNhm+ADO4ZpGbPuFsZ8Kge/UUDkv5W3Jc7znm+eogc5J+kATGzympzfqfzC0Bw+udp3mY/+lv2EI1xU+Ok45AWUAUK6TL/LH2NLe6CC1ehVS3sy/l0AQHuI7gIR77KrbOKv7ek6Y2Iftr+EcCcDLFerV/6lej5BW1JUtdBVXmnKffP8dYbXpX50ffonLDscJl3O1RMqnmPN0jPVJnPKCm47DkVRFXjWA9Sms7+7ygXucu0LX09/qRNj80AC3qz4uAmrV2tKun+Ot1nUuquxSR3pkeB4UojzQxvZYUdp8SV7n1Lbxokf2po3RFn4dAVJBl3KaReGud5gxw61dzTeEQ1qKdETwOVPhcFPm83LaXqsduy2R/KhDFHelLyv4H8w/yQNnv0bktRPov/bd2efp4fQuHJFzHXfIK2/KpaP28hjiuoarhMqF9lkqvjk0qyyDKO+UmuesItygtKrnHIPQMnRe+RxeAkLnQeoT0VTG/uyTSe0R3Lca5JyDpktbcBBVwhs0jh+Jf+KHYF3/8zp59gl6z4tofsrLad5q9Q9vEHujz1/+L4+GqaynEfhRmhfYECxij9MfJIf7C2XRSVBI18Z/1FvDvD8Q18nzNjA99CQzEjD6WXTcj5iPsuzCvqwqYuNpkmx1gwgQ7cSIILl5CwE0YyWUQowvc17jT0s0GtrSpay6bu5yF8q4Aj8yuSfVW8NiJdf+3ItMQEgreL7Q/Q7Bny90r8L+p34cTIEgNoTmR28eE/vmG+X+wVtPPOgHcV4pz96nPAL2Svc8n/Ojt1yW+xweERv5aerYOhaP6dO9ttv9KjT3HaATCGQc4OPfXCCIho1PGCNTyQlThODT4fEhx40b7lsWfGUzYCobQiQE1vz5zQQ01oW4D6k1XUhDdwKQMekEW14B4JmuJtk4ld3wnFBFnfmabKS07XnmmeLMa70j5VQvUOkM1uH02cm79QoqhRedtV3QBE2CvdCYo1O/9DLJXaXYjQ4GjfwEA7CCtmJhPi6O8D/qJKbP2w6ok7Z2l3SoPgJBKcM7JQsRfIaF5y54HCzI1IJ+0MjLSUpZMOgAkqvOo10EaVjVw9vA+Pwwr4t//06TtlcBKXqvYI83lNaEnP2EtNnbj3puF16vLtKST5VZYfQzcrCViR+55Kr/JGXe+V9GYMIluVMDsc8o2k7anUpX3tAzBFNYZcNbwD5ngiMIpOj3FwoE+a1fXyoqTkScDdM0Mb4mOKScvX/YNPrVK0XGzcvKHybUCQIpAOSAjezBjmWLTcMQOmSsp40YqfYFIo+CM54GZWLn3CEIuFYFyf7ITs5aH3gu88qoiP6OKexJmbGgkq3isEowDC9Kco4srOk8XiA7dLS8BLYwdJbAobLbYiOiP97ArpJBnE9hzSt+5/aCq3CcbQMIH5/HNOfmsiuclhHn2QT7Vo0M2SmpfhcZPhVq++JGD9voQZ59K9iWtkuxs0Y1v1vf/rnqrZTa4hZto7YQxJpYB5cnhwK4O7rlJ7lAD8nyBK3JrR+ERulAjlmpc9R5qV+WjljlwfXbKtL3GelqtXxkN+GMjgt7I222f6CGULnJxNHRdfWMentkWCY8EXAloxZxgZv4IDl57nQn7pRz0h2iXKx8eP+70rVdH7Pvo3TqGfs9PdT+uLhk8O58OvRpOgwp/Y3OnMIxGtzPB8uNVNUg4QnH8JRQaMsxXYAeo2MfJlzSadNZvhCpctqk8jn2T9KIcItmDIcVGquujlPeKWARomOYIk6Ve1stMXDJ9dwNv1Iw8YT7p7zVH7LLMeemQD45JeHoU0yIOTkuRpOtWWBRmdq6QmnZhzxDhW8Llrz/Kfw38AB9nP6OMe2l9n3wwZ3HD9OBDecj3cbd5VXXc4pYNLhUqsTaMuQG72yILudBdMwMpNMl9dOSVYZPiWt5zrLMAwalbQL7R2iRQQGZaRR44X7A9wbKuSdghQ8vnGGLCbah+E6rfr57o0fF3r79w//5y1//8O232i9IPzyzKugtbzJWmRVEPGKm/3x072IdswKIH0kT2OFeleAOn8Cg496HgBBlnozgujw/mk9ZbfJZqfsokTj17O6sAnz96BrlRtltUhsPKVfBM1Bz0QaHBO4henfG9UDLCq/4hi91+PkxmkCQAzW6f+T6ipVAX/qeh2COHvPSPc/XCgT9Wfc6r3Sfw2qgP/vep08/8Mp4XoLD/qfsB5RVQf7RW/K5bdRtz4vXbTRTBut/WiAAFpJot5coCAYdbWF88Pr4/1Ig6tdI8j+q/nXJg/hQuZS7c7cd+GedSAaHMm4bOskxdHgpI980HJbwGUACcVIe6kfQziJfjD6xgR9CNtku3fSEPlI2kUoFLXEAjkBQCTowWr8IOSs+vWzdEnmiXyrXDvCUaWppVXgcDhCACC3EL/uP00x/CBQ9LDtRi5ynF4HBesBzImScCDhsvaig1TVnNQDei11MTDqtbEcEzEhJ+wRaYYZpTNsNdXTCgQAmME8/BoP2lGIZ2MlHeEg1IcLiuqg9SU8Qh1VBBGwI/rA6iOd0f9CHzdvesk+QVgcRqQf2I0EjBXi875BmWq/8Ea+XcE7gB9k/aY8jcKwgIjKPMdiRkYGlStil5PavxqkOfRDmw38EgtgTiOdkvSm0lj8SGWf5I6+FZ0JkRRDPy7IS6BX7AvFoGIEgHv/yKqA8/pVVDJlwmWhtFZM69mGTzc3U1zBbrEw74thloG1MB4zd0zBWGvmRMM28hD0YJ24LGikjYtIeQwEENxTOLpAL7+bIGK9M2tXkMdAKYJk6WaGP+YUgbUsfZsQO5JDT9n9E1ELHvhwX0PLusGAD7flACzL6c4QGSH3pfhIsVlXizUaf0JHOcWNLPziqg/QYGfDzbEsJvrLkt6IsrxcLW4ptN40OpS36rGPEqld+QLumEpVFV0HK7/DWyY9k1uH3fCJcYBx/T4Gg7UZGm92kg0vTqXUh7qsbc6aMu+My0LcUah8v/XojO6tVUNj00Yv9Wrrm8JensGf5r0GHnn+X7oteKmqg/junGO0+JZAr+wAAQABJREFUuBDGM8/gT8imt8PTI368J02mjI+6uOJbL3lY71CwnwALc0Ud+eZtE56SjlGlWQJq7ACYt+pDQEvWjW7x3wsv0FUmaEtd18jRsPTcfREGOnZporTpF1hAQW90wYpD/+v7xL0VDHIimevk2zx+k2cOwZ7qjrj/Of7bPJBefFS/e/gRd0Laqy/JudP+MrpyLW0AfO2yIOcQFnJzXFsC3MwI0Ngunaq3tClzbpfyknsOG21CgOtKHP84LAB1tqDgEbEftDKItxWzZ9C33+oRMW0aTSDoDYEgrRJ6qwAQASFeLf89r5HX/QY/XmurId+j+FbC9z2cd2wSTeu4iM+9kHO/ah6bCUhhk8q6nE+QCPryTFnnJjjSPpPBNQlKw4TNJxKK5XRHgrldBoNi340pL5/u7FROIEgN8i0GGilou2t4ZYYDQfzApg/BG4JAX37O1hfkPPnwuVYDvdaHV8hnRRCPhX3Ndhja9uKr16Jhg2jeDCahfODNSiPueyRberD7acJ02w9B7kZ51I5EUCyBoDSL9vDmsN/Yo2Fz2eaOwxlJy2H4AuAAii8l6LhrYyIhcs1qAJ0fyvAO/Z0NWnSNPdCGi+MQt9jc8s+D6GyvcjoTOtJLukCYjhNDxAddoqlAEcLQlz31xbIR4VHC0WMdSEC7KZA1DY7qne5AWex5YXBRaTtD7TF72I3cVGWxSLDfJyTKLAQsZSiT0i7Ri2GkHoEg2ESsf05cXwSJjZv9Z7qRaFXKHeRAoPgPdakj0pO7cv/BqbJmv0U7jf4bZ7OUedlmLYQfej5m6PSEFJR2hRDtEo3kOmijQBCBHYJBfqOYN5JO9P699g3Km8NSZ7nmT56ciQRrxY+eAf6JR8XWKiCCP/MhMCR6Pz6GCUp5ZeMxbtqy0x+2PRZLSaLkmhA/5xlZAkJEyHkrmD559CtBIE9+Cgx5byAFg/yMLMsixctrE5kQM7lih/qV6Ljc4nLcIwtHObmdSL0wjebaqWLOgNJBVjqVlZCdAFAeP6PtqElwSLYMETAn6NWnSFmwQSEMWHCxO/o2ZXGwuDwozPK4rCzl2EYCRxn6HCgcCTojlZtnj8yAcyzZtr5MyDLjCNqUYHYKDcdSb9xjyWeEGhXbxTG+j5dGxr2REtP5A4nIsK4qxOThOemgdRKupAV9PC8HwnPelccXYeMmt0PlUpvG9gz10C14hZgDrhCnTaeUKRdUOWVZcqZQOqoq263iMdvkhg0/5xMXFb+HhA98vsQbqsVZuZjWyBcy3xvQMbY8SpdrTD0+PelCcIytNW6XiumAJaosyTEsprxM5w4cupbDvY/IeAkHVfHNN+cunXac5U2R0ok7y/8KurZx9N7PddD0T+Fn+dG8ey9fKVDBSMj5XcVXmsfOq0NKZ4umizOmOlquIyz0wQ3djKW2pRKdGyfZaXBQPrkvVDhDNGP7yFv1k/ROV56ThvJJd8cZr0NtelHGEIL/mE0IqzzYKF/STcYH5Q2j5VE+fHPKPNrYc/1E/0/51/dAzr0P6elA+BAlONLq8CkbeBye0YGujpI+o7vTQPthunDo6Au7cxYAVl1HjriBZ2ju76qDKmoPfoqu6kC+xKjAqZdvuRClrns+FVwevN9grHsEB4JYFcSPzPq8UbDn7Xfv/vCXv77JiiCCPwoCfSsYL6jhDcY8UsaG0Twi5reI8eP12i+IuZePLkAI/rhhBIT0kW7tspN8bTbNzLgDRmnNXLz4nAdHI5mvx/8GALRA5YSePKObljJOMWxYctcrOEaAJyN8wguAFDQhlELuj2qwwa/bFAdpuKf0vkDct7ANhp5+8I/e+vGbR8TYBuPPCgJ9rQ8vw+ExsG/YG1V4B4L04zf3PjwWxj2PHy0TjFVA654HnbYb25JsJkUXMqbcBAGygID7E1s/fUxfgPstPhpmn+AJume5xlB1XRIopclSOY4znA5I5VTCDL6DYhXt+dIvqAoaMI8mnQS7/HfTPWsJNg5cWcwqXfOtktK+YAr+WSsuHM/FvEhykaeK6xxGTsVd6AAugAbujaioZXsBFTrWrAspHMG/T2R0Z9LIvBHhHJGnuUtpBPoGP/2/ZAl3ITF5TrTy2e8jw9RL3DREejIZtmEICYwS9kUHx8Se0Y/5nMSO3svQn/W4l3f5ZwmnVvHkzWJE8DMZs8G0Vw4J5+WarPYh0KMAEoEe7xckecD8XLAmfu8RRLAI+W5ILlFxbSda2gRqrEyz2zRBKWa5ZAI6BIFYHunlkmwQzccTpWAODOXZ2C+ZFBVF72Ngf3JkPDetngwVBCKWhuZ9IxtPxsVM2JgzvxXISLXCMBtsZCiQ4uaBhQ6wEuJpJ+Gf7D9EQEg1IRwMEp68ya1V1RBEFVF5Rhw+hBKY8fHgqpa3uRBL3sBepC3PJd/c56bReOVMyPzUREvKDd/fw7vPoWoTN/8j8C6reqDuuX7CKuXM6ZrVl3eBi/BFxKI4C9vuXACcuJafSrSxOtioUjZ/xiHYauAz/PCCWnQq30irFninPDjrl99LIIj21k0+Ze2AOqsYPLNTxs9z3EGVLhVZpZ2lTfcvLmHMx0zHpP/udJ/gtvscBsunNP0U3bn7Q4zRI8rx2VUHtT0CkJ3xc2q5liOG60LNozMeK6GyO8My23xM3t/f6qs9qd0tAFoY5VpGmTbW9tRfOv59dC9J2fBP9cWn6t2SXy4h67ebHsfvv6et1/HVsYfne/7dx+DVzlBeYc9qL9G91Md3u+4yX5J3p0t9zVn9IhYYDe6FA/aM+5OH4ZjU/WGQVdFtjecVnd+5vp8QiJC+xyAXDh7vGcSKID5+POxHrQBSsEefv/zluz/8VXsF8Up5B4IE47Xy79mWwo+T8UM1vAoG+UdmydSNHPdXCVCgQx90MdcoKEQQiCCZSERH3rty7mv7Ea2dwTU5jaUunMr8ic2wbv/APZ9B0TK80UpL84HHhk2dsm629OH+AWdkRRA3YJI3F6x/1JvCFPPx/UB+uFbghr2BFL358gt+1OYpB/Y8/Syvh9eKoNf68EP4N4J//TV4AkF/0gbSCgRp1VBfhGM5eiLCgaA0ACPd9KPqFhghk4Hn/pXWpD20HnNpSfo3j4XRv7+9R8PsIR3UOnXV87S85+Fyo/FQHNhZBnRKZDB+KB20cnRSCh60eP9ZYkAXh50vkHkUFGe6NgpgbRNsZJg0Z4bwpd0G7C/M4KAf1k0ETMCaB8EzGhhKB76p5l7adMor4bN86Dyob0q7PwBy06O1PoI6+F3DeP7diNB57nBLYu35hd8LtNLnBEISvEkuTbXhBzDxkxBjr203Qgc5yCw4CgL+ZUjEAAM0NCqf0xU42yNirwoiUKOP9wxS0Jo8Ey879xPB1/JMwZjE8yYATQAann17mFcVEQBikpbRDgapnMfGFAgSzvpsZmzD3tjhYyoDNErtIs9yRk1iuuP0/kAKBLFpGvsEsRv+3jOIR8C0kRqBIuMSMGISdKR9bZSGnyRbnRrXRQ8zXPyLU2JTfMkxH74QTKNq+hNUuMjNBQ76qSMdfXrRpb5jEgjqc8DoJ0h0jhfo+feXBVOAiKoLXGnxTctWLBwWACfFGhUizvUFM8XjAXvG7AP5hMu+io6OsFJV/yHAbTzrj2W4KuER+wwSn2y+bfsE+EbcVapqm/CmsZaHY5EJ7D55ZoRhJ1/LLxFrZCyD7t8Nw4M+0yzCIC5VG/XEZaPfxtcGwax0cAW/lA8Z6ijWlEsOfOz5/QSCcp77nJvGU05xnHH4NDjgj7iQxdGbLqR03YlvbXfGgjwvrE4DXd1LaHgA8xVfdKDX44nzgLzJgPrUdZavkn4Z3V3GWf+Qrn+ALk3eDd+lCN19JQOwYVLOV1UGZvNgPmhMKth5apoOxDNaM+wD48zibjKXjE36VN6LdCefyrZvdGGWlbpwJ5QaD/6rQa2VxeOZShEVc2vQU/ugFd06z0RUOuveJ4vhxcG2yqfuKS8chE01WHWTieiRrkRgSGe95WAej+V5xNwhj3rvFP8d623/h6z/dB/8fS1E94f0ntI+RHfizvLJT/nE3ct32pfq9ddd3ofoT10z+nwOlicyz71Q4eDzNFncacdJtbm47dtqAt9c/cEbeyxwAjThKZ1uAXz/wNMDbC3BI2LsB8TjYG++/d6bRn/H/kDahoIg0BvB/cp5fojWdhXeY4h7EO4xJOvnH//4hx9lFHZxL5JgT76P8xiT7oVmJVDDN/nS44o1j5JxXed7P+XMMcbPD7V4LS1Nm1CU+Y6L8l67DcX6AqC1bbFyF0XvlUBUKHMdQVmbQ2O8yrzSHfWf6/An7m30Q/dnCtx8+Rn3N2z4rDeDzYoffvR+zX6of/5KMG2JAY6nH/Q4mJ+AUKAI2KuvJEff8V5ZREBJn/2Dt1oX06V/p2npXH/aVFtqe2Xrs0AQ9yk0g4DVfylY9Wsk+cme+jVkP5Xpm/BRSXc74TFgzRe8hSFz1lOBCp28T8KsxTDoODTgcoBWcSxATHvIuAyaeuZJf26G8jVfslsAcUhwG4sj37iYkRNtfTE/kIp+7H3mskU+Yj0YD/qFRzOiwN3K49YgjDzoVB+WjT9gOQEvqFWxGTqsoJDaHjPFBdyUKfjxruF0G0RYapfMGG44za9DhnPlLaGRJHIisGn3aKsDlpXYFz73AfAxAG2BMalRTlAokkx48Y3hGmKMMoJAf/N+Pip7pQ/BHHb714dnewVzYEgzMKt7vEk0vJqB2SCaee5ncJoUwHmV0ZTZIwhYouDYVr9gEyn+SJljaCDT1OUgDkEg9ghiMuOZ2c81QX5GEIiPyuwZ9JknTXAzkYqWslcCKSfww6QY30Sbo/HLnHoq5136XLbpD5Kev+mS9AE4/ungZBypqh5C1VgRpLZIcd4eRltU1wG5WRm0jBAs5YsMZI3fwONDU01uVeBRv0WpslNjuCYbccvETbZLyKm8O6FVgYw6Wh/Sq/LWmpvh6QGKyDjRH+Lb/j05UsazSGtbHykGIgLk+Ly5t3GR1Io640VpQqCXlPaU0yAdanPsyrgqrvkFFzFFpT9aM2ErR36BI0Af2naBH/QtVheDNY0wxkUdmldc67+3QBBOwVWkjpvmgd6Pe3xd6Spl03NWe4zwyOoaj490NoAOaJ9tEQdMBBlMJzZM5b1hLlXrH93rQheKJ/acjM9seob/70Q3ttgtKvc8Pc2Oy3p+x7Un/ix/rGmlLV3zwn9pXjnNX5JTvHMa9iSBo6s7VNZwfEL7dByedBaWkfOCuo+KqDjb3MoH8tI1f4m0eOencQCOVLoDpOLJUEwpm5/wlj+c3zk/TP0x7OrBjxEKj+YzlfcZ7FnbTzrKbUnz4u96Cv8lee04dZzlu8wP4U7a0jU/cWe5ePK/J9XuT+V5Rj/XTEYdeJnS89XXuAfqom19P4igzVgEbY/mvEPeOUYqNrqgy/cXVzbAbJ1z9vdhNU9+JPbbwHRfweNfvB2MvYLe6o1h1Nkf6M2b77U6SKuBFATiDcZ5lIyVRKwKyj0Iq4N48oB7Jt1eJBgk2wkCORBkvbRB+wgpIBS7dU8kED/Lpj6Pk1Hj13xh+I4mpe6Sv4sJg+R7AcF8p5vMh5QFVwqYcuhkUfjsJBk6ee4ntE5JDL4fkBDdwuQHbwVzWGHzuVYC8eM2j4a99uvgtepHMFb+8Mr4r7UHKltksCfqawWKvtT+QK8cCOJHce6XkEdwKY+HYedptw1eB3ot1s8tZkwVXnd0aQ542c8dEr7gno7tQWgqP87/dgJBE91ye5eDdiFdnWOhdl38N24s5qSjjKOTmpcyw+cOxd1H8gA66lO8cz1SXCF3MVgWGXP0SKG8Jbclm/eKt4ZFvgoeIIeYqyG/sLZteBTwiKvlV9reFG9obM6KD0FvbK5K+MpViC4mBJ0+mkN6ElVmLvpb46ThAx9SUg7WUi2bEtPR7vngQqejqoU4l9mZuJjAhMeWcbgff1IZutJi5eIfRG3y41uaWLHPwZ6ZaBsQ8mNgROMJGk3OJJxgkCYHwfrxfkEKAAlkGMEgNxsTa5/K2Bt/YGQtc6MyQcob3thZdASACPZ4hQ85EyUfgkCaSf1RnXbzXCz4PxE4kkMdbBGcpZGoj6+mrGx51TOg2re9NjjZ5v/YuGw264bVl+YXuPdP6CM4kRVBEwiaXx4cDhIBpl2S+L3ctn5Bng1PnqbQGLjK3fyQJFAsPGAuPoHCXvATUbBdz50SC64iY/eaIsQXI1fEpdYmXoCuSOjNNxffPzJsyEtCx4eb8IWSTN+9stu5ff2cz5Qn+VOyK8F29Uvwm5CSrbZsCRdK0w2uPGuslBLEwX8UF4sKLt9zcf4eA0F47nATVSdgy2cDe4TcOGF4zmj4jXpJ/WihhlRA62Ys8JmUC+EzAsE+xP9x9AtC/+3g3fJdetoJoD/ign97Y36pAdM2mnd6wZVPafOF6Tfsp4t/742+IKfyKc57xvePwj7FtuqojR/jKR18H6Ot7OYnb2H/aP732FD9fw/Pp9hXuZ9CC80z/c9gL8kLbS5zXuJ7CV6Z4F+ioT27Tdy9hnLDLlJ0YXjiEyyAAnh/PM69RR4P09MGvJhGgaC/fqcg0Lwp7C1vFOtjYtqflH2CeGnNe14rP28eg5+gkvcM8uNiyOVeQ5pkBG8Isy3OuSplU2nytCnX0arzYznX4fxxE0fyNRX1tNPXgBLm8IfjA9NKoZcnXNgeyr2MPSBGcmUyLFZRzxolgkH5gVj3JtzDsH2FAj9f6P6GDaJ5ssH7A2lPIN6E/LVW3HhvVAWAeFOY6woSfaUg0Gs2iOaHcd0Pfckm0QR/tEcQQSZkfyHZ63LRFuWw2qAWuq22NbheQtNiB32gIPhDS4S8BIKk478UkPo10r98RRA3sHR5u9g3PXiPRq8WqtTK9qKdfFSHGsJ+AoIGSAZFXJ968B88umcq4aBEwJnGkAd7RBfSnDCL5Rz0dLYZOUTCKT6Do5Inb9UCwx+6peFpYVz7FHcBIh/ba8jULzQhmQZO+xZdGYUWDLBRMoDcDUa4/hkBvYHf5aP/RWdSi6StOoGYUyIJaZMs2Yp882q+4XVjINt2LRsKtYEbv0s5CcfwsKFd9GdQiKDDWGB7sjdSINhf8ch1ezSJMvxpaQI4TKwK/GiizebPqhMoWpMtlKIXDQGLvjqeHeQbEAL+o1YK0X6xRZFtjR05B4DrcyR8SUCH/XS6kiaPeBEMEkxt+1wfllH6MSto50Pg5486kHeSdVBMKj2um0uffYBe+nD0Z8K3sYZMKxd2gPZTy+SeGN1I+UntddvcroyM7hEkSz05Z6+g4HzELgtEWFzi1VaSBQac7Z+yYQEM1+I+BA1qMkuSnzwei4JNiMkKHV2rugqeE1etqsbgWClsbE4e4oxN4HxIW6ObKDjPeS+8itR+UZLA9Fu5tySXxm9k0V265L0I2NCRJtPbBxuXEj6Fz5rbxCFC56ln+yIExovTtg0PM9G6OAF2k7nIFteVe+HvHWZEaSUUuTEghaJUq8pl+5Cvumh+r4EgNX15nvKHUsZZHT2Ude7B6PFzONdj7U7n/pkOK2713yGMYhRv4D9KtyUh/FK7VD6AutB9qPKSrXeefyKde0jy1vzoDrgrTP1jasE3tZta/7Xyj9lUvf9susr9WP6pej8m55+J/1SbPo3OI0jmnT0O578ynbp/Lb2f5o3ji2UM+ZAv6rtnNn8IB/29zXf77vVnOv5R2Ifadpd9t7f4tvMl/Em3y4vahdQKy4qg1srzJJf5fPVcW0GtkObwpozUSM6VfMvcN0GDPNPo4P1CdX3M1hEEcH7UG8GyD6k2jVagh72BeDMx+RutDvpO+wW9/16rhgR7SzBI+bv3envxez0eJt4ftWUFTyr8oHsS7lG8L6kUc+3NK+Ntg/Vja56O+MMfP5dV2KqbA9rg+V33SQ4CpZ5r8bQPvEuWwwGpysMtfsqt9/pNV4AG6eDcYRPJgVcfwfrDqG5hct8iPeznk4ANQSCtBOLxLn3YA5UNoL/mLch6M/IXvAyHDaK9Ckh10fjNyQoafcFKIgeD9JjZ2hM1chUfcpN1vKRpadpZzJiedkwz1HY3RzRyudqYlU5rRZDuK/5LAapfI/1bAkG9oHeD5KU23rk8kzrHuNCngIquOc/gKWUc42F5+KhSNwj+GXYbOCVwS++dtabQa5vwNMjMRomEDiTBlgseTgQGMXfQWKA/nyBwhK6FtCnwUSCU6gXtgln+WYeziS/J7KDF4pavfQAnkjCXNqqmA8ECkn0RtPkjIwAfBUgOrTl0iJwVTAZ8+gAd/4+9N12Q20aWtj1jy8v745z7v9BvbElevngiMgCQVd1q2Z7laAypCCAzckECRJFokAXWsir4/+TQnVahhOQrplQHc83sP1zaY7NqC31Hk5KPEhH2aTw06VqWmZ9wVDeNjA7WM/zRgeHiydWLQJEBCg0Z+JGdBSDTZzeQnoNl4udxs0RvbNvncVllQTheqGxpZILuQhCLP10E8iNWM4nS1iwC0eboRIY+wVp2RoUXArbCO++ROY/jI37YocnPOqTwfOQgI46DYdIwuThO+GCQfWO8cb7JZ3KEh67smqQoesdItACXzEajBSUcOYtpU8oApwzhWfIX2TNGaJkHVK5C5XhzT1uN7DsAMbjcHMB2gxIjbYXTBevezXUzUbf0WOJTh8QNT+ML+FpmTCVWLyoVNGiO0uEjOkpvXp3h1Zbx43NpW1+wPopoOgeEdEjdlYFQhmrO4My6HYYPlWBtFYN7RrupWNWtq2rcpVKaPPyUNY51gcEFzZee6MvG42zrjtZJfaXcLr0oq5YLcSkpt4QTxWiuZ+Cqvtidl7spz0ufg9saLj7dHd6w/4zSC03cbUgUn55KT1pwV3dvfvXecU9U/ceS3ur7vwv3nxO4jJ0/35+73rPeEVar7YXmpT/mFy2jxt+3J+NR7E2U5yrwqWkMtur87vNL+JfoF2WqVF/zO7/1T/GLeyk//XkJc6ef7T/Ld1zrxTQvnXkfmj6+qA0fGiU8s3erifU1OK7EyiJf/a/ylq6MiaELmO9FvoOSfBUjaN7TI3fEwAvuJ/wkge4j/MoJFnH4dWI9IvYjizz8OrF2A7HwwyIQ7w9it9BP+vz4k3YGKeddpSwevdcCUR4T07tL9bTBB/3QjReDZISdQvwhm8UKkq+JVwS4UMFXclZGlNxYt1rFaWOvzYPQ0S0gc7mtLR6p64s4iCjgfEbr8sILQbom7kIQTzp861dZ8F5TLf74ES9+9Yv3oGo3kBaDfuB9QDz+pTovjuZxMF4ezaNj3/NjOaJ7JxAvhdYiEDuAeBUG90Ii+ToNP+qVissfymfqtR3o/lpYFsFEEVMPgylnISiLQdDeqTFfzEKQdy+okVxcdUxwXk3/6xzLxbrrjGwiO73sosuSVWCWvCG5DUeOQZRclSmNCghYdt4DNdsjnx5aNFxoRblPygFD702PxRafzt3Y4HKC+L0lyOGDGGMurqiyJ4x4iamcDMKvdJYX8WnBro6/BpyilIdnO9VwVlpWvto0Qsv3VaBdUbl3iFyjjTomUscRPdab/sJA+AY5PhAcP98J0eeyYBCYKY79kbbuc7I+kCluL60BdQ5LnTc1UJuSTfPBefEkNcqkTlapyCf7nL5doRHNumij2k8/Q2CIe/VXBbAef4yLo55YiWfanhgYLVlEglZZq60ra+cVZxUTqWAq4beODmzyvRiUhZQ8LkZ7WQbJhIpcF39QYXFbmnLCEdtTjr2A8ktY+DCjHKeNA8WH5Mg4RuPsYhEH1jvcCvKKSMpq5uCFGzlHm3ASGYqgyBxjqtbRmEBYCJeo6x1zGUqxMIiMQ0tacXQYiAEbQVpJZao5xGHiXt/jFwDSCOOwymi1bMTMpr8h0hJ0MKtcQdSRitBqKziR1iI1saxxG4lOk1RHd7VAq7+jxL6xUHkwlr7oVS9UP7ZXOuwODU/5NI07re4cc6pd2lSuHUYyfVyN9WG7kpjW3mu2VgCwsRVUtW3BSvyliZM5leB30AyzjtIc1MBNcts40O+TTNPllK6nOO++9ES7733r/mvM3hgA+hRdzh3Mey+buLoKtZlXKrRZg7wQMqbEWUzkZAM/awqex8wruMqfODtdfU9MiIVdjwfbSB3yStWxCP8BhfGpTaYRxHHX4+Mz11+iPWvVH9F3l32m/19Je6nd+HnyznL9+3fRav+fmzcCv9fKM/nSyM9EJEkn/YXo6vw/tXh83+auk2bNVR8jn330lPMJHcWcrnjKWdZeaM+lzYD/Xbg6+omGFvaQn313lk/gnX6vg809Zq7zdCEmSFH2TAfHeFUkMl8sxaGl073LHEhM6As07WR+XDSsc22TOZN7ZvzI1TKXHhp7wvoPylql8UKQdvF8ZFfPx1+92+eDHvfKrh/tANL7gfgFsff6dTEWhnhsjHcJeRGIxSA/LpZFJH7hmF8zlip98sdoPyYme9lhfjwq5sZwsbI/qwluOI7ro/K00hJpfJG5RoNh9AD5WfXEk+8OsNRhBh+s9KqQ8m/rFRa855RfN/aLof1olxaBtBj0PYs7PPqlnUDf86tgyr0QxG4hlX/4gV9F5h1C2g3kDzqyAKSqFpry9IEXgl65RqO/7DKNIk1TO2O4n3Vx7/5UhZG2NgLkJs/vNPrfL2VH0F4IUjBmMOSt4gziDGsHx5Fh0OeCgVJ3HqxA0tsrcZo0vgyMnBiLrYJvDOmAATJgWgU3Jk102UA4SuNbKjqObQ/n0Ve/bV1469ABG96doIWDv/8tq6SXRxJGKbhcCMt7ya1TxbY0ypT7IhQgaeymcjuKt1wenCePOwzc6AO2KsUpSPWJx5ECkQD/LadDA+mWpopz9BdrJfYZQXBKeRG0BvosdqCMsnkoNWxaLxmqSweg0WMnxLV/OsvqT+be2DLReOm1Ceix1bxfzs0xESeKoyo/0GNnqFJPvlEwhyZnok9WBtAcjEk6QCOqzgdHhXC4Sm7BjKPGjhp0MOZTtgyU8UOla5qYigi2CyXgWWfz7iB1mP+572aDZ9stubS5FlK3E8TiNDYV8DvN+NHPOGI/aQrgIDpX8cJWLEXIlx84mNPeESk8/YKSGE6Lcy6FEmRiVgoKVd5OGZQlsFEliOd5GiTc9VwaPW6snZM6aFNGm6qDOgrAGL/kgEirEGJ9Aufzb/Pbm5E9bElLa6BdnvnIY8YGt6UdM+zPuKWotP2iFq3oYC7oXyvgoM16pqGMJephKY8Tq376B7H1AB6PbUfCITT/JzZjRUIqGYgvV9+XKxLElmGTP1oLxWESsHmkTjRa4ofb2kY4D8+AGqOCI/Z78yvWIMBeY10iPjdfuciwjS/g4IXFaQfxdFwUjMSWWmO2IpZ4isNYsIxjO0oMm7Pk6IN0waFjNNsCdkfHg75RS3byzrJ5xo3B1d8mXg/mQZLlsXttYWrAgMQ/2rqafdM3VQMjcwVM7TQyul/EoYv0Z+LG/qk6RvaxvE1Jqa7f6dRPmeKgfcr1yv5ZuPrxKX2fi8PP35P+nbHA37f0wVtikbY/Q+Y7tNND29tY7fO5lPZMkdHZ8yp555+ca9VRDc197lOR0H0eKIb8Uzif4xao3UPf4aanDLl72jrLtXnRR4OUnuF2zF6f95B9W09iCXtbHxTS6VMowbW8Hlt3I4O35zps2aDfdrT0Dbpp7dNH3RuzhaGFrp5RUdeyDcmAuLIA4e7iC1yKdaU0cQ/IvSuQ2UIaa1bu78bEaEFHo5knDrDNv3gzufqXLvYtlAr5VeG8a5QFIe/s0aNevECaXyj242H65bAff2TBRwtCH/X5B4+N5dGxD8LlBdJZGPrAY2LaJcSvifHIGT9Mw4IQIchdN7bxJa1JfPCXeunhTXRWlqhcj1tqWukO0r1IVWBpyolGrGAur4fgnT38AS2vt3CuVRsWdPjRGxaDvtNPv39HrsfA3mkhiJdD/+BHw7Lzxz8TL5lvxf/WMrwXSDq0A4g/zPGHcpdlk/tcrTH5Ou3akuc1XJ9TUi3JYKClRDN9mAUhxicfr4uIwcutv5iFoI+//OwgMLH4Q1Cmo3shmOD46PBwKoClk0k5DotAmpKbTFMd5S1/xVeeIfQ8uVPoHxCtAOVmaqQirVpH51y5A28H4oYnGxX8Elth/6Z9/t2xUPs1YSzyqz1GYlkJ2ZfvBqpjoChxmiwVjjW6KVOaeD0IhH22iQ6cENtMVCJISTl9paL7lzbDoY/5jGxuIqlGl/u+eo3XwbGVrsorXwmlSvgRl3OEZlarg4vDwZYEttLxrRR6ABS+KZffnnJd5AApORWKO2VyHvFN9vhY1ky3izowcmlH06VM64Y3PSRswaHsqurjS/yvxsktBkAF90XAuylIpe+QaLP4Y0XLo8nibveossrFTKG+tAFEpruCCm2bWidv66JTtUUIylUON3q48TUxuUYhtRGyc7L+RMdl5Z6W8x+gg3CPxFg1zC0UbmNWcZTC2Sbl0YJagZWFtBht1tHv4uHOQJzd4LUB2eNF/u+/MGh8i+F4qOCy6sgsNaN8svGB3tI/fSnx5eTdRdLLP2tj8Yd/6OS891+5xpOrIknEEsf6OkYesmKM0yHjX1YPwfqNfZJ9GOLV9CE0lio7VWePqHJH/1SNG/DiLGFRqnw5WwLt3uVqB7agQ/xvWQjyd4Ha7LlV+Z7nEqcdrRlx07Eb1ygOsv1SwdUvq1AB59h91HWBfKKC3o5A+vflZJfeYO8xFujk3Lrpbv01oxGN4J+J+5Su09X6ObSK3sjmlncXh/4avrxn8ndd1P8duE/ZPP38o2Xi0ZiddktH/396zHYMcu7ToNP/zW9LSjlbXNrOOY/u8y3ct8wFz8/NrbulnKvMLVBe9uft+tLGT81Vb2lDfXxL/jDnTGt2xD89f/6+Nm4Ldz/r02Mf7jgXg2xitnlXfbkuqq5TLv2G3PW6o/LWOIdqf9nrSikHpGvR2Mr9XcowuJ5sokwttF47QMFfFhAoU/GP0ojgH6TRzYV/Tl4LN/woDYtC7Prhl8N4FIxHx37SC6X/oYUhflHMPyM/vyTGe4Ooswj0QTuHkOWXjXmf6Yd5ZxC28WXn9S90nOoTEPYPH9Nol85aWizuBM7RUHmqi85uoEQK7MROIL++QtecXC/xflMe22IRiMfCvtPLoHkxNAtB7Ozxo17zLiAWeL79Vo+CTd3vDmJxSDL8vDzvTv1W7waKLhaYshbhxSbZ5zbV12j4WmdX654XgCVmDmD6Tkdfozto8FkYCo1n8fDlC1oI0ottJzbuRMqKHoOcwDB8OFlzn5YywTVWHcxl1jXW3EZD4cYEeVILzUMt2RrQaTJHPsL6f6jFBlIesOocP+RcOj8nA0q6qJGX+uaUxf+/swikUeNdQTZbW2l7J/ZtQnyZQxYjg76U1802bvFBpC4OSVlbGF+jNrssYDpJSP+xhR+x1X6Jfyhu22qjPi/frEsnI48z6F9XaF1DN0bUZeTuZ99Uphz9UiBl3q3CzST/3H6RcRFDdlUHpyGoDKx/VYCKIttzOfJWYq1gpSNA6xxYshsd3cGqwH/X8Y4ydkaMMhUYykuHYNIQDIEQWApT3jKLPJxRepIv5VFoWvrQdkZ6OxNceVVxSr9GK4+8Mmdu/yGU6ICI6rq5o8JLYHKrwCHfM4mkHxNDszsQijWGSgq24kMjH2A1uO+HXxV1D/LySBX6+mJ/mGTdzYg+j9MqQ0e/yfCJRP1iU96gJAZApCyaF562F+FxlHxU0JKWkNuQnsMZ3jm3fK6J4HZLjphjFi3xE9OjhEzlQ+Uox57mAJ2zv+knLZOHZgBfwvqgJ7sB5wWC9jPaQKdU70f1kwxcWnjkNMrEaUsB47tmHOt3FQVux32EVWgbPW1t6i6VT1Su0sMRsfHzWIgXE+/oAek0zrl/RHSTOChNplLiD/W/ZiHo0n5VztTgERCVqWYsnyB4A6Q/hrVjWmyUhL/P22f60t/gUT0SVjiOjEpYkZeEKs90VcdLPPPltc/OpWO1YizRLtFKbh4XF8aFk3eWr6it62zSH9VX+dNuy4f9E3aaPyAuFnenV2VlH3DDuOPQ84AV7d+Jqz+nD5f2CuBxNkRwlbngVDl51VcMMuU3L+/MK1c8vGf2/qU4nXvLh5x0drnnZpn1+XouEb/ff24m+JkvXjyH8YkkP+vT4/wjvv8wJOwFt1pmFT481fcHcePQ9u9R3xmnS3l75lBbh/RtDR0NAEV1H022QeH1bPtkLEZnVV9idjjU4g1ns2xfWWkccWxV1v88auTuWCgKDb9B00q3dlSAcZGD21o7mcdFBKIj9KMdh7z5ZnGBmO8PaE1cvfBpqmjoodKlXkRwrrIWDtgZBJ3HuH7hkS5V/LPy2uHDL4V51492CH3Qi6R/1Ocf7AjSDiFeFP3+R+0Y4uXSwvGOIXYS+X1D0sNi0M/a0KFM+v+W99n4mhC7+MM5hj/jmzLK0Dg487Ft6sKOv9l8PUqpsScv0jQWglRg7LEI45z7an2+1vWnf85dF038Ohi7gMj5ZTAWgPyeH3K9E4jdPt8pz8KPFod4J5DeDeR3AkHXwss7LQJ54UfPfvELylyL+SPb2NGakJOv0eqkKC5O/SAHPEfHI6PHfQPZ99cUxPT6gXOCp4Ug3VR/UQtBNDZ/MXZ302yFg0HrI5VVN1ORdMerE7ZEpAweecsxyig4ZdBlBA5pZekebhw4/fpgGdRI7+7jNFwqK6+BIHftF/ekzGU91f0lQVv0YQ8R//NuoHhPOUMFTBVK3mdOfR46uufmChkvkGwRl3pPjKrtf8o+L7eqLbmbJ1p8hGl3XC0tPuKVH0sqz4rDO40qLEo5KXMzmBOWs9pfKG0jethVID2ZwNBlosSDZfudW0E9JZTvBFEi/FqX+2g4/l5xGYCScQJOwo6/wFyHPjiV0h8bm/ZEcJUp8H+JpX7quZZxc4GtjNq2Ev09uj9bOXDR8JIUEcDKaefatqo0VYcTCc+afUjbzrFZ2bsM9AfaneAOQfEot7LWT5oZF42nD8R71y8Vq64mzINzP6OyjPEUHhiPx60wQhc8FaUxzFl77xvzB5MxtYwhmP8YazrL5Zc3/lHdY8u1hdht2SSrtClKjAJnLeW8paa2epspAPUJ/vqfvtD2+QAvTiYzGI1KlEfPrz9LF1UOk5inWIhhnpYwi0Ke/8yOzkKTZ7QeGmzhirnWMkdMKyV4HfPYoC2rCfYj1NPKqfMZHVrpkY4E5Z2o7fNa+IoAOcrEwn7Tv0NPFn3uMRHwOwvZKAiPki8yGHxfeCJGjUti8akGZwwntid2x67xvuqbTpDIgTwVvKFcHb9fwx4Xr5t7xOW8eXAeV+rWayr/bNxrtsYlR+lJqOJunZ52PdG3EW9oosDX/j4U3nx4Uyiq71DzUtH66uwJqt1nvBN3litTGrJ3WnlnftoQ/i5ysk8xcA+8O2H03cmnnpaf6itzWXr8DliQFi7GOkeUec07ghCJ/YvwFfxK7fGcew5+jmvLyZNKaf1JpM16rm9LtfRWXPF/Vr5blBa07njrpDsvB54g7EbOzfYQpOd9tNt4WnlsycaV9xhtbGzcM3vPaJlHohW+xt61gWY9ksDV5/qUR8R2TNAnTF11kUr+kFYpcq4xtndgcq/pgg7Ywi9uyVZZKzLcV/n9o+Jzb8nnZ730mZ+G5x1AP3mnD4+K8XJo3hGkXUHsAtLuH14W/X4WglgE4iXSefG0HjFjEYjdQVphou1cB7LY1DL1vDogNNPxTQyfveLr/6SUaJXPXeKgAdL7XMaKeb6mhJcPV5o8IaPX9Qw+mw++/vob32vzTqB3LN54MSgLQHnPD49+iccjX/p8r51A7BJixw+LQDwq9i0vk9bH7xTS4s/fdB/6Tr84xv0ot6T4xzUul2RelyCf6zN8xeHmVEmup7iOxKWR6AKQ74mH3IWgX3STzB+Gv7yFIDW0HU3PEiQPETU2OYSUG0EHnMHgf0SqaW41PLh8EEOylXcZbHmVI/86A8tG4sXqsrMn3WHgKcgD8aQ+ZQ+AnKjWYPoS8ODP4BVQZAbRanOUWJcnGHwWhpZfEjdYkvOLqcpQ3fZUv6F3/c7A9JK3G2lH/YBXH+wHZzekxjPlcbKajLHm0b9OlPGbJh+WLbd0VrcniehPiFiJpcN3bFHT+RXMuG19OdBgG1s0R8nAezAWxAVwO+4ntvr4IkH9IFV2X450smLhaTfE4SCT+dME+TR3gsRDBX5hC33WclFV4Qvx1HKoR+GFFX13GqputKvU1ITbi24H4nSFBQerQ2GVJs9fSQ65W7FoWdmig8kX7SCOzL0zcW8Z9uknbPddcRPbuCfm6MPU9h1tQyC/JzGjE4YNBOHxoqKFydSPpzNQxMMkEKIVaGoo2ePMAEgrrXYIXknGS5qmnG9l16GZY97f8cG+SUoLQSR0xZfWoW7PqHEJgm5vW+XSA7YSf9NBCnk/EoY3qgzbmOshNkp7GSd9+ofnJJ9TKnLRg1V4Scl7dK4D/jTVG0i1F3ZrRVIvDUSVNAe3y5SMnvEEd4vLaumjslXjBhpt1Uneci4yuPD40pPHFY2k6RMrj8d2xS0AVx6gHbPKI3LFVUnxewxdcS8YfVlhFa/c49+dffhlbnWHfrW7xF04eSnHb8qXRP1U2/IFpMqJu/PO+p+Ei7eH3bvfYnFu7/P4dOLPKTcUT0xfDCxfh3qvn+CTd5ZPzOeW0dP0mq8nDvy9q5bsH3Dsc0RP7FluW5y/yLigHiqIkdzGVkKaY78ZgmrbH6HjAOejQOd3ykXdpSKUsNf5evTMmK0d+3eR3RW+al/FGTC2FnLLu1QFquR79SAAaLUBAKd/Z3SAGXdgID3FwbilR9zZ6q3Uc1P9uemQMcfT6LMs3CnSuQ6b/L8wD51n/yBvvQc/OjW/2FYtNC+w9eJKT+72jAPru/zsVMGuY2TX43r1V+80SIqXv9wrTefEHtjuqKb5RSZHo7Xq4LIXg9Lf/sO6qH6EH5wulrheYreOfwKehSAt5LAAxGIOC0I8Jsa7g/xomHg/8jPyWgj6IDo7gN5rl5B3BQn7UYtJP//80Y+c+X1EusZEL/q9OCRb/iVj0bsryYtA4PBLwcp16W6Erz11Her7fOLS2Ki5XJ76ElUHYuO64vE1T9iwS0dE7wby4s832amjXT/+dTDt6HnHwo52+PAT8N+wM8gLP1r04afgRf9GC0YsDvln5EeOnUB5H1AWgLDBziCuw+qbfVUT6BH7pwJlDpcc2iTo7jfqxMF0YgKdAzFypiLX2okhnfhlLQRptPhkcrA4pHM9OByA3Gg4LESHJJiDzkBI1QGk3OAFD/gIoocddQJNeNNBuYlEV7QxuDzCzFfF/yE2BRnpTXOpF+gokbtIxeuUMl9UHjPcGCCJT3ziH6dqXHSLrLoH7wjSkiMavbMIhp0m1wcd0M5UQh0in9Qig9q+4IcU2XJ90knbgQjdJ7Lki8di9Og4tmgbDpH5ZFEhj4YtZKGWQa8fL7Fe9Q/t0KeyLASx8GO9YpC3WcsoFqGbH3mRHlI8o03gX8YhGP7YQ8BE0c3jKD3KGJPhqgJ5IuKSgotPKePf6bHJwzt8sQ6REZsyGXEipY3YZCwXbNZxeIm+IXZLvjM2aavVHzY3MlbKiheDV3ZaSsyib+HGb7bdtg2RKSLnZO3VldabB93Bit3EE371jimLGK+DzzoaSz/dOyBKjV/jx3rFcIDMuhyY7C3Wxp5c8WwPxATVi4HC0GOlUeO/EzkKjQ+Jqs/08sw+Kl0JDTw26Uhw+ud4qJqco/45zMrVLsdB6uoTbedjecpw4IckcnTDz7gbimMU3kZUKHpQAgV+c/Q0dQZpnfwZLtSiph27upy17zKGDrdJBcpnG1w9aOG/hCndGkcXtC3V2mrjOXZEhG48dIk5/ip0PIZv0GhVK5iYVcMK/P+WHUH6ynlIjEOHbvIHwBAecE90GdqgNq/8XfFrdg9Znycv2KpPqD7LD6bG1p1+r1eHc3TeAa3Xv+ZDx83KuFx+88rf8/Kb3/mqv6SvoVm+I1s95H+lvyLwiQh0DM1kus6ljqlzyu3QQmX5z9S/GSfgqf+ZLtvSoefAa/j61PxFfWP3bbheq6Kt0aKFO1VP8825xukZv9iHmJVx5GBe03FAF+5T+PKbnzrOcvnNN0/XDHY+sQmfmLVvG7PmkSy/+B3btnHj870eubP/q8OcM4CLICL/BYRtGwg5+SptlQFuFDzZ13+eZsGmP6BN29dJveaA9Yse5+JdQbwQ+oMqv+pdP++18POBF0bzE/PaEcTCz0/aIfReO4T8aBiPhfmn58ODzy+QsbPIj6BpMekXdhrp18XYxYJ+LzjpXp8dSWC4z+PdQnaSe0vcFNaxUnN9xTOLOr0m5TqIa26i4Vxx4VqIe8ou/HA/zPuAeO2KHwPTe32+Vv1b7Qx6p0UeHgXzI2Hs9nnHT8NrNxC/CMZOIfhaDGLRx4tFYP0oGfp5xIz3AWFbUR/bxDD3fcKMb6ZxUHLX6Xocn30Nqpa2zwyYQ/oucYBEHxETAqKoOC6mQXec+Pn4L+nRsLUQRKgaLEoKgEdFgkI9VYVH0L0QFDn4/DfOWQJZGd5h0bIfh0AfaM0If1s3U+lgxDGyTkZ6GNyYUimmgjLa3VtXTFlg+1UWNn1jZ1/NcntwLh2NT3Q9uY7QVa48qv0T3j0pcEof+3SYBDcmKN4qo+9Q6iIHTPqfyj1Bye0fJ2r6pbn9OwzRNtRk0KtAHf84YclVwGWnianFrV+2p73EoMny1oFs9MNb3WZgnICPjmepZuG17Og+h99UXEGW56DPLrt0EqwDj6/SkB9pSD/irGJ0DsBAkEFfd5QM/pKNXxfaWcEXeSl18epTeMm+4OiLkrM4YcGzLFWxGYWRp7w1PTcl/gyAxZ/C8z4Ff/pNRZYto4NzUY7xg5shr4Jk4plg5uElmOauURlaOOgdXRTKc5GDNAzdTB+Ca3GP+o2gBP1AXpn1Hgd9XlFI4hfHQsZ2vpzsgg45x6QVxVYeC3ExOvL1VG3KpT+otHNbqpLxU1V4xaJhY6mdnI0NZrf1KpP56SpbPVvmDDFjrn5QAn3XiTdJ6Ci3emtt6y965TMPRVLHUZFMeuiT0pynElr0ehGIPjniomsSz6fLzhdaIA4Tnt/fwicKnpCW/t27i/SnFKr3NdsYeoYr7ZOO3IC3qsU/Zf81Gy/pg/5H9J42z3P0pP9V/isCzyLQ+bO8e730v/J/TwSenc+mPZkwnpCeOv1sHnoKfEKMDR2lJGOlVpsjRPms3xXd+Xfs/l63NrN1oOEX6LOWiJb/vg6LALiNtQrp6j/q/oxu7mnzt765slZD52pvGoKB6CMGLAZ9ZPGGn5dXzi+A/aRHv1j0ee8dPyz86NExLQbxmNgHPQr2gd1CwvDiaP+CmOTYIeRfELMOXiStxR5heUk1P1vvl1V7pxALQvrodQLYdJIjXJOyNoRrPHbFdSj3jFno0W4flc1Ty7OhQDgvArHww44dLdZo8YadQSz+vPOijnb4aPWGn4Tn0a938+gX7wLyO4FE/4adQF4g0uNg37yTDj36xcIRj5LxTiAvAGURimsvkq+RW6Y+zTCzdcKsnuHel2g74qYV1TzCHHvv2vtgOpa+c50Yqd6FoG/lzP/ol83+GUmPnmHqX5f4CTo1TwYToQRL4XOj60oCwHC3ewJlIchLKnZ2u31i047wIptTAv2sRvp/bPuIdU4vsgzEuAZNVGjjq7QN1mgknLwaq9JJBcmYgWYpDk7oHB8c9vhef8eEfAUcIXuCWFZWNAAVg7+LxxlCBtQHVSMCZSXDBgcMNXccbfNNsBjeVqick5QTGd9YSMtWvvi7lKPP/yaXAZ8wjht+iju0LYMErmODY2qmjGPV4d1PKACfzGUf9l07RtOoG6bVddJCwAb/VS7f9dFsWvXBINmHliNnkhcM4aMykQhqaR5xxp/Nuv55h7FbX5Q3FowxuEW8rvfq0zhtr1+TQ2q6xbAVmhGq1ge6GJx7STPbixJ8vK7sbsGmUAp602jopb1HwxlJZo6FOD2yJ+4oH5rjJrzVWFkfbOKtihrZYUfsk3Z/lML4PcxIrLOIEKdRgnZJ9zomReMzfoGwWAeB5esoIdA/V0PrPNl8gmQ/fCaqIR659cUu+GDN7ObS1/VpZVScLTRbB3s3OTRhqpfqLUVDbHE8NeLTWd+i0PMvNPpk+1uc43ZoCAKNyO5USWgtL+5BdKwWY7f0ICnuh+Ypl+LqVE5c+WiMfeVePFdNTLzNxc9p6css6+vAI404PPTFJ5q8TpEd0CVxkhZuuJ7CT8DQ77h7vcrv9Hv9c3HFv5Rf4nKpPI/Zk6Yt1S/5WgDqkQfnws5Uekyf0leJE2fdZfyV/xWBT0SAefQcPxmgGp5PBvoFJ733ek3d6fd6cf/K/K0+3HH3en2+0+/1PxOH7jO5/qR/npBOsVW+qVv0h8IxT5W3bOg6pt+7afvi6LvmvJ6o5DlewOZqNFJnOfjqppZrRHRuh37jvs0+GGEht2uClZdVp6W5djEEbSkYxzVCrovshw6x4b/hq1Ie92yIiYszSOmaghJ+at3GL3rmp+XZveNHv3gPkHb9+PEvdvzwrqBZ/GFRhwUidg353UC8MFq7iD7q8TAWkT5qZcmLP8Kw6yeLQ7xLSGXxf1XunUPsGBJf/3NNKV/wBxd5D49f9KxVGNqfRaD4TZkXM3M/yS945WXQPBbGL3nxM+4sBLGzJwtA3hmkhR+/CFq7gKB/xy4gvwiaR8bYFSSsF38k6x1B0qvcj4PJDgtA2CX89ABFrhWcHMgpT1YWPcA9NmnEU5l65ikUJJ2bX0wVgKtt4rI/uv8Wk5+P/5/vvpSFIC0q0HgHrpFKBNxYBi+BgO9AEFj968BI+DjOySvZBhDK/A9/eOgZlG1QS8dxZLAp14d/5UFLOUjKJDAk83RIp0VfMOIPOMiFtlzslQawUvbemC0Xrk4N+xdmuLgcq6aGtYsPJeCRubJiXUf+M/h8ojKRcMKqPjkncOIoeYTskqKhMrHyxzamPDczPnlsePzGvIqnrrR8lFoHOmchSeB1n4dN5EkUEJn4UbqmBTDZch4HV9TTmvXCcePwIFakxFp14FznF6NomkcouR0F8SxlPG2/n2FKi10pFAF9jDJSHGs8mNqhhGrAcXjJDyDl0X8tH6IvFZ8YeiYNbe9aQkj+ewzMeW/9/aWAJ0rHftDyEX36MNbIl8QUVh3exCx+KeZm6nCChHuoQyN1jFQuRB99kOLo3qSH0gJMwdlZPiVKTzs79w01Jg/5CePNB87T6Nzn1YxZMXbTpyR91iMtnvsQHRsZz9Hlo4JeDUv3sOPjOjsOajhLCx23PD55Z3mhDyy0K6bebI3Db9OoukzbkC+SfECQne710q/51YNjPA5s8Zc6edkOMYZxOExljqfzUQBLzlZP5tNcTSD210LQxOmtGfE80q26OMSbz0v8BfwPKnSM2KVL5XUn39rOt+Jet+bh7DmpLj6LsXkFfErhX/y/IqAIdBo9g/GMdvL/Kv9rI5Dv3W3TdU0AvtSciaCn/bN5YUvuUvGb8nrprtfXo2ugwEVjUeQtv6D3b7oyOyCXqx7Re420tC5aPM+9AQpSz5HqKk3xvG6Br+sAi4HjGiF4SNC5RqZM01wehmlglDABD6T/2KKmaK1HnzyuRc5Pw/vDI19asOGXwnhnEDmLOX5htMo/a1HoZy38sHgUHotJKoue3UVaFGKHkO4XWSRiYYh7R5eF4zrI10JsLtA/BgW+co3Drh4v+MhhNgFwz9/NAP7hIWjCcX2Un4dnISiPhnnxh8e6eA8QO4J42bN2/XynHUHsHPIOIAifdXkAAEAASURBVHYIiRY+u3+0IKRdQP5ZeL112vZZkMIG/xR637/in7wkjk5yGD6pQyqtEEFkcOY2Dxm4E+1Noj9Sc1wcD90tmUScslbSmOXRsC9sIYhArMA6bDNARGSI+L+DpLJoeq2zcwKcAdRcXeBBhcaRSym9EdKwVCGVJp3u0HEk5UBOeihztApsWo2JdJzJPqg8eajx2Mehe6eMJKGxu2cNojFBtmOTciYSGOYmmyNZbhvgJVl++bFp1YuPq+wGJP4s/nhXECew4spJ7QUhTQ5VZ1mpjCs6OSlLmbf3QcWfv/EibkXRVSF8M7j9sC58EKl6w+WEQ04fnfiLT0EJuieSVHVEephTMsU4bquTQNzjTEwF2/pSsUA0jm5VqHvcUfJ/1cAr4VMSeayYJvHIkNfWQJshghkfpjJ+8MWZsAlgFkDsgq+lEQ/J1FV8KIygUVO2sk1/EHmJYJ/lA6ItD5Zq/hJhbxMR4UyH50+EtuUugYwy6wpy96KJkR4Y0Wb4nuOi559GpI3SB2hCxLlldXCOznLLH0YVDdDdYrT4FVHucbSUiQBvkno9dQnvcRuA+1c4rC1cBUs76xNspPlEn0tLd1yWxigVHQU5p1KiTGnG4+RQnKwcX6kRORViDC3BcDRtVw0XLbEIuz26pG6Fiz78QB2Kpp0nH659GUiAVKLUYuMUFGTJUXX2P7XMKmKOxshS282yLBCncqjEE0qlgrXkGi/UxQUwqayOAWRcloOlAUW7d3/GcUtrGvQFkCtf8IFw6Zo0/dZ2OlCptNi8kHvOnOnQC9i43zHUPczKsEBoyFC92FHFdTEqV93Q3V033olDn9MoXXiI0EiH0fLDyPHUxwWq4eRL+aDHxilLuerv9Hu96urWnf+W+oPszU/zH0Bv0fwX5r85Ag9j/QgGvPMcKavn0sk7y89wtXPiTj3ln7KU68OZF0NeHWf5pJ3YlstvXvo9h99U/16SKbZ+IlcZypX7HFzl+b5qGV2k6kstx99Do4n3eazz1an7Xs61Q5HN5dcC9uoitF0rgCuKyHF0XFQwznnbmKsWMI0tee7fciWba1W+53OdhS7rsynold3e4ZXvKcZj9EMjFyf3bHMt1OvlXlcg12sxnsLg6Szd3nkhiHs7L+Cwe4cFHC/esDCkRSAv+GQhiPcBsVDEog+PgPHz8eHz62GDEd+LROiahSHfT7IQxH2ldwQ5GI4NscvfCHWlLh//pgWbLgThM9dpvQf0Yg007gn15UfOz7t/rcWcLOTkHUEsAOUxMX75i/cAzQ4gFoBY+NEuIBaGeEH0O+X+JbDm6MWG+6O5Ay1PbvkQOs5Tba+k1nsgJKXWOkBEhhL979UO5UQjsfnV/agaPNG5v6HMy6K/nEfD1CCvghEYfQgKIeHIoEhYQi+HgHlgIBBoBj6hQ9hBDCtHiDHQk8Exhmkb0Yw2y0NXOsuxCZWTjVMtttBcXXQQFeSWrGmGwMQLJYjoINNAViGDjaoGnOkqKwBggpscniijViWVpeCkBGGg6IY7d9nk2Ie4acFbL7OCkgeeT9isFBM7b+3jBFa/tVW0Fa9pB/q8e8d5VlUhZuUWTNsNTl63IcI3RUe1B4fyys78VvjKsW99KHgpCQCOn997Mb3CslcoWD2Af2iiH5LjZ1Jz0FMeu2v3UG3BbnkXtpr6i27HHpbGmtVGkOKpxiyjwB7qU9UxCPeIVVSCvOUBU13+De3MhndKVQus644gEWZso6K4PYahviXNs9CCZq4gV0wQVZxWyFSlDN3+GQCIBC4EeAtDKf9DNcSH9HeVo+KSxr5oGQfI2OoFRaULhw8M8COCtOeDEgbcMYeThh7+0dhUfbRESx4+VGxijJD5M+cmEtSFY3xVNrmOomWuCw/oPVUmipa6O8xmijHTDlJ6pvWRVjtp8fBLHGuh6sh/VwpIfrRaEuXt0Rj50kfpypDu2Q0SXCQoVpbxuXSr7KopR8WQcDh6/KjAxc9KqmdH0NhYjC+vQAz4lREi7H6D8KnULniCe/GUfYI9o/sWs3cV6zy7M271z8GtQXPT0Sq6LvpUf0ubLzJV9iR/hjvj9ETkzSTr+bOUvdnqX8D/yxHwefnCyfnauH82jp/F4d+Fe+bLv5v2R2KB7D2d+s7y78Wdcq/pA7eu0PhSXuNnFUbVp+rRdCgYOebcyJ5+hLR1ZiGIeq4duh8+MvN9Z40JHnSuB6zHZd1LHXyKY1WlLASRQzzu1AKC7BuHLCrA//XXv/lxrrzUmV09LNboPo9HxVTmca6PLArpXbvs6mEHUB4Dg5ddPywO/QKdxSF92BWUhaTU4UmldVuH9Pjev2E5TlruIfPIF7HQ3eUs+OhdzaqzEyj3kl9rASdYvcj573q/jxaDvtFuHnb5+KMFHxaCvNCj8ju9FBoMi0a8EJpHv7z40x1A86iZbWJL9+PEnisw7tFJk5keyj6Wt8cV/pafvqZKzNFJ3p5zGBSD5tkAQx8lQNyPe1MGGH2+/fs3X9hCkJruphIhCm04MSpPdFiJaTtHNUV5BQfE4FY+MshWmhKBxAzBZt3DdeirDF/6D1/sHvqtLJ1hXdaHbNphPrgyyZ3iPY2g5At9BjkfBpxGBs84rrpA7K+Bzs0G9IwqpKOfEnZCgZrk+hAtdtDRZd8KPvNpryNDLFgIEs2PgqnMCvAvXsmFeQpyomRyyg6nnMCczPiM73nJtXKJ4ZMP43xczXFpVdUUg1UmRmIiskyPSDii1/+hL11T2DdtS0M4tyomX1eVGMa1GMuijMoihlK68pv+WzUNHeJeNKn3RY9eBmlUSy288vdEU0C5A1/Iai7OfDegSOVqRzVvvMjPiAJEx4ksEdopJD8PJcj1c8Uh91qyRU+m6ztdZrBUaz7HTCBSwS/moTotvZ0ZkjOlA2HhxSithkf1gkgSUvzgOICbC/i3xmwxS4l4wo/IUFPL8exvjPW8xr/aDt3CMy7bzacmG+m4haEPTcRzUnNKGn2pb6Ix52GzxkqyBTHfjlASs3zTRBKZ1pWMYHVCo9w6vJVKnLiCVVGJr13KAxDNZNPMmkOpmwZln2flR09raFs9LtamI7xGXpy2wjq67bg0fVh5UFzoWOGIeCEojboJf1lVFhsZazTV8XghZG11Q8K4vSeTSh99z3AXOYxW5sL451Tw/y0+2a3DL+qktj8VHz+przKfsvsSrrZjDSf0OXxb9DcUrOtB4RsE/89DCNh/ZcP/UM95mL0w1j41nj/X8FvOzZfOkbutfyfuz47LvW1nvTFre09ey+V9yq8/E+crwTnd9rXXs4EkGrjXBtqa7Lb8Y1vEG3YyedDrnDLm/E/MZDT/J0xzHTRB6H1jRV0fZPVip1cd+LOuDF0O2Bjq8sU/+65yfvY9f/jncS7ujX3/xyIQC0JSxkuetcajnURaENL9YB71YmGIhSDhdH/oF0ir7sUhKfkZGf2SGIsZvn/kMbTRt/54O22g8bkH5t5XbdeHhR/fQzouqfvn2//2je+duc/8xrt72BWUhSLeBdQdQvx0PItA7PphgciPkBlHOY9/STz34YozoSbq5Fw5KpN95RRu6QlpIczjoLiRFUvsTTZScVHFY5H+gCY8sfYVkGmpg+HDzioWs/73+x+s4c8+/MtfFv3hKw0kWkEDnadJU1MlN0rpgIYRagYLpSzm6OjAocu3b1EkvpMUrMDLkAc3AVVZY1m55FWhzujnxGCA1ymrHjloDGgStlNXJmGLjx9k1P3elTWCMrjwhhPYg0uDk4HM4PSAb7mLQuBEw3/yS6qNEsVeCMpTIeODT4nOjvBwrOHv4zueuz2qZzufcp+4TAic/AoayROaBjm2eGDP+firCu1yO3Uiw6QMhuQWGU/FNdNzKAPc8EeOqk8ck0N031Mk4JNqx212hWAV0DV4gS13CAMbcnWRo6LiReMHZZJ3eFAX0DjbLBeAYT5Yv1jVV47D2cpGqpTxFtahaCkNDWvWrbzl1pE9Jalv7+sn+Vk26Omh99QXQyNd246ZpWVZFcaeG20T/aqKxVgN7dHPuwv1cbfJsZzxiK2cm8HF1y2DNmyYgkuuxepGqSSl5il3W1aHXT0Mz020owtmvSZZHoP5spZWC4W3jwLgQNWDIbVOucZc3DF0XIWrv0CdrG/0jr6qxRjjdeFUOMfgMisIZebH6I+I+QsUGkdjpHc0izL2B2KvTzmXPTpi6MTZMkqrzdpLNTKq0Dp2ROAM9ysR7QftFLE+He60+WPyIcMq+m29HVuiqbGOIOTgKMim/TjmGsvjx25mls/VppCPdqFXQP9HK/PKr/mO+JTTQv9fT8x4RIADrb+khCUxU7m4C4YKgmI6tqOkXXhih2U9De1LONtC9Sv6ahcb4J7psn10VKEJ10PZr+kAc+LQ8JLKjMcIVAb8H0oytmIxitDddNopfflHYYjVUbn/3vwIyn9vEF5tucfPGkQDnYHmc01lV+8YQQcWoUvlavK1cw5kRV/DgSG5R6fy4lwQ6D6Xnvg+EGc9X/6IvrYBha/pO9t4lk9/quPBn2m3szH4SR1Shh5wFnkSC3ik4lo28TjYlpk6WIbv2rvCMWYc5RG4wKZih+DHP/Koy3XJmnyNQ4arEu5P5/pmysiRHmNh43FBRaStpQ1GBu5ZV9necY009DaRHM/Ml2yu4VQQge9Y7u/A8Bh2FhnAsOAz934qe4GI+z8J+T0/3h3Eog6yenRM94Mf9RP03BeyCMSCkF8azcYBybFglF1A4JETVgZz34ZhOeJEy9xoX+f4fnEWgXgdjBeFlHtRSAsh2WBAnQUe7qG1sKN7zr/zGBgLQ1r8+bvuq3mMil8Ayw4g5SwS+X5bOhWvbMDoQlBCyHUZodT/fCjcUknOWynGbUrDYG02pWmwipSIA/9UCIecqiJOf9G75MQuffSlLQSx1SxNp9VTSkHHnVYUPUxEV+ep88F4EWcFToSLICpGeE4QL2wIw8BncYPBzMB0V1DXIGYRyB+dWF70AMugt/IYsITtptO8uMTgptsEYSGlJ2VutvA9gwt6/7LrZxo12L1KyYDVdjcGrwcqOAasmpDFIBVWLFJkYPiEoalK00zLpBx/LYZf43PQ+Uu5WqdqcKGr7JFHRiyYDCbX6i5149Gnf9bC6izuyai376mchSBWdtOG7urBSceEQNkxIqM0zl/KpmEvad0PltBcONr2WuKEQt3vfzTspt2OhpaFoBIw0vKWedE9QV/2vBxydO5FIdoTbo5wH63GPoigUn9EntKnlrNc2Z1nkWXX76UtXR/TBxvHGVNvrh5uzFnaGku1lA5eZBG7Wuybx4WQJVZo5WGg1aVbJ3VMEeul5IZBVXpiaXH3L9ijyzd37oB7HQuycWuDx3NYcBeAdjM/zUk1QzFzD+PU2n3IMsQaNKJhgnMTW5TPEeaRN07UFzBOIqyyCVLGf+zpQ/K0UdS6MBIDWxYO7tRdnRklViNsqPtYFPLRQStt207sv+hkroqegcbPIZ3Z1ibqmHjSyq1MfqG//m/5Y4yL2LGy+dtq20bI0DOhM4B6/zq2Jb7M0nz9pIscs7SzMWuML62H2aFwMEw66I3tAfn3F1/w/e7YW31/G465OOf608At45xLGY+L9KRAE35PstzvEH6LT7Rrt/F1717S9+CaCD43R90xtC4GkHuJdwH+Cytt471N9XPR3+r8C7jqa9Nqt/WX8rfikL/YuFSeaD/4q7ga+wQv0mLf2rjklxgUvnGuqfP8lRq9nuctcJcKGupbY/FWXP2I/1ef7Wsvrp+61FY/ZVa184vvT+AnCd9zXXBRcalc9Inzmif3WBS7FULpnHByz7IQD18wm4+NZykiG3fBiLyvHbihf0nJQVfRo8qkoUNzI9Gea5yLnTUKNa8jouD67k7O4R8+4KHLKnD/rVs83QMPjbrovp+FJvmfB+x7Xt3jeqGIRQndJ/v+WcLsEOIxMR4ly8ugc5/IL5GxGNQdQCxmsAjET8in39MXbQPtzQJPfzKeNnL9lrZSpv3cD38dxtR1j6n7Z99H657ZO39YFPK9dHYDeRcRcl0w4r5UF2zV93ddOFu/ncETIgy/3u28pPKoE9fSU5s6DHPJzzR9IZJLwjUnxq5ZVvFzlb5R/BV3fuXsf76UHUHvtRCUBu8AuN1H0FLPYEjXEEhK7kEFhgFFgKAruZDuuJDEYuD5o0MHo1crebZxFoAyaLVANc9DciKg3yuYlOkqPVNJh6H/Nw1yOgezPuHcY7SHFD8yuOJvBx6rJgxunlFkm5e3r32jt5x75XIGssrZ6paFLxZSnKQ8Oic3OTHCJDcjzT1Q8Q+aDl5VnDbVP1hR7RbYa9pDowRNrnZ6QUirvl2ZtD5k9bE/vBSashrplVkpzaJQTjgxVI+fcZl+RIADWSYv8kXHkSgXAmdypDRiolISxcRgwFnfAaTXACUfGaNEtVg0WbMIhyjqYnCIhtuuZO1fAeDqGbSdxkQIgrhpqvU7oR5tCUqxFBrl+kipvGPXQYBPj/Ru0jP/oJXeHPRZjvT9WMnE8M4dDYAEMNYq4zt94arFSosOauW1peGUSi3l2j5xXvQoYYK8YnYoR0PpHhviWawHY+PnvkCAaMCMHXxJir6jYtz2OVLiL+DwVh3Zjads/5ZgCxM7V+ld4RxjCFWms4Bzzicpc5D+e3xWf/KOwYptC/jCBQPdF2pj7aiZNP4ZeR5iO7GNP55hJIMOXwyp4HFJHRszSLeNYNczicYNGLzNcUQ/9kxImSNtd/vD6xSaF/OH7+UwT5qRtYoGhIsDldHbMWI7OAjQDqTAApxlo8a81NNP+IEOm0KhEpRDkWlbL1UbMJ0Sf/3ie+NLTwwDPp9qqflPQnjG5xxL19ieqGvZ/b1Df2WeNRz4V+FOW2d5+TOOLOdn3D74d8ctBS8U7saYZ4ByCM/n2FDgkOC8NRl7E2gzauUlXcU945+8s3zHnryz/Bpu8Sacq67CazrejGs8pP+pvvIPhU9xB7/F13An7yxXtvnJO8vlN3+NVwz5p3AN88KVcCq5lRekBWLWMgXqS2GZCWyOB7zsi41RVh18b/BFsFK1LIIUHjLLmSsOdbCqaapWciJr9tD+ULSOpaAaH2AyJtCJqxMX6FhfuAvzUgFi/y7UXal6PHqtHcVZcuxaZqsKS7yGtqzH1uq72LhHDjK5xhne2IqujWeuS8+QK02110ch3o5ybF8/1M+tM35vfdchpHncdpjPM/fiAvfA6Iw/tVcd4KRf//kezT1bvDZZB3/HKueaixc4k8IrTjxU6ODrP+GwR9333NxH8hHXPz3ve0QWLdgZxNM++icav0SmtYvI/iL/5p7dBnVY/rtN+J/vLdN1wcY1GxGAQ+fRdr8nCJqYLAh5swELQa7PRgo/VUOZxR/pES/vFOK+m3vS0SX5v+uHms7rqniBwZ1sX1XyRMvuxK8N21w6Uf8vfbkkI+B4yn76ihriie8UISjerDPAIpZf5EIQSytuq3KXqB0lVx1sOhGEuk9HLt+1Y0eBifzOozHdxvjmMpwAMuA9MGcA85jThw8886iPVzMzgH/Rm89Z2WRwE3QWh/JT6pxUlNlRhFXpVMbaD/yeIHEIR+WDbyLk6TEIGeC8xIqB59VJFn/8MiueZXyXn7vzSqbeZu4XXAknzHoPj9SiwwNXo4xc/wnMusmQOfuXWOEgvmb3U7blmYRIZJ2nTYzcEXfuxTDFLDGgnXysXgebtQ555BvO+DYnIzcubntjgK/xCuUu2dhJs9rt2FTB2+4EGBec3HiVwoxDiyk6+qnbntq2cCgUfXVYihotSxVCVV89p2oWG/j+JNGCfJei4XkaNxa/9chvT6JSfowaSuhnm+lO4Ya3qSmNU65Uy9aPt5V2YFRPC6Ydd3XmP2p+gImAXrQRZsduEajHKtZjEQ0z3sYjKM9S1MRv6zeI0rTLHaFoyGj4OfavL0bGvBxRYZVHg+uJMzHlfHFynrI1Dp22RARs2my8DaX9PpdU9+1/wMusgxO1sdMYEzTbiKJVHHnA+JbYQtTHoFGzLGTEZO5RWQIen14VAiv9NjFjgTKqkk05bfSoMw9uMNvs0KQ8Jc2T0pUFD9CxA88fHZiDoHt+lu+4z0WFd1IKFZyO8GQ8XQsdGjpvyTGTRuXpffFVZu7xXAhfiQsCkuck5cbj6PjbuQkdmBkxt2WbRYfib4CLOkwu1gxxVDpRp4wu0ybHVtIAXDmUUsQLYMo063onLN8DX3pivDUStNWheKHRqx9O4MTMpFP4oFvdvV4bd/q9Xtw9fyvuLveW+mu6OYHgkxhXZ5uhXepTyUWCeFP3+XsBInnoCy/nxiPO47lkfBl/MYOJmrHO43CeFwd5FVGJqt+dxo/fLX8I1o+zmc/a5VObNo+su6SVU9/EpqTi7PJpzISilJd3kF4rVrz5S9hT7RN3L2LFFveS7jvuouQzKsuOFDrmQ8h3Q75vikGtMTZeoIgKMPNFKDrWuV14g0ejzzK5XkAppcdBXgPIyDY4HMY5s8q/mYVcM7CO+inR8bKkDxy0qMi1wmWcGseZDGYM4RcEEiTKBqSNC7dAAK/J8CFZlQ9XDDX8brr4VeKRgwW+cK4cgClW54lb5QWfWFB3s3U4griu+eDPQFnXWsUpn6sDUCst2cEFNYG0I0TQRndbRhp21UM6whMGNH3vh4719AlCyO00lSH63k1MfPPHZV1LqO57Z9PZEcTVRUOi+2xV/BnFuc+WDJi5GNzyc18598reRKGNFbxwOvfKvV/U3bP08ktY3EumMfhPpLbfuG6K7pGh0tq2ne8Sf89wX1mcELnH7D2n6r5/lhaBWDTyxgTwU/c9s8rY8kuodUmloj/EgUS9qeXmpQM6aRkXo8dzgsqOlyQAEtQjeTzIibN/6AgWfgLPiFkLQQogZX7x7It5R9D73/Q84Yw4wpMmH1FaxYQ6F86UGSa5GF6DVQqQ942FEYm56/BU6GNgv+gn73gE7AM/i/fzx6/ez2KQ33SuZx0/6jlHFof883isbgrr5xpljIUkFkIYxzmx4rVPON/cxGnskrJIw4DUoGbweiDKe5XzMiveYP5OCz4sAuln7vwTd63Plja/6CrPOUqNW+8BrVBInQezTwR4GlRrYUjxYC6LL/iudmvBK20YBxmkx0kgtzwAEUTWSe32DRrt58ymrs/mC4UfUuSTmJVZfPOqq3YJSacXsfDN+uO0sRGMTYxBPFKruFJ3yk6dIzLyZx1BUO9pGWQ0B1cr5uzDMlI+mp4laxmQF4MAuf4pyWfantHi8+ac9ZbvedF3H4orPzlUkDsiRIx0yp9lM//QoT2CVsrRHv8ynp74enGhGnDjwlh1NGwtgxHhit4INJ0S5mh8Q2OcrzQ011f54C+gJH1R9QLPuHrTHJkq2LRSztwwg5mLIuSoLHm1lWDq5HGPzrzjuk/w9DPRr8jKp2CedFPtBx8y5x3eiJmLik0z3k2QHZ3vJCyaDl5TiOdl5WwRpuL5WWVyf4QjF8XyzLkUXWeVyZckZs1BFmivJwzmmdYJw1w0fK3lOM/DMw+JbhnmZGkhZvE38f/bbzOfTXdMkxIQ4ZHpIiNOmB93qbo+osKG0fnafPePobKdfCkX3hJzSBb//1sWgtTjjiFtX+FpmI58fQ8dtLO44ygqikw4Ef/XyxorR5v2WNI5d9CvrdwM8C/jrlKfru0A2w+bae9tm9Fz0FVkjmiyT62c+V3FyfsXlvH0P8SV1eq3+tQof8r/6iseQ89kyn/GW879kwqnzfrx6OegyAzaSEqZaTetrt4pp61i7nn0lboMlqA8Wq64q6Vr7RAd6df4V3RqL7XvGfYttKvvL0vYz5uzt+rT8XTXeJe581+rP/bZnXKt93oqOvsdzHVBZqf7PJnrje3BVX7TaYO1ydy+hortUydWrGManUxHF+CWsTjiDU027v7gQRZsyLleJC8Nj/Rv1TM69zXNXLPhO65iBqwyCvjJ9ZjldVHn16noHtObJ7h200IQtrpJAhXIkp9x8jWYaG6b2sL9q2vK2x4ocylpXO6n024wvq/kOk+FlEXT/ad5gvm+e/Tl2jD6MMWHtlW/amkvBSXYTWcZhq89hkiWiIGmlXNM0fXSp1KE0Vlsw7YE9LEYuQpsQIFPALkWZiHoi3o0zDcC0+ieALTXneNoJcoZEAl1uibdlqAR3hm0ktX/DEApcnA9KLPqye6en7XYw0LQ+w8fvnr/UZ/3H776qMWgD1oA+vD+41cffnnvl1/x5vSfZ5HIgxw90pmXZGEjgx43NeY9yCljnwM+x2Pl3GzMIP07zyhq0GY3EAtBWgD6jp1A33ohiMUg7wz6Vo+Kffu1ngdkESifPHKF7Axu6fW2NxniJoaTyAP98IMY+aZKA8g/8adtezzS5hg7XooectZBORF2zsml+m8scqFnGkfu3SkmCKCUVVtOXv7yzglJOdv1PJHaBrQYaHwijL7oaWb6HDBr0yfx4IUb2/Vx6VsyjBLs4LxKASaDofr9pgIfPcasA41IxxPu9a3NSjP+DPtTDvfWnvV7+V4/HbCTJ+FJ2Q0QvdjmJ/QZ7eS/rdwobYvyffohFs62jE4YJdNvriZ3xYdrHXh6+0BUxyYZtasngLGeT/jimV1M6/la3DrupThfKUKc5p7+qhyYhGe8eXzedcEevn3L12rGJ63lE10+xwwXReewzzV0Tjno6G+zQss5Dq0fUBsTmRyZDzXv6WC+iP5rj5nYVUFbbjWz2DPIebw0c6jLmoe5mPAz5JpjssieuHMhQayyBiRbWKFCShCtN6HDHs+VJwZpL3OX6JqHvPXXuWj6uVBFJHS2DOOh5ubMUfQAumQDWSsX33XsYnxfNKyLB9PxDUpy5kcWk0iI4xsFq0I39FUH1TRGZMwltTWt/u/aETQ93aA4XqtyLyS0oRJY1XcU7+CpD+4ZF9ZScMfd61Vwp9/rr+A8zsVHhM/npP399Ezq1DYRITvJiDEQ55yylgbvjrNgmUY+P4y+U/ypFOdB7VJGoMBD2OTSsXjinntgKiKHmleQn8+aJn5S8I6716vggf5CG38v7kGuhv+F+elD+6bdeumne9vvdflsedE7fNyMhTu16jtBhnOeAFBauFR7PP0DctarsdhP5/ZwwV4wufgUxrsrTcRLG8W9ag789BVK63e/S48UQH3uoEP+c3D2v/pcWdIuPCE9M30ReiZzATyptI2PzbpR7GtoizPB9rdviePE8sUGEuO7+XM+3uMuKPOsn6u1XOfA4WrApmrvVCqjuVoAuEpTvjpm7nIy4wabqN0LQVy3YTs0mNTRHeyhwNdu8LZDfC93V1sWgqpPue6Zc6+ZtlG3fh0wof8+OB+V69qU+txvph0xyhUUGLMPR9rWyONiruFy35lrNnhcu3nhSIrQlYUiytJwtMsGejgdFA0YpBPuK7syLCeE6zdh5Ca2hkkTdonHRMRlX99CEaN9ZoTXHWYhSDLwshD0Pew/Pf3LfzXssiOIxk+TyNvJDX0GAlHuhxAycAlMPr4pQQ91IbnRYOHDizfs6tHix4cP+Vk7dv/8pAWg9+/ff/X+J+UsCn3IDiEWg/wTeCwa8TN4yr0QJKUe6NLO/YhPJtlh4GMQmySfVCm6HRmoDFIGIb+uNY+G+cZECz3sBuIn7rQY9J0Wf759x4JQHhF7953eeu6FIb0FHYzfiK5HyTSaeVyMn8T7+t3om+cgsyPIEZzYaIhNW9jp9PE9L/L6ZRY9HEUPzNww6cSxX5wknETSz5mkxhH5S/IdWqg+6kDu9nk1NotB6ONRuMQhJx9lJ8uM4CiH01gOyfU7rTzyO2+0nxBGy7RhoxkrTZZx/SCWKUm7LFa5zkVcO4LATlOKWeK/q1AtjUjrKGu5+f226VMGG6EzP8uVL631P5bTByS0rv5QJyS2bYshzw/E25y7X491cKdGI06C9dwJrc9kvAbI0FcdYX8lWkueXaJY+ZDPYznJH/0NdtpnO3dMELD4MsC+d8pgU/+9C0jUnGcU+KrLnONxyQTE159ieHg+cwQqZFt6Ue3RVPOYUiLr4mfqoug/C+PWZ6HoMR8B6eALjvfseB5mTtZclI9eRMj8rPpHPY7rxSAW32fXInj/VUk7OH/Vzsz4xtwbH90mDFHgqHZ5DnO7j3mMeVE83sfGgg9z0dcsvvNIrnLoX2tezrzHPB28tJlG7kUiW1KZxSbpIzE17sVjIph05siTEMlCVXwtDS68oKASuCRfHNAnU+cPCLTlS08MpRlOl6a+1PLGh0AVs2gXDZ9X2X37eXJvQde/5qdM2wCtZXCUgz+kjiL4Z8kjkxP7SB1GJkvx0n2FeWzeRLcWBQh4fYTR8b5AGKqCm+4KIs85VXZ9OfWWt/S+Uqjc58ic6iq/HCrzmWPl3XOML0V3ZuqFNC+qZlq/80tvDv9Z+oT5ZyJvotXea/qf+XzSquNNBt8AusfsJZG34F5q1zOfT30tV7741vGptLt/J+bOu9fR8RY8mGfzaPXBf8mfYsh/D67zy12P9UnhS1PCiTf2TviM+vO2PVLXN6xZc33xLDLyW1cF8mDreNZOuJkH0bWxLqpqe84ntguyCkcrbXT0hRyb0I/ZVtXhjtLUsO9bVOX883WV7t3wxfeq+GHHlFvHnoetaOndrc6Ycissm/ti8X1ttum05va3OxHEHzt4SAu4vnKiYdOmXmPxHexWwgrI3xX2BkX+LwQ5sv6ozD0n+nVA/fpDHHz9q0lUgsPXJuonwfVhnuWQTsmWmweBPVIiE1qOwbkvalAV9wt4YjWfPD2F1G+6///6S9oRdDwaRiTcxMTfXUUPOil30WtwovAXZoLFjQuDOTJenFElA1303nBoEYgFIG463v/8we8F4rGwn37U56cfv/rxJy0GfXif3UDeGaRdQTwaJjkWgXhnEAub/ss3nULZfqVbu5UuHUbnxesc4zvuMzD7F+q88TyLLHk/kB4H003JdzwexnuCvsvuoHdaEPpOC0EsFn2r3UF+fEzlb/wuoewmeqdfGvuGv3KzKKTRzf2e7RA0OeN4qO0sAn1gsYuFLrWJ092nvM40nyg6fM0OHt8YoYvHurTYxAkl/e0TXxwTB31cpp+mzT7ppGetyqLTdW5A+XBiEg3yOSAbEmTTrzGUevFt4hJbw2vabTFbiu1X2HPMTZrNcLDN9GVhp8y46Dbat8xchTq3RoD6z3ig+MS9JQOvfFzg8yxBD47jroGNDOOumqCmbaGFXt0nCmQS3CKaw2m5+Umj/McTmqvdOyasUl7qf+jjcUHw24jplFQD4HjWqwUqUakoaixxJ7h+EAfkxV10rIE4GMa8MB77GjCwI+KjrHgkxLpx4We+qK/Lm8PdyjOn1Z+hTbuRwiBcFoDA/aYFEmhAzvPKkiLmnOO8owxIk4Ny5kxOqs6V0Zn2YGZ9EVGhhVEoEeSJrAnGMf/x1ybPw2oo7lufJO2j879pwUgvFdScyqO2fD7yaC7zK4vTekTX+UcWing0V1jwnn9T9m4h7xrCAHHASnyzPyqy0O5FH7XRCzvaQqvpJ3MQOzFVYe70o7iaW8m/4dFczXE8qptFa2mjzrzFnMqFX/XJIibRmZgSC2jChqVjaKabpZJcFcSf+2IQchNO63CZICplIQjx1P9bFoJoLS+obGosW7/niU7CSBn8hPAOdZ2+gN/8OUh8lFX5M9DpWHE2foBPDOTB+TutMiLjy5nqP2MNmcwsKi/cECt0b4xxA66yYo88LmQO4YcwzlSfgoFHKXnH5B3vc9Ew4S52o4Xrt52wW5vVvWNRicbsoYkVHYU19xquGESe4ZYXzGXb0ZRw/SBW/nRjsSkIzxgqrupgIeO8wuCGBg7emdBhfhmq3Nty4stD7qKM+kkafXcfa8/Y2lQFur8/YCgtnDAHbJUxZxd0wCeXLfn8UD9ew4EhvaavmBOH8cYF+okpjpxzrji3SfUxCXvx6quJOpz6Kg/P9AkO2cKpMuSLfmScxo+L7c2akjIAh34YlVn6h/DPitmzWNjkYRe/zthSd7phoJ36hj0LCGKqUaVZ/nZwmwG08Td+FAgwwfA112D39ZeEagTe6Ft9t3TO1ZCxAhYg3VZ5yDb2ecUGOnW9x82llUuBv3Ci2OYQEI850n4ph945s3kkQrfN1fDIoaaLNbnmk29cQyIopt9IY+fciNBd1EFzWHTiScrUaZ3dc66y50uog5mCZQMUJ/pV+Oo3XWORoLQdYKn7VSLF6otSrQhHzIm26pTn+6Mxh+YY9TUABgWXoq/xVHSyNz6oakeHcZZFiqfDmwyTDpkEA9fR7aQWpVWNCH6B3KqnhJj/he/+kB6GgvfLw1fd19fW/9UsBH1hO4J64jV3nN2ZDaNyou7ghsapw0JQbmgY5ASNmwjKCpo+7Ab6+OG3vPNHNxc/6caDx8De//jhq5+04PMji0A//qTPj9oVpEUhPSb2QTuDeIE0Nym+YZkbFezw0f/1aWfjr7qN3qJowJRS57ja0wsf3ThIJH+dzq+GsQj0TruB2BnEwg+7g75jlxB1LQxR/96LRFoUos5HNy9g+dl5Pn4JtRTzl2/7wwBSG7ixYpfThw8/eScU5ZxceM5CkH697Bse6ZIv8xdy/lr+jW6keMzLN0nCeIFn2srNcnuF9nIrlL+WK5f5dUPGX+JFyOKQg+GTwoGyBgvDcKKrG8qWG/diHnLLxJ/0S3sAD0njaxWGOHYqZ9hwrlpKRFvQOnXR5aRxOEWrH+pr2d27l7CxF3SmQ0Z+U7XUI+iUS9/TF1N9WlSfqW1kNJbXHGqm3WoM7vcdr1rjZ2jSrv+rfFF/SE0RSYrbp2KaRwH8jRn9d4LrOqxxEUB22ki+A3E0ETFIiVwm9ViNXGCiaBZfotOe0SwIEldfz/qWu2Noj7T4v0qa9NKvo9HjUWXl/WK9LgR97TE7nvqLO098xlcuELD9C3eoSsXlC0w0zSvWSxGu7HjelZAvLrImZTlGG/MDf8XwQpEM+QX8zEHMy5qPP2hnoudZzbV+LJd595c8psuOTRaG/CJ/FoX4sNikuYwLmnq3fFQ8/XiXfGLe8ZylOTC7fZiPModB57Hb77/9TvPn91999z2/1Kg513Ov5j4tJPHTo10I6k+Nfu3HydRkYiDrtsEErn722pio0GXaOf5x9kHzQTE13Hz6CPIhYyBxlaR5fN/Qr8RPH/Qo/bcsBNHDfEiEpu2fMJl+HuCfuJO3hIdI7BXSN6fj2vxtMi86ssVt/vDjNZHT3ytuGqGMks/TaRgja4+abfdeir7RA1NFaByhdizW7oGEC9ApMinnW6cUMJUOvxZac65GVuJCv1XOWNxYl+qfjbso/w+p/J423nvibMrv0XfKv1Ruv+7R8hLyn0//s9v4Ofp6urw1Dq/11Rmpt+JOmc8pP7ax5/RVyyNOZ3o7/4CaNkHwpcYLATn1VeYFqLU7Dm8NhnHR5qMOa7781JeDnHGzli00iGbiNHR41d250qp1KB0Z7lVJzN9853ecjKbFCwM7NdzyEefFs8a0CXtS640MIuePdtDih7UBiIiOouj/vr+BIezoBjno+R6NHoih7yNqLWYZLFm1DvvehGjme4bSJPErl5urgzeodX21lEYWOetpLvLSNeVLXTQ89vc8gnE/dVWfpcAGCMDxa705fuR7uN5PpB5UQm9/wOy1uEpWDa87gtD+zjuCvrCFIFpK42isk3rJHeljexOKPhox3HQD5SaGewLKfhEyNG4+eJRAzI/aBeQbDN1Q8EJodv2w4PPjj+wA+uAFIBaB/r9/vP/qx/c/zY4gLQR5EYi/QkvHLKLgIDc0yuxDHByfIPEBA38SdbfkdiVZmgesDn7ESws6/Ix8dvx850fD+qjY97MY9P33unlhMei77776Xjcw33+vGxgWi7RjiAUh3iPU3TxebNX7KXKDlr+w/6x2/aRH4T7ow7uPFEmPfnIWafwz9nox9be66ckiEP68k052K+lGSjdRebQi/eMJjEnMZxU3PqKnUXPTw81ZdgRB5+bJUHL9S68Tw0lHsaQzJ7ZnfBevcgT8lWSrYJ/hLBp5HmM5LVVrzORoiFGZxGq29NZ/bx4r8WJGnVTVr3rUHCu7XNTrtonGXQqJWq506lt76Z+fVzM5bSJnDPQdEeVvzVh9QvX4ufv+iEMPGu6+xy5caT9NeFwE/dJCEALxd7Q6m3JUruNLw2yhORkMwqP6r8gs2lK1CvM3HE98uVHmmFhagw7J1b+U/YXLQgjUfHWCxoQXaSTuOUIETXUqS5sO0Riz1mF5VEivPpzLJH9BCe8FIcnTNsp+XMx06WURiIVo7fD5qJw52e9k43FcFoM8P2sB6KPezcY72kSHxvzNzkV2C7Eg9NvsEGIxKDHCGu2cj32Ti5r8WMhhUYdHwHDd8xaL3KLxqO33/+//ffWDFoK+1+fdt9p9qXmPnULf8GJ+5lFk0SEZ5lK+eP/uR3DVdhbKx6Z1O87yRDiiYn8mgvChcUx/0AuKLvKmO6RBDME4UfxXQzj0CQel/5aFIH+3q72NRVrvEKy4UcsIuOZB3Y5VMP3hU+yAlF0Sdothjmhflf9aftf1DHvHnPae4U2rE0tYo2KVr1Iv6zsFXgjGoQq0Ryq2ZeyUPmATH4NMRibfW3X6RL9UlgxT1EtGbmIvt/EAfrqJAf8bcW9t79Gq3138K2afH7q/YvYHYjbn1TMNxJX00hwWbr4D/kzcTFZjN5PN8sFz3DEBTZHv6zN5TIgQ6hyvkAX3tYr0jCUVeo0nyEFnxvRC0JI8C0gLMX7Y1ByWaxf7qfgPhdjwBx380Y5j/tU+HKdDx3kv47lchvoNQB49FbRSd5ZtSQ/tiSXIKtvIGODCaBK41S7hFmcc2JRwzttpt305L4UjbPrSf9ChCbNsnJgpx89WkoPHzEVuDRrxXAahz9CN1YF1g/pzuhrNOSI/PWIC1+C6jNa9BnuC5lodvaOee/X/+f6LWQji/T25qL8HKAOjlxKElJPHcfDJ8rNWgzhpuMnwL3r5ZoOdL1/phiKLOB/0Uuju8OH9Px/e//LVP7Tg43cCKf/xJz4fvvrHP7QjiBdGc4PCX6f5KXk9upCbmbzPIv7FDw8H9zJXLf7bePtedboT5jir0h4+olluhvaM6Lzvh4Wc3Hj4fUH6CzU3LLxE+jt2AbHY40Ug3bRoIYjdQN97EUg3Nt9pJ9G3+ks3Nz06wfz4g25cSCzW8J4Nfi3Mj2KovR91o8WiEFsCwGu5yAtI7+bRsyxG6d1E8/Lqb1gIkn3vXtIA9E8wExB3CIWeqh3w3Chy85Ubxt440qc9IfAtyQFxsSfCpkxJWWmdnDC9iVRuCVsCmTNGM5roMfdODnVI2DXBWPnWd5pKA+KQT147NprHyXpDteWt7W2lUeUmbh27hOZiovHKC+2K2Jbj2ZYIrkfo7dFimqMj0lvbW0vRH/n2BX1EFxBL858qH6KBsdZxsAeBpR9cwe/nvkOdVl76e0t0crYCBod93Pwaoy2osJen/4hskEs1Zf8XkxE5SW3kS8C+TXtnFBuwFoKkyGUbnrErH2kRYnbGZSqjH91aHEZ75gW+YPKYFiZ/1hzqxV2V3RvSDZZFFLbxejcMyuevNZzPmJeKLMqzAC9F3vkzOYs/eRRMi9EsqjO/eiFIL+tnd1Dfy8bij+bgLsKz+POBBSBhftF8zoIQO4F+9fuD8hLpFV354bllfMRfL/woZ3HccyILOuxsFO2d5rUfWFRnHv3+By92e0eQdj+y4M2juiwAsUD/zeyG5F1slE1XDg6bPJLrkOig9SGXZcKx8gwsOtEHQ6LsZ9WVI8886b5SHMEY5z4V3f3gw0D6suhRJsiXmrj44eNQPGkuJEXmaSp9iUFopcyRXHpUwBY4006cytDO1P5MH22OxaxgaGd5w1xCh20OfczfUKrecAaMIGOIxFy1nLQTxCd/nKHKfAPS86xFOHcHiIKmhRP+1F3+5G2/zR44q17YmYvGzKnPvlSX85G8ZktTCpqVZOvqdy1iRPZ0dEmFZ81Dz0ViKs+wvxsnB6bJmFvpc/RV/ozzUjQFePX7LG+cI+E5pbjN2yX8sr1V2LyWTt8JoKF1siDlJ+5V30fGHo7QMx8/R1/dec1uHI/x5zGDJ01mjpNPMrcf+io8gk7fwRlaJw/4iXvN938Gru68ZvfSRjficP4svsCr7up5ARZNl0a+HLPqQqj6n40f4wDR0KU7rT7xQzFuXZs1OMhP6hzmqvRF5VJsZ3ZNCvQf/7hGqg3s+nJynMp8FmwwtYZs5rvmcJjl2mYKKWMVaeX6gQ6K1iVjaWdz6PkHFtvGoZgkcfSFNkdUK1XK3BHSldiqAst1OUos4gP+Avf9lZ2FPwoWMDKQ0z4fL/LhiDSihwnjzoN5J2DKZLiQ6vbB9VYfFIkAL0K5MDEGYq69XaKT69zKK5S8zfcYUGMjFzHHF4KrqgHSh2tzyt4RBE/1L24hyL8aRjg0ghIWwrAHtziuc+T5dQLyM0HhRoMPCza6wfj4gXdO5BGo96KxCMSuF24sWAD6STcU7/UuoJ+0+POT8h910/ETf5F2XTcgLASxWKK/WH/0jiIeRXAPKKcT9WkvKs9f2kOPj/i3W4C/jymDQchhJc87dHRTwI0GO3u0iMNfpf3uHxaBuiikvItB5H2PkB8p00JQFpJYCOKGR483yIq3ASo+3ETxV/if1eYP+ss7N1Z/V/tk0gs7X2vy8IKTFpXWY2n+Szl2eHxCO4O6UIWQYtK/WPeczgns3vNf9rIDKItBvjly8xODTmyEmETmge/aPoAj7A79nInGFmJ1uQG+Rj92CiOH0k/7oPaNk+KL1Ph2JRq5ejC8THSZBMPnWPFNeV7C5h17+tEyOTf/rV+l7hqe27q18AKKBm5VH9NbtT9Kbgp+789odAekvNtVmUcKHNAeP4UdEVmkKYAdSwfrSknvjd41IJiIERkNVDTQ8Yhxuj0T3bitnirj1eOZeW2DDaqJiME8Abtc3Nac/qfusW7ANm5N8+UyJ8zonj6VUyzU5FEtzQcqs4jODkrmUXgsrPuvUlKLZrYDe65j8UerFuy0IfZ56b0eNZMPa/cPi/Es9jAfk7Orh51A7OrR3MMC/Qc+opvH4jwLQV0cYoFIiz+uM1dJjoUjHhFjIdsL//I3z9QTBVo87cfPWXhm3lmLQZqreLwLf/O4mL5E9fitF4FYSH/3nXcCsbPS7wpi/tWCOgs/a1GIXUJaJPJuIT1+y+4gP0KmRSA/OsYOIT1WG7uaTx2nyeUjiz3uAXJcViKforvK5Rst8yEdsWe2b9QP3Y0VTV/mUUPJC0G0Tt3ZXn61scRwRkNwl8oWXTjxV3mz31RCrukFM2W/LZfCZ+f7VVjzjoKReSUc214OrMLghDkdRaSQ+dLWWRN9UfficdldiCeRq63aELYkSi0zH1LuqIYTXhHi7+KyuJ0fkgfGYWxZOEQoxtiN+Fh9UPcIMeWT6nDpqf8vKDT5bAeEZ1ae0arzmcFHna9pqKaXrJ/8lt+sT8BPj29pfavCOvDZ+Vs654r54+Oi/UDj1MQ3xuLPxn12qP6AwDPf0/qb0unvRgjukC6n0DN9N02uGveMcdA8f14UZiZyr+uwfFmFQ1heIXom5sYkazjKG3WOfeynfhobDafNVV4Fg2ovZmtbLIqunvixJVKonXWLLd2qfSiunN980RIF+w+RKYH1Zg5LHrGxc6nv2f3wdZub12psHk9k0Mb9D/BqnGNXNLn9jdNL65hPOCo+3MTtvIaXdstL21K4VD0tGOaFH9lHdpVdWT6ua7UYne88pElxOhLoaSnljhGueynvR8N4WfQ3X/3vD1/QjqAsBBESOqa9QK4PwSMIKhMIf3Sz8kGF7gTyzYUWet7zGJhuPnj0C9rPvunIS6J/ZPFDjxywEMRjYdTfszOIxSB+MYy/ROum4xcWj3QF6peVTp/kJs6b+T04uaT3iYhv3BzhG13KgVbgp0vUaQX+K0kR6K6E5mIuyH0DI+3zV2j+8uwPdX38CJgXhLh5yYIQNB4R8zuCuHkBq5O2f/mWNTvGYy4sluWGjB1B73XT99E3FPwVW6Yk+ze/f6iPm33PjiMtCnGj9J0eQeOxibxfQzdELDK5wWksRduSOZ/ANFPxcXTkj8um4dBOiZldbJQmVo5Y4iy4Q60DcQpAkw8GAxNg8PICMqmsswyvH+iknHwp03nVYD0YRpP6bnpxgKPfIJHABWb+M9sVrEgxrZd/5q/zqqE5kmeZeluzOUGguW2FEkvlhbtlomnX71bgn+nu97aw41+LjuwMhHh7oqW1zoyBbXusOJvy6YTKYDe+TNq96diP9BW5J+Wc0/u2FD2RsR58RzQVmEvjKA7tqn7mi8F6nAGTkoWLV1A99mwDPjVTfGTo/6ZD0PxFQlGkMuJ5nHW4/quUFoNYsGEByIs1LNhozvOijRaDpD9fPIqMdUtWL5BnMWh9tBjEQgs/1w7GOzLJpffjryz4SKfm3y7wsNjj9wKxyK6FHRboveCjxfsPei8b8xKP7vqXGlXnsbCfvSDEYhC7O9kJlA9zs9sve7SKMuMmc4x80oRm35gH5aPnw/GXBfIstjN/6vFbzXE8+spfV3hBPwtBnmvJ2R2pOfVbfVic9+5MsJp7wfDi/m/Ufu8S4v1sspsX9suuHOviPjlJ06sdpm+yU2h1kXxXObB2m8BqoD86EuRJ7B5lXv3S01oIujWVaqNxlh2PYeTX8xi/ITA2Wi4OmgevYpmz6WZogMZh0d8BJj49pP+KG0eeIaej7TvwC2bXGNMZ21eMERsWaepVuPRNe5SZNfQFm+/MOtB2dqg9xMzyo4yybCZuo1jZNE1fx8UZtAHyBPskQ+xM6qXHW9GqAvaBu0Yjsvt4AW7ypbQtbV0n7QBPXK2V8j05huI2aHe+6+JX/eBOVWVtEF7NfA6z4FXuuJhcdEN0aPzjxgiIDj+8goP4vKOt3ESwManFwlov/5O5vRSqeQWqsPUjb4MXZBU2yKTDmY77jThKg0PmCY7uW3HETwYxedUj91oq7oIZHaad5Q3KXIWlmwGqozNnVggPuFEV7lMntrHReaIgkaxXbe6vkxpK3TxDjMlckvo6FkRcHbe4Xt0LNwW+NnWr1+bd2atOf1xOP+wo1Rxen8m1ktyZqkgBJD52rwLFoW30AuqwCyw8sw9MVZBv/6Jw1bE7tm9uWry+EO8lI05nUV2SuWawi6N/ESicNlPO3C6W/O0QbhMdCdqBQTXU/oGzKo6NFdea6iN1kq7MzHfzJZNLHrwUZYLVUZJceg2OmXg1NqBr1zrXOIhKG1qUdBwgWfSYkUOVDMlmIzjyFjLXZMV02KFZflOCEUv0TTU0ERDd1xRiOp++BJwd/SOnes6HRmA7uks2Y2OOrnWNXlE5D3wPjwBl/eMe/H9/+CEO/clH3dufw+1P1v5E3Xv94k0XgtK8dDAhc5DoTQeFAemib2C4qeDmgB1BP/2Ul47+qIWen/jr8vvsBuKFz+/nRuODFoeo/6h344D78P7HLADNX6Tfc3Oimxf/Qo10+t0ZODCjyb4wID3Ck3NTlKGEr/in7pnwgU+H0mi1wYgZuqtNIHIygeCkZvWQd0/kUQb+2pxFIFb/8stg+4YkNybUtTDEX7H16FYfWyBHx5qQ+ell3/gpbloE+1m/nMb7NrhJYSdQdxL9wHuHtAvIL6HW4g8LQT98ywtVeRcRL43mJih/Lc8kRafQxrTSuWPGzY7sO15MDDM5+OSbvhU4MUusGjt0OIyDJXzIo7YnFJPImAWuRA+gtyc3EU0qDko/5Tmn34ZAm0iZKEvd9doISla3GUAmV6p5sb8nn30cD6LbLFY+x9KOfXSNCSsYAABAAElEQVRwTPvTtufaTguRCK5lHLyXoT1L4PKJ77arhUr3rUmHNYBGH5pEyzO3Ljjuh8QBTDFWTnLRyR9jXL6+3FzMOdrIQMoiaDxj3OKmoREYY0esBWAyvyQNnvNlfB25KMr4u6CFVT/BE/mqiloo6cnNbYkHRHKusSMoj4Cxw8a7KbUTyO/jYfF8FobY4YNKbsb9ymMWgbQAkd1AWRRiLmCBCCh4vvyYj3m89mcv/LAbU3Mwu3zY8cPcPIs9H2wz7/1h0R4ZPxKmPAtS0qEFJf9yGAv08ouP5zAuOmjELREzPplrVJa/7NZhXs0OSRaB4DOnZuHnHQs+WtjJbh/eg5bFdy8AseDjRR8ttGvuY6HonXYGsROTufDbWRzyziHZySK5dOs9QnlBtRaGbE9h0jzIQtBMh+oNJepkdOiRt+x+Vofzr2V4/3U7giY+tP1p0vhzKs51Do5aTo0pPuKIv87Dy3kbde6Yk37qGMjTjA6V3Et6625lBz59XOotxzaIEb6f56Ez37TdgRdv8ZtKaxzGan+duWNNX+ZXaDcsjjGXHyiCm1gc1PoC65py/nqSMyOA4mxhHeC1rbF91XXUCj1IT4vGbfAupbmREVX/97Dg3EzciycneSelXEufPAlZYBOxtmXjJnTmD3RlQbe/F/kThdPDZzGL5413x079R3k1LEN2ktpV34rBAKMz0kZe4Utdx0sJD/bEWP4NCFW1Rwl/LfeCjWJfYNf0Z+XWaaNvEJuYNa4P50xVnLF9zWnjztZsR05qVjiq6OzLja9p8iA3Pro4nviUizJ3+SNqL6iX4i0LjOTe8gl10TJWYjUxCt5HqwlvU0U8VTAmVd/n6kaeOPeDnFnXcaM2cjYkwVGszE0a54e6FcOfGmooW93opGJ7zgGqprJxKqzQwSJVLrWlj+8WeHZjDAY6AqZZq5UsNW6Uonle9EpJVMiX0RVz9tRFSuhwLh15fyZ3r6K6LqZk7Y8LiEUv12PI4jOxoxxwyv5D5aKbOQhdr9m3OGttER5Q9I7CkVGGruXLgm6+SvHTV7Z2BbX45u9sl00JjiI0KZ5iCKrwB9NWWBCLcSjBJpzyXAW40T/zE7Aj+frc9ehcddlAdRaCKESXXxb95SwE6SLfwWzgEjrHI73lEwUIO68INo8c+KfddTPA418/6RfA/qEdPT9qQeinH7Mo5EfDdOPxXjuFuAFhl9B7/YWZ+ns9FvWBl0XPzQk3Kvz1uu/HcNDlgPsVR/CDM0d5/yLeOmOeUUfXuuOmkxCjfk8MZg8KK6fNudnML23B1c0BizMqkOteJotBLAjpZoO/PvtdFuwQ4gbGNyg8tpCbG25gwPiv3vzl2HceUtozUIs/v2oR7ddfWQj61Tcq/ks2f83WX7V/0MJPF4K+/0ELQtL///zLOt/Jluzrxqk3S266O2a3EpqM+YanbXXMRKUt4Wcg07FEqI/TdeCvuAkM3hMIsXD8RRDVu4EoPqScZJAT/VoM0PpUvFDHDxCmP3ZbhMtXXshl4pR/pVeA+mnrzgdX/p0HvR9wz1Olmj9HhVpL5OBbf5SB+5LGl+h3LS9ph74itc6R9pu4poGqJZWpkkQqElrOGNHKD+rhiKZqa6k+XEWLIs+5GXzpqKYsqdkK6gsD0YZ62Al0SfrEr3X04EGtJzfWh9LB7QQrloa2YNGU+UREOyWkBP5/9t5EPW4cadqVN2227J77v8bTbWuzFi8n3ggkAFaV3O4Zjf0/84F2EUAikUgkSYgZxEIZdld0We3qBbgDqFLTaOkP7hkNBCDjxfUZFUTr9dN6a5Rz3+cHGGCFPuyNfgBCGhGkNiCP4cEAS7XOWslkUX6DQFoU2n0t029r9KX674z+AfQRUOS+ib64QCAB18hWX+Xhx2pQ4tZKiiXEDJjXfY37GYFWIrgPrZD+UP2cQSH1kYBEr18I3BZCw86IHukDrY0A8tRc9XcAQABBjL70To3qd+kX4TcoBDjEdDGXo1+WbGTqFwBK4A31ogc65k+Jrzw6K+mD+Paoa6o29ufk/xYQxL1aB/aZzFDkvRAz9r8h7f6Q1RvfJLBKWnAlWljsO+QfJku0FdhybnTflV3lpCv9GG3U3SsBPM1onme3isEeee3+6OWrRxut3WjR+CjbD9G6vLKVCaEns8lrvJT1C63SrtECt/3fXIX5e4WJbPJbYtAGIX3rTuHdJOzo7KNHijBC8eF0TMytefVXpLF2lpIF4dBhgSPDyj7B20QRdPGOi7IpskmIo3Soanbzi97CWfhO1iY58XG/Weoh0RPfprwTk26+IYtjohdp28hO3W/flPWroj9s46TEs/NJoE11yF5zvfD9Dc/E/nNRGrN7UMch+i5fY3tSpcNyZslzUejpzw7Us0eCW6VbsJddhMqvsNE3706dJonim/uamHv0p62jSNXIbArQDx46DlPHZfTfp/ZO5jetySAbWxS9wqpa9deorF5/rzTMVWR3NBGqd1bi1QYZoN9mzRizJP4W5e8qqwKFF1oKtdqaPMuXXEJyqMM0+PsR+0aG4qWw8vnLxt/AOqPxVsIkrcpVWPLF4vo7q4AfG1cMal8nN/45PZkk+lunMKJ1VYU9Sm+AmorTAvLKtuhOmV69RUVOyihXmbGvS+sUGbBC90dZM/MOzAweRgSdW9Jzn37DiKACglpTfDV0osHE28Pi4WfyTvzFWc5CnIfvAna0A5hG+VzfPmrBZ34APIBBAEBycBj9o5D1gbxLjQAjACDT5XhkqpTkyoFhZBLV8jNO47tBSvjNHccib/C1RobpqOiXi5TxdYLmG8ERndqBqH77pSLfChqtA7nffIrjMHDTGhCy86JHQiHTAgB92Mrd08BwXOSIMH2hL2wKKCQ+nBuNHrMcOyCSScOM5n7P1vGe1qAP+28kmxvr9PSVRgAJANJCqt6VTGsDnXlBVXYxow45Q0w/k+z0FciTXH463AbFeQBcJy1ucbcxbN0+sXdsYZs7pzG1AMfTstQm5PdHmPb0oymgNB2I7d/zBiOx+edSUoLQXENM0shwpduMus5kt8vvdjrN6cBREqin4gfYOmlXn54xRcYfg0MSLWHiJnqItsMyJaudSMcMHIdqSs44/6iW5EUK8XR+/aruVNAk9WuQDtXNmJX6UYWqw39sunpzC9pXAecVvUL6g4rvh9EdiyqvsmnPnFBuqR4WlersSGjtcf1pRPGlryhpri31KOovEQrhsfwmNNzS23pINmCVblCeLX5Zy6cBQRotacBFAJBH7mi0Dn3lo/rCb0wVUwFhMCpH3VSmJ4sHUZ2KIBWF6gNE+yZU1usMwa++GZDe08A8IjMjgehv79uUr6+Sz3QvT0dTBV4MuoFDtai9wR/RIjfXAbeYvwNck/qhmY8W8QuHdHSfY53TH9B3ph+hL6EvpQ+lFekr2YnLwJD6U/rWY/Vzr+hXAXgAgrxovgAfTSU79s6NAoHU556c6qf1hlion6liJyrDNLOs7QaoRJ8NmK++nH45ZvQ1Q2WuHQdhAUKhcG5XUXlpb2iMCELf//WDFx/dhj93iK8/qtgNuw6Ck4cEcZ90NpVxMRiJ1IE4+Hw9ijiF6NgKRh6EHF1eEQhF5Lnt8ga7a+Badzmqd1aF4l1fEl2n3Zp2S+2wVtmpbu7BLruKT/kuwuknqiq2XqYVm9MVTwu3FdVfA6oqVVLvzLeryNPWqLoq3JVS9Dns9c7EHp9zZ2kwVB70iveCByITH+y74g6UiC0OZezQflaei016/JQSO3X9bHJzoz3d3P4M/J3cSV6aO86bohNf6E8Y5wnyRlZL1KWiyH/12NP9idr2+J5ozJN8Oy3pxeeWVnzSYU8ef5/CN0sMZaanHxx9wMw9yf93ohud5pp3hKlK3o3QJLWPWOekuDLpHxFbR/rxPOWNxa2eWIq1h/ClQtVDQieXpZAJjiih0PmKtsNlRYaDI7qYOogq1Chh2kmNcsm2mRonTh1/f+xwmYakyHPM+uVtGip2s5IOFUW3phzPbx0jCm0wY/EuQpFxBeDDunhzFUeaUi4Q2SXJQmbRYXVJojngbkwKeJUdR5fUSWSnbkj7+aZgD/2I+x0NVg5oIo52Q2x1E+VoyXpbh2RxjnBSjl/fkRXwB8u7NqXx+f+HgKAsAMq0h80h5yPmdcvtEGRhaIE2+lpsx4VthwXwXAn8uREIdHN9J0CIrdGZEgYIxBdohTg3ODkARHZ25Jx8ZZ0KfYHW22bWGsLQOBqqT1fPNyTAAzdiUAjRFecrOI4Gr+3maxevqe9HRHE/TDtN8oXfNjKF7aZOzLKFb0KFdKdea0IE1HiJY4ETAyCkXy3eTGhwCGeDuPKY/sBX6Ez/kssjYciz7KOvTte6FjgubJ88RgQJAPLUMNYgAhTK1LA37B4mPuYn+tANyc3LgVwOrMMDQEcw4qk7HLEPxTajgZDVGLBfdSSE6P5CbSNe9fUKS6jD0ZUMaWSgCef8oPTDlabm3duw8zhS2pFIB4ZU1yPB1Ey7f+YoSYQuUgQSFW+CDorsfGKeGDq5K5F274jsubuRbltllNi9spWxW3hKP82CtElLX2eRqHi3Ioy5oUXqICkNCTYRf2R7P9ZUo1+Oih0CguAgP90uwos79KSiDawjl3zfgRsa8njySg4K8z8SEpa0CinDobRJ3PdVXoVtG8JkR4N2BtDmX+tD3adJUgE77u/U730x6KM+sAFBd63f9Po9gDqtb8wfe+5zpoJxP/Ew5mcgSPVYtvSrddke1bcy1YudGgGAWP8HGqOQAHm87g/gOzuKGfBpI38a+EO/AAiUryxujdc1AgyKaWlf2ot5HMMW7SYgQEV6cMfRW7+MKiSEV/2k+UgrrgRTcOnb2HLeI4I86pKpYJlKdsoOjgKDGAnEdFnv2igeRk/Sf8J3ql+mlmV0EF2lR2yy9TzWw3SqNzqMSywSl3M6aFXa6LcfRWnz/xUgCBBIt8jmwD7NIpsYlqv7IgUGVwko25JTcfJ8z+wQ90ubkQtAke3BxZzuxW1mUi418R3i6bSmi/8GomlTtpGrKpN5MnLAVHEorZAzJwFOt9MLGXcuAnkSU1moXXxFm/matF62anY4lZ35TC5hSpi3MxAJZUtPAc5beviHLUg/cTx1DRBY+ihq+Tu0rcQpUzLnvsilnb2j6VRkKyt8nftJvpSa+bjtYK+j54lQcUKO4qvQ+H7LTJVVAu78VXL2EzZLmchNyUNSWsWtHiT7qMJK9FpFm9uTumGYC1PwwHFAR0jWbufZnKV1SdZnm0NqvzYz9mL1N3yP74A+FOKv5+Y+rYLbqrv8TWSquus20Trvf0I70GJI6Yt6DT8RQYnYL+/saXWoc2NDMa9evuujugvXCZa5iJl/lqbCvv5DAPf9fJ914WLJeyJXievUc4ZVJl1KormrGpXJfYeScJBBnEMR3xeKdVrInbeV6aN44Gu8vQg6+Mi7RPU9DSFJjm3Z2CSgdOr1SkYXAxv8xU7Mag9Ksvz2pWjolIi8SlMMwUlXnaZuKusVWRLPEEUISz/LQVbe4BTayg5TfROYqobAQzFU6nTXhubdJsmCvj1ID+qoKHR4WzultO8pvbN2Ljdk/F2wZBWsOsLHOTaEvWgJlUYuP7gcBz8MP9y8Y/7r/H9xRFBZCavJspjAN7ksgVPATl7+kixHggWe2fHrTg7G9c3D0fXV56NLAUG3NywArRFAXhtIwA8jgwCA5OywBlAWLMUhkWPiL9M4FoBA+qkejK7HS9U30MEhcb5+SydAIE+JQD/WCNLR3qconyN6S2wJ7PS6FShNJv/S/TQWBfp+7zw6A0wCLw6LgSA5Ef66rARfmA34yLOA5mkPjBASHSfGgBFpACHlZ5pCvlDjEMlvyRdrAUdv5KTwNftUo35wdnBwMiIoINCpRgXV4ql88WaaGkddHyd8svX8kOXR1RnHR3n+6YRdbHOaroN4forbHqH7LD3pUJjiYQCOa0A5hO0d47GN1VoFnQ/dchBWLvJyPULsoqmYwxU64mSdrAY8iUReL1xcCWdy1UtOF624eQ4wuoq5ELyNz/Zvcexd9iN+6NgRs8/yIwZEkq+wrsHhWvbFDkoEVDkc/YrPxuBKht5zI6IZI2oqj+zGMqs+l5rpCEm6S1Dx4qiwuPIckopuZd26z6Klr0GXYe7plGvC4qlI5w9GTrBEy9Rad6b4mhp2UGGHtWnp4mLI4tCtlS2A0//I96/94UCGOidG9/Cjr2M0TqZwMTIHsFzrq2lqbUbuCDhXf8kfHabeeloZ+qusYTPffK+lAxVnahiLTwMGZYFogHgBQBpx6R0bFc96bdr+XXUZCJJsg0dSCH1YADpAPP2wWgwIxD9V6rTjptgg2CR2yblSMYXOzSbYEDX9Uxn6UN9ZCgGJOLA8/YtHBSmsxaSP6V/p6wB9BPY4ZPF8AUEZNZmpYmdKA/4Alnu9tpP0o6whlJFBLzyyyH2wZI5RQbp/rN+42+susGKthW4bqvqa/h8CgnS/6fbrRx/92C55Xfk8G0r54Sr2qSBZ+uerrVOuOvdExXJPDHlDVMrozD2pY5LqdEmoEC6/BzdC0kq0l+MU4hlXjjNNSZzoRpDS8HBAr3ilCTk6vQQmDLn1o42nBRLXnqVI6NVuxLW83SDSd6lTmuaSFGM1Z8pNtGco0pVKpOTn+lQKeY2Rso5Oea2CcOjsrGLslTWuKZhFVJxnsuqaWH8Uzd8AOFLXuLWUntWgjgNH8VMt8UPVFw/Fi6/iJXLYLJSSM8u0hj4hpylUepWuXWBFRtjrrjJkNXn9Whb7TCdeirR6LEKnaluJbNlNSlFLKGH69VE+z5TLQVSEuI+W7rrtZRSjwi6AuBJlH1hKSeIclbfLN2oOX2N18dKl5/xEpORXSJEfyOGJ5+n+j4+qz/Z6whZlA1RSlXMbXWzXFjBZd3KbjiV/V2HzNWLV4wp2GGe+nawkWz07fHrtiL49UnxSqNfT9FTg9k2WTT9PPr+nDv3d4e/SARY3qelQzUNKVc07mw8FxKCbz7Jy/yOXZDg5807R+Bq15CGrP+8k+tFKu0GJl751H5UM3sr8t6wpbG6dWqlIbDo50f8YRq/BGS832kckcf6+5k8lb0LcyQptP+I847Q2OVRadiv9nFkn5dsW5g+x86FwRFmIgbdWzuRirArIcxlO0wFz2UJhz614EaxIEvWXtz+lIldJc7gso/jz7ltVGASi6pbP++X/FBDEVvAc9UcsyWY0BTgKj3IMvI4PgJDAnc+f77zL183N49HVTUCgq8ubo+trrRUkJwaeTHdo5R55qczXZ75Ke2qZnBceDGrCKcmdIbP7JiAU2GHQRzcjtAYKEcYJyh8ff3lXcUsiRKbTvoQ0TQfydw+5Vf3hm18x4eXFtf3EQxwnxo6MdGG6gZ0XVBMR0MdftRX3lLEG/mTdC0bxZIQQX6a5gZiy4C/ejDASCHSskAWnvRsZzo4WjT4DFNI0MTs4AoeYIpFFpVW+jQhyxyJ1+bpOm4HGbD7ToPP4EurXQpu62cg3NwV1JMj1oBzUlJEMyoOG1QFhc2AjyhDmiNQNk/PiAJb0Ua/LuVBkPSVlX65KunBCovs8Wz1ItaqcQXNov8X0TEUisssrPhfanLB7L73JOZQo/Sgx63GIF9pT9c5yhvJPSYFeJRJPp19k5c3ZG4GtbW5jrnLpPvw/CuseUIYeCx9QNiI7NfkNyk1iw0kpnskqrdA3KHYmWnSKtvhMEtX38MQ37szc28NxJd3a57DiyK4jZVJFzrnexMlr/1Sf/0l1QyntZYLRlOo62w9AXaMt6CcNiBcQJDDoc0CbR43MuVc/mT5X5WTUAEHR9fs3LIz1tFi05DKyB5CJvpWpYewI5tGXyG8/+mN2KjO/AaDY0SAS02Olu0Ef2tDsZiAIE7jJc+shqcJ++BWlWzFk8WNKyQoYnb6IvHQlagt5pPXDnoSeNgYIJICdabeA3u5H1f+dqB88BuhpYJDXUSMuEMijKWvEkKeMZRrtsaeN0f8GsGcaLvcQfT8hP04OiOrng0izQwyAXTS6SI0ByP9fPxgNVEDQfmu5F7gXc3Qz9TTXNbk8D5vySsx9JXn1sl7ympj+iMLDunQMoukHxFagooQ+itBrbrrouhnEJcmv86HTdLkjZZx/ik8CkW9jqEArQ+DEXN9To4HEWcU3+lVZRBEnoIoIbwQFLU1QWe7jK0G5LmvmQgQZTbhiUxEy26H8jaHgGmVSKs+J+98qVmVgrSKHwpZdxSz6sCJiiTDur85noyTLtnG9XZojc7WVM7egaE+FpY7LlLDGnF4k0maZ1kUEaHN8Yztn7tdacvp1LL4Kd4sUfTcsvh2dh+1QrpgqlJAmh2D/2BZwyvyUU2qbreJNmMNJWpF7ARF87cjQMcdD0Lnl9RvKjFs6N7vLiowulYZVR5dAZE9XaCL6gYF7Ooq/wpblKojv1NObPYlwdKf8bnZPd8FQqlCFjWtKpilVqMJWdOJLm6f8PfHKc1vImPmgT6TKItw9zKfTxM+t0Q/36WRyVAZhxRXVhjo5mhwSLTo+L4tGkWJV3BKm9Jw3yiNs/+hA0H6WBDfdqAP5LWlWCL5nFJqPNDkwzX/AzN2y6MVaOdgsNPk+N/nesCQlmkxKKbPLV2Qu28o50Im3tHo3L7OPt7L6i4286TOdLxYS4ExpUtVENwslrYO4nAlBB+zQUasqhs4BXbwG/SkzC7BwaDoaX78PK13PZimjMp7ZhL7isT0twMJVvCjOnPKLbuZcIXhhqzIKsVf+tlv80bGWZ/nj/H9l1zB2hrGl1TgZtr7E9AbLGDgx7HDDqB5CvjYDBLE49I2mgl1pNNBHRgRd3goUEkAkEMi7hWm0EGv/PDwCJMmQjCiqUUXyanjQGI8Qg8vKXFh08N2RhVA9jh8e30XQuFlJc1G4wClPE+yK+OLlgtblRX5RCHNbpLpcUhh8mc0GLooqlYdNTBPlhTok3C/f03rgmGaAU+PRQAr5gu21KHBePNKH3W7ajjcsdirHhmkNp3JmcG4YCQQIhHNyqhsLJ+eEBaMVskhqRgexZtCxp6F5NJAqZVtmH1N7Yznpiu6iA1rFnmkrSY6yB9e4O37JSl5j9EOvODb312B3xhBgbkytXFGweeWU2ZPmTJ6vruMuqmsYvlauKWdHoRQNY+MjEVmJtdoc5L4we9ciqYPnUlCZ2KxXN9GrnG3aqpoUqewRFs+gJHZAZlfxUN5cvsns+s15U3zThoN6zBWVvREgOllztuXOQrZxHr2qz8WcXQIm3hatZ9Fi+yn8278PJUPAhI9KK5QBfB2kaPVPW55WRIHvMj2fw2alU0I/wU10Auh1LypuokL/UU0ZdzrIFCc/c0chNHJdNZXKvYn6N/QEwFHUPxbmF2bTRgQ1IEh96p0W0/8MCOSRloBAbSqtPHHW9Al400YU8czoDzVXkNAjjFQBYDqLPfunaWYPGuXjBanVbz+oDhaD/qr+V/i7t4JXE/z8oxst4p+Plq64u1klsAJZvu7OhJByKalzK0tA764aXA66TYU9nclJh42Tvgr5/IQBGVQHNKefY62ggOuZFsvoyOMz7aYI+COg/OysjZpU+lxAEOljhacCg05ZX0gg/InAINYRYit7FpKmDvqYl+rT0Cv3S+pHLRTJu2fTMw3z9fy/AgTNl4nrUpawfXZPZMLUjnruqpyvfQmY+Ir/UFhlLbfkl4xDBX6WVvX/hKyq9u9E/1t8P1tornzS2X3wnPcb4v0a/aDunzX37+SbzPqDljxf1t/Z7Xfaglb+nT3+Tv/ns1Qk/Ux9y2Zbq5c9oM7Xs/4EQ3e/TKQdGz7RKj3LgrXoxOdrM/PNPE/xmWeXEeY6SvgsmLzdMsVX5aawF22R+tvU/141Wbu2oI7daiaxiUrmrj1L/q680COx/h461RWUSJjm9FzhAXrpt6fD32s+S27xAxVImSG7fLepKArMxZpCnTzlES19JwmbaLH/M76Zu9Vskk7tIsRf+LEGsxQrNV3Irb+RdvAxkLWA/xuHMIeq/b8hfl/mPaN0AGWUVTeuNdAJGk4C+f56rS/NhEwLu73TNDA5LreaDvbxWiDQp9ujv65uj24EBN3dyRnx1DCcEgFBX+WwKNR/AUn6SR4ABF+wc+gCuXJCkEiFWivCNANAGJvRQVzI5loobiBI+qHv5MYoHj43QPxxSMKXHFqmQ4nGqRBaHJeEdrMaG3mkDVs13gBCnj6mJ4WbAkDI078EADEdLCN+BO4Y+JFDwhQH/bxFvHcECzCkgT8aDZQ1grLg6UsBQIwGCr93yhFTrT1Uaw6hnNsu9bAZ8biHMRU0TOafW5I4TiXt4VYrJ9DNt01pqw7F+ef/CuwMJhkh4ZrOlBODBRXZEkTl6pAHR8UGD0UsX/oYdJv1MFvTaZJvoIg8Gg3d4iXd0dQIEXKVht1HI2AXF4fYxMzMFklej5DYOSqvypO9V6Fo/yU+9KcdHL0tJBqNIE8ypBCTxtYw1qFEPY8bwzRBJbBkFFnFuxgEVlmHyIY28Ygya0GKe3YcFQ8QBG/untABWnIUH1US1zWXAXJfqMTIbvxqfTOURxAqTjHYwktN7VdtaJrSpnTL3Fd1UFfKY1d0zOi69G2JIxs7v/CiuwA21MXzxzRbpn+lX/2mBfcFBN1poX2ttcZaQXdM5/IaP/S9bN+ufthrBtEfo08q/6rQ8tSZesqXdhmrEZcAQkzlZUQQgLzXApIM69ba7fYTp2X6X1YgluG6hPS/yaStZqoyCmmU/znk+ohXQf6qKE46xRPXudAx16sGpF7ZSllcJ4Aar80GcAPwDbDutX8EkDNKEqDnnFFAJx4JdHZ26nWCzk9Pj84ZTQk4RB+qOOD7KaD7icAgddISpR9yaZvqa2BQ2oxyaWJ/PhRBL058vHiNfhRcx7LAssCywLLAssCywLLAssCywDNZ4JcDQXcNCOJFPW+7etn1u7uAGhHxu1gfiC/UX9mNRqN8GBF0fXPvUUFX1/dHnwQAfboUIPTp+uhKoFBAIMowAgjHiC/g9TXbPoFluh47GbxUyx1sIBC7gvnV2y/bQkniHig/L9/kGYaxnpFnHrLN44gSMHCoHZ23Raa84mmaBYwwkZFBzbFRuo8KklGIm19VNW08AofpYt5VTJ7MKxaA1u43p9r+/VhOCWtZEJ7LSTmX48I0B9a28JQwfa1mC2SPCBLtTD9vm4wTIz62qedrtkce4RipVo7eLjmEaXU5U5ii0ayj2fsJBzU/S2nWiMzORPkiRTiNbUePFKGFZXOS8MzpUEpUCiglFjuElHCDkjPOZpjq7iUbi2REjEKk1zHixKzJVp1i3FVzNHNwbGND9Jb+C1NPNcUIR2/BLtdId0c3F2DPBmlKNbTCYlO6kbrEIXBjBTCL7dFLWMTILnqF49lLeaWdxal4yJnj4dw/Vy2EPLFzGu4Grrhg5TnhvIq55p6dekkSA2CpLdYz2lF0FwD8ThwAx/0hwI5G+wCUAwjdaTfF21st6syIIAFBAEMs8Fy7Ln4R0ON1hQByGiAEVOYdw5Q2SAQIZMBHoBCjgDTdi5A1iJgSlr44TxrgUVMtETUC3XrTFOnTnwBM/E8VNgYeM8rXIa38t4K/GslJvx+m4pTVW5QrYONgIIN7lQeorrjkU5Wq9ghLdKHvA2Cn33yjncK8mP7ZG40C0kgg9aln56cCgBTX4vqA7cQ9KohdGNV/ArJnZFHbTl7ykE9FAfS37Xf9NBB9FABs0T9lsWgo61gWWBZYFlgWWBZYFlgWWBZYFngeC/x6IIgpA3oRj3uQF2He1XEUcGpwYNiS+JFpBp7yhaPyoClg93Jc7gz8XAIEfRIQdKU1gq40NQwHRwUfNCrII4BweuRk8M6Pa4cjZAfJNhPdYJAWgyZdb+YeJ6DPwsrzF3zlohdHXssrnZBxBXgPfL32LmNidmva+zqOGc4KQgyAWBoSh9RySNAyUqkJjVsoWdD9s/My+NDL8JV08E5gAEH6AeCcHZ/IecEp0VdqHBX93slpYZ0LnJpzwCBPCQMAYloYAJLWCcJxERDEF22mN/gLtuzD12gcI9vFjpTa1uZ0optNKH12gSAVER+aNhtgC/3jvxo1HdhxIpJHWQLqbQUgQ9s9SlTycrWKr/ICZFmihJREJInWmM2rk7lMm/mq1iZRjaZtNYqhclG86uw08VlcJygyEareVLzN60X2hLYc6JMsUw/RuqAp8nd8ld/C3WqqocnG7nWtqo7dayHOcUPs610CN2GuQUjIbweVcs90QmWIRN7mCJOLHKAPITx7dVBG+lvfpoMouaeTV5xz6PsVgGNqKk9qjqE/fChqsNnqpQBpkm4aPPrfbjQFo17HlE6fKf0qSyFxktXvAWJ/1ShJQBt2BmPK7b2mht0KBPr8Ob+7BgSRZm0fwCINENL6P4zsUTmZxtO8FDLSiD6XtAEjQCPRAIC+ETdQlJFK1kNl6L6wImlOhLYBabWTZnKCRrND01PrjKRTipL5uZ06uZ+10NhDDI0nMaxfYjBOLMy1JR/FIi99L1ULfAG00T+WjXPfqD7UI4M0avKckUHqW98a+NGIoLfHjgcoUp/LKEz1swGQWJhf4JIUyBSxtI92+YduTTkHqht98xFC/b10Yzgwu4+tY1lgWWBZYFlgWWBZYFlgWWBZ4Lks8PuAIL+Epxk4LPnJeWhOhHf+knPCdAUcFnYJYz2gq1umhbFY9GeBQTeZGianRMtRtC/Yclh41ZfMfA3Pa3+5Apu1f/QGHheEV292w8HFyBd0Qg7LaXQ7dH5r52UeJ6WFelEnXu5elfvGgqiSWKNhoFsgNDsgckIcT23alNlpHBJrLacALcpxccvstFBOh+KAMDgZnsrA2j8eEYQTEmfl/DyjgQCC/AVbDspb1rLQl23WBfKUB0YBsaaFwjfIEAjElAbv3CXZ2Mhgj21Ke0TUL7QCgqRrs6dNJPXENQ7K8s/6D7JjxdjKp6B4TU8m12b3IKfoc3xQozvlAM3qmFWIoy8+1T3rRhtI03b05ogmTfqmkXAVH7F2jCp77ZErWcqbskeZKks4N6oLnRkaT5FK4Fyu8ghLxr/JV8VKpNviRHLKBiMMnbOrngX4Iog6X4yuYK8hNrKtc3/BbjEYsFuwNayuyaRTXTtIcDVOc4zypVh7rrrcPH8w171h4G+jc0Q1FVvJ3AO+f32aam5Gi2TxKSumIBJZpc2oXXS1F77Uk1ZxZvQk6wABPPtQfb2841rTh/V9RHwEpAG80chJgJ9bQCBND2OtIHYQuyUugOjB/S7rBrHeGoCQRvoYRFIfS/+skUAe9QOoJOSdKWLC7Q0QWR/pgjro55FA6IGOUhA9OAhsFU7E3DAF6lRsvRY2cmjmjQD3n9SAXJNcW6tg0JBOv0xf1eukRheCIl4rCyCU/pgVgrEi15oqWXONhfO9ULRCjwYCYFffCvjjEZeKv9WooFP6WdYSYqqYACFAdWHsApayY6P7VClEn03b/EMfKiJES8UNvDeQijWC1tSw2GedlwWWBZYFlgWWBZYFlgWWBZ7HAr8cCPqM48DLO/r7FBCIqQR8rQYIYgebB9atuL/3lvE4KiwQfS0Q6EqjgS41PQwgCEDo9vNnTWkQRIJTwnQwyWTHm9oC2W6A3rL9Su+XbbsFGcWjF+wAQHkLN3CEWk0vQn6si1FyAnaonGSysGiNCOLF3Q6MOHNo1JN1iRBPfFPUh0NGRQn4ERPrkFBDnAA0Vbo5utE8DqrzcQ7wruwkKBQfdXvHG3kXLAh9ohFBGf0TZ+WdVhoPEKSRQZ4mBh0giC/XrA3EFskAQQGAvOi0gSB0QbMobiulOSIlB52yE5rSYoCa0C1tp5QvZ7pCZ8KsIwECkobGXTLVDCnHYCnKgRBFIw72KmJ5EeoyoTfCRK82HxAcaU0gRbgnciT0eSMruRvSJtGKKyhJg/IUccPxyxIbtX2PbqtGf3jSjg33ZFLRK6tCF9xtfe74rVGgqVBnRUBP9KjFhbMryNM+jqq4KKTrBy3xkhzulp9EFRxhMc/6ON4yXC7xQAwUrUIVlrik8+RDmytN3L2A+gJvO6lE51AE+a4DEEj9HF2Gt3sX6PEVIKgBPwaDBAjdaq2gW8Ahge73jApSZ3rHyCGNymTNH/evGnFpEEjCsgZQGwWETP0M4lO361eaEL2cVhOajtZLz4xb2JrdHyH6VEE3TsOj/MYyRSQQi0h4+pKWtgWojDT9qoq08kglASDEbUsf67LmlQ0xkKYtZ02oWA+wCSsGINcoSUZYatQk6wQxytKjLZkSJvCH9FtGXTYQiKm4AEInBoLolwGCGLUZHbweEQ2zWtSXtjpNO8gj1P830pt1gtaxLLAssCywLLAssCywLLAssCzwXBb4rUAQL+R6/fYLO1+rveOMQKAvD1kgmnUrmBYGEHR5k/WAblgjSCODbjQl7JrQUxnkiAhT8U5h2vO1gCDAG8EUeqnub9yqTXE7G1kM2k6JaH71z3s3795SDBpf00nwPh4ZHjmiuIEfy1HcgJCYVM94X5cjosIUj19CSwMOIa3AkO8eNYSHpBqaY21tFK8wtYeHeLwrr6gkp0bOirwbvjDztdnr/DAtQSODTjx9IY7KWzkm53JU3mkaA2DQW48UyuKm5waCsqOYcCRtU6fFpyXQ+kiu1aL5qrqpqDiuS6yCc0UGlABlSoo0jmZEEWwRG4TSKTOYqYz/5IV3TJ0Z0syPLq0SxBHHpgZlXB0n9EktY0SQZLv+kpc2NG7lcZ1KHhH9T6bpKOa6mo7O51Q82AW9eroXS6SdYYHHbBQnMh1dhBlbRhGVtB0Vlq3KFrv1dpFVtuTt6Nf5KlL8pOe4kqVq7tc5c45TsDiJK68aCdt4sMjU0crGuCHpbAzWeeJQHiJ8RYm79+iskdHuQ6hlo9KCGvjlKCqpcQ+kRypa0ykFUq+5t2VLaq5F8uq6VN4IDUmkHRLvGlxkaJbqyNRPDeaO9H0MX7GJTjIgkHiqr4FBRiPPT5ftRJ+o6WGARhIGmMNaQKwRRP9J6IX4TWOEkMAgxT97zaAs2O/1gpgmxogihfS1XxmJ2WQaCBKNy5pr1C4xaelUi1n7glYbpKMPpbGX20ubFad/7aTGlOuuBBUQ8M9xx0xJBvl5zrEbsi1VMgndX1lPgHg41ZdiPwFB2DP3gMq1vcMDjAOUq48UEHQqoN0L8AtM94gg1mNjBBCjguhnBQqdnZ15zSCmiDHN1usFacdG96v6k+RF/qUMurQmWzdOvnom0g4BUYq3icxKr2NZYFlgWWBZYFlgWWBZYFlgWeA/t8BvAYJ41fYrriKEwoC0fgXbxcvpsPPxVV+n45AAArE20OXt7dG1RgGxVtCN1wtSiAOjqQx2TgTcIIefv+PK8dFrdn68VPe3beJ6rW5p3AT+s+aNX8HLibIHijPRdIWpH3IoVL6mh5XTQh2DC+eEowlo8fgwODA0vjkwxEEbFKIWjoDVI/QPIk4LLQNMys/KKV3OBNMH2DmM3cJwWrwLmKaIvWMKA+CPgKF3F4BBJ0cXb7O2BQ4NQNCZpou9PpbDITP0EUHo7oNW2VJuH7sScZSeBbLEJtC3dkhpStBuyRwEiEpHnjNaNGyqQenSIsyHz7abjVuyJj47g5WWTPNV+pD0AzrCrkqQnvKtHhPQvOpNiD6baqq6OXyimpml4gxe28qb9a66hynhNZUTrBWWQMJDtDm/4jt8JS4V7Ooxp+e4hFUSeSAGewcZ22Nwjbysn0LOyO2lxDY4uUP5lwP6yCsqtMRz1rM4HZUHCUlc5yFxYtyJhm8mUjN9zPaASr2ebtmzRK2MRvtG/6ADMgfXFz28YHQDY0xHPx4a/a8nNiVfBQhSHqDR58+aCqb+8xogSD/AnxuNCrr5zFpsTBFjRGYAIdZey5QwhawNJBNlmhnAUkD4LFwtjSQ/upWOFYZu5dHfEVpTT44ofojTwpfc8I5yUktCbiVTOs9DrleDy5Kf7MYbMRSnJuTUVUgfTHnpLRCIe1Kts8IGOd1vsF5Q2waeEZNeSw1gR2APgPupgCH3oYA/GiWkfpURmAbatZYQU3TZYt67M4oXICi7k0kWTUQfhahcbeQOM+hpahsRpPx1LAssCywLLAssCywLLAssCywLPJcFfjkQdCcv4gsvuHYYcGUC4NTuYPeaEsaP0T52Tjzq514g0K1AIAFBclJwVJjacMM0BoVftBCqXt/1Ho+Tw1QujfaxF8ErP2/bJDkxCoh0o5FBXKEdGNLoJUqcKIWoqrQRCfPDbpeCiF/YkWD5VMGB2MRUtgtoFAX2YOLEZQRKarQcYCwXjgZ9FzFkuhxfr3FecFxos4Cg1hwAIdb2YcFo1rVgkVNPVfB0BQE+AoLevz0/evf+9Oj9u9OjC/28241AIKaIHWt6GEDQa9YJkhz0aapYd/Rq/mjAJ1up9DRnzIB94qXZPvXVPpbEvpTRWbaOYMlVGRcxiVPyBq1dh5To58bVyyfds1tE9dmo2Jl6fEUnppYuHvKJ7/FRnGufo3SD4iImt/xiEq3EHBI5sT3NJyar1mVF39hsbovqLoEzGb2KTrzyoFUc+u5R+RW2fIpYnE+VmsMSVMInATNbGQb2rjgJMeW/yIIZO5/k6L9FtLorr/g2YiKpN7EAS2oYR+5f0sjNObGiQPU1FsdoSfGQW0euvfUTp599Z1GqfolBDl+rsZ4F88PuBial9gf8Ipl6fRY9QBChckykXn7FiRw6iNyjTJ1l9MvnO4HtAn6u7zSqkmlhNwGFANk/K07fytRcACGmh2UdIAAhQCDAH9YN0ugg9UFcg6xThH6M/hl1o5N1cfuiFxpBiyWScg46msp9nPbTlmEKS3LJkgB0k0N5MUBLb4Pel0lY7gPJt3KUpy+iM2VqGHWE5nrV4b3Udu9v1LkC4LDgM+sFnWh0D1PGTtkh7Oyl+lGB7azLBuD+7uzo4vw8i0oLDPI0MoHzp2esFzTAIO/KmEvT2+hWu5ONXdDmWIqwl+U6lgWWBZYFlgWWBZYFlgWWBZYFnssCvx4IEohhZ6S9t/PKzbSuey0KfSeHxFPBNMrnBgAIh0TrArF1/PUNQBCjgFgXSF+rGTnURg/hjAACeTSQQKC4a7zu85aND5SFoHEWvQ2M37ZxDfjJDYCOPi0NDWeGfAeKbUCLzhcnxeJcUVwbSrako0m7AkUjMT5LvqjHCYrWzveX6JKBhTg8zglFFAcMwmlRntJ8vaYl6OGdw4TmeL0fFo6WA3IiIOictSy0QPSHi/OjC/0CBDFSiMVPk3+sBaNfv/ouAClrWSDPP+pQnJRHHxETAb1db4sXjx0oc4cvzhV6qoRBrLJCY5IkyrhpivAPB5caOMopJB7rJawycIUTjgMHmchTWOVnrgITika7ii93SOXshqnV+lUBs9CCHDO59CXHcV9aOIo7ZTZ8Iu1iBNUK29N2Qp60nm0WUeO8rSL0WbnBmRj8P8qHq8ssxkOFKu+AvKY7og4dlOztkujEZ7WG7OLDdpNik9jBOxFbdDePdGi9idM9sV8+FK76uHOKqyTkvt6tqUr+0BQSwROIpJJPnD6q1gfK1LDUBW9uBfThp7s4hdP3igLAc3sjwF2jLa/V737W6KArRgip371lxKX64M8Cg7yQtIB5TysT8BMQSMCP4oA/rAuk8YmWy3TauqWrLw3ATiujm1i5kDoR4eki5Dcfys+FFJE4eTNP+sRQAHHmvLS4KC5qKbFfiaVndzFO9KPIZ5pul6U0/Sp9sdSk33stnpf6lkDf+pppYgKFWAzaI4NYfF/97Fv6WoHtgEGMxPSOYgDxgESaSsbUMgAgRhm90c9/klyP9FMd6I3OdZ0VXUAQRljHssCywLLAssCywLLAssCywLNa4JcDQffyXrREhd94cV5Yu4JpB3fevYZdbOSMMOrnOiAQO4WxLtCVFoWOk6KRQvpC/aBP03ypZmcbgKWMBsLpKSCoHCCcIBwOvV7nrVuxFpcjgtvAf07xARpvS/NizhH3Yrgu8LsYmXqD73HSOuxwlGgTOLk2InFeJDxulMrbGVEGjoeFVY3IiuNDi2o6A4DK9++ypMrBnrUwsvaEpzLgaLD2jxwVtjH2NvH6Ov3h3dujDx8EBGmK2IWcFRaNZrebczkyJwKCXsnROdbXbgAlnB9VibJ22VjdxEAQ7cJ5cb1xkuCx3uK3+vAoP0e1CVHNohV0pkTs0KvkBtRwVmeMyEo2OVXTXtj5ZLuKT0zb4kpB6HwtDb9oRVbT21GUMIzUuB86axWpkmL2bkWj0MRBJjZL5lYG90WOone+sm3jKL5eYKrBUQTAVILm/KJ3IS1z5t3Nm8t3oXOB3bpaHkGT1bmLViEs4qGJsUDn3NRqMXUjbnL2E/DODveWA/nU1BQz59M1F9++vJTP+LrUsNU8Wki8Dp7wA0e7DyJp1BAgSGn6URX77nXN0JqEQBmXQ2ZKmkcGpBzrr91caUSQpoIxIuiaPlZA0I2m3wLAZ2quRgppaq53DVMnSz9Nn+1Qcfe70FQdMom4DvcM0WG0iWc6eijniaO1vrU3TFUmed/98DU+Mdga/b6fxYoHNvrTpg/9VY70XY6rLH1N+l8KMCKTkB/3nHilAhDXS/XDxF8BBAG289PIyezQqNGX6mdZQPqdpoe9FRh0wRQxrckGGOR1gwQGHTMFV2WONTIIIInRRnyg8G5irVZanNpzXiOCuBLrWBZYFlgWWBZYFlgWWBZYFnhOC/x6IEgv3TgQOAV2KljbR0N5blmbAgdEgNDNNaOB5JgIBLrSotAeFQQ4pDQg0Z3WEmLdChaXfsAJ+qZFnfWyDxhkZ0Mv1nZ+OgDUporZE+Ckn0YJ5fBbvvihK96dFV7Hm4PjGC/noZEsx69l9aB8GHPqVGmKjtIYgP84IRT1yWHkKm3+2Smh9vB9/6bJdTgwChHg7/B2eOSkqEKcijev5HDIYeGLtXcFYx0ggT0X786P/vXHu6P3mh52ocWj375jt5t8yT7RdvIeEVSLmlpj5KM5EBuyA4YNByltzNgp8Vkf2lRtr7ZBafGIg+Cjc9hYXMNmqzIeYWeqUlNY8iqcskYUoWVBqFg/R8JKUTd8udrFhWgO8hy3kjIGoY9Gr7iYCsyy+mYbvNWgGsnShDwZFF+F+4xpfOUndJP3WX8Zhfbyi25VrR3vllM029Q2C2VborhaOGWWRTuH8sj+uSOlc71Ly6LVFSp5c03j7qhSo51F4f7JUwFlUrmrVrQK64b0Eydi3Vr1GPSCiqAN+diSkZCONxr9IHW2MYSOowBgDVn0uwA8N9cCgNSfXqnfBWS/ZkQQi/BrlBDbyGftoAYEMZLTfbXqY1aqZBnIV90am6gE/1GaZ4f6OQgTHyBQnsHigEul+9nRfiqumYN40q6ujNTLtDzSjuokA5YkyMQzddWWFh+MxIGx3AoYlKZsQgAg8vhLw1bwTBF7CQjk0ZcBgU7aqEt2aHynUZdvBQRdMNrynUYGMTpI08dOAYLUL58wRQxAiT9F0q+AoI2iaCUVtHTbmhqG+dexLLAssCywLLAssCywLLAs8GwW+PVAkDwI+RFxJIQIfZHj8Ki5XZ897UsgEA6JgKArvlD767S2iSdP6wN5kWiNGHoQAAQIxM41nhb27ZVBoLzC82bNa73e1vWCXaGJ7O7lN22NGurelZgd500/v/gWxDniuCSeF/PKKdocOk+nEs9Lfh2Vh3YcBgoSFQWHY3v4y7QdERyU5pjIEcloIEIV1qiglCuHhS/YTPGSoyFn5TVAEKOCNCWBKWJMDfvjvUYF/cEXa9azAAjSItIeEaSFpr32RbY6RnVV3/ViC2OAIPk/pnnaRIt3K7k9acvU9NYw9FUUof3oJcnwf5xGxXo6tZnwH5xSJ9d91F4wD2IHNZXspocWaBdHMlqGv3Su1L7E5Pza83z//dqaqQ0bDjs6tue4/3OtSiLWr/gsZdx3u7nz9ZpL7MfDuVs+fFCfqntf0j+gtOoSTLoqOqUs0Dw6GQgiVBfhHkBxdwsqUUBQ9Rzc7ZQjnylfAEGXAEH0tQKA6G8NvAuU94ghAUN3WkSa6WCUYQv5r1qPzSOQqEdEg/qSyTObBfcTHxrneUuPMNtitIjuYH4SSeUonqRzpi/MUVyV2qSnREkZctFOGokn2pW2AD06agSmChY4ZEhPdNKvVBAQiNGTbAcP0O7F+b172BuNCNI6bFqQ//ytgCAWj9ZabABBAET0w8fiy9RdhRoRhCz6bN+3Q9k0S2ftPL+AoG6NFVkWWBZYFlgWWBZYFlgWWBZ4Dgv8ciDoTl+W/V1VTgRTDPia/fj43eDPjb5EZyqYdgkDBBIg9OnqNgAQ0xYAgwQEwf9F5R9Zk8ILRfP6zrda3qL1au8RQQBCpKfQn3XbSKAaLUSZ7jmqrB2IyCkDTz5FSHuE4owoSluVnHxOFdRFZgmQ++OoTkUiGyeFgJOcDsebW+eJGJ4WNgChjDvAExSvmAFq2NKeNSjscDAyiIWjNXXh/Yezo3+914igD281OginRQ6KHJZ35xoxJCflDUCQAKQ3mibm6ksNhbW4KfKph3oJw9caMDn6laeiOjYNbGlK6lDgYhTAFApx0pwB7WcO2FoVU3SUNFH2VhiLbl3PXriX2NW3Z7T2oqOEoWjTlSp2j4zMeVpW8bvpf89W7ArdoCk9onOdvxcIQicaNRrm0UBcg0EaireYL3nLP8jWDG0LwFDyFCbrYKmpnnBNhB9Ys56/mXs/XjUiuak0tXqf/yBfCWnslvOD+1/dgA9syQ+AnbBGRlKeX40YMru6Q7Z6/6zpYFeafvtJ/ez11b3Advpehepv2U3snnXaBAjdKfRW8QaD9MTXaCDXE1DJPYGBINRp0Ip3YUz9nUa2j337hy6hKHzwGM9rYoOxYkCx7h+LMMlxje2mS+1hIt40dgj47nyF7AaJvEBqDVYT8A4A7pGRsqWn0gKyszi/+tkzwHXvHAb4k+m32aExgNCZ1mxjUX52dmTarsEk/V2iv0bmfFQzTkRsf7Xm7BVfFlgWWBZYFlgWWBZYFlgWWBb4ty3wy4Gge72M8xWZ3XofWXzUv2+ejsDXaLaIZ72KS+0Sxk5hl54axoggfaX2ehYPHgWEc8NuYawx5Ff0An+AJxrIk1E/AEG8Yev13m/a9bYNPW5ArOfXf734z7TkDJeh2bne0FtyBCqt4pHUqM2Rs9QW7wyyhUcFdQElWC5N+yoNb0afoC78Mpw+/2dUUHa5IR9nBTpVMM0AJ0Y4kNeyYHoY28qzTtCFgKA/BAR90KigdxfaRp5pYfpi/V7hmbY39gKoclRY+4JdcjgIiO0CQQBCeG6xaNMdZwt9yHM53LPEt20NjSwYq+UpKJIJ2EF5PdPMOVlo8SkkfYAPPUyWEMIxEixpkaDqFwHmmdJoP2lnLkqY7mJUoEirCCk/Osw28T/FC18dB9tvTVCAqsNc9v1/BwiKfu0KyEyl72SyYqnG5mr1FHaldVytXIkqMBlocJtzk3wiEamlWV39w8xP1bTljl6zdhWf+dKWQal0hc45UNDXVJnFV/cEYf0AeoTx+Ac447RO7m9Nzz0PAA8QBAhEn3vp0ZcCgq4YfanF+Jl+q8WkvVi01hJixCYIKiN+vBabZeWpyJQv9bkNCFJW0xL+Q0+OGfZOtIG2jSsyWCIzeXO8ODqtR+acQax+tOpIfdRb/Tb3mNLqZzl4rJwXS4qCHfQZw2sFUUbjTdUJAp4DBDHaB7AdIAgQiHWBsjC/1mLT9DAAdy8srSm4TCNjnaBXmhsmzF2jgrJOEPWWxg51OpEea9cwLLOOZYFlgWWBZYFlgWWBZYFlgeeywG8BghjNwzoTD4BAmKRyegAAQABJREFUGt3DNC+mg2X0T5sapi/Tl5cCg+SYsHYFzskNUxXuNSII54bRQNohzF+/9b0UMCiAT4Agv9LbQYae12gDREUTv+AS3vR1+OQwrkCZN/QNrTywYulhvb4jjRLU2zJdZ5LltCclx6zLG+UplSJqaDsiU04Cc0DU2m8MBxAoxO5hPnBQ7LAABOXHmj7eCr6tR3EqZ4W1gT58EBCkRaO9jTwjgtoUsXMBQSeMHgIIEnjEVDCcHU+o0xSGzdQwtU2WlsnFAwDV9KQVL6SbsTfiNKQ1zTyAWY3XQeXD1jMiLYAGXEnPxZ6Kw2kdWhi+COb8NBAEJ3DasHnK0s7kIZnrOmuDzxg9K48rpbjaNa5tJIVW8nfbGB1dk7Jii6JxHWZ5VVbcYiyulJ35aJF4wx4lfvl5aJd7Nwps21M0mlP80RvVixIu7Axt26jBN7j3+aoskkb5UTY1zOfBNVO38VEj9G0q9ezStuVzlXauI5J2iuVRGRoln+sN73aalgEbyQAwrxFBjARipy9CRv18VB/76aPAIEYCNVCI6WH0tZ8fHjUqiN0ZH/uUMJ4E7l5PtwXkkSqAQ7ZqgT4O0ZH+mZBGDJ2VaEfR/LR4YF1461x81KG42WeDxF5Dinjm7J6AmIxdIAg65f3TSS1SSq1sQrF3nn36WFoOCK8yL76al5FBAEHanNGje95o1CVg+pnWBmIqGDuIvb040w6NGnHJqEsB7qzTxlSyU4XwM4qIdYLYVp6+FA3oU+ZjrRE0W2PFlwWWBZYFlgWWBZYFlgWWBZ7DAr8BCPrmaV1ftc7x/YNAIHkp93ff/CUaJ+T6U75KX8tJuWojguyYsMsNX6q1lXG2i8cBAQjCOWHgPC/RChkZhNOc13uloZHmld5v++ZxXOnhKPP23zwAxXgXr7Euio6jvaTjBOCgkKTUcOMaQ6Pj8PpwkJE6EyFOTqst9Lm84pNXEEdGNTE1DMeE0DuHUYYWA8hQRlMJ1Fxa/EqoELvbAAjhgFxot7APHhGkdSzkmDA17L12tWFU0FstZHoir+MEQEhft2tUEXIYZcQIIEAmwjhJOEWJo2amUSiUo2lnijyU8QGvCTBaR0Kiu0cHa+RIBjDY5WjpIdp62GE8xAqfMqmrZO9eL+e57L5GFK8fLKm2zgpd8WhpgRwVWuzOybaQRhTFLE/pntEJuqrmI0S/1EUb0MIUhBBX/lyvOZPl/F9/QrvoXNN2DrU3tNxPaWJvWVe5mhFpkdozeyQ2IRk+PxEtF0od43o9Ja/qqxKHwkismqgzFKQnRqkRI1UtS3zUEq7pmopQJYfNBj/lsRUluO4F/ujxU1qL8Ov3TXRAd8CfL8QVMp2WqWCfLq+PPtHfqt+9vAQQ4sdC/VqH7QEQiCliX7MotHqT7wLe07e6R2gtId7aalAodUcnNDx0UCaH20cjfFTYkgp61iA1zn1eLLF/QJt+itJP1lFXqcAfr3tGpgyOpa0pozMZiUk5QG91drKu+rhcK0+nFaBDP3uiPvbt2cnRmfrUMwFB7Mz47kKgO6MuDQQdqx/WLo3arZHpYewgBgjESEz66vkoLRkRxDpB61gWWBZYFlgWWBZYFlgWWBZYFnguC/w2IIgpYZ/vs+gzX54vWSD68iZfpvk6/UlAEAtHCxC60cKmd1obyDvZPHz1gqWsmgAABFhjwAYAaAaC7DkFBDI4xGu9fag4Lva+Q7DjHIOaQVHcg8TjWuTcBASbsbii44QhodKRxhkploQ++gGu5KhQpeztzGVHfDgtpRG+CPv0yE0wEMRXalwG5asYDgoHjs2LlyxsKkdDDoY2EZOTojWCAH60ffyFRgS919fqC01ZuGBU0AVbHMuB0TyFU4Ag/SjL9LAAQJJjmQGZ0B6L0xzicagANqK7ac4jlmPTdLGNVhZHwrSUwq3sU4xzMVj/ho8BCuWoD9bEgNaeOso9m6GDEZearehMQ3foVd9TsgdogxAawY/rSTiOwTdoh2IBmFRvvw6SsxV1qNh/mUbbmiWk11Nt4XKX3v3a9+tSjcjdEZMXbVY/OVDIDXflj7xdo4S3+BIekr7lQP7uASV3wsgbsV3u+Z4JFxo3bUSokrFNlZ4105MnmxYIBL+6BfdHgOSMvvSizqI9KoMRmF/V97Jb2F+frvTTyEsAdwFB7BZ2fckujfdH91++HN3ffxVYrxFBGn0ZSATAXU8Dw/0YZennM0+H7awHzP2xlOAORvnSH5vkaOHIaHSCEH3ey98SKoW0is+xXp2fA2knMMf3lpkF5hDq1LVq8QEIJSdgD/0aLdLPoUYESR59M30gW8lrcI9D1v0505QwtpGnP2Ur+Qv6WS0g/f69pobRx2o0ECARO4gxKshAkhYLegV6r8OqtZD4qVRZQBCWWceywLLAssCywLLAssCywLLAc1nglwNBd3qRlh/i7d8Z3fOgL86sRcEX6suPmqog4Id1gq4UAg7dAgSJ71aLlt4rvBMQ9O1rHJHvGu0D+OPX+RYP6KPXc89Ngk9v0frxQm1nxGleuMsFIJt40uHLCzklkq5ceETJf38RhgcCX97HMcehNgBI9RwCgiif4lWuQkoS18+kOC44JQMIwjnR8CoOCQlwRKifnBtawoKkgDjHGuVzoSkK7z+wiKkcE8Uv5JwEDNK0BS0YfW4nhYWltc2xCr1BZwlhRBBTGHCU8FdsRupTmjoa6mFnC5p1UWjblu7OgGhCazOFw1cmJNfOsPhbUfMcOrkqMnrkEFfyUyumbKCEWZsuio8YGUOg22fe6EVOHcTRFYlePNoZokxMBW4cAkC2fJEKrWzR62mMkYVwbK96dwyUe5nyaU3sWFJ+Rxg9UjP3efQmXTqWVmnifBXKssWRcJi23R8ixyLFhwy33IShgeoWZZtbZYqedHhG3o9iQ/7Qo+oZ0oaE0r/qSPmiwhc9qwSXsl3+dr1n3tgga/QI5KG0+L+x1bta+03rAQEGsS4Qa/14Kq7Cy5vbo4+fro/++nijPlcAvNYHutZIINYJ+nwnIEj97OOjwCCAd1uN6bUA6/m9INSTn/sNOISD+1F1KkG63YKtNVyPZhUQWXNUmBQSfERYpXbCZG5ZtqlegH7GWZwAckgTT2jLAQBJDeLW0PH0c6GL6hFBAEkFBtHfJk6elgg6eukpXi/cx9J3nmoNoFNtF3+htYJYoJ9RQR/U556zFpt+5ydsKX+sRfm1ixhTxBiBqc6VOstuqsTHGhFUlljhssCywLLAssCywLLAssCywHNZ4NcDQXyVlvZ3+tr8wBoUAngAeS7ljHz6i2lh1xoNxAKm+jrNVAV9rWarY75O3z3qp1FEOCT6DivMB2ekjQpqDgqv8waH/NUaFz4v13y/9Vv2HCa30ZXQERdMvO3AbeDASSDX6XbK12MS4To4Kogsvd0bAKpQJCTuHQZ4oEZeAmveWEV3VgFBONaMCIJISFG+dstGODiiAwZ5RM/r70fHQnAuND3BP3+lzvoVF1o3yNMWNF2BBU7fAgZ5DYuAP4wKYgoD7Z2nhkV+nKboUPpZEfFnS+RybHBy0u5mR5LtwKGM/USAERGHbNT4HSDPfDNxlOpZjS8jguBNTs9XmvgutaRSnB9HhYknxb3h2BBo3t1TgJswzfF9PunSZD3Nl/t68FFmXwFrNiu9W9l/PT10in6kS/eRFzWmtHVG+3GvQJo4muZp3Jw3xxtTL1kSbZeRuReb6zksby4S7sie6RWfpRXtqXCntrnoJotEDlgAYDh4hjw6CCCIuAgPmn5rIOhB03C1wNqDfp9u1N8KeP9To4IuP14dfdIoIHZlZHF+FuV/ADSC/wvrkKkffUl/GzCIfjdAECG1pp+tnsojkiCr/tGfDn2hk1KQo0eK8FQ4GHNX5CqOO6TKDb6qBYDc4LmfEf6GoAN8+nl0jwI1xi2RcqyLRn+X9sGn8l6PTXFPx5VdXI7+VYC7mDHPscBzRvscaz22szNNE2MkkACgi/fnRx8uNB1Xawexs5hDjQ56o6lk8B4rZHrYoYNdwwCb1rEssCywLLAssCywLLAssCywLPBcFvjlQNBngCC9SzMaCBDoTiAPI4I+aWHoS01T+PjpJruFaScbjwzS6CCmhD0IALqzc6LXftai0NSEOCaJe0oCr/EeGUTIV+q8yduZ5rU/b/Xt7b7erHEm4ItJ47jgHlT+jqnxCXwkkvUi0IkyPbOY7GcgKkCQdoYpHUQkar+EqlyUE7KaHAuVJlN+onJknKeljQF9zN8AIcVfdHCIVmj6wquMm2KED2tVsJ0xo4DeMSqItSs0VczbyCt+fvZKQJDWC+JrNSOCZEoviNqAoEwTi7nS9riB6G3drAuOFo4UlBwjKpocJ9rbWimGwUdjLQLqFG9iRuDKRvKpGPWWPHgCBqXm+YwOuX6hIj7uYrQrDStEVtxQhSpSulZ+0pEVCZSoNPEcW76i7ucNPmpATtVE3bQxsgdfkwHfYA3xl55LV+7r2aK7tmj3T+lmnTnt8qU5g7pt3FxijpdYwm2JOWfES2soo66Rv42V7ruS90s+pdNoVTg6n20GPHHoEFc9I6qK2gCBuBUCAgkMKiBIwzAzykf9qACeT9c3Rx8/XgcI0sigT6wNJBDoilGY9/dHX7yIv8B6gUgsyg/w4/7WU8ICwhs24eZrQBD1U3cAXYBd8tCrnhQnJ4O2fAruc5mWEvCZqZ1DrTK7OfUcRzq8cHDvMaoHbXIfJlQeD7AOpsJShibZ4ooYDHJ5AT9TX/v92xdRBX0JDDJADhgk/jcsHK0RQYA7jAyij/2g0UBMD3tvIEh9LFNx6WtPTzUFF162lBcQpM6Vuuuoq34swhMYUbGucFlgWWBZYFlgWWBZYFlgWWBZ4B9Z4JcDQbcsDq2Xce9IIwAIIOj2TguXMhrIzsmNnREWjr6Rc4Jj8lkjh5jScC9n5ot+37/r06sBH6YpAPjUFDG9OntkEDReowF/CFoc0/hNW0S/cNdbdwsJcKQ6H5HpiL8gQo9QQ9J74EbK4RhR5RYI2r7wVw3wIrs7zHxa11Fapt5Wt/PgVdoLmYrRNBwWTeYwHV6+0/PF+qtH9bzVIqYAQexic6Ht49nR5r2+VrOI9DumLchJ8ZpB/loNEJTRQIwIKhAojg+axQFGP+qw+dyI6Gh682wStJaE0YZJm5FlYpNCmmxJRVSyTDt0QjZ8FR7iMU18BUWge7QkZ8QSHxVW9SUzrmL4E1e9vXhRSvfGV+3Y1NMk/q3S4SuAp8IYpbTqCjSb6Vq4zoTF9XvC0i33aum11aXZScTitlmeuPDj6iCFVI4tPTnIm+mDu0ptw6p/S02q5FQ4eLalKr/CwbeNVX5CznW0Z4mkbuzYrPIqDH/lcf+nz2NhaN3nIrBY9FchQh4RpH7zTgtAP95rbTaFLBT9p0D3vz5dasFojQ5Sf8tOYtcagckITdYRugd414/+1SOAXr4xGPTCYBCj/dL/2sKuH/AHnVtIdLo+aG6t0ynnWm9MZyH9gnEPIKGOxCAO2jZvplNz0nnSefL5idYAydoDrR5g1gNiJBD//RdDChgIcpWASCrvsoyS0s+jMOlb08d6TTWZhF0XWTT6WOsFGXTX4vyAQGwl/047ijEiM4vzn3gK2RttOX+iH4tNUxV1lg60YG0fLyOsY1lgWWBZYFlgWWBZYFlgWeBZLfDLgaAbgRUAOnfaBeyebeE/PxzdCAi60jbGn67YxYb1KrKF/DVfqbWA6Z3WEdJyFXJoBARpXpnXBmIHG00PKyCoRgLhtPAan9E/cgXwJgwE4QIQ5y2b9+zmJrT0cCGSbyadypEpp6LoCSlVJeOG4WrMBw4a1eIkoEdGyaj2Vm94S4ak2aPD+VJ8BwiKDspJJhwGQOycdCelOStK47hkpRAKfBMQ9N271bCAaX4AQfpi/QfOiX4Cgfha/UFxpoixboX8GQNIfK2egSA3R1LRqdvGjUUt0ZoZq51Jb20rznZ0CW5aUW24kXiWmNe9bZIGFIR96pjjRRvh3ALuIad7keTOPKPkc8eqll75wQrK/gcz/+vE0i1hjVraVjuuQrXI+U4UpeRsS25TuRZ/x1kSt2VH6u/KD05icM8S/1nprawDcp4S1+kqQ7ECr5Xw06h8RgQJw2lAEOv9ZEQQOzTeqjMFCPpLU8L++uvq6KPi2S3sUQtG3wh4/6p+ViCQUB0DQYwIYt5Tmx5WQBB97guA+A7sCFpBN/3cjW0b2FJS2Pxm23l+5gII4iAs2xQtVGcfPMFXPxgqTu9MnJWUkEr/OHrsLAxN35wa6auZCuva1aAAP/SvAdsNDNFHa1TQq5f0efrrozIs/syuiwBBb1kjSEDQOwPuZ5kqxi5ijArSyEsAoFMB8Owg9kbDLw08qSbX3Zq9to/35VqnZYFlgWWBZYFlgWWBZYFlgWe0wC8Hgq7lnQDofL7XLmACgu60MCm71Vz+pWlh16wPxOKlAoJuNSLoWiARO9jgmMiDfwQI0tbHjAjy9LB5faCKdyCIb7p6ZwcEMgoRJylv9eVYtFAB7kF75XeME05V5XRiiwS6SKni4SV+LBod2cZGFAVEwXHKyCA5GzCXf9Jqh2B+ATh2T+RkwMKIEzeBsAqRIa4ARyRwUKRx206eUUE1WihgEDvdfNe0Ly1UqlFBrFHhLY29nXybusCXaoFE3k1M+cfaQQwgyNvPqwHa5Vj656s5+rhJk+5RHr1iOWIFRMCbRhDZPdIqqG5Wt3ucMttkt8iURrZ9yxTupSeWKKt8+BobpVp8UObcTfmWcDscB3hwzTWgQNTkDp60f+g/17OVjj3DF5nb3N3Uj3mweYEuZf9dCf80jeZzu36ufLW3Qtq4+1SNvJJpW1Si2TjJanc04a5J6Uo/ffW2Jbvwvci+NluWkvNUTZVf4bb0SFV+Qs7zwXPc0srivrBN3CnMfMp0UU7VMygUX4AgTV5S5F5DhB5ZX00gEGuz3QqEv9Qi/H82IOgvrc/G+kDXNwBBt+L74hFBAYJQhAdf08EYCcSoIPepoilkVJCno3kMjXSlG1KJcc8njX55XrZ5bkC11U1Lws3q6Tml/J5UfFPWBXxXhKUMBhNxwhHfAkGyuQplJFC0ZY2z9Nc0qpVzA7VhAaMw6WPVR2P5Fy/YTl7lJYBdxDQzLFvJqx+9uNCIIIB2RgR5Ou750Vv1u+wk5l3GxOMRQQKOvDB/k5O/YBphpLTErWNZYFlgWWBZYFlgWWBZYFlgWeDZLPDLgaArFiuVc3InIOhWI36YFnat9YCutEbQX1qrIlvIZyTQtYCg2893Wiha32K1YClfuFmv85uAILZGBpKYp4VVHAeFV/mMCsroIF7j/abf3BGnm0eRD9R6254O8qGELxkjDTUjgHBF55LlmqZ0/Ac7GJz0ywLKcolIT0c57nxlJu5624igUoLWcpQzXeBPaBgnTgl0A0Ff+fLNNLEW6sv1uReEfnP0VjvaGAjSrmEfWDia6WE4K0q/Zw0h/U5PXtmhYQFUb5EslV/qyzcTGNwcSY/7aQ3SWGj2XKHR5Lh/aL7bZjPolFaVPYbEyv93QqRF7ra0acpMXl2tmXOOU3Zf0tC0ubYUwSCtaOWnbNWfTGxQ17pyEm7reZpvW+rvUk/Z/Efl3JwdhkO0HZadZDOGqXMcMyXtFjs65Q/jdXmxzG7GVKY9geHrxRyZS1FiTm85k5qlHsoftJ/nHGWejnEnlV3MdUi8aNs2KuUG6aROjCL0Rjx+MxDEws8PAoIY6cOui0z9+qT12Fgo+s8/P3m9oOsrge7qa2+0UDRrt2mNaO82xkhMT7f1AtGAQFkfqEZeAgql11Gfq3q9a5gjaSs6RUkrap6mdBg4h2mke2yU3rBZ1FxIlpuTXSCaVUkYoqnX9jEP4NDg6X2awW5ALn7q8xACAGTe9M/fANy/Zp0g8gCVDJIDBAnN8ZbwAoTOGRHUpoUB/Lxjl8a3bzVKiHXYtJ281gZiS/kTLTDNDmKMKAIMYmdG16taFxAkI6xjWWBZYFlgWWBZYFlgWWBZ4Fkt8BuAoHyhZiQQawAFCNLUMNar0FbGV2xlDP2GkUL33sFGmJGmODDtgMVP9cqtqQoGggQGaVC+nACmieEI8d1Ucd7eSVeoN2scFJLw+b3e58kHMe+w7cavGGTFyEkuciKR81wiOckWXbL5soxTbufCb/iNRwFf/wMOSIrmVuSfSu8AQQFYYC5XygWtg8sL8MlIAnlxAEqsY+Ev13Ja5LiwsCnbGrMrGItBv9WXaNYH+kNOyTs5K3y1/uD1ggQKyYE5PRUQpMVPAYJO5NTki/cAguSrRBF7YQ0QI86Pdrm9SZrVTSbDKkPSwXVTWfG6aLsuzhGfRZvv504lusJDpQL8STaG1FFheJMix+0LsZ+ROw5dT5oq3RGVO4vc0R5ntAanjamzZGxp0XpLC+e/Q6PM7zu27YweRUtYqdho57ZoisciJEZbBq0xtdwhb59eZYaUwTPHDsmY80f85zlHmb+LlZattapirsW5EKoRU58GI09gfuov1Y+wbbzXCAII0gLRjPS5ZUSQgSD1t6wT9OdHTQ3T6EuNymT05bWm6j5oxBBAECOCvmoEpoEg1gPSqKCXL9QhCCDJtFwUoQ/mSeH55afnpylNgD6AVATj+RDJPDPFzDrV0YRIQj+orh95TpEwnthiALThUFlXhAw0EV0PLCMkfTRwRwRLqdL0c8jNiCBxOkNgj2QhgzZ+02LRNUUsdajvVR1MEQMIesVC+/oxxfa9poK9ZQqu+tf0s1kv6K3WaTtTf3wmUD7TwwQEqXJPwZW5PYpU1S8gKJdrnZcFlgWWBZYFlgWWBZYFlgWezwK/HAi61Bdp/ddIn3uPCLq505SEK40IEgD0UQuXXn7SlvE32TL+WmAQawg9AgQB/nhL5AYEaYSQHRCDQZoq5ld0vT0DBOGY+OU9o4H6az5gg22nzOZfFCggb945jfyEheNQjExqrWO3ZOVAb0CQHAQcDA9YKh1VLw5GgBypJfAHx8bSHFdpJ3TS/2oBtXaN7ezIySHEuan0t0fZjDROy6OaGCDoTCN93mpkEF+iP2jHsA+aplBfrdni+MMHvlqfe0TQsda74DcDQVoZxJUXUGLTuW4rGh1gaTYtC23SKF/sZqjWyB6KOksnprNlDZKSMoUpYmaXafImjkRdF5mKlGylYudcwxSdz2bdiLKYSDHd19ICtxW7nSIR+rq6rh15COPY8CGnMpxrW5htw5e8+RzbYiu1x/VS/4ZjTvyCOG2po9pVtHZ/Ozu02bbFVaUTYu0cu9K2fLsWbPeSmKr8Lv+h9NCB+yMlt/UOjipfbSA9xyu/wjlvjle+S/s6SvdWjfk2VYrSCtf1RlODMRLUgSCBOYwIYsewu88aFSSQ50YL9H+6vDn6//7UGkEsFq3RmNder40RQQKCxFtAkIrrFhXAzkgg1gTS1LCMhWR9IGCRgPCAPR5v436AMtVTVYi6KJzDwHwlFM7tGyVaRucbBtjGKK1+zscch2v80NBAEH0knaqNi6Yp20O1y2v0qG20kb803w0giVdlANfZrfGF+ldGF9X0MFndkNgrgeaAOK+FmTEVl770bQPa3yn+nlGXmi7GiCBGaJ4KCGJtNtZkYzTQG9X5SiYHkEL7tVh0ruw6LwssCywLLAssCywLLAssCzyfBX4LEKSP0h7pcyPAhy/RTA271NfpT5dXmrLAWhV8oWZaGEDQo4EgRv0wIuibAKCvekEnrOlhHiFkVwKnBDBoAoD0Uk0aVwPQh9d+Dl6wfUoyngg80HVUmFRRBpViP+Ie5Xihh1sgkPEpuUO84aOHQ8VwLpofszs1rPSEJ8fsYjWStEWX+DUS1MEgTV1g+gJbHctpefnii6YgCAASEHR69trTwwB9PuCY6Gv1+w9v9dWaxaM1dUEhfB4RpEVMj5tzk+kPcekKCIq1mmXxHH0o3dpXWtJqmm2EotiUTLTZpKdFtd0gwOHSCp84yJ5kmlunAmJ6KckMW4ERFcIxCegFRmTWIPHYgXhdnnJ245wfkgfNJRwG6Bp1HI5JqopUHYd5oMrmVkxtVDUtusNu6kQ7pOOU7ehumTn/78ofyh+02frUkpwRGzUNHQ7lDr4R2+UbEgbPUzH0eIo/Oo42HJKxW/chHmhP8Zk+VWHcooQUvZ4PS8l9vQWCNL1L6wN5apjAHfpSRgPd6Afg/ufHyywWrUX6b260Zpt2cbzRSM0AQVqTTTcR8piK+8JTw7JOUI0GAhCaRwPl0c+9Vyq2XqG1tBqg1hWDSc0KovUmcS8X+yac75g5A4EltIUF9jQQJ1O7yDNk1fiVrs6XOs0L+JOpYXRh1sN0yoldQBAjNwOwM+qy9bkGgvQXSCgQ5V5rRNCZRlW+01Qwrw0kMIjdGuln3wMOqc8916jMcwFBZ14wWiOCVIYdGlmPDTAJbRcQJCOsY1lgWWBZYFlgWWBZYFlgWeBZLfAbgCB2DfvWgCCBPkxJ0Bbxl4wI0uKlfJ2+EhB0e/uoxaQ1fUzzwr484Fw0IIhpYPp9sYPClC+tF6TX80wPY8qC3qDlTWRUkF/hE4dHpst0sbxgbyzZPBDzbDJI7FMjrfJw/+EZ6ZZoWI/0kHzAAdwKABJrFmRoCwT5C3NzdtrUsBothB5VTyRVLYRxUgIYqTyLKeGwaDTQ968aGfTlQV+bv2lr41d2Tk5PAYKOPSKIL9RMW7iQg/KHnJU/BAQBCJ1o/YoTjQZivQumhzFtwUCQ2yIcq5qMXiiktEGt0rPxdVACNa24Wy/dSgC24VrS7uT5rJM5zBc6Ig4eZJe4xpASsWUHZqRTKGGuIo2rlYR6QGCjwtSuYJqj9KaN5E7qVju7Dgjw8U/4olM3mcpTR9LVipKn1jRS7rlW3V5wuI2DrfIrHDkjRh7Hrs1KJ/KKp/hGnu3eiu5ylZUHN+X3pRWtiem3Qckrugv/xGmur2TMxXKvzJQRh7/qm+UMjqH/4KtaRoncX0n73mrMvq6NLfeTJPs5AxjX+BT1i2Tz+DO1CyDoka3gHwQCAQTpdy2A/dJrBF0fffxLYJAWjgZ4vxMQdHsvIOj+m0ZgvvAC/aw1BPCT0UBMvWV0UEYB1WiggE+qt+lFmJ6q2kXrp7hk/90RjiZww/wDGsCPjzkkXv0m/RTpAoIAcCggY+lw3+mKAYLo6zjbvGSKRitUQMBPRgVJHiA75Tv4/u1Iaz772Xyt/vL0/LW3jAcAYrFoto5nFzGvF6SRmKwhdH4yRgQBHr1SefpcBdI0QJAGF61jWWBZYFlgWWBZYFlgWWBZYFng2SzwG4AgTVFg+3h9eb4V4HN5LTDIQNC1nBM5JlqvghFBt4wIYo0gfb1+ZMt4gCC9GrNb2Ldvr70YatYKwikBAOLrdOJ+XQdk8Wu7X+VtMEAgXAIOv/+3ME57HKiim2nDFUqdw01qlBi0cOHMWXYqUNwUISg1lqbyxc8bvzwogyGEiGhAEA5eATzl6yDSYlv1aVlL2BOTQDkpLGoKEPT924O0+RogiBFBAoLOtVYQo4H8Y0tjgCA5Kn/88U6A0FtPVTjRAhWMBsIxea0KUZ2v3VixWuGEHCFbVip0O5hAQ8IfvmaxnleRKVS0wJOU7hJGskitySODK1yyyjjSCJu0MrMjPxef6UPeiJXUIT/tgo78mT5KHY5x7UqlZpEdxtS25dthmZL7IFMyfY+U4U2qVkyFHZ0tUTzQKr7LP6fnsjP9qfjgP2xz6tyvu6hPSZ013S/9VKl/j156zzrN8X8mte6ckqrSBkvUimaq3rZBaleVHP10obmfPGpSlABBAnSE5Dxo0fg7LRTNNFumhbEgNID7XxoR9PGj+lyB8OlrWUOIqWEBgrwem2TT9zIi6EjrAwEIfdcv9QBVSEX6ZOsZLa1Hv2+qN+gtUIkWV5mZamGbk4V2nqRy3rA5QQdqM+gMT/2g8mzqp6mxOQoIEo8fQne+LS/PMVekgCD3d01RgHADQe6bdb0MuKuPNRBU6wSlixem436WkT8AQawJ5N3D3mtEEOsGaToY28ufaxt54ifqlxlN9FpYG+sL5dmVDMkBglvHssCywLLAssCywLLAssCywLLAc1ngtwBB9wKCPnuxaK0HpK/R11ocmhFBnwQEfdKi0TU17LMcl1stcKrBLHE++Eb67VXWC7LzgQOSH9vEAwwxIigfnLdAUGjllOAacCg9eSKh7Zq2qBVWARwGjqJXGOeGPI/GMJNTBlCci2fRSpsHKTgk/iVul1DeFVJxMux/KL+XVKQAGbGICYnmdjJfqJnCoNFAXx8EBgEEMSLo5dGZ1qJgIWjWp2A00B/68bWadYL+0GLRHwQI/UtfrVmz4sRbyGtqGF+o5ZnMQFDXpdkAiwcQQQ8dMLQDp4bmmdTiIx9q+1WeZFaZiCB/Oki2aiaqiRtOCbGTVkzIr7jD2HgWR/6cnuWVy14inEfDXGZwbnUv7m3NLqMirXgx9XCWEaCnZ/WIa9wwDju7HUMllSGxr0MX1iO7fCWkylZ+hb3gE5Fdvtg8zCVz1q74CfePyt3Nmbnd9l2GZ0xH60O6/7iSw7rvUCW2X9JWRW/bqNIVVY9ASBb3UqbQakQQo4G8dbwWivbUsOwKdn2tNYLU7wIE0ed+VJxpuHf3AEEC3hkRJHlftCab5bJtPLDvHhAkmjpQpoflHi4dEsYSFe8tEHmK77QnZdKqxOt8kLEyFQrMkdGaBo3ewGl1nqEr3atmFE+TCYhjJD7FkMI/L/BPTGUoRuiuu4E+fUqYQCGNvZI4LKEffDIN/SFTwwCA3moXRhaMZkfGdwKCPmiNoHfvjgUCKU9gEEAQ/S0jgth6HuC9dD2RHCXXsSywLLAssCywLLAssCywLLAs8GwW+OVA0JUcknt9pb71LjUaEYQTovUpPl5deUQQ2xpnaphGDHmdIAFBfmfPF+l8fc4aQSzNybbyGRGU0UAZFYR9eA3n9V9v0Hqjziv/1k0Y9B+5x+WAxDmpFDXk3bwoFYaOEwElL/NxLCpuoguPl/0AQSqgQsTtKgMEEdcPafFbogfAAPKy/hDlRLcKjRfHhh3DGhD0TaOCcNkY4cPaPycAQZoaxjbGAEHvvYW8RgSxi9gHjQiSs2IQSM4JI4I2QFBvV6xspUWLk+QGKKUjBkp8OqM3bakwfDDTyiqkWONLZBKwEy2+hEPCzIY9A7rZSEqV1uEifxzoQLrCOaf0a83LRTHDyKFtakvLm+NDUmI/ypt5RxszhW6T1xK9jc24vm8oePCotlV4kEnEyq/wKb6iH+Lb0ra2HnYfXDwv2+tT0gkP5UH7Fcf2rsi9NloQDUq/Cp/Sq/IT0uZhGdMQrJ8vYVVSYRMKAA4PZLRhoEqAoO+aEqb1gUT48iggSAuzsT08OzGyOxj97l9/abFoLRrNSMwbAUAsKO3F+cWfvjXP43fvFIZGGRH0jVFBHQBqPA1Rtw5dx+ShakjI4KhQ0c4LPfYkVgcS/v4QTzo/2xD+TAGrssiFpwFDFkgef1ic0In+EkAI7epfdkrE/mjsvyicKMMC/EQAgQwMZRcxaNTN7mGUOdX02neAQIz8YR02jbi80GigTA871YL9AoaUz1pBjAh6LcSH0UDH2ZzN/SQjgtbUMBlzHcsCywLLAssCywLLAssCywLPZoFfDwTJyWCNIC8EfX2rreK1PoV2B/uoxaI9NUxA0PWtaNdavJQRQZ+/yKGhvW20j0KmhnkkkF7Ns418gUCAQkwh42hAkL2o5qyYnlPe/0XXS3b3Bab8metpjpIC95CCc48T4KodAySBArGdehqCylLcItiaGB6d+I9Tb0BBTqKi5Rj5a7WKVl0UxheyeDs08DcgSNPCjgQEITBA0Es7HZkaxpoV7BzGbjYJP/xLQNC7t0enAoHslBDKQcnUMGA3Ki4LoytHHJ/o54aEfPA851tjGtIlpMjMA6XxHZRXxN0y+/Qye1zusiZ8UKjjKRmRlSubOOdM2au8Qf+VsehULaJm2pV2cH/8v3iUfrO9S9Nch3+udZX/5yV/rsT2ztimfk7CU1wH7rsSr7C3q2gbMXlumJqVrkJgkDq1r+pEWBuIhaIfhaQz2ufmTkCQ+lp+TA1jNNCfn648KvNW6wPdqaP9rKljX1Tuq0dattE+jAjyVNugE0wNMyAvzbJgvxRSnXXdqJ8j6g761BLl9lY1xv3GzVIssJ326CZMtTmdvwJdizYtzACRWTnBo7DSrZ+tZ1xjggxuI47HiDQATzO0qP5CIfxIcrweG+OoBKHBq874peo8OTnWzmEnGv3DqJ+APh596XWCAIAaECQeFvJncX6mhtFP16PLYtELCJK517EssCywLLAssCywLLAssCzwbBb45UDQtZwS7xrm3cI0NUxgEFPDPrFrmKeGMSIIcKgBQbdfPb2BVRK8DpBAoKwXlKlgtW4Q6//U1DB/9/VbdDkhhLzv80rf3vuJ62Udmv0A58ynmTrH93k2Utvbe2g4DzgF9VJPnZSvcIqX3yJt2JHGjrJoTDpQw8QoitVIKwwECesy+OM6Q7f4IEiqhW2OWSPoQcsFsWvYV4/yOZWTwdQwtjZmRJB3DeMrNVvJ66v1B40I+qD0Fgh6eaSBQZKBgxNXCahtHFW/Qinh0TAos2s6NWJDsikA8HS4HWlBL9hlFH3UWDGz+BTJvQgizSR6lJK/qjhszti99tEtZboGrZpILScRIhQk5Lo0kRO3o2Kq/JZ1MGjqHcz7p8SS5XbYpockpD0jZzdNjiUojF0H738am++BfdnYeJ+6X+euxqXtPufzUqLbYQ1LpwqfqrnyE1YK7hZv4kn1o/cRjWIebEXfl/uMvs9AEACQRl4yKuge8F0jfq4/09/eeTQm08FYnN8jgjQqiB3D7tU3eyruI5uiM8kJ2QJ9OhDEqMwC5JNPXYBC6I0e3OtRnTTxtKDCaD5axeP440MlB3uTRom5YGoqOV7Hp+eHz+sDWY7k9aIYVD+ndapQVO5BA0HEpQDAjik01Y2EmTKAQXTUmoaLxZQGAOIANDo51qL87AzWAB8AIabgXgAEKX6mLeQvNC2MncNOtG4bgDujgY4XEGQbrtOywLLAssCywLLAssCywLLAf8cCvx4I8nbGmhqmNSlu5IxcafQPi0V7rQrAoI9bIOjmViOC5MgcyRmp0T+Z/sVXaUb/4JjglCTMyCFe0QEXcEYCVyQeI/p9X9EeTo7GrpnDQ2mcg1EGvqRnyRBxF5LnUKfNqAyIHPZueiI0/Ar+NeAnPgpAAxnNeVYYuTgnbVROE2O6ytsF83SFtnaF1wj6Ivo3TVWIo8HaFecGgs60HlDWCPrgRUy1ZpAWi/6gLY9PtJ4QU8Jee7FoTVugPjk3WJRRQW5CNE+dNMv1l51iPfRyrNrQyiRQLgycKkzK2ZHQ8k350WlwF5dFUrsimLE0c35rwFyqWbmKtzBSKJurG7KpESr5yqn4Tuk5SZU/wSZxsuTPMEo4Oh3WG7VK91mL/3acOmerbusrXcP1NF+VSvsqBf/hNh2mptzTpUruz4al/c/y/wxf7qphCcWcUIt0D/R2VSMIfW/kaSMKIONuQ9zEs1uYpoap73zw4vwPR1cCgq61OD9Twz4K/PEujVqb7VKjgz5rt7B7AUCMCAJAYsoZ08Pcw6jvZRQQfav7V8UDEqUugKD0CqWD2HS0nshhKL0lSbazqR5FpIaQID7QmtF+ZUWCDdBKE68j8S0QRJ40afIqFMn0AEFNxvS85YoABSmGOvqXkT7EAI9UxvzEWYtNgBD1KP5CgDtxdg071tQwAHemfr0V6PPurXZqVD97oTWCLjRC6MwLSStP03RPtXg/I4IAgxgRpIoQ48WiRVrHssCywLLAssCywLLAssCywLLAs1nglwNBt3Iy7jWUnq3hr67YPl7Az1VGBH281PbxAEEaJZRdwx6Ur6kK/kIN8APgI6dDABAjhL4a/MlCpp4SJuelHBJCOyJ2MJrD1MzWXvt5x/Zhloq3cBuEc/9dnBo4miQl4kDkHd4OxCFHvAQpr/hLjCXpFBBADlmNBlJoF1R5aY1Kyk9DVKAuxV0/NL5ICwRSmRdHLBbNrmEaESQa4A5g0JkcFKaGZRQQ08M0EkijgD5orSCPCHr3TrysV8GOYRoNpHAAQXGKaLab51B1VQIHCcUUDkBD2os8HyRdxuXTqgikeMko65bRZgm7ceoLP34f8nOMWFELICGncqnBI7BaqQrqGmH/umKUgb+04noljebVbiSU9LR1F9yJfYoPCVV2lCP3n/GlrPW2gpGLnMNH5Vd4mGu0lvzS70dlKo+QI2Vix0oNenGbtZ0GrWREi6q9eItvcFVO6jlEHxz/LIb+z3vsapf7LM+L4nN1U9wzQHXXwU034XV7FHYgSH3tw0Om1rI4//WtRgRpR8YbjcZkp7BP2jHsz48Cgq5vNHVMU8NYv01TyACCOrDuPrUBQQUGieZ+2PWq/j0gyE9vs1Lide/QM3FfVjOq75rtuWsN8kKjpRyULgmVJlRf2I1VPArVKbhGF24yXFwnG7nCyOCcegwDOc7zV0AQf1k8HUzhC10ErMUOjfQe9L1p3VePoHzTRgQxPSxTw07cz3pEkEYCAQQxYuidwKJTLd5fC0WfgPzQv0s1Tw2LQkPBFVsWWBZYFlgWWBZYFlgWWBZYFvgPLPAbgCAtXqrdaG7klDAS6PpWjoiAoCs5Jn9pvYpPf2WUEOsG3Wix6BsBQY/azthTFRoQdCQg6KtWTQD88VdqLWZKPq/vcWB4a+Z1nFd2jrgbuAC899fHZ/I8Vcg8uBaH3rYj4VDOKBEexBTAgKhoYWKrYRsYNyF7cDZ9JbkBQBAAD1jvpxxDaMh+iWdCqF/qih5pNQX5Mq0t5AsI0pfqU+0CdnamXcO0MCmLRf/xge3jNVUBAIjpYZ4axjbyb4/eMCJI6I9HBL3KehlUmYVQh9ajHbKgEiyeWjT0M/hBG1CytSca05BcG9utxymlg+aoTNptyvaEPA7EdD502JZhFEDZbs6ra0U1rSrdP8RKu6oAyqDPjqydPmXNnLFB2SLlkDAfvW4pVvxzfsX/Pb6031doVqyE9pDMw/qVBcL6s3xd8IHILEPXo3MkVrkV9mxHoA4bU+Iwn9n6tZj5IiH5T52Lf85/inbIbrNOc3yWR3zOm+PhaxQ9LLlXdX+UEqAoHEqH1Poz8XJ/16ggug5GBD0I0GE0JcDOZ43A9NQwjcK81qLRHwUAsS7bXxoNdKnQQJDm7N5rnSDWFqIfpWdhyu13VqhhShjPJ2AQI4Ia+CPtVDc89DqJo2JUhc5BmwKTONItEHrOxTWXaLZQoeKh5SV1ew2gi8+M3F+RY37RTG556DDwIvHJeBt+cb9wZwWjV0RLH6vy6a0iz2uwoY9HXzJFjNFA6BFAiH7yWMgOABCjLwkZBeSpYeprPTVM6Xfv2oigAoLU53pEEJVJPS8Wbd3RfB3LAssCywLLAssCywLLAssCywL/uQV+ORD0GedEXgJfpdkmnq/U1w0I+vPTJ00RY0SQtjnWukGAQYwIenzUWjfaHcxLcWr7eANAAn8SsnYQv0wNizvQnBI7J7y082rOEReiXITyq8gpGvEc9eadnEpVLmFoyu9eReoqnjglSfW42JGYtF31EiRqA0UUw7EzGIRsJb7J2fimkVRugdLUjQyAAscllRj50UutAwhyOS0U/f1B0gUECQA6O9NoIIV8iX4vIOgPgUDvmRambY7ZPYypCx8AgjQ9gRFB3tK4gCC0dL3RttplHey4SF/yJ4vayXIbaIsEcFRo5xJ+FWLPZR3I8tEi2OJvD3ixy8EyoRuQMh/SVGPxUhSSj7iEnW1o49xWpPFKyqTcnFfgTq7IkN4Ltkjx7dJ300/zDU0p09ukKm3TWaldob8pPZzuUiD22bak8iqsXLdqc71oaQ7kVDyxsvyglrx/PyyZ812zK+3vrnvxV6sqXfqb3irqz1J1WKI7y2CFckkrHiCIvkKbmdPPsli0+k4DQVoDyKMvNTUMQChAEOC7Fum/utbUMO0aJiCI3cUeBCJlZGXAn6wRFGAogBBTw3hWB/iTXic6EM+PVsXy9Fw55rA1sHFVTmPsJZIu3tS0DyuJLgHdVl1YyvWkI+K1zVpN0zOMZdOdcwXbP8tNS9JDlS5qFSAQV8N9LZPp0AMZyhMQxDprrA8EEMT6QO9aH8vOYRcCgM6UZroYI4bOzl+rr9XUMAFBrBFEP0tNCwhq12kFywLLAssCywLLAssCywLLAs9mgV8OBN3pc/WjfqwRdM3UMH2dvpJzcqUv039qatilpit80lfrW7Y5BgjSzmEP94AgL4/k12g3nHyRzloVfKkGBFKoV3C2NY4rMByUcg30at7y8nKNBSe/Sileue0lkHXw2M+N1FEMGUgJZznlo8YdsWIwr9hdAlAkIpozoqScFL7PAwIx0sajbYpH4jwoSIXT4moFjgg/RhGxfgVA0OPRKy1ienr60lPCzk7eyAHR1DCNBvoXI4LYMQynREDQHyxken6uEUECgl4xIighYASOkEjN6Wr1u83QWvtpjD0tQhxVQCm1w7+0SRkuhUwfyBYQhIRGMdkSizBnpqqUrTN85uG6pJDBHxOnAq5z8FCc3OLYAhVVeYTH0QytQCDEqWlpkRKpE6nbo/i21JEqsOdHfGUv6oCPWg/VV3yRHt1HTU/F0q5hif+U76nyZenK31q8qIQ/1jxXGWmlOWVm6TOdvOc6Usf+PZTaU2vOaVtpOutWusBX9M5XBLXM91ylCR3nukdCBg+q/xMY9JVnTOSvXouNHcOyThCLQdd6bFsgSH0ui0U/AARp1zABQY8TEPSNPonexQtGp+/16B/3tTyveWYZ7meYWiFWmYEf0lF1vhpzPO2gpaGmZ97naA12DcRLagv9QNRzEZmDhzQS4W2hi+nkcNBSb7QZOimmPm0AQarHIJDAdqsLCMQITFvBVgkQpP5WQNAZO4dpKtiFRlz+/+y9O4xmy1X+3TPd05e5nLEzJBLjgAQiAsgQQiYykAAGjBCIAERAAiEgLoIIJEIChCBAyCESFiAsi5QUISKELAFOgM/2mUtP93T3nO/5PU+tXfXufrun58x4+vyl2jN7123VqlXr3bt6r2dXrYqz6AKCAIcEBMlZ9KEAeu8aho8gnYyziDaXhkkJ85gamBqYGpgamBqYGpgamBp4pxq4HSBIhgbbFT9/xowf/FYIDJIxwlbGbCH/RD6CUsbSMYAgdr/a1ZIyLXnQsrJaDrajWUH+Qm0gqH291kt+jJMWyjDhwBhJ2N77ndfBIBc2mqJLjZRwXac716oRojJnYqiXIQhNa7uhBsAqptHFdVIhRLIAaI/aBoAEBDEryNafDZCUQ5Nz09zCFHPv2DVM/is+0vKwvd1X8g2kGUFaguAv1ArxEfQp7WLzmB3DBAQ9kKHyac0IevhAQJCWkWlC0DIjiHa8NEwRt7kRdgOs+i1yH9YA9pb6DaDFwQyG0BHCaAzTTRNed0EIG3FFlET4Js+qFp3BqCJrYbVPkpobrNC7e9mIHZBDozkKCCLVc1vhEKSM+7G3UKDPQLY1enM6+niZheXdEI7EFsJLVW9Kd6niDTK6JiJaT19X+bJEqV11xlT1cMwruqtC6rwJffEZ20pPRi5jKfyTrtzi0UPqttIWmBvxqqTQ0RrbAF+UwXItQCGes3M9Z4BAgDrsGnYiH0FPNQsT0J0x91sfykcQY652DnuiZbknGo9P5E/ohUB3A0Es/dIDkiVijK0Cge7u6jEFeA8AxLKpGmsNBFku8jL6lDYDCpVOKqwer9PU7s+ZS7mkw61SwUzOXHRaz32FIYZms42GV7u48GonWht+Zlwty8KozRkQCMrWQ4/HjGek5SNIeg8YpPmpYgwQdLCnmZeaCeSlYZoB9EjjKrMuHz0WEISPIJaL3c+MIHwE7elPGn6CcNJPP2hXK3T9F46W5zE1MDUwNTA1MDUwNTA1MDVwOxoYbdm1BNhsTGr4f+m4XSAIP0BtS2N2rmFG0DMMFBkmng3E0jHNCDo9ydKwM4FAsmt06E3ZX6hrRpCME9J6bV4ME72SlwHCl/IcMTHK7ORdv85G4CB5qTP+nMWl05bBMnAR0QIWuAJl7VCUG8jZXHTD+B9hm9rjMlOI2H2VtDI4OF/JyouvoPAIbcUJG+/Wc5aF7QgIYjbQR8wIUj4Oou83IIilCY+1ffynWBbWloY9lGFCmh1u9gGCZP/tMitIJ2bRdiCoyYDlggwRzPHWc7UPCIT8OdESB7R3vDxMOmh6EMlSVvHkDNc01TIGHZMDz4GUNpFrPJbqpi3DdaTYjFN75ElpNyjDeyynXyU7v7F6vcmwpa6j2ygb+K0ZXUdH25cEXzO4lKYn2+XdJP24dF0bcBjbuqrV0IUyrbpnm+K8RararXbegtVKc3AO11yrpbHXtJZeVbtOiTR1lEu1qqrQUY9rurdI+2TMS/zs4kJjZZaGGQiSM+ingO520C8fQfILBBgUIEg+gk7OGhDUZgQ1f2yMp1kORsi4mzDLcgP6mEbtM84iV3YbQ/J6rhJXhsrzNFAGbfWPO6JTiUaJKqNe63ELqdnoFyZJQ0ndrQe0PkoGJezrSHWtyzBzddMSU+8UEKvTgtfsH8ZYMlga5nE3y8MMkTUfQfgA8oygh/sNCNIMTAHvjL/MFiJkRtCBfLexYxjLwyYQJHXOY2pgamBqYGpgamBqYGrgE6SB//qv/9r553/+553zczYJ6Qe25qNHj3Y+85nPaPXNod7l9na+4zu+Q+9/R53oExi7HSBIBsqxAB7AnuMTGSfyCeSlYU+f7jxtQNAzzRKKM+nTzAgSCKRNbWTc8DpeQNC+3s75Wh0fQZkdhGGEkREjhdf3y0BQfgne9f2+P4TGXlLsa74JDxktihRVOyZNKyBQYTdTqwVRN2SAnBg6GDw5sTb69+fiJUrVYYcaA0ENECJdbRIiS9I2yVzHssk4yfIw3ayaFbR79yJAkJYiPNRShUePDgT6NCfRMkxwFP1QANAHDzFSjmyU7EquXYFBAYL0Vdxy5ut4+kBfmgxkIE+U0zqR/tN3gCD8l0Tp0EVqgCDiVHRY3Q87OtfqVEGF1mQrb/zEf90+TUYutedyMVQYyVRE0o2EbzXHbzhQuTBlolZl5M5vOtZuPOhbGnafKp7SfnV/xSuGMNwT7xSJWVdN9qt4Qem+07HW/oY+w6pdey9p8erjXdAVj2qla7V63EsSqxpjSIm75pBf55N7dDk3Zcwd3/OuplNfVbj0kQTE5CjOGMdRIAzFlOkR83h3obHiXGCQt49nRpCBoBPNttSuYRpbvyEgiF3DvslOjZqFeSIfQS+0NGzxEWQgCCAnQFBm/2RpGONsxlpCeiQayUPbiIEvN8/aRBbfkMgmGh2UX3VQy71qdeCcXo41yINLOJU+R7osUR1ylkYrT6EV1tIqX+4m5S/x1nokVw9E7vaoawAIoF1/lBoYpM8UkktAkAaHu8rjo9C+fAQBrgP4PNC4y7iKc36W5BoIWpxFZ2mYfQQJDGJWUH1U8tKwUQUzPjUwNTA1MDUwNTA1MDUwNfDeNfDlL39558d+7Mdu1O7v/d7v7fzoj/7ozvd93/fdiP42iG4BCNLSA70/47Pi+XOdmhGEYfJEfoEwTp7KKMFH0DOcSWO0yFcQM4Jk02jJQlsaxvbxd5kNJIfRNkoyIygwiF7V/ZWXV/a8wrNkgqPsgYQxIQL8JD7SuIIuqVkGROWGcjN3ZbTYaIACoy30dS2j/64MHgz18WymEKQ6qCjqAoJe4fOHNABPlauGjZfqCTJTiFEimfARxKygi3Ot7LgQ0KNlCGxXDBj0SLN/almY/AJ9IFIso5cAAEAASURBVKPkgQyWxwKCHugrNdvGGwiSEsYZQRhE6AV7jR4iDCH5PirfifTfcktOLw2z7BSKUIwwvO5g9TRduNqbXKIMy3C5WtqPLi+XVk7s6nSgurGULZzbb7zovuiXDlWVa0P669+1UZGWlLqGX8UJ3/Qo2ceaV4NBb8r9XdGP0sHz9T293K/S1ruS6d3xWfdu5NxHmuReR1s/P8+3jyJWuABBvnHFtZVlVg4zJwGC2DnsYuf8TMC7xluWhAUIOt35hkD3b31DQJDCDwW+AxQdn5zvnMpX0Et2DVsDQYwqbazNrCAB8LWzmO9fZEjvAlMDDGUkyv3HL1j3d7qzvrqUQcVHjd5VK7lpgc7mLOoKyV9Y0J710ksBrJaj6a7SNaaaov3NIJ4a4ZsyxloxBgBibGUelsdgb2cQbcgfG8tqD9g+XkvAGFcJcRjNMlxmYLI07L7GWUKcRR/IRxBAkGcEyS+bVuL5ONBVf+3mMTUwNTA1MDUwNTA1MDUwNXCLGviHf/iHnZ/7uZ/b+cY3vrHzPd/zPXqne2zbdlezJk5OwC60AYuwja9//evCLi52vvu7v3vnl3/5l3d+5Vd+Re+AD29R8u1Nv38gSMaJ7JKdYwwPAUHHAnyey2H0kycvbJQECMJvkMAgAUTHmjV0qqlA5+yAo3fu8wu9ivOSfpdZQQBBmR2EkYLp8BEgkQ7H/UpOXl7nbRO4DAq+dufABE8cirz2tyIDHolv5pOXnHAlHgM+1L4qE+MiFIOxi9XWQA/MWZaFUT/rCsM1XKipegBBBoOa82cZINggi1FkI6Tk4Rt+mzEEkY0UZgSdif9HBoLYttiOSzUj6DG7hD1+KEAI56UxVh4/eLBzX8YJO4ZpszB/mV6AINLIqhODC2nLSTR5zmhlJN1rdCAZOeP0eiGjsk+AoALEqHflQYNRqKt2upbZMxZC1E0l+CNDHQOrZh+SQ9/GEtdsTaZuw53MTwxVo/M0g9VlW7uQVC1+ffSUu4D8KlkxukEyPei8XQUd66gyJ1Z9TN6266YuwmWbfGu6bbwqb7P+tv6O3EruzVqlreL5yQo3ZY1s6cflkss5rS+twM95ERECuPi+I4RraUiPudI+AYIaGMTSsBcabwHXsyPjqWYCPdn55jeZEfTEvoIA5jMj6NyziDyKwEu8vSMjs98AggT+3GGs9RIxnnhmY0ITWQiBoJHpwrK12X5Kl5zVFZOtLgGNkhmuVWvMsxKUUZzqXkh+u92tp4xUrZEid1Kyk0asVFM0ely2jm/FyMGRHhBnhqZCja/2DSQ9k3dH4E/mQ6GFV3K034AgxtXmLBpn/PhleywAPjOCcCAtQEjlh4cFBGlGEJNdW5tzRpAUMY+pgamBqYGpgamBqYGpgVvWAEDQF7/4Rb1Df3PnH//xH3d+5Ed+ZEOi//mf/9n5t3/7t52vfvWrO3/4h3+4lH3lK1/Z+dznPrekPymR9w4EvbCBoi/ULEUACNLuYc+FoD35UA5M9XX6CbOCWCr2rPwEncp3hbaQfwkQxLbIev/2jKA9vXgDBHHy5VmAkEz4AECE9dquuAySepVPiPpj5BAjr07SOWIUGNwwr54Lbb2kh1+uoUjLifdr+KusgIgGgAA6bAJB1IF78VQonWUmDU6zAYMAgmgn4EokJR2zzbKJLr1GYTpZGrYrIEgAzwc6+TL9SMvCPqXwg8ePtERMhonyWcLwWA5NjzRraL99lQYM2hVQgy48G0gNELodyeCuILaOiiM9AJF7oT7bbtKlgCATQwMXgDBO/Y6jIVg0S0iDOsw7UcvQddUyHUCMYabS0rlTV1863zQUgzJx9yO9WZpr4tCC+31dM9Caf0VaGmk6n8TQmvWgCk2DkN34QKfretfq9cac3wVhNNk5rSXtJcS6bha1L7lj2WatT0bKv/clUdb9b/eF6DZKhkR2p2q9Vb7vM5eTp9/agEvLV5pRIFvIAwZd2Fn0qcbbAEFy0C+AnSVh3/ymTpzzK/4cH0FeHqbdwwQcLUCQeQP8qB2D7c1hdAOCGHUAhwizBLePQlkqxqiBbzHuyYymQ9dUtuUQS/dWhOmhQmeQpnbOxD2CKA8Cek5U5ZabslCRXXmJ+6pLGC/gj9tUHtmOE6W9hMnWuALI7jPgfNO6aDXuqn3SBoK8OyMzgrJNPEDQY5bjyln0Q/kMuq9xNuNuB4JYirvPhNfW6QkEWf3zMjUwNTA1MDUwNTA1MDVwqxoACPqpn/opz/zZBgSVcNief//3f7/z+c9/vrJ2/vu//3vnO7/zO5f0JyFya0DQCxkex9oR7AW+gk7jI+hbchLNVsbeTr7NCHqu5WHsZnP2MrvfnGlyyyYQxLIwDBG+TBuqUMirO6fSMggwapTgP2/XXByHpuIUcUDDCUBDWGaGoqYmTD6x8SB384DDZq5ShRZIDgMfqlJAUICQTR5uTQbHAgTJsMMAYYZQyriqJ8rrvbEpaG1oDpUIOC88u+eRloSxaw3+gB7hKNpfqJkRdD++LPRl+gPtHsYuN3taGsaKLZaHlY8g2mBHHAw7DjSO4eW2ybMRRh4FyldeZjNJSnW/gKAYaJSLA9W2AUHwsL4ggFvYjjoF8rt8bFJuA4Lyu4aufqWSKQKlrbr2NlRH95R17szWx/pd3fHKg6DLh3og6+0o7szO3THT9XrkWY9LG6EvfklVXhpZ1Vb9kepyfJP+cvmb5IxNwXdMj/qAZ+meeNGVLJWmjKPyi3JdHqpPzvVy3zd7UZL2flVOepifW/fSSEDcIIcC56MF3Y8LDfcms3EAzV9lG3mFOIN+rhlBTzTD8pnODwX+fONbH+78f1oW9lTgO0twAdyfv9CMIKHttQMZs3oYRz3G8qzaPxvPLPEAQLTnWUMOMx8xIBCyMZLmGU+voG39U3jVUb9tehcqx10QgDec4FXUPHeLIlLJS7xaedObBVjqqKzluyqkYlEc4d3bIZ9xrIFA+svCMl2PsRqPDQbdiY8gdgy7J+D9QIgOPoJYGvZQMzA/AHgX6M7yMGZg3tdSXcB5luvuH+5qnJVWCwiS2gD5DtWC3AbNY2pgamBqYGpgamBqYGpgauAWNQAQ9LM/+7M73/rWt7bOCFqL9ju/8zs7v//7v+/sP/iDP9j5zd/8zTXJraZvDQg65gs0yxEEBB2famtjLQ3DN9CHT2WUCPx5cqwt5J9itBzbgekpjkwBhAwEafaPfcq0GUE2SDA2ZJjYL0VmBGUmEG/QvL4TNtjAVnHl8d6/fstOnfGXiSE90ilu62s0PMb4WJt4KysSVQ+YIkOj2Uk2aNRQbwVinWonW8fXbCCF5BsMojg+g9Ij6mCUYL5hpFAf8OjcDkgxSliW8PgxSxI0IwjHpY8EBOkrNc5M78vT+cOjQ3k5v9d8BMn0045hzAhCLoNACgEmSKOX6KZpWYkOvCALp66IoQszmZitYGBDZUuf4YciDAi5SirBnMrwbdkJSDUZXII05G1ShRYWyS92lT+GHWxBluLX2yXHh1ihf0q28au8CqvaGIa/chCrmmoE/m1b3GJAJrouXyscgk260Fex5e6XynYYrWxkOTGKNMbXlGPZGL9Mt62l3lPqvv6oFm5G/Xp+74+ifub84Ot2uZMu6yeYqp5lgAq6HPzDLMJP9QxiEBbPNjdFaUBXLw/TNvLMwGQZ7lOB78wIwkH0N76l5WE4jRYY9Fzby7N87PmxfATJGRsjCCAQvtm8jXzblZGdwwywNxCIwctAu8dWxdUu5RmBEPoyEFSSEpbY9Yuu05VPWCe1CvBJXvRn2gYEkUPbrmWZ9Eybuak28hchKG9nAd2MMHm2DIeJABqWhtUYLAVptmV+HHYM0ykg5y4+gjRuHspHELMvDQaxFFdjboAg8jTmalbQA4HuzAw6OBAQpFmYu3uASDzvakryHCq8Vw+4BZiXqYGpgamBqYGpgamBqYGpgfetgdFH0HUzgkqur33tazuf/exnnfylX/qlnT//8z+vok9E+P6BIHwEyVp4IcOEpQgnAoJeAARpOdgTZgQ9EwAEEKTzGdvHMyNIgNGJ/AS9eIEjU8AQ/AIxd57ZQBgmfJkG5OHFHyMkX6oxAmyQNGOJtGn0Um2j2vQ2K9qPUUYCySEOvSmGvFYjFpgtjMq5JgxdjJQyMBRKbDi7FUU2W1EdWQNeDobx4ThomCq0NIYJNPClxxRiKKEJDBSWK0CjDWxkhGhZgvwBOZRvihglD7QcTF+nWRqGUSIw6P6B/FVoRhDGjGcDSUjvhiPeuwisA9uEGCFyEfajdJJQxabJzKZG1fSKocnv4dMAn8qpMBzQrA9yMKAJU7pZZ6QvIAhK9ARlcXRcid4kslTt/FrQ0Fe30AhTUnRvFq5BnZKKML1CrxGiy769jdBxj4zllRCP/G+F696PdRJPqyXFupxSeI+Srmm2p3tNyvMbjJTVbn6Zkn+kqHinrJxParjuxXX3zAbtktDz6/FLPaw8h+K0hOij68SjgMrYOezi4iM7jvZSXAFBzwT0PNXsH2ZefuPD+An6kGW4ciQNOH8sQOj0VM75AYHMg3aQWmOqnvv4CWJkUdz5GXdpM+MwYjIK9fHY94rv5S5npKWOO3Htz1e1xtDjmpmEEwyyJKvuHvi2ccq6gU5pst1k6lmH7gf5KgDJEr1rigSqeg7pkdEk0RkIYtxlRpCBIIVtrGVGj4EggTpHWhr2SLMuAYJwzs9MzMePmZGpNOD7/XsabwUGaexlh7E9gUDUZ2YQKkOkCQRJtfOYGpgamBqYGpgamBqYGrhlDQAEfeELX9h5Knc2NwGC/vd//9fOov/mb/5m5yd/8id3vvSlL+k9Ty95n5DjFoCgj7T714Vn9xgIMiAUIChLwrRTmMAgto5/otlAgELHMlxeaDbQC5aSKTQQJGfRd7RzWIyPXRsevOgDe3i3Gi8J4NXdr/JWt8EiGTM4O/Vb9lKGQVIH9Bw9xBBIStd8pg8JV1XUHJdGH7Omaprpkhg5turN0GcZwdCCROvSmFLGidsw8JOv0bYQlLssU5DFkF4ECErPFRevXX2dBgwKEKTZQNoh7FMyRh7XMgUZJ3y1xmHpfc0EOjrYtwFzbz+7ht3DV5BkteNoida/llefouWmJItcJp6NGcuPUSN5iVf30Kv0496jJ/82ybNiS4EiWqo01aGR8YBiUPVSZKqlQeQt/pEn94HIkW2pVTThmHxqciB/5bRQBdWE+9MZLRyJXFtmAl2oO/AjezzgwUF7FafOusm6h8KuJE/d8drYVbMuWvMi803oqv5Yp3hEHlJd39D1fMquOm5OeRWH28jf7Ftp53pJ6n5CM9ZjVatQueAQBjd0DX3TuG4M4e0aBxsQpMIXWvblpbgCegCDmAX0TYAg79IoIEi+2gCBvEujZl+evxIPYRue6+I2AwSxNAygnabBTAI3SxbAFIAijcE+PTNT8viZBlZpsgViUTo5S3ecs/1CTc7AOok7z+Mkz2rxhlD6KqZkSy6OeDiLvM5AXunFpNajytrYRA3Pf2xsLb3iJHmu/Px7LBYw76VhgEEAQfgHknY0ngPo7Gl245GAdWZe4hD6kWZifiAwqJaFecx9sCfQHd9s9wQEAbyrbZ3MCNJA67bYNWx/7KPS85gamBqYGpgamBqYGpgamBp4vxp4UyDoiTZl+d3f/d2dP/mTP9n5wR/8wZ2//Mu/3Pmu7/qu9yv0Na3dAhCkLY21VOFUyw9eyjjBd8XJS4wTfa3WkgXPAmIm0FO+XgMIMTNIu4dpNhCAENsgv/pIs4H0ttyBoHyhxmdFds0pACghholf43mZXoAg3rQ5eL0vEwNDwa/7rQR6/nMhnwNazAedS9AsicqDrI5Wr2oTcmKfOM+XFneZmFaZedCOwB/iMj4SV3vkYf3ZmMEIwQAKLcbKXaxEQp/6Pi8D5VAzfD7QbKBPf0ozgDwzSH4qyl+QDJUjfZk+0lKGo0OMkj05LJVhwimD5J5mBtFzDKQmssOlD7RdBZEWiXVgOLUYsjZQKDmtgn8XqivtpWEKRVtgBiWbM4LCEPqAcGl6aT7MN67oKuVF1YTaoErCM5SW/KJfMqJ3J4tnlW3jSf3K77zocumlal8O0QF0vd5lmqZ2G6ir0o1qSXAtaVbUG8mb0m1UWhK0sNH4UtJzmxG+lGxGOt1mfmRP6UhzdYub9d9napueI3P6vpb/kmy+QXSXm5EuC0PVVBljnZ8ZPweq3e4TRiPuGVw+v9LzdqGTZbXeGUxbxAP2fPjk2MvCvqXlYU8YbzX+ZgfHgO7nmg50fiYgSHWBkQF5AIEYd1l+C9DEjCEVK97HWdMNgBA3sJd8LmOsIZbWVTRw/X0AYY3OhEuPm/J4PnxU2hSLopxaRgeIGf/RDUMoZFzUCYNCzuOZHmYEqU6BQoylAYIYexsgLwCI2UE1K4hxlg8996SqXY2ZD+QDiFlAzAb6lLaMf8DSXM8Ikl8g7xam8ZZZmALgmRHEzo5MijQQJHHRzqFkuCeZ5jE1MDUwNTA1MDUwNTA1MDVwexoACPr5n//5nf/7v/+70YwgtpT/0z/9051f//Vf3/m+7/u+nT/7sz9zeHs92Gz5doAg7UpzJkviVEBQgUHPpKhjA0EAQFkStvgK0uwgjJenzwoIwvDQqzFGiR1FK62dxDwTSC/MWbYADS/SAoz0LwZKMwQ8IwggiJfresFOPCZEM1b0As4/H47HJCEndFgO+i9jwuaMM1Oy8BVxOLe6xa542MqDCa0MwAJGB1kuIA5BDBA1qBSgEHnUDRAU2izKII4G7t7l+z1GyUfaBeyujZFPf/rhzqfxDaSTJQsfyCDx12ktY9jXJ+kjLQvb18n28QaDZJnEZ0UkikG29DDNIod0lARy1SFZW5IwcSLpUanXBqOqsI18Y4jN5grWhHnDs3hXW8kjFelIbx7V/mZ58QktswoMrDkpyg3ijUSjS/2UbPKCBeJWu2lh21V3l+ku11/zuI7fWDbGN/tAl5rOV6LQh1GCnh5zV5WULDpCjquoiy5UV1+Lz9UUaaP6cRP663i9j7JRJ11ejxZuvudt6o/8un8CBJERiQkoB4QBpoDOeS0knjzAIoCaHS+pPdYyXID3Y834+VBLw/ATlF3DBLhr/GV5GA6l7YtNKM+Zdmk8OwvgxPh5R7MwgWXsCUc8DQZ5CGTM5d4KIO/t5Z1mhzFJ6jG6ASqWXGwcpvdoA5k53O8WplQJFQLA8LuTZ5YtHPks+SrzMTKlUPLdZexn7LHc6hsKAggibXBHaZULj7bszH60HLqQBxhkOmnBs4GYGQQYxGwgxSnf03cKDZ3ysdaAIIHvAO7MwHxAiNNohQBBRwd3DQQdaUaQZ16qHnU9a7j98AeSYQJB7TedwdTA1MDUwNTA1MDUwNTALWkAIOinf/qn5WLhyY2AoGfPnu380R/9kR1G/9AP/dDOX/zFX+x85jOfuSXpLzf7/oEgzQbCR9CZfP3glPSlHJQyO+j5i2PP+nkuMOgpX6jZNUyzgp4+Zxt5gUNsKa+lYWw3f3GhF3kbF/gHwggRIGSDSIZIWxIGHIKRQL4RBdHrfXojXUZEDC5oOUSkk6/YxDAeqIcZQ8R5pA1kUAPDAOMhRhdpDupQK8vKCEmPR6vnLFkhHKraZ8GEt6WiPfOFzhaMaAUKkYcFo5ClXzlijtEeRgnOnXFAumcgaFczge7vfPoxQBC7hslAYftiOSwFEMKfBcAPO93sy0C5J2uELeT3tCbMPivUig0kGkLW1iJxH86I3FVkQsTkJJOLhAvoQq+c6zzramGqLH7Coqeuj0ZfSYX5VTBFqXy5PL+vm1CxdCWyyhvYLNHMfojhuebX64XPVYxGSa5rb6RbBNgSKbrreFHNdLoU/WVWlKR8XdY112PFp8J1nXDbzO21kz/WHeObtbbLtKbpHLeXfNJyo4vSCHdoz1nrrqjog/XUMkxHvKUXOkUy7hFhjNFY2G5QAugALS60zAvfavbLphlBJwBBOObX8rBv6Q/Zk6cab18AwOvUWHvy8qXGZ83cFAhE+Er1/UywHFfjKMvGwE+YNcRTRznjLXHGS3y3+Rl3nIe4TvqfXiMr8rmfutZzlVIV+IAiNOS7TJcxbgLluCyJIV4tUJnxnH+SxWM3utFJRxSpOOMS+AvjHFNzkJxnzklCFWbsEvhzoWXKjMfemRF4TBrQjJ6MmQJvtNSLGUGPBfo8ZFkYS8Q0M+hh27kR5/yHchDNsrCDQ4B3Lb/VLKJdwPs20DI+zhlBUvE8pgamBqYGpgamBqYGpgZuWQMAQTfZPr7EZObQr/3ar9k30E/8xE/s/PVf/7VmgO9X8a2H7x0IOgEE0qfkczmgeMnMIG0DBiB0/KKWJ2gLYwFBAEAGf9o28gaFWL6grY9lmwj2EPijJQleCqZX8PgK8hwYxRXy5dev7wp5k2dejEMMEIAj8mKMKOFlDjH9ZSwYgYCcL8hJ24goYwLrgLoYEarMP1tGLUUZ3GmvgCCS0OZodWR0xHAb82NOub7p1RuLinTQYXjoaABQhZhi/mouEsTPFu+ZCcTMHpZ2MdvHvoEEArFM4dFDLQ2TMYL/CrYvPtJ6BvxUeFmYwnuiv9dAIHYNs1GkplGj5WtiR9ZowrIt/UROcqwhG1uuieEVQl3VK/iZCfoiKxzbr+Ny11vqEAkHKIlBm1qU9SNGW9LbyinpslS9gbIaqKIlrPaG2kO1hewTF7mJkEOfbih/cX3zmpsNrPlUulNdzulln5zYVXpY7mmJWj25ina5MUVQtO6h0tQhj/EPEAPwgpw2OgTYEBX3P0AQftleAAJpiRg7MOKU/8MPdepLxRMBQs/wHSQQyE6jNXPIMzYZm1+qvh9QPf2A75xKn+sEkDeO4nY7EFRjMeMnYzFAUEbEBq7r+fZYu3RqidCJHHTKfUoSijYsuN/OXVWrZIUw+Eh952AcDrKskI8FZq/LMpxCCyUFjLmSWAMvtRsm09pnfEavzMjkVCVtGX9HuzLSRC0LY8w90BjKkq9Hj+WcX0A7PoIeMvuSLeMFCD14cM+7it0/0rhbQBBgkJeX0XKOA7U4t48vbcxwamBqYGpgamBqYGpgauB2NAAQ9Cbbx3/961/f+YEf+IEdwl/4hV+wj6DbkXx7q7cCBOFb4oIZQTr54oyRwrKw5ycsTdDSBcWfekbQsUK+UgsYwlcQp2YFnWvJAl+lMUZe2T9FB4B42dccmLz024ABAGogkN7Uvb28jQAMFx3YAuKlwCdGC4cBHNULENRD2xN6R8dEkDkgO4CaqqsgOYQ5zMPGRHKgh9DghEKcuTqvXW1UKJ82mmVnnk7K0stsobQHrcEfeOqkbb5GI70wH33M5uuyTDIBOXxpPmB2jwwT+wbSl2n8VODE9KEAIL5QEx7IL9AB4I98VTAziBMACOenDumWeMfobP0c0ukLRDqamJWoJHqqA6lN6A4nl2j56MmvQpoyX0K0XMMs5priI/NNAVzDzbhK1XPr5p2cMLbRCLvWTv3WJCNFq4+sC1Uj3hJQp3j14m396aWJXa6V/OJYPK6iW/Mr+vTj6lq9pFpac1qn35auS7bm3NNIlXbyi/eST3asaxP5x1TkvpzT+tMKrJsVEbc6+QFiiOjUDZ5HgFB3pgg6EPRKy8IEAnkWppxFa1x9Ij9BT54KCGLXMAHsmRHE7mEvBc6LVuPz6Skzirh/NbKwFFch4BPgEGOvRyHFyed5zkLUpDOWBnQPuI7EOf2MtZTzJHuNK9VVh+4nl/rNM9apqtrbuJBqPCihnYS5km5ju8o89vgPQOjsu0zKtHRcFDMApHhqKbSAcGv+gYwcZS7UXcAgxknJub+v8baNofcF9thZ9AOB7uwSRprdGTkFCB2K7uhQ462AoH2hPXdXM4LowgSC+D3mMTUwNTA1MDUwNTA1MDVwuxoACPriF7+4881vfvNGS8P+7u/+bufzn/+8hf6N3/iNnT/+4z++3Q6sWn/vQNCpXmxxXnohb51n59qdRl+VXyp8wc5gp/EFdCww6JlmBLGFPH6CMFCeaikDYNBzAUGeSaTtbFhixtfuGCH5Up1lPQX+GBbROz3LwvI6bw86kgFjhiPLGmQy2NhRhl7kefUnYBkE37DvygC6q/r4l6AaIAuGAlaXZ/TgeZQsbAROF2NIyHgZaFWiKjQUEAhi18dos0llCi5iUcxknIifxYKGuMvgo7TI2KkGX0CYSgFu+DItAEdqYCbQPTn4wT/QkYwNti1mqQL+gR7pK/VDfY2+j+8K7RQW42XXX7L3mEWkU2x29tQofXZXaM8S9jDy0htk1qH+cBSdVa0scjdLktF+ClfAQISGuokRWWJKrI8Yb+Yc5iJYIgvxdRxCZMW2qmmdRBw1N6nLUl24Kr91kiCSVNutYKElAsX1koRP2BanDRbXJtY1RhkoG9PXMmqFa343qXNz2auvcL2JZNWDaPFmstwWVclK+6MW179+lY26WGRuhdt040dM5ZAQNybRKi5PBPm6Z5m5cy405/QlTvqZ5XNhgP3J0+caUwUGAQRp6e0zAUCkX2j57almDp2IlmVkgO5uT+Nfxs+7AuFpW0BQA+IZfWg3s4ES35HfNsZS6AL8kOZoPdKY6lh1cBkIQqWKnbRlJXDjilbYC2l5faCbDkSlGnLVEd2FV9VHXsY7RFM0YRt/P2K8x0eQZgS5x3fxDSTNiI6lXYdaVovjZ8ZTwJ6HGm+Zfekt5LVL2IP7zAZih0YB76JlWRihx1upCOA9f2MiYXwElbQznBqYGpgamBqYGpgamBqYGrgNDbzJ0rD//M//3PnxH//xnX/5l3+xqP/6r/+6873f+723IfaVbd4KEAT4YTBIM4H0gdrLEE60NAED5PiU8GWWhckoMRDkGUKaFSQwCKfRJ3zV1pIF+ZqWQSIIRe/l5ScoL/gyPmS06JXab/F37LNCL/4ACjI2vKMOoShYOkAYHuhJpjoGig0BzHbFWRYlfgBDNgqMiOitX0BUgBxAGeoOh4oBj7y8gEo+kBUjQgCDQgMnkt8zhJxLWYwOepSEzCsZc5n9Q2HMLQClO5yYZuoaIt317J0YIwFxZGQIBGKGz6FmAzHj5zGzgfAPhK8KzQJ6oKULfJ1+oPWKB/JXgT8hzwbSzCB2C4P3nowb+NML90R9pUsYh+6a+46c6FYJnTb8GkHvX9QUo4x+h5+rl47cQGuHOOxol/Za+6ZXPDnQhihVrVXlkdsp8yu70hUXaiA/xU2IVh9j2jkqsxwkoGuZlRzrJc8EMPSRvKXikkcEWdfUFqU1lLquslw6P7KS2kbX5VqqviZSMkaC1xC/tnibTFflXcespHrz/lzH9d2XjVq73M/N33mkHSVxvVZI3Ecxa2EADIYh3T0ai6IXFeomtRNkhTUjiPGWGZgZNy8Ermc8BWz37EvNusysIMB2gCA5lhYIdKJB9uxc/MQeEMhL0Vpo8MdO+j0K+Q4OKJ/ZQR5/JRWO/PPMV/7Soy4zscp2v1ui6UDFy1Fk9fxXWPmESzUinMuDS1xnG/eL0HVdid8n4x5jGNX0R9JV0IDHEQFAAPegb8wEAoRnfATAYfYljvYPtMwWcOe+loJ98IDxNsvClvFWABHAPGPuITOCRIsvNsZbfAQZCEIo/Z06UCj/0/OYGpgamBqYGpgamBqYGpgauEUNjEvDvvKVr+x87nOf2yrNf/zHf3jb+L/6q79y+W/91m/ZYbTt4601bifzFoAgv0LbcLnQCzZfqs+0VuxUu9mwvfGJgCAcQj9/LjBIy8Oe4hdIxooNFgFBzzBSTrXlvJYsnMrH0EvV9dIF+X2IEcKbOyAQjqQBdACAlNYbNqAOhn6MmRj9BQyxXM2ARQOBoGVGj5AcVSUOLxk0/BNLMXQfWN6FlVSGvG0OlYauAUGQ+3AjpqUt7z6j+sgUQwXwJ0aH+TXQJ+BPyii3QeJKfJHGcEj1mg0E8INvn31AIJ2HLPmSwcFuYB/gF0gg0AcCf1iicMSMIG0bf//wQLR8lWa3m8wgslGiBnBciqHjbqs9RLBdhSg6kIFfldBlhHWIsIAgKOnFtsMgEgViAm93swiVbk1VzhCmXalKB1rbPGAVB6+b+ZupsQHXaMXFrUKYDTUdr99ryL8iCnlxGtlcQW7asU7oes2UheNlupFrlXJXdhlGinV85L0uexfpbXKQd5MjpvpNKG+Hpn7j7a3f/H6pm2XRy0ppDAE5dd8vQBC/b8Y2t6+HifERIOiUJbWMmTqP5Rwap/zMAOq7NGq5mMbb5/LXhh+hEznyP9a47K3kPSsIXjzD8C/wJ+PuBeOv5AEs8thIKABIZDq4MCanjJwc4uMI5ToHxZFTZa3UlOSR7kf0WdTk5y5XpPFD1vHw0EnG2F6Luy3pjDGoz4JUG6rU/nKpHhoAyNfnhzvnOpnBA72AIAHuR3a2r23hBfDcF9j+gUEgZgUxC0gAkJxDs2TsvsZpzxw6SggIT7v4CMrfngiZpWGbfRj7M+NTA1MDUwNTA1MDUwNTA1MD334NAATV9vFf+tKXdn74h39YLmvwE6nZ9wq/9rWv7Xz1q1/d+fKXv7zz7//+795d7DOf+czOP/3TP+0QftKO9w4EveT1WS/dvIxjnAASAOawlXyBQVkipuVhLAWTweJdw7SD2BMBQ8SPWUbG0oUT+bCQcQM/dgsD4AksoLdyg0HMCEo8X6QxYDBcYsggA3WVkfqUuB6mBC/lOgcQyC/nMmay3At7AANMu+rACCYEHAppFgAJQClAjvJ5lzdZDLfFsFCmWVDXTJAjcXqUvB5ilGBypSw7zADUeHcv+Zm4tyd/E5rRs89XaWYDHcopqUAgln/hpBSDBCAIY+RIZfc1G+iQHcNkxLCUbE8+Lu5JdgCgOJ1GF+oPktA3RKtDGaTVA+cQ3yh3GZkpMQC2UBKhXquhoJEl23SKrhm2fLLRoYstV0nRCBTAD3Oz63dpbeHiPokuNOZGTZ3pUw9bdqvp/CJf8m4Wua5atVo0lb6K8+vpQvF6urTQ6Wi5UpRtSkLJZg40H/8YW7qOS9rkCf3kHVfro0t9+S69oh+N2ZX9VLlnA+nGBY/OEXBluZf1ADAunmusOrc/Ns2iBAiyc2gtC2M8bTMu7aSfMVdlJwLlnwPM63ypeizhBSwHVIpPNdrBRxAAUMAg4oyntTzMTpkpRzBmZfqAvnoEj4q3Yso8CCQdgLeVKeBXj6+0sZ64pJFwqyLnSZ4W+m6V/NUmZJQVOa0wjjLuJ2xl5HkmJ0rWeIO+AWvYqVEzgjRkGoxn23hmYjKWArqz5OsBMzAFvjPrkmW59+WP7YhZmAJ/oPOSME35YeYmQBAdsTYlQ9Q0dw3jd5nH1MDUwNTA1MDUwNTA1MBtawAg6Atf+IJWKT29kSi//du/vfOLv/iLO5/97GdvRP++iW4HCFIveQEHSMFwuJCFgT+KzPRhtg+zgvQ1mllBWsLwjBlC+AjCoamMludKv2ArecAjzQxiFzH7+hEvYAFepf0F2qAOQIxesGvXMAAjXv31lp2ZQTGiMA5Sj5k/0GNwZFZQloSR5qu2SrASkB+DQLJ7VhAFymsX89jwEUSRD4hUz8QwIemKzjeJJEREJKJHnNCzFCwVmAkUgwTDwYCNuolfoD3N6mFZwr7AoAMZIwaBmPEjo+O+QJ+HMka8UxhAkL5MY7D4C7bKDgQi4eOC7Y/DEyBIcuhEowrcZwWKK7XITU6ZV6ELcSgpwwG0e6lL9RaaO/odnCZOA9uOlg9dkUR/ELd2JUt+N5WIsHjxG1UdqB2vjEGQ3hV6FpkgSzvwpvYgO1K3vNClfIwnp1/XZa16J1Cs+riItlG6NLnQUbyNtniHX1Kvp0tj6XNxSF6lKOvxlI1XyqqdN6GLnMWpala6OJJu94tjVf4+wpKwwqvbLGmrF6POxloj3bZ4KbL4WPEjIeOeblyPQwyk7ZfxL6QkYf1aOHdmnGX2JTN8WIYLyI4zfmZcPmWcVRzH/Iy3zM58AQjPDmIGkACSBCKpHZbTAoMwKjCWvvpoT6HaayC7wSGVBfCpkSNOo6lTXdgEhJBePXXh0uOlT5dzqretngiKhnBpQ/ElQRz5hzaqDkXEFwBIEdI89/TS9dxLxZkRJCBIGlWorQkYe4VzsZSWmZcswWWm5aHOhziJ1tIwA/AecwW6CwhiiRhbx9/DN5CAd5bjCrv3UTLw29GOZwRZmpTP69TA1MDUwNTA1MDUwNTA1MD718Df/u3f2u/P61r+1V/91Z2f+Zmf2fn+7/9+2eKHryO/tfL3DgSd6a2cJWE2Ungp1wsuNoxnBOH7R0sRcGh6fMKXaoE9LzQzyEZKfFh4qZhmBB0DBMlQOcaZqQwUL3/QB1pvdSye/q7qr9B+ndcbfQyX9r3VxgD2AMZIhQESQodZABCEOeCQ+k6rkqwD/nnXLzWMYYEx5kOBOTYaNysRkIJ6/XCrLVl1qZx8SO1YGlNLhgeaymd/TC4ME4yR9AYfPruAN4A4ngXE7J72VVpLEVj69fDoyF+iHwr8Yftidq8h31+uNWsI4+WegCC+SjMriOVmAYHoKcZQ+jD2k44jJ3rrgEvoJWzrG4HKG11mT0HfimE+HFYReXS35VsXA02iNpNEg37ICXWrOlBHRttwyDCULNF2D/SyHoPGui/ipUjtLfEqrLAKqgeV30MoqrSoKa28ohzpknc5h/yrcymFa1rZTgfN5nE13VrCzXofL7W+ZyJr51VtJn8z1am+vbF+F5S017VXMq5/0bFnnWYLpypsoeuNlVWlxp3rgCAe0AJoWIbL84dfNs/4EeDD2IpvoGMtCSP+VE6jGVeZFVRj7ImW4gIAnWn2Jacd9PuZYQYQ/HEi3cEhj+kA740mXQDlCAgUAKiPAdD7EH2/T1eddclK8yIJVc9nrOipsOXqcUt99yzOLTqFD+OY6wuJYRGwpgGZgaXmjxQOoltP3VvAIANBGTftX01jKYA7y7+Y+fPovnYKwzn/I/lhUzz5LNMVECSgSHi9ZxEBBgEoAQL5qFAtzqVhTSczmBqYGpgamBqYGpgamBq4RQ2cnTEJ5dQSlP1LIhhC3jeJ73lCxsEtSnqzpt8/EGRQAygDQ4CX6xgEbFXMbjZsW0z4AkOEr9IChjBIOhiknW0AgPz1WqHK8XnxCqfT+nLt5WE2QHhVj/FBG27HcAav9aQTWoRmoAQsSlmMicwOyrIy4nDhojd2VcTxM/XtnyOMnG87QwZCgCSRu2KMDP8sNKFDPU+EcCMvBkgAINpQWg5KddGptDpL7wwEcbMJsWFZ2C5flnXjHeH4GWOE80g71BzJCNEOYfdlmDzka7SWJxC/z4whfZXGT4X9CQkE4qs2287bUTRdFX9E86k+Vjel6BLZfaCLUKVPTix9Si/JI6ZQ/xc+LVeBDwNnInOdRkdN6lw+Sk+UbLYSWn4DlYisqld4iZcJx9xO2SEA8clPr0jaG2v0eHQWiuvoIhcU1dpITV6lo9mi7LlQlNZ7vOi6RMRGfpslm6lOt41Ptf1x+G22U6loq1JXhZGlJNpMXVXn4+ZvtgKX8R7oXPuvVr9B3f9wyNFjpLtuW/EVgemo2qpXS+ONYplU7llBBipClSqMq0r7wUQqLWTSQ8cKJ5bkMq56jNWMH2YGHbft43EcjbNoZl0SHgsceqGZl+dCj07PcNB/YdAd/z8ZazMrCP6ZCYQMjK2ZAVTLxuimRpXluef59whS4dIxCDPWUicaS6xS4/1Ca06n6yYMmNKoyEchOgPc91/SVVo9A93SFc839assmlMd/sAw9mowYYcwzwjSGMDS2V0hOMyixMca46jHVi+7xQ9bZgM9ZKcwLcsFHDo6ZAdHnPNrFlCrh5NpwHepTrJmbEVs5PauYU1OsuYxNTA1MDUwNTA1MDUwNTA1MDXwthq4NSCIN20MCV65MQrOtYNYtoXHmam2k5eBkplBAoO8nTzLFHTKYPG28mwpryUNLCE7xYdFO8XGs4IweAI2+RXfRke+RCtttKFCXvUjR97CiadOAUAU4+uno30xMgIEYSSoA3RCRwscpxnssPgJIt6ME9jroCW/6ROBDgPAmRJecb4+G8UgJkNE3+KVDiAU8wujBZ9AII8yJHAMzYwgloJpJtChHEDfFwj06P59+aoQGKT8B/oKbWelCu9rFhB+gQwEmYf8VMiwuSv8DCAospc2EFUy0U0u+h/5lVYMUKrFUkCyCNKp1DWVq7fYZtxAi0rcDiyKx0JNhF+M341GcqLSqgNF8gdRIl74tXjouKoR2hnzadjptLLQWh4VOFxyh+qrgg2mvRmoNpprrCpv5FJ5IRlrJt7uKvGDMqnNOr0msSobOYUipZ1fcjfpqnbvS9Utugorfx1WeX5DSvN7rum+HeloqHOu3iBTP4qqh52u7rtOTazqb9JRUjnEt9GFonQSKl2rmkLzNgGJ1pKi/N5+FAHXNVTkaOUGY5SjB8rjm8ZahinIAI6Y5fNCsy1f4BAaMAin/AJ+nrB7GOAP8QKCBMiznfxLTb3ESf8rL4fNbCCDQW4L4EdtAcKXfzZJTrnz6UVbOiYxJAT59IF8/XcmsubwPVh9FQ3UHmKclzt0GU9bHVhxeHgVXaiSRwMerw2YVSsqUx2qxTkzvoEkbZAk5QbOIvzolaadejzGISA01FO5wj2AIPyrKWQ5LktuH8hJNGPwQ+8Yph0aAeJxFn1f4LvWgB1pVuY+s4DEYM9+3SSDeCGZ/w6Iu35VZwgv0q5hrXMqn8fUwNTA1MDUwNTA1MDUwNTA1MDbauAWgKALGyO8pMd28Su+/FfoS7MMjJdefqCvzyfZupit4tlFjFlB3lbeAFDzZWGj5cROo1n2cCpaHKGKxc4FSxh4j8YAwADxiboE6PDGjblgpIZZQ1gDNgcStykBjaSEZnkJJ96OxhsDY1xukFlOofGruy5lWNhwoR3yXDgwswlAGq2IucMWl5Xnr9nMBOJrtMpzavmWGBkIEgjEF2m+NB8eNP8/zAbSl2iMEJYlPPSONQKC5MT0vs5DGSLZIQwe7DQmQ0g735TPIUQcT9slWGztHLuAIYXUNr7GAuXl6JnQcVRYcf8ESuS+ILcdqtprk1c1E7oMmZZjjIe86le4kG5EhlKiAxuiyJcsXRvpiqxxa4VODUxa6dsFY4tjO2/H9c1qv5s+dUMdfpf70lvp5WPv30zmN6Gu9roEb1I7tG8paTWtMJqpDHF3hp43xp46Nx4aCHTW2KUxwiOGQBBGD4CgU826PNEYewoQJFDIs4AE/AACPTMoRMjSsROBQhpjBQTZh5vqAbq/svPoAnoYywGCdALuAwQ1MCgjFaCRnh2PwRY+8tAl52fssMyqzzhJvVBGm5Txj8e8xgnKoa2jYsla12ecDmCWMRpG4VV8HGr8o77j8Ad8t5DAMgKBmBGkPwOM34BGzMY0kA4QpDGYsfVIINBDAfDsGAYA/0h+2ZiRyUzMWorLsjDGa5b02kE/Kqs/LxFN7Smig6VhIpvH1MDUwNTA1MDUwNTA1MDUwNTAO9PAeweCXnpmS15wec3GibBft/FfIWNGOI6XeL3kC7SMDsCdEy1NwFCpZWL4CXqubY/xZ8GXbL5un56KVnTMKjrXMjN8WQAOAQYxq/+VLJG0GiMFMMggT4FBCmNqZEkDMulVX0ELLSUmgajECIMkFwIykrY9Rry1VkYF1oN7qgznLUiQKzZ6apNOaMOl0tZbA4H4Ii0msMAQwbfPvXvyC6TZQN4FjNlAMkTwC/RAX6QxQFii4B1rtFzsED8W+syMAaMqBpL2d3dj4DQjB38VHNV7wupz+osmchC6nL6RRQcvHcmzalrZOk41629dV/mbHKtmQsr4il65pfuFjQpMw6UTJRPDdZ25Jct9J1+Hi/UbtKTzRrahcLYvmLWb5b3s7WOjFDfn5j6YHMkqdZlXlVzmvNkjfvmb9HKTX7tfrJ1e4mds4Vb5hFcdJUvRQreOX1V3W/6ok14+cuy5xMaSm2pis9YGv+oOnBdRxkwVKGkQiCj3Pg9OE4MgIHeNX/w2nDkAZRgnGVcNuLdt4gsMOtb28XbQj+NoxlqNvaeaFcQy3NOX2kXs5Ss7nWZcDdiuMVPjZ2b+sDuYnOzf1RZazmMJGeJKKj1r+HDLzExC5CEkgtTjSRlHyzOtkgBFS09auUigqqOXk5vn1NX5GyBFpT3VoV7VbSGzfTJ8US/jsEPPxDSM5llAnompsZdlYXb6rNmVhAdt6S2zMI80DgME4ZPNoJDGYWZsHmrHsHsee1mGy5IwwCC1p5BnyE9F6y8BK8wnEFS/7gynBqYGpgamBqYGpgamBqYG3oUG3j8QdHGu12tetnlJ570+iIOXcskgYPcvdrbB3w/+KM5kdAAGHcsxk5cyaBmDv1RjpGgnsdrhJlvP66s1hor9DKX+uRgz04jlDMKF9JItI0WzX+7c3dPJbCAAoXyOrd3ByON1HGoMMccNGCQPuX24LG/sNsQqXzWaldNysDLSZ/qdZQgUwZn6MaExOLIDGQZL/xItcysU0k98U+DIGQNCII4uGBUH9/ALJCPDy8IO7BvokWYD3deX6Eec2saYL9VHMlaYCQQYZAelAEk6AZT6l+44ikbC6jFhREW29M8GizMNmem3TE6zpKjRDmrTz26iUUAOh0M30PNS0q4qa8UtY6Om8+7amKz6VR5yG9OdVYtdF4ytxTiDut2yrvgRv6ditFTUvdXKgbTnkkJD+bWTWpeTy1G8k/o41y5Dj3U+m1L1/JvHruZwU9mjC/oKry7lmnM01u4tUa7LS+bXtdvLt3Ho7Re/Hm6jp7Rz7LTb8nrpVbGNWq25RSKnSwZCTmmlRRlPjbb4Bm21ALaV9l26kR9SxtdTja2A7SdyuM9OjfYLhONogT6eGaSltwBBjLcn8tnGTmNsO+9x+YzlvBpTAdndqcwIYibQXcbWOwBBAYgAglgWlmVk+bUZL72MzOMqDJIfnS49d/6S157x1iCVfISaRnSMVTeS0VFYWCA/z1W3nm3G136HxRdbUDY0eeG/DPbJJtBmr83C9LjLklyW3bIcTLMvDfwwMwh/bBp/WRLGUjGc8u9ru3jPwGRGkMZwj70ShPGXowNjieMjaAJB0c28Tg1MDUwNTA1MDUwNTA1MDbwbDdwCEHQWsKMZKrGu+VIcAwUDgV1p+pbFADr6eq2v1i808+dEX6Wf4yiaZQtybPqMXW5knLDMAV8XJzJWXupLN9vRGxBqM4Rw8XAu5gaCNPvljvb7vcOuYshhMEhv4QKGYm7yRo6h1d7MlcJoidHQw8XqoKDZIfws8RlEVv6Rh6HhkxYWtjFIoMIMigFAHl+tAYLKKLHVpPqiExMMBmbs7CrCcoQDnENjiNg5NH6AshTsvrYrxlcFhgmOSr2bjQAg/FkcyAjBNwVbFt+TUSOVZJcw+Ja8hO1UEFAMS6oZZMuXdZUtS8PSya4P0kVvJmXwLdnkuqFBhdE1jesgv0WdzoVcm7kuB7przTgfmqWOSEuMJQ+C9VGFi3HaCSwD5UVDe2NScdI56h4hFTpiVO005AzsFK+y6+iKpuqO6cojTH6EXUSmoB2Ub2unygmrvMKxbLOFpLbTXe4jdBxdxkhT9SuEpmiJbzvCY1tJ8orXyKf/Ip2Gll7HC+ril5qXa/DLpzfVv200vfYmv+RzHeU1QzPVpSHO3OtV1/c9RbrJXY+bndgSLgOOs6HPDmCAQZpJ6dmUmfHDGPoC5/zMCjo+1pLcjLVZnpsZmAGCmuPoZVaQ2mjLwYS0B2j3ro3xZsb4CSAE4O+ZQ0ojh/7rgnwlMxnuha4JecodX+j5W0Eizz81No+UrfPgxm/vX0gJZhFyVCsG65VnX2yWLDMwGYcBgXAMzfjrWZgaOPHxk+VgWvIlv0Aefw/iF8jLcVkexnIwdmlU+aF2CsNBNFvNUw//QCwbZixXMABBkQlJEHECQf6Z5mVqYGpgamBqYGpgamBqYGrgHWrg/QNB5y+BOdyFLM3itZy5MDEM/PX41d3MCtIUHrYtZqnX6QmAkJybasmYHZtqa3lmAx2zy42/YrN0DCAoYNHJS4FDAoTOmFWEI2mF51ouRlt3PfVFcIdCQSB6uTf0obdvTAKMEuU3EKiZDU1il8BBNDJNbEEoTkRdwlihhRzqpTujFMYbtDpt0BC3EQI1YA9JzfpRXowTgCDqKO1TUdVBbJYSeIcwDBFmA+1hgODvh1Bfn+0gmi/QMkJkiDywEYJBojwMEQFAWUpGXb5GAyqpR6iDNjglT/qGvBYDES0zacuubsbjiNIilmsNVm04bhrXTNr0VFNe9EO4OqjUeLRoJ1BGePYsEzsJV2Tsml9zlwqX9i7zGXlWfJNqkbVlEzAjqA6nK7GEIS5ZoB7pxvhSpUXGsjH+8eh8x62rLuneiyXLkeva7ZTRPemr+HTazVjnT6yOkUsoXi991b9Kw1VOG53/mjoSjLQlE78b91bkqTuxKPtdR0n92r3u2OaY+9p4FzWkjA3LjczYQnbaI5ukwVg9i4AVORK37MrzkKMCWDObxzMvmRXEjo0n2qmRWZc4kWYZmIB2xtnnL9hFDP9s8RcEMOSluIyrOgHpMysosyjZSh6gx3AyM4ICK9vTTmYFQRchTFdgu9HxyO3x1dHqi0JVQv7UldZbGlbpGL9EI2gdzW9DjXafiieS+TB/5Zsh2iuGbSYmEnsM1l8m0WrYzfirAZIdwvY1s8ezexQC9ByyJExjL/H75ZdNM4Cc9kwgzcSUI+m9fcZcgfBiyLKyPTHHLxu/Ge1YDt9rRNOnAEEuhGAeUwNTA1MDUwNTA1MDUwNTA1MDb62BWwKCeLnWiy1fhQl1+nWdr8Z6Vef9my/HLOuSrWHnpDE6+Aqt7eRlqLCrzTG7ibFkQcYJ6WfaVYwZQyf6qg3NaYFBODqVsYMjabCZ2g0M86AcR5MXcyFyWTYjG+gYGfkPfWYNEQdQInRhI3OcF3g6JCvDxkgDhByHLr0VDQYdzrMxQohTz5CY2rFGbBzclf8IZv+wO82epu7cE+izLwTnHrOA5BvoUDOBDALpa/TR4eHOoUEgQCG2KsY4ESCkZQuHMlpwLI2TUu0yb2Mkyxxi5AAC8XMYCGpy0jsOQn4X/1KI1gAsl6sSddxl/56Uc1RtRf1bK4hiTGsSitpJmuocbkJh+G9wcjkXZOEfNFcBQSWBQbZRnoWLpU4D1eiKrlGIBm6NKBbkmsuSDkOSC1OXLbwGyopuUl7Foah7mHswtdc8oEp5p98WW9cbenqJvHSaguvaXff+EqsbZtykBzdhte5l70H6O2oyZdVXalZ83VLuwZRe3ULnt51mzVXphVARx/UAEhogSNQSt2J8zDjt+1TyKGSctU8eJf3oqbq42G9aQHZ8qmlmUPOxxk6NOOVnBpABIY2pdhitPIChLBPTGMsSXC0pA0QCUNKKX4/ZwCg45+d5jwPpAoiQgzinDgNA6Ey0Hkcz6jAO54QoOnWvGD+oxhhJyIke6JTVklynrZ+mq7TmGsy+MUddCJGwcRLfigeQ118dOqJyLQcz+B4Afk+yFvC+L2D9iHEVEIjZPzqPBLg/ZLzVbEzG3gdaErZv/23yyda2ixcLAfosC8tsIGSo5cJdPsXcj52dfegkyTymBqYGpgamBqYGpgamBqYGpgbelQbePxB0cSqDQS/Y9cbrnujNGINFccx6ijFe8OlzpgRfrwFxLhTHrwW71wD0sAwMMOg5X7FlpPC12qeWkZ3wFVtfuNkVByfScSQt0MW8m7GitmizzAKDFRZMwml5WmTEQGmHjJS7GCp6MddrvEEkp5vB1SrQCR0xUsowMdhDAYaLDBB8Ab3Sp3Rm1TjkpR+kRaHYRQqBQWUxCPb9AABAAElEQVQ0eCmBlyMIBGL2D2CQTkAg/AIdaXnYEYaIjI9DfYFm9g9xAKIjOSc9Mr2kllHDF21vN6+GmBFEGz7VLl++OawZ+mV5lWGx3bGUQdRkJYrMXGxqtbjrUAAP/cfU4ghLtQBdWKbDQxI6lFCsipSQI9WKo+SHqQrJR2bLAx0ZLiLCseJkQKdVDEG7Qle0RBVfkmaozOLZqtwgoAZs1kflj+GahnTVLTry1vHLdFWLkpsf8OV4fe0Y50W7rldpM2sXeFb+GB9piI9lzYw3SepyjXSh6+lRK6GIjImbxXLptYqyuHYZId5Wt5hEnkolLPrwD8VVdJU/1lkUxJPTCAwetwcnz5HkUqWAJ6kdUBt5eYrJSz4s6nlgHMz4qvFHMyUBgM60TOxMIctqAYEAhAoMiv+gOI722Mv467FV9TSOaYi1Lzb7DfIwluW+NesnMz5rnGdMZRYmkils4yodYbku8gOMtN6YynHJjNwccf7fxlhlpm8aZemgzhpLKy2WGuM0brfnmB2/akSq2Zg7bVYmoVFvc9WSMIE2+wLh2R4ev0DszHgA6M6MnwKBBPx4KS6zMFscIJ6Zmt4mXgPtXvMJlLFWPVeniKefCom0fnuGkJJ0Ryt4p48g6WIeUwNTA1MDUwNTA1MDUwNTA+9OA+8fCDo/MQjiF169ndswUX8wGHgRjsHQwCDlnwuQAQDCwGCXmjOWielLNL4tRjCI+PGJZgaxTExfsV8ABinNEgeMHJxJ4zMIx9HwU+ADw8JfzB0iR6X1hk6e0n5B1+u6jS2MCWYFSfZdhTZYtAQiNOHpV/vWsWA7fCdvRovCbAUvA0wdusB5kQ0XlWOZ0aKNFgdytxE/FP6qjFPofcAdwB8BPjIyDPQo9LbxWpJw5FlBGCA4jxaNACHZLTJctCzBfilqe3gZIrLBcDqNMbLL8gS1ru75rJ7UD2S7iT7VD9YIoKfzBoyU8EyvpTKF7bAuZZjSV2VxcnFIXKSRIHFnKc8cKiRzOUINN2hov3gZxFroGo+V3L3YLfSkY9vyVOAfubUikmp7VfljJUv2dWUkuapspL2eLv3Jdaz1ruJdwh57V7w7n97H6sn21jpdr0usam3mjqlQrLlexW+sSfxqujXHdc1r0gwgPnS3aSz088O9zNlATG7L7ujeCcmCNDpU6PFNUapkvMs9BesLZTImngvMAQh6qTHojBmUQnZqmZhnX2p8faqt5Y/ln+25lt8WyO5lYqK3Tzcc/Auo9/iqIY8xNj6BGM81yDCw6TQ4pFh8snUgiDEVP0PMfgRqNxjiZ671BXBeB/6BDAQpVBOezYNeym9QwDKVNGCd3jImMLPH4zViOI+rxl9SBn/Ez3WkGOmWreLxDRTHzjjk14xKloIx+4exVX5/cACNH7YjdmfUsrCHGoPJZ1YQYy/+2PYAgTwDMz6BLAv81R2Pn2rfPVMGYcCq9BUJ7yl/zgjiV5rH1MDUwNTA1MDUwNTA1MDUwLvSwHsHgs4uBAQJALH57pd8Xv7VHb5yK90WRtl48Uu+LpoM1AEcfb32tvIyPvABxJbyLAV7gbNof8V+kbTAoGNmAtmRNKGAoXMBQSwRk3XCV3DZLTYoJEwzWjAe/N9GU2YIIRui2XqQMbHXjAkBQcqLAZZyusGrvMhVhyuGiv7J+vKXaeI2XgIGvRII5K/RrZwy81BT/mosKwF/EhgfBwZ09rW8IF+i+SK9AEFCeg4PZHgw+wc6naYXDV+y2SaeZQl80QbwYUkCABAzm8Texgh+KmwkSUbLwMW68CU2Zyuje+mfY65PzCAQfP27Wgvkhgg2YQXbpbqpRB/9KBS5q5vfEA+X4eqacIq8Cq1y6pmqyiutH5uSnm2qKl2qUBkBLtF1corw1+JQ8QqhGOOk3+Sg7sc50t8u8jpdfaz8sY23kXfk01sfY5sUtF/tXdfXohtr97wYy+HTufTYWGszvq3/naKXrnn1klCn7R7vPKLpKu98eqxoqz8VVv5myL1d97VCASEBO5j5Esr4EAOIznOXcUpcS2g9UOEietVZA0HwuWA8VMFLjY/4UQNsfwkYpGW47CjG9vGMsd5SnmW4bUw9FtjOjCGWiJ2K3r6GGF/PAJay5Mz7bkmGAEIabMYlYTxn+GdTeNdgegD2ZZalZwu1frVn12OILukHuuGMPnq8dKbWqUDH9bwyvgG8ZJwir5eLY25O0QJVWWuUqx7LcQHfPQtTYBDgzyG+1xhvBfjgE6h2CYtPINF4NibLd+MUOn7YGhAFf+SxXP0XtzrUssdhJdQLFWoGp2SWluYxNTA1MDUwNTA1MDUwNTA1MDXwzjRwK0AQfnHwDcTrNi+6vO6yPMDAC1n9Vdw0ng2kN3+MFfxanAkZwkkpBsgLgz0yWAB6lPbOYacnBoBY2mCwiCViLc4SM3/9xmCRsZJt5QkxiPLqTdwGB6IMh17RBQRleVgZKwkzm4nu6P0+/bEBFoOCj8zLtvDmh9GB6aGlaraQ1K7qAhhhIADUeDcZfcHe1ddkDBAvRZD/H2YA4aPiyMCPwB+Wg7m8AUCaCXSocjsz1WdoDJF9+wQSqKQ1Bl6OoLb4Os4/lkhgGBHaSJJ8tG0FWNZ0yAaVjSp+sXZQF8FVP7+iQtV1+UIErUqh0xFDNjry7+7cXEjbZ5RCsVxOSjfYLWl+r3BhaVjxa3cU1czDrTXZzakIQ+GrG1jyaVzZTi+RhS5kuSJnVStKwquPsbRqhprUNh6bVJc5F8eqP1KMddsvMxa/w/jYUtcJDYzyVbpk3ay1XZzUj2Z4ohLrfDdb6zzWvEuOTjHGri8dKa+LV5slY2grt/++xaPoKqz8pd5y3wa0Jok/G4PGPHd+fhsQhGZ4/rgpF63z7KGhFrYxrtjyXIqbxj/GRWZLakYQM3s0e9IzhAQOHQts9zIxjaH4DmKZGGCQl4153NVsS429XiqGA2mNrWeaGQSghCNp2mZ2kNvyOKCxHvkMpCMr42dA9QBClW40pqYOPBz4bwHRcYmYEumriQB+aZkDSsY3hcqwc+aWT+9DRxn3luowFhJ6ae4djb/sEKZxV4D8oYF4/AFpFiYzgQwE4YONOMCQTs/QZBfHBgIx1oo34yrjb7aob2LRDrK0thGQ4bc+PCC5l4ZBM4+pgamBqYGpgamBqYGpgamBqYF3pIH3DgSde2kYM4LqNV2v3oPh8hFfiHXgNJqXYsdlPMhO0Ut+lnXVMgSMDb5Ec+Ik+qUcmHqGENvMA/5gpLBcTCG7jVF+JqeoNnJk4HhHMRkPFwKHLoT+MEtISS+X8Ndltxk5kSQ+Jtr0fhsugEKD4RVxqZDTEXogkKIsmJZn/lqOgBGWIyG9ZskWgBMgEMsSAILs70fGRmb9YIgIFOLLNIYJM39EY6fRzB4q56SquwsIJOsjIBAzgDB0kDnaxeiwEWLjR+2SP/TDhpFkR7qAQUgbWUOH8UQ9fkd4hjdU44G5gwp8toJwSYL4khYf+MFY/3sY0nbtNaBBtqW+Yq7nqp3OFW2INhYOirLy1unKT7i0gVE5HkO1ITpSKL4u6Tx6LFWuplyxHJLrOlVUvPNLVe63I6yWwrtSJVelb9LyZp1KocHi0vOGO+cSa6hHyksEzng9xfZ623NLws3S7bmbNFtSoMg6qE0040Zm8MRZvfon8QOk8OyRKNCkel/PnsY3ZfEM9oMy8VMeYyxALUvFAHMAzQFzAHgYVw2yt/A54yo+2hhblQeNHfUrZHt5TpaXAQQxppo/v57aecUz6OdQckpeZAdQZ3ZQxlMBW4z/LBGjzMJyTX1VNmjOEw/ABNP0KfmUtwwKxUOnxj3i+euSODcGQFDGvOQZGHfTzJ7UeCkUZp8ZQVqSi08gQHjvzNhAIJaJPdRSMINCKmcsZncwZmEeCggy+CM+Na6qB/RCstTR4xJHsuY3JJ5DS4MVAUCax9TA1MDUwNTA1MDUwNTA1MDUwLvSwPsHgrQ0zFP59TLMF2Kb7Hr5zeswb7v6Fut0JuinoyqXMSGbQsaKZgXpi/UZS7tkuRjYAQwSsGPfQRgiMkCYCYRfoFODQ3IYrXyDRvrSTflLpbO1vAwevmCL14X4XihkZg7GEMZFvab7JV2v8HEUjYFCvIUu1KWIFYn9Fh70QeS+YJTECAICIp5K2EE2FjA+BAJ5i3gZIOwUBthzaP8/MkaYESQQCGDoqC1XoJwdbDxzSMDPvmYC4ZjUzk0BgQpYUrxpXCHy+H8zPohnVpCLEFidCN4ROV3Xv5l/NcisD7qx9E9cHHdpXaiZro46HePIEk3ANPys48aieKqo0UUGdEhe6ZFoWiNW8YWz6ExdhY1ZyyuyagwBK54ag4xqV9Xaz+fSFWmrMQa9bZrqqSZG68u6DA4l2shtW3zk6UotIzw3SrdVf8u8TSm39YMGNqkuNzlKuZa7fu/N/GrpdZwvt5WcscWraN4sf7skm7m0upmzpQ0I7jDTRXe1HxjAatLcfwAolGsccuCr8hOa3iypq7Z0AeghnrsPOvJ9NViDGx5mSQK2e1cxg+1tZqWW357IP5BnCAloz0whACGBQRpPAdw9M5MdxQDn5XOIsdqzLWnbgFDNwaHhgDuRJaAP4z/giUZBEdT4ipzpYxM1/ZH20ifkRyeEGz1Tgtk9yYMmh8AfA+KMHYxXomGMlN6YKclyWZzq7wqYYot3xlX7WmMWkMZZO+VnDGb2j8bgI4Agjcf3GZtVjnPowwPGbmYEZTx0DzKYqkUJmv8Sh8XQyKer/0NPDkfCCQRFG/M6NTA1MDUwNTA1MDUwNTA18O408N6BoItXp3phr9278qIb25zXY9IBgACD+JfXZDpMHIBGs4NkqLCMASPDoBC73QjMwccFAFCWNQjw0cwgO4omlHGSWUMNJDpjdhCgUGYGncsfxrksFi+PMH+1xmdsjItmYRiYsPElo0TGgl/edW1oCUKGVHXcM+qKAf3LF3slVc9fvU2RHhcvzzgyECTwZu+eQaA9vizzNVpGBrOBDtke3iAQ8XveSp5t4bNFMcvA2CJeX7NlgNzT12wMRYwRL2lT89ZyGRoIiazIh2hlqJDQ4RlAhBBZB5LXfXKx64QVxgt8qJh4KLiGWVOFjc0l3okSEylljY31RPVwSEgcMCBi5ErlktXxVpp60HQ6cxmTVKCFbXmVmU6GTnkmHXVVdUtQ89x26RK1nrpv4ViVw6xSI5dqZsxbx7fVi+aovb10zePjpEs27pUev5pT0YwUV0vnO7CRxngfe0OLeRa3cR1b2Bbf1urIfVud6/J63eulSSmtb6Nb8lWY/gky0DMI8OFxqfV5GVd0j1oHqhhddL4eZcQHAIkhzQ2621xcwR2iHWjtM4gxENBd4ekpTqAZQ7UUlyVgBfywW6PAH3ZqzFIxgUQA7l6GC/iuMRWwXgj+hWdbAkTlt2RJF3HPDrJISB3IppbbIhR5yU+K66Iv6QJox0OTQnfJPVC8PbO0VuNJQBd6qFp0W2cAIAE/GjP1XyCQfMBpvGTMvOeltWz7rrHWM3wAfgDiMx6zPIwdGjd8s+EcWucB/AQEZRmaesB4QZsWPj2IJipOzyDgfwCw6hdLwzKTCZp5TA1MDUwNTA1MDUwNTA1MDUwNvL0Gbg0I4vU3M4Lai7zeym2w6DU4M4L09tsMg3pV9ku/LhgoGBaeHSRDhThAUIAhgTu17EvgDqCQZwoxEwjnp8wSqmVjfLnGFwZlgEIqt6+MNjOIHXX8Ms5FRguATZYuYLQgnw6KTBcpk5k8yiBzTzR7iKUcWB/M+IGPyxTkSzRLEZTfDIg9OaVmVs++jAnPCJJPoH0vDZMRoq/O5RDaW8kb/GFHm3yBvsf28IA/DVQCDIrcJV1kWlIIKFlVRQX5FVymfmGaYUBRTnGb6hTjCp04E900c80ZjWF10PoxC7HR72t9xixLruj13/lElzP5uvog5AQQzDHISgZ8dYR+jMfw69WKo8l9cVU17BKqIkTjZ45VhdDlBGq/8jura2Ix/UIQJj2HWJe5NdFyqk9puhooUSpNuF2c8F7u2bHCa+Ilx2vIhuL0Yrsci+pMn972qmOdKksfN0vGfoQu90HVgWPVIC880s4YD11RkvIvasIx1xlvcRnlgk14dz2ty0NTEgCYcJ/pN1QICOTnpPhwz/rsnENLuh0GXFKPW9r8JIXpqAuZQiLIAn/AduhY2pWlXoBBLMN9pWVgAYCYZcmyMJaJ4TuImUHHOJJW+UnzIRQ/QQ24Z6wWz/LBxlgbYKj1rc0QiiiMlZkFFI0hJ/dx04v4lN4qpIS6HIx5BlQy5CqHvx4chBrXGK9Uxsl4mdk/mYHpmZQadxlP8bFmH2yaFWQAiDG4LcM9PGo+gwQO4ZMNsAgQ6F4DksSijfNIrX+WSdJK9mi6y4tkSMw/hyJ2v3SZQJCVMy9TA1MDUwNTA1MDUwNTA1MD71AD7x0IeqUZQa/YqlcH78P1SuwXfL3450Wfl2C9oUOzhKF9pbULAEAsX2CpAXFFnWaJl3eruWB3MBkusmKY6eMlCnzRFuBjh6bE2zIG/FiQ76VizA5aAKUzYx42vpqgfpEv40Tv617+ZYMEo0TC6D/9oWMOFS0DgC3nbXzg+0dLvgz6LHkySsjzV+kYI3t8jcYYEchjB9H2PaGvz/ifYGYQSxAo8zIwxUWLEbInIYUh6eu2WtYJuGPTAoPI8kgyDEPkHA607WVr5LU69IaZNpz0x/HWM4zIRkpF8wZsMl0rCyPRUV9tUhZ9wpl4eDTBlKe23LZoBUhhzJUwjdKk1KZ+jtwxtFFHaJMOC8WHcvq/cTgN/Sp/IVL+usif9ulTZF5Ir41EB5DALhLmOub0sjH3MuPX0Y3lcCL9JkdJVnW21V/TVK9G2ss01/er2iMc+0APiteYT5tjeoyPvC7zS45/Q0WLd9W5qcaoR5vrY81vs/y60pGbpBvuXQNB7m+ncYxxqWWtgaDlmVOTWVYmSUQMXeoSRzpdFEEy6jCuMsadKfKS8ZSZl/j+kZ+1E5bcGvDRTEsBQM8BgeSkn63mWR627CimMRXn/HHSjy82gHwBQA3M55cDcGIsr4Mx3pIhDjItJxSSb0N1FjzyKwp05H/UZQwkbKf5e1zJM8usScZJxmNm73i8NfgjEF7pA4FAOIhm/MVZNDMwPStTY7B3ZWRZmEAh7+rIUlzqagxmzLV/IZxNK27x/ZvlPrWCkckC5eI4gqpvBrAirPsqnCog/UA/o1MDUwNTA1MDUwNTA1MDUwNTA2+jgVsBgmRi+AW33ucJeUUO6MMrsYwDGTZ5Vc6Lv2l0kf1go4EQIyVfmPOlGSPJTp+FM2F8XGhmEEaHd7HBganSL+3YVOAQRoxnBsXJdLZLFoDEUgbRUtdLKcSrdvxC0bgxRVauGCS0Z0MNYwaBFHbDrRklsgaYDcQ2xAA8gD7M/NkVEAT4Q4hPIJZvmcYzfJTXZvrYEbRnAQkEsg8KGSJyCA2vfRkr9ikEiOT6MmwwgKQ+DIgyghzSAeQjIK6DMBp30nFwESA5H6IHAKJe6Kom9WiANhxz6FwITR0WXK8Cgty+6amiVOOHZMjMUaCT46RVxpJB/wbQkYeMOszPsaQCbik3xSnZCgQtlVaREk7ZQ9SyKmtsD7YjyYqRk5E2dBGpBKNmxVOzeG3mXuZ6M7r8RtQe+UWHvWzkPtKRv5ZwM72mHjl9vPi6X2spe4s9tq5zVcuX6TZ7Q711e1fxui6/SxaqtELu5fY2+fTy3NrhlLGmU0LFg2JZnXBGJ1CMOvAg9C5bivuZEr2fXT9o4kB9XUzreoyvAdtZInambeG9BFeAEMvEWC527KVi+AnKsjAvEdPsIMCgFwKLANntdFqhQXbxAaTPsjONsxqzDU4xzmb4NDC0+Zy0jg3PLTL2cQHZNcbSH/4BeitNf1jmldmQSTMeBKRh3AQ0ZwaPxlqDOIyr+FYTuGMQCN9ryms7hh0oDhC0z9IvQCGNwfdwzC+fQPdUb0917mmct8+hu68MMvlps1ySSULneUNOZNTFEhN3wmnnenYUv1nGccCleUwNTA1MDUwNTA1MDUwNTA1MDbwrDbx/IOijU8mOY89mnChVL/3ALFkWpjze5vUyrPfgZpiEfvEt0YwGXpQ5/VXZPPnqDC1tCBDSDB/AHX+VVvhCRox9B3mJGLOB8GWRGUGAQXYc7VlBAoLEIGBQ861hSdsbudsNb7clK6a2dfZbvuTGKEEGQCDAH4AdL/kyaLOXL9HMECIfgMj5MUL4Og0oVF+p+QLN7CCAH0IbL6YXqIRBo9MOpgGBJCI+JQg58U+BMeQDgXRwlQqXg1LOgBntF6GO+nXHABfl/Zey4QIv0ZgzoeNpHyjJrcBDdJDSpsMWD0dyqw5R0RKoGidH+KZ+yxKNuYVAV7aPd1PkqCh1aReZoR0PZIIThASkx/IWXxgqXcIUIaCVDq4uUiS6c/YVl8uS9IbRY+t747uNCeJyuN1ES6KWSrCd7upctDSWbjBTYixLPLKuW6Msmtmss43fSFfxq+iKb5ey6wBJqnxdn3Tk7fTkXW4v+q+y1Kma5H78g7aK06i14khZydPpqlRlLsz9DVgCddFFHy1VgW7I1KFuOGe8ZTxLfSQK+KCQR3VJlyzMGhRD1Qe0AchltuVLwCBAdZ0sxcUnW20j7y3kazaQQHbAIMZUgHjozwDmXZdxtsbkgPeM0V6O5smikdOyS/zSDQ/YEkfk4fAY6zRjbfpWPte8RLaB8YyDAc4ZWwXesJxLYy1Luw4YXw36aKwlrbhBH8362ZfzZ+cJDMJ3EONwxuIsBTOgJH5ZInth3RqYkqNvpM5Pk7B+M8YN5yuSPDpAjlJtMOHjhmd5hoF7OC9TA1MDUwNTA1MDUwNTA1MDUwNvq4FbAYJwFs3hl3pf9KqM8WLwIC/CvDIvr9CiAZTBMMCQsV8J5WHjlGFlNtBQ3aH48bVZcRsZxPU12svABAaxrMG7hwEIyVhhqQO+gs5lrGDonMvA8fIFGgRUcrtu0G3SNjOAXrEMTTTwVk5khJiXecnCCz5Gyi7Gg06WIbCkC+BnF4fQLZ6lCVmSwE5hBoJkbBgMAvgRvWn48oxPC2YQwVfWhqIBmzB21HJ9PUYV2R0HWIZUjk2dtbxWTMAvUZTo0rNtlEV+50Ia4mbCEDqefrfSxqd+qxaKFy1kaZh0haJ0OIgAPe38sVUXLTLSF1fhB7l0UOaWNkt8k1Cr1XF6k6SnhraHKIBVHcTMbSxvedB0yhUBhT46ReV8+0JLuoX9VbJtIR16lNKq++3vR7vb3Gxa+3a3eVXfKh9RSoYxL5q5+lp11hTwuFxWgI6pl3u9tNHaXZpfIh6PqNOBID0xPNMmCXDB3RtQiGdwfGJaXI/ohZ4RDaEG2ZnZc6at5QFzXipkeS27hjHL0ktuBay/0JJctpe3/zWVAwidtLEVX2zMEmL8hdeFZm+yayM+gwoQirzIrVMNLxohbSX0S5xLuxcBYgz6sBQXR/nM0GGc1LjImKtMAz+ewQPgExAIvz+e+aMZP+wSBtjO8lvAH5xF78tRj5fpKn9X469nAIkpcU7AGoAf2vQsRAke5/uG0zJGSWSrXRdC/3qq53jQuKLQ8MJIzm/FjCC14YrKmsfUwNTA1MDUwNTA1MDUwNTA1MA70MCtAkF+Hbbhwas9s39qQVJek3kJviBPEeLMvDEuI6NkWWpUZoGqiFV7uS5jAboYEQZ1WN6A0SEfF/ZzgWGCw1NmAgEMyUCJkZPQyygAeeChkJPmzBNgSeksRYMG2WpWkLrTXtyRycaHAR9mBWFk6GT5AUCQnUIL6BHos4ufigb6sEysZgVhuAAOEQpHkrER3xZ3ZB3Y+EB7aoh4Dhl6kpO0daIQs8IJBciPfARAVhxUjUlCWcAV6DZAIBWhAGiJEsLTdQlpDC4pcJkbcTv5DalCu3AJO12b4OFjFpCZT3aPS3sxjVyki2s7TKtm6kLSKa1WlOqZKiRRR4sXO2cX8UingjEp/x9uRHn0Z6OsWLfsDdaXCFNaLQ5Vt0TfnOq6Gr2sYoT9GHM3+1AdLtnzW3LdpOu8iI3cX0d3XXlxinzXU25KcHWq+hqKUVJyehtX04Um5TwT1+ui2gl955++9fTYnp/bVPQ1WKQo8r9JSY062jOnGxSONW4t96ufWWgBSwjrGabH7fkkVGXqM1aAcQv/8TiqodSAUO0oxuwgQKETz7AMuH6C/zWAIAD3c52Msxp37YCa2UIG3wUMMQsTAJ4xFVAI8IcT2eUzyCHtWzXk112gUMIzJgL4eBZQS5cvNsZfzwLSuIsDfsZaxll2WmSJrbd8Z6mXZ/7IF5DyWI6LL7aDBhTBC/DHOzIKFAqwFCCe4Qug5g5b0ktGACD9dcjYiSYlLNrMMlV0Tbp0Tj+MHin0j5Ay/saJF5o3yOT4vEwNTA1MDUwNTA1MDUwNTA1MDbwbDbx/IIjt4wXv+MvzaJDrJZ7XY16LeQH2q7Mir/SSHCAG4CUGQF6QMQ5MjiXgGn6ljnVEtIEcqk89KBQa6GFZgr5I+2u1DBGMlXMZJZTFIFHaxgjAjto1yGMOiyGCTK/E55V8BBkAwkJSXhksxOmjeySrgRlA9v8D8AOws3vPwI/9BHl5AuUF9mQmUJyYZsmXfV2o3AaILA9mA9FtjATCrjV0IkjNShKdympZWICapguRcZgsUfGAC3Ir191V3ASkc9KfOtw72m79RBDK0w5UoUYnxLn2r/tKt3ZcSiegskEHoXLDzPnrS6gRE/NUfXQbpJdWnU8H3DIFPlTTcTh0ahctNKRaOV/qG29nmZDiRgw7lVefN1gU7RD2VqOPJsxAQTR6rMzw5ErtOsb061pNnapd1CXL0tfGv9OljdCt26u7Bd6hKH4VlqRvGxa/Ct+MH7XqqJ4n3flVbKTtNMTy1OSXSXyTtlKd7qpfdjO/t8z9MMpXJZHDMtRPwP2GzhVWVmTstP1eEQX/G63jSzvi4ueuSQ/Ldg+EX92jLgDacAP4C/LOjXr08PfDkq8sFwuwcyqEiOW39rumcdWzMDXGnuJsWjMvAYqoU8vGmIXppbvMDhI/g+uMuzzaHn/VT3ej+jL2M+Mb/WCcc+jxsfkAwiG/UBqPpcywBGz3rExmADHjR6f9ADUgSGMxs4Dsf02hfQAB0osHOzHap5v5wTPjK7Myl5mXqMi/DzIzLgQQYn5rP6iHpgcASOMMVb0kzCCRdK/ucmjCEZTzmBqYGpgamBqYGpgamBqYGpgaeGcaeO9A0AVAkJaGleFs06oZI7wa57TtIjpehrMkgdk42c4d48evzFJCGTLRR764KlfFvEO392hsifAVL5Yh2G+QMvnybAMGQwTfFy6TbyAZOtAtAJToDGaYTxjHMGFZgzIlG7QiMl1JCFiDMPFRwddqGRTKs6NoGSNOK4xxkTLPHhLaE+OFL9mqb1pmFhHXqf6x281dgDSacPcxNKIZkuAU5INj2MRQous8uilDA/o6zLIKWsgXbf7RvwH/SAP+DYq3zdONdlxNzGFlHqJPvFpsoY244qM8yRv6xNNHrmK0HMRpE7mSnxwIOt2mEUZRmVWdeiCn8nCkZWcMUeTL0XTje7jybhIWgy5nr7UuG9Pb4tTcxqdzvHkM/lfxuq7s5i28X8qSucJtra/LSNex1sVYVjRjuKYfy14XL97w2JSpchYOrRkDPMSdbvfiQkSkjZEqX56nqrvQtXZ1Dy+AkctKHp4gxTU4MMIEn9EY6TGUkGVijKuMoYylAoEYW5Vnp9Jt+Rjgu2cN4S9IwNAZMzCZjak6ZwBKjLnwBGDX89xnBCne5Ce0ZpLhBCMAjx/jm0+NjR5nGWt1Mr7uKWR5rWdcAggx60enl34pn+W6+weiMQhUgJEAIWZyAvwwjgqRYfw1+E4ep9oEBGJM9onGQeHVD/6hMXWkabrCGpMtOTXamCle7h3jE73MMYGg0sQMpwamBqYGpgamBqYGpgamBt6VBt4/EHRxolfjvBAHmGgvwW1GkF6bY3BgCMjwwADhnT8zb3i1xqDhqHq8TLdjiVRG0RLGiClH0nzVjsHB8gYZIM1wMQikF3cDQbzMMxtIDa6BINoH0MJwkXQuN01716+ZLWWcYJD4xFjBOGnp3bt7LY1BAY2MBBkYXuLgNEZIjA54Ynio2HYChkc/Wg+tnGiIMpHbQFn0RVGrFyoSLSZi96GllQipjS5odIZU8bDxb0ifGlPaq7hJqwq/Z6NxG26rMSGARypbZjdJY+STaDLTbo7ippTKqRvZKXXLrYruINXtLEiEQ66NMXlEl7JKtHKIhyiAGcTFeywbay7sIB+OYnWpWdMUh97kdrqB4TuOdglWalm1c1O6VbUrkyO/NRFlHO9OF721Hksb4/X17YaCESa/N09Axdecct+Gbq3bail1Lss09Lwx8HOiC/d+3f8V5pmCF8BCPSPhvVzFp2QhTN16liKPJVaUUKOP6Q3U6OY/1/gIYBM/bIybOOhn90X8/mjbeY2tOIs+JWQpGMvGBALFgbRAIAAkwCMDQg0I0ljMbCCPreqXQXY/3xmLEaD6SD+Q0mOHwl1m1jCGImkD0AHS7Vwfnz8FBmlZ7r79rWVZLr6CmAXEsjFmZt7TzE2AH+Iei/fSDmMx6vSYrLiTtKdsxmXGA2uKH8aHOsJvE61Z1ibxEqeOIaAWWnr316wyI8h8G8sZTA1MDUwNTA1MDUwNTA1MDUwNvKUG3jsQdC4gCOCEIy/vAQDu3ImBgbHBO7Rf/hUPCINRgHEA4KL3ar0k+4Wbl37i1PGLNjEYc+F1nJfx5BGzDyIxgDeziygGGMLpqfP0RftV+yrt5WQqc9g4pXFYYlipunjEbxEv+5KN6f7i6RZ5qS+jxPJimuifLAb+AfogO7OCCjTC4JAaZFQoVH3AIhseivNFmwODgzKfZCieg7bppQ6HaFKHLm6ZBHwpJl+xRu1UslTa6jpdfCBU/5ajtZMlZ3CEdXhbsLRcDVlP0JgNEbddrfd66CGlDpiAYM1bpy6qXzS8lv5WJbMsvuGR+QsVb4QbJGlzizJEDGErd1XFJYy7j1DO02VFNmab5poL3DfEMe3l3ORczr+G9aqIuhyXW0v+J/VacnfZ30YLvZeX+VZZu71zK1fma8OSKnxrROrVrm6vaAC+63Ybqat8I9TPyNOArIDY/l2dN1JpDGCgEFM/WypPOjTpZyoR1/+loHFUZhsnPODwrGoJr3kyitN+ajFOM6YiC+O0l44ZXGfWJU6lL+QsOmCQnfE7fpalZRpzs6sjgHzGZ4+tbbwnz62oEcdIqK20HJEjkqRTJOOq4p7GkzE0II+AHgE8+4A+9s2G358GANkHW5bneqaPxiKApIA+6K2djNFq0kUt081IGkIKy9NdBoqS2UJbWOr3A41GxxlDkV+MOFsVzwjarNSrz9jUwNTA1MDUwNTA1MDUwNTA1MDH0MCtAEG1NMxGCS/uvG3rxTfvvXrjVcRGBvgKL8mkGxDEmzZ0fmluL/2kla3/vuQFWlmVT7EJErHxco7R0pyQinVmANmQiYERQ0Q83C68xC3TQMILQADrxyUImjihX+RVwgu+LQh1T9+VLTNp3vFdavlFIuPCBgx0lcesIFO1WUKVrzxF3R0CDsJIrThyANpA7zI0qBj/XZEa0DsjCdKWuyXdF2emnnnSv9Clj3DvPBcjxpnQuRgK66kn83uSzlK+4kNv0R1pl7ptx8ysOLQy04i0jqq2kZ863DGwhQ0+OIpDqqb9YrNUX/il3JwUXVSo35+ff0kvDEauQ11kUJH5bKUdyy5ThhOydw6XqVaMN5Lh0LPgU3mdZy8ndl0L28qu47eNfrO1y6nid7kkOaPcxb/Cq+pcxTO8+n2b+lf9vpe5j3xHuUbKotksd2oZW65rt9cvORmDDJoro0AZbko/z2alcYRHS4MOtWsMWOoToa7KfIe2ggDxdU9mbHZdBi8rJXVGMIg2GafBpTjxreYlt/hjUwY7jNlZPz7ZDBJp5pDy8AlkMN5jLTzybOnqZ9ayIaMAIQ7PFCKSZO5iZNJ/j5l0FKAdIF35XppbOzFq1s+eff4IBCLuZWOE8QPkpbhiUrsymq0Vl257rBZ78sGt0cmuloLRDiCx8ylHvAael3aV1WRugivJ2AwtB7zy+/C3Qjp3x5uPoCIK6bxODUwNTA1MDUwNTA1MDUwNTA28lQbeOxB0oRlBGAi8UPMP+wfwIzuG5W2XF2C/A8uY4MDgNhDUCmKQ8NKMcRMeoRyuGy/OSlTavPVqTgM6LgQGLb6HmlHlWUAiKDmIhB4mrSIyN8ME4IVcepFmWmOiKWDHAigb46HihCJRoBAwiCLrhSwZGCrzDjOLEVf1XQW7w/obZ8bEGKQgRomba+24rSZh7M6U0mwO5Fcv0lnxyA9gqsoTYTcyI4ekClfkTczsohOLUlqLHlfN1u8ZmWGic2hPiUW+FkkAnypyzpjo8cX5NTQrg9vVmswrsQbWQ0mL2rhTfCgJq+W6LkEe8iJXjy0VtkY26Ypn79vWSpcyq96lgvec8XZyd11s47Pu4zYaurumG1XAc74uz3NUt2O/Ndd0uW3DLW1XneSt6Uf51mURM7mMKrlrSHPW8wl/xxUJGAQd8uYZ5ln0s0U95eU5azwarwJV3IYed9/XTbQEXNNyZmKKN0rw0SmIIQsyMV4zpuI/LUtu41zaoJBmAtUyMvsTEiBk8EizgpidSWX6EpC99c9yqYg2q0ykpQey0z9iigsAYiwFs8rMSwFCAnyyc1i2kPfuX/bZho+2ciitMRgA3uMtfn+Ii5H40GWPbYzPDLxkK5Mx2o6fKVfa47XKqCYJmz6tVeWlHiWNBVEf/m0Uow1mxxYHCt0GkXlMDUwNTA1MDUwNTA1MDUwNTA28Iw28fyDIzqLx2+I357waE/erMwZM7xkzdcjHMChwxqW8cPuFOTz80q3LULUzgW8RDLmhjeECOGDjQ5lAOtgjlsNhXuKHqokiM4RuVcYX0UUCXuZplNBB4rr6sDwqdWHEs5Gl+mTRNxsgplNe45OyzqKxcUY3iiJ8jI5Q+KpLmiMSHnC+FFP1JbeUQL+GfBIULfKLcdVJRCmVF29CDGzobbBC7Pqh+P/bO5tQy64yDS9NOhEqg4RAE40xgqgDSUcTsboRO53Q3ehEMDiSjByYDgkEMgoOYtOt4EAzUFSCPzNRIbSo4EgtgzpwIg5EMRmoINrGgD+glZSVxO99v/Wu9e19zrknlbq5qdx6d3L3Wuv7X885Z9fa655zLjLCnPNToOEPm8UgnWgX8i2qNEhFzhkhYKvgaTHPqC1Nqm/2uw/0UScqRZgunSEWvW3anYUuPDMybBHjIJ/nmmOb3SrlkQ8Pmlct5qDa1zEOsq0xD+7zKV9NFHahkLAYSrSwg16PZTdAM0qXrsSp3XhS0mIjZhpBrNe9NmE0xvMHqfgajQ7aMe456MN+aCIYr4FdN2tEub1OvEjwIx8GzBEsssy8fmLAL5PGRnts9uCjYvHVQdwcwhibP/g4GK7xzzyL7xTK/LgCQ7aeD+fF6zSVWR4BZH5u1nDOs0Zs6ODdUNzQic9t4eNg+KgtPjLGL+WPDSBuwGMcuy25GZ8tponNJEDL13zIMd8UsY8NmrHxE0ro+UOrfnWNfxiy9kGRjwNNyknXUmSLCpmlp8tfCBRbd03ABEzABEzABEzABEzgfAm8CBtBZ2IRjx0evP0di2MsoPOGh0tu3NxgVmjZyVYbNd2DPrl4xuobC/CIIXutoCMMul2MTClA/DggHz4cpOAZ3unjYwlhH/KnS7xFgDBn5WGTJgiCPubELkcp62PJ0zSFkAWLeTMQw25HlXxgph90EENtdMdkoOgTy5iK3R1KPJWB9/TwVkWCRYzQdLlcOYwT6iRXlILHAHXokBPHRQcn6DjJhUe3zADcHIMau2wbZkWAohRvkTPj4IzsNGM/RsUd2nmEYhqmHevsFqGb1rO38C8WU67eInEIlUz6GrPayq628tndIoI8llZ41KSFpubCuNaB8baj+lf9OtYy+mbktf3aYvsMZtS1fa3loH7147N/hixuozqVobbbaJjRwroLeBnhEy01G0yrY7gpD637awSy8XIpj9h4/kYHNtxsKC1yjTghx2YIjmyyX/0QpBNYvTbytU+/OOWrCHHwNhkEjJ/wxRWdx6gH77bMiPhifmzuxL5PyPBRMGz2hCw2hPhF03hHJfL3d1ZyLmHDObKunB9ya568JNBAiaPF7nnYo1Zci/hf9LkhFLs2+QX9IY2NH35cLFrMg9/NFvZo8YM5YaZgFt1+RGAcXYckmQPX1pQnY1SJ/0QKfmCIwthNe4gVfPQzENnCvOuxLcRNqWh9mIAJmIAJmIAJmIAJmMBhEDj6jaBn/5bvCkH1WumOmWD5HEec1HKIcSykKaMtFuFYR/ebkZDlhgeVixNvUEqesfZGBwERG//14LrRGLlGp4bVsnzc/qRy2PYsuFuJKkv6GmSzzznRJf1wVsESR6vs0V0dkW/UkJ0kmkFGrB5TpiUF/fVlp9BX6pksbnDk2LNP9otIq9rOd7hKuhFun37lIHOUzP7oTMPFdOZgzhem8lM73VOHsZKlDiP8wGNGTR3O0k3J/p4yqN302JYpraRRu+mbkl2xqx9s8ANZ2u/y2py/LOGLvxaoGLVFX5HZXZxSmyJFm6OUZFxIlxZphwvBVnlXP9cGr7tlnFkbevjB1WNKl/VIPmNI0n2wy9Qz5LVrXsNCwYMe/UVf+9KPNpKoWl0Hl7EjJ+P06zOCZcARAh1do1lz1MfNnh6bm0HR1zs7YZsf94WM3ggQ/zZEnyfI4ugFQcxrM+NRI1UM5mMKjT66pdcpWvzwy6Oj7vweoBhjTtDF5k/qEYf/quQ1lnPMXDxrDLfOvodAVoTigeuz9ClB9TjUog/jOmYEKOLIK7zSYW9q9zU/PXw2ARMwARMwARMwARMwgXMhcOQbQfE1obmgV5VjPdyXzrHw1+9SabJa+HNxzBX3XHhj8T5W4eGk5bVapVKrBXu/xwh7JEH+7rHLscQesVayMZ1uwHplXNucSEqiEN20DJPuWOMpFm8+UGNVDkd0+mZN6OlDpgVRD6T5YpiifhYYhIII41RBEkeMKyMBDaN8FOkUdtHCdOGb7oyHGNTBrgYMeRx8JkC/qVoJw2jru4YQpR+Ko1bydSu92qGHAEfMsHf5vAuJypsWNKRcupTUszQZRY9FB1INR38dHwpEUaRheJ4d5VmHOdw8iKZMNbJk6+w5rjfEyWy7fY24PdKmdDwG3Vkx5nM6fJAuFHgOMLOMFK7rN5+PtJYVnfs+8banfreDTzw/kK8/ynrOpcFIFsNSCF9LoaNjz6vX6A4XzolB8+qLaNh4yesSCkC8jDWe/zFMO06H+zfy07spY2un2wTFUGKPR+/8waZQRuibQawNsi7VNSE+Xibu0NArfFEpXPCDI+uSBNqMxbLxbh90+H/4cfMnNoX6JpDkDASbcB3IYpybdhkvE5ZHIsP22GzoG+JxsFrMB0ERpvtwcytksM2ZoZ+emj7+alhKRjh3TMAETMAETMAETMAETOC8CBz9RtCz8b0QWMLHypbL6r625iz6yhdL+HqkGLJcQVPLL/TsVimoLhm/L7jhpq6McNMAWZ5SyrEMNjxSwVTdRjGXfiPAchbdWIv9aXUOi/yenDcpCIBxSa7ayJbyOMX/lMsJ8hBkHQgy8+sGJONMC4zniC481ZvyKa29jDQlGQnjfC+VboAgiUe914YR7ho5pAt6q0OhpZqhl4bnYqdYiKB4apMiY88vy1XwaQ4Dcck50mWcRrgh2d5R5FqSLKFTHOhnf5t1eun2creFos94U7K9p7xr7S75+dthFnl12MY24z+37BlpSSNH8bwrYryWcPCGPVpsjMiOpLqtNkzky4+9ljiIwWf7fHFClPHgtJIvroFDjXer9PnRniH6CcnK3KUvomqtPqcXJ5bKkzS9rTIYw1ap1I9xzjvZQc366QuZmCl2vrYRDFxpJpXa7ptKJNaR1kTW/aXJVrmSIKzp3UPgE204gEeb7/m4pB3tZZum/Zx5GQzO/VAP/56gr9c+BtLJNqlI2uNtsaJ9qGXhL4ueBN0zARMwARMwARMwARM4HAJHvxEUWwD8wuCxzO0T6QtfLpP7Qls3X1wr42YiVv9ctIcLWvxWFz3eSoQPNhK0eEZUhMkbhiKHi4zU32KntT78IwXjoB2+0eURMoghH/lkn2KazZMM034j3jQc6dZpmW+HncTpE7lQPw7usqDW0MT/YJZxZJC6TrPr0j3t0ieD5TlveqBVDFlWq9qXbdrhZh4xuDEIM4VBHwOaL4TU7D/l8+RgT9WyP9q0CB+WrptNDkIkauVGMA05N/lXuynLHmpVRWplg1aZwIWPYbTo1dvoGmPTt0qWfeVTu9TOkepXC03tT0tpUK3qzb5sai71c16yg3QySb98RLEZkhtBiA377XXUePvsUCejxynjqY7MXPOjn/NWS89q2PtZXw5yPhlb1aRG3mmRjyk3KbiBMrU5H8UM61DN61RGRkTK+pDXL8mi5fUQrt1XevjhmPEwiB/YZRPncsQLV77Uhx1SQgg5+xynD8YZR6+daSM/WHKONeEINO0zYp5HLhVTq1VSxEVM/OCINoe9AxEE3X5sSEOGYxWHqboOjZjBdISRL4T92CIa9rJRy2nHCbFxjcxrrbRuTcAETMAETMAETMAETOD8CBz9RpC+EBSra62aF/1Yd3MVnItg9Plb+CLjwpzOmHzcOkQc3m6ipX1CURwhol3Xr3WyGWGRb9Q3tLMDHY4NOwlSvT3etBkpdsbLOAs7uMeh+bBPySx56GKiyS+clAO0op836ekI1ciBfvjlb7nzxm2h67bpeS5nRNl24AMkvTyi6RPEg1QTr10VDmbRn48pgwzrqhsstgWuuWp/RsqHOwLqdnwWmN8tMkzTMoaqJTca0g/nTJDavPmHL6Q4Zvrs6ZxRoJePLLPdFS+jZmR51ixTn70Zv+ZaW2W8nOMuO1Ves86+Is5ZQLesDDfBVSaL/NCRIqBFFJ3hk5zFjMpyWj8GjBeJkAvnUROeQJDwtRTRaJAyyKudNq8XzzkY1YMOfUbRn8/bNMp4UR2eZ12payB0OvgF7xGm5pJOLUsPG2bbkutc7TribBQPweNQrezLEPWXAtM0HDERFpV+SXvKoINJPQYn5YUyAy7iSSaVzBBP79TKP1SAL4uGa1oqH1iPA91Q6x0/sMQP/eArXfRVn9y762IekiE++tsOxMcBnng3FT4axt95pNhnEzABEzABEzABEzABEzhvAi/CRlDeluk8ZqAV9RBEp66aIdcKebGExg0TlFrOR6+vsLUwh1bHOqTkGy0NueTfUC0Ei4CjwF56LwQO6k4Thlm4K/AW4RYR51nnWFOoj7uTelOvpPpC1QJ1lIhNIBy4+UHhW3NDVY9wAffV9KpF9EdVQ94z6Q1LIS8R+ruYqmg4rjvbilzbYPw87VAVSXKOYJJzSbablA5KU2YYUeqoFpzxJVG8TWtpklx65atLvmiX0RaUN3TVb1sfNazjbbNLmd7LsFl56iUX06xdfGulypvMN+e4WcNkU3VTqoiqJMa9HERXnoVvONfXHHQz3rTka6HHkjTnlEKpdtqFYm6shGcfI5cO9Jk7X6gUV73skIt2EjyPduEfA9TNI4JPHn1uShgGOefowI5wu2M000+6DKnz8IV7hpYq2y5jg5NqgnaLfYrwNeRQ4wyuKZ2uvRcN59jjoMF4bFhPh3hn6irfDLEoo7iEQx5rWU/H+ebj740gsXJrAiZgAiZgAiZgAiZweARelI2gxW/ysRLGKp/t5sTyhiOXy1gYvyxuerRYTqf0Gb/FVZx0WdxAUMQVffooDnLUG43MOWuRbm1Hi54ni8qI+u297kY4VjgF05gTzwFCsbxVPcO0d1SH2rUeY97f9DhZ4rxxzvkFy+jkDa9IdKJ9iPvL9J1nWUKS/TgXYVpClBa4mZOadeHUj+G/FMQoNAwULYxoqIy9pT7NoJZ2JOt6hg4DssqgFEk+8kzp1l7OJ1QJL9xyXnmzmslUh2Y8fMJNuj6ZrTkgnHa9PwSaEAQ4lLPeXkMGvWyW8eCVh+w0Ph+7dV0Zc5Td61xMjPRgl3Vu2PayphyWlaZyKkYdd+fS1DjKKTU8c2sAkrgyZUlS9xa587WSTyQZbebl8wxB8gkX/rItIUMHT97op0NVsp/XM/hGXjY9Vy8Qo5RkrBKgx66SYUyh5qjUaLcdazvYDFv5lOnJfsyZCbLOYrYtFcgjevwkaxrtcaJ6l00PNzfTtqadwl4rSuDUNL9uMdJkiflZrRCSh2yHUcrJo+tkMhNmT+E4GjXHKBTx5llSwTuC8D1BPkzABEzABEzABEzABEzgsAgc+UbQ37i0jds6Lnr7anfHbPJmKJXzJiPGXHDDN/pjgTw6i5sV+tEecUanOi7u2Xg/ACeFo0sOUtddIVK4ak91v2lEP5ywoB/xhlPxl49iKnfI18eoQQrVoPHOthgyfoyjnalSz9vK1Xy2hRw3xqFEDHJWMITq/aXd5LKIKfssIXyjg5ugaBEGm38HH3JcW+32U8qlxzLO/FLotEptxGRYzAyd3TmWsZcjeOHmt3ore8bNCqFfsF2G6c+mslFBvSKtjDlkxOhl/G0WkMlql/6c5CqHk+Up4tcMKdsXMz1Ut9r0WkdTyqXVMsOMlzSoJeyVXRguNxRCAOfVwXhKTN1iUKy3OIdWtWabNjUCr4e9PkVAq37tKT2vF4hdA0EZTtMvx/3JBO3GIX/E23cwFexw3SvH2nXYsbOlwOK77qoeyGt/aTdjps0cr2Yfw3xG0h+Fhimsa83cGA8B/tgZOJBtN6p2le1CnmEX9UKPEDgQr/whtZDnv5P/EJ8L80ZQMvLZBEzABEzABEzABEzgcAgc+UbQmVzeRvWx8ObqXMvgPiEuyPvqmqK5VB4Lfm0MDNeIFWYawgOLcY4lZKz1DTMtqZknOOg2NftTt6snu5pMsaVb+8pWdix5zAH1H3jI/UCjUA676NSY0de2DNjVIz8tUe1XBgy6lC3f3QB+PfE6uOQ9Ia3miSWOxxkbQaHTdgky9qhjKhhDnvlyRmmX9eU5/Wq/xlGMbTKWGY7SKRuS6luBpo7WrAe9tTy1yzNqwg3f9kMVb2rTr8rXMeZYPT2r5SW5xoq5y65WA99ddoqnNjfy0mPAobJGlPXudm1d66+6Kt8VDfZp1+uqj0E8ARVjvjNnW6SZNePJq9viicznv+RrYmk3nu8lBTeQ5Qa5UiFkt0MLu7yOStqV6wbq8FXIhXUMVAPKZdlr/z4e/j3eVrMaTwZyxBi+OIZdKazLUWjSwhyzphqC/nFi3YonIeTU9SsHHeOElrZxnaAsHfgQjaJStu1c/9lZfBwsPhqW18yMf2k0ZyP+///ud+3J06fb02fP8nG67PLL2xVXXNGuvupKlsJNn7AtpXCAMZ+B0cF1FRtBl/pLgoKKDxMwARMwARMwARMwgcMicOQbQXhHkD4atthwiEVv3nRhalgKa3WvFkvj6EPFFqtk9HV0O5lLXFve4SBGxuINRtfnhgMGmTtvPpBxBtyVDov15Y1bxmDo6T6Hi0DFFhZZHm9+ein0O+i0DldT6pZWN1NIkDc+4VUN0cc8eiIOlZQO0uVmC1Ul8WoW8tzSylLOasO0dMcgZHxeREs1TixSnWSvOzs8xNDnPLQ91MsIIfR9Ogu8cFPOzCdDSRWjx4wg+E9UpnfaZf7u05teGkeZT/qMIpnssq3PQNkv2/W7VfSYo3LFkgfqYp7BIiXjRlaGagGrQ1VceGwcxU55hw3vZk92hAAACfhJREFUoCNrONbZ8HGA8AgPzp35Zq9WNd+llXpVhxH6+eY+9MS2zgiB0xJa+aYMuinBCGNFEVtKGSJsswQ+/4c+ZIqCFs9VbQRpowJ+skE8HHoIclTOMw2F8GN6mfQaMBzdEnzYD6Ucew3dgM0icNpBRNduoNcehotZhBHdQzFew92X/qHFI5F9OqcDBUmPflJ1w3N5Dg6+iJEFZsvaoxstNoF+9uhj7Tvf/nY79Z1vt/97+OGesbWT//wv7YYb/6n96y3/1m699bZ27TX/2M6s3jXVy405Ri9+oL483g7kjaCB0R0TMAETMAETMAETMIFDIHDkG0FnY6m+sRE0VtVYBsdqOu5KuajHWhjDlHJhz9srrpZD2FvdTKVlUoHP+pDbWr4cw2qb99LqXEbKe75R5a9422uo9W+z7FHKnT9vn0pwdjXenmRImWGVZuHaB7oJm6bRGzrdNOet3LAZHZguopb88h0iduCqG0N5Klwdqw8n6WsPct6cVn13ehnfFhDW4Th8oeMApxo9hhtH2Az7VMqLISjaNfMerDuMMDFO3xlBaVWNXlPdVeps5SZjaSXHWMlksw5UbcO8DukiP8Xey2kYbnZUy6ZmKcFdOh6ocdR+6Kq+vDaG+c5OTkZlaGtH5inPXHokMZK97DbbjAs5yykO0qilTUQcGxVlatzoiHERZSrFK0FUVxqUs5y3+Awr2QxBzrEMs4iSTzq44hoO1VTPHuzG9SPES43mFhFYA07dIrp47UpOddcOU4wJKToHHPKlSS2g1HP26afb9773vXbvPXe3n/30pzS9Kt79c+LEFe3MU0+1x3//+5Hh3//jP9t//+//tJMnT7an+dagWTOMWHP/C5uXxUbQJX5H0GDnjgmYgAmYgAmYgAmYwPkTOPKNoPMv2RFMwARMwARM4AIiEDs3Dz30UPuvu+4aRd1///3tTW96U7vuuuvaX/7yl/aLX/yi/eAHP2hf+tKXaPPqV7+6ffOb32w33HDD8HHHBEzABEzABEzABEzABI6CgDeCjoKyc5iACZiACRxbAngn0O23396eeOIJzvFrX/tae/e7370x3z/+8Y/tM5/5TPvgBz9I3S233NJOnTr1nN6VtBHMAhMwARMwARMwARMwARN4ngS8EfQ8wdnNBEzABEzABPBunwceeKA9+OCDhLFrE0ik/vznP7c77rijfeMb36Do5z//eXvDG94gtVsTMAETMAETMAETMAETeMEJeCPoBUfsBCZgAiZgAseVwI9+9KN28803c3p33313+9jHPtZe8YpX7Jwuvgj6kUceae9973vb9ddf3+688872gQ98YKe9FSZgAiZgAiZgAiZgAiZw2AS8EXTYRB3PBEzABEzgoiDwzDPPtK985Svtfe97X3vlK1/ZPvWpT7X3vOc9e+d+5syZhncC4XjVq17Vrr766r0+NjABEzABEzABEzABEzCBwyLgjaDDIuk4JmACJmACFxUBbOh86EMfah/96EfbG9/4Rn7fDzaEfJiACZiACZiACZiACZjAhUzAG0EX8qPj2kzABEzABC5YAqdPn+ZHvPDXv/AXwn7yk59csLW6MBMwARMwARMwARMwARMQAW8EiYRbEzABEzABEzgHAn/961/biRMn6PG2t72t/fCHPzwHb5uagAmYgAmYgAmYgAmYwItDwBtBLw53ZzUBEzABE3iJE8BG0Dve8Y6GL4y+8cYb249//OOX+IxcvgmYgAmYgAmYgAmYwMVAwBtBF8Oj7DmagAmYgAkcOoGnnnqq3Xfffe3Tn/40/wT8qVOn+OXP+xLhT85///vfb2hf85rXtLe+9a37XKw3ARMwARMwARMwARMwgUMj4I2gQ0PpQCZgAiZgAhcTgbNnz7aHHnqo3XPPPe3aa69tn/3sZ9u73vWuvQh+9atftde+9rW0u+uuu7iRtNfJBiZgAiZgAiZgAiZgAiZwSAS8EXRIIB3GBEzABEzg4iOAdwHddttt7bLLLmvY1HnwwQfby1/+8gNBfPe732233norbT7/+c+397///QfaW2kCJmACJmACJmACJmACh0nAG0GHSdOxTMAETMAELioCv/nNb7gB9PWvf73hT8d/9atfbSdPntzJ4Mknn2zvfOc72yOPPEKbX/7yl+3666/faW+FCZiACZiACZiACZiACRw2AW8EHTZRxzMBEzABE7ioCHzxi19sd9xxB+d85ZVXtm9961v88uhLL710wQGbQB/5yEfahz/8YcrxDqJPfvKT7ZJLLlnYeWACJmACJmACJmACJmACLyQBbwS9kHQd2wRMwARM4NgT+MMf/sANno9//ONjrp/73Ofam9/85nb55Zc3fKn0n/70p/aFL3yhYdMIx9vf/vb28MMPt2uuuWb4uGMCJmACJmACJmACJmACR0HAG0FHQdk5TMAETMAEjjWBxx9/vH35y19u995772Ker3/969tjjz22kN10000NG0VvectbFnIPTMAETMAETMAETMAETOAoCHgj6CgoO4cJmIAJmMCxJ4C/IoaPhX3iE59ov/3tbxs+Cnb69Gm+K+iqq65q+KjY7bff3u6888524sSJY8/DEzQBEzABEzABEzABE7gwCXgj6MJ8XFyVCZiACZjAS5jAo48+2n7961/zI2HYBHrd617XrrvuupfwjFy6CZiACZiACZiACZjAcSHgjaDj8kh6HiZgAiZgAiZgAiZgAiZgAiZgAiZgAiawh4A3gvYAstoETMAETMAETMAETMAETMAETMAETMAEjgsBbwQdl0fS8zABEzABEzABEzABEzABEzABEzABEzCBPQS8EbQHkNUmYAImYAImYAImYAImYAImYAImYAImcFwIeCPouDySnocJmIAJmIAJmIAJmIAJmIAJmIAJmIAJ7CHgjaA9gKw2ARMwARMwARMwARMwARMwARMwARMwgeNCwBtBx+WR9DxMwARMwARMwARMwARMwARMwARMwARMYA8BbwTtAWS1CZiACZiACZiACZiACZiACZiACZiACRwXAt4IOi6PpOdhAiZgAiZgAiZgAiZgAiZgAiZgAiZgAnsIeCNoDyCrTcAETMAETMAETMAETMAETMAETMAETOC4EPBG0HF5JD0PEzABEzABEzABEzABEzABEzABEzABE9hDwBtBewBZbQImYAImYAImYAImYAImYAImYAImYALHhYA3go7LI+l5mIAJmIAJmIAJmIAJmIAJmIAJmIAJmMAeAt4I2gPIahMwARMwARMwARMwARMwARMwARMwARM4LgS8EXRcHknPwwRMwARMwARMwARMwARMwARMwARMwAT2EPBG0B5AVpuACZiACZiACZiACZiACZiACZiACZjAcSHgjaDj8kh6HiZgAiZgAiZgAiZgAiZgAiZgAiZgAiawh8DfAbCp0nlblyzIAAAAAElFTkSuQmCC"
-  },
-  {
-    "quest": "Si considerino i 5 processo della figura seguente e 3 politiche di scheduling: FCFS, SJF (non-preemptive) e RR con time slice pari a 2 unità di tempo. Qual è la politica che garantisce il minor tempo di attesa (in coda pronti) al processo C?",
-    "answers": [
-      {
-        "text": "FCFS",
-        "image": ""
-      },
-      {
-        "text": "RR",
-        "image": ""
-      },
-      {
-        "text": "SJF",
-        "image": ""
-      },
-      {
-        "text": "Tutte e tre le politiche garantiscono al processo C lo stesso tempo di attesa",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": 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yZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE+ClKnTH0AAAAcaURPVAAAAAIAAAAAAAABHgAAACgAAAEeAAABHgADHMP7OhiKAABAAElEQVR4AdS9SbMly5YedLK92dz36v8welXCaMwwjDFTZhStoChpyABJyCQokFQwAUmAGQZmGAMmTBipQB0wYMKAf6Cq190mM2+2fM36fC2PHfuck+/mw4zIjPDlq/d2+wqPiPPgf/xH/8+nb7/97ubbb7+9efPDDzc/vMH59u3N+/cfcH68+fAB58dPNx9xPnjw6ObmwUOln5ASBvbm5pNTwp8+IX8DGvGVPnhA+IH/CUYOqfEFg4MHOXNQQkfJGA71EwCePMz3CXljcy0ekcNriU9bdlo1/a6rS1O6lh9l70I4+rvM8Vmp6gx1t8klh/TBUW9q5phCwawrZK2lrk7KSuqMPKEz3ZgWjUKp3VKghFrai0m5hD+tSlevAcNuS3Whtuz6opb0k507uWup7ZMaDus3PlfTyp7aoShxIYxI47VQix7tqxZFTm42x6ynwEM9DCyljVbbd9ZQbDI34eITKvik4Kz6p08PHz7A+fDmEc9Hj26ePn168/QrnEi/eoIT6cNHHOvwFOf79+8xF3BOeH/zFvPDu3fvNEdwfvj48ePNp4/QjxOg7NBUmtsweMBAH+JHeXuauN5Si52SuUvE3GWdzSprKqHkklI+B1vE+NYPqJTRA8yCYo43zpSuGsPW1vofjDaN/h7ptBRrAw7Kym6/Fq+ly64rr+Tal1sVie2evNPnzxW54sQs8qiyK9z3R686v6+f91e9cU7/N8JJ5ku5MvVMOCbnONv6bBiqv2d+zVh/gLlB/2rsc354/NjzxOPHjwE/vnn0GLhHhB9h/niIueSR5hTb/IS54v3NO8wV79+/q/mi5ox3wGPu+Pjxg+cNTho8VgGydoAHwhXhtIKX0FAQeWnVRfPQkp8yzdPQpEcoKbkmPVKkF88gp++F5jmEOfMmjWi0t4pATA1bZ+FPBkrbhJ3pdrm6bFZeScwgE7pEF/5E0ZCnTbGCbc538TnlHyKy1PnW3/Yblzm4+Q3FvW26W0ykhmMh4crEGW6bB08n61BhcPg3G/CCj4gT3lt1nyq5jpzqq8xnbT8VPFgMFG4FcSupZZJLiprdKj149IOFD655ueYQHTz6x7TO6Rvh1kM41PYzmD1djNRQJNva+LQO4UKFc9AHjJMPIDPPuejjzZMnWA894dzGueTm5i/+y/+U0nl58N/9yf/96Ztvvr3hyaCJwdPbt1gUYUHkwMlB00dV9CMUAsoSNDGdQRMDJXRMLzBplM73qUpilQGViiOZHMYIqFzD4jBTIdnVLysxGC3OxAGM5JIOnUCFX9htQA2+E3DzFwur1tPQpZjLndJuqWxba2soCPq90GpK6sNdIt7QImBnl3lLlY9DhQNcs3VtDobSkB9fa2jOGLAPzhHu41IXK6o8qTI1tyux5DVSSrNQUy9lpu7AnQZanEt8ATIsPl0mfsADjM6kUrDowaZ0nRcfLrNuXLIlHBanx344o4DFeam/hBeHxeIP0S3jyYrB02XgxIApwRMXTG6KBzcfuAjaAiffXNGNFcwVnB4473zCDRbBzAOgVec7cCJC3uiyXDaAalHNrOrZy3Apshg3Ram2phJKLilFGnYbOS87UkKIPiSlDA/mS9bMnT/gYyEpRfcxbd3WSd04pNPgXVezdjhm/lhLeocWsd2TN86J/dLRS8wdtgf5x8i2mi7H9gO8GJq+UD8CuPD5DvV3kG/3ZDPWmV0nc6bpygG4MAK3i+sIvR9imRuUsh8DycCIcwHTBE5Mn2hxkeDpsQIn2uW4f//hnQIkBkm60fIW6wrebNENl/cKmj5mAbN7gxz9z4xJmEdS5/o68Ot31PLNA6+6qnZdGz4SlJ+ECYdnpkUfrpB6yLJm1j8TPavE1O51pDtNjSzN47ciXLZbc8HB7bJWjg9iCTcGnFNhSVxLzFreUcmpcGvf9Uz89HDgM5nvgiun36iVC2CvWsss5Fnh2rZkFssConikrX3vL4NF4BW+21QfVdyWX+qHwr0QB2kQl8x2O3jrs62tIfW9yl70RzRE48pkNc42r4SPqWDwFh+lpL4uDZOynGbmjqOcBFf7VPKcdypgUuD08T3mBwdPvDn61dPHOp/gphCPf/9f+V2l8/Lgv/5f/q9Pv/71tze//vU3Nz/88BaBE+4MKXDi5Ie7yR9w9wj21o4Tg6METrWj9Ampqh8DhrCLXrjVFFWpVVGuyC5SnEpxk1Y1hlxVxwrISVImCnd+U3glaabWKkyhxbNg0tuyaFcvo8NdiMQmhY/ElNl4awlPUsrFKaY4Uw5pDB90peNd2KEOH6yVaGtgLrRALwYni7s1gOGMZk/oh1mVHMVBEipGlhPALt4uE5WplkqpKLgs1vJqx4SatKmNiY2lQIDoc7I/ZRv1tdGtPbVDq8HESuqGeZVrXcOBdNofaIGy16UweeYHLHDkxYy8/qO3le/sN7wD9Ah3irkgyo4TAycHT19pN4p87FodOHkhxJsrvKu8AqcETAieeJOFTa3mJsx/TLkztfJ0bPppx3TlxVl5nwv15Ag0pEJSGvGdzlwoZNtht43ExwVWtwDKJNmPE0Sp/aLP6bqCrykRminhyqvSbOPOa1RI2hYWStm2eqcuMYA/IlcFloXiOOavCt5KOGq5bTjcqmgUYI41yaTz367g/zfUW5tqVGiPm4Ec9cQCe75FijqS3pFyV5q7zwyeGCw9YtCE8+mTJw6eFEA90TyiXWUsPt5pZ+mtgqa3DJpwQ5ZBlHeqETgpaII/NR+40pmndZ/Ll4Ov5h2lX52lZc1TWmexp64lN7nP4E0BGI55yhRuuHXURA7vWleLiBfYoS4l6IEYhZMSHC00vEGlc6iWdUoYx+tOjaqJvauKYlO9plQG175NjfSAxyWu+ympR/ox39qlro0O7QfkGaNwvlxaGMRUzkRtPt4uvZe3eM/c2/TfI7PMTmVYqyz8uQ5zN1ON+tNSWkPrD+9aTIDBuOIZ86wxoALwWqI4Ob8UX1K1KPnK5aUmwFao9v22EpL2YK6doVxBk3a8MQ9hDfOxAidEOiTefPXVk5vnzzi3OXD6q7//z12YePBf/M//56df/+rbm1/9ioHTOwVPnPQ+IFp6j5MBE/7r9KN3flyPxdPjehU0eQeDRU7A5AAq4QXpLD+rTf+QSX1Mr1JpxE2Y+VVVa7ItDHSHxgGX+tXgo5KjIiDC3wAthDEpcWeHpTcuZZbWEyFzzyuZtsDpokKoz+dsfPuZ+qv6PLEYb46T0L7bZC/6uityXapW4cpRU9UY27LEBKUBhioumPsoeKIW0eVh5zgOqMWyGm0qaLgh1h5ycU7AyqhqJ6/1D/oAyXfBK7qxe82cckuBSwdB/T8YkAMDF4sTFdxKUyvlnZKCQ1K6+8Sulh0nptplyqN6T79S8MSAio6S9z2DJDy++w6P3rx7xwURH9Vz4OTHblC8BE/sJzCnIGnBFTQpb1/Isx3pR7O8EwZzixACsRFSNdlZ130QTv6YqpSLzvL2wZalkTa0FhONAj1CIwV95KwShTYuwkxzEpx4i1y/RvumYbCT3jyDcAKC776sko6fu6oL7GfpnLW8671/Lgadqg9UgwZjXeG7v+bP4vxtqF+VW8DKD89gN6ZD3ufewXsAOd9GtufeGwVEerRXj+z5UT0GTU+fcnHRKR/fyy6Sd5r49Aoe/cfNWAZO3HninMHH93IDhS3uLg9v47D8oi/+pxLFseVhnAdhyREm42IW0yJP/BrExbuYFhADIw0t6SAR3M0CsfMx57kEc+HiHTwAiTYpDDMF7P+LC4COcJFBMHQNzWEr+8ySOjhKQTBKW+mSPwPKotT1GvUoHM1TwyXO9ROeS3oonRbPZm6s75oR0Ma0UZi5bu12OSuKdNIL9QNx5vMgfw64zA0fu3PdS1PajxoCXxe0HV3XD2VwSBeuNYR3zS+cZzLXjJQS4W249RhaBT402MBPkeqQWT+zZzBwyk7TB+yM8wbwp084ccOHO0/Pnz29eYHAifMbjz/68//C1Cj4wX/+P/1jBU7cdXr7w3sFT+/efdB7TdhwggEHTZ7cuJDCiR0n7Syh0F4GIK/GSrDklNVAOmWZslJSMcxBPJhVCUKB+/wwPhOtBlmxBo4kU8FWGMObWusp1GKOwMa6ZTYfz9jPcNAgdBxEzibDnDSMMYmaW3e8g6Mu8s86HPLNBugwFS37JFGm6Et8AaGYR3LgnfKgiBsX+wOEMGA68MnOETfzA9ag4oByB4EqGhgMNHNhYKc7V9dVJAAnk4q4lrgKIwvzErLSpS/Y9qdre9KqnqRwtRxyS9E0dQWe+gInnSLGXWqmZ6Q1ne8y6G4y3l18irss6zE9BE4MpBg4uS0YOGFO0KIH7zghcPKudAInTDi8waLAiX1knrC48jN4mj4PuNr8ompUoJSK+mftBd96gtn7JelNOXKnZRZ+9TnXWepu0TeAenfdylV/q9EOntKlhJd5UiHyRYs2YnkIbVDXOX+WyIHHPh3lhooGZexosckN3a1t47iPylVvbeW+0GZrCM22vN4HhsAXAtN9uy/8poqvlSzd4xq9S0vL5qrrJtKZ5TOANFdSloPzRN6J1KN62GFy4MTHernAwC41brpwR2oFTgiSfnj7BusJBk4+GThxkcLgqYM5z0oc0PRIdgXQF8w/xlQltldGJF+pikQ4eHIZ5nzRR+hIN3xzNDQZDPMaDearMrTQoLcMjXn3mozAx7xZJLPrJV8wsyaAK3So5pw5YnCUbmeMaI/sQ9Oa/eRncrINePgVdfcWPjqXejzih7l7gJJGVVzXclJPpfeqzJ1limSl103cowS/AUvMSzTGk1rfnmsbbEHTOm3qJTR/d6bmrNdW5xyioWU9QZ6JI2vyhls485OK2JdmWFAq4TLVGhqF5O+wAyfMQdhpcuCEdy75qB5OBU7PETg9583jx9L8N/7gX1wWAjz4m//D38ejet/hUb3vcUcIiyIET+/wUQgGTPooBDoMJ52P6jgOmtauEkqUR/O0uGVQpSZIAAVPIccdKx9pHua64mYPNwfpSwhgw5lwOcQmHHZx4rIkqFA135pdfbRRx8ZM3OQN00yXgFjNPXCT9QwuVgcEZCh7SiYMyhY0tY3VxdOroqNZluWJmnBXUrEO+83Xk5maQYRQIeD/KkN5LmVusvAtV6rN4P0kTZiaUCa+S8eiTVLPXRNL3cd8MMBPp5jZWE0c3cuOHtmMbdFNZxFBteoYYBqYPCWEQrBcab9IL3ojBjT1XIPJblq751FpCmgoaLwk7iEc4W5Tdpy4COIC6GntOPGFbz6VSz7vLvE9J8wP2G1i8MSPRfTHIThnQDtt3PukFz7mpKkKIroLAjgZprRhOV0HHK6u4ca0wkuc28NtY822c1GnU3S4cGnPjFs/V/sMZ5nHowE+gieu4ZgzpnJBQjD1tkSAazjh2hCgsUOWqC91tOfQeIcdk+9gusWx2BLLhRq2KJBicuu2Q2S+ELjF0meQoPa3pBlO1OitgivZKgG2rxh3nzgwp1hDZoChSif7GR/X48cgGDw9xQdk+DEZPdr7DIsMwMT7ZeuP2l168wMCpzcMnt6sd6f1eC8CJzWMjHF9Qb88pm3fbedWA8b/wWNqp3SxcCpa9ff+oWg6WddhGc2O4ZX8lfpxJ4K06bzGk6g8SmbkmV7tVr/l4t0nhq6OC92xhDQgeSYsI0YM9GQXR3uv2gauvB5CKUdSCV69nNhMfW4yw8DCTwuGde3Ja3F+DrC0wuSCh4Iz3CCfg1uZzsoSMWovC7exhf1LpVuhpuEJb91nWdYYQ2fyP/LsMotxAyYPJFZ2AYM7uObTbxbQsikyM8PygFu3C7kVdVjxj16oTPfTgRNGPEwxYOLjeXxML4GTdpwYOOFxPQZNLxk4feXA6Y//wr80LQl+8Ef//f/66Ztfv8LHIRg44VEcnu+wCEJnYR9WiiKuwEkV68fxPqlU+6N52pGaPNChIrDzEcBlVZFK0T6pDldWzGCvlML+Lx1anJEX9HCYnlxNXVKKy2qB3UoLN76Xm8uZAxAbbPx5NH5ij/DORd/CMeABOsrYpSQBlIu1FKiOom2md5eJ3G30aE21TOSRUPW62pRqFs8CuhnVSPZXV7HkZ8b8HFheEJIjPzpUzKN1Op+r8U0tyKZKCplmgGARpaLhxdIocSx8TA5s98JwJZ3MVqi6ArjV2WQ7hae+a3AETY/7TOmfFyhYqIPMvBZDiIwcONWOk3aesuPEj0OwLXATRUGS7xbrMRy8r0Acv6jnbW/uOHHegGakHTzRJ9tuHPNyIw4rtb/oCwKcawLywWdOmNVw0NTSs5Ybe9b2zekyW+Vel8tM3GkHR3u2HUHLTwLOrOuVwMkalmCZTd10VZhQ9sAeCVdRtBS9tGzdPrgvmMYHqTyY3s3cStxZ75Pb1CGzFjzVsiO/t/99lN+TB2btxubMPYUn21aLi8BxqwPJGceyWvOyec15yX/AyPcDrixTHW9mrcBJO01cXHx18wyBE8/H2InyY7sMnPChqTevcXbg9E47TrzZwsCJ+qQUBan5gThTaiyx3Qozy1O4cLsizCfvVzvLijTuF/JiLInZcl2ZZ+Vv3Kp/aWitzRHc/MWNFLkAy+QuUV6skllLsMwZ1nWgUy2pp+ZUCSMmdbaYa+xXWjoP2JNOBsZl34Cuu5rlrwzrsoQKVQI1l8fuhcEmtKorUFiVHs1BJvQr4tfRd/aniMZCpSc+hPOLpTEphdPghHdrs6+wMfUbX/I7bZfbc9BP20h2mWt2xeiug06roW9hyxNXPhR69TP3cRd0Ky75F4LA2ckbk8YzeOKNYAdOWLsocHqLlDtO3AXnjtP7mxcvEDi9eIabQn5U7z/7iyc7Tn/9v/2TT99+8xqB0ysETXypk1/T40TGxSwnFwRNTNduUsOiLzx3mepEST1tWJ5jI6cAcB6rW2UTPvXlwvKqgguoCQjKRJ2DbsKlxwkbjE3iZlk4ARNniu0Z3q5XCeDa7h7dxuh6kF7VUXm13HCHMp1XElCTokdv6oD04AiXkokiWodpTSrekJkKVXgk6caWKUklJ7KxTSnwFPcobBvi4jm6zTj1QVLZwlVSJW4lgpaVC/xGkQ5i2BfJWkqXVPIjLXDTs/gJgBJ2wNO/hk+kawL2RLUUbJrPM1PXhMsXV+QSXZoBCGYH0svYDmhYEfzHdvAnQh9iAVSP3NSCyI/q+at69JcLonyOPC95K3CiXgVPDKB4QrNsMR2nXK08YNGWxwSW14L3UsJTF2RJuC1XtoDVs6RjamzOYJOaIkkYnXaaY/RZII030DyXPrJZml4lUt3TJvM4VZCiyZUdF/ntt5t8Mh+qBKUxwYI0ag43ra8S7KygXc+BeGd2eh/mM1xoM/0xlpeNUyWFVMXt/WK2yvTly8DpB19AGwqIEXOiqLAnJAUjS8LlNpuULYoBUnCe1t9ktQYuOh7VJ8i54/RV7TQ9f/YMgdOzCpx8I+UHBk6vX9+8RvCkHScEUJw3PvIGDOYSmtRnzzHgOAQ4H6yDLmkgsi7BubXhdJbrjHl0O3PErmMbPMHvvwf5CW99BU2/6MqwaE3ma7lltTwIPRxMccaNsMPHoJKGtNIiKBlMlAwuKWUWSwoH3PRffkQ5mKeHRM+ih22lnihtI4Llx7Ir5j1H1GIvAyu/lBu41f6Bd2av6SPP0nnp1lSxw1DYOu8SbM5dyf+3ue5NB7saT8S5HOE7pgepk+yshwmfsFa/sElYArvsMUXGsK5xSzzSJN4znca5ttNAzPF0sMS+zuhFeYwB2UWqAEm7TlzT8MkZvHOJvHedGDg9U/D0DGsiHn/8F07ecfqr/83f+/TdN2/wd5xeY2LDByFwYm6DEk5KtbNUKUvlCSl4VtjcccLMytlVARRpnpz4qJ46rFJeQJrHyFPKB5E+3dlryFfPZ5JJYE66wa0WYLOwxpDux8SZZjfmtSSM2sW33GSY8Ma0FXvO5XJPrPTVMva55NfEV76tOoitSpMtXSWNJGVPStSAwzhwm/3Qp9PBSXfpmvTycbEBmO3Uf++h/ejnv2cXYYtuBZsqDzD5zLtJxL3pX0l6uJIBp/jMfMJ6Yit9MDan1QlH1Lpp6LR+w7alU8+Ew0Sc8dEeClPaYbMymHHwhNqs4IY0PobHBYwe09OnyPm43txxso7sLvGRPQdO3O72jhN1Z+eJKds5KX1gXv/kJiCk9rhT8hnJOYbHsTTIsyA8IuxcXZvfbboRRybU5idRudI72yZckRIv66w0Ni9wQRZNvIE1HmygxwHyA78Kdoaj8jP9ZVQiZcvvmzoz4biyO3qidDHeDsxmmPDtUr8l6lkxMIizgN7nmzPmL+dX94kfp7P7ya6HY+n6cdY32c3OZIgr/FYlR16OWS88/Fly/9037jJxrnj+/LnOPKrH9wW40/Q6gRNgPrbHj8p4d5qLGd+04W43ddM9lasmXtchxxQdwxn8GARp21kXXXrqS6EoH67GuUqcz0/sYqOAMo2hhvkblZlK+Kgf6U6PHqaBzTx9jndDzQJFw2XxFED5jRZ8CpVUlqftgpfC9kxeTtbdC+RgM3IHNUaHSMEJW5Hr3oJnZkw3b9q+crcmZ7qmwBoHly5Ntg1uXz5DSBru8mYz80Uzs0+1YvivIuAi11ye5m2oZa5A2wLJeq5wNhodhpyZH5nmFFPRySSNpXb1s9a0QW5TFqhPrXeU57YP5huMAe84ca7BzlJ9IOJjBU4fP/KDNQ6eGDi9fIkdpwROf/jPb/aYefAf/ld/D38AF4ETdp1wQ6gCp/zgJEBCKu8ZFJnmwIolI08FSxU0VfWIl0FTAid1QF5YvuNRuKorUInwaRKu+p80CjzpOqepNwSk1ObGGcjCE9PWmLMdQju8EUxeV8tclVx8ExD3MO33mMzRnYr5YhoTH7H+oaEWlt0p8ZfHMBJdlaqNJpnCauPYdCtaZ+G2wRJrpJ3Qe7YR4yGrCrZUyYLL6l2XuSaVQExeTZs7LMYwFztJw5FyAl8jdMkcWSOiNH2N3A3Hg4guXUs2lLa3SBfAlDYcabGOtt/wpWdNOCTWLlCnnEhQdr74jXHbO071d5zwzoLecZKb+DtOelTPj9nwi3q6e1yBk4Im6leefRKBFHzLj5T6KweR/3dKt8pXJdW/vOA5lij5pFOQcOPTokeOzpOj+Zd0OSNKXSZXZIhb45R6imnVdxsqEgteylkvq9TBk1Z0pgJHfjnYtoYJgHYgJkzDPC0V5dwmEFzri7WN7Z6ZHyN7TxP3Y0uxWB9VdiXVr0zmdTHeT+/ncnFcXZG5d13tjbm0dd9ZqAtgs85OuXQdrR/ycvqAg/b8xrDPM3Di43r+IAQCJwRPz5/jDi2CJz6q5xssCZxeecdJgRM/Rz4CJ+jKO5aaH+CjLKet6Ddq0eOMZWAx5aCuW1BE0jhYfpfC/JJbxQqO5SLcOim02KTvgMDvcNPbA7JOvERxseYjhfnGkas9suQxv+kDcdELYCIQl6SUWeuKbf2QMgwfJBQrTuXlYDHWjETL68gVX7JOk6PkhK3JXdKCuxnzri5LdjEcdRzzpddJhFZOAPQsi+fiO3/l7MsUmHBE9lIEez39XP6p6cz+Tj/nAJYEmS6O4cZlT2ydg83IqQOY22SjRUOanAA8vA2TLpyA6i10j3zE8Vh+M7N7ox4tFG/IEMjp/AqcgOeY8CN5vOnLd5zyqF7vOr14iUf17gqc/srfReCEHadvcOIroQqePnyQ1zDjHSROEf70OPEOnvLuUwdNpmE6RCHNw9JiDaVFlIMnFChlgqbtAJ4afJCJRzMbg6v/F01M9buwOIxc1zROa1+kYZE4azB1g2emhQsC8YJ+QMD0ATMKy8akKnMYro4VWwfX1w8NKxdyktxmmggyjTBSgemMhQ9ZrMyQjtSOGGaehwxNASKTRzro/rElHYeLZjjX/EAmT7aogkCVqqCpYMJDmOCBpJ+20mnSMlCCqQtmQVOZyRPrxXaRxJBTc0+Z0FtQmGWewMoM+FJuL1TTWxo4tH3ySWU5Gabg4SN10kcY/zhZ+eMQD/ySd30dK4si0niwWhI4ecfJnyMnzrtN9a4TAybtZkF72Zj9YMF02ZorlRW6SGt1BmaaY9KCS0oaj9mmsWOKqeEJv2nK2alVl5PfXNbO+vAYsbe6ErerhAjbhUpxKjHMuvfheiriwpls3kKW7gsDIkcbM12HzIFfxKMc8zhDJiuOqceYH3+9TWfX19G/H2/XGlD7W/ljJ+mXstN6pLkL1oQFwaHbKqX4rrNcpywTbNiLY8pN+Mh4RjOOxeKuEoMnfRiCH4fAjtOLF9xxeqa/6ZR5wjtOM3CqR/Vwc0XzEJTpS31YK2iu0HwBX2QKo0t1mJT4lOlaOssRHuII4yy9k2vuxmbWqJKC31AJLjFjFxfwE7PYhJ9ehKKxH9WFdHc54zaD5xCXhEVJXkUDCyU9H9XvgFRxfo8B43veIX44Eb5iD2UfOyaaVgJKcCmBqEla6irZsat6QY29yT/bphmmjsBJLW290Zi0NS/MJraww5tm2H1pvGt+yE5SmzyBIpP0hOUqahqZ8BRIb544wjv/bIOdUnJw79TDC2RLux8e7TovLnRK9ksHTwRIK5zB6suLJDpJ8mbZDjB7NXBYg6zfXHqPQMmP63E8UIa/uXykDk/M4PTHId5CLO85vcf7TRU44ZPkPP74D0/+jtNf+Tt/8olB0zfaccLXsxA8OXCqnSRObCppgiHi+YMEdzSZNR+DKkeO4SUPAidc8kcx0xKqL7mFy2zB4MK4UvOx6MdjLchAOKNnEj7KHfNTdoNn5ihEm6KfMQ3cVmAIrXwa1LigPelVLsiy7R8aPxLliZjXYav4OrGCWQ9raC3dAGSUCHbkkpGjYUJ6Ycb8q0BF3+pEuOhIIeLd8qR+G63A5XG5gonErWnsk6nKUCgi6iTRh70q34qflPvZ7HpP/SfNj1vbhs6h/8wXezQljIk3k57afKCKtkxwkVIaJPi0fe2GAQm3PPSZYX8cIn/41l/V85f1/HecrNsLIn8cgp8Vzh/AdeBUj+oxcJId2qqaUFplWgmcWjC9pJOcUxo2jvkcKUjSI5550+Y17RFuc3Wf2/DlU1sIXzDOsxkJCVsXNW3YSmkmbBWWhZN+ekTAp+cuEUoKsCuieJayKl3lm3shuv6CgkOaow+OSZNxWY9ODyL9JdLb9MqDbUx8CYvRwbLz/yz7hMP3JdPZJ67rdTvdVjPXZZtyTf62MkYmaWvboXM6fxP8cYjsOPk9p+d8JwA7TvzjuJwn3uPvvfG9plevETjh9OfIEzhxrsA7TtClx4SRemhgVKhi3G6kqzaRalzLpVm2wm+OH+kkFi4dXfzGuR08lq2m8M74GqaBy5wyuS9rbO95ET9RVz9T5WcYR5rfEqVgSz5Fo6SkRWOGawoqsFcTJm75qoUk+c4Ot8tOsWU3hQyYXAqJGdgheoKFTPxI2tLTNmSboXROS7tu128EkrYrC7PEFgZME6aMmayzBC760UFm6W2blxBlDnKXTFcw08CEw25czURBXqTdD+HHXa5cY9nkYFGmz3xCTRaaqdaWAMK/0+hqaDt82babAyhH3RiWBtJ8PngQPMck18284cvTO07adRqP6r3UjhN305/SAQRO/6zSeXnwl/8uv6qHwAknvxLagdMjmEUAdCVw0o4TOpF3nJwed5tYAZwMeaObC6rVQAAvqpdlXEcySUMYgz4opZx0GzHA1ThNnRC9aO6GgrWXU/eUNhzbU5oU5xvbkL6gAA51GHYk8tYktjoTVci8fWA2hxalcIqLVtcI5K84aasyAnXRVVB6LPHljCiFN1fL2H7y8YZ5nKt4hpUNLmlEmC5ce8U6WGiWrOpklXHKX4VVm6LuP13xu+lkMja0YHY/pOziEh573DnnY6XLA8wyEyBplDd3MKOigGp6S6KUo+0bD/b0KUlClnyTF+3M4Ig7SwyYFDwh5Y4TT+KtD+8+YnLg17D4x7EZOPGxGy6S/G4TAyYuiHiDxH3SZlAr7bKLRBcMIS1vq2KcJ+6Ip0DhRhpM0yYfYR6x5hyvttDSTQGt2EN1agnJos6cQwpAdOIomPxSSGWY/FQJgJFaPeuFkE/DS8j4RUdWRlYyGS9K13dHKVSnjbYiOWqli1Raj/nN2BfPwAe78cU1S7EKc83ANfwXcOUu1YdKTq/4ApYvVSxbCwAP4FMfDzxLm5l5o4WB0yN+jrz+dAHnDe448UzgxD+UzXeaXvEdp5PAyTtOcEHjhsOi5gn1+TjGMcWx5l1vudITKLLkC28cTT4p8TUTS3biS+ZCp2U8NosnrNtom3UVviPukIctYQZaHnEiwXHiHbBhrtkS88ziA0A4+TUHaRI78IuJunBGALmlH7ieO0S4zAudXzZmBlxqN9VWM65FTZFASWkGCsjoDf9RK/PBJS0zUNTjadMqhoVZYgsjb0pLJWZSt4w9sUeY6ZRHNqRdUeUm74RPmW9BxkjSyWocR86dh1w486NxLvtBU5NBmHY4Zg+8Ixtaxn2YFUhRU8l2ek1ZHEhKI4DlLHE+LU147kIxiNoDJ31Vj4HTDd9xeqcPQ7z8Gh+8qXec/tYf/jOQ2Y8KnH44BE6crLiT9Ail2XecEigpcPrIDl47UEhdAb3bxErlpJhdJxWE5QC+qwQI4XbHzpE9JI7c5D9VQ2tpiUshYGrSFtQM1lVVf02x2Ft+6mr/h181obHw0sw0EyFpytuHriFxApkUmlGnqFWluFTJ6eS5o5mcpFNqop0ZnAtH2D9UrjMRQHZqxsD2c97RVr8NOj9I19wari79qh8qQJlkxnWXAKpU35HYP6vPBEyR+D3pjZ2Q7C8rw9GBa6wheQowuVh2vmwq2f1YKgWYW2DYVqUOGhhKY4lPK0TteXV/9pOlyzx8t4mB06NH/DgEX/R28NSBk63w+h53VPJhCO84nQROZcP902131iXZH5eHAlIapjjnD6cZhY/M6i/g7aFtHSzZdkhoKRGprGxsyUTLnlqCdv0PXsKweHAR1SzDH2hcd8AcPFVv5pAlsdpj901tt9qJEk2XvThaaVOJmByANYjKsSXHum++Xf6oYwl9FnCp80y8fGhXzph+BA6KlyO/NSPn/t1lbvkV8dnKwX2BdNlZAJQOePk5cJM+XOA4W58j5x/AvQic+KjeE9xM+agbKvk4xKtX3HHCJ8nxlT2/F8nfLS5eqI8n+iNvtmCRIC90qVHGMcbfo+VeHE4qLdLlC/GhJQWK80npbT5DfR380rGMNgugnkPP6c18QgdKWP+4laew6/8tukHW4xkTI3f9Rrr+yErPWZfUrvXEgInl3aDQk1cq2oK2OtrryzzETW9sueXLbJWr8RtUZd/1RC84RZhzFLSWjPXICsBjWlYkv8+bRVFiu0O8SmSeRS0R29jqovwrhsskbl1Qpu4JXzDeAxEjSacIcfXbNNHXYBduUa3xMB/dy934knSp3Pqe+yE8bKDoHOtHGfIRF8J0JPBIVZaRlyTzPDnnVMpH9Rg86ct6/LMqfEyPH4fwByL8jhP/xIJ3nP7Wv/dPg38/EDj9b7Xj9IN2nPiBiI8f6vE7BE56t4neM4BSAZBqIvKjesQlmMr7Ty4tC+sJK+89qALge6phucLylPaFEyDChrrEbGRVzREzG2SnuSprxthkXVZzl3tHUeXVxcqpCZdm6IzHSFVw5msiY7VqUjNNZOKkuQBNGsbEgbUwXQszWNkGQGxSwlMu064HDizqLBsFr2AJjKKLHNvFL97yRGYGvcwuV+IG0oUrUSVFty90p2pLKk1k0FTQkIzNgRJovPkDT97UxZRb1hey3EI+UFKzOBfczLEdyJPeQ9/LPpIFS830y3qXvY0EhTHVQLTK1mAovAWkBhero57UpXF8VMaB08P6Y5YOnp5iAaQdJwXR1PUJfxQbj+lhcuDi5yJw4sIHurX7BF7WgSzZjRROaerAKTwTT9qFnhY8+z3nG0m7JOQxhKv+B7+ZckaCuyPRFu6jNPMTZ2u8+o8yExIPLoKZ6oQdEtaB/BqjriPXi/3xmN1963ojnjnqsMKpepcifVIDM/WNkEWveo182sMW5jU6Ju5+cHTfyS0Tv7mdO/WT4d7O3Evb3Uz3Kc6pT2rpu/V/LodsTYMDXr4O3JUKU//GDvTFo3rccXr5HO8FeMfpPQOn9x+14/QaQRO/rLcFTgqSahGj3xk0EeelOtNPNcIw/3iksdBxNmnhluvBMw1MHhyZP9T3iTjQidpwoS/l4uBljsfbO9elrOTpg0ijZKzc7dhlPV8Bx7WC+Ey3GHBAEq+8eAYv8zopaLxU8HIwK+1SDYs2sVgJaN2nlLnjLEpMlelElhI8rDeGV8mKyEQlqV5o2IFTZMwaPudCg+Flmy21MhFSKmxErvBMgeXzpm4pKNYiHtFTUVu/wH4eIkaSTmnium9NyiVcPitJ3zLXVndbuY9apg8TLj6gFnb1X9DQWYVnakCMzdtypsfudOYII786bmgpF+ccnPo95jtO/D3ml4EdOCloyo5TfRziWb3j9Df/4M/F+EoROP19B076OMQDLJDqHadPfFTPO04sWYIjuqFdJvlYMHGf8APNSlB1sPg+udukRVWVA3VXtFQMGZY/C6Dms+McC85V41Mq3EknbZo1fXKluqcbi17A6lzIN7ygMmZmYn23yHnB8DmB08pTqnoKiyQ/arFD0tKmDgKta2E2fCDjxVEVtHQzn5MgO3DlCde/rlfT+rO+MdA6us8SB3/orB1WJr4vHJWHToFWBTQIUlP1aZUbO0X6KIalMz0oKTnDY7PO4Vp10rri1DGd7pZf8QiswUyu6LQm+LJcWEBYkMbeQG24aDnS01sn3ThaYfHcNqAPE3z0xoHTo95t4qN6+BstvKPMwIoHxy+DJu468d2mFTjVV/X0d5zAo8f0kNKEFkKSziXlZXuUXqWNZ/vYz+KRr6RPHupz3nNN9De+MaOwVXBLxoPmNNR1Zm3TDq350UW9mwEGVo/GCVOeD4c9iqowXiRmnLJe2AhOzTOkRLOrrEdQXCFGgdoHpVhPPva6oPE+TUOe6oSnTMtaB/mPxxnuyHNXvu3cxfnbpn+J0vxmPqaV1ARXVDTPkaG6wBFd+S4VIY++yapGF+KCJtFpN3BS68ndYc0XeEzPX9Xjny14isdZ6lE9BE+84aLACcFTdpz6UT3emOVX9bhgwbm5CM9SSBHgmP7zgnP8/rlfRxi07WA+ZxFKn9WHP+kmXLJHHPLQ0f42NLFTyuN2YgY8ymIvauRqrj3X7RFEWkZvwVUMrSFAXWuICpbEDx7jS7cWYIHp16wLwCIZd/nonskJoCjdh2WnNtHKlJPWrzzqosiVlvSqI+Z97vNbaqL4tzLEINNot5dbTqIbxkyH6+qWizU2D4z3yi4l4J7wiTDMXFoKJulRjvhJm/DknbYbXq0B1MVcMcUXPPVPeDEIEEUXKEbanGhV5tX3wVoEJQsmA9XEz6RSfYKX8wuvMkmkAic+tlfBE99zYuCks3ab+Lhe3nHKo3p/4w9+L8ZWisDpH6zAibtN79/z08Mwh8DJdyuxw8Q7z7XjlKVFv+OEUiFoCl41oZKytNSDOA+XdMCxckw9Qb4rQ3VkyeVkgOYKptJU/IYO9zE1U7AzN3GZokQvwkYfPq9Jn91tMpVwOuF6SQ2FVIMyrUmuJz175CKBgZNLTSStmnaY88nH9ni0H8qOS9WqEsNrMcVWKLwei4BhZ5k2jcr0vLl8GaoFSkIc5ZKSwMnQ5dSFcaWndFYxgQRjqRT/gotfSWweccZn0mcNRdnWpksMpbyz/1iLrxZc5RgF2et/crulODYae+Z/U1P+vUM1vUpZpTA+2jtFycEYS/EveQdOeGcBj+vpMb36+03accIiiJ8qp33KcafpcseJd2wcMM2PQghe9RuAVm2Z7bDmC5FNS/uoa4uX9nc5slcPLUnrVOb00nVWrSDrl6yxbi+jNbbY933/2/2FVaO6xUUeImhiXkdS3eGifS4UWY8JooBSIYmzSF+DI4GwGXQtXmCbXdadja9dAvuq/KzHGm+USqmTDsUFrsJcku6Fsfz0+F5iX4zJln9sKX68O10DDU2t59hwXPYTUrpUDanXlFjrTD+KPqW+i3ngbZnFC+XsW5wPescJgVN9hfPlyxfadeLNlvfYbeLjegmc3tQfwOUHIt7hxotvsoxxQCMo3O4fS8O+S6LhWdaGxUAmFroO7rAOPLGg5feg9RX7RXKQjfziW4aAmbAZtrF5SYZIykNPbKvLWcYOtjyfU1lGacHVfl5DQF8FRdSXdUUe0wuNeR0XxSzE8K/rzCLsg3pbpFTsfbLKMuYWS7HuAxE2n9OCFxl58RKfdlTPQ968YXVNBJeU1OncMFyCCyORlYvaizS+J71g+GzE3Tapcv2WXOhPWZMeGYJPeqQnP/0wnHUDOfbxGJmZTv0TnjyGV1nS94COBNdea/0VJOmnMP2cflP/zA84DQbUGj96RM8BVD4SwUf0+m84+T2nY+D0n/67v0tD23EROGnH6T07LYMhB0/+FHkHR+y07PharlfQ1B2ZQVZKTT42gk9ZHmVjXpwXOCNY4APpIt+2pP1waT17BV/mpp1IbcqANH5cUTDljAIMoODIdglQQzWxeTIjI3BacBUNlZGqU70wg3rWbh65h24HS9ZhG7guegFKpAnSVB6vDGjipg0ZdepOzM5MdvIJUOrdJuLmkby5SZEf8YUpTuI4GOVraGLmRSzLP5NxherFOmFJxK4yuCRPRvYcHkknHD4xgAN5lb/yixzLSOW36brSxPKM9JkPX+SdZ85jBIDkl6EIVGq5pk49DZteeVV4lzY0pjpxmROh6Lg8wqM32XHS54X5UQjuOD3Go3rYdWK1qMWg/z0+BsHg6d3cceLHIRAM+C4yAyhwV125fmSpykWY7ZGUaMPTc9FXMUnHfLLaMzKWzJXYawc1sBQ+OjW+0IPeHoXmfs33wVbwBCZ/EQweAJ7ntjBRu2SRyOCJd9vtQ1Ka3uoqdBFYn/RjpMpawh66JD36AKk/uyS6GcJ6lh6mNSaSSknGiXVZb65nuNDuk1repb4P/5fmsWVVyZdWfW99s/Sz7c4UTN6iA3WCBbHbpts/+ocEOtHItVJBoSQlcsLIygx6P3eo8XEI7TjhxopvtjzFHdoXOvmO03vMCfxoDAMlPqb3Ro/q/bDecdJdXt5AoE9lZo0F+ZMLS0TDKWNS0gMnLRn0adMOePDH1kY/FNNajrJytQwwORMybl6XwAU79AtnOyojwNl+S1aM+dWgEGGnWkMQlmylWF8kYHIKMtccwpPVsPTbfJviAAFZCssK54p1gKanh0gDnPqMd+IDvyVKjnxRsOC0Be25NGGR7a0NqYc6E0Q1p+2WnbI6qYaXdWQNT8wlT2sItMp5KRiWz0jvUtJ0z1cp35mJM9rETfhMnrjYS1q4lQ0QXcd89Iae/EkqFvZP0xIsKQuk0yEHxOodgkmL/aSDX+DAB6wG9C6TgyaH//w99o6T3nW68ftN2nF68fTma/wdp2fPHkvrf/Lv/O7R0M2Dv/x3/sGnX/Nz5PiqHoMmflXvvf6OEx7V0+N3DJ5YBHdePaYHDDsugyelBXNXSp0feacu6hxoENBBjnWc4FalUQDMqQfKTDh2ZHIpbKZ4eJSijqVHAY0RC3ehkPxNzQJRKKBN6R+DdiUy7DSAZath7kIleKJJlXt2JAambIdMMlJHT1qXYFRyYeNMu7CVJZ20WkA9GTimspsU4snbMbie9h2qyXbQL2oXW/745V94SD8nDczOzmtwSNM2VArXzEUA53YwnxPkooc/qXlasMu9iZtBjloyliPJug7OcChJIxcupvY5vlwUQaLhYqZ1MNdjokpxqMhoT5WFP3jq4KEmh7a83/QYO078OESCJy6AuOtEObcXd5zeasfpLVI9qvdDfRxCgRNrYAZOyMV1GaOmeaYujFv1IR4anZ7vcqCOA7TYGdiAlPQxmVi/ySetukXFRSZWSeG/FTahPA6amLouWUQ9pseUBtMAsuPdJj9T7XryQnHU0XKDnlUGFcg6VF4wISGUrvqVdzIqZzwWca25OPnyzOMi9bs6YGpE3rvKcj1BhXSaLp2hWsEqYtBJP1d/5O6d2rLMLFvxZiFKG/FH3DR0Ro+uyXcNNq/a8RrLwg+9AEeuOKafbuWI7votvOOaU5qX/mmlYc8XqBm948SbLfwcOQMnvxOpwOnFy5snwPHLmwye+F7Tm9c88Y4TPgyxdpzGjRb1bplpW13/LFPKmJR+n8Crz4WWtMqZ/r5kQYfJZXUBB7lRTYtFuD3Xmow/UqNmGYQ/s2yuX9v2NRqYZr46wJmrmHK+UYqE64kFM2+6RjiVM09HUtQ0rpwUg91UnRaTTTtwAp4BFA9q6gMw8MJJtinGS6BlwJPf6OakDurM6fWG6yo4c9s2cTyQBjQC13JypYtQfoZO/IQHHykiYfRcZ9kFPit3XenWLBc6LwoLjomb8IWwETDdc8LRj+SZ3qXrLrrNrfKo/0GG/50YGG7O/intupCB/sS3TcAZkUyHmcap8YjgDRvumSLVJ8rrAxHrMb3accLfcfoa7znlHac/+vM/K2WdPPhLCJz0OXIET3xUL+84cbGeHSfd9UUpHTQxdaenKx4ovRul2lBls7Tkq+LOgojSTkDJOlJHtJJKmuNwsC4ZmfOlcdXTrUeeNE2au9tEVLpPDVh0SKBclQP/lPOiaDMla7ThBkONsVXZgXQmcGJjWm51MiEYNDF4AjG2ANg6LRNiJI100W3S2nJNzTIvZ3B1mjZjEOE7AZ2KWw6Rmzoow9SHIVOIIVRVL3/hlvOsr3Xaa9J4pM6cdcmELzPCD5vywUjJ+0Jmn9YTP2YPKIWRQlalou6CpUJ0a1EhVoEiCB81Ml3n4gnpkEYL0YE1fuSKLkMiHHQncFKXLsyLvnzTSBS5P09uHHlbS3ZLsPDnQkhf1eOjeofACe8ysC/ovSX8UjJw0o4TAyfcTWbw5M+RM2Di58hZF7TDtMuavmXvWV5645TONpwSIZWz4UlKbsKHY04OB1K0D2/AQYupjU41JkENzZbS4xkk8VE9/EOdrN0mDM0ET3TN45lOQK8UZFwegydygIfmK1llI45o1Wf4WKd9Ciu+1AeN8z//wSn4aM8R7rFvI5cFSep2LVqq/rodZN4Xiv5GB+1FsJUs1CUpmN9CaquqhqU9nrRvw2FwTXyEIsN86BMXvmtpeNnygSdvdBJ3SXe7TX7CLeP2Jm5qbz2ZY8nRB+nm2emRc+ou5H7PHacOnPwHcPWoHgInPaqHwInBEwMlBk18ZI9wvqrHx1XznhP92O0SkzKlRJ0ntY8Dfs0DxIfW3Ku/D1rjwJciD3pLn/m5UaeCrQUml+D6HU/p6Ovsm7vndCrzVWDPKQmU5PhaS4B3wdBcjw8vHB3g75Z+u+QN4LKIxD7VDKj6rLm46ob1xT8tI0+qvjVvFJ0anC+dkaOp8AfHvHDFK3eII0Ccgyam8UssoplHtiQ+dZirFFVmORgiUs6nyS4giC313LuhvkDmmk3jPW/fZuaszOQPPum5jh538SNp+Gd+6gp+4iJzSwp2STAtYHU95XfZ/B4TGz63aewP/sgP0uriwuGixmbn5Y2FCp6wbtbX9Ph1vbXj9E7vOClwqs+RnwdOfxuBk/4AbgdO/HtOCZqy08QOHPh814kF8Y+2q4ilqYEHaE1SVbiUlVIeLIKq2TkkUgvo4IM5WHPzCuKgL7wYNbSASrqowPjflF26BVwqpYQPQACVh3PChtQmAAWJlG4qyiUMf3I+rIYkjSaJlw4JQAWDJtRrVYLsSm0txuSFJ1P7RGEwiIewtXVKHI70RqRZXDGdcGQmLnApGdqX11Tufkqmcko7ToAzCbHuSOIlrgZa+bhOPfGX8Jpwm8G1xnzaIz1o4ORty1AVUZqYR9qVJwfF1hd7Z19ZhuVtswwoVKIM26/9j+EOgQLjfaSInp4vuitRUqajJmSIl+pL5BnCXvjnMT0GT/VxiAqenvDvtNSO0xY48TG9dwiasNv0FneReWdZX9JDYMV/CZ7oTMrqdotxpvTc6Q7HW9AtLF47zh/RHNGV/CFdssRX+cubLBhcd2EsT1Vp4bdVW/LP9iMFTo9W0MTHltgl15lFCm2VLtr3TQ2MVRTKfYU4usY8OQinTEiFM808lou80pJnsjzl2K1/WDEJ0qJD44Z6aYN1a1tJg9/bSYr7YpHO3wapQFPAtX2bCNz6LR5y6KA/uGk4OLJOfETP6BN3RYziYgsv2z9wdDONzTMaVJyiI9PS1NSsDU0seXyY7n5JjvDvqfoVTHnHiX/HCTtOmB/yqN4LBE0MnvyoXgKn2nH6jMDJvy1xLWU7pvH9iEdebhMfWnhBmmNsoSOzEJey0JnamNCUCL7rb6euHBW1soVm/epA0p4XIxreI8i/8Wvu4joh80zBvdsEWi3FmD4ML/UvmbZEo8sHeVBjds4VcId90O+2uxhbnZJO2VnPC2d+lh3/xbN4hYgvM+Wcz7xnNa0tKStc0sEfUDy8SHHnbHjld/rOO5hUZrXrdZbJ/hnwpUJjfO3iNLQrv4YnV2hJd0mWveeT+JE0vDM/9QQ/cSUD1Ak2ChfRv5nNqZ+o5hJ0GTjFblKyFSxVuAySUCEr5YVjqIMmfpI87zetz5HXxyG2Had/+2eQ248H/0EFTt/WjtPbd3jyjx+HYKCkD0TABZRMHZ2fJ2dwhOphnpWvVHkqBo4jFek6iweMPirtgQp0t6IkyUgLqYk1FheGHONQLTG/gKXTGHs/JOAnh0OccdpuQEqooQ/C5g/vkob7O9/uCflxkkWRLmH4s95twl9Rr+BJ+CVsnWoDtgNsyDIuXjzBvhxmADUDJ3HJJFVtdYKcqdAt9WgFtG2Coe7QjSNjeJJa2P6BqixzhJm6HgGVr0TI55WSJzjDECvfIGTRSpGRUl54MM2ZvFO3Mmi0K56k7JOBBa5L/CfC9UDIteSUcPKk8WDbG+c2ONLNZc5LmL79dgIn++EdJ8I41Ri0iHphteGSR81455hBE99Z0CLoGXad8LjNU/ydlid4z4nsCZy4w+RPkTNw4o4T/mC2Aif0Pn0pi1OS25Q2ypNl0wA1kuZ041t4uixHxTvlgKgj9OST2moZF9KeAK8FA1Ace6IUr5jVImh/EkKnZXPqMT3MawibUHfIoTtlp4mwxw3kwN4LE/YQT9R6TA859RWYtWXmq3loR0h4a6J4TbfcduMhulQO+ghPYdz/7JB2noRn38epOo3+TmPXzpP3SxyuN2syXMX6Eso/U8eZ5eCmn8FR/cTH3Bl94M5EIso0Dcu2c6VPKuAooM7oDW6Ib1JNb6ilJ7TDURI71G/YvgXv1PM+etH6HDnmhy1wwschXjBweow5IYETd5wQPN0zcOq5l75VadY8MHBxPTzHtPr5Yitgn1NCnbV2wKUKgo6e1TaT0Mypx0ldcNiSli55Ua5ceoR5BXfJ3X6cmwrmPKY5Dcq4rqj8SjkN4HxYN3Q4X2nuo8w04klPw78Jc34IzN8CwDBPLzQ3sWDEVWq4lFc7uFu1jgwDtUfaSgrkIDXhIMy5yDjPw6Ez5YG0fDefsX2V0s4Csu3gk5IFcNRuEiUDQXFPkQPf52eHstgGKnNDULveiZ3wzuXc7fS0g3njC9KIbQxBkju8E1cWibpEm1jXNNkc78FNRvdtYxqObeIHHJvqTyXjRD/77j/1W1y/yRxH3v3mu03j7zhh54kfh/A7Tk+k5Y+uBU4MmrjrxEf19sDJnZeLPA4WvE6OtHA1YIw3XbBqITWItPhcTub3glX5kJhAp56PvwAAQABJREFUyRzUOqpH6GNeyCm0Wg6c+G+SvYxeEljH1r6YamCFiwyRDo7Wy6cMJmKKj1zpb+kMTF0lqDnCmcwqffQI+Ecfa4LbfaEufPFZgay2yDlp0S7Ovrs/AqdygF6y7JrE4whQ7kACfKE/rCHwuCMzBYlY4tjWBx6S++t75stCnD+s2c2gtJRxN4LlQEH4aNd7/FV5wmunQuWhTRyy7VRlQL5bjr0Bh+paXjCHo4XSN4lNm0geMtHDtKtkwFITXbSUU9qWFefkiXhu/aEsLZThsaRgJv6Ycnmt0g6pLim5F52VW5qD2wIn0vA/ZWa7sG31ovdD3z1m8PSM7yvgD7754xBcGOFRPWjW32BD5+O7Te8QPHG36QcETXz8xo/q9WN66pN0DkeXD1pUrbz4NK3bxKWZdCqQ0JIpJZVHcjzCXuVVpwOP6kQLDGZWCyBTXmjBgZ/pqhP3X7AiH5WEuOOkXSfykYbBjO6uenXwBH3SVe2BxUxuaKAWy53MHXQe2t00omlBgry+Usgxgzo37Eeb0g56LFJjCpOBfKSXbFMEdMwzxbglzMUHYdkC5DFRqaqiS6iJCTz8f+shuds4poKG7xS7TeVvlUYf4x3T9nk3G54T+gnqVFZjldaiK2kpUJaXwqsNnTXmzBB7J4+S2aCJbzqxPhrX8xi9C96p+xUX4fk7TvMP4H518/z58xU4+R2nPKpXgVO948Q/Z+B534sW+mC77Lfl0qx/zdvEL2KYBi60ka65Y7Cf6orM4Ju2Ug2TLPiM0LXm6jvnEU0kXMq8El2AW2KEzcNnfVg/XD9wztE6An+0XLDmIM89fm/DC0TNSeQtfqZagEIHdam+C0C2jkCLAT4Qxz7mx/Qw9dS8hHUJMpqjPlS61icQKTnOOdXt91TtAd3ia37amqd79/Bn0Elb1SU8RNfRlIUqblNCP6bNTah9L76w72y/Qa4UsbjjyDg8oItjYic8FCzwdrqsX5QFCIpdEKcuEme+DAIl7AkpLqnPhX1kBlhU93dndji6yklnafOiLNWLCu+e4rHB32PnveP0EY/q+e84OYhS4PQ1Pw7hwOk//rd+1mYLwjtO/xDvOL3uwOk9dpzwSXIuQn3Cq+rkKIJwGI5IbZruGab3OFULBTNPupwHPApHyjoGPrhoV34wn7BGxCl5B5PXSvRwHpjkQFhTnYjILTkiyoM1sCk/ZFp6yVE+KjzVQAsnrFpkPUL6CBMe1qk3Dx8bfvzkExapCEmBEy9Mc/BkQnr7Fk9fvvuIF/N7ISU6ePT3MJgqio5/lidPD0Io1X9e4Fl6KmGKVZsJBsY/lMWrvJgWL+m+845ACTMyT3+hzTsYD7kgx0me+PEez3/m7/8Q5smFN9xsP6GHulkiHrwKIs4AMITtqQolr5in3EjVZ6nAeGtsOZcRdMoETWuAzctrpJySW4edAcg6JuZAF5MvkyJY+unT7UdzNOdyE6LzPabYj0wHTrTRI5WFow4v/N1m+rwwOp9e9EbgxJ0nPobTgRMWOfiB5KN5+SiE/qAlXv7mjpP6Ihfy+OdgnjbLIzm8vAKWCJxq44LFG9hpj1bmeYQeWMhxcR2p76hBkK82inXdbYWE+xbpvEubxQc+s4x3vVgX6sfVp20N3oBd7zg9IJ0yOeGZxjdT8K3AiRO0akQpYZ9IYJMHW0L9GCQtRrD4+KCTNxgw3nGDgeOD8IeCPxJWQMUJn/YyVscNCzjkIAqOJWjSeIHdGgu0TlgpnWGeckx1CnnlwrJcP0ylHh5JU/prsubjtTkamnqk9t6Xtk+RPXfE2N4a2hc24k+0OO27oacGpKV1Ukf0oH8EJBdgZ3kNIbboe2CkASnHI+zO1bWRq89vdGYGz3KGuOArpUmNFcwZmCv0VT18gfOZ5ouvFDS9wB/AfYxdagZOecfpNT5Fnh2n7Fjnpll+F+KSh4ULtsZ/3FgFPhZ85gMzxblkC1R/p7XJN/OEj0d4g7fSodqEVXfMurYjwby1QIqC4rUGl7lh/V6Trv/Ecx7h10+xVsBw5sd8PF8jfcKbXmwP/v5ipYaT6wB+tZBn3m8SbQuy2JY8Md6hl+3KY17FgLnDNM4tnBserkCJ8xTnJ356/t27D3gvHjBSzVWao+g7NLLOOeewPEThSPFVK6ITS+u3nce5CbzyOzLUwYP5HGUwWdTOjqFTJOLiReLiFDYOkyOCScW5Zcgl7G98geu2c4sekVLGpLZ4KbXT7+XXpmRmznVV14HqpjcuFoeeZnPzFculTDXvrFPI6rd86JA4Km1YcHMODNndvCNo4u+0eDhW/Edwteuk95ze6cMQX3/9fAVO/9G/+bPytBMHTtxxQvCkHScGTvo7TgmOuPilPzRlnIswYblHF/E/JeMPt+Fe6AJVE5gdb0eOULQc8VslHYnMR7AYlUUBgrYIKhuVt6q8Bo47bjgogbPKzlQZAy1LbNliykWQbKEemGqRpWAJkx3Sx5jsHj/hBAgY6dOvHuDxqAdYqIIfVYY1mxZF/OvrnJzevHmPRx74R0fRwJyUcCqowsIvu04uB52wI17Mmr58oT91so0WHlJdaYbDpxScnkDFqIuDJUzcmrSdPsZ7MXw35jEX3Y/4RxIfY7Lnlxm9oOZuxWs8usFP1OZHlMGTfkBVaZiapY/9Cv9QlJQored+RM/Re1RUw1kgmo89C3hO2OCkTHglUuVJGcmawx98YE4eFDrW91oqpUisNTqO6aRW11/lOvLOvL0npjUsV4Ha6eYJbgZOwVFTaotlT8CbwMk7Tv5Klh7VQzuCrXY+sOOkx/Ow28QXvXFyQaQdJ5RfbZi0PI7XKfOwjhJBMZWvUizPCpc8svI6KfHHoyyBpH5afjAw4jEXtjLJtgWNZ4IgjksFi3hM8Um9/M5dONU99NEqFw/aceIiRv2UdQj9dcND7xFg8SIZvq+odoMdpdAh12sMSiMROCHC3WQvQjjm32Me9qkbDIC1SKnUwZR3bN2HfeMi7fkADnXgRP220+9Jlt3pA2DVnQItuHXXgTq57egRQy7aYzFvl6kZ6RZe6xHDvS8pa7yIYHQljW9Oz4sXnqEz4lQ74ZgZac8T1DPtAPb/Qje9Oo2UW30ZcWca2qnSOg/Iyqb2jzydP/MvfrJsbB//Haf8AdzLwEnvOOmGWO04YY54gxssmTP4qO/1wMllm32ni0RalX0r4MQFHrxdPFTPpFPJMb8prkx4Jm0oDRqo1HBQTs3rOZhMzFeqxRuyMCE6UrUBfgs1nzJoAj+bWgET5pwnCJY4N+tx6qf4vcXL68QxOOJ0xYDpA190xxMduZGTwIk3bDlfeWcdNuvmkMd9vHZ5hWOwVOdDzCmc/7jD9EHrw49oU69JuC4hzFPzlm7ycP5jgWCwisui56Q1tUfapOYoyahd6AdPyCO1j8Ex5ZHArnQZiWvorOscAy6y+zvxoSW1TI8H+x1Ni72BoWNxfR4Ql6FTXeQ26dTZKmeXYBdbSnf0aS6VMomzPi517VOQ6bpeskJp6Rq0KT/heDB/tzfc0BH8aqtReVn3cC1kEfrAflmPtcInvhO4Pg6hL+u9Rf0jcMJuE0/eGOLx1/+NnymdF3yO/B/W58ixoNVX9XA3gZ8j50cJYFLBEmwarrxcMcyOatcgoxpIydzpVWW6BA/zyI/c9GfB1+hStbgOwFGomLvywo8OugKndmbUe5cJZJVPukohMIbqWmjVEWAOdLmCVIszTHqcuDjJPWGwxPQrftL1IRoHXzTDyeAJcYYmt491R4d3c77/7u3Nq+/xNzHe8A+Q4u4OFlB53OEDHcZpb2jY5dOzm8SvKK58ol91sq3Ky1RKpc3DSdZ8JFG5C5pFGneWHCzVoxv1BxG5COXJu5MK8uDHmzc/3Hz//fcoz/eYZPm4l9+Z8R9EZM3hXy1IVY+0xuLRNGgrrYljBlGZYNlXzUtfIUP5A78sqVguJ9h0SH8CaFmzRXpQLOIzTMXMVj8IqzguLyJDMGzH9CixbIgQbrdAlKRGrDU+IhU7L5aLLqfQAYB9gG3LwFa7LGinY+DEH2cefmQMgZMe03PQ9APakguiewVOVCLjvPiMt3TGcNMueFbtSwm1HY6qH5D1A8t+z6CJjc86IJ4SSYlj4IQJVLtM6HOsA06Sz7jbVsETd9wQI0kH60wLDiwevPDgIgT9B91NJ3Vj8UK++QEYNwbxVWIwZPwBknvsnwyc3tXO8lssQN5wfOgDHFicIF2BFBae3OVL8KQ7wVwA0S/OMWjPi8CJYwI21njReIBt1YpT1VuNHZb4XkdV+xlvk6jfR+OC2dPmVLXsxJWbXAt5CxB+p8m57BRrjK3aS3WdW7RGju2tI+ltMuyXoqcm2E9LIDTlC7npZG8ZiGV4GFzKBq4ssmzWGoPhST6+dN4clYdpWs/HIXhTjOPkGd6J5E4132/ijtP6OASCJ35FjzdXsuPE4OkycKIV2uC4INyzGnOyrousEzUOCVQ+8Alfybt6wkexE96h3fQNURkp3AlApYYvCEJw/JOJsjU3aU5AXi557tACsAIna8Qj/KgYzk+8ofMUwZLeRUXd87FqPVr9lDtPN5jLKOEdp48IoHIjh2sKP+VSgROmA++YI/CoHaf62VuesjHU3zCvMHjSPIPUO038wioCJ6xHGCy9QfrmzVuc/G1guzOAwvsFqV/NPfCNxQZWVZCqWHMRCWkP/4ZH3nMTacTzCB/7TGDqJXx20Godi8W4Dp5IH3zMrfGUsVE6NrZkkhbPvRM4dPBJ3eQueZlbggfPI9z0YC7T8FR6UQwiwrNLe7wSt9MbP/lL8WCdfBNuKY+JzsMS5WdnLaKbCjZkhhfb0+qicJ5Z0AkTOGn8VeDEXScFTnhU7+MInJ5X4PSv/6wsdYLA6R8hcHp98+vacXqHOwoMnPw5cnqKAArGPfTlCmB37k7B51KJz+ozAJCTPKu4a66hdmZCprsCJv4SM6mAozgDCQK+A98kOuTAiRVJGWudg4/llQRIgsViPslLMHLK4OJgk1Xhicd3i3THCDtNDJg4+T1F0PQV/rgW/8DW8+cPb56/4OegGVxBAwIsLozevf2AxeqHG75/xpPB01tMSpyYsiXO9x3kCxuoykDbHPTejSJdzijlRMNJU94BJnR5eEIib86ocAeFtPTQX5bHj3bxR/T5c5+c3J9xAYryvld/url5/er1zbfffodHQr/VS8P8IWV5EgTSDwdOCdiFYdepkwVz/yNiLQSrL6buO2UfpmzkqMhlZ19lGVQBROVAHdIepXwGZmr2C7wFzHD1Sr95WP+9REpC9kpgc3XQoz393D5aqEos2ywyD/4Ys/x8LI2LIH9Vj3+3wDtODBq4MCK7+hFW9mqv2m367MBJVuMJvI0jsGC4ad0o5ezipZIqKYqWlnJZQQG7WEljYITGF/dQIyscJwqc8IgsxqS+IIgx+fz5s5sX6L98rvkrLkyA4x1aBj0cMurzMKC6A4Ipu55sig57MsAUNnA6tQ6x48KUuniyD3/6iMAJN0r0SC6Cp9evcaPkFcb7q+zsOYjiovM9xot2oSp4UvCGBY0ei8VY5I0Mnl7scLdXDqG8rAnAyROucQNPVK/wSj6xlnNkvCd/3xSlr4O6fdylS5y8sHkitKXRlTTEc26WR4fKTKjlWPc+kjLXes59bXp0pS2ta9KNyVWUUmquug5Dc6EWOaf20e1UmOW/81P/Lms7xB3HS0te+tJ1UfJwgV5c7jhdCZzyOfJ7BU72JG1iiy5ze+986t0SuIrhSJOni0UA+OZve+uJ7M7e9COe+fZqUan/DL9wlCGTF25wBmONIxI4zhU8mAKvm53qF8Rj7sFcw/mYJ39Xn/MGD+bpZ1jU8ff2q6/4lIefWOGiMMGTdpowPz3EO9QMnPiEC+czri+U4qL5Q8Z9SR3RM/U3zC2sC94o5ZzyHh8O4+855yo+BcOAyfPVD5qzlAeOu1CZa1iU46mq4LhU0Wd72d7Z3OQ2SXtZxn1myoeeQslAMk6rvj3eTO+x16w7nfgTXRvujN76dujo56Re16NuIdYhD/ZLiUGfqhc86YQrf6moJCa/UYcpaGk+AuYrxUPNlJ9wy1tm0gQPHebFmBErLklXjaAng2iRGnugPczHVDBePiJo2j4OoR2n59pxel6B01/7137WbhV0ETi91VYsf9S5gHVHVseHeQ91poVPukpkHuumu+RDeVwHgrcfAGHOL9R0PC4xB45ZqfnBhJB0lTBZNMnp5W0ixSBFYik5Vze4gRRsYhnMNCkkWcAEXtQDphmnzOJfnkPmI3pcjD3FRMfF2fPnuGOH8+XLhzcvv2YgBV5OcAie3uNxvLe4e/MWj+n96lcIan/1CkEHHnngnR3cgdb7D/WBBZm2BzAIT+kyKjwnXMAhZ0CDR/KR+dkS5Olj8XHCBNoTlOnUy3x2nZ49w5Ym7jpy4fkSf22ZJyf35ygjJ3sF4phHv/+O5fj1za9+/Q2CqNyJzN8CYqeGHe04OXCiz6xX1r1rmSlOIQ17cnZfdF9znyVf5MmjvNqVMCkuf7EB42M+qkfLha10aUQ+tAuWxbsDtGuZSCbd+ZIb1Bo8LFEOl4I5l9T+AF5iBEKj38XLYgNU+1Ub+lE9/igzaPDfZWFwz7vH5MuO0x44sf3q4xAs2ehvtrzXkN2SZVrX6WEWeKeFh17DCSW+VMlpT4hYO7BV4KQ6KHGq0T0DSbKuPuIPdmI8opxf4ZEX9V3cNX+ByZLj8xlwupGB7qWFBhRQB/sO64Uwi5LUMPwRDvpFZ1qngi3zWx5lAbsCJ4wPLkbe/vDp5nuMDY6V73D60VbeucfdegRN/EDHez2y5/dIMgb1TiFuOz9k8KTHahw80ZkET0r56AwdrJNlWfAsCLA8qus58xlXt00EaONuXTJfIud2rccsE96tlQonNeaPMqz/PgK3nnP7TXed4QpR65q01hxIvVUs6bemcNz0wXGUXAD7Nq9ssi0vkSkbHUyjh1Dgs3TKH+kup+yiwJovtOPkBfy+48Sv6nFHdDyqh/ec9ITBtuOUd1vtq8qz2qTGeBXD3pDDpS60E08ihQr9hBdKEhTseiKzaUXmGj51M/ln3e145yiTE79zDJ5wrpsraRdO3ugA6hP8OeS8gYSBDudiPqK3bk7yJiX+SCdPzlV+1J87SLRDYexU8SYs8miqDpxq90nzGecjnLRBKV7Y/3QC675IKuc6Bk4PsJOEnXEET29/wCsECJz4FMxr3OD5/nve6MG8Bdg3fMBIzWifpTO6aaxg/34jLy9sC16vvL3rPDnN65R++YhsZaWPhk6O+pH02DPPPg4t0/Sz9j3qPuZP7F6g6PPxuFuP2yVy1rHjdlpyl2nsH9JTF8LTWtZwbdQVqH4LSR1qpvyEW0k7ErrSocO8aR/wS4SXnDQJ+6og4jw2OPYYPPFjKnzHSYFTPhBRj+r95Cd8x8k7TlcCp3+87TgpcNLfcarAiZ3f5rfULqHjojQe4ixRwSrAxLuIuV6UvQgTb1iKRG0oWg6pBIaGTKoQpCf7wTwrEZVOWok1FxE+XedmEK8UlRxlY4f8nGAw8PVjinphQ2e3iT84XKDxDhGDphcvnt68ePkUke3Dm69/wkeFGGTxhwmBE7fCue2NwOmXv/ge5yu9g/aK7whhckrgxBfFq7fAdpfD5WpSypcFQ1I6OOuG/rLcpM9TBSFJ5QVH8XHHiQETv6r0EotOPhfKDsdInQtQTvacaDnhciH4y1/86uaXv/w1JtdXmmi5c/GhvrZH7XwUhKcMyIj7kPofq1oFAY51rnqnI55YHcwT3oMnTd6UA7/bV86zmD5IKtC45JDWJGsy8UU7m6kiFl1KY6Q1RMcp+ybLDEosxuaeGl0i0kwPb0oq+aGTVUp5ptwx4YLbgZM/DpEdJz2utgInv0/jwKnecdKd5AqcsAjgrhQP/ti0N/GKFFklwBJVHnC1MenCH/OSqItoBcPIHIu25PEmCv3hAgWH6gImKZ7ASdaQZ//U7igmyK9fsv/y62B49Ah9l4E/79Lyq5eI5yFv/fSeypyWZWbqdKDkvGFO0BRBCZUStjz7MQMnrDM7cMLO8rff4kbJN99rjLzCTi0DJz0qieBJO0/1HokDJ+4ycfcX7xQyeOKukxY6WCWpz8MoU5xM7WjNUXE6qZqRPD/ukBrpPNNzqb+HkyVbyrysN/sdytSBnnAUC5vKO2Wv6YuAU6u7prR57VZ8OfIf8sh2n20dhM4WbjvHWS52J+1gc5GCTxqbzAd3TCNceJjjQtWBE5804KL9uOOUz5F/wO+Xb45dPqqH+QJzvuaKqFYD0wBtulzxJvlOyy8xmNeYwEwDAwSfWA994bzPDLkyc5nYs3ldVXjBbK6sNewMH2Orr3ppwiZPToKAcWa+4SO4fvS9AycFUJynEjjhaRUGT5qnNFf5nSc9wqc1hXecdCMIgVjfEHJ5bRJ7VZiq+Gg97ttiXnLeRXKdcseJv+W94/RegdL3mLMUOCGAYvDEx/hUctR5FWdLSSTeTNbtNiPs33PmPUse6fZIfJpHSedxTI3dr/IKqIxE58/Gn3F2Ur7a2V3dKQ4sceXAvWfNFNbyZGeZubgOnP2ZxMDRxvyEQ69UukifPBM+8P/YLFRL+zDhOd2KJ9ymusChK5WOppG/24d4nCIb1m+9EMxzTVDvOPH3GLB3m+qrevW4ntax/DgE1gE8/trv/67SecGO0/8+AifcTcDg6L/jRC9jutOFU8RvvKsGcE1QWgithhk1pkKBe6A2hypDrTkaCuaQbroqsyZK2LroaV2J1JSdBvtMDHXUCdaUiS3SQ446eMY4FyM4K3BK4JEdJz62pseAEDwxaPr6669wl/urm5/8lIETH9fz5MbFGt9lYtDEuzo//7PvcX6HnadXvrPzPT/igTvOmOUYdCzz9EUuT5/gHg+vqAkUOxjj9gQJ6wQfAN3Rqbz0ULXKzJQLb+yW4Q8f8hl3Ljp/+tMXNz/9yQvgEDi98ONOeDUGd8tv8Ijeq5tf/PxXNz//+S/x2CHL8koLwryzIY28A1aBE23zb4Kx6dguahtOxGRkcKT2zSTL1AGTJ17CcJxy4Ss9KfhqR7LlWPUEZuFtzWWeMDDVp6Z4OQdtE9twbNJctMX0ZWqO3mreOVIT07f0c9fWpQUt3qGGbevA6fxRvQ6coB39jI9T7jtO+8ch9Dgf3UOd0Oq0fDmmSE+d2FPVF3xKIJW2o8rtYOfEkbqnJjcDJEAymTieXJyIWylpDJzMxztOCJzqy2DcNWXA/xP0Yf79hpe8qYGTH2zxHduSk779kmlGtks/DdoOgybUBidpniw5aEDIp/xdFAdOWJBwx+k7fqjne4wXvA+IMZKdJ+428f2nd/ikc4Inf6QC7zggWHLg5Ef1HDz5UT31f44bOipna1zLA8J0iHQkajg5SCcPxzX8gQ3Zbv9rMpf4btNdn/0jrmWCS9uTapjQONI4QkWeZR48AYOE8+7FISTtUgWjVtx0hSdpOOPfJb45boEgdu5TG1/uX5gIIintGHadEw6t0jUP7j6x3vVor9634Y7TM9x44AKeNxzGO07bjtNtH4ew/vRBloGjJMeEZ/uH7v6cXOSYBi4aiuXfAeabdtpnBr3rpfQgcQ0d6ivZZisoBKY8GZE4cGJ+vQ9ZWsUjEdL4+8ogB0EqbvAweGLApBOP6DFo0q4TbvDwMf+nOP04ngOj3IQlzvNYBU+1A8WfWZ60uQImvluN01/3xLwPmIFUDu848fec7yxjfYIA6dUrv4PN4Ik7T9/h5CN7LBLrnPevWM/zlFWqleq01zHlWuqIiyeF1/qTLco8j6RHWERcZFCpobqedAS9J1v8+9wUHdF5SKcLB9LMXs4drKM7dA/yOes0PuFpueD6Ldgpd8jszPfP1XySeZuCma+OcCvtwoZX6cnc1PVGmTolzhnEZ4KmLXDijhPeacrjegyieF4GTr/XbhXkwAnvN/E9J9zQxI8yBw/teyFKs+yQ7cLIuySiudPyBzh0WkhDVNp1YUrIZK1jomh1iITlMpXQlATL+NHMgtKCVbHS7IXNtGGYuurk4KdgMdkjXqeUeR00cTbCsEDd8OQjev5L63wcj5MfHwvCH9jilzt+4sDppz99gEmQEx9e8sREx/ebGDi9fvXu5s/+9LubP2Pg9Et+WAF3dFbghJ2AuqvuyiyPTjop3b84VF30sUqMfHyeKZBWLwXgJbtksZuGmffFS/zVeAZOCJh+isUng6eXvBuG4ImPJjJo4oTLheDP/+yXKMsvUA4vCvkoUl50p04+aqRdJ06IOnFPQPXPfkUH0r9uD5wuHiWl8uqX0lFt55KzSICqTCqgYFKbg9ZnXpQiL9HJIv6mRFPrEMMdF0jhf2sJu0eldVGztbspM1LJa3ykOFypi+lxxyl3j7kQYuDEH2pV20ngpLvI2AXx58i948T2ibWkVGA4JXAa702XR3LqywRO8CO/1rAeix00yRTK/6kCJy9GZuDERQmDJ45HVgPv0MpLK1P9qU6rQpV0MUxHHksQnLyr5ZOdeNUNAC8s+E4jxzzu5uLkAoQ7Tt/hRsP32G1i8MRxwh0nv3jNx/X8rhPHC8fgQ9xa5ntq2nHio3radarAiY6y73M+x+4WxzZaH2dgO67xtZyjEPHH4wy382wqdlLloiMp6+G6lP2NopYhPnK3iEM5ZVuOjTNy7VMhxX6q8MTHTVfTRytbv5WWrc9Prvs0SgKQud319qkqoowbLz8lED6kUpn87qvecWJfm4ETFvMv6u84PcYCf/8ceR7Jxo5pPaqXz+nT0fia35u0k627bBnB9sS4g1ejaKTnHFxQuOsBTUZqfjovbpiGIopN5gEPsAWCZIq1hmSZ4qt3gfP7y7zYLcM68U6y38PMO076OAQf1eOj/tgp981YviPNpwcgox0lBEuA+dVefoxKH6TCh6i0vtDNIOruwMmBkj/6wA8/+OSXPf20AduJJza6tTvONeIPeFyP7zLpkT0+tod3M73zxJtq+MFnMfjbXbIzBcVtT57VXmk3pCj7wo/fbXL7aJ4ezZThkfQIMy+DSg3Vlc5tB/smcWf0I+8QnKYH+gzc5zVzZD474xdumL5weQnFiaSLMADScA59g3gAj3oodMRNkYPSwapmLVnDlpuwMbuORcdYWfBqm9imzDilgqPe50XgxI85ZcdpPKbHR/fy5NR6VO/3fy9GVvrgL/3t/2PbccJmhgZIHn2KaUoY9o+uYFUKXTOOFZo7O6T34QG0Y5p6Bk1e0lUPR2QEN3xlJJAmjjeFlDZOYDyjG7QyYi7qQbmCFiezpjolMcbD78dg9GMAGT/agMkLQQQnOT7G5p0m3uXG+TsPEGwgAMGOk7fWeUfHnyF//f27mz9l4PRPvsMjbgycuJDCjhNmMO4C6FE9mpUL9MteuXNlYRSPSU49UKb8LllP1JbRzhnoGtygF6fkqU2TFZBctHnH6eUKnH6iHScvPPmSPYMmTrgMnP70T3+OQPAXuIvOsnyPyfa1AyfsnNHWI8zsXAwyaNKOEyyxP/GmlxaZ9ET1nR0l9rs+9/fy6DXkdVfKsEsCnKuiaqtUCisRUscJMDSC/KHTf/KM45C1rdDp9wVDiCdp8+5Bf7NWKYAIb9oWKVCmNz/52OSqCQAMJFjXXHA/RsfL3Uz+MD954q/LUdo7Tnz0xoufN/xAxHhUj5O9P0RSnsD28KSqgVZz0LPkAyMPnzyUBi4iSVkAHP0Dg96o4lfZRKcD7KWHHSfIkbwemUPej87ysTzeyMCOE3ZPOTbzuJ5euuYdWpw8aN06KkXX4wJkxPmCxQtm7gi/x6Ssrw8C5p8ToO/qy3TzI28APAS+blhhrLx+jTu3dYOEH1ThIzAdOPELe/4a5QcMqtPAqYIm7dyiQjUfs2L5flONHZZEu8kuEd0FXy4s5dnReNf5Gc8ZruWa2jg1XxG6XY3Q/COw+RtnnqNMqaqk5YwY+epLO3/l1H+OlOlpaRvqNu6TCmKP5P/tuCa/MVXm1Ccr0DWXzbasRsEtKR0L79FJ9hYfFx+H+Ao7TgycsNv0nDtO9XeceEOF88Wb8Xec+J5Tf1XP7zilPjymaKUsAWEvluXyYCagLVebz8VPPvyTl7joBwgdUrN0mW5sIzV0LhibTqnLI/QETcxzXsragzMhceFLynnFY9Qf8GFQxA/28OQ7TfyiHh8z9qP/T/Flva9wMnDSF/SQri/48kNUoJHnSf3ZE+5AYZrA7zcsw6T/FAI/+sB248l31PxRGtI4xrQrhXmKj/Fxh1x/vwlBlj9H7tcKXvODEQigKM+5Zs1zsJF7vOmeK02bK2W7wSnAWmMAcp8gfrZp8klDDU9SKViXrl3WeHKABCZPduKYN26fYwbfuZllbwLHeWvSAu92gh3pMJ36G9QC41TSyRFc0kkb8LAzsPcDT1VTIftzq5hwYw01zY6svOSPziXPdJ7U5fHl8Uaax53XAKSxb/MDZfWonv6OUwVO81G9f/XPUdl2OHCqHScucrdH9dRZuUjlwSHuc8EqUeOE1wxDXB/68a5s6i5pc+3QpE9dcmlnhdnJXcQSMoWe8yBynqxIYOquTzs9ywS6yiTOYsmwKyOh864uYP2DTxwsCZw46fFjEPxwwtcMmH76DAETFmsMnHAycNIdISzSGDi9fo3nhxk4/ZNvcSJwwrtODJq+/+6V/uCcvkbH2YgFq+JzsQgHVB+eeFGOclHlXnDLUFYquABUwMK7UbVNrjJEqCYTq2dl6DGCPKr3k69foEw4ETjpjr2++PNEQVMCpz9D4MTg6btvv1XwxMCJz7vzK4H+UcYL+bhrqckTtrWohL0ETpmMSXeLcpLtM4GT+mn6oVa1LGFv/bNEnKSU4spUR/pBUYw0l+nGsC67XoNDuhQNXBqHqCFUTTYZBzwUDbAZPBLboJky6vx436WgHxdjO7ON3c4Mmhg8deDkHScGT/Qxf3eFCyGf/EjJ/qie6hLmMk7snXqVO9esA+h025kezzmGveM06V1iQeDh0T8waQdoBI1kllF01bUtLY2kQz7BExchDO5Zdr3jhPecXrzg1/X4xao8qof3BRQ4cVRTFj0JXc4LDy9A9KUqx/vlAxkxjvFYnT7ogAGQv8XEv4XyETs/3hRz4PTpA75Yxa9P4tQnfvGeAAMofRyiPqTiHScvPv2o3juNPz7K40f1aseJgRNOBUYYA+5ycBhBmoInOLYHTSxV1enqMsaJsC6NG914Ue8PRE96qyW7TXdNveCIHNvA8DWZXUNyLa9GCvoiTZ86ElblLEK5sfIGzuQ5OA5szA6XTqgDdaYzZCtJnVSDiwipYkrKbOCZknPmS2wkHlv5PeMCfn9UT4ETtmf51UfeKHDgxB2n6x+HiMmMXVWIiuMy9TxBR84qa+DqN9hz0MCvMlziMl/Rj8s+zfpInQCK+GJs2jKxAaFHj1M/nlcLOOlnKcPbCvQbjEpn6t1k7hjhoz1YR3D3SYFQpYT5xV4FTtxp4tdC9QVfvrvKACunH+njn0HhHIapQ3OlPvjAL+XxKRftHnEHybuFDKbyJ0UYPLHO+P4T5yr+bvN9bO9Q8WYv2h3rFgZV3N1W/aJorLJU20xd6lQsU5waVIY949pmt/+BX1LBsf4CJyWuj/5FoHWf9snehPPOR/XO1Ud8S9fY3LCXmTvns+Fi6vFSSxxLOjmIO8NPnjIybLXMRJ7oWeunqa/hWQ/nc+fkbVuLd9Mf+kwJz5Oes8U53oivcQc9XgMgcNoe1XMApZuo9w6c+Dly3Cg4D5xcSR7irnzB1ckJu3JrsMhdoOpYExTyqe6k4Tmmk56qEc8kRGjVbBBIS8js9nCv1FQmfjKicxjqXTRIDQazzB8a2iorTAVzceJTf/CWjzbUO07cYtdW4O8kcHroHaeXmPAwkflRPU5gflTPgdO3Cpz4ojifI+aPE+/y61E9mo7/1Wk42eYcNbKDJaOkdGw/YOHWY0YoL+xxMqFdPeaF8nGHiI/pvcCd+q95ImhiAOXFpxfgnGzxO4pgCY/q/fwXeM/plwiavsOddLzjhMnZO2f4uxPwmfr4uAfrm/flODnoDhcmasL6Ahn0qTUZpLLgSmsByoAKssQ7iAJdfQO/EmwPBVmkU4cPtSR1r0G5UycnzS2pKAiKqXCLqSgjz0LoSH+s7NUE/BHZeCJPYhgax6K4lFMIdFYHUGxn/nBygX2248RF0R44eSG0gqex4+Tdpmof9X165DaQNRV/1IE8lhf0pLxHHj59fuDE8rn8KhsvyOsHSHVtT+yRTFDA3oGVnyLnQoTB0ws9/uIvQvJOLneHubDgxyEYJPlRP9/R9ZjmXV0sZngnF6neKUAqF1wcfAETX8ZDXbHeFPjgHSV92EWLDvrJlQsfqfOuE8eJFiH8oqb+sCTfa/KdX38cwoHTewRk73HbN2NcwROiO32WXIGTd2w5F3nugkMfaQuNjtL/ZoGTa47X1Y2Z+cwjvZV+8LhL1/yxjcxnmrxkt+lLfGHsU3vajDtObd3EBZ0ugCS6y1cVLLnbgFOdSwHbtKSHHc5tfZzBxBlv3vAkben+PcvuR76qhxsN2XHC3K3+fes7Tnxn8rjjFP9RCJXDhfGMloIlpU8ncLm8+nu7XtCUMcrtDDxkz+u368FTGxkp2/hSfgUXXvLzxI8X3qvIQs6zn0tJThaLv1ICccMvNz+568T3GXVikZCbsQqQECTpkTy+EsD5qnad9Ige5iYGTfxCKHeo+MEb3rzl3MZdJ/Kw3Nlx0vtKfGcJN274mDDfReaNHH7l94N2njifoCQIirwDxeCJO1bQwQ9HIMjihyMIqx30W4zSUIbFJ5iU5SS+yitiJk/hWBOui71Njes+0HzSMfVtsKmx12ODvpZTYjFsHOFz+olqGzi57nPYCUOhdj9O+Iabm8sb67F+JpG00Cd+wmVk2JrUW+E7VHuOMtOar9gJTo6mw2OJgC+p+I9yzF+eHGMeZ1nvY/wpcGLKwCl/wykfiOCOk993Xh+HON1x+i/Ho3q3Bk7tAksgd1Qiwy4VYM0wxPVhnPOp26TNtUOTPnWdtvus5agpIevp4eLK7Uok+1o0L0MpExcaoHvWJKR/wqmEFICForMzk5c2tUiHX15k8cs4/jgE/3YTd5x+yh2n3+E7QQ+x6/QIPz68o5TAiTtOuOujHadvsEvzjd5x4oTGT3nzUQjvOMEgjFUzCKaPXkj5/YfqdUjopz0nTjLM4qD79JmH79izHJ4w+Mf08oEA2cSPnoIcLrpH4PQS7zp9jaCJ5wt+aQ8vDvOOPuuEO0avsFP2y1/+EgEgPg7Br+pht4k7F9RJ/Xy0iEETdeKdVAVoCZr0d6vQZLrzhYmadezTgRKWteA3rBSlEV2VAzxSLxa5oEzbujpUTlcMrqwgnjyOMFAjuDqduKofdG1ST2oW+qTa+o01TK6rx5BpnvRnYsgQnZUiYSl9JAWOEwaQrIPbd5wYONXfcULbcKGzgiZ8Lat3nPJ+E/sKvKjy00pZquKnDuKRvKAny3M4hX6YWgnd/Osa+qHyC00VOvZ3nFj+lNv0WNUfwK2FiP4AMB+B4R1dnByv/lsoCJwUZHbQpDu8tSjhgoR/YoAvZz8BjvXqSuanxblLzK/j+T0l3sV9h4CIwREXmDefMOAZODHgr8f2+L4BqhsLFb5zgJ0qPAbAlH/zjKeDJqdb4KSAqd5vgsO941R1qR0nrKzg3G8eOLn+uvqrwo2+9eqx1Sxqkeqara/phNhPNW/t6Cu5+/hSBm9ldV/ejZTcjoRvB4Sy5/JdxtZ1Ln+mk2Or5ZqDDtgJ6wK8+FLjkUsa6eSZknfmw9Mp9bPfeNHOHY8ETuNRvXvvOPERGZmVAeuusrg4wI85xFzCCVyXxcwiiL6KX/WyWAUMfuTNCxyA/5e2d0+37Eay+0gW35nJzCSrqjUaS/os24ORZFlzcPsPqa0B+DUfv6RxqKuLzEwy+SbL67dWLAD73JMkuz5r37sPgEAgEHgjEAD2DneGaJ4IxUHlfoC4cc6QQhwn5vFqsoYAldQFx6T1o9pOJpvz1v3VVwuej1zHpJ/xtjz1TV60UdP2Ag9u2btVz9omBCa9fHKBRU1MtE6ciyLvIzj9bIHpq69yycOXWuhkS/03GqOzLS/b71wujL+jUfpZ5zPprzAjQLFQSn4ydxJxJdGpTFIFF0xPemRbD3v8HM65MbgOc/oRbrtj2+5QPX/rt+M96/suUpgMo2lvccd/EjD+K/ozmjfYf2sfdr+NH0TLAlwe9gND1qa15ukL7B78xBnCb47gRL7af4X0mQ/Uu+tzJoh+f/syb/Fj88Q77WDgvr67naXdue0xD/J7apz+GsHp3Krn1VCiY8KZyj/TDrnDBplvu1OHnYeUyj6V/ExSG0uxThP7vefIt6E/WKdHA565XNgwsLm7ZigZ2ExOW8ZfxB0u6WNCznPWoTS40DKqEZp2kAHETUVhckbHllv1WPWR4KQb9Sw0IThJaPrkk3eltdGgxHechMtEiYsheP/09xKc9L744isJU2zfmW8fiSm25rkyik1MsoEtbwhOrKZjGi6Yu2Nw4G79DLuilDTCPBPl/fbyhp8lrHFmg0k01x3TibOnnRv10DptwemRhaZcNvC+6UKbbXmvXr7Ut5xerb3vrKKj9qfDWBonTWR/ZCsA2wDEB5267XJzy48nlhaiWPVKfaN+cYnEz5oc7sskIjBRFpxz2oLTlCnFpMcpduLJTxfeQLHXPZjTgI13euHNM3U/Dn7J6OOZeApp3az7jabDXX1D+eQxrRSe7SevTd+OxQ2CAPUCDUU0TlqZ5Htc0rogQLwngff9B1v19u1Y5xmndb5JUbSd735CPKeyXZgPh66Ek8OyC+8qOBEkqVyBwbGDyAI1eayyBDv1SRVLwHDSPCm14rnO+TD13Fyl+vyuBCYmh3wct50rcXiSIlyEpm59+Ug3R36kSyTQIufMgc7oqf2aJ9WVL7/6Uhc9sC31KwlQ3CTJtiUEIAlPWp214PQWwoxeC/650MF2se8FAwlYCFn0Cd2ih7aJQ/gVfiNAITSpXXql+hCcyBUmMX3VpyWnaAfNEWfV9AGy+zn8ChrzYXW8xZ3CKf5teNyDcqV1pdM+7cKnaZX+Ff8h3hHRGc7BSsMe+0fg3Q9scGzXMKvu3aDdnQApoTt0bG8Kf0POzrs0XX7JA/IqjxOQMI6xsdYctMUN8PJWnJqDK9LUmcvlEHzkXH0F/QaXQ2Sr3q9pnDibm0U/l/tEA+vh3xEp0rZz0sTL8yYzvknOhLthf4cdXBvCHbzNy4OAxtzzAzk38kkMjxs3zsIwZ75hwemmX5qyc30nn+VufuDFDg/c7rcZy8cuq2HArRmX2Q/fcj6TRdtTcMrCJgucfDKEc618vgCBhz7mZ/VP36mv+lZ9lsZq9VtfvtLOEF9Mk4WbjG8aV8kQ9yfKQ8ZcxmG9XHKDAIWZPAcPe56OD3FRVoefgXUrjcsvNE7cXZ43eBPP9l8AWUJ702mdF4utCGd5CVaM+J9lucidEbzR7rQ0aW/EOvl4A1JZkPdi+YJ6RnLaiwTsHrz+jUBmrfX6LeYvkSZm+wcpdojej2j5Cz0hhDfzr/thSgdzv6ll0/bcBpkzqxb4PQUnbtRDePrNGqf/+JcXCE7eqqdVTi0soH59KDgxEQVOMsJOJjptAAM3/8D6yL4dq9iSGcV5aJ7+R/Dm4jXAyuUD7EBtJjWbgXgq01wQpY7ZWJMWBCd4b+gWSBsU7tUZuDNpWtVUTGpWhTThYiJGR8UqD+ebnkpoevoMjdO7Ep605e0RE7YRnDSp4rYaVOcRnPThWF0OgdCE8GQtDXHrz5Ml0acDRGvjD+ZpAojGgL3QhiE4yD/fdxnehj86BV5PgHXbDxPObLegM+VsRla9fcB9hCfShiCCYIbA5Fv1rHGK1onvOllw0qqkc1c/aCmYQPKy/eh7bgbjUN083v6nCSs0f2Ci6Fcd9rKLH6/Ua9vACFDUU8lW7rjpvNE2WYBSWVAuKTeNJuKV18KTCyblm7JNPuYXZuC4b9z45bmahL8+zlSBxjwGjeCFbnwfBL6SwrVQlqWUjUsK88jU/+m+xrFrcMougzF5zeszTjroHWG3t+rlGwbVMnq72Zxz6rdZfKuehFzqDgNk8rMxY+YNl4EnAXf8xNgWnEjV4CeBSp7cBwh3nS7SwTMTZIaZIWYwZU7w5hFNP20GAXJW0n3ei3qdduuwbK0RMosayioJTkw4suWFmyN9mYRMn1+UAPW72a5HlXv16lXeOdP3lbSuXNXL4Wk+cB2BiQNUFZ4wFVD1mPqb64Cp77TJnJPqrWU/aYvBnlClbedsU/qB5A45JFuFJrYGejGIzNDr+gmOHrLKP6cb2HjFGrdxC0i51xXzgrC8HkAFuMImboUw/3JmArlI2OL26IAbPxgKVVBNxbCssie+a6wJK9iAt++2gbPpJEQBhQf7DDOQglQnT5zF65B7s6FwpXFBIubEHlrYi3iNa8NLALxbXPwK2/aWBfWNHQFsqf4A7awFp9E4ffSxx5uMHecHcPeCSy6HyKLcnqymrF3OkyHtB/AxN5d+dKcZDpt+W42c/LB7/QT2IA+F79qRSBb2mQf22sRlA7KhR6Cx3sv3wZ/FHOYeJLUpaR3H5GWczhO3ce2Xfjtu7HqFemrG+QgubvoqXrTgvkRCc48nTx5rvvFY5iMteLLo+Z77QLbhcQ05l9Lw+QNu9Xz56kv1XWypz2IPN3qmj1ek9CeeG8luTbnKyQKU+i3GYdmdutW/NKWTrJVychK/m2eVd/3O8PdhQO/SMukzPHj7cWktQCyB3doXUgKXjU3qDbabcG/AAmzM34Ku/Pll3DJX84x0w7YtsSfqMiDzinAS+avs1NsSjR1348O+H/tP/AlWvFtzhwmt7Z9colZ03o/5/5vg9J9uBKfsW92CEwITrMPGFFjt08Drb5yp9GE/KT87rMkLU2wm3su84pEtzQrsE9DW9bNLYYE60pwc74yt0DQZ6sgUy6XBpvET9xKOzEm4SfeIXTE0HKY7lc3G7xBY6MAkFCE0LcHp2cdvPdP7VELT02dSo3/M+Z4KTrnmcwlO//mlb9Vj0sWtNUxm5+o0a37yrShNgr3PmTMXDGo5p9JvvUTNn33T9NrNsnzwTqnRlrxOkrM9KN+L+UFaoax0Z5UbAaoPt3pxOcSjjxGY8j6RyTa9D7Vdj8G1hcc2I24JQ/PEJDCarA4gyiPnU/j7TtI7HfX3mHq9TUkHTnGzbSAHUhHsNGlXVsDST+6wEZ5SHj3jRD3egpPKx3WWAnehe9JPWe5SdYlPEusT35UY+TZE05cAoVnahrVuxOGQwS3NuN74a7Tg3qFO7Vs09w18B8yEJ7wIgO/BmfxGYOBsjMqRb7IswYlb9bxVj0Ew9eKyVc83ZuUqeX8okQmh6364mVgU83YnfZOC1Sse/oJ1whRuE6J56RS10k7ZmZrTVFyZdDZ9z7wh3fIOB2MXPUhmEUACEwsP5ItgTEgoZTpd9kJnIiKNk9rwR7r4hMsj+JwA227RIPNNFQQpzg1QxWjzL6VhfaH3lSYhrN6+0vYXFj64rpePRGpjrqKo4BRT4pngaIWkfUIondcLFyqLmnx7Qtw7QaRha5qAki4BbUs/xpZAv4Sp8CTb6vOumY7P9sMaQH4Xrsps2e01/neA2/ti25jwlcfpwjrlM2Abxqe+2XUvjLNkfiAxMSSgQjXswEt8+TetFw/nWiHLhL/lwHKPduJJPsmef+O6iC7h7zluwi8UYm7slPfyGMubeQkCdPveT3PxUpdUa+gvvLU1W/UsOGnBxWectFjGYt1fJTi5nJOedJdJTFrpSo4sTWRwx+eAy5rsusJO8OG/6u6CCfFX82TnGdi3T3L9JHjiy7404dTNhF4Lmkofed22G9+UbeAJQzD6qKyaUy4IS3Jjqr6//c7PhuHPWab35/Y9hKanTz+x8PToMYs+77vvt2ZbghPaJhZ3vpLg9OLll+q/Xi0tObfseXseZeC+nsUdRar3oUkfBt70PS43uIbnmPY33GD/ONdW1indhh5jxMLHp/kk6zHGruAOy8/9+II3v5dAhWHmXW13V65NdsXzC5ZV0X4BZ2L7ZQyQkp43kzzTe0utfrfsT5qPur+1O7c0/nr3LnvFv1khUVei8rP34GzclgnoN2FMobD2HMH/LyI4/e3/8Z/+grZpa5xuBScqbhpA2QhbmejU7qSqUE+WWzxnITe/YvLbxDrl66d4AC4Yp0exDbvxmEh3swv3SJxQ/MuozFNBjhhcMaHFi9A48S+UQOLkN/FGeNKEx3kwgcQDHRov2iQ+VMfVoGicnj2X4PT8kYSn9yI4PdLZCuHQ0eVWPQ5pRuP09xKc0Dgx2UJ48mqdovXWidlalC1GuVyB/ed9rVXQLG4JTtpi5woctj0xy7c1WAnkPBMCioQmtEISmr7/nmuoJTxxIB2BRxogTefMA9osb9Njq54EJoSmx48fa1KZM04WnJQVPD8iBIkOmqfGRzouq+YaDUgT8X/HywF5CVCnyUo9W5247cdCFAdTLUBpi4C1TipxysDlUKFfBcBstq85SgZE0waAIS/DXkr8tpxT4g668OKS83gmYw2R/eJXR80j2APrgTPWK+X6Hy1OoNb3nQZgemYCSSeUPNeKpOpFNE5oUfieUT5oyTY9zqfx/IUtkpJOLTipLrBND/u3OutEXXD+aXKf/CYAHJTT2uuGouzTE+6+g2CBJ1UnfsKI7GRl/WL6d0A2aPd9CbpCPeTKlBUv7SECE0ITbliEEwlNpEZ9BW2Y7TBsfWF7HjdH5nZMHSSV8PSxrjF/JAHKN1Zp3gA+QhMvExBWb7mSn+vF0Sb7WyfeoofAdApPEZosOJEUdVfWCKsMvLDhBQ4O12cBA17JUkKRDr9jx4M/fD2JOTVOhssvGS4TvDvP+BcN/LYS6JPVt09gdzxuEQl/Awu/AEkLxpUvx7142H4rnEANErMxOJAI4t4pkMPuk5GGCC6c3H9CH98d4kq7cNpHaciS/8VnfR6aK5DjWDQWQ1hWym1/wIvZu9JJPIXVPEMWFpN0ul6pb/YtnNI4Mb4sjZO0TewysODENlL1C+4jfInMb9A4KQLok5YuntgeRq/sujTA7VP7mGW93tBcdlnGYcM/Z7hi3qkfprH9T5Lb3nDgFRffw+1CnJ5vov4tghNUVjnIbgFJ4YEtwWmEpghUWfBh3uFrzLXg8/STJ5prfCLh6YkXe+iv2M7HDg7GULYSf6VtepxveinB6UUFJxZsR3BK/05/oogRmqZvQeNkzbmFpRvBSePurqWUtVPDjx47YlU2NdcCrV/LcLtNb+rMCiQqDX+hu+Jo+BXdsiTcDk2DbWle7I3hSip07v2uRnvP8wo7Yr961GWEiVj2h/gnU6e9BAK7+pQKZlMs+xWpBP5q0+TmZ9sh1/g36dQPuYVoXLxm/rLxH4arX2tL3LS1zPkz96ddMKZnXF+36ul2PbUCFfVv3Kr3t/+7BCd9/Pau4DQrBkTdl6TYrtRhJmnbDKxJIMWyH4NyM6JmMB7+xj+Zc8miBwEFWLBlIdIhenIEbDIRwcnCE2iCUzDiM6GgQ0dAB+F/kPTEF7NVLCDw9Tq/JgwNTy8dXFXnH/hAJrfofWSh6fmnCE7vy87qD9eMVnDSGScJSV+/luAkoenv/7POOL14/db3CE46I8HjrXecM+Iwu7bkbXO+8+APmWpFaSbH1jxx3kozOg9Sw3I0TpqUeYKMdkECCR/bRMiZrVmY1jzNtj1u82MSBx1WGz/WwPmYrXraAmCNkwZRLog4BSdWynO2g8qZvFnsjs8AAEAASURBVCEdOZehCau3G5IubnZE04TApFeC0rcyv5Xpq5qVB/DGxNP0fB1qhH0lQXxRhqyCyaRjdyucDh7iq84Se2sD5doSxZSb/LEtfrjypA7kV5BlOe0OfKDjLuKY1Lff8qx6PCxNmMQgGibTOo4n9tLeZjkiO3gREFo3WEW2xmm+y4LgdL0cIoITH7H8TgLTtxJ+MaP9uLNVz2wRY1/4ws4jU//hODDbgbmsBmYj9oZraiCAvb63qNa8gXCTd82XbYYyGUJ+WGiCsggGB8EpHW0mJwhOushFK7UITk/UjvngMyaTEAQpNFJop9A4vXgRwemFzvWhdXr5krNOCJ/UXxZAJDD5cogtOFnjJJi4IUOcTpKRj+jSbli0UPuT4GTfqUdMJDy5JV+cj6ShExYlaAlNpUtb0EM+EdYm9ptn+m63icE5UY8sXgHvwZbnL1p2iaYqeHpENqzHtPVz8oBnMMkDO2QGY3JwwpN/DVlzssDODZsAEF7PthZPEIB1jmU57d4uEFf0i+rpv4DLQjoehql3ORo+Ct4MCXLmVeO6NVdA429X8ZRMotDY4auxR3CKhvo446Qt4mzfpV+w4DRXWnsc0aLL2qqnjvrkirJLnVVptQBvONk8HWle7BWWQAEfMAILuNBNTP4PYIN4g2n0BYPKlVJDBXzrX9wxXZjq8ZROc6gf6bnHLYfh9hFZ3P4fOP22raCtl0WaaJqgy8sch28jRlPuq8s1T0BgeibB6dmzCE70V/T9CE1onbixt4ITfZYFJ23dy3hLf6XyoTG6QdKPMM7GJBXpYxCaZF+vS9dpaRnLQYlj/MJT/1uTIMB2nUm+p6WXoHPbQd8QfhqV8VpEEziwAFNPi4BZe2Papn0u3hfHRhzb7oseeAlQvm/9Bv6A9C3+6T7tJ70SwTztvxD9Gfwfaxcbi5PTvuI+CNZ/Atjwzw2fR5AHaTBdZhcz57f5JsEpQtP+AO7HmhPluMLf3fsArgWnXg6hBvSDG1EaiCegTiqVsi+cjv0yESVVwOtvi3/uCU7B2ji3tlIC3qwyzmTkBd+dbSGDsEYbOOeBCu9k4hKcBDMCA1SJY7bxK9RioDSgtIBygE9XkDDgc1McW23ovOjMvFqN4KQXwen5p4/f+vQzBKcP3nr2qbb9PEYA4vsLaJy4jnw0ThKa0Di9/EJXg6Jp4WyEosvVpHyXAUFJr7bmYedgPwLL+whOmgBb46SZXLbscX6DTlok/MMWt5wZQhDqx07RMqEZ8uA3V08DY+D7QUIMl0QwOJILH0lo4nAwW/bYQ82WPX9JHsFJPPWBPgdQf9JWu8lw82DBSaOBO3/1uRWcvpeQxhY9BKZvlW4mmgiTOefFxDNbnr73F8y1bU9kFYW1ThaYlEnLdLnI7fpKGfFQZtQGSjJlWdOlOxOvwFrWNcGYZ1nkPu1D/wo7EIZ+yTw0B/cMspBap4lzI+w2k9a60IeJTiajVUFwYuuNtJRrq140Tv4ArrfqkacRkj35GcHJdtWLU3BynZ/2Y+4ubSl53Ty/To7GT/UxQbYZ/uOfVD60lzK47QaW4ARwsge85s9JBRQeD+/yACd4BERwwi1ztr+cgtMnTz/W+US9as8IT2ieLDhJDkJ4Qtv0xRcIT690KQoruAhOaJwqOHEZxBaavGIrLdTbFpzmoojhLoMsPKlGIjy1HxuzExGnTRmR9DpVCjN92SzseLJjDMglN1IdY3eU/Zn8O6oZHNT3rH6/CFueDyy3cYpng/TD/xRqY2w+nPxAcqWYsA4H9CzLuJN/pQYsz91JDLTm2dYjrIH8CDbg7VvbrSnUghqokdyYba+Adxg5FjPLcgAXceFt+07fhi2miWA9d/wVDcVAWfiMk7b3Xm/VOzRO6oiX4PRbNU6UnstZrW1M2Dk5WeztxAvhYfov+XTgXuGhtsf7Um+MNd8Ev/Uvr4Lbq/63pugNI81PYsiCDW00+ZC6jM9On/GByJKw2BVWwZYwpT7qFJwQnuiHcsOeNE4SmJ4jOD3/ROO0thhrqzGCU88Mv+Z8E6804xacJDxxqc1e6FH5uA9RxIep2c24WfCR3S+8p98hPS1feG66djqB3XuMLI+a4IydfLBdbmdz4JccT2Q7DMEbvoiC3K8bRWhvh7svdB4+VzoN/xAPyG6P9/0Xnw+8J/0X8gO74J6w016kEjjTVJhw7gVp0L/SXMUB+Qv9I96hffov+9GfTaHfcFI6p5nxO/jM/dtGBNfcfGucTsHp5jry3y44UbCs0mfaQC5GiCL6E6bUO1XkAvg89bdj/dBJXfJqfO7BEgsIodhscJAHAQQw7IHHBCc0XPNgPwQn3C6MxJBf8MBOo7/XGNqUTM+BwCd9E0ZRRAhJ4TDx+p32H3Nl8Qf6ejcTrc8+k+Ak4enZpxKcnrNaLcGpW/WkceE6cq4f/5MEJ96XL/SdLc74aIsaK3+/Y6+5tukhnHwgyfgDHVi38IQQJcGJG+98OYSEBYQTa6gsoIhLlZllCHH9kxKIwNEJCZNOnylCeNJE+VufyZjtFt66p1v90DxpSwY9DpoKzjTxLads1ePgqQQpvRWcyB13zhpUEZ7IU17nmnhx5z+H71mptzAnofMHVi6VXgRGPs73tS/HyHcm2PKEEPWd4AhXXjVTOqJxUo5Tf1XnKvxjpo4S634oPsrTxXia00jjE18w+9i2nQFf3Irn4gblAFw6gVI9zcE9guAb7gMkdX4WzlnPwV0eEzD+V8GJyyF02Ns36umckwTu9xC8JTgRWTSR0jipPkTjhFCt+qDXl0OoINFaEpMnIrKUiz2xgetwTn8RrmQaFLjDuC9R+DGTuI2Pe9GO5/oNqeljFMHERgA/Pf9VeKmWAPDTL/7EpkqlDvYdT0p+PjRO71toeirB6QnvE4QnBE8uedHUQe8X0jjxZhJSjRP1lnbFLT5MOPTdsjXZUCDV28vkw5kEZ5MQmw8FpzMd2MmJ/rUvszltYpWNyQpzzNJZpuD28k+gbS24aMe3zy3+rX/cze17vuKHOqA3VWGXVvup24kHaXWqY0w4yg9uyqQ4XwwXtuNfXoAO9nbsxT3CCm+HO+BFPeJeoMOSsPfCBYn0b/qCHXwdZA6PG1oq2Idx3OAsHkvxxn/6Kraz/k4VG+HpoeCUyyEQmjjnFC1FPqLaRRe2e7c/2eXgUkt5U1opcKfnhosyl7Quz2uGJK1XGAEveTiZeIUZi5+bZ0UkOPa6a27Isi3CGydEtxsOSaoFIfHj+itA/hir+uy0GF/gU3iyJlwoFp40x/B3olgU5qIn5h3qYvoxXGucnj+NxkmLPE+kJWcekbGZM07aqqczTmwpps96qb6L7XssWiI8uTVZYMpYmv5E9sKs1UZw2v5OF6lyucZFwpMqMkDox7PgC1aEmuMxeWUCztZrK13jhOMtsaGxi8Eeq7gGzd6DMyObfBqoZmnGDI37fldMUbqN8BZhuW/SXPglmuLULBLmCTvtJwHsfc+wN8FvvP6xTsd+sHDr3vkbymexnfZzMSiYt2kBWljS5THcMNrFreDEd5wkNLFVT7s47m7V+5f/PFEdv29H48QtKnwA97hV75h0UgAZglI5Uymx00BkOmVkRfAO+mOdHGt6HiIYMlhjT6JxXIKdSA21cvbWc0KuioqbzFMK3LnIfUxgje2fpoP0gV66NCMQjLTNmdwswUneXqmX+juFNoKThCY0Ttyoh+D02WdSnUtweq4XwYn9yNY4WXCKgPCn//ylBadX0gr6am5tTeNShm7PQ2j6UIfVUSsiqLwvk6vIe+hfDOih41J3pt4VE6HJHa3s2t5swQmcHoyPgJYzTv6CuLdc5FwLg6C/JSPhCeHwA7Z3abKNsNQLIhCaHj3SzXqjcYIFJte9FW9fKLD5oIP3x/ukceOhTiHUfa/0fi8t1Td81dwf5/v+ra+0as/Kvb9rxeq9JqE/oM1S0XIVquvqCE7KLTUGYDVD35E4Hkoz5Xma/bbX3fImXKtACWGesFVnNkLpG3LUu41x2obYQbOck5pGVlhCBjn+Jy1Kt36yK28pf2/VQwDXZMhnnFSOlKUFbzROwul5tCU0oXUawYlJEoOAzzmJfPJ5sSYL3JXDbXf5GDxcjb3405wcNlzL6hQMLeICdDwMxZmEBIgbJIcw8g5UIeoIbjxwdz7RR8DpdLYWnH5S/nSr3ghOuuAl2/U0EdGCyAcfRnBCeELb9DlbXtb75dI4IfRbaLoITtE2ZeLBBIQ0IEjJzL9g4kd8XTVOgjWNwXbS6I8yYdkTGfoyC7gQJIzDKYY79RUiq54bz2QT5PIbuH+Fd6BujwVcCdl+2JY/DuGQDvdX2E9vpd242zQuSKqw1NmxynBNs9kw9nRklwgDvvc79Ha4Iil8/gU47fWvmXjCF/YLwYTl12jBbciLeQY77YOUMm2Ik86v2C+03oQbupSHP1+gPuP99xlz6C9yOQSfpXhXi3X0CafGiUUC72BQv9GtemwLI886gaR+53MRqpvmJ0xReus5WTPw8FtI5OOb4CBd/VbdXuEfRLJ8XMZ20faKV7NoddcEftrLASkLL+mzSD9u58T40U5Dt1yDEjzGcux56ctz5kkBZoHH/YQ1TvmEAsKTNU4VnNA46eVCKb4Zx9ziKjjl0yHROLHzg/6K3jGCUec67qs81tLHjNC0tgWTnrP/mpSIcdv2jxPq1A+K8+KwJyf6Cx88g+AyX8iGX/yNe+AvdyzNZ1yT5bbUnnqKa0ES8O5vcbbnSX9DsT3E3f5nejb0IS3w7uEGNjm9CdSmqFOPy4PMe2SKf2PeRb0LdEkm9OG/rY1fKBvout0o3TTsOHAv7sIP07RwdwzHPmM54/q8DzVOP6ld3Gqc/pljO3/uCE5zOQSNgcmmU0NFTWUtKxWaSG22QCXVxT8jiX1ypWkTxXtP865NA5wLZhHOwM7ZetQ8Qq3aBixvBCcyUo8ykWehOa10BNDSu0i1y9x0tj9VNB1HbsPKFicK621trWFlCG3T+3oRnH7/+ydvFpxmq943aJz+XoKT3i9fsjWKstHVyBq42JLHyw1ffOEY4Sm353CuKRMmOlnfyuWENd3iWEmis2UglDwDh3KzTZDv12gFypohbrCTNkdCU24Ck+mtF2i+uEo83+T4QAMomi6251lw4pY9CU0IT90jSt5y5ak/pCcNkm/xYUsHqi5lLmXta9S1VZGvoL8jPrjyFs3aD3NwFa2SBScJSdz+w7YCzou8Vh4hQPFRUcZjrlWttnRpnVSXSWMm9pTpfuhzz1KNSxCj4dOyJgz2PLuuFCLT3kN/o8oD2DUWgw56B5WxDoGDzlA2rdjT2q5hE2Dj1nfjRuMUwYm6ZMFJAlMmQvs7TvCYrXpZQV7C0yE4RWhS2hStX6I7eE7ayw0m/UVRYg8OAbd/e84zNU3BvclRBt3EYzvk9EzTToRTaAsWlODxK//hIDAtrlA3s90WTS8LBe/4IoiPdUEE2qanCE5qz96qh8bpEJw+/+KVhacvbgQnhCa267FNr+ecMumg/9Cr+uoJiScd6Yc6eMCPz2YqEWzZy2BALult+pwKoalNZ+Dc9DL5AX0KwRksrFR4eVyfIWny9WlNxu3g9RhzhTnhCzFlBIudOBvN/vykBCh/TyqdcMEmmJPZWBVRySZ/CJNIY1KewdhxNUTNk8k7dtMrDfwbLnW+7k3/SiMTXerQhnfye4Y57RvzsB3hV14c3nfzfPEKYvk+7CfNe/4HfcLDt7f2WnDSmOPLZLbgtG/Vo79gKzXvmy+HCM+UEeWWep5qGMbwWc/JvoGH30ISl79Wjw+av5rni+6OPDUOd2E1i3y6T3v8Wx/hPnXCqXfeUsmXoEEMk5amdOELgJ3X47j6goznlJH6hBuNE5dDMLZztumZBSe26mmxVoITl0P0jFOvIkfjhLaJM5qnxql90jYrKLEoSV+jMlxCU+aOpCjpwsTGD+Ud006g/j9Me4PU57Q39we2yrw4Zy43PGb9C4v7bDstsdZNMFNPlk8D3zGLc/U66cfnPt41FK4bfhXsfkjwbnBNLLCUgQHrZ9d9KB5U75FZoballDdEtjeEXeBluYcqHk7/N9gv8S2+y/9hepDHTS+C2bFSY7r80n7YqleN0+1WPZ1x0rya5+/+5T+1ef78IwSnzULYSUdHI7pqnCB/pNqxHW4Cr+fiMLSYMeN/wSrCoiGAWyGAB57BWjW3lKaDcWYSbOKpt7MaWpPGBafbxFHAmG64VM8KTkw4c40zBfa2VIARnHQmSYIT15Cjbfrs99qqp216zznjNBonriTnjBMTKwSnf/j7r/TqI5qv9P0mRcdWNG+V8BWjGrw0gUNoQvNEB8k3G2gUTHgRUPL9F910JH7kYT9XGvW0TKBddghNElQQdD7SS3b50ggJO5x1yvmibySgcJ341xGcpHHio7jRTiA48UVyLomItgkTetAil36U1ii3AupSB61esR0QwazXoEdw4pILXVRAekTvPaUl32XShRESuNA6oV36CoGJq1M5zIoQJRN/BgAEp3wEdz6GywKAyofm06vKz2pC0cEfT8sWd7/xFVh8N6ZsDYSXnxKaOnjxB7Ypmc7UuQl8GJeAZ5SNxbTuDw077HBxoVvYreDE9hu26FVw8pk5b9VD8N5nnH5dcJo0bjaO+Ik9HFDngkL9K0r9BzZtepMizITfgSYwbU9/EyaueK1shtAU2oI16kYi/4lBJn1E371V70MJRghNfL/pE32HzcITgtNTNE7XrXoITp9L6/SFzG7XY3Litn1onG5v1YvgRF8CN+eKrRMhMIMA9poDX7kqr8kPT2bcjzGp4eVfdB1kTLBxOz7MPipPw+WuebWe4AaaOLbTtjvh6af2AA5hkMSTyjHlqbRnZjickR88d+rZeKUKEFoP5Tcc7ngORsB58Fz9JyZhNS9OM7j3aJsDJ6W8OFmOrfV0hyPp13gfsFVAs2Dcvxzq9L1jv9C64984bdIWTsGp/QWCk865aqHsKjhxju/cqpfPWnRcJL1NcsoaZqbtT2m7npaHk73xj9clEYsmtM6ncf0y7BLJgbrhqQ+4C6tZ9NN92vHf9dGpPfqrVefb3smLSUNTsuqUKBG0b2/Ywzy36mmZVVv12MXCDpUITs+fP/MZpyd8SmEEJxZjq3HiG05bcNIZp/XtORZ69pzvFJDoV5bgtLRO4T+1vyloqQhq5klD7DXBsJ/TuMNdilPJbN6QqymK4saMf2HGmp8TNrhHMdWa+hLXbpvL9yR4x168eN2re+NzJyygk8cDRWSvlE/c2zB1pwQOKrae6btH9YpfWgOVM5CH8Gu4uBYHGWQMvIacVB1Aqkef017YNpsjpym74wLWNncrOAmucfS/kOBE5aex7Og3K6QsDekqOB0pdkh+DljTZ7/TEXsxV2YL78Q6SZkEAAfaIQM/fh/U3GTamnhMgW60pns6isUA3SaO81W89meCw2Sn31+p4KSV6ovG6Z0ITtI4oXXiRj3ex0/Y4qOB6UZw+rMEpz//6bVuu/lO8bCKg2ZIF0BIKMk2vQhP3qYnoYPb9fhIZs4pcQNdziohQHUSTH558ozwpENFaHYI/3gEH8OERH7kJjs+vCsB5WuuU5bgpLNOofeTz1K9K3445/T4EW8EJ2/Vq+CkOss5JQRBn03ylcy5nQ1eOSvDdelM2NGiPflEN/Tp/ejjD8Wb8lSCHR17btb7UcKSBCfdONivnH+pzp3b9374IQOAhS3zj7B0R3Ciakxr3IJTSpbC9F/rhAu35U1AF/YxQJsYP/KiDk49DJrBgZW+EQUqQs1BvTXueGdIisfENqE28hWeOAuL4KTaqnL3WbklODHxRwDnfBxb9d4sOLGa3K16mQQpfaowTqXZ2LyEOWIPB1twIsvKVf2VOmAD3lSSaqfkIjgFkf4iA65tDX7N5mngK+uTLWFPEXmAlElMlDOLHQhPnG/yWQKZEZx09bhu1kPbxEes0SAjND15ytYltKV8fuCtt/78+Su9IzihfZLmCSEfzam36q3vOAl5XUmu+u4JCn3P9D9OW3MCcwYB8VZe4Tev+J+8Q2jKSr7oQNMvWEIo+pC95Iko9VmThsEDvq2JvbjLHIQYF0fCCgT0rDeEpXhWGSoR2H0+cxJUv8aT4hz6K83yJSxI+imHKx0OPGFsv/cT/7bYhi2/hFh213lDDBtWzTv85k26Tr/SwORpHHH9yu+k9ddSIaoHoTv2oROkO/5HaGiRlmicesap27QRnDjj1O84VeOUPr5jULfqMQ6t/FMcbrHOHJVWM0lw19PysNg7mT7tQUydeBO8xOJ/P89XREWWuWGpE7gLq1n00/3Q3vaaNCeMa6vSnT/mEWn3TT/cGgdzkobp1zD1UXYrxGzVQ2jStyR8uye7SBCcuIr800+fvvVcWicuhuAym30debbqbcGJxZ6XGmOvl0Nc+qb2UzIjODH/gXf6mml5l75aXnp2u1CqlIi2cewkyiYpxu0AMc7flPNAVjYPvsIGVHdD1n01q9kDq6RSN+K61pOFUaIyS++ksL2v4Tf8t9uGvqJu7NewZ/z41B2zdecMc+XpPtXgX2mVRoqmfgO18wYmrwU5BpkFO1O0gVSD9Zz2BXxgIQ3Hu+LKrpE1ZqpdnAui/wUFJyohqWiTh70NC1z+Tl3hpOpIOU67lbBVRsti33o01FnYC7OeEyIGcWOrZ81BWoHHbQMgKjsmIDxyK6PbGJPe6QCguxo/3SZhCN8Xf2i0wzsEJ+/lTgfmyyF0vonvvKBx+v0fPpHgxA0371nj9Ei36jHZughOEg7+/CcEJ93G9aX2GE+nxNY4tDkf6kpkPryJ1onLIXwrnzpJvoPE9rq8+egsg1f3n5tb9bRMqpg4o3Hg20tP/YG8x9Y+WXiSf76ZFHoITaju/W0nCU9s6XuXq9C1tx1+IjRJ8ySBh5dJuFeztKKFtomwX/tsEtqrr7XyrgsGJND9oJfzVQy8CH6f8K2J50/8bSi27PFKtpJgxDemdG3qCE5fitaXr7RCplUyridnux5vtFTVMEXYtNZJ5UQzcl1xNVF9nQaW0jx+Dae8AyPPpqBlBBqYCQk2dA+0+POL3xlGNFfDxp843vDc8WpLnJhXzT/p1O+kWlg1TpRxtupxycjxAVwJTv4ArgJU2Kb+oCnE9PYbrSb/VsEpSWjs5Dlc1Yw9eSQP9SPGn95yJ3/ghCPIapOh6/5CYXB125l9SqAm/n71e8Dc+N0dAIxHVnIlNKmscqveTxac0DbxIjB5q56Ep08+kQZKWie26uW83ltvfT6CE5qnU+OE0PTN1+cHcBGcsuiyt+rhDqcxyxdmBCcGAPMKf7LFHmwAFpzsw0Sm72Ce5HZQU2n6QbnmUbwNX5ihN86HCCJw0mHApp/dWzxxC+B/MOmXKEf1p17YQfgbmOFOmPEIRFA/wwaGa0Hd5NHgFHUBJugDAx4nkE3cfhXS5kz8FdC8gyu4n2HPPLuPPXjHT2ngwWgQ2Q57aRrtwU/DNy3l8wHiAhQTwGF33VlIYzn8D9xhWTyfH8D9x2qc9hmnLTgl2pQXv237wJNPiyNbAmuomDe/6hdWmMNr5zVA6FzzfKPeDb28k9/gFK9mUU731d4UmUPX5YQ50582y9wDLPvYfqacUJ782YxdVS0wFnlUtghO1jiJFJdIITxxFfnzT59JcPpEQhPnqrfgxO6UbNX75qpx0q16vQWUPtc9jfve9CfWQllwgsMRnNzXkEOCnVkA4/NQl9y+lQ+5vEpb9MmTgZP+1nXI3D6rPB/QD3J6xDsBFzH89Cp8SOx6kzZNjK0jZySn/ZarhDmhxt4/p9dhP/m8Rz/+K81HSKfB7pMGgLpbh06YUraI3YvPBOdn0yk0kMIFtXWgB7j4mIATU34Xmi0HD3IbtBDkPuwnzYd26Mz83GHiTslmzNShkbQTxsy7Gqd7Z5z+6YOofmWrXhoKlZB8TmVMBuxzTcFxcsXswwo7qcYgHb9SYIN9yTyCvflRCAe6hAz6mlzhxL+UMJvBZgoEP8EAl86LV8+iQ4c04eLh35Clgk5noj11nnAy2wd/tuohNFVw+sMfnlp4eo7G6dP3pDbP5RAITggr37IqrReh6fM/6VC5hIV2TNyAhsCERoYPb36k94MPJ7y0VmytoxNkdfurr77S+9qTXZ9NkpDC44sipHFA6GEVkW12rEbxcp05MK4y97kkzjqJJtoiBB8+fsoWPuh1BRLB6REaJ3jyK540Af/+ey53+Nlp4aAp79fzosWCBoIejcNCmASxZ+LhmTr4J08f+0p1bnmjXnkrnrbjve4lEcqTl9qPzdaCfNcpghPyKlsaszUPwYn6i0n1U0REptdlLWtqwPXXfvLhrxjkW+zzGyTBRMQPxMa+/JZlvOquuYOOTcbhZ+vhlu8R28ItbNOI7YQnxaqlAjIoMWC1nLNVL8IuWr8KTr0dsqvGNr0FR1tHVb/3BJh6PvkldpNvmxungHz3I05sjXvbcc87uDvlDUM0Cdec8NCQCI68EQ6wTcB2cCHtMPMLSx5IjJ9AXCCxV6bQOtHhSnCSRimC06FxQnCS0LQEJxZA9KJx+lwaJwSoF7qO3BonbS214OQzTrq50NeRj+D04FaqyQvxuSbo7n/og1j4GV4xSRPoepI+LCzmCOiJzGGCpDxcabZ7/WCZR3SJ4nwMmohO+MU+/q4P4+Fw9IsiKbhvYpQmPpNoopm4xLx5lolGNKvRe5JF4vxHci5xbsfiThbT2l53bRc6qw5PXYYzYLzq1827+F4wwTcn1BlHarNafWBub/bDDhv8JGaTwFUL3r/2KPiF7wf4p++b7ApkXn7Bf+iG9fl8gSbh18shfknj1DNO3apHmXOpDOkVcf04z2AkGROmzBeRq55inCyGaaB6FmKcB95hVfjgqQSDp99k97g3ePlvy/ZMeNyF1Sz2dg/n4yE4SSSczeLjBMAvPE6bxXR+COYg+Mmi/8ad/omgW+NkLTm361lw4lKqW8GJW/XeLDgxb8hWvSz2XK8jJ2r4IRXMdWQyrvLtRPcxzJfkxg4qbFxZNqDtwW1i2vgSnrzYkLQjRPlRlJP6TQ9Kzoug7F8wecJn7Ofv4W+qJnmla3TaesOd9g2r7WpOoEaD56Jzxby4wF94sez4T2KE+jX3iVPc1K6G3X1NI8UsDuF5JmxR6rZX6R6hDNrwGzQ5FyG85hHsCHIhMfDVLTTIXRPaxztjo8dJZ2TGTLtnJ0kEJ9306Vv1cqMeGihfDqFbcjm6wPN3/+q/snn+vP23/xsfwD1v1dM6BUoSGoRS5NcNpWwlNW4407CTHcCFfzflRCkPErBqQ0KdzAxWQEI3KbnuYzZkEYs9pgOdMOylhMlLZso02tmlAkgnIItQGhachrWHvQ2S7SI4MakcwYlzPHRoFZyeP3/0FoLTH/5IJ/b+aJyuW/VYkY7g9OUWnDwR4mOlusVOW+I+1u0fTOI+1kFPBKesdPMF8O/09W/2KvN++dYrvgDuLXYIKWiulDrRYnuctTz63tMn+njt7//wqbRgz619QuhZWy+kWUIw4RKGr73NKFqs76V1Qrhi8m3BSQKT+dEZpY91sI6b/779lo+n5uN6r19ry6GEOEyEp290Zgoa3+mlDNB+QO9T8fDp7z/11gJfk61tgG+/rUsrEIZUZF8rb7ianK16L1/qIOurrzQZ5YO4fGQ3l0T8rNv1smWPjpzyo3NPfWaQ8gtM1pZpSvbqii8+PDVlW/UYOPT1TDyx+xdgLfKuvebyimXInGF2e7niFpUU9SmsbswTVnu36kVwSp4jOH3gw97ZqlfByRNcZfpVcIrW6apxymSyvDd/NneTPWYKTsLN3pqzYUz4lz/48xQ3WR/8UNHvEVE1Tgu2/DI58YA9vfEaKkTUPAs3t+4xoZseUCtTCE1vv/3jEpxYsHjGNr3naJ2yXe+TZwietActcejdGid9w0nb9DjrlK16+R5ZriIXooUnrdYuwWlWbp1HSWE+fEtCjgGA+lQelWWeaAmdEEle0nvRNrmOCoG2QEZC0k/tC7DAg7CMnf8LdMdCmWxatCqcvqzGC0u4KzjFL3VGPIv5TKp+Z8GJehohpBOqSati3TFsFpJjyYfJCHkWuvFO26IjJsNHtUpxW2hSO+Dj37kVVKbTtEIqKurM5t/20Tq1zdX/ZCfZlDyAp8R/crftLVfjmYPt99C2ebvm1MAvWfIm3OS1a5LS0oWyXEdOfedGVT5B8aatehWcbjROCKFJhH6TZ+Y/CUxSDvuuSifTpz1BQjT2M0Xpm4FPvNg20Wv2JPjxe1JyqZtOEE4/INt99s3Aza3rSELu36afXP4FwYm66Sj4pa6RczLV9qMdl6l5Bi/9xF/UZ/2OMlsap1wOgcbJl0PcaJyy0Jp5w0t9g+6ltxb3OvLvRkCCAfoOvSMw1e45E/PGQ3ByFoudnddOgXjebTrfmNxtPW2ERRNyLGVMWqGV0LHDycMn+A7Xvu6CdPgP7VYDaG/6y+Z4LyTe6DgoNBpwS+pN4VY9F+Lg7vwi0EnstJ9+t/BGVjimW7E9Nv3NczCLf9C+8H/1t2v4P+2N3VQmyI7z8MWPcQzDv4dlACt76n/XbDrGNE2N2y7cmefP2Ol+2sITffnN5RBa1Hlwq96bBadvNPnk0H8/gEv5ITjRCLCnC2jyYuKvlOnf5iQ7sJUFk8S6J1EhIL9luWTFr2dU6SmYrXUf5iJ9wFYseDYzZTfK7lAD2JO3shkMwpZ4zAkeZtRpVNv0FwlO0VDljMT7ujHOGicLTs8sPKFx4vVWPW6Vk8aISxMiOH0njZPOSehF42TBTL0k2+oePdbtdbzS8jwawemd3+VDu1ya8PKlhOEX+h4DHSCCkwQWhBReHn8LSp2XL3eQRucTbdP74x8+e+uPf/zMGiMEIc66MDnm2xucIeJckYUWCWHffMMXxb+zoIOwU43TI2nCEJq47Y/tAd98o0sd9L6Wtuo12i8JTfBirZMFJ53BEk80KgQ5Vpmff4bg9FzbGJ+KzkfSrH2keLj0InXS33OS8EQ6X7z4yi+r+BacpN3iSnIELJ9vch328KLwMV2+VDK9LkE1svxRsinlbWt5t8yn9NvbOjedo/JoXZN7oy+KdBAbvG0niWVfmML7BdRJgYMdsS8yJ6z2TuKughPbzCQ8aSL0vgReyh58a5RGcFpb9Ubj6LqhfEAr5XxzZxFm/YtfORGx2GUxI/5RlsUMsH5H21P40li4AgBjGHC4I99NbQdI7HUTwlGoJTleY5uKi9N46sEmHZkIy43g9NaPenMd+aPHuRwCgenZ833O6U2C0xdfRNuE1glhP1v1aIe9Va8aJ0zS/lBwoiNOHqPFZnWsWw6UAwwSpGu9SRfA5JFokkc/T746nXI70SI36V7u5rjh8r88oU1b+rXHYxeM6Qn6aJyqkSdNFkYw9TqNIOtVYiwwqT+o5inbh/FKusiP83EwUuwoy5/Mi3uHKIap6Mf0Jp/JiwqrS1gyrwy2euUP3ZLOZG94c0HUD35IS0zsw+BiJPEq/mEk5vK+Qd9cg+4gG/XGdvresZeUQ93xH2rNb9LAtmmEJ3YlsNDyoa8j1wdwR3DyjawaMxgbrpdDPBScIE89Sh4OMym8xpxMHNYyzxivZVwS8SBDVqqWJQFXnpfOjX/BV4IgBdHh7+Z+/MPVSXS4l4fb64ogmMkFfvlLO43GpemTaXL6mQri/knACE6hG8EJ4pl7sJjqyyE0Fq/ryEdwOs84easeO1S04MqiK3OGzBvYBs9FHwhOJF8jjllgbghPuPO631q36sE3/rSj6wt3VOq0CwSktHELU7Lj9pa9wTG+fpqbk/yCxyS+PhO3nSccADyNKaM0nQ7Aeihb/vIfMz79vaVZONRKsWb93mBe6jvBHfMmcwl2L15gt/A3uYObfhnC5bHmLaWhs7xPutvuPs18Dmx7PeBskTK+fsDVe85jFinDB9E0H4Qezxr486qiKpGmmUob2CE4qYoJ5/8PwUnfCcp3nNA4UXg0DCaqsELDkGXsNtVapytYJo2hMDIjz5klQ0RYeWoWc8AyVnDZi0WXcvEwuqETkFAJWX7rXqa9RdEIQ3lqUuMJsXu0jnBDJ7FN9O1UGGAtcMxWPRWYNU4SjN7Tdr3nzx5Lu4PgxH7j97U9jhtu2DKXyyF8g5zPQbBV76Xf119+qySkQ0FweiwN0aPHukRBq0aPpHn6UBonH2L/3c/q+L7RtiAJTV+81ir3C3+Eky17vRXNA6E7K7ZcICBxtfKTt/7mb/7w1t/88feWuH126oN3IwQqPd9995OFptev9SFan3V67cGRb0pZcNINeBbi2EJowUnb/eT39dfgI3Rx+x3bBnVey8JTNE58WJVBFmGTAQDeLDhJeEJwIo2PHj1W3rwvf11k8fa7nnh+PdeSewuUJqZMRvlYLhontumhnaqg1Y591eNERGRTt9JRUv7utlSo6b4CcUOcIsZwPdkVbHwcaGNRVZYr1FI1x26/jWHntTKt0CelAoOqFB0kTvvCO2jW+psEJ18OcQpOEkxVTnzY8jsEJ9n9AVwmwazCKz88mSRi7MNATYMHlnyYFKyBQ+5lnwl+8WVC5xSc4qUwanOhlF/DjRwMEa1Ftti9prviGv8yKt5dK8aM8BTBickI15F3qx5nFS04oXXSeaffIjhF45TLIXodOaZavtJSgUkmq7bisTwnb8njbCn4GcHJq2agiWclw0nCdIoV0oDJS7UHaKaOYAericYqOy8wmyYyRMeO4SCEbds5/MYf30XaaZDbvEA62/QikE/dqSBV4clxsHrOqvRMrKafgcweqBPPoBOr43H/FgcA22KOvaDxpbYm6baZR2aJ5hWT/ny9vfRHeS5BIvwprxGMKhwRo+JINlI2EQLtj0fzwniNO3kDKGU9JT+4Rj1oGg9cLG98Tt879ks+3PEfusnvCH8PNU69VU/fcdLOBQQnFlQqOH3zrXYV0Geon/flEFqE8w2vmts4xkbbdMrcbMUWlA19Y3LxKL2LVWEHHmN+z3p+8U8M1xjPMIM8ROtKqLjK+aYhuBx2b2CC+Jd04xHTgsO4E2oCLZ6pV3oJgZZJFrLwonHSvMOCk4Qmzj9zOQQXQzzT+WE0TreCk2+q1WUQF8HJH8CVEKzxlXrJPHSNp/QhzHkQ9NyfzHfomKcIRhpgN5pl2j1tKu0KZuG9iyM2tXDK4sgpOCXtziD/nHm9ssKU8BZNiPrBXkfM4A8MvgZvArSKhGe50g6DCG44LrbolHxBjiBUoc7fmx4HHf7KZvJqwtwNevS5yx9KZaTmGWth5b5mQ5VQzGKHwriKcjce4eT/4OOIX4krzUXm8LZnEZRfy1pLTcJ48LpLZSgSHn+9Eopsqr4Fhjswtxu0sqKHxomP3v4s863ZsofGiZd5LM+/v6dx+p+0Ve+FhCZ/APeicUqDcGFO1BBp0sKiUqWMSVIwcYN1pFbW+APnwXWFnO4jpKjEdWKnkp1YjQ1Y4TIV6AxHzEerkgMEvQ4SzCv+0AKNsOsHi94JB4+DmYYmr2qc2MvNZIveBgkXbRPXbT97yla959oWh+D0wQhOfGtBGie9XEfO1jO2xv35Ty90Hbm+p6C9x12B8sdmnzy28PRYezEpZLbq5favn3VZggQnCU2ff66VbglOn3/+wgILgxkv/Ea7I8EJDYMEp2dPP3nrb/7JH9/6JxKeoMfk8ENdONHtNAhzrxGCJDjlrFLOTSE08XLDn7VNbB2UnbBsvQt+LobweSsJTw4vrRNb9XxeSoNqtjWKMeXtKTg9fqxOXml97319rO8d0vieriXPVj0moUnjK+WVJvZcHjGC089aXY/GSan1AgBb92Slg6cSVXhyjOnkUrL5JZPS8eGmwZ0PhPx/AmP3ACIrwfJjuGmZ5oYb5cBZFckhNt5y2tIWuKrgeAseghf0tBdw68lkT3VWLwMUwi0XhLByfNE4jeDUbUmZ/FR4Sj36UZMkhCa2X9GWiCGDjZ3DbeNtbpD3ZVFcHfa0TwB9izdhxbOpDUm3PNtLZExgxWkGDJ4pk/7NRCJZdMhHOfRGICG/Kzj96K143KhH++BiiOdonCQ48S2np7NVr7fq3W7VQ8h/k+DEdr3eymmhyavO5IQ4brLIYwlM3pOtTh7+7C2TZNLH9Olk130GmeyFBFFzhofgkJ30J83OONLvDBTGQloWedVes7FOMJywNnmPaTtuosGbtCAwyUSjnT6TSbXsM7lCKCHc/lAzCyfkiOgQNYRsjAXHhAGHkstDvGO3oZ/8xxte+MPUqx+b4QPeonVnwh8NGfWB7ZhaKZ/+j/7UW4tIo5+ZKCqN5hkhi9mt+RoUOc2543O08iDu+DvPhL9IGhz/prjmULwxTt879mG1HA83Q2Pjm3/yXXnry2SkccoZJ7bqbcFpbe0+BSftTMh27C04ZQI96Ww0RyKTbtgIZ0HZXN4k8uosPUGXdVkO2JHPUwimc6BO7Cf51ItANua24RNXOd9cD5YAR1IP4mBSa/MX+yx6AMP7ElHafPootX3ojgCF8KTapwAVnNiSf9U4PeE7Trog4j0+gKuFcjRO9E25IELb319+eWicctFU2qvY8HCIUARTFZpoqxqfpS23aQFIF2WpMrttq117hwJtSXY/Cu/FBxZI3D4QmhRe7tQ5zKDyu5MfYNsJeeX8Mjh+bTsJnfwD333CEHJbd9hg8evFOXjWG/+pp/KjZBxXoohdv3nKHZHETimcXG/7lOfE3TSaPwdN+J3gFeGA5B6UxF3/W3NYsxG/pKE5Vv/wGYwSLi3htO9ceVW/oWZnYaW53UnfnoEUw2bRZD7AOPySYPFW2DUDhlTSYVxPhtRPuyzSFkJDsGknTJgQmPgIrhckR4hi/vtEL5/F4bkrOP3t//ofdcbpG23pQuPE93Z0pbPiaaOgopmdybxmKxXMlfBIblaEJ/krgY47uE7MohAP/4ZSAQl6Q2A8W/AL12hAiz/xN5oiYroEwatnqnbdhjqjS2t8BEsI/creip5Oa8dMQ/PrgZYOYjROIzj51jsLTtE4cZ7IgpOEpydonNBIqYPzVr3ROP3Dn76Q4PRCgtPXJED/+gCntq4hTDyx8IT9Yw1gh+Ck8vz8C10q8flXb33x+Reyv9Aq0pcWmiI4aRBnWxwCjz5gy/aspwhOEpr+iYSnJxLGluCkzhfhCYEEbZM1Tr7kYQQnTbpNRzfoEeZjNE4SmnjZu9ww3KgXbdNonDjnJMEpwpxu19NgS97S4fOtCW7/eSbzyZMnuur5Ew3UfOVcH/fVlj2Eym98AQaCk86O6GX7Xs84ITDlcggGHjSj6eQ9v2flPaPM1AfKeP/FRVZT4rdvigAwPv2NddeZ8TQ4P9C/2n71vNMK3ZALcLQ4weSdmGV5iOq66vpqP34QnEg6Ezmdx1Ed4M22m9QFbjbsGadObn2Tor+/hcZpBCe0qkx0lbEqutT9g4dYd9pJQWAwgIunlsJw9zXCStYm3TDCvNepb0Sn01QMSzj/rnDloDTVvo1LO6dfUkfrrXq0ZQlOWvygnldwQuP0bAQnNE5cHoHgpCxVu5vLIaQR7a16t4LTzz+jaWKL3ghO1jqpD0M7RD74PybpYHXsJzp6Vsg0AFC2ebd9Ajm8SlmhRI8+XXV/C07pt1w3QhjqRJC3JWXiICginiMfFyw++cW/r8L6j0kRAk0nREOrkxMmbT/p0homVn2pU8Toeuo6yhaxLTgBz8SG6MyU420caffhxZTKd4gu2ma6/bbNrIh7wUiZZhO+ZsKXST8LYXMLqBYYfDaTflDvZJDbhdOixQUiQ+hgksgz3MoSzp1d82O/QfDkb+W/gybMhBvIfSNRnbHdxzM0ERJVnmGgTpnwEsEp5fBLghM3pdJHrK16WhhDW8241kl0xsojAlvLQE2Ap/0W/75b2XP/Efz0OuuNfW78r0QaUqFqNcLFsYKE66tfFzoW0gPLtBenedq/7K4Hl3wARq3ebT43f6rWq7k7HvVbLNr6+4gSjt6v4MTFS9yqZ40Tl0Dp7DDtT8ITi7MITlxBzvkmtuphZ5seFznlfKKroH5IYcZX+hjOIL+D4KSFTRY4GafRTpJXLIoSx498euTHH9zGO1dyihGcVL/YvcKukmic7qVbUe5KOrnX+rHN5FfqrOsPdZfcmnbT8kv/0XAlN3hCqoDXUoQG9GzeBEtoYfqfEHkTF3Yeew5LQ+ugA67rZNETSL9CGlh6xU2qKOXNuAWepsMnMrJwUhFCxkt9wprxz0BcY8E87YAT64YHNaw6kgAaboGGTskZq4mWecJrX0wdeEPdhtsDNvz7ssAFvTHdJhjPBfLL3CXaJsbVap8iOGn78Wic/t2/+qeO4vx524KThKYITpxxUnNjDjuTzRSUo3c4WOJJNidV3UJjP3FUnGAKFy79yGdlAIBixiwWPi0S7Pt5CHUFIBOSRUIdnkp6B5YXiI1lI6QBDTdtVRNu+S34VF2Tkl30cl6CDlWvJ5IRmqz+o7AEZxXIe43VgfEF788+ezYapw/f+tSCE5cpsHVOGidpTth6hhblH/70uQSnL7z3OEl72wdxn+hMUoQnfXRWghMaHiZ4aJ1eIThJaEIb8zmCk94vv/xqTXhJWldKEZq24PRHCU9/1HkntFhsuYvGicZsjdNraZ0OjRODY+kQP0KTb/mT/UMJchGc0Dbx7aUbwUnb/djyx+180GGw7WQl2wm4OOOpeHlqwenDD3XO6b0P1Bm/77zhsgpofv5nJqejcWKrnq8jR3BCWIrQ5EmUOnoWuhCelEmqBx5hXMqkjz9qQM1dTxmApq606gBZ9cEk8rOr1ApS31LH7TimHSTIGbAham6/I/pV28uasW94Av+s8uX5qnHSROhGcOKME6vHhHWdFt2ej2OrHkKUy0wdxVVwAn8nvWnbKUia4m5qag6zRjHnQZ7fk8a0wDQHleumUFtN0j92E5C9hGoO/SuVeNKTRWii41XHyFa9O4KTL4i43ao3glO+4/TLglO36mW7XoR8T0zEu/kS26SDpNCnIDj9rBe3J1BTlTNh2ml2GPfjSjYaJ1floQnloSkyKbjpq1xPVJDxT7yiOjkFau3QunmUda6GMi0sOA4WaTQ5Uj37ndpdJ0YRnBhv9PFq3axJH/CjBiBMYH3oY5iE8ZFueIIXzNTnlJXjxMfCWTLEwmK8N8/mRzTGFKbp0Pekrsd0vfa2yKyUW3AST8UhPdXQ0la4EZRJqHsQxcmiGVvTeHmal2kfJ88KISeQpsHpGv6cXmcyPAtHnVh4IIQ9IH99FjjxXD3vuxTdPGeY2lMHtuB0fsepGqdH7jNchreCk/p3+gxv1VsLLUl3Y90mjCxmBnzr3tjbFl6bhxs+tklKUwTU/XAsK/NP/wk5xkn/zVhnmAczISdDYVdySkcAg/GgTeUviOR94PZzWGDpBfHqCnrtKUvaTzVO2aqHwMTY+vzT2aqnMf6+xuk846SdIf60CZ+fUN2bMRQOvfWXBQGEJoQlhCYEJr3vaax+T1s3SReLoiyMpD30G5BMWumQxL9paB6FtkntCnptL0aYn7QFxyyyzoj6LDN5Bc2db4HRX8BOSn23IYKWFpkpHCF6wXD426W0aYKYfCZ8MFL3asfMu+GiK0d5K69Q4AEv3OEwaH6GP/W7BYfmeBt48naGDd0TIkzNWZ0CgRdFQcXbEJ4Y5Y+trtpPd+lsSsS1yseowb+kd2eesQlzodAoAFdmsLn5xStPYCGJfb9Z+MRNXcsLntewRM+XQ1h4QuuUbXsPBad/prDXx4KTt+r1jNNonLK9iYlnC6tmCDyAixuze2RUo1rjLIBL4gEQaj+X/NrgscX3ikM1oNIchQW2yG7KE8KGfsYZtI1Ipb590sAYrILNL3E186NqnvhpaOpZGDQZ+K0G9OitidcpOOk80Wc6w5OtelfBCa3UjwhOEgz4YCwapz/9/edLcIINNE7RxEh48jmnEZz8DYfZqncRnLbGiVVAKnAFniU4SUhhmx6CkzVOCE4SgNAAMak4BSc0RRZ6dNYle96zVc+Ck4Qnzlt55V2TmK9eI+BEyPFWva/YrhShKTQYVPVNKK1GNa/RNFl4Ek9PJDh98skTbSn8yEITWieEyiU4VePEVj1pxTjn9EDjhABFp6PM46Y9Jlf6SSEqheQpv7VRR5ddtrMmue5MPcFnPafjtC+ExiHAoh/YgXLHuontaru6pQtrTuBBwfWU6CZg6/dVcELjpMPeFqDZetMP4Gr1UAFTJltw8rYbT4QYAEdwUn6QJeuFB9xHLh5s2fogVWVyNc6d2obdYYDEn7a/n6t9u8ZmAqf9TSEDzxY95fVonBCePpDGmMWBj/XttHXGievIfbMe2x2Z5J8aJwn2/o4TwtNs1WMiovqKwBSNE5qKnHNC2Edoor4mbeKX/3k524TQxHY9w4TeG7VcpQnlJO6ccd3XPOpnrSon30J7DeAkV4VHD14BgvJDq7LO5YCjx2VsC/EMHfvkp3WA8KWPoNT+xv2F6htu6hZdpleiv4+QQf1igpWFFPjRhTcK/64+m8AtnaHJ5IpEukBtUNfsnElY+UA4q1czck3URIMBFCpNN5M57DFTv6Ntin2y1v0eZ015+VxChafSgX8WF3jdhiaPasfsMyyaj/CKX/JXCS3a8JkyCvL2O/EcwOPsCvrLFpHZlMpXzQZlYjuXQ6j8/C1Bb++V4PTReR05gm/POHEjW85Dtly9FdN5LLq3UTgqOAk3/t0/ZWSZOwuvhDZ8oW6LUF1XBrLqzcZ4aBPBHcO2PUS8hTzEdXFeygaGkl6X+aXdp32tumDyAxMNaPFSh21WAy2zk0SGOrRNLMpytum5NE69Ve+JLrnhA7j0C9Y4eVfIN8cZp3wAl+8tonECx7sLsvpIDVW87DhB08TChrb960VoYjxhlwiPhWnNaTjnhuaRumCYxnwyNgspatNi1sLTJAz6/DcXe94xbX/8xtNGEe3FT/Kq7R1vStLtjTK1ZdM3swrjNoq/04m/aOlx32Oexm1oaNp6YSKexEeEyyROKK40DpGJwyQc5gp3RhAyCDZtHTdBVr44aHjE2r4m+KTGOZtf6pHxiUH2oVdYvHD1BVJ7sPq7qvFQDKn4BpR4aw9tfifSmhMkUP3aTZ7dwW0YzOVPmghNmNgZw9smoEObwd9jqQSnBxon7br6aK4j/3f/+p+DfHne/tv/5f/9y4tjqx4367HYF42TWt1i4QwnthQxLNUfX2/fM0MnLnDc/Kj62J4swb2fUMO9SWzbFb5DTdNQmODyuyldY5icT2Ahrthlsd0VfNOmdqYRkenFFnXlvDsrBhK9bHtzoWrArcYJQeBnhAELHtyApdtttCJJB/ZU3yf67DOu/kZAeCg4oXH6lu1ommBFcJLGSWeCyiO3zD2RMIHwhLbpsW7X41a7fKRTl0OoPL+YrXqff3FonPz9nRvByTcjcV7jaS6HkPB02apHmvRyOUTPOH2tfevfeu/6d55AsNr6oSbcnVCybZBrmRlov9KHexGcOHCK4PSl3q+5HGJu5qMTpTP1RGXy2KtiEp7QOD15oqtTlU720r+jlSxv1ZNQSf58pY7eN5aNximXQ7CtBu0S743GSZWPjser0uqkKUfyNN3a/j3LOvZgtWa4HAgZS8Eyz0r1EEwMPI5pesDCjkp/BAx24wm38aae33tu4dS7wBTTxHkrOHGG5ENfRT5b9bSCjtbJHfG0Aa6xZ0JLeXVCmMWBGUzFDOTTXpI1pLZZVPPk+Qor5zVPzFt7cmK3+dsw43ajnPSn4wmha8RDXGEGTmhiiAaH3kSaXK4jt8bpd/6GGoITZ5zYpse15LeCE8JTzjhJ6ysLzMXQAABAAElEQVTBiTMDFpykJfWtehL+f/6Zj2WznQXBaV4L+ep3VX9bcnQeLkdY7BknbvmTG2GJyRF2CzrA+JPp9ChNlMlP+v4Z/TowTySMM3Sxu24YecqQGzq3dshlq+Dy5N+vJzKKKAM2noEz4QAHOK+3hKrvQ7PJFi/f5KkMssYJwUlCBmfoosncFwm4L9UqNxOrfGibCZp4QjiiAz4e4uM5TehHAMJn6swRHjr037TB8CyBafXh2BGWIjAV7vgVBmHpkW6S47MQbHfm22fvqt0YXzRoK9w8ystKfc9t7fZBJvp/8TZJUBrGRv4N36QAaMLDccqZX56GHadh+ZGP64Rc1yw7cG6ti5o8EhMYFpw0SUazRjnmo9mjcZLwRPrRLCAI00d4wq0xx5fKLI2TygTNhdLoWM6ohlHS7NSZ319i+hJ4lf2RG7C9nCf2yuPmnDxP/wQ8f8XvLyOcyGM/ZyICJVnL7xJAtFe6bSuyoKoHLTzjyAmEH7xo/zQH7PQH6bcyUVST85yjgtOn8wHcxxKaeNE4qYpbKMp2egQnzjjlNl7mHcxDKMtu6UveqQ3qD4GJhTe0TB+8T1tguzKXVfF+qDzj+4ssiuhbkKLh7ZtacP2BcV9tBFppU23X0TaRvJ3dSpgeFlvesZCmROohX1yPqEtTn8Kbvc2fhTH3FyyeBt4w1Xg3rGMhE00PgwBkKuF2ObgMBAk5fvWCa7zBjREv+TsOLRjQDzRkyELtjCOkwAlJ/IYw0YC7+MMNOf8OHfwHvzE5HD+tw1BpKkKeEH0huSl4JASkpxgNW3d8d6iGTp8bYgNbhiy2h/dQiL2/y2cJd4JM+Em5goFVeEIkVqVxhKYMFWkPCE9roVH2c6vez3+J1onzTU+YU/dyiH/9XzeBy3z7f5Tg9JKteppsX64jn21Obfo1GxJ3t+DVj/IqrHiYRznKnsQlwcUqbOfLyp+ijHkLTwXY1aBFuikS0xFqKuGFpyJTsRrfVMasIKajL880WAtMKhEmF7/7nWJ16ahwFM6DpwaPaFHY3sG+yr94wKEDQ4Pye2mc0Do9f/aBhKecccKPFSA0TmhUWJX+E2ectF3vS844Majrj6tfc74JlTtb9R6pk3pPHRCdUG7Vy4162q5nwakaJ63+aWscj1eANQDmUoAPNfn7RFeRo3HKrXoIQdD8y0wcejnEa2mQunedzq8rrQhuTCbROnHz2PtamWeg/VKCE28OnSI4fTlXkbN3WoOqaPygSTkTnAwQbyk/csYJYe7JY7RquhxCq1gITnTUaOO45cfXkXPVs162NX6nSzUQ8Cw4zXecul0vCwEUD509E68KTilzfvtHOfOHuV9yrY98V0UpTOZZqQqeqhf0UiX8JhCoAhzVtMHNQn7G+1KbFxqW2+B266fwxklVJa/Jh3clNFEXmARV+8hV5JQreA4jXi00aaCjrLbgRMezJ6ekg2TxzmLdykH4O1OMm2fD4LKc2usXfoqndmdr3Q2y3e0P7HOvfBoEc/w7VLgDVrvV1ELxqMNdgpOu21c95yY9BKd+x8mXQ4zGyYKT6iUfwaV+IjRxzilnnDgzII2TBCc0Tltw0oRgBCe21iU/kka7xItyW6/6FL1MmNBkpywrOKlclSngt+woox91cBXhiTSmnQ2eJhVMQzLYONCEEz33bQh0puZyhWZe9SGEZXtNCkG0E2e1POZCfvSRnlwpU/qhZYQnr167biE4oZ1Bk1wNxXfqPyW0SHtBW0XbhLaKOosGKqvIsAZvxOtfukjzV6EpCzKwFjyfoTDf9NuhhS99NLj7jdCEhmSPAWx9ygo7mllr+6Xx7wU7FZzQUNFGevvoDxImEBZCa+dfONav05ASoISTFnx5Jn2yAY+/Ezt++A9ODLkXaFtPP0P7U1rXMMR0+1CvMm684TpyaQW7LetbBCcW6iQ48rkJ8oPtmNY4WXAa6g+iEaOKB3Zdr6Z8b3lpJj0IPpn3AF4C9a9Z+C+Z/xjcRechBy4CJwykw3+snD+clMecepF2RCmbgsLKNlZM+gG3+aVxgj6rJNTXtw+N0yf+wP2zTznjVMFJfdDSOB2XQ7xAcELjdCs4pc/v4gvnmdg+/57K/qMPtZDw4SOb/oyIPiUCr4wdCE60hy4mcG6KOkFfshdoIjyROPddCr1zSXVPcdAHUAebB+AhjLQvSd/UUBnjwKff4KEo8xIubbHm6scWHnTI6xRaNVfQ4QmPoYF7l0nxgQbPfHoule2+gvqfQNDPmznJhDKfjTt0xocx1+mYvsLpaPyJuxlHChJV+KwWrdPwJE3xC224tpmY+AXKszHqTqj6gVKcwBy3kUsDHAOMG+vGSobkV9wOosyGAWZ7/UABRu9eGGZe2EFISsnfCk6Eod4cZ5wuW/W4VW8uh/jv/8Xwsg0JTv/PA8FJ/byiJrps1QO9bDUVZtUZhR+pEesyMvdw6gjmJ7BQiB3wpni1nzEk/PV3FxdwXP2rG/Mh9eFpjFPAoyH5oQLG4koLFa96ZjQOjsJnAJmJpwQdDgVbkJIfWeKBdhoJnReNxoKTBCO0Tk8k6CAcfKr9xk+fvqcb7Vi9pANiSwpnnFid4VY9nXH6BwlOeq1xcgP5WQXKhRC6ilwCBd9yYgBHcPF3nFRsTM5e8GFYf+Popa4j1616WkXqNdJkwbpGHI2T3k90OQTfcfrDHz/VwfdcxfiBrhjviivaHL4txXec6PQYFBkIvRKsSfY64yTh6X1pmzirRT5Z2/RVBCfSgNapnScdJ50qK1LkEZ0bYRCYfL5JPPFhxUf6wCJbdbQBWvnIt6G4zhmNE99x0u0/Sic3EH4roYnvTfU6ckzqcYUnCtBboJh48eqPEqfMY/JLA4ub3/0aPG5BW2cKxtyVe0PJ7HkaD07Su0mM7cBtmCBtzHso8HgPbpjyM36Kz2SUI8D0RnBiAnkjOKk8WUEHDz4JmMEvH1Du9cIXjZPT08EIjpJGpxX7JKg8jNNG/ODyXipOzNqLRzqA1X31D/Twu1c+DYI5/tQKQmHSKT8UnHLlvr/jxBa99Ur4/AjNCh9z5nKIW8GJrXrSQqhds4prbRNap6VxUuM9BKcOmhlU1Wbpkj3iaVLE1gPsbCMUs/ixUOGyFefAokVB06SD2Tr7h/BEGpkApA7UVDinVz82k2dujxIuFML1IFVB9UijNmW2zis5p1K/XK+pC7AmMoSlr0HbxJaeLTixKCN2hNhtbSyiWHBS/4KdhSfqGOlH8KKfhactOIk60v3xQK+TqGwLYwKpx8zMxJ/FAtEKT0zCCMMgKmEJAUdxIvx414D7cfmZWU1EVbA9E0i/++jRIy84eLue/BAc0LhwWyjfrUPDTn9umvIjXypYKmtgzK/HI1mv/uQe3Lkmgrwme3Yc4zPpo0z8mG4dR99wgdf/1lxU5DF20Sao856yVDpzOUS0Tv0ALn30XY3TnIkkHyqYpl9ZMSwmEpN+zWvN5b0tYm1zOrYF2z47gGyHP/n860+Qrri/KeCQvoNLuqYcbJwotpMDRSL9p70cD4a88KYt0/7VrGXHhBBvzjixiMnCA2ecPtXZarbqITjlQioEJ2GqLX69btX7WuPqiyU4fcvWYjRO6j+o37SvcEm7zCLb+zrP9OgjzUc+1vuRhCe+v2jB6S2PHd9rKy5b/L/5hm363cKZCye8QKMEVBgjg+hh3JdMkonRi7SqYxaclGbyJu2dtts34RIs7d2Xt6j/KMy5Qx+GwKXCdTwUMjQbn5zykSspjd9k+ODYILzxwAR9csao4c8Yiq99Ss4phr77YaU9/RoatdhNRTTceuFFbwVE0mottvoq93Wi7T7FDJkLAvqxQXi52ubqF4ykEOYTsnkwdIzUvuXEwN4XpLEbxT8Oyc/RAxnmGC4o4u7GvXPVKZD/SWVgJMThDrdjww0+cx1QsKuCzxuNU/xznGa26r1BcPq7f/PfmO/zR4LT/30VnDjjpIbkCecMp2VrBwwrET52puLec5OdE1vLJLoL3OoG1cSwvAQJ1WvMKdJgFdcVTNjxI1x8zpCnPd7lc3CL4EYANwJg10tlpaIvHhUkA0gGcL7KTcNE4MmHGsOBQirIFJbo0LG5AQvviQZcPkbHFeBPnkgD9eRddTIM5DT0fR05E6x/+PMXb/1Z7yk4Iejk+0YRmh49YqveBwqryYy+BfW1Pjb76tVrvRzy3B/A7R5jctxpEN9VqVsLplv+/vCHT6M1kiDWD+AyAfmeK9IlNCGw9EKHHyTw0Jmx2vSBtFPWOOmqZjpqPvbLRM7nm6Sl4hsRfL+JD+B6JdIryzM5QlJX3SH/SD9C3NOnbNN7bP4+UnqZfIlrlYnSJx4sOInmy5dfSXDKd5y+ldBkjZPqb7fr7XrMIgCPyocRRm87XuBpqKzm1wVu7GlwuPfjTmg7ZVPFcASpU/kNGLRSip26ha3POFagwmUuvGWx5xU1flcYaKTV6P4pz5k0pw5E46SD7kvjpPLTIGiNk8ovnS2CUw/3qvy9V11l10mlByHSpI5cDJM28rEmkcceXspHXP2F0TCb1FzTG6wmZptJX93FOt2HfXc+jfRqjj99D6HcB10Ep599xsnfKfNWvWzTeyA4aYy24KTLWf7sTwK8Go1TryPfGqe33iA4uaNU4vwn00KRyiMaJuWNNMusKrufgUfjCEwYv8rzOZPAIMv2XzTZrhNqR9lmpzbAoE3YeVd8tA/iczsRrSnjCiWKOH0IHRs5pQK+fcMHvNM3IjhFw01f5YPjU0eyVU8r0NZMRCtuwUkDEQIHD2G7Mg09C38Hf1R0/Xsi0UnUT9RHZoV6ms5c4hDtlc9bUVDi/SdWHhUX8SH45HtEEXgsUDEOqHZbc6ZJKO2FhStetE+kB3r0iaTnG/VvX756pU9DvFLfORdfiLbbwaraqWVmHOb1eiLH0AHPeLsmxg0s7YjWddZSIxaVQP+IZ5gZEgm4GLTzUo5Ko7dc6gxLdyusM04aB0j7uVXPgvD0Fyy+tGxcV6B+iSplGB6oiTwXxuI1vwl6ELCVvNFzgC+B5FH/N6I4wPhejF8OcRvP1Y1L4R8U1ZE+k8dN2ot448anXmPXkOn+gWFNTcJ9BHExWUTjxCdQGJPZAr8EpycRnNjl8vN8MJ7FR1/ipG31L6Rx4mXekfEajWHOr7G4UB4RlvkYMhrkJ490fOCR5jQSniI45YwT9Z+yjwYWrRMv2/a+cX2nPVtzTRaROPXDqy9peSk1FpzUzugLul2XfqnjkIUJzVUI24fFmoRDcEq+4lv66c8mjOJuvruWmM7kv/OczD1xiEXUHB0/8jMKZt4Vj/qP3ByqPgUe3Z9mGzJpJ03uj9S3OH3uM5nvpE+Dzo9d1Jl+kbOEpJk+i3QsVoatZcQrfMKm+DyyCIAeJ3DMpKMwB7nBEcbCLV7M0nEA4zwITx5tb9k2BuC4KAHZWBz0UxNH7fhvNz0ifqeJPTM/LTCqDJjLocSo8HQrODGmruvI54zTf/i3/y2RXB4JTv/XCE7ad6pVye/PM04WnDabTe6aVlBLzOaYMjL3cGpWRJ2vOLn2ms7LGM2EoCdkYjhjrm8KrC5i338bGhu/F+oHW45B/Nt/IYkv2V1kDJR627DsofDERmfUgZyVnGwjQZukQUUdUSYvKcC1z1Kdmlc5JTg98hmlRxKYtGL5Mdcbs+JP55EOECHlW7Q7Wun5/M8vNPn6wteRW5MlBpl4fCRhCTponNDIoHHiuyIIcGiE9n7lLzV4s5VNWzHnHAGt25MZNVZvz5JgghaMm/4+0+FR6L8vbROTjHwJHsEp54rQ9qAp8tYLDZIWnKxxyla9jzSh5HxTtuqxiqUrzL/mNr5v3XHCR8MzOcrqSVeVMznq+S0Ewhwy1fktjQrIr7yvEeAkVH71+ru3XiE4vUKLBV/ROP2kgYC+vRon5aqbT+qDSpARhrrL2/JOqft3asDUHirHqiCQ0JN6Ent/Q4v6cXkO50mp9exNuBf4TfSb5PbYsB3SsPUzuDKadOrAFpxyvsn5jTCsAYp67JYqZk/BiXKLRkAd9tFGvMgg3LQfh3TOdSBLmuFPfpv1YRhGm4qEHY/DqD8g2f0P7ITXdcLG7jhP+EEaK3kzIMy2XSYg2ar3s+t1P/DM2aZqnXzGSecVP+xWPSlIudXSgpPMFw/OOGnLrIQmf/R2aZzQPqlu0u/KZPCNkJAFlQgO6lfYkqM3550kGLCN0LjwHCFIhuq/pvrehqMb3qQx5vtwpPAda2670LO1ONUgeSJjHNrKDEMzIakARflloUh8iqrjcp8pvpgU6M9tQZ4+S8RkR/WNSRZ1jH6D/AaPPsZ1SttAs2Uvmuh+TBU8BEdriZs+0TJMbdn82l8lRZpdJzWhsGBP/hA+eOmv07ex4AMf1M8KTp74q1+z5mSEgGrdoRthKxraaJwe+6IEaKGNYoLI2Q206q8kOL189TKCE5NOC05KsdPtkqDAXCY2Zff8RwjgkPd9Mpo4y8iOeSmbhAdmawP8ZtMh72BveATo1EWnX5NRtif+dsEp59eYeLtM1Dmbun52LAf7SvdO+R3WBrTCHsQCWz7XwAKf/m/ASpjleYS4gV2Jv8m1AgXBCQN2k0ajpSw34uSC86M5ssOlrVO30z4QmrC72mhyaMFJC6m+JRXBSVv0Pv30uV40Tu9Z62TBacbK1xKcuJKc88gVnF5bcOoOkwhOaJ6Imz/mPOuTJk+0g0bvJ1pMYHGXBU+e7lbgbPQ3vk03GicEMsZ/L2rA+GQVdX+NKxAYOFthGZdoZ237zImizd0mYVub4M9nKtXvOOeSOW7zFWpqOk32V5QwoYhtDOzIeZPHTf9lXKEnfE35Olz8SWf7lvQn6qcEo0/3XFJ8Zi6VK+Kz0MScBzz6J8Kz+KUFTPolTC/ICIYAJX+YhV9nl3+2PbCpQ52Qm38yGLyzfmGve2gc7uRDcU4TSjscrlTGk/6BcUEdhh0IfP1ZaCr8DaZpxM8jlco+HKXH9NjtHBHc9eI446Rxk0shEJ68CHlqnHR3wEeaU/P8h3/739k8fw7BKWecIjjB9jnZVELgbSpQki42mYDqMYutcDKbRHvyo5Qs2GSW3WTM8ohlvBVkeSwyEEqhbVAghW7zNvSqKytoY1CMROoApFtWZXC36DEJoCHSOCIQqXNSZfc2D2lsenXz+3z7xisv7MNlUsJAI1zIa5LDqpAFG1mYeLEd7tHHTLTe1sse/qSc3+++Y+DNdeQv9A0mPmL7tW6ic2USH6xwfugbnfjeEq8EDK/iatVCwk6EHHV2Erxef62LGBSWgZytdXRiJJiJBA2W25HYdvFIB+KeP3usLXK6wa5nXMQr23tySxKCU1bKrXHQ5JmOgMaOhoKDdPuMExMLhKC3FS9Xq//s9LDKxJuJd3iJcJqOuNtnSA9nt7ixihUtBmtvv9Mk8Eed03gtoYltjGzV+3I0a3zX6XsEJ27Vm4GgglM6haw7pLApFHK6dSC1xfk7jXVqArUhrxuArHFRSeJw5RnrVLLWQkNdrP4pJZcjfqUwoX+zEWpFD5UrrH5JrdMqfhsfuCSfOvDu1AML0JoM+VY9lSflyoTXoRQWwYktlVk9fIPgpLbi8nQo7DvODETJtg3dfIbTa3mcvsnuI5W2JqczQBU7OP69NPojbFFP05nTsB1GpvOdSQhb9lgQoEOlvp+Ck7VOEpx8q55kAlVZC07+JIC27KEV7a16nM9jq94SmpbgRH+q7aj+iCQTfYSNLCYwwGbLCf0PZTeTfb7pJL4ykQCOYKR80cvAjOBE+0VosuCkPEFwavtnRTbCczQJWRBSnESiBw0vdT2akPSDnShEGAFP/poME5/L3P2lg7t1VHMFbxVWSIsf5TvhPLlSf8K5Sc7R4c62OWgyEdoPpUT6Ej/9mF7Rg3f4zKQrghPb7ajsfM8GQY8tqO6nmYS5n3vfPGdbYCYk1PNOcqj3FcCY8DR/3Gdqmx5b9dimRx+FVhy+aSOsrlvb/0BwIreobE6FWKPMZRePrm3kRyq74QIbG5zUxri3PXCTU4jf/rjC30G/AxcT8OH+Qukkrdmql23eaJw4d0vZVlvH4hhaNy4E8DfgXK6nxqnpurJAriRvAsd9h6N4zm/7FjBX1l0w6vg1/+KJ0h1CLrdfY2aReBOiqChRSecgG7UQzG2PbcM6tlAehsqiaq33MO1BDYnGiT6Lb/OxRc/nqkdweizhiaMDHS+/1kKkx1QLTi8sPLFLxOeRtBhbjRMLGvBBG3xfZY6QhBD9XLtEnj99JsHpE8HUR2pOAiu0JdoR9SGapghi3yE4KZ+hw+u+w/MttWExZc2M/FsUjEnvamyi74jGGcGiAlMEiGwlbH+B0Kj2PuMaYfJOXGQ/9KeGWYslPshZzwEnYoxVmljgd9Kf/g4eSQde8mkfTMEMfeaT7VOiIUp/SX/ixQi3KY279CWCZZGMsiF9CqvXfaT6cfoX5lAIToZ5bGYRU3wwBjtd/JRvMdYEeExMGp1OY9GH8Gx4XIXbc/zjAy5/O0zhp0m0wgHNz7LEtZxlLlikIKkQ3Dj1v2OeNCgDpyQmFx7dCk52M54rHIuh9wQnXw6B4DRnnP7n/+Guxun/vGzV2xonWJjJpvgNy45NqcMv9pjJIOOIoyYv2SC/lTgHNTi4g6EE9mnoM0j9iDeFdYXg2j4Jab6KdofY1X8QqHj8yewqY+yZEGRykgGYxpsBhIE4QpM1QVxPy/Y9T3SY/GgqpDlCXu0JllD1gfA5E4QAhcCk8dsdX+PinA5qc64jf8FWO+03/kaaGnIZHAZ+4vpABcvV5Lx0XnSOTAa8NYfGJQEMdTidFcIODfcnNTJqTVeBPXlQmI8kxH2iKxif6htOCCtMAuhk2OLzgzpKH9yGpzl/wGBIJ9VJCPzkA7jwllv1EBS/+5Yb+fR+y75/nWHQmwYvXqxibt5m8kP+IDBxyQQ39eWbDnykj8k7mi99iFd8fP16BCd18lycwWUa+QAughOTRuWWKl4XAFzeqmYp96m/qTjOV6zdUjq1YODUzemIj3q6rbty7Y+xpj5Ck7zmaQ3H7GuPv/Jnx1rKxPnwSfT4UHfGX04g1Odf1zgRlLzPZDITw0xsW36dqLr+ziKDhoWkWWEbLf48mIUZ4B844jWnNm9/PIkHGLQYSuA4l8cJuXY+txRPt6gMU60dpmuhiVoj4UQvbbsaJy6HqMYp2/U0gWDLLYKTXmuc/szlJdlOugQntSGEpyU49UY9xUB9z4ckGTyzbSPbTehvBNMKcvuTH39iqyvnA6Ox7QSqA7cFDwZeteHv1acw4Krlu+0jkOXCBhYm1Idp4GbiS5/WeCmuaphY9exkwuUovwh2mRy4LiyNCrhHCSsj3T/LjOBBHNFU4dPJD/1JvueUugbcC1gjlOF27RjaoZV+I9r+fHCTuKmTTOJ/1GoibcBCluJkYcCLA+rzvCVVfSn4CE5e0ZXQlLqOyYQvH+y0YKX4q2Wj3+P85UfS9jOhyyr4O85jthwiOLFqj/DkSY5okUewnpxRjsDYITg5L/CfyY/9XUVVI4WKPzXRW+KdE9TKPOnXxnHXOMpj+d+DLU9ZhlPzycSc8St1JGNQJsw940QZuAw1ziA4eYuXxx76fL3K204EieVNsV/a7yDe6zHw8iNC9b/Uu/of5q/5H6jXOjwe/5jwJ63YF5d2ukgvtkKu5v5oNcjxc5HI1TYfjfMWnhiz0JivyyGscXpqwelTCVCPrHGiLBE+Ml5acNL292icJDip7iI4MfZ7h4km7Wz3pR6nPUcw+Ug36H0kwemz57rwSu9TfbCesZuXB3zaVi4MySUw3XWSNk1byAIKwlL6rWxDI7/dZlTGtLO0tSz80KYJn3Y77VftODSJGf7U3hHsEOLU1gjTdtX6R9w8XVDCnniZm2Qhw+VOPdNr4UiBMIG3b8TdOWK0aENYOPSdXXwkP7Kt8Cf3t2jSLOCpT6Fd0QfDJ32kt+dp8Zo5WG4epSz0aqGYPIwQlb6KdIfv5BnpSJ8RG50If65D0+9kjjR81s/ohdG2tt1hTWXoLL/i3JpEVxiEx75ALQX8Yt+tBDcZvv2KczHxVx4HzT2kw7m3VL6nl4zGCXu26sGW6hpaJo0RGgFEImedfrvg1OvINTHlZj2VkTPrtwtOsNgwsqxMkV3PXcHJOE7xIBlzBb0hERz5puDHKaPumjvyg8JhTSwNf3jISqMgFVRyKmHfVsY0mAz+rNQswWk6Ce/rlQDygQZlvscUAQp8TXa0Ap0tfuzV5UveCFuaCI1gRWFmvypChgZeVqUlILzSiiV75RGAyGQqFZ1HJgDasqf4EDQ+lBBlIUa8kA53PjQ2DVgRmpgAZHXGDdyNXKvY4oPBkDNKj3WT3iMJLBWaaFTfa8D31kFP9pjwaQWRLTV66QAcpxo7d95zE5+1aB9FMHxX6fzhe0269Uao4XwMkyImSEwkpqErTRbkPDF8Zzpd8pBVaQnv4uMHdR7WKEmr5C0FWh1j+x9XkrMvG2GTOBCuVHQOwzebHF40eNpJuJhxu/bINdVg11NyOX9gMQj5SQWJXb/bObVPBHaNGtvRaUBpx73I/FWWUC+1kDiiuk9T6KQqeCwAZBGACehdjROjshJJ/c+qYVe4suJF+XuF3x02+Q6uTGIhnM3EaYYmfnNt/5NNUtTcI+SdR/lreLO2IewOcJeA3A+ITMA7pItbDEzHJos7YDpcvZzdy2qqriMfwemTMblVz1v13qeNb40Tt+r1LB7nDxGavlUbV4+g7QPU8dRzzAzeEV44eM2EnAmpF0XcVulL9CoIQtOPIzhRVAzavEzuaePuv7xSqbahRRRe8qearPe4DYvJCILTmPRpFp4kWFGe6QOnPD2hV6ZOvloYUVyUNbffuT60DsjMUzOuhlkTDIEdfvrbaH5Y/GAgS7ycPcpq93G5gJjodhb6sPRDbLsjnghOTNR/sMZJ/Yv45K3gFOEpfSbxZDLD5EvCkt5o5ys4BY7mifwjb5nsoGn5UAfgsdOGmJghNDHB4XML3e7URQf3d06vftwIoUXflBomi9oT+SELrysjFTC1EdNeNcEPkuETQNDbh1C3zz1Yca5+iT51y3VDgxn9BVoGFuyicXrTB3CZ3O1v91Cfmg+N7TSd5BMgu7m5svQAI+VO3r0JcfqUXyG2Qj+0HHwszxs+ftmZUKnTD9NZyJS1SQVGnbtOoqgS1B0m+7vdq3q7/UfjrNg0MWQB12ec1EbQOH3KTb4yHz96VwudtHXdFar5HmOmx9MRnF5yxumldrp4a312GLgNSnCiPToO8cXWWwSnj9UOPvtUn1jR+1xnlJkDsUAM69UcM/bTB7mN0E700se4fgkx/UeECuYIcWs8UVIoV/oqz1dU/7q9j34C3LV9bYRzso/xnnZOG6Wu0v7dN5JRPM7D5CP52UUR8ruLgT8p8tUHyk71UpJW/PBlDbHMlMkIT+6LEw+tlsWQnn+EX/gmfbQn98FKW+d09CXpr3SVu/pvdv0gdEVYYuyQ8Ml8DFN9TRZmWJTIHLJtAF7D7TZdlzwfUirU95BHPMyFJmVjAuU5/eMO5B5+fBpmmQbXz0Tzs0BmdDxop3HXtIfmx3CZd1AHrzD3jpSD4NNTenuehSVnBr0l5cR8E5M5Sy+G0HijT3wgPC3BSXNann//b/5FI1ymtupJ48QlAhKe0DZFcBLL3O40LMjCf57UcrlVKWQHp2aTtXAJ0cwZM+VEmKG3jIQquOaOOMRS8CuQLIWMORUBnvpUk1B3zGTtFYaLtLexkLFbioekG5gqtgWnGbDdOBEcuAGO73qosfKhTN53NcnhreDEDph3mdyoQ+MyCJWsUpDGV1U4kyq28vB+xQdjdcCYG/Gar5lIabJDpyWBiW171kChkp6VFWewAnjbCaswaqR0BkxsySlPstRwEJLekcCCIPeRP1yrGR/lqrBqs4o3wg4N1Wp2bfmz1kGCE3mTCQu3jEVoevRIgtzHUt9bmyZNkRRcXIPMrV7f08GpA+gqCuFJFPntTk2dOJ0GAlOEyv+PtTfp2TfJ0rvyX1mVWTmP1XwBFnwAJCSDZWEGWdh7VgjkBbJpQELYCAl7hSeMbTAGhAADMuAWksXGK1ggNgy2QEKIDRskbLfppqsqMyvHysqq5Ppd17ki4n6e5/1nVjf3+95PTCdOnJhOnBPTjfAXOlCKvmDVSi+3/0R52ibKnVfHUJzUBrjO2UzhEEyiOJGek20zWKabjKoEegSih18RYJN2UT/MedreKFWH7/ZoiGmHBDm4JukMitOQ992z4dzbVvgj2BUoyyRtLzPTIlJE4nYgYXBFEPKWBrUp16kYOSsXIRK+gPAYxalbBbyVQhW0laf0G0rP2wZkpsxAo8Tzr5TtMl3Xn+TItF4DiPrwIUZLPLkCLHi2jR5289x57Fg7Nn7lgmG6THqwWozy9KZWZ1Ga+rLqFMWJ2dEXfA05q04f6tZHn8XTeTwUpw6AXnGa1Sb1SBGI4tQVn8yudlDNOQUGWbbsscqCoLFXnNiKVqWJCRomIhBQ6fcIFV9qRZZBNn1faYgRcV7AM52Y1DuCiRhVVk+kOMEzwIHwQJ+gMqfcaFudwaWSrXhYIAA+cdyP3ACIlIgVLmzKF9PtYfB3i/RqOOpU6wyU6PfKEHQpDXgFwg6r5jlvw0CXdAj/8itWOXTtu3ytqAn2ZdVbJgkmz9qmzENeUYy6nQZevNv8KE5iZsYuWimjXqzjFXoYu/KSCy6Y1DkVJ3heBEInpp+Mm6y6pYWB2C1NuFXcyYbwpXxc2F5pokXyX65UxQm8V+6ATx9TXceYj/wIuvd3FSlN8wuVOcLe2qongZRxj1UnyiErd11xyqoTgp13KKgd5iOqUP8oJWfNYefPomhZzlDsCSjfcJO7gEy4QZ9EUiyJ+QAseB8EXNJ62gHXC41P4aA1qKDXO7gYYxyFsDzUCe/tSpPHdfFthENeePvaqqfbavmOEx/C5RbfV/XCK6gT3ipOn7BVz5dKfWTFyeO9lB52q1joV1uGv5A2ipMvB5Fy8oP3dCuv3nf0aRPS5MO7tF/3aTXqTMIiA3QCTv1J/ZSMkJcqS5jph+nnHV98xkltbysW4XFVSOiv2OFFlCH1RV/PjpxXZrxDKaHwMrkUvgDvzAoPW9d5uvJlxUU8EJxeCaZzUu7q7xzZIA1kKuqHvBofZUMdCBf5oocDCB+GRk8OKY8XxQlFE/lNsiM7C4hP+TJh7NUmyz8oTFKckA1nG2xW7tS/VKbmYaI1NDkb8yMi/GDSwjCR30N/KAwvwt4n/KTumoRizxtc9bs1jzgURBoxQLfN2X7xVVk5vDJC3CNcOeTE0zhOiYzrCUcV9ZZ1YwKHAoUZpUlwF8VpFCgpT/2OU7fq/Rv/wu813vPn2Z/8j/7G1x9+FMWJ2foqTmxMiaApkqj3xnIBOHlSNjk1gelbcJvkyjlT+MSP/3hu7AVbpuF24vJvHIcYcXz0K7gdLveAPlKcgnIAimrogLll8K8QEOWpHcNKj2cKNIBYSRrFgT3vnM3x4MzVu8zoMPsc5Yk+yfjqfgvlSp506HgMrD6HoI7FTELOE/EtEIStTzRTk+8vQaq3CUrRQahCYerrJWkpMN5qQ8fWth+28YA/zTCM2+kr8c7aYJInZtNfUqeFUcFIYRoocV9Iecq2i3z41oyPmR3hZQDlfVXKG6tNUZxYCZNgogsmlLxfhLif6bXSJNwVyFr1pYHtfTBzlErKJ2esvrbC5G9b+Zp2buijbPSKiWCiOBXWipMbIvU7DEKNwQzFJuWQturmCJje2GnTBPJrJAoc03GwH4+dtDr9jT2hINVjI3aCC2LTAPc/E3MFBHbHOMNPe9Ja0WTZcZyfcTqOfhgszjNOCM9WoMTEEaJh3pQTD3UexYmVPbbqIcRuoTrtGKEvfcVCs+LVBEeEBqhKyeJ3fSY3LvtrCK4jNw5s3mNeXVPwlyLJQPAAkbwaG8TbTgxSjQnjZcIExYmPPL8hxemNN189FCgmMFgdYqveMylMbNPTBQEoTrrAhJsuvW12lBivOI3i5Msg1Fb9QUkJoHzomW0mnDfM5IRM9SevYnsyRkKDzjehPCFGZzU7/bgzpxlIM1lhZU19mXZKGvAIb2MpfitRubGqggmHwBncU7e7/irMe3CHqam+aAu5zCFxcJvvTHugXFODokAF7P6CuQvbECnv+Gtcdxtk9ac80atIckMXPAtaKR8Oovf6YxD9XIzHK+Na7YD7efui2nOFk557gHfxlN+Dl9eCG4KY3rZ70kZ4oo3Td1CcuF0PGvg+FJmxYCNezRYnvtvFqpOFJSllKKBpw82lMqiS4KWfnS80OYxCcIEdhSUrLTJiQEp1wcdy/J695rQXRH43dZCQG1jBLMWJtiMh76kVJy7WYJeD60wCXhQnnYlFaNR4Rzk4syWh5kM6Gvi0SbnxlL+cJRJ/B6vMFmA8bn5Xjovwlwy/Ab9zmu+1ku9C8aAA3DPGjp+eTs4tAmkS049k0gXTFyO4x64WInRRnFB0dR25FJp3UJx0jvlVbSnmJl9g6auMnVGctINDitNHH+ViE/iV+4D7geqPFSfV4YtOVxMR6j98CPo1FKf33/cnTVhx4nMkyBPQaYVD+bZ86cnT3b9cV4IBLlfZExaetftN2gwKe1Y8oRteieI0ipiVEq1oDW+gqikuPn/gLbWij0ni8kYmfcITwjNzbp3JbPqk5A7zsygtXs0STZFZZuJZ4wD9nsftTolRlp1Msl30KVvpXsr/Ws0Wrdjhl15x4oiF+G/PhUED5UG9+niC5HK+UZmdCp3AnvNi4jWcFXO+VYee7IIxQNf6wdLOFRrjJq/wopjY+YO37Ad73ae57cTJc5qn/UAh7KZrYlwNQhIeGP0aTWMkfMc53YEJ9fHPGSflRvwaf3A7XIIaZ4SdllaYfsE5YW/TQ1j9Krfq6dgK9cHzZ/75f9jm+WPFaa84bcUJpekXXnUKeMjCTk6mcFW5LuQxQwrk3DyJMvEwXBpTKFfYCXEqK2QhTMUuf1um2sxcWoXFIgBZbxWnhe6KaLnoCB1Ia+KXxjyrRe7E3eLEcjCKAytOHCpDAGVGUh0CIWcpTlSYkhEBQmczM5FhJJz9oQOw1cPLsOoQXN35hV6EgD50WJQnhDhWmCwMLAWKM1bsXc7sMUWdK4wT2275eamdYhoPOry3bjEDpQ7o5WF1bs40WXnyDAfnpXTJBB2fVSwx0AjaozhptcnfgGL7oD+Eq2n3Gd29jKwB8yuUJsVDMYOhtrFYiVPa/o6BaBIP8pNVpHwQOIoT5weiLHlVDsVOLwymilNXlhBGlXnhYfWUZ7qPHCiH45XmqDQpCjc8he3uDZzei1+ilnZHEkha5xBuRAMnr3TZ0y17Qcf7NBK0qUgYdMR2G3W5bRkgg8ae9rb905ap8wifzHQhROZqWYR1rTi57ELDEh7VPlGaWHVi0PJqBAIh5SPTKxMi0m6XGUQo3fwPRcFpx82P6bzxq3NTX5/bImwpxKwr0ON6hGSju1RJWguB1F6YL6s9bEFBEHldzJX3Dd43dTOlFCn3efq73o9+whY93Wop82N9R41vqdFmvV3F2+YYePV6u57oU3v19jP33SpMmYDIbXRRnFCeWHmC4bNHG439e6LLn0bQoOvDxarXzuAyqeBPCKgvq2epj0kAseI0aSg9r2yJp9EPy0WtDE0dt15oN+WDEdDoX+pPEjR4mR3NyvneVufKFww1kPNXEVpSI/BVwvxjvgSPjJDOSj0fBOeyBfFCvQjhCOW0vQhPUYbYKrQVJyly4jFffJmP6VqZB73etao67R2li3wkf/Cm4f0WJhFwRjBzu4fvoVh95bRzhX8+lcDZNHLRcxyn4mQhyXjT+GhNPBVYSPv2BRcEU9YmfNwZ5s6W2dKN2bpzAnS6edrjknJDCdd7eB7WFdt++mHM8KQc4w68grMjXXHStkUO7kf4ZcUJvsyV04fipHGjE2alp/TVbDvAvalv6BOmCm/BnvaCX/wEu4ALcJq/0/ATF/YzMXDnvYWKm9xT2n0HChRVnsYrdZJxMv1QdrVjv4yj1Jf6Efwqky/f1dZifQJFq05vvcV14UwAMbmalR7G2d7Ei+LEjZDwLeoPAd/tXiZ2VolYcWHsQDGx3CPZ5wfvv/fCr+hlxSlnt5Ub4fe4IDkgfanjdHCR1UyAbH5VxYkVKvq5X6V5Kk7mQRqfPOHsvhnZKRN6CMApeW8lFW3wBnhr5IvwH/MPT76I76qcvqdyY6KbuF96EYHJFyYBNOaJh8IPUUxI+0XLWOKXsitGXpVHeBKm/OVuGEj3dkLxEdEMX9hbsbOLgUlweLn7muqPXWDQ4u9oauzohDpHJrwDCMVJL3zRfQvlyW0s+XdzWU0QavSo3EI1chF+8PBQ2twkSv0ciZ954h9H7Zg8T5kJ5Vc9YDsKvnwIbbjMFY5f3wV840duEjemOKzKAgWKuMtPShPt8nbFqQrUuo5ccjzPn/rnfo/N8+fZn/qPz6162t5gAVTJWHEqGSE5EckJ/k75Ypo8/WCupxl3tIlHoClfUMtygi/PhZBqLURD5eNwJzChB4ysrojxWqhk2RUUXB6kZIW5VWGqiZ87K8wJ4WQpTpmlzxkfrga/UZwkQHnFSeMqcijZNn71bfVBdyZmVvzOTD6rS36ZtdMWPWYUEFz9iG7fEGXlSUyR5V1WClCgrDxxxipCMDPKXcEJUw0zdWcXHaEFeigLuaf0oqyIEYlhRDmhk7LalG8v+OYkEU8+9oqbhMi54Y9zV6/qZVsMxc6bMmUJvMIJJgF69UCLmRqcHLdehBiEPl5WvUzL3EjGrYMwEc42sZ2QJe1uOVgrpcZCoYcxJCkmBFTzvKSjhFoeTlduhxiYn7x2YqewLg8RoBaaY2LLa+/107HPZkFX6NXS4JWug3FBwyPs1/hxJXaikquJ60zTFkZQV3tGEGpbWrcqqi6KYW+HoF1IUdXrgWTq0+WJvWU75dQ0jSfJm47ifUT1Iz/Dr0I5Yi9rA1U25O/ukV8rwGETccW/lunGAMBmullBjuLE6uprr/M9HxQorvTXCgSTJVacviNlKUoTH33Ox5+5xERXbU+bzioTitOs7ou+F7UaFN4SpSYKLTxGq1zqTz4bOYqT1AM1R4QYKU5W5jKZ4hVp1a0FEAR/9WP6CmmLe1n4teLklUWdcxpehsLF07rM6rAGYtVrnhHMLCAgROOOcGCFA34mgQC+gRnFOnzCvFP1UmEF0+Uq1OmDMtXeosgjyLDVJ8rgVyovtr4h2IUfqgzFD41LcZi88SU5Et7BCbWkDeznOhvqLaXjn9U7lFF4ZF7nQUR4oiDV7XZcoa15cr6YNDIv7urVKE6enJHixEST+PcnurCmZ5yoB8qHLhH0aV1uVapz+xPmwPY5wbgdj0mU5Q4e+fgJznEAPtaGEl47ZsNTUoeHgTyyj82Gf5y86syTdmonXXFCefJWLY17uVUPgbiKU66dptzMM1CcxPTdnjZR10TqKpF1PzAvKOxIGV9LZ1wNB8/wpgcoXT+/k/B7nJtKl7fS3snvsF0ryjgTfudjMPy3p5uCnO5X7jftmzLdL+Hvkj3oR5rE5czRm2++LqVJV5G/qc+X6NZbbgCltVX+4CryT9VuuWzpY0/2fGwFuJMi7Q8oQrQDFBHknkwYvybF6V0rTm9LQVNy5oPgZ5wnTiZjxbWYmFX/xKTvZRVd2/oFSBr0l56tziRtlCzzNfEq0jaP17hOW2OyOX2UCVTxHZS7KSorTprMZmIl/CKrbFVsvHtI/BQFk10u2sij+vnaioovtWK3jSYBmAiALmiGT8E72CKcCWiVuSpk4x8FSvioVzdl4fRWPfGOlLdM9YvkSXxbuFjhoDxRnDKJLcVJ58N/KuWJT630NtYcmajilIkk+hjnTKkrypr8085cDi2MKZPKQ2ohpm+b0BqaL+0xOWjsiXPk7WEcwJ1zxztJOO0OTAHZOhTLTh4EuVAQqzFrEiX2cM3TTtSM2+gHDTfV8shiAmUlnsRqk012FLDilDG9K05/4g//bhK6PM/+9F/mVr2ecWLFSa8GwKU4KdHJgiI2FyGJCjCblXdIJF7s11QaVfF2j9/oBrjYcZ52IV8Q0wzrYcir4nSJaURnBRQVneP2cUzRR1gVJky7EVAUFkaF4qTOdp5x0uCd2+ByxonZHFaduCCBVSdkErLOvmNfEawVF87+0NG795XZkhxKjmCamX1mE3IjXuiVFi1EnaXh+spu57HiJDoiAKNQvWyGEOWJQS/MlTxAC48FXXW0mNRfGGkGOykrM2vY2UMrcWJuPbxdYZutMj3jlcsqtuIkPjNlKFNpRXmKX+vWNAkQpm8/VQ8DbOiIcsSV41n9QmmCmTEgR7FiJQvFibKd7qLckUkJdqOxuq3Shahf178ScVns8ki5CEfDKaQhyF4l2P4EkUZbJXae+MW+f5fcrjbg1lfwDWLb6T2QA0Es5cKRbyJdnLcAcvOfDLju2449c3ijOMHE2arHAJEnTD+rThWMpTjBqGk7es2sJw05V1pNEzyTvEhRnMH8SxmLnKNUlNZ+ApA6LPAOvSpO+ENowjd063LCDQAQ5Y7AEGGewe0VXYbCBAG3SVaJSp9nAP6OLi6RwqSX1QcOWXPTGgOwv6mkyRILSKw2ITz7TB7CDltAUJ6kKFEPbAWel1uqrCCN4tRzDJx3Il1en4WCN+ntai0rXJ2hzG2arDhllSnnmiJMoLRUcEFgYRC20uEyoo/QPzPTDE9B0ULgoL1T//S9nvFBUM5MtfKpJ59ngG9GMUT4ceGrTKmv8iToJgxhyvmRQghPRGliK5FvCRVPgm82DuXDjDLCURV36EdpQsjw6rjqj7YIvzTPVPuu4pSrzCWQqezXipioI+/wf6/Yqd7Mi+DXenm82s5WPcV7oStOoo1VehRlFKcPPvjIAhdtf72Ul+KbH90oTvYzdheKbKeJM3EN0h+BuIrqBsz2+J6/B4isoWKAr0EPXAibaZtsj2SLuHj8seLEqqWFS63IMQvesSO3gWWGvePqQl/CQ3C804HHviAvFkdrXIdQv1gunnGNf8LvYRydmCvqsjTI5iP8F4CHjo2L9mfOtRM6YkwBeJB4oDhtNDuOyolY6VsztjOGUk9CgZkVJyZ6dPnT6/p2pJQnvtnIGSQ+GaJRVu1ZE7cSvD8Vj4rypNVxtd9PNeFjgdw8Xn0BfqCxn75OH6X/s+L4uhQTruVHcfrBe+96KyAKiJqDCzUr10rHlzdFMcKPiRn6O6tWCKkeSyRnEYbi5KML6sdWnmSWd8Aj00R0eYL86Y/QibyEHRrz6FMvsxOI6/LDL1Q+lBvlJNOfQIHnIKdJ9hB7cp/nLPXn2lKdyVrasla5Nd7RvlGQstNHn0tRQVMWlsdEl/mj/LyDRnBtgJSZ6YRWvfmm5pcpR6XP9+V6ydfLo8ghN/4UxUk3CaM4mRbttglNXDLEbcmShRhThNNKk8ouPLDtuf1iisQtRq3GBRjebcVJ7S6tiRZ1vrjqLg5M/Hgadrrrf5pTFNM/L825UQGf8JiCclihaxrwDnZzxnLRmOlN2BOfcZPJRty/+DrKEspTV5wYy5kEreL0r/+hf6gJLvPZn5Hi9BNdDJHLIbriBEJm5nnJgru7IjWHJCm7FSe8Q5JJMfzCH4sraeAOe6030HY2JTtWebVyzxjyczgxHoTL29QPwqI6BbpiMwh5EWNbCtMMnvhBr4UHdQgP8GrwHXyzlYZ9qpz5QeDJrDSzGDAv+hAVBqMyI2HWZTpi9tCyFMxsSWY0a3qmXwwhzIBOEMUJpmjGKO7kbTYIAghYM4tauqCzwggdHuWJP/55PJA1v2aQo8zRwcWMGACZQfVy8JjQwvYXRpqmAyPJdpnctISbciAZysxlKngLWKQzdhMxMOSny9v4V3GCsbPHlwHYl1WgMLHSJD8unWB7UFebVF1gU3quLFmZEXfhQ4jbLbT4BTIE2nSRjFZC+Oq8oKQV2c+O/AjEZelcYufJb0zS45nfM2j8HMxPw+wReEDGJt9tM1Y7t5+j3f1cw5unNYCoPVwVpz0LfypOyvkMXhEaO0g9VpxUby7fEHOWWYsPfFfK7gh/7DFldOK8RySg/F9wmF89SnSICurzl+iNIBM4vRVGGCi//0o+KfB9nefjG2ZsT/VBbJQKrTp9pi22KEterbVALYFfbdf9XrNT/tjtXGJCd6Ltpo/mDFL6MueptvJk/Azyej0Rov6cm/6gBWUgyhN93pNgElZQnNjmBh30hWzjZTsvq2O6+paJBWdXExXuY1nxjiCTMmDyAdpyhbl4iuJ5pUomfcB8RIpTV3ngFwg18DEe8yGfjQmPgmflDNTgFwx90XBWDDmDwSysLhxQ/+7nGfwRTZUlilnrBz6DEI8QxwUP8CeEkyhO2loMv1Lh0m6+K9jFL2fWmPMF9mOLJIqdBCHymnYcxYkVb69sK92kLcVJab5kxYktO5ShvlsnAYuXrZkf6jMSVZw8udBmJLg0u/KjNi8aLm0wL/00bnkRB//hT/j0cQkqCJOYj572uB2ecl+/ExAjvuAJzvjS9qM45Va9tVVPCuv+jlO2dXnc8A6F2aqHcKe20Ms/kn8SmLScxEnEaX+Uo0bdtBpK+G58nInTN0neQSWRR/EJKZ1YA/lL/O4YtMGTloXEIOS57wpJghuF3W4mho4cgBKgJqvhrm7sOePEFl4mIvp9RL6RyMUN8AqQIXQzxnZy5zNNUphvaZXXArmHeiYm1Y8kmJMHJgtQGJgwBS8vStP7Up7efvMN9SP6srCrP4Kb7bu5FCeKQxSnn3tyF3pel+Ll5q180O+szA1dtosHbMUp/IqSgsfQH7vqFHqznY4+CV9AqXtFn2zxQzkqIjyLMrTSYxkp/BR5jXyyNdifPUGZ9Hcw2fUT3kKc7/PtMrV7eE/5SXkcY0MnYCir8Ea224V3MHlG/+B1OSJHih96p45wfl+7C15iRVDKbVa99B1MyTvwcPNB+pVeJoXIu7cSqqyQrSxfKU0a6Ri2J/P9paFQejIlJ2VsdImkcCig48UVty3zE98NV5j61904pHM24vgvnwtfw3dC7vyLr2ZhnYuJVzvUacw2aP1kCqf7imTxvdrEypOUz1lx+kbF6c/+J2zV4yvn3KrHIMGKE5U9ipMS9QBSOqdATYYaprOnSljky2K/E95lqR/gXGFYcS+gO8slaCFs5Z7g8nM4MR6Ey9sVNgiLigZ9+xgEGhW2GvxFcRJ+AVmAUGOnA9FpUB7SeTKzk6/TS7AQY8rWkzAzBAWVpjtmZlEYZCJcsG+eVZxf2GSmM520txT568amS0vFIoI3Qhx0IADRgWtGAMavwhG0Qjdv4if3MDB3bNJWXs3QxIzCTOng6ey9UpaOWiGEkq3QwexrLqlgFpL0teSs9KljytWdWPmPAEH7Shm3Dpof6OsDjJmumWNpygpTZq8ya8VHcYFF8HQRK0UrTqQ8+fUoYmJ2/dICqE//mE754KbFrPYBVNrK9lteAn/Q5ozEiNP2QKnnctZuMYTgNoDTtk3ATXPch6EcyHXEu9gPwIuVPOEB42j7SXugbdCGc15ttx3qQrEch9WmPbs3M3saSNJ2BOXBNWSrKkr+UY6H32C9kPctHK4rqFl1s3Ge0cnfo+eItoPHk1rss234TDkDp4yBujfYsS3PKz0yuUUSRSrbTjJZkm1l2V7mAQ5lQgoTigBbPrylFD7rdps2zFmZCCUoQPCW8JfcBkfdwHPY/otyxmp2Zo5fG+Ut56/CC34GLxdP52wgwhDfgkOY6PnHKAkIT6JBwgJ9/3Mpep9phQxYypmXOAgDT+YG1QAAQABJREFUKNnhM6ELO+2G8i4fsbLIuUyt9nQfP0JdeRPCBtcXM8mDEH0qT5Q25cfLxJOVUZUrbewLXQrDKjOKEy9CR+jjwo4qly/P6j03gepmUq845Uym26nywpkCK32i3bcKqnxp93lzPpQyR3mamteYyIU9zOwyYYOgwseLtconoezll3V2QvzXipPy2dngjzVj/+Mf5+PlCFzhd2mv4U3Y6cnTm5no4VFZp/3iTj8NHXGfbb9N1uET3Sj4OZ704XqQYp7mb7Vxez8KFyXKG2/Hk7XipDLIrWqcccoHcFFUL4rTjCHwcibcqIsUrigoESUKGpSOn5px3f1eymJCv8mvyTnh7Vi4H8VXRe3wZfvlLeWl1/oQHqMnz+d74Cf9klDzhEZZUruRoT6qehq5OAJieBGTtwjmKDisktMfWZ0GL5OzjPdMrPhV3/e2fAnm8HzKBBKy4jSKk/qHV5w0QVrF6X0pTihPb43iJBBPnNAW6DfdYuZtb2ojtBMmWzl39bY+nmseIz7D08nkrjxhVnFCpnIZiqgqTpFXogDSxuiPlAX8xoqTFJ3ymrOOSZP+G5OJAd1kp7TYastOgWyx1uqblCcUKvoy7RycvFl1Df+o3Af/7ooWfDEyFX1CCo/KYt1QrAkgxt7Kb6/xTU7Vj78TqAkjJo68Re8LVptyztNnPUdpojx9Ll4T3Kkn1RGDrx43mWkrO7/Tr8xXKGeVo2DMj2SqFclxtsHYz7HRyP2zcaXdyvPSX8/wxBpyNoqxXfsDUCbmMBvlEYaBF8gpF8UeykZLWeGQ6RUnyWC9jrznhVGevPVe2++pB56HK07/5n96u+IkYdWCqBo9jF10qasgM8zTAhE5LqghV3ZXgqAW6IoycYBxpcldNIW5MV1ZhTHCVl89E8G+HXTAfYtY4K6YidZKgtZbSh0XeIWZWSjTZfRtfCl0OhsNnplfBuCZqWTmAoYihsTsg7eAwNRgZk4/TAchpeeEPIszSpJvwEOBUcfvzJwFGis13j9pukAFPgaydjzMMpZlQoeZQmit4kTOPTgrnyt/GswqBEMbHbFbs+icWfnCRMELfeDpFaHe3oXwjRLJO7O3qzpUqFbAVaaUpfPoOgCLGrLyEvqcMUcDDkblA+fQI6EzdEUgDFOaLXoai2mjCPA0rggnwuWyn5EErEqH+qXumzxl6ccmvg3Zpm2JGNgJSnsrggRt5nOAnoqSwHfqC9FExhi/MY6AsTZApq11T/ANOfYV7YUiv9Dt8mbgEKPfq5VZKaQOqRMeFL4c+E87CLPOKmgUVuF2vQoWkvoS14niYVQ26jdeCTh/H9AfL34TKziItPOFa8Hd4jjSB87PIsBcyV4r/oA4PeCcKZqPYOnTAuT7KN/j9ihmB1lh1ovQz+DLWShPNvyMrbZRNLMVV/1bE1O03V6ZT5ttOe6tdEzCoMyyZU+mtrKwdQABCNw5a4WSwayqlAwxeZQn4JlJZhDPihM3UrLiJIVDgz/KD4rT9/hG1Aza5LF9i/NYfDcO5an1RDthcPf2QU+KkE4UbARonvAKzVrPKhuKU6/dRdCpAugtddo2Q566Fc4CjduPbuty/qI4eQukhDwelJefMes6W+E8gTN8cvFh5ctbBT0Ly5muXA4BPwvP0YqTFKcoT1Eu4d2+6EArR1GelDcUJ6bLaQzqA/4UAitO3IYIbgk/NEOvcklxsOKkPBLB39/TeTJuUfzxBx9KefrAZRveR7Tyn/CoNMHttxQnpF9jVFgIsdvwiRT67Cu8R3tfrGZiFDygwYZ9+29bKCxkTJIPv4jiRNlsxYmdBjlDQln+bATifGeGmfG0fwRnxpA13pDk7sRJaOXBCU7iTxum+haHwa88oRltLh1ax4l+cN0FHWnchZ3xv9FOyvkbMneMVYFpRzsAm1Il4ZvEKS6/aqMZPyNnWHEimhoCfAp+RB/hemWUJ++KsdzCanH6Ln0kdcZKCOeqo+x4V4HyTxF4wkf1C6+yvCGc4LIyJoH/Pd3Y9/6777zw5huvKxwY4ZZClm3+WokVD0IxA/dXkmlQnDgf9e477+p9x/ISchPDThSnrFZlNQnFSf1SfI3xKvKYJk6l5OT2zJFRLJ+wIibFSQSw/Y2thKw4IbfAd6Hf8ZWnbK1DbgMePofi9DPxQPHBj/XO+dQqTshr0PGatj2ySuZPEigNtht69WkmXeCZKG9NE7nF5SvekdUiJoC+8BhMWdJ3KAvOarqexM9fkfL0BTzvc159G46VQF4ruFl1yu6P7GKgjtxUMal/tynaG3Z+qG0M0eY/FKfwoYTHf8svwCcO8Nen7oGxs35A1l5zSDiDBuFQuFyxiOArI7sJH2cIl8Oc1WZCNteEg57hsGrv/hJ+btOL8gRvYmJn36rH7dg8f/JXf4/N8+fZn/vP/ubXP/l4b9VjPyUz+BrbhTDCqNpZKmTFHGauFh5yUziQZ7lVJnZeF6CD9eN/HIcd53OfMAPHUbyAH5GcEO68Ozyp4+2KcUCriAK7T9Qg5Elh6VgS8JV5KwoTgU6N4JSVpJm9pTMjhNBZ3WHpgHmjNJEH0gSxOu8oSnRChIacF0KpUukJRikObBQMr0S5syccyikJaEEQSppJP8JQ99oOTTAF6FKHgZ7ETv6dPmkOMyGvVdas3Hl2XEqLaPXqF0LIlAkYvEfYQlVWu6w0jpvycLXoh7z7BX/tyumqUdEV+mA4isaPHs/yMGsDHZ69wYzg6TNNKlK30ykaipBEozjJKhqMy/goM9qsi5lfvXqSVIzprNTCCjcQzvjaqeBEc00EYtAlJKHxaoDiL2ZQBA1rIpiHn6yHa9Ksz0HPGf0SowEDq6iUAcXhNqHyoc1YSJcwiwLFpSI++Er9+WEyJYo0AyEDGYJrhP8ZiFTuahYU0X6hY9yLCgD05Le+3850FQalMQyq+8jKW+umgU7vLtGrxxmn7dKZITVnjLJLHGjJbZlZ+eH6XRSpfJw2M5dWJvQdIcxsve3NTOrXMNgZtEDNbC68hk8IdOKDwTjb9FjNlYA/itOL2p/NAI/SlLMK+pjlrDh5mweD91Kc2B8vxWlWnIwfoUmDf1a2JIQoXQsnqlNu0/pIW8y42KJPBvasqqNcVGHoSg1lgzKBwOUVIa1aoTiZb4pvEN+wUrJ8iQOKk/DQt+GFyTv9uleMK29S/jLz96rbKSt0wK1tw2qD4VVZgQ+/fdGKlQU10UMbRXn1OS1nRuU2ipOVJ9kp6ypOLm8UVCmflB+CD7ye1W0EmFxEM4qT8CE0IpiRv96ql88jfKmt71KctOL04x994L4DjwoPpJWVB217/OiX8KsKz5u3RAyg3ygOTZCo51N3mueExHHxCgaHb/9tmxRuwmn3Ec7bNtMupWx6xSnny6ysq46Y7GJVwfUg4dD1UMXJ/J/xTUlMn2peTIXz0cwI5rCaqP4I+DH3c0ChXFTb4RRu/FaoLMFo2k7vxkj0ui4Q384BAtIYRI1kZzPaum8gUa4J4yz04uWqH3gSChQm6fABXIZ7xnxe6ozJF/reWnFSOP2OMT/9RQqu+o0nS9XHsjKODDRjsRWT/Q01tuSzUsJtuu++o+9E6eUMFTP6DB9MFn0xgj7b/3zOUxMaKE6k+/prr7/wnr799P6777nP0e+g1ROkCmf1x6tOMj1B7XBpZG47jEsZi7zi5DEqkyT0SV5/JkWrB6yy0S65oCLjVlAwBpZ3WHGSG5q5jj23oUZ5YiIJPkJctiijOL0m2nM8QdvrxAc60UUf6djqSTLiKe2lQKo8si3yc+e1K/nljVacVK7sYsikV24RzioYZciKYM5vVqkEv2XxaSppIarcaTvlH/jnL3zGcpJ2PRhe45FaSuy7hY1bxnoCz+9Kp2H2HHwr3K2xEBcz5C6iFUa7BQS/8U9CeOo5Yeuun0vAUIWD0yY6JsKhTI3hEW2QRas40W5kv3zHKYrTn/4Xf+/g3MazP/9XpDgdZ5x+qq0dX+kWj6U4jUCEUHQ+WW2ikE2KyQPk9jVAerKs7dRUEM5LiRh0/QQAoAEjDp43caxVy9vm4DXMEKxCss3RDqalBjUQR5ICgiYFZJCTCaPnFXzJXasjKEesMqmjePCEOgF5kDGzYuANShKJYhR8FhiE135VlJSwBTRTFuoIx4/0ZTE9lAHZgZ4M7ukEZpBKkE7rWXHMcYehhrbmAxnQJbLyC37SYlZp8r0EG3V+7OOf8kGAC4O6mEq3Ch35TnUkD1bQmk/yREb0Y/ocDwWPfDnA6a2vd1u4hC6E9DBzUPS7TaN3Gh/+4KUcXFBiCMZpvJQz4XqSjCz1wFP2EZBx9UndjGvApybiWRRtk0ZOCfPwq9fpyc/4G8EAm5ZF3PjfGUc8wR6ugWw614ilf7VRlUUP+4fpzyoCgrWUKNpRywfhn8GMAYoBFtMDLu1Fb8peppKGVzgLmPzJ9DOm286VtHvXqpcEXZzGi38QFn2RGLaNvJ4rTj0wd8zg7y8m7xGJch5wi72qv8zkMnmQFaAXtYUufggqCBooSnq5sUdCwFY0hZkyo53oBW+UAOHURIi3fEixiYLSLbBRnMDPB/x4UdQ4q8CWwb3ixGpABH+f/+NGpllxYsYX/F5x4sbN6aekzS2ebEH7UKskH3zwgQSHjygglwJKRm5dzCUM3m7HXnwJzZwrIv5nn+kCDK82MaBLcRI+eCeZY9Xb23gF64+lagbYipPaE21or3BzzTf5k1IqxekNCScIYcxC+1E5RWlXW6T9CT/xeeiHVPlS3JR+hCm+0aGIVKcAzKeGX3uLjnD7e1az4oRgiZJKHYSPIkjlcHaUp2xfpm0g4LzKd/M8Sw6Nz3QuQtsJ9Y25j/TNrh9JaeLNFrW0n0zoQAwYZlinDdiHPIRnOzPkyYIMsETY8QzOT7yWcxCNO1hx1FYMjVD/QjwKx8+UqGKsOKk+vUJNmSE4qy0g9BHGgX++15fVC61ccPOpFdjwjL3ipJR34rHe5MXBN36lOxEOBNcAu1bosuCteohhmMuPAgi9gC/Y7bttl9jf0jEpnEiWPSV9V6kNl1kribVoPMRRQ/LgZSwVR9G7hUR4vs8/Mjkm/mAeIz4PaMZ09UMpJ1ZYrDChZHRSQzSLX9FPu+OkvIMLa17ldlHV/zvv6DtRuoqcM0v+yKiUJ5Qw+EH7JQoIkyyZiJXi9Prr/v7T+++9H5pEG2O2+YIVHRQnrSZpt0kUp6yUw1fo14xLVvgEY9rFU+juOW+kFScpTNx4iuLUy3JQnlBoYE/08SpONcHHxS7wQCaQWIVfK06KZMVptur1G1FMoHhySOWB8ndOSiG/kJ9+YoaVJlb/P5UiSb1kFwA8PP3Il0RYcXpZZYfSBPwX/pwFWwdzbjZnVuEtvOGD1Pv5ysnjdoM/T3iKZYBuzZtbXQnJjY7FEfjd0ozAGGrb6Rl1vBcNOJvuBMkYNrY9Lq26/Y+WrvcuOv7nE7iATZwJDleFy4Im/YF+wY169BkmFlht8jmnWW1CeXrjjVf9EVz4O8+f/Zf+MZvnz7O/8J9fFSefcWLFSenQsBBQq0StiO6Yw/RFkskd5l/Sa7pnEtFxMJJFV5L9F9bDIpiAycDO73jYrH2iUBN649swKMhzmeWXV6vGFVOwoct0DvF0QK/IqOHD8HnAnsae1RsETx8mlhLVB1RumKod2wkg4ihBaxueE5Bi5BIUftKzXRZnwwTYp4wCBhZ0JMI/afBGMYDpxD3mCj/dRuH05sdMyAKGaKLOl8KmRoA/zLVlUZO4MJ79okSNsgZNaZ0rMWc3P0nPOYMu5UM/FlSclwhPRIyCBD1Dg/IPCorBqJS/oKxJLBdeTJDjHrxOjAhKOyV5gMva8rBZNKd3YzlycBusyBZXkK/86h1FDBfpynye4kR6wOzI9snP8gyeI+Q2/Bq080uRqNR3vanOchHBKE4aXC8rToLvoOQVp6U4VbkWbvcRkayyTX1gz+uMDNn2K+U7K5tUF2ach3WHHzZw3T3O252vPR6BmzaHJrX+Uj6JNOXWyDb5yayV9BApn2yHiQIFG1BXVNnCP5nNYrIhEw70e5cPXd14lAZ8U/Yon5mIQJBHkWW2lEGUK2p9hkpCPXgFLep0IxXnnLS1jXNNr2o/PB+dxk7dsWWQCTCu6ecilS+8TUZ76rWihQDCilP7LQodt2Wi7HwopelHP/qRVp2iOFEMCMTcnoailJsz2VLyahQn+dE2EDB42YrDzCrC8pSgBQ0OkfubP1Ka2JLCbZxehUMYQzDSduB8qy5li+Dzhrb8vK6XrXNWeJR5K1ls2bVSigCksk2jcqviXAIvNHhln0FMhJQfoyx1e475jXBSFpQ3kwUIgihPVpxUqcCkDFlF0aqT8sVqCs+rugyEffCUD/xXta60daPep8xYfzKK048t2MCvTKdLZep97M5x+UYVJ4WlDVKKLcnTHHu9IAhElyced96GeeR7+p32UIJSuxWnORd2UZw440TdbMXJn4xQmSGMWtnVGJixRPjPJERTi6BZSPANUAOX+fzwex4x8A+iBXbzSSdxgbsJWzR8e0swCGn+L2VQEW/Xt2xn+mPHOKsd++LpcnjIc+FGccLNUOzvvKmtRmmSKTt1at6jekH45u0kT1aC6WOiWg04uw4ipKMYsELD1uCczfn+C+/oGnKUJz5Lwqw+L6s3nDVkhcRbzKQssDINz6F/st3tPSlN73nFSbIUExYi2O1oFCcrB+Ix7aPwApgmf96qR/tSOhmjWHnJZA1tlT76hhWnl9UGWVVDOSSvyBORXzKZEjkOvlPFCeUpitOccRK9lA/y3laYZgJJ/I1JBFatzX+VNunz0K5o8/ANLnJgAgtFDD7lXi4ew+SSeT0TUhLYwcNkUy6pyG2Hn7B1UC8rTj2L1i2NlCetAj7n1iGTv9X+RUPal6EUIjlNbyZyxLvc+RTDV+EPDogXVF475id+/F7C43H1c3wD5mfBBCxNOr8icB4sdcg848i/IQFurhrnDCUs4RfFibYp5Yk0uj0vH8DNqpO/yyjlqYrTn/uXf9/QtY1n//Z/8Td9OcS6VY8DxUtx2kqThelLDsjNkCUaTrIZ1utOLxasM0/FJnEqdT3LuizTAIAgTv2vdsd3QhN+y3lNxbSJsbdCXPxuVcVNUoMfnDwyMxsj6OmoeFvIH1g6Xb4/RCNcuXZ+TbfQpzTAR6ojPC18irPitVHIb8g6qAsNwJKMHpeh8QPVssF08PjFPhDLsUAKKtP0Q5dps4eSwg3Q+BMmJhBhRQ2QclDHXytGJI6f6SGenhJ04h176XCZion3Vj2XnaK23G2K0YUWIxVFiu3imPam+j/DgaJcnP7QhDPPFGKdmI9GqTP8tDe60uTPT/1wm66HEcSj1AYA8Dswi67GIfzOs4EyV2JP+F3D48pvyzaCs5Rd1R+D0ks+HxOhnYPzXIJgCvSzFScu54iQu1dQMghR9uYTMplncGrUc5KVR3NdvwYcWVCKbS6n7539UdQCua7riHkPfvpgb1knz/waoj8zsCQP8lReoiCFCasIJYQwENMnhE2mJxi0eutJCAT4yb8HKeMlTb1ut4JX+2aw9dYxFCe2wOjltj72WyOkg5sVYXByAxSrXaw6sR++t+qxzQ2hiC3XCAm+3ECznD/1nvpDcVIGKqTkMPjnOpcjxemHP3zhA23X68MKVVeZuNLY7xyMZosKM8JdqfJ2FLZlyQ/cvLQtK066FML7+Flxkh9tioGf7XSsUCBQeUVCeYOvoji98cYbVhxzRko37ElAgCdHwMOMG+X054qHcPHJJ1LgpDipYF2HTOCgLGUrDqZeVVj6AWUYIYd8ZiuT+oCUyyhYfA+LK4EjdPlWTwlq1ByXV7yqq+g9Bsx15ChNn3yiFScpTj/8YZRQZrrpF+aZzPI6Nu0r9a8gmobdbn1WnhJW2NQFofXHB3uf2t2wQKZn7BfbjvU4NFSdsWlzPFacJOBRTllxkvL0SHFSm2NVwW2Kej0UJ+qXlUKTlkxHZksSB8WAiMJma4UsDwHsHEz0Jw1D7p8Nd4MiLWYHr2THy+E3cQ7o51oTbf/aNri2nfwdeVwu+Q1szHHIuzFsjsPcCwGR+PpBiYHPe1VFygH8IauqjJnCpTeTNxnb4UX2ZqyXkkGdoaSwNY4+lxWnbP9jxYkVHRSnt9/WZIe2xrl1K01v1WNShotaRoFiZRuljD7MBQvvvssZp3fVrjQWiS76a5Wg8ghgmdiAl6BktDDgMz3j1DGKkiF/8NLXxDu94vTaS5pAyuQH/bG38MITvDpleFbjbxUn+Mmx4iQ6yPvaruwJoUwidIcAWyI9OcWKXvmMiMq4yXkl3dinlSNWj3jgQ/AQ80gUJylN3aLtK+KlZPFR4kxOSXGSO0ooF9/MyqDqx5M3woVCRKXzt7oIVUxi/gmM4QSTDjh8aSlOpgzq/IdrP9PI7HHaC0EcHv3S+Poc1gGYJm2iWqWCHvedCaKbHjptPHGId43rdig/TI2cfk2S4rHi5O15WmU6FafXWXE6FKd/61/5/Yp3fZ79RRSn44wTK05faqBIZ4kg5K1SQ0+ZfdCkYspqAdlvSHUYldgCdOG1YA9iGm6vCR8/u1Y8WcZuUCcoj6FvAocSvCfAccYuI/64HWAzaOsOCs+ki4l0xUm+jmJFQXGrNNHZLdxPS13ZEbpgJC2YUWhYdGkgCTU3/k6H0tPfkOSo/Bj0oHNSqCBA1D60j4kgM3HOmOMVcONelK14oQwnaScPEZBFn4i7vGBa9RZ70zvxhCz9FlamitBM80DhJKGfpJ02gSa6WDGnrSGAOrwwNUEMmEtTFiMjcD9Ft3w2puWFBbgGWWkaz/rhtJCN5fqkZJM2ylOfM6rxrwSAuCNMfpcYRXPj/whm+1ESUZxYMeQwfmbdrUCxYqEBKsIl6BHAYdDZyuEDqRKGLLSq/Xa1xG1iBlwZJjN9Ahw88vR/hcj43v4uXnEb8C3d5M3Pg6LbJVBbzQD31ziGVuNynQ7C6QOdVc3H9Ko0KRLMXC/KjTbsySSN9BtwBbd+3fbBqZe0VGgMuJlt5FAzZwdYoWHlSSshKE4C7CqWxmT1F4QEDeS6mIItZr0YAqGB+kFxYsbWW6YkwHg/vQRfVlkYaK04qW67Z/7HP/7xCz/80Q+98mSq9QNNHK7mEoAoMwhHuuJXShPCAkLyD3+keD/8kVeuUC5oKxbSaFtqS74UQooTM7W8CAbdShchO7Oo3sIjJQghnW08b7z+hpSTHpjWRRGUPaUJT5awgEDF/v6sdPxcwsVPfLkFipPbr/D4kh6VB0KZBT4UJ+W9+XM/kB/CSwSWCD64eaN8SujSbDWz0eSP+mU2+1Sc4NYoTbwffsiKE2W5V5zSDkZAcU2m7mkefYXYlZo2PG2DRuMHX8Lrj8lTU1Yy5aeWmvV/yjzhTnvgaau0FdrVi2o7ubAExUlCs4Q9FHwUqq4UuE4RmL1VL+fNELwRwKk3P+4XB8nyPFNOTQN5+uJufm/9CXv8TFIKvIlz4yT84nVxHKE3/o9Tvfct9kYvXTGbr5o7/rNzTHHkYtil4aajqI7tgR8+FHe2zqUOkVm4jZO6pO2vMhER0OF2avTiNupnrjN4CSvDsy0skxBZcUJp4n37ba04jeLk9JT2z6Vo/fRLVqD35Ah9yNv+hfMV8ZS3335bH+Z9y7SAl6cKE+NOzlbr6nK2huqcJHSTLzLqHRBabWJM6vhEHqo40Udff4NLMV4WbxIt6rucWex3H+FRrHKB07d5ahIKvvSBVps+/FC8RMoN37PiYoaukMM7rpcp5dwYvBCeyJa9jqPhIenzvuRKStut4kRGoGMpY+KNxU+6n1hx+tSrTXyYmNUqVpxYeYL/wZ/oU+bnrs89KURjdlVi2kLpwkVYcaIQu+o0BXqjOAUmcfjNAyzPxFl2W8Y1MK6o8b/YC9seMe4Sa2cJvjXbYuG4etzWsQC3YUMdMPWH71BO9Av53WzVk5SjMtIqqL7hdFWc/gDIL8+zv/hf6la9j7/Id5ykNHE5BFsTVB9q3AxOrDrFpKCG1HtTHXuT3CyIdPfcZIFc7ooYOgiq79hLoWHtF5gdv54yWyYE6tmNo9TIcxWsQYAyHFV20lNBxvU70cl/lQTHA51f/QqQAX7PKoC3BMk/JDm9kBek16Yiv4mDf+Irih8quHhARniDFnJ5xH7mZaIXOGbjjq8NRV2YhPyaviB2JuSAVsgNnH4Vd1K90LkTuNIPvuAAgrystKFj4XAoP3pIZZ5lwe0IMfWbJWegF8YJG3fzsYJBxhsP/64weT96FG6QRYdcsq9yX/4T+Rzs5LVycqw4XaI8TP+R5yXWA0ofhZN6/FsUuYwjs+8w6szqZ1YPxk/bbnF6UIJJM+s4A1Vn+7sCGQVK+XT7wIwdAne7EhWH/wPineYq04cA3+yZvrzhdolsm6jaAMuW8l7pLxD8M6g7LyhFZsDyVn2GGccE7zPq2IpTVpiTVvoLGfSfBmC3eQYv0lfBIJAygCKMclbgDb18kA/FifM04LFyobrI9gNWZ/ShRylO2aaXmWRmTjnYTR1ZcWLmX6/PUC3FSVSIBuq2VwVbcZIC9OGHH7jOqCsECys8KE76gOabWgVCgfIhZglMxP3N3/wtv6TBChTKTFZypIxwKYTy0xv1UMBoX8xeI6SwSuVr0HVOihUo/Cnt16Q4sZUnq1z61ouUNfPb4RPkLcohbTLvRz/5SIe6PzLOpu+bPzUZ8F3OdUlAoQwod7dXZdC1Ak4JHRV4UBavihPb9Ko4acVJBCKUcfOfb9WzIvZMwo3OIlhx+thb9VCcKF8NIy7PtCFyt8dSh6k5UdZG7NynjcgHzzESD6EnXnEncODSkLZXWtbhfmQtZwhVhTAqOZyKMozihNKEkOlVUbYm0VatDL9iBTvCKzP7c3uY6hZBed2qx3gqIW/xA+c5KTY9XJui0zdw199vCp9yvUZyCrdF8xDT4bloOvzu0D7XY2FwXReN633Fu63TkEowLSI0Nya+206bJDZP7PChsQ+vIpxJXt/+a8XpbGeO6h+nJWxRnNi+h1yoibNRnLLidFWc3kFxegfe8Kr6WLYys135K12Q45Uh2gFb6tTnM+nxC/EtnY/S5MjrevswSZ20pAyJz6EcMFn0siZegPc28skXW3a7A8KTe6LPPEt5gwfCo+ijr7yqG+p0pTffkvqSFSfxRpQnYLp1sd/e5NzUhx9+7DcfAs65ouafMo2ixZZoXo2Zej2RNJNJKE9MgIGfFwXVk1hKl7NOrDixVQ9awceEllea1J+YvMl5tO9ZYeNSCFacfsJWPSlOWXHKGafkQ5Nz6lfwL08WmT+4pt08Ml4dLUVyiXmIx7OBo67deqLkpS4UBn32jw+trW0D24Ib2w4fD2AG7DImN6rAiBNCbZkf+kpC4lH7rZnQaB/JwZb34/bYPGOxCkqFLX/1B3EilT/b6XUOdpQm3Kw48Xar3l/4ow9WnP6dv8p3nL5QpeiaSFabpI3/jMshvDzLbMPMOISrKwFynEJuhx9ync2drVVagofRn+5ktr/XAg3cWVkteNLd/oPPCco+Ce/0wR5XCrWpxaQxFbY4LcSUzgEHrgMsXo2140j4QXlSPON0obREgmShNJmT6pCfBoOfMEOT4x/pKHLoMkgQYr0I5UF2PysF+MZp1+DHznMpexpVw4doh9de2mSaYpmT8uCy4Z/mI2UTgXOVAxAt/EYRIuPyT2m+BdrAm24iOJJQxgxU7RMuw9jsXbzEKBxlUfyPzR1eZIrjeriPiH6YZ4el1FwjRFRwfQr7wNyJHoGJG4+N/wCQdREw3k0r/sTqDJVXnCRYXledsuJUJpSZ5Kw45TyKBj8NpgyAeYUfoYi24WYk00lhJs1tirrxe0Tzrttr6Ld1fVP8oWbQxaXSWOjPNhFPl5YgtoIT+hXX9bgVp2wHAKcKgdcMGzgwpb1Bn/8kbHPLnem1nz6qKoUityu9IgXlNb0oDFlxQVCnUP3BZykmuUZVqzOigYGWDyai5GS2VYqTFYsou19KYLDipBUUVpsyaxvFDYED5Yf3A12f/UNt1ePjrZ0wArYrRW/qGy18pwUFymewRBMD+d/9u7/pl338CEmkHaFCqxMSIFCWtuLEhyO1dUZCFLCePZ2zSfleHFfD/kICT76VgpL2lrYBvfXWmyOIICD021G5EZCP/HJ+4MOPPtQ2uQ+tOJEGQhbnqRD0MTNTnjKHxqzeUVNqp6qhni8jz6fiBG4LaKME0C2zRWkrTlxS88knX0h5+lzllxWnH2kFr1v1aPJpmxFOSA8+jv8aY9QmdltUIgAtHqBW43ayYcLz7lusYs0DAp6acd3+XvvjFdZpkq4EM7ZJsbJkwRAhj616s4pIfUdx4gZCXT+t9uTzbrKjPFHX5DOK0x0Fy6Opp0bqXV93pPGsH87aH4c/ZDcP42xcj+NQZ6S104mt6ROfZ4fHnXB+i3eZh999PHyE6xa9kZ6ee/QjZZrMeu0urOAU5pUW8QIL2tQtf5gEGh4fViOQBaO8VDGh/lCiicsZJ1abWBVHaXr33TetOElXUDshr0xuIJzO2GHeAO9S3zOPYCWd85Kvxk/+jDWdVGna0AH/+L5eeCT9X8kb90+Fk7bF5Ak0MkvhSRO1VVbp2VL7slbj/S02XevN+OWbo5W+FSDxTiYN+awDF+1AL/2X7ba+yU5KDqs7mZzJZTOegGG1avoDyhP05ZxSVuKZVOgOAHgJMrV0T/eNnsUkXy5pKU70Kd5MYLJS/12lrxUnvXxQ29ej67ZOVqF8xkn9qzxeWVadjOIED5k6pY25vdp01VK7fjOexY6fVN0VFshpC9N0zhY2IQEzvjbRa4gBpk0BRl5Pv0sfn3TAFCu/y3PsddcMOn7JAa+fEcDsJ/szCkK7Pzwe49bLrh8rTShPel/Qq9bu8RbFibNmPH/+jzxQnP4SipNmybhZj33cXnHSLUJUiJczVSNdXg9ZyvgI7bhdCVMwDp9yCfktpKkQO/Uz3qbKGE6/m8BbYLkDMXAktOgJRpfRKsC75IjQ0EQoRjc2ebWigRSyvIAm3qZQtOBwPKpIxCRxgP0sWFkuqTogpUQI8ZyVFX/SEhw5XniE1bEmzyt3RUWqDbvBZYKWn10rOq60tQNRy8H5CzwwyeeYN7SZ0BA4EaaMJgdGeSQRIBIfcBlTEhP0CJigs1TSZRLhLKltd5Fsp0A3XpfuCjv9g9G/RxkkbXyJdNJhSP9clFjDBW+qRnanN3472o1tiFq0NXjTWJ978wqzU5JtgjJQhtl2pj0rT1wyEMUpdaEtERpMLBRpcMotRwjHMwBqsENpiuIk9LQx/mX6b9zQaHybhCG7HjJ52u7iuvs9i2NiXGDO8EvA4TAdh3tb3RrGWUyY+N8oTu4wokBmGLQUpfEzY7bSVAUKSgXndpQ20xlCBrzWBRdBZHveqy+8+aYUpzejOL02AgB0WIBhNUkzo1/9nNUZKU7aYpKP42bbCbOcrPpQRwz4S3HSAI2wgPCL4gwNCDAIFQi7H+hWPS6HsOKkOkXAARffK0EoQIl5U0oTJithr2s2l334f+fv/Ibe3/TWLBQM0rXipHR8bTfCNcqThAmEH84QRdiR4qSzD9y2xS1T0IDyBE2cn0L4ePPNNyWQvePXwo2+m8WlGBUa2H7z6We5stdnrbRaxseHictWMswINbn5LbfgEX+2udBO50VogW4O0iMYMqnwM6008fFbC16zcqdCt8DIFkZWsGizCC85i/CZhS7Oi3ElOTPVVhjcWNOWqEcetxz5u//YR/4IPjyGX4Y8htc03DUfPGerddzLzyAqwiOMrrmfOOK3A9I2IyjTbmg/vSyENlGlmnLztiq1uyhOEfCwp16vitN9N7/vlUPRJvHWds3AbajcB86dpQdwp9eR6oM4p1fK/fQ58aRu4rNxLpLl1Zg263CEM25qfgGvJC4R3BoIarli+lXE+pEidlaMEP7dxhf/UW4UuNqS0DN54ssixAc80WCez2qS2oNgubiFbXBMorz37lu66OFtbb17Te2D7cO0GWiEB24FzErT8JZnz5igYKzRx6tRftRXGF8ygfKl+QA0EJ+ry7kGnDaHUsaLbNrVZm6/hDfKy30SZYXVJFaSuEjnc91Ox5ttffDGKE5MGKIAsqrPy2QKt2KiOH3qD+HmCvBTcSL/lB9t/qXvoeywqsV2Zil35jm53IHtf/Aq4FmQ4IWP+KZB8TseyhtaffMoPIvdAyq/l8XnPtWZyY8/ZvsvEzI6F6XX15GzSq9tsPAwyoChZinB5g+pyyk6xMvjoW2J9+sP/gMF5kNjx50npn8Vf6NInBNmx5moyxBs0NjHbWu5SXdjXVFsIeQMw963kDs8FCXc6D0Ok0vau/wpIL1sxbNbShPt8nI5hLfV69zd6xrXtNMDpZvnT/zqP2Lz/Hn2l36ND+Bqqx4rTjo855tHtJRZjZwbcqgYV84I5C1syHIvBKN7pgoiVO8s23+HpxAFZLiQ4sKMdX53oItmlw+IrjAuq/GjfBx6ibBInIgxFGVDbQpgHOdjGLW6DXuGLlTL84S7xbTDBt8A0Hx4LHBixuVffq545GGA8VWBL7zLUhhi80yAjIc5ESpjU/hK67SMnfZX78EY9Mcv4Wcahm+kG7iNjYADueGawq1JYBFinvbbsJVzAgJq8OKM9+2vQYp2ogFD0zhaikPiBli2FWdDJVFiH0/AD4+nrbeUxn3rexN/0XH6E2fi0Z5thea8KE75eGr2a+OGEbc2fR25BPAObAxyMO2uOlXwRADkWW47Sa/+Dp6f+MUxrQY40f8wCwZ8FHLiAegRTFJZZWDn8+IVB+bxKopjOT+yDYOGGbM9jyeMGSgYdRh0Ctwxg03lTvn6dioL6BFiEPDZnsZV3ChNKE9viJG/psPNzJzSsn7OpQ/iy1z1y0w+abwkwYBB2ttOJFGgHPjsjwQJrxAivGrARkmz8KuZUpSCZ3oRTAwjBQGFiVUSbtWD5yM0cUlCZkNf0oDCKliuCc/tQ9/3DOjf/lu/8cLf0sstUGzLQsm24iTBAkGiW/2iyOScQg+bsyrh7ScSJFB4ELKreNEOOf/wKz94/4Uf/Mr7wsMMN2e5RDcXQoi+z6Q0MYbxfvDBj33BBThJ8xUUNp0To0xf1esVKNGEwoiQ5dUgtVnKkDbKxRoooSiL7huCQzH7jGvGNVvdj1dS/RaUhB9ctBGUo84M8w2YnJP4yIJdt73vtkQ7SrsCl/uL206UaODah2hwq+WYyez2uPwVN7hrJsTeEzv2/iactPdz+jWAdh0eQXuxUjnKcJTpfW4NpZMypU68/dLfmuGbXvkYMQIrQjMCK0/xkrsrHaWoNOA+7Yx547axw2KjfLCN/wrGv8/CUI8xF/BEv8a5Ah9h9P9L4NVFkCnSj1OeZI7UjthHXANs97Yd4MsabJUIaCrA2xy7qZC9W+2sOMF/rDylThxLFeJ2OfXllVn83FcQ0qM4sSrCZA/vD37wjq4WZ4LjDfGrF7UKpfbyEnyO9KKEuU0Lh1ez1Qx+rm8I/fwrnSP8+XemnfQslEwmUJQ+uSIPTKC89eZbFmxfEl6xBodnCxx9WXxRr6IIPnlihZlLjqCBs0F8QJaV924B9CSALt7x7YBcq64XGvmA9U+kpHyq1R7HE1/rRA+8wvlXGbz0kiZmfOPo3oocBSi3c/oCH6VPRXz50xyFycpXJhXCY8JvXI7cqKdVr++/ou3X39ctnZ98JaWJl0tnWH3ifBMrufSpuWSDMlKZgot6pLD4c6Gp8NwqRnY3IYRSoFErEi4/WvBuxYTnsW3wuC7A7ZfwDYc94Ym3f0+YRrnx28CytW+a8kkCe98CT7icoSjhwdzcUC7yZxz2pUpadXJfpV3B87PaxEoTShUmbQDlie8m8vxrf/AftHn+PPt3pThxo5636nHrCLcHWXEKg7PiNMqTtxU4dsjMzJjsVJReF7qpTgEuUFkIc2VNeMrbjhNM9vitIiHPhjCSFV44B7ZRGHZBNxbkLfDlSTo7+eXtBrdcR9qH39U66d0newFrU8CzdqKYtgUpn/wbanlvz/EawptvMuL0V+TA2e+01qPmTgFbsFJPV/8dcuv/lPvEH/sF5XIsixDtONf08d9hsROvcZ9vJubEL+hDso80Bu4Exx66bJM9JnTsNjNlZ1Tb17Q+RvaQklvPg7IpidPnFnrcZ3r2CiNxTP0wOCzaBcsKUw+kemsT25o0MFDegKI4dcaNAeRLuW8VJwAvwh5ux3eqrjrc90/DH4ee8M7WmTdFfYTxjLPtt5B1YxZpTWJhv33lJXAXHxYzYUz9Y4dJ2w5YtgbAoB1BzJtm0xbjW9sQWiSQeuuHTGZTcymClBMpTm95qx6KB9f+SuDXH3KnZFOtImUmH6HGSpNmNlkpYase+JbiRH1JmeHV0OrtJb6WW+mhOIliCRwSPCT0/kQffuRGPcwIF1+5rbMVxR+lRQmR8sT3ld56S/SJRq7X/b+rOEkwQTD4SkKyzxZJyN7buZiJZstcrvD2YXMpWAgyfAOK7Sdci47ihILOOQy23KA4/T2/8oMXfkXv66/rsPfrzBBLMVS+yfunnCn6iO1xn/sbVGw3RAFDUXr1VW33E70+JyWas7IqBVTljsCVGWq1OypUr8tRHzOm/PyowvgIJd9mYlUrWxr1cV+Bv8KZC73gogxRnLiBi3MRbKv5SB8T/slHHzsvnq1XeMbItilS0FiZpI0jAl/CoSmz7QIDBujhO8Zj7wOXw+QpnHmIwVPztD7oa06jsNs0JxNuhGwrw67TWcVT/q8rTqM4qfzzvRmuos6WPa9EWhDnfJ7+hJN3UdIkTfP5Q8AZmPyteAZN+N7CXviaLZONNzxwu2MrPK77OBuGsIT7l35/9xzxZXU9k1tAH4Ff4h9x1zjfFC+Ah6NI0zpoDmCxudAJRnYE7Bet/KBYwIOyApH2JfJoq7Q/mVkhEsPRA+2puyhPTNRwhf8ret+X0lTF6Y03+A4bKycoBVwqMqv1RlIlig/k8p25F9RfXzAf8VXbwwPgC2zZdTsRrW+LD7z19lvqy1wuozFLK1oUpHdGaTJJ83riY/TD0Bj5lPyTl194RZsJGrYTZzVePEb0+0IYCcleQdeqEe3qY84V6YUeJoPo94x/jIOUBzSpyBSXFW3OXmVF3vZRxDhzKvZqOOj86Rd8wJvV2JzrJH8cZWDyDAXOt6dKcXrl+1p9euU7Ggueie+I7k/hcXpFy6f6nhMKk682Vz6YuMyiBnWe/uQKppypaD1ub7a3EQCXCRpajL+DacjK7RvO3sePW5h/nNoOURu19/ZxisuvKIcmg9EwHz6NVVNA7l911zwiK5OlANOv4sSuBuExGEWJdkwDAQfjRy6E0Ehgf8L9XTIpT2zv5Pmj/9Q/YPP8efbv/dr/NNeRs4TJAVg0d6Exg2N5Va/taGhX0lIxu7LSv1MYzdru8/Ln/9KLKfzzqStmcSwIe1xhUlvyG2AbR8RVN422kMly+IUhnIHbvtFtG6EX18Wx455QG4SueTwHHQlIKL8Jku0SgbgK2YV7hMMkDty1XjwfATQt4T3pmfi3NVW0Na9RTvynPdCrTi4JHXAm4aTjCHNBTGpn/heuUlKTolH87SzJMm/xNuj0H5ptCIn/iwwzb33OhOK3Q0Bxj9mIn/y5hb9174hHyDXJnfXTH3C5PShJmPaBVA1+uVWMrQddcRLNajs5uxDlKVv15lY98QaEV2A62Fogwc3Ln0nTT/43yZfSAAjo++cke4fiG+hHcTbcAhuvE/q0N0ZT22baPu4I086f0Sa+uSJMGmYsMJtGNwwbOpnxkmms6gCYmfWVcqARGEEdASarM7m2O1vhEEw1mPrWvHzjRLKMhQNWnLio42uVP/ERTr4rM2dzUJyy579bLFFmECZeZNBEaRqT+rGSpUGYrSm9fteH+sEvgODULPIoTm9qq54/eKnvtjCI/7q26f36r/+mZ2dRpBCSHUcCAYeeK1yzpYWVCc4GQBfKN4oSKxS82dKVFaueR0KR7FY9XwGulbesODEQ6uO7Wgn6RNvNuZjBl0Pow5Vcge0tM+vcgQQbCUVeMdGKE7R11ttV5Ypj+8/UhQSatl++3/SF0vjct3KJVtFLW48y+bL7kIrQ8FaslA8ENJ9JkBJF2ZIW9TYNJCZO0pU/dUAwSphnjWW3Hz/rceNSDNFmJrrHY9AYl2HtkK1xa9Zru+9s8ohfQ2izwYdwvbfq5UY9t1eXccqW+vSKk8oo5zAyw0/depV6ZAkwmvcoH8li02v6OzfO0spLXPzuGNvvansEMWXjoJPfPIK9Yru6Wsbx3WPaCXXAYFVGzQVcsSdc7Qf8lHlDyOwZuvwfWspnEod4myfhQHFidQLhPwpxVpwMmfYMjShNMnv5z06KuOB8pkkatghrIkNKwlvawvu2ziKyUv7aa1wFztkhJoWyrW3HpyjSH1gh8tl6rcZklTKTQeY96sOn4sSnCXi5ZVNJqv+pZYoOC/4qWDUtyapqT3rJY9ttCg7FJQoH7TDKYD574FV60c+nCFj1oWX5I7VSmKKgsHWQ8Y6dWIx11CKPeIAmEXxNOhNL4ivmCVLGzIs1ieVyYthQfn1jNQsTc8kM2xGz4jeKk9LmXE236rHi9AXK1ue/UF8Sn2SLn/IAb+V1fxLPTl6m/cqwDdNvx1+Tq5+UDWMZ9YdUbzgHw08U3kfh1i/GbbwNW7LXAa+4hTl960dU+1NpdhTqhFDAcspikHrUJHLs2yeUhwLsfbU6aFj3PPljxs7EprfqWX7J+aZTcaI98Pyrf/B32zx/nv37v/Y/7u84ccaJ7TiccRLubi9AeWLmASJTyJu81TxdGFNwyqxhj5Rwl1HS2lNk+o1lIHHUo+aBBOujCsNvlWDjLY+N8kRVMFAabwOPeOOV8Cf8DXMfVmyla0OkHAnHb5Exlg03GOxx42vnlGGDxtzYJ4GFpoCkue0TfDFaO5u4BC9aL9DPc+x0HsZ96Al9+btN/3kp7ZIs0prECh0um9PbCIdGGvbplt0++rlGuboSKj+AGzSoVjmCd/zGWLgJet5T+BPmkd9KoDQcEeyln/IrE4qbPwZOCdBebbLiNAdUZYepQyjlVuG7h3bZ2pUZ/zDuDq6YMAkLnZhBsMyDLFl3Tgx3DXyuizzt2M8FDeQCXpYnMLi0FHaa2FUWFKCiG8P6waJXQVGcHDBlvZl0Yg1zh/8JHgWG1SbKmdlflB/qId88yk16sUcw4fIHeKgfJePtk5oBpcwRThCCjAfhW/YKsZ1dxXSbRGGa/GAi0HvmUkz/CylBrBAgZLBqgzADnvJuFCBWcLhZ77333tUs87ua5f3qhf/nN/7fF35Db/bga1ZX8fO9pGzzY+8/cVHIrbxIcTF9oslnGiRoW2niwggJKbQ3lAjSZfsi23TyTadcy87KUNoY3wxiK13OOH3yKecSPjHOri5RplbYZDKzi6D0HSluzhNlpTSybTHl1pVWKzsq25xx6u1dEaJI2yuF39FBC8Wn1nnpFwg0lFs+cvmZx1HqzCxGsCisbjDTxvAnrpuR667hxHHIwJPUtMM0uOUPDfupvXFrAiF7/je4bUcPXODLYgjKi/JjFZCy9c16UopZJWWmnZVOeAL1Sl1yJbzb0tRtFSdkCedVZNIek0r8VooOuyHx1rnyvGINBPm/9TsjT3iS/AbYM17txL8+i5Srt1wD6yTDD8n+0+QBeI8ftI99CZlnZTl8hggt3cRdAOYP1GcUKCZSqAnaJpC8FI7otYFJ3GABpHHhWShPvFwQwc2fnujxd+Vyxbf5kuDWA079UQ7eZjfKRJSUXiTCtvBcxGCS1C+63RblBn7HStamu30m9EMtWXB+oFdOT9SI37D6i/JD/zbvZbIJBRDFRwoU8fztMfHCXMSjCailoCiueEJ4T+OLh4ufRQkqPylt0GCUXtV3fi1jh090cshbBtWX2B7GubHvidejdP6MW66/ROliQSM8KLsDMilWBRCTyZwouqeptOXv6iNjfqZARNiqX/kTOi0nBTb1nTjHr9FQonrGXszxzC8QV/+kSzszalfsFcIx7UUL6SObk9s+DZmMyZmwcmJMXo1uNu1mchM4banHtOKkFSfLMVwM4TcrTlzI1K16f+yffXDG6T/4r6Q4zeUQbNFDaeI6chq16sJMH3MpTlAsRQUyVyGLjMmZjYQB2OzETgtKQ3bRCdalMYEYBx5cj8JBbjhMHsW580vI/jXAdmKbpB1yBqeFBeWKcdvwEnAX9yYWUIMOm/52fs8kD+8gvvwK8gLcwJ2B2AJ3D1raiZfQUlGzGJfpgCdDn0+ukCTmPSXgX1iXZaV6sUwLid83wF6BTmDspYPyf/TUV2atY1lOotlx4i4u+S3AI3z5Jc92zk/bwQFSZN9oPj/OmcdALopkoUzTpexw/8oMtw75ajA6V5vYUsVAUMGNrVMIyNmy19kulrpnxksNPQPKHlQcF3/e5mx3iPpMAR0wO+RiW3k5fBfew++xVZAGPmOc9sZqKqdJeVF6lEfgWi5BqhoFvCtOJOToAIt5lmED0lcWFFa/EloQLihvhHorsWyd9Ewuh48jgHxP55gQdEgLPBzER0hFCFD04FCg0BiO80mrfmZgVTQ9kxes4uXw9uwq6FknvnciBaqKk+o++edcD7PJnLuK4oTyRBo//K0fvfBbusY8Z5U4L6ttKCY027tYmXhFbSrb9zhflUspiItAg1DdrTCslDGYEZ8XpYebBvnmC2cqvGIlQcftSnmPYiN6NSObLX+fua36+ywIQ5oVdrqsNCmerwdmGyBKawUeu6OUQDYPtDF5SLv/0sIOAhSzzlz2gFKMApqx0PD66SofM9Tntj7CXWsorU6ARJKQm5R+MCkXrwYCPn5YeUhLkfUfOusbNJuO4DXWgKThX+0THOOEPcGu/tAdxSlnnbJ6yIH6HIhnFdFCCIqTZtN7tom2QHuijlOmlF3yM0W9+hX+TpWs4njqWYFYrnTumNt/gV+wJvz8TXIb+lHyrofbgCeinN6ks3jhJu0G0xnjJkjO54eSwBHHfAf36ZnRp30LvuGVZ9oVf6ttKo6jwb+hW2iAtRE4miGr5axys62WVSfOCmXFJJM9XsEVTCYjoEXPoKYstiIxPICJBylM9DH6GjCmSYQyiURb824IVrGGZ9Jn3G+mbwE/pJM5/p03K0tWImZMGtyePHQ+whsYiXL+Ev4645vjRTmBJlbCUEhcjoqbckv2+KWc/CwL/Fr8hO3B5iHKo3gKdHdlHr6WiSVuRg0tuYlP/NnnWqX0WfETDwKX8OSMsegRP+KN8oQdP+UEU/TqfyrxpIty4o8gTLlN70G0wupymU705WvPQFzDA3HvJ1jAz3a2cNYysTAYU/WESluvP85YIBLgXJCTvKon2+kLSpf2zpicFVj4O7ycc05ZfUJ5eo3vJ3I5xKw4/fE/9I8H9fH77D/8a//z12xx+Fi3dqAweZueVpjYK6wyd4HHjB/Z8DvKU0oBzxRIshkY0qkbO5S7gRk08Cu+AQy0bS7cOp1jOWoe/q5t4bYXv0n1/C30UyYNp89px2+7BTNgG9oQLqexLRjc69lR7bXi32ZnRTgsj2CU5+29sCnSTULxGWSBa7yaR0qH9Qzd+E/fA3isT9GU4BV3We4xLJ+d5PK6wXLjf+tsIhtRbOfvFeNubkc3XdGDz871Iz/bm9ZBw4p375egB3EO0OdZH6EOfEK+Hmaz2gJt231PUJgA2x1hHeWpSpNNC+05BwIo7d8DGYOZ3gi5c8bJDBveMIxbsNiJ07c4MO+fMzdT7qeXIlxK6nQM3A34fRLL54S8tRdxTbWv/qgAAEAASURBVCLVLlMfBmzrJmZi5xfG7KeCCtH0eluA62LHcJDDFO66UB1M3WRmFqE0WzcQ8BlAGVhzyJiVqcRjAPCBbQR4lffCAe7BhyLLAK+KCH3mk1hNxdSP8LAd2wOxPkdhBSZbQSLw5qKG4Mzqj88N6Yard955W+87xv+hLpP4SOei2J7FR09RHJyqflBOso1FqxVSVrwtUUKO2wcDu+j06pPbEoP/FphIl+1hvtRB7dLnnoSPsiI++YtiMzSzyiGFD3yc46qgH3sUJQsq4NC77OoDuCkZfigyCx+ygIstOr34AMEHmgEELnSkvRNGWdJHqjAAk37nypFVlejHqdlmGNk8kcE2SnATksiGMQ7X7cST3cQCZgjcsdl5+al/zQTatX8uMU6HU1J6KJ6Umetz+ISVYgm1UZwQRrSKiOJNXbC1CMVJihRl4gtHKDulSd2WfvJ5pSxBQDx+FPIw8KEnRXc8OJTaJHhN+QJ4xJnkHPw0TCPcQcgjdZx07/LaiDbvYl9Cl+O5SBAceU6gbac8KH9aYvs2ObTd8fJTmjHN/4hnuMTzNmP1Ra88+XwlKzed6Elfdzq0+WZLuChz+g19hf67xxf6DxMn8AT6GAmHrq4ge8VYaaLwZPIDvqg+Qx/WC33JKcJy4sIf7Tdp4zjrXcFRYhQfOPMj9WX3bfEoTF79iOccbsGSBk9hDBeP+Pt3+IlwGbfGUUx4QVedFo8U36I8eekqecMnUYTah+Db8L8oSRl/o0Bt2ChOJi70Y3UOZXGZkFv89CgbbTU4n/9MZVIkBhz38yMlVOka2uWW2Jdoxln/mkCc9h0jvlA+4aofj7025efx0dlVHce8KE5cCjHKE1v1XtNqE8oTl3zw/PE//Pt2YmN79pf/67/x9Sc6+Mrrw7IMom4oNCCRosFW6ZukZRIZf4fjSKEN2Xg0C9sOyKXAcD4q7O23w2n8oGpYTXlZGMAdv6kSpV9qahL//lmNXEGP7KefC2JQFOsZjv10G7SA4LdH6Dy8S/pgfmAc2d2hO6erGKmgm+fqs127xG4iLGcT3XEIqu8Cu7GUqub2Ns7z4jcll+ElL41V8ybR5SyGM9XtV1vNRitWaKcsd9s5IBRpxTtoi3VjcIwFiGscFz/BO0rjNZ1vZ15QrSjb1/QjsIs408ePk5x0lck1oMxg41vT2CagVQG2LDBIMRAZh9LwwOYBjhnBKE8wcAvnMj3bpXRqVnki+9BjOloWi+ZdDJd+s7NyQAr2triM+wLypMNlskLPBE57E6hJhLEvxQm+d8apXdyw0WTabkVK4Ut5CgHhnMWcHgN83gghWYGKUmW7AqkOK1eCreKgYdV15LilFlwkpULnD7sHaBDwjALVGUkLL4fA3/r1OQMJwNRp2gtnsLLyw6oTlzawhY4wVpr4NgkrC46nmeNsG0H4gXZeCQmCNa4p17QLSG05phwg89pGUZYys4s/T9paVqwqePXjzCiVCPjeCokpIcTpo7hBh/ysNI0iENiUD5TwunxEF+VDWn0juKStV7ly2MBW6Oo5BPKfvAj/5B+/82n2u2XQedxFItDAt0zi3DhSy8V4iTiep1/sp8900CK4mE5FdFN333F5zSURvjBEtyb6gHwVpyiVUZyy/RIFkvMcS3GyQKwkhDit303VaZ40TTVfaDkdbQenX8vp6iffVVTLIpAphyPRxzg3tm8K35BP2FrRK/Un4L6F94HqITR8Jk9NXLFTCs0Lpt2ukNZIYjrGRHfJFRZTL23C/WmUpzXBM4J/FZmmVawd46MgRXFq/4opziWBl6RNkRJnEsQTIeq7ppn0nc70Zff3vfpDGuY3J98ZBc48XOH+w9Sb/ERxWpNOSj+0hnLs59v8YNLvob2rPphQP8U38YRPvKk8onyZcupET/hU8zHxB0nSDk1JBz5UXrR5VP1Mu4eInVdoDTr9puLx0rDgGrb9m382rIrkWz47jgr7iPMIwfZzHhb09l9ey0KYXupyTBV23EpORayXdqtsz0dwM/mYFSeUJitOa8WpitM/sVKo5dlf+ev/69ccrv1MX1XORRCqhFGKlIzgRILdiUIhmfSB2fai3KbD5Mw4DbX8xzSUciDXg2d8Cy8I+7iwb2PI7QTw3/iSdikgidOOO08qnUalZ4HQOcZDZrxrWa4GTNzpUHIlbvDf/x4d6T5w+6xsyrLsCT6daX+haRG0sTywjSD1IGR7nSngG/y3voXf/re20hXIHdqYhzmgNvRjBuF0Fcuez409iAbJgfa0Pi/UjPgEvtiTtuPvn5Bl90lb6S2CR6kKxv9nvMJ/s3mP8eqT1syv/AmCgZOUk3PCe+BhUNHLSlPOOWl2H8VJL4qTmZai5ztO2aa0tzF0Rk6DhRq9Bw2ZrjsGPdtTl8kVYTt/abvbjc3p2XL1L+lXXyIki3f+Dz1cIhNyEHKBdUrjc9rFC8PILtA4zoPXZVGhV2lUeXKspompfljnCpu+WeZu3LvqSMfC6wwAyXyQKIqqN/bdluXWPzRZgWDkSCNIikyQ6Y3w0i0kCDFs2c52TExgOptrIVnbZdg2x6UNnDug/eSmJwnHUphYtUKRscLh3QsoTyZlpb45NfTJdbzgw10T+kt32tRuYxW2KohEgcqWsOYZPMaF8sQMrworChX2Q6k6ZsVpp5QmZrfloAg6PZmZMGCsxG/PAqcsIxhlskG3OK28pfzJWx/S6EOZuJxNx8CcAAJsOTVOywX3Bt22DffYb/vKth07mmxOE1PlZgVT5ZjZ8fAKJlu4AATBj/KhDDinxnk5rzZZmUZxYiWhq3Uk0VaQvFLW69lFtLxOi4P3zxkk+zXyUdwDR/iZ2Bmdtni6b+3fFH4Lf+N2sicfugn/ls5LWT2Mk/wlK7d5hc9MJmXEtnxc3yfKpmU4wwe2bdH9SW22l0Ckf0Wp2meQkkrxBmf6U7fm0o864UZ4xoIpb0VvP6YPr7SX4iQ/+jlhMt1xlVjigGPHgQZqILyk45TKZPqpw0XAhiECBCUe9kWfy4MYmWgJf4jyVPoT6uj+8SoRkzFStJSo0zVfYjLHK2dTdqI5z03ZyROeXBpSbsObUNwUsMsSYP0DjFUmf3jaC/pBr/fx8HZNGxy/nccjW0hQdBItluVZjzEPf1sPt+m/AbdzYJwx2THnJY9WmlSk2ClaxtMoTlGYxMUF/ou5VU9nnCQT8fyRf+YftXn+PPur/83/9vVPv+ADf+z16/a8YqbiyGHekt5VKPxbIAkD7vpQGY0HxYbYP0F9jWJXQPyrODEBtr1OIFdt4znheO9U7dLP5VnhlC2wi0iB1Q+r/Rs+QHfhie5G2UoLmkuaZ2YH00346VR+Vj5Pe2AqJAGz7A56jHmhmiRw3/pN0ANj43xunNYvGHaUSzpPxV/gsrQcU/agaqyaD0h0pYGF9xZu/OSN7XzathJDrtuoBo7npmewjFEcwTsIFLba2JkgdsrJYA8Tu4V+6N752LYAxp02DRGy4TVJMTj06d5uGIoP0Gt/uvdZs9VC26OABQ/x2VvtbUgI1RKKKxjDaMq4XW9i6NnKwExPygD/2pt2TZcdJA2s/bErzqZUvhdHYydeXYrm50nQFVDIxrw1F+DQRbj8zGt2mG3jvNhxzOu+aQ1p0nRlOIPOYxKgjAmPv02S1GpSym6HoSAhF1BnPPlFoap9zMMdZQHBJlvA3PicFOki6LKqki1m2Y4W5SlKCIdmhd8KBucYOMCcj8py4QNnDsAf2NnS5o8lz6wq52XBrQEdoSL5gXLylH6AUOPte8qYV4EQIKBV/gg+xOm2RAsmgyv40v5qrwJlmie+hTfbSa8C3Qhawm+BSQVIenlaqnFBqYgwxW3bbfOYpG2FqSYzzxKMWqa08dLwCH/8XBrOL3nG1XQbjmlaqfWQaMNwJ5Dtj3y338W2HMtyxaYy4y8CceqI82M5O8a5FilOWqFmG1/Kgg995pzcXoE8zjh1xYn8OAckNxmSrVRsH8IfPLsYduDB3w5Po38K3zW9R0iDKfGfDt/pPcfWxJ4EuQJcXUQan/uAJzE2zm3+6y6q5q/+RXgNT02lHcpO21DfyYROJyWEIf8AuGb9a8TFnnGF9kLf7rY884cmPG0iVVqETT/9lr5S3hHFSSkRYdLadAV+UJqnqOdaAfHEiPq385LE6O56wpehyXTJz1yAwLGTr6ZXZQXT/CLzRRo2kuf8MhETnkHeeYJDJjxWPOikw7ibxsAKe/BjKC1oKz9iQgcaQ+LQ6QhEGboc3VBKSw69NZc4Le/9lPLt80vbFg2Dy8Z4Gtlpv8G+gpblBqBOwvWST54Ugk3yF3lHpS32mvxSblKWXGYyPd7qcghdcvKK3ipOv/pPPrhV76/9d//H119yIYTerCyRI1LJnlEN0a5ITJO1aBIMNAILjWMuRSbeCQtoIEyxvZ3M2O4MRx/YoKJ56UmOb+BPiNhD2xBr6NOe6DSdBtm2QGTJv4Lv7YmzYhfEDdO+qrCFKinMb2h7HHYAJqNH+UyeAFnWKfGZtn4uZgU6/EgYd+Ic6T60HpEm/FG8Xa07dNsm4j2q+xQFE0blYnXbD9Adtmtc44YRPZXICCILzbJk5l/OaWFXvHZt2PbJnU7CUg0bjmih5AE9KqxdXg+S+xZeV6xXV6LTBuVPkIke2g4S94Cna2WtNEkY0gFfbsfinA1M27xHPwxs+2BrBGQYP4w7wqRSE9z9S/ImwrSU0kXGKojQ6nAnelMIK0L8lzPRboAfOCdC0s/vA6jDa0cITWk/u43sOjTk/KzsyL1WopbiBCa9RzmJc0+ZYcbucGoPpl4/w7FlTpzYLwmQhg33ZVntJhOmc8IuipMJJJT8UF9b8eiWNCs5Ev5zXgcadB25bo9je1sOMO+LRFCkshc/g7fbidpKlWwr3FK290UWbSOlFYE8h8tzfobtOChOuUACYR2hgPgoZxGyImiV9tW+lO8IYuEDVryU3woiVl7IucvNPyYiwglWl+AQNoWXkpQfpRqhJ2087Rp7lcKtPGVVhTMIDMpERqhrOm4CxqYfmsTgJpyyoL7qmbwBTLuRb4hvFJuguD6nz2l/BHWE0+8ePCtNpZ1tjtSX6snKUy7r4FIAlF3qCkHUN4DptsNcncz2zWzxpX4oJ9q4y8XpTbk/SPtJr5bZLYDr8NZT7qf8D1CjfA7cN4UfqH771ps6uK+Rw+ewPj/BAE6RPR/0OaFnfOwpqt2/3HY9AcG4QZpNN32O+l4NV0HwHysR4hf06z7pJ0c/JS39VcEBNXwBON72Gffv8XMMJWfex0qz+pUfZyI8qJMgFpyFNPiQqgU59Lmvj3ICvW63E4Yz6UMdp2qC13l3ExcAyFJQtmMlfPMpU+X4oV98gjiBdlzSMBpCZHfROq3gcnryNK2lLWj9i1w/FmI5Dx2ztxIhCIORqYDnt46aZ9gvYaco9ISWM40JSPD97yX44riBbdjIxTgpE6D04yJsm6niRGl4/IvS5IlK1SKXQnAtPGMdzz/9+/9+m+fPs7/+P/yfX2uM1EAn72HYDDj9zgd+qchRnATmipvKSEEwCO/qvhS8ytt5OFIlE/ePsxjvA2BhXX5EvkdQuBNvGtjpU/uRlrzsOr0m4vJaFbB8jMgN9kCZUCojnlNtA7FpvmIpgsNsXh1l4u3oAwgWOjvOYqw5IBgKv406utad/xHrgXUa5IMQ4y/NE36b5or2gMQdllir/A7Y1fkX8CMLERBUHj0nRaf9EewDv7b3m6CNadtuQFQ7R0bOwKejnFDfYC/umgd4C9Jek9iRJtYoT/omBgqTrj/l5Sak7+l14xJa0EQglpDoW4FQoiIgmQF7YKEVpu13FsyM3H5Dm42xD00HOSklJbbBTtjk69LMboJvnIlw87thtu0KclKUvNODDO0fcRqDhOPUfulngyJhimQ3kfU6f5QTdg21lJ0ESQsFFrInTLBLaRKcYRUnnLYUCXH+MzAoBSelhG2O24O7+DjCQwZh8gAENOXpoHu6o/yw4iS8I9CvLVq+8Y/tnRKYNclmAUhkdmXSK1ezSpmbnxCOEC30uAyaEsJNz3XlJimUJgR0pynTwjarVnOLYIUOlZBxGRN51ptyJRWn5HLA//om7UI5TskBlliMexPPTmMCa4Q9yqQP8Z1/D8IjFE3devZZgFnNSpk76v4hVOHAyJxyNqRgFm1NsHBDD3B9XB51fKN5ZGDK6puiQB91BY35OHHqi8sAuO3QCjR1K9QoTtx22NsIc2Miiq/agQTlph76+e37gIozkzfBl6CL4wYQJ4X8DQ95fN7zTeHPi/uNYS6Ulkxb8Blrh60CPIO/hf1R7hbWtrFHeBSxcsMZvIpLlrYN/Gi3vKTnMpOn054IyWr6ivuPed/GTJz14i336vfCW15mmKZBCv4nbuJYqaJP6U3vDV1Okz5Knk0rylh55KZ/j2+JF1ioTztvnjF5dlnGHl+Iis1A+iEvTrseMsmLJ01k+o8oYz/AbCVuSN/0B2YoID3HxVf2g7DEVdnL0yMbFbvIO+0T14gXgF2/9M9K/8R58bxDeZ/i8+AnjEh3YJQmxaFf/nEApHyvsZe24BL5hRUmJoKYQOb5A7/r77V5/jz7b/+X/0vlz3YIBgoGq2yRyKAlO+ecRnlCJHVlgUGCJHZTErJsN3F484jAMw+n3Rl4MjQZLBLnMxH0G1fC+nuTUL2/hamiew6RE2TCDSmPSy6Swg0KPIF+AKkQN9XEe95vK9kwN3k2B2sKVWgmtTvutkrvkhq+N1gv4cthtGdOml4hhCX/9ZAZzGmg471QLEsCllNxbD/KZykrDSNKcA/WwyCyyuRR/QClaIl5jd/kE21c9Tywx3qNu4Mft0rCN6rYnmoVG9cvYwPnTuEaE3/RZZJLX+lP28Glru0B77u66vq7L+nF7DcywC40YMrNayhQvGIxvAqU3O8yN9yypx7iR2zcMm5oDTVD04RvMNuI9OTjmAJ7CDloG7npP4QtUOMMkFt67SsiZXot15bxbmNFKLP9sX3WhEz5MHh6AM2Ma2c+EzswKbgZZMXc4xYxyRA1bHBo8Ds+oal0mIsP3aFflX5xJ0+CW8JDvgOVVR6VhBDy+gDzWmnIagODPXIPbQGFie+MEA9lGzdCAs/umynM1gl4m2636uGOEsFMMwp7bqFiZjpCR3CCN8p/4F0alEUSJNXYh/6E8yucwltcXTklTa9UuXxiT95BCG+ausMlnH3IkV/XZ/pGBRTHc5aTrvOtn5QCeIJrlcPgTXULKhGSFPngjzhN/LdlNvUz8iO/SX6oddko8RzKh1dw3olb9lglpK5SDr5oyp83yfdn8j2cqTvK6EiW/DhDz8vR7yyzz8McSqbMQ8dzytblfxD//5d1FUgs5+8lCbeFi8/VsfDg/aDQKOrD3+CD87RfYp9oBIsT2NWCp+zST2ibtIPhWaYifu4vlJ8QXCbXhNOTRCTqZ8MvnPL3iq4GIPrVWnFaeQHHRJfReOErXBKDjDsTVAKEvvKl5EZXq2vF1HxHBJJGeAOmcPOmQzpMLpsLPzLylJPzOflwadtfPzWFe/OGe5oth7ucUlYT0XEo+KYNTTzJd+xJW/HK30kUXDuZid/6Id75GuNAN1bNA8kvbT1xxL59QvtDlAYq/SfcaT9ibqSTbzz62muAXYoqO9qE+JJMt0GZ/iCy+dmLhv1df98PjgRiffbf/+9/Wz2BSkdhmoazlCcqTmEoT7NVr4xxlfUiSsSZaP+Mfad3yeaABP4SsiPIFrACk2nsLcSAOvbTKBa+jWV52XKJejoOO0Xs/CoGDf7JZ4MVfJlnnOdgCJiJpewba/I8btNjzMJkvxEOAK+g1qhTinUWJWbtDXtomtinKV44luWoofGzsVAsi8t0uUZJMlXjuXK1gI5EbohNmcjzefWj6GfrWWhlWfGLdwXWo+YDGh54FXqZlzSeRL7Av73l+bhoQ5uRC+uAk1/ITrhuLNJX2K00YfKNDPEMwihOXmRflCZMdlXk5s0IyxkECivMjjMmCY77zNNZZNTJ7leJd8I+sq/4ws0zRhwrME6HH+3iArtBaBx5DgCX0pGGQVQwlGnBY+q3Ho1v9zhWv8RN6cucwbjKk038HcU/sg+s6SfseA/aNU5eaXLlFYA4TtZ1yo/p7+AqMwIAW7E0BszWlvO8EJkDJeH7SmpWHLSlTgG0C+QKVqksNEthyiplBJ2j+EODSUtdQ4sFIQk3uaEr5xbqT9uAlqXkeJAjQ+R58q245MF+/x9779ZzS5Jch52e7p6eO0nDkEmJFgXBMGwYMqX//+4nAwYMWKRfDBgyL6IuI9IzPX0lvS6xIiOzau/9ndM9BAm4zqnKyLisiIzMyqqs2nt/OjLiajT1FL86hBVJiOc3fYzDix3nhdfDypGujbQmiMepPasq5OARVL3FEnjazcTRThURk0HCB/tiH7RPiqhvDEPIwLr0CrFiIo39t7tVHHTCGPGP/cV882/p8A019x9g3lDTEKr//gzGQN5QYxHNPkxepJig2Ri14kFLHrBj/r2U5UM9/cRfjYTvxeUFZIwJytzjPl50l8JVVCanJcc0typES2cdulu2FHSlrsjQ1zgXguMkRM4ZdmX3c/gcNcSpvl7yjHEiYiM2dKxKm+xZOPmGP+cKTbRx4GGron1pXuM5jHFqn7bvhR39wY5heQ70ue+baMSkOdox0p4OjIPpG5b6KCDnnoqTgTv6AmVQ4dAJN/k0jhnUYAxp6yojZ5m2de4YiwR1KPyJI99ya99Ume1QMEIREr0QTPGIuKkt/vtSnpErkjKO33ssh26LxLY0b2xt4PhF85pg+12bNc/5a9HEfPKPyXM+41rIdv/qn/18uSzqo//lT/8C45RKWDhhAPhpGxdR7Dw4VWdwKOCpIl3BnwJgOW92CQg/clUdSJZ0Q0CoOpVwIyFdyh5tpUDv2SYd3nLSnCuxIK6yDhKiSVNT9TlAd/MSixnaEGUz1M1/FgiUI65SxcjngkP+6mbMYqMveVHBA3CT5WbWL3Zh5GztegiXd6Et3vRZ8d2EuVwgIsilkpJuepwt3zcwVFwKoW5YFrn1yeFL9Qc4Zr8lkwfAW0wS1FEu0wPz0EvVvWDdtmDSAcS+4s6/wq7FE0r9VXY/bOm+0MIJJrlB9ncZkHHysLPUhK5JYtQRhPuXConI5WoHKculUodDXUbLZsdK7c5m+o08ZexuwUtp6iqXCELz4zBWXFDMrGabsqQ+g6jgm0ZifBHzxZyJkoXKHYkymkuDSqhnI9/96J7OWWcsqsIKHacnlIqFBrSBpm6AfROsG2EumrRw4mKIT2XlrFxxYVW60OEFhjv9cVxwz3ecWNKepUN126jr9Q2j5sZ5DNzaebEiphZS4FkXtmxDL5hmTNWOsle7eHAmlSeIwCl/KizXx3CE61yqL6hMbRmxZIy2tlUjU0CRNiOABCGauKxWqU9oqG6ejOpAH77xAhwq9kll/Of5ZERp0510WEOl3LuUY6m98bAbCCuAB4JyI54V0l8s/Wtq7CvenNqQ40a/0ss30zWO3H+U735t8cAxhU9Etr053rm4UbvDZpv+3rdLvB4/d3EwOqlfbEq7+BGnpJ2admnfYVAwnYUmSgDv7OfgshTdDlznmDHL5w8rRRGhx0rAhIMDy8QZSPYJb3D19ody7TyOLYOPLIl4Lq1dGZUOfdu/eIKwXm6WydKYZzy0KWzRkHnuYJxlx4D5n+W2ebFAliU8soU8Elek7WRPPWPGwhp1TBxt63ZIKge0xqY4QttWfIpUje8KAHiRd1DSW1yj7Mfn0l1Xnm7u5XatvSb8i5PEvOt2rfVBIA/UVutA2BJH6qjOxZOvUexb9uucz4j5L3/3Ryy27aP/9f/6D3+nX0zik0YunLiAgrO8LrQHeqkA4hwJqD5cgFZb9aJab8onfbGw15NtEx73rfF3dtcuY7klN4QzmwxbIQ7S9sOsTSivSnhUnXRM73hj5Hrcl/L1ZER+xqLpZfsqZcncWSamh2WCTTkUhRXAyS/eJrqx9+C1YeeOetilfmdj8fD2hHxgHwvmrmNsItKjHFghWYY+tB9W5fOVr4fWI95HOggoMU03fZGQvCZKKDCeLJzw9ZJ3n/DmNQ9qCov3z1og8QaZPBxQmFYdPJYHzRBXv9Jij7+mdvFzUSKwNcW+HGabpvDWZjBDppy2om+AT12pIGHMGbc2oSJ2FVVOBerxnJUdKm3fyaE2DZ3X5KL1aBOVOGIdm7Fx4yoF1k2oL4iHvsr3jHjz6g2xwIBzSxYrWbBo4dRvD9ii8s1S+iltT3kWTr5h9g2ObnLoXzu1YucSx+K5zrYyFi6WWLpOGXfHIQNlmWCu8ahrVqqTD9+qAiTsICnTVQmPEE3TMbbYpYw8JXUmzTo3tVsla75eIhzxyQkw+015LQfllkNhAZcDyqjPjXSR2wcNwrPW+x3j+7TKeFQ2u7H2NPuLdm4jxwT7vvKgxjjmE1s2N8ytHVvlRrlY5eaxwgvJo/a/MPvu4s7pgrphdX9T67atZcQi8uBovCCPb21jp7yJNd6IqesA/WSnHvb4CyG/EHELVGzEQ4X8xqOwFGPLkjNXzimdzeUomMSK9zrrxeG5lc13scYJr2AuupF/lEQWg/qMg/EKGfguUS9f02TSHUkT8UJb7+Ss2WrJd2pF3fgTcy5Qis/i3I1prGk+6d3vtfZWXcbZsV5h7jkAfyv+BEjzOQ7YOu045OqXIeGYdCXoPj3ns9/H977P7aP/7f/+j1o4+fPkXDihy4S6Sj8t2wNQcxhNbU3eNLRl1GUWkomUhTGLU+S6h9OUGXtyJsqiX2ss3ZzwSncFPy+yQ7NVpZbegsJs86Rtm9N3IoGeQYLequlpmRQib8QKYomv3lopuuUqtsUu7qqJuoE7NAw/wJosQsXEKXqymDDVZ7kp7F6fiJbiVJr00uiJrvPXwQ8lksM+5FluSof5tGfCNAIe+Tptj/pLswS22fkmxqylwHOdbefH9D7mWye+ccIcwIUT+dTMhKcLGxi8986FY5WLH31NktQvd1X0cOx2RCBfrgzW1oq22biuXGwOxqxOeoM6HEhvKDMnmoVUbpZo6LWtNBUk9XsXx2Mg8hogujFAwlhyyzihT1uJLV+5SKcPudYtb1ZiPKC0cELncfHEzvNcBknHkwUL5n8smLRoyd25kdp3fHVJPfqp3Qsox6+2wK9iSHuQBC+K0hqPv5kb0Vw8jfgUK2MZCyjqeTMG6fAimjMteY5GRKUqmrJuOWN+tVmnEZWHDgDG6UPpjQeNJ7bbibwolCEFaVuG6jidJ9e2yIfZq7iXfF0/xGvAlU/y0w7T0qzAKqax0FU3IxbPFcgNhxxbsRoCgDh6FHTk5YvFDWtId/IR7K7l2gPcB+w7hO/OO+I9qgf+kA5yKdWIhyzilB47eyojW/aL6hw0Ubao63yHKu2zs4/GLVAB1VgNhhz6nPER4x6a1CImb2zNL3PKYEvz+GGpaUBYli9tU46vFIYwYYSVsd2aGsCRujxtyKV+bDQXos5SgZZC5K1IfrYBmhjEUsOcs6h2OeCb1w7BGZj02f6LIipVeiexlNp8wsTP4i0qMpb33KUhNx3Tg/Yt9Z0ac/4uuK/JFwLK7NaLJwg4vthN2Zh77pmv+MYpD+3Un1C8XTj97//Pf/47fVyjPstNVAMTfd85puhYm6JDcFVu0cBsxDb7xrYT9iqVTuxTEtFmi2Ow6vvEEWZKqF8tIjzKE0P1Ys7i0DuqAC2PVSz54i8eY5iX97YGwTbvmh3xGExL59A9Gp7qWTbmU+LxYJ8DcdKBi7/ZFI+bGa8nTuqIO0UAmrwlWlR8XcpWqSi6Dk2xON4tc9HRHlA8wRYrZMqKEAqLs7RNpc13OTp1n9VXfz/TgmwLxZV5UWKPMha23wsnfmfFCye8eFZ6ZIWDJxaXxNXNOBrENnniAc2bJNXNc3vNU6SQza2yPvxUdNI7lKch40bdGrveXmultp7ySUvhUdcnkTRArpI3BRFk3jFALkyVoiyFaI2x5LxFIDLFk2JO+dEBlsHgZ+ntl1akVJKHnTJx6YeCmKEmCBz49F9vALCyMb5vUajOuxDh4w5GD85wB69PaRNNfqVkVTkQW77luvzQV3a1RHUHk/Zk0cS4vQuWbuwLscgFXZtVMvuktn2SYI11E8yLcSgyjiROAjSZU1o4JirbVpbCi5waipnq0Re9DoVSmIsvio5LQQUOq5eXrnKAKuN2LFQsQ6qJnO1zzG5X4UDHGlUfBcfTo61tmnAcj/TJZ2gzPMJzrLBfFUXJ09/SpxL+c6uucgXHYnf9JDa54nSwK+SpUdy+e7+RxQFUF0aYH1pOPxMjHqY8POoVf4plvjNmzdbm6DiFo0WTPWmNM/mYcTCSqSUFaw21u76jlSyhp/HNkjbF3P3ZR8YEfVKVH/bFh3NRwxwVU9ag3vaFaQQr2Z4C9iVK/xeOxx/CAAj/ZYt26pJQB4T0pB/pGiO2czxG81VY8eHgORh2UsShXNL/uQWLuh0nDaSKA/jSIfjYUjNbLXZA0MkcSHX7ZJvs28fCLN0sVqmfWIkfH+KjttfN3Y9v0ZkWA/Fo39QyjcgT/EU4BAOSLZAEPJb8OpHKaklPDcJDjnhdZJ9jZ8n7HIaleblwfx+/GHpuH/0ff/FLfMeJmlamQrcHILn8bAEwkrG5Wl5GQTKqKbsn6I8YYwEwIEUWVNGrgxa18AvthLjg3CqE2UE2wcxHOsnmkdAJh9aseIuqwgiTV51bKJu8eNJWR9xdbsunlKacSCvegnIx3YPDarGG2pUzhFsLw5dFmU16k6eS0FA6ZxGgFA8IpTPSvpSkw+rFeumcVOGJLbqCLT2PffPUg7t4oQ3fZAY25R1nGZvKRCYXT8b9aXdX30fQobGCGgJPDoNRkwNajXH2CX5FRn8/B1/y5tumPHVJP7DU4gkH0t5J42NZqHDRlJLu1VbqieZheZ4pnmdN8kPwob4Mi0pvsfpIs+2b2GEmu+kZmNRb0vG775wz04UL1f7O5138yLEWJVQnDWO7w/lrQvXOJxPK1gGXfc08aayKIg8Xa360Wli89RAs0TvW9sDYAOc3T1w4+YcW0GOg2UbHoDhq8URALabErADlFeo1du2bDiGnD5LarC9ocD0W2FP0w5sMxMvFmdSqBO26UQqh4SiT3ILdtnxKB3R0G0NNXPlMpP1xdBjoHwwYsyIFIbMqY+OAfHSktAlFfgUYqjBtMc9aeqxY6R0V1svr4c7xUSN9Qo7cDte0z7bHFO4sbUi/vYVG6ehaAgKRla+UlNJE40D9iZg4zqBH/54PaFSGNDi2jlO+E8BS2i2X/IE6DK+RL7RlH97W/jCflHs8VLxyHppD1RHweGd38jwaTjyOotkHS270hTLHm7WkYbVlBmrZLCoKsWmzIqKpErz8odc6OWFefaHzy2ju7xob8EoNfDlEc0KdoRXLaiPdsZ/sz86FU0kgBsfg2mhbPlCS5j9u0sUxG7mSR6/KyDW2q9IuQqAkmXMyNu5l+yu39LLEomCI//Kta2rFSDWGZ7E0wwrP7aWo2s04XLU+DdRmzu9i9UHQ0mfOqdcioqniI0lQJV+84ktzHfb8L/6VWkhvsWF+rtvB0/Vo4VKqnZ0DgqMsFlk4UVs6yL0XTBwnvpdxXJVb6P0Bfpr83D76k7/8JXABIb8N3wn3zQAcS7QCMNAMtmgU4aYMqmzAZF1IUehm7eGx3d7cpatjWxAFIFx5FMamFTdii8yBtXzIRuctcsjLxhHgeIRizfB34TXu6BE0uldfdnnyUX9yQx60IM+68aZvc3KkLnN5sRED/Memuw1C9BQhIvAq+8ZzcquJVUDCAR6FJsI4+nBWEWCrr1aoRT3QzkYsPdsy8n1zncdIznJgtM6O8X61hb+QF2Us6EStwRN7BOwz71wk8dfRsnBinR/X01bJ1qKJsKiTpZ1f+lad32mxzHXLcXRfUTmbyMJGofy3rGIU5rApua1kpDHVGk3Q4/PtlJ/1ZT0lRbOge+ZNhLVJSyOzMtintWJXvqvNakZp4ZyVnHZsO61HSS+W26+OwPICxD+kYIeFEljFaJ7WYbxIqM9wQe0/ruvbFTuAIdTpH/BY3ABbROEyEG7BJ5ttErMOqtOYXLbDePmpdebOi3LYUSeqJgsEEcgHDyKsRylhubGcNvILluTEBg0QqxGvsBqXCtTzolM0IKWl3Dtu90NFQQzhM4DwomceFYghNboottxWzTwLI2cWSavv+dxdBtGkrjWUP77ioS51DKz6eZD8iYLQ5/WCbhiFBAfa8ENS+YUix4d+jay+FEmf/dFQ3ojoppDjjVZunZChaEihlbOrY+uUeARG/+dmFo8l7LbdKEerRSGmx/Cmp8QN3lTdK2Uw7XOO77yJbHoDPZ2UunXcvxMh2CmPEKl6lziwu1WCPmIAXCM2QSwCYusShPBXSZHnS/ioPs8cxxtVjjfOYx9jp1fGkVKED/SCLY4WVrp4SawpHPjz9ciorXPO04zr2I2yjpqrGEGBeO7y+Ff7SkDxnj3UxJhcaBXO6Zd1bsacWMWPzEqlZ7w+d6HqHHNux8Y6inKJEv8Q76pTybXwUqfhhUf1yV0Kkjw+TB+M4bFmJIzb+duIxRwY0l0SmHku1pk39IiUrX9wpMYKxwtzo2trBfiHP/1J1Lv86E//6pc0AYNuPYBSVh+iE+jVnnP6G6Gi2TJgXkk2NVY4hLVlxKuSJlvE44RMF7MMvTSlPaqb5+Lf8a4+HVr4KQd0EkLWpKtNii2BD5cLCczIJ2zlNjmW6EZvmBz+S6KcLm+b/qgktJQRccA82iiZJ9vUi90jOXtdWxcg9L8YBfZs4USVHjukO/8DY5Azl80eE2bkHk/V7q3911y0SwZTYsdED/FylhMnsgHQdoSYutTBtp0nZnnsGev+fJh+ykbZm3xYor16WoYbZC+cuHjyjbifolWeYca2ezJGqZsh13Mj7hskzh/15oluUZHHJE4VClY7M3bIlbEK4rSyRDzEatrcqM2Mtu0krshTijA2hNIejjTOa6x0LNscufDiy3OD+W0DP+tHD6LpHDIKRVJ+PQyIgizAN//5V/D8Yz49dhaMz1fFiQsI+Zjm2V9548SSNy/u1/IXvyh1Q1xvtQJLsfKDcs/Datvkpw16mgfflK03ZcaoJhmA6HLG0jsFK09A7IGgVMhOeZGetKEPGexricEwsOkgXEgrfmukT6iRscd26l/HQwxs5T96ZDnmVUrNXUUSW4IGpT6Z3I4e/rhoUhSyoURS2OgfS9kDD2rxa7T9OOPbJa7NnI7wtvjabjqqGDQ+QK+/f8O/5+U+y68q+hf1uHCqcQYcpZOZrby6L+gpOXJpvY6giOgwj6eMdedJkm3uvCrv9pFPr+EtP4lenFZtYik6gKovOaNb2+Lf857Lt1gad+Kb3lGm3F43nOrnzQZ5bKuRtCYp1F65l4DjdJ1bFOd8pj/SvGnVwhu/6twLp+qz6CrCGUw5nfKtm90k+yo/8VcDrzRYcF40ePBmGcXtfEVD1NR+21/X0E6Gre5yGjyV0N9ygjj0wAE54Uaf3I3jo/xKuuSKjTGVvmzUJtikbWk4mmqvpa8IyFtb5JO78rtrxgqu37hZUUfG+war2fWa8NpmSqoPC5G1SDly8yZ0j9M59fUQYxHzFhdN3LloyjWK7v7oF79oryGwcPrP8IFJjTsvonQJYw8gq+0LJ/Jmk0E7E8GUfGpY7qb0sRXMGcYi08hWAwjpPd1Lavuj3tAHv6utsNzXYFuMUKWroiaS1g1OATd+bNmRYIq/C9eUNPhpfJsPWfNuiDXCD2HiM5tod4jile+rHJkHc89/mpS+Qf0S+/BduWORkzpD3KKr1zKhhYMPdcl9aSw1aF7x1l0X4SiPjtvH+oKIjLrYIJCs2axFO3TqsnhwCEBK2oROSdNgpQzcqk/tSFe59HROS0CerdhXmSA+qT9q6puguhhA0/NBLYY4LxCDJQjeDPnvOeXJsice6mj+oLYrtLrZjtEkdR6Mfxp0W6/EpkqXl20wB3lRI6NaKcoKu4UuUhRsYx1twR39Op9tySOtrdqBlxCSPmfj4wECVwtRARj/6cZVv4LqGxTJS0ewuAA4VsTFDuPCiXM8+o1/9M9zPC/UbDENmXdS5vHGhz4cvIEJo0BYMg6ExVi0kdZOPojSjZ9+moe3mYqLZtxTFJ04yFfciolgjlLArWtjVysSVFhnDsRXHO0KktoYP/6tjFux1KVEj6w7jhi6TDSsOS+rJI8pyGYSxwuPGpEylsq9DB0f5elHaQ/g+JX6OHQbHilUHANKYSSSGWc3zs4Zjf8ALsbGD/AH3z7Bzr/vxfGiGxDNC/67Tfr7W/pp+vwdLmSN/akAmUFmH17ZFx0Mx2sa0wQYFXTFoSJqXVYLhqpEjd2KJlpPUYD3wF+ZMV5tU+2eAW6Dg45dxXfaiB3QlFSatIz60LG0n+lv0kTZ6wFZ6EWhMLUk4TQEoBpN/QY0MdiHni+YTdLKaumn311yDvKDFP3tONph84ggURTjUSg8LMd9PkpPhjhkW7ELpeyFE32qgt85JJ91ySVYMreiG81x6geLnF/BZg4YW29ySHhsPlo0aVskPueEMVRPCdPxdYwAiReW9M2D/I+yW6W2lJFwY02cGbH5OgoUNrX15Un12Ke0ku1i8axcdot6pv9IljymXFkOxyXmkTiqks1DKrA5q16s1n0LBLxG6W8Vcj7jtQ/bv/y9/0rlPHz0f/6H/4S+4kUUO/9hMNOY6K7TE03oubwLoWgV4aeUwjpsbDfJUEXbwa2+TdPJq1zKpqLHmlADvcV8WO2jwsLduA1WE6AQHeapN9IzCguioVKDciFRY/GtH4zU1oS32+3ju/IilXgMwl1p1B2x9AC8+IMCGcngyoixtGwJD8cVlwqOM7Z9xDrFh6U0h6rFAgDpcdom0nMQFxO2bMhtQ11ELxPbnX1APWNFbo55PJpyOWnqTZuTnvVTl3VuwVu0rcxP3qXah6sNccTVmDWCLgD15KwXTvzDb7iI6cIg79VXvOHmnFBjnkWeJvtmfE0+9KNeoRL/l03Cm62e8StCGctatrHpUsa2ouqe39Z6TDikW7nxKAqliOBi1Stz7tU6CR0NJdilaq3lBHVgBGUS5kaC85KqOAhzwkDFeVxM/hmJ3LAKM1eJyrtuXDj5E572ehvgB2TsMwCC7X9R8hso93X+rh9jUoR1UMygE+tqZ8XOhdFgGpPjo944sX31UVBibNvIteJjjNoRU/+jhYKxKTAMM4/gMUapVTzS44F160qMA0vim0tYLmJUlH/LzaRAUhLegmEg8EDYFeiJSvW9vpBowBjMUYwVa+JNSRRtMFn2Zqm+DqV4LTr3FY4KhjDio1X6m7S/m8a/3+U/hPwJ/n6BFk76Q8hcOOH+AX+7iX8I+dtvvkH5jf7wbS+gOI/UWyn1J+LUeNd4qEDKv/zS6bZFp+JcVWmdsa+mWLHVmwj4IQ/7KC8xnck/9J9VVwgBeVAuxVs4W02lRzTNIYMBbWTX55uh51xt3Gi6lnyyz7ipyBh1BTwuKOqhTenZIbIHGPrAKFGpaxB0+Yev4086rOm8hxMyuFWzVOOh5J4fpEGmY4py2ODzvpZQaiPplpGgoGSsSW5eARYiRhjaxI8b8xpJGcPivuOBsXHsm1xtNMU/RVF+Q7cKsMljthgPfaCija65qR8436KuZaoEFvpysKKKvco6WFNIxtNx1VM99cxf3Oi9ufwAU5ukPSmZktCZOTtNltAQ+3JpC48bZFfzkXE4Pj7GnKb5DddXbv/df/1PVM5DLZy+Qcf4Yxu6CVJHAYgo+g+Xii2uq1QRHmFN0yzb6MewUA4F0hOCWlWfbNMepK0QxIabFnFz8AZ+x3GjEmirw0H7IO66uElvNngaFr1M0bHytTvUKdGsJhrJp8zOV63OnqaXo7ZdgS+h9S9ZdNpPzIHEJ+pbFNBNnScw6dSHWZHxn0EL9pa3Gv5UEwgPtoll6qsvcspAw//L1yhyM9msGSHp1NMW15fPGK6TMhzHQ81oT3ppmVp+Vj08ciZtjXUMPjmmq5tUfWwZO5fKVlhA8gXAF7hPPv303af1PSde9HRRUACwQj9teyV7LpzyER1dnCCXm7ITzDjMeE3jSF3qqAQ14hymi5ThRFqiC3VgHdVWv/IZU7go4S4eM1psTC44PdaiRalpo9wdyTOfpZDQuc4/aoGCClKjQ7T77++hv/rRWoQocwPDKHSz2gsntIoLJ/J14wRlg+PmwtcB9rfOaWJnsKlvZCVbsnXhVs185aX59GN+xo8w60JvsxEwGWky4oqNnwry5oEP9xiqjjav/MTvSOW2aHJ/4YigHXMZFt7CJWyD2sfQMX5ipkD/pbfaWmZsS9ycc2epdLEUZSMzxlr/GFND0X0qBCi6o1JMXWsXkzC+OXGdcuqFds75ozF+C/mx5gp+tJcLJ5d84PKtFk5/q4XTN19//e4bLJ64aOICKn9LLB+PSb6wGqsFWRqFudZCuZ8tsUb0IB5BD27CvsovSouxqGV+R605AdIZ3J3yHQ827auJAB3lRX4CWkFWnYs2gnLRgW3zXM9KELWqrjaCkfMtsFXm3J/nlMYr+xO7Ps0Q//Db5zPwSK8HKdZVaHTHf/RZNP2r3u0jmP7jQBkLHAIAn1RNfCWoG2Rj4ThsoCFzY7FiuGCuhgeX2P4eTNDvythTdviTOnGtY/fQEZ+95vOdHC2cKIkQOo4IDMShdoLBawYXT2p8kK1Y+gY/1spiylvpBp2CZtm0jif3rG/KR2U0QpK32Pq6uIKZGKZ9XFjR0PWt2Cp44JjhP5bYMWVVfzvFmdO4eOL23/+Tf6pyHrBw+o+w5cWSiye/Pp2g7i94UyQVwWoBsHICGtYDbrrofrzphTSvRIG3s40ZkTq4zcLd/alWOpXyq0KZ3iGcvL2FhCrUu8ZePaU1klziwWB3qKfXAEWeOkvo+r9pVJZ1J2cY7LzoppSi8Ioz6YESL5sdI0AbuHWpWg7TN2j/R8lRxi2M6Fc5nLTeVKncDxRB+QwYxrJJHaXBjFRxK5ngpH2XPuoQC6dnHoIFMHTqdrGOIwb1VurUCJ1yWS188nbsqT3pXc82vrCAZlXKaC0nWux8s8DJ4lMsnvRRveJx5DHNmQ/UZ+y3yn0WTrxZ0gRUMukr3NItfbK4daz0Y5aOVosNWY59xUzetJg0ZW/bCvWivPNd0xH9TU8I15uYPIQBMoumlNKMHGWPmWoVGwu2oNLOIFJd9ixD0Qf+c+c/lPONk889ajeoaPKpm5vWHgfCJxI3lvUGKtcAlLTlDdDtxhixa4FHlwTiAfS+2UN4tHGs9OpxQ4q7zCVHVZjgg8kbLI81LpyotzCJR+XgUiaxIWsxS7kyg5LtsT4tA2Vccu635bHk9EOyS7bgujk+6l3lCl0mbLSiqnYYRwtfxhodwtBfgg4Ayvh2TDTYOELYDysH9g1p4QU2+sbEEY3hzTDfDtwtnBivv9uEhRLfNvWOt09688T+dl+yCWoL3bJvYJtxQb/xqTKBVJmMNLsDvkiskk540L5ueAM+I6rfo3IXYGR3ZekzlA6bre0KFQ7QMXdcIcf9QdoppQBWeUD2Ne5Qkyl0VytZYa0AoB83yjbrDJ592HMfxwk+uYBxkvOOuOpzIhEPD3G0cKI1bQOqAKhrnxrrCgGHxCowHqrtlONfi4VXcRWeTIiJ3W+eTLNZtGVw8im/y0iYxCt0hpl4E7P9lnfKaV5VI8lJ+fUDq+XhjrIxj4qtyoYEHCXayMTeERadrpCNDqU+zMzJ0UpHN0RY5QCqBpbVofe4mvZEI3lN/b5E0OV6RpAcVJNguktZ9YPBiEpTfez+ztsm+a3Gf/IJHyJ/qjFM/v/w+/9M4nmohdPdGycDa0zRIj3Rwa0gEQfHXbpSdJx0R4AYp3jE1emrKpQFPQWwT5qvCvTvbciaR8ngi7Sw45PG0lkULam7gU00gj/dpuWil4c+5RfLeFK+yxoUS9cZGYYrESMmArWXnqSGVfVDQJPnAQFS0tUAMIbeoDfchoghSv9HaV4fTSzcAtpPtlJSYUnM3E1dS8RuOdgjWqkytEx+ltphEFJaeW/V8hwtlpMmerbYskwUpqNB/uMtuDQfNKs0KtYVIbqVQeSbnOix7bm45Y0Tb4i0mKoTQxZl54sYOILFBYhPl7F/q19oGxckhpS+ZVk0Q+UW/9v4AT+48SNlHmbQYjZCq7wPofBPA0De8cPjJ4kqJQo07ROMlHCeRrmxGaf3eRa3Gvqy6bYhsc5V1jxiHKDTyT7gjzfw5iQ3KLnxXOMrFG9S+NSfO+27HQxNWxYwuamtOuTEyDnCWGVCPkC08+0R+IzHOUlpZAptU/rEEIPft6rvvYwFlHzRBjtjZS6yaOLNt1nOG2E6Dtl85JuT+uiFlHHdyj9GEn3ScyOi/dEjvMhRaRD7Vp82S1f2zIvMZCS6YzZRoKyUJhsrOz7IEFlxrj51PMyFfSpG2nGrQvCM/uLHavMok7IPjKAKa+qSZjuZOz1YqQct+UEZfrSFtBdOHmdr0VRvnLCIynyht5/AY5iMVX2icbQ715g6A0Fjp5aaWgwXd/JIADaNL9gvGHAmf3V8qG2lh2KaMwyFMuPpuf0ESD0ljTdDR3TwVgDQHabiD13m/8yLz2Vqerw1QMWoPisj2ZOmD/nxdeUH+KVFvpXhRh32tfqUfY+dCyfpyz0OY4v/qT/EIKkvw6ItdbMwBkCEtmT37QW841g+OP85UYpGGB5PbqPbQTzj7zFPHn2z7nYsPzwH0rbgJL4z3vBdVlzAZR4nL7XwiBOVxE1Zok3ZSiXczxyj3R8bQW1cyPfaG5d9PxgzvsE+yLoeLreH3NU7rH7jBFt7rr6ofmAu1R9IGu05h2XhlDdO/+Mf/LcXf1g4/QfY4YLKmx9cwDiYOagakO4ETltGnuhTuiOpwy2la3WUanXLMpOwREPdQFc+PRfGshR167PiMfCd0+Xy9LV7IdAAK/JAXGChSmFYSrLHaiXp+KyJ9VaeGBba1rEmGmumZr3dOjKWoaWnijkOZWSB7Fo4bzZ2IKCyHHSELvkLV9pQ9Klz0G0xnYC2ZdkHhKXs2wr1qVN2ivsAHCYhV2ut288Jdki4pJzMCE46iHdl4qC30NSb9J0defFX8piAHZKSRe/6nByUd7JLiROFPm6AmxZ9TA9vnPh9BT395R2cNhjwP+3HTh7nCi6acjHIkzwa0I3sfDDUOAZdLFbKwAWOIhafet0n1YANgwrHFoiDfam23gEoPg/gU9S/QqY8DJgAqDxAZGxeFk9Rp6jpASduCTJOVKK/Vh/wbQ8/4lL9BZkm/vaHemFsCye1YzqjknfP+76w04+22RywNDeAJ1+82dUNLxBqbBDK44zKBSEj2yg8szF+snCiEa14cw4h8VGAKz7H1d9qnHHhJweSUZd58QWPdiM/erALBYbBgQNZdAlrR6J0IKy3GrupVsl4NhvGq50uHJNVpQlVl+YVpny0I4t4HLHRjDuWTCCycIJOuehfp5NdHaBq1MLuxtz4okm2ihHm8GVmsaJxKbeFk77nxO8DeGeb2U986+TvN/nNkxftXEBlYe7cpU3uP7YV7tIEEGxGZa7icJAVqnizhY6dvextytRACCJbRCk/KzqmKE1kIs46alu1PcpYNchZWlIVSSctBg4BSxk+SgFkViHfiOFLU2bFF2Mc0tkQr6xZ3nnnGG8gUHEBW5M6q8yHmG0nhw/ktHPhVPjCIR4I3Wfq5pUtlAR6wSSOeTrH6J92VcqDYidwBaQY60Cc7INvLPofDyB0r2ue+PQtlVCnAABAAElEQVRLR4qZ8G6f3VR8wibw8i1q+pQ9uStut5l1t20hSPlAWzrU02Y1kZJWjiw89Om6tsTedRKlPtQkPnVjc1/amjl6y+ZwjzjR6tfmy0aeHro7BKiu279kHSUC4a4xpQIHBJExw4UT9yyc/qd/erdw+qu/AkYWS3PhRPDqdvuoXCe4KhEZxOqIrRyZlOY6jAECpYLxkBRC8whRYqFRZ9UXVVbS0eFkLNWlQ4p6QzbITa+nJuGe4Jvqqhxgu+kUNvoWC4GWp6m/XJByRiKvlfmugtrOp7YteJxeCgdFTgbhF1uwS13V80SLnYRlIPPYdbkGMkMwO0IGMBHA73rp7MVUXnRRav2EAL/MRx6o3E7GTfpQpkptiX4hBTFlNFMGO2M49chZ3vFu8Eptak96Iia+TFqaMKSAOGB0XTjhyTFuhnnR08ZJhv9Ycs8Tb4SlJ8i5+Y28dGmryMF/82YDqXecw3yQzlQ3ugm7GoqLXJSVaDN4qO5nCbUgbxVIayXiJlEw/K5VHQ21+dwZOhGM8sYMPtspPNhekzpoSpQbufeNdWQ+99ivtMEuZev7ab/fOC33pRBFlL4WuK8NQG3Wywqwwkfpj3mSQRXoWLX1ky9ZeqKo1ojDx3Ttzz6AUHANJd+80TkWdAwImHStUjTzU7ErGEhTUi26WxSlQlltMpFh8CPZS4agVtvAFkVLs9pilvuuerCAFBHo7mU2BXml2P3ovoSf+GCeuRfC0ZTitzRaD8tEQBxFUYzmwzJojCU3xLyp0I9CcPGEnb+uxwcuHmf5RT0slr7xGygtntCHWz8WsMdTcuCcztzGPxtBLcY2eXtt4tCiNiYWm46zcSUuCWrnLOAes9ryuqgGeE2UUdyn3LDFfAv60mHEXat2zmBWRiaXNJztQZwKqnu07cHTjXGDbiD2Gyk/1Kk3TtSlAWTE4vjVm1OOBzPFv3Mur3O80wE9K4C6RoEjPQGUnA90+OAhoHRPnGe7YqPBQmPrjEE8g8k3yJTm8kidaKtaUMaz73rLFiOpx0eYs1yxrMZMOemhs9GUwUHiPlSLHbF0w6Pqwy2JkMKIXcaPEJj7e8RHFhftNyvCErpyVzbzOst+YDAqoaq40CY1Cwf/4A0+eYM5jdu/+sO7hdO/Xwun9bZpDjAGsBq9phVG5OjEq6Q8So4ioMWWdHPveJREtdquDl7dFG4liAaKoQJhPdtSDWcYLdYjakxLsLviXzlAeulzKXROF8tNeRTQxmdGbLjFOXQC+6hcySgcFTWQiJ6OGJh3pOKIk6EgFpKUkqIMWjaU40u82erhM/J+hG5l2Rxk85qYZ0wzB7F13gr+ldnK2QSY9PDR5MJPn7XoA4mRpucIc6IoTfYrd94A5ztO+chN/kip+2nduPZHhbSA8o2Q5w3OEewpl8+DWdIeB4vFwbFqRbIgNreVRc4Rq9ZWVpZu82SY2rJZ/Xgiy3yNU/m++3y6sdyfE9f2Mz5z9uMaZ8N2tD/jRDcB1daVM9os//bFPqUPHCoP1Gcf8aaV27KHgnzl1sv9Rzv3oxcrqiUmwComlrw50Q1KwQhdDkJBKTlfrFBcpOnGid6CL6FtdAS/y9JZupUdNNhN1nFgoV7uLSnPY8wkFuWrKvlcfKGauwGY5biKLj+s9dv1YdNzPOUJylELIL4YWszUn4m18Nn2tD9/n0QA22EEs/EfV3qcJpenKuOCjAsnllws6btOXDThrefH/QdwPVdwrHGxlNJvnVBH/BmHSQPx4t9tcxvZirQ14ThPzlCNDItqbCSPZG5ZgA9uVYheh2Sc8mm1+NGdPmefHt5K/Wo/4Xdp+Z0KcfqwtE3O3kdqye0u373vsr2W80Hcio95Dm7nHOEwIta9cHJJu+hm/OrTCVw41XieeZWfcajMiKPxgE7UAz8sjCQjBqQ8chiwjzlf5uFf+rx9Uxe+Vael/hMBZI0j1UA7S9VC4oYTUFnVAbyWT35j0pH9THFyI16LmwC76Dd12bQrLzPWAyqQWztncI/ogRkMJf6RPvhu+jW+tn9i+xbRRM51VT0YBymhqBGjkoFhh0z9gNJzGxZO9fD4j//57cLp32OseBBxQvPgYsmGaigOOp5dKqiKsMcDg9AW3dRZgqf/h0zVnTdZkbA0Hc7ELroDqfoT1YzHG5SDVY3qtk2xumAyFn3nOz26tECVYhW3bjb9swJDGNl8ty7IeLiU6twNDqc+jHIyp3x1UhA4k8aEC09xVIzuIlQq1GTQ1WJWpJGxusvhJarT4YWW5ws3jI6l/IV/VpsPouNo/01MtYf084geml0FADLWGxDRUOWyQmWRfuZFKAunj/Fz5Lwh8qTBWzxowtY33qhljsA9eG7GM3/IgxMqm2vAO+dh1oJB9Y5XkTTAXYsbL0RsWzmCZhQe+SePIus7HNLZS7svHrbNWKeltpLfIZeCgOylbFJhKcNCBdaOz3wE2RIdTz3geE5nH6oC4MqlGkaU4bR47stcDyi3TTzSteaG9dDXDQgUMVvZonkUvvyCcoKnuLyBBRh5Tkkt0RMeLZev5RAq3kBseVsq0RglhJKXdfXfUHhIziZcF04rwwS4D8FcHkXh4P4cLqvdPB8d4Y67NCv+xXhKrTm+fB7anVvExHlBN8VYKGWeWN+zQzyMDTvvJfpje1pA+SN8esjChy7pRPlK/6GiNvJgHRTHpuwcPBkN3tQhgJLZ5ZQuo+hMh3ealvsYuVqzoJqK3Iyuwbjp1p1Mo7foIWG9zAOPrLr/HuK8FszFU/DUBlRWW0CzGeB54VQfv/RI9pjN+KgxovHi0dByRjPHR7cLjnJO+EGSJx+OEevAigT1KgbRBARPejhkjLZv8qiTsmgW3LYWqrHFEW0dObhoR4YeUoyOMtxVTg93OoO3+VwIb6YANSFCu5xxvECUwaFfvIeWM7+lFP8Pbd5DkCypBLDrIGZc5COOEgqdelaxrv6uGB8I4b6I27/+oz9UOQ8f/elf/iVw5mBaF0vi9xMBRwFbuYBflpwoDec/kgv60LPUR6XZZ91kg3bAYdoD2atjwksZ3WuJADoGYlw1Ns7U3QRn5cDdxHXibTxULr6LcefzJi93aqeLtLVdDaPw7su0ZxgI3DnnxOOQYE0AgQRpRFEsFzi2yuo7arPWD9PoUm7tO9lLjfqmSZ30lFj+/NgB3avVAL6gPjOTcqK+h71y7WGHvXi9mj3hKMM74NK+QPMcp7gELNTHvMjhjVP9HDl/HCI3RJRrkoGNb4TqCWE+rsebIz504WKKyHBgF+WD7t57g63/d6gh3oIqHTZ0KDf5KFdnjAZpru1xBK4gJk4x1mhfQp8/hFnSBjW7qsvGfSRXlkFEa7tZehJ2nORDx0ruV3CEVTpKyWIsYRwiYb4NZx/SiOPFC2YCiScsuZLcQaVuFo5rO8K1wOikJ7WMyM9WGs0469HrDIXhcnXAwd+rq8aA4QyFQ29iqdxQnULKKlbnEnVA1KjZLI0/WYsTqvtzqNmXnfgY7ShVAKm+qQSG/0M742gaRo65oj4CpQUUFlGcK/irnOsNNdrL8VJvODlv+G2TF1J+Y817jOCTGG0ogecSyFov+iyHvtinHuWHYcaCTHM+yfjFYfpamKFSXkJq1GnvsCZn0Y1kpba/J5Z2ziIjLf6yuxtHS3pSdwils4Kt8WJG8HMW6m0PmBwj0gDN0uPC8fKaoQf16Cd61K5xY//p/x4nAPA9Cccnd2LzLOszTfiKdOhyUlQM8sFxmZ0+Q0+fQmCwgSqGUQC3+KmUxpC4QWBY3Vib2otKYm6gqb+Ek3tPH67vTMPTorezdQ/XXBitzBZXPNJBbG0QzPWsf/804ePC7yccR8+/M6wozjAoR59y3PbfRwTr3/yLm1/V+5O/+AtBrAHFm6Q1oMxHQN3qFYwCgrVjAJ+EK4ygdhS9ccDbvllJ8sFWpxQvorNcGJPqAAYTvBiHe/fm585U+iW4kS8WT8NjO30iiAvrMEn1ghXBWUaxSuGHB934O0t2lBcyUN76Fpr8j36SDQ/sM1VYFME4Bhl69dsSxqb/DhTja58rb4taw2g0BRHPGgN4y7biuNP2uL7BfWK2zoU7xGe8+OlT+Znym2Qr36UeF4e1zmPmb8hpq37GzVDeOPHzvfo1JEwe2WirhVPdDPX3nDBPaOGkN9aEnvjDUYDeWHpoTCwbPuz/U7XG1sMIHvVtGzSx0iVM82XeGCD0n4dmdkvTP1dJVA4JXNBVR1CY1Drnzs6ToNyXOSnpd/bHGrMBp4dF+/ajePKe60A9SFsR2U5H6td2NKPZB58xxWpR1o7qkpOfWhCdn1UjxdbGekhu+mOpTdxlu5lslYF7R064S8wL/8705EX77O/o1fC+eIn8/UuOHVspjwmggSzXXAFF3RBXaXpfOLHPOEfkjXQ+sjcfvnCQr/E7HIq5ZGvcMpih17EVofzzcOqAl8ZROuilemOzhKcn1O1HLimF+TmWr3EYRvktw91roxW+9Z8dbTGPjuy02dp8Ci/1GcdF2M2a4yS0ljIcF0iIxkUWTgWTvsz1hKX/OW7VNS7Y/xw/jGVldo47BsLFEzdp8TATijjUbvDCtj/ot49CT51Y8rlsiD+3YN3nNNKyqFSupd1EmvS0s9HktOYts6WPiY7jXiWwLPvh9r1qcZnbGwXm/IYdlsKo/Ib3/ZXrCkbM9144pUFoQN6g84Eyt4cLpzWQPGBdzwBDqUmQzZ5pyXBAWR2jESxX1KN813cNxwQp3XVY2nvHiE+zpfqCSkDTZvHCnZwVO6A3QVxN5hnJOrmp/Viz7E7zuHhrKQfsq7SETsurCjuIG5ahqTdpu4TRtGP/8L8UTavuwzXK0lvdamOxpe0TKn/nYS3YKFy5cwvOut1ZZn1z3nJcEdxpx9/eY1NzeQ23OSK6FvHrsvrpeWSvYaTBNLNPHm0ZE5AzUl8UZsw8z3CRw+43TvglGXxUb31nwTbsEc0BeUoIXGLpiWGVDEE9N+HllZL321bYG5hAloxVxuFSQnHGYZp3mpqIyVGWEYCn+VLC+dMQIERXLyxBq5v1qJcaqPXplH77DX6puFg4K7ZQ1GB/EqqMSAovOq6bGR7nA9LHjvbnxtfjhmDUil5o83ncfN9dgWFqaxxHiAuBIKkxKmofW7GmZGWljW109keL7/0vHJi3bhNHIEe1A2riUGD1OdZ9uFebnAPPPN04f8qSFwTQ3kYwLQOhtwnzrROf/KvOsVfWCCxvojlH6I/i1tyROUP3GOzf2QjRxajzb4695/mbQIxj1t10tc6NWVAV8uPkTIUrJoEyTls6TVZGx03pyHM7jnXKEhzVVq/2TXHolNF1t2xBRXSU03LSu1r3M9iL5nXEdX1MD0610BmmRFS/s2+zUw67yDI+uGgizS2R8K1mFur2K4ctl3IObLT/h1M+iEf/BB5xkBbf3la7bA6obXP95G4qDytvsXqq81R4uE3ykIw7s+ZBj3TXD5hZbZ0mbqWTCboDOfjfXzV3tvHkuoN8/H3Qw3+NW71J94mDhdPNH8D9k7/4c4xhD6QezFX3myeOL8iP70UnSLoVvaIFJ4umZLZKBGLq4I/YWyICB/9vjcibISLOJ3fx7myWlDbRAHcKJj2hm6YdT7e1TTrcoNtN1yL+gNL95bPf2VYQca7SfniMR98ggSE5Dim7BdbWsSYeBWfGfmNEQYCL7t5t2+aUKuryCVOMqfgvVkfxqH7mWrF95wP7Lx4n2B1vyXlOvP8GG/x3lt/f+mIBoGT4lJ3xqY10P+LmxSE7//gt3zpx58WJb52IrXMfNryI+WaIY8831X7zxI9cOBfxccbyvvVXOJF3OdpkX5A4pKvrZzlj58jukbHhOLy1qXTFvAiWPJzzQmw/LS0DF4xd8UcMtsnBKIvkwPiRp6TSTVs6OZy5Ka9d/FVXn6PfM2bkS35J4V/jID64VAzTdemqoBdAE52bsIbunh8LomuLB8dWok0BBhdlnx/hMYKm7zH3WO51LtyO4yJ5G+Mmpo79QEjaH7m8s1O+D5xZjc3dOE6/ssxH9Uz7+06krbMaoTmCY4fzBvYsnppfY63HUDcGRNEtU6ALe8Ztuo2vopPDWMnL4RnsafugTu8dwSs8PAqPyqLaeiItb0P8rB8dx1VD/VNe95wuF4sazlarWqx+Zg2N6Pgrp5R58cRxkUVOWoscoc/jf9KcPPiGgLzMOX29QTiJSNclXpvoRAHkUxEKpmPUZFQxLibxC4txQJAYzpJayhni2jYC1GbJIacsrKUak62M2sYcFclP/0P+XmR9uoqYz/zy3izylA/9vFR4aPlbEzjlDiy01iZi7QHfdY/T7XGbeY7B/pt/8QeXmD/6t3/+ZxpFGlQ4rO80cZBjCGHB5IG12yYwjkAFR/GIxjwGm4BnGRrSY3BE4hJH/2/nkTdDxHDcgp132u3SNjLxVLh0/VTYyq9MVjPPSBbeWyl3GLTrRO4vIzOILZBMbeiN4st72UlZfE8k6StaMV7FLMKRqa9WQ4rpQkfIhM/ShNk1Buy7vHScTTh0VBlNuGd5NHA4/zDS+PEyMe54kVd8z1Si2mUpo2AelIuWfSABkMr4AXCNTxz0e3c9Y1A/sa/wd5w+5aLJi6fLwgm9kQuaF0tr4ZTFFAOwjyOUD6hW9A+7OnLOSxwoKoefyHsQDRkTf5+zGX/11bQLLfuqqBPNEHn26qG7+ZULW83RIPZ0HxW53Co3+TnkiVmJiKzA2xEJ7BoYfDpWdZTd5xw34hNQhq7TrOr+rovH0tKhum3VVTKNBWQJibCiK0OePMgtdJHjUIYOpfgDrMjt2lL4QhyqA3SFM/xP+UN6i2NqnYIHjmNyiF09mIA8UWO+l9PubRZ9Zsh02TMdPVfgppU03yqst0/p92WjewYumvTx3hpL/Ehvf7wX44BjQwODkZ8NO+sLe29n2RrhKpoctkP1ItCOZ6jT9BGdzKYM/CN9NnN63f1vKIIQp9kmuro0NmqcYeCXt4xp5vvl9kRHqav8LXQhapyQx/GhBQ7HiFvIcs7TG12GnG/yyYZvNW740IbQbtGGKVxiO5b4oTa3Wiu4Uke3asWR8ceyv3tX45B4cjFHyMhdtWrDV8UC85+n8Wo7OPY9GE1OB818QXgMxFLlg9g2nReo/9DE6V/Gleb5TVNatSKOvDlQyXm5jTMo3C+c/uzPNIbXIIJTDBDtY9GUMbM7dEBaJJVAhQ58lkn5CJqDUZGGl9LhsxbOOhHKJgKrHkc7t0oFcmg8q6ZtK92l/RBqBTNt79QV/VK/hPFQDrA7PAOUkM7xn5mWMgptKVGZrrcFVtDVgBhQ2xY8cdUHKslGpGbKBQ+ysrqtJCcCdalRwlDjaRuts5ijJjdHwbFH2pPl4ktFE+JCLR6Kk2fJc7n8rQ48QByNgyp6gJpz5UvlATvmjPVZvNF7S3k7ftCmM4Q6yTe++qluhNYfwF3fcaJcSADLE8C+uPEmCH5Ud4dZl0FD/xrBW1rzNh21hW5Ov2Ue+SO0+5zNmGf2Jm3Eiz0688IbztXXPiHM3a7qD0YC3Q4ba111E10kquMQ/gjjQno+oCZ6C/2JXmZSq9ezcPJPSNNYfQpH7Qu6zDU/qpW/6WOhvc+HcKLrzWQhLSBmL6AglEs61BZB6ndldKoc1cYKr80Z44UpVtu0bog7G7dVGiBHLUZd3nizrBsf1Ts/kVW5+VrIi9r1n8XFNCy7av1iaN73jSRkmi98Q8y3CvqSvkr6W0YcF979RtoLqH3OkFxjgrqJd2uYmEK95Cj6KGXSAEMwyA6NMRe/CWMM7XsydjfSF94vFoYagCsBF10zdPa1zP6m1ysnyruvHSc69+XEZ+8CqUJe92e7pe5FwMoPDGXcMOm3NtUHnBJ9beHbybHQRghODecZv8kiTt8QM6TZj6LdRo6vuVmPDfB1jWL9iAmIfHrCY5aYO+55rzJxFz3GVjFXCEcsbURH+t8cEtLeTRTTpvS0MmIZ911s/aNNMvh8ptO2b1Jq7b8XYqaLVzFu5t0EK9aVzzHCnYunjKv7hdO/w8IJ8B5ELIvG4BVN5/DOndsMLinuz+NT3noMKoGlJELolOBosFsSLsKnsrbIU7+WK6qcuFedx5zzBHusuSR7m5f/pbGoV/HfyZPHhTKoCFni/7YgSigpYZacrJPfdjIGBv95Y85zwrnsCaRusHP2xkJ21VVqB2j2nbt09SH1ln/qeEvJWvpBESkuRdjRtRF0NztDbbxiqUisdzYL/ACNILmegKBXzg5BqnGa+igZx4xliN6bvBs7iu7w79wi6sGnbfbLG6eaONInWjjVx27Iy4IpJQOP7p3/927YE4P4OcuYhJ/6Wd7nbI9/2YyEFXOzr45UUfPYsl3UJn+xcNrGxoa5SRKNy+pYRQv6GjXVTntoacHEccFFExdPXjix5Mervv1b/tFcoxF1hhM+b6a18MbHPJeL3PxkAebSwdZxhlPAzu39/LFsp2G4gxcSmCG3wDs7LV16hJuNDHzbSKG4I8u35GKOSBpxIzafsVvxbboRb8wHupvOk0qZu9gqPUewb/IRFtK9I3vut4XPsaF/KDNHnGXeXku3xthCGEMJzBN/6pHOWLxNDRXSpJHnDfOhIY3HVjiDs42MyX9Fb1BP/Fs0j0ZmfkPxfH20zbG3bB5pP+d3ztj/pZrcMwbSTHE+qpcbUC56Yquyxg8hEjnHh3bMQ3wDtB68uH9pJxwagaadsdb4CJbGVCPTgDqJAbYan8a9jEu1YY3ped9ipMfHtHFqrPwsbnJHzltt2OZpt9Cu1MSMFW3bPok6TFt+8P8xVGeTQru8aRVyeW7JGcuMW+r8dhZOiCyrO47TFTADS3ApGUbolGvgkBNuOlsWN40kf23xSvtFL/lz6m5gP7fgCZdIffI900+HPNK5k99cR5Z5hCzxXwunNJvlpFFNThSxZDikBBE4O+CEARsclPatlCBqLnmUIkvbmSVvJLWpVnFFkjI6DIQqPPbEpnpryAdr07ZgN96wEOa0ib50ZmWCxmpPTsM60q5eiYk7pHGRcog+iLwbO+pc+N9CUG6Z1+XGfcx+vvmo3ouFk767QExe7Ap0nUe7n+Xx+6Hi5yyDHn7qZ3mfM+SrkzOSJONZr/NigqIzV38WtRjSdDXMlBRN2qAbJ+fWQLEWj0NTIdaoZL/MetkObUNIyYslv3HKoonQvOHlzYvfEtiA/PIKOc2ZM/7x0/wqI8NlFOTn7/jwxoQLsG+/pa+xzYCqnRqTy8lQnuQ0DH/wRLq+0od6q6Q/m7FEgpu6wY8N67Er3hRBuqqLwqgJ0INy+ozdjU1EF5Qb3YvOE0aZu9gqmh/8dgmtGG+cev5A285ziv2/xsH+pik3qr+NhRNbeJsiNEmtyjij4hocD4yodGyVmsm99TcVHtAb1BMQi5bCpJZscU93c+yxT77L1v2M3CX+fd703SD1cvOpcVLXE9EMoHKfaDReesygO0RXt0CJerP/aB/bglJdvMYpjVJ4tXBKDGd+/v+F05mRf5j1jAdGF9plRuqIO4NmY1lvjl2Kv/+FUy0edKpUpCtgBpGAUzKM0Cl5DpnmMdx1Wi45re+3eKX9ou91r9x14l9ljzj/sBZOiJLNxoTRzVfd0a+cRj5KGlC3N2jzP/pE3bKV5Fm+1FOPHSVFd2+WNuOz9LZMP+Ayu02cW3iylIeiVvjwervFPmMjdZb7OLO55aVVMZ/A8wIUv8GV7qhEPjHueFP+Vjrnzq4P58M/Zb4QMa9L033sPn3rG6cslFgS008IDZr+o/PpZ3n8fqj4Ocugh5/6Wd7nzDmy7kjSaYy6zouD3/3ZRBTM6KOIqTRp22yczZkl8+gzIflmidj5H+W1FRNZSnDohZM77G/VtuQnH2NJS/p0bhj748Lp009/+O6HP/xUqvKNBRd/hvqbb7+pv+ND+tuGEtE4qHGeKWb83yb6YigGAWpfBSXCXAeysCUz9kjOluZuKCXZYsN67CYvesx96CaAH5vIznLKYzd5p/5Zfx/d0xb1MnexVRT7P6aF003run3s6M7U7JOk/NZ4MNt48d5quixMbVBPQCzaFXYeaztn+hothtaOM/XeQvc4Hnmc8y2vsUpryamvHQunjCH5kVLNU2Ou6uhKvvoLyEMvGNIvvtoGBsu8raKeYkayXy2cqJu2BFf2HRRrz7fOz1AL5mCtMQjmW21WLibSPT0x0/8cbz3mHrSp5few/6C5s0mhXd60KuNrtCg5Y5lFP8VvXzjhWqpBygEJz3TOklsVRTigbRFhNRx1ClVtBh465Ro45ISbziZAGlRgN4Wjsm1HeKN3z7ob2Peag4tFYzw9s3dMOKZhA4LkrbyAg3+YjA7BJEWlPMglnQ6jEerTrZ+cgBm9bgGV18YxpZynBArrGmt44tgbSTFLJsPyKX5rmqBf+JRZiQaa4uKk54mPVf5zuKWuYrMZ8pMfG7lFJT2WOsuMM9luAPatAHwIXJVB2dnNBbHBDbVH/KHyJtIxP0DLCVtIPp+d04Cz7X6CXB+14i/rjb/jRHllwQskLpZqwaQv8fZHKjhhDOxJx9n3WPb5Vn66Hh8v/N/OJxcbMG425/wquPRCM0xknNkSPLFbaQPcudGliiULa2k6B+itOv9ZsgXpP9uUfsYG+k8dJ03TPoXtUxjCcS6EQQjsxLUvvnH6GIumsXDChMTxoUXTN7V4whunRwunxhU0fdMJN9Km1vHCsF4Uh3iRoFYF+unbxWyXu+JyK4p2y2bh7GpJr7m2Wfi7rmsTk5xrfHdWO+/E2KUva2XuYqv0DW9uPHUtqI9ui0ZOznNqjp35sIU3tOcbJ16zmLOMVcY6WzPHx6N2aCw+Eg7ADSudknQ/s49sBhYeyveBoNkF5gXAzE3cLl6MXaZmPbU4JluOm/lGQjFXzmYePd7rut6vZ6BhA40NjRMwXNLhuhqvsUJ2ZaZ0V2jQ59ixM7NLd9ozJ11HIqSyvSXNL/G5z4RZ1zSCsl3B0KAkc08oObfbeQ5EKflJneXq/+RpSuFSPk/H97q7pWuJhe3JRqprJ3QptTxG/4jK2aTQLm9alXE22128LJqSw8cLJ6JrULLYB57ra+wokERV3XBdOCXQs2SU97y0Y0lDeTC3WTX0LHIaTg+nzl2d7XvfbZqYBsYzGDRutebG2yFvqCYOa/Fr0URaOw7Fd734KJZ1bMpI+td41BeMCYYaPEWTkcHUoFaSE/nhgTwVLlUZSQs35SnXxRWNqCgl/rDDaKBI1wdXsSaOCtvh8DhiXv4dV+qxlXofIlVaVuWgpu0hensVIMYJWrVuayTW1miLxjrLQmdfcueTQP0Uef0kOScOfrlX/Vj6utnBBaZ/fQj8vG3SYoqYA1vV8vOygN37bK1Nf4etWtcKj1GZrf7bDhvGG4wDm5SjPshIu/S4isZZ0jg80Ihl1ChsHNGocjbhv2yM2O12Ptic5EV8yK0fKyrwfy2c1H6isI5NPjhmvAAKBkWiIVcJO/rhX1nn2yYunqLDt1V846Rd4wa0FmrUqDhWEzQOZSznibMUhp51jHGhZx5LuJlCPtuyUK5+EgF12oYp2raTMb1dZQzv5E44Wp9yIy7ce/lEudKnzVXDnKSv215uPUf4+yGa/6E45w6OX/EHMMdF7+z/Gkv+kQjLONb1cT22Wv/3SM+2J77hhhBv3zqNjPftZpvmYfc+7hfOAmlKQFe05lCxKi4i2cvUlq9QAyCsDykB45h93CFq7jpEGitQnGNHdugEz1M1VsBkf6KWpkouHscK9iycPP5+UHrDnradBMTDN13Y/cMS9R2ndH75EiZt1DYHz/nL41eRdu6r5kL6k3M0XCI5mUoX2uEMW7b1omWGtBL/Mx3I5jmZc3p4ubWWvJw/imEZvtZYunfUq2jubN7CW/lzhDd+kMONmzqYfECU73LS27/+o9+/OP3o3+LHIdhLHGy5ueoJr/hzADmQ4Nj1vnCa4RQ9vg+k0Snzpbd3sLF33tKN51ku6XnTMbUe02zf+26yKLPN/oRCcEcX7a5eydm9wlytFAM8PdyJP7aBdNoi2q5sucu370V1RDTCxpg4kHiCqiTPdfOk5YN0LLddyci/bOif5aKl0fRA5O2bJ8jRhNZ9f6IcyhB0VcN1O42qtoFMHOZGs03NHseOP7kfMpKRH+z3ru44VRvMkIo4YVdMOofZMvDTPre9Fk7Hz5H3wqlseuEEAF1U6sdjciNE4ODGNRs46S2+S+vX2Jg2F7UNk42x9mbTlSZu4nA0U+Ma35Iyjqt8RRfZ4thg8UmlViWKzA3Tkx4CFWOf0XgO2kZ2gvFiwHM0jNQ/LAygPqFnfjFa/qnDKHlwX15py70w9g1EEqherpM4bx21cPqMCyd/VM8x+MclNG5wE6JxopjcBuVCbaEvbJ2LMNU4C6Rwc2gbqlE/Nje6g9U5Ea9shqnzNAxAxkZcp1bc1now31keRDrZkJZ5x77kDmkEVtpxv0m2SsM2UcNB9agGh8yEn/E1FT1PrIVT5o1ZEoC4wdT4G3OE3zr5wQvHAgaET12OVzo7xqv8AzGxuv49HNNQQg3wkIn/oacoPlR4JXgA0I6bWEDDZPbjyHbr3lhXfqNypxHZXrbbJiBvuok2MsczFr3EUzQ5XjRO0gcpocnx4vtPzhWhUfbDuqIrAb659dsj+7I3iTU/2Sv/HuHH+BQF/7B7j+PViB57tM54ZoO2hRNAyy1F3tiUgdPsalPmX/GTiCgdZaeB/NLVWXHaPfA54dxqtyV85b0qkUd2lmnTFv+pdNTrDD64z6vx81zrw6WJySm8tnrLSTqg8qtxwMV28W8XTn/CX9UDuvc1YFOfMjZj9eUK5n7htOTrR/UHrwadEljsTZrG3A7PldDNRuwV4dJ6QbH9o2UvtC2WTWkiSe21Cctm++5wX8mdu9lKnBT0gV3covvMVodZ7qAckHVNu7MLp4MqGetQ1qDpElGyP7peRuFJsNtJA/rbJhfnreDSyMmqCRRsLqC4jchUf/9DITA354Y2VDMgcRuTtxvtsn4gAXtKdAN8+ntSd7pm0ta4WtxFBSoneOrs346jxgM5zK/4ipP9wM/yum/1N5y0eMJFRk/peKFBe0pXNz662UGvjBui/IBAxlQ8t38ERfqMesoZt+R0VtuiwtnLyPOLkqnLfyq7yW0cVKH6im83dm0fs7FhmW3ZhzMwmUhtLEOTAr2qKw45XVF1RHgAxb4WijDLHnnjuaL+dYeJLqf2HH16ISCfqLYNbmRV540t+Fzo5C0R3hr5LaPxafx3mIDii+74Hae8cUpzHDP1gC1mxar4Sbsdab/C2ypBmq0IbZltyGPduYnGXiKOndG19iKiay2/JQR2YA7TQQ7ze+5Q+G7kgB+kMCvc5/gwkl36Jdrk8wls8ks59/lRveLFxP6Qn5on1qKJY8s3wCzZKSppiLrsBELqbIUE3/2AWHsrcnAkShwnn0LJ7gQN+oy4MwSPuRibH4UMBkmaltrSXtTUNnfIilycRU27SW+RzkrTTdhMkJknV1/G01jLcEBp5wzQmsiBfkBGH+kdb6v51rp+WEZjp3LFt0g/wJvubPHDZ/TEzRjtT1Lg2kZ92WE8zy22tKGcm8csJByXKlpL8jmMxDBXTRNJmwgq5lRnqSw22DKySSPYBHpH1idUy6RViju9qd9UjN9en8QdY+u2RdgvyufteGH8JvGK63oXNnPu1Fc8ld/+Bcjql9uF05+OhVPeOLnUmMGgycDZ41Vgla+cLtZAWOLPLiZ91qldPBRnKtOgpUXd3RNl2YKeMvxXpU7cascr3cinzcyD5HdYN+0LlspH8saCwvbWDvnioMZ/5Y162snjXnUR5SknAcosvKxY8i6YYwWkyYczgfpCJWnLpA5SQrLCJ4+bjEz6WH3H2Gqz6mIkryz5oaFIUspsqwTpUQnlxEM02cpLG2iCZU16ann7tdJyuKgyByPwll00pKh8l8l3LeJPnlfFfgqcUSiS0ec6j8FtFnRo7i/s4lfRuGjSR/U+1kev/MYJOAXmN0u48dGNNXF4A4SdN9yguVu5fCcGlHOjT8U2maJpv5iDVJyzvmlJYGnrNEHNQ1bG7OlNDbWkU/x1aHeKPUrNbRe7Pdg5BdzmMgwT2oHaY3FUtiknYIVLXV3ggWMeddgP7gP3Azjsj7gkAX1ViaVFEvRrkaSF0t9xgcQFk3/M4ZtvUGJXKb4XVISlP/2jT9S8cMJ3nDB+uBC3L8ZJEiV4fPL7A3wXSnXyKORGHR8cn46RVWnN65E44vIYm6vaznHM5C3brkg1qKrUYVmBoRxEWpW0J+yjXL42pNZaPpd82bSaiLivFoMXDkgxeRg8WqFKzrLZNRJ++mXFAxvmeeyoqJ6HLpGVG/uq8YgB5TlCJecM1tFGDiTttBqxUGbWFmuxvnuRhhKpc2Uyfr+7k1cIsxem7vMILMXxVm3lbSKSXuqLmtxTf9ZHioq9x74QQaniMrcriqr6eekCqsZQRw2drzHffP3N15pzvvr663dfcx/zkO4JiIWNb7m1cGJ/JsgKjfON5fjRGn2E+LN3P/zsh/ouJt8+6aN7BNnGgj95kZ8713UtC/8tcBpy2/NgXiAhqzjFv7WPhaGIJrU6TPPWhNI8L5tfRCJSOXUnfRp9UB1B+v8HWdPIMer4wRgPDZE8pfGhAgOoTDoQxaO5r8ZlL56g+sf//L+5IH30p//uzzGHwRETgcNaNJF2/0c+rRVYRbduOaCRM6YHVpLzqGQbIlseJm8Olnm7EO1pPenIX5Vs3/tu02Yzv4GabbnzcytvHLSoc2rr3Iiz9CKIOpC5E01vIxtC4VWJIk/q93ikJJZiYjI1wEDwfz1lrFEvnvjsP/6v0nLa7uhzMlmi5TM5JYdD32WFTqilegA/qSZZNJa9QQLFmBPL7IfIHyJvnW6tO5v7PN+jrkiWfE4Bd/IZc6wYh+xE4IBY9Y+kk6CuoG1+6WgtnPzGSQsnZQYWsPMNDxdOrGPHDRBLLaioQB7L2kKlTI5Peeoskyva3NmF98qGsdxt4c5Y7njTHq0S1GazVezphqXs8ZSIRs+TxaRo9umKJZTSuuzBnn1GgTV5Q1r5Zz+Qj9JuygMq/Ke+02I3b5VYctH0DfryW92sfIObF920fOWSiycvqtDnRjdORdYLJ9yk6KYEvliSrxJ/30nj6xMvrPSxQQbH/5WfNY6dlYKWTtNN2GjZUG3YFWarg0g/Tt6mlkCmwiN6dc/CLTAXG/IjlO+Z76DkuduyAn1wSqwYOn5ksu0tZp0s5tjXgPGxvbomSAYd+pFXjsPMESprvIHPseR4orzCoPFdXw2N70bOthVdTW9cxY/ayadCZK383sREDb1Q5/3N4i4nc45d3EXtNnuNWsmtPVt+1TranjDlZquI0zGxz92xzhNUVS8+6fblASV7Min76uuv3n311Vfvvvzqy3dffunyK81BX2k+oi13jjU+hOHiSGNV/Qhn+lpu5h7IofPZZ5+9+9GPf/zuRz/6kb7H+zHmIvJpx2uftio5V3HnxnjmOEW1tmv7I2HZ507rg7mMp2rTbSNVGypvrWFi6h0iVRlZopu6k76z+xDeXXzvg/PbiGn6fxVf+0fCOJ78HxTGgnaOgxoX771wYn/PxdMMjJ7meNBNgfo8XUft0I9KapQsKuUkjTHKEs6JpVSDoOrSjPRFWQN8nNIvDCA+bVAv1iAWzGzL4oaqDCjwir7BSueycIINdKTNknLadIfRDgzyuiTJiY12JZsdSFUbCDgDS6UGE8Q1qOSYg4oB6MJZMhQabBRRtm0KBh769rGk5juk6CQS94q4S83tHtgUXdy1HBiMp0GMGXH6xvZUjOS+bJjk6lDbYrnk91AeVcd357zil+4udw3HnS1NtZLBVJ8zo5xM9E+NgFn1J8tP+bbp/KgezQUBK0wEeeskTNWLx49jDT8KoGxDvywVkwOr8F6auAuW9oJgKz9wWyAD4AaNOb+wrx0hzjrAqHS6KGJ4C2eGEnr2WUzUr8q/2+2LBs8zILGPpegjb2a9CPJHYP5WP+CAN0vffo0fcvgGNy68aflS5Ve6efnKb530kRkvlmtUuC2A9a/q+TtOpL1o+tg/OIIbFf/iHt5I4SbGiyncuOQhDFEc2iTStE7XIOQ3Jm7c3kYZt8KCuqNaTUTX7lTNG32+jbJhmmgeg3zfkgTFEoGMWNRXEb9yW2PF42Ypa8wRmR1FHX5fDv2nhy7sR/4rW7rSroW8x4vGHOYIjzvPGRiu1jxjK/7JpvZ33ZyWmZ9Jfzj6o1jt78Sd3NATYdKHLUTbmLuKxdkR1vV28U3Z+2PEREdQ34JMTjkX1Jh3ah6SFOqam+rakXmKD+e58Rgex8gXmHe++PIL77/58t1vvvhCC6mvsaDigxyOa9rk+01cOHk8QqBx6XGY7zV9gu81cdH0k5/85N2PsWce4sf3epHEMV1b81BnXByv3Cpc0QqiqLuC58Jl2wEuYp1XxVUmnZ6L3h1j2sZzSupP+Z39h/AU3os2vcSt+eKl3gcovCW+zguSpT6rknzt77tw0oCBZw9oDxqM+2PwVGvIz0DBmaWAJdq6rpTN2wfW1Nsz1A0De9rcWTzyvCO+rr3voCXitFH7VxLa4Yy/mSF6AM2WTRrtn5jIM6VaOHE2g0xy6mjHocuiKcpAnzLGED5pGaIArPJfpTzSKS6SnKAcAMp6wkMW+e4zloWBIltNragygLmt+plLS5bltCJNN8vaUvKyxT51aj+0kaFze4d58va82cOu02fGNcgV0EEhiAmimMJwbJdWM++NYioZkyUP2HU+85swzQQftvOjevxbTnoihwuSLkoypbHfOPm7L0AHSBZS/phX8Qi+jacO7CWhsGbjzXhq1zZD9473FORGqPwNTKmsJKM6+pZC6UYhJfggR80M6g/m7D2KtMmuZjVgKx4ImFpN6gBgvyVEyj1fl2b1QXQLVXHyZsC/dFcfxcNiyW+YuED6qm5afqObFi6gvvziS7+F0gLrW/ipmwn4zNuHc+HEGxZ+JCY3Kj/60WfvfvwjPPXFTQxvWrK4Un6UCzUYYToxfdQkl+jJtcScSVNSmZxs0ZMRrFW29A26nXCYp08aqYHMufRrOqsNPpA4/BiF4M8clOyRymi75u7kMiFyjoGOxpPoeuOkxVPePlGZQByLOOLAsZJ5QosmLZ68cFK4NU7jhuXOmgHfNrxMl96iJuocOXOcPMPc7R/V4i9l9IKcMvwZyaKn9aSXFSlLcHygQnaLDses7mO2NQn9wZtR2Nf7LkA4JV9vw/PApsaA+DU+vpXs23e/+c1v3n3+BeYelKI//43ePn2Nt1Bf482T7j+4UOd37vR2qD76y3HH/1nQ4+HNp5hnONdwwfSzn//s3U9/+tN3n+GtE9888SEh5yDuGtgIlmPbDwIwnlHPx0kZpzYVlHB/sDGGO3kwHpnx5PqOG+PnNpEm/Qz+g0YC+/sZ6AtZ4n2h9sHi9NujHLR/KKjPqiQ/e8bGH9/9qt75UT06ZD/7ox8+EVW/y5IfQ0B/hbduKRZvducaWFN+zU83DKJlM5GWzbqFuJcvzVeU2/5K65Snk5SimzzN+E9bta5PnGtOxGlM1ED7fgKoolHqw79Arr5TB9KGHceSB/4/6pJLLCXrsQ43yn+VoTWQGCvfZle5BllsEA/tCoeFN/rgyRZfk18ajK+2ULGafGGHgZI6J2+Jl8c5ToI/7eaYu5OHJ+wRa3xNefuazCg+Ksd51CpaFbOWUZSIDTxjXlmoPJdvdbviBb/ipob7zjc++gO44ztO/Pw4PQoJNn5q7JIY+WiYP6pXbyKoV/gd/3sQ9KXtPXJ2588hvAfIEeOtfdIOXZNnrOQOpcLsU3vKhlp6dYYw+zTppDe2lTJe2qOTVmYBKxwwmcssisnLRyz5hol/X4kfv+NTXC6a+Jbp66/9luk3dePyBcovsGji4kl6vXBSJGqqb2DwfQIskjh+/EeUufj22yZ+TIY7F00/+elP3v30Jz/V9+h4U0MdpiTtcEXRs3UhwKa/JIxlaKvwaE5bhVHlVT+Wm2R1VMR7mUTvXOVZrAIbUeyaD+x3pTfWtsBpE/CUNzgZSDciJSrxIw8nPPtI/VRl6j+oG1XVYcUU0g3HHseqx1w+oseS/CoZs8I9Yg6746T8jKiFB2GsA3FYz7YB862wh5ezSn93Pu/hJzf0tJ706cn5vXpbelssBR8v1GLfcDNv+VqUxJfUnHJr+Zg5WH3Ofh9jLZ9S0AObemjDjwZ/+23+QDZozEWckz7HQunz33zuRRMWT19g50f3+D2nbzBX6TuS+Pivf1qc480fq0trNEa5mMeiKg9ouGD6+S9+jsXTz9/9GA9uuJDinEQ5d9mwGRi8dwunvWOZtZlNt38eG28yRz4mO/StTYQvSkXDEw/bjGzSLyD2Jr5SLvns4zeabGrfpc0b0IPKjO8uF+0fQs0KVZKfnWOC23daOLHvLyfPey+cMnHNpkx6ZaFiBiM2a2BcLVZkV9nCfE3xpH+tdWqkk2R6Y79aEMs9Snfi5C1aFDCbQ3zknR/P88IJMvHIBzEWUay7PeDzPyv4LyZLMY+SVThTTF3CFwPggWXePOWNk+q2WXbGwbE2OQadqXvyTSePrCm8m9KaPjKU6IXaedRbGra6P9LOSRZ1o1Rxb3CjAnLUADVrN3BiHb5emGgcHSZ7NbUjx4yNT/f4zwNC0ak7+fQOOz+mt79x0uq4bGDPp4L1hJAY/SSZPO3GDr6ad9eehLi1fymKWtVHiSs+fZ4qd7xT51n9gX3F7WI4HaQH0NbAJ0PKejruJj73bKlBhYgUMNvqU5Dno43iXn3CfqFuMakjPbLUf/ijtPX9Jd6I8LsEX/Gtkj6e94UWSV9+yQWTPy7D7xfwxqV/IAKLp8TCgPjjAHryiye3n/AJrt408btNx8IJNyw//enP3v0MNzH8kjbfRPGHSDz+0I6RhLTLYx2Bd25IdOWgGc2QlppT9Nym7aaa0PbDdZzt8oS2YBZVnSiD6prD2FVanPKJQi3JB9M2p9XIhWwgv6oQbm0Ayvxi+OUk/dTjqcaVFk5FSwY0utHec8VcMHEOwQjCWFQ4I6kd3pUYoa/IWk0t2GurUTs1WmSBGIu7aw+VEjzy8oj/HJmg0dgRyN05FQC4I2VhbuWyAxX4Bxob+wMrc77J3K+5SHh8u4idD13Q55xH9NDma/8IBB/IcH7J/nm9aeL886X4X8LGb7m56P74U88rXDgZHldZjifuYPDI/Gi86o3UD/SmiQunn//8F+9++rOfqs4FFN86ffrpDzWH1USkdGX+UR4Jpi2JZBm6REcR+43dOBu3K3c2z720qQmegyfrqD+qpoUpH+ld+C/adNE/Geyk3+Y24rvz1DmHcM17oMZ8pnGBGL/zwont3BL8nRdOd01yNp1Xy6PFMrS1clxR3cuj96r0ifdK65T3hEHBCqXV9mF9RsjOomr4KW3Omr7D5KrwvVCCXS+e4FQLppRQ5sBhLCj5T//J0ydtlkw6JbcBHeI/g+LCyJXFY0BcKHHuQqmBloVT1WPCcm0KBlWWOR7UGOzWmnoye3GI1bRbvEfGHaY74pGaJuirEPjlYnp6vXBqrwtyAixuU+oL1obpIIdAPW67xFbjwGPV8kwSa+GE7znhKRxvhu9+HIK2fHKoC5WeHvPCWAsq4teuhDxri4LeI3ewFTJw3rq5Pbv2HW/XeF67tUe4zn9iS2KDxfZc29ScJqi/Kk01AanGYTHaDfNLGa193hGpo+ncL6b7FzpQ4s0L+47fF8iNit8qYZHEhZLeMHHxxJsWL6b8Vsq/sqePWsF+ecTpz5sTPAH2z9fz4zNZSHnxpDdO+JjeT/Cm6Wc/w8IJOz8qo4/L4IckGJ9+hY8NqXNPOa42Mk3FpgY252R9qsHcJam61TpX5hYTleRss1ti5dc21tQRh9hFNXXhhImKx8nSF9YY09POftIy1yIfkFEzVAmmfJzxlaXNROfmztlrHnPgATTxR8PjyGPuKQ0Dxq6d47HmiswZsxR25SQ2kyd6HO5yEt5Q29o+5TNXrT8GV+SvbKa8cUDc8YM59XY6Gss6HOot7rK6nZuWeLfRjcIQirxDPXXes35Aciyya1myz/1z4v7hma+xaOL8wzfZ+j4T557ffOGP52kOwoMbyP2Lnlw04Wyv+wr+Qt6neOjCeUcP7oCthRlL0SnhvPqWb5x+8YtfYOGExRMWUKT5nacf/tBvw/3raWjvGAt76znuyeEh+64xa30eTWaN88ma9J3Na08D4SZ2hTxUHpHsuqP7Hqnu/Bdt2pVvajcx32h9OGvEd5eLzjmEmu+qnPNbxsQbF05IJJzifsglspp7craikyzCIb31o3qekNOMlPe5ScOmFulZX5aO6l62tF5RznW38JV6y6fd6C/JHVMiS9mm1WnhpxxyLo5SrZz7TRP4WjhhYsn9DJ1XZ7H/avYiob6kTBOvcHCgftPDCRwq/3SMAR46pRdO5EPMRVNNbJrgWOd/7CtwYtNR9nJr5xRi82RbVHFUbFrmPDqqMS1ctUW1sAiG6c1xp3ZbCoaXg7EVLxxWjblpRVzl8roJnpiMURAHMl1Ii6KgoyxMndPo7/BZEpMXIfbrp/jFs+3HIfDWgIgeItDGQmm+ceqbIC6ccNHa8Gn0dAPyHu6uDfPEuQtuane6d7wb04esG3vlv2OGArcqXOGxFcTaa1NsyZ3c/QwJhQNf+RUENCjWCbZUfF5X1squddAfvBHhd5vyww/8GN5v6mMxXDTxOwUs9VSYv7Cnt0vA55gRXoJJ6Rjig5EohmoUF0Q/xBNdvl368U/wxqkWTj/+8U/e/QRPfLmoynzRGGo222cQFUUroTqUg8W4UsIh27lyMq9qF0753/lu71uGNO3cfwuhs/USYNm8ooi5ZUGV9iRzsjaOKhtnuFHDbXfJN3yBl7dLdMz6Za9c04Nd8aaZYwJzwzZ3cL6w1hxXy45hCEHx3B0ubbtTesKjvTJo4onma9GMdNKBTnlFOiXT+qpNjjTW4aIUhJQeJK7Fm2utccH4YMYByZmI8wH7+lv+qQN8NI9vmLjzbVK+x/Trz3/97te/+vW7X/3qV/XmGx8NxlvxDAF+PM8fAeaPz/zw3af1s+K55hD3G330DwszvU33Wy19jxNzGN8u8WN6XDj9zu/84t0vfud39NZJD3A++5E+/ofBzP/a2AyRxchcZG6ymHLP1rTbJC/O/eVjWRHr3svSEZXAT/ZRf1RNt6V8pHfLf9GuWxsynaiH4u9HgBbh/10Ot3xDQXN2lXNey6B4j4UTfGrSQ4nkMD/aR4ucaIc1F04KVcIZMmlFpnJdXKbOACcp9SUnlaf4i3vYfFDVLfnQMcDeWbZM1F0QiTgtTz265IeXMjKlArjma9FE7X7bBHkWTtVnuMN1h82FEoLUjY1EDJIEiyo7cNThSoNLLuGLJyffPrEkrxZKKklnh1wfO5YcerJHqU3OQLHkMX5dm8doLh6pV5txC/mV8ibvMEV0bdMRbly0ZLVB7Wk+iaW8IVY/WnWTbNZPKzBDpp+qWFhjU2GO/q/YMknMN0750qwWVABRK3DIoum2xBgSP2MMVhxWt9uj2Jv/xPYAhOZMc0nfbn/AqXqPac0942cDI015Rb9IirH1pXjkcLYrH2pmtZXnGHRkU/bSolhJL724hw5vMvikVx/P45NePOX1l68/x/cKft2LJi6ciMHzmQsf/lDIJ/y7J/xeAT/SCd6+OdfE/xa/ysdFl2jU2TH+HsGn+I7Tj/pjMnwCzP1H+N5T/GReSZs0Ljm++b9dNlFZWXXGtNccpW2dr+KoUL4e2FhvSm3NRgAAQABJREFUx4s+y9NPZJMfOrJgprzy+RADGwSnLFhtezBSzbSSevRdvj4nkivl/Ghl5omU0oFBFlPyiTrLxO/7BvrlzXPeUmPRhLo/qrcaC47tYrwH/+YazWf7Azd5G5gD32wof2UX+cS64z30Ow0PetpcMcHx/8NqVZfNorpFE3yZfO+U+p59yr5m/6vP+XeaauGEOejXn3/+7te//vW7v/l//+bd3/w19r/5a/wcueUcL5w7eC36jG+G9Jaaf4vpM71x0vcj2TzgfoPFkd6MA5u/yqc3WfoIst9qfYY33ppz8ODmd3/3d7XzzTcXVPyxGr4tz9/s0SgkLvKkH5rgSaGcJXEok9Ya75fkHXypI85nm+a/oUBv2un/t7zNyCb9W3b79wafDKa8OIagrzuk2H9jp/7//Ee/fzXbfxyCY5GDHWXddIsuHq2TXJcOZ1s4ZQb3iINFQmbpoIiz+K6dR48Z2+4IieC0+PA62/xdtmUPnFuo1QJ10uaMnCXfRFXJR/VmyU/KafGEftIbed2roO+0aGL/MRbsWUypWrzuT9YB1O1XRV2jmBhWDSKGyBsdhYpSJztK3kzpy5qUcXHFkjYKsBqgorArQStnFFLGY0rTrkUqlSeHXTu1JwYSIdq1edCt+kl1nkZMG296Nb3h97lB4E1yenpan+fGU0UIlWeE4nz73KYNc80+zA8I5KlePqqXN1Hpp9z8PCz5ZJnjSn24/NzFp7FzI2i+cG4UbliJb4rueFP+ir6zd2+lz2Y/T7TIJ8/0I0m3uU14brFiixWLcxr9lKXs3GcspgQKI+XbI92UjCe9XDj9+te/0s3L57iJycf1eFPCP2TLj8XwqSxvWtavUM2TmvH4ppg3LvyBCf/IBG5+6u1W/gglcfjxGP5ABJ/88saFdY0xzh9osHdOHav9yoOTwYSM7VE2hwpImgLNzFucXT+1O/RHPR6bWU77N9sdfUa8iTPxg9nyEChDUj96sh34E6vpyo+LicI8pn8elAChDrf45NjovRZOc+6QbsVEmzXOKdm3PZpdllr8pv6o3LAq5ke6b+Ur/kOZfjZfh/xDq8/yRMw9FmfFcZjeLkEfGsQb7LrvOQ4YF8q8EeLHgfmG6Vd40/Rf/vq/vPvlL3+pnYsg/kkEnrP8LiQXTZwn8rCFH9Pj4om/2pmNNvn7T/yOFBdknM+yc+7K3PN7v/d7734XOz+ux4UT+Vyc9bWu4tR4x7xEPrd6rCG6H2D/f+y9a6MsN5GuuY0N2EAbbKC7v033pzkzZ878/78y53ybHmj6go3xBXzDE88beqWQUlmVtfba3oZ2rpWlUChuCkWqMlKZWSex4+MgiRtLi/OKq/DKo9jhmKtErxDOyMjYeYVqXoto+fKW5iCQp1upscf3bYf1fuKkZCmDnLHO3RPg7Nh0dmiDzkM8HZWYzFbLNGjGZ239FFcLzlmCh3nleGJd9j9d5mEi2x4kowfr4ZD10b72wsmSXgjRGn8Qfgb/A8wGVtIEHAgSJZKmVsq+wJMI0551BAUN/GzdZhDsIROT+OBfZeB03kQZSE542t5PgKL9LHEKzU12U4otgSlGtFqlbLSFSkTbD9O6HJK35IGki2NrtRk5mrvYDnT3Df+hc7R7hVRCtsfGEH8VknntuLjLE6bIHkqNcdS6eQGEHF81XhMnfm8HDzm+60kPuLUuHMmT+9/Hd7bylv0RUTkotntmPdZ2dK1/3Y4j1yWM+w1xhkR+DuamqCPW9t6wxNnAA61fmmjL4U151Q5g+2jwNTr8js+7WQnwyW0r/CYTzxbohCKu9OoWGcpInv4Uzxhk4vNF3A6TCdPb77wdJxc8j/SObpEhoWL1yaGH3P7CCd1+k4mTrvrGbYCczDgUkMmJCm+0ejcSJz1nEKtOWt0Mubr4EoKRXb+wRh/xVGzqavY3PxPtz971Qip/iQDhpjwvb5FU+WcSVv4rPJIlwsvUs/qm1GNTG7vEm/JDQP5PPracvLjC+ECERxPW/K866GZEMKUqYrGtNhCXS/KUcZ3xmjEbsBWWckgtyBNwx19JJ1nF3krzVHine9L3VMGVLx279ZPJjnZUTMDPbFSVjmEaxYKk7vHlgkreqhcrTpp7Pnvx0UcfZeL0hw/1ncL3CrH047iNjp8xyMQpXydOMkXyxC3l3vIlN/xY7heZjMV89nEkZR9//LF2LgQy7yDn/fffe/He++9r/lEyFs9e5s9v5KqT5s8m2LFNvM9OSwdyDOx8ecDbJzZ4U648qZFjbEP8ilAeMpevSM23Ktbuc7lVHo32PyWxV3d49onT//vbmMMyuAnwsTecVixyAqyKM1lKkwRPJ4ZQ0maTRzkmWOOCbDNaNWjUMaQFy3QyGrhL20Z+5ePgvrXlJH+LorR1UQF02H3V0CSxUKPuPg4eywxCyUkKrTJFUyZOgaMtkiKX0OpLii+qrxnD2J1MaXCRCxFlbCqnitDyvxweOrA1PnIyCVjL24F8k6sy4Jl4sl0XacQHj0S1j9AhI5u3sUXbKD0OlGBzj0+TNI6ZbyDNPzALNNkz2sLyUbkASY9t6qyBNW41uOMRngyP6pzMChG3+Kt9jt0sq40hgzFt+1nihPPp15oorXXFmRIncZyMWetF99nUq8t9qly1rxVvWO2utNIjUNE7OrVPY5ccJ+ZXcZfhMR+ahTEBTi0ePzCMQza10V/pWgAOk3Ps/GwTt7FwwvLJJx/rJCNPXj7VahNv22PliNWhn/Lq8EhsONkYr+9tb7RK47AmYiITaE6IuIr8xRd/jsQsXinMClYkY7xJC936IcqQi+yfxzMG7Kw6+U2OvvDCCTodTJ+UCE90+qT0GZ9Qzf6OXoPXJj4xBF2WWTTYdK+9DNs35u/N2hCqO/FRuxjMU0xv2Cb5donEuJIUniPwYYfbvKGxgqfHRfLkfED8ec9nm6h77oBS8a3u6yOZ18+NTZVE/XT/ZtMrWYdXW3vDEwGrXtkvmLKynNebkmlMF+ozO6bgeiajqi77P8c6jdKh3GJC+IhGbhdm1alfwIm54o9//OOLDyNp+sOHf1AscOmN+YC5IueivLWX+SJXnH6kt3e6G6yk6/a8uFij2/5CHjJZyfrDHz7SxRn/CO77v3w/kqf3+/zDHMeKFM9RsTpuO+mB4zyA5uW53MZQkPR5pnFR2D8FNcAND5qkf1B9KxBjWsf1VSrd+u/ZFEYvSkc8cl18Rdj/rfS4u4TneuKklacYbvQz0bXJrysOoCdOshFLqjVQVpzbmHjdVsrSSbBslczBKIk6+U6ay58b+ZX31mQEHT64vJlUJR+l7xKiXnQ0fXP/1Gx+K1RC2ihYXUJilFoPoP6X2OEpq05KlOKhyW9i15fUIXHq6luAVaVps8YpHa6DWCtMgWTViUSprzgpeRqJEwkURnb+MC2l8xk7MqNMn2YLGOFEmaPBp1rjo0FJ1j8rr5F7Srem7l4TkB6dcVdqU0yoT8HFcdKZGzQQ0ZKE9bOTPwDMx9COMeyw+gbYXpdweXKgrIkTq006mQ089Ijw76/4pMcl7R1mpBZ9O+ueNA7YULxrudbn+q5c+c7GfKWTrGn8UrqHe6frERx+P25hndDZVvuHa7PJPYhakqXf7fsiFP6v45W+vMGKH7MlaeLEgiuyXnXiRIbfVmEcSZb0Bqr40Ujgn8TJxfS7J8huOvPhf06IOBnijXwhn8Ts409efPbpZy++iB/UpY1VTd16E1eM/ZzB38XtMr4lkMQqf0OFvtOnVNDUqDfDV7R3E0pPj/FReYasE+ZJ0muobOJsWLE2LnX5LKlHP7PeY3phGbIb1BgVWYsQ/KikNhwPvNsdE11u6KvPPRKHfC/5GUnVPV8EbY3zLqOa1mJibVMdfs8Ni+2Vfte32v6ycHXxDTOepqYI7309kVRIC0XDPrNhkhof2MT043H08YwBGuto1IoTzznFzxz8SW/y/LPmIVad2EUbH0qctNqdt/j6Io5eDhErTnrGSdQvlITpmc2QJznxrBQJ0wcffvDiww8+1POZSpxixfv9X/4y9vf7c05KxuIFNvyUAskT9tMTb5o/5K/46PGXDtzFUpJmu2WoJPYnxKjseMDpGBtk3wpkG12+KqW7Pj+7rhzMw7TU9bRh0hgHsvt7md+gv504KVkiyL2PA2FNnJpNqBs73m7BlY53W+C7+Wmtje34ZaRan2BsIhWmpS6wfMwCKr90zM0tiBNZP4vACfRkMCG3laZZQuMjhQdl63fz17CvfQlJVuVtwsVv/PCBb9XT6lPQqCyJE2+aYf8mTph0csPqU3xpsdEXJCJt2EEDrWwITEjjxJgSTG1FSQEGHAkTyVO/WtzawR2fbwp5bbUpNaCs2aOywGFf/kGR+OQZn0DmT/h1fa72bewaJIuRk/eXttvVPMxu8eexK2vw5zhgAzXaNJaMbexr4lRv1YP9VuLkL0aX0lvG7nZvbrQuvlM8LLgb3FP8dLoH+DvPBsgx2DQ8CbUfS+uYniEN+Uk986hb0zgzZvKYVpI+X09U4gQjnwP4VMmNzf5Z/NYJK0Lvxqt79Qa8SJ5IerhNj9te0qbU7fHmt1n8jNPHkTSRmJE86XXDkUyxEV8kSr947xcv3nuPq76ROMXzCj9GNg+Cx8WYTNaDuHcczhwwxyoYt8uK7obsq9rLR0R3qw3I/IXsOw6uQbvWw/ziDMCDNzYsh043V+Hrutn3eFBt0WwcSTG6Vx7HxlTG91B/OUTYg41ub8Nc1T4Nnk0/yFjtPBA8EbG6944Zj2tp/rrHuNrh4+cen9ofNNq6GEO2PpYBcyyz06ILazH2JE75Iod8JfmXccGGZ5I+ZgU85gto2eFjXuCCDbcM/yTexMmtvm/xOvK2QqToCWIu+mTi9CclTB/xzNQf/vDig9///sXvP/hAsnLFKRKn95fEKW4b5s2fmn9InPjDAFlB6bhWgM911RT6CXXfcYy0xlYs0/Lc2GoTC+p0rGW5ZXgQOdkwKZsFqfsz6pXVbpjxDDrnnhx0CTHGKueFnN8YP9XbQJ4nTqxIhJ55N25ecfJBopBpqyHZy2ZaU+bT86lNlaSzoUKVPjYphbJ1IgUdJugR5Clk8A/ID/Q1EVFwiPgTbDGA6rKNPi8Nh6rCXeKkoYttfc7DYeLKA6Tx0dJ4pkCXn+Vx5R+iZrWp79EWK08awEiglDiRNMVtMoJL4gQNvteKFd94sSGPLb0iMJUFmCtIQRE8fgmEcC150slOS5qcXCFOYZBim8CQrro9b+egteHU6V6zKwa/oMHXGl5zYXv2ZpzHzuScPfMJNg+xW/zhw2ZW/TIb4rJdY8q4xn6WOCkqQhZffr41yytMnAShyDrcV5dD32OQImDj1peReybzMcuS+r7/nyJ1z8PYsHk8gX28AvtA3fVP4xJH0Vdxi0y+/KFdleU2ltj/xEPU8UA1b8TziQ7PH/38F3E73bs/788l/ZirvHFiwQmGNDabHBvcjsfvQ7F/FG/IQjZvylJiFvJZzXpTideb8YzBe7pdhpUnnmHgdpx8+UT+9lOVL/uj4zVOa3vCfKr3KqmNDd+l/6bPbv+g/G5D9WCocLE6O1gQFQz/nLBVKmD7uuIVgyGfuHP7WuLmGpceu1Hm/EHd8wc6RvtFA6thO3ix40Byr/3AcB9xZnmoepZtd2yfCfaRcNZ+F3/RaPdZZYwppccSEZpPtIqT480c4TfgCY4kihdBMC8xT3waP42AEOS8EXc8kNDwgholT9y2x0shImni9t5cHSKmv9EqN6tXvOzmD5EwsfOyid+TOMXOOYmer/SKU9yqx9yTryj/WTy/ycsm8qKQ7cdH3ecxV+R0kbFPpcY5tH2rsSW67M/lg68JQp+0pOIu/smABqnZYiEXx9nkf31l63QYvuuq5jQ65TGrPmfsYGr+P0+cCPy24sTCRFT1QUkw1RUn6rmFZJ0Jo4GtlU3ZcyZOvXNokRrrRK+MBdBWW7pNecZuEnHAp4NDWPepk3RALb3PHX0CjENKLJNYHQrNS8NKHyCd0zxRJhgtDSfagLMEn22ZNIVJWnWK8YqkidtzeuLE6hMGiRcf5q0X/faLZlWO7fBKMoQOEqO4skjpZ5q0stSfcYKG9pAdNBJHMbo5J03uUBkDaZXTwtboSh2ddLbtopXNZdZe3+dtO8bxslpYnLM23amHt9PHZ3ThR1kVH/Kj/FqIWztycmzf0JcHJ7B59Y2T2EytmQyQtSZOeZsWcYXcoIEuK70sGh8CLacypRp9VvRleCfzMvOBMPz29OE7SLuF0FgHwdxzY3dGMA4pMfuczxZwYsFJCrezcHLBldnP2u84cQKjH7KNE52fR+LEA9S/iFUnn3Bwi4xe5BDtPmG2zehgnsk3ZpE48bB33nrjZ6hYkcrbP9/UA9rcMvNerDyRNKFDcRfxxi046lnrFrLZrdNfdr3ECNEG3eKhtI/GFGaPieXbGrw04pk+awRUuIlvPtsqC/K9f47U8jU+K/LwnVwWH273vNHrEBQej10vfZse8cKFvCgZMo1cG+ejNY9jNM7FjlXCvfaV/mp9HZEbJlwVOejwz6jdhR6jXsQ9YHi1yfAbjGuLE0rGmVUmkiXtXIAL2CuPvESG35TjGUlkwM65xQ9JZiJJYm5g/tHFm8D9gJ2VLJ6nDeL8TahInOKnFD4icWJ+I3GK1aYPYycJeydecMNvyfFWPeY2Lgr9LG5F/ulPSZxixSn0ML8pJhd3qC/g3Kca5Aut56VGDpM6ZN8s5FN1cju64I7ypbcT5UI/g/iXtu+VCBid3nVxmgOCwHVK+VxjHYY1/99MnPJEKAOX4FUAR+mkyUHlEnV5C4lNa2UfbOpLm5yUuAyK1j762d04cbaKOtgpEkh1GwGdLpjVbIlu0JTdKoN/QKaLMpyx4ldpST0stP+6FJIcVRgcgF7TYLm1K+oKgy5g2uHQroQJfOD0jFPALWnSfeRKnPJHLPtte/HFZR0kTEw+byrTsWS6GYrU1+ivOoDCkB27kyb48na8xHHLnpMpJU1KsmRwGju6GlDI3dyyl13lM8ckvZ2fsHdbunOE5ePb23D8dkvr3WS3uU6fpq1XTwUOvzVGj9sk52YlfNf04LvhPzNl+5gk9itOtIs3ZPXEKb6wDOeXl8cn9aTegK3qKSU27/gqPhXtqG7itnJvckRjYeJwOAzQPf7SfmPUC1UBzdBtMCKiIo1pZY5p4dTYMX48Z0TixD4nTuA+0wkNiQ0nD1yN/WU8B8BJhh/QduJEUo1O60UX8jkhQgd7yo+HveM5g3zl8Cc6cRJfzA08nP3LX/1KiRNJEzp4615e9d0lTvGWLf40r2TfrT9r6ZiMmO6ksIzWtkeBDG8DahghDliTf8fK2scK3zZT/rlHHi6Qr1tcWWKOedQYe/5aDKyl6SmJi3X3801+9onjSjQBUNbjrMq6DDf7T+nvtZ8yvu4G/POoDRuGkxDfUN5QNqgFtVhBdB6jcQE1YNqIua/joi2JEzvfG7wwhpdX6RgOQi7wMm+QQCkE+Ag8cxErS8wL3A3BD7S/IRwXaEviFG8L1a16zG1x+zEr3rpdL5InLhBxvsKteiRO+XwlryP/u3z5TXs5hN6sF7IxALuxX7He4jyrYGPPRlD7zSS0ykELmfiNmypGpoqo7Vs72bMDcTr5N7dll5i1ls2IKNXayoxL5jjok+j//qft7zj9hvOfCOo8ODN4FUM9kD0BIkoTHCVC5ekUbiVNY1O6tCGgGSMDG7wNsE4ZHBYjXKkgLrY3+gpG1ufPoFcAz3x5iDiys3Rt5h99rvhZ2mjxEOHH3q8umNbkzP7jjfjjAG34lUd4EqQQZypWmpQ0UZbEiQkpMl1d0eE1nUxKekFEW3FK/pY0xYTCpKLXUTfd8DtRxniNNc7HvjhxEX3wZOIUBoGLxClXm5JGyZNYotNRamvlSJpCtnCMgp1juJZwR90knTbxkv1tfrg/k85uXLGzEoz2yfzunEpb4KaLMXvK1o9TvgyGA7socGOS8BW+ecUJ3YwG//mll1eLgeGnZAP2ngrEleATPpG12zr+pH3HU3F7qZViA2+YfOxuqC+hnjaiFm3uGB2OS47PZbOfPCacmHA7zJw4xTNOZcWJFzRwskLC9KtfxbMAkeCQ1HCrzC5xQq/1rIkTt8pw0qJXAsergXmWgQ1X/jJWm371axKn9/qK1o9JnOLkSIlZ0LhPfq6OLqqvMd+wuZ3ozM3x5npDtxuSqZlHsJtrufFjbf5uwe6ny/vWeazuUcrPiy8qzvCurLIde7Ws84btqe2V/6kwdt3a7rXf4n2dbfbXYzZs4mPjng3VDTWD2pBjwecH1EmQGG9uz2P+ceKkF1fFcavEKM4l6JdXo7wKhVxfKIHOO6tH6IiDuX/f8EIa5jVuOa5v1eMCDju3CJM0cZHGb/Tk5Td+Y6hXm0jScjopK9yhy/HiUue9N7zTw8/OWWm7/zuw/YYfrauAp9erzJ15eU72dPnfRU76vD2HsjOiVHsrNc4xiDmOSXQjceIkiBOg3T6fFPkA/jYSJwZCpqtTOSzZlYT92YPViKkMDkXJzJlftQ6fLF2r7MKdnKjNEpMrcRzYUbdAlxqiRtGMZtAYLP60mTai2LIodVueqKIWNE6e9CpyPd8UaAYxEqevucITzzV8HW+w+Uu8kpxVJwxCIokSD2Lz+k0mJGlutmi1KmTklxklitmDKnZu59HExdVfTmJirnFCpYnO+GhiMUu8Mjbg2OTzSHLt+yyjQbikSCoUuzVK+wRZ07bWp8bnr+CLwzZsGHZWotE+zN8KKkw4MKv3KAvTBKYt+C73qTEqtGvY29jmrREPJE4k6Lq/t43YpMdjt2q9Vvccs1J3fHZubb5ZL6Nwk+7QuGHkmPH4HOgvIJ46pina3HlM+gu9qrWfPPa7xInEJk84PtOFFv1OUyRPJEy/jsSG0r+lks8gkdhwq16eTKTejC1OfHxVmZMVJ068JIKdkxufFLHaZPk6eYkTGr8ggpMYeuc++UIOcQpOcwwdBVaHPTiON9fVGB99EuoyabEHTaUSJX81W+1nhU86ECRJdY+WmMI/+ujC5HvhWjv+3+ydIQAOUd9OpThs84W+Y9ptVhjFyGWcwn3PvqphB6d9u5bE3Ws/53zdLTnlPeqfDf0mzDdUN7o7qAfUvNtiggjy3JPPNbEanStOSp5jnHsyRMC1jbZMuBQVkkGznq2OeUfnH5yzBNLzCc83+e2gzD1+Y+gn8XtOrHhz4Ye35/FMk57fbD+FoFWouIWPea+f17QOWacuCDf70JnbuFxju12KwmSrcwpRkpjwZD4y/TOWQ+P+SPs+ccp5jUkwhzs9didxYgJjssuT/oSpe2LLSKDOxmn47la9GmAjJOqQJZx0DX8WZC1oHLNQH8PWnWzEhyK4JL/pau0cmq2hl9WMCW59tmhLcmk8pXHyjYW4jFb+RNc6JQwD1fBpa8hRFCc2W00TJW1c7G+lfgCX3IikpydOcZUnrvSQNPF7TgwuSZMSJ67iROLEazgZB3DoZeLS3r7YPNYZSE6cIlvioo/eqhelkiWWz8Ouvodt0KiPdD4dwIqT/Z5Q4PtqITCeqTStnuxdDthXu3WFsxoP7oQdtEuYNKrR3twQeAtyOQnMipoY86ds4UOpzbKPYxeV+AzB0BHA7uUQ4M2bX2q5ygTsEyD6BI3+ugOoPX2zzlVCx0vf2vqK6puOPHVUbKH97vrjZUYF45PH2CzBftK4hK/WxInEhpOMXIXKW/VyxedNrQj9+te/VslJB3s++8aFlrxVj1tzM4Yzjnj5g5MnbtH7Q/wuy4fxuyzoYP9zPH/gq8q/isTp7//+7/Va4J/yuvPY0YF+YpDNfeqJEzjPLUnQjgsPTou3Hn8Qxca81igtU2g+1i0HZcV+R+vu9z3zgq6RXuXADWt89zhr8eb6XGLLmK0ceyqZL2Jsbt+qd68v19pzGIcdK9e99pX+u1HP42y25cqIbmg2rtlQzapqrRxj5uuzfSAEqyEVcdzrbXrcARNx8HXMFWy6UBPzCaWTqK/iDhkSLc0V/DxCwBE5imFEJm3MQTEXaG6Icx1Wzf1jt5pv4nY93tLH78jx3BPzC7fo/Tx2VppInni9uW9DZqWpvjoddYprXyQm5jE4AweA2vnmZvkgyYxSrVfmo6yjzyU/a0sxr8vVKWev/W0A+BVPV/+q70ZEqdZWMvaMdQ53Er36xMlGyOcoTcWjpCFxMtDtu1GUjPxwzKbEI3HKKgwTGFyTp7IxD3bLytI1KAyPiWASKsvdu9pinOYXR6KFBZeH0AOTGAbKdiIt6o2X9khLGl8raUNmJEtOmuJbSSezJElc/eUNVyROvlUPep5pIkkiYeL1wpwI6XknnWCEuEiynDjp5FidCF38h31MMroKE7lTMOq8hEkslqGUSHkCkrjWbk/a306eqBsnmp5A0X23RUuZqC0rKL6FrQ/a0BVd3W9JO5naCYucDiLoVFi2teZbVF3FBrDfdOKyGJbVuPTRDizKPDnO26XyeZdcWTAtt/JyvzplxkbCjIl0tBJTclw3Rl1E2faV3HiXa/uz1/t4rZI5Dp++2e8vIUGsyDnKYjxSsm91I3Fidcm36t1LnEhsuKWuJ07xnIGfA7DOoTfjwYlRTZxY1cq39/0p5qS8XYekzPI5geHHdmviJM82554lTlV362nr8zJgmohSmHlOx00Np61PH6pn51z6eEu+AyFornJpfBeZwjFXtHhzfS0rm+aE0K/SidNSQq/2BC7bWPUcYY6JI9YYbP5r3PZz3r1R3bSfdH9DuXfTJqZyDNtY0x7/6ee4Za+tRjMHkTTxHcL3A7flkgjxveM9acfb976Kt33md05814RcEiydg8QYer7hBTROmFxyQQh9vLCGZInVc14Kkb9Px/NN8RML8TY95h3uoHEc02HZHfL93DbBJJf1uDlxoL3l5uZQV93sL44m1dXe/G0CuzH3Keu3acer0oXvcz+MQntURFNaUgUJY+Lxz+FOvtuJE5Nc3tGlLyHFP8cA+FbSQR/AWnFqZoFPE1O50G7T6FTDE/aBlby3P81NeVxxClwP6p2cyt3a6Q9Ht3ZwMvLwmdjGM8iESFtKWwOtDZ9p62qSYz1gdOtL9GrgLSFxg8v1wBDd7fY8rTyxosQJbZScbHCbnhKnL/IFESRPdE6rTTFR6DdZImmiBKfb7xARvH2PYGCy8qbl8uBlWVurSUqYwvdKkGLyodTzTlE6aeKCrzbk4PHyx6pW/OWKE2UjFS6pB6/bbpVpazGZOa9sU6Xgd2CThS3aXGZtH2/Rm5ms81Y8o9ikRGG4oS4U9/vUbFeBTbar4psFUh8WhdCx4uQrgHyhxDgEG3vGRZ4k18SJMcz+NV3yWeq60J0tieeYtdF4l2v7I/WHZKg7OVb1856+MVbJZfrEzzi3PVIybnMseiwYszYeUeaKU74IgpMLEhpKP/f0l7/EW+/iRIZb8UiYnNhwYsHzTZzY+KqwdQ69qSdPcL6WXFab8na9XHniBRTYwAUdVpz+4R/+IfT8Sicw/G4UehR/ulUv/NJcw6qmHsANpzCnaJ4C7q7LOBtjucRdzG39eMNXV5zbhF+hbdqZrP52tuh49n14oI65xh2aFnu6W8H14mHHX19lUtKUF1vyFj5clvEan92Hz+3KESv0C0OP/fvWB++sk2Hbusk3IA88RrhcOY91UW507Ci71A6YaiAM5fEX3w3MOXHMYqviI84XSHB87Odq0leam7hoS+LE/MLxz1vztCIV5yr99j5dbCHZiscOIunKN/v+QHqUGMWcwmqTL9D41mBWmmwTK01aQY95R7fsxW17PO/keY05xckTPXRcK845j1HAxGf3WwfskEvlxKWKJHfeqb1jXw3gcavSd7ja/mT42+zYxkjUt1Hsreqr7VIZH/pnnHN3Hab/cf5yiBb0IZGTIO1KotrExgHRPOsvqdPEqUcYFsmqENhKmZ6wAtPtwp9/mJvyWRKnkOMDK7Vm59bP1uXZsIZMW+YmarbVfsoJL7DiG1/kphvrSXV4GbyUllg+yVcav1ecKEmWlDBR5hfTV/HL3Pw6t2/VY8kb/aw4seePWcaVHhInnnUKHBsnxEx0fNnpRJlBDxWMNePV7wUOck5knEBpFYpJpr9hL2BEQtNWksK6/GsJk748MUrtOCd1YUfqBBdgXYkS5vyj+7yQ2I8FdQnE2tyabQtXxu+M3OmnXz521LFgydEM3zxxu98n68zSdrm0WsuhLyNxYiWSN6wxgNiYMo6JUyZR8g7zA/5yCfwS22qnRRnv0vhHy4f4p67kmOX4XdO6i5Md7pq0mQo5Q1aOExTuXya4Z2/V+yhWoPKFERzzejnEkjjlVdnxA5EkT1WndaOPHX0kZCRNdc8rwPzO05eROP1aiRMJFElT3jaTiRMJGlvKRR5y44uIIybmkrxFkHaRxUfqzdo0UEJxopUxbJlJee/T/bpHF9ox4W9rC9+u8e0xv1UyKH1YwiOOCcdFzh/tlr2IEzbacmMcDTXUMxbEi63L2KmWPqOiK6Lc5aC1FQU1kEXWeZyZ02VhOgGnU7ETmkSPMZnJhi5DOY5Bz8VbEqfYFCuRlDDuTnKYZ7h4Ar1Xj0iauG3unfihW9/N8FUkSeZR2VarJTmcxkUanpvkbXokSx+0+YYkihdE8KO4eVvfD+KW4F+++Me4UPPrWEXPFe64TS8Sp3xb3w970rTGtuK5TTS0ZaeyX4Ld+Wx5+LPLDE7HwcNCXoJhZ/4O9xIqBmvpYAHVvup0+4q3sF27caapJW0+9iu+Hwdijg/9B2WMtcam1eE5SZx+G3HsK0FMZt4BtJChA2JMbOAzfeGEegx7UyjrwLOVMthyazgM7O1u25dDSgpxvVMH4lyWqV3CxaRgg1wKK5HGuOx6klVVpFWJpgE3TXQSEthWkiixmfdK4pTpUvor+xmfjHysOumqrJKn+FIiceKKTZygfPF5JE6RPOn2u5Y4cStdJk6RNMXrPXW7XiROuuISNjHJ6cox8RD+yeQm9PDPeOnqTFRIkqJQ2VaZtLSt+4KjTc8/QT86ik9iWlVS5qRJJ0U9cRq06ZxwGDqCS7oA72w5pMuoYfcdvrU5JVgOpWFTtv67aooeU6Nh2FStwKZaH/SXoGC9xe/4k88xQP9ZTvKbHMZWz5gQE3HynKsLOXiWlSc+eXIsmASYE1v+mnx/gR79NWm9WxnH5iANFSFWn+XYHe2PQDv5p/ypsjXnmN3y/SpHk/AR+TKj36WNCd6u0Wj18daJTIwTJx++VY8fpvWKU0+c4kSFcedkIl8OMZ5xqldmeS6y6+wHJT4hBjIODolTPO/0WdxOkydCX+jqLytOJE79RCZ+CDdv1+EZp/Buulnzkccqb52x/yFLfeqsPDINlDDpe3ganwWr9fxjO2Ybctu2afqrRaWL2gC0XuCPK3vtdMZDxkXOHZxjnCVOxO8YvwFViU+HFQFl7K+O79M13uBsnZs9PKJYnIfG8ONWpLEut0QzMmRfog6ivdbBLch0MX5ecWIscyXnTX3f+8UxJE7stZ0fufXzRoqZsJY44Ta7fOSAV5XnTx3oWcpoo87FGHbPN8xpn0adF0Xwcqwf/fhH2llB/4d//McXf//rv+9v1yNZyxX2/E0o4sGrTnkuFOdVbQxUtNjJIuenEq6zfy/U+vCGwA5f4HtukjGSQ/ION1qfCFVfbkRY5+oL482ya19xa/3sTMnj6y8bjUQw93kOpW3ct4nT//P//Tbu7soJjsCNuIzADqb4oGTPk2hw+kdkO/gw06Y2GOUVh6xWF1jbaDrZLJVmwy4ri3CtYW6vtTgQUnlhZVpYkQOTLWt7sDdUlW6hE66yepSirIkT9E6c0muJQZ4ndyVNOsDgbX/I+yZObEMHJ0esOuntee3146w2fRm/b0CZP4Sbg6rb8iKbYaUpE6e4BSeSIU6I2PILjgmwfcHhNHcKG3zVt8EkTpkwtbZInHhl6BvtbXtalRJ/+jq+OmMcciemQmMbA+DYRvcDTt1K0W1DUp1/NjETwVXeiYmKhblcCHZyo08H6gMCObWji9wL1XY836TkWGbTl1GDFS+di4Q04wlzOHHlxFn3nscYanUBfslwbMR4RbwpTlripBGU/Oi79QTXtttd9x0g5Gz5jXd5R8xpc7fzlGI0TIbkoOO1y9syWOJccFVWl3ygmQwRyySr+4RxGOPOWHEhhbdPkShl4pQvbeCWFnCsSPtWPSdO+VY9btXLpIYTCuYKvkxSLyYcPaETGb0cYqw6cYLDVWCSJ26bceLkFSfeqnc/cUJ1aO4OstfsF5fGN3oxHJgGEb0YHZrw/xUrO1/0kwnG/sZuf2nOiQq3jXv+ye+WrAOzjflC1QPuOKKD7hGI4VXsFKa1Xppm8LmMKFJrNAJXFRUeLHlMj7ohU7eyCjZJK+mvZtVGas6FbKruxme2Nms53iG9fTcw/3DHAnez0MZ4K2mKkuSH803ODVgR5k4H36qnAxE7g8e39TFnfBHnMfzOU158+fLFn+PHcv1CCP3gbbv1+M+sQkUbDtUryOM15L/i1mOtOP36xds/jp9X4CJN3Bbotwpngpe36ylpijkuTAgbcEXzEjFPNT6OMx4NT9tSpj6vC2gmwfAgZ9fh09GOCMBiXdY261nbjK+0W7gQrv7byVxxVSai1vYivpIKPm1TQ3y4DGrGXSPMeAMnYr/iROKUwV2/cDNwwMf50UicQlYGFMZLixTFR5YoPOCjqYzU4Gs8FJvN0mk6g83W+qdqpTVnvv3P1JSaQlTusJWitgsuozbrSsqOK3Sj/+GdSHhMQ3k1cfJteulf5AR37L5N7y9fxaQUJ0CUSppixemLSJ7AkTwRbXoRRKx4vRUrC06eNIFosohXfGpiIw5InvBR81Oowsd5j3FWcjUprGfFiVv0WtKkkklRK1HqoIYBSX/5Jm8DdPKk+MIw7YMWB/Vb9CocJHe36neIg/9pWxVU4SZtI5f+bLcDGuaNgC3zBhmst7g1aqFTahnHZtcwrxkkOTFWMbhjxancqsegIyd2n/i4VIxEvCAp5Rc9Hs+N6VdQtn+l7f2QUWvrI3U6dZG+0w2Ph8euMYvsSHvvpG3f3g0ZukO0bUmfMQY0Z8lY4TNOOFhx4u12Spw++oNKrUIFjpMarzjl7zj5deRr4pQXWNAw5lwZITuwe584ecVpTpzqyyE4gRq36qEhY86xpYs2VtpdWn1S4eRPP5rYZbatn3ufr1R/+/V08ewrfHNlxzsj/ojB3B2HtUza45j5GFc7H8+w0Zvd+O5wk7qjeVPzUyrVsxWuqiqcOnxcrxorZYOr0EZe+/mIfyvt0Fx15jRaxzkvwmfixMU3tmN7yGh2Mu/kRZP4gVvOQ7g4E40800SS1ROnuPBCUvT5nz/XqhIvneFFNH/Uj95GGbfp6e19sdpEAvSzv4tXkMetwPz8Ac9sssLN6hYr6FwcVMIUV3j9+AF13T6IDRHvvniM/fYfJhuevQDVY9s0TJ7Xrog4H+Yr3J2mnJIPXEC7flVb3V5xXcAZ0Ij9XbWS7WQaV2mrTrdXXIUr3xa2z8WUnB5bSjU3mu2K0//8zb9OK05MfNrjqgClV5tyhSDbHPUyXiMQivWPAWnEscR8rR8so2P6uXsVW+GUUmhbY6WpsBI1GTpjhSphUusVLpqOYBJ2vDWorG2CwcaAhL8cQA0TeJKpUetwoNQSWQo4vbWOW/0sI8q8PS+SEm7TiwTpL19+nVdo/hxXaCJxImnixCgGMvhTDknTW2/mrXpvsbqgFae80qNJLixyKbPoYdiSAYVRYQ1BFaZkwpSJU4UzcYIpeUmW9BfJU16JpIZjvCed6VlxgjXrwKqBecJWB+MR9g3fDTPw2el2aLoh6FRINDS2M+5UE5/5r3HkQO7qBbRaCMGvjOWjiZPmhZI4SUPrf9F2qyenbZ2/G52kqYOutYal/VTgtuEis8iqtx+JxMrXjGg+35oEsrHMWja2drqU1H3TxlrHLxdCoq4VJ96qR+IUzwR8pBOOP+bte4Fj7nDixKt79Raq+IHa/AHct3ViwxzBycVhi9jBFM8NmTjFSU37EVxeTc6PU/qtevmMU761j1cF5+vIc8WJExqmFXY2n2gDc6GG+a9vusW31wI4+khzlB3ay8ozw0k/4/6r1dLFxc/hAI/tvRJf5RQQ0Ugcxu4xXMvdXLnijiP6+Gi4J7ux3eEmDWGA+Sf8M1R2cs/662N7p1ZttWEnONoZ1xwbiIemAVUhDWYMN+iJPwigYux0m3/MJcB5u3d7C28oT19DN+gRnnexsOIT5xAxx7zRnqPM885YLY9EiNVqdt6gx+14rDZ92Fa1P/nk48DnLXoeT1aU3n337/QWvffefy/ms1+++EXMZ8xxejsoyVFLkDSvtXMgzl90LiRfZZ/U/ainW+nHcMjeN6P9DCoikqQKPWMC3xQe+G/x3GjTqfvS/mifbtoyNU6VRevLVx+WTkyidny0GAXXxrmNy2niRKAroFlqDc/1eoMJYvAMHIU2ATaXssE9CApODFGv/CklP7vQgWzSkrPLHO0VaumYLWhNRcImQqLHoy+dtOBGa1WVsOwttFFPEW0woKp9Ekxb/CnpgcC0ic96whMmzhgyYQosiQ+DyqoVfYp9JE6RIEXSRKLEStMXcWWGUokTK04xgDE1KXl6KyYov1nPt+p50rHpioE0k88wD/0uA27J0xtvZdKkVae3wq6YfHQVh1UoTrTEE6ZG4vS1Vpy4x5kTupxgcZQ8GXQpHx3s6cCOS9WtMYrL22EgLnM+Rhi9qKoeY35m6mFLHsuLYRzvoVG+JQ4DqC+HUDIdY5kDwdjlSSwn2Lz5iMT3LHFKTUUfY/nghs2HLVBpNfZs2g8Mz4A4qMnO1M+naAmP27VPYW/jlqw5hukT+aX5SeOu+TyfcfLvmihxihc4sPJEIsUD1qxK+zbNn8cPRLLqRAKVzx68E7HBrS2c3IwVJ0ZjbBlDxBHy9ZarOLHhivBHcWWYlS7fesOVX/9OFG+3InHK56jyNlH64z75ZFvuCqTx1j3qWFLtSctyTsvRuurwlGme0cPvEnQr/us8Xm2+zQNl9nnXf2Re2ZGShybzD+cMzBUNZt4A18q0hza49tuNpj1Dw+5Gb+eXHa4Krrn5TmalfZWw5r3FGbVa4TaMJ+ZUygofydV6Y3DMzTjmWJM4cf6R4+zESclKJEO8Rc/HMdqIjVydcoz8Jc4bYiUokhtfoEFHPueUidMf/xgvf2B+ifnrgw9//+KDDz7Qc068KOLzLz7vK1e8/IF5jP1dle+++Lv4/SbGG9ka9xhQ5mEnS7ZXF38D737JM2Xw8/gQ9tk+pji0Yy9IL2ZdoN7NkDPbpPpB4Z38VThoNvNZat3nYThxoI15DoA+ALS+3EicmPByEnPpJdc8KAj0dOt0LCkhaUqLchmRmhN0W9Qm/ta6+c4rHNhvHWaYSydOYAflgMatcoOPyagHSiMtmCDsrYOpQPilbw1ETB+E0mxRtI0VJ1MK2zgTthxqWiWK/sdpi/yg5Kn5HVn58of4Qork6CsSp3gV+S5x4gtMMkImV1X0W04/iNuyYiKhjo+rn9t6T3ddDkFYxr+TJkoSJCdMUSppchnt0LMfEyfiLSZNOSecBWmjTZ50YMchJo0I6JFtMxCPsD9AO8XEA3zPT4pvU2oez9UH4LPdY4pfp8Qpxm+8VS+PWU548pXTfOGNkyBJC2X6s1KNaerXWDbwanHmR+NdXpX3UnST6whQQjXLp8p9WhwPbfUwMOxxtm8oPU7c7kKCxK15Spzi5OOPcfIBTolTjCfzAVdj343fPdEPRsYJB4nNO2//JBMnLohMiRNxkc5xfyidOLHyxG001Hn24BvdevMXveXq17/+lZIzyQ8deftMvpyC/sx9yudhQBpP/LKNOrVpoEBEuycU1YS78uH+XKF9HTT2+053+uQYn7d4kOM+7/hpu7IjJ0OC+WXEn+Owlkl7HDPw3m63mmoujz3PdvevUu9wU3sz4ExmpX21cM7Xq47qnw7fNbZThrgKr9IZyzvtSAga73w/sFN3IuIyV5MjjjgniI1Y4CKcHiUg4YpdP0bbVpz8fcKLIPwGPS7IfKjnmeJWY+awmF9o13NTodMvmeAWYCdOP4v5jPo78dtN64Yl6MRGEirtMc95fu/9Lz7N42OV9HL1KQ5vu7wrKiZ13BXglvip7QEFnfRVOOdKp55AY5/X+c44fbHQqdafbylxohfS2krXKT2xJtw/pxFLbB+MqPYOdYYZuJ84mX5I1YG5oMGNrcIDa6gfVCAaafa66ajsvT0OyUh2fGBm6XqtJY7Pa4lTTEJxpYffb/oqfrtJiVOcrHwRt+vxzBOrTiROktdksupEwuQryVrVikDhLwOGks6FdykF05R4bp0BnpImrha1k25Klt/FF0UmTqxU1BWnOXGCVrEaZeoLmzuc+rDosW0zEI8JuEw9xcRlrldByJdZysWmo13ZLt+GoxnH7xOnk3Go4ZNBicdOiK+h781n96TkuCWVYY+zx5rSJ6pOnEiSnDh9TELzp0ic4rkBPePUTiD4kch34+os+zvvxK10sZPY6ASDCyyh1i6xLiwxzG00JE3sn3zyiXZus4ELvvfj1plf/vL9vqLFa4h5xoGTl/VWPWQyb2iLjrqvtmDUobBVSc5n+tlj5XK0n0EvOz5ncp8Lb1/v5KVPjn29xYMc93nHT9uVHTk57+Sc4/jblUl7HDPw3m63mmoujz3PdvevUu9wU3sz4ExmpX21cM7Xq47qnw7fNbZThrgKr9IZyzvtjSWPU+abHHfY8K2TEScmGUOplZhg59zkK36nKUolVZxYxCZcJFOsivNyGW7T+32sMLEzd/GyiC9ilYlb7nSbccwhP43Va5Ikfq+JFaacw97RCyF+FG/SSzuRPvqVyVImTmnfiHUotRWf5vHhhucppzgcpt0UXky6Sbc23hI/tT2goJO+CuesHXimun2eJmcPjIvgjQAORa0/32LiRO/GrT5pRfZ4eyxOI5Z0fTCi2juUTYfP64kTrCk5DvFx+DRlBRN0G6OK5mlSaaTytU+qKntvj4PyNHHCsuPf5cQpVpu+5IdveQMNt+rF/mW8IIKVKCalyFiiS+jnWQFWmXKlyTBl6lJG1HwePYr/b3xAND9l0hTOiMSorzA5afphTELcK6xb9pJfMuIE6GuW56fEKUchptyMUcjR4R0Qg6kLbkBWL34254u6whfZHyCbYuIBvucnDb+2rvpLbdaR7TmsERMBfJ84zR7qtSlkMv44Sl9muzef3ZPtwxE6wx5nxyClT1idOHHldkqcIpH6PHYSpzy5eVMPVJM8sf/kJ3GlNhInvb6XxIbbbWKrOlbYiRMlJzuc9HC7DX1mf++9X7zguYOf//zduEIcP3wZJzROnLCB/qR/iNHcmYuF627PQXHfZdRmvk45ZnKZ1Lc+k+8Wxetts893VqRPjn29xYMc93nHT9uVHTk57+S4Of52ZdJOBxeoabvdOpH2yrHn2eT+dcIAdripvRlwJrPSvlo45+tVR/VPh+8a2ylDXIVX6eM4P7YkxtyOLX/nhGdFsIsZy2LOUeIUpV/q4Dbweq4pkqNPPv0k5qz8fab/+I//ePEf//mfMa98oqSMc4y348KLnpWMhMnzlksSKF5LrueaYm7J28vbvNL6TuLlBM/6bTd4bcWn85xjjpcrpzi0U++ILCbdoZybb4mf2h5Q0ElfhXNm85+tZp/X+c64mBgyhFt/ThKn30Xct2AKzxH8Wc+S822uJPhWPSzvB0hdiuBg6R6E6uUSJyR0cdGBDtOwbPvECaLgmqKh4QIJujc14YmFhq23ZrVjGl5FhdGWgvQp3zTWRkZ7Jk7gVSulMYn3ZyYzkaDEXx7QbSlZSZBv1WO1qf1oXKw4fRlXeEmalDixdM4eYyjdYUsmSSGTZIkEKkpuAXTihG6CB30yPaou1SOaya/YY0LK5IlbfUJGJE5KmoDjNj7Tkxzlfc3jzXrUkfyNn2dCHizeAQXbgbJMIq9/NN7OsNZ7w0sDktwPjpcW9xICWiSX43oWFu1hbPo2I60mTowjzzkxENmneOO9bqvIq4R8sfXnF6BAT1Dr0wwobMMP+MjmL+KVp+IrvNI9e119ys7UzyfpaT4ZkSzhk6gjZjSL3TI0gK1NY8C4Bjf/1NtO4uI3UpHQ5G0useKkW/V4cUPcJhOyON5/wglInHBw0kHilM8g/TifgdJzCiifdegEKG7F4zadT2OVCR2c8GhFK3TQrsQsYuoXcQvgLyJ54uFtVrJ4FTkrTZqHWuJE4NC1XG0q3lC/Sz0sqS5IntqefUoPNac1d90q+hfoLaLX1HYl7lf7H+FJf86+Qt6VHZcQfsQHOhn3szJpoD3f6kieU80ts+WjbfUJLTvc4Ij2ZsCZzEr7SuGwQ3ProqT6p8K3591KWeFFeFRzLCt+pq+1jLHwVENmvOQ5Cwepfe1jVN8hER/5DFP+ThPJFN8zvEgm56bPM3FizoqdF81wqx5tP9Ib8n6oCzs/bT+kzW15zF0/ZY9b85i73mqr2ZyjjBWxMLINKvOOkidOZuhz/GEreHbo+vh3oPrkPrxjs+/CM0OBkSFyx7NqukJTeYp4D1NtFlxprhgx21D6cpD83ULUeLS3jSNe1XfK2O4kThwoTHh5wBhWls4kKPxwax5UCE7ho5Su+MgTr6yZJmWbopdDbEcZEGfrwJDi1ixPE6cu98hJ09o8T05zLTUlh1oasyRXWIRxOFThvZ3DRIdKUDWIBCj+2BKaP+fEKSciUZCYxa7nTuKEZSRO/PBtrjx9HaW+vOJ3nuisbIpSCVNo42QpYSdOITkmkAyg5jPUyDqpS4gm5hR2EicmGb2xZpRKokicoI1dSZNeDkES15KoSKZSOsbFP/J60DZVGI0MNpdZmz7Pm3bjWFmzd50fIFCJrXQDFm1nAB8Vjp1BUqA99raGwv4UUPbrI2yKvzxYh6TWThwxSeBy/a5XvBKak1hOckl+acd6+PVFF3HmL7yaOKV49x+Ol9sO9jZxZ/iX05bc92XLW0E8DfxzqF5kXPAfw4YdLkNCH2fiUCLauEeFFzPoAepYceL2Oa7gfvxxe6tePPfEipQGOuT8JJ454sTjZ5FAacUpTj5+HL9/8sOIC1ac+vEZtHlBLVaSQ75uE44THr3hKpKm/O2m/P0VPEZcsbLE7TO/iNUmXhfs1xDzfJUuvnCiElv/AsvoC0x0CCE2ElBwxi41YtjbGEuQpikEJjwph/5KYL9W3N8O7D6nH2df0XZlxxs19vTdEwjPGYyLcWOMzn2YMX3e7hbbTn223BTEx7FlhxscwdMOxSNnpXoC3A/xK5KTGF+s2xEzKKa2Sc3UMhi20EzrsV1JTaV2KuFrnVfEHSmcT1CvJkDimCBJ8lvzcpUpn2n6LH5f7k+xWv1JrFp/zMWYeHveJ5/EW/VibmE+Y7WaZ5r0kwYtcfrpT0mY8mIPK1HQ6Pbf+C7jHAX7etyFQY5pnWOFre4HeCdOPUag7xSrB67Uqwdu05vS5W3qx1vdz5XzDL/STXX55VVZOmm6XOHU+N7mcc1pIRmMI14VsNl4JXFqgRUezIQp61p1yqOmB56cLAttpUubzJegcS49sZqmlXdGrHeoSKwSakB3TZPMju1sNHeS1lwnpwqbKXGNq/mDNk+wglufWd3pW4OZPvLPlNAE5tBuupyA6L8O7piESHZ0yx16go9nm/RQZlw15ofiuFWPkxheEsFObsJqE51VDhI8khf8eWqciRIaU0fq1tihIizUZNNg9Slg5cUxxPkK0bArJqc3SZ58q5C0fSEAAEAASURBVF572x5mQsvV43yIM1ecdOIdPpRPwzDFanwwz4oHPkAZnbDwDdwVjeXQtBvLmSjHFBu8leE1apRBN0gH1CflQRlQyp5Qqpzhj5SPY6LHTTw2He3K9uxvxsO04tQSJw2K+POLzrdXOGmipHuWf9/P13pieSv1GX6le2r9lnyOijrqT9Vx5DvGwT0/ej6k9Bhiu+ynREmUKqLGrbo+OVHixIrQx5nc+MdpdTEjLmi845ORdgLyTqw6KXGKpIfkeuhsMdGuGls+L6BAJm/Sy+cXYjUrTlq0uhQrTNxGw2oTv7HCSQ17nqTE/HOaOEVPNMm2PrW+0fdhT/qi+0G9H2Nmnwl952NHeys27oj7q2h2nx1P1ej0cc4Tt2B4cmgyFp0k+SS51qv8HewYzhHfUcy4bv+M7jW3d0QAO9zU3pSPGb62PhGeOnRVMsf0xCjlR8xsU2+f1HTsTHypxrhCeJRhNCVzZR7T7aVTzJ0RWO6Dj1HigYs2n/P7THFRx2/5ZP7IV49/GskS8Cd6DTlvzvtcz0vGBZ64oMPOylKuMGXClC+ceVvzjZ/N1Mus2tzibvY45nGDOOmgnr2Kc5GgxX4uILLlMWHOY9/dclbij912Jglq7zu+l8FZp8tV1hl+pXNdPZsd5KbXWrZT6lMbfOyn6Tk+xmnANQCJ3644/a/f/i6+M/OAyIDOEyEOEHYnUL5Vz18gcnC3TloWI30GnOgIx93xNnjORizYauBlVwYbkEM+4dY2yTty0TyT+LCGv8JNXscGlxgHt77TG1m3lQQFnMhSP5j8o8FQlM2Pbp3KGFkf2E6eaCdpQvZfYjWJxKmuOPFa8r/E7Tm8GEL6gw4LdKte15x6u67QE5oyMcO8qGG6YiIgxYMwqVoCSZza1ZxpxUlJFMlUaJXisJM/3qzFqlNbcUovy0FKrgji+kIJWfEsiROSmh7A3RZ6MNWbqHcsjSiLyoGGYNjxbJFbQqt/ybLFL+bEwB3tynZZr3Gfn3HyipMmEPhDDmOWP0qYq04k439riRNO9/xWB0DHmxDzeFeap8H7GMjROZfoCd5lUuY4DfuRnfby5ipfzf00rtrq6m0kTvkM0qf6UUlumeGB7HdidYnb9XjQ+h2dnJA48RxS/tZSzkGpUbdvtpMfbp/R26944UQkTZzg+LX1JEdvh4wfxxVgbqUheeKEJ+OMFWtOtMLLTABhs4pUkZ89aco++iCDDh6XECveCVhtHq8sZ381kpOi0g6fnhD/DaDd3/S9/ZYdSx/n+NyCoU7XM07HlabvEycclD71sena7dIzQmfu5EdMbxKg9nk4A3+Pa5Yx13JsZ1zWGHskR6TE8Zx3omRcNQPUnrrzHOCbmJciYWJ1KS64OFmiZKWJUj/SHW16MyfS459nln7yk3jjp+anLHkW06tQJEz5u4RxUSbORZwIMVHIkihzvskEicSKeU2N0NDORWqSrWBIntrj7EPFnMFN41lzH4nmIdEZdnnK/MQGW+9yFXOGX+mGfQGNykr2Wurqww2bMi7DbNEkoXH5hRJmZ+N+xWkkTvmllF88EZ+hWZNfKfGAv0Qw7JspcaIV/9nakTjlZJrt49N0A3MGDZn78Xn5xElubkFc4WZRM1XTV+9M0kFxljj5qLD9lPknruarwGwSJ1PkiUoeyD6g5WXx5IoTyRPPKbDaxBUc/RBuJFM822SNdAE9rDIlvz5jEKFIbTFVNJsCHSj6mzGQpWKDlhQRZchjYiJRil1v1GPVqcE1cYJXv+Ok5ClOvmO1Qs8wSHlbceLkibnK8rG0wM1QWbv76IFfG0PvGKnaYLj1J6rtOCGw7/BAK8MtRKWPjQk5VaolFZ6IXr5i+0MFPTnY1drVg+gHfdGtWLGqwEluT5zkcPhJ0E8Sp7DW8l2+bAfO5Ozwu3F4iv6d7FlODcS55VqtjneFj9z3bHGfMwQzDuFZ+Ty+nKQwL7DrpIS33bXEiRWoP8eJCa/15dlIkiSSJq7mcmKSJyS8+e5HuoqLbu9egdTV4pDBSQ4wSRq6TMctebw9j6vBrDTp+amA86puzB9dZvqCujdAYpggVKmG9B9tyesynvmMvg5fICdlWWaKTpx1fF+O+WznnzE+Y+x3OPzIXKERC0AnxxoPLraM2/TA39p8dKzxfIunj+8Jkdtr8w43tTdDnjVaJNMSXVatO/gY+zuqirMPwbXTi9r8RBipVXKKyTFvLa1Zd6JE4hERM3S1NsaV738uxnPBhRWlzz5rtxDHLXl+sQzJk+eTL+LCzo9iHsnnIn+cc1MkT/xmk19DzjzDs0/5HRbnIzovaY8SsHoUwc2fkqa2qpQrY5kkKabbBRzBLZkqPRi9qcjRwy20i7Oz2LZYysl3W8mPIT1yKsP3rl+WYuMWhue2cxF/udr7E3ZWeCfAY1LnO+OIkxzo7PD/+Od/PIh44yxx8koTB4VhuD3gGPbXmjjZqVm6hrMrXHzlgFGwDRoNT1Td3ENdJIFtpA4syiMcmEa/a2cw1+RJMlri5BUnbovxW/XyZRA8S9R+uwkZTXe/1c+zqexPW60fcxhnxl632AWQMdBOSuhw2zXxkCgxSemFEJE4qQQXRI0OObpC7eeclDhJk2gUqy1xgoc6m8oGS1ait5898JdWx+yCzmrJeq2Thts8GoGDuJs8oqa/dVvrte3lYNtCaXhIzLHN/kZfAjhNnOAPRk52fKLsk1OfAFm+y6HnadCZnB3+bMyfonknf8hpgTwQD0Iea5fn7Lft8LGRBwWfKTHHtErFN+zII5FhVUm3wZA4xcqTXhcet+2B4wSFnRMT/75SlvlWPSdOHO/2OSvIrHbDx1Vj5Oh24dBFrHhFiWSMK8S+tYbnp5CNnLzyi9XYmtZn2foXhSJQcWjfZQld9tHl/cRJmqwo1X3/GR7wmFbf2zHp44ylM9jjF8MUG7E4Eqc6Xxhv2WvpEU4Za+t53fZD0cJoIq7tbtj11W2U5athK7PSXobVQVvo8hZ3zr8+ykd5i2em8lf9bY4rrR4dl8mjWnwMLLES3/9ycOtja2T8FQPtfIB544/xvCWvGP8ofpfpo/hhbp6//LTdrse85ZVt356XF3RImt7WhR5eNa5VJm4x14XbuEWQVSR23XbOxdxIoMIerz4ZT4LHhV7aiRHmtwByvGV/i6fWjWnEpsref5K0o5O/hsfgNlmWzYa92CdhhzbGoI7XA+JsZGOxrQ9IeKWkvY9hWIWrUtk8ja17ESVga3P53Umc1KM0tkdL7dkC1xBqXAtFumhq615rpGt9g/YUVYXPk04Tkt8OlayPUrW1jtxIjhib/ENA0s91t6uMQXTipC+tzqsRzlv1OKmNxEkPaMdzTXqmKZImjg4tObdJDHnU+yUoOhdk8ps72ruYyVLexpm3Z+UqEV5qTGGbfgCXqzeafLKscAon+c5VJm73IonSBKqXQ8gJilFd4NEVnyV2/Q3WBzgBx/cYiE4wUA1C33az7Gg8yuvDumW9jwydW7XNfxsB5z3YEG9ROSnS5C+p7Ho1JGmyvzGGAfTEKV4A0FecIAhm+JU4sVoYsVZPhKynlsAvs52N1YqXr8JG/u5titl7RHfbz/UcfVyEddd3II+hQvIUcJwM5hjZP8Y7yQFPIkPi9KfP4paYz+I2GG7Z07NO+dpw3xLDVVuu4JLsvP12Xs3NW/XiGadYdbJMdCCTvSZO6ACHTr/8oT/E3d50xYoWOMLLtrZJqI8lbdoE0D9G0LfTph/VFB+UyOHPF3oUtBKagrqei46WbPEPBvt3YI7Qo3qOEl4Pxnbv+i3f4t+2Y6HhtfRxgK88V7jUGDb8rperf3OUxzGznUs1vG2MN0J3/bH9K3nSFmyoTskF91JgSOvdeUSyPdGZL1shjkdU3ZRs/S6T2EclfXMLccGByV0uwvHRjmHOTzgPYOfizYfxtrw/fBj7R3+I/SMlUZ+1FWzmEskJWfl85Lt6AcSPY/5gDvlhW2Gqz0qSHEl9nEvouyzuouDFNkqeWpL0ZtR9d4VL5rZMnM6dgNz9Rn9vxQuM9s5GQmsa4m/QbtgfRUl6Hqyd9ZbGud/DSphdW/l3PTatlfbYaQjxrEQmbrru6VH7DRlpMPOZBRtuiGhQWyP4v3YrTv/zN+vryPNL2KtM+NYwajy5YdyTVpx6r7vVw/Pux1JGNzpmQB0VQBc6KAeqNlemDpuUL+bd5pxCgnYkBdftKzgnKrTRF/cny7metZQCPCVNDGj8WQolX0pfx6163Eqlk5ZIoJQ4xcBByavGdb9x8EqWJESLkqbUo26HvT6O9AXHrRWB0JceSY9OmHlGKW+zsE9IkvLqTrtdL+rgtMfEpQ11yAreTJyIsUyeMkDD1qDJhzijh8BmJbFpcC/xgAlSQ/s04YTsFcduRwC80sQp5Nupk1L0GjECpaPc9OQyj2GNo8bVOkaJWelC/N0Sp3j4318iXIVLI9t8QJx95xKn8Jj+r3ju7Oh+xMm39KSfDtLsch9kQdBRB+LHEbYo52iOo8RQ9hOBEOskh9vyWBli57XkH8cb9jh54RY+cJxc+HaYH/8oruS2q7n9GSedlGTMWCbPKXwWyRhXj3OeILbe6HK4Uuxb9Fhp4s19JGNsaa7HJj3jPqkvIsC30Li9lRBGOyTxKZ1OnKAHRwub/aLKxY/Ks507TuRUvhOS7xzaNud4pM9sJG1jB1vrM5zTXY5XxoK/O3K+B7fz5RZXj5RbB43MbeNso0u565N6kQ2Fkjgp1aazokrr00B/cYr7quQR+48o7S67quaucEt0mQx5ppFK+jjakZDGrrhoxzDPPvJmPHZWmX4fv830+9//pxIoXjfO6tOf4hY+buNj45kmvpfee+89/XA2CRTPSzI3KRlqCY/HWSXmxE47F3DeKt9tyOI15ZTZlvKZL6Fnox+9L8LER8hzt4zKMvsO3I+jQjC8NaDS3L8Qmslq8lw30T25ctSbx+ks8EiV7b13HcAVpdLEVP7aanzFVc0ZP3JvR0+HScNWfsukyfiK2/F34fB4IIO596Xh1Aa+1f/7aeIUghwonBcD54IFwTMSJw1m1GWgyjS5K+5dwESfeCGPemwuVXF3VRm9b1UXg8q63OJyEjosqOgKm62Uo3kfrqMdplGTRVEdmOhGrYj82ANj8Fv6bkA5jK5HGYPnHXG05BZlRIdWckhyInHSF1WUcnjYAaVWnJhYIoGSVOSppZXNXo1RwJSOBckLhN+4lbrii08G8BkyWCGKXcvinFQJJnEiUQsLmrl41qtXwDkppSScpj4iC5ayw69rVk2OVHe4Aw0914UsH01bwQDahgXdqnuePe059paU0Xbb+nPp+xZ8PMZSNFKV+hgDjR+f4XB9mfQvl3brQrRBpflAifM4CdKJesSd5BAjUKboxhXFEzfsLsIkZYPC/Ph/Xq8dTXanbuuZ+z+kzH1JWYkbNIIm8da50Gyq2X/m67AgdtXxS4xpJk75o7I6pmO8+D0nVp1IkkiauC3m4/K8Eyo4eWDnZCPftsjV2jy50C04cZyyOXH6Mn6oUrf6ffFn6X8jbn/hFeOsWPFsU/42VD7blKtYeWsNMpAk343gEYY2jW4QNK8lqtVkQXz0/iIJ2uaHRpztSGJSeXCrPMi9ulW+qzyvm842p5tmX9E2diyt9Rmm1WPwcolTHfWAb7o/xz4t43PdsPGIA7PiD/1veg/sq7iH6lVahdN3e1FHBxwxe06Oi+fbjloTk7cEo4fjOTw+nBsEHD66WMq5SlyA45lK9g8//ODFv/3bv8X+O602seLEhRy/pZPzCf9Uxs/f/fmLd+N34HjlOD9sywUe5qm83ZfzzWIbMRu8fK/9iBfbBC30+ftPCQvXXnrDyySUOMXcpT60eaT3BWTpkoimj+HkAQ2CYVmDTBRVg1AnzFw+eO9DG+IqdCPsyGHNs7YqprYcjpPWiNwdzxk+2TYcGxS0oFfbTTrhAznVU1H7bBxRZJwanXjPde7IPnH67b9plJjsIqbbpNeSpTgHzy9k49OUMQ7NAGmQCcW8TJzEEQzJY3rIVvrC+giozm8YitcOycyGHBQHyeptxPTstcrsMhpyZRV69Nc2GOPea5DkC1oSy6cgDn7+MkqlEdj+p9SXFCew8nGUDKIk5eSVQXBMnIbMZpFkhTwmNsuipM5Js+A8cZafpAVF2EeRiRKTlZInEjVCAJLYsUo2yseqCSftfMSe9yBbnkTHR9JKSJCxiSfB8tmwGKPNZSEBjH707YykE6TdvXpC39vPgK6yAV1ObxBnR0etwmdiz/DZxZDNPxWpKaPGWDZmx4Fu1VPilCfNueLUWTMGevJEjEQCTdzxJ4WpZ0g+s+4CPjvQbRwcttqYiBWDV8qHiENgU7dq3aty/5fW2pcKW/hC/lA1+uMu+ZiFH5yORxKgdjVW2Gj4Qs8j5YscuFUvk6f2o7VxwkIy5LnEK1ajzCQM2XTFidPX8TY+3sj3ddymx9VhTnQ4SfkJrwqO1SZ+X+Xv4neb+O2mXMWKE5mgSePt5Iyl3n/Q7lxB9sNbfaRhJbK8bHKEOM7huLpVnozxa5yV7xrH66eyzenf2ae0jR1bsy4o2ogPt7sn+Z2RcwRxQh0fujSdy9m/bQzV2GaUijLTphzxMffBpPv+9dboh+FXUTbh6stVRSczqmScO6W2eGyfr0eT9D6Ted6QnnDkqlcxERd2mSf+HKvUXMT5z//4zxe/+e1vXvzmN7/JH+ZuzzfxsivmFWJLq91xSx4vlflZzCXvxPOSSoJi1YhnldisS3EUrvUFHlaUWN1mdYpb+/i5Bd/m93a8QRQ53O4Hnec55gx9v7X5WvJTyRwf0c5ZljdBo2p0K2mofqtw2NurCdTPRdBJtQs4tG8jaCK30S4PIgaikQzKAQ2i+1DnEtBrg3FCTZVBcxGqXQUex3jItWiVLWZBQ9QI//s//8NB0xv/04lTNPnLN+a4NsllSfz4oBgTHJpy53NYoEp8tMRJVsfQdetNnaWpX6YcjihSur6wrMCF4gAqwBZaV13CZMtd2iei6YRuDYaGA1OwiIp6DpbKrCU2OsWfaJYOdn0hVzAHcNc7dEgfCQyymjwmFMS5LgWhB/ZMmspYc2KMbCVTY7Uh+yNvFRubTOuyjuyC1Iij29kBGdxtgj9CJ+uUsizNvPOpYG8+k9A79G4ulhjlIeulGkpfOuFDQNVU4RRi8S4fEr0QO0YUGE1Vx0WvHC/ps/yB0lx1YnUhb7tMHyatvvScRJeTINSmXOjcJ5eLURerQ85Fhotk7utFcpFdt2X4tMof/KN94ArlNOjX/Zd94oR0+N/99MoRJThODLiSm88k/Ulv1uPV5B//MRMnEinaueWXHTttK/yWa6t9QqyTYcXUX+JEJF473k5SePU4v7FCqZOe+qO3kWBF5DRR9k3WrdN6KJkPvM1wNpzxnM2hlnWrrP3dyT/jrXxnNN81vG1O3xZnh6G0jR3LqbvcJ074K+eM/N5wQq5Y6fMEMnIb/nVMgB/waG8MJ4X7cdKsfuzazOdyR/M8uOrbCqf0q/2E+irtq+6TRwl7uk0tZtSr1k1+FiF/OuVL3YrH7zb9+7//+4t/+Zd/0a65KC7e8LwlccJLIZi7lNxEYqPbfONCzHjmMpOd9Fx+Wr+TIBImz0f95RJxq3C++CZvGbZ88yBp6ktTgB8z7lff13G0NxqTik17OSkd4OB1P6qUc3jw9WOmqLwtC8JCPMHnGu2HpKj85zy1ZeKYhYlsRk3UVcwl2N5xOSmw6Cj1XYHi2KW/GXGaONmx3ydO4drJuzkuG1T6vg2b/Se6TuwRCaKGM8Yl7AyW/togua6WjktKPnPziUY5gJcvI+Tkf0DIccKEtqh7F1EIhT33nPzoU991wkx9rDLQJ7o19aXIlUrpaiZLR3OEmd2EedqbXZE4cTcQuL7iZNobpfrZLYL52marKrVxLtV2XWQVVeBJWvavtAJWFRVeyE6rjkUIBKOyxcZoG/GTPvs+cTpz6PDZGYXxw6fGUA7+0T5wjXIz0HnBYDQceMyqg+R+4uSTAt5S9cXnmTzxXFN/PXmcsPCmPd1yF+3cfucXPfhkN20YMUzdduUq8xu6mssP5nJiwnNNrDZlCfxT3frHbX9e0WQWyfBMuZbXujcV6mrv99S0rWhOaUeU43xLeIKsPLfsWtkr39r2Xa3b5vTxiDvspW3swrS5K/HEltvdP/z1feJkb9Sy+rbCSfNInF2l9dhWK54LHrPBIrEerNEUR7lWqEmeePsvq008w/Qf8XzTb2PF6be//e347abAK8mKxInY8gU9EiB2Eh0SKvDuG6X9Aay35gUvtF5hInHKhIky5yjLg85z5JDluWn0zd2yrmyp47jzyK496EC3IuUM3ln+0A9kG4ALB9XYBibrgWnf/67PJUZU+wY8oJlDtalxqkzSNpxHVHRolhAkE2LTfpTSMasHXHcpQjvReqKUFmyhjf9G83+erTjhWIRS5i1ZDW4rT/hdNNBRSc3xiVZ3CrhufxsrTrVH7jm42lv7RO2dqFA0XMF0sXgv/6ong5IBFJXLzlKAFNxVqoWaeVJyBkLALRAodUudNKQWuMY4IyjHWrdjMe7tina+1EHNECVTQs3m1IMq6XGbSKUF0bm7Lbub9LJtTpxyPWxmMWsts3/ZH3xwdcOcdTPOpdqvi1zFtfqQ1oZioqviKzwRXag4HhlQHa76YKisv+FDlmPCX06U3684DScPnw3cHho+re2Df7QPXFBuBrrGBrCHbeJrSnL87idOb7YfpNRqEslTrCzpJRFxdffTTz9TAkUSxcmMdz9nsCZQUh12yfQwkNsBeQ0wscPtMDzHxEkKiRKvHs9b9rIktvIEJTte+2TYZfWjfeKSNsdupaswtMyAbPdoK5/hyrOzyXRrWfnWtu9q3Tanf9NntpW2sYOl7vL7FSf76VpZfVvh5H4kzq7Semyv2Xedyt8m5sgYoTb6BU3MfPru8a29zCfMMVyk+f0HH7z43e9+p2eceAX5Z/H8JUmVVpwiyUKmExpup/PLHMAx7xCIa//UFokVL6ji1mAnTl6x8moTyZNXyLldjznSq/OyunVw+Hn0eODm/sJ33IY/RvugG60DN8sfEte+pnfdDv+Q0bH+AjFiKtE+LDBcMRO5KxPBqAzIhPfLY5+CpwjyHH5f0qCoXjDsUlQ5gQ09oU96HE/UG81DiVNNoPA7A+k9zaNnufM5LFAlPl5z4oQZzVNjKdS27cuzYDX15PhA0m8mhfafZJ0ovSJkxx29xGBVL0LfB1DMblfl8DFEDwiiSYaDAayUUeZui8QdH5Tyg8Y74T7u4CKBgoqYEHGjl76AUaC/0OPAS09k/KQCCGNDRjY2e5KnzYeIir0oaiywHjf6A7YJ7OWRcsWgYd0qrsMWvRJfrqekyczGW0VX+LLohTDHMJCMGY5unXCMu/QYPZI4wVtXIVJWiwkr6uVi2MWq7btIfpfM/bxLuBA8YseOduDsH4akR9QI16K3xkfanbwTX6EHpM3t7isnAdpJmrj9MkoezOZnC7786ss4eYnnDOIE5k9/Ym8nLSVxytWnz/UbUJz0cMLTAykgXXyJgw4dPqHJxGkkT/79Jj1fEKtQ+SA3J0PjCrHtryVw3WafjBb3dWAGBE9tr/Cgug2Zx769TZ2t5rlC+12hqXPnaj/1umOzaMLBzPWcqLrd/cFf36842Ru1VCQ3RIVNM+YJY87KqzGpsToT8hL4MotJSsYAYPaL9uhNn5ucDDGP+KIMryBn1ek/Y9dcFPMPbY4dSYv4czLkOc3xVvtmmBI63iacK07jdj2SpjFH5TNO0PgZJ/gsB934ePh59DincNfrOBoHt7dd+6AbrQMH59Cbcqpdliy6/l0L/yxD7WlsZSmwtc+la4VwBieCURnQTH5WO+tTPX1jjnl0q17YwjnhOVRVSk+Z66x2mzj9r3jGKW7AikHKgXLC5DIDZwTQGEw6k3t2a+3ca0qcMGPx1JXEafRrZu8D1uUyFQx/S1XRhx8P/ijtq5dMa/wIkoSO+KLcxplo6jj80aBAgHCGCdgM2s6c3GGrekgZndExR0mr6iJo+BCrNkqpkh4kHg8I88XjlAGOLftpO/PEKmUR25k4DeoJGqY39IyAf1I1MVNJgh3NDndgfxgRUm1ilAarmB2utl+GQ5XHcYwR45k9c+lxepnECZsyVpCd8i/beSC8zt+6cpAAgrHfb6cNG/KrtuDXI/vwCW2Dxr4/cgQmzOsWdiD9KymLohy/lG25HtM8ecgrsz7h4FkoboPhZQ78WC0nKZ/r1j1WmvJHcH01mMSJdv0IZfD4ORXsyGMzjt1IgJDt+OEkxFdx345XBnOS4ltsKLEteZFCv/TZ4OFE+rIbQ+OyLA6ShPFhH0BhmNYKD+pvH7pn/22LRizdprvWap+c2VQTo0Gb3x/U2U1jjT7xdenYoe44NS3lwI0Y4Jh5ru2sb5Zf+2XcqylrzFb4TNv5WA+fnfEmPscI+La+q/KsrY4OvNWHYbXIOL6BaXcsUDKnsOt3nCJ54jXkTqZIrGqcpP0ZY8RZjTXrRE+OcR7jptMqVawm8WY9zUMxHymZarf8+YIP85d5LJMO2Ccu3feXK+25LMeoGP+odPNlad+vUg59ELm1uwwujumV+Yn16stVxK22ThuGPMUaewQ5Fe71HixNk/XQ97LT+n/88983olG8QeKEo1lIUHBT89v0/hpv1fOI21tRCuX66HuHakBtySxTE0Bn6wAHbd8aPA12ae+iOkOzL+qVxzD0O54+S9DeCawoEMJRZiCgzoGapfGdGRJt2R8mu6jGrgNRoqkUvNqpq3G2v4rN5qQzbXtVofopG9M++rLuMqLZ1osqf4NEBltTnZXdZ9ixo9nhduyP4ZrU1seVd9ullehqvany2GjccjA1DsY7JnziS3nvVj146xcbde9p3qvx3q7r7kdty7F/Lm9e7Us7LoohaZvxLjmEbst07EqU47j5GFzl9/gR6RZrnE8EdCtdJDckOGw+geHNVZykfNmuAH8ZiZSTJZdr4lSPKJ3AtMTJiZlOVOKWGl77y9uwfDXXePRP/WtHX+2TYXV9M4zmvzfO+MHs9knqN5ba69uqTY9YYf88wnOP1rbsfKpxxpdlx7F4F1xttx7HmMvXnzjdHvPR/9t07t/Tyyq/wrckjmO7Ul2NA49b5T2Dr8qE37PYGU9YLTWUzE3QsRMTxAM7P3T7Mc9XfvpJf6bSsWK5dWxqX4BNs/YnY5Lno/KijhOodU7yvEU5x3HarH56Yl2VvFQd36R/RhRk/XGx5nOJ5AFXed1fU7MtcJnnYpXvZWCPX5Wxw9X2Cj9Ca77avQr39ogdbe5ylPq2KHOc9T5L4oSyEUeaOtMAZtK+Aecuo+MjB2zQyMhO/3KAfSApVtG81Vc3dt5rah1MOxLhuswjhTAV3eCpf6XdotYeG1/5Es4XXrp98AWmIIcPUEZba6SMXbWpLDQSOjR7fO0XNasP8RGNtIumwVlpljX11JoFQZxtktfgbEMndvAff7plOUrq3s3cxKuItuM2kPDWramsqAIz+ZZqAU/QheJRMCXKvsXGpfqo4CN9Mb6Po8Yrv7xWBl95yy+SWKWIW7uYOODFP/4NLq1YxBdfrkjHlZXYkiYVdl27cVuVPlRfPWR9sqBIyvgpiGcAizNPpaWfanP6wniX6a9Kt4X7xJWt9qtL82SsH08gGDt2TgbYGVd+W4kNGey8tUo/nM1JjJMoPf/E1eBcafoqbunLVwOPV0qn7piXImnK34mLsp14ZOI9VqBc9wlK2psS+sTQbOpYAi42jfg67OAbLssNgbjbHJJS5IuGnmDjXlfpL+dH9K8x8AjvGa3t2PnUv5PjmBJtuD0irMeY2yzfCZNLnwxT39k/cPVYq7AlP610/864a3uFz+hfDu+YdXlP2pg7KuXwWcXOsPvicm6da1fkVQ6PTuULS/thLbgxQGs6SscFF2g+i7fr8YO3xlGyQWe7XYKvMHU2y1Ylgpg45udNuF3Pcw+/1bT+EK7nx5o0wdumoFmuhD/XhzyS5naR9mhHXATM5zLsP/n+7X4apKHDcegy1e78fNGgiQx/Dh3Z9IjsR2ituHavwm4nQIRvXZaONFTxRd16Xz5xKgfF0Gpn78rEOQjnsTR978qTgdbf5LfY8IpAe83lRouDaSUZ9bMwbMIGIRGrjS+VvpX2gu3NBtxWecEZb7pabvsOgRpyAplgmpBIYEhQlT7gYnJSCREf/MeAMqZ+3sk4Ee6sLbw5IzXdmNH+YNPvOIHDjFYCAq+bg3rF7+pST8NGDj/S1ds7sJPyHLimoPXtOSSeylj6ohjXgdjGrh+U+Ns/gMsXS155q4kTOjJhGl94a+IETT+Oimzwz7UpLkLYEJ99WeUn3W6wV8p79erECu/4jrakP4x3Ofy0k5K4oiu68VS/Mq6c9Dpx4m12bPaMxjBU9R+4jtsOWIEaO1eF4/eZIrHySc1kn+I4juDQ8wYJmvT55Q9zmbZw+3b2jTFyv5C5g2WnjU3F+oSXLcsNQTarvc00srGhJ9i411ne68dqW/XV2vbUOuPDtrPlv1ri9FQf3udzrHLMyNv3WUQx5o7KcC8OPKaV5zlhz1K2I6ycjmN0mWbV699JYq7RLcNxwWblTxc5LqNkrmoSdVxn06TTtrjvNTESrAs8JFM5P0FX92qnZVXc88F4Jr3TutHrj+uwl10OP62yep8GaZDYApcr11x/LHYHr8cETIUHxR56hNYSavcq7PZ2AGbXs0Oa/BwL0FnvaeLkZ5zGFykHQPtdp367XqrvjsfZQtnZuzJxIkt2xwpm9T68LOB+V5H9gu1B71Gb+2RSKGa41o78C7EI/IWtSmO/1+Pabn5w5dR+o1zjnfhJQHISIA50lxBXuDJbb+2xcW+0M1b8lW/ZC07gjk9Jh08Lgy7+q24FavQQXAZttieNaQ8SRXvEHjFWrZbqn07K1fpWcdnbnhtIBerb1pZn1Lf2hXHC+fwDt057clhv1cuTJXyTgtbECXy9Mojlpl3h5+qVbb2nx3TPo7c6ssKrdHw649JO413Ofpo5qAWdkLOw2ucjzx5jP4zEKR98Bq+2ogLb0cGYOlFy6aSJdvepa2zHLfUut8ADl/33cU0/2Wq/OoweyRDJ9KHDph076sPUOleku6EqbYVnjtdXe8Sm7qdnNNf6XVbRxA/4uvNdy/cCuNpuPseMy9e54rTrk+2kzJhsQVUbXgmMntR1z66qfjfmO5x5XnWfytTRj2Hs0V+UfUtDVF37Cy3x8RU/ihsXZxgI201pepeSL9FjJQrBqW7orH6Bt++bOLbsbm8BqpyCfiYQe9PmOfJGP64rMo9LJA+4yul9mpptgcvKsYdv+W3PMcaT9qv8joczmWf42r0Kd/oUrJjT4Ui8AZR4Me154kTAx84CKUHok2LBbtPb1FIUeG0q7exSCuQj9vYsS+NgRPtWwI57FLBWdb4xdxwKrMTlRoGDqZLMcK1tBIAqJBqAShZt3aaKD3jFT/XCN+GrMuS5MUqBCgqgRGSQelIqDCKmntvB7iJYpDHwlIoVgiBiQt0Gzv/JD5arUoRhkcsWuOgExEaX0GczE+QkpVVg4H/b2Bmaql4/I/+mxuiBabAD3dY40x5r7vxLCzqKXjFLPxTj7cAFdsx7AiNx4na9case45GJk4Y6xrrfptdut+FLj82yXK6mnNUfpT+T82rxiyPbsUdcNnc29fh0tiT7Z7zL4a+Z2rWgk45B75anlHlcjVUn11X2aB5Rjc2MKz874PEG5zoHeNajs4NNsYJ9yN2V6QtacJIdNfo42o/+mS4cNZ3Wk7r4XLdmR0PP9MXwle011at990yovrpHu58/V67hD9tR+fJH03M+oF00wcL8C/x6EyfHEn0a/XAP3R+11k6ZQLF45OvNzw6gK/VV266pyb6u88w1Xqiqr65zrZRVSrWlWTdpUU83fo8jX2K5WJ/JdUuGggG/eK+6+7zTkFf9ZzqVTTYijK86ZnjMT8/luyGf/hdPFnDCD4YbkJldImHAlbHPHVNzxqPjstKfwfd9d+SsPBU+UiYmw8a2nVHt8VP3ICli8iwWXENSxM58Bg7bcofxxYv/9k/bl0P8Tndc9dWm0IjSkTxl8KxBqwOmn3A2A4JvOAQct2Z4i3rpTT3gTPHU0v0Xv01BgTrSpBbdqx4HUyWZ4Vpbuee6nN9QghdWmzdzadyE6u3VQZOMqZJiGpP8INhSHABZH2OTbOPgalabrUeZe9P4pZp4GHv6OBryv41xFzR1sydNrafYKw2tjAtCJX6yn9PYVmmnDZVogU94hquL3al+ElBaJ/z1ShOKoJcXdlOtY1pE7kvrqMePNseEEydu1Rsvh4Aij39Oor/+2l9y+ZzCyyZOkj6cT/U7uNl5w7QaRsPP6adBFZ5T34x3aXylrDB0sevAAp98HqdKucKmGTYlBXhObustVzrZ5ejrnUnYvLZBJf3AJhU59kk3rLQtlodY6L11eiGzIXFQpGxBlan7wFKy7CZH1fpmilHzYVbpKjwoXy/0iE3Db7dtTj/ZA7dp3Yodqy3G1ZL5i7kb3OtKnHZ+qLbfgne89sGrLRmPHJNq33PrnPs3jq/n1FMO7xS7C7V6sBblnjmwEzkc9v55A/vFpdlEO80P8/G/0puvlye29PYNsNO5IbuDOngq6AfOblvH7I7Q0mxZLjWjlvYBdh2DNBptgctBfwbd9fWGsfJUeEN6mIN2NLdwc/eO/fqmxgLNsTOfhWLpxj7b+N/+6dcHVfFWveuJE9x2vOL3ZuJE0mSDXSIAKbktx4DRj5XqcGGxKoRbl8tC1kGRJUEna4DrPsg7zw1Azm/tHbagwNu8KqLiBhxM5nPZEYXbDFEKHB8ZBIF1ALiEe9snyZCApgC490L2gPFkoliQbc3WViRzlQPT6Hu2YFfiFKRoKsFqhkVKEb1tyfazTxRutjkOG436NRPvuWea81oRiKCXE3auprX4OO0h0zppvEvHhF8OkQ/z84ppX/SISAnTv0+chstrGNmPxOscR3mcDPxoHzxD5oCgi70NnGk9ToNuhub2oQsqH1eU9SRX+DI/rEE52YBNEQfdNnXWFMOW2Q77oPE1nvTJOB7cx8SnrIGjPmir71ddw4qEfIglT9bu8awyvq36I3bNvjm38BGZlgLPymdcLZm/+GYAV2PKcnIVgVXL3F/FrXo7P1Tbd3DF7fht/6spe0SG39Dg+qvR5v65fG4t46hskunU2iXiqShOnvo5jm7GxuNT4cKe80/MI6Zz21SXwqrVVKE3/41o5TxfLo1d54q/Xs/+zvQDNyxd7Rg0M++uVmkTrhh7eYoFEQztx8Hb6Unc5O9zsqnFPC6nxlK5115Ib4KZntT+DfKeOLk5SkVqi0FssB3fucTpbKD0dTyP+uixOzow04HZK/qSDqIzOYXfwSTShX6pisv0RUQH52mioUOIzXbZGRiughRYDZkMmCpDBFAwdjkD0OBnQEACUdCGmOMpT8pAVBIZgqH1qvlU/Q/YLhZwMA2+ZWs0aiEwo1m7YcpuexIn7SLnSdWQdCIsx1ONQ/KhP9Urg+xxKATTx0Xd43Juc/QY9Tj1wYqxLzBS8Pn3idOZP4+B0EM0WIYvy/HQRGWb8S4rz04ndLG3icvy60ReuWp7xa+w+WsJTYbhMRjnXtv2wLYG7BNXdcaqNOq2L0uYi6zSDqtpB42wfExbVZnw0X4z1Bb67q3Cxn27pf2QWh+xZ/hpb/E9n+y5hh2rLdTXncFnBgf/15o43fLDq21zHLp8NdocJy6fXYvML30AbMdYnT9GPDJrjE12FRmOu7NycCYEv2nXNtsx4TWvgpmPvYlmqaDD/nO5kFyoRq+Lm8QQcr2NpoFz2/3yyJOiKz7h7v3a1BUMK47GdqItMMZ32zwh63hVeCKKyiMyV17X3U2XxlP+FSdOdaBqlxI+DdIN24RypQTmUfqMsS45eOPlnSjzzJIIORtQWppMWjat08Hf26vSblMHivAhVMEmAfHRSn+5wVADdWt/Vy5qPrT1PtmmMEMHIeZo39k1CUtBjSxNS6nA2NXLPGKa4L2/Utijn6EhFR8Yt77YdEnsB+4nILKzT2C8zqI+RR+yzM7UfgJ7csL/3ydOZ749BkIP0WAZPj1+GWeb8S4rz04ndLHrwBq0PjmtHKJrx2Q9tiuNYbevpdtR58O746aAH36wDOgMuwQ3fDJg+4L2wGZBLxu44xl0laexRuH4HZgZquZX+yo8c3y7tdrnR2yqfDuLH5G18sO78htXS+ZSZm1wjs3K55Uml9/FFae1799OfYrKV6rSceLyWZWpG7UvIZ0DElQemP0ob1Wp95HPgQ+cYnYxBzl4lwIUb2Du9qnxQesNHvO5dNtZabq1PKPf4v2MQm30xBe47BmN3TuV8kmw7U1my8XnDTZKBMOCas1VxfW4v8VT6c7gHX+l3bXvcO6eeuxKIXymxCmeXQjhODvL0NDqjC/43JtmcID6WJweVSbUHACXEBe6fosf+CubFCVhEXOF8y5NC2D3p2hq/ZslzAGZbcZlv2f6XgvB1RsdH8jKN3cvmCaD4DogJFh8XUGTErOO/lpjw0q1pLS+C9EbOxDohDvGqokHmEpdMvpH5+gYAY0+rQrpjYwDA9DlzGQrVuyDdStrfarcHr+K630ryJNeFYqLoATFx7MJPOpVn3zcclCXzf1Nl4TvAzhPnGKYQ06+KMBl3n4D3jviLbeougs+heeu0GcjqH6r8KoAP6y49Mfo30wz8Bu+CL5sT6GMk44NDdgIGmi80+4twVE3njLlCKroMPZIH9ILzYCv6pr7aH6XNV6Gb+7xFIMEVlvWNuq1V6a95Z+djOfG1T4C264reirvGf0j8lYZ5nVJO3DddzgnT5bnhMnlcydOZ36odtvOXWk713LlX9ufo55zRY3Mp0gdx8x97tu0T+5zmXNkA10CF3se5eNY7+3TEUl7+uHMBuNHiSR4bvRJIlMu1HVz3LisbTsYOtO63NEdcWvfl/pSTWtrnxaCo4KLmJApSsuL0uAkwf5yOTW+dMXjhyDDLm8Jv0Jzxu9uujRdr6urrb8ULZ7R6R2e01v1vo7gmF4OwZHNv/YMnAygVA1em0o7upYFxoh+sBR8CBj4Jm8pPOQLOo+bhrTEA40QNnTfCnb0pWjbsHW6IkocG9pCMgUptu7srcExtUv2TsGME08XHoD+s+xedhXjgj0l1M8gaNvZuNQLJ90fEpFyYJ95h8z1YKWlxal4OiyWwSe6ZtfLFZbpckjbToijS53wyNmbHgMkaJG2VB8TeKTO4zU6EUlThxey9HmMWAC3E6d8xilPgkie5sQJsWc6FpWH6tb3B6rnQTxdVw2GhPFdPwaaeTv5A8c43O8Hc0ryDGLPDzmZpwxkQTfkD9lJlwFl3kpnHBwdHuokyLZiT24uC09rcbHzidssI2MubXfbsI3+dKyBKI10SdM4YCyzMEyg56Sks18mkm+lMvqW6ka/z9VfoTnnvtbSYyDIK2xucP55AvxO3Ts0TpoqL3Z7nqB82cTpqh+qDdV+46vdbt+V92Jqx3MPN729tROPOAZ11s8z/Dg2ukABNdbc95niWLve52JzASMoCKB+DaYerVXbzp6j7iH4nH7QVPkdXoTahy6T7szK1hrN0D/C0/ULaPKrqV1lB5ZhLPhZ2IM1z96W18oaHH0erQY+ouYxPo8lZQ7PfX7zXLXKvYU+4YoBt+hUNT4oZVe2W+9J4vSv38TLskri1L68FCwOmloW87s9qahpbgSyAkuKg4wTtgi6ANaz9gvkj5CoGy2YEm7cvX9DmkJxh5+CcdAbsvn2VMeXg3ttM43KnfwQ2nkExAelCiGK77PJMutEMLqTPNDEqJm0l8I0YhWdMYD4n3mO/BIEXTS5tR48FW8K6EzbDXkSYCkuh5Dqi47tfUvMkatTPg6UMZ+Yn1GJJ3u9HTNOXHabJwbKXeKEmYQd+1hxGm/U6zqCwD50udO3wz1Kv5NxFfc0XTUQKnzUupNfcRU+cg9Mft0NXXWcBpXn5OF72qCt9IbRzZ6hl4HmtpTJYFfpVBMx7H5sdaRKS73GDEVDdvYnKbD1SFsNVA+a0OyN6Y9l7WeFj5TfFmbu3+jrUX/1z7H1eTDVJxW2dCdN2ZbxBexYupI4OYmiP7za/tjn2SfWXcf8ni9sz+DleKA2bFYtkZWsw7v+98ZnArLvZ1Fb/TAU7vu+px1co/8Vt8JP6vPOh+DiB7fZ2swxTSkeC9of0bmjrbKQd9jauM/46q8Kz1RzLemq/ys806618EIf5j6hDaLa3LHVrg1Pp7sKpIyQ2hhcRlVgNzAQFb4q/xpdHS/GczemO0lX6Xa8pae995Uuu9/6jIEdnO3bJ07/GolTnFflilMJkpDKAU6QzGVVHXBqb0g0N+0qsz6cttYH9Sw16JqYVozmA4ImGdFp5lpHD2AhUJV+mqIDRkQZuEIxGhrttg2q1m6zXQ4B7moMlpEGJjumyhAMT9CLxXxNUvq9EiQd49kNm6AuICQMGGr6MWFWc0Tkj4kykY3eLb0UkDWDrmU56zXOmq6XcHo3V8a2a5Rd/s3+VY6EO19puitiDFDhejmwx2Eoz2MXYHei0uwOGzDjrbd++OKt+B2nt+KX1d98M9+qBz6P/ZRVkyfNFy0hs57jF8ptD2Qcur+3aU31lHLoeYqOlWetp4+GXaPdeuuYBPUg3UCH1hgDxZZKQYHIUn6PMejyQ56O20Zbv6A8Rqicv4yazI0tA3WwajQJGu3u80Jgk3t5FivJP+SlHNddgk279dn8UXXuerUhayxN1o6pCn1JePimzju1T0PB3g9rO/XKPzpw3tch4wjlXKAoGqIUL46ljJ2kExyKfvBGvureYyKrogNOlrL8WvUah0lnK9wPl4mf42RuM+coh9Gj/4lzzK/l4E3I7Sv+ueojBvYS7407XEPGPX/sdcxYj/mMvV0bfh5fmMERTueV4pTMSVg322pdoxx6zvri8auypKKxNluKSUPmHho20X6md/BmzI76gG7xLm2+cg7z0iR5BTdiviCH0j6HStSepFD726ESBtyrdpzL6tsi5qXBKj/hq8faVbpqYu+ekUP96HoNXiYM0xDH1Nv2v/9vvzLYyzf+183EKSd4Bw4BNwddk9Pvj0eZFRquB0k1qMKDa1gW7VTUgbKyIlynavbMbpprg7ZDhWCA2VfRDGRnARgBnWgsXHETQzJNqOxTQzU99hjYOmAT41SB0UauQlJalTkdaZZTBtKS6ig075taZZc5GKb2rHSquS14aothlSVIYQKHHwZNQq7Pgh+pIXOWso6fWm/276hvljjaz8Qk/oxr8D8V0lSJkhhjw2s/q2x8Pf+OU30deR7zI2niOMmTIkrLHTC4Kn2q1AbBj9AemB9ApJ7btuzFXeOxH2YZ8r5Q+/aZutamQ0KhUiK3HRtOXlcLIdfxA10TNMYntRjvo7LaJ02H8EwtKY5Gj3PiK/+Ah5D/n733YHckubUEb5m2st0ttaTW7KyZb/b//543Mzuz37735J58+3KLc4ADICIjySQvb3W1VFmVBAI+EIYJZpIXeu5zjhbxFK10PU5/LX6n0jrsak2Xuy4m7kBbNSonK+5taKv+rWnwt+43OWSt+ffph3QF4Qt4P1c03XUCD4cXSyqevGgCDX2d+zu2t30a+W7/1GuPXXKdJrzmsqQcij9Sb9Ma97utzaN9PSq39bCmXNpnH6VacMwl5smmcBrHU34E19HsU9XvQd8Hcl/pHhz56ybGHkVrJJr4RKhUbViwzVJTpk5Mkrmrq/jGWBWHYHCnJiLA4fZ7sCF/Y4DxG8bwhP2jcjKx7lr1acOHIjoOkZZg+f2///MnMp3w0b9Y4YTvjuPCCIOAMeNgBPR20ExN7bSgtyVGQ8/GUpA9OcIFYQG4W5JGtphYl6lXw0ZBI2w3YsgzHCCnDvRx4Hv/kzQy4SkVLPISQ1JOHeKbSmmVQn4YQf5KomQdkz+DpqxWlyorhXV+t+P6Jdf71nVSYuVw2bPSpm7opR3TIV4EKoDK/bfZ3IupPJzG3MVoRcMizQxj2T9JjTB1RnK2VqaKdk47zVyGWMfoIzpYm+s0v82qr78qnJ7kHSfLlYUHE3g2HzaqeKoLIN8z4A22/dwGWz3uvAivkdZyTaChl+Ru2+9m6AR6PJ7K8WjOPJNAeNCcxoSKQzfxAZIRuDfamLQLUZmmOPim3N+c5rEBTzpAenx6w6ArBQG3xPsL8gorgE7fy0PaDPVHuenN9tTO6ETYQPURjCFng+R5O6f1B2M3aFSuRmP79FHOW8p3z/1+DlYWiuZ6aPuan0e6/+Fk+ehzS0VTH2PMg/GOU/9e5OoOeO//sTGrHuxjipe9q45SoccrCyuaeLeANW4ra97v0zKz3ulcnbPV8zNbPtfWx9lKq4om5NBG884fEO/jWut09LuSGb1XP1w29bkpTZvT6ZSMhi9u+S6I/vkhODcb3VVGT0t25WF/Hx3NoLUnK8mKldJObv613jOnUnwgKD9H19pRuR7u2L1xAAYelDwghyHaff7X/+3jbpp4FE4+YKigUEThXZDG7QWDgkmrE1rA80hcwQHqhJTjY7KwQbu8XwikNSKi7cmQHyoeaQSYtGbP3TSCxY+W4g6oJgVbB5OeSJmC6SZajMRMKfUMWcQCUb+GUD5ASAM7iIzGOIUTUaE0XJeYPW1y5FVQtF96PbMIA4EUl7p5d5Gqi5fTwXeu453i5h7Ds5HB6RFtJRfuVyR2QZZmK8aMLmK+zdyVuWtop+eJLO551xh0/ooWXQlnlGiO9zZY5PrJ06d379j55AlOa9vjesgH1HHiqTx9uNI/Ofb9AXsEvOmCyPcM9WofMsIza2irjXhfx+Gp8xiv8YfVSW0zVFYK27OpebiRREDW+cdIAMcGefZztkUbEIsZDTncoWLqegLNJKLj+IV9yUCXos2OVCHqs638+xzw+eI8yNAaELdFbO9FPRbck3M6LKOfONTf7q9iOGbvQID0dfXLEMbQ2DXpee7s0lO+Oxf91xiN9HOt0lNOpeG5rcfxeq7hC20VT9LRHSd8n+mFfRfg5UvcdcJc8Q9dJAdYc6lTR3zd11EGrTl2SYx05FDzRhKATiNWaBe4F74dy9Fc8WuMFdNqTEt+tOOtbmPFvy+tffCCOQBzFqSKJ+4pRtob2z4eGFucRau5CLPVz9prwqEct5GDhlb+fXJgMbgpey2s8G6746mUUZDSTazEG63meiM2s30u9Nw0kUTP8VOw9bHbL/4tMU9GjffatsfRE7eW26Nm9vY61OnA5Sroav6X//TRxsWjf/n331huvWBCkn0SmxyobPvEBY4jIRskGTEgPcsdoONKEKDjBaEpvqyIJrqg6F0Ozn2iZRBkDy2F1BTZjxQKAWsjF/NRE9k4we7LapZHe9AppZVoFjmLMF1+yfBAEK9i7j574dSLJgXQ54x63Ps0unSJRztFk8ZHc0M+Zthtdl+aJ5J/bAguDiE/ykniQmjhu++Fteg8+eHzEuu9Tys95Ra8c/lRlKOdbqF7E73TSrPPBVE7TbjmAR7VQ9H09Km+4yS7Xjz53SYUT37hIwjbsFUXRMBJlduTUHGcFJqYmm8T+UGa18SnQHyEtDr7nqCxk2RBHw+bpzEw6R85NjHQdZHqeUe+/ZQV6QIK7+MlGuSlKyiedB26X80I+TFt6nc7wktmi8nHyOk56fgo1VtIEVa0p6r6KvvMmE9GUzttUzrd/puIe588MozZ6rimL6kTOe12wdOcAy5ZwfHHIyo2FU+A+FW9lyia4smWbh/4Xl9muWtdP6QvAABAAElEQVTbirX7Ea3bXNE6/75493/Y1mJMjuhe5euI4ZDJ2RcbA3MX8wPzVD9JtIpDaxY8ndAXXRCupC85uodPF8r5SHq8SKfTLsFrHlhMVIxORmvcTzIT2JCOHUsV5MLVV/FHdzf2V7Jd6Bxfsj3noj0UrPyuPZzjr7XW1PybTZ3dkykcMMav+/+/PvtJ1yR+onAC3wcSnzZrQEnV4LoIXuOAV80cxzXtIPDIvkSKgDwoQVftgVLH1EWboWvo1RfeOJHFM8hwYKzRLH52Ifoh5nKCUTYEU95t7RUSFlFzBrRTRh7DClIPEVrod9cErQ5XAl8YeNmHwc1s2QSNBGoXy7EKXjFLatNnysLSnFOSli8ujfEH21tdEIUTYgH0QxpqXwArdHoavVkr+KRjbl5gGqJH5FsIcrfjZc+aLHT+imZmRU7EdThLyDMsYA+iCqfHvNvkF0Nmjo/p6W6TF05eROFt0W35nAO+/TS5+1jhOV9XzB2a9gOwO74jfjXZ05QJvdiOazLz1B37urar/nDtx6YLPemCrotYGO08yYDe7aC9VziBh0N2fE06DZNbK6LPPOeO8XffshdWdsHgi1KjzV3FYKCPiGvoa4sZ89MPwWiuwKC3EngYmmI/Z11yPc8dl77k1D4KpSfY9UDrc04ygkcKJy+i6kOXbh84V8mBYZr1DrcxUQ7YV58O271QcDVmMHHW78H5KTuzH9Hha+aBds3R0zl/pgqeny618omYQNfepBgBdSIu6QIKj4XPsKXHRntJ2UY7ipbNvgNiEvVDGQg4s7vojEvV6LPaubjbvb4zUxr5mh2fble/Z7kLDc3qQ9t7PPc7RWz852NLmSWqnZGGnWxLJOhDX0GjE0EJ3939n7/6cTUCWxZOGA06A+TpA9AHoeNuS13rELi3Mf080L4oRBs3DnUIUDh8dNx94hWRtgVVDMdow9BIFonoExC+APEYVxN2oKV814X+/mGRFdOSNrSNQ88h4lG4eO9r6nQBE3O6KfO/G9mNt6LIXMBcuCa3+wQh3fXBNoWkA2OjKINBWl28YEzSftMNUS+cYNp529eFzRWpdy79leDwiGTEVNxj2Db6fb0pnEnwlCVozvwVrZvsfH+D0mhjjviQVkReOOFukxdOeFQPh/72SL/jNBdOsOs2Yc9tlmWaWb/0ebWWWFI1TwEdn3Mzqx2KZlZinzbECwjymuvXdDu+MqWeoF/KqSDkQcdFLE7QccyQRLy0Oa2LEye7F89dShPx+Mwu5ggo2YlEBoXRBmTUAxdTbNGa+G7Twlwc8jezInaQo3+MwcjYL8Z4ILJnZ7YL2WUgW8EbUEZfiHttdJQrmTGvRd+TL4kt1nU6LknQdIImGad5Wx+0SAd7hD/6/yoe1avvOGEu7h17/VrLb/N2RH9PRv1a+7odtfs/4rPL7OFjdJhMWls+saA37/mjzrFWj10rq6CNOfeN3EVolLTYq7oXRIbHh1VUI8b57PKDHShDHgIGV0ePdcU/RVOe3YPsC0pTPUfb8JktsT14sUr3t2fU6df2Xf2W9WvtSH8Pzn7uKzfr90x1HHLu2waL4yVIxnIM/49f/mg2f8fvOCE5+G6Tz21MfpOLNnA/QfTD5dTqs0U4YD/R8jaC5ol24LDUEymZFR208VCwM9XjpV0sLLjvB/qF9vxRCTtegm4FckUbcDU6fyHqWwkYk2A0e3jCEXvphVFjikZI/aDEwIzj04IhCgV5KF7Pf1ELe0SjoacuWNMpZe+0b7fH8Q3TsfWVI8NgrU7DFjkaFM41TF/2RtHwjnnIfyP3SAt2LzmUurXOnjVpdf6K1q2CL3lBmyc2QDyRVJkwSX9ErwonXQht7zjpjc7tcDZy0GW3xXBkMjRx7Ac8Tuj1ecp5hLGTXre1wK95E/BQWqIWdk+RRk3L0SA8tsRSf3KsLAhqmjh4Kpwgrz7lmMqIwZjd9Mm7gdjkQUe++H+VO3iKO4cmjv67j2jQAl5ga6vvQ6H55nI1nNDZHpcUNwzcTMgPPKE/2XaEThztPntcFUdTKeK9sPN+NMZw0/HuttM7rjHvssK7nGhHYOkpl6UFnvgFwXfZuuNUOojRP2DRfoHCyXHO1T4sqVbEmjPJHJBzY7bWL/sytpYT9+HhuX4ox4hEuZ9xRdn5oklfvFNzp3T2sa6vbAJyh8KY86kD3x1gRfSu162Dfl3hZGsM/yOBfcUpru7nGly26QjO5qNPngV7Fl+1j3+u03vV8ZVVp+3lfKVRfV1xbRyPuVwrT9Tzc94VxpguT3APueP07y8+tHCH9o6L//0XP5x6YKL4VT0kBS8w7njIkR5JC75z3ANke3vEdd8A8bg8EuHJGCH5EbSStYXyVfZAiaiLOWNKBhPVmBm709icaJ5J8yC6oMzMbdEH6BE2Lwi6HdVQqGCq/8C7BbRxdJrwzUIx02W9MLegUfEWXxlARZGYEkATSW2KQPfok1g0Ie0a/dWZ4jknaFHgdlqYOgSwQc26PiXcG1/nObKyPKVxtrlSEW1SFXmC3eIxjbFnTcdM+SjH3QvMhljH81x58lhF0+JRPYvQv5vgj+zp7gX3DJqET5uFmidTj1pEE6eavdeirvS6HNaIr5NOlXaHK0udv48v+3TOnZkbUjHIb2PpPorrGHPcVjHGsz82Jd2E0ZV0GQjtRFBa+X2PqQzAb40lVeyFewxDqghnH7Ah22UPuSidTseCTBsDY250fYy58wHlT/NAPHKa3Gxx3T4WzVq3YjrFF2/O/dyWXO9H0ab5FYzqOwjH+zLrjW2zZASdisHbnTf61HzjXoGCyS6oeRfK5gJoe8fuXNlTGPrZ58muQqzNY7L7Vh6Csx2zPhbzHJnbiGhFm+l7y/F8jzxnXZ/7gimCw13CmP4vaFQxSldqjuq9BHNinGfsS0uA5hTUSQ7eXp8ht3LbTELk5OH7y3ZcSmmaR6dES8mTE+2jKi4++es2G76X7yayQJH/LXmVw63UZZSVnz0Lp8Z3T0dZEhzlrJPoZw8COPvOl0H8P3/64dBG49G//Pa3lmOf2EiQkuTQjDSatCXDdt6x6Q6B9xMxehuw47BRNFoMvnREc4jX3t+ktgxh4eahsASTYUiKTcxGTzSRrgdjweh8kOMoco+qqBCbvGf/sGi7VtksffGTEoOTbSixEZSB0Tw3VH4GUtfreBsMkjtPhiYoFdn3zcmFQEOvcejTGPFLnuxN3pxq3V3E4HZ7rssa7VvTZUSXNYduMgxP9tcaK/2RNnSANleWujPxV7RuO/hYa9ErzBOtc0yIvrFCBhfj/pie/3pW3nGKIFU4QU+fJLs9+C17Y+57nD2+2+Hz3nE7y5hHFT/yl4cNg+fVKcA7v+OpQ0TjJ6rblJ+E8BWisy32F96xiExMfEHQuivFSb54cm9QfBojHUJ+eve96HYWrLgRrs2Ice5VBp/BUHtoDTraEFwsXhfBGkd6UkH8iQsJKW8Wv7QHR6k/Uu/bUqSjnQxxJLPFMV3QQRJPc2RHjGTJnpLZ5/V8uRTs6QRF9kVDn0QD3+eNzRRDcHrBVIWT0yE5HzXmbmPmV/tUHk/rlg9YOy1b/h4SO9UX+e35PUUDD7LjPNmOqWxcCmW3Z1F7AiDoyilx0IIguu8v7tnngstkHy1+Xz2YWBWhW4cDO4OeOiKkeI8wiURKZ6SvWwpAEFLNttDOXhtaUnWNs2QORDkaiDsNl61874g18jgHe2cut9XMLlH31X0sxWwed/rQ6IxdvGes42m3EJ9PcJG0MvuffvZBNQJ79N9++zu7JqovdSPZnnAPlDi8Ns+ORkd64ZR9YwSmJAjM/yEwn7iCiLVwxNUntvDen3QD4cWRCwy8kzOzW2p49lWX8GYnaZPDA7OzVAsrKyOtRYFU7bitHnZtbVBlG2PZJDpOoe6tazm+4dLUSE3riWztrCi0Yi+yhtmBw4erfgcw+c0+aCFNne2LC/fuIpeu55rQkU9imINbQxvKYLPFtBHcECKmXSfBWNoEcVaU4ETPJnJoDfbL30gxa/RGxfBkwhoomh7jbzjZH9BC0bQpnKzj6Dv1+chN4KSXoWG+RQ56zlYzGmOz7R/shwGCoUHKvG906Vvh9BqBcNUhn5HjecaQH47nPGgf63FJhjlteXw171nyF45P+e32fV6PFPl0k2HYRGqnQ4891z3/0iNvMxSdUDbdc+dVLEMfmkpDQ7jpAw0BAowFpQymYiJGE12w/D8Uthrnc77O64zx11isLI+yK4nTtK2+5xF0O5Fx/g845VhzxmP0/UZ3mkDDibbm2CqW0/1baRyl1dyQf8V71MLDyrW53hyt5seKBpVOVx47rZm9CpVNKPdotfdpZMUDvetQryVdPME51vEX0WTVQx9lt/PWpfZfR/19ufDWBCwO/99o16HzVr9vZez7vhw4l8jOlvoamXn3b2Poa68+bW+UuzyuLE123AxzC852XKwLp9/9zuasT27N5xGaNUySxVj4F8fLmzZVjwB0P10icG60haNPmsCCIw0tP8ZEiroPuZjd+b7QnC32c1JC/ycLKZG8UYK5mHS2ViTQdQ2n8fRgQp3vOp0iXJuPrAIONIxzZ+7g69hhDAo9rjAQk+OI7W7Bu1netInIgy7m9Et+RV9GEcE4mOcrdGttyBLskJO96nOwG3R7rYeBlqUuvY+nhVkxGfu6F3HCPvqHPmEe4Iu4mg+CLmaFEn4Ugj86UIWTJ8Ul0H/o8LSnKmSLdsDD5Fj2ATrnI6+1DX+X6Hj/znu4XEI58vCjE8prBWyGPWZ4kM7aWyhHophL5ZTQPsCSokTRNlzzkvO181LBx0ByVIsYNf4y3WGZ0ocVMqgxkITTs3+nBlUmuqPqmVHdpocX9uUmuVBuhhoa6rLCdQ3p3vfeFl0QvIc+rvF1VCfHYNkJrIcl4yBxq4+48uRkxAiaE/wnD6ZHp9u5jflU5yqY0/1aadyf9l34vC7q7bjAztE5c53PfS0tRxvRFAKGtiiE9lIUw9u+MeLoS5oiIjvT1Eqhbd/XOUqFCdnqTwInmt6NjPCE5AmWqU9dPiEs1j19yswuvDyiXVMLRh/zebxH8YrjtFzXKh1QmamRlMKZRRmnnL0s5H/9yfupJ+TRf2PhVBM6O2aW+VZKqCikhjYua+VFsC9kfceJW6wpQAYTu6AmriCsCxd0Gl79UD/V3oN9sa7vOlXMHltYqoym6bbWk9a1M2fJrX40UqDpoLE6reNNBGhz2qUyPiMO/Q51xSc4WV02NWoDc6eEv8Su7Kkr9GMNti3+onsPUUwVzbW9vYxQ5ocNGkToYE7JlqyOlOKmoT3Ew2v29gSL3scm1Iv5IJj1zrqEPmKM4JOQE8YjUP/1gwOP4w/f6o6T8gQV13WoT5ERdo5/TsTqzIIUzG0GtOaP63j/FGN5vR02jlnkrG1CY8zOz3wMYSjWypfGwp/z90eZjFu/V4OxC1+cu83vYHpqnNdZ5L7t5ohBx3YsjEe2yxS/dJp6Q8XXjPO1ll1qS6+hFkboSV2BEVZO1efO7jlb8bvsLfFrfF2is55f6kHlRJTL4FZfeSTEjsf/Adv87H40t/dgl+346b51ycvwVX4fytdlkY3S65i2YwKtVZ9Gaw/X6stRe6RogMIRgfjEY8Nw0KXArWPgjBsChfb6vkcvy44dlZv11F6Pk7gHYOvgonsnDDTFE1LXsS6L5FIfl+TsmvHpOkOWFt0SnzWM+IJTx5aF03///e/tB5f802jJewfNilmnA0EIkCAPgMLBtAUOwFfxCmKPpYQh6CT+rpOO3mnhvieHRQemI42CewPCBZvyDDyUkmjtjgd76KPT6gKhNKTZLYcFt7oJdpbs7T1cFgOGU0nr56LB3ebBMhCCyRNhMtubGsVOy17LcWMOpF37FrgSZroNZQPtZZFkxiU7wnWUCCv7ikYctG+45paTwyJ9yLo0zkPdITsvWRL9TaSot8cwJuorMgW/GBrPjePscXQ7Cyd7TA96VTghtii8Ql92YEt49QCe4wg0KYk0gfA/j2aKJrKnc/m4ydIhaIO8CSEU+/40z7lsR5J7lOLpD4L6F+ftbtPLF9P26mMhPxpPuK+tpVsW3WmQp441RylYGHsFPmZJHT621Q6MIi6Hsa8DDTv9/2DexfyV4xzBM6YIrONl002mMTcRbPTN0Z6Xrqv+7/G77K3wa3xdqqP5s4658rLmn6Nu9ec8+hhirkE2BsHMdhwxnjr3ojjdtz2t0/Qe1yw5+zslO+te0/Y1M0zkjZkxpu14QOGh49wENRF6DxyvfVI8QagW1/Bx45gse7PrklLTLOUvzkGfq2nltsiRvqVH6+SiW8neRzbZ2Rc9zLkuksPmQ/BIfi4eV7O90mGWTnRLRdM5uf3CiZvcOKFzbptVGt7cbUBEdpKJrBjOILGtEnEacbSDHputdxSbgstSR2qQJg6+NdhuTCfFKwLIIJJTlMCW6hOxlMyO8zwPk1xy090YQoonYoLd+KX4ymHEFyHkeKEdDffSLoAGoVBcgIq6MIrR4EjLnhy0rbzCnsZWIcDy4zAoL4DEjZ40IUmRBYPUdyNhikzZ4TwL8TTT7HR+szqi3bBxys4o1lv9jUP0yYzIN4a+hrBh+RABjp4RP3+pLR7Vw5pE4TQf0tfdJrRnW32ap5dEZHFDYBKV++QmAr2h4YYYorS6zDZ2V5CNzl/RurT4TqvBlg3nTykN4VEHkXq+fAyQRxVNL61owglrXBf+QhzjMe+XsEyRcIEEVttx6kC3gjaZqT+hn0XToiMjKfSbmZoDI2+c81JA3hCf54+vSiU52SFDRnudI1x21O4QPJ2gn5Lteq8TvyamyvcqUvR5RT9K2+qvckga5hX+7zhEnHvnXjSn+7bVKtf36vRk+Ja2ummtgU4r3NfZaRlfO6XzXWE9SuKRMtFH6K3V2I57y7Y3m8tOE6kxh3yN1Ujf2krKYcHUaF6KNsauHhf/JDaJVy9OaU1Kp0Sv5h2L5Brzq/E/ZWdvXzmns8nS1KXOJ97nwyQLX599/N7G5aP//oc/8I5T71TiZlWP4w2TJGczvJQn23KjXTTng2M0CxB3mTwhwHEipi5PMdLUn55A0aDlBzZnYD0dU2t5sTD6pK3BRONbfzsLnMal6uZlG6iJdCsrfEVrlptTjQu5GZ/p+//yhbYlyC0nsxl1VKYd9tceNS78NqqNsG+fQquchGMAnmYi8bCM4QuxMNNbISSQ8anPxoC+qWAObjWNkva3XJndwPBzgQYDyfA2Bh+IoJzHHNDaFhRbd5x8Te4VTh69CidEzLl1plPDnKFsKLTkzSMzmMyGIYMOImiEmOUjDTI6ZOigjom1WSQjBlf6yEUTAdrEqn/Yr/zEI3ov7AR8+fK54S/cgA2KxoHjg/ad75saL9iWzdwfGw0TfrARoWVI2hN7SocOVGcGMu0YL9gE+eJEygd/zKDtWt4hJkd9EYwQR9Cdp00XyX6PGmxV373Hp2QX6q+FdG1MWrurIK+1WbYwb6oFTDZXcKZJUzFqrs9QcluItbGlrihjnFPQK4UDNPXngOiVIqf7533fT8DDx3esWz3CvBw01YEepqzHafRc/1JQthbDenUOxgnTXZ3EFyHsymve7wp0RqTlEvtjhruxW+CXRXKNxyP5uXZ8u17OuJ0uFb9d2+/ILgun/4HCKawIURuJSbyvDmZMXrZQb+ieWPDrROe8g4K1Mbt8aMUkd7DdzEvWL0Kq7VgmBotWIW6E9hgSFH/skZsUT7Kn4fhLWRVdLYRG00XNaZMWRouhqWscoZ54DmQ32vSDLMrU45oHNNptON43xy1XlLBaTsQwaLwYKrB5Rp/UhnDf+4aFsupfywl1wy7wOjyY7C/n+aRYwoW12Ir43WN7kXN8jKlxynkRIXvhZFmI9el3nDw3LoJ1Bswh9JVy0cPUACQzELGAhkNj4MSZW6Irzhijy3Zaadda63zZ7LRJZ8PqBGW064z4PE89d1E0vQB8wfPlCy+cHsXjkhoLh/GBk7kWXV66feEpE+OZspF770H0nUB56DTXGnoYsn1cXaPphYx8Vt5BgWefZ+L3bIo26ITp4o1Y7QmjJeXA+e531LxV60yACzcapwXrEKnnXwqVB1GugRibUU+xCoKbucV4hnzuocbHnPF5brnh/2rPe8/ozXO56l+Xm2PMILrQhXjNkwsV7yE+9vN037d9vofje6p6pGHExr+3hQtCyvFOcd0txejT/INkn3uued2r+1s4GMxVtF2y44P41BjHdGJOzYOXeqa1zNRk7VbNoz29xN818Vccx+d+6eQ8aiRErEgESTvh4LOP39109NH/+I//yPfF3NDMoozWJDDvItKMounQ8dpAOw/rwdoWoC8CQRgDPsdWtNOLxjfkKbh1qN1F68vG9ZxxxAzdQQc0VKtGbPTuolRcwGvPWdjazbjqX0ptAyvzaaYJkRbDGfwc002QTS+sOiX66kFl36qf6bhiOYw1n0CzGZ7NNMmCTUSiNU8sThFNzufpFNvUpO1NrD6OTva+L211vYXdzv6u8Sk8hsOLGMeWucIa8+81+QX6+lE9t4z81Lzy3q5y5rRVNnqENYiaeTGDF4pdT+zSz8laE0tCAaV/jY6ZSDVD7P+2z7Ivt67Q9y/kzX+R0AomFU1WMLF4evGcinxskuOhu0yY6x1H22OAguwzf+6SNNB5VuAm7DG6mOFo5kBV/OBrjJ0arwQdRwQ4nOb2nJI0NQnds2IGKULeSMn/wNhtoK8jU/2Xh+5zlLx/65JYHzKO+/dkm0fYVMwr2Odd94+c1AlOtbvciMc8GolnW4rrrOBZgXX/z6pdKJBLbtK7ZB5Nqq+1OYxSrDvSGj7ITNGd4q02hFuML3ye9Nti7OPQt5WON/HL0RbIzWxeHsX3TuPiecDk2ktLcp8HbRg8F01OyfnVR6vC6Y9RODULfVEn3vgVBbzI02nIzdVk1XGHriOaAgXstI53Gcd9M56XxDLcrtwEFHlne7+m+FIne0OVvshGG97ShSDh4CwM5scOdQ9pc4NvNpyxINIwijcqyRniOJCkimtwCIR02Ylel2yzda6vpbTCwjJAuk9E13RkzXefYK1fGK3mxDK26Lq8CCo6jSTtV1B50Si5DZzsbvjfMWEeccwMnwbO6blCLpFbh8D9j+DOXZAO7AjvMmtalxDeo6sR0Ziu7Lhm15Ot0teMb5NLQgGlf42OmZBam4hjrLIvt8illJwGeZz4bpMXS3hMzwqnKJ4gBZ26A4ixmIqmaEvWLbtexzWmHniPzXFGxsFE5owG3I4esfpHfhgXDc1QAebcAGom3bn2OuZkSE/oyr/blsE0sETmPEMINKcDkrLUvQWx5+ScvVWs53ReH1+5Gj1WzM63V04U0QWlpXwA6gSv45Id4bHx7jqz7867FL+lrXO+a+2UpPJWlDcTG0ZJC9ZC1XUL+IPM1I1dXrMllVuNybmY5A+wj0MPqeNd/mK8JeBmNi8O4vulcNU84Jvg2E+lfjMfdgZiWTj9P3/8Y70nYpNrPvrC7r/e5iLmhcLwJo/cTsOC6OL5G5lkPQnOWyWk0zrewgvUN+Z5mfZ+ZHhduQlUhF1A8Stu46WO91O97YusWxCurBLSWRpyo42mWLQBycYGhgnJkx80gsQN6QMZhhT7aLdTHQ8z7DvjX9gabZxqWbQKWDAIbJozkb/rwulUL2oeuJRinnU8VZnBmR0XcxvyvQnyKAiDfsFCLOyL69FjjfmJ+X6+cAojCdy+bCZ5NfWM2eUqe1rn++up68lH6ZfdTpMcoPQ7f0Vb6RhNan4VTqExVtmSvudULUDlyUqnuuPE7zh58QQZFUosnnCnCY/u4Yc7MEatiEJAfF9o8SiHiJXhKmYYxpHr12JluA69Hx5/qoAV8trD3ITLDeYyt/ABjh9pSwTCorbQXcJ0xT23frrJ7Hcj+nyGte04NLGboMrTEWOrWI/ovR4Zn1Ozr4o55lxMMNEFpad8AOoEr+OSHWGbPCNj2Zr9LoUuIN7a3inXuRSbkPLWSG8kOoySFqxFqusW8AeZqRe7vGZLKrcak3MxyR9gH4ceUse7/MV4S8DNbF4cxPdL4ap5wDfIsZ9K/WY+7AzEfuEEu7ImH9YWabXAfYXAk7wJwoDoTrOtVlZT3pMQ/M27p1lotI43Q4H6xlzROlmxs9Xdy0ATWLGrDy2W1PEeqV99kcl8h7roIGx3l1Imae4VdG1AKTMjEQtiV/x9nAo3wWqkFcWeBCKyBJuFS+b8m54k13AYxzSfyHDHCZMPHJ2w2KbEMD/kbTkOLU+0IeGAGknnVSyT2LY52d0KOGUZk7F6X/Z070OP8NoadkrFg7b1nl0e4T9n4YRsr8ZfmWzsNniVT+g3WTSZ39Em5PmPd5ziblP+QIT9ZWFomX2cftcp7jihcGpFk2S0pgRdn2b8JfYW7j0Mz7CADDf2BvYj8IzY5NQ/6odZ0dAMFWDBHdG0VVzDitpSmRLaDj3MZjcltkjvv7h6vBHtFV9yt4A9J+fsPXQs5/yf5msvGKUqZufbK4dRdEFpKR+AOsHruGS38Pox39q6jDL34zLty6Rr7ZSe8laUNxfLUarlnNct4CV/0YVdXrMltVuNybmY5A+wj0MPqeNd/mK8JeBmNi8O4vulcNU8QHKnN5k+D9ow9LelITHrwulPccdpsBB6RnPyYmh5ZS86o3OltCOaZMB23BPQ8fDXwJykqe8p6RM8nZLurUbrIUiT7IhBtAGCx7cHQoYeJovq+n2RDSZagxceuiLILSUiDXqGGUjrQbPkqExJBzA34kHRGsnoZqTZadVjUNHPvGCCGcQ92N40R2O9Fe70httZmheg9X4BV5R9/B0Xp1vaxkduxDxrVCzAJu7Uz+6l49Q6KZsZTDXqTO6SeSukpcLDsziW88ByHMkFIG4IYA+RNpr+9g60228i2ZMtbU4Y/EkcyMx33l78PktmnTQYhlf8FU1xADa+zAkyO873/jXZ1INw7xssKk/1U+T4Zb1XKJ6smMLB3CP/vMuEO05+B1AFbfKZNNk3XxEbAV8iPlqdYtWggAyc//WosMuCTjHAhQ3ySF+8mEKEs2CCpLhHdurQYfc6yrmFTjN7QxNto2ge1wTrUjfF9+dnueH6quYbiK3HBYFW7C6jeahOFB9TKsYOc2cHl94efA1DNrne7/skeLPmag0pXzdz8oCG+ge8wn3kuaWc9Cy5FBoWsFP7nEq5A8jGdujs0btJH5NRsofW8a53GB9NU+3eNg87/74KXrk2mVh7aQlG+jUEgpmVJifasnD6n3/6k94VJUe4XdDNIr2pDWinVk1aCfrQRqP0Ti2KLW/TxbSMNOxuNv1KPDUQrxo2IEIHCLo4hbmI00Vd+zYu3aQjUwWuttJubcUI43IJXMdMCxNS0w9KDOalC7gdTCPORjtFPXMjuZGYX0XtnOD3Bs1ubaeIsdL6LNb6BZbYgtTMhuc37VI3DCTRkIGUnpsEaGY07CqnTWAfTduJ7MtOHLrLvkA/G5Mkmiu+fE56RnZOfzUam04bHYx55Lozk9v1F2GMytmCfa2DfV8pvkDGOLqA2wNlFX+XfGC8p9rwHte2z4gVCq6ki0D2wITxjz9Dbnj9LSdw/Se7kf8smFRABQ08PLYH0zV33Y/T4NZzlevVmk6Bi8AARRRNBPJMm3RA06OqIxprp46vEYkRGd3InFroxnCEH5AV2sBfNNxE9yWjRpMDwYX+W1LPAHLW2yOufHJ+xmQTbZTENNOkwVhqLjX6rLBon4plIf49I42J9nRtZ33m8R692xuja032KIlbVwA5ziujY1dXEkEzwcOyWzObuLYiSTmcVxrVh0qpfn/E7O519fx4nV6n9w/u+20h58EiweIJsqeUs5eF/H7hBM3BiidN+97SGjxs7jp1O4hAUczQONwRRXd//XWcOIvgUlhv6rNMtOVCUHqt0OtvuWI7zZW2fFGcv1qAPX5sJn4AdtyadoHjFHudY4TSiuZqNAX2XuGUqjWQsMhj3gZS1riFGxa6jHGyo56Eya5IKyf50zsifYYCcBUwFYvRmk7H0/8UX9Ed06gl3RC34166rxymLrzELehNR5eCRTRH5asrF7WEV/wVrTQQUE+FpM/N00yvIdv8mhUZoqsxVvdXfle+eoQz7r5Hm7PMpTZn/Xu1e2gNV0yC7kOJkqBD9JGrnamMgskSB10UUbLBXNhLfqcpHtF7rF/W08+Vw5nJwTo9UtEwuW0DRj5fIByIiDOdekaM2BKmao2zkTaH3DtUayOG0JeH1j6YCm0l2NXHtR2e6SCk9pytDP+T07ZrvycEewPaDiUr2CU1n0ETDii8y67wf/wh6zPYM7CXmz36Km9bmsZsy7mW0tclcesKIPa3zqP9bTd33JrgYdm1CfkWXEvVfNzjD3Ttl0a8Z3hltgW4srlaT6UM7PZjOtr/frdaejeDBt7AV1ex4SwGY104/TnuOC0tycNsDe2gDcVTRDDQpCsIVeGCodfAOHH2goPC3kYcOnIhKB9RODl5ZqJ3oPlr9jV0Re30traqe/KVw4SYWl/MhW8z9jqEMMUOO43PCwsTeWROPcpw1Mw3cXPSfIboYDBphsBubwfOOFdmXMWlNopOoFrwUoRItlw/7IvaL6D0Zg3BjruiXs2A/Ze+qIQZRHFzGppGUZtWxNMoCzSEDsmaujkqXyul4rIzC49rkvTKZh92n2ejRdeIV6kTWj4Cli+zW6aDLCWzHjxBCBdeVlZYH4cVv2jHbZbODTB1U6ayrXgEITAnKYWlTRnTiJ8lBwZ9PylkCYEWfhTCXqKAwpjo1/Ug5WNESRPLD0LozmIgpLXxheHZi8LUIKlNBvh+8oINsYEeZCBSG417S64dqjVK1piPdLSoYb50NFSkoXvyM3iiA1EMElU7zbxFdjNQuRrHyucd1PAerfk3vl+70X6xL7zgrmMyRp+nZa/jrmbVdZau16ocw4Zys2fv1Jrb6qz6N/rb6lxO0XULvZl5wMHzJS4vkd0JVb4Fd8TO5nrQa4m/QYhuugW4srlaT0NMbKw0R6mHX0ejvzel1dLLkJbtlj7y2W7E6Mzy7zj9LxVOU4/bXDFONxb4qjgq76EjPUE4ES4I2vYYJ07vdsddb73hhJy5oafZnVY8TcxM6MSbgvH55jCEKHnBgZmNmrSKGVC4i/GSZKgQpN7kZjfGwl0mqEmVIq0NK6k2DmZEkFyTDF+0K/+uHxwnDo3UGhXUMvMuvvADmUxO4zf7jVr9wEgEY5wfZo+69hI2ur6HJIqgU9FL2RRFNrIdyKwJ8nruzZpjG3YeaeBG1k1aPtyeiEgH7Xa8HLXZnR1c5AQKOY9SsMw0dvc/CJxpbMa0yV9rs5m4Cl3mLLuvAqJgOYFmChbZMLfJlc/5g/ZmHmFS2ukA0AsomFSeOHJyYYIZK2m2P0B5PihkLyGcWlK2ROMfBYC/hGzQiLrgJt7mh3FZG96XMTTZFbqIeiVGmmThU7iEh4+VhlzMktJ4C/cyUONoeY70iQYovOv3OSJcsMvt4fKzx78v/ZJY7utrrV/z8NaxvI790ncC2x6iGw5tR6luVbdXtOLeDMuYzli8KN+eTFq8WTcUqFld2VytpzNd2mXf0taukzeM4e9hHlRLdUaZNNtkCk/2gHz28XtDG41HLJyApTYaftR86UMr3GCukE6Dbmsn3ukzjvZ4jIPdg+u46+xvErh4MBmFky6CQFMbpom7Ur2mYiDOmam9TavDxbHiFnTpVybTB9ljHWVmGszyhAngBuRKECzGAKQGEq0YanGbr0DFkSSVypo322uzsBF1nizODkQXNPVmrKj1Zg0H49xwl3w1XepM/XUJcEY7RXdseG1xiF7xiOLwok3YVBhJH6jR3L1bDD1y0LvRcTnhXJ865hcsExEKQ163fPcryytv4q2g21tdLHW3l+Z65elS2qYn2XVbucpzD7JP4sW60XrXypeNisscwIclA/nAfNcpmWEN9KQptoCDHJWtN+gQO+U9k0rRol/WJ8aGvuE/+wiacEVT0G3FqwHOr2KfxTKWs5IuIHnA2dfbwulgEg+I9Xkk3Kedz02YEF3mhnmNuSTGSXhM6qSJg8whvoM6txXz2fswcfg6vW28Za2Pki4DdwsnLdJSfzBsiOukl6P5MYvN6M26csbmvJZOduUM85a2zrh6Y9jabVqah9hE53uEBlVwkLy72ymc/iwbeDecVNTsFg2nGGiiB9QK8nf9xocdyXa800DfO3pcHT8ibzLmpl9b1ELo/jsOFf8nD/ObsvfHdIZwekO4IOIIvLnCAGuQ6Usy1hjoUKdAeLaxgihoPGWacI42mKGPmP2iAgTxSKSE/Ei8w27Z42vSgdIicUg0Pg2B22kdl6eidX/Q6+M48qB7rg+wW7blrXJQlExLk19pNo0zKLPiMoa2zzm2evdzFKG7v+Z164eUlsWNXycg5741nLCWaz+yeUJ0FUiN6yaIELe5lDYTWZm6Ga28zCuxXOiiR3A5l0qcmOwWFBaCSoZBoN7sc79ypDdFWhA5IHjiu+XwY6CvTM5Fo0Etx9kQ9ikhLDgtZdzo4rXmVGGuvxA2kgfMfkaIR/JIW4x79OI+zCYNZjLWrv/hqNHf7FcmNCmXI5hH0HLbwh2CanQ1mnGRfL44o9ZJE9xBpb/DPkze87lHP2z4noI9L4fn+wU+z6/TC4xNon1Waet/xQFb3HGapuTpcZWwexhzNAXRmrCpmAQbm2jZKokVbdZzw66j6DYyJJzmDjoVQqwq54779aBxdeMhbF4dzGtS1Dt2S/PgWfTbFE4wXTOpOeoTInCuFtEb1CridGj0tLaiJXMHUTfB7viOOMkhZ+7oUW6lknGCMDP9zUBvx4JSLfnQG0JCQ4QJQnxyhQHWINN+FE4DLexJFSKPYcogTTZIG3QjaVAUh7gYZvHBC34TW13Y+wLsem4viyOxaFHGuiXRoNeEB9xtFh9v2l3W+dsxKQ1KbOYx7IAz2tp988xQS74wj+Hy1zRqY1f40s49nLnl/rr0QOIqt12683dzlQoR9JmupfiEdF8Ti035F1zJ3JqmdbjXJcXicE9qjKpL5TLsIprvls6eE5G7aM5nzRdCKLpu6ZdXxQw7Wp3aS0BLvs1RPqqHHQrzFe2Yt4KQn4++Ngsv/7M82hWnNcLHSm6gNZPwoxSUjO2SSppgMf9BsZ6FlqB79rbGx/IcLkZa9+vOJIfW0SE1yXtGulZfzdcVba39MNQxJ7fv90P2r0erPcwLJxvBPvBIXZsaM2ub2SYcc2HM00KjqSguwVl6lZMVbdBrATRXg0g1zktQtgVYGrW2yt79sVqn97f1fbFw9n07OvK2cNqMaMxMm5WcmDU7XVKrna2ZCR3/Bzaw8VA7YC4CIGoIQjNwiEsVZDswwBpkEo4WTmaS5mxROyzTFS84FUd3fapwcrnSY1z24gtQVoo/F07OEV+XZrAimltMPTZl13nqDfsSu+0ocaoFV6MvWB3jdz+7m2aql5/CXPfy1zT6D1o4ISOWpermRSnyYd7Lsq0S2hW8yPSVwrUy113qsQhfS/Z1P0sMbXbfc8B8tCuNhk79MXmlTfogmUK9aZaXmvNO8y3HVmOKGIL/SHiczIRo5r1sTKFYU/uPx6vA3OZWmgqpQ/5i7S71zKQOeHFP8gfO28JJ+bkvrHmEOeXWtrSee8y/8np0SDnxSu1m2Gq+rmg3c3jAUOWkTeQDekdFHrJ/PWJdSqlgEsw42zzocyL5A9KE442k8jQIZqPbVFyCKRTIKicr2qDXAujRDTLZOC9B0RZgadTaSnM3QGqd3sDY98SE3rlbmofIRec1qAZAcJA88qgeFNokKf1uMXCuFtEb1Cri21ijp7EVLZk7iLoJdsd3xEkOOXNHj3IrlYwThJkJiv9z7sz3NiToJUMyhPlLgnvTFQnUJlMYYA0yhS8pnMwXTZo7RFLmC/P4PYzhNfuPWCPeAB5iNEKJNO5QzpWOSwUtgNP0KnkYKpuFdX44az3hSLSdsaQLk9YAF/N4tYEsxNxMBlh+Chs8XdBIo//AhZOlo7p5QW7Ov2noDc7H7ConF8Tjou5lWKGDjZo/23ikOyjYJNpKDhLWMCFNtj73RZvF0ZYcZVwf873mvLxaX4hGOzqgLWrYv8CLUzoj3Abi7rEz+iFsP4OjHFvuJCwo7mh20Fjw5z4Le1s4tQT1vF2B1zyqdbqiddPF92nUefv47WLuPrR3nKN1/kPjq5hu6fMh7fdR0qWEQ7sS0eJXZ7Jdc0esLUxhY7mXYTvYKuTWB5biEpzFVzlZ0Qa9FkCPbpDJxnkJirYAS+NIftLRYaSvw8NK33NBvd+0NA89Ev01FU41xHybyhUiusGB1ugZtmggdDwFFoi6CVbHF6JJCjlzQS+zK7I7sePQ8X8w55yRLwnwdTHB2NoiA8+VK5YwRhZeMMAaZOqHm4EW0ooAj3k9NpP8OXJAswCeznBKSuoMNowaIRUShKS73VAL6wDIix8eoxePqcYLOG8lLeQFRBd0uqxKCu0Yg7goHCXGlrQIaXi0DvpqA5mHK+2kevkpLKUuRGTUxkvonoV7OHPT/XXPyTonXbrn7OwbjBTP9U1yE+y+JpZP1xgszrsrfcx2z7W1DvfcjfNnK0XKYixld+Ofsk0h5j7ljNw4o6r0ALVeDFZOPTbPHXA7nWRQeBKKNvG8v2ZFomMUdK0dwsNQxPs6MCEdmhuM7ziCYGOx21SWv6DYrxE6WXRv/uO+9n62BN2zwzWPMKfcWNHW+0jnD0N6NpbbxS1Xq71rRZP8Q0Oto4fy8+D2W+C65APkyGmCNBltXH1OdHbhmr81B87OHamUkb41JNX3vmwmcnYetAAWrtKOI+clKFfdU2pIPp+fyd2B5kPYPOD2OxXhWFsELc1DPKIPhRMkFsN3/schwvRCd7TIlSKpgFo99CyeIrGLa0VKH50fTnfAag3uiBq5O3G8KIX17Hgk23jwRq43c8HRL3RCL00DycaIS5RwlPFW0Aa+5ATh0S+6H9nPBA+Fk4lA1U8aYUv5K4qRnRPhh+10YUjiLluvYyYoZoYx+QYV0koLWPE1pUFVVIOANfgNLmOLD9h8k9zaXX2caMY5dch+c1WBhmLJgNC9nt1wB9cy7PDhC6fBeTXG7hR9B+ubbu+vp1l92lE+TI6sHonNXPYZdNjFNYLN19zTsf8VeE5Z85c6xcYEKnrDFF7qiDDYAbeMDZg1OFYkshFFjBtyu5Y5BG5njaW3OR+9U+EZdDjHSxxEW1v0CSKOITbyz+tBTOui3J7Qayz42/rUo3rIB63j5R/8qCz0mXbfTtc+YGMbLooWc29y0vkT63ZNmwPX7Aea/4IIqOO3C/A2lnw9tAl/G7NnrZzzSL7NB8ll8STLbTrWfKg5JLFTULYlU3sDKI3bfE0cqVJ8M1/MxIYmDZk3p5N5SQQ8zZ2EvWm2pVW5Wa+lpf5BYrd9UOV7L6bx1PDNHRKdo6pBmIWi/dkni58j/5/2d5xWb3OjjW458N3iqcvCitpz8TR62LYuW1ylXykRTUlUe4S99+I4rb+K47D6xLZcDo1GlLjx8fPjOPqrWmToyiE3BNnxhcsSBYsYhROLJ2TYcBODG/981R0i/nyTMx5wcYA4LvsZlCONbAQ7XNrxekVUXjMzMjKoSnFkvvTcZH+FuPE3vqJwAo8dcBs+Hu57tRlc98ZXOXLL9jrEU/F3/5I95lMGBa1bhcrUCMvtSD/Q2jV9hc2eZ/X1Yd7EF+Ow01fFscO+KVm+ek5FGx1N8WeuDREeULbGfSmoBsSH/S7je0cZEwY5jBP/YC7XDAl0q/WPmGkL0PYN7wOcwVs4pWO+wGTMUed1X2SuXkyIcogl+IWV3ZXq1GmKnJ1nzST8yafbDwrvOBlO5iixjON7T6x5uJ6n13Ww9oGyXzRLryZaM7+iNfbtUE7RNhkOWlZ+uDZ8sh3UfL1iZ9fBA4VzLqPij9c0sX/1pdbmhuaE4JHQ5QeyiRPJVtG734VxjXlnrWjJl59wddr8aW7aBFKh577Vc9LxQe+Kxi1tXeH+O1Hhe515bmke4hCd16dnhu2Kwmm2qLZBem5thgW6aD1O0eziWhF39gJva23BPUIqR0riSssjU3wuIdoIuzY4pbPtU/luYqGiaCDT5ITTrOiSMWj/WSAhDDh8+ZKFE/BHrwwn3wonQjPC/4/uHgfE4kFO0eZBeuAGGJXp0pFBoSUhTAa8rcJJBRL1TIQQIua0eEmd5sFo06KGop1eBjLwSKRL2qv/hwce+/FKYg19nrnVQaJCNXJ6HUTQOO+3Gyr8ZOG0CGfj+AShvExCV9pFjvocP/lGM7m8pHl0g38o/0OslkTPoyPAtXIHOTSY1yoR0Ia85lb2K+hQka30QTNOVa4dhkSbMFpPsEPXgOaMfuAURLQd2Kv5ozGzH0WTfi3PCB4sInZXkE6cNmjB94/sJRjzYTSXL6Yw9izsKy/pJP0aRXjAcaxBlMVwbiRRBJ1jLTqKvYTMUWIO/x+lrfk25u5+vZNNWNH4OSQlaN2Hz5dOqZEaqbdoXdNX6QAKv0Ust7bR94Fb2z5lr/YZLcqS7hS/5Iu9y0SSNy83mzC+DB32ObWZQLkRuD013QucYMwUT1DTPmaaeQr/8qMxLgh92UljNDqEDhbOMkmZ9cuguRYBtbkrjVozinnfwHHOLW0d9/rdSnp6++sYT6bf5kzio0i2dgqnP2veh6APY22KIBttsK6hXkHRwlzqOT2bwd4DscT22Pt0XGBsnGwIG/0xam8pBraQkMFMFQQy5uwuBDxspTpoWv6OS59DSHG3wTZ3h5DTTmFFEwqnO95xEsQ2Z6eJIm5eLljMT+x8bNXSY4NYQCyczAfd8EXdMkVuRhEb8YjMwzElWGZU2S+PzGn4NR2Kml1uuuFEv7JDWdlySROMIDwixujR+QUP/Dmr5MJsxqD81U4aslJJgS1SIoVRKuMsHcYSzZE9tkpD2MRnc6JJ9CHh1MVjrrqSx6xpeEz/uNTRDV5vfMcth+SQ8t6v2ZLWAOiOw2dQfY7aXNcBjKaLRAr68wh3PECXvOGyk/2AciuMXubai3y7ddpR7gVN002bXfdHxFwC8r9JhEfaNZzFEwomw2EoT1hzn8BMnXYQOvoh+7Ct7lAIwjxcl3yRAOkCMdgHPFBAXoIP135AqFBgzkOMbAUTIPxP8rIpvu9BUThRuySasZuhOZ4bi+U387aRebMJFXfk3sJ1mvet4+rJikZepcNFNY5SXAoNzN3G/hhsVSQLKHwr9d1Tao1cEssyqWEgBmAeh8n8KQvFw7r2pQus6G5saJs/urSJgb0E1yTaSOZQpOdjA1sYI766j7xr7jRIwKYg9yq72CGM/UK2NN4JTQm4q5sOrbSXGIDOCVdNSOhGW4wRqoNGLQ3E62Lqy6h0XeuWtq6L4PVrtfT2BDMQ8Ab+ot0j3i+cBisaxhrE0Uvxy3inCS9u1+ebWWPtoQsre6IjXYpDn4bGKG8tqXTGuEiaRJjyfjhd1gXdTrU0cWtbAU8npF2WZQdNKktG524RslzAhlvR9IqF0wsWT15E6a6T/XCE9Yin2Xry+DGLJ0D77xuJ0blADbpn35TgCxc37hI0+PfeIEsMLRTVf7DtEszEqniCIPkmy6IJ0IlhrtmlfVpmbAoMPfCRwTx0vrc9nqJ5m68ZK1Sl0/gLdJRqrW4r9Bo3+rEwuCEtDEGGY7kRflhC78A9PD1U6MsxXcTJebmgnyXlUJxORLcvHPAlZrrNK8SpWDXN0rQFoXWOYgMfWlAWguEWfMqYkiAKJ4ggt71wkv/UDZ0aA/eMx/Q8ph6f20ReZEdFk+48ce8wY7QCo4H7ivWLG9h9bJsHIQqo9BUZz3RylRvRYggWDMO3n7ZHmS2eIaG44JcH5IGoGXSXA1H9pHTKtboz8gAWNzzquHRGFcq3A9mPXZPlG+P8fTsyZotd4WMcdXT8FM0GR+wRavzTOtg7sqPm0Do/DiUuWUDhxX1zsEzNxSHFIhr0Wk4bOoiw4e/nTt/aEcWh7x/AgYknm5lb88edweYA90XsKSa0mjveZx8X4LDBfRH2Dc/9ywVzqsAW9iqdaPvpNiD+EtdPsEcYMVu7xzKkpnVIsa6n8aCl7q9ht5kSiNUb8pOseyC3tHWPMF6raktvzg0FIJ7gTFdb8EDhpIF3qJYMVASds8I7LbRblHp7LbtbbGFhKzRTulL6O+JtNFRmHPPJHFSz66bRxubirXTXaLCqhTB6gHQ/wY04cQFF4eBjpfNxGkA/UTS9eomi6cXdqxfC7aIk7kBhc0LZgbtNT+wiR8UT7zzxoqfFZc6yD+aHmxKioS8GAgEevgC933iMTneYELHwl+ywt9H5V/zEHdHADPIFJAwGQB7xD/Ly4T2QL7AKhwkcmw1B9iAKeweOUWpsqd8wM3E664wXBdXFjLYid5Fb43MH7mlfQ3hPM6nO4d1kOdkDwrk5UA42Muenk+F9c2HhfKPFoJkq5h3n3sKM1hIiYrHB9Yaiwwh28garGfV/WApmE67iyp903kR2//NEsRXqa5RsyXhMGZsjeE27QHHwgiPuODmO/QPrHhC2604U71KbFRRKvBCJoukxoA8YLMK58UMO8siPe6dPMxox2w4QPMAcR09ya5vV6lrQ3YY5S9+ZfskyFFENwkfGAbp4HtYtX7Mvu0bLt4X1vToy3oibwF5yjDuunq1o4ElZcoQxgBrHHCcKD5InG6Zvs+SkSGdqzGbYZd4UvK+Hy2Lq+Wj5TDSRjdlaO7CB02W7Ref4q3+A2rlYx97GK7VtMmHt98IGDEUhbejxtD0JBQ7OFzxfEMcGIdvQxhzlfmt705MnOJ/Y+ZT7kj68gvxL7nGyCRtmO2xhPnuMFc88nRA7jgDE60W9KMoupo6aQGl5P6AjP7v6FzBuaesCt9+paEtvTzBjEk9Qgc5t0XcKp7/ou30hNwyjdBss/iaiNgWagqFdZ+Tst67RadaYBU8FLK2t7aUKdqRRkxnUsgjcZbqVGadEmPJrI0kA6nTLfJunrNPR5uZgGwY2Cj/jQufFc9sEUDj5eWfQCCymUKbgsxzAp/bpy9MnvlHpsT0sep24kDMvjIUbFXD+d4jI/NC2AmVYRqBRPJkxPITD4slw5MXvNIFvshYD5OnFrxzpw+3aq9GwuHV69LA/nojZaYDbo28QHd9KNgqCygN9PHAoPwdER5HB2ch627IMHMq+r4lr8pXp17xaG6k3ZKw/yPg6tJXHEDlPrVDg4ZPSRLCK0gFZeXEAWYhTJ6RCnmsOarY58J/hKGDwZl5H4GYDZL35I65MGV24H8YnBvzGAZT+4uJBH7rwwsT2D34Ca3uIfzDT7g5ZH/2O9RMvorJw8r7gwgQXKo9xsWI477RFXtyn+0Xwio10xmU20L3ob8cjbI/ZGIjd1zV8iGuI9JEO+TXIfUh52EDp3wbWnNmzlwFnjHuS96MjGZcfytuuZhT24Cv33C2jWyv9zt+1OzMyfOVLcBYc2+fzP8qjJR3BrcTDUXy6Z2ebo2P9bQpLdLdPGrxZa0Efo/OWv3qMoqAv3LvwinVq//qBFuicI+YHkPuJ7RfA4RoWpUV7sGNFzQv7YPiFXee8sP3p+XM77boHuDHdpum5DRVLgE/vnj59evfOOyic/K4/9ijtm4BejDlkzIgvghaM5gDYB/oE+ZTkoLZtqLODFc8FhOVnq3g55Za2Lvf+3Wi09G6GSTxBRTi3RT9QONVE4KYnzYTF30SzlBGx64l2Dl6j02xi9dkhK4IlsZcmSZSGJl7X8GXmMrVReHEAC10WG4MfpkWGuICOU9OYUV5E3MaLoomfBAu3jeSlISdTegAAQABJREFUbSCvUDw99xO434F6YT8Q4X/nCeWK33HShY+30R9uVrhYi9iqD+ZTtERBwIyAHnCzjILo0RMWSCiOVDS9jGIK/LzbZJuX9yzy4102O2aCAbhdj8vtwxd81knpkxuKxomSlXQ0948eC/3ti3bO7htTF9rgzdmG95bgY30+D9fl3uxm+jWv1r66fa4L0wO0t1lXYHEAG36k2XJABt+07dPPfLQNC9IOrTV90sk3blBx1we+7GUsnFwP05O8KHxgaZiyxue6wfoOFQGtLfYt9Ll32EUILkRe2D7C0/CXOG2vkQ3oqggE7LbQF3zC++4779698+479iGNFVCPUUDhoxuLZ1qHiswh+hzZQ7/RHzQBRGfTOBGzfKNfspXypid/gNpzIAvpOjpe1PtgPd61Hffp6bi9//KJBF5+KG+7mlE49eHsOh2XjRVNvF2Y4StHgrsaxsA8OsVf82rMrtNfWz1OLf9d50h/u/w+vrTfBzBVzefCLdaPL0gIeoKdphYzzwbWLvzpn0wrBmqbb8wJnPyghUUN9hNJmykThI2XeJLG9kMWS7Y3PbPrm2+//Zbnc2tDEDZxV9z3pid371ix9NSKpXeevnP37ru2H73zDvemJ/zgeC6cUDThhB+zRcfeR9jeOzSnBffkztKbi+o+cuOa97bfArilrWb2jUZbejdzWzxBdWRui75bOEGAb0KhSVxaqxW1pKUCrfXWJvKRudOq6bQjcJock19WBEtJaRIsTsXrWpp4XZIbSBgtuhPU7huPOILlDVLQGKFodgVlLNxxwt0kQGwqVixF0fTi2TMrnvz0QupZ/FS5XfiYLAsnc2rXerbB2CZjkDUPoJ0YSm5B1kAbfRUEAhESDDNtFjqP7MKIX3o3yOIJhVEUSgWtB614Ih69DKvmGFj8oy8EgAsueJ1ODwTRmG82iPeXTu94l9ngGixjuN+NxJKgN4Qlc5fYnO3K/DMz1uM6Z+S63JuVTL/m1mzZ2/6W7PKuotcwYOop081CXV0wiDd03oFBEYFiKwonyby0Nc03b6xpW9u8g2wuYDu2L1i0w4hhF3RcUECPNPlzwRCbiebb1gy/jE19ezF/uABBsfTc9pBn337jFyXPQHvGC5YehAomOOA6MeiP+L3iBcoHH35w9+GHH/rFCy5g+JhMfNeAvj0H6AqP1sFhPBUfhEIGfO+3McP/sFalA3buDRD0XZRKdGq0w8clsgjVnJ88MAZ7AruMPQWjn/N3QnWXtRNjhNfj7zjMVd7L+IpW3B1s6NbxvHj6B+UdB0XuOh0viYfEvG+vz6/WwqpPGuCRN2cTbae13c8IpFtH/J8XIt2S+I+wD9rEwemP0uFDFvcNGq2aMD40woc3+EDnW9ubVDB99dVXd199/TXbmhmwg7tLT61Yeu+9d1kwvffee4b7yQ9y7DIFfriP8AMYbH/mDb4IYz9FH1RIoQPsk/dE/jSnBXs/L8KRlDhkG/uU1tW97cu4wVvaambfaLSlt7b/iFg8QXVkbot+onCyAQspvwMglaSKcAGULlQ6ftTENTrNNlaFHbIiWBLg76VK0g418bo0H0ULY0V3eW/3jaoonQp1b49FE+ICnTwWSyqczA4umqxoUsH0/Nm3huO04skgiijbcXg+Ml3sS49xJ4sQuPnk6TTcPEKS0EcWVrzIqy+1s+/GY6GEQucVLoD8sZxHj5+aQexKuPNkJ+GqiHInvCsFZ3b6jMOr2eO/2lQZkNFGiCZofmhM1AbstI53mQ1eg8coNvwdwvkLpZVic7Zi/9PTanxPpeK63JvFTL/m1toL38CDlSrWxvQDj2/AxGDTpdvUdEGT9U9CbYbbJ576oQhNa8jjmX1eJBj0R1HsLnLsW+kXobaDvuETcjDS+NKlOO0E0+Q8FluHoOO/+UTBhP3j22++ufvGLki+tgsTXKQ8+xb0Z/Qhf7mewqTTccHxkhcpP/rRj+5+/OMfE3/XPunlp722H+DCRRdJT7BXROxDrK1/4oPELjDfwL3P3DfYbXtRktCf6Bf0FKvv0RFwJkptSJ47jsvu9idcKKZ9j8d9Vcf3rV3LWcZpoTG6CHGY6yf6t7R1LjCNKeWO5+Rc/lduu07HV7IPQ/P+PbzvyuOQ3uxU8XOZGG+WRRu7nei+PmPtiWfXJ5JQvySPPVDfl9SegDnCbcxC4BKOdY4PdXB+Y3vTV3Z+bfvT559/zvMb+5CH1wymiH2G+43dYfrggw/8fP/9uw/ed/zJU3x45Zcpbl9+PG4Ll3swH4+O74vrLtR81x9Z0pwWNNJ1h5Ji2pV9zwUM3tt+i+qWtprZNxpt6e0JZsziCaojc1v0t4WTMpHp20uVprJDTbwufapwgvkoe8Kja2YxFFSAokHGtVgwyYJdmKBY4hm4P573zAsmu9B58eybu+eEhtum8so2HBRPuAOVD8nFY4C+SaFoisKJu4HFgc2Fn5DX5uaPGKmgMcgiB88Qv2MXg/b8sJ2PDH9k8M6KKBZOKJ7mAgpRmGP0zgTZaxZi1p4LJ5/lJodAKS+IJnA/NCZqA3Zax7vMBm+DimiOHnpTOCrvcs3ZZYr/JNLH8n9d7i2Fmf42pxaZ1Rs/WKliOKYfLxzsHRh3i1Z8EiFo//2DCLwZ2mmfWPATV5v+bBsfb8zoC2zhkRQUT3jTlt3qZ0UEmk7OcbMDZylhASdOS+6/F0587M38PLMLEhRKX39tn+R+8cXdV19+eYdPdb/+yj7VNR7ji3hoKl5kn/FZPO/aJ70onHDik148JvMeHpWxT4HxPQOHXkzpBydginlAotgHGQ/r7Cdo1kafot/WGyi6igYn+CbKg3mhpssG1YFLNPwU2gM7JefxnZJQTPsyx30hJw93YL5M1pFGkIK+4YO1IK5ok+Vtc+jaHMhW3Ck+R/a4a3rX6fha+mGo3j/M74ceU8U/pFdEQg1uEWdZyxJnHug9Zq5P8LBG7XFm7guQgSl7kR7mA05eZ8QHKsAxdXDi7g9/AML2wm/xgQ72JtuPvrDzS9ub/v73v9/97W9/YzHFfcR0sd+8b4USzh/+4Ad3P/ihnR/a+YMP7Q74D+xOFD4Exl7sMXvchfsjwGib77YHa79BF3QgQ5rTguJdDFtyI/OwzjzA1r3tt4BuaauZfaPRll6kdTjEExRzbot+ReEEVXkVlLlzsMt3/Jye+NfoSNdgrBBZESyJvTRJojQ08brGkcJJliyYQL0wKjqyKxpkHD9fOMXjebjTZBc4z61YQsH0PHDceXpld55QQOGP49qWZH4AwxeLKHz520hexxB/bB/N5MUeNzR/1Iaflltl9djuKuF8YhdEOh89fddqJhRP79gXqMYC6iXuQqGHpuuevYBSAQbnWTihcrMWcg1Zn3cGGWSMBXFjgdtwp4y0FV9yA9TQwCb9Dtzdht4cdgWWjOZsyf9nJ8Y4n0nDdbk3o5l+za+1I788cF6qWBNTDr7tPgsh5UwAMuDlnGPD20CxpqpwshlvRLTdDwqnV0PhpDdtXnAYj22EA5ygoqJPxgUBPyjFoCwG/KM/X8vmjMUZHoX51j7F/eabr61o+pKf5H5hFyZfWAH1xRefWxH11VA4Vc49BnMJ47SNQgmP6eFC5f33/RGZ998FRAFVj83gIgeP8PHTZttf/KKJEdIWo48+sq/sJvrveUcMkEay6V9pCL7GV+PAfQQDwEMwmrugy3V8V4GMys9aTjGtuaAe9+Uzbt/S/TiYL5MFa5MU9A0f0S+IK9pkedvUmG45JymYI5fmpet0/KSjezEXiUXUV8R+WRjl92x6S1TLKV1B109fk2Bor8H8570mg/wXfXIAecNsjmBO4MTjddgHuAdgfzSa7jI9s7vduMOE8wsrmHSn6a9//SsLJ9BVfKFg+gEKJjtxx5vnj35490OcP/yhfWiDH7ShawbvuY6cW0iICzRAfniF4sk+LHK6X4koAUiN5rSgeBdDJsa1KuW19u5tvwV0S1vN7BuNtvTaoI2hiico7twW/W3hpEwQ7qVJQpVtTbyucU3hpIJIHgC9TIBlP3ub8vjEd7jjZHeSrCDiY3lWID23C58X335999w2ExROzwyK9/L5t/gYx/Tt0T0UTlFEsZCy4smqKp9U1lVsLvg+Rm5kvNiLYgkXOSiY7E7SY7uz9NRujz995z2rk969e2IXTU8Mf5TF07t158l0+GMR7KXuOnmxBIcqoABZRNnmqYA8v9EmHaztmBg1D40TCB1PgRWCtMdh25bQs5BvBGelZoHmbGa9bVsGjuX/utyb+Ux/zKudnOONX0dhPv3gm99N4lqCSZfAfMs5h3kaXSGKJj6IiPXFDyfw/Ah0jQcb+PUovHHnYyLx5g1f7hNv8O5LUP4Ae5zJRyciLhUpfCQFv1JlvvBp7td2YYJi6e92UfJ3+zSXZxRQsCNbhGhHYtiv6A8el9F3Cvjpb3wK/AE/CbbvPukRGoMosvhDEnbhhJgUl/piDvHfjhEqFq5R9MlFPBqT9/i8KVvXFU6wEYOX0O2eelWe9mQU0x6/fO5LFEejUJTbYZjHkzVrkxT0Dd/EV/1b0SbL2+aVXTuX/62jmDPBuEZ/ZfM8rSfXcc3387rXSpTPs+kN0ZUcaNgBCCnga5Q064TuoLsEZD3HyK3yizmB03/5rgon7APf2B2mZ7jLZNcxuMOku0y60/SXv/zlDicKpydWEOFDGHxgg2IJd7s/+ugjO39695Of/MROf3T4KQon6xNO5tmC8nyzM4xL8fkPUeDOPyOvmCPtSI3mtGCwLgfMn6tFymGdcYJ6b/tumq+3tNXMvtFoSy/SOhziCYo5t0U/VjjBy2BBXgVl7hzs8h0/pyf+NTrSNRirQ1YEmwSExma2SjoxW3ld2t+UXaHoKW2MLu8STilpaINWdMfVxo87ZNFkF1CP7IIHbXyP6RXuNvFOkxVOVjyhcHpmkIWTbT54fA8yru96bhd3mszPY7ONcO3kxoJPZXAhRJpvbmDwuWQrcvC3W/BrWSig3rFC6aldALF4evd9w9+7e2y/quV3n1A4tcf2rCjy+11118kcmdsooAjND4onBAIYQTHHCtCozgcCFLLjMdPm9igdrTYciOLooTeCo/Iu15xdpvhPIn0s/9fl3lKY6YeffV/9jb8S78p1kYA7uHGEKc03SpIGzPWwtjB/WSzwzi4KKSMZHaIomHDhQQjcTn4HKnC9uavv2Brgjz7Dv8LpUDFJlt+lsh+AwHeYvvzyi7sv7W4T7jThEZi//+2vbH/11Zf2nadvNoWN2+rZgf/BmxVG/p0DFFIffoC7UB/6IzT2iTA+AX7/vfd5JwrFU33PwXLB9NgOxT0PF1qw67lzEmjYEaLPxaaY63ks6jP3jwxwCNQFd18lK7grODA0NgPRGopnpo/tS3x5Xkb9W7bmceXUtRf3kSltLld9XNGayj56Yfc0I2t975vuHOqFr453mdvjfZwd73P99v5gsXweSa1+aXeOBbrIE6EHzTWZd5qMhj2Mi9cXNPlYF31tYF484Q/I+M+R++K/4wc5+PEHFEy6y4R9CXea/gpoRdNf//JXK6y+jh+EeMqCCYXST3/607tPPvnk7meffHz30ccfsXj66U9/bHL2vco4GJrdcUcH0AceRDw+FU7+gxEpkdlDFjWnBcPK5aDMp32Mk9bWve23iG5pq5l9o9GW3j79GbN4gurI3BZ9t3DCm5GOxNOKeIKSPAe7fMfP6Yl/jY50DWKV2CErgiROL6d4XXS8y1Ramar0BsfFr2UKyZKGbRQzLJBIx1u9n5BjoYOCyU4WQFY4PbK7R/wVPSuacGfpxTdfsXBCwfTcvqsAyILKeOBDnnecYAc2LSwvnDxEXLwZwy/iyPM2vPuBGWH/WuGEgukdK5ZQMD197wPD37c7Tyie/PRH9vy7TrjjpMLJI4BDP714gh8UUREEIPjMn2fDcSO3nK42g6M0s1RHGw7GUJyTWH8jOCk4MJuzgf624RnQnDudj+tybzYz/fCz78svDUIcb/pDOHZxYP8yhmZG8w/y0IIM/4wALNjcZZFkhRLv3OqxWIOu51ov+CgdPvWMwskeqdOjI7obBXOQhp4KrwqxAvIl1Ga1KT2zu9X4VBef7n7xuT2ahy9c20UJLlD+Zhco+DI2fhwCv7aHO0n8kYd4pAZFHw7PiL0iVuvjc/sgB3o48WMQuCONL2zzOwf2+B4+DcbFDT4Zxt0n3JXCI3z+a1h+5wnjwXjZN7ygh34QZS69rTwzCSBRnIoUEJ97GI2CbHmp1FBu+UK3EhRcSt6YeImvys2Ng1ia4zxDeBFiprRJK+eNFPO6Uy7Dc41dpnZMmtMl8tjxY9rXSw1ViSfUp/pDjmnNLV+7Hv5Jj4tBhjz0CbEe8c8aGCfdGeeeZ3KcD2HD98HyBh7/5pvtFcDdzqt4TNjufscHOX/9K4omK5ZsX8KdJkLsUXaNwx+FsA9vsad8/PHHPD/99Od3P//5z+8++ZkVT1ZI4e4TPpzBwRi4X7HBPYOMeAGfj+rxe6Yt1uArg5rngt3GRXi50LIy9dgDgS3yf5H9JnxLW83sG4229Oa+pYDFE5zpagu+5sIJbnO6KYYLofQvVIM4VrQdsiBI4vRyitdF/Y3YKVVYzGtQ1gwK5VYDPcQ0DhdLA8YKi84fYBZOVjTxjhMe1bPvOKFw4veacMfpKy+a4o4TCifw8Gt7Xjh58RUfetsnyRaaNQS5RnFNxJCxjSF9sSkSx4LGBRjuONkf1LWLHhROKJjeQeFkv2DzJIonQP5YhN1Gf2WbFgoni9w21iig+MYBZ14soXiCY8IhEKcPFz9tM1ltBiuaGT+9CbXhQBRHD+Tn8uMancu9fH81juX/utxbVjL98LPvy1dAiGMdMKF6xboAzdrNRJ97zsddJFurtn7xiSxFsYaw7uKOE/7+kf5oLARgA/IqmnB3iF+WNuiP2HnBRt9wb7boN9ZFC4f9k0/0hDHZhQO+cI0CB4+7fP73v9n593w8D4/pwactVurr+0oqnngRAqPGRgz8yWDrI21ZAYYLHhz4DheKInxJG8UTLm7wiTBOfnHb7kLhF7AgA9vD43qx70T2aY/+4NN4Pc9kItQ4wTeBHJa9vcONnnhdXNyekL4Raxy9faPo7Os9OL2Q1whRsEexGRdjrmhd5xzO8TwndA9+t9/xe5g8r7qYW6/Htw8e9w6L8uwsovg4J32ZuQXEzH9cr/4BCmmk+9jjw9Yu05Ojwgl83WXH/qGiSYUS7zZF4aS7T9i/sG9g/8g7TT/72d0vfvGLu1/+8tO7nxn+sd11UuGEbUF3kQBtI8lQeg91x8nHw+c7+JLp87njaewSpEJI+/CktXVv+y2WW9pqZt9otKW3BjAiFk9QHZnboi8Lp//157+YvKYGh87lByvFl7GCp3iQWvFXtLK4xi7UicUhLXydZ++QzB6fdBOKyx820xzpIMlKQWAuF9IMIjXJVeGkYsktwRPOuNNkF1O441SFk91NUnGEoikKJ39cz+5A4VE98Fk4edEEe/huxRNbmfh5ZOL4tBsXX7wAMxcRHzYXfoIEaK792WUsahQ7j23Tssf0WDRZ4WRF07t24s4TiiYUVfi+k33szB+LYNFkWi/sDeMlTvTaiijPl4onZMpo3DXAAy4Z49VuYjw/VpvBiib5NbQ+NcbYaowFet2bXR/7hdG3pJ0M9FHC/Lkyj6k22pudSgxv6v7fKfJLejeROBDjWnw4+V0oKywAccCK1huKBRRO/II0CymsSbvQgF48pqfCCX8MUhcXjIFh2f5g6wL2zCrM24E9QziWjeOIlzasKPraHsHDL+fxcRgrnFAsfc47T/6jEFjj+EOS+CU8/bwvfuRBd4e6zRf4swhmE9+R+vOf/8wTX/BGQYUDd5fwuB4KJ//+wUd3P/qh/fqePbKHR/jqj1TiAxmLXPEO44s+IJ8EMMuj91M5YYIlYBDZWO0dTWSNcrgrj2uhc/y11uugevrYiUPuIu27shyXlkoIaqykNLdFX8l2Xsc5jp3wwHj31/EHdZvDUvOnpnsyrwyhbK4MyHrvq2iQr3mAwSal0eI6wMjYTxCzQ+DY6xymba7nVjjZtQT+6cB3PrHfYW/E9y2f216iwgjFknDeCW8f7vzNcNxx4r5p+riTjULp53b+8le/uvvVr355hztP2G8+/tjvOPEuvn1vCX+CwR8lVBTeTc1d7GUontAH0PI08Tmz0ilLF2KVimYbPt3Ove23cG5pq5l9o9GW3s3giSeojsxt0R+ocIL5eVrJpeCKv6JJfg9eoIOVbQc1DD2leYrXIxkf1StOfYgES25teGPXhjEUTorPFikWKmVGSBqLpviOkl2E4bG9l8/t+0ssnPCDEFYo2SN6+JGIZ4QonPAre3pUD/b97zk9sY2GfxDX7gTx7xvwgg2felvM9p9bG+zjwg2PCvFxIXxx3TgM1/uH7zjlHSe7MHrXThROT+37C+/Y+Yjfd6rCCcWTF072K+lmB720Ko0QhZjnCjGoWDKafZfKcwk6/KIZkGjh5O3QxNuDfZw6vicver5BiHAI+pgfEn0rNGWgxvu63Ju5TH/Zmpyw6WLxNm8TP9uxp9R6N/FhTpoLyMf5AkUTT34LwO2Ya74h40OMKJxwEeA45r9foOQaNH2sw93CCf4Zg0UZ/cveGR2+aMsKGlyc4NfyvvjciyRclHjhZL+iZ8UUfo4cPyP+Ab6bZEWP/1rVh3y0Dp/w4mThZysWWfFfwXrGT4l///vf3/3hD3/g9xTwGCAe9cMjeR/YfvCjH+FxGlzIfHz3Uz6y9xMWT/zZcvsulC6CUDzhGMc3ehN9o4BeRGPX+eKcoHOfCZt9nKS+C2UX+9TJ4xz/pPKDMcf8HXOjNC2lrZu6+OpyokGn4ysb5/jXxLzycwmt++z4JTYuls25BU2fP76tDIyLzZbC/pykB+xNISxYurGVgGADPY8ZNBGrIHe12OuqcIKyCUHfrjeQV5zYg6CH/zzCPoqVZ/YEDX5JTz/+oMKJ+5MVSp/bBzP6zhMeLcYdbi1NFE4//5k/ovfZZ5/dffbZr+zO06fca7DnoDjTtYweLWYnMgzvp72aXP04D/oOXWVTMNQ2uRH9MFQeTKFsIxa3MOf+sN2F4C1tLcy/kaSW3p5gxiqeoDowt0W/rHCC1mCphlcGC57iSWqWmduSOwcP6jF2vnX6Ot4xe9Aatf2N2A311BCnIby4xXrldmH00MhbX2ibRdtUvISINi0A928E4U6T7TomijtHKJzwqB7uOOHX8+wxPRRLUTAl/AZFEx7Ve3b3OOyjNsIFylPbDPzTY8Pti5N4bAh0hK3HivwTIHz64sXTc164WUwodgzUd5zeZ9GEwgl3np7ahRIe3UPhhJ8oNwd8TM9+FJ0FE4om3jJHj3n1iSz5xsW7TMRx8YgNy2AWUianHQUaDbdmHLXpiHIUYtPEIXhE77o3Wozr2+P6DPg4XZd78zqk322tYqkVawr+3+YrlMMAVG0ORitNYFpCTqcXO37HiXefaMNkYg/go3q2Hr1w8vUIG/40iV9sqGDiXWDuAxUd14QpIJwhJxYYaHqBjWd29xkXJyia9EgMIB7Xw90nPML3re0bP7A7QfybTHZnCH8LBScKIN0d4sUE73LZhyBRjOECB4UTTjz697n5+Mb2JO4z9sgufhQCRRM/Bf7I4U/sLhS/64SfKLd9Anbxwc7QD6xIdi47A8QPJt9HoIYGuZcAoO+spFRCusAan2yshUCl0X32d8AZ83c8AOZ5T9y66Xzvb5fte3HHZ1OneNfGPPu4tN39dvxSOxfL5/zyfHJFJ+1ia5PC/pykC+xPoSHYDeTYGjKPGTSxvgSzcDIDyJ9O2gtDoKGoUuFEmebwuf1QDf6gLR6/0+N52E9wF/tz/FkEfKBjf1cOxZL/qYQv+OEMvquJ71Zir8IdJ5y//uzXd5/9+rO7X3z6i/igxgsnfXeJvnHny/qgfqKP+PoBlrLu7EMO+xFOHMjonNU5NxS85KUlv2zHfgd/CvASmzuyt7S14+KNI7f0bgZPPEEFP7dFv7xwkuYKjh+7riQWtJoim94spNekbmMtQWpkgdKGr7RWtBMWc8OBTCbZjCSeXmoBlD1JARYeD6+ZptP8rd5lyIuiaSic7CIIRdPLZ363Sd9x4o9D4A6UXQD5HScrnMwuTlyU8IvbViTx+WD8YUo7edHGn+z0T6axyeGT5Ge4fc7NyS66DGfBg/rNQnvy1C528Mt6dtGDound961wsk+oUTzh5C/s4dNpK55QNNmf4/XCyTYt3DZX73xULFfIm20ULJZQNNlGJtzpGCnIIJt8AXJ4c8FGuD7omSyPAdbL/lrHqfs2T2qdYr7lnc2Aj811uT9rPAV8uvg8xWQFJlrOQc3FmC4VE2T9RLHEDyNsDePC4QXu5pJGi5y//GlyrE08HmfrUW9yXCN8k8eFh9kx3B8z8bmcM1rTlT6zC4ZAwmczPknVxcnnf/+cX7LGxQke0UMbFyX8vpLJ8cvWP/2I3x348EN71M4KKXzXiT8jbnejVOwhTj7aYnexUIDxjpMVTn+yR/b+YidoDMHigg0UTfg+Ah6p+Zl9QvyxtVU4YT/iPhQXKuqFcpFrPjstiegmHOE/+S7kuO2mGqdUUcKScB5xkyHX9Tt+3sytJGqu3coi9tJmKz/cE83mUfI77vwaJ8nHzEudor9JWM9jx193jH3eHvZ9xZzEruP/Y43MzvgebESMG3FD2hj6vgYT2N+s0CCGdYd22BQM2yqatO+hLXlAfGDjxdFX+Xge7i5hT8KJP4T7zIor3JXCBzw4cWf8SxZUX/Gu+Cf2oQx+Ue/Xv/41TxRO+GU97DGYm144PWfM2EeZhOgf7qDrMWF+SGUfEuOYCyfQWipoF7SrD08X1ctura3VmrrW1y1tXRvD69Zr6R0HzgIRT1CxzW3R34DCCaFomggqvKPwoF5kgdKGz1pz+4j3ntgBT2OJTAtrkDZXaPvZCyXQehu4XXEZ2U6Dj/OOE76/ZH+g0oonf1Tvy3bXCT8W4Y/q4Y4THnjDB8QonN6xT39xgYafDMb3lABx1wkFFTZKfbfp2TPcPn9umxY+1THc2v64EEKxQsz+fpP/FHkUTiieeuFk33N6bLbxPScWTdar57ZJ4pG/59YPzwZ7Z9Fhs8AnOw5RMLFoasVTLfy9/Jr6zuH7uHtciZhXkgkNVXsl22l6o+i08/h+HOd130pgdHBcl/vj+ZN9jhbe6OmTnhkC56Otl6A45ETz8YU+T9N03IomPnJn6wjFE4ooa7sJm+22QPlhRtx58fkO+9IvSGfTi3H9osAnOxI0SOADEF2IoGDiJ7v2K1V6BOaZ7RcIBh5R4KC4+eTjT/gdJxRPY+GExwrtTrXFrD7hQgeP6fHknac/3P35T3+yveOZ/ez5c/6NJ/04BC5q8CjNzz75GQsnfI8KRRlsch9qkSMPufaHD+pCyAcIaUIGCMHp48enBCju4+WaHXfK2dchpdIXPKt9UwH175ZGYzq7yU3hhOmhvmJMtp6LX7wVrbjfPdbz2PHXHZkv12GCHQshVfqAdHw0ozVCaKxUl5ipcheAib72go8cQYfQEL2Tiw4x5RFQRRKKJv0yqOgsUmwfxL6kO0m6E462Ht+jHPzayaLJCibwsY/hxP6hX9UbCifbxz6yD4AwV7H/4UMe2MCJA3NTJ4ska8OXF1ZeOPEDXOaEKq4X6L3ndks+0u1Hra1725dJg7e01cy+0WhLLyb1cIgnKObcFv1t4aRMGJxy2Tj7aE9sx2HM226Vr/kySJpxtOvshRLovd3/jtMjW9SPXtn9Gz6qh7/RFI/qffNlPK73pX/HiY/tReFkn+YMhZMVMvhUG39DRZ8gP8WdJ7tgw+LyC7pXLJrwKQ8KJ9xt6oUTiqsnT/BHb+20AsnvNrXCyYqox3YhhNOqMntUzzYu69Xbwgnzap4LoL09jmfA15fe/I7rXSYp+xwtvNmaur/fGmYh8I0I78igx2vy2daLy8OeigzA5/YmzjdyXHoYD/Z4x4UFiT8iAhr9yJRByTaS+Tcf+G92dIIPXAcuHPSpLr4/gB9ywN9EQcGDi5Dntk/oE1ZchOAnfVHYfPCBfUfJLkzwN5n8wxa/M+SyukP9kjb+ZIUSzt/99rd3v7XzP/7wH/b4zdf8e1DYa36CX9Wz7yP8kr989Ut+LwG29SMRKJrwt108qx45LlxQoPE4XDghD6FiwFFZnaHLHXqtdJr4PewccnZaqI/tacnj3JjOrvC2cDqeuBtI+nwdJtgxq6mi+Qi1jo9mcq+IVZHqEjPVqwons6c1J1MsmuyaBfdvuC9RhhFYceKFFPZA7EsqmPQ9JhRTXjB5aaZ9sBdYeqzvPbsbjgIJ+xa+4/RrOz/9+ae8u40Pa6DbCyfFp/1VRRPajJN3pHyPlwwyChyHsqs2ide8tOTLJrMfjXvbbzHd0lYz+0ajLb01aBGxeILqyNwW/W3hpEwYrMnaiGfQntiOl7FmtaGjWWjW2Qsl0LNtOxFw3G3i33HCJ9S44/TSbjm/+JaFE4qnFyyccMdJhRPwb+xOFIonu+NkceB+Dh6xwd9Wwa9l4ULGL4beZSHFL33bxqDvUaBQQtH0rV1w8TY57jjh+05+44vfX3pidp7gRyJ4t8m/4wQcj+o9sV/hemQ+HtmFEO44vS2cNAOGWSPiW3g4A76oHuLCsYcg+xwtW4dcrd7gxsE3ongjdS4uDmCBL/Umaxf9/qZVhRN+oAFv5HwztzXNTzlND2/gfifHCyfg0NWp+Nwe6E7JixJ7wyeOeD0Yh4bj7jEfbbFHXHDBgbtBeJxOFyL4Y7hP7W40fOKRl08/9Z/0RWGD4sl/Vc8/dME+wk9irW/u7yUvfvSzwb/593+/+/d/+7e73/3ud/5ojf2BXewv+BI3vtf0K/7y1a94cYOiCT9Agf2I34dqhZP6re8fxDN3SkN0HsByzv8O0dBYxA4aOtqQAYUH6wjwoV3YOqJ8WxmN7y2taj7R5tvC6ZapPWtL8/Ws4CyQc7LP546PCtyrtFaMleoSM9VrCyc3Ub698Ik9iWTnIQbMXz7ma/sS9iD9GARwfd+S+5yp6GfLsT+qwII8PqTBB0C4jtHdbP44hO0v+ODH95ufcP+EL5z9gL3cYwLH3SauLYtPvISmTDyMAL/X0ZJflmpfv7f9FtwtbTWzbzTa0rvZ7sUTVEfmtuhnCycuGkmfg8MngDX059SqF5fodKsH9SILkF68D1zz1skgenITz5AS8YCnZm1V0PQzCyVrs1ASxOLF5zWA9gkN/ogt7zjZxZZVRfYDEV+zeHphBRNOL5xQPAFHQeXfc2LRZIscn+j63SYUTu/xYggXLLio2Suc+Mcyo4iqwsnieWJ/e8Ue18NdJ/4wBIonfs/Jv+vU7zjpO06842Qb03NsTsyO99Yfy8PMQ7Liogyl3ttH9XwOvX1tGfAF9RAXjs2Jv3kagfPU1p/PVyxFw2wt8T3TXpwerwR4cT7erHC3xH8u3L7jZ+vWf93JiyY8iqK7UHiUhbK8wxI/FoE3czv5iahBHbCrE3sDPCIunLhYmWmg40ch9P0AFEy86LALD9LsYgU/BuPfsXpnKpx0x6kKG8WDGJQZ3FnSRQ2Kpn/9//7V7jr9ht+fwqfI0MF3p/BF7s/swgYXOPhU+Ac/xN95+iEvfrJwMrs4uBsgB9Hfo4UTxygjwxi6Pbcoyx0CP3D4MIegbKLZ8QN27ini/bunkYV6pN041tFFlzAOOjp+igbeSlY6x+CQ+GMqR6TMrFvG2jmi8JAyV8YwxF3jsxxAC1/rVXNoUDc+h1jjbBD/+lyAHnIFO9BlO9LilIohC6fgu3E4cT19eKTCCcUQ7j7hbhP2qyf2FQJ8kIN9AXeigevDGXz488c//pEn72bjQxk7sbfggxn8UAT2mx/br3li/qFo4t4YsYCmsxdQ3Dvt+gQH+cgAUiD5oIsPePXRkl9Zc3+wCZ+3Om5p61YxPbSdll7Oue5PPEHx5rboy8Lp//3LX0O+BooLJrS0yGQk4fescOp9wtLnsZep7OSERIpKrS4eXDIEMpUlOVoCHaf/cp5fguEt3umAOnHHyVa9icejenbHiYWT3W3iHacsnOyRm6+scPrKIO426Y6TXbzhrhMKJ95xskIJPznMu074pNce1cPmhB2Cm51tHHw8j3eb/DE9/6EIu+Nk31HCxd9jFU54VI93mexTY0D7hBp3nR7zO07+qN4z62XecTLbL7D5Ru98RltwLJKQNIySF08qohwqoYImOh17m4O/Ke6Ng3uEKc4PhGL/jhy76+Kk8n4cJ9XeMocM+JgOpJs2NLYcLc7X0bxvfZwsxvAx1TzLN1m92UbNg7XFQokFlP/wSi+cMOuoa8UCvo+IT1p7kZJ2JWf2M07EOJ+IzHyCzl/TwxeqrUhC4YSLDnxay+8M2B0hxIE70fgABXec8IktIO4I4TtO/e849QsNxfSt/SqWHrP5Nyuc/u1f//XuN7/5DX/qHN9DgJz/tPkPeGHz2a/wy1ef8tf28It7+NRYhRtttj7SX67JaW36AHEIeOGWOTADNi5ZNJl/P2YY5LSv9gJulq5sLWR3SdfolDGNt1M2AZXgNViERrATJsZmdazoK9pK9zztxv2cHI45nZivuXk+lin/m9RMfMTfSSYvFcFNF00eKv1Pr0gGOohRumxnCzyXBNSPQZAEm9iv/n/23oTBjtvI1iQpUqIkq722l57//79mXvfrtt2ybK2WSJGc80XgAIFM5N2qiqYtJpkXQCA2BJZCJJDIxgge3j6H48QYwYMXTtbjxtGJVwg0HsUDFYU4TrFa3h78fP755/FOJWMWThKO0x9+/3t9APf3sc04TgbVgxrk5rxGc6h2Aas3YNJWkPJzVZw4QCKBYZ/AD6wbf2wM5HQW6JWJO/PvPFvZSvqnEC3mrQaOojvPoe2xTRt+wnGaqs74PVx26H8qx2kxHXYv76W8MNJM1f8oi2wMJcOOvRfCtoKjd1JFeXcHSWlWmHJ4sWshnPYUmRWnJ5rgsOr0RitOj3Qc+ZsXnKqnic8POE16obI5Tq/0BJj3Fl5rq97TcJz08rme4HjFyY4T7y3kk535HSfewfChEH7HiaPJOZb8RzlP6TjlVr0PP8ZpsuOE85SOE1v1fDhEbNdTUXLViZKrlDFCYJh8qhxhtCkGj7biRNjzFZ0NCWC6VoNNVjO2Xl9uGRFKHafX2AO67BMj+yB2rMcBwXvwwgK3dt0FqyXIdRu1JWHbWpsmFW052+2s/rGN9qj+x5J3fHxRfbk7UOpj3XFSv/YFDZMEO004UBNPIZLmD3nXUwqiJel+C8+OEydX4TQxScFhsuPE6VR81wmdmIDwMIV3BXhii+PEUeQ4TxwO4ae/1XGyjvCHN/d//7dWnOQ4/VFb9vyEGJ3im056sPKH37PixLdWftcdp+d8/03yuXtZpX/KaqtOYaBpIM0hlNpp5Wfm1stPhfQxJjo2lgsu46ema3xg9JjY3e0yf4fXcaNc4zqKD4yrY10txt9jaupnda3gK9iKdg97gPLthdBcdFVZC6S3DJrr2cKrzUv8pOrCK6jmdJLESApjfrOhhzb6V+QPZMYeLtuT0AdDkBPtoPEyj/yUwfyOEw94uJERu2HiEKscF1h18nuUdpo4yRPHCicJ54n3JxlXGLt4IMNqNmMI45vt6jbpEL0d52+/i9xhau8+dQ9Y3IUG+puuNFmQWiYV5u5l+Tfx3hDdJ68N63c2Wcy76wfOc+hCbNOGn3WcsgJHNZqQ0A1vwITXJa1pBm6NGddhzbskfiFd0w3sx5qQ76givxfgEsGJ0xjFwNKokgu/VUrhXcFhNPLyXjlO1XnqjpMmV3mqntwQfaPpjU7VeyPn6dX32mqjOx0nnCelY7WJI8vTcXqq3hgrTh9yMIQmSDFJygGJ7zg9eZLfccpJnZwjDoTQbefJTlOciqc5Ht9o+oCtemz5w2n65Gd9y94zpR8LPjlOsj/OE0eR8y2ndJxwijAMThKh6qg5TrECxSpUOE45WA3bTsYU3biOBoh92zXNaBcRE+sBMc46POa5xk9oaROn0N7nnbRA/oE+iXKnTNdt1Fa011pvOP2FfcRrPs3ZbVZtveXDM44m18oxe+l5IuuP4xJXZowI0NohCcchtqvRFxpfiQ7+RYXgrXTKCIXFS/9w1MQ3HJt2EER3nL74ax4YIecJOiYq6Tj9UhOPjeOkdxaZuDBJcdmqnjw99lZAVpr++/+yVe+PsbrFU2KeIJs/T4R5gZvT9ZjwMMH5mO+/bR2nZoeUp/JHefO3Fz3MPsobNgw7Y2syw2gthGpDP6W3eeBvrpC3gV2crPxr/DyDLEvFsyIOa94N8Ukd2u5pHtTJ9roUtqXbp2uZanyPeVfI3q535Xh3+rVO1d4lfso8gVZwi2qnyDqaSOscBzh06BdhRwSeHOlyPb/hBVpRwzxi+5zGhe81V+GBC+MH71ryegBXHEbDjhjNNVh94sENDhMPfQj93Tg7TowlOE2sZPPwB6eJVW7G0NApxoNgvfxBxRxrmAGM8RvYe8dpabJ3GpgtsqlY2h8Q5zl0QbZpw5eO0/+JrXqVc42bdBNKQgjpki6g6SyM67BnXBi5kK7pFp1AccJxCRD5DUkZNXfgDSNXGPGxYuJhY4cxAGIO/xTJ77jPOk5MqjTRihWn9p4TjtMjrTblihOOkwadWHHCcfqmO05v9B4FTlMeRc7hEDhOfC8Gp6mdpsfETE/F0YjBjGND7TQNB4ptRprsyWnigIhwmuJUPb04Lqfpo08+ffRMYRwOEStOzxn5tOL0oVaZ2KonOkLNIu048deZL0xFvTQHKcY24eTL53W1CeuNGlr9kRZCDHyE115uGw4voV//gTtHiZXfX3e1wJm/gXdl3ybdyWZbY047LM1SBJpqlEklI0P8y4YtvvAUBOdJCec5DAT/oVa/HI5Ttv34wy4OtFOLcTtMfrCHf8qJJ63qzzhOHD3+rZwnvrMUK07Ncfpe7xVAi2PDljmOI2fFicmHV5y8VY/JS+gg4YReGcNx8vsJOEz/8z//Hafr4aRxEEW8syBaJjlbx4kJD4dQMCbxQKfyj/KHrHfEccKst1xRH2P8qmPZOXau3xnPrc9hy60iZoIrUqNtnSaa8ai37bWCbXGcnsuZ5VqwNPq9hLPMe2F5ZyZrnapta/y0ONXQ1NQ2rWVHvM2/xnHymEMYZVDlTfykCnBghIxN3IxN/lSCt++hWIwHGpPYEeNty15pqo4TYxBjSDhOcpp+K+eJ7zd9qjkJq+WMIVyTXZseWzgOEofR1DEIa09p4RgG/c1XMQ788hp96pq+Y+qj8D55Hcl41+DFvFMfQE/nObTu27Th9+c4IRwpXdKoegs7Do3r8BhznXMhXdONZs5ummzuhWMpwDmOvZidXINCECVlDgc9s0UGlf8AhMgcOoRDfqy9SDfHF+88aXDB2I9jm15u1dPpD7FVD+dpWnH67luB5DjpnYPXGpDexIqTPq4px+iZBph0mrRlrz095htObRSIgSVO/JLz1LfqadUpVpm0TQ81Xr/WZkIV3KtNzz58HqtNH+npDtv1nmmg8oqTTovIrXoq2ivRpPPEseQqtganHJTbqhOOk+ABa/nhPIXhGExy8Ks9YDUYrGCtMk4GbhsOTyK3zGkgvoQgcCjc++uuFsh+dFcux/S1blc1BqzD2+DhPl655qqSnKTA7hSDug88ODt5q7HHH+rqNCQsOZvEoeV1egAShczJcZLTxHtIseL0+V/CgcJp+rtexmZ8wWmy48RWFxynPPUujyNngrJ1nFip5pQ9Hrgw+eH9hD//+U+x2vRnnarnQyg4PCJGYJWN9xD+4z/+n3gyHC9wa4sN2/hyFVwncbbyE2KDnPi4Z25LTVnTrlFnzYbDFsIXnxg3gnRLX9M1LpL7vvIPRuN6mawo01IPtyWHQrqM5ZLbFnjpOFrxatz8VjDnbcNR1lGmqLot4j2mh8x7ZHpHVmudauXW+GlhK/sP665pa37+jR545KFfhAM8YLUvUnmlAqEK3o0+xibFeaiC88QNjLGEKx0nHUKlMYZDcmjf/Vtx+macV5yq48RpoNwcT/5pvJ+5d5xs3ygHuhSdP2A8Y8xpumO/fkunbTwUvfWnGHrUKPKS4arubhV1n7xu1eFt0xXz7sZG5zm0btu04Q/oOCFiVL8FrsOKV+Nr7D30QppmBf7k3tVxQofZqNJB/8fA0gaFSdlB4c4Ak8QkL2+cJt9yTUpccDp128ZTHafHOhhCx+fFqpMdJ7bovfzum9yqVx0nTWyeajB4pjAdJ225iae/OenxQMi2IZbJeeoTjhMOVBxBzoEQTMLQEgdHL69rtempHKOnejeBFacPm+P0lJUnb9XTUeWx4qSShuOkiQOlk+snpxNHKEvt8I3y8zhQQaRvOk759Aec0fmz/kd6GH0FG7mnY7STkHMared6wO2AiyLU+fvrrhZof+vuyuaQflu321rLnqvWkk2RZrO74NG343WF1achyv8R788ExAGazM/2TjwmDcrbieg8m2ilq54hXxMRtuvh0Pjwhr9qpYkVJ5waP+mFA04TDgwrTjhOhBwMwWoQq1FMUHjQEjqhl247Nn5yzOTnc01q/iTn6X/1/oHfSfCqFjr9/nc4TnrHqW3Vw3mKrXqSgfNkh9G8I936ZitpBPDKS2FUSE6CgPtOQ2O5ZvCdFbdW3aabiPsIJscJhudkUY4jwc5weAG7I1YH8GiHB3kGV5waX+UbdhRO9dmQ6CoPeQ2ZDynlOt7HOlVj1Pia/6o+jFlajUE9rHkZH7IYYWiTwAdegbUGSxnybzh/6xN50CYDcLh5zzPmHJp3MI5wc+V4o63BvCOqiy3HK8eJVWy2+zKODMfpF/HQ5xPtfoGPbVpDb2O2HsgIx4lXFzQoY79+Ky/iLQT3lH3JP3sNA5aRAJmD8s4yBqu761t4/TNEi3l3Q63zHLo827ThD+w4WYzD0gIMivAITuapPDO5BEe4zQpqivfiOCF9GFY6hBpM/kfOyB8wYr0zBAI/404OGmgEmw6H0EAReHKcwoFqK05P4lS9veMUW/XkOL3gHafiOOEw5c3hEG21KZ4ec3oXK0g5WOEsxdMfbe9jix5pTtBjlyDb83CYNLSoMJpAySnqjhNOE86T7qcaqNJxeh7vOcXhEBo5cZxwmF5rQNKbWuKRK07hLFFyyeDmO1IMZLlVL1+Qx9A5gHhQyfpfDSormJTulwfODlCE9sEVYVRrpgN44mfF6wR6y6JO3193tUB0jbsyOUG/qtuoOQSrM0fcnTraTGOmOLS+6VtMDNia5wuybN9t77z6oFmBE21YgCk0MSE6NAN0nWp+i6MDk5DuOH39zaOvv/laW+dmxwmnigunaes4JYyP36bjxOqSHZt8sIHzRP/NJ8dMgHDKmODgQH3xhRw0pdnGF9uANabwHgLHBttxmrbqSU53mGQDJj2WRw+NK4ofJQ+5Mkb8dxhllj7oFDRhXFdS45GcMr/H7yuyldH47hyn8/KiCEu0LH8reGIciF2SN2DyN6/GpjbGTjy3UYPreFvjq3zDjsKsL3KHPktVjhjcAB8ybyB+IJLzOq0qesBW9TCpujBqlTmsX2siOZAHboSFKe3IeYCJR79lpYi0aQgjngRwIm2HyXFooA+nR3HPUVhlYmypod9x6o6TTgT9RWzV08E2vDbQdIBnyG46VJnAuXCceKjsMQdbxq0829WWdjoIb/lJkUFpnoxXi+pZcr+z/CXXy4H/aPnnNC3m7X86TOM8h1u40w4vcJxGFZroMJTULrhHKvaK1wpWaYifwjmVt+WjtPRSU5Tjwe/mis6Siu/yNqhOjmKKIoiSMoeSxBo4pHECTN3CLhdMO0w1nqtOkQdu3DhPuuU0cXMc+WMdDqHlpUevdJIeq06828T9Qs4Th0NwMARb9ThN70OcJ0I9neHdpqdKx5K0Jj3pIOlDt5r0vHjJkrm+L8NqU3v6w9Y8HBycpsePeTlcL2qWFSdWm1h1wmF6Gjen6tlxau84iR6n6Y2crte6R6nFmy2AKqY/vkvI0yqW6HPQYzDJO2qzGXTVcVewan0PkBVGnNbBv/y/rbAtdqaPeK2xDaWe31/3YYHoRvfB6IDHqn47TG2QdxyjpZTm4nEAPO74g6/3Ejsdsmhm6gv53Sbi6XxEFjx166fxHukYB0CCt0MbIcKiiDDAicmBnnqwVe4bHCcd+evDIVgNwmli1QmZ1XFimx4rTsBYiYrTNxkzNKkYDlM+lbXjZMcIvp9//r+P/sJ2QBwnpTm9z0+VeXn79zpZ77ea4PCkeOs4IYOxyU5TTmIkizLr17Z1yDjZDJJR7I7DiE2wZVCmTYPF9FNtNmXcIXHEU/CouOtY70gCUKGKH4k8I2pqlx2XNtcTPbIaWwfscprOcBOZdcnyrfTYkN0pOcu8E6t7Iz6v06JySgMYdXKg0sKoVeamZU1MGFXoVsbpYYXFWKQmGX/DeUha+qy4hSzhJK3DFNP7rZKUAx48+MkdMD/2FaeTjpPGF95x+kS7X9iut3KcQk7MbyhPjhXoEztx9CFwO05oZXva6j1c2BH8i69mPPODzrIu4XEN7iX8rsdZ9/nr+TwMRTNvMq9GFsR5Dq3BNm340nH6z7991fAL9zONovWNxlfkS4mFX2Bu01ZrG1a8Gt/iHaVFU/VR/OEdp4Uu7ZjiUYKmlIIcLuQxyJAaVuIG+lg0TjvMkQrvQms21XFq7znlVr3cosdWvRfffh0rTq/kAOE4fahVJhwmtul9pJswnqrw9FjKscrExzhxmHCcftBqFQdEpCODzmOl6fETDSrhOOl4Yp2cx6rTh2173s5xiq16z9o3nKS+HCa9HCV2T3NDIs6U2CMntwKyv5kn9HwMlMmTHKeYqGFBOul8Y/FrB4/6BwL6aCdiL87xTz8ZRubpnx2v0+g/gdzWvu9Y0nN1mmPPtbJoQ5ddK/6ua4dqeDS+wjB79DwNoHvrj3L7V5BpZL3tRp9vbXv7Bzts0fI6vRRED9/Aiac21ol8+pY+/BiO0w/tA7W84/RFHg6hgxuq47TdqveLX/yivfeUH8kOh6Y5TjhL6filA2hdmJBwGMRf/pKnX7G6hTxOzIqHMXo48+86eOK32qb3m3Jk8POPNY6wVU83T5CfapxgWyD2QG4YrBmgb7GRLOTGRdij6ThlRrNH1FUYvXF5yMB14FCymm7XSD0kiYyD3CKyyup2qkDUsv0mOOPsBIjE3C8Hzgyf6U7lzZhO1XLVuPOPQ0ZwLvrbP9OVVTB0XtfJqkRZ3qyrWmE1PsYZOIzqTnlD6uBfYVss0nEPRkrb7gqDQDXQxiwespDtMmZ2jk3UU5RV+bQTbtJxS05wFSzHDeYmL/tK9v9+nqfqsfrE+MBq02e6eSjD/cs4VQ/HiVP1GD+4Gm9k9PlNzsFscw6iYOxh3DEMytClh6Ptk3eXy3zh4fZ7Kb/r+9alnK/De1f02Godbc3AamjBnOfQaNu04UvH6b/kOLnxB6Ia67VXbWRdq97c4HYNT+M6vFabRteskP6LmuXWKtGbE3ippMFCFEGUlAM+dA0zNsY4QVxhp4jSidVpBe8OkuJPquMUIoQMHrpqEvQIx+mVPin7ihUnbbHRe06vvtcKEytOOE3hOLHiJDiHQ2iiYmfpI30T4bluQt554hhyGv0LPXXGWeL9BEImU/GRWgYYOTePcXh0P5HDg+P0+HF+v+kDJjlynJ4913tNzz/pq01PFec4clad+neccJI0gD3W0aKP9fFc1tTSaVJR+D4U71K1rYE8XfpAg9inFr0AAEAASURBVFc+cc7JE3pu7xhqmn3DuBf81HbqePCVPvzL/5cxNf0FYn9CKNnOby0wdXHJdZ3tL+N5JNeyCNlnz+W2SFy9hCDCmh9ARIf4YRfjRz5U4kmx+WNtx8n8awg+afBXd+SnsBwvBPAqEH2a1aavteqEI8MqECtPwLm57DjhMPGOE6GdGV7UtuOUTtNYbcoVKERmGeH7hRwny+CDu2zVY8UJfX6tSc1vfqMP7PbDJz7tK1voEAfXSB6hHSeXGz1xznAGWc3LdyJbnTQTV9tkh26VEPUQP7B5wKvJeyAJaebRni4V4/qp+CsYNlt1Q+ogr5E/YJXriJ/LH5g1VstW4xVnH7esdZn2+O8SpNbpNfq7zG2QaUVyPeV4sS1n5b+yboXNYxVjXI5zgdMRPbtpct1OCB1vSrichF7poQyPeYhbnBXriIgf46Gu5iYapzhNL07WK45T3aoXx5Fzql5znDiSPB+8pAJe0bLjRGhZYIALP3QC3vNS8Rhdh82T562/o5aix13N5r70uFrwguBd0sXq9eYJoBpbSec5XNIYqPC941SMoV6hVJpuY9eKNcWHoRkUyErKAR/oMWY0xh5aUmTKXTpO0qevOoUI4RbHKbbpvZbj9OOLPBwiHCe+4fRN3D98y4qT4nKcOFXvtQYdnKXncnLCcdL3WIjHR281UPHi5UvhvcRxaitPL1/qJc02aOR4gePEkyMGlLZdj5UjOUA4Qk8/ktP00cePcJj8jtMTHRrxge7HWnWSq/folYzBitNjDUrQdadJPiHvU6XzxEuivFf1owYwOU7C/YAVLuk4tjYRzxvbh42Hyc/G+kAoTMeDH7z0L/+3SjvDzfRn0H5i2auecLkJLh2Ar7P9ZfV5pKVlEdbSoWumE575CXE5on1G+6W9IaFSAPMfbj00oT+2tr2KQx0ye9/MP+whV7BovyBxpbBwVJgsDMdpbNXbOk5sy+MgCBwmtulVx4nJRD7IyAkO+tmBIk65PCn529/yCHL4f6lvOPEdJ7/jhPP0q1/KcWqHT/CdFbbreUtgOE7NaUImvOvEh6LZAavOE2YN2ynftge31UYPgT38RXu7W5s7p6Pb5Dm8mr+iWcHQfTWuDruO/AGrkkb8XP7ArLGstYTUeMXZxy1rXaY9/rsEmcaG1ncv0c9lntvbaHsjf3Cr9llZt8LmES/7E7DAKYiMPqmDwhDfwkVDsnzCiAsnxjuNk74ij4Rw4hWC9kCXlSbenST0QRHHjhMfwP00xg/bIR+05Jjrhy7WB3Ee48CPBzQaO2thgccN8h2vUdrocVdzc5muJnwAgndJFxevNM+5ewjBeQ6XNAYqfO84FWPk5CJNVxtxRdnGh6HbwND+QA74oIgxozHuUywQY2Bk0FisOKlKzztO+Y4Tq01viuPEihOOE3c6Tvme08d6ivsxDtNznZrFyVntY5Z+0ftHbc/j5kvebJOLbXoMalIPB4dW9yYOhsB5ytUnbabTqlG+s/REq0sfaHUpHCgOh9DyeDpRHz96Inmv5HRxIMQbrTi9weGSMxTfhBJ/wh9fpsMUB1JIB9L+2KZf1MyJ2phYZkcdf8CH1U/H6iDpeA6E4kVdxv9WaadZySarWj9D9C+ffTebXDoAX2f7y+rzqGosi3CULtteQAQkJM8YLgcPJiJOu2pqJB9hwq85ToSmcVt3CNw3Oiad6ZtE8Yr260K0tuk//tVxYiUovuNUVpzgj9PE/fOf/zwcJ0J/S6U6MalXTnZyDGErjPpxW1H68ks5S3/9WzhNX3755aOvvvpycpw4KpjVJpwznCacJ449x2mqjpMnMcizbSg7cnzjPLGKRdlDL5Wj2g0zRL200OZ52JCKvlubO6cfdrj2WtGsYOjutlpluA5q/oBVzBE/lz8wa6yWrcYrzj5uWesy7fHfJUhWZ5b1Gv1d5rm9jbY38kdpK/+VdSusjS6dmDxggVMQu+PkhrMNO4ccv0iiBzc6xvtQWnUKeGb2/Mlx2jhNrD7NjtPvdfjMb2N8+VSrTThO5HMhJ2RqzhE2aPIjs/3kWJMPghhX/DDIFq0fwq10t8TNE9pp7L6Q2apuLyS9d7R3SRcXrjTPuXsIwXkOlzQGKnzvOBVjqPcolaarjbiirOKdIoiSclsB/N0s2YonRookTue9m+OE0zQcp9yq9304Tl/rM09s1fsh3nH6WA5T3nlqFg4Ug0mu6Gg1SKtSHCTB+xCpWTpMdpzQOY8iV4nkQHEk+UscHu0c5IO2T3RQxOOn4vlcH7GMbzl9qvee0oH6QE+x2aL3Sg7XG02AOBwCB0rznXCaeKcJRyk+uKuVLla7eOfqmVaymLT1CZtoPYGKgZbB1oPzqpIOYDFgtjzHg18MXVlplwxiUY+tTg9E/UTB2c5vLfyldeq6u0xO9MTLUBdYlhV9Iyu+t72YQiiDMPJbm3A5cJxotzEeFDVim5l4MQbkH2evPAlV7RqnwY5DTCpaWycPfbb3UFutt+kYOqmfwj8cJ52oxwERdpwIgXPD144TK03cK8epOiXEcZzQk3LgzNB3v/rqq0dfaZUpnSbFleZwCjtW8GbV6ZcKcZy47Tix8kSf93fm0CvsNwqY72SyOq77VaxQp+MUumhsse3yxfLcXhmtMuqpMHqwKBVdKvsB5LhNXsN6RbOCZVPb609d5MXY22KOHCgyaA4QluCorZZT40vkBhw6ZfO/lO4Uz7eXV3Ve1clak1Hmub25nnIs2dKa/5GFDM8RbaYmL8YVwEaMKDJ1uz1sQ/B1WbbjpGkj9FUfOx6IDZd8+rlfI/gzjlO7Y9ue4k81T+CAGd5ziq16en+yb9XT2MLD1+AvOSFfemfIOBqaWGTq0vC8zTl0bBgx/qGv7rtelYM4Xs3uPnS4WugBwbuki1UszXPuHkJwnsMljYEKTzhOYLXKW9bhElhYz51CM+Qpr/PeQNdJ0zpcY52GirZZxe82cUDEuFpmBPm8ZOSdj/HyY14Ou7gOjpyWvXKcWNJhECJvuvt7TlhNCuLBELbDIXjP6Q1b9XQ4xBudqvfj9zqxim846f6hrTq95J0CTYjYrhdOE6tObSsOL2I/ay9hs2UvnCYNTkx+olxaIcIscVQ4YiU+DorQt5zC4VH4UvcL3k1S3uOnOE7a/ifHiY/gcljEh3rS8xFPe7QChePkm+834TjFgRCa1OE4vcRxag7Tix/yVL/4GKYcsni3Qvi8j1UHrRhsex2kyc/9xmB5gMTAFYOX6uvcILYdbA9Y/kTBtJx6bdM1bxvPznLJIHyqLrdcM9064jrzJLTKWpUmJhJqFD3PfZ4/sM1xip2uSFEe7Sdp0mmKAxzUyfrTTdF58h9tPiYVmlgEvUYEZG3uKID4jlKGkMDDSaNffc0HcPWe0xccDhEfwOU7TnmqHvSxVU9jBI7NL+XYEPKQ5SNtueUkTuplODGUFj0Thj5MNHCOvtIqE9vz2KaX71V9FRMf6/yLn8NfzplCb9XDaWM1/CPJx2nyO1VZfpWq9HW2FjOR8umfOE/0WWzGJMm2w/YMnX3bcRhp9TOstsq9DXYLz8tpsGVeDrdawmvOGzQzbmfVwMXUBVEW7hnEM2vACmqJnssvqCU6670tR0Fs0SO7bfnsKd8VSNbB0PeormoPdx1UWJZntketA/MdkvYWyLwynhWUGLeUrm0m8ZHZG8XUX7eyug5i4njoqP5aL9uEMcXvYIfT5O16/8v7TjocQuNFOE5ynjh0hm85VceJ8YQxgTmEu0TwLvKrHmhB2o5T1en9ilO1xojXNjag/9jY1O7mpuVm0ENrOtEYqPCM49QwN0J6h3DHKAy30WzsDTppsWO6JS1p4zosWddEm3z+qGKhHbei3y5vI8f5hSQxknXHrvm1MYUDJCx32FCoOUSZN5wnDoggH5mRBx6EOtb4se50nFhNyhWncJx++C4cpxffpfP0Qo4TztMrTZhyq54cJzlMH2sliJuXvtN5ao6TVp2iFclJ0SjTJEu6xL7m47esDOEohZPzSu9DvXr0gyYsOFDapxeHQPCeEw4TjtNzPen5SEvlzySL7XncOE84Tq814vvEPhynF22licldPAHXZO6jeCdLxyDLefJEaOU4VRtL6RjwCLk8GGbq9C9tJNqJjB7hAfo1PA9Y/IuDabu+juLO34bZ4oFu63WLefd6QNZlV5c1ZiqdkDxPMUYs9acMdpx4H4gLFoEHnfp1bIuVw8G3nrw1BLpYDeaPvZwm30GvH6wacnG2WlwAstV2x8UwEnjKw9HgA7g4T5x697m+rUTIu0fc4OEgsVWOLXT+AK5XoXBkojzoL1w7efn+IVJzogGclSYOhLDj9I1Wupj8JP3j3Aoop4kVLRwnbpy27jht3nF6Iues9smYRMUDFm0xZsWpOU52mjiNjzGD9yJDV+mr/3mH9bIe0lKTxYbx3noMPaoupxWgXHkROm6aLZ/MT5ItrmnOhaqBaP+pZ0RFkrBj2nP5a8pbdVxze/eg+/JdXzfbOq6lPJW3by2VkjjjU4b0mVlXp3roiNuuGsZ4qBxs+k9HFSTHkBwDA6E3qI7eI4wd+ZmUF3mqHo6Tbpyoz+U80dd/9ll+1iA+gPvvOlVPY9hnmoOwms2nFLzDxg/RYY4OFC/DoR3WA+axuSuiiMew0xauFMfxyqOOb8cUc85tfWvmcV+pd0kXl2nUqCDV2Eo6z+GSxkCFD+44Iav3tUmrjeZFqX3UuA73GBdBmvzgotWmJbeKc4KpaVdFqrAar40pnSMJEAKdskUUcr5cOk3Axql6KNPgW8fpDStOOE68w6QtMFpxehWO03dacfo2T9bTZAjn6Uc5IUxIcJ4+4R0GOTKffCrHSelwnPQkOb/3pPempBaHN8QBDkw6sJhg/eCGFxrAOLL8B54A6cZx+lEI2lbHjeOEs4Tz9Pxnn4XzhBP1Rjw1E8xVJ0qrQTLecWJwihUnbfGBn1bI+KbMD7o/0ntTzzWRw3HyhOgaxynbIHY+f7llRKgiO72izLpb5byHpQWqzR13eM5G9DL3NMX8x3RBdvd6GHIW7CdQlyV9rJNhhGOikbHejoQfjkU4P7U8/MFOOj/V/FEHojiODI7hnx4YiIeEd+sEffBQF6Wxc7crxpqWjEB56Th9G84Tp+rhOLFVj2PC//5dfpw2TtBTf+Np7a9//Rvdv4otdDg26ThlGXCOvEomleJCB+CUgdUmnLIvdUjEN998++hbbR8mj8kLd7xDJceJrTVs0QvnTI7T9I4TzpPGpujzcpwkuZXuUYwTuTJdHCcpwjjhsYJPGeC0stok0VlHmKlxsd6ZHLyrnC7wrUSsg8NTQmk/5Ls0Dk1TeYy8aCdGuTp024c38WTg/nDE7lz+Ed0o2zHGP3fOqBeX47L6qXVryhqeyz9t2dSq/SrIEW3wJydzFToS2b1BZL4bSCPtqCIKHoS6GRe4er7Y9FGuseSwKA6uYgxjpfxzndgZjtOfc9seK0ne8su34f5dNyvmvN+E8/SR3ulm/OLOYUSMJTDlpx7d9q1Q1i1O3WtlQJ1ozypbU63l3BZUHrdwvL1v3abvKap3SRfr2dsUgGpsJZ3ncEljoMKfruM0umOaY2OxbtcNvNhuHxVRRa9xkGtjqo5Tjjhg+07niTSvJVZHKidBwgvnqa04heOko8btOMlp+vF7OUo6kpwVp1h10kdxX3wnp0rOyFhx+jgnKpqsxEvYGlCYyLzWig+n7yH5iQYX3lnSrCM10bj2o/K5X/zwUk6N3on4u1aGtOL0witO2qaH4/RMW/VwnNiih+P0sZbOib/RKXoSJMeJAyU0meFmm54GqXzHKd+NCMfp798/+rvK8lyHTeA4fXSD49QHQZVgddV6Gfkeuhxmzjleg/6nGtu2+moH5zmseas4vbD3xB1Crbe718uxnK3gqr11QL51YCoQ8c5SEf7zB1aT91yVaWnBg7bRhLOk1SaeqnKT5rLj1EP1neC3+aOdsqGQlqFo07YojbyXeuDxjRyYb+XI9K16+jjtt99+p1unckruMz3ceKp+/Ktf/TKPC9fJdz/7Wa4IMfmwfDtO1hXBwKx/fsfpL7HqhGPGzTyKMYeVpdiqp22AOE6x0oTTpBVmHuZEqCfEz9Tvw3GKLbqyoxjEnEY/P7BNL1ac8kPdseKkfDtOXqGDRsNM2psQM7XfiE5tTQq2C7q3f1mmwyMNWikiyPhcJugqD+OkHY64nodnHSRv4klxzlbn8s/L/VfDGPWxr7d1WaPd77JqHe8yW3+psgbOGjq3D+MwvtSLVNwdvNVD/bSAOlpj4jRjEmNGjIWtgOQFqRqX2xcRxpk4klzOE4faxK0Vpz/LceJDuPT3T/UwmMMgfvPvv9F34uQ4tfcn03Fiu/Gz2GWDGrRJRHrXi3UgjAyFGR3vnYb66NXoI33Hn2Im8a2pyxi/e32r1NtlRXhQLLe1ELIxr/McWpFt2vD3jpMtQVis1O1aYBV1FT81QIBfG3Y4QADhT6/MSA8zH8cpV5mMH2E4TTw2lcshp4kte29e6ajx2Kr3w6OX3XHCWWpb9fQUOVec6lY9OU4aYGKrniYpHxbH6ZUGJfSNE/KUp6mbbknXzINJF/eL71/o6fT3j77XnY4T7zipLO0dp2da0cJxeq4bp8mOk1ekwnESOlNDPwlmOZyXP3HMWGny1qHnWr36GMeJj+xqYGRSxACJjttb7OIKs8p+MQAauAlrnYysMWyNGNVEPb2/ji1wzj7Od3jMKXPohb0nLpFdf3evm9NyqvCqfZWPDjGxEEKEEAXb9gck2qpAcp4Ube0WpGyj0MSkICYGPimuPcBQWw8HQA8cvPKEbPeBEKV0ym8a1vZqpZtu9LFvecdJTpK36rHixIoQW/jIZ5WGmxUnntr+5jc4Tp/p/QGe2uq7bOEE6oRM6csdh8loTMIOpOHBHYdPaBsNx5HTn7/XgxDK4G15TGo4WQ/Hyd+JincacZ76nU+I2abHoQ/0y7C3ytMdJ8nCaWIbMbax40TcNyZhrEmrR7D5qe1gxKF/u5flOVxJz3KQM9r/gA2K1v4mvEozMC+PpU2zgQ/+5+x0Lv9y+f9KmLXOavy4jLVrt0HmELnafLSTgb6SWPFqfh/XGjl56DJw9u3VW/UCx4q3/mQYYXWcsm9r7tP6LuOo42xj5qEM70EztnCz4vSnP/0pHCfwWLnm/o0+rs3N+5mxVU8PbnlF4flzfY5F8x1sBz5qxcNbvWqAbOtCRtgiQgodGgcNKS7o7+OqXKTV1SzvS4+rBZ8geJd0ypprym7M6zyHLtI2bfiNjhPkSN5IN9dNSKPsV41fSD/LuUxml1cjTXZyWDTNoluXUmCV1Sp+znFSD5tKzDQn+mEYCEHjJm/cWCDzkiadJnX1R481iGifXnOc2Konx0lb9V5qcuLDIThZ76WckJdaHeJwiNyqp/eb9GQ333PSu0is5HzIpExHg2u16ZUGJU6e4dtLT8NxkmTUlWOEw/RST3jjRfLvfgjnKQ+GYNVIZRQvVqme6SAInKaPtDz+yWf/1hynn2nrX1tx0irWj2LKa1F2nPykmoGRp8hjq54GOzlN/R0nJpLYc3G7bjz4OzS8hkcdu9YU8VM8Kr+fdpw2eupyvsNTuOS5Fzrc47v+7qd+juVY8lZzKIAh3zrE5AKg2fUwsePvrKIRBlJyhS6ckLLihOPB5VOg/K6TnadwpuxUKUxtoBDPYItepHUBagm2ubCyxI1Dw1PbdJz03pNO2qPvRd+Sfkw6mHzgQOE0sQ2GlSG/O5RlH4dZ8K4W/P3QI58Iy3H64q+a7ORBDjhEOEps0/uV3j9gK82/6eGKt+8R4jyxnQZcb6+x44RxGYuY7GAjtu7wsOV1OHDpOOH04TxxjXaSZrFJInP34woLyp5rHh3woBHr4HArrJag1HFW+ha5lH/QuS3skC8ApC3QLfWzbUabXjMx3jr3pwoddZIW2KbXdsmufNQ+TMPfyBYX2xibnKVwJSn5jpwRW9ODnzgWVATQTxvY7Q3cADXFglZMmAPkWDJC2ku/eeCkO9+vluOkOQIPfb5g/NKWvT/+6Y/hPCGd7b44Tryb6Y93f6a5CCvmH3/MSjerTuk4xVgiJfqKkx7ehi4aWxjLGEBT92HL2o5rvJb82ni1niRdS977+NWED0xwX/a5q5rZRhuXjXmd59CytmnD7+g4mc02PNCqoYUyS402dFu2y8Z0jqYwKTJXDbO+KNi5FprCaY425Ipa4x05Bgokl9wYdUiPm/z5jq4dOEEbnVkdujlOHBDxWitObNV71RynF2zN06l633/7tW59x0lO0ytNZl5rsPFWPba+ccIeg0icNKOXqNkfjNPCzWTtmVZ62HLHxUQlDm8Qrxe6v/87TtMPj77TdjreUcJpei1n6Ik+cvuEJ8U4Trzb9Olnjz7h/YXYqvez2MbHqhOHQrBC9WMMVAxczYHSU2P2MTMhYrsekzgcpg/Fl2PJP5Be3NVxQr9VB/VgTf7qWtEEnqpj1UZWPN7DbIHSrg2aQuc7nDIXid4LF3mAzuUfkB2A6YrXX+jQ/tATa0wESVZFxYTwq7vBGRIaZtCyauODIWj/dpyY/dBW7TjhWOA8ObQjpSVos0758UcfvZpukU5nw44T7yDh3ODYcOrdV7pZ7c05w5t0bvp3lvIDtTxwwalBPitPvphoMAFhVYljx+H3+ef50jYfwo2VKTk3TGxwxOL9KUIcJ40RlBFnkPLEEeSSkdv1WH3yB3BxEPPzBRxU4/GKMCZeGkjCVjFG5Ep52BkbYO34L52H2la/hDVzxOFz+jqLcJp8yr2Elwqjy+1uIs8c/dJ2Mqc1z8ypicw+/F2Pk9bP/B3CxnkzyzUfYVvBGT1Sx2VbIBeQed5KX1g9cDTrECErMxzrX21c4wfqRruPn0AYUqNLbIhG7oi1/lMwyYuxJWArHQTT/8SzAyJkFTSwS4HXjhMr7bQr3QqJsyuFb0zS3/+q78P9VePK5zhOf5TjpBtZPNjxGPPrX/1aD3/y4Bnec4qHxR+PFafY1yMixhKPX/EuUxsrifsKPZrObl8OjXNrWK3XrHMVq/vS4yqhd0R+mzrTLvpVjS2g8xwab5s2/IEcJ9gXzRbS12N2obGGU3iUfwSfiId1Qrs9zdJxgsVC/865sKloNZ64QgzcJOjOU4w6YI975TQlfjpUOaOx46RJQt+qp4/WarveC604heOkbXp/x3HSKVZ8w4l3l95o0vJcTg2rN3wEN06r037f3PqWAxQvpr/UqtNjTV4+5GS85jjlUrbeZZLDxLtNOE44TThPrBr5aPH4+C2rQ3GanrboxftNOE56h0FPfTTj0qrUM23Rk+PEYRC6NdeKAThCDVSx8iR9c/L4Qu9baAVLTtNTrYjFU3YNXjhOXO58DgPYfo7/6CTCiqbW9y0DWJX/04rThk9dznd4Ctd5WcdO7cNz+XuKFWQ9Hq0w9zAm49mNs1y9zaGa7oAKgZA/zBGDYKM6cI4KD+eJrW7qg7GK0pSjreJQ8J4TDstTHZYQfaLF+ZRAuAnmKxHoAvkcpuMU7xtpxSkOb9BTW1ae4ptLX36l1SI9DInJyas42pdVJ1aHWG367DMmHxoXeDiiO7bO8cBEk5rc4vI6VrK8jeYv2kbzF73Azel6Vo3jgtn+x83EBgeKFafU801zCjkQ42k8GWacwlFDBvIoEw9x8vZWQY2JUV6NkzE+5MMV15hIlB//E9TGD+fvQ2tLTsbPkvQS7rldD6nyV9RRosjobW5Ccz7jemZgN19rGufWcNBXqG1CaP7zeLrXf85PbitYlXO5npVKWjWlbqWfuT1kalSK7VilZZ0NnMyrtq3xSrmJi4VtUbnV+EyROTU/R7GBRR76Jc5KD8FaoeJbdfoDzz/qJusn207wCV6MV7qFB763IoMbDlQbY/yw5K966PO3v30ZD37++D//E84T9HzGAOcpHs7ooUyMXXKaePdpu1WP9htlaA9uw0bo0PSIVSfpZp1T79G+nB5WuS1WrSerXM3kvvS4WvAdCN6mztlGm7Ib8zrPoYu0TRt+B8cJFhvp5hphyVtID9AOXmgmXjWxwlnBKk2LF3mrhhlcGs6FHCchhX0bSGq2OAZTc07s/Dil4vTcuNI5mp2n5jAx4MDZvTxWnOw48dHaecWJE/X4jhMrTrHapCfYb+QUfaiJ1oeaZPFEl/izZ5pwMZBp1gV/JmzcGqk0adFJdhqANGxFmRhEXnzPSXq5Ve87vd/EHZrpY7aPeaGc7X06zAHH6WO26P1MTtO//TwdJzlR8YFcHCfJfKkjzV9oksg3oljTDys0UzAJY1meieQT8c67vdckW2073TaNOWMQDLuuf1Y0qUTir9rJmtN7qKx9xgjOd3gGvWe7z3RAiZzKK2hnor37ncFbZTMRiC5ZmACLi2YteGaxtUztOZyn8RTTeITxxDOcJz1QaE9V0xlJjrHSunWc5ECFI6W+rC7VL08AeODR9Ws68kCCbbDcfGcpjgzXJOQrOU3EWY2KhyN6QIKTlA5Tvt/Edr1PPsFxYrsL7zqNp8J2nHhPKo4HltOEQ8ZR5Jym5+13v5AT9rvf/z4+UMlRwb9s33BKW2k0UUG8isbxwUmX346ijN5Wg+MEjW3k4tNvwZv6bzdOw+rpbrJFxBwzK0lm2Ex0Km/GvCx1xG/uQ1HXO4bGSVuQ3ao/MNc0OyYCDPo517qN/Nk+zh9Uq/F2BRsU2X9q+tK4+V5ezks5Pxzevkl67Dglc2/nJbaag23hlgFeje/pctwxfItLbowtgbDSQ7BWqK3jlCSjh3rFiTDGSY2V1GFgdDasOOXhELzjxEOfXDH/Qu83/Sm36knJj2InzfM4hjy2AWs1+9P23hM7bHjHiYc+nb+k5EVbHncpXIODhTINndS+0kbmFbHCMst8BS2o96XHlWLvhP42dZ7abjW2SuA8hy7UNm34HR0ns3FYtSnxhfQJ1BOFxizPhtBcSNflrBvZ4YrTKR0KzxLtFTFI5RSEmtY1sXGcmrsg1ArToKF0veEVuJpY5V9A4WhwecJEKw6HyBUn3m/KrXp5HDkO1GtWnOQMsVXvqVT4gCe3up9GiDOCbAZBJmx6D0E3jZqJEadbPeE43xggHscR5DHx0orTtzhO3/5dtOk0PdaJec/kaMWJep9wKEQ6Tp/+mz5uKeeJrXscOPFYgxYl5N0ovgFFSbMemYTxpDi8uICGXXCqmGumERXBBNXi6zrd4gRh+Vl23MI2Bu2C/z56ygLFcEs05ztcIl0AdB8CtcYvID1AyaZ0m15Q0c42zZHeFOplnlLxFJWteEz203EKHErRikEafkwyONmOVZ/4fggOF309nJS2lS1Om8vtck/1IALnaViDyYd4oZdDFIS5fuD9QttgOVjhG73T9PXX2lanrXVf6uktE5Gvv/o6Do7g1D1WenJ7yyfaTvdZ3Lw/wNgwHKd8oOEDIli54mVtbnhzEAXbbuMTCHLEeO/gP/7jP+L+ucYFtumR5wkTWrKyRHnjPSc5hzhSvsDDaaKM9aI/c3QE17AF9lVKd/RnG9thZXAmvhwvZklnODxM9nqcs22YCKbc2kbXNCv9Bv2cawuPfMtJ6zt/UK3st4INiv04X/NOxc338nKe4vZ28ob9hrxaZwU6olP7K+BtlO7fmFWebiUTeqm6HJEyd4tLGp4JL0SdmWCtUNVxcrbriHR3nOjb0b/zRFHjhhQJih0xbRszYxVjFqvbPKjhRp9wnPQAl4cycTBEG194CITj5PcmGQ/QgdvzDp9cynhDiWJqBA6KtCJWO9QyWNdbwsY6SJu0q9jclx5XCb0j8tvUudaZ69HqO8/hFu60w38Rx4ni1Gbn4i3CZpllhSmvcqnxBacBajyr0Ws8ETXxD4bmmhjhGNVRTEOQYRG2NJMdpzUyiKXcDk2uwvFS+CacIlacdHADB0N050nHket9px9/0FHkcp7IfyJ6aOUzyXlKJ4qxLZ0nHCdetn4RxuAIYLYDMZgwsPARSSZdfGjyhb61xHY97kdP2EKnWyfqPcVx4htOsVVPjpPeccKB+uQzOU7aqvdYjhPvQPFOFEeYcyJfWj7581K35bESxhJ9TDqFpvG0Df4aRpuR/cdgVafOG5W1jTFobmCl8m4ZwDbcfkLJYrhlqZ3vcIl0AbBWWI1fQHqAcr6dHBAKHKXRT/KgIyVu/KFXHLj3zPO0NJwD+mxSBnJvg+A3Gk8e0klIuuyDuRoTW/Xk1LCdjW16OE4uh2VCOzlOkkY/AZ7bYF9qq20eQY5z86VWnGIFiveT5DxxUARq4sQwFvgkvHjHSfJw2NDJVzqEb2LFipOuuFnVeqHxh/eb/BFKDpv4wx/+EI7Tv2lsAM7WmrCNdMMeMZER7zgdj22Iike5sKdurzpRntjGI8MT51/YoeEFDDhMuV1BEbfml4XBY4kKXy6HmXpbv1HenbDsZ1VnmaRfa5qe3SOVvgMjkmWt+cOkxc6FqOIavII5z+Gluhp/y/NaevN5m2FYc9V8GFuWihi6ItoThA0aydQO9qhTM06S+jsIgMIrx7KVHglLPPVZjXsuTB3/0M0rTYwTsRVPYazAlwc/4JHH2MUchdVxVslZ1Z4cp/ZQB6eJmwczOE088MkPa4+HPnF6qMa3WOHmQJk2rvFaANqPUjGGZFnTGmmHbVsb1rkuNuQgs6Yu43Nfelwm7f6w3pbetc625nWeQ5dumzb87ThOlrYIowN37c43lnkANL7DFHBLRQSHpsfMba/0Nj/JZmgvkshzNQU+FcdU6RBVKeEQQSXj4CwlhxIGvDlN4UBpCHrFahJHkusjkDhJuofz1N554iO4cqjeaPIm5ODNitPTGDSYjDEBeRSOE84Tg1zuK+bJL+8b5EpQlEfq8MT3pZwe7sc4TRpownHSNj2263E4xIdadQoH6hMOidDHcD/+NFabOHmPb+Wy2oTzlLbhWTEDWD5dzoEM2Uz0cJo0uAqVNuDbdot25BHZwGVIHWDLo2sesubUEc17eFrglF3BcL7DW+1W+1GNX8/v8nZzmnfyaTh99qh2Gs0tJwU4T/QpO09gM3mI8Up4Dj1M2ImoK07g2KGwsxTp9mAjHCX6B7LaXR0n6EOOZPugBn9oGgeKwxxwmDggIkI5UHzkmpUvVpP8pDZWtxov98UcmymP+jUrWXLE2PKHHCZAbDPkPSbeb8Jx+t3vfvfo99quFx+r5CGLHCd4oLf1pO8z7kALzPlp7ywL8BinENyaVi+/xijsY36MM/kAC+tTOddd8FlfhjtcYz0ctPapGkfi0Glqp3dWBvsP/rNpyBhyLWplvxXM+O/DhQXo3wuwQdkPndqE7h8RZv0sefWqm2VtccmlTW3hs9Tst9F3Azl5Ji30eTNOcLNFmVMyX2oMqQ+PPJ5VxynGK41Vf9MhEThOfMcJfoxT3HacGHN46IPjhAMVt8YbHvz44XAcRiOnyYfe5Ap+N4QKOUrZY8q+r/ZbJKnn1NRszaPUfelxxP8h4W9D915nFGRjXuc5dFm3acP/4Y4TPW60x01prGUL1wMCNHu6Wyrimq16ljgb1tAxkIST0csx8o2BY8Rdr4DJKBmypYd8cyIcTlOsHinNu0tsxWPlKVaWcJx+4EjydJZ4z+nvmsh8r217r3Cw2IqnyQxPc5895WmyQt1PFX8pJwzHKSZWwsHuTD7CkYkJWn7fhW878W6S5jnNaeLpt245Th9oVelpHCzxSRxLHk7U80+0GvWxjiPPD+SG4xQrTvoWVWywyRUnnvTw4c2Ul3I58YaT+9JxGlt0apuo8WpPx90msr3NNjcOYR20arzivI+vLHBs08R2vsMVj0tgtR/V+CW0M865NjNjn0oVPfoMUuUUGBm+7TTFthX3e+HQNn2rK+ii9yedJwzWFbx0lrJvdDrBwc0tbJy0matU1XEKuuKEIClWnjRJ6c6OtufF6XqsPrFlT+PGd3KAOCmPFWecFMajGB9CXk54SJPHja5McIDRj5mUcPwvL2nzfhNb9TgY4re//a3ePWBCoy00etjiMqJXlhO7aHTgR5dtgX2yLLkSNRynZjM5TCFf40svs8od9RCc+EmePXlBBJ3Wl+EO11gPB3WfclglDZ1y7Kt5t8eHLbKOZk7IHHKdN2gMyXoeqfexiyzAmLJArP1nkZ1TCWXEO8UNYcWnVl2VtMUlPfepvVS3OfDov52GtOYX7tMeL9g+zMMc7nzPU3Ma9WOPN3acGLf8cIYtezhNOE/gxfbe5jgx5rDi5NVyO1B1u3Gc3CknCmcLWjtPlCbmANi73VFCN22NB6s2vbfCeYhZghkyz5NMGPelx8T0LSYeWv+p7VZjq4zOc+hib9OGLx2n//zbVzP+RoiJ96ERHe4xdhBJCmHxc5puPShAs6e7pRLeRcdJU4VYeXLVhjNVHSdZj5WpN1pByu16mqxwSATb8nS/ZPVJW2V4z+l7Jj/atofThPMEHY7SMz3RnRwnnC85YtxxwpcGOyYfOFl5HLIcG60wPdYqFI9vGYRjtSlWnOTwcBy57jxdL1efWIHim1A4VELQkeRPc8VJ7zi9LCtOHADBihPOU77jwLY9TXjailMP6yCmFrBuG9vWln/gz+HWQavGt9zep7cWmIeNba7b8Aj3GJdBan+v8cuoK9a5tlBxt3E1QV38pA59zIkkP8pTgAzfMVFgskBDLlfQCjc/jkskM+GeK1VjJYYcPhXQHQJkJHqfXMRkRBMInthKePYPIQ2aFIBcVpNigqKJyHd60PJ3fSyblaJvvuLdp6/7h3JZkWLliJvJiW8OcEmHUCvPbSJR/QsmIR/pfSzeh2ICw6oTT4J5/4BTr+K9Jz1QYdIy6LOvRrqVjVJ68gQ8xgmNI7VM2ApbM7niRELuni+bcdlW3cid/2URZO+vCqvxPebDQFwqh1spQ6dst9v869KzDbKuZg7IGzIzb4UnrKU9Z27vUxsLqJrVqzdAuvoe1pFKVnWcyC9ZiV6qLvPqb+cYdDG2DdAUszoe/zJM3YnbaWL8iD6rfhsr4HKa2OKLc2R4jDfq3328Ei5jEmMW41Sc4qnPKjC28uCVcSdWuDXmcAw5jlLcJf6R5iM80CHMQ2j0qkF7PYEV/envv/RF57hsH7Xd+2q/Zgn/SW5KPPt7X3qcFfRACA+t/9TGq7FVHuc5dBG3acPv3XGi8CHsSKIlOwy8LIXbpLO2YW+0Uwa0GysAuWEwTscpFd9zrEJHs56LOagM9zpRUo98V1U6QsY2VjpD4LC6FGEzztJxEh7HjMeqE1v2tF3vtQacdKDkJMmBeqHBBeeJ8FVs69Nx4+Kd2/TSceKkvQ/kSP0ox4lVJ76lFC+oa1BjqwxOk1eDWMbGyZHLJP00iZOjw4l6uV1PjpNWleJ7TnwMVxMiTtIDxha9N3K43sgJ4whzjiLnritOdpw84SFknokJMmQAI512GyH2k81uqHsofY3avW0AM5+fXpj1cVxu5zs8xjydU/tRjZ+mWuW67azyjmDZ7LZlSD2i7VWVFLcMQjtOu0mP8KCNmy5V2nDSlT/a6nVk23mCFzjcdiwcMjFRBv9Fpb6BwxWrRmP7micvPNX9QQ9a8lMDOE9690mrTzzV9apTTmLa2KCJS0xmNPaYR+gl3eizrDQxTuQnD/J7cbEtTxMXts5w40ixRS8+bi3nKvs8q0hi0spJn6YAloFM8PxkmG3EpJEdOKw2tU8ZEGLTzMdo7e9T542cW66sgz3lit8Ktqe8G8Tt0eERt9SF9pBXj7T0Wtdo1ybZhStbZFumDo+u0zw3VFs1N9m7ZIi1bBFfS79j+LAA+nC9ztomijTTQM8Y0K8WLZBuh+TvvtApeqTqM+grNFHJi7GnU47IUCXHH49RwOFEOvt0PoTx6tL3fOpEThMf0I7V8HgIojEHp4mbhzZt7PmelSnh8mkF3nfihi/jDneuMuUpoHEwBO85tXed+oqT5iecEIrj5JWmoNd8ZrpGgUaz1nhytq4mJscJt1YwxPUY8SDnvvQ4YP/g4IfWf7RjFWVjXuc5dGG3acPv1XFyZ4R5bWMWdhi2TefnaOgQ+wsLbKwAhL+iV16swPg6R+2GPSigHFSGZ2i4Q3Cdg3NkbOBwESx0Aa4VJ9Lg6H/GvVUv4U+Ew3tL3XmS45Mn6KUDxUrUS57ecGiEJkav27Y+eLGq81TvNj2Nl8s12dEE5Ec5TbHixOCklayYqDAJahMhnubEVjpWnbzFTnk4TvGuEw6SVpVwlISorXlaYWIQ0v1GNG8kA+cpHCdtv+MDuGwBCgdMYRwOwTtTwotj0qlL2ggmiBWuHIixldtEVh324mIwy9gtv67bxukWFj9RGtv/qPjOd3iEdw5eK7fGz9Ht891+9jnHENPs25x0if8ZBgdFB35MF8J5ItYv/viSgIy4ukIfv5SG3idOBU1r3Inb8tWHqmNB3CtV0Ad/EeM0xYckFUb/oi82/tC81PtMTFb4wDUTF26cJ38s9yUPYbSVBhwerDA2hCzxYCUqr/wOkx0bv4wdH97mqW+buPhdg9gmw+RFT4hjey4HQaAntqD8lEW8kROTJskEz+8xuBzgZj6Ok8pBWXQDB0eRiKehbWNbpql+RQDf/bWCgXUE33O4DeL25PCIy9DD7XLGHPkDjt1Gahs7skPCjwnXdFvuSp8r0oLEdRxZQX8TkyXn+wZOY0FhftI+Ks6WTl1QVytnKW6P9ojqJP5n3XRwkZ2w+puZO5lIpO8XWqKpSwLdzgKv4TpOn45dLerTXvXGGWLcYWsw71d61Sn6f+DnOEDaYxGOFtuJv41VcX8H7mm8yxQPZvRwxqeAetwhjLGHgyTa9jwOwvJHxhljuJYt2MA+pmRZ7/Jrlimzpi7jerK9XMbiH4r10PpPbXRjXuc5tCG2acPXjtOXm616xt6GZTQdeoxY7Txb0in9L+84UVrbhdDVkRMaHJh6keb9o8RL58hOXeSR33Cc5uWfdJ60vU4TGg6LeCPHh1WoWInSROI1L1tqi55GqsCFliPJebrrkPkFK044TjEJEV+eksfJMzzFkfPjlac4MjzqTmWKbzjJeZJD9Fgfq8VxwpEaDpNWmTQZeq3812o3ryVIPlNs13ulhlIdp3wizmrWmOgQl7q6MnTb8qBc7QdOaZpJk8Qz2kEqZLW8Gj9Afw/uFpjbcQf3iPMd9owrI/QhXzVu2OXhuv2cpjdN/vFP3Bz0W7urKrV4p1E77NMM5W0tEe1W8AyTGBzocZ7iIj/aeEQyr00ociKSTkbqNyTADT39nlKGPLCQRkKLiUm8S6CVZp2YyaQkv9eW33uK0/FwnDSxYeXJzkwqlb/AuJHjp744RLENRs5Rd5LiKS9PevWUNx7E8FJ2PiWGzodBwCdO1ISvyohcxiUmNUyEPtQ2mxwvKIcOvGjvQvCNlx/bpCvLTLmz7CKIeI4TaePU/pbfQS/27eoRAxSuYCX7zlHXs8NTDNEl63yPtdITe+0xgWDT9TVsvM4/Rbuh2BapzRc2WLeZeMt7x/RhAX0sYCRYmPLQvkIftIwP6JmF8TARmidI0Q3zSG5gQTB+GreJTZVJBjhxT0KtT5BKt8aphDk2acx5M1arvSWPd5wYa7iB5UOahicejFN+KBTjQcOJ8Uq0FDVPytO7lW2c8UMcQq+AM36Q9rflvMrUT/RlMqTLVup14TavjMhzOot786/lpMyauoxl1+8y9HcO66H1z1bYir0xr/Mc2jjbtOE3O07HhbRGGYbgTaey8B6+U45Tmsql6DpGZED1ZyEgs2FHvuEOk8/I9yAHBAeGa+RuHCfZb+A0p0k0wHwPxwkHKp0lYPV+rAlHpBs/5LEjJld1LEMrQHKacJ5YFo/JkfDjSbAcoZzU8N4Rjo0GFqmuOY2UV5w7HKfmNMXqkp7asLqkwUVvSsX62SvJDOdJZHorQjCRMvjwlJltfwr5J4DuHITJIY/fsFYxLIPw3B75ox2kHS4UOCWQWAJ6fs9QxHULrMYrzvv4ygLDvqvcYf9zeGvqAW2VG4AaHxiXxtwOLsUHzzSEGU8dsg2XFiPwdqKhBhXXm/JCZVqj2kTx6A9CVQiPSVbj4TZPXjg96t+Op16hbPJKsdHeoeP2ii6tPPSU2JiUaKtbvLekCQnb95ic+JSrF23Fia0yrk/zIvTTY0WDP+PGWKnOeDzR1aSEBzboYBxCT3B8gic8Y5LEwTAaz3Ca0IdxKJ4g65ssWDzwpH887EFnHCdWnXSTx0Xo7Y3xoIZ0ywuEm39ahYSMFRPyB84K4+4wtx+H5zm2IXCDuNITO23QlDxtO9t2QdhYnaYv8lSkXqojp6nzJHIsc3ClT43UPy5mJVq4UH1pJ6FHn5Xiva9HvJTErFf2WFVoIY2odEkW7bfINCo5mXush/VbhTyU5cEI7zhF31X/xgH64QfdGmvo69yMSzE2tTHX4xVzlDgEB3jMV3Iu4FXonLfw3nTOWXgg4zEmTtRjfOKBcDysSTzsnTer3lnSATPAFlBopAK6JVqrXhpczWLZTq7m8o8jeGj93U6jhBvzOs+hrbBNG3674wSHZYOpGilOg7a0VRiZSXNuIMv8ys2yHA4Bt1SCV3Xgsuc4eJPr/KpNpZrhY3BJnMxNHjgseTnEE0n3QHgqdMahzPgyHQNQOkZ53LidJLkmGlDiiHOF8NPmnJCJ04RMwtRQdSU+bNWLPcRtIELbOI6cd6DkBMVgpFDQIKNeumZqE0+ejNUmtuThEKWDpK00kvha8qRdhK/1h3BynMJ5ShcpD57I9oMjhdXT8qFwqiw+/ZLszFHYUIgQDYtnA8IEhijbiJ1Lk+G0eTr9Pjy2QBj2ODtrQfnn8LYs9nWUGEfwLf06XdvEGmMPrTTR7lubcjtyCGXO8TZlRWXf4ICo38GL9p5tPppma59MOho06KPkLc+TCpydwAJXt3WpYY3zgIJte/1CmbhFH45KOlDw96Tmpd6fxBlhsuKu40kKvMF7rQc3XKS5wzmyo9RgqUeWPleMwGP7Xa5OxUQH/YQfZZcO6JGna/0YY5BXnMIgYgWeHSeH6Okr9aHMyZcCpB7GuDUsNlQ0UwUWbLfpW2Ud0aUtj3K3cGy1vlZ6YqctdhsXJ/iUaLadYZXLpbafVD3jOEWdViEH8ehLRyY4oHk4MIo0ZZbm2ts/bZI0tS67raayLZgGaAF3IUtW2KppWGWBajGEVafIa5kRNMWgN17GSXNrjOl9vH2nCYdJ/TfHFB7iJsMRIjPhhqG2+zfjDm1sG1ZnKucy+fDGK9fRLhkbKASzgWYL4L3NFvt0hMC//Wdi2aRfw63rdg3RO4T70Pq35pglrsYWxHkObZZt2vArHKfGgqA3KrMhrJrUeFOqkFeqjAvf+S3c4xxBZlkdy629A0bkgELOBTipwBHOXM7B8yg2irPimA5MimzxYJRuSOiiwSIpDUODjE9wDSA4XHnntr1wwFh1Aq5BCQeK/XHMleKWLMyk4UUxT7o0gPGUR6tOPOFFf8YrBqN0mnhSnA5Ubq+DVs6PcPjOEprlIRF6rwmniVtPdXAFucNhEkWuQKEONFysWDFYJb8MBSYf+cpHz+hc2z+e5FOQVudBSxK2DUY0eEXYf5KOZLmCPpUa0MpnQC+OhS4XY/+rILpuXR4b1SFwW6bCjO88px0ewZ1/Rdjb3xU0oErdKF1TezfoS8XM2pRL8GyrIz8nDrTz3I4XkwGo3eZqcQUzODVWv4oJhxyWcJxSL0g8gUAedx7p3eiBRX+CC0rxXz90NlQmwDYRT91I50vZ+QTYegRvjQ9c6B7jRhAGKH5Cn4jBUBe8Wxhp/TDB8ZY+VqlIc4cebVIVDpEmU8Cf6aAZtuLAK8YRnD0mWsKNp8+MR4x5usLmGVE8x5qWEcG1P5RnXE4pdHRkKrYEThjXJbb8mk2DieI1eR3jBXbKirZRcwV2zrp42b4qyRQP4uQwwQ8Sozkd01zJMux0r6Y60P08GC2aJpviZXIDbAxbD4r2bxndTlPBFvQCTShmEKHqrpBYzgo/+qZoyEvZM5b1CagSfUwRBXGu4K+o5fQHNZp/xJyi0215m5/hpLOve8yrIbKcZvwgzgpUPkDKdOTToFv5uxmaQUYabr461IARnsgaSBmbUecUGHvIzAHdz1+21XnM8xIv4XE5zmX6X85vizmVfGGqKV/ETjus/C50nAppi64LWbUZ8dFBBqwq0R7Nto435ZxIHPAyxZlGtKIuu2fONlKLuXMY9kyjeiWJKutxZWUcGFeGzndewHGQqG7tGw5nyY4UzhMTiPRuHuksiJhE4TzBkwlV1BGTHt350rfeJVCcgYg7ttVowhGTGU1qcKIe815TPBXWipImbfH9GCHbccrDIorjJGHpQLUVJ3jrH2qFJijTSpmTOGQHkqBtYFPMeYkdRCJr8MbD1V/bqdshJkJy0BsxUvnzeOuYOW+B66xLwqbpJaj/Mjj+Y5gFSpvb9gmzVZxXi06e8yv8/uK9TdzCsqg8tbPWtjK7IFEa2pA6HgGy459CTxCi//HAgnbfim7eEQrmMDjrJxwnOQyEQSPm4HhyEH02JggwTNlmjh4VliLpS7pCheQFP2DpGOVT3qQVNXktkU+AcZ5Ushg/KJsdGehGHvzDBsoHBx68sxTfUuGAiPbh28TJcqbjlEeM+5CaKH/jYVmhuxRuJaE07QIYpdOfnQydc204qIm11B15ntdhSJ1xozVEnc3wU6lGc4hiWXsrQuI6j7IbtfBaU1WEBVHJdpSuMK7TNNeYf+Y7JLz9GAVshVwUb2XHxG40pSA92rKyLHumR3/iXPZRt2g2STNKhOTkOEYIaBLccFJ+fk8u5xTBtSlrWf1TDI3LEb9g2n6ME+MldAKYHyg13kimANvSZrqNTzWgpm8wmEw6JSb+e9x9tiEzlzkFzh5iygzPlXVVNzOHVeqc1BXNbbDz+t/G11RTyzxRLONtQ/MhXDpO/7U5HMIdJwgbt5S7kl5hxAe1p/7Bxz/Bj4m7AZeGVc6WRnmnsoW+yn47jtOmoJFMWOrkeMNTsHWSjOcwSy/ralISnSNCxTEqcU0q4o44W/Vi7hY2yEEDMupJ/4QT7xLE0x74JRscpJiIaQIWT2nCcWK4yae3TJTynSjpqzyOJc8VJ/I5GEKlkMJ2nODMjYr50nuWJkYxhHJhgnaHgyZpOcDpF3hLEYNudDziAU24ooHuRpaJQBg0DZ9A+SmnwIh2phv4hUmrdCH6vwTa6P0UJwzfykV8a5GaDxr5Wxzg93f5D+5NHHfqousAZmykQ0a0U8WEOiYc9Lu8wwmIvph9L2lAFwHsoVcQE5/G2s4CTpfbKO06+6tC+i50PCmBk+Jc8ZvRhDeoQSkpKIItdHaGYoLSmASeiAhHfjpXYd9WNr+DYBxM5XIzqUIhDojgY7mxVU/6xkMdyclJVzphOE/o4u842XHyxIzxJLdduwRRMP2EkhnCs9nBudeGM3esZv7XcroUHxmnLgx6Kn+VtyXYysj0Fhq1HcCWs7DlnqbKN12FLeK7Ip3memkVRKm3RV+IfzsgFGnK7IqXgC04sRtNC9BVTf/0ZZItww2VxwjAdQzfsicdfTjCxOa3Xp739bEgOn7SUV/RlgjigVJTLMZAgOev4NvQHK/6O+68ypE+nE33jEGCSKWlwBPqlKisR/wCFJBntDm1zx/sHXM5nd6H29rbY+whez32OPcDOa//3eRMpT9RrIpHvKatwUWOk0mDQeOylmuow5Rqwe5AFu5w+zE2w0+HRcamyQVdzV4wWmU/vONkS1ghpTuoWqfElT87TtCS3wlbmiBcEYXk6SYE5pCnuoGTPLHBsMOYvIXzpFWrzgY8jS75BLutOsVKk6jbH0xwY7vQ/nmBAAAb/0lEQVSMwtwixDY9nKa8w3FCK+E3LYvjpAzyMui/MTGBX/vn2AiJcbXfpkukW8GmzhgFEnoISmlTfvByPonGxLHOn7zrr8Htetp/Vor6R3dfw5eU6mGttvpjeolWgaMmlK3ogCJU32C4OAqjW4pD9rPW/5TOfiS6Ql/baX9aHPJrv23jBu1U/3MLChODdECCh+LmFeyr6q19G94fTiS74GldKxmyoKGfRn0nksqVuqWRULalo8yhfNqg4cMz3z/A0eMWT+nkOgItnMQYw3I8YkyynBquHSfXF9oq3sobiShBxi79TS75mxYQZSQNu5TTtXhb/jKMr7ClE7eGW/5Zt8nNsmhHQPjR3eOJ5ZyRuj1mickhBB0zO5M9Ec6Mp6y3myiKXKO/lSzkJerGbiyFI3fESnaJeowAFH265W3pSEe/ixCkLQaQLFSODYmTzbTiCof/rU+6z4NdL+dXWMXNuNtmxVrHU7P8XWNU6EbfnmX6mt8zW98o6YOouWT2nAIWkD24c2P8PX0d6HeS6BzPk8RXZa7q9ioGZ5Cn0p8oVsUjXtMWsXSc/u/BilMdk6uTYWYZWiOHQ7A70Ba/8p3zjlKD92GrrCgLNqvsWqZV/oLNFSCb32EjLYWvzpDlR9hwRr4tWXkpHngJy4lDgwEPB0ouS+yLS7hlpCYMawnX1C0nKySbmrklT10zngKP/cCZnROmXpTmLOVf1lyRikkKkyAR+A4HSolQqcGbuAhiINAsET3Hr1OpfcKTaup4fVIkDBfUCkah4qcP1FVuL3TTwnkTfwOvCK3GFST/9Kj1j+5oTZcW62Etls0h28GlGlU8N6cKm+Kh/p6/HR/oPZkYfU8xw4NZoy+m6ByNJwJgTBqijQqXcL4TJmDiCL+wnNR2wvkODV+FgaOfHEOzDL2+pZzHriwnHEL5HLIKQ3SO1TF4CU7fxR5cYRfKGuUkL8tCOidMieR4Hc+DAdml1MNxQtK1l2R3khELrWuy49xX5Ii5jYScFr9J5J5/LWmyhP9oR1nmFd1NCuyI5tLs5UwEZ7I77sy0g/8xkaLMpfpXRQt5iVaMEacT6TqLR9/qeMc05ET/g6ku971M5e/U56rkjS70Z18rPjXfeNsw6Wib25wL0meNUhGqgBqvOE1mzT6jxkAdsU4i0ALas4ns+2rNXuhWsw/j56QeEl6VcUn9XsVwgzyV/kSRKh7xmjbLCxyn+FMX1J2BIw7NrYVpgKGZ0WoHStTEaf2ncBm0BbiIVrwSL9EFUYBWKOMP7V7TIz6n4S65sTbpUnBPLMC0bg4D1nHNw2HhHTjWXfmBQsjdVp7YtsfVcDPhX09AICRueP6RpF77C+d0USloFIdJkc7S+IOq0oGsOzk7TPpQr4mqAyaDAP8g6nHSCZ3CADceDkZHhKZoGFHbCWzy8xqxPWTwc9714Z7/9TwuoRh1d4xNlTz0lVYvtr9J4H0q2jS6WaUr6Jdq53hq8ZFSooa1D7ScYrWkH4CWVoXDM0SqYrO7KRVx0hvYYNBj2WasmUhHtONM/ahAQ15Jl8EjoLMposCJvZEBnnUd7Ab+bJvEBa/D3fB7WLkMLdZO08gfVOdilabGz9Fdmr/mOcaiYkCXGdYFvJS0ZivUOWNOVU4th4pvF2P0tVchF+mevrb2UaQ93rVyL8U/J2notOZ4jt5UwWeJ3CScE2RG50K1kRWrCgs1+k/m1PwqAjh9r+bXZghuzcv0FgJ0f1U+59rJXgr8qkEvkNlQ1phbqHh3UI6towQ9I0FVjYF0QWwQRmwkd7R1rB62qgQbnTYcqq03WS1Zea0xroUOPZNyjGnXcTqve+F3rhjKr5YiXtPmtHac/vblhOtuEUDnODSnCK2VG1KmjVqnqrVRj4KbfmK6SSxwtjWwoVglF1ymiUJ1ZFb0K9jM06UGs8Yb5Sj0Lt98tuGYjCz4IaPzdL5hLSS/rDg1TXrgeh58elabzIyVpjQ5DlHKsuhMSfNAoATECTLuCQt40R4UibgYJO2QSYyDGvwHeRzaUGBn6n7ZGUNQSkOr7WV5CR8YS15b4gvSg+MFyDegZF2srLlndl9l2nNOyNBixI5w1/D7t1afZK8FnoVeTC/Vt80TK9SJhnn1MPpxqjD3iLRf0E89pdm1BTlXUAvGbPrxcePUM7Cob+KbUlo+4B5vnXqLuyHtSeNtx06kIdtXj1pnZ7Sw50e6IHmQ2eBPyYpTSQvS+DtUJFUFC25EC5/MKnQddwXrmTdGjngmfKgsBauONX5K8oJ943yKquTtGdwynmQ59rxGMeaeUBR4K9HoLVv1wuRDw1OKnLPJWS61TZ8SdEFe79sFdyl/W96CX6PQwnPLo6ZrvNISX+mzxanpc7asuNv4OVlp5lPaFo7eNhCgNNbojwALnwttWbgvo4dlb+1jiJnH2yWzAjxtl8G1kNxbtJapxi8VcFWdmakqalUq11itWsNM6vAPv/7I0R4+/r/FcapdotXP1CY6VY9YJSpv/hOaSjgfAuVPmtW8zrBEFvkBWsAL1Sq6oghY0yfzJ+VWbCbYzLPS1ngjmQo+55vPNsy+OOMOBQQPnjUfGBgtj3zdCWowM7AwpxOrpbIuqc9+kyMaeLkoyXkwigmKR5MIldfSnrxs6c0r2ScvO0zjqQoNv+WZPwSLa9kZ0wCBPbQdxOadkMvkDOrLYiu5l1Gexzo9EM70S/vMKDenwsydek518NnI/Voq29eturitX0gv1UP7UgTk00/gUHWpdTbFAxMjpUz/ZjjgRov6bELZMUt/yy6SoevbYXAona7LTkVDbsmONDTby90Q0aW4kSIv4A2p549CTOx6vgs1KzDh7hIVt/GvYjzuTFpa+R2zAuhMhnYjdwUbubfHVnwHbFLb5e56XiA1WA1+UMypUzwOMKXUQc4hs9oWK5KL0v5i1ay3Gl/pl+a2hqfVWdFXirNcXLeV6MZ479+Ffin/wkqEFp5bHjVd40Vsj6506pklcs6OBXUZPSfnXP7EtM6uW4uf+mO1yIW2nPgvEsvyh3HTwlXMEnfB83w7rlwXDO4IqnrW+KVsr6qzztR/EzsgIm6nEbZiGzZjPnq0dJz+qztOg8wKTn14ZIvvMHAaYKQR6j9YSVLz5vh54+GMTYK3ZTqb9tDusBKENseFrKi7eC2JJzqJtNC3yJjdy5ntzDPzlvghQj/m20XKWs7DbsQDYLh5KlwJa7amXqYbZP2HFfURImANOyeIezSJMGlSkEoBLOiTKHkRb5fyeh0RL3wNP9delvnBJ5lJg91l3pmR5d4h3TNgpcetItxXL6Ff2ucSwgtwbObROi4guiPKqfKs7DL6xh0Fr8gXlZqtLvue45BW3Wo88nqHsiWTciUyYJIbbTh2zEYi00TdHxfEk1wZJtJpoI59yl6w1v+4k4C+02Ml3tnNERWroRd4KetGl4I0RwvJnMFwYwkZegV8i1fTgbmYKFWc+4/Pei75Bwo/KrDL3EJTL+kMFNIebw8x+iq0WPKC0hW+Qi6wKuWoTZq3W30hf6vRlX6n+sFWuRV9xXE5OwzjVOCl7b4zOI5MfbyhVVGdslZQB85qGQzPLY9tund+EV1jO8tYhVu7rsoGXdflwI41v8dXAhssTLMYD+amXzgd2PKEiGXWtryBFGJSVhWzxF1wPV8XleuCwR1BVc8av5TtUZ2fph9/lyqeayzCVmzDKh7xE47TILFyU7sb2YWnjWzFMm3U8UcLEuOOeBquwgvriI68fVfd4h6nY1KhbIfGHNwFOV9Yk/Vwoh/dFWYdp0cK/6Uj1BFnSxk8yxLU/HqYmK67ljIwJ0WZ6r/rhpu6k7e9IYQ/GIHV4oYT9gET+p5WLB6HD8t0tbemclphHngRTHrrWesMTl7L/FQ2EHZ2FHS0C8oM2goryO/1576kzHV+WsWlfU6TXJyLma/R5WLGZxBXZTrS4wh+RsTl2ZtKrTaJZtjGhqpHjacgj3amyPBICZc/tukhX3e06aaL8yv9JJPOSF9ud8UjPuFuMkNcyJPMNpMIsa3/o0cDz5QeAFLTllfK2fNnsl2qkOzyBMi/QWmIS5wm8/Cq99saC4ac1NV6TGE1pO1Tyn+CMti4fiaeVySKqE51DU/rd0Rj/m79Xchbjqz0S3Nbw9MKregrxcTFRqlA120lujG+6rtVVGdrPTogI1vcSEu/Jdy0tZ022EoPo18Srmx6VCddtwM7Ov8ancZ4gLZprLmY5tqzLynWSZxVmdvEK+hqlS1xF9yPbDZQK9cBva9Y1bPGL+V/TZ0Nnuu/Qa6xCFuxDRu0GTtwnP7W8a3Y1OZ67pYdaRsa5YZLMEicX3EdTxpSxxc8B7djvOMcSYlMh8bsmk2FJfdyecmj4td4k7Tg32VbmROhcR0GKmLMt4g0aJSBCREU+il42emHRUZWxqjLcauWXbeNDXUC39FeGodkHPj6ScvHqXvVUi3eSHp5rKDhEuAyD00De/mDjodXM8weo3C2vl3qIbd7y9jrcx3rXrdXkJ200xV8tqi0BVfdNu+h07VMRzZxW31oXSr/6CcCpF3ab2uL4K10GlY01bFVXe4IaUy6DRvNmL6MtLwmmdZFIkJX0oat8M2khSGy9ZuUkf2JeMQi3BBFcSRNckrvs7QMj4u8YaZkwa3lBPHYcSoG2XMMnnvdVogL2FaJBcoedEafUZmNlHoaUfMLLktWCVxmmfhEaFETiphdYyO3D3hs9Qj+BTi10RXBpMh9JlqJrEsr+Cj/iK2lJqHJdwUV0aZ7pTG2bLfptbAz0GSyk4cOW8qusDPGKGQIoemC54bGeVGgTR6FHvmV4/l4stLvhueujTRWR3BLcn7oszKOEXvY2kQQDCXmbl5KN1A6h1sifRzfEktUFTHrsUXep7PIRd+J2x7/viC1PDV+LX/X36V0R7JsgQibQQ3b8l46Tv/5VztOSVYVm9rVjqurTw2r1N5AsyNlPNRxHJqa3qp6kCcaczDFkGfIHAa+llpN53DGGqla/jFUjHzH9nysiUNjKmygPQ04xnc46IzvcCo96LpHnpJ9STknJuZtzuAaP0IqwZkhNhPUJ1lRrxHPuqQ9BIYihNjK9kpIck/6YACTvEM2cehCWA+LaLj2bXpj1anpkmQ3/0p0L//MhBzyMpzzzqdsg/OYiXGblPPc3Q+v1ec858swsl5VudT5O3J1nTb6XGYjj1Mb4oNk9IHWtkFxNPsGaUPoAxlf6lf2qVaarVjXd/bT1kdseodbopZGfkUJfQTb6mM9V2zoL9G9WwhO9P0SImSSowNr/FHcfX8b9immWomeYMm/SanCQnICxoqTERxOrO6QSH719zpmJ/SpWWGiZqdiLstayq/0RryHMOrvQt7ncN0W3N4XRbsHjc+wUFmq/axLpzqn1MYWLlOnVyRYnOGzYVPJL4y7H8/oZ8T28cl4Dmcuc6ri9OnH3ONngptT1SqlZqoCvQRNyJQHbNCdGtcmFcWjtolRp5V5iVc1J0bXJTy2b6lmXUbuJX9yT5f5nhQfKi1jtVw1XpG3f4Nq3n3FXWMRtqITN7zKWZ6ql45TolfDpvKNfMUtsmxsNSfVXEWb/ywbD6KMp9EqvMnqwTpv1UCq3E7eIsHlQsdprrBTXF0KS6u4Nd7yC2hfKjILglm2EPxB02JGj7B05T5yMXAOvkY3r+AiQ5rvqOvEpG6wc9RR4KlulQYvuCqSNCmntxuI0LeGwYgXMCIn+MIkJQnWI8RHYnKaIL3jlZpVW5qhc8gbceeeC3vZzyFu8q+XtGGwSNrurpsFyoOBZpkq3UMU8AbtV/Wzgh2xtk2P8g2fy59QWrP/VGfLbr+lnS91ucJxsn4RYnPb3aEVLGGV2dGkU4U77rCQ92h8g6kJHHpIhRgzCIc6JnqD46TPJJzia9xpbOjAPU+yLD/QeqGIZGJ2nDpCoN/Pj3kydjp+P5x3XNx+xnBZUHLsLoAHi15TznO4zq/tYireA5vURrIepKsuzr8mrLyCTgWKMk0FmzneZzFX+p8Q3bqbR6ym66xeSUnTpmzl2acgBfMhoqvxFjmrMlf5zndY847idavemCNMpR6k91SBu7YTEob0KubS4ebIZr0iRykeJFbLVOMrYdfUz4r+HMy1F2Fpx4ZX+qXj9H+++GvDHSRVaY/R8x+xUW1pANK6B1joJbGID7qqYo1X+gFfNZKh+cCLmDKSy7rBbbDV6YCYm8MtVqZn7SpujTfaAprpyCezIDQSB+AHTaAU6pJ26XpdiV8ti4dC8wp8JZLOuEhMPagb7NzrqBkdnmDQPhwC6e2l4SVdow9GHPmlq+VnIgAWqURjHpyFOgoztZ5Be13MlnM4qAfEdhx552O97OdRJ4whdQLfKWG717qHYTHlnfifJqZF+FLpHqKAZn9FuKqfFeyIpW16lG/41ubAo4+09pzWab+lQpa6/IMcp1GWUZdL/Roi/YVqrjYiru9md9i2GcRq06u143Qoa6jTm1WViTpTugsN7ULb945TmOFefyabn+F8Dtf5tQ2Uan9r44n1oDhVlzPFW2ZXXoGgAkWZpoLNpL3pzuCbUiv9T4juI5VxHK6FS9OmbMV77zitrXUtdNd2gsGYodR2sptWHQhb/Y1K1MrtgPgewLVMNb5ivWq7K7xbYW6zEZZ2bHjle8JxGuhV4f73fWQXfja2KjOi/Ohu4H+449R0TnXWDa4UJqJzw1oWupO0YrZ0xa3xffZMRz74C5pGCv6YRxXqPkK5bFoV6hVWB/3h2NBYgx+8W5zooEs93KgDPyq3bdULXLQVHv8lz//gU+l6OugXjlPAYQgmV/I04L3jlFa55tf2r+141O01nO6Kq1ZWmupdud2FflX+FexIhm16lG94tXmHKRJ9JUKg2dir/BoHI67R4Tu9s2o4+mfre71zC+uE/avMika85tV4let40nr8SR3o1kAyBGbsDF+z2hR37/iRcShrRgtcs6x1U+Oj7GAm9nvHKe1/n7+Tzc8wPofr/NoOpqp3pZ+Rc9ds6wGfqsstfCuvoFeBokxTwWbO91nMlf4nRHt0aqFHq1m/kZKmTdnKs09LBuKDxFbjLYJWZa4KON9hzTuKv19xOrLMdfDaH2p8xeWa+lnRn4O5zUZY2rHhlX7pOP1/X3wxcLM1Bk1vmC23ZCl/dO80AGndHdwj6oSO5x/TgUTaeVXNbXzG2ZIM5Td0LQNq/pBzzZwCNP3MlXXIudMMfhW3xhtqAQ0as3GmQ8MzDPyeldQJMydKl3HXWaC3nwySgSczYRGTI0aEiRGJ4Be4YWwhEgoBnLjBbw3ClMADPUKYl/o1H0E7EjqDBmG/lND/cNMaf7JAu4/rkI9G+5E3YudlTsqfR79njJ2mroBiO9fTPYs+w06a7ZQ7Q/JA2avyr2BH4i8bo9Rsi83Ni9ZR+4cbe8WtcdOl7UbbGrGO0btR6Bfmbja33R0Okh6rMisa8VEMSY3/K+nJaow8La32Bw+aIXqZt0M42Wli5aleKXcja5M0vvmR7vVT5HXBqc0YYlCsZ1Yu5nyX0PxU7og6fYbnQRlPUzH+7jFSon4vFL3ncB4ys05ZM2zFI/FWOcCS3lyyYIviHZHfH9wqdANKi6bIJfp08l6oCZJ6XsJoVaKb6ERU6Eq0S9jC+ni1UN1ESdMQWlD51Hj2BVPeHs7tvUpInqvx4xRNHQPParWcH1QdSnxjt03yrCgj9HHNgAjHmEqy8+6RRN4kA7iyT2Lzu6IYufcZq+Wq8SrjtK4V8/a4a6w6+sAMr5zXjtNf7DglSfy2Ftcb3opbcLbB+WMx4tRDkhgGsuMb3KrhFE/8znbKK4kjBClQJPZ4oVxOdkb+YaELygqnlTyE86N0QbNOhcmIHsTKA+iBETXujtS4dlEZSbEWjgubePGrH9IxUAZKw6PSZVPMGvjEA0/FUB5Y2S4S3wNtKNYYZ5XAIwD5DSfx8GV4psFrOcFSP25429AMbggpA5dF7Vmcy99TGHLVINyIjvUw10tCcSmMSnQ0OdvwEnb3hjPrdW9sb2C0qpsV7AbWZ0mir7SacG+BqMqv8c6wVuSy8yem+1GENjm0lR55nXFGqswJNcaUhh1B691bBo0f/XaiFxxdgMcaM3HLNj+1x3jPaWqXgq1krGAqTefZ5IUIhG6vDsu8ZEd8gdtpT+V1pEUk6VwnC4QToCtkNjsGs5V9qs1PSLw5K1RtZTWTk+pvcE2zDYUG5mHxTsrYMrs9HWKaIrN559Rawqxk52Vk9a9aPoNrOHOoOVfGJWj621zIj0qS8EF1hBesWt8aOI3u3gpQFD6I1nHMKCuY8wjP5VfcHu+FLONZzyTSERgAW86AGTKRnEmsx5EcW7ekJ/5EFNTUJ8fZoVsdC9cyC4t7ir4tOVZ3K4+Wags4BJd4TZt+6Tj9v+E4FfT2F6w3MGVFbkExwzS6K7M1j96hanNxHNyMZ2j44LiKmWaVF7DeWAeGG1OTtvtT2cs3SDaxZYEHzvIvPdlJN+lccNclPidriM2Y7BgkcFO8la6Wqca7ToVN1a/jhp6amGzqiDTiwOu4hVdvhhMdYwh00rEXuke6jNC/gXuu5aBPlDN+isTborbTRF1Yd/kTwrudcF0dahl1epj7QBmy5DtizGV7fUs2oWm5b2Qza79F/kq/bjvMeMaOrv/AM67DUrulmU99uKNOj94G9lK/xhfarX6kY0SKMOOgm0+EKr/TjdUuHfChhtEibNPPjFsBhxtM23K4W6H1hLVPgHPtNWhcJ2c5TOUb9Ofotrab8WX9y1nNpBel1vzXMi9XxJiTSS7S556RrAhsmzIZXKbZ9Dem8rKatZ8Zdi68TPSeSxlnnFlZ1fjIH9CVqiN3FO6NJ1ti4vHO/Agv7g+VqMRPt/cxthSS9XhSEa6IVzNO9dt5DKuMzpcwW8lhJzkTWdts3ffcTivLKm+2X9G1Exj7gH/Hu7/Iunz3x39w2pcpLZAttVqDeE2bx4Hj9JeB21pINXSABoZ5lRCjN8NPo2eBg93zKEjmJajRFo6r6FlDd/6ilr7mOkLH1h1tL/NEoSPrKB/4prIK/tBiK/GIX5ZnxhYXoUtKyMo84gI2NtEsOsuMdNmyVY9D1UcG6HW3fNeTkcHrqCk0ZAb3YJhcocvq0BQnIw17SB31adxWHlB86hbCgnn8dIl3iwwd0oaDW8kZwHc8Nux4QtF9pZ1AvmuWrPiOGHK061GmNMV9tqfBexujD1rSiCVW1a3Gq+2i65ywpeu+dzHjOiwKWQ9Akzyl+7wnZkoDczSbASssI4qoLr9lJmwenTovRcIWAaiKCtrFtEhPz1KTiswxdkSjq+yCRAD9TzbOJHR85jtS5/IHZsa2+FWvLW5L78q25XFAJ/C2/mbMC2TPBFem1vy3beC8ja8U+zbRqYpSPxktgBO6yDpzbk0GiwqYUQ9Tl4meyTtNj9QiBe7ISdKRztjFjhOcW7G245yV8ljl9KXh6ba+7gvnaC6VbbwxLgHZ1XBDw2YywlS98+OahnhRsLbXuu/tKjY16XJme4xa7ghd6QP+A/HeYuvy3Rv7wmhfpta6w2zVGsRr2kxWjtP/DwAA//8/a/80AABAAElEQVTsvXef5UaOLVilkldLat8zPW9n3u5v3bz3/T/NzOz+vS3Xanlbi4ODAyCCwUvezJtZVd1iFRkI+ECYS9Dl0//340+eP/FNRZSGe/5cOMDkquPTAFn68Snh508aLXBgfuowaE8NdgwOFzfKXGAxRbLWuTr26cZ3cmb7uuAhTGUrm1202mf8ab8i03mvhl2feWBlb2fqcZNp1NCE6XM/UuI56M5ikHU0Yu5cXhpEEactYxZ0Z6Qg+9fkxxYnY4wF2DdbgXa7kPn5Z9990IVf9LSOSz+KvIDKtojEVAS3HOJ8ecvD+fHyun4Tz7gucXzvKTw7Vs7y7dmZ8fLK59dicF20B37bXWwhK1vV/5i3gY1S9oEd4L6YizCXMpAl1gZVCMCeTIqC2TTjxZPyBkCD45NIDUNM0p60o5xWlJAfscE/OAJGGVMZfJui0zu8YbwRwqN2WtcQo40U479B3wyx1l/j8GaGXhpFPgxz8F526zgOfTx1+IJe2F7OhQsyE6nEV9Ba/fOFeyVdBjibWX++d7JV7DeDVvOg4zp8yehRn41dvwhKKHdKLsKMFLmXq9Mll9r5UWdbz73V2OhejnFY9aC4d/R3F24EH8X8RmZMzbZNjABHbY8G4F6XD3/+3VsCs3zKxCnY2whRsB210uYqFHBUBFvpIA7CAWSdpIBzkEH+8nYU6BXdQpaRcLvNhNrXUCfAMRCzzlFBdJiLSO76CTTqjJqr620bPfEhIZPTUPCYmBpJ1KJnEGTswG6B/+RC4eqM5jyzU1IGPJj9Pw5InBzpRx7ITNWdF2K0OSZO2ZDUQR+2+GTYBWi7k4FRTIDfcnTulw9WH718nj28RxqTe5aO6JI7yyf+syX01vyyymJwLdch8VnJebJvsfpf8zZ4TdbbFdWEDUifhJzLhbnRTwq4m/Bx4vd5XG4M1Hnupv9tYUlb8mvQAHtFUHyWKyuIctJ1oDJ7OylPPgBneFfy1+K0Ah3by9jsmMh47tDvi17rx7p9X80vr/w8Zi95uo5Pl+iB6nDnmWAM9zY/JuphtWYLWKsmSOWgyFxb4de4wj5W4rTqk447mie9rUd9VqE/6q8+DxgTSixXp+7CBt7zaYmv8A965O0Yi5lZXCZqk7jVBl23rizbcWsj0LdoEyKg30BFYy67K+vE6aOPQyZUec3g1NTxXR1ghbmVDra6s1VdAfMuOrHain+0LH0el5HUa9YG2CnuTrx2LVJAqtWjtqpVs2CZck8zoMV3LwiXg7x9ay3ea+kyGRWJbTzUxxCgEOIufgXQJ6CRg3s0XI22sWoWtDuXLEYJMpRmlTBxBvsdJ7NiZW50K6qs3C2kYdQ0CWKZrU2TR0Br8hHrg9HX8+PBzB0qvlufHKrdYdC43SFrpK7Gzr7IHuUOePq3unJLZYNjpb8NzKMxVv0fuiRb2mJGy+Ji7gAlV1Q2+SIaFB0sv2ZzqBPXj13Z0gAZkhRA1kO+1Z8OV7Zpq5HDCcPjvzsLHvJ1b9aw+FSuuWbsXcc+reB4ZG9oYZg3mRAbpcfa7Otd6uzzrV6NhXRkV3n5X9CauawUtOZ8KGx5eE2/Vizk19b/0ryluVQxSImXw5AfKKsKleyoSgHRVdIQyQMuJLY4nQ3MZZp4QMBsbhwSjoQtfXSn+munLwZ28agsYukRrhzrF3pA3UqP2K0u6VS51rCmhh/ljtjGMoxe1jyK3KdWv1tntdzBswsiCEcPiX6jO06e/fm3bwrM8un/44lTsFvhw19VjTrgBZdoQjkUPPjyVqWxqVOSbiepTm48TRvAVWBXuElsrJrfOB3etzKyn6sxOEc6B18jdpSJ4J4zNnE1q5E4kWGh01BaygC5pAWdGhR/SAeX+wgh6Cq6S4RZjIHtOKAHkAnlVkIeQiHIznY8uUs/eEiu8vnP5kMkT84f8ZMllPt+dK4ZDn8a2qxmraBENWrhOjT0cyc8Evyi7fdm9j7BKHqMrdtc2duj7+FXOoBbjY09XuEZg5hf1ygQr5WaOtI5l9X/sLaNescIrnWhiazFB3MVM2qCbbnaGXWCt6INfJ1BzjlDVDquw6Gkx2ZBZvCCiXHqBrsnMyw+lTN9W6/YGG3lzEVViONFhq1Bx5hMiLn8XVSE5mvs15jbcWuFDt+GOK34Gu4an5rYDUF25OLnZ9dGH5PZOY17HBqLDhsZmiTBhcSGxxGmp89zqR39q6Eqeld2Dicrc3mkqdOP4HWrV2OpcObPqgGLyXk8nmXfRuQcwF3Xy/icOEFEGh0+rXPX2IbQ9W+IG0RxF7RhuinifByxjN/dq+qFct9xplI0lc6xMPXPvzlInHyg2cEVxahjsTcIZaWV1kjWhIM7AUcAGAhxdb5o3ALlWq4NoDUE3uyoC2OXi7vIrtzkycTQRZcN71LDo0FVqzioPjQlcRZDKUx8Kq7BKF4I2h797mzGrBJ45wj6rnswCSPaFX0ZRn+ED947AxwVS5yYPHljBlMajwPydCWNjRJhZkXd4grT2zEqfNha2e12yq+OfRyYAey91eHb+hCax6KZGAlem8bsCtcULMF1dNfYUhBzprGt+64kBghytjfxgdxpbHW0feSyeVv4ghqTkCobSWCdkABDRvfLGjT4FzoGnJS0cklP+wlQYqoCuZIf2JzBDvBP8FKqOZXgrH2uJ2MAZpn/WR8cCZaFikTFoJgiORvZ1kPOCR3ecl7E0I/05oDX+M6xjnok47FZBWhkhxGJzJTHrdvsOeNuOFXdsPa+VK3pbaruNnNHcuBnmGmtbPp0GPhQAb3ziOE8jhp03MptMbJxrly02FX2lS00WWeRRJtby1uMj7SFifJNY1FMKotjhMoGOasuPmngeFFN1MtljbEDvsvkRi37BTXyA4B1/nms/BreWds28jHeo6EDfafxy8Tpv+Y7TrFK6IeSVSwegwnzT1Z6iQHW69GM7OmgRv3agLi01M8RmuvhLtjLp5npcr1MFaQlpmMGLUkwq4Ddj4pdkgehKyumblQr/Vo0oI9w2jMAMEMfvoVZ9i2Uxh68LuEwJDEGwCJbIdwLsNnGfpINKABBxCidEyTVGz+e0EPyJFMJhJAVF/2QrWK/E1SeSdxbpkqUI1c2Z+KqqhpVmPtAFb/7aLm/LEZb76bbtnL2D9ZiS0AIlEF3WuMlyRknrOP2DmMPk2s1EmZ5mB/sSNHMONeDz20kbEyDPLQ3ogWfoeBxVilq4qXL2aOyEGWfhubewbBuYqs4SHXaEhD6O93nS7frcEeE8AKl5kv9ptRkjLLmSvdgI9UQjW9lPzjL5YjyitdUNW0mGbXEj9TmRFsjiU31ah+0NbjLnof37XcdfUx2/BEs96YhtCsmb1TuMj4C4fJvzegA27nvNfpufONl5L3pI/25JpSPK//oU/EIynEmxLiajdiwhRkwy20xKXoOMKfHKOH3H6KjJaKIW/VZp6dhU7zVntQEaN84NZCTcgkY/euc0Fdt6h40mWLoogN85M8JFZMng/oHqxz53Q1fw9vlNO5aRINs9wAjMANtJ1iHiRMGYw44TQSO0MJ3z7zrZQ0lBoDqYBSs0jAx8Jyv0IPWSxXJX+JxWkSEJkavDmWNobt2lXQIDvOr9U7Xe8aPPZ7sJ2egAeI6TGn5glLxU1kLkMn5AOB7RaJDA2DZU0nN66NkWbpRaIGipUDyi8cTJ2NtcYPgGdtlYG2r6ISOdEKLNK3HQXGoHbONqqtBKotyLXRs61qN9+NHi/YXqvvpnqWP+qzTOyw9K5xoe2X1MsaDRsQeN/GMSeMJsZ1p0BgL7P1ccm38hE60qWFLQWIbdXBfCgaky9fasJ57kOj+NaNbcMd8Wm10X4e2Gq7H0EGXKz9lUeVJtd2/QYR6NKZ6zDpb2R+wuSSu6cUr+uiG2VYzanCU0FWQFB0LyZdjzuJI98YGFMMCgkfnvVoouBFqr09X6i/Fhk3vD25tW3erxGnP59k/dYdKtWmuF36m2LrjqOdP8LONVWjBIfE7lbPPUKL51hXKjz06HNt6d7yG9V46u/Z3v1ZwH9uls0fO4G54UtJj0uGJzasX1AR7cRS00nQ73JHP3dI1vJJTP/eIiobyeSxIA32n8ScSJzMHTXaQQv9B9oowYd6rsCRr+6VTcuWk/NXBgGOTjvBkv4CI/9tnWVHUEtBqUBen4zqTk1p8NjTT08il6RZQKeZiYvXWf7Rg17gsdgrfNvbod8phadHGfkMtGuR6g15sYs+StkzGDaoMPagWZ0FyDlSuwCyTg8BqwZxYrFoWtrQtZqNzahu0zRorhiO18LOdrrTDM9+5+r6dc/IPwYVWaeW4fwsve4jh2sfqzN3pm/6FJBlmsWVdfc+eVm3JOiKNlXEImSaaw32U2NTGfu5RNbjpUzgU/1GR5EaZUhCKxNaEe5w67BLWiOZCk9oBJ/Mb2Ym+o+U82gygSWMMYXVj+bLORVxKoC4qFW4cX6N9cnVch7sOwSu6fvhD29VNku6UHxHL2sqPJeOEvFouxsGVvTRZvXX14iBoxi57DS11x2nkZa2oTekAnlu66G/n5Zoz2oRi+mTAlhTrVzePc4WpDi77b39ExPlXa9AK17XswZfGDv0YnHE1uU65Wxfoxn1Jv3zqYblyxZOKTQmdW73dV4M7Q9PQfe5wY0lwjz6o7mZT8uGAPZ+2FuucdUs7xvQxNzQxfnw7TnegVlr/5Xdvb9BP/VE9G4FQogG3KsfJIj0W/rTYu0KwGq465bx28sxBvqTFA7kNv40+2Lt2wHeP92Q5AHr45aUbrEpj6XqL4T5QKI8+xAo2xqCMI3QatCqj533he/7cMhZ1dDjqbZfTsiEele6+cTofSgp46bDVg8YSAj2qwS+iuQxXfFAKB5HBHhDjtiLLl5GzaohVj1eH6RV5Jw9DAbBBiTaX5j2o+iMauGE88nkjYIgzMmd4VrrP4NAqjqW9Vp3Rco6n99FKotM7LN4VTrS9snp6HLl7/MBnT3OIoJOSvYGJm4Gxv1Jb16zh52O4c1BXxzS43DC2qDh5IFDnalI1R0cJEka/g3nHfMo3OiQSH+J3KcYmdY0dXmtejpHBx2Mda81aJ0EteBmzpkD07sJwin1mQDV9Myj9M35Vv4ZX8qdlWgMR4btGWXZvWzbnlooveyvpod+mFrLN4hyNHEzFgXk5fo2Dw4R+yorKVGDkDQ5E/FYmU8GwBXw/Jlviidnzq/MD7uOlw+Jb6jEn5FWnd1jy15QerWr4NaJLXvYxSf0sSMz5xVB2k9BWrteLVXyaEKSWE+nW7eo2j+Ajn7v8NbySy3EgxFTqwtPQrZt4U+i//f6dSdrCicRJA8tLDb6p3EzaTcI0W0W9cHdpPLyVb6PnpRdTZd66DAbNa80P8vbhOsqj1rV3zm5nbM+oI/mgaCJ13cl3b4BG2O4Ol2L9rqIcfddiY0MNC6Dtw8MEElRTMBDcBEraohWLlDcOJXePJHBOgOHkJBAIooMICtRmDtfwTus2gRi3wSUjzW0dudEEtll4xjBqZkrWVYJSOoVlWXhpm8vue4dnPsVyxl+uH9pHv1xWcZK61bJZqIxl7otj5ZdiMkpT9z5/78cOS8s1vqm1cyldu2UKCDDOBq7kLvdha6/rqbpDGMsLpTmfTWZFd5Fcz0uBYjzGihr2m3Fh7Jpol+swfRjp5ckdIW9vt9LhCzrNz2Ukh+Ad6er0Eqz+7XHq8NovyZUm+NhscPFdC5/ASv8JVrPa7EJgqq51HLdxHpxQe0r12uBLh2Xf5Wxctu6ozau1bK+hS974DdA4Ulk6zAP7vxr/Wg/AS7nginUHtaVccLvcuJiU2Qnq47HDA5s5Mdub26y6ykH+RCXH3zZQJ6TXLL2PN3PJRPYSpx6HPXhlsfOKnu0C4oZtk/7D8ppzkWt4szkaoVtPnNLWy2z+EJSS+9c/vleVgCJxQs2GIDT4JDCA/6OOItVbpVsg/HzAQZ94VGJAAL/euvriKJugX5KHzOBjKMHAhNnVAA2WsQiTF1xly9KZ8hGK0oeu4ITO0Ym71GCkLVxWBcbdsANKn0AOB8LNhBz6PTsBuMXmOv1gRJTFx8nJaKcdjxFsGR4O+EZAvVLYZKBa9wdUw5eZwaZke9lcmvqpcxWsNqtMSrdpyPKOHGrFUKtGppoR0DI/KR+ZvHZ6vErWHOoyspTk5lvn2zTMBI7o0jmXvVUdHjtwlqp677vCriCM1Y4fKk4o+swrua2MKJvSWNX/PTYtpBuRRJhgWjIB6XH6UEmJJbC1Ra3bY4gjABf1p1f0z9ZvF3EZCSp2xZvOLVBsqWRn88TzWMIDt6G37UyLa6BUbemmnOSyMnu1FQqFl/QOQl33QNhUhviaWEkWtBEKhOJSblEmkycx7Ck4wB+Jaz651SNm+Sybjf+4pSYUjTzFKxs3KpurN9JYatRXbN66dStsx7EfahSU9jU0/q6ZpqZsrYU88+8HtDt/DATJig92+C84mx3HaAChcnLTCb/KPbGhje7HyOm+7djvugc9oUIr9tSc0cAdatLHUjVG1Wt+UJSj22Jwki6ZtfHerpnjsuTMfaJebp5gNpbejiOJwdmhciBJp/Zc0x2nVOKq7RAmevz+7U+/SjYBLXGyiYHBpYGXJSaM/ZMHKl0DrfSkKfnkgSxZeWlRKrkmwKnqiDV9j7ecxMCvO070N2LThQ0umQ6CacM/NCTkzMGmYdId1UOGldgFIfdD9Cp7rNx3O2AgELZy4z+XPPV/eSGdxKReB0DrdOiXbpZpB7abTfAhqu5PHMsm1cJE2gMxTSUwiBS9o2kBmGY+GWhD+toYF4dIkBdOpSGe5sWCovZ2ihVlHx0d7jwdpsbS22l7sHPbobk9sJJOnf2oxg04k1zFbBGJwUaGpGFXP0iNPIJDp48k1Kptvb8K2yVK1UgvfOfehxkX0gWr3JcyipjmQAp/UbiIg/hGFnGo9hVU5tOPUjnKuDwTJ7CMY7jHeVDgFbjTbTaOBHOmD8wllfSUaL433DUgtStYUap6QVGFsvxz9qm67+ElI1BC+tCnqw7a8XF0A7rCnhcB78heRF8UHceA+mvQt5AnKgiNvpSXsuoAtUyURyvH8X9bs+y/HgzMu22vzlabxExa1medYOpWOjwqMEv233p8RG/kSafr5MZREITZz8RslAWit2u26DSbKOiPzgfRzju0FQ61yQWa9pUPc18PukzAbXdjKyVX4tQWlv0YivQnZIYX4yVFn86YnNs2y5TGmXK+Pg3d04Ju+04OnBGqDitocq2NkRwvUB34Hrv/9Z8/mISNjY/qEe+Dxiz5UO9lg0tDNWCdOIGzeGz0plOlo6B5wJJiMrad6xzyYkb1qYopx8SpfCmIVjQNZzsrvplHGkpH+OEEaOj1bbXkj6Cux/QOzommkrpQA5tPfohgUNjuolEyVlxcPHKuQnpUGkUggKo0p7XAhQ3XLxhssivYvSg80LHRBOzIVPCmXXEuSvkZpD4BFtzWFLV9pMqUvBypaE1sU6awZ09jku71oxRVmbrLShEvQC4XwvI/2Q1PEvpJ2AAaDRTHBlPxjjKpItBD2BuRfgxUKdqU6zVgw+Z9JuwlmWtsS9+mbK6rWSrBu43PpCGZDUi48SxwvU19PKWtlBnHbroand9l07Yx5Vh0AUf4KgUxytQY2cRQMmiCw9UW6S0MmpzOUneXN8aiNl5DdnzXN8CT/YFmFV3hd3wGb+Za1CcfFxyG2vNwD4+1zDy6wo/oxqV5Nh22bE+TCSxldpGHYmaN/8vUrGxq10Zl0Df4WQ/q0fBTvCv5e+Cu6Z+7mNkbk5rze23ew1/2AevDyNGrHU6u6KfVXAaPZFjWkf5Div/mOKp90EETWGO2rRJfpwFWPemhCHXtQGETL0rQfv7ZPlsxB2Lig1zXkz6Cb3/UQ+zqbWz1rN1iagzkUbRpQj5dbXASGO1PxDtU3cvR1ctasn2X2bbUazynQ0u3XE3oYlAxaPDfS9jtY+5/+/OHG1cscfokx5QDGIiYHvzvixjG3GLcQb0r7LQ9WJa7z8CN/L2ZmBCSqlKTIjHGVGxwNCkOeCzMz/HugJhUQmwrO2qCr8U/01AnvfPAOusHogdf3ettNJUMO0y2rdltdAwGTE0NBI+f04GNdruPijfKpiv8hyGOD4dwAIIlNMkO0lQfhLBpZD+gQAUb7VZJnJN0MLWwleoN32GxbcpwR5Y29B3E2F4y7duD/2FIzbeSzdu3rB4sEUErp/b1rLiBSwk4Es53C063g6IPh0uGGs7wDLZQmbZucyK16ti3Rej4y5qqf7pMabIgZKV4E3UV0C/8ZcxMQw7pCd4oj8BKlp7ZMRDlaUliTGrO9LGVNs0ptgvlSgN1SUdpjrmF+IQYilJh4wJjww11G9BQMhS2BoSO0k8EjmqvoFAZrP0Ukpx+hO1SdgyV4xMvtcCK271O66Trmqq8VwlZxXGlZxPAZNprWknARtjxottMNTcAzH9oKcODzqP4HtEHZV6xXtuxteW9HYZ+XtJ3m/h601bG9jrcXLq/5RqDPbQdjonSAhD9PmBGZyhP3eTGsdYuipLe1OQaM69P81pWa1FoajECDfzaV/pFQ/LUN9mlflAqyfKa6XYaKxn/K1cnSF+5aV1UeaX4i2C3QYA+P7t5DO8/oI/NtbHSvfO+1/xDGb4IzzFRDv7v/+03G1ubxIm2YqD7oITMduCnNVCbV4JVunSruHNAti0ni/FJVeIaH8Aj+YndYmL/TGkNeAQkrTi7bKmcdah+hi6e7qdw0rMpw53qqpGjotLwA7Pas6X3QfDaa0xqwCX/6BvlO1wx8t73qtOdVfZQyhGLMBYb/NOiE4NTtlS6/XanBjJ9w3BRzNrQ6SxrWG4ZddS4ZgdWdmaOtV1phYOUOPsDT/bqyebqbPrO9Yrvar5Wn8OAeFV2XIf36OC567aOefm8pq+trXhXuLX0CWzrKPW+SkjHEHdFHV5pVixrFNgw6spCqPsvGeo2Zv9vTlkJvs57yaZo4q+SFJ9zBsKedgxyzgOWlDHbQ0y2DZBu2USZ/gNeTC61k7yhs9kBfrs1hgYWH4JFXV1/0R8Cgr3wv6lfxWSFayK7fVtNbbainV3+lvCRr0fxPaKnr21sbKOYXC8MON2OHQ+r74wh+uwotlClWKjcUb9Ed/2yr3IWGPCTMdFUyinp57qGIzkUK9G7LdBeew0GsN6AQmN7vOAHrdM7ruNdm+t/zfn7HSfxQVa7+IsPiV9cACZRTYW3wDzQlpE1K5cTJ7XjgRy5Su1dfEHsH3K76BP6HsbhA4DwxX0Cre1g+z/+l9+iGLan/2l3nPRDiDULBrl2oTLVU7QaTV4SEqZoIKHGEHsb7AWtN7bDXXQT8CYfjic7T2oRpAiUU+B7Wqy2Auto0VLNAOz5BaZRHh0w4wZVVWkmK7IzuTHBVpILSlRTwsGAsRFxgFP+vzGFNrYN+kad3kOBGttvyDjzc23NBhceeMQ4uFHUwGgyzh866QmPRMUx6KNN6NzZgh9Uatvha+g93ezLxuhg0+q2Li9us7Tcg03BM89d6ps5cUHJzNvrR3CnXzBxSFrFvOM6fFmZxXERyPPyl7U7NfS3nh/Glo/nUNPhWfMcOx8BXWkIzM1xFikOfvyowgm0k+2fpcr6xm4ErMeoRjENdBnagB3urjl0gM/3FpGut7ygr6jDQtcfTeqsRd9v1sDvlRWv4uYMWoe2orfFeAt3VUbojL5yuMTIt+XpGPbbKoKl5zGh3q8ru0d0j0lvoCl5eVpXLTpux37vTs1LpXvzBgw9Bh1O4QNAumfbcx1qVhdyunrQl3I+YLGq8V+Pkey7fqPj1x9050FphM7f7XUYeqSLU5vRqISnuFO/obqcOGgbsQ0/gk93ppDU4RwGG630o6Mf4FCR9ZjsWiBfrSViLHlhHq9E3xxb6/3c4WPJ8xwaIxcl9NuAEv+95FgA7L0dPP/nv/5uoyoTJzRaDXfDWedgFW3U4OodRfoUvAikYVM35SvCvZEdNhGfgt2eN6dMhirq8mMcnIVoTAvji0AwHFYP4uBX91H0br2bW9MzRmZSFkNqq6hjFup6M7dkRJQLQleTsITdD8NiIMQg4KAgQWyQY+xda6rpgPoXhukPj+JBe9lm2nI7zWbyARdmZF/lqBE+QYrYYWxIWS9nYaNJb2cb4d7nIwW17E8nbbXV3aaF8a06x4BT3Idt2tEhtPpU9Uvlirfj2FXVxpG2xl+yt09bx3yOxVxf6dvnWdtY6TiDq34ex1QM71Qx10XosRTOZ3CFVeixNIXJkoCxhENof58jo7BPezAPaMWMpWhW4n9rgMY+bcAO3xVwZUF8zU4s+l1s6e4GhVMJi7BDyzwWv51UOSrwmijFcAEymZk/1RNozbug5z4kGVS5r0vx3ecARf07clUzPYUeiS+g1sdNN7+H7zx78HEE9ySvwJ/rhEHhtW2qvio1KxyomiPFOc7eu8QkbVlbE24GBlwYGHCNF5MWtBWdvsOGUSdH1S6VUOlrAJIT/MN6cGJy4u9M/vwzrUsGOvsud0VH3SXQ12HD3YPN8EMy0oM65LG25bZtVpJuC0T7TOkUxo2ZHs+RSB0j7rFq6I+trd6/7Iaj1m113BqTX9bDWPDdLCRc/v1f//b7jemn//kXe8cJaDs4gNLrEQBDor7uJCrvgUo+V9J0QadvNECtYZMWndnxISuJTVltkoJUnbymg2ycIKolXTYNkT4brreleDtE5474+kDxhnUVM7xo79DEiT882Pe1Cfsg9bod/D+JPlAGvWo7tG8dckwckpoAoxtLUQxCDkZQ3Af1BsybnN45ozd0RGOiu8W+oaHqp87R4MmfRtkFR53dm5VIo6etul6/khAu2Q0BeLQrruvLcZxdll/xrnDQ0vF78GVr+9RV21e4PQ2dt8N7/PfCR8e1no+RPGrlGB9xqHW84jiM8654K579kONHWdy0LruaA11SP8YshMxR/Es7xiw+lDhhwYmLr3ux+D2zEwslTuKVDZQdBxhiiAfiQKsqnbuLEu7ODFHvBDVa5VaNY9LmDv1maDQQ/7f+pNcJ7Bt1lohz5+qi51aeLv1wsMZ2t7DCdfoRvI3gkcSV9EV8DzWcHEdjP13W2ufJHmePRYf3+H2NCSeOfOn0xbBNE+Bb3ZGS/7BpKwX5zUnNAZ/7wPZ4I46KZZYl40rQ0OYc9PxsaxBwLmtybtPXJm9x2hDdzboyHjQmy3bZdD/Dxy4PSW9L88VM32xT/KQQqqH/kolZhrLNQSl7xHLtUzmg2APT4eJ4PCgj5YFmsNXn8k3lfuIELba7MpQBeGkHVNdBYdfGWPNWJ5/rkE7TkXUD/H8i3LjoQXRd+4eQHRi6viJw8uI4D0Py0670lZ+lYQUd8ynolJb+lS7DLcjubbi8IRtixLW2NTCtCRelYiF08oVW9nj5RVs0mjCERic8xh5pX1UMsjJ38DueC8LFxMn1ygtUFG+Uk1Hobdu2TUG8LBbjc1e6WSjwOm7KyY2jdpSVy9A4zq7nvSQ/0+b6ZWtHVPXpyHc+Lmv50ramX92G6LDe1x2WvRjaqma5h3eGlSJJBk0sOW6crprPjFIlZunYKccYK1mSMMvy2+g257iPiRNi+ZozQoY8OyZjfsXJgLFrDSr+aFM1bSLJP6A7E9YY4Dod9Xlzo4nsGhJ5A8DbtXElEAdGt0vbvgApG0M3aMHdVKzm1Qp3N+0PJLUN+KGha9u06sFx/h2aTIazvb2nf+ULlA/4ychAA+9Ed3mPo83/WANQIk5P/R0mcHBjuKVxVjTiNad7yTUIusDLeQ+djgfOVfgh6c4tFLBUSP/cBa0foQdJmP3zeUwtUMG68QvvyOHQjAz4u1Xm6EDLiDN7G5OBGBnv5sAdpPbGnVQp9qh3WPTHLDN0GA+Il5XyCaVg+PR///c/bFzjHSdosV3riAa4l3ZwsoiDCvZQJ2XwwjPSTEfqJwyDJQdiKC7kYKkqYgSmw+RI+zHMMNA9EPOwG+xIT/epLK6gsrOizgND+te8i2agH3NbSvsK1pjQPlQ7CjFOLaShvmHrPA73OJyATSl1xjEGngagTq7YJvQI+OtI87AjbyfYWyE/xLNxeh+RIgYkLHb4oa3Dwj1MWW29n/4+wY80rXhXuJWes3wr2bO48zHRWNjTfJl+VVtivPSR0WF50OercCj38J1nA8uAlQLhRo9PDWNCbkfMG4UjYtRTQph+FZtYN6MBkOk7NELS+YOGu1Jkp84Odw/KYpuOMfedVo1rYkup8KL73UQGUAkiRZYmBv67VdzLiNmgYXXG2RnMoa1PW0wXYQ+MmBdVq3FTHqxwRX3xUJ8HZ725b5u6zVXv9lF+1qeBD3NxQBxXBv7mQMcn3OjS7OuCWWVpF1eMgN98T5xiLiBu2XZjcC9daa0r0odFRPMIcq4r9KQfwew6pS8VNEC0EPT+M+UoHTZWlc/js+VdJzyhN+TnuUzT7+Ds1Uy/Td1jonYse7n5sein23ixryX7d4dFcQa5wzvsD4rOSCFOGFsYD7AY46L7dy5xMo2u1EtWMMi3Qameid88b2jyuRLTlSV0oE6EyhDyIplZ2zmGQgpueEov/fNh70EpfyFUfF5LPSM+0RuAzei+dBZMsl4HvMe7IJlsF99KGtWR5MpHNlDtgjCLvvOSB8BiUdnIAVKit9ExcYguRBCbKAeeHz3e/CqNFj0MRLen0mr8JxXQxTHCknX6P9IkcaqUi4Ovs6QioXKm375+dpwdWe4T/C6895U/snkN/WxMjviO6Ne0WZOnj4wOq33b+U7KHl5yy7IZgLwPYRu/OZRnIeNxET+Ia2bqdc2zSiZqergSZ/Y5Gw1QTL00E/AGnJzVepSvbCvGFC+d3YsBlgOhYivRMbJTONkbdEaF3OR1uMRW7PfGbXyRu+ylpX41f0tM4S3pgr4F84OiNm0Oa3v4B3XmpHKN6ZPsyXafNsnmXq/ed2hKfzp7AkhfJuPCq9wbblgLYFclVoen+IKvf2DBVoi2CKZ/odTlIItH8GxLWwZDLnenjvRAeZF6O1KwKYVv2LheORC6icUR5zP0x2t+YEggFRB8KnJA3esN8WYItxum6MNst9W3Tt7Mjz1FF/vAhPo46PCevofAtwhRPeKk8RnjDR3svRz4f1/fcfqU7TWNvnijxCDjfw4mYKy+t3VawsGvYALPQUmC8DJUdVrZ7/cLjrifolODD3kf7KPG2R6tXm5ntf+YjzGHTfkD6Q6XthXk3kbHzXTGGFdwRGltMzDRAbFux8YmSZUioSy9PhK6wqCZLv5PGuU92rXY4eVxxF57GOOk8aE5uRSe+rsUgLnTC8WcPKCVn2rFTikRkDdC4XnGWpHY0XUj9Hr8Xa/8mgVoxbvC7XlxDe+ejkv4szE54juiX9WOGDt9VHRY7eHwmSk18HJ4SeBSaWqkKWYgZ7KN3dJoCjCvoAcHyEgIONuO4oCpIH2cFlBQ80pxgt6cNhKgBTfteuygd6AkB5YOuwhwAqyc1BVtIECAUnMbpYp2uuZR96BuZJOKByzDoDvx6MYfsF2letXPol6iiedFlEfz46xP17QPNoexOBm57+i4S5v2hqVukl7yF+6jReChbYPsPUx8kc4/N441KvbBN8XB2LlulA7w9Zgu5c1e54Efqy1two57Sa6UtYBjFUW983Kd0bxl6Uc0FJvLEYTm+23mWVMx2G6Ku316BGIT7HAxNA0PC2b8dsxkzI3e4R32m6F7hKRUuIw14sUKfTM4qk/+/b//UWJZ2qN6q8TJ6Dmwa0Cn1AT0Tk84PGOdAwOBreDGYAEO+lKQyvf7XU2enPBq108NmhQo+1Z+DNjZjU4c4LV8sYwDQz6rLL49aJQfuRgqJk6MXbQNRYARVYvtFTZLvPVT2c4uMpWuV6qjhM8Zb1s4Uc/ECZQYid4X5ie5S78WAV590l/7hiUYsKPbobGj+HetLg5ENqBTI2DuH/Cqd57bw1f5f8G8YnqBJUkr3hUuBSbgGt5J9FT1fEw0FtZqj/Rc1Y4Y27CkkaGyW+fQXlGoIIZ+F9mFOy/HPufbZvgao9riMmleTh/EyTyQzudxlqTYqZT+7qzPX0eYHfw3JdoHvuZfxy9huWxEff9i5MvG5Q9a0REH1IoHNahsaoHiNrIJ+7ClzkLdyotw4GGbtxon3eIRvfM+Fqwxfl97q7G3p9PnyR7R8PcdGXdp0zA05Zs5ormjUqS5xBpFnihtAuOLm/2Ok/p/9s/j0dYP1buNLit54IRX2WU6LBngOoy6ZFU6znoBdcdZw7xtOACOBRN9Xn3lHBC90yadXbj7I7zbC1NlG9Ruf8kgFQ9c4ndg30RvU4f3JW5DkUsqu9acux5QO6D0vmcJ3v9xt8QJ3YKAzGar60giPdkaO3CSR9lhRNpZUxCu9kHJeh2b4kIGBN0j0oY//zFCTpT9kXMrO9KrtpYvOqBxYMgplSPvXBtlZypiyalSJzvBoy6xhassFbTVJIGiEBMyk2jG1gAniR4l/PZ/PvC2iZMPSDMFHj9aMXpARf8IiZPPKsWvwn8VdDROurIlb8yJsQ+61AR7/064G1fPzC2a3J+vRzqWsVi1Y+ofxUllF2EoVxQqiVB3kSU882kme2mqukveDvVJmhaH5qjqW3Og8IE7K3cTJ8ilclfC+QvhssG1wephTvHYtGdyx+kTDo2cLbphHYxIeuNSHIJHKlVKdFKc6AcF3Inm6wtx4kFb6MqP5tUR/eE9HC0crRMj935NY32foyiYMZojhSXUR8hMO1u/uk1hdJgnC9xAn5wBjSsBj7jy0RMnsK/6Hjj4q11/j0ltUNllZ1yndbc6vsPgkY7O3+E6h2EgvL+8kdV3oLDfIXkpOl3zFt7zZfYZktEtaS7rrrb5sL7ytDX+AJj99sBYebxq3wO4M/RMi1CayrnrrtkBpSEdHx18z8QpbW2AvhAknF7WYENQGVjiHDY+q8VgUNlDvDG3RWR/SH+x+CRoHVaUgvY6uzgKOss7DgwFQ2XpI5QNaJNx5lFdd5oihkArbKmGdspaQdRijMkrvYUaSC4a+qTGyoaOCsZbRNsH3l7iBB5sLGNshhOyg+edH+aOk5oQBsML1Mwfd2xovdgetZx9XBlfetmCeURf6RRuKQti6N+lS8EDl30OdlhmVzjRUI5zsyiX5NBmtVsjuCQVGnF0CnuzdU0n7vhSI8DXRpOoNbKJm9Jsi5suOVtoKQPZXJSbLECTP0qcJJF2IBaRcL2+potrW5Yc1ytyyM9tvNiMLV6anbIJpuauuKqUpey8Il0PpbKt6AVSMJvnxkTX99u31fzqYqrvqw0rXFEfF9qdF1e6cU2f1qwcjWhOjdhFbW8uB+temxT33XEaQ7LTV3eiOp0mOa/RLl+lXA8ftYNN7eCVDzMMn7EjcZqTJ/D2jXa4BnV9nUd42VZZPOHzFEvFTvyux5rleDYPy6pv4wxeIhd85QEg2RuxrLntiTDanIhWxWq+3Va4LddjY1btu6UPq1avcDl3Pbh2QOnjliV8Ok6cwGXaNTjUsagLBgu31o2gR6dJtvchcZwcI+yClDXQt1TANgT2cpGucEJ4I0JCC5LKlaJt21Zcqxis+YAdB4YaB0qHnRMH39iJqq1KNhQhQrx76XojDmWhoLAwFBXhkS/DCW4nBV0FyoBdocOxSNrIQ9vxjDNLPK4HSzjgqH+QNDiNlUJ/5hl/D8IaiH80xvaOsFs/PoRqDq2yA0F4w02+qB7oF1CMHq4dWHpZwcxWDdKNPuAXlY1+RxC7oS3kHxLV52uHu809PHgYhrEVl/hdBnIAYqtxIwz0dg7gx57ckI1jKzPKcfwDh+kw6wt7rli0KH3uwAPhqaMfPWlyFbwYwxZynnVb3cfebvA8ty/prTfNJ1L73NvTnXrsjG2OZNKsrUUrCMhWc/bBs5mYCk8Cg7KtzAHZOqIc6PHcavr7wazaucK9qBb3cXgfH65pE+bjaqz0UX3Rl2kNmHn32iQfV7ZdRwxP0b2sIZtmRBfCW2NItsuAdrcDNmV3VQrn64i1S4nTJnkyPxQf8EpOpXzJMvhBx9+Z447GVINk0z2H/xFXlZBN/Wi087CUnaZNqG4iccWXqLRXmC2U9htppauRrYVwdt5WuJnnceurtt3Sg70Wb/D6PfHA2gGl9z1L+LSTOH3GIWMaY+ywRN1HSwyqrUXoZDdp0AXCWRu/BqMGq0pnH4xKISixSU+0iVghxRSlN95oK/IKN4lfqqoNl3g6bTswLjmASdqlt/AgbTFD3eNoEGkqJTvWqb4H0aYYkC48aO9rnxgqpm64iaVKLG1cbFj2xAkYjEenAPK6lyDkFi3BxyHwz7+0IxwNX9sPrpoqcnynOQPKvPnklcJ0vhV8J1+utLGyu8JR7XnfoWM7RkfNqW0anNWE5MBgHIXvXUP/X6+k90mHz2payaiVKDVyu77DOLaTiSO5KdRkN/lzsfBZQ5kQYAi3gcy/mm4t0l0nzYZlDLpjoQ5J08+wY/99bnee3lCfy3ILwqHAUYou6ZtaIgj0IyXWfTItYmqaRK4vu8sunY55zckbnrWZo/GylRptkX7S2FbZo2FW7VzhHs2hl8BQm6HXe6MuP1gM+vxdxVtq3IHF0Go5/jBT5fAgH0i2S61rHKZfayZ8mXeIw1/sSJh++umni3ed1B7pkXy44YVoKJE0PXv2zHcQJS+bq1K6nFdNQdlhMalcxBGkQktYAijR7l4nLB87pfR0LGBSOr3D6fTOb9Cs7bHqqzbe0vYmrO23aUNDFD1odkDp4zSRO4nTR5Y4xUzxTjStrtgHswmjDoIjrT5s7CKnB14DQSXQoqP0uwmhU/hh9KSdhc1oF00lY1iOgi7BaOEbWMjzUPp5XiQn6BmRy4MIi9G40R8uNqCSzlK1UYK1bqdOnIzWYgXdCmHqcAN2QEljKYJFUYMO+lm3pMnxoMUHIoIPeIxOlhqwackNwAdbRs0GDbKEm82BLnIEp8+blrk3Eu/xEW6vlE979It4d2Pry0WZE8Rr/Je6I5kVXf0tHVl6/2TtHoD1f/TZXZT0vunwka5LvBy11KCx2/Wt4kQ6G8KYdYnQtSAMqMUwWfuJmNGWrwEXApgnRmaoZntv4dZPYLyNZoL67WFa/LHIuOMEGk5S+iZ/gBPcS7Zz0cBQ4pQMRvm3in+3m3A7WUg1SbwSGMZj95mw2nVG6/5YmaW3dnIBdtbBqVn4hddX7VzhXrijj+gA5s6dek1CF+Y1mrEahz3mUjM0uQ2zpBsu4ca8xhHrbYuLnu4LNIBkuvwX3yYhfMGuO0HOZ22C30ialDjlXaewLRclD/TQrhYX8Shpev311z1xEl42UeouF+ynza5LZwjexJ2gQJFtXGPkKWPi+J1INjMu3w+9bR0/w7TWj+SQF6yFL209nPU8dv1s++7qV0XfNLBjBlUDHRQLmP+uIHA+RlmCtL7jtJs4xSQ0Cz4ZN5agkt3TJ6sGg0pwiY5y3kE3pBeEA+y4ok4xaHLi0Yjp8gs2sZ8p5f8ZXvFcMzCOeOel1uuIZftnQfQwOs3x9EThUF8N64CxeJhCFypej0ZE70J1YjD3smrYYUG0mr8U6gOvkif/sh54gQ8HWNqxHJQR86HfcYKPsCjfuvUUuQyEiC4QdOZu/qgfutxdxkSXXzZ8YLi+co3/0n4ks6Kzz3rkQlsfPDJwh/K+se3yHT5y5RIvWqsWawzP+raxGsfqaqxvZTBPmuYOB3r2k3XZwjwR3PQINH1FxV1nGUCrkEitt/TTGHyNsZKJE95HxDxeJ07wRf7NbtG07G/tjnTwkXcv/hsN7UQhm7lhOokYAiOfVWqNOqcrY3nIXvrVdorIGZWHil4Iw6qdK9wLce4FGcXcuVOvSWieRFM7VnO/x1xqBrE2zDo9L7A05k4XWi1i23jBs+Y9uUDTuYGSJtwJwibeH3/88Qn2OYESD0q1BaV24VVXqcRJd5yER6k4yfZQoodaQ5/2QHQYhqcNurmVgtWqKvuTeFZLT6J2AVrsx3G1yMa09XBX2SMRrmnfXVzK6HtY1CelKelCGYv/roDVxxZLkP8OEyc0awqBYtQXmIkFUtdsR4N8peuagXHEq4WJdlTjMqWjrQQWiVi0FBO02+OhoLAKtMsZwAWjLXbtnYWSguU4XbGFI/GxePGT4/ibTXw8T6W/52TcY+IkXdRSbU+t5hMSJ/rknnr/mce9T6Hm7ObyaGvZkGjHlC+i7pd39kUqc4EV4v7lNf7L2pHMik7Xe+RC2137R86kmuiwCX+22vumw0fynbfDaClGvbfYDm0GHKkMOp7LJ7gbu+AUXfw0OpqRbyoxR7CxPsZu7j9RVaZjbohOJq2ZTT1GxNqB/0eJU13BxUvfnL/QUzsMRGCaLYGKBevgI+/p+LcThYynlF9bDkGRzyoV+3NKM5aH7KVfbaeInFF5qOiFMKzaucK9EOdekFH/3b2LbXX1wRpba0IZ6TGXmqIa1IZZpy9+LmOlGaS5HhjK1wU/jndv5BP86EkTEhrgQMf+/fffP/nhhx987wnUc3w0Ymo35Lo+6N3U8ZhePKonuuTGFqznb/rdA9HhWYnVoZ9bRfKhEyfYk9UqBYEavrT1ENgXuVWcHsaLjL6HoceC9pIu88bivytg9bHFEuRTiZPGp5c44H+UUFJbOaMBBtogH8yio5x3Z5GQK0ihAMYix+WAbmGQWyudg8z5ivw/L9En0LHU0SDiggQ9bKfXbRKgVndnUEeyoYQD3HgQJwKiuLgM5Ixq+8/g90duuDhhkZo36KA4IfmL0v/QnSVMviB6WTjSbbF0eeBjcHZ92aFyEAup+TQkTtHu3qezk5fqMTx+SZy2QVJfbinErOjsMvVXk7xr/zQVAO8y37qKLt/hzrOCxauy86C1GMfYVjFxwvLAwZfD/EBesU3+ZZhrPpSvwMVADz9Wfopjw+0nBDyJwewrvdUozH6sKW7GCpzM+KN65iwujmANwCa7SpzIxy9m+TphfDqRKe1bCPYi5NAaO6BFULbixqjWGnhSZKXGcaUq/aBPlFjFa0+X4rNHL3x3usNyRmVJvEzQqp3sh96Wl8njh/cF8+fqXusCB2vsahz2fuiqsrWtOzp9lSN0uuSrRQbhvMR8xNzHnSPA2vvcx+Nz2OGb1onvLHH6/rvvKoGy+o/x+B50YVP7nmINiTUHCZg/jvfasyev+ftMcT7Skibd3YK9vrtS4AxwPCD7L59Bf9nvOHkb4GfsqI9rpHrNSjC9BBti/ZCbWuw2FrYGOpjMHY+ZB5FjRLFaJk7/8ZfPGE07Yk5Coc9NAA4TqQFLLI7R8OCjZMi6XHG6PiwYbRIRBg/1Sz5XFdebylPZIgZBC164lWIBZD3VXA1s27+v4ppBcYbXIhfGWHpSYTgmF0wykAAxCbLFCv881tZL3k3RV9IS/YATmp9/5suYvnhZAoXSxF0H2DGYfEExRbyTZItSx/lJkC1YljTh6s5rWLx8UTMuKyGf/KFPeo3kdI4l2CHF2+E+MolT7FWC6+otfgVMbW5DVMz4UE+uNXDel1krHDDcjF6buQp7ZizNCo9k9ujqq9TXAttCnOTTQOi5s44u33w6su/2jF/lzI/uwjjGthcTJw6HsRWMGRku6XC+YHaL01jR2EMpWItet4FB1uvOb+ZRYu7/hCu5OMH58Sc/QfnRyh9++PHJDz/+4I/M6ITGL6xwUWitY7KEdmDO63EYncTwpEgnMBU3v9Bin9nEesFwTo0bLFhlCEDxnpqtzs4+GNQ0G6fAoRvLh3AuVVRfJGoDXOr3kbnbAaXX5ZBK0DuM+ovYyscadyvci/Dt5bCJXrq6p0LAV6dLwjanl5t1BnphhzoMrc5zbeIE/3yNsXMRJjx47M7OT7BO2TrjyY6dEzx79polOm8MiRN+87/95tsn3377je3fPvnu2++efIckyu5A/Yi7UPYIHy6o4s41Nswj7K8jaXqDut6wUjvWnzfeQHJG2uvPXrd1ymT83ISy+AO9arxDoRP62Q5Fg/EDryDwrLaa35KVhapDjl014rq+0tOxl2HNtCoByYZKQ4nhsroHpd6lfdc41Fob7a1GDzQpNbJ88nFhY0HbfuJkmnA13hU6TBHNQw4iqWml8Zb6WUZ8mEyiAY6J5HZAACBeVr0mIVaSQY1LxCBc2EHpoKvzPCy89fVu9ixqIch4ecJkd2SYYFjigwXlORIge7nSyopx3HGaOgn9iQXI5Wxh6y9k6mRJ3YI2YGdihKQISRLrWIieeaJkJ04obUFUPZMnO93ko3pcchQTutQXItoBHT74VeroN/iLTWUE44oiAuBqpmA0LfuUxhTgsS9dG2D1YSjo5K36O2EU22uEj2SO6Lu2rM+mFu+yrgjH8V1JFe4u8ikT4620cZ27e+I0RkIxtaFu22IgBIp0eiEZ1OQnSsHAj/yOyR8D8XLNQNJkSZKdjCBR+v77H/wk5Vs7WcFJyzc4cfnuW0+o+L4B12zMSZ+xZgj+eJJkJyQ4SdH+1ltvPdH+5htvPnnjTZ64YD0AP+9QQ56JE5s6xkDRGrFjje3d4oDPrZEbmOT7A3fVehe5LqMI7ZX3b9l1GtCfKwlDBn5JXon8nePQY+q1DM7QZszpATFWjJYr68S3G+N155TeJthVXpM4wWf45cmNnY9gXfnRLsD4uYWdY+A3nXec7NzB1oE3LKFBkoMY+HmIrUdff/X1k6+/5v7NN988wY51iHeivncduNiDtQzrD5rlemyNefPNN7nuvPm2l2++hfqbjvd1yNYiJGw4R4EfWH+kA4EwbVFnMLReKkhOdx5yCz+XtU5XJFt4g119XDz7embKoh5qup0O54hz5L7NheZdVLVzl+Ui4b7yF5Ubsbeywy43BMcqUed4QJ1jCyW2f/+3P3jZD0//4//71H8O8W1qn7BmxQ1NZf+BdgXNm7A7THjXFZYkq8HYS+pqygR6qQq4AEeDXKgfOt8C353p5AeEbzUwcpH09rOddtrDxQYl7hg9/9HLn6zEQtUTJzRx9oV3mriYcWHj12z8CrRfIUK0zZb998GEkx7f+XWaZ7bo+ImQ4Xi12fBx1RlXgHjXCQmW0bFAeb/hiB50j4ajKBinsEf/zHz0m0oIddiVHB40OsFo8M5Q6VyHKp1Bi9+Ku2vrcDPe0SsVd8DN/XxGxZHMEX1l4/o+GrW8KPnB7rRmcBTTz/MxQX+3Po9mdvkOZxTa2Ii1ezOH6SvHIOAtH9dK6AcdOy9IcN7jijDeKcBJCZKlr776xk9avrITl6+++spPWnAH6idLrlwu7kZDH3bMfyZLlhh58sTHZd55550n77777hOUb7/9tpc4qdGV4Dxpwf27bGcCGS9ErWPnGmJF+ZEL+NwaqYFJvj9wF613kZGnkNV4Ugka4F4H7hE3c4tr+GQzOvg+LZ40vvLVsae2keG83m/mQF90+Vaj6aqJtlbchLrK04kT1hfT7OuMXdDFeYTeVcLdbFyk+dnWG879uODiidPrJvPEz1nA8+WXXz75CrutP0qgcNfpu+/sEb7vv4vzgpY4mU3eWeIFGlywefstJk5+8eZt1rEOoU5ensO4L7aGaf1FCLS2IUhcLxkNhUf05ViHkG3S1+ej5MnBI/uxR7tTu54Rv6yFmm6nw+lLe3R5qecKZLXzCqHGel/5pmoD9qh2eGBUgLy0g//nbxvmi0+ZmDdXJE6mxSy60ZwUg9nsiwSah5gM2gjXDzz4fYJBLw2kCpfZ4JoyYzhaA2i3yYQzHh85pXKJFPFFleV7xsdcwbPD3BA3W4gsaUL50892YoOEyUruSKawuHCBwYiYY8ZFIU6gsLD9xFvqujrk/QNjZhK3LXF1homRXVm2kyQmUUyavA4czhNHcAAAQABJREFUbpnjBMp2JlNImvjyp2nIH1b3BXrtH7ZecjLpRA/vUDiLH7jQVP1qyH8FLnf4ZWpZPPQl/S6NieKsQsNvvt1lMdqT4ZjZOnnY9hu06r427iO/kkUUtKN5GrPbplYvF22L6zHvcMq0sGvurvjga/dXfTaWNo2Nj8kP7y7jJAUvX39rJyW4y/S1XdnFScuXX37F0k5ckED9HFeKfT3BE7ymB35g97vNPt/5mJ7wSJree++9YVcShZMX3ZHmFV+1uDXYfx+Ev1yqneJaxUi0l6Ps7ezwyrsaN/wdEE/hiVHS3PBHqqVKZRMV6nQZtoY5kfYxVk5r+vtntDgj1BXubXD6fF4FZKCXomTdajQSOmFJSLEEuspbJU7+JIytJVojkLTokV6sLf5osN2h+uLLL558+QUSpy8jcfom33fC3atVI3AHCRdrn9lapIszuFDju91xevedd/NCjidTtgaB79nrOF/hHXA0XmsJSu8jO/nAOZQ2jG//dzCgV2vQNvRat3u0ZYnlSs/I0WqhptvpcI64PIdssncEr/JvYeO+8guVbV6R2qPbYadagDxGOngdB6xZ2I0r+vpk4tQe2YMFs2jdbIPIzdWh1xuxgSmjyY5yBSdj2HMjXVFa1Y9EIvaBJh8tGnk9YCPqxdcqqIoTfEL8ffOBz6TI7zJF4vTTz3ZLPBKnH5FIKXFC5uFjYWysf6XGeMCHq8r+GVC7IoSrQvlMcsRPV4iZGPGxG91dQunJkj2q8+yZ3XrHiZTBnjj5I3y8To9H9aIBbAaOapIWJHcU7iJxYo/BhdaNOXao6/IxJ2bYITf8kC9b+X0KeXufbKVHX9U+8lFz9iOIR8ZWBg5w2eYDvk5eybC7tg4etb/rvQ98Xzu3lFcUUBYsaG6lBpvKmc56j3mHk7up19QB38yLdmpXn1UJJfQDPLgogv1HXCSxOY+TFTyS9429V/ClJUp/+9sXtv/NTl6+fPLFF1/4VV/OP9jA2MbBYhB+oOQJy/hJYSRNv/rVr568//77Tz744AMvUUdChR0nOEqe0N5lm2DI/h9taiv4Zj1Hso9Pb52aI+myFx7zZOkB6TAZnvaTom4q5Q+ArcoDASM3O1i3fes4dtCxnn8gjphG0eIWrMCMfb4NzEBf9NlWo+lAPywJC/0NdbfECRdp4l1JvJ8Ud6yx9rgL5gvmqr8PbYkL8LibhLtKWHe++OJvfgEHj+nhrhPuXGntUsIFea5HFoDQhwu8OEd59polUHi/yR7fQ4KE9QhrEdYgXMDBjgRKSRZ0aVfToRs7zo+wKXQY4+C9tK3oWwnqv7TIrfTs2o1x0O10OO30NWJX2TnCVf4tVN5XfqHSfzIWU2L9UxIBYndaxf/joPFgFqKvTyVO+eUx8wBOYKLjMDgUFad1SmMiLeSDRwNSchz8CIEUAm6blAjljWtGhF+VzkZeE7NtkiNyJfkCcfKRJyt0RLEHzSayDX5/n8kfz+Ndph+VOP1kC1VPnCx+GKDR/9kuvOyNRQG31PEoDh7bQQLlOIM97H4wWTyOhztOtij5i5Y48fEFiu81Cfe6JU7+6I7Rh8QJ9v2fzFvgrSkKP0vxWA3OGl3JE6Q0DFgqRtK3Kk2fDLgCHIRQuZXbp5DXx+tWLDHuWXfP4dKqnqQrnTFV3Bm4y0LEGJV/3fhK31H7u/x94FvYua8OyPfIAFadpWpqqfpTpfDrUvFd9kGo1hgu3tEmfNQuHjiJ2eTOuitYLzDPmTT9GB9/wHtNX3+D9wl4twmJ0+eWOOExma/sXYNv7CVtu6/k6wcmk/9DGU75I7h2soKLIvDBL77YWoLHYt59l3ecPvzwg0yedPLyhr1rgLUCawh0aYcOblz7qt7jh/aLz5oYvoCjw5QYeYl70Uf4pK3DwvWScSCm2ryuo/2N50h1NwO4ic6ksT4pDpsYG9ysFGiI1j2jmn/QmofZDhXuFizEBPP5KDYuH1wT86StNIFwsjO6ytOJk3mN6QvvMW95QXb6m0x4BcD++YbCfMKcRWKECzj4MASSJiRPuJDznb9naY/nRfIC7fgIBN5pwtoBO9rB4zD023/o1TnKu++9m2sQkiclUP4OlN2Z4oVhrkNoAzbpRalNsa2xLkor0aY+AUZSq3mkYKjhJtDaIJsTZV1Fuxulw0SL4YLNJn8RdOVX+jcp3K7XE8OVVbVKZRdf4TJY1hZvidqEuAP20pEX3nHCFX7XjpLMMQYdr4HUnQE9N4dxMHkgg1bjAoOcBOoCjwaP8ChrCy8K4VAo3sBkG2SSNQFjavDATPn9Y5NLcKsgMQNAgYrFykoodbmAvQhZ9xswFx/ebcKdJVucPGnCVRlLmuxq8o9W6gVwxBqDAIPUB0OY1t9f8cTJkyY+qsfECVdZ1F98l4GJk14At4UL7zPFzoQJixkf0/NH9SKx8pMr2M5R6qq9phCJxpK8OGXNcQgRhSTKaMbFgu0NKyknq2vRy1T4kYqWCpKaANhKK0Z8bR0u7FnovgvPGfkhhubYUfvP+g4+jc0eH8nf0o509vK0/tbf6EX1pMquk/CqTw0HdJ+AITjOS80E8IqBpfpKJbCAdcIQ3FWYPFS4N9YGPZqH5OYHS5j0bhOSpC8tScJjerjb9Dc7aUEihXeewON3h3A3Oe4e6yQDhtyXaC708qrxd3Yl901Lnuzdgrff8RMW3HX68MMPc3/rzbf8MRpccIE+7H1D32hPO51hgtFFGqcKseLE7gsnJ7kXV1XnHnuw7/+2TUPidKz6gCNHT+Nb+G2owgZUCI4R12D+Npcb2PT/44DV/hYsdnbM2fvFAlqHNa6ZWWrWxIFcY9CpYEMNdOH1u+bz1pA8j7A73Haxxu8W+TtO9oRLvD6gdQv839rHH/RRCN5xsrvddqfJv6RnSRUGGM4p8Ggd7hThwgwu0GqN0N1zv3ATF4XwiLE23HHCGvRBXMTBWgQc9OB9KHwwQh+KQOs156gfWiIiVmhdke4z5X7oe6TPaCqePZ0dTxjHbgeNKD23hsrmPTXfw8feWnmxwmUcwhb71ir4b/NBO3Ts33G6NnEyT9yZ9CgAL+hJrAPuOwYhNg12wTFKHe8M0yHaNGFlVOVITpmB3CsBJ+Mov641+QTXChKrxcj4tbCsdQMrn1I5ghXskAaMIxYELEC80/TTcyZMTJqQONlnO23P95tMhw8AfMBK/kCDXaHxr+rhjpMlTng8D6VwsoxRpJMmlDjZ0aM2TJwimfIryEycQM87Tu3DEN6YUKwYoazTRSZNwGBn81W6dIWE1YvHbG81xvVeEpJfezwax7v0TljYrXEwELvUKZhdeeTtvqpr5DOOpu6o/fsWR0rX0/WLq9OFu2V5lf6YhxyV9GId+Z0+1Tx2oVES/aD21zwwG8Hm/cTOMj7YJkEyaId2etaOdjdANE+ccFJhJyJ4JAZfqvrG3m3Se004YUHShBJ0PR7zpiU5SIT8nQBcDGl3iWDJT4Ds7jWSrG/87tXXPvdxYoN3DHCXCfuvf/3rJ7/5zW98f8feOdAL3EiapFN9Ip9VV1vVdrawYo3wKjZVRgCxZir+LTQvHpR/lz3Z973aLw23S5xm32ALuBmvuE/0xhZDly5GP2w9Vwv+Mcqx/RGsNkYbeKeAtPDX2O/IldbWUd2/qxMn6LYG+Med7JwiEygkTnGuoRKJDs456v1KPiKMNQmP6fF85CdPkrCWIGnS476oa534/gf87SesafwiqC76+HmNJW644/ThBx964qQ1CGvSu/HYHhIyXeCl+1gzHMKBWw+KcFeU6/DfT+laZ83S4ffEzx/NYRe6n93LzR6tXuY9oO418ILYXsuWeOlvpXtvcwG/Odph7v6Jk40od8IOKod2pIfypp1sQdbl4scsFHCQQksKDypLU0eveaFj4B/YWsWZWr2rXsKNN8HBUkhx4CSLdFUja2SLlu02qY1f1MQfUNyOtsfokDjhLpMnT0yWfvrJTnZwx8neW/jBYOcPm1gT8SJ3nF24VdD96k88ssdPk7NfAIPXW+IfheBHIDxRwpUf7PbRBz/psRLvNjnO7zLp6nR9UQ+N8pOfCAqjxjgBVt35vKZkSaUxmWyIZ9QuA6V1y0faFn+A6X24YF36NyFZnZALXUco9Kkid8S7ol8jf56X83tlb8Rt+erk2Pr5IM6jrutrV+vXPHJTtb6cGkVzWyYhX5x9fpjynHOtTY1f/cAFHTyYH5yzXtoMYdsYXztanY+w4CQCyRA+Eax3B3CVF8kSHtH7wl7IZhL1pevA47mY77hr9PY7b1sSZAmUJUN4VMb/tICtC5iq/HiEJU520vLV1/YlLLuD5Z8htjvfaPo7JvsO7jzZVd7f/fa3T377u989ef9XfN8AJ0H+aK/p7f3vyVi0i22N9cPXBsam9yFh8JAmGc6PigmpL9Oxde7SrVgtlsvFiFTbl2quRs5+8eIb47lStkM3NV2T+gzj8srFfGX0lcSNvbZoQjAc8i1EgerxRl0xB7whOjIObQB126vECRKdR2q8X0FzotXsPAL2/akWS5ywBvldIdz1trUIF1uw4663r0P2mB4fE7bH9Gw98Xls+t6yu9f4wMM7777jj9nhbhGSKG3QgWQJO9Y0rENY45BMgQZ5PaL3W6xBv/mtJ1HvxTuX/Aqw3XXCnW+sO66Y64l8oK05uvLgQhmB2kquInhBz4K01UmmwvMcK0X7o7yJfFkBawX/n3ZwHVFiN7QKUs0Lx/G3Rr8hKLHdIXGafpzjB42TwzSuPNrgwIdJBHY/+ISCQ8PEBmLYVoqMoTc6+DkmyG8eD1rWFfFEudDZ5QY/U8SE9laWEJYVb7yxD2ayAq7YHUcpB+0A277jMT0kTvlukyVPlkD9aIkSdrzs/cOPKL8PfXTC/+ibDQCMAdfsOkuvKaR1mjXYGJyft7BxApV3kAz2OhInJFUokUg5D+408Ut6oPEqjunwVmsS0wn4Yv+dxhK+oudA56M7GC++Wemg6i4ZtFesGMbRPXxH/BjBuykZ5Tk3V5q0cFS5b1fjdNZzqzbPes/W720/BqKvKzEQPfwnHNjy5SB26b5AC84MIPRzdlpvR5+LD6VijjIfg9HjMChxYcTKSpx+8JMTfLkKydLnn39uO98rwHtN2PFHI9+yx1iQ8OhdJTzWgqu8uvOkR1twpxoflMGX+b60L2F9ha/yRYlHcPA+ApKjDz54/8nvf/9733/969/YHagP7U7UB64PevW4ntqD5AlbbyvGO9vMtYvh2Y5drndYu9oa58xj7Cn/y3GMAEcsx5ooiBvw/A0RtsrLdPaD+oz95WP6l+6oED4QdHHtY1fTcuvw3i0HpzeYoC4vO0O/Ggm/+vk6gF1MqYTpO0tu8GGab/wu91//xnUI71wCBz5eVHn2BAnO+7Z++N1ru+iCEhdz/IzCbCA5gsw39oixHvXDGuSP/5k+rEFIut6z9y5/+7vf2gWc39ljwx88+ZUlYEjCYMfPW5A4xQbd+ntPbIURerzEuFf2IF4puqfS8U3vkTtYK1/VzT2vwx2a0QJlZ4+rcZwcESaGC2tc7Rrf/2P37ziZZp4foAxNwJnL+jGbSxDz5LaNKh/Qvangc+bSBbImW2cd4WzaiA73OrKSaZx678h1AYfBF7wLnZ198FUiHqeVIHGDTLOTEgnIDyurIeozixOvGuNO03Pc8vbEiUmTEickS0yemDjBtiZOJU5mMGzSoh0BYHM8DtyfWiKEk6NMhDwxikQJsNHw4jg+N46kKvdIqFw2XizneAi9bi8whgIkq6hx7AGD8eGF+5iwcwf+FSzGMXH3BmiS31VDl7/kk/g0llSu7ELPStcKt5J/KNy97cfg83Ul4fPecjSDXwO6ZDG/+kLt8WXQnQnrpvxXX2A+U+41p4GOHX/4GgmH77ZmJByPyOAEAyckSJjwPhMSps//+vmTv/71r/4lPZx44GQGSZJfocVjdn6igq9RvetXeXGll3Mdc99OivwR35/9aq+/L2WP2UAfdtzBwgbfcILiV3vtiu/v7K4Tkig8vgd92PG4nja1B2XFh3HiGsE4gs4N7Zc07q4rprTd+YrrF2gbAY5UjTPSFVhbp9u4HGXBs6ZLRH0KuQ6Pen6p3ToCNfZ3NGtxUkcZm3ocEjoVXEq7rBSwX/vY0bqGCzf4+BQu3uBOku4Q6e4S1qLPPv/rk89szfgWCZDdOQIvLqi8aV/Ge//9X+X7kZ482bqEL+P5nW+b69KHL/DpPc0v7C76V/i0ud3NwtqCNQYyWINw51vvOSF5WiVOWEOw6zFitH9//C+jMwSyorTDew06OuhI59X+XuPDI/De338FCr+i2y1xEUiNXY1b6/Ds84PEiRpe/cQJQcqwbCOWGPAE38EoHBYgiVyVOIVRs5OmMkmSH1YmDhMVMjwp8h8bT5zwN5twgjTdbfrpO7sNzrtOKDFUPCUxJZ442ckWB4SpDAd0suED1P3CAY/g4MQDC4clT1hAIhni32RCnbu9/eQwkizSWNedJtPietyPMPo0YgYX0D7QHA46fQLGWh5xRhclHHzO8AoehnF0D/85Nhinu6jp8pd8Ep8WMZUrmz5Gq6OS5ZL+ZHpA4N72o023TJwUR5TY62Q/xn7EA/NY/qsv9IOOH3XFHCcnSJTwLhNOOlAqccIVX368gY+u+NfzPLmxq7xInOyuE5Kc7+yuEe4cIcnxl6nxXkB82EGPxyCpgl1Pnswh6P75p+d+QqQToc8+++zJp59+asnT55aoMVnDnSqd9PzpT396gh0JFE5moBMnL4qF2oQQVFwqcQJeMZlh1KUHsNZPwSx/OY4RGNcRjbORh3HFil2bFmhgMI5Zdjpx7C/1We/f4v0FeogIKOa7utWd6ihj7L16TeIEG5p7vezrEv7Ytt5F0t9swl3vTz/71PbP/AIMEid8zlwXVbBu/Po3v37ya3vcFzAu6mgdwlqEtU2P6umOk5dInOwiEdYQJGHQh3ecfmv7B6bnXVvn8Ajg6/aOE9c0rTFcdyBX6w/XabUR5eHWAqkwH8qcYQi9RzrRB6/ydn//FSj8im63xEWYtH5p7PqCFjH8n5fvOFHD30fihEBlaLZRcwzowXMwxoYFSCJ3TpxgzJS4TfkQ5ZA40dD2jhNOjCxxssfzfvqZd5h+tMTJ7zrhzpMnTlzE0Pk++eOzwZ4XGY6azQ2DOVAAMmFC4sSkSQlRJEqgx10kuxYTMGRwd8p4nSZecHIhMgsw5PuYOA0Uo1tU0rEGG67wHjTnfRUPwzi6RwM4n+8eiy5/ySfxaRFTuXIdela6VriV/EPh7m3/xolTj6kWaZWYqz4FEEsLCI7yX3L4kedjLPVlqX5ygj9H4MlTXOUFDXeacNKCq7M4UfnsM7vC+6ndGbIrvUiekPTg8+R4LwAnJzjB4CN1dsJid4b0CV9PcvzjLza7zVd+TIYfh8AVX+yffPLJk48++siTJ9ShG5uSpH/+539+8i//8i+WPP3RcPzbTkisEAPo7JuvXYbn2qR1pDgUm8IQqnFa8UM0f9nmCGzXEI2zkRN9AMyWX3wVc2Hwm8KYo5/UVx0uzl+gh4iAYr6rW93JznW2PkvOJk7qe8xX7bIJH3QhB39wG4mTHtHDRZzP/vrZk08+/cTXDafjjpOtWVhr8I4kLt7wow6/9os6uIiDtURrINY6rGvQi7vpX1rChMQJF4NQomn4qBWSJyRfH9p69j7+rpzpkB76jAtCGOdch9QO1OddbbtYtkAqzBf5zxJD75FO9clZtS8b3/39V6DwK7rdEheB5BRgX2PQuH0in6wTp798ZufmeEwKylXiZJUatdDpZT98MQXM5HcwvTJzCQ9AeCldoB1OaleQzSt1swlj6aiWFsBKyW0g0ILeFWz4Jl8lskycShHjM9rPweBsst9LM57JEx63o21PnvJRPSZOeDTPPwrxoyVNcccJSRM+EOFDJTrfFwD/5CYWBC0CsGPKgwd+4e4ST1CwgBg83UlymkWa/4xuiZFdmzGZSqKULOlRPtfrvUMp7ymLm5uGC/7PAGwtVBpbwjvJDxVfp71ih3Nj/rhROY6OWZccs/yeX+Kby5XSPrdF39Mr+kOX97afA9EGn/3va8vZkTjzcdoRy/mIOYQ5wR0x8aGO0uz7PyshBw/2Eif/w7Z2ElGl7joZzj4K4Vd77Q9NIlHC1d1PP/2M7zhZHQmOf9TB+HCi8lt7DwCPtSBpUuKEExle6cU7jfQZ/iF5gqyu+iJx+vjjj/1k6Es7ccHJDE5ukBxhr8TpT/6VLJwIAT9f9fXWWqOxFvlKEe0HHnVuihRiFaihEFLlQPwHryiGYxg01zuWuBqfndbhUVYxx7kCd/B2uMv+At8+Aoj1xU1DgIuLs3aJs4kTBNH3fLrFzgJwhdaR1d84b0SCo0frlNzgDrUnTnaX2t9Vssf18Ggf/qQB7hLh/abfxDqEu0R45+ltS3r8j9zauuGJk39sAl8Jtb9BZwmTPnSDu1o4x9Da438WwdY3XQjyxEl30O0cCY//9YTJz4MMh7b1nY07OLZAKswHEufIofdI5zgXz6l+Wbhu47sChV/Q7Za4CKR1sW2xxkV/x4/uhcTJZognSqaNcy0SKK/jBeNY/OJLKb74wc4wMasrMVhzg6zzEqPJrDL5doFs4i5HsxaWu0yHuwrgg9YVdJaAB18lcipxGpWhczgoyjbeXUo/0p8w4nXEHu852WMx/nEIPI7Dd5r4XhPuNvU7Tkyc8Je0EQydkHBB4MLgPmBw6AoL7iYpcYp3lvwuEh7Xwx2ouNvkAwuDS0mTl6xDJ/D4l0mWyyG4oKlkDZFJjJpr7R2GVOLBOQ03x7xah2EcLVzH+GBUFsQboLp+9NeRP+5N84ljd+0IdHV9e/Ba2lpN55Lc5RN5Q+Aa/U9jUPbEaRyoW//lam8WR7HNBAN8vhiwKjXs4SMew+PGuXEqcfI/dhuJk8nrUb3vLHHCI3SfWnLzSSROf7M7UF9/zfcK8DdRkCj97ne/90fpeuKEkxgkTr6OYF6b7wgLfJR+fBoYj+l9/PEnbgN/UPdv9i4V7niBH7JInP785z/743pImrDjajDbhXedGBO0WbERDBphL/ygC3zCVL9qLVHSKY5Xv6w2btuCmB1vnafDiDmkZ1z1yUr3vk2OZPhLn1l2/zu80v3YuP22PLYn97d3GFt1cxszWntg/ZrECfyeaMQ5BepQn/qs4hdn7NE63B1ScoN3m3jH6dP4g9x2Eccu/tY7Tu/nO076HDnWISVDfkcdCZmtMV/hwzR2h7t/pAb9CV7owyN6uDD0nt1xegcXgnDnyhMnO2uxtQkXhHx983Mhjnlvk+nQuFCJ9lXjvLZ7UJh3Ga4hRECPdA5+XqP/kXhX68xtTStQtuYsFCcuAil/PG7obz9/JvF//usfNhqe/sdf/urzqxInMvNHkT+MutsExnHf6MPP3hYZXnoTBMPA6e0yb7dYcJfpsIwCF/gSEnEohwXIRayVUQ6M3nYqG2SCCZ3DAR123T6igvqIYxiJZ8yROOGP3uKOUyROkTAxccJ7TvgkOd9xgjw6X4lTlTzpYdLEhMkTo1g0MmHKBMoWEztR8rtL7g7aZ3K2s0QdvY5/xFfiVHjyGKvz9dIw0fQhZgqH8Wu7ashI6CUqh/Yt/HroxU76tUjAhWt8kvzC9XZi5FqtTq4j/V2X9F8j0+XPwlfpj4YgacqRqMZNBuV/RzPWbdS7EpsXVoK/7/iR9i14kDTBV+2g9cQJJw2gocw7TXZiopexcTcIF1yU2DBxshMVT5w+9Q9EIHHCRyH8C3ymB4/F/P73f8wPOCB5QnKDxAmfJdfdJvgtv/CRCP/j22bbryCb/k8/wXtO+FDE5343Sn7+0z/9kz+q98c//tGv/Eq3Hr1RPNBWwNr2YEQWm7oEdrCWsp6LiMfbGf8ODmzjfkN6rNZciqvK4lrJAqe9OEdoK1d9UH0yjuWjdowWHqe2bcfj2H0IK4fxVfe3eVYzxmaR6HvONTmw+BhpiZPEdK6Bu0NYJ/BHuPVOJNaIT+xiC+6CezJlyQ/uSuHrnq/bjvVBf7zWEyd7vLcnTq7TznvwqDEfF8afRcBnye0LfbbDpzftb9G9+Rb/rpzeb4IO7D1x6smTt6WNe40LlXm6pkZeKI/CeEF0S4oOOtKZfm41vBSYh/evAmWrzqbNiYlAcihzncOPxe0SJ/+0bS187kxaH/0ys4UIns5/OKFLukE7xoyjWWtw5++wVALX8F2JWFrZfda7Ol28vJAixKopMBCdUwNGRERGvhSO2QR0kOZ3nOz9Bdx1qjtO9pWaH3THyRInu+qSiZOdg2XiZFdVcMKDky4/8cEVlUiU/IpKwH53CXeZkDThqnLeccLjeOY8VlJfTZEgoZ040UNpNcNX4gTY/sUdJ8DO4yUhQ0TNSmuit7wOgYDcuM0xHakvf62Po+5tjYuOvR3c9WuRkPYzPnV5yc0l9EjXXM68L6ouv07bjwGnu00+IncG4RxX2CCujXlXgLkBGspxTwGT5V1mxlSx7YmTcJk44U6TPhBhyQzpSGrwHgDfL0Bi8zESJ9txNwjvGeAxO61lv7G/c/KHP/zR9j/klV7dFcJV2/TXZm+3rw9T4P0p3HXCo4CeOFkdj+DgBAd+4sMQuOukxEkve4+Jk0cOh2HrY7DgWiPYLYgXYofFxBcU16F+GBS+ohW2bd95trXisuUUTSU5KqajhK66j9iq7ckh/uoHjRWVkD5qR1l4PGi/LY/nw60sHcZX3d8mR80Y6x/R9xxyemOKtcx/2G3+QReofJrlqc9//8pe/D05rAu4K62PQ+BreH+zv+eE9YjnJU/tcV77+032ZT2sE1iH8Flx/G2m1/1vSj7z96dwRxu7HvXD1/lwVwsfvMG6orvl/pVQe/QPj/vhTy4A7xeU4TfOgayEXf9iX7RF42Eu29KyFx3Ht+hc5DtNjA66pFe+ntb5Ahgfxce4Is+RODYyx3kE0rrbNv4W4zf4ZokTr15yIdTiN09MMzd612vh6SzTWe4K66QG8uVBhsawHZYV4CZ8CYtpLDu7JworAZxQQAyxGsXROdsBYyc47oeYUdquTvdHdfjIzs+WOOFvOeERPX+fCR+FsMTpe7/zhMSJH4ugvKmJO076cswzPMeLpChvT/N9Jl8sbMHwBa4lTfqSHhMgS5J8JbVG2N9aYl8DhzYiDsAATxpq/Cdeq3m4gKUMS4Oz6QIaLni9yMM2tkm6AGxjb6bnTrog/yqRVm2V/+oH1Y9K6VK54teaANoM7/F3/CXd4lv11VrubuNDdoYyxsewxhyMme6TYI11xV5rAeh9l23MBLR3vuuEOYw/OI0TAsUZCQmv5jI58a/dGc7pNrl4x4mJE67s4h0k7H6iYicrOMnQRQ587Q5JDZInfVUPV3r1aExvT/qIi2pmD/vn9sEJJE1I0JBE/dV2XAmGD9ihe5U4QT/alPotLkf9TV5FFpGjjOJSC4tROpuC/IqWq7ismqJYbmkKhkrEZw1DFjTtW13EdHliMAe5nrM/lMiP+JU+ya1oK9zW9orrHO6Wus5ZfDiuwziqy1vft1/gZeLU6el5yHsRsPocc1LnF46z9Qh3qL+zv/OGr3jiXSd8IAKP7OFCCz5eg/VCvEhu3vvVu0yaLHFCAgWcJ1ZmC+ueEickXND7nX3kBu91Yr1509YVvMvkCVj8HaieOEmPj29rUE+acKKCs5U+JhJeBgL8D7yF3W4nfXpg07dUf43P1/DSR1tj0lnAVRM6MRFIDtvoa6vcLXEyrVCMdY9/RNFMx/tN+HHUhFQpZ1hiqE1beLnmn3jvUB1Oagb5MLwIXLRw4PbKxvnGInWGyrtOjdynjccufjjEggHADhIGpToWytvufhgtH9dpj+rFO056RO+HH771O09IpvAJYB8ouONkxp7Z5zYrceKnN/0qS0ugUOdVIUty/O4T323yDz/4XaNKiHTHydsaf6wWOP7jHSjBwiKZAr/aDjy2aKLDfvD4RpCjCK7iCeguY2lvAt5F18ahlwix1065eEQXH0rxquy0DiOGiuMMdz7A4pvxl2z4mM4xUZIrmT39JXUFFHOYI5YO+Hqz8EVau0+CKU8O4HwuoDQU6wbjuWpsphvqPWnyO/110okfeiQYmLPOGnHHCYQSJryT6uu0a3niJxB+BdZOKnA36KOeONmJCx6d8T87YLrxN5b++Mc/ReLEdwJw4jEnNt024l2JE77ax6QJX+7DSREencFJDE5wkDjhcb2642R/0NKuICsxg17FbK8fRcfaULBL+tiCHGWrk7T2gOtV3/biMrdrjE2najSqrJivZIDT3rUIXsmQxn5QX6A8On842zbZVrnvgzjOlbfSc87aw3JdjGV1PTo/HakZY2tQoZf0RDqfHfw/Vkf2O/oaG2KKdQub1jWsOVgPvrCPOeDrnkiauG7wkT09eox14Z138YW9d/Jz5EicXJeNJ60r0OUfn0DSZGuNLQC+BuBxPN2t0ufMcSEIOrDDN/W5mptlxEV02Ey4B8q94UGyDXVbMOzKDl1U7bamHkrbNT5nvK90BmOQm4/GjbSoPAnVFIixgDGB3+Lo/+U7Tv/5kb3jFF/Vs7Fmgw1X7XzcuTG/0xRXFDER+w6GPjnN3MbBRKSnibkZ0E9k3IPBDRlWKbNHdfEtShNlW2VIZfEihqst+qKRwAiFUWLR6Z2+kzj94Hed4lE9S5xw58n39o4T7jjhGV4lTzrhwhVr3HVSAqU7Uba62VjpOx7Ri7q3OBIgT5iszXkHCtFADJBgCUapOhdN8iB22FTzCg8eMz80JPn34jkw3qjSx/SNVN5czTWLTzfeF6IOd54Z7nywu+qLXBdM2OOHtQKKvJz6NKod62OCA2M2X3UTOCWz0F9KzkN0h8rSNTVepalLWqge40Vq5xHdSyOgxI6rnT77vZ2VjGRszSZOQDBvlTjBJOiZLBmsutNMI05CvrOrsbgii/cJ8Lnwjz+yO072xTt8eeqH73/0ZAyPvyBxwuN0uOOkEw3/+pQla3VHCDZo123F3SYkb/rcOe40+R0nJE72x3V1gqPECY8CSr/eWaB+HzIRybGArSF2Rmad0YVPGCHyDbA2jNu/lw1xOLMpVlteBUOl4rgtIQs92re6SmakcRyyPwhrrMh/lIJH2Yep7cej7J3hKe5XC9odNTUM2oyxnmt4tXRPB/AaI9arniBjPQCMX/qMq+nE8PV3Io3+tX3MAWsGHtnzdy/xCLHBngTZRWCcu7yNJMcuruAOONYMJDx+ocjkM2Gy96KwxjBpindBbZ30d6RCDrLYkTjx4xN89Djbhqd77Fx33tJ3tHGz2o/ci5CNDAc1yp/Tco7rwOAjk4dYPuCijHHHDZDgamxiIoh0JcYp1rv7JE5a2PjisJm3gaVH9uDCetFbdH16WY7fEtJXr6AzB1MCwMoBlcD1TXiVnTbBwcIJBCMypJL8q982/V2LUaMpbEkTfVVXM+bsh/GOE/5uk99x8rtNlTjhy1a2bJlOdn5PmnBiwrtPvGLN5ClguwPFxAmDB4mOEiYkP7FDp8He5pY86e4bqKIDAh9lBaPljBPpqgFv2yJoParr8UbRWx8f09ZdfO8L0Fn5LtPhS/KdT/AqNhyjHLeCMZbZpdO8suqE4ajonb1wajE8MMy320L/lukcxi/KGGuZCeWTM0U33uaU4E6X02ADHTQs1IAVOyVCupOjmCtxQindaAnokHHYjx59h3Ay8c233zzBs/94vwmJ00eWOPnL2JY8gf4GXqC2q7u///0fLHH6J3/HCVdqsSOxQaKG9aNv+j2AjziR+dHescJJEJMmvONksF1Jnh/Vwx2nOXGCblxdxqa2jrZqxKjdHruItb6uR9ntuAu2rvKVhVfxWTVGcdrSNBpV1pjtMZWccNKnUnSUM676of+G8YQaNLVBZdf1kPDs52zriD7zv2r1mkXN8xoGw7p8NnGSTsQO8wx1rgd4tYBU63GOkZiIWt/wXhISJex4fBhrE9YMXOTBmoVPnL/xJr+Kh8+JI3lC4tQTJtxNR6KltfKZnb/gHSYkXODvXwfdJE7mK3zxMYmnqbC38Yko9TGh8xbgV1sL5Yp8iDvSLwX3tSM9j1my6+l5j+lD+IDxxg2Q4LKUmAikfHO/rHKTxGnvrlO50aEITEellx15O/h84gSbK2c6rsMLH52M4a2hi1Jw8cd6UQhwxXtLAxL+3CBxwsvf/qEIS6R8oLhbdscpkiV8oQZ/++B1OzkBzq9ae6nEyRKiuNvk7QHs/5g0MZECpidORou7Ts5rcNJDGrrwr0q2nji3VOHw2I7x75HVIlwCDwc9pq1rW6FJfr0co3mNfF/gCrYRNnaT/9j4DxCc0g8Rxrb/n5l3PO+dvWKZ1DjLSmbFt9J3ArdNnEwIjZ8C0N2oONn4ZrB9BqQ54Uwo6ZE4Qa9+yJWQYCwCRomESTv0Sd4AqvfCZpfVNYbxeV+coHz9Tf2BWiRO/vUpu+KLkxx/dMW+mofEiY/q/SHeDeBjLau7XDAo33AigwQMfyeKd5rqnQV8tU9XiHHHiXe0/uBJmRIzrlOVmEFvti3ssIHVZtDFY+y+sdT4rIGg8EjHq1yqX4/aoNhs+TRaVa5jKjnFWaXwvZxt0Uf0A/Y6GdU4VhtUdl0PCc9+zraO6DP/q1avGdE8r2GA5Tq36xInSuKIPlXihLXMEHFOovmKVRX4J74m9cTp408+9gsuuMiDxAmbn7vY+QuSIOy4W+R3pCxZQtLEd5u+i7H23D8cgT9wiztL4MeXQpF06Y4T7qBjvdPHbmDDx6mtsfhqMW4U9K2PCZ23dHqHWyg7+hRM2XMaznGdMvtoTEMcH3hB9vHlLYuxN7UyezgCSXdifOJ35RZ3nPYSJ8yJvsk4cEPHTnwlM3AVeoB2hZPrtolTql0D4Q4nkPxXWSJzbEB5sMTpe7zjZDu+nIXECcbNJQxUPyF5w64WW8KET3zqXQX8Ne1nkVT5o3t2QobkKAS9ZBtXiZPx+arakitPmhCHSqZclzlCPahVnAQTg6P5nF2dQJPg4maMj7I99g/6NY3q8+w6uYi2F4SP5NeLnS1G1UWuAvFS4uSxs7r/c76ReZaVD2yXamO5UOMMK5k9/aPG4xoitJs4DeOVI10aVzEboh1OoxDvfMfJ42k/5DgB0ckmSvDv7nEXynVi4begQQ8SJ36q1xKnTz958pe/fOSPxQgHPtxVevutd+Idpz/633ICDicYWDOUrMlfTla2Cj4iccLOdxX4vgK+2ve57Ti5ge/YcacJd5zwSCCSJjxKAxtz4jTH0sdUIOWD4gB09XmNzVFGGl/9srfrUmsUpy2PRqPKNg5jfHUZxVllpwmebdFH9AX2XxInxelFl+NKHN7UMMBSkNv5xIlzzo7e35jnmTgZLLwtXLHemcGwyS/rfe5f+OSfSfjE71rrC3mQ5d3uZ5n4YD1C4qSkqSdOaADWEqwtv7K1BQnTpTtO/c497jbB97aYeCz62NZ5SwZpAlooJ8pxlbLnNJzjOrb5mBxDHPHj94Abxg03H30bS6JqHNKdGJ8Yp4+ZOMG7IThyN70Uwjl75QBeKkiZx0+cNH3U+SrTpXnuOYGdM7fF6ve448SE6RtbSOxrMvjCnpX5w2pu4WoNFhK/3Y3kyW57M3myO09W1yM4+qoWVzS0B6eMaCfgSJ4woDy5QuLERIupE4+QeZqP8KHJkmd8qAt4Ugg1uM5+jMQ4UZKc2S4JPmD5mLbu0ow+z87KS0aLxBm5PZk5PrpLgl4DzXf0of9nX6a9qZr43tmJbMBKbk9mxdtUnQGpGrOAm0pO7jAQRdKMtWJW2IQcYA183hdWJcy7RIqfko1egtb1A86kpt2NwkdepAcJzVdf2x+GtLtL+Y6TPRbjj8PYlV3MS1yFfdf+Rgr++C3eb8LX9fQeANYP2elxk884SdIVYCRO+hS5PncOmnxB4oQ7Tkyc8Adwf+V2euIEXmzSL5u97TNdMsDXMoJxSGmOecKv+rG39VJbEL/1JrzKcczOcuoHlbPOmZ/0WAOsAypxwolp4cF3ti2zzbvW176WtiN6cb6aUEyH0fkaBvGrS/LpxAl9bP/wG6C1CnefsS7gzxSgj/3OE1aaGJNeml1cvPF3nOwRX/+6nn1YBneg/NFie1wPsvyS6LO8Q43zFyVLWsNQ+liyBoKOxOl925E4efL04a/zs+b94xA4/9H6+bP5q0f15CciMcD5azCG0Pm2qKsw7IbWGRekz3FdUPACSEMcd9em2ziG8ciNY3PWKqq6k+7E+LTKYeL0Xx997ncm8V6cjVHb8eMt2Ixi0GOxsx2TgnVcRbAtrY9uDQFa8tyl25eK3DCfgCM9NSewcnSla4Ub2+U1Z8OphgyoHHkRw3lj5zSCi6I+78AQh3hzxwJkV5/tj+D61/PiHafvv7e/WWA7Eyfcdfo2FilTa/rfsDtLfqsbzwnjPQYkTnarm3egKnHC4sEEKa6xYxx4K+EkaGixeFjnZ8mZNIHGmBi/r7hWy5XXGxo6rPCtIkjdhhyCxjhJEiKIw2Nuj23v2rb1eXZGVvxaJI5kjvgVHx+pGqem1McrRnD0l8q0t9eNvbOTeQK67BF/553UnK3GbLCxzY2lt9gaWlowmpOHAXZixlCszkROZ7MDSqKJV7z8j13bp3t1MoITEdGgDrqx64f/NftUuX8VE4/ZRuIEWSROX1rSxMTpE/8wxCf2SMw3drKBEw54jqTpHduR0CBx+r0lUFgnkDzhvUjYkL1sStjviRO+jOWJk30cAidA+Nww/jilNiROeFwPdt577117nIZ3nHQBR3woZ3toO3B9U73HBXQOvdZBXegVh+e2rppTYRrjRV7hVDLWoM0xhx5+MIhjbbal+M941OHnmDTVnSe2AfSV5MPhLvkLq0f0h/PscTQvw13DoC9ppz8O4f0c672SJ7zv+JN9lAZrA74OmhfWzAHw84uhPz/50i7o4GILHvHFWoGP1eidSNwpx7rjF37tAjASHtyhxlqBu03Y/RFkS75Qui1bLyGDR/VwMcgTqPgohGDoAA07LyLz4rKSpj4o+3hgmFqwWpetsY3hBHhJfxe/ha2u77HgIZa1QD2IeVtZQi8gwWUqMRFMuhNrnFWOE6ePLXFC0nQycVLyhAWPi185MwSm0P7DXNXe7R0ujhHKJhq6w8VVrw7V1WGeiYhnLUfqJZrkWxnsONmwZbYRBBI3xwZU/giJT3bVsahrB0S6L0p41ME++PA8/gAu/o4TPkGOr+ghadKu5IlXd2gHC4PvljC9Ze8w4ESId52sNBoWJXxA4hk+DpFtiuTZ6tVOS5bsbhLrlUApYRLeS29Gj4/B3hyWjFAcvSCsBYs1tj8o3phVTNnKxz5a79C9xzZ8yl6fh11AeC0SoHW48wrudMmrH7ybLRAqERTBgJJvDpYxkVNWWEr/iH0BNTQit1pTNBaVTDlL8IIG/5OHgSNOSBdoFQPBttfunjD5SYEt1MBhQ2zdHmzaiQL+sLX+PhvqoGmtxh0fJE5ffvXlk08/wefIP/LPkiNpqsTpPT8xQUKDxAZ3ni7dcZJtnKTAN53I6A/gIoH64gv7ap/9nSgkbuJH4oQdd7R0IgQ7lxInyHJDsAUHSpR5jBle42/kfPVrR+1SvNZ8iB9jKD5ERDDKgp2SfVf4bR+IBglstM01gOOQYxd4JlPiIf/qKP/Z/VubK5kj3OznzH9En/lftfqwtMn5FtpOz+ue4rOy04X2PgUNfWv/0N9YE+quk9XtzhMuwCNhAk10fKDGP0NuX9/Eu5Df2ntN39t6AUuY0m/YawZah955+50nb7/DD9Vg3VLihAtCSLawzuDvN8GPt+Kij99Jj4QL7zj5I3x4jM92PSasx5HdIIwuNoWoVvgF0z1RtNGP91T4kon3udXhh3AT45Abx+RsQ1T9nGiNcb+wBh49qvdfljghafLkybTVHScOXL/ThAkRd5z0Y+yTJK2XWwqIBhoo42ATRWXJ7kPdUIcpUYkTbDV6mmi4pZEjehMKVrYpDTQG4hCfefv/2XsTJmmS40xvBiABEMcAGJAy2VJLk8lWpoP//9csKZNMJmkJgCAAEsdgDsgff/2N8IyKrKO7+ptvBpPdVeHhd3h4RqVnZmUxOZogKKZ7Yun7JcmkpB7mgrM2LArxeyj5e0367SZdbfp9LCJRQEUx9UndqocmPMniKAokLz7f/S6FE0/QUgGlwimKp13hVFeOpIkRu3hS4QQeHOWU4eTNoQVurLzQY8vhCT68F3mEJACjEANOlZuYQn/X2/vky3bsGbzjXgef900COqgsElslhWx0y5OX9R8NuRndeJt4cK2/zNtu3zj4d82fd0XLScaYAGI049Tys/iIDXtFjy04xwzSurE4B8vFZhkfgLjlYAOYjZBaP0+ectE0rgwFjh+ahP+TKJx+FwcoFE/8jhNPr+K7Tp/ED1Bm4RRXqHzFqV8R8prBGjH0lsO2TYtfs3DiitOvPvi3+BHcLJzCLgczyPOiMMPGxx9/nIUTxRN2ch2KM8neun7jrrW7nNrhrun4qtCujYu4edvzzUzuvIYdd3SsuE6zjc7Xcew32OdFDubnV7bqw7v3b2rpdPsyqS+Dbum5RX+Z1fdHaixr3aWZMrXaiTg+vhvvTj5X+iIAe73Kq05RMGWh5JaCKq5EcRWaEzpclc6r1P/2qyy0Po1b/JDXiRQVTX4cOQUOL9YR1hvkudJE4cTrT/EbTp/E97w/j6tdyHMyyTJ8X/OjH8XjzD/SI8nzQRFRPP1NrD/+LmckZA1crfOvhWd+braYPAuUnf7+LM3vjx7vX1qmemSf6yN5qC2z80K5qf5Qtz/pX3RuF05xqx75ErmaebMtnILoe5O1CIYzuShe+DMX2yTtksDBcnup4xIzhhmkDovzWDit0pf8DpY4O73Dq57qF4sON3djEM47XdfC5GiCwNqWJ5b+xEmu4hz4WTjFF7E/02828f2mT/4URRNXnaJwUj9u1WMyYyMJZuFUxVMsPD4g0hUn3cr3rSic5pjqilO6w3jiVd9pEo+vOEVvXIWCAp/5adlaO4YoXH8XbzDEv2KUxod00kUW6+m75CbZ9ifmKRD5/yJFZ1LP83PmWHMwkPk3zEQv4YFozAJFFr3DOUk1FyN7cz0QMnGOT2vRmqM3rlmUfiNk070vq5UXmi/DZ2sNsVXhpLjS1z/9/XgSDdsJXYVPHXjUWdq+rui2vNBfRQnFE7qsLw9YqqjhwGIUTnGbHsVNnrWN4ikEonCKK051q56/g+SDDooadKp4YjY0HtvizHIvnP41rmr9mitOUTT97j9+FwdEHMjo96e4kkXxdLtwwi1dOdvPP2vipOjzqCEm6S8W6rkyg+BcJE/AzrnMXssf55Hn2e3UxRxZ3xHWfOjWPB0zzCtO/nza+xdrRJ/YMtbtdPuPwrf03KI/au9949/uIXMKD59p9xdOGqXnbVxViuMQr0FccWKdoDDiyhAFD+sPhdOvomiiePLtfJwI+k7dHfM3cYVJL91Wx7ELc4QO6/ldXG3i96DQ98e4apU/yRIn+lkgKKA4zkGOJ+z9uJ7Ml0/oi+8//SAKJ65KUTyNjc+neqEjYxZvhCn2msH2bOCwPz5b+Xuk79Y+dot+z1Bq1oIV6DLrB6am07FP29G5o3DSD+BWnkWe1EFzIJw8eYm1rjgZp/ZyCB60/Onv5nXiuTX+WjuGGUwdlszZwczUeClzzH/T3U7JC6hYtAPtxiAc8Vk3JkcTBMV0xTn7fSBFz2nPnZcvLvo7Tnr0+Cyc6ooTT9XLK05r4fRXo1j6TlxxcuFEy9P1dEaZs706UJHrlQcEqoohPfiBw0NG7zbgeiCEH02eS0wOz/GpFj0edk2AOYgIi5S3GSdjoDd4A+52EM3ThvmVqN383lK586/LPM3XQ1Blgf0y0a2Fcsum9+fkHZNCzkpv5meAXhfAGk6WzN3iLaGz2A31lRuS+nLffQVbIdWg+27qUBNHfsB2xDiA7LmNYfRY5qigpcA6RiE56ND3nGbxlLErIV/Fcetb9KyNAxUXNSqc4la9uBLEI38pnCh2/hiFE36NK075O056eIPPxHLgAQ922Po4gLGRRVjo4wCIp2Nxy16eCY7CiXH4ihK36FE8rYXT2a16HEQRyb7N/HEeqp34zv2XC5/HQ/HUPHZYc+v57S2wX46o6fQ7TB/bnOyjfaxw8pyiZW6r/kl5DLql5xb9MWvvH/f2I7TtXp3+aOHEaGP2cs4/z5Ptx8KJW/Aodlh3cm2IYocHQ7hwynWsCp3vf1/fQfL3IGm9RjBHrDmsK/6Ok58QSsta9Bm24qoWvOhl/ckHRVA8tSftcbuev/OE/4TCOZsFfgSEMfEPrVZ4WJ++hauxaTL0/nQT743CW/vZLfqtgeScJVNm5AX7yPMKtGOfdqNzZ+HEj4DNqwwc33D7nhY/Fj6/5kKIJ3UcdHDKA54JNqEDYyXIEXetN4a6MMVtM420T7jG0KUHc6d3uDMXHGSNaAg3piOO+HnTxITskSXIimnyjYFYjlYTf3nFybfqccXJV53mrXrI4ed4DHkUSVkwjcKJ7z75e048briuOIWDFM8US/JCsHboOHAKvIumLLTgxVK1KrIYTeGzZRj0aXkrGDC27AVe9nwbVGD1Lya/i6l4jaQtQkcN+GhvoAM4p3SuI2w/B/aa6WK6kBnCK3Dbo5scOwYWg8Aze/o3U2JWJ9RPlqI32B8kHhOt1woEE475UPq7ZYaCM//Vyki9lztqqnNg+HI6cy9glNrGbhpde8q6l3+BSDj7irfXRMu7ZT5yc2tCatVvoYwztnGQAIwX1p8/YF1Xm3zVCRWeCxdOHEjwVD1+t4mDFH4EV1ec9AVrZP7me/GghuWKk8/EUji5OIN33Vw4YceFE1/45moTB0gcgOQJmviC988+pnD6Wfy2yj236mEpojoCpbHZ/lxflWOzb46/7PY8Hko4xbXDM7ccOeeaW/Cej7W1jFodM6h40tUmFVAqpODZ+bfDwWtbwC/dlEYXO9tB3TPsHBS+Z525ijXHWkg63R/ZjTPXn94HHjJND3P9OV8viNuF82pTrF1/ioKJ24ZZJ/JBEPEwCAon1gyeqJd3wMRaw5XuH/5QBY2+kyQ4b7+rq+qshdigCKNY8i17LqC48sRvQTmfWL9+0gomHlH+03j9gO861VUnjwsZdDtf+SDjs4zh1aehWZ/aOj9bGJ+q/31Sds9+dg/P2ZjyWCOJQCNDB/vAVLBH7AHi9UDhxELWiqeTwsnJRCKlQ8MDLW4ebB5EZKoNXwN4Rko0g6V6+zjybnYTuEE+uNR1d3hwtx1HI5wUQyiMyOzFmZO2wQSv2hke9eE1zYXTfKoehROFEg+HmIVTPlUvdno25Cmc9ANyvlVPBRRFE0/Y44CGJ+9l4RS3xmiOqniSlswL8KNgyhVVRVTyR18HkxWTpDNQv1AETe3FdPRYAYtRzTFgqUHyXaihBR7eS90BNztBvc4wWVcoXdjtlitj9WOeL72evPe78bjP7JcKZWvT9Lku+SN6wp6LNg5/MNEe4MxrDNSYY+AevfnmyI9QZoqMHwnvqtcm6aHCiRjzR8iA48X3/+g/lmSK1Cya6tG+cbCQmqpY8oGEb9Hj4IDY+kOfgmac4aVwigOMX40rQv4ytp6q97344jVfvuY2Ot+q5y9Wc0DDrXasEWxz/pjzD9IGB0MunPI7TmHnd1Go/T7scuLNvyH3MYXTx3+bP0p5++EQOdq0ub5NH6DIjyNulfjL65/HQzuXPqs7PPO2RyvzOHJL/OKBftaH5qtN+blVB6E+dqAVT9vREtNzqxDV2NYRe3/vXp1/a+AAAEAASURBVPl7+e63/H5xXkY8/FMKpKOdnh/ji/sHetHG8saaF7g/R6vjFa1Fn8W6xVrG+vBJvH4fBQ0FE7fpcYIlX7/59Qffqe8wse581L+L9CM9zIH1jTWPOXIu8T0n9PKEUL7H6avcPHSCPmsgtwDil4slCiZ+FJfXWeGUhVn4zD7EGqc3QjWDNaEKxGubUNj1v1bd+yz/kv3sERl9ghIBoJ61isrA1CT6MzptkMfc6SDkB//4D393EcoP//kX3KrHFSflxiieAkHCkDj9ipMTdiRUU4nR4UCmgG7taCxvAPqKk0KxT+YRpr39IdT5Oiwxsfmddgg2vcLtPrRqHhovIHHGVrzGqewYU6oRPmMdV6byV615HDlP1fssnqoXr7Vw+uST+D2D+GBCEh1coh6Fkx8MEVefOBjSrXo8ijMOjPKgyLfqYby9UJZ95vNYMKUhF0oUULDWbXzWIZxp6o0rUChI/eRaCuebdxLn06QI2sV35bmnbzv38O54HvHjEd6dLePOYmL6rj3smzkFNStSthNJXI+P4T4Ow7QDjgnNPhrAl/ZB7xNdtN7YTse9U9gOh9GHC6eIp2JNvkfhFGF25t87BkdMhZOKJg4A6LNT59WfOIDw7W8UNWklaMQ4P/RjDfD3APKs7B8oYn6fByn8lhNnefUUK/3+CT9++73vfi8LJ56qxwMcKGz88AYXaX0Oc47DHnZcOFE0uXDy2V/4WG94qXDiitNPQ7fOJLMOoZ/xrNuaC7Z/5CPfSLU2cUeGv8jeeTz6vt9h0kv52wNmXG+he26M7zLYVvE0rzb52OGbwqlH6t3C2z1EKZCOdPrdhVPJZx5EccMalfMfKz9zTeHE+sVVIV8ZmgXTb6KIiseRRyH1vXpEOLfO+feXKKBcRPXCSfmltSdvOY7jHq5wUzBxNeu3FGbx49sUVtiGnzXn41Y0fRxXvX8QV7ZY4yjWvHH8lE8BjKtlPBTNtmpPSbYWMou9uo2wxfYWml/t2tMVeO14RPEjMv4M5eBywtPayPMKt2OfNlgDHy+cdACrW/X0YaTCiQVQO4IWRPFNV7zoguFQAY/cdq5nw/cUTtgcobp0YORq5+mwRMTmdwu5PardfYZrco58+MWOmf61won+h9GXHk0+T9T7In7LicLpsyyc4tL3p/yG07zilIVT8Ng+Z3pVPMWBC7/lVEUTT9bjQCZpLpxImFH04GeMzUVRzqcO0HwrX9Kgp/vE4fg65kD0giycddPGVs7a5+RCFdzZCubdm2Lm3svbR3bGnZVH/HiEd2fLOMVkBMboqy3jtJxbBG6N/xp9jkd5Sp8/NsFuAyf0oCVw8nbN5onIc9HN14vCKWg98oYVX2LsV2ZvXXEyl+Jx1dlgTe54owBywcSXqoHRn7foHQqn+TQ6DlRUcNWXseOJdn+K7z7qN5v+kIUTV51UOHEgo4fJfDevQH83b6Obv7PEQcUP4ovT343ChnUEOzooYgw5x/FBwROyOCCieKJo4jtOeate3KZH8QSfT9RwsMJ3nH7ykzjbW98v4EmfqT/GdLE5GBeEhsi8a/0AsfmXvF0ffwa19v0Ox+xqcRgtMZw5rdw2zq3p9L05N/LKQ+QkeckLPC3bzscdDl77BfyS7V75e/le4sP7ILPdK5QC6V6n3184hYL4zxM6UTjR5idBzDXfdfLaxdUg1gMKm7w9L9ah3/yWK0/x+03//tt8wh1FDLfn/eQnP84rRBRQKp4+GidXOKh1PvHEToojXnmVKWzklazQ/ev4niVrEusm/Fxp+riuNP3kp9yqpzXItyQTAELB0sFJaucrCAqouSlgep/Y10L9c/m1ut53+ZfsZ4/I+DgkZjP/1niM2axJdOzTRnReWTiF0VzofNVpLn4k11zkKpGi0eDCcKZg4ROW65Z5JAjroNf+/la9EZqVvfqNXsEjyHPrsLAeDT2Nz9xDgRG5841OAfWZtKAV47S9FE7TH3yJOciCiCtO8XsFn38SL11x+lMUT3qyXvTj6XoqbOW/CqO/UpHEj9/WmV//AK7pnPHN7yyNeWNMHhetiiY9XS/6oT4LqC8mPOccfudA3qw08wJSj14OTb6SU9pCtoLl1nhzOI/cf2l71P+4lkf8eIT3mifeya/xrDTGaTm38Nwa/xl9joX8JR3yLVumUfSizYlNt6Zsdi/ezmya0bbcX9tb8iv/RX/kYcSnfHcBBW+mcAkZxqZe4gDWwyLYDy63sxikjjxpO78AzQd/Xj3qhVNcnfnrKGTYf7mFzvpcNLnoQpaDCh7T+8f4PoC/U0BhoytC+uHIv+bK9Lf/Ooqaj/UDuHHL3g9+oFtkeEiE1wn8Y8OeX+jX2eS4FTAePqGHQ/xb6udgCT6fsMkrTlE4cdDi7zHwBC2Ksr+K4ix1Vy5lB1sGorV9UIe4ZmcWddAdE+Cv07YbV4/Ljn4cvyInmQ4v8a25hq+/0GV7K9528OGbK06OxvvT9n1peNV2pE6/q3AKWeeAr0qPwikMcLVJa1f8CPe/x08ixBWhfJJeXZkG5vflwH+/TqT8KAqnn36sW+kooD766MdxBeqjcaIX/V7nxtoYV73zNr1Yb1jjfsX3OOPpnqxLn8b6BH9ecYrCKW/Zo/3xT8KmHkd+fKoea4eK/MxjiqbM5xhQtGeb94kz+i28drc2GbcEvsL0l8TqEZk8Rsj4AF3O2cBUuB37tMF69/IrTix8+vC5fauerGPcg9PhAviiBdRzzo4+Y+59cIMuWbPWER4jWttoQ6jhNsGeI7EdC7qd6vtYjdWY3XNLnLEbrxuF058pnD6oK05cdYriiaKJ7zplG0/U44qTbumTXg548jaYuvI0rjLV1aZv5wHTt/PgS3OnS+1zrEDxqifnZQHVr0LlFSfoxRe8nnvrEybeScgMgjgyAg55tAnyBl8S4QdQb7bBuwtwyjz2Jn8ek+ncj/jxCG+3scLHmKzUfV+xh3aM6a3x7+laG9DmMQVmwjE3TOOgLXNlPPK7bW9zcr5Wfmo6gTIRRfPaogwUwdkIh2HFVz3D9BIeXEiEDv3TOVCMkLyuOPGbJFn88JSoz/hhyA9jf46zuu2KEwcSKCXMPhPLgQKwCyi+lE2Bk2dj49YYXxHiYGM8fSq+48hBxd9GYUOBkz8YGT8ayRlZn3DJPZOBhbH8XOCKU31Bm0KMq1lcdUL/H6LPgQubv7+AXvRTOHHQwlUnfqxyXtFK1SnDmyLe4qTkT7pjr07rVf45Jkn/Gr3t8p+cYdvRLocuXsl0mHyFe8WxZsxXcpS9FW9b+LEWTuB8Fl90z66l9v57bJPrceheHffyPe7B+yFxGfHwS9OdDnb6awonEokUycIp1i2uSvcHQuRvyUVx8xsKp/+IK04UTnVrMOsOT938Wby4MsSaRAHlNciFE+uiXxRnLpy4RS8Lp1iHdoUTBVTqjKtZ/h0nfitqBkKfZZmjdase6x39sSCdTOdr8ke7VJuMExtfB/RL4vSITMxWhQnI8IzcwFS4Hfu0Qe4+p3Cat+nxYakFkUXOjsg6xj24MB3E8gq2wUtHO9WBDvqFmw9uEG8WS9vBcLPQ8EOo4Y4Op5zY+jto+kNB8vE2YzNQuZDMniHiid143Sqcomgi9p9/EWdQ4sUtexRMfNeJ4unTOKtMy/25vjeXRYaXvkNQ34uIs7s6UImz1fy+Ck/UCx7NHXNTV5fG2JjNwOVKyhUk8+pqkq5UTZmce5Iv/4T3b7IkLsKVPBmC6FTYs8m3oGZIzeX4uiW+JeQwvrB1vr5Q/CE/nucz3s5Y3OM74xwxrdimFiFPVezjQ85KxGOizb/A07IZ5/mVxO2529u09Ovlp6YTqKWW15Ye7R1MGO13b8n+3Vw5bl2XWZHn9Xn8UOShcIqDg9TGvhoLO/u09+8kxFvGnLnwBAWOg1UeBUzhxMELt8dwVhaYs8DczoIdiqyP4qCF2+i4rYUzvfzeCQcXXDHiR7PzAwVjEaMszGKt6Y8XHrfgxMELeF78EKW/v8DBUN6qFwcv34+CzN+h+qsYC+OR6ouoENyTjGeyxE/Mcupq7M7HVPo1eutz+7JhzXj12KErQsg7b4d8dk7SntGSUG/Kw3lrHp9bKpp0LOExuLXs2rc901/a3qvnXr6X+vFly7WlbbqiKc1+p99dOJEv8e/1KNs8ucOVIdae+E52vH7zm/juUT0Q4he//MUHv/zFL/MEC2sRBZR/r4lb9fSzBT/74OO4Ak4R9XFcgeIkMMVTFk61XnEyiUePs77xW048kIbb/vgdOX4SgbWNK05fxNpmPZy0yYLsx3zP8m/iKX7fyyf5jYDU+jly2MUTDD1AQ2ACr8mfvu9NjV9P6CVxekTGxyBM2IRnLMc0Vu479mkjOq8snMJkJNH+ihMf0nZE1jHuwR0+5gYf/OalFWwtL219cIP8XuPBgTLTcEOo4TZ7iNj6e7c4lKT+GZsyF40mZ/YFKca5R94snLg3vBVOUTx9+mncslffdwLmx3F9tpkPKuaDF7+H8u28HUZXl7S4BZw0HYhl9JLfRdAsjKC5ePpW/jAlT7cJerzoZ/GUrewlP/SSSz+A0R9/AfAeG++CZswmbfKYl5bc63Ml3Eve8ec12yN+PMJ7zSe5/JjfGfcUidhWi41b49/TyVl56DHR5l/g1YrB9D6eHa7T9zYnx2vlp6YTqKWW15Ye7R1MTO33aEO94W7JsQNn/aYnf+liP3bhxBnV/GHHXJdgiD0uT3ZwYiQ6tcme5tgHMgwnb5cJHb5VRt8t+G1+gZoztRQ4f/rkT/lI8vxOwY/iOwZxRpbvGHBViCtG3FKHTW9aZ/S0rCzC4qzxr3/9mzwY4gDJt9FwwOPvM/mAiDO+XMniRUHmH8hlYAr/HBP2GJdjOec/OGHOcPBWMhXgOGwfeWqfvw7tHP9LR6M4OVfQ4tju1gbH3u2RX/NieXuEjzrB6qtMLpzc1yyvY+n9Vad1v6S9V9e9fC/x4X2QUdQXT2q3AdvpdxVOIZMxCx0+iUPLA6l46aSNrnb/Jk6msD5wcuUXv/jFBz//+c/Hg2S4Qp0/uB3fp6Rw4umevH72t3GFGjh+wiB1xlqCvXzMeayPn9XVLAoz1jGuenMyiGKMB0TwFD/WPvzwVSxfcaJl/Um7sbZ5/MrdyN+QyXzMXK7I9AClxPHtNfnT972j1q9f7yVxekSGYxBtQIZnHAemct+xTxvRua9wCi2RI/khw2eOXmGOhIlq+1A4ce9nVuC1k6UHXojbAlwHw+nq8JKeeWnL62R6+ZvqDRk513hwIowt/RRccAtPhHM4OSFQvTdh4tg3TY4xnahYSw34/oLffQ4F4gPoz5+OK07crkex9Fm8Pv2Mh0ashRPyKpy0sAHrKpQKH8j2uR+0gGPEKqKyQPpAxdK3PtQVKtq1iHIxRQK6qEKH+sKlZujxp9jJfkYkVmv1RJtwsBYFiJis8RX+sfdHdsYzzf3D/ozH+Ed4LbO2L/F5xh9tFdUx76sF9c/tzNh7PLlWsF6EKO8dv2o3bcW7f25XHK+Vt53TNhOxqDEmRUt9w27BAiuU5Lf4Ep85Xv3Ex5uSXMiy00QS77nyk50oUD6Lq08UIopwSPDPSYtWNIHEvuXZzymeiFcWMSHP7zn5lpl/jy9lU+BwwJG/exIHGdw2x8HED+I3nX74wx/lQQz9/M23oI25CZ34h2+fxI/o8j0F9FCYceDCI4d94EHRpd9jie8uxMEKLwoyvlfAi59DcOGk8CwRIY4VS9EVhQwmiAxHDryCHUj96zOssF+X5lb+Xx+nY+tcVd/zSv5kQHlXZ+RTzkHDJSfzUi/63vBRLw4+j0WTrjzlTCaPZdwiZ9vGvba9V9+9fK/158uUV+TLA6dDcyjpgT/wFb3jBhy8qPFdK7nu5F0t3841ghM+3Krnh0BQQPE9SF5+AieFU57YjcKINcFXhVgrPv6Z1gx/zxL9mUNxNcsnZyiO9D3LP+SVp1yHYi3iahS8bC6cdOvfT2IN4uRNrEF1Usj71czd+hyrXN4GJDXPt9fkz2tkpwfvP/SScWrZ2STryXD9CUEWT3gy99wFa/3pG+sZn6tCXnkceWiJ3MjXLKCUNKNwCgYSMBOWwglrq/V0QIML03BoG3x0i55ONZ5ifUkzLtQM7V1LN34GF39X1FRU/Np4PYrG1Meb6LOxnflgPK0n+4jjatMHH3LFicLJxZOKJj1pj9v3Ah8HM34qjF1hPmK9GR9yTl7mPe0xv0DRF0pFVBY9UfhQIH3rW3FrH8VS3NqXi2MWUNUvvA7m6kpUGEQ+Cy2SkStQ2QaWoGZgHado6xSXcod3cPjXeOiebF74VrLHuuKf22d/ea7GZ2nz+B179Bq3wjub8B5jO8dq/PiwCQWZvRUM07veHa7Tu28db/i18tZz2vZ5XCaVTFyzMfvxZjx6PQbRJgVI+xf8ZajZQ86yiilrrh4xnrfgpm69mc8t/JanZR/lxabC6/MoaH6fBQ5FDoUTRRSP8c3vI/3+D3nrLoUMV4EomHhxm17+gG0c1ISB1Ictbn/Jwql9x4lbY3iCH7fHpP0o3Dg4+ehHH+V3pvyYYb7HwC0yPLGPA6Lpa+lv0QSTY8R22PX8k2cZwkDnalG+4aByEHYFt4U4/f8qv3lMLxuD4psRS1B955BCuOKUk84t7E7+Sev+yEfNVR4zVPE0jiE8L9V22beA7e8t3ffy3dLz3tLbnB98DHzfR3L/OTCoc+RpDCEv1coHvgLAwSfrg24T5jtIPARCJ24olPzy7b3WxpWlH3HSJn6/6cdxq/CPf6or371w8lcRPo+TQfnUviiQWHu4wpRrEE/5jJd/w4kTSPlEvbjtj9uQf8xtyPHACX67jqd6cmKIjbztr+hoDekDt6Ob9pH8eYR3Y+orgzob5xn+GQMjf7XtM9lUf8x43UufovNw4cQ6xkvfZVLSHK44RfGkxAq3VuuBcjByJ7JXg4+hFCU9FQz2NVuvd/YauwOG3TbLXVFHd6Ul1lGNNUBT3HZqt9lh84DTSxNvnsJH0UTxxI/g6ntOfroeT42poikuXeeZ6nqcpjW75RHn3rxA5IdZTHrOcy4UygE4R+EUxdJfReHEd6NUOMV3EiigsmCaOBdVo4WeRZeLJrdaYA/xysIp8PU3aOOegV1MPRp8nmMz1vno/lu2O/tvae8e3R4/7VwckFQsTT/T1elzfKwLkjCONv8Cr1YMpnf9O1yn4xo5cLbdku8+n+m4iu9p5IE2ATyzd71VfBsjfA054Kaz7Y4SDIWZ/yHncdJ+UVf60zA6+W+6EYbPONpZjFThFAcxPqjwLS2cmaWI+gM/Vhuv1BG6kM2rTPkQmeMP4NoWB0V5YBRFEmd70c2Z37wyFv7qR7a/k99j4rtSfvnqEwUZt8n4lkJsSrejmt2Wt4pJjwscCgP5fZTjoGnwStXX4t1jetlgiJHi1ONl2C26DdP21xkNvDf5qPh/Uzg5Ku9Bu+wj6VHbbbz00bKOr1vHTHhCtTNmipEznLBx4cQaoSd56pHknLT5Dbfu/ToeHR6FFOsG6wku5g9yxwkXbvH94Ud6uieFE2uFfnMybMY//Mjx4jucnLChNYwPyPHdqLzazYMm4kqTfhvqo7y6ld/fjCcOsyyTt5m7o604tCGuMXE/w7iLrxlai19/KdturDvcM+MxcxfocvIGpqZB01GfIdF5YeGkg2gnkgonnfkcH+AkGSNdPBgO9CgkT0+Uyw+5zu6F/Ygbho7o6PWDj27lgjERXU+HF0VNWGMq3mzC/0Y/gqa47dRur8PmAaeXJts8hT8UTnEJ+s96QMTnXGXKh0Ww8HBpmgMa5it+MHNspYsFgb9aGHxlit9bYJ6zDVbfjunb7SiQ8qxzK5wojr5NMZVXoJbiqRY4iqZ8+MSheNL8a+dxnKI9FE66RVDumzYGcwVwzGCx7ivsTyLpQOFJyp6oxgsU7dg3MyyKjenXTCLHWjA38kc9jzvzKfOKDBbRuOoOccsMxA44m7qDHyXYeK/tmTszV3FpK94Wm5hrJhN2XzHuWh13cPauFA69AxBHKDNn15QwtHYbgemaD/TIHn74Ci/zwdOtOMjgu0wUOLz4MjXFEgczf4irTcB8p4o1gTnS1WP0aV8kV3JOg0abD68InRRL3IrDwYqDRRHkW/F4rDlXmCic9F0nfnDy+3kww0ELB0O2hf/7TeOSffKxxSxFoE/Z5MOb4pvce+1fPexLRuT4rC25ItzaEhdw/WWcW9Poe9M8kSe+VU/wl3XFCb88Nvu4a+/h2cl9ZXBtHxk+Ox0C4ayiZd1YtxWjfsOWfo4TOXFBQcP6wMsPi/lj3NqbRVRc/c7iiYdDxMsFD+uUry59N65I5+10UUS5aNLtyZGT4Rz5BH8WUHXSxn2uiPMzC9wqzI9780jz/F2ouPrtkzecuPHT+rRWkKcMPt48rFpD1lhc9iuQLZ6XPMKk93fwncl/lfC7fWqHe+aYZu4CeSKnhYHxlGVbnyGsd+0z9h//4e+mYEEf/vMvfp35knlCrgQAzEG0Fz8VThRTvtoknpFPzQvvl0rrstLo9kCBu5Y5nTYUWPzQvrxwsprS3xWZFG2/SuM5OIyv8QZ39dx2Yh9Hh80DTi9NtnkK3wqn/J4T33WqokltHPTwJK682qTHEaNPC4Lnlj6LWsxlW3QomCi2XEB5/vM7THG7nZ7CF78DFbfx+IqTCicVT4mLoopCKn+gM4spnR1SYcWVJt06xNzrxbgdp2hH4RS8iYcGpHbkG2LvzaZ94b1xZ3HEC1SPt/ZRxd30RexGd4555NaaZ6Fh0JaJM/6GkbvIL/P/LtVisu/eFQOryM0WRmWoRPx+jLO5aEPZ0DeAFJOeOTeaN+8v0caC3hd13NvFc6zDAUDPW/XiQMIHJ7TcysIP4P4xiiYKpz/EgQwPiPCBjg5yj+u+PwN8wPJZPt2KM746eMnfY+JgJQoifjyXp+b9KG654ftSFE++/Y8DFp8JVuHEGHXFKQNx8qbp8JrmOMGsGAE5HkS2w9C+2YiA4jbbgCphnG89Tsa5TQ0Lv+UtR9z1cuHkduYTvJ4fy711u/q52rtFX/m/cn0vDN1xp0PgvBrlvjN6k9n0iUGmYcd6U8UMhVPcBfNp/FAtD3Kg9frCGsTV7t/GU/AooHxFiu9EoRK9fO9JhQ0nZrntX3es6DfygifyTOsUJ371ck4xLG4F/n6cpNE6FCdv6nubfvQ56xS/aclaxJayNZx5ONjGl1x+a4Ez6s72kGcvV3OntS+X7TDWcmWHe6aXMyeBLudvYCr22i3qMyQ6/TP2jsIpTIRGJaNawSQnNBKT1nxtqHgSToTJRLodHIunCty1jOm0ITzUdWAmeP8o6BwdXnW1flc0RDjzOjoRFMEeZaMUaGa3naPZsqJBNo1Wr4hyUQs3CqcoiqJoytcXFEpccaorTdHXAuIzx6hDU/yxsOTceZERj87QqGjSlaqYb/hirrVQ6aoRV5y+zSuKIg5yfKWJloOf/A5UXZESTlejxOeiybfq+cPacYp2FE5kT53lznzSAVW49N5tudC+d15Nh7xA0c7FAbribvqUuAeK3Ki58PgzvwopGD3iM481r33jX9K+zP9HLMVAnXfVOmPRYpjW8EE7cR8IYCsD2eHBVPyxB+ScMW/ztrscb54Nkzgaejw1x9Yl25wk4YoTxZMPWvIMcJz55TeYeLgDRRQPiMin68XPGnySBZQOdiiK5lVsrx0+08tV7nlCjSfvcbDC1SauNOWtNvGULD9VL59gFUVTHrBwoiUOjDggmlec7HtvZwQZax8vUdeYFSekTF9js492t/OXAjuebiOKlTi0hh0N49yCN49x7ltG86S84PNofUDEmCMvJBZ843b1czV3i77yf6X6Od1zzofvDeV9RC0r+XFb+6bCmbELXUxpfu8ob6HTrbs+saIHysQawlWiyAuKpfzuU1395uE1f4zfosynicZ6xRqJ3jxhlMYCjhYc66PyrK0JYRxanuiNYxJO1PiHtn8QJ3F+ECdzwPlqOLcT+2l9OO50TBu1EqcPHuhoW9AG7gEgfBxbAwfuawRkXizj2eEWlld1Z+YCXWbtwFTsNR219kXnjsLp33INi2Nk8iMTB0Tk9EhKF0u9oifBnGQeYaV0JC6Y8mh4OLlE3WVLx3W4K+mwdPZ6p0sV1Yar7fILPIQ7np0U0YaLA3yxDoFmwzi3jdR1bGHboI1XDEyY6ufjG+JDKH7Pie85/ZnvOsXriyyW6koT/bpNzx9Yc3Fx4cSBkA6AKJry0Z55lUrFE/PKnKcLJFFeKarb8rIwogjyd5d0VUlFUxVPFFJZTG0KJw6S+IvwaOdxnKLNwUKF55vCqWfOS2EvULTK49mi0/TH9Ede5lzFlBWQy9MBlsZBt0CgjXvMJumhnLRc+k4exd+zt9TdfFZuLlYqBrbu1lyHfnQOfXQfEJJKVOnNwinPsi6FE0wxmblfExVUtflFE3Nt99nX9XAIfRdABzVx9jdur8vfQOE7AVE85a00bimoonjirDBFFrflHQson1HmduA6uAmjfDeBs7nciqcDlh9WXw+a4OxxFk1xhjdvvckTLj6ZsgnIRZAY7xyzxpoBGbns/CKMFUrYTjfznzJ87QiOs1vlD8P0euDWOOUXa4dkemtaD5Pm6JvCqcfky4SZo9N9YabB4Enehsf3nNOzQcAbNtJO7J95giYKJx7e4NuElRPaf1mcsMH6QvGUBVQUTb+rH80Gz4t1J2/74/bhOCYBRlDmjg56veQ34XKdiaLoB/Ej23lbXpzE4el53LL3nXgYBA+/GetQrEXIOD6pOyzUkBjY2ahfjq/9KBW8gfqXO/Z8Sa8VXfMO1+mvhZVdaAHyzE6tA1Ox13TU+kYe37pV759+HoUT6uPNAK2KJXC+2kSrg23tAMU/fclUmwFp2TC8hFl4pWUTHqDl3ELoCjosoeuFEzw7XV1PwcnW8aW/GxAqahqYu94iDNyO1nXvYHD1ar5o4sHr4RAUTn/+c7yqgPoi4PyuUrb9ipPO9GkO66xfXjUsOG/NU+GUcztomnfygKKJOeXKUxZHVTDx6FE9GKKKKPD50nedzgonnV1WbJQrjlO0DDHi903hRByes3l/pJ2LA7r7HDxqKzIy50p5gnQuT4UULJ3knjfDbo2/t13l+tju1XEPn/Umb/Nf+bloqOERTWfywpHdI23GRHOySGAz/nVryiyavO/k0hMixIM/Nu+ngmUt6cFzKJziQCS/D8BBDWd942oSxdNnUURRSPn7T7T+PpSLJ38JW1eoVTgBEy8XQfMKk77QzVUnzu5ypckHKtwWoyvUrB16KT+PUXK+5gDrzTlAq3kir5Fz23Myw9jFT2HrPWX4WhEcZ7fkj+Ae84lTbOl3HCExzniHSbn3TeHkeHyZbZ+bufI0j2YaHJY4rzPmTNlcc4yptuQzF2J/Zu59ZZuiiatLrEFs8oX1Qvs9a8onUSCx1lA0ccWJ71yy/lBMae3h5A0/taI1iztn0tbQJ71eVyiaKJD+JtYdiiZuEabl+07w/FWc/DWv1y3a9I91pPRSiCWu2uw86e3PXWeL/5PUv1dqev7ZsR3OtGe0/lzkE2DCU/PYD0buQqv1jXXum8JJAbkM2QhdkAreFEjQeo5bz/MKJ/vR24BzQoXTxAPzig8jCqcsomJBoliiiDoUT1qo/CVvfUE3tERhxKKW328CrjM4blGfH3g1yGCtBUrFUxzKhVt1drjOhOuALRbBeHjEh1E4+eoU/W/nQZGvSB0PkjDhRVTmYsAMj+T95oqTQvKEdy9QtMrj2Wa0d8l90y65JCbyhY0cPcJHevKY18Jiufvd+i3Qx2bcM1rrTV3d1xrzwUbh2F1rDT6Q3TnSpqJt+CGH3V444RN9jIhMxGGTLs0vc6sXdk3j9pgseqpootjxD0jmLTN8P4kzw/GiePLZXloOaDgI8kGLiyYOhAxjKw9I4qBE3x3Qd5v0ZCx+A0rfIeDgxHzAHpN9dos+NsVmiVyfD3Glnlw3Kpged8YneW6/WeY259eBwzF1S6wF95hP3MyrjiMSnjPjHR3iOT5vIle+uVXPkXn3bZ8brRaLDzMNck0x9TWFU643sZ7kCdmcf1vW549PmED31ak8aRO36f0x1hyfuPlT3Dbs4mnojPWLDbd7/qko4mrTd+Jkja4usR5xModWJ385VuFYxCek4vgkirhvCqcM6Zu89fyzgR3OtGe0+nREE5Bzb2oemMp9r3vpV3S+KZwyVm1lGEEcocvgig1cxyc2dk61/f3LLJxUPJEOVUStxVPddqcCKcZDkcRfFU46SKDvFyNr4yZxWJbc5sC5NSheceJIixVIuDiYUzuLqCiusrBigfItfdcKpwpwrdTfFE4Z8Ke8eYE6zFmGWzE3/TFj5I0kfMAZmMwnsIKP9MSXkGXEcf/7Kmff3d6v6TrnQZ8HikjbRYaGwhHNyuJB6sCRNhXt1xZJ4kfefsL+FfsTfcfW8XZMkrd4pBOLzAl3D8xb9fLBMXkrb909YLgVUz5AGWeN60xvFlyhqxdN4LDNQUue6Y1b9fwACD+OPAu+8AY+F0/7wolxx1g1gGjV571vHjM48VpGUTadKM9Idw2XsGUuKV9HjLPRreNIy3iFn/Og+NLvuOQsnPGOFvH05w358k3h5Mi8+7bPzXZ/mGlw2F9eVDhFPmDDJ1WUB0ermUcULlHAQM8HUsU6wkMkeHQ56w5XoeYJHE7ezIfWoDsXtrCD66wv6PQVJdYhfiOOq9xcdfJ3mtKxvp6XrIu49Iv1B/wm1wP9tO2bK04t6Z4W1amIz0dt+Uk5CQNbQLkR0x1brW/M/WNXnMIY//HSwwGAudWDRZD2xq16GMy0kxPlWuoccNHl7y54HQfsAKChw01jQ3fpxlFgYzzoKnxeceo8IRYKdzpvF06YXCW7bsO0HUYu+ikqvJLAfLTxQVS+jtv2sngKPEVTVDf5YcUHVv7xwSV5t9Nmmc9x8qbkycW2zWfmRBRNX3wun+bIgHjF4kWb8oJZGGcBNQun5IE7lVg+OzV05PUdJ3Sn3njPIYSl92lTPN8nj46+5DwGKj8UKt4z7sIfJe7psR6Iz+PPHA2c8u2SDvfgtfA9phqP5Y3qYzPuGa31pq7ua435YKNwzuIDbelUhgd2KtJcHBmTL1g4K6qTEdoL2GEyvuET6zOTYE2aX/j1skZixlrwGQ+RqeLI63goyTnhinPGtnih+8BHjxxX33Jq0avb9bDpK0m6LU8HLRRHfEmbzbJr4dT9NUzL5jY7yxv+TrrHjBxjETON47OIX3TX3Lpg+FohZiaSWWwzlhM2jtYv864042nZiOc3hZNi8WW/e65yXnbOtHTo+8vdhRM6Q0faqX3X+3vfryKLki/ZK6fsDnx6cAQPsdFPHOTT+LgCnkWTCqe88h0nclJv7ei7wonbgr/713F7cHyfybcJc5U9H5DFcVHmp9YQ3RkTxxz4xF+6SavAuLWvT2lD94i1zDxF7fuoZBe/He6ZvsfqU+qADE8LA1Ox11TXnEfnjsLpV5l+vAnQohefpUquAPyEtdwZSLhMPDkRqZdAmDq0Yw8BO7ykYz7gvtUIBmrtH5QMLoB+h90qdWA8dLq+gFMQXMOXspbiQ8PtwumaJ81G2nO/2jGghh9FnRNBbX445XeeWAy4Za8Xt/Bwb3G0Nblg5nisn2GRNPFO0ggAk3hoyMe0x9yjC/6AEwie7E9e68grR3XliURUIYX+WqSwNWykB6WYwgl9FE9s2KB/z3Yc0z0Sr+FRDF6j4W1lvUBpTrBF/NXmuzqAD2wzBxByDGj1l0ilReLo859vahF8ZMtpTQ1DKsdE70VjGGoOgPIOnfEaqTSAhmtiQb6WnZ0meOpbXYeOD+DVAmcnKRnfEHesmxfig6uUJg/xj1celLBux4t1HFy8cYZMw8y+9OpEGcUSvLrtF37LWa8KJ33fQEXSt/N7TDxZj7O+um1X+3AWbWHXfD7Ysa85l+F3b/vYbsOKmYY180SRVtRv6xA3fOj5em89JoL7XHjsmXptvQbf+Q5w2wuUp8oz5c3MO+cRujIPAd7RZn/PzN2in8m9z/g+pm1at1To9PUjN2mxY3SeOe7A1roDTseMHHuwhYGyUZk2+qJHHvCXx5rKEz8F1LcQ83Abn8zh9mIdg8oTxscfP4WQJ2bihA0/m8Ia5Bcndsi7LJ4YgxYKrTc+NkFL6pJXGg6aGUINwA7f0Z6J+Ji5q1hj3Wlfdbjnn8eyw5n2jHZmaWbWhcqRw85LTXLOP3N9X+EUWlCUHxaZVJX4lWD5QUpS50GzFsPKu5ByQqmtNGt4FMdrbOYbiBPAek/IDT3qjMDdL9WcGgoyCtI8FHFWAlTQhkiMMuHBJJmD9ZVWLBdNs4kB20q+MnjhX+34wU9hpOKp2nY7nj6UxDOdnzqHhwEAZzKTNPwxaF61RzPfzgMdOAWJ/MDPeIOOFvSkNusZhVMrlqC1D2PtRIELW9Jgu9JmH1Jv6t+/abxH2pvvoBr40eh71PP4aZnOnKNqsyfkgx6Tf0cR5YTyId9Hbkxez4/oR/n7eotRxhSCHuNtHZVPtxmvc6yDh3txDVS3doQn8xr+3AMCmbeKoIMxxssS2VZsFW0sXW4j1vDGa1yFTjhwcRCRPpXiDmsu2ae91ksHVrrefgCs213md5g4UGHDd9YNeDnwgY+DG1rT3XqsbpPhwTf5jr8hGIOaByl9Bu5T6rHex/1+c2U8LlxUTGKKYjOsfOusno/epoQER452GSLvuVCefFM4HePz7nrMmzevI+5nO8ljnQG/Hsz3tecgn3NdmNCV+00wRwYMZPcBc6JMDuvLnAlqFjl1Eli3F3NSGDx5FesSRVC8ohOZq5zVGlQPqomvCPAQiHGiJr7HJJmQLzl8wBfdFZNQ5DK4eGs7zPAdlpSgvb71fWrH6ch02gGXtjr1qwuP+LUh7HCN/GpwZhbQIbKpe2Aqzp6v9Cs6DxZOoY7/eOUZgwBGstaHXyZ24p1bnuFMt3Bq9g+jT09Nm1wHnkNn8h7Qm86oK4J2v1Q6JG1DAbjCD0UUTo13kINhXVlSmwXdysT5e7dpO0s77E8888AWS0m8xULCX7Y62Ela8sAHPTH5nodh4V56WG7OxAEPkfc6YAthVI1cKDjMJQ7d5U4AUx5jLGZZJOVtR6ERvaNoasUUNrGIMuCMbTk34lx9WDabY9JJHlfHPRve2X22jZfq8wKluKOF+Ku1TvO4f0+7jpl+vlJYMLmgvJTGIQPvPUYueI5SOSZy5XpalJa7mC4sniJGwjeOo3tJsFW3IEcZFMiOT1pMTl5vzf0k5gr+gK3aMXQfmR7NQU//mIegE+/NK+3xFlvaKW+OOiQvruM7fBy8KLfCb06SxAFLL4zQnAdBdaADr9YE8lCj7611GSeLfbRrxLpPx/GS6N8UThXBHsIRshlLx9vxHywBGNdb6NdklEPKO+afzya1ypk1x7q9t4Tt85mNW/QzufcZ38d0Iw3GOsN4xsduDS5lWUeWwXouU6aoiSvGtO99Hb0pLz1dm1Y7KQePjswbjmsCjjfJRpu5lHmltQsp7ORDH+L4QmtRtAVDS30h0/VgM12LN8G1T2CvthG/ZDT2ejtkrrBNC5Np4OauOYlfUWgXix3umcOrTAmVQCOqw8TAVJw1teRCIMiFOF5VYnzwwfYHcP/p5//qnKykzLy6q3CSF57h2Qqq/urhcL2AQV8J1rfiL/ujrgjSudSZocCnEHTzGCdbp4UTghYZbtkDt4NQwIVA4MHZpulug9QHWAZz0lIq+LJggt9wgAErYQoPqmSBxpjCzeFpwkKwiLDxQAi1oQ31uWgVnGd/hGvrTLBLJheiLJjiUPBQOJGgrWgiUVOmWmz2VXvA0psObd4ck07yDtFxbwHvbF+z8678sg8sCLbp1rSzdl3cJMd8S6KPGdj9zLvKlWSFhkhrz2w+hI90yIyxYw8JP5fZYz9ojUE7Y91CTzjf5mG95Yj5WjiZRptxBAi+iiq9GfuaB/ujlrlBNt+S13T7VXte6nrkbegJf9L3LJ7Yt3VFKe3Gmw+ec8aKFzsB8p6yxxzVgQ7UdVvzErr9oDUs5XOE9AdtVbrpP8K7EX+vUMzD5ebYMA+CNQcTj4xxvTXereXps3ke8mRezsmXWzg5z+TdfF/9npT3F3qNzzfSYK4vMfzxsVuhSFnmcglN30/GGlNrDaw7f9FxoSnSjnVICySScARn/tdayUTiAydiSOp80XCC2+sIN/kfjy/Qhp58O7SBKrvR4Gya72Ma/hcNnrfY0q1SvMb+Ley9K50jfs3gDtfIrwZnblUOLRpHrGsqNaXKn8yBm4XTv0ThFEqdg5WntwsnHEnrTiK1SjuIxsPXYEjrNkbRCTdkGmuvK/ZSWwOlIWgpBI9fATZFo8hAolRpnMVUOMhTsCkQId4PjIXtNju9wX2AlspZoxOJMeA4m1I2euIY51ZJwkJTyqod/VRrYkiFK+llAAnn1Ufwsq0FTHylMcIneRVMsYhdFE5KUi12eagYokiFXA7dbaBH/kyfbKe3Mw4T6x1iYt4O2tk/s/bWC8dqV3EGS9zVrjy7fvdzwsoD+D1m54J0VJ6QLAtP7yfxFW/2x+0rVD1BdMakK/Ou2zO372eUP31jLLk3RIvMHFvpt/BokaYwgX58JaXmAJitzxf9tEGb9rqXUKf96cfEiUPvcofckkZa+SOb9m0nAy7t44MUZbuX2eWvYrOOLZSgWSYbbD4Rzt/v5TvX8P5QljQox+Z897gbtvd9bgxDM1/HWUZzpysFLp6+zCtO9tX+df877n2Hd+N4xOfjalOSMw0Oq9H42C22lGWNWQz2/cRwchXjzmfugTGv1TmPkj98qqWgtVofcNffoUodYce6PBS1eu+00CBzmx0Cin21DMzG4ciApeXp747tGvunG3qHCncx2+Ge6dLMUiBHdVoYmEoH5VrNL/N8q3D6rxROaIlXNrS1c2TLQTL9/GDmrFHAlXTw94Na+VCeOEHT18Alc3Yu305p1nUp0jE+OAF3KXGqXCqGMHz1WpQ8Vjh1L7qiMz9sF+dXnuh3FfJY7zVH1SlK05WY3m9w6MwDttS92iiDsNem6WbeyQ0vWuQEfeWDeETHZy9QuYNsiqbxuE+SNAV4rwIK28N+KOuwnTppnZsmv/UOajtuV/vG9/Zd+4RtbM7FITHdpVO4+zph5YKFPGbnAvgVFu+Us4x1vKTtY3qJ/PNl5vjQXXvSYbfWHMiy6S3BNU/sCcU4eRTTnERoRWCvCasj3o67290YO8360SO1xpSP2V1x+77zQ614tC7YP3QqRvjQ+TWXkjHeB9p9DOYzD63HQ3vcQl/FUa39lg9H3n3vUuee76uAPYbHsdDaYP8dX/dpjeut8W6h9U1zcl44wet563JvAa++YWOHewvbz9T5DJ/XPST9a1PX6evBu2m7fWLFrXPbfY+9L5c86eNdDvjkKl3Wo/wv33JtMj4krN8tKnNrSc7hlNEhoDxGM8oan30vU8N2acxm+L/keed5Bmx/19g/Q/eXpWPErjmwwzXyq8HMsdQC5KhOtQPT8iuzrhLtjsLpl5wQGnmEQnLKCTmLpjAfhN3VBSc+bmXCp3/lUXoouOVqcoy3MYqBeQjo9YaT/y4FyWzjtPHqykrJeeFUDFbBnj62Dg9kAIO5kNgEXPFFDlqSm18jjqvIMNkJwP0V3dC16ixroxk2Qtaw2yyes6AOzdHCkBYBUZ9a4h0gEjF3kmj1eGX1yZNcg6CL8dimQrkjepoR4kt6T3/T1+mAF92JeT8h5sD+u73H077ATXjmhHV4vSADyJPZF2w+2mfGbPrULdyGXyp3pnk7poiD9gVJHeDs1H64KMU357zY2s4ALxMIQROZ0o63Wp3gSkJ7Q2/nK8G0JZUzR5rYAGUuPRo4gPQ30cinYwe6O9221xLJSqdkpc+8FE9969+P6v74qob1EpuMoZgqVpe+Wzf2rm236NdkX0NzTKzjOX70OHTYc7nHea7sU28Ndz/nnKiAciHsOd2NZYezzmvtav81vI/oumbny6B5nziL4zbL23R3+u7g3fQz/YwZml93xaDsZ9zZbxkE/wNvOPfoVJn2h520GjbLWrbxRltIQE5G5ZN9iy0b2EpPmZPdMu5coEXHW2+2sYv9W9t+K/2OYde/w3X6a+E6Ig01QI7q1DowLcdIuvQr5vp24fTfonAKLflCL3D86Xg4IJKqrjI5wTqvXHHKpenybuLmVSnZKYbZjFFM1CNQqynY3+7bBqONR5uK3J9q7i+ckLFit1NPBrd3E7bdC4IQoab24ckQIullvtkOkx4s7oojEDDNW/jsg8aUCppMUMcey9zb5IRz7isn9D3LYKJfvLTyF8fxRy0JeXgFMUnJnIxgEEijnlPh5IdtqPfu33c7e+48796Vhy0q9ogp7o7zLUV9zBOe8215rw30DTs2bjuv4de206f7Nb1E5rb2y5ggM/N4asiUr+76cZz5nruM9oOxY8HvnSt3k9pPSlmPObAPTm3VY+58SQveVJd5cdRp2Vstuv26lV/45bsWpkxIlQ7b8hh8sA2dzYWT286PTN/ye1Yll4tNrS2dx/Aqa7zbW3TzPbP1mFedr/dFsdytAes8YNu43hrvdvUVH9/Hwmn1E//7doveed9HuPu/y5PjHlIjcDpEt9PHoUAb6KTv1zuzav7hmRKm9bb764NV43LXDd/SPftofdWm/jCRVsg5K68Tuu5C8O/i+Wo+tPSzjm/p2xY+TD9s/Bgf+J+92f9d7J9t613pcxy7vR2u018Lz0wAclSn1oGpqdXHRM05c3/zVr2Lwklm8kpTaO+JlTDJCR4fVuuBCpPlnVv4Jlz5XjzVDD1H9L09H5zAPy3dkB6MNh7tQ4VT058qrNBto18FbfeEKdRpUhs9RPJwZ9gl6jCuvDA4cQRr0kJ6GzTNXqrFXAGZEQG71VXIKMdqcVJeNP8SlD/pfIKhm4Gkj/I3u9FPZI6ATiLqgFP+pLp42+aOie+g3e3sjP2rsOG78mi29/jdxzxhrQFd3msDOMOOjVvzr33jX9JOn+6XfonMbe2XMUHGu5myWlo0DwWrGe/aHXre54436LV7hOKuUeRj3Pf+wJkHtBwsqJPCWESjY+M2ieNtrxPe9eX9WKKWo6DTC1+RoQBis7xyw3x6hLl5ze+iyTLIe+zWpTZ0O07ZXsYMPjbZFbx7v0XfybwWx/h22+t9sV6300qPqbHG9Raa/TPe/LSaj9u36nUZy624e/r25RbvLb5b9Fv6v2x693+XJ9tPq5YGnd4O28awJt379CAdAM2/uHd+sNh4lcNn7Z5zHRn0WkBztYo1wyfxh7Gzz1/wmJ8O64oTT/kdwu1rB6UnafiWPsmfxt7VdfTTYLu7i/3TjLxjRT0nbXqHM+0ZbeZLKgJyVKfmgalk0FJb883cP1Y4hbr8jw8uWhKVlg/ZhN1WPgZN20zFMHmB+8sonBg2Y/f4Kww3mwiij652vKFOk9qIzAl2cu8i4hx8MOnw2r4mJ2ayEoe+rzxx0II+8chl9KDD8n2OKw8yOTioCVrkwyyc1EejNulIVdZHm//yEz5smZz+pyMlWytH9VLtdgGWwXfy3mNjg1+2T/bjVjtj7ZgrsvfImWeOX/lgPK3Xhw47Nm7Nv/aNf0k7fbpf+iUyt7VfxgQZ79o92t1+xyd/vU187aPgvc2dxphop33H121jGvOUtFrToae9MIVvfnW5VT/yHgdtL2ZyXx4+yn/4/dIVp6Mt24THV5k++0w/epn+lV/Y8csy0K2747654nScwdlzdrltlIrzxOznCTqxdms4EfGm+fimcHI83lXb54E5WLdLTHC0NOj03cH7pM/1ZrVB37bX1rzH/XSuOQMfPqVb8ZYnemJ9y6vV8aPc6LTLIWmVh3Gw7nrtNQO6/d3q7pfWI40MbaR1PvWXg+dlm+NfCE/qWv8u9k8y8c7V9Jy08R3OtGe0kZ2lBsjw1DwwNcVaypSHJMALCieZOV5x8kIYtPwAjD4+LNblg5PN7fA/gc2+LIahq/jvbmrXKfnF6rmWZLRRC9MaN0UPV2fMOslNBKV3e1AaQuG6h3fdoS41drWxVymOIP1EumjhVQYM3TWbMSocn4UTDHmbkPWSLPwd5KVGttrcZw64cIrhZ1+8F+/pFFh0i2obg5Rk2a/RRkjEDLZvXvA67l3A8v3oi+2+3ifmZq9bNnb0Hc4e0R7pxNxjUAvPzuYqN/mmHHMOfm7KASEnTM4oPybnZb/THoE9nkdk4HX+PSp3i3/NgxHdiIHh7rNxq154Jq0CHciJYxDHOZi2FXPp7DCY43wMmZgk6869EFtyVGqQrAnvbefphZNyzRolazlfccIXDk78vUcGhJxu5eN2vi8+cOGErHX6N6K6vRxZ8HQ+8d9/xcmxGQNeAPu/oN+0yxh22+t86To7HDOQ3cu5x491nvCr8xu2v/M2PeZFB7suiGnZzsZxhrfutT2L08pH/xrvOoad/PuO6+PbxbFWk+MwWhp0+u7gfdLXteWo0j374Ba8fdy14DwPasNO/eXa0Aqn4LSZkhlddueLwyp099v04GZZS99qfQOn3aD2g2kCUm4zBsY8r7XuXeyfZ+XdavI8d6s7XKe/FiZntCl7Vn2mOoVmzsWER+eOwukXI29SGR9AYeV64VRupYAyS0lMsuGisy3alpBK0nUIr+yHD7YGNOD07YruUayY0Yrcn7I3C6fBivXhwcBeB8Jei1HydhUB9650UTgFNvcuqBRNxN7xnxajvIlOzBdPAKmrT4fvVZRy5k3JLD1dA7BcRA+wiiZaJY9wky8g9NlzJQUGjClbyTbexAaPnJqt7WPh/dwyDg+5dplnD4k/xByRHPPLNCi+t1VIznxdTvlgiubfMRh5EeSOM7dx7u/abmtHfwT3TF2P2PXa7Wi7Rce5Ty5ilB/mo+3yos79EZ1rXN13C8/FtkxknpFlD6wcsaxb5DtsfekfPubL65Go8B9fqSV4VTixAMjeLJw+//zzLJwonpC1/luFUy+oHrni5HHsxpbeLnEy/2tbjfu1Wl4i72xyKx32xy1Yx944pQZzNmVME2auBy6aiOu9hZN1vLRdfVn1vJa+6ru/r/31fv7JecvnyblCR5vbT52WAp3e4dQafBN31GurOz93+xR85u1040QPrfYtjte+oGiKotv8ocFmL9oP2V+ns0lPnaisxKW1rgCko8k4v6fy4D83uZqbYg9ANn/NzgPq3gtWx7s7s8N1+mvhyM5SAWR4ah2Ymk/NdeVkdF5cOJFQ43a9OOamf3jhQ1p3+tLG38g2ZxhyYqYVPAfwDMhXJ9BlbzaxOprKwsnhqzZdNm6yv/PCSQMZDoyQGhN7VR5e5d7l2/QU+xl/M7PQVNHEHGYhBW2OuSeN5S9sIoF8iB3yIHEgwdtma1ORcsG6oTo7bBtEzl21JVF86mH3fd4e889jcduj0ke5o+9w12TIjf6B0XkVW2Eu9R7mrLHOh4iUpPMgus4PKD0mht1K8vK927ykPoZ5pq7HLIvb52da6NoaudPoOVABZf9dECDh+NH216rNtJTJHbTnQPeo749xVpY9MRKmy4fV6K8Wpi/2k9a+et+2Hrf2hydccSVD1tXmWeU4OKJw+vTTT/OFHHp59cLJX/a2XlpsT/uPXHGaY0PPuu1wK8+jfcfsUbnX83vu3U6NjvPEKGeM15yKav9NmzLHvPT8vIvCyT5NX47Qa+lHbY/3XpJH3o8etyaJbvMys4OnpUGnd9i2+wF91wtdeZCQ2U9b54xz41JX05f7fpz2pWiqwimPFXbave9ssN8sAABAAElEQVTSLgPIFaYS2PYP3sJf8qs/aUoKElxUJ463M/xguAFYvsf5hsh7T97tczvcMwcSK1CpAzI8LQxM5b73sfSLfLv5Had/iStOWRhJaSqMtzRHG4kkerQB60rUMBsMsuxEngEpj5BHdbXR3LlZ7x3sqXN4MAWuGTsUTiECb5iUe9PJGkXQAjfRfa0pe+Z0O924DqG3KTZzqclmVZkx58AGggonnCf2JICSQIpcNOF8zkSNcwwGfliRLR0pmUig6ZsWE+UBajI3EiiuyZoqpBlFpSxtFGliEzHyJljFjTdsJZswthN4+K3HZBXOkHQzwbDaWeVXuuN0iV+t0fcg3Hae7sgZveM7v/Ws9J4Xgs2p1jqOcnPMos++42M5+s4LdBi2Fc+b9F+L0bQxdVvL2k7eleL+bR3mvLe9ZnMdl627tQ31V6ypngO12PN+6X2koniMucWsJtqIOm/8x5sYUgc6ayDey2DBI/C2Bw45SWYn3rrfVg5NGx84qWMECn7Nv9YP+NCo22ZW3lk4casehRNXnDjxI70USyqMdle1yn/GkF8Cz4GCxGhsbtU7f3e+Hjmm/0d87zmuHfd+wWsMZl9hIs543PGe0xbKnA/xaQ67DPNLDOfL3yNhLgcekcrLmWMpCuXu7d6Yr+NaDdyrZ5V7rL/PrTMdt3zucpe8l7YOcT4Iz07n6XByVFps9wU5kPv/1HYJKb2UU8z/IR+KHVWeD8OZO/V9+2S7cK4Q1fik1fAAndlRjls/qMRbn9e87BtZWmqM9BZKMsRoivHY7LFHHvdcND0iY9m13c7TwqQhKTIL6dXdHuOu7AzfeV4Dz3kA2kcysTVsxwC/RtFUc/2P//B3F658+F//5Zepl/Wr1jAZoh/s+p0etbHkJc9wJAXKcsx2QvNNxkjC9NAtV0tub6X1NmPtChRwFzKnhoqQxZDg/n40uiix6JGp9eRFxbzhDS76QDvwZqFtg2lgEny1qc5JFzOLAS+E2WyHgw5gvxqteCVD/ALBv3W4Ddmc81CRUPpb8wkO1bG5PTiPwlwJUvtQbtVuU0F2GJWwbqsb+stQMr/sre+wUnfUCf2anZV+qeOor3t5dP+cr8ucwiNwI0qNteuGseJZMmN+HVi37ZNmp7XLHcfC3JMP/YU78sPxlEz3rbmMl21MRwq98GjQj9Q+p0fK2/W6zWvj6i47puxDVzcpzFnRmDV2yyOLDtjUJjBUjjgN9AAyyPgOT+pLB6dmoPytk+54+jsR1h9amfhhFyDjYt1CJM/kTWLaHn6kQunXWeU/5xUnbtPzrXo2Ihn818v4jEW8GU+rQaLXL3M/2irWj0g5RmcyS9jO2BTPU+pbEGZcM4ZhYrYKqawWX4Y5YP7GoIlX5We2wK1gMo68yv/KoWqOo2Lu7tum/dfxP6rnPmvsBhqg2y73EptnMkxDmeomcq04IHpnE+Y+HR3uYsCTRjL0HIE6FQ8o2SajryLBzYY+pxJjBEZWORVU/hkgjM06PW/wT1L2mk6ICy51gY8tVVt/IZJwfHOBM7BdBGSY6GoH3wnQeROWi4N7lzcQz/BDMIGuvVOmkbN86ty34Ht13Mt3y94ZPVacIgEZ3nB7+NGyhpEkWThx4q2S8H//z397IfjhP/1cP4CLbqsfOx246GQ/3vLWPdwYDKFvCKHbXhztJD96TuhHbve0w7h32h6yt+zbp+HcRlqDSkKy9zEtclJXSqPZjxJVouT7lql0rO4cbBex5I9q3KP49BUncEwyco4ZdmzLsNvgKjUlgmBslg0shOKRHskyg3PugVMwWXJu3R/CoaSKplQY9LBSQtNEYsZBO0omTwcxNGwOLfcBjs+e2443u4utVX7GIbwaTlnP3grYwTrm55z3lNLc7PE88vc4em7F4fmvXjShMHXK/53OowzjaE6EBmKwvtDfYzPHDuVymzaOuuFc43+Gu9T6XMzOj2nhmJ99FMeYBt8U2kBBjX9sDR3RmToknfEO6RFj0F2mNBd6EPML0qkPhtCqQaX+tAIt6ThRSsRZ+ssIc46jGPBW/NaZpEaXqdI/7ExhF060n39O4fR5jI+Dbnh4QxZYhoCdV7SmyX6skYkopxB7xTbifEWHx32F5UC6pvNRXQfFL+w4bojbPiHscKoOZI+1vnTvOGvClZ+RIRdP5Q2cc4cMqrn1PM58sr7rg7Fv17mO1DOZM/xR+uW9HPcYqPS8xOZOZofDnufpXq81e+LucF8LrCuH0qcp4C7TfTLbwMmxzAWSAF/HVszwWk6KK3fgTZkhMfgGf2ASnm+tL7nJW3oWF4ZP3TdYLwRr3LhVxzJN1SEmZWnbpEzp7vJmHv4YEe0O18hwEKrt1nOjw1vmG8gxrzf4ID/Ce4e6C5YYceGADF+wzXl0ntFSNLVb9baF0z//4lepNwMb+tMECWkb4FQx6dECyVNUz0Z2Y7azLdpwOwDrG6RN1tmeWw/E/V2mohr60EvHugN5wEPrWxE98CBt2T1GizYmWzqSJsNKP7GQ4pRC3iZkDG3XBve8RU80f5BFL3dctPSXdFnLxU4SBNEKyI49qeT7MK85HoaRHGZDwyiUMI8ScPjKxkIGTgJ9zEluMYAnOXmrTVMxjBl9Zzvjc4/ApS05QtxmSsiXI+/evylj63s+U+9qNYkZp+v8FcRoChJ7djwnpaExNHCjflIZCYu3zxwmnMMLXLVWMOOwjr/PT4ctSXuJv1yA5VeFpgs/ET76cT6mjckx7AFcMi0xuxxjiRBzwOlAdGL8+i8mmpqrxMv31JlBij4cBa8Phyh0aZjjhl8f2MdxHHqHjtxBbr7kW3mXfip3Yp2Jzxy+58TLt3mxJhyGmn7P4ZumsRQxPNdKgq3HN8ZvvY9LrxIzIHudkz7mbFXxxv0Ru4gbY3dfczaNq58Mcz4nOaHMzhiob+/X3DKHMc545R9tDtttiI4wzMyQ6kFoliaPfW3EA3hJX/VJF+N+i03jZOirXVnb2z135nI8c74e9n9jZu/lUXPnGefSNgPp6off5qt4bOMSgpKNd8ctWtaHL/IW3qM/tsOqlnDZyF4RzXOUjF4bzORxXgYx6VsmqTILmV0KGvcwt8OZKFrIg5hOjKGbT619o7fX6pCtcrM/jShUsz959pCnT9Qpd8TvZMV7m28nexs3x1xxzNjs4hN+2G3acIjc1O3mfD9Wtv633RWn/+OX/ybtoReDQ30BWuSKRsKGLiW4GUCU3PRYFpMXsHgLO3v2ugjVTGxAozOAI/NUNqUTd0GYco1+GMvkGNBhZ15Urh4deIcGAAuef4R3XeY+qBha4IxXZp2LJzilQcmIBt+iB+vUaDssJGMBQ9wbCswf4EiOhEJPFE+HLVVPrboSQT98G4VT2PJKgt1UQCymX4qR+27LfqnfpNfBldsdxnyb63weL2U7b4cvOcFoXP19z3cPVgM5DOfQsY6J3I09Z2NDmFKh59C51AsmrxSwPrSzy+B3Mdnh4HU+ugXXtxXvvttrvJ32DHi1eTamC1sx+dqnLigTcSPR+3Ts7Ca95nQH47v99/zn75awb0ILT8RDW9NvfUV3TlzYr/Fd4Gt088EN3YcismNYngOjekgEunYvSeUIE3TYcFUrrWjnq27ZvdIQh7OxXBFbSPi/oA7dW/QD85t2Rl70+c4wMl8ynTwBK0ecM3M+yY0Rsxi4Cifdqjfmt+YUjZ5b02i1YfC++Ntv5DosPeCS4u70b2AErHwL+dXdEZcHNe3HlIO60LTnhW3Pf6EgEGMKirj2L2RCNTzJtzHTUfbPLbp6XFgfxV9KD8bCAv+xNrA+rJvtoDtf1pQEU6cUtuS0cOYY7Awod17zgdhvpqTOYOGQx7hVYocfcsW847GeGa+QusZogWinTEM28JHc73PXVGz3vU43fCZv+mvbOVaCowBtw8REM3D+nTP067u6+PG//g8/ozlsH/6f//pr5U5pZRIStBX6haQFLXpB0FFZb5eTeG1i8Xq/JWW+NaZFxvYbR4Lpz4rsfQuKcQa68yzwolOeKCYaf8VqEavg1C58VLKM5iB55ITkUiOkMsuPhdMskJDk9YXYyjnbiiUl5d1HszbwEwQaMx7FVMLjalbxdQEXSkvRxAqSNuENt2RCPnb4YG+or/jiCyKv2O7dWe/KhfKj83Z47+YcwBzVnvM2VpHTe3EfOl1D8W7oisklYWAG0PUBHwncTuUrA8Qhr1IH1z4mQZ+hGIr7/HTYDCvOfbdnfMY/q81d72L8mwGdGNzHpDHvgtPIgI5+5lEz3WPR/TS+t4J9gOGHLUgz79DRYT1q3ZdDcrU5EOic/5MxoNPFExrsj9cFxUb5wYFRFuStGN/p3pnKQ690y2tmLj1y+s734Vvx35y3E7235fb7w4m6Lfq2jRbrrYYjUnOv3CDZnA+ClRckRueLnhhTlcdEqyKYnZ4poS9/xTP6QUyObGHE6n2b58q52qVMA2e67IORD0DezOP+s9pp83GNfQyWvh+HxP2xhDt3HYA7tuQN9Ye25A5WyZem78L/nPeN5UIN2cqnkTdNJyB86M4/TWb2k1b9w1xUXg79KY+ewIxcDWTA1yLTPSePe/p2Gn6sfeGENc0ttHU7+I+ta8yr8A3+i3m5kBfiXr4urvD3SHfqs2EHxe0m7kqWTJrMGfKGV7tV73/5+48vHPvw//rVb5jj2OrMTsDZLVvKFU2M8MVeM5W9wVtAMyO2S7xY7g1g5+uwtByTyPRuM3eB5lWA9j/ZOu+R7dBrbLYCvSISKoOh8UjWCLVdzrp3OEuZ59ivgimz0NK0cbiQXbjnFaeJz/xIlUSkR8XeZdtVjgEFhaLJV6MGHmYLlF+sGLlqHFvbS+6MP9Y4xFGrhQbYW0WWoq2jTR52Q8ueITkVE/s4hK8AZ/YuRa7Z7dyTrw3kEZe6sivwrcVsR9/huolbdKbPZ5UpnpgspmOOeWrb4SZV0Nl8dT8M0xrues50dJ63hu8Z6+rDPTIjba7m/OAa8XFMZswUu+yzGsTjwdlSMuMq2PGVvDwGlvmWz0HCf4/hSEe/ZT1nwk1PUQBP5Q+FE99vysJJupO6GfcGFVr4TJvaj56i6famsd+7Hkw+x+C2BXE8wm9ez4vjda+ta3zWSTvgPPsauQAO4aRVv/hE8wRrrjz/+Lt7oarjZx8otpwwdM64Jj7ewizv7k5fy7cdLSUkmHKOo30Yyt4QWP1+iSnPC7Idtq4dzrRntYd9qaZBH/nzREW31Waq5q5TF7jWgIFtxtDj+cqTKpsdHx7no2OxtlN35Nbo9Kwp2PqzDU5yufGv4KCFE4L7u3Gz7fJdM1JDV2daYOfwgr7Z7XIdXgUdtxW/6z+S24/o3dm6jpvrhXd32hnPHunQBJH/aNcXdv7nv//phbkP/+9f/za0SKtyZBqdudJxzbwEMpGteTcJO1ylpcXubMPPtlhaaK/fVAVk9grC9zmUC/IpomTwxJunAT/Slwu9ef4z2ffLijXt26mupDMbFAsloL2B8/ia33eakePjTxKWk12VMIU7kEIn/V3h1HzpxVJa0EoaLvmArJRGnBLK/Cl/syDDDv25EVvHl6FN6sFBCRD/KZrQS3fQWznVzdzLO/g0+K7i1fCtcZ7Rz/B26BbdfIztcMWJua3JGOM2c7Q7nMk7mx1nmDbTr7L5mrxp77q9Ns4zX27JOPPdnunpsekxg1+xI35xpYm/jKX2U+sDx4k3NukSPN9jf8t9uGGqv+Inh2zTtw8Jd4bS4QOjtXDqrIYXNxL9jMLJ+m+1Hq9a4nJL4ki3/BG775mX+GmTPeNXqcm3Ui775u2t7vnXvgYeswd64pRDXSP+nL0IECEyHTnBc23IGF6J4/QBacVC/k04KSNO02/w2HPb/UjkG7zJjaNvNmNf3D9r55ilZ+UzfcW/RX9MjV2JAebDWNxfjJ6gF67WrflZDgli4jhRx62fvOY8WhI7mQe1roF3v9LErGrLTkcOX03LNo0l2xh7Fwp44IcCcMLyPugLbDVnvKY/s+05f5Z/z8ynZ+q6Nw7sc233TzHPRrZFTN+S15+Fcz37L//pJxfmPvx/fvMfNVeR9JEc5IdypdRnn7dKAKGLB32SGZrNNxACLiemZdbC27vypWPuk+sSwIdJs9Iay8p7q3/mwRjjQa86llkLp8SvM9sc6KomjJQ0HcY1dktfbQqJWHVku/sRSZF/GJpUcarf8Tn5yRpzneTpifnUQgzNkMUYra5CWStqSJ70PtUkc4i5TY56U2rqXShN3UFbFwjVqXTgjvEZ6NtA6Dlqui1yi2P4Fu7nCM6HcUvVKf3WeHf0Ha4buEWHl4Nbfwmc+WUaxnhLWe93uNsCPrO34td+ykoB7+/Fdm2cZw5ek+kp0+EzXY6R8m1+GIAfL/ZZCqhch6ZWuvk6US4/j3vJznftkpNv+FT2Vhv2YBROiFZOnbiS+bbSuNpUK03I8z99WHlf2/e41/a1ei/ltW+B9/zdsul4X+q6xJi3t8oDkkH50G132DLg8EkvwRn9gROt803YsqlkO6/w2pZb4+gbd9bC27c1PzvtXcGewzN70D2eMx7w9/Bck7+LlvvtwslOG/inFE65m9a+Wo3XBKzO3DrmkT2KDKjPV7XExHFxa97RKglGFyBtgk8aLdiwSbNsOxwKOrd51vaoShLwmO9IP/bSx918HNlOe867e/PrVNEdhNPY3yH7UpYZmhlNQW1miin9i4DS+oXd/+m//+jC/If/77//fsyUcie6ZSODGjAMyh2xppbpx+C/0B4ITww06135Os9Kc1+ymSZG3d1uJ6z5f4/W1NFkMK6klRtEZuhZ+AavZSSS73NiG3IBu7pmJWsT2eTdXLTtlYWTaVKcNUp4q2XFxnyQgS6PRK2ka+4lXEJQgsdXlhJbskFK6ECDIQgxmVk4lZ/wjcw66E/u5ErV9XaWR53nncEM58LD69ZzzitM1znvo+7ye4fr2l5CvyajqwIRiZicnB/aMtj37w5vF4QrO4Ttdx0ZxisyuGC5Pv5nwN2Prb4Wgy39BHmmt49D+5YjfKIo0AeZSjwa8KJxVckfFBRPR12HfjM3fWzIEJ34ox56k4Y9MGrTF7qxBYY3/pMfmZQLM5lXybV/W+kqnCR39PIoP/064tdej6VsTa3W4XaVvdlnfGOPucldcxdxahO0jt9aJksGvdDTd/PRWt9lCxGZnCGa5F350OGNWPQX+LP+pAXP+E6b+K2vt9hdbRvX8YaRNey263vX8Fn+4Me9ObTn0z71FuNZ4zYyiLTKV7y137+58OEsQRvjYUxl4MON3JpHqWI4JIVyifeeqzM+OZ6uu8HDj8SFYtOqXUzJYLwPvMwKXzugaW4zZkMSWVF4HzyN/hJwnbOX6ECm9vqXiis/FulX61z0zW5FL+egRTLAGWNDJcUc8V9zRWv4f/zvfjhVm/3/+90f4lRx9Eo/eeGk6a1h5JxDljng6JxsQ67Ru96GTn/60Pq96ge+Gx0PfssWY+35veM5ys+dDt6DbHU6rsOdf8UnrSYMuG81LYGa0Zi4zgkMZX0Jr0IFmI2UtRezVYzp69XtWGuK94mHNxmnHvGElq7ASJgzEXrxBLGYQ6iLHQrFThj6voJAhepkyl84oGNuXirZ0Xc4S16jmae1Oa0xdzG33s+9b7uF+wxumi7A8GTZ2VDE/2VCJC8anI4X2t4doo/11OrJOLpsX4O2w3LAm5Hkax8CJqHL+gy75Ydv123kaAv19M1z3YirgtafciCVX2k7u5q50Q+c+Wu5gCs3DffS5hqGs8LpXP5SJwbx6WxbddnnM/5n4j1v9+lUvM27+m28xjp51VdcRhgqHLfsEwvHYwd3GvaTJ45FzKvWnl22q3331xZJcN52sMiTx7zPbb2/oHUPOybd7r24LvMyeM77vfL9vCi/fxOBPsQaPRd7lZLv1MQYbwiOGVlkzOM2lV0YkrzzAZ4OHxyw/tQhRdIdMDTTESpYXAct5XAdbQ3ng6fBR7kgxL9w/d24Rf+mGxE/6L9kuX9e+75xqee5mLez1SM8YeeKozwpNS7mqNaJnif/8Lffvxj4h//td3+MPTjw8VI+zB0aQ6mctpIFDQM8WMbq5dbloA7ZxrryNNKW/0aWdPFzuPl+9HyfZOskp0xbjDFkPaM1UF646/bcuUtKczfmZPYGlFWKe7Tz1e0lnGy5uwWfqCpQgHXlKVurS5x8EmoQJF/64JDWTg9krq7G0c6iaV55ksfoYDP3Cme/E0F8Kds+T85dWZzuk3IudBdlzU0J3fLvnL7XV1qXnE9sDC3XilwnBIPv+7Vht9L2bt6vjec1Htwzlnt4ug/X+HPfqrzJZreYdmUF9/Ebph0v9trqD/FuJ+hjK5vTzyWvB+MRmPwTf/Al0crJ9CX6MjU/jw6LwlRzyDOjkc1VJluU5b/JQ7cRO/9Mo7WvHWf4lqz5nt1ezNkNA30MZz6bZ7ZT6WHXj5Qwjzl6H/22sYPn3Cp/kjfAvN23PQzEOmzDrW3RrvDEzXxCrvOtekQ39u1ajRv9GndChTStj9lxEt9eDuytzWO/xXcX/ZAIJcESwVzUFac2uguVfUwrsY/dIdpdcULOvG5TVxn2iuVxz5yYeTBsO/BSKjWpJ96SRpvEIbIDksWEciBxCdujNvMTZSkPWe2GPhgXwONc0KN7i27Ge/nM/5r27WwdZiJcPPbJF2Hc1iiId+V29+0//+xvLob54b/8/hNJh6ZUWDacjMPIIbl2eXR7lrsKe2I77tN23KXMbTtd1yl8jOU4NOgB67ITr8UYWkC8jc2g20FIXvVW2m4N6nKe9O6up1u4I0X8xtEK7nYnbCjaOnWkH9ZFSjTFv1+vsgx6kbO3xhOVgTRx+CEB6GEhlQvucSxMkw2NO5WN48sBPeZ7rNcAQmRIDeAe+ft4Zp7u+Xf0HS6z+wH/1i/w57rRJu24T790Mu3Qbfn9mPYxeStsH/POxo6+w1m2jykj0eJrnst2rlemoccvcIa7/sE7s3VA08fb82A9U8YYtbbJqsFamL4EiXYMr5nZ6RFfYwp5erri1PQkbvJ1XR0Ots12GccNU6KG32cMgb+97l8RTnnvC27P+aetzqs4dF/FN+fBGod8EzfO82det8TTMd21Z/T4vn/Kmd79s263mSt2JJDury38xnVZ4yfO0Nu2GtPMQ7LV46R1vPDiHvjZ3hKrW1v3PnlDJGNcV50sX1nm7mj7uIw84LqBCMqZR6cypRS549wv+3HaKWOehJAZejUhpa07VahNk4dS4COOQ+JsACWf3w0zXC3NkG+4Ds6p2hsw/Z45ld69nm7ztbB9igC9VtUV+ciZg/pjJDmGZo6N1ZxJwHKO2d//9LsXdj78+R/+JOnQkIqqNadwVi9s5tcRFYSDlxYfbcvJgQMYCdqwHbeXu26rqdqDF74fvXfAunDHGXaAk6902rMDrRQNWld8F3x0WL056dq9zNNbw8fxXZq0Z3GpfRRQaC184KQp+mZFSSI7AmQsTCMd6XuzL7RoVltKzJStKR350ls1u45nw86D+/R6/I27ha7nfONI8BE7t3h39B2u+3CLniNbvpfg8awteo3rNm7Bt3y4lF8+IC8Z3hxza5xn9DN87luVM9nsF8eLca2xo2+c4d5fFbBHj+1gc5PTg/EInI8pRoU/wS5ftIIA703xgXfUTW/VD8u8VW/6bz63O1lwu80x2tFeinutztfK7/xGZ/zn1vUbJ8IlXRi9E1/HuLeaO9E6HiloKffgFSdk7ad8V34bNt08mWGb8YmP93exKYl7Lnv8WHdsrsErjf5rtxmj25rGbthi6dv1LN2PUgbuMGhjY8wcEwylHWbG9tuI05A78iHX8wBqH+OULwXZNGWalFLa8IXZNaNw4ijHjrvdCQRuWzjdkOmq+pg63nCnd9j0L6N9Oz/WeVr7yjWwF5RNzPeF0x+jcHrwO04Z5LCo/N9YunMWDvtP75T8SGoGmCN8ua0zl7rGwwFCCfTJNawPkC4pZhcMlxTTL70Qb7yX0JS9mNI2yaJpUfLSZP5jK33CDd1mObgDlde3hp0snIJXBZToo5jqsqVv6B/A0dAslrAEzXS3R4ywoWySKw+68XcPOw9eZjkGM+LzMg07qVs+7eg7XOrmwGln5AqOfXU9Wwx74tu+3ffpK+oOH27X+C5p86DvkvYcTI+bhtYSdGPi3jFb9Bp/n5cOW/ZWi+/2v7cdbx09Cy5tXR+zdaztbmxpOxhZVwV7DstqM+V4t5RKE6teRLJwCsDiXcb8blHS4VRab45Tx53Bj/CyEOiz5Ezb8/A7v3bjFZ/8sm/ZLgmw07d6i37bmPMGDs5Jyx5I/lPGrXlXze7P+Nkf2v6C0/0eb/MPTW18Hrdpz2w19kuNxjtecHR47YvfmX2p700xuwARv4q9bXfvYiaNHu06PrN0fE6LgzMkL4EhU2Y8nXPulQeXkoGxfreJav6Cb7TobNXMD82wHv/JVY6cSGTMurK7ZEJgzd+uA3hH3+FWuXfRf7d+xBGrk6EGR64QZ+dkj7nH3338Tz/5jtGj/fAXf/zUWjI3nIBnrSUzj0Y2LJ6Z6Urb89C2OrvoMjB5H7fTdZ7BU2ssuhumGcS+UG8Yw10XTxtqoq7rR97bCK4Rre1TDmxet7BO2DrdJnUGtfRC9Qvp4s6rTcLrrEjhS6qZsYQoF0+GsEb86rAVTY/FUfhxyiboc0hT6EuCZk484kAbwBJGtOz2g0fs3OLd0Xe4PqJbdPP6SVgXv+XUxrUbn+WP7dzPjnj1bvl0i77T+Qiu6781plv0nd1bMj11OrzTteLsu1rFGdivzh/Y0Z0QqJbHg+N+YB0fumXfrS0frdpsl/ea0HH25ItYO+LiRW7mo2Ne4cRgnLgv3xWvS/wOcy/vvXw7G2+Js1+0edAR05AzsUyH+Xa+ZFQjwI5rbzts2YEbt+ppnow3X2+n/fIziPKZvpzd9a3DPO7TllhHvTm8y02M7sa+w3UHb9E770vgHrOxCjgvyBeURjto0Z3HJ9Ni3/c68/C/BcXqp/QRGjKgy7Bl7O/aDg3kaMqVYLObPO67tYGhYAHK8OEKkp1ZWEe3JZ3j1g57BlsHPJ6O6/CePveTzvtlwHv/numJI4nODqvnnDxQTubpjsKJhS4UjyQR3Pvphq25bR+wjwy9mbkQs81LnpPRXWh4HLHTXEtBKJtJ1/L8aKSc3ek5Ms6eeXsiHZedyWtIk+7g027TIPHD/2CTLclZwjrVwmGuaJMVb4TjapQ/WaQlugYOigppsaHBzGoHOWRNQQ1w9g+rR+eG63mbc+1ejX2u7pURXxvlZjg7Px6xdYt3R9/h+phu0c2L7/m7O3XLHgUUm8fk1vy327m/rbzdpw5f41tpr+1j99aYbtHPfLhHzunj9kzXDq+YKb6O37c2jxHOtSMM2IbWkpbDTfk9PsN+xscP7eJLvoLPfk3rZUwLQ+hR3y29VbcLJ/N0umHR9mMqiy9oFNt7BOc47+F+dzxjLmry8zPPiRBu3OP3jLHi61iDN8yIJl92kmYe0+Bbt+6DYfsN74Q1H+YxbdXn/unnuxle2GrM58K7se5w5xpmLK/xvJTW44eOsde0vNBiMY9gdscZfe6nEhQ6T4bm4Wo3MZAAlklYlM5rn9dWnBWvNFc2OxxMqWtM3KVf1uM2OcoBwdXpTpnZbSWcTEfEurx5ltbjWdCje06/f20ayt4IOPfxtQb7PHVYeslJsJeUQG7maVs4Hb/jhOJQWhq909IahkNbWBiWN9bMdtHe5u227MtRzW0dR/6X9aaVSLZScVxURwBEpbt3+NQBkmfovuBa9B/oaawwxbevYsY8pZ1kVeIMQk7k0QsVS4VLmYBz8PNWvu6OpYUrf7JzD2xNfcHtGif8zJ2t55k9eNu2xWIOaZjc+fPIeG/x7ug73HAogDP6iqdo+uJzfsmdAkrrhceztl3/Dl51w9NxE748ILpX347vERw+eFxncrfoO7l7ZXr6dHinc4fDf8eRdls4wZPCbqUp0GPr/nZ4MCzAGQ868/ekgr/7JvFuEMz8jMpe7Varbmp3iifwXpY7T4dl5+Xvt3Q51i+zoDy/JnvL/jXZTrOfvWVusl/T0Oe/y57B9o1W86D1AX73BRc95s28lt3rPsYFH49+H/vWYR7333V7fUwv96bH8uVaziXXuLVPs0gQ5GL+SpzWMMCkFAONHBbCzIXLbqc3McC2IhzlG5/9dZtyLXnHPJzY0ZDkmOzZyWakwJWSfTtJ2+xeShMfmBQl6dpGbIj2MQ1kA27RJ+txH5r4CY04TdQrods2X2mgxD0rboV27xDhnKt4y/Zo/Y7CKVSVVgdrbYfK5LMVt4N6BbiP13ZRZJ+k9D75Kw7cRepWYpqHzMx/h3+QyPvV2UbcgyS4tbudnBsbg5jGRi+NXyoQvdTMuoqUsW63TVV4NK8yBT5PgaA8XhUASYE7cJYS63QLusPFtjRd04TnoO5fDBbFV7rKrdu+XVHxAKnZmcM6yPe8J94z3w5sF53bsdnruiZ3Rtvhs3CK4olb9s4KJ5w+7stzGOs4dzbg7njB+3FZc+c37rVt13mcr0vNt+iXEsSo5cmGwanjdsNyFWX/afuL4qlvSStEcA5Snyv76nYwNSBH08a08qJZfmTGZ87TZ3ObHd5CmfQJM9XOzy7zZuGUt34hM+m2L9nrsbauW611nvFdjOOM8QR/S/6W/RO1F2jb6W1OReZKsMe0zEy4ED9F2D/aDlug45iXnJvkNcf19uiv8hqJju/969rejupxvp2Ft9XseB73GvV4Zz9jE5zgmG/16j0n2LBbydK7J06Zh1OklMzGvoIx7Pagv/tS4j3Hj49Dv2IwZE31VSPtLDd2mlrrcGPGz1A5tDQex4Ie3Vv0wRjALd5DrLrgi+Hrn9kvVnsQ9EyA7LB7m/gyD33iS9+2cPqXP9TjyHlARGw5eS2RCJpf4uC9aR8ZMqkdupyUJtsZF7hPVnMnuO6TX9Q93O1W9gcNx8lIA4ma+C53zQFs9TjN6y5nUrbh1nxrH3xoNzpbdZQ2B4KVZDsLl+iOwglSejtUCmM99Aw7Mas/Akp/dApSv9uccOOtBQYrz9qUW/b5WVrP9Bxj0ee85/ul9P0LTdd5qeeYZ6ZfkzmlcSBVCnJ9yIIprjpFS59Ciu36uErB0uxs7nCIneGt8hbdfI+0Xeet8d2i7+yuMt2e+R17+h02/VZrnbS8fMXJeMujW7vdnG/TaO2rW9PWfvJKwCyHFhuyhR35lDgZn7yxC+VeVLvS/GwoRHGC/6ZwmmED6nO7m5/OY17anILRvizfbI+2w/aw45i7nNfkNcf19uiv8udsPF3Tml6d9mw4xxTZq/bZ2t+NPsd52dtypwTHp75pszUkHz3X2XMwOotx14Zkni638NtXtZXHC8/Bh0bTmhoyjCZt2JDbxtzApIbw4EpF8SaFjbOBlYQMydHrcWycA/TYBmIBbtE7+yO8Xe618FvZnfnlWXArj9VzpNsomIfNPO0Lp8PvOKEkFJYdO0BreJoJC8OfjbXJeFiwt541XoPdnv0R7boty7+27VbYfbzNhXYM3qSKh/BdZjJcQqk5lNqC2xbcS6HEtNKihI5xklh6Y1fd5u7pjlv4Jzwh0DZAK7jTj4WeKH/Oy1udK+BS04DU59HPEeGJmd2G1Ax+yD1nU8y6n8/Ru9dSdmJIzxzLvbp2fDscvnd8h/fjioNUbtWr4inXC45aY+v78Zlsx9uW204TTD7cP1/nei4134vpOm+N7xZ9Z7PLdFvmnXvEcU8y/VZrnWopmmIPjH3r4opTKPIu53101W1f3UI/g1fZ3veY8CN9qX1EPjbOmPqc/UqBueYdcwL8N4XTjNtFHIPU58mcjj99y2QO5LyAfFnO2RZth22345i7nNfkNcf1dvqq/Ol9JNe+cNd1Ppv6/7P3Hgpy28gabo/knNMm23vO+7/WzWcd1lGSlebWV4UfKIBg6J4erXaPKHEAVEYBRLMIEPQ6We+N9NrSX408+bG72kqFgFnt4vr0fNjUIEOfkyOyMMH2qmN03mcyb+KRnX1K30hEyk50Mt45rXBKa+3E3Kf1Vklg17dz0RSjUCFfka5UzSWrXlIzpnv4TH8Obea7S/6+dWo8waNxNmvDrxP/0g6T/jENnP6/R09CAksakG1/pDTKhrYWFaypNw1hgYGKtolS6OmEfkx7baBmf2tfBZl1ZT0VPpNwOSyrQEotW6bmR/HhMIfWOo80Q1kuCZlZclQsQzJrnT42AtEk9UZaoAasQUhxaOsyoaM5N8qCNp9Ls8ksI4NoWtBkEFcpGaSiauYgKaBhX/gp8tXOYjt04Z/Aw3sfF9yyb6Np5cCUVK0VqhVwYTQZXu9WrRX6Y+BzfDKj3YPN8KNlCpy0u14eM476V3qUZh0j7JhM8/KVfLxmS1xS2x3imK1Zg3UxEzzWGYpcnZzvuddLWabypPnM3NKhVLhcJ+X7tIwy266RuFovt8OgsoegLg6lVioyPdmQT+BE+9SzSJKdFHO+oC9KtuXcvR/ij61jWz+cSxtmPKFHtJG6au8jLqa21ZY9I857g7dFu5eQ/pbCBb60i9JRmJebP8I12eaSx+Za76AXLSIib0pe0eGaqNOobx8wcvyLysXncn2tS1SAv7Utk4UN2/COpqF11GzNCLNIna1c1E49sph9pbWjjcd+UPFF9Mhv4Ogbgfd7rUpTMwu7UJqxnpegMGjJA6TQOL3VizTkxN8l01iBCYX0LlFnQfbGnbOEdcS6XjvgVQrRreS7SLM78HDDKofquV+ngdP/+3sJnIyfTh9KWwf3C6E0JsimZtkTGKSmR7E6sANNLdZMLwK7CmTKX6lX+At3u8GvDMuMFC0wTXZugAqd8FVclVWIlgijaJ5z+YO89m5SFWYcjUgio+1UsrSQeEDi+da+IAMtOWNaKATGSjdO8rFFSNKct1JntHDA4cN6yfGfNoAFJriD6p98Aed8JVhk9i/M8FezbSFiAQjaczh6EZdz9nKidMwP67Rr/IIrnekeYYwTvkzP7lZjHIl0pKPsY8oEIX2kyk/ILgZtyVyzaaZsS06m35O5h5esmb58leS8ePbSUSblEYaMTnZcMF37qQ6kNc9YQBkBhYfs4ijCmw7GghgNZY9mwXzokTXlMspXU8hoktA1fgBX9LJzYc+ZgCwn52d+PFP0gnxPZta/YD4DID05jWG/jKfm4t7L28LhjS5A/4A2UtlLOuYz3bZ02RIWYXOccMne6FNRFl1IVX/Y03F1/KA4ivZ3gM/1lutqjhygpb5AyRb5h9QMklQMiRJVbOnkDvYZLkPU1tiS4ZJfOkktrmVcjvcdUeRalTY3Yz3n/SLlYRHO2TOv5JX+Q2cs6Kj7jFY8pHZvIydZyalTOVPWvF9gUjP1SiWVdhdZ+CryNc9oTHmVZoaL5LWmGUj29FY7zQOn3x5HXzbO6CNkyLuoLnVIgYcJyx4xd06jm+KnHaDxqOOic8qfe2oYlv7qFj3qkxDL7AESH4CMkwuyWjjhqzjXkgh6xGBDuViBFhbIuxikcNQZJ/AFFk3jHIU/MNxAcAQ+BKt9g1X2kRa8DHCCgHmNa1steZy3GJs7ZegwbDG0tUjYGp5UKFiIxGSpVOa2z/lEWrN7eBFmnwi2mqaG6C6DVYYRIZ+N8MvLR+uJhhntHmyGn1mrd5voV5p1gq7vZ8E5g4GRLlLlg+Maf9O1tSJuza6R/Bzb9mTu4We68hWS86Oda+Uss+mf+6fKLx0eevHMUsf72NHo1uwArmvb82UkYzzg3/jeVbZ7KTN4MjzGmdjwQmMOeNmdaS/JZzktb3ZUp10idc6zXffr1clapNgfKXq9PjWF4tiR/SD/0I3It3LkxzIaBFvTZpZVlPwT9hrc/7c048VkmpV9hWmzOSutdS3XWcbl/Hk2h66+HbK0y/NhZvgve7HWI4nOMOWVJrKSzdKWWEH2fmOgy22uftHBJGwtxX1mjnuxmKW7lDmLUYbLu56l+545j0FTA+21b/VO4lmVexCx3hYhQD47KG6V7FpyVhVMELhpdBU+lJ/Dn/nvUsg0cPp/fnvkMmJACyZ3pMlycWWQq/ml3A6SB7Pai6Aw60ufWtZkkKBidXTUy8UlKSJDeDE2gWpWP5kSUhF9ZgfdEdMYBnBbVvhqXZ0xEfWITqxLFL6wuJ7KXjMlmIqys3i2XNZ+pTpn8YvBC2uVQLtW7cqR5jwEKosYuYLl1PI1sAj4OGCo3Fqk2Fi8GRq8NlJWU3X+2icMk/OVsGS2cJk2/KJ6ZMyYp349TD7toVulI3q2+Hvc0TrCNaPdg83wvQWtxJhRg6cy6wR2NijPYNBKH6nywK9xHJF3pC8ckTPau1Zf0e3hR525G+a85B1JkbmrNwsqnR0enaAj77nI29gQcpUGjr9rRx7Tyfu/0gcUPME7+kHygn/0hJV90GjfnjNAsU2cd0ujnk0GLnKVDXSV3Fq9s/DRlow7L2/edFdGim4v1xQvHjtCTtDKPnxEvp3gW9lLEAEtqRcmf6LdDUFTxx+zlZwDNlPTOJF436Atz+ED9OOgbTuO2R66chu49B3Z25obNrdNtWdquurl2msd50173DjXb0KQ02xpuqLe1hPcDaTjWfpMq9IiR72c3XR4nzKKcoe1oHVAcnauie575kwIbf0i+DJ3zxUYo28sPcEFpea/OfOR8WfO2UOvJaeXul26qfekPR1+jHbZ8/jptBI4/a4+WK9Z79QmTw4lVZ6eGqp6QyipcwWmtGzqFPuOC55gaT0j8+V8taDrmYXPKwFFu02v9EC9Ems1yZTLfLFy0XebxaMvkp5MtBDNxV2AhYVinl0KrNUp2d/6hlE73Lksz2UOjHJBWdlbMJkkjKdVrgiUNgkuzP+AK3g3IsqtOUxTqA7qUrnWIsVO96TyTfqYk2+mfcCIMzznRzm5vN4Pcr0LR6pLlnE0X6+howwbdLP6zWBrIjJtzov+KEz09R0nxofXIHCa2S9br5Eif73vLDWc0/Yz2lyf3A1zfqn1PMiot8qOirowaMYTRIUxHkDj40LQ7lkRevSXXxHGwThngVP2RZMdfK1suRuCJmackF1r05HcpTD6S7Lm9gl7P+maLedrw+9wRRrtQLHADXfUkyEnLJB9dKXaV1JfgqrRxNirckhY/s1+Vj7sDQsrDIuL0YItpTXIEZpGffccPrHau6C9Ot9V27Xk9zYn+ycG9rSqZ+OZsOyCvB7ef0IO5awnBOQ+TF7nA+/jW+0seXSb0nU83eLJRkctA5LvgTJNzecLpQKXGWRWuTJqSXY2ZK9PHK3znuJrydnTE/jqqUKeysV3giiFMH63CktJvvn83R5gpZv/+9ffnY9O5ycknucPMPsDaEgduPmndbnssJyfs0etMt1avvJ3PVNeCbupxOwpgepTZZyZsUuwXlCZtWh3UKOQLQbOBJmx5GtdC4vbbvlga3IUTAkf7EblvkgpCIMpWEFSSLG/TZxlS8HbmbyQSl0Qf8oh+EBboji94+RUpc5uA6Z5bWQTyEJQU+no0zy+VD8lkgzL+USyyEa3Vl0yegKTmZnsjPxd+5xUrdVtDS4+pZku52d4wfzGqRaWmXFHPdVV6cixhPOjFlTYNLNrlLFWvgvvmswMl/z1vpOpI7+s75JGkDVa6c3dMOfFf2k66nXZZdxHJvj+dGiFVRofO/gYcuDJTK4mkPXQWEnq/0ofuLHAh35B3Wv9AXQHHBwJ7vQ5cBrwTn+3P6O/JE12qvwq0jVbztWdbZfP3d3uT5NGWxwUmptJ9tEn+j7U+hRiRTfmZypHW6GRzcqLT7RKBR/TPfxIf63yOfW+i856Td5FiPGu2Tu7zme0GXaRKYwpUZmuPyELuNoxp3oIA0znmm5kuBwniPHF+z28a0wJnv3Q3Z4mmprNF0oFLjPIrHKPGLEUsYCEC6vUBZ6r/aB5E94epLboofdVUp2U9nrUJhm79it1RuBEpzFFdubOiWqV3QzwrSlrZ3Wcd69o3dFhYzno9XfJk+lzXhxu59idi0ciTPCKVPJrZLByre/uX1rFuGTItF5FR+hKPAwMnIXf6+hoNBs0BU/BJQnioB1hTjK9qDKpnUYkSKV1gKAtjY5I2U5MqCmC7XCY7AiA18CuylyT0avRfi6hu4Bn/sqwnA/u+d+pH5xUdUt8zX0J+Ppk1+q8B1/Dq2ZTfPoBwYe3t2xH3r7jxKwTRzdeSOBOir6pzo4Pmg4wFPbwA/nBYvQXiCf9Y0XGzAcz2Ap7B1aVlYLc91Un4ljBq1fq2Crt7YntOhGmujgMv4Dnn/OpPFcrXh+3jITU/5U+kAMnJCz7hlG7M4KvaTEgiDrj5NwNfYWcbB9F3Ut7jErOKK/ZOYoY7Q5fh88Zvx1POjKW8tb1KBvoEuTXTonO9Gtys73Kh81hoWDIVF6p9OS04kzhWh0z/bl51WmNbw+/xncUvid/Dy89I53KZbTg8i/XZHAIT0l5pZKJ70eYcKS5PVyPjy25L0GlMYd8HGpTUs4cPEEhfCGvCbZwlp7kqeezIZV6zPRE7d5IdAmfssKupdRbPu4cssYwgRe3TTACaTxV+TrpzM8z2DW0RT+SpyLt+pb1Ax0KoChbiwvcpauBk5xJ6sykyLA/gVMKqAgXbacid0SMCwNnDprBQtSSJ9PmvFTLJOlzuJvpleiWtYnnrmmr3VzS+tBb/Ddhm9Ut9ARPeMYu5lK3FjxxgRvW4KGXvKjHpSolFClOq9YUmaXhi3Vq96BSXwhk5bRiwdfleqgXrFX01jtsC5RiWLJy7cjYHHaLq1jrxUoGVS4U4gzLecmapX2dMkWunxRk/OuXn9V5BsNywZWu1WaKN9/3rZS/40SfsdmGew2cTP9oQFeBPXxHfHahjoEHOGe0M9gBUU6iaisV37SdhNxIZ3zVPrsE8vWHGHD5lGjxOI5fEesDjAsZLlqlkkNZPYoUm5Ryo+P40uBhLzQObv3YOJCSaX18eRM4uU/UDl6Y/Jn1A28Hc6n7mjZxF9Myy0PtscQERPoZb9XusxTqRqsxuLV3lp9tVj5s7vsBPBmfZSgvPJWc1U90d01VtzU5e/g1vqPwPfmX4MXjrcX44Ma0NhMesPKknPK7UuFzfWbtIbqc0rcYc/xvFDr56BhPaKXbGcsf5HKWnlT7xF4/bzKa1boXajjLHRdU2ahZ1A7+Cj4rU9yywtPabIXgIvDMvzPYRcKnTLQdiPCY+khHWvzvZMWX6rkdnRVWAycI5dDoMACAuVhPMx56x7dmdBB/mkOwJixqsEqW6BosckuezJ/z4pRtKnsaplvWOr/nK6AjO7dQfOxsOT+TMx+C9+3IddSNS0vRdHt6YJVGP/BIHeyQsMswHjwJq9RI7JBLZI13GhxZnBltT1m0hbIkBaomNrKCMGeLRAOGl62jahjy1MtIMbvcYNnnhTDS/qqUx5mcF2HnsxmBCIc0qiuLM3ICkzGZ7DXKZx9ksxZw84+qssBlRsuv4Ud4nW0q7zfFOBI+1DgyiJ4WJVfplGjDLuj3eNdkHoWfU58Z7Qx2VDd0tJ3aL/PN6x0/hr3O9gM544GWc6YncGjtr492HXH9B7/kQEteh/INT32iRqTYVNN0LTscXDmRF/YHveRHavKMzrXaOKixp6e5W0n1GKWETSP0X1tes1VWzWwO15tvWfHoPoca/4urpTNYw1oPKO1PonZfS8UnHspz+5ohwoedpS8Vo4Rb2r6sS6OVFddNc51mkvfwM55zYO06XefasmGGE4xrTfnR14KT5jxWPKBv2QOSjMvW0Zpju0hGjC1B3WDZjsAFf7Q3eZ3Sk+VLDqlROon6t5cCJNaNFEJGw8lxniAX0MkpNnSwiZoMmrd9rkz4J/NcI599K3kzmHB3T+ljSOFPzhfJasxCUZp4raW2AydEosw7DWkBCBaGFLzjoHEqSvVoDqFBolEarJItLoSE8WzmWcuLpzkpdQIZbER6H0j0W2ny6RaZfL1JA1IXnhOGUyuPLtAKgDpVoeV1ExP+5jbA55GsjnwfMuaUDFebQ0JI+xN7gizoyeu0tVap/QtefqSofDO45YpKZBGpemrZKMs0022VClykmm3yGpYKy3oXhf3xn2LxT1A0/ziq4DN3g6/lokqyNlNNYOeJzsJeST5fJ6PCDmeOU1U6+Mhk5T28WOgb1/qOEzKP6BWNUtly3+nmdTAon9HOYAOb13+ko0zL0e/VfiMfGF0Xe34Bn2nIo0MnOtAnZZRlU6RNV7ODa7/J8NnHuMgqL7TSIXnA0BX/LO+VWNZSS22UOlepMLwcso0rOFRrzFHZyRZ/si0gs28oj3hgs2Pkm9G8atie7Ws241pwfvrkH/kD1sOXyKSf9iC/dYpNPJRn9mWY8m6n+sNOKj1UKNta4feQyXWaid/Dz3iuBcu6cz7Ln8EFI22/mst+4vih7WkvrmWCp4yXTm/X0j5qY3DSaTmR1pQ+Blyplwwm/pwqX5knmdo3hj49IZ2DqoA5WtBFTSZ8CxoxW7qFgyz8kRhqVoqWbVZJ7pCZ+XgGu4OKCWt4I/pJ75lc8rxWR02kANqccYIAx3rXJ0Wi/XFYSYPGVUEYtADT0RxCY0SDNFgjnMECu+TJtDkvae0iEsTSYiaQawZO6mJJ0262DstuUxg2tblIsnHCDnVi6IOHn39QnPZ8xgMmTw3guCAzrB1FRmSCy0GBcBJvW8u5Bm9ja2/tiGZAt1EpHE7orP2foitWB2KJERoMco6aMji6pQb0PDiDhWEOC3v1lxQ/FMtFF+yI7g7RdcCdAj5oFmZiWZ1gSX+CvjbZvfpXvPlTVamwlVrs4cVWtyKnH6kPGXLWz2cw5EiXUsleS0WndI3u2vA1+2d6ZrQzWObN9RGt+intBl7tl/mUF7/TlWtHcqDJ+LH80tsvHqBIT6U34j05XPtBQxpyKOtEH4fKQQtA13qk/J0d2OI3Wnaz5fb5UjzxBIdksloUv/mVrBRVDuiliydDVW9gM3ymzfnMl+H/yvye/Vs2u5/tCR1dyfPzpllUr3Q9h0u/t0fqD8DHU4LEQ3lmX4YpH/aFgVuwNZnSfV9prtNMxx5+xnNNWNaf89KxBQPHpUXKu4kPeKpbykqdxvCsUCBPwPTg4cPTQ7ueBQOuQ+2Z2zJwjYZy39eAqF+FPUkkyNKP+37iiMmfLDvnJ6R3AnU1OniNoVB8SteMCB+MVCiSsrjG1/gvhavtMv8MlvF3z0c9oy+NdR589iZwSu5OvvrPCpwi9IjAieApzuj+4Oxo14FlreBXe1CQzYOI31xokOGGt9z0RmqyjLgOZPi0RjmjogiKUFesKKTAg9YtNwO8XNKAOUGxM/K6NSTVRebVWJIWCOzU8bxjPpggI3UgiTxfvDhfSbpX/4o3P6kqFbZi4R5ebG8CJ3miT+u1k8AzWEJ3/Vi06qe0G22i9st8Oa92ixTq1p+Fy/TKN31xFYtW+mSH6Csem3T9lR8idGZ54lEKTnjMa3WTNlG2FB1xKnAy2lK1sM2KJfMmcGp+k08apM/VtuvBXnJ/vwmcJp45H7TXDnv48zWex5H157ykbMH8MrQ/dlW3saAwwjc76VsPh8CJ3xJd1HGt95s6BAwd3S2DTJRG14esFy9e2lkGiTJySi6pDtlHWfjIi2JLX6O5NCcLnb+ZtStOfErXGGJYHKlQJGWMrWvcl8OzjyVlBhPuOmnUM/rrWGf1rpK+CZySy5Ov/vMCp5f+jpM9o7HAidPDD+v0utkxP5TrwTuoXwzpxgY3mX9wkQ8WlvMbXxtklCqAMgL+x+EMZHV1FSU+98XMUVXqFkEZlvki+cgbjYsrqeOzOJhMPv9qrlzNJQm4F+hGlQAAQABJREFUeLwUfy65GOeDCfJU6awg5V/D7F79K94cKfdV2Ep99vBi837jfcX6i/WjGLCif4lGqXAqK5UupYKvpaJTukZ3bfia/TM9M9oZLPPm+ohW/ZR2A6/2y3xj3unyRVMIJF8yW9r3eelQKvmySWXpidSg2OeGtpEi80g//BVuqoPF/m7UL6pT5CcveB1MBqlkcv/lZVdkeVI7or6R11/xqEw6tTMTrOQz3wrJKwfP6peN2LIZ3M2bwCm76+L8Xjvs4S9WfJAx6895sR+B6TrLPOIjzSd96+GDhzbrZHcxvitreV3AmLlOud61LFczzcxkGYvxBb7pUQ44Vtyenj9/eXr69MXp2TM2q2HciFEGGWOfZ8brxYsXLuSBKXiIArhgKUfOC3attPNb0rknX3xK1+hj3Bupwh/Bo3F1TcJl8NHPSJnBLpO+xhX1jH431jn9FrgxhXZF1BWW6qWbaOuU4w+QuZ2+WY7WIDMnzWCJuXNsps15adJFqbKnyVf/+sAJi8wf1WFL33W2Q40r/SqlInEScvhpS2AiYHpR0pcG50SL0TBoeIG0XAyUOSTTUC7VbNJAFkETO6SBNLhS+Jy4pEVTKGFw4UQzI5nyVEDzYQVmum8dBjn0Razb5CLsDwc9KQz2XMGLzClUH6ePP7O+kdDT7LTvOCXWDcdE50DxLy3u1T/j1/KLCpjTj1T7TeC08JwDZv0rhoFJ/yoicttkfvK0BfgjbSI5SrOFwJCXT67GsK1QFryAskWp6N0ebLLTb2pYRsfNDe8uWMohG0RbNERSXKE6xTXfUawUCofxY3fUBdKoBzAfwgq36qY0C211ytBm9xq+p46S6jrD/atge/Zv2extxtDubdx8slcXI/cj/G1tQskKlKOtyC/P4AqayNO3BG1ptln5sDGIt2BIEb5JvP/cXjvs4e/bwqw/56V3DgNL65rfa5t7a3v7gqL1nZf2hrq0O22gICUCJ+gIcuJQeypoIpiJMcYCJwueOGSTZBaota8Cp5ceOEV7xzgluQFDBvc9L+wM3ehjJowj972cDz3X+xseS/ImfT5ha1Z8SitiyMhPPRglUjS/znr680vyceacwTL+7vnwRtR56RlBPPUgmz46Py4MnOjIfeeci5fzhVU5N0bAovMJH/RLRzZ8pl/SNduk2VP3woorKjhuRIoFnlx8YeCkcowyzAOBYbAg1/4UjmXS6hlyCTMiOCK1gMmDpxeRtzIw0Zxu7OL34Ml4S6eoaVFVhq9oW5OtpXmMWd7ZjNUDJ6N3EZgRplimBEK+bI88A0xLCY4o3xY4qQdMOMYDJ7PUstTExbp7wBmbH3gsCp4rDs1+jXxlCK6+WGRtJ20wqZVbZzgk/4CcsHZdz4WY1mfmAkb8WA6z5PlexpQ2kbwJnJIzUrb1rwQs2TXc6OuRjm4464oxBKn/WTsa0Sgrl5E7nlyVDrOI4wU3ErbMxW9obGDQTKLscTq/ioseU/gWy27e4mntw9NbJdVND7oVTJFPQ6aPLaoTOOWXXhshyMFmg9upPGWDRuBUXOKJ04wygm8J3YdEHYoCRq2Dhud22Ndydwq12ZqkLXu8PWxIr+0y1HGLt+mLNpId0U6lr3n7tXzj2c5lvcq7jaURtmBIFn5bS2Cj76idj3CcR9P3o/N4r0mt9kFmzmcde7biJbVvlUMbR8FT4WkDxgrGhRhf2owT5OA1ZpAqcPLVNdYP+d156WNTfEMw3pMi+Im2As8kEvFQtDfXKGd7qIMeDr1jRT50ch8DnyeLfINeJ3e4dyV70Cw+pTNr1trSamfkEnh8/Jrp2IKF7xvFWG6Ya+VivAnvLD0jiKf3FzjFoEaVWgPI2UBznjJHg2UntfwcH7wjv6DR6VVS2mwSxNK4uhNgyCZ8sqS7SAaO9WKSpW7cLjazuXA62YR2Jrj5ybDB6KFIBE8ETs9NLjNOSplijgAqAicbgGwWyvOWi2GLbhK5sFNwVFj+ZbmZyb3K8jVwwlAPlqgRpw12twRN+XzLyz775AHTW6YxAicPmmrgRNDEWURa2pyPz8JrnivObD6FVF6FMY4JSKjVNPqOKrxKVhTs4L02otmSKf+J9jrpzCdZ8gx/FIacGa3kvwmc5Ik+nY5NiWSGn/k50816TwwrY5+zq6dcJmsygy99uNhuQriB4Hz+/Pnp+bNnvnzFYQRRjBP1TDc5pgcdb7/9djnfqnl/h4GAqtwg6UYodDdntHrp6k9DQiHLPMp7an9I42RcizGtzjiBQ0ahKeJqkv1bgTsZ19td8zA0n++wb15Pe7zn4vfqN+sf0kEfAq+0DM1Ce7rFD4H0t77j0NSXWr9ygQf+ZJ3Kh53R6bdgiBf+gKpq/xHac2nm/ehcKdehVzshLedH6Vs269rzQMgvOJfmIiTTN6ExIbSBxgX1DVPstGpLzWKTPnxIP6R943pnXGJ5XT6B+YXuNAQ/0Mc7kYw9khtyWj9AP7zCQ8vh6jzX5wvoaknU+qC46OJOLD6lowT5fIRHGUESdnzsmsvahsrfUOX8Ntdl2FZnvLL0jCCeXiVwKp02Euuc9stTO7SbYKpCW6qRHJ9ApTGi0wU+51tjrTmxyWwdVxdN1mPmFJsH6MxfiUT16CzpLpJEvJ2t+t0xlbbZbTos4DBPJpt62sqUMt65kuwbu6khcPJgyQOn5yVwembwCKBOBj/dvDBNdlpqz1Esz0BCGv/CCLfG4NiBXRHEkPLPwYYpfcrL1MEPTxlUOC0w8uCJgElBU5+/vbGynTHrVJbvmXMuD5zmF3j2t9t54E+0yX5buKjWJVckZzk5P5IjaFfYyLRb3huMZvgZDEVTuDl4zeoaOI3jRe2/zfz59dp0TnU39poTndKKeAWZtTqMqqP6631hiZ/37axvrfdkGtkx843oSKPdIlBiiYxuQAianj19enpqgdPzZ89t5iluTrixcD6e8nJSNmVx7d2c3nnnnXq+++47p3fffbcGUARVEUTxtJeHLrKyvxpy/eb2Nz7JIPV6eRrDl8OMtNIon/QiKfADEMTOIT+OZDObRxqVz6EVzyXpmq2StWUHbQve25g8owCNlA/wuZzy0p3TMU85wxL7NJvtVX4rHXEIFWyqIAG9Z6gTJfi1spf2v2vpz3LUBsByPtMov4Wvu3M2YuVcLrz4lV5DQMR3nLgQs0zah1OBE/0vYhlt+MASvGd22ljFOMWDHju11A/6hw95kPOOnyFrPXDCQPRLL6mOlI3rQIgrpmePQGZe5sl5mZX9KVifUkfVE3/32GuWen/eoyIzutUbryw9I4indwmc4uJtCqMcHRkj3BDTEt0dW3LFWz47pzk9N4hola4NYJlHkuJCUklpc5IgpHJNhpW8o7wyDkiWXNZx5Lyqs+k2i3sDKm0P3ispaPIleQRNdj44ETDZefvU05MHTzZwWPrS8KQEUMxEETzFSQvKPkvdPMpYSsEGFkoeQFnGj1ILyJxFQRODHTNKJVDy/NtGRPntArfyTeRvb2xmihknO1/aFRqBk6VyUb1qZUuxqsAjCeKcdxPtT7wQqtKxNHxBxa5xZDk5P8p2D4/AO5fn114TO8PPYJXDnKymqbCVjI8RFp9rrNA1qTSzzWDgZYvSzDPLi07pjOY+YWv1mOk8hzbzz/jUJp5yIZQxZUbbZFlbOinBEkFPn3pgZEvzPGDym5Fnpz/++OP01M5nT+2m5MXz04vn8Q4A/CyPae8ERF+nHQicCJbee/e903vvv3d6//33T++9957DgLfZJ8aQdJTLpaubobHZR4GSUtIQqrTCTIZ8AI7xxXGWunjHO6jSRemyv9I1cl+7P15D3pqtsn1LB23geE8rR2kblfdT2UA65gOGDG+pfWGFotkd/RuwYDnNeQkXTOW1NPrOeXatyZrBox/fn/yZzjWY2mWG38JBn/EKnLIc4fvU2o33Ia2TAReOttHpgZWX8RHj1gvb8CGCpT/+eHp68uQPH6sieHrm41ssxXvgY88HH3x4+uCDD/xhTcx2x9ij9peesHXZjwJOQJVrs8xL3hIzh6iuc+wc6r1kYod6j9I59wyKMAlsdYfyXPv26p/xOT+z6q6wZnv0mVGe/OTppYFTXLghWgpdYOnIwCq8Epuzpb043tze2iBZ2jupNZJIenxAZzCEz+CyTfIircb1YJWoRyGRRSFfBGek1ScIXOp1vyCu0p0hu/LZhXvL0jxbjkfgdLJg6VZBE/mnRvnMz5cGv7W8PY8xlQRNBFAKnEizjZan2RiYCJo89RzedkPlc+9fzmp0tww+LNFjxqkESiWNoOkdwxFEWWqBk8MscGL2KT6CSwAVNzRK28iE5qKbXBmxlJqwCiOv403g1HqyfJLT7D/BZzDhSPfwom0z03Tz2XghSl0GuQ8GTrqUNo55TnRK51T3C52PPXOd59BKwoxHrUyqumefi3eWetBD4JPOmGWywMhmlZ4yy2QnQdMfT57YTckTL8fTXN55UvBkM1D+MrWNL2VcwxaCI04CJm5WPvzww5oH9tZbb9VZJ9le7SxdQpv5gI9Lv40BotVQGqk8UvpWkeOBUxk7bsvTGehlr2Rdmq7JWdTrUgWJ764y12yVii357sLaFvS54NrikVyl0j9LgUU7tlR8W2nWr7xS+JQnzXnJFEzltdS7Uxi4RnInuHxyJyFXYt6z5Sg+2rT9Dsg88QtvreT9ibZosGg7YC1oAs8Dn3i4Q7D0+DHn49OjR489ZdwieGJs4wENY81HH310+uyzz06ffPKJw9iIApnSqxRdcXIPJGuVlgEFrgVONJEi48ghPxyhFU2zwiBJjeBKRX8sRVATJvsvsU+863qjrcHv065LOYJp9uOVpWcE8fRVBE4Y3Y0hdbpg7oylg9RIkS7x4ZY5vA2AQRV/m5M6aC4s81RC3jNsscYzsnDJNINIjoQp7Wld5hzVE44lOdsGjdgAgsCpBEzMNN3+4efJAqmTB09P7WlwCZwstTcVrL1i5imCJxvMasXNIDOMax1/R+Bky2gsR5m0/Cfxw1MLmsCeyhK9GjgRJN1GsHSroOn0rvHZyawTQVN530mbRXBzE9uYG2sddLAlNHquwHOfyPlimtl8voPDF+fzSWdLs4ycbxQtR93k0Qa9a27mk1HmjGYGy3x7eK9tWqLnl1bpt/Nr09ramZqfso6cz3aM+aCjr46YV1deq9/MgrHOM5oRNpOv6novKpUf6WZlYNxU5OV45J8/j+UupB4wETTZSdDEyZI9ZpsInhRwKWiCX0tjkJ8DJ4ImblxIledmRsET7RfmlxqFg7xzqG7qCzxB3r5m5BUj898kKyPcFQinm7PRy5eVRx9LimxW+VrpXeSu2SrbtmTLhZkm2g33yreStEylWykUyue05Z1iKShBRr0qjykswGZw4ZLYRTaGqTZOLQjuCLhkTLijyl12tcMa4RZeuDGVrAwnrzL47Itorxgf1HYvXsRyPMam33///fTbb7+X9DfPx7I9Hhbf1pnvzz///PSnP/3p9OWXX9ZxR+8uoTPr0WwUsGyXWQZpHAwpys9Sx29SDLJnQuawZEVnhOBK59xrUNmqdI1uH6522qbUmC/fb1Nfgm1tJ48oDWkqRVr+blR/dVe96LBFaCm4OMtjhM6uEk4gbUqPOEO0bTDr5JbCvBHmPM1RWVI4JEO6PPUcSEx61yFFL4ujHEwda/FZxvc8RZIzdZyZpRBNEpdPiMESPQKnEjC9JH1i9bDg6fTE2olZJwInOz21wKks2XvpM0961wkdYQe/e/GNDnzL0xZCIvvnKTDatLmFPDcmdcaJ5XkeLLEcj1mmOG9vSQma3ov05h3DM9vEGcv1WBbos00m1K1RarXhH4fnXCl2BMzhKe+E0L7SwGnSjm7IGlxWklKPVpeMuUs+++eonBnPDLYnL8YIo/LxgiT8oHSPP+OP6BeN0sz/KvOX1O8cnhltPyPT96NMH03Qxm9wzy0AekGgxDtMZUkeqT+ttQDpyR8RLBEw/WFPdT1welYCJ59t0owTARiBVJSRzVmX6tnyvI8//nhxavOIeCHcrm4PiEqLYTDdhtQO2jafvoy4VrdmKq3zcF3B56kFWz5OQBv0IRpbnW3zz17fyr7Ogvb4Mu218ns612yV/k3+cKmT5mG38dBOktRS+RpI1p/zwgmmtEk5lpMtpMqLM8NGnGiUZvzMllwn8fwnprO653qu4TN85ivwyxPJDU4ptxll8CzLe/r0D59h+uWXn08///zL6Zdf4vz1119jDLOxDF7NeP/5z38+ff3116e//vWv9p4TD23e9iV7LtFskR7SHDgJT4ruekz6uXCOEj6xCH/XtBMpPSZUcKXn65EwpedLgAMfHjvm48Ux3qNU2Rs5L39ZfyuilJafiIWCKwdO2Uktv++8oA26xjdaO5ezHBThyxdrk1Pd0UA5x1U9kjDoZpqS72HB1FiznAbteYogRzeaqmoCajghbcbJl+hZ8OQBkwVLLwmadP5hLHEy41QDJ59xYgaKJXvxr6u4GYqvOQmWfG0wefOEJXEan+dNA6kHTkbhM04eOLFUj8BIgdO71iYKmgicOA3vs07MPPH0mGV7MdsUZcSWAArdpSU850r7C3PWP4JM/jIVB464fM7jCbEznhlsZgRO5LzuMfPJnoYpjznyXOv0Y2idwNqeM3yhdM+OjJ/alAgyPucTySvLXlK/c3hmtFuBExUP1zf/I0MzRTlYarNLFiSVGSZSLdXj3Sbo/YXrsrQPWS7PZsAJnOJswRSzSQRHzDwx28RJAMVSGU7gCp64UaH9ahtiuJ/RfMIFfqAtLSzeSkvP9f5rYwwvnfugoOuNJ8mtbxYRq4lkrxHM2gbaPb41eXeB7+lcs1U6N/lxKYT+x93rbJkn5yUz61Qe/1sLiMTThgu4yh3RTkH6SZUXS4aNONEozfiZHTOYeP/T0q26ztqR+m/ztPaFTqc2qImyS6ltKBrSx48fedD022+/nf75z3/6+dNPP1kA9bMHUBqreCjz8Sc25nz8yelvf/vb6e9///vpm2++8XFHs93Ii4P+Etcs4xG86gONRrTGUa6BwrxIfLgBmlgWRBcCOpHJDsGVni9ewpSeLwEO+e0I9zm0R+QtabI3cl5NM7nzW6n+m8Ape7deOBkYnhv915ejEcLthddBfeP0PI2u45PqwjrnAWmn3aj45g8ET8wwWdCkwOnm9rHhFTiV2SZ/x+mpDU7tHSfksFwvjtAWF3r82NCZ/TSCBzZ74wOKcTCTk/OnulSP4IkZpxw4xdK821sFTO+btJh1umXWyU/ejYpZpwieCKQIorCw2JVSXWRKjdjtJM1H1CVDyPftssQW/46I3fIodyxvCaCOUc8tqnNx2T9Heac89IOjAgqdfuBwN5eWfnSUniNualMSkPE5n0heWfaS+p3DM6NdD5xoteiHMbzZ9W4ZTi3PU1DETBLvB3AzEmm8J6Cbj1iCVxrTpOLnB3V2iHHEZLJ8rwRP8Q6UjU30HTu5QdFTX4InBU5to4h37AlvfL/FeUqLeX29A4VOyYvZcMaJOIBzNLzGLoNjAz24BGYGgDI806rk/Ft/pGONZtY20O7xrcm7C3xP55qt0rnJj0sh9D/UL7gyT85LZtapvPqlaEgbLvpuhmW6rbz0kyov+gwbcaJRmvGySzjSGSzj/5PyW3WdtWP2z8yPkpdT8jzUIc2n/JiXFv/6K0vyfvVA6Ycffjj9+OOPHjwROHHq4RCz3l988cWJZXoETP/1X/9VAyft7Ckb0CNbx8BJNmhM9XLp+w035IQvXVnFgeqiYrs6jD0JFlzp+cIlTOn5EuCQH49wn0N7RN6SJnsj59Wa/IIpX7hXqv8mcMrejSs/QyzfPNdyGQp5NELn9r5dXGbmdwCcTjchLqAZDwFTXORlU4gSOJ1ePi6Bk6UlcLrxGacInG5vmHUqM01lW/KwHWXS1Kd0Zn4UucXwYEmpfVC3wlgOx1I9n3EaAyeCJmad3jObFTCRfhAwD5psNord9eq7TgRMCpxci+Gyhe2HMF9sOe8M8Kg6Ang68XfCd+2Y4PvZLDfn9zmjdlNjjzCv0sx8skpcEFMe+sEe44DXjx6XB/1cP0xKB/LN4tSmxJHxOZ9IXln2kvqdwzOjnQdOce1ScfGQcnJDocCGGSUFSr//zo3Ib/6eAO8McOomBjkEP3zQNtJ4N0kva4NntonAiWBLL2bXgMvwzC5xE8P7TQqc2DDi/fc/sN2u2g57zHTXwztP1IG2bSczTjHrJNqGs9FIs1f0XO+/lqbAKa5Qg5X+KRlb6V7fkp9HGXt8I/01yns612yV7k1+XAqh/3H3OlvmyXnJzDqVp3m9EURU0oaPlnKqIB4o50XpJ1VelBk24kSjNONlk3CkM1jG/yflt+q6147yuWSMqfykYEfjDnT5ZDxh7GKM0ewSAdP3339/+u6777pZJ8nkgc1f/vKXE8v0vv32Ww+cSAmaGI+YVZI94iFlDNFJufWF1id1DYCfHuUaYZzRIZDKl6ZJZL0WkSW40vPly0Kl50uAo/lrn/8c2n1pM4rsjZyXvyZ3fivVnwZO/9evv4cE102nDSM8GTpxM69ocKKmbd8ZotWPvMpNsnIzWQFb8swuglQLiVymqmzFNNkt1/VRowwHdW4vPqtiLJP5BQ91E+IE6vloD0My2+Snrd/lPSdmmyxYurHgiaV6EThZygYRFjz5jno3bAhhgZMFPb4deXn/J+Tzl1NPcAvUbzi4AJj3gc8CJg+aSkoeedjrL2DnwIl3nCxwsnebbk8RNEVK0FTKN4a/saDKgyaW6rE9+TJwCmuMzaxwS43GSyUd8460PwktUEmTgxcYr8wAPVKUTKVHeETjtVLhaunsmjkifMY3g23Jaj92RmUu0TWpdIt3xO3pzvicH+W8ivLqNb2j/KhfZvJz4OTXiHWn7AfJJuXk5oMbD87YhcqWvFiQ9KsFTTzBJWB69IhlMI9cDjcWnO/aTcY77xD82LdQ7N0AbjwUoNDIOXDSEj8FaNwIEXDBw43Mx7Zs5hNbPpN32kMHNC7T/EUd3HYqbf9VJwVM0MXRjwfMhMWWxobHFzFqICBOK8dVamXvmzuNIy3rA0oVIF8LIJtVflXpnt7RztGuTX73qXFYypHdkvlyPuvr80iI1iCno6dp+AwX7ZhK75iKboSrLPyYgp/pnV2LI+9/Unnmg1y/GV4wfCg/AxMcfvIZx/ikACrngemhDA9mCJw4WaZH8MSpZXq84ySdjDcETWwK8e2339hSvf/yAKoPnNwS+6N70QicJEPpoq+WawDuxZFxrQvrslmQnwtIIuu1iIwMz/nj8mW40uOcI6XadYSP5aN0I9/xsjyhtHFy7y6oUseuVH8eOP3yW8cbg0NpjNLhA9aRFYKsKeebkco1R2W6nBdlpI3eylyEFd1yAuWLUrCWDnY3xDKXSLOWnFc3DdcXhsQnoT1PQKd+BJX4Gx+DDThmnCJw4ptNJ99Rj2V6MdPkAVR5z6kFThY0ETh58GS8HjyZLBeOL7kB0Qmw+ZcfxbjNYJYJXpvpYgt0k0VK2Q+32YjrUj0LnOy9Jn/PyWabbn15nmabPjBbYunerQdP2iRiDJywyY0MHaWkvqAUZM6LGNtH/s652dGFqWtHCTqUqtGUHmIqRBja1/Mc7jXamU/WaLfgl8iJvloGJHOJrkmlW/qEO6q30cWPXitLUqSr11tPdpXSOfXcUrhqcyC8O7uXrUy9x1N2kOoprp7YcvOhmSUCJpa+cMMB7MmTx74RBIEMwRIzQu/bBg/vvWffYSrvJfG0Vlv52tOaOov1zDaPQDZnDqD09PZ9+57TRx99XN95YgaKAEqBlYKxekVwSZX64ascOMkNAQ8O8DETZgEUCD9sFIsBwUqMaAXjfbOQ7CRr/WqH7V+C3rNV/WLNuD3+cKU5r7gx0+f8KH/UG+1HAy+PjBv5ltQjJMYCoKM9rbxOM0oby+fbM0q4W3nU3+p0N7nncI82iDfDlcc+2SiYUvGBB6agiSBJD178oYwtBdaYwrhCwKTgScvz8ox5G2/e9130vvryq9PX33w9XapHR9bwIDtls1LZGX2+9Fn1f4AlX+lyJnXxLTJYcr+XCNmkstPlQhKaVDnFWM5s87yEKZ1TXQKd1QM5a/BLdMx55IVyHz3c+1VsrnLOJ6GXBU4IsJYdO739KiXROZ/AJds7SbRKl/RAMk/Oz3rrwraFSLlpgegBiSxb1/IiKDeIagyBk7TG04Crdib+4GuNHd8uMALfTY8ZJ7YiJ3Cyp8PMOvkZG0TUwMmCnJhlItDBVgIg/vNENgKmG5v1ie8wodEGEQg8R8no/SRoMlkeNJXUlgB6f3BqsvZ+k21LHrvo2WxTmXHqA6cPjdqCKYInC5xiZz3j8+86NZviRifsKOLdrnGAA9f3iaCewQIjBysNKH+jJZfwRrGWE4/SNboZPHw+w9wFtl7/86WeK4vBX/3bfwji16DC9i1oP2R7tLJtTGd8smmGuzbsWrqmcryb4eTSZ82/1H88xUuqGxJuRhTQcKPBTlQETDqZafrDdtODhneQfFaofIPpQ/t4JDCCJs7QF55j+3KW6iloImVGi/enyOuAT9uRa8MIyvkJcGxMU6596lrqh4zQ2Zbpla7lcOF91kkDBUA7CJYUMCl1/7kvg2brL3r/XY49W9Uv1uqzx9/tWFrcknlyXjpmOmcw0ZNG20YD7dE2vjZ2zOyATnClGdbkzHPZpjnF/ULX/JDrcr8WNOkzW7J/wPuvW10mS5tyP9O3KbZzAtc4lceReBgTn0dgPOFU0MT4xTjGqWXH4HPgxPtNnOyolzeHYMxhptt6hOlXv2h5+VSp17ys1om8fNH6nCBdmsaYvVFEvsn8Jh0TuyOJ7HAdvHDMYJ2wriBFSjvknQudL4u0GezOijoB4YHw7dIbgniqaivt5JxOu4FT6dvO5gLp8JRIM9JhWUvOO3v9s3SQaJVW0poZecZy12vMwtG0Kqhm5KYKmGcSWbYu8gmJTpdQYBlVJGd+QGHjhNCRhckS6VKDk97alLUHTsw4lcDpQRc4EUBZ8ORL9bhhsQ0hbGaI0w+E+k2A7RjjwdNblrKhQykbzC9UyJyB2SZ4OdHJS9/ItTwps2AEVlYdlt1F4FS2I/fAieV4zDAx40TQ9KHZYnnNOj1IM04lmIugiSCq9xx2CZT7Qc6bAj9msMDI70oLgyXRkkt4o1jLiUfpGt0M7g0yQ9wJtl7/C8Sa0/uW2JZB/65jBHnIy4VZ4asiWhuvkhRErqPyStd49/WvcZ4H37zGzxC1am84Nfxsyqh3PqVCduiGhMCJGwsCI4IlPa1V4MTNB0EQT3w/sECJJXXshPeRf3/pI595IvjhpiNmnEKTPx32d5zabJOW/CEz9L+wmaW3TW58EFdL9gigYhngO2W5nj3KYSkedfR6oiP6RdSxD5zwkdp94YPCzy1c3MYhsvRmw4V/og5bfyV/i+Z1wB2xc7VPlQrsydgLnBCTZcz0zWAz/6n/gtvn6ceObEOWLbhScDmfacd8tmfE3Xd5S/dR+69t46xNsp3+61bGJr+cDek8XHvl4rZW4/J2uMapJ4/bpxD4LALjFeOWHsZovGKGHFw+9aAGn/BupXbyZFe92Bziaxu/YgyLwCnaH/p84qulX83wMnzU1J3a973Oz1S8HGJVuUvxTQdohWzHlCYJnuFnsCY95yRIacZdJ5/rgsSxfB0tWQp9jjJ/lp4QxFNVW2kWY/lp4PR//vyr8S45XKAaVR0/C1yZcdp3iHQp3XfinsxwUDZuzMtNI3woR6Ud2KwbvROyuu4+EZ/5ZwNNpzlVQHzBY1r8w6Is12P2h+82MeOkmabHpwc1H4ETM1K2iMYClTJrZANDNC9BiQImS21r8Bv/SC1BU3kCU4xixqkFTgRMOtm5z74X5QFVbFrh7yvZLnu+ZI8tx+2bTgqa4h2nCJxOJXBi1okZJ2yJtHzTCTs9iJIHwhgGWEdZMfeDnA/KHi9YpGogpQ0b7biEN4q1nHiUrtHN4NSxr+eM6lxY9slWn8t0ezqO0tKFq87kkgrbU2T4o7okSvRKBZ+l59gx478EFpd1csZBIWu20mMcZ4JJqXc+JV78uiEhIOIGhJOgiSe3pLoRAc73mHi/gBsOntR++umnp4/LduLv23I9BU66FtHBR3BZUoN8bly4kYknwbH879kz2zzCAjJ2z2PWiuV/yHXZpkc77PH+1IOHthWw3mHCZWxAYxVu9YvACZ+im1S2UG8t9wPuSBK7xhQwKTWA0LBtHrlf5fyMST6f4a4B29O/p2PPvj35RwKn0QZvCxx+4bFn80zsWj0EVwpvzs9kCXZOPY7Ttt816SE9zn/c/iz/PvJjO/mvm1+cPmJZnWK8om7+e2uprl1wPk69eOnvWP5e3rV8xG6f5H3nz5jFBgcsxquXzsfYozP8Z1+LtOXGWg6cAyce4MSME/c/4T/6QIwdjC9z77jNhnL8Cg2cXX9K3X7K4vggijYfdE+ZEk3FW6bmAy/VShPXSlYClK6Q3RGc/ZPzdxQ7ZW99Ei8sPSGIp6q20kHidQMnWqvTOnScQXkUs2Utv+fEPfy043X65aYOuCw4WdA263K/bHJ0MbmQBu5kIiNsWyGAejDe9RpM8pltioHHAic+fOvBkwKnRxY4lfedLKDxd6A8yGELcuPzKeaYxbnxnezeNoUWMHnQxLIbgieCJgKqqDF/Y6leLNO7dXmx1fmtBWkROFlgZoEcy/luLWjy0+XwnlMETgqeTqePTGBeqmeB0wOzwwMnNogw+8qsk49MXjZQObBLA1ruBzlfaUUoQE3lf6UVUfy8hDeKtZx4lK7RzeDh5RnmLjD5ZLfPmRLR7uozn0bP2KZ0nd5vjS655IgtSD5sTzJDPEoTapptg+kUfS/AS3TOeGobmG+1fJd651MVEH8OnLSkhYBJ30AhyCF4emoBj4841lifffbZ6Uu28rXTA6cPbcbJXrbmhoOzHRE4EXDlwOnXX3+xoIylNL/WJ8JclppdQv7nn39ms1qfmtx4hypuZmyjCJbQqO9Ymuum95zoT9RPdcSeTOf9DbwbuhI4gZMep5v/QW472jjUYC2X7WnQ6+V6W86Xu2ffnvxZ4IQVW3x7Oo/U4lwZa/YIrnTP9myb96kjHcaYzrE32yJ9d+WXnFeZymZdLZ767wbXX1yrTqNr14wT7UtbvfLSgibGKo1H/vDFZpV+s10/NW6RaikfD3jwHQEPecYfZr813gFn1okHM3z4lhmnb7/9xme+NdbAH2e8HwnP2qHRBKOjbmuU6XpI44vq2nFFp/JxKJE2kilTQ3tONN041Ya2qdxBRBSroCn2WsDc33P+WvKzHPXJ8EbviVzy/E71VwKnX4wXTnGHehdYGrcZUUyrmjNPy685RX0lpOzTF22bgzM0vVxx5bQanIGth3XQoA3rUr6Z63z1YoLXyYqOknQicyHTZngZmGM5bZFuFcP3cdpyOQucTgRPFsDETFMETpFnximW8rEZhAdOyLdApC3PI1iKj9TahLbnWbJH8MSNiZN7P4igiSV6Pttk34iyz8+ZHZyPLG+BEzj/RpQNjvZ0OGaebBbJlwDGUj2fcfKgyYInlu094B0nZpwUODHrRGCnEyenAcyK3LZrXMj9Kuexm2MGC4waRWlA+RueXsIbxVpOPErX6GZw6pk71IzmfJjqH9fDvl2iP1/TCoepdK37qlcErLfhYgwyCbJf6arQgpjJ2OO5Nv6IDSON6kePAed4SwMe14dosFf8upHgxkI3INqR6p///LHMDv3mNyTwc/L9k6+++srPvFSPp7icTQ9Pim03LLvxUeDEjc3PP8fOVwRo0kmgxw0LT3y/+OJz10EARUDGyWzWWw/fio9Plusirvu4RhQ0cXMT9W91VPvIfuHpiD7LpMEDuZb3a+MO/VP67iNtvr2+dPWJNcnbus1h3hTNcZk+57P8PZ2ZlnyWcy7vKGssS7bSUd9In8tHxtMjNFnmqP+u/KPsu5V9pDkuIox3+nLFRncpYviNhcTb1DLRiwSLwOmFBU6MGb9w2sOXX8o7mCz9fVxmy+HnpA1j9uitbtaJ4ImxiCNmyN+xD+C+2sAJ3YxdiwMHjEcBTb09ETFln9HVMY8x8MghIUqP8JxPc8m1d76W4GjjB/1t3QuOUbUtnVF++zmf2emPm//jp1/s9RI4xR0ELqA0djMiMV+4VK/1n6YvOzRpqNk9fJNZWYbMxB0TUDAFglBAR+qHAsUgoJIMaCzC9KnjZ0QFZon0yuekcbINuc38KHCyQObGghhmnCJwYlaIGSmW6ukfP0YERcwqMdNkwRIBkwdN75V8LJsjuKKecdErcEIWOi1wsqDp5S3feXnketjq3N+lMptZFMhHcQma4qSjETzxXhOzTcw6lXecLI0lejYz5bNOFij50x6CJwxIgZNx0vbyf+4HOW9kfsxggZHPlRYGS+KiWsIbxVpOPErX6GZw+n7r/zOKS2Cqf3THY3aJ5xJ9C56iUpfDAr8D2LNF14TEiF6p4GvpyL9Gd9/wPTsyXnXzHmMXAjjNQoOreF0kZrz4c+DkNyD21JbAiW+gsJUvT3b5lhM3HAQlrP0naNJWvr6hg73zxNNbBU7Rb6OhfebLrn74WaZH4PTTT2wVzO5X/3TZv/32uy/ne2iB0VtvPayBGcsB2YSC813bxU/yddNBqvpFyvikwEn6Wx8XbSxtLj7wBzLlWvPLzf4Yy6X98z77hdrxvnSoT6zJ39ZvTnP/NX8jJ/PkvHTs6RTdKEvwc/jFs5bKPqVrOmf8R8bTS2yVLUfkz+wS/wx3KUwyz7KpXFDeRcodRNYv35BKbuTLMj0Lmpg58pnwsuU4QdTPv/zsy/d8mbGNLz5G2Djytn0eIZb+vlcDJ81GMQ4x7kHLeMaM03//939fZ8bJKlWH2ahsrmaX1zjmwOKfjiBdShKVQOV66zi6wi5tNdSHvI53WZAFSpcU14CobyEr568he5ShPkft4x5vpIiy+7FUuwtpEvlq4EQrjRVpivmh6ZqpaBydHOVorxHXrGiiGs2ou1Erh33K92mT18NbaWZ7w5KT6OxgBTCizPpDZ5IrIxJIfF0quh5YS5ptcoDRIs5vTuw9J2Z3CGBq4GRBU8w8lcDJApxYqkew46GM899YcOKBk800KWB6YEHNDSdbgxNQMevkAYsu9xQ4WWBG8HRr25974GTBEzZE4GQf2cU0txWPxbI/vufk24775hBaqmffc/Jd9cqMky3XiwCqLNfTTY7bUV1idhWbvJFyP8j5oF/vR2oYpZKvFh/hwm+l4lG6RTvi1OOUjvhLy+GT6fW6KnLpx1XSLcTghmlX3+Bfb7vGtKxX2H4Zb5P7r8gt69KsCN+FQ1U3UnqLX2tcb3Zz4Lh6fRQ8Yvx6tMRouIngKawCJ3088ocffihbk0fgpMCFwOkvdrPxZ/sGygf2oVo2dYjAhm87lRmn2m2xMbYlV+DETNYPP/xoN0E/lneofvHAivGF4Oyrr77076t8+eWX/h4CwVkXOBkd4qmN15n6+ZkDJ68kf6iq4TmD7qW/E2pAd58jjMrGJog4HB58AXgN/hbbioX3YtBWf0Oh96VVzTgZZHFeoaONAg4A/xeEJbkPN+h6rpeFAP73+ta59zGqn9Lg6G1ek7JXlz38mlzV+a78a/Ivg4dPDtsUhKGqNFfqBp0J6oN1DDNexieCJk59m4kA6qcyc807Tn/YAxkCI2anCZhyykMbD6xsVooZbvLIYjxjlrst1YsP4HZL9cw6vv+mGe3OWHDeoVsv9HrlyuX8yMz1AMx9MvTjobhgFWBF/oJ9RlcuxgWtZHepBCjtkFcr5Gsv56+mIAmqfc0bYN0LOVjK+STqtBE4QTZzWijM10YVONUSF13QzORpQO317TtxLqvaQmbqmwzM+eCcS010ZOdErrBSykEOqFBX0rFP8BBVmox3mdwg2Wl53xxCgZN/wykFTj771AIni6jCHSbYAycPjFimR8D0vqcPSNn5jsCqbhKBLVjDEj2W41kQRuDkJ7tk2YzT7W/m6xw4lXewjMOX63nwZJtEeFDGLBPL9GzW6YbASbNO4G25nu+uZ8GWB03MWGHwyoyTO8n/mP0mshy57+S88JGqTcKXPY5SwSe5S5oR0mSOmP3ynqI9/L4GVWmT0tUsdS0hm1JWkMyOgJKfVsjocQcVajDMktbbPFP9++RVR6WqH6nnGQvsZkN4OU90XlNoLMNNCbQKnAieZoETNxrcUHDy0UgFTmwKwXtI79huVMwW8QTX9RRb1G5sEEHgxIl8grIff/zBnxiz9IabHtnn8v/yF5/Z+tDen/roI9vu/N334kbHlvJB56f3i5J3RQRO0VGiX6lNqanorLdZ0cdMRxu9s/AnzsxbfShRryit9biSPq/ijqy9usqmuRhzqivB1+0wr0fBk5kVPX3jXOaqrIQqv2IJcnlW9VO6JmmGjz6zXpc9/Kqu4r+QvC5/j38Nn+FHpMflNWtHSeL3U3nSVAChYk9Ur9uO02gYnxQ4EQAxbsTYYQ9ebMaa87HttMf49czw2imPhy1a5svY4h/w9neifvf8C6NlqR6nAqdvvo3A6R0b4x6y5BhjfKyha8f4Ue0b+nP4hcol36Rs5esymSD5pqNpBajlvgYdcllkph/gzlXGykHCio4QMLsGR/67lPO1lfN3kbnGq/EuxpCVkWTwWw5pclv8fb5U72ejQcIgJbl4uA7C1qwlWd8cMsorbNWihm88SVCXbbQdWIUqUwDSDMz5oMkSlQ+qQptZRJDEd00hBzlPZhx0HcYbYaFV4HS6tWDmZXvHKT6AW5bqETj5jBMBD1uGw2ynDww8JWZWicDp/dODGjwROFkg40v4ytbksLjJETQhy4MmWyL40ma4YqmezTjZksDbEjwR0L30d51gJPix09+jstksn2H62OAET5pxIniywCltEMH3nAiY4n2n3tn0jToG1IyRV7KGX+9H4Y9oJvLjUWBV5oiflSVH6YxmC7anbA+/Ifsck5ojO4F30G5yGrcGsE54Kqy3WSIq2Zmsc/iXEl8/iPqo6qr6kXreCPYCJ/EqcOLmwnenssDpBwtsvv/uu9P3dpPy6FHcaLwkcCo3GgQ2f7XAhuV6BDR8BNef0lrgxOYNboM/pbXFvW6TLdg1fp70KnD6/ofv/Qbo57Lshm9EYRN1Qy7vHaCHj+J+/PFH/hSZnfU4vZ7Wf1Rf1+flEjiZjLF704VF3y3Vox96/9b6geiX4WNre5c1SrvvPtHGq2tqblfc3H71iTk2/LeGi98SsL3FVpPG4n5uxcj19CO2lTtJFdz9xlboZZnoR3v1nOOjv6zXZQ+/ZrFqHZLX5c/5xT3HZug5kuWnzJ/zfT8qknVBWepZlQujrs0sx8cmo2PsIGjiZAlxLCP+oSzb++fpiT2MeW4fwoWODWt4B/OTTz6ps05/2LjDd53qt+lsUxp28+Sj3SwxroHTN9/UYKo9AAqLOk+WLu0w79P+ONc6hqyvmQQTLqeJbvBHpjo7X8R2bZpUVXmT67HjqYRkJKDzREdxjULuWzl/DdmjDPVTxpDpOKIqJ8Yc0mRf7QROScKQnbe7ac7S4TFQc/3EMiNpshp+34mNdjBtaUMlyMblfOsmIpX0zsGJxbMiEpOnhcgT+6O0o0n6DuObLG4E7E7J/GZL9RaBE5tEsFSPHfXKjJN//NaW6lnwFPcMBE6cvNv0nt3sRMBEEOUzTsw2+ayT3Zi4fRjPxg+aceL9pnjHiY0heMfJgyZgFkC9NDrO6J7lPSUL0vjQbR84MevEDFcJnHyDCFuuZ1sVR+CkHfbM0cXXMXhZUb6vmQQzYoHX+xEVMwu9frWSqZUKTHoSZj0rOUrXKdcxWwq3cOsSHXOWSaZnomoC2lGa0Y1bA1jG5vx6m2WqyM9kncO/lPh6QqgnfVX9GitrPUEUfG63ijda+YmAiJeun5XAaZxxoszT2gic3vYbiy9ZqmfBzZ8s5anuB9q8wWec3rLldvQXrrm47mjpulzGbnL+yYyTzTYx8+QfrLRlNyyh4caHkxsZPkr5FwvO4gky25Lbrn0ETvY0mGveZSstuqKTtn6FT/IRPO6acp0LG/b6JZF+GaMMjedE7OkS0qEPFNbthFntenc9zZRtja1PNI4+l/tPjyml9uNQ0U1nyTVApTmambF2v8dHBa3QqX5KV8i87404LrlZPxHdHl50fUpPj+PSfiD+Xu6ydI58t2pNsAnq28QBpjA0+Ljj4xM2ZK1c080u6DTbxNjhM0o2q6TAiaV6eseJmaYYz25OLO/98ksCp09j1zwLjBjD4t2of3rwxI6efAKB2fL3bLb8b3/92+nv//X30zdfl8Dp3diIBuf3faG0R7JTFjuoVqAQiE6piGtaEO6G7ItKcFkmi3UJBpjZMIEtrRiJWp+8zLhtruzvnN/mugyr30D6a99ni7yx6gZOPw9d790InLaNY9e05TGD0YaCK+05Y5AB1vD7Tmy0XY2qaOFz12h5PW8M8gYX+4xbekTtqQjFqFSV8jQ4MmnNO0oSxZw8Yaiw1TKFVk+X+8CpbEFuS/Y8cGIrcgtkbixo8vec+IaTn5ZYUOSBkc3y+PK8ReDUNodAp+tndoulevbNKIKjCJR4amy76r20b7740j0LmpiJslkpTt6rcpOpFkFaFzgx66SlegqcCK54z4nACRvSu06IkNMsbXkBE8wsFn69H2GZXT7Fp1YYjmJ5Ez/gZ0XVVumM5lxYNiDnz5RzlkkTPQaaQM8wonFrAFtjXm+zJcdM1jn8S4mvJ2RWz85SjTcJmP0gfgUrecbpR20OwTtOdtPxiMDJdsfzj0PaS9fcnDAb9JWlbNzwIe84aXMIm3HybXvtgqvXnNnAzc2TJ499WY02h+AdJ39XwfRxc6ObI76r8q0tmyGA+vTTT/zpsQdOthzwnTLjRO/zf64HXdafvE+3fpWq3rKgF79VAPULMPC7zP5iodRDmvhjOelQuuSS7y7TM+da1xb61SeW1gQk958pzUTBBNSzOsEulfM0qla/6Q1Pr+FwSfVTusY4w6//boSUPXyvq9WUPs7RatxT7pWapG3KvbbP3LP6Z3yW1drHaoATvCItzbTxi+IERsp7TfFuE+PC06exscz337NU73sbN36KHT9t4xoe/GjjGh7m8A4mgdM7NlaxFI/PKWiJnz+osVluZPrGM7ak7282zvz97wROX5/esXGMzyLw/pNfg4wvuXKU4r/ZGLaCdion5E8hCAR/Vw5nKLgma4W4grcow4SQ29EFosrwTFZfMB1PT11Koz+mRBcDc9/K+YsFbjCq/eijrZ8mhpl/Eiz76vLAic6SJbn+pKXYExDBlSZjLdv6Y8PvO7HQLmxAdpMTmkSkFIqWn1SkSshUYhFMaSUOZUmlUXjlKmUlrRY6quFhrjhT2B7qIQusNbkNHHSCFjiVD+ASNFkQc2NBTARPtozvxnC+VE+BE7NOFpjYu0RtY4hhxomgxbcQtxkntwHF9h0ogqcSOHlqgZkHTCVw8qDJYbzvZEEUH8W1ysAdQRPBEzNMBE2czDgRNJXAyWe6CJ5sJz6zL95ziuDJfVIcEwOcsXPojqPL2sVeaUvGifMfrMKHwPxPRjbYGvtAHUXJUTolOhOYDcj5M8WcZdJcj3x6puZC3mSu+zxI96/9ZoEGwwahSzRdGf7vnN/zWXThvpGzHxxjQsbASTNOutGgTODEh2yZ7eFGxLcj98DpCwuabHOIDz/wZS9sF/6233CYv/mf/M6Nil7QJnDyJ8AWMCl99DuBk73gbXRfe+D0d1+ux5Kb+J5TfCeKd6nip7v8NR0P/P1Ha81FQLTWwrk/KK9fAJUTbzi7AuRVpRVxOIOOiZ7Cn9w2HYmOqVlat64xJM6unaarjaENNuQmCiagxGTYbYJKuySL+uW/lfiCTO6rOT8TNcPvXo8maNu/WZNqG31cmGWLCrOeStI6RWCO2B+U+/1Asrq2caBBHEjKb22cAZSlDa7leSzvZexgVppZasYMlt35pwwMzsHYxOmfSbAHOh/buKHNbH6xnfe+++57O787/WQBF+9FMR75jqAWOP3Vxpu///3bOuPEQyB/x8kuxLGt3UpdoFHRcIs6suNKXUhUrULVJxkZ3urxy9I+lcnMYl3EDGaIBV1pnqXaBOn7ZEJcJZv9nfNXEZ6ERDcMb1qP838JPfUN+PwTk9vi4sApNE1aorNGBTl/pLcfr2yNk480kqE04StvgomsyoJIhEqxvqxTdboG79gTZyYTdZWwUG8UTkRKxgtVdCaPwKjhG64PmhzOwIOUaeCUZ5z+KEv1eAFb33EiiLHg6UEJnCw44ftNvpOe76an4MkCG95xqoGT7dSFVpYF+oxTC5581omgiVkmC9ZIX9jJjnsvmZXywKnYjK4y43RTAqebFDjFO1BsEBGBU2wS0YInqm2G8D/+yFFKAdd8G+zXL0Y8aba5Q/0PktNRYFVmQq1mJUfpKuEZiGxAzp8hAtJNk47JXfflmba8Ib+zB9b7bRMdrcrfcg0a01rgxJIYgicPnOw9J+j8HSe7OfnClsIw4+S73vmME4ETm0PYduIWPHHhtWsv9HOjos0hfkoBk8842RPgPOP09dd/O33zTcw4ReAU7yt44LYWOLVqWm6r/2ac8mPaCVsUuHQ2L5/CMW+TpuvI9bPQI/aFVQkQihdWHmFNUs7PmgL9sh9mPpsne6Tkr1yxvXaZ4edtvfTCseCJCkWl8t+ltGtB9Lt3TF5c22tOl6wyxuhKcQdZe6lvWhrvMNk9SD24/4uHwOAYMzgJkPyDt/ZpBL2rxPJhx9tMNu8jEexw8lCH78B9ZJvKMAvFyZK+f/zjHx44+YMaG2+eEzh9ZBvPrAROjGU+jlXbyMS4Nmv/IJNP/A6pNKFgRdBQLNDVJPf2VaIR4TpCEfx1LK6ZkSHKR3WdWYW5sgV0+Zux7ucF81mAVs/WR5VbCBoq23j734CVwIkP4GaWUbykKx3xfbkNrpm+5XVtlZ7XM3elxtObl+CVXjDqobpEGpga9iR8Za4ZcTqgFCSl4qSqcllGlfI0DxZBVFlMSLnsytAJXkGTNAWPZGrGyYOZlxYY+XecCJwieHrAEj0LZG4seCFwihknmzF6UIInD5p4qbsETmVzCGZ+IpBiNsrec7K/2HZjYRAzTi14YoMITguOCJr8tC0/mW0ikGKrcj9Np0eGVDIHTp+YzNmME0FT210vluvxjpTNkHHIaaQ570gDCWZI5dcvRnzbBv0iIiXh+6onYdazhcfkXu+olTKROX+mhlWTjspsPj1T8xvye/LA3k2Zj7uleUXbBU42u8RNCgGTAiffjcrgbcapLdVrgVNZqmc3MH7DMdQPXeghcGJJIIETywF510lLZx49euzvHfCU+W9/I3D6xpfqReBU3nFixutQ4IQBs36cYcorXeMZKmNFLp3Vy2cgl58DLF1cO8oPDEOx03OMpRgnTqVzjwzq7lQ8Wqes5BKezvttCUYWe6f8nk0zfPy8N19vGdD3iZFSjRypX7MjyT2Ut23qFc7qnykkK25Gi0/cQZYPR5VVMrFjHvSSSZ6gifGCsYKToMnfZ7IAKMaj320Gineq0Xrrs+DaQY+PZseHsz+ov/mMMf/zP//jwZMe1BB0fUTgZCfjzbfDjNM8cOKeIq5d2at6l2qVYrRd/byBiJSqiVVeSY/1piXzQq/py2P/kqO7omboDnbQ/I5nrzD6E/oZbE/OHj77tAVL5DKmSJlUNFPl/H9w4IQz8ATVVZUjBapgxbJ2CB+l/LdiakbUScLE4RowIl0PnDTjlEW45HJlZpaBqvwAAEAASURBVLhkEjiRv30Zsz8sm/OgiWCJ4MmX6rExBO84pRknC5xubdYpvuGkwCmWz8WW5C1wilknLj8qHoHTjQdPbPpQ3nWyjSkImgiemGli1kkzThE4Gd00cCJosuBJ25Gzk5/NRvl25WXGybcm93edykYRNEo0HLnI5xRQcVa+AAPWedEo1ZiRauBHXDtEY5CRvRENOfEoHdAXFbPynD9T2KpJx2Rmn56p+Q35PXlg3m+bstxmos2Bky99KYETy1oInpgJ4maFwMnfGbDgxd9xss0hunecbAkfy1vYVW926AVvboS4efnRgrO6OYTd2OjbKtjDu00ETmwO8cnHn9iym4/Lzn3v1F310MFoRJ2U9nrX+nGGK680pPZyliUundXLZyCXnxtYurC9QSO3APR6luhRQCvXO7lm6TnsTdDxXO5fx7hmPjjC2eqU1q4fYTxEs1ePNfyyrdfVrdOqlejVOlpOkOundv+Q3Lomv/XZNZtCSMjKN6QGlwJLmVnyzR/sPSaodEW95L0mG2sYL/6w4Ihx4ZdffylLen8y2BPbRe+J4x/aplHMKPl3m+w9S95Z8vHiYx602LcgXc+tL837x//8wwMnf1BjAZjPONlsE4ET4w3vVLIhzbvvves7hRI4+djC+EKlvbraJZRybp9ivVddfgme8MbgTZEM4LE45R2JhrI//h/lY0pruIEjiufoGsVPBZ4JnNk3g50pdkGe6xn9jrYDmjGFbVLRTJXz1w+csnTsoRHrkJAta3ldX05c6jBPGk9f7wSfMsqoSKEmIGhcARdVFuGwARHFFDiJoQm0dilMnh4PnEKE8RqfxJHWgdfgnvfgiVkgZpzs9NmmCJrYFIJZp1MXOFmQ9cDsuGHWiWCErXxZksdHb/Xx2xY4MeMU7Uaghf12jsv1TO/LFyVwMn0RPPFkyE7bnIKlei1wMnndUr1PzAaeEpV3nOwDubHrHjNOTJuzs18JmjwtTjZndINFcVKMEwx6hU7kQ7lhh0atCPxLYQ1fCSPTyReP0oH2omJWkPNnCls1aS7zPgaxMy1+Q77igToWdPjo+xmX21BwAhVuYAhoCJwIlJhxUuBE0PR7WaoXgdM7py/tA7VsGa7AKT6Ay05U8R2nzoxSQI+W3tTvOJkezTgxG4Ud2FV31TMdHxM4lRsh7aqHSMYi/8cNTLnIKfvhybwft8s448nbmUEhafqXS2d2+XSwrjCKkSKlgR/HKqCdmJ58EDogY9DqJAwUA//di2qHNUl7+DW+JTx55TWZcVra2EN0vfXQlVKt3n232Ir+M8Bjn63dzmTUG1KrT+QBUuA3tX2fKXwDrCwdtq3Fn9r7jsxCP378yD50+7M/aOEzCc9tG3F2xEMM4xHLd31nTwuCPvzATnvfkneXmJkmAGNM4b2m/ylL9fj8AbNXbFYDHe9o8k26b7/9xt6t/NoDrvcseGIjnJuy1I9+G6cNEX6fRBknldTrR5kjRqF2t0bdh+Ngsy74BjGzYncvBEHRtXftdbrg6QC9poPm90w7pZl9M9iOmF10rpb6ZKQZU8RMKpqpcn4jcEJYJs02SoPShFuwqNNBk+lbnosijgYTpE8TvvJAkeA9QymJOFKoDwdOYk1yJWWBymaoUp5uBU7tkmvsBhO/22p/rOz6gJcbjn6pngVKZbbJd9Oz4IWleqcbW67nm0OwVM+CJ99Zj6CJJ8U8ZWE7crYlt6BJAZS/38RyPbOIGSqfcSK1oItgiK3GbYmgL9dj1smCp5c5cLKAjbK/E9XNOLEULzaHuLmJwOnkemPGyQMn37acnfUInjgVPJk4OcjS6rUC6wY2I9UxDvQBX7ScyD3VwN4B1wqyKTgT1baORLiRzcJzfoNlDbVqzlLufQxga2a9gZ/vgdlNWW6zhm9jr2DcWCio4SaFmxUFTgRPHjixc5UFPtqlihknAiffjpwnvbYd+bvMOJXACd2SLzvYzpybFYInZpu0FFCBE4GbDr4RxdNfdPh3nOyp8Lt8K6psRw5d3J5Yarr89MFAUAiW/Xj+8wVdofVkwofCdHDpzC6fCquZxLTILvXIV5m0E7VkyaSWTwT196JJSNiB7zrFmf1Z8h4+027nW53uY8YJP866j2y6pB7RHMluCZulHdl9t9rMgOOwmZ/U9fINqcYD7lu4elglo7HnZXmvCRpmgp5ZYPTHH/bRWlui95s9uGGGmjGJExp4mWliTOBbTL6zpwVBH9jJN+WYbWLGiDGL858WLH33j+/8YdDPfM+JGScbi+B738avv/z5Lx44sWTvPRvLkMEmOHU8I4AiYCrfpmOUoQ943fkztBd4jgzO+XyZOuHKn45nhWYEd4FT6jp7fbbTJb4O2DQJ3SB3z83sm8HuqilXqdxB0xtNbMYULZOKZqqc3wmctsyeaIHcpfe4pUN6vC687odgqrrn6+s+4Dp+VTlSKLcDpyar2dYJ7FUbalFtMXq6EjgZU7YjaZ0GTm6ByZu943QiUFoETgQvNhvFcj2+5cRsk7/nZO8N2UbkPuNE0OSBU5p5IqDy4InBosw2WcDUB055uV7ZhtxseOkfwVXglGeceMepBU7sqndz03bVc5zPOpXNIXxb8hQ4UXk5qIzeeW0voGU/AwZjPtQXMqzP10G/gjd4LpBfxR7OLJQc5qyEq1VYyp75scp5k/mXe2DZP/u+3+Nz+7ZvpRDQMNsU7zh9b8ta4kaDwIl3DF68eO43EwRPzDT92W422MGK2Sb/jpNtDsGNxrhUj77DmQOnH2wJoGa0CJx4AkzgpJe5a+D0JwKneA+hbnfOduTu8fIX+RUCDH1qkpoRoKTR+WNIDp4QsUY/sFsRCXmHpUrRXVd78nbwFV0ytVy1DZlEoN+b9MuUsAPfdYp748Qe/rgVycn3MOMUduR+NLPsXG8mm2fiMsxIz6DOnBflW03IheY1/R1tKyS9YXzjL/LUHwmS+GffnvTgiZmnsjyPQIpxQLvo6YO1BD6+m57NOHHQjxhrCJA4IwCy3T0tECLg4SEOYwnBEYHTzz/9bB/zjs1uYnOJX33Wik8cvG/fcfqTjTNsSMNMt4Iwxhtms96y8SYCKAueTCdVjn4c/cPHnuoHSvEPO5sP+nyMNVBsH5l/m7Jh3wROzRezXPYp/ZAj0owpnLVdS9lp5/mVwOnXidQmYDPX/bqEJXsDqK4xfgS3jz184u7sUHUiRUoOWNTN6yxGFdOepFbQkJFkwL3KgvHKTQKngg47ml0S3884Gd5JzEKyNuD4kxj/AC5L9ezpbQmcTrcs17MgipQZJwInD55YqscyPQuAfDtfu+B9OZwCJ73rxOwTgRODhnmqBE4RNFGPmHE62ftVzDi99PecmH2KoImZJjaNYFc9p8VwjPbNIUrgxMYQvrOeLdV7oKV62jyiBE6+NTnbkpcZJ5MWBzZZrt4tlbKDQPTHzQU/tP2NZ5bnjZABYUsPeT1LE9PD0JnPlrDXs1L/O62a9c88xm7huVnRDYvecWI2SLtQxVNfC5xs+Uws1Ystf32pXgmceGrbzTjRDOV6fGAptqBD7zkRNPGyNqle+gbngZd9RPevf7EP4NqyGZ9xYjmNnXxfhR372uYT0SdVTx+bUBuDgdSvdoj2G+PGOqezrnI0hC4dT/OlIUQnKBM0GYdzdVwzjqmoDEz5WsFq1Jz9sCH7hGqLNco9/BrfEt7q5L8lS4KrQa5n83GToulSHY+zXkhZrp7Sfbb1cz1vq5nxxxhkt6iOjDQCJ3t4Y8HN8zI79MSWCz+295h4iKOxIT5fYNuI28yTHq68/fZb5QO2BD/tZHzQ+0mMKZwESz/+8KNtSPOjf9PpN/uuE7Pf2omPB0DMNvFOJbvssdseMsETiEkm45P6g3vM/OCuYIwrV5fnmJ0yF+UWzPlCuu3EgX+XuBC8CZy2PZXb4TUOnKz7VEu9i3mt1PnWqhgXHtjGM6fdwyeuPoopiDAOKfuBU+iaDQpJS6uuAXuVxREuoA+cXHJB7wdOJZwzemj9/yJwIniKGad54GRBjC/TU/DEbJNZ4bvrWcBiy+Ni1imCqJMHTSVwKsv0CIK0u55/P8qX6z23GSYLmmzZHjNNPtvEjBMBVQ6ccBjvU5V3nE43HxmA4EnvOLF8D90ETXYy2+VBnQVOWqpn2v3wBNtVLqnhBQrCQn5R4ARvaaAsbAaT+o7uNSzMquNmLiuwd72+hrX7X2XSVmCEI7bwCpyYcRoDJwIobjI04xTbgcfmEAQ1LNnzGScLnLqlLdxIlIsvB07o4MyBk7YXxg5koCMCp7/aUsA/+RNgPUl+aIETJ6JjHM7XOLcsNgyUcUH61zpC80lwOd1swJgI4NKpl0++XCoQJiGUTgQdAWWbpqIyMOXrj2gzKmGPaD6bZs/ne/jjClud/jMDp1S/4065E2Vum3ZtzEVm2hnFjD9gQ+BkfRQ4s0IKcgiYOBlztIyXAErjhAIngpkIfJh1iq3INQZBg1xkMt7wAVzNbGsGHZzGG8YxZpsY03ifkpOHNQRPzGhBx0MjZp98hPELKcYe+YJxx//Z9co/jtyKNX/GRVh5XNqxP28Cp20/ZZ++CZy2fRXYPoopHOFG+vK/KnCq15GZosvNAyK3CTOLjbXFjwZO9i6TL9WL2SYPpOxdI1+mRxDjgZMFWD7rZJc6P9AWlMRyPZbDEECxQQN53n9iOZ8dNiDpHSffIMICKN6tiuDJUpt5ekkQRbBEEFXOCJy0g47JqYGT6dCMkwdOBE3MOhG8ETSVGafyjlMfOBXv+c0F9hu5/dFgRmk8LptxQkptgCRyAismJaLXMzsxPQxdVmDLn69n5f53WTW7UclttoUnYAGfAyfeJWDGyQOn3yxw+vU3vwnhKS8fuOVbKfqOUwROH9hNTHsnAO9LvwInbo7QwVIcAqcm395jsJskjrhR4Z2DP53+aksBuaHR02QCqgicGIt8GPK0/YnbFd20SH/D97nmE/p76fM5SOnJuxKXTr188uVSgZALobQTcbyQbZqKysCUtzaNQ2mz6Ljy8yj3fL6HP66t1elN4HTca1uUuW3atTHnyLQzihl/wJaBE/waGxgf4p3K3z3YYYaJk8AHOAGVdMfstI1HvpyupciDhnFN4w28yOBEBg+ICJwIvuD//PPPPWgicPr000/9VPBEAEXwpNkn6SfV6Tq57yjXvNLUSyHxC3ABC8z07zm0EvAmcJIn5mn26ZvAae6jHvoaBk7pZy6N/3T9aF6lVKRNlOwFTjaLZMHKDcv1auBUgicFTrZBhAdQDwiceNeJQQAles+JpXkELARN8ZQlB052p2XEsUzPU1un7BtT+EueEUSxYUQEThZE2Xbl5KFVZw3Z6IjNIXzWaQycig2xKURsDrEMnNxwr4P9iXpYuna8CZyKZ/II0jlr6Tv9WHRkbwqvjQdmNyq5zbbw4DgVOLH97zxwehZLVmy5CoETy1tIzwmc9B0nBU7o0Y3MQ3tKzBIZblT+bB/XZbbpS5PPDQs3LvGuAR+07AOniA/ozHG7opuWXP9ZQzWflPEDIh8EZ9Q9DG318smXSwVCL4TSXsbhUrZpKioDUz4cY2qaUQl7WP05hHs+38Mf19XqlH4Yj7OfQXk9m48rbX3zOM9dKXM99/Rn2pneGX/A+sBJvJoZ4qEKD1E4fXmdvdPEu02///7I3nt64u8+STY2aPZJeVLhCcY03jDGcBJA6f0p8PATgBE4MZ7xMOizzz63cnwLKmafPvExTrNP6ODk+n6QNougLhp7dMmlXtqGA6Pr4DCuHEfpMvubwCl7Y5nPPtW9aKQZU/gmA2amyvnrvuPUBSvNiuh4y0oJUsf8+uMjzCxtcmfYCvNaQpurG3mgBClNEvActiAlsO2HGtjyyNLBugscaH+UetAhiRClvNvhhJ0FlwVOBExxnmw7cLYjPzHb5IETs04ETnYi3J3AoBAbMPh7TR44WTmQltqBA9wJBE7YWVIPnnh6bWV72vPSZ6GYebJAiu88+btQ0JajLMNj+/MImliqFzv5xYwTS/kiePPAyZfrPRyW6jELZoaXgQzJGtSKlkVy74GTG7FQ+/oBootN7GpXgZB716vo3qSv3gO6URg15zab0QgPjpObF4ImnsZys8JsE2c8qf3l9Kxs3kCAwwcm2SCCwOnDj+xbKBbs8P2TeBdA70JiUfQlhhd2ynry2L7B8uSxyyV4YnthvsvCzQy8fqNi32z605cWOP3JArPPCJxsmQzvNxk+bpQscHKbY/SJPJ05blt0/bde3HLFHHdV84nwlpbs6qXhnPGn/lqIHXDHmBGJ8dysj22FKec7ORNd1ZaaUfU6zmsW1KfWZO7h1/iW8Fan+w6c+G2ZeHdp0pUgXjP/fb2SwKNiSj0P6d/xSbu2QrnfmJZxBhy76HHdhmNtdmhYqve7BTkET2wdzhK7RzYmPbUx4g8bg/wiw0hrlNjpLl4zoI3oX75Dn2088dw2s+E7UIwt8D+yoAm5Gm88cLKgycczC5zYIZTg6dPPPj199uln9u24T+rGNNqpj4c46PDTg6akO/cSdyJ1Lz1n6EC6YYdi66hitogG3DxwwuaBcCh2uqDtABA3AS03CLlDcTY2zGB3UOGsuVpqh0gzpmiZVDRT5fyxwClzJIduV6pZMXeI8FaNKl+wbck9doWnyszUAYSjD5yCpmcJuWFbj1lKzJDSB2uljJeBw45sacvT9WVX03N54MRgExtEEDjdeuDEkj179+hBpPaRJyubTT4oWDDCZhFsCMHSPV+mFx7C5mgc7JKdllfdPGiy9mPHHA+cmGFiBipSeEx4OQmK4ltNNyd7x4n3nOqME8EUgZPNdj1gxiuCOZYS9jNO2JmDJ2Nz+QZeOfYGkBnbeptTn3s6Woe4HwW7pjcD5tfr/Zj1RupxD4w3KJkzt9mMTnhwnAROehrL8hiCJmaEePL7q5088VWP4Mbiyy+/sCezX1iw85EHPOxOxVK+t2zpi+jcniKfp8mP7Mkx34T64Xu2I//Ol+E8teU5z+ybLbxH4MtkPvk0nv7ajNPn9vRXW6CzTI/AqbeboYeHNdGZ7Rlwvf7dhsXFbtBinHgqAGMNVwOi7MyUX1w2WccCKcaiVMWVdMpeWXXzUwErUsCbpImwPc4VgYfBaps1hj38Gt8Sbn1WwPLDuFa3Sif6M1KXmdu38K7p2hN92JbSn/fkXRWf67mnv9Cu+aFdW6UbljGAVntZvq0EjYuxP9wv6F1LHt5wMkPEuPOLLa9jwwjGCcaQ3LHbN5WaJSEn3pmKBzUmyz6zwKwVMhnjOAmcCJrYKY8HQcw2ffXVlzaWfXL6pARNvFvJGUv1eM+J1we4Dm2cscApliEz5sRR7z1yQwuZGiujE3iRPUqXGZ3HddqfpDuaLAEyk+WP66q1HCTcrTgbG2awu2np66lRJNKJBybuylQ5f2bgNJG8WrNGO3dIw7cLr8FWxU4RE75cy8oTQKivFTgheqaq1skHpfsPnNhZ78Z2uGPGyWeaSnrry/UsgLrhtFmoBzYY1W3Jsd68wVVWgiZuRhzmVyE1izN8Br0dXmH7Q0rVPHDyYdJvamxYNFzUuQU6JXAqM043J7YiZ3MIgqbybhVBU3m3Sd9vusUesy3kKGiSjYZyOw29csQAsoJcAcfviFdyoJjBBpK7FHHyfR6b5jfl8+v1Pg17I3vPA3U8WSHMbTajFR4c/ZvtxnVTwVNegiZmnvzFbHs5m5ki3wXLZpM/tRuLLyxoYonLJzZDxNNZluzxvgCBTj58x08bD5DN7BVPkpH7g20PjGz0chPz3rvvnT4rS2S++vIrC8y+On1uARoyOX1HqxI4eX3M5rCdXQGjI1MnXf9Kw5bSl31cC0jzSevneYHE5qXhIpJMVXifSZTTdMpezbMa+eBVAVMZFTgRdpCzijg3oz61xreHX+Mb4brhcXh7ojgd+SduGMVtlmc2X+LHs+yIH5xNu66OzD+Me/oT7cwXurZU57hOGWdiB08PniyPb6t/yzhEcMTJg5rffrNZIpspIpBiKTFnHEgO3spvEL/jsLGE8SSWHtsMuo1bemBDqjGOj+M+tGW/jCuMOxE4fWWz5/H5AwImLc97h1nvMg75R3Gxu4xFzHpxdH4oFc+2OVH50/XfjBjy8t8A3izmT7Jko9ZskbB9XVFDq7lYrprO7JvB7qo011PtEGnGFC2TqmaqnN8PnCp1lprz+1VbOqTn37tuD2iYk1TbhQ4A2nsLhF+mYdtC0IJwQcHAAJUL4OOx/dHKZSbHiDXzBGXgQ6rnhUeuBypaJmeDi71fpMAptibXMr0nZgMnM0+WPrDTgqdbAic/LbhBuA9oDAg84Y2BQRYwPEWQGZdQu5CMkTsPD5yMIirrNzfYD1/ICLmx6QMBkgVKNuMUgZOW6hncgiZfNviApYIWKBEs+Wk75ngwV+T4IE5elrh3rLw8nHQJ3oWoLruEdyaI9q1i1qtSSe6cGVQ2eaE8fPYqDGma3+T2PaCbkxnlOL6OtBkvHDcb3FRw00JAoxezFTg9tqe1z225HTNTH9u7SMwOMfO0FjjRY+haCpy4+WGHPoInvsvyk7/0/YvR2D+7wPgWFDNYBGNffmHLAG0p4Kc2+8RmFLFMj41r7Bo3wVyP8MQZgRP6ol6pr6ZIyHGOaviu6xs4l3N+5mPGG/6XP41kn7HRDrkFazPV9SzqN/B3xYWwhaUd+TUKYd9MUrTbDHMJjD5Tj0XglJ3WUVYWZWJcVynS8TdiVqemoeV6KWNp246R2supilP8NYGzaqzpH2ijmIGlrlyfxUZdq1xhBCwETsDwrU5IkcI24YwzjEOP7WPcvkzP8s+elsDJiFwbvM5kf00R2pDJOOaBk9Frl1BmuXlg4+842fK9JxaU8e2oh/bpAz5x8PkXn/tW5F/ZLPeHfJfug7QhhAVNMQaxq56NQcxScZZZJ7cfOzhU4SiZfW5hKbWkeabBZrlB3Iykh7kris5B9awf98wL8we0ajMIHqguLc7sm8EulS++7FO1Q6QZU6gnVc1UOT8PnP45fscpS8x5mbed9g4Rv1IugG3+faxk7VAWRaJWusUVLOcZ6NSqlKeafWmamm4bUKpdTU8XRIXAsrzb/FUDJwuAbFc7BU4nm3HKgdPtrT019sDJBo6T5W8en14y6+Qfw7X3kDx4Mpvsx8hnmnxgU+AUtspO2tCnqn14MOitBVncqFh6eklwY4eVi6nBTCCmHfqYVfJd+0rgVD5+G1uUs5aYZXrcLLVlegRytwRyHsxhSQRMnpo9cegCL8UrJJe0+eVqW5uvjLuXiz6LM/tRvj1LwBvie/QANwmzox9bgyLTjnj1bT2l5YaF5Xn+foEFUKQET7wjAI53DXga+4lt2cuSljFwettmnOgt6jG+a5/dpBA4ETT98gsn2wv/7DcyPo7Yshc2heCdKT89gPrCdHziQRNPhblZ4YCe+nAy0xRPsRmvAqde29xTLGEsCwH8rYd7sZBkj6aYq9L2mcJUa9pjLyll/Ut+s9/HOOldUnSQibCDnJ2Ycwpj34J3BjtH5ozWWr2Bc+BUfwMaOvd9QdXnVR7TbHPOi8570pnOnNkhef/O6cI/1jRqH9U5Ul2zFtj4Tp4vyzuLPJylb9uvuP0hsGIs8qXDT55akPNHnW3SjJPTc915G/AnlvqhxwMnC75Y2heBV2wKodluxiHefUJPvJP5lu/eqe3IWZanXfTAa7ZbYxDjkM9+u80EUG6E2+DtmLtmtTG1cPJPgk6zSVSp65SsA/pIKJMSZtFOCXcsK6FKj3EdpZrZN4MdlbdGl31a+6mPJxlTuCdVzVQ5fyBwytJyfs3UJbw5JPO3vC64JedRSJO1yVF+XUWtdItnb9Bd4611cgF94JT1RtAUNwIMCByBL7DSWh6SeN6a3wciBqY+cLr1wMmW6tnHb291EjBZAPWSoOn0yNNbD5zYJIKZJ4TayYDgg0O2zkD8M5A/cbGMDR1GHwHRDUHTrc0MvWQpnRH53UcJbtxWvTNFMBQ76vnSPJbp+VI9ZpxiCd+NzTTFjBOyTEaZbUJ5zDhhV5GNQcVL/qNmpWsftf2uLXgqD/9PEa8QmD35LzfmFdb730PVrD+2cbWvg2hneI1n2r6XmxOeznrwZAHTLwRPdvIhXN4Z+MOWvvCSNIHOhx9+YC9Q890TPhhpO9+xXbktaal6TLg/1LHxyWecTAY3MDz5/f13k2c3MTzF5f0lZChw8p2tbDbrIwvOWE7z4GE84VWtqI9OD8yohA9ZPjo5WdRLHNF/3S4fKwR3Nr/WfHgqYM/vdvlMkPNN9jm5rH/Ox7iLnoO6JgIPcs7VH4DWdk+0M1hCX5S11m98FwVOib9Jqrlsc85XAsuswTNNzusazLB/93xcSstepbq6l/1CTNdrWa5HG/rMjc/e2H2Eb7Zgtw6GZ8wgAHpaZpmePYvvMTE24Xed7j8r68ExY4EHToX3sb3bxIwVM928K8XYw/tSjDvQaRlw/gAuYxvnGDChM+xlvCLY40z9ICobTVq611ofkX/22r/rpUs3T9nfBE5Tt1Rg9qnGkUgzppBPfJ6pcn4ncMqScr7adSjTOlSW0fJHO9YhZQuipsd+fR0riNIFSwIES3ZZQm5ka51cgAVORYTrLIo9MbwHRS5LARQ/l8XWqtoy9h943DxoqR472bFcz86XtiSPU0ETAZMFSx44edD0yLZueGRCCJo4/3/23kVbsuS4DuueGQDiy4TFZZIAAXtpedmS//9rbJqyJZIiJUoUIQqiMY8ex46IHbEzT55XVd2eHmiquyoj47EjMjIy6+Q9deva3SoHdWDrjxnhhoXLk8/sIIPNw1u7i4Q7Sf78YIeib/HxOhyicLDBoSra+EO6kOHgxD+uiztOv2dYv2utHZw+48EJH82LL6jwO0zmKz6/a1geF2JjfDNtopuPrsnRsOZtZL9Jjws5wGui38TXOShm+ehxLD2y/EH2fAZWdXlWwys5cbCH4Ke8ePovZttFBi44+FE90PjoC2T46MpPfoyvCf/Ju9+1wxOe/8wuNuInuNgXujb4Q50vf/NlHpjwlcD4BfDf+Edy8DtRP/rRj4eDE78k4vfsIzO4WOHvEyBrqErEzCfixtO3LfPLqo19WvNsEsTVobnQV5nxuNrYQqj0gFTjm8BUaYfewxzVV7jgbfkVygiwCJ6ZmRVf19d5J+qKR9mj7bBPfkcHp0djX9mNObpWISucR3mPeOxKNKo7Xnc+PwYauCByxqyNg5Fdq1iwfhDBD0X4RQu4ZMi1jY/Sff017jzZ7ythX7JDE/Ym5IqHlvBr+4HvAXEdhI8BfmO2+MIZHJpwePpHu8v9K3zRhP0AiB/fi4NT/MFt/P2mP/uzn9sfwf2ZfwFE/E06u/bww1zsG1g98I1DE/4kAmL23cga32v8pWdAU9LcpkI9MkTu2CM32zPAVPvh4DTlbepqjrmPRKuS/ZyrltIHByedOaWnyC50e6NQnKb5Zn4B6kGV9JXFTs9sj0BXBX+kT1mNCQD2UxU81B/fAHHHiYckUxS9PEQZK+yAA7FNu20c8e1S9tlh+6ge/obSt/53nOLg9OED7jLZx/P8o3p2l+lbe9qB6Zt3v7b21wZof6gWX1FuXxse365XTugs4shNC3ebcGCKw5O1/g18ttHYgen9t/YTZz84gcbdorhjFB/RMx4+emd/G8o/kmcf1es7TnZwst93wh2nODyZrm1Q9pJPjDoPTZmsOlYOn6vRrHrYF16wKe6r1dztq7xMwsUcgD7BL8O+B4S3iqPHsfTI8gfZ8xlY1WRcVMS8qJx077vtHzI++dNaPyD5XaFf+8UGLjjqTpHdNQLOF/bxOfxU1j/a8js/8QsOfIQFd5CGB0rYfPjvLPiFTPzxSXwpBPzi8PUT+2IIfOQPd5z8CyfsI3q/b3egfvd38GUx2GtsTLJAI16DzUPT+uDEtcM6zTUuOIjTw0sVWpCPdu/RuST+nmbzFb+5e9SMyz7bsJuG02ALZ2cruo0fozonbb/itfQxatgjv+cHp3V+FpP3WKouW93xOFTgogCxPvGIhjTa3msg14MT8pCfxpX9KA5DfoCyu044OMWdqThsMWasfx6e4neo4o/f8gc9uHuOL7zxjxzbD37wET5g4Vvy8PyTP/mTd7/85S/e/exnP7d+/CAHd7mxOVStpTPscfyoHsbgDxuoi3PcQ36os2iZJ4g4loVasC6C/nBw2s2gCzTPnNtoVZIYi5yrltI7B6f/KjqKpvRxwCrdbhYjjhaU2r2OTn8+qrr8PrlQDO+5NqwjKbkSmC6u1cEJGBbW3sGpDlPmNqI3IuNfH5zybpP9EVwcnPzQhDtOdmj6tg5NOED92g5NdnDyrynnwSk/SghHmSp/08XmZhcycTFjXw/shydr7SD0mf/NJxya7OBjhyfcdcLfguq7TLgjxUMTvtbTDk3+O074XSfcbcJFEr4sggcn/OTaLsJ8N827TR6LvfhmzbI3Uzw8F5BFd/fV9WZpXlTN7OzHnC8NdyweZ4+j+jg+19GeXWadJXqN+gP3NRmY90juqWxVTpoyjQAyPv0CxPr4KAs+0sKvBMbXAvvvCOAulB2c8HdX8MAyjN8D+MIvJvyihlc/ULDyjSqJj97gozY4QOGBwxAuQPyOVf7O1E/tiyH+R/tqYHwMkF8DHAjj+vT1iLjxkZ48PLkvw/Qx+rKRlYQfrPh/vEREiIGriz93YV9loNcPjakx17rBVfwjvZCtMFc8HZLIF87OVvR5TMcaq/pa8Y5Rrkhlbn3I9h4u86oIrP0znspnrLmvuq+gR3xOHNtXeDjGeMRTVJq9SsnBi6/NXFnMvfI+YN0GIw5Ofkc51lLkwaKJ//E7jLa+cQeJP9TBvhF3oGNMiB37QByecEcLtYE/r5C/42QHpfnghH0NByfc6ca35f3xn/zxu1/+4pfvfvbzn/kPg8DHnSXfFxmMD872Lb/jhGsgGzji9LFkwGDYY0qJ81YvXsVhkpYrrRuAbr6dk2BfjWonhhrVszhr/HENhM6Kt7a+zs10u4Hn36hoVZJ4i6GqltLrg9M/2MGptIjGFk6UTqcHTexx+zZcWAcQT4rSd46JkbA9Aj+PrRLlMMB0Tm4WvrPkwUn90DcOSHVIyqRDVjwDC10jAth/2qJ3nD7Yx/S+ta8f/9YOTXh++GB3l/yuEz+qx7tNPDjhK8nt7zrVR/Xs4ISf5LmjvkCIW+S824SDUx+aPsOB5x0OTT+xgxRafC0x7jrxAGUtvvDB7zaZPD+q538A1z6ih4/pxcf38FE9e+ICDAcnv+NkYgRj/33IkQDjoeccKPSj5M1yylRde2PSY5wswmxttFJ9micLzbA2gT6Nfx3gOCc+GQR7MMy32BgrJK43Mn7LWt2HNI+kVU4aMsqZDh6WqIMWhxt8wx4OT/iIHp44OPEA9dWX+JidfY24PWsvH9acFUTWBOTwCVx+HTHuVOGCBb9LgN9jwrf04Ysm/CN69m19/8y+YQ9fT86vNh+n0gCTwQsmH4MNyC+qbJMI1xFDhmHScezNr1Bt/2NWmtccUKKQgjmfo/7YU58bKHXuZltfgbbHR57FXzlrpmVAFF5PrnKx4j3vuQYXU3IwLNa1+lzxVP42MauHoPf95PgOxrVFe4YT/sZ1tsbzkPploaRYWPNUiXc29HG4wSLVj+HGPmG6qY8m9gyzy4MR1nn8cAaVjHUeOH5Ygo5fV0WV4w/g/je7s4SP6v2XX9kdp1/hj+nal9zYN+zVwekLfFPeF3ZwsjtOv7A7Tj//uR2c7Pc0jY8DksduL/7P47Lfy8IdcL82sfjMJ5Sqnoz2cXDIJ63HnzoOf6J/TWyTM9VNTFcwH/dDULbXormqtVoLK95VvD09HT/zH61K0noxVNVS+uLBiYhs98Jc888SUoW4Nn8BN+POkXMUbI8c+GLi6t5VDGDFqzEBwEE07V3r2A741h/w6EOe+tYELnCgYdOOW9W2aeDLIT74R/Xi4OQf0cPdJnvi4DR/VO/De3xUz37Hye42+V0n/5II3G3Cwckae/LN2Lcq6+AuUNxpwl0mHpx+bDTuIFlrByfcSXqPAxIPT6D943w4OOHQFB/Vi7tO+B0nHpxChj9+GwcmHJxs5LIbfVsfzcDg+TSS+YnkgLF8LFJvehjXUt2Z1+Z83/6OhIs5bHyC75i/UPc4J8hZPR4K8wy/0B8iar09ZP3pG+n4ej/tnKqcNPRaN8aoByfKvraPxXxphyMcoPClEHjq4Ql/0wnfrgcd2PMJP/TFNj5ag2UcvtHi96HwN1J+x776F9+c9wf5DX1o8cSBCh+hwUWNYlbN5bKHjL4xGvoATf+xdl2aazzqliXL1m1OS1oUYGAP5ix6x6/qS5dPWQ2Hp62v0tshhljKWePYDOxYvoY9+E/IFe95bzk4DoftAph1oKIVT+VvE7N6aHrrixM3jbFN3oQ6y4k63casUl17HEu9O9u6xMEp9IHDJzgrXMYV36CJL5SAnmvbC/ab8Ac96PhhzBRwhwoHJHxcj190g4/q8eN7uPuN/QV3vfE3nH6Bg9PPfpZ30O3axhwBk/7Zarw4OOHay0fnY8prtYN6ROR8mDVJoYr1IIGcLkyT2R4XOocsgrI9VL4tXM39incbeDLQ8TP/0aokjRZDVS2lfzg4TYmeu1iovQ3MUvYjpZp3Lrxc6RsM6sbBCTg9LcHLvjWha4SzYvHGwQkXMTg04fcHcKcpvk3PP6qXByf/GnL7Zj20/FY9HJzi95zwrXpxcPJ1Zo7CF16xIHGLGgcn3HXC4Qd3nWyTwYEJByJ/2h0nfFzPDkt+d4mHpuFjerCFDj6ah4/qxeHJ70jhd6D8iR0ydknfjiIQO0RlHnzwoLXvYdrL/sPnjyaltrPZpPzanBfYUwQXc4BsAn0K+57xcU5QD/V4KMwz/EJ/iKj19pD1p2+k4+s3mM6pyknrmz5HOB+coIOLDlxYfGUHJ/z9JjzxdeT4Jrxf2x+l3Ds4AZO+iM83cbTAxgUJDka/Y1/7i8PT79ndpt/3b+jDt/TFk992NRycqsa4EYSvHw5OzHTkt3pzvkxg1VHityC6Dht9xWvpo1QOjsNhu4Cb6xEqK56aHsUc7wWqjbyP/fs9BcDYpvHdB7xpUcVSdqtxQthj1ZjLzIjA0hwrOuhvhx8QRN0GLvav3sOAijj4/tuYjdgyaOMuVmDgSyJwSPon+yZQfLz4H//RfvhjbfDid5zgC/vRH9nfjPvTP/3Td/iSCOw52H94cApUxBA+ozYwdlx32RN3uTxIaP5wcGK+7rarNbfi3cWd9btyfAZdHNdcKkmrRYmrltI/HJzmTE99LtSJPXUjpZp3LrxYZEw5W7ypxaMPTugTBwsyda0JXSOchQWMu01YxLjjxIOT/TTYv00PPxXG3ab4XSf8Hac4PFlrX0n+rR2g8G16uOP0Hl9H7neb4o5TvdH6roZefEwvDk/46F0cnPxOEw5NfmDCXSf7qJ0fmPJOEw5Rpsu7TvGRPdP3j+tBFwcne+adJv9SCPMV36KXGckE/XBwslR9lMf4BrZ1yYo1SZbmVueIc4Z/ZHsuq/V2rvq91NDx9RtM51TlpKHXujFsHpzQoxw8/yieHZ7iG/Did57iEPVPzgMfByscshRjm0wUh+1f6ZsHJ/69FPwuE/74LQ5R5PHiBT8NRux8WvQNbxdf4P9wcOqUDHNba7JzNuSvzV5GDf4TdcV73mEOjkNjuwBm7atoxVP5XsxHdns2inudjjXj+gdju453T/NonEA6G6vaF+3XEG4NBH/LgIxyHniADRo6eJhK6YDG9YnagUdo2sIe+wIOTfh9TXxk79e/th/+2A+A0MfHkPGDIT5+ar9XibtOOEB9YR/Tw8f1sE/xwRjZhx/G4HecIjAP1iO/OGc2ekIKVawHiX4PGAAySe1xkF7ocFBsL5jcUFnV1Ip3A3KpquNn/qNVSZouhqpaSt88OMHBAn0Z8sRktU8Ic5FOVi/qZsw5co6A7ZGTWCOaspV2yAvPupyk3AnEaNSFTWwrVMmfYsRWAyBuKU4DVw9OfrfJP66HgxO+ijwOUEHbHah34OHQZPR7o/0P4H5jmw++GAIHJ1zkZEw1R4gKGwkWJb5FD3ee4uCEwxC+HCI+mhd3muIAFYcm/z2nOjTF3ST/kgjccfK7Vjg44a4T7j5Bnr/XhFFig/LWxPbwqDSpzolYQ8NeS16cgfD5m0xgVEMdtKPzcWoyfcUoM4pNoIvo3oZ1vmlNib4d6nHOnx3Vx5yzZ2N9xF7Hp3MFWveolZ7q66EHfFww+GHEDkT45jt8XA8HJHw07ze4EMFH9OzQhI/p+cHJLlDwZRHwEyWAV5tbDAovEYyvL2DjiY/h4a4TnvhWPfw+E74CGPwf4SN69nedcHiCruMmBiAdlLVm7ergpGOGBc1j3FG3hHA5XuyhPwhXeUjxOtV8CjSfa7tGKGoFpQG44kqpEDaExtHbSGPkrGzsXsaw+mlviXq0sYrj2e44jyKdDQUz3zEGDjpzfcwKQx5F+KidQBTJmiwGCX/vjfHV0Iqg0tu1EZfkd+FqLz9QVXul+w3WBuM1EYcb5vTo4BS4ERP049M17AMu3ku8tUMTzl3Q+f/sD+jij93+E77sxp7/9E/x8WLsafjBEHHx8WB8mycOUNh38Hfl8OUQfAzjMCb8RBy49kIc9oSSPTG0q9PFHRN+YjSgnn1ELjYonvNn/HBUbDcenmKsamrFe8qJGWuemf9oVZJedoZKTbbQPjk4BdJ8qzXdTM12AlFs95OxE71709An94fdxExzemB7ZDovorVuADte+uAkxQKDVcdOv2h5aAoedXB8IJAfJdyesfCWcWwo+Jgenl/awcl+d8kPTjgsBe0HJ/9onvU/sz+O63+/CQcnfEwP8wM/nKeIKK4ogsZdJxyi/G82+YEI35ZnhyT/Eoi+w+SHI/xeU/59J/92vToY5aELd5hwN8ruVvkXQvjvQRk+NiZf6OYTtGnw4dcWebAjr3MVFPmr1nOmgK401io385X92/MiuE2Ih47vaR9CmfDaGpWcuXvkvuNoSrwNTLEXlT3yu52Tvai+O77mY2++VIeRzrrcQyCHDE/8ICYOJHZ4sgsM/xsq/tE9+1Y8b+0CxP5Oytf2xDde4eDU35YVk2xIBuj/3TUujHhw8o/i2df+4ssffoxvtsKByX7Ki5/2+oUL/+itHZxs47L/Ujh5uOA65h/MHPcs0efAEYw/i5HV2vuLnltWCG1JCpgnD1EhJnOzsaTCFOdGb8mIuSvREkuCKcXXEb5lO5z5SVdv57EGWL5WI9H6XslXvHmNUGeoQzJf3nJc1r5d8l4e9QpwyH0VB2oDA9s/OMU+5EvfYYflb524zhk90oYHMOxf8SU3+CZP+6GP/R05/yGQf635V36nHBsArstw1xuHp9+3jw3jLjee2KvOHqgHrwkEaM+YLrZhzdlcY7W0qbXmdS72gX3t8pNKUC3evplJCMr2UPm2cLXmVrzbwJOBjhVzj0e0KkmjvaEaH9pq8cuf4of/4+P9X9q36ulhSelRde71JJ5tOusk7UVOPxo6eVfaxE1zemF7hIA1MqZspR3Aen3PSYqMM+7USwj4jyflxMZiBM+euUAdD13888/aooOf/OKJg9NXeXCyAxK+/AEHp3f2MT4cmqz1A9Nn1n6Gb8b6YE+ztyfWU6wpvL1nRHZFEXNufb+6wKaCA5PdHcoWf7PJP4rnhyV87XgcmvqQhQMRvyUvv2rcD0rQjY/2ASsCgG5e0LA1LX94nNUh4ZGmQvFWhM+fDXV8XK/T0e4teh1cU2d+rmueIa3X4cLK5gXVEY+mrFCctRtRCTrnRNlrz/aOPbvfZr7mZDVnKl/lQW2oCx6e3vcvm7GPwuFQlM9v7I9Rxu8/2V0o+7pfHKjQ94/rpT5svRqyPqxxTHzjHf5wJH6Si7/19IUflOLOEv4mFH/KCx3EgAsXj1EWbL3vWA15GaE1uX9cxgbp+jVYFhprc25DkTsrevcPTrAibsYElj5arNwp1hQx5EHzrEMHXE/ZLyzKgaP0Ge4D8oJnLPcwaF6h75qrhtK7BrcEYx21KddJcx6jTnHkwgHr5/v6iLeCnJ8aiA3I6dhncMBBvsGKNuQYtltO0+vrHWs+wD01o33gAPcr/5Kb+MO5+Gief/yY+5XJieG/c5kfFeYeVbEcJJ+xGFCsrIwpr1wi/gN73TGmYR5aHQuP1576wRj5UD55Y0tdtqP02Z7GQqwVj7JHWx0nrp3xiFYlib4aavKgrRa7BydsuqzVegNL/HWznUAW6qy/TtAq6tlSQ59lR30ZvanRE9sjy70xjDYWV4YGzCCb55yUg4YOfTddCo4QizF4+FtPTnkbm4jfMvaLFzs42Z+2xcGJBygcmHBY8ta/chwHJ7vDhEPTZ7jbZGhyaLKZy4hwZwnzbn1TiRaR2MHmW8hwgMLTLnbyj93yDlMcqkKO+XVd/7IH4+WXTOBjf3FYwuEJd54gg2/8fpM1Tps/0P5AnKTRshMRh4Q81RPaxyF9I7X+rs3vaP/d92xQ8+MkDbN69X2uqndIaN5KMUOJZoqruveD+37OS2XlTQjkZDUHV3KldtjXfalZlOC7vRyE/KPAdqGBixHQuADB30nBV/7iUKW/54Q7T5xdtPwJMA5Cn9vTWzs8+U927QDlvPwJbxyu4sAU8QHBgvO6wQtis8Ye3kJURPDLeXbR9O4APEYXCoBjxL3PpMtQOXltvAxt1G/xyEdUKlsaTybLLkGi9bxtsKgDAKWXgI8zC3oa2+OIC0sdnNIL1SdYuj4Ac2VNXXV3ivVbcnDqvGVheIPrh+hrHrAWmHO2kc+0zeTCRu2Yc9j4ekJrTOjg4BR7lf3pBNunQGMP833M942w9j+PYHe9cecb+1P90MbEYyz01vXgsRiWR1mYV+sy9K5qt/d9ai9etYA/j9cT1pLjODgPbNvuWWov5j3+M/7GMeZ1tFUkqQF7NdTkAUexDg9OAEVtnB2cdMCg3UEV1RDaskP7cxMsoiXELjNqRUafw5e9atf2skCDqviKyG0DaFtebyvqDQsT33ZnD2Jb65NtEL6Z2IYAPNxxeucHp7zzZLTfYXqHL4DA53rxkby8y/QZftqDL4IwfD+zYNOx3PjmYwy/kkBr/z/ERYtvEeDbwankOPCgb20cnILG8my8wHVsfAgZhy13mocnv9sEO/MDvj30a8idATM82Hp80XE/g9A725dOuQC12mpThpQ12ZofhxrCXbo80WCulrYjs1SnDXXU6vQ7X3UrlCKkwk072eWnJnL20P29+WiNNfWo3Rrt+8O9Ou7zerbJ8r0GrY0f+02239jvUOL3n/wOlH17ld+RwgUJdHwfinzBRxyG4g4SD0782N57//gedMbDUpQUqwRO7cEmW2dlPL4Plg7sQskRqj5zL4KePLCfETI+HhxC8kR1h2Sc9DqptXgS3O3mDjfj6WlvuZ5owPau32v6sc9T13oX3XlN1QzQ/kqrM6T0Fdutzvl6GG2g/2jsp2tULkau5nGM7tPrRa6wRqIw5hnT/MeYjwuIeBgpc6QYyDHvhrPlgQlt2IUP/BAnfrcprkmwP6ncOzsvMZc2Kh+QvbDd0Qe79qsDHYp0TOS9rLXEYaQ6F0qPfjgfbEfpnd7VMV3Vu+N7GF+uM8zhck5WQ00ecBTr0sEJl05auBq4DlZp6JxuGKaztVH0ifYBT7wL3fIhg5C96gLCgYpnM1OqmXUTbhtqP/IwL/1WTj3oAExxbaoRP3KgrR+wcGiynw77oSkOUjgsxaEJB6W4y+Qf0fvMsLHz4L8vpLiI8bs/fjiyQ4y7iTnHoRmHKDcAzcOLt3FgwnKMJQk94OZL+oCvuOsUByX/CB8OTiYYfrcpzN2VCbtlx32C7YDOnZSSNzWeRoJPMuvOdVr1slX9hDhZG4xof3jU2LR3xnlFd85jOTzJf+kJsYslOjP5iM2M8X3rXxuzrZjD+mAtcW+KFYZcwCx+ahs/xQWtd5zcf+6rqBE9OPlPc7HH2IUJZIiBLfPcnpKTWB5RhpUsVxjGy7AJlm3XKtB74FTnKHlwIn+C2elu8QbFFg/sRzo9DrHOPTA4K2fKU1owXkR2fDG3V2CH+bti4Do6Q0pfBlgqdvxL8cCk7iPxn9rIxQjWyG/Dg2sWs8X1No5rXTPM86i76o32yLH//iV+sIMf6uQTlsw/sPHUu0zkQe/MN3F8RF6G9sIWADuP5UX6QvfM/8LkBmvKl1j6EKQ/XlM9V5B3xnRHdwj3oDOM7e0PTr8Wf3EBPSZzjJQDZutSK2QBGQ1Oelx0W7VA3JePFrtTbjC7shHivDcP0vvBVB9K52pzbPAtw5MfbDXgCd8HbX3kNROAw1LoWOsHJxySwMOdJRyg0McdJ/5Ok5FWPJgn/DPC/uOukdF5Bwkt4N2F33WK+R8PTOBF5Hg1oHwGxa7Bl8jvNkHgTDtA+V0mw6FS6vqIQePB1oio+VQqARV8BGEzv0oKIRo28czjbDLUMYQe46z1gv6O/3PkaVBqwJQo74DejHVP13JwBO0RrcZToR5Zj05Z3yP3qIc1cSR/vezj+jsenMZyXKqYgy1W5Dv40PCZyvkGHu4qxYWIXJSYU9jNtjg4oab8LpOtcfQB6A1e8tEUOVMLbLAyXKXB7DE3UlMwZL0K1/xzlN5WH/p7D7EfVIKf4YVkUB0kg+XljuSrbC4enCKUIaCCeBXRewfmvFF7bpoX1P2c6F4QX2Q0Yu77GvW0p7Fmtat4oEfdQTR09uO4MubUkRwO4N/TDnOC0en7bue0B9w8DLb5q6G3rur1nyrAXoW6YcsaQr3yqYcn99igK5fOI45vSj4ojup4jnMn28WloNdTchS2hloEzS61Pby2V3iCjLzWPZsT2mvbPpW7T2/Gv696WTKOhzOBdpQ4oA5XPFCTLUTLb9X7y1/Zwam0iDa3goyCzG4NPldNwYj6QHJ1GXPQHTphIUFNygPicSdxGe+x8gWpxLmHqXzQOg78/lLLCbbmwdLtkTM+HQ0HJNjGs778wQ9OPEyZSjri5hFzhYMT7jLlE79vZCYBHwck0HbCcXiPAH2JeqQhyvgRk5n5GclN0EEQZOaFDHhgI/4UoxcPFxhprYupIHxqcoDZ98ZjNdNsVXREVx2bktJHNndlvRHftrxuwDTtWVjOzlRoepSH5Vg2OT/3tMTxADZgDCukx+JB95nOfnzPoJ7b3vF7PE/wFcmKNUEara3brAdfkvaCg48fnPCDmTxAIZZ6euidfNj54cmqKlqsH1eKtT3pp2RsCGct98ph/VaHwGrevK7s5iGY3J3yhzZqO9NqpzLhK5t0iu/MGU2HthIn3IsHJ1j0+MX+hWTXmXl61ZglvjF/URT0A7VRLoYXyI79XPlMV9fROdpKgwVvspPSWll/ijzOTa21KchVTle8yay6K10elD7kH6pFHw/GAhp2ePLgRB7as0fhcP/JNq9ids25h+0qpGAYk5RE2WnxF1NKxmvngQIyXHe38gk/O34lhJeQw/hfgoj3j370PIBSSeosUqdaSu8fnAyrN14gEpVtc5BY57JlrFZY6ozso7b0i6B2MIpdBOUn7UK/R3JieySecFeYygOtWcHBqR/jNhN2lEeLEg8T6zsBfj7l8FR3mfzwFLaYpJip2DyCloNTHqAA60/+nlMemmAdvtN1zHqH75QZw48fmpSG0APIFh/bQ+TkgR36W1jTcTHaoKMl7dL1+jZIPhg7+3utLmCl9/Sf4ddmfAtEBnVmpyla6F4e37y2JyyPaJXgCvUkEMHz9VF2FGwYFHgb/pN13dWAcamTYRxHcwnpulID2nHxAABAAElEQVTmdZVeBdH3t9W8stYCJ0YQvN5TPHWYayNw8PGDkxnEVwJbmz/NjXz7TGUIgRf2sTrzXnYuWeoeZ863MCLKgIssAkqriW5e7G8Jlrp1h9vY/KgeNbZtY7Ur4W0NhpCY75XamjdhT123+e/y4NQ1ozV+P79j1ldrZNTo3pHus3HgXXB4rOZ9UHiyM7l7Em1jHvD9yquaWXGV0xVvtot+7FGUcQ7Q4slDVPShFfEAH08cnOiLLbH2WvqYLoJsa9CE6uR1DvYwlR+1DXuzU8hSMpnCJ79Za3mZL4m2GbbW0m15sd6IuDoPd9xrGvsdCFyVJGInslyoltK7Bye86QQOKaIm1xt7if9VhOVRiPWEiEJAVk1UgEWIrg242EWovOml2JjprhVfQOkbPuHUj9KUYyTFtyQFHVErHfqUoxc6kQmO0lp+dM+DwU9bjEe63nFyPrGB5EEpWniMu06YL3/mwQl3m2oOIcsQvAU9P+DT4PrwFH1X8zjgCwo0BA0dGxF5HjdrEHpGtzANS9n7vfGkGM0iyNoARU1JXcBKq86r6LNY1n4Wg1orSo7XCpfH5/WyxgDXI6oiEb0KFXMs/D3S9GWFt9YKu6XDNC/rQHSfJi2WGlYRT6OeAlypFZ1PpT3MzCFwiBUt+nCfewymypKoByefFSjBFpphUHYRvIPYNLP1Veu4wYGtUd4JjttpbQk7wV2F8SV0uFsWVBeZ7x7VDSJGmBFGoSTWqiljqV3hnZgwRyu15h3gWXyaDrcZGFvbacdsN29AdX2Z1wzl2pivBdNYPWj6AULLr+HNWh3/LHlN/258OrbXRLBF6UxuZS/l5D4BTPhUv+u8dw3txbG222oj73jizhM8z/MAHD5hfQfXvcVmhAL07nYVOvv+iwJpwo6QzEbX/JFqydyPOgvJ2qU7KNO3JK7Ow50YdEz+3mPG0aokEbcpGepWLZYHp7/61X/zH8ZhQmJSumXQHKQvdhQiBGypZC1qi3Um7CYxLwwYdEoObRK3QbbUmT39bC0f4GRG9zDJZwsPnERNjr5NNt3TBXtilH1NrXF4cLK7TFUcfgABBi0DJWbMfuoiv9vkOsPvOGHuLBKY29Mj8RfwjRfsIPQ1D0E4CPXhKRU8jIjBORVWjsjjNUm2rDPEtj04AYEARlUhOXK8ZJzCyXEsBFSSOl5iUu8F7bypX4M8iH0G6PTMEu9fHp/kZAW0Ow6GupqbFZDxllgsuD2b5Icbq5WTce/AXGIPoXB8lywfV1rmZAeu5lTmjPZotzR4AIvBIHfA4MEJfJXTLXHCkomw1pVt7SfLVzbYjg8s6hJpXLu0S6euNNiU+WqSm1d5cITg4+AUhydjnhZJY/U2I7wOvykRDzG3hlCi3A5KXj8nKo4RNXYw1T6U8p3YO0qH9LWvnd9eb+djvh5DY/Wgdcpafh1TNTt+5b6GXq2XK8g6viv6d3UWS+8uxCX9WOtdrj2DYb7OfdfR2smxnJioi/mpeNSbW9VZ0VVvTGK2vQqbWtmf8tScPi4YPVQzC6N5jsK1BaVxncbzuALn43GEraWOiTUZ70QqSbvFOFVL6Z2D0381HWy78c8zJ7t4DdCSD1+Yg+JtY9f3v3WEsOc7AgDNuwa5gEwdKO88HGONcmCVYOcas9d6sx8E9RZd3Bye9TM2bzrOykNZNAZs2x4KbRc0SgM8eQ6BESHn1eeUH9UzGQ5N8GDmvm6z9S+CACQe4AU10uTBBTTg190l7bxBKTqub6S7pW70ocD1PdQiBP6gMcyN7i4VojXY+eGsvc0p63q2+XT6iwEdBYfc7oz1aN0q5JneYT4dyGZob37E0RhmjtOaoLIv+kWmzhUfZfMEMcR5ENYTLjam4xzu53NvrmiPlk84adpWv8+RYeOrw63jf5cJOqG4WWLEhEakAa/2zARhGwhO80HhMZZDjodC17CO96MtURGNMmO5eY7F6WLowantW2emArlcjo5m5Rt9BVI6IOiP7SmwKVoGS03pYr6QGGss587wux6edxYl1BmYvxxilt/xOMZ/x/Ka7qOxvfX+lcvy2iCe0IpZ456Qyzjx9nPfdUTXa92tXuujBqMOua+tapK4bGl/qXUH0IxR6qq7ZH+kBDCHDWyoHs3ZY/ViTjroIZr2CnYq7egOhi/oPDQXJ37H8WQ9DtfEE8A0VrVX+hc//fFkaNn6y3/4R7ukNgR84xq05dDEZHKQPnH24v7YCuTRpAeWhtOGzl2LWqmoabTG9xQd2J8X3Baz3M1E+mkLY6hvpUvQmwqUh7k0oAHL/KEfPLwaYCtE317baTosUPRpYHPl84m+Pe2wZJdHiMBo8gJK9wceqEzSbkbSRS4EDB7wb08fKXmInbEUL3Wh6TzYpZb3UV9RY/7qMhfAiz2SXtRfyMfX1UaqGohw2K3Oi0XNr9PHi+M6zlIzcjiLfGzJ1AyW3s5Yl7plFIRiOycZblsARUzW7I4okaLkWTNK1zbkvlVb07YO5k3c7tUs9+HRqa2SKc20RzvT0Q8+8GD73v8wJA5O2B/GR/UzEb6+XYXYPV9hCQ1MniRMSMYaK5y+EtX1Upk2bF21oqGhtTr+Ue77mMmjhckoF5AkTX6mQqOreq6fyj6WrSH2wWGY9MH2QNh5bIpmb9N2vllbr/Wjg1X6dV5Q91Ger8F/FGu9nl83zhnp0ThnnLmvWYz39Flj1e86Gtew9bhJDGa9bihGyz0OY1Oapq0b9sTeq13Kae96hq2/nz7rUPeZdi+eZzBpO8c7zBeV2HaayXmTdo7pFU50XH49KqCjLAUnY6XNn/3hjwQpSD84eeHiHQOart33QnQ7xmDnQtwgAoIeIYSNK+FVBc4cdYN18LoYqUHm2+6uHWPeVbj6bjmF79E4LwWDPDr6GsNvpRpNEZgJyCNnxS4CI2j7Go/JQ0VlacQDEjT84JQXEq4KXkB613QLoYj0Yv2ZlU7NeUi+9ZY07CaLDMn5Tqc87X0xGR+jj3/AMKrsitjZXKE/Ps42JI9gKNjR/tPvSY4zPadjqjX5RqN7EJ9zxXYvujP5nt0j/CoNSfMjOHdsVuNbv9Ho2mgPtEc709EPfuznhuF/rPazd59/Hl/iQiSutmgtAVFYuaqx64IniSk5iORnQ6x+P6CXbhmrcwbcsjYRdobp0RuECKCVe93WQvRAjoge8sia9MeuR3Sob8LMw9ZXCPBaKiP8tpeKnYmmtsqv5WgdDvP1UjeaCaWfdWJ5knlyZK2zJ+DvYmken3D7kOlbzJuPP3N7NmOulhOha2fOSehhiDJp6GUXLaYP4+ln9JmYwAgD9RXTPkY6+yeGL0wzSLfO3tUto+uExqL0dYR9zaM4OXq2wwD3IZ+WHMX0LLiPpSbKekUL8pK3ZTIvP/8fvhDjIN//5X/+L4FOj9bG5KWuVWdD5sZTRQ+dlqbFormio2Z7+uSzhQ0C9v9BK4zRXGTHYSreBMCu5yc6eZ0PdHumQOSdwGb6RUZiyfEkAVNgDSPx1g4yFT/9tKrYco7a34hkaPmjINfMw1TNs5g5mbrlMoMStTkKc8cRmhbclXGqdtKMETqVu8TnWLGw+K8cUWiMULfXtCudmagBzoLux3gPR9bKnyQlsWc+Tsfk+X3DwTyIzzd0tnsRUn5hevcg7vMlzfeN71vo2KT0DaiLfuS3j85PXFBAAh6fvjqt7+sMc3VycKp16i6QCGA5qtPjUo9E1fq3bkectDOUa0phBmT3wsY7yYI1rZoyjWSOlzawhICHQVqCb4+95Jmo3LnitZchniXG5F9g4e+Kz6EmykA97/sQdw+Tq5RpTHvAK7u1bg3K8tH0Wvc+17MzBDN6OcveMqIl81psEc813Ue1hvCGzqOIW7uGbWqrNXFyHjgnlXsjtKLdapfnO1Hsa/615Na3ghxq0m2BUh6mQLKb4mhU18Ykw2pJU44+dtc+FtyItR0MsS/077CGUp8M26MM78ExTNBHW6uovsiZIOqY+MP83WlX94tEKdbP/uBz8RLk+3/7n39llWYo1LQ2Jk+RoWz9+G9tyPxV6NWkx+ky9DfeN4w9PfpDu9XhhULA5UBcLehFXiacLeYmNDIK3mySFiLJEIzbMgDKYKTTfTT5Wn5MNeVAGB+joHtNtXFuR3UoEp30pdENoabq9sJkjMbH67pRUiFVVNLWpp7rJD7nCZFGtPbqslSgO+uGnAy2xGf/vHWLVeGem34iGjLmTNPpmCypU0ZfO5YH8bmO2e4FFW+OMu49xVfyuW5eiXkT6+pP6pg/zRPpkNn6tJo/OzghvPGHPJHz2td83azmIXmDqHcPr76pACOuKSG0Z5tirV7mZFKJ/cXusAdBZ2zBFlrcbnBEdkYirl17rZ90rbpKz35ie0qNQbEz0dRs/Wn0d9K9Ca5qayN5HSNqZt/TXBlDyldhnCqsjKIy32ojXoa0ZK5ju8Nt2Kau2HPtQrdoS77mP+iJmeCoqdjX2Nq362GxzGHk+5GzZ5k7b58VR/rwRmw0trZq5ct1XphFjAe+hnyY2oulPW5TNST/Ac97PtdQYzbXOte5PS6jFFppwgkPc96225z86e/zh280Nvh/+/c4OAWD74MFUoR6gW70WWTHm7ZJJZtKdxikxI+ztE8v4Ck/ghzfeI2XKvMvmYYntVeacRy07i5j8TfDSpIYkcdWREx2tSnzMCIWvTmzH11MNrNCD4nAroJ7RZR8FZppO3uWSRCjqAXBz9diU5stwknaB0m6ovJ5izFxZNYWXg4HjTG3bPUjugfkWDcHip+6qJKROdhLRemdDGjK7508Ha/xfb8eclwl+hvinma8We4NcM/qWf6YuFX1PevhlfaaI9Ixh7bdW46RPawrzBWen9nH9D6333EaH8wxW0rZn1vKrc15JCeyB/2gxmxy3yEerYAjtJNj5tlztQEUHTx5eIKxKGDwr3pUjIFZXcUnU9ySBTWl1UzpmD8qc+QYlYCqwUeg76z1s5TXhQiH+Abxe6Y8kOOMM6PHWhngJaXFYMwJ/SykD7MOwzkU3nc5wllvZOwD1tiD8Dw46ZTbHdZLqmGb6f0t6E0MhMzYNEQX2UuoBBG0hF4Gs2TsH8YrcCRjiyxwsl/WHsVDr2zL6TikYl8mzP46xHXNK/57LEk5fHMdQ10uEkRttrBZHpz+zd//g+1XhpaaPplG6yYGmQKFP0TALVujgavuc2PlGzSkeJAfPb7SLtr2o3L6DF6/PzNCtiaXE8giR1Ag8EQLW0lCezvG4WqUW6fyVy5EOGSzHeSo3RrcCr8wUteg4ue3KcifZm7HSPnBSCusItJJNM4d/I+nc2ipZdFlQw5bWnTfVVM/GrzGczsmk7hSaAKtH8Bs3OY/Si2wVm6vwCcUmkXlXEEYdHr9bGMMzpavAG2v3NfRd/F5UehR96KugCgHQ+lSeGvCA3t08l8RnFXNBfeaG9B8IgLSvjchxwYITHwpBL5RDy23xNBBV+soaHJWss1IXbnvNrk8x1HDMR1iDvbKFLrsoOydaUUxUcMdJzodrAd36IibjWyfEZh4XZRuYo7ZYp73MVuiczoHqCNXuq2/e4rTcTWS7XvnVcszPZuh+D+nsQx9Jv2lWF4Uj9WFYExkuShiUni2awG/OuZlSOVEHRZzaRLMrFYbv1OZB2+UhvJeAblLqxZ+TM9GHOtv9p+A2A+Bp4vUsMMnW3eIl+UjtZYyMvfCpVzdk4dW+WcYandE7+FohpS+sy8d+T2UWcJzRg7V7gp1HEEnZ3Y29XGtsrVt73/ye5OBid7/m/9kByc87BUTV5MHOuGGjduVWewAZCnN4NHnBRRaPgMC/QSrJvC2cirCF+kysphjCMFJuk4dFqHQbaU4SrfGknJ4iyLbpY4zJaYBnny2iiCjG8akOpgXAzRzHp74N4+2+Uy7ctUXMENIOc+jl+7Flz6g31aEjDb45LUlKUpWbY+54wde+2o+8TCnLQ/ujN26j1HEE+vZpYiukDqlPeorlpOOjf08lCfi38Ef19kUk3S3cyPCA5L4bGdV8tnO8rfvR9avZP/tY9n3wPygnWnf06000PoSspf4KnI7PPmXQ9Subw6guKgjcd17645esse3pgZgHS/d7EDSplHm/QAafC7uOPnA1foFtHwUbx4Lh8H2wuLdBMR5dEHltDPxXdTknXV+NeWsPoxTUrrJx6OMs5hXcXq6a/Ie9by1W/naaj3OmevwcaQTSzpioi7lyio2y5ctvNT8mKyre+u/XBoBmuuD7dYidGY5/c3tyr73lLUUXB3LvtbptnoZ58gHZauYdIqKPko4wV7UrmJ6FrrGYUC6j6DnDx3fIoDRvqN56cGpI+GWrVFBmnwLEEXJp0sWQSveqBO4/Tr7Cct4XQzd1fMiQVWdViylN4oTvI3HXR3ZaCxmXqrks1VfkTOXcGdQcdF2GeJKAEUsAb6b1nIVBEOJtoSFXoXmHCvBNGjN+TJI+0QXOBZutZARLcZM7bY2qjsXN5K9eSb61ZaxLfQlpoV0lzVP5zjqXbO1AOtpLZm4Mo5rBpbza9jzG1A5vmhf+kIQk62InCR/r531X9fv5F3Lzus830Xq3MASFxXx9B5orDv/j7Vio7Gh8cshcHDCSKNq8BpUt0CJR2UE9s6iLjXMSovexa1De7ZtBSq5rT6KFz236JfEAIMHJ8xc4sIeA3/VQ+L0v4M34VLMVsOYVJfdIY/QSCAdj9JLkDdg8mLzCvTVdHt9JiDfd67gX9U5i3kVp6e7Ju+qp3O9la9zq+sauvyuWz2gSUdM1KVcWcXmEmQLzzo/4BNyL6rY33qv2awVMQzdObiIg37ZipmQCPh839DxiHGRTFcxFsQZxsJkl7XC0iwUfT60XR93BauY7mLM+jUOE+g+IhtmmywCGO1b9bGDk80yALcFGVnuDXvMuvMtOBQinyi6RbwZIe1Dn2FbbyOn7LiNNLi/4Y4T0zPjHqNBqrHcPjgVPP2zdeCUMibrZt7LbCBMz82hj6jCTqwH7V6o4TOsQqWPPBKPi9i3+TeD7oXdCqmdzpHM1qZJVkXf1jkcZ0S9lLIojWTX1bnuaLnXm3DmIe2ZXeL3nF1Sn5VssJfDEcXO0Qy47e+9gWz3ga3toxxis51xyEcbT2gEPeu+TZ/JvJH/BwLhOO+aqt0qP5S7zPKGAxPmmR/T2x6cEAHWQa8FrSHPBlOiwfaG46bxDkIYwUqbEWLsKewR3Vak0OLQhMOg0RU45UdoD8rqSn/0wRGz1cXLObnlMYG0CpW+hfWE8t4esYKs9K+EwqtaMV6lU+TPkmcxr+L0dNfkPRtB2698tfR5Spfh82gHCHTERF3KFfaewGSL3t780MU2Cuz/ViupwHarFzorOX2iJQ371o1AQzau7ZWfV/A0J8/irbB0ior+OEPz4axienacNQ4D0n0EvXAqHhYBjPatuzw4/b/4qB6r0iyjCMOIhYOWdMENu9o24+/xB3UtOH36RX6pFuGQMQ7yuu3xCU/eeRh6xcUkOSPvQLippqW1EVM/rrz9UAd29lzCLpmmTz7bgGj/oIAb8u3YIIeMMSOWpG0+yIVWPILTOIFLPR6a2NIqfGQM8GcGKERwggvNRhlplYHmoy2dk/WzjTr1Er4/CgSrCYPQ1nadUIfxHdsJxEQSJ9kKN2ne63LEB4A5mAONY5dmP9hKp/N0DAGpvomEdm8V7Ec7v4rDWSR9z3AXp0u4zxzKzCbM0MZTYN+YjLFNGX6ZT47/DqDazPScH+/nSva9GYcn//2mz+XvOLH259aisuF7BnKKs5nCNbv4b21g2Cxtli9sac+2ORPk5S6RMENxcAJm1L16vAx4XbHeExlDmyILnolJ1PM1Cdo0qcijdxKIFm9Vi5sQhLHdG0Q4keOew6gnJXTlB5w1WlN3OutoYTWy5r1vlB72xji3qldD2FpuOV2PW9krOfdjrszfC4OOYE56F4HrMRQq7ztzF/O/Cza8B4RrWKzqjO8XjVW+fY8Y4xqHQdkKt/FeQXVMz6MdYXGm2S5T9nwIG4SjmDbKNxk1FrPz9xzay94iF4uUVjvaB3v55RD/z3/8+8A3CxQKN3IWIHnkq4fQ2RZSbKootMUTs5OZGzffxlG+IaTLua1IdohMAdxh63Xz5LkF8dBRetUP3qhlkeUbZaAqNvRXD9UROoGjiddNvuPtYwBF/iM/agkV9O3pLmz05cqIpJGTsIJ+MGNagq6icGnyrPjSHEb2IEL6G/qUjxZu5nmL+Hp+XRIvXuRmZyqrudtfeOFrXy4+ipzHRMEiboo27ZEucwSjoH1dVK42YKcMXR+HyuL6ak6uYWfOjoZ9GNhW6FBZqHPta580WqW3iC/kDOOUpL7QxREUx3mkozLqM0fsY/Fi5qIfd5xQhvhSiPf+BRF20PDhccDdav0orX6DTpuduVR9ZpJtyMae6s/0cu9wpcDwg1PuNVHXxGY7I2afw9Y1emLCjXH0E3iAK0intZc+T5rewwOMI4TZWWgn0Kfi/T3BZuCi86t6CMazkxc69zN1NJzr8R6h/DbLhjq7PFCbJU4UAEif2GtdsT6UR/OjmLi3zS3xiIHWQxvAEChrAm2uKmtUl/zQ7YKnT/Vxl27su5bH+qvxzxY6Tf6O3kObVS/174zlSnyXnIrSZjwuE66MT7iCkKUrehD+7A8WfwD3X//df/KceREYGguG9QU+n/RA2bhARm9Iov/DmzLo7GOnZV+LltjRQoecIhxv722iYnIzpuXOHSdEy0dTK3+t2XrXFxFjgy+jEyKaxnPpMKjJLqyhlnlJKB5KAJy0w5R5EHFwKqbjOFYdjiBDaYROUEaPIbr3MIZgfoZkfM24PDbGTlDGAz+YO/STl2+k6HdtjMjRg5x4Kie28kAHf086a1M/+FetGE/UPmy7hrYe1vFv9dYc+BBJ0gNPxCvy2H/my5vV+NX5Cn3L07VDmi20V/SKt0V+EWcY5v3xPRNFbAFDAJfgkB99wihylnxfR1Yr+XE9HJyCVvj0yxqCqOi9mMC3p/9PnWwUWWlmlG05UaUNvacd/FhfoO1pe03UdNvs+qhYVTedL1gVltn1utkqAragiyZnq1+4QmjN84d2ELd1U2L2NLnaO3qs5/Btv46v5YHl7zmpygxBouNfeb0T08r+4/LWufi4May9neV5acVrFS90nbWVdo+dcx9t8MmDJWFXKJRzX2Nf7Wc76DYmrxXot987tzmgrGOnv9nHnf5RrHdwVPcqps5SXo4pzC36qk+A3tG9FYQpD2PSXl0/NuK3i0DUnpp/9oc/Ilnt+3/9d//x228/5KZkVigqLS6nP3yIPyxWZkakbrDGYsJWjk1s+cQ277I4UMF+b8Mb+fARfgw53NprLwKwOGy2yQuXIge/MWa6JU2NOrQ3+RgABMNDI+n4oDJKwHFvmMwNJnWj3b6arTMzM/waXlsNtjW4q/ilZVOy/323KZC6fuJQwk3D38DMgG9k0YYN4o0Hol48wYoRWQubiMMjxCr1lepKomekB4ioESjsEBN9ggcda2ga3XylHmpPBeQrL2iOcSs54yim0rMdA+mW9TvW92zH/jwW8s/ayc7dr+JkNIbHEA+hVxg2SxP7ythYZ3AX9gFCPtuQjzLlgX6zxzSu8HMpUS8JSXNwFZC5hC2fSDCGEnjB9znCTaY8NPnXkbsTGXStvXHNxfoSPQYXzuEpkKpPBW2l9ox9LauqNdoHMnmmZ3tM9GgztxqL0L43oU/9ceyiuSDN47j5RN5TM2ZhYSa+VtKqgyHlHKtGurJ+njePCYgr3p6nTknndKNbIsmS8WrsG4M1405ca4SPwa3Bfgxnl33czbUDe03aC2tzWvNTt2LReYr6wNopcRF79oyVbQdA0xEPOK0LHQTc6xXx0D/0Qrd5qjvi0N9jrebhMYS1FceyktZUuZA96yzyv7Lf410dy1Fse9hnfBmFzezYc1sZm0qJO/BE9xc//QlVqn3/F//h7+zPLaOgoqiihRyFQ14U0VAsLiOOeElWJDCKMgoS9PruE1G03U6A2destj/E2A/tCG3qffHd2kE1VuNTp2XbioIs5WMQNK5WIjGe9pQGmuHRpYtUTpolEX1y4QzXOI7hb/z4fL8dP4z28MBzZT9KeRx01UWsByf4oQ+01gsHTtuLPYCgz+Q5INHBy7hAeGwm83hoC0E/3r+3gvQLNh6afGCmEHF0vG2jVMjVf4wjdJQmR3lKK+oRfWSjccR4nWNBquQIfbsWjrQtdXsJkovgRpA4TgPaH+e8BHZjaMdOhV3j9huWzbaA7tOAafsJ/jXdAf40Sa/x+QQKc8VcssVG4P+y9TlCHfrvOMUdp3Zrg86hajk5y180KUobgszb+dxI/U0wHctMRWCbGquQU47fcfL9BvbB63bGZL/1iELJlXYTUxphaDo87q0hps9jD5pWXcrXrI+xj6Q6JqVps+JRdrVljTFHbPMngmNJDaClaVyrpbdOxuD7bmcObu7fxXsLfatMTeklF2Zg/300ZTvicE8KuJ4nrZ2Yu3VORvtAIY9tcq0JjLkW5nHBjv7Rqn/IQg5U4o2xjX7D+91X+r9rd6R/FVP3IKfH4R252Miu+oThHd2NoxOGl5+PowrR3/NWZjp+ytuenHfv/ud//jvdSer9X/z7v7M7TigSq31/hsMoHPAgiydsWCzQ7XeCbcb97dCLEQXZTxRh93Fx7xzWpvfnF8dyJv2wNSZiLoOmlOsLYkpmxEHDxFPzPX808cUkcRR/JJivkYvexpmrxOJVbdUjzSnveYEFPr4RubK48q6TH5zsHOIJprn3eIAyieQGB0yfb5hYjOEpDOPgJCCuY8aZ4PKN3Dimv7iWvzA+a+MOGOR8gkpsOzg5PR+eAJJXDNs8uYd66cWp8SodqjE+mm3llOy3V2yYB7YYa9P72CHpsZxq5lyu9DLOhdshloU80I7H6fuBuL0ec6InQDRZg+4y/Oo6Ulpcvp48HPJuol4fx01E5sfXsX1awFvD8DY3btexRYR1NNxx8mHJwHOY3iTde5foSYy1jrmeRbYlA1QPAludmVOBWPxJSyhVz77fxHsMVlw/lG5uU5QDiXRLz6iKKRURGsMb9xtFOvZT64tAaUortor4SlrHpDR9rHiU3Wk5vGjt1QdGLpE42pl/JKeNQTZJg4/YqnOlP2IIO6649+6I99ksTk6Xafr+khZKK0jXTOxDkDWvNVf24JHPti2CWmFRp23CN3W9tXFgnVKHMtiSpox4z7TEfAZja9s53cpifOBv9qMnS/L6WI7jW8V8xuvdoAtxM77VmBO47Y0hefhf/uh3N67f/8Xf/gevAdQ+CLbY6UkHHyGA6f/jxeh+iCcwvYvk5BOMmXYeVCFrpJlyuTOpxNaYjMflGlDTvlGKCfFjA01Bq6d4Nlj1Zx6Ru10vsI0zNyi0Ioij+kHHXCjNLKE1gDo4GWkXEPWTVzMJ+Dw4pS9v7OoFFz1AjbiDth44ebMofDIy3EUMxAQCeiQ2+dCEDeNC37wgPsQVjqNxPZfaix6eYBIxAOtscQ7zCneJ6+RAb2Xj6MLi+PWqBfPTaJyx5jxKBfZ23IpncW5DSFYKFvJGOBmnig9xGnFFca+BrNaOYQe81WD5KWIF8zre0s0TA3xdZIdIzOOqRTbB93VkReN3nOxvOOFryb1I9BSTQ/Wmhj0nJfvZlJriHEZrFrPtmX7Kw1d5FCsbF/aXoeipx1bUJ7LX5rmumrZdc5kttp7/Fgt17KtrP7fDtDy2EvgHSd1XjvbeI9kV150fyVANjtJiGCR5ir4nD35Ji1Dbj0GrY6U/hu8TH5lOvuufaIe4piAIHxFxRDbUrivZSpmGz/phCwdhV0DuM/Y0l9Z7BHj68HU44avcadhg/8MTjGwdyV7mPGhcUJ99gnf3MWPetV/rb3M76+nYInPxOmyXs9Gl/rlvwmzHnjFQ4Ua7svTtf7FH6NjVxYAhtfMv/qffVzWn4+CUv+OEOgKot6CNqKfdlcLDnYJ03WjHNydo2SNXBUrSC9P7TGrw3M4ChM7Ro+VrPY8swhOYZAB/Zea8lYAQZzKRb3wTQ1ok9fCBCxkqFJGMHkswfJYwQd4ldOQJtvaMqvE2Dk5QDdx4xSGpH07XhU7WACY5ZtxMU586YeCYsQDAIKK1lKOG0k3FZ7H1HSdTrVqhJnIRvuG/abgInTChv3QgzSgnLhSU7n7VtWAcku56xjq04HSF0g3Tg1G2w8phswaqQNRxMP215IPVoqP2LZ65vWZb54jqKrEZQs2wqM2oSKmlI6xnZZqK8v0sqNmzzl8AdQjB3EUe7fdT/RMFzCnayLHvy/bzC/zww/+GU35RhK8RSUKWlimqW5nxIosYVQc7xUi6zUq4NVFO003RNDmOSSmrkX3oKk3buYXdFb3ZbtvnEKNlb6s3ctK3qZdFEdDs6NZRDsoj9M1e1277vI6+jm4ModGc2phQrgLyFGklb16PAzYre8V6CzpjsaYz+RZ+FLPHz71BpXv0HV1g0Ava3jdjv5l9cB5qb4E938OEuYoh9jXuZ/DV80iaWLNf7Ve85q/06dswG7WtqEc/LXmcakxghFfyHkc9tlyOjgmpKI4wVtnZ6r/1OGaPHhWvUSH0MXWsRTmBlx40L5mDZfwU/a9//Aezm3fv/+JvcMcpirvbKEbUY/BMjjffnFSvU0bA1qHbWXgKz5E8FCe42C6glzKnQ3v16rolCJvqkkCcpIcWF93G2JgZY8NTw0OhAKbe2nkDesK6u6V4KFGJxiAOnI0BczbMpsQQ0s5a8FENOKg4dMjwGlQZipS41mrcLMY2DptMMOcYzByNy73vOJzJAODBKeIwmROM0gM3XrQ+EJWHkUEXUb5AZGlNch0rabawCnoYM9irx8at4qwMglfpvKa+D7SRRECxrjbCZKydHtvMWGuMWcv7mxwttZZM7jkQVs6cvuF/iXyNOYeuMVxD2Gp99DcQC5p5XLUeoQ0UcfF3nPzwVIOHMMbB9eRdf4l5qNlwInvZ0GabiRNOgc56GYyzg1bOrF3BO55XeaocWylOWF3XV9uZHofFPXbWmvvi2wAKIwmOSrRmAOuX1UJWWVrKiukOwkv74ntKaZ0QbblVjBiXkZYZpcUwGPIUcSVvHnMWtb2yV6y3oKdYuvsWzgTTHNlwr1beLd30wqGw1X1T31d9DrDvUNHtca0GnjJBxxypPdR1T3Pz5JE+agcPFQfjUSn2UEEaYosYRPoQOY63MWf+Q+AHRod1YCnQYa9hzjVod30s1zGJre3G2qcyuTqmYVKBkHNOHcxzsiD93/70D9EMDzs4/Xv/HSfcUAJeFKS0+dPKDy6EbaYc8UBf4HxBSN+9ewwZBYrUI4p+JDStJNABQkcwCrqnQTQ3KOCusC2WI7MZZuwvAA1swW2zQ3lGMgBoZxUpFjX41Iu25yD6rpKHpw6GVoJr6syIc82wpUm5jro0b7WZwF/4VD/EQBwhDT0erhgvLT0G60QssMaGCkQCgAe/4O09juSw5yPo3pSR01HGqINbwmY7S/g0X7Sq2j4XirdYTMTRmAmocao+MUY5raJV2SjpXuoQrgW3qJiDxLLGqXxJ7i28O8oRug5Aa+IO0qjb62Tkv1Uv9vHYI6LWYhzkx3pCDdgzvxwCBycuYc8AZAjQXiojRWTkPj85Kz5xolBkEcfDNZiLmgc4E4JjbnkHACkyG/8/2Z4b7mpklkwuVJM7du2/9ybmKefHLJsCjIIqvePiCjthOprJS/24dgWmVis5sKY43cRG5e0k8yqZeYoLo1m+jeH4PUTx3orexvQ2nh7zc/c9avSivXEPRd593/GP+WOeYp8KHuZcbU2qhY8EmQnqpfeyUQf8EWGRVcSQbPpjO2qH1lxNo85re5vxvha+0JDDo0dJmaiNcmlsJMpY51U1SAverk/qbluxNuHY29c2R/TFuvMChUUI/vef/3Rj/v7//nd2cLJC6y+IQEGiEKMY+TEPtPEIOeJyDtlwQ8euyGjQCdo3d9PxNnXKRtVdduNFYthYVRJGyeE+P6ru9KaAJYZJ4vbvkdCzx8pwZZNQUfgwYkYXADnQuMMTYK2lMfVmQ64u4M3UYh4dCC0R2YYfr6uMH8NvKY5FiNk4yURDOQ9wqLC46wQhozKy/CX41BzLicMWxlnT3ip/AvZuyhFsqRaxMgirVPE5O1ffxVkLmDlIlVbt0Snnzi32TNS86BGn2DNxC7ONY5lUsiLF4jJqvvVfTXlNTqC6DibRpe5xPV6CuK3EmH0NWlLZAog0DkyIbTg40ZPNH3LB2GvJOZ9KgRU9riH0dPKVbruPQmXdxNYxxXG2H9eAx9E8GreU8AaCczUKpnhNqCH73GSeQW8f9Mh21FhZjBpTDzVkLNppLE1TqrbN63cYlWMb3YnRTGUaRqPqqW37KvFAbOX1/jLovVVn6/+tPI249MtWczZqau+aVlsQPThjT2vc9xyb2PjzB9ybPsQf4s4J574DLO5X7Yn7Tu85jZ88D349Ap1z+mELH0qz4rvGUa9v++ixvI2fM3wdn9PjVFpQoXG+No/jR5577avXtNv4PcYrqU5WMXcI+sBgOCClzexf/eKfb4zt4PS39a16cUhioaJ9N3w+ngln2xVE74YP/7W9kt9tUHjtN+VNVMKIsdBeBAPZC2hgo+Om9iIQnCK2KtvYX2EUULsRd6yzp92sQ4En9aZ0WHgdSYzgjlqTMMz2X9PY54bFNiHC2BeFQTd6GPbG1FGQilbeYu2qpw5SVDLsxjgIs2KLaEZNrRlGSB77tIDjmUdZ8K+tVdXdwyPu1VaSspiDRpn9Yf1ROo4v+CWkkrXMj7CKFHw55Jb4BjHkUmBr37mBdVd1VVfP+t3P593orurHPDFutCsa5YLx1sHJ/p6TlpDLukikXkKtp2avLlY1dHUMd/UWvjLAa+U4jmFVB3sRXZvfEX+LNcsX46FR/jCM77NsKY6Ws8N2lB6gj4rs6aJMyEI2QnZsWlirXpouO9G00UsvyM7rLAPWzFPzUd44qjPTR3iz7vepz7yzfbvY5zxjDem+Q8/cV3qNxTd/oo/DVPPDghi0Zx8taciK9qm0l9WU5mYQsWL/g+VRbihju4YFyqseNY5XAW5w5r1mozCkbnWTQQ+fW+tzjs5xr32ZsE73OdhKQ/erlZw8+Mli8JhAj8Wxc3D667+1m00Wun8kL4rPvsHWixC+WZy848RJrbh0rHRYhYgg8B/R4RHtpl92ofWK156MQJMwnYG+8xjas04NjFBsAdljbfmRK7U90uMbVRTgvlXMIZAsQBlwHUYmJ1rQkyi7DmJ0+FwtKrWjdvAsGxVqEN1vq9jbwrLilKufsxgDsxw18LAdkN2bSNiNEVNL26j9cz21Ac21E/z79jNe9HWcTY++RsvO+f4ccH5Hy/2Y2x91rO1wRpjdXs/Faqpqz5nsz+phUv+kuttalRxciJT2nf+uM/DI15a0583mCF9Hjm/U858Ad3HUWqUPhiMqhk9uEcYwUO3eLwSCXmuzzjblNsVAOcd/Bfxta+v6XHeeYxR8X2GLsQTNQbM9HiVzcqhF5wUZBNnwzPejEUfRmy4YUbZMSK9JrbXmPkK1f1o/e/FHnE+x7blhdNvxU4L21XWueFxvbOGvD0iY99inYNP8iIk2bGGLB/tzG0LXmPYgl9Sehi1pVVtLPN+/cO2SOWQbkC97pe8AvL43vCwAAdLVuL3Go5StGB6QlT/TUbrXvuAdl+uBlxTpAhBYlyo26JxPjwm0PV0l+f9ydcfpz//6b+yOUxQiP5YHn9u7T1HcNblDMB1JJ8QjijgzgBhS6EZobRey9WtjruUVk4h7Mrbrh6GzjSyJ8V2ygCznacsW3ZwGlyg/Vas5kpUSCc8pLLCgwcRPdyjsFrmp/Ih85UvzrDTRCocMa5FngRWJkDn/jBOSxl9b9znp8TtOEsEB2f4zzANdipBT0ndaGrG9Y7unqzMZ9Gqedq13Bt3z05bHuFJnXhE5Rg2voZZU4e+kZ5XzVZxL8E+UuYq/8nAhZtqrDWm0SgNO+25r8+MXK354sjtOeMicsTzoJxROXof5E7ATs2fEy/gkjrMomJc5hiXurGT9M709fEKdyakX35YaoxlfOUIZ9MnOTIvCPiBqP5ZF2J74460ZUftBt83o7MK7yGhws7eeH41G6Zvgn6h6T5XOwzbYdW62elc4igWadT23kPkPanD94IF+W4cmyBSHtuofPPLZDuXeg1ezvFYylqWEexsVZszA7esrGERcx/kk3t22xnHX8A30dTW87uCEQCN3w/zWxInXZ1Os8y+wlSrio81C8JhAY5491FBaH5z+6m+GO056eIJvFpPSNc6Kwr1UbyyucO68GkDwmMQyXBAxJuovFIQ1F165g45AgD/IJjm6lx8boHYlLs199JS38uHyBabqujgne+1NtE253pQEdy8OLWhBKXKV48IvLSEqTot0SUtQNDNWx/cpHpz6ApQhX2sXY71meKLV2aLiPE/kz63OCWUrHmVeTcthkDm3bXlEuZVudpPygWjSZHebE0o+lbaXg8Ya+bsy3raPEbUNLipYo0H3vKEfT59nA6mP6gHQn4HHOvDooHczcR3PbNhIcIfHvm7I915pjx2jaQCmBds9AOevc954He8eTOju6a3xG4tz1Jx9ynzkmDgj4VV9z4Oe+4GuFvv+UncNwVBMCWiN2OrNU7n6s9FXt6liPU14BP0ieOpNaVF5A1Iz8gbwBdlr6twj13oZP0koHvcbQILGWsGfQGgd5B78uPMd/F7Pq/cy/LRfPwU16PhU2ss0pfQX/s1lL3CvXNQhcaIFR1aZ65/nEuP8Pj80bUUPwwa3JLeGGikPMMyHrv0N5uDzhpsu/A3kgAJ8+kBg9uwaiT70lwen/+uv/l18q558VA9+4/mtFSfoKKixqAzRc0fP8Nt0RATn+E9+tpMdgtt7jJh7WiO/4gSbrkcVD/1GGJP11HWg0RXd9thH+YTgXdqUzHBnXsQc3HBLDbZlHUTGNnE3uLN8r8/cUk74cQGk1EOKuIZ5NFbkhdZEyzbZw88x60eeZiu4k+UD3XA2lO5FlDkX18zgL3JyTf9xrbP4hjlJNyveOgLsCbMEjA1zVtrtew0dmJ+NJ4Dn3Gr/AHw3qmcF6n/EOqq5K2NdzVXMCecGrdCYG+7lFopHZkH4wQkXMvw7TibgGvN1iv7DNUv/OnZDWwx+W09qM9Kz+QqvStGn/Xzu1f+Mf7Rmt7pjrOwpPnm6Xtby1lQq/rhvcNZzsxpv8/arUr0Qf8sjJ2IG2goxd/ALCZrr3SO9YMc49tpNVANm50PnYQ/rWb7GovSzuHv2XU8fw1tHESlun7EHaa6xv+geEDJ+TI8yTtVcG5ir/gE/9zjDsAGXFww+O9wbGFfgR7zkRQ/2YaQ+cXhK7Wz/+2g8Ezb0TOM0aHDXkkmxuvM8UNAoSTHdVBja1h7Yq46p7kEVChT8mbto1iVjBey//OUfbdDf4+DkRTgdnKIwUYsoTLagy6XkrcNrh4woFkl5rvt+bVOyHaIxdxSEzfgkymX2IC+d66GIJyELqF0ppL6xKV8QnBxkiTnwTIuuejGDN2sxji2fPvcl1Ni2zK1KeqtR7kRzBzQ2SY5jGYcJm2+j685AT16G7qpmVvHDiPhsB6BFZw9noTqxOGqwZVCT1rPdO/Gt8hR5OIov9oQxToxNxzdKr/TO4j6Ttw+NXenn4mv8O5T639qtau76OFG7gT9sy9kBDrGijT7WrG/qZut7k/9+k1H+U+DENFiXOnz4oK8YxZzL0NER0jd4TcMPdNG2tsbf3HOqMUY8t0SIFWYRS9A9/4q/NExm66211vgd01q+wGoTF2KO8GArA3Z+v0yGKZApaNUVJeYca8cc8zmbDe9LNJqVvN/7ibhBIS61H2EW0oCp3pR+xMM1m4rD1JW+Zn1Pa5yfe7av0MY6730HiD3PkOH3KiMLkXu94wQ5p6r3jogK/XjOf9xb9hkMXqY08CLjxEaLB1uPj0ZuHgBDHb/5rMUYv+tXSV18OngIiFK2g3C303nunI8I0lsuDpHvekmBqK6gSgxhPn0P9boLC8a7Pjj9pR2cUIg/HJzOpmJfXrMQcwBFnax+Uxv5M6DacP0OvDQId9iUgiHu3Yz+Yl/IIpidWX/ekKjCgmGfrW9W8ECHq+CAS4ODljrcHFWVsGwhu/OLvHvxA0fHrL6Vht6Vh2Jd0f8UdVa50rpZx9xvgC3HjHJWm3uHOsvnmbx9DZXTbIuPa0aYE3k2BsWeTHe65/mcDc/j7HrdxsM8oVUa80OeUbZHmS3+Gxi/IMLrgbzcxbY1MsZ3Nj7GEPXBj+iYTwn9fF7mHHX/0H9NpxFFt+0V6hD/CsBCp3OyEA6sMdcUnd9xoiZbHfx4KUiNs1amy1S1B1r7gTR70Qj2fA06WiB7Bjf4HuGAqd6E3g7lhpcDVXOh0EofWD0s6jX11p7mEJHL9sk9R7V8z7G56OkAHYcpylo/9q3u21LOva1bfDNf8INQ7aCJGy146p/64yQxhxhRPHpc5Pw2tj1ey+lmyJSyvZYB5H3vMSAt1ajBdg+p+fIhpWaCUnzEhL43eIk6dIOM91+t7jj9n3ZwikNTbNBRiBe+HALINQZ4jkcnJ6MxdvOsU7PQNmm62wz2u1ohQPx4VGjoLFxBXjoLOcwuPwqoXSmkTUVBNVWsIgZZYoJHPt14a3kc+qbHfui311X+mKdyPhF7Nhs7Buf23bFqmhAzvml+dC2pTxZ9yANLdTfgE0OxKNrEbgLFVJo2e+0Ka0/3U+av8hR56Lncxr+6mMMcbed8a7vPOcvpmbyRNfamz+zP5MRf5YyyvfaOzfU44K3HR9+0RzvTvi7JN1PEVc/8KmDMf/DW+JjnXMZ06frs6FjpnzYhi8OT1lnrEeVeqz4HS5bkHPCgdN7ZxT833WhcHeuuXo6pd3hUwbYORsdMBLhBn1mM9tte+wTSFk0PTup9i9ScQS8KpIUvoMZ5VG9Jb4fxAq8Nwfc1cN7YlazRt/bU41vX7Hq/4FxoC7qfgRuYMT+NH5joz08fuKm3buA0bvuAZCyzsQ68B6yAgHZRv81Ej9fGvhkypWyvZYLzPGsvUQaf1GA7Iyz6qTrAiBrH5HOPF/+Pl3jaaxXG/sEJxXfxjhPwqiA9OHcBtrx5khcF6sLQkAqkTkuXlKn1Br3UGJgVG7l0I6sDYW+mgHq0u9oqUL45K9Qcu8pmFyoLul9B0RWvAfDGZDPngpKZHq2i1XlJj5jv2fnUd1vJGTYj2GzyCzs6aiLRRj+wdfsATx3EFyQXVsGZw5DlW3AJynRJeM53dOf46RtASi+BhTnjiOh7RTLnGnTkYSeBroh5VAvQZLCd5cf9wDu2vZ5zjb3pM/szOUewyhlle+15TtvyFXEAgzik577HZOnBePxjevk7TuBzjGwZnWKQx/U2t5BTn7qG7P+w2HxmPIiVXltcowyvpzpMtJycVsY1VGpxbOw/3FoI4664j7TNnenKEDKDDqD0GpGGbDETzz3aJ5C2aE8fnBDeZlKfizkgEWvnIRClvx3K804T4bf54LSqV691SS0T6fuK5Rk1FFOMVp/URNt7WXPBix784ssi+gZACBhPTKdiJydraywxCdbU4EM45jBsO47fXsrHncMdc4Axg7PlHmVjfi+h7hJlSDM12NLyoD1TJb61VhkxrVl/XpDkm4vjg5M56sLbueOERDEgthU7I7EYvBLZjxY8L/SyozwA9pJa8DeJcgO70ZUjQe46C5kr3H0xMIUqeghk1KGL0jVG5SHtIFM5bPiGxEUNVVeffEF3tgWvHj4h1WsCBdS9otzfbGOKFTM0fe59uww70/f40o6bGUOlLczglX2HilEZN7VXQYWXeq3IL+hmgG172cbyPeehUL5fhOabkfsULiuAGv2mRU5WYHY5uy2dqeP0ndsDb38OdCKb3teP6M7kHMMqZ5Tttec5bcu9OM78Qq62oPkEOunQQY4tN/hvdv6sgxP6EY/6DDvJu0yT6zkcbQM7UPAaNRPrE+CiF1KoPPbwWP1lsHeOYKIfu9GgdqkTcV9SPVXyGCSuPYMzPY1J6TUeHbL1qV+rXuS2T2R2m3++TwGuvY70ypXXGQtwpfAgLyC3cQacRbgRadQPOhUzwkfb2ROVJ0ii70GMe8Oe1qN87g2wV5p4mkmPFHuOCbm/+P4Dnj/DCnRg0Tr2EEhNhNdxT5P9rmKgqdsQP3JF30DqhxmEuFiAOH6/KtWXEBzbS8CeAUH+aT/kxDNCydNt+VCkjT8Il5pq1bSpQjtgtnbOMaFXIVokPRLvX5LkdPb/j18svhzCP6rnBWeO/K5TFOfqyyEQVRWkd/CCR4TnVDoLXvCrQDGYGsPKxsFe++Ju7KXdOT7CWITymO8CsreKogVKeFMYrkRepC56sa1oFhvP4fKjeqAxJ9G2jlLEV55MxMBmp+aMDGvdj4zFRQZeujX3kIQiIsOchy3oBEhd2qKLMVffwcGDPm2SudMwZy5eDnplmNgmGsJfqQqvxiG87yPJfGvskYejBMacqk3QzCXbrQY4LIG11DX2RZNkPQ8ae9Nr3QY8k1NzlTPK9to7Nqs4zuxVTnu0PhNo64kIcx3aRHt20OLpB6dYz6gBxQQQ0BTbx5pT7faJA37Zdvpd3SS5nNs3BMRNpfsNAp4euherFOO4+8ho75ot9S+PlfO3REEme1RKr9U5ZrY+E2vVi9z2iTg6Fpo/cnAacrOYU2I/2lZdbgBYyCroXCn3UZoZ6pbUo4hqd4TVsiG/av4ErZhKE3KVRd9fUDP+3yop946Y8og3aOw5QNqiwIb+0PIJg6LNknVKH0C7Wgd0vfUOlLd77Mf3dj4H5Ei+s/ixtpYzG2xb8gi1ROmSNUhqsD33EjUDvWMbHyZesg6tMFAcfXgyhB8OTpLvIZ3DJInSVdLAgOcX+QNwAghv5Yo8n0TMoGOx9TlNoGgcjgcn6+BCAIXC1rW6csp2wJeYSkEI1Q020M3HbJfB10J3w1ZiTG5tbG50jhlOkszN03rMBwTyM49R4Fbji9oNIKPa1OtYJZxJZ9uNPLTtVuP7wal5k3C3cy9CJ202l0Mnk+1sF/217ax7jEHtfayhGlx91N3in8nDJ+qU3osgY7dtm12VEoxxBPtoTlayWGcxT30RIX1bzYje598IflTvMwMjXrThn+vWsXK/cYkHm2vXcRx1iZEjqdoxq1jTNhW+P4TCg6/wOz7IYTtKv6Nelt2l8bruWtNzJ0OY+yJKkvXO9nQ73UJMnPaJDG+zfPfgFMPt+NydFuHk/9Huas9DBdajhiK8Ej5OELZbUo9jtuUZVh8y2uZ5ivsCkDh/l7Jm4aJ+ML3Rgo4xbKf8CFH2NNuLPB60iCbNgB//00f6XI9efJke92Lhrs1ezGUuXgx7HU4mYXtwAgwy8pqsLFGiFDJe1VA6xYuG83YWow8TL6wJp61O8I2PmYMfDk6LBDtrmKQ9pTXfp7Hm0oiiRV94K1fkxTxFzxe7QaBHOREdbnVwyo1DNzPaoHUce3FsA5lxS5c6xQgCfmUowRSQWuzC8y0sjfbiYoHGRRuMzSBttgcnAY8I8lXfpo21pzbYoNMj0vxv1AYGNmgyiiDje9XWnEnU53nQ8Yth5XLMSedKdV9D72NvCyB0x9g0ijM5dbf52fqiLtuwYe+4XY2p7UdfGgt1OA60WHOx7mLOom9/nM8e1EcNzH9DBeuHnjxjwDEbtwfl/6MNrLgoYTzA9H1GcKCHVRrxNX7wGwv9+w9Gm5bpV7kez33gWxaeJRvKKx4xb9eQzsfGoNiO+b/mZdRqn8iyZjr0dEdur146BaR8Z2adlcILArPU9gAAQABJREFUiNUeB9iRv4nENJK3HdpDURGmW1IPwU1GWyyus0nxZV2tT2Yv9odzF71PvHtnXxVTczHPSeit8ejffeJ3nCwI50kNeVZ8L4r59po1ZtfujM2RBJ89trP2W/XHPLyVlwNcSfz+wWm2fyxLS6uhnJcas3Ppx3tdMI5tfZh4YU04bdVx6+CEgqs/eBtFqH8AF4GwWD2oiilGOU42eBPf9N3EX0IGnNHOkV/34mG0ryXwiXhpI8zeoDhAEYL08QZv5Yq8qNXocWGjR3kgMIdxpPCvkodbzJ08qcvWMcyB47EdkLXY1nOCYchQCB3cCL7nMoOmvtaNeWogsWNsNpABO6GMZ1R3RCfI7SFro7JgtK87ddjjafsF+CfP2hvzHp8D6vGTg5a5YIu6VPnr6TX+ukhadx3UmZzRj7lZ+6KutlnqytqlO5ZQGX1ueaM81jLnCG08YQcaFxl2WYuA8N9a/tAC9IgVvtwO1hkYW0o9C2JLDO5j1EPLC2rNXMCu50Vt17QitUYOzxmrOFrztdScm4fRMW8Xjc/HRyS2h1vpJa/tE/mf52DYjYdxMAK26uxluVNQRIdimB7NW0UCZeFvzSe0a13NVOfvmu2x1jbAXKrHZg9KdZ6YJW8v1ixzj9b3HosjpgjjICKCA22Z2g4v9zTubd3CKh5pa53wFzjle1OzsFLfjbLlhuwtXxnnW/o4xLak17iH/Bd3Mt/jT2rS3bUof7sagrIluz5P7M2P5xkt6gGFZs/TgxP+AC4PS/H3nKx0UPzmL54syOB7iMtYzGE+esLJy7bsyIeB0kSINhbLvnzU7l65Ies+BC0vt+HTXjfODUJ4q1DI0/HGJEZ2IPd+RoN5sV9H83ni3+CKA5TNFZypvwD14sC8oAus+NfD60IDLwDgpx6Jg76zU5iaBhqjGIswrU2m+HOMFow9EBFe5SEBBPwgFUWSiMaeZ2pUl/YavhhUkiMDKvk0aKzbbSTzOHutrnSvJpK+6JCtzUaTWwePTNQCRVlH/vZlEeS+XD2w1K/mZmUL3qP2WzvOIea2xxBzEns55wdy0Ljj1BMT+4KtPwOI+oj4mkYfD7MNIl61Y74jMonPedFnXIwVjkRT4g7orW91vEcrYuJkjFvJHsZFfgKiqTQUMWJU1nbko/Zb9eicLefrvr+Ym0jAuGt3lunFD8moOXHjPWOAp3xReTlZdXcZOSPTX5S7bLtWRHaYobH61/rXuESEttLXrFda+l69ks88nUOfUy72WTH7nIvYb+zgZHzy2rdVieGw1rgfUI/Q0PlgP91Hq0/Iwz4zbQD1wyHkyf8f5ws+MRTU6zBGMN/wMY/xDV2toZnslK7vOK1M57zM/ZVN8AbN42nZBxkkRGQ7CLsDXzbeGDJaY6B/dsdJD05eJF6A62/VgzcUoz+GeHqk46STzxYAQh8sdA4knN1/3QnvPtANC19eg+M0Fp6OntDk6ZiZR8iw9KmDHuYAdwJxWPpgF0HxRR69cRAXLXDw9E3DiuGz97gxnpjG53zutYqFgvIHagREtt6DH/DQQg//vUXfBe0L1pkTSU3GBV17GPb8cLyZWX3qZ+s+S+gEx6hcxVRaddb05G+t9J1xV2NlMJwP9M/GfCYnJia0fTI3y2lMk8UENdhT1Fg69IP4jmDP5G17PSdto9Sj9is78nROOQ9om465iH5caMwxBUZzgT3zIKXP1hwp+hy54d9/l8pxiR3zozZn+DPuXt+R97eDPbPL/MjNNv4VgI5vJX97HoufrW/Rt93qmHuekAM+A9K92Au99fhjnRX/dgSPGXSsV+0lwpjiq4a7eoTpltSuyUWB4ih90VzUep6EeUIyU1AjDRxiae5Jo/Wn2aDF9Qltwi7sWW9oaaPhQHd1cCJW2MBiPDitsBSXfi0Kf99Ai0fEpprfLc04Xx0F5xHLumhxgvxtH6qp9FZTOUvNFbwandJEZbtjAD82FubRx4X+2cHpz//qb+zL9KwsLvwdJ7geCqdi6lF2QpuHoq1HHZyEV0IlOBjlXacrNJicuboOe645OBb15O+F4nx7iTa0LAPe90VeUO/9oOSHJpszbBrfTD9xwUpnGG5rhcCftvjByf2ED8Jyo2FL/l7r+OancuvFh4LjH7cEDUfW2hMP1s6mNRmj8Ra4Ow9ibcW0YbunMco9w+l8H3uLNW4nI+ZK+2PzmOOVXx2n0me6K3nz4g2m8xI5WU8lZ7utX0mNPtvXUU6wYHrVHEXjFdMFe6S6K0uMPXmHXBpap2SSxzaC8oHE+rdEYMyYCW+D8HFGLsDIofg69W6t0+hRIXoIjTXDtiVBdQ2gPw/Goq09wSN3o4iEsazsXM1fZkQwad9awDJuClY2qvsoHTlg7e+jHNbevtkLJZqhoB/JSc855hHh4YXPMVx44Vrk+L0aTeCyUf3Neh3zHReRI4/0kUTtuBoz9SpgxVE6gthytsFxtDVhW5UNp2xEQl7sN+xZheQ1AFrWjdNmy7Z+CMzrmawS7BKwCQy1R33FHrd3eAp/sIkfGscPkOUahbFPSYJPPFCvqGGvW/RZ0C79NF6Yz1dG0zNnY55yQz8xH+yhHaxUcEirVSnu+Cz5KaGoSouh+7AX/PckZm0ZzWtXaC+/HOLP//pv7JMbURx95+LCHScgVjw9yk5m8zwy6ONRs6DyEI2vIY+igORMf7Su0GC5MFX5aPlsb0LO7iIEd0R+tPbq/5Nrjf/i5DR2LN5v/NCE9pt333yDu059uxrAvsBND/PBn/Ly8OS8aZi6AdFeN4me18kQycV/OzBxU+IvmqNF8r0IGVO2xGZbqNPGlJko8R4xznEkfZoJqddGKTsfRoylpUfUBv1I+SPLYj3vOa0xm8LuvKbxmbx9qM/OzTSdrb6krs42jXOe2x0F0jbmUGu1D1GVb43s77eB2Lj7mjuSA9MWNQWUmLMFzwT4l/9jS/Z8xHxgzD4y42Euou+/yDoGBwjDcjlsfOIA5Nal697g05+sn4jLWPkoIuMONiDdLj6cE8xUFYsaiyu0YIOdDGkQbz5I+jgckhJrMT7pnpAxrk0gJ1ajOPI58j5uzwY8jblHJIJmLsLDvIMdSqyB6IPXxsgvUVlTsEQ1qQy840djHul17R1phYx1fq6ZI1iGwNGdo6iGQvm6VeFlWlFmoyPZrLvtj3V6hNXjb8oqwEx8fu1lxIKM+0bgxl4QMYD+5ptv3n2wZ/wgOK5pIGUUak+atcXf20Qf17FeaUZTD9ci/rRP3ERrseCHvOlgMxcYB2QYhzcxynlMUPmUHxj/3mN3LExKGm7eKvcAB37ka2BlZw5pqbkf9gpywVuitp7ie11CFPWJorh4cEJ9RKHb9XfS8+FpUTgVW0fRE9W8Ln2LrWZB5T2eplremC09oyo0U5wniraqQ94zbUc8oaSjPTn5Ok7SWNCgqQNkbAzYXLzFRlObTczht3KICtvE8ALZ4gEzNpw+fO0tKsYFGzx888FHALkxff550Sg+fs0xdInJWvMW2xInwmpw9dCxr+Tk6TzTF2V7bdmYE8/yVWcV9B7yd8s/Gn+N2UKc53OO+kyu+u2z53FnStVM6MvJL5v2WayJaMzSrT1oVI23yJG36nmdrAQv4q1yvscjny1C4DjRjjRlza/Fh/q3wuAPYNjG70PFwDCX0PnM13XuI9Z332ihxtbJyL3GZgqxzkI7gEUXjEG/p691J1sIlnOHgLMAFYY5KcALxBDTBf1Z5RGfM8ZT/cyDYmhOtA5Up+lxLpmPaCFboCH9JtE69E+3NOgJNWIeKVvZXXowbiifzwmiz8cSX+TUO2lHmLF3YnoUiJhuMbccUTdyGMVQJ/uWy/VmWJHfOLzEYYa8aCHnHLCNaOya5utv3n399ddxgPLrm28qUERCW20RPesLLfctGKIPXVybfP6ZXZd8Hocmp/16hbGgLVdOMCeskWjha9T7PvTGPEfEHNdu/JKQnbfLXdP0cCgX+LH+aDXNB9nX25OJIn4Gwhx5azw7X6Pg3N3OHae/tRxaQSzuOqFIXGZEnOKjX8FXbIyiF4lRpTbQNQsqF1UnVbYt6ll7r4/wzHpwP+vWEGbBzb5GvDE1J3ty8tlGuNbz/x69L37F7IPTh3df49BkG04cpOxje9bHwck3EGvp2fFREABia2RsBOPmwzmHKh9aWKD5/AwHJduQPrcWT/S9zYNUHJ78QzMORWxsqngTHVZNBEOX1XrM1dsnss5dgePa1w5J2TAnV52FlzP471S+SmeN1xOPedwPkXO+r7GVYH7j8cibzEEwW1fyJkafs9KIJ6HNit7fQ1HlEVElr6JXc7LiYRnHOsQa13lk7aON+Yi5iLWHOPt3nCgHF+P/UHew7ae/efHi7w2QQiF9wnf8sAS+cUESLWMKecQHumOMe+jo49Fx6xiUdlPXjReTSY8kQhs2E2dkzBa42nSN0vq8jTGc6x1pPOL3CO+WjPkQI81JsE1pyzRRMD3zTsb8RJ8GbAvJCdag155x2IbW2euIeap9QV3n8dp8eOKYgimElE3cs+4Y5tg7s90JZDLbYm45YTKMoDZIwq2twmawpEG1yC32Dz6Qd32SzzlA+/VXX737yp48PPm1jMdkvux/2wcduUAc2+sX1qofmvya5It3X6D9wp52iPochyjrFyYDypajQ1wR4yPvZxPod9TFGOcH8z7zqy82dclewqsEs7jWh4tDjW3Ya6AN9xA1tB3bXtJH5Cj2NbwpxR2nUD0+OJmvODyh4I32gplbFlFGWvH1CHuSmlfRwaxmQeWJ543yg445VL7qn9Mr+wod5o9Dh/MBLFgDpMgHfqgO7l1uL1j4zCXbVPcNiQelr/FTmm++ts0m7zzZwQm3u+PZd5BoyzYwezPgBsEWeqDpG60+eYfpiy++iM3IW9uc0MrhSW95Ehut/mSIMZnDIpVY5UzlpHuee1yU7bVhY1LPebR7ur89/M5zjX8aHOcdbKXRx/wdPTjPZzqjHLN8daZHy6u9CtvDf1tfV2Na6XUdt3TNw5qETrc2O7WMdB6C5tzFPh7zmHRefOBiJS5c4ie/oHutInG9D/CihHuBtqiZue/7h89xzLXWFWmOx0eVYwNdD+NZBNUlYaPA0KcHeMHsH90wB5PqSZfxnaidiiPnp2qvV9jkRlebCJffIseMI/lJexu1gGDrbT0j51pDyzqEF9D+SarUO2ru5VzGMIEqjtKPzYX6SXpbjlMEz3bNQbm970zHvI6k94xRfuRraxNrNxC4ZyDH9I+WT/qJ2ohPz3z55ZfvvvoSh6cvfQ/C3uN3vG3sWxwihAwJ0vmMWN779QiuSfD80Rc/iusTHJ7sGuWLz7/wC+Q5JiATK+Ibsdvz94Ni/jVajk95Ay2TOa/tQe9yhwXMtg03nKOya7NLFOtgo0wf3qIuoZH7mdH3Dk5W5Pxq8trw7BDlt9chA9MebOfFHL791YNwZX8hD8ak2bZWUOSzxV7d9Kx9pb+yHybrGfgBqKMZIFNn4LVqXQZQznjnlibYlHBwioucuND5yjYZbDR+0YP2K7vwsRa63MRgX3OXYNoHzX5MdWx6iINPXgz5xsMN6Ue2If3INidvc3PKwxMqMsaBN8zA9zti4ovj8tZz1UllTgadnQ7zBTHHsaNa7Cotc+S+7jgslO8jETmu8csQNI9Ki8phfrWO1Ib0em6YeLbUfk0b9ZxYtQe9BvvVKKucxzyNuYEeddkiFuY35oF9rL2gQ94/vIhfyo594uuv46e+ffHyle0z4x5Cv7yzzB+UsMUeQZo63Dfi9y3zNzez+Bi7jw5jwiAmGVh8UJ99tByz8nzA+SYVmYuaX+oOhtvOyudW6xrnEf/XkA+0ekstpbGaUsGZo3LMSGhHHqa6M9FoES6i3rLmTAMHJox9WIsVzZq4lveV9xFPcYK+F0ejqS+hx2S2+isod0MHbO8B6/hny/163Pe1ZxPLFntL7C/0Bf98khe1EHq4RvnNb37jTz9A5d0n/AAYBUN/wLDewAtZzwV0uN/8yK5J8Pzxj39cLQ9SaKE3xxXxRX0wRvpn7N+XNuZjNY8n9R+GPszXvF1yfth2Brcck61CbpNbFOZ381CWyZkn1zXZpYNT1mFsavmRvSiYPDQ5L4rXB6kjLTojgVOPMvtF4xePM3xvVc5hJS8HusWh3o3WQFaJYyg3kI5Va3CjWrzpBG81YkjI7xZB43+0IyI+TsMvhbBDkx2QcGjCLW48v0TrP7mJn97gcMXnvAlc2Qy4qXAjQsuNhxuSb0q2MUXLn+rEnScUIDCQnvh4nt0Fw6YqmyE2QuYp2nF2mJc5D8/3Jz8XHI0W/z97Z9ogOY6b6Zrp+z6n2x57v9v//+/s8WXd5/RZdx/jxQPwJUCKCikys6qrZkeZCpIgCIAghCBISSEJclVNkN10TWAX/XTFCdmh1S6tgewKFrhBtF4/ezZT4bIzMdleGqMSov5kB0R0Nz2g46wPcHZpP5uKqt89DjFGo9y6NmlTaYQ+11/8GhtSbstjwhK3+8YtMkxafvnFJjB2uj+xMgsw2uGWfPAjKGLVlttf8Alebosp8hGeNpwIouJWGfyIezijo7557yirP1Ehlp56nzsk9YEP4T+PWsC/6Khwwe4qTS5ris+G9/b6Su6uwoqwK0KrsC4MvegFxoVCAGR7pJ1kx8XXN/uzFm5rKrdUg9XbpsieC14T8KC4MJehBbLOB7Ld/LC2W5I3J3expTTy3Bi6NEfqqfoLm2jj3RZs1aVqL8DCPjJoYo7y5MkTOx/b+dR8EH7ol7hzxmix2OrKtu67PTGvMf8VcGwtF3k1R8HfvPHGG5vztdcIomwHygKqOq+RrFU+n7O0+e/KfmqbFy1/U3nnK+K6wCmu+31dME5j7cxvrL1FaeLTKTV4148JFDKRWiX/Nm/Vtb28Ve9//hfPOGGMOsnE5DyNOwzTwG6gLgAFHUWzqZQmnbiTQrq3U72IkAILuDpSa2+UN3LuciZ2XYwbEZ0aZaemiigeuTyJlqkLHXJDQhWNOpOdCIb+3ra2I2BisvPUnM1TczxPzQn9Yis4sYJcH7jM2/d84BtNGdGc4liAkeKIOGvAVB3T6+akXn8jVnbYfQKXtpzw0sp25MOmYE/Z8eixD/xgKE3CZ5WkJTjrC2wSsyIlNGSvdVM+UaeKOypOdjJTTflSkL0+C1dppVXtZgWnXjjrSyP5j/UHHajMLuZ36HS2O/UXaT6bypV+V5xinEa5aav2SmkrnWoc5jRwLGBqt/fiIzRRefrUJi92PrUJjPuTp7YAY4sxWnyRbPCbgyX8gk5fRLFbY1R+ZQqoXvmz3SbT5PdekTfigsHH+2TweggnYVEvewt4H+iOVqb3BtvWd8QbZ0Y598k8C9455pXvoDoZBQhLEQI4tOldirGJL6KwucBL+ytTABcBduYF3Ba1SOYw+9BYLcXYlc/JLj8kC5WD/Atst6kGlxwLtJOg1oOup5PNboLWFFzlvwmZa9pUk1m1m/WH7pEPuOYoagdcJzBwOOVXuFPm8ePH9x49euQBlM9hbD4DnF0nAiTZiy/2mE/Cb5EPOWIOoXmK/M5bb711T+ebb755j5M5C/X4KM1paKdDsom2ytQ/T/1Lnpukt5VTuob3fG3vyxPX+6V61dXrtPJS/W1T0ax8BprtmnU9uW1S2/wZdnxt4GRWyL8ZZBg2RpN5iBuchEOZotkUVN6kpIYP7TgEV5kUWMBvO/CdqpEzdYhsB3cxOuQWmezUkojzt5ro2RZF8ExDXrWbG8op+fNN7Dix02QB0xO2us3Z4Hwe2/no4aM+IaqTn3A0Jgdyu9GEwaBz6d1lsbICJw+YmPzYSs0br9sqTnNAckpvvMnKjjklS32V2RzTq7YS7do3Om5H/mO94ej6lylG5GLQW/EPWBrYVmd3BzFe7TAxLx6JWdESKt3V2iGfqAP4zgoH8qd8KUj0eWyYemA8VtKtHaTsijQuCaVrGkDHS2fJbNX4ALagk122tov6A4rPqjrH5DKHFR4wnbQmL32SxjgwBvEMQZQjz24TPiF8x9NY7bWJC5OXx0/Mf1ga9ew45ctnJCW86qTD/YRNPl5vE5K+oIKv8AWVSDWhwUf4b8q1hRlGRH3x0Wl9wwAp+/DZhxXdT7gcgRgiWYdziDMXlbSpsJoXxm1TF+Ykkbvnr3GvAqCrflSEDfsR0NsV/cYoyB/UNJjEFCAZuq0Zc9Lu642NbBK5Rq5N0iWw1V1IsB2OLvsOrvCoRpbbHaV9dv2YZGnmyEdtHV/9O0I+Zn8W45J6VrpD9+iXuri91wIeiBgMqWfdB148akCA9PDhg3sPHjx038PuE7furQIngi1uJeZ5KPwYdDjxQZz4ljdsAZfA6O2337737rvv3nvnnXc8gFLg5As7Pkdhp5x2zFXigBZzrBo4qa72QbAXMb2tnNVEy/T+YlfR2+Uj6+t1mtDLra+prTQrr4FGGKXbZeDg19yILXAyzNbwP//946EZhT/9zy9sx4ldUONEv90I63NNuq3KUo769STP999Fs8gSh3JjWnGNvZBLGrDbDnwnaOT8sp1YVcV23JtmDgwm3Ma6t7CUaJm60CG3y5TSktNFXQOn2GmK1eIHDx7cu3///r0H9x8MTgiHgyPygXa6xhtnYwaiAEmGQzXyaEKEM3qdLe7XX7v3lgVNb5lDImjCMXGS9+DJ6thtes3wWWV2OkbfndHVgROts+9O7M4/kn5cJxqFmVGz/EQPhPIw9aHNDm33+Mx8j8qF6AHJlC/bJCz5VFjms417gVpsTeMy4EsMEwsEpUm95gJXkIPLSGjDF3AHDhlTxEK+RDlQVCI+81zqd49VyNp8+IBEW/1GW9AJXOlcX/yZMiHIW3d/YWfJJiePbXfp0cOHfj589NAmMExeHvXV4N7eBqj7MuOtiQqOIm6//btPVnxhxYIl+YW33zL/8DYrv2+3CU2s+L6C7ym70t4f66H3xftG/9pYYVP2B3+XoYGp96GWvbmGtoM/Bk6DGu+scMZ+1Z07Y9r6u+I98KoIRT2yFcgIv+u86Zhvgsha6pmaAjDtdlwXKHwAI2Z8LwVORZTWMBI+J5JZschJZvVhgeIg4VGofd/DvwzfSH8ZvdXOrc71M7Cq/KeY3QKpmkySGX12wrGfsAvaaScJHdcxkfzAOZmPaAGHOct9m7M8Mh+kW/Y8cGJXyXeW4Ba7VNxO/IvthsftxtD6e3/lOLfhKUB677337r3//vv33nv/PZu3xO5T7jhxV0zcYswikA7kQn75Pefq/aB/50ZLtP6o9LZyVhudr+29PqG3y0fWhxpDlwm93Pp8rdnDgDyWZJAug33Ibt3PAQQ27DgtAqf/9cXX1l9jZMES/c48X7Jh3EqRZRCpyVODoTQr5cY0cQUfemiFgN924DtVI2dqENkOnlTZ4TfKHBiM8zfCl3uc9ZJX7dC6DnJc0H+3B7Z57iDeQvOr7zT5PcKPn9z7+eef7/300492/uyTIHagtHqjwEnGwkqLJkBD8NQY+uvGtYpjKzg4HYImJkWs4uiMwMm2wt+ywMkCpnqrHqSwq/WtethYjFHIhJYM0LvcM02iu06S/pHN0YfN8VIGTvSCvpiFuVGOlln1EPXqtfofYyaoUqmHNHQlPLUTZqZVp2qftetclW+Jcejpx/4uaTwn4GFfmtcYxyGEq0GT8qHD0Ht88cfKKT6cshZPuM3liQVHTFAIlB74pOW+r/qy8suOE2MzjEkTAr+E3H7aFwx0NflhcYXbdd+03Wf5BlZ8dWpCw4ovfoIJi/8WnOhZ16QTUnwSh2SBd/y3tMk02FE6D2/Lx7MOnAY9da7rTBN5XXlDaO2/SAx8qoDtcly3YVwbhZ5xT+HAOjYAokzgpEbR1iynjdk6cJJHUBqt7LMARoodY5lJuZbVHSg8AKv+d8RTmSLsKXx4bhEn1W0RMPh2VPkFe1bpStZLOquy1cAp5Itb6SRr+KYInHSbMPMWThZ+43mnJ77bzbzB+ZpAqI9b93gMgXY1wGHnyO+MsYVb/MxbNhchaPrwww/vffDBBwGz4An/pB0n8MMPxTOXyEcQxo4ZtGt/6V/to/ryIqa3lbOa6eHXaVNA1dVaJ0m1ypfQdaubQMNS1HLBoV10PqYtSOK7xeVinB0W191//vtHItTTP0XgZMbiQVI4kwii1oETLbtQLo8Rt5RsXt5gqURa8o6rMnj1CLh/HnuT2nA33wdowdLF3215RcXKw5TmDAjHQoQOq3WSWe1Cu0EQmTUJwoHEhOW3WDlut9r89NNP93748Yd7P/34k9+2p8BJEycmI77aS2qOQ+f6t1hy+1u32fhKcgueFED5hKjdrueOyHecxlUcd4BM4JojZCXSrakNhBsx466yd7kVovvP6BMeXDSXyccwT/LcKHA6YHRZjEXtoLBFfYBkV1FSn0dZKs5aH8Frz0miI10OgRMTqD2hqk7Vbg9X8CqjYEN60dOP/R3aPffCkc2FrOtxoG0941lEuhBjEHrnmo+TnSaea2KFl+eWfskdJlvl/fn+z/fu26SFQIrnnPArLKrgG2JhJVK+UPyvCPWL0XpiCzYEW+By/eMrCJzwDwRNrPxyuu+w3Wn8hSYttPG+kJr8Gl9S6mQjso/O35DJ6+jfS/kN1arCZhNTLe4mlVxnqRXVnW1yiCcdVcSBTxUy1DFMCtUubIqSK9fB0rHGxWshbqfr1BmNoSks/BZR44sfgH2kreyUDdbSnkyAs2M2ytapLTOIW9WxRDoNnAQ+aLfi6+q72C61UPt5sckdVK5k3fP7sKuy8eIGFncTf9ST/BJ+hgCIhV3mLQRO9x/c9+e0CZ7AE13ujGGRhcGjHb/7JDrwx1cQPLHjxGMDb9p8BN/jO07meygD16INPirmPnGrHnw4kbnSxUrRheSA14t+3FbWOloXv06LInKsC3CThTJ6zorKK6G3ywVNUVZaaDYBXE98p5k89skg+zi7nTUZ14HTl98wk3XDmH/HSTtNSgvbyDZ5VhcYYsQJaskPo9AkC2rjJx0YIa20hi5RAQpd6S7izSuODEZGXObYnZnE6qkGtAtuqEXBqLxf2OaYInCy1WNuubGJC0HSjz/+eO/777/3lDIneLTjxFlwKx0THJ04HZ3IS14pu078eJxw/bkmmxBx683bfgtOTIR4OQT3FnOLXjgkBU7NGVnA5A6pBU4uT3NKKKTriUI7Qi/SjqB76eIC2UNdwEP1+7yQNw6lViqDKvkXpKdm4qF0t8VBheRo6QG5lXzA9uAjc/ECGnY01lMKAcLOwk4Dd4spSNVpV68qd9KVvAPqWR8zNPojCugevvsDd6mvGrtIx8AJqlxrdfWUFdSYqNizkOYvHtgEJXaabKW3TVoIqH6334WjrT+bZM8oMcHg2ue2FvkFeDJ2LIIwwbn/M7fZ3HcYXzr4C93K++6779mK7/s+gdEuFHXyJ/I7pBzqMykw7CJsKlat9c0Q/R51Z5YJstOpnw54QT7Uv7sTJ76/g170mnzYVuNSL66Gktdew/E26Q+qbVaZlffUkWwMmh1D2k/jpzFbphIrWUcuxe/jCG2N+YyusmRSuaaDHmrFH5Bf6xxBRjveE+1SP/fanIPP/KtNJYWV/FmLzQUdBR5+PTqCDWwbW2hQL3+EL8KHEDgxd2HHCRgnuPIPWmiBx99tAUg7QpJJeK+99krcMtxuF9ZuNwGTdppWvkftERea+E5LQnplovjCf2ocbirocBnOprFDVOOwUz2Aw0yCcOU1IN24kFbXvNGWUrNT11PZccJpKkCXDv/j3z/ctP/T/7bAqe82eQCFUYfhKGBSumndjWpTYwCUopP6pv0+qWllqhaHhB6rLrcZcVtJTZQukW4HPDIY9aXMsTtDxJJonmpAO9QQ2kUrA/OLmp0bG6gInH7zN2EpSPrhhx/ufffddx48PbaV4CcWUP1qt+ZIDr+dxp1IeeuVTXTkOOKh7Zi0xKpz7kwxIeLFEG+2VWPuG2b12N+oZ88/vWZ0FTT92VZ/4iirOC1o0q4TfQr9aRKpFq2pJUdfmokpDSXkmpz0s9dmHOfGywct8kft9cUxXhd73M7CXYOBLEPaaTrLp7JSNatOTbBIpV++VJSvGCGA26dVBw64Fafma13NV5xtfpZ3g+H8qjJqfoP9hwHcrtv1vifEXl+9R9aW+jgzcPLYwQiif01g2Gni1ISEnaX7bZeJnaaff47VXvARid1nFkb8FlybgMQrfPP1vfAUbfwOPodJDzyAc3CrzJvmH7Tqy+0ysfP0ri24vOOBk1Z95Xtopz6rX9YTtyH6EzoLnxD189jKjsLorATJF+ZAt3H936VI6jM0s7/Bq/GpF2FDietzlKPqVOMARs+b/H0MnAEAOuUdc+6Qh/bFE6J2pLRRHgBF5s6/oc3Jcf3c4o8puz1OnT6SvUr6bOwnxq7yIb+yjxVsbqe2Gn+NsvoOnKCH019qZUETryD/0e6SqYETdeiGBRteNKXAh/mFP/PU/Iyr02jKh7Dr9DpzEb9l7y3b+Q4/hg8DHotA7KazEKR5Tiwcw49T/VSqPq36+iLCrrGplfzVRPuUfYVYYFVXBbzMSr7KZ4l4I6BbWmsJhwWXuJDcd3m2jTu+DNmwJcl4i8CpbrmqJ3axNXmKf1OlpXKmuihVpkYw0Gu+NJ+y6kSAs83hYAlV6UT3LopHMqi/EkGpeKscqX36f4O6jvkSEnYzCgMQ0P7a3qrHK4Qf2lv0eF6B3abvvvvbve+/+95XcqjDSXkQZAbBSxx4wQMBT3UmBEzufMzx6NknnBQwv3+Y56HcKcUbsuJtWfbck1alWZm2bfJXGp5vdzaxkT9v3Qh76rfqqWuWhq5kWFkxjn/Cx1xX0gg+WTrHA2Lio/Qkg47G2Lbx7bCbZpChyDGQHQrBsYE8cY9hkrRUEkRxbBt14qNULRjbmg97jeuCfKlMNM9FVdRfQBtazfIOlRQ6u1UfVrANhWcCGDm3kim7wrvoZKzCa5UiVUGmznVBvV27nRI41h69c43VW3oJmHxn2p5vImDqwVPbfWIiwcSCSYd2hwh+NGnR5AS+CpzS53zngZmePfA20PHbZT7wXSeCJ26dIZiCxzZwij5hg/CgX3QG22CBD7hrJTINx4FU2BG4pK3kaXwErACeUzbkayI/E5557WQfB36JINUsr8vQueQtejViXmofThtYZKxPgQt3P40f9qczXxBhtcAb3kYZVLexU5J8K3bwA7KuD9zAss9Er0SGfEfpmaH69gXv20jmWbESlxgflfbStRQrv72CQXWGq6zUR7XYQw2cfHHX/NIPP/zoCzDshP9ib8xj95t5CMEOvoRFHF+ktTw2NB++U2D2iI8i0GLuwh0wtOEWPW7h005TzGu0WBx2TLu0f2w3OGQfZo4vbvncuG91qB5ljQWRa/MQak9vo6fk18lNmZNCyGnQuu9ULKg7OfsglR9rKbqTLUDmysCJ26rC8SmFSB7GsckjA8s6cnzhuXRD2twvCHaoPkp7n6MRbNtcHDChK91jcgv4Rf5GV31GhJUYgkXaPm3w/DAd+xeJ6zoNAJ3DVz9+y4oNW9ycBE5/+/Zb33XSb7EwyeH2PCYquoWG5w3idpz4QTgFTu5UzOlk0CQHYwGVyeUTHnNemvi4MzK64JPnOYg0PLvwrCP0AXnzjOAp6qKr0kP0O/sataY76USATbpts0G5ADimXxuLl9Jad5Snp0NvjxpcqIf/JIOT3tIXJNRoJf+PtDLo9RXoefFROiLID+QYBx7lvaPWXUAbmh+O08BOva4kVrBaf/f5kWOWVn2pelA9qfJVOsH0cgjqBNM4sAvERISAJm7n5c159lxT22nidj2CH2B6IxXB0nvvxbNJ8Ta8WCyRT4CHAid8zrfmbzihAw92tvAF4BOAETBxu96HH35kJztP77v/YEIUNMPHQFf2p2cumcGgE9mK+he4oUvB6L/wmtcBZMdgFAF6jp9VvmfBNm0m+xl6bNwSoasi9ZQS7em0DUokZYyiX4xBjAPc/fQxC39fgybZJBxTUkpxDDIVhKEvQi4+dF0fiC7ZJYROj2unFJ5Btg7DM2bVpT+2vbUkw1g0akewbX1cu4w2dZz4DQVO+AkCJ3wGu9Y//PC9v1mPO2nwWwRABD6c777Ly6je9TmLgqTaN7ddk5NUO0n4IHwMcxVoySf5wjALxCw4WfejTQ2cTOI2WErDNta66sp+QTJVL2uRysW1QKi184tfFugOkp726vfgldceTsCPdF8ptbw3qfDGQaRi8H38MQS3IUtz/nrngZMJ0ELR6gyy40eBkyTPFtucvkRVs99md9CsycqIFqoUkyvTRumAoPWku/lVLyos8gjeLl6nHQxS14Hlt+qZg+Hh7PseON33gOnbbyJw4rWdT+21nejHV4DNiTCRift+3/G3X3HrHY4FZxMvjYig6RVWbXz3iJRdKMYjHv6m7lVe5QkOqXapLGWnSY5NF525zO44kSXuHwaKS/Wu+ie9cmh21CB5rMYya0NHWVYudKXSKg2nuKrZg+3x2sOf4ccyzS3W5YUcnXTPdNuDRu+rVbtVJpqziPoJ6DULXg7ngzGNQowvZQAJj9r6OdapfcVY5S/bQGsxiLrqywq24raF3bRlegDRNErxL4BfC3wgvsaGVA69Iyrj7floOMDJW6IJ6+/mH/ABnAQ2/myT+QrfbbLb9XgFOTtR3NLLQgrPLYaPiBc66HY96vhC0ckEiDGG5t/+9jc/9WYsbt8LUbjl7y3fZSJ4+uijj+59/PHHXsYfQXMbOGV/NdZuS21MBZt1EvBqU5YPjZoog0G4bM/ro19vPirPhut47URfpSfve+++ZVo+rs9RnqrTbG84rROeYF8YWLNLUk6xIIW2TtmhUqvouFCpx0YmiGHeFannG3S3viOavGsKidFyJ2ht2lwBqON0UqIrqK9Rj/u+lmQzFkb+EkzjPUsRbcIeYhFegdPfzSc9sWew49lsFnwJnnhWkqCJk3kJC7z4JHaqOfEnPr/QDlFh6D3xMQwbxbcQLClgoqxTk2P0E3Ob8DuQW/UFvGNdFmH+wOyxnLpa10LWWs3h1pgJXdlG1u7nKq99LGrWdjq2EbWW9l2nEauTwi9oXFsa9hBBNK1uueMU960ne2PYZMtXjGft8Y7TnhISTp/i6BkBrkuPmh/VX+ImHaCMlq/odql5USyUCmcsC1u1kAy6aZQwoVWcvFaYXScPnMzh/GzndzaJYfWXlN934vcOQGeS8qad/pwBTsjeNIMT4nklnnvSKo0cCykvhvAyTsoDopw4cdueOx+Hm+wtsHIH4/kmpyVyRPryVLn31HC89zTxL92YlB07gKAQtkLj7ZF2tK0bISZBk3WEH5UWA3+xieRUegn5DO0JxzpxiXLXqSE53oR8SZcp6cTTKmSjGtsoxxdmthtzdSJR8yPWWOryj+DLpS6uOqv0crNae30LWq/GIij5Z350Vq4H5LU6v5agwZgyMOBbHX6hU3aw6hvUcLU48ZvdzsuONC+QYXcJH0HQFAHUfV/tjTft/WoTlHiNOLtNvNSBEx+hRRf3CVzvdmq8CZzi9uB4rpLJDwGUVpbxO5r0fPLJJ/c4eUUwt/jyamAmNbo1OFaAoy+9z3TZfUJ7OUQYaOqkay4yrTraoCw/lFKo+ai9u09kvztqlyklo9W1M8jhXW79bsgav8qj6tztTZVd5wawfBRjnDBKrFEHXDRe8vdKBV/xVvuz6SDfhUYu2aCMNXLgrevuDFpMLzV2Z9SXhEJP13NbjdEerI8r15b6CEuK7dpVqp1qdp3YccJ/sNDiz2Xb4wW84ZM6Aid8hxZ58RmcLOpo8cbtlV4zvroI4AfMUvD0e3E5r8F/6S6a6mtoFIcWhVQm9bmO2/71uqx0nkf++NrQICFNzYd0CbFr+8S141Sk/yBx+jN5HTVB73u6F5UpnQIn1YqM60lj2lK3KbMb6fA/d18OgaHxQoh+ax42V2/Vi0nsplt9x2nVmZvuOAUtCb3heQPAilZXIPRW4l/DpxrMQFikW58OWK3EkLNBHPIc0Z8YWG1p81YaX+21SRGrv99+/Y2n3J6jhyy15R23zXDrzAd9NYdnneSM0sGEc1FZ9dKnpxibBUkpV8gnJxMVcWny5SmHVPvlOPYBPc6Y8DWbM9IGFcrFVHJVJCNnx9n22VK6Tshd587JFJq7jvdRn6PeaJoIK/UctV9LkwGSJuzSodJVu+HSma6dFT6w1Tjv4Q7wTp9en9V/UKjYNT/QXxYCu35WtFVf0AkndXGST0e+0mfiNk7WFjxOfETcDtN8hL0IgjdYcWvew0fxBqv4IcnfbZLyji+svG8/GBmB07t2m0wETqz+KnCqcnObDQ91EzCxYIP/IZCCL76HdgROLNh8+umnfrLzFM8evNEDJ2h7P2xs1B8Wc9AFB30JOKXEoVQP2TfTp9RVH3xDrfna8vb5m107N+WbligdVUp1jLoS6XtDTt1kK+kdyNC+KdUT+1BdpDZG5XpCu9BWsKTUYW2XMjnePCcZjiicxYNO6+YRyZvXF9PL0bs5uTMtNUZncCvOyj72YMDrKZ1XmPIKnEiZt3Crr3atCZ6Yx+hWPp7F5sdr8R3sVHMSSGlOIj6eIgMdIG12BpxTcxf5L5V1F03Qwb+E7SIr8ukIGtBKP6y6FzGN/lySrBjiwh9mrV3bJy8KdHaT43wrrphLVw2URK2ljh551biMjYzrqdkIg0/Zz3OBk7FrQRMBlLZT9WyTJrsbpdx54BS9iXG6pKCNJBcBKyNaKfEikaPKYjSm+gFbpSn4HXAoCK9WyNkAS8PUAP/ZJyeszBwHTvd8AsRkhUmMfhSObXBWcFhRTmeSu0pyNDXV+FSji90myVXuEbVOoQ8Lg9wR8TY97ErOs/ZLRhv9ZITsbO3BOzpW4yxZj9pSH7iBmbo+0/ImOKvRXtEZLHWFMMDO9Lf303VrzSdRztAYmHohJ6n/DJyqdkK59XOo7YORULmSsOe8pqp9V/vUdQOFmgeH64wAJm6HeWyTknjlLz+QzS16rPSy24QYnExQPvzwfV9UwS/wTAE+49VX421UWhCpsrBqrOcrv/7663ucBE/4JIIqfEesGr977y9/+YufTIAicIqHtsO/2EJNm5ioH5UP/Qm493Toa9ce/WiFNo0qfrNeSzXfW98qc7Pr5jYs1VO+G7Z0qu4SwRAbcrUhtZbeKff2zgY7bFiWiTrBnn/g1GWT4DvpWTya9/7t0LoTcBknqfNO6F4gorG6gLKsWtkHiDOcsl9rblr5PSCimj86nuHqu58U/8OteZxadKmBU8xX3vM5CzvV+A18FP5CcxLNW+DnOkWedtL36rOUr2kGT2Hzf7KJmjXf9BNa19iT+v9HpMdyFkPswUZKmrUva+BEX6wX5SJzG1UXG9z1pHFtab1zCvQLO07BwwMln9RiOM9vx2k9yKXH6uwN0xX9NAwjenesBlJ2mXWJI2gyrgPjXu2ZxE64HAAQ5bXqQXpux+mptf6TT1T0vIECp3iW4d0hcEJfo2OJQApHxW01HGFwkbo8tjI8twEn8OziM3v63RwlztJXk1qePgETTcc3JVnTOCytemzQZRJtx6qgI2Jj3VzqPK0CuZ7tcU6miwZjAl7TP/Wn99N1CxHVRFpppk7jy2TErCXqQ2f/DJyqXkK59XOo7YMxQFuB64dsXEc5FhU38tSpXinXFWPCiyFY0eWtmwRM7Azx6t/H9ma9RxbY8LtNenCaFzh89BG3xHzgCyq8NjyeQ4rnBLac7/ktNQRJnF999dW9L7/80oOnB/C01WTkiSDsHQ+aPvvsM58A+W3C/nxl/oSB+hHPSObqrmwr6pFiRyemr6aybo8hs65npauevGywsCqkXrkr2YH3qiNY//t1utWF9E+b3t51KsV6RauTzTG58hZOG6qMl3aalDqs2STYe0fnu4cguORS+UK6prnt/wUSd1NVWLrK7obqRSrR9+u56ZpbEa91ys8p7YD5T49oYZ5y++4nxS+x+835zTffePBEvu842W3CLPR+YOcnn8ZtvgRO83NLfe5R/anxou86w2eEXQtGyiMH3M7n+f6IgXp99L0nvBcrpS+Xj2KIi0lp1r7MgZNpoKvBxtEV0nrW4K6nZiNmAGEr2EDdcfq3vd9xgpad7DppN8ANvhm7voA3A3FHO07rQe493rC9FrCi39QXpO6IVSVjqu9i9pxdyAsb3eJ1SDgevuh8iEi7M4hbIfUQJSu7PLvAag3PNn3DyyEs5VYZVoQ5WL2JFZxYUeZWPQ+cbGWZyZE/z4TB2FkfvsQpcdZVHnTqp+Ob82GleAqepHdSOUuCJ/9VcU9jYieHm/g4NykBPspfTtV+xtqDb/ECwjBdHKi54Y3KJzt1yWCM79m+VRG7Pk0El2ISJeoDWOlrnCqtzIdtUn7WgRM8qoyUD49TF/ykiEK01tR8QdnJNj167bZl1W8SaG28k7L/TBMvcqIxp7rm8AEETQ8ePPSAidvoInCyV5M/eeTXZrzy9zXfif744488eCJoYkea3Whd+3CM6yOlYJLDAg4ngdMX//Vf976yXSd8Ea88R/UESW/b+RcLmgicWD0Gxsltwvw+HDxyUSj8C34IIxVPL3a9BE5KYrlAD8O2RjnsmXv21/Yg0TMshJ3AQPqpzGQPDusIpoeWX13PtFE7paFTKdaodRzhjpMr6HIqYIo0F2JXfKvcTn8ArAtdvnX1AN3iVnsYUJ9tYWKbI/is2DJG0L6e0+VxYoxDZr/KpnwUww50N5PbAUETtmE+A7/BTjULOX6br81Zvvn2Gw+iqGNuw2/BvWdBE89ja8eJ3evxZQ/MT9ozS9ZZD6LoseXlt8K3hL0C16nnuvE7kZdNR9/q52V9VMw/Pr+191mmaog1H3gJGa/tmUot31Q/yatSm/OyX6VzPWUoTdQKutup6gVvtuA+R3Zxs8ApHVzcqpfljah/SOCkHm+kKYBReSsjGjFK01tku2SWsctvoHSbHSc8FE6Kgcc4ddHDhd9xwsF44GSTlJ9/Li+HsPuFCZqYOHHoLVas2PjD2pbyGyvvtsBJzkiBkjugFkQ5zCY2pDihmgYeDiecD0Y4H1p18skcu5k4T+9X9ok2Plauv5lClpP8lk/QSFzlVjagunUaOl/XAT22oJBzLSNjd+64xAedn6NSsXoba+vNJxpV7qq3y44xxhE+1wdO0Uds/PwxCX264bpdQNd1kM6a+co+w7i2SUpOtw9GpaNxBVd5y21wg5bgSkWpBk4ETZxMUL7//jtP/U169jY9xouXNLxhAdJHFjR9+snHnuoHcHlxjL810657P/y6FRdu7Y5dZPwQt+l98eUX977+6uu+kkw9b+5kceYzu1Xvs88/90kQgdRbBGb+7FTsaNGHenrgZDpoFtLrQi+Bm5JYzlSSGjabpDI/GupVhtbavIhJ7elWvsEe+sVlfW/51fUs3UOtt3c2aYdW0eoyrc9BQJez+nhsQHClG4mbfQc7o71BuAHAiNyO0h3bSiN3tm+hkjPYiUObPtw+jugt689qkXHaP/L7sV1lfp0pr6ZetoLbgy/E23f/73/3OQv+glv0vrOFnO9trsKteiz4siseO0715RDv+qLO/HIIn5cwR5nmJVqI0ZxHjyJo7oJf8XmLpdh53K5Hnh5nmv2/pIvEelFy/drdFaj2p+ajQUL+yMBpZbMrmDqJ1Cm5Qxt6QEu9yMSAuw2wYYABuC1YXjr8z+MdpwiS5oApypNASPXcAyd6qx67WtrHQraiQCngqEWtv0m+SjbwbOL51/9K1Mastu/8m9OhOzwnhAMK2nHBx0pvCZx+sh2n5oRI646TfuyWFRuCp/da0MSuEztRBE7+WvISGMFLO1ARIMXryLVVLodVn3HyLiK3dUJfku44bYLmq0/eD0w8cNRX59UMeXMBCKmlg36nOoqdTKk7alNQXe5a3uYvDGRB3ue5HO3SUtk9PtF+1U+13Et7GyPhVCZRol70s5Ix3D/CNqm/LnBKmpfpV84p09ofVNyzebPzC6hNG45xGXNNZGyTnFb2kTBr1Qarj1mTILjU+qQpCRTQ8BpyPYP0/fdz4PTE0C2wsaDmTfthbHab/vKXT/1WOt62ycPZ7hPaxERjpBRe5H1ibBMjAqcvbdeJlBdGcDJBggYnzzh9boETL4lgN4sdJyY1/LQBPoX+9pMdbFuMcR7+yXVNPQXhRX2r9iR1hU2qpmcMUPOqfxnTHPNVj2Q73rOuCMNs+TqG6r10T3lo35TqiX1EXaaXAqcaNCkP/UsyxxAbfRBvexiRm1FaSXhLYdD9RHbQ84J8qP5IE1FfaWl8K2xBfhek9muEvLbqt7ja1C4KFj4iFllY8GXuwm153BnjL7SywIngCRg+g1uI8Q3MT/AVvthru0+8rEY6UcCjeYjmJfgaFmR8p7u19/mN+ZgxeEJvsmNsPnq70pnq1vp4saAr+UcJhxEaq6yUtX9U4LSy9xWsio7UKbnXWJOElHqR8rSNvw2wz3XP7jhB2a9ntlHb7XpMfhQwKa0iev6FCZyQJtWTcgJDKQmpuVWLWn+TfGXlxtuZWMb+a32lvwePgQknhYNyJ+QdisH2wOmXDJzcEVnAhCOqgRPt3HGYM2GlV84obpex1WB7NTAOBRyckIIk5ftryQ2nruKIpr+avO040WU/jSd855N+Bw6fkZ8v9L0x8wb+sT+uwlnRmPkIt6au4wrY5EPuDXgB2Oe3O+ILKjO/aLvq36LxBtTbGRmnNIkS9eKRlZf10mzTuL2cgZN7io2uBGja8KJZnsCn09o+GhnE/7e0ZDORtpYDmgrhA4Q/C8OqLZMVAqf79/PHsWPH6Xt/JonnkrgC37Kg6U27LeYT220icPrk409sl4hgygInfEJbTHEeXNOWkZ349d2+O3hO4SvbbSL9wZ6jYofrV3vGyicr9iwBt+n9y+f/0gMnvxWQV5KvAidjQN84dQVEuY2V1y0CJ4Rkiz/+raDWVHDM5YC+fJ+yg3WPBruwMYsDpUR+dT1L3+AO7cXKdK66wLXX0geka9XtwUo+l2j+XwGT1zUYzSRV5eUcGFune/uPSvs6apLuulYXsdH9TNY6eqm3Vf6aH/nkuCRcjG6myZV9JG3MSPTpUuT9s8GzNlphA36aX+K5S15K85Mv9Npc5W/f+XxFb9VT4EQAxByFYOmdd/JFVlAc+BtPzVXwNQqcmOdwqx+LxSwOcRJQ+Y4Ufs3szOc6btdVYvxO7W3kV7At1h8P2bcTyVb7WvNRn5A/InBaKN7F2oPXPqXkapIQcnZWMp63DxtYdBaBUzx2Ih2ud5y++sY8HEZo5wsdOKm3SqWsVZqqovZZGrtzKg4k+C1k1Hjt4KpFDBaDEf1y52BtfHxwT2QYYB99btXDAUXgRNDkzzi1wIlnGeJWvae2evN3n7zgVPgtJyZErCa/jjOxVR0cjRyPO5K22hu/1WTPH5iTYQL1anNk4czi9htfMbY6nxxZ0BWih6z1drzoUe+aG3DrpqpOpTGe0tjcpLxYolTpIiigg2zo/ADp0Lb2+Vb5yV+niaR7XTv6068HY+tSVFF6fQCTj0k42S608mi2aYCXNnDqisleaViaNrwirr2CcyIbep4U7VfxDGN8jEOTRSksQv/CD5wVrsZJgRO/4cSOE8GT/94St+q5b4gXOvAWKS2gfGrPHrEr9MmnH/tiChONV18riymlr+INP02Kvv32b7bb9I0/q/CD7W4ROPkzliY2evPA6V8scDIe79gq8ts2GWLRhgUYTtGsKSwp++G6CVqBU3ak3DdSV4/V9bGC1TYvbn6UPHs6wkP+rjOK/do1zJaXndTeSsZGWkIAAEAASURBVO/AavtO38eRyriVxXF8bCK4Fc28TU+LsdOtepVpyXuPGOMGqzIUtOeQ7T2+O15OEv1vSdZ+1nzFrPCaD5zQ2BZeKdw2v/pONFgjy9grP3PyOqvHT+CXuBOGk/nK9xY04Zc48Rf4KgVO+AQFPKS6MwY60BQ9UvWd+YsWdLmzRi/C4kd0337L/I3RIXh67XX7eYX2bJTa5uDIB489khua+/eilbM/e5LVftV84CfkeQdOuvKRo+b3+lHhSJ2Se42RcIiTGusTXr5L26aBbteDxn/89QMnVT/+9H8scLLNJfelCpzCILlIxl2n2tDzuztOSKkTTJfaPrcwr11aY7ShXu23+YCsP1OBS/LrRjeCoq/5GAxX1YZXe0Ub10gTcGzTGlkbJ+8pMa7BDV+6VODEaz1xQtpxYuVGgRMTFzkWnApOg2DHnYflcTIEPfAfTsPVj8cRMHmbtnXOSg4njoyTOmhwcuBC6YHblHfAwfERnfa8VEOh6nHQRbQaPtf1SW015us2A1kvVDm2tSNkxadirHlWK6j5lL/SmPPBU+3Otak0usxGwqmIVEOq9Kv8l/WCnYYsL2Xg5LqYFIE+mnqjRp8LvKrgRb7qMavjKq7+TboXvlLpNtrCf7pW+6DmdRTPCbDj9LTsOLEbzTNOLKo88ZOm/ryRrex+am+u+uwzC5wsgHrDdoK0KKJFFclDqhPZNIGBNrfbEEBpMsSuFvXgETj967/+qwdn7DbhQzw4Oxk4iSd60IskyFf9pCr2ro09OJRe/COlTztMWMqvsXJI98GG2a/TbatRv5V+wS1jzxhgizEVsNRo61TwpLFXqrEqFN2WJHlyZYxrSRjPI63S3RE/jcGCdO1nzYuzYEqB1zxjsIU56E4/NHaVaHzTN1fJ+NfKkmf8edb5998icGLuwpyFRRwWWZi7cPKmPW7TA4+FWxZ160m/nZbRI8DSKRj1zEOY13B7H76M343zRxRs5wm/w2IN/g0c+bboG/Ybuk39Ro/+MFMsOjybTdn3WtRRqvnAT8jzDJzqtV7ze32Y4UidknutkWlhSquL+gHLBhZ9+dkDp9h1gsaJwMm+AM2w9XY9DEm36WGUm6NLNHeScj2tuMEd22wHeqyXY8h0I80ESNU8a4NfOROEcb4So6XbXjFg9Mo/vA/CcT/Lh53w8FO9bPf+615hAic5HiYs3KqnwImVHSZRMg52jl6z32V5ra0kK2iCNG7PZbE8+B4MgW/O6403bIfJbt9xB2QrOaQ4IR4ilxPCEYkOqXacHKgP73D0En4c6l+ihCGrvEylKKss2UQtQGV9TBKj51zXvRQZyVbB0s0AE/EKbPl9u141krEsCBVQ9EHtz7UpzcMuARgJpyJSDanSr/Lv2Xk0C/skPwZOCQ+8kVmlWfOBu/c50hixLtWNmNVoVuPq2E29QbV+VlqXeVYd1lZof0UR/P02UKBV4FRctZEea+BUd5zwDT/8YC+OsYCGoIrx9tVYW5Hl2SMCJwIoLZSEn4gXwsg2xJdJB98N8GIC89134Xvkf/BB/IaU6nm+6a9/JXD6zIOmo8DJextMXSfiG31NPUWfGSxgtOLQtaE0oC/vZ/VI6mSkqx7KHry/3cEZZsvLTqo+vI0p0Km2tHLtuG3HifEP3jbBCkZO/lLQBN+kqX7YyGWWUeys/pjMSqO3lERjMJGu41Tz4ibYnFIvWOCGzqoeuR7mY6yfa/fLXXxlGmofd5WnegebGDGvjNv1mJMQOOGXfrLnIH/68af+HCYvusJf8KPc9O8Vv41X/ieu+fBt8TZP7V5BDx+EP1IwROCkH87llea8SZh5C3fbsMuuRWPwOXRNSK+pq2nQHPvF/ZD8+xLW/tR8tEjIyxY41R5bL7A7QH4ZkEvP0zFtkNGXnzcNnDAcDC9SY+LlSJ1patSKuihJlZc4lOv9561+aCPcmgbeeuCpa3Rqk2U+BU3jXyLeGqiLbZdQE2UlPV8QyEca/2P/nLYHs6ZuGwsvt0GmAc7iFzsVOP1oDohnGDRxwakwOcLRcKBXgqHX2u5Q1bPGPfxevISCW/TAZxLlD4ubs2H7GwdE+u678SOZrBoTXOGI6I+07zJ7Kfrl/DxrH+C1PrGT5nl7xg6ceu7qdaqA76XjqJ62rt9LRKhz0bfMVvSrfr2h01ZbpQC7xhzj0kfwUVu1u9RirOtyGgmnIlINrdKv8l/WjXwEOgz/EfgJTymSYaVZ84m7ymX7VW3qeV27N35L7HLtiu6a+w60K3tF3a96qxjbSudK1TL0I9y4RvSFD67wpceYXMSO0xw44SPYcfrFTtq9847dxmKLILHj9NkUOOVtvOJDCm9S/MZvv8UtwwRK7HYTQJHnZAXZ/ZQ91/AvdpseO07sPBE0MYm5tONE3+GhtPJHb6qLPsdgCRbX1PXXhzN7wT40pimW6cTVErpZ9TL1YK3CqZPp+S3Npuui7+DHNUzOPkpd3/EzmO6lgKZOBU+U87sl8o2ak69yRm8cHB+rjlGzQSxtrsiudDA23xNgxDpT6iJPJNV/pZWWYHtp4HbK/XpY0QAmOrX+fF52gAllJ2pO+VrPWOHpZAfYAv6A85H9TMLP93++98BuI35si78ETVroBY9DL4CgvU4CJPwbuPoNOeY5nNRxgKsdJ4Indp0+thOY3yZs/k5zFuY4ok1b9JS66r2i6qU4UvY9cdUn6ms+8BNyx4FTEu6CJSjt2JTf66/PJEWedc0S+SgJ5mzaWPuY3y5wwkDH2/QwKg6lUaBz6qBSr7GP2MKPUql7boETnEPmW41BdODiZ6hGQ7FAbVVVW8LCofiAeSpopq5vf40nYxKn47PjZB0jaMJ5EDjpLVZMXAicSKmTI6Idkx2Cm9ftjCAn+Is2O4yuN4zJcjVwYoLDbXn+uwrmfPhtBSY/b7db9qDH6rRsAiN1utmd7oz0FqaO0/oGfvQv5EKIkKQQ2cs67l6lwS/Uw/eaAxnnYwHq/Q1ctZlTasVf6Uw9y1vex22ytZtNFKUPidOQoh8BrLwu6yjGmn74mLfxVLnyl30AqzRrfsSfS5PAc7Vb7gbYAbVPHXgpY+qtNrjmvoVWPa7Ji+qq7RaWNKiL64O+1BMc6ZHnGpl8MJnIwCl9g9+qZ7tOuJJ3uP/fA6dP7a137Dh96gGNFkTwGzrhAU8FTkxgIjD6xZ9RqEGTAif5oc/txRB//etfTwVO4qO09lP5sCVsDqy4DqiLg3LAGuAlTdS/SXzvZ/R11cvUA2oQhqUtLzvpVI2UWyR0lfdK409Ku6bb0H8EzsDOBk5xBwLPBvi/U69yRm8cnAituEkG5E3tIWDT/00L7/UGehuAizyRHfrf9Nt5tHEQjlLqa94HbAMLKsIL0rdTWr3OukkZm2YhTYywV9dvkx9JKPufzTHwFwQ4BD0srBBAuY8wX0VdnX/SFtnxM/gz0t/sNj5u58O3EWxxKoDSfAdcFmZ4xomg6RMLngigKPMTLMxd/hk4+cjwMRxponcYOCXRBa/JLufrYGhxptCYlcApbDTgXRTs03j1s3zP8aZojlO36mHccXveP0LgdEbBt8XpQ3CRkA+BoU7m4V9UPmjWWg5OhNwx8YHDKafj2wCTurMxR4Pz4cFKgqd4xsB+ALcETrT32+5sZYWXQfCCCCZFTH6AQwscHeLBM07Ue1Bk7dh5whlx8jpzfoeFE1rCEw13kkaTL8vhgFcDhDE3p2p4yOC8mzGPX+ADleMCF8UGS5w3FQ6oOlhjlHFy+snBRLYjy14aiioo3eMwwoPuCNuWLver4nd6JoZLMolT+8FY6Bh1s+UX9aOt+lfqPP5FR5VmzYvnOsXRrWsCOlfO5TKGl8hsu9glX1BslLY1SxZOe4tb9V3z0EA/AaNdcfamjBkXfCYO+PIaOMWiyrf+uylPbLeJ33JClzzjxO16BEyff/6Zv1mPRRJ+gJKfMeDaVqAkXqScWvn95ZentsP0o+94a7eJ158/fPigL+CMgRN+hGec4qcQ6kKOeNAP8VEqOVQnu1GabVHyYhBp+JIc6tNSXAauWeSql6kHQ+vXoGG2/Ia2kYs2ZPiPFPxK39nawmjsBMy36oWd5m5T2KAeAYDn5vug9YE+Gsc8KlOHToABOZudzW36v2k48dvUXw+YRXYdFzLDmBlc5TmliWDRPCiPsFbj16m3CMDic253pJtuTkZrsI7WQdpXGtBXWcGP/AYBz1N7gY3vIrWdJHDBUxtS8AmWFDSBXwMnBVDcgvyr1RGAcTsed8Zo5+lTe1vohx/G7XrMYQic5NtQi/ghb+hENqB0obwXEDSP51bE2p+aD8yE3HXgNF8B2E89Wv0WrSKdyDeqc+Dk4GaxYsVY8wpyT3OBUDr8j387fDlEGPs/A6cT43IFimzA09FK/MvJxqulwuQCbgzkgMoOIIOsyYN2nBQ4MWEhcOJ1wOw6EVjhQLBOdoNwFDgTPQyuFRfRg6t+t4nUXw7RgicFRqxM43R4IxavCeUWPgIq6jndLJHbaPkKIw6wdYdE+Uwjh9PidAOW42q6Kc2vy3p7NRFHlZHFYFtwIuzkdFEpBa3m1YyxzaMWal4YW9jY/kjQo/rg02kaO+c4sY36ANY+6UtlrbAYOzhoHAM/4epl4+rFpBntEmc/V2VaY9UO1XxgH7cvVItKRUlpwbIs0HXNiGelQnPV5rJ8+kJvjr4NJm3mduHH14ETAdSTJ3FrjL9Vz65jAideRR6B01/CT9i1Xq/tmQdlJjAETbz2nMWbDJryVj1f4DFfFM84/VvbcRoDJ3yR6CtFd+R1arIeZa91hZrbGCY91ISiB2UH+CX5rNfGUmQf+7C5VS+rDssXCopycgP9ZrrexumG3sVXuFGV45Evh2hyNB8OvoInTYAVPGXgVK+XyFfIeJ2oh0pNsgFZkp5P1af9FoXXPtJVNVVk0+KmbR2znjc04XaYtax5KWOEBXlgK7iYr+vw28Koacqc9Q1RVZZqx4iWM323DV/UiZc7MD/hJwuA+5i0VGXZD36GW4IjjZdCEDhxt42CJuWBc+K7mK+wu8QbQz/95FPbfbIdJ5u//DNw0rhuBzohdxk4yUDcKsR8vMwFraiCXZXKJs2mWju3LeUFNT66Pjx4srkuc+E6H36OgRO91omkRQvP9Va9pqU/MCk9Dy1oFJtMOEQfOLTUvrCoklMaHElbgXF8G1zQ+T2EX+z3EObAiR+gJHDCKeFoaKNdJhwGu0Xv2MnukyZGwbetajdn60ZkeX7HRb/G7W/Ss2Ap3qZnr/Zsb6fpKzet0/QBs40+RN550L9muJM6HNf7R+c4LNGXRgCu/PT2ajNza3JswWqwm2qslIJY82qobkS59UmVYRG95J0tJbJj+yNBj+qDeKcp3UxiRX0Aa5/S8az4tHE2FuDpdA3LmIM9veq5pBntesWFTJVpjZb0Ky/hHrcXpqWtq5cpgg9GxQK2cwzq27a5LJ8mQc1vtMGkzdxOY1B/ADfeeveN70b3wMk6Ga8jJ3DirXrxjJMmHPgHfZnQozpm8GRig4/B11wOnH71wCmfcdoJnEwl9ZpX30j/GTgVm/KxD/sZTKqhDPbQr0HDbPk6jjJdb+N0bQxaWjh2GHX1GSdGDBlEM4KmetdK7B74Qpr7h6QaPdheB44B0V6lXipNGs8md/d8ele8W7WUPah638uDXeui9ZaecJQml8zt1WksE5Nc8ugmJSdJatVojbar9vACPgRD9nMqv5nvUBulcKu4tOGFEXELMun4jBNBEy+iefz4kd+2RzCF38J/MVfh7XoETx9xq96779lO1HbHKaSPPoZeZANKkerFP/bGNCWv/an5wEjIyx44MaLRm7DXlm8w7BVd+cmcum1K/AGBky4s0ppvQ/b/UeDkxpveJbSRFukKYYLgg4a2yheVmsmJeFoDJ3DtXyu5OA0mLZzsNilw0uoMhsBEiJO3y3CfL9vXHgSZU/GdIpja6SLyAYtmVB44GQ3wXjNHxOqwp/Z2Pn7nRUETfKKjMfZODbJQbSl5Z4UGAo3c0H8HOBCUgtQrTmboQ0elU+MhuUbocUljpZQWNS8KZUjBELikFVbzgTK238pfCFn2qH6iaeyc48Q2eAaw9gkbjEMppcyrvtqsj3xv15oXPaiNU9rgCX9Mq0xjjUq1QzUf9cftRcfS1r1KpeYTE+i6JnFaLlW2bHNZvvAX8AJPuHMKJ+kWH8FvOD148NAXU779NnajV4HTp/b7TfyOEy+JmAMneEBTk57kDyzg+B92szi188Sijt7+WV9H/s47/wycmkUsE43fshKgX6hhc4NJtQayCS/2a8swW36g30zX24QD6LYVrAJBYx5pe8bJbDG+1sM+oOt2gq1wtu+tnjb+TUxLGm0HNEHI905ZpoM70LGf/cfd8uvdcMHX32zDuLUOrmDZ96a/TrxnOsql9nt1g30kpZ7LYURHoafIxfjX9jMP2cLvvotkQZClspu9VIyjPgJxzX/8lj0Lmh5Z0BQBVKTgMidhfkLQ5K8mt7kPt+9xMo95xV55/mf7Paf5CJk1/kpnrBezPOt7K+WqPwnL3D9C4ETvY86Zdtp6aJcKuvLT7EQ/gKsFOlr+x7+9TzIc0+84NYfXHZ3KkdISQ+yHe0tdpKt0hkFgAesEVRed6eCeUb3SXvHCZeK7p8nZdOaloj6ExnX6oHkPyEdXpGY5EV+pU+BEu4bIbhPOQ4ETzzgROH311Vd+y17cF/y7v+SBByLZbfK3y/CWGXMgPJ/EbXs8XyBnJp6kzsd46VknTw3XA6VX7dY8czgKmsCNwEn9iM7IZsyKwjk2I6asDrsOHL21oc6rm0JCLVd/Sp9Nq8v2km9ZuQKaSIwbh8Zhznul1yvnGLXQ8rV/NR/VZ+RPopNxZcWQ6zS9H1Y1sY36AGaesROZnjFA5qVHUp3UC67WlWGtq/nEXeVkX6s6YLVDNR/4dcz2KHR4616lUvMdz3muaxKn5VJlBli0MZDsa9MWfBsU/2NwhOuwht3yzsbGgtt5eTnEg/sWOH33t3vfttt4Y2X2kY8PzzLxil4mFtyu98knH/vtLdziwoptXJ/5+yn4I4fRA+TgsJTfZFHgpOCJVWBfwLEV4s8sKOPNegRn0CZ44vnIeLFMu1Wv9sXJMt554mPEk9T9iHVW9qO6sM1B2S7my/Kh/uzKa313A7DPVS+9GgOhtiOQj8JAvw+hZaLhoGNkqGMQ+TYOTQ6oOk3LcFueB03NF+i7xb/HGsxp8uEyembKC0YaMgek5ivO7fKpr0rnGfAykk3dlVHX9wC0QtrzXLMqrygf05h5DLYxsAn6zYSsRvrxq9DLQGivU7S9pSs5Flr4jcffOS1o4jebhK8UtpnH3+R1Tx28tdMdc6DYbWKhxv2d+TzmP9gi/lI7TiwaK3DityyZv3DOR8jtvZmrXviydL4vqMatYiQsc9cETtDKlpWy5/vcn1La6bJFVm/InAMkVbHFliSfrNX1ZLxICZoycEpbWwdOX9oP4Bo93rFPRvfF17xgCBzMm+ibwEm93UnVg6K0RskStQHCl2TWjLndihGtlwJ/2yoV21HvLNN4kizYcBHXQ2WlUceEIPSNzvVDsqQc6Acq/oyTBU8ETnqr3jfffH3vqy+/uveNPevkTsmcB7tC7ixsi5o3y+BESHlO6Z233/GJy8DHnE2MNWMRAVEET7E68ydfpbG8OTOfyJjR/Rnja+c8gG6o9MNkdj6Wk2rCeGnrI99x6Gc/RpV18KXMsok9LNiPIVsKHWGbqWPkcjeUmq+t6FMeQ6GBV7CoGtsmlW3unOzRji+gRsEynlU5wcolrkHwE6NBJ9+wlRhbx2Kso0FPgdejVRso6GzpV+yaD4GjH2E3tTbzU8e8ImFdD9lgzGX3Er6C9es5aXuDjtszBp5wNuVgNcpW20SekavXmnRB69kWuYWOycRDe3MVt+/yDCTpo0cP7RXAD83n/27By+sewBAwEdzEjhNvzIxnnLjOoesTYhZw7PvCDxPHZWkP17LLBO3v7OTFEPzQ7mN7WNt/w8UG+DP7/abP/+Vzf9YA2uxq8WOU+KdXbfd68CHBofGJcXY59CAvupNqTJzwKALQDBmr7oPUS/Ppol+QfzSSqaehB0eZ6dg4BNUtbbedQjeyYWs4g7A5+frw/YyBeRVXq65534FswVP/7oJvOb2BNcN+4lDailU8r6qAhnNNYrwvHvTvIsLdVp7nFZg5LHv9CLwT3SwdOZKi8tqjHzjVqjTOMPJW+WHGEjuRbhc2N4nF3ZhvqJ3sqKZhe9ig0Wy2SFsFT8yBFDTxw7qc+D5o0OZjezEE8x6/24YdJ1tE5q6ZV1kAtt+KqkcVt/dhyFTsMe9tR9BQqhodKkrhiEZBXWSjtdvLLrNVRRvHgTkFOwts1RKHkvDMdeH63F+QJLjAHvipxbWp7FHtslw4ujE1e6qBk9sXbwa3Had/f08kevqn/+OBk5HkDaEYdAugal4wWsmQncLZwKkrTcqa0y6PZaLOB72PVuloRb2YFw+j2PnXBtC8Cd1KY84XntGBQBAbqx5dc+KDmHWCW/BkY6IHLd3ReOCEIwicX9lxsgcmcRo//WQ/JGe/vP31V1/f+/LLL33nyX+B25wLW9LcmtffLvPpX9yJsOrLThT1uauVb0JymZDbJk5saetlEXgiOS9wPK8JjcsWMGnI+2H6zjRqohvgGg9rbx+tz3c/PsFLEkU62PNYdbEk/YNU83OjkWeM2YizgkFzxNovybj2MbImcDtt1G1//Nej1xuw5s0UyzEUfMyolD59nB2FMfealhYSQ1Z4A7AUsl76ntOCXLJT51qN2hbEdXbsZuAYrIN7hqoVr5S7Y1SlLtuAmdc4pZG2jZrx9YdZoWVn9MdH08u9hVUzeWDXBx/BLtDfCJwsffjgvk00HvjE4/X24hgmFtxOx7NOb9uCCoGNdpxYIGHnQLsHGmt4c2sD9d+1l9MQOMWtwz/6Q9rgcPLDt7wgAj68jIJnq6BP0KS36uWOUlGuteVQYEWa/cbuvHr6MN3nSE11L0Fxp0+SPMZcpUiXTTpKq+1IPdMxnGbTtYCCkWrVn5Rx4vSjtMEuFDhFPibFbjcgW33lHCNLReYorY/aco2xBz1q2bl7X46w97icgBvpzmuJvld7Vibaj36HcVjZy5L9SeB4zTXZmujw6/UGqz2KOmzEfIkHTviUuuOEANEe3DhSfs1FsD1oKHiKxaGHHjDhe1hIZidKfdeCse84WdDEQnL4Hlu0Ycep2DA8XeYGizzA2pOQTJ/7NcIYU/VshKaurqU30xnKxqzSG6/AhlkXlYd+1paBO8oe9U5zrBhEGAtJc9kkq8dmi1I3kUVdgkw6pzlxs37SVfdrzF/NprhO8jvoUuDkwZKZqqXaSlewlEYeDNOQjZuD1ENSnYgruGV74LJTD7ofahOdETTSqcNj5aIUtPyz869o0LuWZm2/l298PYm82IyOq9UNZKzfXuYzcqn/mLD83X6bBbmDlv0egv/wpE2M7N7en3/+qQdOX3z5xb1vvv7GJ0UETwqc+A0m3iwTt+N86rfu6XYcAjTn1+0AXiEHEyNuy/uzBU/qR6RtrDA2N0LDJ+XP0no4bZM90qgJlKDh+AaoeL39SKqDz2bUvIp07oLb5zD3b4VZ+UmXI54kS+jYJuHr3FkbTrxO31iv+tDrjWHNp76SlmTyMXNw1GXZrLU1VKo2cxpoW9rCU3vJHGnYjnC26Uq/W9i2XYMsxEk9GM5QP9Ld/SJB7+26Ci613UDQByBrIxf82/WGs/dBcqKdburonvsAHpR+ZCe30rEjROD04P59P1l88UURmzzwWyeffRa/46TdaCYXmiQjHQs4Mb7xfUEf4IcY39sP37Kj9Z3dEvijLeKwkPObPfztt/MafW7R+/yzz+99bDtb3BrIM5a+6msrvh44EYC13a3QTdNHM8TgE32HYYCN8aQ22rr+VxVB+OX4XPQrO6v+j13ZNpkgvdgznYDbTdN1AYZdtXHx21kMRzZR28guSH1ibOm842TG00l7pvEL6x6rblvqnGaee4Tnvu/h3RI+9/WseDn2ewIkZflLpbTwsdpreiU8Ze5axrH5EXUGpypF6pWyDQInBT9Uyn4iH+hzn+mDbA98Pcddd5v0fCV14ICvwOmDDz7IW/V80WYbOElPLrrxi5SuWG7uj8S09EJVwZp7NFR5ATpnaW1bT5A2PJVe+MYJzxGELGylI27DMmDW9zEfUS+U4q6qJUKSXVZXYPCtkJ28v5o8JUd2v9wZX06CJv8+ZUEoYFDa33E6ETghXBh1EcploIc6qav5huuBizSR9TLOhtUS4VEkXzta8w19N7GOq87497xgTvcaer3hiUzllnn1Vyn964PeRWHAYMGHdpzal1BbXfFax4kfkiN44nmF2J7+yZ9v+vKLL+999fVXfqsezuNV21FihYUfrf3EAqdP/TmGTzxw4rmn1+23WuKwS8qE4qyHrzC2HSfg0QcTgn8ZXk2twvsRnQlSRpILlj/+/XASQQdilvN6m5p5riNF5safVYwbE9lpmOO5g7AAhzz09raHFDnT2YGb83AtN9Yau7E1gfkIqaXZNqhL3xB85S+izj/T1ilujtYO+FJ0bDIaSXeZXhC28BF+AR1nN7KUa5bWVh8oPTPQ3DS32i7tZSVzYQXujGf+DLqMXd15GcaVlvwbDrfrPrHfSeH3Un74MV7e8L0FTj/f//nefbud5Rd7ba9YfGw/FCnfEM8g8TtL7Tfa2nMA8PbxZsUYX9FSJkBxqx6/E/X9vfu2o8VLKYCzcMNJ4MTJJAa63KYH/BW7XeZVC56Y4PibjUygpe4Mjv78miPv5eirgTUY1nbV2jFe4g/r09CtKKCPfpg+dETt0EBVu6m3LjQc0cvoOhbN6nMAPl6MQWuDXTDeyzScwtgFY9AlnvlW0TvSruj7Fdjofu2m5jasNsSWgJlD+rYl+gAM3FTVTCuRGQMOpVmTuaCzTyMxt7klXZEy1o27NwTcx6DZCL5DgZNsBuRKt3Whto5r3gTH5minW/X0XBOLNQqc2I3CRlm4wedwDoGT+R7mRvOOU/gVY+t8kAp+kfK5OqKeGilhhQVM4zLW91aWIW8cR4Sblpriq0ddXhGVnctQAclc48hMoh45VhWq/LrPTalCynQknfCJJ7q8zHduKukb3MeXzpq2W9Dk3z98D7UB/Y9/f7cQiWzcqncYOIUxI2A16hh/ejiflY/VuawVBzkpz4dwZnjtbM3PeFmuRvf8btVL/qETlaOvfjH2frf+u05HPNoGGpM0DIMvo/gBOBxFU6ih2WqL3YYTgdNj33G6b5MhXgzxxRdfeCqnwmquHojkNhl+6JKUoMkDJ1t56c4CDtUaTRjq3AGZQfnR+qE+zSk4FUa50qx58OrBRa2/qBrrK+41+ZnPNW3vGvfuZFldDyuY9aBtxUunmY765Xd9Lh/Y5IhBOcc06gMncbN+bDuX9vAEl+7mdKYzl4U/wy+Wh36Gnmrflcdew89dpLapXMmkfoKseqWdn7EDNp9zG+qZPOh3Tbh9hUkFzx79zI6Q7VI/ecwzSHG7DLex8HwTEwy/jdf8w5v8QK0/g/Rqlwc++CJO3SqDr4nAKW7T0zMH4LKzRKCE3yFwgg87WbpVhokNPgofo8k4ClV/oaFDuqhvPhIMnKo/tfnHSaudpXH2K9gzzU5vYJDyCaO+jB4V3J6HzbUxqmMl/aN7bGKVAtsbG7Uf+d5daY/v3XE4SynGZsRe2/mIE6XajyOdCVfpih6wIzrrdmuZk1atTzsNWWI+o+cluYsm5jXH1676Ij74Hm5F5rzPDrq9EIJHFr6zhRt8EXTxK/iZeCnWRxY45e84yf+AU3UBfZ0zfK2PgEquSzjUqR8Vr1qG866Vt8k39V8TOF3qR5Cr1ELyVZ+q2Lv14XQqqhnlWIzSErjU5ar1iqb3031bG2+CJzvzO4gdp2Xg9DV3aBlz/VhpODc6CYw0b9sLcYD7ofQFC5xCvalkU4V1pMncEwAbYK+9XSZ5dwvgQkQO/89616WLkbBAcuwcA8bBnASOArkZaxuhIXAiaGLX6WvbaSJw4pXk/M4Tt+JgCFpB1lv16uQIB+JfhMjZTpi4VJbWL0zVk8rAlHodhica9LrnU6t7F5HDW9+CuWnD2t/FEWRuQ6t+GdxOorvp0579ruANZt13DfiY0IcYH/UmVb2iISyzve4EKgw4ZekpfYiwVu1UV9M9PGy+WWW3izO6PINT+ff8oIawndp15ZGL/6sPH4+gS9tVvyU7KfzAIY2dmVwZo1640FKZyYNe28vtvLr/n8CJMs8//d5+WJJFlA8//MBXZbWooueQ8BGiKVlFWz9AyWov9PFD4slPGehHt7XqS+DETlM82xRvtfLb+cxPVR7w2Rz004DCC5tNHW7w/6EAsrOtsbkG2ofb4w37vdWnEQVoZ7ycI4Jb/76YxgvbxCY4la8pIlGuB+O4f+zVjTT22481E+uxcrd0M15rcuv+zDpZtx2hl/U26nmP/nasRx6XSjPNUZ7wUYkTOlS52gdzGpVVD1/3cU1dlTY4sjECJl8Ush/gfuA/ufDAfQ8LRPgi2rFgw8IN8x5OnvPGtzEfCh8UbwnGqeBZaFNPZBH/2+gLOkeHrINUPI/aHNY38w3abRy80WTXYt4I7vGnVbQcG1y+tsIelrKGUrdVI3mr3wC8TbWZLZEGWTT1bxHgZbzNwfW5Lt+vHOvA6Yuv/QVJdBoBKHiqshtpdjqU08quPUnkEiCFM8sPK3c84ewZRdbTPgZOvJ2IQZUmh5rrg93RXD1QK2iqVFqq7jRbeSKHld1IChwR7ExJmrzC8zrTATtO9gAlwVNgG8z+6o6TAifeqveF3apH4IRT+cWcCv3Xw9hsVeNAPrAJEm/Uw4HwQ7j6kVsPgtqqIvrkrIGRvjCVMuERjvCUqj0ph1L6ELZkuZZRGk++O7K3iXY9e0Wm6Lm1amJcQSNQJevVDXuD7G+AtrJVK+jNTmXSetbo4613rgP7CF3E+NKujs2spz42awY+loNvMLzwIyFbbV/zlZz4r+pXMOErrbSUVx9VvjodVBtjVm1Bea5F/u/qoL+1X8rDwnlaBlg94S28mjLJYFeaZ43wEUwoOCNw+tkCp4d+ux6BDhOMCJje6TvU/uY7bquzU9c69DV54RZAv03YVnxZ9b3/832nyauGwWFy4os29qIJ/A5BE5MXVnk5eaOV/5aK/Ij3q/VzUqj3i/rorKfZ16lNu8Qq7kTuzoor+7wz4jMhGd0Mp9z6rKpr5ZIu1d5Jmr7NsOxkx8kCW1+Rteddp+8IcBlveMo2anmURRdLCgyLPIZCgjc50dlUdMAldXWknczY9pjXDpkGPurTEf2x/WqsKv9R37Xm2eU1hvCOk2uSfmXfsi7voiF4Ai57qRLSz3qCo10m5jf4HxZudKueUnwR/gX/xYtuPvroQ/M9BE7vuT8Cjm/CjvFrHJWP5jCCO8LBh/p/CW20qcTU6LoMBia9k6OpPqn5t9WW9OGdJmoSu01BNqmqdk5j/GdollkKHY5SHFVQKoYGUdjjc1GPblvW3tNmZ+bfPGhqzP/zfyzeqve/LXBiIOOMDPncZcKYZfyB52K2NiGyOkSqfNR42XADnvXrzqzqNeF0IkZHqeiP6UC38Q2JZrmovExrpHxtqfJreb50Zv2YCHkhqQ0DCL8oa2x8x8mCJ4uiXBgbmTFw4pkCm7QQOPFWPQInnAong+zPFNhrh997731bUX7f0wim7JXABs+JTHwp1hXg+iUJnDMmPeNqMXDGQE6H/HxWTSKXDL7n+Q1dFMB/0UNtdzZ/2/aVj+SssGvyY/sY27H9TezxqE3Why6qbkMG17UJkilSZTvJOMovaKRho4LFNSv8vVTYSsUf3tCbD9ERXPhKBa/ppbqKt5sf5Ah9VdmUN4lXKtsle6mi9rPLb6zxHaHn0A914wnVHFO1hR630XHyALUWWHi+iYCHN+u5n7DJBxOJN958w1/cwK2979r5jk0w3HdYUKUfi2RHWZNjaH5vu0zsNHF7HpMYnptiIQY/QDDGq3+hxY9vv28LN7zJ8xUCJ3AaXvqM6OtSR97n6Cf2jE78AD43UJWjL+pn/FuU65jdgsyJpgd2VpRwE5nQqWyoC+O6tQoFTZ7GZFNjJlx4avIr+1A55Flf27JV0Ym0dGasaKXh4lxizEBdrzP8cnkt8+U2z6a26qnmV9xuMv4rOtfAwn7MFbodMD6hO409tKIuAmwCpvBN7Dqtdp6YS6Sfw96wJ916rKBJtwWTug/yH8N97H6LhSB8GTvpLNqQZwecU3Ocasfo1cvGCwukfKRr+nUXh/MzQnfKs1wmuqJsVBbiGkwIi1qBYlQrYs0LK9IzNrgJnGhaSMqmBuDIxksrXtG2EJva+bj6+MKz2dnLFTipc5Gmocpp1YGu+VET2U5wfWGKvuAx/CrdOF2IEqDKT33i+SDJExzdkXcaamOp/7f7SK0+nI3dqlcDJwugcDqsvjx5ohUXAqf4Adyv7a16vDQCR4KDUmAUKzD2+002edGzB7rX9zV7lkGBkZzKnDK5Al+p2oIHD5yOHJHGg1TnqGuNb/TRjf8fMHDaXtQa66qNbggVeJA/apP1ciKeYl8BWKRrltmHpClM7DgnJfXLMsYVPLVXqrZKJY/K1sKzSRdI8uZK2rbJ1uSO6kfsRSnZQc0RBnlavcs14C5onQRV/XT55Q+MOfzBoU4nsg3j23RDPbhc//iOxzahIFBiVdafCbAdqAcW+DxuEw66yE8OcB3jJwia8BGv224T13m9rqHJYg6BE68h52QH/DeDIaT7FnawzM/opxDq7X/yKxt/0fq6UpfrQ/VKDdHhU4Nm3jFqjjsh3GGRMZG93iHZNalLdmb91FHtSLCjVDY04DkQ+4rdJn9RUPPxvGkVBUv/CpJqqnzIE/Y70G8FjVcU1RGlqxaXFLHFjzHawo8h+zIft11hVLkv9W/bVnqmpua3mOEjVvDnAWOs63irHGk8F/m7PdvE3EW39P7WbhfGrwifPobPIVCPYD0Dp1983vPYntFk/qNF4qdPf2nB2K8WHL3dfQ8+iAVjv8PGd9DfbLSrLsOnEqxVX3ek67vUKRYBv+ssYyuBX0/V1LYoHRJo55CFFeltpcRrLmgUUPqFAuySZybsLcvkou1+Ox9XdA2Kp5Z/eQKn2rHIp6HKaWm4RsVEKeuynfBkgJUHdbTJdsK+Sbp2yC3ocYJNhtvuOPmKDLfqsQVoiQVOv/pKMoHTE5/AMCH69lsCp689gGKSxMSGH8rVc0ev25usdN/v669nEMRkqQZOlJnc6KTMGTtXvEo47h32iZVNqqJ9bH1rMiR9uiMIK3anIHhN5Szv/UMFTrLf2lPysz0Cu4k9HrXJel0b6SRCjjYsfVxUpjYOZFU/RE9pYHAN5HUQuHJke2kj3hPJ1wElIxoF5NlLbUA4qp/pbcpDN2PMsp/ZZ+vxzYZvwxCaybTLb6zxItTppC7qSSGUZS/1+riFioexn5qf4JkmFlQe2A61bmvBR3AyeeFhbfrzpk0udG2zSMLbp/AhjVkEY+Z/oOG/D2Vv6uPAX4Cvl9FwazArvZxa6cWHyEd4P6yd98Vlhkromlw9os9ASp+9OOI3dTiVmq+07jJfx+wu6S5ppXlsq4sabiKT7GgkbER9XCxwIm1B0zB+0XCz24QMZwOnmedalsS6Sf+y9TW58GfXtNjDXcnsdr/XYIJX3Jqf0Ly44rXCexYweAf/0B152QGBkXbAdasd85cIoJ5aMBWvEAc/ApjYuc4fqv1vm+889V0nBUukun0P+tINCzXyPbHz9K4HU/gnTvBmPQEbbTt805E93pUedQkrvZbuzeU8b+e4oEtuSDJrHFRepf+ggVN8kXPrHofZfxyW9nz/kmOo5+G2cv8dpaxPhVb8zGe9BlOMG/9NQr2+TLPSIA5PiHLgH9EU7n7qFBZkApT9cdlcvlEe1+HQvrUx6+dP9N3x/HdsZ7virSF/eqgbB6TJD5MYdp34PRUmNQRPrMJAg1MBjk+GWjCEU4rAKAMlJkDAlMrZMJnSBEgpE6HX7JXmBGJyOqQc8NR4zumsWfrEkDleUV84gxm7lgtyBbf8cftsZOJeOC5WDu0u01nJe552Mjpqk/XSvfTrEmBnTRTVqxw8qpzYD1A+kq5DrKj+Mt4BC3tT3oH2oXqVlYq/ynM6tws5q3xji6P6EXunNHQzeamvtJJOKmyH2ilw7WfXibF2f2BMqOdUXaSMo+SLfO2/2jA5efo0bt/FX/B8E2n4iYc+GQGHAIpFFH95Q1s84ZY68YAeExvtOP1gD2Nzqx6+gp0p/EIGTLxsIgIn34UyXwEOtHSiGJe+wfYU5fybLrxN73PzGaUh9FRd8wXljrK6Lu6I3BGZwSYXyK7I/ets0aKDQl+NQEJDkbbw54tvO4GTbEwTZKX9B9zNZjjAOzrCzmY5tq3O0Nq2uhZyd+O7kjf6ek6milvzq9YrXiu8u4aJb6ShO/LuL8xnENgoyCHg0e11uv0O/yN8+sg8Iucg8fa7wMWXRQBFCl14cMRcJt4kTOCEL2KniV105iqqBxdeISul8COaw8Cfa0K6VhqYz+jT5Dm2/JF3XLeCnWtd26wvyfV1CnRbc5nnnt6CzqJtAaWcBaiuTmkdR6r2+PZmRjzoW6pbQi01R9fb7j/j5M8wyaGFocczTjKqmjaWG80ZM4fRueygm0CH0zbqJWyjNrRxrNTWYNSJr9woSGnWOy6eahGCju28jgsokY5zxdqiXfabxlEdsBhAG4yim2Bg9da48nWc2hHqjVi8vjNug6EFMH6jhR+4xdm4A7KV5B++/8F/5JIAKidETwwvnJYcUU3Jv/pq3reOo5KzIlXQpEmRbvdTijOijtOdjm2r8yAxMsqYqxGTV1lpVzgq6a/OHnXacU5mNrRPtYtr4BTqCST1P1Hp09wvWYDSxD7OHbcJc0LnQS30jxgjjFrhBibluiqn8Rx5YuvqJ/mwz4AJLnpzWfA6VjXv1IKoUA/Tuf1hgz2EoZt1zKpOsu+VzCzyXr9rmznf+2Gs8QvQ0Kk6pY7hgxdyCk4q3jFhiQmH3oSn4IkAilteuPWF4Kq2R65Kh/HlO4KJCrtY99utf9U/aKVXExcmL/ITvN6c/ohH7fcKpnqva7oYYCq0FDWgBddEy08oL28RG3hG0rv5hNYKBxTIedPAKexEPqEQPsiu7YNGsucDAjeuHunf3fdBpbvW9WWR67VR85dbRW3lfQb/CCf4j7oRD6Uac8r4CoIbTuYrnPigWMB51G+1A85chWee4MH8gzmF5iDoDZynT+ONweTxaxzwAR8/hK9h15tFG27RY9FGO+iiqTYpr5Ppi7/w1zhFGvWrz7saD+seHVmxWMKu5QsR9WkmOOthrqeMZGelW8vmPRxJDyArlPKerCOBG5baoMY4B1//IdxX0vesA6f/WrwcwmTIwCmMkXGMs6isZ9VLS4fdpejMfw+wwB0VqvbZ+Vp/PJhdkGYQGIZozql40CbbNUlP2+u+TMFvtPuA+eBwH9p0pH5UkRcrEOk9Vu6IcuNWGioImnwFx5yHtq37b7VYAPWg3apHnbbHkT31E7qizLZ4PPwdzyjhXLQ9jtPSpMffkPVOvKlGqzisMCtwklOCJry0CtR7Nxmry2Iqsl73C0aBk9qcSWufzuBfwtkf30utztZhD2ET2UK2qDRrLufO40s/qJ+8D4NSJHIAKRxDPsGApE7iwV5gOmSjlMGby8JTfS0rL15KBVea/AVZp3vt19gH0EG9GjOlVSfjBEJU0QOH1fJx9dH7Yiy5PkK38Apiqo8UuUbfoXoxxleEH4jbewmQ6uSFSQw71HouUhMdrmFO8a1yMGnBv0AHP4BPiIexP4y3d1rARNDEMwbsSsdOVqwcSy7RpTzLLJxe13SxgVeA5Vk4RCMczbSj8JJ/Vl3ddVfqtZ+0USTnzQMn2YtfCaeug9GOU5bMPUs9wCXpr6/tlOR8LmmiUlnn+fa1Tc2fpVD5n22zwqt2IppzGu1Cd9ThS/Ah+B/tFOFnHjwIn4P/wI/U3SP6yHN0f7aFWN3RAgyfQ8CkOQ2pL9i2AEt3wkTgFL5H8xN2m6ABPnJVv4bM1KmeVLBIvbj7IfxdhFYhXY145TtCXxwjwrJ0lmdtfNRmLV9Q0OWrVHSPaI71C9uvoKZ3aI/txO12qWQPNs3XtHGPZ5zieU64LAOn/2WBk3+nM2YMlv+nsQMyWy91xaFYnXWLj3ZY3oOkCWa1YQfAwyjVYmwfUHUmcQrPCux5F8QU3AFF2QIqFQ5tol2HnDTWS0ZFf9ZkrN/oisEhLcc2cKLSsAxNtOCpwCnGSeV4QJtnnXiTFU6Hh771yuFH5oxwTsDlZNQeLtIAEvnba2y24Q6IL8n2MOar9rpgJjuvtR2lt21y9JZtefNGvrft4cu3LOVHMl/jeSmbGNHeo3brQMhtXNQRmLoOSOgjOol8VFkbSYVQVx2hs6uaLJBDVGlmgXBrkDqmFILwu5bntfjSOfxCV7E9TZkj9VedFXm3GcfB7rayAoqxBgmcKHspFBqtS94B5UM8lZaqlg26W3hCMCf6caeHq7nSzPyoly1Xdddt2ulscS5Bui6MpY1O07HGwGDR4ZaGXAFTXtSjzCqugiFfqWXBxW/di50mdp9859qeg9LD2vgNJhe0o7+aaKjv1EOLU4ETwZOCJQImvRSCBRjd/otkoqG8+iOp55T6Fc4aFpbgPbeP0MBM8eUquwnJqJ6F6K6nSVNuY+h97xmnsEPEmu2j2kqO9ZnrGBkmORb9TZqLyluCkvaxvOdYjXRWNnuGTm1X80dtsz9HmOfqMYtqitCfeahMKh+CH+HRAX4mhWAJf8OCDXMUAif8CLic9E8LsbnjFIGT5jPyZzVw0uMEWsQhle/B/2Ba+FMO2ah6DU9O6Amv6pl+7x+03a+lJnS2+DIYlXmZyFB7zHNAbwX1aSNJlcNwN/U7MMiKJvntUeXcUVIHW6blPTlS6pbZRYj6FGSbXFZA/tOBk+vJKLmRkxpL8sAzzTwI4IwapXt2ekXrsXoOrudneJQlvKNd/QFDZzq0zAGceQot26WdbOmA7XpRs5JmuwLsfZaTbH0EzqC0+pQP+pKx0iGfcGSIC5xV31j5DZhNZszB4ETiNpxf7z0yJ8StMzgjreIQVPG2K02CYlybo6PbIZ4bTr+P3eR1p+W36hE82QOVFhzJKb3lW9+81tPeumUvnJBji8ApjFC/C+Y9sz44K3g5Q1QSeN0GssuLUXUqy4/efll7PXA15sHjelq1RdhM6WStPOzx2j4HEjsFyS49hd5D/xp7H5UmWrXPJCmbbnaTFWaTFGp9XjfSpVLHjAadguwAwJp3R/2DMhozS8swZJ/U91E8ddNqh3Yj1n6p68LYcs3ATyetVB9pyCiYqKpMKh/CrTAKjPAbTFq0+4TP4PTAyurqBIX2OqGPLJQ1edFKLxOVeBg7fjOFMrf14k80EZJ8l3Qj2YVLeh6GzuL0TCXyEudlczHaxRhP9Sla7aJ6deDIZ6DB0HmswsZb9doCG4tkhsiJrdfnmWSnshF4SvZd/kOF+A5AL1xHZ9uei1HX5qoWWPI4xq00sl2FbvMrO95irSG3abuieFZmtZ35R/vQ00xL17f8Br5CCy3aqeaFNQRS8jnCZRLrO05mZwQ8zDHgjU+SzyEFPwOnV22OYou57Vls/A5+SQs2Pj9pxi3ZZpkrjvqqVDqY0yB5cH31Riubqt8RO9d1A3cu5XrtpEum4xXYkS/ccOY7p7ZveWD7OgnOTc2tRYUtJQs8r2r1lnTMkVijaclKONX2xgJEqiYaM0/tg/6cC5z+r92qBy378ExTEnmMaUzBywGnLg5JZ2kPAgQDQ3nSmm+tQ/ooXP0pIZTONCvPShz8aJP9oH6kQ3msDxorWNRE/8YLMWA+KO1WvWpw+4FT8oqxqLfLIFcLpnwy027BMSfizshWcZj8+ITIVnB+tRMH44GTtSPYCppBJzhxIXDKgOyZJ3NYOBGcDs8lsPPEq4l54xa37vlpO1HsRr0Gzqvm2MzZiYZ4uH3BxHh73+FjxcwHgPI8ApLtKHVaR0gn6pF5ddzKTI3gSJbez8eab2Id1SfmKif50VOchuX5wJb+et1EJPUy2gxo9E31q1SwwN32I3mGXiTLJMIfWHRrDf5F/KN+5ZijsyvF90ui2UnLw09n6ojxhLYj2WdrU9iFfmvgZcEOb+n0AEqLLvnsE35Dk5saOGmSwkRFctSUyUqdsBAwMWkRTJMbUtrpqHnBsn+CRLqCz7CiDqllJPISl6SrW3jK/d53J+Fqk8LDpqxOP4BbX0nuukfhNpzIxrO4sWAWZWA6QSrDvi8HHDWIO1jSw071IfhS+1pX80dEr8GdbfaI9lx/pJ8Zf698jcyVRsif4wmdmZZB3C5o58GQ2QY+R4u8+BjmK5yC4W9ECx7yGQp8gEXQxHwod8GFC57mJvgd5UWHVIfkVQocOjpVFj7wS8dRvdpWfoKNF0b6xl5PpoEHKY5kGgiUghHZ4dKR6vgJOLdZ9zklTPEqLPOiO6StkWMJNQkNqIedUPvSSn0I+s3XGH0fd+avZiPMYznWt+ptAid3bX0Mw4ANZg8C4/DiFFvKkqqlc9lZV5ya90oXNnLjJ3o6drKSRWkYvijFoMJTfKEp3EijKBgtlY8+By3Bap+jZv7s5HtF8PZBuTJwkqwxDtp1Qi7OmLzY4NiXVdTxlitePa7b9riFD4fkAZPV/V4mPChXdF1UE9NMJsbD8vqSxNEQQL3SXh7Ba4m1u+SpB0y8fS/ewOf9pMtlAEMnxg9wq/Kc83TunW+UGAUwzx2Biezn8MEKmYSf47uCiOw19KOnojbzS/iY28qR9ZfqEmsvJ9ljfJquDBhwS2nYy54dSMkWGRmNpxAoq76mg30ZsurUTmlcp/CUpmf+CVeb55s2/t2/JfexTzlGsq+qq6zN9nu54Nj4WsIIzfqUvpSCxYEaxT/KGuccAwU/m9v38B82ocFn4Du04KKgSana1/7LL/A8gYIoTVyA4Ut8YaXJiWy1PWWO7A8FB/lHs9IEtFzFV/5P7QUzQi6mJdBLleZ4hhXNgVNR00G/Rsy0yQb3JO2FAXDd2Yd/J1jqrySnbONJpevcCPmOkwnqdupzBn3HaJzlOw5EFM86+FOTld1MKFMxeypdXqIx183liXgpJp8CnLKha1NdPyRTBxxkZOcHaIfV5/uVpELuEF7t5zSxbez5a/aA34jAJ9+uh78RTH6F9vQRG6snvPViGnBX+PJDSgmm3PdY4zN6A0d4StWfuSx4T8uYdljNLMxDuqtoaE1HJ9lAvazrIwFqMqS1OqlGLsutyYS8g2XIBbFkBQ9Q+/QkkY51aPqvPVD7AdgQXMDdikAq3wXZ38bDx5ruxJjfcMcphksDycVMvqaMZ73IM3gy5n1iUTuiPGnNtz6ZwPMRoIBLlhkny0UVhVQMjgBKo1XSrI5cedGLNHGTI/2oOqg123zwRh5zA15dDWfQX2tceSovB0E5gybLtwFxaa1Ot+Qx8anOSE5G8gXd2ucip4zIQHHrXjgwjEoPa+q2G9+Vsltw5NygX/vn/Ew4l7Mxd07BziCh52zTK6DUWhwn2f4YNzHU/4Ts5W5CP+34PJ+Rf+gmYDU/Yp0phSwxNvRFsnm/TM3NjQS8qV1toC879JGcRAmbDOCl/K6cA//tmEvW3fbPvMJk6n3eype6GQURXOlYe6bUeFnC+IgOqexxTitV4cd4p9zoU/4LnDhzQiO/4elv8eO5guEoJYtOAAAWQElEQVRHyMufdFmMJr5BfkGTlprOcki+KrP6A6zmK86cF16mDaN8Yc5tXsqy22AYYg2ccmTP9ErYSifTbhe91yrfUi2moeeal96ZHPuOk4moiXLaF7Kd9YPpn/Z6tLKda3Br+5pPGmtZ17jZ6kwu1Jn6r23O0Je+a7vb5M/wrPRn/mqvFNyaV1tgnPId8aZfe4aSt+i1hWDqRZ80Ah5uCR39QaUjuvChjXyQ5iSkqqtpzUNDBzSqDIIrVZ3Kt0kr30t03FqaiGvLudR6rrNxaCCle1OtS/Lt6yEkZMz45vLPKGzyDpg+NnRFJ0iN2D3mAAxC75Hns6fUj4fz8bE2eBvzWwVO8IYhthSnA9zoZV9KEbbmQ2718ChFXuFkp0ZYyJG1qxwXW8KzvYBKAyeNodIe8xqAxE36q2Ana+dc8EYm/tBXyBrwNa0qC2NAOb6UULbKkrFzNJJ8YWli423KLhNljtRP0K4w1bm8IWLIa/mxLpxLODYqvWcdxxlNHya5QxpZy0dZaKLvxAR0nfXCfqbx30fY1rg8owhbpAZJ2XZRNhVjG8Ztg3ICUBvV/ImmDWW+NpBLNigZM7VGbYA8sY+Qu/HG/ibWaY9hT3MZdNne1LQXk39j3msio/oJ/PyKGwddWe+Pbe33VnOVhuUHxaYeat8rPeC1bqLWi8JR2iuGDH3IU5ObOZVvUVrlgZxk0uTFb/Ntu9KX+Tdh6JMTCn/SoBcT0fXUGnfN/aMFTq6FMJLo44l9+R2bKloKs4Ng0b3q5SdgrbGVz58npLKfeCuv2VLfdcrrf7gGBtm8c+0j/VOF1vxsd7VuzGPTI4RSbV/ziXncLnGvzLmNdivtjV03C1k7ApmdtsKJ8VLpcrrSy+UW69oqd9Wlw9Wk9Auc+RSabGxOVU+qPkIj/BP02H0KJmobuKln4PWYy5J9r73azu0Ev0kqnmfaVnc29uRM68AJDdXP0nYieka2fV0EsVqvvNLCecgu6xm7Sb50XDSvlTI2LFD5gYUXnI/RddLx0W/TI4DiuOJWPcM2XrCT4rjAwgkCg1ymUQIgwWuqPPiZr51cKWkF63yGTBOmwOj/eGwArQ/RNvpT87SmLFiklebYl1qzl48JTkwJ9KUAbg06a9uQOfU/ORofoJBL/UVn0hvtelvHTdrCE65qVM5UNakHN8NmAAN9UKuaTPzoa9Bw4+31XvIG25EBH2jURN/WWGAeHerLHt4o1x5WwI9orVrfpE3S6QozUM0nxjq3jyt5SKXbCoNewMnM1I2uk27jV9gA6fbQbK+WoaTyTFXlWQ7Blape5eeeen83SrkoRrtUHOeo/2tCW36y2RjDrD/ST62vefHVuK/qNClREKWgSSl946Reeb1EgACqPp/g/MRMzFvqvWl1kWT/QBlL2dhltgbRPuCBa4M2N5rLSebFz7XrD0HpRu1KzdeOlMu0gqfWFBsFT+Qf1CTq0LNOBU/VXnzsfdcp7EG2YHNaPIT7D09FlrQL2DMGhN8sA8hx5HVV26g208QDlriCI9/esapTH/baXA1vKvd2+6IkWcNvVp6wmjuqr7gtf1d9qvqq+Zml7AUc5WebqralukoTUwUOTIGTfBM7V25qVqc2SmdZvNx0Bo7wxJN68vOxgs04Z8vieQa/Bk7H+JcNKmvdAo7J7WBc1kXoTjhzukNyqXMD7qALrhQ06513kL5lT51AQXN5jC6pn/58kyGQNn43CpyCPwZlcvjJh/JzP4xhD46QThIqjXbRKmESsFJbwWr9mDeB2rHWbfISXjXWMd8xLBN0a32v7f0U5Dj1gTl5q17qDp2FHJqUIFeAYpfN6baBl8NBGtehdT34FuMwXOGpbUgPTsu1TPCOyRAOqTundm+xlw2eK4s5FkEpP9OAU35qt6MjCMLzr3LSOp07aO8y7Ys8sHF9DpDjwk3ajFSrcDU/Ym1LW1zJEkOrsQ67oP1Y3yhW1Tc7TPtLruhRdkqqE4wKzxbbXPKvTBNP9Ql5zrmu0rV8a2mEi07WGDP0LB76ONLJqn4Phl3gF+YTfI2hrn8FTDUlX0/1C3oKnhwWBrh7bXf5TJ7VtS+Nin73EY1uki8Kr41qvhN5mTLRL7qhrijd68XapkqrqjspcCAWuIyNTn2HgCYbwU70PQVP+YFIwVxcBw2P2jzgQ6nImJWek01O4GVxhbuC1car+hWstnnW+X5t7DA6qt9pdhp81H/V2ygz1MPhstlwck2T14lvkI9QSl3aV9gCtIP+fzcfFYvOWtRJ3zPeSix7RBjJNwjWCkk/7NnltTqltc0KVuuvyV+SaaZzPnCalD8RqrU+VlP9NcVjXYy+A9pHbZb14RAWoslHKDWU7vDoXe1ta95Q4aPTMvH8rYKnxu//48AJZRWlosquy5ov+nZlB1LiNqW3ZA8+YmXJB8jlCEcgmYJOlY82cxk520l1Y+5j64PfXubgeWvrzcUn6IEbRmKTI/AwEBCp8KMrxUtAjSMf9h/b4OGkuB/ZagieCJokV0uD1vYzHIRT7PKD5dwlQpQCanxDtF65JXoEsabexwt4ZxwXervJcdN2yWscEx+MrDydq+JLpv10IkvXuxiRmXXGqIZJkuYJJeEqnaj34ixPr2gZ1c/w51buOoDjWXs4i5e9mPUUes16XRNKs2bMrfS1gtGV8ANcb3G1eEo+mDhhuu9jW3aVKMfiSaSavAALm7HFHQKnFjxtVbi9OjtPV92iXt0ssvU21Hl/yCS3RDXY9UMCsSuOZ8mg9KlJdIpbNpv60VqLiI85KAJU9LAH2UZObMNu3DaaPSiP7WY+hKA8HI4zQKwAL2ArOYLm3GKvvOHXEPfgorOqX8GE/zzSwc4XDI/qF02uAh31X/V8H5TLz3m4bDacXNGSk1R29Ir9buSf+bFbFm/4zbCy6g8BaIu+5i+CzS+LcJ80+am5o6JV4Q6bZPx/5V1Jdiy5DXzPex/B3tr3v4/vYgaCQQRI5FD6pd50dmcRxBAYM1WpkvRdHvSUH/wfM5DXO+O4Gl7pXivtkujVO/emFZHMa9TYB0k9sNFr7/uhagzpGQsAZcuN84xeBUV2e8bDcqoqpvCH+OZDU/yY3vT3N39wqjVnXVnQVeOhknQW23XPZlVc7bqLMpo0O5aD4T+ql41PuRDrjSPuSqGOYcyhpB15nA6ODVRDNpTjpoMhGTcnHMEfSYbmLAA/UBs1mGVAPushyW5I8QAVNmmflQt4vkx8wicu/Utvxn18mpd1kebrFbXRVfLa6HuKXR+BfvLbqg3NK/6bGN02a8j7gWYkY1FMlN/hj147dEQJHphcQesa2Ncr5PSfsbqu5M77S+kt5+o7Bq2yYldzqbtT3euqukFLfPSGdcj+udwRVS+tLlv0CgjAgcSRmw4z5bPn0Aa2eo0Vb1jwlzvx1z0DbfCgE2+Gxhsi8YIIfwyAr8HNa8PklDDEI5+B7/a5YfSEyUxWngL94rpF8kXkayjlrnXXjMwt/V0eexZp1F7z5VpCxszlqTe80PQ5wAz4TDgNXY6WBTTIOW5Agso44IcrXoHx9nir+6TXyTve27i+oXfM/gb6JN/UP94+5S/5uFvwPmIeIjbM1+ox5wZ8nP+IB6X8xFt8QQBbJ2WcG/G07vMne63Q82PfAxuH1iMRSuP1Oy+MZwurhdbV0QqNuedkooOMXh1cZzRetz669hXt/cza9tqtPPrSxLIgTLaKiexqv0N9qiqm8Ic5/PGD00CVz1wxsJM/CNGSr7hxQaw3vYhMiWhN7JT5gBrSHN7k3FFZmLdmPlieR9KJCc/Jv4ujytwHJNGkWRMfjPx9qayT5J5PxsDYUOqwQMNxTh8RxTT0GBJz3qRoEeojw5EjzvGd4nEMavJjE3vKpQe501CH1YyNW7zOY9clWxkzNu1ksu0F9XJVvi/Vf0Wti6Hj0bnVfEXT8ZbwR4T8c0QwO4Sp/HtonytoRudjQGufNTOhkwPcgqf/vu+St8Z/BfOuFRFyFzd5vDrfB5n1RT1phxU14HliSU8S6Wp/rB6u3S/2f+Mt3Dv4ioHfhAEuQ8SDEx+e8OYFgYMPN/rz1TEPYRDJgIrDQ8FArv2Mq+hJbrKlD8W1YVSKjurwK7TfWQn/y06a0C89qgyNzWJNY11jWpecXbT548PwPmN606pV179W4OVsJzoGhSM2q7d6q6zyOjCrlmzxW82LWKZuTG9Tu0/wL9z+mH32pUI9yav257un3F3uNDztsyLvihmrHsb1ECUdrMDbMcXXKvmVLvRwSM9pjwN87jV3+yBoLqH5p0di+212R/3E49Xs7pjYh24nCN7pVXW6NGkEstnXRpVe1/VvGsE74zGNQUKOm4m4yG5txFxfAxCPzkF858EJXtDIaGYMrfYMZMkinJnQw4MTI/fknaYUrypwcu6oLExX787SLxzII8dDcZR85ePC9JfcmseOL714SzBUtbKnstWq/NFUWXJlnNO/ZFMpBgBqc68YtFIETJ3EjNcByU+U5i9WNsMGHGFpHZWL/4jEuFJm+IN0e5dEGogJ/2HT1lzJuuX36ee5Qw0+8zvbMY0yj8qH+A74TvZJPO6ftOJQ7trfoZY5jMg1G1qz35oHrVe46T9jdF3JnfdbNGrwaZ/jqi6ha6MVGvfH7tNrBhniQh28FtKpPaHOrpvecd3qAChpYK3TPzmQ6lg5HxN/8oUF27iPzL9uBXFAr5iBP41soY4xFNDGGl6P/BnPVBzYmVk6Kv+m0/tfFHDvr2nE2IX/GuBKMdM5NGb7Dv6njH5eWHMkJfl6g4ufWjDn+wNTPECjI+hLNL7pf/RsRFryk09m0M1Mlxt9dJKTt+vm3AzdEgttd/0TkRy/Nl1H9sWPKzzQMVfQsXq7yZI7U/SFjcTH2uQPHeVw6BvjSmfVxWLxmDVb+ypo4BIbwSWI/F2tYQ/1mZP0hKtVfrXHuuuWe40rfon2Ob/0pTyUUOd76Px0zgS3+hWMrHcvF/fdWrFPm16OGMZ5hlIBZn3U8LYOE0M9D3/4GmWfOCmG//7rnxUfUfzP/gFcSNU4rvPHyGJgOUQud3ohrze9M8kQeKZX9EKgxeXUVD3uVqXmF/VOp/L2CwJS5VM1sWPMnc2uq2JLV6v0JNca/FIzcEYDh0voSA+rsI5BsFpJf+lG77I+gT708R2d8DMvPmDiixy/0LHX0MURmMCJ3VgdLkojhtZQLC8eTxGMjWLGTZS+Do2d8Wt7xXLnQLnc6TzJTj+qndYnhD+Ty7+NTvYhehovF062GRhamA7WZV913xD/AnKwM6bet+TXCN+RuJ9Peu12jER5cMV8f3K4b9HwoVM8YILWXten3twqLq7QyygWOUMjBrH06VHi8o/R4L6hQ/MTOBNM+tLBGhZTuZULaOjeyZV7zSk90TZrgcmEc0ZMOnhp8lVKcX0VdAebdRY7uyHOz1fV1xHwI93Mi7PncwUaB+SoPc794Ul86LE/oPLweZxfZKaQ/oo8zVqqw+8UXc/pJ91OvvOuZkB+tO52b/ZX2Le2Mf+fTcnoZHuZ/EnsiPE2/hmndLCKVn7wfxeDZB6/MMLW8sJeMvnSPEOm90LyzVsUYhLnu6tiF6pi036t0ZrtJrCEJHasTfxqW/2fSVf5M6TH9GTbyxHDGUfvOesTs7ArTRj40YnG/vGDE+droKNJcWJg88ZHmtGA5kH9Ra8kPVnRWmVb175wVYe75Ty2b4fam+iomYtzM9YrO2jvMb/W9Qen4Wq0b16c1tQVjo8B6IxNuSsH+NfJuyB1ocdhWaCh1z04ETN9OA6tR/1djIG5OK7qIR8r71UPBypOXPAHtMdKfNXwDpT1dds77U52lQsw/wS383XyNKeeq3jQVj9OS3F4L9AOa0wl5i3ih5xnyDba7ZyuMZw1crnbfZuufpDHs4dqI31ev9zVfOpO+nV1v7p24IfnqPmMizLGKR6QoLe+QeLNHjLhrdQsIOH5g9NAvxxNyHDt8mjqNUQpn1oKNGTgyf5envl7bWkzLTl7MYfg6A45Mw03K2s3/Ar9fO18wc0Wfq3cz/EVO2rshx6ccvUfqZoPTsMAYWF2+EeDMAf1BKbmDrQfGofgAWjEwCj4WuRuuNFX+Jva2PqcOn1qVt1OnjzVMDlJKTatKXlP7b15a/mpXR/jU52eo3kTh3Sw1nre+U+ZYscqLETmfO3pg34w37pf6uEfvc9DevX6SPnPKcXmCDX3lHS6KSX1Rme32fdZuzPfq9h2DO0/jSd9CwEr4jhjcY2k1bcxF8lMasJk/wcD86ZPnCYNg48+cYK3+JKDdZ0YToZBHr1PFoOieNCQzejWCpWOB349zsIt4Kq4dr38xKGB8ljmLaFYZdMqFSYGqtRjVFH7q2Eb7ZoYWMcZ//MijWaiiUOSGZIS7jKHTojkE981Uc/SWjVhPHSNeuhmkbUZNtM3tRBFsLjVK9mxc0yJuSqmysWONqqCgUXItj9Nv8bJuN/4Yy1X/X8QhXJOU2ASN3m/R7n/zJ3+XNZHcPZycGL4Yo2Zh47qlDTwxN+xNZc7H3vJnmM7rWV7Sr7L6WPDPHGm/PXOs/dDM6aaIZfqB7VlTaUDbPD8kwHwYFt0bN7YKWjlwfsBPoVOafR3boHleLL0+JU7ZLt/6cc6ioM7QIfncpQSeqzDrCiXUKP9HtcIOOyUB/fF/1c36tFXQQmmFBpoK0MjvWJVK9QVh1bR5aEJn0DhDcb8JIq94MxxJq7+OERAHy+l5ys/xaU1Ilm2xWZxQSyAwt03vK6oe41Fqyd5xfZ4qwS7z7BO+59w6v3iLQLvK6d2rS/rWLXgb+fnPaGrj2Pq2skV6Il5FZdioHyvM2YUvDjja5X0Oev8nSp+QwCSfC/kevW6SMlJ+fWDmdzrcVqQg/hYq65OV1Ynf8//1Ljn1PjVi86mxvnk90kuD9X/4oq4WD0WzZTWzUSqo9jwFf6wzgcn/QE1WP3n3x/8qB5+zyRcjhc0nSc4GkwMIoPRGrvJGyM2tis6KsZrxzPxJGvhFuipGJx7ecUixHMDFWd1WXKtojnwwt+EN9vRLkrjipk7NHFws6kVwONX9uL5ClqnI4QrY0gHNwweE3UEofi8jou2Mu2YBn9Jus3MuLjnxpxcIv1UoOqx1m9RVOO3+qfenpPi0HpafIvDmtO/11/4q7diHGv3hYBzFveHuEdoj3sHc1or7ixNmk9+Jdd6hNUwPtFtzF+zvKZpNCY6yqxez5qnQkt5zH6/Qf0k07pqOq9zAMoGOn7KmWxC1xqBliherLgX+DdehCv7fHORcQGTGEHhJQ7Fi43sUw9c1eheznyor9ywy4HSbM6Zi/ziW4Cp84u/46T6RUi/9dJdOz/ylTWXea0puXpA0oO4VvGRs/qLf8+PvyvLPqLXfsrPvmomfA7q77qesaaNozXFcbHRbu+0qSzySb4UbY6Tl9R7nLT5U0q9+RSnj7Wvr+4Nfk13vNLfFdCJyZjZ8w5zmW6EYsYqWvk7b0jjlgGZ5ldzDUjo5nshOqHuOYdbCLGVT2wUR6e386Tr9rvOm71w3uh2Ou7f6U43e6p7b6+FgmsmnuLrfT7V3uU+U07P2KZq+IkZGAys8Q0h/qSGYugenP4POB59bO/942UAAAAASUVORK5CYII="
-  },
-  {
-    "quest": "Calcolare il tempo medio di attesa (average waiting time) dei seguenti processi, assumendo una politica di scheduling round robin con time slice = 3, nessuna attività di I/O e context switch trascurabile:",
-    "answers": [
-      {
-        "text": "6.5",
-        "image": ""
-      },
-      {
-        "text": "6.75",
-        "image": ""
-      },
-      {
-        "text": "7.15",
-        "image": ""
-      },
-      {
-        "text": "5,85",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": 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yZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE+Cvy4kAsAAAAcaURPVAAAAAIAAAAAAAABAAAAACgAAAEAAAABAAAApm07n7/lAABAAElEQVR4Aey9V3+cR7buV0gECIAIBHNOIimSkkglKo000oQ9exzO8bm1P4Z/vvOV/Rl84w9g++w9Z+/ZEzRBGuUcSJFizjkiESBy8PNfbxfYIhpAA0R3v91YJRW70f2Geld1rVrPilX/6/91fHJ8YjJMTk4Gb04Bp0D5UaCutjosr68Jjep7tzSHp7e1hPbmunDkXE84cr433O8ZCRNa3xNa596cAk6B8qRAS1NdaNO6XtW6LGxf1xi2r28KDwbGwg9a40fO94TRMfZxns3XeXnOsI/aKZBuClT9z//HN5NjDhjSPUs+OqfALBSoF2BoXF4bmhpqwnM7W8Pzu9tCR8uy8MWJrvD5j53hdtewAQYUA96cAk6B8qQASoAOgYV17Q1h9+bmsGfzitDTPxq+PNlla31kbMIAg2BDeT6gj9op4BRINQWq/sv//uXk2HjUTKR6rD44p4BTIAcF6uuqDSw0CTQAFl7c0xZWt9aHT47fD58c6wy3OofMujA+keNk/8gp4BQoCwqsXJFYF9Z3NMiSuCI8vTUBDJ8e7wz0kdEIGMricXyQTgGnQJlRoOq3/9tnk6MOGMps2ny4ToFHFAAwNGNhUH9pb1s4/HR7WNNWHz48et/6jfsRMLjm8RHV/J1ToLwogNVwdduysEGAYf/2lrB/24rQ1TcaPv6BdZ4BDLIuuHdxec2rj9YpUC4UcMBQLjPl43QKzEABszAILAAaHDDMQCT/2ClQ5hSYCTB8JMDwkQOGMp9dH75TIP0UcMCQ/jnyEToFZqWAA4ZZyeNfOgUqggIOGCpiGv0hnAJlSwEHDGU7dT5wp0BCAQcM/ktwClQ+BRwwVP4c+xM6BdJMAQcMaZ4dH5tTIA8KOGDIg0h+iFOgzCnggKHMJ9CH7xQocwo4YCjzCfThOwUcMPhvwClQ+RRwwFD5c+xP6BRIMwUcMKR5dnxsToE8KOCAIQ8i+SFOgTKngAOGMp9AH75ToMwp4IChzCfQh+8UcMDgvwGnQOVTwAFD5c+xP6FTIM0UcMCQ5tnxsTkF8qCAA4Y8iOSHOAXKnAIOGMp8An34ToEyp4ADhjKfQB++U8ABg/8GnAKVTwEHDJU/x/6EToE0U8ABQ5pnx8fmFMiDAg4Y8iCSH+IUKHMKOGAo8wn04TsFypwCDhjKfAJ9+E4BBwz+G3AKVD4FHDBU/hz7EzoF0kwBBwxpnh0fm1MgDwo4YMiDSH6IU6DMKeCAocwn0IfvFChzCjhgKPMJ9OE7BRww+G/AKVD5FHDAUPlz7E/oFEgzBRwwpHl2fGxOgTwo4IAhDyL5IU6BMqeAA4Yyn0AfvlOgzCnggKHMJ9CH7xRwwOC/AadA5VPAAUPlz7E/oVMgzRRwwJDm2fGxOQXyoIADhjyI5Ic4BcqcAg4YynwCffhOgTKngAOGMp9AH75TwAGD/wacApVPAQcMlT/H/oROgTRTwAFDmmfHx+YUyIMCDhjyIJIf4hQocwo4YCjzCfThOwXKnAIOGAo4gVVVIdRUV4Vq3uh/WubF3k9MhjChfyYn1fWJXlLVEESX19cEXnO14dGJMDyiPjaRPEPKxp9rzJX42VICDNW2nkKyppjMR0ur4FMb16itV/0xwbr133zB6e43SCjggKEwv4S4PSe8pSrwd7EbfAR+gkwAf0Eg0P/enAKpooADhgJOR11tdWhYJqFbHS4EH8rmRaPjkwGhe0R9HGZh4KGAA5rnpTeuaghb1zaG9R0NOZnora6hcP3uYLjbPRzGNPZxPQ8Mz1txKbBUAAMbeUOdAKzWU10NG3uyuWevqUJSnt82v/Ox8YkwNjYZRvU6qldvToFiUMABw+JTGZ6CQg/FHnwFXlqr98Vu2jolB4ybPDCW2UeRB7w5BdJEAQcMBZwNtPMtjbWhpanWBO5qU4c+YkZDI+Ph4eBYeDg0LiFEgogYRJqYxLM7WsJLe9vDge0tU4ABBhvbicsPwtFzveHMtX4DPSMSnsadyUXyFO11qQAGNvYVWk8rltfa5p5Y70Tm7B9lAak+qd/2EFY1NnZZ1ga1fof06s0pUAwKOGBYfCpjVaiRPq9O/8BbmsVbUPQVu41L+dAvOaB/aCxRICIPgCK8OQVSRAEHDAWcjI7WZWHz6uVh85rlpsFAwEErGltP/4hp5+9IQ2/Cx7CsDXLvSUsDLLz5bEd4YXebyWQ29KzxHznbE7482RWOXXyQuCZhKXHAUPTpWyqAoVZWhY2rlqs3hLbmOtvYsTQUCzCwqfcJ4Perd/ePhs7ekdD5YMTdkor+i1+aN3TAsPjzXldbFZYJIDRKubdBvGWDrOlNAg3Fbigh7vYMh3vqDx6OhYHh8TAgAOHNKZAmCjhgKOBsbF3XGA7ubA3P7WoJtdJgINwAGmK72TkUzt94qN4feh+Oht7+hFHE70v9+sq+leGdQ6vCy0+3G9DJwgo2tG/OdIfPjneGo+d7DTCgbXXAUPxZWyqAYZncBbB20dnYcfdrWFYjl4Li0HxEGr8uAQRAwvV7g+HSrQH1hw4YikP+JX8XBwyL/xOAhwAWUEDsF1/Zt3VFgM7FblgX4CUX1e90DYeuvhHxmtFiD8Pv5xSYlQIOGGYlz5N9uX9bS/i5BO6fH1wVEHbQZKAlje3izYfhyPkec+tBs3C3ZyT0DYzFr0v++vqBjvCrF1cHgANgAfNtdvvqVFf4+IfO8N3ZbnPNMMDgZtRsEhXl/VIBDLj4vf5MR3jjwMqwc2NzaGqokTYQwPDT32WhiE680W3F7dwS0D97vT8cu/AgHFcnWNGbU6DQFHDAsPgUhofgirS2vT68ur8jvCbeQsxesRsKw+Oy1NMv3xkIt+4nfKbY4/D7OQVmo4ADhtmo84TfzQ8wyD1JoMEBwxMSfQme7oCh+IDhXAYwABocMCzBRVeCR3bAsPhETxtg+DEDGG46YFj8yfYrPjEFHDA8MQlnvkB+gKE3y8LggGFmavo3M1HAAUNxAQNWhrMK9AcsOGCY6Vfpny82BRwwLDZFg1kp02JhACxgYbgiCwOAAZdlb06BNFHAAUMBZ8MBQwGJ65eeosBSAQzEK7y6f6Vc5NrDjg1NoRGXJPWcLknCEHyOF91c+dWJuyEl8FzxN7gk3e0eCreVpOCCYo/Y4H+81OcWhqlfor8pJAUcMCw+deEfZEZa3bYsHJbr7WHF661bObtLEolLcC22eETxl8fVFUkZhaRGC1kP4StzeS3iknTqcl84oX71rlySOofN/XHxn9iv6BRYOAUcMCycdnOe6YBhThL5AYtAgaUCGHjO55Wx69Cu1rB57XLVN6mxwoK5QhhqtKHXK2bIYocy8UMzpUskG8lDpTMcVGaS7M3+8amh5sL93mH1EdMCYmWge2KwxynlfxeCAg4YFp+qxEUBGqDtc+IrB9VXKbvhbC3y26aGWlNIxHow8RxAAjWWqNdCXB/p06m1NFsj89r56w8Dro5YFpKMSSOzneLfOQWKTgEHDAUkuQOGAhLXLz1FgbiBoSl7aW+bacnWtNWHD4/et35D5u2o6Zo6qQzfIPyTxWTfthVhvbSAZDih2FJ2quL4WJYqUYJAYoWoNaEAC0Wu1t2nFKkPhpWlbNSE/5kqOCMAcCwdlwHLknTbsyTloql/tvgUcMCw+DS1wqpkSWqqC0+Lt+zd2hzaV8wOGJoBGK31YaVABlaGJF36o7FRP8FqtQgkPJDlIKZJfXTE9HcDUlhcuTMYrtweCKRZJxtbl/iMN6dAmijggKGAs+GAoYDE9UtPUWCpAAbcAKhpQm2T9hWZOgyyIuSyMEATK/KmDChr2hrC2pX1M6ZLvCqf4YtKkXrz/qC5D8zkQsDnaAIfDo7bhs7GjiZwLneDqYnyN06BJ6CAA4YnIN4Mp2J1NP4pELBRfGXT6gZzUZrhcPu4VeCCTEp0lDTNytSWrYygqGP/4KjxittKkXpLMU8oGWZrnJNkSpTiQiCjb0BF3MRrvDkF0kQBBwwFnA0HDAUkrl96igJLBTAQk0DVdIAAmkE0e4+n+o1EYQNva64NbO67lIJ1z5bmsHVtY/z6J68EGn6vIoRnrvVZdVVcj3JlPgIYUFhxVJrDQW3waAWp0u7NKVAMCjhgWHwqRx6yTAXcooJhJtfFeHfmAV6yVW6RxDusVUeBERvujdFCQI2lc3I1IhXzbI0YqlisDevEyOhkqoq4zjZ2/27pUMABQwHnulCAwYKsMpFWqh1tUVePB17xWJYdPvNPfG+veT5z/nUYelS4bdyKt1ngqAS7qfHEN7ox957MqGNdK5vnJORx2FIBDHmQYuoQfJPZxFeqH9zVFl5SMCPuTLna5z92hg+O3DfQACAYATB4YEIuUvlnJaSAA4YSEj/r1qvl7vnUxiYpIpos+QIJGLJrN+CGhPXxjhIk/HD+gRU2vSxXI29OgXKngAOGAs5gIQADGlUqRqMFwUUjdv7m8yp9j7AzoRirUflcoy0dlRCEXyV/o8lAFspHIJobMHSrcNt9pYXtkStHMHcOxoe2Bh9yAk/R4NDIMEMn+IvxIJgxxpn8xQs4LRV3aQcM06fUAcN0mvgn5U0BBwzpmD8HDOmYBx9F8SnggKGANF9swICvNgABYYgMMUnQZ/LKZ436DCHdMjQIKAxK60/mF0ydQ+q4USCwE7wJcJjL2jAXYPj6lADDsfvSovRa4Gl1dZA/aFJ9t1kZJAANBKoCGR4MKPhLVazxyyQrDWMCxOSTzrKAU1QRl3bAMH0aHTBMp4l/Ut4UcMCQjvlzwJCOefBRFJ8CDhgKSPPFAgwI3GSDwS+brC8t8uFe0VgXGgUSGuqrBR7UAQwS0mul0QcwoMUfHJaf9XAioONrjW9lvwI2EdrpibVh5hzRcwGGowIKX5/qCqeu9AkY1FjwGGNq1vgIBiOtZV0WYCCYi4wRVLPuE4DoYyx6D7BxF6WF/xAdMEynnQOG6TTxT8qbAg4Y0jF/DhjSMQ8+iuJTwAFDAWm+WICBYM91HfWWSpICM6RzW6nUb1gYLM+8XIBwSUKjj0sQcQS4CJnrT8YVCMBAkOZ9pWu7dnfQOtaH6K6UiwxzAYaLNx+Gs8obff3eoNJWKnWlskVg5ajPWD9qa5LAVCwj3It81AAVLA34eV7VOMhQg78ngMFBQ65ZmPszBwzTaeSAYTpN/JPypoADhnTMnwOGdMyDj6L4FHDAUECaLxZgIA6AHNH0Hesbw+r2eqWKrJ/KFIMbErnoM+EC9kRRACdWYUw9Fqe6odSRxy48CMcu9oYe5Z2nsAwp3XK1uQADRazuStjH3aiteZnlsm6QhQG3KcYMUEjqYCYxFcQrMA4sDfQfLvSGI+d6rWquxTJoGHO5SeUa51L/zAHD9F+AA4bpNPFPypsCDhjSMX8OGNIxDz6K4lPAAUMBaf4kgAFZG/ej1uY6CeN1Yfem5vCUOjno25T5hc/qZFnA+pAI5tkPknyWaOwTYZ2A4yGlayPXM2neSPdGQa/bSvd2r2fEshcZyMi6zFyAAWsB7kVYDpJ81EncAuN5PN1lHAvjiO5RJy/3BVJaMhZclXrVsXh4mx8FHDBMp5cDhuk08U/KmwIOGNIxfw4Y0jEPPoriU8ABQwFpvlDAEBXz5HjeLovC9vVNBhQ2CSxQtj6JZ0jcjzgW60J2tqHks/hgdrUk0FmWBmIYsAzc7x0JZ672hRMS2ilHTzwDlohEsE/OnQswWApKAQDOJVYhcYlKbApc4fEKvFwbdylAAXEWl+TSdEEd16bonuR57eO85f/qgGE6rRwwTKeJf1LeFHDAkI75c8CQjnnwURSfAg4YCkjzhQIGXIvQ0mNROLS7TXnkW61KLWCBWIHYEMABCuZ2JAEcYZw6B2Z1UMYi3IJqlLrIUptyTZ04JbCrMMyR8z2BHPTfqWgVhWLQ/vN9bHMBhnicjUPnjevNpF65hI3D7p8U18plCbmtCpg3ZeW4ojgGLA0/XnowZ0XMeE9/fUQBBwyPaBHfOWCIlPDXSqGAA4Z0zKQDhnTMg4+i+BRwwFBAmi8EMDyUBQBXI/pexSy8vLc9vLCnXUBB2YfIPCRNvon0+geXIIsHUCwCr8QkkDbVajLofDaYte0NYZUCpQ08KLYA2DBOWlVJ9Wev9VsswylZGe7KLQl3pWwN/1yAIQErSfXbzozVok9jIiYCNyUCs7m3BWgrtoFUsMQ3xNbdN6KKmKMWNP3tme7w7Zkes3zE7/01Pwo4YJhOJwcM02nin5Q3BRwwpGP+HDCkYx58FMWngAOGAtJ8IYCB+gQI1suVLvXA9pbw2v6O8LKq1MYCbcQGoNGXLt8Cjq+T8ejeQLhxb8hiEgARpF8lg9JOVaBkDLs3N2eyKcmNSQI72n+ucUPZjS7dfhgu3RoI5xXXcE6xBAjwsc0FGMyNSeADkIFb0/kbDwNWA8ALMQk7NjRa7MU2uVQRc9GubnUZMkEXPCuZm24pjuLT413qnfZM8f7+mh8FHDBMp5MDhuk08U/KmwIOGNIxfw4Y0jEPPoriU8ABQwFpvhDAgFtQW1MS1HxgR4uBhRfklhQb7kfEDlDB+erdgXBOVgKChq9nAwbVRAAw7N7SHJ7b2SrQsMIEdgKoAROxdUnDj1XhpjInHc2UsL/dqRSn/CdAMRdgsBSpAgedioc4pXiI01f77VpJAPOoARbGsGtjcyD+YtPqBrOSAHpwUbJYBj0L1o2Pjt4PH6lqNOAhVoCO4/TX2SnggGE6fRwwTKeJf1LeFHDAkI75c8CQjnnwURSfAg4YCkjzhQAGXIU2dDSEDauWh72yDAAaSKcaGy5HpDHt7R+zYGEEdVyLeuWO1COtPt/X4tIkSwJC+g5ZGdD0b1vbFLauawztyrAUGxr+h7JIEAT91cnu8KU6NRoYA30uwMCxl2WhuHx7YKq2Q3cfqVqpuTAROhRzQd2IjXqWA9tXmLUDZou1BLcp7gEAIgD7Y4GFT451ykoyaPEUgCJv+VHAAcN0OjlgmE4T/6S8KeCAIR3z54AhHfPgoyg+BRwwFJDmCwEMePjvIoXqxibT0G+XwL9Ngn5sCPl35PaD688ZafSPK1D49BVlOZJJAOEbywAeP1ynXTEE1GtYv6pegdNJ8PR6gZHYYsA0loIPj0jDf7QzXLz10DIYjUlgf+1AR/jVi6vDK/tW2jUfT5VKoPJ3ij348VKfXJkUjyCLBUABlycNZaoew8qWuvD6Mx0GQEgLG6tWMw7GSywD7kh0QEjiqjQeh+mvc1DAAcN0AjlgmE4T/6S8KeCAIR3z54AhHfPgoyg+BRwwFJDmCwEMtXLX2a/YhQNyI8IisFECdraQT90DNPpo9qmngHWB1KS5GoHSKxprTdN/+OmV4bBiIbasbbQCb9nCP1aGD3EJUudauEWh4Sd+YjbA8L2yK31xoiv8cL7XqjcztlyWAeIX3gAwPLPS7s+4GpXtKYY/E6z91ckuWTi6LL0q1hJqMnjLjwIOGKbTyQHDdJrM9Imtw7gYdRAgPu0tjjl5Vd2ZzIAZOy6VtHJ4jsyw83pxwJAXmQp+UKEBw9RvOxPr9+i3nSgEy2B5FnwO/AaloYADhgLSfSGAgVoGh55qDc8rbgFt/CpZCEinGhvC9WniBa70KWB5IFy9M2hZhuL32a9kWuJ6LY0S2J/tMKGdStE1NdWm/c/wIwUtj4VPfuiUW1CnAEO/WQkG5VY0F2D45nS3WQWo1kzwMlWcqa/weGtpqg2v7l9pfasAS4tAzAqNKd6fmIfvz3ar94TLSrF6XzENuClNv9LjV/a/oYADhum/AwcM02ky0yesQ2KKaNn1XGY6Pg2f25g1EGq98D7WfInjj4kdKomHOGBIwy8vyM223jwAdskLIHH5bfqJUu+B4vrudA+rD0mZ9kDxgb2m5Mt39Pbb1j/SHU79rjmX3zZuvJUGhPOlix9Xego4YCjgHCwEMBCU/Mq+9nBYnViGVgVAt6jHRoDxDxd6reO+c7tr2AKX4/e5XhGefn5wlfWnFIBMpqIkW1FyNIK+uQQphgALQ8xe9OocFoavTnUZyPhOwj6uSHQyJz3esHJg3SDbk8VRyOLQ1rzMNnqOxTJx/GJv+FEuToAgajPcksuVM8bHKZn7bwcM0+nigGE6TbI/QRiJyQdiBjZW7piynpFQAZfCKHRnn5eW9xYHpVioGvVaas3oFYFqTPyHZAoWhyWvRj6rlOaAIR0zWWjAwG8bRR9xfvppG5hnLY5oXWLBN9CgBcoa9eYUKCYFHDAUkNoRMLwtYT1WQoYZxEaF4yPSPhyVhp5sRXfVGyXc/0zWADqVnhF86LHdE2A4Ik389+d6LC3qPWnjOxU/MFsDhLyZueZTm5rseg26ZtQqAhA+V/zAZz92mYWBWhBUhMYqMJtL0nwAw0uqJ/HinrawTRYO6jLQ0aTQcIk6KYsJVhNiKKj6DBiqoL0+edAC/euAYTphHTBMp0n2J7gFooholfWP7GlkZkMAsSrw4in9shgOii+gBEhjQ3heqU6qZhQSzctrbKzEUXUqNTRKCDoJGCqlOWBIx0wWGjCQmGS99v51HfWhXqBhmbIeAuTZ65ER+F2zZ7M+vTkFikkBBwwFpHY2YECjj8ZgLsBAcbZ3Dq0Kv3h+tZk+ax87B4ZBgbNv5Q6EJh6wgJvSbI0Uq2Q8eu3ASlWPbgpNukezYghiHAOMh1gE+gWlaH3AZgtgULDzYgEGUsO+AGBQXAYuVh0t9VOAAQvHWeo4qGPhuKi6EJcEHBwwzDarj75zwPCIFvGdA4ZIidyvZC/DgrlxlbpcH8lkhuaSFM3UU0GBQcaztMYSbZfiYfu6JsVELTc+uUbP0yu+RXzXFfU7srziFkJNmEppDhjSMZOFBgz8pqnBRDr0Ju3TgHviCqmTRNwiv22AMevTm1OgmBRwwFBAarPo35bw//ah1QILSSpRKi7HhoUBS4FZGGQ5YJNGW/ZLgYVfvrjGBGuOj4I9593VJviNMhN9c0qAQTULYBr5AIbXiCEAMMglqZkYAoGGeF0Aw5cABgUdX5CwAGCgFxswUPwNmjhgiL+Q/F4dMEynkwOG6TTJ/mTzmuVJJjb5YZMIYcuaRgMMZ68laZqx8l1XYUdcHtPYSDX99JYVqvHSpBTUpKFuMF5IfNcZJYJg7NSmIXtbpTQHDOmYyUIDBpR6L8sij1UeeYCYv0FZ+n6U2+4xue3y2wYMY3FYyg3ZyDIuShkbZRmUjIArLIu4J3pbXAo4YFhcetrVoqvNM6qh8I6Ef6wF/LgfF/7RphPoC2iIgb4wiHeel4VBIAPGlNPCcLrHQAMWBjbEvACDLAyvCzBQRA3zPZaMuMiKYWF4SdaFF/e0P3JJkjtBpFN0SaKmxCXRxF2S5vejdMAwnV4OGKbTJPsTBG0UGvukxcT1cW17vSUZoPo7tVBI2cx6vHhzIPu01LzfJ8DA2J9SCmrAAhYStK64NVJA8pqeA7dGBwypmbKKGUihAcNu/aaJYXz56TZLDgJgwDXwmGIXAQz8rrEy4G2wlNtyuVqvbk9Sx1tMpoiBWyVK1ei6tZTpU4hnd8BQAKoiCJO141kBhl++IMCgTrxAzOgRb4npn4BhQAPBzPjeAhhigPIabeIWoCy3pNiwQphLktySqNCMS9JcpklzScrUQWCDxcRJj4ABf8gvFL/wxQkFPWtMfYop6BtY3BgGNCYv701cktoUv0CNiAgYuB9Bz9R1uCx3JCwnVHx2l6Q467O/OmCYTh8HDNNpkv0JwjYazIO7Wq2Yo61HHYDyoad/xDK7fCPFBGsyjQ13DdJPw8+oIE+hS8DBKQEGgM41ZY9DsJorviuNzzbTmNzCMBNlivt5oQHDHhVsjYlPyHBIrBFZCwEMJDwhMyKWPwTjpdrw0yD7IgrQnVJ+IM/QqEWFXHVWrlvISt4WlwIOGBaXniYE1yltKS5I5pIkawEuSfzAY+q/eEtccL5WLMK3cjEy1yK5F6H5p14BMQfr5GPcrIVAzYLYSDd6VBYJgqUxTcI0+CxXM+cn/YPwRBD1m8+uMhM+Ac8EQkfvKADDZwQ9q2P1eKiYArT+i5UlCQ3JYcVDkCmJGAaCLMn+FAHDVFpVPRc+yDwPFhc3KOaa1emfOWCYThMHDNNpkv3Jni3N4ZDAAlbQjtZHqZvZZEmscEIFIamvgrY+jS0Cht0SrrAuEIsBYLDkCQIMCFUOGNI4c+U/pqIAhv3KlKj9ErAAaMDFJmZHXOqAIZGlgiVOeWZnixSzrYrLrDEFI8HhKDmwxOCB4W1xKeCAYXHpaW5HaPQxl+3XZhyzEyEdmwCfdT98bSlYRrYhhGYCDAEIL5GCVNp4tGZo/ih8FhsaM9KPsiiu3k1SkOLPmKshkAMKyLz0lqVVXW2AgeBrehTYCTr+5Nj98AmAQegcFyVAxFx1GPLNkoQmgBiKV9Rz1WFAq/mTwm0KVOzt98JtueY012cOGKZTxQHDdJpkf0L+eITuvQIOa8nIImsm5nzckfD9R5lBUUgAfBqbA4YkKLZLSqaPfqDoZmcYke82RevcMlvYX6wDhsLSd66rJ3JNVSDRAana8V4gLpOkDcQvfJtx2QZYeVtcCjhgWFx6hjqlTUVARiuA2R8h+RVp13M1AvQ+xxXoZGfolwtQv7T6WBMOCjUfVPE2AhPXtjdYLEM8H0vEGZ2H5u+Sqj2zKLA05GrETDCe5kbVYXhutYGGnRIUqvUZ38WGReFjNh11AMPwaJJOMV/AgEsVGhD8LLMDjVjY3AWLwmuZLE0ABnOJkiWF77AidMv3+FPVgKAWBFpBwAogxlt+FHDAMJ1OpQIM9pvXP7apaVjRqmjWMklyCOVWG0Cv9tljQ2dZ2jksjkxDACQPOy1xbeQY/ZF1jWwhke/4m/vwiuthcl27hJ1HZiSKOG5f/yhomA0Xl0BqoVzXOozBlZmz7FqMg2eYqXFvVCM2PnufjMVOYTw6kTElxyXPau/thOSqPBpj5z7xuTknuzlgcMCQ/Xso5vtSAwb2yMRPf8T20Ckew/rKrBmWS/aaYU1lr7O43OIayz4WWtqx9ppZy3ofj7F7aCXzGq/J9WNL7s2/Cb+Krs+sZTtXX8EJeB/5XXIdXU//2Zkcq/Pj+PjDPtdnSTxosNgrPCfekLs1rtwUjbUELlLCIlddEi+Lze6rK/DqbeEUcMCwcNrlPBNBBfP4Jm3IBC/t2y5f220tOY89eblPWv375gqEkDw4PGHWALR+T29tlvtOksGEa8UGqAAkYF1AAwhwIBViroWAe1Nbc21YLZeDl2SxwGd5s7KhVE0Vg0kWLnUXPjx6T1oqAQa5JFEghuJHcwGGuSo9W2YouWeRL52Aa7I08SwIuLhEmVCgNO9YTbj3x7JyEHRpBWqkKfCWHwUcMEynUykAAxsjc0HcEa9Tv3PtevzWAdMAYdY664vNEJ/b2NgIsU6yNrRs1JJteEgAfjhTD6FRFkhSLVLQyTbBzHWtmJMuxcYLqEDbDICn0FMSs1SrWjCZbV3HoZHDLRDrJZo6hCDGiHWBNYi1kwrwbMDJSKq0IU/Y33xm97ZvHv3D+K2gmlkwk+JT0IRnTbr4SqYwXGLlTI7hmeuVa57ia1HwiFZOFBEUgxwTnbhnbA4YHDDE30KxX0sJGIhjuNU5bLFGVJSO6dqhAet9SGuTNR/38Egb1hY1HeBJSbFDrTT9zzojA9OYzmF5scbiOrYCchk+xrpkDcJn4C1o8lmXrNtlWr/wK1Yv/IfvE2uX+IxkEIR5mo2LcxmneJpYQWjg+rb+H/FN+CSd44ckE0UeAH9ijJG3buhYbqniqRdFIhcKThLrkcR4diu9MrWcEsUD12PM8CFvC6eAA4aF0y7nmSwO0v0h8KO9I2UhloJc7Uf5CUdBmR8zCwlBhxzjaP8I6CGoD/eB2Fg85uMvP+OTlx9YhiXck1jo9OxGrnX8e7l/3GCxWMAoYAAczyIEhHzw/b3wwZH7VjiNxQUzwCowWx2G72RZ+PzHTguQ5BoESmcvSJ5leX21BJL6qYW9XnEZNZJ2YEZREMBv+oMj9wRa7pvfITJUtiCV/Uz+fjoFYKBWW0PMGWCI7ys0h570G/LlhJ4w+6XSig0Y2CjZiNkgbZPMbJTwA+jOb52126XEBmTzYaOOG2OcE4RoK6QmCyXvY7O6KEpzzG7ZoRom1DHhXszphPa/kTEJCeIf/G3WBAnpABMT+qUM4PhV4gUAB/sF6B/GhAAAE8DtkWJRnI97IxlYeB4T6nWfyDAQRrotKHrUzn+c33C8uWNq3eMGyRygNBjMgCQ2c5Qi0AFQxDGMCT9t6ER1W+7GP1gdcbdBKII3IgAxvtgiP/MYBndJir+JYr2WGjCw/8M/AAisnxjj2Ku1Sd0RFIAPAQLqsQEsYnZE9gtba1pnSZKDUQMNk1pfLDG+WyblQoPAQMLPaqRkTJQQCPHEN1KnCZ7DuuW68XpYO1A0MAZ407qV9WGNuk63YrCcG9O2Iysk3hgJz+Re7GPD4g+s+YSHJe7J3GtcDAcewDE8N8pH9rqXn15pfzM2njnGemAlFdu1c/gcTwr3XIi/iIW9OmDIk25sxOydpDllI2yUIMxnbJp0FgSLigBfLAR7BBoojIR2newWsfGDTzbACQss/BSXJAndmNPGtCmymNerwiOC9V5d44DiIPZtlYWC6+siLDKqMCOgn1HOdBbHiUt99nncVE1o0MFcgyBj3IC28qpOZdTYWEQsIDT8FG37XP3anYGpRUbg9WyAgQBDAiPJStKjzZ1NnmeLLWowYRrP7WwNz8rVapWsHQgjMBYEB5jena4huUN1WhwFAsvjgki8nr/mpoADhul0KTRgiL9hXpdnrAJsYmQAQ2sPH2AzNcCgtY2AznpjrbHhI3gjDCPUJzxk0oRoeAZpQhGm0fRhDbS1peM5jvSndAPc+sCuqzXMtXlv1g3xKDblROAes02b1Km4JiZra9LAPcIF6x+wQOV1lAesRTKwYMlIniNJToDVgmxmZC+jR+sFoCc2np/1DaihzksUJHhWeAMbNvyBDn3gD/QV5sKZgCR4HDQlgJnx8xrP5556RBMAHDC4hSH+7or9WgrAwL5PzCN7PpWeUUCw3rE2witorGf4SVLtXOtHygmCgNG8s57Zh6kgjRIPywD6eiyKCNa9A1ICCF9wTQPyAgEtWsOrpHhC8VgrEwL7NWuXPfq2eEC/1vN6XZPkLE0aA3IHa9cAi3gFaxUZZK2OQdMPoOnJjBHeB+9ANoJf4MINv0gAQ2KBgDfFOlMADSyzjIGir5yzWYBhvzw49smDA0UFNGJ8uFWTKYlx8hn3gY/wN7zX28Ip4IAhT9pFNwM2wo1K44fmnh8pP0YWWdTG8YNnkbDxozljocYFza2IDyB9Kj9gYhiOnOs1DT1AIl6nTRsomzVpA3Ejel5VklmICNkcB9oGTaM5vqyKyFck5MMoWIQjo4wlMfUzjq2qGokbUntLIhQgSEWhAT9IrnFN7k24RwEA7pKdSAdwzFyAgfSn13UuTOdOt7oEDRZ1sukrKEmCTQQ/mA8RhBAqeBiOMQYnJsIYvlIhuq8V/L3Ui9Hk+XP8yWEOGH5CDvuj0ICBzRGhvVavbIgI5Pze2QDR6LORR1M7a5b1NKw1G7WAN7RmqGaOny2afvgIvAXLJEXJEOLN3C8FAsI2Gyf8AWFlta7PvfWnfdanzZ6kCdwHVyM0df0SKtgc2aCxKjJGPk/W9qRdj7XGGjQQovFzPsIDNQwAOnzO85gLgwQGxmGbsdwWeQ8oQTiIjXE9paxFWEXbxPsYC/QhkJo1juaRdS8GYDRareuv1HNibeA3zH2S79FGJtcGYOEmSeee0V3RAYMDhvi7K/ZrKQADz3jbwPyQAQAtKxPQTe7IWOaiAo59mWQFVyUXwBceiD8AzBGuydzIHsx5KCiPy8sBTwf2btwF+YyYQ/gPz7ldSsZt8nYgiQvXx33JarRIVrjfO2yeC8RqYqVMFJVYIhJFIGPmOihRkFk6Hwxb+viYhRE5wACHKTMSCyNyFrwQoIPw35cZP3zgXreULXqNSlDSKZOwAWt6rWQe+BfnciydZzeQozFDD+Qt+Egi/zA6b/OlgAOGPCnGIqOziFh0pCNkQwTBjkpIBzyQrhRBJTlWmyUbIMJxZhdks2axsJCvKA7hvHIFozE4q4wk8UeMVjEBJ1Wmlf/Zc6ssw1BcjFyLY9EOsCAAHp1auLcl/EeBnTGg9QS04BJFtiUTbjQermPn6x8WD+kTWUhs6KQhQwiIbS7AkJj5yO40annPKbjGRs8Y0UhS4j4GVZqZU0yK+8dGcZW7YlScd1SWCqwVaEe8zY8CDhim06vQgIH1FLX5CMnkTsd1ENCAoM0Gi/T76NcuH2BtZolZXNZBrfnvVEuFDvhnQ0VJQIIEcrBbSmUJ+PAS1jjads43jVxGiIefACK69b0BCv2NpYBjsAbg6od1gjEhnDcLxJgfsI67q80XQb5LoGKTXBY3SanAhntRIIZq6wjyfMY4lulZETDuic/g/kjNFHgFGju0mLGx3hMXgXazrKI1ZLmfE58jzgotYT3gQLxpo65L4DX81AK5zQc6Xinxd4a3AnqIlSLzCYIQCgk0jw4YHDA8+rUU910pAAP7J7/9weEx20NRJsD3s3mMlrWkgmDrFxDA3s4apcMT3pIs8eZzSYAwnhK4GH6SSTZyRQI1Ajq8CCsh1ghcmZF16FgEk/uPm3LvM3lGEOv0BingFXSMchLZhbUMT4KX0JaLf6EwRbGAUhElZYzJgocmLtuNiVI1i19yNvxuQEoG1js1py5KuUL8JsAHZeomyTXIE9zXGG1yS7s3fA6eauBE1yDNKl4UJGihQStv86eAA4Y8aYavHSgdRPyc8pfT+ZsfNT36/SWvSQAPiye2ZKNOfAZPZbT5LNLrWgjZ+YI5JQr3uzevCC/IunBIGZO4F8gfQSheFfScaOLGMmZ7LAwTJsgwDjZ9Fn+7mIWtKV0cISMGHlIVFQvHCcVCIHDQ8TOObS7AYGBJDAbgQO52aidwPs9AB/mvkXaTcUTAw+ATLWewjCxRe8grggrmVm/zo4ADhun0KjRgwIqHZhxFwTPKA47LHZp14xPaXNmPWBesB8ADx7Ku49q7rAxnCN+kSE40YqO2tgngIwUx1rjoP8wax2eYTZjPsEQkG2qywUfXJj40QCFNP5s/n7NhYukkNmJKe6c1i3UB1yK09lggN0vYx+f4ws1+syIgIJj2TkADawnuAvg74xaBgsE0mOJftwT4YyP2iowlbyhzCfwKQQHhgc36R4EM+CRKjPV6tlW4FYgvkT8dVyPjG7oQv2VABTs6zwg/IFYL6ydBjIwZ4OCAwQFD/N0V+7UUgAGQjWtRdLExoVzrCc06dZ94RXDGdQjBHMslFkzAOimS4Rs/P7TKisJiPeQcAADZEekcj+yAFZTnAwAAGKhxQAFa+Br8jHMQvAEaZGt6SwDkTQEREr0AQrg/+zvyDnIKigZkERSRibvjUGKx1Fjh0bEGjA5PZBnxK5N/9D0u3lFWwAvi1JV+exZctakjw/jMJUugRIca/yLOwcap6xj/zYw5enMApLiXt4VRwAFDnnRD6MVvDlQLWCDtaXtz4vcLmJYsMIV2I+pFaI6NBU5H62d5gqU1Y+NDO4+lIDZOYaGwAGKmJTSYaO+IRWAcSOMcZ2BFC3wE8502XYQE7hGRPsIMixJmwxmMB1OfaQq0oBBY0N6xobNpA0C4ZmxzAQYEGBYo/pTJ4sRfUFJHZnzcG5csMiFESwuLlfPo3P+oYjBIE4sAgwaTcXibHwUcMEynV6EBA5sgv23u8+KeJNAcgM9mRypj3G+o3o4JPcYxIejbwtVw0foBkOls1mj2+TIbMHB8kzb3CMxZO1ybTRjgEdes+Qzrfigo4FEI4jQEAGKjoiWEe5uJXp8zNix8DwQCsgEDlgD8f3kueA1jx2qBa8GQzrsioEPqVayiZ68l7pB2M/1DxdVYpR4BBVoQ5PzN6S7jMwgTz2Zimdjo4QuMKalyrxSRem+KEYENAFmNjufZ0S7iwnVO44rgYZ/cttAyetCzBz3H31+xXksBGFA4IISzb6JAwLURuSHyINarKTAUn5D4/hMrlZE1ZMXENZBaTG8JNMBX4CEcN1/AwD4PYPh4CjAkVguE96gsZR7gVaxt5BjkEZQNxEYB+FFeYE1dIQukeWbo2XCb4jvcrhJFTOIlkTxTjXll4IVAvOaODUk6aGQic7eUUgN3RjI9YSHBKsmzJy5JCZ/EbZuskigmvS2cAg4Y8qQdgT+4GrC5HswABjbSmRoLBh3ZpORfFjrCPJ1goS+UJ/gLmfT4YZswkCWkZ1+PzRqkD3oHVe+VfzMauqhJYINNegIgss/lPWuWfxLEn/g7IzCgeaQDFEhBxiKMx3BKbI8DBoR+mj2XXTd5Nu6RgCTGkxwTr5Ecn4wBmuAjGV0wvlOF66/lanBK2kPTCmhsgA9v86OAA4bp9IqAAUH34K42K+6Dr22uRqYvMoSR9Yv1GJMH5Do2foYQjgadDZvCQbgSPbWpaQrE40LEBnira1BrlkrEyy2AkLlik0RRYN/LteeYtF7HLwi0iz/8BDAILCB4G8DWskAZACBnDaGRtzWjDTwGQLJhAxiwKsIjooBhigWtK9ZdAuzFAzJxD/y9JWNhAOtHwFCjdcyzIVxg7YAP0aIfNTwD/oH20hQUuh/P/7YEEirbQx94H8/5mej7ufgdwZakaia9MgKMHscADHS42TkopUuVaTcZP8+N9QGQkfCrMUuu8KWEFfinAwa3MMS1WOxXAMMugWM6bojUNorrg7Hwe01chLW2ta4RdOdTABH3xlceq/SM4GxKR63j+4oFuIVLoIRrhO4kaUCSIhkvBLZgUwwKEJCFkQ4PeFOAYcolSetqIYAB+YG1PAUYuKYsirgWReHfxqnFHfkSr8RS4Y6EoiSmnUexgUwAn4KvEB+FQJ+AH2X+k1LBKl0LYGDNOCpvCJSMxC7gzghg2Lmh2RQVKFJQkMAXE5fvAfN4gJfD07k3sZa3dB9vC6eAA4Y8aYd7DYGDW4Sko0tSLsCAuBs16GzQUQtoqFcavRvaHM8J6WIqtGwHLCx2zhwN/2U0bm0SeizQRwFIAAbQOR1XBzZmBAWYRNTix0uxUGEUjAOtBB23I6wcaBdZWJdl5seXmcbizW6vaWP/5QurTRhCe0nud8YawQ9WDRYpC54NHlNn9li4lgERCQ4Ee3MemhG0rmgVo7mUIEsT1BQLwrW8zY8CDhim04tNhzWSAIZWS72HG0uuBmD4h9IKJ4ABl71Ek5fr2PgZ2nN+63Vaf09JcKDmCv7+o/qNs96IIUiyG8nlZ3WSWhnBG2GYoGj8eHHhYy1+f65HvrW95j70OGAgiQK+/1gREL45Hkscmz33YX2bG5I2X3vNAAbWqQEKWR6iHzNrj3NYa2gYEULgMbkAA+fCPxDsMf+T9Q2AhAsTPAQh6BsBfgIgWfd0AMNhpTg8rBgMwAf+x/A9Ehp8o46iYY+sMAhEtMhLuCYAa5msCpE+CARYc5k/AzniNVgXEFSoSk9gOPVt3MLgFoa4Jov1yvrFmgZQ4JX05+zLsbFOEYCJKQRUE5v3pIABUE58X7dihm4JXF+T8IvlLaYYBcQYeNF42A+QQZA9WCu4D+HOxH7+mmIOUAIAxAsFGLAmRD4BLfg7qemSKCxQfrJ+AQ5s9+z5KA2wWhLXCX+iIwlkJ35ATsB9O1o+yZKEpwfyGM8Mn8AF0xK4iFfgwREzPyFrdfcngdBxnvx1/hRwwJAnzQgcJBBoq7RxpAd9Vj9SUpQ+rlFnAZgWUJstP160DSwa/Akv3iSjkAINtTmyoBC2WRQzycgwiZjxCLCC+Q2hA1clFgsCEb7F5GGOWj6AQ2xcH6F+QIFSBDnekbBxS0yGhQdix2TXr00dxpGrIbz8AsCgXMfRzSqCD2ouRBCC0GKZVkQjtIIJuHjkfwg9WLB0EH7igjFgqB8BCIYCZiIPNPTwNj8KOGCYTi+E4QisiQFCiJ2pgCIa8AgY0N6zWfF7nK2xziKIxhK4Sh3QbMBYaw5rQVQW4EoYAT9ZR0gEQOvRBkbK0K8lTH91stv8fKcBBq1t1giCN7wDjT6mdTZgYg4YB5svnXtECwOaNgALmUmSDbTPhBjboPVwgBvGhZYuF2Dgnggn0OJFWVCo74EwT9Al/AJXRgIfiX8iQxS8EeD0DC5H8nsGMKEUQKj5nkxw6vChCGoQaABm8BMDBHJdQkOJMEbaxJjRhbEBLOAhJxT79f53d8M/VLOFWjekU3TA4IBhtnVaiO/4jbKGidkhbomO8BubAQYJq+x1aMSxMjwpYGANcI0rkiMuK/AXFxv2cBQjdO7/svZpADvJWBAq2P9Zo/A31jHfU7MAmaGQgIHU7IyPAGViNNHswxdRUmAJIPMjHaAFH+DZ4BPER8HbYpYj0sejcKTxLGRowsIBbyepC8kaYswUylN4IjINvIkOnTifSyCjQANevS2cAg4Y8qSdZR8Ro7CNUQxit7RpaP+jIG0/Q/2DTz8/bjZCNIMIyTAQsiIR5Iy2L2roWSz5NIQCszQIIMCscG8gtSufxYxMAAsWJEIMiJ1rcx8WGBt8jBFA6wHyhpkxTo6baRhkgkJQQNDCugAowcQHUEDjaa+6Nps5gAErjKVSFFOAIcEIxvUd90GAINUjplqCF2EofMZ1GKO3hVPAAcN02mF5Q5OGEI+GnN8wGsFcjVom30pbThKAmOKUTSbfZmtQmzCFjqaa1qz+N4UCaxWQT481UfgO3oBrkNVA0cZOauTHAQPKACyBdOIdYtAxygjQNZYOEiM8rzgKrBwRMADC0dphPURoIbjazPGZ5yJVIkI3tMkFGAAnuD2wxnFNRDvJc7CmASrQ69PjXeZOuG3d8rBN4GO7fIt3rE+yRQE4uAY8D0GfoGXWf2ND4soFL6HGhClcMmPiWdEekqJxrwAQbkf4RjMXqBIIWPz7t3fDe9/es++xGCXCGi5fDZZNivsQ4IiwgkCF8FEpDbCFayzabOI3eH6UTx8paPWjo53GZ6HTfH67lUKbYj4Hbj/MAco7fp+sH/a+2FgzuA0BmIkHorMW8225XJKQJ/hdw6NQuJFZkN93TN2M58NbB1dbDBGKEhoCMrzlS7nwcf4Lu9vDC+IThQIM7EOMBxdjMjSdlfURjwrkDWSEJPlDtWVVgqdgaYiNmAOTkcTnsLzyN5ZMQADAAVkmylXwPDr89J3nV4e3n18VGgUYOA5Z63PxUqzGZFXytrgUcMCQJz1B8fj0kj0E898aMW6EEdwSzF9XmxqCOggWjVnULiZC+0SiTWQBiJnMVyhBuCBlKxo4xtAqv0V8+/ibe7NQbTHKRxg/YJgDJkhbZHqPlYGFh5ABM0sElaT4y2wbDOAI1wDAAKCFzX1Cz0cmhbh4qfsAisclAdowPmMcEp44BjAALdAw8GquGhJmEGgiE3DUn+ePcIbDHDBMJwwCKXRhfbBe+Q3j3pKrEYxHikCEXMAz63g+QlcC2pOiQ4AU1gLrABce0gpmuwxZELEEPwRvBGiyhxDPRNFE1sQ0wKBrnNbGi7BAfAGCMOCBtUSbCTDg33xeAINzsGwCNhBgaMjnaEdnAwwI+gglBEUTc0DmJqwRKCXgeYAsrCJcP6lI32TCU8Ib623zj/EQFtwtzSh80fioNneKyOF/HS2k0CrhIfC22ozVosHmjPEy6CnAICsDwgYxKQ4YHDDw8yhmY98lZTCxA9QbAUDAA2Jj/0VJR4/acvbcfFsuwMBeiRCORQ9LA0kAEMSjohDAQOwQAnQ2YEjqG3WbTPC8LK2HpFxgrIttYQA4RfchlAnwBsYLf0MpAl9NEjZUSfnQISXESitIG8eP9QCLKDzQFJGSU6LSA0UHdARA8JkDhnx/SYt/nAOGPGlqArOOxaJgIEEafUxjJhhIM2badK0KfJhNOBfaRRBmszM/fr0x85j9nedNsw7j/rQYp6Bh2HteEY4AL3Qp9qdM/DAuxmDjQAjS+QhCcTx2wVn+iS4XPDONf7kG/ySvyTUZG4sYhjBFEwkAZGMC9Q+KcSZl3R8JYwhldqnkxd77PwujgAOG3HSLaya6yGV+xtMONrO4fof5WvwevwA8gE2Y7B+xQCGC80oVSwQsoDk38KA1YWZ58Q7ANGsDQQJtGK4DuQADAvaRTIwDNVss7kGCP9pDWgQMaA6zLQwI/MeUyhQh+3anXBG16WYLLXMBBhPyBTKoAQGIeUUdC41ZbQSI0CKS05z7PC3BHWsArklWi0Z8kbFi2eA4tKt0+BH8AYELAEfVWVwtV0n5grsTFlOjZf2jonfQKzYEECwMdPODdsDgFob44yjiK/tg3IfhMfCXyGsYBlta3Gd5na8CIhdgQPEGSD+mDpBnTeOtwN4PD0Bg/4XAwi9eWDOlGIFHkNQE9xwUiM/tajF//0IABtyDovL0QwVZv//dPYvdiLJGQjPoFgwwYLHE6stYokt1pBNeCcgsxFxZNjbFNeCRgMWSGAYHDPzKStMcMMyT7vzgWaR00HEsoMKPHYGDH3q0KgAiCtlYhEnqMgT1xMqA61DU3CcgJgkuLPQ4EtesTN7ljNUDoQhf6miFKDA5CvmIqb62A4biTw+bFnRn/a9pTzKoARLMJUggIbEyJIXXohYtbqhYPeYDGBDMCcgmwBhNW8yuxlPPBBio84CAgdCOGyLCxbwAAxlLZBXAKoFmkmrzAIYo3OMOcV7xFFx3q1yScLXClYjxIOQj2CQZ2BItI/c2oABIEFiwWIWM+xFujCg7cGcgyJoc8fWylgIuoFlsDhjcJSn+Fir5dTbAwJrGyohVlMw/rDdkkZkAwzfKQvi1AYaJqeyOBQUMWq8fHL1nboMkRshuUV7BsklF++3iJ0nKZvFLrf+oeDGwJdiFhQaAQFFZ0k4T30AMV2xkpnKXpEiN4rw6YFgAnbU+E+2+9rIIHrK1CpadSNIxP/xCt7gI2WjJRWxuQ7o3YAEB3awaxRiH0UT0EKIy8KCx4L6E+5UBJ8ZSaGIs0es7YCj+xKP5b5Wgi2tgkilFLjly37NNT5sf69FMchqarVG9sj4T18EasxDka2EoJWC4LzctXH/oAIZNyviE7zC+wndVIIogQ6wouFrx7ChU0LgmQdGdBliIeYAXYk0gOHuv4iZw6UBwgY4oOeAZHGPWHvEMtI58D2iIzQGDA4b4W6jk1/kCBkADgAHh2SwMWTEMUy5JUt49v7twLklTFgYAg5ISvCcLw+OAgTmDFxKHQ+V54j6IBSEBA0oE/ia5DDwEOQY5CvckOpaGI8okhyKCZAlYTzjWAUNxV4IDhuLS2+/mFFh0CjhgWHSSznlB4nXYsNjoMK0fUIIAAn8J5EVgxspHWlFiliJQRvOO5QHffYIAywEwoMWkUNJOZYWJKSTJP48llefDUhLdkBD6eS42egItSYFKWkncseikfSSJAlmXKNoGgKLhsoj7IjSLaWktw5UsFlgeYnPA4IAh/hYq+XUmwMBaMpckuedQYR3LIRY4LJjUQXhbNRHezophwAWQuiVfKp6A9y8IMOC6SH0DrHjEFMW0q5fkWhjjLuFruFYSs/SMguvhbfC7JDYzdx2GfAED82aWWYsDqU0SuCh4nEQusb5C4raI+6aUCJwgTQLJHgjg/uZUz1QNGqy5U4BB/AV+SswV8WAEPifFMDnflZWQcTGaA4bFoKJfwylQQgo4YCg+8dGqb1Ua063aVJ9SekCqsa/TRhtT/1lKUeVLx5xOYG+zNmnqqeDnH4s8lQNgoNAk413fUS8LQ3MgcxodUIQQgvUwCWaUm5X+xuKA9YG4Cyran1NQdHtzUikaC8XBHqt16QAAQABJREFUp9oCaW5pAAvivaAR/sn4aWOxRWNK5hmEIISX2BwwOGCIv4VKfs0FGADT1CGJQc9WhEzrhjTmAACE+9ef6bA0o8QCYa1jPX52nKKJnQbGSWVKVXqUFrhFck2CkwEVl+TCSKp0XAdZ71yPNM3ECtHhYQAGXIxJ0vB44bb5AAasihRxRIkQYy9jFkgSU3AtrLXENaFUgB/g/ohr1XeKybDELVLE4M5kgEHFIqEBvITxAxYADcRh0aAFwKNY3hZ20wr9xwFDhU6sP9bSoYADhuLPNRnE2EhJS5rUM2i0jCkUVyLOgLorBPzSccVZpxoquPLsUupTNPRsguUAGEitStYiNnTGHXO9E2/ARsxWjPsAHlhkRCG4mZzqpDf9UdWgryvWAVoBlCjsdkA1GgAcMXMbLk+J5vSB1WlIskzVWR2GZ3QsQktsDhgcMMTfQiW/5gIMCP9nSKuqTlpVUpACsqkFhTWOdfJSVh0GhGOsf5/80Cnh/r6BAyrSs35jhkWshLg70uFXpOglyQF8ijoTWBbJgIZlEeE+AgYEcorBEcf0VqbS83wAA66GdECLxVkqQzRuiVhgKd5IvNQhKRZQMNRnkiTwzN+d7ZYioteCoUkQwXNHwIBChvGRhRFLBEkkzMIAWFBPYkyT10r+7RT62RwwFJrCfn2nQIEp4IChwATOcXlysJMHH99+NliqOSNYAxbYdEl9yOZ+Sj1mTuI4Nvbt65psgywHwEC6WTSCuAkgQMS0r2gGSc0I8ImNYOwklWqSyvWCNHxkdaJGAvQiBeoB6geok2YZdy3OATAQnD0s7WWbtIYIQQAxABk0i80BgwOG+Fuo5NdcgAGB91ompTIWOVKqUk2alK6kdmV9RWsArjwADPiLZWBTvRT+fknWBdwByd7G+sVKSBAxneDiHlkGyUy0BuWGrkfdlQ1yFeLauD49CWDAXTHGWVoMg+IVmgUaEjeopLIzAGZMAU9WVyaTZIGkEiRLgJdQWZ7kD32yYmLJxBpBKtm3ZWHAZYrkKlhA4CV0CtQS6wBwMuuD+A11n7wtnAIOGBZOOz/TKZAKCjhgKP40YLa3YGBl+9gs9xmCDnG9oXI6gjC+/2gA2bQI5rP0oTKxk0mI4mRo1soFMAAMcDvCBYuK2YelpeQ5Hg9KxrqAjzUAAKBBJpfo4gC9sDBgNcDCQHAzoAGrBOCKTqV3rosgAM3IOoVAFJsDBgcM8bdQya+5AAOuOQjzdLTrCPcP1C05gAB9i9bJavEXhHEE5J5+VVqX4oKaCGQrG5PgfIigZ7kDrhUgIEkB6xfAjvsk6xRhGwEeMIHyAyE81ptALRABw0JckgAcKB6wVMAHcOEk/ouxWryXrAPUiSIuCsUClg0ColFI0IlhwM3oq1NdyThUGX6VngGwAGjA2gCows2R4rDEd/D80IhnAxBdvav6FXr1tnAKOGBYOO38TKdAKijggKH404AA/DRacAEGfO3xucfnFo0WmzN+tmzqbO6xWBu+w2j10LahTS8HwIBLEQ2BAbcDgpZxa0CgbxNA4pliI1f6V9rUCbJksyaWgfzvj1ySmsOzO1vUW82FiQ0eYQGLDMcDTABT+CYjJEAnemwOGBwwxN9CJb/mAgwI27jW4GqEJh6LAbwmceNJEgjAW/S/rSfqNFDlPbHe9ZqC4lnij7T+sNrhIgi/itfEu5D3sXEd61r5vHIvAANxRrgkzTeGAaAQrSHwENyncDmKGY9QHMADutXXZlIvAwJY/dz/hNwbqWj+qWIyzBIhGhDfZIBBFa6JKcOtqUr8wmIVlJmNRAoobu7K7ZEK2UelzDipqvPeFk4BBwwLp52f6RRIBQUcMBR/GizoWYGBgAV8+4lNQCOWbLJJLRQ2LHKJm+CrTa8WLZtcewj4Y9NDo4Y1As0Zm3BPv4qk7VtplZVxY0IYJ1UpgX5oCtGyReEaQZyGYB1N+LuluUMIWCVhHp9k0hpSvM02ze4RAzGRUrhGkeIU1x8LMBYY0JAyLkUPw8VMHYYIGDgPwT8GPSN0AJqmhA7FMlxSRenPKEKnTR2gxPPznAaYBATQGibFo9oeZUnSPWPqRABErXZ9ngn/ZjruFQRCQ0M2e4q2vae+R+5KjB9tJWMhywoCB0GhHId1h8BQ6FUpjd8cGmR+Z7h14RKH3zmC1EdHO01bSynNbMGvUp59KT1HLsAAjwckjNi6n0zWhJgNlgfWB3Nu2nodQ9wRFrsr8vu/LBBv1jv9LrbjDqkK72QrI0aBdWNBxTofvsU16EOy/GFpYO0ulzsQWdAQwok7GtKahl9NAwy6VgQv1GGgcFt2WlUAD/FJWC6eV3wCcQo7iOXSvRkDioPIB5I4Jlweq8w60C8wAe/DHemIeBrWSToWSArAvba/w6wV8EruQ+M5ADi4bVErhlgyalicEm/QV94WSAEHDAsknJ/mFEgLBRwwFH8mMNsjvFGADMEV33xcduoJ3NPmzoY1SsVSbfDmG5zRtuNug+sSDW0dQjUmfjbhHgVMU1GZOAH8hnEHYBP8VtlB8N+lcBsCIlo4NHM0NlU2X4qrUekZsIAQD2AgUxGb5D3FEdzvHTaLhp2kfwAMMWibjCiABu3BswIGBFYDR9roCYTcIaFjowQF28B17gWBjI8RXtXZ/BFguCaZXKAXwgrBjId2tQaKtaF1RMgwP2MJOggj+Bjjb8x3jTonHoNLA4HUgIX3vksAw14Ag8aB4EPHvYLK1nQCr3E/6Ba9KqU5YKiUmZz9OXIBBtYJAb2sK9a8rQ/xhkQDX2WWvL6BxP2GtY+Afe7aw9AtJYStAR2ICw+pSFn3gE3WcJLCtNosn7hJspaxDAL4yfiGxc8KMkqwx4UQ0DAbYMB1kUrP732fVHqOT8oeRaYlshklSodWszA0Nya8gedIrA2TZikABMEfbwj0Yym5KGUEtRjOi8fAXOF+rAeyPj2/u93ipIjNwCoRG8oaKtzjGkk82fELqjyvV07mfG/zp4ADhvnTzM9wCqSKAg4Yij8dCc2TDTABDCvCNgnQtpFL2I1aP7TmbFr497OBmQVAmzbfozFkA0ZzBijolSYNawH50jHLI2RzPTR1R8/3WOBfr3yT2dDR/tEQHgArdIKS0eazkVIN9risCyekUesWEOl6kGj8I6UQsBH+ERoISkZDD8ghH7tpJ9FQku9d444Nn2aADMfvlLWA84nfiBo/ziXIEmsJ/tAIIFwTWtWpkyoRYYGxsrFjQcl2tUCTiNAPuEGw4Pskm0qNXQNhwfLGy4KBlpR0ttsEQtC4Y+3AmoBAcOZKUoEazSK0qpTmgKFSZnL258BVhyJrKAGapTSAD8Av+C33KtgXhURrJsbAuID+QUMPMGCtkx3otNYAqUixSMBndPpUvRQKJ1JbAQUDCgl4DNeHpxB0DK+4Jzce+NVqVbHH/RDAbhYOBRbDrygIByB/lZgmWUVJuxyTIJClCNehbPcfsh8l9VqqLUaBOIWkOnwCSHimBPwk6WAZN/EagB8yJGExhBch/HMc/8BD4L10+NcaFDjqERAwfngAFlaCpgENKDXgSUa32afBv81BAQcMOYjiHzkFyokCDhiKP1sI6mxy0D7WVsDawOaLEMwGzQaM0GxBitr82HARhBEAcAEgQI9jCMZDwEejFgOoMd1zbTZqc6+Rlo2MQ0ngoTT3ui4NVx0EZjraQwRs7kF6VwL9ACtsnHTuHxuWjghe2HjpbKS49Zjbk87vluCQLXCTrSTmS+dZcQlA+2g+wzr3vs69pI0ZgQWXKcASDWEEelGHImaMwu2AsfKMPArPw7Phy0yQIrSluBuvnIswghBzXtdn04/355lNeFKmGJ7RXBAywdYxkDM+c7m/OmAo9xnMb/z8tkmiQI9ZgkhdHGMIsDawduA1rDCWGdZM3BuJiwJwE/TLemHJw2fgN7j3UbCNBAwAbIKO4VVch+9jPAFWPqwZWAjhVStkBWAN6xZ2LfgVSgUCq7EaYq1kDZrrpY5DwMelEd4TG+fb/TV2AAg9ZmvCAskzcYz+N/DDvXHZtKBs8ZUegSF4EfyBpsNs7FSJpv5Nu5QkbYAodfgJNAFEWaC4zrsnRcQdjQceSoNu3uZPAQcM86eZn+EUSBUFHDAUfzrYYJMA5kzl0kz6P0z8iR/tpAnNuNuwcdEBD1F4ZsuLgjZAAfM/QjNCOZo4fHtj7IOlHpQ7wKi0e9FtIG54bJzURECwMH9/CdfcA4HdXAi4rwQGswLEk3SOFVtDINdGjUDOhs0uOiKpgDFzPq/RkgGFGQ/nxeMR5vmbxqU5B6EGN6v4bHwHrRB4ODaOlTgO4jnQPPJMY+o2ZtEBWhl9oUHmXP6mCrTFhej6jJexRyGIMXEdzkXY4HqMPQIrxlHuzQFDuc9gfuPnd218QOuDQF74DBIyv2WEdtZRVFbEdcbvnd99tALYOtD6jd/bJfQP64nrsw4b4Fm2DnUT3SA5V2tf64a1z/1qBDBYWzotA05IXZq4D3IcvI44BwB9Zpi2RuEDjCG2x+/P+BmHvdbBG6JiIHFFgudhfWVMxov04KznbH4En4t0SPgudKsxRQVj51iuwTgiX8hWmsSx+Wv+FHDAkD+t/EinQCop4ICh9NPChsjGbhuYNkK8bNmw2FTZvKxnCeylH3FpR5AICwn4QNCP2ksDD6KZt+kUcMAwnSZL8ZOodECQR5MOr5EnkQFm1k/GsDcnaeBZ0YrJwaYoyPArVmC+15nzRjMcwP15lvg88TUK9/DPfDlBosRILJGR1/Ka8Jb8aTLDUP3jDAUcMPhPwSlQ5hRwwJCOCWQDjFk/GFGyYbHxJhtfoTfgdFAhv1FEQQENKnTBfcleAVf5Sgn53apijnLAUDFT+UQPAp9JrJt6g0id/G+gIboB5nMDzo5Zljg+Ctgsv2LxqmhFjZZE+IG5agoAMZ58W7T2wlcYOxw34ScJb8n3On7c7BRwwDA7ffxbp0DqKeCAIfVT5AN0CjwxBRwwPDEJ/QJOAafAE1DAAcMTEM9PdQqkgQIOGNIwCz4Gp0BhKeCAobD09as7BZwCs1PAAcPs9PFvnQKpp4ADhtRPkQ/QKfDEFHDA8MQk9As4BZwCT0CBqv/l//xm0rJo5O8u9gS381OdAk6BxaYAgMHyaSsF3nM7W6ySJkV6KLBDTmxSTVpQXOLcudi39+s5BZwCRaAAqXABDaTFpLjXbnXqcnxxsit8qXVO7vqYFacIw/FblBEFiHtIaytWvERan7+cxlX1f//h0mQMOCungftYnQJOgYQCZIhI0tOpOJYKaW1V/m4q6ZKv/vyN/vBAxX4sAMw5s/9knAJlSwGUAs2qsUGueWp/ABwGlaueolQX1UmtGQNgy/YhfeBOAadAailQdebqAwLK+d+bU8ApUIYUIMNELJqTFAZTHn/lte4bSArdkC6P5nihDCfXh+wUyFAAxYClj5RFkRoW5MDHO4ACdax11rfv4/5zcQo4BQpFgaq+gREDDIW6gV/XKeAUKDAFZG82i7P+Ia0nQgWFssYyxW4s1Z5LEgWeBL+8U6CwFGBN41oSlQNJCsmk3seY3JEcLhSW/uV9ddshyvsRymb0lbvZVg2PeJWcsvkd+kCdAjNRQPsBWwJCBbmseY+HArms3bIwE9H8c6dAGVFgao1n1rn+Zm3HdV5GT+JDdQo4BcqQAlUKkqpcOFSGE+JDdgo4BZwCTgGngFPAKeAUcAqkiQIOGNI0Gz4Wp4BTwCngFHAKOAWcAk4Bp0DKKOCAIWUT4sNxCjgFnAJOAaeAU8Ap4BRwCqSJAg4Y0jQbPhangFPAKeAUcAo4BZwCTgGnQMoo4IAhZRPiw3EKOAWcAk4Bp4BTwCngFHAKpIkCDhjSNBs+FqeAU8Ap4BRwCjgFnAJOAadAyihQ9XBgVFmSPFFSyubFh+MUmAcFkvzsnFCjGgw1KtpGMbcxZUymFsNkUrdN3/o6nwdR/VCnQKooEOsw8EoNBuoxkONwPFNvxdd3qqbLB+MUqDgKVN280+9SRMVNqz/QUqIAxZyiMNGwrDbUqwIswGFoaDwMDo0ZaFhK9PBndQpUIgUACAAF1vayuhrr1FkZHB6zdV6Jz+zP5BRwCqSHAlWnL3R6JYb0zIePxCkwbwpkaxybGutCs3pdXXV40D9ifXRUVgYvtzJvuvoJToE0UaBWlsPa2mpb28sbakOjOhWe+x6yzoe9QGOaJsvH4hSoQApUnTh7PxEl3M5QgdPrj7QUKFClys41Ku+M9rG5SYChaVmok2DxoG8k9PYNh5HR8cQZydf4Uvg5+DNWKAUMLGQAQ+PyugQwyB0prnPXCVToxPtjOQVSQoGqY6fvufIxJZPhw3AKLIQCwgmhmtgFgYYVAgwrmuvlrlAdeh4MWx8ZyQCGhVzcz3EKOAVSQYEIGOrljtTYWBuaBBpGxyZMKcBad8CQimnyQTgFKpYCVUdP3nXAULHT6w+2FCiQ7du8onlZaFGvk1DR0zsUuiVIGGBw68JS+Cn4M1YwBbAa4mpI/AKuh/QxAQbAQncvgMEXeQVPvz+aU6DkFHDAUPIp8AE4BZ6MAg4Ynox+frZToBwo4IChHGbJx+gUqFwKOGCo3Ln1J1siFHDAsEQm2h9zSVPAAcOSnn5/eKdAySnggKHkU+ADcAo8GQUcMDwZ/fxsp0A5UMABQznMko/RKVC5FHDAULlz60+2RCjggGGJTLQ/5pKmgAOGJT39/vBOgZJTwAFDyafAB+AUeDIKOGB4Mvr52U6BcqCAA4ZymCUfo1OgcinggKFy59afbIlQwAHDEplof8wlTQEHDEt6+v3hnQIlp4ADhpJPgQ/AKfBkFHDA8GT087OdAuVAAQcM5TBLPkanQOVSwAFD5c6tP9kSoYADhiUy0f6YS5oCDhiW9PT7wzsFSk4BBwwlnwIfgFPgySjggOHJ6OdnOwXKgQIOGMphlnyMToHKpYADhsqdW3+yJUIBBwxLZKL9MZc0BRwwLOnp94d3CpScAg4YSj4FPgCnwJNRwAHDk9HPz3YKlAMFHDCUwyz5GJ0ClUsBBwyVO7f+ZEuEAg4YlshE+2MuaQo4YFjS0+8P7xQoOQUcMJR8CnwAToEno4ADhiejn5/tFCgHCjhgKIdZ8jE6BSqXAg4YKndu/cmWCAUcMCyRifbHXNIUcMCwpKffH94pUHIKOGAo+RT4AJwCT0YBBwxPRj8/2ylQDhRwwFAOs+RjdApULgUcMFTa3E7qgSaq1EOo4v1jbVJfhWp1vuQ93VtZU8ABQ1lP35yDn5xkIU+GiQn6eBgfHw+TExNhQp/zXVzCVVVVoaq6OlRbr7HXmpqaOa/vB5QHBRwwlMc8lXqUE+IN8IgJ+IT+s5ZDFphznGIs4ighm684P5mTahV9gAOGSpneyBDGtcCH1UeECgQaHm+TyA91E2GyTicAHKozr48f6H+XDQUcMJTNVC1ooIAEhIDR0dEwNDgQBtV5PzZGH0s2dIGFaoGDZXXLwrJl9aG+oSE01C8Py+rrp75f0M39pNRQwAFDaqYi1QMZHh4KgwPiE0MDUiygVNB+b0qH+Q0boABAqK6pFU9ZZvwEvsLn3pYmBRwwVMq8AxjoIxIcHtaEqgEhg/HpDzdZKwayXAykQWgC0ACAqOFEb+VKAQcM5Tpz+Y0bUDA+PhaGhgZDb093eNDbY6ABwWBkZFgbeLUsC1WhTmChcXlTWN7YFFasWKHeGpqaV0wBBt/o86N3Wo9ywJDWmUnXuPr7+8QnukJvb7cpGibGAQw5tIdzDBu+UldXF2rVG+EpLa3GU5yPzEG4Cv7aAUOlTO6oUP+YTIhDckl4UBuqetVlbXi8YVmYbJLGslFoQqBhskFgoX7+zOTx6/rfpaOAA4bS0b5Qd8aqAFCg379/J3Teuxu6uu+Hvt7e8OBBbxgWeBgZHTFLAxs4vba2LjRIA1jfsDy0aHNva1sZ2tpXhpUrV4V2dTZ9b+VLAQcM5Tt3xRz5rVvXw+WL58KVKxfC+FjCR+An823V1TWhqak5NDY1hVWr1oQNG7eob5aro7s5zpeWlXK8A4ZKmEnJ/ACFMCTBoV8mxO66UKMecgIGgYRmAQaBhskV6i3qeu+tfCnggKF8526mkY+OjJhFYWDwYTh98ng4fepYuHb1chgY6A8PH/aHMbkkjRPHID/lqPEjdqGmtlbAoVaAoS2s7FiljX5t2PP0gbBn7zNhzZp1M93OPy8DCjhgKINJSsEQT586Hr756tNw5Psvw+jIqJQKIxbTMN+hwUfa2ztM6bB1266wb/9z4Wl1Pve2NCnggKES5h3A0Cc3pH65JsiyUNNVF6o7l4WqsRwWhmUSMgAM6pPto2GiYyxMtsk04a1sKeCAoWynbtrAo6/xwMDD0CdLQo9cC7787MPwxecfhrNnTgTckOgW7Gy+xKzxxKUwcVPGZ3kytLS2hY6O1WHtug3hldd+Hl597e2wfccu0w4CLCLImDYA/yC1FHDAkNqpSdXAvv7qk/DXP/9b+Md7fzaXRdwWsVTm2yJvwMUR/rF27XoDCy+/8mag46bkbWlSwAFDuc87soI8iqq75IKkXi3AUN2n3i8tQC4Lg8UwCCzIHWlilQDDGvWVo6RD8FamFHDAUKYT99iwCWxO3JBGw53bN8LlS+etAxTOnTkZcDUgloFj6hSEuLyh0dyPLChalgY+TwDFcKhXsDPxCwAHNIP79h8MO3bsTgQACQEERvO7wU/ZW3lQwAFDecxTqUaJooD/vv7ik/Dun34X3v/7H40nwDPgLRbrZO6Ls4+Q41AqNMi1ccvWHdb37N0fDjzzQjjw7PNuYZidfBX9rQOGcp9eAIOAQc1tWRVuLwvVPQIOypBUNSw/w0Tx+NMnVFakyWXqypQ0vlpgYf2wgQY7yEHDT2lVJn85YCiTiZpjmKRCJLCZ+ISzZ0+G77/7Mhz9/qvQef9e6Oq8F/r7H0xdobm5xVwFWlrbzT0JF6VBndff1xv6+pLjamprQv2yhrBx05awUf7HO3ftDfufOWSbflPTCsuA4mkSp0ia+jcOGFI/RSUboEEFy4g0Gb784qPw5z/8a3jvb38wa2MS8KxkKJl0y2aYnGWkZFuDL8Ajdu/eF55S3/XU02HHrt1hx8499t0sp/tXFUwBBwzlPLkZ60KQ61HNjfpQczMBDGFcgIFaDLkAAx8rKxJ9Yq18GzcIMOjVLAwABgcNZfeLcMBQdlOWc8Ajw8OhW4HNXV33w4kfj5of8rfffCa3gpEkG5IWZ7OsBk3NzfItXhU6FIhIMPNYJviZ2IbO+3etP5RLE8AD/2WCn1vb2s3C8PKrb4bD6pxPgDSWBm/lQQEHDOUxT6UYZbZ18qsvPpaF4b/JJelPlg6VNb58+XLLnkYWNWq1zNYACzVKpUqw804BBEDC5i3bZJ3cGNat32jAY7bz/bvKpYADhnKeW2KVZV2oUirVmpsABuVcJzuSAYkZJH8AA7UXdND4WgVORsAADyG96gynlTOZKn3sDhgqY4b7ZRm4eOFMuKCOGxLBi2dPn7CHw91gxYqWsG3HU4pF2B1Wr1kb2ts6DAjEIk1YFm7euBZu3LhqLk235dYEgCBrEuBg85bt4eXD+CH/LKyTW1KrrBOkSvRWHhRwwFAe81SKUeKOSNzToDoBz+/JHenTj9+zOKaOVau13jeG9Rs2qW+2xAizjbFaLkmkaQZoEAdFb5XSAf4Dv4gxDrNdw7+rTAo4YCjneZVloYp0qsqOVHNLgEGdGIa52mSmBHRiYZCVAQuDYhsMMMyufJjr0v59CSjggKEERC/ALXE7+u7bL9Q/D5cunJXgfy3cunnN6isQaLhu/SYT9k3g13tSp1JrAVMiAc/UZ7h08az6OQMcZ0//aOCDobLJc/5Lh98IL738hvklr127IaxavaYAT+KXLAQFHDAUgqqVcU0Cm3tUo4X6C/CQjz/8W/j6y0/Ctm07w9btu5Ql7UB4+ulnw959z+RnVZRYAc/A0lCrnrgpEdvgKVUr4xezsKdwwLAwuqXjLFkWqgalDVCvuSOwcFcuScqW9JOGRUH/6UjeTDVAw8RKBUOtEWBQ8LPVZFBBNwMOU0f5m3KggAOGcpilucd49+4taQXfN83g1SsX5Zp0L/R0d5nrEXnQt2rzP/T8K+HQC4cl6K+Vm0Gj9XhlXJJuKzD61s3rBhhwazpz+rjSsD4MA/oODeHefc+Gp9V379lvvsnbJEx4Kw8KOGAoj3kqxSixLNy5c1OWxZvhh6PfBNySfvjhW8Ug7Lc4hH0HDoa9Tz+TP2AoxUP4PVNPAQcMqZ+imQdoYEF1F6z2QqdqL6hXqcrzVAMjYE3QK2ChavKnoGGiRYChVV1pVSf1Otmq7ElexG2KfOXyxgFDuczU7ONE2H/v73+yYMUb16+YkE8QNAGHBB4+tTu+7jPXAPKhU6wtNuIV+vv6LDj6/LlTFgdx6uQxEyIQJnBrMreE9ZvDc4dekqXh9fDMsy+4i0EkYMpfHTCkfIJKODzcEa8oq9qVy+dt3R8TWDgjCyPZ0SxL2oFD4iN7rZMu1ZtTYCEUcMCwEKql5ByAQlUvXbUXlB2puleAQdaG2CxWgQBnfUTV5ypiHgANmWapVSngRl0GUqwqa9IkFaC9lRUFHDCU1XRNGyxZTCaU4QSQ8BflT3/3j78LxB+QDhFXoxdfei28KOGejR8fZDppU6c3WQ11wqRSKJKS9eSJHwKA4cL50+pnzGWJgOlGZT85rHzq7/zyt6rP8HMDDLgfuG/ydIqm6RMHDGmajXSNBUsk1kSKPJ4586NZGK9cvhieO/hiePa5l8J+WRi2bpd7kgqweR2FdM1dOY3GAUM5zVb2WLEYUHOhk66UqrIsYF0gpWpsWAsMFOi1GtclVYOuGs36XqlVLcWqQILFM6wbMfBgFolHuCJezl9TSgEHDCmdmDyH9fBhX3jY3x9wQ/rgH++GD95/N3QrUxJpEGsVu/Da6++E19542wADwYdtyniUbVnIvo3lYhdowKLA9QAOuCj8cORrszQsq28Q2GiwWIa33/nn8OrrPzefZgAI/sre0ksBBwzpnZtSj4zkBke1xo9+/7UlTmDtwwOef+FV9VcEGA6FjZu3hk3qM/GOUj+D3z/9FHDAkP45mj5CgQWagQVqLyh2Iam9IECQVaxtAusBLkd6rcYS8UAZlAANhgi4ANYHddVlGN84bH2yRRYGsig9whV2L/8nvRRwwJDeuZl7ZJPh3t074d69Oxaw/MXnH1pl5/7+Psts1NjYHH7+zm/CW+r79j1nwj1F2wATMzVAA1WiSc96547iIj55L3ymjCmAB4IWq2uqZbV43a6LhYECb6Rr9RSrM1E0HZ87YEjHPKRxFICDrz7/yCrCAxbu3rkdenu7lRVNSQ4O/8zqr5ASlWxJuDJ6cwoshAIOGBZCtVKeA1hQbHJQnYXq+4pboPbCHQEGczmSWYD6C5k20SY3ow51gQazQnQlMQ6WJCnjmiS4EEJdBjBsUk0GHRsIgyBrkreyoIADhrKYppyDRLhng79y+UIg7gAt4Q9HvlGF1lGLU2iXRQGw8Nbbv7GgRS6Sj+sQsQ+kWezu6rQUi++riNM5FYOL7dDzh8PP3vpVpiZDh4rAdfwkgDoe56/poYADhvTMRdpGclOplD/64K/hww/+Em5evxp6H/SEocFBsyCiFMDCQKIEsqI9bknM5ifZ79P2jD6e0lPAAUPp52B+IxBYsFSqSqlqgAELA4ABADBVrC0R9scBC3Izmlg5Gqrv6bh7cl0ii9Jjhd0ma1X1eYOyJVH1WQHQAAjrj7DH/MboRxeVAg4YikruRb0ZgIE4Azo+yOfOnDTBHrcBai2Q+vQVbfj0nbv25AUWGCDF3gANpFr921/+Xf33FgQZB0+w8+FX37L4iDVr14U1a9abpSF+76/po4ADhvTNSalHBP+gXbt6Ofz9r7+3dU6mJNY+sVGv/+wX1gl+bm/HnbHDLIycY9s7NRcy8Uvx1b7TZ96cAo9TwAHD4xRJ+99YElR3oWpYgKFzmdKpAgSU9eAnBgHZDUibukaF2bAaKHVqtYAFx1phN8UxWCxD5pxJWRPG18klSfUYJsmYVK8vyJbkPCPtvwYbnwOGspimnINkw//um8/Dt+onTxxV4bWrCn6+GpqVAnXT5m1hy5btSqP6ivkhb9m6I+c1cn1IISeyJpE95S+q+vrun35nheDisaRXfeHFV5Ut6eWwaePWsHHTltDS2ha/9tcUUsABQwonpcRDgn/QyY70x//4F/X/Gu7LvZHPyIb05s9/LUviry3+acUKXA9bfuLOWCXAgHsjYMFeM+95LLc2lHhyU3h7BwwpnJRZh0Sxtoda4Apwru5SdqT7AgxyNZpqxCQIAGA1IOuRVXLuGAs1WBfuqlMJekjxDOrm2sSJyqRELYaJDlkjAAxNCoZW3IPHMUxRNdVvHDCkenpmHRwb+2ef/iN89sn74cdj34fOzrtWnbl95apARecdO3cr9enzlv5046ats14r+0uqP5NlqV/B1H/+479ax4phaZd04O49+8KzB1+2a1PfYevWnXJLWpl9CX+fMgo4YEjZhKRgOBMTrPMkK9qf/iDA8Pv/Tzzkno0MK+WzypL03MGXpHjYYZnV6lXxPQIBYhlrMqmZlykuihgmEiI0AyxUELKpqUnHPrJApOBxfQglpoADhhJPwHxvX0WxNoKXFcRcTSrVngQExOsYWFiuYGf1CQGFidWAAAU9C1xYHAOAQelYq9Wn4h0EMgAKVpOBegy81zkW/Bwv7K+ppYADhtROzZwDw23gH++RGelP4djRb80i0N//IKyRKxJ1FyiwZsXWVKWVGgr5tgmlVqVTsA1B4k8CDaRZBTAAUnBvwq+ZdIs7du4JO/T3SoEUb+mlgAOG9M5NqUaGJZF++dI5KQV+F/6stR4BA9WZN27cYp3salXVsiIIAMTG3/XLyJpWH0iuQGHH5paWsEHnbJJyYrXcFGtrayzmIYKMeK6/Lk0KOGAos3m3LEfdEv4BAACHPr0i/Gfa5DIJChRkWyHBv12v6pMryJIEyNCxBjL0qvchZlQSD5loxLIgkEFWJawNqxTL4IHPkaypfnXAkOrpmXVwCPV/ffffVX/hv1nq0+HhYcUfDMtFaKtVZKYqM0Xbdgk8EM+QbwMU0KkA+0cAgzqVn+1z3XPb9qd0fVV+VfXXpwRKduseHaom7S29FHDAkN65KdXIcDscHR1VhrVzxkOo4RIBA0J+Y2OT6q40WypVLI5YHuEBNFyQGhqWWwcstCvxAckPdu3eK0XFAdVs2Dn1vWdWKtUMp+u+DhjSNR+zj0brnMJsFrwsF6Oqfgn+A7gXPdIaTDbIREmwszpAgaJsk8snzKpglgVAQ6Yq9BRgUKyC1WwAbAAYFMtAPMNU4LPHMsw+LyX+1gFDiSfgCW4PYDCXoT/8q2VISjSGo2GzYheiBQBrwM5de6XxW5f3nQQXLK5pcHAg45L0O4uRGB9LXJWIh9iz98BPOllUvKWXAg4Y0js3pRrZSEbBcEkpk0lugPKhSy5JFIKEASxf3hga1CnWFoECPIfijhwBaKhRquVlGStDo9yQsC7Af6wrjopYqhWyPET3pFI9q9+39BRwwFD6OchvBKxuNeIXLID5lgKYAQsq1BaLsSEkYCVA2B9XdqRALALBy8p6ZBWgARsCDDWq20APioeIbVJxDLggARhixqRA8LPXZIgkSu2rA4bUTs2sA7P1qo37j7//r+EP8j0+8v1XtqmzoW9TRVZiFw4om9H2HU9ZX4hAD2CgejSBzwRVj6CRVAYlhAEsF3v2yuVJ7k5YGuYDSGZ9MP+yIBRwwFAQspbxRSfD8NBQwCqJS9LflTr5vb/+wSwMxDYAGhoUs4DLEbVbYoAz/CWJcZKsgEuTLA8WzyAXphr1jlWrlX51nbkykXDh0AuHZd1cb+CCOi7eli4FHDCUy9wrpICYA6wENQIL1F+wwOVYf0FfmwAiqwKBzhRiw7JgbkUYIIh9AFw84HxVdVXWpOxMSZCBzEpYJex8pVidbBBgIMWquyZBntQ2BwypnZpZB4bGj80bsPAf//b/GGCIJ2yXyxABi88896LAw065EO1akMsQ6RX/CmCQ5vGkXJJwdxoeHjKtYRIjsU+uSc9Zd8AQqZ/OVwcM6ZyXUo6KtcyapvbCl198HL78/MPQ09NlCQ8mFAyN5YBOxiTAAH1CbkljdLkyPXz4UPVa+uW6OGCpWIeGBqbiGYijOvzqz8LLr/zMFAxNinPAvcnjGUo546W9twOG0tI//7tL4A8S+KmjQN2FmttySRoWALDaC/pO/08BBqo2bxpKBH6zHOg2WBMUlkDcA4ChGsABgBCmEFKYGseEXJhIwzqubpmSGrBSCDR4Sy0FHDCkdmpmHFh0D0DTB2D4w7//vz8FDLIqkN3kWQEGy2Ik0LCQGIMEMOCq8G8W9IyAgUZy0+atYddTBFUngGHf/ufcwjDjbKXjCwcM6ZiHNI2CAo+4MfYKJFy8cM6qxT9UlXisBgADMh9FwID7EVXeEwvDhCyNw3Jfum8V4akMffv29XD71g0DFZzX2tZu2dmwdJIUYcOGzRYQzXW8LU0KOGAoh3lHXpc7kQU841JkKVIR+DEkZoR9AEO0EAgsUH9hUsK+pUblEK6hbhaGG7IwqAM4iGMw0JGhw2Qj2ZWUjlXF3qZiIBpBFd7SSgEHDGmdmZnHBWCgAxjInT4dMOwOBw8JMAg0bN22I2xR2tOOjtUzX3CGbx4BBlkY5JI0BRhUdwHAgFvSvgOyMAgwULzNW3op4IAhvXNTqpGRZQ0+gpvhQ2VEo2NxGBslwHlsCjCQYtUAgzIjTWQsm/CCmzeuWb904Ww4depYOH3yuNVvQVioV0D0LsVO7Xpqr8U6wSP2yRpJKlZvS5MCDhjKYd4R9GVZIJ1qNelUu7X4lSWpauwR0o+1FyZbEpeiCVyKsAwAFiJg4C31G2SdwEphAdNYKWRpiA2Q8ZP0qqRZlZuSt/RSwAFDeudmppEZYNCmjNsAudNjDAPH8x1xC2ZhkFtSrJOwUAsDMQx//XMGMEiYGIkuSQAGtzDMNEWp+9wBQ+qmpOQDglcg3MNHLD5JMUoxsQGWBLIbARZqaiQvCCzgTmS8R0CD7EoESHfKynBZQdMnjx8JJ6RUwKWpXwUfUWaQlnXDxs2mUHjhpdesMjzX87Y0KeCAoRzmXTzBaigAEkiLqjgG0qlWxbSoWBcU3DxZL+uAAANZjuiTy2AmagCGTCP4uapT1yAtKylZuY6Cp2MjLSuZlSZIsYqVgVoOqsvgLb0UcMCQ3rmZbWRs3GzqZmH4vVySvkuCnvmctKfPHnwh45K0y2IYVi0g7enQ4KAFPb+rtK1YGNBEooHcvGWbWReszoOCnknf6jEMs81W6b9zwFD6OUjjCMwVWQHO8JIk2DmxOkzqs8SqoD1fYAFBQHjBWuQ98IdBxS3cuHYlnDh+NPz44xGrNn/71vXQ29sdKCBJfZZ9+w+GN976ZfjZm7+0eIg00sHHVHgKOGAoPI2f/A7yCCKrUfUdWQZUgyGp1CyrAPELNL1MxGJtAgyTmbSqFqycOSQ5UP9mAp+r+siYpOuRZlWgITazVCg1q1kaVCmaeAYKwHlLLwUcMKR3bmYbWdy0rUKr3JKOKktSsulPmFXhAFmSnpH/sCo+Y3FYaJakd5UhiW6AQVrFMWkhyZIEWLDUqsqQZFmSPK3qbNNV8u8cMJR8Cip2AHdu37QYp1Mq7nju7Mlw/typcPPmNQuAXt7YaHzol7/+HwKdqtDeliYFHDCUw7wDGAhUVnYkAwwI/bgRZQwIAIbJOmkV6AQpU3tBKVUDAc+PNwU/Vw3rBNVuqBqUe5J6dh0H0qhOKjMS15pYo1gGuTZNCDiYleJx8PH4tf3vklDAAUNJyL4oNwUgvPun31k/+v3X5iZAMSYEegKR0eztlA8xfsTziTEAjMAgyH7yJyrAUulZWZJwM0ALiZsTYAGgAHDYrfcLsWAsChH8InlRwAFDXmTygxZAge6uxC3p8uUL4fix76zq/KWLZ5PCbarjcOCZQ+HX//Sfwq9+8z9aXMQCbuGnVAAFHDCkfRLZ95H9M4HKxC+Q8cjckbLwwKTVS5CgzysGA8BCLgE/cz0Ldsal6bGgZwMfmWtZPQdlXAI4TNVjyHXNtNOwwsfngKF8JxjA8Le//D78TWlPfzj6tVIbDll6ww3yHZ4S6FUrYY+E+rXrNub9oAAGijMNxErP//EvZmGwz/UdFgvSqe61StKKZVA8w0JiJPIekB/4xBRwwPDEJPQLzECB/v4HgUxJd+/eCt9+/ZnSs34UTp78IdQrW1J9fYPqwTwf/umf/7N1Mih5W5oUcMCQ5nkXULDaC6RDBTDclJVBgMFSoUZ3pKzxS0TI+iu/t4IeOQ8k49KUhYEUq9RjoD+Kj855nn9YfAo4YCg+zRfrjmQ5+eD9v4QP3/9zOPbDt+FBX2/oe9Br8QRkMaJHSwPBh/k2c22SNYGsKeby9Id/CbgbxEblaISA/QcOhh07cXnabb7K8Xt/TR8FHDCkb04qZUQUeOzp7gzd3V3hi88/DJ989DezMlhaVgEEeMVvfvs/WXfAUCmzPv/ncMAwf5oV7wwsCdRfkAtRze1MsTXFHEiWl7dBbkF/MQc3TizEamIYFPysInCTpFf1Im6LSeJFuZYDhkUhY0kugsafYktffPZhOKGAw7t3peW7cyu0ta0MW5ROlYJtZEui46aUbyOlIqkV0Rz+WS5JuD2dOnls6nSsFwcPvWzF4bjuli07LO/61AH+JnUUcMCQuimpmAGRfrm3p9sCnT//9B/how//Fn448rW5H1ElmloMv/ntf3HAUDEzvrAHccCwMLoV5SyLNSCrkXq1gp4JfLb0qjNYBRZ7UBMtypDULtCgLEmka53U317EbbGp/OTXc8Dw5DQs1RUADEe1MRPwfPLEsXDt6sVw9crFsLyxyQolbdy0Nbz4MukMXzfwkO84KehENqQHslZYpWelVj196vjU6VgtXnz59XDo+Vd0n01h/YbNYUVL69T3/iZ9FHDAkL45qZQRDcsV8kFfj1k3P/vk/fDhP/5ihSSxJgAYSL7wz/+dA4ZKme+FPocDhoVSrgjnVT0UWCCLEbUXeuSKpJSqlhY1AgaMDBRr4xVXITM9zHNgGWuFnWpVox+dT2rVyRUCDaRqJcXqSgEGisF5SxUFHDCkajrmNRgAA1lJzp45Ec6ePjFVPIk0iBRqW716XXjtjbfDq6+/be5JVcqLmE+lVYoyEb/QIxeD9/72B/X/0D1O2ti4BhWkueZLAg1kXyLgubGpeV5j94OLSwEHDMWl91K6G7yCwGcqP3/5xUfh00/eU/Dz94pfyMQwCDB4DMNS+kXkflYHDLnpUvpPCRcALNxV6tP7AgtYGlQvoWo0K4iAjEaUULBXTlCfj6dSBAtgAIKfKQSXhQcstSrpWqnLkEmxam5JpaeOjyCLAg4YsohRZm8BDDdvXA03rl8N58+fDt9987n14eHh0NTcHFpb2sJb7/wm/FydjEY1NVIeVIsPxITqMzzvgGIXiIfovH/XtIUf/OPdcOHc6anzXnjx1fDm2/8UXn31rdDS2q7eZhlRZricf5wCCjhgSMEkVOgQ+lSojdSqd+7cNP7z1RcfyyJ5LDQoQ1KDKj5jYfi1MiT96p88S1KF/gTyeiwHDHmRqcgHIcirARRqbiqVqtKpIsxXKaZhqvaCvreaCUp/GlSgbZKsSLV6n4Un7CKz/cN9ZFUg41KVKj4Hqj5zj0yz9KoUcmtU9eh1imVYr+BngQdv6aKAA4Z0zcd8R9PdRbDh/XDp4vnw6cfvhU/UH6hoEhVV2azf+eVvre9XakNcBMhaMpeVgWqt9wUWbt+8Hj7/7APFSHwQrihlYrUAR011dXjp8M/smq+/8U5YvrxJLlCNXpBpvhNX5OMdMBSZ4Evodlgir5o75CUlX/hGLpJfhwvnz8jq2GS1GEir+stf//fhF7/yOgxL6Gcx7VEdMEwjSYk/MK2/xiBB3gAD2ZEEGKJgn50IaVLa/4kViQWAuglkMTLgkO8j6F5VuCEJJFRTQRqXp6Gsqs8GQpQtSW5IE0qvOr5hWC5KGcDwCFfkezc/rkAUcMBQIMIW6bIDDx/KfahfsQuXApaAD99/N3R23g0yPsiiUB1efuVN9Z9ZtiTch1ar19XlLp6ExYJ2/dplK76EuxPBzqcUH9HVdc9iIxobmy0u4s23fh0O69r4KANEsF54Sy8FHDCkd27KfWRYF04pjSpxVLFw2y0Vbmte0RpWNLcElBVv/+Kfwzu/+K3xi3J/Xh//wijggGFhdCvcWez3Vh8BC8OyUHs9AxgIVEhkgal7T7QqviBT1XkSS0C9QMN8shhlrlk1KsAgUEJxuKp+uTtYUIRuwy1xd9J1xzcJMGwasuBnc3tywDA1D6V+44Ch1DPwZPe3jEZjY+aWRLzB3//6H+H27Rsq4iarnlKj7j9wKOxT+lNckkiBumPnHlkFGnPeFMBAByR8981n4fvvvwy3ZGXA0jAyMhLaV66y9KnPyyWJ2Ahck3BxwmIxl5tTzhv6h0WjgAOGopF6yd3o2tVL4euvPg3ffPWJlA1Xwu1b10OPsia1trWHVrkskn75Lbkw0mdSViw5oi3BB3bAkLZJl8wfJMAHxSrUmEtSkk71J8NEWFeU8oSCkCmuNkGdBACDXJNyVnf+ycmP/aFTSN1aff3/Z+87/OK4kq1ryEgCiQwSkkA5WtlylCx77bW9tl/4/tHv/Z5315ucrWwrWjkHBEIIIXKaeedU04DXsmEEM9M9c1q+HmCmu2+fmr59z62qUxVWDHJS9BxeBvZhijSwHgNDnljx2T0MVEtiPQYSE5GGfwMzN7+KMOQG98U8Kyf5XOX7Gh4Gehn40O5//szrKKxZu87Y1m/YjGJuO1FsbSdyG2qmPANlKNAWkIRxKCONQE+dmuqXUHOBBZjOnDnhx3ne1+efp4Qq285X9rmsKtWSRBQW05KZO5YIQ+awjeOROWaE9ztlUZn3RLGD4uISeAwDr2EZkpZd6QjhjVwUKILH0mu0oKjjJBYpRvD5Uex75/YNLDAcs9PIoXra0w11tWc2hgWLlVBPa4GK2lYUeNx3gGptb3ioZBzxUp8XjoAIw8IxXNwjMJ9gBDPxEdzcPchh6MLKf/es8AO8FVZ1pndhciUIA0gDiYKHI6WTw8CeY95PgjJdSRqJ1tNVoPk+zwdykKwfM9ZlSMGrkaJ60lKwChEGIpTzTYQh5yZYlA4w5+AUVvlOYpXv9q3rvsrHmgw1NXXuGWDhtrD6c2NTi9dqYLLyJLwQbIMD/V7DgfvcuHHZZVSvX7vskwjKJq6oqfXkRcYjsyAcvRXp1HZYlIvUQV4aARGGl4YuL3fkxJ+eAHoQec/3IOSQk/1y5D3VrMCY4eNG8FpVVY0FA5KHMnguA8nl4eFB637cZd3dnZ7fdBUqbVRrGx4acu8mQxW3b9/t3s2Nm7Yaq8O3tW9U6GJefpvmd1EiDPPDKXufYrE2yqmiFT0FYehB0jOIw/TmE3h4E0AQqFw0idyCJBKSFxQmRMLQAU9GB5KrmcfAPlAxiWSCG8/FmgxsrMmAlqxh+engbf0/twiIMOQW/8U6OxOVL108iwJuZ+0acg9uQjXp9s1rvqJXUlLioUSbUHBt89YdXmiNBKK5eZU/3DkJYI4ClZBuoDGB8SG8FI8woQjDlFa1rkEY0nv25tvvWivqO1C2dQUmFdrigYAIQzzslI1e8p5muCJrq7DduH7Z7qJ+y927N60KeQerVq3xe7x19Vq8trl0MoUNGMoYSi6zUNutm1ftJtp95E91IGeh4+F9dD+BMacYCxJ19uYhjBcQRuDiQjUU27hAIY9kNiwczXOIMETMLizW5pP2Z4gr7oMbESFCbNMbPQkefoRE5HoQBqoXNY4vbLUfBKG4E8SkC+cjYRhFHgNVk0LCgDyGJOVVoZbkeRMNIAwgKx7+JC/DtGly9YMIQ66QX9zzUgudycoMR7p65YJdhA76zz+ftQmEDkyAEFAxqQUkgWEC9Q2NTiA44WdowQQqOw9AGrHz0UMnCT1Pul156XnfM1uK+gqssbC2bb0dfP2wJzo3NDbZMiQzLkGBOG3xQECEIR52ynQvSRb4cOa4cO4MFI1Q+PHq5Yv2EPLMlGgur6jA2NBgdfVoWBSoQ42VahRlZGgSG3OjGL5EoQWXUkW+1BN4Jvp6g0rPy5ZVIXeh1ppbVtmrB99CrZa3bBWIB8cfNhGGTFs4uscXYYiSbTAOJBiK1I26C6y/0A/SMIzJ+2zlIoQHceLuCkl1wcSdoUkL2hAGxXN6I2FAvQeed7omA70aUGHyBu/CJORV2aYTrEUaFgT/QncWYVgogtHYn6Sgf+C5T/yZtMzQJNZlGECoEcONGHa0FA9zEoDKykoPPaDEagqhCUlMIsaR1MxJwCBqMDAumSuJnFQ0Na+0xqaVngOxd99rXt2ZK4VMXiwtneW9jAYM6sVvICDC8BvAFNifA49h0kOLTh7/ztiocNTdjfCix52YRCQwsa+YmuAHdRRYgI05DFRCYygTxxLmPIUKbQxZJJGgMEJzS6vRM9HWtsF27tqHfKe9XkCyGF5Oejq1FS4CIgxRsX2waBBM1qlYxPAgL9Q2FR401U/3LkDaNMkKzAwPQuIzXxe0kTD0gig8ZVVptH42EgYyhakjUy2JidYkDK6YNOoyrgsKhVpQp7VziIAIQ4hE/F/D8KGrVy7aiePfumpJD1b/WIG1/3kfHvT0JmDBAA/9F22JRKB2xAd7SWmJVZRXumdhbft6z1tg8uLWra94QSatFL4Iwej+TYQhurbJZs9+RRiwsMCwpLBSMxcKQuW1cDzh6683PPdRUT4BIlGKpOhQXrmtfQMWF7bYBjS+rt+42ZWSfr2//lJoCIgwRMXifP5z4s7cBc8ngMQpqjt7AjIn7py7Y8Lu1ZdJFGrGkVOAicNUHYYFXQbzlymnOojWh8JOyJ0o6sXKI3MZQtLAc6MPThhYkwESq6z74HkMymVYEPwL3VmEYaEIRmf/8MHO3APmMNxC8ST+zMRGrh729rLIW497E8LJQNh7riCGXgOGFDAcgXUb1qxtd5WlVchbYJhBc3OrJz+G++k1HgiIMMTDTpnuZTBGQLkQCwcUR2BjKOMTeBjYerC4wHwmFoQcGxt1rwEJxOyNiwUcL5ggTW/E8uW1Xt+FNV4YftS6us3zIOoRutjQ0PybMs6zj6mf8x8BEYao2NgTjYOJuysWgTQwPClgCiALWOHn5DzJPALkLrB51WUUVSOJWNDGxQdIq1JeNcGcicfwcKD57yAx7mkgYcE/5jC4hwGkgXKrnscgwrAg+Be6swjDQhGM3v4MK2K1ZyZC371zC0XdbrqSCRMb76ENI98hlEcMe0+vQgWSGpnYyKTmdiQqUtlkNR7+ravbPaaZIUzzqRQdHlOv0UFAhCE6tsh9T/A0htcgDFfsRaXmx10dnpPA8eEOqsYzAZrjCBuJQ7iFnkWOF1VMZEajgELoVWAII3OcarHgUM68B4wZKuoYolfYryIMUbE/CQOKKHv+APMJULSNE/ZwS3FSzrAgVl2GQlESHgYjWeAqP9tCNxZwBjkI1ZmozJRALYiEEwa854QBpGEZCEsDchjQ5GFYKOiLs78Iw+LgGKWjBInOSE7Eg57KJay6+uDB3amk6DseezyBGGR+LpgAJDwfoRJJzExk5gSASc6s39DY2IwJQAuSnKuUsBglI6fZFxGGNAErgI9PJvE8nkx67ZWnqA5PsYOHD+7Zfaik3YfXgblPJBUMU8z4KasAACYhSURBVCLBYK5TsecylPh4QcJQhYToZpAESqa2IxyJ8svMceJ4wQd/SDAKAE5d4hwIiDDMAVDW3qaTAOE/rLrsuQsMD+IkfmpjOJBP2lkwjYpFlSAL/BmpBiQSC954fhwmMQaSgFAo9sE9CwxXwt/9/DwJFJqSqMHAxOtplaQZXrPgbugA6SMgwpA+ZlHfw70HCDlgKMFz5C709/fB4/DMnsHr0AevA4uzjY4g3ABqJwnEITO8oARxyCQM9DAsxwOfCkp8+DNJeunSKngWyqN+2erf7yAgwvA74BToW0FYYpAATa8jldY4XlAytQ9jBX9n87wGLC4wjIm5CqGnMVA+qnApVo4VrN1QAUGFCngVWLcBkUvY9IAv0K/Xry5bhOFXkETgD5z//x4HCO/f8HWxuzyf82fq3It9LQVwPBGGfDYyQw8wHHB1kGpIWFHkQ5+VXYcHB0Echr16a1FRceBhAFngA5/VXkki2LhCqFXC+H9HRBjib8NMXkFAHoKHdzBeoBI0xomgCjRUkKCARCWkioolgdIa6jJw3ChG9edfjhMaLzJppzgfW4QhztZT34UAEBBhKIyvAXOIUkkSiGBFkd4FxiYXURnJwwxAGrB6SK11TgC4iSjkz3dDhCF/bJmNK+F4MT427iSBks2s1cKaLfQc0MPAqs+/JArZ6JXOEWcERBjibD31XQgAARGGwvkacOWQW+hpYPwyIwYCL0KwUhgmKIos5Nf3QoQhv+yZjavhOBGKI/CVCw5F8Ci4BxKv8CVoUSEbhsiTc4gw5IkhdRmFi4AIQ+HaXldeOAiIMBSOrXWlQiCKCIgwRNEq6pMQSAMBEYY0wNJHhUBMERBhiKnh1G0hkCcIiDDkiSF1GYWLgAhD4dpeV144CIgwFI6tdaVCIIoIiDBE0SrqkxBIAwERhjTA0keFQEwREGGIqeHUbSGQJwiIMOSJIXUZhYuACEPh2l5XXjgIiDAUjq11pUIgigiIMETRKuqTEEgDARGGNMDSR4VATBEQYYip4dRtIZAnCIgw5IkhdRmFi4AIQ+HaXldeOAiIMBSOrXWlQiCKCIgwRNEq6pMQSAMBEYY0wNJHhUBMERBhiKnh1G0hkCcIiDDkiSF1GYWLgAhD4dpeV144CIgwFI6tdaVCIIoIiDBE0SrqkxBIAwERhjTA0keFQEwREGGIqeHUbSGQJwiIMOSJIXUZhYuACEPh2l5XXjgIiDAUjq11pUIgigiIMETRKuqTEEgDARGGNMDSR4VATBEQYYip4dRtIZAnCIgw5IkhdRmFi4AIQ+HaXldeOAiIMBSOrXWlQiCKCIgwRNEq6pMQSAMBEYY0wNJHhUBMERBhiKnh1G0hkCcIiDDkiSF1GYWLgAhD4dpeV144CIgwFI6tdaVCIIoIiDBE0SrqkxBIAwERhjTA0keFQEwREGGIqeHUbSGQJwiIMOSJIXUZhYuACEPh2l5XXjgIiDAUjq11pUIgigiIMETRKuqTEEgDARGGNMDSR4VATBEQYYip4dRtIZAnCIgw5IkhdRmFi4AIQ+HaXldeOAiIMBSOrXWlQiCKCCSu3e5NWQr/RbF36pMQEAJzIlCUSBhJA9vSyhJbuqTUSkqKrH9wzPoHxm18YtJ4g+senxNKfUAIRBaBkuIiv69JHCorSqwCbXIiGdzng+N4jOsOj6zxctyxRI7Pr9NHA4GFjhCJrieDYgvRsKV6IQReCgHwBQtawsrKiq28tNiKihM2OjppI2MTNjk5NUwsdLR4qd5pJyEgBBYDgXBRoBj3duBtKLZkMoX7fMJGxya57qdNCAgBIZAxBBIjo+MiDBmDVwcWAtlBgISBWzipIIPgZCKZTAYTCU0mAoD0fyEQVwS4MIB//M/vc7/pU74gwHtdmxAQAkIgkwgkhkdEGDIJsI4tBDKOQDCHmJlIIDSJ/0gWJjGR8JVHzScybgadQAhkFAHe596mQhDxC1f7kvAgTnsRM9oBHVwICIFCRiAxNDymqUQhfwN07XmBQMJXG82Kp3IZ+PskCAMnE0nFKuSFjXURhY2ALwOQMPAex/1NLwPzFrgoMDmZLGxwdPVCQAhkHIHEwOCoEwaxhoxjrRMIgYwgEE4kGKpQXITESMQ4kzBMICGSE4mQMOgezwj8OqgQyAoCJAn4z+9t5jFwcYBrAbzPJ3Cf6/7Oihl0EiFQsAgkevuGlcNQsObXhecFAtMTCfNkyJISJD0XmY2PJW1sfNJzGfLiOnURQqCAEXDPwpQH0RWTQBqYujCOe3xsHB4GeRIL+NuhSxcCmUcg0d0zJDW2zOOsMwiBjCEQrDoGK49lpUWulMRwBaokUT1F8c0Zg14HFgJZQyAUNJhWSYK8Kr2H4X0uvpA1U+hEQqAgEUh0dUNWVZsQEAKxRSAMU+ArZVXZGK4wQsIAyUURhtiaVh0XAtMIzBCGIivlwgAJA1wMfp9DPlmEYRoq/SAEhEAGEBBhyACoOqQQyCYCIgzZRFvnEgK5QUCEITe466xCQAgECIgw6JsgBGKOgAhDzA2o7guBeSAgwjAPkPQRISAEMoaACEPGoNWBhUB2EBBhyA7OOosQyCUCIgy5RF/nFgJCQIRB3wEhEHMERBhibkB1XwjMAwERhnmApI8IASGQMQREGDIGrQ4sBLKDgAhDdnDWWYRALhEQYcgl+jq3EBACIgz6DgiBmCMgwhBzA6r7QmAeCIgwzAMkfUQICIGMISDCkDFodWAhkB0ERBiyg7POIgRyiYAIQy7R17mFgBAQYdB3QAjEHAERhpgbUN0XAvNAQIRhHiDpI0JACGQMARGGjEGrAwuB7CAgwpAdnHUWIZBLBEQYcom+zi0EhIAIg74DQiDmCIgwxNyA6r4QmAcCIgzzAEkfEQJCIGMIiDBkDFodWAhkBwERhuzgrLMIgVwiIMKQS/R1biEgBEQY9B0QAjFHQIQh5gZU94XAPBAQYZgHSPqIEBACGUNAhCFj0OrAQiA7CIgwZAdnnUUI5BIBEYZcoq9zCwEhIMKg74AQiDkCIgwxN6C6LwTmgYAIwzxA0keEgBDIGAIiDBmDVgcWAtlBQIQhOzjrLEIglwiIMOQSfZ1bCAgBEQZ9B4RAzBEQYYi5AdV9ITAPBEQY5gGSPiIEhEDGEBBhyBi0OrAQyA4CIgzZwVlnEQK5RECEIZfo69xCQAiIMOg7IARijoAIQ8wNqO4LgXkgIMIwD5D0ESEgBDKGgAhDxqDVgYVAdhAQYcgOzjqLEMglAiIMuURf5xYCQkCEQd8BIRBzBEQYYm5AdV8IzAMBEYZ5gKSPCAEhkDEERBgyBu0iHDiVMkslgzY5ZsaWnHzJAyfMOLM0vhYFrajYrLjErAjN/873tcUNARGGuFksvf6mMA6wJZNJm5gYR5uwyclJb/xbuBXhvi4qLrJi3NfFJSVWWlpqJXjllvB7P/ykXuOIgAhDHK2Wuz5zzOBYEY4ZycmkTWL+EI4n7FlREcaLYowXaBwr2BIcR/B3jRm5s11UzyzCEFXLsF+TEwFJmBg1G3pqCTQbG0y/xz6jDElCqVlJGYhCmaXKlppVVJnxleQhwSbSkD7Aud1DhCG3+Gf67CE5GB0dtf7+5/b8eZ8NDw/b6MiIjY6O4PQJf7gXlxRbRUUlWoVVLauy6uXLbRleOdHkJEATgExbKrPHF2HILL75dPRwgYHjRf/z5zYwOGAjGDNGRoZBILDYMEUcOFZwzFiyZKlVVVVhvFhmZWVlIA4ziw35hIuuZWEIiDAsDL/M7k2iMDZkNjpgid57Zr13A9KQzlmdACQsFXoTissDglC6xGxpnaWWNfirFYNIsNH7oC1WCIgwxMpcaXd2fHzc2AYGBuzx407r6uq0vmfPnDwM4m8k+SQDfNBXVVVbdXW1NTQ0WlNzizU2NoIwBCuIIgxpQx+pHUQYImWOyHYm9CBwoeHx4y57jPHiyZNuX2h4DvIwNjbm4wk/V4WxgkShtrbOx4z6+gaQhyVOIjieaBMCsxEQYZiNRtR+HhsGWeg3G+6zxOMrQevvTLOXDEFCVBO9B04a6GGoCFrlcktVrjCrrDFbUoOf8Vq+zKy0Eg2fmSIbaZ5QH88yAiIMWQY8C6ebAEEYxYOdHoQn3d3WjQf+k+7H068DA/02NDQET8MQ/Qt+r5ZiVZAP+yVLl1pdHSYAjU3WCOLAVxIIkgmGHDD8QOQhC0Zc5FOIMCwyoHl0uNCjkARJ6IMHsqfnifU8eeKLC11dj+xpT48NDg3a0OCgjU+FNYIvTI8X1dXLrbam1mpqa62psdmampqtrr7evZX0QHDM0CYERBii/B0YRfjRyDOzQYQjPTxrRQ/PmD17kH6PPcqIk4owLGkqb6G03FIkDyAJqdo2s5q1ZtUtloLnwZbUBrkN4b7pn1V7ZAkBEYYsAZ3F05AMMPSInoQrVy7Z1auX7d7dO/as75n1oTEcyR/84whb5K2Nf4w7LkHeAokDvQwramrw0G+wnTtesR07d1lr62qfAJSXV/hns3g5OtUiICDCsAgg5ukhmMs0PuU5uHPntl2+/LNduXrJuh9jkQFeBpIIeim5EOHhSEnkReEfxwrmOpEULMVCw1KEJG3atMW2bt1m69dvBIGog/eh1jhmaBMCIgxR/g7QuzDUazbQbUV3j1uCref24vUYM80UZxvwJqSatqJtM6tfb6kVq81WtCJECeFLmIQoTGnxIM/EkUQYMoFqbo7JlUJuvb1Prauz0x496rBTJ4/ZyVPH7cb1qzY4OATPwqAnQJMgsAW7BInR4UpjZSUnAMusBquGbx96xw6hbdm63ZYjr6G6eoW8DLkx74LOKsKwIPjyemeSgUHkKQwODNr582ft2LHv7OSJY9bztAfehSfujQzHi8C7yJXAQEiBZMMXG6bIw+49e+3VA6/brt17rHX1Gl9oYC4U95NnMq+/RnNenAjDnBDl8AOZJgy8NJKGIoQpLYdnAd4FehlS9Ru8GcOVypEQzXwHbZFFQIQhsqZJq2Oc7IeqJlwl/PnnC3YJ7ebNG3YLrbPzEVYJg/hjrgjSi7AM8cd84KfQGMI00N+PXId+nwCUlZd7MuOWzVtt85apxp/RmOTIMANOFLTFAwERhnjYKZu9DBcI6HW8cf2aXceiwrVrV+za1St2/ca1qUTnEQ9F5OIBQ47K4FGgihoXGp4964UXs9cJReixXLNmrbWvW28bN2xyz+ROeCcbEaLEnAY2kYZsWjha5xJhiJY9ftmbbBAGnhGhSinPW6i01PKVZs07LNW8PQhPYlI08hu0RRcBEYbo2iadnjFJkUpIzFs4f+6sffPtl/b9d9/4Q50P9iF4F7gqyElCXV29taxc5bHGrqIE+UQqoTAhmkmO4whV4gSzGLLJnCQwPnnT5i12+PC7dgiNk4eyMiqhYLFAWywQEGGIhZmy2kne+1ww6Hj4wL799mv7DmPG3Xt3kb/Q7d4Fl1rGogCTmtva2q2tvd0XC7iYwHHkHj577+5dz496PqWotGwZFJOQ09DS0mKH3j5ib8E72d6+3pbBY8mQJRGGrJo4UicTYYiUOf6tM3MRBs4UGVLE3ASGFTEfgUpH4cYlBEwwuJSQSKF+AwYWS04EUq2T49PvTX+cxwNBYGiShyfVtQe5DfQ8aIssAjQbB3G+lpUVeyvGZHFklBNQavYHYS6RvQB1zBEgWXg6FULw44+n7Ouv/+WTACYyJiGDyFVBV0GCV4EKSK2rVjtpCOoyTCDn4bk9fHDfHqBxxZGehkEkOTJGmW09Vgw//PBP9uFHn1gz9qeXgaFL2uKBgAhDPOyUzV4GikdjduvWTfvrX/7X/vLn/0EY4yNfdOB7tVhYqANZYGjRhg0bPS+BoYrThAF5UXfv3vYxo6PjgT3qeOiLDSnUf+JY8/ahI/b22whnhIeSYwbHHXkls2nhaJ1LhCFa9vhlb+YiDFQ9QtG1FGoqeLIyJ/bl1TPH8KJvJAqTlpiA4tI4VZcgwziMRGq0BMmDf2ZmQknVpFTt2iA0iXkN9DTUrZs5pn6KHAIiDJEzyUt1iEnO164FIQUXL5yzc+fO2AW8eihAaZmvEm7cuMk2bNyMBzdUTDAZoKcgDEkiQejAA7/j4UO7ffsmQpmuG0ObwtjltW3t9u67H9i7771vqzGB4P4rVsh7+FLGysFOIgw5AD3Cp6SHgPlMXBS4gfCjL/76uZMGSqhOokgbvy9bt+1AAvN2JDJvtpWrWm3VylYnC1RL4/69vb32DPlSd0AaLl284GGQ4WIDFyO3b98ZNAgnbN8R/Mx9tRUmAiIMUbb7XIQB3oSU11VYAo9AkLScqmqavqIEiIIl4UmANyEx0gfFpedQXHpiib6HZmyo8+CfIWkItzIca2l90Fr3mK05ANKwI3xXrxFEQIQhgkZ5iS51Qzb12NHv7ejR7zwOmeEC9BhQ8pD5Cm1t6+zNtw7ZW28fRigSPQRL4CEI84tSPnHoxOpiZ2eH0UNxDMc5ffqkTww4OViFCcNbbx3GMQ5jpXGD/84VQ23xQECEIR52ylYveU8zVPEZlNSuQUXtb3/7i/3ti8/9dy4SMM/pyJH37J0jf7BXdu3G4kKdLzAwd4nPDG5exA0ezFu3btgP339j33//rY85rN/Aui8tLSu97dq1x0MZD7/zrnsrg731/0JDQIQhyhafizCUhLKoVZZq3Yu2BwpHrTNXxBAkhCIlSBpYAI5Vogd7LPGMhOGBJfCzDfeCTIBIhBuPWcH6DPBUrAZZaH/TUqt2h+/qNYIIiDBE0Cgv0SUmNX/55T/sy3/93ROdKYdIPXUmIa5Z04YchK22f/+rtv/Aq8hJqLPSqSTE8FQMaaK60jOsGv7440n7DvkPJ47/4KuQlGllHPP+AwftABoTnymbuGZtW7i7XiOOgAhDxA2U5e6RMHRhzGDeEqWXv/rqn/bVl//0cCTmGtD7eARk4ciR9907QNlUhiHODiniMbjdv3/PfsTiwunTJ+w6vJw3blzHcR+5F5Ljxu7d++z9Dz5E+8g9nlm+VJ0uIgiIMETEEC/sxpyEAXkL8AhQzSi19jVLtaHVtM06FAkDBgR6EOhlYAgSw5H6OtzDkHh6BzKtt0Ag7s/sQ68Fj1m6FMc7aKn171hq9b6Z9/VT5BAQYYicSV6qQwwn8rACrBLeuX1rqppzv1HmcPfuva5YwpCkjQhJoswhH/xcLQw3JkCOoD4DazScPfeT/fDDtyAMR42eCxZ9q0C+ws4du/w4O6ZCDHgsbfFAQIQhHnbKVi8ZisjaLHfRLl2+aMfdO/k96rMWocJ7s61cuQr5B4fhkXzHx4wwl+nfk5Y5Reju7ppWVzp75kc7g8aQxuXLV7iHc/eeffbxx5/aR2iqAJ0tC0fvPCIM0bPJTI/mIgxQNkqxMjOrNK87hMn925aqWz+z/4t+Yg7D80donZZ4dMFYDC7RdXnmk8iL8DAnehra37DUxvdARl6deV8/RQ4BEYbImeSlOsTwo8+RtPjnz//H7iMciV6BsbFRTzw8dPiI7dq111WRmHw410P74sXzHt508uRRTCiohHLH+7QRscybQBJ2YQKwb+9+rDy+8lJ91U7ZR0CEIfuYR/mMJAz0BlBKlRLMDEP8CZ5FehHWrm13adTXX3/LXnv9Tagc/X4eIvMWHkJpiWpLR0E8vv/ua+RPnQ+KuSFJei/Gik8+/U/79LP/xNiD+kzaChIBEYYomz0ThGEcoUkDCEViLgOqRyfunbKiR+dnUHDCUAq1pbIgHGnTH0AYDs68r58ih4AIQ+RM8lIdYkhSEFbwDw8zYOXWSUwKXnvtTbQ3UHht2/SK31yJh1cuX7JTCC9gmAFXCtkmILW6bt16l0jcu++AvXoQxZkQm6wtHgiIMMTDTtnqJZXTrl654lXgSRjOnDltZ3760WuztCPfaR1CDg++9rodPPiGtbW1/263KLhAD+ejjg4Qhm/t22++8gJwVFQiAdm7dx/Iwn/Zp5/+lydN/+7B9GbeIiDCEGXTZoIwINHZhpkA3WeJ+z9a4s5RSzyAl4Hyq9xYk4HSrEUITVqH/IXN73u4U/Cm/h9FBEQYomiV9PtEdZOTJ47acYQRMQ+B8cUJhB3t3bcfD+wDvkpIz0IpFJNmxyG/6ExcdTx39ic7e/YMJhWXPMZ5eHjY8yGokHTgwGv2xptv49gHXrS7/hZBBEQYImiUHHaJIYjMXbh8+WejR/E8VNV4z1MkYd26DVBT22QHXn3N7/W1c+Qq9aMGA6vKc9HiByQ+fwNJZ6q0kSxQXIEehk8/+2/7DKSBkqzaChMBEYYo2z0ThGF8JMhjoKzqg59AGI4FnoaQMEyHJMHD0IaQpE0MSZKHIcpfExGGKFtn/n1jWAAnAFcu/WyDkEtkrDFzFDZt2mIb0RiKxL/NRRZ4RsqzMhb5LCYQV6GgwjY6QsLQ5qSByc+vvwHCgImAtnggIMIQDztlq5cMSeJ9zcrO7mH46TRCkk7bEiQ3e7VmhCG9hpCk1z0kab2PHRw/XrRRbekBEp/vP7hnx48d9fwnVplnojS9DHvCkCSEJSkk6UUIFsbfRBiibOdMEIZRKCX1dyEsqcsSHeetiKSh8+cZFDzpGQnPZUvNQBRS6w8r6XkGnUj+JMIQSbOk3SkmLFMZiZKGLLrkdoWHob6+0RoaGpDoXB387Tce+uEJmcR4+dIFO3HyuJ0+dcJrMtxGYSdOMNajeNOGDZtsD3IYSBp27twV7qbXiCMgwhBxA2W5e7yfWWflzp1buN+R9AzP5PFjPzgxqK9vsGZUaqZCEpWSWOWdiw+zRRJmd5fCCE4+rl7xhYYw6TmQdF4OwhAmPX82Z/7U7OPq5/xCQIQhyvZcbMLAmQQlVHvvQxnpnic7Jx5dtMST6zMooFp0IKsKadXVWH10WVVNKmYAit5PIgzRs8nL9IghSAwzYGXnVOjxQ7BgMVRPiuD5m49ngbc4ZNE8POE76KqzrkOYzMi8hy0o4sRCTtRlf+WVPfh528t0VfvkAAERhhyAHuFTkjAwjOjRo4cIS7qEvIMvvXHhgWFELMr44UeBshFV0aiSxDHgRV4GjhGhR/IKQpyuXLmMnIYHLs1aU1Pjsqp//PBj++CPfxJhiPB3ItNdE2HINMILOf58CAM9AZRV5cSeqkZ1s5Kbkpg9oA4DKz3bBEKREI7ktRcgo5rovQfiANJA4kDVpHArR02H5SstVY3WshM1GEAWGjeH7+o1ggiIMETQKDnoEicQkxMTNjE54QXbqMn+HdROnvb02NOnPZ4MSc8CJRK3bdvhoU5MgtYWDwREGOJhp2z1kgsMvLd7nj5x78A///6F/f0ff7X+5/2Y1Jd6/gELPb755iFXQ6urq0ddBdRvAXHgAgS3gYF+L9B2F54Khi8yB+L+fRSMBIFgIjSrwzP/gYsLDG1iiBP311aYCIgwRNnucxIGeANQL8Eq4Q1g+BBbzdqZK2LdBdRfsIkxs6GnlkCRNoYjJeBhMJIG/s7kZ0qtTm2ppXWWatjkzRo2WqoeE4oVq8O39RpBBEQYImiUHHQpqMMw7LUYGJrAyq9ff/UvGx4e8tbQ2ISHPmKa33gLuuybPJ+BWu3a4oGACEM87JStXpIwDA4O2ODAgOcs/RmSzJ9DkrkXiwP0RnJiv3XrDtu6bbsvELBQI0MS6X2geAK9ka6MBA8F5VkvXDhnF86fxeLCU+vvhzAKtl2o/0IlNXooAu/kNng8S7J1iTpPxBAQYYiYQX7RnbkIA2sloBaDVVR7NebUKlR6Xj5rAjAJokCyMD5sCeYtkCz0dwZF21i8De8nWNSNIwdXHJjwXN1iqZW70KDPvrwVvzebgURoiy4CIgzRtU02e8a8B64KPu/r86TFL774sxOGFO5xTi5Wo2K0V359931fNWwEgeCqo7Z4ICDCEA87ZbOXvOfHx8ft1s0b5oThf/+/daJC89goJJnhaaRQQkvLSi/cxnwltmXV1VZRXmFJjAm3bt1AuwnCcCUQXEBo0wS8lHymMH+BSmpvQByBks4tLau8GNx8QiOziYHOlT0ERBiyh3X6Z5qLMBSB6bNeQmmFewFSK1o9PGn6RAxFCr0MzF3A8RLwKLisKlWS+B5jpSmluhQThyXwLtSssVTTFoQhbbHUklonI8bicNoii4AIQ2RNk9WO9ff3e8G3e/fuQC3llB099j3qMJyaSnYs8voL73/wob3/wUd48LeipsNyq6qqzmofdbKXR0CE4eWxy9c9JycmnRh0dXV64bbTqL1yE+SBYUUdDx9OqxxRNIH3/KpVrVZeUeHeBy4iUMqZjftTUrULbUVNrTU2NFnr6tUujsAQRqouLV9eg7yIFS/MgchXfHVdv0RAhOGXeETrt7kIg88UizDhh3eAVZ/pbWANhXCj58CmPAgMTQJBSGDVwZL4mb/7+/gMSUf9hqmGEKTa9iAXwo8HUkJioi2yCIgwRNY0We1YT88TDyk4f/6cyyxeunQBK4fXPPyAIQgbUeH5o48/QfvU6F2oqKi0cmmqZ9VGCzmZCMNC0MvPfTnp99AkhCU9RJIycw9Yj+EU1NGYxOwiClg4pDoS73c2/hwmPrOS/Ci8FKNIlB4dHUE44yhymzbb9u07bdv2HbZ5yzbbsnmr1dXXg2SwBsys+UV+Qqqr+h0ERBh+B5ycvzUXYVisDv47YXDyAOIAxSQEQ7oHYrFOpeMsPgIiDIuPaVyOyMkCwxL44OeK4qlTx+0k5FRvI9TgwYP7WDnsAjmgLGujhxUcOnTEDh0+4goqVExRPHJcLM2hmDU4AtWs0tIiKyspglRuykZGJzHpmwjWf+JzOerpIiHAdb+RkSHIMT92SeYzqMdw9Idv7cSJYyABIAQgAkGYEeKM8LCYXYmB4we38JU/U0Vt5yu7bSfyFraCNPD32to6Jxkh0eDntBUeAiIMUbb5PAlDcMv//oXMHiR+9Ul4EFLLGtAazerWBepIK3eaQTHJkCfxC6/Fr3bWH3KNgAhDri2Qu/OnoIz0tPepJyqSJJyYqhTd1dnp+QycLLyyK0hc3L5jp68eshBcZeUST4xUPHLubJfumUUY0kUs/z8fTvSZu3T79i0sFNy080hcJmk4f/6MEwWSBSqo8V73Cf8s0sD9+V54HL6uXr3W2lD0jR7JV71S9EFrQi5EKO+c/6jqCn8LARGG30ImCn+fB2GYD1kIL+U3SQMGkJQTA6gu1a6F2tJrQXVn5jCUQ4WJoUnaIouACENkTZPxjiURbnDvHiq0Imb5MipEH0PeAmsv9PcjZwlriQxB+OijT+xDhCK9glVD5ixUI+mRYQna4oWACEO87JWN3nKCz8ZwxIsXzkPp6KxdwjjAWgrXr1/190IyUIR7PiQN4VyAZCFs4bFqkMNQV9fgeQtH3v2DCyVQMIEeSTZthYuACEOUbT8XYWCysqsb4SamUhKalcye3E/RCQworMWQSCF/YWLUbGzIEmOo+MykZyZG830qJDFXoaoJSc9bg8RnSLR6IjX+xskH3ZnaooeACEP0bJLpHlEZhd6DoaEhnxywcNNlL7h0Cb9f8gc7Czc1NjXbO++8Z+8cec82o9prWVk5SERFsNKY6U7q+IuKgAjDosIZ+4Nxgh/WUXiI8EPWUWCFZlZ+Znji48edHnpIAlANgYMl8CpWQlI1JA0EYHwcKktj49aPegyUY6WkKklBOVSUWLBt774Dtg+N1eFbIMFMGWZ5JWP/1XnpCxBheGnosrDjXISBoUQlZU4SXN2INRiW1Mx0jESAKkh8ReG2BMkC6y70P0bFFsisUm6Vyc9sIB+ulkRFJJIGtuYdZijcxroM4fsiDTPwRuUnEYaoWCJ7/aD+eh/kU1m4icoop0+f9OJNT7ofWzcacxba2zfgQb/R9u1/1R/6XCWkZ2F20mP2eqwzLRQBEYaFIphf+9Mz0OmVnjvsxs3r9hMU0U7/eNK6H3fZAMYHJjIzrGiD11xZO1W1uRaEgIXbsNiIfyPDw16jhQpJ169fsxvwSgzjb1yQ4DjRjtCktrZ1SIDe6WMICYS8DPn1PUrnakQY0kEr25+dizAgjCjFxGRWZ27ZEeQeoI7C9BYSBgwsNg6vAgiCDT5BdeepSs+UVuU56G0IN5IQhiBBqjW15lVLrXvLUq174YGA94EKTCQW2iKFgAhDpMyR0c6EYQM9T55YBwouPbh/z44fP4r2g8cvT8JjmJxM+org7t17kL+wx/MWOHGoB4nQFl8ERBjia7tM9JwKSNeuXrarqKHw88ULror0E3IXWKiRYgb0JO7nYgHaFqgdURmNCwlUOmKlZxKGwcFBeCkH4JUIKj1TWYkSqxxfuChBL8OKFbW2GwXcPvjwY/vjHz92L6WSnzNh0egfU4QhyjaakzBgUl+2JAhHWr0fE/t9qKMwqyozHQwYFNzDwCJuLNTGYw50WwLNeu9aoucWCMSDmc9RohXEIMW2aneQz4BXT4CugPcBikraooWACEO07JGp3nBF0UMIpgo1XUDM8sWL57xoE1cY+/qeWdWyKs9TYHXXvXsPeJVWThQam5psGd7TFl8ERBjia7vF7Hm4aMBk5hNYKOCCAROd72LSf/fubZ/Q10MGtbGx2fbs3YdaCvtt3foNPjYsq6pyz0ERw5nxL1RYI0m4QQ/DjWt29cpluwIi8uD+fa8KzcrQuyCc8KdPPrOPP/7Mw5rofVBo0mJaNR7HEmGIsp3mIgzwBKTKkJRcucJS7W+ivYGk5fYXX5FXdIangWFJowNBEbeO85a4e9ISj86DVCSR4+AMA14E5itgQIHXItkKIkLFJKgoGYu7kaBoixQCIgyRMkfGOsMJAlcPh4aGEat82r75+l/2w/ffQA3puSsicVVxVetqa0XbBc/C/gMHfWWwHHkLZai3oFCCjJkmKwcWYcgKzJE/CQkDFw8omfrFF5/bF3/93FWRKHTAsaCpqcXWgyAwFInjAFsrlI94/7PN9g6ECc9UWWLRNoYm0Vv5w/ffgoScc3JBcrAL3sr/+Oz/2Wf/8d9YeFhmpajrovEk8l+VRe/g/wEAAP//48ZoVgAAQABJREFU7L0HYxtX0izaSMxBoqhEUTnnnCzJOe2uvbv27vd9972feO+7G+x1TrKVc86ByhJFiTkBRHhVBwRFWYEJYQassY8AAoMzZ6oxg1Onu6sDzS09KdPmTQSiXWa9bWbdLRa8fdgCbE+bno01Um6p0iqziqmWWvSmpRbvsdS0xc/ef9mzZMIsHnUtcOeoBa79Yny0VNICKbyXevZ1SM1YYcmG9ZaatcZsSoNZ7RwzHk+bpxAIBMwC+IePJSUh10LBgPVHExaNxi2ReGZTTw1cgxkVAslk0th6errt0cOH9vDRAzt96oQdPLjPjh09bMFgEC1kdXV1tnzFKlu+fKWtWr3GVq9aa8uWrxjVMbST9xEI4ppmC4WCFokErSQcxPcilb7OY/Hht27vn4xGOG4EEomExeNx6+vrs3//6/+zf//7/9qpk8fdawMDA7Z48VLbsGGTrUdbwfvBipU2c+as1x6vv7/fOjvaraOjw/bu/cm+//4bO3rkkCUwX0jieKtXr7VPPvmrffLpX23KlKlWWVVpZWXlr+1TbxYfAgERBg8bNReEAcTAknGzRBwE5AgIw08WuHUYryUGCQPeH9xS9UtAFlZbasZKS9XNN2Mrq8m8rUePICDC4BFD5GgYsVjMBgZi9ri52c6cOWWnT5+0a9eu2K2mm3b37m2rrq6xmpoaa5w7zzZu2GwbNm22uXPn26xZs2369Bk5GpW6zTcCIgz5RtybxyMpiGKC340FhC/+/Q/74ot/uAWEzMICJ/dv7HrTdu7chfvAPNwX5tvUqVNfezIkICQN7Pfnn3+wb7/9yg4d3A8y2udeW7p0ub3/wUf2/vsf20zcV6ZNm4Z7Tu1r+9SbxYeACIOXbZoLwsDzdV4EeBRAFAJXfrBA00FHIgIkEiQUg1uqbqGlpi8DYVhmVr/YUmhW/vobT+azeswfAiIM+cM630fipdrX1+tWE5uabtj3333t2sOHD9wPPInE7NkN1tAwx3kWdr6xG5OFPZgg1Fk4HEEL53vIOl6OEBBhyBGwPuuWk/qe3h54BDrsyy//aV9+8U+3kJA5jS1bttsHH35s77z7AbyO09zkvqKiMvP2Kx9TuNmw/fTT9/b1V1/Yvn2/Wnd3l3V3ddm8+fNtz563XZs3b741zGm0+vrpr+xLbxQnAiIMXrZrLggDZyAkBQg/coThKj0MGcLAkKRhhIEkgd6FmQhrIHmoWwDCoFUFr31lRBi8ZpGJj4erhVz1i8cH7P79e/bg/n27eu2yHTty2I4cPWQ9+CFnGFJJSYktW7bChR4x/GDlytW2ctVqq6ysRIgaQ5WCEx+MevAEAiIMnjBDwQdBT0AvCENHe3uaMHz5LzsLz2Nm27p1h3340R/t3fc+cAsHXDyoqKjIvD3i43DC0NXVCcLQ6TyWu/e8Zbt3vwXysMAaG+eKMIyIZPHtIMLgZZvmhDCAELwsJIkEwnkYQCgGN+ddmL0WYUmrkMMw11JoVladeVuPHkFAhMEjhsjiMBh2EItGnXfh7Nkzdu7cabt06YLdvHnDbt647vJVqqqqXTzxps1bbTMacxdmzJhpM2bOdEQCmS0utyWLw1JXBURAhKGA4Hvo0EOEATkH9C58+RLC8NHHIAzvfugIwxSEI42FMPz80w/29ddf2H54GDo7O4ykgSGOjjCANMybJ8Lgoa9DXociwpBXuMd4sNESBoQJuYTnRa9Ken5GAiwxYDbQj6TnPiQ7H7fAzd/c40uTnkEUUnM2WoqkoQZJUzWzkVU7smtzjGep3SeIgAjDBAH02McZFsAVRIYctLW12uHDB+3I4QOOMLRjVZGvMX6YoUhzsNK3ffsbtn3HTpfsWFpaZqWlpfIseMym2RiOCEM2UPR/H1G3kID7Q2eny2EYHpLEe8fmLdvsgw8QkvTO+1Y3rR4hSfXO4/i6M8+EI9Gz+QtyGL7++ks7sP9XkIUu1+YvWGBvvfWuvYnW2DgPIZANLtzpdX3qveJDQITByzYdDWHgBJ4qSQt3pdu0RS85o8EwJChqWKwbqkuPLdCF1nzR7OE5Czy+glCkFJKeSSzYILfDZ3PWW3L+dkcarKIOx0GLlLn39I93EBBh8I4tJjKS4T/azFG4cf2aa5cunXdk4d69u8YE6FgsagsWLLK16zbY2rXrbdHiJbZo0RJ4F2YM5S1QNUtbcSEgwlBc9hzv2TBUkfeAnp4eqCT9X6eUdAqqaUNJz2vW2S6X9LwbCc9zXfhQOun51fcE9hmNIukZZIQehm+++dIOHtjn/ubry+C9/PjjP9lHaBRSqK2dYlVVUkwcrw39+jkRBi9bbkTCUGYpEobyKZZa8Ea6Mc/ghQ0kgHKqbL1PLfAEIQ1Pbpi13bZA212zroeOJwQcWcBTN9nAX3O3QK4VXot5W+BZwM2BkqqhyAu964XCIiDCUFj8s3V0EgZKJrJduXwRXoVDdhT5CsxhuH//rpM8ZBIz26ZNWxCj/CFW/N5xCklUSqLMIYmC8hayZRFv9SPC4C17FGo0GWLAifw///F/7B//+N928sSxwZynuC1Zugz3h622cdNmo7rRsmXLMcmf6cIYM4uBvx87iYLLV0BuFHMYvv3mP3b40IEhErIOixOfff5f9tfP/supsjF3KhLRXOD3OBb73yIMXrbwSIQhXGqpMFb8kVeQmr3OUg3rEDaEegnPbYNeA8ioutyF3lYLtN4ya22yQPcTEIhWs/6OQZ8CPohEylSo1CxcAsKwFYQBnovGTfgbx8Hx+L42byEgwuAte4x3NPQedCAuub29zc6fP2tHDh10hKGtrc29xgRoruzV1NY6+VTGFO+AdGI4FLYQSERohGvTkQlo+JNQ8Ae/pKR0MNdhvCPW5/KJgAhDPtH29rG4uMA8p2+Qa8DwIdZhYIgScw5mIVRxyZJlthREYdUq1mNZ7cKIysrL3aICr/+MB5Iqa+yH9ReaHz205uZHLgTywIHf7NzZ06j5EcL9IgQCstk+/9v/2N/+/j+DffD1V3ssvI2eRjdeBEQYxotcPj43EmEIhjG5B8vHZD7Fomps8DY82wbJAkONHGFA/kK0xwIkCWyxHuQzIJchjpyGzIaQo1QZQpzYz5wN8C6ANCCHAXcOkAVINEJ5RZu3EBBh8JY9xjsarvBdu3bVrqMxwfnChXN26eJ5yKf2OVnVSKTE5sxpdJKGCxcuxqRgqS1cuMj9oA+fBLzq+EFcw2miUIJk6SkuBpkERJs/EBBh8Ied8jFK95OOfMQDCBti6NCZMydRk+WO3b1zBxP6MqdgRPGD1avX2Zo1a53XgaFEFEWgZ4AkgGHIvOcwT+HBg3vu3sP6LlevXrFraPRqclGBOVH0Vvz1r393rRT9k3BkSEc+zlfH8AYCIgzesMPLRzESYcBFm+IEPoCLn7kFYVRefC5kCESBdxaGGjHZOdOghuQUkfi6e3/Y4UvhrahtQJIz2uw1Zqz0PGN5egfOTLV5DgERBs+ZZFwDaml5bIcO7XcFk65evWx37tzGBOC264sripzcZyRUqa9eWVn1O/WT11+fkUjYyiGvSMUUJkxT+YSP2vyBgAiDP+yUr1EydPHChbN24fw5O3/uLLySVFM76xYYQvA6kjgwx2ntuvW2GnkNi5HnxHyncoQuMqwxBQn1lpYWtGZHEk6eOG4nTh6zp09a4NFsh/BCr0uWpkQzCcOnn36GSs+fOQKRr3PUcbyFgAiDt+zx/GhGIgwMJHKkAY9BeBpIFkgehjaSBWwkBSmGJKXzGAKuDsOzeguuDxeGhJCjqnrUW1hkKSZPT2PthYXwXGBSIbKQxtKD/4oweNAoYxgSZRLZuKK3f99e++23vXb7dhN+uJ9Ya+vToZ7SE/05NrthjvshH2tIEcML6GHgiuHyFSttA6pCs25DOuzgWZjC0AH1xFMIiDB4yhwFHwxzGVjpnZ6FK5cv2fFjR+zYscNuss8kZi4ysNJzutrzXJs5cxYkl2dZCTyVvOa5XtiBECaGMT2CyMJNFIZsunnTSTkzPDKAkCMqIrHmAonHzp27XQikchcKbvqCDUCEoWDQj+LAIxIG9kHSwAd6GhguxJbZBgkD/3QF2Ugc+Frm0T1Lf64MBdnKalBroRGF2laaoaWqZphVQhmJ72nzLAIiDJ41zagGxpwFNtZY+HXvT7b3l5/s4aMHxoquJBKZjauCXO2rqKhCWEE4HYqEnITRbkHcH4LYn7kO27bvtPfe/8ipqXACkA5TGH1foz2m9sseAiIM2cOyGHoiIWDOE+WXOdnnYsM+LDY8evQICkrdLoyRSkaVaHysKKd3sdKRBZIBbqz1Eo3G4E3oHsqB4ISC4YvV+My69RuNCc8sCsm8iMUIg3RkoxgA1DmMGQERhjFDlscPRCGB2ttm1tNiwVuHLXD7kAWeNo1vAM5DgAmBm13yMWgpxjGywTORqpgGcjDNUlPnm7H+Aou1URmJic7PhTmN7/D6VO4QEGHIHba57pn8vbk5nWzIMKS9v/wIHfQf7elTCBLkcHvn3Q/sMyieUCYx43XQRCCHgGehaxGGLIBYZF1QCIHeBCqpHdj/myu2du/eHXva2urqtQzAU8Ck5gRyGEkwMtvw/AO+zr8ZxhQKhxy5oOoa8x22bdtpW7dut4WLFrucJ4ZCSoUtg+LkexRh8LLNmZTc1+ESlINNByzQdBCEAXKoY9xwO0hP+qF8ZAw9Yr4Dk5uRr+C8CvQgoJaDoQCcVU1H/gIKtLE5VSQkOpNUaPMsAiIMnjXNiAPjjzVDke7dvWuXr1y0A/t+s33791obfvBzuZEwfA6ZxDRhSKsliTDkEvGJ9y3CMHEMi60H5jEwNIkeyhs3rkEw4ZojD1Q8egQv5ePHza7RE8F9SS4y23DSQEnmWqivUYFtzpy56VCmefNtEcQVFqLVT59ulfBOVMDDOfxzmb70ODkQEGHwsp2hYGSDYUnBm/stcHOfq6EwliG7NQXOKCMVqNlQgVoKJAm1UEKqMatm9eZZluJjOUgDlZFY14FEgaTChTjhs/y8Ns8iIMLgWdOMODAShlu3blpT0027DGWkI6jqzMrOnADkcnOE4W//7YoxUX2JXgYRhlwiPvG+RRgmjmGx9ZDxGlAetae7x7oRivTw4X0nmHD7VpNduXLJtYcP7g8WYYsOeRo48Wejx4BEoWF2Oj9q9eo1UFZa72o4VNfUuDovrCDP+4PuEcX2DRrb+YgwjA2v/O6diEH2NIrWi2rMl1GZGa2redRjGHJAckYJEuBqNkSgpERSUFoJ6VTkJzBHgY8leJ3vufAjkYRRg+yBHUUYPGCECQzhMbTPm7ES+ABhBTdvXsdK4XXEFMO7mMON+uxbt+5AjPIGTALSxeAUapBDwLPQtQhDFkAs0i5IHNiSSeY1tEH56LG14J7ilNaQGM2/WZyNLZlIWtLlNOInPxyxMPKhGII0bVo95Fjrbf78hbYAcs0NDY2D9wbWXFB+U5F+dcZ0WiIMY4IrzzvzooYEqquh0NdugT6sOsZ6xz4IN6NM5yukWEsBEwSnqhSBJ2GoIBtfY+ONQR6FsYNcuE+IMBQO+2wcuQ/yhb19vUhU7HGKJQwfGB46kI1j/L6Puro6xCjPcpMETgYyq42/309/ewcBEQbv2MKLI8l4G0gK+nA/6evrs27UWOjq7rRe3FuiyGdgknMc+QwkDdyfimmsq0AJ1nIUditHYnR1dTVarRNY0L3Bi5Yu3JhEGAqHvY4sBLKCgAhDVmBUJ0LA0wiIMHjaPJ4dHIkB8xcyVZ25GMG/+TqJAlsYngYtGnjWhJ4ZmAiDZ0yhgQiB8SEgwjA+3PQpIeAnBEQY/GQtb42VidEkCVRL4nM28AUnz0yykMlNIGnQJgRehYAIw6uQ0etCwCcIiDD4xFAaphCYAAIiDBMAb5J/NBOuxMfhzxVyNMm/GGM8fRGGMQKm3YWA1xAQYfCaRTQeIZB9BEQYso+pehQCQmD0CIgwjB4r7SkEPImACIMnzaJBCYGsIiDCkFU41ZkQEAJjRECEYYyAaXch4DUERBi8ZhGNRwhkHwERhuxjqh6FgBAYPQIiDKPHSnsKAU8iIMLgSbNoUEIgqwiIMGQVTnUmBITAGBEQYRgjYNpdCHgNAREGr1lE4xEC2UdAhCH7mKpHISAERo+ACMPosdKeQsCTCIgweNIsGpQQyCoCIgxZhVOdCQEhMEYERBjGCJh2FwJeQ0CEwWsW0XiEQPYREGHIPqbqUQgIgdEjIMIweqy0pxDwJAIiDJ40iwYlBLKKgAhDVuFUZ0JACIwRARGGMQKm3YWA1xAQYfCaRTQeIZB9BEQYso+pehQCQmD0CIgwjB4r7SkEPImACIMnzaJBCYGsIiDCkFU41ZkQEAJjRECEYYyAaXch4DUERBi8ZhGNRwhkHwERhuxjqh6FgBAYPQIiDKPHSnsKAU8iIMLgSbNoUEIgqwiIMGQVTnUmBITAGBEQYRgjYNpdCHgNAREGr1lE4xEC2UdAhCH7mKpHISAERo+ACMPosdKeQsCTCIgweNIsGpQQyCoCIgxZhVOdCQEhMEYERBjGCJh2FwJeQ0CEwWsW0XiEQPYREGHIPqbqUQgIgdEjIMIweqy0pxDwJAIiDJ40iwYlBLKKgAhDVuFUZ0JACIwRgcDjJz2pVGqMn9LuQkAIeAaBVxGGaDRh/dG4JRK6wD1jLA1ECIwTgeGEoSQStEg4aMlkyvpjCYviOtfv+DiB1ceEgBAYFQKOMIxqT+0kBISAZxEIgDUEg2aRSMhKS0J4HrDo4ERChMGzZtPAhMCoEQi6azxgoRDIwjDCwOucCwPahIAQEAK5RCDQ8lQehlwCrL6FQK4ReM7DAMJQMowwxGLPPAxagcy1JdS/EMgdAkMeBqwM/J4wRHGd6/rOHfbqWQgIAbNAZ1dU8Qr6JggBnyOQJg3mVh/DCFXg3/F40rVk0ucnp+ELASHgrum0JxFeBngQQ6GAIwm8zgfQtAkBISAEcolAACsTIgy5RFh9C4E8IAB+4LYAJhIMXTD8n0J8cxLLjlp5zIMBdAghkGME3DXuLu3AEHngIZnHwOtcmxAQAkIglwgEELKgO00uEVbfQiAfCLjZhDmywFXI4YTBdIXnwwI6hhDILQKDZIHXNi93d53j0S0KgDRoEwJCQAjkEoFAW3uf1iZyibD6FgI5RoATh/QEwozhSFRPYbxzbAChCgMJS/AK13wix1ZQ90IgtwjQc0gPYiYcidc6vYjuOkdIkuQOc4u/ehcCkx2BwN0HnW4qofnEZP8q6Pz9ioCLa8ZkAnMJKysNW1lZyOUy9PUNWG9/3OIJxDeLM/jVvBq3EHAIhJDszLyFMFSSSkuCTtwgCclkXuN9aBnCoN9yfWGEgBDIBQKB67faMveZXPSvPoWAEMgxAo4wMHcBsqoV5RGrRAuHA9bdM2BdPbFnCZGaSeTYEupeCOQOAcqp8rqmB5ELA+VlYdRYSQ5e5wNDhCF3I1DPQkAITGYEAhevPR0kDJpNTOYvgs7dvwiQMHDlkWFIVRURq6oscZOKzu6YdXRFEbKQUEiSf82rkQsBhwBDkOhdKIF0ckV52DUqJHXgOu/siokw6HsiBIRAThEInL74WB6GnEKszoVAbhEgUcjENVdXlVgNGgu4tXf0W1snCAMKOylRKbc2UO9CINcI0LPA+gskDJVYGGAjYWjHNd7WERVhyLUB1L8QmOQIiDBM8i+ATt//CIgw+N+GOgMhMBICIgwjIaT3hYAQyCUCIgy5RFd9C4E8ICDCkAeQdQghUGAERBgKbAAdXghMcgREGCb5F0Cn738ERBj8b0OdgRAYCQERhpEQ0vtCQAjkEgERhlyiq76FQB4QEGHIA8g6hBAoMAIiDAU2gA4vBCY5AiIMk/wLoNP3PwIiDP63oc5ACIyEgAjDSAjpfSEgBHKJgAhDLtFV30IgDwiIMOQBZB1CCBQYARGGAhtAhxcCkxwBEYZJ/gXQ6fsfAREG/9tQZyAERkJAhGEkhPS+EBACuURAhCGX6KpvIZAHBEQY8gCyDiEECoyACEOBDaDDC4FJjoAIwyT/Auj0/Y+ACIP/bagzEAIjISDCMBJCel8ICIFcIiDCkEt01bcQyAMCIgx5AFmHEAIFRkCEocAG0OGFwCRHQIRhkn8BdPr+R0CEwf821BkIgZEQEGEYCSG9LwSEQC4REGHIJbrqWwjkAQERhjyArEMIgQIjIMJQYAPo8EJgkiMgwjDJvwA6ff8jIMLgfxvqDITASAiIMIyEkN4XAkIglwiIMOQSXfUtBPKAgAhDHkDWIYRAgREQYSiwAXR4ITDJERBhmORfAJ2+/xEQYfC/DXUGQmAkBEQYRkJI7wsBIZBLBEQYcomu+hYCeUBAhCEPIOsQQqDACIgwFNgAOrwQmOQIiDBM8i+ATt//CIgw+N+GOgMhMBICIgwjIaT3hYAQyCUCIgy5RFd9C4E8ICDCkAeQdQghUGAERBgKbAAdXghMcgREGIr+C5Cy1GvOMeDeS//7mt30locREGHwsHE0NCGQJQREGLIEpLoRAkJgXAiIMIwLNu9/yNEExxT4LE0ZMo/DSULA8FfA/YuTEnHwvmVfHKEIw4uY6BUhUGwIiDAUm0V1PkLAXwiIMPjLXqMabYYspB/5b3KQNGR8DRnKEARXIFnIkAYRhlEB7LGdRBg8ZhANRwjkAAERhhyAqi6FgBAYNQIiDKOGyrs7khIkkjGLp9CS0cHWb4lUwlJsjjCANKQGCcMgSQgYCUMQ/4YsEiy1cLAMjY9ogRL3nnfPWiPLICDCkEHCn4/JZNKSyYQNDAxYb2+P9aElEomcnkxJSamVlZVZaSmu+UjEwuGIBYPBnB5TnU8MARGGieGnTz+PAO87/f191t/XZ7FYFPecuMXjcXcviOCeECkpcfcH3iNCodDzH9ZfkxIBEQafm51kIZXChZ/osmi80/oSaPF26423gTjELJmKu/a8lwFEAf8FAyH8GwI5iFh5eMpgq8VjuvF9hSl5/wsiwuB9G71uhCQK8fiA9fR02+Pmh67xBzyXW01NrU2tq7cpU+qsvKLCyssrjZMEbd5FQITBu7bx48hIDlqftthTtK6uTouSPKCVl1dYRWWVVVVVu/tDLe4RpaWlfjxFjTnLCIgwZBnQ/HRHmmCOKCThQUikMNkYeIr2xLoHcPEPPLbOgWYbSNDLMADCMOD2JWlIb+lQJHoWgoGw8yZUl8ywqsgMq47MtOqS6WgzBr0MIBXwQmSCmPJzfjrKWBAQYRgLWt7bNxrtN7a21qd288ZVu3H9ivM05HKkM2bMsjmN82x2Q6PV1E6xmpop8DiU5/KQ6nuCCIgwTBBAfXwIAUYbxKJRu3Onye7euWlPnjy2nu5utC53P5g6dZrVTau32bMbbdbsOY5ADH1YTyYtAiIMPjM9J/2JZNprQK8CvQlsPfFW6x1Aw/N+eBnYSBRIKJLwQNhQHgNPOJ23MBSSBNJQFqpGq7GK8FQQh3q06UMeh9JwlYWwD8kFP6PNWwiIMHjLHmMdTSYMqbn5gZ09fdzOoHV1doy1mzHt3zhvgS1estwWLV5m9fUzrX76TKvEqqI27yIgwuBd2/hpZJnQo+6uLjt/7iTaKXv08N5QeNK0+hk2c9ZsLCbMtYWLlrrGBQVtQkCEwWffARKAgWS/a12xR9YWvWftaAxBInGIJrvTXgWQikwY0lDugvNL8ISH+QtcPgN8DY4QRKw0VGWV4WlWGZlmU0oabErpHKspnQVvQzrHIR2m5DPQiny4Igz+NnB3d6d1IyTg7t1bdnD/z3Zg38/W1vY0pye1eMkKW7N2o61avd4a5sxzjWFK2ryLgAiDd23jp5Ex3DHa32+trU9wv/nFDh742e7ebkIOVczlUdHrOBcLCvMXLHH3h5W4R9QhfFGbEBBh8Ml3wAUhwY1IskBywNYevQ/CcMcRBnob6FVg0vNENiY+l4dq4W1AjDPIwtSyuSANjfA8MMdhKpKjyybSvT6bAwREGHIAah677EYYQA9Iw+1bN+yXn7+xX378BnHFjxFGyEG4f8Y9GvbBBYPM/SPT0YqVa23T5h22fuM2a2ycb3PmLrBahCZp8y4CIgzetY0/Roa7AO4H7W2tLm/hwf07dvjgXjt86Fe7f++OS3qm2MJc3AvmL1xiS7CosG7DFlu7fgu8kDP8cYoaZU4REGHIKbzZ6zwdWpSw/niHPe2/5VoX8hSYu9CLcKSBVFodiUnOE9kYdkRSwJb2NNRbTclMm1a20DUmRGvzFgIiDN6yx1hH09/X68IB7iKe+JefvrGff/raxRSnkoMT/bF2iP2dV3GQKHASwJaCKkpmW7V6g23dtss2bt7uQg+4qlhdrWs7g48XH0UYvGgVv4yJZCHdmCd1+dJ5u3L5vF2/dsm1Nngb+D6Vk0QY/GLT/I9ThCH/mI/riExeZu5CV6zZ7vecsXvdp52XYSDZBznVaPpm4FYjJ7YiOZTfgFClSLACIUqVjjg0Vm20uVUbkAw9c1zj14dyh4AIQ+6wzUfPmVCA+/dupwnDj19bS0szrmnKrT6b5I9lLOnJAaSU8fkBKDBRiSkJ0pDZ1qzbZDt2vGWbt+60GTNnozU4VZTM+3r0HgIiDN6ziV9GlLkfJEEKjh89YPt/+9FOHDsEb8NTa29vdQsWmXMRYcggocffIyDC8HtEPPg31xmZ0NyD1h67b497r1gzWhRhSAlLJ0Bnhs2k5EyCcihY4nIPQkForEMRKeBkVJm/kF65pDciTURYwyHtoWDthkwYRCYJmiShoXKNa0yG1uYtBEQYvGWPsY4m7QGgxOETO3f2hJ09c8I6O9vTiwDjJAyUR2QydU9Plws/YN/0ZITDUEVDzYX1G7baG7vfsS3bdjvpRMqrlpVLJWmstsvn/iIM+US7uI7FvIVM6OPxYwft0IG9dvrU0aFEZ8o6cyOxEGEoLttn82xEGLKJZo764krjk35In/XdtNbobeuIPbTO6AMXhsT3nsmlmlMyKgmWO++AUz4K18BLUA3iEDESCBKKQbqAfIg+kI5u1xjq1IfGHIkMoaiGzGpNyWyXwzC9fIlNL1/sVJRydJrqdpwIiDCMEziPfCztSUi5H+8n8Cy0PH5kUdZhYAjBOMfIOOUnLY+sGXUdbkKm9caNK9aBlcQyaKxTZ535C3ve+sC2bt/t/i4rq1AdhnFina+PiTDkC+niOw5FFR49vG+PHt2HCtsxO3nisF2+eNaFKrJoZLp4ZLq4qwhD8dk/W2ckwpAtJHPSzzNPwL3uM3Yf7Ul/k/MspGVTn4UYUCiV4UTMPXCF10JTrCJS58KJ+JjOSyh1XoYMyUjLspIotCHU6TFqODxG3z1OhpUkhApJzF2oK5sPxaQ57m+qKGnzFgIiDN6yR6FHw1XCh5BJvNV0HXUdrtg5eCzouaCXoXbKVCQ3T0Uo0hu258334WHYhQrPIVfJNYAwRG3eRUCEwbu28erI0qFIKXuKOguZfIVLF88hh+Gsuz+wWCMrvXNlIlNAkiIISnr2qkULOy4RhsLi/9qjJxgyhGrN9AQwZ4G5C0/7b0MJKS2r+syzQKJA2dNSKBxNsVpM9CmJSuJA70IJ8hAYphSCl4FF2NxNBISAikqxZK/FQBK6UfSNhd9IIvh6PBVzfdSVLYBa0lxXk4FKSTyGNm8hIMLgLXsUajS8rrlSyAWBa1cvuZXEc2eOozDTLbQm9968+YuMbTUkVddB/YRqSfz+uOKMIgyFMt2ojivCMCqYtNMgArwfsDgbi0Leu3drqMYLc6XobejoaHU1WKZNn+HuDWnvZrPNmTNXhEHfopciIMLwUli88SLDgziZ74932r3BROe26F0sBmSKsaUDFuhdKEXRNYYgMYRoRvlSm1GxDK+x4BpDkUAU3H8oupZZRMRHUQIOkwtkQYAcMD+CjaFJsWQPPA29Vlsyy3kXaksbXD/hAEKaQDi0eQsBEQZv2aNQoyFRiMepiBS30yeP2G+//mCHD/xiXQhHYGPxpbXrN7u2ePFyWwDpxEZIKHKTd8HB4Ol/RBg8bR7PDY6Egdd9d1eHW0CghOohtM6ONoQ/9mOhIJgu3gj51EQ8bteomISFhtmo7Ow8DEshq4pFBcmqes60BRuQCEPBoB/5wCzExlAhKiM19yHRue+ydeJ5ZsuEIdF7UI3JfXVkpvMGMNegHo1hSG7lcIglZD75/CMnGn2JDkdM+pHTEANZIFFhxWcWbeOjNu8iIMLgXdvkc2Rx/Oj39fVYX2+vHT92wCkusRAcX2dS4yxMBHa88bbtfOMdRxRmzMCCwDRd2/m00USOJcIwEfQm32eZm/DwAfIWEJ548eIZp4507Oh+F35UVlZuNai7snTZKtcoknAW3khWmp81u+GZh0GEYfJ9cV5zxiIMrwGnkG8xe6EDhdke916zlr7r1jnwCGThkSvOlhkXqy4HLV03YTq8CmwMH6pCleZKTPJJJNIrhxm3QuaTzz/yWOkwp4xSUgyhUAMIZapwXguGNGnzLgIiDN61TT5Hxh/9p09aXNG30yePutXEE1BEyYQp0Zvw9jsf29vv/cFmzWqwangcqqqq8zlEHWsCCIgwTAC8SfhRLhRcvHDaLpw/7R5vXIP4wfXLjijMnDHbZuIeMAf5Cg2N8yCI0GanTh62U0iG5uvzFy5F4bbl8jBMwu/N605ZhOF16BToPaeNgpChx31X7XbXMbvbdRKKSP1uUk8Z1MzGcCPmFJQEK21e1WabW70JhGGeIwrBIMjCCJ6FTD98TB+T/zLMKf2vC2NCCNJY+hnep57nBwERhvzg7PWjUAnl7l3kK6CdP3vSTQAunDs1NOyFi5baR3/4q338x89tWv10KykphTJSydD7euJtBEQYvG0fr40uFovZ/n0/oubCTyANJ53oAQu0zZ270JYsW2kLQQqmTK1DmwZltod27MgBowciTRgGKz07D8NmVXr2mnELNB4RhgIB/+rDInERsYdMaG7uuWRNXUfsDkhDumZC3L2e+WwJCqtlaiXMqVpvcyrXuYTn9ET/9V6FTB969D8CIgz+t+FEz4Dxym2tT6F+klZAuXrlgl29ctFu37oOT0Kty19YjJhkKiNRTpVKSaEQhBBCoYkeWp/PEwIiDHkC2ueHYf0V5i7wfnD08G92BI3VnV2BSJCI5SvW2LoNW2zZstUWDAWdSto9LDIcPbIPpEGEwefmz+nwRRhyCu/YO3dUAepISSQjP+q9aE2dh0EYjjuikJZDTSc6s+dyqBaxVgJzF2ZWLEdbgVyGGYN+BRGGsaPvz0+IMPjTbtkaNckC22PUXGBRJuYv3Iak6sMH9+wJ5BTnzV9oc+cttKXLV7uCbRs2brWKiiqX9Khk52xZIff9iDDkHuNiOAKrxJMA3L3dZGchp0yltObmB86jSK/ixk3bbfvON23V6g1IiIYgQnen3bh22ZEFkgZ5GIrhW5CbcxBhyA2u4+6VngXmD1BS9VHvBUcYGJb0so1kYUppowtDmgb5U9ZMqETNBW2TCwERhsll79+fbbpSdMIol7j/tx9sH9SRWIehs6PdqaGsWbfJ1qzdZMtXrrFFi5bZQrTSUskj/x5Hr/8twuB1CxV2fFw04Mb6KxcvnLFLaNeRs8C8hd6ebggcTLdpaNt27LFdu9+zVWvWp4u5QWL1EuoyHDu833kZRBgKa0cvH12EwWPWcdKIkDll/YWH8DDcciFJx186yloUU6Mi0rSyRSAODZBBTddeeOnOerFoERBhKFrTjnhinCT0dHdZNyYEDD86tP8XOwBlpNanLRZjtWj4G3e88ZZry1Fzob5+htNeD4fDI/atHbyFgAiDt+zhpdHwPsCFgySam/wjtIiCB49RNZ75CaWlZc7DSFWklbgPrFi1DgnP80QYvGREH4xFhMFjRmIokiuchuJpDEm61XXU7naffOkoWYF5VsUqm1m+3EmfUhmpVIpGL8WqmF8UYShm677+3KiAxCquDD1iWMGRw7/a4YO/WjdIBHXWy8sr7L0PPrH3PvwERdrWubAEehcUivR6XL34rgiDF63ijTHxPuAqNQ/E7CSUjvb+/A28jT/Cw9hnfWizZzciDAkLB2gkCjOgklRVXS3C4A3z+WYUIgweMxUJAwu2xVHd+VHv5bRK0isIA2stMNF5duUaVHiudZWdWXtB2+RCQIRhctl7+NlyVfHO7ZtoN5DkfMFOnzpmZ04ddbUXKioqbcqUOnv/o0/t/Q8/tWXIYdDmXwREGPxru1yNnJ4FNhIDKiC1Pn1ip09DUhkFG48fPehCD+ldWASJVIYh7drzrk2tq7dK5DCFwiERhlwZpkj7FWHwmGHThKEPpIGE4ZJLeL7b/UwacfhwZ5Qvs7lVm2xO1TpX1ZnyquGgZBKHYzQZnoswTAYrv/wcqbV+4fwpo3wqFZJu3qDW+hV4EkrcxIDxyLv3vG+7oY5EWVVt/kVAhMG/tsvVyF0YEgq0kShcRh7CpYvn4Gm8ZDdvXrW7d5psxswGl8TMxQImO29AY+0V3h9INB4hf4FNOQy5slBx9SvC4DF7Uj41BrLwjDCcsHuvIAyzKlba/OqtjjSwHgO9C0EUa9M2uRAQYZhc9h5+tgxDOHxorx1BGBITHR8jXvlx8yOrnTLVGhrmOnWkLdvesC1bd7nqzsM/q+f+QkCEwV/2ysdoXRgSqrjfu3vbftv7nf2KRgLQg5ymWLTflkFCddnyNbYaCc58XL5itQtLDEBEkZ8VYciHlYrnGCIMHrNlhjDEEn3WTA9D93EQhtMvHWWaMGxzhCECwkDSIMLwUqiK+kURhqI270tPLpGIW3wgbn19va4404F9P7kVxq7OTuvs7HDkYOUqJDciwXEJEh2XLl1p9dNnvrQvvegPBEQY/GGn/IwSeorJlLW3t7p26+Z1t3Bw+OBeVG1ud96DEIQN1kNCecOGbS7JuWHOXLeIkBE8YF0GEYb8WKtYjiLC4DFLijB4zCA+GI4Igw+MlOUhRrF62A+yQHLw297vXbt65bxbNeTK4arV653W+pZtuxxRmF4/0yoRiqDNvwiIMPjXdtkdOfMWDIQh4cKO7t65ZdcRhnQW9RbOnj7u1NFKy8pd6NH2HW+5+8AyeBaqq2vRapwYAscjwpBdq0yG3kQYPGZlRxjgXRgKSep+XUjSCpvnQpI2unCkMEKSQoGIx85Iw8k1AiIMuUbYe/2z4FJb21N78rjZeRj27/sR+QvXjKuHbJu3vmHvvPdH5C68h8THcisrK8Prujd4z5KjH5EIw+ixKuY9qYhEsjCAqs3MPbiMvIUrEDy46WouXLFwJGJ1SGymEtLOXe+4tmjxMlfVfXhldxGGYv6W5ObcRBhyg+u4ex1KegZpoErSne5jkFV9VdLzUmus2uiUkkpDVS7xmWFJ2iYXAiIMk8ne6eJMD+7fxariZbeyyAJNFy+egbxqi1tB5Erixs3bXaIzizSRKEQwiQgGQ5MJqKI7VxGGojPpuE6IikgsxNaFRYOzZ6CKBq/C9auXrKWFNReakeg825YuW4m2CsUaEZa4Yq3NnN2AcOXgkHeBBxZhGBf8k/pDIgweM/+QrGqi3x71XXq9rCoKtjVUroWs6ipIqk61ivAUeBrKPXZGGk6uERBhyDXCXuo/LaN4+dJ5V5X1OIozNT+6b80PH0BKdcBNFjhhWL9hq23bvscRhwAmCqy7oNoLXrLj2MciwjB2zIrxE10IQ3yKwowkCMePHrBjaFRHY5hiLBp1eUusubAV4YjTcS+gp4GhSNyG3wNEGIrx25HbcxJhyC2+Y+6dhOH5wm30MJx4aT91pfNsZsUKo7xqdckMFG+b7rwML91ZLxYtAiIMRWva504srbnOcIQkai0cs72/fGcHEIrElcaurg4rLSlz0qmUT121eoOtXbfJJTs+14n+8C0CIgy+NV1WB87qzay9cqvpOnIW6GHAHAESqtx4j1i1ZoPtfONtEIbdyFuqcrlLpSUvRh4MYIGhxVWCfuS8lcx/OAOPBavBNzYusHkLFltGOGHq1GlD51CGYpCs8cL6DtyGk5ChnfSkKBEQYfCYWVOppMVTMUcaWIfhVucRp5T0smHWljRYPbwM09CmlM5xrTxc+7Jd9VoRIyDCUMTGHXZqJAoJ/Miz9gKruf7849f226/fu6JNUYQp1NROsTVrN4MobLYlS1c48jBv/qJhPeipnxEQYfCz9bI3dhKF82dP2rlzJ+3WzWvWhJoLrPROssBt4aJl7h6wEsIHpai3UILK7qHQi3LrVFpra33qcqEe3L8DL8VV56moqZ1q06fPQv2G2TZv/mK0hS5hOnMGsxDeRMnmaSAWznsZhPcS/2krfgREGDxmYxKGRGrAEskBe9h70W51HXZhSS8bZjU8ClNK5trUUly85QtBHBZaZaTuZbvqtSJGQIShiI077NSSqOocjUWdvjpDkX747kv79ZdvjcWb+OPPUKQdWFnk6uK8eYts+oxZ+FGfPqwHPfUzAiIMfrZe9sZ+8fxpO7D/Z1fNmaFJbD3dXUMHmDW7cWixgPUW4APAxH7o7aEnvG9043MZAQV6G1jDhd6D6ppaqwVxqJ8+w6mslZc/C3VejtoO9GAuWLjE5UQwN0pehiFYi/qJCIPHzIsIZYQcxI2hSQ97L1hT56FBwsB30isImSHTm1AVnu7CkWaiiNsshCdVl8wc5PovuUNkPqjHokJAhKGozPnKk6GMaktLsz1B7DITHY8c+s1OHD80qIwUsTmN823X7ndt1573bNbsOe5Hn1VdtRUHAiIMxWHHiZ6FIwyou3Lo4F5HFlpJGJAEndlYtJEeAnoAXrdxcZIJ1Gw93Uyi7jDmR5QgfKkMsqzlJA7IfSB5iERKhrratGWHy49aiYTqIDwXVF4SYRiCp6ifiDB4zrwgDLiQeTE/gofhZsdBEIajjizgnedIAxOcqY5UjmTnuVBLaqzaAI/DHAYVTthF6Nyb4BxyNXruC/LCgEQYXoCk6F7g9djZ2T6ojHTRriDp+dLFs3bt6kWrrKx2ccpc8dv95vu2B61u2nT88Jc890NfdKBMshMSYZhkBn/F6bKi+6H9v7hCbQxFojpad3fn0N7MLShHnkHZMK/A0JvDn2D9Mc4CkAhxjKOIWwwyrTF4MOkxyMgzkzwwpGm4whrvL++89wfbuGm7C3VigTgRhuHAFu9zEQYP2tZ5EjBBaO69YrddDsOJdF4DchtYpyGzsaoz6y6UhCptXtUmkIbNVlc2z70WCkYw2Q9mdh3x0ZERHJNEBUFRzsMBETbcKMKQY6Mc48RJyIiD0A7jQkCEYVyw+eZD7ppEVdcn8C6cOnnY5S80IXb5/r3bUEh64JIU61GYbQmkFKm7Ti8DVwW1FRcCIgzFZc/xns1V1FygOtIJhCW+LCRptP1ynuFqOiTwi++IwwCkVuP4zcfMAV6D0BBxoCRzZi4RsLfe+cje//ATV+slTA9DmB6GzPujPbr28yMCIgyetFo6+Ki9/66rxfAYxKErjlWEgRaLJp65HnmRBg2FmoIlNr18KdSSliKfYR7Ukupdc0XcRuFt4NHiyX6XaB1L9Lpj9Ce6nPeiAnKtZWFUhwRpYBsLCfEktEU4KBGGIjTqsFNigaYYVgAfPrhrhw/udaEI9+/dsfb2Vhd/zCRHKiMtW77GKSMx6bmisnJYD3paDAiIMBSDFSd+DrwP3HBF2q5aR0ebdbS3WV9fz5g7Jlno7+uzvn5UjO9oT5MPeCwYjsSq8FUIR5o6pc6mQCGpFIUf3ZIh5hNr1m609Ru32ZIlK0AsuKiokKQxg+/TD4gweNhwPQNPrSP20Dqi962l/4a19F13pOHZkNOr/pzIM3ehOjLTJUBPL19s9eVLrASVnznBH4n9u1hGEIT+eJf1xlutK/bYugYeo7/pNrVsrtWUzHakJBQoGfQ2PBuBnhUeARGGwtsgdyNIWV9vr/Uif+HOrRuQUv3W9v78rfM2UEedoUrr1m91dRdWrFpr8xcsdsmIGcnD3I1LPecbARGGfCPuzeNRRrmttcVanz5F7kKXa1HUXxjrRq8CyUY7GknITagtNUEpiWpr9FhSRGHuvIU2d+4Cq4LHEjMJF3o0u6HRGhrnuX0YisTfH0YgaCt+BEQYPGzjaKLH+uLtmMTjgu4575Kg20EeGD7EPAeoLrvRkzbQC1AaqrEakIb68kWQW12M16pRyK0Mk/3SIeLAfTMbE6vZEgh1IjnpGWhFewJSwoaqsajtMAUKTJRspaeBuRLsT5u3EBBh8JY9sjkaEoK2tqfWDvlDFmfa99uPaD+4H3r+WEcQY7xjx5u2feeb0Exf75SRqI7Eys7aigsBEYbisud4z4Z5Bs4zgEUEFmtj0jJrKox1Y+7C08EciFtN14zJ1BfQ6qbV22zIpjbOnW9Ll65yoY5T4Gng/YatBuShpmaK80LwmHxN2+RAQITBw3bmRH4AoUIxEId73Wfsfs8Za+2/4/IZEsko6EJGNSngSEEkAHUDEAd6G2ois6wCEquVgxN9hiexPfM2MAwp6vonMemMPXKNHgb+zdAnVo6uAmkgCakrWwDZ1gUgDYqN9tpXRoTBaxbJ3njo/WP40b27t5HwfMlOQhWJykhURWFiIsMH9rz1gWskDNU1uP6ra51ySfZGoZ68gIAIgxesUPgxuHyDAeQbMFl5sC5LMpkY88Di6INF4Cineu3qJTt96igKQh5xMqqNcxfaAngrWcth5ap1NrWu3i01khyUIKm6DCFKVE4SWRgz7L7+gAiD582Xggcgbne7TtidrpP2BKFJsWQvSESv8zQ8P3wQB5cEXeXyD2pKZhmLu9WUYsUxgAsc3gEmSqe3VDpXId4JD0a7tUXvukaPBpOeU/A8MJm6NIRYRoQmNVatt8bKDc7r8Pwx9VehERBhKLQFcnd8xhlfu3LRmOh49fIFu3TprGtcZaSCCRWSmID43vuf4Md9HUhExHkX9EOeO5sUqmcRhkIh783jOiXDCQyNIY2PHt63R4/u2+WL5+zokX127Mh+F4q0YOFSW7xkOcIdt6BtfkGiVfeXCQDv44+KMPjAeAwbaum7Zo/RWvtvwRPQbJ0DzSjuhhhm52XIeBrM5RiE4WkIgxzQG1ARrnOeghASo9M5CBk1g9Sg94Lko8d64k8RhvTUBhwRYa9JEIy0bGtleJrNraYK0ybkM8z0AWKTa4giDMVnb04GSBa4CkhlpFMnjjgZ1fuoyEp1pPKKCpsxYzbqLTTa1u27oYu+2yirSk10JSEW3/eBZyTCUJx2LdRZDREGkAYuRBw7vN+RhpmzGmw+7iVMaiZhWAvCUD9CTYdCnYOOm18ERBjyi/e4jsbJu0tEBlFoi95zxOFx39XnJveZjpmjEEASNBOh08SB5AE6yvAsBC0jf5YmGPRcJFFVOg7iQZWkAYQo8e/0uynnkYgEK1xY07zqLcZGr4U2byEgwuAte2RjNK56M0IOGKPMvIX9v/7g4otZXKkTxZWYeLhs+WpbvmK1U0fi4wz80AcRMvAs7DAbI1EfXkFAhMErliiOcYgwFIcd83kWIgz5RHvcx2L4UNoT0AnVpAeoAP0ASdD98Q6EKw24pGWuSP7e2zD8cE4OFTKsw9OTSEQQezT4ucze6cQm7l8CslCKsCR6GFgUjo35Edq8hYAIg7fskY3RZBIbWZDp5x+/RvvKWLDJEYlEAiRhlW3Ztss2b9npEhRJIGprp2bj0OrDowiIMHjUMD4dlgiDTw1XwGGLMBQQ/LEcmgnKcSRB90Ex6Wn/bbRb1h1rcTKoVFHKEAeGL718c74HvEXKkAlhSlOMZ38zpCld14HeCdZzqI4g6Rmyqkp6fjmqXnhVhMELVsjuGKiv/uRxszU3P7Ajh36zI4d/hfb6VRwE1ywu3zXrNtruPe+7Qm1Tp6a10isqVHshu1bwVm8iDN6yh99HI8Lgdwvmf/wiDPnHfFxHpDeAiimxZF9a+jT2xNpj9601igJOCFMaGEyEJnF49Tbcv8C9MsTh2SfCqLXAROfSUBVqMMxzykh1pfOcpCpVk5gboc1bCIgweMse2RgNKzg33bwKKdWrdv7cSTt39qQ9QP5CABVXWXV10+adLtn57Xc+dkpJpVBLkpRqNpD3bh8iDN61jR9HJsLgR6sVdswiDIXFf8xHJyGg5Gk03oPiao9AFh6guNsD6090IkSpE4SiZzAngQnR6VoNGT8CDzbcv0CfA18JIlSJeQ70KlAZicSANRdqS+agEFyjy1sgUYgM5kKMedD6QE4REGHIKbwF6fzunSYXgnQJYUiuoNLNa9aOegyswFpRWWUbUGl1z5vv246dbzt51RAkVkkktBUvAiIMxWvbQpzZAAQVmqGQxMWJy5fO2/FjB+z40QNOJYkFIBctXobK8Zvhzdxk06ZNL8QQdUyPISDC4DGDjDQcyp3GmbeAROV+V5250/oSHenCa6ih0Ivia5RJZegSyQVKvMGRQOLwonchnRyNVGh4FViYrRJ1G6iqxJwFPi+DylJ5qNZKw1XYBxMSNJcLMdIg9X5eERBhyCvceTkYPQtOF/30UXv86KELTaLmOlWRZqMtX7HG/ZCz9gKJApukDvNimoIdRIShYNAX5YFJGB43P3Tt6tULUGI7bCfRpk+fZXPnL7SFkFZdvWaDrVq9wRVzK0oQdFJjQkCEYUxweWtnhiix4jOVjSi12jXwGN6Gh9YBrwMrQg+k+hHGBCUk1FVIZzCkSQMDkeh1oJISi7mVwHvAis5pb8JsV3eBtRdIEjALcZ/11plrNMMREGEYjkZxPGfdhSOHf3Mrfkx87u7qcqFHS6mMhDYPK4BzUYm1Yc684jhhncWICIgwjAiRdhgDApRsbmlh4bZmVxTy9OljKNx2DIXbZlhj4wKjl4FF21asXIvCbdPG0LN2LVYERBh8bFkXakS9dngdGJLUB9UkNlZr7h1oc0nSmSJs9DAM9zGQNJAwuCRneBjoUaiMTHOhSGUuh6Have9jeCbN0EUYis/Ujx7esxs3rljTjWtOWrW/v89VVk17GOa4Qkp1qL46BQnP2iYHAiIMk8PO+TpLKq51QaKZUs3N8GLeuXPT7ty+iUrxNcZ7y7T6mViQgEezYS4KRFbla1g6jocREGHwsHFGM7QMaaCCEsOU+PispkIcfoSE80Kwr2eEIf2Meu2szUDSwCrQLo8BeQr0OrDQ2/MUYzSj0T6FQECEoRCo5/aYvb091tnR7mouJBJxJ6fKsKOKSuQYVVSlE51Ly6y0tDS3A1HvnkFAhMEzpiiKgVCKPRaLGZOf+3C/oSezC57MkgiET8rK3D2G95tK3G/CkUhRnLNOYmIIiDBMDD9PfjpDIviYUVdKDzSjikTCADrgwo1YmwHJknhJBMGT5hxxUCIMI0KkHYSA7xEQYfC9CXUCQsDXCIgw+Np8rxp8mhhkirk5AjG46zMvA18gRRBZeBWKfnldhMEvltI4hcD4ERBhGD92+qQQEAITR0CEYeIYeriH4VThxWEOBia9+IZe8RUCIgy+MpcGKwTGhYAIw7hg04eEgBDIEgIiDFkCUt0IgUIhIMJQKOR1XCGQPwREGPKHtY4kBITAiwiIMLyIiV4RAr5CQITBV+bSYIXAuBAQYRgXbPqQEBACWUJAhCFLQKobIVAoBEQYCoW8jisE8oeACEP+sNaRhIAQeBEBEYYXMdErQsBXCIgw+MpcGqwQGBcCIgzjgk0fEgJCIEsIiDBkCUh1IwQKhYAIQ6GQ13GFQP4QEGHIH9Y6khAQAi8iIMLwIiZ6RQj4CgERBl+ZS4MVAuNCQIRhXLDpQ0JACGQJARGGLAGpboRAoRAQYSgU8jquEMgfAiIM+cNaRxICQuBFBEQYXsRErwgBXyEgwuArc7SL2+0AADrNSURBVGmwQmBcCIgwjAs2fUgICIEsISDCkCUg1Y0QKBQCIgyFQl7HFQL5Q0CEIX9Y60hCQAi8iIAIw4uY6BUh4CsERBh8ZS4NVgiMCwERhnHBpg8JASGQJQREGLIEpLoRAoVCQIShUMjruEIgfwiIMOQPax1JCAiBFxEQYXgRE70iBHyFgAiDr8ylwQqBcSEgwjAu2PQhISAEsoSACEOWgFQ3QqBQCIgwFAp5HVcI5A8BEYb8Ya0jCQEh8CICIgwvYqJXhICvEBBh8JW5NFghMC4ERBjGBZs+JASEQJYQEGHIEpDqRggUCgERhkIhr+MKgfwhIMKQP6x1JCEgBF5EQIThRUz0ihDwFQIiDL4ylwYrBMaFgAjDuGDTh4SAEMgSAoGzl1tSljL+r00ICAEfIhAMmoXwTygYsKqqEqupLLFIJGjtnVHr6IpaNJZwZ5XSRe5D62rIQiCNgCMM4aCVREJWURG2yvKIxeNJa8c13tEZs9TgBa7LXN8YISAEcoFA4OK1J5n7TC76V59CQAjkGIFAIGChUMDoaaiqiFg1CQMmFh3dMUcYBgYS6QUBzSRybAl1LwRyh0AY13Q4RMIQtAqQhYqysMUTKevsjlpnV8ySWhHIHfjqWQgIAQtcbWoFYdBMQt8FIeBXBEgYMmFJnEiQNHBy0dU9YF09MRuIgzC4S1zXuV9trHELgRDIgiMMuLbLQBbK0RIgDLzGu3sGnIchfYXrOte3RQgIgewjEHjY0u1CkrLftXoUAkIgHwiAL5gjDXhSUhKyUjSGJ/VH42gJiyeTijnMhyF0DCGQQwQyiwL0JkbCIedpSCZT1o+QQ17nWvjLIfjqWggIAQv09MW0HKEvghDwOQIBw38gDplJBQlEIpEEWcB6gDyIPreuhi8EsCgweI2nPYrpa52xhgxLSuA616qAviVCQAjkEoEAwhU0n8glwupbCOQYARIFbnxIP0+TB8Y0kysMrQgMPXG76x8hIAR8hACv7cFL3XkU+Xf6+h52nesa95FFNVQh4C8EAt098jD4y2QarRB4HgE3ieBkAs2pJSFkgauQcXgY6GVgRJI2ISAE/I2AIwz4B9GGzpMYRE4DV/uYx8Br/dnKgL/PU6MXAkLAmwgEWtv6tCbhTdtoVEJg1AhkJhOUU2VjaFIslrQYFJKSmFBoEwJCwN8I8Brndc3mFJOQ/JxCKFJsIH2dizD4274avRDwOgKBJ0976dXU6oTXLaXxCYFXIJAmC/QwMOmZhCGd9Mz6C2xcgXSbeMMrENTLQsD7CKTJAryIVEsCWaB0MsMOY4PXefqHHOeh69z7xtQIhYAPEQi0POkZus/4cPwashCY9AgMJUOigBuLOlEpiZOLKJRT0oRBMUmT/ksiAHyPQMa7wLBD50kkYYCHIbMwoGRE35tYJyAEPI1AoLmlR+sRnjaRBicEXo9AJhyJjyQLbGlZVRAGSKsOeRhe343eFQJCwMMIDBEGeBhIGEoGCQMlVaOxuEuA9vDwNTQhIAR8joAIg88NqOELAREGfQeEQPEjIMJQ/DbWGQoBLyMgwuBl62hsQmAUCIgwjAIk7SIEfI6ACIPPDajhCwGfIyDC4HMDavhCQIRB3wEhUPwIiDAUv411hkLAywiIMHjZOhqbEBgFAiIMowBJuwgBnyMgwuBzA2r4QsDnCIgw+NyAGr4QEGHQd0AIFD8CIgzFb2OdoRDwMgIiDF62jsYmBEaBgAjDKEDSLkLA5wiIMPjcgBq+EPA5AiIMPjeghi8ERBj0HRACxY+ACEPx21hnKAS8jIAIg5eto7EJgVEgIMIwCpC0ixDwOQIiDD43oIYvBHyOgAiDzw2o4QsBEQZ9B4RA8SMgwlD8NtYZCgEvIyDC4GXraGxCYBQIiDCMAiTtIgR8joAIg88NqOELAZ8jIMLgcwNq+EJAhEHfASFQ/AiIMBS/jXWGQsDLCIgweNk6GpsQGAUCIgyjAEm7CAGfIyDC4HMDavhCwOcIiDD43IAavhAQYdB3QAgUPwIiDMVvY52hEPAyAiIMXraOxiYERoGACMMoQNIuQsDnCIgw+NyAGr4Q8DkCIgw+N6CGLwREGPQdEALFj4AIQ/HbWGcoBLyMgAiDl62jsQmBUSAgwjAKkLSLEPA5AiIMPjeghi8EfI6ACIPPDajhCwERBn0HhEDxIyDCUPw21hkKAS8jIMLgZetobEJgFAiIMIwCJO0iBHyOgAiDzw2o4QsBnyMgwuBpA6ayMLpAFvpQF15GQITBy9bR2IRAdhAQYcgOjupFCAiB8SEgwjA+3HL8KRAFxxX4z0RIA8nCYBNvyLHNCte9CEPhsNeRhUC+EBBhyBfSOo4QEAIvQ0CE4WWoFPq1VIYoJMEXMs/HM6gg+AKaIw18EGsYD4pe/4wIg9ctpPEJgYkjIMIwcQzVgxAQAuNHQIRh/Njl5pOpuAWS3Wg9Fki0WzDR5h7Hd7AMYQhZKlBiFizBY7mlglWWCqHxeaDMvT5EKsZ3IH2qgAiIMBQQ/DwcOh6PWwItGo1ad3eXa319fdYf7XevBYNBY4uEI1ZWVoZWbpVVVVZdXW2VlVVYJwi4loeh6hA5RECEIYfgFmHXKSw29vR0D7Ye4z2DbWAgZvEB3FOSCSstKbWS0lIrL8c9A/eKKtw3SkvL0EotEokUISo6pYkgIMIwEfRy8NlAss8C8WYLDjyyUKzJwrFreLwxziORKITABcIgCdWWJFEI11siMseSYbZpIA5T8V7tIF+QB2KcQBf0YyIMBYU/5wfvJzno77f29jZ78OCe3b9/z54+fWJtbW3W0dluYRCFSDhs5RWVVldXhzbNZs+eY42Nc/HY4MiESEPOzZTzA4gw5BziojpAMpm0hw8f2KNHbA/t6ZMn7r7R3d1tvb09IA4DVlNTYzW1tVZfP8PdK2bNarApU6falJpat+hQVIDoZCaMgAjDhCHMbgeBZJeFok0WjN20SN9ptKMW7j85zoOALFjau5AMTbEkyEESZCFRutQSJUsccUiFZ4M4TAdhiIBchN3+6dAlkYdxgp73j4kw5B3ynB+QP/aJRMLoXejq6rTOzg573Nxs169dsevXr4I43Lfm5kfW8qTFSkpKsBpY4n78Z82abfzRX7xkqS1btsIWL14y9H4YpCLjjcj5CegAWUdAhCHrkBZVh/QosGXuG1F4IG/evG43b6A13XD3jIf371t7R7u7n0SxCFFfP92m1dfbnDlzbeGixbZo4RLcP2bbjJkz3cID7xmhUMjdN4oKLJ3MuBAQYRgXbLn7UJow3HRehXDfKRCGI2gnxnlA5i8EnZchFayAJ4ENxCE8wxJoqfBMPGeb5VoiMgsehykgD6XYD6FKmdyHcR5dH8sPAiIM+cE5n0fp7OzEamCLPQEhePTw4dAqYfOjRyAKD+FtwI8+iER3FxYYwiELh8IuFImrhTVYHeSP/pw5jdbQ0GizGxrc6uG0afUIO6h0oQckDtr8hYAIg7/slc/RcoGBHgO2lpZmeCHv2wN4Iu/fv+vaI9w3OuChJFno6+21PpCFOPZlCBLDF6dOqbP66dOdp6GhYQ7uG+k2a3Z6AYL3DW1CQITBY9+BNGG4AcJwHUThpEV6D+Px+ARGGQBhoLeAoUlpb0OKhIAtVD9IHmZbvGyNa8nIXLxe4xrJhjbvIyDC4H0bjXWED+FBuH79mvMm3LhxzW5glZCvMZSgFz/4sRjikOMDzgORCTcKYiWQeQyMPa5C/kItiEMdSMLatetszZr1bgVx+vQZblWRK4fa/IWACIO/7JXP0dITmc5R6LUrVy7ZqZMn7MyZU27B4SkWHeihJJmIIX+BHogkWzKFcMawa/RSMm+B+QtzEMo4b958d79Yu3Y97h/rjYsNvM9om9wIiDB4zP6BZCdCkkgYbjjPQqT3EB6PvWKUvIBfdxFnJFkzj8+6cdpLwRoXppQKzQBZWJ0mDSWLEbbEMKUG8AUmRSPxiU2bZxEQYfCsacY0MIYT8Ief7QbCjk6dOmGnT5+0W7du2q2mm/A4PB0KKeKPO5ObmeScTCVdKAJXDDOTBv64czLAfVavIWFYZytWrkKI0lJbsmSZ+xy9DJoEjMlEBd1ZhKGg8Hvy4LxncONCAj2RD5GrcOH8WTt58rgjDBRJ6EHjPaWECc4gBiGGGQ1e+9FY1GLR9OIDiQTbzJmzXA7U/AULbfv2nbZt2w5HInivIaHQPcOTX4W8DEqEIS8wj/4gYyEMKXoAmNT8gidgkCBgIpGu45AcpBXPEwcqJKVICoKV6ZAkhCYlIgstQfJQugqvTYenAUnRoerRn4D2zDsCIgx5hzwnB+SPelrVpMfOnztjhw7ut8OHDyC5udUlOFPdpLq6xjV6ChguMGMGrtlEmmR0ITwpkxTd29PjVhOTiaTNnDULk4DZtnz5CtuxY5ft3LnbJTpmVhdzcjLqNOsIiDBkHVLfd8hQJLYnTx47rwIXGeiRvHPntt27d8epIfG+wgWGumnTXF5CRXmFlWLyHwoF3SIEk6EZ3tjX12sUWKiAeALvMzNmzLQtW7fbli3bXC7UdPzN1xTO6PuvzbhPQIRh3NDl5oOjJQxu6s8QI6MHgKRh2Da46mCWsEAqgTfSxAHBScN24tNBDwVmnHBOupYoWWADFbtd4/NkSaMLW/rdB/WnhxAQYfCQMSYwFMqmtrY+tVYoIB07dsR+/ul727v3J3gQkMyISQGlD2cOJjXTS0CPwdKly20A4UkMNXj8+BGIxlk7f/4MnjcjZrnDJUwzaZGN+3766Wf25798jnjlGW4SwRVHbf5AQITBH3bK5yjTXoE4vJBN9s3XX9rXX33hkpvpcaC3Mb0oEHFEgWFG8+bPt9raKY4QhCNh97nbt245AQXmOHQgx4EEhF6Eqqpq27R5q23atMVWrVpjS5Yuc95J3ku0TU4ERBg8ZveRCQOTmFlPoQQT+TqEFM1E8jJUjoY2kgI0RxpAFlIDIA1orOuQ6nWPQSgxBRJd2I9EIrORNAQcOYiXIp8BzXkaylZBVWmJC0ty4UkveDMyn9djoRAQYSgU8tk9LsMHnKIJlE0YinTs6GE7ceIYVvwq3KofFU0yP9rz5y+wxrnzXWJzJpeBnogmqKHcvIl247pbaeRkIAm9dU4sFixcZH/845/tj3/6szVAdrUakopMetTmDwREGPxhp3yNkuFI/f2UXO5DCON1+/rrL+yr/3zhkp7pVWCOQiZ5uXHuPNwrmMjc6K75ctxTgsGQIwpUW2OCND0Sd+/esS4ILvTAQ8nflWXLVtoyeCaZx7ARxIHkgSRE2+REQITBY3YfiTBw0u4Kr6GuQpzSqExWLl3+u7MYJA3JGEhCDLygz0KJJxaMt6A9tFD8voUG7jky8fwHQRoQnpSgchLyGgYqtqG9YfHyje51vqd8hucR88JfIgxesMLEx9CKHIUTJ47aieNH7dKli3bt+hU38ac3YDrIAmOKN2zcbBvRGBpADXWuAmbCEriiSA8FazQw4fHIoQOOcDCUiUnSc+Y02rvvfWjvvfeRW2lkWBNrNmjzBwIiDP6wU75GScLQ2dHhvALXILf83Xdf23fffuXuARwDxQ82bdrqJvrLl68cVEGa7jyLnPTTi9DT3WPdKO52D0ThwoWzaOdc7QZKODM8knVcKNO8fsMme+utd+2tt99VQbd8GdiDxxFh8JhRRiYMSHIM0bNQh4n8Zhuo3INJ/fZhZzFIFuhlAGGwZBRehd4hkhAaQDG46FUkVl9Jex1sAMQhPuzzVFJiTYaIIwuxqvfxuAueBxZ5Q0POgzZvISDC4C17jHc0LK6095cfEYr0g/MOMKyIsqrz4E2YP3+hrVy52nbsfMPlIDCsgNvwBER6ETLk4DDIwveYQPyC/rhayB9/EoRdu99Ce9MlP2cKu413vPpcfhEQYcgv3l4/GgnDY3gHeJ+4fPmi/fzzD+7ewWudoYZcTHjv/Y/QPrR16zY4uWVKLg/PQcgsNtxFzsPx40dcu37tmqvfQM8D92c+w8ZNmwe9k5+65GmvY6Px5QYBEYbc4DruXkdFGBCClIQkqvMAVL7tJvbPDkjCwA2PzF9AOJLByxBMtFswiTbwwEm2hqHCFBy4C0/DfXgdHqf352cQcuSqQ0OGlVKrA2UgJeWb0sXeUPAtBaKizVsIiDB4yx7jHQ2rsv74w7f2A1oTwpKewFPQjmrOq1avQQzxWigdrUVowAZbix9/hhINJws8Jn/8SRoYjnD69Anb99teJE7vQ9hBszUjv6Gqsso2b9nmEhm54sg8CIY2afMHAiIM/rBTvkbJ6z1TmO3ixfN2FCGMR48csiCSmaejcjNFEXbtetPe2LXHFXGkYhrzoIbfN0g62EgOLl26YJfRzp497RqV2Spxz+C9huFIn3zyV/vk07+IMOTLwB48jgiDx4wyMmEox2o/i60xZGinxaresxi8DM9vadLgkpxxM8BUwhEHl8uQeArC0ORauP8sJFtPwdtwCdkL3C/T0snQich8hD0tdiFP8YodON4OSK5CblWbpxAQYfCUOcY9GFZv/vbb/9i33/wHyYg3QRbajXkNW7fusK3btmOVcKMtQuVmVm8uh9LJyzZOIjgB4A//cYQ2McQpnRdxw00U1gzqqlNmdfXqtbZ02e/DGV/Wq17zAgIiDF6wgnfGwMWB9OT+lBM7IGm4eOG8TZkyJS2fDJGDTZuZd7DV1VXIiB/8/gx4v2hHwvPdO8xhuA1ltoNuoYHhSVRMYr4Dcxf+/OfP7dM/f+ZCmn7fh/6eHAiIMHjMziMSBlRrZqIzSUMM+QWx6g8QlvTmqM8ikOyGZ+Ghy2WI9B5DYbiDrt5DINWPfIc+cIZn4UlJhCAxnyERWYDjfGgDVR8YlZO0eQsBEQZv2WO8o7mPxMNvvvnSqZ1Q9aQTiiUMJ3rjjT1ulXD9+k3uh38u1E6oif667drVK3YKXoYzSJ6+cvmSK+bECcbKVavR1tg6eCrWrd9gK1asel03es9DCIgweMgYHhgKr2cqolEZjd6BJoodwDPJ/KYVK1Y7FbVly1a4RQG+9rqtC7KqrAbdjLDI/ft/RWjkT8iDOukWJsoryh3poLoaSQMlWrVNTgREGDxm91wThnR4UifyF7os7CpJozAciEMwgYRoNOY7ZDYmVydR3C2BIm6xmj9bFC2BsCRt3kJAhMFb9hjvaEgY6F2g2sltEIYOJDQyHnknCcMbuy1DGJjTMBJhuHr1crrwG3TZr1657AgDvQ+UR1yJEKd19DQgtEmEYbzWyv/nRBjyj7mXj8jrmQsLt2/fRLsFD8Ftp3LEXCWnbgTvIYnCDBRiq0bl99dt3ajhwrAktv37fkXu0w9OqY0Kbc7DsHEr5Jg/g4dBhOF1OBb7eyIMHrNwzglDJuwIbshw/2mL9KQJQ2jgJvIZmkAa2oYQcbUZkPzMAm7R2v+x/in/A8Kwcuh9PfEGAiIM3rDDREdBacNvQBiop86JADXRHWHYuRukYZAwgCww72A0hIHVXk8PEgYSCFaEJmFgKBJDk5gIyVwGbf5AQITBH3bK1ygZSkRFNLaWlseo9PzAKRyRMDA/adGiJc4bwCJtI0mhMvSRykhMoN63b69LoOa9gyFJlZUVUGbb4sjCnxGSVCIPQ75M7LnjiDB4zCS5JwzPTjgUu4X8hcsW6r/owpIi/ScQqvQAoUmDeQ9MgEbyMxWZorV/hYfhM1cB2lx1aKolMddBW6EREGEotAWyc3yX9Pzjd/bj99/azabrxgqsrK2wfv1Gl7/ASf6qwZCiykpIHP9u4wQigYRnhiqcY6XoQ/tdEuQ9EJH79+666q6UZGUC40oUfSNZWLhw8e960Z9eRUCEwauWKcy4eL1zQaG7u9sVaGQeAkUSqGw0CwUe6V0IQT6VZGG4MtLLRks5Zi5SMHeK9V+OII+ByktUY6uprXVSzn/4w6f2hz9SJUnFHl+G4WR4TYTBY1bOJ2EIUDkpjvoMUEsq6f4R7QcLx64DEVaIZi5DOvk5Gaq1aNXHyGP42CknMbchBZlVKippKzwCIgyFt0E2RsBwgF9//dl+RXXn69evuRVDrvhxpXDRosXOO7B1207btn2HmxT8/pgkCjFUi47Gonb82FEXVsDwAk4k6K2YOrUOsqy7nCwrC8BRqpW1GbT5AwERBn/YKZ+jpIzywMCAq7PCSvHRaL9TMaJngOFEQfw4BILB55SRXjY+SjpfOM8q8eeG8iLuIMSpvr7epqFtQB2G9z/42N5//2MRhpcBOEleE2HwmKHzSRhcgjOIQSDRamWd/7DSjn+gRsN5kIV0dei0ahL+RZG4WOVbFoWEa7xsLZSSGl2zgCo+euHrI8LgBStMfAysuXDkyEGnUnL1yiW71dRk/NGeORNxyFgtXArVE9ZR2L3nLZuGgmuhcMhCIVyDWGmkT3AAxdm6EFrAeGTKK/7003cIL/jVTSg4qWhApdd08aX3bMGCheh3NmozDK8SP/FzUA+5Q0CEIXfYToae6ZHINJ4vn1OCOZGIO4WkEyeP2ckTx5yqGj0N9DpwQaEBjV7O3bvfdvceFoTTNjkREGHwmN3zSxiSOHt4E+BpKO38p5V1/BOE4RwSn6mY1I/3OA0hYahELYZtFkOLl61DHsOydPIz8hu0FR4BEYbC2yAbI+js7LRryDVgvsE5KJ9QpeQ8QovSccSV0FVvcHkH6/Dj3TB7jtVNm+a8BvQsJJMJhCV0GaVZmQtB1ZRz0FNnxWiGI3CyyUrR776LSs8o5NTQ0OjkFxm+oM0fCIgw+MNOXhwlE6TjWDQYGCQIzhuJBQZWl0+HI92Eh+Gcq/TMnAh6JfkZJ78MCWbWgmHhSIYyukUKL56kxpRzBEQYcg7x2A6QV8IwSAgCiU5HGEo7/4VE6LOuAnTQqSWRUIAwBMqdZ2GgdC2qS693pIGPFlAs49ism5u9RRhyg2u+e43291sLvAwtLS12Eqt9vyE86QAkDtOrguYKKC1YuMjlHSwcfGQCNL0HbE+etjgJ1csgCffu30VI00NUin6MBOlyKysvQyLkcvvwoz/YRx/+0erhWWARp9LS18uz5hsDHe/VCIgwvBobvfN6BOhJ4P2lv7/PhS/FcL9g/gOLszU1QY71xnWEQV51oZDcl78pXEzYTY8m2rLlK+CNnGnTZ8wYMR/i9SPRu35GQITBY9bLL2FInzwlVks7/20lHf+2iCMMXagK3YU3M4ShLF28rWRFmjCUb7F4xRYQCekxe+HrI8LgBStMfAz8oe7t7UXrcdrqB1Gl+fChA44IMAGa7zOEiCoojXPnuRyEuXh0hCE+YG2tre5H/wbyH7hC2NeXnhyw4iuTIJnkTLWlN9Bqaqa4ZMiR1FMmflbqIVsIiDBkC8nJ1w/JAZWUnqAxSZr3mY7ODrtzG7Kst27ZfSwwPMQCA3MZapHkXF8/3d1jduzYZTt2vOHqv7i8CIgtDK8UPfmQnNxnLMLgMfsXjjB8aSWdXzoPQzDZAXnVDiCTIQylKNi2FFWflzjCwIrP8YrtIgwe+e6IMHjEEBMcBkMAmMQYiw3Yvbt3EJqEgmuXL6eTEJGQyKToysoqtEool0xxIUVUMUkmkpZASFJfX68LMWBIAUMO+L0oKSl1BdpYJZohBcxdoJeCXod0qJKECyZotrx9XIQhb1AX3YEonnCJlaAvXoAMa4tbUKCiUptrra6iPEkE7yFLkCu1GiFIK3C/WAp51sVo0xD+GImUKOG56L4ZYzshEYax4ZXzvQtFGEo6v7LSrq/ShAG1GILIa2B+Azd6EhIlC1HxeSFyGdbbQMUui1fuEmFw6BT+H04MuerDx5KSkGshxKz3RxNQzWBSWzoXpfAj1QhGQoDhR9y6kM/AiT9/6H/88Vv78Yfv8GMPQQJnZ9qaLZ2bkP4IExpxraLWAvtgnQauCNbV1dkHH/zB3v/wY0cY+DrDkEaSWRxpnHo//wiIMOQf82I5IkOPfvvtF7Sf7R4klnlfaX3a6nKfuLjA+wGTmUkK3ti1x955533bum0HcqSmYmGiztVz4D1H2+RGQITBY/YXYfCYQXwwHN7HRRh8YKgRhsgf7ow0YvOjR1BIuuW00ZnPQPUSKibxhz0UClkYP+4lgyt+6RwH1GDA52OQVI0hmZH7lKLAUlVVtW3DD/+27TtR1Xm1zZw1y2ZBHYnFl7iPiMMIRvHQ2yIMHjKGz4ZyB5WgDyDE8eCB30AY7lkLCMPT1ieQYaZHM+YWGng/YGNhx/WQUV2NZGeGPM6dO98RB95P6LEUcfCZ8bM4XBGGLIKZja4KSxj+M+hhQH0GeRiyYc689CHCkBeYc34QyqJ2dHS4mglMQDzvdNHPuvCkuwhRYi2F9CpgxHkPqqqrXYhSCqFMDGfiD39XV6dr/Jsb92dIASu/rkAOw+o1azEhWGfV+GwEBZgkkZhzs2btACIMWYNy0nXEHIVjx464RiU1Rxjgwezp6bEe5DQwFDKz8DAT+U5zoKJGQYW1qAa/FgUj5zTOdUnQzG8QYZh0X5+hExZhGILCG08KRRhKO/9jJV0gDH1nkfCcIQzDcxgWIYcBrWwDQpJ2IiTpDYUkeeMrgxu4PAweMcWEhtGHGGIni/rgniugRK/CqVMnXFwx44sRcGRV1TVuss/cBVeFtabGkQUShH4kObeiMnQb9NPTiY09UEWJukRpJjGyjsP27W+44m38u6q6yhGPCQ1aH84bAiIMeYO66A7E/KcLF1iULZ0LlZFTZehjJ5Kfeb/o6+txydD0IpRDVa2urt62bN3uGu8dDQ0NNhtyzvJKFt3XY9QnJMIwaqjys2PhCMMXUEr6YlBWtRMehk6ccIYwlLnaC3EmPpcxhwH1GNCkkpSf78RIRxFhGAkhf7zfhsn+2TOn7ezZU3b1ymW7ceO63bx5Az/Q6ZyFKVOmuIRE/ngztri8vAJyqeVGDwNXB/shm0h1JLbbt5rsGrwUfKyqYqJ0NUIL5iLUYLNtRLjB/AWLoIIy16kn+QMdjVKEQd+B8SJAzyMXIx4+fODyo6iaRO9CL5KcuVBBQkF51aammy6sMYnwRhIH551cvNTVYVi3fgPqwGx06mrjHYc+528ERBg8Zr/CEYZ/odJzug5DMNmNWgzdQOYZYYiXroK0KhoKt8UrNiP5eZPqMHjkuyPC4BFDTHAYlDT8+afv7acfv3c/3k8gpdrW9tTlITCEiIXX9ux527W6oUrPIZCF9IFj0SikEttdWNOJ40ftV9RxOHL44GCuQshJJa5YscoVX2J8MosyUV9dmz8QEGHwh528OEpKMmfyo/ic+U58TKuyxazp5nU7dPiAk3Gm94FFJJkPRS8DC0SuX7/J3kfBx/fe/whEQvWXvGjjfIxJhCEfKI/hGPklDCAEUFUJQEK1DEXbWO053I9Kz6k+EIZ0CASHngpW2EDZRrRNjjAkykgcVoMwqNLzGEybs11FGHIGbV46ZjgRG/MUvvrPv+0///mX3b1zx9VjoNdg3vz5Tgedsqg7d+52tRQYjsRteDwx6zFkVg4ZzsTCbwcP7nchBww7oAIKExjnzZtvm7dsQz+7bNOmLegj6PoZ3ldeTlwHGRMCIgxjgks7j4AAvZIkDolE3G4jKfrYscN27OgRRx74N5WU6MVk27Rps/35L5/bp3/+3IkpjNC13i5SBEQYPGbYvBKGVBxsYADhR62OLJA0hPsv4LUoSEMMyKSXLlPBKotV7LZY5R4QhbWWjCyAzOp8zFbCHkNvcg5HhMHfdmeyMif7DB/68st/uvYQ4QN8jURiCyb3m7dsRzjAeluE8IDFaKzS/PsJfhK1GNgXG5Omz509g3bahSZdv3YVydBdrnbDlClTUbxtj73/wUe2a/eb8ECEXZiBYpO9/T0SYfC2ffw4usxiRSsSoJtu3XSVn0+cOGrHjx21y5cvDYkscGHh87/9t332+X+LMPjR0FkaswhDloDMVjd5JQxJEoN+C8RbBj0MIAzRi3iN9RdAJga3VLDGotUfW3/VR44wpML1lgrVgzCEMrvosYAIiDAUEPwsHJoVmVkwifkKX37xD/v3v/6B1b1HrmeqGP3hj5/aH9E2bd46GJ5U48KMXnVorhxSBeXuvTuYADTZAUgpspGQZKRU34bO+l/+8jf7APUZGGLAeGW+p827CIgweNc2fh8Zw5W6u7usG4sKP/34nX399Zd26ND+oUUJ3nv++7//X/uv//5/QBjK/H66Gv84ERBhGCdwufpYfghD2nMQHHhkwYG7ForesJLe/Wj7LBS7jVNj7gJbEInNiJEOTbX+6k9BGj6xBEKRUsFqvFbl3sc/2gqMgAhDgQ0wwcMzSbmjvd15Bb799iv79pv/IHeh1a3u0ZPwxz/9xf70pz/bRoQF8Md6NIXX+HmGFNy/f89++fkH+xntxvVrQ6FHb775DsILPnNehkzYgSRWJ2jIHH9chCHHAPukey4IcONjxjvJsCL+zcbrmAsAXAj4vRfyVaeYznHod3kOP3z/jf3ny3/ZfiwyMGSJCdDr12+0v/39f9nf/+t/uRClzMLDq/rT68WJgAiDx+yaH8LA3IUUiMJFi/SehJQqWuwKvAtXEJ70BIjwhoSbD3IU2JKh6Rat+cyitZ9BLWmley2dv6DKj174+ogweMEK4x8DFUoeo129esV++eUHN8HvgsxhBWKHqyGbSrLwRzQmHvKHmm2kiQBXC9tBQtgvSch3aJcvw3vILwu23bvfcn2+8+4HTqa1GnKtLMykzbsIiDB41zb5HBlJQSaUqJd1FKB4RA9BEvmIfJ0V3nk9UyiBW+aaf90Y+bkMOfgehOGLL/5p+/btRWG3NIlgLYbPPv8vF5JE1TUSknBYIcmvw7QY3xNh8JhVc04YEG4UQI6CIUch0nvUIj2/oh0EUWixUOIxkp17hhBJBcotiYTnZHi2IwvRms+dvOrQDnriCQREGDxhhnENgj/+rvYCPAFXrlyyfb/ttd9++8WFKLEwG/MN/kQPwyd/QQ7DhlEfg2FOJA1PEZv8ny//jWTqf9nFi+eHPv/Grj32hz98Ym+/8wHqOdS6mg5lZQo1GALIg09EGDxolAIMKaNwFI8PWEtLiz1peTxUrDGBiT8V1Gah+NrMmbNczQTmJo2GNPBexO277762L/79f6Gy9our7dLf3+eqPv/1r3+3v372dxRw4+JCmfNkFOD0dcgCIiDCUEDwX3bo3BKGFBSReiwYf4S8hWaLwLMQ6T2CROdTKNbWBbKA5pKd0yNLIhSJ3oVEZL7Fqj9C+xjJzgtfNmy9VkAERBgKCP4ED83faOYrNENS9erVy/Aw/AgPw49u1bCysspqMJn/9NO/2ieffubCAkZ7OGqrd5EwQJr1q6+ovPRvuzSMMOyCh4Gei3ffo4eBq5HyMIwW20LtJ8JQKOS9dVx6E7ggQM/C5UsX7BIa6ytkvA4LFy121ZlXrVrjvIaj8QZkvBZ8ZEjkP//5f5wscxzCCwMDcWMNhs+R8MzEZ96XIpEwPJ3yMHjrm5H70Ygw5B7jMR0hd4QBMxP8z5CjUP8lhCNdckXaIv1n8PyyBYyeByY6M3chvSXCDSAL8+BVWGoDlW86laRkZG7mbT16BAERBo8YYpzDePIEq4SY2F+7dtl+/OE7tG/diiFzCxiS9GdIGVLScAMKro12Y2XobhRrYr8kDJRr5cQis7GewycgItRVTxd2Y5iB9NUz+HjxUYTBi1bJ/5h6e3tcrZVWVHRn2NB+eCWvXrviCjgmMeHfuHGzWwjYs+cdXNuVboJP0vC6LR2SxJCmhH391Rf2j3/8b/t1709DeREb0CfzF/6OPAbmVaW9FsHXdan3ihABEQaPGXVEwsAwofAMt/LPisuxqrdRefmNYWcBLwJiGYcSl52bEbUWGIaUjMG70OyUkJi/EIpdR+5CExKf74EwgE24DTHOTHS2ELwJrOy82gZK11iiHAXbyta5Yw/uqAePICDC4BFDjHMYrJHQ0dHhkpK/+eZL++brL13SM3/kKyoqnEoSlZJIGBg2NFLSM1cJmUjd8vixS3r++efvn0t6/v/bO/unuKo7jJ+7u4QQjIEAeQFCgAAxSaMmMRrbabVOtPWXTqcv0z+xndFf6tRq7bQzdsaXaN40alQI5E0IahPIG7DsS5/ne1hg7SpxYXfvuXnuzAm7G+7ecz+He+557veNN/tfPPcChAiDnn+N72zBImAzfJJVV6XKIazLbhIMdcEc+4MwoxpTJDOxAeMN/gkXok8+ueByCHxm8DIDlJ/D9c2UyXRLYqMFkW5J3+eaxHovnIduYx76F+aLN994fanoY8rEwRGkVaVY+P0f/mTzBe85WCjEnpU6uLEEJBg2lue6v21twbAZYqHDWq7lCYiFZ12u5diq4zKgOQuBsIifrLOQs9dRYRbuSDMuDXekdHbcWir3LT67BXek2ZX9KRaiZms5FGvL4vsXW55C7YVeV9i0xzIkrfyyXsWBgARDHEah+j7wZr2A4EKmVX3tr0yr+ip8k7+2GzUznjAFKoOTKRg6OjqtfV9GI4oFNmZHGr805kYRSH369Clr/IyBimzPP3/S/ea3v3Mvvviy+SLz+1SHofoxrMeeEgz1oBz/Y7A68/y8T4PKZAZvvvk39/FH5/HZvLWBwUETDY8/fsQquY+MPLYcz1ApdTKfKbKi/JUrE0i9jAJuH7zv3j/1DlwYP7WHCJwbvluH4fuER/zpqYfrISDBsB56Ndh3bcHQDLHQhmDkNqQ4PYCn/kesNsJyV0wgoLZCYW5JODDAeR6WhSmXZhpV/sxNwqow6UXFsmXBf0Mx2mSVnYupVrggwQ0JtRdYtM0h+JkVn1XdeZl0bF5IMMRmKKrqSGmRzxv2q6/8xb3yyp/dJBb3dBPgxurOJ579mXviySNu794Ba5UClHnjL8K6yO+jUDh//ow7f+6sxUYwPoIuDLRaMBvSL184iQDGP7qXXnr5B588VnVC2qkmBCQYaoI1uC/l9c25gbEMb/z9NauZcO7saSQ5uGuJDjo7u9yevn43MDDonn76hDuOxtd8UMC4g+8u9ksPGM6fw3yBOePixc/cl0jAcP36NbNmcq6hhYEBzwx8Vja14P5kNqzDEgwbhnJjvmgtwcDqyoWIi/dWPPX3MQblcQWMRaB1wTeLS8DrqDCD+AVvZUiZVWEGv8MCbaWNTkloCHTOb0IlZwQ6L7YchfXiuAkSCglnTcWdSsTi8lOCIS4jUV0/uNDHxeiuX7tqVZ5pZbh69YplSuJTw6HhETc0NOKGh/e74ZH9bgQ/t3d0uEdat7otra0mEgqFIp4uzrlbN2+aMBhFZWdmRWJQ5A0EVLNxkcHsKWzPnPipe+75F9wzz3h3xu8uIqo7E+1VSwISDLWkG853lx4wsBL8qVPvmuvQx6jozsKMfOjAhwK0RHbt2OEY+HzgwCHHQOj29u3WKBxSuGlwPpiZnbH0y9evXbHKzl988RkeVnxl8wXdJDs7vUWTMQwnT/7KXBgV6xTO38pG91SCYaOJrvP71hQMVkwN2QkgHIoRFgsooFZMsYhaaeNjRgoBuiZRAizFMyCGgRmQLAuSVXiG5QGLlNLGmAUXpVw+02suSLktEAqbRlyheRDigYHOCHCyys7yWywxi8tPCYa4jMT6+sH0qm/BH/mtf7zuJibGzUeZtRS2tbW5tm3trqenF+kND1uKwz17+tyunbuxKNhphZVYuOkW4hbohnTp0ijiIfzPiYlLjgHQbEyHeAALiIOHfmJZVJimdf/+g3jiuL5+a+/6EJBgqA/nEI5C0cD0qmNjX7oxPhz49II7c+ZDtA/M8tAMqwDruOzp2+v6MFf0D+yDZbLf9cFCSQtBBrVcGO9AgXH5ymV3lQ0PKa5dvWzZ1ZhljQ8y+vu9RZNZkmjlPHHiZ0qnGsIfSI36KMFQI7DVfu3agqHab660H+MVSuJjM4RHC0TCPsRF/NxnREL9hWKmA6KkrdLO+iwmBCQYYjIQ6+wG4xbeRXXVd9DGRkcRh3ANNRomzYLABQJrMtgTQyz6eSPv7d3junt6cOPPuzxu/qy5wExI9D3mzZ8xC9PTU0txC00mOOiewLZv37Drw2Kiu7tnnb3W7vUiIMFQL9JhHIdzAucM1mFg0cf/oG7C22//2+YB1migoGBNhu3btzs+YBjcN+L2DQ27FiQ5yGTSSJe6iMxsXnBcu34VGdV8TQfGMtEKwUBpWiceQzt06LA9aOD8w//T9nASkGCI2bjXUzAUYJkopraZIMg37UYGJLg4oc5CDtWcWdGZsRIOrk8UEtriS0CCIb5j82N6duf2bdzAv7DGqsxc/H/++UXvmoS862ncqGlV2LW723VgIdDW3m5F1wp5X+H1LvKyT99ATQeIBGZQoUsB/ZrpisBFwwCeMh576mn3FJp9B9yaKEK0hUFAgiGMcapXLykYWJyRGZOuwjJw6v133XvvveO+QuwBHx7cunXLsqwx0xqv886uLrgY7bAaCqlU2iyTFBxfozGr2j3MFfw+FnJsb+9wO3ftsmKRhw8/6Qbh0rQbDxd2Y+5RcoR6jXD8jiPBELMxqZdgMGclFGUrQCjQ5SjffBCxCgctdqHItK2ZLlgfGLfAmAXlW47Zn0lZdyQYynAE+yabzWKRP+NmcfNmmkT6J3+AjCUzWPxTADCegf7J9CFm5pIMG5/2YeFQclHgd2SzC97qADclXMBLrgj95n509Ogxd/TYcavsTNcE+SOH8+ciwRDOWNWrp7QS0Jrw9fS0O3futDt79owlPKArIq2MjE3yFoM05gw/b/AzNs4Z3J9zBt2T6NZIq0QPLJd7+/phkRhCdqTjCHg+ZpZIpnNmADT31fZwEpBgiNm4R4W7SHl62aWyV1zT/HlUY/7QZVCRuboNFzbiEorL8QeMe0CudaZNTTE96w4IhG6Igx4IhiFYF4YgIHbi/2hVaMUhJRSq417fvTh/+5uAwwIwbS2ditz8Qh7+rLwJrMSq1LdnOtqPIcAgRC72swtZpFgdcxcufGRteppWgxsmHLwgyNqNnb/P5u/ffhGQgm9yGi4FdnNHgaVWBEUP9A+6/oFBLACGETjN4OkR1F3YYgsJPS38MSPU2N+VYGgs/zgenYt+ZkbjgwbGLY2NIX4JcUzj46MWBzUPyyTrNnDeyKMoG62R2MOHL+K+kYalgalW+QDCarzAXYmuigOcLwaHltOy0koZYS3Bv0FtDy8BCYaYjX1UuI+Up1NIfzqJSszIg0zRMH+hql4WrQAbYxSaTCAUUfStmNqKuIR2iIXtaJ1wR+qEYEDD+2IGjW5KZllgESdNDlWBr/NOEgx1Bl6jw5WsBBbAjGxHNyASmN2I2ZOY4pCuRrMIgubigNYG/3QRdVb8H4AFMm5ashrwBt/JTCldO1wv/Jd7exEkjexIzK5Ev2YuELzI1DVeo+Hc8K+VYNhwpMF/IecMbpwPWEvhJuYNpmS2AGbMG6W4hFkUZVs062PWFVjY1faLLACaVsZHH93ma7wgKxLnCsY89PT24rMu+5zVnbnZXGOv9M/DSECCIWajHqFmQoSCaiyqls6OetGwcLGqXtKa4IuwIaA5DaGQetRcjViELY/GFKoWw1DKsuQfVVZ1LO3UOAISDI1jX6sjcyFA6wFdBOheMIGiblwETN+YdFNTU+ZrzEUCG60EERpFwJYtrWZVYDAzs6KwWSpV+B4ziFFbuAQkGMIdu3r2nPVWpqY4T0wi1eq4uzwxYRZKWhruo3FeKSINMxf/TMvMGIcdyLbGGi99cEViNrbd3d2Id+iqZ7d1rAAISDDEbZCsZsJdVGC+A9EwjSJrX5nFoapuWtVmiAYHKwMCl4sRMyFthWiAUKBFgW5HtDrARQmzR1WH0E6NJyDB0Pgx2OgeeFcDLxr4lJDBiTcRyOhjHGbcHIQC3ZdoZfCWAp/ZhHEJbAyI7lx6OritbZvFLGyGu4G2cAlIMIQ7dvXs+T0kP2B9BVojvYXhG3cbFgYWeuOcYYIBhgneN+i6yPnCLAywLnDO4NzRhlTOjzyytZ7d1rECICDBELtBorkQiwATDnMouHYPr+9X1UuLXTC3ItZQYPwCA5jpnkSBgDgGplSlmLDPqzqEdooBAQmGGAxCDbpQcjeYgx8yi7LZDR83/QULas4tByoiesHu/j64MWOB0KUARfol0+WAwdL0VdYWLgEJhnDHrp4950MEuh9xnihZIbOYN3IIamZws80rdEnCjYNJE9iaMT+UYhiam/18QYulNhFYTUCCYTUNvRaBAAlIMAQ4aOvosrc++IBnPi3EnX/JQOiDEkuBzPI3XgfkGO4qwRDDQQmgSyVrJX+WrAs+6hmOB3BlNJdG3EQ0XwQwmA3uogRDgwdAhxeB9RKQYFgvwfD2L4kG/vSbdyn07km6+Yc3omv3WIJhbUb6jcoEVouG1b+h+WI1Db1ei4AEw1qE9P8iEHMCEgwxH6AadW9FLKwcQE8JV1gk7ZUEQ9JGtP7n8/9zRsk6Wf++6IjhEZBgCG/M1GMRKCMgwVCGQ29EIJEEJBgSOaw6KREIhoAEQzBDpY6KQGUCEgyVuehTEUgSAQmGJI2mzkUEwiMgwRDemKnHIlBGQIKhDIfeiEAiCUgwJHJYdVIiEAwBCYZghkodFYHKBCQYKnPRpyKQJAISDEkaTZ2LCIRHQIIhvDFTj0WgjIAEQxkOvRGBRBKQYEjksOqkRCAYAhIMwQyVOioClQlIMFTmok9FIEkEJBiSNJo6FxEIj4AEQ3hjph6LQBkBCYYyHHojAokkIMGQyGHVSYlAMAQkGIIZKnVUBCoTkGCozEWfikCSCEgwJGk0dS4iEB4BCYbwxkw9FoEyAhIMZTj0RgQSSUCCIZHDqpMSgWAISDAEM1TqqAhUJiDBUJmLPhWBJBGQYEjSaOpcRCA8AhIM4Y2ZeiwCZQQkGMpw6I0IJJKABEMih1UnJQLBEJBgCGao1FERqExAgqEyF30qAkkiIMGQpNHUuYhAeAQkGMIbM/VYBMoISDCU4dAbEUgkAQmGRA6rTkoEgiEgwRDMUKmjIlCZgARDZS76VASSRECCIUmjqXMRgfAISDCEN2bqsQiUEZBgKMOhNyKQSAISDIkcVp2UCARDQIIhmKFSR0WgMgEJhspc9KkIJImABEOSRlPnIgLhEYi+/e/9YjG8fqvHIiACSwS8YHAuwoumppTb1JR2XFxks3m3sJB3+YKucP2xiEDoBJYFA67tTCblmtAKuHuXrnNd5aGPsPovAvEmEN2cmdM8E+8xUu9E4AcJRPhfigUKB1tIQDR4wVBwi4sSDD8IT/8pAoEQSOECt7YkGNKZyBULzmVxjfM61408kIFUN0UgUALR7J15P89otgl0CNXth50AhQIFAzcKhkyagsFhEQHBkCu4giwMD/ufiM4/AQQoFnid82FAOu0b3QN4jedyEAy6hydglHUKIhBfAtG9uaymmfiOj3omAg9AAAsJ/hb+SXMxAbVA/ZDLF+GOVMBCQpf4A0DUr4hArAnwKud1XRINFA68tPP5wpLboa7zWA+gOicCgROI5hexqtAmAiIQNAETDDgD/xTSLypoWaCPsy7woIdWnRcBI2DXOAUD3plogHrgwwAaEHmdaxMBERCBWhKI7kkw1JKvvlsEak6gJBZ4ID6B5JKCPygVuI7QUoJMtIlA2AT8te3PwUQDrnJe2/ZIQNd52IOr3otAAASib+YWtZ4IYKDURRF4EALL4sErBomFB4Gm3xGBkAjg2l6+ziUUQho59VUEgiYQXb6zIMEQ9BCq8yKwQmB5IbH0kS7uFTZ6JQJJIbD6Otc1npRR1XmIQLwJROO3l7Ikxbuf6p0IiIAIiIAIiIAIiIAIiEADCEgwNAC6DikCIiACIiACIiACIiACoRCQYAhlpNRPERABERABERABERABEWgAAQmGBkDXIUVABERABERABERABEQgFAISDKGMlPopAiIgAiIgAiIgAiIgAg0gIMHQAOg6pAiIgAiIgAiIgAiIgAiEQkCCIZSRUj9FQAREQAREQAREQAREoAEEJBgaAF2HFAEREAEREAEREAEREIFQCEgwhDJS6qcIiIAIiIAIiIAIiIAINIDA/wBwte1OdwhUrQAAAABJRU5ErkJggg=="
-  },
-  {
-    "quest": "Calcolare il tempo medio di attesa (average waiting time) dei seguenti processi, assumendo una politica di scheduling Round Robin con time slice q= 4. Nel calcolo, si consideri il tempo necessario ad eseguire il context switch trascurabile:",
-    "answers": [
-      {
-        "text": "4.85",
-        "image": ""
-      },
-      {
-        "text": "4.25",
-        "image": ""
-      },
-      {
-        "text": "4.5",
-        "image": ""
-      },
-      {
-        "text": "4.75",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": 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"
-  },
-  {
-    "quest": "Calcolare il tempo medio di attesa (average waiting time) dei seguenti processi, assumendo una politica di scheduling Shortest Job First preemptive (SJF). Nel calcolo, si consideri trascurabile il tempo necessario ad eseguire il context switch:",
-    "answers": [
-      {
-        "text": "6",
-        "image": ""
-      },
-      {
-        "text": "5.75",
-        "image": ""
-      },
-      {
-        "text": "4.5",
-        "image": ""
-      },
-      {
-        "text": "5",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": 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"
-  },
-  {
-    "quest": "Calcolare il tempo medio di attesa (average waiting time) dei seguenti processi, assumendo una politica di scheduling First Come First Served (FCFS) e che il processo A esegua all'istante t=2 una chiamata di I/O che si completerà dopo 4 unità di tempo, ossia all'istante t=6. Nel calcolo, si consideri trascurabile il tempo necessario ad eseguire il context switch:",
-    "answers": [
-      {
-        "text": "4.5",
-        "image": ""
-      },
-      {
-        "text": "5.5",
-        "image": ""
-      },
-      {
-        "text": "7.5",
-        "image": ""
-      },
-      {
-        "text": "6.5",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": 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"
-  },
-  {
-    "quest": "Calcolare il tempo medio di attesa (average waiting time) dei seguenti processi, assumendo una politica di scheduling First Come First Served (FCFS) e che il processo B esegua all'istante t=6 una chiamata di I/O che si completerà dopo 3 unità di tempo, ossia all'istante t=9. Nel calcolo, si consideri trascurabile il tempo necessario ad eseguire il context switch:",
-    "answers": [
-      {
-        "text": "4.5",
-        "image": ""
-      },
-      {
-        "text": "5.25",
-        "image": ""
-      },
-      {
-        "text": "4",
-        "image": ""
-      },
-      {
-        "text": "4.25",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": 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"
-  },
-  {
-    "quest": "",
-    "answers": [
-      {
-        "text": "72",
-        "image": ""
-      },
-      {
-        "text": "73",
-        "image": ""
-      },
-      {
-        "text": "74",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficienti per rispondere alla domanda",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": 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-  },
-  {
-    "quest": "",
-    "answers": [
-      {
-        "text": "23",
-        "image": ""
-      },
-      {
-        "text": "24",
-        "image": ""
-      },
-      {
-        "text": "25",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficienti per rispondere alla domanda",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": 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"
-  },
-  {
-    "quest": "",
-    "answers": [
-      {
-        "text": "18",
-        "image": ""
-      },
-      {
-        "text": "19",
-        "image": ""
-      },
-      {
-        "text": "20",
-        "image": ""
-      },
-      {
-        "text": "I dati sono insufficienti per rispondere alla domanda",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": 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"
-  },
-  {
-    "quest": "Il seguente Resource Allocation Graph (RAG) mostra un sistema il cui stato:",
-    "answers": [
-      {
-        "text": "Dipende dalle scelte dello scheduler del sistema operativo",
-        "image": ""
-      },
-      {
-        "text": "Presenta deadlock",
-        "image": ""
-      },
-      {
-        "text": "Non presenta deadlock",
-        "image": ""
-      },
-      {
-        "text": "È impossibile rispondere",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": 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"
-  },
-  {
-    "quest": "Il seguente Resource Allocation Graph (RAG) mostra un sistema il cui stato:",
-    "answers": [
-      {
-        "text": "Dipende dalle scelte dello scheduler del sistema operativo",
-        "image": ""
-      },
-      {
-        "text": "Presente deadlock",
-        "image": ""
-      },
-      {
-        "text": "Non presenta deadlock",
-        "image": ""
-      },
-      {
-        "text": "E’ impossibile rispondere",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": 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"
-  },
-  {
-    "quest": "Il seguente Resource Allocation Graph (RAG) mostra un sistema il cui stato:",
-    "answers": [
-      {
-        "text": "Sicuramente presenta deadlock",
-        "image": ""
-      },
-      {
-        "text": "Potrebbe presentare deadlock",
-        "image": ""
-      },
-      {
-        "text": "Sicuramente non presenta deadlock",
-        "image": ""
-      },
-      {
-        "text": "E’ impossibile rispondere",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": 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"
-  },
-  {
-    "quest": "Il seguente Resource Allocation Graph(RAG) mostra un sistema che:",
-    "answers": [
-      {
-        "text": "Sicuramente presenta deadlock",
-        "image": ""
-      },
-      {
-        "text": "Potrebbe presentare deadlock",
-        "image": ""
-      },
-      {
-        "text": "Sicuramente non presenta deadlock",
-        "image": ""
-      },
-      {
-        "text": "E’ impossibile rispondere",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": 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"
-  },
-  {
-    "quest": "Si consideri un disco magnetico composto da 100 cilindri/tracce, numerati da 0 a 99 (0 indice del cilindro/traccia più esterno/a rispetto al centro del disco), la cui testina si trova inizialmente sul cilindro 11. Si calcoli il numero di cilindri/tracce attraversate dalla testina del disco, assumendo che la sequenza di richieste: 24, 16, 77, 49, 82 venga gestita da un algoritmo di scheduling SCAN (non-ottimizzato), che la testina si stia muovendo verso l'esterno (i.e., verso i cilindri con numeri più bassi) e trascurando il tempo di rotazione.",
-    "answers": [
-      {
-        "text": "76",
-        "image": ""
-      },
-      {
-        "text": "87",
-        "image": ""
-      },
-      {
-        "text": "46",
-        "image": ""
-      },
-      {
-        "text": "93",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  }
-]
\ No newline at end of file
diff --git a/data/questions/so1_unive.json b/data/questions/so1_unive.json
deleted file mode 100644
index f39cee8..0000000
--- a/data/questions/so1_unive.json
+++ /dev/null
@@ -1,1193 +0,0 @@
-[
-  {
-    "quest": "1) La tecnica di gestione della memoria con paginazione e tabelle delle pagine multilivello porta _______ dimensione della tabella delle pagine in memoria e _____ l’overhead di memoria nelle operazioni di gestione.",
-    "answers": [
-      {
-        "text": "\"ad una riduzione della\" e \"può ridurre\"",
-        "image": ""
-      },
-      {
-        "text": "\"ad un aumento della\" e \"può aumentare\"",
-        "image": ""
-      },
-      {
-        "text": "\"ad un aumento della\" e \"può ridurre\"",
-        "image": ""
-      },
-      {
-        "text": "\"ad una riduzione della\" e \"può aumentare\"",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "2) La tecnica di gestione della memoria con paginazione basata su tabella inversa delle pagine _______ l’indirizzo della memoria secondaria e la tabella ha una dimensione pari alla cardinalità del _______.",
-    "answers": [
-      {
-        "text": "\"non memorizza\" e \"numero di pagine riferite\"",
-        "image": ""
-      },
-      {
-        "text": "\"non memorizza\" e \"numero di page frame\"",
-        "image": ""
-      },
-      {
-        "text": "\"memorizza\" e \"numero di page frame\"",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "3) La tecnica di gestione della memoria con paginazione e sostituzione delle pagine ‘Far’ sostituisce la pagina più lontana nel grafo delle pagine da _______, dove il grafo rappresenta le pagine come _______.",
-    "answers": [
-      {
-        "text": "\"l'ultitima pagina riferita\" e \"nodi\"",
-        "image": ""
-      },
-      {
-        "text": "\"qualsiasi pagina riferita\" e \"entità\"",
-        "image": ""
-      },
-      {
-        "text": "\"qualsiasi pagina riferita\" e \"nodi\"",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "4) Nella gestione della memoria basata sulla segmentazione si possono verificare errori di traduzione, nella traduzione si controllano i campi _____ e si può avere un’eccezione di _______",
-    "answers": [
-      {
-        "text": "\"bit di residenza, di protezione, di lunghezza\" e \"overflow del segmento / protezione del segmento\"",
-        "image": ""
-      },
-      {
-        "text": "\"bit di validità\" e \"validità del segmento\"",
-        "image": ""
-      },
-      {
-        "text": "\"bit di residenza, e di validitù\" e \"overflow del segmento / protezione del segmento\"",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "5) Le strategie di sostituzione di pagina globali rispetto a quelle locali _______",
-    "answers": [
-      {
-        "text": "non ignorano i comportamenti dei singoli processi.",
-        "image": ""
-      },
-      {
-        "text": "tengono conto dello stato del processo",
-        "image": ""
-      },
-      {
-        "text": "ignorano i comportamenti dei singoli processi.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "6) La strategia di sostituzione di pagina a orologio _____",
-    "answers": [
-      {
-        "text": "è una variante della strategia FIFO",
-        "image": ""
-      },
-      {
-        "text": "è una variante della strategia LIFO",
-        "image": ""
-      },
-      {
-        "text": "ignora possibili collisioni",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "7) Il modello working set nella gestione della memoria con paginazione si basa sull’osservazione della dipendenza del _______ dalla quantità di memoria per le pagine di un processo.",
-    "answers": [
-      {
-        "text": "tasso di page fault",
-        "image": ""
-      },
-      {
-        "text": "grado di multiprogrammazione",
-        "image": ""
-      },
-      {
-        "text": "tempo in cui una pagina è caricata in memoria",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "8) Il working set definisce un ______ durante l’intervallo di tempo [t – w, t]",
-    "answers": [
-      {
-        "text": "tempo limite",
-        "image": ""
-      },
-      {
-        "text": "insieme di pagine riferite",
-        "image": ""
-      },
-      {
-        "text": "approssimazione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "9) La strategia del working set unito all’algoritmo del clock per gestire i page fault si basa su una ______",
-    "answers": [
-      {
-        "text": "lista circolare",
-        "image": ""
-      },
-      {
-        "text": "coda di massima priorità",
-        "image": ""
-      },
-      {
-        "text": "lista doppiamente linkata",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "10) Nella gestione della memoria virtuale, la tecnica di traduzione dell'indirizzo da virtuale a reale si basa sulla tabella delle pagine. Per migliorare le prestazioni della traduzione in alcuni casi tale tabella _____. E Per la miglior gestione della sostituzione di pagine si usano ______",
-    "answers": [
-      {
-        "text": "\"può essere tutta o in parte inserita in memoria associativa\" e \"bit di modifica e di riferimento\"",
-        "image": ""
-      },
-      {
-        "text": "\"può essere inserita interamente in memoria secondaria\" e \"bit di modifica e di riferimento\"",
-        "image": ""
-      },
-      {
-        "text": "\"può essere tutta o in parte inserita in memoria associativa\" e \"variabili globali\"",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "11) Nella tecnica di paginazione per la gestione della memoria, la scelta della dimensione della pagina di piccole dimensioni ______ la frammentazione interna, _____ la quantità di memoria per mantenere il working set di un processo e può ______ la dimensione della tabella delle pagine.",
-    "answers": [
-      {
-        "text": "\"può ridurre\" e \"può ridurre\" e \"aumentare\"",
-        "image": ""
-      },
-      {
-        "text": "\"può ridurre\" e \"può ridurre\" e \"dimunuire\"",
-        "image": ""
-      },
-      {
-        "text": "\"può aumentare\" e \"può aumentare\" e \"aumentare\"",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "12) La tecnica di gestione basata su DMA permette di interagire con il dispositivo ______ e le interruzioni ______",
-    "answers": [
-      {
-        "text": "\"indipendentemente dalla CPU\" e \"sono ridotte\"",
-        "image": ""
-      },
-      {
-        "text": "\"tramite la CPU\" e \"sono ridotte\"",
-        "image": ""
-      },
-      {
-        "text": "\"indipendentemente dalla CPU\" e \"possono aumentare\"",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "13) In un file system quando ad un record fisico corrisponde un record logico si parla di file con ______",
-    "answers": [
-      {
-        "text": "record non bloccati",
-        "image": ""
-      },
-      {
-        "text": "indipendenza logica",
-        "image": ""
-      },
-      {
-        "text": "record bloccanti",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "14) La variazione della posizione fisica di un file rende un hard link non valido e un soft link _____ ",
-    "answers": [
-      {
-        "text": "rimane valido",
-        "image": ""
-      },
-      {
-        "text": "viene invalidato di conseguenza",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "15) In un file system la dimensione di un blocco influenza alcuni indici di prestazioni. All’aumentare della dimensione del blocco si osserva ______ e _____",
-    "answers": [
-      {
-        "text": "\"un minor spreco di spazio\" e \"un maggior spreco di tempo\"",
-        "image": ""
-      },
-      {
-        "text": "\"un minor spreco di spazio\" e \"un minor spreco di tempo\"",
-        "image": ""
-      },
-      {
-        "text": "\"un maggior spreco di spazio\" e \"un minor spreco di tempo\"",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "16) Per migliorare le prestazioni del file system con il metodo della allocazione non contigua e tabellare si usa una tabella per memorizzare i puntatori ai blocchi. La sua dimensione cresce con ______",
-    "answers": [
-      {
-        "text": "il solo aumentare dei puntatori ai blocchi",
-        "image": ""
-      },
-      {
-        "text": "il solo aumentare dei blocchi",
-        "image": ""
-      },
-      {
-        "text": "#blocchi x indirizzo di blocco",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "17) In una allocazione contigua di file su un dispositivo di memoria secondaria, considerando un disco, i record logici sono ______. Questa tecnica permette generalmente di ottenere ______ prestazioni; inoltre può dare luogo al fenomeno della ______. ",
-    "answers": [
-      {
-        "text": "\"fisicamente adiacenti\" e \"buone\" e \"frammentazione interna\"",
-        "image": ""
-      },
-      {
-        "text": "\"fisicamente adiacenti\" e \"scarse\" e \"frammentazione esterna\"",
-        "image": ""
-      },
-      {
-        "text": "\"fisicamente adiacenti\" e \"scarse\" e \"frammentazione interna\"",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "18) In un file system le tecniche di backup e recovery includono backup fisico e logico. Il backup incrementale si applica per _____",
-    "answers": [
-      {
-        "text": "il backup logico che memorizza solo i dati del file system che sono stati modificati rispetto al backup precedente",
-        "image": ""
-      },
-      {
-        "text": "dati critici o sensibili",
-        "image": ""
-      },
-      {
-        "text": "risorse limitate",
-        "image": ""
-      },
-      {
-        "text": "ripristino rapido",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "19) Per effettuare backup e recovery in file system si utilizzano principalmente tecniche di _____. Un backup incrementale si basa su ______. Un backup logico ha il vantaggio di _____.",
-    "answers": [
-      {
-        "text": "\"esportazione delle copie\" e \"memorizzazione dei dati critici o sensibili\" e \"garantire i dati in ogni copia\"",
-        "image": ""
-      },
-      {
-        "text": "\"ridondanza con copie multiple\" e \"memorizzazione dei soli dati modificati all’ultimo backup\" e \"mantenere la struttura del file system\"",
-        "image": ""
-      },
-      {
-        "text": "\"ridondanza con copie multiple\" e \"memorizzazione dei dati di tutti i backup\" e \"mantenere la struttura del file system\"",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "20) Nella gestione dei dispositivi di I/O i metodi che comprendo l’ I/O programmato con busy waiting che è caratterizzato da ______.",
-    "answers": [
-      {
-        "text": "complessità esponenziale",
-        "image": ""
-      },
-      {
-        "text": "la necessità di performance discrete",
-        "image": ""
-      },
-      {
-        "text": "semplicità",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "21) Nella gerarchia di gestione dei dispositivi di I/O i gestori degli interrupt ______ dall’utente e si trovano concettualmente ______ dei driver dei dispositivi. Un driver di dispositivo si trova tipicamente ______",
-    "answers": [
-      {
-        "text": "\"non sono visibili\" e \"sotto livello\" e \"nel nucleo\"",
-        "image": ""
-      },
-      {
-        "text": "\"non sono visibili\" e \"sotto livello\" e \"a livello applicazione\"",
-        "image": ""
-      },
-      {
-        "text": "\"visibili\" e \"sotto livello\" e \"nel nucleo\"",
-        "image": ""
-      },
-      {
-        "text": "\"visibli\" e \"sotto livello\" e \"a livello applicazione\"",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "22) I sistemi RAID per la gestione dei dischi per incrementare l’affidabilità usano un meccanismo di ______, per incrementare le prestazioni usano _____ ",
-    "answers": [
-      {
-        "text": "\"ridondanza\" e \"distribuzione e partizione sulle copie dei dischi trasparenti\"",
-        "image": ""
-      },
-      {
-        "text": "\"trasparenza\" e \"copie scalabili orizzontalmente\"",
-        "image": ""
-      },
-      {
-        "text": "\"ridondanza\" e \"copie di blocchi di memoria salvati nella memoria secondaria secondo algoritmi appositi\"",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "23) Gli algoritmo di scheduling del disco di tipo SCAN includono il C-SCAN che _____, le varianti ‘Freeze’ e ‘N-step’ che _____.",
-    "answers": [
-      {
-        "text": "\"aumenta inizialmente i tempi di risposta a favore di una maggiore organizzazione dei dati per accessi futuri\" e \"permettono di velocizzare la ricerca\"",
-        "image": ""
-      },
-      {
-        "text": "\"riduce la varianza dei tempi di risposta, a scapito del throughput e del tempo medio di risposta\" e \"prevengono l’attesa infinita / riducono la varianza dei tempi di risposta.\"",
-        "image": ""
-      },
-      {
-        "text": "\"riduce il tempo di latenza\" e \"possono portare a una lunga latenza di accesso se le richieste sono disperse su posizioni diverse del disco\"",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "24) Il primo algoritmo di scheduling SCAN ricerca del cilindro _____",
-    "answers": [
-      {
-        "text": "tempo più breve di seek in una direzione preferita",
-        "image": ""
-      },
-      {
-        "text": "tempo più lungo di seek in una direzione preferita",
-        "image": ""
-      },
-      {
-        "text": "nessuna delle precedenti",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "25) La formattazione di un disco con la tecnica dell’interleaving si applica per gestire il problema ______",
-    "answers": [
-      {
-        "text": "della deviazione del cilindro tra le tracce",
-        "image": ""
-      },
-      {
-        "text": "della fremmentazione",
-        "image": ""
-      },
-      {
-        "text": "della latenza casuale",
-        "image": ""
-      },
-      {
-        "text": "della complessità di allucazione dei file",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "26) L’algoritmo di scheduling del disco Shortest Seek Time First fornisce ______ rispetto all algoritmo FIFO. ",
-    "answers": [
-      {
-        "text": "nessuna delle altre",
-        "image": ""
-      },
-      {
-        "text": "throughput maggiore",
-        "image": ""
-      },
-      {
-        "text": "throughput minore",
-        "image": ""
-      },
-      {
-        "text": "latenza maggiore",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "27) L’algoritmo di scheduling del disco SCAN si basa su una ricerca del cilindro  ______",
-    "answers": [
-      {
-        "text": "cercando i dati più vicino alle posizioni correnti delle teste di lettura/scrittura del disco.",
-        "image": ""
-      },
-      {
-        "text": "in direzioni variabili quando si raggiunge un estremo",
-        "image": ""
-      },
-      {
-        "text": "cercando i dati prioritizzando le posizioni più distanti delle teste di lettura/scrittura del disco",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "28) Nel sistema operativo Windows, gli oggetti sono nomi di ____",
-    "answers": [
-      {
-        "text": "risorse logiche",
-        "image": ""
-      },
-      {
-        "text": "risorse fisiche",
-        "image": ""
-      },
-      {
-        "text": "collegamenti simbolici",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "29) Ogni oggetto nel sistema Windows: ",
-    "answers": [
-      {
-        "text": "condivide delle risorse con altri oggetti",
-        "image": ""
-      },
-      {
-        "text": "nessuna delle due",
-        "image": ""
-      },
-      {
-        "text": "\"può essere con o senza nome\" e \"può avere puntatori e handle (hanno significato diverso)\"",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "30) In un sistema operativo Windows le interruzioni sono organizzate in livelli di priorità che includono i livelli, nell’ordine, dal basso: _____ e i thread sono schedulati con una disciplina a _____ ",
-    "answers": [
-      {
-        "text": "\"hardware e critiche, chiamate differite, chiamate di procedura asincrone, passivo\" e \"Priority Scheduling\"",
-        "image": ""
-      },
-      {
-        "text": "\"passivo, chiamate di procedura asincrone, chiamate differite, hardware e critiche\" e \" priorità di code round robin\"",
-        "image": ""
-      },
-      {
-        "text": "\"passivo, chiamate di procedura asincrone, chiamate differite, hardware e critiche\" e \"Multilevel Feedback Queue Scheduling\"",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "31) Nel sistema operativo Windows, il nucleo NTOS include: ",
-    "answers": [
-      {
-        "text": "Le tradizionali chiamate di sistema",
-        "image": ""
-      },
-      {
-        "text": "le interfacce utente",
-        "image": ""
-      },
-      {
-        "text": "nessuna delle precedenti",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "32) Nel sistema operativo Windows, il livello Executive si trova:",
-    "answers": [
-      {
-        "text": "sopra al livello nucleo",
-        "image": ""
-      },
-      {
-        "text": "sotto al livello applicazione di sistema",
-        "image": ""
-      },
-      {
-        "text": "sotto al livello applicazioni utenti",
-        "image": ""
-      },
-      {
-        "text": "nel Kernel Executive Layer",
-        "image": ""
-      },
-      {
-        "text": "sotto al livello nucleo",
-        "image": ""
-      }
-    ],
-    "correct": 4,
-    "image": ""
-  },
-  {
-    "quest": "33) Il sistema operativo Linux ha una organizzazione della memoria basata su ______. Le tabelle delle pagine sono organizzate ______. La memoria fisica è divisa in ____",
-    "answers": [
-      {
-        "text": "\"algoritmi di scheduling\" e \"in colonne\" e \"blocchi\"",
-        "image": ""
-      },
-      {
-        "text": "\"paginazione\" e \"secondo uno schema creato al momento\"  e \"aree di utilizzo\" ",
-        "image": ""
-      },
-      {
-        "text": "\"paginazione\" e \"su tre o quattro livelli\" e \"tre zone e in pagine\"",
-        "image": ""
-      },
-      {
-        "text": "nessuna delle precedenti",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "34) Il sistema opeartivo Linux: ",
-    "answers": [
-      {
-        "text": "e di tipo microkernel ",
-        "image": ""
-      },
-      {
-        "text": "è di tipo monolitico, ma con componenti modulari",
-        "image": ""
-      },
-      {
-        "text": "ha un kernel organizzato a livelli",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "35) Nel sistema sistema opeartivo Linux con porting si intende:  ",
-    "answers": [
-      {
-        "text": "l'esportazione di componenti del kernel",
-        "image": ""
-      },
-      {
-        "text": "l'aggiunta di feature da un kernel all'altro",
-        "image": ""
-      },
-      {
-        "text": "il processo di modifica del nucleo per supportare una nuova piattaforma",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "36) [GENERAZIONI] Algoritmi di scheduling di processi basati sui quanti di tempo",
-    "answers": [
-      {
-        "text": "III Generazione",
-        "image": ""
-      },
-      {
-        "text": "IV Generazione",
-        "image": ""
-      },
-      {
-        "text": "I generazione",
-        "image": ""
-      },
-      {
-        "text": "V Generaione",
-        "image": ""
-      },
-      {
-        "text": "II Generazine",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "37) [GENERAZIONI] Definizione e uso della memoria virutale:",
-    "answers": [
-      {
-        "text": "III Generazione",
-        "image": ""
-      },
-      {
-        "text": "IV Generazione",
-        "image": ""
-      },
-      {
-        "text": "I generazione",
-        "image": ""
-      },
-      {
-        "text": "V Generaione",
-        "image": ""
-      },
-      {
-        "text": "II Generazine",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "38) [GENERAZIONI] Interfacce grafiche: IV Generazione ",
-    "answers": [
-      {
-        "text": "III Generazione",
-        "image": ""
-      },
-      {
-        "text": "IV Generazione",
-        "image": ""
-      },
-      {
-        "text": "I generazione",
-        "image": ""
-      },
-      {
-        "text": "V Generaione",
-        "image": ""
-      },
-      {
-        "text": "II Generazine",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "39) Quale di queste istruzioni dovrebbe essere consentità solo in modalità nucleo disabilitare gli interrupt: _____; leggere il dispositivo che calcola l’ora corrente: _____; impostare il dispositivo che calcola l’ora corrente: ______;",
-    "answers": [
-      {
-        "text": "\"anche utente\" e \"anche utente\" e \"solo nucleo\"",
-        "image": ""
-      },
-      {
-        "text": "\"solo nucleo\" e \"solo nucleo\" e \"solo nucleo\"",
-        "image": ""
-      },
-      {
-        "text": "\"solo utente\" e \"solo nucleo\" e \"anche utente\"",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "40) [architetture ideali] efficienza e prestazioni:",
-    "answers": [
-      {
-        "text": "monolitico",
-        "image": ""
-      },
-      {
-        "text": "microkernel",
-        "image": ""
-      },
-      {
-        "text": "a livelli",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "41) [architetture ideali] flessibilità e modificabilità:",
-    "answers": [
-      {
-        "text": "monolitico",
-        "image": ""
-      },
-      {
-        "text": "microkernel",
-        "image": ""
-      },
-      {
-        "text": "a livelli",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "42) [architetture ideali] isolamento delle funzioni:",
-    "answers": [
-      {
-        "text": "monolitico",
-        "image": ""
-      },
-      {
-        "text": "microkernel",
-        "image": ""
-      },
-      {
-        "text": "a livelli",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "43) In un file system che contiene un insieme di record, un record fisico corrisponde a un record logico se si parla di file con :",
-    "answers": [
-      {
-        "text": "record definiti",
-        "image": ""
-      },
-      {
-        "text": "record non bloccati ",
-        "image": ""
-      },
-      {
-        "text": "record bloccati ",
-        "image": ""
-      },
-      {
-        "text": "record di dimensioni variabili",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "44) L'uso di link in un file system permette di creare dei collegamenti ai file. Se un file viene ritoccato fisicamente nel sistema, un eventuale hard link al file ______ e un eventuale soft link ______ .",
-    "answers": [
-      {
-        "text": "\"rimane valido\" e \"diventa non più valido\"",
-        "image": ""
-      },
-      {
-        "text": "\"rimane valido\" e \"diventa valido\"",
-        "image": ""
-      },
-      {
-        "text": "\"diventa non più valido\" e \"rimane valido\"",
-        "image": ""
-      },
-      {
-        "text": "\"viene collegato ad un diverso file\" e \"è indefinito\"",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "45) Considerare un sistema operativo della IV generazione, include la caratteristica \"sviluppo di interfacce grafiche\"?",
-    "answers": [
-      {
-        "text": "no, nella V generazione",
-        "image": ""
-      },
-      {
-        "text": "no, nella III generazione",
-        "image": ""
-      },
-      {
-        "text": "no, nella II generazione",
-        "image": ""
-      },
-      {
-        "text": "sì, nella IV generazione",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "46) Considerare un sistema operativo della IV generazione, include la caratteristica \"gestione vincoli real-time\"?",
-    "answers": [
-      {
-        "text": "no, nella V generazione",
-        "image": ""
-      },
-      {
-        "text": "no, nella III generazione",
-        "image": ""
-      },
-      {
-        "text": "no, nella II generazione",
-        "image": ""
-      },
-      {
-        "text": "sì, nella IV generazione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "47) Considerare un sistema operativo della IV generazione, include la caratteristica \"memoria virtuale\"?",
-    "answers": [
-      {
-        "text": "no, nella V generazione",
-        "image": ""
-      },
-      {
-        "text": "no, nella III generazione",
-        "image": ""
-      },
-      {
-        "text": "no, nella II generazione",
-        "image": ""
-      },
-      {
-        "text": "sì, nella IV generazione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "48) Considerare un sistema operativo della IV generazione, include la caratteristica \"scheduling di processi basato sul tempo\"?",
-    "answers": [
-      {
-        "text": "no, nella V generazione",
-        "image": ""
-      },
-      {
-        "text": "no, nella III generazione",
-        "image": ""
-      },
-      {
-        "text": "no, nella II generazione",
-        "image": ""
-      },
-      {
-        "text": "sì, nella IV generazione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "49) Considerare un sistema operativo per un moderno sistema di elaborazione con connessione ad una rete. Il sistema operativo: ",
-    "answers": [
-      {
-        "text": "aumenta il livello di comunicazione fra moduli",
-        "image": ""
-      },
-      {
-        "text": "usa un approccio a livelli",
-        "image": ""
-      },
-      {
-        "text": "supporta funzioni per connessione alla rete",
-        "image": ""
-      },
-      {
-        "text": "nessuna delle precedenti",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "50) Considerare un sistema operativo per un moderno sistema di elaborazione con connessione ad una rete. Il sistema operativo: ",
-    "answers": [
-      {
-        "text": "aumenta il livello di comunicazione fra moduli",
-        "image": ""
-      },
-      {
-        "text": "gestisce un sistema connesso alla rete",
-        "image": ""
-      },
-      {
-        "text": "usa un approccio a livelli",
-        "image": ""
-      },
-      {
-        "text": "gestisce un singolo client connesso alla rete",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "51) I sistemi time-sharing e i sistemi con multiprogrammazione in che relazione sono fra loro?",
-    "answers": [
-      {
-        "text": "il sistema time-sharing prevede anche multiprogrammazione",
-        "image": ""
-      },
-      {
-        "text": "il sistema time-sharing dipende dal livello di multiprogrammazione",
-        "image": ""
-      },
-      {
-        "text": "il sistema con multiprogrammazione può comportarsi come un sistema time-sharing ",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "52) Considerando gli stati di un processo, se un processo è dispatched se:",
-    "answers": [
-      {
-        "text": "passa dallo stato \"Esecuzione\" allo stato \"Pronto\"",
-        "image": ""
-      },
-      {
-        "text": "passa dallo stato \"Esecuzione\" allo stato \"Bloccato\"",
-        "image": ""
-      },
-      {
-        "text": "passa dallo stato \"Bloccato\" allo stato \"Pronto\"",
-        "image": ""
-      },
-      {
-        "text": "passa dallo stato \"Pronto\" allo stato \"Esecuzione\"",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "52) Considerando gli stati di un processo, un processo che riceve una notifica attesa di un evento:",
-    "answers": [
-      {
-        "text": "passa dallo stato \"Esecuzione\" allo stato \"Pronto\"",
-        "image": ""
-      },
-      {
-        "text": "passa dallo stato \"Esecuzione\" allo stato \"Bloccato\"",
-        "image": ""
-      },
-      {
-        "text": "passa dallo stato \"Bloccato\" allo stato \"Pronto\"",
-        "image": ""
-      },
-      {
-        "text": "passa dallo stato \"Pronto\" allo stato \"Esecuzione\"",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "53) Considerando gli stati di un processo, se il processo si blocca in attesa di un evento esterno:",
-    "answers": [
-      {
-        "text": "passa dallo stato \"Esecuzione\" allo stato \"Pronto\"",
-        "image": ""
-      },
-      {
-        "text": "passa dallo stato \"Esecuzione\" allo stato \"Bloccato\"",
-        "image": ""
-      },
-      {
-        "text": "passa dallo stato \"Bloccato\" allo stato \"Pronto\"",
-        "image": ""
-      },
-      {
-        "text": "passa dallo stato \"Pronto\" allo stato \"Esecuzione\"",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "54) In una allocazione contigua di file su un dispositivo di memoria secondaria, considerando un disco, i record logici sono _____ .",
-    "answers": [
-      {
-        "text": "dipendenti l'uno dall'altro ",
-        "image": ""
-      },
-      {
-        "text": "anche fisici",
-        "image": ""
-      },
-      {
-        "text": "fisicamente adiacenti",
-        "image": ""
-      },
-      {
-        "text": "fisicamente lontani l'uno dall'altro",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "55) L'allocazione contigua di file su un dispositivo di memoria secondaria, considerando un disco, permette generalmente: ",
-    "answers": [
-      {
-        "text": "di ottenere ottime prestazioni",
-        "image": ""
-      },
-      {
-        "text": "frammentazione interna ",
-        "image": ""
-      },
-      {
-        "text": "di ottenere scarse prestazioni",
-        "image": ""
-      },
-      {
-        "text": "nessuna delle precedenti",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "55) L'allocazione contigua di file su un dispositivo di memoria secondaria, considerando un disco, permette generalmente: ",
-    "answers": [
-      {
-        "text": "di ottenere ottime prestazioni",
-        "image": ""
-      },
-      {
-        "text": "frammentazione interna ",
-        "image": ""
-      },
-      {
-        "text": "frammentazione esterna",
-        "image": ""
-      },
-      {
-        "text": "nessuna delle precedenti",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  }
-]
\ No newline at end of file
diff --git a/data/questions/so2.json b/data/questions/so2.json
deleted file mode 100644
index 1a96255..0000000
--- a/data/questions/so2.json
+++ /dev/null
@@ -1,2896 +0,0 @@
-[
-  {
-    "quest": "1. A quanti gruppi può appartenere un utente nel SO Linux?",
-    "answers": [
-      {
-        "text": "Ad almeno un gruppo",
-        "image": ""
-      },
-      {
-        "text": "Ad un solo gruppo",
-        "image": ""
-      },
-      {
-        "text": "A zero o più gruppi",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "2. Si supponga che nel sistema esiste un gruppo \"studente\" ed anche l'utente \"utente1\".\nSi supponga quindi di eseguire il comando <code>adduser utente1 studente</code>.\nQuale delle seguenti affermazioni è sbagliata?",
-    "answers": [
-      {
-        "text": "Il comando genera un errore perché per aggiungere un utente ad un gruppo si può utilizzare solo il comando addgroup ",
-        "image": ""
-      },
-      {
-        "text": "Se \"utente1\" non appartiene al gruppo \"studente\" lo aggiunge a tale gruppo altrimenti non lo aggiunge",
-        "image": ""
-      },
-      {
-        "text": "Aggiunge utente1 al gruppo studente oppure genera un messaggio del tipo L'utente «utente1» fa già parte del gruppo «studente»",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "3. Si supponga che nel sistema esiste un gruppo \"studente\" e non esista ancora l'utente \"utente1\".\nSi supponga quindi di eseguire il comando <code>sudo adduser utente1 studente</code>\nQuale sarà il risultato?",
-    "answers": [
-      {
-        "text": "Da errore perché utente1 non esiste",
-        "image": ""
-      },
-      {
-        "text": "Crea utente1 e, oltre a creare il gruppo utente1 lo aggiunge al gruppo studente",
-        "image": ""
-      },
-      {
-        "text": "Crea utente1, lo aggiunge al gruppo studente e non crea il gruppo utente1",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "4. Supponga di eseguire, come utente sudoer, i seguenti comandi: C1) sudo ls /home, C2) sudo su --command=’ls /homè. Quale affermazioneè corretta?",
-    "answers": [
-      {
-        "text": "C2 da errore \"comando non trovato\"",
-        "image": ""
-      },
-      {
-        "text": "C1 e C2 sono equivalenti",
-        "image": ""
-      },
-      {
-        "text": "C2 esegue una setUID mentre C1 no",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "5. Quale è la differenza tra i comandi sudo e su",
-    "answers": [
-      {
-        "text": "sudo è un comando che permette di eseguire altri comandi come root; su è una scorciatoia per invocare il comando sudo",
-        "image": ""
-      },
-      {
-        "text": "su è un comando che permette di cambiare utente. sudo è un camando che permette di eseguire altri comandi come super-utente ",
-        "image": ""
-      },
-      {
-        "text": "sudo si riferisce ad un gruppo di utenti. su è invece un comando che permette di cambiare utente",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "6. Di quante sezioni è composto il man di Linux?",
-    "answers": [
-      {
-        "text": "5",
-        "image": ""
-      },
-      {
-        "text": "7",
-        "image": ""
-      },
-      {
-        "text": "9",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "7. Supponga di voler creare un file vuoto e di voler settare il tempo di ultimo accesso al \"2 giugno 2020 ore 12:00\". Quale dei seguenti comandi è corretto?",
-    "answers": [
-      {
-        "text": "touch -at202006021200 filename",
-        "image": ""
-      },
-      {
-        "text": "touch -cat202006021200 filename",
-        "image": ""
-      },
-      {
-        "text": "touch -ct202006021200 filename",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "8. Quale è il risultato del comando touch nomefile?",
-    "answers": [
-      {
-        "text": "Crea un file vuoto con nome nomefile",
-        "image": ""
-      },
-      {
-        "text": "Aggiorna, al tempo corrente, gli atttributi atime e mtime di nomefile ",
-        "image": ""
-      },
-      {
-        "text": "Crea un file vuoto con nome nomefile e ctime uguale al tempo corrente. Se si usa l'opzione -t o -d si può specificare un altro tempo di creazione ",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "9. I premessi di acceesso della directory /tmp sono <code>1777/drwxrwxrwt</code>\nCosa significa?",
-    "answers": [
-      {
-        "text": "Il bit SetGid è settato",
-        "image": ""
-      },
-      {
-        "text": "Lo sticky bit non è settatto",
-        "image": ""
-      },
-      {
-        "text": "Lo sticky bit è settato",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "10. Supponga di voler mostrare l’albero delle directory con radice dir1 e con profondità 3.\nQuale tra i seguenti comandi è il più apprropriato usare?(uscito 2 volte)",
-    "answers": [
-      {
-        "text": "tree -d 3 dir1",
-        "image": ""
-      },
-      {
-        "text": "tree -L 3 dir1",
-        "image": ""
-      },
-      {
-        "text": "tree --max-depth=3 dir1",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "11. Supponiamo vogliate visualizzare l’albero delle directory con radice nella vostra home. In particolare volete visualizzare solo le directory e non i file in esse contenuti.\nQuali tra i seguenti comandi è il più appropriato?",
-    "answers": [
-      {
-        "text": "tree -d ~",
-        "image": ""
-      },
-      {
-        "text": "tree -d -L 3 /home/myhomedir",
-        "image": ""
-      },
-      {
-        "text": "tree -a ~",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "12. Si supponga di avere un file di testo (filein) e di voler copiare in un altro file (fileout) i primi 100 caratteri. Quale di questi comandi è corretto?",
-    "answers": [
-      {
-        "text": "dd if=filein of=fileout bs=100 count=1",
-        "image": ""
-      },
-      {
-        "text": "dd if=filein of=fileout bs=1 skip=1 count=100",
-        "image": ""
-      },
-      {
-        "text": "dd if=filein of=fileout bs=10 skip=10 count=10",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "13. Si supponga di avere un file di testo (filein) contenente 1000 caratteri e di voler copiare in un altro file (fileout) 100 caratteri a partire dal decimo. Quale di questi comandi non produce il risultato atteso?",
-    "answers": [
-      {
-        "text": "dd if=filein of=fileout bs=1 skip=10 count=100",
-        "image": ""
-      },
-      {
-        "text": "dd if=filein of=fileout bs=100 seek=10 count=1",
-        "image": ""
-      },
-      {
-        "text": "dd if=filein of=fileout bs=10 skip=1 count=10",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "14. Quanti job in background crea il comando seguente?\n<code>sleep 30 | sleep 15 | sleep 10 &</code> ",
-    "answers": [
-      {
-        "text": "1",
-        "image": ""
-      },
-      {
-        "text": "Nessuno, da errore",
-        "image": ""
-      },
-      {
-        "text": "3",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "15. Quanti file system principali ha linux?",
-    "answers": [
-      {
-        "text": "dipende dal numero di filesystem mondati al boot",
-        "image": ""
-      },
-      {
-        "text": "1",
-        "image": ""
-      },
-      {
-        "text": "dipende dal numero di dischi installati",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "16. In che file è contenuta la lista dei filesystem montati al boot?",
-    "answers": [
-      {
-        "text": "/etc/mdev",
-        "image": ""
-      },
-      {
-        "text": "/etc/mtab",
-        "image": ""
-      },
-      {
-        "text": "/etc/fstab",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "17. perché il comando passwd (ovvero il file eseguibile /usr/bin/passwd) ha il SetUID bit settato?",
-    "answers": [
-      {
-        "text": "Per consentire a qualsiasi utente di modificare la propria password",
-        "image": ""
-      },
-      {
-        "text": "Per evitare che un utente possa cancellare il file eseguibile passwd",
-        "image": ""
-      },
-      {
-        "text": "Per evitare che un utente possa modificare le password degli altri utenti",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "18. Supponiamo di avere il seguente makefile (memorizzato in un file di nome makefile):\n<pre>merge_sorted_lists: merge_sorted_lists.c\ngcc -Wall -Wextra -O3 merge_sorted_lists.c \\\n-o merge_sorted_lists\nsort_file_int: sort_file_int.c\ngcc -Wall -Wextra -O3 sort_file_int.c \\\n-o sort_file_int\n.PHONY: clean\nclean:\nrm -f *.o merge_sorted_lists</pre>\nsupponendo che non esistono entrambi i file merge_sorted_lists e sort_file_int e lanciando il comando make, quale target viene eseguito?\n<b>Adesso posso scrivere in bold con l'HTML nelle domande yeee</b>",
-    "answers": [
-      {
-        "text": "merge_sorted_list",
-        "image": ""
-      },
-      {
-        "text": "entrambi",
-        "image": ""
-      },
-      {
-        "text": "nessuno dei due. Va specificato quale vogliamo eseguire con il comando make <nome_target>",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "19.\tAssumiamo di compilare un file .c nei seguenti modi\n<pre>gcc file.c -o file1.o\ngcc -g file.c -o file2.o\n</pre>\nperché le dimensioni di file2.o sono diverse da quelle di file1.o?",
-    "answers": [
-      {
-        "text": "perché file2.o è stato ottimizzato, per occupare meno spazio in memoria, rispetto a file1.o",
-        "image": ""
-      },
-      {
-        "text": "perché file2.o contiene informazioni aggiuntive rispetto a file1.o utili per il debug",
-        "image": ""
-      },
-      {
-        "text": "non è vero che i due comandi di compilazione producono file di dimensioni diverse",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "20.\tAssuma di avere due shell aperte, etichettate come shell_1 e shell_2 e supponga di eseguire la sequenza di comandi che segue\n(shell_i: cmd indica che cmd è eseguitto nella shell_i, i=1,2).\n<pre>shell_1: xterm\nshell_2: ps -C xterm\n#restituisce xtermPID\nshell_2: kill -s SIGSTOP xtermPID\nshell_2: kill -s SIGCONT xtermPID</pre>\nQuale è il loro effetto su processo xterm?\n<code>\n(NOTA BENE: la risposta 3 viene data come corretta all'esame, anche se errata)\n</code>",
-    "answers": [
-      {
-        "text": "Il processo xterm viene prima mandato in esecuzione in background e poi riportato in foreground",
-        "image": ""
-      },
-      {
-        "text": "Il processo xterm viene mandato in esecuzione in background ",
-        "image": ""
-      },
-      {
-        "text": "Il processo xterm viene prima portato nello stato stopped (T) e poi mandato in esecuzione in foreground",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "21.\tSi assuma di avere due shell aperte, etichettate come shell_1 e shell_2 e si consideri la seguente sequenza di comandi\n(shell_i:cmd indica che cmd è eseguitto nella shell i, i=1,2)\n<pre>shell_1: xterm\nshell_2: ps -C xterm\n#restituisce xtermPID\nshell_2: kill -s SIGSTOP xtermPID</pre>\nQuale è il loro effetto?",
-    "answers": [
-      {
-        "text": "Il processo xterm viene terminato con segnale SIGSTOP",
-        "image": ""
-      },
-      {
-        "text": "Il processo xterm viene mandato in esecuzione in background",
-        "image": ""
-      },
-      {
-        "text": "Il processo xterm viene messo in stato stopped (T)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "22.\tSupponga di avere 2 file hw1.c e hw2.c contenenti il seguente codice(uscita 2 volte)\nhw1.c:\n<pre>#include <stdio.h>\n#include \"hw2.c\"\nint f(int argc, char *args[]) {\n  printf(\"Hello World!\\n\");\n  return 256;\n}\n</pre>\nhw2.c:<pre>\nint f(int argc, char *args[]);\nint main(int argc, char *args[]) {\n  return f(argc, args);\n}\n</pre>\nQuale dei seguenti comandi di compilazione genera errore?",
-    "answers": [
-      {
-        "text": "gcc -Wall hw1.c -o hw.out",
-        "image": ""
-      },
-      {
-        "text": "gcc -Wall hw1.c hw2.c -o hw.out",
-        "image": ""
-      },
-      {
-        "text": "gcc hw1.c",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "23.\tSupponiamo di avere il file eseguibile (ottenuto dalla compilazione di una programma C) mioprogramma\nQuesti due modi di invocare il programma sono equivalenti?\n<code>$ ./mioprogramma A B C</code>\n<code>$ ./mioprogramma < input.txt</code>\ndove input.txt contiene A B C",
-    "answers": [
-      {
-        "text": "no, nel primo caso A B C vengono caricati in argv, nel secondo caso vengono inviati sullo stdin",
-        "image": ""
-      },
-      {
-        "text": "dipende dalla logica del codice",
-        "image": ""
-      },
-      {
-        "text": "si sono equivalenti",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "24.\tQuale è la differenza tra thread posix e processo linux (uscito 2 volte)",
-    "answers": [
-      {
-        "text": "Thread concorrenti condividono codice, segmento dati e file; i processi concorrenti pure",
-        "image": ""
-      },
-      {
-        "text": "Thread concorrenti condividono lo stack; i processi concorrenti anche",
-        "image": ""
-      },
-      {
-        "text": "Thread concorrenti condividono codice, segmento dati e file; i processi concorrenti no",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "25.\tPer mostare il pid dei job in esecuzione in backgroud quali di questi comandi è corretto?",
-    "answers": [
-      {
-        "text": "jobs -p",
-        "image": ""
-      },
-      {
-        "text": "ps -p -u",
-        "image": ""
-      },
-      {
-        "text": "jobs",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "26. Quale di queste stringhe non è valida come identificatore in C?",
-    "answers": [
-      {
-        "text": "_voltage",
-        "image": ""
-      },
-      {
-        "text": "rerun",
-        "image": ""
-      },
-      {
-        "text": "x-axis",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "27. Quale di queste stringe è valida come identificatore in C?",
-    "answers": [
-      {
-        "text": "_voltage",
-        "image": ""
-      },
-      {
-        "text": "x-ray",
-        "image": ""
-      },
-      {
-        "text": "return",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "28. Si consideri la seguente funzione  f\n<pre>char *f(char *a, const char *b, size_t n) {\n    size_t i;\n    for (i = 0; i < n && b[i] != '\\0'; i++)\n        a[i] = b[i];\n    for ( ; i < n; i++)\n           a[i] = '\\0';\n        return a;\n}</pre>\nCosa  produce come risultato quando eseguita?",
-    "answers": [
-      {
-        "text": "Copia esattamente n caratteri della stringa b nella stringa a e restituisce a",
-        "image": ""
-      },
-      {
-        "text": "Concatena al piò n caratteri della stringa  b alla stringa a e restituisce a",
-        "image": ""
-      },
-      {
-        "text": "Copia al piò n caratteri della stringa b nella stringa a e restituisce a",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "29. Si consideri la seguente funzione  f\n<pre>char *f(char *a, const char *b, size_t n) {\n    size_t l = strlen(a);\n    size_t i;\n    for (i = 0 ; i < n && b[i] != '\\0' ; i++)\n        a[l + i] = b[i];\n    a[l + i] = '\\0';\nreturn a;\n}</pre>\nCosa  produce come risultato quando eseguita?",
-    "answers": [
-      {
-        "text": "Copia al piò n caratteri della stringa b in a e restituisce a",
-        "image": ""
-      },
-      {
-        "text": "Copia esattamente n caratteri della stringa b nella stringa a e restituisce a",
-        "image": ""
-      },
-      {
-        "text": "Concatena i primi n caratteri della stringa b alla stringa  a  e restituisce a",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "30. Si consideri la seguente dichiarazione di struttura\n<pre>struct point2D {\n    double x; // coordinata x\n    double y; // coordinata y\n}  pA={0, 0}, pB={1, 5};</pre>\nQuale delle seguenti assegnazioni è corretta?",
-    "answers": [
-      {
-        "text": "pA -> x = pB -> x; pA -> y = pB -> y;",
-        "image": ""
-      },
-      {
-        "text": "pA = &pB",
-        "image": ""
-      },
-      {
-        "text": "pA = pB;",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "31. Si consideri il seguente ciclo for\n<pre>int scoreCount, a;\nfor(scoreCount=0; scanf(\"%d\",&a)==1; scoreCount++);</pre>\nCosa produrebbe come risultato, se eseguito?",
-    "answers": [
-      {
-        "text": "Legge una sola volta da stdin e  poi termina, qualunque sia l'input",
-        "image": ""
-      },
-      {
-        "text": "Legge  da stdin senza mai terminare",
-        "image": ""
-      },
-      {
-        "text": "Legge ripetutamente numeri interi da stdin fintanto che è fornito un input di tipo diverso (ad esempio un carattere)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "32. Consideri  il seguente frammento  di codice\n<pre>int *ptr = malloc(sizeof(int));\nptr = ptr+1;</pre>\nassumendo la malloc assegni a ptr la locazione di memoria 0x55c2b1268420 cosa contiene ptr dopo l’incremento?",
-    "answers": [
-      {
-        "text": "<code>0x55c2b1268421</code>",
-        "image": ""
-      },
-      {
-        "text": "l'incremento della variabile prt genera un errore di segmentazione in fase di esecuzione",
-        "image": ""
-      },
-      {
-        "text": "<code>0x55c2b1268424</code>",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "33. Cosa  stampa su stdout la seguente chiamata a printf? \n<code>printf(\"aaaaa\\nbbbbb\\f\\rccccc\\r\\fddddd\\reeeee\\n\");</code>",
-    "answers": [
-      {
-        "text": "aaaaa bbbbb ccccc eeeee",
-        "image": ""
-      },
-      {
-        "text": "aaaaa bbbbb ccccc ddddd",
-        "image": ""
-      },
-      {
-        "text": "aaaaa bbbbb ccccc ddddd eeeee",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "34. Si consideri il seguente frammento di codice\n<pre>char **mptr, **mptr1, *ptr1;\nint i;\nmptr = calloc(10,sizeof(char *));\nmptr1 = mptr;\nfor(i=0;i<10;i++){\n    mptr[i]=(char *)malloc(10);    \n}</pre>\nPer de-allocare tutta la memoria allocata, quale delle seguenti opzioni è coretta?",
-    "answers": [
-      {
-        "text": "for(i=0;i<10;i++) free(mptr1[i]);",
-        "image": ""
-      },
-      {
-        "text": "for(i=0;i<10;i++) free(mptr1[i]); free(mptr1);",
-        "image": ""
-      },
-      {
-        "text": "free(mptr1);",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "35. Si consideri il seguente frammento di codice\n<pre>char **mptr, *ptr1;\nint i;\nmptr = calloc(10,sizeof(char *));\nfor(i=0;i<10;i++){\n    mptr[i]=(char *)malloc(10);    \n}</pre>\nQuale delle seguenti strategie di de-allocazione crea un memory leakage?",
-    "answers": [
-      {
-        "text": "free(mptr);",
-        "image": ""
-      },
-      {
-        "text": "for(i=0;i<10;i++) free(mptr[i]);",
-        "image": ""
-      },
-      {
-        "text": "entrambe, ovvero sia (1) che (2)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "36. Si consideri un file contenente un programma in linguaggio C. Si assuma che è stata inserita la direttiva #include \"stdio.h\" . perché la compilazione potrebbe generare errori?",
-    "answers": [
-      {
-        "text": "perché cerca il file \"stdio.h\" nella directory corrente",
-        "image": ""
-      },
-      {
-        "text": "La compilazione non genera errori a meno che il file non esista nel filesystem",
-        "image": ""
-      },
-      {
-        "text": "perché il file stdio.h potrebbe non esistere",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "37. Quale delle seguenti dichiarazioni di variabile inizializza una stringa?",
-    "answers": [
-      {
-        "text": "<code>char r[10] = {`L´,`9´,` ´,`4´,`a´,`p`,`r´};</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>char r[] = ``L9 4apr´´;</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>char r[] = {`L´,`9´,` ´,`4´,`a´,`p`,`r´};</code> ",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "39. Si consideri il seguente frammento di codice\n<pre>\nFILE * pFile;\npFile = open(\"myfile.txt\",\"rw+\");\nfprintf(pFile, \"%f %s\", 3.1416, \"PI\");\n</pre>\nAssumendo che myfile.txt non esiste, quale delle seguenti affermazioni è vera?",
-    "answers": [
-      {
-        "text": "Il programma genera  un errore in fase di esecuzione",
-        "image": ""
-      },
-      {
-        "text": "Il programma genera errore in fase di compilazione",
-        "image": ""
-      },
-      {
-        "text": "Il programma scrive sul file myfile.txt la stringa 3.1416 PI",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "40. Cosa fa il seguente segmento di codice se eseguito?\n<pre>scanf(“%d\",&num); \ndo; {\nprintf(“%d\\n\",num); \nscanf(“%d\",&num);\n}  while(num!=0);</pre>",
-    "answers": [
-      {
-        "text": "Stampa il valore di num almeno una  volta",
-        "image": ""
-      },
-      {
-        "text": "Cicla infinitamente se num è diverso da 0",
-        "image": ""
-      },
-      {
-        "text": "Popipopi S.p.A. > CD Click s.r.l.",
-        "image": ""
-      },
-      {
-        "text": "Genera errore  in fase di compilazione",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "41. Si consideri il frammento di codice\n<pre>i=0; c=0; p=1;\nwhile (i++ < 10)\nc=c+1;\np--;</pre>\nche valore conterrà p al termine dell'esecuzione del frammento di codice?",
-    "answers": [
-      {
-        "text": "0",
-        "image": ""
-      },
-      {
-        "text": "-10",
-        "image": ""
-      },
-      {
-        "text": "-9",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "42. Supponiamo di eseguire  separatamente i seguenti frammenti di codice\n<b>Frammento_1</b>\n<pre>close(2);\nif (fopen(\".\",\"r\")) {\n           perror(\"main\");\n}</pre>\n<b>Frammento_2</b>\n<pre>close(2);\nif (fopen(\".\",\"r\")) {\n               printf(\"main: %s \\n\", strerror(errno));\n}</pre>\nQuale delle seguenti affermazioni è falsa?",
-    "answers": [
-      {
-        "text": "Il frammento_1 non produce alcun output sul terminale",
-        "image": ""
-      },
-      {
-        "text": "La loro esecuzione produce sul terminale due stringhe identiche",
-        "image": ""
-      },
-      {
-        "text": "Il frammento_2 produce un output sullo stdout",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "43. Consideriamo queste due line di codice\n1. <code>printf(\"main:%s\\n\",strerror(errno));</code>\n2. <code>perror(\"main\");</code>\nQuali delle seguenti affermazioni è corretta?\n<code>\n(NOTA BENE: la risposta 1 viene data come corretta all'esame, anche se in realtà differiscono di uno spazio)\n</code>",
-    "answers": [
-      {
-        "text": "Producono stringhe diverse e la prima la invia su stdout mentre la seconda su stderr.",
-        "image": ""
-      },
-      {
-        "text": "Inviano la stessa stringa su stdout",
-        "image": ""
-      },
-      {
-        "text": "producono la stessa stringa ma la 1 la invia su stdout, mentre la 2 su stderr ",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "44. Quale delle seguenti funzioni di libreria alloca memoria nello stack?",
-    "answers": [
-      {
-        "text": "void *calloc( size_t nmemb, size_t size );",
-        "image": ""
-      },
-      {
-        "text": "void *alloca( size_t size );",
-        "image": ""
-      },
-      {
-        "text": "void *malloc( size_t size );",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "45. Un processo può allocare memoria nello stack?",
-    "answers": [
-      {
-        "text": "no un processo può allocare memoria sono nell'heap",
-        "image": ""
-      },
-      {
-        "text": "si mediante la funziona di libreria malloc(3)",
-        "image": ""
-      },
-      {
-        "text": "si mediante la funzione di libreria alloca(3) ",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "46. Quale è la differenza tra la system call _exit(2) e la funzione di libreria exit(3)? (uscita 2 volte) ",
-    "answers": [
-      {
-        "text": "_exit(2) chiude tutti i file descriptor mentre exit(3) no",
-        "image": ""
-      },
-      {
-        "text": "_exit(2) non invoca gli handler registrati con  atexit e on_exit mentre exit(3) li invoca",
-        "image": ""
-      },
-      {
-        "text": "_exit(2) invoca gli handler registrati con atexit e on_exit mentre exit(3) non li invoca",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "47. Quale attributi di un processo sono ereditati dal processo figlio?",
-    "answers": [
-      {
-        "text": "parent pid, timer, contatori risorse ",
-        "image": ""
-      },
-      {
-        "text": "working directory, descrittori dei file, memoria condivisa",
-        "image": ""
-      },
-      {
-        "text": "timer, lock, coda dei segnali",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "48. Si consideri il seguente frammento di codice\n<pre>pid_t pID = fork();\nif (pID == 0) {\n    Blocco_1\n} else if (pID < 0) {\n    Blocco_2\n} else {\n  Blocco_3\n}</pre>\nQuale blocco di codice (tra Bloccco_1,  Blocco_2 e  Blocco_3) verrà eseguito dal processo figlio?",
-    "answers": [
-      {
-        "text": "Blocco_3",
-        "image": ""
-      },
-      {
-        "text": "Blocco_1",
-        "image": ""
-      },
-      {
-        "text": "Blocco_2",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "49. Si consideri il seguente frammento di codice\n<pre>pid_t pID = fork();\nif (pID == 0) {\n    Blocco_1\n} else if (pID < 0) {\n    Blocco_2\n} else {\n  Blocco_3\n}</pre>\nQuale blocco di codice (tra Bloccco_1,  Blocco_2 e  Blocco_3) verrà eseguito dal processo padre?",
-    "answers": [
-      {
-        "text": "Blocco_3",
-        "image": ""
-      },
-      {
-        "text": "Blocco_1",
-        "image": ""
-      },
-      {
-        "text": "Blocco_2",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "50. Supponiamo che la system call\n<code>pid_t waitpid(pid_t pid, int *status, int options);</code>\nsia invocata con valore di pid uguale a 0. Quale è il suo comportamento?\nScegli un'alternativa:",
-    "answers": [
-      {
-        "text": "attende la terminazione di qualunque processo figlio il cui gruppo ID del processo sia diverso da quello del processo chiamante",
-        "image": ""
-      },
-      {
-        "text": "attende la terminazione di qualunque processo figlio il cui gruppo ID  sia uguale a quello del processo chiamante (ovvero il processo padre)",
-        "image": ""
-      },
-      {
-        "text": "attende la terminazione di qualunque processo figlio",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "51. Si consideri  il seguente frammento  di codice  (i numeri a lato sono i numeri di riga delle istruzioni)(uscita 2 volte)\n<pre>1.    Pthread_t tid;\n2.    pthread_create(&tid, ... )\n3.    pthread_create(&tid, ...)\n4.    pthread_join(tid, ...);\n5.    printf(\"joined\");</pre>\nquale  delle seguenti affermazioni è falsa?",
-    "answers": [
-      {
-        "text": "la stringa \"joined\" è inviata su stdout solo quando  il thread creato a riga 3 è terminato",
-        "image": ""
-      },
-      {
-        "text": "la stringa \"joined\" è inviata su stdout quando entrambi i thread sono terminati",
-        "image": ""
-      },
-      {
-        "text": "la chiamata pthread_join(...) attende la terminazione del thread con identificatore tid",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "52. Si considerino i seguenti frammenti di codice  (R1 e R2)\n<pre>R1: strPtr=(char *) calloc(SIZE_OF_ARRAY, sizeof(char) );\nR2: strPtr=(char *) malloc(SIZE_OF_ARRAY);\n    memset(strPtr, ´\\0´, SIZE_OF_ARRAY);</pre>",
-    "answers": [
-      {
-        "text": "R1 e R2 producono lo stesso risultato",
-        "image": ""
-      },
-      {
-        "text": "R2 dopo aver allocato la memoria la inizializza, mentre R1 no",
-        "image": ""
-      },
-      {
-        "text": "R1 alloca nell’heap, e quindi dopo è consigliabile “pulire\" la memoria; mentre R2 alloca nello stack e  quindi non c’è bisogno di “pulire\" la memoria.",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "53. Consideriamo la seguente invocazione della funzione realloc\n<code>strptr1=(char *) realloc(strptr, 10 * SIZE_OF_ARRAY);</code>\nstrptr1 può essere diverso da strptr?",
-    "answers": [
-      {
-        "text": "si, la realloc modifica sempre l'indirizzo di partenza dell'area di memoria ridimensionata",
-        "image": ""
-      },
-      {
-        "text": "no, strptr1 è sempre uguale a strptr",
-        "image": ""
-      },
-      {
-        "text": "sì se a seguito del ridimensionamento della memoria allocata non è possibile trovare un numero sufficiente di locazioni contigue a partire dal strptr ",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "54. Supponiamo di voler modificare il comportamento di default di un processo quando esso riceve un segnale. Ovvero  vogliamo modificare il gestore (handler) di un segnale.\nQuale, tra  le system call, o combinazione di system call di seguito riportate è possibile utilizzare?",
-    "answers": [
-      {
-        "text": "sigaction(2)",
-        "image": ""
-      },
-      {
-        "text": "sigaction(2) seguita da una fork(2) che esegue l’handler del segnale",
-        "image": ""
-      },
-      {
-        "text": "signal(2) seguita da una fork(2) che esegue l’handler del segnale",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "55. Assumiamo di voler settare i permessi di accesso 0600 al file filename mediante l'uso della system call open(2). Quale delle seguenti chiamate è corretta?",
-    "answers": [
-      {
-        "text": "open( \"filename\", O_RDWR | O_CREAT | S_IRUSR | S_IWUSR);",
-        "image": ""
-      },
-      {
-        "text": "open(\"filename\",O_RDWR | O_CREAT, S_IRUSR & S_IWUSR);",
-        "image": ""
-      },
-      {
-        "text": "open( \"filename\", O_RDWR | O_CREAT, S_IRUSR | S_IWUSR);",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "56. Si consideri la system call\n<pre>int open(const char *pathname, int flags);\nnel caso venga invocata con il flag impostato a\nO_CREAT | O_EXCL | O_RDONLY</pre>\nQuale è il comportamento atteso?",
-    "answers": [
-      {
-        "text": "Se il file non esiste viene creato ed aperto in lettura, se invece esiste ritorna errore",
-        "image": ""
-      },
-      {
-        "text": "Se il file non esiste lo crea e lo apre in lettura, altrimenti lo apre in lettura",
-        "image": ""
-      },
-      {
-        "text": "Se il file non esiste viene creato con i permessi di esecuzione (x) ed aperto in lettura. Se esiste vengono aggiunti i permessi di esecuzione se già non settati ed il file è aperto in lettura",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "57. Si consideri il seguente frammento di codice\n<pre>char* file = argv[1];\nint fd;\nstruct flock lock;\nfd = open (file, O_WRONLY);\nmemset (&lock, 0, sizeof(lock));\nlock.l_type = F_WRLCK;\nfcntl (fd, F_SETLKW, &lock);\n....</pre>\nQuale è il suo comportamento?",
-    "answers": [
-      {
-        "text": "mette un lock mandatory in scrittura sul file file",
-        "image": ""
-      },
-      {
-        "text": "mette un lock advisory in scrittura sul file file",
-        "image": ""
-      },
-      {
-        "text": "mette un lock bloccante in scrittura sul file file.",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "58. Quale è la differenza tra i seguenti frammenti di codice?\n<pre>C1: int fd, fd1;\n    fd=open(“filename\", O_RDWR);\n    fd1=fd;\n</pre>\n<pre>C2: int fd,fd1;\n    fd=open(“filename\", O_RDWR);\n    fd1=dup(fd);</pre>",
-    "answers": [
-      {
-        "text": "Dopo l’esecuzione di C1 e C2 fd1 contiene lo stesso valore",
-        "image": ""
-      },
-      {
-        "text": "Dopo l’esecuzione di C1 i due file descriptor puntano allo stesso file, mentre dopo l’esecuzione di  C2 il file filename viene duplicato",
-        "image": ""
-      },
-      {
-        "text": "Dopo l’eseccuzione di C1 fd1 contiene lo stesso valore di  fd; mentre  dopo l’esecuzione di  C2  fd1 contiene il valore del più piccolo file descriptor disponibile",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "59. Si consideri  il  seguente frammento di codice\n<pre>int fd,fd1;\nstruct stat buf,\nbuf1;\nfd=open(“filename\", O_RDWR);\nfd1=dup(fd); \nfstat(fd,&buf);\nfstat(fd1,&buf1);</pre>",
-    "answers": [
-      {
-        "text": "buf.st_ino è uguale a buf1.st_ino",
-        "image": ""
-      },
-      {
-        "text": "buf.st_ino è diverso da buf1.st_ino",
-        "image": ""
-      },
-      {
-        "text": "st_ino non è membro della struttura stat",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "60. Supponiamo di avere il seguente  frammento di codice\n<pre>struct dirent *dentry; //directory stream\n    char *filename;\n    DIR *dstr=opendir(“mydir\");\n    while ((dentry=readdir(dstr)) != NULL) {\n        /* Memorizzai nome file nella  directory  in filename  */\n         }</pre>\nQuale delle seguenti istruzioni deve  essere  posta all’interno  del  ciclo while per  memorizzare in filename  il nome dei file  contenuti all’interno della directory mydir ?",
-    "answers": [
-      {
-        "text": "filename = dentry --> d_name;",
-        "image": ""
-      },
-      {
-        "text": "filename = dentry.filename;",
-        "image": ""
-      },
-      {
-        "text": "filename = dentry --> filename;",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "61. Quali attributi di processo sono preservati dalla system call execve(2)?",
-    "answers": [
-      {
-        "text": "Memory locks",
-        "image": ""
-      },
-      {
-        "text": "Timer",
-        "image": ""
-      },
-      {
-        "text": "Umask",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "62. Si consideri la system call execve(2). Quale delle seguenti affermazioni è corretta?",
-    "answers": [
-      {
-        "text": "la execve(2) permette di generare un proccesso figlio del processo chiamante senza utilizzare una fork ma semplicemente eseguendo un immagine contenuta in un file (execve esegue implicitamente la fork)",
-        "image": ""
-      },
-      {
-        "text": "la execve(2) permette di sostituire l'immagine di un processo con quella di un file eseguibile o di uno script di shell eseguibile",
-        "image": ""
-      },
-      {
-        "text": "la execve(2) è una estensione della funzione system(3). Infatti, execve(2) può eseguire un qualsiasi programma, incluso uno script di shell.",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "64. Supponiamo di aver mappato un file in memoria con la system call mmap(2). A cosa serve invocare la msync(2)?",
-    "answers": [
-      {
-        "text": "Impostando il tipo di mapping a MAP_SHARED la msync(2) permette di scrivere le modifiche su disco prima dell' invocazione di una unmap(2) o prima della chiusura del file descriptor. ",
-        "image": ""
-      },
-      {
-        "text": "è necessario invocare sempre la msync(2) se non si vogliono perdere le modifiche fatte in memoria.",
-        "image": ""
-      },
-      {
-        "text": "non serve invocare la mysinc perché quando si chiude il file descriptor tutte le modifiche fatte in memoria vengono scritte su disco",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "65. Quale delle seguenti affermazioni sui processi Linux è falsa?",
-    "answers": [
-      {
-        "text": "In un determinato istante, non possono esserci 2 processi distinti con lo stesso PID",
-        "image": ""
-      },
-      {
-        "text": "Per creare i PID dei processi si usano dei numeri interi che crescono sempre",
-        "image": ""
-      },
-      {
-        "text": "In istanti diversi, possono esserci 2 processi distinti con lo stesso PID",
-        "image": ""
-      },
-      {
-        "text": "Ogni processo può conoscere il suo PID",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "66. Quale delle seguenti affermazioni sui processi Linux è vera?",
-    "answers": [
-      {
-        "text": "Normalmente, il processo figlio, una volta terminata la sua computazione, attende, con una chiamata alla syscall wait, che il padre termini e gli restituisca il suo exit status",
-        "image": ""
-      },
-      {
-        "text": "Un processo diventa zombie se termina prima di almeno uno dei processi che abbia eventualmente creato",
-        "image": ""
-      },
-      {
-        "text": "Ogni processo può conoscere il proprio PID, ma non quello del processo che l'ha creato",
-        "image": ""
-      },
-      {
-        "text": "Con l'eccezione del primo processo, tutti i processi sono creati con una fork",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "67. Quale delle seguenti affermazioni sui processi Linux è falsa?",
-    "answers": [
-      {
-        "text": "Digitare un comando sulla shell genera sempre un nuovo processo",
-        "image": ""
-      },
-      {
-        "text": "Esistono file che non possono essere eseguiti per diventare processi",
-        "image": ""
-      },
-      {
-        "text": "Affinché un file possa diventare un processo è necessario che abbia i permessi di esecuzione",
-        "image": ""
-      },
-      {
-        "text": "Qualsiasi computazione eseguita dal sistema operativo è contenuta dentro un qualche processo",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "68. Quale delle seguenti affermazioni sui processi Linux è vera?",
-    "answers": [
-      {
-        "text": "Eseguendo k volte un file eseguibile, si generano k diversi processi",
-        "image": ""
-      },
-      {
-        "text": "Per poter lanciare un file eseguibile, è prima necessario aspettare che il comando precedente sia terminato",
-        "image": ""
-      },
-      {
-        "text": "Tutti i processi sono sempre in stato di RUNNING",
-        "image": ""
-      },
-      {
-        "text": "Un processo è sempre un'istanza di uno script bash",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "69. Un programma scritto in linguaggio C:",
-    "answers": [
-      {
-        "text": "Rappresenta le stringhe ESCLUSIVAMENTE come array di caratteri terminate dal carattere ‘\\n’",
-        "image": ""
-      },
-      {
-        "text": "Rappresenta le stringhe ESCLUSIVAMENTE come array di caratteri terminate dal carattere ‘^M’",
-        "image": ""
-      },
-      {
-        "text": "Rappresenta le stringhe ESCLUSIVAMENTE come array di caratteri terminate dal carattere ‘0’",
-        "image": ""
-      },
-      {
-        "text": "Rappresenta le stringhe come array di caratteri terminate dal carattere ‘\\0’",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "70. Quale delle seguenti affermazioni è vera?",
-    "answers": [
-      {
-        "text": "Linus Torvalds ha riscritto i pacchetti di Unix, creando i pacchetti GNU",
-        "image": ""
-      },
-      {
-        "text": "Tutte le opzioni sono false",
-        "image": ""
-      },
-      {
-        "text": "Linus Torvalds ha scritto il primo kernel di Linux all'inizio degli anni '80",
-        "image": ""
-      },
-      {
-        "text": "Richard Stallman ha descritto per primo la licenza GPL",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "71. Quali delle seguenti affermazioni è vera?",
-    "answers": [
-      {
-        "text": "A. Nessuna delle opzioni è vera",
-        "image": ""
-      },
-      {
-        "text": "È possibile montare un filesystem solo se è dichiarato nel file /etc/fstab",
-        "image": ""
-      },
-      {
-        "text": "È possibile montare un filesystem solo se è dichiarato nel file /etc/mtab",
-        "image": ""
-      },
-      {
-        "text": "D. Ad ogni filesystem corrisponde un disco fisico o parte di esso (partizione)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "72. Si supponga di avere il seguente frammento di codice:\n<code>FILE *stream = fopen(NOMEFILE, \"w\");</code>\nQuale dei seguenti frammenti di codice ha lo stesso effetto?",
-    "answers": [
-      {
-        "text": "<code>int fd = open(NOMEFILE, O_WRONLY | O_CREAT, 0666);</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>int fd = open(NOMEFILE, O_WRONLY | O_TRUNC);</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>int fd = open(NOMEFILE, O_WRONLY);</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>int fd = open(NOMEFILE, O_WRONLY | O_CREAT | O_TRUNC, 0666);</code>",
-        "image": ""
-      }
-    ],
-    "correct": 3,
-    "image": ""
-  },
-  {
-    "quest": "73. 10. (questa domanda ha una crisi d'identità) Quale delle seguenti affermazioni sulle syscall di Linux che riguardano i files è falsa?",
-    "answers": [
-      {
-        "text": "Chiamando la syscall select, è possibile monitorare un insieme di file descriptor, ed essere notificati non appena ce n'è uno che è diventato disponibile per un'operazione di lettura o scrittura",
-        "image": ""
-      },
-      {
-        "text": "Per richiedere un lock su un file (o su una porzione di esso), occorre chiamare la syscall ioctl",
-        "image": ""
-      },
-      {
-        "text": "È possibile usare la syscall select sia in modo bloccante che in modo non bloccante",
-        "image": ""
-      },
-      {
-        "text": "Le syscall ioctl e fcntl ammettono 2 o 3 argomenti, a seconda dell'operazione",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "74. 11. Quale delle seguenti affermazioni sui segnali Linux è vera?",
-    "answers": [
-      {
-        "text": "Tutti i segnali, se non opportunamente catturati, provocano la terminazione del processo, con l'eccezione del segnale STOP",
-        "image": ""
-      },
-      {
-        "text": "Per un processo è sempre possibile ridefinire il comportamento di un qualsiasi segnale",
-        "image": ""
-      },
-      {
-        "text": "È possibile per un qualunque processo inviare un segnale ad un qualsiasi altro processo dello stesso utente",
-        "image": ""
-      },
-      {
-        "text": "Nessuna delle altre affermazioni è vera",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "75. 12. Quale delle seguenti affermazioni sugli errori delle syscall di Linux è vera?",
-    "answers": [
-      {
-        "text": "Per stampare su stderr la spiegazione di un errore verificatosi in una syscall, il cui nome sia contenuto nella variabile syscall_name (di tipo char *), si può effettuare la seguente chiamata: perror(\"Si è verificato il seguente errore nella chiamata a %s\", syscall_name);",
-        "image": ""
-      },
-      {
-        "text": "Per stampare su stdout la spiegazione di un errore verificatosi in una syscall si può effettuare la seguente chiamata: printf(\"%s\\n\", strerror(errno));",
-        "image": ""
-      },
-      {
-        "text": "Per stampare su stdout la spiegazione di un errore verificatosi in una syscall è sufficiente chiamare perror",
-        "image": ""
-      },
-      {
-        "text": "Per stampare su stdout la spiegazione di un errore verificatosi in una syscall è necessario scrivere uno switch sulla variabile globale errno",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "76. Si supponga di avere il seguente frammento di codice:\nFILE *stream = fopen(\"file_esistente.txt\", \"r\");\nfseek(stream, -100, SEEK_END);\nlong pos = ftell(stream);\nQuale dei seguenti frammenti di codice ha lo stesso effetto?\na.<pre>\nint fd = open(\"file_esistente.txt\", O_RDONLY);\nlseek(fd, -100, SEEK_END);\nlong pos = lseek(fd, 0, SEEK_END);\n</pre>\nb.<pre>\nint fd = open(\"file_esistente.txt\", O_RDONLY);\nlseek(fd, -100, SEEK_END);\nlong pos = lseek(fd, 0, SEEK_CUR);\n</pre>\nc.<pre>\nint fd = open(\"file_esistente.txt\", O_RDONLY);\nlseek(fd, -100, SEEK_END);\nlong pos = lseek(fd, -100, SEEK_END);\n</pre>\nd.<pre>\nint fd = open(\"file_esistente.txt\", O_RDONLY);\nlseek(fd, -100, SEEK_END);\nlong pos = ltell(fd);\n</pre>",
-    "answers": [
-      {
-        "text": "a",
-        "image": ""
-      },
-      {
-        "text": "b",
-        "image": ""
-      },
-      {
-        "text": "c",
-        "image": ""
-      },
-      {
-        "text": "d",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "76. Si consideri la seguente funzione <code>f</code>\n<pre>\nchar *f(char *dest, const char *src, size_t n) {\n    size_t i;\n    for (i = 0; i < n && src[i] != '\\0'; i++)\n        dest[i] = src[i];\nfor ( ; i < n; i++)\ndest[i] = '\\0';\nreturn dest;\n}\n</pre>\nCosa produce come risultato quando eseguita?",
-    "answers": [
-      {
-        "text": "Genera sempre errore in fase di esecuzione perché non c'è alcun controllo sulla dimensione delle stringhe",
-        "image": ""
-      },
-      {
-        "text": "Concatena la stringa src a dest e restituisce dest",
-        "image": ""
-      },
-      {
-        "text": "Copia la stringa src in dest e restituisce dest",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "77. Si consideri il seguente frammento di codice\n<pre>\nsigset_t set, oset, pset;\n...\nsigemptyset( &set );\nsigaddset( &set, SIGINT );\nsigaddset( &set, SIGUSR1 );\nsigprocmask( SIG_BLOCK, &set, &oset );\n...\n</pre>",
-    "answers": [
-      {
-        "text": "Prepara una sezione critica (ovvero dopo la sigprocmask può inizare la sezione critica)",
-        "image": ""
-      },
-      {
-        "text": "Disabilita tutti i segnali tranne SIGINT e SIGUSR1",
-        "image": ""
-      },
-      {
-        "text": "Termina una sezione critica precedentemente iniziata",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "78. Sia mylink un hard link al file myfile (<code>ln myfile mylink</code>).\nQuale di queste afferrmazioni è vera?",
-    "answers": [
-      {
-        "text": "myfile e mylink hanno dimensione diversa",
-        "image": ""
-      },
-      {
-        "text": "myfile e mylink hanno lo stesso numero di inode",
-        "image": ""
-      },
-      {
-        "text": "myfile e mylink hanno un diverso numero di inode",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "79. Supponendo di essere \"loggato\" in una shell come utente1.\nQuali dei seguenti è un path assoluto?",
-    "answers": [
-      {
-        "text": "dir1/dir11/dir112/filename",
-        "image": ""
-      },
-      {
-        "text": "<code>~/utente1/dir1/dir11/dir112/filename oppure ~/dir1/dir11/dir112/filename</code>",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "80. Si supponga che nel sistema esiste un gruppo \"studente\".\nSi supponga di voler creare \"utente1\" e di volerlo aggiungere al gruppo studente.\nQuale dei seguenti comandi è corrretto?",
-    "answers": [
-      {
-        "text": "<code>adduser utente1; adduser utente1 studente</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>adduser utente1 utente1 studente</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>adduser utente1 studente</code>",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "81. Si considerino le seguenti dichiarazioni di variabili:\n<pre>\nint vect[10];\nint *ptr = NULL;\n</pre>\nQuale delle seguneti assegnazioni è corretta per far sì che ptr contanga il puntatore al vettore vect?",
-    "answers": [
-      {
-        "text": "ptr = vect;",
-        "image": ""
-      },
-      {
-        "text": "ptr = &vect",
-        "image": ""
-      },
-      {
-        "text": "ptr = vect[1];",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "82. Si supponda di avere 2 file hw1.c e hw2.c contenenti il seguente codice\n<pre>\nhw1.c:\n#include <stdio.h>\n#include \"hw.2.c\"\nint f(int argc, char *args[]) {\nprintf(\"Hello World!\\n\");\nreturn 256;\n}\n</pre>\n<pre>\nhw2.c:\nint f(int argc, char *args[]);\nint main(int argc, char *args[]) {\nreturn f(argc, args);\n}\n</pre>\nQuale dei seguneti comandi di compilazione non genera errore?",
-    "answers": [
-      {
-        "text": "<code>gcc -Wall hw1.c hw2.c -o hw.out</code> oppure <code>gcc -Wall hw1.c -o hw.out</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>gcc -Wall hw2.c -o hw.out</code>",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "83. Si consideri il seguente frammento di codice\n<pre>\npid_t pID = fork();\nif (pID == 0) {\n    Blocco_1\n} else if (pID < 0) {\n    Blocco_2\n} else {\n    Blocco_3\n}\n</pre>\nQuale blocco di codice (tra Bloccco_1, Blocco_2 e Blocco_3) verrà eseguito nel caso in cui la fork non vada a buon fine?",
-    "answers": [
-      {
-        "text": "Blocco_1",
-        "image": ""
-      },
-      {
-        "text": "Blocco_3",
-        "image": ""
-      },
-      {
-        "text": "Blocco_2",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "84. Si consideri il seguente frammento di codice\n<pre>\nfor (i=0;((i<n1)&&(i<n2));i++)\n    m2[i]=m1[i];\n</pre>\nquando termina il ciclo for?",
-    "answers": [
-      {
-        "text": "Termina solo se n1 è uguale a n2",
-        "image": ""
-      },
-      {
-        "text": "Quando si raggiunge il più grande tra n1 e n2",
-        "image": ""
-      },
-      {
-        "text": "Quando si raggiunge il più piccolo tra n1 e n2",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "85. A seguito di una chiamata a fork(2), quale dei seguenti attributi del processo padre non è ereditato dal processo figlio?",
-    "answers": [
-      {
-        "text": "groups id",
-        "image": ""
-      },
-      {
-        "text": "coda dei segnali",
-        "image": ""
-      },
-      {
-        "text": "descrittori dei file",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "86. Si consideri il seguente frammento di codice\n<pre>\nstruct stat *s;\nfd=open(“filename\");\nfchmod(fd,00744);\nfstat(fd,s);\n</pre>\nPer visualizzare su sdtout i permessi di accesso a \"filename\", quale tra le seguenti opzioni è la più appropriata?",
-    "answers": [
-      {
-        "text": "<code>printf(\"New File mode %x\\n\", s.st_mode);</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>printf(\"New File mode %o\\n\", s.st_mode);</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>printf(\"New File mode %s\\n\", s.st_mode);</code>",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "87. Si consideri il seguente frammento di codice\n<pre>\nint n=2;\nint r=2 * (n++);\n</pre>\n<pre>\nint n=2;\nint r1=2 * (++n);\n</pre>\nQuale valori assumeranno le variabili r e r1 dopo l'esecuzione?",
-    "answers": [
-      {
-        "text": "r = r1 = 4",
-        "image": ""
-      },
-      {
-        "text": "r=6 e r1=4",
-        "image": ""
-      },
-      {
-        "text": "r=4 e r1=6",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "88. Supponiamo di avere la seguenti variabili\n<code>int x=1, y=7;</code>\nQuale delle seguneti espressioni è falsa?",
-    "answers": [
-      {
-        "text": "<code>(x & y) == 7</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>(x | y) == 7</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>(x || y) == (x & y)</code>",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "89. Per visualizzare l’atime di un file quale dei seguenti comandi è corretto?",
-    "answers": [
-      {
-        "text": "<code>ls -lc nomefile</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>ls -lu nomefile</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>ls -la nomefile</code>",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "90. Quali attributi del processo sono preservati dalla funzione di libreria execve()?",
-    "answers": [
-      {
-        "text": "Memory locks",
-        "image": ""
-      },
-      {
-        "text": "Timer",
-        "image": ""
-      },
-      {
-        "text": "Umask",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "91. I permessi di accesso del file eseguibile <code>/usr/bin/passwd</code> sono <code>4755/-rwsr-xr-x</code>\nCosa significa?",
-    "answers": [
-      {
-        "text": "Il bit SetUid non è settato",
-        "image": ""
-      },
-      {
-        "text": "Lo sticky bit è settato",
-        "image": ""
-      },
-      {
-        "text": "Il bit SetUid è settato",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "92. Si assuma di avere due shell aperte, etichettate come shell_1 e shell_2 e si consideri la seguente sequenza di comandi\n(shell_i:cmd indica che cmd è eseguitto nella  shell i, i=1,2).\n<pre>\nshell_1: xterm\nshell_2: ps -C xterm\n#restituisce xtermPID\nshell_2: kill -s SIGINT xtermPID\n</pre>\nQuale è il loro effetto?",
-    "answers": [
-      {
-        "text": "Il processo xterm viene messo nello stato  stopped (T)",
-        "image": ""
-      },
-      {
-        "text": "Il processo xterm viene terminato con segnale SIGINT",
-        "image": ""
-      },
-      {
-        "text": "Il processo xterm viene messo in background",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "93. Supponiamo di aver dichiarato ed inizializzato le seguenti variabili\n<code>int x = 1, y = 7;</code>\nQuale delle seguenti espressioni è vera (true)?",
-    "answers": [
-      {
-        "text": "(x & y) == (x && y)",
-        "image": ""
-      },
-      {
-        "text": "(x && y) == 7",
-        "image": ""
-      },
-      {
-        "text": "(x & y) == (x | y)",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "94. Si consideri la seguente funzione fa\n<pre>\nchar *f(char *dest, const char *src, size_t n) {\n    size_t dest_len = strlen(dest);\n    size_t i;\n    for (i = 0; i < n && src[i] != '\\0'; i++)\n        dest[dest_len + i] = src[i];\n    dest[dest_len + i] = '\\0';\nreturn dest;\n}\n</pre>",
-    "answers": [
-      {
-        "text": "Copia la stringa src in dest e restituisce dest",
-        "image": ""
-      },
-      {
-        "text": "Concatena la stringa src a dest e restituisce dest",
-        "image": ""
-      },
-      {
-        "text": "Genera sempre errore in fase di esecuzione perché non c'è alcun controllo sulla dimensione delle stringhe",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "95. Si supponga di avere un file di testo (filein) e di voler copiare in un altro file (fileout) 100 caratteri a partire dal decimo.\nQuale di questi comandi è corretto?",
-    "answers": [
-      {
-        "text": "<code>cp -n10 -i100 filein fileout</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>dd if=filein of=fileout bs=1 skip=10 count=100</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>dd if=filein of=fileout bs=100 skip=10 count = 1</code>",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "96. Sia mylink un soft link al file myfile (<code>ln -s myfile mylink</code>).\nQuale di queste affermazioni è vera?",
-    "answers": [
-      {
-        "text": "myfile e mylink hanno un diverso numero di inode",
-        "image": ""
-      },
-      {
-        "text": "myfile e mylink hanno lo stesso numero di inode",
-        "image": ""
-      },
-      {
-        "text": "myfile e mylink hanno la stessa dimensione",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "97. Si consideri il codice\n<pre>\nstruct stat *s;\nfd = open(\"filename\");\nfstat(fs, s);\n</pre>\nCome faccio a sapere se il file \"filename\" è un link?",
-    "answers": [
-      {
-        "text": "Se S_ISLINK(s) == 1",
-        "image": ""
-      },
-      {
-        "text": "Se s.st_size == 0",
-        "image": ""
-      },
-      {
-        "text": "Se s_st_nlink == 1",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "98. Quale tra i seguenti comandi è il modo più corretto per verificare a quali gruppi appartiene un utente?",
-    "answers": [
-      {
-        "text": "<code>groups nomeutente</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>cat /etc/groups | grep nomeutente</code>",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "99. Cosa fa sto ciclo?\n<code>for(scoreCount = 0; scanf(\"%d\", &a) == 1; scoreCount++);</code>",
-    "answers": [
-      {
-        "text": "Legge ripetutamente numeri interi da stdin",
-        "image": ""
-      },
-      {
-        "text": "Legge una sola volta da stdin e poi termina",
-        "image": ""
-      },
-      {
-        "text": "Legge da stdin senza mai terminare",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "100. Quale delle seguenti funzioni di libreria non alloca nell'heap?",
-    "answers": [
-      {
-        "text": "calloc",
-        "image": ""
-      },
-      {
-        "text": "malloc",
-        "image": ""
-      },
-      {
-        "text": "alloca",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "101. Si consideri il seguente frammento di codice\n<pre>\nsigset_t set, oset, pset;\n...\nsigemptyset( &set );\nsigaddset( &set, SIGINT );\nsigaddset( &set, SIGUSR1 );\nsigprocmask( SIG_BLOCK, &set, &oset );\n...\n</pre>",
-    "answers": [
-      {
-        "text": "Termina una sezione critica precedentemente iniziata",
-        "image": ""
-      },
-      {
-        "text": "Disabilita tutti i segnali tranne SIGINT e SIGUSR1",
-        "image": ""
-      },
-      {
-        "text": "Disabilita i segnali SIGINT e SIGUSR1",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "102. Per visualizzare contemporaneamente l'access time e status change time di un file, quale dei seguenti comandi è corretto?",
-    "answers": [
-      {
-        "text": "stat nomefile",
-        "image": ""
-      },
-      {
-        "text": "ls -la nomefile",
-        "image": ""
-      },
-      {
-        "text": "ls -lac nomefile",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "103. Consideri  il seguente frammento  di codice\n<pre>int *ptr = malloc(sizeof(int));\nptr = ptr+1;</pre>\nassumendo la malloc assegni a ptr la locazione di memoria 0x55c2b1268420 cosa contiene ptr dopo l’incremento?",
-    "answers": [
-      {
-        "text": "<code>0x55c2b1268421</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>0x55c2b1268428</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>0x55c2b1268424</code>",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "104. Che cosa si intende per sudoer nel gergo Linux?",
-    "answers": [
-      {
-        "text": "Un comando per essere aggiunti al gruppo sudo",
-        "image": ""
-      },
-      {
-        "text": "Un gruppo che permette ai suoi membri di eseguire comandi come super-utente",
-        "image": ""
-      },
-      {
-        "text": "Un utente che appartiene al gruppo di utenti sudo",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "105. Assumiamo che quando viene creata una directory i suoi permessi di accesso sono 0644.\nQuale sarà la umask?",
-    "answers": [
-      {
-        "text": "0644",
-        "image": ""
-      },
-      {
-        "text": "0022",
-        "image": ""
-      },
-      {
-        "text": "0133",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "106. Se una directory ha i permessi di accesso settati come 0222, quali operazioni è possibile fare su di essa?",
-    "answers": [
-      {
-        "text": "Nessuna operazione",
-        "image": ""
-      },
-      {
-        "text": "Operazioni di scrittura ed e possibile visualizzarne il contenuto senza vedere gli attributi dei file",
-        "image": ""
-      },
-      {
-        "text": "Operazioni di scrittura",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "107. Assumete di voler visualizzare il numero di inode di un file, quale dei seguenti comandi è più corretto usare?",
-    "answers": [
-      {
-        "text": "ls -l -n nomefile",
-        "image": ""
-      },
-      {
-        "text": "stat -f nomefile",
-        "image": ""
-      },
-      {
-        "text": "ls -1 -i nomefile",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "108. Quando si esegue il comando ls -l viene mostrato, come prima informazione, il totale (vedi figura, ma non sul bot :p)\nQuale è il significato di questo campo?",
-    "answers": [
-      {
-        "text": "Dimensione della directory espressa in numero di blocchi su disco",
-        "image": ""
-      },
-      {
-        "text": "Dimensione della directory espressa in numero di file contenuti in essa e in tutte le sotto-directory",
-        "image": ""
-      },
-      {
-        "text": "Numero totale di sotto directory",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "109. Si consideri il seguente frammento di codice:\n<pre>\nint num = 5;\nint *numPtr;\nnumPtr = &num;\n*numPtr = 10;\n</pre>\nDopo la sua esecuzione, quale sara' il valore contenuto il num ?",
-    "answers": [
-      {
-        "text": "5",
-        "image": ""
-      },
-      {
-        "text": "10",
-        "image": ""
-      },
-      {
-        "text": "0x123AF345 (indirizzo di memoria)",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "110. Si consideri il seguente frammento di codice:\n<pre>\nint n= 2;\nint r= 2*(n++); // r = 2 * 2, n = 3\nint r1= 2*(++n); // n = 3 + 1, r1 = 2 * 4\n</pre>\nQuale delle seguenti espressioni sarà vera (true) una volta eseguito il codice?",
-    "answers": [
-      {
-        "text": "r < r1",
-        "image": ""
-      },
-      {
-        "text": "r > r1",
-        "image": ""
-      },
-      {
-        "text": "r == r1",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "112. Si consideri il comando\n<code>gcc -c file.c -o file.o</code>\nQuali delle seguenti affermazioni perché falsa?",
-    "answers": [
-      {
-        "text": "Il comando produce un file oggetto a partire da un file precompilato",
-        "image": ""
-      },
-      {
-        "text": "Il comando produce un file oggetto",
-        "image": ""
-      },
-      {
-        "text": "Il comando produce un file eseguibile",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "113. Cosa produce il seguente comando?\n<code>gcc file.o file2.o file3.o</code>",
-    "answers": [
-      {
-        "text": "Un file eseguibile a.out",
-        "image": ""
-      },
-      {
-        "text": "Nulla, la sintassi è sbagliata",
-        "image": ""
-      },
-      {
-        "text": "Fa il linking dei file oggetto ma non produce nessun risultato finché non si specifica l'output",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "114. Si consideri il seguente frammento di codice. Cosa fa una volta eseguito?\n<pre>\nscanf(\"%d\",&num);\nwhile(num!=0); {\n    printf(\"%d\\n\",num);\n    scanf(\"%d\",&num);\n}\n</pre>",
-    "answers": [
-      {
-        "text": "stampa il valore di num almeno una volta",
-        "image": ""
-      },
-      {
-        "text": "cicla infinitamente se num != 0",
-        "image": ""
-      },
-      {
-        "text": "stampa il valore di num se num != 0",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "115. Cosa produce il seguente comando come risultato?\n<code>cat /etc/group | grep nomeutente</code>",
-    "answers": [
-      {
-        "text": "Visualizza su stdout tutti i gruppi a cui appartiene l'utente \"nomeutente\", incluso il gruppo \"nomeutente\" (se esiste)",
-        "image": ""
-      },
-      {
-        "text": "Visualizza su stdout la lista dei gruppi a cui appartiene il gruppo \"nomeutente\" (se esiste)",
-        "image": ""
-      },
-      {
-        "text": "Genera un errore in quanto il file /etc/group non esiste",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "116. Nel caso in cui la system call <code>pid_t waitpid(pid_t pid, int *status, int options);</code>\nsia invocata con valore di pid uguale a -1. Quale è il suo comportamento?",
-    "answers": [
-      {
-        "text": "Attende la terminazione di qualunque processo figlio il cui gruppo ID del processo sia diverso da quello del processo chiamante",
-        "image": ""
-      },
-      {
-        "text": "Attende la terminazione di un qualunque processo figlio",
-        "image": ""
-      },
-      {
-        "text": "Attende la terminazione di qualunque processo figlio il cui gruppo ID del processo sia uguale a quello del processo chiamante",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "117. Quali dei seguenti comandi permette di creare un intero path di directory?",
-    "answers": [
-      {
-        "text": "mkdir /dir1/dir2/dir3",
-        "image": ""
-      },
-      {
-        "text": "mkdir -p /dir1/dir2/dir3",
-        "image": ""
-      },
-      {
-        "text": "mkdir -m /dir1/dir2/dir3",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "118. Supponiamo di avere un file di nome filename e di creare un link a filename con il comando\n<code>ln filename link1</code>\nquale delle seguenti affermazioni è vera?",
-    "answers": [
-      {
-        "text": "filename e link1 hanno lo stesso inode",
-        "image": ""
-      },
-      {
-        "text": "link1 occupa zero blocchi su disco anche se filename ne occupa un numero diverso da 0",
-        "image": ""
-      },
-      {
-        "text": "filename e link1 hanno inode diverso",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "119. Quali dei seguenti comandi change dir usa un path assoluto? (# indica il prompt di sistema)",
-    "answers": [
-      {
-        "text": "# cd ../studente/download",
-        "image": ""
-      },
-      {
-        "text": "# cd Immagini/../Immagini/faces/",
-        "image": ""
-      },
-      {
-        "text": "# cd ~/Lezione1/esempi/filesystem",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "120. Quali sono i permessi MINIMI che devono essere assegnati ad una directory affinchperché sia possibile:\n- leggere il contenuto della directory inclusi gli attributi dei file;\n- impostare la directory come cwd;\n- attraversare la directory.",
-    "answers": [
-      {
-        "text": "rwx",
-        "image": ""
-      },
-      {
-        "text": "r-x",
-        "image": ""
-      },
-      {
-        "text": "rw-",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "121. Supponiamo di avere il seguente makefile (memorizzato in un file di nome makefile):\n<pre>\nmerge_sorted_lists: merge_sorted_lists.c\n        gcc -Wall -Wextra -O3 merge_sorted_lists.c \\\n        -o merge_sorted_lists\nsort_file_int: sort_file_int.c\n        gcc -Wall -Wextra -O3 sort_file_int.c \\\n        -o sort_file_int\n.PHONY: clean\nclean:\n        rm -f *.o merge_sorted_lists\n</pre>\nIn quali condizioni viene eseguito il target sort_file_int? ",
-    "answers": [
-      {
-        "text": "Sempre, se invochiamo il comando <code>make sort_file_int</code>",
-        "image": ""
-      },
-      {
-        "text": "Se invochiamo il comando <code>make sort_file_int</code>. e se sort_file_int.c perché stato modificato dopo la data di creazione di sort_file_int.o",
-        "image": ""
-      },
-      {
-        "text": "Il target sort_file_int non verrà mai eseguito",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "122. SI consideri il seguente frammento di codice:\n<pre>\nint x, y, nread;\nfloat xx, yy;\nnread=scanf(\"%d %d\",&x, &y);\nprintf(\"x=%d, y=%d, nread=%d \\n\",x,y,nread);\nprintf(\"xx=%f, yy=%f, nread=%d \\n\",xx,yy,nread);\nnread=scanf(\"%f %f\",&xx, &yy);\n</pre>\nAssumiamo che, in fase di esecuzione, la prima scanf legge su stdin la sequenza\n<b>1 w</b>\nQuale sara' il valore di nread dopo l'esecuzione della seconda scanf?",
-    "answers": [
-      {
-        "text": "0",
-        "image": ""
-      },
-      {
-        "text": "2",
-        "image": ""
-      },
-      {
-        "text": "dipende dall'input letto su stdin dalla seconda scanf",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "123. Si consideri il seguente frammento di codice\n<pre>\n 1: #include <stdio.h>\n 2:  ....\n 3: \n 4:  char str [80];\n 5:  float f;\n 6:  FILE * pFile;\n 7:\n 8:  pFile = fopen (\"myfile.txt\",\"w+\");\n 9:  fprintf (pFile, \"%f %s\\n\", 3.1416, \"PI\");\n 10: close(pFile);\n 11: rewind (pFile);\n 12: fscanf (pFile, \"%f\", &f);\n 13: fscanf (pFile, \"%s\", str);\n</pre>\nLe chiamate di funzione a riga 10, 11, 12 e 13 vengono eseguite tutte?",
-    "answers": [
-      {
-        "text": "Sì",
-        "image": ""
-      },
-      {
-        "text": "Viene eseguita solo riga 10 poi genera errore ed il programma termina",
-        "image": ""
-      },
-      {
-        "text": "No, nessuna",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "124. Cosa fa il seguente segmento di codice?\n<pre>\nscanf(“%d�,&num); \ndo {\nprintf(“%d\\n�,num); \nscanf(“%d�,&num);\n} while(num!=0);\n</pre>",
-    "answers": [
-      {
-        "text": "stampa il valore di num se num è diverso da 0",
-        "image": ""
-      },
-      {
-        "text": "Il ciclo do-while entra in un loop infinito",
-        "image": ""
-      },
-      {
-        "text": "stampa il valore di num almeno una volta",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "125. Supponiamo di aver inizializzato un puntatore ad una variabile intera in questo modo\n<pre>\nint num=5, *ptrnum;\nptrnum=&num;\n</pre>",
-    "answers": [
-      {
-        "text": "ptrnum = (int *) 10;",
-        "image": ""
-      },
-      {
-        "text": "ptrnum = 10;",
-        "image": ""
-      },
-      {
-        "text": "*ptrnum = 10;",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "126. Quale dei seguenti dichiarazioni di variabile perché non valida, generando quindi un errore di compilazione?",
-    "answers": [
-      {
-        "text": "int goto=1;",
-        "image": ""
-      },
-      {
-        "text": "int goTo=1;",
-        "image": ""
-      },
-      {
-        "text": "int go_to=1;",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "127. Si consideri il seguente frammento di codice\n<pre>\nint scoreCount, a;        \nfor(scoreCount=0; scanf(\"%d\",&a)==1; scoreCount++);\n</pre>\nSe la sequenza letta in input dall scanf è\n<pre>\n1 3 7 2 12 w\n</pre>\nQuale valore assumerà scoreCount al termine del ciclo?",
-    "answers": [
-      {
-        "text": "Il ciclo non termina. La scanf va in errore quando viene letta la w",
-        "image": ""
-      },
-      {
-        "text": "5",
-        "image": ""
-      },
-      {
-        "text": "6",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "128. Si consideri il frammento di codice\n<pre>\n  int K=10, c=0, p=1;\n  while (++K > 10)\n    c=c+1;\n  p--;\n</pre>\nche valore conterrà la variabile K al termine dell'esecuzione del frammento di codice?",
-    "answers": [
-      {
-        "text": "11",
-        "image": ""
-      },
-      {
-        "text": "L'esecuziuone del frammento di codice non termina perché Il ciclo entra in un loop infinito",
-        "image": ""
-      },
-      {
-        "text": "10",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "129. In quale situazione le system call dup(2) e dup2(2) hanno lo stesso comportamento?",
-    "answers": [
-      {
-        "text": "Nel caso in cui gli passiamo gli stessi parametri",
-        "image": ""
-      },
-      {
-        "text": "Nel casa in cui invochiamo la dup2(2) settando a NULL il valore del nuovo file descriptor",
-        "image": ""
-      },
-      {
-        "text": "Nel caso in cui la dup2(2) venga invocata specificando che il nuovo file descriptor deve essere il file descriptor disponibile con il numero più piccolo",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "130. Quali dei seguenti attributi di un processo non perché preservato a seguito di una chiamata alla funzione di libreria execve()?",
-    "answers": [
-      {
-        "text": "Groups id",
-        "image": ""
-      },
-      {
-        "text": "Memory mapping",
-        "image": ""
-      },
-      {
-        "text": "File locks",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "131. Quale attributi di un processo non sono ereditati dal processo figlio?",
-    "answers": [
-      {
-        "text": "Descrittori dei file; terminale di controllo; memoria condivisa",
-        "image": ""
-      },
-      {
-        "text": "I timer, i record lock e i memory lock; i contatori delle risorse ",
-        "image": ""
-      },
-      {
-        "text": "Real ed effective user e group ID; working directory; ambiente del processo",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "132. Si consideri il seguente frammento di codice\n<pre>\nchar* file = argv[1];\n int fd;\n struct flock lock;\n fd = open (file, O_WRONLY);\n memset (&lock, 0, sizeof(lock));\n lock.l_type = F_WRLCK; \n fcntl (fd, F_GETLK, &lock);\n</pre>\nQuale è il comportamento della system call fcntl?",
-    "answers": [
-      {
-        "text": "Verifica se sul file file perché gia' presente un lock descritto dalla struttura lock. Nel caso in cui nessun processo detiene un lock su file piazza il lock",
-        "image": ""
-      },
-      {
-        "text": "Verifica se sul file file perché gia' presente un lock descritto dalla struttura lock. Nel caso in cui nessun processo detiene un lock su file restituisce F_UNLOCK nel campo l_type di lock",
-        "image": ""
-      },
-      {
-        "text": "Verifica se sul file file perché gia' presente un lock descritto dalla struttura lock. In caso affermativo il lock viene rimosso ed il lock richiesto dal processo in esecuzione viene piazzato",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "133. Un processo puo' allocare memoria solo nell'heap?",
-    "answers": [
-      {
-        "text": "Sì, mediante la funziona di libreria malloc(3) e calloc(3)",
-        "image": ""
-      },
-      {
-        "text": "Sì, mediante le funzioni di libreria malloc(3), calloc(3) e alloca(3)",
-        "image": ""
-      },
-      {
-        "text": "No. Può allocare anche memoria nello stack mediante la funzione di libreria alloca(3)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "134. Supponiamo di aver utilizzato, nella nostra funzione C, la funzione di libreria alloca(3) per allocare un'area di memoria.\nÈ necessario liberare tale area di memoria mediante una free(3) prima della terminazione della funzione?",
-    "answers": [
-      {
-        "text": "No. l'area di memoria allocata nello stack viene liberata automaticamente",
-        "image": ""
-      },
-      {
-        "text": "Sì, ma mediante la chiamata di funzione dealloca(3) e non mediante la free(3) ",
-        "image": ""
-      },
-      {
-        "text": "Sì, bisogna sempre liberare la memoria per evitare dei memory leak",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "135. Si consideri la variabile globale errno.\nSe una system call termina con successo, e immediatamente dopo la sua terminazione ispezioniamo il contenuto di errno, cosa otteniamo?",
-    "answers": [
-      {
-        "text": "Il valore zero essendo la system call terminata con successo",
-        "image": ""
-      },
-      {
-        "text": "Il codice di terminazione (con successo) in quanto non c'è una effettiva differenza tra codice di errore o di terminazione con successo",
-        "image": ""
-      },
-      {
-        "text": "Il codice di errore generato dall'ultima system call o funzione di libreria la cui esecuzione è terminata con errore",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "136. Si consideri la system call\n<code>\nint open(const char *pathname, int flags);\n</code>\nnel caso venga invocata con il flag impostato a\n<code>\nO_CREAT | O_EXCL | O_WRONLY\n</code>\nQuale è il comportamento atteso?",
-    "answers": [
-      {
-        "text": "Se il file non esiste viene creato ed aperto in scrittura, se invece esiste ritorna errore",
-        "image": ""
-      },
-      {
-        "text": "Se il file non esiste viene creato con i permessi di esecuzione (x) ed aperto in scrittura. Se esiste vengono aggiunti i permessi di esecuzione se già non settati ed il file è aperto in scrittura",
-        "image": ""
-      },
-      {
-        "text": "Se il file non esiste lo crea e lo apre in scrittura, altrimenti lo apre in lettura",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "137. Assumete di voler visualizzare il numero di inode di un file, quale dei seguenti comandi non produce l'output desiderato?",
-    "answers": [
-      {
-        "text": "stat -f nomefile",
-        "image": ""
-      },
-      {
-        "text": "ls -l -i nomefile",
-        "image": ""
-      },
-      {
-        "text": "stat nomefile",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "138. Supponiamo di avere un file nomefile memorizzato nel nostro filesystem.\nQuale perché il risultato del comando touch nomefile?",
-    "answers": [
-      {
-        "text": "Aggiorna, al tempo corrente, gli atttributi atime e mtime di nomefile  ",
-        "image": ""
-      },
-      {
-        "text": "Crea un file vuoto con nome nomefile in sostituzione dell'esistente",
-        "image": ""
-      },
-      {
-        "text": "Crea un file vuoto con nome nomefile in sostituzione dell'esistente e valore del ctime aggiornato al tempo corrente",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "139. Si consideri un file contenente un programma in linguaggio C. Si assuma che è stata inserita la direttiva #include \"stdio.h\" . perché la compilazione potrebbe generare errori?",
-    "answers": [
-      {
-        "text": "Perché la direttiva dice di cercare il file stdio.h nella directory corrente, mentre tale header file è solitamente memorizzato in un altra directory del filesystem",
-        "image": ""
-      },
-      {
-        "text": "perché il file stdio.h potrebbe non esistere nella directory /usr/include, dove la direttiva dice di cercarlo",
-        "image": ""
-      },
-      {
-        "text": "L'inserimento della direttiva non genererà mai errori",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "140. Dopo aver esegguito il comando\n<code>cpp helloworld.c > hw\n</code>\ncosa conterrà il file hw?",
-    "answers": [
-      {
-        "text": "Un file identico a helloworld.c",
-        "image": ""
-      },
-      {
-        "text": "L'input per il debugger relativo al file helloworld.c",
-        "image": ""
-      },
-      {
-        "text": "Il precompilato di helloworld.c",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "141. Quale perché il modo corretto per controllare che due stringhe str1 e str2 sono uguali?",
-    "answers": [
-      {
-        "text": "<code>if (s1==s2) { printf(\"stringhe uguali\") }</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>if strcmp(s1,s2) == 0 { printf(\"stringhe uguali\") }</code>",
-        "image": ""
-      },
-      {
-        "text": "<code>if strcmp(s1,s2) { printf(\"stringhe uguali\") }</code>",
-        "image": ""
-      }
-    ],
-    "correct": 1,
-    "image": ""
-  },
-  {
-    "quest": "142. Si consideri il seguente frammento di codice\n<pre>\nint i, n1=10, n2=100;\t\nfor (i=0;((i<n1)&&(i<n2));i++)\n    m2[i]=m1[i];\n</pre>\nquando termina il ciclo for?",
-    "answers": [
-      {
-        "text": "Quando il valore di i è uguale a n1",
-        "image": ""
-      },
-      {
-        "text": "Quando il valore di i è uguale a n2",
-        "image": ""
-      },
-      {
-        "text": "Non termina perché n1 è diverso da n2",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "143. Supponiamo di eseguire  separatamente i seguenti frammenti di codice\n<b>Frammento_1</b>\n<pre>close(2);\nif (fopen(\".\",\"r\")) {\n           perror(\"main\");\n}</pre>\n<b>Frammento_2</b>\n<pre>close(2);\nif (fopen(\".\",\"r\")) {\n               printf(\"main: %s \\n\", strerror(errno));\n}</pre>\nQuale delle seguenti affermazioni è vera?",
-    "answers": [
-      {
-        "text": "Il frammento_1 non produce alcun output sul terminale",
-        "image": ""
-      },
-      {
-        "text": "La loro esecuzione produce sul terminale due stringhe identiche",
-        "image": ""
-      },
-      {
-        "text": "La loro esecuzione produce sul terminale due stringhe diverse",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "51. Si consideri  il seguente frammento  di codice  (i numeri a lato sono i numeri di riga delle istruzioni)(uscita 2 volte)\n<pre>1.    Pthread_t tid;\n2.    pthread_create(&tid, ... )\n3.    pthread_create(&tid, ...)\n4.    pthread_join(tid, ...);\n5.    printf(\"joined\");</pre>\nquale  delle seguenti affermazioni è vera?",
-    "answers": [
-      {
-        "text": "la stringa \"joined\" è inviata su stdout solo quando  il thread creato a riga 3 è terminato",
-        "image": ""
-      },
-      {
-        "text": "la stringa \"joined\" è inviata su stdout quando entrambi i thread sono terminati",
-        "image": ""
-      },
-      {
-        "text": "la stringa \"joined\" è inviata su stdout quando uno dei due thread (non importa quale) è terminato",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "144. Supponiamo di avere una directory\n<code>/home/dir</code>\ncreate da root con diritti di accesso\n<code>1777/drwxrwxrwt</code>\ned al suo interno il file filename, creato da root, con diritti di accesso\n<code>0770/-rwxrwx---</code>\nSupponiamo quindi di eseguire il comando\n<code>rm /home/dir/filename</code>\neseguito come utente normale (non root). Quale delle seguneti affermazioni è corretta?\n\n Scegli un'alternativa:",
-    "answers": [
-      {
-        "text": "Il file non verrà cancellato perché lo sticky bit è settato",
-        "image": ""
-      },
-      {
-        "text": "Il file verrà cancellato perché lo sticky bit non è settato",
-        "image": ""
-      },
-      {
-        "text": "Il file non verrà cancellato perché il proprietario del file è root ed  i diritti per il gruppo others sono tutti resettati",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "145. Si supponga di avere un file di testo (filein) contenente 1000 caratteri e di voler copiare in un altro file (fileout) 100 caratteri a partire dal decimo. Quale di questi comandi non produce il risultato atteso?",
-    "answers": [
-      {
-        "text": "dd if=filein of=fileout bs=1 seek=10 count=100",
-        "image": ""
-      },
-      {
-        "text": "dd if=filein of=fileout bs=1 skip=10 count=100",
-        "image": ""
-      },
-      {
-        "text": "dd if=filein of=fileout bs=10 skip=1 count=10",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "146. Supponendo di essere “loggato� in una shell come utente1. Quali dei seguenti è un path assoluto?",
-    "answers": [
-      {
-        "text": "dir1/dir11/dir112/filename",
-        "image": ""
-      },
-      {
-        "text": "~/utente1/dir1/dir11/dir112/filename",
-        "image": ""
-      },
-      {
-        "text": "~/dir1/dir11/dir112/filename",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "147. Assumiamo di avere un file eseguibile il cui proprietario e' l'utente root ad esempio\n<code>-rwxr-xr-x 1 root root 60296 feb 22 2017 /bin/chmod</code>\n Affinche l'effective UID del processo cambi quando eseguito da un utente senza privilegi di root cosa bisogna fare?",
-    "answers": [
-      {
-        "text": "Impostare il permesso speciale Setuid bit",
-        "image": ""
-      },
-      {
-        "text": "Nessuna risposta (0 punti)",
-        "image": ""
-      },
-      {
-        "text": "Impostare il permesso speciale stiky bit",
-        "image": ""
-      },
-      {
-        "text": "Non occorre fare nulla perche' quello descritto e' il comportamento standard, ovvero l'effective UID cambia sempre in quello dell'utente che esegue il file.",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "148. Si supponga di eseguire, come utente sudoer, i seguenti comandi\n \n C1) sudo ls  /home\n C2) sudo su -c’ls /home’\n Quale affermazione e' corretta?",
-    "answers": [
-      {
-        "text": "C1 e C2 sono equivalenti",
-        "image": ""
-      },
-      {
-        "text": "C2 da errore “comando non trovato�",
-        "image": ""
-      },
-      {
-        "text": "C2 esegue una setUID mentre C1 no",
-        "image": ""
-      }
-    ],
-    "correct": 0,
-    "image": ""
-  },
-  {
-    "quest": "149. Che cosa stampa il seguente frammento di codice: <code>\n \n void func1() {\n extern int myvar;\n void nestfunc1() {\n myvar = 0;\n }\n nestfunc1();\n myvar = 1;\n }\n \n \n int myvar = 8;\n void main(void) {\n func1();\n printf(\"myvar = %d\n\", myvar);\n }\n</code> ",
-    "answers": [
-      {
-        "text": "myvar = 0",
-        "image": ""
-      },
-      {
-        "text": "myvar = 8",
-        "image": ""
-      },
-      {
-        "text": "myvar = 1",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "150. Come si dichiara un puntatore a funzione myptr per una funzione che ritorna un int e richiede un int?",
-    "answers": [
-      {
-        "text": "int (*)myptr(int)",
-        "image": ""
-      },
-      {
-        "text": "int *myptr()",
-        "image": ""
-      },
-      {
-        "text": "int (*myptr)(int)",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  },
-  {
-    "quest": "151. Che cosa stampa il seguente frammento di codice: \n <code> static int myvar;\n void func1() {\n myvar++;\n }\n void main(void) {\n func1();\n printf(\"myvar = %d\n\", myvar);\n }\n </code>",
-    "answers": [
-      {
-        "text": "Nulla, il compilatore da' errore perché si e' usata la clausola static al di fuori di una funzione.",
-        "image": ""
-      },
-      {
-        "text": "myvar = n+1, dove n e' un numero casuale scelto dal compilatore",
-        "image": ""
-      },
-      {
-        "text": "myvar = 1",
-        "image": ""
-      }
-    ],
-    "correct": 2,
-    "image": ""
-  }
-]
diff --git a/legacy/docker-compose.yml b/docker-compose.yml
similarity index 100%
rename from legacy/docker-compose.yml
rename to docker-compose.yml
diff --git a/legacy/Bot/bin/Debug/net8.0/JetBrains.Annotations.dll b/legacy/Bot/bin/Debug/net8.0/JetBrains.Annotations.dll
deleted file mode 100755
index 6361fca55e2f5a4ea2b2877bb57e16bd2b2a3f63..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/legacy/Bot/bin/Debug/net8.0/SoUnBot.deps.json b/legacy/Bot/bin/Debug/net8.0/SoUnBot.deps.json
deleted file mode 100644
index 7582c96..0000000
--- a/legacy/Bot/bin/Debug/net8.0/SoUnBot.deps.json
+++ /dev/null
@@ -1,104 +0,0 @@
-{
-  "runtimeTarget": {
-    "name": ".NETCoreApp,Version=v8.0",
-    "signature": ""
-  },
-  "compilationOptions": {},
-  "targets": {
-    ".NETCoreApp,Version=v8.0": {
-      "SoUnBot/1.0.0": {
-        "dependencies": {
-          "Newtonsoft.Json": "13.0.1",
-          "Telegram.Bot": "17.0.0",
-          "Telegram.Bot.Extensions.Polling": "1.0.0"
-        },
-        "runtime": {
-          "SoUnBot.dll": {}
-        }
-      },
-      "JetBrains.Annotations/2021.3.0": {
-        "runtime": {
-          "lib/netstandard2.0/JetBrains.Annotations.dll": {
-            "assemblyVersion": "2021.3.0.0",
-            "fileVersion": "2021.3.0.0"
-          }
-        }
-      },
-      "Newtonsoft.Json/13.0.1": {
-        "runtime": {
-          "lib/netstandard2.0/Newtonsoft.Json.dll": {
-            "assemblyVersion": "13.0.0.0",
-            "fileVersion": "13.0.1.25517"
-          }
-        }
-      },
-      "System.Threading.Channels/6.0.0": {},
-      "Telegram.Bot/17.0.0": {
-        "dependencies": {
-          "Newtonsoft.Json": "13.0.1"
-        },
-        "runtime": {
-          "lib/netcoreapp3.1/Telegram.Bot.dll": {
-            "assemblyVersion": "17.0.0.0",
-            "fileVersion": "17.0.0.0"
-          }
-        }
-      },
-      "Telegram.Bot.Extensions.Polling/1.0.0": {
-        "dependencies": {
-          "JetBrains.Annotations": "2021.3.0",
-          "System.Threading.Channels": "6.0.0",
-          "Telegram.Bot": "17.0.0"
-        },
-        "runtime": {
-          "lib/netcoreapp3.1/Telegram.Bot.Extensions.Polling.dll": {
-            "assemblyVersion": "1.0.0.0",
-            "fileVersion": "1.0.0.0"
-          }
-        }
-      }
-    }
-  },
-  "libraries": {
-    "SoUnBot/1.0.0": {
-      "type": "project",
-      "serviceable": false,
-      "sha512": ""
-    },
-    "JetBrains.Annotations/2021.3.0": {
-      "type": "package",
-      "serviceable": true,
-      "sha512": "sha512-Ddxjs5RRjf+c8m9m++WvhW1lz1bqNhsTjWvCLbQN9bvKbkJeR9MhtfNwKgBRRdG2yLHcXFr5Lf7fsvvkiPaDRg==",
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-      "hashPath": "jetbrains.annotations.2021.3.0.nupkg.sha512"
-    },
-    "Newtonsoft.Json/13.0.1": {
-      "type": "package",
-      "serviceable": true,
-      "sha512": "sha512-ppPFpBcvxdsfUonNcvITKqLl3bqxWbDCZIzDWHzjpdAHRFfZe0Dw9HmA0+za13IdyrgJwpkDTDA9fHaxOrt20A==",
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-    "System.Threading.Channels/6.0.0": {
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-      "serviceable": true,
-      "sha512": "sha512-TY8/9+tI0mNaUMgntOxxaq2ndTkdXqLSxvPmas7XEqOlv9lQtB7wLjYGd756lOaO7Dvb5r/WXhluM+0Xe87v5Q==",
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-      "hashPath": "system.threading.channels.6.0.0.nupkg.sha512"
-    },
-    "Telegram.Bot/17.0.0": {
-      "type": "package",
-      "serviceable": true,
-      "sha512": "sha512-YvQ9lqEt1bTafu6BJPTbYWDHxyHP+TK8PtjTjNV/6VQw3XxVcZnGwYkJ1CdYW3lJHmHjYxzhBlhhOGNtqJ3U7g==",
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-      "hashPath": "telegram.bot.17.0.0.nupkg.sha512"
-    },
-    "Telegram.Bot.Extensions.Polling/1.0.0": {
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-      "serviceable": true,
-      "sha512": "sha512-OsUbHdHIMmldevoRYzArh5uJDVs1fzlpj+T3mddeP/ELhhhHLmcjon0ZEypgf1KFEj6QWbuZHkijauIW1LZlqg==",
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-      "hashPath": "telegram.bot.extensions.polling.1.0.0.nupkg.sha512"
-    }
-  }
-}
\ No newline at end of file
diff --git a/legacy/Bot/bin/Debug/net8.0/SoUnBot.dll b/legacy/Bot/bin/Debug/net8.0/SoUnBot.dll
deleted file mode 100644
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diff --git a/legacy/Bot/bin/Debug/net8.0/SoUnBot.runtimeconfig.json b/legacy/Bot/bin/Debug/net8.0/SoUnBot.runtimeconfig.json
deleted file mode 100644
index becfaea..0000000
--- a/legacy/Bot/bin/Debug/net8.0/SoUnBot.runtimeconfig.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "runtimeOptions": {
-    "tfm": "net8.0",
-    "framework": {
-      "name": "Microsoft.NETCore.App",
-      "version": "8.0.0"
-    },
-    "configProperties": {
-      "System.Runtime.Serialization.EnableUnsafeBinaryFormatterSerialization": false
-    }
-  }
-}
\ No newline at end of file
diff --git a/legacy/Bot/bin/Debug/net8.0/Telegram.Bot.Extensions.Polling.dll b/legacy/Bot/bin/Debug/net8.0/Telegram.Bot.Extensions.Polling.dll
deleted file mode 100755
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diff --git a/legacy/Bot/obj/Debug/net8.0/.NETCoreApp,Version=v8.0.AssemblyAttributes.cs b/legacy/Bot/obj/Debug/net8.0/.NETCoreApp,Version=v8.0.AssemblyAttributes.cs
deleted file mode 100644
index 2217181..0000000
--- a/legacy/Bot/obj/Debug/net8.0/.NETCoreApp,Version=v8.0.AssemblyAttributes.cs
+++ /dev/null
@@ -1,4 +0,0 @@
-// <autogenerated />
-using System;
-using System.Reflection;
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diff --git a/legacy/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfo.cs b/legacy/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfo.cs
deleted file mode 100644
index 7ef4872..0000000
--- a/legacy/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfo.cs
+++ /dev/null
@@ -1,22 +0,0 @@
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-// <auto-generated>
-//     This code was generated by a tool.
-//
-//     Changes to this file may cause incorrect behavior and will be lost if
-//     the code is regenerated.
-// </auto-generated>
-//------------------------------------------------------------------------------
-
-using System;
-using System.Reflection;
-
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-[assembly: System.Reflection.AssemblyProductAttribute("SoUnBot")]
-[assembly: System.Reflection.AssemblyTitleAttribute("SoUnBot")]
-[assembly: System.Reflection.AssemblyVersionAttribute("1.0.0.0")]
-
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-
diff --git a/legacy/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfoInputs.cache b/legacy/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfoInputs.cache
deleted file mode 100644
index 2cfa414..0000000
--- a/legacy/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfoInputs.cache
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deleted file mode 100644
index fe34a67..0000000
--- a/legacy/Bot/obj/Debug/net8.0/SoUnBot.GeneratedMSBuildEditorConfig.editorconfig
+++ /dev/null
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-build_property.EnableComHosting = 
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diff --git a/legacy/Bot/obj/Debug/net8.0/SoUnBot.GlobalUsings.g.cs b/legacy/Bot/obj/Debug/net8.0/SoUnBot.GlobalUsings.g.cs
deleted file mode 100644
index 8578f3d..0000000
--- a/legacy/Bot/obj/Debug/net8.0/SoUnBot.GlobalUsings.g.cs
+++ /dev/null
@@ -1,8 +0,0 @@
-// <auto-generated/>
-global using global::System;
-global using global::System.Collections.Generic;
-global using global::System.IO;
-global using global::System.Linq;
-global using global::System.Net.Http;
-global using global::System.Threading;
-global using global::System.Threading.Tasks;
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index d8d035c..0000000
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diff --git a/legacy/Bot/obj/SoUnBot.csproj.nuget.dgspec.json b/legacy/Bot/obj/SoUnBot.csproj.nuget.dgspec.json
deleted file mode 100644
index 244f051..0000000
--- a/legacy/Bot/obj/SoUnBot.csproj.nuget.dgspec.json
+++ /dev/null
@@ -1,81 +0,0 @@
-{
-  "format": 1,
-  "restore": {
-    "/home/marco/RiderProjects/so-un-bot/Bot/SoUnBot.csproj": {}
-  },
-  "projects": {
-    "/home/marco/RiderProjects/so-un-bot/Bot/SoUnBot.csproj": {
-      "version": "1.0.0",
-      "restore": {
-        "projectUniqueName": "/home/marco/RiderProjects/so-un-bot/Bot/SoUnBot.csproj",
-        "projectName": "SoUnBot",
-        "projectPath": "/home/marco/RiderProjects/so-un-bot/Bot/SoUnBot.csproj",
-        "packagesPath": "/home/marco/.nuget/packages/",
-        "outputPath": "/home/marco/RiderProjects/so-un-bot/Bot/obj/",
-        "projectStyle": "PackageReference",
-        "configFilePaths": [
-          "/home/marco/.nuget/NuGet/NuGet.Config"
-        ],
-        "originalTargetFrameworks": [
-          "net8.0"
-        ],
-        "sources": {
-          "https://api.nuget.org/v3/index.json": {}
-        },
-        "frameworks": {
-          "net8.0": {
-            "targetAlias": "net8.0",
-            "projectReferences": {}
-          }
-        },
-        "warningProperties": {
-          "warnAsError": [
-            "NU1605"
-          ]
-        }
-      },
-      "frameworks": {
-        "net8.0": {
-          "targetAlias": "net8.0",
-          "dependencies": {
-            "Newtonsoft.Json": {
-              "target": "Package",
-              "version": "[13.0.1, )"
-            },
-            "Telegram.Bot": {
-              "target": "Package",
-              "version": "[17.0.0, )"
-            },
-            "Telegram.Bot.Extensions.Polling": {
-              "target": "Package",
-              "version": "[1.0.0, )"
-            }
-          },
-          "imports": [
-            "net461",
-            "net462",
-            "net47",
-            "net471",
-            "net472",
-            "net48",
-            "net481"
-          ],
-          "assetTargetFallback": true,
-          "warn": true,
-          "downloadDependencies": [
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-              "name": "Microsoft.AspNetCore.App.Ref",
-              "version": "[8.0.3, 8.0.3]"
-            }
-          ],
-          "frameworkReferences": {
-            "Microsoft.NETCore.App": {
-              "privateAssets": "all"
-            }
-          },
-          "runtimeIdentifierGraphPath": "/usr/share/dotnet/sdk/8.0.103/PortableRuntimeIdentifierGraph.json"
-        }
-      }
-    }
-  }
-}
\ No newline at end of file
diff --git a/legacy/Bot/obj/SoUnBot.csproj.nuget.g.props b/legacy/Bot/obj/SoUnBot.csproj.nuget.g.props
deleted file mode 100644
index 311e8bb..0000000
--- a/legacy/Bot/obj/SoUnBot.csproj.nuget.g.props
+++ /dev/null
@@ -1,15 +0,0 @@
-<?xml version="1.0" encoding="utf-8" standalone="no"?>
-<Project ToolsVersion="14.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
-  <PropertyGroup Condition=" '$(ExcludeRestorePackageImports)' != 'true' ">
-    <RestoreSuccess Condition=" '$(RestoreSuccess)' == '' ">True</RestoreSuccess>
-    <RestoreTool Condition=" '$(RestoreTool)' == '' ">NuGet</RestoreTool>
-    <ProjectAssetsFile Condition=" '$(ProjectAssetsFile)' == '' ">$(MSBuildThisFileDirectory)project.assets.json</ProjectAssetsFile>
-    <NuGetPackageRoot Condition=" '$(NuGetPackageRoot)' == '' ">/home/marco/.nuget/packages/</NuGetPackageRoot>
-    <NuGetPackageFolders Condition=" '$(NuGetPackageFolders)' == '' ">/home/marco/.nuget/packages/</NuGetPackageFolders>
-    <NuGetProjectStyle Condition=" '$(NuGetProjectStyle)' == '' ">PackageReference</NuGetProjectStyle>
-    <NuGetToolVersion Condition=" '$(NuGetToolVersion)' == '' ">6.9.1</NuGetToolVersion>
-  </PropertyGroup>
-  <ItemGroup Condition=" '$(ExcludeRestorePackageImports)' != 'true' ">
-    <SourceRoot Include="/home/marco/.nuget/packages/" />
-  </ItemGroup>
-</Project>
\ No newline at end of file
diff --git a/legacy/Bot/obj/SoUnBot.csproj.nuget.g.targets b/legacy/Bot/obj/SoUnBot.csproj.nuget.g.targets
deleted file mode 100644
index 3dc06ef..0000000
--- a/legacy/Bot/obj/SoUnBot.csproj.nuget.g.targets
+++ /dev/null
@@ -1,2 +0,0 @@
-<?xml version="1.0" encoding="utf-8" standalone="no"?>
-<Project ToolsVersion="14.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003" />
\ No newline at end of file
diff --git a/legacy/Bot/obj/project.assets.json b/legacy/Bot/obj/project.assets.json
deleted file mode 100644
index c6e325b..0000000
--- a/legacy/Bot/obj/project.assets.json
+++ /dev/null
@@ -1,280 +0,0 @@
-{
-  "version": 3,
-  "targets": {
-    "net8.0": {
-      "JetBrains.Annotations/2021.3.0": {
-        "type": "package",
-        "compile": {
-          "lib/netstandard2.0/JetBrains.Annotations.dll": {
-            "related": ".deps.json;.xml"
-          }
-        },
-        "runtime": {
-          "lib/netstandard2.0/JetBrains.Annotations.dll": {
-            "related": ".deps.json;.xml"
-          }
-        }
-      },
-      "Newtonsoft.Json/13.0.1": {
-        "type": "package",
-        "compile": {
-          "lib/netstandard2.0/Newtonsoft.Json.dll": {
-            "related": ".xml"
-          }
-        },
-        "runtime": {
-          "lib/netstandard2.0/Newtonsoft.Json.dll": {
-            "related": ".xml"
-          }
-        }
-      },
-      "System.Threading.Channels/6.0.0": {
-        "type": "package",
-        "compile": {
-          "lib/net6.0/System.Threading.Channels.dll": {
-            "related": ".xml"
-          }
-        },
-        "runtime": {
-          "lib/net6.0/System.Threading.Channels.dll": {
-            "related": ".xml"
-          }
-        },
-        "build": {
-          "buildTransitive/netcoreapp3.1/_._": {}
-        }
-      },
-      "Telegram.Bot/17.0.0": {
-        "type": "package",
-        "dependencies": {
-          "Newtonsoft.Json": "12.0.2"
-        },
-        "compile": {
-          "lib/netcoreapp3.1/Telegram.Bot.dll": {
-            "related": ".pdb;.xml"
-          }
-        },
-        "runtime": {
-          "lib/netcoreapp3.1/Telegram.Bot.dll": {
-            "related": ".pdb;.xml"
-          }
-        }
-      },
-      "Telegram.Bot.Extensions.Polling/1.0.0": {
-        "type": "package",
-        "dependencies": {
-          "JetBrains.Annotations": "2021.3.0",
-          "System.Threading.Channels": "6.0.0",
-          "Telegram.Bot": "17.0.0"
-        },
-        "compile": {
-          "lib/netcoreapp3.1/Telegram.Bot.Extensions.Polling.dll": {
-            "related": ".pdb;.xml"
-          }
-        },
-        "runtime": {
-          "lib/netcoreapp3.1/Telegram.Bot.Extensions.Polling.dll": {
-            "related": ".pdb;.xml"
-          }
-        }
-      }
-    }
-  },
-  "libraries": {
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-      "type": "package",
-      "path": "jetbrains.annotations/2021.3.0",
-      "files": [
-        ".nupkg.metadata",
-        ".signature.p7s",
-        "icon.png",
-        "jetbrains.annotations.2021.3.0.nupkg.sha512",
-        "jetbrains.annotations.nuspec",
-        "lib/net20/JetBrains.Annotations.dll",
-        "lib/net20/JetBrains.Annotations.xml",
-        "lib/netstandard1.0/JetBrains.Annotations.deps.json",
-        "lib/netstandard1.0/JetBrains.Annotations.dll",
-        "lib/netstandard1.0/JetBrains.Annotations.xml",
-        "lib/netstandard2.0/JetBrains.Annotations.deps.json",
-        "lib/netstandard2.0/JetBrains.Annotations.dll",
-        "lib/netstandard2.0/JetBrains.Annotations.xml",
-        "lib/portable40-net40+sl5+win8+wp8+wpa81/JetBrains.Annotations.dll",
-        "lib/portable40-net40+sl5+win8+wp8+wpa81/JetBrains.Annotations.xml"
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-      "type": "package",
-      "path": "newtonsoft.json/13.0.1",
-      "files": [
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-        ".signature.p7s",
-        "LICENSE.md",
-        "lib/net20/Newtonsoft.Json.dll",
-        "lib/net20/Newtonsoft.Json.xml",
-        "lib/net35/Newtonsoft.Json.dll",
-        "lib/net35/Newtonsoft.Json.xml",
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-        "lib/net45/Newtonsoft.Json.xml",
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-        "lib/netstandard1.0/Newtonsoft.Json.xml",
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-  "projectFileDependencyGroups": {
-    "net8.0": [
-      "Newtonsoft.Json >= 13.0.1",
-      "Telegram.Bot >= 17.0.0",
-      "Telegram.Bot.Extensions.Polling >= 1.0.0"
-    ]
-  },
-  "packageFolders": {
-    "/home/marco/.nuget/packages/": {}
-  },
-  "project": {
-    "version": "1.0.0",
-    "restore": {
-      "projectUniqueName": "/home/marco/RiderProjects/so-un-bot/Bot/SoUnBot.csproj",
-      "projectName": "SoUnBot",
-      "projectPath": "/home/marco/RiderProjects/so-un-bot/Bot/SoUnBot.csproj",
-      "packagesPath": "/home/marco/.nuget/packages/",
-      "outputPath": "/home/marco/RiderProjects/so-un-bot/Bot/obj/",
-      "projectStyle": "PackageReference",
-      "configFilePaths": [
-        "/home/marco/.nuget/NuGet/NuGet.Config"
-      ],
-      "originalTargetFrameworks": [
-        "net8.0"
-      ],
-      "sources": {
-        "https://api.nuget.org/v3/index.json": {}
-      },
-      "frameworks": {
-        "net8.0": {
-          "targetAlias": "net8.0",
-          "projectReferences": {}
-        }
-      },
-      "warningProperties": {
-        "warnAsError": [
-          "NU1605"
-        ]
-      }
-    },
-    "frameworks": {
-      "net8.0": {
-        "targetAlias": "net8.0",
-        "dependencies": {
-          "Newtonsoft.Json": {
-            "target": "Package",
-            "version": "[13.0.1, )"
-          },
-          "Telegram.Bot": {
-            "target": "Package",
-            "version": "[17.0.0, )"
-          },
-          "Telegram.Bot.Extensions.Polling": {
-            "target": "Package",
-            "version": "[1.0.0, )"
-          }
-        },
-        "imports": [
-          "net461",
-          "net462",
-          "net47",
-          "net471",
-          "net472",
-          "net48",
-          "net481"
-        ],
-        "assetTargetFallback": true,
-        "warn": true,
-        "downloadDependencies": [
-          {
-            "name": "Microsoft.AspNetCore.App.Ref",
-            "version": "[8.0.3, 8.0.3]"
-          }
-        ],
-        "frameworkReferences": {
-          "Microsoft.NETCore.App": {
-            "privateAssets": "all"
-          }
-        },
-        "runtimeIdentifierGraphPath": "/usr/share/dotnet/sdk/8.0.103/PortableRuntimeIdentifierGraph.json"
-      }
-    }
-  }
-}
\ No newline at end of file
diff --git a/legacy/Bot/obj/project.nuget.cache b/legacy/Bot/obj/project.nuget.cache
deleted file mode 100644
index 5f88de5..0000000
--- a/legacy/Bot/obj/project.nuget.cache
+++ /dev/null
@@ -1,15 +0,0 @@
-{
-  "version": 2,
-  "dgSpecHash": "SEGQ12m9AW+QER7Y2rV+jE8A1HXwy7fhpFirLSBhBNS7WqVkdPM2naTyYcFc+MOC1gyeO3WdP0+uVYxDWUvDRA==",
-  "success": true,
-  "projectFilePath": "/home/marco/RiderProjects/so-un-bot/Bot/SoUnBot.csproj",
-  "expectedPackageFiles": [
-    "/home/marco/.nuget/packages/jetbrains.annotations/2021.3.0/jetbrains.annotations.2021.3.0.nupkg.sha512",
-    "/home/marco/.nuget/packages/newtonsoft.json/13.0.1/newtonsoft.json.13.0.1.nupkg.sha512",
-    "/home/marco/.nuget/packages/system.threading.channels/6.0.0/system.threading.channels.6.0.0.nupkg.sha512",
-    "/home/marco/.nuget/packages/telegram.bot/17.0.0/telegram.bot.17.0.0.nupkg.sha512",
-    "/home/marco/.nuget/packages/telegram.bot.extensions.polling/1.0.0/telegram.bot.extensions.polling.1.0.0.nupkg.sha512",
-    "/home/marco/.nuget/packages/microsoft.aspnetcore.app.ref/8.0.3/microsoft.aspnetcore.app.ref.8.0.3.nupkg.sha512"
-  ],
-  "logs": []
-}
\ No newline at end of file
diff --git a/legacy/Bot/obj/project.packagespec.json b/legacy/Bot/obj/project.packagespec.json
deleted file mode 100644
index fe4ca64..0000000
--- a/legacy/Bot/obj/project.packagespec.json
+++ /dev/null
@@ -1 +0,0 @@
-"restore":{"projectUniqueName":"/home/marco/RiderProjects/so-un-bot/Bot/SoUnBot.csproj","projectName":"SoUnBot","projectPath":"/home/marco/RiderProjects/so-un-bot/Bot/SoUnBot.csproj","outputPath":"/home/marco/RiderProjects/so-un-bot/Bot/obj/","projectStyle":"PackageReference","originalTargetFrameworks":["net8.0"],"sources":{"https://api.nuget.org/v3/index.json":{}},"frameworks":{"net8.0":{"targetAlias":"net8.0","projectReferences":{}}},"warningProperties":{"warnAsError":["NU1605"]}}"frameworks":{"net8.0":{"targetAlias":"net8.0","dependencies":{"Newtonsoft.Json":{"target":"Package","version":"[13.0.1, )"},"Telegram.Bot":{"target":"Package","version":"[17.0.0, )"},"Telegram.Bot.Extensions.Polling":{"target":"Package","version":"[1.0.0, )"}},"imports":["net461","net462","net47","net471","net472","net48","net481"],"assetTargetFallback":true,"warn":true,"downloadDependencies":[{"name":"Microsoft.AspNetCore.App.Ref","version":"[8.0.3, 8.0.3]"}],"frameworkReferences":{"Microsoft.NETCore.App":{"privateAssets":"all"}},"runtimeIdentifierGraphPath":"/usr/share/dotnet/sdk/8.0.103/PortableRuntimeIdentifierGraph.json"}}
\ No newline at end of file
diff --git a/legacy/Bot/obj/rider.project.model.nuget.info b/legacy/Bot/obj/rider.project.model.nuget.info
deleted file mode 100644
index d8bcafa..0000000
--- a/legacy/Bot/obj/rider.project.model.nuget.info
+++ /dev/null
@@ -1 +0,0 @@
-17116412128528733
\ No newline at end of file
diff --git a/legacy/Bot/obj/rider.project.restore.info b/legacy/Bot/obj/rider.project.restore.info
deleted file mode 100644
index d8bcafa..0000000
--- a/legacy/Bot/obj/rider.project.restore.info
+++ /dev/null
@@ -1 +0,0 @@
-17116412128528733
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_10/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_10/wrong 1.txt
deleted file mode 100644
index 03abba5..0000000
--- a/legacy/Data/Questions/ingsw/1122_10/wrong 1.txt	
+++ /dev/null
@@ -1,10 +0,0 @@
-
-int f(in x, int y) 
-
-{ 
-
-assert( (x >= 0) && (y >= 0) && ((x >= 3) || (y >= 3)) );
-
-.....
-
-}
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_10/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_10/wrong 2.txt
deleted file mode 100644
index a820d7a..0000000
--- a/legacy/Data/Questions/ingsw/1122_10/wrong 2.txt	
+++ /dev/null
@@ -1,9 +0,0 @@
-int f(in x, int y) 
-
-{ 
-
-assert( (x > 0) && (y > 0) && ((x >= 3) || (y > 3)) );
-
-.....
-
-}
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_14/correct.txt b/legacy/Data/Questions/ingsw/1122_14/correct.txt
deleted file mode 100644
index 97f2744..0000000
--- a/legacy/Data/Questions/ingsw/1122_14/correct.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-#define n 1000
-int TestOracle1(int *A, int *B)
-{
-int i, j, D[n];
-//init
-
-for (i = 0; i < n; i++) D[i] = -1;
-
-// B is ordered
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-// B is a permutation of A
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; D[j] = 1; break;}
-
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-// B ok
-return (1);
-}
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_14/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_14/wrong 2.txt
deleted file mode 100644
index 4a9e2c8..0000000
--- a/legacy/Data/Questions/ingsw/1122_14/wrong 2.txt	
+++ /dev/null
@@ -1,29 +0,0 @@
-<pre>
-#define n 1000
-
-int TestOracle3(int *A, int *B)
-
-{
-
-int i, j, D[n];
-
-//init
-
-for (i = 0; i < n; i++) D[i] = -1;
-
-// B is ordered
-
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-
-// B is a permutation of A
-
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if (A[i] == B[j]) {C[i][j] = 1; D[j] = 1; break;}
-
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-
-// B ok
-
-return (1);
-
-}
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_2/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_2/wrong 1.txt
deleted file mode 100644
index b0c70b4..0000000
--- a/legacy/Data/Questions/ingsw/1122_2/wrong 1.txt	
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-
-initial equation
-
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_2/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_2/wrong 2.txt
deleted file mode 100644
index 50c4137..0000000
--- a/legacy/Data/Questions/ingsw/1122_2/wrong 2.txt	
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-
-initial equation
-
-wy = false;
-equation
-wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_21/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_21/wrong 1.txt
deleted file mode 100644
index 93791b3..0000000
--- a/legacy/Data/Questions/ingsw/1122_21/wrong 1.txt	
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-
-initial equation
-
-y = false;
-equation
-z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_21/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_21/wrong 2.txt
deleted file mode 100644
index 826c225..0000000
--- a/legacy/Data/Questions/ingsw/1122_21/wrong 2.txt	
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-
-initial equation
-
-y = false;
-equation
-z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_23/quest.txt b/legacy/Data/Questions/ingsw/1122_23/quest.txt
deleted file mode 100644
index 63f2e9f..0000000
--- a/legacy/Data/Questions/ingsw/1122_23/quest.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-Si consideri il monitor seguente che ritorna true appena i requisiti per il sistema monitorato sono violati.
-<pre>
-block Monitor
-
-input Real x;  
-
-output Boolean y;
-
-Boolean w;
-
-initial equation
-
-y = false;
-
-equation
-
-w = ((x < 0) or (x > 5));
-
-algorithm
-
-when edge(w) then
-
-y := true;
-
-end when;
-
-end Monitor;
-</pre>
-Quale delle seguenti affermazioni meglio descrive il requisito monitorato.
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_24/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_24/wrong 1.txt
deleted file mode 100644
index 835a5ac..0000000
--- a/legacy/Data/Questions/ingsw/1122_24/wrong 1.txt	
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-
-initial equation
-
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_24/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_24/wrong 2.txt
deleted file mode 100644
index 5a7d171..0000000
--- a/legacy/Data/Questions/ingsw/1122_24/wrong 2.txt	
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-
-initial equation
-
-wy = false;
-equation
-wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_31/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_31/wrong 1.txt
deleted file mode 100644
index 8f1589e..0000000
--- a/legacy/Data/Questions/ingsw/1122_31/wrong 1.txt	
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-
-initial equation
-
-y = false;
-equation
-z = (time > 20) and (x >= 20) and (x <= 30) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_31/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_31/wrong 2.txt
deleted file mode 100644
index 8fd5deb..0000000
--- a/legacy/Data/Questions/ingsw/1122_31/wrong 2.txt	
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-
-initial equation
-
-y = false;
-equation
-z = (time > 20) or ((x >= 20) and (x <= 30)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_37/correct.txt b/legacy/Data/Questions/ingsw/1122_37/correct.txt
deleted file mode 100644
index deba1f5..0000000
--- a/legacy/Data/Questions/ingsw/1122_37/correct.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-
-initial equation
-
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_37/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_37/wrong 1.txt
deleted file mode 100644
index 6a0d3e9..0000000
--- a/legacy/Data/Questions/ingsw/1122_37/wrong 1.txt	
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-
-initial equation
-
-wy = false;
-equation
-
-wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_37/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_37/wrong 2.txt
deleted file mode 100644
index f2a9214..0000000
--- a/legacy/Data/Questions/ingsw/1122_37/wrong 2.txt	
+++ /dev/null
@@ -1,20 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-
-initial equation
-
-wy = false;
-equation
-
-wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
-
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/Questions/ingsw/1122_38/quest.txt b/legacy/Data/Questions/ingsw/1122_38/quest.txt
deleted file mode 100644
index 24d3f68..0000000
--- a/legacy/Data/Questions/ingsw/1122_38/quest.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
-<pre>
-block Monitor
-
-input Real x;  
-
-output Boolean y;
-
-Boolean w;
-
-initial equation
-
-y = false;
-
-equation
-
-w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
-
-algorithm
-
-when edge(w) then
-
-y := true;
-
-end when;
-
-end Monitor;
-</pre>
-Quale delle seguenti affermazioni meglio descrive il requisito monitorato?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_1/quest.txt b/legacy/Data/ingsw/0120_1/quest.txt
deleted file mode 100644
index 5922b9f..0000000
--- a/legacy/Data/ingsw/0120_1/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_10/quest.txt b/legacy/Data/ingsw/0120_10/quest.txt
deleted file mode 100644
index e11a044..0000000
--- a/legacy/Data/ingsw/0120_10/quest.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_13/quest.txt b/legacy/Data/ingsw/0120_13/quest.txt
deleted file mode 100644
index c1cd6d0..0000000
--- a/legacy/Data/ingsw/0120_13/quest.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
-int z = x;
-while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
-return (z);
-}
-Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_14/quest.txt b/legacy/Data/ingsw/0120_14/quest.txt
deleted file mode 100644
index 36947c2..0000000
--- a/legacy/Data/ingsw/0120_14/quest.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (x + 7); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
-Quale dei seguenti test cases consegue una partition coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_27/quest.txt b/legacy/Data/ingsw/0120_27/quest.txt
deleted file mode 100644
index 864cc93..0000000
--- a/legacy/Data/ingsw/0120_27/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
-Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_29/quest.txt b/legacy/Data/ingsw/0120_29/quest.txt
deleted file mode 100644
index 576af1a..0000000
--- a/legacy/Data/ingsw/0120_29/quest.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_38/quest.txt b/legacy/Data/ingsw/0120_38/quest.txt
deleted file mode 100644
index b420aaf..0000000
--- a/legacy/Data/ingsw/0120_38/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
-Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_48/quest.txt b/legacy/Data/ingsw/0120_48/quest.txt
deleted file mode 100644
index 22c683f..0000000
--- a/legacy/Data/ingsw/0120_48/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_7/quest.txt b/legacy/Data/ingsw/0120_7/quest.txt
deleted file mode 100644
index 031c331..0000000
--- a/legacy/Data/ingsw/0120_7/quest.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_13/quest.txt b/legacy/Data/ingsw/0210_13/quest.txt
deleted file mode 100644
index b420aaf..0000000
--- a/legacy/Data/ingsw/0210_13/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
-Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_14/quest.txt b/legacy/Data/ingsw/0210_14/quest.txt
deleted file mode 100644
index c1cd6d0..0000000
--- a/legacy/Data/ingsw/0210_14/quest.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
-int z = x;
-while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
-return (z);
-}
-Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_15/quest.txt b/legacy/Data/ingsw/0210_15/quest.txt
deleted file mode 100644
index 22c683f..0000000
--- a/legacy/Data/ingsw/0210_15/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_16/correct.txt b/legacy/Data/ingsw/0210_16/correct.txt
deleted file mode 100644
index 293ebbc..0000000
--- a/legacy/Data/ingsw/0210_16/correct.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_16/quest.txt b/legacy/Data/ingsw/0210_16/quest.txt
deleted file mode 100644
index 5922b9f..0000000
--- a/legacy/Data/ingsw/0210_16/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_21/correct.txt b/legacy/Data/ingsw/0210_21/correct.txt
deleted file mode 100644
index ad21063..0000000
--- a/legacy/Data/ingsw/0210_21/correct.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_21/quest.txt b/legacy/Data/ingsw/0210_21/quest.txt
deleted file mode 100644
index 031c331..0000000
--- a/legacy/Data/ingsw/0210_21/quest.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_22/quest.txt b/legacy/Data/ingsw/0210_22/quest.txt
deleted file mode 100644
index e5fbc81..0000000
--- a/legacy/Data/ingsw/0210_22/quest.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
-block Monitor
-input Real x;  
-output Boolean y;
-Boolean w;
-initial equation
-y = false;
-equation
-w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
-algorithm
-when edge(w) then
-y := true;
-end when;
-end Monitor;
-</pre>
-Quale delle seguenti affermazioni meglio descrive il requisito monitorato?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_39/quest.txt b/legacy/Data/ingsw/0210_39/quest.txt
deleted file mode 100644
index 36947c2..0000000
--- a/legacy/Data/ingsw/0210_39/quest.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (x + 7); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
-Quale dei seguenti test cases consegue una partition coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_43/quest.txt b/legacy/Data/ingsw/0210_43/quest.txt
deleted file mode 100644
index e11a044..0000000
--- a/legacy/Data/ingsw/0210_43/quest.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_45/quest.txt b/legacy/Data/ingsw/0210_45/quest.txt
deleted file mode 100644
index 576af1a..0000000
--- a/legacy/Data/ingsw/0210_45/quest.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_5/correct.txt b/legacy/Data/ingsw/0210_5/correct.txt
deleted file mode 100644
index e582263..0000000
--- a/legacy/Data/ingsw/0210_5/correct.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_5/quest.txt b/legacy/Data/ingsw/0210_5/quest.txt
deleted file mode 100644
index 864cc93..0000000
--- a/legacy/Data/ingsw/0210_5/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
-Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_1/wrong1.txt b/legacy/Data/ingsw/0324_1/wrong1.txt
deleted file mode 100644
index 4bcd55f..0000000
--- a/legacy/Data/ingsw/0324_1/wrong1.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_12/quest.txt b/legacy/Data/ingsw/0324_12/quest.txt
deleted file mode 100644
index c1cd6d0..0000000
--- a/legacy/Data/ingsw/0324_12/quest.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
-int z = x;
-while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
-return (z);
-}
-Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_27/correct.txt b/legacy/Data/ingsw/0324_27/correct.txt
deleted file mode 100644
index e582263..0000000
--- a/legacy/Data/ingsw/0324_27/correct.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_27/quest.txt b/legacy/Data/ingsw/0324_27/quest.txt
deleted file mode 100644
index 864cc93..0000000
--- a/legacy/Data/ingsw/0324_27/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
-Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_28/correct.txt b/legacy/Data/ingsw/0324_28/correct.txt
deleted file mode 100644
index 4c75070..0000000
--- a/legacy/Data/ingsw/0324_28/correct.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_28/quest.txt b/legacy/Data/ingsw/0324_28/quest.txt
deleted file mode 100644
index e11a044..0000000
--- a/legacy/Data/ingsw/0324_28/quest.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_28/wrong1.txt b/legacy/Data/ingsw/0324_28/wrong1.txt
deleted file mode 100644
index 6dafe94..0000000
--- a/legacy/Data/ingsw/0324_28/wrong1.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_28/wrong2.txt b/legacy/Data/ingsw/0324_28/wrong2.txt
deleted file mode 100644
index a3d79a4..0000000
--- a/legacy/Data/ingsw/0324_28/wrong2.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_31/correct.txt b/legacy/Data/ingsw/0324_31/correct.txt
deleted file mode 100644
index 293ebbc..0000000
--- a/legacy/Data/ingsw/0324_31/correct.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_31/quest.txt b/legacy/Data/ingsw/0324_31/quest.txt
deleted file mode 100644
index 5922b9f..0000000
--- a/legacy/Data/ingsw/0324_31/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_34/correct.txt b/legacy/Data/ingsw/0324_34/correct.txt
deleted file mode 100644
index ad21063..0000000
--- a/legacy/Data/ingsw/0324_34/correct.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_34/quest.txt b/legacy/Data/ingsw/0324_34/quest.txt
deleted file mode 100644
index 031c331..0000000
--- a/legacy/Data/ingsw/0324_34/quest.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_34/wrong1.txt b/legacy/Data/ingsw/0324_34/wrong1.txt
deleted file mode 100644
index b14ac60..0000000
--- a/legacy/Data/ingsw/0324_34/wrong1.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_34/wrong2.txt b/legacy/Data/ingsw/0324_34/wrong2.txt
deleted file mode 100644
index e4201ab..0000000
--- a/legacy/Data/ingsw/0324_34/wrong2.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_39/correct.txt b/legacy/Data/ingsw/0324_39/correct.txt
deleted file mode 100644
index 4a8e634..0000000
--- a/legacy/Data/ingsw/0324_39/correct.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_39/quest.txt b/legacy/Data/ingsw/0324_39/quest.txt
deleted file mode 100644
index 576af1a..0000000
--- a/legacy/Data/ingsw/0324_39/quest.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_44/quest.txt b/legacy/Data/ingsw/0324_44/quest.txt
deleted file mode 100644
index 36947c2..0000000
--- a/legacy/Data/ingsw/0324_44/quest.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (x + 7); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
-Quale dei seguenti test cases consegue una partition coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_45/correct.txt b/legacy/Data/ingsw/0324_45/correct.txt
deleted file mode 100644
index c37d6ae..0000000
--- a/legacy/Data/ingsw/0324_45/correct.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_45/quest.txt b/legacy/Data/ingsw/0324_45/quest.txt
deleted file mode 100644
index 003d1dd..0000000
--- a/legacy/Data/ingsw/0324_45/quest.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_46/quest.txt b/legacy/Data/ingsw/0324_46/quest.txt
deleted file mode 100644
index b420aaf..0000000
--- a/legacy/Data/ingsw/0324_46/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
-Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_48/wrong1.txt b/legacy/Data/ingsw/0324_48/wrong1.txt
deleted file mode 100644
index 4bcd55f..0000000
--- a/legacy/Data/ingsw/0324_48/wrong1.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_8/quest.txt b/legacy/Data/ingsw/0324_8/quest.txt
deleted file mode 100644
index 22c683f..0000000
--- a/legacy/Data/ingsw/0324_8/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_9/quest.txt b/legacy/Data/ingsw/0324_9/quest.txt
deleted file mode 100644
index 47201e7..0000000
--- a/legacy/Data/ingsw/0324_9/quest.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
-Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
-P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
-dove b è una opportuna costante note da dati storici aziendali. Si assuma che b = 0.0001,  C = 1000000, ed il rischio ammesso è R = 1000. Quale dei seguenti valori meglio approssima il costo S per lo sviluppo del software in questione.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_12/quest.txt b/legacy/Data/ingsw/0613_12/quest.txt
deleted file mode 100644
index 36947c2..0000000
--- a/legacy/Data/ingsw/0613_12/quest.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-Il partition coverage di un insieme di test cases è la percentuale di elementi della partition inclusi nei test cases. La partition è una partizione finita dell'insieme di input della funzione che si sta testando.
-Si consideri la seguente funzione C:
-int f1(int x)  { return (x + 7); }
-Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
-{(-inf, -101], [-100, -1], {0}, [1, 500], [501, +inf)}
-Quale dei seguenti test cases consegue una partition coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_15/quest.txt b/legacy/Data/ingsw/0613_15/quest.txt
deleted file mode 100644
index 22c683f..0000000
--- a/legacy/Data/ingsw/0613_15/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5].
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_16/correct.txt b/legacy/Data/ingsw/0613_16/correct.txt
deleted file mode 100644
index e582263..0000000
--- a/legacy/Data/ingsw/0613_16/correct.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_16/quest.txt b/legacy/Data/ingsw/0613_16/quest.txt
deleted file mode 100644
index 864cc93..0000000
--- a/legacy/Data/ingsw/0613_16/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ1:  Durante l'esecuzione del programma (cioè per tutti gli istanti di tempo positivi) la variabile x è sempre nell'intervallo [0, 5] oppure [10, 15]
-Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_17/correct.txt b/legacy/Data/ingsw/0613_17/correct.txt
deleted file mode 100644
index c37d6ae..0000000
--- a/legacy/Data/ingsw/0613_17/correct.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_17/quest.txt b/legacy/Data/ingsw/0613_17/quest.txt
deleted file mode 100644
index 003d1dd..0000000
--- a/legacy/Data/ingsw/0613_17/quest.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato x era maggiore di 0 allora ora y è negativa.
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_23/correct.txt b/legacy/Data/ingsw/0613_23/correct.txt
deleted file mode 100644
index 4a8e634..0000000
--- a/legacy/Data/ingsw/0613_23/correct.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y <= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_23/quest.txt b/legacy/Data/ingsw/0613_23/quest.txt
deleted file mode 100644
index 576af1a..0000000
--- a/legacy/Data/ingsw/0613_23/quest.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 60 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora è concesso l'accesso alla risorsa (variabile y positiva)
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time < w e ritorna il valore che z aveva al tempo (time - w), se time >= w.
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_25/quest.txt b/legacy/Data/ingsw/0613_25/quest.txt
deleted file mode 100644
index c1cd6d0..0000000
--- a/legacy/Data/ingsw/0613_25/quest.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-Un test oracle per un programma P è una funzione booleana che ha come inputs gli inputs  ed outputs  di P e ritorna true se e solo se il valore di output di P (con i dati inputs) è quello atteso dalle specifiche.
-Si consideri la seguente funzione C:
------------
-int f(int x, int y)  {   
-int z = x;
-while ( (x  <= z)  &&  (z <= y) )  { z = z + 1; }
-return (z);
-}
-Siano x, y, gli inputs del programma (f nel nostro caso) e z l'output. Assumendo il programma corretto, quale delle seguenti funzioni booleane F(x, y, z) è un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_27/wrong1.txt b/legacy/Data/ingsw/0613_27/wrong1.txt
deleted file mode 100644
index 4bcd55f..0000000
--- a/legacy/Data/ingsw/0613_27/wrong1.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_29/quest.txt b/legacy/Data/ingsw/0613_29/quest.txt
deleted file mode 100644
index 47201e7..0000000
--- a/legacy/Data/ingsw/0613_29/quest.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-Il rischio R può essere calcolato come R = P*C, dove P è la probabilità dell'evento avverso (software failure nel nostro contesto) e C è il costo dell'occorrenza dell'evento avverso. 
-Assumiamo che la probabilità P sia legata al costo di sviluppo S dalla formula 
-P = 10^{(-b*S)}    (cioè 10 elevato alla (-b*S))
-dove b è una opportuna costante note da dati storici aziendali. Si assuma che b = 0.0001,  C = 1000000, ed il rischio ammesso è R = 1000. Quale dei seguenti valori meglio approssima il costo S per lo sviluppo del software in questione.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_33/correct.txt b/legacy/Data/ingsw/0613_33/correct.txt
deleted file mode 100644
index e940faa..0000000
--- a/legacy/Data/ingsw/0613_33/correct.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 3) || (y > 3)) );
-.....
-}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_35/correct.txt b/legacy/Data/ingsw/0613_35/correct.txt
deleted file mode 100644
index ad21063..0000000
--- a/legacy/Data/ingsw/0613_35/correct.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_35/quest.txt b/legacy/Data/ingsw/0613_35/quest.txt
deleted file mode 100644
index 031c331..0000000
--- a/legacy/Data/ingsw/0613_35/quest.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 40 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se 10 unità di tempo nel passato x era maggiore di 1 allora ora y è nonegativa.
-Tenendo presente che, al tempo time, delay(z, w) ritorna 0 se time <= w e ritorna il valore che z aveva al tempo (time - w), se time = w.
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_35/wrong1.txt b/legacy/Data/ingsw/0613_35/wrong1.txt
deleted file mode 100644
index b14ac60..0000000
--- a/legacy/Data/ingsw/0613_35/wrong1.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_35/wrong2.txt b/legacy/Data/ingsw/0613_35/wrong2.txt
deleted file mode 100644
index e4201ab..0000000
--- a/legacy/Data/ingsw/0613_35/wrong2.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_37/quest.txt b/legacy/Data/ingsw/0613_37/quest.txt
deleted file mode 100644
index e5fbc81..0000000
--- a/legacy/Data/ingsw/0613_37/quest.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
-block Monitor
-input Real x;  
-output Boolean y;
-Boolean w;
-initial equation
-y = false;
-equation
-w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
-algorithm
-when edge(w) then
-y := true;
-end when;
-end Monitor;
-</pre>
-Quale delle seguenti affermazioni meglio descrive il requisito monitorato?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_43/correct.txt b/legacy/Data/ingsw/0613_43/correct.txt
deleted file mode 100644
index 293ebbc..0000000
--- a/legacy/Data/ingsw/0613_43/correct.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_43/quest.txt b/legacy/Data/ingsw/0613_43/quest.txt
deleted file mode 100644
index 5922b9f..0000000
--- a/legacy/Data/ingsw/0613_43/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 10 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà: se la variabile x è nell'intervallo [10, 20]  allora la variabile y è compresa tra il 50% di x ed il 70% di x.
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_49/correct.txt b/legacy/Data/ingsw/0613_49/correct.txt
deleted file mode 100644
index 4c75070..0000000
--- a/legacy/Data/ingsw/0613_49/correct.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_49/quest.txt b/legacy/Data/ingsw/0613_49/quest.txt
deleted file mode 100644
index e11a044..0000000
--- a/legacy/Data/ingsw/0613_49/quest.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-Si consideri il seguente requisito:
-RQ:  Dopo 50 unità di tempo dall'inizio dell'esecuzione vale la seguente proprietà:
-se la variabile x è minore del 60% della variabile y allora la somma di x ed y è maggiore del 30% della variabile z
-Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_49/wrong1.txt b/legacy/Data/ingsw/0613_49/wrong1.txt
deleted file mode 100644
index 6dafe94..0000000
--- a/legacy/Data/ingsw/0613_49/wrong1.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x < 0.6*y) and (x + y > 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_49/wrong2.txt b/legacy/Data/ingsw/0613_49/wrong2.txt
deleted file mode 100644
index a3d79a4..0000000
--- a/legacy/Data/ingsw/0613_49/wrong2.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x, y, z;  // plant output
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 50) and (x >= 0.6*y) and (x + y <= 0.3*z);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_6/correct.txt b/legacy/Data/ingsw/0613_6/correct.txt
deleted file mode 100644
index a98afd2..0000000
--- a/legacy/Data/ingsw/0613_6/correct.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and ((x >= 30) or (x <= 20)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_6/quest.txt b/legacy/Data/ingsw/0613_6/quest.txt
deleted file mode 100644
index b420aaf..0000000
--- a/legacy/Data/ingsw/0613_6/quest.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Si consideri il seguente requisito:
-RQ1:  Dopo 20 unità di tempo dall'inizio dell'esecuzione la variabile x è sempre nell'intervallo [20, 30] .
-Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_9/wrong1.txt b/legacy/Data/ingsw/0613_9/wrong1.txt
deleted file mode 100644
index 4bcd55f..0000000
--- a/legacy/Data/ingsw/0613_9/wrong1.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-block SysArch  //  System Architecture
-
-SC1 sc1
-SC2 sc2
-SC3 sc3
-SC4 sc4
-
-connect(sc1.output12, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_10/correct.txt b/legacy/Data/ingsw/1122_10/correct.txt
deleted file mode 100644
index e940faa..0000000
--- a/legacy/Data/ingsw/1122_10/correct.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-int f(in x, int y) 
-{ 
-assert( (x >= 0) && (y >= 0) && ((x > 3) || (y > 3)) );
-.....
-}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_10/wrong 1.txt b/legacy/Data/ingsw/1122_10/wrong 1.txt
deleted file mode 100644
index 03abba5..0000000
--- a/legacy/Data/ingsw/1122_10/wrong 1.txt	
+++ /dev/null
@@ -1,10 +0,0 @@
-
-int f(in x, int y) 
-
-{ 
-
-assert( (x >= 0) && (y >= 0) && ((x >= 3) || (y >= 3)) );
-
-.....
-
-}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_10/wrong 2.txt b/legacy/Data/ingsw/1122_10/wrong 2.txt
deleted file mode 100644
index a820d7a..0000000
--- a/legacy/Data/ingsw/1122_10/wrong 2.txt	
+++ /dev/null
@@ -1,9 +0,0 @@
-int f(in x, int y) 
-
-{ 
-
-assert( (x > 0) && (y > 0) && ((x >= 3) || (y > 3)) );
-
-.....
-
-}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_14/correct.txt b/legacy/Data/ingsw/1122_14/correct.txt
deleted file mode 100644
index 97f2744..0000000
--- a/legacy/Data/ingsw/1122_14/correct.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-#define n 1000
-int TestOracle1(int *A, int *B)
-{
-int i, j, D[n];
-//init
-
-for (i = 0; i < n; i++) D[i] = -1;
-
-// B is ordered
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-// B is a permutation of A
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if ((A[i] == B[j]) && (D[j] == -1)) {C[i][j] = 1; D[j] = 1; break;}
-
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-// B ok
-return (1);
-}
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_14/wrong 2.txt b/legacy/Data/ingsw/1122_14/wrong 2.txt
deleted file mode 100644
index 4a9e2c8..0000000
--- a/legacy/Data/ingsw/1122_14/wrong 2.txt	
+++ /dev/null
@@ -1,29 +0,0 @@
-<pre>
-#define n 1000
-
-int TestOracle3(int *A, int *B)
-
-{
-
-int i, j, D[n];
-
-//init
-
-for (i = 0; i < n; i++) D[i] = -1;
-
-// B is ordered
-
-for (i = 0; i < n; i++) {  for (j = i+1; j < n; j++) {if (B[j] < B[i]) {retun (0);}}}
-
-// B is a permutation of A
-
-for (i = 0; i < n; i++) {  for (j = 0; j < n; j++) {if (A[i] == B[j]) {C[i][j] = 1; D[j] = 1; break;}
-
-for (i = 0; i < n; i++) {if (D[i] == -1) return (0);}
-
-// B ok
-
-return (1);
-
-}
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_2/correct.txt b/legacy/Data/ingsw/1122_2/correct.txt
deleted file mode 100644
index ad21063..0000000
--- a/legacy/Data/ingsw/1122_2/correct.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_2/wrong 1.txt b/legacy/Data/ingsw/1122_2/wrong 1.txt
deleted file mode 100644
index b0c70b4..0000000
--- a/legacy/Data/ingsw/1122_2/wrong 1.txt	
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-
-initial equation
-
-wy = false;
-equation
-wz = (time > 40) and (delay(x, 10) > 1) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_2/wrong 2.txt b/legacy/Data/ingsw/1122_2/wrong 2.txt
deleted file mode 100644
index 50c4137..0000000
--- a/legacy/Data/ingsw/1122_2/wrong 2.txt	
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-
-initial equation
-
-wy = false;
-equation
-wz = (time > 40) or (delay(x, 10) > 1) or (y < 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_21/correct.txt b/legacy/Data/ingsw/1122_21/correct.txt
deleted file mode 100644
index e582263..0000000
--- a/legacy/Data/ingsw/1122_21/correct.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 0) and ((x >= 5) or (x <= 0))  and  ((x >= 15) or (x <= 10)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_21/wrong 1.txt b/legacy/Data/ingsw/1122_21/wrong 1.txt
deleted file mode 100644
index 93791b3..0000000
--- a/legacy/Data/ingsw/1122_21/wrong 1.txt	
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-
-initial equation
-
-y = false;
-equation
-z = (time > 0) and ( ((x >= 0) and (x <= 5))  or ((x >= 10) and (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_21/wrong 2.txt b/legacy/Data/ingsw/1122_21/wrong 2.txt
deleted file mode 100644
index 826c225..0000000
--- a/legacy/Data/ingsw/1122_21/wrong 2.txt	
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-
-initial equation
-
-y = false;
-equation
-z = (time > 0) and ((x >= 0) or (x <= 5))  and  ((x >= 10) or (x <= 15)) );
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_23/quest.txt b/legacy/Data/ingsw/1122_23/quest.txt
deleted file mode 100644
index 63f2e9f..0000000
--- a/legacy/Data/ingsw/1122_23/quest.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-Si consideri il monitor seguente che ritorna true appena i requisiti per il sistema monitorato sono violati.
-<pre>
-block Monitor
-
-input Real x;  
-
-output Boolean y;
-
-Boolean w;
-
-initial equation
-
-y = false;
-
-equation
-
-w = ((x < 0) or (x > 5));
-
-algorithm
-
-when edge(w) then
-
-y := true;
-
-end when;
-
-end Monitor;
-</pre>
-Quale delle seguenti affermazioni meglio descrive il requisito monitorato.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_24/correct.txt b/legacy/Data/ingsw/1122_24/correct.txt
deleted file mode 100644
index 293ebbc..0000000
--- a/legacy/Data/ingsw/1122_24/correct.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-initial equation
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_24/wrong 1.txt b/legacy/Data/ingsw/1122_24/wrong 1.txt
deleted file mode 100644
index 835a5ac..0000000
--- a/legacy/Data/ingsw/1122_24/wrong 1.txt	
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-
-initial equation
-
-wy = false;
-equation
-wz = (time > 10) and (x >= 10) and (x <= 20) and (y >= 0.5*x) and (y <= 0.7*x)  ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_24/wrong 2.txt b/legacy/Data/ingsw/1122_24/wrong 2.txt
deleted file mode 100644
index 5a7d171..0000000
--- a/legacy/Data/ingsw/1122_24/wrong 2.txt	
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x, y;  // plant output
-OutputBoolean wy;
-
-Boolean wz;
-
-initial equation
-
-wy = false;
-equation
-wz = (time > 10) and ((x < 10) or (x > 20)) and ((y < 0.5*x) or (y > 0.7*x)) ;
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_31/correct.txt b/legacy/Data/ingsw/1122_31/correct.txt
deleted file mode 100644
index a98afd2..0000000
--- a/legacy/Data/ingsw/1122_31/correct.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-initial equation
-y = false;
-equation
-z = (time > 20) and ((x >= 30) or (x <= 20)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_31/wrong 1.txt b/legacy/Data/ingsw/1122_31/wrong 1.txt
deleted file mode 100644
index 8f1589e..0000000
--- a/legacy/Data/ingsw/1122_31/wrong 1.txt	
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-
-initial equation
-
-y = false;
-equation
-z = (time > 20) and (x >= 20) and (x <= 30) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_31/wrong 2.txt b/legacy/Data/ingsw/1122_31/wrong 2.txt
deleted file mode 100644
index 8fd5deb..0000000
--- a/legacy/Data/ingsw/1122_31/wrong 2.txt	
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-class Monitor
-
-InputReal x;  // plant output
-OutputBoolean y;
-
-Boolean z;
-
-initial equation
-
-y = false;
-equation
-z = (time > 20) or ((x >= 20) and (x <= 30)) ;
-algorithm
-when edge(z) then
-y := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_37/correct.txt b/legacy/Data/ingsw/1122_37/correct.txt
deleted file mode 100644
index deba1f5..0000000
--- a/legacy/Data/ingsw/1122_37/correct.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-
-initial equation
-
-wy = false;
-equation
-wz = (time > 60) and (delay(x, 10) > 0) and (y >= 0);
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_37/wrong 1.txt b/legacy/Data/ingsw/1122_37/wrong 1.txt
deleted file mode 100644
index 6a0d3e9..0000000
--- a/legacy/Data/ingsw/1122_37/wrong 1.txt	
+++ /dev/null
@@ -1,19 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-
-initial equation
-
-wy = false;
-equation
-
-wz = (time > 60) and (delay(x, 10) <= 0) and (y >= 0);
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_37/wrong 2.txt b/legacy/Data/ingsw/1122_37/wrong 2.txt
deleted file mode 100644
index f2a9214..0000000
--- a/legacy/Data/ingsw/1122_37/wrong 2.txt	
+++ /dev/null
@@ -1,20 +0,0 @@
-<pre>
-class Monitor
-InputReal x, y; 
-OutputBoolean wy;
-Boolean wz;
-
-initial equation
-
-wy = false;
-equation
-
-wz = (time > 60) or (delay(x, 10) > 0) or  (y >= 0);
-
-
-algorithm
-when edge(wz) then
-wy := true;
-end when;
-end Monitor;
-</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_38/quest.txt b/legacy/Data/ingsw/1122_38/quest.txt
deleted file mode 100644
index 24d3f68..0000000
--- a/legacy/Data/ingsw/1122_38/quest.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
-<pre>
-block Monitor
-
-input Real x;  
-
-output Boolean y;
-
-Boolean w;
-
-initial equation
-
-y = false;
-
-equation
-
-w = ((x < 1) or (x > 4)) and ((x < 15) or (x > 20));
-
-algorithm
-
-when edge(w) then
-
-y := true;
-
-end when;
-
-end Monitor;
-</pre>
-Quale delle seguenti affermazioni meglio descrive il requisito monitorato?
\ No newline at end of file
diff --git a/legacy/README.md b/legacy/README.md
deleted file mode 100644
index a936ac3..0000000
--- a/legacy/README.md
+++ /dev/null
@@ -1,44 +0,0 @@
-# WARNING
-**We are working on a completely new version of the bot, written in Python. Stay tuned!**
-
----
-# so-un-bot
-The official https://t.me/so_1_bot questions repository
-
-🇮🇹 Raccolta di domande con risposta multipla utili per esercitarsi in molteplici esami!
-
-### Struttura del repository
-
-In Data/Questions sono presenti tutte le domande attualmente presenti nel bot, il nome del file corrisponde al nome del comando sul bot.
-Per aggiungere o correggere domande potete fare una Pull Request a questa repo.
-
-In Utils trovate script, sviluppati da vari studenti del corso, per creare o validare i file delle domande.
-
-**Nota per gli admin di appinfosapienza:**
-Al momento non sono presenti dei test CI che testano l'integrità del repository prima di un deploy.
-Quando accettate una Pull Request, entro due minuti verrà lanciata una nuova build sul server di produzione e al termine eseguito il bot con la nuova versione.
-
-Non essendoci test CI, se sono presenti errori, un commit contenente errori può mandare offline il bot (ad es. se il bot non riesce a fare il parsing di tutte le domande all'avvio).
-
-**Per i contributori:** 
-### Struttura dei file
-
-Il bot accetta le domande sia in un singolo file (utilizzato ad esesempio da Sistemi Operativi 1 e 2), che in file multipli (utilizzato da Ingegneria del Software).
-È in programma l'implementazione del supporto al formato JSON.
-
-#### Singolo file
-
-- Le domande sono separate da una riga vuota
-- Le risposte possono essere su una riga sola
-- Le risposte devono iniziano per `> ` se sono errate, per `v ` se sono corrette
-- Non ci possono essere righe vuote nella stessa domanda
-- Solo la domanda può contenere un'immagine, non le risposte
-
-#### File multipli
-
-- Ogni domanda è in una directory separata
-- La directory ha come nome il numero della domanda
-- Il testo della domanda è nel file `quest.txt`
-- La risposta corretta è nel file `correct.txt`
-- Gli altri file contengono solo risposte errate
-- Sia domande che risposte possono contenere immagini (max un'immagine per domanda e una per ciascuna risposta)
diff --git a/scripts/docker-compose.yml b/scripts/docker-compose.yml
deleted file mode 100644
index acc5bbf..0000000
--- a/scripts/docker-compose.yml
+++ /dev/null
@@ -1,15 +0,0 @@
-# The configuration used to deploy the bot on the production server
-# You can adapt it to create your own instance
-version: '3.8'
-services:
-  pim-a-bot:
-    image: 'wapeety/pim-a-bot:latest'
-    container_name: so-un-bot
-    restart: unless-stopped
-    environment:
-        - API_KEY=${TELEGRAM_TOKEN}
-        - ADMIN_ID=${TELEGRAM_ADMIN_ID}
-    volumes:
-      - "../data/questions:/app/data/questions"
-      - "../data/config:/app/data/config"
-      - "${ACL_DIR}:/app/data"
diff --git a/scripts/validate_questions.py b/scripts/validate_questions.py
deleted file mode 100644
index bfa804c..0000000
--- a/scripts/validate_questions.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import os
-import json
-import logging
-
-logging.basicConfig(level=logging.DEBUG)
-
-for filename in os.listdir("data/questions"):
-    logging.info("Analyzing " + filename)
-    with open(os.path.join("data/questions", filename), 'r') as f:
-        text = f.read()
-        try:
-            data = json.loads(text)
-
-            for q in data:
-                if type(q["quest"]) is not str or type(q["image"]) is not str or type(q["answers"]) is not list or type(q["correct"]) is not int:
-                    raise Exception(str(data.index(q)) + ": Some parameters are null, missing or their type is wrong.")
-
-                if q["quest"] == "" and q["image"] == "":
-                    raise Exception(str(data.index(q)) + ") Question's text and image cannot both be empty.")
-
-                if len(q["answers"]) == 0:
-                    raise Exception(str(data.index(q)) + ": Question has no answers.")
-
-                for a in q["answers"]:
-                    if type(a["text"]) is not str or type(a["image"]) is not str:
-                        raise Exception(str(data.index(q)) + ": Some answer's parameters are null, missing or their type is wrong.")
-
-                    if a["text"] == "" and a["image"] == "":
-                        raise Exception(str(data.index(q)) + ": Answer's text and image cannot both be empty.")
-
-        except Exception as e:
-            logging.error(getattr(e, 'message', repr(e)))
-            logging.fatal(filename + " is invalid. Aborting.")
-            exit(1)
-
-logging.info("Parsing successful!")
-exit(0)
\ No newline at end of file